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In this week’s episode of Deep Tech Catalyst, I sat down with Eric Donsky, three-times exited Deep Tech entrepreneur, and today CEO of Atomic13.

In our conversation, we unpacked TearLab’s journey, a venture path at the intersection of Deep Tech and lab-on-a-chip diagnostics, from identifying an overlooked gap in eye care to building a clinically usable biomarker platform, navigating long development timelines, structuring capital around technical uncertainty, and ultimately scaling the company through clinical validation, market adoption, and the public markets.

Key takeaways from the episode:

🎯 The best opportunities often begin where demand is real but not yet explicit
Some of the most valuable Deep Tech companies are built not around markets loudly demanding innovation, but around operational or customer friction that is widespread and poorly solved.

🧩 A product must work for every user in the workflow, not just the buyer
In healthcare especially, adoption depends on solving for multiple stakeholders at once. A technology may be clinically powerful, but it still needs to fit seamlessly into the daily routines, incentives, and constraints of the people expected to use it.

🧪 The invention is only the starting point; the real challenge is building the full system
The scientific insight may define the opportunity, but commercialization depends on solving the interface between science, product architecture, manufacturability, and reliability in real-world use.

💸 In Deep Tech, capital strategy has to reflect delay, iteration, and technical risk
When timelines stretch and technical bottlenecks emerge, undercapitalization becomes one of the fastest ways to destroy optionality. The right capital often comes from investors who understand the problem deeply enough to strengthen more than just the balance sheet.

📈 Technical success does not scale on its own
Clinical evidence, trusted validators, reimbursement logic, and a credible market narrative all matter. In regulated sectors, scaling a company requires much more than proving that the technology works.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Study the Friction Before the Market Names It

Some businesses are built by responding to an already visible demand signal. Others are built by recognizing that an existing workflow is underperforming, even if customers are not yet asking for a radically different product.

TearLab belonged to the second category.

The starting point was the belief that analytical functions normally performed in centralized laboratories might be miniaturized and brought much closer to the patient.

At that stage, however, this was still a technological possibility, not yet a business.

The challenge, then, was not simply to advance the technology. It was to identify a clinical setting in which miniaturization could create enough practical value to justify building a company around it.

The question was not just, “Where could this technology work?” but “Where could it materially improve an existing workflow, decision process, or care pathway?”

That search led to eye care.

What made eye care interesting was not explicit market demand for a point-of-care biomarker platform. What made it interesting was the apparent mismatch between the clinical problem and the tools available to manage it.

The early thesis was that, if biomarker analysis could be performed directly in the doctor’s office, it might not only improve diagnostic quality, but also change the speed and economics of decision-making in routine practice.

This is where the business logic started to become more concrete.

The company was not entering a market with clearly articulated demand; it was identifying a setting in which a real problem existed, but the category of solution had not yet fully formed.

That meant the opportunity could not be defined in technical terms alone. It also had to be defined in workflow terms.

A point-of-care diagnostic in eye care would matter only if it could fit inside the patient visit, reduce ambiguity during diagnosis, and provide information at the moment treatment decisions were being made.

By that point, the concept was taking shape around three linked elements: a technical capability, a specific clinical bottleneck, and a care setting in which time-to-information had practical value.

The ambition, however, was broader than a single test.

From early on, the idea pointed toward a more general platform logic: miniaturize part of the reference laboratory and make it usable at the point of care. That made the first application important not only as a standalone product, but as a beachhead for a broader diagnostic architecture.

The first wedge therefore had to do two things at once: it had to be narrow enough to solve a real and immediate problem, and structured in a way that could support expansion into additional biomarkers over time.

Seen this way, the company did not begin with a fully formed business model. It began with a sequence of design choices:

• First, identifying a technical capability that could change how diagnostics were delivered;

• Second, finding a clinical environment in which the status quo was weak enough that better information would matter;

• Third, defining the initial product not as a generic platform, but as a tool that could fit into a real workflow and improve a real decision.

The venture became credible only once those elements were connected.

A Product Wins Adoption Only When It Solves for Every User in the Room

One of the important commercial realities in this case was that adoption did not depend on a single user. The product had to work for at least two distinct customer profiles inside the same clinical setting, each with different priorities.

The first was the office technician or lab technician responsible for running the test as part of the patient workup. This person was not the ultimate clinical decision-maker, but was essential to the success of the product in everyday practice.

If the system was difficult to use, slow to operate, or disruptive to workflow, adoption would be limited regardless of its scientific merits.

That made ease of use a central design requirement.

The test had to fit into the normal rhythm of a practice, be manageable by someone without specialized laboratory training, and generate a result without introducing unnecessary complexity.

This is a useful lesson for Deep Tech founders.

In many cases, especially in healthcare, the person who physically uses the product is not the same person who benefits most from the result or authorizes the purchase.

A product may solve an important problem in principle, but still fail if it is too complex for the person expected to run it day after day. In practice, ease of use becomes part of the value proposition.

The case also shows that workflow considerations can be commercially decisive.

Better decisions, better economics, and reimbursement

The physician represented a second and distinct customer logic. What mattered here was not primarily operational convenience, but whether the diagnostic information improved care in a meaningful way and whether the economics of using the test made sense.

For the doctor, the central question was how the result would influence clinical decision-making. A new test was valuable only if it helped improve diagnosis, clarify ambiguity, or support better treatment choices.

In the eye care setting described in the interview, this was particularly relevant because several front-of-eye conditions could present with similar symptoms. A more informative test therefore had value not simply as an additional data point, but as a tool for differential diagnosis.

At the same time, clinical value alone was not enough.

The physician also had to understand the economic implications.

• Would the test be reimbursed?

• Would it improve the financial performance of the practice?

• Would it make the clinical process more efficient?

These questions were a key part of the adoption decision, and this point becomes especially clear in the discussion of practice economics.

A broader point emerges from this. In Deep Tech, it is often not enough to “know the customer” in the singular. The more useful discipline is to map the full set of stakeholders involved in use, decision-making, and economic approval.

In this case, the technician and the doctor each required a different narrative. One cared about usability and process reliability. The other cared about clinical utility, reimbursement, and revenue logic. The product had to satisfy both at once.

That dual-customer structure shaped the go-to-market logic.

In that respect, the commercial challenge was not separate from the product challenge. They were tightly connected from the beginning.

Key Challenges in Developing a Deep Tech Product

The broader vision was compelling, but turning that vision into a usable system required solving a much more specific and difficult problem.

In particular, one key challenge was tear collection.

In order to generate a meaningful diagnostic signal, the system needed to work with an extremely small sample volume, roughly 50 nL of tear fluid.

But collection at that scale was not straightforward.

If too much manipulation of the eye occurred, reflex tearing would begin and dilute the sample. If the sample changed through dilution, the signal would become unreliable.

The same was true for evaporation. Tears could not simply be collected and sent to an external laboratory because sample loss during handling would distort the result.

In practical terms, this meant that the core challenge was not only to detect a biomarker. It was to collect a very small tear sample in a way that preserved its integrity, then move that sample into the analytical system without introducing clinically unacceptable variability. That requirement shaped the entire product.

This is an instructive pattern for Deep Tech more broadly.

A company may begin with a central scientific thesis, but the hardest part of commercialization often emerges in the interface between the science and the real-world operating environment.

In this case, the key bottleneck was not the abstract idea of biomarker analysis. It was the highly constrained physical act of collecting a stable sample from the eye in a routine clinical workflow.

The three-part architecture behind the platform

Once the nature of the problem became clearer, the product logic also became more concrete. The platform was not a single device, but a coordinated system made up of three interdependent components.

1. The first component was the disposable chip. This was the central consumable and the economic core of the business model. The company was structured in what was effectively a razor-and-blade model: the chip was single-use, and this was where recurring revenue would come from. But commercially attractive recurring revenue only mattered if the chip could perform consistently and be produced at scale.

2. The second component was the handheld device into which the disposable chip would snap for each test. This device acted as the collection and signal-processing interface. It was not simply a holder. It had to manage the practical interaction with the eye, support sample transfer, and process the resulting signal in a way that reduced noise and stabilized the output.

3. The third component was the reader. Once the handheld device was docked, the reader translated the processed signal into a result that could be displayed.

What matters here is that the company was not solving for one isolated technical feature. It was designing an integrated architecture in which collection, signal processing, and readout had to work together seamlessly.

A failure in any one part would compromise the usefulness of the whole platform.

This system-level view also explains why product development timelines became longer and more complex than a simpler diagnostic concept might suggest. Each component introduced its own design constraints, but all three also had to align with a future regulatory path.

The device had to be developed not just to work technically, but to be compatible with a point-of-care use case where reliability, usability, and eventually regulatory clearance would all matter.

Turning a scientific concept into a repeatable clinical tool

The most substantial technical work centered on the chip itself. The team’s objective was to build a chip architecture that could collect a very small tear sample, move it through a nanofluidic channel, and interrogate that sample for the relevant marker with high precision.

That alone would have been difficult. What made it more challenging was the requirement that this be done using a low-temperature plastic substrate rather than the higher-temperature silicon approaches more typical in microfluidics at the time.

The reason was practical.

The company wanted the platform to be suitable not only for osmolarity, but also for future protein analysis. That meant the chip architecture had to be compatible with attachment chemistries and biological components that would not tolerate high-temperature fabrication processes.

No obvious development path existed for this. The state of the art in the field was not yet aligned with what the company needed to build.

This is where partner selection became central. Early interactions with technically strong organizations were valuable, but they did not lead to the required solution. The more suitable partner was eventually found in Melbourne, a company willing to work on a longer time horizon and able to innovate around laser ablation in plastic substrates.

Together, they developed a polycarbonate-based platform with a hydrophilic pressure-sensitive adhesive that could support capillary collection and movement of the tear sample through the nanofluidic structure.

This step moved the company closer to something commercially usable. A clinical product could not depend on occasional performance under ideal conditions. It had to deliver repeatable results with a tight coefficient of variability.

Diagnostic accuracy depended on chips performing the same way every time, at volume, and at a cost structure that could support adoption.

This is where the difference between proof of concept and product became most visible. The company was no longer just demonstrating that a biomarker could be measured. It was trying to build a manufacturing-compatible, clinically reliable system that could eventually be used in routine practice.

That transition required advances in materials, fabrication, fluid handling, ergonomics, electronics, and quality control at the same time.

Deep Tech Timelines Break Simple Plans

Because the company was developing a system rather than a single component, partner selection became a strategic decision rather than a procurement exercise.

Different parts of the platform required different external capabilities.

The chip, the handheld, and the reader all had to be designed and built with an eventual regulatory path in mind, especially because the commercial goal depended heavily on CLIA waiver.

That meant the product could not merely function in a technical sense. It had to be robust enough that an untrained operator would not introduce errors leading to misleading results or patient risk.

The implications for design were substantial. Product architecture, usability, manufacturing tolerances, and signal reliability all had to support that eventual outcome.

This raised the standard for external partners.

The company did not just need vendors with technical skills. It needed development partners capable of understanding why the constraints mattered, how the system would eventually be used, and how the work being done at that moment would affect later manufacturability and regulatory feasibility.

Why founders should raise more capital than they think

One of the clearest founder lessons stated in the interview is that early capital planning is often too optimistic for the realities of Deep Tech execution.

A company may begin with a strong vision, but it is unlikely to know in advance exactly how every technical problem will be solved.

In practice, development paths shift, timelines slip, and costs rise. For that reason, capital should not be raised only against the ideal version of the plan. It should be raised with the expectation that setbacks will occur.

This point is especially relevant in a case like this one, where the company faced multiple layers of uncertainty at the same time. There was technical risk in the chip architecture, product integration risk across the three-part system, manufacturing risk in achieving repeatability and cost, and regulatory risk linked to future CLIA-waived use.

Each of these could extend timelines. Together, they made undercapitalization particularly dangerous.

The lesson here is not simply “raise more money” in a generic sense. It is more specific than that.

Founders should assume that development will take longer and cost more than early models suggest, particularly when they are building against technical requirements that have not yet been solved in a standardized way.

Dilution, in that context, should be weighed against the much more serious risk of running out of time before the company reaches a meaningful value-inflection point.

This case also shows why milestone planning in Deep Tech needs to be connected to the actual structure of uncertainty. It is not enough to define milestones as if the path were linear.

Milestones need to reflect what has truly been de-risked, what still depends on external capability, and what setbacks are plausible at each stage.

What emerges from this part of the story is a more focused view of execution. Deep Tech timelines are not difficult only because the science is hard. They are difficult because technical, manufacturing, regulatory, and partner-related uncertainties often interact.

That interaction is what can make simple plans unreliable, and what makes capital resilience such a central part of company building.

Capital Works Best When It Came from Belief, Relevance, and Timing

One of the most interesting parts of this case is the way early capital was sourced.

The company did not begin by relying on traditional venture capital. Its first meaningful financing came from people inside the market who already understood the clinical problem and could see why the proposed solution mattered.

The first roughly $6 million came from key opinion leaders in eye care.

They were clinicians with enough technical and clinical understanding to recognize the platform’s potential value at an early stage.

That made the capital especially useful, because it was tied not only to funding, but also to domain credibility, influence over the market narrative, and access to parts of the ecosystem that would otherwise have remained difficult to reach.

How opinion leaders became investors, validators, and market-makers

The role of these early supporters extended well beyond capital. Their influence also had practical consequences for development and validation.

When the company later needed to run a large multi-site clinical study, these same opinion leaders made their clinical sites available at cost.

According to the interview, this reduced the cost of generating that data significantly relative to what a conventional outsourced path would have required. In other words, the value of these relationships compounded over time.

This part of the story shows how some forms of capital are unusually efficient because they arrive bundled with trust, access, and market-making power.

For a Deep Tech company operating in a highly regulated environment, that combination can be especially important.

Using milestone-based capital to keep technical progress aligned with scale

As the company moved beyond proof of concept and began to see product traction, the financing strategy evolved.

At that point, the question was no longer only how to fund technical development. It was how to fund manufacturing scale-up, clinical validation, commercial infrastructure, and broader market expansion.

This is where timing became decisive.

The company was operating in a favorable public-market environment, while also beginning to see stronger revenue growth and adoption. Rather than continue on the same financing path, it chose to partner with a public eye care company as a way to access larger pools of capital.

The logic reflected a growing recognition that the company would need substantially more capital than originally expected in order to build manufacturing capacity, expand sales, and complete the clinical work required to support broader adoption.

The deal structure was milestone-based. This created a staged path to full consolidation into the public company.

As discussed in the interview, that approach proved especially important when circumstances changed and the founder had to help redirect the public company narrative.

What this illustrates is that capital strategy in Deep Tech is rarely static.

The right source of funding depends on what the company is trying to accomplish at a given stage, what the external market environment looks like, and how much uncertainty still remains.

• Early capital in this case came from actors with deep domain relevance.

• Later capital came through a structure capable of funding larger-scale execution, but still organized around milestones because the path remained developmental rather than fully mature.

The broader lesson is that effective capital formation depends on fit. The best funding is the one that matches the company’s stage, reinforces its path to validation, and arrives at a time when it can meaningfully expand what the company is able to do next.

Scaling the Company Required More Than Technical Success

To recap, the company’s path to success required a combination of clinical validation, market education, and a credible adoption narrative that could be understood and repeated by the right actors in the industry.

Clinical data played a central role in that transition. The company did not rely only on the technical logic of the platform or on the novelty of measuring osmolarity at the point of care.

It also invested in a large multi-site clinical study to strengthen the evidence base around the product. That data had more than one function. It supported the clinical legitimacy of the test, but it also gave influential physicians a stronger foundation from which to explain why the platform mattered.

This is important because adoption in clinical markets is rarely driven by the product alone. It is often driven by a combination of evidence and interpretation.

Even when a technology works, the market still needs trusted intermediaries who can explain how it fits into clinical practice, why it improves decision-making, and why it deserves to become part of routine care.

In this case, the company benefited from having leading figures in the eye care field help shape and communicate that narrative.

The interview also makes clear that product traction was connected to broader market-shaping activity. The company was not only selling a diagnostic tool. It was also participating in a shift in how dry eye disease was understood, with osmolarity becoming part of the disease definition itself.

That mattered because it helped align the product with an emerging clinical framework rather than leaving it as an isolated innovation looking for relevance.

What worked here was the combination of several reinforcing elements: a product that solved a real diagnostic problem, data that supported its use, and respected clinical voices that could help translate its value to the wider market.

The lesson is that for a Deep Tech healthcare company, scaling often depends on building this full structure around the product. Scientific validity is necessary, but it does not automatically create adoption.

Key takeaways for Deep Tech founders

The closing reflections in the interview are useful because they move from the specifics of one company to a more general set of operating lessons.

1. The first is the importance of understanding technology readiness level in a precise way. For a pre-revenue Deep Tech company, TRL is not just a technical classification. It is a way of explaining where the company truly is, what risks remain, what must happen next, and how capital should be matched to progress. In this view, founders need to be able to communicate not only what they are building, but what it will take to move from one stage of technical maturity to the next, including timelines, risks, and resource needs.

2. The second lesson concerns techno-economics. A company can solve meaningful technical problems and still fail commercially if the economics do not work at scale. This is one of the more sobering points in the interview. The claim is not that technical success guarantees business success if execution is disciplined. It is that many Deep Tech companies still fail after raising substantial capital because they do not ultimately meet the cost and economic requirements of the market they are trying to serve. For that reason, founders need to understand their future economics early and continuously, not only the technical feasibility of the product.

3. The third lesson is that customer understanding has to extend beyond the present moment. It is not enough to know who the customer is today or what the current market looks like. Founders also need to think about what the competitive and commercial environment will look like by the time the product actually launches. In Deep Tech, long development cycles create a gap between early assumptions and eventual market entry. A value proposition that appears differentiated at the start may be less differentiated several years later if the market evolves in the meantime.

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On this week’s episode of Deep Tech Catalyst, I sat down with Richard Wang, Founder & former CEO of Cuberg, a Deep Tech company commercializing next-generation battery technology for electric mobility that was acquired by Northvolt in 2021, and today Co-founder & CEO of Voya Energy.

In our conversation, we unpacked Cuberg’s entire journey, from the early shift from technology push to market pull to financing a battery company outside the standard VC path and ultimately building toward a successful strategic exit.

Key takeaways from the episode:

🎯 The Best First Market Is Rarely the Biggest One
The real opportunity lies in niche markets where technical requirements are non-obvious, willingness to pay is high, and the product can create meaningful value before cost competitiveness is fully mature.

🤝 Strategic Investors Can Solve More Than a Funding Problem
When prospective customers become strategic backers, commercial validation and financing stop being separate challenges and start reinforcing each other.

🏭 A Capital-Light Model Can Extend Survival and Increase Optionality
Avoiding premature investment in internal manufacturing infrastructure, relying on external prototyping partners, and combining equity with grants and other non-dilutive funding can make the difference between stalling in “the valley of death” and reaching the next stage of technical maturity.

📈 The CEO’s First Job Is to Keep the Company Alive
Fundraising, customer conversations, strategic partnerships, and timing all matter more than founders often expect. In Deep Tech, survival is not a side effect of progress, it is the condition that makes progress possible.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Moving Beyond Technology Push to Find a Real Commercial Wedge

The story of Cuberg did not begin with a market-first insight.

It began in a far more familiar Deep Tech pattern: a promising technology emerging from academic research, accompanied by the belief that its technical novelty could become the basis of a new venture.

The original foundation came from PhD work on solid-state batteries.

The underlying concept was compelling: instead of relying on the liquid electrolyte used in conventional batteries, the architecture replaced that internal medium with a solid material capable of conducting lithium ions.

From a scientific standpoint, it was easy to see why this looked exciting. Solid-state batteries had long been associated with the possibility of meaningful advances in safety, performance, and next-generation battery design.

As a starting point for a company, it had all the features that often attract a technical founder: a differentiated concept, strong academic roots, and a clear sense of being attached to a major future trend.

But after roughly a year of trying to build around that original technology, it became clear that scientific interest and commercial logic were not the same thing.

The issue was that the specific technology did not make enough sense as a product platform, especially once the scale-up journey was taken seriously. Manufacturing complexity became a central problem.

When examined through the lens of what it would take to move from a laboratory result to a commercially viable battery product, the original approach looked much less compelling.

The pivot that changed the company’s trajectory

In 2016, the company pivoted away from its original solid-state approach and toward a fundamentally different path in advanced battery design.

That choice effectively reset the company.

Once the company stopped anchoring itself to the original technology, it also stopped being constrained by the assumptions that came with it.

That shift changed the company’s posture in a profound way.

Instead of beginning with a fixed technology and asking where it might fit, the company now had to think more openly about what different markets actually needed and which technical pathways could realistically serve those needs.

In other words, the pivot did not just change the product direction. It changed the logic of the company from technology push to market-driven problem solving.

Becoming technology-agnostic was critical.

Once it was no longer narrowly focused on a single technology, the company could evaluate opportunities with a different kind of discipline.

This is why the pivot stands out as one of the most constructive decisions in the company’s trajectory.

The business was no longer organized around proving that a scientific concept deserved a market. It was now trying to understand where unmet need could define the product itself.

Innovation became tied to usefulness, manufacturability, and strategic fit.

From the mainstream market to a single, clearly defined niche

Another turning point came through a less obvious interaction, when an oil and gas company working on high-temperature battery applications reached out to explore a potential collaboration.

What began as a potential partnership quickly became far more important than that. It created the setting in which the company could begin discovering what a genuinely useful commercial wedge might look like.

The customer’s application was highly specific.

Batteries were being used downhole, alongside drilling equipment, to power sensors and electronics deep inside the well. The environment was extremely harsh.

Under those conditions, the available battery option was a single-use lithium metal battery. Once consumed, it had to be discarded and replaced. That imposed both significant cost and significant operational friction on the customer.

What mattered was not just that the problem was painful. It was that the requirements were different from the assumptions a battery researcher would normally carry from mainstream markets.

In traditional thinking, especially in automotive, battery quality is tied to highly demanding metrics such as very long cycle life. A thousand cycles might be treated as a minimum threshold for commercial relevance.

But in this oil and gas application, that model did not apply.

For a customer already relying on a disposable battery, the value threshold looked completely different. If a rechargeable battery could survive only ten cycles, that would already represent an order-of-magnitude improvement over the status quo.

That is the kind of insight that is easy to miss if a company remains trapped inside the assumptions of large, visible markets.

It showed that some early commercial opportunities in Deep Tech may come not from the biggest or most obvious market, but from a market whose requirements are unusual enough that an emerging technology can already solve the problem well enough to matter.

In this case, the harsh thermal environment was challenging, but the low cycle-life requirement made the problem much more tractable than automotive.

The opportunity was difficult in one dimension and forgiving in another. That kind of asymmetry is exactly what can create a viable beachhead.

That is where a real value proposition began to form. Not through claims about next-generation batteries, but through close engagement with a customer whose non-obvious constraints revealed a commercially credible entry point.

The niche looked strange by conventional standards. Precisely for that reason, it was promising.

Matching Markets to Company Stage, Adoption Speed, and Strategic Fit

In the company’s early commercial thinking, one of the central questions was not simply which market was largest, but which market made sense at a given stage of development.

Automotive was highly cost-sensitive, with a high threshold for entry in terms of manufacturing maturity, reliability, and price competitiveness. For a young battery company, those conditions were difficult to meet early on.

That led to a different go-to-market logic. Rather than starting from the size of the addressable market, the company began looking at the relationship between a given application and its current position on the cost curve.

The operative question became:
“Which customers are able and willing to pay for the performance the technology can deliver now, rather than the performance it may one day deliver at scale?”

Markets were therefore evaluated not only by size or visibility, but by how well they matched the company’s stage of maturity.

From that perspective, automotive looked less like an initial commercial destination and more like a later one.

False starts, dead ends, and what became clear

As the company moved away from obvious market assumptions, it explored several possible beachheads.

Some appeared promising at first but turned out to be less attractive once the commercial dynamics became clearer.

That process helped refine what made an early market viable. Medical devices offer a good example.

On the surface, the segment looked attractive. Performance mattered, reliability mattered, and the battery often represented only a small share of the value of the overall product.

By that logic, a better battery seemed likely to command a premium. The company even received its first purchase order for samples in that segment, which made the opportunity look concrete.

But over time, the fit appeared more limited.

Customers were interested in performance, yet they were also highly risk-averse. Regulation added another layer of caution, and qualification cycles were long.

As a result, the path from technical interest to meaningful adoption was slow. The issue was not that the market lacked value, but that its pace did not align especially well with what the company needed at that moment.

That experience made the screening logic more specific. Willingness to pay remained important, but it was no longer enough on its own.

The company also had to consider how quickly a market could adopt, how burdensome qualification would be, and whether the commercial path was likely to produce usable feedback, revenue, or both within a reasonable timeframe.

Seen that way, the false starts helped clarify that an early beachhead market had to combine several conditions at once: economic willingness, operational accessibility, and a purchasing dynamic compatible with the company’s stage.

How different sectors serve different stages of the journey

As these experiences accumulated, market selection became less about finding one perfect industry and more about understanding what different sectors could contribute at different moments in the company’s development.

That mattered especially in batteries, where a single technology can potentially serve multiple end markets.

The company did not treat all markets as interchangeable, nor did it assume that one sector had to perform every function. Instead, different segments began to play different roles.

Some sectors were useful because they could support development.

Manned aviation fit that pattern. It had a high willingness to pay and, just as importantly, customers who were prepared to fund development over a longer horizon.

For a company still advancing the technology, that kind of relationship could be valuable even if broad commercialization remained slower.

Other sectors were useful for a different reason: they could move more quickly once a solution existed.

Drone applications fit into that category. They may not have offered the same type of long-term development support as larger aviation players, but they were easier to access and faster to commercialize into.

In that sense, they were useful in translating technical capability into market validation.

Over time, this led to a more staged view of go-to-market. Some sectors were more helpful in financing learning and development. Others were more useful in creating early commercial proof.

Larger and more cost-sensitive markets became more relevant later, as the company’s cost structure and technical maturity improved.

That is how the company’s market logic evolved.

The objective was not to identify one market and remain fixed on it forever, nor to pursue every possible application in parallel.

It was to match markets to stage: development-oriented sectors when the technology still needed to mature, faster-moving sectors when commercial proof mattered most, and broader markets only when the economics made them plausible.

Financing a Battery Startup Without Following the Standard VC Script

As the company’s financing strategy took shape, one of the underlying observations was that the standard venture-backed startup model was a poor fit.

Battery companies tend to face high barriers to entry, significant capital requirements, and long development timelines. Moving from technical promise to commercial scale can take longer than many traditional venture investors are set up to support.

That mismatch influenced the company’s approach from early on.

Rather than building around the expectations of a conventional VC, it chose to pursue a financing path that was more compatible with the pace and economics of the sector.

In practice, that meant operating with tighter spending discipline and a more constrained capital base, but also with investor expectations that were closer to the actual development cycle of the business.

Turning customers into strategic investors

Once traditional VC was no longer treated as the default, the company’s fundraising logic shifted toward strategic capital. In particular, it raised from corporates that could plausibly become users of the technology themselves.

That changed the financing process in an important way.

In a conventional startup model, raising capital and proving commercial demand are often treated as separate challenges.

Here, the two moved closer together. If the investor was also a prospective customer, then fundraising depended more on showing that the technology could solve a real problem for a known buyer.

The early oil and gas partner illustrates this clearly.

The customer already understood the operational problem. The company’s task was to show that the battery technology could address it in a meaningful way.

If that case was persuasive, the customer had a reason both to work with the company and to invest in its progress.

A similar pattern later appeared with Boeing.

By the time Boeing led the seed round, the relationship was not simply financial. It reflected a view that the technology addressed a real need in aviation and could become strategically useful in that context.

In that sense, the capital was closely tied to real commercial validation.

This kind of alignment depended on choosing the right strategic counterparties.

Another important takeaway is that the strongest fit often came from downstream users—companies whose businesses could benefit directly if the technology worked.

Their incentives were easier to understand, and the relationship was more clearly tied to a concrete need.

Pricing from cost structure and delivered value

The same logic shaped how pricing was approached.

On one side, the company needed a bottom-up view of cost: bill of materials, manufacturing assumptions, expected learning curves, and how costs might evolve with scale.

Without that, pricing would quickly lose contact with what the business could realistically support.

But cost was only part of the picture. The other side was the value created for the customer.

In markets like aviation, battery performance could directly affect the economics of the end product.

If improved performance allowed an aircraft to fly farther, carry more, or operate more productively, then the value delivered could exceed the incremental cost of the battery by a wide margin. That opened up room for premium pricing.

Pricing therefore sat between two perspectives: what the battery could cost over time, and what better performance was worth to the customer.

The relevant benchmark was not simply the incumbent battery price, but the economic effect of using a better one.

Crossing the Valley of Death With a Capital-Light Model

One of the company’s key operating choices was not to build an internal battery prototyping line too early.

The technical process is complex, manufacturing quality matters, and there is often a strong instinct to internalize as much as possible.

At the same time, doing so would also have required major capital investment at a stage when the company was still proving out both the technology and the commercial path.

Instead, the company chose to be selective about what it owned. Rather than building a full prototyping capability in-house, it worked with external manufacturing partners that already had the relevant equipment.

The first efforts involved prototyping labs in the United States. Later, the company moved to a more sophisticated partner in China that could produce higher-quality samples.

This made the capital-light approach very concrete.

The goal was not to avoid technical development, but to preserve scarce capital by outsourcing expensive capabilities when that did not compromise the core of the business.

What mattered most was not owning a prototyping line, but being able to design, test, refine, and validate advanced battery concepts in a commercially relevant way.

That also affected how the company moved through the early stages of development. In deep tech, the difficulty is often not only the science itself, but the cost of reaching a demonstrable product.

By avoiding a large infrastructure build too early, the company reduced some of that pressure and kept more flexibility as it advanced.

The timeline from early funding to advanced prototypes

That operating model was supported by a funding path that developed in stages rather than through one large early raise.

In 2016, the company raised $900,000 in pre-seed capital from the oil and gas partner that had helped shape its original commercial direction. That funding supported a small team and early lab-stage technical development.

Alongside that, the company secured roughly $500,000 in Department of Energy support through a fellowship-related program. Together, those sources provided around $1.5 million across 2016 and 2017, enough to sustain the first phase of development and continue building the technical base.

The next step came in early 2018, when Boeing led a $2 million seed round.

With that capital, the team grew and the work moved beyond the earliest research stage.

In 2019, the company added another roughly $1.5 million in grant funding, bringing total new capital across 2018 and 2019 to approximately $3.5 million. By then, the team had grown to around twelve or thirteen people.

During that period, the company also moved from very early laboratory work toward prototypes, first through U.S. labs and later through the Chinese manufacturing partner.

The shift was not only financial but technical: it brought the company closer to a form factor and performance level that customers could begin to evaluate more directly.

When the company nearly ran out of cash

Even with that discipline, the path remained fragile. In mid-2019, the company came close to running out of money, with only a short amount of runway left. What helped bridge that moment was a combination of grant funding and additional support from angel investors.

A California Energy Commission grant arrived at a critical time. But that timing was only possible because the company had applied roughly a year earlier.

That reflected an important feature of non-dilutive funding: it could be highly valuable, but it operated on a much longer cycle than equity. Applications had to be made well in advance, often without knowing whether the funds would arrive when needed.

That made grants less a reactive source of capital than a long-cycle pipeline that had to be managed continuously.

Even after an award was secured, there could still be delays before funds became available, and reimbursement structures could create working-capital pressure.

For that reason, the company still needed more flexible capital from investors to absorb timing gaps and cover costs that grants would not reimburse.

Scaling Through Partnership, and CEO Focus

As the company moved beyond early prototypes, the question of scale became more concrete. In batteries, that inevitably brings manufacturing into focus.

A product may work, customer interest may be real, and early commercial signals may be encouraging, but those elements do not by themselves answer how production will happen at scale.

From relatively early on, the company did not assume that it should become a full battery manufacturer itself.

Building and financing a large manufacturing operation would have required a level of capital, operating capability, and organizational complexity that did not fit the company’s stage.

Manufacturing clearly mattered, but direct ownership of a factory did not appear to be the most practical objective.

The working assumption was that the technology would create more value if it could eventually be scaled within a larger manufacturing platform—one that already had the infrastructure, operational experience, and capital required for industrial production.

In that sense, the company’s role was not to recreate the entire battery manufacturing stack, but to develop the technology far enough that it became strategically valuable to a larger manufacturer.

That made the long-term path less about building an independent factory and more about reaching a point where integration with a larger industrial player would make sense.

Samples could still be produced through external partners, commercial relationships could still be built directly, and early validation could still happen without owning the full production system. But the route to scale pointed elsewhere.

The path to acquisition

The acquisition emerged gradually rather than through a predefined plan. The relationship with Northvolt began early, but at first there was no concrete discussion about a transaction.

By late 2019, the company was still focused on raising a Series A, and a strategic investment seemed more plausible than an outright acquisition.

What changed the situation was timing. As the financing process advanced, the opportunity for Northvolt became more time-sensitive, since a completed round would likely have changed both the company’s trajectory and the structure of any future deal.

That became a major forcing function in pushing the conversation toward acquisition.

What followed was a more intensive process of engagement and diligence in early 2020. In that sense, the transaction grew out of an existing relationship, a fundraising process already underway, and a moment in which independent financing and strategic acquisition became parallel paths.

Strategic investors, negotiation, and IP

There is a strong takeaway in how the company approached strategic investors.

The most relevant partners were usually downstream companies—potential users of the technology rather than peers or adjacent suppliers.

That created a clearer form of alignment, since the strategic investor’s interest was tied to a problem it might eventually solve through the startup’s product.

Those relationships also tended to revolve less around conventional venture questions and more around commercial terms.

Discussions were more likely to involve issues such as future pricing, preferred access, or exclusivity in a specific application.

In the company’s case, for example, the oil and gas partner received exclusivity for its own market segment, which was narrow enough that the concession did not significantly constrain the broader business.

The company’s experience also shaped the founder’s view on confidentiality and IP concerns.

Early technical founders often worry that large corporates will copy what they are shown. The view here was more selective. With downstream customers, that concern appeared limited, since battery development was not their core business. In those cases, openness could support trust and momentum.

With companies much closer to the same part of the value chain and had the technical ability to replicate the work, more caution seemed justified.

Taken together, these choices formed a fairly consistent pattern.

• Scale was approached through strategic fit rather than internal manufacturing ownership.

• The acquisition emerged through relationship-building and timing rather than through a pre-set exit plan.

• The CEO role centered heavily on keeping the company financed and operational.

• And strategic negotiations were handled with an emphasis on relevance, proportionality, and the specific incentives of each counterparty.

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Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we explore one of the most persistent misunderstandings in MedTech company building: what happens when founders apply conventional startup logic to a sector where validation, regulation, commercialization, and financing are deeply overlapped from the very beginning.

I sat down with Jon Bergsteinsson, Founder & Managing Partner at LIFA Ventures, to unpack how an investor evaluates early-stage companies, why many founders ask the wrong questions at the start, and how the most credible companies align product development, go-to-market strategy, regulatory planning, and fundraising far earlier than most teams expect.

Key takeaways from the episode:

🏗️ Validation, Prototyping, Regulation, and Go-to-Market Must Evolve Together
In MedTech, these are not separate stages that can be handled one by one. The strongest companies build them as an interconnected system from the outset, rather than treating commercialization and scalability as downstream concerns.

📍 Go-to-Market Strategy Determines Regulatory Strategy
Regulatory planning should not be chosen in isolation or based only on geography. It should follow from a clear understanding of who the product is for, who will use it, who will pay for it, and how the company intends to enter the market.

🧪 Clinical Development Is Also a Capital Planning Exercise
Clinical studies are not only scientific milestones. They are budgeting and financing milestones as well. Timelines, sample sizes, and study design all shape capital needs, and founders often underestimate how early those assumptions need to be made.

💰 Fundraising in Medtech Should Be Framed Around De-Risking, Not Venture Labels
Rounds such as pre-seed, seed, or Series A often fail to describe what is actually happening in a medtech company. A clearer approach is to define each raise by the specific technical, clinical, regulatory, or commercial risks it is meant to remove.

📈 Scalability Begins with Early Commercial Signals
Revenue is not the only meaningful sign of traction. Buyer interest, pricing feedback, reimbursement logic, and early distributor conversations can all provide strong evidence that the company is building toward a real market rather than just a promising technology.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Market Validation is the First Real Milestone

In an early-stage MedTech, the first milestone is market validation. Before anything can scale, before capital can be deployed, and before a regulatory or commercial path can be chosen with confidence, there has to be evidence that the company is working on something real.

In the strongest cases, the company is not only trying to prove that something can work technically, but also that it should exist commercially.

This is the stage in which the earliest version of the company begins to take shape.

The goal is to determine whether the underlying assumptions are strong enough to justify further development. Without that foundation, every later step becomes more fragile.

Prototyping is a continuous process, not a single event

Once early validation begins to take form, prototyping becomes one of the key tools through which learning continues.

But it is important to understand prototyping correctly.

It is not something a team does once before moving neatly into the next phase. It is an ongoing process of refinement.

A common mistake is to treat the prototype as a one-off milestone, as though the company can build an initial version, freeze the design, and then simply move forward.

In reality, prototyping is iterative by nature. It evolves alongside the team’s understanding of the product, the user, the clinical context, and the commercial environment.

Each version should bring the company closer to a solution that is not only technically functional, but also viable in the broader sense required in the field.

That is why prototype readiness is such an important marker. It says less about completion than about progress. It reflects whether the company is moving through the right cycle of development, testing, and refinement.

Regulatory strategy needs to be a priority from the start

Regulatory strategy cannot be left until later. It has to enter the picture early, because it influences how the product is built and how the company prepares to bring it to market.

But regulatory thinking should not be approached as an isolated technical exercise. It belongs within the wider structure of the business.

The key point is that regulatory choices are not supposed to exist independently from the rest of the company’s direction.

They have consequences for how claims can be made, how products can be introduced, and which pathways become available. That makes early regulatory awareness essential, even before all answers are known.

At this stage, what matters most is not just knowing that regulation will be important, but understanding that it must be developed in relation to the company’s broader plan.

Go-to-market

One of the clearest mistakes early teams make is treating go-to-market strategy as something that can be solved later, once the product is more mature.

In reality, market access and commercialization logic belong near the beginning.

Founders need to understand who the buyer is, who the user is, how the product will be sold, what the pricing logic might look like, and how early commercial traction could eventually emerge.

This is not because every detail has to be fixed at the outset. It is because the company cannot make sound decisions in isolation.

Product development, regulatory direction, and commercialization planning all shape one another. A MedTech company that delays go-to-market thinking too long risks building technical progress on top of weak commercial assumptions.

The strongest early-stage teams begin asking these questions while they are still validating the problem and refining the product. That creates a more coherent path forward.

Funding and scalability must be planned as part of the same system

As the company develops, funding becomes one of the structural requirements that keeps every other part moving.

Founders must start thinking not only about what the company needs to build, but also about what kind of financing pathway can support that journey.

At the same time, scalability, distribution, and manufacturing cannot be treated as distant concerns that belong only to later stages. They are part of the architecture of the company from early on.

A business may not be ready to scale yet, but it still needs to understand what scaling would eventually require and what assumptions must hold true for distribution and sales to work.

This is why the company has to be built as an interconnected system.

Validation, prototype development, regulatory planning, go-to-market logic, funding needs, and scalability are not separate boxes to check one after another. They are linked parts of the same structure.

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Why Go-to-Market Strategy Determines More Than Founders Think

Founders often speak about regulatory pathways as if they were mainly a matter of choosing between geographies or selecting the fastest technical route to approval.

But that framing misses the deeper issue. Regulatory direction only makes sense when it is rooted in a clear go-to-market plan.

The company first needs to know who it is building for, who will use the product, who will pay for it, and under what conditions adoption is likely to happen.

Without that clarity, regulatory planning becomes detached from the actual commercial reality the company is trying to enter. The risk is that the business chooses a pathway that may be technically valid, but commercially misaligned.

This is why commercial thinking has to begin early. It should happen at the same time as initial prototyping, market research, and early product definition.

The point is not to postpone regulation until later. The point is to avoid pretending that regulation can be designed correctly before the company understands how it intends to reach the market.

The go-to-market plan shapes every major decision

A strong go-to-market plan becomes the foundation on which many of the company’s core decisions are made.

Once the company knows the type of customer it is targeting, the intended buyer, and the type of user it wants to serve, it becomes much easier to determine how the product should be positioned and where it should be introduced first.

That clarity also affects the selection of countries, regions, and market segments.

A company should not decide to enter the United States or Europe simply because one regulatory route appears easier than another in the abstract.

It should decide where to go based on where the strongest commercial traction is likely to emerge. The regulatory pathway then follows from that decision, not the other way around.

This is a critical distinction because it reverses the logic many founders instinctively use.

The question is not which regulatory path looks most convenient. The question is where the company can build a real business. Once that is understood, the regulatory route becomes part of a coherent strategic picture rather than a standalone technical choice.

Early confidence in the buyer matters more than founders assume

At the center of all of this is a simple idea: founders need to become very confident, very early, about who they are selling to.

That confidence does not need to be based on perfect certainty, but it does need to be grounded in serious market understanding. Without that, the company is exposed at every level.

The product may be built with the wrong assumptions.

The regulatory plan may support claims that do not matter enough in the market. The commercialization strategy may arrive too late or fail to match the buying behavior of the intended customer.

In MedTech, these are not small corrections. They can alter the entire trajectory of the company.

That is why go-to-market strategy determines more than many founders initially think. It is not a downstream commercial layer added after the technical and regulatory work is done. It is one of the earliest strategic choices the company makes, and it influences nearly everything that follows.

Clinical Studies Takes Longer Than Founders Think

One of the most common mistakes in early-stage MedTech is underestimating timelines around clinical development.

Founders often assume that once they are technically ready to move into studies, progress will depend mainly on their own speed and execution.

In reality, that is rarely the case.

Clinical development is shaped by external approvals, third-party timelines, and institutional processes that the company does not fully control.

Even getting approval to run a study can take far longer than inexperienced teams expect. Ethics committees and other approval bodies introduce delays that are not necessarily predictable from the inside.

This means clinical planning cannot be treated as a simple extension of technical development. It has to be approached with the understanding that major parts of the timeline will be dictated by outside actors.

That matters because timing is never only an operational issue. It has direct implications for hiring, runway, fundraising, and company credibility.

Clinical work should not be the first real test

Another important point is that clinical development should not become the company’s first true testing ground.

Not every medtech company needs animal studies, and the development path varies depending on the type of product.

Some companies can do a great deal of their early work through bench testing, laboratory environments, and controlled preclinical validation.

But the broader principle remains the same: the stronger companies tend to arrive at clinical studies only after they have already done meaningful scientific and technical work.

There is a clear preference for companies that have built a strong base of laboratory testing before moving into first-in-human work.

When founders can show that they have already explored the solution thoroughly in controlled settings, it signals a more disciplined development process.

It suggests that the company has taken the time to think through methods, technical assumptions, and performance expectations before exposing the product to the complexity of clinical reality.

If a company uses clinical testing as its first serious attempt to understand whether the product works, the risk of poor outcomes rises significantly.

Study design and financing strategy are inseparable

Clinical development becomes a capital planning exercise as well.

A company cannot build a credible fundraising strategy if it does not know what its studies are likely to cost, how long they are likely to take, and what the major cost drivers will be.

The more serious founders understand that study planning and financial planning have to evolve together. If the company is preparing to raise capital, it needs to be able to explain not only why a clinical study matters, but how much it will cost to run it and what that capital will actually de-risk.

That level of specificity becomes especially important in MedTech because investors are not just evaluating the science. They are evaluating whether the company has a realistic development plan. A vague understanding of clinical costs can weaken the whole investment case.

Early conversations with experienced partners improve realism

This is also why early engagement with experienced CROs and other specialized partners can be so valuable.

Organizations that work regularly on MedTech studies often have a practical view of timelines, cost ranges, and operational requirements. They can help founders move from abstract assumptions to more realistic planning.

These conversations matter well before the company is fully ready to launch a study.

By speaking with experienced operators early, founders can get a better sense of what it will actually take to move from one stage to the next. That improves both internal planning and external communication with investors.

In that sense, clinical development is not just a technical progression toward validation.

It is one of the main places where strategic planning, operational realism, and capital discipline come together. The companies that understand this early are usually much better positioned to build an investable path forward.

Fundraising in Medtech Should Follow De-Risking Logic

One of the reasons many MedTech companies struggle with fundraising is that they present themselves through a framework that does not fit the actual nature of the business.

Founders often anchor their company to conventional venture labels such as pre-seed, seed, bridge, or Series A, as if those categories naturally explain what stage the company has reached. In MedTech, that picture is often misleading.

The development path of a company does not unfold in the same way as a software startup or a more conventional venture-backed business.

The company moves through stages that are defined less by generic financing labels and more by technical, clinical, regulatory, and commercial milestones. Trying to force that progression into the standard language of venture rounds can create confusion rather than clarity.

That confusion matters because investors are trying to understand where risk still sits in the company. If the company describes itself in language that says little about what has actually been achieved, the fundraising story becomes weaker from the outset.

A MedTech round should be defined by what it is meant to de-risk

A more useful way to think about fundraising is to define each round according to the specific company risks it is intended to remove.

In practice, MedTech companies move through a sequence that might begin with prototyping and early technical validation, then move into early clinical validation, regulatory progress, market validation, commercialization, and eventually scalability.

When a round is framed in those terms, the logic becomes easier to understand.

Instead of saying the company is raising a seed round, it may be more meaningful to say that it is raising a regulatory round, or a round to complete early clinical validation.

That immediately gives investors a clearer picture of what capital is being used for and what the company expects to achieve before the next financing event.

The point is not just to rename rounds for the sake of presentation. It is to communicate the true structure of company development.

Every financing round is, in effect, meant to de-risk a particular set of unknowns. The more clearly that is articulated, the more coherent the company appears.

The right amount of capital depends on the next real milestone

This way of thinking also changes how founders should approach round size. In theory, every company would prefer to raise a very large amount of capital at once and then move forward without constant fundraising pressure.

But in reality, investors commit capital based on what has already been de-risked and what they believe the next tranche of funding can credibly accomplish.

That means round size should not be determined by generic expectations about what a company at a certain “stage” is supposed to raise.

It should be determined by what the business needs in order to reach the next meaningful milestone.

If the company needs a certain amount of capital to complete a defined regulatory process, or to reach a specific validation point, then the amount raised should be tied directly to that objective.

There is no automatic rule that each round must simply be larger than the previous one.

Often they do increase over time, because the company is taking on larger and more expensive de-risking steps as it progresses. But the increase only makes sense when it reflects the actual cost of moving the company forward in a credible way.

Medtech funding paths are usually more segmented than founders expect

Another important implication is that medtech companies often need more financing rounds than founders initially assume.

It is not unusual for a company to go through four or five rounds before it reaches the point where sales begin to scale meaningfully. Some will go through even more.

That should not be seen as a weakness in itself. It reflects the reality that the path to market in MedTech is staged, capital intensive, and dependent on multiple layers of validation.

Early rounds may be relatively small, focused on very specific technical or clinical milestones. Later rounds tend to become larger as the company moves toward regulatory approval, commercialization, and scale.

Seen through that lens, fundraising becomes much more logical. The company is not trying to fit itself into a venture template borrowed from another sector. It is building a financing strategy that reflects the actual sequence of development in MedTech.

Investors respond better when the story matches the company

Ultimately, the fundraising narrative becomes stronger when it mirrors the real progression of the business.

VCs respond more clearly to a story in which each round is tied to a defined purpose, each use of capital corresponds to a real de-risking step, and each milestone makes the next stage of the company more credible.

For MedTech founders, this is an important shift in mindset. The goal is not to sound like a standard venture-backed startup. The goal is to present the company in a way that reflects what development process actually looks like.

When the financing story is aligned with that reality, fundraising becomes easier to understand and, in many cases, easier to support.

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Scalable Medtech Companies Are Built Around Early Commercial Signals

Validation is not only about revenue

One of the most important things founders can understand early is that commercial validation does not begin only once revenue appears.

In MedTech, some of the strongest early signals come before sales. What matters is whether the market is already showing credible signs that it would be willing to adopt the product if the company succeeds in delivering it.

That can take the form of written interest from buyers, confirmation from payers that they would consider purchasing the product under defined conditions, or early feedback around acceptable pricing ranges.

These signals matter because they show that the company is not operating in a vacuum. They indicate that someone on the market side is already willing to engage seriously with the proposition.

From an investor’s perspective, this kind of traction is meaningful even before commercial launch.

It does not replace revenue, but it helps validate that a payer exists, that the buying logic may be real, and that the company is building toward a market with actual demand behind it.

Reimbursement can define whether the business is viable

Reimbursement strategy is another area where early thinking matters far more than many founders initially assume. In some cases, it can shape the viability of the entire business model.

If there is no reimbursement code available for the product, the company may face a serious structural problem.

Creating a new code can take years, and not every startup has the time or capital required to absorb that delay.

This means reimbursement cannot be treated as something to solve once the product is ready. It has to be part of the company’s early commercial logic.

Distribution only works if the economics support it

Distribution strategy is equally important, but there is no universal answer. Whether distributors make sense depends heavily on the type of product, the margins available, and the markets the company is trying to enter.

If a distributor is taking 20 to 30 percent of revenue, the economics of the product need to be strong enough to support that structure.

A high-margin product may be well suited to that model. A lower-margin one may not.

That is why distribution cannot be evaluated in isolation. It has to be considered in relation to product economics and overall commercial design.

At the same time, early discussions with distributors can still be valuable even before launch.

They may not generate immediate revenue, but they can serve as another form of market validation.

They can indicate whether there is appetite in the channel, whether external sales teams can imagine supporting the product, and whether the company’s vision makes sense in the context of real market infrastructure.

Geography and manufacturing shape the commercial model

The right distribution strategy also depends on geography.

In the United States, some companies may favor direct sales, while others may prefer distributors.

In Europe, the picture often becomes more complex because of language barriers, currency differences, and local regulatory realities that can make distributor involvement more important.

Manufacturing adds another layer to the equation.

Some products can be manufactured flexibly in multiple places and therefore support a broader range of distribution setups.

Others depend on very specific production capabilities in a limited number of locations. In those cases, the structure of manufacturing can directly affect how the company distributes the product and what kind of commercial arrangements are realistic.

This is why there is no single formula that applies across MedTech. The right model depends on the specific product, its margins, its manufacturing constraints, and the markets being targeted.

Scalable companies are built by testing commercial reality early

Taken together, these elements point to a broader principle. Scalable MedTech companies are not built by focusing only on technology in the early stages and leaving commercial design for later. They are built by testing commercial reality as early as possible.

That means looking for credible buyer signals, understanding reimbursement constraints, exploring distribution dynamics, and thinking through manufacturing and sales as interconnected parts of the same business.

Not every early conversation will lead to immediate traction, and not every hypothesis will hold. But from an investor’s perspective, these efforts are important because they show that the company is not only developing a product. It is learning how that product can actually become a business.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we explore one of the most intriguing compute questions beyond the current AI stack: what happens when the dominant architecture is no longer enough for systems that need to learn continuously, respond in real time, and operate under severe power constraints?

I sat down with Peter Olcott, Principal at First Spark Ventures, to unpack how a VC investor thinks about neuromorphic computing, why biology remains such a powerful reference point for the future of intelligence, and where founders might find the first investable openings in a field that sits between scientific ambition and practical system design.

Key takeaways from the episode:

🧠 Neuromorphic Computing Starts from a Different Model of Intelligence
Rather than optimizing the current transformer paradigm, neuromorphic computing revisits a deeper question: why biological intelligence can generalize, learn continuously, and operate in real time with extraordinary power efficiency.

🤖 Embodied AI May Be the First Real Commercial Wedge
Humanoid robots, drones, and autonomous machines need low-latency, low-power compute that can operate safely in the physical world. That makes embodied AI one of the clearest early markets for neuromorphic architectures.

⚛️ This Is Not Quantum Computing
Quantum and neuromorphic computing may both sit outside the mainstream stack, but they solve very different problems. One is designed for highly centralized, exceptionally hard scientific computation; the other aims to bring intelligence into distributed, real-world systems.

🧩 The Biggest Bottleneck Is Training, Not Just Hardware
The core challenge is not simply building brain-inspired chips. It is figuring out how to train these dynamic, spike-based systems in a stable and scalable way—something the field still has not fully solved.

📈 Investability Depends on a Staged, Full-Stack Strategy
The strongest companies in this space are unlikely to be isolated chip plays. They will need to own the broader system—training, inference, and silicon—and enter through emerging markets where traditional architectures remain structurally weak.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Neuromorphic Computing Begins with a Different Model of Intelligence

Neuromorphic computing is often presented as a futuristic departure from mainstream computing, but the idea is older than the current AI wave.

In many ways, it reaches back to the earliest stages of AI, when researchers were already trying to understand intelligence by recreating some of its underlying principles.

The field has always been tied to biology, not as a metaphor, but as a source of architectural insight.

Even some of the earliest neural-network concepts were rooted in biological observation. The original intuition was that if intelligence in nature emerged from networks of neurons, then perhaps artificial systems could be designed along similar lines.

What changed over time was not the disappearance of that idea, but the direction taken by modern AI. As large language models and transformer-based systems became dominant, AI in silicon moved further away from biological inspiration.

That divergence is part of what has brought neuromorphic computing back into focus. It is not trying to incrementally improve the dominant architecture.

It is trying to revisit a more fundamental question:

Biological intelligence and digital intelligence are not the same thing

That question matters because the contrast is not subtle. Today’s AI systems can appear remarkably capable. They can generate language, solve complex tasks, and often give the impression of reasoning.

But they do so through an implementation that is very different from the one found in the brain.

Biological intelligence combines several properties that current digital systems still struggle to achieve at the same time.

• It operates in real time, with very low latency.

• It reasons, but it also acts fluidly in the world.

• It can drive a car, play sports, react to unexpected changes, and move continuously between fast perception and deeper thought without switching architectures.

That continuity remains difficult for current AI systems, which are powerful but still constrained by latency and by the amount of compute required to operate in real time.

Another difference is learning itself.

Human beings do not stop learning once a model is trained.

Learning is continuous, cumulative, and inseparable from lived experience. Biological systems adapt from birth to death.

By contrast, most of today’s AI systems are effectively fixed once trained. They may be updated, fine-tuned, or retrained, but they do not learn in the ordinary course of use the way a person does.

That gap is not just philosophical. It points to a practical limitation in how current systems are deployed in the real world, especially in environments that require continuous local adaptation rather than periodic centralized improvement.

Generalization and power efficiency

Generalization is another defining distinction. Biological intelligence is extraordinarily efficient at transferring knowledge across contexts.

For instance, a human can learn to drive with a relatively modest amount of experience.

That ability to adapt from limited exposure stands in sharp contrast to the enormous quantity of training data and compute required by today’s AI systems to approach comparable real-world performance.

The issue is not that current models are ineffective. It is that they often require orders of magnitude more pretraining than people do to reach a narrower form of competence.

Neuromorphic computing is, in part, an attempt to understand why that is.

Power consumption brings the contrast into even sharper focus.

The brain operates on roughly 20 watts. That figure becomes striking when compared with the computational resources required to sustain state-of-the-art AI systems.

Even highly capable digital models consume dramatically more energy while still delivering only a subset of what biological intelligence can do.

A human brain does not just produce language. It coordinates movement, perception, memory, sensory integration, and real-time interaction all at once.

So when neuromorphic computing looks to biology, it is doing so because biology appears to solve an intelligence problem with a level of efficiency, adaptability, and responsiveness that current digital architectures have not matched.

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Why Building the Brain at Scale Isn’t Simple

Once the field is framed in those terms, the difficulty becomes obvious. Neuromorphic computing is compelling precisely because biological intelligence appears to do things current architectures do not.

But the moment one tries to reproduce that capability in hardware, the scale and complexity of the brain become impossible to ignore.

The challenge is not just to build something inspired by neurons. It is to understand what must actually be replicated for brain-like behavior to emerge at scale.

And, simple but not obvious, copying the brain at a small scale does not necessarily produce the outcomes people imagine.

There have been serious efforts to build silicon neurons directly, using analog circuits to mimic the electrical behavior of biological neurons as closely as possible.

The intuition is understandable: if one can recreate the neuron faithfully and wire enough of them together, perhaps intelligence will follow.

But that view runs into a brutal scaling problem.

The human brain contains roughly 86 billion neurons, and the greater challenge is not just the number of neurons themselves, but the density and diversity of their connections.

On average, each neuron connects to thousands of others.

The result is an immense web of connectivity whose complexity is difficult even to represent, let alone reproduce in silicon.

Connectivity is the real source of complexity

That connectivity problem is central.

Neurons do not simply talk to their immediate neighbors in a neat local pattern. They connect across regions, across functions, and across long structural pathways.

The brain is not a flat network. It is an extraordinarily dense three-dimensional system.

Even storing the information that describes which neurons connect to which others, along with the strength of those connections, requires terabytes of information.

This means that the challenge is not limited to computational logic. It immediately becomes a problem of architecture, density, and physical organization.

Where Neuromorphic Computing Could Win First

If the long-term ambition is to build systems that bring intelligence closer to the flexibility, responsiveness, and efficiency of biological systems, the most credible early wins are unlikely to come from trying to beat existing architectures on their own terms.

They are more likely to come from environments where today’s architectures are structurally disadvantaged—where power, latency, and local adaptation matter more than raw centralized compute.

That is why the most promising early use cases sit in the physical world.

Neuromorphic computing is particularly well matched to situations where intelligence must operate directly inside machines, in real time, under strict energy constraints.

These are not edge cases. They are the conditions that define a growing class of important systems.

Embodied AI as the most natural entry point

One of the clearest application domains is embodied AI.

As intelligence moves out of the cloud and into robots, autonomous systems, and physical devices, the requirements begin to change.

A text model can tolerate latency in ways a robot cannot.

If a prompt response arrives half a second late, it may be frustrating. If the same delay occurs in a machine interacting with the real world, the result can be unsafe.

That difference is crucial.

Physical systems do not just need intelligence. They need intelligence that can act continuously, respond instantly, and do so without carrying the energy burden of a data center.

This is where neuromorphic computing starts to look like a potentially well-suited architecture for the next generation of machine intelligence.

Humanoid robotics makes this especially visible.

These systems need compact, power-efficient, low-latency compute to coordinate sensing, balance, motion, and interaction in real time.

Yet the compute requirements are high, and the power budget is limited.

Adding more traditional compute means more energy consumption, more battery weight, and often more mechanical stiffness. In other words, the computational architecture directly affects the physical design and safety profile of the robot.

From that perspective, the most compelling target is not simply “AI for robots,” but the brain of the robot itself.

A neuromorphic system could become the core compute layer that allows a humanoid platform to behave more dynamically and more safely under real-world constraints.

That does not mean everything must become neuromorphic at once. A more realistic view is that the first successful systems may be hybrid.

Different parts of a machine can be optimized for different computational tasks, and neuromorphic chips may enter first where the performance gap is most acute.

In robotics, that could mean low-level control.

Actuator control, balance, inverse kinematics, and similar functions require fast, efficient feedback loops.

These are domains where latency matters immediately and where there is value in specialized compute that can operate at much higher update rates, potentially closer to kilohertz than to the low hertz rates still common in many embedded systems.

Running physical systems at very low update rates creates obvious risks. In fast-moving machines, a delayed response is not a minor inconvenience; it is a core limitation.

Neuromorphic chips could also play an important role in sensor processing.

Vision, audio, and other incoming streams may be preprocessed locally in ways that reduce latency and improve responsiveness before being passed into more traditional architectures.

In that sense, the opportunity is not only to build a monolithic neuromorphic machine, but to distribute intelligence across a device in a way that mirrors biology more closely.

The human nervous system already works through specialized regions and layered processing. A machine architecture that adopts a similar logic may be more achievable in the near term than a single all-encompassing neuromorphic platform.

Another reason embodied systems are such a strong fit is that they expose one of the weaknesses of current AI deployment models.

Today’s systems typically depend on centralized training pipelines.

Data is collected, labeled, sent back, retrained, and then redistributed as model updates.

That process can work at scale, but it is poorly suited to environments where each instance accumulates useful local knowledge that may never make it back into a global model in a meaningful way.

Neuromorphic computing becomes interesting here because continual learning is not a secondary feature. It is part of the promise.

A machine operating in the field could adapt from repeated exposure to its own environment rather than waiting for centralized retraining.

A local system can accumulate narrow but valuable knowledge tied to its own operating context—repeated routes, recurring obstacles, or environment-specific signals—that may never be meaningfully propagated back through a centralized training loop.

In a conventional system, it is far from guaranteed that such experience would ever be translated into a model update that materially improves that individual unit’s behavior.

This is what makes edge autonomy such a natural application category.

Drones, delivery robots, and other untethered autonomous systems all operate under pressure from power limits, intermittent connectivity, and the need for immediate response.

A different path for human-computer interaction

Beyond robotics and autonomy, another compelling frontier is human-computer interaction.

This is a very different use case, but it draws on the same underlying advantages. If the goal is to create digital systems that interact more naturally with people, the challenge is not only to generate text or images. It is to sustain fluid, emotionally responsive, low-latency interaction across multiple modalities at once.

A more human form of interface would need to process tone, timing, expression, visual cues, and conversational dynamics in real time.

It would need to react not just accurately, but naturally. That kind of interaction is difficult to achieve efficiently with today’s dominant architectures, especially when the system must generate and interpret audio, video, and affective signals simultaneously while adapting through use.

This is where neuromorphic computing opens a different possibility.

The architecture is not attractive merely because it might be cheaper or smaller. It is attractive because it may be better aligned with the kind of continuous, context-sensitive, real-time processing that natural interaction requires.

If that capability becomes technically viable, it could evolve into a universal interface layer embedded across devices—from phones and vehicles to household systems and everyday consumer products.

That market may not fully exist today, but the logic is already visible.

Whenever a capability becomes broadly useful and repeatedly needed across many devices, there is a strong incentive to create custom compute optimized for that task.

Natural human-computer interaction has the characteristics of precisely that kind of opportunity.

So the near-term promise of neuromorphic computing is not that it will replace everything current AI does. It is that it may solve classes of problems current AI handles inefficiently.

The strongest early markets are those where intelligence must leave the cloud, live inside physical systems, adapt locally, and operate under real-time constraints.

In those settings, neuromorphic computing does not look like a fringe alternative. It looks like a candidate architecture for making AI truly native to the real world.

Neuromorphic Computing vs. Quantum Computing

As interest in alternative computing architectures grows, how should we think about neuromorphic computing in relation to quantum computing?

Both sit outside the mainstream digital stack, both carry a strong sense of future potential, and both are often framed as breakthrough technologies rather than incremental improvements.

However, the two paradigms solve fundamentally different problems, operate under radically different assumptions, and address different bottlenecks across the computing landscape.

Two architectures built for different kinds of intelligence

Quantum computing is rooted in quantum effects at the most fundamental level of physics. To make those effects usable, the system has to be kept under extremely controlled conditions.

That usually means highly sensitive hardware, deep isolation from the surrounding environment, and specialized infrastructure that is inherently difficult to distribute. In practical terms, quantum computers tend to be centralized systems.

They are designed to tackle extraordinarily hard computational problems that would be intractable for conventional machines.

That makes them powerful in a very specific way.

A quantum computer is most compelling when the task itself is exceptional: discovering new materials, solving unusually complex scientific problems, or unlocking categories of computation that cannot be addressed efficiently with classical methods.

These are important use cases, but they are not the kinds of tasks most people or most everyday machines perform continuously.

Neuromorphic computing sits at the opposite end of that spectrum.

Its ambition is not to isolate compute from the world in order to solve impossibly hard abstract problems. It is to bring intelligence more effectively into the world itself.

It is concerned with real-time behavior, low-power operation, responsiveness, and adaptation in physical systems.

Where quantum computing is about solving rare but extremely demanding problems, neuromorphic computing is about making distributed systems more naturally intelligent in everyday operation.

Centralized breakthrough compute versus distributed physical-world intelligence

That distinction matters because it shapes the entire economic and technical logic of each field.

Quantum computing is naturally aligned with centralized scientific infrastructure. A company, laboratory, or institution may use it to solve a breakthrough problem once, and that result can then support years of downstream value creation.

The computer itself does not need to be embedded everywhere. Its value comes from solving a narrow class of extremely important problems at the frontier of science and engineering.

Neuromorphic computing points toward the reverse model.

If it succeeds, its impact would come not from a few centralized machines, but from widespread deployment across devices and systems that need intelligence at the edge.

It is meant for robots, autonomous machines, sensor-rich environments, and human-facing systems that must operate continuously under power and latency constraints.

In that sense, it is less about concentrated computational supremacy and more about making small things (or, one day, big things) smart.

The Real Bottleneck is Not Just Hardware

For all the attention neuromorphic computing receives as a hardware story, the deepest constraint may sit elsewhere.

The instinctive assumption is that the challenge is mainly about fabricating better chips, denser architectures, or more biologically inspired circuits.

Those are real issues, but they are not necessarily the first one that matters. The more fundamental obstacle is training.

The hardest question is how to train the system at all

This is the point where enthusiasm often gives way to scientific reality. If neuromorphic architectures are modeled on biological systems, then the question is not only how to build spiking neurons or brain-like connectivity in silicon. It is how to train such a system in a stable and scalable way.

That remains unresolved.

The core difficulty is that biological neural systems do not behave like the architectures that underpin mainstream AI today.

They are highly dynamic, oscillatory, and spike-based. That makes them appealing from an intellectual standpoint, but much harder to train in practice.

When attempts are made to extract information, assign weights, and produce stable learning behavior, the result can be unstable networks that collapse rather than converge.

This is not a secondary technical detail. It is one of the central scientific bottlenecks that determines whether a neuromorphic architecture can evolve from an interesting demonstration into a viable computing platform.

A startup may have beautiful hardware, and compelling biological inspiration, but without a convincing answer to the training problem, the rest of the proposition remains incomplete.

From an investment standpoint, this becomes a gating issue. The architecture must show not only that it can run, but that it can be trained reliably.

The training issue becomes even more significant because the broader AI ecosystem has already built massive infrastructure around a very different paradigm.

Today’s leading models are trained through centralized data pipelines, large research teams, benchmark-driven workflows, heavy annotation, and tooling stacks refined over years of transformer development.

That ecosystem cannot simply be lifted and transferred onto neuromorphic systems.

This matters because every time the architecture changes fundamentally, the surrounding infrastructure must change with it.

The challenge is no longer limited to the chip.

It extends to data handling, training methods, software tooling, research practices, and the people capable of operating the system.

A neuromorphic company is not just introducing a new processor. It is proposing a different training regime, and with that comes a change in organizational capability as well.

That is one reason current neuromorphic systems cannot simply take existing large models, download them, and map them onto a new chip.

The accumulated value embedded in today’s LLMs and related architectures does not transfer in any straightforward way. The field is not inheriting the current AI stack. It is, in a meaningful sense, starting over.

Memory density is the second structural constraint

Even if the training problem is eventually addressed, another structural issue remains: memory.

Neuromorphic systems need to store parameters, weights, and state. But once the architecture combines memory and compute in a tightly integrated way, a new trade-off emerges.

The density of that memory becomes critically important.

This is where conventional digital architectures still retain a powerful advantage. Modern transformer systems rely on specialized memory technologies that are highly optimized for storing enormous amounts of information in compact spaces and moving it quickly.

High-bandwidth memory is not just an accessory to those systems. It is one of the reasons they scale.

Neuromorphic architectures often lack that same density. If the neuron-like components and their associated weights occupy too much space, then the system runs into scaling limits very quickly.

The aspiration may be to create a compute-and-memory architecture that is more brain-like, but if the density is too low, the result is a platform that remains confined to relatively simple applications.

That does not make it useless. In fact, there may be many commercially relevant simpler applications. But it does impose limits on the claim that these systems are a near-term route to very advanced general intelligence.

What this means for startup founders and teams

These bottlenecks also reshape what technical credibility looks like in the field. A founder does not need to manufacture full silicon at the outset to prove whether a concept is viable.

Many of the most important questions can and should be explored in simulation before large amounts of capital are spent on hardware.

That changes how a serious neuromorphic company should be judged. The strongest teams are likely to be those that have been working on these foundational problems for a long time and understand where the real points of failure are.

This also means that team composition is unusually important.

The challenge is not simply to gather chip designers. It is to bring together people who understand the interaction between architecture, learning dynamics, memory constraints, and system-level behavior.

The field is difficult precisely because no single layer can be treated in isolation.

So the bottleneck in neuromorphic computing is not just that hardware is hard. It is that the architecture, the training method, the memory model, and the software stack all have to evolve together.

What an Investable Company Looks Like

Once the technical ambition is brought back down to company-building reality, the picture becomes clearer.

Neuromorphic computing may be a frontier field, but that does not automatically make every company in the space investable.

The path to a fundable business depends on whether the team can translate a scientific and architectural breakthrough into a staged development plan, a credible product scope, and a go-to-market strategy that does not ask the impossible on day one.

The timeline is long, but still within venture logic

This is not a near-term software cycle. It belongs in Deep Tech venture timelines.

A realistic horizon is closer to 7 to 10 years than to a short product sprint, but that still leaves it within the bounds of what can be financed if the roadmap is constructed properly.

What makes it fundable is not the idea that everything must be solved upfront.

A credible neuromorphic company would need to advance in stages, beginning with narrower problems where the advantages of the architecture can already matter.

Full-stack matters more than isolated brilliance

Moreover, neuromorphic computing is not just a chip problem. It is a chip, a software stack, an inference system, and a training system that all have to work together.

A company that only brings one piece of that puzzle, without control over the rest, risks becoming disconnected from the actual value creation.

That is why the more compelling company model is vertically integrated.

The business has to deliver the full solution, not just silicon. It needs to show how the model is trained, how inference is performed, and how the hardware and software interact in a usable end-to-end system.

Otherwise, the customer is left with an impressive technical component but no practical way to deploy it.

In a field where the surrounding infrastructure does not yet exist in mature form, integration becomes a strategic necessity. It is not enough to claim that someone else will build the tooling, the model pipeline, or the deployment layer later.

The company has to behave as though those layers are part of its own responsibility.

The best markets may be the ones that do not exist yet

The other defining characteristic of an investable neuromorphic company is market choice. The instinct to attack large, established markets can be misleading here.

Trying to enter the data center and compete directly with incumbent architectures on their own ground is a poor fit for a new technology that still has major scientific and technical hurdles to overcome.

A more coherent strategy is to go after new or underserved environments where traditional architectures are structurally weak.

The right markets are likely to be those where centralized GPU compute cannot go easily, where energy is constrained, where latency is mission-critical, and where intelligence must live inside the system rather than be reached through the cloud.

That may sound niche at first, but it does not have to remain small.

New compute paradigms often become viable by solving problems existing systems handle badly, not by trying to beat them everywhere at once.

In neuromorphic computing, the opportunity may come from entering spaces that are still emerging rather than displacing incumbents in mature ones.

So an investable company in this field is the one that pairs a real technical breakthrough with a staged roadmap, builds the full stack rather than a detached component, and chooses markets where the architecture’s strengths are genuinely native to the problem.

That is the version of the story that can move from scientific fascination to venture-backed company building.

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Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we turn to one of the most practical—and most misunderstood—questions in Deep Tech: What does it take to build a successful go-to-market strategy for advanced materials startups?

I sat down with Michael Bartholomeusz, advanced materials industry expert, serial entrepreneur, and current CEO of NOVI, to unpack how founders should approach their go-to-market strategy, how to distinguish real commercial traction from surface-level interest, and how to build a company that can scale without breaking under the weight of early commitments, poor margins, or a weak exit story.

Key takeaways from the episode:

🧭 A Materials Company Has to Start With Market Reality
A strong technology is not the same as a strong business. The real starting point is understanding the market, the competitive landscape, the buying criteria, and the value drivers that determine whether a material can actually win adoption.

🎯 Early Progress Depends on Real Adopters, Not Curious Experimenters
Not every interested customer is an early adopter. Some want to test and explore, but only a few are positioned to evaluate, adopt, and scale a solution in a way that creates real commercial momentum.

⏳ Customer Discovery Is Really About Timing, Ramp, and Roadmap Fit
The key questions are not just whether a customer likes the solution, but whether it fits their roadmap, when it could be adopted, and how quickly it would ramp once approved. Those answers shape capacity planning, fundraising needs, and execution risk.

📄 Early Agreements Should Help You Learn, Not Trap the Business
The most effective way to work with early customers is often through phased agreements—development, pilot, then volume—rather than premature commitments around pricing, exclusivity, or service levels that the company may not yet be ready to support.

⚙️ In Advanced Materials, Margin Discipline Is a Survival Issue
Overly optimistic financial models, poor yields, excessive customization, and weak operational discipline can put the company in a hole very early. In this category, margins are not something to fix later; they have to be protected from the start.

🛣️ Exit Strategy Works Best as a North Star With Flexible Off-Ramps
A credible IPO path can provide long-term direction, but founders also need to stay open to strategic acquisition and private equity opportunities as the company matures. The key is to match the story—and the investor—to the real stage of the business.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Great Science Isn’t Enough

In advanced materials, the first mistake is often not technical. It is strategic.

A founder can move too quickly from invention to company-building without spending enough time understanding the market that the technology is supposed to enter. The science may be strong. The material may be novel. The performance claims may be real. But none of that, by itself, is enough to support a strong business plan.

The starting point is more practical than many founders expect.

Before building a go-to-market strategy, before writing a business plan, and often before deciding where to focus commercialization, there has to be a serious effort to understand the environment around the technology.

That means understanding the market, the competitive landscape, the buying criteria, and the value drivers that actually matter to customers.

Doing the Homework Before Writing the Roadmap

A go-to-market plan is not something that should be assembled from assumptions. It has to be built more like a map.

The company needs to know where it wants to go, where it should not go, where the obstacles are, and where the real openings might exist. That mapping process starts with disciplined homework.

For an advanced materials founder, that means identifying which markets the technology could serve, who the existing competitors are, what alternatives customers already use, and what factors actually drive a buying decision.

It is not enough to say that a material performs better in the lab. The more important question is whether that performance advantage matters in a commercial setting, and whether it matters enough to displace what is already in use.

This is where many early plans become too abstract.

A founder may correctly see multiple possible applications and assume that this flexibility is a strength. In practice, it can quickly become a source of confusion.

Without a clear understanding of where the strongest value sits, the roadmap becomes too broad, the messaging becomes too vague, and the company starts trying to serve too many markets at once. What looks like optionality can become drift.

The discipline, then, is to build the roadmap around commercial reality rather than technical possibility alone. That requires a close look at what the market rewards, what customers care about most, and where a new material can create a meaningful advantage over incumbent solutions.

Using Customer Conversations to Pressure-Test the Thesis

There is no substitute for the voice of the customer. However thoughtful the initial roadmap may be, it remains only a hypothesis until it has been tested through direct conversations with the people who would actually use, evaluate, or adopt the solution.

That process should not be approached as a search for validation.

One of the most useful strategies in early customer discovery is to avoid speaking with customers merely to confirm what the founder already wants to believe. The more valuable conversations are often the ones that expose the holes in the plan, the missing assumptions, or the reasons adoption may be harder than expected. Those are the conversations that give the roadmap credibility.

This is especially important in advanced materials, where the path from technical promise to commercial integration is rarely straightforward.

For instance:

• A customer may appreciate the innovation and still have no practical pathway to adopt it.

• A company may like the performance profile and still decide that the switching cost is too high.

• A technically superior solution may still fail if it does not align with purchasing logic, qualification cycles, or product development timing.

These emerge only through repeated, direct market contact.

For early-stage teams with limited resources, this does not necessarily require a large formal process. Industry conferences and sector events can be one of the most efficient ways to accelerate customer discovery. They create opportunities to meet potential buyers, compare perspectives across companies, and gather insight quickly.

But the quality of those interactions matters. The goal is not simply to collect interest. The goal is to understand what customers value, what they worry about, how they make decisions, and where the startup’s assumptions do not hold.

When done properly, this work does more than refine a go-to-market strategy; it helps articulate the company’s vision more clearly.

As a result, the roadmap becomes more focused, the value proposition becomes sharper, and the founders are in a much stronger position to decide where to commit time, capital, and energy first.

Early Progress Depends on Finding Real Adopters

One of the most consequential distinctions in the early life of an advanced materials company is the difference between people who are curious about a technology and people who are prepared to help bring it into the market.

At first glance, both groups can look encouraging. Both may take meetings. Both may ask questions. Both may want samples or technical discussions. But they do not create the same kind of traction, and confusing one for the other can waste critical time.

In the early stages, a startup is usually resource-constrained. It does not have the capacity to pursue every lead, customize for every inquiry, or follow every apparent opportunity. That is why progress depends less on broad interest than on disciplined selection.

The entrepreneur has to identify the one or two customers who are not merely willing to experiment with the technology, but who have the intent, appetite, and internal pathway to evaluate it seriously and take it forward.

The Difference Between a Tinkerer and an Early Adopter

This is where a great deal of early-stage confusion appears. A startup may receive attention from companies that genuinely find the material interesting, but that interest alone is not enough. Some customers simply want to explore. They want to test the technology, learn from it, and perhaps imagine future applications.

But they do not necessarily have the urgency, commitment, or internal alignment required to move from experimentation into adoption.

That kind of engagement can feel like momentum, but it often leads nowhere. In practice, these customers behave more like tinkerers than adopters. They are willing to play with the technology, but they are not prepared to scale it. They may not have the budget, the process, the roadmap alignment, or the strategic reason to take the next step.

For a startup, especially in advanced materials, this matters enormously because serving those customers still consumes time, product, engineering attention, and management focus.

The more valuable customer is the early adopter reference customer.

This is the customer willing to evaluate the solution with a real intent to bring it into use. There may only be one or two such customers at the beginning, and that is normal. In fact, there are usually not many. Most companies prefer to follow rather than lead. They want someone else to take the first risk. They want proof that the solution works in a real commercial environment before they commit themselves.

That is precisely why early adopters matter so much.

They do more than generate initial revenue or technical validation. They create the reference point that reduces perceived risk for everyone else. In markets with a strong herd mentality, customers often look to one another before acting. Once one credible adopter moves, others become more willing to engage. Without that first reference point, the company can remain stuck in a loop of interest without adoption.

Why Focus Beats Chasing Every Opportunity

The temptation in the early stage is to talk to everyone and pursue every signal of demand. This is understandable. Founders are trying to maximize opportunity, and advanced materials often have multiple plausible applications across industries and use cases.

But without discipline, that breadth becomes a liability.

Every new conversation can start to look like a new market. Every request can begin to feel like a path to revenue. Every potential application can appear too promising to ignore. The result is that the company starts reacting to every shiny object. Instead of building commercial traction, it fragments its efforts across too many directions.

The better approach is narrower and more deliberate.

Once the company begins to see where the strongest early-adopter potential lies, it needs to stay with that path. That does not mean becoming rigid. It means avoiding the instinct to convert every expression of interest into a commercial priority. In practice, the company needs to place a small number of focused bets and follow them long enough to understand whether they can become a real adoption.

This kind of focus is not only about efficiency. It is also about learning quality.

When the company works closely with one or two serious early adopters, it gains much deeper insight into how the product is evaluated, what features matter most, what objections arise, and what conditions would support scale.

That learning is far more valuable than a larger number of superficial interactions spread across many uncertain opportunities.

At the same time, early-stage go-to-market work still requires flexibility.

As customer conversations develop, the company may need to adjust the value proposition, refine the product, or even change which aspect of the technology it emphasizes most strongly. That kind of pivoting is often a sign of progress, not inconsistency.

What matters is that the company remains anchored in real customer evidence rather than pulled in every direction by weak signals.

The startups that succeed are often not the ones that began with the perfect commercial thesis. They are the ones that stayed close enough to the market to recognize where the best fit was emerging and flexible enough to move toward it.

Time, Ramp, and Roadmap Fit

In advanced materials, customer discovery is not only about whether someone likes the technology. It is about whether the technology fits into a real product roadmap, on a real timeline, inside an organization that has its own adoption cycles and internal constraints.

That is why early commercial conversations have to go beyond technical enthusiasm.

The critical issue is not simply whether the solution performs well. It is whether the customer can realistically absorb it.

This changes the nature of the questions a founder needs to ask.

The most useful conversations are not only about performance specifications or potential use cases. They are about timing, organizational intent, and the path from evaluation to scale.

A material can solve a meaningful problem and still fail to become a business if it arrives outside the customer’s planning cycle or demands a scale-up pace the company cannot support.

Asking Whether the Solution Belongs on the Customer’s Roadmap

One of the most important questions is also one of the simplest: how important is this solution to the customer’s roadmap? The answer to that question reveals far more than general interest ever could.

• If the solution is already part of the customer’s roadmap, the conversation is immediately more concrete. There is a defined need, some internal recognition of the problem, and at least a possibility that the organization has allocated attention and resources to solving it.

• If the solution is not yet on the roadmap, then the founder needs to understand whether it could become part of it, and under what conditions.

That distinction matters because in large companies, adoption rarely happens in an improvised way. Even when a technology is compelling, it usually has to fit within existing product plans, qualification processes, budget cycles, and internal decision structures.

A founder who misreads curiosity as roadmap relevance can spend months pushing a technology into an organization that has no realistic way to adopt it in the near term.

The more productive approach is to use customer conversations to uncover where the solution sits in relation to strategic priorities.

• Is it solving a current problem or a future one?

• Is there urgency behind it or only exploratory interest?

• Is there a clear internal sponsor, or is the conversation still at the edge of the organization?

These are the questions that determine whether the opportunity is real enough to build around.

Understanding Adoption Cycles Before Building Capacity

Once roadmap relevance is clearer, the next issue is adoption timing.

A customer may genuinely want the solution and still not be able to adopt it for several years.

Product introduction cycles can be long, especially in sectors where new materials, coatings, or components must be qualified well in advance of launch.

A material intended for integration into a mobile phone, for example, may need to enter the development pipeline several generations before it ever appears in a commercial product. That means the startup could be waiting four or five years before seeing meaningful volume.

This is why it is essential to ask not only whether the customer wants the solution, but what their cycle for adopting new technologies actually looks like.

• How far ahead do they make product decisions?

• Do they introduce new materials across the full portfolio at once, or do they start with a smaller set of products and ramp gradually?

• What does their internal ramp behavior typically look like once a technology is approved?

These questions determine the shape of the company’s operational plan.

• If the customer ramps slowly, the startup has more time to build capability.

• If the customer moves quickly once adoption begins, then capacity, equipment, staffing, and capital may need to be in place much earlier.

Without understanding that timeline, a founder cannot make responsible decisions about production scale.

Just as importantly, the company cannot afford to fail during the ramp. Missing a customer ramp is deeply damaging.

If the startup reaches the moment of commercial adoption and cannot supply what is needed, it risks doing lasting harm to the customer relationship and to its own credibility. That is why the discovery process has to include a realistic view of how demand would unfold, not just whether demand might eventually exist.

Matching Capital Strategy to the Customer’s Timing

Once timeframes and ramp behavior come into focus, the capital strategy becomes much clearer. In practice, customer timing, company capability, and access to capital have to be thought about together.

A founder cannot decide what kind of agreement to pursue, what production commitments to make, or how aggressively to scale without understanding what financial resources will be available along the way.

A bootstrapped company will make different choices from a venture-backed one. A company with grant support or university backing may have a different runway from one financed only by the founding team.

In each case, the commercial plan needs to reflect what the company can actually fund.

That is why customer discovery is not separate from a financing strategy.

The two are intertwined. Once the founder understands when a customer could realistically adopt, how quickly demand might ramp, and what production capability would be required, it becomes possible to judge whether the existing capital base is enough.

If it is not, then the company needs time to raise additional money before the ramp arrives.

Seen this way, customer discovery is not just a market exercise. It is a planning discipline that ties together sales, operations, and fundraising. It helps the company understand not only whether a market exists, but whether it can meet that market on the right timeline and with the right level of readiness.

The process itself also depends on relationships.

These insights rarely come from a single meeting. They emerge through repeated conversations and through the trust that builds when people move beyond formal discussion and speak more directly.

In that sense, customer discovery in advanced materials is about building the relationships that allow the real constraints, priorities, and timelines to surface.

Early Commercial Agreements

In advanced materials, the first commercial agreement is rarely just a sales document. It is usually part validation tool, part financing mechanism, and part test of whether the company can move from technical promise into disciplined execution.

That is why early agreements have to be approached with care.

A startup may be eager to secure a recognizable customer, show traction to investors, or create the appearance of commercial momentum. But if the structure of the agreement is wrong, the contract can do more harm than good.

The central risk is straightforward.

In the early stages, the company is still learning. It is learning what the product really costs, what yields it can sustain, how quickly it can produce, how much customization a customer will require, and what kind of operational burden comes with delivery.

If the company commits too much too early, it can lock itself into terms that assume a maturity it does not yet have. What looks like progress can become a constraint that weakens the business before scale has even begun.

Why Phased Agreements Work Better Than Premature Commitments

A more resilient approach is to think of commercial engagement in phases. Rather than trying to secure a single large, fully defined agreement from the start, the company can structure the relationship so that each step creates learning, and each subsequent step is negotiated from a stronger position.

This matters because the first interaction with a customer is usually exploratory in both directions.

The customer wants to see whether the technology works in its environment. The startup wants to understand what the customer actually needs, what it will take to serve that need, and whether there is a realistic path to broader adoption.

At that stage, both sides are still discovering the shape of the opportunity. A phased structure acknowledges that reality.

It also gives the startup a way to create commercial movement without overcommitting.

Instead of pretending that everything is already known, the agreement becomes a sequence: first evaluation, then pilot, then volume.

At each stage, the company gains more information about performance, cost, operational demands, and the seriousness of the customer. That learning improves the basis for the next negotiation.

In this sense, phased agreements are a way of preserving strategic flexibility while the company is still building knowledge.

For a startup with limited capital and limited room for error, that flexibility is often what keeps an early customer relationship from becoming a company-level risk.

Moving From Development to Pilot to Volume Production

The logic of this structure can be very practical. The first phase is typically a development agreement.

At this point, the company provides a small amount of material or product for testing, and the customer contributes some level of non-recurring engineering support to make that possible.

The amount may be negotiated, and the customer may push back, but the principle is clear: if the customer wants the startup to do work to get the product into its hands, there should be some support for that effort.

That first phase is not meant to resolve the whole commercial relationship. It is meant to establish enough technical and operational evidence to justify a second phase.

If the product performs successfully, the relationship can move into a pilot production agreement.

This next step introduces small-volume production and begins to test what it takes to move from development into something closer to repeatable supply.

Again, the terms should reflect what is known at that moment, not what is still uncertain. The pilot phase helps both sides understand volumes, expectations, pricing logic, and operational realities at a more meaningful level.

Then, if that phase is successful, the company and the customer can move toward a volume purchase agreement.

The important discipline is that each agreement should point toward the next one, but without locking the company into binding commitments too early. The startup should describe the intended progression and the contemplated conditions under which the next phase would happen, but leave room for those conditions to be negotiated when the time comes.

That way, every stage improves the information set for the next stage, and the company is not negotiating future obligations based on today’s incomplete assumptions.

Fixed Pricing, Exclusivity Traps, and Early Service Burdens

This staged approach becomes especially important because some of the most dangerous terms for an early-stage materials company are the ones that seem attractive in the moment.

A customer may ask for long-term fixed pricing, some form of exclusivity, or strong service and delivery guarantees. For a founder eager to secure the deal, these requests can feel like the price of entry. But they can become serious liabilities.

Fixed pricing is one of the clearest examples. In the early stage, the company often does not yet fully understand its yields, true production costs, or the economics of scaling.

Agreeing to long-term pricing under those conditions can lock the business into a margin structure that is unsustainable. If costs turn out to be higher than expected or yields lag, the company may find that the more it delivers, the more it harms itself.

Exclusivity raises a similar issue.

A customer may want privileged access to the technology, but if the startup agrees too broadly, it can close off the rest of the market before proving the business. If any exclusivity is granted, it has to come with meaningful volume commitments behind it.

Otherwise, the company is giving up strategic freedom without receiving the commercial support needed to justify that trade.

The same caution applies to customization and service obligations.

Early customers often want the product tailored to their needs, and that can be reasonable. But customization consumes time, engineering effort, and money.

A startup should not quietly absorb all of that cost in the hope that scale will solve the problem later. If a customer wants a customized solution, there should be a clear understanding that the customer contributes to the effort required to build it.

Service level agreements also need to be handled carefully.

Delivery guarantees, capacity guarantees, and aggressive performance commitments can sound like signs of seriousness, but for a young company, they can become traps for the business. If the startup is still stabilizing operations, any guarantee made too early can create obligations it is not yet equipped to meet.

The broader principle is simple. Early agreements should help the company learn, prove value, and deepen a real customer relationship. They should not force the business into a structure designed for a mature supplier before the company has the economics, capacity, and operating discipline to support it.

In advanced materials, the companies that survive early commercial traction are the ones that structured those first agreements in a way that let them keep learning without breaking the company.

Margin Discipline Is a Survival Strategy

In advanced materials, margin is not something that can be deferred until the company is larger. It has to be built into the business from the beginning.

A software startup may be able to absorb early inefficiencies and recover later through scale. A materials company usually does not have that luxury. It has to produce, qualify, deliver, and improve under real physical and operational constraints.

That means weak economics at the start can become a structural problem very quickly.

Avoiding Overly Optimistic Financial Models

One of the first places where margin problems begin is in the financial model. It is common for early-stage materials companies to build plans that are simply too optimistic about how the business will perform.

Yields are overestimated. Costs are underestimated. Engineering effort is treated too lightly. The number of people, the amount of time, and the degree of manufacturing difficulty required to reach stable production are often assumed to be lower than they will be in reality.

This may make the model look more attractive on paper, but it creates a dangerous starting point.

The company begins with economics that are not conservative enough, and that distortion carries forward into fundraising, pricing, hiring, and production planning.

When reality arrives, the company discovers that it is spending more than expected, producing less efficiently than planned, and operating without enough room to absorb the difference.

That is why early financial planning has to be grounded in caution rather than optimism. The goal is not to build a model that looks exciting. It is to build one that gives the company a realistic chance to get airborne.

If the business starts from assumptions that are too generous, it can end up trying to scale without the margin base needed to support that growth. In practical terms, it becomes like trying to take off without enough fuel.

Yield, Rework, and Operational Execution as Core Economics

Once the company begins producing, operational discipline becomes central. In materials businesses, yield is often the defining economic variable. It is not a technical side issue. It is the difference between a business that can improve its economics over time and one that continues to bleed.

For that reason, early management attention has to stay close to the fundamentals of execution.

Yield improvement, reduction of rework, control of process variation, and the application of basic manufacturing discipline are not secondary concerns to be handled later. They are among the main levers that determine whether the company can hold its margin and eventually expand it.

These are not always the topics that attract founders or investors.

They can seem operationally dull compared with product vision or market expansion. But in a materials company, they are often what determines whether the business becomes viable.

A team can have a strong technology and real customer interest, and still fail because the operating system underneath the product is too weak to support repeatable economics.

There is also a pricing implication here.

In the early phase, the company should be careful not to adopt the mindset that it must buy customers through underpricing. If it enters the market at economics that do not reflect conservative cost assumptions, it may never recover the lost ground.

A more durable approach is to understand costs realistically, stay focused on operational execution, and establish a pricing basis that allows the company, at a minimum, to avoid digging itself deeper as it grows.

Then, as yields improve and execution becomes stronger, the business has a path to reclaiming margin rather than chasing it.

Leveraging the Supply Chain to Scale

At the same time, scaling does not always require building every capability internally. One of the more powerful ways to protect margin and reduce capital burden is to use the supply chain intelligently.

A startup does not have to be “every animal on the farm”. There may already be partners in the manufacturing ecosystem with the capacity, process discipline, and quality systems needed to produce at scale.

Working with those partners may mean giving away some margin in the short term, but it can also buy something more valuable: speed, reproducibility, lower capital requirements, and access to manufacturing excellence that would take years to build internally.

For a young company, that trade can be highly rational. Instead of trying to construct every layer of the production system from scratch, it can leverage the capabilities of others to accelerate adoption and strengthen execution.

Exit Strategy Should Be a Flexible Journey With a Clear North Star

Different investors want different outcomes, and the company itself may evolve in ways that make one path more likely than another.

That is why the most useful way to think about exit is not as a fixed destination, but as a journey with a clear direction and several possible off-ramps.

The company needs a credible long-term vision that is ambitious enough to matter, while remaining flexible enough to accommodate strategic acquisition, private equity interest, or earlier forms of monetization if those become the right outcome.

The point is not to predict the exact ending from day one. It is to build a story that is both aspirational and believable.

Building the Company Toward a Credible IPO Path

A strong way to frame that vision is to start with an IPO as the North Star.

Not because every materials company will reach the public markets, but because thinking in those terms forces clarity about what scale the business would need to achieve and how long that journey might realistically take.

As Michael noted, a materials company in the United States would typically want to IPO above roughly $500 million.

Below that threshold, there is often less analyst coverage, limited liquidity, and greater exposure to market volatility, all of which make life difficult for a smaller public company. So if the company is going to position IPO as part of its long-term trajectory, it needs to build the plan around a scale that clears that threshold in a meaningful way.

For a materials business, that typically implies a long journey rather than a short one.

These companies do not usually trade at the kinds of revenue multiples that allow a business with modest sales to command a very large valuation.

Revenue multiples in materials are often closer to 2x to 3x, though they can vary by sector. In general, the path to a $500 million outcome is tied to building substantial revenue.

That means a company may need something like $150 million to $250 million in revenue before an IPO starts to look credible. That kind of scale does not appear overnight. In many cases, it is more like a ten-year journey than a two-year one.

Framing the business that way can be valuable for both founders and investors, but it has to be done carefully. That means the financial plan has to feel grounded, especially in the early years.

The first 24 months should be very conservative, because that is where credibility is built. The later years can be more aspirational, but they still need to remain connected to a logic that the market can believe. The company has to show not only that the outcome is attractive, but that the road toward it is coherent.

Staying Open to Strategic Acquirers and Private Equity Along the Way

Even with an IPO as the directional target, the company should remain open to other exit paths that may emerge along the way. In practice, many advanced materials businesses will encounter acquisition opportunities before they ever reach the scale required for a public listing.

A strategic buyer may decide that it is easier to buy the company than to recreate the technology internally.

A large industrial player may see the coating, the material platform, or the manufacturing capability as important enough to bring in-house. That opportunity could appear relatively early, and it may come at a valuation below what a long-term public-market path might eventually promise.

At that point, both founders and investors need clarity about what is enough.

• What level of return justifies the years of work?

• What outcome is large enough to make an earlier exit rational rather than disappointing?

There is also another category that becomes increasingly relevant once the company begins generating meaningful earnings: private equity.

As the business matures and starts producing a compelling EBITDA, it can become attractive not only to strategics but also to firms that are building larger platforms through acquisitions.

That does not mean the journey is complete, but it does mean the business enters a different part of the capital ecosystem, where it may be folded into a broader materials roll-up or become part of a larger industrial consolidation story.

Seen this way, the exit strategy works best when it combines a long-term public-market vision with an active awareness of these intermediate possibilities.

That is what makes the approach flexible. The company can build toward scale while still recognizing that a well-timed acquisition or a private equity transaction may become the right answer depending on traction, market conditions, and investor expectations.

Matching Investors to Company Stage

This flexibility also matters because the investor base itself changes as the company grows.

One of the more important disciplines for founders is understanding that not every investor belongs in every stage of the journey.

A great deal of wasted time comes from speaking to investors whose mandate does not match the company’s stage of development.

At the beginning, especially for a company coming out of a university or a laboratory environment, the most appropriate capital often comes from high-net-worth individuals, experienced entrepreneurs, or very early seed investors.

These are people who can write relatively small checks and who understand that they are backing a founder journey before the business has much commercial proof. In some cases, universities, government grants, or institutionally supported early funds can also play that role.

Once the company begins to show initial customer traction and can answer basic commercial questions more convincingly—who needs the product, who is buying it, and what they are paying for it—it becomes more legible to later-stage venture investors.

That is where the path toward Series A and Series B capital begins.

Depending on how the company performs, that may be enough to reach a self-sustaining position, or it may require further rounds. Some companies need to go all the way to a Series D and raise very substantial amounts of capital before they become profitable. Others raise far less and still achieve a successful exit.

There is no single template.

That variability is exactly why founders need to understand the broader ecosystem rather than fit one canonical path.

Market conditions matter. Macroeconomic cycles matter. The company’s timing matters.

A business that emerged in 2008 faced different capital conditions from those growing in 2015. The trajectory is never shaped by the company alone.

What does remain constant is the need to target the right investor at the right moment and to articulate a vision broad enough to accommodate different return expectations.

Some investors want a billion-dollar outcome and are prepared to wait.

Others are seeking a smaller but faster multiple. The company has to build an ambitious but credible story that can speak to both.

That is why an IPO North Star, combined with realistic exit ramps, is such a useful framing. It gives the business direction without pretending that only one ending is possible.

In the end, the exit strategy is not just about the final transaction. It is about how the company is built, how capital is raised, how expectations are managed, and how optionality is preserved over time.

For an advanced materials founder, that makes the exit strategy less of a closing chapter and more of an organizing principle from the beginning.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we step into one of the most provocative infrastructure questions of the AI era: what happens when the energy demands of computation outgrow the Earth?

I sat down with Jasper Wigley, Investment Associate at Systemiq Capital, to unpack how an investor thinks about the shifting shape of compute demand, why space is being considered as an energy and constraint workaround, and where startups can position themselves in the stack to build traction—and capture the upside.

Key takeaways from the episode:

🚀 Compute Demand Has Split Into Two Economies
Training is optimized for power and performance, while inference is increasingly optimized for cost per token and energy efficiency. As models move from text into multimodal, physical-world workloads, the compute requirement expands again.

🛰️ Edge Compute Is Real Today—Orbital Data Centers Are the Bigger Bet
Processing data on satellites to avoid downlink bottlenecks is already happening. “AWS in orbit” is a different category: larger, more ambitious, and still in an early testing phase.

☀️ The Core Space Argument Is Energy
Sun-synchronous solar can offer continuous baseload generation, and higher harvesting efficiency without atmospheric filtering. Space also avoids terrestrial permitting and local opposition, while adding a potential sovereignty angle for compute and data.

🧊 Cooling Sounds Easy in Space—Until You Size the Radiators
In a vacuum, heat rejection relies on radiation, which can require enormous radiator surface area. Thermal design becomes a gating constraint once you connect it to the economics of launch mass.

🧱 Investability Comes From Stage-Gating and Early Customers
A credible plan de-risks in steps—lab proof, then subscale flight—while anchoring early commercial pull through specific LOIs and a small set of concrete use cases, often starting with defense, satellite operators, and eventually hyperscalers exploring off-grid compute.

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BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

How Compute Demand Is Shifting, and Why It Keeps Accelerating

The clearest way to understand why the idea of orbit-based computing is even being discussed is to start with what’s happening on Earth. Compute demand didn’t simply “grow.” It changed shape. And once the workload changes, everything downstream—power, infrastructure, and the investment logic—changes with it.

Training vs. Inference as Two Different Compute Economies

Before the 2020s, a lot of the growth story in data centers was driven by cloud computing. That era rewarded an infrastructure stack built to optimize for a particular kind of workload and a particular set of constraints.

The dominant jobs were not the same as the high-performance compute workloads now defining the AI moment, and the system design decisions followed accordingly.

Then the “ChatGPT moment” arrived, and large language models moved from a research theme into something broadly present in society. That shift didn’t just increase demand; it introduced a new split in how to think about compute.

1. One track is training. Training is fundamentally optimized for power and performance—getting the most capability out of the system, pushing model quality forward, and competing on who can produce the strongest results. Cost and energy still matter, but in the race among the biggest hyperscale players, training is often treated as the performance frontier first.

2. The second track is inference, where the center of gravity moves. Inference is increasingly optimized around the cost per token and energy efficiency. Once a model exists, the question becomes how cheaply and reliably it can be deployed in the real world, at scale, under constraints that are far more operational than aspirational. The economics are different, and the engineering priorities follow.

This distinction matters because it makes “compute demand” a misleading single number. Demand is not only rising; it is bifurcating into different economic regimes that reward different architectures and different infrastructure choices.

From Text to the Physical World: Multimodal Models and New Workloads

There is also a deeper reason the trajectory feels so steep: the workloads are expanding beyond text.

Text is one thing. The leap comes when AI has to understand and interact with the physical world—when it needs to work with data across many modalities.

Vision is an obvious example, but it extends well beyond that: sound, temperature, and hundreds of other types of signals that show up in real systems. As the model’s relationship with the physical world becomes more direct, the complexity increases dramatically.

This is part of why attention is shifting toward foundation models in areas like biology and vision, and toward multimodal systems that can integrate multiple streams of physical-world data. The point is not that these are fashionable categories. The point is that they’re harder.

And because they’re harder, they require more compute.

It also helps explain the renewed energy around robotics and adjacent domains that once felt perpetually “almost ready.” When the cost of compute comes down and the performance of the infrastructure improves, it becomes possible to train and run workloads that were previously unrealistic.

A set of applications that used to be constrained by compute economics begins to open up—not because the ambition changed, but because the underlying capability finally caught up.

From this perspective, the compute story is not a temporary surge. It is a structural shift in what AI is being asked to do, and therefore in the scale and type of compute required to do it.

Two Distinct Space Pathways

Any serious discussion of “data centers in space” gets confusing quickly if everything is treated as one category. In practice, there are two different tracks that share a similar setting—orbit—but operate with different assumptions, different levels of ambition, and very different timelines.

Space Edge Computing

The first track is space edge computing, and it is already happening.

The idea is straightforward: process data directly on satellites or on space stations instead of sending everything back down to Earth. You can think of it as putting a kind of “brain” on the spacecraft.

That brain performs an initial stage of analysis in orbit, then only the important parts of the data—or the insights extracted from it—are transmitted back to Earth. This matters because bandwidth is not infinite, downlink capacity is constrained, and raw data volumes from space systems can be overwhelming.

So, if the spacecraft can triage its own data—identify what is relevant, compress what matters, discard what doesn’t—then the entire system becomes more efficient.

• Technically, this tends to involve custom chips, custom ASICs, and hardware strategies designed to operate reliably in the space environment.

• Commercially, it is still a relatively small market compared to terrestrial compute, but it is real, growing, and already producing companies that are doing well.

Orbital Data Centers (ODCs)

The second track is orbital data centers, which is a different proposition entirely.

This is the more ambitious idea people often mean when they talk about “AWS in space” or “Azure in orbit.” Instead of edge nodes designed to support satellite operations, the concept is to launch bespoke platforms capable of delivering full-stack compute in orbit—something that resembles a true data center capability rather than an embedded onboard processor.

That distinction matters because the maturity is not the same.

Orbital data centers are, at best, in a testing phase. They are not yet a widely established reality. The system implications are bigger, the engineering and economics are harder, and the path to scale is still being worked out.

The demand narrative behind this track is also different. It’s tied to the broader AI compute hunger and the fact that, today, compute is supply constrained. That constraint is visible everywhere: data centers racing to come online, multi-year grid queues, unconventional power solutions being pulled back into service.

In that environment, the question becomes whether orbit offers a path to unlock energy and compute capacity that is difficult to access on Earth.

So it’s important to keep the two tracks separate.

• Space edge computing is about processing space-generated data more intelligently and efficiently, right now.

• Orbital data centers are about building a new compute frontier in orbit, driven by the escalating demand for large-scale AI workloads—and that frontier is still emerging.

Why Space is on the Table: Energy, Constraints, and Control

The underlying argument for putting compute in orbit isn’t that space is glamorous.

It’s that, when you strip the concept down to first principles, the bottleneck on Earth is increasingly energy—and space changes the energy equation in ways that are hard to replicate in any other environment.

Solar in Orbit: 24/7 Baseload and Higher Harvesting Efficiency

A useful starting point is simply the sun, and the difference between harnessing solar energy in space versus on Earth.

In orbit, a platform can operate in a sun-synchronous configuration that effectively tracks the sun continuously. That means solar generation can be available twenty-four hours a day, seven days a week. There’s no night cycle in the same sense, no daily drop-off that forces the system to compensate with grid dependence or oversized storage.

The second factor is efficiency.

On Earth, even in strong solar regions, sunlight is filtered through the atmosphere. That filtering matters. A panel on Earth is not receiving the same energy input as a panel in space, because the atmosphere changes what reaches the surface.

Once you remove that filtering effect, the same solar technology can, in principle, harvest far more effectively.

The implication is not only higher raw efficiency, but a different marginal cost profile.

If the system is designed so that energy harvesting is both continuous and more efficient, then the marginal cost of energy can look meaningfully lower. That doesn’t eliminate the question of upfront costs—those are real and heavy—but it does explain why the energy story is the center of the case.

Escaping Terrestrial Friction

The second set of reasons has less to do with physics and more to do with constraints.

On Earth, new data center capacity tends to collide with jurisdiction. You have land issues, local opposition, permitting regimes, and practical limitations on where power can be generated and delivered.

As data centers become higher density, the infrastructure footprint expands, and it’s not surprising that communities push back—especially when the resource demands are tangible, like water or local power capacity.

In space, those constraints change. There is no local opposition in the same way, no permitting path to navigate, and no land acquisition. The friction that comes from operating inside terrestrial jurisdictions largely disappears.

There is also a sovereignty angle embedded in this.

A spacecraft operating in orbit, outside a traditional jurisdictional footprint, can be framed as a more sovereign environment for storing or processing data. That perspective may become more relevant as the geopolitical importance of compute grows.

Cooling as a Potential Advantage When Thermodynamics Cooperate

Cooling is often the point that sounds counterintuitive at first, and it needs to be framed carefully.

On Earth, cooling can represent a major part of the energy bill for high-density AI data centers. You’re paying not only to run the compute, but to keep the system within operating limits, often using water and climate-dependent approaches.

In space, the environment is very different.

You are sitting in a vacuum, looking out into a universe that is effectively near absolute zero. In that sense, the thermodynamics are on your side. The theoretical promise is that you are not constrained by climate, and you don’t need to move vast amounts of water through the system to manage heat.

That doesn’t mean cooling is “easy.”

The physics are hard in practical engineering terms, and solving them at a meaningful scale becomes one of the defining challenges. But the point is that the basic thermodynamic context is not working against you in the way it does on Earth.

Why Space Over Other Harsh Environments Is Still an Open Question

It’s also important not to treat this as a settled conclusion.

People have explored other harsh environments as a way to manage cooling and infrastructure constraints—underwater deployments, mines, and polar regions.

But those approaches don’t remove the same bottlenecks.

Even if cooling is more efficient underwater, you still have to generate energy, connect to grid infrastructure, and operate inside permitting and jurisdictional regimes. Generating power in extreme terrestrial environments—like the Arctic—is not trivial, and you still inherit the terrestrial constraints that space side-steps.

So even if some arguments overlap—cooling being one of them—the full rationale for space is not simply “space cools better.” The deeper logic comes back to energy availability, continuous generation, and escaping the terrestrial constraints that increasingly slow down the deployment of new compute capacity.

The Binding Constraints: Thermal Design, Radiation, and Launch Economics

The vision of serious compute capacity in orbit tends to trigger an intuitive reaction: the hardware works on Earth, so why wouldn’t it work in space?

The reality is that the bottlenecks are not about whether compute can function at all. They’re about whether the system can function at a meaningful scale, for meaningful workloads, within constraints that are brutally physical and brutally economic.

Cooling Dense Compute Requires Radiators at Uncomfortable Scale

The thermal problem is the first place to start, even though it can sound counterintuitive after hearing that space is “cold.”

Cooling a chip in space is not inherently impossible.

The issue is what it takes to do it effectively when the compute density becomes serious. On Earth, heat is moved away from chips using air and water—whether that’s traditional air cooling, immersion, or various approaches to on-chip cooling.

The common feature is that you bring something to the chip that can absorb heat and transport it away.

In orbit, you don’t have that. There is no air and no water moving heat for you. In a vacuum, you are largely left with radiation as the mechanism to get heat out of the system. And once you accept that, the engineering consequence is simple: you need radiating surface area, and a lot of it.

At the scale implied by high-performance AI compute, that surface area becomes enormous.

The cooling solution begins to look like a giant radiator infrastructure—potentially measured in kilometers squared. It’s not a showstopper in a pure physics sense. It is a hard design space, but it is still a design space.

The problem is what those radiators imply once you connect them to the launch and deployment reality. Size becomes mass. Mass becomes cost. And cost becomes the gating factor.

Radiation Hardening Shifts Cost and Performance Trade-Offs

The second constraint sits inside the hardware itself: radiation.

There are already examples of compute operating in orbit, and a major part of what makes that possible is how chips are packaged and protected. Radiation tolerance is not a “nice-to-have.” It determines whether the system can operate reliably in the environment at all.

But radiation hardening isn’t free.

The more you engineer hardware to survive the space environment, the more expensive the compute module becomes—and, in practice, the more you risk sacrificing performance relative to the best commercial terrestrial parts.

That creates a tension: the most powerful, most cost-effective compute available on Earth is not designed for orbit, and the compute designed for orbit carries a different cost and capability profile.

So the constraint is not only technical. It’s economic. The moment you change the hardware requirements, you change the unit economics of deploying compute at scale in space.

Access to Orbit Is Improving, but the Mass Problem Still Dominates

The third constraint is the one everyone reaches for quickly: launch.

Launch costs have fallen dramatically over the last few decades—from many thousands of dollars per kilogram down to the low hundreds in some contexts—and they are still coming down.

And yet, even under optimistic assumptions, launch remains expensive when the system you’re trying to deploy is physically huge.

If the orbital data center requires multiple kilometers squared of solar arrays and radiator surface area, then even “cheap” launch becomes expensive in absolute terms. The math is unforgiving: if mass is large, cost is large.

That is why these constraints are tightly linked.

Thermal management pushes you toward massive radiator structures. Power generation pushes you toward large solar arrays and storage. Radiation pushes you toward more specialized—and often more expensive—compute packaging.

And then launch economics forces all of it into a single question: can the system be designed so that the mass per unit of useful compute becomes viable?

In the end, none of these issues are abstract. They are the binding constraints that decide whether “compute in space” remains a compelling story, or becomes a practical system that can be built, funded, and scaled.

Where a Founder Can Play: The Component Stack and Its Bottlenecks

Once the constraints are clear, the opportunity landscape comes into focus. The most investable entry points are not necessarily the most ambitious “build the whole orbital cloud” visions. They are the places in the stack where a technical breakthrough can materially change the feasibility curve—where solving a bottleneck reduces mass, cost, or risk in a way that the entire system depends on.

Thermal Management

Thermal management stands out because it is both fundamental and leverageable.

The cooling challenge in space isn’t about inventing cooling from scratch. It’s about the scale of radiator infrastructure required to reject heat through radiation alone.

That scale drives mass. Mass drives launch cost. And launch cost drives whether the entire concept can ever be economical.

So if a founder can improve the thermal layer—specifically by reducing radiator mass per kilowatt of heat rejection—that becomes a platform-level contribution. It makes everything above it more viable.

There are several ways this could be approached.

1. One route is materials: high-emissivity coatings that more effectively convert heat into radiated energy in the vacuum of space.

2. Another is structure: deployable radiator architectures that can fold tightly for launch and then unfold into massive surface areas once in orbit—ultralight panels that behave more like infrastructure than like traditional spacecraft components.

3. There is also the internal physics of the radiator system itself: heat pumps that remain effective under radiation constraints, thermal storage materials that can buffer heat and dump it in sync with varying load, and other engineering strategies that improve the overall heat rejection performance without ballooning mass.

The logic is simple. Any company that meaningfully improves the mass-per-kilowatt requirement changes the economics of what can be launched, and that position can translate either into direct commercial pull or into strategic value as a target for integration.

Power Systems, Storage, and the Nuclear “Backup” Conversation

Power is the other foundational layer.

If the appeal of orbit is continuous solar generation and improved harvesting efficiency, then the supporting infrastructure—solar arrays, energy storage, power electronics—becomes critical.

The system is only as good as its ability to deliver stable, reliable power to compute modules over long periods.

This is where design choices matter: deployable solar arrays large enough to support meaningful workloads, batteries that can handle storage and load dynamics, and architectures that are compatible with how orbital platforms are actually assembled and maintained.

And in parallel, there is a more speculative but important thread: nuclear. The idea isn’t to replace solar as the primary driver, but to consider what a backup reactor capability could enable.

NASA has already funded fission studies focused on the Moon, and the broader concept of nuclear in space continues to surface because it could offer a different reliability and baseload profile.

Whether that becomes practical or not, it’s part of the emerging conversation around how orbit-based infrastructure could be powered robustly.

Optical Links, Orbital Assembly, and Maintenance as Enablers

Even if power and thermal problems are solved, an orbital data center is not useful if it can’t communicate effectively.

High-bandwidth links between satellites and Earth become an enabling requirement.

As the ecosystem shifts from radio frequency communication toward optical communications, there are problems to solve—but also clear opportunity.

Optical downlink is not just a technical feature; it’s the bridge that turns orbit-based compute into something that can integrate with terrestrial networks and deliver value at scale.

Then there is the question of how these systems are built and sustained.

If the platform requires massive radiator and power structures, the assembly method becomes central.

That’s where modularity and robotic assembly enter the picture: launching components separately, assembling them in orbit, and designing for repair, replacement, and maintenance.

A founder can play in that layer too—orbital assembly robotics, servicing capabilities, and even “maintenance as a service” models that support long-lived infrastructure rather than one-off spacecraft deployments.

Taken together, these are the practical wedges into a market that isn’t fully formed yet. The system-level vision may be new, but the component challenges are concrete.

And for founders coming from university-driven technology push—whether they’re working on coatings, batteries, structures, optics, or robotics—the critical step is to frame their work against the bottlenecks that determine whether orbital compute can ever graduate from a demo into an industry.

Turning a Future Vision Into an Investable Plan Today

A future market can be a powerful story, but it can’t be the entire story. In the early stages, fundraising is always partly about vision. The mistake is to treat vision as a substitute for evidence.

A claim like “we’ll build a data center in space, and someone will find a use case later” doesn’t clear the bar. The investable version of the narrative starts by forcing specificity: what is the path to capability, and who is prepared to pay for it along the way?

Platform vs. Prime Model

One way to ground the business plan is to acknowledge that there are different models for how an orbital data center ecosystem could be built.

1. At one end is the traditional space prime approach. A single company acts as the prime contractor: it designs the platform, integrates subsystems, launches the system, and sells directly to customers. It sources components from suppliers—chips, solar arrays, subsystems—but it owns the system architecture and the customer relationship. The organizing principle is integration.

2. At the other end is a cloud ecosystem model. The parallel here is to providers like AWS, which do not manufacture every chip or server themselves. Instead, they rely on a broad ecosystem—chip companies, OEMs, power vendors—working against shared standards. The organizing principle is orchestration across a stack built by many specialized contributors.

For a founder, the choice of model isn’t a branding exercise. It determines what you need to control, what you need to buy, and what kind of company you’re actually building. It also shapes how you stage development and what milestones you can credibly hit before you need serious capital.

Early Commercial Proof

The harder part in Deep Tech, especially when the market is still emerging, is commercial proof. Even on Earth, getting firm commitments before a product exists is difficult. In orbit, it can feel even more abstract.

The discipline here is to start with customers anyway. As early as possible, the goal is to line up a small number of concrete use cases—three to five is a useful target—where someone can credibly say what they will buy if you deliver a specific capability by a specific date.

In the earliest stage, this doesn’t have to be a paid contract.

Letters of intent can do real work if they are written commitments tied to clear milestones: if you demonstrate X capability by Y date, we will buy Z amount of capacity.

That structure forces the company to translate ambition into a deliverable, and it gives investors a basis for believing demand can exist beyond curiosity.

From the conversation, three customer categories stand out as plausible early anchors.

1. The first is defense and government. These buyers care about sovereign and resilient compute, and there is a game theory logic to why countries might want to be first to do this at scale. If a team can navigate the nuances of selling into that world, it can be a meaningful route to early traction.

2. The second category is satellite constellation operators. These players are already drowning in raw data. Across thermal, SAR, optical, and soon LIDAR, satellite systems generate enormous volumes—petabytes of data. If machine learning and increasingly multimodal models are applied to that data, the value of processing more in orbit becomes clearer. Early prototypes can be tied directly to this reality by demonstrating onboard or nearby-in-orbit processing for satellites in LEO or GEO, with different constellation strategies influencing what “in-orbit compute” looks like in practice.

3. The third category is hyperscalers, and AI companies thinking about sustainable or off-grid compute. Engagement here may start as exploratory, but the signal is that the theme is entering mainstream discussion. Public comments from major technology leaders suggest that orbit-based compute is becoming a topic that serious organizations are at least evaluating.

An investable startup plan, then, is not a promise that the market will appear in five to ten years. It’s a staged strategy: de-risk the technical system through progressively harder demonstrations, and de-risk the commercial system by anchoring early commitments to specific capabilities and timelines. That combination is what turns a futuristic category into something an investor can underwrite.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we move into advanced materials and specialty chemicals, and look at the question that quietly determines whether a scientific breakthrough becomes a VC-backable company: what business model actually captures the value.

I sat down with Tony Sun, Director of Corporate Venture Capital at GC Ventures, to unpack how an investor inside a chemical incumbent thinks about market pull, pricing power, and the strategic trade-offs founders face as they move from lab-scale innovation to a scalable commercial reality.

Key takeaways from the episode:

🧪 Why Specialty Chemicals Feel Fundamentally Different from Commodities
Commodity chemicals remain cyclical and price-competitive, often with stable revenue but squeezed margins. Specialty chemicals, when truly differentiated, tend to be priced by value—shifting the TAM and the investability story.

🏗️ Business Models Start with a First-Principles Choice
The real fork is organic versus inorganic growth. Venture capital only makes sense when speed to market is critical, and the market is large enough to justify a risk-and-return profile built for rapid scaling.

🔗 Owning Product, Manufacturing, and the Customer Relationship
In long chemical supply chains, value leaks through intermediaries. The most defensible path often means owning the final product and the end-customer relationship—and, in many cases, controlling manufacturing.

⚖️ BOO vs Asset-Light Is → Risk vs Control
Building, owning, and operating concentrates capex and operational complexity, but tightens control over margin, quality, and customers. Asset-light models can work, especially for formulation-led plays, but only if the core know-how is truly protected.

📈 Scale-Up Has Two Distinct Meanings—and the Proof Points Change
Some businesses need 10× manufacturing steps toward commercial scale. Others can replicate small reactors regionally, where the real validation isn’t manufacturing scale-up at all, but customer pipeline repeatability and the ability to scale sales execution.

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BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Framing Advanced Materials From a Venture Capital Perspective

Advanced materials can be commercialized through many paths—licensing, ingredient supply, toll manufacturing, formulation plays, or fully integrated product businesses.

But not every path is equally investable from a venture-capital perspective.

In many cases, the economics are shaped less by the novelty of the science and more by who owns the customer relationship, where margin pools sit in the value chain, and how much capital and execution risk is required to reach scale.

That is why business model choice matters: it determines whether a materials breakthrough becomes a defensible, scalable company—or a great technology trapped inside someone else’s P&L.

Commodity vs Specialty Chemicals

A key distinction that influences these choices is whether a product behaves like a commodity chemical or a specialty chemical.

This is not a naming convention. It’s a difference in how pricing works and what kind of strategic control a company can realistically build over time.

The venture focus naturally narrows toward materials that are high value, tightly specified to an application, and closely tied to performance outcomes—areas where differentiation is more defensible, and margins can prove more resilient.

In that context, materials become the asset to access higher-value end markets.

Moreover, for founders, this framing matters because it connects a materials innovation story to a clearer market pull and a more concrete path to adoption.

For investors, it helps explain why certain advanced materials opportunities can justify serious attention even when the broader commodity chemical backdrop remains difficult.

Focus on higher-margin segments as a strategic hedge.

As discussed in our conversation, over the last one to two years, a clear pattern has emerged across many public companies in the sector: revenues can remain roughly flat—and in some cases even grow—while margins still get squeezed.

That’s a common dynamic in commodity chemicals, where supply–demand cycles, capacity expansions, and intense price competition can compress profitability even when volumes hold up.

And that shifts what “strategic” means.

So, if the commodity base isn’t delivering attractive margin performance, the incentive to accelerate into specialty and higher-margin segments becomes stronger—especially when there are plausible pathways (even if complex) into more value-based pricing dynamics.

In this framing, the move toward specialty isn’t just trend-following. It’s about identifying higher-margin businesses that make sense within an existing supply chain and can be built.

That’s why themes like energy-efficiency materials, bio-based chemicals, and other specialty materials remain active areas of focus. They tend to be high-value, application-specific opportunities that can help a company shift its economics versus pure commodity exposure.

In other words, when only a limited number of players can reliably “do the job” for a specific application, pricing becomes less about competing down to the lowest number and more about what the product is worth to the customer—what is referred to as being “priced by value.”

There is nuance here: “specialty” can mean different levels of scarcity and differentiation depending on the product and who else can produce it.

How Fast Do You Need to Scale?

Business model conversations in specialty chemicals can get overly tactical too early—licensing versus manufacturing, direct sales versus distribution, asset-light versus BOO—without first clarifying what the company is actually trying to do with the technology.

A powerful question to ask is:

If a team believes it has a genuinely strong use case, what does it need to turn that opportunity into a business, and what kind of growth path should it choose to pursue?

And once that choice is made, it shapes almost every downstream decision about capital, market entry, and business model design.

Organic vs. Inorganic Growth

Growth can be pursued organically or inorganically.

If you have great technology and you don’t have meaningful capital behind you, there is a natural tendency toward an organic path.

You do what you can with what you have.

You find early customers where possible, you grow steadily, and you finance the journey through patient capital, private funding routes, or whatever resources are available.

In chemicals and materials, that organic path can be very real.

A surprising amount of success in specialty materials comes from companies that begin as small operators—sometimes operating out of family-business roots—where growth is measured, cash flow matters, and the business scales as capability and demand prove themselves.

That is not a lesser route. It is simply a different route. It assumes time is available and that the market does not require a rapid land grab.

The inorganic route, by contrast, is fundamentally about speed.

It is the choice to use outside capital to move faster than an organic approach would allow.

If the technology has the potential to be large, and timing matters, the logic becomes: raise money, build capability quickly, enter the market faster, and position the company to own the opportunity before competitors do.

That is the path that brings venture capital into the conversation, because venture investors are designed for high risk and high return outcomes.

The core promise is that additional capital can accelerate the company into the market in a way that materially changes the outcome.

The Venture-Scale Logic: Speed + a Large TAM

This is where many founders underestimate what they are implicitly asking investors to underwrite.

Venture is not just “money that helps you build.” It is money that expects a specific kind of outcome—one that can justify the decision to pursue inorganic growth in the first place.

If a company is raising venture capital, the expectation is that it is aiming high and going big.

That means it needs to show a credible case that the market it is targeting is large enough to support venture-scale returns.

In other words, it needs a large total addressable market. Without that, the fundamental justification for taking on venture-style risk weakens.

This is not about producing a slide with a big number.

It is about demonstrating that the company has a real path into a market that is genuinely expansive, where accelerating entry and scaling faster than the organic baseline can create an advantage that matters.

That is why business model discussions are so closely tied to TAM.

If the opportunity is narrow, a different kind of capital structure may make more sense.

If the opportunity is large and the window is time-sensitive, the case for venture-backed, inorganic acceleration becomes more coherent.

Why Business Model Choices Are Inseparable From Market Timing

The trap is to treat business model selection as a static optimization problem, as if the “best” model exists independent of context. In reality, it is dynamic.

Entrepreneurs have to understand what is happening in the market and adjust accordingly.

The model that makes sense when time-to-market is not critical may be the wrong model when a market is opening quickly and speed matters.

This is why understanding the venture-scale lens helps.

The question becomes:

• Do you have time, or don’t you?

• Are you optimizing for lower risk and a measured build, or are you optimizing for being first to market and owning the space?

An organic approach can be sensible when the company can afford to move carefully, build proof gradually, and let commercial traction accumulate.

In that context, licensing or early partnerships might be part of how the company learns and generates initial validation. The trade-offs are different, and the growth expectations should be different.

If the market is moving quickly and timing is critical, the logic shifts.

The company may need to raise capital, build its own capabilities faster, and act with urgency. The objective becomes to establish a position early enough that the company can defend the space and build a durable relationship with the market.

In practice, both routes can work, and both routes can fail.

The point is not that one is universally superior. The point is that the business model has to be coherent with the growth path, and the growth path has to be coherent with market timing.

If those elements are misaligned, the company ends up caught between strategies—too slow to seize the opportunity, but too capital-intensive to survive as a measured, organic operator.

Owning the Value Chain That Matters: Product, Manufacturing, and the Customer

Once the growth path is clear, the business model question becomes more specific:

Where does the company capture value in a chemical and materials supply chain that is long, complex, and crowded with intermediaries?

In specialty chemicals, the structure of the chain often determines whether a startup can build venture-scale outcomes or ends up as a narrowly positioned supplier with limited leverage.

The practical insight is that “being in the supply chain” is not the same as “owning the economics.”

Many different players can touch the product before it reaches the end user, and value can be diluted at each step.

That is why the most consequential choices usually center on 2 things:

1. Whether you own the final product that the customer buys.

2. Whether you own the relationship with the end customer.

The Chemical Supply Chain Is Long, And It Leaks Value

In specialty chemicals and materials, it is common to see a sequence of roles between the origin of a technology and the final application.

• Feedstock suppliers sit upstream.

• Formulators transform inputs into usable products.

• Application developers and OEMs define performance requirements.

• Brokers and distributors can sit between producers and customers.

Each layer can be necessary, but each layer can also become a place where value is captured by someone else.

For a founder, this structure matters because it could create a temptation to build a business around the easiest entry point—selling into a layer that is accessible, even if that layer is not where the durable economics reside.

For an investor, it matters because it can cap the upside. If a company doesn’t control how its technology reaches the market, it can end up doing a lot of work while other parties control pricing, customer access, and long-term account ownership.

Why Owning The Final Product And The End-Customer Relationship Becomes A “Must”

The most direct way to avoid that leakage is to ensure that the company ultimately owns the product and owns the relationship with the end customer.

In this framing, it’s the condition for building a large enough opportunity.

• Owning the final product matters because it keeps the company close to the value proposition. It allows pricing to be tied to the performance delivered, not merely to input costs or contract manufacturing terms. And it creates room to build a defensible position as the product becomes embedded in an application.

• Owning the customer relationship matters because it determines who learns from the market and who can scale commercially. If the relationship sits elsewhere—through a partner that controls the account—then the startup’s future becomes dependent on external incentives, external priorities, and someone else’s willingness to continue pushing the product.

This is why, when the goal is a venture-scale outcome, the logic tends to converge on the same conclusion: own the product, own the customer.

When Manufacturing Is Not Optional

The next question is how much of the manufacturing must be owned in order to make that strategy real.

The instinct from venture is often to prefer asset-light models, because capex and operational complexity can become a heavy burden.

But specialty chemicals do not always allow the clean separation that software companies enjoy.

In many specialty chemical businesses, the core know-how and defensibility sit inside the manufacturing process itself.

It lives in how the product is made, how it is formulated, how yields are achieved, and how quality is controlled. That manufacturing process is often the heart of the IP—something the company wants to protect carefully.

When that is true, owning manufacturing “to some extent” becomes a strategic requirement, not just an operational choice.

If the process is central to what makes the product hard to replicate, outsourcing manufacturing without a clear protection strategy can weaken the company’s moat.

Even if a startup can technically outsource production early, the long-term path often pulls it back toward greater control over the manufacturing layer.

This is also where the logic runs backward from the endpoint.

If the long-term objective is to own the product and the customer relationship, the company has to work back through the chain and decide what it must control to protect quality, margins, and IP.

In some cases, outsourcing remains viable if the defensibility is elsewhere. In other cases, manufacturing control is inseparable from the business.

Starting Points May Differ, But The Destination Must Be Strategic

The important nuance is that this is not necessarily where companies start. Startups often enter through whatever path matches their capital, timing, and immediate constraints.

But even if the early path is indirect, the strategy should still be designed with the end state in mind. Because the supply chain is complex, there are many ways to participate.

But if the ambition is to maximize the total addressable market and build a company with meaningful leverage, the model tends to consolidate around a clear destination: a business that owns its product, owns its customer relationships, and controls enough of manufacturing to defend its differentiation and protect the core of its IP.

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BOO Model, Asset-Light Paths, and the Risk–Control Trade

The business model conversation tends to get framed as a binary—either you Build, Own, and Operate (BOO), or you stay asset-light.

In reality, the more useful way to think about it is as a trade between risk and control.

The more control you want over the variables that determine long-term value, the more you tend to concentrate on capex and operational responsibility.

The more you try to reduce upfront exposure, the more you accept constraints on margins, execution, and sometimes even on what parts of the value chain you truly “own.”

BOO Model (Build-Own-Operate)

A BOO model concentrates both financial exposure and operational complexity. It brings heavier upfront capex, and it shifts what the business has to manage day to day.

Suddenly, the P&L is not just about the selling price and gross margin as an abstract spreadsheet concept. It becomes about utilization, yield, and working capital demands—variables that can quietly dominate outcomes in chemical manufacturing.

That concentration of risk is precisely why many investors instinctively prefer asset-light models, especially at early stages.

But the flip side is that BOO can also provide tighter control over the levers that ultimately determine whether a specialty chemical company captures the value it creates.

Pricing, margins, quality, and—most importantly—the customer relationship become far easier to manage when the company controls production and delivery rather than relying on someone else’s infrastructure.

So the question isn’t whether BOO is “good” or “bad.” It’s whether the company’s strategy requires the kind of control BOO provides, and whether the team is prepared for the financial and operational load that comes with it.

Building Around Where The Core Know-How Sits

The more practical anchor is not the label of the model, but where the core IP and know-how actually live.

In specialty chemicals, defensibility often sits in the manufacturing process and the formulation details that are difficult to replicate.

If the heart of the advantage is embedded in how something is made, then the business model has to be designed around protecting that heart.

That doesn’t automatically mean full vertical integration from day one.

It means being honest about what must be controlled tightly, and what can be delegated without losing the moat.

The “right” structure tends to be the one that centers on the company’s unique point—whether the uniqueness is primarily formulation, manufacturing, or the way the company manages the customer relationship.

When Asset-Light Can Work: Formulation-Led Companies And The Protection Problem

There are situations where an asset-light approach can work well, especially for formulation-driven businesses.

If a company can source existing ingredients, keep the critical formulation capabilities in-house, and demonstrate a clear value proposition to the customer, it may be possible to outsource manufacturing to a blender while still building a strong business.

But that approach only holds if the company can protect what makes it special. In this particular case, it’s mandatory to ensure the IP is not easily reverse-engineered and that the “copycat risk” is managed.

If the defensibility is porous—if someone else can reproduce the product simply by observing it—then outsourcing production can turn into a strategic vulnerability.

In that sense, “asset-light” is not a free win. It is a model that must be earned through the ability to defend the differentiator without relying on ownership of the plant.

When BOO Faces Commodity Dynamics

Bio-based chemicals offer a clear example of how a BOO-heavy strategy can become challenging when the market still behaves like a commodity market.

About fifteen years ago, many teams built around a seemingly straightforward thesis: develop a bio-based route, produce a drop-in replacement for a commodity chemical, raise capital, build a large factory, and compete head-on.

The challenge wasn’t that the technology was uninteresting.

The challenge was that the pricing environment remained highly competitive and highly sensitive to oil prices. When oil prices were high, the economics could look reasonable. When oil prices were low, that advantage could disappear.

A number of companies were burned by this dynamic.

What stands out is what the survivors did next. Rather than abandoning the broader ambition, they redesigned the business model and go-to-market strategy.

They started from a more niche specialty position in order to capture higher margins and build a more resilient economic base.

At the same time, they continued development with the intent to be ready when the cycle and market conditions were favorable again.

The strategy became less about placing the entire bet on one huge asset at the outset and more about building optionality and timing the bigger move.

From Single Application to Broader Applicability

One of the best-case scenarios in advanced materials is when a company gains traction in a specialty foothold and then expands into adjacent applications over time—building optionality and reducing dependence on a single commercialization bet.

In this way, the business can evolve toward becoming a broader platform in advanced materials—something that can support multiple products, partners, or application paths over time.

That platform orientation doesn’t remove the hard decisions around capex and operating complexity.

But it changes the narrative from a single, fragile commercialization bet to a more strategic progression: prove the value in a high-margin niche, build credibility and revenue pathways, and then expand into adjacent opportunities with more leverage and better timing.

In the end, BOO versus asset-light is not a universal choice.

It is a decision that should follow 2 realities: where the core defensibility lives, and what level of risk the company is willing to take in exchange for control over margins, quality, and the customer relationship.

When Is the Right Time to Scale?

So, when should you build a factory?

This question often shows up as if there is a hidden formula: a specific revenue level, a specific volume threshold, a clean milestone at seed or Series A that reliably tells you it’s timeIn practice, that cheat sheet doesn’t exist.

The more realistic approach is to accept that scaling in specialty chemicals and materials is inherently scenario-driven, and that “scale-up” itself can mean very different things depending on the technology and the market.

The key is to stop searching for a universal rule and instead build a decision framework that can survive uncertainty.

Build 3 Scenarios

A disciplined way to do that is to build at least 3 scenarios.

1. One is the base case: what you genuinely think will happen.

2. One is the best case: what happens if timing, execution, and market pull align unusually well.

3. And one is the worst case: what happens if key milestones slip, if adoption takes longer, or if market timing turns against you.

This is not a pessimistic exercise. It is a planning exercise.

The value is that it forces the founder to think through how the company would respond under different conditions, and it gives investors and shareholders a realistic view of what the journey might demand.

Transparency is part of the strategy here.

If the company only communicates the good numbers, the relationship with investors becomes fragile when reality becomes harder, which, in deep tech, is more common than anyone likes to admit.

Sharing the range early allows everyone to prepare and to align on what decisions get triggered under which conditions.

The “3x Time, 3x Money” Rule

A great anecdote from our conversation captures the reality of Deep Tech execution from a VC’s perspective: “Whatever the entrepreneur says, it takes three times the time and three times the money to do whatever he says…If we are lucky.”

That multiplier is not a precise forecast. It is a reminder of the execution friction that shows up in materials and manufacturing businesses.

The effect of that mindset is not to make planning vague. It is to make planning robust. It pushes teams to build strategies that still work when things take longer, cost more, or require more iterations than originally assumed.

2 Scale-Up Approaches in Specialty Chemicals

Usually, scale-up considerations fall into two different cases.

In the first case, volume is inherently critical.

Cost reduction depends on economies of scale, and eventually, you cannot avoid building meaningful manufacturing capacity.

For these technologies, progression is typically framed as a series of step changes—often roughly 10× increases in volume or production capability from one stage to the next on the path to commercial scale.

In extreme cases, teams attempt 100× jumps, but those tend to be rare because confidence in the results declines when the leap is too large.

In this “volume-driven” category, the scaling plan is largely about managing the cadence and risk of manufacturing expansion: how big each step should be, what it costs, and how long it takes.

The scenarios help the team prepare for delays and capital needs, but the underlying reality is that large-scale production is part of the destination.

The second case is fundamentally different.

Here, scale-up does not necessarily mean building a bigger and bigger plant. It can mean building one small reactor and replicating it. The business grows by creating regional production centers that serve local customers, then repeating the same playbook in other geographies.

This model can look simpler on paper because it avoids a single massive factory build. But the critical validation shifts away from manufacturing scale-up and toward customer scale-up.

In a replication model, the core question is not: Can the process scale to a huge plant? The core question is: Can the company repeatedly build demand and convert it?

In simple terms, the operational hypothesis becomes: With one small reactor, one business development person, and one salesperson, can the company generate a predictable amount of revenue?

If that unit works, scaling becomes replication.

You don’t necessarily scale one facility to ten times the size; you replicate the unit across regions. But that only works if customer acquisition, sales execution, and pipeline repeatability are real.

This is a different kind of scale-up, and it requires different proof points. It demands early validation that commercial capacity scales in a way that can be translated into numbers.

The manufacturing challenge is still present, but it is no longer the primary bottleneck.

The bottleneck becomes whether the company can consistently build and convert customer demand across markets.

And that is why this model, while attractive to many, is also difficult. Successfully building a business on that basis is rare.

It requires a level of commercial repeatability that not every technical team can achieve, and it forces founders to treat sales execution as the thing being scaled—not merely the product.

The Final Takeaway: Define What “Scale” Means In Your Case, Then Prove The Right Thing Early

The common failure mode is to plan for the wrong kind of scale-up—building a manufacturing roadmap when the business actually hinges on a repeatable customer pipeline, or assuming replication is easy when the real challenge is commercial execution.

The more investable approach is to clarify which camp the company is in, build scenarios that reflect that reality, and validate the correct scaling constraint early.

In some businesses, that constraint is manufacturing capacity and the economics of scale. In others, it is the ability to reproduce sales outcomes across regions with a small, repeatable operational unit.

Either way, the path forward becomes clearer when the company stops looking for a universal threshold and starts designing a scaling strategy that matches what “scale” actually means for its technology and market.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, I sat down with Mitch Worden, Vice President of Corporate Development – Energy at S2G Investments, to unpack what “growth-ready” really means in the energy sector—once the first proof points exist and the real work becomes scaling deployments, building operational discipline, and creating the kind of predictability that later-stage capital and strategic buyers can underwrite.

Key takeaways from the episode:

⚙️ What Growth-Ready Actually Signals
Once an energy company approaches growth, the question shifts from “does it work?” to “can it repeat?” Standardized deployments, real operating systems, and a track record of hitting milestones start to matter as much as the underlying solution.

🏢 Customer Concentration Is a Resiliency Problem
Every company starts with one customer. The risk is staying dependent on one customer—or one customer segment—especially in cyclical markets where demand can turn for reasons unrelated to performance.

📉 Cost Curves and the Path from First-of-a-Kind to Scale
First-of-a-kind is an achievement, but growth capital is about what happens next: clear buckets of efficiency gains, a believable path down the cost curve, and an active dialogue around milestones as repetition accelerates.

🧭 Scaling Under Real-World Constraints
Utilities and industrials come with long sales cycles, and many energy businesses come with long funding cycles too—especially when CapEx is heavy. Grounded planning is a core part of being investable at the growth stage.

🎯 Building Toward the Exit Before You Need It
Strategics and public markets reward predictability: durable unit economics, sticky revenue, defensibility, and a clear “build vs. buy” rationale—plus integration and culture as real variables in whether an acquisition succeeds.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

What a Growth-Ready Energy Company Looks Like

A growth-ready energy company is no longer being evaluated on whether the core idea works in principle. The question becomes whether the business can scale in a way that is repeatable, disciplined, and economically defensible across real-world conditions.

By the time a company is approaching a Series B or Series C conversation, the most important shift is that the market has already started to render a verdict.

There is some evidence of adoption, some proof that the solution performs outside a controlled environment, and at least an early signal that customers will pay for it.

The work now is to show that those early wins aren’t a one-off outcome of exceptional founder effort or a uniquely favorable customer. They need to reflect a model that can hold up as volume increases.

From bespoke execution to standardized deployment

In the earliest stages, it is normal for companies to win by being highly hands-on. A first project may be custom. A first deployment may feel like a one-time build.

A product may exist as a version one that gets reshaped through real usage and iteration.

The growth-stage test is whether that early, scrappy execution has been turned into something more structured. The company needs to demonstrate that it has moved from “first-of-a-kind” delivery toward a standardized approach that can be repeated.

That standardization is not just about the product itself, but about the internal process: how deployments are executed, how projects are managed, how performance is measured, and how the organization supports delivery without reinventing the wheel each time.

That’s where scale begins to look credible. The underlying signal is that the team has learned from early work and can now repeat the process at a more efficient clip.

Proving the organization, not just the solution

At the growth stage, the focus widens from the technology to the company’s ability to operate. The question becomes: Does the organization work?

That tends to show up in very practical ways.

• Are there operating systems in place?

• Are there playbooks for execution?

• Is there a supply chain discipline where it’s needed?

• Are there clear KPIs and milestones that the team tracks—and can actually hit?

This isn’t a request for corporate bureaucracy. It’s a way to assess whether the company is building a track record of execution that can be underwritten.

In other words, whether performance is becoming predictable enough that an investor can believe the next phase will look like an extension of the last, rather than a totally new bet.

When technology risk stops being the main risk

At this stage, the appetite for pure technology risk is limited.

The expectation is that the solution has been proven in market conditions and that the remaining work is about scaling it—driving cost down, improving efficiency, and making the economics increasingly competitive against the status quo.

That does not mean the technology is “done.” It means the core technical uncertainty is no longer what a growth investor wants to be paying to resolve.

The company should be able to point to evidence that the technology is viable, that it performs reliably in the field, and that the cost curve is moving in the right direction as production, deployment, or delivery scales.

The key test is whether the company is becoming an economic solution in the marketplace. Not a promising experiment, but a product or service that can win because it is efficient and valuable enough to outperform traditional alternatives.

Customer Concentration as a Resiliency Problem

Customer concentration is one of those realities that looks harmless at the beginning and becomes structurally important as the company grows. Early on, it is almost unavoidable.

Every startup has to start with one customer, and in many markets, the first contract is the hardest one to win.

The issue is not that the business began with a narrow base. The issue is whether it stays dependent on that narrow base once the company is asking investors to underwrite a scaling plan.

At the growth stage, customer concentration stops being a simple go-to-market detail and starts to read as a resilience question.

The more the company relies on one or two customers, the more its growth trajectory can be limited by factors outside the company’s control, including a customer’s ability to expand.

Even when the relationship is strong, the business can become exposed to the limits of that single customer’s ability to expand.

The goal is to build a durable customer base that strengthens the business over time.

A diversified customer base tends to signal that the company’s value proposition travels—that the product or service can land in more than one account, across more than one environment, with a sales and delivery process that is not uniquely tailored to a single buyer.

That matters because scaling requires repetition.

If the company’s growth depends on one relationship deepening indefinitely, it becomes difficult to underwrite growth as a system rather than a hope.

This is also why the idea of a beachhead market matters.

Penetrating a beachhead is often the right early move. But growth-stage readiness shows up when the company can demonstrate that the beachhead is not a dead end.

There needs to be evidence that adoption can extend beyond the first customers, and that the business can “land and expand” in a way that is not overly dependent on one account.

Diversifying across segments to withstand cycles

In energy markets, customer diversification is not only about reducing exposure to one buyer. It is also about reducing exposure to one segment.

These markets move in cycles. Certain customer segments experience cyclical downturns, shifting demand, or macro headwinds that can slow purchasing decisions.

If the company is tied to a single segment, a downturn in that segment can effectively become a downturn in the company—regardless of how strong the product is.

A more resilient business is one that can expand into additional customer segments that are not synchronized in the same way. When one segment tightens, another may be stable or growing.

That ability to move across segments—without losing coherence or becoming scattered—adds a layer of adaptability that matters in growth underwriting.

Diversification ultimately points back to a broader way of thinking about scale: adaptability.

A growth investor is not only evaluating whether the company can execute in its current environment, but whether it can keep executing as conditions change.

Customer concentration works against that because it reduces strategic options.

A diversified base, especially across segments, gives the company more ways to continue growing even when specific customers or verticals hit friction.

And at the practical level, it ties directly to what makes scaling believable: the capacity to deliver more services into the market, support scaling economics over time, and sustain growth even when individual customers—or entire customer groups—cannot keep expanding at the pace the company needs.

Cost Curves, Techno-Economics, and the Path from First-of-a-Kind to Scale

At the growth stage, the conversation about techno-economics is less about presenting a perfect model and more about demonstrating that the team understands how costs will move as scale increases.

Once a company has reached a first-of-a-kind milestone, it has already crossed an important threshold. The remaining question is whether that first success can be repeated—and repeated in a way that becomes more efficient each time.

This is where cost curves become central.

The investor is not just underwriting the existence of a solution, but the trajectory of that solution as it moves from a single deployment to many deployments.

The point is to understand whether the business can become increasingly competitive against the status quo, and whether the path to that competitiveness is grounded in tangible operational levers rather than hope.

First-of-a-kind is a milestone, not the destination

A first-of-a-kind project is hard, and it often requires a capital structure that doesn’t map cleanly onto traditional venture assumptions.

Depending on the project and entity structure, equity may not even be the right instrument for that step.

But as a proof point, first-of-a-kind matters because it provides a base layer of evidence: the team has built something real, learned from it, and can now move into repetition.

Growth capital is typically aligned with that next phase.

The expectation is that the company can take what it learned in the first deployment and repeat the process at a faster, more efficient clip.

The underwriting logic rests on the idea that iteration will not only increase volume but also drive down costs through learning, standardization, and improved execution.

Efficiency gains

A recurring signal in growth-stage diligence is how teams talk about efficiency gains.

The teams that inspire confidence are usually not the ones with the most intricate spreadsheets, but the ones that can clearly articulate where the efficiencies will come from.

That clarity tends to show up as “buckets”—specific areas where the team expects to cut costs or increase efficiency over a defined period.

The buckets might look different depending on the nature of the business.

A software-oriented company will have a different set of levers than a Deep Tech company, and a project-based developer will talk about different constraints than an OEM.

But the underlying requirement is the same: a grounded understanding of what drives cost today and what will drive it down tomorrow.

When that understanding is present, it becomes evident in the way the roadmap is discussed.

The narrative isn’t abstract. It reflects operational reality: what changes with repetition, what changes with scale, and what changes as the organization becomes more mature in procurement, supply chain, manufacturing, or deployment.

Underwriting performance as the cost curve bends

From an investor perspective, the goal is to understand the material level of efficiency gains the company can achieve over time—and whether those gains are meaningful enough to change the economics of scaling.

This is where first-of-a-kind becomes a reference point rather than a credential.

It establishes a baseline, but the underwriting is about what happens next: the ability to improve performance, reduce costs, and demonstrate that the economics are moving in the right direction as the company scales.

The key idea is that scaling should not simply multiply the same cost structure.

The company should be showing that cost comes down as volume goes up, and that the solution becomes increasingly viable as an economic alternative in its market.

The techno-economic plan at this stage is not treated as a static artifact. It becomes part of an ongoing dialogue.

The expectation is that the team can articulate the areas where cost and efficiency improvements are likely, and then work actively with an investor to execute against those milestones.

That means maintaining constant communication, using the investor’s support where relevant, and treating the plan as something that gets refined through real-world execution.

In practice, what matters is not whether the founder arrives with an impossibly detailed model, but whether the team can explain, credibly, how efficiency will improve and what the organization will do to make that improvement happen.

The plan becomes a living roadmap, and the milestones become a shared framework for measuring whether the company is actually bending the cost curve as it scales.

Scaling Execution: Systems, Sales Cycles, and Capital Reality

Once a company is moving beyond early-stage experimentation, the question is no longer whether the team can build something impressive. The question is whether the company can operate in a way that makes growth predictable.

At this stage, the founder’s scrappiness and charisma still matter, but they are no longer sufficient on their own.

What begins to matter more is whether execution has been converted into a repeatable operating discipline.

Growth-stage investors tend to look for that shift because scaling exposes weaknesses that are easy to hide at a smaller scale.

When the company is small, a few heroic efforts can compensate for missing systems. As volume increases, the absence of operating infrastructure becomes a limiter.

The organization has to work, not just the product

A core pitfall that shows up in growth conversations is when the company is still running as if it were in the early stages.

The team may be capable, the vision may still be compelling, and early customers may be supportive—but the business has not yet demonstrated that it can run through a playbook and deliver consistently.

At scale, it becomes important to show operating systems, execution processes, supply chain discipline, and a KPI framework that ties the team’s work to clear milestones.

This is not about signaling maturity in the abstract. It is about building a track record of success that makes future performance easier to underwrite.

In practical terms, the company needs to demonstrate that it can repeatedly meet targets, that the internal machine is functioning, and that the business is being run with enough rigor to support rapid growth.

Long sales cycles

Energy markets often involve customers like utilities and industrial companies. These end markets can be slow and process-heavy.

Sales cycles are long, and the path from initial interest to deployment can stretch far beyond what founders expect when they are used to faster-moving startup dynamics.

One of the most important signs of readiness is whether the executive team is grounded in that reality.

A company that treats long sales cycles as a temporary inconvenience tends to get caught off guard. A company that treats them as the environment can build around them.

That mindset shows up in the way the team navigates the cycle: how it moves through procurement, how it manages stakeholder alignment inside the customer organization, and how it works to shorten timelines where possible.

The question is not whether the cycle is long, but whether the company understands what it takes to move through it, and whether it has a credible approach to improving velocity over time.

The funding cycle can be just as long

A second pitfall is failing to match capital planning to the reality of the business model. Many of the companies operating in these markets are capital-intensive.

CapEx-heavy models carry different demands than software businesses, and the consequences of misjudging capital needs can be severe.

Growth-stage readiness includes realism about how much capital the company needs, what future funding is likely to look like, and how the organization can weather periods where capital is slower to come in than expected.

This is not only about avoiding under-raising. It is about demonstrating a clear view of the financing path and the operational implications of that path.

When founders and executive teams show that they are grounded in both the sales cycle and the funding cycle—and can plan accordingly—it changes the confidence level a growth investor can have in partnering with them.

It signals that the company is not merely building technology, but building a business that can survive and scale within the actual constraints of its market.

Building Toward the Exit Before You Need It

By the time a company is raising growth capital, the exit is no longer an abstract concept.

It may still be years away, but it becomes closer in a meaningful sense because the company is now being evaluated on whether it can mature into something a strategic acquirer or the public markets can rely on.

That changes what matters.

The defining theme is predictability. Whether the destination is an IPO, an acquisition, or a later-stage capital handoff to infrastructure-oriented investors, the business has to demonstrate that its results can be repeated at scale and that the performance of the model is durable enough to persist through a transition.

Repeatability is the foundation of predictability

A company becomes attractive to buyers and public investors when it can show that it can do the same thing not just a handful of times, but at a much larger frequency and volume.

This is a different mindset than early-stage proving.

The questions become:

• Can the company produce the same outcome ten times, a hundred times, or a thousand times?

• Can it execute repeatable deployments if it is project-based?

• Can it deliver stable unit economics if it is product- or service-based?

• Can it illustrate, through real results, that its growth is not fragile?

This is where internal discipline and standardization connect directly to exit readiness.

The more consistent the company’s execution becomes, the more a future buyer can treat performance as something they can underwrite rather than something they have to gamble on.

Durable customer relationships and “sticky” revenue

Strategic acquirers and public markets both care deeply about whether revenue will survive a change in ownership or operating environment. It is not enough to show fast growth.

The business needs to show that its customer relationships and contracts are durable, that customers continue to renew or buy more, and that revenue is sticky enough to persist.

That durability also links to the acquisition logic around accretion.

If a strategic buyer is acquiring the company, they want the business to be immediately accretive, which means the revenues and margins need to be real and maintainable.

The buyer is not only purchasing innovation; they are purchasing a working engine they can integrate.

This is also why margin improvement over time matters.

A track record of gross margin expansion signals that the business is scaling efficiently and that profitability dynamics can improve as the company grows.

That kind of trajectory supports the idea that the business will continue performing after an acquisition or after entering public markets.

What scale tends to look like for strategics

The thresholds that matter vary depending on the business type and the segment.

A project developer building renewable assets will be judged differently from a company providing a new energy source—and differently again from a business selling equipment, infrastructure, or industrial services.

In asset development, the quality of the assets and the strength of the pipeline become central.

Scale is not only measured in megawatts deployed, but in the credibility of what is coming next—how advanced the pipeline is, whether projects are at notice to proceed, and the strength of the underlying agreements that support execution.

In businesses that are fundamentally about producing energy at a competitive price, levelized cost becomes a core reference point.

The company needs to show that it can approach cost competitiveness, and ideally that the market views the product as something that delivers equal or higher quality at a similar marginal cost.

That’s the point where adoption begins to look like market pull, where you’re not relying primarily on a green premium but showing you’re economically competitive.

Across models, revenue scale is another gating factor.

Buyers tend to have minimum expectations for revenue magnitude, and while it varies by industry, the idea is that the company has proven enough commercial traction to matter.

Alongside revenue, buyers want to see that margins are robust and defensible over time.

“Build or buy” and the strategic value of being already in-market

A frequent strategic question is whether a large incumbent should build a capability internally or acquire it.

Many strategics have deep technical teams and significant R&D capacity. They are often capable of developing solutions on their own.

The acquisition rationale tends to come down to time and leverage.

If a company is already in-market, already serving a critical customer base, and already expanding the services that customers rely on, it can represent a faster path to strategic positioning than internal development.

This is where positioning becomes important for founders.

The more a company can show that it is serving a customer segment that is strategically important, that it expands the acquirer’s service set, and that it strengthens the acquirer’s customer relationships, the clearer the “buy” case becomes.

The story shifts from innovation as novelty to innovation as leverage.

Integration, defensibility, and culture

In acquisition conversations, there is rarely a single decisive factor.

The company needs a defensible technology set—patents or other protections that differentiate it from competitors.

It needs a defensible business model that proves it can deploy at scale, not merely demonstrate technical promise.

It needs to show that it can integrate into existing sales channels and operating structures as an additive solution, strengthening customer relationships and expanding what the acquirer can offer.

And culture matters more than founders often expect.

The history of mergers and acquisitions is full of failures driven by integration friction.

Being able to show cultural fit—an ability to “slot in” without breaking the way the acquirer operates—can materially influence whether an acquisition is perceived as seamless or risky.

Exit readiness, in that sense, is built years in advance.

It is shaped by how the business runs processes, how it builds repeatability, how it earns customer trust, and how it demonstrates that it can keep delivering performance even when ownership changes.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, I sat down with Alexander Schubert, Partner at SciFounders, to unpack what early-stage investors look for when they back scientists before the story is fully formed: how to pressure-test whether a tech solution is truly unique, how to communicate it to a broad audience, how to think about market size, and how founders can de-risk the “final boss” of adoption.

Key takeaways from the episode:

🧬 Differentiation That Holds Up Outside Your Own Lab
A rigorous reality check requires scanning adjacent labs, industry programs, and startups—and being honest about whether the work addresses the real bottleneck or just adds an incremental twist.

🗣️ Turning Technical Work Into a Venture-Grade Narrative
Data isn’t enough on its own; the story has to make the problem, the population, and the “why now” legible to non-specialists without losing the strategic edge.

📊 Early Economics
Order-of-magnitude thinking—benchmarks, reimbursement context, and care-cost logic—can be more credible than complex forecasting, especially before product and pricing are real.

🏥 Commercialization Risks: Therapeutics vs MedTech
Risk differs across therapeutics and MedTech: therapeutics often de-risk after clinical/regulatory clearance, while MedTech may face a second hurdle—coverage and clinician adoption—best tackled early with clinicians and payers.

🤝 Designing the Journey
Investor type shapes exit expectations, early valuation is best approached as a range rather than a declaration, and disciplined budgeting and collaboration choices determine how quickly milestones can be reached.

INSIGHTS & ANALYSIS

Deep Tech Startups & Venture Capital Annual Report - An Analysis of 2025

As every January, the year opened with our annual report: Deep Tech Startups & Venture Capital: An Analysis of 2025—a full-cycle study of the year just closed, designed to start 2026 with a clear, disciplined view of what actually changed.

Read the Report

The premise is simple: advanced technology shifts gears when prototypes become systems, pilots become assets, and balance sheets start to carry factories, grids, supply chains, and regulated distribution.

Those shifts are measurable—through round sizes and terms, project scopes, unit capacities, offtake structures, manufacturing footprints, sovereign programs, and the increasingly explicit linkage between compute, energy, and industrial policy.

This annual report exists to make those dynamics visible, so that 2026 begins with a grounded understanding of what really happened in 2025—and so challenges can be tackled with conviction and opportunities pursued with intent.

In January, the first three chapters were published:

• Chapter 1 — 2025 in Data: Q1–Q2

  The opening ledger of the year: month-by-month numbers across AI infrastructure, energy and grids, critical minerals, space and defense, biology and health—showing when the underlying structure of the year first came into focus.

• Chapter 2 — 2025 in Data: Q3–Q4

  The second half of the tape: where early signals hardened into contracts and infrastructure logic—fusion PPAs signed before electrons, microreactors shifting into factory-line manufacturing plans, robots priced as opex, GPUs treated as collateral, and interconnect reclassified as defense-grade.

• Chapter 3 — Control Points Across the Industrial Stack

  The thematic pass that turns the tape into structure: semiconductors, photonics and high-speed interconnect; advanced materials and industrial chemistry; quantum technologies; AI infrastructure and data centers; and energy systems and storage. It traces how limits on bandwidth, power, feedstock, and uptime hardened into financial constraints and then into investable bottlenecks—showing where value really pooled in 2025 and where exits, M&A, and sovereign capital are starting to concentrate.

• Stay tuned in February for Chapter 4, which completes the 2025 map across defense and space, synbio, agrifood, health, and biology, and the evolving exit architecture for long-cycle deep tech.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

What Real Differentiation Looks Like (Outside the Lab)

In early-stage life sciences, it’s easy to mistake “strong science” for “real differentiation.”

A technology can be impressive on its own terms and still fail the test that matters most in venture-backed company building: whether it is meaningfully distinct in a way that changes outcomes.

The discipline here starts with a willingness to be uncomfortably honest. Not about whether the research is good, but about whether it is truly different from what the market is already moving toward.

Moreover, certain themes become dominant—first in academic attention, then in investor appetite, then in the number of companies formed around similar approaches.

That pattern creates a predictable risk: when a theme is “hot”, many teams end up working on adjacent problems with variations that are technically elegant, but strategically crowded.

In that environment, “we’re working on X” is rarely enough.

What matters is whether the work changes the core constraints that make the problem hard in the first place.

A practical way to pressure-test differentiation is to ask whether the company is addressing the root issue—not just a visible symptom.

For instance, in areas where there is a lot of activity, like the current proliferation of different CAR-T “flavors,” the field is full of strong teams doing exciting work.

And yet the question remains: are these approaches tackling the central reasons CAR-T is difficult to make work, and difficult to make work financially at scale for a broad patient population?

In conclusion, differentiation is not the ability to describe a new twist on an existing idea. It is the ability to point to the bottleneck everyone is living with and explain, clearly, why this approach changes that equation.

Scanning the landscape: labs, corporates, and startups

The most common failure mode at this stage isn’t a lack of intelligence or effort. It’s insulation.

Scientists often become deeply focused on the trajectory of their own research and miss how quickly adjacent work is evolving around them—sometimes in the lab across the hall, sometimes in a corporate program, sometimes inside a startup that has already translated similar ideas into a development roadmap.

The antidote is not more conviction. It’s more context.

Building an investable company requires actively mapping what else is happening:

• Which labs are converging on similar mechanisms?

• Which industry groups are quietly pushing related programs?

• Which startups are already positioned a step ahead in clinical development?

That kind of scanning is not a distraction from research. It’s part of doing the strategic work required to turn science into a company that can defend its position.

This process is also where founders can discover something valuable: that “uniqueness” often doesn’t live in a single metric. It lives in how a technology fits into the overall competitive landscape.

The moment a founder can say with confidence, “Here’s what others are doing, here’s what that means, and here’s why our approach is fundamentally different,” the story shifts.

The company is no longer relying on the audience to infer differentiation; it is demonstrating it. (And here, of course, communication skills are not optional.)

Why forcing a single research thread into a company can backfire

There is a subtle but important tension in scientific entrepreneurship. Researchers often feel an implicit pressure to commercialize their own work—to take the project they know best and build a company around it.

But real-world scenarios repeatedly surface a different pattern: many successful companies begin not with forcing a specific research thread into a startup, but with stepping back and selecting the best opportunity available, even if it is not the most “emotionally owned” by the founder.

That shift requires humility and curiosity.

It means looking beyond the boundaries of what you personally developed and asking a more strategic question:

“What is the most compelling technical opportunity to build a durable company right now?”

Sometimes the best answer comes from your own lab. Sometimes it comes from noticing another group’s work and recognizing that it may be closer to a true commercial inflection point.

This is not an argument against building from your own research. It’s a warning against treating that path as the default.

When a founder is too attached to a particular project, they can overlook how crowded the space is becoming, how much of the “core issue” remains unresolved, or how much of their pitch relies on incremental improvements that are legible mainly to other specialists.

In the earliest stages, the goal is not to protect a thesis. The goal is to find the sharpest wedge: something that is both scientifically real and strategically distinct enough to justify building a company around it.

Turning Technical Work Into a Venture-Grade Case

A recurring gap for technical founders isn’t tech competence. It’s translation.

Academic training teaches you to let the data speak, to treat the numbers as the argument, and to assume the audience shares the same interpretive framework.

Fundraising and venture building are different.

The audience is broader, the incentives are different, and the job is not to prove you can generate strong results in isolation. The job is to make it unmistakably clear what the work is, why it matters, and why it can become a company.

That’s where storytelling becomes less of a “nice to have” and more of a core operating skill. The point isn’t to oversimplify.

It’s to structure the narrative so that someone who isn’t living inside the science can still follow the causal chain:

• What problem is being solved

• Who it affects

• What changes if the solution works

• Why this specific approach has a credible shot at changing outcomes

This need shows up early with investors, but it doesn’t stop there. The same narrative muscles are what eventually help founders recruit talent, engage partners, and build trust with stakeholders who are not evaluating the work the way a peer reviewer would.

Making the market legible

In biotech, founders are often closest to the mechanism, the molecule, or the platform—while investors, collaborators, and future hires need a clear view of the opportunity it unlocks.

The trap is to assume that the market story is self-evident if the science is strong. It usually isn’t.

The market needs to be made legible.

That starts with clearly connecting the technology to a real, meaningful population—often framed as a large unmet need—and explaining why solving it would matter at scale.

The emphasis here is not on building a perfect model in the earliest conversations. It’s on presenting a coherent, understandable picture of the magnitude: the kind of opportunity this could become if the technology delivers.

The way that picture is communicated matters. Scientists can get pulled into details that are persuasive inside a technical community but do not land with a broader audience.

The challenge is to focus on the few points that carry the weight: the size of the unmet need, the reason existing approaches are insufficient, and the reason this approach changes the odds.

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Early Economics

In the earliest stages, the point of “running the numbers” is not to build an elegant spreadsheet that pretends the future is knowable. It’s to develop a grounded sense of scale.

When a team is operating with a molecule in a lab, and the company is still forming, precision can easily become theater. What actually helps—both for founders and for investors—is a view on orders of magnitude.

One practical way to get there is to start with industry benchmarks.

If you’re working in an indication or sub-area that isn’t yet intuitive, looking at comparable programs and comparable companies gives you a reality check on what “big” tends to look like in practice.

Revenue numbers from drugs in adjacent indications can help establish whether the opportunity is plausibly venture-sized, even before the details are fully known.

Another anchor that often clarifies things is the cost of care.

For patient populations where there is no therapy or where existing therapies are not optimal, the current spending profile can reveal a lot.

• How much is the system already paying annually?

• What is the burden on insurers for that cohort?

Even at a rough level, that framing helps establish whether the potential value created by a better solution is large enough to matter. It’s not a pricing model. It’s a sense check that prevents founders from building toward an outcome that can’t support the kind of returns venture capital is designed for.

The underlying idea is simple: at this stage, a sophisticated model doesn’t reduce uncertainty. It often just disguises it. The job is to understand whether the opportunity is likely to be in the right magnitude range, and to be able to explain why.

This isn’t about performing certainty. It’s about demonstrating that you’ve done the work: that you have a view on the market, the commercial mechanics, and how macro changes may affect where the company fits.

Finally, the real value of the exercise is the founder’s internal clarity.

Running these numbers early is a way of validating that the problem is big enough, that the company is oriented toward venture-relevant scale, and that the next milestones being funded actually move the business toward a credible commercial path.

A Case Study from MedTech

In the example discussed during our conversation, the guest described an early-stage MedTech company that did a notably thorough job thinking through the economics very early on.

Rather than relying on a highly detailed forecast, the team anchored its assumptions in real-world reference points: comparable technologies, reimbursement codes and pathways, and a practical breakdown of payer mix (e.g., Medicare vs. private insurance) alongside treatment volume.

They also sanity-checked the model through conversations with clinicians and stakeholders in integrated care systems, and paired that with an “optimistic but realistic” view of market penetration based on where adoption dynamics actually were.

The takeaway was simple: use credible benchmarks and reimbursement reality to establish an order-of-magnitude opportunity, and keep early models grounded in how the system pays and adopts.

Commercialization Risks: Therapeutics vs. MedTech

Commercialization risk shows up very differently depending on what kind of company is being built.

In therapeutics, once there is a drug that addresses a major unmet need, and it can move through the clinical and regulatory pathway successfully, commercialization is often less of a central concern. The path is relatively well understood: generate the right evidence, get approved, and the market tends to have established mechanisms for uptake.

In MedTech, the profile can invert. Regulatory approval can be a major hurdle, ”the first boss”, but commercialization becomes “the final boss”. Getting through regulatory requirements does not automatically translate into adoption. What matters afterward is whether clinicians will actually use the technology, whether it fits into their workflows, and whether insurance will cover it.

Those factors determine whether the product becomes real revenue or stalls after technical success.

This is part of why MedTech can feel harder to underwrite from a venture perspective.

It’s not only that clinical and regulatory risk exist; it’s that there is often an additional layer of uncertainty about whether the market will move once the product is “ready.”

A founder who understands that early and can speak to it directly can materially change how investors perceive the overall risk stack.

De-Risking Go-to-Market

A common commercialization trap in MedTech is the chicken-and-egg problem between clinician adoption and insurance coverage.

Doctors may be hesitant to adopt a new device or technology without coverage, while insurers may hesitate to pay unless there is wide adoption. That dynamic can become a major bottleneck and compound venture risk.

From an investor’s point of view, the equation starts to look like this: clinical and regulatory risk are already high, and then there is an additional risk that even after those are navigated, commercialization may still be difficult.

The way to handle this risk is not to pick a side and hope the other follows. It’s to build awareness early and work on both fronts in parallel.

That means talking to clinicians early—and broadly enough to avoid creating an echo chamber. If a founder only speaks to one group in their immediate environment, it can create a skewed view of how broadly exciting the product really is.

In parallel, it means thinking about what insurers want to see in order to reimburse—whether comparable reimbursement codes exist, or whether a new code might be required.

Early Stage Execution: Team, Milestones, and Capital

Team building: recruiting top talent through vision

The ability to attract talent is evaluated indirectly, but it matters deeply, especially for technical founders who begin as the scientific or technical center of gravity.

Scaling a company requires pulling in people with very different backgrounds—machine learning engineers, biology experts, experienced chemists—and those individuals will not join just because the science is correct.

The common thread is narrative.

Recruiting is similar to fundraising in that it relies on making people believe in what you’re building strongly enough that they are willing to change their own trajectory to participate.

For many, that means leaving well-paid industry roles or walking away from secure academic paths.

To make that leap, they need to understand the vision, the stakes, and why the company is worth betting a meaningful slice of their career on.

This requires being able to communicate with different audiences in different languages—without losing coherence.

The founder needs to hold a broad vision that is legible to varied specialists, and deliver it in a way that generates genuine excitement rather than narrow technical persuasion.

Capital efficiency and milestone velocity

When a company is raising a smaller pre-seed or seed round, the operating constraint is not elegance. It’s speed to meaningful milestones.

If you are not raising tens of millions of dollars up front, resources need to be deployed primarily toward the experiments and execution that unlock the next inflection point.

This has implications for founder compensation and early spending choices.

Founders should expect that, in the earliest stages, they may need to take a pay cut in order to create more room for R&D that accelerates critical progress.

The trade can be rational: if hitting milestones faster enables a stronger Series A or better terms, capital becomes less expensive later, and missed compensation can be recovered over time.

In the same spirit, equity is a powerful tool for early hiring.

Using equity to attract key hires is not only a way to preserve cash; it is also self-selecting. People who value equity tend to be more aligned with mission and long-term commitment.

Collaboration that accelerates vs collaboration that delays

Cost efficiency in the early stages often triggers a natural question:

Should founders leverage university resources or shared infrastructure to reduce burn rate?

There are cases where universities make strategic sense—especially when they have highly specific models, such as unique mouse models, or prototype instruments that aren’t broadly available commercially and that fit the company’s needs extremely well.

But for everyday work, the trade-off can be time.

University equipment may be cheaper, and instruments may be sophisticated, but they are heavily used and will prioritize internal academic work, as they should. That means startups can become second in line, and timelines can stretch.

In early-stage company building, a slower turnaround can be more damaging than a higher cost.

For routine execution, contract research organizations and service companies can be a better lever because turnaround time is often the real bottleneck.

And founders can treat CRO engagement as a process rather than a single vendor decision: run a competitive process, compare multiple providers, negotiate pricing, and use the fact that service companies view startups as future long-term customers.

When vendors believe a company can grow into something meaningful, they are often willing to offer strong terms early, especially if they know you are speaking with multiple alternatives.

The example of cloud providers offering substantial credits illustrates the same logic: discounts today to win durable customers tomorrow. Similar dynamics can apply with CROs and other service partners when founders approach negotiations with that long-term framing.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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This week, we stay firmly in the mining and critical minerals universe, but flip the lens. Instead of asking how to build a mining tech company, we ask what makes one investable.

I sat down with Jun Qu, Principal at Main Sequence, to unpack how a Deep Tech investor looks at mining and critical minerals: where the real upside sits, which risks matter at each stage, and how non-dilutive capital and business model design change what’s possible.

Key takeaways from the episode:

🔌 Why Mining is Finally Venture-Backable
Structural demand from the energy transition and AI, looming supply gaps, more open incumbents, and new non-dilutive capital are turning parts of mining into genuinely VC-compatible markets.

⛏️ Where the Big Opportunities Sit in the Value Chain
Three areas stand out: exploration tech that finds better projects faster, processing and refining that make stranded resources economic, and operational tech that boosts output from existing mines.

💰 Business Models That Capture Value
From subscriptions and hardware-as-a-service to licensing, royalties, and tech-enabled operators, the core question is how much of the resource upside the technology provider chooses—and manages—to capture.

🧩 Cracking Adoption in a “First to Be Second” Industry
Industry insiders on the team, aligned strategic investors, and a focus on agile tier-two and tier-three operators create a realistic path from first pilots to credible references and, eventually, major miners.

📈 How Investors Assess Companies Across Stages
Pre-seed and seed are mostly about the team and showing the core technology works; Series A and beyond focus on unit economics, repeatable deployments, and scale, while the market is still maturing on growth capital and exit pathways in mining tech.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Why Mining Tech Companies Have Become Venture-Backable

For a long time, mining sat firmly outside the mental map of most venture investors. On paper, it had all the wrong characteristics:

• It was slow-moving, built around multi-decade project cycles, and long permitting timelines.

• It was deeply risk-averse, shaped by safety, regulation, and a culture that rewards reliability over experimentation.

• It was capital-intensive in a way that did not fit the classic VC model: large upfront checks, long payback periods, and assets that looked more like infrastructure than software.

Venture capital, in contrast, is built around fast growth and disruptive adoption curves.

The ideal customer in that world buys quickly, iterates quickly, and, if things go well, scales quickly.

The traditional mining customer has been the opposite of that.

Even when there was technical interest in innovation, the combination of internal processes, competing priorities, and perceived risk meant that adoption tended to move at a glacial pace.

Given that mismatch, it is not surprising that mining and critical minerals have rarely been seen as a natural home for early-stage venture money.

The industry could support large balance sheets, long-term private capital, and listed companies, but not the kind of rapid value creation that venture funds are designed to underwrite.

That was the baseline from which things started to shift.

3 Tailwinds Changing the Equation: Demand, Adoption, and Non-Dilutive Capital

What has changed is not the fundamental nature of mining as a physical, capital-heavy industry, but the context in which it operates. 3 reinforcing forces have started to reshape the opportunity set and make parts of the value chain genuinely venture-backable.

The first is on the market side.

Demand for many critical minerals is now driven by structural trends, not just cyclical swings. The energy transition is the dominant driver: as power systems are electrified and electric vehicles scale, the volume of copper, lithium, and other critical inputs required by 2040 and 2050 looks dramatically higher than historical baselines.

On top of that, the build-out of AI infrastructure, especially data centers, is creating additional load on power systems and materials supply, adding to the pressure on certain commodities.

In a number of cases, this translates into markets with a scale and growth profile that can support venture-scale outcomes, provided companies can actually bring new supply to market.

The second tailwind is behavioral.

A growing number of incumbents are acknowledging that they cannot meet projected demand with today’s toolkits. Existing technologies, processes, and cost structures do not add up to the volumes implied by energy transition scenarios.

That realization is pushing both major players and tier-2 and tier-3 operators to engage more seriously with new technologies.

The posture is shifting from “we will wait until something is fully proven” to “we need to play an active role in developing and scaling what comes next.”

That does not erase the complexity of selling into large mining organizations, but it does change the willingness to run pilots, co-develop technology, and act as early customers.

The third force is the emergence of significant pools of non-dilutive capital aimed explicitly at critical minerals and enabling technologies.

Governments see supply security and diversification as strategic priorities, especially in a world of rising geopolitical tension.

The result is a wave of grant programs, debt, and other instruments that sit alongside equity rather than diluting it.

For Deep Tech companies in mining and processing, this does not just feel nice to have. It directly alters the economics of their development pathways.

Taken together, these 3 elements—structural demand, a more open customer base, and strategically motivated non-dilutive capital—are what start to make mining and critical minerals look like a domain where venture capital can sensibly play.

How Strategic Grants and Debt Actually Change the Capital Stack

For a deep tech company building hardware, manufacturing processes, or new forms of industrial infrastructure, the capital stack is often the difference between “interesting technology” and “investable business.”

If the only route to building a first-of-a-kind plant is to raise the entire amount as equity, the numbers frequently stop working.

A billion dollars of equity to build a facility is very hard to justify on a venture timescale, even if the underlying technology is compelling.

Strategic grants and concessional debt do not change the physics of the plant, but they do change who pays for what and on what terms.

If half of the capital stack required for a pilot or first commercial facility can be covered by non-dilutive funding, the amount of equity needed drops sharply.

That immediately makes the opportunity more attractive for early investors and creates a clearer path to venture returns.

Where the Venture-Scale Opportunities Sit in the Mining Value Chain

The starting point for thinking about opportunity in mining is simple but easy to overlook: everything ultimately comes back to bringing more critical resources into production.

The value chain is long, the technologies are diverse, and each commodity behaves differently. Moreover, bottlenecks do not show up in the same place for every mineral.

For instance:

• Some commodities are constrained at the discovery stage.

• Others are constrained because known resources are not economically viable to process with current technology.

• In still other cases, the constraint lies in how effectively existing mines extract value from ore bodies already in operation.

So any serious view of where venture-scale opportunities sit has to be specific: both about the segment of the value chain and about the commodity in question.

With that caveat, three broad domains stand out as particularly relevant for venture-backed companies:

1. Exploration technologies at the top of the funnel.

2. Downstream processing and refining.

3. Technologies that improve operations and extraction at mines that are already producing.

1. Exploration Technologies: Filling the Top of the Funnel with Better Targets

At the very top of the value chain is exploration—the activity that determines what projects will even exist in around fifteen years’ time.

For certain commodities, copper being a clear example, exploration activity has lagged what would be needed to keep future supply in line with projected demand. For more than a decade in some cases, the level of exploration activity simply has not matched the scale of the challenge.

In that context, venture-scale opportunities emerge around tools that can accelerate and improve discovery.

This is not just about doing “more exploration” in a brute-force sense, but about using data and computation to make exploration programs more targeted, more efficient, and more discriminating.

One direction of interest is combining multiple datasets and applying AI to help identify high-potential assets—including the kind of tier-one opportunities that can materially impact future supply.

The other is more subtractive but just as valuable: identifying low-potential or “bad” projects earlier, so teams can avoid spending time and capital drilling out assets that are unlikely to be economic.

For miners and others focused on discovery, that distinction matters. Being able to eliminate dead ends sooner can make exploration programs more efficient and improve how scarce capital is allocated.

2. Processing and Refining: Making Previously Uneconomic Assets Economic

Moving downstream, another cluster of opportunities sits in processing and refining. Here, the problem is often not the absence of resources, but the fact that many known deposits are not economic with today’s technologies.

Impurities, complex mineralogy, and other characteristics can make projects difficult or expensive to produce—effectively trapping resources behind constraints that only new processing approaches can address.

Technologies that shift those constraints—by lowering costs, handling impurities, or unlocking new process routes—can have an outsized impact.

In lithium, for example, there has been significant interest in direct lithium extraction approaches that can reduce the cost of production and, in some cases, make resources viable that otherwise would not be.

The compelling aspect of these technologies is that they do not just improve operating performance for a project that would have been built anyway. In many cases, they can change the binary question of whether a project is economic at all.

That is a very different value proposition: instead of incremental optimization, processing innovation can make an asset viable.

The same logic applies to tailings reprocessing and other forms of value extraction from materials that are currently underutilized or discarded.

Recycling approaches can also fall within this broader category, where chemistry and engineering innovation changes what is considered feasible or economic to produce.

Across these examples, the unifying theme is the same: taking assets that the market undervalues because they are difficult to process, and using technology to unlock that value.

3. Operations and Extraction: Getting More from Existing Mines

The third domain focuses on mines that are already operating today.

These assets are on stream, producing ore and generating revenue—but there is still a significant question: “Are they extracting as much value as they could from the ore bodies they control?”

Given how long it takes to bring a new mine into production—often on the order of fifteen years—this question is not academic.

When supply shortfalls loom, one of the most immediate levers is improving the performance of assets that are already producing today.

This is where technologies around data, sensing, and precision mining come into play.

By capturing better information about the ore body and the flow of material through the system, operators can adjust how they mine and process in ways that increase recovery or reduce losses.

Data fusion plays a role here as well: combining different streams of sensor data to provide a more accurate and actionable picture of what is happening.

The common thread across these operational technologies is that they aim to maximize output and recovery from resources that are already committed to production.

For companies worried about meeting demand, that is a powerful lever.

For technology providers, it creates a clear link between their solutions and measurable improvements in recovery and yield.

Designing Business Models that Really Capture Value in Mining

At the heart of business model design in mining technology is a simple question:

“If you are the company bringing a new solution into the industry, how do you capture as much of the value you create as possible?”

The answer depends heavily on where in the value chain you operate, and on how responsibilities are split between you and your customer.

• The further downstream you sit, the more familiar the models tend to look.

• The further upstream you go—especially into processing and exploration—the more business models start to incorporate commodity exposure alongside technology economics.

There is no single “correct” playbook. Instead, there is a spectrum of options—subscription, licensing, royalties, joint ventures, and full asset ownership—that can be combined in different ways.

In practice, the craft is choosing a configuration that fits your technology, your capital requirements, and the kind of upside (and exposure) you want to take on.

1. Subscription and “Hardware-as-a-Service” Models

Closer to the operating mine site, many of the models will feel familiar to anyone working in enterprise software or industrial IoT.

If you are providing a software product to support mine operations, an analytics platform, or a data product built around sensor networks or robotics, a straightforward subscription or annual recurring revenue model is often a natural starting point.

In some cases, this extends to Hardware-as-a-Service arrangements.

The provider may install sensor units, autonomous platforms, or other devices at the mine, but instead of selling them outright, charges an ongoing service fee.

That fee can bundle together access to the software and data, as well as ongoing services such as maintenance and support, into a recurring commercial structure.

From an investor’s perspective, these models are relatively straightforward to evaluate because they resemble familiar enterprise and industrial technology revenue structures.

They typically scale with the number of sites, users, or units deployed, and can become repeatable as adoption expands—provided the solution becomes embedded in the customer’s operations.

The trade-off is that, in many cases, the upside is primarily linked to commercial reach (how broadly the solution is deployed) and fee levels, rather than direct participation in commodity economics.

2. Licensing, Tolling, and JVs for Processing and Refining Technologies

The business model equation starts to look different in the processing and refining part of the value chain, especially when you are bringing a novel flowsheet or a fundamentally new way of treating a particular ore or brine.

At the simplest end of the spectrum is classic technology licensing.

• A company develops a new process for copper, lithium, or another critical mineral.

• An engineering, procurement, and construction firm may handle the design and build of the plant.

• The customer takes responsibility for building and operating the facility.

• The technology provider licenses the flowsheet and receives a fee, but does not participate in operations or put capital into the asset.

The appeal of this model is that it is “low-touch” and comparatively light on capital for the technology company.

There is no requirement to finance plant construction, take operational responsibility, or manage the day-to-day realities of running a production facility.

The downside is that the upside is capped.

Even if the technology is central to making the project economic, the technology provider’s share of the value is limited to the licensing terms they can negotiate.

That is why venture investors often approach pure licensing with caution: if the total number of plants you can license to is limited, the key question becomes whether that stream of fees can support venture-scale outcomes.

Further along the spectrum are tolling models.

Here, the technology provider’s economics are tied more directly to what passes through the plant.

The fee might be linked to tonnage processed, units of production, or some other measure of throughput.

In return, the provider may offer ongoing operational support and maintenance, and supply proprietary components, reagents, or catalysts that are essential to the process.

This structure can allow the technology company to capture more of the value it creates.

As production scales, revenue can scale too. There is also room to blend fixed and variable components so that the provider can choose how much commodity exposure it is comfortable with.

If the provider takes on more operational involvement or puts some capital into the project, commercial terms can reflect that.

Joint ventures are a natural extension of this logic.

In some cases, the technology company will not just provide the process and operational know-how, but will also contribute equity and capital to fund the plant. The project becomes a shared asset, with a tolling or processing arrangement layered on top.

This gives the technology company a deeper claim on the economics of the facility, but it also ties up more capital and increases exposure to project execution.

3. Royalties, Commodity Exposure, and Technology-Enabled Mining Companies

In exploration and early-stage resource development, business models can shift away from selling technology alone and towards becoming, in effect, a technology-enabled mining company.

One way to do this is through royalties.

A technology company uses its tools to help identify, de-risk, or advance assets owned by others. In return, it negotiates a slice of the upside—often framed as a percentage of net smelter returns or similar.

Over time, this can build into a portfolio of royalty interests across multiple projects, each tied to the role the technology played in enabling progress on the asset.

Joint ventures are common here as well.

The technology provider and the asset owner can agree to co-develop a project, combining the technology with the mineral rights and capital. The precise split of responsibilities and economics varies from case to case, but the essence is that the technology company steps closer to being a co-owner of the resource.

Some teams go further and eventually choose to acquire assets outright and become operators themselves.

The logic is straightforward: if your technology helps you identify undervalued assets—and you believe you can operate them effectively—ownership provides the most direct exposure to the commodity upside your approach unlocks.

That shift, however, comes with a corresponding increase in operational and capital intensity. It pushes the company into a different type of business with materially different execution demands.

4. Hybrid Approaches

Across all of these examples, one pattern stands out: most companies do not live at a single point on the spectrum forever.

They experiment with hybrids.

Some processing technology companies combine licensing with elements of tolling, or pair a tolling model with selective equity participation in specific projects.

Some exploration-focused companies combine royalties on certain projects with joint ventures on others, and in a smaller set of cases may choose to pursue ownership where the opportunity justifies the additional exposure.

The important point is that there is no universal answer that applies across all technologies, commodities, and stages.

In practice, the right business model is the one that matches the characteristics of the technology, the structure of the segment it serves, the willingness of customers to engage, and the capital that is realistically available.

What is clear is that mining technology companies have more tools at their disposal than a simple choice between selling software licenses and building and owning entire plants.

Much of the strategic flexibility sits in the middle ground—where business models are designed to reflect not just the technology being delivered, but also the responsibilities taken on, the capital committed, and the level of commodity exposure a company chooses to accept.

Cracking Adoption in an Industry That Wants to Be “First to Be Second”

One of the defining characteristics of mining as an industry is its relationship with risk.

There is a well-known saying that captures it neatly: everyone wants to be first to be second.

In practice, many operators are willing to adopt a new approach once it has been proven elsewhere—but far fewer want to be the first to validate it on their own operations in a serious commercial way.

That stance is understandable. Mining organizations are often large and complex, and bringing a new technology into an operating environment can create real disruption risk—especially when production outcomes and reliability matter.

Yet this cultural reality makes life difficult for early-stage companies that need their first pilots, first field deployments, and first commercial references to prove their technologies in the real world.

Despite this tension, there are clear, repeatable ways teams can break through the “first to be second” barrier.

They tend to start with who is around the table, extend to which customers are targeted first, and culminate in how the company sequences its own de-risking milestones.

Why Industry Experience, Advisors, and Strategic Investors Matter

Selling into mining and metallurgical companies is rarely just a matter of having a better product. It is often about navigating a buying process that requires buy-in from multiple stakeholders—sometimes across different sites and functions.

This is why industry experience on the team can matter so much.

When founders, executives, or senior team members have worked inside the industry, they bring more than credibility. They tend to understand how decisions actually get made, where the friction points are, and what evidence different stakeholders need to see before supporting a pilot.

That experience does not have to sit exclusively within the founding team. It can come from board members, advisors, or senior hires brought in early.

What matters is having people around the table who have seen the industry from the inside and can help shape a go-to-market approach that reflects how mining customers actually adopt technology.

Strategic investors can play a similar role.

As more mining-focused corporate venture arms become active, it is increasingly common to see operators take minority equity positions in technology companies.

When that happens, it can do more than validate a solution. It can help streamline early piloting, reduce friction in adoption pathways, and accelerate de-risking.

Tier-2 and Tier-3: The Practical Path to First Commercial Proof

Even with the right people and the right relationships, not all customers are equally suitable for a first deployment.

Large miners have said this openly: for an early-stage company looking for its first serious pilot, they may not be the best place to start. Their internal processes can simply be too slow and too complex for the pace a young company needs.

This is where tier-2 and tier-3 miners—and other smaller operators—can become critical first partners.

Their organizations are often able to move faster. In some cases, a pilot can be approved with only one or two key signatures rather than a long, multi-step internal process.

That alone can make it much easier to get an initial reference and build momentum.

Smaller players can also be more willing to explore new technologies because the economics of their projects can make improvement opportunities more urgent—and because they may have more flexibility to work with new commercial structures.

Arrangements involving royalties or shared upside can be difficult to negotiate with a tier-1 major, particularly on flagship projects.

With tier-2 and tier-3 companies, there is often more room to experiment with structures that align incentives and help a technology provider share in the upside.

The result is that working with smaller operators can provide a faster, more flexible path to proof.

A technology that demonstrates its value across a handful of sites—at increasing levels of scale—becomes much easier to present to larger players later.

The majors may still want to be “first to be second,” but if “second” means following credible references from real operating environments, the conversation changes dramatically.

How VCs Evaluate Mining and Critical Mineral Startups Across Stages

When investors look at mining and critical minerals startups, they are not applying an entirely different playbook from the rest of deep tech—but they are applying it with a sharper focus on where the risk really sits at each stage.

Science risk, engineering risk, commercial risk, and capital intensity show up in different proportions as a company moves from pre-seed to growth.

Understanding how those pieces shift is essential both for founders planning their fundraises and for investors deciding when and how to lean in.

Pre-Seed and Seed Rounds

At the very earliest stages—pre-seed in particular—the center of gravity is almost entirely around the founding team.

That is true across ventures in general, but it is especially pronounced in complex, industrial sectors like mining and minerals, where the path to market is long, and the operating environment is unforgiving.

At this point, most teams do not yet have a product. There is usually no revenue.

The “company” may be no more than a founder or a small group of co-founders with a clear idea of the problem they want to solve and a first view on how their technology will address it.

Investors underwriting pre-seed rounds are therefore asking questions about people more than anything else.

• Do the founders understand the technical domain deeply enough to push the science or engineering forward?

• Do they understand the industry well enough to navigate customers and partners?

• Can they build a team, absorb feedback, and adapt as they learn more about where their technology fits in the value chain?

By the time a company reaches seed, the picture becomes more concrete.

This is the point where investors are looking for early signals of product–market fit, even if those signals are still rough.

Some teams will have started to generate revenue, others may not, but in both cases, there should be more than just a concept. The technology needs to have moved beyond the realm of pure science.

A key shift at seed is that science risk should largely be behind the company.

The core physics, chemistry, or algorithmic approach should have been demonstrated to work under relevant conditions.

The remaining work is now primarily around engineering execution—building systems that are robust, scalable, and deployable in the environments where customers operate.

Investors at this stage will often look for early techno-economic analysis.

Even if it is still based on lab-scale or small-scale tests, it should offer a plausible view of how the technology will perform at commercial scale and how it stacks up against incumbent approaches.

The numbers do not need to be perfect, but they do need to point toward a cost and performance profile that could be genuinely competitive.

Seed is also usually where the first in-field pilots and demonstrations begin.

These may be limited in scope, but they provide the first real test of how the technology behaves outside the lab and how customers respond to it.

For investors, these pilots are about more than technical validation.

They are also about seeing early signs of how the team executes in the field and how it starts to build relationships with real operators.

Series A and Beyond: From Science Risk to Execution, Scale, and Repeatability

By the time a company is raising a Series A, the questions shift decisively away from “does this work at all?” toward “can this be scaled and repeated?”

This is where investors want to see clear evidence that the company has found a specific problem, for a specific type of customer, that its technology can solve reliably.

In other words, product–market fit should no longer be a hypothesis—it should be something the company can point to with confidence.

At this stage:

• Pilots and early deployments should have moved beyond one-off experiments.

• There should be a pattern: a defined customer profile, a repeatable use case, and a consistent set of outcomes.

• The company should be able to articulate, with some precision, what value it delivers—whether that is in improved recovery, lower cost per ton, faster discovery, or some other metric that matters in the context of mining and minerals.

Series A is also when unit economics come into sharp focus.

Investors want to understand not just that the technology works, but what it costs to deliver, what customers are willing to pay, and how long it takes to recover the cost of a deployment.

Payback periods, contribution margins, and lifetime value become central to the conversation. These metrics are the bridge between a compelling technical story and a scalable commercial business.

Alongside that, the company needs to show that it is beginning to build a repeatable sales and deployment playbook.

Selling into mining is complex, but by Series A, the team should be able to describe how they win customers: who the decision-makers are, how long the cycle takes, what evidence is needed at each step, and how deployments are executed with acceptable risk and reliability.

As companies move beyond Series A—into Series B and later—the focus intensifies on scaling what already works.

At that point, the central questions become: “Can the company expand into new geographies, replicate its success across multiple sites or projects, and strengthen its margins as it grows?”

Organizational scale also becomes part of the equation.

Teams may reach one or two hundred people, and investors will look closely at whether the company has the leadership, processes, and systems needed to operate at that level.

Growth Stage

Across this progression, one emerging issue stands out: the relative scarcity of growth-stage capital dedicated to industrial deep tech in mining and critical minerals.

There is a growing ecosystem of investors willing to back pre-seed, seed, and, to some extent, Series A rounds in this space.

But when companies need larger checks—often in the 10M+ range—to fund scale-up, plants, or global expansion, the options become thinner.

This creates a genuine gap in the market.

Exit Strategy

The uncertainty at the growth stage feeds directly into another unresolved topic: exits.

Unlike in some more mature segments of enterprise software or consumer tech, there is not yet a long list of clear precedents for how mining technology companies, particularly those with meaningful ties to physical assets, ultimately return capital to their investors.

There are not many examples yet of mining tech companies following a well-trodden path: building a certain type of business, reaching a certain scale, and then being predictably acquired by a major miner, an industrial conglomerate, or another strategic buyer.

That does not mean such exits will not happen, but it does mean they cannot yet be treated as a given.

As the mining technology sector matures, and as more companies reach later stages and begin to test different exit routes—acquisitions, IPOs, or other structures—the market will start to reveal which combinations of technology, business model, and capital structure are most attractive.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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Deep tech often lives far from the spotlight—down in mines, factories, and remote sites where safety, uptime, and unit economics matter more than headlines.

It’s a world where technology is judged not (only) by demos, but by whether it can survive harsh conditions, move the needle on cost per ton, and scale across a small—but global—set of demanding customers.

Today’s question is: what does it actually take to take a mining tech company from a lab prototype to a successful acquisition?

On this week’s episode of Deep Tech Catalyst, I sat down with Alexandre Cervinka, Exited Founder of Newtrax Technologies (acquired by Sandvik in 2019) and Angel Investor, to unpack how he built and scaled a mining tech company from early experiments to a successful exit.

Key takeaways from the episode:

🎯 Niche Dominance Is a Strategy, Not a Slogan
Picking a tightly defined global niche—like underground hard rock mines—and committing to dominating it gives every team “vector” a clear direction and filters which opportunities deserve attention.

📊 ROI-Driven Sales Win Complex Industrial Deals
In B2B industrial environments, no one buys on vision alone. Building a living ROI model around concrete use cases—lost equipment, ramp congestion, cost-per-ton improvements—turns abstract digitalization into numbers that internal decision makers can defend.

📡 Direct Access to End Users Shapes the Roadmap
Partners and distributors can open doors, especially in new regions, but sustainable advantage comes from direct relationships with operations, maintenance, and safety teams underground. That proximity is what keeps product development anchored in real operational pain.

🚫 Saying No Is Core to Strategy
Attractive adjacent deals—like large open-pit projects—can quietly derail focus. Ruthlessly declining off-niche opportunities is what keeps the organization aligned and gives a company a credible shot at truly dominating its chosen market segment.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Anchoring a Venture in a Single, Global Niche

For a small, mining tech B2B venture, trying to serve multiple markets is usually less a sign of ambition than of dilution. The alternative is to select one specific segment, understand it in depth, and design the entire business around the goal of dominating that slice of the world.

In this framing, “strategy” stops being an abstract concept and becomes a constraint: every engineering hour, every sales trip, and every hire should reinforce the same direction of travel.

The question is no longer “where else could this technology apply?”, but “how do we become the obvious choice in this one market, globally?”.

Choosing Underground Hard Rock Mines as the Beachhead

In the case discussed, that beachhead was underground hard rock mechanized mines—deep metal mines with complex, safety-critical operations.

The founding team did not start inside this industry; they were pulled toward it by partners who saw a clear fit between a technology stack (hardware + software) and unresolved operational problems underground.

What turned this from an abstract opportunity into a true niche strategy was direct exposure. Visiting mines, and sitting with operations managers revealed 3 attractive characteristics:

• The operations were complex

• The incentives to improve safety and productivity were strong

• The number of relevant sites worldwide was finite and mappable

In other words, it was both painful enough to matter and bounded enough to be “dominated” by a focused startup.

Within that niche, the mission took a clear shape: help underground mines become safer and more efficient, and in doing so, reduce their environmental footprint.

To justify investment at the mine level, the system also had to improve cost per ton: fewer minutes wasted at the start of a shift looking for equipment, less congestion on ramps, smoother traffic flow, and better use of existing assets.

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Commercialization Strategy and ROI Model

The entry point into mining came through strategic partners who were already embedded in the industry and could see how a new device might address a range of unresolved problems underground.

In the earliest phase, these partners acted as translators and guides.

They introduced the team to mine operators, framed the issues in language the industry understood, and opened doors that would have been difficult to access directly.

The natural evolution was toward direct global sales.

The company moved from relying on partners for access to building its own relationships with operations managers, maintenance leaders, and safety teams underground.

That proximity to the end user proved critical.

It not only shortened feedback loops but also provided a much clearer line of sight into which problems mattered most and how the technology needed to evolve to address them.

In the context of B2B industrial technology, the lesson is straightforward: partners can open the first doors, but enduring value creation depends on direct, long-term engagement with the people who actually use and depend on the system.

Building an ROI Model Around Real Bottlenecks

In this environment, selling on vision alone was never going to be enough. Underground mines are capital-intensive businesses, and each new system competes with many other potential investments.

To win those internal prioritization battles, the technology had to be tied directly to measurable financial outcomes.

The company built a detailed ROI model in the form of a spreadsheet that captured the different ways the system could reduce cost per ton while improving safety. That model was not static; it evolved with each customer conversation.

Sitting down with the economic buyer—typically an operations manager or equivalent—the team would walk through the assumptions, line by line, and connect specific applications to that particular mine’s context.

Some applications were simple and easy to grasp.

One of the earliest use cases was around lost equipment.

At the start of a shift, crews would go underground without knowing exactly where key machines were located. By using the system to track equipment, mines could start each shift with full visibility on asset location, cutting wasted time and improving overall efficiency.

In another case, the main bottleneck was traffic congestion on the ramp—a critical artery in an underground operation.

Better traffic control, enabled by real-time data on truck movements, allowed one mine to increase the number of trucks reaching the surface each shift. That single additional load per shift had a direct, quantifiable impact on cost per ton.

As the company expanded its footprint, the model accumulated more of these applications.

Over time, it captured around 30 different ways in which customers were using the system to improve safety and productivity.

When visiting a new mine, the team could present this catalogue of real-world use cases and collaboratively filter out the ones that did not apply. In most cases, a subset remained that clearly resonated with the mine’s operational challenges.

Crucially, this exercise was not only a sales tool. It was also a structured way to learn.

The discussion around ROI surfaced how each operation actually worked, what constraints mattered most, and where incremental improvements would be most valuable.

Those conversations, repeated across many sites, helped shape the roadmap far more effectively than abstract product planning could have done.

ROI-Driven Sales in a Complex Decision Environment

With contract sizes measured in the hundreds of thousands of dollars over the lifetime of a customer, these were not impulse purchases.

Each sale involved a complex decision-making unit—a kind of internal jury that had to be convinced the system deserved capital ahead of competing projects.

In that context, ROI-driven sales were not a nice-to-have; they were the only viable route. Internally, mines would run their own financial analyses to compare this project against others vying for the same budget.

If the vendor could not clearly articulate the return on investment and provide a structured way to quantify it, the project would struggle to advance.

Mastering this dimension became a core competence.

Over time, the sales process evolved into a collaborative exploration of where value could be created, grounded in operational data and economic logic rather than generic claims.

For founders building B2B industrial technology, the pattern is important: the value proposition becomes tangible when it is systematically translated into cost, throughput, and safety metrics that matter to a complex, ROI-driven buying process.

Founder-Led B2B Sales and Early Adopters

A recurring theme in this story is that sales was not an abstract function delegated to others; it was a survival skill learned under pressure.

After an early angel round was partially burned on ventures that did not work, the founders were given a very clear constraint: there were roughly six months left to generate sales or accept that the company would be shut down.

That ultimatum pushed sales out of the realm of theory.

Cold calling, which is easy to romanticize or dismiss in the abstract, became a daily reality. Rejection was constant, the hit rate was low, and very little about it felt glamorous. But it was the forcing function that turned the founder into a salesperson, not by preference but by necessity.

Sales effort only makes sense if it is pointed at the right targets.

Once the niche had been defined—underground hard rock mechanized mines—the next step was to translate that definition into a concrete set of accounts.

Rather than thinking in terms of “the market” in the abstract, the team built what was referred to as a hit list: a named, finite set of operations that fit the beachhead criteria.

In the context of B2B industrial technology, a beachhead market is often not thousands of customers; it can easily be a few hundred globally.

That scale is large enough to build a company, but small enough that every account can be identified, researched, and tracked.

Within each account, the focus turned to specific roles: operations managers, maintenance leads, safety managers, and others who formed part of the internal decision-making unit.

These were the people who both felt the operational pain and had a voice when capital projects were prioritized. Reaching them required a mix of channels—phone, email, and any credible way to get an introduction.

“Playing in Traffic”

Direct outreach was necessary, but not sufficient. The team also needed to show up where potential champions naturally gathered.

That meant identifying the “watering holes” of the industry—technical conferences, trade events, and professional forums where operations and engineering leaders spent time.

With a limited budget, classic marketing was not an option. Instead, the company invested in presenting technical papers at conferences. This approach served multiple purposes at once.

It positioned the team as serious technical contributors rather than generic vendors, created a reason for people to engage, and kept costs relatively low compared to conventional advertising.

The mindset behind this was described as “playing in traffic.”

Rather than waiting for interest to appear, the founder spent long stretches on the road, especially in the first few years. Within North America, that included long drives—up to twelve hours—to reach remote mining camps, along with cheap flights, multi-stop itineraries, and nights in low-cost hotels or even airports.

As international expansion began, the approach evolved. In these regions, especially where language and cultural barriers were more pronounced, the company initially relied on distributors to provide warm introductions.

Here again, there was a filter: the right distributor was one already selling complementary products into the same mines, with established relationships and a sales rhythm that could accommodate one more offering.

Joint road trips with these partners, visiting multiple customers in a week, brought concentrated exposure to new markets without the overhead of building a local presence from scratch.

Selling Capability (While the Product is Still Unfinished)

One of the hardest phases—especially in B2B industrial technology—is when the technology works, but the product is not yet fully finished.

That was exactly the context of the early sales efforts.

The company had a technology stack and a clear sense of the problems it wanted to address in mining, but the system was still evolving.

Under those conditions, the sale depended on two elements.

1. First, the ability to demonstrate the underlying technology in a way that made its potential obvious—showing what it could do rather than only describing it in the abstract.

2. Second, the ability to credibly convey that the team could close the gap between what existed today and what the mine actually needed.

Early adopters, in this context, were not self-declared.

They emerged from conversations with individuals and organizations that were frustrated by a concrete problem and willing to make a bet on an unfinished solution.

The fact that the team already had a track record of building solutions in other markets mattered. It signaled they could move quickly from a technology demonstration to a deployable product, even if the domain was new.

This is an important nuance for founders: in the earliest deals, customers are not just buying what the product is today; they are buying the team’s ability to deliver what the product needs to become.

Scaling Commercialization with Capital-Efficient Operations

A striking aspect of this story is how little early capital was spent on building internal structure and how much was directed toward simply reaching customers.

When the first angel check arrived, the priority was not to hire a sales team or invest in marketing infrastructure.

The priority was to fund founder-led sales: plane tickets, long drives, basic accommodation, and the minimum out-of-pocket costs needed to get in front of decision makers.

This approach reflects a broader allocation principle that is highly relevant for Deep Tech founders: in the earliest stages, the most valuable use of capital on the commercial side is not branding or polished collateral, but direct access to customers.

Every dollar that goes into being physically present at the mine—seeing operations, sitting with managers, understanding problems—creates learning that no second-hand research can substitute.

Leveraging Distributors and Trade Commissioners to Enter New Regions

Geographic expansion added another layer to the commercialization strategy. In the early years, most customer contact was direct and concentrated in North America, where language and distance were manageable from a Montreal base.

As the company looked beyond that region, the cost and complexity of going it alone increased.

Here, the company leaned on two types of leverage.

1. The first was institutional support. In the Canadian context, trade commissioners attached to embassies abroad played a practical role. These officials had visibility into local industrial ecosystems and could introduce the company to potential distributors that already served mining clients in their markets.

2. The second was the distributors themselves. The goal was not to sign any intermediary willing to carry the product, but to find those whose existing portfolio and customer base were already aligned with underground hard rock operations.

Once a suitable distributor was identified, the model was hands-on rather than arm’s-length. The founder would fly into the region and join the distributor for intensive road trips, visiting several customers in a single week.

This allowed the company to benefit from warm introductions and local knowledge while still maintaining direct exposure to the end users and their feedback.

Ruthless Focus as the Path to Market Domination

One of the most vivid images from the conversation is the idea of every employee as a vector.

As a company grows beyond 20 people, each individual brings energy, skills, and opinions—each vector pointing in a direction.

Strategy, in this view, is about alignment: getting all those vectors to point the same way so that their force adds up rather than cancels out.

Focus is what makes that possible. If the company is clear that its mission is to dominate one specific niche—here, underground hard rock metal mines—then every team, from engineering to sales, knows what “forward” means.

Product decisions, sales targets, hiring priorities: they all reinforce the same trajectory.

Without that clarity, vectors begin to drift—toward adjacent markets, custom projects, or one-off opportunities that look attractive but dilute momentum.

Saying No to “Attractive but Off-Niche” Opportunities

The real test of focus is not in what a company chooses to pursue, but in what it is willing to decline. In this case, the pressure showed up in the form of adjacent opportunities—such as large open-pit mines—that sat just outside the defined niche.

Salespeople would sometimes arrive with what sounded like a dream lead: in the case discussed, a huge open-pit project, possibly worth tens of millions of dollars, with strong initial interest.

On paper, it looked compelling. The customer profile was related to existing clients, the ticket size was large, and the potential short-term revenue was hard to ignore.

Yet these opportunities cut across the core strategy.

Open-pit mines, while part of the same broader industry, are operationally and structurally different from underground hard rock operations. Serving them properly would have required new product adaptations, different workflows, and a shift in commercial focus.

The response was deliberately ruthless: “No, this is outside our target market; we are not going to respond.”

Saying yes to a large but misaligned deal would have pulled engineering, sales, and management attention away from the chosen niche. Over time, enough of those compromises would erode the company’s ability to be the best in the world at the specific thing it had set out to do.

For founders, this highlights an uncomfortable but important reality: serious focus means turning down opportunities that look attractive in isolation but are strategically off-axis.

When Years of Focus Make the Hand Fit the Glove

Focus also compounds in a quieter way. By committing to a single niche over many years, the fit between the product and the market gradually becomes more natural—“the hand fits the glove.”

In the early stages, even with a clear niche, the alignment is imperfect. Each new customer surfaces gaps and requires some degree of adaptation.

Over time, however, the repeated cycle of deployment, feedback, and refinement within the same type of operation builds a kind of structural expertise.

This accumulated fit is not something that can be replicated quickly by a new entrant or a generalized solution. It is the product of repeatedly solving variations of the same problem in the same context, and folding those learnings back into the core offering.

The moment when the company began closing deals in places like India and Russia was a concrete signal that this had happened.

These were not “friendly” markets. They had local suppliers and different operating conditions. Winning there suggested that the solution was not merely adequate, but truly competitive on a global stage within its chosen niche.

What It Means to Truly Dominate a B2B Industrial Niche

The endgame of this strategy is not vague market presence; it is dominance. In this context, “dominating a niche” is not just rhetorical. It has a quantitative benchmark: serving more than 50% of the relevant customers in that clearly defined segment.

For underground hard rock mechanized mines, that meant aiming to become the default provider of digitalization systems for operators that fit the target profile.

The company did not set out to be one option among many, or to own a small share of a broad, ill-defined category. It designed its strategy around the idea that it could, over time, secure the majority of serious buyers in its specific, global niche.

This framing has two implications.

1. First, it raises the bar on what counts as success: a handful of impressive logos is not enough.

2. Second, it reinforces the logic of focus.

A company cannot realistically dominate multiple industrial niches at once, especially not in its early and growth stages. It can, however, build an exceptionally strong position in one, provided it is willing to align every vector—people, capital, product, and time—around that singular objective.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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When does it actually make sense for a Deep Tech company to go public, and what does it take to step into the markets without being crushed by public expectations?

The answers to those two simple questions are anything but obvious. They sit at the intersection of strategy, preparation, and relentless execution.

So, I sat down with Adam Bergman, Managing Director at EcoTech Capital, and explored what IPO-readiness really means for Deep Tech companies.

Key takeaways from the episode:

🛎️ IPOs Are About Deep Capital and Signaling, Not Just Liquidity
Going public is less about giving early employees a way out and more about accessing the deepest pools of institutional capital and the credibility that comes with being a listed company.

📏 There’s a Real Threshold for Scale, Profitability, and Market Relevance
Deep tech companies that can’t clear basic bars on size, margins, and long-term growth potential risk sliding into micro-cap territory, where many institutional investors simply can’t or won’t participate.

🗺️ You Need a 24-Month Plan to Educate the Street
Successful IPOs are built on years of relationship-building with investors and analysts, a clear milestone roadmap, and visible progress toward EBITDA or cash-flow positivity before listing.

🎯 First Movers Get to Define the KPIs for the Whole Category
Early public companies in a space effectively teach the market which metrics matter, turning their own operational strengths into the scorecard that later competitors will be judged against.

📈 The Roadshow Is Short, but Early Misses Have Long-Term Consequences
Those final weeks of meetings are about converting trust into commitments—and if a newly public company then misses its first or second quarter guidance, the credibility damage can shape its stock and financing options for years.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Why Companies Decide to Go Public Today

For any founder building a capital-intensive company, the question of how the journey ends is not theoretical. At some point, there is an exit.

• Sometimes that means a sale to a strategic buyer.

• Sometimes it means ringing the bell and stepping into the public markets.

Both paths matter. Profitability is a powerful advantage whichever route a company ultimately takes. But the specific role of an IPO, and the logic for choosing that option over an acquisition, has shifted meaningfully over the last twenty-plus years.

From Internet Mania to Today’s IPO Market

The frame most people still carry around for IPOs was forged in the late 1990s and early 2000s.

That period, defined by healthcare internet stories and general internet excitement, was a time when simply being associated with the web was enough to generate extraordinary investor enthusiasm.

Many of the companies going public then were not especially capital intensive. They were software and internet businesses where the primary assets were people and code, not factories and hardware.

Yet the public markets were hungry for anything with a dot-com label.

Institutional investors wanted to show exposure to the internet in their portfolios, and being public was almost synonymous with being part of that moment.

In that environment, the logic of going public was straightforward. The markets were open. Investors were eager.

Fast forward to today, and the landscape looks very different.

The number of IPOs has fallen sharply. Fewer companies are choosing to go public, and fewer remain public overall. That is not a small change at the margins; it signals a deeper shift in how both founders and investors think about the public markets as a destination.

Access to Deep Capital Pools and Credibility

With that backdrop, it is natural to ask: why pursue an IPO at all? In practice, two reasons still stand out.

1. The first is access to capital. For all the growth in private funding, the public markets remain the deepest pools of capital available. Institutional investors operating in those markets control enormous resources. For businesses that are large, capital intensive, and still in growth mode, the ability to tap those pools can be decisive.

2. The second is a form of validation that doesn’t show up on the balance sheet, but it does influence how a company is perceived. That signal matters in concrete ways. Some customers—particularly large corporations and government entities—feel more comfortable awarding business to a company with a ticker next to its name on an exchange like NASDAQ.

Founders working in sectors tied to energy, transportation, grid infrastructure, storage, or agri-food technologies often face established incumbents with long histories and trusted brands.

In that context, being able to stand in front of a customer and say, “We are a public company,” is not a vanity point. It can be part of how a younger business shows it belongs in the same consideration set as much older competitors.

What “IPO-Ready” Really Means: Scale, Profitability, and Investor Relevance

Public markets operate with their own thresholds, constraints, and expectations. To be a real candidate for listing, a company has to clear some basic hurdles of size and financial performance, and it has to matter enough to institutional investors and research analysts that they are willing to spend time and capital on it.

In practice, “IPO-ready” is a mix of scale, profitability, and relevance. Missing any one of these makes life as a public company significantly harder than it needs to be.

Valuation, Size, and the Micro-Cap Trap

One of the first issues is scale. A company that is too small may not fit well into the way many institutional investors are mandated to operate. There is a very practical lower bound on valuation.

As a rule of thumb, a company will often need to be expected to be worth at least 500 million dollars at the time of listing, and in reality, a billion-dollar valuation is a much more comfortable line.

The reason is not ego; it is the mechanics of how funds build portfolios.

If a company’s valuation falls to 3 or 4 hundred million dollars and stays there, it begins to drift into micro-cap territory. That is a category many large investors are either formally restricted from owning or simply choose to ignore.

For a newly public company, that is dangerous ground.

So the minimum scale question is about avoiding a structural classification that shuts the door on exactly the kind of investors a capital-intensive business will depend on over time.

Share Price Dynamics and Structural Constraints on Ownership

Alongside overall valuation, there is another technical constraint that can materially shape a company’s life in the public markets: the share price itself.

In many cases, a meaningful portion of institutional investors tend not to buy stocks that trade below certain levels, and $5 per share is often treated as a practical threshold.

Below that level, institutional participation can narrow quickly.

It is not unusual to see companies go public at $8, $9, or $10 per share, especially when they are pricing toward the low end of their IPO range.

If something goes wrong and the stock drops below $5, similar constraints can reappear: many institutional investors simply won’t own shares trading under that price.

In those situations, some companies attempt a reverse stock split to lift the nominal share price back above the threshold. Typically, this does not resolve the issue for long, and the stock price often drifts back down again.

Revenue, EBITDA, and Financing the Full Business Plan

Beyond scale, public investors want to see a business that is already meaningful in absolute terms.

The numbers will vary by sector, but a useful benchmark is at least 100 million dollars of annual revenue and, ideally, 10 to 20 million dollars of EBITDA.

If a company raises capital in an IPO but still doesn’t have enough funding to execute its business plan, investors assume it will need to return to the market for substantial additional capital before reaching profitability—and they price in that future dilution and uncertainty.

In recent years, those situations have often been hit hard by investors.

There is a persistent concern about whether non-profitable companies will continue to find financing in an environment where not every follow-on deal gets done.

That concern shows up as a valuation discount, or as outright reluctance to participate in the IPO at all.

From the company’s perspective, this means that preparing for an IPO is not just about hitting a certain revenue threshold. It is also about being able to show a credible path to EBITDA positivity or cash flow breakeven, and ideally about not needing constant returns to the equity markets just to keep the plan alive.

The more self-sustaining the business can become by the time it lists, the more comfortable public investors will be in backing it.

The Lens of Public Investors

Even with the right scale and improving profitability, there is another dimension that matters: relevance. Public investors have no obligation to own any particular stock. They can choose from thousands of listed names across every sector.

A company seeking their attention has to answer two basic questions: why this business, and why now?

In public markets, attention doesn’t spread evenly. Some sectors simply feel more “exciting” or easier to underwrite—because the growth drivers are (or seem) more familiar than others.

For Deep Tech companies building industrial hardware, complex infrastructure, or highly technical systems, that dynamic matters.

The challenge is not just proving the technology works; it’s making the business legible to investors—connecting a credible present to a clear path toward scale and profitability, in language the market can benchmark and follow quarter after quarter.

At the same time, public investors think differently from venture funds.

Their risk tolerance is lower. They benefit from liquidity and can move in and out of positions quickly. They are not committing to holding through a decade of volatility. For them, owning a newly public company is a trade-off between near-term execution risk and long-term upside.

That is why it is not enough to talk exclusively about a distant future.

A pitch built around “in twenty years we will be enormous and profitable” with little evidence of traction today will struggle. The story has to connect a credible present—a functioning business with real revenue and improving margins—to a believable future of expansion.

Planning on a Two-Year Horizon

Deciding to go public is not the moment to start thinking about the public markets.

By the time a company files, the institutional investors who matter most should already know the story, understand the business model, and have seen concrete evidence that management does what it says it will do.

That kind of trust cannot be built in a few weeks. A realistic horizon for serious preparation is about two years.

That may feel long from a founder’s point of view, especially in fast-moving markets, but it reflects how institutional investors actually work when they are considering backing a new, capital-intensive business they have never seen trade before.

Why the Pre-IPO Journey Starts 24 Months Out

The starting point is accepting that most institutional investors will not write a check for an IPO the first time they meet a company.

Particularly in newer or less familiar sectors, they need time to study the market, understand the drivers of the business, and get comfortable with the management team.

If the first encounter happens during the compressed two-week equity roadshow that immediately precedes an IPO, the odds of attracting those investors are low. At that stage, they are being asked to make a decision quickly, under time pressure, on a company with no trading history and limited public information.

For many, that is simply too much risk.

Beginning the process roughly 24 months before a planned listing gives room to do something very different. It allows management to meet investors in a low-pressure setting where the goal is education rather than allocation.

It creates space for a conversation to unfold over time instead of forcing a snap judgment.

The company can introduce itself, explain what it does, and outline where it aims to be by the time it is ready for the public markets. Investors, in turn, can ask questions, follow the company’s progress, and decide whether the way management thinks and executes matches the promises being made.

Operating in Sectors with Few Public Comparables

The standard way public investors evaluate a potential IPO is by looking at existing listed companies and examining how they trade: revenue multiples, EBITDA multiples, or net income multiples, depending on the maturity and profitability of the peer set.

In many of the markets where capital-intensive innovation is happening now, that peer set can be either small or misleading.

• On one side of the spectrum sit large, legacy incumbents—big, established businesses that may be profitable but are not growing quickly.

• On the other side are small, underfollowed public companies whose stocks trade for a few dollars a share, with market capitalizations in the low hundreds of millions and little or no research coverage.

Neither group is truly comparable to a young but rapidly growing company with significant expansion ahead of it. If investors default to those benchmarks, they may end up applying valuation frameworks that do not reflect the real potential of the business.

That is why the education process has to include a thoughtful conversation about comparables.

Management needs to be ready to explain which public companies are the closest reference points and why, even if they sit in adjacent segments rather than in the exact same niche. Investors need time to absorb that framework, test it against their own analysis, and decide whether it makes sense.

Owning the Narrative: Designing KPIs That Play to Your Strengths

When a company approaches the public markets, it is not only its revenue, margins, or cash flow that gets judged. It is also the lens through which those numbers are interpreted.

That lens is built from key performance indicators, and in many emerging industries, those indicators are not handed down from above. They are defined, often implicitly, by the first credible companies that come to market.

That creates a real opportunity.

Instead of being forced to compete under someone else’s rules, a company can shape the conversation around the dimensions where it is genuinely strongest.

The way it chooses and explains its KPIs will influence how investors think about the entire category, and how later entrants are compared.

Every Industry Has Its Own Metrics—and Its Own Room to Define Them

At a basic level, every public company is evaluated on a common set of financial metrics. Revenue growth, absolute revenue, gross margin, EBITDA margin, cash flow, and net income are always in the frame.

Those numbers are the language of the markets, and no business can ignore them.

But beyond that shared core, each industry develops its own set of operational and strategic indicators. These are the metrics that translate the specific physics or economics of a business into something investors can follow and compare.

• In biologics, for example, it might be the percentage efficacy with which a treatment targets weeds or protects a crop.

• In robotic systems working in the field, it could be the speed at which a machine can move down a row to harvest, cultivate, or weed without compromising quality.

• In solar, it is the percentage of sunlight a panel can convert into usable energy.

These sector-specific KPIs are not preordained. That is why it is so important for management to step back and ask some fundamental questions: where is the company’s true competitive advantage, how can that advantage be expressed in quantifiable terms, and which metrics, if adopted by the market, would highlight that edge most clearly?

Learning from Early Solar IPOs: How First Movers Set the Rules

The dynamic is easy to see in the early days of solar IPOs. Back then, most institutional investors and research analysts knew very little about the sector, so the first credible public stories helped teach the market what questions to ask.

In our conversation, three case studies captured that dynamic in practice.

1. One early deal was built around flexible solar panels. With little else to anchor on, flexibility became the reference point investors kept coming back to—because it was the first concrete comparison they had.

2. A subsequent company came to market with a different angle: high efficiency, centered on how much power you could get out of a panel. In that framing, flexibility wasn’t the point; efficiency was.

3. Then another company arrived, positioning itself around low cost—arguing, in effect, that cost was the metric that mattered most.

What emerged from that sequence was not a single definitive KPI, but a vivid illustration of how early public companies in a new field teach investors what to care about.

Each one defines success in its own terms, and those definitions shape the questions investors ask of everyone who follows.

For a founder contemplating an IPO in an emerging or technical industry, that history carries a clear lesson. Coming to market early is not just about capturing capital. It is about defining the scorecard by which you and your competitors will be judged.

Keeping Public Investors Engaged

Finally, KPIs have a role to play not just in getting a company public, but in keeping its investor base engaged afterwards.

Again, public investors do not behave like early-stage venture funds. They are not necessarily locked in for a decade. They can exit after the first day of trading if they choose, especially if the stock trades up sharply.

One way to counterbalance that pattern is to give investors a clear, long-term framework for why they should stay.

Well-chosen KPIs help make that case.

They give investors a way to see how the story unfolds quarter after quarter, beyond the initial excitement of the listing. If the metrics that matter most are improving steadily, if milestones are being hit, and if those trends are visibly tied to future growth and profitability, it becomes easier for investors to justify holding the stock rather than treating it as a short-term trade.

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From Preparation to Roadshow: Turning Long-Term Work into a Successful Listing

All of the preparation that goes into an IPO—the years of educating investors, refining KPIs, hitting milestones, and moving toward profitability—ultimately converges on a short, intense moment: the roadshow.

For management, those final weeks are when the story has to land.

The company moves from talking about a possible future listing to asking investors, very directly, to commit capital at a specific price and on a specific timeline.

The roadshow is not the beginning of the relationship with institutional investors. It is the culmination of everything that has gone before.

When it works, it is because the groundwork has been laid carefully, and the story being told in those meetings aligns with what investors have already seen over the preceding months and years.

What Actually Happens on the Roadshow

In practical terms, a traditional roadshow is a concentrated sequence of meetings, presentations, and conversations with institutional investors across major financial centers.

It typically runs for 2 or 3 weeks, depending on the size of the deal and the geographic reach of the investor base.

In other words, the roadshow is the period when the company has the full attention of the market and must use that time to make its case as clearly and convincingly as possible.

Moving from “We Plan to Go Public” to “We Are Going Public Now”

One of the biggest shifts between the long preparation period and the roadshow is the tone of the conversation.

When management is still 18 or 24 months away from an IPO, meetings with investors are largely about education. The message is: here is what we do, here is how we are growing, and at some point in the future, we expect to be in the public markets.

During the roadshow, that changes.

The language becomes immediate. The company is no longer talking about a hypothetical listing; it is saying, “We are going public in the next two weeks. We would like you to participate.”

That shift in tone is only credible if the intervening period has been used well.

If investors feel as though they are meeting the company for the first time, the request for capital will feel abrupt and risky. If they have been following the story for years, seeing milestones achieved and plans executed, the roadshow becomes a natural next step in a relationship that already exists.

In that sense, the roadshow does not replace the long-term work; it compresses it. It is a focused, time-bound attempt to turn familiarity and trust into investor buy-in.

Articulating the Story Clearly to an Investor Base That Knows Your History

Because of this, one of the main points of friction in a roadshow is often not about the underlying business but about how effectively management can articulate the story under pressure.

Institutional investors are being asked to take a risk on a company that has no trading history. They cannot look back at a decade of stock price behavior, prior management changes, or market reactions to past earnings reports.

With an already public company, an investor can study the record and decide how management has handled good news, bad news, and changing conditions.

With an IPO candidate, that record does not exist in the public domain.

The only evidence available is what investors have observed privately and what is presented during the roadshow itself.

That is why the quality of communication in those meetings is so critical.

The Non-Negotiable: Hitting Guidance in the First Quarters After Listing

All of this effort around storytelling and relationship-building ultimately converges on one unforgiving reality: after the IPO, the company has to hit its numbers.

The projections it gives the market for its first quarter as a public company, its second quarter, and ideally its third and fourth, are not just targets. They are tests of whether management understands its own business.

From the perspective of institutional investors, missing those early projections is a serious red flag.

The logic is straightforward.

A company that has been preparing for an IPO for years should have as clear a view as possible of its near-term performance. If, despite that preparation, it misses its guidance almost immediately, investors are left asking how well the team really knows its own operations and how reliable any future projections will be.

That is why the discipline of setting realistic milestones during the private phase is so important.

How Missing Early Numbers Becomes a Structural Problem, Not Just a Bad Quarter

When a newly listed company misses its numbers in the first or second quarter after an IPO, the impact goes far beyond a single earnings print.

Investors do not treat it as an isolated misstep.

They interpret it as evidence that management may not have a firm grip on the business, and that any projections further out are less trustworthy than they appeared in the prospectus.

The immediate reaction is often a sharp drop in the share price.

This is why thinking about the IPO as a long, deliberate process matters so much. The roadshow is not just about filling an order book. It is about stepping into a new environment with the highest possible chance of meeting or exceeding the expectations that have been set.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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Autonomous driving is getting real attention again—not as a sci-fi layer on passenger cars, but as a blunt, practical tool for sectors that can’t find enough skilled operators to keep tractors, forklifts, and trucks moving.

Behind the robotaxi noise sits a less visible but critical question set: where autonomy actually makes economic sense first, how much data it really takes to reach “good enough,” and which business models can survive the long, expensive slog to OEM integration.

Look under the hood, and a few structural challenges show up.

• Early traction tends to appear off-road in constrained, industrial settings long before fully open roads are viable.

• Simulation can speed things up, but autonomy is still a “data, data, data” business that ultimately hinges on real-world experience.

• And the most resilient teams are moving away from full-stack hardware dreams toward software-first, OEM-aligned platforms—while carrying years of retrofit costs just to be taken seriously.

To unpack how autonomy becomes an actual business, I spoke with Ivy Nguyen, investor at GFT Ventures!

Key takeaways from the episode:

🤖 Autonomy Solves Labor Scarcity Before It Cuts Labor Cost
Early adopters are less focused on removing drivers from the P&L and more on filling roles they simply cannot hire for, especially in remote or hard-to-staff locations.

🏭 Off-Road Domains Are Where Commercial Autonomy Starts
Controlled environments like warehouses, yards, farms, and factory floors offer bounded complexity and clearer liability, making them more viable than open roads for early deployments.

📊 Data Is Still the Hardest Bottleneck
New model architectures and simulation help, but real-world data remains essential; winning teams are those that can acquire and generate it quickly and economically.

🧩 Software-First, OEM-Integrated Is the New Default
Instead of full-stack vehicle startups, the emerging pattern is autonomy as a software layer integrated into legacy OEM platforms and distributed through their dealer networks.

🏗️ OEM Partnerships Take Years, Retrofits, and Meaningful Capital
Reaching scalable distribution via OEMs often requires four to five years, multiple parallel OEM conversations, and $10M+ of retrofit and field work to prove both technical performance and customer demand.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

The Logic of Autonomous Driving

In practical terms, autonomous driving is the ability of a machine to move from one point to another without relying on a human operator to control it in real time. The operator is not sitting in the cabin of the vehicle, and may not even be physically present on site.

The core requirement is that the system itself can handle the act of driving.

That definition applies across a wide range of platforms. It can be a car, a truck, a forklift, a tractor, or any other machine that needs to move through an environment in a controlled way.

What changes from case to case is not the fundamental concept of autonomy, but the environment in which that autonomy has to work and the constraints it must respect.

Understanding the Environment, Not Just Following a Route

For a system to qualify as genuinely autonomous, it is not enough to replay a fixed route. The vehicle must be able to understand what is around it and make decisions continuously as conditions change.

That includes recognizing obstacles in front, to the side, and behind; distinguishing between static structures and moving actors; and updating its plan as new information arrives.

The machine has to chart a path between point A → B based on the rules of the environment it is operating in.

• On a public road, that means following traffic laws and adapting to how human drivers actually behave on highways and city streets.

• In a warehouse or factory, it means respecting internal safety rules, right-of-way conventions, and the layout of aisles, shelves, and loading areas.

The bar for safety is high.

An autonomous system must avoid damaging itself, avoid damaging the infrastructure around it, and, above all, avoid harming people, animals, or anything else that might enter its path.

The entire autonomy stack — from sensing, to perception, to planning and control — exists to make that kind of safe navigation possible without a person constantly correcting the machine.

Teleoperation as Part of the Autonomy Stack

Autonomous driving is not an all-or-nothing state. In many real deployments, there is still a human in the loop, but that human is no longer physically inside the vehicle.

Instead, they might be nearby or in a remote operations center, ready to step in when the system reaches the limits of what it can handle.

Seen this way, teleoperation is not a contradiction of autonomy but an integral part of how autonomy is made practical in early stages.

The system does as much as it can on its own, and when it encounters a scenario it has not yet learned to resolve, control can be handed off to a remote operator who manages the exception and then returns the vehicle to autonomous mode.

Why Autonomy Matters Today

When the Issue Is Not Labor Cost, but Labor Availability

Autonomous driving is often framed as a way to remove labor costs from the bottom line. The assumption is that if a company can take the driver out of the vehicle, it can immediately improve margins.

In practice, that is not what is driving many of the most serious pilots and deployments today.

For a large number of businesses experimenting with autonomous machinery, the problem is that labor is missing.

Companies cannot find enough people with the right skills to operate the equipment they depend on, whether that is a tractor in a field or a forklift in a logistics operation. The constraint is the availability of skilled operators in specific markets, not the hourly wage.

That simple, but not obvious, shift changes the logic of adoption.

When a business cannot staff its operations reliably, autonomy stops being a theoretical efficiency gain and becomes a practical requirement to keep the operation running at all.

Infrastructure as an Unexpected Barrier to Deployment

Operating in remote regions introduces another layer of complexity that does not show up in a simple autonomy business case.

The areas that are most eager to adopt autonomous vehicles because they lack people are often the same areas that lack basic infrastructure.

Electrical and power infrastructure may be limited or unreliable, which complicates everything from charging vehicles to running on-board compute and communications systems.

Telecommunications infrastructure can be thin or absent, making it difficult to support connectivity, over-the-air updates, or teleoperation. What looks straightforward in a connected, urban environment becomes much harder when the supporting systems are not in place.

These gaps shape how and where autonomous vehicles can be deployed commercially.

They introduce additional engineering and operational challenges that may not be obvious from the outside. The autonomy stack itself might be ready to handle the task, but the local environment is not ready to support it.

As a result, real-world deployments are full of surprises that sit outside the pure software and hardware discussion.

The Less Obvious ROI: Reduced Damage and Consistent Performance

Alongside labor dynamics and infrastructure constraints, there is a quieter set of benefits that can make the return on investment for autonomy more compelling than it appears at first glance.

These benefits come from the way autonomous systems perceive their surroundings and the way they standardize performance across a fleet.

When a vehicle is covered in sensors and driven by an algorithm-based system:

• It can maintain a comprehensive, 360-degree view of its environment.

• It can process multiple streams of data at once and react based on that full picture. In practical terms, that can mean fewer incidents.

Some operators are twenty-year veterans who know exactly how to handle the equipment in every situation. Others are on their second or third day and are still learning.

That variation shows up in damage rates, near-misses, and overall efficiency.

By contrast, a computer-driven fleet tends to deliver a more consistent level of performance. Once the autonomy stack reaches a certain standard, that standard applies across every vehicle running that software.

When companies analyze the economics of autonomy in settings where it works well, these less obvious factors can be significant.

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Where Commercial Viability Begins: Off-Road vs Open-Road Applications

A useful way to think about autonomous driving is to divide the world into off-road and open-road environments. That distinction is not just semantic; it is one of the main reasons some applications of autonomy move toward commercial viability faster than others.

On the technical side, autonomy behaves a lot like robotics in general.

The more control you have over the environment in which the system operates, the easier it is to make that system work reliably.

When the space is constrained, when the types of objects are limited, and when the patterns of behavior are relatively predictable, it becomes far more realistic to engineer a system that can handle most edge cases without constant human rescue.

Off-road autonomy benefits from exactly this kind of constraint.

The vehicle is operating in a defined footprint rather than on an open network of roads. The number of different actors it must interact with is smaller.

The set of motions, maneuvers, and scenarios it needs to handle is narrower. In some cases, it is even possible to carve out a dedicated lane or a protected corridor where only autonomous vehicles are allowed to operate, which further simplifies the problem.

In that context, the autonomy stack is being asked to master a contained universe. It still has to perform robust perception, planning, and control, but it does so within a territory measured in square meters or square feet that does not change very much over time.

The vehicle is not being moved arbitrarily from one environment to another with completely different rules. That boundedness is a major advantage when the goal is to get a system from prototype to something that can be deployed and left to run for extended periods without constant intervention.

Off-Road Applications: Where the Path to Deployment Is Shorter

These characteristics explain why many of the most promising autonomous driving applications are off-road. Factory floors, warehouses, rail yards, industrial plants, yards full of vehicles, and large parking environments all share the same structure: a limited geographical footprint, a defined set of tasks, and a smaller variety of interacting agents.

In these settings, the system does not need to be prepared for “infinite miles” of novel conditions. It needs to be deeply competent within a known layout and a known set of operating patterns.

The number of scenarios it must recognize and resolve is still large, but it is no longer unbounded. As a result, the amount of data required to reach a commercially acceptable level of autonomy is more manageable, and the engineering time required to reach that level is shorter.

That difference shows up in practical terms. A system designed for off-road autonomy can be tuned to the specifics of a single rail yard or a family of similar warehouses.

It can be deployed knowing that the environment is not going to surprise it with an entirely new class of obstacle or behavior every day.

The company building it can reach meaningful performance faster, and the customers testing it can see reliable operation sooner, which matters when both sides are working under real economic and time constraints.

Open-Road Autonomy: Infinite Edge Cases and Expensive Data

Once a vehicle leaves this kind of controlled domain and enters the open road, the problem changes dramatically. The autonomy system has to cope with the full complexity of public driving environments, and that complexity is vast.

Highways have their own patterns: merging traffic, lane changes at high speed, long-distance visibility, and a particular mix of vehicle types.

City streets behave differently.

There are more intersections, more pedestrians, more unpredictable stops and starts. Vehicles range from compact cars to delivery vans to heavy-duty trucks. Motorcycles and bicycles cut through gaps that other vehicles cannot. Pedestrians may jaywalk.

Debris appears unexpectedly in the road. Animals can cross at any time. Weather and lighting conditions change hour to hour.

Each of these elements expands the universe of edge cases the system must be ready to handle. Training models to recognize, anticipate, and respond safely in all of these situations requires large amounts of highly varied data.

Collecting that data is expensive and time-consuming. It involves driving vast distances, capturing rare events, and then painstakingly labeling and incorporating that information into the training pipeline.

That is why open-road autonomy has absorbed so much capital over the past decade. It is not just the complexity of the algorithms. It is the scale of the data collection and model training effort required to make the system safe and reliable across such a diverse, unpredictable environment.

Data as the Central Bottleneck: The Real Requirements Behind Training Autonomous Systems

Changing How Autonomous Systems Are Trained

Over the past few years, there have been real shifts in how autonomous driving systems are trained. New approaches have made it possible to get to “mostly autonomous” operation with less data than would have been required in earlier generations of the technology.

Architectures that move beyond purely traditional pipelines are now being applied to self-driving systems, from passenger vehicles to trucks in logistics corridors.

Some systems still lean heavily on high-definition maps and classic perception stacks. Others, like Tesla’s self-driving effort or newer entrants training autonomous trucks for specific markets, are using model architectures that can learn patterns of behavior directly from large volumes of driving data.

These newer approaches can compress some of the data requirements and make better use of the information that is already available.

But the fundamental reality has not changed as much as people might hope. Regardless of the exact architecture, these systems still end up needing more data than most founders expect at the outset.

The early impression that a system is close to ready — because it handles a demo route smoothly or performs well in a limited pilot — often hides how much additional experience the model will need before it can be trusted to operate with minimal human intervention across a broader range of conditions.

Synthetic Data

To address this, a growing number of companies are turning to synthetic data and simulation. The logic is straightforward. Instead of waiting to encounter every relevant edge case on real roads or in live industrial environments, it is tempting to imagine those edge cases, construct them in a virtual setting, and generate the data needed to train the model on how to respond.

In simulation, a team can create rare but critical scenarios on demand.

They can stress-test their autonomy stack against unusual combinations of events that might take an impractically long time to observe in real operations.

They can iterate quickly, adjust parameters, and see how the system reacts without putting physical equipment or people at risk.

This toolkit can dramatically accelerate the training process.

It reduces the number of physical miles that have to be driven solely to “collect edge cases,” and it gives teams a way to explore the limits of their system more systematically. Used well, it becomes a powerful amplifier on top of the real-world data the company already has.

However, simulated data is not a full substitute for real-world experience.

The scenarios constructed in software are based on assumptions about how the world behaves. They are abstractions, even when they are detailed. The gap between what happens in a carefully modeled virtual environment and what happens on an actual road, or in an actual yard or warehouse, can be subtle but important.

It Is Still “Data, Data, Data”

That is why data remains the central bottleneck, even as tools and architectures evolve. Whether a company takes a more traditional mapping-centric approach or leans into transformer-based models and heavy use of simulation, the constraint shows up in the same place: the quantity, quality, and relevance of the data used to train and improve the system.

From an investor’s perspective, this is an area where founders often underestimate what will be required. It is easy to focus on the elegance of the model or the novelty of the approach and treat data collection as a secondary consideration.

In real deployments, the opposite is true.

The autonomy stack advances as quickly as the organization can access or create the data it needs, at a cost and speed that the business can sustain.

The companies that ultimately pull ahead will be those that treat this as a core strategic question rather than a downstream operational detail. They will be the ones who design their programs, partnerships, and early customer engagements around systematic ways to gather and generate the right kinds of driving data. They will use simulation to accelerate, not to replace, the hard work of learning from the real world.

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Business Models in Transition: From Full-Stack Autonomy to OEM-Integrated Software

The First Wave: Full-Stack Autonomy

The early generation of autonomous driving companies grew up in a very different financial environment from today. Interest rates were held low for a prolonged period, capital was abundant, and the venture market was willing to back highly capital-intensive plays with long horizons. In that context, the dominant pattern was full-stack autonomy.

These companies built almost everything themselves.

They developed the autonomy software stack and also took responsibility for the hardware platform. In practice, that meant buying vehicles off the shelf—trucks, cars, and specialized equipment—and then modifying them heavily.

They added sensors, wired up compute, and converted the vehicles into drive-by-wire platforms that their software could control.

The result was a business that had to carry both the cost structure of a software company and many of the commitments of a hardware manufacturer. Significant teams were required on both sides. Engineering capacity was split between algorithms, perception, planning, and control on the one hand, and mechanical integration, vehicle systems, and physical reliability on the other.

It was an ambitious model, and it was only sustainable because raising hundreds of millions of dollars for a first commercial prototype was considered realistic in that era.

Today, the backdrop has changed.

Liquidity in venture as an asset class is more limited. There is less tolerance for indefinitely funding highly capital-intensive models without a clear line of sight to commercial traction.

The combination of slower exit markets and a higher cost of capital has made that first-wave playbook much harder to repeat.

The Shift to Software-Centric, OEM-Aligned Autonomy

Against this new economic reality, the business model is evolving. Instead of trying to be vehicle companies in disguise, newer autonomy startups are deliberately focusing on the software layer and aligning themselves with existing original equipment manufacturers.

Legacy OEMs—whether they build trucks, cars, tractors, garbage trucks, or other specialized machinery—are now designing next-generation platforms with autonomy in mind.

These are vehicles that have been rethought so they can be driven entirely by software. They expose the interfaces needed for drive-by-wire, and they are architected to accommodate sensors, compute, and connectivity from day one.

The emerging division of labor is clear. The OEM provides the physical machine and the industrial capabilities around it.

The startup provides the autonomy stack and the data that make the vehicle operate without a human driver. The two sides integrate their systems so that, to the end customer, the result appears as a coherent, next-generation product rather than a collection of separate components.

This approach is not just a matter of capital efficiency, although that is part of the story. It also reflects a more realistic view of what different players are good at.

Startups excel at building software and data systems quickly, but they are not set up to replicate decades of accumulated knowledge in vehicle manufacturing and support.

OEMs understand hardware and field service deeply, but they rarely move at the pace required to build cutting-edge autonomy software on their own.

What End Customers Actually Buy

From the customer’s perspective, the decision-making lens is much simpler. A farmer considering an autonomous tractor, or a warehouse operator evaluating autonomous forklifts, is not primarily focused on architectural elegance or the details of who owns which part of the stack. The first questions are straightforward:

• Does this machine do the job it is supposed to do?

• And if it stops working, how quickly can it be brought back online?

Reliability and service dominate the buying criteria.

Farmers cannot afford to lose two weeks of harvest because a niche autonomy startup cannot overnight a replacement part or dispatch a technician within hours. Warehouse operators cannot halt critical operations while they wait for a single vendor engineer to fly in and diagnose a problem.

These customers are accustomed to dealing with established brands that have dense dealer networks, trained service technicians, and proven spare parts logistics.

That is the real advantage legacy manufacturers bring to the table.

They already operate the networks that can reach end customers quickly with the right parts and the right expertise. Their brands carry the accumulated trust of decades spent delivering and maintaining equipment in demanding environments.

For a new company, recreating that footprint from scratch is not simply a matter of money; it is a matter of time and institutional depth.

When autonomy is delivered as a software layer inside an OEM’s product, the customer can experience it as an evolution of something familiar, backed by a support structure they already know.

The purchase becomes less of a leap into the unknown and more of a decision to adopt a new capability from a partner they already rely on.

Scaling Through OEM Distribution

This is also why the OEM-integrated model is more promising as a path to scale. Selling directly as a startup can work, but typically only for a limited number of early adopters who are willing to accept imperfections and provide intense feedback.

Those customers will tolerate systems that require teleoperation, frequent updates, and close involvement from the engineering team. That stage is important, but it is not enough to build a large, durable business.

To move beyond a few hundred deployed units and reach thousands of vehicles across multiple geographies, a different distribution mechanism is required. That mechanism already exists in the form of OEMs’ global sales and dealer networks.

They know how to place products into markets, finance equipment for customers, and provide ongoing service. Once autonomy is integrated into the machines they sell, it can ride on top of that infrastructure.

For the autonomy startup, this model shifts the emphasis.

Instead of dedicating most of its capital to owning and modifying vehicles, it invests in the software stack, data collection, and integration work needed to make its system compatible with one or more OEM platforms.

It focuses on building something that an OEM can adopt and distribute, rather than trying to build a parallel distribution system of its own.

In that sense, the evolution of business models in autonomous driving is a response to both financial reality and operational truth.

The capital environment no longer supports the first generation’s full-stack ambitions at the same scale, and the market signal from end customers is clear: they want machines that work, supported by service networks they trust.

The Reality of OEM Partnerships: Timelines, Capital Needs, and the Path to Scale

The Multi-Year Journey to a Meaningful OEM Partnership

From the outside, partnering with a major OEM can look like a discrete milestone: a negotiation, a contract, and an integration plan. In reality, reaching that point is the outcome of a multi-year journey that is far longer than a typical venture funding cycle.

For the teams we’ve discussed in this conversation, those that have managed to secure deep, strategic partnerships with equipment manufacturers have not done it in six or twelve months; it has taken four or five years.

That span includes the time needed to build credible technology, to demonstrate it in the field with real customers, and to convince multiple layers inside a legacy organization that the partnership is both technically viable and commercially attractive.

This timeline sits in direct tension with the way venture capital usually operates. Most rounds are designed to provide twelve to eighteen months of runway, perhaps stretching to twenty-four.

Founders are therefore caught between the slow, deliberate pace at which OEMs move and the much shorter time horizon imposed by their capital structure.

The practical question becomes how to progress far enough toward OEM readiness, within those constraints, to keep financing the company without giving away the entire cap table in the process.

Despite the difficulty, OEM partnerships remain central for any autonomy company that aspires to scale beyond niche deployments. They are the mechanism for accessing global distribution and service networks, and for embedding autonomy into the mainstream equipment that customers already buy.

The challenge is not whether to pursue them, but how to survive long enough and progress far enough, to make those partnerships realistic.

Why You Cannot Rely on a Single OEM

One of the hard lessons that emerges from this experience is that a startup cannot afford to depend on a single OEM relationship. Large manufacturers are decades-old institutions with incentives aligned around sustaining and incrementally improving their existing business.

They work on three-, four-, or five-year planning horizons, not on the twelve- to eighteen-month cadence that defines a startup’s runway.

Inside those organizations, the people interacting with a young autonomy company are navigating their own constraints. They are typically not rewarded for taking disruptive risks that could destabilize the core business.

They are rewarded for keeping existing customers happy and existing products on track. As a result, even enthusiastic champions inside an OEM can find it difficult to move quickly.

A reliable way to introduce urgency into this system is through competitive tension. When one OEM sees that a promising autonomy provider is also in serious discussions with a major rival, the fear of being left behind begins to influence behavior.

Suddenly, moving first has strategic value.

Timelines that would normally stretch over several years can compress because the alternative is ceding a potential advantage to a competitor.

For the startup, this means engaging with multiple OEMs in parallel rather than waiting for a single partner to move. It is a demanding strategy, because it requires building several relationships at once and managing confidential conversations carefully. But without that dynamic, the startup risks being pulled entirely onto the OEM’s timeline, with no leverage to bring decisions in line with its own survival needs.

Taking on Retrofit Costs to Prove Technical and Commercial Readiness

Even with competitive tension in place, OEMs insist on a high standard of proof before committing to serious integration. They want evidence that the technology works reliably and evidence that their customers actually want it.

Autonomy has to be more than a lab demo. It has to be a product that performs in the field and a user experience that operators are willing to adopt.

To reach that bar, successful startups have had to absorb the cost of retrofit themselves in the early stages. They buy their own tractors, forklifts, or other target machines—sometimes secondhand, sometimes new—and modify them to be driven by their autonomy stack.

They then take these retrofitted vehicles to early adopter customers who are willing to experiment.

Those first customers accept a level of imperfection that would not be tolerable at scale.

Teams send engineers into the field, sometimes literally camping on-site during critical periods such as harvest, to ensure the system keeps running. They fine-tune behavior in real time, resolve issues as they arise, and capture the data needed to improve the stack.

This phase is expensive and operationally intense, but it is precisely what convinces OEMs that the autonomy solution is more than a theoretical add-on. It demonstrates technical viability under realistic conditions and surfaces the kinds of user interface, workflow, and branding considerations that matter to end customers.

When an OEM sees working machines in real customers’ hands—and hears those customers affirm that they would buy such a product through their usual equipment supplier—it changes the tone of the conversation.

The Capital Threshold

The capital required to reach that level of proof is substantial. In many cases, it takes at least ten million dollars to fund the equipment purchases, retrofits, and field operations necessary to make a credible case to OEMs.

The exact figure depends heavily on the category of equipment involved. Retrofitting tractors can be comparatively less expensive, especially when secondhand units are an option.

Because of this, there is a clear narrowing in the funnel of autonomy companies as they mature.

Many teams can reach a compelling prototype using simulation and limited real-world testing. Fewer can raise the capital required to run a serious retrofit program with early adopters. Fewer still manage to convert that effort into one or more meaningful OEM partnerships that open the door to large-scale distribution.

Compared with sectors such as enterprise AI software, where the primary investments are in people and cloud infrastructure, the bar here is higher and more unforgiving.

Autonomy companies must fund physical assets, carry the operational burden of real-world deployments, and stay alive long enough to convince conservative industrial partners to commit.

At the same time, there are reasons for cautious optimism.

As sensors, compute, and supporting technologies continue to mature, the cost of achieving key milestones is gradually decreasing. Talented teams are finding more efficient ways to structure their retrofit programs, collect data, and negotiate with OEMs. But the underlying structure of the challenge remains.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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Nuclear energy is back in the spotlight as a practical response to a world that is about to consume far more electricity than it does today.

Alongside the engineering, though, there is a quieter but decisive layer: the product and financing logic that determines what can actually get built, who buys first, and how quickly deployment can move once safety and regulation are factored in.

Within that layer, hard truths emerge.

Generation IV designs are framed less as scientific novelty and more as a credibility reset around “meltdown-safe” outcomes.

At the same time, nuclear still prices as a premium product, shaped by stalled learning curves, fragile supply chains, and planning burdens that materially affect timelines and capital requirements.

In that environment, early adoption is not about serving the whole grid. It is about finding the segments that already pay the most for energy—or that value speed of access to power above almost everything else.

To explore what makes a nuclear project commercially viable—and investable—we’re joined by Will Dufton, Partner at Giant Ventures!

Key takeaways from the episode (TL;DR):

⚛️ Gen IV Is a Safety Narrative That Enables Scale
The defining promise is “meltdown-safe” design and advanced fuels that reduce catastrophic failure risk and reshape how nuclear is perceived and regulated.

🧱 Nuclear Still Prices as a Premium Product
Costs remain high because the industry hasn’t been building at scale, supply chains have degraded, components are complex, and regulatory and planning overhead is structurally expensive.

🏔️ First Customers Are the Ones Already Paying the Most
The most credible early markets are the “high-cost power” pockets of the economy—places where electricity is scarce or resilience and autonomy justify a premium long before the grid does.

🖥️ Data Centers Care Less About Price Than Timing
Hyperscalers face grid access constraints and build behind-the-meter generation to move faster than competitors, which is why firm power options like nuclear attract renewed attention.

💸 FOAK Is Equity; Cheaper Capital Comes After Proof
Risk-off lenders look to historical precedent, so first-of-a-kind deployments are typically equity-financed until multiple projects prove repeatability—while the nuclear supply chain itself offers parallel opportunities.

🚨 COMING SOON…

Our Deep Tech Handbook is Coming 🚀

After 100 episodes of Deep Tech Catalyst, we distilled the recurring patterns behind what actually gets Deep Tech ventures funded—across capital, timelines, and risk from lab to market.

The result: a practical handbook for Deep Tech founders (and the teams and partners backing them) to turn breakthrough technology into a fundable company.

It will be released soon…

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BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Generation IV Nuclear Reactors

Generation IV reactors are best understood as a design response to the two issues that have historically constrained nuclear power: public trust and safety risk.

The core proposition is straightforward. If the dominant concern is the potential for catastrophic failure, then the next wave of reactor technology needs to be engineered around preventing those outcomes by design.

In that framing, Gen IV is compelling because it aims to shift the conversation from “How do we manage the risk?” to “How do we reduce the risk profile at the design level?”

What “meltdown-safe” means in practical terms

A central claim of many Gen IV designs is that they are “meltdown-safe” or, in some cases, physically incapable of reaching the conditions that would cause a meltdown.

The emphasis is on inherent safety characteristics that reduce the probability of the specific failure modes that shaped nuclear’s modern reputation.

In other words, the value proposition is not merely incremental efficiency. It is the reduction of tail-risk scenarios that have outsized impact on public acceptance, regulatory scrutiny, and project viability.

Alongside reactor design, advanced fuel types are often presented as part of this safety and risk-reduction package. A frequently cited example is TRISO fuel, which is described as improving safety characteristics and reducing the likelihood of severe failure outcomes.

In addition, these fuel approaches are often discussed in relation to limiting the proliferation risk associated with fissile materials, which matters not only in geopolitical terms but also in how nuclear projects are perceived and regulated.

Testing history versus deployment reality

A second defining feature of Gen IV is that many of the underlying concepts have already been tested over decades. Several reactor concepts have been tested over decades, including historical deployments such as high-temperature gas reactor work in the United States in the 1970s.

The practical lesson is that technical validation in controlled settings does not automatically translate into commercial rollout.

Technologies can be demonstrably feasible and still fail to scale if the surrounding ecosystem—manufacturing readiness, supply chains, regulatory pathways, and cost structure—cannot support repeatable deployment.

That gap is visible today.

The number of Gen IV reactors operating commercially and connected to the grid remains extremely limited.

This is not a mature buildout phase; it is a transition period where the promise is clear on paper, but widespread replication has not yet occurred.

The investment implication: a safety narrative that can enable scale, if economics follow

From a commercialization standpoint, Gen IV’s value lies in its potential to make nuclear expansion more politically and socially tenable by addressing the failure modes most associated with nuclear’s reputational burden.

If safety outcomes are credibly improved, the technology can, in principle, support a broader buildout of clean, dispatchable power.

The remaining question is whether that safety-driven advantage can be translated into repeatable projects that regulators can approve, supply chains can support, and customers can buy at prices that sustain a viable business.

Why Nuclear Power Still Prices as a Premium Product

A central constraint in nuclear today is that cost reduction has not followed the familiar pattern seen in other complex industries. The underlying logic is simple.

When a technology is built repeatedly, organizations learn, suppliers standardize, labor becomes more specialized, and a predictable “learning rate” drives costs down over time.

In nuclear, that dynamic has been weak for years because the industry has not been constructing new projects at the cadence required to accumulate those efficiencies.

When build cycles slow or stop, the system does not merely pause. It deteriorates. Capabilities become fragmented, institutional knowledge disperses, and the industrial base loses the rhythm that makes complex construction repeatable.

In practice, this means each new project can feel closer to a bespoke effort than a scaled manufacturing process, which keeps costs high and outcomes uncertain.

Supply-chain fragility and the complexity of advanced hardware

Nuclear reactors are not standardized consumer products. They are intricate machines that rely on advanced materials and specialized components, many of which require tight tolerances and highly controlled processes.

If the supporting supply chain is thin, inconsistent, or geographically concentrated, costs rise and lead times stretch.

That becomes a strategic issue, not a procurement inconvenience, because the economics of a reactor are shaped long before it produces a single unit of electricity.

In this context, “broken supply chains” is not a rhetorical phrase. It describes an industrial environment in which inputs are hard to source reliably, vendors may be limited in number, and reconstituting production capacity takes time.

When the ecosystem is not operating at scale, the reactor developer bears more integration risk, more scheduling risk, and often more direct responsibility for ensuring that components can be produced to specification.

Regulation and planning as a cost driver, not a footnote

The other major factor is the regulatory and planning burden. In nuclear, regulatory requirements are not marginal costs. They are a defining feature of the operating environment, shaping timelines, project structures, and total capital required.

Compliance is expensive, and the planning process itself can be prolonged.

Even when regulation is justified by the stakes involved, its economic impact is still real: long development cycles increase financing costs, slow iteration, and make it harder to benefit from repetition.

This is why nuclear is an outlier even among regulated sectors. The downside risk is large, the oversight is intensive, and the pathway from concept to operating asset is demanding.

The combined effect is that nuclear projects carry substantial “soft costs” in addition to the physical build, and those soft costs compound because time is a direct input into capital intensity.

How high build cost translates into expensive electricity—and what that implies commercially

When a reactor is expensive to build, the electricity it produces will generally be expensive, especially in the early deployments where the industry has not yet regained momentum on learning and standardization.

From a commercialization perspective, that reality forces a clear question: if the product is premium-priced power, who has both the willingness and the need to pay for it?

The practical implication is not that nuclear cannot compete, but that it cannot compete everywhere at once. Early market selection becomes an economic necessity.

The near-term opportunity is most credible where existing alternatives are already expensive, unreliable, or logistically difficult—conditions under which premium-priced clean power can still be rational and even cost-competitive relative to the status quo.

Who’s Willing to Pay the Most for Energy?

Customer discovery in early nuclear starts with the highest-cost baseline, not the largest addressable market. If nuclear power is expensive to build in the near term, then commercialization becomes a disciplined pricing and segmentation exercise.

The objective is not to win the broad grid immediately. The objective is to identify end users who already face high energy costs and limited alternatives, such that the value of dependable supply can justify a premium price per kilowatt-hour.

That framing treats nuclear as a product that initially competes against the most expensive marginal power in the system, rather than against low-cost grid electricity in well-served regions.

In early deployments, this distinction is decisive: it determines whether unit economics can work before the broader ecosystem—supply chains, repeatability, and financing—has matured enough to push costs down.

The case of Radiant: remote communities and diesel replacement as a pragmatic entry point

One of the clearest early wedges is remote communities and industrial sites that rely on diesel generation. In these contexts, the alternative is not cheap grid electricity. It is fuel shipped over long distances—sometimes by road, sometimes by air freight—burned in aging generators, and priced with logistics risk embedded in every gallon.

The cost per unit of energy can be extremely high, and reliability is often constrained by weather, transport capacity, or disruption risk.

In the case described of Radiant, this is the commercial logic: if early nuclear power is expensive, it should be sold first into markets where the incumbent baseline is already expensive.

The argument is that a reactor can be cost-competitive not because it beats grid power everywhere, but because it displaces diesel in places where diesel is the default. That makes willingness to pay observable and the comparison set concrete.

Beyond price, the value proposition is operational. Reducing dependence on fuel delivery can improve resilience and simplify planning for remote operators. In practical terms, the episode points to remote mining operations, isolated towns, and similar environments where energy is both costly and mission-critical.

These are niche markets by global electricity demand, but they can be credible first markets because the baseline is expensive and the need is urgent.

Defense as an early buyer

Defense is another segment with high willingness to pay, but for a different reason. Military buyers often evaluate energy through security of supply, operational autonomy, and the cost of failure—not only the nominal cost per kilowatt-hour.

In that framework, paying a premium for dependable, deployable power can be rational if it reduces exposure to fuel logistics and increases flexibility.

This procurement environment differs from civilian grid markets, but the commercial point is consistent. Early nuclear products are most viable where power is both valuable and constrained, and where decision-makers are used to paying for resilience.

Data centers: less about price, more about timing and access to power

Data centers are a distinct case because the binding constraint is often time, not price. Large operators can tolerate high energy costs if electricity availability is the gating factor on growth. What they need is access to power quickly enough to bring new capacity online.

That is why “behind-the-meter” generation matters. In many regions, grid connection timelines and capacity constraints make it hard to secure power at the required pace, so operators build generation next to the facility rather than waiting for the grid.

Today, a common solution is natural gas turbines, but lead times can be long and costs high. Renewables can be fast to build, but they are not always baseload, which creates challenges for continuous, high-reliability demand without additional firming.

In that context, nuclear and geothermal receive attention because, in theory, they can provide firm power behind the meter. The strategic rationale is competitive: if a company can secure dependable generation faster than rivals, it can build data centers faster—and in an AI-driven race, speed becomes an advantage in its own right.

What this segmentation implies for early nuclear go-to-market

Across remote diesel replacement, defense applications, and data centers, the common thread is not ideology. It is economic and operational fit.

Early nuclear products are most credible where customers already face very high energy costs or severe constraints on access and timing.

In those environments, premium-priced, reliable power can be rationally purchased, creating a path to initial deployments that can later support broader scaling as learning effects, supply-chain maturity, and financing conditions improve.

What an Investor Wants to See Early in a Nuclear Company

In nuclear, technical sophistication is necessary but not sufficient.

The first screening question is commercial: is the company building a product that specific customers will buy, at a price that supports the business?

Because nuclear projects are capital-intensive and slow-moving, weak demand assumptions are punished more severely than in software markets.

If the initial customer segment is not clearly defined—or if the willingness to pay is vague—the project risks becoming a technically impressive effort without a viable path to early revenue or repeatable deployment.

This is why early nuclear companies need to be explicit about who their first customers are and why those customers will choose nuclear over incumbent alternatives.

The logic must be grounded in the customer’s current cost structure and operational constraints, not in a generalized belief that the world “needs nuclear.”

In practical terms, this ties directly to the premium pricing reality: if early energy will be expensive, the company must demonstrate that it is targeting buyers already paying high prices or facing urgent access constraints.

Modularity as a financing strategy, not a slogan

A second major consideration is modularity. The concept is often discussed as an engineering preference, but in early-stage nuclear, it functions primarily as a financing strategy.

The advantage of modularity is that it reduces the amount of capital at risk at each step and increases the feasibility of iterative deployment.

The most financeable configuration is an “atomic unit” that can be built at the same physical scale as the eventual production unit, that has credible unit economics on its own, and that can be manufactured repeatedly with a high degree of standardization.

The strategic benefit is that scaling becomes a matter of replication—building one smaller system many times and linking deployments—rather than betting on a single, large, first-of-a-kind infrastructure project.

From a capital provider’s perspective, this matters because it changes the risk profile. If the first deployment is smaller, the upfront commitment is lower.

If the design is repeatable, evidence from one build can carry forward into subsequent builds. This is the type of structure that can eventually make projects more legible to non-venture capital providers, even if early financing still relies heavily on equity.

Why “small modular reactor” language can mislead

It is also important to be precise about what “modular” means in the nuclear context. The term “SMR” is widely used, but it can create false expectations. Many so-called small modular reactors are neither meaningfully small nor modular in the sense that matters for capital risk.

Regulatory and safety requirements can drive designs toward large physical footprints and substantial civil infrastructure, even when the output is lower than traditional gigawatt-scale plants.

As a result, there is a spectrum. Some designs still resemble major infrastructure projects, with many of the associated financing and construction risks. Others aim for far smaller units, which can be more attractive from a capital-at-risk perspective, even if they introduce different engineering constraints.

The investor question is less about the label and more about the underlying deployment logic: how much capital must be committed before the project produces value, and how quickly can the company prove repeatability?

The non-negotiables: regulatory strategy, government support, and team quality

Because nuclear sits at the far end of the spectrum on capital intensity, regulatory burden, and downside risk, early-stage credibility depends on how the company responds to those constraints.

Three factors become decisive.

1. First, regulatory strategy is not an operational detail. It is core to the business model. A company needs a realistic plan for navigating licensing, approvals, and planning processes, and it needs to demonstrate that it understands the timelines and costs involved.

2. Second, government support is often essential. In a sector where public policy, national security considerations, and regulatory frameworks shape the playing field, alignment with government priorities—and the ability to work constructively within those systems—can materially affect the probability of execution.

3. Third, the team must be unusually strong. Nuclear requires not only scientific and engineering capability, but also the ability to mobilize capital, manage complex stakeholder environments, and sustain credibility over long time horizons. The requirement is not generic talent. It is the ability to attract attention and resources in a sector where both are constrained by risk perception and institutional friction.

Taken together, these criteria reflect a pragmatic stance. Nuclear can be transformative, but it is not forgiving. Early-stage companies that succeed tend to be those that treat commercialization, financing, and regulation as integrated parts of the product—rather than as downstream challenges to solve after the technology is built.

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The Reality of the Capital Stack: Equity First, Cheaper Capital Later

A recurring misunderstanding in industrial technology is the belief that first-of-a-kind projects can be financed as if they were already proven infrastructure.

The logic is appealing: if an asset will eventually generate stable cash flows, then it should be possible to fund it with low-cost debt or other non-dilutive instruments. In practice, that assumption breaks down at the point of first deployment.

Banks and other traditional capital providers are not designed to underwrite technology risk. They are structured to avoid it. Their mandate is to preserve capital through predictability, and they achieve that by referencing historical outcomes.

When the historical record for nuclear project financing is mixed or negative—and when project timelines, regulatory exposure, and construction risk are all high—risk-off providers rationally step back.

The result is that early projects tend to be equity-financed, even when the long-term vision is infrastructure-like.

This dynamic is not unique to nuclear, but nuclear amplifies it. The downside risk profile is extreme relative to many other regulated sectors, and that pushes cautious capital further away.

Even where the strategic case for nuclear is strong, the financing reality remains: if the project is genuinely first-of-a-kind at a meaningful scale, it is difficult to make it compatible with low-cost capital from the outset.

Modularity as the bridge from venture risk to infrastructure finance

This is where modularity becomes more than a design preference. It can function as a stepwise de-risking mechanism that makes later, cheaper capital more plausible.

The core idea is to reduce the size of the first bet.

If a company can build a smaller unit—at the same scale it intends to replicate—and demonstrate it works, it creates an evidence base that can be carried into subsequent deployments.

In that model, the pathway to cheaper capital is incremental.

The first unit is still likely to require expensive capital, because it is the point where uncertainty is highest. But once a unit has been built and operated successfully, the second and third deployments begin to look less like speculative technology and more like repeatable projects.

That is the transition that risk-off capital needs: proof of performance, proof of process, and proof that a build is not a one-time exception.

This is also why building one small reactor many times is structurally different from building one large reactor once.

The former creates a series of learning events and proof points, each requiring less capital than a single, large infrastructure commitment. Even if the ultimate scale is large, the financing journey becomes more modular as well.

The specific constraint in nuclear: limited availability of non-dilutive capital

Even with a strong de-risking story, nuclear faces an added hurdle: many banks have traditionally been cautious about lending to nuclear projects—especially in the early stages.

That is beginning to change, but the starting point remains restrictive. As a result, the menu of non-dilutive options is narrower than founders might expect, and it is not realistic to assume a wide range of low-cost instruments will be available early.

Non-dilutive funding in this context often implies government support, but it should not be treated as a guaranteed substitute for equity.

Grants and public programs can help, but they do not eliminate the fundamental problem that the first deployment is a high-risk activity.

Where non-dilutive capital becomes more plausible is after the initial proof points, when the project can be framed as replication rather than invention.

The milestone logic that turns risk-off counterparties back on

The gating factor for cheaper capital is not ambition. It is demonstrated performance.

Risk-off providers look for evidence that a technology has crossed from hypothetical to operational, and they seek precedents they can defend internally.

Until multiple projects have been delivered and operated successfully, the “financing tap” remains relatively off. Once a few have come to market and proven that they work, the basis for underwriting begins to change.

This creates a clear sequence. Early capital is likely to be expensive and equity-heavy. The role of the company is to use that capital to generate the proof points that make subsequent deployments financeable on better terms.

For founders, the practical takeaway is that capital strategy must align with a realistic view of counterparties. Banks are not venture investors, and they will not behave like them. The pathway to their participation is through proven deployments that make the risk legible and defensible.

Supply chain as an adjacent opportunity set, not just a bottleneck

Finally, the financing and deployment discussion connects directly to the supply chain. If supply chains are fragile and regulated inputs are difficult to procure, there is not only risk but also opportunity.

A nuclear resurgence requires supporting infrastructure: enriched fuel supply, maintenance and compliance capabilities, and technologies that improve the performance and economics of the existing fleet.

From an investment perspective, this creates multiple entry points into the broader nuclear renaissance beyond reactor development itself.

Building in the supply chain can be attractive because it targets clear bottlenecks and concentrated markets, often characterized by limited competition and high friction.

In those environments, technology-enabled disruption can be meaningful, and it can allow companies to capture value even while large-scale reactor deployment remains early in its cycle.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

Subscribe now

AI in healthcare is moving fast—from diagnostics and imaging to drug discovery, virtual care, and advanced therapies.

Alongside the technology, though, there is a quieter but decisive layer: the way capital is structured, which influences how long companies can stay in experimentation mode, how they absorb risk, and how they pace their path to scale.

Within that layer, different models coexist. Traditional closed-end funds work with defined fundraising, deployment, and exit cycles. Family offices and disease-focused foundations often use more flexible or evergreen structures. Each of these approaches creates its own constraints and opportunities for founders and investors working in healthcare.

To explore the patient capital model in more detail, we’re joined by Everett Kamin, Healthcare Investment Director at Gemini Capital Partners, Chief Investment Officer and Board Advisor to the Silverstein Family Office and its Dream Foundation, and Impact Investment and Board Advisor to Macmillan Cancer Support.

Key takeaways from the episode (TL;DR):

🧭 Family Offices Play a Different Game Than Funds
They are not constrained by a 7–10 year fund cycle and focus more on capital returned over time than on IRR, which changes how they underwrite risk in healthcare.

🌱 Evergreen Structures Match Healthcare Timelines
Without a forced exit clock, patient capital can stay with services, devices, and enabling technologies for as long as it takes for regulation, adoption, and reimbursement to catch up.

🤖 In Healthcare AI, Data Comes Before the Model
Investability hinges on data integrity, security, ownership, exclusivity, and access duration—only then does model defensibility and the “flywheel” effect really matter.

👥 Founders Need Both Depth and Versatility
Experienced, “Swiss army knife” teams that have built and launched products before, and that target real unmet needs rather than crowded categories, stand out to this type of investor.

🎗️ Foundations Bring Capital, Credibility, and Access
Mission-driven investors like the Silverstein Dream Foundation and Macmillan Cancer Support act like VCs on the cap table, while also opening doors to hospitals, ecosystems, and non-dilutive grants aligned with specific disease areas.

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

How Family Offices Differ from Traditional Funds

Closed-end funds are built on a specific logic:

• They raise capital from external investors, usually over a defined fundraising period of one to three years.

• They then have a defined deployment period, again often one to three years, during which they are expected to put that capital to work.

• Finally, there is a harvesting or realization window of similar length, during which they are expected to exit investments and return capital with a profit.

The life cycle of a VC fund in this model is often seven to ten years; for private equity, it is usually five to seven.

There can be extensions, but the structure creates a clear cadence and a real sense of timing. Limited partners expect their money back, and managers plan exits with that horizon in mind.

Family offices mostly deploy their own capital and operate differently from other investment firms.

They generally fall into three main types:

• A single family office revolves around one principal and that principal’s family, managing and deploying a pool of wealth into chosen strategies.

• A multifamily office brings together multiple families, often to pool resources and share operational efficiencies while co-investing in strategies or individual deals.

• Then there are more institutionalized multifamily offices, where several families are invested, but professional money managers run the platform day to day, with a chief investment officer holding real investment authority.

There is no external timetable forcing them to make an investment by a specific deadline or to sell an asset because a fund is approaching the end of its life. In practice, many family offices also invest in funds as limited partners and sometimes as general partners.

They may have a liquid book, investing in public markets; an illiquid book, allocating to private equity and venture capital funds; and a direct book, comprising operating companies or minority stakes in businesses.

Where a family office often diverges from the classic fund mindset is in how it evaluates success over time. In a fund, the primary performance yardstick is usually IRR, an annualized internal rate of return that builds in time-value-of-money assumptions and tends to reward faster exits and quicker return of capital.

In a family office context, the emphasis tends to shift more toward multiple on invested capital—how much capital has been returned versus how much was originally deployed—especially when an asset can be held for twenty years, collecting dividends and cash flows along the way.

IRR is still relevant, but in an evergreen or continuation structure, the compound effect of cash flows and capital returned can become the more intuitive way to think about value.

Why Evergreen Structures Can Fit Healthcare Timelines

An evergreen structure does not impose a fixed period within which capital must be deployed or harvested. There is no obligation to exit an investment purely because a vehicle is nearing the end of its life. Instead, the hold period can be adapted to the specifics of the company and the market.

Capital can stay with a business for a handful of years or for much longer, as long as the investment thesis remains intact.

For companies on the other side of the table, that creates a different type of relationship.

When they take capital from an evergreen, family-office-backed strategy, they are not tied to a predefined fund clock; equally, they are working with a partner whose decision to stay or exit is case by case rather than driven by a pooled LP base.

With a traditional fund, the timelines and expectations are more defined; with evergreen capital, the flexibility is greater, but the pacing is set more by shared judgment than by fund life.

In healthcare, where the development of services, devices, diagnostics, and enabling technologies rarely fits neatly into a seven-year cycle, this flexibility can be relevant.

In practice, this leads to an investor landscape where different models coexist. Some actors combine control and minority stakes, mature and early-stage companies, services, and enabling technologies, under an evergreen structure. Others operate through classic closed-end funds with defined horizons.

For founders and investors, the key is not to assume one model is inherently superior, but to understand how each structure interacts with the realities of healthcare innovation—and to choose partners whose time horizons, incentives, and constraints match the journey they are trying to undertake.

AI’s Role in Healthcare: An Investor’s Lens

One of the clearest areas where AI is already making a difference is diagnostics and imaging. The raw numbers may look modest—a few percentage points of accuracy gained, systems performing at roughly the level of human experts—but the context matters.

Clinicians work long shifts under constant pressure, and fatigue inevitably raises the risk of missing something. AI introduces a diagnostic layer that never gets tired, running in the background to double-check scans and test results with the same consistency at the end of a shift as at the beginning.

Even small gains in accuracy, applied continuously, translate into fewer misses and a more reliable baseline of care. Inside the lab, AI takes over repetitive tasks such as counting blood cells, handling them in seconds, and freeing technicians to focus on higher-value work.

Coupled with the ability to process very large datasets, this makes it possible to detect diseases earlier and more efficiently, using patterns that would be too time-consuming to track manually.

Accelerating Drug Discovery and Clinical Trials

The impact extends into drug discovery and clinical development.

AI can significantly shorten the discovery process, not through a single breakthrough algorithm, but by supporting many steps in the workflow: exploring chemical and biological space faster, prioritizing candidates, and revealing patterns in preclinical and clinical data that would be hard to see otherwise.

Around these core scientific uses, similar tools streamline the operational side of clinical trials—documentation, payments, data flows, coordination—reducing friction so that projects can move more smoothly from concept to clinic and from clinic to real-world use.

Automating Reimbursement and Administrative Work

AI is also starting to reshape the less visible but extremely consequential world of reimbursement and administration.

In many systems, staff spend substantial time on the phone with payers to confirm coverage and map procedures or devices to specific reimbursement codes.

Software can now query policies, interpret rules, and perform much of this mapping automatically, compressing what used to take multiple calls into minutes.

That shortens the time between a clinical decision and action, helps patients and providers get clarity sooner, and reduces the delay between delivering a service and getting paid.

From Wearables to Advanced Therapies and Production

On the patient-facing side, wearables and virtual care platforms allow continuous monitoring instead of occasional snapshots.

Signals that were once captured only during sporadic clinic visits can now be tracked throughout the day, with AI scanning the data stream for early signs of trouble.

At the other end of the spectrum, AI is influencing how advanced therapies are produced. CAR T-cell therapy is a concrete example: originally extremely expensive and slow to manufacture, it can now be produced in a fraction of the time and at a much lower cost when AI-enabled processes are integrated into the workflow.

That shift alters both the economics and the practical accessibility of such treatments.

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4 Elements That Make an AI Healthcare Startup Investable

Building on Solid Data: Integrity, Security, Ownership, and Access

When an AI company operates in healthcare, the first questions an early-stage investor asks are not just about clever algorithms. They are about the data that sits underneath everything.

The integrity of that data and the way it is handled form the foundation on which the entire investment case rests.

Here are five important considerations to stress-test your startup project.

1. The starting point is understanding exactly what data is feeding the model.

How robust is it? How representative is it of the populations and use cases the company claims to serve? Is it based on a narrow cohort, or is it built on a sufficiently broad and reliable base to support the clinical claims being made?

2. The uniqueness of the data.

If anyone can access the same datasets in the same way, the advantage is fragile. If a company has built, curated, or secured access to something that others cannot easily replicate, that strengthens its position.

3. Security.

Healthcare data is sensitive, and the expectations around privacy and protection are only increasing. It is not enough to say that data is secure; a company has to show that the right protocols are in place and that they are being followed. In the current environment, that is not a side note—it is one of the big questions.

4. Ownership.

An investor needs to know who actually owns the data being used. Is it the company, a hospital, a third-party provider, or some combination of the three? Are there clear rights in place for the company to use that data in the way it intends? If the data is shared or licensed, what does that license look like? How long does it last?

This is where details such as data lockups and exclusivity periods become important. A company might have exclusivity over a dataset for a year, only to lose it in the second year. That changes the durability of its advantage.

5. The consistency of data updates.

Is the company working with static data that never changes, or with live data streams that are refreshed regularly? Will it have access to those streams over time, or is that access precarious?

Taken together, data integrity, security, ownership, lockups, exclusivity, and access duration form a picture of how solid the foundation really is. Only once that picture is clear does it make sense to move on to the model itself.

Designing Models with Real Longevity and Defensible Moats

In AI, there is an uncomfortable but necessary assumption: a better model will always be built. Even if a company has the best model today, it is almost inevitable that someone, somewhere, will come up with something stronger tomorrow.

That reality shapes how investors think about model longevity and defensibility.

The question is not whether the model is perfect. It is whether it can be defended long enough to become the dominant, default choice in its niche—whether that niche is a specific indication, a particular workflow, or a clearly defined discovery process.

The goal is to become the de facto service for that use case, so that when people think about solving that problem, they automatically turn to this company’s solution.

One way to build that defensibility is through a data and usage flywheel.

If the model performs well, more users will be attracted to it. As more users come, they generate more data. As more data flows into the system, the model can be retrained, refined, and improved. That, in turn, makes it even more attractive to users.

Over time, this creates a situation where it is not just the model architecture that matters, but the accumulated data and experience built into it.

The moat then comes from scale and embeddedness.

A competitor may build a technically interesting model, but catching up to a system that has already become the go-to in its space is far from trivial, especially once that system is fed by ongoing real-world usage.

This is why many healthcare AI companies are choosing a specific spot and trying to dominate it, rather than spreading themselves too thin. The priority is to own a clearly defined segment and build a defensible position there, rather than being vaguely present in many areas without real depth anywhere.

The Core Team: Experience, Versatility, and Market Insight

Beyond data and models, the composition of the founding team is crucial. Investors are often candid about this: they do not want to pay for other people’s learnings.

Every new founder will make mistakes, but a team that has built and scaled companies before will skip a long list of avoidable errors. That experience compresses time and reduces risk.

In practical terms, that means looking for founders who have already formed companies, created products, navigated regulatory and legal structures, and dealt with issues such as intellectual property.

• They know how to file patents where they make sense, and how to think about defensibility even in spaces where patents are not the main tool.

• They understand how to move from a concept to a product, and from a product to a sale.

• They know what it means to get something into a market and then expand into adjacent markets.

In the earliest stages—seed and pre-seed—teams are small, and resources are limited.

There is no fully built-out C-suite, no large roster of senior executives to lean on. A founder may find themselves acting as de facto CEO, COO, and CFO in the same week, while also speaking to customers and working with engineers.

That is why investors look for “Swiss army knife” profiles: people who can wear multiple hats, who have a broad set of skills, and who can draw on personal networks and resources without always needing to spend cash.

What matters is not only what the founders know, but how they behave. A team that has the energy and drive to run through walls, combined with enough experience to avoid the most common traps, is far more likely to execute.

When that execution is focused on a product that answers a real gap in the market, and when the team can articulate why this gap has been overlooked or poorly served until now, the investment case strengthens.

Solving A Clear Unmet Need

Finally, there is the question of what the company is actually trying to achieve. Investors are not looking for yet another version of what everyone else is already doing. They are drawn to propositions that address unmet needs in a way that can be defended and scaled.

The ideal is a company that is not simply competing in an existing crowded category, but defining or creating its own category—a space where it can grow without immediately facing a wall of indistinguishable rivals.

That does not mean chasing novelty for its own sake. It means identifying a problem that is real, important, and currently underserved, then designing a solution that fits that problem tightly.

In an AI healthcare context, that might be a specific diagnostic gap, a particular bottleneck in clinical workflows, a neglected patient population, or an overlooked part of the drug development process.

Whatever it is, the key is that the solution is not just another generic tool, but something that clearly answers a need that others have not addressed properly.

When a company brings together solid data foundations, a model with a credible path to defensibility, a team with real experience and versatility, and a proposition that tackles a genuine unmet need, it stands out.

It moves from being one more AI healthcare startup among many to being a candidate for a long-term partnership—one that is worth supporting not just because it is fashionable, but because it has the ingredients to build something durable.

Partnering with Mission-Driven Foundations in Healthcare

Foundations as Strategic Partners

If you are building a company around a clearly defined disease area, a foundation aligned with that indication can become a specific kind of partner.

Their starting point is often the impact a technology might have on the patient group they care about.

That lens influences which projects they look at, how they evaluate relevance, and how they think about the potential long-term role of a company in a given therapeutic area. The capital they deploy is still managed with discipline, but the analysis includes clinical and societal dimensions alongside financial ones.

Networks, Signaling, and Access to Ecosystems

Another dimension highlighted in the discussion is the network that often surrounds these organizations. Foundations like Macmillan tend to be connected to hospitals, patient support services, nonprofits, and other stakeholders in oncology. The Silverstein Dream Foundation plays a similar role within the diabetes ecosystem.

For a founder, this can translate into easier access to certain conversations.

Being backed by a foundation that is already embedded in a clinical and philanthropic network can help with introductions to clinicians, hospital systems, and partner organizations that are familiar with that foundation’s work.

It does not automatically guarantee adoption or success, but it can shorten the time it takes to reach some of the people you need to speak to.

There is also a signaling effect to consider. When a foundation with a clear mission and established reputation decides to invest in a startup, other investors can read that as a sign that the company is relevant to a specific disease area and aligned with the foundation’s understanding of the problem.

These organizations are usually selective.

Their presence on the cap table suggests that a company has passed through an additional filter tied to medical and mission fit, not only to financial potential.

Because they are active in the philanthropic world, these foundations also tend to know the landscape of grants and non-dilutive funding connected to their indication.

Once they are involved, they can sometimes help a team navigate toward programs and funders whose missions are aligned. Outcomes are never guaranteed, but they can make it easier to map opportunities that would otherwise be harder to identify.

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Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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There is a point in every hardware startup’s life when production has to move beyond the lab—not yet to mass manufacturing, but to a meaningfully larger scale. At that stage, the real challenge is learning how to ship higher volumes of actual product without burning too much capital along the way.

The question stops being whether the technology works. It becomes whether the company can industrialize it fast enough, and intelligently enough, to remain on a credible path to bankability.

Demand exists, but it is still delicate.

The one-million-dollar question is this: when is the right moment to move from first sales to a factory that truly makes economic sense?

To unpack this from a European perspective, we’re joined by Vincent Prêtet, Venture Partner at Aster Capital!

Key takeaways from the episode (TL;DR):

💶 Series A is for Demand, Not Steel
Use Series A to grow bookings, fund working capital, and learn to ship reliably—instead of rushing into a big factory too soon.

🏭 The Workshop is Your First Factory
A modest, “dusty” workshop is the training ground where you stress-test the production process, increase throughput, and quietly de-risk future scale-up.

💸 Working Capital is the Hidden Constraint
Customers pay late, suppliers want cash early, and banks rarely help at €1M in sales—so investors must bridge the gap until revenue reaches €2–3M.

📈 Build the Plant When the Numbers Earn It
Around €5M in revenue and improving burn efficiency, a right-sized first plant starts to make sense—without falling into the Gigafactory trap.

👥 Hire for Industrialization, Not Just Innovation
Scaling hardware requires industrialization experts, a real HR function, and access to experienced talent inside relevant industrial clusters.

Before We Dive In: Big News!

📙 Announcing Our Book!

A Practical Handbook for Building and Backing Companies That Actually Get Funded.

Hitting 100 episodes of Deep Tech Catalyst has revealed patterns that are impossible to ignore.

The startup books we all know were born in a software-dominated world: cheap experiments, fast iterations, low capital intensity, clean feedback loops.

Deep Tech lives by a different physics. It doesn’t care about “move fast” slogans: it’s fragmented, capital-intensive, and the risk stack runs from the lab bench all the way to policy and infrastructure.

Through Deep Tech Catalyst—by sitting down with some of the sharpest investors and industry operators in the world—we stopped looking for universal answers in Deep Tech and focused on the questions they kept repeating.

From quantum to materials to biotech, these worlds look unrelated—but zoom in on any cap table, IC memo, or founder call, and the same fundamental questions keep surfacing.

We’ve turned those patterns into a practical handbook for people who build, fund, and partner with Deep Tech ventures.

It will be released soon…

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BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

What a One-Million-Revenue Deep Tech Startup Really Looks Like

At around one million in annual revenue, a Deep Tech company is no longer just an idea, but it is not yet an industrial operation.

It is in a transition zone:

• There is usually a product, or at least a product that is close to convergence, rather than a vague platform or a collection of experiments.

• The company has moved beyond pure R&D into something that customers are actually willing to pay for, even if only in small numbers.

• The organization at this stage is still heavily weighted toward technology.

• The core of the team is typically made up of engineers and scientists who know how to make the system work under controlled conditions.

• Around them, a few product-oriented people may have started to appear—someone who can translate the technology into benefits that a specific customer group can recognize and value.

But commercial capabilities are still emerging, and the operating model is not yet built for repetition or scale.

What exists is proof that someone outside the company is prepared to pay real money for what has been built.

What does not yet exist is the ability to deliver that same thing many times over, with predictable timelines, quality, and economics.

From Technology to a Product Customers Understand

Reaching one million in revenue usually means the team has gone through at least one or two product iterations.

These solutions are not academic exercises; they are how the company has learned what its technology is actually good for and how it creates value in the hands of real users.

Each iteration narrows the range of possibilities and sharpens the definition of the product. This period is about convergence.

The company shifts from asking “What can this technology do?” to asking “For whom does this product matter, and why?

The answer takes the form of a specific device or system with clear performance, clear use cases, and a clear buyer. In other words, customers can now look at the product and immediately grasp why it might be worth purchasing.

For a hardware startup, this often means the product has been built perhaps ten times, not hundreds. It is still far from mass production, but it is consistent enough that early adopters can use it, provide feedback, and justify a budget line.

The company has learned to move from a one-off lab artifact to something that can be sold repeatedly, even if every unit still requires a disproportionate amount of effort.

The Starting Point for Industrialization

This converging moment—around one million in revenue with a first product that customers recognize—is the point where the industrial journey really begins.

Up to now, production has been a lab exercise: talented people working with a lot of care, improvisation, and manual effort to get each unit out the door.

The constraints are those of research and prototyping, not those of a factory.

Crossing this threshold forces a change in mindset.

The question becomes how to move from craftsmanship to reproducibility, from bespoke builds to repeatable production.

At this stage, the company is not ready for a full-scale plant, nor should it try to build one. What matters instead is recognizing that industrialization is now the central challenge.

The one-million-revenue mark is not the end of the technical story; it is the beginning of learning how to turn a promising piece of hardware into something that can support a real business at scale.

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Grow Demand, Not Factories (Yet)

By the time a Deep Tech hardware company reaches roughly one million in annual sales, it often becomes a candidate for a Series A round.

The exact figure depends on the specifics of the business, but the order of magnitude tends to be similar: enough capital to move from early product-market validation toward robust demand and more reliable production, without overcapitalizing the company.

Alongside the amount, valuation discipline matters.

If the valuation is pushed too high at this stage, it can create problems later. Future funding rounds may become harder to structure, and the company risks losing momentum if it cannot grow into the expectations that were set too early. On the other hand, a valuation that is too low unnecessarily dilutes founders and early teams.

The objective is not to optimize the headline number, but to find a level that keeps the company fundable over time while preserving meaningful ownership for those building it.

At this stage, valuation is important but not the core of the story. The real question is how this capital will be used and what trajectory it enables between Series A and the next major inflection point.

Why Working Capital Becomes the Invisible Constraint

A common assumption is that Series A money is primarily about capex—machines, lines, and buildings.

For Deep Tech, that instinct can be misleading. The first real constraint is usually not a lack of industrial capacity, but a lack of working capital to support growing demand.

As the company moves from producing a handful of units to shipping at a meaningful rate, the cash cycle becomes unforgiving.

1. Customers rarely pay upfront; they often pay later, sometimes much later.

2. Meanwhile, the company must purchase components and materials months in advance.

3. Because it is still small and not a priority customer, it often has to accept unfavorable terms from suppliers.

4. Orders for parts may need to be placed three to six months ahead of production.

This creates a structural gap: cash goes out early for components, while cash comes in late from customers. However, one million in revenue is usually too early to leverage debt, so investors become the de facto providers of working capital during this phase.

A good rule of thumb in this context is to reserve part of the Series A—and potentially an additional dedicated amount—to finance inventories, receivables, and throughput growth until the company reaches a scale where bank financing becomes a viable option.

The Job of Working Capital

The goal for this capital is clear: support the company as it grows from roughly one million in sales to two or three million, and create the conditions under which banks can start to ease the working capital burden.

In practice, that means using equity money to finance both the components needed for increased production and the operating expenses that accompany that rise in output.

Once the company is in the two-to-three-million revenue range and has a visible order book, the conversation with banks changes.

At that point, the business no longer looks like a pure technology bet; it looks like an emerging industrial company with actual customers and repeat behavior. Lenders begin to listen. They are more willing to consider credit lines for inventories or receivables. When that happens, the role of equity capital can shift away from bridging the cash cycle and toward supporting the next phase of capacity and growth.

The bridge between Series A and that moment is therefore strategic.

Shorten Your Production Learning Curve

Crucially, Series A is not the time to pour money into a large factory. Building a big plant too early locks in fixed costs and process assumptions that may still be wrong.

Instead, the capital should be used to grow demand and to learn how to serve that demand at an increasing rate, using a modest, flexible production setup.

As the company moves from making one or two units a month to higher volumes, new problems emerge. Every increase in throughput exposes previously invisible bottlenecks.

A change in a single component can ripple through the entire assembly process. Minor design adjustments can force rethinking of workflows, tooling, or quality checks.

These are not edge cases; they are part of the normal progression from prototype to production. This learning takes time, and time costs money.

Employees must be paid while they experiment, refine, and stabilize the process. Mistakes will be made, and rework will happen.

From an investor’s point of view, funding this learning curve is part of the job. It is not enough to finance parts; one must also finance the time required for the team to internalize how to run a more productive operation.

Series A capital, used well, therefore serves three purposes at once. It fuels demand generation so that bookings grow month after month.

It finances the working capital gap created by that growth. And it buys the time needed for the organization to learn how to produce at a higher throughput without the safety net of a lab environment.

Only once these capabilities are in place does it make sense to consider larger, more capital-intensive industrial infrastructure.

From Lab Production to a Dusty but Real Workshop

Between the lab and the factory, there is an in-between space that plays a decisive role in the journey of a Deep Tech company.

It is not yet an industrial plant, and it is no longer a research lab. It is a workshop.

This workshop is usually financed with the five to ten million raised at Series A. Instead of pouring that money into a full-scale facility, the company rents a modest, imperfect space where it can begin to build real products in a repeatable way.

It is often a dusty place, not optimized for high-volume output or long-term efficiency. But it is real. It has enough room for a line, some tools, storage, and the people who assemble and test the product.

At this point, the company has already left behind purely lab-based production. The prototypes are no longer built under the microscope by a handful of experts, improvising every step. In the workshop, production begins to resemble what a future plant might look like, even if on a much smaller and rougher scale.

Learning by Doing and Increasing Output in the Same Space

The workshop phase is where learning by doing becomes central. With demand starting to grow and bookings increasing, the company must find ways to produce more units without yet investing in a large facility.

Instead of expanding square footage, it improves how work is organized.

An example discussed is a company producing 3D printers for silicone applications in medical and industrial settings. When the investment was made at Series A, the founders were convinced that, in their small workshop, they could not produce more than fifty machines per year.

Three years later, in that very same space, they now believe they can manufacture around three hundred units annually.

Nothing fundamental about the building changed during that period. What changed was the way the team structured assembly, learned from bottlenecks, and optimized their internal workflows.

Step by step, they refined the way they produced the product, discovering how much more could be done within the same physical constraints.

This illustrates a broader pattern. Before spending heavily on new infrastructure, companies can uncover a surprising amount of latent capacity simply by experimenting, reorganizing, and iterating on the production process inside the workshop.

Validating the Production Process Under Real Conditions

The workshop is also where the production process itself is validated under conditions that are closer to reality than anything in the lab.

As volumes increase, problems appear that could not have been anticipated on paper.

When only a few units are produced, many issues remain invisible. The production line is not yet stressed enough to reveal where it will break.

As throughput rises, each new step exposes fresh challenges. Changing a single component can suddenly disrupt the flow on the line. Small design tweaks can force adjustments in assembly, testing, or quality control.

The team discovers dependencies and sensitivities they did not know existed.

This is why it is so difficult to foresee all industrialization problems in advance. The workshop forces them to surface. It gives the company a controlled but real environment in which to encounter these issues while volumes are still manageable.

The cost of stopping, reworking, or modifying the process is far lower here than it would be in a fully built plant.

At the same time, this phase is not only about process; it is about the market as well. While learning how to produce, the company is also tracking bookings and demand. It is finding out whether the market truly wants the product at the expected pace and whether orders build consistently as capacity increases.

Designing the Future Plant Through Incremental Improvements

In this sense, the workshop is a design tool for the future factory. By wrestling with real production and real customers in a constrained environment, the company learns what its industrial setup actually needs to look like.

Layout, workflows, staffing, and critical steps in assembly and testing are all tested in miniature.

Because the company has not yet committed large sums to “steel in the ground,” it retains the flexibility to adjust its vision of the plant.

The production process evolves first in the workshop, and the plant is later built as a scaled, more efficient expression of what has already been proven there.

This approach de-risks scale-up. Before any large capex decisions are made, the process has been shaped by experience rather than speculation.

The team has confronted real problems, solved them on a small scale, and gained a much clearer understanding of what it will take to run a higher-throughput operation.

When the moment finally comes to plan a bigger facility, the company is no longer guessing; it is extrapolating from lived practice.

When to Build a Factory

As revenue climbs from one million toward two or three million, something important shifts in how the company is perceived. Up to that point, investors are often the ones carrying the weight of working capital.

Equity is used not only to fund development and operations, but also to prepay for components and absorb the delay between shipping products and getting paid. Once annual sales reach the two-to-three-million range and bookings build consistently, the profile changes.

The company starts to look less like an experiment and more like a genuine industrial actor.

• Customers are no longer isolated early adopters; they form a base that generates repeat business and referrals.

• Suppliers recognize that this is a recurring client rather than a one-off buyer.

• Banks, in turn, begin to see a track record they can underwrite.

At this point, working capital can gradually shift from being an investor’s burden to being partially supported by credit.

Banks may still move cautiously, but the door is now open to conversations about financing inventories or receivables. Equity capital is freed, step by step, to focus less on plugging cash-flow gaps and more on enabling the next stages of growth.

The two-to-three-million mark is therefore a turning point in credibility.

It signals that the product has found a place in the market, that demand is not purely hypothetical, and that the company has learned how to deliver consistently enough for traditional finance providers to start engaging.

Using Efficiency Metrics and Net Burn to Earn a Series B

The next significant milestone sits around five million in annual revenue. At that level, it becomes possible to say with confidence that there is a real market.

The open question is not whether customers exist, but how deep that market goes and how far the company can grow within it.

To approach a Series B round responsibly, it is not enough to point to top-line growth. The company must show that it is moving along a path toward profitability.

One way to think about this is through a simple efficiency lens: for each increment of new sales, how much loss is still being generated?

In practice, this can be framed as a kind of net burn multiple adapted to hardware. It involves dividing new sales by the company’s losses and watching how that ratio evolves over time.

When that number moves closer to zero, it indicates that each additional unit of revenue is accompanied by a smaller and smaller loss.

Efficiency is improving. The organization is learning how to produce, sell, and support its product with less cash burn per euro of incremental revenue.

If, before reaching five million in sales, the company can already demonstrate this trajectory—growing revenue while steadily reducing the relative size of its losses—it becomes much easier to envision, on this trajectory, a Series B in the range of ten to twenty-five million. The story is no longer just about growth; it is about disciplined growth with a visible journey toward economic self-sufficiency.

Avoid the Gigafactory Trap

Series B is the moment when it starts to make sense to think seriously about a plant. Not a symbolic workshop, but a dedicated industrial facility with meaningful capacity.

However, the temptation to jump straight to a “gigafactory” is one of the most dangerous traps at this stage.

For a small set of companies, extremely large plants may be justified. For most, they are a source of serious economic risk. A factory that is too big brings heavy fixed costs and assumes a level of demand that may not materialize on schedule.

If orders fall short of the capacity that has been built, the company ends up carrying infrastructure that its revenue cannot support.

The more prudent approach is to right-size the first plant.

That means designing a facility that is large enough to serve the expected demand over roughly the next one to two years after Series B, based on concrete projections rather than ambition alone.

The plant should be a tool that fits the company’s current and near-term reality, not a monument to a hypothetical future.

By keeping the first factory at a reasonable scale, the organization preserves flexibility. It can learn how to operate a true industrial site, refine processes under higher volumes, and continue improving its efficiency metrics.

If demand grows as anticipated, additional capacity can be added later on a stronger footing.

Understanding Demand Before Committing to Capacity

A crucial element behind these choices is developing a clear, evidence-based view of demand. By the time the company is approaching five million in sales, it is no longer guessing about who the customers are or how they behave.

It has seen how bookings evolve over time, which segments respond best, and how long sales cycles really are.

This experience allows the company to build more grounded projections for the first, second, and third years. Those projections, in turn, inform decisions about plant capacity.

Instead of building a factory and hoping demand will rise to meet it, the company uses emerging demand patterns to define what the factory should look like.

In that sense, committing to capacity comes after a period of deliberate demand-building and observation.

The company does not expand industrially first and then search for volume; it grows volume in workshops and small-scale settings, validates that demand is durable, and only then scales capacity in line with what the market is already signaling.

When done this way, the first plant is not an oversized bet. It is a carefully calibrated response to proven demand and an extension of lessons learned in the workshop phase.

The result is a more resilient industrial step-up and a company better positioned to sustain its growth rather than being crushed by the weight of premature infrastructure.

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Building the Team and Culture for Industrial Scale

In the early days around one million in sales, the organization is still largely an extension of the lab: a small group of highly skilled engineers and scientists, sometimes supported by a few product-minded people, all focused on making the technology work and delivering the first units.

As production increases and demand becomes more regular, headcount begins to reflect what the physical product actually requires.

• For some companies, especially those that are manufacturing-intensive—such as small aircraft or similar complex systems—this can mean building a workshop with one or two hundred people directly involved in production, even at relatively modest revenue levels.

• For others, where the core activity is assembling components into a final system, the workshop remains smaller, and the number of employees on the line is more limited.

The structure and size of the workforce depend heavily on the nature of the product and the complexity of the manufacturing process.

What does remain consistent is the underlying shift: moving from a lab-centric culture toward an industrial one, where daily output, repeatability, and reliability become central to how the company operates.

Hiring for Industrialization and Production Excellence

As the company moves into this industrial journey, one of the first intentional steps in the HR plan is to hire a team dedicated to industrialization. The task is no longer just to design a product, but to design the way that product is built at an increasing scale.

People with grounded experience in growing industrial capacity play a crucial role here.

They know how to walk into a workshop, look at the way assembly is done, and see what needs to change as volumes rise. They can work side by side with the operators on the line, understand their constraints, and translate those day-to-day realities into better processes and layouts.

At the same time, this industrialization team serves as a bridge between the current workshop and the future factory.

They challenge and refine the ideas the founders may have about a large plant by confronting them with what actually happens each day in production. Instead of designing an idealized factory on paper, they build the blueprint from lived experience on the floor.

The Role of an HR Manager in Protecting Throughput

As the workforce grows and production becomes more regular, another role becomes essential: a dedicated HR manager.

At low headcount, personnel issues can be handled informally. Once the company relies on a larger group of operators and technicians to keep products moving through the workshop, that approach stops being viable.

Turnover, illness, and personal circumstances are no longer isolated events; they directly affect the company’s ability to deliver.

A missing operator can translate into a broken link in the production chain.

Without someone systematically managing recruitment, onboarding, workforce planning, and day-to-day HR issues, the risk is that throughput becomes fragile.

An HR manager’s job is not just administrative. It is closely tied to protecting industrial capacity. They make sure there are enough people with the right skills on each station, anticipate staffing needs as volumes rise, and help maintain continuity when individuals leave or are temporarily unavailable.

In a hardware company that aspires to industrial scale, this is a strategic function. It supports the transition from a culture where a few heroic efforts keep the line running to a culture where the system itself is robust.

Locating in the Right Industrial Cluster to Access Talent and Experience

Location is also part of the people strategy. Placing the company inside an industrial cluster that matches its value proposition creates a powerful advantage in both hiring and day-to-day learning.

A rocket startup that sets up shop near other space companies benefits from an ecosystem of engineers, technicians, and suppliers who already understand the domain.

A laser or optics company locating in a region with established photonics players taps into a pool of specialized talent that would be difficult to access in isolation.

Young graduates from universities and engineering schools connected to the cluster see a clear path into the sector. Experienced employees from incumbents only need to cross the street to join the startup.

This proximity to incumbents and peers brings something else that is hard to manufacture internally: seasoned “gray hairs” in the industry.

People who have spent years inside large industrial organizations have faced problems that early-stage teams have not yet encountered. They know what it means to run production at scale, to maintain quality over time, and to deal with the realities of supply chains and operations.

Bringing that experience into the company, whether through direct hiring or informal exchanges within the cluster, strengthens the culture. It anchors ambitious technology and fast-moving startup energy in the practical wisdom of people who have seen industrial systems succeed and fail.

For a Deep Tech venture, that blend of youth and experience is one of the most valuable assets on the path to scale.

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Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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Automotive is in the middle of a long, complex transition. Electric and combustion platforms now coexist in the same showroom, software defines more of the in-vehicle experience, and the path from lab prototype to qualified component runs through some of the most demanding manufacturing and safety standards in industry.

For Deep Tech founders, the question is not just how to build a breakthrough—it is how to make that breakthrough fit into a large company’s programs, economics, and timelines.

How do you approach a major automaker, navigate long integration cycles, and design a business that can eventually be cash-flow positive in a market where price and cost discipline decide who scales?

To unpack these dynamics from the inside, we’re joined by Shobhit Gupta, Investor at GM Ventures!

Key takeaways from the episode (TL;DR)

🚗 Mobility Is About Choice
EVs and combustion engines will coexist for a long time; what matters is giving consumers real options in powertrain and in-vehicle experience.

🏢 Two Doors Into an Automotive Manufacturer
Engagements typically start either from a business unit or from the corporate venture team, then grow from pilots into broader strategic relationships.

🧪 Readiness Before Integration
Automotive timelines are long; OEMs first look for technologies mature enough for pilots, with hardware held to a higher readiness bar than pure software.

💰 Cost Curves, Not Just Prices
Early-stage unit economics don’t need to be perfect, but there must be a credible roadmap to cost competitiveness, often built by leveraging automotive manufacturers’ supply chains and joint ventures.

📉 Margins vs. Cash Flow
Hardware margins are structurally lower than software; the real target is company-level cash flow positivity, achieved by systematically working down costs while scaling deployment.

✨ For more, see Membership | Deep Tech Briefing | Insights | VC Guides

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

A New Era for Automotive

Powertrain Diversity as a Response to Consumer Demand

The most visible change in mobility today is not a single breakthrough technology but the expansion of choice. From the perspective of an automaker, the primary responsibility is to offer a portfolio that reflects the range of preferences and use cases across its customer base.

That has translated into a dual commitment: a robust lineup of electric vehicles and, in parallel, a strong portfolio of internal combustion engine products.

Electric models address the growing demand for new powertrain architectures and future-oriented mobility, while combustion vehicles continue to serve large segments of drivers whose needs, habits, or infrastructure constraints still align with conventional platforms.

The point is not to force a single answer, but to make sure that a driver can select a vehicle that genuinely fits how they live and travel.

In-Vehicle Experience as a Core Part of Mobility

For most consumers, mobility is no longer just about the drivetrain. It is about what it feels like to be inside the vehicle every day. As a result, the cockpit has become part of the competitive landscape in its own right.

Materials, design, and overall look and feel now carry as much weight as traditional performance metrics for many buyers. At the same time, the software that runs on the dashboard has moved from being an accessory to being a core part of the experience. Driver information, connectivity, and digital features delivered through in-vehicle displays all shape how people perceive a brand and a model.

When mobility is viewed through that lens, the product is the combination of powertrain, physical environment, and digital layer.

The evolution of the sector is therefore tied not only to the introduction of new vehicle architectures, but to continuous improvement in how the interior space looks, behaves, and supports the driver and passengers.

Infrastructure and the Multi-Actor Nature of Transportation

Behind the individual vehicle, mobility sits within a much larger transportation system. Moving people and goods from point A to point B is an infrastructure-scale challenge that extends far beyond a single automotive player.

In practical terms, that means the sector is shaped by a broad ecosystem of stakeholders: automakers, suppliers, tier-one partners, infrastructure providers, and other entities that contribute to the overall system.

No single company, regardless of size, can unilaterally solve the full spectrum of problems that arise when transportation, mobility, and infrastructure intersect.

The current phase of evolution is therefore stakeholder-driven. Progress depends on coordination across different players who each control a piece of the puzzle.

Opening the Door: First Interactions Between Startups and Large Corporations

For a startup, the first question is often simply how to enter a large automotive organization in a way that leads to something real.

In practice, there tend to be two primary paths:

1. The first begins directly with a business unit. A founder might meet an engineering or product leader at a conference, an industry event, or through targeted outreach. That initial contact can lead to informal technical discussions, early evaluations, or a narrowly scoped pilot. The engagement is often modest at the beginning—limited in scope, defined around a specific use case, and owned by a small team inside the business unit.

2. The second path starts with the corporate venture arm. In that case, the venture team identifies a promising technology and then introduces the startup to the relevant business stakeholders inside the corporation. The first step is to map the startup’s product to the right internal owner: the person, or small group, who is closest to the problem the startup is trying to solve. Once that match is made, the CVC team works up and down the reporting chain to make sure there is alignment, both technically and strategically.

In both paths, technical assessments are left to the engineering and product staff inside the business units, because they understand their problem sets, requirements, and constraints better than anyone else.

What the CVC arm does provide is navigation, coordination, and leverage. It helps ensure that the startup is in front of the right people, and that internal stakeholders understand how a new technology might fit into existing or emerging programs.

B2B → B2C: Turning Technology Partnerships Into Consumer Features

From the startup’s perspective, the interaction with an automotive manufacturer is a B2B relationship. The conversations are with engineering leaders, program managers, procurement, and supply chain teams.

Yet the ultimate test of the collaboration is almost always in the B2C realm: whether the technology ends up in vehicles that consumers can buy and use.

That arc—business-to-business partnership evolving into a consumer-facing capability—is the throughline for many successful automotive startup relationships.

On one side, the automaker and the startup collaborate on technology development, validation, and integration. On the other, the outcome is a concrete option in the showroom that drivers can see, evaluate, and choose.

For founders, understanding this dynamic is essential.

The first meetings may revolve around pilots, specifications, and technical evaluations, but the real end game is always the same: turning a B2B engagement into a durable B2C feature that lives inside vehicles on the road.

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The Long Path From Pilot to Scale

From an automotive manufacturer’s perspective, integration into a vehicle is the ultimate goal—but it is never the starting point. The first step is to understand how ready the technology actually is and how well it maps to real needs inside the organization.

Early conversations focus on two questions: what level of development has already been achieved, and which business units could realistically benefit from the product.

In an ideal scenario, more than one internal team sees a clear use case.

That diversification matters because different units move at different speeds, and having several potential sponsors gives a startup more chances to build a successful engagement over time.

At the beginning, integration is not the immediate ask. Automotive development cycles are long. Safety testing, engineering validation, material standards, and regulatory requirements all sit at the high end of industrial complexity.

A product that will eventually go through a manufacturing plant and into vehicles must be fully tested and validated, and that simply cannot be compressed into a short timeframe.

What matters most at the beginning is that the product is far enough along to be evaluated in a pilot setting. That evaluation can start in different ways, for instance:

• Sometimes the automotive manufacturer makes a strategic investment and, in parallel, works with a business unit to stand up an internal test or proof of concept.

• Other times, the startup has already run pilots with third parties or other customers, and the results from those external deployments become an input to the company’s own assessment.

In either case, the condition for deeper engagement is the same: the technology needs to be at a point where it can be meaningfully tested, not just described in theory.

Software vs Hardware

Readiness is not a single metric. It is shaped by the nature of the product itself, and in automotive, the contrast between software and hardware is particularly sharp.

Pure software offerings can often be engaged earlier. If a software solution is critical for a business unit—say, in how it manages processes, data, or internal workflows—an OEM may be willing to work with the startup while the product is still in a relatively early stage.

In those situations, the value of the relationship can include guidance: clarity on requirements, continuous feedback, and an understanding of what the automaker needs to see in order to broaden deployment later.

The development then advances with a line of sight to a specific customer’s expectations.

Deep tech hardware lives on a different timeline.

The bar for maturity is higher before a meaningful collaboration can begin, because the path from lab to factory floor involves more steps, more stakeholders, and more irreversibility.

A hardware product has to fit into existing manufacturing and supply chains, meet strict quality and safety standards, and survive the realities of industrial-scale production.

As a result, the level of risk differs across categories. For software, earlier-stage engagement can make sense if the strategic importance is clear. For hardware, the expectation is that the company has already moved beyond purely theoretical concepts and is in a position to support pilot-level testing.

The Manufacturing Learning Curve Behind Cost Claims

One of the most common patterns in early hardware pitch decks is making ambitious cost claims.

Founders often present a vision in which their technology not only reaches cost parity with existing solutions but ultimately beats them on price. On paper, the argument can be compelling: new materials, new architectures, or new processes that promise lower unit costs once they scale.

The difficulty is that many of these claims are made before the team has gone through the manufacturing learning curve.

The theoretical cost model may not yet reflect the realities of production: the machinery required, the number and type of suppliers involved, the yields that can be achieved at each stage, and the inevitable surprises that surface when a process moves from the lab into real-world factories.

A large automotive player like General Motors, with decades of manufacturing experience, views cost through a different lens.

Having built and optimized production systems for more than a century, it understands where efficiency is gained and where it is lost. When a startup arrives with strong statements about cost competitiveness, the natural response is to dig deeper.

The conversation quickly turns from slogans to specifics:

• What exactly drives the projected cost reduction?

• Which third-party manufacturers and supply chain partners need to be involved?

• How will their capabilities, incentives, and constraints affect the economics?

• What are the push-and-pull forces that will either support or undermine the claimed price point once the product is integrated into an actual vehicle program?

These questions are not academic. In automotive, the viability of a new hardware solution depends on whether its promised economics can survive contact with the full industrial stack.

Moreover, each segment, each technology, and each supply chain path introduces its own variables. So, integration is the destination, but manufacturing maturity is the road that leads there.

Pricing, Unit Economics, and the Cost Curve

In automotive, price is always in the frame, but it is not evaluated in isolation from timing and maturity.

When a startup enters discussions with a large automotive player at an early stage, the expectation is not that the product already matches the long-term target cost structure of a high-volume program.

What matters more is whether there is a credible roadmap to get there.

The sector is intensely competitive. No single player controls the majority of the market, and even incremental differences in cost can influence how a product is positioned and scaled across a portfolio.

For that reason, cost competitiveness becomes critical when a solution is considered for mass-market deployment, especially in segments where price sensitivity is high and margins are thinner.

At the same time, a young company faces realities that make its initial unit economics less favorable. Volumes are low, manufacturing processes are not yet fully optimized, and many of the learning cycles that reduce cost over time are still ahead.

When CVCs decide to take both investment risk and strategic risk by working with an early-stage company, it does so with the understanding that early price points will not look like end-state figures.

What it does look for, however, is alignment. The manufacturer and the startup need to agree on a path by which costs can decrease as volumes rise and processes mature, without compromising the underlying capability of the platform.

The roadmap is as important as the current number: it shows how a technology that may initially be expensive can, over time, become suitable for broader deployment across different models and trims.

Leveraging Resources and Joint Ventures to Cut Costs

One of the advantages a large automaker brings to this journey is the depth of its existing supply chain. Over decades, it has developed relationships, joint ventures, and internal capabilities that give it a granular understanding of how cost moves through the system.

When a startup plugs into this environment, it gains access not only to manufacturing capacity but also to cost-improvement levers that would be difficult to access on its own.

A corporation with in-house expertise and joint ventures in this area can help a young company understand where its technology could sit within that stack, and how economies of scale, process optimization, and integration choices might influence long-term pricing.

The goal is not merely to negotiate a lower price, but to design a path in which cost comes down structurally.

That can involve better alignment with existing manufacturing flows, more efficient use of materials, or smarter integration into platforms that are already being produced at scale. A strategic investor could help spot these possibilities and, where appropriate, open doors to partners and facilities that make them real.

To recap, startups are not expected to arrive with perfect unit economics on day one. What they are expected to bring is a serious view of how their costs can evolve, and a willingness to engage with the industrial realities that will shape that evolution.

With that mindset and with the support of a large automotive player’s supply chain and partnership network, a promising technology can move from an expensive niche to a viable, cost-competitive component of mainstream automotive platforms.

Beyond Gross Margin: Designing for Cash Flow in Automotive Startups

Realistic Margin Expectations in Hardware and Software

When founders ask what margins they should target to be attractive to an automotive partner, the temptation is to look for a single number.

In practice, the answer is more nuanced.

Margins in this sector are structurally different depending on whether the company is building hardware, software, or some combination of the two.

In pure software plays, gross margins tend to be higher. Once the product is built and deployed, the cost of serving additional customers is comparatively low, and the economics can move toward attractive levels relatively early. This is one reason software businesses in and around automotive often reach appealing margin profiles sooner in their lifecycle.

Hardware tells a different story.

Physical products must be designed, manufactured, tested, shipped, and integrated into complex systems. Each of those steps brings cost and complexity.

As a result, margins in hardware businesses are generally lower, often in the low double digits and, in good cases, somewhat higher—but rarely approaching the levels seen in software.

For an investor or strategic partner looking at a hardware startup in automotive, this difference is not a surprise; it is part of the baseline expectation.

The question is not whether the company can replicate software-like margins, but whether it understands what “good” looks like in its category and has a plan to reach that level over time.

Why Cash Flow Matters More Than a Single Product Line

A unit can look attractive on paper while the company as a whole is still consuming cash at a rate that is unsustainable.

The more decisive measure is whether the business can design a credible path to company-level cash flow positivity.

That does not mean expecting a young startup to be cash flow positive in the next quarter or even within the next year. It means insisting on a roadmap that shows how the organization will move from investment-heavy growth to a position where its operations fund themselves.

What matters is the intention and the discipline.

A startup that has thought through how its cost base evolves, how its pricing relates to that cost base, and how volume, efficiency, and mix will eventually produce positive cash flow is in a stronger position than one that simply assumes future scale will fix everything.

For a strategic investor, that mindset is a key signal.

Reaching company-level cash flow positivity is rarely a straight line. It usually involves a series of operational decisions that reshape the cost structure over time.

Sometimes that means reallocating resources across facilities, consolidating where it makes sense, and investing more heavily where the impact on efficiency and cost is greatest.

In other cases, it may involve changing the footprint of the business itself—shifting where certain activities are performed, rethinking manufacturing locations, or restructuring parts of the operation to better match the realities of demand and supply.

Throughout that process, the guiding principle is consistent: continuously work down the cost base while preserving enough margin to move the overall company toward sustainability.

That requires attention to both sides of the equation.

Cost discipline alone is not enough if pricing is not aligned with the value being delivered, and ambitious pricing without a handle on underlying costs will not survive contact with the automotive market.

For founders, the implication is clear. When engaging with an automotive manufacturer or its venture arm, it is not sufficient to present a compelling technology and an attractive gross margin on a slide.

What resonates is a thoughtful view of how the entire business will progress toward positive cash flow—how the organization will evolve, where it will seek efficiencies, and how it will structure its operations so that, over time, it is not just building impressive products but also running a financially resilient company.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

Subscribe now

Optical interconnect is moving from backbone networks into the heart of AI infrastructure. As data centers push past the limits of copper, the question is no longer whether light can carry the load—it’s how to deliver speed and power efficiency at a cost the market will accept.

How do Deep tech founders build optical solutions that clear today’s speed class and still scale economically?

To unpack this shift and its commercial implications, we’re joined by Henry Huang, Investment Director at Micron Ventures!

Key takeaways from the episode (TL;DR):

💡 From Copper to Light
Optics offers higher throughput and lower energy per bit, moving data over fiber and waveguides—now extending toward chip-to-chip links.

💸 Cost Is the Gatekeeper
Complex component stacks, thermal sensitivities, packaging, and yield keep optics pricier than copper until manufacturing scales.

🧩 Two Paths to Innovate
Win as a best-in-class component or as an integrated system—either way, manufacturability and economics decide adoption.

🏗️ Selling to Hyperscalers
Align with internal roadmaps, prove fit within existing programs, and show a credible path from current foundry partners to volume production.

🚀 What It Takes to Compete
1.6T throughput is table stakes; efficiency (energy per bit, bandwidth density) and repeatable yield turn performance into product.

🎯 Focus to Win
Start with a sharp beachhead—earn revenue, harden the process, and expand only when the cost structure and reliability hold at scale.

✨ For more, see Membership | Deep Tech Briefing | Insights | VC Guides

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

What Optical Interconnect Is and Why It Matters

Optical interconnect is a simple idea with far-reaching consequences: move information as light instead of electricity.

The function does not change—components still need to talk to one another, data still needs to flow cleanly from point A to point B—but the medium does. Rather than charging and discharging copper traces and wires, information travels on photons guided through optical fiber, carved into on-chip waveguides, or steered across carefully aligned free-space paths.

The moment the carrier switches from electrons to light, the entire profile of the link shifts. Loss behaves differently. Distance behaves differently.

And most importantly for modern systems, the relationship between throughput and power consumption changes in a way that opens headroom copper can no longer provide.

Why Power Efficiency and Speed Are the Decisive Advantages

Copper holds its status for good reasons. It is proven, it is reliable, and decades of engineering have wrung out extraordinary performance.

But physics does not negotiate.

Driving an electrical link is a repetitive act of charging and discharging conductors, and energy is spent each time.

At the scale of a modern data center, those losses compound into real power walls. Optical transmission redraws the equation. For a given data rate, moving bits as photons can be markedly more power-efficient, and the achievable bandwidth is higher.

That combination—lower energy per bit and higher throughput—is not a marginal gain. It is the core advantage that turns optics from a niche upgrade into a requirement for the next generation of infrastructure.

As more data is produced and more of it must move—AI being a major driver but hardly the only one—the system that can carry it faster and with less energy becomes the system that sets the pace.

Copper remains the default today, but the trend line is clear: where power efficiency and speed define the limit, light becomes the medium that keeps the entire stack viable.

Where Complexity, Yield, and Thermal Realities Bite

Optics Still Trails Copper on Cost

The promise of optics is straightforward: higher throughput at lower energy per bit.

The obstacle is equally clear: cost.

Copper retains its dominance not because it is superior in performance, but because it is cheaper and deeply integrated into existing manufacturing flows. When budgets are tight and deployment needs to happen at scale, that price delta determines adoption.

The economics are not a mystery to practitioners.

An optical link is a collection of specialized parts and processes, each with its own yield profile and failure modes. When those elements are combined, the aggregate cost structure is harder to compress than the single-material, highly standardized copper path.

The Bill of Materials Becomes a System Problem

An optical link begins with a light source. Lasers remain the mainstream choice, though alternatives such as micro-LEDs can also drive communication.

The source must then be modulated, which pushes design into materials and devices optimized for high data rates and low drive power. The signal travels through fiber or integrated waveguides and is recovered at the receiver by photodetectors. None of these components operates in isolation.

Each introduces constraints that propagate into assembly, packaging, and test. The engineering challenge is not only to make each block work, but to make them work together repeatedly and at yield.

When a single stage underperforms, the penalty shows up as rework, scrap, or tighter process windows—all of which feed directly into cost.

Thermal Sensitivity Turns Into Operational Risk

Even with solid devices on the bench, the system changes once heat is in the loop. Light sources generate thermal load. Modulators, particularly in optical communication, can be sensitive to temperature, and their operating point can drift if the environment moves.

Put the pieces in close proximity, and interactions multiply. What looks stable in isolation can become fragile when the full assembly is powered and pushed at speed.

The practical result is additional control, compensation, and packaging complexity to hold performance inside the window that a data center requires. That overhead is not theoretical; it is part of the real cost of delivering an optical link that is reliable over time.

Manufacturing Remains the Bottleneck to Scale

Cost is as much about process as it is about devices. The semiconductor industry’s muscle memory is built around CMOS—flows, tooling, and supply chains that naturally favor silicon and copper.

Silicon photonics fits some pieces of that puzzle, but not all.

Specialized fabrication for photonic devices, advanced packaging, and precision fiber attach are still less standardized and less scaled than their electrical counterparts. The consequence is visible in yield curves and unit cost.

Large incumbents with packaging depth and fiber-optic manufacturing discipline can narrow the gap, but that expertise is not yet ubiquitous.

A Maturing Ecosystem

There is momentum. Major foundries and packaging houses are investing in silicon photonics and optical assembly, and capabilities are expanding.

That is good for the category. It does not, however, equal immediate access for early companies. Big manufacturers will prioritize big customers, and their slots will fill with programs from hyperscalers and leading system vendors.

For a startup, the path usually begins with smaller foundry and packaging partners who are a better fit for stage and volume. That is a workable route, provided the roadmap never loses sight of mass production.

The question is always the same: can the process move from small lots to meaningful volume without breaking yield, and can it do so at a cost that holds up against copper?

Yield Is the Core of Unit Economics

Ultimately, the business case reduces to repeatability. A prototype that functions is necessary, but it is not sufficient. The market buys parts that ship in volume with predictable performance and acceptable margins.

That means aligning device design, thermal behavior, assembly flow, and supplier choice around yield targets that make a price point possible. When that alignment is in place, optics becomes a credible alternative.

Until then, copper remains the default—not because it is better physics, but because it is better economics.

Landing Your First Customer: 4 Tips for Founders

Across new approaches—new sources, advanced modulators, packaging breakthroughs, and full-stack systems—the common thread is focus.

Component specialists win by making their block unequivocally better and then proving that it slots cleanly into existing roadmaps.

System players win by convincing customers that an integrated solution reduces complexity, accelerates deployment, and holds its performance under real operating conditions.

In both cases, the bar is not a demo; it is manufacturability, yield, and credible economics. With that in mind, here are 4 considerations to keep in mind before approaching your first B2B customer.

1. Fit the Roadmap Before You Pitch the Product

In the AI hardware infrastructure stack, the most important buyers—hyperscalers and high-performance computing providers—are not discovering optics for the first time.

Many run internal programs and maintain deep optical interconnect expertise while also engaging external vendors.

Winning a place in that environment begins with alignment, not invention.

The task is to understand the customer’s roadmap, the specific problems they are prioritizing, and how an external solution can fit alongside, or inside, efforts already underway.

Without that context, even a strong device struggles to find traction, because the decision is less about a single metric and more about how a technology lands in a complex program with defined milestones and dependencies.

2. Manufacturing Is Part of the First Meeting

The practical barrier is manufacturability. There are only so many fabs specialized in silicon photonics, and many are small to medium operations that can support development but not immediate large-scale production.

Packaging and assembly capacity are similarly constrained.

The ecosystem is improving—major players are expanding silicon photonics and optical packaging capabilities—but those gains flow first to the largest customers.

The consequence for an early company is straightforward: expect to begin with partners sized to your stage and volumes, and build a plan that bridges from those partners to mass production as the product matures.

3. Prioritization Favors the Biggest Programs

Access follows gravity. Large manufacturers will prioritize large customers, and slots will be claimed by programs from names everyone knows.

That is not a reason to stand down; it is a reason to be precise.

Choose foundry and packaging partners that can deliver at the pace your stage requires, prove repeatability with them, and keep a clear line of sight to the next rung of scale.

The conversation with a strategic buyer improves when the manufacturing path is not theoretical but evidenced by runs that hit the target specifications reliably.

4. Cost Discipline Travels With You

The refrain does not change as you move from prototype to pilot: cost, cost, cost. Even with a differentiated architecture, the economics will be judged on yield, assembly flow, and the degree to which the process can be repeated without surprises.

The right partners at the right time help, but they do not replace the need to design for manufacturability from the outset.

The stronger position in front of a hyperscaler or HPC buyer is a solution that already lives within their speed class, demonstrates competitive energy per bit and bandwidth density, and shows a credible path to volume that brings unit cost down as yields climb.

When those elements are in place, the conversation shifts from “can this work here?” to “how fast can we roll it into the plan?”

The Economic Sequence: Throughput → Efficiency → Cost

1. Throughput Is the Ticket to Enter

In this market, speed is not a differentiator; it is eligibility. The industry cadence sets the bar, and individual companies do not get to move it.

Eight-hundred gigabit links are already treated as commodity. The working frontier is 1.6 terabits per second, and any serious contender must show that level of performance.

Demonstration matters in the concrete sense—delivering the stated throughput under realistic operating conditions, not a lab-limited experiment. If a product cannot live in the current speed class, it does not join the conversation.

2. Energy per Bit and Bandwidth Density Decide Who Stays

Once the speed bar is cleared, selection shifts to efficiency. Buyers compare peers within the same data rate on two fronts: energy per bit and bandwidth density.

The language is practical—energy per bit for power, and how much performance can be packed into a given silicon footprint.

Clearing 1.6T at excessive power does not earn a slot; it simply moves the bottleneck to the thermal budget. Hitting the same rate with lower energy and tighter integration is the way a new architecture proves it carries a real advantage.

3. Yield and Manufacturability Turn Performance Into Product

After throughput and efficiency, the decision turns on whether the device can be built repeatedly at an acceptable cost. Yield is the hinge.

A compelling prototype that ships poorly is not a product, and buyers have learned to ask how a process behaves outside a controlled run. This is where foundry partners, packaging choices, and assembly flow become business variables.

The mandate is to show that the same performance can be produced at scale with yields high enough to support pricing that holds up in the field.

That means designing for manufacturability early, aligning with partners that match the company’s stage, and proving yield improvement as lots grow.

A Credible Path to Unit Economics

To recap: the economic story follows a simple sequence:

• First, meet the industry speed class so the device is relevant.

• Second, show energy per bit and density that compare favorably within that class.

• Third, deliver yield data from runs that are large enough to matter.

When those elements line up, cost curves begin to make sense, and the unit economics become credible to a hyperscaler.

The value proposition is not a claim about potential; it is a set of numbers that show how performance translates into power savings and how manufacturing translates into price.

That is the point at which a technology shifts from interesting to adoptable.

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Focus to Win: Niche → Revenue → Expansion

In optical interconnect, ambition often outruns timing. The total addressable market for full-stack optical systems is enormous, but the path to reach it is not direct.

The companies that build lasting traction begin by focusing on a single, well-defined use case where their technology delivers a clear, measurable advantage.

Narrow focus is not a compromise—it is a strategy for survival.

By concentrating resources on one application, a team can refine its process, validate performance under real operating conditions, and demonstrate early revenue before chasing scale. The alternative—spreading too thin across multiple end markets—usually leads to dilution of both capital and credibility.

The Test Equipment Beachhead: A Case Study

One instructive example comes from the modulator space. A company we discussed, which works with thin-film lithium niobate—a material prized for its high-speed and low-power modulation—chose not to launch directly into the mainstream communications market.

Instead, it targeted test equipment vendors, a smaller but technically demanding niche.

Test systems need precise light generation and modulation to validate other optical components, making them ideal proving grounds for new photonic technologies. The volumes are moderate, but the margins are healthy, and the customers are sophisticated enough to recognize technical merit.

By serving that segment first, the company was able to mature its process, refine its cost structure, and accumulate real operating data. The discipline of shipping into a professional, paying customer base sharpened the technology faster than internal testing alone could have done.

Earning the Right to Scale

With those fundamentals in place—validated performance, stable yield, and production experience—the company could credibly step toward higher-volume applications.

After demonstrating its advantage in test equipment, the next logical targets became optical plugs and communication links. At that point, the technology was no longer experimental; it was a working platform with a known cost profile and proven manufacturability.

This staged approach mirrors how Deep Tech businesses often win: by earning the right to expand. Each stage of commercialization is a test of readiness—not only technical, but operational and financial. By the time the company moves into larger markets, the core process is mature enough to withstand the pressure of scale.

Discipline > Velocity

In an environment fueled by AI demand and record infrastructure spending, it is tempting to move fast, broaden scope, and claim a large market story early. Yet in Deep Tech, discipline outperforms speed.

A narrow beachhead that generates revenue and hardens the process creates leverage that no projection can replace. It anchors valuation to evidence, aligns investor expectations with operational reality, and allows the company to scale on its own terms.

Optical interconnect may promise vast potential, but the companies that capture it are those that sequence their ambition—first mastering a specific problem, then expanding from a position of proven strength.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

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Grid technology is at an inflection point. After a decade of flat consumption, electricity demand is climbing fast while weather-driven disruptions strain supply. Data centers for AI are concentrating load, interconnection queues are long, and the legacy grid wasn’t built for this volatility.

The result is a real imbalance—and a real opening for solutions that harden infrastructure, localize generation, and align with how utilities actually buy.

How do Deep Tech founders capitalize on this moment—commercially, technically, and financially?

To unpack what it takes to build investable grid companies now, we’re joined by Brad Jones, Principal at SE Ventures!

Key takeaways from the episode (TL;DR):

⚡️ Load Spike Meets Fragile Supply
U.S. demand is rising while weather events drive outages; data centers dominate near-term load and make the problem urgent.

🧰 Two Hardware Plays Win Now
Fortify supply with real-time detection and fault visibility; shrink demand by bringing generation behind the meter at the point of load.

💸 Capitalization > Opex for Speed
When solutions qualify as capital projects, utilities earn a regulated return, enabling multi-year deals and sizable upfront payments.

🚀 Start Where Buyers Move Fast
Hyperscalers are the quickest path for behind-the-meter generation; with utilities, integrate into existing control-room workflows to reduce friction.

🧭 Land with Thought Leaders—Be Ready
Pilots with first movers shape regulatory and market norms; success accelerates adoption, but going big before you’re ready can backfire.

📈 Stage Readiness and Bigger Early Rounds
Pre-seed is team + scenario-driven market analysis; Seed favors recurring revenue on top of hardware; Series A rounds run larger, with traction judged alongside margins and cycle time.

✨ For more, see Membership | Deep Tech Briefing | Insights | VC Guides

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

The New Electricity Curve

After more than a decade of flat consumption, U.S. electricity demand accelerated by roughly three percent last year. On paper, that may look modest; in context, it ranks among the highest growth rates seen in a century.

The shift is not a blip.

Electrification is becoming the most economic way to power useful work at scale, and that reality is now showing up in the numbers.

The immediate implication is simple: a system designed for incremental change is absorbing a step-function in load.

That gap between what the grid was built to handle and what the economy now requires is where meaningful company building becomes possible.

Weather as a Structural Supply Constraint

On the supply side, the grid is contending with more frequent and severe natural disasters. Wildfires, hurricanes, and flooding inflict billions in damage on infrastructure each year, and a majority of outages trace back to weather events.

Even a heat wave stresses the system: air conditioners spike demand, infrastructure overheats, and components fail.

The legacy grid was not configured for this level of volatility. The result is a structural constraint on supply at the very moment demand is rising, which sharpens the opportunity for solutions that harden assets, speed detection and response, and keep power flowing under stress.

Why Data Centers Dominate the Near-Term Picture

Multiple forces are pushing demand higher—consumer EVs, commercial fleets, and the electrification of industrial processes among them. In the near term, however, data centers stand out.

The buildout required to support generative AI is driving large, concentrated loads that must be served reliably and quickly.

That concentration makes the challenge visible and urgent, and it creates room for technologies that either add resilient capacity or bring generation to the load.

The wider story is a classic imbalance: rising demand, constrained supply, and a system under pressure.

For founders, that imbalance is not just a problem to analyze; it is a window to solve for, with solutions that will be adopted because the economics and the physics now require them.

Resilience at the Edge and Power at the Source: Where Devices Win

Detecting and Preventing Infrastructure Failure in Real Time

On the supply side, the job is to keep existing assets online under harsher conditions. The most direct gains come from seeing problems before they cascade.

Camera hardware paired with computer vision can spot the earliest signatures of wildfire ignition, giving utilities hours—not minutes—to act. That shift from reaction to prevention protects transmission and distribution equipment, reduces outage risk, and preserves capacity that would otherwise be taken offline.

Similar thinking applies to instrumentation on the lines themselves.

Devices mounted on distribution infrastructure can identify faults and emerging hazards in situ, feeding operators with the kind of granular diagnostics that make targeted maintenance possible.

The common thread is simple: resilience comes from visibility, and visibility comes from sensors that translate field conditions into actionable decisions fast enough to matter.

Bringing Generation to the Load with Behind-the-Meter Solutions

On the demand side, the most pragmatic move is to relocate generation closer to where consumption is exploding. Behind-the-meter systems do exactly that, shrinking the draw a facility places on the broader grid.

Data centers are the clearest use case. Their loads are growing rapidly, interconnection queues are long, and utilities are cautious about adding new generation methods at utility scale.

Small-scale nuclear configured for campus-level supply, or solar paired with storage designed for the facility boundary, changes the equation by delivering power where it is needed without waiting on grid upgrades.

For operators, the benefit shows up as reduced dependence on external capacity and a smoother path to expansion. For the system as a whole, local generation dampens peak demand and eases stress during volatile periods.

In both approaches—hardening supply and localizing demand—the hardware works because it addresses the specific friction points the grid is facing right now.

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Designing for Utility Economics: Capitalization, Opex, and Cash Flow

Selling into utilities is as much about matching their accounting model as it is about proving technical merit. When an investment is capitalized, a utility can finance the project and earn a regulated rate of return on it.

Those costs are recovered from ratepayers over time, which makes the purchase easier to justify internally and more durable once approved.

For startups, this isn’t an abstract detail. It shapes fundraising cadence, go-to-market choices, and how ROI is framed in the pitch.

A solution that qualifies for capitalization moves through the organization with more momentum because it fits the financial incentives that govern how utilities spend.

The California Precedent

Capitalization decisions are regulated and evidence-driven, and the specifics vary by state. In California, the Public Utilities Commission determines which categories of technology can be capitalized, often after benchmarking against incumbent approaches.

Wildfire mitigation is a clear example.

Undergrounding transmission and distribution lines is widely capitalized, but it can cost well over a million dollars per mile. Faced with that price tag, regulators evaluated alternatives—computer vision and related technologies among them—and cleared new options for capitalization because they achieve the same protective outcome at far lower cost per mile.

The lesson is straightforward: if a solution delivers the required resilience at a better cost profile, it can be recognized in the same capital framework as the legacy method, unlocking adoption at scale.

Utilities can earn a rate of return—up to roughly ten percent—on capitalized deployments, on top of the operational benefits the technology provides.

When Purchases Hit Opex, Everything Slows

The flip side is operating expense. If a utility must treat the purchase as Opex, it draws from a budget managed to tight margins.

That reality lengthens sales cycles, introduces more scrutiny, and reduces appetite for rapid rollout.

The product may still be compelling, but it competes with day-to-day spend rather than entering the capital plan.

For founders, knowing which side of that line a given offer will fall on is essential because it determines not just price sensitivity but velocity.

Contracts, Upfront Payments, and Extending Runway

Capitalized programs also change contract shape.

Multi-year terms of three to five years are common, which is longer than in most technology categories.

In many cases, the utility prefers to prepay a large portion—or even the full total contract value—at signing.

The cash arriving from a single, large, capitalized contract can resemble an early financing round.

Used wisely, it extends the runway and reduces dependence on external raises between milestones. For teams building hardware or hardware-plus-software solutions, that can be the difference between a thin cash buffer and the flexibility to execute.

Positioning ROI for the Approval Path

Because commissions benchmark outcomes against the current state of the market, the burden is on the company to quantify impact in the terms regulators and operators already use.

That means translating prevention, resilience, and operational continuity into credible cost comparisons with established practices, and designing the product so those results can be demonstrated in the field.

The tighter the link between observed performance and the capitalization criteria, the faster a utility can move—and the more repeatable the sales motion becomes.

Hyperscalers vs. Utilities: Go-to-Market Choices That Reduce Friction

Customer selection is a pacing decision. For behind-the-meter generation, data centers are the natural starting point because demand is concentrated and urgent.

Hyperscalers are adding capacity at extraordinary speed and are actively searching for technologies that let them stand up power close to the load.

Utilities, by contrast, tend to move more deliberately on new generation methods, and interconnection queues can slow deployment on the grid side.

When a single private buyer is willing to purchase and operate at scale, the shortest path to traction is often through that door.

Fit the Software to the Control Room, Not the Other Way Around

Integrations live or die on how they meet operators where they already work. Control rooms run mission-critical systems continuously, with limited screen real estate and no appetite for yet another standalone interface.

Solutions that blend hardware and software—whether camera networks that detect wildfire ignition or line-mounted devices that surface faults—gain adoption when they plug into existing distribution management systems and deliver insights without forcing retraining.

When direct integration is cumbersome or impossible, product strategy should adapt to the workflow rather than fight it.

(E.g., Allowing engineers to interact with AI agents via familiar channels like email, returning outputs in formats such as Excel, mirrors consulting patterns utilities already trust, and reduces friction to near zero.)

Product Choices that Streamline Capitalization and Onboarding

Design decisions can smooth both procurement and regulatory approval. Simple, learnable interfaces lower the barrier to day-one use.

Shareable links enable collaboration across siloed teams that must weigh in on capital plans.

Thoughtful back-end architecture makes it feasible to offer a constrained version of the product at minimal cost, giving stakeholders hands-on exposure before a full rollout.

Because public utility commissions benchmark outcomes across many voices, technology that clearly serves operators, planners, and regulators alike is more likely to be recognized for capitalization.

The result is a cleaner approval path, faster onboarding, and a sales motion that aligns with how utilities already buy.

Selling into Utilities: Who Matters, When to Pilot, How to Scale

Who Actually Drives the Deal Inside the Utility

Access at the top helps, but it is rarely sufficient.

Utilities are deeply siloed, and the buyer for a disaster management solution is not the same leader responsible for real-time grid operations.

The people who carry a deployment from interest to integration are the directors and managers embedded in the specific function a product touches.

Reaching them is not straightforward; getting in front of utilities is hard, and warm introductions from trusted nodes in the network often make the difference between a stalled conversation and a serious evaluation.

C-suite sponsorship can unlock doors, yet progress is determined inside the operating teams that will live with the technology every day.

Pilots with First Movers—and the Risk of Going Big Before You’re Ready

The industry has clear thought leaders who set the tone for innovation.

In California, PG&E has been an early adopter of computer vision for wildfire mitigation and a first mover in standing up a mission-control model for disaster response.

Their pilots become reference points. Regulators look to what worked and what did not when they evaluate what should be capitalized, and peer utilities often follow the same path.

The attraction of landing a pilot with a first mover is obvious, but it comes with a responsibility to be ready for the pace and scale that can follow.

If a solution is not prepared to roll out quickly and reliably, the same visibility that accelerates adoption can backfire, creating brand damage that complicates customer acquisition across the region.

Turning Early Success into Regional Momentum

When a deployment performs with a recognized leader—PG&E, Xcel Energy, NextEra, and others—the signal travels.

Pilots that demonstrate clear outcomes give regulators tangible evidence for capitalization decisions, and operational teams at neighboring utilities see a vetted path to implementation.

The sequencing matters: start where the organization is ready to move, prove the outcome in production conditions, and let that performance inform the broader conversation.

Done well, one thoughtful engagement becomes the basis for momentum that extends beyond a single customer and shapes how a market modernizes.

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Tips for Grid Tech Builders: Pre-Seed, Seed, and Series A

Market Analysis that Anticipates Policy, Incentives, and Climate Risk

At the pre-seed stage, readiness is defined by people and judgment. A capable team paired with a market analysis that does more than size a category on averages is the baseline.

The work needs to stress-test scenarios that reflect how policy shifts, incentive structures, and climate risks actually change the go-to-market.

Geography matters. Launching in ERCOT and then pivoting to CAISO can force substantial product and business model adjustments. If those shifts aren’t anticipated, the next raise can arrive under pressure, including the risk of a down round.

The discipline is to map pathways that connect technical milestones to regulatory contours and grid realities before committing scarce capital.

Revenue Quality and Recurrence with Hardware + Software

By seed, a tangible commercial signal begins to matter. Zero to one million dollars in revenue is a reasonable band, with a clear preference for dollars that recur.

Hardware can and should sell, but the revenue model strengthens when the system is anchored by software that renews and can, in principle, be removed if the contract isn’t extended.

That construct keeps the customer engaged beyond the initial install and creates leverage for retention.

It is not feasible in every use case, yet where it fits, recurring software layered on top of hardware reduces volatility and makes the business easier to scale.

Why Early Grid Rounds Run Bigger—and What Milestones Count

Post–Series A, round sizes often look larger than in conventional enterprise software. Twenty-, thirty-, and even forty-million-dollar Series A financings are not uncommon in grid technologies, a pattern that rhymes more with today’s generative AI rounds than with classic SaaS.

The bar, however, is not a single metric.

One to five million dollars of revenue is a helpful benchmark, but it is evaluated alongside gross margin profile, sales cycle length, and evidence that deployments can move from pilot to scale.

There is no rigid template for timing a Seed, Series A, or Series B.

What matters is demonstrating that the product performs under production conditions, the economics hold as contracts expand, and the commercial motion repeats with less friction over time.

Disclaimer

Please be aware: the information provided in this publication is for educational purposes only and should not be construed as financial or legal advice or a solicitation to buy or sell any assets or to make any financial decisions. Moreover, this content does not constitute legal or regulatory advice. Nothing contained herein constitutes an offer to sell, or a solicitation of an offer to buy, any securities or investment products, nor should it be construed as such. Furthermore, we want to emphasize that the views and opinions expressed by guests on The Scenarionist do not necessarily reflect the opinions or positions of our platform. Each guest contributes their unique viewpoint, and these opinions are solely their own. We remain committed to providing an inclusive and diverse environment for discussion, encouraging a variety of opinions and ideas. It is essential to consult directly with a qualified legal or financial professional to navigate the landscape effectively.

Subscribe now

Biomanufacturing is entering a decisive phase. Once a frontier of synthetic biology, it now stands at the intersection of engineering, automation, and industrial scale. The challenge is no longer whether biology can make things, but whether it can make them competitively, at volumes that matter.

How do Deep Tech founders navigate the shift from the lab bench to industrial production?

To explore what it truly takes to build scalable biomanufacturing companies, we’re joined by Nima Ronaghi, Principal at Breakout Ventures!

Key takeaways from the episode (TL;DR):

🧫 Identify, Engineer, Produce
Success in biomanufacturing begins with the right organism, refined for speed, cost, and stability, and proven at scale.

⚙️ Continuous Fermentation Is Gaining Ground
Advances in process control and automation are enabling steady-state, modular production that lowers CapEx and localizes output.

💡 Sell the Product, Not the Method
Synthetic biology is an enabler, not a business model. Viability depends on cost and performance, not on scientific novelty.

📉 Scale Myths Persist
The real cost bottleneck is downstream; capacity is scarce, and premature scale destroys optionality.

📊 Economics Defines Endurance
Productivity, yield, and purity drive unit costs; disciplined scaling builds margin before expansion.

🏭 Build Capacity Intelligently
Use contract manufacturing first to validate demand, then invest in owned production once economics and customers align.

✨ For more, see Membership | Deep Tech Briefing | Insights | VC Guides

BEYOND THE CONVERSATION — STRATEGIC INSIGHTS FROM THE EPISODE

Find the Bug, Engineer the Bug, Produce What You Engineered

Effective biomanufacturing begins with selection. The objective is not to invent capability from scratch but to identify an organism whose native metabolism already aligns with the intended outcome.

Nature’s repertoire—enzymes, pathways, and regulatory circuits—offers starting points that can accelerate development if chosen well. Selecting the “right bug” is therefore a strategic decision that shapes the entire program.

It determines the direction of optimization, the likely constraints, and the level of effort required downstream.

Projects that begin with a chassis predisposed to the target function consistently progress with fewer surprises and clearer cost trajectories.

Rewiring for Speed, Cost, and Stability

Once the organism is defined, the work becomes one of disciplined engineering. The mandate is to reconfigure metabolism so the desired product is formed faster, at lower cost, and with greater reliability than it would occur in nature.

The modern toolkit—CRISPR and related editing methods, complemented by computational and AI-enabled design—has materially shortened the cycle from hypothesis to construct.

Yet the standard for success is not a schematic or a data point from a single run; it is stable performance over time. A viable strain delivers consistent output and resists evolutionary drift, sustaining its profile across operating conditions that resemble those of production rather than the bench.

Why Scale Is the Real Test

The decisive proof is production at scale. Laboratory quantities demonstrate the possibility; industrial quantities determine the viability.

As processes move from flask to pilot to plant, the realities of oxygen transfer, heat management, residence time, and downstream recovery surface in full.

This is where the sector’s imbalance is most evident.

Tools for discovering organisms and editing genomes have advanced rapidly, while the translation of those gains into robust, economic tonnage remains the hardest problem to solve.

Ultimately, biomanufacturing is validated not by a sequence but by a process capable of delivering consistent, cost-aware volumes. When that translation holds, the scientific concept becomes an operating business.

Why Continuous Fermentation Is Rising Now

In batch fermentation, a vessel is inoculated, allowed to run for hours or days, then stopped for harvest and cleaning before the next run begins. The cycle is labor-intensive, discontinuous, and difficult to automate end-to-end.

Continuous fermentation replaces that stop-start rhythm with a controlled steady state. Fresh media is introduced while culture is simultaneously removed, sustaining exponential growth and creating a more constant production environment.

The shift is not merely operational. It redefines how capital and labor are used, how uptime is managed, and how predictable the output can become when the process is under control.

The Hard Part: Control, Contamination, and Drift

The challenge has never been in articulating the benefits of continuous processing; it has been in maintaining the conditions that make those benefits real. Tight feedback loops on pH, dissolved oxygen, temperature, and metabolite accumulation must hold within narrow bounds for extended periods.

Contamination risk is ever-present in biology in a way that it’s not in traditional chemical flow processes. Strains evolve; without careful design and monitoring, evolutionary drift can pull performance away from the intended product.

Finally, most facilities were not built with the infrastructure required for true continuous operation, creating a practical gap between aspiration and implementation.

The question is not capacity in the abstract but whether a given plant has the specific capabilities, tooling, and know-how to run continuous fermentation for a particular product and downstream sequence.

What’s Different Today: Software, Process Control, and Smaller-CapEx Plants

Several conditions have improved in tandem. Process control has advanced, with more sophisticated sensing and automation making it feasible to sustain steady-state operation.

A new wave of companies is addressing the software layer of biomanufacturing—integrating data, controls, and optimization across scale-up and downstream, rather than treating them as isolated steps.

Strain design is more stable than it was a decade ago, reducing the drift that undermined long runs. Downstream costs are coming down, and capital can be leveraged into smaller-footprint plants that would have been uneconomic before.

These developments do not remove the difficulty, but they materially change the starting position for teams attempting continuous operation.

Modular and Distributed Production as a Strategic Fit

Continuous fermentation also aligns with modular, distributed biomanufacturing. Smaller, more automated units can be replicated and situated closer to demand, enabling localized production that was not practical ten years ago.

The combination of improved control systems and lower site-level CapEx opens a path to scale that does not depend on a single, monolithic facility.

Where It Applies in Practice

The relevance spans categories where economics are sensitive to cost and throughput. In pharmaceuticals, higher prices can absorb greater process complexity, so the cost pressure is different.

In chemicals and materials, the picture changes.

Inputs for adhesives, chemical starting materials, pigments, and even complex vitamins such as B12 compete in markets where price discipline is strict.

For these products, the ability to run closer to steady state with consistent quality can influence unit economics in ways that intermittent batch processing struggles to match.

The case for continuous fermentation strengthens wherever volumes are meaningful and margins depend on efficient, reliable conversion at scale.

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Sell the Product, Not the Toolkit

A recurring misconception in industrial biotechnology is that synthetic biology itself is a business category. In practice, it is an enabling method—a set of tools applied to a specific commercial purpose.

For instance, a company producing vitamin B12 through engineered microbes is not a “synthetic biology company.” It is a vitamin B12 company that uses synthetic biology as its manufacturing platform. The distinction is more than semantic; it defines how the company tells its story, who its customers are, and how investors evaluate it.

Markets ultimately buy outcomes, not processes.

Buyers care about the quality, cost, and performance of the final molecule or material, not the biological elegance behind it. The more founders frame their work around the market they serve—nutrition, specialty chemicals, materials, or health—the clearer their path to scale becomes.

As the sector matures, investor discipline is moving in the same direction: evaluation based on market traction and cost trajectory rather than novelty of the toolkit.

Pricing Power and What That Means for COGS

The economics of biomanufacturing are deeply shaped by where a product sits on the value spectrum. In pharmaceuticals, prices are high and cost sensitivity is low.

A biologic drug can tolerate expensive fermentation and purification steps because its selling price is orders of magnitude higher than its cost of goods. In contrast, most chemicals and materials are priced as commodities.

Adhesives, pigments, vitamins, and chemical precursors sell into markets where margins are thin and customers are unwilling to absorb price increases.

For these categories, cost discipline defines viability. The difference between profitability and failure can rest on small percentage changes in yield or downstream recovery.

Every point of inefficiency compounds across production volumes.

In such environments, the production method—biological or otherwise—is judged entirely by its ability to meet cost and quality thresholds.

That is why the most competitive industrial biotech ventures position themselves not as demonstrations of synthetic biology, but as producers of essential, price-aware goods that happen to be made more efficiently through biology.

Ultimately, credibility in biomanufacturing is earned not through the sophistication of the science but through the clarity of the commercial proposition. The winning narrative is not about the method—it is about the market.

3 Myths That Derail Industrial Scale

Myth 1: Biology Is the Cost Bottleneck

The instinct in industrial biotechnology is to focus on strain engineering as the primary lever for economics. Better enzymes, tighter pathways, smarter edits—surely that is where the cost unlock resides.

In practice, the largest share of cost frequently sits downstream.

Purification and processing often account for a substantial portion of the total cost of goods, rising from a third to well over half, depending on the product and the complexity of the cleanup. When purity thresholds are stringent or byproducts are numerous, the burden compounds quickly.

Again, pharmaceutical biologics can absorb that complexity because selling prices are high; industrial molecules cannot. An antibody can sustain elaborate capture and polishing steps because the value per kilogram is measured in the tens of thousands.

A vitamin such as B12, priced orders of magnitude lower, cannot carry the same overhead. The conclusion is not that upstream work is trivial—it is essential—but that a credible economic model requires early, disciplined attention to the downstream sequence that will exist at scale, not just at bench.

Myth 2: Capacity Is Plentiful If You Scale Fast

A second misconception is that capacity is primarily a matter of finding open tanks. The United States remains underbuilt for industrial-scale biomanufacturing, and many existing contract facilities are designed around pharmaceutical needs rather than commodities.

Overbooking is common, but even when a slot appears, the decisive issue is capability rather than volume. A plant must possess the specific tooling, process understanding, and team to execute both the fermentation and the purification you require.

Producing an intermediate is not the same as delivering a saleable specification.

If a facility lacks the downstream operations your product demands, the apparent capacity is illusory.

The practical question is therefore narrower: who can run this exact process, end to end, with the right controls and the right recovery steps, reliably enough to meet cost and quality targets?

Myth 3: Go Big Early to Win the Market

The final misconception is that speed to large capacity is the surest route to advantage.

Scaling prematurely hardens process choices before they are ready, locking in inefficiencies that become expensive to unwind.

Investor enthusiasm can amplify the pressure to move, but thin-margin categories punish haste. Each percentage point of yield, titer, or recovery matters, and optimizing at the wrong scale converts small variances into structural cost.

The disciplined path sequences risk reduction: prove the strain and process under conditions that resemble production, understand what downstream truly costs, and advance when the data indicate readiness.

Moving forward too soon does not accelerate success; it institutionalizes avoidable problems and consumes capital that should be reserved for building where the economics already work.

The Economics of Biomanufacturing

Volumetric Productivity as the Capital Efficiency Compass

At the core of any viable biomanufacturing operation lies a simple but unforgiving measure: productivity per unit volume and time. Grams per liter per hour becomes the benchmark for how effectively capital is deployed.

Low-productivity systems require enormous fermenters or long residence times, both of which erode economic potential. While precise targets vary with the market, certain rules of thumb hold.

Processes operating below roughly one gram per liter per hour will struggle to reach commercial viability outside niche applications. Between five and ten grams per liter per hour, the economics begin to align with industrial expectations.

Beyond that range, incremental gains continue to improve competitiveness but are not essential for initial market entry.

Productivity, in this sense, is not just a technical metric—it is a proxy for how efficiently every dollar of capital and every hour of plant time is being used.

3 Levers That Drive Downstream Cost

Productivity is reinforced—or undermined—by three interdependent parameters: titer, yield, and purity.

1. Titer measures the amount of product per liter of broth.

2. Yield measures how efficiently the feedstock is converted.

3. Purity measures how much unwanted material remains.

Weakness in any of the three amplifies cost.

1. Low titer inflates downstream volumes and processing loads.

2. Low yield wastes raw material, particularly if sugars or substrates cannot be recirculated efficiently.

3. Low purity extends purification steps and consumes additional reagents, time, and labor.

Together, these factors determine whether a process scales gracefully or becomes economically fragile.

For commodity and specialty chemicals alike, high titer and yield are essential just to reach break-even economics, while purity determines how competitive the margin will be once production stabilizes.

To give a sense of magnitude: in many processes, downstream processing accounts for roughly 30–70% of total COGS, so improvements in titer, yield, and purity translate directly into unit-cost reductions.

What “Good Enough” Looks Like Before You Scale

The transition from laboratory to pilot should occur only when these fundamentals converge to form a credible cost trajectory.

In practice, that means the process must already demonstrate the productivity and downstream efficiency necessary to support market-level pricing. Scaling prematurely does not correct deficiencies; it magnifies them.

Each run at a larger volume consumes more capital and locks design decisions into equipment and layouts that are costly to reverse.

The discipline lies in knowing when the data are mature enough to justify expansion—and in resisting pressure to build before the process has earned its scale.

Target Gross Margins by Category: Specialty Chem, Food Ingredients, and True Commodities

Benchmarks for acceptable margins vary by sector, but the pattern is consistent. Specialty chemicals typically target gross margins between 30 and 50 percent, reflecting moderate pricing power balanced against meaningful production costs.

Food ingredients tend to sit in the 20 to 45 percent range, where volumes are larger and pricing is tighter. True commodities—ethanol, succinic acid, and similar intermediates—operate on margins closer to 10 to 30 percent. In these categories, every incremental efficiency matters.

The industry’s history is filled with examples of promising technologies that faltered not because the biology failed, but because the unit economics never reached parity.

Investors often push to move faster, yet sustainable scale depends on reaching these thresholds before building. When margins are thin, progress must be measured not by the pace of expansion but by the depth of optimization achieved along the way.

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Building Capacity the Right Way

Own the Manufacturing — But Know Where to Stop on the Value Chain

A persistent strategic question in industrial biotechnology is how far to integrate along the value chain. Complete verticalization (e.g., doing everything in-house) has intuitive appeal, yet history shows how easily it can become overreach.

Companies that attempt to master every layer, from microbial engineering to consumer products, often find themselves diluted across capital-intensive disciplines.

The lesson is not to avoid ownership but to calibrate it.

Long-term competitiveness generally requires controlling one’s own production, both to secure supply and to protect margins. Still, the line between strategic control and costly distraction must be drawn deliberately.

The goal is to own enough of the process to safeguard quality and economics without extending so far downstream that capital and focus are spread too thin.

Where the Margin Accrues Should Dictate How Far You Go

Every decision about integration should start from a single question: where does the margin actually accrue?

In complex, multi-step value chains, the bulk of profitability often resides either at the application end or within a specific proprietary process node.

A company producing an intermediate for an eight-step manufacturing sequence rarely captures most of the final value. Understanding this distribution of economics allows founders to decide where to position themselves and how to justify the cost of deeper integration.

Vertical expansion makes sense only when it aligns with the flow of value—when owning an additional step directly strengthens the company’s pricing power, defensibility, or access to customers.

CDMO First, Steel in the Ground Later: De-Risking the Path to a Plant

The decision to build capacity should come through staged de-risking. Early on, partnering with contract manufacturing or development organizations (CMOs or CDMOs) provides a way to validate scale-up and gauge real demand without committing to full capital expenditure.

These facilities charge a premium, but the expense functions as insurance against premature investment. Once the process performs as expected, customers begin to engage, and volumes justify dedicated infrastructure, it becomes rational to move toward proprietary manufacturing.

At that point, financing shifts from pure venture capital to project or growth capital, supported by tangible evidence of market pull. The sequence—prove, produce, then build—remains the most reliable route from concept to industrial footprint.

Determining the correct initial capacity is among the most difficult choices founders face.

Too small a facility leaves the company unable to meet demand, too large locks in cost before revenue can sustain it.

The appropriate approach combines customer dialogue with disciplined economic modeling. Founders should engage potential buyers early, not for letters of intent but for concrete discussions of volumes, price thresholds, and timelines.

Those insights feed into techno-economic models that translate product performance and process data into realistic cost curves.

The objective is to identify the point at which production scale intersects with viable unit economics—the level where each additional batch reinforces profitability rather than consuming capital.

Scaling in this way reframes the factory from a symbol of ambition into a tool of discipline. Each step, from outsourcing to owned production, is guided by data, demand, and margin logic rather than momentum.

When the facility is built on those foundations, it becomes more than an operational milestone; it marks the transformation of a scientific achievement into an investable industrial enterprise.

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