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Welcome to the 64th edition of Deep Tech Catalyst, the channel by The Scenarionist, where science meets venture!
Artificial intelligence has the potential to reshape the energy sector, offering solutions to some of its most pressing challenges. While AI is broadly applicable across industries, its role in energy is particularly critical, given the increasing complexity of power grids, the shift toward renewables, and the growing need for energy efficiency.
In this episode, we sit down with Felix Krause, Managing Partner at Vireo Ventures, to discuss the key challenges and opportunities at the intersection of AI and energy, including:
Where AI is making the biggest impact in the energy sector today.
How to approach the first potential customer to validate the market.
How investors evaluate AI startups in the energy sector.
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KEY INSIGHTS FROM THE EPISODE
🏭 10 Areas Where AI Will Transform Energy
Let’s start by highlighting 10 key areas of innovation at the intersection of AI and the energy sector.
1. Grid Optimization & Stability
Historically, energy came from large, centralized power plants that supplied a stable, predictable flow of electricity to consumers. Today, the grid must accommodate distributed energy sources like solar panels, wind farms, and battery storage systems, which don’t operate on a fixed schedule.
📌 How AI can help:
AI can analyze real-time data from the grid to predict energy demand, optimize supply, and manage grid balancing efficiently. Machine learning models can predict outages or failures and suggest preventive measures.
2. Building Efficiency & Smart Energy Management
Buildings are responsible for a significant portion of global energy consumption, particularly for heating and cooling. AI has the potential to eliminate energy waste by making buildings more intelligent and responsive to real-time conditions.
📌 How AI can help:
AI-driven energy management systems can optimize heating, cooling, and electricity use in real-time, tailoring operations based on occupancy, weather, and energy prices.
3. AI-Powered Energy Forecasting
Renewable energy sources are inherently unpredictable—the sun doesn’t always shine, and the wind doesn’t always blow. To make renewables a reliable energy source, AI must play a role in forecasting and energy distribution.
📌 How AI can help:
AI models can process weather data and historical production patterns to improve short- and long-term renewable energy forecasting, enabling better integration into the grid.
4. Energy Trading and Market Optimisation
Volatility in energy markets and mismatches between supply and demand can lead to inefficiencies and increased costs.
📌 How AI can help:
AI can enable predictive analytics and automated trading, helping optimize the buying and selling of energy in real-time. This is particularly critical in wholesale energy markets and peer-to-peer energy trading platforms.
5. Decarbonizing Industrial Processes
High-emission industries need smarter ways to reduce their carbon footprint without sacrificing productivity.
📌 How AI can help:
AI can help optimize energy-intensive processes by identifying inefficiencies, recommending alternative inputs, or suggesting operational adjustments to minimize emissions.
6. EV Charging & Load Balancing
The increasing adoption of electric vehicles (EVs) is placing significant strain on the energy grid, particularly during peak charging periods. As more EVs hit the roads, the demand for electricity is rising, putting existing energy infrastructure under pressure and highlighting the need for smarter energy management solutions.
📌 How AI can help:
AI can optimize EV charging schedules based on grid load, energy prices, and renewable energy availability, reducing strain on the grid while lowering costs for consumers.
7. Battery Storage Optimization
Efficient energy storage is essential for managing variable renewable energy sources, ensuring a stable power supply even when solar or wind generation fluctuates. However, poor battery management can result in inefficient energy use.
📌 How AI can help:
AI can manage energy storage systems, determining the optimal times to charge and discharge batteries to maximize their lifespan and support the grid.
8. AI-Optimized Carbon Capture & Reduction
Identifying and reducing emissions from various sources remains challenging-
📌 How AI can help:
AI can monitor emissions through sensors and satellite data, predict high-emission periods, and optimize the operation of carbon capture technologies.
9. Supply Chain Optimization in Energy
Energy supply chains, including fuel transportation and distribution, often suffer from inefficiencies.
📌 How AI can help:
AI can streamline logistics, predict demand, and reduce waste in supply chain operations
10. AI-as-a-Service (AIaaS) for Energy Companies
Many energy companies lack the technical expertise to develop AI systems in-house, creating an opportunity for AI-as-a-service (AIaaS) platforms designed specifically for the energy sector.
📌 How AI Can Help:
AI can enable smaller energy companies to integrate AI-driven solutions without requiring massive R&D investments, while also standardizing AI-powered tools and processes across the industry.
🚀 From Idea to a VC-Backable Startup: Where to Begin?
One of the biggest mistakes founders make is building a solution before validating a real market need.
Founders must shift their focus from “How does this work?” to “What problem does this solve?”
As we often discuss on Deep Tech Catalyst, successful startups solve problems that customers can’t ignore—problems urgent enough that businesses are actively seeking solutions.
Features may become important in later iterations, but in the beginning, what truly matters is problem-solution fit—the alignment between what you’re offering and what your target customers actually need.
Three Key Takeaways:
Customers don’t buy technology; they buy outcomes.
It’s not about how your solution works—it’s about what it enables.
You must define success from the customer’s perspective, not your own.
If they don’t understand your solution, they won’t buy it.
Even if you’ve built an incredible technology, it won’t matter if you can’t communicate it effectively. Many decision-makers in the energy sector—and beyond—are not highly technical. They may come from business, finance, or policy backgrounds and need to understand your solution without a deep dive into the technical complexities.
Core Insight: Your ability to explain your product simply is directly linked to your ability to close deals and raise funding.
The “Mother Test”
The Mother Test is a simple way to test whether you’re communicating your idea effectively or not. This is one of the hardest things to do as a founder, but once you master it, you’ll notice that everything—from fundraising to customer acquisition—moves faster and more smoothly.
📌 How it works:
Imagine explaining your startup to your mother or someone with no technical background.
Then, ask them to explain it to their friends.
If they can do so in a way that still makes sense, you’ve nailed your messaging.
“You need to be able to tell your mother what you're doing, and your mother needs to be able to tell what her daughter or her son is doing to all her friends. And those friends must understand more or less what you're doing. If you succeed there, VCs will follow, and also customers will follow.”
🚧 The “Proof-of-Concept Trap”
One of the biggest dangers for AI startups in the energy sector is getting stuck in endless proof-of-concept (POC) cycles. It can be a trap that many founders fall into: they secure a pilot project with a large corporation, hoping that it will lead to a full commercial contract, only to find themselves stuck in limbo. Large organizations move slowly, and unless the right decision-makers are involved, a startup can waste months—or even years—waiting for a deal that never materializes.
“The worst thing that could happen to you as a startup is you find a sponsor for a POC, like a small paid POC, but then you are stuck in the POC loop. You're just chasing the next step that most likely will never come because that person is not even entitled to approve bigger budgets.”
How to Avoid the “POC Trap”
To prevent this, founders need to do their own due diligence before committing to a POC. Just like investors evaluate startups before investing, startups should evaluate potential customers before signing a pilot agreement.
Ask direct questions like:
What happens next if this POC is successful?
How long is this POC going to last?
Who is paying for it?
Who in your company is championing this project, and do they have the authority to move it forward?
🧐 What Do VCs Look For in the Early Stages?
Investors aren’t just looking for promising technology—they need to see a clear, structured roadmap that outlines how the company will grow, achieve key milestones, and ultimately generate exponential returns within the typical VC timeframe.
So, what are the critical steps a founder should take in this sector?
Nothing beats a strong team.
Before even talking about early sales strategies, investors place the biggest bets on the team itself. Many founders believe that having a fully developed product is the most important thing in the early stages, but in reality, a great product without the right team won’t go anywhere.
The ability to hustle and engage with customers is just as important as the technology itself. A team with technical brilliance but no one who can communicate clearly or close deals will struggle, no matter how good the product is.
Beyond just technical and business expertise, investors love seeing founders with industry experience. If at least one or two people on the team have worked in energy or a relevant field before, it’s a strong indicator that they understand the challenges and can navigate the ecosystem.
One of the biggest red flags for investors is when a startup claims to be AI-driven but doesn’t have a technical team in place yet.
Beyond the Tech: Understanding the Business
While having strong AI expertise is crucial, it’s not enough on its own. Investors also want to see that the founders understand the business side of things—particularly in terms of hiring, financial planning, and scalability.
Investors aren’t looking for detailed five- or ten-year revenue projections at the pre-seed or seed stage. Instead, they want to understand:
What’s happening in the next 12–18 months?
Who are you planning to hire?
What key hires are missing from the team?
How are you prioritizing resources?
Core Insight: The ability to adapt and allocate resources efficiently is a major factor in determining whether a startup is investable.
Attracting Top Talent: How Investors Assess a Startup’s Ability to Build a World-Class Team
One of the key factors that determines whether a startup can truly scale is its ability to attract top-tier talent. In AI-driven businesses, where the demand for skilled professionals far exceeds supply, investors need to feel confident that the founding team not only understands the importance of hiring the best people but also has a realistic plan to bring them on board.
You need a talent strategy.
One of the biggest misconceptions among early-stage founders—especially younger ones—is the belief that they can do everything themselves. While having a strong technical team in place is crucial, investors want to see that founders understand their own limitations and are actively seeking out the best people to complement their skill sets.
📌 What investors look for:
A founder’s willingness to hire people more experienced than themselves.
Evidence that they are already networking with top talent in their field.
A clear plan for bringing in key hires as soon as funding allows.
If a founder can already name highly qualified individuals they plan to bring on board once they secure funding, that’s a strong signal that they understand the importance of talent acquisition.
It’s not just about money—top talent in AI often values equity, mission, and innovation just as much as salary. Investors want to see that founders know how to sell their vision to potential hires, convincing them that joining an early-stage startup is worth the risk.
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