Nvidia is buying the layer above its own chips

Nvidia sells the hardware almost every AI company depends on. Over the past nine months it has been quietly acquiring positions in the companies that use it.

The Three Moves

Nvidia is reported to be in discussions over an investment in Perplexity, the AI search company, as part of a financing round that could value it above $30 billion. The round would reportedly be worth several billion dollars, with Nvidia among the prospective investors.

That follows a $6 billion technology and investment agreement with Poolside concluded this month, which included an investment component of roughly $1 billion and moved more than 100 engineers onto Nvidia's Nemotron model programme.

And it follows a comparable arrangement with Groq in December.

In the Perplexity case, Nvidia is reported to have first explored paying billions to license the company's technology and hire specific talent, before pivoting towards a conventional equity stake — the same sequence reported in the Poolside deal.

One caution before drawing conclusions from any of this: the Perplexity discussions are reported rather than announced. Nothing establishes that Nvidia has committed capital, how much it might invest, or whether the talks conclude in a transaction at all.

What Perplexity Is Worth Now

The valuation is doing a lot of work in this story, so it is worth examining.

Perplexity's annualised revenue has risen to more than $750 million, from under $250 million at the start of the year. A significant part of that growth is attributed to Perplexity Computer, a cloud-based agent professionals use to automate tasks on a machine.

A round above $30 billion would represent an increase of more than half over its last raise roughly a year ago.

That is roughly forty times revenue, on a business growing threefold in eight months, in a category — AI search — where the incumbent is Google and the challenger has no distribution advantage. Whether that is a sensible price depends entirely on whether the agent product, rather than the search product, turns out to be what people pay for.

The Pattern, Not The Deals

Any one of these arrangements is ordinary corporate investment. Three in nine months, structured the same way, is a strategy.

The shape is consistent: identify a company doing something valuable on top of Nvidia hardware, explore acquiring the technology and the people directly, and settle on a structure that secures access to both without a full acquisition.

Taken together they describe a company that has decided selling compute is not sufficient. The reasoning is not hard to reconstruct. Nvidia's position rests on being the place AI runs, and that position is under more pressure than its results suggest — Qualcomm has bought its way into the software layer, AMD holds large commitments from OpenAI and Meta, the hyperscalers are all shipping custom silicon, and OpenAI has just put its fastest public tier on Cerebras hardware rather than GPUs.

A chipmaker facing that has two options. Defend the margin on chips, or own more of what the chips are for.

The Conflict It Creates

The difficulty with owning the layer above your own product is that your customers live there.

Nvidia sells to every laboratory and cloud provider building AI. If it also holds equity in an AI search company, a coding company and a model programme of its own, it is a supplier competing with the businesses it supplies — with better information about their compute consumption than any of them have about each other.

This is the structural problem that has attached to every dominant platform that moved up its own stack. It rarely produces immediate friction, because customers with no alternative supplier do not complain loudly. It produces regulatory attention eventually, and it gives competitors an argument they did not previously have.

The open-weight dimension adds to it. Nvidia is reported to be putting significant money behind open-weight model work, and the Poolside engineers went to its Nemotron effort. A company that supplies the hardware for closed frontier models while funding open alternatives to them occupies both sides of the industry's central commercial argument.

What To Watch

The question is not whether these deals close. It is whether the structure keeps repeating.

If a fourth and fifth arrangement follow the same template over the next two quarters, Nvidia will have converted its cash position into a portfolio spanning search, coding, inference and models — the entire layer its customers occupy — without triggering the scrutiny a series of outright acquisitions would attract.

That is a considerable amount of the AI industry to assemble through investment agreements. It is being done in public, deal by deal, and each one on its own looks unremarkable.