The Time of the CPU has Come

Finally, the market is coming around to what we started hinting at in this newsletter two months ago: the time of the CPU has come. Gothmog from The Lord of the Rings trilogy already said it (kind of):

Whether you’re a LOTR fan or not, I deeply apologize for the terrible meme

But if you’re a regular of this newsletter, you already saw this coming. In my 2026 predictions, I explained my belief that CPUs were set to return to vogue due to agents.

And the reason why has nothing to do with the AI itself.

Today, it’s not even March, and we still have pretty much full visibility that my prediction was true: CPUs are definitely back in vogue as a fully-fledged AI trade, after years on the sidelines as everyone and their grandmother simplified AI = GPUs.

And what does that mean to you? How should you navigate this newfound opportunity?

By the end of this article, you’ll intuitively understand why CPUs are now suddenly so important, and also how the key companies in this market, NVIDIA, AMD, Intel, ARM, and Hyperscalers, fare in benefiting the most from this new huge market need, with one clear short-term victor that could soon see a revenue explosion that I don’t believe is remotely priced in by markets.

Let’s dive in.

The CPU, Back in Vogue

In the latest NVIDIA deal, in this case with Meta, something “strange” was announced: Meta would be buying standalone CPU servers. That is, not just GPUs with some CPUs on the side, literally CPU servers. Like the old times in cloud computing.

But to understand why this changes everything, we need to do a quick rundown on AI hardware, even if we’re only focusing on the key high-level stuff.

AI workloads from the CPU side

While GPUs have captured most of the narrative over the last few years, catapulting NVIDIA to the most valuable company in the world, GPUs aren’t standalone computers. Actually, they are external components to a CPU.

Often forgotten, AI GPU servers have CPUs inside, too.

But what is a computer? Computers are just that, machines that compute numbers. Each machine has both compute cores and memory: the latter feeds the data to the former and stores the results.

As you might guess, the compute core’s sole purpose is to perform mathematical operations (it has a bit of memory too, but it’s very small). And the more a chip (a bunch of cores) can execute per second, the better it is (throughput-wise).

Some companies build cores to optimize for energy consumption because more throughput means more power requirements and more heat, which leads to more cooling, which leads to more power requirements… You get the point. This will become important later.

The types of computations in a standard computer vary widely, so usually, these compute cores have to be somewhat general-purpose. This means chip designers have to allocate different parts of the chip’s surface area to different ends.

NVIDIA’s Vera CPU

Naturally,…

Enjoyed the introduction? Subscribe or sign in to continue with this edition.