2025-04-15 00:06:48
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Soundtrack: Mastodon - High Road
I wanted to start this newsletter with a pithy anecdote about chaos, both that caused by Donald Trump's tariffs and the brittle state of the generative AI bubble.
Instead, I am going to write down some questions, and make an attempt to answer them.
Last week, OpenAI closed "the largest private tech funding round in history," where it "raised" an astonishing "$40 billion," and the reason that I've put quotation marks around it is that OpenAI has only raised $10 billion of the $40 billion, with the rest arriving by "the end of the year."
The remaining $30 billion — $20 billion of which will (allegedly) be provided by SoftBank — is partially contingent on OpenAI's conversion from a non-profit to a for-profit by the end of 2025, and if it fails, SoftBank will only give OpenAI a further $20 billion. The round also valued OpenAI at $300 billion.
To put that in context, OpenAI had revenues of $4bn in 2024. This deal values OpenAI at 75 times its revenue. That’s a bigger gulf than Tesla at its peak market cap — a company that was, in fact, worth more than all other legacy car manufacturers combined, despite making far less than them, and shipping a fraction of their vehicles.
I also want to add that, as of writing this sentence, this money is yet to arrive. SoftBank's filings say that the money will arrive mid-April — and that SoftBank would be borrowing as much as $10 billion to finance the round, with the option to syndicate part of it to other investors. For the sake of argument, I'm going to assume this money actually arrives.
Filings also suggest that "in certain circumstances" the second ($30 billion) tranche could arrive "in early 2026." This isn't great. It also seems that SoftBank's $10 billion commitment is contingent on getting a loan, "...financed through borrowings from Mizuho Bank, Ltd., among other financial institutions."
OpenAI also revealed it now has 20 million paying subscribers and over 500 million weekly active users. If you're wondering why it doesn’t talk about monthly active users, it's because they'd likely be much higher than 500 million, which would reveal exactly how poorly OpenAI converts free ChatGPT users to paying ones, and how few people use ChatGPT in their day-to-day lives.
The Information reported back in January that OpenAI was generating $25 million in revenue a month from its $200-a-month "Pro" subscribers (it still loses money on every one of them), suggesting around 125,000 ChatGPT Pro subscribers. Assuming the other 19,875,000 users are paying $20 a month, that puts its revenue at about $423 million a month, or about $5 billion a year, from ChatGPT subscriptions.
This is what reporters mean when they say "annualized revenue" by the way — it's literally the monthly revenue multiplied by 12.
Bloomberg reported recently that OpenAI expects its 2025 revenue to "triple" to $12.7 billion this year. Assuming a similar split of revenue to 2024, this would require OpenAI to nearly double its annualized subscription revenue from Q1 2025 (from $5 billion to around $9.27 billion) and nearly quadruple API revenue (from 2024's revenue of $1 billion, which includes Microsoft's 20% payment for access to OpenAI's models, to $3.43 billion).
While these are messy numbers, it's unclear how OpenAI intends to pull this off.
The Information reported in February that it planned to do so by making $3 billion a year selling "agents," with ChatGPT subscriptions ($7.9 billion) and API calls ($1.8 billion) making up the rest. This, of course, is utter bollocks. OpenAI's "agents" can't do even the simplest tasks, and three billion dollars of the $12.7 billion figure appears to be a commitment made by SoftBank to purchase OpenAI's tech for its various subsidiaries and business units.
Let's say out the numbers precisely:
We can assume, in this case, that OpenAI likely has, in the best case scenario, access to roughly $16 billion in liquidity at any given time. It's reasonable to believe that OpenAI will raise more debt this year, and I'd estimate it does so to the tune of around $5 billion or $6 billion. Without it, I am not sure what it’s going to do.
As a reminder: OpenAI loses money on every single user.
When I wrote "How Does OpenAI Survive?" and "OpenAI Is A Bad Business," I used reported information to explain how this company was, at its core, unsustainable.
Let's refresh our memories.
It seems, from even a cursory glance, that OpenAI's costs are increasing dramatically. The Information reported earlier in the year that OpenAI projects to spend $13 billion on compute with Microsoft alone in 2025, nearly tripling what it spent in total on compute in 2024 ($5 billion).
This suggests that OpenAI's costs are skyrocketing, and that was before the launch of its new image generator which led to multiple complaints from Altman about a lack of available GPUs, leading to OpenAI's CEO saying to expect "stuff to break" and delays in new products. Nevertheless, even if we assume OpenAI factored in the compute increases into its projections, it still expects to pay Microsoft $13 billion for compute this year.
This number, however, doesn't include the $12.9 billion five-year-long compute deal signed with CoreWeave, a deal that was a result of Microsoft declining to pick up the option to buy said compute itself. Payments for this deal, according to The Information, start in October 2025, and assuming that it's evenly paid (the terms of these contracts are generally secret, even in the case of public companies), this would still amount to roughly $2.38 billion a year.
However, for the sake of argument, let's consider the payments are around $198 million a month, though there are scenarios — such as, say, CoreWeave's buildout partner not being able to build the data centers or CoreWeave not having the money to pay to build them — where OpenAI might pay less.
To be clear, and I’ll explain in greater detail later, this wouldn’t be a good thing, either. While it would be off the hook for some of its payments, it would also be without the compute that’s essential for it to continue growing, serving existing customers, and building new AI models. Cash and compute are both essential to OpenAI’s survival.
OpenAI has dedicated somewhere in the region of $19 billion to the Stargate data center project, along with another $19 billion provided by SoftBank and an indeterminate amount by other providers.
Based on reporting from Bloomberg, OpenAI plans to have 64,000 Blackwell GPUs running "by the end of 2026," or roughly $3.84 billion worth of them. I should also note that Bloomberg said that 16,000 of these chips would be operational by Summer 2025, though it's unclear if that will actually happen.
Though it's unclear who actually pays for what parts of Stargate, it's safe to assume that OpenAI will have to, at the very least, put a billion dollars into a project that is meant to be up and running by the end of 2026, if not more.
As of now, Stargate has exactly one data center under development in Abilene, Texas, and as above, it's unclear how that's going, though a recent piece from The Information reported that it was currently "empty and incomplete," and that if it stays that way, "OpenAI could walk away from the deal, which would cost Oracle billions of dollars." Though the article takes pains to assure the reader that won't be likely, even an inkling of such a possibility is a bad sign.
Business Insider's reporting on the site in Abilene calls it a "$3.4 billion data center development" (as did the press release from site developer Crusoe), though these numbers don't include GPUs, hardware, or the labor necessary to run them. Right now, Crusoe is (according to Business Insider) building "six new data centers, each with a minimum square footage...[which will] join the two it is already constructing for Oracle." Oracle has signed, according to The Information, a 15-year-long lease with Crusoe for its data centers, all of which will be rented to OpenAI.
In any case, OpenAI’s exposure could be much, much higher than the $1bn posited at the start of this section (and I’ll explain in greater depth how I reached that figure at the bottom of this section). If OpenAI has to contribute significantly to the costs associated with building Stargate, it could be on the hook for billions.
Data Center Dynamics reports that the Abilene site is meant to have 200MW of compute capacity in the first half of 2025, and then as much as 1.2GW by "mid-2026." To give you a sense of total costs for this project, former Microsoft VP of Energy Brian Janous said in January that it costs about $25 million a megawatt (or $25 billion a gigawatt), meaning that the initial capital expenditures for Stargate to spin up its first 200MW data center will be around $5 billion, spiraling to $30 billion for the entire project.
Or perhaps even more. The Information has reported that the site, which could be "...potentially one of the world's biggest AI data centers," could cost "$50 billion to $100 billion in the coming years."
Assuming we stick with the lower end of the cost estimates, it’s likely that OpenAI is on the hook for over $5 billion for the Abilene site based on the $19 billion it has agreed to contribute to the entire Stargate project, the (often disagreeing) cost projections of the facility), and the contributions of other partners.
This expenditure won’t come all at once, and will be spread across several years. Still, assuming even the rosiest numbers, it's hard to see how OpenAI doesn't have to pony up $1 billion in 2025, with similar annual payments going forward until its completion, and that is likely because the development of this site is going to be heavily delayed by both tariffs, labor shortages, and Oracle's (as reported by The Information) trust in "scrappy but unproven startups to develop the project."
Based on reporting from The Information last year, OpenAI will spend at least $2.5 billion across salaries, "data" (referring to buying data from other companies), hosting and other cost of sales, and sales and marketing, and then another billion on what infrastructure OpenAI owns.
I expect the latter cost to balloon with OpenAI's investment in physical infrastructure for Stargate.
Based on previous estimates, OpenAI spends about $2.25 to make $1. At that rate, it's likely that OpenAI's costs in its rosiest revenue projections of $12.7 billion are at least $28 billion — meaning that it’s on course to burn at least $14 billion in 2025.
Assuming that OpenAI has all of its liquidity from last year (it doesn't, but for sake of argument, let’s pretend it still has the full $10 billion), as well as the $10 billion from SoftBank, it is still unclear how it meets its obligations.
While OpenAI likely has preferential payment structures with all vendors, such as its discounted rates with Microsoft for Azure cloud services, it will still have to pay them, especially in the case of costs related to Stargate, many of which will be up-front costs. In the event that its costs are as severe as reporting suggests, it’s likely the company will find itself needing to raise more capital — whether through equity (or the weird sort-of equity that it issues) or through debt.
And yes, while OpenAI has some revenue, it comes at a terrible cost, and anything that isn’t committed to paying for salaries and construction fees will likely be immediately funnelled directly into funding the obscene costs behind inference and training models like GPT 4.5 — a "giant expensive model" to run that the company has nevertheless pushed to every user.
Worse still, OpenAI has, while delaying its next model (GPT-5), promised to launch its o3 reasoning model after saying it wouldn't do so, which is strange, because it turns out that o3 is actually way more expensive to run than people thought.
Reasoning models are almost always more expensive to operate, as they involve the model “checking” its work, which, in turn, requires more calculations and more computation. Still, o3 is ludicrously expensive even for this category, with the Arc Prize Foundation (a non-profit that makes the ARC-AGI test for benchmarking models) estimating that it will cost $30,000 a task.
As of right now, SoftBank has committed to the following:
SoftBank's exposure to OpenAI is materially harming the company. To quote the Wall Street Journal:
Ratings agency S&P Global said last week that SoftBank’s “financial condition will likely deteriorate” as a result of the OpenAI investment and that its plans to add debt could lead the agency to consider downgrading SoftBank’s ratings.
While one might argue that SoftBank has a good amount of cash, the Journal also adds that it’s somewhat hamstrung in its use as a result of CEO Masayoshi Son's reckless gambles:
SoftBank had a decent buffer of $31 billion of cash as of Dec. 31, but the company has also pledged to hold much of that in reserve to quell worried investors. SoftBank has committed not to borrow more than 25% of the value of all of its holdings, which means it will likely need to sell some of the other parts of its empire to pay for the rest of the OpenAI deal.
Worse still, it seems, as mentioned before, that SoftBank will be financing the entirety of the first $10 billion — or $7.5 billion, assuming it finds investors to syndicate the first tranche, and they follow through right until the moment Masayoshi Son hits ‘send’ on the wire transfer .
As a result, SoftBank will likely have to start selling off parts of its valuable holdings in companies like Alibaba and ARM, or, worse still, parts of its ailing investments from its Vision Fund, resulting in a material loss on its underwater deals.
This is an untenable strategy, and I'll explain why.
While we do not have much transparency into OpenAI's actual day-to-day finances, we can make the educated guess that its costs are increasing based on the amount of capital it’s raising. If OpenAI’s costs were flat, or only mildly increasing, we’d expect to see raises roughly the same size as previous ones. Its $40bn raise is nearly six times the previous funding round.
Admittedly, multiples like that aren’t particularly unusual. If a company raises $300,000 in a pre-seed round, and $3m in a Series A round, that’s a tenfold increase. But we’re not talking about hundreds of thousands of dollars, or even millions of dollars. We’re talking about billions of dollars. If OpenAI’s funding round with Softbank goes as planned, it’ll raise the equivalent of the entire GDP of Estonia — a fairly wealthy country itself, and one that’s also a member of Nato and the European Union. That alone should give you a sense of the truly insane scale of this.
Insane, sure, but undoubtedly necessary. Per The Information, OpenAI expects to spend as much as $28 billion in compute on Microsoft's Azure cloud in 2028. Over a third of OpenAI's revenue, per the same article, will come from SoftBank's (alleged) spend.It's reasonable to believe that OpenAI will, as a result, need to raise in excess of $40 billion in funding a year, though it's reasonable to believe that it will need to raise more along the lines of $50 billion or more a year until it reaches profitability. This is due to both its growing cost of business, as well as its various infrastructure commitments, both in terms of Stargate, as well as with third-party suppliers like CoreWeave and Microsoft.
Counterpoint: OpenAI could reduce costs: While this is theoretically possible, there is no proof that this is taking place. The Information claims that "...OpenAI would turn profitable by the end of the decade after the buildout of Stargate," but there is no suggestion as to how it might do so, or how building more data centers would somehow reduce its costs.This is especially questionable when you realize that Microsoft is already providing discounted pricing on Azure compute. We don’t know if these discounts are below Microsoft’s break-even point — which it wouldn’t, nor would any other company offer, if they didn’t have something else to incentivize it, such as equity or a profit-sharing program. Microsoft, for what it’s worth, has both of those things.
OpenAI CEO Sam Altman's statements around costs also suggest that they're increasing. In late February, Altman claimed that OpenAI was "out of GPUs." While this suggests that there’s demand for some products — like its image-generating tech, which enjoyed a viral day in the sun in March — it also means that to meet the demand it needs to spend more. And, at the risk of repeating myself, that demand doesn’t necessarily translate into profitability.
As discussed above, SoftBank has to overcome significant challenges to fund both OpenAI and Stargate, and when I say "fund," I mean fund the current state of both projects, assuming no further obligations.
The Information reports that OpenAI forecasts that it will spend $28 billion on compute with Microsoft alone in 2028. The same article also reports that OpenAI "would turn profitable by the end of the decade after the buildout of Stargate," suggesting that OpenAI's operating expenses will grow exponentially year-over-year.
These costs, per The Information, are astronomical:
The reason for the expanding cash burn is simple: OpenAI is spending whatever revenue comes in on computing needs for operating its existing models and developing new models. The company expects those costs to surpass $320 billion overall between 2025 and 2030.
The company expects more than half of that spending through the end of the decade to fund research-intensive compute for model training and development. That spending will rise nearly sixfold from current rates to around $40 billion per year starting in 2028. OpenAI projects its spending on running AI models will surpass its training costs in 2030.
SoftBank has had to (and will continue having to) go to remarkable lengths to fund OpenAI's current ($40 billion) round, lengths so significant that it may lead to its credit rating being further downgraded.
Even if we assume the best case scenario — OpenAI successfully converts to a for-profit entity by the end of the year, and receives the full $30 billion — it seems unlikely (if not impossible) for it to continue raising the amount of capital they need to continue operations. As I’ve argued in previous newsletters, there are only a few entities that can provide the kinds of funding that OpenAI needs. These include big tech-focused investment firms like Softbank, sovereign wealth funds (like those of Saudi Arabia and the United Emirates), and perhaps the largest tech companies.
These entities can meet OpenAI’s needs, but not all the time. It’s not realistic to expect Softbank, or Microsoft, or the Saudis, or Oracle, or whoever, to provide $40bn every year for the foreseeable future.
This is especially true for Softbank. Based on its current promise to not borrow more than 25% of its holdings, it is near-impossible that SoftBank will be able to continue funding OpenAI at this rate ($40 billion a year), and $40 billion a year may not actually be enough.
Based on its last reported equity value of holdings, SoftBank's investments and other assets are worth around $229 billion, meaning that it can borrow just over $57bn while remaining compliant with these guidelines.
In any case, it is unclear how SoftBank can fund OpenAI, but it's far clearer that nobody else is willing to.
Before we go any further, it's important to note that OpenAI does not really have its own compute infrastructure. The majority of its compute is provided by Microsoft, though, as mentioned above, OpenAI now has a deal with CoreWeave to take over Microsoft's future options for more capacity.
Anyway, in the last 90 days, Sam Altman has complained about a lack of GPUs and pressure on OpenAI's servers multiple times. Forgive me for repeating stuff from above, but this is necessary.
These statements, in a bubble, seem either harmless or like OpenAI's growth is skyrocketing — the latter of which might indeed be true, but bodes ill for a company that burns money on every single user.
Any mention of rate limits or performance issues suggests that OpenAI is having significant capacity issues, and at this point it's unclear what further capacity it can actually expand to outside of that currently available. Remember, Microsoft has now pulled out of as much as 2GW of data center projects, walked away from a $1 billion data center development in Ohio, and declined the option on $12bn of compute from CoreWeave that OpenAI had to pick up — meaning that it may be pushing up against the limits of what is physically available.
While the total available capacity of GPUs at many providers like Lambda and Crusoe is unknown, we know that CoreWeave has approximately 360MWavailable, compared to Microsoft's 6.5 to 7.5 Gigawatts, a large chunk of which already powers OpenAI.
If OpenAI is running into capacity issues, it could be one of the following:
Per The Information's reporting, Microsoft "promised OpenAI 300,000 NVIDIA GB200 (Blackwell) chips by the end of 2025," or roughly $18 billion of chips. It's unclear if this has changed since Microsoft allowed OpenAI to seek other compute in late January 2025.
I also don't believe that OpenAI has any other viable options for existing compute infrastructure outside of Microsoft. CoreWeave's current data centers mostly feature NVIDIA's aging "Hopper" GPUs, and while it could — and likely is! — retrofitting its current infrastructure with Blackwell chips, doing so is not easy. Blackwell chips require far more powerful cooling and server infrastructure to make them run smoothly (a problem which led to a delay in their delivery to most customers), and even if CoreWeave was able to replace every last Hopper GPU with Blackwell (it won't), it still wouldn't match what OpenAI needs to expand.
One might argue that it simply needs to wait for the construction of the Stargate data center, or for CoreWeave to finish the gigawatt or so of construction it’s working on.
As I've previously written, I have serious concerns over the viability of CoreWeave ever completing its (alleged) contracted 1.3 Gigawatts of capacity.
Per my article:
Per its S-1, CoreWeave has contracted for around 1.3 Gigawatts of capacity, which it expects to roll out over the coming years, and based on NextPlatform's math, CoreWeave will have to spend in excess of $39 billion to build its contracted compute. It is unclear how it will fund doing so, and it's fair to assume that CoreWeave does not currently have the capacity to cover its current commitments.
However, even if I were to humour the idea, it is impossible that any of this project is done by the end of the year, or even in 2026. I can find no commitments to any timescale, other than the fact that OpenAI will allegedly start paying CoreWeave in October (per The Information), which could very well be using current capacity.
I can also find no evidence that Crusoe, the company building the Stargate data center, has any compute available. Lambda, a GPU compute company that raised $320 million earlier in this year, and according to Data Center Dynamics "operates out of colocation data centers in San Francisco, California, and Allen, Texas, and is backed by more than $820 million in funds raised just this year," suggesting that it may not have their own data centers at all. Its ability to scale is entirely contingent on the availability of whatever data center providers it has relationships with.
In any case, this means that OpenAI's only real choice for GPUs is CoreWeave or Microsoft. While it's hard to calculate precisely, OpenAI's best case scenario is that 16,000 GPUs come online in the summer of 2025 as part of the Stargate data center project.
That's a drop in the bucket compared to the 300,000 Blackwell GPUs that Microsoft had previously promised.
OpenAI is, regardless of how you or I may feel about generative AI, one of the fastest-growing companies of all time. It currently has, according to its own statements, 500 million weekly active users. Putting aside that each user is unprofitable, such remarkable growth — especially as it's partially a result of its extremely resource-intensive image generator — is also a strain on its infrastructure.
The vast majority of OpenAI's users are free customers using ChatGPT, with only around 20 million paying subscribers, and the vast majority on the cheapest $20 plan. OpenAI's services — even in the case of image generation — are relatively commoditized, meaning that users can, if they really care, go and use any number of other different Large Language Model services. They can switch to Bing Image Creator, or Grok, or Stable Diffusion, or whatever.
Free users are also a burden on the company — especially with such a piss-poor conversion rate — losing it money with each prompt (which is also the case with paying customers), and the remarkable popularity of its image generation service only threatens to bring more burdensome one-off customers that will generate a few abominable Studio Ghibli pictures and then never return.
If OpenAI's growth continues at this rate, it will run into capacity issues, and it does not have much room to expand. While we do not know how much capacity it’s taking up with Microsoft, or indeed whether Microsoft is approaching capacity or otherwise limiting how much of it OpenAI can take, we do know that OpenAI has seen reason to beg for access to more GPUs.
In simpler terms, even if OpenAI wasn’t running out of money, even if OpenAI wasn’t horrifyingly unprofitable, it also may not have enough GPUs to continue providing its services in a reliable manner.
If that's the case, there really isn't much that can be done to fix it other than:
The problem is that these measures, even if they succeed in generating more money for the company, also need to reduce the burden on OpenAI's available infrastructure.
Remember: data centers can take three to six years to build, and even with the Stargate's accelerated (and I'd argue unrealistic) timelines, OpenAI isn't even unlocking a tenth of Microsoft's promised compute (16,000 GPUs online this year versus the 300,000 GPUs promised by Microsoft).
Though downtime might be an obvious choice, capacity issues at OpenAI will likely manifest in hard limits on what free users can do, some of which I've documented above. Nevertheless, I believe the real pale horses of capacity issues come from arbitrary limits on any given user group, meaning both free and paid users. Sudden limits on what a user can do — a reduction in the number of generations of images of videos for paid users, any introduction of "peak hours," or any increases in prices are a sign that OpenAI is running out of GPUs, which it has already publicly said is happening.
However, the really obvious one would be service degradation — delays in generations of any kind, 500 status code errors, or ChatGPT failing to fully produce an answer. OpenAI has, up until this point, had fairly impressive uptime. Still, if it is running up against a wall, this streak will end.
The consequences depend on how often these issues occur, and to whom they occur. If free users face service degradation, they will bounce off the product, as their use is likely far more fleeting than a paid user, which will begin to erode OpenAI's growth. Ironically, rapid (and especially unprecedented) growth in one of OpenAI’s competitors, like xAI or Anthropic, could also represent a pale horse for OpenAI.
If paid users face service degradation, it's likely this will cause the most harm to the company, as while paid users still lose OpenAI money in the end, it at least receives some money in exchange.
OpenAI has effectively one choice here: getting more GPUs from Microsoft, and its future depends heavily both on its generosity and there being enough of them at a time when Microsoft has pulled back from two gigawatts of data centers specifically because of it moving away from providing compute for OpenAI.
Admittedly, OpenAI has previously spent more on training models than inference (actually running them) and the company might be able to smooth downtime issues by shifting capacity. This would, of course, have a knock-on effect on its ability to continue developing new models, and the company is already losing ground, particularly when it comes to Chinese rivals like DeepSeek.
As part of its deal with SoftBank, OpenAI must convert its bizarre non-profit structure into a for-profit entity by December 2025, or it’ll lose $10 billion from its promised funding.
Furthermore, in the event that OpenAI fails to convert to a for-profit by October 2026, investors in its previous $6.6 billion round can claw back their investment, with it converting into a loan with an attached interest rate. Naturally, this represents a nightmare scenario for the company, as it’ll increase both its costs and its outgoings.
This is a complex situation that almost warrants its own newsletter, but the long and short of it is that OpenAI would have to effectively dissolve itself, start the process of forming an entirely new entity, and distribute its assets to other nonprofits (or sell/license them to the for-profit company at fair market rates). It would require valuing OpenAI's assets, which in and of itself would be a difficult task, as well as getting past the necessary state regulators, the IRS, state revenue agencies, and the upcoming trial with Elon Musk only adds further problems.
I’ve simplified things here, and that’s because (as I said) this stuff is complex. Suffice to say, this isn’t as simple as liquidating a company and starting afresh, or submitting a couple of legal filings. It’s a long, fraught process and one that will be — and has been — subject to legal challenges, both from OpenAI’s business rivals, as well as from civil society organizations in California.
Based on discussions with experts in the field and my own research, I simply do not know how OpenAI pulls this off by October 2026, let alone by the end of the year.
OpenAI has become a load-bearing company for the tech industry, both as a narrative — as previously discussed, ChatGPT is the only Large Language Model company with any meaningful userbase — and as a financial entity.
Its ability to meet its obligations and its future expansion plans are critical to the future health — or, in some cases, survival — of multiple large companies, and that's before the after-effects that will affect its customers as a result of any financial collapse.
The parallels to the 2007-2008 financial crisis are startling. Lehman Brothers wasn’t the largest investment bank in the world (although it was certainly big), just like OpenAI isn’t the largest tech company (though, again, it’s certainly large in terms of market cap and expenditure). Lehman Brothers’ collapse sparked a contagion that would later spread throughout the global financial services industry, and consequently, the global economy.
I can see OpenAI’s failure having a similar systemic effect. While there is a vast difference between OpenAI’s involvement in people’s lives compared to the millions of subprime loans issued to real people, the stock market’s dependence on the value of the Magnificent 7 stocks (Apple, Microsoft, Amazon, Alphabet, NVIDIA and Tesla), and in turn the Magnificent 7’s reliance on the stability of the AI boom narrative still threatens material harm to millions of people, and that’s before the ensuing layoffs.
And as I’ve said before, this entire narrative is based off of OpenAI’s success, because OpenAI is the generative AI industry.
I want to lay out the direct result of any kind of financial crisis at OpenAI, because I don't think anybody is taking this seriously.
Per The Information, Oracle, which has taken responsibility for organizing the construction of the Stargate data centers with unproven data center builder Crusoe, "...may need to raise more capital to fund its data center ambitions."
Oracle has signed a 15-year lease with Crusoe, and, to quote The Information, "...is on the hook for $1 billion in payments to that firm."
To further quote The Information:
...while that’s a standard deal length, the unprecedented size of the facility Oracle is building for just one customer makes it riskier than a standard cloud data center used by lots of interchangeable customers with more predictable needs, according to half a dozen people familiar with these types of deals.
In simpler terms, Oracle is building a giant data center for one customer — OpenAI — and has taken on the financial burden associated with it. If OpenAI fails to expand, or lacks the capital to actually pay for its share of the Stargate project, Oracle is on the hook for at least a billion dollars, and, based on The Information's reporting, is also on the hook to buy the GPUs for the site.
Even before the Stargate announcement, Oracle and OpenAI had agreed to expand their Abilene deal from two to eight data center buildings, which can hold 400,000 Nvidia Blackwell GPUs, adding tens of billions of dollars to the total cost of the facility.
In reality, this development will likely cost tens of billions of dollars, $19 billion of which is due from OpenAI, which does not have the money until it receives its second tranche of funding in December 2025, which is contingent partially on its ability to convert into a for-profit entity, which, as mentioned, is a difficult and unlikely proposition.
It's unclear how many of the Blackwell GPUs that Oracle has had to purchase in advance, but in the event of any kind of financial collapse at OpenAI, Oracle would likely take a loss of at least a billion dollars, if not several billion dollars.
I have written a lot about publicly-traded AI compute firm CoreWeave, and it would be my greatest pleasure to never mention it again.
Nevertheless, I have to.
The Financial Times revealed a few weeks ago that CoreWeave's debt payments could balloon to over $2.4 billion a year by the end of 2025, far outstripping its cash reserves, and The Information reported that its cash burn would increase to $15 billion in 2025.
As per its IPO filings, 62% of CoreWeave's 2024 revenue (a little under $2 billion, with losses of $863 million) was Microsoft compute, and based on conversations with sources, a good amount of this was Microsoft running compute for OpenAI.
Starting October 2025, OpenAI will start paying Coreweave as part of its five-year-long $12 billion contract, picking up the option that Microsoft declined. This is also when CoreWeave will have to start making payments on its massive, multi-billion dollar DDTL 2.0 loan, which likely makes these payments critical to CoreWeave's future.
This deal also suggests that OpenAI will become CoreWeave's largest customer. Microsoft had previously committed to spending $10 billion on CoreWeave's services "by the end of the decade," but CEO Satya Nadella added a few months later on a podcast that its relationship with CoreWeave was a "one-time thing." Assuming Microsoft keeps spending at its previous rate — something that isn't guaranteed — it would still be only half of OpenAI's potential revenue.
CoreWeave's expansion, at this point, is entirely driven by OpenAI. 77% of its 2024 revenue came from two customers — Microsoft being the largest, and using CoreWeave as an auxiliary supplier of compute for OpenAI. As a result, the future expansion efforts — the theoretical 1.3 gigawatts of contracted (translation: does not exist yet) compute — are largely (if not entirely) for the benefit of OpenAI.
In the event that OpenAI cannot fulfil its obligations, CoreWeave will collapse. It is that simple.
I’m basing this on a comment I received from Gil Luria, Managing Director and Head of Technology Research at analyst D.A. Davidson & Co:
Since CRWV bought 200,000 GPUs last year and those systems are around $40,000 we believe CRWV spent $8 billion on NVDA last year. That represents more than 6% of NVDA’s revenue last year.
CoreWeave receives preferential access to NVIDIA's GPUs, and makes up billions of dollars of its revenue. CoreWeave then takes those GPUs and raises debt using them as collateral, then proceeds to buy more of those GPUs from NVIDIA. NVIDIA was the anchor for CoreWeave's IPO, and CEO Michael Intrator said that the IPO "wouldn't have closed" without NVIDIA buying $250 million worth of shares. NVIDIA invested $100 million in the early days of CoreWeave, and, for reasons I cannot understand, also agreed to spend $1.3 billion over four years to, and I quote The Information, "rent its own chips from CoreWeave."
Buried in CoreWeave's S-1 — the document every company publishes before going public — was a warning about counterparty credit risk, which is when one party provides services or goods to another with specific repayment terms, and the other party not meeting their side of the deal. While this was written as a theoretical (as it could, in theoretically, come from any company to which CoreWeave acts as a creditor) it only named one company: OpenAI.
As discussed previously, CoreWeave is saying that, should a customer — any customer, but really, it means OpenAI — fail to pay its bills for infrastructure built on their behalf, or for services rendered, it could have a material risk to the business.
Aside: The Information reported that Google is in "advanced talks" to rent GPUs from CoreWeave. It also, when compared to Microsoft and OpenAI's deals with CoreWeave, noted that "...Google's potential deal with CoreWeave is "significantly smaller than those commitments, according to one of the people briefed on it, but could potentially expand in future years."
CoreWeave's continued ability to do business hinges heavily on its ability to raise further debt (which I have previously called into question), and its ability to raise further debt is, to quote the Financial Times, "secured against its more than 250,000 Nvidia chips and its contracts with customers, such as Microsoft." Any future debt that CoreWeave raises would be based upon its contract with OpenAI (you know, the counterparty credit risk threat that represents a disproportionate share of its revenue) and whatever GPUs it still has to collateralize.
As a result, a chunk of NVIDIA's future revenue is dependent on OpenAI's ability to fulfil its obligations to CoreWeave, both in its ability to pay them and their timeliness in doing so. If OpenAI fails, then CoreWeave fails, which then hurts NVIDIA.
Contagion.
With Microsoft's data center pullback and OpenAI's intent to become independent from Redmond, future data center expansion is based on two partners supporting CoreWeave and Oracle: Crusoe and Core Scientific, neither of which appear to have ever built an AI data center.
I also must explain how difficult building a data center is, and how said difficulty increases when you're building an AI-focused data center. For example, NVIDIA had to delay the launch of its Blackwell GPUs because of how finicky the associated infrastructure (the accompanying servers and cooling them) is. For customers that already had experience handling GPUs, and therefore likely know how to manage the extreme temperatures created by them.
As another reminder, OpenAI is on the hook for $19 billion of funding behind Stargate, money that neither it nor SoftBank has right now.
Imagine if you didn't have any experience, and effectively had to learn from scratch? How do you think that would go?
We're about to find out!
Crusoe is a former cryptocurrency mining company that has now raised hundreds of millions of dollars to build data centers for AI companies, starting with a $3.4 billion data center financing deal with asset manager Blue Owl Capital. This (yet-to-be-completed) data center has now been leased by Oracle, which will, allegedly, fill it full of GPUs for OpenAI.
Despite calling itself "the industry’s first vertically integrated AI infrastructure provider," with the company using flared gas (a waste byproduct of oil production) to power IT infrastructure, Crusoe does not appear to have built an AI data center, and is now being tasked with building a 1.2 Gigawatt data center campus for OpenAI.
Crusoe is the sole developer and operator of the Abilene site, meaning, according to The Information, "...is in charge of contracting with construction contractors and data center customers, as well as running the data center after it is built."
Oracle, it seems, will be responsible for filling said data center with GPUs and the associated hardware.
Nevertheless, the project appears to be behind schedule.
The Information reported in October 2024 that Abeline was meant to have "...50,000 of NVIDIA's [Blackwell] AI chips...in the first quarter of [2025]," and also suggested that the site was projected to have 100,000 Blackwell chips by the end of 2025.
Here in reality, a report from Bloomberg in March 2025 (that I cited previously) said that OpenAI and Oracle were expected to have 16,000 GPUs available by the Summer of 2025, with "...OpenAI and oracle are expected to deploy 64,000 NVIDIA GB200s at the Stargate data center...by the end of 2026."
As discussed above, OpenAI needs this capacity. According to The Information, OpenAI expects Stargate to handle three-quarters of its compute by 2030, and these delays call into question at the very least whether this schedule is reasonable, if not whether Stargate, as a project, is actually possible.
I've written a great deal about CoreWeave in the past, and specifically about its buildout partner Core Scientific, a cryptocurrency mining company (yes, another one) that has exactly one customer for AI data centers — CoreWeave.
A few notes:
Core Scientific is also, it seems, taking on $1.14 billion of capital expenditures to build out these data centers, with CoreWeave promising to reimburse $899.3 million of these costs.
It's also unclear how Core Scientific intends to do this. While it’s taken on a good amount of debt in the past — $550 million in a convertible note toward the end of 2024 — this would be more debt than it’s ever taken on.
It also, as with Crusoe, does not appear to have any experience building AI data centers, except unlike Crusoe, Core Scientific is a barely-functioning recently-bankrupted bitcoin miner pretending to be a data center company.
How important is CoreWeave to OpenAI exactly? From Semafor:
“CoreWeave has been one of our earliest and largest compute partners,” OpenAI chief Sam Altman said in CoreWeave’s roadshow video, adding that CoreWeave’s computing power “led to the creation of some of the models that we’re best known for.”
“Coreweave figured out how to innovate on hardware, to innovate on data center construction, and to deliver results very, very quickly.”
But will it survive long term?
Going back to the point of contagion: If OpenAI fails, and CoreWeave fails, so too does Core Scientific. And I don’t fancy Crusoe’s chances, either. At least Crusoe isn’t public.
Up until fairly recently, Microsoft has been the entire infrastructural backbone of OpenAI, but recently (to free OpenAI up to work with Oracle) released it from its exclusive cloud compute deal. Nevertheless, per The Information, OpenAI still intends to spend $13 billion on compute on Microsoft Azure this year.
What's confusing, however, is whether any of this is booked as revenue. Microsoft claimed earlier in this year that it surpassed $13 billion in annual recurring revenue — by which it means its last month multiplied by 12 — from artificial intelligence. OpenAI's compute costs in 2024 were $5 billion, at a discounted Azure rate, which, on an annualized basis, would be around $416 million in revenue a month for Microsoft.
It isn't, however, clear whether Microsoft counts OpenAI's compute spend as revenue.
Microsoft's earnings do not include an "artificial intelligence" section, but three separate segments:
As a result, it's hard to say specifically where OpenAI's revenue sits, but based on an analysis of Microsoft's Intelligent Cloud segment from FY23 Q1 (note, financial years don’t always correspond with the calendar year, so we just finished FY25 Q2 in January) through to its most recent earnings, and found that there was a spike in revenue from FY23 Q1 to FY24 Q1.
In FY23 Q1 (which ended on September 30, 2022, a month before ChatGPT's launch), the segment made $20.3 billion. The following year, in FY24 Q1, it made $24.3 billion — a 19.7% year-over-year (or roughly $4 billion) increase.
This could represent the massive increase in training and inference costs associated with hosting ChatGPT, peaking at $28.5 billion in revenue in FY24 Q4 — before dropping dramatically to $24.1 billion in FY25 Q1 and raising a little to $25.5 billion in FY25 Q2.
OpenAI spent 2023 training its GPT-4o model before transitioning to its massive, expensive "Orion" model which would eventually become GPT 4.5, as well as its video generation model "Sora." According to the Wall Street Journal, training GPT 4.5 involved at least one training run costing "around half a billion dollars in computing costs alone."
These are huge sums, but it’s worth noting a couple of things. First, Microsoft licenses OpenAI’s models to third parties, so some of this revenue could be from other companies using GPT on Azure. And there’s also other companies running their own models on Azure. We’ve seen a lot of companies launch AI products, and not all of them are based on LLMs.
Muddling things further, Microsoft provides OpenAI access to Azure cloud services at a discounted rate. And so, there’s a giant question mark over OpenAI’s contribution to the various spikes in revenue for Microsoft’s Intelligent Cloud segment, or whether other third-parties played a significant role.
Furthermore, Microsoft’s investment in OpenAI isn’t entirely in cold, hard cash. Rather, it has provided the company with credits to be redeemed on Azure services. I’m not entirely sure how this would be represented on accounting terms, and if anyone can shed light on this, please get in touch.
Would it be noted as revenue, or something else? OpenAI isn’t paying Microsoft, but rather doing the tech equivalent of redeeming some airmiles, or spending a gift card.
Additionally, while equity is often treated as income for tax purposes — as is the case when an employee receives RSUs as part of their compensation package — under the existing OpenAI structure, Microsoft isn’t a shareholder but rather the owner of profit-sharing units. This is a distinction worth noting.
These profit-sharing units are treated as analogous to equity, at least in terms of OpenAI’s ability to raise capital, but in practice they aren’t the same thing. They don’t represent ownership in the company as directly as, for example, a normal share unit would. They lack the liquidity of a share, and the upside they provide — namely, dividends — is purely theoretical.
Another key difference: when a company goes bankrupt and enters liquidation, shareholders can potentially receive a share of the proceeds (after other creditors, employees, etc are paid). While that often doesn’t happen (as in, the liabilities far exceed the assets of the company), it’s at least theoretically possible. Given that profit-sharing units aren’t actually shares, where does that leave Microsoft?
This stuff is confusing, and I’m not ashamed to say that complicated accounting questions like these are far beyond my understanding. If anyone can shed some light, drop me an email, or a message on Twitter or BlueSky, or post on the Better Offline subreddit.
I have done my best to write this piece in as objective a tone as possible, regardless of my feelings about the generative AI bubble and its associated boosters.
OpenAI, as I've written before, is effectively the entire generative AI industry, with its nearest competitor being less than five percent of its 500 million weekly active users.
Its future is dependent — and this is not an opinion, but objective fact — on effectively infinite resources.
If it required $40 billion to continue operations this year, it is reasonable to believe it will need at least another $40 billion next year, and based on its internal projections, will need at least that every single other year until 2030, when it claims, somehow, it will be profitable "with the completion of the Stargate data center."
OpenAI requires more compute resources than anyone has ever needed, and will continue to do so in perpetuity. Building these resources is now dependent on two partners — Core Scientific and Crusoe — that have never built a data center, as Microsoft has materially pulled back on data center development, which have (as well as the aforementioned pullback on 2GW of data centers) "slowed or paused" some of its "early stage" data center projects. This shift is directly linked to Microsoft’s relationship with OpenAI, withTD Cowen's recent analyst report saying that data center pullbacks were, and I quote its March 26 2025 data center channel checks letter, "...driven by the decision to not support incremental OpenAI training workloads."
In simpler terms, OpenAI needs more compute at a time when its lead backer, which has the most GPUs in the world, has specifically walked away from building it.
Even in my most optimistic frame of mind, it isn't realistic to believe that Crusoe or Core Scientific can build the data centers necessary for OpenAI's expansion.
Even if SoftBank and OpenAI had the money to invest in Stargate today, dollars do not change the fabric of reality. Data centers take time to build, requiring concrete, wood, steel and other materials to be manufactured and placed, and that's after the permitting required to get these deals done. Even if that succeeds, getting the power necessary is a challenge unto itself, to the point that even Oracle, an established and storied cloud compute company, to quote The Information, "...has less experience than its larger rivals in dealing with utilities to secure power and working with powerful and demanding cloud customers whose plans change frequently."
A partner like Crusoe or Core Scientific simply doesn't have the muscle memory or domain expertise that Microsoft has when it comes to building and operating data centers. As a result, it's hard to imagine even in the best case scenario that they're able to match the hunger for compute that OpenAI has.
Now, I want to be clear — I believe OpenAI will still continue to use Microsoft's compute, and even expand further into whatever remaining compute Microsoft may have. However, there is now a hard limit on how much of it there's going to be, both literally (in what's physically available) and in what Microsoft itself will actually OpenAI them to use, especially given how unprofitable GPU compute might be.
Last week, a truly offensive piece of fan fiction — framed as a "report" — called AI 2027 went viral, garnering press coverage with the Dwarkesh Podcast and gormless, child-like wonder from the New York Times' Kevin Roose. Its predictions vaguely suggest a theoretical company called OpenBrain will invent a self-teaching agent of some sort.
It's bullshit, but it captured the hearts and minds of AI boosters because it vaguely suggests that somehow Large Language Models and their associated technology will become something entirely different.
I don't like making predictions like these because the future — especially in our current political climate — is so chaotic, but I will say that I do not see, and I say this with complete objectivity, how any of this continues.
I want to be extremely blunt with the following points, as I feel like both members of the media and tech analysts have failed to express how ridiculous things have become. I will be repeating myself, but it's necessary, as I need you to understand how untenable things are.
I see no way in which OpenAI can continue to raise money at this rate, even if OpenAI somehow actually receives the $40 billion, which will require it becoming a for-profit entity. While it could theoretically stretch that $40 billion to last multiple years, projections say it’ll burn $320 billion in the next five years.
Or, more likely, I can’t see a realistic way in which OpenAI gets the resources it needs to survive. It’ll need a streak of unlikely good fortune, the kind of which you only ever hear about in Greek epic poems:
If those things happen, I’ll obviously find myself eating crow. But I’m not worried.
In the present conditions, OpenAI is on course to run out of money or compute capacity, and it's unclear which will happen first.
Even in a hysterical bubble where everybody is agreeing that this is the future, OpenAI currently requires more money and more compute than is reasonable to acquire. Nobody has ever raised as much as OpenAI needs to, and based on the sheer amount of difficulty that SoftBank is having in raising the funds to meet the lower tranche ($10bn) of its commitment, it may simply not be possible for this company to continue.
Even with extremely preferential payment terms — months-long deferred payments, for example — at some point somebody is going to need to get paid.
I will give Sam Altman credit. He's found many partners to shoulder the burden of the rotten economics of OpenAI, with Microsoft, Oracle, Crusoe and CoreWeave handling the up-front costs of building the infrastructure, SoftBank finding the investors for its monstrous round, and the tech media mostly handling his marketing for him.
He is, however, over-leveraged. OpenAI has never been forced to stand on its own two feet or focus on efficiency, and I believe the constant enabling of its ugly, nonsensical burnrate has doomed this company. OpenAI has acted like it’ll always have more money and compute, and that people will always believe its bullshit, mostly because up until recently everybody has.
OpenAI cannot "make things cheaper" at this point, because the money has always been there to make things more expensive, as has the compute to make larger language models that burn billions of dollars a year. This company is not built to reduce its footprint in any way, nor is it built for a future in which it wouldn't have access to, as I've said before, infinite resources.
Worse still, investors and the media have run cover for the fact that these models don't really do much more than they did a year ago and for the overall diminishing returns of Large Language Models.
I have had many people attack my work about OpenAI, but none have provided any real counterpoint to the underlying economic argument I've made since July of last year that OpenAI is unsustainable. This is likely because there really isn't one, other than "OpenAI will continue to raise more money than anybody has ever raised in history, in perpetuity, and will somehow turn from the least-profitable company of all time to a profitable one."
This isn’t a rational argument. It’s a religious one. It’s a call for faith.
And I see no greater pale horse of the apocalypse than Microsoft's material pullback on data centers. While the argument might be that Microsoft wants OpenAI to have an independent future, that's laughable when you consider Microsoft's deeply monopolistic tendencies — and, for that matter, it owns a massive proportion of OpenAI’s pseudo-equity. At one point, Microsoft’s portion was valued at 49 percent. And while additional fundraising has likely diluted Microsoft’s stake, it still “owns” a massive proportion of what is (at least) the most valuable private startup of all time.
And we’re supposed to believe that Microsoft’s pullback — which limits OpenAI’s access to the infrastructure it needs to train and run its models, and thus (as mentioned) represents an existential threat to the company — is because of some paternal desire to see OpenAI leave the childhood bedroom, spread its wings, and enter the real world? Behave.
More likely, Microsoft got what it needed out of OpenAI, which has reached the limit of the models it can develop, and which Microsoft already retains the IP of. There’s probably no reason to make any further significant investments, though they allegedly may be part of the initial $10 billion tranche of OpenAI’s next round.
It's also important to note that absolutely nobody other than NVIDIA is making any money from generative AI. CoreWeave loses billions of dollars, OpenAI loses billions of dollars, Anthropic loses billions of dollars, and I can't find a single company providing generative AI-powered software that's making a profit. The only companies even close to doing so are consultancies providing services to train and create data for models like Turing and Scale AI — and Scale isn't even profitable.
The knock-on effects of OpenAI's collapse will be wide-ranging. Neither CoreWeave nor Crusoe will have tenants for their massive, unsustainable operations, and Oracle will have nobody to sell the compute it’s leased from Crusoe for the next 15 years. CoreWeave will likely collapse under the weight of its abominable debt, which will lead to a 7%+ revenue drop for NVIDIA at a time when revenue growth has already begun to slow.
On a philosophical level, OpenAI's health is what keeps this industry alive. OpenAI has the only meaningful userbase in generative AI, and this entire hype-cycle has been driven by its success, meaning any deterioration (or collapse) of OpenAI will tell the market what I've been saying for over a year: that generative AI is not the next hyper-growth market, and its underlying economics do not make sense.
I am not writing this to be "right" or "be a hater."
If something changes, and I am wrong somehow, I will write exactly how, and why, and what mistakes I made to come to the conclusions I have in this piece.
I do not believe that my peers in the media will do the same when this collapses, but I promise you that they will be held accountable, because all of this abominable waste could have been avoided.
Large Language Models are not, on their own, the problem. They're tools, capable of some outcomes, doing some things, but the problem, ultimately, are the extrapolations made about their abilities, and the unnecessary drive to make them larger, even if said largeness never amounted to much.
Everything that I'm describing is the result of a tech industry — including media and analysts — that refuses to do business with reality, trafficking in ideas and ideology, celebrating victories that have yet to take place, applauding those who have yet to create the things they're talking about, cheering on men lying about what's possible so that they can continue to burn billions of dollars and increase their wealth and influence.
I understand why others might not have written this piece. What I am describing is a systemic failure, one at a scale hereto unseen, one that has involved so many rich and powerful and influential people agreeing to ignore reality, and that’ll have crushing impacts for the wider tech ecosystem when it happens.
Don't say I didn't warn you.
2025-03-25 01:01:14
A few months ago, Casey Newton of Platformer ran a piece called "The phony comforts of AI skepticism," framing those who would criticize generative AI as "having fun," damning them as "hyper-fixated on the things [AI] can't do."
I am not going to focus too hard on this blog, in part because because Edward Ongweso Jr. already did so, and in part because I believe that there are much larger problems at work here. Newton is, along with his Hard Fork co-host Kevin Roose, actively engaged in a cynical marketing campaign, a repetition of the last two hype cycles where Casey Newton blindly hyped the metaverse and Roose pumped the bags of a penguin-themed NFT project.
The cycle continues with Roose running an empty-headed screed about what he "believes" about the near-term trajectory of artificial intelligence — that AGI will be here in the next two years, that we are not ready, but also that he cannot define it or say what it does — to Newton claiming that OpenAI’s Deep Research is "the first good agent" despite the fact his own examples show exactly how mediocre it is.
You see, optimism is easy. All you have to do is say "I trust these people to do the thing they'll do" and choose to take a "cautiously optimistic" (to use Roose's terminology) view on whatever it is that's put in front of you. Optimism allows you to think exactly as hard as you'd like to, using that big, fancy brain of yours to make up superficially intellectually-backed rationalizations about why something is the future, and because you're writing at a big media outlet, you can just say whatever and people will believe you because you're ostensibly someone who knows what they're talking about. As a result, Roose, in a piece in the New York Times seven months before the collapse of FTX, was able to print that he'd "...come to accept that [crypto] isn't all a cynical money-grab, and that there are things of actual substance being built," all without ever really proving anything.
Roose's "Latecomer's Guide To Cryptocurrency" never really makes any argument about anything, other than explaining, in a "cautiously optimistic" way, the "features" of blockchain technology, all without really having to make any judgment but "guess we'll wait and see!"
While it might seem difficult to write 14,000 words about anything — skill issue, by the way — Roose's work is a paper thin, stapled-together FAQs about a technology that still, to this day, lacks any real use cases. Three years later, we’re still waiting for those “things of actual substance,” or, for that matter, any demonstration that it isn’t a “cynical money-grab.”
Roose's AGI piece is somehow worse. Roose spends thousands of words creating flimsy intellectual rationalizations, writing that "the people closest to the technology — the employees and executives of the leading A.I. labs — tend to be the most worried about how fast it’s improving," and that "the people with the best information about A.I. progress — the people building powerful A.I., who have access to more-advanced systems than the general public sees — are telling us that big change is near."
In other words, the people most likely to benefit from the idea (and not necessarily the reality) that AI is continually improving and becoming more powerful are those who insist that AGI — an AI that surpasses human ability, and can tackle pretty much any task presented to it — is looming on the horizon.
The following quote is most illuminating:
This may all sound crazy. But I didn’t arrive at these views as a starry-eyed futurist, an investor hyping my A.I. portfolio or a guy who took too many magic mushrooms and watched “Terminator 2.”
I arrived at them as a journalist who has spent a lot of time talking to the engineers building powerful A.I. systems, the investors funding it and the researchers studying its effects. And I’ve come to believe that what’s happening in A.I. right now is bigger than most people understand.
Roose's argument, and I am being completely serious, is that he has talked to some people — some of them actively investing in the thing he's talking about who are incentivized to promote an agenda where he tells everybody they're building AGI — and these people have told him that a non-specific thing is happening at some point, and that it will be bigger than people understand. Insiders are "alarmed." Companies are "preparing" (writing blogs) for AGI. It's all very scary.
But, to quote Roose, "...even if you ignore everyone who works at A.I. companies, or has a vested stake in the outcome, there are still enough credible independent voices with short A.G.I. timelines that we should take them seriously."
Roose's entire argument can be boiled down to "AI models are much better," and when he says "much better" he means "they are able to get high scores on benchmarks," at which point he does not mention which ones, or question the fact that they (despite him saying these exact words) have had to "create new, harder tests to measure their capabilities," which can be read as "make up new tests to say why these models are good." He mentions, in passing, that hallucinations still happen, but "they're rarer on newer models," a statement he does not back up with evidence.
Sidenote: Although this isn’t a story about generative AI, per se, we do need to talk about the benchmarks used to test AI performance. I’m wary of putting too much stock into them, because they’re easily gamed, and quite often, they’re not an effective way of showing actual progress. One good example is SimpleQA, which OpenAI uses to test the hallucination rate of its models.
This is effectively a long quiz that touches on a variety of subjects, from science and politics, to TV and video games. An example question is: “Which Dutch player scored an open-play goal in the 2022 Netherlands vs Argentina game in the men’s FIFA World Cup?”
If you’re curious, OpenAI’s GPT 4.5 model — its most expensive general purpose LLM yet — flunked 37% of these questions. Which is, to say, that it confidently made up an answer more than one-third of the time.
There’s a really good article from the Australian Broadcasting Corporation that explains why this approach isn’t particularly useful, based on interviews with academics at the University of Monash and La Trobe University.
First, it’s gamable. If you know the answers ahead of time — and, given that you’re testing how close an answer resembles a pre-written “correct” answer, you absolutely have to — you can optimize the model to answer these questions correctly.
There’s no accusation that OpenAI — or any other vendor — has done this, but it remains a possibility. There’s an honor system, and honor systems often don’t work when there’s billions of dollars on the line, and there’s no real consequences for actually cheating. Or, indeed, no way for people to easily find out whether a vendor cheated on a test. Moreover, as the ABC piece points out, they don’t actually reflect the way people use generative AI.
While some people use ChatGPT as a way to find singular, discrete pieces of information, people also use ChatGPT — and other similar LLMs — to write longer, more complex pieces that incorporate multiple topics. Put simply, OpenAI is testing for something that doesn’t actually represent the majority of ChatGPT usage.
In his AGI piece, Roose mentions that the OpenAI’s models continue to score higher and higher marks on the International Math Olympiad test. While that sounds impressive, it’s worth remembering that this is just another benchmark, and thus, is susceptible to the same kind of exploitation as any other benchmark.
This is, of course, important context for anyone trying to understand the overall trajectory of AI, and whether these models are improving, or whether we’re any closer to reaching AGI. And it’s context that’s curiously absent from the piece.
He mentions that "...in A.I., bigger models, trained using more data and processing power, tend to produce better results, and today’s leading models are significantly bigger than their predecessors." He does not explain what those results are, what results they produce, and what said results lead to as products, largely because they haven't. He talks about "...if you really want to grasp how much better A.I. has gotten recently, talk to a programmer," then fails to quote a single programmer.
I won't go on, because the article is boring, thinly-sourced and speciously-founded.
But it's also an example of how comfortable optimism is. Roose doesn't have to make actual arguments – he makes statements, finds one example of something that confirms his biases, and then moves on. By choosing the cautiously-optimistic template, Roose can present "intelligent people that are telling him things" as proof that confirms what he wants to believe, which is that Dario Amodei, the CEO of Anthropic, who he just interviewed on Hard Fork, is correct when he says that AGI is mere years away.
Roose is framing his optimism as a warning – all without ever having to engage with what AGI is and the actual ramifications of its imminence. If he did, he would have to discuss concepts like personage. Is a conscious AI system alive? Does it have rights? And, for that matter, what even is consciousness? That’s no discussion of the massive, world-changing consequences of a (again, totally theoretical, no proof this exists) artificial intelligence that's as smart and capable (again, how is it capable?) as a human being.
Being an AI optimist is extremely comfortable, because Roose doesn't even have to do any real analysis — he has other people to do it for him, such as the people that stand to profit from generative AI's proliferation. Roose doesn't have to engage with the economics, or the actual capabilities of these models, or even really understand how they work. He just needs enough to be able to say "wow, that's so powerful!"
Cautious optimism allows Roose to learn as little as necessary to write his column, knowing that the market wants AI to work, even as facts scream that it doesn't. Cautious optimism is extremely comfortable, because — as Roose knows from boosting cryptocurrency — that there are few repercussions for being too optimistic.
I, personally, believe that there should be.
Here's a thing I wrote three years ago — the last time Roose decided to boost an entire movement based on vibes and his own personal biases.
The tech media adds two industry-unique problems - the fear of being wrong, and the fear of not being right. While one might be reasonable for wanting to avoid the next Theranos, one also does not want to be the person who said that social media would become boring and that people would leave it en masse. This is the nature of career journalism - you want to be right all the time, which means taking risks and believing both your sources and your own domain expertise - but it is a nature that cryptocurrency has taken advantage of at scale.
I hate that I've spent nearly two thousand words kvetching over Roose's work, but it's necessary, because I want to be abundantly clear: cautious optimism is cowardice.
Criticism — skepticism — takes a certain degree of bravery, or at least it does so when you make fully-formed arguments. Both Roose and Newton, participating in their third straight hype-cycle boost, frame skepticism as lazy, ignorant and childish.
...this is the problem with telling people over and over again that it’s all a big bubble about to pop. They’re staring at the floor of AI’s current abilities, while each day the actual practitioners are successfully raising the ceiling.
Newton doesn't actually prove that anyone has raised a ceiling, and in fact said:
...I fear, though, will be that “AI is fake and sucks” people will see a $200 version of ChatGPT and see only desperation: a cynical effort to generate more revenue to keep the grift going a few more months until the bottom drops out. And they will continue to take a kind of phony comfort in the idea that all of this will disappear from view in the next few months, possibly forever.
In reality, I suspect that many people will be happy to pay OpenAI $200 or more to help them code faster, or solve complicated problems of math and science, or whatever else o1 turns out to excel at. And when the open-source world catches up, and anyone can download a model like that onto their laptop, I fear for the harms that could come.
This is not meaningful analysis, and it's deeply cowardly on a number of levels. Newton does not prove his point in any way — he makes up a person that combines several ideas about generative AI, says that open source will “catch up,” and that also there will be some sort of indeterminate harm. It doesn't engage with a single critic’s argument. It is, much like a lot of Newton’s work, the intellectual equivalent of saying “nuh uh!”
Newton delights in his phony comforts. He proves his points in the flimsiest ways, knowing that the only criticisms he'll get are from the people he's steadfastly othered, people that he will never actually meaningfully engage with. He knows that his audience trusts him, and thus he will never have to meaningfully engage with the material. In fact, Newton isn't really proving anything — he is stating his own assumptions, giving the thinnest possible rationale, and then singling out Gary Marcus because he perceives him as an easy target.
This is, to repeat myself, extremely comfortable. Newton, like Roose, simply has to follow whatever the money is doing at any given time, learn enough about it to ask some questions in a podcast, and then get on with his day. There is nothing more comfortable than sitting on the podcast of a broadsheet newspaper and writing a newsletter for 150,000 people with no expectation that you'd ever have to push back on anything.
And I cannot be clear enough how uncomfortable it is being a skeptic or a cynic during these cycles, even before people like Casey Newton started trying to publicly humiliate critics like Gary Marcus.
My core theses — The Rot Economy (that the tech industry has become dominated by growth), The Rot-Com Bubble (that the tech industry has run out of hyper-growth ideas), and that generative AI has created a kind of capitalist death cult where nobody wants to admit that they're not making any money — are far from comfortable.
The ramifications of a tech industry that has become captured by growth are that true innovation is being smothered by people that neither experience nor know how (or want) to fix real problems, and that the products we use every day are being made worse for a profit. These incentives have destroyed value-creation in venture capital and Silicon Valley at large, lionizing those who are able to show great growth metrics rather than creating meaningful products that help human beings.
The ramifications of the end of hyper-growth mean a massive reckoning for the valuations of tech companies, which will lead to tens of thousands of layoffs and a prolonged depression in Silicon Valley, the likes of which we've never seen.
The ramifications of the collapse of generative AI are much, much worse. On top of the fact that the largest tech companies have burned hundreds of billions of dollars to propagate software that doesn't really do anything that resembles what we think artificial intelligence looks like, we're now seeing that every major tech company (and an alarming amount of non-tech companies!) is willing to follow whatever it is that the market agrees is popular, even if the idea itself is flawed.
Generative AI has laid bare exactly how little the markets think about ideas, and how willing the powerful are to try and shove something unprofitable, unsustainable and questionably-useful down people's throats as a means of promoting growth. It's also been an alarming demonstration of how captured some members of the media have become, and how willing people like Roose and Newton are to defend other people's ideas rather than coming up with their own.
In short, reality can fucking suck, but a true skeptic learns to live in it.
It's also hard work. Proving that something is wrong — really proving it — requires you to push against the grain, and battering your own arguments repeatedly. Case in point: my last article about CoreWeave was the product of nearly two weeks of work, where, alongside my editor, we poured over the company’s financial statements trying to separate reality from hype. Whenever we found something damning, we didn’t immediately conclude it validated our original thesis — that the company is utterly rotten. We tried to find other explanations that were equally or more plausible to our own hypothesis — “steelmanning” our opponent because being skeptical demands a level of discomfort.
Hard work, sure, but when your hypotheses are vindicated by later reporting by the likes of Semafor and the Financial Times, it all becomes worthwhile. I’ll talk about CoreWeave in greater depth later in this post, because it’s illustrative of the reality-distorting effects of AI optimism, and how optimism can make people ignore truths that are, quite literally, written in black ink and published for all the world to see.
An optimist doesn't have to prove that things will go well — a skeptic must, in knowing that they are in the minority, be willing to do the hard work of pulling together distinct pieces of information in something called an "analysis." A skeptic cannot simply say "I talked to some people," because skeptics are "haters," and thus must be held to some higher standard for whatever reason.
The result of a lack of true skepticism and criticism is that the tech industry has become captured by people that are able to create their own phony and comfortable realities, such as OpenAI, a company that burned $5 billion in 2024 and is currently raising $40 billion, the majority of it from SoftBank, which will have to raise $16 billion or more to fund it.
Engaging with this kind of thinking is far from comfortable, because what I am describing is one of the largest abdications of responsibility by financial institutions and members of the media in history. OpenAI and Anthropic are abominations of capitalism, bleeding wounds that burn billions of dollars with no end in sight for measly returns on selling software that lacks any real mass market use case. Their existence is proof that Silicon Valley is capable of creating its own illogical realities and selling them to corporate investors that have lost any meaningful way to evaluate businesses, drunk off of vibes and success stories from 15 or more years ago.
What we are witnessing is a systemic failure, not the beginnings of a revolution. Large Language Models have never been a mass market product — other than ChatGPT, generative AI products are barely a blip on the radar — and outside of NVIDIA (and consultancy Turing), there doesn't appear to be one profitable enterprise in the industry, nor is there any sign any of these companies will ever stop burning money.
The leaders behind the funding, functionality, and media coverage of the tech industry have abdicated their authority so severely that the consensus is that it's fine that OpenAI burns $5 billion a year, and it's also fine that OpenAI, or Anthropic, or really any other generative AI company has no path to profitability. Furthermore, it's fine that these companies are destroying our power grid and our planet, and it's also fine that they stole from millions of creatives while simultaneously undercutting those creatives in an already-precarious job market.
The moment it came out that OpenAI was burning so much money should've begun an era of renewed criticism and cynicism about these companies. Instead, I received private messages that I was "making too big a deal" out of it.
These are objectively horrifying things — blinking red warning signs that our markets and our media have reached an illogical point where they believe that destruction isn't just acceptable, but necessary to make sure that "smart tech people" are able to build the future, even if they haven't built anything truly important in quite some time, or even if there’s no evidence they can build their proposed future.
I am not writing this with any comfort or satisfaction. I am fucking horrified. Our core products — Facebook, Google Search, Microsoft Office, Google Docs, and even basic laptops — are so much worse than they've ever been, and explaining these things unsettles and upsets me. Digging into the fabric of why these companies act in this way, seeing how brazen and even proud they are of their pursuit of growth, it fills me full of disgust, and I'm not sure how people like Roose and Newton don't feel the same way.
And now I want to show you how distinctly uncomfortable all of this is.
Last week, I covered the shaky state of AI data center provider CoreWeave — an unprofitable company riddled by onerous debt, with 77% of its $1.9 billion of 2024 revenue coming from Microsoft and NVIDIA. CoreWeave lost $863 million in revenue in 2024, and when I published this analysis, some people suggested that its "growth would fix things," and that OpenAI's deal to buy $11.9 billion of compute over five years was a sign that everything would be okay.
Since then, some things have come out:
To summarize (and repeat one part from my previous article):
I'm afraid I'm not done explaining why I'm uncomfortable.
Let me make this much simpler.
Okay, simpler.
CoreWeave's continued existence is contingent on its ability to borrow money, pay its debts, and expand its business, which is contingent on OpenAI's ability to raise money and expand its business, which is contingent on SoftBank's ability to give it money, which is contingent on SoftBank's ability to borrow money.
OpenAI is CoreWeave. CoreWeave is OpenAI. SoftBank is now both CoreWeave and OpenAI, and if SoftBank buckles, both CoreWeave and OpenAI are dead. For this situation to work even for the next year, these companies will have to raise tens of billions of dollars just to maintain the status quo.
There is nothing comfortable about my skepticism, and in fact I'd argue it's a huge pain in the ass. Being one of the few people that is willing to write down the numbers in stark, objective detail is a frustrating exercise — and it's isolating too, especially when I catch strays from Casey Newton claiming he's taking "detailed notes" about my work as a punishment for the sin of "doing basic mathematics and asking why nobody else seems to want to."
It isn't comfortable to continually have to explain to people who are all saying "AI is the future" that the majority of what they are discussing is fictional, because it reveals how many people believe things based entirely on someone they trust saying it's real, or being presented a flimsy argument that confirms their biases or affirms their own status quo.
In Newton and Roose's case, this means that they continue being the guys that people trust will bring them the truth about the future. This position is extremely comfortable, as it doesn't require them to be correct, only convincing and earnest.
I don't fear that we're "not taking AGI seriously." I fear that we've built our economy on top of NVIDIA, which is dependent on the continued investment in GPUs from companies like Microsoft, Amazon and Google, one of which has materially pulled back from data center expansion. Outside of NVIDIA, nobody is making any profit off of generative AI, and once that narrative fully takes hold, I fear a cascade of events that gores a hole in the side of the stock market and leads to tens of thousands of people losing their jobs.
Framing skepticism as comfortable is morally bankrupt, nakedly irresponsible, and calls into question the ability of those saying it to comprehend reality, as well as their allegiances. It's far more comfortable to align with the consensus, to boost the powerful in the hopes that they will win and that their victories will elevate you even further, even if your position is currently at the very top of your industry.
While it's possible to take a kinder view of those who peddle this kind of optimism — that they may truly believe these things and can dismiss the problems as surmountable — I do not see at this time in history how one can logically or rationally choose to do so.
To choose to believe that this will "all just work out" at this point is willful ignorance and actively refusing to engage with reality. I cannot speak to the rationale or incentives behind the decision to do so, but to do so with a huge platform, to me, is morally reprehensible. Be optimistic if you'd like, but engage with the truth when you do so.
I leave you with a quote from the end of HBO's Chernobyl: "where I once would fear the cost of truth, now I only ask — what is the cost of lies?"
2025-03-18 02:07:55
Soundtrack: EL-P (ft. Aesop Rock) - Run The Numbers
In my years writing this newsletter I have come across few companies as rotten as CoreWeave — an "AI cloud provider" that sells GPU compute to AI companies looking to run or train their models.
CoreWeave had intended to go public last week, with an initial valuation of $35bn. While it’s hardly a recognizable name — like, say, OpenAI, or Microsoft, or Nvidia — this company is worth observing, if not for the fact that it’s arguably the first major IPO that we’ve seen from the current generative AI hype bubble, and undoubtedly the biggest. Moreover, it’s a company that deals in the infrastructure aspect of AI, where one would naturally assume is where all the money really is — putting up the servers for hyperscalers to run their hallucination-prone, unprofitable models.
You’d assume that such a company would be a thriving, healthy business. And yet, a cursory glance at its financial disclosure documents reveals a business that’s precarious at best, and, in my most uncharitable opinion, utterly rancid. If this company was in any other industry, it would be seen as such. Except, it’s one of the standard bearers of the generative AI boom, and so, it exists within its own reality distortion field.
Regardless, CoreWeave’s IPO plans appear to have been delayed, and it’s unclear when it’ll eventually make its debut on the public markets. I assume the reasons for the delay are as follows.
First, (and we’ll talk about this later), on March 10, OpenAI announced the completion of a deal with Coreweave valued at $11.9bn that would see it procure AI compute from the company, while also taking a $350m stake in the business. This arrangement has, undoubtedly altered some of the calculus behind things like valuations, and so on.
Additionally, Coreweave has now released an amended version of its S-1 — the document that all companies must file before going public, and that acts as a prospectus for would-be investors, revealing the strengths and weaknesses of the business. The new partnership with OpenAI does complicate some things, including when it comes to risk (as we’ll discuss later), and so it naturally makes sense that CoreWeave would have to release an updated version of its prospectus.
I’ve spent far too long reading CoreWeave’s S-1. For the uninitiated, S-1 documents are, as a matter of rule, often brutal. The SEC — and federal law — demands total, frank honesty. It’s a kind of hazing for would-be public companies, where they reveal all their dirty secrets to anyone with a web browser, thereby ensuring those who invest in the company on the first day are able to make informed decisions.
The revelations contained in S-1 documents are, quite often, damning, as we saw in the case of WeWork. It laid bare the company’s mounting losses, its debt burden, and its insane cash burn, and raised questions about the sustainability of a company that had signed hundreds of expensive long-term leases in the pursuit of growth, despite having never made a profit. Within a matter of weeks, WeWork cancelled the IPO and its CEO and founder, Adam Neumann, had left the company.
Sidenote: WeWork, incidentally, would later go public by merging with a SPAC (special purpose acquisition company, which is essentially a shell company that’s already listed on the open markets). SPACs exist for one reason, and that’s to allow shitty companies to go public and raise money from investors without having to go through the scrutiny of a full IPO. At least, that was the case prior to 2024, when the SEC began demanding increased disclosures from companies that sought to merge with SPACs and enter the public markets via the back door.
Unsurprisingly, many of the companies that used SPACs (like failed EV makers Fisker and Lordstown Motors, and Virgin Orbit) ultimately ended up in liquidation, or winding up petitioning a court for Chapter 11 bankruptcy protection. WeWork, for what it’s worth, filed for Chapter 11 in 2023, exiting bankruptcy the following year, albeit as a much smaller company, and one that was no longer listed on the vaunted New York Stock Exchange.
CoreWeave’s S-1 tells the tale of a company that appears to be built for collapse, with over 60% of its revenue dependent on one customer, Microsoft. In early March, the Financial Times reported that Microsoft has dropped "some services" with CoreWeave, citing delivery issues and delays, although Coreweave would later deny this.
The timing, however, is suspicious. It came a mere week after TD Cowen's explosive report that claimed Microsoft had walked away from over a gigawatt of data center operations, and likely much, much more. For context, a gigawatt is about the same as the cumulative data center capacity in London or Tokyo — each city being the largest data center market in their respective regions.
CoreWeave is burdened by $8 billion of debt (with its most recent debt raise bringing in $7.6bn, although that line of credit has not been fully tapped) that it may not be able to service. This figure does not include other commitments which are listed on the balance sheet as liabilities, like its various lease agreements for hardware and data center facilities.
Worse, despite making $1.9 billion in revenue during the 2024 financial year, the company lost $863 million in 2024, with its entire future riding on "explosive growth" that may not materialize, and even if it does, would require CoreWeave to spend unfathomable amounts of money on the necessary capex investments.
CoreWeave is, on many levels, indicative of the larger success (or failure) of the so-called AI revolution. The company's core business involves selling the unavoidable fuel for generative AI — access to the latest (and most powerful) GPUs and the infrastructure to run them, a result of its cozy relationship with (and investment from) NVIDIA, which has given CoreWeave priority access to its chips. As CoreWeave’s own S-1 notes, it was “the first cloud provider to make NVIDIA GB200 NVL72-based instances generally available,” and “among the first cloud providers to deploy high-performance infrastructure with NVIDIA H100, H200, and GH200.”
CoreWeave owns over 250,000 NVIDIA GPUs across 32 data centers, supported by more than 260MW of active power, making it competitive with many of the familiar hyperscalers I’ve mentioned in previous newsletters, despite being a company few have ever heard of. By comparison, Microsoft bought 485,000 GPUs in 2024 and aimed to have as many as 1.8 million GPUs by the end of that year, though it's unclear how many it has. Meta likely has somewhere in the region of 600,000 GPUs, and according to The Information's AI Data Center Database, Amazon has hundreds of thousands of its own.
In short, CoreWeave's position is one that at the very least competes with the hyperscalers, and is both a fascinating and disturbing window into the actual money that these companies do (or don't) make, and the answer is "not very much at all."
Furthermore, CoreWeave's underlying financials are so dramatically unstable that it's unclear how this company will last the next six months. As I'll get into, CoreWeave's founders are finance guys that have already cashed out nearly $500 million before the IPO, but did so in a way that means that despite only retaining 30% of the company's ownership, they retain 82% of the voting power, allowing them to steer a leaky, poorly-built ship in whatever direction they see fit, even if doing so might send CoreWeave into the abyss.
If this sounds familiar, it’s pretty much the same arrangement that Mark Zuckerberg has with Facebook. Despite only holding a small percentage of the company’s equity (around 13%), he holds the majority of voting shares, as well as the role of chairman of Facebook’s board, ensuring his position as CEO can never be challenged, regardless of any pressure from shareholders.
CoreWeave is a company that is continually hungry for more capital, and its S-1 cites potential difficulties in obtaining new cash as a potential risk factor. It intends to raise around $4 billion at IPO, which presumably will go towards servicing its debt and fuelling future expansion, as well as funding the day-to-day operations of the business. However, as I'll walk through in this newsletter, that will not be enough for this company to survive.
Sidenote: Remember when I said that companies have to lay out all their dirty laundry in the S-1, including potential risk factors? One of the factors cited is the questionable future of AI, and the failure of its customers “to support Al use cases in their systems” when those AI use cases are deployed on CoreWeave’s iron.
Again, Microsoft is Coreweave’s biggest customer. Essentially, it’s saying that Microsoft might not actually do a good job of getting people to use Copilot, or the OpenAI models it licenses through its own ecosystem, and that would, in turn, hurt CoreWeave.
The same document also mentions the usual stuff: the reputational harm that generative AI poses to its creators and those linked to them, regulatory scrutiny, and the uncertain trajectory of AI and its commercialization.
And, while we’re on the subject of risk factors, a few other things caught my eye. CoreWeave cited “material weaknesses in [its] internal control over financial reporting” as a risk factor. As a public company, CoreWeave will be forced to prepare and publish regular (and accurate) financial reports. While building the S-1, CoreWeave said it “identified material weaknesses in our internal control over financial reporting” which means that “there is a reasonable possibility that a material misstatement of our annual or interim financial statements will not be prevented or detected on a timely basis”
The good news: It’ll be able to fix them. The bad news? Doing so likely won’t be completed into 2026, and it’ll be “time consuming and costly.”
CoreWeave says that “negative publicity” could harm the company’s prospects, “regardless of whether the negative publicity is true.” This is a fairly generic statement that could apply to any business, and you’ll see similar generic warnings in most S-1 prospectuses, as they’re supposed to be a comprehensive representation of the risks that business faces. One line, however, did stand out. “Harm to our reputation can also arise from many other sources, including employee misconduct, which we have experienced in the past.” Interesting!
Anyway, I have a great deal of problems with this company, but let’s start somewhere simple.
Paging Doctor Zitron…
To properly understand CoreWeave, we have to look at its origin story. Founded in 2017, CoreWeave was previously known as Atlantic Crypto, a cryptocurrency mining operation started by three guys that worked at a natural gas fund. When the crypto markets crashed in 2019, they renamed the company and bought up tens of thousands of GPUs, which CoreWeave offered to the (at the time) much smaller group of companies that used them for things like 3D modelling and data analytics. This was a much smaller business, and far less capital-intensive, with CoreWeave making $12m in 2022 with losses of $31m.
When ChatGPT's launch in late 2022 activated the management consultant sleeper cells that decide what the tech industry's next hypergrowth fixation is going to be, Coreweave pivoted again, this time towards providing the computational muscle for generative AI. CoreWeave became what WIRED would call "the Multibillion-dollar Backbone of the AI boom," a comment that would suggest that CoreWeave was far more successful than it really is.
Nevertheless, CoreWeave has — through its relationship with NVIDIA, which holds a reported 5% stake in the company — an extremely large amount of GPUs, and it makes money by renting them out on a per-GPU-per-hour basis. Its competition includes companies like Lambda, as well as hyperscalers like Amazon, Google and — believe it or not — Microsoft, all of whom sell the same services.
What's important to recognize about CoreWeave's revenue is that, despite whatever WIRED might have said, the majority of its revenue does not come from "being the backbone of the AI boom," but as auxiliary cloud compute provider for hyperscalers. When a hyperscaler needs more capacity than it actually owns, it’ll turn to a company like CoreWeave to pick up the slack, because building a new datacenter is — as noted in the previous newsletter — something that can take between three and six years to complete.
CoreWeave's customers include AI startup Cohere, Meta, NVIDIA, IBM, and Microsoft, the latter of which is its largest customer, accounting for 62% of all revenue during the 2024 financial year. It’s worth noting the speed in which CoreWeave became highly reliant on a single customer to exist. By contrast, in 2022 its largest customer accounted for 16% of its revenue, suggesting a far more diversified — and healthy — revenue base.
Although CoreWeave says its reliance on Microsoft will decrease to 50% of revenue as (if?) OpenAI starts shifting workloads to its servers, the current reality remains unchanged. Broadly speaking, CoreWeave is dependent on a few big-spending “whales” to stay afloat.
Per the S-1, 77% of CoreWeave's revenue comes from two of its customers, the latter of which remains unnamed, and is only referred to as “Customer C” in the document. However, based on reporting from The Information, it’s reasonable to assume it’s NVIDIA, which agreed in 2023 to spend $1.3 billion over four years “to rent its own chips from CoreWeave.”
Once you remove these two big contracts, CoreWeave only made $440 million in 2024.
These numbers deeply concern me, and I'll explain why.
In short, CoreWeave is existentially tied to the idea that generative AI will become both a massive, revenue-generating industry and one that's incredibly compute-intensive. CoreWeave's future is one that requires an industry that has yet to show any meaningful product-market fit to grow so significantly that compute companies turn into oil companies at a time when Microsoft — the largest provider of GPU compute and the hyperscaler with the highest amount of proposed capex spending — has pulled back from both over a gigawatt of compute capacity and (reportedly) some of its contracts with CoreWeave.
CoreWeave’s three largest customers have, according to its S-1, increased their committed spend by around $7.8 billion during the 2024 financial year, representing a fourfold increase in the initial contract value. For the sake of clarity, this reflects future spending commitments — not actualized revenue from providing services to these companies.
While this might seem like good news, that's also nearly four times its current revenue from three customers, and as Microsoft has reportedly proven with its other compute contracts, big customers can simply cancel contracts on a whim.
Put simply, this dependence on a handful of hyperscalers represents a fundamental — and potentially fatal — vulnerability.
Sidenote: On the subject of vulnerabilities, the updated S-1 prospectus talks about a theoretical “counterparty credit risk.” What does that mean? Essentially, it’s when one party defaults on paying for services that the other party has paid for. If you don’t pay your mortgage, that’s counterparty credit risk.
CoreWeave is saying that, should a customer fail to pay its bills for infrastructure built on their behalf, or for services rendered, it could have a material risk to the business. The S-1 gives the example of its arrangement with OpenAI, where CoreWeave has agreed to provide certain services (and build certain infrastructure) in exchange for $11.9bn over the course of the next five years.
Although CoreWeave talks generally about the risk of counterparty credit risk, and only cites OpenAI in hypothetical terms, it’s also the only company named in this section. Which makes sense, because the chances of Microsoft or Meta becoming insolvent in the immediate future are slim, whereas OpenAI’s entire existence depends on its ability to raise more money than any startup in history, indefinitely, while also never making a profit.
And, as readers of this newsletter will know, I don’t rate its chances.
One last note on risks: Perhaps the biggest, in my view, is the fact that there’s really nothing inherently special about CoreWeave besides its existing infrastructure. Cloud GPUs are incredibly commoditized, and the core factors of differentiation between the various players are price, availability, and the exact hardware available. In fairness to CoreWeave, it has some strength in the latter point, with a close relationship with Nvidia that’s afforded it access to the latest and greatest hardware as it becomes available.
The problem is that, for the most part, with enough money you could make a company as equally capable as CoreWeave. And, indeed, CoreWeave does effectively the same thing as other hyperscalers like Google Cloud and Azure and AWS, as well as upstarts like Lambda Labs.
So, tell me, why is this business worth $35bn?
CoreWeave simply doesn't have meaningful demand or revenue resulting from its services. $440 million — with some of that revenue likely coming from other hyperscalers, albeit those who haven’t spent as much as Microsoft — is a pathetic sum that suggests either not enough people want to use CoreWeave’s services, or the services themselves are not actually that lucrative to provide, likely due to the ruinously-expensive costs of running hundreds of thousands of GPUs at full tilt.
Regardless of the reason, the company selling the literal fuel of the supposed AI revolution is losing hundreds of millions of dollars doing so.
Worse still, CoreWeave is entirely dependent on its largest customers, to the point that their entire business would collapse without them...and frankly, given the precarious nature of its financials, might even collapse with them.
That's because servicing this revenue is also incredibly costly. According to The Information, CoreWeave spent over $8.5 billion in capital expenditures in 2024, and funding said expenditures required CoreWeave to take on onerous amounts of debt, to the point that it's unclear how this business survives.
Forgive me, as the following is going to be a little dense.
In simple terms, CoreWeave's operations requires it to be in a near-constant state of capital expenditure, both to build the data centers it needs to serve customers from, to purchasing massive amounts of power, to acquiring the specialized GPUs necessary to run AI workloads.
CoreWeave has raised a total of $14.5 billion in equity funding (selling stock in the company) and debt financing (loans). Many of these loans are collateralized not by money or real estate, with “the Company’s property, equipment and other assets,” which includes the value of the GPUs used to power its operations. This is a new kind of asset-backed lending model created specifically to fund compute-sellers like CoreWeave, Crusoe, and Lambda, similar to how a mortgage is backed by the value of the property.
The problem is that, yes, GPUs are depreciating assets, a fact that will eventually become problematic for these companies. They eventually slide into obsolescence, as new chips and new manufacturing processes come out. With constant, intensive use, they wear down and fail, and thus require replacing. As noted later in this piece, as these assets lose value, CoreWeave is forced to increase its monthly payments as the collateral is (presumably) no longer sufficient to satisfy the outstanding debt.
In CoreWeave's case, the majority of its raised capital was raised in debt, with the majority of that coming in two Delayed Draw Term Loan facilities (DDTL). This usually means that while you have access to a certain amount of money, said money is only disbursed in predefined installments. These installments may come after a certain period of time, or when the company reaches a certain milestone. DDLTs work unlike personal loans, where you typically get the cash upfront, and start repayments immediately. The contracts are custom-written for each loan, and reflect the needs (and risks) of the business.
These loans can have wildly different terms based on their collateral and the incoming revenue of the company in question. All numbers below are an estimate based on the terms of the loans in question, and I do not attempt to factor in the actual cost of interest. For the most part, I've worked out the terms of the loans and the repayment schedule, and given you what I believe will be the lowest amount CoreWeave will owe.
For the sake of both your and my sanity, I'm going to focus on just these loans, as I believe they are, on their own, enough to potentially destroy CoreWeave.
CoreWeave's first Delayed Draw Term Loan (DDTL 1.0, as it calls it), came from titanic alternative asset management firm Blackstone (not to be confused with Blackrock), and Magnetar Capital, an Illinois-based hedge fund most famous for its involvement in the creation of Collateralized Debt Obligations, the security product that created the Global Financial Crisis of the late 2000s, raising $2.3 billion. This loan has now been fully drawn.
The effective annual interest rate on this loan is a little bit more than 14%, averaging at 14.11% in 2024 and 14.12% in 2023. The reason for the variance is because of how the interest rate is actually calculated. It combines the Term SOFR of the period (which is an average of the interest rate on treasury bond activity outside normal trading hours), plus 9.62%, or an unspecified “alternative base rate” plus 8.62%. At the time of writing, the 180-day average of SOFR is around 4.6%, although this can fluctuate depending on market conditions.
The terms of the loan require quarterly payments based on the company's cash flow and, starting in January 2025, the depreciated value of the GPUs that are used as collateral to provide the loan, and CoreWeave has until March 2028 to fully pay it off. Interest accrues monthly, and there is a final (though unspecified) balloon payment. Per the amended S-1 document, CoreWeave has paid $288 million in principal and $255 million in interest since the inception of the loan.
It’s tricky to actually calculate the monthly payments on this. The previous balance repayments aren’t a useful guide, as the loan wasn’t fully utilized in 2023, with CoreWeave carrying a balance of $1.3bn. There are 13 quarters between December 31, 2024 and March 31, 2028. If we divide the outstanding debt of $2bn by thirteen, we have around $150m. That only covers the principal, and not the interest — which, I remind you, stands at an arse-clenchingly steep 14.11%.
Nor, for that matter, do the previous repayments include the increase in principal payments starting from January 2025 to reflect the depreciating value of the collateral.
As Reuters reported in 2003, CoreWeave used its H100s as collateral for this loan. Those chips are, at this point, nearly two years old. While it’s unclear how the resale value of these chips has changed over time, it’s worth noting that the cost to rent a H100 in the cloud has dropped from between $4.70 to $8 an hour in late 2023, to just $1.90 at the time of writing. That will, undoubtedly, affect the value of CoreWeave’s collateral.
As the S-1 notes, this loan has a final balloon payment. It’s unclear how big this payment will be, as the filing doesn’t provide any detail. Still, regardless of the balloon payment size, it’s not unreasonable to expect that, from this year, CoreWeave will be spending around $250m each quarter to service this loan, or $1bn annually.
DDTL 1.0 also imposes liquidity requirements, although CoreWeave is only required to have $56 million in cash on hand to keep this loan, and that amount goes down as the principal reduces through repayments.
CoreWeave's biggest, sexiest loan was also co-led by Blackstone and Magnetar, and allows it to draw up to $7.6 billion by June 2025 (with the option to extend for a further three months), with several fees (both upfront, at closing, and annually). These are calculated in rather esoteric ways.
Take, for example, the yearly fee. This is equal to 0.5% of the difference between $7.6 billion and the average outstanding debt on the loan, or $6.1 billion (at least $75 million), whichever is greater. In essence, CoreWeave pays a fee if it uses the loan and pays interest on whatever amount it chooses to borrow. The loan must be fully repaid in 60 months from whenever the money was drawn.
Per DDTL 1.0, a series of bizarre interest calculations based on standard/prime interest rates are involved, but DDTL 2.0 can also calculate further interest rate increases based on CoreWeave's credit rating. And there’s a huge scope for variation, with the acceptable range starting at 5% and ending at 12%. That eye-watering 10.53% interest rate I mentioned earlier — although far higher than what a consumer with decent credit could pay on a mortgage or car loan — isn’t necessarily the highest it could reach. It could get much, much worse.
These mob-like terms suggest Blackstone and Magnetar don't necessarily trust CoreWeave to survive, and intend to rip out whatever guts are left if it doesn't. CoreWeave's S-1 says that the actual average interest rate being charged on the amounts borrowed was 10.53%, but again, this could go up.
The number I've put above could as much as double to $1.52 billion a year (again, with no consideration of accrued interest) in the event that CoreWeave chooses to draw on the remaining $3.8 billion, something that I'm fairly confident will happen based on its aggressive capital expenditures.
DDTL 2.0 also has a brutal covenant — that if CoreWeave raises any other debt, it must use that debt to pay off this debt. This raises the question as to how it’ll manage to pay the DDTL 1.0 balloon payment, should any future debt raised be used to satisfy the DDTL 2.0 loan.
On the subject of future debt, the updated S-1 prospectus says that, in order to meet the requirements of OpenAI’s $11.9bn deal, CoreWeave will have to take on additional financing. How will it accomplish this while also remaining compliant with the terms of DDTL 2.0, particularly when it comes to how it’ll use the proceeds of any future borrowing?
The S-1 sheds some light here. The company has created a “special purpose vehicle” — essentially, a separate company owned and controlled by Coreweave, though technically distinct — that will “incur indebtedness to finance the obligations under the OpenAI Master Services Agreement.”
All of this might seem a little dense, but it's actually pretty simple. CoreWeave made slightly under $2 billion in revenue in 2024, but somehow ended up losing $863 million. In effect, CoreWeave spends $1.43 to make $1.
As of January 2025, CoreWeave's obligations under DDTL 1.0 will likely reach $1bn a year, if not more. Starting from October 2025, it’ll need to start repaying the DDTL 2.0 loan, and these repayments will depend on whether it draws more capital from the loan, and whether its interest rate increases or decreases based on its perceived risk. Regardless, it’s not hard to imagine a scenario where its debt repayments surpass its entire 2024 revenue.
Furthermore, CoreWeave has made a lot of commitments. It’s planning to invest over a billion dollars to convert a New Jersey lab building into a data center, it’s part of a $5 billion effort with Blue Owl, Chirisa and PowerHouse, it’s committed to invest over a billion pounds sterling in UK-based data centers, it’s committed to invest an additional $2.2 billion in data centers in Europe, it’s committed to a $600 million data center project in Virginia, and allegedly have exercised an option with Core Scientific — a deeply dodgy company I'll describe in a minute — to create "approximately 500 Megawatts of Critical IT Load at Six Core Scientific Sites," with the agreement "increasing potential cumulative revenue to $8.7 billion over 12 Year Contract Terms."
In short, CoreWeave has committed to billions of dollars of data center buildouts, and the only way it can pay for them is with burdensome loans that it, as of right now, does not appear to have the revenue to support.
CoreWeave spent approximately $2.86 billion to make just under $2 billion, with $1.5 billion of that coming from the cost of running its infrastructure and scaling its operations, and the rest coming from hundreds of millions of dollars of interest payments and associated fees.
These numbers do not appear to include capital expenditures, and by its own admission, the vast loans that CoreWeave has pulled are necessary to continue funding them. Worse still, NextPlatform estimates that CoreWeave spent about $15 billion to turn its $7.5 billion of GPUs into around 360 Megawatts of operational computing power.
Per its S-1, CoreWeave has contracted for around 1.3 Gigawatts of capacity, which it expects to roll out over the coming years, and based on NextPlatform's math, CoreWeave will have to spend in excess of $39 billion to build its contracted compute. It is unclear how it will fund doing so, and it's fair to assume that CoreWeave does not currently have the capacity to cover its current commitments.
How does CoreWeave — a company with roughly $1.3 billion in the bank and more than $4.6 billion of debt available to draw and an inability to raise further capital without paying said debt off — actually continue doing business?
Before We Go Any Further, A Note On "Contracted Power"
"Contracted power" does not necessarily mean that it exists. "Contracted power" is a contract that says "you will provide this much compute." This term is used to deliberately obfuscate the actual compute that a company has.
As of writing this sentence, CoreWeave has "more than 360" megawatts of active power and "approximately 1.3 GW of total contracted power." This means that CoreWeave has committed to building this much.
These figures will become relevant shortly.
In 2017, a company was founded with the goal of mining digital assets like Bitcoin to generate revenue, and to provide mining services for others. Several years later, it pivoted into providing compute for generative AI.
Confusingly, this company is not CoreWeave, but Core Scientific, a totally different company entirely that went public in 2022 in a disastrous SPAC-merger, and later filed for Chapter 11 bankruptcy that same year. It exited bankruptcy court in January 2024, having shed $400m in debt and restructured its obligations to creditors, and once again returned to the public markets, where it trades on the NASDAQ.
In June 2024, CoreWeave made an unsolicited proposal to acquire Core Scientific that it rejected (three days after announcing a 12-year-long deal with CoreWeave to provide 200 megawatts of compute), before signing an extension of an already-existent 12-year-long deal in August 2024 to deliver "an additional 112 megawatts of computing infrastructure to support CoreWeave's operations" according to CNBC.
This capacity, according to CNBC, will be operational by "the second half of 2026," and would involve repurposing existing crypto mining hardware. Here's a quote from CNBC about how easy that'll be:
Needham analysts wrote in a report in May that almost all infrastructure that miners currently have would “need to be bulldozed and built from the ground up to accommodate HPC,” or high-performance computing.
Great!
As of now, Core Scientific holds, according to CEO Adam Sullivan, "the largest operational footprint of Bitcoin mining infrastructure," and per the above analyst quote, it's very obvious that you can't just retrofit a crypto mining rig to start "doing AI," likely because the GPUs are different to the ASICs (Application Specific Integrated Circuits) used in crypto mining, meaning the server hardware is different, which means the entire bloody thing is different.
Nevertheless, CoreWeave's S-1 repeatedly mentions that it’s made an agreement with Core Scientific for "more than 500 MW of capacity."
Right now, however, it's unclear how much capacity Core Scientific actually has, despite both its and CoreWeave's suggestions. Core Scientific, as of February 2025, had approximately 166,000 bitcoin miners — which, I should add, are likely all application-specific chips that only mine bitcoin!, which means that none of that has (or, potentially, any of their data center operations have) anything to do with GPUs or compute for AI.
In fact, I can find little proof that Core Scientific has any meaningful compute capacity at all.
Once you dig into its financial filings, things get weirder. Per its most recent annual report for the year ending December 31, 2024, Core Scientific made $24.3 million in HPC hosting revenue (referring to high performance computing, which includes generative AI workloads).
That isn’t a typo. $24.3 million. By contrast, it generated $408m in revenue from mining and selling cryptocurrencies for itself, and $77m for mining crypto for third-parties.
Sidenote: Assuming it’s possible for Core Scientific to repurpose bitcoin miners for AI workloads, how does that help the business? As noted, mining crypto for resale, and for external partners, provides the overwhelming majority — nearly 95% — of its revenue.
Core Scientific has run at a loss for the last three quarters, losing $265 million in Q4 2024, $455 million in Q3 2024, and $804 million in June 2024.
Core Scientific has one HPC client: CoreWeave, which is referred to as “Customer J” in the 10-K form — the annual financial report that every publicly-traded company must publish at the close of each financial year.
Core Scientific, according to its 10-K form:
"...was contractually committed for approximately $1.14 billion of capital expenditures, mainly related to infrastructure modifications, equipment procurement, and labor associated with the conversion of a significant portion of its data centers to deliver hosting services for HPC... [with] $899.3 million [being] reimbursable by our customer under our agreements."
That customer — Core Scientific's only HPC customer — being CoreWeave, with the expenses expected to "occur over the next year."
How exactly will Core Scientific, a company that was bankrupt last time this year and lost over $265 million in its last quarter afford the up-front capital expenditures from CoreWeave's expansion? Core Scientific has around $836 million in cash and cash equivalents on hand and is still in the process of cleaning up its already-existent piles of debt, and even then...how does any of this work, exactly?
And given that the company recently exited Chapter 11 bankruptcy protection, it’s unlikely to receive capital on favorable terms.
Hey wait a second...in Core Scientific's latest 10-K, it proudly boasts that it has "approximately 1,317 MW of contracted power capacity to operate and manage one of the largest center infrastructure asset bases." CoreWeave's S-1 says that it has "...total contracted power extends to approximately 1.3 GW as of December 31, 2024, which we expect to roll out over the coming years."
Core Scientific's only customer (CoreWeave) is contracted to build 1.3 gigawatts of capacity, and mysteriously, that's exactly how much CoreWeave, Core Scientific's only customer, has said it’s contracted. While Core Scientific has said a chunk of that capacity is reserved for expanding its cryptocurrency-ming operations, it is still an extremely suspicious coincidence.
Nevertheless, Core Scientific, as of right now, does not appear to have any meaningful HPC infrastructure. While it may have seven data centers, that doesn't mean it’s able to meet the demands of companies like Microsoft and OpenAI, both customers of CoreWeave, as evidenced by the fact that it made $8.5 million in HPC revenue last quarter, and $24.3m for the entire financial year.
Somehow, Core Scientific intends to spend a billion dollars building HPC infrastructure, a thing it has yet to meaningfully do (it has, as of November, broken ground on a site in Oklahoma), and somehow deliver over a gigawatt of capacity to a company that will allegedly reimburse it, at some point, somehow, with the money they do not have.
What the fuck is going on?
To summarize:
If CoreWeave makes it to IPO — and it may do so as soon as next week — it will raise about $4 billion, which might give it enough runway to continue operations for a year, but by October 2025 it’ll face upwards of $500 million of loan payments a quarter, all while trying to scale up an operation that doesn't appear to have a path to profit.
The reason I've spent thousands of words walking you through CoreWeave's problems is that this is the first meaningful tech IPO in some time, and the first one directly connected to the AI boom.
CoreWeave's financial health and revenue status suggest that there either isn't demand or profit in providing services for generative AI. This company — the so-called backbone of the generative AI boom, and one of the largest holders of NVIDIA GPUs, with a seemingly closer relationship with the company than Meta or Microsoft, based on its early access to the company’s latest hardware — does not appear to be able to get meaningful business for its operations outside of hyperscalers. While it may sell by-the-hour compute to regular companies, it's clear that that market just doesn't exist at a meaningful revenue point.
If NVIDIA is selling the pickaxes for the gold rush, CoreWeave is selling the shovels, and it mostly appears to be turning up dirt. If this were a meaningful growth industry, CoreWeave would be printing money, just like how the automobile created an entire generation of billionaire oil barons, like John D. Rockefeller and Henry Flagler. And yet, it appears that, outside of Microsoft, it can't even scrape a billion dollars of revenue out of being the single-most prominent independent provider of AI compute.
Furthermore, it's unclear how CoreWeave actually intends to expand. Core Scientific is a tiny, unproven party that has yet to build an HPC data center, one that has to front the money for CoreWeave's expansion in the hopes that it’ll be reimbursed. Building data centers isn't easy, and Core Scientific's previous work as a Bitcoin mining firm does not necessarily apply thanks to the massively-different server architecture involved with running superclusters of GPUs.
CoreWeave should have been a positive signal for generative AI, or at least a way for AI boosters to shut me up. If generative AI had this incredible demand — both from companies looking to integrate it and users looking to use it — CoreWeave would be making far, far more money, have a far more diverse customer base, and, if I'm honest, not have to take out more than five times its revenue in burdensome loans with loan shark-level interest rates.
In reality, this company is a dog, and will show the markets exactly how little money or growth is left in generative AI. NVIDIA's remarkable GPU sales have been conflated with the success of generative AI, rather than seen as a sign of desperation, and a signal of how willing big tech is to spend billions of dollars on something if they think their competition is doing so.
Really, the proof is in the use of those GPUs, and CoreWeave gives us a transparent — and terrifying — expression of the lack of excitement or real usage of generative AI. As I hypothesized a few weeks ago, I believe that outside of OpenAI, the generative AI industry is terribly small, a point that CoreWeave only underlines.
Based on its revenue, how much could Amazon Web Services, Google Cloud, or Microsoft Azure really be making? Based on reporting by The Information, OpenAI spends roughly $2 billion on the compute to run its models and a further $3 billion to train its models, paying Microsoft a discounted rate of around 25% the normal cost. Even in the most optimistic figures, given how much bigger and more popular ChatGPT is than literally every other generative AI company, how can any hyperscaler be making more than $3 billion or 4 billion in revenue a year from selling AI compute?
Without conceding that generative AI has a future beyond the frothy present, one also has to question whether there’s even much of a place for massive hyperscaler investment, given the rise of new, more efficient models. I’m not merely talking about DeepSeek. The largest version of Google’s newest Gemma 3 model can run on a single H100 GPU, and according to Sundar Pichai, requires one-tenth the computing power as similar models. Separately, Baidu’s ERNIE 4.5 model reportedly has one-hundredth the computational demands as GPT-4.5, while delivering similar performance, and its X1 reasoning model allegedly outperforms DeepSeek R1 at half the cost.
These numbers also suggest that OpenAI is likely charging way, way less than it should be for its services. If it costs CoreWeave $493 million (yes, this is napkin math) — this is its "cost of revenue," and that amount only includes rentals, power and personnel to run its services — to service 360 megawatt of power, and Microsoft's 7.5 gigawatts of power is, say, 70% OpenAI's compute, it may cost Microsoft over $7 billion. It's already been well-established that OpenAI's costs eat into Microsoft's profits.
Again, these are estimates, as we don't know Microsoft's exact costs, but it's reasonable to believe that its contracted compute with CoreWeave was likely to facilitate OpenAI's growth, which is further ratified by The Information's AI data center database, which reports that the upcoming data center buildout in Denton, Texas is, and I quote, "...[for] Microsoft [to] rent to use by OpenAI."
And you'll never guess who's building it. That's right, Core Scientific, which announced on February 26 2025 that it was partnering with CoreWeave to expand its relationship across its Denton, Texas location.
It's unclear how this data center gets built, or whether OpenAI will actually use it given its new plans for a "Stargate" data center in Abilene Texas, and the general chilling of the relationship with Microsoft. Furthermore, Microsoft's indeterminately-sized cancellations with CoreWeave pair with its own retreat from data center buildouts, which coincides with Microsoft releasing OpenAI from its exclusive cloud compute provider relationship, which coincides with OpenAI's plans to build gigawatts of its own capacity.
How, exactly, does any of this make sense?
While I can only hypothesize, I believe that this move is Microsoft's attempt to disconnect from OpenAI, dumping its exclusive relationship and canceling its own capacity expansion along with contracts with CoreWeave, citing, according to the Financial Times, "delivery issues and missed deadlines," which would make sense, as it appears that CoreWeave's infrastructure partner does not appear to have expertise in building data center capacity, or any actual cloud compute capacity that I can find.
Think about it. It was reported last year that OpenAI was frustrated with Microsoft for not providing servers fast enough, after which Microsoft allowed OpenAI to seek other compute partners, which in turn led to OpenAI shacking up with Oracle and SoftBank to build out the future of OpenAI’s compute infrastructure. Once this happened, Microsoft decided to (or had already been in the process of) massively reduce its future data center capacity at a time when OpenAI’s latest model necessitates bringing hundreds of thousands of GPUs online.
Even if you disagree with my thesis, how is Microsoft going to support OpenAI’s growth any further? OpenAI’s latest models o-3 and GPT 4.5 are more compute-heavy than ever. How, exactly, does canceling over a gigawatt of planned capacity make sense?
It doesn’t. And I think we’re about to see what happens when the world’s biggest startup becomes desperate.
2025-03-04 01:12:42
A week ago, analyst TD Cowen revealed that Microsoft had canceled leases "totalling a couple hundred MWs," with "at least two private data center operators across multiple US markets." The report also details how Microsoft "pulled back on converting negotiated and signed Statement[s] of Qualifications (SQQs)," which it added was "the precursor to a data center lease."
Although the analyst added it was unclear whether Microsoft might convert them in the future, these SQQs converted into leases "close to 100%" of the time. Cancelling them was, therefore, rather unusual.
TD Cowen also added that Microsoft was "re-allocating a considerable portion of [its] projected international spend to the US, which suggests to [TD Cowen] a material slowdown in international leasing."
But one crucial, teeny tiny part of the report was missed by just about everybody, emphasis mine:
As we highlighted in our recent takeaways from PTC [Pacific Telecommunications Council conference], we learned via our channel checks that Microsoft 1) walked away from multiple +100MW deals in multiple markets that were in early/mid-stages of negotiations, 2) let +1GW of LOI's on larger footprint sites expire, and 3) walked away from at least five land parcels that it had under contract in multiple Tier 1 markets.
What TD Cowen is saying is not just that "Microsoft canceled some data centers," but that Microsoft also effectively canceled over a gigawatt of data center operations on top of the previously-reported "multiple +100W megawatt deals." If we add in the land under contract, and the deals that were in-flight, the total capacity likely amounts to even more than that.
For some context, Data Center Dynamics reported that Microsoft had 5GW of data center capacity in April 2024, saying that Microsoft had planned to add 1 gigawatt of capacity by October 2024, and another 1.5 gigawatts of capacity by the first half of 2025. Based on this reporting, one can estimate that Microsoft has somewhere between 6 and 7 gigawatts of capacity at this time.
As a result, based on TD Cowen's analysis, Microsoft has, through a combination of canceled leases, pullbacks on Statements of Qualifications, cancellations of land parcels and deliberate expiration of Letters of Intent, effectively abandoned data center expansion equivalent to over 14% of its current capacity.
Sidebar: Let's explain some terms!
Letter of intent — In this context, it’s a statement that an entity intends to lease/buy land or power for a data center. These can be binding or non-binding. A letter of intent is serious — here's a press release between Microsoft and UAE-backed G42 Group signing a letter of intent with the Kenyan government for a "digital ecosystem initiative" (including a geothermal-powered data center) for example — and walking away from one is not something you do idly.
SOQ — Statement of qualifications. These set the terms and conditions of a lease. While they do not themselves constitute a lease, they convert into signed leases at an almost 100% rate (according to TD Cowen), and are generally used as a signal to the landowner to start construction.
Tier 1 market — Essentially, a market for hyperscale growth, helped by favorable conditions (like power, land, cabling). From what I can tell, there's no fixed list of which cities are T1 and which ones aren't, but they include the obvious candidates, like London, Singapore, etc, as well as Northern Virginia, which is the largest hub of data centers in the world.
Megawatt/Gigawatt — This one is important, but also arguably the most confusing. Data center capacity is measured not by the amount of computations the facility can handle, but rather by power capacity. And that makes sense, because power capacity is directly linked to the capabilities of the facility (with more power capacity allowing for more servers, or more power-hungry chips), and because chips themselves are constantly getting faster and more power efficient. If you measured in terms of computations per second, you’d likely have a number that fluctuates as hardware is upgraded and decommissioned. When you read ‘MW/GW’ in this article, assume that we’re talking about capacity and not power generation, unless said otherwise.
These numbers heavily suggest that Microsoft — the biggest purchaser of NVIDIA's GPUs and, according to TD Cowen, "the most active [data center] lessee of capacity in 2023 and 1H24" — does not believe there is future growth in generative AI, nor does it have faith in (nor does it want responsibility for) the future of OpenAI.
Data center buildouts take about three to six years to complete, and the largest hyperscale facilities can easily cost several billion dollars, meaning that these moves are extremely forward-looking. You don’t build a data center for the demand you have now, but for the demand you expect further down the line. This suggests that Microsoft believes its current infrastructure (and its likely scaled-back plans for expansion) will be sufficient for a movement that CEO Satya Nadella called a "golden age for systems" less than a year ago.
To quote TD Cowen again:
"...the magnitude of both potential data center capacity [Microsoft] walked away from and the decision to pullback on land acquisition (which supports core long term capacity growth) in our view indicates the loss of a major demand signal that Microsoft was originally responding to and that we believed the shift in their appetite for capacity is tied to OpenAI."
To explain here, TD Cowen is effectively saying that Microsoft is responding to a "major demand signal" and said "major demand signal" is saying "you do not need more data centers." Said demand signal that Microsoft was responding to, in TD Cowen's words, is its "appetite for capacity" to provide servers to OpenAI, and it seems that said appetite is waning, and Microsoft no longer wants to build out data centers for OpenAI.
The reason I'm writing in such blunt-force terms is that I want to make it clear that Microsoft is effectively cutting its data center expansion by over a gigawatt of capacity, if not more, and it’s impossible to reconcile these cuts with the expectation that generative AI will be a massive, transformative technological phenomenon.
I believe the reason Microsoft is cutting back is that it does not have the appetite to provide further data center expansion for OpenAI, and it’s having doubts about the future of generative AI as a whole. If Microsoft believed there was a massive opportunity in supporting OpenAI's further growth, or that it had "massive demand" for generative AI services, there would be no reason to cancel capacity, let alone cancel such a significant amount.
As an aside: What I am not saying is that Microsoft has “stopped building data centers.” I’ve said it a few times, but these projects take three to six years to complete and are planned far in advance, and Microsoft does have a few big projects in the works. One planned 324MW Microsoft data center in Atlanta is expected to cost $1.8bn and as far as I know, this deal is still in flight.
However (and this was cited separately by TD Cowen), Microsoft has recently paused construction on parts of its $3.3 billion data center campus in Mount Pleasant, Wisconsin. While Microsoft has tried to reassure locals that the first phase of the project was on course to be completed on time, its justification for delaying the rest was to give Microsoft an opportunity to evaluate “[the project’s scope] scope and recent changes in technology and consider how this might impact the design of [its] facilities."
The same Register article adds that “the review process may include the need to negotiate some building permits, potentially placing another hurdle in the way of the project.” The Register did add that Microsoft said it still expected to complete “one hyperscale data center in Mount Pleasant as originally planned,” though its capacity wasn’t available.
These moves also suggest that Microsoft is walking away from building and training further large frontier models, and from supporting doing so for others. Remember, Microsoft has significantly more insight into the current health and growth of generative AI than any other company. As OpenAI's largest backer and infrastructural partner (and the owners of the server architecture where OpenAI trains its ultra-expensive models, not to mention the largest shareholder in OpenAI), Microsoft can see exactly what is (or isn't) coming down the pike, on top of having a view both into the sales of its own generative AI-powered software (such as Microsoft 365 Copilot) and sales of both model services and cloud compute for other models on Microsoft Azure.
In plain English, Microsoft, which arguably has more data than anybody else about the health of the generative AI industry and its potential for growth, has decided that it needs to dramatically slow down its expansion. Expansion which, to hammer the point home, is absolutely necessary for generative AI to continue evolving and expanding.
Cushman and Wakefield's 2024 Global Data Center Market Comparison gives some chilling context into how significant Microsoft's pullback is. A gigawatt of data center capacity is roughly the entire operational IT load of Tokyo (1.028GW), orLondon (996MW), or the Bay Area (842MW).
And, again, the total figure is likely far higher than a gigawatt. That number only accounts for the LOIs that Microsoft allowed to expire. It doesn’t include everything else, like the two data centers it already killed, or the land parcels it abandoned, or the deals that were in the early-to-mid stages of negotiation.
Microsoft is not simply "walking back some future plans." It is effectively canceling (what it has loudly insisted is) the future. If you think that sounds hyperbolic, consider this: London and Tokyo are respectively the biggest data center markets in Europe and Asia, according to the same Cushman and Wakefield report.
In the event that Microsoft believed there was meaningful growth in generative AI, it wouldn't be canceling anything, or at the very least wouldn't be canceling so much.
Sidenote: Without context, it’s hard to understand how big a 100MW data center is. These are some of the biggest. According to the International Energy Agency, small data centers can consume anywhere between one and five megawatts. These are, for the most part, average-sized facilities. Perhaps not for cloud computing giants, but for other companies, it’s par for the course.
100MW is, by comparison, huge. It’s the equivalent of the annual energy consumption of between 350,000 and 400,000 electric cars. Although there are others that will likely dwarf what we, today, consider to be a large facility. Meta is in the process of constructing a $10bn data center campus in Louisiana, with a proposed 2GW capacity.
Still, whatever way you cut it, a 100MW facility is a big, long-term investment.
Canceling cities-worth of capacity at a time when artificial intelligence is supposedly revolutionizing everything suggests that artificial intelligence isn't really revolutionizing anything.
One other detail in TD Cowen's report really stood out. While there is a pullback in Microsoft's data center leasing, it’s seen a "commensurate rise in demand from Oracle related to The Stargate Project" — a relatively new partnership of "up to $500 billion" to build massive new data centers for AI, led by SoftBank, and OpenAI, with investment from Oracle and MGX, a $100 billion investment fund backed by the United Arab Emirates. OpenAI has committed $19 billion to the Stargate project — money it doesn't have, meaning that part of $25 billion to $40 billion funding round that OpenAI is currently raising will be committed to funding these data centers (unless, as I'll get to later, OpenAI raises more debt).
Leading the round is SoftBank, which is also committing $19 billion, as well as creating a joint venture called SB OpenAI Japan to offer OpenAI's services to the Japanese market, as well as "spending $3 billion annually to use OpenAI's technology across its group businesses" according to the Wall Street Journal.
In simpler terms, SoftBank is investing as much as $25 billion in OpenAI, then spending another $3 billion a year on software that only loses money, and still hallucinates, and shows no sign of getting meaningfully better or more reliable. Whether Softbank actually sees value in OpenAI’s tech, or whether this purchase deal is a subsidy by the backdoor, is open to debate. Given that $3bn is equivalent to OpenAI’s entire revenue from selling premium access to ChatGPT in 2024 — which included some major deals with the likes of PriceWaterhouseCoopers — I’m inclined to believe the latter. Even then, how feasible is it that SoftBank can continue paying?
To get the deal done, Microsoft changed the terms of its exclusive relationship with OpenAI to allow it to work with Oracle to build out further data centers full of the GPUs necessary to power its unprofitable, unsustainable models. The Oracle/Stargate situation was a direct result — according to reporting from The Information — of OpenAI becoming frustrated with Microsoft for not providing it with servers fast enough, including an allotment of 300,000 of NVIDIA's GB200 chips by the end of 2025.
For what it’s worth, The Wall Street Journal reports that Microsoft was getting increasingly frustrated with OpenAI’s constant demands for more compute. The relationship between the two entities had started to fray, with both sides feeling somewhat aggrieved. This, combined with Microsoft's data center pullback, heavily suggests that Microsoft is no longer interested in being OpenAI's infrastructure partner long-term — after all, if it was, it'd fund and support OpenAI’s expansion rather than doing the literal opposite.
Here's a question: if generative AI had so much demand, why is Microsoft canceling data center contracts? Why is Microsoft — Oracle's largest customer as of the end of 2023 — allowing SoftBank and OpenAI to work with Oracle to build the future rather than itself?
As mentioned previously, TD Cowen specifically noted in its report that Microsoft's "shift in appetite for capacity [was] tied to OpenAI," which heavily suggests that Microsoft is, at best, less invested in the future of OpenAI, a statement confirmed by The Information, which adds that Microsoft had been trying to "lessen its reliance on OpenAI technology as they increasingly compete in selling AI products."
At worst, this situation could suggest that Microsoft is actively dumping OpenAI, and is having questions about the fundamentals of the generative AI industry.
In very plain terms, Microsoft, despite its excitement around artificial intelligence and its dogged insistence that it’s the future of technology, has canceled data center leases and over a gigawatt of other datacenter infrastructure. Doing so heavily suggests that Microsoft does not intend to expand its data center operations, or at least, to the extraordinary levels the company initially promised.
While Microsoft has reiterated that it intends to spend $80 billion in capital expenditures on AI in 2025, it's unclear how it intends to do so if it’s pulling back on data center expansion at such a large scale, and the company has provided no tangible explanation or elaboration as to how it might do so. While hardware upgrades could account for some of its capital expenditures, it would be nowhere near the $80bn figure.
Again, hyperscale datacenters aren’t cheap. They’re massive, billion-dollar (or multi-billion) ventures.
Microsoft also added in a comment to CNBC in the same article that it "[continues] to grow at a record pace to meet customer demand."
Quick question: what customer demand?
Microsoft said back in April 2024 that "AI demand was exceeding supply even after a 79% surge in capital expenditures," and CFO Amy Hood said in its next quarterly earnings in July 2024 that "demand remained higher than [Microsoft's] available capacity." On its most recent (January 2025) earnings call, CFO Amy Hood once said "Azure growth included 13 points from AI services, which grew 157% year over year and was ahead of expectations even as demand continued to be higher than our available capacity."
Riddle me this, Batman — why does a company that keeps talking about having demand that exceeds capacity decide to cancel multiple data centers which, collectively, account for a significant chunk of its existing capacity?
Let's see what Microsoft had to say when asked a week or two ago!
“Thanks to the significant investments we have made up to this point, we are well positioned to meet our current and increasing customer demand,” Microsoft’s spokesperson wrote. “Last year alone, we added more capacity than any prior year in history. While we may strategically pace or adjust our infrastructure in some areas, we will continue to grow strongly in all regions. This allows us to invest and allocate resources to growth areas for our future.”
Sounds like Microsoft built too much capacity, and in fact has yet to see the consumer demand to actually reach it! In fact, a couple of weeks ago Microsoft CEO Satya Nadella said in a podcast interview that "one of the things is that there will be [as a result of data center expansion related to AI] overbuild."
Microsoft also in late January said it had “$13 billion in annual recurring revenue” from AI — not profit, and “AI” is not a line item on its earnings, meaning this is all related revenue put into a ball with no context. Either way, this is a piddly amount that works out to about $3.25 billion a quarter. These are mediocre numbers and Microsoft’s data center pullback suggests that they’re not going to improve.
Sidebar: Now, to be clear, Microsoft, according to CNBC, also leases data center capacity through CoreWeave "and other providers," though at that point the reporters stopped being curious enough to ask "how much" or "who the other providers are." Luckily, with the power of research, I found that The Information reported that Microsoft planned to spend "about $10 billion" renting CoreWeave servers between 2023 and 2030, which is not a particularly high amount when you consider that OpenAI burned 5 billion dollars last year on compute alone. And, as I’ve noted in previous articles, most (if not all) of that spending would be massively discounted, meaning it’s significantly more compute than if, say, you spent $5bn with Azure.
From what I can find, as of the end of 2023 CoreWeave had around $2 billion of revenue contracted for 2024, meaning that $10 billion across seven years would be meaningful revenue for the company, but unlikely to even touch the amount of capacity that Microsoft has cancelled.
According to The Information, OpenAI plans to have Stargate handle three quarters of its computing needs by 2030, which heavily suggests that Microsoft canceling so much capacity is, on some level, linked to OpenAI’s future plans. While The Information reports that OpenAI is still forecasting to spend $13 billion in 2025 and as much as $28 billion in 2028 on Microsoft’s cloud compute, in addition to whatever capacity it gets from Stargate or Oracle, one has to wonder how it intends to do so if it needs the capacity Microsoft simply isn’t building. These are two huge numbers. For context, $13 billion is about ten percent of Microsoft’s cloud revenue in the 2024 fiscal year, though it's unclear whether Microsoft counts OpenAI's compute as revenue.
Nevertheless, I have real concerns about whether OpenAI is even capable of expanding further, starting with a fairly obvious one: its only source of money, SoftBank.
Let’s start with a few facts:
Doing some napkin maths, here’s what SoftBank has agreed to:
SoftBank made about $12.14 billion (1.83 Trillion Yen) in revenue in Q4 2024, losing $2.449 billion (369.17 billion Yen), but made a profit of about $7.82 billion (1.18 trillion Yen) in Q3 2024.
Nevertheless, SoftBank has agreed to effectively bankroll OpenAI’s future — agreeing to over $46 billion of investments over the next few years — and does not appear to be able to do so without selling its current holdings in valuable companies like ARM or taking on at least $16 billion of debt this year, representing a 55% increase over its current liabilities.
Worse still, thanks to this agreement, OpenAI’s future — both in its ability to expand its infrastructure (which appears to be entirely contingent on the construction of Stargate) and its ability to raise funding — is entirely dependent on SoftBank, which in turn must borrow money to fund it.
On top of that, OpenAI anticipates it will burn as much as $40 billion a year by 2028, and projects to only turn a profit “by the end of the decade after the buildout of Stargate,” which, I add, is almost entirely dependent on SoftBank, which has to take on debt to fund both OpenAI and the project required to (allegedly, theoretically) make OpenAI profitable.
How the fuck does this work?
OpenAI, a company that spent $9 billion to lose $5 billion in 2024, requires so much money to meet its obligations (both to cover its ruinous, unprofitable, unsustainable operations and the $18bn to $19bn it committed to keep growing) that it has to raise more money than any startup has ever raised in history — $40 billion — with the cast-iron guarantee that it will need more money within a year.
To raise said money, the only benefactor willing to provide it (SoftBank) has to borrow money, and is not able to give OpenAI said money up front in part because it does not have it.
SoftBank — on top of the $30 billion of funding and $3 billion a year in revenue it’s committed to OpenAI itself — also has to cough up $18 billion for Stargate, a data center project that OpenAI will run and SoftBank will take financial responsibility for. That’s $48 billion in cash, and $3 billion in revenue, the latter of which, like all of OpenAI’s offerings, loses the company money.
OpenAI has no path to profitability, guaranteeing it will need more cash, and right now — at the time when it needs the most it’s ever needed — SoftBank, the only company willing to provide it, has proven that it will have to go to great lengths to do so. If OpenAI needs $40 billion in 2025, how much will it need in 2026? $50 billion? $100 billion? Where is that money going to come from?
While SoftBank might be able to do this once, what happens when OpenAI needs more money in 6-to-12 months? SoftBank made about $15 billion of profit in the last year on about $46 billion of revenue. $3 billion is an obscene amount to commit to buying OpenAI’s software annually, especially when some of it is allegedly for access to OpenAI’s barely-functional “Operator” and mediocre “Deep Research” products.
As per my previous pieces, I do not see how OpenAI survives, and SoftBank’s involvement only gives me further concern. While SoftBank could theoretically burn its current holdings to fund OpenAI in perpetuity, its ability to do so is cast into doubt by them having to borrow money from other banks to get both this funding round and Stargate done.
Sidenote: On the subject of Softbank’s holdings, I recommend you look at its most recent Group Report. In particular, go to page 29, which lists the ten largest publicly-traded companies in its Vision Fund portfolio. Note how all of them, without exception, trade at a significant fraction of their peak market cap. If Softbank liquidated its assets — and, I admit, this is a big if, and most likely a “worst case scenario” situation — how big would the losses be?
OpenAI burned $5 billion in 2024, a number it’ll likely double in 2025 (remember, the Information reported that OpenAI was projected to spend $13 billion on compute alone with Microsoft in 2025), and it has no path to profitability. SoftBank has already had to borrow to fund this round, and the fact it had to do so suggests its inability to continue supporting OpenAI without accruing further debt.
OpenAI (as a result of Microsoft’s cuts to data center capacity) now only has one real path to expansion once it runs through whatever remaining buildout Microsoft has planned, and that’s Stargate, a project funded by OpenAI’s contribution (which it’s receiving from SoftBank) and SoftBank, which is having to take loans to meet its share.
How does this work, exactly? How does this continue? Do you see anyone else stepping up to fund this? Who else has got $30 billion to throw at this nonsense? While the answer is “Saudi Arabia,” SoftBank CEO Masayoshi Son recently said that he had “not given [Saudi ruler Mohammed Bin Salman] enough return,” adding that he “still owed him.” Nothing about this suggests that Saudi money will follow SoftBank’s in anywhere near the volume necessary. As for the Emiratis, they’re already involved through the MGX fund, and it’s unclear how much more they’d be willing to commit.
No, really, how does this work?
In my opinion, the OpenAI-SoftBank deal is wildly unsustainable, dependent on SoftBank continuing to both raise debt and funnel money directly into a company that burns billions of dollars a year, and is set to only burn more thanks to the arrival of its latest model.
And if it had a huge breakthrough that would change everything, wouldn’t Microsoft want to make sure they’d built the data center capacity to support it?
About GPT-4.5…
Last week, OpenAI launched GPT 4.5, its latest model that…well…Sam Altman says “is the first model that feels like talking to a thoughtful person.” It is not obvious what it does better, or even really what it does, other than Altman says it is “a different kind of intelligence and there’s a magic to it [he hasn’t] felt before.” This was, in Altman’s words, “the good news.”
The bad news was that, and I quote, GPT 4.5 is “...a giant, expensive model,” adding that OpenAI was “out of GPUs,” but proudly declaring that it’d add “tens of thousands of GPUs the next week” to roll it out to OpenAI’s $20-a-month “plus tier” and that he would be adding “hundreds of thousands [of GPUs]” “soon.”
Excited? Well you shouldn’t be. On top of a vague product-set and indeterminately-high compute costs, GPT 4.5 costs an incredible $75 per million input tokens (prompts and data pushed into a model) and $150 per million output tokens (as in the output it creates), or roughly 3,000% more for input tokens and 1,500% more expensive for output tokens than GPT-4o for results OpenAI co-founder Andrej Karpathy described as “a little bit better and…awesome…but also not exactly in ways that are trivial to point to,” and one developer described 4.5 to ArsTechnica as “a lemon” when comparing its reported performance to its price.
ArsTechnica also described GPT-4.5 as “terrible for coding, relatively speaking,” and other tests showed that the model’s performance was either slightly better or slightly worse across the board, with, according to ArsTechnica one “success” metric being that OpenAI found human evaluators "preferred GPT 4.5’s responses over GPT-4o in about 57% of interactions. So, to be crystal clear, the biggest AI company’s latest model appears to be even more ruinously expensive than its last one while providing modest-at-best improvements and performing worse on several benchmarks than competing models.
Despite these piss-poor results, Sam Altman’s reaction is to bring hundreds of thousands of GPUs online as a means of exposing as many people as possible to his mediocre, ultra-expensive model, and the best that Altman has to offer is that this is “...the first time people have been emailing with such passion asking [OpenAI] to promise to never stop offering a specific model.”
Sidenote: As a reminder, GPT-4.5 was meant to be GPT-5, but (according to the Wall Street Journal) continually failed to make a model that “advanced enough to justify the enormous cost,” with a six-month training run costing around $500 million, and GPT 4.5 requiring multiple runs of different sizes. So, yeah, OpenAI spent hundreds of millions of dollars to make this. Great stuff!
This, by the way, is the company that’s about to raise $40 billion led by a Japanese bank that has to go into debt to fund both OpenAI’s operations and the infrastructure necessary for it to grow any further.
Again, Microsoft is cancelling plans to massively expand its data center capacity right at a time when OpenAI just released its most computationally-demanding model ever. How do you reconcile those two things without concluding either that Microsoft expects GPT-4.5 to be a flop, or that it’s simply unwilling to continue bankrolling OpenAI’s continued growth, or that it’s having doubts about the future of generative AI as a whole?
I have been — and remain — hesitant to “call the bubble bursting,” because bubbles do not burst in neat little events.
Nevertheless, my pale horses I’ve predicted in the past were led by one specific call — that a reduction in capital expenditures by a hyperscaler was a sign that things were collapsing. Microsoft walking away from over a Gigawatt of data center plans — equivalent to as much as 14% of its current data center capacity — is a direct sign that it does not believe the growth is there in generative AI, and thus are not building the infrastructure to support it, and indeed may have overbuilt — something that Microsoft CEO Satya Nadella has directly foreshadowed.
The entirety of the tech industry — and the AI bubble — has been built on the assumption that generative AI was the next big growth vehicle for the tech industry, and if Microsoft, the largest purchaser of NVIDIA GPUs and the most aggressive builder of AI infrastructure, is reducing capacity, it heavily suggests that the growth is not there.
Microsoft has, by the looks of things, effectively given up on further data center expansion. At least, at the breakneck pace it once promised.
AI boosters will reply by saying there’s something I don’t know — that in fact Microsoft has some greater strategy, but answer me this: why is Microsoft canceling over a gigawatt of data center expansion? And, again, this is the most conservative estimate. The realistic number is much, much higher.
Do you think it’s because it expects there to be this dramatic demand for their artificial intelligence services? Do you think it’s reducing supply because of all of the demand?
One might argue that Microsoft’s reduction in capacity buildout is just a sign that OpenAI is moving its compute elsewhere, and while that might be true, here’re some questions to ask:
Microsoft making such a material pullback on data center expansion suggests that the growth in generative AI products, both those run on Microsoft’s servers and those sold as part of Microsoft’s products, do not have the revolutionary growth-trajectory that both CFO Amy Hood and CEO Satya Nadella have been claiming, and this is deeply concerning, while also calling into concern the viability of generative AI as a growth vehicle for any hyperscaler.
If I am correct, Microsoft is walking away from not just the expansion of its current data center operations, but from generative AI writ large. I actually believe it will continue selling this unprofitable, unsustainable software, because the capacity it has right now is more than sufficient to deal with the lack of demand.
It is time for investors and the general public to begin demanding tangible and direct numbers on the revenue and profits related to generative AI, as it is becoming increasingly obvious that the revenues are small and the profits are non-existent. A gigawatt of capacity is huge, and walking away from that much capacity is a direct signal that Microsoft’s long term plans do not include needing a great deal of compute.
One counter could be that it’s waiting for more of the specialized NVIDIA GPUs to arrive, to which the response is “Microsoft still wants to build the capacity so that it has somewhere to put them.” Again, these facilities take anywhere between three and six years to build. Do you really think Blackwell will be delayed that long? NVIDIA committed to a once-a-year cycle for their AI chips after all.
One counter could be that there isn’t the power necessary to power these data centers, and if that’s the case — it isn’t, but let me humour the idea — then the suggestion is that Microsoft is currently changing its entire data center strategy so significantly that it now has to issue over a gigawatt’s worth of statements of intent across the country to different places with…more power?
Another counter is that I’m only talking about leases, and not purchases. In that case, I’ll refer you to this article from CBRE, which provides an elucidating read on how hyperscalers actually invest in data center infrastructure. Leases tend to account for the majority of spending, simply because it’s less risky. A specialist takes care of the tough stuff — like finding a location, buying the land, handling construction — and the hyperscaler isn’t left trying to figure out what to do with the facility when it reaches the end of its useful lifecycle.
Sidenote: I also expect that somebody will chime in and say: “Well, that’s just Microsoft. What about Google, or Amazon?
I’d counter and say that these companies are comparatively less exposed to generative AI than Microsoft. Amazon has invested $8bn in Anthropic, which is a bit less than half Microsoft’s reported investment in OpenAI, which amounted to $14bn as of December. When you consider the discounted Azure rates Microsoft offers to OpenAI, the real number is probably much, much higher.
Google also has a $3bn stake in Anthropic, in addition to its own AI services, like Gemini.
OpenAI, as I also noted in my last newsletter, is pretty much the only real generative AI company with market share and significant revenue — although I, once again, remind you that revenue is not the same as profit. This is true across mobile, web, and likely its APIs, too.
Similarly, nobody has quite pushed generative AI as aggressively as Microsoft, which has introduced it to an overwhelming number of its paid products, hiking prices for consumers as it goes. I suppose you could say that Google has pushed genAI into its workspace products, as well as its search product, but the scale and aggression of Microsoft’s push feels different. That, and it, as mentioned, is the largest purchaser of NVIDIA GPUs, buying nearly twice as many (485,000) as its nearest competitor, Meta, which bought 224,000.
Ultimately, Microsoft has positioned itself at the heart of GenAI, both through its own strategic product decisions, and its partnership with OpenAI. And the fact that it’s now scaling back on the investment required to maintain that momentum is, I believe, significant.
I also recognize that this is kind of a big, juicy steak for someone people call an “AI cynic.”
I have poured over this data repeatedly, and done all I can to find less-convenient conclusions. Letters of Intent are likely the weakest part of the argument — these are serious documents, but not always legally-binding. Neither are SoQs, but as TD Cowen points out, these are treated as the green light to start work on construction, even though a formal lease agreement hasn’t yet been signed. And to be clear, Microsoft let an indeterminate amount of those go too.
Nevertheless, it is incredibly significant that Microsoft is letting so many — the equivalent of as much as 14% of its current data center capacity, at a bare minimum — on top of the “couple hundred” (at least 200) megawatts of data center leases that it’s outright canceled. I do not know why nobody else has done this analysis. I have now read every single piece about the TD Cowen report from every single outlet that covered it. I am astounded by the lack of curiosity as to what “1GW+” means in a report that meaningfully moved markets, as I am equally astounded by the lack of curiosity to contextualize most tech news.
It is as if nobody wants to think about this too hard — that nobody wants to stop the party, to accept what’s been staring us in the face since last year, and when given the most egregious, glaring evidence they must find ways to dismiss it rather than give it the energy it deserves.
Far more resources were dedicated to finding ways to gussy up the releases of Anthropic’s Claude Sonnet 3.7 or OpenAI’s GPT-4.5 than were given to the report from an investment bank’s research wing that the largest spender in generative AI — the largest backer (for now) of OpenAI — is massively reducing its expenditures in the data centers required for the industry (and for OpenAI, a company ostensibly worth at least $157 billion, and which Microsoft owns the largest stake) to expand.
Microsoft’s stake in OpenAI is a bit fuzzy, as OpenAI doesn’t issue traditional equity, and there’s a likelihood it may be diluted as more money comes in. It reportedly owned 49% in 2023. Assuming that’s still the case, are we to believe that Microsoft is willing to strangle an asset worth at least $75bn (several times more than its investment to date) by cancelling a few leases?
How many more alarms do we need to go off before people recognize that something bad is happening? Why is it that tangible, meaningful evidence that we’re in a bubble — and possibly a sign that it has actually popped — is less interesting than the fact that Claude Sonnet 3.7 “can think longer”?
I do not write this newsletter to “be right.” I do not write it to “be a cynic” or “hater.” I write this because I am trying to understand what’s going on, and if I do not do that I will actually go insane. Every time I sit down to write it is because I am trying to understand what it is that’s happening and how I feel about it, and these are the only terms that dictate my newsletter. It just happens that I have stared at the tech industry for too long, and now I can’t look away. Perhaps it is driving me mad, perhaps I am getting smarter, or some combination of the two, but what comes out of it is not driven by wanting to “go viral” or “have a hot take,” because such things suggest that I would write something different if three people read it versus the 57,000 that subscribe.
I am not saying that Microsoft is dying, or making any grandiose claims about what happens next. What I am describing is the material contraction of the largest investor in data centers according to TD Cowen, potentially at a scale that suggests that it has meaningfully reduced its interest in further expansion of data centers writ large.
This is a deeply concerning move, one that suggests that Microsoft does not see enough demand to sustain its current expansions, which has greater ramifications beyond generative AI, because it suggests that there isn’t any other reason for it to expand the means of delivering software. What has Satya Nadella seen? What is Microsoft CFO Amy Hood doing? What is the plan here?
And really, what’s the plan with OpenAI? SoftBank has committed to over $40 billion of costs that it cannot currently afford, taking on as much as $24 billion in debt in the next year to help sustain one more funding round and the construction of data centers for OpenAI, a company that loses money on literally every single customer.
To survive, OpenAI must continue raising more money than any startup has ever raised before, and they are only able to do so from SoftBank, which in turn must take on debt. OpenAI burned $5 billion in 2024, will likely burn $11 billion in 2025, and will continue burning money in perpetuity, and to scale further will require further funding for a data center project funded partially by funding from a company taking on debt to fund them.
And when you put all of this together, all that I can see is a calamity.
Generative AI does not have meaningful mass-market use cases, and while ChatGPT may have 400 million weekly active users, as I described last week, there doesn’t appear to be meaningful consumer adoption outside of ChatGPT, mostly because almost all AI coverage inevitably ends up marketing one company: OpenAI. Argue with me all you want about your personal experiences with ChatGPT, or how you’ve found it personally useful. That doesn’t make it a product with mass-market utility, or enterprise utility, or worth the vast sums of money being ploughed into generative AI.
Worse still, there doesn’t appear to be meaningful revenue. As discussed above, Microsoft claims $13 billion in annual recurring revenue (not profit) on all AI products combined on over $200 billion of capital expenditures since 2023, and no other hyperscaler is willing to break out any AI revenue at all. Not Amazon. Not Meta. Not Google. Nobody.
Where is the growth? Where is all this money going? Why is Microsoft canceling a Gigawatt of data center capacity while telling everybody that it didn’t have enough data centers to handle demand for its AI products?
I suppose there’s one way of looking at it: that Microsoft may currently have a capacity issue, but soon won’t, meaning that further expansion is unnecessary. If that’s the case, it’ll be interesting to see whether their peers follow suit.
Either way, I see nothing that suggests that there’s further growth in generative AI. In fact, I think it’s time for everybody to seriously consider that big tech burned billions of dollars on something that nobody really wanted.
If you read this and scoff — what should I have written about? Anthropic adding a sliding “thinking” bar to a model? GPT-4.5? Who cares! Can you even tell me what it does differently to GPT-4o? Can you explain to me why it matters? Or are you more interested in nakedly-captured imbeciles like Ethan Mollick sweatily oinking about how powerful the latest Large Language Models are to notice the real things happening in the real world with buildings and silicon and actual infrastructure?
Wake the fuck up, everybody! Things are on fire.
I get it. It’s scary to move against the consensus. But people are wondering, right now, why you didn’t write about this, and indeed why you seem more concerned with protecting the current market hype cycle than questioning it.
There are no “phony comforts of being an AI skeptic” — what I am writing about here, as I regularly remind you, is extremely scary stuff.
If I’m right, tech’s only growth story is dead.
2025-02-25 00:42:35
Soundtrack: Mack Glocky - Chasing Cars
Last week, I spent a great deal of time and words framing the generative AI industry as a cynical con where OpenAI's Sam Altman and Anthropic's Dario Amodei have used a compliant media and braindead investors to frame unprofitable, unsustainable, environmentally-damaging and mediocre cloud software as some sort of powerful, futuristic automation.
Yet as I prepared a script for Better Offline (and discussed it with my buddy Kasey, as I often do), I kept coming back to one thought: where's the money?
No, really, where is it? Where is the money that this supposedly revolutionary, world-changing industry is making, and will make?
The answer is simple: I do not believe it exists. Generative AI lacks the basic unit economics, product-market fit, or market penetration associated with any meaningful software boom, and outside of OpenAI, the industry may be pathetically, hopelessly small, all while providing few meaningful business returns and constantly losing money.
I am deeply worried about this industry, and I need you to know why.
Putting aside the hype and bluster, OpenAI — as with all generative AI model developers — loses money on every single prompt and output. Its products do not scale like traditional software, in that the more users it gets, the more expensive its services are to run because its models are so compute-intensive.
For example, ChatGPT having 400 million weekly active users is not the same thing as a traditional app like Instagram or Facebook having that many users. The cost of serving a regular user of an app like Instagram is significantly smaller, because these are, effectively, websites with connecting APIs, images, videos and user interactions. These platforms aren’t innately compute-heavy, at least to the same extent as generative AI, and so you don’t require the same level of infrastructure to support the same amount of people.
Conversely, generative AI requires expensive-to-buy and expensive-to-run GPUs, both for inference and training the models themselves. The GPUs must be run at full tilt for both inference and training models, which shortens their lifespan, while also consuming ungodly amounts of energy. And surrounding that GPU is the rest of the computer, which is usually highly-specced, and thus, expensive.
These models also require endless amounts of training data, supplies of which have been running out for a long time. While synthetic data might bridge some of the gap, at least in situations where there’s a definitive right and wrong answer (like a mathematical problem), there are likely diminishing returns due to the sheer amount of data necessary to make a large language model even larger — data amounting to more than four times the size of the internet.
These companies also must spend hundreds of millions of dollars on salaries to attract and retain AI talent — as much as $1.5 billion a year in OpenAI's case (before stock-based compensation). In 2016, Microsoft claimed that top AI talent could cost as much as an NFL quarterback to hire, and that sum has likely only increased since then, given the generative AI frenzy.
As an aside: One analyst told the Wall Street Journal that companies running generative AI models "could be utilizing half of [their] capital expenditure[s]...because all of these things could break down." As in it’s possible hyperscalers could spend 50% of their capital expenditures replacing broken stuff.
Though these costs are not a burden on OpenAI or Anthropic, they absolutely are on Microsoft, Google and Amazon.
As a result of the costs of running these services, a free user of ChatGPT is a cost burden on OpenAI, as is every free customer of Google's Gemini, Anthropic's Claude, Perplexity, or any other generative AI company.
Said costs are also so severe that even paying customers lose these companies money. Even the most successful company in the business appears to have no way to stop burning money — and as I'll explain, there's only one real company in this industry, OpenAI, and it is most decidedly not a real business.
As a note — I have repeatedly said OpenAI lost $5 billion after revenue in 2024. However, I can no longer in good conscience suggest that it burned “only” $5 billion. It’s time to be honest about these numbers. While it’s fair to say that OpenAI’s “net losses” are $5 billion, it’s time to be clear about what it costs to run this company.
The New York Times reports that OpenAI projects it'll make $11.6 billion in 2025, and assuming that OpenAI burns at the same rate it did in 2024 — spending $2.25 to make $1 — OpenAI is on course to burn over $26 billion in 2025 for a loss of $14.4 billion. Who knows what its actual costs will be, and as a private company (or, more accurately, entity, as for the moment it remains a weird for-profit/nonprofit hybrid) it’s not obligated to disclose its financials. The only information we’ll get will come from leaked documents and dogged reporting, like the excellent work from The New York Times and The Information cited above.
It's also important to note that OpenAI's costs are partially subsidized by its relationship with Microsoft, which provides cloud compute credits for its Azure service, which is also offered to OpenAI at a discount. Or, put another way, it’s like OpenAI got paid with airmiles, but the airline lowered the redemption cost of booking a flight with those airmiles, allowing it to take more flights than another person with the equivalent amount of points. At this point, it isn’t clear if OpenAI is still paying out of the billions of credits it received from Microsoft in 2023 or whether it’s had to start using cold-hard cash.
Until recently, OpenAI exclusively used Microsoft's Azure services to train, host, and run its models, but recent changes to the deal means that OpenAI is now working with Oracle to build out further data centers to do so. The end of the exclusivity agreement is reportedly due to a deterioration of the chummy relationship between OpenAI and Redmond, according to The Wall Street Journal, with the latter allegedly growing tired of OpenAI’s constant demands for more compute, and the former feeling as though Microsoft had failed to live up to its obligations to provide the resources needed for OpenAI to sustain its growth.
It is unclear whether this partnership with Oracle will work in the same way as the Microsoft deal. If not, OpenAI’s operating costs will only go up. Per reporting from The Information, OpenAI pays just over 25% of the cost of Azure’s GPU compute as part of its deal with Microsoft — around $1.30-per-GPU-per-hour versus the regular Azure cost of $3.40 to $4.
OpenAI recently announced that it has 400 million weekly active users.
Weekly Active Users can refer to any seven-day period in a month, meaning that OpenAI can effectively use any spike in traffic to say that it’s “increased its weekly active users,” because it can choose the best seven-day period in a month. This isn’t to say they aren’t “big,” but these numbers are easy to game.
When I asked OpenAI to define what a “weekly active user” was, it responded by pointing me to a tweet by Chief Operating Officer Brad Lightcap that said “ChatGPT recently crossed 400M WAU, we feel very fortunate to serve 5% of the world every week.” It is extremely questionable that it refuses to define this core metric, and without a definition, in my opinion, there is no way to assume anything other than the fact that OpenAI is actively gaming its numbers.
There's likely two reasons it focuses on weekly active users:
The latter point is crucial, because it suggests OpenAI is not doing anywhere near as well as it seems based on the very basic metrics used to measure the success of a software product.
The Information reported on January 31st that OpenAI had 15.5 million monthly paying subscribers, and immediately added that this was a “less than 5% conversion rate” of OpenAI’s weekly active users — a statement that is much like dividing the number 52 by the letter A. This is not an honest or reasonable way to evaluate the success of ChatGPT’s (still unprofitable) software business, because the actual metric would have to be divided paying subscribers by MONTHLY active users, a number that would be considerably higher than 400 million.
Based on data from market intelligence firm Sensor Tower, OpenAI’s ChatGPT app (on Android and iOS) is estimated to have had more than 339 million monthly active users, and based on traffic data from market intelligence company Similarweb, ChatGPT.com had 246 million unique monthly visitors. There’s likely some crossover, with people using both the mobile and web interfaces, though how big that group is remains uncertain.
Though every single person that visits ChatGPT.com might not become a user, it’s safe to assume that ChatGPT’s Monthly Active Users are somewhere in the region of 500-600 million.
That’s good, right? Its actual users are higher than officially claimed? Er, no. First, each user is a financial drain on the company, whether they’re a free or paid user.
It would also suggest a conversion rate of 2.583% from free to paid users on ChatGPT — an astonishingly bad number, one made worse by the fact that every single user of ChatGPT, regardless of whether they pay, loses the company money.
It also feeds into a point I’ve repeatedly made in this newsletter, and in my podcast. Generative AI isn’t that useful. If Generative AI was genuinely this game-changing technology that makes it possible to simplify your life and your work, you’d surely fork over the $20 monthly fee for unlimited access to OpenAI’s more powerful models. I imagine many of those users are, at best, infrequent, opening up ChatGPT out of curiosity or to do basic things, and don’t have anywhere near the same levels of engagement as with any other SaaS app.
While it's quite common for Silicon Valley companies to play fast and loose with metrics, this particular one is deeply concerning, and I hypothesize that OpenAI choosing to go with Weekly versus Monthly Active Users is an intentional attempt to avoid people calculating the conversion rate of its subscription products. As I will continue to repeat, these subscription products lose the company money.
Mea Culpa: My previous piece focused entirely on web traffic to ChatGPT.com, and did not have the data I now have related to app downloads. Nevertheless, it isn't obvious whether OpenAI is being honest about its weekly active users, because it won't even define how it measures them.
In summary, both Operator and Deep Research are expensive products to maintain, are sold through an expensive $200-a-month subscription that (like every other service provided by OpenAI) loses the company money, and due to the low quality of their outputs and actions are likely to increase user engagement to try and get the desired output, incurring further costs for OpenAI.
As it stands, OpenAI has effectively no moat beyond its industrial capacity to train Large Language Models and its presence in the media. It can have as many users as it wants, but it doesn't matter because it loses billions of dollars, and appears to be continuing to follow the money-losing Large Language Model paradigm, guaranteeing it’ll lose billions more.
The Large Language Model paradigm is also yet to produce a successful, mass market product, and no, Large Language Models are not successful or mass market. I know, I know, you're going to say ChatGPT is huge, we've already been through that, but surely, if generative AI was a real industry, there'd be multiple other players with massive customer bases as a result of how revolutionary it was, right?
Right?
Let's look at some estimated numbers from data intelligence firm Sensor Tower (monthly active users on apps) and Similarweb (unique monthly active visitors) for the biggest players in AI in January 2025:
To put this in perspective, the entire combined monthly active users of the Copilot, Claude, Gemini, DeepSeek, and Perplexity apps amount to 66 million, or 19.47% of the entire monthly active users of ChatGPT's mobile app. Web traffic slightly improves things (I say sarcastically), with the 161.6 million unique monthly visitors that visited the websites for Copilot, Claude, Gemini, DeepSeek and Perplexity making up 65.69% of all of the traffic that went to ChatGPT.com.
However, I'd argue that including DeepSeek vastly over-inflates these numbers. It’s an outlier, and it’s also a relatively new company that’s enjoying its moment in the sun, basking in the glow of a post-launch traffic spike, and a flood of favorable media coverage. I imagine that when the dust settles in a few months, we’ll get a more reliable idea of its market share and consistent user base.
Without DeepSeek, the remaining generative AI services made up a total of 39 million monthly active users across their apps, and a grand total of 81.7 million unique monthly web visitors.
Without ChatGPT, it appears that the entire generative AI app market is a little more than half the size of Pokémon Go at its peak, when it had 147 million monthly active users. While one can say I missed a few apps — xAI's Grok, Amazon's Rufus, or Character.ai — there isn't a chance in hell they cover the shortfall.
These numbers aren't simply piss poor, they're a sign that the market for generative AI is incredibly small, and based on the fact that every single one of these apps only loses money, is actively harmful to their respective investors or owners.
I do not think this is a real industry, and I believe that if we pulled the plug on the venture capital aspect tomorrow it would evaporate.
Another counter to my argument is that API calls are a kind of “hidden adoption” — that there is this massive swell of engaged, happy customers using generative AI that aren’t using any of the major apps, and that the connection to these models is the real secret success story.
This isn’t the case.
OpenAI, as I’ve established, is the largest player in generative AI, making more revenue (roughly $4 billion in 2024, though it lost $5 billion after revenue — again, OpenAI lost $9 billion in 2024) than any other private AI company. The closest I can get to an estimate on how many actual developers integrate their applications is a statement from their October 2024 dev day where OpenAI said over three million developers are building apps using its models.
Again, that’s a very fuzzy — and unreliable — metric. I imagine a significant chunk of those developers are hobbyists working on personal projects, or simply playing around with the service out of sheer curiosity, spending a few bucks to write the generative AI equivalent of “Hello World,” and then moving on with their lives. Those developers actually using OpenAI’s APIs in actual commercial projects likely represent a vanishingly small percentage of that three million.
As I’ve discussed in the past, OpenAI’s revenue is heavily weighted toward its subscription business, with licensing access to models like GPT-4o making up less than 30% (around $1 billion) of their its, and subscriptions to their premium products (ChatGPT Plus, Teams, Business, Pro, the newly-released Government plan, etc) making up the majority — around $3 billion in 2024.
My argument is fairly simple. OpenAI is the most well-known player in generative AI, and thus we can extrapolate from it to draw conclusions about the wider industry. In the event that there was a huge, meaningful industry integrating generative AI into distinct products with mass-market consumer adoption, OpenAI’s API business would be doing far, far more revenue.
Let me be a little more specific about why API calls matter.
When a business plugs OpenAI’s models into its apps and a customer triggers a feature that uses it — such as asking the app to summarize an email — OpenAI charges the business both for the prompt (the input) and the result (the output). As a result, where “weekly active users” might be indicative of attention to OpenAI’s products, API calls are far more indicative of consumer and enterprise adoption.
To be clear, I acknowledge that there are a lot — a non-specific amount, but a fair amount — of app developers and companies adopting generative AI. However, judging on the revenue both to OpenAI’s developer-focused business and the lack of any real revenue for any business integrating generative AI, I hypothesize that customers — which include developers integrating OpenAI’s models into both consumer-facing apps and enterprise-focused apps — are not actually using these features that much.
I should also add that OpenAI makes about $200 million a year selling its models through Microsoft, meaning that its API business may be as small as $800 million. Again, this is not profit, it is revenue.
Sidebar: There is, of course, an alternative: that OpenAI is charging way, way less for their models than it should — an argument I made in The Subprime AI Crisis last year — but accepting this argument means that at some point OpenAI will either have to become profitable (it has shown no signs of doing so) or charge the actual cost of operating their unprofitable models.
The Information reported last week that Anthropic has projected (made up) that it will make at least $12 billion in revenue in 2027, despite making $918 million in 2024 and losing $5.6 billion somehow.
Anthropic is currently raising $2 billion at a $60 billion valuation for a business that loses billions of dollars a year with an app install base of 2 million people and a web presence smaller than some niche hobbyist news outlets.
Based on reporting from the Information from two weeks ago, Anthropic made approximately $918 million in 2024 (and lost $5.6 billion), with CNBC reporting that 60-75% of that revenue came from API calls (though that number was from September 2024). In that respect, it’s the reverse of OpenAI — which, itself, points to the relative obscurity of Anthropic and the fact that OpenAI has become accepted as the default consumer entrypoint to generative AI.
This company is not worth $60 billion.
Anthropic has raised $14.7 billion to create an also-ran Large Language Model company that some developers like more than OpenAI, with a competing consumer-facing Large Language Model (Claude) that has an install base of maybe 2% of the five free-to-play games made by Clash of Clans developer Super Cell.
Anthropic, much like OpenAI, has categorically failed to productize its Large Language Model, with the only product it appears to have pushed being Computer Use, a similarly-useless AI model that can sometimes successfully do in minutes what it takes you to do in seconds using a web browser.
Anthropic, like OpenAI, has no moat. While it provides chain-of-thought reasoning in its models, that too has been commoditized by DeepSeek. Its models, again like OpenAI, are unprofitable, unsustainable and heavily-dependent on training data that's either running out or has already run out.
Its CEO is also a sleazy conman who, like Sam Altman, continually promises that his company's AI systems will become powerful and autonomous in a way that they have never shown any possibility of becoming.
Any investor in Anthropic needs to seriously consider what it is they're investing in. Anthropic has, other than iterating on its Large Language Model Claude, shown little fundamental differentiation from the rest of the industry.
Anthropic's business, again like OpenAI, is entirely propped up by venture capital and hyperscaler (Google, Amazon) money, and without it would die almost immediately, because it has only ever lost money.
Its products are both unpopular and commoditized, and it lost $5.6 billion last year! Stop dancing around this fact! Stop it!
Perplexity, a company valued at $9 billion toward the end of 2024, has eight million people a month using its app, with the Financial Times reporting it has a grand total of 15 million monthly active users for its unprofitable search engine. Perplexity, like every generative AI company, only ever loses money, and its product — generative AI-powered search — is so commoditized that it's actually remarkable the company still exists.
Other than a slick design, there is little to be excited about here — and 8 million monthly active users is a pathetic, embarrassing number for a company with the majority of its users on mobile.
Aravind Srivinas is a desperate man with questionable intentions that made a half-hearted offer to merge with TikTok in January and a product that rips off journalists to spit out its mediocre content.
Any investor in Perplexity needs to ask themselves — what is it I'm investing in? An unprofitable search engine? An unprofitable Large Language Model company? A company that has such poor adoption of its product that it was prepared to become the shell corporation for TikTok?
Personally, I'd be concerned about the bullshit numbers it keeps making up. The Information reported that Perplexity said it would make $127 million in 2025, and $656 million in 2026.
How much money did it make in 2024? Just over $56 million! Is it profitable? Hell no!
Its product is commoditized, and it makes less than a quarter of the revenue of the Oakland Athletics in 2024, though its app is marginally more popular.
It's time to stop humoring these companies!
The Wall Street Journal reports that Microsoft intends to spend $93.7 billion on capital expenditures in 2025 — or roughly $8,518 per monthly active user on the Copilot app in January 2025. Those figures, however, may already be out of date with Bloomberg reporting the company is cancelling some leases for AI data centers. If true, it would suggest the company is pulling back from its drunken AI spending binge — although it’s not clear to what extent.
Sidenote: For what it’s worth, Microsoft responded by saying it stands by its original capex plans, although “may strategically pace or adjust [its] infrastructure in some areas.” Take from that what you will, while also noting that a plan isn’t the same as a definitive commitment, and that the company paused construction on a data center in January that was reportedly intended to support OpenAI. It’s also worth noting that as part of these cuts, Microsoft has pulled back from so-called statements of qualifications - the financial rundown and statements that say how they'll intend to pay for the lease (this might also include financing terms) - a document that's a precursor for future data center agreements. In short, they may have pulled out from further data centers they hadn't fully committed to.
Google is currently planning to spend $75 billion on capital expenditures, or roughly $4,167 per monthly active user of the Gemini app in January 2025. Sundar Pichai wants Gemini to be "used by 500 million people before the end of 2025," a number so unrealistic that someone at Google should have been fired, and that someone is Sundar Pichai.
The fact of the matter is that if Google and Microsoft can't make generative AI apps with meaningful consumer penetration, this entire industry is screwed. There really are no optimistic ways to look at these numbers (and yes, I'm repeating from the above):
These are utterly pathetic considering Microsoft and Google's scale, especially given the latter's complete dominance over web search and the ability to funnel customers to Gemini. For millions — perhaps billions — Google is the first page they see when they open a web browser. It should be owning this by now.
47.3 million unique monthly visitors is a lot of people, but considering that Google spent $52.54 billion in capital expenditures in 2024, it's hard to see where the return is, or even see where a return could possibly be.
Google, like most companies, does not break out revenue from AI, though it loves to say stuff like "a strong quarter was driven by our leadership in AI and momentum across the business." As a result of its unwillingness to share hard numbers, all we have to look at are numbers like those I've received from Similarweb and Sensor Tower, and it's fair to suggest that Gemini and its associated products have been a complete flop.
Worse still, it spent $127.54 billion in capital expenditures in 2023 and 2024 combined, with an estimated $75 billion forecast for 2025. What the fuck is going on?
Yes, it is likely making revenue from people running generative AI models on Google Cloud, and yes, it is likely making revenue from forcing AI upon Google Workspace customers. But Google, like every single other generative AI player, is losing money on every single generative AI prompt, and based on these monthly active user numbers, nobody really cares about Gemini.
Actually, I take that back. Some people care about Gemini — not that many, but some! — and it's far more fair to say that nobody cares about Microsoft Copilot despite Microsoft shoving it in every corner of our lives. 11 million monthly active users for its unprofitable, heavily-commoditized Large Language Model app is a joke — as are the 15.6 million monthly active users to its web presence — probably because it does exactly the same shit that every other LLM does.
Microsoft's Copilot app isn't just unpopular, it's irrelevant. For comparison, Microsoft Teams has, according to a post from the end of 2023, over 320 million monthly active users. That’s more than ten times the amount of monthly active users of their Copilot app and the Copilot website combined, and unlike Copilot, Teams actually makes Microsoft money.
Now, I obviously don't have the numbers on the people that accidentally click the Copilot button in Microsoft Office or on Bing.com, but I do know that Microsoft isn't making much money on AI at all. Microsoft reported in its last earnings that it was making "$13 billion of annual revenue" — a projected number based on current contracts — on its "artificial intelligence products."
Now, I've made this point again and again, but revenue is not the same thing as profit, and Microsoft does not have an "artificial intelligence" part of its earnings. These numbers are cherry-picked from across the entire suite of Microsoft products — such as selling Copilot add-ons to its Microsoft 365 enterprise suite (The Information reported in September 2024 that Microsoft had only sold Copilot to around 1% of their 365 customers), selling access to OpenAI's models on Azure (roughly a billion in revenue), and people running their own models on their Microsoft Azure Cloud.
For context, Microsoft made $69.63 billion in revenue in its last quarter. $13 billion of annual revenue (NOT profit) is about $3.25 billion in quarterly revenue off of upwards of $200 billion of capital expenditures since 2023.
The fact that neither Gemini nor Copilot has any meaningful consumer penetration isn't just a joke. It should be sending alarm bells throughout Wall Street. While Microsoft and Google may make money outside of consumer software, both companies have desperately tried to cram Copilot and Gemini down consumers' throats, and they have categorically, unquestionably failed, all while burning billions of dollars to do so.
"BUT ED, WHAT ABOUT GITHUB COPILOT."
According to a report from the Wall Street Journal from October 2023, Microsoft was losing on average more than $20 a month per user on the paid version of Github, with some users costing them more than $80 a month. Microsoft said a year later that Github Copilot had 1.8 million paid customers, which is pretty good, except like all generative AI products, it loses money.
I must repeat that Microsoft will have spent over $200 billion in capital expenditures by the end of 2025. In return, it got 1.8 million paying customers for a product that — like everything else I'm talking about — is heavily-commoditized (basically every LLM can generate code, though some are better than others, by which I mean they all introduce security issues into your code, but some produce stuff that’ll actually compile) and loses Microsoft money even when the user pays.
Am I getting through to you yet? Is it working?
One of the arguments people make is that “AI is everywhere,” but it’s important to remember that the prevalence of AI is not proof of its adoption, but the intent of companies shoving it into everything, and the same goes for “business integrating AI” that are really just mandating people dick around with Copilot or ChatGPT.
No, really, KPMG bought 47,000 Microsoft Copilot subscriptions last year (at a significant discount) “to be familiar with any AI-related questions [its] customers may have. Management consultancy PwC bought 100,000 enterprise subscriptions — becoming OpenAI’s largest customer in the process, as well as its first reseller, and have created their own internal generative AI called ChatPWC that PwC staff absolutely hate.
While you may “see AI everywhere,” integrations of generative AI are indicative of the decision making of the management behind the platforms and the demands of “the market” more than any consumer demand. Enterprise software is more often than not sold in bulk to managers or C-suite executives tasked less with company operations and more with seeming “on the forefront of technology.”
In practical terms, this means there’s a lot of demand to put AI in stuff and some demand to buy stuff with AI on it by enterprises buying software, but little evidence to suggest significant user adoption or usage, I’d argue because Large Language Models do not lend themselves to features that provide meaningful business returns.
To be clear, and to deal with the “erm, actually” responses, I am not saying Large Language Models have no use cases or no customers.
People really do use them for coding, for searching defined libraries of documents, for generating draft materials, for brainstorming, and for summarizing and searching documents. These are useful, but they are not magical.
These are also — and I do not believe there are any use cases that justify this — not a counterbalance for the ruinous financial and environmental costs of generative AI. It is the leaded gasoline of tech, where the boost to engine performance didn’t outweigh the horrific health impacts it inflicted.
When a company uses the term “agent,” they are intentionally trying to be deceitful, because the term “agent” means “autonomous AI that does stuff without you touching it.” The problem with this definition is that everybody has used it to refer to “a chatbot that can do some things while connected to a database,” which is otherwise known as a chatbot.
In OpenAI and Anthropic’s case, “agents” refer to a model that controls your computer and performs tasks based on a prompt. This is closer to “the truth,” other than the fact it’s so unreliable as to be disqualifying, and the tasks it succeeds at (like searching on Tripadvisor) are remarkably simple.
Next time you hear the term “agent,” actually look at what the product does.
Generative AI is probabilistic, and Large Language Models do not “know” anything, because they are guessing what the next part of a particular output would be based on an input. They are not “making decisions.” They are probability machines, which in turn makes them only as reliable as probability can be, and as conscious — no matter how intricate a system may be or how much infrastructure is built — as a pair of dice.
We do not understand how human intelligence works, and as a result it’s laughable to imagine we’d be able to simulate it. Large Language Models do not create “artificial intelligence” — they are the most powerful parrots in the world, trained to respond to stimulus with what they guess is the correct answer.
In simpler terms, imagine if you made a machine that threw a bouncy ball down a hallway, and got really, really good at dialing it in to throw the ball so that it followed a fairly exact trajectory. Would you consider the arm intelligent? How about the ball?
The point I am making is that Large Language Models — a cool concept with some interesting things they can do — have been used as a cynical marketing vehicle to raise money for OpenAI by lying about what they’re capable of doing, starting with calling them “artificial intelligence.”
Revenue is not the same as profit.
I'll say it again — revenue is not the same as profit.
And even then, Google, Amazon and (to an extent Microsoft), the companies making the most investments in AI, do not want to state what that revenue is. I hypothesize the reason that they do not want to disclose it is that it’s pretty god damn small.
It is extremely worrying that so few companies are willing to directly disclose their revenue from selling services that are allegedly revolutionary. Why? Salesforce says it closed “200 AI related deals” in its last earnings. How much money did it make? Why does Google get away with saying it has “growing demand for AI” without clarifying what that means? Is it because nobody is making that much money?
Sidebar: I can find — and I’ve really looked! — one company that appears to be making profit from generative AI. Turing, a consultancy that helps generative AI companies find people to train their models that made $300 million in revenue in 2024 and reached an indeterminate amount of profitability.
While Microsoft may “disclose” it “made $13 billion in AI revenue,” that’s annualized — so projected based on current contracts rather than booked revenue — and does not speak to the specific line items like one would if said line items were not going to make the markets say “hey, what the fuck?”
Put aside whatever fantastical beliefs you may have about the future and tell me, right now, what business use case exists that justifies burning hundreds of billions of dollars, damaging our power grid, hurting our planet, and stealing from millions of people?
Even if you can put troublesome things like “morals” or “the basic principles of finance” aside, can AI evangelists not see that their dream is failing? Can they not see that nothing is really happening? That generative AI, at best, can be kind of cool yet mostly sucks and comes at an unbearable moral, financial and environmental cost? Is any of this really worth it?
And where exactly does this end? Do you truly, gun to your head, your life contingent on the truth leaving your lips, believe that this goes much further than you see today?
Do you not see that this kind of sucks? Do you not see that generative AI runs contrary to the basic tenets of what makes science fiction cool? It doesn’t make humans better, it reduces their work to a stagnant, unremarkable slop in every way it can, and reduces the cognition of those who come to rely on it, and it costs hundreds of billions of dollars and a return to fossil fuels for some reason.
It isn’t working. The users aren’t there. The revenue isn’t there. The best time to stop this was two years ago, and the next best time is as soon as humanly possible.
I have said that generative AI is a group delusion in the past, and I repeat that claim today. What you are seeing in the news is not the “success“ of the artificial intelligence industry, but a runaway narrative created by and sustained by Sam Altman and OpenAI.
What you are watching is not a revolution, but a repetitious public relations campaign for one company that accidentally timed the launch of ChatGPT with a period of deep desperation in big tech, one so profound that it will likely drag half a trillion dollars’ worth of capital expenditures along with it.
This bubble will only burst when either the markets or the hyperscalers accept that they have chased their own tails toward oblivion. There is no justification for any of the capital expenditures related to generative AI — we are approaching the limit of what the transformer-based architecture can do, if we haven’t already reached it. No amount of beating off about test-time compute and connecting Large Language Models to other Large Language Models is going to create a new use case for this technology, and even if it did, it’s unlikely that it ever makes enough money to make it profitable.
I will keep writing this stuff until I’m proven wrong. I do not know why more people aren’t more worried about this. The financials are truly damning, the user numbers so small as to be insignificant, the costs so ruinous that they will likely cost tens of thousands of people their jobs and one of the hyperscalers CEOs their job (although, admittedly, I’m less upset about that), and inflict damage on tech valuations that may rival the dot com boom.
And if the last point feels distant to you, ask yourself: What’s in your retirement savings? That’s right. Google and Microsoft, and hundreds of other companies that will be hurt by the contagion of an AI bubble imploding, just as they were in the 2008 financial crash, when the failure of the banking system trickled down into the wider economy.
I should also not be the person saying this, or at least I should not be the first. These numbers are horrifying, and I have no idea why nobody else is worried. There is no industry here. There is no money. There is no proof that this will ever turn into a real industry, and far more proof that it will cost more money than it will ever make in perpetuity.
OpenAI and Anthropic are not real companies — they are free-riders, living on venture-backed welfare for an indeterminate amount of time because the entire tech industry has agreed to rally around the world’s most unprofitable software. And like any free rider that doesn’t actually produce anything, when the money goes away, they’re fucked.
Seriously, why are investors funding OpenAI? Do they seriously believe it’s necessary to let Sam Altman and OpenAI continue to burn 5 or more billion dollars a year on the off chance he’s able to create something that’s…alive? Profitable? What’s the endpoint here? How many more billions? Where is the fucking money, Sam Altman? Where is the god damn money?
Because generative AI is OpenAI. The consumer adoption of this software has completely failed, and appears to be going nowhere fast. ChatGPT is sustained entirely on deranged, specious hype drummed up by a media industry that thinks it’s more remarkable to write down the last lie that Sam Altman told than say that OpenAI has lost $9 billion dollars in the last year and intends to more than double that number in 2025 for absolutely no reason.
It is time to stop humouring OpenAI, and time to start directly stating that it is a bad business without a meaningful product. The generative AI industry does not exist without OpenAI, and thus this company must justify its existence.
And let’s be abundantly clear: OpenAI cannot exist any further without further venture capital investment. This company has absolutely no path to sustain itself, no moat, and loses so much money that it will need more than $50 billion to continue in its current form.
I don’t know how I’m wrong, and I have sat and thought a great deal about how I might be. I can find no compelling arguments. I don’t know what to do but tell you what I think, and why I think that way, and hope that you, the reader, understand a little bit more about what I think is going on.
I’ll leave you with one thought — and one particular thing that bothers me about generative AI.
Regular people, for the most part, do not seem to want this. While there are occasional people I’ll meet who use ChatGPT to rewrite part of an email, most of the people I meet feel like AI was forced into their lives.
With that in mind, I believe that Apple is radicalizing millions of people against generative AI by forcing them to reckon with the terrible summaries, awful suggested texts and horribly-designed user interface elements of Apple Intelligence.
Something about generative AI has caused the hyperscalers to truly lose it, and the intrusion of generative AI into both Microsoft Office and Google Docs has turned just about everybody I know in the business world against it.
The resentment boiling against this software is profound because the tech industry has become desperate and violative, showing such contempt for their customers that even Apple will force an inferior experience upon them to please the will of the Rot Economy and the growth-at-all-cost mindset of the markets.
Let’s be frank: nobody really needs anything generative AI does. Large Language Models hallucinate too much to be truly reliable, a problem that will require entire new branches of mathematics to solve, and their most common consumer-facing functions like summarizing an article, “practicing for a job interview,” or “write me a business plan” are not really things people need or massively benefit from, even if these things weren’t ruinously expensive or damaging to the environment.
I believe regular people are turning on the tech industry thanks to their frenzied attempts to make us all buy into their latest bad idea.
Yet it isn’t working. Consumers don’t want this shit. They’re intrigued by the idea, then mostly immediately bouncing off of it once they see what it can (or can’t) do. This software is being forced on people at scale by corporations desperate to seem futuristic without any real understanding as to why they need it, and whatever use cases may exist for Large Language Models are dwarfed by how utterly unprofitable this whole fiasco is.
I want you to remember the names Satya Nadella, Tim Cook, Mark Zuckerberg, Sam Altman, Dario Amodei and Sundar Pichai, because they are the reason that this farce began and they must be the ones who are blamed for how it ends.
2025-02-18 02:51:09
It's been just over two years and two months since ChatGPT launched, and in that time we've seen Large Language Models (LLMs) blossom from a novel concept into one of the most craven cons of the 21st century — a cynical bubble inflated by OpenAI CEO Sam Altman built to sell into an economy run by people that have no concept of labor other than their desperation to exploit or replace it.
I realize that Large Language Models like GPT-4o — the model that powers ChatGPT and a bunch of other apps — have use cases, and I'm fucking tired of having to write this sentence. There are people that really like using Large Language Models for coding (even if the code isn't good or makes systems less secure and stable) or get something out of Retrieval-Augmented Generation (RAG)-powered search, or like using one of the various AI companions or journal apps.
I get it. I get that there are people that use LLM-powered software, and I must be clear that anecdotal examples of some people using some software that they kind-of like is not evidence that generative AI is a sustainable or real industry at the trillion-dollar scale that many claim it is.
I am so very bored of having this conversation, so I am now going to write out some counterpoints so that I don't have to say them again.
I KNOW. Stop saying this to me like an Uno reverse! I'm talking about generative AI!
OpenAI and Anthropic are both burning billions of dollars a year, and do not appear to have found a way to stop doing so. The only "proof" that they are going to reverse this trend is The Information saying that "Anthropic's management expects the company to stop burning cash in 2027."
Sidebar: Hey, what is it with Dario Amodei of Anthropic and the year 2027? He said (made up) that "AI could surpass almost all humans at almost everything" "shortly after 2027" in January. He said in one of his stupid and boring blogs that "possibly by 2026 or 2027 (and almost certainly no later than 2030)" that "capabilities of AI systems will be best thought of as akin to an entirely new state populated by highly intelligent people appearing on the global stage—a “country of geniuses in a datacenter." This man is full of shit! Hey, tech media people reading this — your readers hate this shit! Stop printing it! Stop it!
While one could say "the costs will come down," and that appears to be what The Information is claiming, suggesting that "Anthropic said it would reduce its burn rate by "nearly half" in 2025, the actual details are thin on the ground, and there’s no probing of whether that’s even feasible without radically changing its models. Huh? How? Anthropic’s burn increased every single year! So has OpenAI's!
The Information — who I do generally, and genuinely, respect — ran an astonishingly optimistic piece about Anthropic estimating that it'd make $34.5 billion in revenue in 2027 (there's that year again!), the very same year it’d stop burning cash. Its estimates are based on the premise that "leaders expected API revenue to hit $20 billion in 2027," meaning people plugging Anthropic's models into their own products. This is laughable on many levels, chief of which is that OpenAI, which made around twice as much revenue as Anthropic did in 2024, barely made a billion dollars from API calls in the same year.
It's here where I'm going to choose to scream.
Anthropic, according to The Information, generated $908 million in revenue in 2024, and has projected that it will make $2.2 billion in revenue in 2025, and its "base case" — which The Information says would be "the likeliest outcome (???) — is that it will make $12 billion in revenue in 2027.
This is what happens during bubbles! Assets are over-valued based on a combination of vibes and hysteria!
Dario Amodei — much like Sam Altman — is a liar, a crook, a carnival barker and a charlatan, and the things he promises are equal parts ridiculous and offensive. The Information (which needs to do better work actually critiquing these people) justified Amodei and Anthropic's obscene and fantastical revenue targets by citing Amodei's blog, which at no point explains what a "country of geniuses in a datacenter" actually means or what the product might be or what he's going to do to increase revenue by more than thirty billion dollars a year by 2027.
But wait, The Information says it got a little more specific!
Anthropic says its technology could transform office roles such as generating or reviewing legal paperwork and automating software engineering. It cited code repository GitLab and legal search firm LexisNexis as examples of customers. Up-and-coming startups such as Anysphere, which develops the Cursor coding assistant for programmers, are also major buyers of Claude software.
So, just to be abundantly clear, it appears Anthropic's big plan is to "sell people more software to some people, maybe."
Anthropic is currently raising $2 billion at a $60 billion valuation primarily based off of this trumped-up marketing nonsense. Why are we humoring these oafs?
When you put aside the hype and anecdotes, generative AI has languished in the same place, even in my kindest estimations, for several months, though it's really been years. The one "big thing" that they've been able to do is to use "reasoning" to make the Large Language Models "think" (they do not have consciousness, they are not "thinking," this just means using more tokens to answer a particular question and having multiple models check the work), which mostly results in them being a bit more accurate when generating an answer, but at the expense of speed and cost.
This became a little less exciting a month ago when DeepSeek released its open source "r1" model which performed similarly to reasoning products from companies like Google and OpenAI, and while some argue it "built the model to game benchmarks," that is quite literally what every single model developer does. Nevertheless, the idea of "reasoning" being the "killer app" — despite the fact that nobody can really explain why it's such a big deal — is now quite dead.
As a result, the model companies are kind of flailing. In a recent post on Twitter, Sam Altman gave an "updated roadmap for GPT 4.5 and GPT-5" where he described how OpenAI would be "simplifying" its product offerings, saying that GPT-4.5 would be OpenAI's "last non-chain-of-thought model," and that GPT-5 would be "a system that integrates a lot of our technology," including o3, OpenAI's "powerful" and "very expensive" reasoning model, which it...would also no longer release as a standalone model.
To break this down, Altman is describing his next model — GPT 4.5 — as launching in some indeterminate timeframe and doing something probably quite similar to the current GPT 4o model. In the case of GPT-5, it would appear that Altman is saying that it won't be a model at all, but some sort of rat king of different mediocre products, including o3, a product that he would no longer be letting you use.
I guess that's the future of this company, right? OpenAI will release models and uh, well. Uhh.
Uhhhhhhh.
Wait! Wait! OpenAI released a new product! It's called Deep Research, which lets you ask ChatGPT to generate a report by browsing the web. This is almost a cool idea. I sure hope that it doesn't make glaring mistakes and cost a shit-ton of money!
Anyway, let's go to Casey Newton at Platformer for the review:
Generally speaking, the more you already know about something, the more useful I think deep research is. This may be somewhat counterintuitive; perhaps you expected that an AI agent would be well suited to getting you up to speed on an important topic that just landed on your lap at work, for example. In my early tests, the reverse felt true. Deep research excels for drilling deep into subjects you already have some expertise in, letting you probe for specific pieces of information, types of analysis, or ideas that are new to you.
It’s possible that you can make this work better than I did. (I think all of us will get better at prompting these models over time, and presumably the product will improve over time as well.)
Personally, when I ask someone to do research on something, I don't know what the answers will be and rely on the researcher to explain stuff through a process called "research." The idea of going into something knowing about it well enough to make sure the researcher didn't fuck something up is kind of counter to the point of research itself.
Also: "I think all of us will get better at prompting-" Casey, we're paying them! We're paying them for them to do stuff for us!
Nevertheless, I did go and look up one of Casey's examples, specifically one about how the Fediverse could benefit publishers.
Let's do some research!
Despite Newton's fawning praise, the citations in this "deep research" are flimsy at best. The first (and second) citations are from an SEO-bait article about the fediverse from a "news solutions" company called "Twipe" and is used to define "broad cross-platform reach." The next one is from reputable digital advertising outlet Digiday, but it's used to cite how sites like 404 Media and The Verge are "actively exploring the Fediverse to take more control over their referral traffic and onsite audience engagement," which is plagiarised ad-verbatim from the Digiday article.
After that, the next three citations are posts from Hackernews, a web forum started by yCombinator (here's an example). How is this "deep research" exactly?
In fact, this thing isn't well-researched at all. Across the following paragraphs, Deep Research cites the same Digiday article eight times, before going back to citing the same Twipe article again. It also, hilariously, says that federated posts "can simultaneously publish to [a] website and as a toot on federated platforms like Mastodon and Threads," a term that Mastodon retired two years ago.
The next two citations are about Medium's embrace of Mastodon, followed by yet another citation of the Digiday article. Following that, Deep Research cites two different Reddit posts, a company called Interleger moving to the Fediverse, which the report cites several more times, along with yet another forum post, the very same Twipe post several more times, and then the support documentation for social network Bluesky several more times.
I won't go through more of the research paper citation by citation, but you'll be shocked to hear it mostly just cites Twipe and Hackernews and Reddit.
For now, Deep Research is only available on ChatGPT Pro, OpenAI's somehow-unprofitable $200-a-month subscription, though it's apparently coming to ChatGPT Plus in a limited capacity.
Not impressed? Well what if I told you it was very compute-intensive and expensive? Oh, one other detail — the entire thing’s on the very edge of comprehensible.
Here’s a bit under funding models:
"Memberships and Donations: A common monetization approach in the Fediverse (and across the open web) is voluntary support from the audience."
Nobody talks like this! This isn’t how human beings sound! I don’t like reading it! I don’t know how else to say this — there is something deeply unpleasant about how Deep Research reads! It’s uncanny valley, if the denizens of said valley were a bit dense and lazy. It’s quintessential LLM copy — soulless and almost, but not quite, right.
Ewww.
So there you have it folks. OpenAI's next big thing is the ability to generate a report that you would likely not be able to use in any meaningful way anywhere, because while it can browse the web and find things and write a report, it sources things based on what it thinks can confirm its arguments rather than making sure the source material is valid or respectable. This system may have worked if the internet wasn't entirely poisoned by companies trying to get the highest ranking in Google, and if Google had any interest in making sure its results were high quality, which it does not.
I'm sorry, I know I sound like a hater, and perhaps I am, but this shit doesn't impress me even a little. Wow, you created a superficially-impressive research project that's really long and that cites a bunch of shit it found online that it made little attempt to verify? And said report took a while to generate, can only be produced if you pay OpenAI $200 each month, and it cost a bunch of money in compute to make?
Deep Research has the same problem as every other generative AI product. These models don't know anything, and thus everything they do — even "reading" and "browsing" the web — is limited by their training data and probabilistic models that can say "this is an article about a subject" and posit their relevance, but not truly understand their contents. Deep Research repeatedly citing SEO-bait as a primary source proves that these models, even when grinding their gears as hard as humanely possible, are exceedingly mediocre, deeply untrustworthy, and ultimately useless.
Furthermore, nothing about this product moves OpenAI toward profitability. In fact, I think they're doing the opposite. Deep Research uses OpenAI's o3 model which can cost as much as $1,000 a query, and while I imagine these prompts aren't that expensive, they are still significantly more so than a regular query from ChatGPT.
The whole point of hiring a researcher is that you can rely on their research, that they're doing work for you that would otherwise take you hours. Deep Research is the AI slop of academia — low-quality research-slop built for people that don't really care about quality or substance, and it’s not immediately obvious who it’s for.
Surely, if you’re engaged enough to spend $200 on an OpenAI subscription and are aware of Deep Research, you probably know what SEO bait is, and can distinguish between low-quality and high-quality content. If you were presented with a document with such low-quality, repetitive citations, you’d shred it — and, if created by an intern, you’d shred them too. Or, at the very least, give them some stern words of guidance.
Let me put this in very blunt terms: we are more than two years into the generative AI boom and OpenAI's biggest, sexiest products are Deep Research — a product that dares to ask "what if you were able to spend a lot of compute to get a poorly-cited research paper," and Operator, a compute-intensive application that rarely completes a task in minutes that would otherwise have taken you seconds.
As an aside, SoftBank, the perennial money-losers that backed WeWork and WireCard and lost more than $30 billion in the last few years, are trying to invest up to $25 billion in OpenAI.
Everywhere you look, the media is telling you that OpenAI and their ilk are the future, that they're building "advanced artificial intelligence" that can take "human-like actions," but when you look at any of this shit for more than two seconds it's abundantly clear that it absolutely isn't and absolutely can't.
Despite the hype, the marketing, the tens of thousands of media articles, the trillions of dollars in market capitalization, none of this feels real, or at least real enough to sustain this miserable, specious bubble. People like Marc Benioff claiming that "today's CEOs are the last to manage all-human workforces" are doing so to pump up their stocks rather than build anything approaching a real product. These men are constantly lying as a means of sustaining hype, never actually discussing the products they sell in the year 2025, because then they'd have to say "what if a chatbot, a thing you already have, was more expensive?"
The tech industry — and part of our economy — is accelerating at speed into a brick wall, driven by people like Sam Altman, Dario Amodei, Marc Benioff, and Larry Ellison, all men that are incentivized to have you value their companies based on something other than what their businesses actually sell.
We are in the midst of a group delusion — a consequence of an economy ruled by people that do not participate in labor of any kind outside of sending and receiving emails and going to lunches that last several hours — where the people with the money do not understand or care about human beings.
Their narrative is built on a mixture of hysteria, hype, and deeply cynical hope in the hearts of men that dream of automating away jobs that they would never, ever do themselves. Altman uses his digital baba yaga as a means to stoke the hearts of weak-handed and weak-hearted narcissists that would sooner shoot a man dead than lose a dollar, even if it means making their product that much worse. CEOs have the easiest jobs in the world, and no job is easier than Satya Nadella waving to the Microsoft 365 staff and saying “make them put AI in it, pronto” and telling Microsoft CFO Amy Hood that “we must make sure that Bing has generative AI” before jetting off to Davos to yell that he intends to burn more money than ever on GPUs.
Sam Altman believes you are stupid. He believes you are a moron that will slurp up whatever slop he gives you. Deep Research and Operator are both half-products that barely brush against the fabric of their intended purposes, and yet the media screams and applauds him like he's a gifted child that just successfully tied his shoes.
I know, I know, I'm a hater, I'm a pessimist, a cynic, but I need you to fucking listen to me: everything I am describing is unfathomably dangerous, even if you put aside the environmental and financial costs.
Let me ask you a question: what's more likely?
That OpenAI, a company that has only ever burned money, that appears completely incapable of making a truly usable, meaningful product, somehow makes its products profitable, and then somehow creates a truly autonomous artificial intelligence?
Or that OpenAI, a company that has consistently burned billions of dollars, that has never shown any sign of making a profit, that has in two years released a selection of increasingly-questionable and obtuse products, actually runs out of money?
How does this industry actually continue? Do OpenAI and Anthropic continue to raise tens of billions of dollars every six months until they work this out? Do the hyperscalers keep spending hundreds of billions of dollars in capital expenditures for little measurable return?
And fundamentally, when will everybody start accepting that the things that AI companies are saying have absolutely nothing to do with reality? When will the media stop treating every single expensive, stupid, irksome, quasi-useless new product is magical, and start asking these people to show us the fucking future already?
Generative AI is a financial, ecological and social time bomb, and I believe that it's fundamentally damaging the relationship between the tech industry and society, while also shining a glaring, blinding light on the disconnection between the powerful and regular people. The fact that Sam Altman can ship such mediocre software and get more coverage and attention than every meaningful scientific breakthrough of the last five years combined is a sign that our society is sick, our media is broken, and that the tech industry thinks we're all fucking morons.
This entire bubble has been inflated by hype, and by outright lies by people like Sam Altman and Dario Amodei, their lies perpetuated by a tech media that's incapable of writing down what's happening in front of their faces. Altman and Amodei are raising billions and burning our planet based on the idea that their mediocre cloud software products will somehow wake up and automate our entire lives.
The truth is that generative AI is as mediocre as it is destructive, and those pushing it as "the future" that "will change everything" are showing how much contempt they have for the average person. They believe that they can shovel shit into our mouths and tell us it's prime rib, that these half-assed products will change the world and that as a result they need billions of dollars and to damage our power grid.
I know this has been a rant-filled newsletter, but I'm so tired of being told to be excited about this warmed-up dogshit. I'm tired of reading stories about Sam Altman perpetually saying that we're a year away from "everything changing" that exist only to perpetuate the myth that Silicon Valley gives a shit about solving anyone's problems other than finding new growth markets for the tech industry.
I refuse to sit here and pretend that any of this matters. OpenAI and Anthropic are not innovators, and are antithetical to the spirit of Silicon Valley. They are management consultants dressed as founders, cynical con artists raising money for products that will never exist while peddling software that destroys our planet and diverts attention and capital away from things that might solve real problems.
I'm tired of the delusion. I'm tired of being forced to take these men seriously. I'm tired of being told by the media and investors that these men are building the future when the only things they build are mediocre and expensive. There is no joy here, no mystery, no magic, no problems solved, no lives saved, and very few lives changed other than new people added to Forbes' Midas list.
None of this is powerful, or impressive, other than in how big a con it’s become. Look at the products and the actual outputs and tell me — does any of this actually feel like the future? Isn’t it kind of weird that the big, scary threats they’ve made about how AI will take our jobs never seem to translate to an actual product? Isn’t it strange that despite all of their money and power they’re yet to make anything truly useful?
My heart darkens, albeit briefly, when I think of how cynical all of this is. Corporations building products that don't really do much that are being sold on the idea that one day they might, peddled by reporters that want to believe their narratives — and in some cases actively champion them. The damage will be tens of thousands of people fired, long-term environmental and infrastructural chaos, and a profound depression in Silicon Valley that I believe will dwarf the dot-com bust.
And when this all falls apart — and I believe it will — there will be a very public reckoning for the tech industry.
