Investors who bet on Nvidia (NVDA) have seen their plays pay off in a big way.

The chip giant has become a leader in the AI boom, designing the advanced chips hyperscalers need to train and operate advanced models.

With Nvidia firmly in the lead, Jensen Huang is now turning his sights on CPUs—a semiconductor that’s seeing renewed interest from AI companies.

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The patience of investors who have followed Jensen Huang this far has paid off.

Chip giant Nvidia (NVDA) spent decades proving graphics processing units (“GPUs”) were useful far beyond their original purpose of video games and visual rendering.

A GPU is built to do a huge number of similar calculations at the same time. That made it perfect for training AI models, where systems chew through mountains of data and repeat mathematical operations over and over.

Nvidia’s GPU chips became the workhorses of AI. They’ve helped train the AI models that are dominating the headlines this year. Shares are up more than 1,100% since the launch of ChatGPT.

Huang isn’t done, though. He’s now turned his attention to the central processing unit (“CPU”).

CPUs have long been called the “brains” of the computer. They handle broader, more complex tasks. CPUs can manage instructions and coordinate the rest of the parts within a computer.

Nvidia announced at the end of May that its AI-focused CPU was in full production. And in a word, the market seems underwhelmed.

It’s because CPUs have been considered far less exciting than GPUs for years.

Training the biggest AI models requires brute-force parallel processing—running the same simple calculations millions of times. Nvidia owned that market through its top-of-the-line GPUs. It’s what made the software giant the biggest company in the world.

But there are tons of AI models out there now. And that brings Nvidia to the next logical challenge: using those models.

That means running agents and coordinating tools across millions of users. In that world, CPUs become more important.

In May, Nvidia launched Vera, a CPU designed for AI. Nvidia says Vera is twice as efficient and 50% faster than traditional CPUs.

Customers and partners already include Facebook owner Meta Platforms (META), software giant Oracle (ORCL), cloud-computing company CoreWeave (CRWV), and computer maker Dell Technologies (DELL).

The CPU market used to be a two-horse race, controlled by Advanced Micro Devices (AMD) and Intel (INTC). But based on what we’re seeing, Nvidia isn’t content to stay in its lane.

In short, Vera could be a huge growth opportunity for the biggest company on Earth.

Nvidia minted roughly $216 billion in revenue for fiscal 2026. Analyst consensus is for revenue to grow 32% per year to $861 billion in the next five years.

So Wall Street seems to understand how the CPU market could turbocharge revenue. The problem lies with investors expectations.

To get a sense of what the market is thinking, we turn to our Embedded Expectations Analysis (“EEA”) framework.

The EEA starts by looking at a company’s current stock price. From there, we can calculate what the market expects from the company’s future cash flows. We then compare that with our own cash-flow projections.

In other words, the EEA shows how well a company has to perform in the future to be worth what the market is paying for it today.

Let’s assume Wall Street’s estimate is accurate. If Nvidia’s stock stays where it is right now, at roughly $200 per share and assets grow 32% per year, we’re looking at a Uniform return on assets (“ROA”) of roughly 66%.