When people talk about “AI” these days – which depending on how healthy and well-adjusted your social environment is can be very little or if you are on LinkedIn all the fucking time – the main focus is on what is called “generative AI”, sometimes shortened to “genAI”.

Generative AI (in the way most people understand that term today) are stochastic models that are able to produce something that would traditionally have been a human work product. A new text for example, a summary of an existing text, an illustration, a piece of program code, a video, whatever.

A few days ago I wrote about the infrastructures that the current AI boom will leave behind and this text is a bit of a follow up. Because with all the narratives about all the magical things that “AI” will supposedly do, will (quoting Sam Altman) “solve all of physics” for example [EDIT: I had a reference to Eric Schmidt claiming that AI will use 99% of energy in the future but that seems to have been a bit of a misunderstanding by the reporting so I cut that reference] calmer minds might wonder what those systems are actually for.

Many people will know Stafford Beer’s famous heuristic for thinking about systems abbreviated POSIWID: “The purpose of a system is what it does”. Beer kinda shifted system interpretation from the usual future tense (“this system will do X”) to the present tense (“this system has this effect on the real world now”).

For generative AI this purpose is quite clear: It’s about putting pressure on human labour. If you can generate somewhat passable prose or images or software code using an “AI” you can either try to run your business with fewer people (who tend to want to be paid) or (more realistically) you can push people’s wages down by always pointing at “the AI” when someone wants a raise or does not want to do unpaid overtime. The actual problem generative AI is trying to solve is having to pay people for their work. And that is the effect we see: Working conditions getting worse, our media landscape getting gunked up with generated slop. But as I wrote in “These are not the same“:

“So there is a structural reason why those companies probably can’t be economically valid: Digital spaces are built around winner-takes all scaling but for genAI providers that is economical suicide.”

But while generative AI has been sucking all the air out of the room for a while now, there is a different kind of AI whose situation is a bit different but whose power can be that much bigger: Discriminative AI.

Discriminative AI is not any AI that discriminates against groups of people (though most AI systems of course do that), discriminative AI are neural networks that you can apply to big or messy data and get some kind of classification from it. Give the discriminative AI system a picture and it might tell you which objects it estimates are in the picture, give it a text and it might try to guess the level of education of the person who wrote it.

Technically generative and discriminative AI systems are not that different: Both are basically lossy compression of the data used in the training process. Both are the most dominant patterns in the training data crystalized. GenAI reproduces those patterns, discriminative AI applies them. And here the actual purpose emerges.

If you run a somewhat complex operation you care about, say a business, you probably will not just have a statistical black box run it the way LinkedIn influencers describe the future. Because we have digitized many processes before the current AI fad happened, build ERP systems and workflow engines and all that, all in order to “optimize” and “rationalize” existing social and business processes already and those things are very deliberate and controlled.

Those digital automations or workflows are built based on domain knowledge about your company, your machines and organizational structure, the market you are in and the requirements your clients have. They are mostly just very complex rules – often expressed in somewhat byzantine source code in some enterprise software system – that operate on data. That is why all business software looks like a hot mess: It’s not written for you as a person to work with, it’s written for the machine to get the data in the way it needs it.

But the world has this problem of being very messy and chaotic and very badly structured. While software is usually built based on a somewhat well-defined domain model (not an “AI” model but just an abstracted description of the objects and structure the organization deploying the software considers relevant) those models still are often too simple, too abstract. You feel that every time that reality presents you with a situation the software developers did not see coming. But now we have discriminative AI.

Computers cannot see. Cannot perceive the world. They can only operate on data that might describe the world if interpreted in a certain way. And creating this data often used to be hard. Sure, adding a thermometer or other simple sensor is easy but how could you add more complex interpretations of the world? Say you wanted to determine how “lazy” your workers are, how would you formalize that? For some jobs you could define an average amount of production per hour and compare people’s performance but that only works in very specific cases. Solving this is the promise of discriminative AI.

As a serious person you do not want your organization to be run by a stochastic parrot: What if you hit a fabrication while processing a big order for your most important customer: They won’t let you off the hook by pointing at the “AI made a whoopsie” sign. You still want defined, clearly modelled, transparent processes that you can iterate on, that you can adapt based on the changing landscape you operate in.

But you used to need expertise for that. You needed not only expensive outside consultants to mold your ERP system into something bordering on useful but you also needed the domain experience of your workers to tell you what characterizes a successful production run and how to detect a flawed run early. Now the hope is that one can extract those patterns (and more!) from all the data being constantly collected: You don’t need your workers, you have all the sensor and machine data and know which productions runs failed so your can create a “soft sensor” that just gives you a binary “OK”/”Not OK”. A simple data type that your computer systems can operate on, that you can built your business logic around.

Discriminative AI turns the complex and contradictory, the wild and weird world of physics and chemistry and people into simple data. It is the tool to build tools. The mold that you can use to shape the world into computer-processable bits of information of higher abstractions.

Sure, computers can somehow process all data. Obviously your computer can handle a live camera feed, we see that every day in video conferences. But a video stream is not what you want if you want your digital process to handle the world for you: You need to know which relevant objects are in the feed. The video information is of a lower abstraction, often just recorded in absence of better data, checked by people in case things go out of line.

With its totally agnostic approach discriminative AI promises to take any form of lower abstraction data and refine it into higher abstraction data computers can use. It is the foundation that Marc Andreessen’s “Software is eating the world” screed will be actually materialized on. Discriminative AI is the ultimate adapter, no the ultimate mold to shape the world into simple data. To process the world to feed it to software systems.

And that is why my outlook on the lasting impact of the data centers and infrastructures that the generative AI hype brought with it is a bit bleak (yes, quoting myself yet again being the pretentious bastard that I am):

“The AI crash won’t leave us with infrastructures that are useful to democratic and humane societies, with useful tools to do something productive with, but with infrastructures tailor-made to suppress it.”

A data center that Microsoft had build to generate slop can easily be repurposed to datafy more of the world in order to automate even more, pull even more decision making out of social systems into centralized, often opaque machines run by god knows who. Because the tech works the same.

All the things I described above are the things companies like Palantir and others are pitching to any government and big organization willing to listen. And those listening do not always have democratic or humane values as priorities.

There is another problem though: By datafying more of the world we are narrowing down our modes of interacting with the world, of coming to decisions – not only as individuals but as communities and societies. With more and more of the world being fed to the machine we lose many ways of engaging with the world that are deeply human but don’t fit in with that mode of abstraction and rationalization.

Human beings are not rational. And while a certain level of rationality is helpful in order to be able to communicate with one another and find consensus, many other aspects of our lives are just as important: Feelings, spiritual beliefs are not easy to abstract and put into well-defined models based on logic. Those are ways of being in the world, being with us and others that are core to the human experience, that are core to how we make decisions, core to how we think about, hope for, strive for better futures. And that gets sanded off by applying neural networks to the world to turn it into small standardized data bricks to build workflows and processes on.

That is maybe the most lasting effect I see the current AI fad having. A further solidification of the cultural logic of computation at the expense of other modes of being. And if one thing has become crystal clear in the last decade or so it is that the (neo-)liberal belief that everything just needs to be based on better data and more rationality will not keep Fascism at bay. It will become its tool and catalyst.

Before we go any further: I hate to ask you to do this, but I need your help — I'm up for this year's Webbys for the best business podcast award. I know it's a pain in the ass, but can you sign up and vote for Better Offline? I have never won an award in my life, so help me win this one.

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.

How Much Cash Does OpenAI Have?

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:

• Incoming monthly revenue: roughly $425 million, give or take.
• Theoretical revenue from Softbank: $250 million a month. However, I can find no proof that SoftBank has begun to make these payments or, indeed, that it intends to make them.
• Liquidity:
  • $10 billion that it is yet to receive from SoftBank and a syndicate of investors including Microsoft, potentially.
  • An indeterminate amount of remaining capital on the $4 billion credit facility provided by multiple banks back in October 2024, raised alongside a funding round that valued the company at $157 billion.
    • As a note, this announcement stated that OpenAI had "access to over $10 billion in liquidity."
  • Based on reports, OpenAI will not have access to the rest of its $40bn funding until "the end of the year," and it's unclear what part of the end of the year.

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.

What Are OpenAI's Obligations?

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.

Compute Costs: at least $13 billion in 2025 with Microsoft alone, and as much as $594 million to CoreWeave.

• In 2024, OpenAI spent $9 billion to lose $5 billion.
  • This figure includes the $3 billion spent on training new models and $2 billion on running them.

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.  

Stargate: $1 Billion+

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."

Other costs: at least $3.5 billion

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.

How Does OpenAI Meet Its Obligations?

OpenAI Could Spend $28 Billion Or More In 2025, and Lose over $14 Billion while having an absolute maximum of $20 billion in liquidity

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.

SoftBank Has To Borrow Money To Meet Its OpenAI and Stargate Obligations, leading to SoftBank's "...financial condition likely deteriorating."

As of right now, SoftBank has committed to the following:

• At least $30 billion ($7.5 of the initial $10 billion, and $22.5 billion of the remaining $30 billion) in funding as part of OpenAI's recent $40bn funding round.
  • This assumes that SoftBank finds others to invest with it. SoftBank's filings surrounding OpenAI's funding also suggest that SoftBank is, ultimately, on the hook for the entire $40 billion, but can syndicate with other investors. Reporting suggests that syndication will happen with Coatue, Microsoft and other investors.
  • If OpenAI fails to convert to a for-profit, that $40bn figure is slashed to $30, although, again, SoftBank’s share of the final sum is contingent upon whether it finds other investors to join the deal. 
• $3 billion in spend on OpenAI "tech."
• $19 billion for the Stargate data center project, which SoftBank takes financial responsibility for.
  • Total: $52 billion or $62 billion, with at least $20 billion due by the end of 2025.

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.

OpenAI Needs At Least $40 billion A Year To Survive, And Its Costs Are Increasing

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. 

SoftBank Cannot Fund OpenAI Long-Term, as OpenAI's costs are projected to be $320 billion in the next five years

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.

OpenAI Is Running Into Capacity Issues, Suggesting Material Instability In Its Business or Infrastructure — And It's Unclear How It Expands Further

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.

• On February 27, he lamented how GPT 4.5 was a "giant, expensive model," adding that it was "hard to perfectly predict growth surges that lead to GPU shortages." He also added that they would be adding tens of thousands of GPUs in the following week, then hundreds of thousands of GPUs "soon."
• On March 26, he said that "images in chatgpt are wayyyy more popular than [OpenAI] expected," delaying the free tier launch as a result.
• On March 27, he said that OpenAI's "GPUs [were] melting," adding that it was "going to introduce some temporary rate limits" while it worked out how to "make it more efficient."
• On March 28, he retweeted Rohan Sahai, the product team lead on OpenAI's Sora video generation model, who said "The 4o image gen demand has been absolutely incredible. Been super fun to watch the Sora feed fill up with great content...GPUs are also melting in Sora land unfortunately so you may see longer wait times / capacity issues over coming days."
• On March 30, he said "can yall please chill on generating images this is insane our team needs sleep."
• On April 1, he said that "we are getting things under control, but you should expect new releases from openai [sic] to be delayed, stuff to break, and for service to sometimes be slow as we deal with capacity challenges." He also added that OpenAI is "working as fast we can to really get stuff humming; if anyone has GPU capacity in 100k chunks we can get asap please call!"

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:

• OpenAI is running up against the limit of what Microsoft has available, or is willing to offer the company. The Information reported in October 2024 that OpenAI was frustrated with Microsoft, which said it wasn’t moving fast enough to supply it with servers.
• While OpenAI's capacity is sufficient, It does not have the resources available to easily handle bursts in user growth in a stable manner.

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.

Any capacity or expansion issues threaten to kneecap OpenAI

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:

• Significantly limiting free users' activity on the platform, which is OpenAI's primary mechanism for revenue growth and customer acquisition.
• Limiting activity or changing the economics behind its paid product, to quote Sam Altman, "find[ing] some way to let people to pay for compute they want to use more dynamically."
  • On March 4th, Altman solicited feedback on "...an idea for paid plans: your $20 plus subscription converts to credits you can use across features like deep research, o1, gpt-4.5, sora, etc...no fixed limits per feature and you choose what you want; if you run out of credits you can buy more."
  • On January 5th, Sam Altman revealed that OpenAI is currently losing money on every paid subscription, including its $200-a-month "pro" subscription.
  • Buried in an article from The Information from March 5 is a comment that suggests it’s considering measures like changing its pricing model, with "...Sam Altman reportedly [telling] developers in London [in February] that OpenAI is primed to charge 20% or 30% of Pro customers a higher price because of how many research queries they’re doing, but he suggested an “a la carte” or pay-as-you-go approach. When it comes to agents, though, “we have to charge much more than $200 a month.”

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).

What Might Capacity Issues Look Like? And What Are The Consequences?

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.

OpenAI Must Convert To A For-Profit Entity By The End of 2025 Or It Loses $10 Billion In Funding, And Doing So May Be Impossible

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 Systemic Risk To The Tech Industry

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.

Oracle Will Lose At Least $1 Billion If OpenAI Doesn't Fulfil Its Obligations

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.

CoreWeave's Expansion Is Likely Driven Entirely By OpenAI, And It Cannot Survive Without OpenAI Fulfilling Its Obligations (And May Not Anyway)

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. 

NVIDIA Relies On CoreWeave For More Than 6% Of Its Revenue, And CoreWeave's Future Creditworthiness To Continue Receiving It — Much Of Which Is Dependent On OpenAI

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. 

OpenAI's Expansion Is Dependent On Two Unproven Startups, Who Are Also Dependent on OpenAI To Live

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 - Stargate - Abilene Texas

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.

Core Scientific - CoreWeave - Denton Texas

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 was bankrupt last year.
• Core Scientific has never built an AI data center, and its cryptocurrency mining operations were built around ASICs — specialist computers for mining Bitcoin — which led an analyst to tell CNBC that said data centers would "need to be bulldozed and built up from the ground up" to accommodate AI compute.
• Core Scientific does not appear to have any meaningful AI compute of any kind. Its AI/HPC (high-performance computing) revenue represents a tiny, tiny percentage of its overall revenue, which still comes primarily from mining crypto, both for itself and for third-parties. 
• CoreWeave's entire 1.3 Gigawatt buildout appears to be being handled by Core Scientific.

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.

An Open Question: Does Microsoft Book OpenAI's Compute As Revenue?

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:

• Productivity and Business Processes, which includes things like Microsoft 365, LinkedIn, Dynamics 365 and other business processing software.
• More Personal Computing, which includes Windows and Gaming Products
• Intelligent Cloud, Including server products and cloud services like Azure, which is likely where OpenAI's compute is included.

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. 

The Future of Generative AI Rests On OpenAI, And OpenAI's Future Rests On Near-Impossible Financial Requirements

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.

Financial 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."

Compute Resources and Expansion

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.

How Does This End?

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.

• SoftBank is putting itself in dire straits simply to fund OpenAI once. This deal threatens its credit rating, with SoftBank having to take on what will be multiple loans to fund OpenAI's $40 billion round. OpenAI will need at least another $40 billion in the next year.
  • This is before you consider the other $19 billion that SoftBank has agreed to contribute to the Stargate data center project, money that it does not currently have available.
• OpenAI has promised $19 billion to the Stargate data center project, money it does not have and cannot get without SoftBank's funds.
  • Again, neither SoftBank nor OpenAI has the money for Stargate right now.
• OpenAI needs Stargate to get built to grow much further.

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: 

• SoftBank somehow gets the resources (and loses the constraints) required to bankroll it indefinitely. 
• The world’s wealthiest entities — those sovereign wealth funds mentioned earlier, the Saudis and so on  — pick up the slack each year until OpenAI reaches productivity (assuming it does).
• It has enough of those mega-wealthy benefactors to provide the $320bn it needs before it reaches profitability.
• Crusoe and CoreScientific turn out to be really good at building AI infrastructure — something they’ve never done before. 
• Microsoft walks-back its walk-back on building new AI infrastructure and recommits to the tens of billions of dollars of capex spending it previously floated. 
• Stargate construction happens faster than expected, and there are no supply chain issues (in terms of labor, building materials, GPUs, and so on).

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.

It's Time To Wake Up

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.

The 74 published an op-ed this week, penned by an AI industry consultant, invoking Sputnik and claiming that "AI education is the new space race." I'm going to set aside its problematic call for "AI literacy" for another day; and although it's really the main gist of the author's argument, I'm not going to engage too deeply either with its xenophobia. It's not the Soviets who pose the threat (LOL?), but the Chinese apparently; but the gist of the argument is similar to the one made in 1957: the American education system must invest in rigorous science and math education for the sake of national security. Or at least invest in "AI literacy." (Also LOL.)

The author points to the National Defense Education Act, which was passed in response to Sputnik and marked an unprecedented injection of federal dollars into the US school system; and she argues we need that again, but for AI. So I guess I'm sidestepping too how utterly tone-deaf this whole article is considering the Trump Administration is actively trying to dismantle the Department of Education and axe funding for all sorts of scientific research initiatives in and adjacent to K-12 schools and universities.

The passage of the NDEA, as I argue in Teaching Machines, was a boon for the nascent education technology industry as it generated an incredible amount of interest and funding from the government and from philanthropies for education R&D, for all sorts of content, curricula, and gadgetry. And as I've said before, Sputnik was, in its original instantiation, already "the Sputnik moment for AI," and the passage of the NDEA initiated a great deal of interest and investment in AI in general and in education – that is, in intelligent tutoring systems.

But to talk about the changes in education in postwar America solely as a response to Sputnik, solely as a boon for math and science education, and then to call for changes to education today so that we can echo that very "arms race" isn't just bad history or bad policy or bad metaphor – although the latter should give you a clue that it is, indeed, bad: a "weaponized" vision of education is not a virtuous or just vision of education, folks.

As Jim Wynter Porter argues in a 2018 article in Multiethnica, one of the "underexamined aspect of NDEA legislation was its incentives for curricular stratification by 'ability.' About 50% of its total spending was for increased 'intelligence' testing," used to group and track students based on "their natural, individual differences." [emphasis mine]

Wynter Porter argues that the passage of the NDEA – and specifically its support for intelligence testing – must be viewed alongside that other momentous governmental intervention of the 1950s: Brown v Board of Education, the Supreme Court decision that ended de jure school segregation. The discourse around the NDEA's passage, he argues, "worked to justify durable structural changes that remade or maintained divisions by 'race' (and so 'race' itself) via racialized tracking." That is, while race-based segregation was ostensibly dismantled post-Brown, much of it was then rebuilt through tracking and through discrimination based on "individual ability" – ability as decreed by intelligence testing, a practice with its own racist history.

Tracking students into different classes and into different schools – dividing students into "bright, medium, and dull" as Wynter Porter puts it, into Honors Math or AP History – has come and gone and come again, has been rebranded and technologized in the decades since the Cold War.

That individualization and "intelligence" work hand-in-hand should be no surprise to those familiar with education technology – its practices, its history – and the ways in which ed-tech has, on one hand, promised a technologically-enabled meritocracy, serving all students at the level and pace they need, while at the very same time re-inscribing educational inequalities through an algorithmic sleight-of-hand that never has to mention race or racism. (And by re-inscribing educational inequalities, I should add, by extracting data from students and schools and dollars from communities that might better be used to build human capacity rather than buy gadgets and software.)

"The new psychometric individualism could still accomplish the work of 'race'," Wynter Porter contends, "not only because of the neo-hereditarian principles around which it was structured, but also because 'race' was embedded latently in the tests, in the structures of schooling itself, and indeed in the broader knowledge production practices and social matrix that surrounded them."

"Individualized learning," particularly when enacted through technologies that defy scrutiny and accountability through algorithmic decision-making, echo the legacy of "separate but equal." Individualization surely has great appeal – it is a core tenet of American ideology, after all; and most teachers today who embrace it do not see themselves as reinforcing discriminatory practices. But in the school and in the classroom, individualization operates through the establishment of a hierarchy based on "ability," often determined by standardized testing – a way to avoid saying "IQ" perhaps while continuing to practice ranking based on "intelligence."

This ranking is at the heart of artificial intelligence too – not simply the rating and ranking of machine over human but the ranking and rating and sorting of humans.

AI determines access to content and to opportunity and – this is crucial – to human support. AI technologies offer a less valuable, less human educational experience through the ranking and rating of "intelligence."

For a research project I am currently evaluating all kinds of generative AI models (mostly for visual artifacts but some text based ones as well). There also is somewhat of a push at my employer to use those systems more because of “efficiency”.

So we all know that LLMs fabricate facts, meaning: They produce text that is factually untrue. Happens a lot, those so-called hallucinations are a structural property of those kinds of systems. But I kept wondering about something else that I keep stumbling on a lot. Let’s call it “true to the letter, wrong in reality”.

So look at this ChatGPT conversation:

I prompted the LLM to generate a Python function that would take a list of items (like a list of numbers) and return the sum of said items. And I got some code that does exactly that. Cool, the LLM works. This is often the “middle of the road” way of using “AI” coding assistants: Offload simple tasks to focus on the big stuff like architecture decisions and such.

I also gave a self-hosted gemma3 model the same task which it responded to with a bit more code (we’ll get to that in a bit) and that actually got me thinking.

Would you consider the response ChatGPT generated correct or not?

It very obviously gave me the code I asked for, so it’s right, right? What did gemma3 generate?

Even better, right? It has documentation, it does the thing. Brilliant.

I don’t think that either of those responses is good enough to be “right” though, but ChatGPT is slightly closer.

Because the correct response to “Write me a function in Python that gets a list of items and returns the sum of those items” is “use the built-in sum() function“.

Which is the function that ChatGPT used just wrapped in pointless code. And that’s one of my core issues here.

Sure, functionally the generated code might be okay (for this simple case) but it lacks understanding and knowledge about how to do things properly. How to limit code duplication and pointless indirection. How to keep code maintainable for the long term.

This stems from two related properties of those systems: They tend to follow the letter and they have “improv-brain”.

Following the letter just comes from their lack of understanding, knowledge and competence. In their training data there’s a lot of garbage code used to explain simple structures that should never be used in reality but they just regurgitate it. And success is just measured by if the output formally produces the correct output – which again is a very surface level understanding of what characterises quality software.

“Improv-brain” relates to LLM systems’ tendency to not challenge input but just roll with it. Again, this stems from lack of knowledge, understanding and competence but has a bit of a different bend: LLMs try to go “yes … and” on whatever you tell them and rarely challenge prompts. Which is why a suboptimal prompt can lead them astray so easily.

But I think it is important when evaluating “AI” output to use the right metrics, to not just look at the surface but dive in: Is the “AI” output actually right? Or just kinda right? Would you accept that code from a junior dev? From an intern? Where is your lower bar for quality? And if your team accepts those kind of bad pieces of code, how will your code base look in a year? How much competence gain will your junior devs have had in a year? Or is it just faster slop?

That’s what a lot of the metrics and statistics of “AI” evaluations lack. Some depth, some thinking about the long term consequences on the product and development team as a whole. Because a lot of correct answers are not right.