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Wall Street is still trading near record highs despite lingering worries over inflation and the wars in Ukraine and the Middle East. The roughly 15% correction between February and March – which for a few weeks raised fears of something more serious – now feels far away. In the end, the U.S. market and other major markets did what they’ve been doing for years: they rebounded.
There is, however, a detail that’s starting to draw attention among investors. And it might be the most interesting point in the entire current market. The rally seems to be driven by the same names over and over again. That’s where Wall Street starts to look unusual. In a “healthy” bull phase, you’d typically expect to see constant sector rotations. Banks lead for a while, then energy, then industrials; tech cools off while other sectors take the baton. Today the opposite seems to be happening. When Wall Street rises, it’s often the same few stocks that move higher. And when it corrects, it’s almost always the same names that fall. The same pattern holds across most other markets, too.
Most of the excitement is still concentrated in a handful of companies tied to artificial intelligence, defense, and advanced tech. It’s almost as if many other sectors simply don’t interest anyone anymore. That’s odd, if you think about it. Truly strong markets usually broaden out over time. Here, the opposite seems to be happening: capital is getting more and more concentrated. In a way, it’s understandable.
Plenty of investors are chasing exactly that: the next stock that can multiply in value in just a few years. It’s a kind of constant hunt for “the next big trend.” That’s probably one reason we’ve seen such violent moves recently in companies tied to quantum computing. Firms like IonQ, Palantir, Rigetti Computing, and D-Wave Quantum have suddenly been thrust into the spotlight.
But there’s a catch: many of these are still highly speculative businesses – extremely volatile and a long way from having truly mature, stable operations. And that’s precisely the point. It matters less where these companies are today, and more that the market needs to believe in the next technological leap.
Wall Street is desperately searching for the next macro trend. And there’s another curious detail that deserves attention.
Even the world of cryptocurrencies, after its explosive boom in recent years, seems to have reached a kind of equilibrium. Following the huge rallies that briefly brought back excitement and speculation, crypto has, for months now, lost some of its appeal among many institutional investors.
That doesn’t mean the cryptocurrency market is dead. Far from it. Many retail investors are still betting heavily on another explosive Bitcoin rally after the latest pullbacks.
But the feeling is different. The sense is that the really big money – institutional capital – is slowly starting to look elsewhere. And that’s often exactly how major trends begin. Quietly. Long before everyone notices.
Outsized returns rarely emerge once a trend is obvious to all. They usually appear much earlier, when almost no one is looking in the right direction. Years ago, investors who understood the potential of Amazon or NVIDIA weren’t just buying tech stocks. They were trying to anticipate how the world would change over the next decade.
That’s the real difference.
The numbers matter, of course. But markets often look much further ahead than quarterly earnings.
And that’s where one of today’s most underrated themes may be hiding.
Or perhaps one of the most ignored.
When we talk about artificial intelligence, we instantly think about chips, chatbots, or generative software. Almost nobody thinks about energy. Yet AI consumes enormous amounts of electricity. Every time we use a chatbot, generate an image, or interact with an AI system, somewhere in the world thousands of GPUs, servers, and cooling systems spin into action.
All of that requires power. A lot of power. And the market likely hasn’t fully internalized what this means on a global scale. According to several estimates, a single AI query can use up to ten times as much energy as a standard Google search. But the most striking number is another one.
Goldman Sachs estimates that by 2030, data centers could consume as much as 4% of the world’s total electricity.
To grasp the scale, consider that some next-generation data centers already use as much power as an Italian city of about 50,000 residents.
At that point, the story changes completely. The real bottleneck for AI may not be software. It could be our ability to produce enough energy to power it. And if you look closely, there’s something almost ironic about that. For years, Big Tech built much of its brand around sustainability, “green” goals, and carbon neutrality. Now those same companies are engaged in a global race for energy. How can Microsoft, Google, and Amazon remain “green” while building AI infrastructure that demands staggering amounts of electricity? That’s where a topic that was almost off the table until a few years ago comes back into play: nuclear power.
It sounds counterintuitive, but the digital revolution could end up pushing the world back toward nuclear energy.
Not long ago, few would have seriously bet on nuclear making a comeback as a strategic sector for the future of technology. Today, that picture is changing fast.
Microsoft has backed the restart of Three Mile Island to secure stable power for its future AI data centers. Amazon, for its part, is investing in small modular reactors, or SMRs, which many see as one of the potential energy pillars of tomorrow’s artificial intelligence.
These companies are becoming energy players. And Wall Street is starting to notice. But there’s another piece the market may be underestimating.
The race to build out artificial intelligence needs energy now. Infrastructure, however, takes years. That’s where the market may be making its usual mistake. Wall Street likes to price in the future early – sometimes too early. Building new power grids, plants, or small modular reactors isn’t something you do in a few quarters. It requires massive investment, permits, bureaucracy, and industrial capacity.
The real bottleneck may turn out to be this, not the technology itself: the speed at which the world can build the necessary infrastructure. On paper, everything looks straightforward. In reality, building out energy systems is a slow, political, often messy process. That’s where the real risk lies.
Wall Street may be right about the trend – and still be completely wrong on timing. The danger is that energy demand explodes long before the required infrastructure is actually up and running. At that point, the issue wouldn’t just be economic. It would be geopolitical. Because whoever controls the energy needed to feed artificial intelligence may also control the next wave of global technological supremacy.
The U.S.–China rivalry is no longer just about chips and advanced software. It’s about energy, grids, industrial capacity, and national security. That’s likely why energy has suddenly moved back to the center of U.S. industrial strategy. And it’s probably where the market is slowly starting to shift its focus – even if only a few people fully appreciate it yet.
If this thesis gains traction in the coming years, some of the most interesting companies may not be the ones building chatbots or AI models.
They may be the ones making the physical infrastructure that will enable the entire revolution.
One of the most closely watched names among analysts is GE Vernova, the energy business spun off from General Electric. The company is directly involved in turbines, grid infrastructure, and power systems that could be essential to running tomorrow’s data centers. The stock currently trades around $1,040, and Wall Street’s stance remains very constructive. The average analyst target sits between $1,090 and $1,150, but some firms, including Jefferies and Baird, have pushed their estimates up toward the $1,350–$1,400 range. The market continues to reward GE Vernova’s record backlog and its strong exposure to turbines, electric grids, and energy infrastructure tied to AI data centers.
Constellation Energy is also seen as one of the best-positioned players in a potential U.S. nuclear revival. After the latest pullback, the stock has returned to the $303 area, but investment banks are still broadly positive. Morgan Stanley rates it Overweight, Barclays still sees upside, and UBS has kept a Buy rating despite trimming its target price slightly.
The average analyst target remains in the $370–$380 range, with some estimates still north of $400.
Wall Street, in other words, continues to see Constellation as one of the main beneficiaries of future AI data center power demand.
Then there’s Vertiv, which specializes in data center cooling systems. It may sound like a secondary theme, but it could become critical. The more computing power grows, the bigger the problem of heat from AI servers. Vertiv is probably one of the “hottest” stocks tied to the entire AI infrastructure story. The issue is that the rally has been so strong that many analysts now see the stock as stretched.
The current share price is around $339, while the consensus target is lower, roughly $278–$281. That doesn’t mean Wall Street is negative on the business. Barclays, Evercore, and Citi still view Vertiv as a clear leader in AI data center cooling. It’s just that the market may have already priced in a lot of future growth.
Eaton is also frequently cited as a potential indirect winner, thanks to its exposure to advanced power grids and electrical distribution. After the recent sell-off, the stock is back around $401, but analyst sentiment is still constructive. Wall Street’s average target remains between $430 and $440.
Some firms are particularly bullish: Bernstein sees the stock going as high as $509, Citigroup has raised its target to $464, and Morgan Stanley maintains an Overweight rating. The market continues to view Eaton as one of the best-positioned companies for the big themes of electrification, energy management, and grid infrastructure needed to support growth in AI data centers.
The interesting thing is that just a few years ago, utilities, nuclear, power grids, and industrial cooling were considered “boring” sectors.
Now, Wall Street is slowly starting to treat them as potential stars of the next global tech cycle. Of course, nobody has a crystal ball. The market may be overestimating the energy problem. Or new technologies could make AI far less power-hungry than most expect.
In fact, some analysts argue exactly that: more efficient algorithms could sharply reduce AI’s energy consumption in coming years. That possibility shouldn’t be dismissed. Still, major trends often begin this way – almost invisible at first – and then suddenly become unavoidable. While everyone today is focused on software, chips, and robotics, the next big boom may be taking shape far away from the screens.
It may be taking shape in the energy needed to power the entire artificial intelligence revolution.
This article was originally published on Money.it and is here re-published under license. It can be seen in its original here.
