\u201cThis is really the first time that technology chips have become an investable asset class,\u201d Nvidia CEO Jensen Huang said to CNBC. \u201cThese are revenue-generating assets now. They\u2019re productive, they\u2019re long-lived, they\u2019re fungible, they\u2019re flexible.\u201d<\/p>\n
\u201cThis is the very beginning, like what it was when I started in the mortgage-backed securities market in the 1970s.\u201d<\/p>\n
Huang said something very different<\/a> about Nvidia\u2019s own last-generation Hopper chips last year. \u201cWhen Blackwell starts shipping in volume, you couldn\u2019t give Hoppers away,\u201d Huang told attendees at the company\u2019s AI conference, hyping up its latest GPU architecture<\/a>. \u201cThere are circumstances where Hopper is fine. Not many.\u201d So to now be told that chips are actually \u201crevenue-generating assets\u201d that are \u201clong-lived\u201d is\u2026 quite frankly, it\u2019s giving me whiplash.<\/p>\n At least for right now, Huang isn\u2019t wrong. The price to rent old chips has been rising, and Silicon Data projects that it will continue rising through 2028. Here\u2019s a fun anecdote: One cloud service provider nearly doubled its prices<\/a> on Nvidia Blackwell B200 chips for one rental customer during its contract renewal.<\/p>\n \u201cThis 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,\u201d said Larry Fink, CEO of BlackRock, to CNBC<\/a>. 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?<\/p>\n 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<\/a>. You may also remember that it, uh, didn\u2019t happen<\/a>. But the cool thing about memorandums of understanding is that you get to make a big announcement, and then it sort of doesn\u2019t matter if the actual thing goes forward. Still, let\u2019s assume it\u2019s real, because even as a trial balloon, it\u2019s telling us something interesting.<\/p>\n Let\u2019s back up for a second. Why are we talking about \u201ccompute\u201d? Well, according to Huang, \u201cNvidia compute is not just a chip.\u201d That\u2019s because there is also software, called CUDA. \u201cThat is what makes Nvidia AI factories different\u201d from mere dumb silicon, Huang says in a tweet<\/a> \u2014 er, post on X. \u201cTheir value is not fixed at installation: CUDA continuously improves their output; the installed base remains productive well beyond its initial depreciation period.\u201d<\/p>\n Okay, but the chips and software alone don\u2019t create compute \u2014 they\u2019re only useful if they\u2019re housed in massive data center infrastructure, which requires warehouses and power supplies. Huang appears to be discussing compute without those things, dubbing Nvidia\u2019s system \u201ca complete AI factory platform including accelerated computing, networking, systems software, AI frameworks and a global developer ecosystem.\u201d Notably absent from this list: brick-and-mortar facilities.<\/p>\n \u201cCompute\u201d here isn\u2019t referring to the entire data center stack; it\u2019s our old friend, the GPU-backed loan.<\/p>\n Leave aside the risible idea of an \u201cAI factory,\u201d where electricity presumably toils in the silicon chip mine. Huang is downplaying data centers partially because that\u2019s where most of the financing has gone so far. \u201cBlackstone 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,\u201d writes Rubinstein<\/a>. Huang doesn\u2019t care about that \u2014 a lot of it is real estate and irrelevant to him. Huang cares about people buying Nvidia chips.<\/p>\n So \u201ccompute\u201d here isn\u2019t referring to the entire data center stack; it\u2019s a buzzword-y way of talking about our old friend, the GPU-backed loan. I can see why one might want to switch to \u201ccompute\u201d over \u201cGPU\u201d because everyone knows that a GPU has a much shorter lifespan than, say, a building \u2014 estimates range from somewhere between two and five years. I suppose \u201ccompute\u201d also covers TPU-backed loans, so there\u2019s that.<\/p>\n Earlier this summer, Broadcom put together a $35 billion package<\/a> 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 \u2014 and is doing the same thing, for the same reasons.<\/p>\n So now Huang is cheerleading the long life of Nvidia chips. As a \u201cpowerful example\u201d of how compute can improve over time, Huang points to the pre-Hopper A100 chip, which it introduced in 2020, and which \u201cremains in active commercial use,\u201d he says. \u201cCustomers continue to commit capacity for multi-year deployments, extending A100\u2019s economic life toward a decade.\u201d My goodness, that\u2019s very different from what he said last year about his flashy new chips, isn\u2019t it!<\/p>\n If Huang is out here in front of God and everyone saying that the depreciation schedule is 10 years, then I don\u2019t see why banks wouldn\u2019t believe him<\/p>\n We\u2019ve talked about<\/a> chip financing before around these parts<\/a>. You may remember that no one can agree on a depreciation schedule for chips; it sort of doesn\u2019t matter as long as Nvidia wants to bail out the companies that buy them. You can, in fact, view Huang\u2019s 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<\/a> for chips. IBM\u2019s Arvind Krishna says depreciation takes five years<\/a>. And here comes Huang, saying the economic life of one of his chips is a decade! My, my, my.<\/p>\n This is relevant to the lenders, because it determines loan terms. For instance, the amount that CoreWeave \u2014 the pioneer of GPU-backed loans<\/a> and an Nvidia client state \u2014 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\u2019t see why banks wouldn\u2019t believe him. That\u2019s pretty useful for anyone trying to get loans from this consortium, I figure.<\/p>\n Huang cites price increases on compute \u2014 including for the Hopper H100 chip, which came out in 2022. He\u2019s not exaggerating about the price increases, as self-serving as his logic may be. They\u2019re 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. \u201cThere\u2019s just been a major shortage of inference chips, and that\u2019s reversed the expected trend of decreasing prices,\u201d he told me.<\/p>\n On CoreWeave\u2019s second quarter earnings call<\/a>, 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\u2019s decade depreciation cycle refers to.<\/p>\n This new compute consortium seems like a pretty good deal for Nvidia<\/p>\n And right on cue, CME Group, a derivatives exchange, has announced its plans to introduce compute futures<\/a> in October, assuming the regulators approve the two contracts in question.<\/p>\n 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<\/a> in a saturated market. But as AI gets integrated into more things, more normal companies \u2014 on top of frontier labs \u2014 will need to run inference. The pace of adoption matters \u2014 if it is too slow, this model may run into trouble.<\/p>\n 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: \u201cThe return is in the usefulness of AI,\u201d he writes. But if my understanding of what\u2019s going on is right, and \u201ccompute\u201d in this context is just the old, familiar GPU-backed loan, then the return on investment is what it usually is with debt: interest.<\/p>\n So there\u2019s that. We also don\u2019t know what the contracts look like, and the details matter. (In the Broadcom contract that appears to have inspired Nvidia\u2019s announcement, Broadcom is not backing all of the debt, just the higher-priority senior debt, for instance.) Based on previous GPU-backed loans, I\u2019d guess that the contract from whoever is buying the compute is included among the collateral. That contract is better or worse based on who\u2019s behind it \u2014 Microsoft will surely pay its bills, but OpenAI doesn\u2019t make money and needs to keep raising, so its contracts are riskier for lenders. Plus, in any agreement, it\u2019s 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.<\/p>\n Last year, when I talked to Stanford University\u2019s Vikrant Vig<\/a>, 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\u2019 GPUs \u2014 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\u2019re starting a neocloud \u2014 that is, a small company that rents out compute such as CoreWeave, Crusoe, and Lambda \u2014 from scratch, buying Nvidia chips gives you support that you can\u2019t necessarily get from competitors such as, idk, Broadcom.<\/p>\n \u201cIn effect, they made Nvidia\u2019s product cheaper without really cutting GPU prices,\u201d Felix Wang of Hedgeye Risk Management told Bloomberg<\/a>.<\/p>\n 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 \u2014 the big new neocloud player<\/a> \u2014 said it was working exclusively with Nvidia chips<\/a>; later, we all discovered that Nvidia had a $21 billion stake in SpaceX. SpaceX was evaluating alternatives to Nvidia<\/a>, but its data center buildout requires a massive increase in spending \u2014 so if Nvidia\u2019s investment may have locked the neocloud in.<\/p>\n \u201cIt\u2019s going to be a major sheep herding exercise to get them to follow one approach.\u201d<\/p>\n But there\u2019s also another interesting side effect of this financing, points out Burke. Because it\u2019s 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.<\/p>\n 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\u2019 jobs easier. It also invites more scrutiny on how much customers can make and the accuracy of Nvidia\u2019s modeling. \u201cThe forecasts I\u2019ve seen are very bullish,\u201d he says. In some cases, the projections are for $70 billion a year in revenue per gigawatt, which is not what anyone in the field is getting today.<\/p>\n So the terms of the contract may demand specific settings that increase the fungibility of data centers with Nvidia chips, both because it makes it easier to model revenue forecasts and because in the case of a default, that makes it easier for lenders to offload the collateral. \u201cMost data center operators are highly customized and it\u2019s going to be a major sheep herding exercise to get them to follow one approach,\u201d Burke says. Conditions on lending may serve as sheepdogs, corralling the engineers into specific designs.<\/p>\n This also shores up Nvidia against competition \u2014 and not just from Google\u2019s TPU and Amazon\u2019s Trainium chips. Inference can be run on old Nvidia chips, sure, but it turns out CPUs can also do this work and CPUs are cheaper \u2014 like, one-fifteenth of the cost, Burke says. So if you can use CPUs, you not only can spend less to buy chips, you can also lessen the demand for GPU compute, driving that price down.<\/p>\n Don\u2019t call it circular financing<\/p>\n Nvidia is bringing in outside capital because it appears to be quite sore about the accusations of \u201ccircular financing,\u201d where it\u2019s a major investor in the neoclouds and AI labs that buy its chips. Remember CoreWeave, the neocloud propped up by Nvidia? Nvidia invested and saved its IPO and has promised to buy any extra capacity CoreWeave might have. Nvidia \u201cagreed to spend $1.3 billion over four years to rent its own chips from CoreWeave,\u201d making it CoreWeave\u2019s second-largest customer in 2024.<\/p>\n It\u2019s not just CoreWeave. Nvidia is widely invested in the neocloud companies. Plus, Nvidia is paying $30 billion in cloud service agreements as of its most recent quarterly filing<\/a>. Jay Goldberg, a senior analyst at Seaport Research Partners, thinks these numbers represent Nvidia\u2019s backstop agreements.<\/p>\n So if the new memorandums of understanding are finalized into deals, we wind up with a different situation. Instead of (say) Nvidia giving CoreWeave a dollar, against which CoreWeave borrows five dollars and then buys six dollars of Nvidia chips, Blackstone is giving CoreWeave five dollars to buy Nvidia chips.<\/p>\n Let\u2019s look again at who\u2019s in this consortium, shall we? We\u2019ve got Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR \u2014 so exactly one bank, Goldman, and a bunch of private credit companies. Private credit has been financing the AI buildout in a big way.<\/p>\n So, you know, a lot of liabilities<\/p>\n I am not an expert in finance but it seems to be the case that private assets make more money for the finance bros<\/a> than public ones. Because AI has been on the rise, there\u2019s been a large-scale freakout in private credit about the threat from AI to software as a service, a sector that a bunch of private credit funds had gotten into pretty deep. So now there\u2019s a new interest in asset-backed securities<\/a>, and a loan that\u2019s backed by AI chips has the virtue of (1) not being an SaaS company\u2019s debt and (2) potentially being the infrastructure for the thing that kills the SaaS company. That might make GPU-backed loans look attractive.<\/p>\n The AI buildout generally has pivoted to debt. As of the end of July, the hyperscalers \u201cand related companies\u201d like Nvidia had issued about $225 billion in bonds, according to S&P Global. \u201cThis segment has seen almost 10-fold growth in its bond issuance through midyear,\u201d S&P noted, and could issue $400 billion by the end of the year.<\/p>\n What\u2019s more, the hyperscalers have made $1.5 trillion in lease commitments<\/a> \u2014 and $1 trillion of it isn\u2019t on their balance sheets. There\u2019s also an estimated $1.5 trillion in purchase commitments<\/a> for chips, electricity, and so on. So, you know, a lot of liabilities.<\/p>\n Apollo, at least, has seen a big opportunity; The Information reported that there\u2019s a new guy there in charge of AI infrastructure financings<\/a>. Arranging AI deals is \u201ca growing source of revenue.\u201d About 60 people are focused on AI buildout. The development of the data centers themselves is financed differently than the chips.<\/p>\n I am pointing at Apollo because it published something interesting recently<\/a>. In it, Torsten Slok, the firm\u2019s chief economist, notes that the further away in the AI stack you are from the end user, the bigger your profit margin is. Models and applications lose money. That\u2019s not a problem forever \u2014 Amazon lost money before it made money, for instance \u2014 but it adds another layer of risk to these loans. The neoclouds are the layer one up from models and applications. Should those companies be unable to figure out how to turn a profit, they are directly exposed to the risk.<\/p>\n AI evangelists compare AI to the internet, as a technology that has the power to totally reorganize their society. Curiously, a lot of these evangelists do not have a good model for what AI\u2019s goals should be. There\u2019s a lot of talk about curing all diseases<\/a> and \u201cintelligence as a utility<\/a>,\u201d but the most concrete ones look like \u201creplace customer service agents\u201d and \u201cspeed up coding\u201d; maybe there\u2019s some room for, I don\u2019t know, risk assessment in insurance and stock trading. Are those applications enough to justify the enormous capital outlays we\u2019ve all seen? I doubt it.<\/p>\n By contrast, in the early days of the internet, the technology wasn\u2019t ready to deliver streaming music and video, but by 1999 it was obvious to a lot of people, including Larry Ellison,<\/a> that\u2019s where things were going. Similarly, in the \u201990s, we weren\u2019t culturally ready for online shopping, but it was clear to a lot of people that was an opportunity. The problem of the dot-com bubble was not that the evangelists were wrong about what the tech could do \u2014 it was that they were wrong about when the tech could do it.<\/p>\n So even if the most ardent AI boosters are right, getting the timing right also matters. The entire model ecosystem is currently being subsidized. It\u2019s not yet clear that if the model makers were to charge the actual price for their services that their demand would be there. Imagine a perfect AI personal assistant, trained on every document in your organization; it costs $100,000 a day. Even if it is very good \u2014 nearly perfect! \u2014 it is way more cost-effective to hire 100 people who cost $200,000 a year.<\/p>\n There\u2019s increasing pressure on the model companies to make money<\/p>\n Compute is only revenue for companies running cloud platforms<\/a>, points out Larry Dignan at Constellation Research. For everyone else, it\u2019s a cost. And companies are always under pressure to contain costs \u2014 consider the big splash that Uber made in May when the company\u2019s president said it was getting \u201charder to justify\u201d the amount it was spending on AI<\/a>.<\/p>\n OpenAI and SpaceX hemorrhage money. Anthropic has recently been reported to have an annualized run rate of $65 billion<\/a> \u2014 but there\u2019s no word on profit. There\u2019s increasing pressure on the model companies to make money, and to get to a 7 percent return, below which is an \u201cunmitigated disaster\u201d for AI investors, the economic forecasters at Gartner project<\/a> that AI companies need to cumulatively earn $7 trillion in revenue through 2029. That\u2019s almost $2 trillion a year.<\/p>\n Now, if Fink is right, and compute-as-an-asset is comparable to mortgage-backed securities, we should expect a lot more companies to jump into arrangements like the one Nvidia is touting and Broadcom actually arranged. But if any of the major AI model companies suddenly go belly-up, perhaps because they cannot make a profit, the demand for compute abruptly drops. What\u2019s more, if their contracts to rent chips are part of what secures collateral on chip-based loans, those loans are also in trouble.<\/p>\n One sign that Nvidia is more bullish on \u201ccompute as an asset class\u201d than the financiers it\u2019s signed memorandums with is its residual value support. Basically, if the neocloud bails on the loan, Nvidia has agreed to pay up to 25 percent on some of these contracts. This is perhaps meant to reassure investors, since Nvidia\u2019s assets are on the line for investments for companies with little or no credit record. The financing suggests \u201cthat Huang believes his \u2018investable asset class<\/a>\u2019 pitch much more than the market does,\u201d says Stratechery\u2019s Ben Thompson.<\/p>\n And although the price of compute has gone up, it\u2019s irrelevant to the question of residual value, which is the resale price of the chips. If someone goes belly-up on a loan, and, e.g., Goldman has a bunch of compute to flip, who\u2019s buying and for how much? The big boys are all building their own data centers, and in some cases using their own proprietary chips. The neoclouds have a ton of debt to service \u2014 they may not have the cash to be buyers. That\u2019s not settled. So if the resale value falls below a certain level, Nvidia has to compensate whoever owns the debt.<\/p>\n We don\u2019t have a lot of details, but what will matter here is how much the Nvidia GPUs are sufficient collateral for lending, says Goldberg. In most deals so far, the chips alone weren\u2019t enough \u2014 lenders also needed contractual cashflow on those chips. So CoreWeave\u2019s GPU loans are really backed by Microsoft or Nvidia or whoever. If the \u201cAI factory\u201d only needs chips as collateral, and not customer contracts as well, that\u2019s significant. But notice: Jensen Huang isn\u2019t saying that directly.<\/p>\n \u201cWelcome to Jensen math,\u201d says Goldberg in an email. \u201cJensen is now trying to claim that this is a new investment class – stocks, bonds, mortgages, GPUs. And his tweet is arguing that this is a special asset class because it somehow gets better over time because of software, magic and reasons.\u201d<\/p>\n \u201cThat\u2019s naked sleight of hand, in my opinion.\u201d<\/p>\n At a certain point, it begins to feel like this is another way for Nvidia to keep the AI party going. It\u2019s been on a historic run, and investors\u2019 expectations for it are high. Nvidia may have been facing limits on its previous model of endless upgrade cycles, says Leevi Saari, a fellow at the AI Now Institute. \u201cPreviously, they were like car salesmen, saying you need a new car every year because the previous generation was so inefficient you\u2019d lose value.\u201d Now, Huang seems to be saying that depreciation doesn\u2019t matter \u2014 compute doesn\u2019t lose value quickly, like a car. It loses value slowly, or even gains value, like a house.<\/p>\n The number of companies that can afford to keep buying new Nvidia chips every year is limited, Saari says. For Nvidia to keep beating and raising expectations on its earnings, it has to unlock more ways to fund companies buying chips. Enter the financial institutions it\u2019s cut a deal with. If the chips don\u2019t depreciate the way Huang said they do just last year, they\u2019re suddenly assets that might interest, say, pension funds.<\/p>\n \u201cFor the life of me I can\u2019t fathom how they square the circle of \u2018you must buy a new chip every year\u2019 with \u2018don\u2019t worry about depreciation,\u2019\u201d Saari says. To believe that chips will continue to appreciate in value, you have to believe there\u2019s a totally inelastic market for chips. \u201cThat\u2019s naked sleight of hand, in my opinion.\u201d<\/p>\n According to Saari, the actual financial innovation is \u201chow do you unlock safety-seeking capital for Nvidia\u2019s revenue growth\u201d and the answer is the announcement we all saw. The market\u2019s response was muted. In The Wall Street Journal, Jack Ablin, a founding partner at the $260 billion family office Cresset, which invests in Nvidia, noted that compute, historically, is \u201can asset that\u2019s had the shelf life of lettuce.\u201d<\/a> Even Stratechery\u2019s Thompson, usually an unabashed cheerleader for tech industry pablum, noted that Nvidia\u2019s strategy was risky.<\/p>\n \u201cOne layer down, a second circularity has been created.\u201d<\/p>\n And who bears that risk? Whoever winds up with the notes issued by these arrangements \u2014 and that\u2019s often not the people originating them. \u201cThis is originate-to-distribute, and the destination is the general account of a life or annuity insurer,\u201d writes Sascha Steffen<\/a>, the DWS senior chair in finance at the Frankfurt School of Finance & Management and the director of the Centre for European Transformation, a research group focused on private credit.<\/p>\n There are a few interesting things here. Half of the group making these deals shares an owner with the entity likely to wind up with the loans. That weakens the scrutiny the final holder, probably an insurer<\/a>, has on the loans. \u201cNvidia\u2019s announcement is routinely described as resolving the \u2018circularity\u2019 of the company financing its own customers, and at the level of Nvidia\u2019s balance sheet it does,\u201d writes Steffen. \u201cOne layer down, a second circularity has been created.\u201d<\/p>\n One other thing: The amount of capital required to cover insurers\u2019 risks is tied very closely to debt ratings. If an insurer is downgraded because, let\u2019s say, a ratings agency makes a change in methodology, that insurer may be forced to sell their GPU-backed loans, \u201can asset with almost no secondary market,\u201d Steffen notes.<\/p>\n The question now is whether the actual arrangement will really come to pass. After all, Nvidia has made public pronouncements before \u2014 and then shied away. 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