英伟达的新财务策略行不通。

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Nvidia’s new financial strategy does not compute

Jensen Huang wants you to think ‘ah, compute as an asset class’ not ‘oh no more GPU-backed loans.’

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“Compute is an asset class! Compute is an asset class!” I continue to insist as I slowly shrink down and turn into a corncob | Image: Cath Virginia / The Verge, Getty Images

is a reporter who writes about tech, money, and human behavior. She joined The Verge in 2014 as science editor. Previously, she was a reporter at Bloomberg.

Only the British fleet stands before him

I see it is once again time to talk financial innovation. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR are all working with Nvidia to put together $500 billion in financing to turn compute into an asset class.

“This is really the first time that technology chips have become an investable asset class,” Nvidia CEO Jensen Huang said to CNBC. “These are revenue-generating assets now. They’re productive, they’re long-lived, they’re fungible, they’re flexible.”

“This is the very beginning, like what it was when I started in the mortgage-backed securities market in the 1970s.”

Huang said something very different about Nvidia’s own last-generation Hopper chips last year. “When Blackwell starts shipping in volume, you couldn’t give Hoppers away,” Huang told attendees at the company’s AI conference, hyping up its latest GPU architecture. “There are circumstances where Hopper is fine. Not many.” So to now be told that chips are actually “revenue-generating assets” that are “long-lived” is… quite frankly, it’s giving me whiplash.

At least for right now, Huang isn’t wrong. The price to rent old chips has been rising, and Silicon Data projects that it will continue rising through 2028. Here’s a fun anecdote: One cloud service provider nearly doubled its prices on Nvidia Blackwell B200 chips for one rental customer during its contract renewal.

“This is the very beginning, like what it was when I started in the mortgage-backed securities market in the 1970s, and I look upon this as a next future for financial engineering,” said Larry Fink, CEO of BlackRock, to CNBC. Now, for some of you, this may make alarm bells go off. As former hedge fund manager Mark Rubinstein notes, mortgage-backed securities failed when mortgages were overproduced. The AI industry is becoming saturated with data centers, and Chinese open-source models require less compute despite being fairly powerful, both of which seem like potential threats to the notion of ever-growing demand for chips. There is also a far more basic question: Can frontier labs such as Anthropic and OpenAI, which are driving much of the current demand, make money?

Before we even get to the Jensen math, I want to point something out: This is not a done deal. This is some memorandums of understanding. You may remember that last year, Nvidia signed a $100 billion memorandum of understanding to invest in OpenAI. You may also remember that it, uh, didn’t happen. But the cool thing about memorandums of understanding is that you get to make a big announcement, and then it sort of doesn’t matter if the actual thing goes forward. Still, let’s assume it’s real, because even as a trial balloon, it’s telling us something interesting.

Let’s back up for a second. Why are we talking about “compute”? Well, according to Huang, “Nvidia compute is not just a chip.” That’s because there is also software, called CUDA. “That is what makes Nvidia AI factories different” from mere dumb silicon, Huang says in a tweet — er, post on X. “Their value is not fixed at installation: CUDA continuously improves their output; the installed base remains productive well beyond its initial depreciation period.”

Okay, but the chips and software alone don’t create compute — they’re only useful if they’re housed in massive data center infrastructure, which requires warehouses and power supplies. Huang appears to be discussing compute without those things, dubbing Nvidia’s system “a complete AI factory platform including accelerated computing, networking, systems software, AI frameworks and a global developer ecosystem.” Notably absent from this list: brick-and-mortar facilities.

“Compute” here isn’t referring to the entire data center stack; it’s our old friend, the GPU-backed loan.

Leave aside the risible idea of an “AI factory,” where electricity presumably toils in the silicon chip mine. Huang is downplaying data centers partially because that’s where most of the financing has gone so far. “Blackstone has built a platform valued at $185 billion including facilities under construction, and reckons the market for long-term ownership of stabilized data centers could grow to $1 trillion over time,” writes Rubinstein. Huang doesn’t care about that — a lot of it is real estate and irrelevant to him. Huang cares about people buying Nvidia chips.

So “compute” here isn’t referring to the entire data center stack; it’s a buzzword-y way of talking about our old friend, the GPU-backed loan. I can see why one might want to switch to “compute” over “GPU” because everyone knows that a GPU has a much shorter lifespan than, say, a building — estimates range from somewhere between two and five years. I suppose “compute” also covers TPU-backed loans, so there’s that.

Earlier this summer, Broadcom put together a $35 billion package that looks an awful lot like what Nvidia is offering now, signing a deal with Apollo and Blackstone to fund what we are now calling compute, with about a million chips as collateral. Apollo and Blackstone will make money on interest; Broadcom has provided a guarantee for the two senior notes issued by the special purpose vehicle where the chips live. This deal was meant to boost demand for Broadcom chips. It seems like Nvidia took note — and is doing the same thing, for the same reasons.

So now Huang is cheerleading the long life of Nvidia chips. As a “powerful example” of how compute can improve over time, Huang points to the pre-Hopper A100 chip, which it introduced in 2020, and which “remains in active commercial use,” he says. “Customers continue to commit capacity for multi-year deployments, extending A100’s economic life toward a decade.” My goodness, that’s very different from what he said last year about his flashy new chips, isn’t it!

If Huang is out here in front of God and everyone saying that the depreciation schedule is 10 years, then I don’t see why banks wouldn’t believe him

We’ve talked about chip financing before around these parts. You may remember that no one can agree on a depreciation schedule for chips; it sort of doesn’t matter as long as Nvidia wants to bail out the companies that buy them. You can, in fact, view Huang’s statement as a sort of bailout itself. In the discussion about chip depreciation, short seller Michael Burry has suggested that two to three years is the appropriate depreciation cycle for chips. IBM’s Arvind Krishna says depreciation takes five years. And here comes Huang, saying the economic life of one of his chips is a decade! My, my, my.

This is relevant to the lenders, because it determines loan terms. For instance, the amount that CoreWeave — the pioneer of GPU-backed loans and an Nvidia client state — can borrow decreases as its chips depreciate, according to its corporate filings. So if Huang is out here in front of God and everyone saying that the depreciation schedule is 10 years, then I don’t see why banks wouldn’t believe him. That’s pretty useful for anyone trying to get loans from this consortium, I figure.

Huang cites price increases on compute — including for the Hopper H100 chip, which came out in 2022. He’s not exaggerating about the price increases, as self-serving as his logic may be. They’re driven by a higher demand for inference, which is the industry term for