Transcription
In recent weeks, there has been a lot of market anxiety about the sustainability of the AI boom. This was partly driven by the outrage around Sarah Friar, OpenAI’s finance chief, floating the idea that a government backstop for its $1.4 trillion dollar data-center buildout might be a good idea.
Friar quickly walked back her suggestion in a LinkedIn post later that day, saying that she had meant that the government needed to “play their part” in combination with the private sector to contribute to America’s AI growth and that OpenAI was “not seeking a government backstop for their infrastructure commitments.” Her statement, while attempting to calm the outrage, only confused matters even further about how the not-yet-profitable startup plans to pay for its massive AI data center and chip commitments.
Sam Altman tweeted on The Everything App, “we do not have or want government guarantees for OpenAI datacenters. We believe that governments should not pick winners or losers, and that taxpayers should not bail out companies that make bad business decisions or otherwise lose in the market.” Then it turned into a Bill Ackmann tweet at that point, where he went on and on for around twenty pages. At first, I was thinking, who would write a tweet that long? And then I realized that he had probably used Chat GPT. He knew that people would only read the first few lines, but wanted to seem thoughtful, so had it churn out an entire novel…
The core problem for OpenAI is that they have signed more than $1.4 trillion dollars in infrastructure commitments over the last few months, with the goal of building out the data centers that it says are needed to meet soaring demand, but they are nowhere near having the money required to complete those deals. Friar gave the example of having to hold back Sora2 for months due to compute constraints. [Clip] [“I just want to be clear what it means when I say we're compute constrained. It means that, for example, we cannot roll out our new models when they are ready. So when Sora 2 was ready to, when Sora 2 actually launched, there was probably a good six, seven months actually gap there. And you all know, like you said in tech, right, you don't want to hold products or features on the runway if they're ready to go.”]
The agreements they have signed have raised lots of questions around how a cash-burning company with tiny revenues (relative to their planned spending) can possibly make such huge commitments. This was not the first time OpenAI has looked to Washington for help either. Just a month ago, the company sent a detailed letter to the White House urging the federal government to “double down” on semiconductor subsidies, asking that tax credits be expanded to cover the entire AI supply chain - from chip fabrication to data centers and grid hardware. The company argued that broadening eligibility for taxpayer funded subsidies would “lower the effective cost of capital, de-risk early investment, and unlock private capital.” OpenAI and its data-center partners are (of course) amongst the largest buyers of semiconductors in the world, so any subsidy would directly benefit them.
AI is being pitched to governments around the world as being a matter of grave national security and economic importance akin to past industrial mobilizations like the Manhattan Project and the space race. If AI companies can put it on that level and pitch it as being too important to fail, a government funded backstop might make sense. The irony though, is that while lobbying for taxpayer support in the name of geopolitical survival, the same businesses are pumping billions into building models that generate weird anime girlfriends, SpongeBob deepfakes, Sam Altman’s Studio Ghibli style profile photo and, in Elon Musk’s case, a chatbot that appears to have been hard coded this week - to constantly flatter him - in the cringiest manner possible - which caused all sorts of hilarity on The Everything app (formerly known as twitter) this week. We will come back to that in a minute though…
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While all of this was going on, Nvidia warned for the first time in a regulatory filing that its customers’ ability to “secure capital and energy” for AI data centers could potentially slow its growth. On top of that, Amazon lodged a complaint with the Public Utility Commission of Oregon that the electric utility was failing to provide sufficient power for the four new data centers it had built, highlighting the strain that rapid data center expansion is putting on electric grids. I guess the utility agreed to hook them up to the grid, but not necessarily to provide them with the power they wanted…
The question hanging over Silicon Valley is not so much whether AI will change the world, but whether the world can afford to build it. That brings us to NVIDIAS earnings report on Wednesday night. The tech rally that has defined much of 2025, especially after the liberation day sell off in April, began to lose momentum in early autumn. Some analysts trace the inflection point to when OpenAI announced a $300 billion cloud deal with Oracle and Nvidia pledged up to $100 billion in reciprocal investments. Those headlines, which were meant to signal confidence, instead raised questions about circular financing and the sheer scale of spending commitments. Private credit blowups added to the unease in markets, reviving concerns about lending standards and fraud in a market already stretched by aggressive leverage. Valuations were lofty, and the spaghetti diagrams of interlocking deals – with hyperscalers funding AI labs that fund chipmakers that fund hyperscalers – started looking increasingly fragile. No surprise, then, that bubble talk intensified.
Nvidia’s earnings report on Wednesday temporarily eased those fears. The world’s most valuable company—and the beating heart of the AI trade—posted a 62% jump in revenue for the three months to October, far ahead of expectations. Data center sales hit $51.2 billion dollars, and the company raised its revenue forecast for the current quarter to $65 billion dollars. For now, the numbers seem to justify the hype. As Robert Armstrong put it on the Unhedged podcast, “The worry is not Nvidia’s price-to-earnings ratio. The worry is that the revenue it’s earning and the growth rate of that revenue is ultimately unsustainable.” At today’s pace, Nvidia’s valuation makes sense. The question is whether the growth curve can defy gravity indefinitely.
OpenAI’s finances look even more precarious than most people realize. Microsoft’s September earnings filing revealed that OpenAI lost roughly $11.5 billion dollars in a single quarter—its worst on record. That pushes year-to-date losses north of $25 billion dollars, against projected annual revenue of about $20 billion. The company has raised nearly $58 billion in equity so far and was valued at 500 billion dollars last month. The company is talking about an IPO at a $1 trillion valuation next year, which would float the shares on an exchange and possibly bring in about $60 billion dollars in cash, but that is just over 4% its $1.4 trillion dollar infrastructure commitments.
To bridge the gap, OpenAI has leaned on creative deal structures: Nvidia has pledged up to $100 billion in reciprocal investments, while AMD granted OpenAI warrants to buy 10% of its stock for a penny per share if deployment milestones are met. Sarah Friar, OpenAI’s CFO, explained the company’s financing at the Wall Street Journal event. While she is from Northern Ireland, she must have been in Silicon Valley long enough to know that the first step in raising capital is to use the magic word. [Clip] [“The Innovation on the finance side to pay for it is massive!”] She then went on to say: [Clip] [“We've raised equity, as a private company, very kind of typical path, but we've raised a lot. We're building a really healthy business. So free cash flow, CFO's favorite way to fund anything. That is absolutely climbing quickly. But I think the third area we've gotten into is really working with our ecosystem to do some really interesting financing deals. I'm particularly proud of the AMD warrant structure that we put in place just a few weeks back, 'cause it's very strong alignment of incentives.”]
This is a really bizarre claim, as OpenAI can’t fund anything with free cash flow, when that cash flow is negative. She then digs into explaining the AMD warrant. [Clip] [“What we've seen is when someone comes out and says, "We're gonna work with OpenAI," they immediately are often seeing kind of impact on their stock price. And so to the extent that that's gonna happen, we would like to have some alignment on that. And I think Lisa and the team did something incredibly creative with that warrant structure.”]
The warrant deal between OpenAI and AMD is a strategic partnership where OpenAI commits to buying billions of dollars’ worth of AMD AI chips, and in return, AMD grants OpenAI warrants to purchase up to 160 million of its shares (which is about a 10% stake in the company) at a nominal price of one cent per share. When the deal was announced, AMD stock went up 24%, but the deal only vests if OpenAI buys six gigawatts of AMD chips, hits undisclosed milestones, and AMD’s share price triples. The AMD deal would bring in almost a hundred billion dollars’ worth of AMD stock, if all the targets were hit, including the tripling of AMD’s stock price. But it is tied to 6 gigawatts of chip purchases, which she later explains. [Clip] [“So a one-gigawatt data center build today is about a $50 billion investment. That's for one gig. How that really breaks down is about 15 billion is for the land, power, shell, and about 35 billion is for the chips.”] So to bring in $100 billion, they have to spend $300 billion.
Nvidia’s $100 billion pledge to invest in OpenAI is also tied to reciprocal commitments. If all of these deals worked out, OpenAI could bring in $200 billion, but that still leaves them $1.2 trillion dollars short, and they are burning tens of billions of dollars per year, with no end in sight. The unit economics of running the current generation of LLM’s is dire. As Paul Kedrosky explained it on the Odd Lot’s podcast, the incentive seems to be for all players to just grow the top line as much as possible, even if adding more users just leads to greater and greater losses. The models have negative unit economics, which is a fancy way of saying “We lose money on every sale and try to make it up on volume.” In AI, costs rise almost linearly with usage, which is very different to traditional software; there is no marginal-cost magic going on. According to Forbes, despite an invitation-only rollout, OpenAI may be losing around fifteen million dollars a day, or five billion dollars annualized, on Sora2, its AI video generating app. Tech firms have always been creative about financing, but OpenAI’s approach borders on the surreal, where it has become all about trying to find infinite money glitches. MicroStrategy – or Strategy as it’s now called – is trying a similar trick with its Bitcoin investments, which I don’t expect to end well…
Behind the headlines is a financing structure that looks increasingly baroque. Hyperscalers and AI labs are using special-purpose vehicles so that they can borrow but keep the debt off their balance sheets. Tech firms have essentially been reinventing structured finance to build AI models so that they can generate AI girlfriends. That is just the world we live in… [Clip] [“I will always love you.”]
Sarah Friar explained at the Wall Street Journal event that each gigawatt of compute costs around fifty billion dollars, where fifteen billion is the land and infrastructure and thirty-five billion dollars is the GPUs. [CLIP] [“People know how to finance data centers. They typically will have 20, 25, even 30-year lives. Those are easy things, I would say, today to finance. Chips have not been as easy to finance because, number one, I think we're all still getting our arms around what is the life of a frontier chip, right?”] What this means is that the more innovation that happens with chips, the faster they can be expected to depreciate, and so the thirty-five billion dollars’ worth of chips in a fifty-billion-dollar data center are very difficult to finance. People don’t want to own them if they might collapse in value when a new one comes out, and people really don’t want to accept them as collateral on a loan. That is when she put forth this idea. [Clip] [“And so this is where we're looking for an ecosystem of banks, private equity, maybe even governmental, like the ways governments can come to bear.” - Meaning like a federal subsidy or something. - Meaning like just first of all the backstop, the guarantee that allows the financing to happen, that can really drop the cost of the financing, but also increase the loan to value. So the amount of debt that you can take on top of an equity portion for so some-- - So some federal backstop for chip investment. - Exactly, and I think we're seeing that. I think the US government in particular has been incredibly forward-leaning, has really understood that AI is almost a national strategic asset, and that we really need to be thoughtful when we think about competitive competition with, for example, China. Are we doing all the right things to grow our AI ecosystem as fast as possible?”]
Essentially, the problem is that they want to lever up their bet on AI, but banks wouldn’t want to lend, and the interest rate on a loan backed by rapidly depreciating chips would be so high that you would need the government to back the loans. Now, I can tell that this will make some of my viewers angry, but there is actually no need to get angry about something like this, as both Sam Altman and Elon Musk have both explained in the past that AGI will soon make money obsolete. So, who cares…
Now, even if the money materializes, and then suddenly doesn’t matter anymore, the electrons may not. OpenAI’s Stargate project alone would require ten gigawatts of power, which is roughly ten nuclear power plants. Its full buildout implies twenty-three. And that’s just OpenAI. Google has a model, Facebook – or whatever they call themselves – has one too. There’s Grok, good ole Grok, Anthropic, and lots, lots more. What I’m saying is, we’re going to need a lot of powerplants. [Clip - “We're Gonna Need A bigger Boat”] – and we also have to plug in our cars and robots…
Only one new nuclear power station has been built in the United States in the last thirty years; it took a decade to complete and was the most expensive power plant ever built. Bloomberg estimates that AI-driven electricity demand will more than double over the next ten years. Utilities are already balking. Amazon has filed a complaint against PacifiCorp for failing to deliver promised power to four Oregon data centers. PacifiCorp says it is protecting other customers from “indirect harms.” Translation: we can’t turn the lights off in Portland so Jeff Bezos can train a chatbot. Behind-the-meter gas turbines are proliferating as stopgaps. Some operators are whispering about nuclear partnerships. These fixes create stranded-asset risk, as a natural gas plant lasts 30 years and a GPU cluster might be obsolete in 18 months. Lenders see the mismatch and flinch. Tech firms that promised to “dematerialize” the economy now need more concrete, copper, and electricity than steel mills did. The cloud, which was supposed to be weightless, turns out to be very heavy.
So, why keep spending? Well, because the game is framed as being existential. U.S. labs talk about “sovereign AI” and competition with China. Once you call something existential, the limit on spending becomes unlimited. Paul Kedrosky described it as a “metabubble” on the Odd Lot’s podcast: tech hype, real estate speculation, loose credit, and a potential government backstop—all in one. There are some bubbly signs. I remember in 1999 seeing adverts on CNBC for a company that manufactured equipment used in the wafer fabrication steps of making semiconductors. I couldn’t understand at the time why they were paying for TV adverts when their customers would all know who they were and what they sell. No one watches CNBC and decides to start manufacturing computer chips in their garage. I later worked out that they were advertising the stock, not their products. The stock fell around 80% over the next three years. Recently, I have seen a tech CEO being interviewed wearing a t-shirt with his company’s ticker symbol on it, not the company’s name. I have noticed that every podcast I listen to seems to have adverts for an AI military tech company, and once again I wonder if they think that their potential customers might be listening to a Bloomberg podcast, or if they just want to pump the stock. I’ll note that the CEO of that company constantly talks about burning short sellers while dumping his own stock. Even if there is a bubble, it can be impossible to know when it will pop.
As I mentioned a few weeks ago, the big tech firms funding a lot of the AI spending are so profitable in their core businesses that they can afford this gamble. So, should Investors cash out of the stock market then? Well, probably not, unless they know how they will get back in again. If you are a diversified investor with a long holding period, even if you invested the day before the 1987 crash, right before the credit crunch, or right before the Covid sell off, if you stayed invested, you earned good returns over time.
The Economist estimates that should an AI crash occur, it could erase 8 percent of U.S. household wealth and cut consumption by $500 billion dollars, or 1.6 percent of GDP. They show that at the peak of the dot com bubble, the market cap of the S&P was 124% of US GDP. When the bubble burst, tech stocks lost on average 76% of their value. Since ChatGPT’s launch in 2022, American stocks are up 71% and the S&P is worth 175% of GDP. They point out that a crash today would have a bigger effect on ordinary Americans than it did twenty five years ago, as the share of household wealth in the stock market has climbed from 17% back then to 21% today. If the stock market fell as much as it did back then, it would wipe out as much as 8% of US household wealth. Foreign investors who are heavily invested in US tech would take a significant hit too. The fallout wouldn’t stop at Silicon Valley. Pension funds, REITs, and private credit vehicles are exposed to AI investment too. Utilities that built gas plants for data centers could be left with stranded assets. The last time America overbuilt infrastructure this aggressively was the telecom boom, and much of the dark fiber that was laid was never lit.
As I said a few weeks ago, the tech boom today is very different to the dot com bubble of the late 90’s, where unprofitable startups were racing to IPO after a few months in business, burning cash on vague promises of “eyeballs” and banner ads. Today’s big tech firms—Microsoft, Amazon, Google, Meta—are highly profitable, well-run businesses with entrenched revenue streams. They may be pouring tens of billions into AI, but if these bets fail, their core businesses—cloud, advertising and e-commerce—remain intact and cashflow positive. The real risk sits with the private AI labs and their venture backers, not really with the hyperscalers. If anything resembles the froth of 1999, it’s crypto - not trillion-dollar companies with fortress balance sheets.
For AI users, this frenzy is a gift. Competition has meant that the models improve rapidly and their prices stay low. There is no reason not to use these products while they are free or almost free. For AI investors, the economics are unforgiving. Better chips make models faster—and make yesterday’s chips worthless. Every leap forward accelerates depreciation on the collateral lenders are asked to finance. That is why banks refuse to lend; they prefer assets that last longer than a news cycle.
Sam Altman says that OpenAI isn’t and wasn’t pitching for a government backstop, that he thinks governments should build their own AI infrastructure. That may happen. But it does nothing to solve OpenAI’s problem: financing $1.4 trillion dollars of private data centers with non-guaranteed bonds. For now, the company is betting that capital markets will keep playing along. If they don’t, the bailout debate Friar stumbled into will return—louder, sharper, and harder to ignore.
I almost forgot to include this piece, but one of the funnier news stories of the week was about Grok – Elon Musk’s maximum truth-seeking chatbot. It seems that the code must have been tweaked a bit this week and adjusted such that Grok’s output is more in line with Musk’s way of thinking. Grok began claiming that that Elon Musk is more physically fit than LeBron James – a better role model than Jesus – that his intellect is in the same bracket as Isaac Newton’s, that he was a better fighter than Mike Tyson, and that he is funnier than Jerry Seinfeld. People quickly worked out that Grok would say that Musk was amazing at everything, which led to some inappropriate questions and this headline at 404 media. Many of the Grok responses were quietly deleted on Friday, and Musk tweeted that someone had manipulated Grok into saying absurdly positive things about him. I’m sure if he ever catches that guy, he’ll be in a world of trouble.
If you found this video interesting, you should watch this one next. Don’t forget to check out our sponsor DeleteMe using the link in the video description. Have a great day and talk to you in the next video – bye.