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20 May 26
Bertrand Duperrin"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.
"artificialintelligence bubble costs openAI profitability nvidia tokens ROI meta Oracle zillow
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AI Is Too Expensive To Ever Pay Off Hyperscalers’ Capex Investments
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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.
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- 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.
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In other words, Microsoft has spent 4 years sinking (either through spending or allocating the capex in advance) nearly $300 billion into…building OpenAI?
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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.
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f 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.
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And let’s be clear: none of these numbers include the actual operating expenses.
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- 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.
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:
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All four must be true.
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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.
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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.
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These AI revenues have to be completely fucking insane and they need to be that way extremely fucking soon,
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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
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Per The Information (in a chart I love to share!), more than 50% of hyperscalers’ revenue backlogs comes from these companies:
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Microsoft’s RPOs jumped from $392 billion to $625 billion between Q1 and Q2 FY26
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Amazon’s RPOs jumped from $244 billion in Q4 2025 to $364 billion in Q1 2026,
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Google’s RPOs jumped from $242.8 billion in Q4 2025 to $467.6 billion in Q1 2026,
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outside of OpenAI and Anthropic, these three companies do not appear to be significantly increasing their revenues, and the only way to get that revenue is to feed money to one or both of these companies.
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If we assume the absolute best-case scenario, these companies are making a combined $70 billion in annual revenue on investments that now — including the money invested in the companies themselves — total over $900 billion.
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And it all comes back to a very simple point: AI is too expensive. If the margins were good, they’d be sharing the margins. If the revenues were good, they’d be sharing the revenues
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But LLMs are too expensive! They cost too much to run, and said costs appear to increase linearly with revenues.
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The more you build, the more your infrastructure becomes dependent on the continued existence of two perennially-unprofitable ultra-oafs, as your existent AI product lines are, at best, add-ons to products like Google Workspace or Microsoft 365,
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I assume, OpenAI or Anthropic will pay you $100 billion or $200 billion over the course of a few years, because nobody else in the entire universe is spending that much on compute.
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Put another way, Amazon needs another AWS ($128 billion a year), Microsoft another Azure ($75 billion a year, including OpenAI’s 2025 compute spend) and Google a business line at least half the size of search (around $200 billion a year).
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In Oracle’s case, as I’ve explained at length, it has to successfully build 7.1GW of capacity, have that capacity actually be margin-positive (doubtful!), and then actually get paid for it by the time it’s built in, oh, I dunno, 2032?
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- Silicon will get cheaper.
- They’ll start selling services.
- They’re profitable on inference.
Here’s a fun game: ask an AI booster how OpenAI or Anthropic becomes profitable!
Here’s what they’ll say:
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Here’s a really simple way to dispute this: Coatue said that Anthropic’s revenues were 85% API calls in 2025. If it’s profitable on inference, how is it still losing money?
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Otherwise, you have to reconcile with the fact that both Anthropic and OpenAI are both incinerating money and have no real path to any kind of sustainability other than, well, not doing that.
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Anthropic has done a great deal of work to obfuscate how much it actually makes or spends, but I think it’s likely it burns even more than OpenAI,
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Anthropic and OpenAI Need To Make Or Raise More Than $1.25 Trillion In The Next Four Years
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Anthropic — based on its own affidavit from March — appears to have spent $3 to make $1 of revenue
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Additionally, it needs $330 billion to pay its cloud obligations
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The Information reported that Anthropic’s gross margins were 40% in 2025 — 10% lower than its “optimistic” projections, specifically attributed to “...the costs of running Anthropic models from paying customers
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And right now, as I’ve covered, there’s not enough compute being built to keep up with Anthropic or OpenAI’s voracious demands, meaning that they will both be bartering to buy whatever’s available at whatever price it’s available at.
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Let’s say that Anthropic keeps growing and (as The Information suggests) hits $100 billion in annualized revenue (around $8.3 billion a month). How, exactly, does it afford to make that much money? Because right now it’s (allegedly) about to hit $45 billion in annualized revenue, and needs so much money that it’s absorbing (along with OpenAI) the majority of venture capital raised this year, and very clearly does not have any path to bring its costs down.
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I think the answer is simple: their CFOs know that doing so would reveal their actual margins, which are hot dogshit with sprinkles on top.
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Organizations Cannot Afford To Keep Blowing Through Their AI Budgets Millions Of Dollars At A Time Without ROI
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Uber, ServiceNow and multiple other organizations are blowing through their yearly API token budgets in a matter of months, and are currently in the “cope” stage,
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Romack said she recently met with ServiceNow Chief Financial Officer Gina Mastantuono to figure out how to contain costs so employees can keep using their Claude Enterprise accounts for the rest of the year.
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As I said, this is one of the more-normal examples. Goldman Sachs reported a few weeks ago that AI costs are approaching 10% of total headcount costs, and “...could be on track to be on par with headcount costs in the next several quarters based on current trajectories.”
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Enterprises do not know the actual value of AI, and do not know how much they should actually be budgeting,
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Put another way, the current spend on AI tokens is not something that’s indicative of lasting, reliable revenue.
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Zillow Spent Over $1 Million On AI Tokens Through Q1 2026, And Is On Course To Spend $7m to $10m In The Entire Year (20%+ of Its 2025 Profits)
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The real estate tech firm spent over $1 million on AI services in the first quarter of 2026, and in April it spent $749,000 in tokens across Cursor and Anthropic’s services, as well as through AWS Bedrock. As of the end of the month, it was nearly 75% of the way through its annual Cursor token budget of $1.1 million.
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The reality is chaos. In a slide deck that I’ll discuss later, Zillow revealed that while engineering resources have largely stayed the same, outputs requiring human review have increased by nearly 50%.
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On Blind, the anonymous social network for tech workers, Zillow workers complain about Zillow’s code “slowly becoming AI slop,”
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AI Token Burn Is Increasing — and ROI Is Impossible To Measure
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n extra few million dollars’ worth of operating expenses for reasons that escape effectively everybody I’ve talked to.
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Every engineer tells me the same thing: “I’m being made to do this, I don’t want to do this, my managers do not seem to understand, my bosses seem to understand even less than my managers, and if I don’t use AI somebody is going to fire me.”
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One reason Anthropic costs are tough to predict, ServiceNow chief digital information officer Kellie Romack told me, is that Anthropic doesn’t automatically show customers the kind of granular data that allows them to see which of its users consume which tools;
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This is a company ostensibly worth $900 billion dollars acting with disregard for the basic measurement of “how much did this cost, and how did it cost so much?”
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Every Single AI Token Budget Is Bullshit Because You Can’t Measure How Many Tokens A Task Will Take
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How many tokens does it take to do one thing? Is it consistent across every model? Is it consistent across every employee? Are you even measuring how many tokens a task costs? Because if you’re not, that token budget is basically throwing a dart blindfolded.
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Unless, of course, you can’t actually measure how many tokens a particular task can take with much accuracy, in which case every single AI token budget is bullshit.
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You can’t say “use AI every day,” because even if they do so, that doesn’t actually set up a success criteria.
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- You can’t tell software engineers to try and “ship more software,” because that, again, emphasizes doing more, not making good stuff, and leads to an increase in velocity rather than how good the stuff is.
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- You can’t say “pull requests” or any other metric a software engineer can manipulate, because in 100% of the situations where you give a software engineer a number to hit they will focus entirely on hitting that number.
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Before AI, this wasn’t as much of a problem, in the sense that inefficiencies and wasted hours weren’t directly connected to a chatbot that is specifically designed to burn money.
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LLMs are dangerous for many, many reasons, but the under-discussed one is how well they play to a certain kind of executive imbecile. Generative AI is
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Where regular human beings would say annoying things like “that’s not possible within that timeline” or “we don’t have the resources to do it,” AI will say “of course, right away!” and burn as many tokens as possible.
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A Business Idiot, given his druthers, can sit there and fuck around and make an LLM spit out something that makes him feel like he’s coding, which in turn makes him feel that you, a lazy and stupid engineer, could do even more with the power of AI.
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to convince a Business Idiot they can do anything, because executives and managers do not regularly do much work and thus have no idea what it looks like other than when they look over your shoulder, which is why they wanted you back in the office!
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In our world, one dominated by disconnected, self-involved and massively-overpaid dullards, many businesses pushing their workers to use AI are doing so because the other guy is doing it, with about as much strategy and forethought as one would expect from somebody who spends 90% of their life reading emails, going to meetings, or going to lunch.
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Every time you defend generative AI you defend a machine of capital that has burned $1 trillion and created one of the most-wasteful products in history
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These companies were only capable of growing in an economy dominated by the gullible and work-shy. Only a capitalist culture dominated by people who don’t actually do or know stuff have let this get so far.
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Yet even a Business Idiot eventually realizes that too much money is being spent, and the first one of these dimwits to cut their token budget will send the rest of them running for the doors.
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At the end of the AI era, the only thing that will change the rot at the heart of our economy is the acceptance that the majority of companies are run by lazy, self-involved and ignorant fuckwits, and accountability for those who refused to scrutinize them.
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19 May 26
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