The Best AI Energy Solution on the Planet
There are many ways to power a data center. Each has a trade-off of economics, practicality, and scalability. This is the best.
Today, I will discuss the energy solution that I believe is the single best way to power AI data centers for the next five years.
I added this company to my portfolio at the end of March.
Since then, they have produced great returns, but I think they are bound to go higher and without much of a ceiling on the valuation (as tail scenarios can launch it hard).
Their management team talks like a startup. In the sense that they are so cocky and comically overconfident in their own success you can’t help but also be confident with them.
It shows up in their earnings calls, the podcasts that the management team goes on, their slide presentations at technical conferences, and basically any form of communication. They use weird and funny analogies (just like I do!). It’s honestly pretty entertaining.
I don’t know exactly when, but one earnings call within the next 18 months or so will be the equivalent of Nvidia’s May 2023 blowout: numbers so far past consensus that the old company dies and a new one is born.
This is a loooong one. Get strapped in. Let’s go!
The Failure of the Grid
I won’t belabor the point here because this is already quite well known. The grid was built a century ago. It expects predictable slow-growing power demand of 1-2% a year. AI wants to double this capacity, and clearly the old infrastructure is not built for it. But the intuition here is important.
Power cannot be stored. This means that at any point in time, the total supply and the total demand of power must be perfectly equivalent. Thus, before a load (demand) is added, the grid operator must figure out how to add the equivalent supply so that the grid doesn’t break. Enter System Studies!
Imagine trying to model voltage stability, frequency response, contingency scenarios, and load flow under fault conditions for each new data center. When you do these models, you’re basically operating under the assumption of a fixed grid. But the grid is constantly changing! When grid topology is changing faster than studies can be completed, studies go stale.
This is why new interconnections take five years. The logistics of adding data centers to the grid is terrible. The system was never built for it. We need a new solution. We must bypass the grid and go behind the meter.
The Failure of the Turbine
The classical behind-the-meter solution is the gas turbine. It’s the obvious solution because it provides the base load, always on type power which data centers need. There are three main types of turbines.
Combined Cycle
Combined cycle is called combined cycle because it combines two cycles.
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Cycle 1 converts natural gas into energy via normal turbining. Cycle two is converting the heat from cycle one into energy via steam.
The benefits of this are that it extracts the maximum amount of energy from the natural gas by not wasting the heat by-product.
The drawback is that it is very complicated and takes forever to build because you have to route more stuff around.
Therefore, this is usually the preferred gas turbine for supplying energy to the grid, but not for behind the meter. Again, big, complicated, but efficient.
Aeroderivative
An aeroderivative turbine is literally a jet engine strapped to the ground. They cost around $2,000 per kW.
This is the majority of the market. The most “normal” kind of behind-the-meter gas turbine. G.E. Vernova, Siemens, and friends make them.
However, there is also a very obvious problem. During the 1990s and early 2000s, the gas turbine oligopoly suffered a massive boom-bust cycle, almost like memory.
Therefore, the aero guys got PTSD (Post Traumatic Supply Disorder), and today, despite overwhelming demand, they refuse to build new factories and expand their capacity. This has been great for GEV stock.
But although investors love supply discipline, customers hate it because it means that they’re paying higher prices for a smaller source of supply.
It’s gotten so bad, in fact, that GEV is fully sold out through 2030. This tweet from SemiAnalysis sums it up well.
Now, customers are searching elsewhere, desperate to get their hands on anything that spins and produces electrons.
Reciprocating Engine
The “searching elsewhere” first lands at reciprocating engines, the cheaper, dirtier, and less technologically marvelous sibling of aeroderivatives.
These are literal car engines.
Although they cost less per kilowatt than aeroderivatives do, you pay for it with the insane amount of maintenance needed. They are smaller and generate less power per unit, so you need a boatload of them to power any reasonably sized data center (a 1 GW deployment requires 100-400). You basically need to set up a massive 24/7 auto repair shop on your data center campus.
Pollution
Climate change is bad!!!!
All forms of gas turbines are heavily polluting and terrible for the environment. They release NOx and SOx, which are harmful particulates that cause public health problems in the surrounding living areas. Therefore, every single deployment of gas turbines is subject to stringent permitting regulations that take an excruciatingly long time and potentially expose the developer to environmental lawsuits.
The Requirements of the New Solution
So, we need a new solution. We know that. But what characteristics should this new solution have?
Regulatory Landscape: Capital vs. Permission
Building a data center requires two things: capital and permission.
In 2024 and 2025, when AI was less developed and capable, it was capital that was scarce. Companies did not know if their CapEx would yield sufficient investment returns because all we had were chatbots. At the same time, permission was abundant because chatbots didn’t require that many data centers, and people were not afraid of losing their jobs.
Today, the situation has completely changed. AI agents are extremely capable and able to do most white-collar work. Anthropic hitting 30 billion ARR by the end of Q1 has signaled that the ROI question is effectively answered. AI compute demand is going parabolic, and now data centers are both a political and environmental issue. Today, capital is abundant. Permission is scarce.
What does this mean? We are transitioning from a regime where solutions required low capital and high permission are attractive to a regime where solutions requiring high capital and low permission are attractive. This forever changes the class of energy companies that benefit from AI capital expenditures. In order to be successful as a power equipment vendor in the future, you must help your customers get permission (FAST) and avoid regulatory stupidity.
Compute Shortage Necessitates Time to Power
Let’s think about the second-order effects of H100 prices (which were expected to decline into oblivion as technology obsoletes) rising 33% in 2026 in an unprecedented massive compute shortage.
Time to power was already important. AI cloud revenue is around $10 to $12 million per megawatt per year, meaning that having a 100MW deployment delayed by a month costs you $100 million.
Now it’s even worse. Failing to consider time to power means your (luckily secured) supply of compute can’t be used to address this shortage and collect the scarcity pricing.
We Will Transition and Never Return
I believe the adoption of the solution I discuss below would not be a temporary stopgap, but a secular transition.
After adoption, this is a technology that shall compound upon itself, becoming better year after year, never giving the legacy players a chance to get back in.
It has over 10 distinct advantages that will become more important over time as the requirements of AI workloads are changing in its favor.
The TAM ceiling is so large it’s irrelevant. The company has a monopoly moat. And there is a surprising lack of alternatives.
This is an extra extra long one. We have so much to talk about.
I also wrote all this by hand (found that it is easier to read and more entertaining), which is a strange thing to brag about, given that I am trying to integrate AI into every part of my life.
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