AI Is Too Expensive

If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large. My Hater's Guides To Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle. This week, I’ll publish the second part to my ongoing series (“What If…We’re In An AI Bubble?”) about the factors and events that will cause the AI bubble to finally pop. Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week. AI is, as it stands, not economically viable for anybody involved other than the construction firms, NVIDIA, and the surrounding hardware companies benefitting from the irrational exuberance of a data center buildout that doesn’t appear to be happening at the speed we believed. Every AI startup loses millions or billions of dollars a year, and nobody appears to have worked out a way to stop hemorrhaging cash. Hyperscalers have invested over $800 billion in the last three years, with plans to add another $700 billion or so in 2026 and another $1 trillion in 2027, meaning that they need to make at least three trillion dollars in AI specific revenue just to break even, and $6 trillion or more for AI to be anything other than a wash. I went into detail about this (albeit at a lower, pre-2026/2027 capex number) in a premium piece last year. To give you some context, Microsoft made $281 billion, Meta $200 billion, Amazon $716 billion, and Google $402.8 billion in revenue in their most-recent fiscal years for every single product combined, for a total of $1.599 trillion. None of them will talk about their actual AI revenues.

Yes, yes, I know Microsoft said that it had $37 billion in AI revenue run rate ($3.08 billion a month or so) and Amazon had $15 billion, or around $1.25 billion a month, but both of these are snapshots of single months that are meant to make it sound like they’re going to make that much in a year but in the end, you don’t actually know anything about how much money they’ve made from AI.AI Is Too Expensive To Ever Pay Off Hyperscalers’ Capex InvestmentsWe do, however, now know that Microsoft has spent an approximate $100 billion on its OpenAI partnership after testimony from an executive during the otherwise-dull Musk-OpenAI trial, per Bloomberg:That figure includes Microsoft’s original investments in OpenAI, as well as the costs of building infrastructure and hosting OpenAI’s computing, Microsoft deals executive Michael Wetter testified on Monday. It is cumulative through the current fiscal year which ends in June, he said.This is a fascinating insight for a few reasons:Microsoft has spent a total of $293.8 billion in capex since the beginning of Fiscal Year 2023 (which began in the back half of 2022).This means that around 30% of Microsoft’s capex ($87 billion) went to building OpenAI’s infrastructure.Based on discussions with sources familiar with Azure architecture, this is the vast majority of Microsoft’s operational capacity.At the end of 2025, OpenAI claimed that it had 1.9GW of capacity (likely referring to total power draw rather than the actual critical IT of the infrastructure at its disposal), which, per analyst estimates, ($42 to $44 million per megawatt) works out to around $79.8 billion. This claim was made around six months before the release of Microsoft’s most recent quarterly results. In other words, Microsoft has spent 4 years sinking (either through spending or allocating the capex in advance) nearly $300 billion into…building OpenAI?Okay, fine. Microsoft also has 20 million Microsoft 365 Copilot subscribers for an absolute maximum revenue of $7.2 billion…if every single one were paying $30 a month, which they are most assuredly not as Microsoft has been offering discounts on it for years.

Based on my reporting from last year, Microsoft made around $7.5 billion from OpenAI’s inference spend and $761 million from its revenue share in Fiscal Year 2025, a year when it invested (either spent or allocated) around $88.2 billion in capital expenditures. I didn’t report it at the time, but I also had the numbers for all of Microsoft’s revenues for the first three quarters of Fiscal Year 2025 — a total of $8.9 billion of total AI revenue, with around $4.35 billion in revenues when you removed OpenAI’s inference. If we assume that Microsoft’s other AI services grew 10% quarter-over-quarter, I estimate that Microsoft likely made around $17.9 billion in AI revenue in FY2025, or a little under a fifth of its capex. And let’s be clear: none of these numbers include the actual operating expenses.Data centers, after all, need electricity to run, and AI data centers in particular need a lot of electricity. And some — though, admittedly, not many — people to handle the things like maintenance, repairs, and operations. And then there are things like taxes, insurance, and the other day-to-day costs that, when you add them all together, make a big, scary number. You can argue that “actually GPUs are profitable to run” (I disagree!), but for any of this to make sense, four things have to happen:AI revenues have to explode.Capex has to stop being invested.GPUs need to be margin positive, including both their cost and the debt associated with operationalizing them.AI revenue has to stay consistent both before and after you stop spending that capex.All four must be true. If AI revenues don’t explode, capex can stop, margins can be positive, and your best-case scenario is…you maybe broke even. If capex never stops being invested, you need revenues to explode dramatically — to the tune of effectively doubling Microsoft, Meta and Google’s entire businesses, and tripling Amazon Web Services’ annual revenue ($128 billion) — and for said revenues to be margin-positive, because if they’re not, eventually other healthy businesses will slow, leaving AI to tear a hole in overall margins. In all cases, AI revenue must stay consistent because, well, you need to get paid.

Sidenote: In all honesty, I have no idea how Meta makes this make sense, as it plans to invest at least $125 billion in capex in 2026 and has, to this point, not shown any actual, real growth in its revenue from AI, and no, those increases in conversion don’t mean actual revenue.I also cannot find an economic scenario where this pays itself off. Let’s assume that Anthropic is actually at $45 billion in annualized revenue (I believe it’s doing some very worrisome maths to get there), or around $3.75 billion a month. On an annualized basis, this would not be enough — assuming it had zero operating expenses (rather than losing billions) — to recover a single year of capital expenditures from Microsoft, Google, Meta, or Amazon from 2024 or 2023.Even if OpenAI’s entire cloud spend ($50 billion) for 2026 went to Microsoft and it doubled its Microsoft 365 Copilot revenue (at full cost) to $14.4 billion, it estimates it will invest $190 billion in capital expenditures this year. Amazon’s $15 billion AI run rate, even if it doubled, wouldn’t put much of a dent in its $200 billion in investment plans. While we don’t know Google’s AI revenues, it plans to invest $185 billion in capex this year.These AI revenues have to be completely fucking insane and they need to be that way extremely fucking soon, because otherwise the best they’ll be able to say is “our first few years of capex weren’t particularly useful but the stuff we built after it was,” which still works out to a few hundred billion dollars of waste.Things get even worse when you realize that at least 70% of Microsoft, Google, and Amazon’s compute is dedicated to Anthropic and OpenAI, two companies that burn so many billions of dollars that Microsoft, Google and Amazon have already fed them a combined $54 billion in the last three years, with $28 billion of that coming in the last month and Anthropic due another $50 billion from Google and Amazon if certain performance obligations are met.

And there’s no real sign, outside of Anthropic and OpenAI’s compute spend (which is reliant on hyperscaler and venture capital money), of any real explosion in AI revenue. Per The Information (in a chart I love to share!), more than 50% of hyperscalers’ revenue backlogs comes from these companies:If massive, incredible demand for AI existed, wouldn’t these remaining performance obligations be near the trillion mark? Wouldn’t there be other Anthropic or OpenAI sized chunks of revenue? There’s allegedly incredible, unstoppable, insatiable demand for compute. Why isn’t it lining up?Let’s take a look at those RPOs!Microsoft’s RPOs jumped from $392 billion to $625 billion between Q1 and Q2 FY26 (or calendar year Q4 2025 and Q1 2026), driven by the $250 billion in “incremental Azure spend” from OpenAI (including already-existent commitments) locked up in October 2025 and the $30 billion promised as part of its deal with Anthropic from November 2025. Based on Microsoft’s own disclosures, without Anthropic and OpenAI’s additions, RPO would have been effectively flat, as evidenced by the fact that in Q3FY26, remaining performance obligations sat at $627 billion. Amazon’s RPOs jumped from $244 billion in Q4 2025 to $364 billion in Q1 2026, driven by its February 2026 $100 billion expansion of its $38 billion compute deal from November, and its extended partnership with Anthropic for 5GW of compute capacity unattached from any kind of dollar number. Google’s RPOs jumped from $242.8 billion in Q4 2025 to $467.6 billion in Q1 2026, driven by (per The Information) $200 billion in committed spend on TPUs and compute from Anthropic, meaning that it has expanded its future revenues by an unremarkable $24.8 billion when you remove Anthropic’s spend, when RPOs