2025-11-08 00:27:45
Soundtrack: Queens of the Stone Age - Song For The Dead
Editor's Note: The original piece had a mathematical error around burnrate, it's been fixed.
Also, welcome to another premium issue! Please do subscribe, this is a massive, 7000-or-so word piece, and that's the kind of depth you get every single week for your subscription.
A few days ago, Sam Altman said that OpenAI’s revenues were “well more” than $13bn in 2025, a statement I question based on the fact, based on other outlets’ reporting, OpenAI only made $4.3bn through the first half of 2025, and likely around a billion a month, which I estimate means the company made around $8bn by the end of September.
This is an estimate. If I receive information to the contrary, I’ll report it.
Nevertheless, OpenAI is also burning a lot of money. In recent public disclosures (as reported by The Register), Microsoft noted that it had funding commitments to OpenAI of $13bn, of which $11.6bn had been funded by September 30 2025.
These disclosures also revealed that OpenAI lost $12bn in the last quarter — Microsoft’s Fiscal Year Q1 2026, representing July through September 2025. To be clear, this is actual, real accounting, rather than the figures leaked to reporters. It’s not that leaks are necessarily a problem — it’s just that anything appearing on any kind of SEC filing generally has to pass a very, very high bar.
There is absolutely nothing about these numbers that suggests that OpenAI is “profitable on inference” as Sam Altman told a group of reporters at a dinner in the middle of August.
Let me get specific.
The Information reported that through the first half of 2025, OpenAI spent $6.7bn on research and development, “which likely include[s] servers to develop new artificial intelligence.” The common refrain here is that OpenAI “is spending so much on training that it’s eating the rest of its margins,” but if that were the case here, it would mean that OpenAI spent the equivalent of six months’ training in the space of three.
I think the more likely answer is that OpenAI is spending massive amounts of money on staff, sales and marketing ($2bn alone in the first half of the year), real estate, lobbying, data, and, of course, inference.
According to The Information, OpenAI had $9.6bn in cash at the end of June 2025.
Assuming that OpenAI lost $12bn at the end of calendar year Q3 2025, and made — I’m being generous — around $3.3bn (or $1.1bn a month) within that quarter, this would suggest OpenAI’s operations cost them over $15bn in the space of three months. Where, exactly, is this money going? And how do the numbers published actually make sense when you reconcile them with Microsoft’s disclosures?
In the space of three months, OpenAI’s costs — if we are to believe what was leaked to The Information (and, to be clear, I respect their reporting) — went from a net loss of $13.5bn in six months to, I assume, a net loss of $12bn in three months.
Though there are likely losses related to stock-based compensation, this only represented a cost of $2.5bn in the first half of 2025. The Information also reported that OpenAI “spent more than $2.5 billion on its cost of revenue,” suggesting inference costs of…around that?
I don’t know. I really don’t know. But something isn't right, and today I'm going to dig into it.
In this newsletter I'm going to reveal how OpenAI's reported revenues and costs don't line up - and that there's $4.1 billion of cash burn that has yet to be reported elsewhere.
2025-11-01 00:57:29
As I've established again and again, we are in an AI bubble, and no, I cannot tell you when the bubble will pop, because we're in the stupidest financial era since the great financial crisis — though, I hope, not quite as severe in its eventually apocalyptic circumstances.
By the end of the year, Microsoft, Amazon, Google and Meta will have spent over $400bn in capital expenditures, much of it focused on building AI infrastructure, on top of $228.4bn in capital expenditures in 2024 and around $148bn in capital expenditures in 2023, for a total of around $776bn in the space of three years.
At some point, all of these bills will have to come due.
You see, big tech has been given incredible grace by the markets, never having to actually show that their revenue growth is coming from selling AI or AI-related services. Only Microsoft ever bothered, piping up in October 2024 to say it was making $833 million a month ($10bn ARR) from AI and then $1.08 billion a month in January 2025 ($13bn ARR), and then choosing to never report it again.
As reported by The Information, $10bn of Microsoft’s Azure revenue this year will come from OpenAI’s spend on compute, which, also reported by The Information, is paid at “...a heavily discounted rental rate that essentially only covers Microsoft’s costs for operating the servers.”
It’s absolutely astonishing that such egregious expenditures have never brought with them any scrutiny of the actual return on investment, or any kind of demands for disclosure of the resulting revenue. As a result, big tech has used their already-successful products and existing growth to pretend that something is actually happening other than Satya Nadella standing with his hands on his hips and talking about his favourite ways to use Copilot, a product that so unpopular that only eight million active Microsoft 365 customers are paying for it out of over 440 million users.
Sidenote: Speaking of unpopularity, Microsoft is currently being sued in Australia for raising the cost of the personal versions of Office 365 to reflect the integration of Copilot and other features, and hiding the fact from users that they could continue using the software they were already using, and at the same price.
This stuff is so unpopular, the world’s biggest and most powerful software company — and one with a virtual monopoly on the office productivity market — had to use dark patterns to get people to pay for this stuff.
Earlier in the week, OpenAI announced that it had “successfully converted to a more traditional corporate structure,” giving Microsoft a 27% position in the new entity worth $130bn, with the Wall Street Journal vaguely saying that Microsoft will also have “the ability to get more ownership as the for-profit becomes more valuable.”
Said deal also brought with it a commitment to spend $250bn on Microsoft Azure, which Microsoft has booked as “remaining performance obligations” in the same way that Oracle stuffed its RPOs with $300bn dollars from OpenAI, a company that cannot afford to pay either company even a tenth of those obligations and is on the hook for over a trillion dollars in the next four years.
But OpenAI isn’t the only one with a bill coming due.
As we speak, the markets are still in the thrall of an egregious, hype-stuffed bubble, with the hogs of Wall Streets braying and oinking their loudest as Jensen Huang claims — without any real breakdown as to who is buying them — that NVIDIA has over $500bn in bookings for its AI chips, with little worry about whether there’s enough money to actually pay for all of those GPUs or, more operatively, whether anybody plugging them in is making any profits off of them.
To be clear, everybody is losing money on AI. Every single startup, every single hyperscaler, everybody who isn’t selling GPUs or servers with GPUs inside them is losing money on AI. No matter how many headlines or analyst emissions you consume, the reality is that big tech has sunk over half a trillion dollars into this bullshit for two or three years, and they are only losing money.
So, at what point does all of this become worth it?
Actually, let me reframe the question: how does any of this become worthwhile?Today, I’m going to try and answer the question, and have ultimately come to a brutal conclusion: due to the onerous costs of building data centers, buying GPUs and running AI services, big tech has to add $2 Trillion in AI revenue in the next four years. Honestly, I think they might need more.
No, really. Big tech has already spent $605 billion in capital expenditures since 2023, with a chunk of that dedicated to 5-year-old (A100) and 4-year-old (H100) GPUs, and the rest dedicated to buying Blackwell chips that The Information reports have gross margins of negative 100%:
Big tech’s lack of tangible revenue (let alone profits) from selling AI services only compounds the problem, meaning every dollar of capex burned on AI is currently putting these companies further in the hole.
Yet there’s also another problem - that GPUs are uniquely expensive to purchase, run and maintain, requiring billions of dollars of data center construction and labor before you can even make a dollar.
Worse still, their value decays every single year, in part thanks to the physics of heat and electricity, and NVIDIA releasing a new chip every single year.
2025-10-20 23:01:51
So, I originally planned for this to be on my premium newsletter, but decided it was better to publish on my free one so that you could all enjoy it. If you liked it, please consider subscribing to support my work. Here’s $10 off the first year of annual.
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Over the last two years I have written again and again about the ruinous costs of running generative AI services, and today I’m coming to you with real proof.
Based on discussions with sources with direct knowledge of their AWS billing, I am able to disclose the amounts that AI firms are spending, specifically Anthropic and AI coding company Cursor, its largest customer.
I can exclusively reveal today Anthropic’s spending on Amazon Web Services for the entirety of 2024, and for every month in 2025 up until September, and that that Anthropic’s spend on compute far exceeds that previously reported.
Furthermore, I can confirm that through September, Anthropic has spent more than 100% of its estimated revenue (based on reporting in the last year) on Amazon Web Services, spending $2.66 billion on compute on an estimated $2.55 billion in revenue.
Additionally, Cursor’s Amazon Web Services bills more than doubled from $6.2 million in May 2025 to $12.6 million in June 2025, exacerbating a cash crunch that began when Anthropic introduced Priority Service Tiers, an aggressive rent-seeking measure that begun what I call the Subprime AI Crisis, where model providers begin jacking up the prices on their previously subsidized rates.
Although Cursor obtains the majority of its compute from Anthropic — with AWS contributing a relatively small amount, and likely also taking care of other parts of its business — the data seen reveals an overall direction of travel, where the costs of compute only keep on going up.
Let’s get to it.
In February of this year, The information reported that Anthropic burned $5.6 billion in 2024, and made somewhere between $400 million and $600 million in revenue:
It’s not publicly known how much revenue Anthropic generated in 2024, although its monthly revenue rose to about $80 million by the end of the year, compared to around $8 million at the start. That suggests full-year revenue in the $400 million to $600 million range.
…Anthropic told investors it expects to burn $3 billion this year, substantially less than last year, when it burned $5.6 billion. Last year’s cash burn was nearly $3 billion more than Anthropic had previously projected. That’s likely due to the fact that more than half of the cash burn came from a one-off payment to access the data centers that power its technology, according to one of the people who viewed the pitch.
While I don’t know about prepayment for services, I can confirm from a source with direct knowledge of billing that Anthropic spent $1.35 billion on Amazon Web Services in 2024, and has already spent $2.66 billion on Amazon Web Services through the end of September.
Assuming that Anthropic made $600 million in revenue, this means that Anthropic spent $6.2 billion in 2024, leaving $4.85 billion in costs unaccounted for.
The Information’s piece also brings up another point:
The costs to develop AI models accounted for a major portion of Anthropic’s expenses last year. The company spent $1.5 billion on servers for training AI models. OpenAI was on track to spend as much as $3 billion on training costs last year, though that figure includes additional expenses like paying for data.
Before I go any further, I want to be clear that The Information’s reporting is sound, and I trust that their source (I have no idea who they are or what information was provided) was operating in good faith with good data.
However, Anthropic is telling people it spent $1.5 billion on just training when it has an Amazon Web Services bill of $1.35 billion, which heavily suggests that its actual compute costs are significantly higher than we thought, because, to quote SemiAnalysis, “a large share of Anthropic’s spending is going to Google Cloud.”
I am guessing, because I do not know, but with $4.85 billion of other expenses to account for, it’s reasonable to believe Anthropic spent an amount similar to its AWS spend on Google Cloud. I do not have any information to confirm this, but given the discrepancies mentioned above, this is an explanation that makes sense.
I also will add that there is some sort of undisclosed cut that Amazon gets of Anthropic’s revenue, though it’s unclear how much. According to The Information, “Anthropic previously told some investors it paid a substantially higher percentage to Amazon [than OpenAI’s 20% revenue share with Microsoft] when companies purchase Anthropic models through Amazon.”
I cannot confirm whether a similar revenue share agreement exists between Anthropic and Google.
This also makes me wonder exactly where Anthropic’s money is going.
Anthropic has, based on what I can find, raised $32 billion in the last two years, starting out 2023 with a $4 billion investment from Amazon from September 2023 (bringing the total to $37.5 billion), where Amazon was named its “primary cloud provider” nearly eight months after Anthropic announced Google was Anthropic’s “cloud provider.,” which Google responded to a month later by investing another $2 billion on October 27 2023, “involving a $500 million upfront investment and an additional $1.5 billion to be invested over time,” bringing its total funding from 2023 to $6 billion.
In 2024, it would raise several more rounds — one in January for $750 million, another in March for $884.1 million, another in May for $452.3 million, and another $4 billion from Amazon in November 2024, which also saw it name AWS as Anthropic’s “primary cloud and training partner,” bringing its 2024 funding total to $6 billion.
In 2025 so far, it’s raised a $1 billion round from Google, a $3.5 billion venture round in March, opened a $2.5 billion credit facility in May, and completed a $13 billion venture round in September, valuing the company at $183 billion. This brings its total 2025 funding to $20 billion.
While I do not have Anthropic’s 2023 numbers, its spend on AWS in 2024 — around $1.35 billion — leaves (as I’ve mentioned) $4.85 billion in costs that are unaccounted for. The Information reports that costs for Anthropic’s 521 research and development staff reached $160 million in 2024, leaving 394 other employees unaccounted for (for 915 employees total), and also adding that Anthropic expects its headcount to increase to 1900 people by the end of 2025.
The Information also adds that Anthropic “expects to stop burning cash in 2027.”
This leaves two unanswered questions:
An optimist might argue that Anthropic is just growing its pile of cash so it’s got a warchest to burn through in the future, but I have my doubts. In a memo revealed by WIRED, Anthropic CEO Dario Amodei stated that “if [Anthropic wanted] to stay on the frontier, [it would] gain a very large benefit from having access to this capital,” with “this capital” referring to money from the Middle East.
Anthropic and Amodei’s sudden willingness to take large swaths of capital from the Gulf States does not suggest that it’s not at least a little desperate for capital, especially given Anthropic has, according to Bloomberg, “recently held early funding talks with Abu Dhabi-based investment firm MGX” a month after raising $13 billion.
In my opinion — and this is just my gut instinct — I believe that it is either significantly more expensive to run Anthropic than we know, or Anthropic’s leaked (and stated) revenue numbers are worse than we believe. I do not know one way or another, and will only report what I know.
So, I’m going to do this a little differently than you’d expect, in that I’m going to lay out how much these companies spent, and draw throughlines from that spend to its reported revenue numbers and product announcements or events that may have caused its compute costs to increase.
I’ve only got Cursor’s numbers from January through September 2025, but I have Anthropic’s AWS spend for both the entirety of 2024 and through September 2025.
So, this term is one of the most abused terms in the world of software, but in this case, I am sticking to the idea that it means “month times 12.” So, if a company made $10m in January, you would say that its annualized revenue is $120m. Obviously, there’s a lot of (when you think about it, really obvious) problems with this kind of reporting — and thus, you only ever see it when it comes to pre-IPO firms — but that’s besides the point.
I give you this explanation because, when contrasting Anthropic’s AWS spend with its revenues, I’ve had to work back from whatever annualized revenues were reported for that month.
Anthropic’s 2024 revenues are a little bit of a mystery, but, as mentioned above, The Information says it might be between $400 million and $600 million.
Here’s its monthly AWS spend.
I’m gonna be nice here and say that Anthropic made $600 million in 2024 — the higher end of The Information’s reporting — meaning that it spent around 226% of its revenue ($1.359 billion) on Amazon Web Services.
[Editor's note: this copy originally had incorrect maths on the %. Fixed now.]
Thanks to my own analysis and reporting from outlets like The Information and Reuters, we have a pretty good idea of Anthropic’s revenues for much of the year. That said, July, August, and September get a little weirder, because we’re relying on “almosts” and “approachings,” as I’ll explain as we go.
I’m also gonna do an analysis on a month-by-month basis, because it’s necessary to evaluate these numbers in context.
In this month, Anthropic’s reported revenue was somewhere from $875 million to $1 billion annualized, meaning either $72.91 million or $83 million for the month of January.
In February, as reported by The Information, Anthropic hit $1.4 billion annualized revenue, or around $116 million each month.
In March, as reported by Reuters, Anthropic hit $2 billion in annualized revenue, or $166 million in revenue.
Because February is a short month, and the launch took place on February 24 2025, I’m considering the launches of Claude 3.7 Sonnet and Claude Code’s research preview to be a cost burden in the month of March.
And man, what a burden! Costs increased by $59.1 million, primarily across compute categories, but with a large ($2 million since January) increase in monthly costs for S3 storage.
I estimate, based on a 22.4% compound growth rate, that Anthropic hit around $2.44 billion in annualized revenue in April, or $204 million in revenue.
Interestingly, this was the month where Anthropic launched its $100 and $200 dollar a month “Max” plans, and it doesn’t seem to have dramatically increased its costs. Then again, Max is also the gateway to things like Claude Code, which I’ll get to shortly.
In May, as reported by CNBC, Anthropic hit $3 billion in annualized revenue, or $250 million in monthly average revenue.
This was a big month for Anthropic, with two huge launches on May 22 2025 — its new, “more powerful” models Claude Sonnet and Opus 4, as well as the general availability of its AI coding environment Claude Code.
Eight days later, on May 30 2025, a page on Anthropic's API documentation appeared for the first time: "Service Tiers":
Different tiers of service allow you to balance availability, performance, and predictable costs based on your application’s needs.
We offer three service tiers:
- Priority Tier: Best for workflows deployed in production where time, availability, and predictable pricing are important
Standard: Best for bursty traffic, or for when you’re trying a new idea
Batch: Best for asynchronous workflows which can wait or benefit from being outside your normal capacity
Accessing the priority tier requires you to make an up-front commitment to Anthropic, and said commitment is based on a number of months (1, 3, 6 or 12) and the number of input and output tokens you estimate you will use each minute.
As I’ll get into in my June analysis, Anthropic’s Service Tiers exist specifically for it to “guarantee” your company won’t face rate limits or any other service interruptions, requiring a minimum spend, minimum token throughput, and for you to pay higher rates when writing to the cache — which is, as I’ll explain, a big part of running an AI coding product like Cursor.
Now, the jump in costs — $65.1 million or so between April and May — likely comes as a result of the final training for Sonnet and Opus 4, as well as, I imagine, some sort of testing to make sure Claude Code was ready to go.
In June, as reported by The Information, Anthropic hit $4 billion in annualized revenue, or $333 million.
Anthropic’s revenue spiked by $83 million this month, and so did its costs by $34.7 million.
I have, for a while, talked about the Subprime AI Crisis, where big tech and companies like Anthropic, after offering subsidized pricing to entice in customers, raise the rates on their customers to start covering more of their costs, leading to a cascade where businesses are forced to raise their prices to handle their new, exploding costs.
And I was god damn right. Or, at least, it sure looks like I am. I’m hedging, forgive me. I cannot say for certain, but I see a pattern.
It’s likely the June 2025 spike in revenue came from the introduction of service tiers, which specifically target prompt caching, increasing the amount of tokens you’re charged for as an enterprise customer based on the term of the contract, and your forecast usage.
You see, Anthropic specifically notes on its "service tiers" page that requests at the priority tier are "prioritized over all other requests to Anthropic," a rent-seeking measure that effectively means a company must either:
- Commit to at least a month, though likely 3-12 months of specific levels of input and output tokens a minute, based on what they believe they will use in the future, regardless of whether they do.
- Accept that access to Anthropic models will be slower at some point, in some way that Anthropic can't guarantee.Furthermore, the way that Anthropic is charging almost feels intentionally built to fuck over any coding startup that would use its service. Per the service tier page, Anthropic charges 1.25 for every time you write a token to the cache with a 5 minute TTL — or 2 tokens if you have a 1 hour TTL — and a longer cache is effectively essential for any background task where an agent will be working for more than 5 minutes, such as restructuring a particularly complex series of code, you know, the exact things that Cursor is well-known and marketed to do.
Furthermore, the longer something is in the cache, the better autocomplete suggestions for your code will be. It's also important to remember you're, at some point, caching the prompts themselves — so the instructions of what you want Cursor to do, meaning that the more complex the operation, the more expensive it'll now be for Cursor to provide the service with reasonable uptime.
Cursor, as Anthropic’s largest client (the second largest being Github Copilot), represents a material part of its revenue, and its surging popularity meant it was sending more and more revenue Anthropic’s way. Anysphere, the company that develops Cursor, hit $500 million annualized revenue ($41.6 million) by the end of May, which Anthropic chose to celebrate by increasing its costs.
On June 16 2025, Cursor launched a $200-a-month “Ultra” plan, as well as dramatic changes to its $20-a-month Pro pricing that, instead of offering 500 “fast” responses using models from Anthropic and OpenAI, now effectively provided you with “at least” whatever you paid a month (so $20-a-month got at least $20 of credit), massively increasing the costs for users, with one calling the changes a “rug pull” after spending $71 in a single day.
As I’ll get to later in the piece, Cursor’s costs exploded from $6.19 million in May 2025 to $12.67 million in June 2025, and I believe this is a direct result of Anthropic’s sudden and aggressive cost increases.
Similarly, Replit, another AI coding startup, moved to “Effort-Based Pricing” on June 18 2025. I have not got any information around its AWS spend.
I’ll get into this a bit later, but I find this whole situation disgusting.
In July, as reported by Bloomberg, Anthropic hit $5 billion in annualized revenue, or $416 million.
While July wasn’t a huge month for announcements, it was allegedly the month that Claude Code was generating “nearly $400 million in annualized revenue,” or $33.3 million (according to The Information, who says Anthropic was “approaching” $5 billion in annualized revenue - which likely means LESS than that - but I’m going to go with the full $5 billion annualized for sake of fairness.
There’s roughly an $83 million bump in Anthropic’s revenue between June and July 2025, and I think Claude Code and its new rates are a big part of it. What’s fascinating is that cloud costs didn’t increase too much — by only $1.8 million, to be specific.
In August, according to Anthropic, its run-rate “reached over $5 billion,” or in or around $416 million. I am not giving it anything more than $5 billion, especially considering in July Bloomberg’s reporting said “about $5 billion.”
Costs grew by $60.5 this month, potentially due to the launch of Claude Opus 4.1, Anthropic’s more aggressively expensive model, though revenues do not appear to have grown much along the way.
Yet what’s very interesting is that Anthropic — starting August 28 — launched weekly rate limits on its Claude Pro and Max plans. I wonder why?
Oh fuck! Look at that massive cost explosion!
Anyway, according to Reuters, Anthropic’s run rate is “approaching $7 billion” in October, and for the sake of fairness, I am going to just say it has $7 billion annualized, though I believe this number to be lower. “Approaching” can mean a lot of different things — $6.1 billion, $6.5 billion — and because I already anticipate a lot of accusations of “FUD,” I’m going to err on the side of generosity.
If we assume a $6.5 billion annualized rate, that would make this month’s revenue $541.6 million, or 95.8% of its AWS spend.
Nevertheless, Anthropic’s costs exploded in the space of a month by $135.2 million (35%) - likely due to the fact that users, as I reported in mid-July, were costing it thousands or tens of thousands of dollars in compute, a problem it still faces to this day, with VibeRank showing a user currently spending $51,291 in a calendar month on a $200-a-month subscription.
If there were other costs, they likely had something to do with the training runs for the launches of Sonnet 4.5 on September 29 2025 and Haiku 4.5 in October 2025.
While these costs only speak to one part of its cloud stack — Anthropic has an unknowable amount of cloud spend on Google Cloud, and the data I have only covers AWS — it is simply remarkable how much this company spends on AWS, and how rapidly its costs seem to escalate as it grows.
Though things improved slightly over time — in that Anthropic is no longer burning over 200% of its revenue on AWS alone — these costs have still dramatically escalated, and done so in an aggressive and arbitrary manner.
So, I wanted to visualize this part of the story, because I think it’s important to see the various different scenarios.
THE NUMBERS I AM USING ARE ESTIMATES CALCULATED BASED ON 25%, 50% and 100% OF THE AMOUNTS THAT ANTHROPIC HAS SPENT ON AMAZON WEB SERVICES THROUGH SEPTEMBER.
I apologize for all the noise, I just want it to be crystal clear what you see next.
As you can see, all it takes is for Anthropic to spend (I am estimating) around 25% of its Amazon Web Services bills (for a total of around $3.33 billion in compute costs through the end of September) to savage any and all revenue ($2.55 billion) it’s making.
Assuming Anthropic spends half of its AWS spend on Google Cloud, this number climbs to $3.99 billion, and if you assume - and to be clear, this is an estimate - that it spends around the same on both Google Cloud and AWS, Anthropic has spent $5.3 billion on compute through the end of September.
I can’t tell you which it is, just that we know for certain that Anthropic is spending money on Google Cloud, and because Google owns 14% of the company — rivalling estimates saying Amazon owns around 15-19% — it’s fair to assume that there’s a significant spend.
I have sat with these numbers for a great deal of time, and I can’t find any evidence that Anthropic has any path to profitability outside of aggressively increasing the prices on their customers to the point that its services will become untenable for consumers and enterprise customers alike.
As you can see from these estimated and reported revenues, Anthropic’s AWS costs appear to increase in a near-linear fashion with its revenues, meaning that the current pricing — including rent-seeking measures like Priority Service Tiers — isn’t working to meet the burden of its costs.
We do not know its Google Cloud spend, but I’d be shocked if it was anything less than 50% of its AWS bill. If that’s the case, Anthropic is in real trouble - the cost of the services underlying its business increase the more money they make.
It’s becoming increasingly apparent that Large Language Models are not a profitable business. While I cannot speak to Amazon Web Services’ actual costs, it’s making $2.66 billion from Anthropic, which is the second largest foundation model company in the world.
Is that really worth $105 billion in capital expenditures? Is that really worth building a giant 1200 acre data center in Indiana with 2.2GW of electricity?
What’s the plan, exactly? Let Anthropic burn money for the foreseeable future until it dies, and then pick up the pieces? Wait until Wall Street gets mad at you and then pull the plug?
Who knows.
But let’s change gears and talk about Cursor — Anthropic’s largest client and, at this point, a victim of circumstance.
Amazon sells Anthropic’s models through Amazon Bedrock, and I believe that AI startups are compelled to spend some of their AI model compute costs through Amazon Web Services. Cursor also sends money directly to Anthropic and OpenAI, meaning that these costs are only one piece of its overall compute costs. In any case, it’s very clear that Cursor buys some degree of its Anthropic model spend through Amazon.
I’ll also add that Tom Dotan of Newcomer reported a few months ago that an investor told him that “Cursor is spending 100% of its revenue on Anthropic.”
Unlike Anthropic, we lack thorough reporting of the month-by-month breakdown of Cursor’s revenues. I will, however, mention them in the month I have them.
For the sake of readability — and because we really don’t have much information on Cursor’s revenues beyond a few months — I’m going to stick to a bullet point list.
As discussed above, Cursor announced (along with their price change and $200-a-month plan) several multi-year partnerships with xAI, Anthropic, OpenAI and Google, suggesting that it has direct agreements with Anthropic itself versus one with AWS to guarantee “this volume of compute at a predictable price.”
Based on its spend with AWS, I do not see a strong “minimum” spend that would suggest that they have a similar deal with Amazon — likely because Amazon handles more than its infrastructure than just compute, but incentivizes it to spend on Anthropic’s models through AWS by offering discounts, something I’ve confirmed with a source.
In any case, here’s what Cursor spent on AWS.
When I wrote that Anthropic and OpenAI had begun the Subprime AI Crisis back in July, I assumed that the increase in costs was burdensome, but having the information from its AWS bills, it seems that Anthropic’s actions directly caused Cursor’s costs to explode by over 100%.
While I can’t definitively say “this is exactly what did it,” the timelines match up exactly, the costs have never come down, Amazon offers provisioned throughput, and, more than likely, Cursor needs to keep a standard of uptime similar to that of Anthropic’s own direct API access.
If this is what happened, it’s deeply shameful.
Cursor, Anthropic’s largest customer, in the very same month it hit $500 million in annualized revenue, immediately had its AWS and Anthropic-related costs explode to the point that it had to dramatically reduce the value of its product just as it hit the apex of its revenue growth.
It’s very difficult to see Service Tiers as anything other than an aggressive rent-seeking maneuver.
Yet another undiscussed part of the story is that the launch of Claude 4 Opus and Sonnet — and the subsequent launch of Service Tiers — coincided with the launch of Claude Code, a product that directly competes with Cursor, without the burden of having to pay itself for the cost of models or, indeed, having to deal with its own “Service Tiers.”
Anthropic may have increased the prices on its largest client at the time it was launching a competitor, and I believe that this is what awaits any product built on top of OpenAI or Anthropic’s models.
I realize this has been a long, number-stuffed article, but the long-and-short of it is simple: Anthropic is burning all of its revenue on compute, and Anthropic will willingly increase the prices on its customers if it’ll help it burn less money, even though that doesn’t seem to be working.
What I believe happened to Cursor will likely happen to every AI-native company, because in a very real sense, Anthropic’s products are a wrapper for its own models, except it only has to pay the (unprofitable) costs of running them on Amazon Web Services and Google Cloud.
As a result, both OpenAI and Anthropic can (and may very well!) devour the market of any company that builds on top of their models.
OpenAI may have given Cursor free access to its GPT-5 models in August, but a month later on September 15 2025 it debuted massive upgrades to its competitive “Codex” platform.
Any product built on top of an AI model that shows any kind of success can be cloned immediately by OpenAI and Anthropic, and I believe that we’re going to see multiple price increases on AI-native companies in the next few months. After all, OpenAI already has its own priority processing product, which it launched shortly after Anthropic’s in June.
The ultimate problem is that there really are no winners in this situation. If Anthropic kills Cursor through aggressive rent-seeking, that directly eats into its own revenues. If Anthropic lets Cursor succeed, that’s revenue, but it’s also clearly unprofitable revenue. Everybody loses, but nobody loses more than Cursor’s (and other AI companies’) customers.
I’ve come away from this piece with a feeling of dread.
Anthropic’s costs are out of control, and as things get more desperate, it appears to be lashing out at its customers, both companies like Cursor and Claude Code customers facing weekly rate limits on their more-powerful models who are chided for using a product they pay for. Again, I cannot say for certain, but the spike in costs is clear, and it feels like more than a coincidence to me.
There is no period of time that I can see in the just under two years of data I’ve been party to that suggests that Anthropic has any means of — or any success doing — cost-cutting, and the only thing this company seems capable of doing is increasing the amount of money it burns on a monthly basis.
Based on what I have been party to, the more successful Anthropic becomes, the more its services cost. The cost of inference is clearly increasing for customers, but based on its escalating monthly costs, the cost of inference appears to be high for Anthropic too, though it’s impossible to tell how much of its compute is based on training versus running inference.
In any case, these costs seem to increase with the amount of money Anthropic makes, meaning that the current pricing of both subscriptions and API access seems unprofitable, and must increase dramatically — from my calculations, a 100% price increase might work, but good luck retaining every single customer and their customers too! — for this company to ever become sustainable.
I don’t think that people would pay those prices. If anything, I think what we’re seeing in these numbers is a company bleeding out from costs that escalate the more that its user base grows. This is just my opinion, of course.
I’m tired of watching these companies burn billions of dollars to destroy our environment and steal from everybody. I’m tired that so many people have tried to pretend there’s a justification for burning billions of dollars every year, clinging to empty tropes about how this is just like Uber or Amazon Web Services, when Anthropic has built something far more mediocre.
Mr. Amodei, I am sure you will read this piece, and I can make time to chat in person on my show Better Offline. Perhaps this Friday? I even have some studio time on the books.
2025-10-17 23:41:21
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One of the only consistent critiques of my work is that I’m angry, irate, that I am taking myself too seriously, that I’m swearing too much, and that my arguments would be “better received” if I “calmed down.”
Fuck that.
Look at where being timid or deferential has got us. Broadcom and OpenAI have announced another 10GW of custom chips and supposed capacity which will supposedly get fully deployed by the end of 2029, and still the media neutrally reports these things as not simply doable, but rational.
To be clear, building a gigawatt of data center capacity costs at least $32.5 billion (though Jensen Huang says the computing hardware alone costs $50 billion, which excludes the buildings themselves and the supporting power infrastructure, and Barclays Bank says $50 billion to $60 billion) and takes two and a half years.
In fact, fuck it — I’m updating my priors. Let’s say it’s a nice, round $50 billion per gigawatt of data center capacity. $32.5 billion is what it cost to build Stargate Abilene, but that estimate was based on Crusoe’s 1.2GW of compute for OpenAI being part of a $15 billion joint venture, which meant a gigawatt of compute runs about $12.5 billion, and Abilene’s 8 buildings are meant to hold 50,000 NVIDIA GB200 GPUs and their associated networking infrastructure, so let’s say a gigawatt is around 333,333 Blackwell GPUs at $60,000 a piece, so about $20 billion a gigawatt.
However, this mathematics assumed that every cost associated would be paid by the Joint Venture. Lancium, the owner of the land that is allegedly building the power infrastructure, has now raised over a billion dollars.
This maths also didn’t include the cost of the associated networking infrastructure around the GB200s. So, guess what? We’re doing $50 billion now.
OpenAI has now promised 33GW of capacity across AMD, NVIDIA, Broadcom and the seven data centers built under Stargate, though one of those — in Lordstown, Ohio — is not actually a data center, with my source being “SoftBank,” speaking to WKBN in Lordstown Ohio, which said it will “not be a full-blown data center,” and instead be “at the center of cutting-edge technology that will encompass storage containers that will hold the infrastructure for AI and data storage.”
This wasn’t hard to find, by the way! I googled “SoftBank Lordstown” and up it came, ready for me to read with my eyes.
Putting all of that aside, I think it’s time that everybody started taking this situation far more seriously, by which I mean acknowledging the sheer recklessness and naked market manipulation taking place.
But let’s make it really simple, and write out what’s meant to happen in the next year:
In my most conservative estimate, these data centers will cost over $100 billion, and to be clear, a lot of that money needs to already be in OpenAI’s hands to get the data centers built. Or, some other dupe has to a.) have the money, and b.) be willing to front it.
All of this is a fucking joke. I’m sorry, I know some of you will read this, cowering from your screen like a B-movie vampire that just saw a crucifix, but it is a joke, and it is a fucking stupid joke, the only thing stupider being that any number of respectable media outlets are saying these things like they’ll actually happen.
There is not enough time to build these things. If there was enough time, there wouldn’t be enough money. If there was enough money, there wouldn’t be enough transformers, electrical-grade steel, or specialised talent to run the power to the data centers. Fuck! Piss! Shit! Swearing doesn’t change the fact that I’m right — none of what OpenAI, NVIDIA, Broadcom, and AMD are saying is possible, and it’s fair to ask why they’re saying it.
I mean, we know. Number must go up, deal must go through, and Jensen Huang wouldn’t go on CNBC and say “yeah man if I’m honest I’ve got no fucking clue how Sam Altman is going to pay me, other than with the $10 billion I’m handing him in a month. Anyway, NVIDIA’s accounts receivables keep increasing every quarter for a normal reason, don’t worry about it.”
But in all seriousness, we now have three publicly-traded tech firms that have all agreed to join Sam Altman’s No IT Loads Refused Cash Dump, all promising to do things on insane timelines that they — as executives of giant hardware manufacturers, or human beings with warm bodies and pulses and sciatica — all must know are impossible to meet.
What is the media meant to do? What are we, as regular people, meant to do? These stocks keep pumping based on completely nonsensical ideas, and we’re all meant to sit around pretending things are normal and good. They’re not! At some point somebody’s going to start paying people actual, real dollars at a scale that OpenAI has never truly had to reckon with.
In this piece, I’m going to spell out in no uncertain terms exactly what OpenAI has to do in the next year to fulfil its destiny — having a bunch of capacity that cost ungodly amounts of money to serve demand that never arrives.
Yes, yes, I know, you’re going to tell me that OpenAI has 800 million weekly active users, and putting aside the fact that OpenAI’s own research (see page 10, footnote 20) says it double-counts users who are logged out if they’re use different devices, OpenAI is saying it wants to build 250 gigawatts of capacity by 2033, which will cost it $10 trillion dollars, or one-third of the entire US economy last year.
Who the fuck for?
One thing that’s important to note: In February, Goldman Sachs estimated that the global data center capacity was around 55GW. In essence, OpenAI says it wants to add five times that capacity — something that has grown organically over the past thirty or so years — by itself, and in eight years.
And yes, it’ll cost one-third of America’s output in 2024. This is not a sensible proposition.
Even if you think that OpenAI’s growth is impressive — it went from 700 million to 800 million weekly active users in the last two months — that is not the kind of growth that says “build capacity assuming that literally every single human being on Earth uses this all the time.”
As an aside: Altman is already lying about his available capacity. According to an internal Slack note seen by Alex Heath of Sources, Altman claims that OpenAI started the year with “around” 230 megawatts of capacity and is “now on track to exit 2025 north of 2GW of operational capacity.” Unless I’m much mistaken OpenAI doesn’t have any capacity of its own — and according to Mr. Altman, it’s somehow built or acquired 1.7GW of capacity from somewhere without disclosing it.
For context, 1.7GW is the equivalent of every data center in the UK that was operational last year.
Where is this coming from? Is this CoreWeave? It only has — at most — 900MW of capacity by the end of 2025. Where’d all the extra capacity come from? Who knows! It isn’t Stargate Abilene that’s for sure — they’ve only got one operational building and 200MW of power, meaning they can only really support 130MW of IT loads, because of that pesky reserve I mentioned earlier.
Anyway, what exactly is OpenAI doing? Why does it need all this capacity? Even if it hits its $13 billion revenue projection for this year (it’s only at $5.3 billion or so as of the end of August, and for OpenAI to hit its targets it’ll need to make $1.5bn+ a month very soon), does it really think it’s going to effectively 10x the entire company from here? What possible sign is there of that happening other than a conga-line of different executives willing to stake their reputations on blatant lies peddled by a man best known for needing, at any given moment, another billion dollars.
According to The Information, OpenAI spent $6.7 billion on research and development in the first six months of 2025, and according to Epoch AI, most of the $5 billion it spent on research and development in 2024 was spent on research, experimental, or derisking runs (basically running tests before doing the final testing run) and models it would never release, with only $480 million going to training actual models that people will use.
I should also add that GPT 4.5 was a dud, and even Altman called it giant, expensive, and said it “wouldn’t crush benchmarks.”
I’m sorry, but what exactly is it that OpenAI has released in the last year-and-a-half that was worth burning $11.7 billion for? GPT 5? That was a huge letdown! Sora 2? The giant plagiarism machine that it’s already had to neuter?
What is it that any of you believe that OpenAI is going to do with these fictional data centers?
The problem with ChatGPT isn’t just that it hallucinates — it’s that you can’t really say exactly what it can do, because you can’t really trust that it can do anything. Sure, it’ll get a few things right a lot of the time, but what task is it able to do every time that you actually need?
Say the answer is “something that took me an hour now takes me five minutes.” Cool! How many of those do you get? Again, OpenAI wants to build 250 gigawatts of data centers, and will need around ten trillion dollars to do it. “It’s going to be really good” is no longer enough.
And no, I’m sorry, they are not building AGI. He just told Politico a few weeks ago that if we didn’t have “models that are extraordinarily capable and do things that we ourselves cannot do” by 2030 he would be “very surprised.”
Wow! What a stunning and confident statement. Let’s give this guy the ten trillion dollars he needs! And he’s gonna need it soon if he wants to build 250 gigawatts of capacity by 2033.
But let’s get a little more specific.
Based on my calculations, in the next six months, OpenAI needs at least $50 billion to build a gigawatt of data centers for Broadcom — and to hit its goal of 10 gigawatts of data centers by end of 2029, at least another $200 billion in the next 12 months, not including at least $50 billion to build a gigawatt of data centers for NVIDIA, $40 billion to pay for its 2026 compute, at least $50 billion to buy chips and build a gigawatt of data centers for AMD, at least $500 million to build its consumer device (and they can’t seem to work out what to build), and at least a billion dollars to hand off to ARM for a CPU to go with the new chips from Broadcom.
That’s $391.5 billion dollars! That’s $23.5 billion more than the $368 billion of global venture capital raised in 2024! That’s nearly 11 times Uber’s total ($35.8 billion) lifetime funding, or 5.7 times the $67.6 billion in capital expenditures that Amazon spent building Amazon Web Services!
On top of all of this are OpenAI’s other costs. According to The Information, OpenAI spent $2 billion alone on Sales and Marketing in the first half of 2025, and likely spends billions of dollars on salaries, meaning that it’ll likely need at least another $10 billion on top. As this is a vague cost, I’m going with a rounded $400 billion number, though I believe it’s actually going to be more.
And to be clear, to complete these deals by the end of 2026, OpenAI needs large swaths of this money by February 2026.
I know, I know, you’re going to say that OpenAI will simply “raise debt” and “work it out,” but OpenAI has less than a year to do that, because OpenAI has promised in its own announcements that all of these things would happen by the end of December 2026, and even if they’re going to happen in 2027, data centers require actual money to begin construction, and Broadcom, NVIDIA and AMD are going to actually require cash for those chips before they ship them.
Even if OpenAI finds multiple consortiums of paypigs to take on the tens of billions of dollars of data center funding, there are limits, and based on OpenAI’s aggressive (and insane) timelines, they will need to raise multiple different versions of the largest known data center deals of all time, multiple times a year, every single year.
Say that happens. OpenAI will still need to pay those compute contracts with Oracle, CoreWeave, Microsoft (I believe its Azure credits have run out) and Google (via CoreWeave) with actual, real cash — $40 billion dollars worth — when it’s already burning $9.2 billion in the first half of 2026 on compute against revenues of $4.3 billion. OpenAI will still need to pay its staff, its storage, its sales and marketing department that cost it $2 billion in the first half of 2026, all while converting its non-profit into a for-profit by the end of the year, or it loses $20 billion in funding from SoftBank.
Also, if it doesn’t convert to a for-profit by October 2026, its $6.6 billion funding round from 2024 converts to debt.
The burden that OpenAI is putting on the financial system is remarkable, and actively dangerous. It would absorb, at this rate, the capital expenditures of multiple hyperscalers, requiring multiple $30 billion debt financing operations a year, and for it to hit its goal of 250 gigawatts by the end of 2033, it will likely have to have outpaced the capital expenditures of any other company in the world.
OpenAI is an out-of-control monstrosity that is going to harm every party that depends upon it completing its plans. For it to succeed, it will have to absorb over a trillion dollars a year — and for it to hit its target, it will likely have to eclipse the $1.7 trillion in global private equity deal volume in 2024, and become a significant part of global trade ($33 trillion in 2025).
There isn’t enough money to do this without diverting most of the money that exists to doing it, and even if that were to happen, there isn’t enough time to do any of the stuff that has been promised in anything approaching the timelines promised, because OpenAI is making this up as it goes along and somehow everybody is believing it.
At some point, OpenAI is going to have to actually do the things it has promised to do, and the global financial system is incapable of supporting them.
And to be clear, OpenAI cannot really do any of the things it’s promised.
Just take a look at the Oracle deal!
None of this bullshit is happening, and it’s time to be honest about what’s actually going on.
OpenAI is not building “the AI industry,” as this is capacity for one company that burns billions of dollars and has absolutely no path to profitability.
This is a giant, selfish waste of money and time, one that will collapse the second that somebody’s confidence wavers.
I realize that it’s tempting to write “Sam Altman is building a giant data center empire,” but what Sam Altman is actually doing is lying. He is lying to everybody.
He is saying that he will build 250GW of data centers in the space of eight years, an impossible feat, requiring more money than anybody would ever give him in volumes and intervals that are impossible for anybody to raise.
Sam Altman’s singular talent is finding people willing to believe his shit or join him in an economy-supporting confidence game, and the recklessness of continuing to do so will only harm retail investors — regular people beguiled by the bullshit machine and bullshit masters making billions promising they’ll make trillions.
To prove it, I’m going to write down everything that will need to take place in the next twelve months for this to happen, and illustrate the timelines of everything involved.
2025-10-10 21:35:34
Readers: I’ve done a very generous “free” portion of this newsletter, but I do recommend paying for premium to get the in-depth analysis underpinning the intro. That being said, I want as many people as possible to get the general feel for this piece. Things are insane, and it’s time to be realistic about what the future actually looks like.
We’re in a bubble. Everybody says we’re in a bubble. You can’t say we’re not in a bubble anymore without sounding insane, because everybody is now talking about how OpenAI has promised everybody $1 trillion — something you could have read about two weeks ago on my premium newsletter.
Yet we live in a chaotic, insane world, where we can watch the news and hear hand-wringing over the fact that we’re in a bubble, read article after CEO after article after CEO after analyst after investor saying we’re in a bubble, yet the market continues to rip ever-upward on increasingly more-insane ideas, in part thanks to analysts that continue to ignore the very signs that they’re relied upon to read.
AMD and OpenAI signed a very strange deal where AMD will give OpenAI the chance to buy 160 million shares at a cent a piece, in tranches of indeterminate size, for every gigawatt of data centers OpenAI builds using AMD’s chips, adding that OpenAI has agreed to buy “six gigawatts of GPUs.”
This is a peculiar way to measure GPUs, which are traditionally measured in the price of each GPU, but nevertheless, these chips are going to be a mixture of AMD’s mi450 instinct GPUs — which we don’t know the specs of! — and its current generation mi350 GPUs, making the actual scale of these purchases a little difficult to grasp, though the Wall Street Journal says it would “result in tens of billions of dollars in new revenue” for AMD.
This AMD deal is weird, but one that’s rigged in favour of Lisa Su and AMD. OpenAI doesn’t get a dollar at any point - it has work out how to buy those GPUs and figure out how to build six further gigawatts of data centers on top of the 10GW of data centers it promised to build for NVIDIA and the seven-to-ten gigawatts that are allegedly being built for Stargate, bringing it to a total of somewhere between 23 and 26 gigawatts of data center capacity.
Hell, while we’re on the subject, has anyone thought about how difficult and expensive it is to build a data center?
Everybody is very casual with how they talk about Sam Altman’s theoretical promises of trillions of dollars of data center infrastructure, and I'm not sure anybody realizes how difficult even the very basics of this plan will be.
Nevertheless, everybody is happily publishing stories about how Stargate Abilene Texas — OpenAI’s massive data center with Oracle — is “open,” by which they mean two buildings, and I’m not even confident both of them are providing compute to OpenAI yet. There are six more of them that need to get built for this thing to start rocking at 1.2GW — even though it’s only 1.1GW according to my sources in Abilene.
But, hey, sorry — one minute — while we’re on that subject, did anybody visiting Abilene in the last week or so ever ask whether they’ll have enough power there?
Don’t worry, you don’t need to look. I’m sure you were just about to, but I did the hard work for you and read up on it, and it turns out that Stargate Abilene only has 200MW of power — a 200MW substation that, according to my sources, has only been built within the last couple of months, with 350MWs of gas turbine generators that connect to a natural gas power plant that might get built by the end of the year.
Said turbine is extremely expensive, featuring volatile pricing (for context, natural gas price volatility fell in Q2 2025…to 69% annualized) and even more volatile environmental consequences, and is, while permitted for it (this will download the PDF of the permit), impractical and expensive to use long-term.
Analyst James van Geelen, founder of Citrini Research recently said on Bloomberg’s Odd Lots podcast that these are “not the really good natural gas turbines” because the really good ones would take seven years to deliver due to a natural gas turbine shortage.
But they’re going to have to do. According to sources in Abilene, developer Lancium has only recently broken ground on the 1GW substation and five transformers OpenAI’s going to need to build out there, and based on my conversations with numerous analysts and researchers, it does not appear that Stargate Abilene will have sufficient power before the year 2027.
Then there’s the question of whether 1GW of power actually gets you 1GW of compute. This is something you never see addressed in the coverage of OpenAI’s various construction commitments, but it’s an important point to make. Analyst Daniel Bizo, Research Director at the Uptime Institute, explained that 1 gigawatt of power is only sufficient to power (roughly) 700 megawatts of data center capacity. We’ll get into the finer details of that later in this newsletter, but if we assume that ratio is accurate, we’re left with a troubling problem.
That figure represents a 1.43 PUE — Power Usage Effectiveness — and if we apply that to Stargate Abilene, we see that it needs at least 1.7GW of power, and currently only has 200MW.
As an aside, I need to clear something up, because everybody — including myself! — has been getting this wrong.
When you read “1.2GW data center,” they are almost certainly referring to the data center’s IT load — which is the power consumed by all of the computing equipment inside, but not the cooling systems or power lost in the infrastructure bringing the electricity to the gear itself. The amount of non-IT load power required, furthermore, can fluctuate.
Data centers need far more power than their IT load, and any time you read a “gigawatt” data center, know that they need about 30% more power than the amount of capacity the data center has.
Stargate Abilene does not have sufficient power to run at even half of its supposed IT load of 1.2GW, and at its present capacity — assuming that the gas turbines function at full power — can only hope to run 370MW to 460MW of IT load.
I’ve seen article after article about the gas turbines and their use of fracked gas — a disgusting and wasteful act typical of OpenAI — but nobody appears to have asked “how much power does a 1.2GW data center require?” and then chased it with “how much power does Stargate Abilene have?”
The answer is not enough, and the significance of said “not enough” is remarkable.
Today, I’m going to tell you, at length, how impossible the future of generative AI is.
Gigawatt data centers are a ridiculous pipe dream, one that runs face-first into the walls of reality.
The world’s governments and media have been far too cavalier with the term “gigawatt,” casually breezing by the fact that Altman’s plans require 17 or more nuclear reactors’ worth of power, as if building power is quick and easy and cheap and just happens.
I believe that many of you think that this is an issue of permitting — of simply throwing enough money at the problem — when we are in the midst of a shortage in the electrical grade steel and transformers required to expand America’s (and the world’s) power grid.
I realize it’s easy to get blinded by the constant drumbeat of “gargoyle-like tycoon cabal builds 1GW data center” and feel that they will simply overwhelm the problem with money, but no, I’m afraid that isn’t the case at all, and all of this is so silly, so ridiculous, so cartoonishly bad that it threatens even the seemingly-infinite wealth of Elon Musk, with xAI burning over a billion dollars a month and planning to spend tens of billions of dollars building the Colossus 2 data center, dragging two billion dollars from SpaceX in his desperate quest to burn as much money as possible for no reason.
This is the age of hubris — a time in which we are going to watch stupid, powerful and rich men fuck up their legacies by finding a technology so vulgar in its costs and mythical outcomes that it drives the avaricious insane and makes fools of them.
Or perhaps this is what happens when somebody believes they’ve found the ultimate con — the ability to become both the customer and the business, which is exactly what NVIDIA is doing to fund the chips behind Colossus 2.
According to Bloomberg, NVIDIA is creating a company — a “special purpose vehicle” — that it will invest $2 billion in, along with several other backers. Once that’s done, the special purpose vehicle will then use that equity to raise debt from banks, buy GPUs from NVIDIA, and then rent those GPUs to Elon Musk for five years.
Hell, why make it so complex? NVIDIA invested money in a company specifically built to buy chips from it, which then promptly handed the money back to NVIDIA along with a bunch of other money, and then whatever happened next is somebody else’s problem.
Right?
Actually, wait — how long do GPUs last, exactly? Four years for training? Three years? The A100 GPU started shipping in May 2020, and the H100 (and the Hopper GPU generation) entered full production in September 2022, meaning that we’re hurtling at speed toward the time in which we’re going to start seeing a remarkable amount of chips start wearing down, which should be a concern for companies like Microsoft, which bought 150,000 Hopper GPUs in 2023 and 485,000 of them in 2024.
Alright, let me just be blunt: the entire economy of debt around GPUs is insane.
Assuming these things don’t die within five years (their warranties generally end in three), their value absolutely will, as NVIDIA has committed to releasing a new AI chip every single year, likely with significant increases to power and power efficiency. At the end of the five year period, the Special Purpose Vehicle will be the proud owner of five-year-old chips that nobody is going to want to rent at the price that Elon Musk has been paying for the last five years. Don’t believe me? Take a look at the rental prices for H100 GPUs that went from $8-an-hour in 2023 to $2-an-hour in 2024, or the Silicon Data Indexes (aggregated realtime indexes of hourly prices) that show H100 rentals at around $2.14-an-hour and A100 rentals at a dollar-an-hour, with Vast.AI offering them at as little as $0.67 an hour.
This is, by the way, a problem that faces literally every data center being built in the world, and I feel insane talking about it. It feels like nobody is talking about how impossible and ridiculous all of this is. It’s one thing that OpenAI has promised one trillion dollars to people — it’s another that large swaths of that will be spent on hardware that will, by the end of these agreements, be half-obsolete and generating less revenue than ever.
Think about it. Let’s assume we live in a fantasy land where OpenAI is somehow able to pay Oracle $300 billion over 5 years — which, although the costs will almost certainly grow over time, and some of the payments are front-loaded, averages out to $5bn each month, which is a truly insane number that’s in excess of what Netflix makes in revenue.
Said money is paying for access to Blackwell GPUs, which will, by then, be at least two generations behind, with NVIDIA’s Vera Rubin GPUs due next year. What happens to that GPU infrastructure? Why would OpenAI continue to pay the same rental rate for five-year-old Blackwell GPUs?
All of these ludicrous investments are going into building data centers full of what will, at that point, be old tech.
Let me put it in simple terms: imagine you, for some reason, rented an M1 Mac when it was released in 2020, and your rental was done in 2025, when we’re onto the M4 series. Would you expect somebody to rent it at the same price? Or would they say “hey, wait a minute, for that price I could rent one of the newer generation ones.” And you’d be bloody right!
Now, I realize that $70,000 data center GPUs are a little different to laptops, but that only makes their decline in value more profound, especially considering the billions of dollars of infrastructure built around them.
And that’s the problem. Private equity firms are sinking $50 billion or more a quarter into theoretical data center projects full of what will be years-old GPU technology, despite the fact that there’s no real demand for generative AI compute, and that’s before you get to the grimmest fact of all: that even if you can build these data centers, it will take years and billions of dollars to deliver the power, if it’s even possible to do so.
Harvard economist Jason Furman estimates that data centers and software accounted for 92% of GDP growth in the first half of this year, in line with my conversation with economist Paul Kedrosky from a few months ago.
All of this money is being sunk into infrastructure for an “AI revolution” that doesn’t exist, as every single AI company is unprofitable, with pathetic revenues ($61 billion or so if you include CoreWeave and Lambda, both of which are being handed money by NVIDIA), impossible-to-control costs that have only ever increased, and no ability to replace labor at scale (and especially not software engineers).
OpenAI needs more than a trillion dollars to pay its massive cloud compute bills and build 27 gigawatts of data centers, and to get there, it needs to start making incredible amounts of money, a job that’s been mostly handed to Fidji Simo, OpenAI’s new CEO of Applications, who is solely responsible for turning a company that loses billions of dollars into one that makes $200 billion in 2030 with $38 billion in profit. She’s been set up to fail, and I’m going to explain why.
In fact, today I’m going to explain to you how impossible all of this is — not just expensive, not just silly, but actively impossible within any of the timelines set.
Stargate will not have the power it needs before the middle of 2026 — the beginning of Oracle’s fiscal year 2027, when OpenAI has to pay it $30 billion for compute — or, according to The Information, choose to walk away if the capacity isn’t complete. And based on my research, analysis and discussions with power and data center analysts, gigawatt data centers are, by and large, a pipedream, with their associated power infrastructure taking two to four years, and that’s if everything goes smoothly.
OpenAI cannot build a gigawatt of data centers for AMD by the “second half of 2026.” It haven’t even announced the financing, let alone where the data center might be, and until it does that it’s impossible to plan the power, which in and of itself takes months before you even start building.
Every promise you’re reading in the news is impossible. Nobody has even built a gigawatt data center, and more than likely nobody ever will. Stargate Abilene isn’t going to be ready in 2026, won’t have sufficient power until at best 2027, and based on the conversations I’ve had it’s very unlikely it will build that gigawatt substation before the year 2028.
In fact, let me put it a little simpler: all of those data center deals you’ve seen announced are basically bullshit. Even if they get the permits and the money, there are massive physical challenges that cannot be resolved by simply throwing money at them.
Today I’m going to tell you a story of chaos, hubris and fantastical thinking. I want you to come away from this with a full picture of how ridiculous the promises are, and that’s before you get to the cold hard reality that AI fucking sucks.
2025-10-04 00:27:25
What is OpenAI?
I realize you might say "a foundation model lab" or "the company that runs ChatGPT," but that doesn't really give the full picture of everything it’s promised, or claimed, or leaked that it was or would be.
No, really, if you believe its leaks to the press...
To be clear, many of these are ideas that OpenAI has leaked specifically so the media can continue to pump up its valuation and continue to raise the money it needs — at least $1 Trillion over the next four or five years, and I don't believe the theoretical (or actual) costs of many of the things I've listed are included.
OpenAI wants you to believe it is everything, because in reality it’s a company bereft of strategy, focus or vision. The GPT-5 upgrade for ChatGPT was a dud — an industry-wide embarrassment for arguably the most-hyped product in AI history, one that (as I revealed a few months ago) costs more to operate than its predecessor, not because of any inherent capability upgrade, but how it actually processes the prompts its user provides — and now it's unclear what it is that this company does.
Does it make hardware? Software? Ads? Is it going to lease you GPUs to use for your own AI projects? Is it going to certify you as an AI expert? Notice how I've listed a whole bunch of stuff that isn't ChatGPT, which will, if you look at The Information's reporting of its projections, remain the vast majority of its revenue until 2027, at which point "agents" and "new products including free user monetization" will magically kick in.
In reality, OpenAI is an extremely boring (and bad!) software business. It makes the majority of its revenue selling subscriptions to ChatGPT, and apparently had 20 million paid subscribers (as of April) and 5 million business subscribers (as of August, though 500,000 of them are Cal State University seats paid at $2.50 a month).
It also loses incredibly large amounts of money.
Yes, I realize that OpenAI also sells access to its API, but as you can see from the chart above, it is making a teeny tiny sliver of revenue from it in 2025, though I will also add that this chart has a little bit of green for "agent" revenue, which means it's very likely bullshit. Operator, OpenAI's so-called agent, is barely functional, and I have no idea how anyone would even begin to charge money for it outside of "please try my broken product."
In any case, API sales appear to be a very, very small part of OpenAI's revenue stream, and that heavily suggests a lack of interest in integrating its models at scale.
Worse still, this effectively turns OpenAI into an AI startup.
Think about it: if OpenAI can't make the majority of its money through "innovating" in the development of large language models (LLMs), then it’s just another company plugging LLMs into its software. While ChatGPT may be a very popular product, it is, by definition (and in its name!) a GPT wrapper, with the few differences being that OpenAI pays its own immediate costs, has the people necessary to continue improving its own models, and also continually makes promises to convince people it’s anything other than just another AI startup.
In fact, the only real difference is the amount of money backing it. Otherwise, OpenAI could be literally any foundation model company, and with a lack of real innovation within those models, it’s just another startup trying to find ways to monetize generative AI, an industry that only ever seems to lose money.
As a result, we should start evaluating OpenAI as just another AI startup, as its promises do not appear to mesh with any coherent strategy, other than "we need $1 trillion dollars." There does not seem to be much of a plan on a day-to-day basis, nor does there seem to be one about what OpenAI should be, other than that OpenAI will be a consumer hardware, consumer software, enterprise SaaS and data center operator, as well as running a social network.
As I've discussed many times, LLMs are inherently flawed due to their probabilistic nature."Hallucinations" — when a model authoritatively states something is true when it isn't (or takes an action that seems the most likely course of action, even if it isn't the right one) — are a "mathematically inevitable" according to OpenAI's own research feature of the technology, meaning that there is no fixing their most glaring, obvious problem, even with "perfect data."
I'd wager the reason OpenAI is so eager to build out so much capacity while leaking so many diverse business lines is an attempt to get away from a dark truth: that when you peel away the hype, ChatGPT is a wrapper, every product it makes is a wrapper, and OpenAI is pretty fucking terrible at making products.
Today I'm going to walk you through a fairly unique position: that OpenAI is just another boring AI startup lacking any meaningful product roadmap or strategy, using the press as a tool to pump its bags while very rarely delivering on what it’s promised. It is a company with massive amounts of cash, industrial backing, and brand recognition, and otherwise is, much like its customers, desperately trying to work out how to make money selling products built on top of Large Language Models.
OpenAI lives and dies on its mythology as the center of innovation in the world of AI, yet reality is so much more mediocre. Its revenue growth is slowing, its products are commoditized, its models are hardly state-of-the-art, the overall generative AI industry has lost its sheen, and its killer app is a mythology that has converted a handful of very rich people and very few others.
OpenAI spent, according to The Information, 150% ($6.7 billion in costs) of its H1 2025 revenue ($4.3 billion) on research and development, producing the deeply-underwhelming GPT-5 and Sora 2, an app that I estimate costs it upwards of $5 for each video generation, based on Azure's published rates for the first Sora model, though it's my belief that these rates are unprofitable, all so that it can gain a few more users.
