2026-04-29 00:35:07
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Yesterday morning, GitHub Copilot users got confirmation of something I’d reported a week ago — that all GitHub Copilot plans would move to usage-based pricing on June 1, 2026.
Instead of offering users a certain number of “requests,” Microsoft will now charge users based on the actual cost of the models they’re using, which it calls “...an important step toward a sustainable, reliable Copilot business and experience for all users.” Users instead get however much they spend on their GitHub Copilot subscription (EG: $19 of tokens a month on a $19-a-month plan).
Translation: "we cannot continue to subsidize GitHub Copilot users, or Amy Hood will start hitting people with a baseball bat."
Anyway, the announcement itself was a fascinating preview into how these price changes are going to get framed:
Copilot is not the same product it was a year ago.
It has evolved from an in-editor assistant into an agentic platform capable of running long, multi-step coding sessions, using the latest models, and iterating across entire repositories. Agentic usage is becoming the default, and it brings significantly higher compute and inference demands.
Today, a quick chat question and a multi-hour autonomous coding session can cost the user the same amount. GitHub has absorbed much of the escalating inference cost behind that usage, but the current premium request model is no longer sustainable.
Usage-based billing fixes that. It better aligns pricing with actual usage, helps us maintain long-term service reliability, and reduces the need to gate heavy users.
You see, it’s not that Microsoft was subsidizing nearly two million people’s compute, it’s that AI has become so strong, powerful and complex that it’s basically a different product!
While Copilot might not be “...the same product it was a year ago,” very little has changed about the underlying economic mismatch: that Microsoft was allowing users to burn more than their subscription costs in tokens every single month for three years. Per the Wall Street Journal in October 2023:
Individuals pay $10 a month for the AI assistant. In the first few months of this year, the company was losing on average more than $20 a month per user, according to a person familiar with the figures, who said some users were costing the company as much as $80 a month.
Naturally, GitHub Copilot users are in revolt, saying that the product is “dead” and “completely ruined.”
And I called it two years ago in the Subprime AI Crisis:
I hypothesize a kind of subprime AI crisis is brewing, where almost the entire tech industry has bought in on a technology sold at a vastly-discounted rate, heavily-centralized and subsidized by big tech. At some point, the incredible, toxic burn-rate of generative AI is going to catch up with them, which in turn will lead to price increases, or companies releasing new products and features with wildly onerous rates — like the egregious $2-a-conversation rate for Salesforce’s “Agentforce” product — that will make even stalwart enterprise customers with budget to burn unable to justify the expense.
And that day has finally arrived, because every single AI service you use subsidized compute, and every single service is losing money as a result:
When you pay for access to an AI startup’s service — which, of course, includes OpenAI and Anthropic — you do so for a monthly fee, such as $20, $100 or $200-a-month in the case of Anthropic’s Claude, Perplexity’s $20 or $200-a-month plan, or OpenAI’s $8, $20, or $200-a-month subscriptions. In some enterprise use cases, you’re given “credits” for certain units of work, such as how Lovable allows users “100 monthly credits” in its $25-a-month subscription, as well as $25 (until the end of Q1 2026) of cloud hosting, with rollovers of credits between months.
When you use these services, the company in question then pays for access to the AI models in question, either at a per-million-token rate to an AI lab, or (in the case of Anthropic and OpenAI) whatever cloud provider is renting them the GPUs to run the models. A token is basically ¾ of a word.
As a user, you do not experience token burn, just the process of inputs and outputs. AI labs obfuscate the cost of services by using “tokens” or “messages” or 5-hour-rate limits with percentage gauges, and you, as the user, do not really know how much any of it costs. On the back end, AI startups are annihilating cash, with up until recently Anthropic allowing you to burn upwards of $8 in compute for every dollar of your subscription. OpenAI allows you to do the same, though it’s hard to gauge by how much.
AI startups and hyperscalers assumed that they’d be able to get enough people through the door with subsidized, loss-making products to get them hooked on services badly enough that they’d refuse to change once businesses jacked up the prices. They also assumed, I imagine, that the cost of tokens would come down over time, versus what actually happened — while prices for some models might have come down, newer “reasoning” models burn way more tokens, which means the cost of inference has, somehow, gotten higher over time.
Both assumptions were wrong, because the monthly subscription model does not make sense for any service connected to a Large Language Model.
Think of it like this. When Uber (and no, this is nothing like Uber) started jacking up the prices for its rides, the underlying economics stayed the same, as did those presented to both the rider and the driver — a user paid for a ride, a driver was paid for a ride. Drivers still paid for gas, car insurance, any permits that their local government might insist upon, and whatever financing costs might be associated with their vehicle, and said costs were not subsidized by Uber. Uber’s massive losses came from subsidies, endless marketing expenses, and doomed R&D efforts into things like driverless cars .
To illustrate the scale of AI’s pricing mismatch, I’m going to ask you to imagine an alternate history where Uber had a very different business model.
Generative AI subscriptions are like if Uber charged users $20 a month for 100 rides of any distance under 100 miles, and if gas was $150 a gallon, and Uber paid for the gas because somebody insisted that oil would one day be too cheap to meter.
Uber would, eventually, decide to start charging users a monthly subscription to access rides, and bill them for the gas that they consumed. Suddenly users would go from paying $20 a month for 100 rides to paying $20 to access a driver and $26 for a 10 mile drive. Understandably, users would be a little upset.
While this sounds a little dramatic, it’s actually a pretty accurate metaphor for what’s happening in the generative AI industry, and in particular, at Github Copilot.
GitHub Copilot’s previous pricing allowed 300 premium requests a month, as well as “unlimited chat requests” using models like GPT-5 mini. Each of these requests (to quote Microsoft) is “...any interaction where you ask Copilot to do something for you,” with more-expensive models taking up more requests in the later life of the request-based system, such as Claude Opus 4.6 taking up three premium requests. When you ran out of premium requests, Copilot would let you use one of those cheaper models as much as you’d like for the rest of the month.
This wasn’t even always the case. Up until May 2025, Microsoft gave users unlimited access to models, and even then users were pissed off that there were any restrictions on the product.
Microsoft — like every AI company — swindled its customers by selling an unsustainable service, because it never, ever made sense to sell LLM-powered services on a monthly subscription.
If you’re wondering how much services are likely to cost under token based billing, a user on the GitHub Copilot Subreddit found that the token burn of what used to be a single premium request was somewhere around $11, as one “request” involved using 60,000 tokens in the context window, a few tools, and a bunch of internal “turns” (things that the model is doing) to produce the output.
There’s also the underlying unreliability of hallucination-prone Large Language Models. While a premium request chasing its tail and spitting out half-broken code might be frustrating, that same fuckup is a lot less forgivable when you’re paying the costs yourself.
Users have also been trained to use the product in an entirely different manner to token-based billing, and I’d imagine many of them don’t even really realize how many “tokens” they burn or how many of them a particular task takes, something which changes based on whatever model you use.
This is absolutely nothing like Uber, and anyone telling you otherwise is attempting to rationalize bad behavior. Uber may have raised prices, but it didn’t have to dramatically change the underlying economics of the platform, nor did users have to entirely change how they used the product because Uber was suddenly charging them on a per-gallon basis.
There has never been — and never will be — an economically-feasible way to offer services powered by LLMs without charging the actual token burn of each user, and in the process of deceiving said users, these companies have created products with illusory benefits and questionable return on investment.
And that’s been blatantly obvious for years.
On an economic basis, a monthly subscription only makes sense with relatively static costs. A gym can sell memberships knowing roughly how much wear-and-tear equipment gets, how much classes cost to run, and how much things like electricity, staffing and water might cost over a given period of time.
A customer of Google Workspace — at least before AI — cost whatever the cost of accessing or storing documents were, as well as the ongoing costs of Google Docs and other services. The relatively low cost of digital storage (as well as the fact that, unlike LLMs, Google Workspace isn’t particularly computationally demanding) means that a particularly-heavy Google Drive user isn’t going to eat into the margin on their monthly subscription.
Conversely, an AI subscriber’s costs can vary wildly. One user might only use ChatGPT for the occasional search, while another might feed in reams of documents, or try and refactor a codebase, or try and use it to put together a PowerPoint presentation. And the provider — a model lab like OpenAI or Anthropic, or a startup like Cursor) — has no real way to control how a user might act other than making the product worse, such as instituting usage limits, reducing the size of the context window, pushing customers to smaller (and worse) models, or changing the pricing to dissuage users from making big GPU-heavy requests.
Yet these services intentionally hide the amount of tokens or how much a particular activity has cost, which means users don’t really know what a rate limit means, which means that every abrupt change to rate limits leaves customers desperately scrambling to work out how much actual work they can do using the service.
It’s an abusive, manipulative and deceitful way of doing business that only existed so that Anthropic, OpenAI, and other AI companies could grow their user bases, as the majority of AI users perceive its real or imagined benefits entirely through the lens of being able to burn anywhere from $8 to $13.50 for every dollar of their subscription in tokens.
This intentional act of deception had one goal: to make sure that the majority of people were never exposed to the true costs of generative AI. When The Atlantic writes a breathless screed about Claude Code being Anthropic’s “ChatGPT moment,” it does so based on a $20-a-month subscription rather than the underlying token burn that it cost for Anthropic to provide it, which in turn makes the writer forgive the “minor errors” that a model might make, or when it “gets stuck on more complicated programming tasks.”
Had the writer paid for her actual token burn, and had each of the times it got “stuck” resulted in $15 in token charges, I don’t think she’d be quite as forgiving of these fuckups.
Yet that’s all part of the scam.
It’s very, very important that nobody writing about AI in the mainstream media actually understands how much these services cost, and that any mainstream articles written about services like ChatGPT or Claude Code are written by people who have little or no idea how much each individual task might cost a user.
Remember: generative AI services are, for the most part, experimental products that do not function like any other modern software or hardware. One cannot just walk up to ChatGPT or Claude and start asking it to do work.
I mean, you can, but if you don’t prompt it right, understand how it works, or make a mistake in whatever you feed it, or if it just gets things wrong, it’ll spit out something you don’t like, which in turn means you’ll need to prompt it again. LLMs are inherently unpredictable.
You cannot guarantee whether an LLM will do a particular action, or whether it will present you with an outcome based on reality. You cannot for certain say how much a particular task — even one you’ve done many times using an LLM in the past — might cost, nor can you be sure when a model might go berserk and delete something, or simply not do something yet claim that it did.
These are far more forgivable if you’ve not paying on a per-token basis, because in the mind of a subscriber, that’s just another turn or two with a chatbot rather than something that’s incurring a real cost. One doesn’t criticize so-called “jagged intelligence” because the assumption is that whatever problems you’re facing now will be eliminated at some future juncture, and you didn’t end up paying for it anyway.
Had users been forced to pay their actual rates, I imagine many would’ve bounced off the product immediately, as it’s very, very easy to burn through $5 of tokens if you’re fucking around and exploring what an LLM can do.
Sidenote: In fact, you can burn a great deal of money without ever getting the outcome you desire, because LLMs aren’t really artificial intelligence at all! Somebody without any real understanding of their limitations could easily burn $30, or $50, or even $100 trying to convince an LLM to do something it insists it’s capable of.
There’s a term for this. Sycophancy. LLMs are often designed to affirm the user, even when they’re saying dangerously unhinged things, and that can extend to saying “you want this big thing that’s not even slightly feasible, whether technically or financially?” Sure thing!
This is why the industry worked so hard to obfuscate these costs — it’s a fucking ripoff!
I think it’s inevitable that the majority of AI subscriptions move to token-based billing, especially as both Anthropic and OpenAI have now done so with their enterprise customers.
The fact that Microsoft moved GitHub Copilot subscribers to token-based is also a very, very bad sign. Microsoft is arguably the best-capitalized, most-profitable, and best-positioned company to continue subsidizing compute, and if it can’t afford to do so further, nobody else can either.
The real thing to look out for — a true pale horse — will be a major AI lab like Anthropic or OpenAI moving all of its subscribers to token-based billing. Once that happens, you’ll know it’s closing time.
As I discussed last week, Uber’s CTO said at a conference that it had spent its entire AI budget for 2026 in the space of a few months, with Goldman Sachs suggesting that some companies are spending as much as 10% of their headcount on AI tokens, with the potential to increase to 100% in the next few quarters.
This is the direct result of training every single AI user to use these services as much as humanely possible while obfuscating how much they really cost. Every single major company demanding that every single worker “use AI as much as possible” has done so while either fundamentally ignoring or being entirely disconnected from their actual token burn, and as companies are forced to pay the actual costs, I’m not sure how you can economically justify any investment in this technology.
Sure, sure, you’re gonna say that engineers are “shipping code faster” or some such bullshit, I get it, but how much faster, and how much money are you making or saving as a result? If you’re spending 10% of your headcount on AI tokens, are you seeing that extra expense reconciled in some other way? Because I’m not sure you are. I’m not sure any business investing these vast amounts of money in tokens is seeing any return on investment, which is why every study about AI’s ROI struggles to find much evidence that it exists.
For the most part, everybody you’ve read gooning over the many possibilities of generative AI has experienced it without having to pay the true costs. Every Twitter psychopath writing endless screeds about their entire engineering team hammering away at Claude Code has been doing so using a $125-a-month-per-head Teams subscription with similar usage limits to Anthropic’s $100-a-month consumer subscription. Every LinkedIn gargoyle insisting that they’d “done hours of work in minutes” using some sort of Perplexity product has done so by paying, at the very most, $200 a month for Perplexity’s Max subscription.
In reality, that 10-person, $1250-a-month Teams subscription likely burns anywhere from $5000 to $10,000 a month in API calls, if not more. Anthropic Head of Growth Amol Avasare said last week that its Max subscriptions were built for heavy chat usage rather than whatever people are doing with Claude Code and Cowork, and made it clear that Anthropic is now looking at “different options to keep delivering a great experience,” which is another way of saying “we’re going to change the prices at some point.”
I’m not sure people realize how expensive these tokens are, especially for coding projects that involve massive codebases and regularly make calls to coding and infrastructure tools. Can somebody who pays $200 a month foreseeably afford $350, $400, or $500? Can they afford to have a month where they spend more than that? What happens if they go over budget, or if they literally can’t afford to spend the money necessary to finish their work?
To give you a more-practical example, up until the beginning of April, Anthropic’s own Claude Code developer documents (archive) said that “the average cost [for those using Claude Code] is $6 per developer per day, with daily costs remaining below $12 for 90% of users.” As of this week, the documents now read as follows:
Claude Code charges by API token consumption. For subscription plan pricing (Pro, Max, Team, Enterprise), see claude.com/pricing. Per-developer costs vary widely based on model selection, codebase size, and usage patterns such as running multiple instances or automation.
Across enterprise deployments, the average cost is around $13 per developer per active day and $150-250 per developer per month, with costs remaining below $30 per active day for 90% of users. To estimate spend for your own team, start with a small pilot group and use the tracking tools below to establish a baseline before wider rollout.
If we assume an average of 21 working days in a month, that puts the average cost of a Claude Code user at around $273 a month, or $3,276 a year. At $30 a working day, that works out to $630 a month, or $7560 a year.
These are astonishing numbers, made more so by the fact that there is no way you’re only spending $30 a day if you use any of Anthropic’s more-recent models. Claude Opus 4.7 costs $5 per million input and $25 per million output tokens. ‘One million tokens is around 50,000 lines of code, and assuming you’re using the supposedly state-of-the-art models, there isn’t a chance in Hell you don’t run through at least a million, with that number increasing dramatically if you’re not particularly aware of which models to use for a particular task.
Let’s play with that $30 number a little more.
While this might be possible within the slush fund mindset of a well-funded startup or a banana republic like Meta, any business that actually cares about its costs will have a great deal of trouble justifying spending five or six figures in extra costs on a service that “increases productivity” in a way that nobody can seem to measure.
Right now, I think most companies fall into three camps:
Large organizations still have a free pass to say that they’re burning millions of dollars on AI tokens for their software engineers under the questionable benefit of their “best engineers” not writing any code.
All it changes is one bad earnings call to change that narrative. At some point investors — even the braindead fuckwits who have been inflating the AI bubble — will begin to question mounting R&D costs (which is where AI token burn is usually hidden) when the company’s revenue growth fails to follow. This will likely lead to more layoffs to keep up with the cost, as was the case with Meta, and then an eventual pullback when somebody asks “does any of this shit actually help us do our jobs faster or better?”
I also think that startups burning 10% or more of their headcount in AI tokens will have a tough time convincing investors in six months that doing so is necessary.
And once everybody switches to token-based billing, I’m not sure we’ll see quite as much hype around generative AI.
The way that people talk about AI data centers is completely disconnected from reality, and I don’t think people realize how ridiculous this entire era has become.
Per Jerome Darling of TD Cowen, it costs around $30 million in critical IT (GPUs and the associated hardware) and $14 million per megawatt of data center capacity. Data centers appear to take anywhere from a year to three years depending on the size, and that’s assuming the power is available.
Of the 114GW of data centers supposedly being built by the end of 2028, only 15.2GW is under construction in any way, shape, or form. And “under construction” can mean as little as “there’s a hole in the ground.” It does not — and should not — imply that the capacity that said facility will provide is going to be imminently available.
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Let’s start simple: whenever you think “100MW,” think “$4.4 billion,” with a large chunk of that dedicated to NVIDIA GPUs.
As a result, every AI data center starts millions of dollars in the hole, and even with six-year-long depreciation schedules, takes years to pay off… and with NVIDIA’s yearly upgrade cycle, those GPUs are unlikely to make that much money once you’re done with your first customer contract.
It’s also unclear whether the customer base for AI compute exists outside of OpenAI and Anthropic, whose demand accounts for 50% of AI data centers under construction, creating a massive systemic weakness if either of them lacks the money to pay.
In any case, it’s also unclear what kind of ongoing rates these data centers charge. While spot prices might sit around $4.50 an hour for a B200 GPU, long-term contracts generally price much lower, with one founder (per The Information) saying they paid around $3.70-per-hour-per GPU for a one-year-long commitment.
To be clear, we must differentiate between the spot cost — which is the cost of randomly spinning up GPUs on somebody else’s servers — and contracted compute, the latter of which makes up the majority of data center capex. Most data centers are built with the intention of having one or two big clients, which means said clients are likely to negotiate a cheaper blended rate.
As a result, many data centers take far less than $3.70 an hour, because they bill at a per-megawatt (or kilowatt) price.
And that’s where the economics begin to break down.
That’s the starting cost for a 100 megawatt data center. A 100MW data center will likely only have 85MW of actual stuff it can bill for, and based on discussions with sources familiar with hyperscaler billing, they can expect to make around $12.5 million per megawatt, or around $1.063 billion in annual revenue.
Now, I should be clear that most data center companies you know of don’t actually build them, instead leaving that job to companies like Applied Digital, who are also known as “colocation partners.” For example, CoreWeave pays a colocation fee to Applied Digital to use its North Dakota data centers. CoreWeave is responsible for all the GPUs and other tech inside the data center.
To explain the economic mismatch, I’m going to use a theoretical example of a data center leased to a theoretical AI compute company.
The GPUs in that data center are likely NVIDIA’s Blackwell chips. More than likely, said data center is using pods of 8 B200 GPUs, retailing around $450,000 a piece, or $56,250 a GPU. Based on there being 85MW of Critical IT load, the all-in capex per megawatt is around $36.78, or total IT capex of around $3.126 billion, or around $2.67 billion in GPUs.
Let’s assume this data center is in Ellendale, North Dakota, which means you’ve got an industrial electricity rate of around 6.31 cents per kilowatt hour, which works out to about $55.4 million a year in electricity costs. Based on discussions with sources, I estimate that ongoing costs like maintenance, headcount, replacement of power supplies and the like comes in at around 12% of revenue, or around $128 million a year, bringing us up to $183.4 million in costs.
Wait, sorry. You’ve also got to pay a colocation fee based on the critical IT, and according to Brightlio, that fee is often around $180-200 per kilowatt per month, depending on the scale and location of the deployment, though I’ve read as low as $130, which is the number I’m going with, or around $133 million per year. This brings us up to $316.4 million.
Well, that’s still less than $1.06 billion, so we’re still doing okay, right?
Wrong! You’ve got $3.126 billion in IT gear to depreciate, which works out to around $521 million a year over the six years you’re depreciating it. That’s $837.4 million a year, leaving you with around $168.6 million in yearly profit, or around a 16.7% gross margin…
…if you have 100% tenancy at all times! You see, data centers can take a month or two to get those GPUs installed and a customer onboarded, all while making you exactly zero dollars in revenue and losing you a great deal more, as you’re stuck paying your colocation, electricity and opex costs the whole time, albeit at a much lower rate (I’ve modeled for 10% electricity and 15% colocation/opex costs), meaning you’re losing about $3.27 million a day.
For the sake of this example, we’re going to assume it takes you an extra month to get this thing operational, meaning you’ve paid about $102 million that you’re never getting back, bringing our total costs for the year including depreciation to $939.4 million, or a 6.6% gross margin.
Wait, fuck, you didn’t use debt to buy these GPUs, did you? You did? How bad are we talking? Oh god — you got a 6-year-long asset-backed loan at 80% LTV, meaning you borrowed $2.8 billion at a 6% interest rate.
Your bank, in its eternal generosity, offered you a deal — a 12-month-long grace period where you’re only paying interest…which works out to around $168 million, which brings our total costs (excluding the month of delay for fairness) for the first year to around $1.005 billion…on $1.06 billion of revenue.
That’s a 5.19% gross margin, and you haven’t even started paying the principal. When that happens, you’re paying $54.1 million a month in loan payments, for a total of around $649 million a year for five more years, which comes out to around $1.48 billion, or a negative gross margin of around 40%.
And I must be clear that this is if you have 100% utilization and a tenant that pays you on time, every time.
Let’s talk about what should be the single-most economically viable project in data center history — a massive campus built for the largest AI company in the world by Oracle, a decades-old near-hyperscaler with a history of selling expensive database and business management software to enterprises and governments.
Hah, I’m kidding of course, this place is a fucking nightmare.
Stargate Abilene, an eight-building, 1.2GW data center campus with around 824MW of critical IT, was first announced in July 2024. As of April 27 2026, only two buildings are operational and generating revenue, and the third barely has any IT gear in it. I estimate the total cost of Stargate Abilene to be around $52.8 billion.
Per my own reporting, Oracle expects to make around $10 billion in annual revenue from Stargate Abilene, and I estimate around $75 billion in total revenue from the 7.1GW of data center capacity it’s building for one customer: OpenAI. As I also reported, Oracle estimated in 2024 that Abilene would cost at least $2.14 billion a year in colocation and electricity fees, paid to land developer Crusoe.
I should also add that it appears that Oracle is paying all of Abilene’s construction costs.
Based on my calculations and reporting, I estimate Abilene’s rough gross margin is around 37.47% once it’s fully operational:
I must be clear that that 37.47% gross margin is likely too high, as I don’t have precise knowledge of Oracle’s true insurance or headcount costs, only estimates based on documents viewed by this publication. I should also be clear that Oracle is mortgaging its entire fucking future on projects like Stargate Abilene, incurring billions of dollars of costs up front for a business that will take years to turn a profit even if OpenAI makes every single payment in a timely manner.
Sadly, I can’t tell how much of Abilene was paid for in debt, only that Oracle raised around $18 billion in various-sized bonds in September 2025, with maturities ranging from 7 years to 40 years, and had negative cashflow of $24.7 billion in its last quarterly earnings.
What I do know is that it has a 15-year-long lease with developer Crusoe, and that Oracle’s future heavily depends on OpenAI’s continued ability to pay, which depends on Oracle’s ability to finish Stargate Abilene.
I also need to be clear that that $3.85 billion in yearly profit is only possible if OpenAI makes timely payments, takes tenancy of Abilene as fast as humanly possible, and everything goes to plan.
Sadly, the complete opposite has happened:
Based on reporting from DatacenterDynamics, the first 200MW of power was meant to be energized “in 2025.” As time dragged on, occupancy was meant to begin in the first half of 2025, had “potential to reach 1GW by 2025,” complete all 1.2GW of capacity by mid-2026, be energized by mid-2026, have 64,000 GPUs by the end of 2026, as of September 30, 2025 had “two buildings live,” and as of December 12, 2025, Oracle co-CEO Clay Magouyurk said that Abilene was “on track” with “more than 96,000 NVIDIA Grace Blackwell GB200 delivered,” otherwise known as two buildings’ worth of GPUs.
Four months later on April 22, 2026, Oracle tweeted that “...in Abilene, 200MW is already operational, and delivery of the eight-building campus remains on schedule.” It is unclear if that’s 200MW of critical IT capacity or the total available power at the Abilene campus, and in any case, this is only enough power for two buildings, which means that Oracle is most decidedly not “on schedule.”
This is a huge issue. OpenAI can only pay for compute that actually exists, and only 206MW of critical IT is actually generating revenue, with the third at least a month (if not a quarter) away from doing so.
Yet there’s a larger, more-existential problem with the overall Stargate data center project — that the only way any of it makes sense is if OpenAI meets its ridiculous, cartoonish projections.
I’ll repeat the numbers: the 7.1GW of Stargate data centers in progress will make around $75 billion in annual revenue on completion, and cost more than $340 billion in total. Oracle’s free cash flow was negative $24.7 billion, and its other business lines are plateauing, making its negative-to-low margin cloud business its only growth engine.
For OpenAI to actually be able to pay its compute deals — both to partners like Amazon, Microsoft, CoreWeave, Google, Cerberas, and to Oracle — it will have to raise or make $852 billion in revenue and/or funding in the space of four years, which would require its business to grow by more than 250%, every single year, effectively 10xing by the end of 2030, at which point it will have had to find a way to become cashflow positive for any of these numbers to make sense.
To be clear, OpenAI’s projections have it making $673 billion over the next four years, and burning $218 billion to get there. It is an incredibly unprofitable business, and even if it wasn’t, it would have to make so much more money than it currently does to pay Oracle on an ongoing basis.
I calculated that $75 billion number by assuming that Vera Rubin GPUs get around $14 million per megawatt of compute (a number I’ve confirmed with sources familiar with the data center industry) across the remaining 4.64GW of critical IT that I anticipate makes up the remaining Stargate data centers.
OpenAI’s numbers come directly from The Information’s reported leaks of OpenAI’s projected burn rate and revenues, which have the company making $673 billion in revenue through the end of 2030 and burning $852 billion to get there:
I must be clear that any journalist repeating these numbers without saying how fucking stupid they are should be a little ashamed of themselves. Per my Friday premium:
In other words, in two years OpenAI projects it will make more revenue than TSMC, in three years almost as much annual revenue as Meta, and by the end of 2030, as much annual revenue as Microsoft ($300 billion or so on a trailing 12 month basis).
And if OpenAI can’t pay for that compute, Oracle dies, because it’s taken on around $115 billion in debt just to build Stargate’s data centers, and needs another $150 billion to finish them:
Oracle is a company that currently makes around $64 billion in annual revenue, and had free cash flow of negative $24.7 billion in its last quarter. It raised $18 billion in bonds in September 2025, $25 billion in bonds in February 2026, it did a $20 billion at-the-market share sale sometime in March, and despite it being called “closed” for months, only appears to have recently closed its $38 billion in project financing for Stargate Wisconsin and Shackelford. I’m also including the $14 billion in data center debt related to Stargate Michigan.
Either way, Oracle is insufficiently-capitalized to finish Stargate Abilene. It will need at least another $150 billion to get this done, and that’s assuming that other partners pick up about $30 billion in costs. Honestly, it may be more.
I really need to be clear that Oracle has no other path to making this revenue without OpenAI, and these projects are entirely financed and paid for using the projected cashflow of the data centers themselves.
And I’m not even the only one worried about this, with OpenAI Sarah Friar sharing similar concerns after the company missed user and revenue targets, per the Wall Street Journal:
OpenAI recently missed its own targets for new users and revenue, stumbles that have raised concern among some company leaders about whether it will be able to support its massive spending on data centers.
Chief Financial Officer Sarah Friar has told other company leaders that she is worried the company might not be able to pay for future computing contracts if revenue doesn’t grow fast enough, according to people familiar with the matter.
Board directors have also more closely examined the company’s data-center deals in recent months and questioned Chief Executive Sam Altman’s efforts to secure even more computing power despite the business slowdown, the people said.
If that doesn’t worry you, perhaps this will:
She has emphasized to executives and board directors the need for OpenAI to improve its internal controls, cautioning that the company isn’t yet ready to meet the rigorous reporting standards required of a public company. Altman has favored a more aggressive timeline for an IPO, some of the people said.
That sure sounds like a company that’s gonna be able to make $852 billion by the end of the decade!
While I regularly bag on OpenAI for its ridiculous promises, Anthropic isn’t far behind, promising to take “up to” 5GW of capacity from both Google and Amazon in a deal that I estimate includes around $100 billion in actual compute commitments given the scale of the capacity.
Now, I should add that Google and Amazon are far-savvier and less-desperate than Oracle, meaning that they can take the hit if Anthropic ends up running out of money. The “up to” part of that deal gives them some much-needed wiggle room that Oracle simply does not have.
Nevertheless, for Anthropic to actually meet its commitments, it will have to agree to spend anywhere from $25 billion to $100 billion a year on compute by the end of 2030.
Anthropic’s CFO said in March that it had made $5 billion in revenue in its entire existence.
The near-pornographic excitement around however many hundreds of billions of dollars of GPUs that Jensen Huang claims to be shifting regularly clouds out a problematic question: sell the compute to who, Jensen?
If we assume that the 15.2GW of data center capacity under construction (due by the end of 2028) has a PUE of around 1.35, that leaves us with roughly 11.2GW of critical IT. At $14 million per megawatt, that works out to around $156.8 billion in annual revenue in GPU rental revenue required to actually make these data centers worth it.
When you calculate for the 114GW of capacity theoretically coming online by the end of 2028, that number climbs to $1.18 trillion in annual revenue.
To give you some context, CoreWeave — the largest neocloud with Meta, OpenAI, Google (for OpenAI), Microsoft (for OpenAI), Anthropic, and NVIDIA as customers — made around $5.1 billion in revenue, and projected it would make $12 billion to $13 billion in 2026.
Who, exactly, is the customer for all this compute, and how likely is it that they’ll want to buy it by the time all that capacity is built? While many different data centers claim to have tenants for the first few years of their existence, said tenants can only start paying once the data center completes, and if it’s an AI startup, I think it’s fairly reasonable to ask whether they exist by the time it’s built.
Remember: the customers of AI compute are, for the most part, either hyperscalers trying to move capital expenditures off their balance sheets or unprofitable AI startups. Anthropic and OpenAI both intend to burn tens of billions of dollars in the next few years, and neither of them have a path to profitability.
This means that a large part — if not the majority of — AI compute revenue is dependent on a continual flow of venture capital and debt, both of which are only made possible by investors that still believe that generative AI will be the biggest, most hugest thing in the world.
How does that work out, exactly? Who is paying for this data center capacity? Who is it for? Where is the actual demand?
And if said demand exists, how the fuck do the customers even pay?
Despite multiple stories that have both of them becoming profitable by 2028 or 2029, nobody can explain to me how OpenAI or Anthropic actually reach profitability, especially given that both of them had worse margins than expected, even when said margins strip out training costs that number in the billions of dollars.
And I’ve been asking this question for fucking years. Every time we get a new update on Anthropic or OpenAI, we hear they’ve lost billions more dollars than expected, that margins are decaying, that costs are skyrocketing, that everything is more expensive despite promises that the literal opposite would happen.
Even Cursor, a company that briefly (before its pseudo-acquisition by Musk’s SpaceX) claimed it had positive gross margins, actually had negative 23% gross margins as of January, or negative 31% if you include the cost of non-paying users, which you fucking should if you actually care about your accounting. Mysteriously, reports claim that Cursor’s margins “recently turned positive,” but magically don’t know by how much, or how that happened, or a single other detail other than one that likely helped the company get sold.
I also don’t see how any of these AI data centers actually make sense, even if they have customers to pay them for the first few years. The economics are built for perfection, with zero fault tolerance. They must have consistent, 100% utilization and tenancy, or they end up burning millions of dollars and fail to chip away at the years-long wall of depreciation created by the tech industry’s most expensive mistake.
Even if they somehow succeed, these are pathetic businesses with mediocre margins — 70% at best, assuming consistent payments, tenancy, and six fucking years of depreciation to actually break even, which might be difficult considering the yearly upgrade cycle makes the entire thing near-obsolete by the time you’re done paying it off.
And that’s before you consider that the majority of the customers are unprofitable, unsustainable startups.
I truly don’t know how any of this works out.
I realize it seems a little much, but I genuinely believe that subscription-based AI services were an act of deception tantamount to fraud, as they misrepresented the core unit economics and thus the possibilities of a Large Language Model. By selling users a product on a monthly rate and creating habits based on its availability, companies like Anthropic and OpenAI have misrepresented their businesses in such a way that most of their users are interacting with products and building workflows based on products that are unsustainable and impossible to maintain in their current form.
Anthropic’s recent aggressive rate limit changes were instituted mere months after multiple aggressive marketing campaigns based on experiences that are now near-impossible under the current rate limits, and based on recent moves by Anthropic, it’s clear that it intends to start removing services for its lower-tier $20-a-month subscribers at some time in the future. This is a disgusting and misleading way to run a company, and the vagueness with which Anthropic discusses its products and its services are an insult to every one of their users, and a sign that it doesn’t fear the press in any meaningful way.
I need to be very clear that the product that Anthropic offers — by virtue of recent rate limit changes — is substantially different (and far worse) than the one that you read about everywhere. Anthropic was conscious in its deliberate attempts to market a product that it knew would be gone within three months. Dario Amodei doesn’t give a fuck as long as the media keeps writing up however many billions of annualized revenue he’s conjured up today or whatever new product he’s released that’s meant to destroy some hapless public SaaS company that had already seen its growth slow.
Members of the media, I say this with abundant respect: Anthropic is mistreating its customers, and it is doing so because it believes it can get away with it. This company does not respect you, and in fact holds you with a remarkable degree of contempt, which is why it doesn’t bother to fix its services very quickly or explain why they were broken with any level of coherence.
That’s why Anthropic fucking lied about Claude Mythos being too powerful to release (it was actually a capacity issue) when in fact it’s just another fucking Large Language Model Nothingburger — because it thinks you will buy whatever it is selling, and it’s worked out exactly how to package it to give you enough plausible “proof” that a quick skimread of the system card will make you and your editor believe whatever it is you’re saying.
They also know you’ll rush to cover it rather than waiting to see what actual experts say.
AI is a con, and this is how the con works. AI was rushed and pushed in our faces as quickly as humanly possible in the least-efficient yet most-accessible form it could be presented, even if said form was never going to result in anything resembling a sustainable business. The media was rushed to immediately say that this was the thing so that everybody would agree that this was the thing now and use it as much as physically possible, and, crucially, use it in a subscription-based form that would make people experience it without ever asking how much it costs to provide.
The narrative came pre-baked. Because very few people who talked about LLMs experienced their actual cost, it was really easy for them to vaguely say “it’s just like Uber,” because that was a company that lost a lot of money but didn’t die, and it’s much easier to say that than say “wait, what do you mean OpenAI is set to lose $5 billion this year?”
Think of it like this: as a reporter, an investor, an executive, or a regular LinkedIn Lounge Lizard, you might read here and there that it’s $5 per million input tokens and $25 per million output tokens, but you’ve never really experienced how fast or slow one loses that money, because it’s important to do so to truly understand this product. Anthropic and OpenAI intentionally obfuscated that experience and created businesses that expect to burn tens of billions of dollars in 2026 and several hundred billion dollars by 2030, all because most people graded generative AI based on the subscription-based experience.
LLMs are a casino, and you’ve been gambling with the house’s money while encouraging people to bet their own on whether they’ll get a unit of work out of a particular model.
This was intentional. They never wanted you thinking about the costs because once you really start thinking about the costs this whole thing feels a little insane. I truly believe that LLM-based subscriptions are going to go away entirely, at least at the scale of any product that generates code, and in doing so, Amodei and Altman will wrap up their con, or at least believe that they have.
The problem is that these men have now signed far too many deals to get away scot-free.
OpenAI’s CFO has now said multiple times that she doesn’t believe OpenAI is ready for IPO, and has material concerns about its growth and continued ability to meet its obligations. To repeat a quote from before:
Chief Financial Officer Sarah Friar has told other company leaders that she is worried the company might not be able to pay for future computing contracts if revenue doesn’t grow fast enough, according to people familiar with the matter.
This is a blinking red fucking light, and in a sane market would send Oracle’s stock into a tailspin, because OpenAI’s ascent to over $280 billion in annual revenue is critical to Oracle’s ability to not run out of money. In a sane media, this would send worrisome shockwaves through every group chat and Slack channel about whether or not OpenAI is actually going to make it.
This is the kind of thing that happens before a company starts dying. OpenAI’s growth is slowing at precisely the time it needs to accelerate. It needs to effectively 10x its current business by 2030 to make its obligations. OpenAI’s CFO, the literal person who would know best, is saying that she is worried that OpenAI cannot pay its fucking compute contracts if revenue doesn’t grow. This is a big, blinking warning light! This is not a drill!
Yet the bit that really worried me was the Journal’s comment that Friar didn’t think OpenAI “was ready to meet the rigorous reporting standards required of a public company.”
What the fuck does that mean? Excuse me? This company has allegedly raised $122 billion and is allegedly worth $852 billion god damn dollars and expects to burn $852 billion dollars by the end of 2030. Are its accounts not in order? What “rigorous reporting standards” can OpenAI not meet?
I’d generally not be so fucking nosy if it wasn’t for the fact that this company accounted for something like 20% of all venture capital funding in the last year and everywhere I go I have to hear the endless bloviating of Altman and Brockman and every other man at OpenAI and their fucking ideas about what regular people should do as they swan about shipping dogshit software and spending other people’s money.
For the amount of oxygen that both Anthropic and OpenAI consume, both of these companies should be fucking flawless, both as products and businesses. Instead, both are sold through varying levels of deception around their economics and efficacy, obfuscating the truth so that their Chief Executives can amass money and power and attention. It’s an insult to both good software and good taste — the most-expensive, least-reliable applications ever invented, their mistakes forgiven, their mediocrities celebrated, their infrastructure hailed as an inert god of capital.
Generative AI is an insult. It is unreliable, its economics don’t make sense, its outcomes don’t justify its existence, and the perpetrators of its con are boring, oafish and avaricious men disconnected from society and anybody who would ever disagree with them. It requires stealing art from everybody, destroying the environment, increasing our electricity bills, the constant threat of economic annihilation, the endless cacaphony of “everything fucking sucks now because of AI,” all to push software that can only be justified by people willing to ignore basic finance or sense.
It’s all so expensive, and it’s all so fucking dull. It’s offensively boring. It’s actively annoying. Every story where somebody tells you about how much they use AI sounds like they’re in an abusive relationship and/or joined a cult, echoing with a subtle desperation that says “you really need to join me in this because it’s so good, and the fact that I appear to be experiencing no joy from this product is just a sign of how efficient it is.” There is nothing light-hearted or joyful about what AI can do. There is nothing goofy or whimsical about a Large Language Model, and every interaction feels hollow.
Those who desperately look for clues that it’s becoming sentient or “more powerful” are simply seeking validation themselves — they want to be first to something, because arriving at other people’s conclusion is what they do for a living.
Being “first” — on the “frontier” one might say — is something that people crave when they can’t find something within, and it’s exactly the fuel that grifters crave, because LLMs are constantly humming with the sense that they’re about to do something new, even though they’re mathematically restricted to repeating other actions.
This is a deeply sad era. The people that have so aggressively worked together to hold up this industry have only delayed its inevitable fall. It’s terrifying to me that our markets and parts of our economy are being held up by the generally-held yet utterly-unproven assumption that LLMs will somehow get cheaper, that AI startups will magically become profitable, and that offering AI compute will be profitable in perpetuity to the point that it necessitates increasing the current supply tenfold by the year 2030.
People have debased themselves to defend the AI industry, because that’s what the industry demands of its supplicants. To be an “AI expert” requires you to actively ignore the worst economics of any industry in history, to constantly explain away obvious, glaring issues with products, and to actively convince others to do the same. OpenAI and Anthropic do not provide clear explanations of how they’ll become profitable because they know that their supporters will never ask for them — because the only way to fully “believe in AI” is to actively wear blinders.
And I get it. If you accept that OpenAI and/or Anthropic will eventually collapse, all of this seems a little insane. I am genuinely asking you to seriously consider that one or both of these companies will run out of money.
I’m really worried, made only more so by the general lack of concern I’m seeing in the media and greater society.
The assumption, if I had to imagine, is that I’m simply being alarmist, and that “the demand will absolutely be there.”
You’d better hope you’re right.
For Larry Ellison’s sake, at least. Ellison has already pledged 346 million shares of his Oracle stock — or around $61.5 billion — “to secure certain personal indebtedness, including various lines of credit,” meaning “many big, beautiful loans against his Oracle shares.” which IFR estimated back in September (when Oracle’s stock price was much higher) could allow him to secure as much as $21.4 billion in debt at a (they say “conservative”) loan-to-value ratio of 20%, and that’s assuming the banks weren’t particularly generous.
If OpenAI can’t raise $852 billion in revenue and funding by the end of 2030, it won’t be able to pay for Stargate. That’ll kill the value of Oracle’s stock, leading to a series of margin calls, leading to Ellison having to sell shares, leading to further margin calls. Whatever bailout might or might not exist won’t save Larry’s estate.
What I’m saying is that Ellison’s future rides on Sam Altman’s ability to raise funding and make revenue to the tune of $852 billion in the space of 4 years.
Good luck, Larry! You’re going to need it.
2026-04-29 00:33:46
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Yesterday morning, GitHub Copilot users got confirmation of something I’d reported a week ago — that all GitHub Copilot plans would move to usage-based pricing on June 1, 2026.
Instead of offering users a certain number of “requests,” Microsoft will now charge users based on the actual cost of the models they’re using, which it calls “...an important step toward a sustainable, reliable Copilot business and experience for all users.” Users instead get however much they spend on their GitHub Copilot subscription (EG: $19 of tokens a month on a $19-a-month plan).
Translation: "we cannot continue to subsidize GitHub Copilot users, or Amy Hood will start hitting people with a baseball bat."
Anyway, the announcement itself was a fascinating preview into how these price changes are going to get framed:
Copilot is not the same product it was a year ago.
It has evolved from an in-editor assistant into an agentic platform capable of running long, multi-step coding sessions, using the latest models, and iterating across entire repositories. Agentic usage is becoming the default, and it brings significantly higher compute and inference demands.
Today, a quick chat question and a multi-hour autonomous coding session can cost the user the same amount. GitHub has absorbed much of the escalating inference cost behind that usage, but the current premium request model is no longer sustainable.
Usage-based billing fixes that. It better aligns pricing with actual usage, helps us maintain long-term service reliability, and reduces the need to gate heavy users.
You see, it’s not that Microsoft was subsidizing nearly two million people’s compute, it’s that AI has become so strong, powerful and complex that it’s basically a different product!
While Copilot might not be “...the same product it was a year ago,” very little has changed about the underlying economic mismatch: that Microsoft was allowing users to burn more than their subscription costs in tokens every single month for three years. Per the Wall Street Journal in October 2023:
Individuals pay $10 a month for the AI assistant. In the first few months of this year, the company was losing on average more than $20 a month per user, according to a person familiar with the figures, who said some users were costing the company as much as $80 a month.
Naturally, GitHub Copilot users are in revolt, saying that the product is “dead” and “completely ruined.”
And I called it two years ago in the Subprime AI Crisis:
I hypothesize a kind of subprime AI crisis is brewing, where almost the entire tech industry has bought in on a technology sold at a vastly-discounted rate, heavily-centralized and subsidized by big tech. At some point, the incredible, toxic burn-rate of generative AI is going to catch up with them, which in turn will lead to price increases, or companies releasing new products and features with wildly onerous rates — like the egregious $2-a-conversation rate for Salesforce’s “Agentforce” product — that will make even stalwart enterprise customers with budget to burn unable to justify the expense.
And that day has finally arrived, because every single AI service you use subsidized compute, and every single service is losing money as a result:
When you pay for access to an AI startup’s service — which, of course, includes OpenAI and Anthropic — you do so for a monthly fee, such as $20, $100 or $200-a-month in the case of Anthropic’s Claude, Perplexity’s $20 or $200-a-month plan, or OpenAI’s $8, $20, or $200-a-month subscriptions. In some enterprise use cases, you’re given “credits” for certain units of work, such as how Lovable allows users “100 monthly credits” in its $25-a-month subscription, as well as $25 (until the end of Q1 2026) of cloud hosting, with rollovers of credits between months.
When you use these services, the company in question then pays for access to the AI models in question, either at a per-million-token rate to an AI lab, or (in the case of Anthropic and OpenAI) whatever cloud provider is renting them the GPUs to run the models. A token is basically ¾ of a word.
As a user, you do not experience token burn, just the process of inputs and outputs. AI labs obfuscate the cost of services by using “tokens” or “messages” or 5-hour-rate limits with percentage gauges, and you, as the user, do not really know how much any of it costs. On the back end, AI startups are annihilating cash, with up until recently Anthropic allowing you to burn upwards of $8 in compute for every dollar of your subscription. OpenAI allows you to do the same, though it’s hard to gauge by how much.
AI startups and hyperscalers assumed that they’d be able to get enough people through the door with subsidized, loss-making products to get them hooked on services badly enough that they’d refuse to change once businesses jacked up the prices. They also assumed, I imagine, that the cost of tokens would come down over time, versus what actually happened — while prices for some models might have come down, newer “reasoning” models burn way more tokens, which means the cost of inference has, somehow, gotten higher over time.
Both assumptions were wrong, because the monthly subscription model does not make sense for any service connected to a Large Language Model.
Think of it like this. When Uber (and no, this is nothing like Uber) started jacking up the prices for its rides, the underlying economics stayed the same, as did those presented to both the rider and the driver — a user paid for a ride, a driver was paid for a ride. Drivers still paid for gas, car insurance, any permits that their local government might insist upon, and whatever financing costs might be associated with their vehicle, and said costs were not subsidized by Uber. Uber’s massive losses came from subsidies, endless marketing expenses, and doomed R&D efforts into things like driverless cars .
To illustrate the scale of AI’s pricing mismatch, I’m going to ask you to imagine an alternate history where Uber had a very different business model.
Generative AI subscriptions are like if Uber charged users $20 a month for 100 rides of any distance under 100 miles, and if gas was $150 a gallon, and Uber paid for the gas because somebody insisted that oil would one day be too cheap to meter.
Uber would, eventually, decide to start charging users a monthly subscription to access rides, and bill them for the gas that they consumed. Suddenly users would go from paying $20 a month for 100 rides to paying $20 to access a driver and $26 for a 10 mile drive. Understandably, users would be a little upset.
While this sounds a little dramatic, it’s actually a pretty accurate metaphor for what’s happening in the generative AI industry, and in particular, at Github Copilot.
GitHub Copilot’s previous pricing allowed 300 premium requests a month, as well as “unlimited chat requests” using models like GPT-5 mini. Each of these requests (to quote Microsoft) is “...any interaction where you ask Copilot to do something for you,” with more-expensive models taking up more requests in the later life of the request-based system, such as Claude Opus 4.6 taking up three premium requests. When you ran out of premium requests, Copilot would let you use one of those cheaper models as much as you’d like for the rest of the month.
This wasn’t even always the case. Up until May 2025, Microsoft gave users unlimited access to models, and even then users were pissed off that there were any restrictions on the product.
Microsoft — like every AI company — swindled its customers by selling an unsustainable service, because it never, ever made sense to sell LLM-powered services on a monthly subscription.
If you’re wondering how much services are likely to cost under token based billing, a user on the GitHub Copilot Subreddit found that the token burn of what used to be a single premium request was somewhere around $11, as one “request” involved using 60,000 tokens in the context window, a few tools, and a bunch of internal “turns” (things that the model is doing) to produce the output.
There’s also the underlying unreliability of hallucination-prone Large Language Models. While a premium request chasing its tail and spitting out half-broken code might be frustrating, that same fuckup is a lot less forgivable when you’re paying the costs yourself.
Users have also been trained to use the product in an entirely different manner to token-based billing, and I’d imagine many of them don’t even really realize how many “tokens” they burn or how many of them a particular task takes, something which changes based on whatever model you use.
This is absolutely nothing like Uber, and anyone telling you otherwise is attempting to rationalize bad behavior. Uber may have raised prices, but it didn’t have to dramatically change the underlying economics of the platform, nor did users have to entirely change how they used the product because Uber was suddenly charging them on a per-gallon basis.
There has never been — and never will be — an economically-feasible way to offer services powered by LLMs without charging the actual token burn of each user, and in the process of deceiving said users, these companies have created products with illusory benefits and questionable return on investment.
And that’s been blatantly obvious for years.
On an economic basis, a monthly subscription only makes sense with relatively static costs. A gym can sell memberships knowing roughly how much wear-and-tear equipment gets, how much classes cost to run, and how much things like electricity, staffing and water might cost over a given period of time.
A customer of Google Workspace — at least before AI — cost whatever the cost of accessing or storing documents were, as well as the ongoing costs of Google Docs and other services. The relatively low cost of digital storage (as well as the fact that, unlike LLMs, Google Workspace isn’t particularly computationally demanding) means that a particularly-heavy Google Drive user isn’t going to eat into the margin on their monthly subscription.
Conversely, an AI subscriber’s costs can vary wildly. One user might only use ChatGPT for the occasional search, while another might feed in reams of documents, or try and refactor a codebase, or try and use it to put together a PowerPoint presentation. And the provider — a model lab like OpenAI or Anthropic, or a startup like Cursor) — has no real way to control how a user might act other than making the product worse, such as instituting usage limits, reducing the size of the context window, pushing customers to smaller (and worse) models, or changing the pricing to dissuage users from making big GPU-heavy requests.
Yet these services intentionally hide the amount of tokens or how much a particular activity has cost, which means users don’t really know what a rate limit means, which means that every abrupt change to rate limits leaves customers desperately scrambling to work out how much actual work they can do using the service.
It’s an abusive, manipulative and deceitful way of doing business that only existed so that Anthropic, OpenAI, and other AI companies could grow their user bases, as the majority of AI users perceive its real or imagined benefits entirely through the lens of being able to burn anywhere from $8 to $13.50 for every dollar of their subscription in tokens.
This intentional act of deception had one goal: to make sure that the majority of people were never exposed to the true costs of generative AI. When The Atlantic writes a breathless screed about Claude Code being Anthropic’s “ChatGPT moment,” it does so based on a $20-a-month subscription rather than the underlying token burn that it cost for Anthropic to provide it, which in turn makes the writer forgive the “minor errors” that a model might make, or when it “gets stuck on more complicated programming tasks.”
Had the writer paid for her actual token burn, and had each of the times it got “stuck” resulted in $15 in token charges, I don’t think she’d be quite as forgiving of these fuckups.
Yet that’s all part of the scam.
It’s very, very important that nobody writing about AI in the mainstream media actually understands how much these services cost, and that any mainstream articles written about services like ChatGPT or Claude Code are written by people who have little or no idea how much each individual task might cost a user.
Remember: generative AI services are, for the most part, experimental products that do not function like any other modern software or hardware. One cannot just walk up to ChatGPT or Claude and start asking it to do work.
I mean, you can, but if you don’t prompt it right, understand how it works, or make a mistake in whatever you feed it, or if it just gets things wrong, it’ll spit out something you don’t like, which in turn means you’ll need to prompt it again. LLMs are inherently unpredictable.
You cannot guarantee whether an LLM will do a particular action, or whether it will present you with an outcome based on reality. You cannot for certain say how much a particular task — even one you’ve done many times using an LLM in the past — might cost, nor can you be sure when a model might go berserk and delete something, or simply not do something yet claim that it did.
These are far more forgivable if you’ve not paying on a per-token basis, because in the mind of a subscriber, that’s just another turn or two with a chatbot rather than something that’s incurring a real cost. One doesn’t criticize so-called “jagged intelligence” because the assumption is that whatever problems you’re facing now will be eliminated at some future juncture, and you didn’t end up paying for it anyway.
Had users been forced to pay their actual rates, I imagine many would’ve bounced off the product immediately, as it’s very, very easy to burn through $5 of tokens if you’re fucking around and exploring what an LLM can do.
Sidenote: In fact, you can burn a great deal of money without ever getting the outcome you desire, because LLMs aren’t really artificial intelligence at all! Somebody without any real understanding of their limitations could easily burn $30, or $50, or even $100 trying to convince an LLM to do something it insists it’s capable of.
There’s a term for this. Sycophancy. LLMs are often designed to affirm the user, even when they’re saying dangerously unhinged things, and that can extend to saying “you want this big thing that’s not even slightly feasible, whether technically or financially?” Sure thing!
This is why the industry worked so hard to obfuscate these costs — it’s a fucking ripoff!
I think it’s inevitable that the majority of AI subscriptions move to token-based billing, especially as both Anthropic and OpenAI have now done so with their enterprise customers.
The fact that Microsoft moved GitHub Copilot subscribers to token-based is also a very, very bad sign. Microsoft is arguably the best-capitalized, most-profitable, and best-positioned company to continue subsidizing compute, and if it can’t afford to do so further, nobody else can either.
The real thing to look out for — a true pale horse — will be a major AI lab like Anthropic or OpenAI moving all of its subscribers to token-based billing. Once that happens, you’ll know it’s closing time.
As I discussed last week, Uber’s CTO said at a conference that it had spent its entire AI budget for 2026 in the space of a few months, with Goldman Sachs suggesting that some companies are spending as much as 10% of their headcount on AI tokens, with the potential to increase to 100% in the next few quarters.
This is the direct result of training every single AI user to use these services as much as humanely possible while obfuscating how much they really cost. Every single major company demanding that every single worker “use AI as much as possible” has done so while either fundamentally ignoring or being entirely disconnected from their actual token burn, and as companies are forced to pay the actual costs, I’m not sure how you can economically justify any investment in this technology.
Sure, sure, you’re gonna say that engineers are “shipping code faster” or some such bullshit, I get it, but how much faster, and how much money are you making or saving as a result? If you’re spending 10% of your headcount on AI tokens, are you seeing that extra expense reconciled in some other way? Because I’m not sure you are. I’m not sure any business investing these vast amounts of money in tokens is seeing any return on investment, which is why every study about AI’s ROI struggles to find much evidence that it exists.
For the most part, everybody you’ve read gooning over the many possibilities of generative AI has experienced it without having to pay the true costs. Every Twitter psychopath writing endless screeds about their entire engineering team hammering away at Claude Code has been doing so using a $125-a-month-per-head Teams subscription with similar usage limits to Anthropic’s $100-a-month consumer subscription. Every LinkedIn gargoyle insisting that they’d “done hours of work in minutes” using some sort of Perplexity product has done so by paying, at the very most, $200 a month for Perplexity’s Max subscription.
In reality, that 10-person, $1250-a-month Teams subscription likely burns anywhere from $5000 to $10,000 a month in API calls, if not more. Anthropic Head of Growth Amol Avasare said last week that its Max subscriptions were built for heavy chat usage rather than whatever people are doing with Claude Code and Cowork, and made it clear that Anthropic is now looking at “different options to keep delivering a great experience,” which is another way of saying “we’re going to change the prices at some point.”
I’m not sure people realize how expensive these tokens are, especially for coding projects that involve massive codebases and regularly make calls to coding and infrastructure tools. Can somebody who pays $200 a month foreseeably afford $350, $400, or $500? Can they afford to have a month where they spend more than that? What happens if they go over budget, or if they literally can’t afford to spend the money necessary to finish their work?
To give you a more-practical example, up until the beginning of April, Anthropic’s own Claude Code developer documents (archive) said that “the average cost [for those using Claude Code] is $6 per developer per day, with daily costs remaining below $12 for 90% of users.” As of this week, the documents now read as follows:
Claude Code charges by API token consumption. For subscription plan pricing (Pro, Max, Team, Enterprise), see claude.com/pricing. Per-developer costs vary widely based on model selection, codebase size, and usage patterns such as running multiple instances or automation.
Across enterprise deployments, the average cost is around $13 per developer per active day and $150-250 per developer per month, with costs remaining below $30 per active day for 90% of users. To estimate spend for your own team, start with a small pilot group and use the tracking tools below to establish a baseline before wider rollout.
If we assume an average of 21 working days in a month, that puts the average cost of a Claude Code user at around $273 a month, or $3,276 a year. At $30 a working day, that works out to $630 a month, or $7560 a year.
These are astonishing numbers, made more so by the fact that there is no way you’re only spending $30 a day if you use any of Anthropic’s more-recent models. Claude Opus 4.7 costs $5 per million input and $25 per million output tokens. ‘One million tokens is around 50,000 lines of code, and assuming you’re using the supposedly state-of-the-art models, there isn’t a chance in Hell you don’t run through at least a million, with that number increasing dramatically if you’re not particularly aware of which models to use for a particular task.
Let’s play with that $30 number a little more.
While this might be possible within the slush fund mindset of a well-funded startup or a banana republic like Meta, any business that actually cares about its costs will have a great deal of trouble justifying spending five or six figures in extra costs on a service that “increases productivity” in a way that nobody can seem to measure.
Right now, I think most companies fall into three camps:
Large organizations still have a free pass to say that they’re burning millions of dollars on AI tokens for their software engineers under the questionable benefit of their “best engineers” not writing any code.
All it changes is one bad earnings call to change that narrative. At some point investors — even the braindead fuckwits who have been inflating the AI bubble — will begin to question mounting R&D costs (which is where AI token burn is usually hidden) when the company’s revenue growth fails to follow. This will likely lead to more layoffs to keep up with the cost, as was the case with Meta, and then an eventual pullback when somebody asks “does any of this shit actually help us do our jobs faster or better?”
I also think that startups burning 10% or more of their headcount in AI tokens will have a tough time convincing investors in six months that doing so is necessary.
And once everybody switches to token-based billing, I’m not sure we’ll see quite as much hype around generative AI.
The way that people talk about AI data centers is completely disconnected from reality, and I don’t think people realize how ridiculous this entire era has become.
Per Jerome Darling of TD Cowen, it costs around $30 million in critical IT (GPUs and the associated hardware) and $14 million per megawatt of data center capacity. Data centers appear to take anywhere from a year to three years depending on the size, and that’s assuming the power is available.
Of the 114GW of data centers supposedly being built by the end of 2028, only 15.2GW is under construction in any way, shape, or form. And “under construction” can mean as little as “there’s a hole in the ground.” It does not — and should not — imply that the capacity that said facility will provide is going to be imminently available.
Sidebar: If you’re interested in some of the deeper math here, please subscribe to my premium newsletter so that you can see my Bastard Data Center Model, which I created with the assistance of multiple analysts and hyperscaler sources.
Let’s start simple: whenever you think “100MW,” think “$4.4 billion,” with a large chunk of that dedicated to NVIDIA GPUs.
As a result, every AI data center starts millions of dollars in the hole, and even with six-year-long depreciation schedules, takes years to pay off… and with NVIDIA’s yearly upgrade cycle, those GPUs are unlikely to make that much money once you’re done with your first customer contract.
It’s also unclear whether the customer base for AI compute exists outside of OpenAI and Anthropic, whose demand accounts for 50% of AI data centers under construction, creating a massive systemic weakness if either of them lacks the money to pay.
In any case, it’s also unclear what kind of ongoing rates these data centers charge. While spot prices might sit around $4.50 an hour for a B200 GPU, long-term contracts generally price much lower, with one founder (per The Information) saying they paid around $3.70-per-hour-per GPU for a one-year-long commitment.
To be clear, we must differentiate between the spot cost — which is the cost of randomly spinning up GPUs on somebody else’s servers — and contracted compute, the latter of which makes up the majority of data center capex. Most data centers are built with the intention of having one or two big clients, which means said clients are likely to negotiate a cheaper blended rate.
As a result, many data centers take far less than $3.70 an hour, because they bill at a per-megawatt (or kilowatt) price.
And that’s where the economics begin to break down.
That’s the starting cost for a 100 megawatt data center. A 100MW data center will likely only have 85MW of actual stuff it can bill for, and based on discussions with sources familiar with hyperscaler billing, they can expect to make around $12.5 million per megawatt, or around $1.063 billion in annual revenue.
Now, I should be clear that most data center companies you know of don’t actually build them, instead leaving that job to companies like Applied Digital, who are also known as “colocation partners.” For example, CoreWeave pays a colocation fee to Applied Digital to use its North Dakota data centers. CoreWeave is responsible for all the GPUs and other tech inside the data center.
To explain the economic mismatch, I’m going to use a theoretical example of a data center leased to a theoretical AI compute company.
The GPUs in that data center are likely NVIDIA’s Blackwell chips. More than likely, said data center is using pods of 8 B200 GPUs, retailing around $450,000 a piece, or $56,250 a GPU. Based on there being 85MW of Critical IT load, the all-in capex per megawatt is around $36.78, or total IT capex of around $3.126 billion, or around $2.67 billion in GPUs.
Let’s assume this data center is in Ellendale, North Dakota, which means you’ve got an industrial electricity rate of around 6.31 cents per kilowatt hour, which works out to about $55.4 million a year in electricity costs. Based on discussions with sources, I estimate that ongoing costs like maintenance, headcount, replacement of power supplies and the like comes in at around 12% of revenue, or around $128 million a year, bringing us up to $183.4 million in costs.
Wait, sorry. You’ve also got to pay a colocation fee based on the critical IT, and according to Brightlio, that fee is often around $180-200 per kilowatt per month, depending on the scale and location of the deployment, though I’ve read as low as $130, which is the number I’m going with, or around $133 million per year. This brings us up to $316.4 million.
Well, that’s still less than $1.06 billion, so we’re still doing okay, right?
Wrong! You’ve got $3.126 billion in IT gear to depreciate, which works out to around $521 million a year over the six years you’re depreciating it. That’s $837.4 million a year, leaving you with around $168.6 million in yearly profit, or around a 16.7% gross margin…
…if you have 100% tenancy at all times! You see, data centers can take a month or two to get those GPUs installed and a customer onboarded, all while making you exactly zero dollars in revenue and losing you a great deal more, as you’re stuck paying your colocation, electricity and opex costs the whole time, albeit at a much lower rate (I’ve modeled for 10% electricity and 15% colocation/opex costs), meaning you’re losing about $3.27 million a day.
For the sake of this example, we’re going to assume it takes you an extra month to get this thing operational, meaning you’ve paid about $102 million that you’re never getting back, bringing our total costs for the year including depreciation to $939.4 million, or a 6.6% gross margin.
Wait, fuck, you didn’t use debt to buy these GPUs, did you? You did? How bad are we talking? Oh god — you got a 6-year-long asset-backed loan at 80% LTV, meaning you borrowed $2.8 billion at a 6% interest rate.
Your bank, in its eternal generosity, offered you a deal — a 12-month-long grace period where you’re only paying interest…which works out to around $168 million, which brings our total costs (excluding the month of delay for fairness) for the first year to around $1.005 billion…on $1.06 billion of revenue.
That’s a 5.19% gross margin, and you haven’t even started paying the principal. When that happens, you’re paying $54.1 million a month in loan payments, for a total of around $649 million a year for five more years, which comes out to around $1.48 billion, or a negative gross margin of around 40%.
And I must be clear that this is if you have 100% utilization and a tenant that pays you on time, every time.
Let’s talk about what should be the single-most economically viable project in data center history — a massive campus built for the largest AI company in the world by Oracle, a decades-old near-hyperscaler with a history of selling expensive database and business management software to enterprises and governments.
Hah, I’m kidding of course, this place is a fucking nightmare.
Stargate Abilene, an eight-building, 1.2GW data center campus with around 824MW of critical IT, was first announced in July 2024. As of April 27 2026, only two buildings are operational and generating revenue, and the third barely has any IT gear in it. I estimate the total cost of Stargate Abilene to be around $52.8 billion.
Per my own reporting, Oracle expects to make around $10 billion in annual revenue from Stargate Abilene, and I estimate around $75 billion in total revenue from the 7.1GW of data center capacity it’s building for one customer: OpenAI. As I also reported, Oracle estimated in 2024 that Abilene would cost at least $2.14 billion a year in colocation and electricity fees, paid to land developer Crusoe.
I should also add that it appears that Oracle is paying all of Abilene’s construction costs.
Based on my calculations and reporting, I estimate Abilene’s rough gross margin is around 37.47% once it’s fully operational:
I must be clear that that 37.47% gross margin is likely too high, as I don’t have precise knowledge of Oracle’s true insurance or headcount costs, only estimates based on documents viewed by this publication. I should also be clear that Oracle is mortgaging its entire fucking future on projects like Stargate Abilene, incurring billions of dollars of costs up front for a business that will take years to turn a profit even if OpenAI makes every single payment in a timely manner.
Sadly, I can’t tell how much of Abilene was paid for in debt, only that Oracle raised around $18 billion in various-sized bonds in September 2025, with maturities ranging from 7 years to 40 years, and had negative cashflow of $24.7 billion in its last quarterly earnings.
What I do know is that it has a 15-year-long lease with developer Crusoe, and that Oracle’s future heavily depends on OpenAI’s continued ability to pay, which depends on Oracle’s ability to finish Stargate Abilene.
I also need to be clear that that $3.85 billion in yearly profit is only possible if OpenAI makes timely payments, takes tenancy of Abilene as fast as humanly possible, and everything goes to plan.
Sadly, the complete opposite has happened:
Based on reporting from DatacenterDynamics, the first 200MW of power was meant to be energized “in 2025.” As time dragged on, occupancy was meant to begin in the first half of 2025, had “potential to reach 1GW by 2025,” complete all 1.2GW of capacity by mid-2026, be energized by mid-2026, have 64,000 GPUs by the end of 2026, as of September 30, 2025 had “two buildings live,” and as of December 12, 2025, Oracle co-CEO Clay Magouyurk said that Abilene was “on track” with “more than 96,000 NVIDIA Grace Blackwell GB200 delivered,” otherwise known as two buildings’ worth of GPUs.
Four months later on April 22, 2026, Oracle tweeted that “...in Abilene, 200MW is already operational, and delivery of the eight-building campus remains on schedule.” It is unclear if that’s 200MW of critical IT capacity or the total available power at the Abilene campus, and in any case, this is only enough power for two buildings, which means that Oracle is most decidedly not “on schedule.”
This is a huge issue. OpenAI can only pay for compute that actually exists, and only 206MW of critical IT is actually generating revenue, with the third at least a month (if not a quarter) away from doing so.
Yet there’s a larger, more-existential problem with the overall Stargate data center project — that the only way any of it makes sense is if OpenAI meets its ridiculous, cartoonish projections.
I’ll repeat the numbers: the 7.1GW of Stargate data centers in progress will make around $75 billion in annual revenue on completion, and cost more than $340 billion in total. Oracle’s free cash flow was negative $24.7 billion, and its other business lines are plateauing, making its negative-to-low margin cloud business its only growth engine.
For OpenAI to actually be able to pay its compute deals — both to partners like Amazon, Microsoft, CoreWeave, Google, Cerberas, and to Oracle — it will have to raise or make $852 billion in revenue and/or funding in the space of four years, which would require its business to grow by more than 250%, every single year, effectively 10xing by the end of 2030, at which point it will have had to find a way to become cashflow positive for any of these numbers to make sense.
To be clear, OpenAI’s projections have it making $673 billion over the next four years, and burning $218 billion to get there. It is an incredibly unprofitable business, and even if it wasn’t, it would have to make so much more money than it currently does to pay Oracle on an ongoing basis.
I calculated that $75 billion number by assuming that Vera Rubin GPUs get around $14 million per megawatt of compute (a number I’ve confirmed with sources familiar with the data center industry) across the remaining 4.64GW of critical IT that I anticipate makes up the remaining Stargate data centers.
OpenAI’s numbers come directly from The Information’s reported leaks of OpenAI’s projected burn rate and revenues, which have the company making $673 billion in revenue through the end of 2030 and burning $852 billion to get there:
I must be clear that any journalist repeating these numbers without saying how fucking stupid they are should be a little ashamed of themselves. Per my Friday premium:
In other words, in two years OpenAI projects it will make more revenue than TSMC, in three years almost as much annual revenue as Meta, and by the end of 2030, as much annual revenue as Microsoft ($300 billion or so on a trailing 12 month basis).
And if OpenAI can’t pay for that compute, Oracle dies, because it’s taken on around $115 billion in debt just to build Stargate’s data centers, and needs another $150 billion to finish them:
Oracle is a company that currently makes around $64 billion in annual revenue, and had free cash flow of negative $24.7 billion in its last quarter. It raised $18 billion in bonds in September 2025, $25 billion in bonds in February 2026, it did a $20 billion at-the-market share sale sometime in March, and despite it being called “closed” for months, only appears to have recently closed its $38 billion in project financing for Stargate Wisconsin and Shackelford. I’m also including the $14 billion in data center debt related to Stargate Michigan.
Either way, Oracle is insufficiently-capitalized to finish Stargate Abilene. It will need at least another $150 billion to get this done, and that’s assuming that other partners pick up about $30 billion in costs. Honestly, it may be more.
I really need to be clear that Oracle has no other path to making this revenue without OpenAI, and these projects are entirely financed and paid for using the projected cashflow of the data centers themselves.
And I’m not even the only one worried about this, with OpenAI Sarah Friar sharing similar concerns after the company missed user and revenue targets, per the Wall Street Journal:
OpenAI recently missed its own targets for new users and revenue, stumbles that have raised concern among some company leaders about whether it will be able to support its massive spending on data centers.
Chief Financial Officer Sarah Friar has told other company leaders that she is worried the company might not be able to pay for future computing contracts if revenue doesn’t grow fast enough, according to people familiar with the matter.
Board directors have also more closely examined the company’s data-center deals in recent months and questioned Chief Executive Sam Altman’s efforts to secure even more computing power despite the business slowdown, the people said.
If that doesn’t worry you, perhaps this will:
She has emphasized to executives and board directors the need for OpenAI to improve its internal controls, cautioning that the company isn’t yet ready to meet the rigorous reporting standards required of a public company. Altman has favored a more aggressive timeline for an IPO, some of the people said.
That sure sounds like a company that’s gonna be able to make $852 billion by the end of the decade!
While I regularly bag on OpenAI for its ridiculous promises, Anthropic isn’t far behind, promising to take “up to” 5GW of capacity from both Google and Amazon in a deal that I estimate includes around $100 billion in actual compute commitments given the scale of the capacity.
Now, I should add that Google and Amazon are far-savvier and less-desperate than Oracle, meaning that they can take the hit if Anthropic ends up running out of money. The “up to” part of that deal gives them some much-needed wiggle room that Oracle simply does not have.
Nevertheless, for Anthropic to actually meet its commitments, it will have to agree to spend anywhere from $25 billion to $100 billion a year on compute by the end of 2030.
Anthropic’s CFO said in March that it had made $5 billion in revenue in its entire existence.
The near-pornographic excitement around however many hundreds of billions of dollars of GPUs that Jensen Huang claims to be shifting regularly clouds out a problematic question: sell the compute to who, Jensen?
If we assume that the 15.2GW of data center capacity under construction (due by the end of 2028) has a PUE of around 1.35, that leaves us with roughly 11.2GW of critical IT. At $14 million per megawatt, that works out to around $156.8 billion in annual revenue in GPU rental revenue required to actually make these data centers worth it.
When you calculate for the 114GW of capacity theoretically coming online by the end of 2028, that number climbs to $1.18 trillion in annual revenue.
To give you some context, CoreWeave — the largest neocloud with Meta, OpenAI, Google (for OpenAI), Microsoft (for OpenAI), Anthropic, and NVIDIA as customers — made around $5.1 billion in revenue, and projected it would make $12 billion to $13 billion in 2026.
Who, exactly, is the customer for all this compute, and how likely is it that they’ll want to buy it by the time all that capacity is built? While many different data centers claim to have tenants for the first few years of their existence, said tenants can only start paying once the data center completes, and if it’s an AI startup, I think it’s fairly reasonable to ask whether they exist by the time it’s built.
Remember: the customers of AI compute are, for the most part, either hyperscalers trying to move capital expenditures off their balance sheets or unprofitable AI startups. Anthropic and OpenAI both intend to burn tens of billions of dollars in the next few years, and neither of them have a path to profitability.
This means that a large part — if not the majority of — AI compute revenue is dependent on a continual flow of venture capital and debt, both of which are only made possible by investors that still believe that generative AI will be the biggest, most hugest thing in the world.
How does that work out, exactly? Who is paying for this data center capacity? Who is it for? Where is the actual demand?
And if said demand exists, how the fuck do the customers even pay?
Despite multiple stories that have both of them becoming profitable by 2028 or 2029, nobody can explain to me how OpenAI or Anthropic actually reach profitability, especially given that both of them had worse margins than expected, even when said margins strip out training costs that number in the billions of dollars.
And I’ve been asking this question for fucking years. Every time we get a new update on Anthropic or OpenAI, we hear they’ve lost billions more dollars than expected, that margins are decaying, that costs are skyrocketing, that everything is more expensive despite promises that the literal opposite would happen.
Even Cursor, a company that briefly (before its pseudo-acquisition by Musk’s SpaceX) claimed it had positive gross margins, actually had negative 23% gross margins as of January, or negative 31% if you include the cost of non-paying users, which you fucking should if you actually care about your accounting. Mysteriously, reports claim that Cursor’s margins “recently turned positive,” but magically don’t know by how much, or how that happened, or a single other detail other than one that likely helped the company get sold.
I also don’t see how any of these AI data centers actually make sense, even if they have customers to pay them for the first few years. The economics are built for perfection, with zero fault tolerance. They must have consistent, 100% utilization and tenancy, or they end up burning millions of dollars and fail to chip away at the years-long wall of depreciation created by the tech industry’s most expensive mistake.
Even if they somehow succeed, these are pathetic businesses with mediocre margins — 70% at best, assuming consistent payments, tenancy, and six fucking years of depreciation to actually break even, which might be difficult considering the yearly upgrade cycle makes the entire thing near-obsolete by the time you’re done paying it off.
And that’s before you consider that the majority of the customers are unprofitable, unsustainable startups.
I truly don’t know how any of this works out.
I realize it seems a little much, but I genuinely believe that subscription-based AI services were an act of deception tantamount to fraud, as they misrepresented the core unit economics and thus the possibilities of a Large Language Model. By selling users a product on a monthly rate and creating habits based on its availability, companies like Anthropic and OpenAI have misrepresented their businesses in such a way that most of their users are interacting with products and building workflows based on products that are unsustainable and impossible to maintain in their current form.
Anthropic’s recent aggressive rate limit changes were instituted mere months after multiple aggressive marketing campaigns based on experiences that are now near-impossible under the current rate limits, and based on recent moves by Anthropic, it’s clear that it intends to start removing services for its lower-tier $20-a-month subscribers at some time in the future. This is a disgusting and misleading way to run a company, and the vagueness with which Anthropic discusses its products and its services are an insult to every one of their users, and a sign that it doesn’t fear the press in any meaningful way.
I need to be very clear that the product that Anthropic offers — by virtue of recent rate limit changes — is substantially different (and far worse) than the one that you read about everywhere. Anthropic was conscious in its deliberate attempts to market a product that it knew would be gone within three months. Dario Amodei doesn’t give a fuck as long as the media keeps writing up however many billions of annualized revenue he’s conjured up today or whatever new product he’s released that’s meant to destroy some hapless public SaaS company that had already seen its growth slow.
Members of the media, I say this with abundant respect: Anthropic is mistreating its customers, and it is doing so because it believes it can get away with it. This company does not respect you, and in fact holds you with a remarkable degree of contempt, which is why it doesn’t bother to fix its services very quickly or explain why they were broken with any level of coherence.
That’s why Anthropic fucking lied about Claude Mythos being too powerful to release (it was actually a capacity issue) when in fact it’s just another fucking Large Language Model Nothingburger — because it thinks you will buy whatever it is selling, and it’s worked out exactly how to package it to give you enough plausible “proof” that a quick skimread of the system card will make you and your editor believe whatever it is you’re saying.
They also know you’ll rush to cover it rather than waiting to see what actual experts say.
AI is a con, and this is how the con works. AI was rushed and pushed in our faces as quickly as humanly possible in the least-efficient yet most-accessible form it could be presented, even if said form was never going to result in anything resembling a sustainable business. The media was rushed to immediately say that this was the thing so that everybody would agree that this was the thing now and use it as much as physically possible, and, crucially, use it in a subscription-based form that would make people experience it without ever asking how much it costs to provide.
The narrative came pre-baked. Because very few people who talked about LLMs experienced their actual cost, it was really easy for them to vaguely say “it’s just like Uber,” because that was a company that lost a lot of money but didn’t die, and it’s much easier to say that than say “wait, what do you mean OpenAI is set to lose $5 billion this year?”
Think of it like this: as a reporter, an investor, an executive, or a regular LinkedIn Lounge Lizard, you might read here and there that it’s $5 per million input tokens and $25 per million output tokens, but you’ve never really experienced how fast or slow one loses that money, because it’s important to do so to truly understand this product. Anthropic and OpenAI intentionally obfuscated that experience and created businesses that expect to burn tens of billions of dollars in 2026 and several hundred billion dollars by 2030, all because most people graded generative AI based on the subscription-based experience.
LLMs are a casino, and you’ve been gambling with the house’s money while encouraging people to bet their own on whether they’ll get a unit of work out of a particular model.
This was intentional. They never wanted you thinking about the costs because once you really start thinking about the costs this whole thing feels a little insane. I truly believe that LLM-based subscriptions are going to go away entirely, at least at the scale of any product that generates code, and in doing so, Amodei and Altman will wrap up their con, or at least believe that they have.
The problem is that these men have now signed far too many deals to get away scot-free.
OpenAI’s CFO has now said multiple times that she doesn’t believe OpenAI is ready for IPO, and has material concerns about its growth and continued ability to meet its obligations. To repeat a quote from before:
Chief Financial Officer Sarah Friar has told other company leaders that she is worried the company might not be able to pay for future computing contracts if revenue doesn’t grow fast enough, according to people familiar with the matter.
This is a blinking red fucking light, and in a sane market would send Oracle’s stock into a tailspin, because OpenAI’s ascent to over $280 billion in annual revenue is critical to Oracle’s ability to not run out of money. In a sane media, this would send worrisome shockwaves through every group chat and Slack channel about whether or not OpenAI is actually going to make it.
This is the kind of thing that happens before a company starts dying. OpenAI’s growth is slowing at precisely the time it needs to accelerate. It needs to effectively 10x its current business by 2030 to make its obligations. OpenAI’s CFO, the literal person who would know best, is saying that she is worried that OpenAI cannot pay its fucking compute contracts if revenue doesn’t grow. This is a big, blinking warning light! This is not a drill!
Yet the bit that really worried me was the Journal’s comment that Friar didn’t think OpenAI “was ready to meet the rigorous reporting standards required of a public company.”
What the fuck does that mean? Excuse me? This company has allegedly raised $122 billion and is allegedly worth $852 billion god damn dollars and expects to burn $852 billion dollars by the end of 2030. Are its accounts not in order? What “rigorous reporting standards” can OpenAI not meet?
I’d generally not be so fucking nosy if it wasn’t for the fact that this company accounted for something like 20% of all venture capital funding in the last year and everywhere I go I have to hear the endless bloviating of Altman and Brockman and every other man at OpenAI and their fucking ideas about what regular people should do as they swan about shipping dogshit software and spending other people’s money.
For the amount of oxygen that both Anthropic and OpenAI consume, both of these companies should be fucking flawless, both as products and businesses. Instead, both are sold through varying levels of deception around their economics and efficacy, obfuscating the truth so that their Chief Executives can amass money and power and attention. It’s an insult to both good software and good taste — the most-expensive, least-reliable applications ever invented, their mistakes forgiven, their mediocrities celebrated, their infrastructure hailed as an inert god of capital.
Generative AI is an insult. It is unreliable, its economics don’t make sense, its outcomes don’t justify its existence, and the perpetrators of its con are boring, oafish and avaricious men disconnected from society and anybody who would ever disagree with them. It requires stealing art from everybody, destroying the environment, increasing our electricity bills, the constant threat of economic annihilation, the endless cacaphony of “everything fucking sucks now because of AI,” all to push software that can only be justified by people willing to ignore basic finance or sense.
It’s all so expensive, and it’s all so fucking dull. It’s offensively boring. It’s actively annoying. Every story where somebody tells you about how much they use AI sounds like they’re in an abusive relationship and/or joined a cult, echoing with a subtle desperation that says “you really need to join me in this because it’s so good, and the fact that I appear to be experiencing no joy from this product is just a sign of how efficient it is.” There is nothing light-hearted or joyful about what AI can do. There is nothing goofy or whimsical about a Large Language Model, and every interaction feels hollow.
Those who desperately look for clues that it’s becoming sentient or “more powerful” are simply seeking validation themselves — they want to be first to something, because arriving at other people’s conclusion is what they do for a living.
Being “first” — on the “frontier” one might say — is something that people crave when they can’t find something within, and it’s exactly the fuel that grifters crave, because LLMs are constantly humming with the sense that they’re about to do something new, even though they’re mathematically restricted to repeating other actions.
This is a deeply sad era. The people that have so aggressively worked together to hold up this industry have only delayed its inevitable fall. It’s terrifying to me that our markets and parts of our economy are being held up by the generally-held yet utterly-unproven assumption that LLMs will somehow get cheaper, that AI startups will magically become profitable, and that offering AI compute will be profitable in perpetuity to the point that it necessitates increasing the current supply tenfold by the year 2030.
People have debased themselves to defend the AI industry, because that’s what the industry demands of its supplicants. To be an “AI expert” requires you to actively ignore the worst economics of any industry in history, to constantly explain away obvious, glaring issues with products, and to actively convince others to do the same. OpenAI and Anthropic do not provide clear explanations of how they’ll become profitable because they know that their supporters will never ask for them — because the only way to fully “believe in AI” is to actively wear blinders.
And I get it. If you accept that OpenAI and/or Anthropic will eventually collapse, all of this seems a little insane. I am genuinely asking you to seriously consider that one or both of these companies will run out of money.
I’m really worried, made only more so by the general lack of concern I’m seeing in the media and greater society.
The assumption, if I had to imagine, is that I’m simply being alarmist, and that “the demand will absolutely be there.”
You’d better hope you’re right.
For Larry Ellison’s sake, at least. Ellison has already pledged 346 million shares of his Oracle stock — or around $61.5 billion — “to secure certain personal indebtedness, including various lines of credit,” meaning “many big, beautiful loans against his Oracle shares.” which IFR estimated back in September (when Oracle’s stock price was much higher) could allow him to secure as much as $21.4 billion in debt at a (they say “conservative”) loan-to-value ratio of 20%, and that’s assuming the banks weren’t particularly generous.
If OpenAI can’t raise $852 billion in revenue and funding by the end of 2030, it won’t be able to pay for Stargate. That’ll kill the value of Oracle’s stock, leading to a series of margin calls, leading to Ellison having to sell shares, leading to further margin calls. Whatever bailout might or might not exist won’t save Larry’s estate.
What I’m saying is that Ellison’s future rides on Sam Altman’s ability to raise funding and make revenue to the tune of $852 billion in the space of 4 years.
Good luck, Larry! You’re going to need it.
2026-04-25 00:40:45
Soundtrack — Brass Against — Karma Police
It was January 21, 2025. Per The Information, Larry Ellison, CEO of Oracle, had just flown to Washington DC from Florida, and had to borrow a coat “...so he wouldn’t freeze during an interview he did on the White House lawn, according to two people who were involved in the event.” He was there to announce a very big — some might even say huge — new project standing next to SoftBank CEO Masayoshi Son and OpenAI CEO Sam Altman.
“Together, these world-leading technology giants are announcing the formation of Stargate, so put that name down in your books, because I think you’re gonna hear a lot about it in the future. A new American company that will invest $500 billion at least in AI infrastructure in the United States and very, very quickly, moving very rapidly, creating over 100,000 American jobs almost immediately,” said President Donald Trump.
After he was done, Ellison stepped to the podium. “The data centers are actually under construction, the first of them are under construction in Texas. Each building’s a half a million square feet, there are ten buildings currently being built, but that will expand to 20.”
Following Ellison, SoftBank’s Masayoshi Son added that Stargate would “...immediately start deploying $100 billion dollars, with the goal of making $500 billion dollars within [the] next four years, within your town!” turning to Donald Trump with his hands extended. It was unclear what town he was referring to.
Altman added that it would be “an exciting project” and that “...we’ll be able to do all the wonderful things that these guys talked about, but the fact that we get to do this in the United States is I think wonderful,” though it’s unclear what “the wonderful things” or “this” refers to.
It’s been 15 months, and Stargate LLC has never been formed. SoftBank and OpenAI have contributed no capital to the project, other than SoftBank’s own acquisition of a former electric vehicle manufacturing plant in Lordstown, Ohio that it intends to turn into a data center parts manufacturing plant with Foxconn, which is best known for effectively abandoning a $10 billion factory in Wisconsin back in 2021. Oh, and Project Freebird, a SoftBank-built project that exists to funnel money to its subsidiary SB Energy, though I can’t imagine how SoftBank actually funds it.
No government money was ever involved, no funding ever left anyone’s bank account, no "initiative" ever existed, and OpenAI, Oracle and SoftBank have, in my opinion, conspired to mislead the general public about the existence and validity of a project for marketing purposes.
The “data centers actually under construction” referred to a 1.2GW project in Abilene Texas that had been under construction since the middle of 2024, and had originally been earmarked by Elon Musk and xAI, except Musk pulled out because he felt that Oracle was moving too slow. While Ellison said that there were ten buildings under construction with plans to expand to twenty, only eight were actually being built (each holding around 50,000 GB200 GPUs across NVL72 racks), with the extension up in the air until March 2026, when Microsoft agreed to lease 700MW — so another seven buildings — that were meant to go to OpenAI. These buildings will not make Oracle any money, as Oracle is, despite spending so much money, leasing whatever land it uses from Crusoe.
Sidenote: Previously-unknown information from the Wall Street Journal published this week shows that the reason why Microsoft ended up buying the additional capacity at Abilene was because lenders were uncomfortable with providing additional funding to provide compute that was ultimately destined to go to Oracle.
As far as those eight buildings go, only two are actually online and generating revenue, though sources with direct knowledge of Oracle’s infrastructure have informed me that work is still being done on both buildings despite CNBC reporting that they were “operational” in September 2025.
Let’s break this down. Based on a presentation by landowner Lancium from May 2025, the Stargate Abilene campus was meant to have 1.2GW of AI data centers online by year-end 2025.
Based on reporting from DatacenterDynamics, the first 200MW of power was meant to be energized “in 2025.” As time dragged on, occupancy was meant to begin in the first half of 2025, had “potential to reach 1GW by 2025,” complete all 1.2GW of capacity by mid-2026, be energized by mid-2026, have 64,000 GPUs by the end of 2026, as of September 30, 2025 had “two buildings live,” and as of December 12, 2025, Oracle co-CEO Clay Magouyurk said that Abilene was “on track” with “more than 96,000 NVIDIA Grace Blackwell GB200 delivered,” otherwise known as two buildings’ worth of GPUs.
Four months later on April 22, 2026, Oracle tweeted that “...in Abilene, 200MW is already operational, and delivery of the eight-building campus remains on schedule.” It is unclear if that’s 200MW of critical IT capacity or the total available power at the Abilene campus, and in any case, this is only enough power for two buildings, which means that Oracle is most decidedly not “on schedule.”
Sources familiar with Oracle infrastructure have confirmed that while construction has finished on building three, barely any actual tech has been installed. It also appears that while construction has begun on a power plant of some sort, it’s unclear whether it’s the 360.5MW gas power plant or 1GW substation. In any case, Abilene needs both to turn on the GPUs, if they ever get installed.
Abilene is, for the most part, the only part of the Stargate project that’s anywhere near complete.
I say that because the other data centers — Shackelford, Texas, Port Washington, Wisconsin, Doña Ana County, New Mexico, Saline, Michigan, and Milam County, Texas — are patches of land with a few steel beams, if that. To be explicit, every single Stargate data center is funded by Oracle and its respective financial backers.
Oracle is taking on a massive amount of debt to build these data centers, working with a labyrinthine network of financiers and construction partners to pull together the capacity necessary to get paid for its five-year-long $300 billion compute deal with OpenAI.
Oracle has also, per Bloomberg, deliberately raised money using “project financing” loans that are repaid using the projected cashflow, allowing it to keep the massive amount of debt off of its balance sheet. This is remarkable — and offensive! — because it’s borrowing over $38 billion to fund construction of its Wisconsin and Shackelford data centers (the largest debt deal of its kind on record) and said debt will now effectively not exist despite its massive drag on Oracle’s cashflow, which sat at negative $24.7 billion in its last quarterly earnings.
Based on estimates ($30 million in critical IT and $14 million in construction per megawatt) from TD Cowen’s Jerome Darling, the total cost of Oracle’s 7.1GW of data center capacity will be somewhere in the region of $340 billion to build.
All of these data centers are being built for a single tenant — OpenAI — which expects, per The Information, to lose over $167 billion (assuming it hits annual revenues of over $100 billion) by the end of 2028, and as a result does not actually have the money to pay Oracle for its compute on an ongoing basis.
In addition to its commitments to Oracle, OpenAI has also made commitments to spend $138 billion on Amazon over eight years, $250 billion on Microsoft Azure over an unspecific period, $20 billion with Cerebras over three years, $22.4 billion with CoreWeave over five years, and a non-specific amount with Google Cloud.
All of this is happening as Oracle’s core businesses plateau, even after Oracle reshuffled them in Q3 FY25 to represent Cloud, Software, Hardware and Services segments, the latter three of which have barely moved in the last 9 months as low-to-negative-margin cloud compute revenue grows.
In other words, Oracle’s only growth comes from a segment requiring hundreds of billions of dollars of compute.
To make matters worse, every single one of these data centers is behind schedule. Stargate Abilene was meant to be done at the beginning, middle, and now the end of this year, yet sources tell me there’s no way it’s finished before April 2027.
Bloomberg also reported late last year that Oracle had delayed several data centers from 2027 to 2028, but here in reality, every other Stargate data center is somewhere between a patch of dirt, a single steel beam, multiple steel beams, or less than half of a shell of a single building. Considering it’s taken two years for Stargate Abilene to build two buildings, I don’t see how it’s possible that these are built before the beginning of 2029.
And at that point, where exactly will we be in the AI bubble? What GPUs will be available? What other kinds of silicon will exist? What will the demand be for AI compute?
I don’t think that OpenAI exists for that long, and even if it does, it will have to raise at least $200 billion in the space of three years to possibly keep up with its commitments.
I’m surprised that nobody (outside of JustDario, at least) has raised the seriousness of this situation.
Stargate, as it stands, will kill Oracle, outside of OpenAI becoming the literal most-profitable and highest-revenue-generating company of all time within the next two years. Even then, by the time that Abilene is built, its 450,000 GB200 GPUs will be two-years-old, and entirely obsolete far before its debts are repaid. A similar fate awaits whatever GPUs are put in the other Stargate data centers.
Today’s newsletter is a thorough review and analysis of the ruinous excess of Stargate, a name that only really means “data centers being built for OpenAI in the hopes that OpenAI will pay for them.” Oracle is mortgaging its entire future on their construction, and even if it gets paid, I see no way that the cashflow from OpenAI’s compute spend can recover the cost before its GPU capex is rendered obsolete, let alone whether it can cover the debt associated with the buildout.
I’m Larry Ellison — Welcome To Jackass.
Welcome to the end of Oracle, or Sell The Compute To Who, Larry? Fucking Aquaman?
2026-04-23 01:24:17
Documents viewed by Where’s Your Ed At shed additional light on Microsoft’s transition to token-based billing for GitHub Copilot, as the company grapples with spiraling costs of AI compute.
As reported on Monday (and as announced soon after by Microsoft), the company has taken the step to suspend new sign-ups for individual and student accounts, has removed Anthropic’s Opus models from the cheapest $10-a-month plan, and plans to further tighten usage limits.
According to the documents, the announcement for token-based billing will be tomorrow (4/23), with changes to GitHub Copilot rolling out at the beginning of June.
Explainer: At present, GitHub Copilot users have a certain amount of “requests” — interactions where you ask the model to do something, with Pro ($10-a-month) accounts getting 300 a month, and Pro+ ($39-a-month) getting 1500. More-expensive models use more requests, cheaper ones use less (I’ll explain in a bit).
Moving to “token-based billing” means that instead of using “requests,” GitHub Copilot users will pay for the actual cost of tokens. For example, Claude Opus 4.7 costs $5 per million input tokens (stuff you feed in) and $25 per million output tokens (stuff the model outputs, including tokens for chain-of-thought reasoning.)
Users will pay a monthly subscription to access GitHub Copilot, and receive a certain allotment of AI tokens based on their subscription level. Organizations paying for GitHub Copilot will have “pooled” AI credits, meaning that tokens are shared across the entire organization.
For an initial promotional period running from June, July and August, GitHub Copilot Business Customers will pay $19 per-user-per-month and receive $30 of pooled AI credits, and Copilot Enterprise customers will pay $39 per-user-per-month and receive $70 of pooled AI credits. Afterward, users will receive either $19 or $39 of tokens depending on their subscription level.
While the documents refer to moving “all” GitHub Copilot users to token-based billing, it’s unclear at this time how Microsoft will be handling individual Pro or Pro+ subscribers.
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2026-04-22 06:44:29
The following exists as a record of what happened previously, please see above for the full story.
In developing news, Anthropic appears to have removed access to AI coding tool Claude Code from its $20-a-month "Pro" accounts. This is likely another cost-cutting move that follows a recent change (per The Information) that forced enterprise users to pay on a per-million-token based rate rather than having rate limits that were, based on researchers' findings, often much higher than the cost of the subscription.
Update: Anthropic's Amol Avasare claims that it is "...running a small test on ~2% of new prosumer signups. Existing Pro and Max subscribers aren't affected." This does not really make sense given the fact that all support documents and the Claude website reflect that Pro users do not have access to Claude Code.
I am waiting for further comment.
Previously, users were able to access Claude using their Pro subscriptions via a command-line interface and both the web and desktop Claude apps. Users were, instead of paying on a per-million-token basis, allowed to use their subscription to access Claude Code, but will likely now have to pay for API access.
Anthropic's Claude Code support documents (as recently as this April 10th archived page) previously read "Using Claude Code with your Pro or Max plan." The page now reads "Using Claude Code with your Max plan."
Pricing on Anthropic's website reflects the removal of Claude Code on both mobile and desktop.
Some Pro users report that they are still able to access Claude Code via the web app and Command-Line Interface.
It is unclear at this time whether this change is retroactive or for new Pro subscribers, or whether Anthropic intends to entirely remove access to Claude Code (without paying for API tokens) from every Pro customer.
I have requested a comment from Anthropic, and will update this piece when I receive it, or if Anthropic confirms this move otherwise.
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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. I recently put out the timely and important Hater’s Guide To The SaaSpocalypse, another on How AI Isn't Too Big To Fail, a deep (17,500 word) Hater’s Guide To OpenAI, and just last week put out the massive Hater’s Guide To Private Credit.
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2026-04-22 00:28:59
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Soundtrack — Megadeth — Hangar 18 (Eb Tuning)
For the best part of four years I’ve been wrapped up in writing these massive, sprawling narratives about the AI bubble and the tech industry at large. I still intend to write them, but today I’m going to do what I do best — explaining all the odd shit that’s happening in the tech industry and explaining why it’s concerning to me.
And because I love a good bit, I’m tying these stories to my pale horses of the AIpocalypse — signs that things are beginning to unwind in the most annoying bubble in history.
Anyway, considering that the newsletter and the podcast are now my main form of income, I’m going to be experimenting with the formats across the free and premium to keep things interesting and varied.
Pale Horse: Any further price increases or service degradations from Anthropic and OpenAI are a sign that they’re running low on cash.
Let’s start with a fairly direct statement: Anthropic should stop taking on new customers until it works out its capacity issues.
So, generally any service — Netflix, for example — you use with any regularity has the “four nines” of availability, meaning that it’s up 99.99% of the time. Once a company grows beyond a certain scale, having four 9s is considered standard business practice…
…unless you’re Anthropic!
As of writing this sentence, Anthropic’s availability for its Claude Chatbot has 98.79% uptime, its platform/console is at 99.14%, its API is at 99.09%, and Claude Code is at 99.25% for the last 90 days.
Let me put this into context. When you have 99.99% uptime, a service is only down for a minute (and 0.48 of a second) each week. If you’re hitting 98.79% uptime, as with the Claude chatbot, your downtime jumps to two hours, one minute, and 58 seconds.
Or, put another way, 98.79% uptime equates to nearly four-and-a-half days in a calendar year where the service is unavailable.
More-astonishingly, Claude for Government sits at 99.91%. Government services are generally expected to be four 9s minimum, or 5 (99.999%) for more important systems underlying things like emergency services.
This is a company that recently raised $30 billion dollars and gets talked about like somebody’s gifted child, yet Anthropic’s services seem to have constant uptime issues linked to a lack of capacity.
Since mid-February, outages for systems across Anthropic have become so common that some of its enterprise clients are switching to other AI model players.
David Hsu, founder and CEO of software development platform Retool, said he prefers to use Anthropic’s Opus 4.6 model to power his company’s AI agent tool because he believes it is the best model for enterprise. He recently changed to OpenAI’s model to power his company’s agent. “Anthropic has just been going down all the time,” he said.
The reliability of core services on the internet is often measured in nines. Four nines means 99.99% of uptime—a typical percentage that a software company commits to customers. As of April 8, Anthropic’s Claude API had a 98.95% uptime rate in the last 90 days.
Yet Anthropic’s problems go far further than simple downtime (as I discussed last week), leading to (deliberately or otherwise) severe performance issues with Opus 4.6:
One of the most detailed public complaints originated as a GitHub issue filed by Stella Laurenzo on April 2, 2026, whose LinkedIn profile identifies her as Senior Director in AMD’s AI group.
In that post, Laurenzo wrote that Claude Code had regressed to the point that it could not be trusted for complex engineering work, then backed that claim with a sprawling analysis of 6,852 Claude Code session files, 17,871 thinking blocks and 234,760 tool calls.
The complaint argued that, starting in February, Claude’s estimated reasoning depth fell sharply while signs of poorer performance rose alongside it, including more premature stopping, more “simplest fix” behavior, more reasoning loops, and a measurable shift from research-first behavior to edit-first behavior.
While Anthropic claims that it doesn’t degrade models to better serve demand, that doesn’t really square with the many, many users complaining about the problem. Anthropic’s response has, for the most part, been to pretend like nothing is wrong, with a spokesperson waving off Carl Franzen of VentureBeat (who has a great article on the situation here) by pointing him to two different Twitter posts, neither of which actually explain what’s going on.
Things only got worse with last week’s launch of Opus 4.7, which appears to have worse performance and burn more tokens.
One Reddit post titled, "Claude Opus 4.7 is a serious regression, not an upgrade," has 2,300 upvotes. An X user's suggestion that Opus 4.7 wasn't really an improvement over Opus 4.6 got 14,000 likes. In one informal but popular test of AI intelligence, Opus 4.7 appears to say that there were two Ps in "strawberry." Another user screenshot shows it saying that it didn't cross reference because it was "being lazy." Some Redditors found that Opus 4.7 was rewriting their résumés with new schools and last names. Multiple X users posited that Opus 4.7 had simply gotten dumber.
Some X users have suggested the culprit is the AI model's reasoning times. Anthropic says the new "adaptive reasoning" function lets the model decide when to think for longer or shorter periods. One user wrote that they couldn't "get Opus 4.7 to think." Another wrote that it "nerfs performance."
"Not accurate," Anthropic's Boris Cherny, the creator of Claude Code, responded. "Adaptive thinking lets the model decide when to think, which performs better."
I think it’s deeply bizarre that a huge company allegedly worth hundreds of billions of dollars A) can’t seem to keep its services online with any level of consistency, B) appears to be making its products worse, and C) refuses to actually address or discuss the problem. Users have been complaining about Claude models getting “dumber” going back as far as 2024, each time faced with a tepid gaslighting from a company with a CEO that loves to talk about his AI products wiping out half of white collar labor.
Some might frame this as Anthropic having “insatiable demand for its products,” but what I see is a terrible business with awful infrastructure run in an unethical way. It is blatantly, alarmingly obvious that Anthropic cannot afford to provide a stable and reliable service to its customers, and its plans to expand capacity appear to be signing deals with Broadcom that will come online “starting in 2027,” near-theoretical capacity with Hut8, which does not appear to have ever built an AI data center, and also with CoreWeave, a company that is yet to build the full capacity for its 2025 deals with OpenAI and only has around 850MW of “active power capacity” — so around 653MW of actual compute capacity — as of the end of 2025, up from 360MW of power at end of 2024.
Remember: data centers take forever to build, and there’s only a limited amount of global capacity, most of which is taken up by Microsoft, Google, Amazon, Meta and OpenAI, with the first three of those already providing capacity to both Anthropic and OpenAI.
We’re likely hitting the absolute physical limits of available AI compute capacity, if we haven’t already done so, and even if other data centers are coming online, is the plan to just hand them over to OpenAI or Anthropic in perpetuity?
It’s also unclear what the goal of that additional capacity might be, as I discussed last week:
Yet it’s unclear whether “more capacity” means that things will be cheaper, or better, or just a way of Anthropic scaling an increasingly-shittier experience.
To explain, when an AI lab like Anthropic or OpenAI “hits capacity limits,” it doesn’t mean that they start turning away business or stop accepting subscribers, but that current (and new) subscribers will face randomized downtime and model issues, along with increasingly-punishing rate limits.
Neither company is facing a financial shortfall as a result of being unable to provide their services (rather, they’re facing financial shortfalls because they’re providing their services to customers), and the only ones paying that price because of these “capacity limits” are the customers.
What’s the goal, exactly? Providing a better experience to its current customers? Securing enough capacity to keep adding customers? Securing enough capacity to support larger models like Mythos? When, exactly, does Anthropic hit equilibrium, and what does that look like?
There’s also the issue of cost.
Anthropic is currently losing billions of dollars a year offering a service with amateurish availability and oscillating quality, and continues to accept new subscribers, meaning that capacity issues are not affecting its growth. As a result, adding more capacity simply makes the product work better for a much higher cost.
Anthropic’s growth story is a sham built on selling subscriptions that let users burn anywhere from $8 to $13.50 for every dollar of subscription revenue and providing a brittle, inconsistent service, made possible only through a near-infinite stream of venture capital money and infrastructure providers footing the bill for data center construction.
Put another way, Anthropic doesn’t have to play by the rules. Venture capital funding allows it to massively subsidize its services. The endless, breathless support from the media runs cover for the deterioration of its services. A lack of any true regulation of tech, let alone AI, means that it can rugpull its customers with varying rate limits whenever it feels like.
If Anthropic were forced to charge its actual costs — and no, I don’t believe its API is profitable no matter how many people misread Dario Amodei’s interview — its growth would quickly fall apart as customers faced the real costs of AI (which I’ll get to in a bit). If Anthropic was forced to provide a stable service, it would have to stop accepting new customers or massively increase its inference costs.
Anthropic is a con, and said con is only made possible through endless, specious hype. Everybody who blindly applauded everything this company did is a mark.
Congratulations to all the current winners of the “Fell For It Again Award.” Per the Financial Times:
Anthropic has said it will hold off on a wider release of the model until it is reassured that it is safe and cannot be abused by bad actors. The company also has a finite amount of computing power and has suffered outages in recent weeks.
Multiple people with knowledge of the matter suggested Anthropic was holding back from a wider release until it could reliably serve the model to customers.
So, yeah, anyone in the media who bought the line of shit from Dario Amodei that this was “too dangerous to release” is a mark. Cal Newport has an excellent piece debunking the hype, but my general feeling is that if Mythos was so powerful, how did Claude Code’s source code leak?
Did… Anthropic not bother to use its super-powerful Mythos model to check? Or did it not find anything? Either way, very embarrassing for all involved.
Pale Horse: data center collapses, misc.
As I’ve discussed in the past, only 5GW of AI compute capacity is currently under construction worldwide (based on research from Sightline Climate), with “under construction” meaning everything from a scaffolding yard with a fence (as is the case with Nscale’s Loughton-based data center) to a building nearing handoff to the client.
I reached out to Sightline to get some clarity, and they told me that of the 114GW of capacity due to come online by the end of 2028, only 15.2GW is under construction, including the 5GW due in 2026.
That’s…very bad.
It gets worse when you realize that the majority of that construction is for two companies:
Sidenote: I’ll also add that Anthropic has agreed to spend $100 billion on Amazon Web Services over the next decade as part of its $5 billion (with “up to $20 billion” more in the future, and no, there’s no more details than that) investment deal with Amazon, with Anthropic apparently securing 5GW of capacity and bringing “nearly 1GW of Trainium2 and 3 capacity online by the end of the year,” which I do not believe, but whatever.These deals shouldn’t be legal.
So, to summarize, at least 4.6GW of the 15.2GW of data center capacity under construction is for OpenAI, with at least another 4GW of that reserved for Anthropic through partners like Microsoft, Google and Amazon. In truth, the number could be much higher.
This is a fundamentally insane situation. OpenAI and Anthropic both burn billions of dollars a year, with The Information reporting that Anthropic expects to burn at least $11 billion and OpenAI $25 billion in 2026. The only way that these companies can continue to exist is by raising endless venture capital funding or, assuming they make it to IPO, endless debt offerings or at-the-market stock sales.
It’s also very concerning that only such a small percentage of announced compute capacity is being built, especially when you run the numbers against NVIDIA’s actual sales.
Last year, Jerome Darling of TD Cowen estimated that it cost around $30 million per megawatt in critical IT (GPUs, servers, storage, and so on) and $12 million to $14 million per megawatt to build a data center, making critical IT around 68% (at the higher end of construction) of the total cost-per-megawatt.
Now, to be clear, those gigawatt and megawatt numbers for data centers refer to the power rather than critical IT, and if we take an average PUE (power usage efficiency, a measurement of how efficient a data center’s power is) of 1.35, we get 11.2GW of critical IT hardware, with the majority (I’d say 90%) being GPUs, bringing us down to around 10.1GW of GPUs.
If we then cut that up into GB200 or GB300 NVL72 racks with a power draw of around 140KW, that’s around 71,429 racks’ worth of hardware at an average of $4 million each, which gives us around $285.7 billion in revenue for NVIDIA.
NVIDIA claims it had a combined $500 billion in orders between 2025 and 2026, and $1 trillion of sales through 2027, and it’s unclear where any of those orders are meant to go other than a warehouse in Taiwan.
At this point, I think it’s fair to ask why anyone is buying more GPUs, as there’s nowhere to fucking put them. Every beat-and-raise earnings from NVIDIA is now deeply suspicious.
New Pale Horse: Any and all signs that companies are facing the economic realities of AI, including any complaints around or adaptations to deal with the increasing costs of AI.
Last week, a report from Goldman Sachs revealed that (and I quote) “...companies are overrunning their initial budgets for inference by orders of magnitude (we heard one industry datapoint on inference costs in engineering now approaching about 10% of headcount cost, but could be on track to be on par with headcounts costs in the next several quarters based on current trajectories.”
To simplify, this means that some companies are spending as much as 10% of the cost of their employees on generative AI services, all without appearing to provide any stability, quality or efficiency gains, or (not that I want this) justification to lay people off.
The Information’s Laura Bratton also reported last week that Uber had managed to blow through its entire AI budget for the year a few months into 2026:
Uber’s surging use of AI coding tools, particularly Anthropic’s Claude Code, has maxed out its full year AI budget just a few months into 2026, according to chief technology officer Praveen Neppalli Naga.
“I'm back to the drawing board because the budget I thought I would need is blown away already,” Neppalli Naga said in an interview.
…
He wouldn’t disclose exact figures of the company’s software budget or what it spends on AI coding tools. Uber’s research and development expenses, which typically reflect companies’ costs of developing new AI products, rose 9% to $3.4 billion in 2025 from the previous year, and the firm said in a recent securities filing it expects that cost will continue rising on an absolute dollar basis.
Uber’s CTO also added that about “...11% of real, live updates to the code in its backend systems are being written by AI agents primarily built with Claude Code, up from just a fraction of a percent three months ago.” Anyone who has ever used Uber’s app in the last year can see how well that’s going, especially if they’ve had to file any kind of support ticket.
Honestly, I find this all completely fucking insane. The whole sales pitch for generative AI is that it’s meant to be this magical, efficiency-driving panacea, yet whenever you ask somebody about it the answer is either “yeah, we’re writing all the code with it!” without any described benefits or “it costs so much fucking money, man.”
Let’s get practical about these economics, and use Spotify as an example because its CEO proudly said that its “top engineers” are barely writing code anymore, though to be clear, the Goldman Sachs example didn’t specifically name any one company.
For the sake of argument, let’s say that the company has 3000 engineers — one of its sites claims it has 2700, but I’ve seen reports as high as 3500. Let’s also assume, based on the Spotify Blind (an anonymous social media site for tech workers), that these engineers make a median salary of 192,000 a year.
In the event that Spotify spent 10% of its engineering headcount (around $576 million) on AI inference, it would be spending roughly $57.6 million, or approximately 4.1% of its $1.393 billion in Research and Development costs from its FY2025 annual report. Eager math-doers in the audience will note that 100% of headcount would be nearly half of the R&D budget, or around a quarter of its $2.2 billion in net income for the year.
Now, to be clear, these numbers likely already include some AI inference spend, but I’m just trying to illustrate the sheer scale of the cost.
While this is great for Anthropic (and to a lesser extent OpenAI), I don’t see how it works out for any of its customers. A flat 10% bump on the cost of software engineering is the direct opposite of what AI was meant to do, and in the event that costs continue to rise, I’m not sure how anybody justifies the expense much further.
And we’re going to find out fairly quickly, because the world of token subsidies is going away.
Pale Horse: Any further price increases or service degradations from AI startups, and yes, that’s what I’d call GitHub Copilot, in the sense that it loses hundreds of millions of dollars and makes fuck-all revenue.
As I reported yesterday, internal documents have revealed that Microsoft plans to temporarily suspend individual account signups to its GitHub Copilot coding product, tighten rate limits across the board, remove Opus models from its $10-a-month Pro subscription, and transition from requests (single interactions with GitHub Copilot) towards token-based billing some time later this year, with Microsoft confirming some of these details (but not token-based billing) in a blog post.
This is a significant move, driven by (per my own reporting) Microsoft’s week-over-week costs of running GitHub Copilot nearly doubling since January.
An aside/explainer: if you’re confused as to what “token-based billing” means, know that the vast majority of AI services currently subsidize their subscriptions, using another measure (such as “requests” or “rate limits”) to meter out how much a user can use the service. Nevertheless, these services still burn tokens at whatever rate that it costs to pay for them — for example, $5 per million input and $25 per million output for Opus 4.7, as I mentioned previously — meaning that the company almost always loses money unless a person doesn’t use the subscription very much.
Companies did this to grow their subscriber numbers, and I think they assumed things would get cheaper somehow. Great job, everyone!
The move to token-based billing will see GitHub users charged based on their usage of the platform, and how many tokens their prompts consume — and thus, how much compute they use. It’s unclear at this time when this will begin, but it significantly changes the value of the product.
I’ll also say that the fact that Microsoft has stopped signing up new paid GitHub Copilot subscriptions entirely is one of the most shocking moves in the history of software. I’ve literally never seen a company do this outside of products it intended to kill entirely, and that’s likely because — per my source — it intends to move paid customers over to token-based-billing, though it’s unclear what these tiers would look like, as the $10-a-month and $39-a-month subscriptions are mostly differentiated based on the amount of requests you can use.
What’s remarkable about this story is that Microsoft is one of the few players capable of bankrolling AI in perpetuity, with over $20 billion a quarter in profits since the middle of 2023.
Its decision to start cutting costs around AI suggests that said costs have become unbearable — The Information reported back in January that it was on pace to spend $500 million a year with Anthropic alone, and if that amount has doubled, it likely means that Microsoft is spending upwards of ten times its GitHub Copilot revenue, as I can report today that at the end of 2025, GitHub Copilot was at around $1.08 billion, with the majority of that revenue coming from its CoPilot Business and Enterprise subscriptions.
The Information also reported a few weeks ago that GitHub had recently seen a surge of outages attributed to “spiking traffic as well as its effort to move its applications from its own servers to Microsoft’s Azure cloud”:
“Since January, every month, every week almost now has some new peak stat for the highest [usage] rate ever,” [GitHub COO Kyle] Daigle said. He attributed the growth to “both agents and humans,” and also noted that the rise of AI coding tools has led to a rise in humans without deep coding knowledge starting to use GitHub’s platform more.
“Agents” in this case could refer to just about anything — OpenAI’s Codex, Anthropic’s Claude Code, or even people plugging in the wasteful, questionably-useful OpenClaw to their GitHub Copilot account, and if that’s what happened, it’s very likely behind the move to Token-Based Billing and rate limits.
In any case, if Microsoft’s making this move, it means that CFO Amy Hood — the woman behind last year’s pullback on data center construction — has decided that the subsidy party is over. Though Microsoft is yet to formally announce the move to Token-Based Billing, I imagine it’ll be sometime this week that it rips off the bandage.
Two weeks ago, Anthropic did the same with its enterprise customers, shifting them to a flat $20-a-seat fee and otherwise charging the per-token rate for whatever models they wanted to use.
I’m making the call that by the end of 2026, a majority of AI services will move some or all of their customers to token-based billing as they reckon with the true costs of running AI models.
I kept things simple today both to give myself a bit of a break and because these were stories I felt needed telling.
Nevertheless, I do have to remark on how ridiculous everything has become.
Everywhere you turn, somebody is talking about “agents” in a way that doesn’t remotely match with reality, like Aaron Levie’s epic screeds about how “AI agents make it so every other company on the planet starts to create software for bringing automation to their workflows in a way that would be either infeasible technically or unaffordable economically,” a statement that may as well be about fucking unicorns and manticores as far as its connections to reality.
I feel bad picking on Aaron, as he doesn’t seem like a bad guy. He is, however, increasingly-indicative of the hysterical brainrot of executive AI hysteria, where the only way to discuss the industry is in vaguely futuristic-sounding terms about “agents” and “inference” and “tokens as a commodity,” all with the intent of obfuscating the ugly, simple truth: that generative AI is deeply unprofitable, doesn’t seem to provide tangible productivity benefits, and appears to only lose both the business and the customer money.
Though my arguments might be verbose, they’re ultimately pretty simple: AI does not provide even an iota of the benefits — economic or otherwise — to justify its ruinous costs. Every new story that runs about cost-cutting or horrible burnrates increasingly validates my position, and for the most part, boosters respond by saying “well LOOK at how BIG the REVENUES are.”
It isn’t! AI revenues are dogshit. They’re awful. They’re pathetic. The entire industry — including OpenAI and Anthropic’s theoretical revenues of $13.1 billion and $4.5 billion — hit around $65 billion last year, and that includes the revenues from providing compute generated by neoclouds like CoreWeave and hyperscalers like Microsoft.
I’m also just gonna come out and say it: I think the AI startups are misleading their investors and the general public about their revenues. My reporting from last year had OpenAI’s revenues at somewhere in the region of $4.3 billion in the first three quarters of 2025, and Anthropic CFO Krishna Rao said in an an affidavit that the company had made revenue “exceeding” (sigh) $5 billion through March 9, 2026, which does not make sense when you add up all the annualized revenue figures reported about this company.
Cursor is also reportedly at $6 billion in annualized revenue (or around $500 million a month) and “gross margin positive” — which I also doubt given that it had to raise over $3 billion last year and is apparently raising another $2 billion this year.
Even if said numbers were real, the majority of OpenAI, Cursor and Anthropic’s revenues come from subsidized software subscriptions. Things have gotten so dire that even Deidre Bosa of CNBC agrees with me that AI demand is inflated by token-maxxing and subsidized services.
Otherwise, everybody else is making single or double-digit millions of dollars and losing hundreds of millions of dollars to get there. And per founder Scott Stevenson, overstating annualized revenues is extremely common, with AI startups booking “three-year-long” enterprise deals with the first year discounted and a twelve-month out:
The reason many AI startups are crushing revenue records is because they are using a dishonest metric
The biggest funds in the world are supporting this and misleading journalists for PR coverage.
The setup: Company signs 3-year enterprise deals. Year 1 is discounted (say $1M), Year 2 steps up ($2M), Year 3 is full price ($3M).
They report $3M as “ARR” — even though they’re only collecting $1M right now.
The worst part: The customer has an opt-out option at 12 months! It’s not actually a 3 year contract.
While it’s hard to say how widespread this potential act of fraud might be, Stevenson estimates that more than 50% of enterprise AI startups are using “contracted ARR” to pump their values. One (honest) founder responded to Stevenson saying that his company has $350,000 in contracted ARR but only $42,000 of ARR, adding that “next year is gonna be awesome though,” which I don’t think will be the case for what appears to be a chatbot for finding investors.
This industry’s future is predicated entirely on the existence of infinite resources, and most AI companies are effectively front-ends for models owned by Anthropic and OpenAI, two other companies that rely on infinite resources to run their services and fund their infrastructure.
And at the top of the pile sits NVIDIA, the largest company on the stock market, which is selling more GPUs than can be possibly installed, and very few people seem to notice or care.
I’m talking about hundreds of billions of dollars of GPUs sitting in warehouses that aren’t being installed, with it taking six months to install a single quarter’s worth of GPU sales. The assumption, based on every financial publication I’ve read, appears to be “it will keep selling GPUs forever, and it will all be so great.”
Where are you going to put them, Jensen? Where do the fucking GPUs go? There isn’t enough capacity under construction! If, in fact, NVIDIA is actually selling as many GPUs as it says, it’s likely taking liberties with “transfers of ownership” where NVIDIA marks a product as “sold” to somebody that has yet to actually take it on.
Sidenote: There’re already signs that GPUs are beginning to pile up.
You see, when a hyperscaler buys an AI server, what actually happens is an ODM — original design manufacturer — buys the GPUs from NVIDIA, builds the server, and then ships it to the data center, which, to be clear, is all above board and normal. These ODMs also book the entire value of the NVIDIA GPU as revenue, which is why revenues for companies like Foxconn, Wystron and Quanta Computing have all spiked during the AI bubble.
Oh, right, the signs. Per Quanta Computing’s fourth quarter financial results, inventory — as in stuff that’s sitting waiting to go somewhere — has spiked from $10.54 billion in Q3 2025 to $16.3 billion 2025, and nearly doubled year-over-year ($8.33 billion) as gross profit dropped from 7.9% in Q4 2024 to 7% Q4 2025. While this isn’t an across-the-board problem (Wistron’s inventories dropped quarter-over-quarter, for example), Taiwanese ODMs are going to be one of the first places to watch for inventory accumulation.
In any case, I keep coming back to the word “hysteria,” because it’s hard to find another word to describe this hype cycle. The way that the media, the markets, analysts, executives, and venture capitalists discuss AI is totally divorced from reality, discussing “agents” in terms that don’t match with reality and AI data centers in terms of “gigawatts” that are entirely fucking theoretical, all with a terrifying certainty that makes me wonder what it is I’m missing.
But every sign points to me being right, and if I’m right at the scale I think I’m right, I think we’re about to have a legitimacy crisis in investing and mainstream media, because regular people are keenly aware that something isn’t right, in many cases, it’s because they’re able to count.
