AI may be changing how the world computes, but every new data center still faces a very old problem: it needs electricity. As billions of dollars flow into AI infrastructure, the most interesting opportunities may not always be found in the companies building models or renting computing capacity. They could be hiding in the businesses working to solve the power shortage underneath the entire industry.

But the AI energy story comes with an important warning. Growing demand does not automatically create good investments. Just as excess computing capacity could pressure pricing for companies such as CRWV, NBIS, and IREN, massive spending on energy infrastructure can produce poor returns if the economics fail to work.

For you, the more useful question is not simply whether AI will consume more electricity. It is which companies can turn that unavoidable demand into durable cash flows, competitive advantages, and attractive returns on the capital they invest. ⚡🏭

What if the next major AI bottleneck isn't computing power—but the electricity needed to keep all that computing running? 👀

We’ll examine four very different energy plays, from oilfield expertise being repurposed for data centers to geothermal technology that could deliver always-on power, and explore which businesses have a real economic advantage versus those still relying on a promising story.

Imagine setting aside $500 a month for IES Holdings $IESC ( ▲ 0.66% ) for five years, buying a fixed dollar amount on a schedule, a method called dollar-cost averaging. In our example the buy happens on the first trading day on or after the 7th of each month, from Oct 2021 through Sep 2026: 60 buys, $30,000 in total.

IESC closed at $23.17 on Oct 7, 2021 and at $339.34 on Oct 2, 2026 (both split-adjusted, per Yahoo, meaning older prices are restated so they compare fairly after the two-for-one split of Aug 2026), a price gain of about 1,364%, or roughly 71% a year compounded (the steady yearly rate that would give the same total gain). In that example the $30,000 would have been worth about $269,800 at the Oct 2, 2026 close (about 9 times the money put in), and a single $10,000 invested on Oct 7, 2021 would have become about $146,400. Price only: no dividends, fees or taxes, and these are examples, not forecasts.

The highest close of the past 52 weeks was $396.98 on Aug 17, 2026, so the Oct 2, 2026 close sat about 14.5% below it. Earlier, the price eased about 54%, from $27.60 on Nov 17, 2021 to $12.64 on May 3, 2022, before the long climb that followed. A steady plan is built for stretches like that.

Caution: past pace rarely continues. The company's release for the quarter ended Jun 30, 2026 (issued Jul 31, 2026) puts backlog (work ordered but not yet completed) at about $4.5 billion, and about $1.7 billion of that is agreements and letters of intent that the company says it does not yet have a legal right to enforce.

The AI boom looks like a technology story, but the bigger it becomes, the more it starts to look like an energy story.

Every new data center needs enormous amounts of electricity. That creates an interesting disconnect: investors can spend hours debating which AI company will dominate the next decade while overlooking the companies solving the much less glamorous problem of actually powering the infrastructure.

That does not mean every energy stock connected to AI is a winner. In fact, the bigger lesson may be the opposite.

A great growth trend can still produce terrible investments if too much money is chasing the wrong business model.

That distinction becomes especially important with the enormous capital being committed to AI data centers. The bullish case for artificial intelligence can be completely correct while certain companies building the infrastructure still generate disappointing returns.

For someone who does not have time to monitor every headline, earnings call and stock-price move, that is an important way to think about the market. You do not have to decide whether AI itself will succeed. You need to understand which businesses can actually turn the growth of AI into sustainable economics.

And that brings the focus to four energy-related names: Atlas Energy Solutions (AESI), Liberty Energy (LBRT), Ormat Technologies (ORA), and Fervo Energy.

They are not four identical bets. In fact, they represent very different ways of addressing one problem: where will all the electricity needed for the next generation of computing come from?

There is a difference between something being in high demand and something being a good business.

That sounds obvious, but it is surprisingly easy to forget during a major investment cycle.

The current AI buildout requires massive amounts of capital for data centers, chips, networking equipment, cooling and power infrastructure. Our concern is that the amount of capital being deployed could eventually create too much computing capacity relative to what customers are willing to pay for it.

The concern is particularly relevant to so-called neocloud companies, which rent computing capacity to customers rather than selling traditional software products.

Companies such as CoreWeave $CRWV ( ▲ 4.96% ), Nebius Group $NBIS ( ▲ 7.44% ), and IREN $IREN ( ▲ 1.98% ) have attracted significant attention because they are positioned directly in the AI infrastructure boom. But their business model creates a difficult economic question.

What happens when computing capacity becomes abundant?

The comparison to airlines is useful. An airline generally prefers selling an empty seat at a lower price rather than allowing the seat to fly empty. Similarly, a data center has already spent the overwhelming majority of its capital before the computing capacity is sold. Once the facility is operating, the incremental cost of running that capacity can be relatively low.

That creates an incentive to keep the machines running.

If enormous amounts of new computing capacity come online simultaneously, the industry may eventually compete on price rather than scarcity.

This is the risk behind the bearish argument for some AI infrastructure companies.

It is not necessarily that AI demand disappears.

It is that supply could grow faster than pricing power.

That distinction is crucial.

There is an uncomfortable historical comparison here.

During the late-1990s internet boom, enormous amounts of telecommunications infrastructure were built because investors correctly believed internet usage would explode. The problem was that companies collectively built far more network capacity than they could profitably monetize.

The demand was real.

The infrastructure was real.

The economic returns were the problem.

As capacity increased, pricing fell dramatically. Companies that had spent enormous amounts of money building networks discovered that increasing usage did not automatically translate into increasing revenue.

The same risk exists whenever an industry makes huge capital investments based on expectations of future demand.

AI could absolutely transform computing, software and business productivity while some companies supplying AI compute still struggle to earn an acceptable return.

That is why watching pricing can be more useful than simply watching demand.

If demand rises 100% but the price customers pay falls substantially, revenue may not rise nearly as much as expected. Meanwhile, the company still has to pay for the enormous infrastructure it built.

This is the economic problem worth keeping in mind when looking at CRWV, NBIS and IREN.

Their future is not determined solely by how much AI is being used.

It depends on how much customers are willing to pay for that computing capacity and whether those prices are high enough to justify the capital being deployed.

This is where the energy side becomes more compelling.

A data center cannot operate without electricity.

And unlike software, electricity cannot simply be downloaded faster.

Power generation and grid infrastructure take time to build. Interconnection queues can stretch for years. Utilities have to balance supply and demand across an increasingly complex network. Large new loads can require substantial transmission and generation upgrades.

That creates a potential bottleneck.

Even if AI computing capacity becomes abundant, the physical infrastructure required to power that computing capacity may remain constrained.

This is where Atlas Energy Solutions $AESI ( ▼ 0.08% ) and Liberty Energy $LBRT ( ▲ 2.71% ) enter the picture.

Both companies have roots in the oilfield services industry and have explored ways to apply their power-related capabilities to the growing need for electricity outside the traditional utility grid.

The concept is called behind-the-meter power.

Instead of waiting years for a new data center to receive additional grid capacity, a company can potentially generate electricity on-site and use it directly at the facility.

For a data center developer facing an interconnection queue, that difference can be enormous.

A project that cannot wait five or seven years for traditional grid infrastructure may have a much stronger economic incentive to consider alternative power arrangements.

Atlas Energy Solutions (AESI) has historically been associated with the oilfield, particularly services and infrastructure supporting hydraulic fracturing operations.

That connection may seem unrelated to AI at first.

But the underlying requirement is similar: oilfield operations can require enormous amounts of reliable power, and data centers have the same fundamental need.

The opportunity is to repurpose power-generation capabilities for off-grid or behind-the-meter electricity supply.

That gives AESI a potentially interesting position in an AI infrastructure chain without requiring it to become an AI company.

And that distinction matters.

You do not necessarily need to own the company producing the hottest AI model to benefit from AI investment. Sometimes the better opportunity is the business selling something the entire industry cannot function without.

Energy is one of those requirements.

The challenge is that the market has already recognized many AI infrastructure opportunities, so valuation still matters. AESI is not automatically attractive simply because data centers need power.

The thesis needs to be tested through actual contracts, deployed capacity, cash generation and the company's ability to turn this emerging opportunity into a durable business rather than a temporary side project.

Liberty Energy (LBRT) presents a similar transition but with another interesting possibility.

The company has deep experience providing equipment and services to the oil and gas industry, including hydraulic fracturing. That work requires significant horsepower and specialized energy infrastructure.

The same expertise can potentially be applied to providing power for other industrial customers.

That makes Liberty interesting for two separate reasons.

The first is behind-the-meter power, which can help large electricity users bypass some of the delays associated with traditional grid connections.

The second is geothermal energy.

Liberty has been involved in efforts related to geothermal technology, including approaches that use drilling techniques developed by the oil and gas industry to access underground heat.

This is where the investment story becomes more speculative but potentially more interesting.

Oil companies have spent decades becoming extremely good at drilling deep wells, managing underground reservoirs and moving fluids through complex formations. Geothermal developers can potentially adapt some of those capabilities to create electricity from heat beneath the Earth's surface.

The idea is simple even if the engineering is not:

Drill deep, access heat, circulate fluid, generate electricity and repeat.

If the technology can be made economical at scale, geothermal could provide something extremely valuable to data centers: electricity that can operate around the clock rather than depending on whether the sun is shining or the wind is blowing.

That makes geothermal particularly interesting as a potential complement to intermittent renewable energy sources.

If Liberty represents an energy-services company moving toward geothermal, Ormat Technologies $ORA ( ▲ 1.95% ) represents a company with much deeper experience in the geothermal industry.

Ormat has operated geothermal power plants for decades and has built a substantial portfolio of geothermal assets.

That gives ORA an advantage that a newer entrant does not have: operating history.

But the more interesting development is the company's interest in enhanced geothermal systems, or EGS.

Traditional geothermal resources are geographically limited because they generally require naturally occurring underground heat, permeability and fluids that can be economically accessed.

Enhanced geothermal attempts to expand the potential resource base.

Instead of relying entirely on naturally favorable underground conditions, developers can use drilling and reservoir-engineering techniques to create or improve the pathways through which hot water can circulate.

If successful, that could make geothermal available in far more locations.

For AI data centers, the attraction is straightforward.

A large computing facility wants electricity that is reliable, available around the clock and scalable. Geothermal potentially offers all three.

The technology still needs to prove itself economically at larger scale, however. Engineering performance, drilling costs, reservoir behavior and project financing all matter.

That is why ORA is different from simply buying a company because "geothermal sounds promising."

The technology has to work economically.

Then there is Fervo Energy $FRVO ( ▲ 6.46% ), a geothermal company that represents one of the more speculative opportunities in this theme.

Fervo is focused on enhanced geothermal systems and has worked with Google on geothermal power development.

Its Cape Station project in Utah has been described as a multi-gigawatt development, making it potentially significant if the technology and economics scale successfully.

The attraction is obvious.

A successful enhanced geothermal system could provide a new source of always-on electricity without depending on conventional fossil-fuel generation.

But this is also where patience becomes essential.

Fervo is not the same type of investment as an established public utility or a mature geothermal operator. The technology still has to demonstrate that reservoirs can be engineered, maintained and operated economically at commercial scale.

That makes the biggest risk an engineering risk, not simply a question of whether quarterly revenue comes in above or below expectations.

If the technology works, the potential market could be enormous.

If it does not, the investment thesis changes completely.

That is a much more useful framework than trying to predict whether the stock will be higher next month.

There is a powerful temptation to look at a stock that has fallen 30% and immediately ask whether it is "cheap."

That is the wrong starting point.

A stock can fall 30% because investors have become impatient.

It can also fall 30% because the business is deteriorating.

Those are completely different situations.

Consider Fervo's development timeline. If the market becomes frustrated because revenue arrives later than expected while the underlying engineering milestones continue progressing, the stock could weaken without necessarily invalidating the long-term technology thesis.

But if the company discovers that it cannot maintain the underground reservoir as expected, that is a fundamentally different event.

The same principle applies to AESI, LBRT and ORA.

A falling share price is information, but it is incomplete information.

For a long-term position, the more important question is:

What specific event would make you change your mind about the business?

If the answer is simply "when the stock falls 30%," the investment thesis may not be strong enough.

If the answer is "when the technology fails to work economically," "when expected contracts do not materialize," or "when the company's financial position becomes unsustainable," then the decision becomes much more rational.

You are no longer reacting to a number on a screen.

You are monitoring the actual reason you invested.

Another useful lesson is surprisingly simple: you do not need to predict the entire economy to invest well.

Interest rates matter.

Oil prices matter.

Inflation matters.

Government policy matters.

AI demand matters.

But trying to build an investment thesis around predicting every macroeconomic variable can quickly become exhausting.

For someone already managing a career, family, finances and an investment portfolio, there is little benefit in turning every Federal Reserve statement or energy headline into a new portfolio decision.

The deeper opportunity is often narrower.

Instead of saying, "Interest rates will fall, so geothermal will rise," focus on whether a specific geothermal project can generate electricity at an attractive cost.

Instead of saying, "AI will explode, so every AI infrastructure stock will win," ask whether a specific company has pricing power, differentiated assets and a reasonable return on the capital it is deploying.

Instead of saying, "The market is bearish on AESI, so it must be a bargain," look for evidence that its behind-the-meter strategy can become a meaningful and profitable business.

The fewer assumptions an investment requires, the easier it becomes to understand what is actually driving the outcome.

The AI story is not going away simply because some AI infrastructure businesses may struggle.

In fact, the opposite could happen.

As investors become more selective about which AI companies can actually produce returns, attention may increasingly move toward the physical constraints that AI cannot avoid.

Electricity is one of them.

AESI and LBRT are interesting because they can potentially provide faster, behind-the-meter power solutions.

ORA offers established geothermal expertise and exposure to the expansion of geothermal technology.

Fervo Energy represents the more speculative possibility that enhanced geothermal can become a commercially meaningful source of large-scale, always-on electricity.

Meanwhile, CRWV, NBIS and IREN demonstrate why the other side of the AI infrastructure equation deserves scrutiny. Huge demand does not guarantee attractive economics when competitors are simultaneously building enormous amounts of capacity.

That creates an important investing lesson.

You do not necessarily want to own the company that is spending the most money.

Sometimes you want to own the company solving the constraint that everyone else cannot ignore.

The next stage of the AI boom may depend less on who builds the smartest model and more on who can provide the electricity required to run everything behind it.

That makes energy infrastructure an increasingly interesting area to study, but it also demands patience. Atlas Energy Solutions (AESI), Liberty Energy (LBRT), Ormat Technologies (ORA), and Fervo Energy each offer different exposure to the power challenge, with very different levels of maturity and risk.

At the same time, CoreWeave (CRWV), Nebius (NBIS), and IREN (IREN) highlight a critical warning: rising AI demand does not automatically translate into rising profits. If computing capacity expands faster than pricing power, enormous capital spending can produce disappointing returns.

For a busy investor, that distinction can save a lot of unnecessary decisions.

Do not ask only whether AI will grow.

Ask who gets paid when it grows, who has pricing power, who controls a scarce resource, and who is spending billions hoping demand eventually catches up.

Those questions lead to a much more interesting part of the market.

Tip: When a stock is down sharply, do not make the percentage decline your investment thesis. Identify the fundamental reason the business should become more valuable over the next three to five years, then define what would prove that thesis wrong. A falling stock can be an opportunity, but only when the underlying economics give you a reason to believe the market may be wrong.

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Thanks for reading. This format is built to be fast to open, clear to understand, and useful enough to act on, without pretending past returns continue forever.

Caution: Past pace rarely continues. All figures here are approximate and are shown as examples, using price only (no dividends, fees, or taxes). Past performance is not a forecast; this is education, not advice.

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