sport football: AI Stocks Are About to Hit a Wall

A power plant "the size of Manhattan."

Those were the words from President Trump on stage at the World Economic Forum in Davos, Switzerland.

He was marveling at Meta Platform's new data center campus. That project, which is under construction in Louisiana, is set to open in 2030.

Now, while the plant won't actually be as big as Manhattan, it's still a huge undertaking.

It will cover 2,250 acres – the size of roughly 1,700 football fields. And it will house 4 million square feet of data center space. That'll make it Meta's largest-ever data center.

Meta – like other Big Tech giants – is racing to build out artificial intelligence (AI) infrastructure. And investors are chasing it, and other flashy AI stocks, in hopes of great returns.

But as we've been showing you, these companies face a problem: How will they power their big ambitions?

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Energy Is the "Steel" and "Concrete" of the AI Revolution

Over the past few days, we've been pulling back the curtain on the World Economic Forum showdown at Davos.

It's the same invitation-only event Daily editor Teeka Tiwari attended back in 2020, when he showed us governments were just waking up to blockchain's potential. Since then, bitcoin has been up as much as 1,660%.

This year's gathering of the elite saw the usual display of wealth. Presidents, monarchs, and prime ministers flocked to Davos, mingling with the top brass of the world's most powerful militaries.

From the private sector, the guest list included nearly 830 CEOs from titans like BlackRock, Microsoft, and Nvidia.

Now, if you'd only read the front pages, you'd think all the attention was on Greenland and the fraught relationship between the U.S. and its allies.

But a different story caught our attention.

At the forum, U.S. Energy Secretary Chris Wright dropped this bombshell: The world needs to more than double its oil production to meet future energy demands.

The last time oil production exploded like this was during the Shale Revolution of 2008- 2019, when the U.S. became the world's largest oil producer.

This time, the reason behind the coming spike in energy demand is simple: AI.

AI data centers already account for about 4% of total U.S. electricity consumption. That figure is set to swell 133% by 2030.

To get there, companies will need to spend on data-center infrastructure in a big way. McKinsey estimates they will spend almost $7 trillion by the end of the decade. That's roughly the combined GDP of Germany and France.

Yet, despite all that money, the system is clogged.

Heavy industry publication Construction Dive reports data-center construction backlogs stretch 12 months or more. It's not because companies lack capital. It's because the physical infrastructure isn't there.

This isn't just a U.S. problem. It's a global one. According to research from banking giant Goldman Sachs, global power usage by AI data centers is expected to leap 165% by 2030.

It's a pattern we've seen before. Every major innovation – from the railroad to the interstate highway system – eventually hits a physical wall.

Just look at some of the most transformative tech leaps in U.S. history:

In the 1800s, the railroad buildout needed steel…

The "product" of the railroad buildout was transportation, but the "fuel" was steel.

Before the Bessemer process made steel cheap, railroads used iron rails that wore out in months. Steel lasted years, making the transcontinental buildout possible. And the companies that understood this change made a fortune.

Andrew Carnegie's steel operations saw their net profits go from $2 million in 1888 to $40 million in 1900. That's a 20x increase in profit in just 12 years. And it was driven almost entirely by the demand for rails and infrastructure.

In 1901, J.P. Morgan merged Carnegie Steel and nine other companies to create U.S. Steel. It became the world's first billion-dollar corporation.

In the 1950s, the interstate highway system needed concrete and asphalt…

With the Federal-Aid Highway Act of 1956, the U.S. government committed to building 41,000 miles of road. This transformed cars from a luxury into a necessity.

As the highway system expanded, the demand for aggregates (crushed stone, sand, and gravel) skyrocketed. Vulcan Materials filled that need.

While many car companies struggled or went bust in the following decades, Vulcan Materials became a compounding machine.

A smart investor who recognized that "roads need rocks" would have seen the company grow into the largest producer of construction aggregates in the U.S.

In 1956, Vulcan went through a merger. By 1960, its net worth increased by 7x, from $11 million to $72 million.

Annual revenues jumped from $21.4 million in 1956 to $110 million by 1959. That's a nearly 400% increase in three years as Vulcan became the nation's largest producer of aggregates.

Today, AI needs energy…

Just as railroads couldn't run without steel tracks… and interstate highways couldn't be paved without concrete… AI cannot run without electricity.

The problem is: The amount of power AI requires is staggering.

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There Isn't Enough Power to Meet Demand

AI data centers consume up to 10x more power than traditional servers. And while software can scale overnight, power infrastructure cannot.

Building a new data center takes roughly 18-24 months. But securing sufficient power from the grid can take three to five years.

Leaving data centers underpowered for even a few years isn't an option. It would cost these companies billions in lost revenue.

That's why, as we showed you last week, the White House is working with Big Tech to ramp up energy production. The traditional utility model just can't keep up.

When a critical resource is scarce, the companies that supply that resource tend to soar.

We saw that with the railroad and highway buildouts of centuries past. And we're seeing early signs of the same pattern at Teeka's newest research service, The Asymmetric Edge.

Our basket of AI energy "picks and shovels" plays is up an average of 37% since we recommended them last month – and we're still in the early innings.

Based on our projections, these stocks have the potential to deliver returns up to 18x your money. That's not a typo.

To put that in perspective, you'd need to hold the entire S&P 500 for nearly 30 years to see comparable returns.

And while these "picks and shovels" have been running and gunning, AI pure plays like Microsoft, Palantir, and Oracle are down an average 17% since December.

Now, that doesn't mean these tech names are bad companies. But they're approaching a dangerous inflection point. Here's how Teeka put it in the January 6 Daily:

Just like the dot-com bubble inflated popular internet names… the AI bubble is pumping popular tech stock valuations through the roof.

Alphabet, Amazon, Meta, Microsoft, Nvidia, and Tesla are trading at more than 2.5x the entire S&P 500. If that's not a red flag, I don't know what is.

Friends, I've seen this movie before. When you buy stocks at extreme valuations this late in a bull market, your long-term returns get crushed.

Sure, you might squeeze out another 10%, 20%, 30% – maybe even 50% – from these hot AI names before the bubble finally pops. But will you have time to get out before it does?

The big flashy AI names get all the headlines, but they will not 18x your money. That time has passed.

You have to look at the names that will emerge as the new leaders of the AI trend. The companies that will power the data centers that will make Silicon Valley's AI dreams a reality.

Watch Teeka's special briefing to unlock the AI "picks and shovels" names he recommends buying instead.

Don't Watch the Future Happen. Own It!

Houston Molnar