Nvidia’s Risky Business
Listen to this post : On January 1, 1870, Jay Cooke, hailed as an American hero for his role in financing the Union effort in the Civil War, signed a contract that would, if you squint, lead to world war. In 1864, Congress had created the Northern Pacific Railway Company with the goal of linking the Great Lakes and Puget Sound with tracks that would eventually run from Duluth to Tacoma; the charter included 40 million acres of land adjacent to the proposed line in exchange for accomplishing the build-out. For the ensuing six years, however, Northern Pacific struggled to secure financing, even as the Union Pacific and Central Pacific railroads built towards each other, driving the golden spike linking Sacramento and Omaha in May 1869. Northern Pacific had approached Cooke about funding in 1866, but lacked the generous federal guarantees that undergirded Union Pacific and Central Pacific (which, it should be noted, led to an incredible amount of graft); Cooke, himself no stranger to the financial power of the federal government, wasn’t interested. Ultimately, however, Northern Pacific gave him an offer he couldn’t resist: a commission of 12 percent on every bond, and $200 of Northern Pacific stock for every $1,000 in bonds he sold. Cooke soon found that his institutional peers agreed with his earlier refusal, and weren’t interested in his bonds, so he leaned on the same tactics he honed selling war bonds: appeals to patriotism, control of the media, and promises of railroad fortunes, backed by industrial-scale distribution. At the peak Cooke employed 1,500 salespeople and funded 1,300 newspapers (through a combination of advertising and direct payments) with a brand burnished by the Civil War. Retail investors could already buy railway bonds; Cooke made them his primary funding mechanism. This was, to be certain, an incredible innovation. It used to be the case that if you couldn’t get loans from the government or from banks, you couldn’t get much money at all. The problem was that Northern Pacific’s capital needs were endless, and by September 1873, as credit tightened worldwide thanks to a crash on the Vienna stock exchange and the demonetization of silver, Cooke, who had been funding Northern Pacific from deposits in between bond issuances, could find no more buyers. The subsequent bankruptcy of Jay Cooke & Company triggered the Panic of 1873, culminating in endless railroad bankruptcies across the country, a multi-year depression, multi-decade deflation, and, one could argue, the financial conditions that made Europe, four decades later, into a tinder box. Northern Pacific did eventually finish their line, by the way, with multiple bankruptcies along the way; ultimately, they were one of four railroads that were merged to form the Burlington Northern Railroad. Burlington Northern would eventually merge with the Atchison, Topeka and Santa Fe Railway to form BNSF Railway; Berkshire Hathaway would purchase the parent corporation in 2009. If this story sounds vaguely familiar it might be because Cooke is — for obvious reasons — a central character in Liaquat Ahamed’s new book, 1873 , released earlier this year. Ahamed is not shy about drawing a link between the collapse of the railroad buildout and the current AI moment; the book’s very first page — even before page 1 — is about translating sums of money, and concludes thusly: In order to grasp the true significance of sums of money that relate to the economic situation of whole countries — such as the size of the indemnity imposed on France after the Franco-Prussian war — it is most useful not simply to make allowances for changes in the cost of living but instead to adjust for changes in the size of economies. To translate such figures into comparable 2026 magnitudes, multiply by a factor of 1,200. Thus the $500 million that went into U.S. railway bonds annually during the boom years of the early 1870s would today be the equivalent of $600 billion, roughly what is projected to be invested by major tech companies in 2026. Microsoft CEO Satya Nadella is certainly aware of the connection: he cited 1873 as “the book to be read” on the company’s recent earnings call . Perhaps it’s not a coincidence, then, that Microsoft, alone amongst the hyperscalers , still boasts substantial free cash flow — $19.6 billion last quarter. Microsoft is the one hyperscaler still abiding by the dictum used to deny the existence of a bubble: its CapEx isn’t funded by debt. This was, believe it or not, a defense that could be used for nearly all of Big Tech a year ago; then, between September and November, Oracle, Meta, Alphabet, and Amazon issued a combined $80 billion in debt for building out infrastructure. That was only the beginning: after raising a combined $108 billion in all of 2025, these four companies have, as of July 7, already raised $194 billion this year. Unsurprisingly, spreads are rising, and 86% of the bonds issued this year are already trading at higher yields than at issuance. Cover for recent issuance has fallen to less than 2x, from 5x in February. The real shock, however, came at the beginning of June, when Google announced it would raise $85 billion in equity, including a special $10 billion issuance to the aforementioned Berkshire Hathaway. I wrote at the time in The Google Capital Company : It is worth noting that $10 billion is a relatively small amount of money to both companies. To that end, perhaps the primary utility is as a signaling mechanism. On Google’s side, the signal is that the expected demand is actually far greater than anyone thinks, and that the company is ready and willing to fund supply using all means at its disposal, including equity; for them Berkshire Hathaway’s investment is an endorsement of this view and a validation of the wisdom of the investment. And, on the flip side, if the signal is correct, then Berkshire Hathaway is getting a deal and putting its cash flow machines to work building the future. I concluded: Implicit in this analysis was that there was enough compute capacity in the world to be bought; what happens, however, when and if there isn’t? What if the ultimate battle — the one that determines who gets compute — becomes a matter of who can bring the most cash to bear? And what if that advantage compounds, such that the company with the most cash capacity ends up with the most compute capacity (which we already know they will sell, in addition to using themselves) driving the ability to generate more cash? In that world, what company would be your best bet? The implied answer, of course, was Google. Google right now is no one’s bet, at least in terms of the frontier. After the departure of DeepMind CEO Demis Hassabis (technically promoted to chairman, but no longer in charge of day-to-day operations) and Gemini co-lead and former Chief Scientist Jeff Dean, along with a host of other prominent researchers, SemiAnalysis declared that Gemini is Cooked : For all intents and purposes, we believe DeepMind is no longer a frontier lab. We said as much a few months ago to our Tokenomics clients due to large numbers of departures from their reinforcement learning teams and poor compute allocation. Google will continue meandering on and releasing models, but their odds of reaching SOTA again have dropped to zero. Furthermore, the biggest beneficiary of today’s news is neither Anthropic nor OpenAI—it’s Google Cloud. Whereas Gemini and GCP used to desperately fight for compute allocation, it’s now clear that Thomas Kurian won. We expect GCP revenue growth to meaningfully accelerate as a result. From later in the post: We’ve obviously been quite bearish on DeepMind thus far, and if we had to steelman the case for why they’ll still be able to train a true SOTA model in the future, it would go something like the following: Perhaps there’s some world in which this happens, but we think the odds are basically zero. The issue with Google was not Jeff Dean nor Noam Shazeer, but rather their extremely bureaucratic, painfully slow, and strategically timid culture. Remember that DeepMind had an AI chatbot 1 year before ChatGPT but was not allowed to release it due to fears of disrupting their core business. Actually, you could make the case the problem was also Hassabis and DeepMind. I explained in an Update after Google I/O how Hassabis’ vision of the frontier was fundamentally different from the other frontier labs because he believed in world models, not just text/code, and concluded: What falls out of [Hassabis’ vision] are models with multimodality — in contrast to Claude, which outputs text only — and, it must be said, not nearly as impressive coding capabilities. This gets at the point of this entire digression: I think it’s possible that the reason Google is widely considered to be behind both Anthropic and OpenAI in terms of coding, particularly long-running agentic workflows that depend just as much on the harness as the model itself, simply comes down to their research team having other priorities. That’s why the coding parts of this keynote fell on the Antigravity team, not DeepMind, and why Hassabis was barely on stage. From this perspective, last week’s events are less surprising, and were arguably foretold at I/O: Hassabis might be right about world models being the path to AGI, but Google has run out of patience in terms of letting him find out; Google co-founder Sergey Brin is reportedly deeply involved and closely allied with Koray Kavukcuoglu, the new DeepMind CEO, and I wouldn’t be surprised if the company is pivoting to Anthropic’s more text- (and thus code-) centered approach. What is fascinating about Google’s position is that these machinations do not necessarily mean the Berkshire Hathaway bet was a bad one; indeed, it’s arguably good news. This is what the SemiAnalysis article was driving towards, and it’s a point I made last week about Google’s recent earnings : The story seems to be very similar to last quarter , with even more Google Cloud growth: 82% year-over-year (compared to 63% last quarter, and 32% a year ago), with 36% margins (compared to 33% last quarter, and 21% a year ago). I wondered then how much of this growth was actually Anthropic, and while we didn’t get clear confirmation this quarter, I thought this answer from CEO Sundar Pichai on the earnings call about why Google needs to rent 3rd-party capacity was notable: I think on the bridge deal, the main thing I would say is, look, there are — on the margin, there are very, very large customers of ours on Cloud who we are trying to support them through this extraordinary moment. And the incremental opportunities they are bringing to us, while a short‑term cost over a few months may be very high, in the lifetime of the deal, as we bring more capacity on, is highly ROI‑positive. So those are factors we are taking into account. So are you willing to take upfront a six‑month deal to be able to serve the customer in what is a multiyear opportunity where the margins and the returns are very, very attractive over that multiyear horizon? So hopefully that gives some color on how we’ve thought about those opportunities. That customer is almost certainly Anthropic. Again from SemiAnalysis: More than 20% of total TPU shipments from 3Q26 to 4Q27 are being sold directly to Anthropic. This is excluding the hundreds of thousands of TPUs GCP already rents to Anthropic today, and the many hundreds of thousands more they’ve committed to rent to Anthropic and Meta over the next 6 quarters… If you’ve ever listened to an interview of Google Cloud CEO Thomas Kurian, you know he is not AGI pilled. In one podcast , for example, he argued that it’s great for TPUs to become “general purpose infrastructure” that supports customers like Citadel, the Department of Energy, and generic high performance computing. And when asked why he was selling compute to Anthropic despite them competing with Gemini, he said this was the natural consequence of Google being a “platform company.” Kurian said the same thing to me in a Stratechery Interview : We sell different parts of our stack. One of the things people don’t realize is we monetize many different parts of the stack in different ways. Like Anthropic, there’s a lot of labs that use our stack — in fact, most of the large AI labs use our stack. So if somebody uses TPUs to either to train their model or to use it for inference, we’re monetizing that part of the stack, that gives us resources to then fund our R&D and other investments. Some of the labs use our TPU and our Gemini model, others may use our TPU and then buy our cybersecurity protection for their models. So as a platform player, we have to allow our technology to be monetized in as many ways as possible and we don’t see it as a zero sum. We’ll see how zero sum compute actually is — there are reports Google’s researchers have been starved for compute — but the overall takeaway is that whether or not Google is competing for the frontier, they are absolutely competing to dominate AI infrastructure. And, in a world where intelligence is a commodity, TPUs in particular are a big deal. Last month, in Who’s Afraid of Chinese Models? , I talked about commodity markets in the context of frontier labs versus everyone else; in commodity markets marginal costs are determinative of not just profitability but also viability, and I made the case that the frontier labs are well-positioned to have superior cost structures for any given unit of intelligence. That cost structure, at least for now, includes the cost of renting compute, and it seems likely that TPUs are cheaper than Nvidia GPUs; Anthropic may have built for TPUs (and Amazon’s Trainium chips) because only Google and Amazon had the wherewithal to fund them, but at this point that ability may very well be a significant advantage. The fact that Anthropic is straight up buying TPUs for its own data centers (converting compute costs from marginal costs to capital costs) suggests that is the case. What is notable is how amenable Google is to share, even at the price of needing to issue equity. This, however, fits the Berkshire Hathaway model that I wrote about in The Google Capital Company : One of the businesses Berkshire Hathaway used the See’s profits for was on the opposite end of the spectrum in terms of capital utilization: BNSF Railway. Railways require a lot of capital to operate; BNSF consumed $3.8 billion last year; they also make a lot of money: BNSF’s net income was $5.5 billion on revenue of $23.4 billion. To put that in perspective, the total amount that Berkshire Hathaway has made from See’s Candies is probably less than $3 billion (the last disclosure was “over $2 billion” in 2019), i.e. less than BNSF made last year… In fact, you can make the case that Abel is actually just replaying Buffett’s strategy, only this time Berkshire Hathaway is See’s Candies, and Google is BNSF. At the end of last quarter Berkshire Hathaway had $373 billion in cash, and $25 billion in free cash flow in 2025. How many companies could actually employ that cash in a way that generated a high rate of return? It’s hard to imagine a better option than Google. The company is not only investing in AI, but has optionality in terms of outcomes: its Services business benefits from the investment, it is in contention at the model layer with Gemini, and it can sell capacity to the frontier labs. Moreover, that capacity has a sustainable cost advantage because of TPUs, which means that in a world where compute becomes a commodity — as hard as that is to imagine right now — Google is the hyperscaler that is poised to make the most profit. Notice that I didn’t say margin; if that were Google’s concern they would almost certainly be making different choices. Profit, however, is an absolute number, and Google is bringing everything to bear — first its cash flow, then its debt, and now its equity — on making money from the infrastructure build-out. Today corporate executives and financial engineers don’t need to control newspapers; thanks to his new X account , Nvidia CEO Jensen Huang can go straight to the public. From an X Article posted last night: NVIDIA AI Factory Compute Is Becoming an Investable Asset Class Today, we announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time. This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure — with repeatable platforms, long-term institutional capital and a diverse customer base that uses compute to create revenue. AI has reached an inflection point. It is moving from research into production. AI is creating real value, and the infrastructure behind it is becoming one of the world’s most productive assets. In AI, compute is revenue. Huang argues that Nvidia-based AI factories are fungible, protecting residual value, and that CUDA makes AI factories better over time, extending their economic value; according to Huang: These are the characteristics of an investable infrastructure asset: it produces revenue, serves a broad market, improves in performance over time and can be redeployed. Thus the attempted formalization of a new investment structure: The demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly. That is why we are partnering with the world’s leading long-term capital providers. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR are also among the world’s leading infrastructure investors, with deep expertise in underwriting long-lived, productive assets. Together, we are creating repeatable financing platforms to help the AI ecosystem build the factories it needs. What Apollo et al. are, are new sources of capital beyond the investment grade debt markets. In that sense this proposed structure is somewhat akin to Google’s equity issuance: a way to secure funding beyond bonds. The difference, however, is stark: whereas equity dilutes the upside for investors without adding risk to the company, this structure preserves Nvidia’s margins by finding new pools of capital willing to bear risk. It’s not a total free ride for Nvidia: the company is backstopping opportunities with up to 25% residual-value based financing, suggesting that Huang believes his “investable asset class” pitch much more than the market does. That is, in a certain sense, a price cut, as the goal is to reduce the cost of capital for entities building data centers with Nvidia chips, by putting Nvidia’s profits on the line for uncertain investments. That guarantee is downstream from Google’s (and soon Amazon’s ) aggressiveness: why build a data center with Nvidia chips if you can buy TPUs or Trainiums (Nvidia chips are likely better, but if the constraint on new data centers is capital, lower up-front prices may matter more than token efficiency). Nvidia’s bigger problem is one that has been apparent for a long time; I wrote back in 2024 : In the before-times, i.e. before the release of ChatGPT, Nvidia was building quite the (free) software moat around its GPUs; the challenge is that it wasn’t entirely clear who was going to use all of that software. Today, meanwhile, the use cases for those GPUs is very clear, and those use cases are happening at a much higher level than CUDA frameworks (i.e. on top of models); that, combined with the massive incentives towards finding cheaper alternatives to Nvidia, means both the pressure to and the possibility of escaping CUDA is higher than it has ever been (even if it is still distant for lower level work, particularly when it comes to training). The situation today, with Anthropic and OpenAI appearing to pull away, is even more problematic: Anthropic has not been dependent on CUDA for years, and OpenAI is moving in that direction, at least for inference. If those companies win then Nvidia’s profits will be squeezed — indeed, the implication of that backstop is they already are (this, needless to say, is why Huang’s first post was an open letter in defense of open models). This might not cost Nvidia anything in the end: if AI revenues truly take off, then the debt markets will open back up, and ultimately companies will go back to funding infrastructure investment through free cash flows. Right now, however, is the danger zone, as hyperscalers blow through the debt markets and Google at least starts to tap equity. To the extent Nvidia competes through novel funding mechanisms that, at the end of the day, draw on things like insurance floats and pension funds and other long-run liabilities that are the bread and butter of the asset managers the company is partnering with, the risk — unmarked, unlike equity — is considerably higher. That’s why I started with 1870 and Cooke’s ill-fated agreement with Northern Pacific. Yes, the upside the deal afforded Cooke was incredible, but it was incredible for a reason: it was very risky, and pioneering new funding mechanisms only served to spread the pain when it all blew up. It’s one thing to spend all of your free cash flow; it’s another thing to tap the debt markets. And, beyond that, it’s a completely new nerve-racking thing to bring safety-seeking assets to bear. AI better deliver before it’s too late. The current setup clearly wasn’t working. With the existing leadership team, their odds of catching up to Anthropic/OpenAI looked extremely slim. Now that they’ve cleaned house, the new guys can start from a blank slate. Maybe they’ll even acqui-hire a neolab like SSI or Thinking Machines. With this new team, their odds of catching up to the frontier actually increase.
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