待翻譯：Nvidia’s new financial strategy does not compute

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April – 1805 Napoleon is master of Europe Only the British fleet stands before him Compute is now an asset class 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.” 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. Putting the ass in asset 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. 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! 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 when a trained model analyzes new data, according Brendan Burke, an AI industry analyst. That meant the hourly rates for old chips remained high, and in some cases, even increased, Burke says. “There’s just been a major shortage of inference chips, and that’s reversed the expected trend of decreasing prices,” he told me. On CoreWeave’s second quarter earnings call, CEO Michael Intrator said that the company has been able to sell GPUs with architecture from 2020 in a contract that extends through 2029. Connecting the dots, since CoreWeave is so tightly wound with Nvidia, I wonder if this is what Huang’s decade depreciation cycle refers to. And right on cue, CME Group, a derivatives exchange, has announced its plans to introduce compute futures in October, assuming the regulators approve the two contracts in question. Will the demand surges go on forever? Fuck, I dunno. There are all these data centers being built, and it kind of seems like if compute is (or rather, chips are) as fungible as Huang says, that means data center providers are competing on price in a saturated market. But as AI gets integrated into more things, more normal companies — on top of frontier labs — will need to run inference. The pace of adoption matters — if it is too slow, this model may run into trouble. Our fearless leader Nilay Patel has been running around with his hair on fire in Slack, asking how it is that if you put a dollar into compute, you get $1.01 back. Huang does not exactly answer this question: “The return is in the usefulness of AI,” he writes. But if my understanding of what’s going on is right, and “compute” in this context is just the old, familiar GPU-backed loan, then the return on investment is what it usually is with debt: interest. So there’s that. We also don’t know what the contracts look like, and the details matter. (In the Broadcom contract that appears to have inspired Nvidia’s announcement, Broadcom is not backing all of the debt, just the higher-priority senior debt, for instance.) Based on previous GPU-backed loans, I’d guess that the contract from whoever is buying the compute is included among the collateral. That contract is better or worse based on who’s behind it — Microsoft will surely pay its bills, but OpenAI doesn’t make money and needs to keep raising, so its contracts are riskier for lenders. Plus, in any agreement, it’s possible that there might be a clause in there giving the debt providers some kind of revenue share or other way of sweetening the deal. What I do know, though, is that this new compute consortium seems like a pretty good deal for Nvidia. Competitive landscaping Last year, when I talked to Stanford University’s Vikrant Vig, he noted that the majority of GPU loans were made with Nvidia chips as collateral. That, in turn, made it easier for companies to get new loans with Nvidia chips than with competitors’ GPUs — the cost of financing Nvidia GPU loans was lower because the collateral is more liquid. If the deals between Nvidia and the financiers do get finalized, that will make it even easier to get financing for Nvidia chips. If you’re starting a neocloud — that is, a small company that rents out compute such as CoreWeave, Crusoe, and Lambda — from scratch, buying Nvidia chips gives you support that you can’t necessarily get from competitors such as, idk, Broadcom. “In effect, they made Nvidia’s product cheaper without really cutting GPU prices,” Felix Wang of Hedgeye Risk Management told Bloomberg. Nvidia has been aggressive about investing in and providing financing to neoclouds in order to expand its customer base. By funding and nurturing neoclouds, Nvidia reduces the bargaining power of the big boys (e.g., Microsoft, Amazon, Google, and Meta) on price. Interestingly, on its most recent earnings call, SpaceX — the big new neocloud player — said it was working exclusively with Nvidia chips; later, we all discovered that Nvidia had a $21 billion stake in SpaceX. SpaceX was evaluating alternatives to Nvidia, but its data center buildout requires a massive increase in spending — so if Nvidia’s investment may have locked the neocloud in. But there’s also another interesting side effect of this financing, points out Burke. Because it’s in the interests of lenders to have relative uniformity between the loans, that may further standardize the way Nvidia chips get installed in data centers. That may also give Nvidia a competitive advantage in selling chips. It turns out that GPUs perform differently depending on how they get set up, which can make it hard to reliably project revenue for the lenders taking on the risk, Burke says. Nvidia has started putting out guidance about revenue in the ideal setting, pushing cloud computing providers to use that particular design. That would provide standardization, making lenders’ jobs easier. It also invites more scrutiny on how much customers can make and [truncated for AI cost control]