Editor’s Note: Artificial intelligence has dominated headlines all year… but as Chief Investment Strategist Alexander Green explains below, the biggest opportunity may not be in the companies building the AI models – but in the little-known firm making those models work at scale.
While most investors overlook this critical piece of the AI ecosystem, Alex believes it could become one of the most important beneficiaries of the next phase of the tech boom.
He details this below…
– James Ogletree, Senior Managing Editor
Artificial intelligence has generated no shortage of commentary – breathless predictions, dire warnings, sweeping promises. Yet for all the noise, very little attention is being paid to the single most important question for investors: What must happen behind the scenes for AI to actually deliver on its potential?
Because while the conversation tends to focus on what AI can do, the more consequential issue is what AI requires to function at scale.
The newest generation of AI chips is astonishingly powerful. Nvidia’s latest architecture, for example, processes data at speeds that would have seemed impossible a few years ago. But this development has created a less glamorous – yet absolutely fundamental – challenge. These chips generate extraordinary heat, consume enormous amounts of energy, and produce more data per second than most existing systems can handle.
This is rarely discussed outside technical circles. Yet it is the limiting factor that determines how far and how fast AI can advance.
We are building larger and larger GPU clusters – some with hundreds of thousands of chips working in unison – and asking them to perform tasks that dwarf the demands of even the most powerful supercomputers of the last decade. But here’s the problem: These chips can’t operate effectively unless they can communicate with one another at incredibly high speeds… without melting the servers they occupy.
In other words, AI doesn’t rise or fall on clever algorithms alone. It depends on the physical infrastructure that underpins them.
And that’s where things get interesting.
There is a relatively small American company – one you almost certainly haven’t heard of – that has quietly solved the most important bottleneck in AI today. It doesn’t develop models or design chips. It builds the connective tissue that allows these chips to exchange data at blistering speeds while keeping heat and system instability in check.
Without this capability, the highly publicized advances in AI simply don’t work in the real world.
That’s why nearly every major player in the industry – Nvidia, AMD, Intel, Amazon, Microsoft, and others – relies on this firm’s technology. It is not an exaggeration to say that the most advanced AI clusters on the planet could not operate at scale without it.
This is the part most investors fail to appreciate.
Technological revolutions rarely reward the companies that generate the headlines. They reward the companies that quietly make the entire ecosystem function.
During the dot-com boom, investors bid up flashy internet stocks to absurd levels while ignoring the behind-the-scenes firms that enabled the internet to actually run. Cisco, which built the routers that moved data from point A to point B, became one of the most profitable investments of that era. So did companies like Akamai, which solved the problem of delivering content efficiently across the web.
Meanwhile, many of the companies that investors thought would change the world disappeared entirely. Their business models weren’t sustainable. Their valuations weren’t rational. And the innovations they hoped to commercialize were ultimately built – or bought – by others.
The same dynamic is unfolding today in AI.
Investors are clamoring for the biggest names, the megacap platforms spending billions to stay ahead of their competitors. And many of these firms will continue to do well. But the most underappreciated beneficiaries of the AI boom are not the giants creating the models. They are the companies enabling those models to run safely, reliably, and at scale.
Consider the magnitude of what’s happening right now. Global data-center construction is accelerating at a rate we’ve never seen. Companies are racing to build new GPU clusters as fast as they can pour concrete. Entire power grids are being upgraded just to support these facilities. And late last year, a consortium of some of the largest firms in the world announced a multi-hundred-billion-dollar initiative to build what may become the largest AI supercomputing system ever attempted.
All of this expansion hinges on a single, unavoidable requirement: the system must be able to handle the data produced by the chips that power it.
Most investors don’t think about this step at all. They assume it’s already solved. It isn’t.
And that is precisely why the company addressing this challenge is not just a “nice to have” in the AI supply chain – it is foundational.
This is also where history acts as a guide. When investors become overly fixated on a narrow group of winners – whether it’s the Nifty Fifty in the 1970s, the dot-com darlings of the late 1990s, or the megacap tech giants of today – the biggest opportunities often emerge elsewhere. Not in the obvious place, but in the necessary place.
AI will undoubtedly reshape industries across the economy. It will enhance productivity, lower costs, accelerate drug development, improve supply chains, and transform manufacturing. But none of this happens unless the underlying infrastructure keeps pace with the models themselves.
In the meantime, investors would do well to remember that the biggest profits are rarely made where the crowd is already looking. They’re made where essential progress is being created – quietly, consistently, and before the rest of the world notices.