GE Vernova stock just jumped 25% as AI demand explodes

GE Vernova (GEV) stock jumped 25% jump in less than a month.

Investors piled in after its latest earnings report.

Profits didn’t just grow… they exploded. Net income jumped from $264 million to $4.75 billion in a single year. Free cash flow hit $4.8 billion in one quarter. That’s more than the company made in all of last year.

Orders are flooding in too. $13 billion in new business in just three months. Management now thinks it will hit its massive $200 billion backlog a full year earlier than expected.

What’s going on here?

This is what happens when a company sits right at the center of a megatrend… and that trend suddenly goes into overdrive.

Because GE Vernova isn’t just an energy company.

It’s becoming a critical supplier in the one industry that can’t get enough power right now. AI.

Remember the chip shortage?

When ChatGPT launched almost three and a half years ago, big tech was tearing the hinges off Nvidia’s (NVDA) doors to make more chips.

The next shortage became electricity.

Meta Platforms (META) founder Mark Zuckerberg said, “Energy, not compute, will be the No. 1 bottleneck to AI progress.”

AI’s thirst for energy is off the charts.

Models like ChatGPT are powered by hundreds of thousands of high-end chips packed tightly in vast data centers. These systems generate so much heat, they need specialized vents and fans humming 24/7 to keep cool.

And that, my friends, sucks up a humongous amount of energy.

ChatGPT 5.5 now answers roughly 2.5 billion prompts every day. Even at just 0.34 watt-hours per query, that adds up to around 850 megawatt-hours daily. Enough electricity to power about 28,000 US homes for a year.

And that’s just one model!

As AI models get better, they’ll suck up even more power. GPT-4 already needed roughly 50–100X more compute than GPT-3 to train. Today’s models go even further… trained on massive clusters of tens of thousands of GPUs running non-stop for months.

So how are we going to power all these huge AI factories?

Too many people treat energy like a team sport. They’re “team solar” or “team fossil fuels.”

I’m an “all of the above” kind of guy. Cheap, abundant energy is the bedrock of all innovation. We need all the energy we can get our hands on.

Yes to solar panels and batteries, which keep the light on when the sun isn’t shining.

Yes to reviving “Project Independence” and building 1,000 nuclear power plants in America.

Yes to the fracking revolution, which transformed the USA into the world’s #1 energy superpower.

And that’s where GE Vernova comes in. It’s the spinoff of General Electric’s (GE) energy division.

GE Vernova’s bread and butter is boat-sized natural gas turbines—the kind utility companies install to power entire cities. These turbines burn natural gas to generate electricity for the grid.

Natural gas turbines aren’t as “sexy” as sun energy or garage-sized nuclear reactors, which can power whole cities.

But they’re the best way—for the next five years, at least—to satisfy AI’s huge energy appetite, for the following reasons:

Speed. You can install a new gas turbine plant in just two years. By contrast, a nuclear plant takes at least a decade or longer due to regulatory delays.

Reliability. AI data centers can’t rely entirely on solar, as it’s intermittent. And we haven’t built nearly enough batteries to solve solar’s “night” problem. Gas turbines run regardless of weather or time of day. You can turn them on and off as you like.

Cost. A new gas plant costs around $1,000 per kilowatt of capacity. Compare that to solar, which is 2X–3X more expensive when you include storing energy in batteries. Or to nuclear, which is at least 7X more costly to get running.

We love dominant disruptors…

Great businesses often dominate their industries.

Think Google, which still controls roughly 90% of the global search market. Or Visa, which handles about 70% of US purchase volume across Visa and Mastercard cards.

GE Vernova fits that bill too.

It has one of the largest gas turbine fleets on Earth. And demand is exploding.

In Q1 alone, GE Vernova signed 21 gigawatts of new gas turbine agreements. That pushed its total gas turbine backlog and slot reservations from 83 gigawatts to 100 gigawatts.

Management now expects that number to hit at least 110 gigawatts by the end of 2026.

That’s utilities and data centers lining up years in advance to secure the equipment needed to power the AI boom.

What’s behind the surge in demand:

“These orders are directly tied to load growth of serving the big tech demand associated with AI.”

GE Vernova doesn’t just sell turbines.

The big money is made servicing them.

This is where GE Vernova gets really interesting.

The company doesn’t just sell giant gas turbines and walk away. More than 55% of its backlog comes from services. Once a utility installs a turbine, GE Vernova can collect maintenance, upgrades and monitoring revenue for decades.

Management now expects its baseload gas turbine fleet to double from around 200 gigawatts to 400 gigawatts over the next decade.

That’s an energy annuity.

It’s like selling the razor once and getting paid for the blades for the next 30 years.

Here is a little secret…

Big tech loves to brag about how clean their AI is. They put labels on their data centers like “100% renewable-powered.”

But it doesn’t mean the power actually flowing into those data centers is clean.

Say Google runs a massive data center that needs 1 gigawatt of electricity. To claim it’s green, Google might sign a contract with a solar farm somewhere else, maybe hundreds of miles away.

On paper, it looks like a match: 1 gigawatt in, 1 gigawatt out.

But in real life, when the sun goes down or demand spikes, that same data center has no other choice but to pull electricity from the local grid, of which 60% comes from natural gas and coal.

When it comes to powering AI’s energy needs for the next few years, natural gas is in a pole position.

GE Vernova is the #1 pick in this sector.

GE Vernova (GEV) has a Disruption Score 2. Click here to learn how we calculate the Disruption Score.