AI Bottlenecks Are Only Going to Get Worse

Editor's note: Wall Street is obsessed with AI software and chipmakers. But as our Director of Research Matt Weinschenk explains, the real winners may not be the AI companies themselves. In this issue, Matt and a fellow Stansberry colleague explain why the companies supplying the infrastructure behind the boom could be the greatest market opportunities...

The more you dig in... the more impossible it looks.

Everyone is talking about the AI boom. But it's hard to comprehend just how much power demand it will create.

One projection from the Lawrence Berkley National Laboratory expects data-center demand to grow from 176 terawatt-hours ("TWh") in 2023 to between 325 and 580 TWh by 2028.

(For reference, just 1 TWh could power 90,000 U.S. homes for an entire year.)

We likely have a big data-center building boom on the horizon. The catch is, it's unclear if we have enough electricity to support it.

Independent power producers (which sell power to the highest bidder) have already committed all their capacity. Energy equipment is sold out. And the related stocks are soaring as a result.

It's well known that AI is causing energy bottlenecks. But people don't realize that throwing money at the problem isn't enough to solve it.

And according to Gabe Marshank, editor Market Maven here at Stansberry Research, the greatest opportunities for your money could be the companies that make the "widgets and goo" that power the AI boom...

Before joining our firm, Gabe had an illustrious career working for the top hedge funds in the world. And he has dived deep into the industrial, commodity, and energy markets...

As he explains it, businesses that make "widgets" do well when they sell more widgets. (Think tech companies.) Businesses that make "goo" do well when they sell their goo for higher prices. (Think energy companies.)

That thinking set him apart from many of his tech-obsessed colleagues. While they played with the latest gadgets, Gabe was researching factories and oil wells.

Over the past two years, Gabe has been studying how the AI power crisis will get solved. I chatted with him recently to get his perspective. Today, I'll share the three AI bottlenecks that he believes may point to the biggest opportunities...

Three Things to Know About AI Bottlenecks

1. The power grid wasn't built for this – and it can't be fixed fast.

Everybody knows AI needs a lot of electricity. But what most people miss is that the problem isn't just demand... It's also the inability to bring new supply online.

America's power grid is a mess of overlapping regulators, regional operators, state-level utilities, and decades of underinvestment.

After the California energy crisis in 2000 and 2001, capital fled the industry. Nobody wanted to build new power generation.

Then, AI arrived.

These workloads are so power-hungry that electricity demand went from growing below the rate of GDP to surging higher. And supply won't catch up for a long time. Adding power to the grid can take as long as eight years, given all the complications and bureaucracy.

Money alone can't solve the bottleneck. It's a matter of physics... and a lot of red tape.

2. Natural gas is the only answer (but its price doesn't show it).

Electricity needs a source. But nuclear energy plants take 20 years to build... And solar and wind power are intermittent and just lost their subsidies.

That leaves natural gas as the only realistic fuel to power the AI boom – and it's trading at a price that suggests the market hasn't figured this out yet.

Natural gas sits at about $3 per million British thermal units ("MMBtu"). Typically, oil trades at 6 times the value of natural gas. With oil at $80 per barrel, that means natural gas should be closer to $13 per MMBtu.

The reason prices are low today is simple: For years, U.S. drillers were chasing oil, and gas came out of the ground as a byproduct. There was so much gas that nobody could figure out what to do with it all.

That's changing fast. Two massive drivers are converging to boost demand today...

First, new gas-fired power plants are the go-to solution for data-center electricity.

Second, the market for liquefied natural gas ("LNG") – natural gas that has been cooled to a liquid state for transport – is booming. LNG export facilities are coming online, and the build-out mirrors what we saw with U.S. onshore drilling two decades ago.

3. The ultimate AI bottleneck isn't software.

When investors think about AI, they focus on companies like Nvidia (NVDA) that design the chips that power AI.

But the AI supply chain stretches much further than that...

That's because these chip designers don't actually produce their designs – that's too hard. Instead, they rely on manufacturers with specialized equipment.

And today's most cutting-edge AI chips make that complexity even more extreme because they require extreme ultraviolet ("EUV") photolithography machines.

Each machine costs several hundred million dollars. And more important, the technology, supplier networks, and manufacturing expertise behind these machines took decades to develop.

That's why the AI race isn't just about who can design the best chip. It's also about who controls the manufacturing infrastructure that makes those chips possible.

In short, the right semiconductor equipment companies will enjoy a moat unlike anything else in the market.

Find the AI Opportunities Behind the Hype

While software leadership can change quickly, physical bottlenecks are much harder to disrupt. They are built on manufacturing know-how, supplier relationships, and highly specialized production capabilities.

The common thread is simple: AI isn't limited by demand. It's limited by infrastructure.

Data centers need power. Power plants need fuel. And advanced chips require highly specialized manufacturing equipment. Every step in the supply chain creates a bottleneck that can't be solved overnight.

As Gabe puts it... if you want to own the long-term winners as AI spending continues, own the bottlenecks.

Good investing,

Matt Weinschenk

Editor's note: The Financial Times called it "the biggest bagholder exercise of all time." A controversial new rule tied to one of the largest public IPOs in history could change the way stocks are added to portfolios across America. That's why Gabe and our colleague Whitney Tilson are looking past the hype to explain what this means for investors – and what comes next.