AI Startups' Economic Viability Questioned

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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. Subscribe to Ed Zitron's Where's Your Ed At Get on the free list. No spam ever, unsubscribe anytime. Already a member? Sign in We 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 had previously jumped $85 billion between Q3 and Q4, likely driven by its compute deal from October 2025.It’s fair to assume a chunk of the remaining RPOs are from its deal to rent TPUs to Meta, announced in February 2026, which makes it likely that it accounts for the majority of the remaining $24.8 billion. It’s fair to assume a chunk of the remaining RPOs are from its deal to rent TPUs to Meta, announced in February 2026, which makes it likely that it accounts for the majority of the remaining $24.8 billion. That was a lot of numbers, so let me make it simpler: 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. Put aside all the theoreticals and hypotheticals and metaphors and imaginary future scenarios and tell me: what, in the next year, are Microsoft, Google and Amazon going to do about this problem? How do they solve it? 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. Doubling that won’t be enough. Tripling it won’t be enough. In fact, to pay this off, these companies will need to be making over $100 billion each in AI revenue in the next year, because otherwise there is no covering these losses. 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 (and no, run rates aren’t revenues). If the business was strong, it would be a separate category in their earnings. But LLMs are too expensive! They cost too much to run, and said costs appear to increase linearly with revenues. The more a user uses a product, the more it costs the company to run it, and the more capacity they can take up. The only way to capture any growth is to buy and install GPUs, which in turn requires you to build somewhere to put them, which takes time and money. I’m really struggling to see the argument in favor of continued capex investment. You’re more than $800 billion in the hole with, I estimate, less than half of that resulting in operational GPUs and capacity. Said capacity is mostly taken up by OpenAI and Anthropic, two companies that burn billions of dollars and do not appear to have an answer for how they might stop. 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, or further expansion of cloud compute capacity with lower margins and higher up-front costs than anything you’ve ever built. Every quarter is an opportunity to put yourself another $30 billion or so in the hole, all in the hopes that, 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. You are not recovering these investments without either a massive new product line that doesn’t exist today or three or four Anthropic or OpenAI-sized compute contracts. 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). These businesses have grown to this size by providing extranormally large amounts of value from the very moment they were created and impenetrable monopolies — and while there are quite literally other cloud providers that can physically provide the infrastructure to OpenAI and Anthropic (Oracle is trying to compete and may die as a result), the actual “monopoly” here is “being able to deploy hundreds of billions of dollars.” Anthropic proved this when it took 300MW of compute from Elon Musk. Sidenote: I have absolutely no idea what Meta does, and my chaos bet is that it starts renting out its compute to Anthropic or OpenAI when things get rough. Perhaps it does some sort of incestuous deal where Meta gets equity. I really have no idea here! It’s a crazy and stupid company run by a moron. 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? Sadly, I have bad news about Oracle, Microsoft, Amazon, and Google’s largest customers. Here’s a fun game: ask an AI booster how OpenAI or Anthropic becomes profitable! Silicon will get cheaper. They’ll start selling services. They’re profitable on inference. I must be abundantly clear that nobody has any proof that anyone is profitable on inference, but we have plenty of proof they’re not. They’ll likely cite known liar Sam Altman saying OpenAI is profitable on inference from a party from August 2025, or Dario Amodei saying (in a sentence around “stylized facts” that are “not exact” and are specifically “a toy model” and specifically not about Anthropic) “the inference has some gross margin that’s more than 50%.” 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? You’re gonna say “training,” but that doesn’t actually answer the question: if Anthropic’s process of providing tokens to its models is profitable, how is it losing so much money? Why offer a subscription platform at all? As I’ll get to, Anthropic has companies paying massive amounts for tokens — hundreds of millions a year in some cases — that’s all inference. Why are you bothering with these stinky, nasty monthly subscriptions? The “inference is profitable” argument is a bedtime story told to people that can’t reconcile the logic of a company that allows people to burn between $8 and $13.50 of every dollar of their subscription revenue. 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. One very, very specific counter-argument people make is that open source models are cheap, and can somehow be compared to OpenAI and Anthropic’s, despite the fact that we have no idea what the actual parameters of Sonnet, GPT, Opus, or any other of their models actually are. What we do know is that both of these companies lose billions of dollars. What we do know is that OpenAI, per The Information, plans to burn $852 billion through the end of 2030, and that as of March 6, 2026 (per CFO Krishna Rao’s sworn affidavit), Anthropic made “exceeding” (sigh) $5 billion in revenue and spent $10 billion on inference and training. 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, given the fact that it’s had to raise $75 billion in the last 6 months (assuming its new $30 billion round closes), and that’s not including an additional $30 billion from Google and Amazon if certain unknown milestones are hit. Then there’s the issue of those RPOs. Anthropic is now on the hook for $200 billion to Google, $100 billion to Amazon and $30 billion to Microsoft, I assume over the course of the next three or four years. Anthropic — based on its own affidavit from March — appears to have spent $3 to make $1 of revenue on a compute basis, and that’s before you include any and all other costs like staff or electricity or the vocal coach that Dario Amodei uses to add that bass to his voice. Additionally, it needs $330 billion to pay its cloud obligations to Amazon, Google, and Microsoft over the next four years. I’d estimate it needs $5 billion a year for its compute deal xAI (so $20 billion over the total period) and an estimated $30 billion to cover its deal with CoreWeave. That brings us to a total of $380 billion. It’s hard to estimate the actual costs associated with running Anthropic because so much of the reporting no longer makes sense as a result of that affidavit. Nevertheless, I think it’s fair to assume it will need at least $20 billion of operating expenses across that four year period. We don’t even need to play in the realm of “what might Anthropic or OpenAI’s revenues be?” to understand the problem here. Both companies aggressively burn money, and neither of them have any answer as to how they might stop. Numerous reports about how Anthropic will turn “cash flow positive” in either 2027 or 2028 are fantastical, illogical, entirely driven by ridiculous projections, and should never have been reported as anything other than an attempt by companies to mislead their investors. In both cases, reporters should’ve had more asterisks on those numbers than Q*Bert reading Frank’s lines from Blue Velvet. And we have plenty of evidence that they’re losing more money over time. In January 2026, 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, in a process known as inference, on servers from Google and Amazon,” adding that those costs were “23% higher than the company anticipated.” In February, The Information ran another story saying that OpenAI’s gross margins fell from 40% in 2024 to 33% in 2025, a full 13% lower than its projected margins of 4