Posts in Finance (20 found)

Where Are All The Data Centers?

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Sam Bankman-Fried was the smiling, friendly, “ self-made billionaire ” face of the crypto industry. NFTs were the future of art, and would change the way people think about the ownership of digital media. The actual evidence, of course, never lined up. NFT trading was dominated by wash trading — market manipulation through two parties deliberately buying and selling an asset to raise the price. Cryptocurrency never took off as anything other than a speculative asset, and altcoins are effectively dead . Sam Bankman-Fried was only a billionaire if you counted his billions of illiquid FTX tokens, but that didn’t stop people from saying he wanted to save the world weeks after the collapse of Terra Luna, a stablecoin that he himself had bet against and may have helped collapse .  Three months before his arrest, a CNBC reporter would fly to the Bahamas to hear SBF tell the story of how he “ survived the market wreckage and still expanded his empire, ” with the answer being that he had “stashed away ample cash, kept overhead low, and avoided lending,” as opposed to the truth, which was “crime.”  The point is that before every scandal is somebody emphatically telling you that everything’s fine. Everything seems real because there’s enough proof, with “enough proof” being a convincing-enough person saying that “most of FTX’s volume comes from customers trading at least $100,000 per day,” when the actual volume was manipulated by FTX itself , and the “$100,000 a day in customer funds” were being used by FTX to prop up its flailing token .  In the end, the “proof” that SBF was rich and that FTX was solvent was that nobody had run out of money and that nothing bad had happened to anybody. SBF was a billionaire sixteen times over because enough people had said that it was true.  Anyway, one of the most commonly-held parts of the AI bubble is that massive amounts — gigawatts’ worth — of data centers have both already been and continue to be built… …but then you look a little closer, and things start getting a little more vague. While Wood Mackenzie’s report said that there was “ 25GW of data center capacity added to the funnel ” in Q4 2025 does not say how much came online. CBRE said back in February that “net absorption of 2497MW” happened in primary markets in 2025 , with other reports saying that somewhere between 700MW and 2GW of capacity was absorbed every quarter of 2025. At the time, I reached out for any clarity about the methodology in question and received no response. Okay, so, I know data centers are getting built and that they exist . I believe some capacity is coming online. But gigawatts? Or even hundreds of megawatts? How much data center capacity is actually coming online?  Why did Anthropic get so desperate it took on a years old data center, xAI’s Colossus-1 , full of even older chips from a competitor — one whose CEO described the company as “evil, ” and that’s currently facing a lawsuit from the NAACP over allegations the facility’s gas turbines are polluting black neighborhoods ?  Remember, Colossus-1 is an odd data center, with around 200,000 H100 and H200 GPUs and an indeterminate amount of Blackwell GB200s, weighing in at around 300MW of total capacity… which isn’t really that much if we’re talking about gigawatts being built every quarter, is it?    So, I have two very simple questions to ask: how long does it take to build a data center, and how much data center capacity is actually coming online? These simple questions are surprisingly difficult to answer. There exists very little reliable information about in-progress data centers, and what information exists is continually muddied by terrible reporting — claiming that incomplete projects are “operational” because some parts of them have turned on , for example — and a lack of any investor demand for the truth. Hyperscalers do not disclose how many data centers they’ve built, nor do they disclose how much capacity they have available.  I find this utterly inexcusable, given the fact that Amazon, Google, Meta and Microsoft have sunk over $800 billion in capex (and more if you count investments into Anthropic and OpenAI) in the last three years . So I went and looked, and what I found was confusing. So, you’re going to hear people say “well Ed , data centers are being built ,” and what I’m talking about is data centers that have been fully constructed and then turned on . It’s really, really easy to find data centers that are under construction , but as I’ve discussed in the past, that can mean everything from a pile of scaffolding to a near-complete data center . Yet finding the latter is very, very difficult. I’ve spent the last week searching for data centers that broke ground in 2023 or 2024 that have actually been finished, and come up surprisingly empty-handed. Some projects are stuck in construction hell, eternally dueling with planning departments over permitting, some are chugging along with no real substantive updates, some, as is the case with Nscale’s Loughton, England data center, have done effectively nothing for the best part of a year , some are perennially adding more capacity to the order as a means of continuing raking in construction bills, and some are claiming their data centers are “operational” as only a single phase has turned on. You should also know that even once construction has finished, the buildings themselves must be fully filled with the necessary cooling, power and compute hardware, at which point it can be configured to meet a client’s specifications (which can take months), at which point the unfortunate soul building the facility can actually start making money. I think it’s also worth revisiting how difficult data center construction is, and how large these new projects are.  This starts with a very simple statement: nobody has actually built a 1GW data center (to be clear, it’s usually a campus of multiple buildings networked together) yet. There are campuses — such as Stargate Abilene — which promise to reach 1.2GW, but nearly two years in sit at two buildings at around 103MW of critical IT load each with, based on discussions with sources with direct knowledge of Abilene’s infrastructure , a third building sitting fully-constructed but with barely any gear inside it. It’s fundamentally insane how many different companies are trying to build these things considering how difficult even the simplest data center is to build. Take, for example, American Tower Corporation’s edge data center in Raleigh, North Carolina, which I’ll mention a little later. This is a 1MW facility — or one-thousandth the size of a gigawatt facility — occupying 4000 sq ft of real estate at first and expanding to 16,000 if ATC actually gets it up to 4MW. That’s about two-and-a-bit times larger than the typical American home . And, from ground-breaking to ribbon-cutting , it took eleven months to complete. And that’s not including all the other necessary time-consuming bits, like finding land, securing permits, and so on.  That’s a simple one. People want to build data center campuses a thousand times larger than that. Look at how difficult it is. In fact, it’s so difficult that the companies can’t build all of it at once. Larger data center campuses are almost always divided into “phases,” in part because that’s the smartest way to build them, and in part with the express intention of convincing you that they’re “fully operational.”  For example, CNBC’s MacKenzie Sigalos reported in October 2025 that Amazon’s Indiana-based (allegedly) 2.2GW Project Rainier data center was “operational,” but only seven out of a planned 30 buildings were actually operational, and her comment of “with two more campuses [of indeterminate capacity] underway.” This comment was buried two videos and 600 words into a piece that declared the data center was “now operational,” with the express intent of making you think the whole thing was operational. To give her credit, at least she didn’t copy-paste the outright lie from Amazon, which claimed that Rainier was “ fully operational ” in a press release the same day. You’ll also note that Amazon never provides any clarity about the actual capacity of Rainier. Sigalos did exactly the same thing when the first (of eight) buildings of Stargate Abilene opened, declaring that “OpenAI’s first data center in $500 billion Stargate project is open in Texas,” burying the comment that only one was operational with another nearly complete several hundred words earlier.  These are intentionally attempts to obfuscate the actual progress of the data center buildout, and if I’m honest, I’ve spent months trying to work out why big companies that were supposedly building large swaths of data centers would be trying to do so. Unless, of course, things weren’t going to plan. In its last (Q3 FY26) quarterly earnings call , Microsoft CEO Satya Nadella claimed that “[Microsoft] added another gigawatt of capacity this quarter, and [remained] on track to double [its] overall footprint in two years.” A quarter earlier , he claimed to have added “nearly one gigawatt of total capacity,”  with Karl Keirstead of UBS saying that he “...thought the one gigawatt added in the December quarter was extraordinary and hints that the capacity adds are accelerating.” As I’ll discuss below, I can find no evidence of anything more than a few hundred megawatts of Microsoft’s data center capacity coming online. While I’ll humour the idea that it doesn’t announce every new data center, and that there may be colocation and neocloud counterparties ( 67% of CoreWeave’s revenue comes from Microsoft, for example ) that make up the capacity, as I’ll also discuss, I don’t know where the hell that might be. So, to be aggressively fair, I asked Microsoft to answer the following questions on May 4, 2026: A Microsoft representative from WE Communications promised to "circle back" by 5PM ET on Monday May 4th, but did not return further requests for comment via text and email, which is incredibly strange considering the simple and straightforward nature of my questions. That’s probably because the vast majority of its publicly-announced or documented data center capacity doesn’t appear to be getting finished. In September 2025, CEO Satya Nadella claimed that Microsoft had added 2GW of capacity “in the last year,” and acted as if Fairwater, a project with two actively-constructed data centers with one in Wisconsin that broke ground in September 2023 and another in Atlanta that broke ground in July 2024 , was something to be “announced” rather than “a very expensive project that has taken forever.” Nadella also claimed that there are “multiple identical Fairwater datacenters under construction,“ though he neglected to name them. To be clear, “Fairwater” refers to a project where multiple data centers are linked with high-speed networking to make one larger cluster, a project that sounds ambitious because it is , and also unlikely because it’s yet to have been built.  Fairwater Atlanta — the latter of the Fairwaters — was “launched” in November 2025 and it’s unclear how much capacity it has. Cleanview claims it’s at 350MW of capacity , and Microsoft’s own community outreach page claims construction would be completed by the beginning of October 2025 , but, as I’ll get to, it’s unclear whether this is just one phase, given that reporting shows multiple other buildings still under construction . I have serious doubts that Microsoft stood up a 350MW data center in less than a year, given everything else I’m about to explain. Fairwater Wisconsin is also a data center of indeterminate size, but Cleanview claims Phase 1 is 400MW , quoting a story from FOX6 News Milwaukee from September 2025 that said that Microsoft was “investing an additional $4 billion to expand the campus,” featuring a video of a very much in construction data center saying the following: So, $3.3 billion — at a rate of around $14 million per megawatt per analyst Jerome Darling of TD Cowen — is about 235MW of capacity, which is a lot lower than 400MW.   Seven months later, Satya Nadella said that the Fairwater datacenter in Wisconsin was “going live, ahead of schedule,” a sentence written in the present tense, but also said that it “ will bring together hundreds of thousands of GB200s in a single seamless cluster,” which is in the future tense.  It’s a great time to remind you that Microsoft claims that it brought online roughly eight times that capacity (around 2GW) in the past six months.  To make matters worse, it doesn’t appear that Fairwater Wisconsin is actually operational. Ricardo Torres of the Milwaukee Journal-Sentinel reports that Microsoft has said it isn’t actually online , and that while there “...is equipment inside the data center conducting start-up opportunities…the company anticipates [they] will continue to happen for the next several weeks.”  Epoch AI’s satellite footage of Fairwater Wisconsin — which mentions  a completely wrong capacity because it’s uniquely terrible at calculating it ( it claimed Colossus-1 has 425MW capacity, for example) — notes that as of April 2026, one building appeared to be operational, with a second under construction. So, that’s one building in Wisconsin that might be complete, and based on the permitting application from August 2023 dug up by Epoch, the project is designed to have 117MW of capacity, which is a lot lower than 235MW. While Epoch didn’t have permitting for building two, it did for three and four, which are designed to have around 719MW of capacity , and as of April 2026 still appear to be slabs of concrete.  In simpler terms, there’s at most around 117MW of capacity running at Fairwater Wisconsin. The Fairwater data centers are Microsoft’s most-publicized data centers, yet they’re shrouded in secrecy, with the Atlanta Journal-Constitution having to file an open records request to find the site being developed by QTS, a data center developer owned by Blackstone . Videos of Fairwater Atlanta from last November show a giant campus with two large buildings and a patch of yet-to-be-developed dirt. DataCenterMap refers to it as “ under construction .” Epoch AI’s satellite footage notes that as of February 2026, building four’s roof was complete and “all mechanical equipment appears to be installed,” but “there is still a lot of construction activity around the building.”  Based on air permits filed as part of the project (that Epoch found), it appears that each building is powered by a number of Caterpillar 3516C Generator Sets at around 2.5MW each, with building one having 47 (117.5MW), building two having 13 (32.5MW), building three having 30 (75MW), and building four having 35 (87.5MW). If we’re very generous and assume that three buildings are complete, that means that Fairwater Atlanta is at around 225MW of capacity (not IT load!). So, that’s about 342MW of data center capacity being built by one of the largest companies in the world, in its most-publicized and written-about data centers. Put another way, for Microsoft to come remotely close to its so-called 2GW of capacity in the last six months, it will have had to bring online a little under six times that capacity. I’m calling bullshit. I really did want Microsoft to give me some answers, but I’m very confused as to how it can remotely claim it brought even a gigawatt of capacity online in the last year. I also question whether Microsoft is actually building multiple other “identical” Fairwater data centers, as I can’t find any announcements or pronouncements or mentions or hints as to where they might be. In fact, I’m having a little trouble finding where else Microsoft has been building data centers, and those I can find are extremely suspicious. In Microsoft’s announcement of its Wisconsin data center , it mentioned two other projects — one in Narvik Norway that had already been announced months beforehand by OpenAI , and another with Nscale in Loughton, England that was also announced by OpenAI that very same day as part of the entirely fictional Stargate project . If you’re wondering how those are going, Microsoft had to take over the entire Narvik project (which does not appear to have started construction) from OpenAI , and the Loughton data center ( which OpenAI also backed out of ) is currently a pile of scaffolding . For two straight quarters , Microsoft has said it’s brought on an entire gigwatt of capacity,and I have to ask: where?  Because when you actually look at the projects it’s announced, very little appears to have been built, and that which has is nowhere near its theoretical capacity. To be specific about what Microsoft is claiming, it’s saying it’s brought around 4GW of capacity online in the space of two years, and at a 1.35 PUE, that’s about 2.96GW of critical IT load, which works out to the power equivalent of around 284,600 H100 GPUs, which may be possible — after all, Microsoft apparently bought 450,000 H100 GPUs in 2024 — but I can’t find much evidence of data centers that could house that many GPUs, nor that might be in construction.  Let’s dig in. Microsoft broke ground on three data centers in Catawba County North Carolina in 2024 — one in Hickory, another in Lyle Creek, and another in Boyd Farms: Alright, maybe I’m being unfair! Maybe it’s just a North Carolina problem. There must be another that broke ground and got built…right?  Microsoft also broke ground on a data center in Quebec City, Canada in September 2024 , and as of April 2026 , “generator testing has been completed,” and “civil works will continue until Autumn 2026.”  Okay, well, maybe it’s a Canada problem. What about Microsoft’s New Albany, Ohio data center that broke ground in October 2024 ? Well, as of March 2026, “spring activity would resume,” and “beginning soon, soil will be delivered to the site via a designated truck route. I’ll note that Microsoft specifically says that Ames Construction is currently leading it, and that it will “resume the lead role in project communications” once the final phase of construction is done at some unknown time. Alright, well, how about the August 2025 ground breaking in Cheyenne, Wyoming that was allegedly “ due to launch in 2026 ”?  Well, Microsoft hasn’t updated its community page since it said there’d be a community meeting planned for November 2025 and that “neighbors within the vicinity will be notified ahead of construction,” which sounds like construction is yet to commence. Not to worry though, it announced on April 14, 2026 that it planned to expand it to “ accelerate innovation and economic growth ” How about that 2023-announced Southwest Hortolândia Brazil data center ? That’s right, the last update was in September 2025 , and the update was “construction activities continue to progress in alignment with local regulations.” A piece from Folha De S.Paulo from March 2026 mentioned that Microsoft “had begun operating its first artificial intelligence data centers in Brazil,” but satellite footage shows that it’s barely finished. What about the Newport, Wales data center it announced in 2022 ? Well, as of November 2025, a politician was standing on a concrete slab saying how many jobs it’ll theoretically bring in , which it won’t. What about Microsoft’s four data centers in Irving, Texas, announced December 2024 ? The best I’ve got for you is a news report about a data center in Irving Texas breaking ground in January 2025 . Its San Antonio data center, announced in July 2024 ? Well, construction was underway as of December 2025 , and it appears that construction will begin in the summer of 2026 on another one in the area. How about the two data centers outside of Cologne, Germany , announced in November 2024? Well, as of September 2025, Microsoft has… plans to build one of them ? …what about the 900 acres of land it bought in June 2024 in Granger, Indiana ? Great news! According to 16NewsNow , Microsoft officials “could break ground on a proposed data center…in late April or early May [2026].” How about Project Ginger West, a data center planned in Des Moines. Iowa since March 2021 ? Hope you like waiting , because Microsoft itself says that it’s estimated to finish construction in Summer 2028 . Ginger East , announced a few months later? Mid-2028 . Project Ruthenium ( announced 2023 )? I don’t have shit for you I’m afraid. Rutheniumkanda Forever! This company claims it’s built four fucking gigawatts of capacity , but when I go and look to see what it’s actually built I’ve failed to find a single announced data center from the last three years that got turned on outside of its Fairwater Atlanta and Wisconsin sites. To be clear, all of these sites are somewhere in the 200MW to 300MW range. For Microsoft to have brought online 4000MW of data center capacity in the last two years would require it to have completed thirteen or more of these projects, all while choosing not to promote them, with every project operating in such a veil of secrecy that no local or national news outlet reported a single one of them.  I truly cannot work out how Microsoft has brought on any more than 500MW of capacity in the last year based on my research, and think Microsoft is deliberately obfuscating whether said capacity was contracted rather than actively in-use , much like CoreWeave refers to itself having 3.1GW of “ total contracted power ” but only added 260MW of active power capacity in a single quarter at the end of 2025.  However, the exact verbiage used in Microsoft’s earnings transcripts is that it “added another gigawatt of capacity,” which sounds far more like it’s saying it brought them online… …but it didn’t, right? It obviously hasn’t. Where are all the data centers, Satya? Where are they? Why are your PR people too scared to tell me?  No, really, where are they?  So, to be fair, analyst Ben Bajarin, one of the more friendly pro-AI posters, argues that actually all of that capacity is secretly behind-the-scenes , something I’d humour if there was any kind of paper trail to a bunch of Microsoft data centers that were secretly being built.  I’d also be more willing to humour it if any of the data centers that have been publicized as “breaking ground” had actually been finished, or if both Fairwater Atlanta and Wisconsin weren’t so deceptively-marketed. My only devil’s advocate is that Microsoft could, in theory , be working with colocation partners to stand up several gigawatts of capacity through shell corporations and SPVs, but even then , not a single one has any sort of trail to Microsoft? All of that capacity?  It’s really, really weird, and the only answers I get are smug statements about how “Fairwater is ahead of schedule.” But if I’m honest, I’m having trouble even making these numbers add up. Considering how loud, offensive and conspicuous the AI bubble has become, it feels like we should have a far, far better understanding of how much actual capacity has been built. I also think it’s time to start being realistic about how long these things are taking to build. For example, I was only able to find a few data centers that for sure, categorically, definitively opened, and for the most part, it appears that a data center takes around 18 months to go from groundbreaking to opening. And these, I add, are all facilities that are relatively modest — at least, when compared to the kinds of gigawatt-scale campuses that are reportedly in active development.  Digging deeper, I found a lot of projects stuck in development Hell: While there are absolutely data centers under construction , and some, somewhere , are actually being completed , the vast majority of projects I’ve found are either in a mysterious limbo state or, in most cases, under construction years after breaking ground. Across the board, the message seems to be fairly simple: it takes about 18 to 24 months to build any kind of data center, and the bigger they are, the less likely they are to get completed on schedule. Those that actually “come online” aren’t actually fully constructed, but have brought on a single phase — something I wouldn’t begrudge them if they were anything close to honest about it. In reality, data center companies actively deceive the media and customers about the actual status of projects, most likely because it’s really, really difficult to build a data center. In any case, what I’ve found amounts to a total mismatch between the so-called “rapid buildout” of AI data centers and reality.  It also doesn’t make much sense when you factor in how many GPUs NVIDIA sold. In October last year, NVIDIA CEO Jensen Huang told reporters that it had shipped six million Blackwell GPUs in the last four quarters , though it eventually came out that he was counting two cores for every GPU , making the real number three million. I disagree with the framing, I think it’s incoherent and dishonest, but I’ve confirmed this is what NVIDIA meant. In any case, if we assume two cores per GPU, a B200 GPU has a power draw of around 1200W, for around 3.6GW of IT load for 3 million of them. I realize that NVIDIA also sells B100 and B300 GPUs (similar power draw) and NVL72 racks of 72 GB200 GPUs and 36 CPUs, but bear with me. Blackwell GPUs only started shipping with any real seriousness in the first quarter of 2025, which means that a good chunk of these data centers were built with H100 and H200 GPUs in mind. Nevertheless, I can find no compelling evidence that significant amounts — anything over 500,000 GPUs — of Blackwell-based data centers have been successfully brought online.  When I say I struggled to find data centers that had been both announced and brought online, I mean that I spent hours looking, hours and hours and hours, and came up short-handed.  I want to be clear that I know that there is Blackwell capacity actually being built , and believe that the majority of that capacity is retrofits of previous data centers, such as Microsoft’s extension to its Goodyear Arizona campus which it began building in 2018 that likely houses Blackwell GPUs. But I no longer believe that the majority of Blackwell GPUs are doing anything other than collecting dust in a warehouse. Blackwell GPUs require distinct cooling, a great deal more power than an H100, and cost an absolute shit-ton of money, making it unlikely that a 2023 or early-2024 era data center could handle them without significant modifications. I fundamentally do not believe more than a million — if that! — Blackwell GPUs are actually in service.  If that’s the case, NVIDIA is likely pre-selling GPUs years in advance — experimenting with the dark arts of “ bill-and-hold ” — and helping certain partners like Microsoft install the latest generation to create the illusion of utility, availability and viability that does not actually exist. If I’m honest, I also have serious questions about the current status of many H100 and H200 GPUs. Based on what I’ve found, I’d be surprised if more than 3GW of actual capacity was turned on in the last two years, which means that NVIDIA has sold anywhere from double to triple the amount of GPUs that the world can hold. While the Anthropic-Musk compute deal is an obvious sign about xAI’s lack of demand for compute, it’s also, as I mentioned earlier, a clear sign that AI data centers are mostly not getting finished, and those that do get finished are taking two or three years even for smaller builds. While it sounds a little wild, I think in reality only a few hundred megawatts — if that — of actual, usable AI compute capacity is being spun up every quarter. If I was wrong, there’d be significantly more progress on, well, anything I could find.  Why can’t Microsoft offer up a data center that isn’t called Fairwater, and why are its Fairwater data centers taking so long? How much actual capacity has Microsoft brought online? Because it certainly isn’t fucking 2GW in six months. I’m willing to believe that Microsoft has a number of collocation agreements with parties that don’t disclose their involvement. I’m also willing to believe that Microsoft doesn’t publicize every single data center it’s building or has built.  2GW of capacity is a lot. It’s nearly ten times the (likely) existing capacity of Fairwater Atlanta. If Microsoft is bringing so much capacity online, why can’t we find it, and why won’t they tell us? And no, this isn’t some super secret squirrel “they’re building secret data centers for the government” thing, it’s very clearly a case where “capacity” refers to “something other than data centers that actually got brought online. Despite their ubiquity in the media, AI data centers are relatively new concepts that are barely five years old. They are significantly more power-intensive than a regular data center, requiring massive amounts of cooling and access to water to the point that the surrounding infrastructure of said data center is often a massive construction project unto itself.  For example, OpenAI and Oracle’s Stargate Abilene data center is (in theory) made up of two massive electrical substations , a giant gas power plant and eight distinct data center buildings, each with around 50,000 GB200 GPUs, at least in theory. Every data center requires that power exists — as in it’s being generated in both the manner and capacity necessary to turn it on, either through external or grid-based power — and is accessible at the data center site. This means that every single data center, no matter how big, is its own construction nightmare. You’ve got the power, the labor, the permits, the planning, the construction firm, the power company, the specialist gear, the temporary power (because on-site power is slow ), the backup power (because you can’t just rely on the grid for something you’re charging millions for!), the cooling, the uninterruptible power supplies — endless lists of shit that needs to go very well or else the bloody thing won’t work. These are very difficult and large projects to complete. Edged Computing’s (theoretically) 96MW data center in Illinois is 200,000 square feet in effectively two large squares. For comparison, every single inch of gambling space in Caesar’s Casino Vegas is around 130,000 square feet . These things are fucking huge, fucking difficult, and fucking expensive, and all signs point to capacity not coming online.  Let’s go back to Anthropic mopping up Musk’s fallow data center capacity, which stinks of desperation for both companies. If there were modern data centers full of GB200s being turned on and available anywhere in the next month or two, wouldn’t it be more financially prudent to wait for it, even if it’s just on an efficiency level? A franken-center made up of H100s and H200s with some GB200s stapled onto the side feels like a stopgap solution. I have similar questions about the results of adding this capacity — that “...Anthropic plans to use [it] to directly improve capacity for Claude Pro and Claude Max subscribers ,” “doubling” (whatever that means) the 5-hour rate limit and removing the recently-added peak rate limits.  What’s the plan here, exactly? Less than a month ago Anthropic’s Head of Growth, Amol Avasare , said that Anthropic was “looking at different options to keep delivering a great experience for users” because Max accounts were created before the era of Claude Code and Cowork . How does adding 300MW of capacity magically resolve that problem? Was that always the plan?  Or was this a knee-jerk reaction to the surging popularity of OpenAI’s Codex ? Because the original justification for peak hours was that Anthropic needed to manage “ growing demand for Claude ,” demand that I bet Anthropic claims hasn’t gone anywhere. It’s also important to remember that last year, OpenAI’s margins (which are already non-GAAP), per The Information , were worse than expected because (and I quote) it had to “..to buy more expensive compute at the last minute in response to higher than expected demand for its chatbots and models.”  In other words, Anthropic has deliberately tanked its already-negative 2026 gross margins by desperately buying the fallow compute from a company whose CEO threw up the nazi salute , called the company “ misanthropic and evil ,” and has the “right to reclaim the compute” if Anthropic “engages in actions that harm humanity.” Surely you’d wait a few months for some new, less tainted source of compute, right? And surely it wouldn’t be such a big deal, because new data centers get switched on every day, right?  So, let’s get to brass tacks. Anthropic and OpenAI have now committed to spending $748 billion across Amazon Web Services, Google Cloud, and Microsoft Azure , accounting for more than 50% of their remaining performance obligations. The very future of hyperscaler revenue depends both on Anthropic and OpenAI’s continued ability to pay and both of them having something to actually pay for.  I also think it’s fair to ask why Microsoft’s theoretical gigawatts of new compute aren’t producing tens of billions of dollars of new revenue.  Microsoft’s $37 billion in annualized AI run rate (sigh) is mostly taken up by OpenAI’s voracious demands for its :compute , and only ever seems to expand based on OpenAI’s compute demands and the now 20 million lost souls paying for Microsoft 365 Copilot . There’s supposedly incredible, unstoppable demand for AI compute, and Microsoft is apparently sitting on gigawatts’ worth , but somehow those gigawatts don’t seem to be translating into gigabillions , likely because they don’t fucking exist. All of this makes me wonder what Google infrastructure head Amin Vahdat meant last November when he said that Google needed to double its capacity every six months to meet demand . Many took this to mean “Google is doubling its capacity every six months,” but I think it’s far more likely that Google is taking on capacity requests from Anthropic that are making said capacity demands necessary. Similarly, I think CEO Sundar Pichai’s comment that it would have made more money had it had more capacity to sell was a manifestation of a distinct lack of new capacity rather than a result of bringing on swaths of new data centers that immediately got filled. I also need to be blunt on two things: Look, I know it sounds crazy, but I’m telling you: I don’t think very many data centers are coming online! While I keep wanting to hedge my bets and say “I bet a few gigawatts came online,” I cannot actually find any compelling literature that backs up that statement. I’ve spent hours and hours looking, and I’ve come up with a few hundred megawatts delivered in the past two years. Every major project is stuck in the mud, a phase or two in, or facing mounting opposition from locals that don’t want a Godzilla-sized cube making a constant screaming sound 24/7 so that somebody can generate increasingly-bustier Garfields.  I’m not even being a hater! It’s just genuinely difficult to find actual data centers that have been announced that have also been fully turned on.   So, humour me for a second: if hyperscalers are bringing on hundreds of megawatts of capacity a year, then that means that the ever-growing quarterly chunks of depreciation ripped out of their net income are just a taste of what’s to come. Last quarter, Google’s depreciation jumped $400 million to $6.482 billion, with Microsoft’s jumping nearly a billion dollars from $9.198 billion to $10.167 billion, and Meta’s from $5.41 billion to $5.99 billion. While Amazon’s technically dropped quarter-over-quarter, it still sat at an astonishing $18.94 billion. Remember: depreciation only increases when an item is actually put into service. If Microsoft, Google, Amazon and Meta are sitting on tens of billions of yet-to-be-installed GPUs, and said GPUs are only being installed at a snail’s pace every quarter, that means that these depreciation figures are set to grow dramatically. In fact, year-over-year, Google’s depreciation has jumped 30.7%, Amazon’s 24.7%, Microsoft’s 23.9%, and Meta’s an astonishing 34.9% .  And that’s with an extremely slow pace of deployment.  I do kind of see why the hyperscalers are sinking capex into these big AI infrastructure gigaprojects now, though. Shareholders are currently tolerating the capex because they think stuff is coming online, and that’s where the “incredible value” is. When a $20 billion or $30 billion a quarter depreciation bill first rears its head — as I said, Amazon is close, reporting $18.945bn in depreciation and amortization expenses in the most recent quarter — it’ll become obvious that the only people seeing value from AI are Jensen Huang and one of the massive construction firms slowly building these projects.  Actually, it’s probably important to state that I don’t think the majority of these projects are doing anything untoward I just don’t think any of them realized how difficult it is to build a data center, and unlike basically any other problem the tech industry has ever faced, simply throwing as much money as possible at it doesn’t really change the limits of physical construction.  I think every one of these data center projects is its own individual construction nightmare, and thanks to the general market psychosis around the AI bubble, nobody has thought to question the core assumption that these things are actually getting built. With all that being said , I’m not sure that anyone building these things is moving with much urgency either. Perhaps they don’t need to — perhaps hyperscalers are happy, because they can continually string out both the AI narrative and put off those massive blobs of depreciation. But we really do need to reckon with the fact that nearly two years in, Stargate Abilene has only two buildings’ worth of actual, operational, revenue generating capacity, and nobody has given me an answer as to how it doesn’t have even a quarter of the 1.7GW of power it’ll need to turn everything on , if it ever gets fully built. Maybe they can really pick up the pace, but as of early April, barely any actual gear was in the third building.  And then we get to the other problem: Oracle. As I’ve discussed before, Oracle is building 7.1GW of total capacity for OpenAI , and keeps — laughably! — saying 2027 or 2028, when at this rate, Stargate Abilene won’t be done until mid-2027, and the rest either never get finished or are done in 2030 or later.  This is setting up a horrifying situation where Oracle desperately needs OpenAI to pay it for capacity that doesn’t exist, and if it ever gets built, it’s likely to be years after OpenAI has run out of money, which is the same problem that Microsoft, Google, and Amazon have with their $748 billion of deals with Anthropic and OpenAI, though thanks to the $340 billion or more necessary to build the Stargate data centers, Oracle’s problems are far more existential. I’ve repeatedly — and correctly! — said that the problem is that these companies didn’t have the money to pay for their capacity, but Oracle lacks Microsoft or Google’s existing profitable businesses to fall back on if these data centers are delayed, with its existing business lines plateauing and its only real growth coming from theoretical deals with OpenAI and GPU compute with negative 100% margins .  Anthropic’s desperation for new sources of  compute also suggests that it’s bonking its head against the limits of its capacity, and will continue to do so as long as it continues to subsidize its users . I also think that the slow pace of construction will eventually lead to OpenAI facing similar problems. These companies need to continue growing to continue to raise the hundreds of billions of dollars in funding necessary to pay Oracle, Google, Microsoft, and Amazon their respective pounds of flesh.  It’s now very clear that the whole “inference is profitable” and “most compute is being used for training” myths are dead, because if they weren’t, Anthropic would either need way more compute or way higher-quality compute. Colossus-1 was specifically built as a training cluster, yet its current use is “reduce rate limits for our subsidized AI subscriptions,” which is most decidedly inference provided by three-year-old hardware . Despite writing over 9000 words and driving myself slightly insane trying to find out, I still haven’t got an answer as to how much actual data center capacity has come online. Hyperscalers have clearly been retrofitting old data centers to fit their new chips, and based on my research, I can find no compelling evidence that they’ve added more than a few hundred megawatts a piece since 2023.  What I do know is that, across the board, a data center of anything above 50MW (or lower, in some cases) takes anywhere from 18 to 36 months to complete, and nobody has actually built a gigawatt data center despite how many people discuss them. For example, Kevin O’Leary — known as “Mr. Dogshit” to his friends — is allegedly building a 9GW data center in Utah , but he may as well say that he’s building a unicorn that shits Toyota Tacomas, as doing so is far more realistic than a project that will likely cost $396 billion, assuming that locals and bankers don’t drag him to The Other Side like Dr. Facilier .  Nobody has built a 1GW data center, so I severely doubt Mr. Dogshit will be able to do anything other than create another scandal and lose a bunch of people’s money. In other words, any time you hear about a “new data center project,” add a year or two to whatever projection they give. If it’s 2027, assume 2029, or that it never gets built. Anything being discussed as “finished in 2030” may as well not exist. In any case, what I’m suggesting is that very, very few data centers are actually getting finished, and if that’s true,  NVIDIA has sold years worth of chips that are yet to be digested.  And if that’s true, somebody is sitting on piles of them.  I’m trying to be fair, so I’ll assume that an unknown amount of data centers got retrofitted to fit Blackwell GPUs. But I also refuse to believe that even half of the three million Blackwell GPUs that got shipped have actually been installed. Where would they go? You can’t use the same racks for them that you would with an H100 or H200, because Blackwell requires so much god damn cooling. Another sign that these things aren’t actually getting installed is Supermicro’s $1.4 billion or so of B200 GPUs left in inventory from a canceled order from Oracle .  Why not? Isn’t this meant to be a chip that’s extremely valuable? Isn’t there infinite demand? Is there not a place to put them? Apparently Oracle wanted to use faster GB200 GPUs from Dell , but why aren’t there other customers lining up to buy these things?  Also… how was Oracle able to cancel an order of over a billion dollars’ worth of GPUs?  Can anybody do that? Because if they can, one has to wonder if this doesn’t start happening as people realize these data centers aren’t getting built. Pick a data center. It’s probably barely under construction, or if it’s “finished” it’s actually “partly done” with no real guide as to when the rest will finish.  Remember that $17 billion deal with Microsoft and Nebius signed ? The one that’s a key reason why Nebius’ stock is on a tear? Well, its existence is based on the continued construction of a data center out in Vineland, New Jersey facing massive local opposition, and multiple sources now confirm that construction has been halted due to local planning issues. The data center is horribly behind schedule already, and Microsoft has the option to cancel its entire contract if Nebius fails to meet milestones . That data center is a major reason that people value Nebius’ stock! It cannot make a dollar of revenue without its existence! It has the funds and blessing of Redmond’s finest — the Mandate of Heaven! — and it can’t get things done! This is bad, and indicative of a larger problem in the industry — that it’s really difficult to build data centers, and for the most part, they’re not being fully built! You’ve heard plenty about data centers getting opposed and canceled — how about ones that fully opened? No, really, if you’ve heard about them please get in touch, because it’s really difficult to find them. Why don’t we know? This is apparently the single most important technology movement since whatever the last justification somebody made up was, shouldn’t we have a tangible grasp? Because the way I see it, if these things aren’t coming online at the rate that people think, we have to start asking for fundamental clarity from NVIDIA about where the GPUs are, and when they’re coming online.  NVIDIA’s continually-growing valuation is based on the conceit that there is always more demand for GPUs, and perhaps that’s true, but if this demand is based on functionally selling chips two years in advance. That makes NVIDIA’s yearly upgrade cadence utterly deranged. Buy today’s GPUs! They’re the best, for now, at least. By the time you plug them in they’re gonna be old and nasty. But don’t worry, it’ll take two years for you to install the next one too! To be clear, Blackwell GPUs are absolutely being installed! But three million of them?  People love to use “enough to power two cities” to illustrate these points, but I actually think it’s better to illustrate in real data center terms.  Stargate Abilene has taken two years to build two buildings of around 103MW of critical IT load. 3 million B200 GPUs works out to about 3.6GW of IT load. Do you really think that nearly thirty five Stargate Abilene-scale buildings were built in 2025? If so, where are they, exactly? You may argue that other data centers are smaller, and thus it would be easier to build. So why can’t I find any examples of where they’ve done so?  By all means prove me wrong! It’s so easy! Just show me a data center announced or that broke ground in 2023 and find obvious proof it turned on. I’ll even give you credit if it’s partially open! The problem is that I keep finding examples of “partially complete” and those are the only examples of “finished” data centers.  Isn’t this a little insane? This is all we’ve heard about for years, everybody is ACTING like these things exist at a scale that I’m not sure is actually true!  I expect a fair amount of huffing and “well of course they’re coming online” from the peanut gallery, but come on guys, isn’t this all kind of weird? Even if you want to marry Sandisk and name your children “Western” and “Digital,” why can’t you say with your whole chest several data centers that got finished? We have macro level “proof” but when you try and look at even a shred of the micro you find a bunch of guys with their hands on their hips saying “sorry mate that’ll be another $4 million.”  Something doesn’t line up, and it’s exactly the kind of misalignment that happens in a bubble — when infrastructural reality disconnects from the financials. NVIDIA is making hundreds of billions of dollars and it’s unclear how much of it is from GPUs installed in operational data centers. It feels like Jensen Huang might have run the largest preorder campaign of all time.  This has massive downstream consequences. Sandisk, Samsung, SK Hynix, Broadcom, AMD, Microsoft, Google, Oracle, and Amazon’s remaining performance obligations total [find] and are dependent on being *able* to sell gigawatts worth of computing gear or compute access. If data centers are not getting built in anything approaching a reasonable timeline, that makes the future of these companies only as viable as the construction projects themselves. Even if you truly believe Anthropic will be a $2 trillion company and a $200 billion customer of Google, the compute capacity has to exist to be bought, and it does not appear to be built or, in many cases, anywhere further than the earliest stages of construction.  If they don’t get built in the next few years, there’s no space for that solid state storage or those instinct GPUs. There’s no reason for NVIDIA to have reserved most of TSMC’s capacity , either. There’s also no reason to get excited about Bloom Energy, as it’s not making real revenue on those until Oracle finishes its data centers sometime between the next two years and never .  And if they don’t get built, hundreds of billions of dollars have been wasted, with large swaths of those billions funded by private credit, which in turn is funded by pensions, retirements and insurance funds . I’ve got a bad feeling about this.  Microsoft claims to have brought around 4GW of data center capacity online in the last two years, but it’s unclear how much actually got built. In an analysis of all announced groundbreakings and land acquisitions, it appears that Microsoft has only finished the first phase of its Atlanta and Wisconsin data centers.  It is unclear where this capacity could be. When Mr. Nadella said on his most-recent earnings call that Microsoft had (and I quote) "added another gigawatt of capacity this quarter," did he mean active, revenue-generating capacity?  In the event he did not, what did he mean? How much active, revenue-generating capacity has Microsoft brought online in FY2026 so far? Outside of Fairwater Wisconsin and Atlanta, where has that capacity been built?  Microsoft’s latest update on the Hickory/Stover site is that it “will” begin “initial site setup and earthwork activities” as of February 2026, and it appears the contractor has changed from Ames Construction to Clayco. The latest Microsoft update on the Boyd Farms site is that it started construction on April 1, 2024. A February 2026 piece from the Charlotte Observer claimed it had started construction again after a 10 month (!) delay. The latest Microsoft update on the Lyle Creek site — which it adds began construction in March 2024 — is that its contractor, Whiting-Turner, “will begin initial site preparation once weather conditions allow” as of February 2026.  A press release from a Canadian satellite firm from February 2026 said that it had “identified renewed construction activity at all three of Microsoft’s permitted data center campuses in Catawba County North Carolina.” Novva’s 60MW data center in Reno, Nevada. Announced in May 2023, operational as of July 2025 , or around 26 months. Edged Energy’s 36MW Phoenix, Arizona data center that broke ground in August 2024 and opened in April 2026 , or around 20 months. Duos Edge AI’s 450KW (lol) data center in Corpus Christi, Texas that was announced in July 2025 and opened in May 2026 , or around 10 months. Edge Energy’s 24MW, Columbus, Ohio-based data center that broke ground in August 2024 and opened in September 2025 , or around 13 months. American Tower’s 1MW (scalable to 4MW!) Raleigh, North Carolina data center that broke ground in June 2024 and came online in May 2025 , or around 11 months. EdgeCore’s 36MW Santa Clara, California data center campus that broke ground in January 2023, said it would be “energized in Q1 2024,” and opened in September 2025 , or around 32 months . Edged Energy’s “180MW” data center in Atlanta broke ground in July 2023 , and around 33 months later in April 2026 ,  it managed to top off a single 42MW building . EdgeCore’s two-building, 216MW campus that broke ground in August 2023 with plans to complete “as early as late 2025” is, as of March 2026, still under construction. Edged Energy broke ground on a 100MW data center in Aurora, Illinois in May 2023 , and has, as of February 2025, successfully opened (per DataCenterDynamics) “phase 1” — 24MW of capacity — but in its own press release from the same day referred to it as 96MW , choosing not to refer to any phases or separate buildings, something it has done since before the 24MW phase was complete.  CyrusOne’s 40MW Aurora, Illinois data center broke ground in October 2024 , which was apparently so significant that CyrusOne would announce that it had broken ground a second time on January 28 2025 . Confusingly, CyrusOne has another campus it’s linking to the Bilter Road one on Diehl Road, which may or may not be the same one, and as of May 2026 is still very much under construction . As of March 2026, locals were still opposing the data centers , slowing down the process further. Vantage’s “192MW” OH1 data center in New Albany Ohio broke ground in October 2024 , with its first phase to be due live sometime in 2025. As of August 2025, Vantage had topped off the second building , and per its own website about OH1 , the first building was meant to be operational in December 2025, but it’s unclear whether it actually opened. PowerHouse’s 65MW data center campus in Reno, Nevada broke ground in October 2024 , and its website states that “delivery” will happen in April 2026, with “construction/delivery” due “Q3 2024 to Q2 2026.” Oppidan’s Carol Stream, Illinois data center broke ground in November 2024 , with the “first phase” due live in 2026. Per Clearview, it is still “ planned .” Databank’s 20MW Ashburn, Virginia “IAD4” data center that broke ground in July 2024 was “set to go live in Q1 2026,” and as of May 2026 is still referred to in the future tense on Databank’s website . Aligned’s 96MW “NEO-01” Ohio-based data center that broke ground in May 2024 was “scheduled to be opened by end of this year” as of March 2026 . Aligned’s 72MW Hillsboro. Oregon data center campus broke ground in October 2023 , topped off the first building in July 2024 (Aligned also plans a separate building, too!), and as of May 2026, Cleanview still marks the first one as “planned.” Flexennial broke ground on a Denver-based 22.5MW data center in October 2024 , and as of April 8. 2026, a local Facebook group has said that it will be operational by January 2027 .   Flexennial, on the other hand, has been referring to it as “ the new build ” — in terms that make it sound like it was built — as far back as February 2025. If hyperscalers are truly not bringing on that much capacity, they cannot make those hundreds of billions of dollars from Anthropic and OpenAI. The current “AI compute demand is insatiable” narrative is utterly false , and a direct result of a lack of capacity coming online.

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Am I Meant To Be Impressed?

If you liked this piece, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large .  I just published a lengthy discussion about how OpenAI and Anthropic make up 70%+ of all AI GPU compute capacity and revenue . The previous week I wrote about how OpenAI will kill Oracle — and quite possibly Larry Ellison’s personal fortune, too . Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  God, it’s been a long few years, and only feels longer after every ecstatic, ridiculous round of tech earnings where the world’s largest companies do everything they can to obfuscate the ugly truth behind their numbers. Let’s start with the biggest, ugliest one: Microsoft, Google, Amazon, and Meta are expected to spend between $800 billion and $900 billion on AI capex in 2026, and over $1 trillion in 2027 . By the end of 2027, big tech will have sunk $2 trillion into AI capex, with very little to show for it. Oh, I know what you’re going to say. “These companies are growing faster than ever!” “These companies are building for future revenue streams!” “These companies are saying that AI is driving growth!”  Yet those revenues are, in the case of Meta and Google, not good enough to actually share.   While Google CEO Sundar Pichai will gladly say that “[Google’s] AI investments and full stack approach are lighting up every part of the business,” said “lighting up” never results in a revenue number that you can point at, because Google knows that analysts and journalists will read “Gemini Enterprise has great momentum with 40% quarter on quarter growth” — which we have no frame of reference for because Google doesn’t share its AI revenues — and clap and honk like fucking seals. Sundar Pichai knows that everybody is desperate to see him jingle his keys, and has such utter contempt for reporters, analysts, and investors that he doesn’t have to prove AI is actually doing anything. Those writing up his earnings will do it for him.  Meta, on the other hand, has little real AI story, and can’t even seem to get its metrics straight on what AI is doing for the company, per my premium piece from earlier in the week : Nevertheless, I have to give Microsoft and Amazon credit for deigning us worthy of actual numbers, even if they’re piss poor. While Meta and Google refuse to actually explain their AI returns, Microsoft revealed that it had $37 billion in AI revenue run rate — $3.08 billion a month or so — and Amazon had $15 billion, or around $1.25 billion a month . And I must be clear, that’s revenue, not profit. In any case, I need you to recognize how small these numbers are in comparison to the capex it’s taken to make them.  To give you some context, Amazon’s AI revenue run rate is roughly 0.419% of the $298 billion in capex it spent on AI capex so far, or around 25% of the $5 billion it just invested in Anthropic last week . Microsoft, on the other hand, has spent $293.8 billion on AI capex through its latest quarter — making its revenue run rate around 1.04% of its spend. These revenues are deeply embarrassing! I am not sure why this isn’t the common refrain! These fucknuts have spent over a trillion dollars on AI and all they have to show for it is either nothing , vague statements about “everything lifting because of AI,” or pathetic revenues that only get worse the more you think about them.  For example: even if Microsoft were to make $37 billion in AI revenue in 2026 — remember, that $37 billion run rate is a snapshot in time! — that would still be $500 million less than the $37.5 billion it spent in capital expenditures in the fourth quarter of 2025 .  Yet things actually get worse when you think about the sources of that revenue, or perhaps I should say source, as both Microsoft and Amazon (and I’d argue Google too, but we don’t know its AI revenues) are heavily-dependent on their large, unsustainable sons — Anthropic and OpenAI. I’ll explain. Microsoft claims that its $37 billion in AI revenue run rate has grown by 123% year-over-year, which means its run rate, not actual 2025 AI revenue, was about $16.59 billion in Q3 FY25, or around $1.38 billion a month or, if you assume that number is consistent over the quarter (it likely wasn’t), about $4.14 billion. Based on my own reporting from direct Azure revenue numbers, this would make OpenAI’s $2.947 billion in inference spend in that quarter around 71% ($11.7bn) of Microsoft’s Q3 FY2025 AI revenue run rate. That’s embarrassing!  Oh, and capital expenditures for that quarter were $21.4 billion , or around $4.81 billion more than its annualized revenue.  Yet my reporting helps us be a little more annoying than that. Back in January 2025 — around Microsoft’s Q2FY2025 earnings — it announced that its AI revenue run rate had hit $13 billion , or around $1.083 billion a month (or $3.25bn a quarter or so). In that same quarter, OpenAI had spent $2.075 billion on inference on Azure, or 63.8% of Microsoft’s AI run rate. This is particularly funny when you go back to the quarter before, where Microsoft CEO Satya Nadella low-balled that figure, claiming it would be $10 billion in annualized run rate, and specifically said the following : That’s…not really what happened. Today I can report, based on discussions with sources with direct knowledge of Azure revenue, that in Q2 FY2025, Microsoft brought in around $325.2 million in revenue via renting out GPUs and other AI infrastructure, and around $367 million in revenue from Microsoft 365 Copilot, or less than half of the $1.467 billion that OpenAI spent on inference.  If you’re curious, the next quarter (Q3FY2025), AI infrastructure brought in around $412 million, and Microsoft 365 brought around $300 million.  While my sourcing for Azure revenues cuts off at Q3 FY2025, my OpenAI inference and revenue share data goes out a further two quarters to Q4 FY2025 and Q1 FY2026 (so Q2 and Q3 of the calendar year 2025), as well as half of Q2FY2026, and we can make some fairly straightforward estimates as a result. So, based on my reporting, OpenAI spent $3.648 billion dollars on inference in the third quarter of 2025 on Microsoft Azure, or around $14.4 billion on an annualized basis.  While I only had half the fourth quarter’s numbers, I estimate that OpenAI’s annualized spend hit over $18.5 billion — or around $4.6 billion a quarter — by the end of the year, and that’s not accounting for things like Sora 2 or the launch of its Codex coding platform. In total, this puts its spend at an estimated $13 billion dollars on Azure just on inference, with billions more on training. Yet Microsoft Azure isn’t the only place that Microsoft gets fed revenue from OpenAI. Microsoft also accounted for 67% of CoreWeave’s 5.15 billion in 2025 revenue — or around $3.45 billion dollars — and as all of that is used by OpenAI. I also believe this is used for OpenAI’s training compute, as CoreWeave’s announcement related to its direct deal with OpenAI specifically said it was contracted “...to power the training of [OpenAI’s] most advanced next-generation models,” and said capacity was only available because Microsoft declined to extend its current agreement to use compute for OpenAI . All together, that puts OpenAI’s spend on Microsoft services at over $18 billion dollars in 2025, and it’s easy to see how that would grow to over $24 billion dollars on an annualized basis in the last quarter, or around $2 billion a month. Microsoft is OpenAI’s primary cloud provider, and I estimate that OpenAI represents around 70% of its AI revenue, while taking up the majority of its infrastructure. Otherwise, Microsoft’s 20 million Copilot 365 subscribers likely pay no more than $7 billion a year. I also think that OpenAI is taking up the lion’s share of compute. As I discussed in my most-recent premium newsletter , Epoch estimates that Microsoft had around 2GW of compute by the end of 2025, with OpenAI as its largest customer. At the end of 2025, OpenAI’s CFO said that it had access to 1.9GW in compute, at a time when its compute was entirely supported by Microsoft and CoreWeave (estimated to have 480MW of compute).  Considering that 67% of CoreWeave’s revenue came from Microsoft renting capacity for OpenAI , I also think that it’s fair to assume that 80% or more of Microsoft’s GPUs are taken up by OpenAI, though some might now be taken up by Anthropic, which agreed to spend $30 billion on Azure. I’ve also confirmed that Microsoft’s “Fairwater” data centers — which constitute (when finished) “ hundreds of thousands of GPUs ” — are entirely reserved for OpenAI.  Microsoft desperately wants you to think that this is a diverse, booming revenue stream, when in fact it’s spent around $293 billion in four years to make — when you remove OpenAI — less than $3 billion a quarter in revenue, not profit. Booooooo! Booooooo!!!!! As far as Amazon goes, things get a lot grimmer. As I mentioned earlier, in early April , per Reuters, Amazon’s Andy Jassy admitted that its “cloud business’ AI revenue run rate was more than $15 billion in the first quarter of 2026,” which translates to around $1.25 billion in monthly revenue, or roughly 0.419% of the $298.3 billion in capex it spent so far, or around 25% of the $5 billion it just invested in Anthropic two weeks ago .  I also think it’s reasonable to assume that a large part — if not the majority of — that revenue comes from Anthropic. Per my reporting last year , Anthropic spent $518.9 million on Amazon Web Services, at a time when it had around $7 billion in annualized revenue, a figure that’s increased by 500% (if you believe it) to $30 billion in annualized revenue since . $518.9 million is about $6.2 billion in annualized spend, and I think it’s fair to assume that its spend will have at least doubled to $12 billion in annualized revenue, or around 80% of Amazon’s AI revenue. As of the end of Q4 2025, Amazon had 1.67GW of capacity — and based on my estimates from my newsletter published April 21 , 500MW of that is taken up by Project Rainier, a data center dedicated entirely to Anthropic , which is also Amazon’s largest AI customer. I’d be confident in assuming that more than 75% of its capacity is taken up by Anthropic. And man, $1.25 billion a month is fucking pathetic. I’m sorry, how are any of you possibly impressed by this?  God, everyone loves to slurp down Sundar’s slop. You all fall for it! Sundar Pichai doesn’t respect you enough to tell you how much AI revenues Google makes, but because its current businesses continue to grow thanks to its tried and tested tactic of making shit harder to use so that Google services can show you more ads . Nevertheless, people are doing backflips over Google Cloud’s 63% in year-over-year revenue growth ($20.03 billion), and I have a few thoughts: One of the reasons that Google might not want to break out its AI revenues is that they’re — much like Amazon — heavily-inflated by Anthropic’s compute spend. Sadly, we have only a little information about Anthropic’s spend outside of its promise to use “up to one million TPUs, with over a gigawatt of capacity [coming] online in 2026” from the end of last year, and a month ago, when it said it would use “multiple gigawatts of next-generation TPU capacity…starting in 2027.”   Another guess might be to travel back in time to before Anthropic was a huge consumer of compute. In Q4 2023, Google Cloud sat at about $9.19 billion a quarter , and $11.96 billion in Q4 2024 (around 23% year-over-year, but a putrid 5% quarter-over-quarter from Q3 2024). By Q2 2025, it sat at $13.62 billion , and as I mentioned above, accelerated to $15.15 billion to $17.66 billion (14.2% quarter-over-quarter) to $20 billion (11.7% quarter-over-quarter) in the following three quarters. These periods match up exactly to Anthropic’s big jumps in revenue from Q2 2025 ( around $3 billion ARR ) to Q3 2025 ( around $7 billion ARR ) to Q4 2025 ( around $9 billion ARR ) to Q1 2026 ( around $19 billion ARR ), which suggests that Anthropic’s growth is what’s actually boosting Google Cloud. Yet things get weirder when you listen to Google’s most-recent earnings call : Interesting. Interesting. Google appears to be planning to sell its TPUs — its own custom silicon it currently uses only for its own services and some of Anthropic’s — to a non-specific amount of unnamed customers, to the point that its remaining performance obligations jumped from $242.8 billion to $467.8 billion in the space of a quarter.  Nevertheless, that’s a remarkable jump, especially when you try and work out who they sell to- oh wait, we actually know! Google also signed a multi-billion dollar deal to rent TPUs to Meta, per The Information , and is also discussing A) selling TPUs to Meta directly, and B) creating SPVs that will buy its own GPUs and lease them to others: This is exactly the same shit as NVIDIA is doing with xAI’s GPU-related financing last year . To explain, Google is creating something called a special purpose vehicle — a company with one purpose — that it then funds along with an investment firm. The SPV then raises cash via debt, which it then uses to buy TPUs directly from Google . Now, remember that Anthropic deal to use a million TPUs from last year? How about the deal with Broadcom (which makes TPUs for Google) and Google to use “multiple gigawatts” of TPUs starting in 2027? Well, Per CNBC, Anthropic agreed to buy $21 billion of Broadcom’s TPUs in 2026 and $42 billion in 2027 . Where will those TPUs go? Google’s data centers, probably the ones that it’s backstopping, per my premium from the beginning of the week : It’s a pretty sweet deal for Google! Google pays Broadcom to develop TPUs, Anthropic pays Google to buy those TPUs once Broadcom builds them, Google installs those TPUs in a data center, and then Anthropic pays Google to rent them back.  This isn’t real demand! Boo!!!!!! BOOOOOO!!!!!! So, for the sake of transparency, I wrote the above before The Information published its story about how Anthropic had committed to spend $200 billion on Google Cloud and TPU chips, which contained this very important detail: The Information’s story also had this fascinating chart showing that around 50% of Amazon, Google and Microsoft’s backlog (which includes all revenues not just AI) — a staggering amount — is made up of revenue from OpenAI and Anthropic: To be clear, I also wrote the below before this chart ran, because it was very fucking obvious when you actually looked at the numbers .  Anyway, as I said in my last premium newsletter: As I’ve explained, most AI revenues out of Google, Microsoft and Amazon come from two companies that lose billions of dollars a year, have no path to profitability, and are only able to keep paying these companies because the companies (and investors) keep feeding them money. These relationships are utterly poisonous, and an intentional attempt to deceive investors and the general public.  Google now plans to invest up to $43 billion in Anthropic, a company that I estimate takes up at least half of its 2.95GW of capacity, which has cost it around $211 billion in capex since 2023. Amazon has already invested $13 billion and as much as another $20 billion more in Anthropic, and announced its latest round with a statement about how Anthropic will use up to 5GW of compute capacity . While dimwits might read this and say “WOW, AMAZON JUST LOCKED UP TONS OF FUTURE REVENUE,” it’s important to remember that Anthropic plans to lose $11 billion a year both in 2026 and 2027, and that’s based on its own internal (and fanciful) projections!   Let me spell it out in a way that boosters can understand, in the style of Gillam Fitness : Anthropic not have money to pay big cloud bills, because Anthropic company cost lots of money, more money than Anthropic make! So Anthropic only PAY cloud bills if OTHERS give it money! Amazon GIVE MONEY to Anthropic to GIVE BACK TO AMAZON, which mean no profit! And Amazon not give Anthropic enough money to pay it, so Anthropic have to ask OTHERS for money! That BAD! It mean BUSINESS not STABLE, and CLIENT not STABLE.  This bad when client MOST OF AI MONEY! This ALSO mean that Anthropic RELIANT on OTHERS to pay AMAZON, which make AMAZON dependent on VENTURE CAPITAL for FUTURE REVENUE! Amazon SAY it have BIG BUSINESS, but BIG BUSINESS dependent on ANTHROPIC, which mean BIG BUSINESS dependent on VENTURE CAPITAL! This SAME for GOOGLE! Both say they have BIG CLIENT, but BIG CLIENT MONEY not supported by REVENUE, so BIG CLIENT actually mean “HOW MUCH VENTURE CAPITAL MONEY ANTHROPIC HAVE.”  This bad business!  And it really, really is .  Most of Amazon, Google and Microsoft’s capex is being driven into capacity mostly used by OpenAI and Anthropic, neither of whom have the money to pay without continual infusions of more capital. Only Microsoft was smart enough to realize the problem, which is why it allowed Oracle to take over the majority of OpenAI’s future capacity ( which may kill Oracle, by the way! ), but both Google and Amazon keep feeding Anthropic money so that Anthropic can feed it right back to them.  I’m going to try and speak simply again, because I’m still not sure people get this. The only solution to this problem is if either Anthropic or OpenAI can somehow find a way to become profitable, something that I have yet to see any proof is possible.  In fact, the only proof I can find is that these fucking companies are more unprofitable than ever — in the last month, Anthropic raised $10 billion from Google , $5 billion from Amazon , and is reportedly trying to raise another $50 billion from investors , less than three months after it raised $30 billion on February 12, 2026, which was five months after it raised $13 billion in September 2025 . That’s $58 billion in eight months, with the potential to raise it to $108 billion. I’m gonna be honest, I think Anthropic is outright misleading its investors if it’s saying that it will only burn $11 billion in 2026 and 2027, per The Information : If that were the case, why does Anthropic need to raise one hundred and eight billion fucking dollars in less than three quarters?   Time to make up some booster talking points and get mad at them: So, SemiAnalysis — which traditionally does not wheel and deal in revenues! — randomly said that Anthropic had hit $44 billion in ARR , or around $3.08 billion in monthly revenue and…I’m sorry, what?  I know that my suspicion of Anthropic’s revenue numbers has effectively become a meme by this point, but something about this doesn’t add up. If we cut the periods down to strictly those after March 9, that means that Anthropic brought somewhere between somewhere between $4.5 billion and $5.58 billion in less than two months , or roughly its entire lifetime revenue. This was also a period where Anthropic claimed it was facing capacity shortages , but said shortages only appeared to create performance issues for its current customers rather than stopping Anthropic from making money… …which makes me wonder what all of this “capacity” talk is actually about.  If Anthropic is truly facing a “capacity crunch,” it’s choosing to solve said crunch through sheer, unbridled greed, taking on more customers as it struggles to keep its services at above two nines of availability . If it were an ethical business, it would simply stop taking on new clients, much like GitHub Copilot did as it transitions to token-based billing . Nevertheless, its capacity issues also make me wonder whether it’s actually taking on all that revenue, and if so, where it’s actually coming from.  Per Newcomer , as of the end of last year, 85% of Anthropic’s revenue came from API calls from companies or individuals using their models to power services. This would mean that there was roughly — assuming that number is down to around 70% given the ascent of Claude subscriptions — $3.5 billion of API spend in the space of two months, or a few thousand trillion tokens’ worth of spend. For some context, Meta’s “token-maxing” fiasco from the beginning of April involved it burning around 60 trillion tokens in 30 days, but based on discussions with sources familiar with Meta’s spend, 80% of that was cache reads. The Information estimates that the actual cost in that period was around $330 million, meaning that Anthropic needs at least another five — if not ten — Meta-sized customers, or such incredible dispersed demand that has effectively appeared out of nowhere in the past three months , to possibly come close to those numbers. I personally think it’s because Anthropic is doing something peculiar with its annualized revenue calculations. Per The Information : The first and most-obvious place to game the numbers is that Anthropic chooses a single day’s active subscribers to anchor to its annualized revenues, which means it can preferentially select one where, say, a bunch of new people were signed up under a trial, or avoid a day where churn had users leaving. One could easily include those who are canceled but have yet to actually leave the service — such as somebody who canceled on April 7th but would still be on as a “paid” subscriber until May 8th — too. As far as API credits go, it’s easy to manipulate a four-week-long segment based on how Anthropic bills its enterprise customers, specifically self-service enterprise deals . In this case, Anthropic customers pre-pay a sum (say, $50 million) in credits that are billed based on their teams’ usage, and don’t expire or run out unless they’re actively consumed. Anthropic could very, very easily manipulate this by — instead of booking based on an enterprise’s actual token burn — saying “we just got $50 million in API revenue in a calendar month!” even though that $50 million might take months to actually use. To be fair, there are also other customers (referred to as “sales-assisted”) that are billed in arrears for their consumption. It’s unclear what the split is, and Anthropic doesn’t have to tell you. Remember: Anthropic is a private company! It can do all the non-GAAP bullshit it likes.  I keep hearing about how Anthropic is capacity-strained and all that shit, but I don’t hear any explanations as to how it fixes that problem, or what that problem actually means for the business itself. Somehow being “capacity constrained” has led to the company making more revenue, which makes me wonder whether it’s a “constraint” so much as “a company running as shitty a service as it can while billing as much as possible.” Either way, it’s unclear how many data centers are actually getting built, or indeed how long they’re taking to build. What does Anthropic do if it’s 12-18 months away? And really, why do these capacity constraints not seem to have any effect on its revenue growth? I ask because Sundar Pichai noted on Google’s most-recent earnings call that Google Cloud would’ve made more revenue had it had the capacity to meet demand. Why is Google revenue-constrained due to capacity but not Anthropic? While there’s a compelling argument to be made that Anthropic was the customer that would’ve bought that compute, I think we deserve an actual explanation of what Anthropic needs more compute for if it’s not “to make more money.” Also, if it’s currently making as much money as it likes with its current capacity constraints, wouldn’t getting more compute…make the numbers worse? Ah, fuck it, let’s move onto something funnier. Meta is probably the funniest company in the AI bubble, in the sense that it does not appear to have anything approaching an AI strategy beyond “build as much data center capacity as possible” and “ lose $4 billion a quarter selling pervert glasses .” I realize I sound a little dismissive, but nobody can actually explain to me what Meta is doing with AI in a way that remotely justifies it burning $158.25 billion in capex since 2023, with plans to spend as much as $145 billion in 2026 alone . Oh, Meta’s AI app was high in the app store charts? Who fuckin’ cares! Who gives a shit! Oh, it launched its own closed-source “Muse Spark” model ? What am I meant to be impressed about? That over $150 billion has resulted in a model that ranks #27 on the LLM leaderboards in coding ? Now, some of you — including people I respect so much I’m not going to mention them by name! — appear to believe that Meta has some super-secret way of using all these GPUs to make “more money from ads,” and I must be clear that Meta has yet to explain that that’s the case.  Per last premium : You’ll note that these conversion numbers aren’t connected to any financials , which makes them a little suspicious, as 99% of Meta’s advertising revenue is ads, and “more conversions” should be fairly easy to peg to “more money”...unless said conversions aren’t actually converting into revenue for Meta’s advertisers. What does a “conversion” mean, in this case? Are these CPA ads that reward Meta on a clickthrough? Or CPM ones that pay per thousand impressions that just happen to result in a click?  Again, these are ads, which means that it’d be very easy to take that “conversion” number and turn it into “made $X,” unless of course said amount is pathetically small in the grand scheme of things. Seriously though, what is Meta doing? I suppose it doesn’t matter when the Wall Street Journal will breathlessly write that ( and I quote ) Meta is envisioning “supersmart agents” and the following lede that I find to be one of the more-revolting things I’ve read about a hyperscaler recently: You may be wondering what the “glimpse” was, and it was “laying off 8000 people” and “grading employees in performance reviews on their AI use” and “making a CEO chatbot for Mark Zuckerberg to talk to.”This is an ugly, wasteful, distressed company that has no idea what to do anymore, run by a mad king who literally cannot be fired , and those who are charged with scrutinizing it will write entirely imaginary comments like “wow, Mark Zuckerberg is building supersmart agents!” without a second’s thought. The magical hysteria of the AI bubble is such that Meta, Microsoft, Google and Amazon are, despite proving no actual profit from their AI investments, effectively protected by most of the media, investors and analysts. To be clear, I don’t think any of these companies die as a result of the bubble bursting, but I’m sick and tired of hearing everybody cover their asses with the same tired and hollow talking points, so I’ve pulled together a few of them: So, while this is technically true — as I said, these companies will not die as a result of the bubble bursting — any investor (or person who wants to deal in “the truth” rather than “stuff they misread or misremembered”) should be deeply concerned that they’ve sunk around a trillion dollars into AI capex, and all they’ve done is incubate two large, unprofitable companies that have become a burden on their infrastructure, and revenue streams that they either refuse to disclose or are both incredibly-centralized and proportionately embarrassing. Let’s get specific: 2023, Microsoft, Google, Amazon, and Meta have spent a little over $850 billion in capex, mostly hoarding NVIDIA GPUs that will be near-to-completely obsolete by 2030.  With these GPUs comes a massive depreciation problem, as I discussed a few months ago in my time bomb premium newsletter . Every quarter, more GPUs come online, which grows the “depreciation” line on the income statement, steadily growing every quarter to the point that the Wall Street Journal projects that it will eat as much as 58% of Meta’s, 40% of Microsoft’s, and 38% of Google’s net income by 2030. This flows neatly into my next point. Well, let’s be clear: whatever growth these businesses currently have is being eaten by depreciation. Last quarter, Google had $6.48 billion, Amazon $18.94 billion, Microsoft $10.1 billion, and Meta $5.9 billion, numbers that sometimes oscillate slightly down but have, year-over-year, grown by billions of dollars. And yes, year-over-year is appropriate here because this is a balance that has been steadily growing for years. In any case, depending on the company, that “growth” is either “barely related” or “entirely unrelated” to AI.  Remember: Microsoft and Amazon are intentionally obfuscating their AI revenues by using “annualized” — a term they refuse to define that usually refers to a monthly figure times 12 — to define something in statements related to quarterly revenue. As a result, it’s impossible to precisely backtrack how much revenue they made. In fact, that’s probably the simplest response here: if these companies were truly growing as a result of AI, they’d tell you. They’d say “AI revenue was X.” They’d say it in blunt, obvious terms. No annualized revenues, no projections, no fluff, no “AI-influenced,” just a line item that said “AI:” or even a segment, such as “Microsoft Azure AI compute.’ I also want to be clear about something else: I know, from documents viewed by this publication, that Microsoft has these line items fully itemized, and could share them if it wanted to, but intentionally chooses not to. These companies are deliberately refusing to share their AI revenues: and it’s time for the tech and business media to begin asking them why! So much that neither Google nor Meta will tell you how much! Also, three years in, nearly a trillion dollars, and two companies have dedicated nearly their entire sales operation to pushing it, and the best they’ve got is annualized revenues and no segment breakdown.  “Oh, Microsoft has 20 million paying Copilot subscribers,” $600 million a month? For a company that makes $80 billion a quarter? That's a pathetic amount of money. You could raise more money by auctioning dogs ! I need you, please, god , to start actually using basic mathematics! Microsoft has spent $293 billion on this bullshit, and spent another $30 billion or so in the last quarter on capex! When does this pay off? As I said above,  Amazon Web Services was profitable in a decade and cost about $52 billion between 2003 and 2017, and that’s normalized for inflation ! Anyone making this point is either intentionally lying to you or incredibly ignorant. I have done the work to prove this point, and will continue to repeat it until those too incurious or deceptive learn to stop doing so.  When?  Wwwwhen????? Whheeeennnnnn?????????????? I’m serious, when? And how??? Not that they would, but in a scenario where Meta, Amazon, Google and Microsoft stopped spending capex on AI next quarter, they would have to make somewhere in the region of $2 trillion in brand new revenue — all while other services continued to grow — to make any of this capex worth it. Please, explain to me how that happens when it’s taken three years and nearly three hundred billion fucking dollars for Microsoft to squirt out maybe three billion dollars in revenue (not profit), with most of that coming from OpenAI! Please, somebody, anybody explain! You can’t!  But you know what, let’s try! As The Information said, around 50% of all remaining performance obligations, as in all (NOT JUST AI) of the upcoming revenue for Microsoft, Meta and Amazon , is from either OpenAI or Anthropic. Put another way, 50% of big tech’s upcoming revenues are dependent on two companies, neither of which can afford to pay them, meaning that 50% of Meta, Amazon and Google’s revenues will either come from their own venture investments or venture capital. This is not what stable or diverse revenue looks like, and suggests my grander thesis about AI demand is true . Outside of OpenAI and Anthropic, there’s barely any actual demand for AI services or AI compute at the scale necessary to substantiate a trillion or more in capital expenditures. Yet the most-disgraceful part is the sheer contempt that these companies have for investors, the media, and the general public. In a functioning regulatory environment — or a market run by people with object permanence — it would be impossible to add such large amounts to your RPO balance without active scrutiny and analyst markdowns based on the fact that Anthropic and OpenAI can literally not afford to pay these bills at this time. Microsoft, Amazon and Google have scooted along for years on the idea that they’re diverse, well-positioned companies that can build massive AI revenue streams. In reality, they’re the paypigs for Anthropic and OpenAI, providing more than 70% of their compute as a means of artificially inflating their AI revenues, knowing that analysts and the media will nod and smile without a single thought. In fact, fuck it, I’m ending this with a rant. The story of massive AI demand is a lie — a trillion dollars annihilated to create the largest circle jerk of all time.  Venture capitalists and hyperscalers feed money to OpenAI and Anthropic, so that venture capitalists can feed money to startups to feed to Anthropic and OpenAI, so that Anthropic and OpenAI can feed that money back to hyperscalers, who then feed that money to NVIDIA and buy more GPUs.  While it might seem tempting to credit these men as geniuses for creating companies specifically to feed them revenue, but to keep up the kayfabe of “doing AI” to substantiate this buildout means that they’ve had to massively overcommit to the bit, even though the only two meaningful businesses in AI appear to be Anthropic and OpenAI, and that’s only because they’re effectively intellectual honeypots for the entire industry.  Outside of those two, the only other competitive AI businesses are those of Amazon, Microsoft and Google — two of which now have annualized AI revenues of around 6% of their capital expenditures so far.  Google’s AI business is so booming that it refuses to break it out, and while it nebulously claims “AI is creating growth,” it’s not really clear how, and it’s vague about it because analysts and the media are ready to swallow the narrative as long as number go up .  That’s why Google doesn’t break out the number, by the way! That’s why Sundar Pichai is able to bullshit his way through every earnings call, because the media and analysts are ready to fill in the gaps in the most preferential way possible.   Amazon and Microsoft had their hands forced by the markets after their stocks stumbled, and fucked up by sharing their AI revenues. Amazon’s $298.3 billion in capex has successfully created a business that, more than a quarter of a way to a trillion, has successfully managed to make $1.25 billion dollars a month.  That’s fucking pathetic! If we had analysts with IQs above room temperature they’d run Andy Jassy out of Arlington like Shrek.  Let’s look at this fucking chart again :  Unbe-fucking-lievable! Anthropic and OpenAI have now committed to over $718 billion of Microsoft, Amazon and Google’s revenues, despite the fact that neither of them can actually afford to pay for it. The market’s response? A slight (and short-lived) after-hours lift .  Dear members of the media: these companies are laughing at you. They know you are going to cover this in a way that makes them look good. They know you’re going to use this as proof that they’re “doing well in AI,” despite the fact that the majority of their future revenue is tied up in two oafish failsons, one of which (OpenAI) plans to burn $50 billion on compute in 2026 alone . I realize that it’s a lot to ask people to think about things in negative terms, but things are getting a little ridiculous. These are loadbearing failsons with dysfunctional businesses! It’s very clear both of them are doing weird things with their annualized revenues, and even clearer that there’s no path to profitability! Sadly, asking the media or analysts to act rationally or apply any real scrutiny is a joke, because  this is the AI bubble , where everybody is wrong because once everybody admits what’s actually happening they’re going to have to admit they’ve all sounded insane for years. $1.25 billion a month! Andy Jassy should be ashamed of himself! And god, fuck Microsoft too.  I’m sorry, WOW, Satya! You managed to get up to twenty million paying Microsoft 365 Copilot subscriptions — $600 million a month in revenue, not profit! — and all it took was you investing $13 billion dollars in money to OpenAI, forcing Large Language Models into every one of your products in a way that borders on harassment and about $289 billion dollars in capex, as well as laying off thousands of people and savaging the Xbox brand .  Whoopdie fucking shit man! You should be ashamed of yourself. Amy Hood should lock you out of the building. She should turn off your keycard and disconnect your keyboard.  OpenAI is, in and of itself, a kind of psychosis generator.  It was the first thing in a long time that felt like a new thing since the iPhone for the people that entirely obsess over growth.  It was the panacea for the tech industry, creating a new way for Business Idiots to spend money on infrastructure, a new thing for consultants to scam people with , a new series of things to be an expert in , all wrapped up in something that could also be both a consumer product, an enterprise software product, and a new kind of API to attach to other enterprise software to.  In theory, OpenAI’s success would lift everything at once — hardware, software, and even adjacent fields, like services. It promised to both democratize access to creating software while also heavily reinforcing existing power structures to the point that every dollar inevitably ended up in the Magnificent Seven’s pocket. It only succeeded in the latter. The problem is that the system needed to work one day. It needed to eventually make more money than it cost. Every single one of these companies is talking about AI non-stop, and not one of them can show a profit. The only thing they can do is tell lies of omission by saying “AI helped boost everything,” and when you ask for specifics, the results are either tepid or so secretive you’d think they’re hiding a dead body. The only reason Google, Amazon and Microsoft are being tolerated at their current excess is because their non-AI segments continue to grow through endless price-increases and enshittification, and its external business units — by which I mean OpenAI and Anthropic — are yet to die.  Sorry, I just don’t know what Meta is doing. I don’t think Meta knows what Meta is doing. Every so often it buries a fact in one of its blogs about how it saw a 3% increase in something related to AI, then it promises to burn $170 billion dollars and it’s unclear why. It also lost another $4 billion dollars on Reality Labs by the way ! There should be a legitimate inquiry into where this money is going. Eighty six billion dollars and all we have is the metaverse and pervert glasses?  Meanwhile, SpaceX is rushing to have the strangest and largest IPO of all time, all as daily stories leak about billions of dollars of losses and whatever the fuck that deal with Cursor is .  Apparently SpaceX will buy it for $60 billion dollars or pay it $10 billion dollars.  I think what actually happens is the third thing: SpaceX funds Cursor for a bit, there’s a falling out between Musk and CEO Michael Truell, and the company either rushes an acquisition or dies. Remember: Elon killed Cursor’s funding round ! He can’t buy it before SpaceX goes public !  Elon Musk took fucking OpenAI to court. Do you think he’ll care about killing Cursor? Who’s going to be left to sue him? Anyway, that OpenAI/Musk suit is a real Alien Versus Predator situation, and if I’m honest I’ve found whole thing a little boring, a duo of dullards shoulder-barging each other to see who can run a company that neither of them can really describe because neither of them do anything other than pontificate and take credit for other people’s work.  If this breaks OpenAI I’ll be very surprised, but if it does it would be extremely fitting that Elon would accidentally destroy the AI industry, like Mr. Bean sitting on a button that launches a nuke. If I’m wrong here it would be very funny. I’m just not giving it much hope. Nevertheless, this entire industry is only made possible by the kayfabe circular economy of taking every single sign as good for AI and ignoring every possible glaring warning sign in the hopes that they’ll go away.  You know, like last week when Microsoft said it’s shifting GitHub Copilot to token-based billing — something I reported a week before everybody else.  This is effectively killing the product as they know it, and invalidates every single story about its revenue growth ever written. To give you some context about its scale, GitHub copilot is the second largest customer of Anthropic’s models , and was only that large because it was subsidizing the computer spend of its customers. Why? Because that’s the only way to build any kind of AI business.  Google and Amazon realize their AI revenues are contingent on the continued survival of Anthropic, and Amazon and Microsoft’s revenues are contingent on OpenAI AND Anthropic.  They know that if these companies die they’re going to lose billions of dollars of revenue, but that they also have to compete with them for fear that they’ll be seen as “falling behind” their horrible progeny. As a result, they’re incinerating their brands and endlessly pontificating about the power or AI while spending nearly a trillion dollars on capex almost entirely to make sure their competition, which is also their customer and welfare recipient, doesn’t die. It’s a mess, and a mistake, and eventually one of them is going to grow tired of it. Microsoft was already billions under the analyst estimates for capex. They’re moving to token based billing. They claimed to invest in Anthropic in February but didn’t mention it in their earnings in any way, shape or form.  At some point these fucknuts are going to be forced to reckon with what they’re doing.  Until then we’ll have increasingly more frenzied and ejaculatory statements about AI demand that fail to match with reality.  I truly think that it’s going to be like this if not crazier until one day when the music suddenly stops. Somebody is going to blink. Somebody is going to take a step back and give everybody else permission to stop too.  Maybe Perplexity, Lovable, Replit, or Cognition dies.  Maybe Microsoft shifting GitHub Copilot to token based billing in June first inspires others like Anthropic to follow suit.  Maybe AI token austerity begins at Microsoft, Meta, or another large company.  Maybe NVIDIA fails to inspire in just the right way, or the fact that data centers are not opening fast enough to have fully digested the last year’s GPUs finally catches up with the economic mismatch that Jensen Huang always beats and raises expectations.  And that really is the strangest thing.   At the current rate of sales, it’s taking six months to install a quarter’s GPUs . At this point it’s obvious that there are warehouses of these things. It just isn’t obvious whether they’re in ones owned by hyperscalers or the Taiwanese ODMs (original design manufacturers) like Quanta Computing and Foxconn that build their servers.  None of this makes sense.  It hasn’t from the beginning. It’s the largest bubble in history, and has reached such an intellectual and financial scale that many have taken sides on it in a way that will be completely impossible to walk back if they’re wrong.  As things deteriorate, expect them to cling to their mythologies tighter and become more agitated.  And really, we’ve never seen anything like this in our lives.  You realize that Anthropic and OpenAI are insane, right? These companies have promised $718 billion to Microsoft, Google and Amazon, and cannot survive without venture capital funding , because their underlying businesses lose money on every transaction — and so help me fucking GOD if you say they’re “profitable on inference” without proof I will crush you into a cube like a car in a garbage dump! Every single AI business you see is unprofitable, nor do any of them have a path to break-even, let alone sustainability. Nothing has changed about this story. And nobody has been able to explain the massive differences between my reporting on OpenAI’s revenues and their own leaked figures, other than to say “you must be wrong somehow,” as if that somehow invalidates “direct numbers from Azure billing.” If you disagree with me, you really better hope I’m wrong, because I’ve got years of receipts and I can remember basically every article about AI revenues written since 2023 off the top of my head. Not a single one of my critics or any AI booster has put an iota of the same amount of effort into proving their case. The hysteria and excess of this era has proven how many people can come to conclusions without making the effort to prove them. Disagree with me or not, I’ve done the work, and I see no proof that the other side has even started. The world has been swept away by the fantastical ideals of Sam Altman and Dario Amodei, and two giant, unsustainable, cash-burning monstrosities that were only made possible because hyperscalers built their infrastructure for them and funded their excesses in exchange for theoretical revenues and equity stakes that give them paper gains. Their hope, I imagine, was that in doing so, OpenAI and Anthropic would create industries surrounding them — both in the business lines attached to hyperscalers and AI startups that would potentially pay them for compute. In the end, it appears the only way to create any real demand was to literally fund it themselves.  These men believe they’ve created perpetual energy. What they’ve actually done is shit their pants and set their houses on fire. “Year-over-year” is an attempt to obfuscate actual growth in the era of AI. A better comparison would be quarter-over-quarter, which was 12% from Q4 2025 ($17.66 billion). This is actually significant, because it’s a slower rate of growth than between Q3 and Q4 2025, when cloud revenue jumped from $15.15 billion to $17.66 billion, or 14.2% quarter-over-quarter).  I think quarter-over-quarter growth is far more indicative of how a business is going.  Google Cloud is far more than AI! It includes all of Google’s workspace revenue, such as Gmail, Google Docs, and so on. It’s important to remember that Google jacked up its workspace pricing twice in 2025 , and that by Q1 2026, the majority of customers will have been forced to renew at inflated prices. It also includes all of Google’s cloud revenue, which is incredibly diverse and far more than just AI compute. Google has intentionally bucketed AI-related revenue into Google Cloud so that finance and tech journalists will claim that AI is what’s driving this growth despite there being no proof that that’s the case. Anthropic and OpenAI make up the vast majority of all AI revenues and compute capacity. I estimate 70% of all revenues and capacity demand, if not higher. Amazon, Google, and Microsoft’s AI revenues — and by extension their justification for future capex spend — are justified by Anthropic and OpenAI. OpenAI and Anthropic both lose tens of billions of dollars a year (yes, Anthropic said it’ll lose $11 billion in a projection, and I believe they are being coy with their actual losses), which means that the majority of AI revenue and compute demand is dependent on whether Anthropic and OpenAI can continue to raise money. Well actually Ed this is because Anthropic is taking advantage of the dumb money that wants to boost its valuation. It doesn’t need the cash — it’s building a reserve!  Are you suggesting it’s raising money because it doesn’t need it? Like a rainy day fund? Are you also suggesting that Anthropic is taking advantage of its investors? Anthropic has a bunch of compute commitments that require it to pay a bunch of money up front! This isn’t because its business economics don’t make sense at all. I think you’re right that Anthropic likely has to pay up front for its compute. Dario Amodei himself said so, while adding that you have to do so based on how much revenue you expect to make, and that if he’s wrong, Anthropic goes bankrupt! Basically I’m saying, “In 2027, how much compute do I get?” I could assume that the revenue will continue growing 10x a year, so it’ll be $100 billion at the end of 2026 and $1 trillion at the end of 2027. Actually it would be $5 trillion dollars of compute because it would be $1 trillion a year for five years. I could buy $1 trillion of compute that starts at the end of 2027. If my revenue is not $1 trillion dollars, if it’s even $800 billion, there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt if I buy that much compute. Nevertheless, this doesn’t remotely interfere with my thesis! It just means that Anthropic has been forced to buy a bunch of compute immediately rather than paying for it in chunks. In fact, I’d argue that Anthropic is having to raise this money to pay up front for capacity that’s yet to be built.  This is a sign of how much faith investors have in the product! Yeah that’s generally how venture capital works. There’s also not really any other success story out there other than OpenAI that has anything close to a time horizon toward an exit. Anthropic said it had hit $14 billion in ARR on February 12, 2026 , or around $1.16 billion between January 12 and February 12.  That’s $1.16 billion in that period. Anthropic CFO Krishna Rao said in a sworn affidavit on March 9 2026 that its revenue was “exceeding $5 billion to date.” I also at this point think that sources telling anybody Anthropic made $4.5 billion in 2025 alone were lying , as it doesn’t make mathematical sense otherwise. This also means that Anthropic, if it’s being honest about what “run rate” means, made 23% of its lifetime revenue in a single month. On April 6, 2026 , Anthropic said it had hit $30 billion in annualized revenue, or $2.5 billion, I assume, in the period between March 6 and April 6.  That’s $2.5 billion in that period. SemiAnalysis’ estimate is from April 30, 2026, so let’s assume that it refers to the period of March 29 to April 29, 2026.  That’s another $3.08 billion. It’ll get cheaper in the future- okay, are you saying the chips will get better? Because these companies have somewhere between $100 billion and $300 billion of these fucking things. People are starting to pay for AI- okay, but they’re not paying very much, and it’s taken so long that these companies are now burdened with endless piles of GPUs that they’ve yet to fully install. How do they catch up? Just give it time- no! I’ve given it lots of time! Why are you being so generous to them and so impatient with me?  This is investing in tech that will turn into the most transformative tech in the future - you’re a mark!

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Stratechery 1 weeks ago

Google Earnings, Meta Earnings

Wall Street loved Google's earnings, and hated Meta's, even though the latter's core business was more impressive. The difference is that Google is monetizing its investments now (and it might be all Anthropic).

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Stratechery 2 weeks ago

Amazon Earnings, Trainium and Commodity Markets, Additional Amazon Notes

Amazon's earnings suggest that the shift away from training towards inference and agents means their bet on Trainium is paying off. Plus, additional notes on ads, agents, and sports rights.

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OpenAI Projects ChatGPT Plus subscriptions to drop by 80% from 44 Million in 2025 to 9 Million In 2026, Made Up Using Cheaper Subscriptions (Somehow)

The Information reported on April 28 that OpenAI projects an 80% decline in its $20-a-month ChatGPT Plus subscribers - from 44 million in 2025 to 9 million in 2026 - and intends to make up the shortfall using its cheaper, ad-supported "ChatGPT Go" subscriptions by growing them from 3 million in 2025 to 112 million in 2026: That's a load-bearing "as a result" if I ever saw one. What OpenAI is actually saying here is that it's expecting a dramatic decline in its primary business line - $20-a-month ChatGPT subscriptions - and intends to somehow get 109 million new paying subscriptions of an entirely different product . As The Information noted, this would be a 3600% subscriber increase year-over-year. Eager math-knowers in the audience will also realize that, if we assume a $5-a-month subscription cost, even if OpenAI succeeds in what would be the single-largest user acquisition campaign in history, it would still be $155 million short. I imagine OpenAI's answer would be "we're going to be serving these customers ads" and "some of them will pay $8 a month," neither of which are substantive. Putting aside ChatGPT Go for a second, it is pretty remarkable that OpenAI is projecting an 80% decrease in ChatGPT Plus subscriptions. Perhaps this projection is something that will only come to pass if ChatGPT Go grows at such a rate...or perhaps it's something that OpenAI already sees happening, as The Wall Street Journal reported earlier in the week that OpenAI had missed revenue targets for new users and revenue, which makes the timing of this leak all-the-more suspicious. I should also add that adding 109 million new subscribers at any price point will likely massively increase OpenAI's burn-rate. If you liked this news hit, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . I also just did a piece about how OpenAI will kill Oracle . It's one of my best pieces I've ever done and I'm extremely proud of it. The Information reports that OpenAI projects that its $20-a-month ChatGPT Plus subscriptions will decrease from 44 Million subscribers in 2025 to a projected 9 million subscribers in 2026. OpenAI projects to make up the difference by increasing its ad-supported ChatGPT Go ($5 or $8-a-month depending on the region) subscriptions from 3 million in 2025 to 112 million in 2026.

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AI's Economics Don't Make Sense

If you liked this piece, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . I also just did a piece about how OpenAI will kill Oracle , and I’ve used some of the materials in today’s piece. It's one of my best pieces I've ever done and I'm extremely proud of it. Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Yesterday morning, GitHub Copilot users got confirmation of something I’d reported a week ago — that all GitHub Copilot plans would move to usage-based pricing on June 1, 2026 .  Instead of offering users a certain number of “ requests ,” Microsoft will now charge users based on the actual cost of the models they’re using, which it calls “...an important step toward a sustainable, reliable Copilot business and experience for all users.” Users instead get however much they spend on their GitHub Copilot subscription (EG: $19 of tokens a month on a $19-a-month plan). Anyway, the announcement itself was a fascinating preview into how these price changes are going to get framed:  You see, it’s not that Microsoft was subsidizing nearly two million people’s compute, it’s that AI has become so strong, powerful and complex that it’s basically a different product! While Copilot might not be “...the same product it was a year ago,” very little has changed about the underlying economic mismatch: that Microsoft was allowing users to burn more than their subscription costs in tokens every single month for three years. Per the Wall Street Journal in October 2023 : Naturally, GitHub Copilot users are in revolt , saying that the product is “dead” and “completely ruined.” And I called it two years ago in the Subprime AI Crisis : And that day has finally arrived, because every single AI service you use subsidized compute , and every single service is losing money as a result: AI startups and hyperscalers assumed that they’d be able to get enough people through the door with subsidized, loss-making products to get them hooked on services badly enough that they’d refuse to change once businesses jacked up the prices. They also assumed, I imagine, that the cost of tokens would come down over time, versus what actually happened — while prices for some models might have come down, newer “reasoning” models burn way more tokens, which means the cost of inference has, somehow, gotten higher over time . Both assumptions were wrong, because the monthly subscription model does not make sense for any service connected to a Large Language Model. Think of it like this. When Uber ( and no, this is nothing like Uber ) started jacking up the prices for its rides, the underlying economics stayed the same, as did those presented to both the rider and the driver — a user paid for a ride, a driver was paid for a ride. Drivers still paid for gas, car insurance, any permits that their local government might insist upon, and whatever financing costs might be associated with their vehicle, and said costs were not subsidized by Uber. Uber’s massive losses came from subsidies, endless marketing expenses, and doomed R&D efforts into things like driverless cars . To illustrate the scale of AI’s pricing mismatch, I’m going to ask you to imagine an alternate history where Uber had a very different business model. Generative AI subscriptions are like if Uber charged users $20 a month for 100 rides of any distance under 100 miles, and if gas was $150 a gallon, and Uber paid for the gas because somebody insisted that oil would one day be too cheap to meter . Uber would, eventually, decide to start charging users a monthly subscription to access rides, and bill them for the gas that they consumed. Suddenly users would go from paying $20 a month for 100 rides to paying $20 to access a driver and $26 for a 10 mile drive. Understandably, users would be a little upset. While this sounds a little dramatic, it’s actually a pretty accurate metaphor for what’s happening in the generative AI industry, and in particular, at Github Copilot.  GitHub Copilot’s previous pricing allowed 300 premium requests a month, as well as “unlimited chat requests” using models like GPT-5 mini. Each of these requests (to quote Microsoft) is “...any interaction where you ask Copilot to do something for you,” with more-expensive models taking up more requests in the later life of the request-based system, such as Claude Opus 4.6 taking up three premium requests. When you ran out of premium requests, Copilot would let you use one of those cheaper models as much as you’d like for the rest of the month.  This wasn’t even always the case. Up until May 2025 , Microsoft gave users unlimited access to models, and even then users were pissed off that there were any restrictions on the product.  Microsoft — like every AI company — swindled its customers by selling an unsustainable service, because it never, ever made sense to sell LLM-powered services on a monthly subscription. If you’re wondering how much services are likely to cost under token based billing, a user on the GitHub Copilot Subreddit found that the token burn of what used to be a single premium request was somewhere around $11, as one “request” involved using 60,000 tokens in the context window, a few tools, and a bunch of internal “turns” (things that the model is doing) to produce the output.  There’s also the underlying unreliability of hallucination-prone Large Language Models. While a premium request chasing its tail and spitting out half-broken code might be frustrating , that same fuckup is a lot less forgivable when you’re paying the costs yourself.  Users have also been trained to use the product in an entirely different manner to token-based billing, and I’d imagine many of them don’t even really realize how many “tokens” they burn or how many of them a particular task takes, something which changes based on whatever model you use. This is absolutely nothing like Uber, and anyone telling you otherwise is attempting to rationalize bad behavior. Uber may have raised prices, but it didn’t have to dramatically change the underlying economics of the platform, nor did users have to entirely change how they used the product because Uber was suddenly charging them on a per-gallon basis. There has never been — and never will be — an economically-feasible way to offer services powered by LLMs without charging the actual token burn of each user, and in the process of deceiving said users, these companies have created products with illusory benefits and questionable return on investment. And that’s been blatantly obvious for years.  On an economic basis, a monthly subscription only makes sense with relatively static costs. A gym can sell memberships knowing roughly how much wear-and-tear equipment gets, how much classes cost to run, and how much things like electricity, staffing and water might cost over a given period of time.  A customer of Google Workspace — at least before AI — cost whatever the cost of accessing or storing documents were, as well as the ongoing costs of Google Docs and other services. The relatively low cost of digital storage (as well as the fact that, unlike LLMs, Google Workspace isn’t particularly computationally demanding) means that a particularly-heavy Google Drive user isn’t going to eat into the margin on their monthly subscription. Conversely, an AI subscriber’s costs can vary wildly . One user might only use ChatGPT for the occasional search, while another might feed in reams of documents, or try and refactor a codebase, or try and use it to put together a PowerPoint presentation. And the provider — a model lab like OpenAI or Anthropic, or a startup like Cursor) — has no real way to control how a user might act other than making the product worse, such as instituting usage limits, reducing the size of the context window, pushing customers to smaller (and worse) models, or changing the pricing to dissuage users from making big GPU-heavy requests.  Yet these services intentionally hide the amount of tokens or how much a particular activity has cost, which means users don’t really know what a rate limit means, which means that every abrupt change to rate limits leaves customers desperately scrambling to work out how much actual work they can do using the service.  It’s an abusive, manipulative and deceitful way of doing business that only existed so that Anthropic, OpenAI, and other AI companies could grow their user bases, as the majority of AI users perceive its real or imagined benefits entirely through the lens of being able to burn anywhere from $8 to $13.50 for every dollar of their subscription in tokens.  This intentional act of deception had one goal: to make sure that the majority of people were never exposed to the true costs of generative AI. When The Atlantic writes a breathless screed about Claude Code being Anthropic’s “ChatGPT moment,” it does so based on a $20-a-month subscription rather than the underlying token burn that it cost for Anthropic to provide it, which in turn makes the writer forgive the “minor errors” that a model might make, or when it “gets stuck on more complicated programming tasks.” Had the writer paid for her actual token burn, and had each of the times it got “stuck” resulted in $15 in token charges, I don’t think she’d be quite as forgiving of these fuckups.  Yet that’s all part of the scam.  It’s very, very important that nobody writing about AI in the mainstream media actually understands how much these services cost, and that any mainstream articles written about services like ChatGPT or Claude Code are written by people who have little or no idea how much each individual task might cost a user.  Remember: generative AI services are, for the most part, experimental products that do not function like any other modern software or hardware. One cannot just walk up to ChatGPT or Claude and start asking it to do work.  I mean, you can , but if you don’t prompt it right, understand how it works, or make a mistake in whatever you feed it, or if it just gets things wrong, it’ll spit out something you don’t like, which in turn means you’ll need to prompt it again. LLMs are inherently unpredictable.  You cannot guarantee whether an LLM will do a particular action, or whether it will present you with an outcome based on reality. You cannot for certain say how much a particular task — even one you’ve done many times using an LLM in the past — might cost, nor can you be sure when a model might go berserk and delete something, or simply not do something yet claim that it did.  These are far more forgivable if you’ve not paying on a per-token basis, because in the mind of a subscriber, that’s just another turn or two with a chatbot rather than something that’s incurring a real cost. One doesn’t criticize so-called “jagged intelligence” because the assumption is that whatever problems you’re facing now will be eliminated at some future juncture, and you didn’t end up paying for it anyway.  Had users been forced to pay their actual rates, I imagine many would’ve bounced off the product immediately, as it’s very, very easy to burn through $5 of tokens if you’re fucking around and exploring what an LLM can do.  I think it’s inevitable that the majority of AI subscriptions move to token-based billing, especially as both Anthropic and OpenAI have now done so with their enterprise customers.  The fact that Microsoft moved GitHub Copilot subscribers to token-based is also a very, very bad sign. Microsoft is arguably the best-capitalized, most-profitable, and best-positioned company to continue subsidizing compute, and if it can’t afford to do so further, nobody else can either. The real thing to look out for — a true pale horse — will be a major AI lab like Anthropic or OpenAI moving all of its subscribers to token-based billing. Once that happens, you’ll know it’s closing time.  As I discussed last week, Uber’s CTO said at a conference that it had spent its entire AI budget for 2026 in the space of a few months , with Goldman Sachs suggesting that some companies are spending as much as 10% of their headcount on AI tokens, with the potential to increase to 100% in the next few quarters.  This is the direct result of training every single AI user to use these services as much as humanely possible while obfuscating how much they really cost. Every single major company demanding that every single worker “use AI as much as possible” has done so while either fundamentally ignoring or being entirely disconnected from their actual token burn, and as companies are forced to pay the actual costs , I’m not sure how you can economically justify any investment in this technology. Sure, sure, you’re gonna say that engineers are “shipping code faster” or some such bullshit, I get it, but how much faster, and how much money are you making or saving as a result? If you’re spending 10% of your headcount on AI tokens, are you seeing that extra expense reconciled in some other way? Because I’m not sure you are. I’m not sure any business investing these vast amounts of money in tokens is seeing any return on investment, which is why every study about AI’s ROI struggles to find much evidence that it exists. For the most part, everybody you’ve read gooning over the many possibilities of generative AI has experienced it without having to pay the true costs. Every Twitter psychopath writing endless screeds about their entire engineering team hammering away at Claude Code has been doing so using a $125-a-month-per-head Teams subscription with similar usage limits to Anthropic’s $100-a-month consumer subscription. Every LinkedIn gargoyle insisting that they’d “done hours of work in minutes” using some sort of Perplexity product has done so by paying, at the very most, $200 a month for Perplexity’s Max subscription.  In reality, that 10-person, $1250-a-month Teams subscription likely burns anywhere from $5000 to $10,000 a month in API calls, if not more. Anthropic Head of Growth Amol Avasare said last week that its Max subscriptions were built for heavy chat usage rather than whatever people are doing with Claude Code and Cowork, and made it clear that Anthropic is now looking at “ different options to keep delivering a great experience, ” which is another way of saying “we’re going to change the prices at some point.” I’m not sure people realize how expensive these tokens are, especially for coding projects that involve massive codebases and regularly make calls to coding and infrastructure tools. Can somebody who pays $200 a month foreseeably afford $350, $400, or $500? Can they afford to have a month where they spend more than that? What happens if they go over budget, or if they literally can’t afford to spend the money necessary to finish their work? To give you a more-practical example, up until the beginning of April, Anthropic’s own Claude Code developer documents ( archive ) said that “the average cost [for those using Claude Code] is $6 per developer per day, with daily costs remaining below $12 for 90% of users.” As of this week, the documents now read as follows : If we assume an average of 21 working days in a month, that puts the average cost of a Claude Code user at around $273 a month, or $3,276 a year. At $30 a working day, that works out to $630 a month, or $7560 a year.  These are astonishing numbers, made more so by the fact that there is no way you’re only spending $30 a day if you use any of Anthropic’s more-recent models. Claude Opus 4.7 costs $5 per million input and $25 per million output tokens. ‘ One million tokens is around 50,000 lines of code , and assuming you’re using the supposedly state-of-the-art models, there isn’t a chance in Hell you don’t run through at least a million, with that number increasing dramatically if you’re not particularly aware of which models to use for a particular task. Let’s play with that $30 number a little more.  While this might be possible within the slush fund mindset of a well-funded startup or a banana republic like Meta, any business that actually cares about its costs will have a great deal of trouble justifying spending five or six figures in extra costs on a service that “increases productivity” in a way that nobody can seem to measure. Right now, I think most companies fall into three camps: Large organizations still have a free pass to say that they’re burning millions of dollars on AI tokens for their software engineers under the questionable benefit of their “best engineers” not writing any code . All it changes is one bad earnings call to change that narrative. At some point investors — even the braindead fuckwits who have been inflating the AI bubble — will begin to question mounting R&D costs (which is where AI token burn is usually hidden) when the company’s revenue growth fails to follow. This will likely lead to more layoffs to keep up with the cost, as was the case with Meta , and then an eventual pullback when somebody asks “does any of this shit actually help us do our jobs faster or better?” I also think that startups burning 10% or more of their headcount in AI tokens will have a tough time convincing investors in six months that doing so is necessary. And once everybody switches to token-based billing, I’m not sure we’ll see quite as much hype around generative AI.   The way that people talk about AI data centers is completely disconnected from reality, and I don’t think people realize how ridiculous this entire era has become. Per Jerome Darling of TD Cowen, it costs around $30 million in critical IT (GPUs and the associated hardware) and $14 million per megawatt of data center capacity. Data centers appear to take anywhere from a year to three years depending on the size, and that’s assuming the power is available.  Of the 114GW of data centers supposedly being built by the end of 2028, only 15.2GW is under construction in any way, shape, or form . And “under construction” can mean as little as “there’s a hole in the ground.” It does not — and should not — imply that the capacity that said facility will provide is going to be imminently available.  Let’s start simple: whenever you think “100MW,” think “$4.4 billion,” with a large chunk of that dedicated to NVIDIA GPUs.  As a result, every AI data center starts millions of dollars in the hole, and even with six-year-long depreciation schedules, takes years to pay off… and with NVIDIA’s yearly upgrade cycle , those GPUs are unlikely to make that much money once you’re done with your first customer contract. It’s also unclear whether the customer base for AI compute exists outside of OpenAI and Anthropic, whose demand accounts for 50% of AI data centers under construction , creating a massive systemic weakness if either of them lacks the money to pay. In any case, it’s also unclear what kind of ongoing rates these data centers charge. While spot prices might sit around $4.50 an hour for a B200 GPU, long-term contracts generally price much lower, with one founder ( per The Information ) saying they paid around $3.70-per-hour-per GPU for a one-year-long commitment. To be clear, we must differentiate between the spot cost — which is the cost of randomly spinning up GPUs on somebody else’s servers — and contracted compute, the latter of which makes up the majority of data center capex. Most data centers are built with the intention of having one or two big clients , which means said clients are likely to negotiate a cheaper blended rate. As a result, many data centers take far less than $3.70 an hour, because they bill at a per-megawatt (or kilowatt) price. And that’s where the economics begin to break down. That’s the starting cost for a 100 megawatt data center. A 100MW data center will likely only have 85MW of actual stuff it can bill for, and based on discussions with sources familiar with hyperscaler billing, they can expect to make around $12.5 million per megawatt, or around $1.063 billion in annual revenue.  Now, I should be clear that most data center companies you know of don’t actually build them, instead leaving that job to companies like Applied Digital,  who are also known as “colocation partners.” For example, CoreWeave pays a colocation fee to Applied Digital to use its North Dakota data centers . CoreWeave is responsible for all the GPUs and other tech inside the data center. To explain the economic mismatch, I’m going to use a theoretical example of a data center leased to a theoretical AI compute company.  The GPUs in that data center are likely NVIDIA’s Blackwell chips. More than likely, said data center is using pods of 8 B200 GPUs, retailing around $450,000 a piece, or $56,250 a GPU. Based on there being 85MW of Critical IT load, the all-in capex per megawatt is around $36.78, or total IT capex of around $3.126 billion, or around $2.67 billion in GPUs. Let’s assume this data center is in Ellendale, North Dakota, which means you’ve got an industrial electricity rate of around 6.31 cents per kilowatt hour, which works out to about $55.4 million a year in electricity costs. Based on discussions with sources, I estimate that ongoing costs like maintenance, headcount, replacement of power supplies and the like comes in at around 12% of revenue, or around $128 million a year, bringing us up to $183.4 million in costs. Wait, sorry. You’ve also got to pay a colocation fee based on the critical IT, and according to Brightlio, that fee is often around $180-200 per kilowatt per month , depending on the scale and location of the deployment, though I’ve read as low as $130, which is the number I’m going with, or around $133 million per year. This brings us up to $316.4 million. Well, that’s still less than $1.06 billion, so we’re still doing okay, right? Wrong! You’ve got $3.126 billion in IT gear to depreciate, which works out to around $521 million a year over the six years you’re depreciating it. That’s $837.4 million a year, leaving you with around $168.6 million in yearly profit, or around a 16.7% gross margin… … if you have 100% tenancy at all times! You see, data centers can take a month or two to get those GPUs installed and a customer onboarded, all while making you exactly zero dollars in revenue and losing you a great deal more, as you’re stuck paying your colocation, electricity and opex costs the whole time, albeit at a much lower rate (I’ve modeled for 10% electricity and 15% colocation/opex costs), meaning you’re losing about $3.27 million a day. For the sake of this example, we’re going to assume it takes you an extra month to get this thing operational, meaning you’ve paid about $102 million that you’re never getting back, bringing our total costs for the year including depreciation to $939.4 million, or a 6.6% gross margin. Wait, fuck, you didn’t use debt to buy these GPUs, did you? You did? How bad are we talking?  Oh god — you got a 6-year-long asset-backed loan at 80% LTV, meaning you borrowed $2.8 billion at a 6% interest rate.  Your bank, in its eternal generosity, offered you a deal — a 12-month-long grace period where you’re only paying interest…which works out to around $168 million, which brings our total costs (excluding the month of delay for fairness) for the first year to around $1.005 billion…on $1.06 billion of revenue. That’s a 5.19% gross margin, and you haven’t even started paying the principal. When that happens, you’re paying $54.1 million a month in loan payments, for a total of around $649 million a year for five more years, which comes out to around $1.48 billion, or a negative gross margin of around 40%.  And I must be clear that this is if you have 100% utilization and a tenant that pays you on time, every time. Let’s talk about what should be the single-most economically viable project in data center history — a massive campus built for the largest AI company in the world by Oracle, a decades-old near-hyperscaler with a history of selling expensive database and business management software to enterprises and governments. Hah, I’m kidding of course, this place is a fucking nightmare. Stargate Abilene, an eight-building, 1.2GW data center campus with around 824MW of critical IT, was first announced in July 2024 . As of April 27 2026, only two buildings are operational and generating revenue, and the third barely has any IT gear in it. I estimate the total cost of Stargate Abilene to be around $52.8 billion. Per my own reporting , Oracle expects to make around $10 billion in annual revenue from Stargate Abilene, and I estimate around $75 billion in total revenue from the 7.1GW of data center capacity it’s building for one customer: OpenAI. As I also reported, Oracle estimated in 2024 that Abilene would cost at least $2.14 billion a year in colocation and electricity fees, paid to land developer Crusoe. I should also add that it appears that Oracle is paying all of Abilene’s construction costs. Based on my calculations and reporting, I estimate Abilene’s rough gross margin is around 37.47% once it’s fully operational:  I must be clear that that 37.47% gross margin is likely too high, as I don’t have precise knowledge of Oracle’s true insurance or headcount costs, only estimates based on documents viewed by this publication. I should also be clear that Oracle is mortgaging its entire fucking future on projects like Stargate Abilene, incurring billions of dollars of costs up front for a business that will take years to turn a profit even if OpenAI makes every single payment in a timely manner. Sadly, I can’t tell how much of Abilene was paid for in debt, only that Oracle raised around $18 billion in various-sized bonds in September 2025, with maturities ranging from 7 years to 40 years, and had negative cashflow of $24.7 billion in its last quarterly earnings .  What I do know is that it has a 15-year-long lease with developer Crusoe , and that Oracle’s future heavily depends on OpenAI’s continued ability to pay, which depends on Oracle’s ability to finish Stargate Abilene. I also need to be clear that that $3.85 billion in yearly profit is only possible if OpenAI makes timely payments, takes tenancy of Abilene as fast as humanly possible, and everything goes to plan. Sadly, the complete opposite has happened: This is a huge issue. OpenAI can only pay for compute that actually exists, and only 206MW of critical IT is actually generating revenue, with the third at least a month (if not a quarter) away from doing so.  Yet there’s a larger, more-existential problem with the overall Stargate data center project — that the only way any of it makes sense is if OpenAI meets its ridiculous, cartoonish projections. As I discussed on Friday :  I calculated that $75 billion number by assuming that Vera Rubin GPUs get around $14 million per megawatt of compute (a number I’ve confirmed with sources familiar with the data center industry) across the remaining 4.64GW of critical IT that I anticipate makes up the remaining Stargate data centers.  OpenAI’s numbers come directly from The Information’s reported leaks of OpenAI’s projected burn rate and revenues , which have the company making $673 billion in revenue through the end of 2030 and burning $852 billion to get there: I must be clear that any journalist repeating these numbers without saying how fucking stupid they are should be a little ashamed of themselves. Per my Friday premium: And if OpenAI can’t pay for that compute, Oracle dies, because it’s taken on around $115 billion in debt just to build Stargate’s data centers , and needs another $150 billion to finish them: I really need to be clear that Oracle has no other path to making this revenue without OpenAI, and these projects are entirely financed and paid for using the projected cashflow of the data centers themselves. And I’m not even the only one worried about this, with OpenAI Sarah Friar sharing similar concerns after the company missed user and revenue targets, per the Wall Street Journal : If that doesn’t worry you, perhaps this will: That sure sounds like a company that’s gonna be able to make $852 billion by the end of the decade! While I regularly bag on OpenAI for its ridiculous promises, Anthropic isn’t far behind, promising to take “up to” 5GW of capacity from both Google and Amazon in a deal that I estimate includes around $100 billion in actual compute commitments given the scale of the capacity. Now, I should add that Google and Amazon are far-savvier and less-desperate than Oracle, meaning that they can take the hit if Anthropic ends up running out of money. The “up to” part of that deal gives them some much-needed wiggle room that Oracle simply does not have.  Nevertheless, for Anthropic to actually meet its commitments, it will have to agree to spend anywhere from $25 billion to $100 billion a year on compute by the end of 2030.  Anthropic’s CFO said in March that it had made $5 billion in revenue in its entire existence .  The near-pornographic excitement around however many hundreds of billions of dollars of GPUs that Jensen Huang claims to be shifting regularly clouds out a problematic question: sell the compute to who , Jensen?  If we assume that the 15.2GW of data center capacity under construction (due by the end of 2028) has a PUE of around 1.35, that leaves us with roughly 11.2GW of critical IT. At $14 million per megawatt, that works out to around $156.8 billion in annual revenue in GPU rental revenue required to actually make these data centers worth it. When you calculate for the 114GW of capacity theoretically coming online by the end of 2028, that number climbs to $1.18 trillion in annual revenue. To give you some context, CoreWeave — the largest neocloud with Meta, OpenAI, Google (for OpenAI), Microsoft (for OpenAI), Anthropic, and NVIDIA as customers — made around $5.1 billion in revenue , and projected it would make $12 billion to $13 billion in 2026 .  Who, exactly, is the customer for all this compute, and how likely is it that they’ll want to buy it by the time all that capacity is built? While many different data centers claim to have tenants for the first few years of their existence, said tenants can only start paying once the data center completes, and if it’s an AI startup, I think it’s fairly reasonable to ask whether they exist by the time it’s built. Remember: the customers of AI compute are, for the most part, either hyperscalers trying to move capital expenditures off their balance sheets or unprofitable AI startups. Anthropic and OpenAI both intend to burn tens of billions of dollars in the next few years, and neither of them have a path to profitability.  This means that a large part — if not the majority of — AI compute revenue is dependent on a continual flow of venture capital and debt, both of which are only made possible by investors that still believe that generative AI will be the biggest, most hugest thing in the world. How does that work out, exactly? Who is paying for this data center capacity? Who is it for? Where is the actual demand?  And if said demand exists, how the fuck do the customers even pay? Despite multiple stories that have both of them becoming profitable by 2028 or 2029, nobody can explain to me how OpenAI or Anthropic actually reach profitability, especially given that both of them had worse margins than expected, even when said margins strip out training costs that number in the billions of dollars. And I’ve been asking this question for fucking years. Every time we get a new update on Anthropic or OpenAI, we hear they’ve lost billions more dollars than expected, that margins are decaying, that costs are skyrocketing, that everything is more expensive despite promises that the literal opposite would happen. Even Cursor, a company that briefly (before its pseudo-acquisition by Musk’s SpaceX) claimed it had positive gross margins, actually had negative 23% gross margins as of January, or negative 31% if you include the cost of non-paying users, which you fucking should if you actually care about your accounting. Mysteriously, reports claim that Cursor’s margins “recently turned positive,” but magically don’t know by how much, or how that happened, or a single other detail other than one that likely helped the company get sold. I also don’t see how any of these AI data centers actually make sense, even if they have customers to pay them for the first few years. The economics are built for perfection, with zero fault tolerance. They must have consistent, 100% utilization and tenancy, or they end up burning millions of dollars and fail to chip away at the years-long wall of depreciation created by the tech industry’s most expensive mistake. Even if they somehow succeed, these are pathetic businesses with mediocre margins — 70% at best, assuming consistent payments, tenancy, and six fucking years of depreciation to actually break even, which might be difficult considering the yearly upgrade cycle makes the entire thing near-obsolete by the time you’re done paying it off. And that’s before you consider that the majority of the customers are unprofitable, unsustainable startups. I truly don’t know how any of this works out. I realize it seems a little much, but I genuinely believe that subscription-based AI services were an act of deception tantamount to fraud, as they misrepresented the core unit economics and thus the possibilities of a Large Language Model. By selling users a product on a monthly rate and creating habits based on its availability, companies like Anthropic and OpenAI have misrepresented their businesses in such a way that most of their users are interacting with products and building workflows based on products that are unsustainable and impossible to maintain in their current form. Anthropic’s recent aggressive rate limit changes were instituted mere months after multiple aggressive marketing campaigns based on experiences that are now near-impossible under the current rate limits, and based on recent moves by Anthropic , it’s clear that it intends to start removing services for its lower-tier $20-a-month subscribers at some time in the future. This is a disgusting and misleading way to run a company, and the vagueness with which Anthropic discusses its products and its services are an insult to every one of their users, and a sign that it doesn’t fear the press in any meaningful way.  I need to be very clear that the product that Anthropic offers — by virtue of recent rate limit changes — is substantially different (and far worse) than the one that you read about everywhere. Anthropic was conscious in its deliberate attempts to market a product that it knew would be gone within three months. Dario Amodei doesn’t give a fuck as long as the media keeps writing up however many billions of annualized revenue he’s conjured up today or whatever new product he’s released that’s meant to destroy some hapless public SaaS company that had already seen its growth slow.  Members of the media, I say this with abundant respect: Anthropic is mistreating its customers, and it is doing so because it believes it can get away with it. This company does not respect you, and in fact holds you with a remarkable degree of contempt, which is why it doesn’t bother to fix its services very quickly or explain why they were broken with any level of coherence.  That’s why Anthropic fucking lied about Claude Mythos being too powerful to release (it was actually a capacity issue) when in fact it’s just another fucking Large Language Model Nothingburger — because it thinks you will buy whatever it is selling, and it’s worked out exactly how to package it to give you enough plausible “proof” that a quick skimread of the system card will make you and your editor believe whatever it is you’re saying.  They also know you’ll rush to cover it rather than waiting to see what actual experts say.  AI is a con, and this is how the con works. AI was rushed and pushed in our faces as quickly as humanly possible in the least-efficient yet most-accessible form it could be presented, even if said form was never going to result in anything resembling a sustainable business. The media was rushed to immediately say that this was the thing so that everybody would agree that this was the thing now and use it as much as physically possible, and, crucially, use it in a subscription-based form that would make people experience it without ever asking how much it costs to provide. The narrative came pre-baked. Because very few people who talked about LLMs experienced their actual cost, it was really easy for them to vaguely say “it’s just like Uber,” because that was a company that lost a lot of money but didn’t die, and it’s much easier to say that than say “wait, what do you mean OpenAI is set to lose $5 billion this year?” Think of it like this: as a reporter, an investor, an executive, or a regular LinkedIn Lounge Lizard, you might read here and there that it’s $5 per million input tokens and $25 per million output tokens, but you’ve never really experienced how fast or slow one loses that money, because it’s important to do so to truly understand this product. Anthropic and OpenAI intentionally obfuscated that experience and created businesses that expect to burn tens of billions of dollars in 2026 and several hundred billion dollars by 2030, all because most people graded generative AI based on the subscription-based experience.  LLMs are a casino, and you’ve been gambling with the house’s money while encouraging people to bet their own on whether they’ll get a unit of work out of a particular model.  This was intentional. They never wanted you thinking about the costs because once you really start thinking about the costs this whole thing feels a little insane. I truly believe that LLM-based subscriptions are going to go away entirely, at least at the scale of any product that generates code, and in doing so, Amodei and Altman will wrap up their con, or at least believe that they have. The problem is that these men have now signed far too many deals to get away scot-free.  OpenAI’s CFO has now said multiple times that she doesn’t believe OpenAI is ready for IPO, and has material concerns about its growth and continued ability to meet its obligations. To repeat a quote from before :   This is a blinking red fucking light, and in a sane market would send Oracle’s stock into a tailspin, because OpenAI’s ascent to over $280 billion in annual revenue is critical to Oracle’s ability to not run out of money. In a sane media, this would send worrisome shockwaves through every group chat and Slack channel about whether or not OpenAI is actually going to make it. This is the kind of thing that happens before a company starts dying. OpenAI’s growth is slowing at precisely the time it needs to accelerate. It needs to effectively 10x its current business by 2030 to make its obligations. OpenAI’s CFO, the literal person who would know best, is saying that she is worried that OpenAI cannot pay its fucking compute contracts if revenue doesn’t grow. This is a big, blinking warning light! This is not a drill!  Yet the bit that really worried me was the Journal’s comment that Friar didn’t think OpenAI “was ready to meet the rigorous reporting standards required of a public company.” What the fuck does that mean? Excuse me? This company has allegedly raised $122 billion and is allegedly worth $852 billion god damn dollars and expects to burn $ 852 billion dollars by the end of 2030. Are its accounts not in order? What “rigorous reporting standards” can OpenAI not meet?  I’d generally not be so fucking nosy if it wasn’t for the fact that this company accounted for something like 20% of all venture capital funding in the last year and everywhere I go I have to hear the endless bloviating of Altman and Brockman and every other man at OpenAI and their fucking ideas about what regular people should do as they swan about shipping dogshit software and spending other people’s money. For the amount of oxygen that both Anthropic and OpenAI consume, both of these companies should be fucking flawless , both as products and businesses. Instead, both are sold through varying levels of deception around their economics and efficacy , obfuscating the truth so that their Chief Executives can amass money and power and attention. It’s an insult to both good software and good taste — the most-expensive, least-reliable applications ever invented, their mistakes forgiven, their mediocrities celebrated, their infrastructure hailed as an inert god of capital.  Generative AI is an insult. It is unreliable, its economics don’t make sense, its outcomes don’t justify its existence, and the perpetrators of its con are boring, oafish and avaricious men disconnected from society and anybody who would ever disagree with them. It requires stealing art from everybody, destroying the environment, increasing our electricity bills, the constant threat of economic annihilation, the endless cacaphony of “everything fucking sucks now because of AI,” all to push software that can only be justified by people willing to ignore basic finance or sense. It’s all so expensive, and it’s all so fucking dull. It’s offensively boring. It’s actively annoying. Every story where somebody tells you about how much they use AI sounds like they’re in an abusive relationship and/or joined a cult, echoing with a subtle desperation that says “ you really need to join me in this because it’s so good, and the fact that I appear to be experiencing no joy from this product is just a sign of how efficient it is. ” There is nothing light-hearted or joyful about what AI can do. There is nothing goofy or whimsical about a Large Language Model, and every interaction feels hollow.  Those who desperately look for clues that it’s becoming sentient or “more powerful” are simply seeking validation themselves — they want to be first to something, because arriving at other people’s conclusion is what they do for a living.  Being “first” — on the “frontier” one might say — is something that people crave when they can’t find something within, and it’s exactly the fuel that grifters crave, because LLMs are constantly humming with the sense that they’re about to do something new, even though they’re mathematically restricted to repeating other actions. This is a deeply sad era. The people that have so aggressively worked together to hold up this industry have only delayed its inevitable fall. It’s terrifying to me that our markets and parts of our economy are being held up by the generally-held yet utterly-unproven assumption that LLMs will somehow get cheaper, that AI startups will magically become profitable, and that offering AI compute will be profitable in perpetuity to the point that it necessitates increasing the current supply tenfold by the year 2030. People have debased themselves to defend the AI industry, because that’s what the industry demands of its supplicants. To be an “AI expert” requires you to actively ignore the worst economics of any industry in history, to constantly explain away obvious, glaring issues with products, and to actively convince others to do the same. OpenAI and Anthropic do not provide clear explanations of how they’ll become profitable because they know that their supporters will never ask for them — because the only way to fully “believe in AI” is to actively wear blinders. And I get it. If you accept that OpenAI and/or Anthropic will eventually collapse, all of this seems a little insane. I am genuinely asking you to seriously consider that one or both of these companies will run out of money. I’m really worried, made only more so by the general lack of concern I’m seeing in the media and greater society.  The assumption, if I had to imagine, is that I’m simply being alarmist, and that “the demand will absolutely be there.” You’d better hope you’re right.  For Larry Ellison’s sake, at least. Ellison has already pledged 346 million shares of his Oracle stock — or around $61.5 billion — “to secure certain personal indebtedness, including various lines of credit,” meaning “many big, beautiful loans against his Oracle shares.” which IFR estimated back in September ( when Oracle’s stock price was much higher ) could allow him to secure as much as $21.4 billion in debt at a (they say “conservative”) loan-to-value ratio of 20%, and that’s assuming the banks weren’t particularly generous. If OpenAI can’t raise $852 billion in revenue and funding by the end of 2030, it won’t be able to pay for Stargate. That’ll kill the value of Oracle’s stock, leading to a series of margin calls, leading to Ellison having to sell shares, leading to further margin calls. Whatever bailout might or might not exist won’t save Larry’s estate. What I’m saying is that Ellison’s future rides on Sam Altman’s ability to raise funding and make revenue to the tune of $852 billion in the space of 4 years. Good luck, Larry! You’re going to need it.  For a ten person dev team, that’s $75,600 a year, and we’re only counting working days. If you raise a mere three months to an average of $50 a working day, that raises to $88,200  If you add a single month where you go over $100, you’re spending $102,900 a year. If you spend $300 a day, you’re now spending $756,000 on tokens for ten people. Enterprise deployments in massive organizations like Spotify or Uber with AI-pilled CEOs that allow budgets to run wild. I’d also say this is the case in large, well-funded startups. Smaller startups that use the subsidized “Teams” subscription. Individual users paying a monthly fee to access Claude or other AI subscriptions.

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Four Horsemen of the AIpocalypse

If you liked this piece, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Soundtrack — Megadeth — Hangar 18 (Eb Tuning) For the best part of four years I’ve been wrapped up in writing these massive, sprawling narratives about the AI bubble and the tech industry at large. I still intend to write them, but today I’m going to do what I do best — explaining all the odd shit that’s happening in the tech industry and explaining why it’s concerning to me.  And because I love a good bit, I’m tying these stories to my pale horses of the AIpocalypse — signs that things are beginning to unwind in the most annoying bubble in history.   Anyway, considering that the newsletter and the podcast are now my main form of income, I’m going to be experimenting with the formats across the free and premium to keep things interesting and varied.  Let’s start with a fairly direct statement: Anthropic should stop taking on new customers until it works out its capacity issues. So, generally any service — Netflix, for example — you use with any regularity has the “four nines” of availability, meaning that it’s up 99.99% of the time. Once a company grows beyond a certain scale, having four 9s is considered standard business practice… … unless you’re Anthropic! As of writing this sentence, Anthropic’s availability for its Claude Chatbot has 98.79% uptime, its platform/console is at 99.14%, its API is at 99.09%, and Claude Code is at 99.25% for the last 90 days.  Let me put this into context. When you have 99.99% uptime, a service is only down for a minute (and 0.48 of a second) each week. If you’re hitting 98.79% uptime, as with the Claude chatbot, your downtime jumps to two hours, one minute, and 58 seconds.  Or, put another way, 98.79% uptime equates to nearly four-and-a-half days in a calendar year where the service is unavailable. More-astonishingly, Claude for Government sits at 99.91%. Government services are generally expected to be four 9s minimum, or 5 (99.999%) for more important systems underlying things like emergency services.  This is a company that recently raised $30 billion dollars and gets talked about like somebody’s gifted child, yet Anthropic’s services seem to have constant uptime issues linked to a lack of capacity.  Per the Wall Street Journal : Yet Anthropic’s problems go far further than simple downtime ( as I discussed last week ), leading to (deliberately or otherwise) severe performance issues with Opus 4.6 :  While Anthropic claims that it doesn’t degrade models to better serve demand , that doesn’t really square with the many, many users complaining about the problem. Anthropic’s response has, for the most part, been to pretend like nothing is wrong, with a spokesperson waving off Carl Franzen of VentureBeat ( who has a great article on the situation here ) by pointing him to two different Twitter posts, neither of which actually explain what’s going on. Things only got worse with last week’s launch of Opus 4.7, which appears to have worse performance and burn more tokens.  Per Business Insider : I think it’s deeply bizarre that a huge company allegedly worth hundreds of billions of dollars A) can’t seem to keep its services online with any level of consistency, B) appears to be making its products worse, and C) refuses to actually address or discuss the problem. Users have been complaining about Claude models getting “dumber” going back as far as 2024 , each time faced with a tepid gaslighting from a company with a CEO that loves to talk about his AI products wiping out half of white collar labor . Some might frame this as Anthropic having “insatiable demand for its products,” but what I see is a terrible business with awful infrastructure run in an unethical way. It is blatantly, alarmingly obvious that Anthropic cannot afford to provide a stable and reliable service to its customers, and its plans to expand capacity appear to be signing deals with Broadcom that will come online “starting in 2027,” near-theoretical capacity with Hut8, which does not appear to have ever built an AI data center , and also with CoreWeave , a company that is yet to build the full capacity for its 2025 deals with OpenAI and only has around 850MW of “active power capacity” — so around 653MW of actual compute capacity — as of the end of 2025, up from 360MW of power at end of 2024 .    Remember: data centers take forever to build, and there’s only a limited amount of global capacity, most of which is taken up by Microsoft, Google, Amazon, Meta and OpenAI, with the first three of those already providing capacity to both Anthropic and OpenAI. We’re likely hitting the absolute physical limits of available AI compute capacity, if we haven’t already done so, and even if other data centers are coming online, is the plan to just hand them over to OpenAI or Anthropic in perpetuity? It’s also unclear what the goal of that additional capacity might be, as I discussed last week : What’s the goal, exactly? Providing a better experience to its current customers? Securing enough capacity to keep adding customers? Securing enough capacity to support larger models like Mythos? When, exactly, does Anthropic hit equilibrium, and what does that look like?  There’s also the issue of cost.  Anthropic is currently losing billions of dollars a year offering a service with amateurish availability and oscillating quality, and continues to accept new subscribers, meaning that capacity issues are not affecting its growth. As a result, adding more capacity simply makes the product work better for a much higher cost. Anthropic’s growth story is a sham built on selling subscriptions that let users burn anywhere from $8 to $13.50 for every dollar of subscription revenue and providing a brittle, inconsistent service, made possible only through a near-infinite stream of venture capital money and infrastructure providers footing the bill for data center construction. Put another way, Anthropic doesn’t have to play by the rules. Venture capital funding allows it to massively subsidize its services. The endless, breathless support from the media runs cover for the deterioration of its services. A lack of any true regulation of tech , let alone AI , means that it can rugpull its customers with varying rate limits whenever it feels like .  If Anthropic were forced to charge its actual costs — and no, I don’t believe its API is profitable no matter how many people misread Dario Amodei’s interview — its growth would quickly fall apart as customers faced the real costs of AI (which I’ll get to in a bit). If Anthropic was forced to provide a stable service, it would have to stop accepting new customers or massively increase its inference costs.  Anthropic is a con , and said con is only made possible through endless, specious hype. Everybody who blindly applauded everything this company did is a mark. Congratulations to all the current winners of the “Fell For It Again Award.” Per the Financial Times : So, yeah, anyone in the media who bought the line of shit from Dario Amodei that this was “too dangerous to release” is a mark. Cal Newport has an excellent piece debunking the hype , but my general feeling is that if Mythos was so powerful, how did Claude Code’s source code leak ?  Did… Anthropic not bother to use its super-powerful Mythos model to check? Or did it not find anything? Either way, very embarrassing for all involved.  As I’ve discussed in the past, only 5GW of AI compute capacity is currently under construction worldwide (based on research from Sightline Climate ), with “under construction” meaning everything from a scaffolding yard with a fence ( as is the case with Nscale’s Loughton-based data center ) to a building nearing handoff to the client.  I reached out to Sightline to get some clarity, and they told me that of the 114GW of capacity due to come online by the end of 2028, only 15.2GW is under construction, including the 5GW due in 2026.  That’s…very bad.  It gets worse when you realize that the majority of that construction is for two companies: So, to summarize, at least 4.6GW of the 15.2GW of data center capacity under construction is for OpenAI, with at least another 4GW of that reserved for Anthropic through partners like Microsoft, Google and Amazon. In truth, the number could be much higher.  This is a fundamentally insane situation. OpenAI and Anthropic both burn billions of dollars a year, with The Information reporting that Anthropic expects to burn at least $11 billion and OpenAI $25 billion in 2026 . The only way that these companies can continue to exist is by raising endless venture capital funding or, assuming they make it to IPO, endless debt offerings or at-the-market stock sales. It’s also very concerning that only such a small percentage of announced compute capacity is being built, especially when you run the numbers against NVIDIA’s actual sales. Last year, Jerome Darling of TD Cowen estimated that it cost around $30 million per megawatt in critical IT (GPUs, servers, storage, and so on) and $12 million to $14 million per megawatt to build a data center, making critical IT around 68% (at the higher end of construction) of the total cost-per-megawatt. Now, to be clear, those gigawatt and megawatt numbers for data centers refer to the power rather than critical IT , and if we take an average PUE (power usage efficiency, a measurement of how efficient a data center’s power is) of 1.35, we get 11.2GW of critical IT hardware, with the majority (I’d say 90%) being GPUs, bringing us down to around 10.1GW of GPUs. If we then cut that up into GB200 or GB300 NVL72 racks with a power draw of around 140KW, that’s around 71,429 racks’ worth of hardware at an average of $4 million each, which gives us around $285.7 billion in revenue for NVIDIA. NVIDIA claims it had a combined $500 billion in orders between 2025 and 2026 , and $1 trillion of sales through 2027 , and it’s unclear where any of those orders are meant to go other than a warehouse in Taiwan.  At this point, I think it’s fair to ask why anyone is buying more GPUs, as there’s nowhere to fucking put them. Every beat-and-raise earnings from NVIDIA is now deeply suspicious.  Last week, a report from Goldman Sachs revealed that ( and I quote ) “...companies are overrunning their initial budgets for inference by orders of magnitude (we heard one industry datapoint on inference costs in engineering now approaching about 10% of headcount cost, but could be on track to be on par with headcounts costs in the next several quarters based on current trajectories.”  To simplify, this means that some companies are spending as much as 10% of the cost of their employees on generative AI services, all without appearing to provide any stability, quality or efficiency gains, or (not that I want this) justification to lay people off.  The Information’s Laura Bratton also reported last week that Uber had managed to blow through its entire AI budget for the year a few months into 2026:  Uber’s CTO also added that about “...11% of real, live updates to the code in its backend systems are being written by AI agents primarily built with Claude Code, up from just a fraction of a percent three months ago.” Anyone who has ever used Uber’s app in the last year can see how well that’s going, especially if they’ve had to file any kind of support ticket. Honestly, I find this all completely fucking insane. The whole sales pitch for generative AI is that it’s meant to be this magical, efficiency-driving panacea, yet whenever you ask somebody about it the answer is either “yeah, we’re writing all the code with it!” without any described benefits or “it costs so much fucking money, man.”  Let’s get practical about these economics, and use Spotify as an example because its CEO proudly said that its “top engineers” are barely writing code anymore , though to be clear, the Goldman Sachs example didn’t specifically name any one company. For the sake of argument, let’s say that the company has 3000 engineers — one of its sites claims it has 2700 , but I’ve seen reports as high as 3500. Let’s also assume, based on the Spotify Blind (an anonymous social media site for tech workers), that these engineers make a median salary of 192,000 a year. In the event that Spotify spent 10% of its engineering headcount (around $576 million) on AI inference, it would be spending roughly $57.6 million, or approximately 4.1% of its $1.393 billion in Research and Development costs from its FY2025 annual report . Eager math-doers in the audience will note that 100% of headcount would be nearly half of the R&D budget, or around a quarter of its $2.2 billion in net income for the year. Now, to be clear, these numbers likely already include some AI inference spend, but I’m just trying to illustrate the sheer scale of the cost.  While this is great for Anthropic (and to a lesser extent OpenAI), I don’t see how it works out for any of its customers. A flat 10% bump on the cost of software engineering is the direct opposite of what AI was meant to do, and in the event that costs continue to rise, I’m not sure how anybody justifies the expense much further.  And we’re going to find out fairly quickly, because the world of token subsidies is going away. As I reported yesterday , internal documents have revealed that Microsoft plans to temporarily suspend individual account signups to its GitHub Copilot coding product, tighten rate limits across the board, remove Opus models from its $10-a-month Pro subscription, and transition from requests (single interactions with GitHub Copilot) towards token-based billing some time later this year, with Microsoft confirming some of these details (but not token-based billing) in a blog post . This is a significant move, driven by (per my own reporting) Microsoft’s week-over-week costs of running GitHub Copilot nearly doubling since January.  The move to token-based billing will see GitHub users charged based on their usage of the platform, and how many tokens their prompts consume — and thus, how much compute they use. It’s unclear at this time when this will begin, but it significantly changes the value of the product. I’ll also say that the fact that Microsoft has stopped signing up new paid GitHub Copilot subscriptions entirely is one of the most shocking moves in the history of software. I’ve literally never seen a company do this outside of products it intended to kill entirely, and that’s likely because — per my source — it intends to move paid customers over to token-based-billing, though it’s unclear what these tiers would look like, as the $10-a-month and $39-a-month subscriptions are mostly differentiated based on the amount of requests you can use.  What’s remarkable about this story is that Microsoft is one of the few players capable of bankrolling AI in perpetuity, with over $20 billion a quarter in profits since the middle of 2023 .  Its decision to start cutting costs around AI suggests that said costs have become unbearable — The Information reported back in January that it was on pace to spend $500 million a year with Anthropic alone , and if that amount has doubled, it likely means that Microsoft is spending upwards of ten times its GitHub Copilot revenue, as I can report today that at the end of 2025, GitHub Copilot was at around $1.08 billion, with the majority of that revenue coming from its CoPilot Business and Enterprise subscriptions.  The Information also reported a few weeks ago that GitHub had recently seen a surge of outages attributed to “spiking traffic as well as its effort to move its applications from its own servers to Microsoft’s Azure cloud”: “Agents” in this case could refer to just about anything — OpenAI’s Codex, Anthropic’s Claude Code, or even people plugging in the wasteful, questionably-useful OpenClaw to their GitHub Copilot account, and if that’s what happened, it’s very likely behind the move to Token-Based Billing and rate limits. In any case, if Microsoft’s making this move, it means that CFO Amy Hood — the woman behind last year’s pullback on data center construction — has decided that the subsidy party is over. Though Microsoft is yet to formally announce the move to Token-Based Billing, I imagine it’ll be sometime this week that it rips off the bandage. Two weeks ago, Anthropic did the same with its enterprise customers , shifting them to a flat $20-a-seat fee and otherwise charging the per-token rate for whatever models they wanted to use.  I’m making the call that by the end of 2026, a majority of AI services will move some or all of their customers to token-based billing as they reckon with the true costs of running AI models.  I kept things simple today both to give myself a bit of a break and because these were stories I felt needed telling.  Nevertheless, I do have to remark on how ridiculous everything has become. Everywhere you turn, somebody is talking about “agents” in a way that doesn’t remotely match with reality, like Aaron Levie’s epic screeds about how “ AI agents make it so every other company on the planet starts to create software for bringing automation to their workflows in a way that would be either infeasible technically or unaffordable economically ,” a statement that may as well be about fucking unicorns and manticores as far as its connections to reality.  I feel bad picking on Aaron, as he doesn’t seem like a bad guy. He is, however, increasingly-indicative of the hysterical brainrot of executive AI hysteria, where the only way to discuss the industry is in vaguely futuristic-sounding terms about “agents” and “inference” and “tokens as a commodity,” all with the intent of obfuscating the ugly, simple truth: that generative AI is deeply unprofitable, doesn’t seem to provide tangible productivity benefits, and appears to only lose both the business and the customer money.  Though my arguments might be verbose, they’re ultimately pretty simple: AI does not provide even an iota of the benefits — economic or otherwise — to justify its ruinous costs. Every new story that runs about cost-cutting or horrible burnrates increasingly validates my position, and for the most part, boosters respond by saying “ well LOOK at how BIG the REVENUES are .” It isn’t! AI revenues are dogshit. They’re awful. They’re pathetic. The entire industry — including OpenAI and Anthropic’s theoretical revenues of $13.1 billion and $4.5 billion — hit around $65 billion last year , and that includes the revenues from providing compute generated by neoclouds like CoreWeave and hyperscalers like Microsoft. I’m also just gonna come out and say it: I think the AI startups are misleading their investors and the general public about their revenues. My reporting from last year had OpenAI’s revenues at somewhere in the region of $4.3 billion in the first three quarters of 2025, and Anthropic CFO Krishna Rao said in an an affidavit that the company had made revenue “exceeding” (sigh) $5 billion through March 9, 2026 , which does not make sense when you add up all the annualized revenue figures reported about this company.  Cursor is also reportedly at $6 billion in annualized revenue (or around $500 million a month) and “gross margin positive” — which I also doubt given that it had to raise over $3 billion last year and is apparently raising another $2 billion this year. Even if said numbers were real, the majority of OpenAI, Cursor and Anthropic’s revenues come from subsidized software subscriptions. Things have gotten so dire that even Deidre Bosa of CNBC agrees with me that AI demand is inflated by token-maxxing and subsidized services. Otherwise, everybody else is making single or double-digit millions of dollars and losing hundreds of millions of dollars to get there. And per founder Scott Stevenson , overstating annualized revenues is extremely common, with AI startups booking “three-year-long” enterprise deals with the first year discounted and a twelve-month out : While it’s hard to say how widespread this potential act of fraud might be, Stevenson estimates that more than 50% of enterprise AI startups are using “contracted ARR” to pump their values. One (honest) founder responded to Stevenson saying that his company has $350,000 in contracted ARR but only $42,000 of ARR, adding that “next year is gonna be awesome though,” which I don’t think will be the case for what appears to be a chatbot for finding investors. This industry’s future is predicated entirely on the existence of infinite resources, and most AI companies are effectively front-ends for models owned by Anthropic and OpenAI, two other companies that rely on infinite resources to run their services and fund their infrastructure. And at the top of the pile sits NVIDIA, the largest company on the stock market, which is selling more GPUs than can be possibly installed, and very few people seem to notice or care.   I’m talking about hundreds of billions of dollars of GPUs sitting in warehouses that aren’t being installed, with it taking six months to install a single quarter’s worth of GPU sales . The assumption, based on every financial publication I’ve read, appears to be “it will keep selling GPUs forever, and it will all be so great.” Where are you going to put them, Jensen? Where do the fucking GPUs go? There isn’t enough capacity under construction! If, in fact, NVIDIA is actually selling as many GPUs as it says, it’s likely taking liberties with “ transfers of ownership ” where NVIDIA marks a product as “sold” to somebody that has yet to actually take it on. In any case, I keep coming back to the word “hysteria,” because it’s hard to find another word to describe this hype cycle. The way that the media, the markets, analysts, executives, and venture capitalists discuss AI is totally divorced from reality, discussing “agents” in terms that don’t match with reality and AI data centers in terms of “gigawatts” that are entirely fucking theoretical , all with a terrifying certainty that makes me wonder what it is I’m missing. But every sign points to me being right, and if I’m right at the scale I think I’m right, I think we’re about to have a legitimacy crisis in investing and mainstream media, because regular people are keenly aware that something isn’t right, in many cases, it’s because they’re able to count. OpenAI’s Stargate data centers account for 4.6GW — with 1.2GW in Abilene, Texas; 1.4GW in Shackelford, Texas; 1GW in Dona Ana, New Mexico; and 1GW in Port Washington, Wisconsin.  It’s safe to assume that with big tech’s hundreds of billions of dollars of capex that its data centers will make up a large amount — as much as 6GW — with most of that likely going to Anthropic or OpenAI. An indeterminately-large chunk could be Amazon’s Project Rainier in Indiana , which will “eventually” (per CNBC) draw more than 2.2GW of electricity.  While Amazon says it’s “ fully operational ,” it’s fucking lying, as it also claims that it has “nearly half a million Trainium 2 chips,” with each chip being 500 watts, and 500,000 times 500 watts being around 250MW. Other reports said it would be up to 1 million Trainium2 chips by the end of 2025, but that would still only amount to 500MW. Anthropic is apparently the primary tenant.  Anthropic also agreed to take 3.5GW of capacity of TPUs from Google Cloud , with the first 1GW coming online in 2027 , and also agreed to take a gigawatt from Microsoft made up of “Vera Rubin and Grace Blackwell systems,” meaning that these are likely data centers that are currently under construction. Anthropic and Google also announced in Q4 2025 that Anthropic would use 1 million TPUs as part of a new deal with Google Cloud, and that “well over” a gigawatt of capacity would come online in 2026 . Microsoft is also taking the 900MW extension to Stargate Abilene , and considering that most of Microsoft’s GPU infrastructure already goes to OpenAI, I can only imagine that’s where it’s going. I also will add that Satya Nadella claimed that Microsoft brought 2GW of capacity online in 2025 , and that its Fairwater Data Center cluster was “going live ahead of schedule ,” only to fail to clarify when that might happen or what said schedule was.  Microsoft’s also been relatively vague about the actual capacity, but based on there being “hundreds of thousands of GB200 GPUs,” that would be (assuming 300,000 GPUs) about 583MW.

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Stratechery 3 weeks ago

Tim Cook’s Impeccable Timing

Listen to this post : It’s the nature of business that the eulogy for a chief executive doesn’t happen when they die, but when they retire, or, in the case of Apple CEO Tim Cook, announce that they will step up to the role of Executive Chairman on September 1 . The one morbid exception is when a CEO dies on the job — or quits because they are dying — and the truth of the matter is that that is where any honest recounting of Cook’s incredibly successful tenure as Apple CEO, particularly from a financial perspective, has to begin. The numbers, to be clear, are extraordinary. Cook became CEO of Apple on August 24, 2011, and in the intervening 15 years revenue has increased 303%, profit 354%, and the value of Apple has gone from $297 billion to $4 trillion, a staggering 1,251% increase. The reason for Cook’s accession in 2011 became clear a mere six weeks later, when Steve Jobs passed away from cancer on October 5, 2011. Jobs’ death isn’t the reason Cook was chosen — Cook had already served as interim CEO while Jobs underwent treatment in 2009 — but I think the timing played a major role in making Cook arguably the greatest non-founder CEO of all time. Peter Thiel introduced the concept of Zero To One thusly: When we think about the future, we hope for a future of progress. That progress can take one of two forms. Horizontal or extensive progress means copying things that work — going from 1 to n. Horizontal progress is easy to imagine because we already know what it looks like. Vertical or intensive progress means doing new things — going from 0 to 1. Vertical progress is harder to imagine because it requires doing something nobody else has ever done. If you take one typewriter and build 100, you have made horizontal progress. If you have a typewriter and build a word processor, you have made vertical progress. Steve Jobs made 0 to 1 products, as he reminded the audience in the introduction to his most famous keynote : Every once in a while, a revolutionary product comes along that changes everything. First of all, one’s very fortunate if one gets to work on one of these in your career. Apple’s been very fortunate: it’s been able to introduce a few of these into the world. In 1984, we introduced the Macintosh. It didn’t just change Apple, it changed the whole computer industry. In 2001, we introduced the first iPod. It didn’t just change the way we all listen to music, it changed the entire music industry. Well, today we’re introducing three revolutionary products of this class. The first one: a widescreen iPod with touch controls. The second: a revolutionary mobile phone. And the third is a breakthrough Internet communications device. Three things…are you getting it? These are not three separate devices. This is one device, and we are calling it iPhone. Steve Jobs would, three years later, also introduce the iPad, which makes four distinct product categories if you’re counting. Perhaps the most important 0 to 1 product Jobs created, however, was Apple itself, which raises the question: what makes Apple Apple? “What Makes Apple Apple” isn’t a new question; it was the central question of Apple University, the internal training program the company launched in 2008. Apple University was hailed on the outside as a Steve Jobs creation, but while I’m sure he green lit the concept, it was clear to me as an intern on the Apple University team in 2010, that the program’s driving force was Tim Cook. The core of the program, at least when I was there, was what became known as The Cook Doctrine : We believe that we’re on the face of the Earth to make great products, and that’s not changing. We’re constantly focusing on innovating. We believe in the simple, not the complex. We believe that we need to own and control the primary technologies behind the products we make, and participate only in markets where we can make a significant contribution. We believe in saying no to thousands of projects so that we can really focus on the few that are truly important and meaningful to us. We believe in deep collaboration and cross-pollination of our groups, which allow us to innovate in a way that others cannot. And frankly, we don’t settle for anything less than excellence in every group in the company, and we have the self-honesty to admit when we’re wrong and the courage to change. And I think, regardless of who is in what job, those values are so embedded in this company that Apple will do extremely well. Cook explained this on Apple’s January 2009 earnings call , during Jobs’ first leave of absence, in response to a question about how Apple would fare without its founder. It’s a brilliant statement, but it is — as the last paragraph makes clear — ultimately about maintaining, nurturing, and growing what Jobs built. That is why I started this Article by highlighting the timing of Cook’s ascent to the CEO role. The challenge for CEOs following iconic founders is that the person who took the company from 0 to 1 usually sticks around for 2, 3, 4, etc.; by the time they step down the only way forward is often down. Jobs, however, by virtue of leaving the world too soon, left Apple only a few years after its most important 0 to 1 product ever, meaning it was Cook who was in charge of growing and expanding Apple’s most revolutionary device yet. Cook, to be clear, managed this brilliantly. Under his watch the iPhone not only got better every year, but expanded its market to every carrier in basically every country, and expanded the line from one model in two colors to five models in a plethora of colors sold at the scale of hundreds of millions of units a year. Cook was, without question, an operational genius. Moreover, this was clearly the case even before he scaled the iPhone to unimaginable scale. When Cook joined Apple in 1998 the company’s operations — centered on Apple’s own factories and warehouses — were a massive drag on the company; Cook methodically shut them down and shifted Apple’s manufacturing base to China, creating a just-in-time supply chain that year-after-year coordinated a worldwide network of suppliers to deliver Apple’s ever-expanding product line to customers’ doorsteps and a fleet of beautiful and brand-expanding stores. There was not, under Cook’s leadership, a single significant product issue or recall. Cook also oversaw the introduction of major new products, most notably AirPods and Apple Watch; the “Wearables, Home, and Accessories” category delivered $35.4 billion in revenue last year, which would rank 128 on the Fortune 500. Still, both products are derivative of the iPhone; Cook’s signature 0 to 1 product, the Apple Vision Pro, is more of a 0.5. Cook’s more momentous contribution to Apple’s top line was the elevation of Services. The Google search deal actually originated in 2002 with an agreement to make Google the default search service for Safari on the Mac, and was extended to the iPhone in 2007; Google’s motivation was to ensure that Apple never competed for their core business , and Cook was happy to take an ever increasing amount of pure profit. The App Store also predated Cook; Steve Jobs said during the App Store’s introduction that “we keep 30 [percent] to pay for running the App Store”, and called it “the best deal going to distribute applications to mobile platforms”. It’s important to note that, in 2008, this was true! The App Store really was a great deal. Three years later, in a July 28, 2011 email — less than a month before Cook officially became CEO — Phil Schiller wondered if Apple should lower its take once they were making $1 billion a year in profit from the App Store. John Gruber, writing on Daring Fireball in 2021 , wondered what might have been had Cook followed Schiller’s advice: In my imagination, a world where Apple had used Phil Schiller’s memo above as a game plan for the App Store over the last decade is a better place for everyone today: developers for sure, but also users, and, yes, Apple itself. I’ve often said that Apple’s priorities are consistent: Apple’s own needs first, users’ second, developers’ third. Apple, for obvious reasons, does not like to talk about the Apple-first part of those priorities, but Cook made explicit during his testimony during the Epic trial that when user and developer needs conflict, Apple sides with users. (Hence App Tracking Transparency, for example.) These priorities are as they should be. I’m not complaining about their order. But putting developer needs third doesn’t mean they should be neglected or overlooked. A large base of developers who are experts on developing and designing for Apple’s proprietary platforms is an incredible asset. Making those developers happy — happy enough to keep them wanting to work and focus on Apple’s platforms — is good for Apple itself. I want to agree with Gruber — I was criticizing Apple’s App Store policies within weeks of starting Stratechery , years before it became a major issue — but from a shareholder perspective, i.e. Cook’s ultimate bosses, it’s hard to argue with Apple’s uncompromising approach. Last year Apple Services generated 26% of Apple’s revenue and 41% of the company’s profit; more importantly, Services continues to grow year-over-year, even as iPhone growth has slowed from the go-go years. Another way to frame the Services question is to say that Gruber is concerned about the long-term importance of something that is somewhat ineffable — developer willingness and desire to support Apple’s platforms — which is, at least in Gruber’s mind, essential for Apple’s long-term health. Cook, in this critique, prioritized Apple’s financial results and shareholder returns over what was best for Apple in the long run. This isn’t the only part of Apple’s business where this critique has validity. Cook’s greatest triumph was, as I noted above, completely overhauling and subsequently scaling Apple’s operations, which first and foremost meant developing a heavy dependence on China. This dependence was not inevitable: Patrick McGee explained in Apple In China , which I consider one of the all-time great books about the tech industry, how Apple made China into the manufacturing behemoth it became. McGee added in a Stratechery Interview : Let me just refer back to something that you wrote I think a few months ago when you called the last 20, 25 years, like the golden age for companies like Apple and Silicon Valley focused on software and Chinese taking care of the hardware manufacturing. That is a perfect partnership, and if we were living in a simulation and it ended tomorrow, you’d give props for Apple to taking advantage of the situation better than anybody else. The problem is we’re probably not living in the simulation and things go on, and I’ve got this rather disquieting conclusion where, look, Apple’s still really good probably, they’re not as good as they once were under Jony Ive, but they’re still good at industrial design and product design, but they don’t do any operations in our own country. That’s all dependent on China. You’ve called this in fact the biggest violation of the Tim Cook doctrine to own and control your destiny, but the Chinese aren’t just doing the operations anymore, they also have industrial design, product design, manufacturing design. It really is ironic: Tim Cook built what is arguably Apple’s most important technology — its ability to build the world’s best personal computer products at astronomical scale — and did so in a way that leaves Apple more vulnerable than anyone to the deteriorating relationship between the United States and China. China was certainly good for the bottom line, but was it good for Apple’s long-run sustainability? This same critique — of favoring a financially optimal strategy over long-term sustainability — may also one day be levied on the biggest question Cook leaves his successor: what impact will AI have on Apple? Apple has, to date, avoided spending hundreds of billions of dollars on the AI buildout, and there is one potential future where the company profits from AI by selling the devices everyone uses to access commoditized models; there is another future where AI becomes the means by which Apple’s 50 Years of Integration is finally disrupted by companies that actually invested in the technology of the future. If Tim Cook’s timing was fortunate in terms of when in Apple’s lifecycle he took the reins, then I would call his timing in terms of when in Apple’s lifecycle he is stepping down as being prudent, both for his legacy and for Apple’s future. Apple is, in terms of its traditional business model, in a better place than it has ever been. The iPhone line is fantastic, and selling at a record pace; the Mac, meanwhile, is poised to massively expand its market share as Apple Silicon — another Jobs initiative, appropriately invested in and nurtured by Cook — makes the Mac the computer of choice for both the high end (thanks to Apple Silicon’s performance and unified memory architecture) and the low end (the iPhone chip-based MacBook Neo significantly expands Apple’s addressable market). Meanwhile, the Services business continues to grow. Cook is stepping down after Apple’s best-ever quarter, a milestone that very much captures his tenure, for better and for worse. At the same time, the AI question looms — and it suggests that Something Is Rotten in the State of Cupertino . The new Siri still hasn’t launched, and when it does, it will be with Google’s technology at the core. That was, as I wrote in an Update , a momentous decision for Apple’s future: Apple’s plans are a bit like the alcoholic who admits that they have a drinking problem, but promises to limit their intake to social occasions. Namely, how exactly does Apple plan on replacing Gemini with its own models when (1) Google has more talent, (2) Google spends far more on infrastructure, and (3) Gemini will be continually increasing from the current level, where it is far ahead of Apple’s efforts? Moreover, there is now a new factor working against Apple: if this white-labeling effort works, then the bar for “good enough” will be much higher than it is currently. Will Apple, after all of the trouble they are going through to fix Siri, actually be willing to tear out a model that works so that they can once again roll their own solution, particularly when that solution hasn’t faced the market pressure of actually working, while Gemini has? In short, I think Apple has made a good decision here for short term reasons, but I don’t think it’s a short-term decision: I strongly suspect that Apple, whether it has admitted it to itself or not, has just committed itself to depending on 3rd-parties for AI for the long run. As I noted above and in that Update, this decision may work out; if it doesn’t, however, the sting will be felt long after Cook is gone. To that end, I certainly hope that John Ternus, the new CEO, was heavily involved in the decision; truthfully, he should have made it. To that end, it’s right that Cook is stepping down now. Jobs might have been responsible for taking Apple from 0 to 1, but it was Cook that took Apple from 1 to $436 billion in revenue and $118 billion in profit last year. It’s a testament to his capabilities and execution that Apple didn’t suffer any sort of post-founder hangover; only time will tell if, along the way, Cook created the conditions for a crash out, by virtue of he himself forgetting The Cook Doctrine and what makes Apple Apple.

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Premium: The Hater's Guide to Private Credit

A few years ago, I made the mistake of filling out a form to look into a business loan, one that I never ended up getting. Since then I receive no less than three texts a day offering me lines of credit ranging from $150,000 to as much as $10 million, each one boasting about how quickly they could fund me and how easy said funding would be. Some claim that they’ve been “looking over my file” (I’ve never provided any actual information), others say that they’re “already talking to underwriting,” and some straight up say that they can get me the money in the next 24 hours. Some of the texts begin with a name (“Hey Ed, It’s Zack”) or sternly say “Edward, it’s time to raise capital.” Others cut straight to the chase and tell me that they have been “arranged for five hundred and fourty (sic) thousand,” and others send the entire terms of a loan that I assume will be harder to get than responding “yes.” While many of them are obvious, blatant scams, others lead to complaint-filled Better Business Bureau pages that show that, somehow, these entities have sent them real money, albeit under terms that piss off their customers and occasionally lead to them getting sued by the government . That’s because right now, anybody with the right lawyers, accountants and financial backing can create their own fund and start issuing loans to virtually anyone they deem worthy.  And while they’ll all say that they use “industry-standard” underwriting, no regulatory standard exists. This, my friends, is the world of private credit — a giant, barely-regulated time bomb of indeterminate (but most certainly trillions of dollars ) size that has become a load-bearing pillar of pensions and insurance funds, and according to Federal Reserve data , private credit has borrowed around $300 billion (as of 2023) from big banks, representing around 14% of their total loans.  The eager, aggressive growth of private credit has even led it to start targeting individual investors, per the Financial Times : The FT also neatly summarizes the problem of having regular investors involving themselves in the world of private credit: And those high returns come with a cost: a lack of flexibility ranging from “you can only redeem your funds every quarter, and only a small percentage of your funds,” to “you can’t redeem your funds if everybody else tries to at the same time,” to “we make the rules here, shithead.” When an asset manager sets up a private credit fund, it often sets terms around how often — or how much — investors can pull at once, usually set around 5%, because in most cases, private credit funds are highly illiquid , as despite them acting like a financial institution , they more often than not don’t have very much money on hand for investors. Why? Because the “private” part of private credit means that the lender directly negotiates with the borrower and values the loans based on their own internal models. Said loans generally have little or no secondary market, and private credit wants to hold them to maturity so that it can continue to provide ongoing yield (which I’ll explain in a little bit). Things were going great for private credit for the longest time, but late last year, some buzzkills at the Financial Times discovered that auto parts manufacturer First Brands and subprime auto loan company Tricolor had taken on billions of dollars of loans under dodgy circumstances, double-pledging collateral (IE: giving the same stuff as collateral on different loans) and outright falsifying lending documents, allowing the both of them to borrow upwards of $10 billion from private credit firms, including billions from North Carolina-based firm Onset Capital, which nearly collapsed but was eventually rescued by Silver Point Capital . After the collapse of First Brands and Tricolor, JP Morgan’s Jamie Dimon said that “ when you see cockroaches, there are probably more ,” the kind of sinister quote baked specifically to lead off a movie about a financial crisis. Seemingly inspired to start freaking people out, on November 5, software-focused asset manager Blue Owl announced it would merge its publicly-traded OBDC fund with its privately-traded OBDC II fund , and, well, it didn’t go well, per my Hater’s Guide To Private Equity : Two weeks later on November 18 2025, Blue Owl said it would freeze redemptions on OBDC II until after the merger closed, then canceled it a day later citing “market conditions.” Two months later in February 2026, Blue Owl would announce that it was permanently halting redemptions from OBDC II, and sold $1.4 billion in assets from both OBDC II and two other funds. The buyers of the assets? Several large pension funds that had a vested interest in keeping the value of the assets high , and Kuvare, an insurance company with $20 billion of assets under management that Blue Owl bought in 2024 . This is perfectly legal, extremely normal, and very good. Private equity is also the principal funding source for private equity’s leveraged buyouts, accounting for over 70% of all leveraged buyout funding for the last decade , which means that private credit — and anyone unfortunate enough to fund it! — is existentially tied to the ability of the portfolio companies’ ability to pay, and their continued ability to refinance their debt. This is a problem when your assets are decaying in value. As I discussed in the Hater’s Guide To Private Equity , PE firms massively over-invested between 2017 and 2021, leaving them with a backlog of 31,000 companies valued at $3.7 trillion that they can’t sell or take public, likely because many of these acquisitions were vastly overvalued.  You see, when things were really good , asset managers raised hundreds of billions of dollars from pension funds, insurance funds (some of which they owned), and institutional investors, and then issued hundreds of billions of dollars more (at times using leverage from banks to do so) in loans to private equity firms that went on to buy everything from software companies to restaurant franchises. Said debt would immediately go on the balance sheet of the acquired company, creating a “reliable,” “consistent” yield with every loan payment that the fund could then send on to its investors, on a quarterly or monthly basis. The problem is that these investments were made under very different economic circumstances , when money was easy to raise and exits were straightforward, leading to many assets being massively overvalued, and holding debt that was issued under revenue and growth projections that only made sense in a low-interest environment. In simple terms, these loans were given to companies assuming they’d be able to pay them long term, and assuming that the sunny economic conditions would continue indefinitely, making them tough to refinance or, in some cases, for the debtor to continue paying. And nowhere is that problem more pronounced than the world of software. The jitters caused by First Brands and Tricolor eventually turned into full-on tremors thanks to the SaaSpocalypse ( covered in the Hater’s Guide a month ago ): The SaaSpocalypse is often (incorrectly) described as a result of AI “disrupting incumbent software companies,” when the reality is that private equity (and private credit) made the mistaken bet that every single software company would grow in perpetuity.  The larger software industry is in decline , with a McKinsey study of 116 public software companies with over $500 million in revenue from 2024 showing that growth efficiency had halved since 2021 as sales and marketing spend exploded, and BDO’s annual SaaS report from 2025 saying that SaaS company growth ranged from flat to active declines, which is why there’s now $46.9 billion in distressed software loans as of February 2026 . And to be clear, it’s not just private equity’s victims that are taking out loans. Over $62 billion in venture debt was issued in 2025 , with established companies like Databricks ( $5.2 billion in credit per the Wall Street Journal in 2024) and Dropbox ( $2.7 billion from Blackstone in 2025 ) raising debt just as the overall software industry slows, with AI failing to pick up the pace.   This is a big fucking problem for private credit. Per the Wall Street Journal , asset managers are massively exposed to software companies, and have deliberately mislabeled some assets (such as saying a healthcare software company is just a “healthcare company”) to obfuscate the scale of the problem: And as I’ll explain, “obfuscation” is a big part of the private credit business model. If I’m honest, preparing this week’s premium has been remarkably difficult, both in the amount of information I’ve had to pull together and how deeply worried it’s made me.  In the aftermath of the great financial crisis, insurance and pension funds found themselves desperate for yield — regular returns — to meet their payment obligations. Private credit has become the yield-bearer of choice, feeding over a trillion dollars of these funds’ investments into leveraged buyouts, AI data centers, loans to software companies, and failing restaurant franchises.  In some cases, asset managers have purchased insurance companies with the explicit intention of using them as funders for future private credit investments, such as Apollo’s acquisition of Athene , KKR’s acquisition of Global Atlantic , and Blue Owl’s acquisition of Kuvare . More on this later, as it fucking sucks. Asset managers offering private credit market themselves as bank-like stewards of capital, but lack many (if any) of the restrictions that make you actually trust a bank. They self-deal, investing their insurance affiliates’ funds in their own equity investments (such as when KKR used Global Atlantic to invest in data center developer CyrusOne , a company it acquired in 2022 ), value and revalue assets based on mysterious and undocumented private models, and account for (as I mentioned) 70% of all funding of leveraged buyouts in the last decade, of which 30 to 40% were software companies purchased between 2018 and 2022 , meaning that hundreds of billions of dollars of retirement and insurance funds are dependent on overvalued software companies paying loans funded during the zero interest free era. While a market crash feels scary, what’s far scarier is that the present and future ability of many retirement and insurance funds is dependent on whether private equity-owned entities, software companies. and AI data center firms are able to keep paying their debts. If private credit fund returns begin to lag, the retirement and insurance industry lacks a viable replacement, and I don’t know how to fix that. Fuck it, I’ll level with you. I think asset managers are scumbags, and I think the way that they do business is fucking disgraceful. The unbelievable amount of risk that asset managers have passed onto people’s fucking retirements is enough to turn my stomach, and if I’m honest, I don’t understand how this entire thing hasn’t broken already. If I had to guess, it’s one of two reasons: that private credit funds have yet to escalate their risk enough, or we’re yet to see said risk’s consequences, with First Brands and Tricolor being just the beginning. And Wall Street is prepared to profit, with S&P Dow Jones launching a credit default swap derivatives product to bet against a collection of 25 different banks, insurers, REITs, and business development companies. Bank of America, Deutsche Bank, Barclays and Goldman Sachs will start selling the derivatives next week, per Reuters, and I’d argue that enough demand could spark a genuine panic across publicly-traded asset managers.  In any case, this is a situation where I fear not one massive catastrophe, but a series of smaller calamities caused by decades of hubris and questionable risk management resulting from the unbelievably stupid decision to let private entities act like banks.  This is the Hater’s Guide To Private Credit, or The Big Shart.

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Stratechery 4 weeks ago

An Interview with F1 Driver and Venture Capitalist Nico Rosberg About the Drive to Win

Listen to this post: Good morning, This week’s Stratechery Interview is with F1 driver-turned-venture capitalist Nico Rosberg . Rosberg started his F1 career in 2005, and retired after winning the world championship in 2016; Rosberg spent his last four years as teammates on Mercedes with his childhood friend Lewis Hamilton in one of the most intenst teammate rivalries in F1 history. Over the last several years, however, Rosberg has reinvented himself as a venture capitalist, founding Rosberg Ventures , with a specific focus on leveraging his F1 background to build connections between European money and Silicon Valley startups in one direction, and startup products and German businesses in the other. In this interview we cover all aspects of Rosberg’s journey, from having a steering wheel in his crib, pioneering the use of sports psychology in F1, and his decision to retire on top of the world. Then, we discuss how F1 builds connections, the similarities between founders and drivers, and how he realized he could leverage that in a new competition: winning as an investor. What I found particularly interesting is how Rosberg’s background and history seems so varied and unconnected on the surface, yet are clearly linked by a consistent ethos of maximizing opportunity in the service of winning. As a reminder, all Stratechery content, including interviews, is available as a podcast; click the link at the top of this email to add Stratechery to your podcast player. On to the Interview: This interview is lightly edited for clarity. Nico Rosberg, welcome to Stratechery. Nico Rosberg: Thank you very much, Ben, it’s really an honor to be on the show. I hear so much about your show always especially when I’m in the Bay Area. Well, I don’t normally interview venture capitalists on Stratechery, but you are no normal venture capitalist, which you use to your advantage. I want to ask you about that, but needless to say, that made this an easy exception to make, particularly since I’m a big Formula 1 fan. To that end, I always start my interviews talking about the subject background, we may spend a bit more time on yours if that’s okay with you, it’s pretty fascinating. NR: I understand. With pleasure. Okay, good. Well, you were born in 1985 in West Germany to a German mother and a Finnish father. Your father Keke was the 1982 Formula 1 world champion. Was there a steering wheel in your crib when you came home from the hospital? NR: There was actually, yes. (Laughing) Oh, that’s funny. NR: On my Facebook page you would see photos of me in a go-kart when I’m like three years old with a helmet on and everything, so yeah, it was an early discovery of that passion. I’m interested about that because obviously your father was tremendously successful, is he immediately all in on, “You have to do what I did”, or was there ultimately a bit of humoring you, “You can come along and try this but I’m not sure you could ever measure up to what I did?”. NR: There was a go-kart track near our house and he was going there with his friends even before I was born and then when I was born, and then I was six, seven years old, we just gave it a go, I enjoyed it, and I looked pretty fast also. So then he was like, “Maybe this can become a father-son hobby”, it just went from there and then you start doing a race here, a race there, I started winning the races kind of immediately and so that even that hooks me even even more than when you win, of course, it’s amazing, it’s an amazing motivation. So that’s how we just kind of got going and it became an amazing father-son hobby to share. We spent a lot of time with each other, we traveled in a motorhome to the races, so it was really lovely. There definitely is a bit to driving a car very fast. On one hand, of course, you started early, and you see the history of Formula 1 drivers, they start early, but you took to it right away. It’s definitely like father, like son in that regard. NR: Indeed. I think as in every sport — you also see it with golf or tennis — you have to start pretty early now it just gives you a head start and in practicing those skills. And I think, yeah, I guess I inherited some of those genes from my father because we need to be very good at hand-eye coordination, that’s super important. NR: We need to be also very good at processing things very quickly because we have things coming at us at 220 miles an hour, our eyes are flickering left and right all the time, just taking in all the inputs that we’re seeing and also feeling, so I think that also probably has to be a strength of ours. There’s a lot of stuff in your background about your parents really pushing you in terms of academics, learning lots of languages, all that sort of thing. Was that unique to you, or to your bit, it always strikes me that Formula 1 drivers all come across as very intelligent. And to your point, there’s such a high degree of information processing that’s happening on, is that the norm, generally speaking? NR: I think you probably need to be a bit street smarter, at least, to be a successful F1 driver than maybe in some other sports, because we depend so much on this high technology car, and if we’re not able to understand the car, set it up properly, be at least street smart about all these things, then it doesn’t matter how talented you are, you’ll never be able to go fast. So probably I would say that in our sport, yeah, that comes a little bit more to the fore than maybe in other sports. But in my case, actually, my parents pushing me at school was the contrary, my mom and my dad would usually come in late at night and say, “OK, stop now”, because I was always very hard working at school. Somehow we had a group of friends, everybody wanted to achieve, and I wanted to achieve as well, and I had to catch up because I was missing half the week every other week because I was racing. So my parents were more actually telling me to stop now because I was trying to make too much of an effort to catch up. Interesting, because a bit I want to get to here is you’ve had such a widely varying career, even since you finished racing, you finished relatively young , and so that has been a theme for you all along, is like you born with the steering wheel in your crib, but you’re interested in more than that. NR: Yeah, I really always enjoyed the academic side. In fact, if I wasn’t going to make it as a driver, I already had a place reserved for me in Imperial College in London to study aeronautics, that was my plan B of how to get into F1, which would have been as an aerodynamicist. Right, design the car instead of driving it. NR: I don’t know if I would have gotten there in the end, but I think I had a good shot, so that was my plan B was already set. You’re most famous for your rivalry with Lewis Hamilton but as I understand it you actually met him quite young you were teammates in carting as well? NR: It’s a pretty crazy story because the McLaren Formula One team wanted to set up a little go-kart team at the time, and the two rising star drivers at the time was Lewis Hamilton from Great Britain and myself down south, and so they actually funded our two go-karting seasons. And so it was just the two of us driving for the McLaren Mercedes go-karting team and we were winning all the races and championships. Unfortunately for me, more often than not, it was Lewis winning and I was second, but there we go. So it’s incredible because we were best friends at the time and we were 13 years old and we were on holiday together all the time and dreaming, “Imagine what it would be like in 15 years to be in the F1 team together, winning races and championships?”, and it was impossible to achieve that dream, just seemed so far away. And yet really 15 years later, we’re in the Mercedes F1 team as teammates fighting for races and championships, so it’s a pretty incredible story. I mean, why did it seem even that impossible, though? I mean, your dad was an F1 driver, you’ve been racing in karts. What makes F1 feel so far away? NR: Well, come on, you can imagine if you’re 13 year old and you’re playing in your regional tennis camp in the middle of nowhere that you look at the television and you see [Jannik] Sinner and [Carlos] Alcaraz fighting for the Monaco Masters that’s going to look like extremely impossible and far away. Right, but there wasn’t a bit of total self-belief that, “I’m going to be there, there’s no question”? NR: Well maybe Lewis is a little bit more like that, I’m more sensitive, more insecure, less self-belief, so I never actually really believed of myself that I could get there and be good enough, which has pros and cons to think like that, because it also is an incredibly strong motivator. When you don’t have that self-confidence, you just fight so hard to prepare to the best of your abilities all the time. So it has pros and cons, and it was nice to see that, of course, someone like me that did not believe until the very last corner, I was still able to actually win in the end, so that was reassuring. I’m curious about this mindset bit, because this has been an area that you’ve actually talked a lot about. In 2007, you stopped working with your father as closely as you were, went to work with a sports psychologist. At what point was it clear to you that this mental aspect is going to be super important to your success? NR: That became clear to me in my first year of F1 because it was mentally just an enormous struggle. We had a bad car, so we’re either breaking down or finishing well out of the points all the time and it was a really rough start to my career. And this is with Williams at the time? NR: Yeah, with Williams. At times it was almost as if like, “Oof, I might not get taken on for the second year”, because it was such a rough start. So mentally, it was incredibly hard because my dream is at stake, my dream is to be an F1 driver, to win races, so that was difficult. So I decided that, “I’m spending four hours a day on training my body, why am I not training my brain? There must be solutions out there to improve my mental state”. So I sought out help, and I found a psychologist/philosopher and this was incredible for my life, for my performance, I worked 10 years with him. In the winter, two hours every two days, so it was like an incredible effort, it was harder than the physical training was actually the mental training. It was a combination of learning to meditate, learning to visualize, to learning the power of repetition, and also learning to understand myself better. “Why am I scared?”, “Why am I anxious, jealous?”, because then you cannot switch those emotions off very easily or almost not at all. But when you understand why they’re there, you can really adapt your reaction and that has a snowball effect, because when you react in a much better and more appropriate way, it has an enormous snowball effect on your life so it’s these kind of learnings that really helped me so much. Was this pretty novel for an F1 driver to seek this out and do this sort of training at the time? NR: Yeah, it’s a bit like in the startup world. Founders are not really allowed to admit that they’re scared of failing or that they’re working with a brain doctor, as some like to call it at the time in F1, so it was not something that I could really tell anybody about this because it would look weak in a way, but actually it became my superpower to go through that process. And now there’s a little bit more acceptance now, there’s been a couple of other drivers talking about it. I think even Lando Norris, the world champion last year, he sought help in the middle of last year as he was struggling mentally, clearly, and his championship was slipping away from him, and he went out and sought help and made enormous progress, and that’s what got him the world championship in the end so that was great to see. Lando’s always interesting because he seems to wear his insecurities on his sleeve, they just come through sort of so tangibly. Did you feel a lot of like sympathy for his sort of struggles and working through that? NR: Yeah, totally. That’s the state of mind that I can very much relate with, and that’s what people love also because he’s very authentic, so that’s really appreciated. At the same time I wrote Lando a direct message on Instagram and he never replied, but at least I wanted to see if maybe he would read it, because I’ve been through what he’s what he’s been through, and one of the obvious things that I would change if I was Lando, and he did change it a little bit, is to not always talk about the glass half empty, even when he was on pole position he almost only spoke about that one corner where he messed up rather than like, “Hey, that was almost the best lap of my life”. I mean, both is right. “Hey, that was almost the best lap of my entire life”, that would be correct or, “Ah, damn, I messed that last corner up so bad”, that would also be correct. You know? And he just says, “I messed that last corner up”, and, “I need to get my stuff together”, and that’s just unnecessary because it’s repetition, and it really ingrains itself in your mind that you always, if you say, “I make mistakes always”, you’re really going to believe that you make mistakes always. So that’s something that he could quite easily just adapt, even if he keeps on thinking that that, but don’t say it, and don’t say it out to the whole world, because that’s a whole tsunami that you’re setting off there repeatedly, which is not going to be beneficial to your performance. You’ve talked about talking to founders and not being able to show weaknesses. Have there been any examples in the times that as you’ve been an investor and talking to different companies, where you’ve identified someone and been like, “Look, you’re kind of a Lando Norris here” — maybe that’s not the words that you used — but, “Let me talk to you about your mindset and how you can shift that”, has that come in handy yet? NR: I really enjoy that because founders are really very similar to high performance athletes. NR: They’re extremely competitive, their drive is unbelievable, they’re very courageous also, because you have to be so damn brave to bet the company over and over as you’re innovating and pivoting, so there’s great similarities, and that’s why I really enjoy speaking to founders. Just now in the Bay Area, that’s very often the topic that I speak to founders about and they enjoy that as well, to discuss that kind of topic mentally, how they approach that and everything, and so that’s really enjoyable. I think I can really add value as well as I learned for myself also, but I can really add value by adding from my experience. The more founders that you talk to, is there a bit where — if you go back to F1, it’s very visible who’s the best, like it’s very measurable in a certain sense, but it’s interesting at F1 because sometimes you could have a great driver who doesn’t have a great car, and yet people will still say, “That person is excellent, they’re just limited by their circumstances”. Do you get a similar sense in being in tech, dealing with founders, and being able to separate the circumstances from the person and saying, “There’s something there even if the circumstances aren’t allowing it to show”? NR: That’s one very, very important ingredient for a successful founder, because actually it will be often many, many years until there’s any validation as to what he’s building or she’s building and the best founders have to be extremely resilient and not feel the need to bow to consensus thinking of people around them or of their board or whatever. They are the visionary and they have to believe with such high conviction in their idea, in what they’re building and see it through. Because if it was obvious, then everybody would be building it, and most of the time, they’re creating something that’s just not obvious to sometimes anybody except for themselves in the early stages, so that’s absolutely a very important trait. However, in combination with an extreme curiosity and desire to learn and remain open to new ideas and everything, so it’s a balance that has to be found. And again, that’s pretty rare to find both attributes within a founder, but usually that’s the case. Is that tension between the sort of insecurity and confidence and uncertainty and curiosity? Is that what you’re zoomed in on, what you’re looking for? NR: Yeah, totally. Because sometimes it’s like it opposes each other. Right, it’s a paradox. NR: Someone who’s very self-confident their idea will be will be completely arrogant and just so sure that their way is is the right way and that’s it and then they will not be very curious, so that’s why you don’t find it in every person and it’s important. I think these two character traits are very, very important. Continuing with the background, you have a YouTube channel that has 1.46 million subscribers, you haven’t posted on it for a while, but there used to be a whole host of videos. But I went back, scrolled all the way to the bottom, and the original upload was in 2011. A lot of people didn’t know what YouTube was at that point or barely did, how did you find YouTube and why did you start posting videos? NR: As an athlete, there was an opportunity that suddenly that came in those years, which was to connect closer with those out there that were supporting me. Were you the first one to really do that? NR: No, not the first, but I joined some of the early movers and it was amazing to see how you could directly connect with your fanbase, and there was also the belief that, of course, with time, Formula 1 is also about marketing and that can give you an edge over some other drivers. If you build a big following, a big brand for yourself, and you become highly relevant to brands for sponsorship, etc., then a team might choose you over someone who just drives fast. So there’s also that element that to be a successful F1 driver, usually it helps to really try and excel in every single domain that may be relevant and that domain plays a role, as well as working well with the media, because the media is so powerful and that’s a game you also need to try and nail. I’m curious about the sponsorship angle. F1 obviously has huge amounts of sponsorships, it’s an amazing sport where people will willingly wear gear with a bunch of sponsorships on it — I guess all racing is sort of like this. But right now, now that tech is huge and F1 is huge, there’s a lot of tech sponsorships of F1 and I’m just sort of curious: I’m in tech, but generally a lot of these companies are enterprise companies , a lot of B2B things, and this whole world of sponsorships and what goes on around that is somewhat foreign to me. I’m just a blogger here in Wisconsin before in Taiwan, what is in that game and how involved are the drivers? Is that a huge thing? You have to go out and actually help win these sponsorships too? Or you should show up to a bunch of events? I’m just curious, how does that world work? NR: So a few things here. First of all, because of Netflix , the sponsorship fees that the teams are now requesting are like 2-3x from what they were just six, seven years ago. Is that just because it’s more popular or because they also their logos also show up on Netflix? NR: Because it’s so much more popular and because it’s now become relevant in the US. So the whole tech industry has become interested and you’ll see most companies are now also sponsoring. I mean, look at just the Mercedes team , of course, but look at the Audi team also . They have Revolut, so the bank that’s come out of the startup ecosystem, ElevenLabs , the voice AI global Leader, all of these companies. In fact, I’m actually, because I’m so deeply connected now with Silicon Valley, I am more and more also kind of casually supporting some of these tech companies with sponsorships in F1. I’m just presenting one dev tools company, multi-billion dollar, with an opportunity to sponsor a team this week, I’m just sending that through. Because the sponsorship fees have increased so much, a team like Mercedes has $400 million in annual sponsorship revenue. $400 million! That’s so crazy. And then you add their share of TV revenues on top, so they get to beyond like $600 million in annual revenue, and because they inserted budget caps in F1, they don’t spend more than $300 million, even including driver salaries and everything. So they are so hugely profitable, these F1 teams, or especially the successful ones and that’s why the CrowdStrike founder now, George Kurtz , he just bought 5% of the Mercedes F1 team. And that stake, I mean, the Mercedes F1 team was valued at $6 billion, unbelievable. you know so so he paid three hundred three hundred million dollars he paid for a five percent share. Do you feel like you were 10 years too early? NR: I missed that train, because I think with a bit of effort probably at some point I could have had a nice little share in a F1 team somewhere, but I completely missed the train. It’s incredible how this sport has become has become really a business case now, and these these F1 teams have become investable assets, which never used to be the case, so it’s quite phenomenal. So these sponsors, we drivers spend a lot of time with these companies then, they invite all of their customers, I do dinner with them then even during a race weekend or the next morning for breakfast. Monaco Grand Prix, I’m at the Hotel de Paris having breakfast with one of the sponsors, so the drivers do spend a lot of time with those sponsors. And apart from that, the sponsors want visibility because visibility for their logo is just an amazing credibility stamp, and also they want to bring and host people at the races, so that’s what it’s about and I think it works amazingly well. I was talking to Michael Cannon-Brooks , Atlassian is now sponsoring Williams, and this idea of you actually have 24, or this year 22 , around the world, pre-planned, clear places to meet customers and bring them there. He’s like, “It makes scheduling very easy, it’s very straightforward”. NR: And for someone like Atlassian the customers are there anyways in the paddock, because the C-levels of all big companies are always there. To make deals in the paddock is incredible, an incredible opportunity and even I myself, so I do work for Mercedes F1 and they don’t actually pay me in Euros, they actually pay me most of the time with tickets for the F1 races, because I too, I love to host the VC community at the races, it’s such a great way to get to know people, build friendships and of course, yeah, it’s very important for me to really build relationships in this ecosystem. That’s super interesting. Speaking of Mercedes, when Mercedes rejoined F1, acquired Brawn , you were the first driver alongside Michael Schumacher, who was then replaced by Lewis Hamilton — two pretty impressive names to have as teammates to say the least. The rivalries between teammates is the stuff of lore in Formula 1 but is it actually underrated how intense that is? NR: So the norm in F1 is always that a team has a number one driver and a number two driver and that’s clearly kind of set in stone, and that’s the way you go racing. It’s very unusual that a team has two number one drivers, the most legendary such pairing was Ayrton Senna and Alain Prost at McLaren, and that ended in total disaster after only two years. They were crashing, then one guy quit, and it was just a total mess. It’s okay and not too bad as long as you’re racing for like fifth and sixth and seventh place — but as soon as you have the best car and you as teammates are fighting for every single race win, it just becomes so hard because you’re always going to push the boundaries and go into those gray areas because there’s a championship at stake and that’s your childhood dream and that’s what then happened between Lewis and I also. It kind of just spiraled from one going a little bit too far, then the other one paying back and then back again and then crashing and it just became very, very tense and difficult to manage. It was a very uncomfortable environment to be in because not only are you kind of enemies within the team, but also the whole team as such cannot really take a side anymore and they need to stay neutral, so they can’t really support you either anymore, so it’s a complicated dynamic. Well, you lasted longer than Prost and Senna, because I think you made it three years with Lewis Hamilton. Is that right? NR: Four, actually. We would have kept going, I had another contract for a few more years so it was kind of borderline manageable, but only after Toto Wolff made us sign a contract whereby it didn’t matter who was at fault, but if ever we crashed together, then we would have to split the bill, the repair bill, 50-50, and my most expensive one was $360,000 and after that, I made sure to leave extra space when Lewis was anywhere close. (laughing) That’s amazing. Why did you decide to retire? I mean, you finally win, you overcome Lewis, and then you’re done at 31. NR: I gave it a thousand percent, really, much more than any that I thought I could give. Total life commitment, insane intensity, the whole thing, mentally, physically and I achieved my dream, I achieved my dream in the best possible way, I beat the greatest of all time, I won that Formula 1 World Championship with Mercedes , the legendary car brand, it’s not possible for me to do better. I had a young family at home, a child at home, baby at home so it just felt like the right moment for the most beautiful exit possible for me that would carry me for the rest of my life. So it was a bit of a rational decision in that way and I just felt that was what I wanted to try and do. Of course, it was scary because when you make such a decision, you don’t really know how it’s going to go and how you’re going to feel. But now in hindsight, for me personally, it was really the best thing I could do and a great decision, which I’m very lucky to have been able to exit in that way. And a lot of founders listening, because I know you’re very popular with founders, also your podcast, they will be able to relate, it’s kind of the $10 billion or $50 billion dollar exit. NR: Once you put your life into it and you’ve created an enormous success and change people’s lives and then you go out on a high, I think that was my dream to do it that way. You made a lot of changes before that last year, too. But then there’s all these stories of that last year where you won the title, focusing on things like jet lag or like your nutrition and all those bits and pieces. Was that just like, “I have to figure something else to finally get over the hump”? NR: I tried to perfect every single possible marginal gain possible, that was really what I was about, and it went from working with a Professor of Sleep at Harvard , and who now has created a startup based on what we were working together at the time called Timeshifter , actually, which is a nice anecdote. And so there, for example, the secret was eliminating jet lag for the whole year because jet lag is a disaster. As an athlete, the difference between 99% focus and 100% focus is the difference between coming first and second, and jet lag just destroys you, and we’re traveling from continent to continent all the time. I managed to do a whole season with absolutely 0.0 jet lag, and it’s pretty simple. Of course, it takes a lot of discipline, but pretty simple. The secret was one-and-a-half hours maximum of time shift per day and then blackout glasses in the evening, two hours before needing to go to sleep and then also immediately upon waking up, 10,000 lux, like a light, you know, which you’re staring into, which you also see with Bryan Johnson , he does that and then, yeah, I mean, as long as I followed that, it was incredible. So I eliminated jet lag from my whole life for that year and every detail I worked on in that way, you know, really everything. So you see my helmet was black and it was bare carbon because I realized that the helmet was 80 grams and every gram counts in our sport so I took the paint off my helmet, just every single detail. I really tried to work on every single marginal gain possible. This sounds absolutely hellish with family and little kids at home. I can see why you once you accomplished it, you were done. NR: Yeah, of course. I mean with a little baby at home it required a lot of a lot of a great commitment also from my wife Vivian at the time and great support and and she did that awesomely so I’m very very grateful for that. Now you’re sitting here as an investor, but we’re a decade on from when you retired, what was the path to get to where you are now and to realize that, “This is what I want to do with the rest of my life”? NR: Seven years after retiring was first of all, just trying everything and nothing, trying to figure out what could be next in my life. And it’s hard because as an athlete, you are like CEO, you know, you’re top of the company, and you feel like being the king and then after your sports career you drop to zero. There’s nothing there and you cannot use your skill that you learned for something new, it’s just gone. And it’s very hard to accept that you really start from zero and you don’t even know if you’re going to have success in something new or not. So I tried a lot of things and and now finally I’ve landed on what I really enjoy doing and it’s being fully into the venture capital ecosystem building my own VC firm, Rosberg Ventures , out of Europe, investing a lot in the USA or even primarily in the USA. So super exciting and yeah, and I hit the ground running and I’m able to win also pretty quickly, which is what is really motivating. What made you realize there was this opportunity? If you sort of zoom out, this idea that there’s money in Europe, there’s opportunity in the U.S., someone needs to connect those two things together. But was there a specific conversation or something that came along that’s like, “Oh, I could actually do this and be good at it”? NR: Well more than money in Europe it was money in my bank account which was just sitting there. That makes sense. NR: And I was like, “What am I going to do with that?”, because it’s really really hard to invest capital across generations in a smart way. It’s like super, super difficult, as most people will know that or many people know. The way led to the Yale Endowment — everybody who’s interested in finance has once looked at the Yale Endowment because David Swensen is the gold standard for investing capital across generations. And my light bulb moment was then seeing that David Swensen had by then put 20% of the Yale endowment into venture capital, 20%, that’s $8 billion, and it was by far his best performing asset class with 21% yearly performance, 21% IRR. So that was my light bulb moment because I said, “Wow, I love startup anyways”, but I didn’t know you could make an asset class out of this, “Let me try and replicate what David Swensen did”, and I believe that with time because I have my unique angles, including F1, that with time, I can also build the right access by adding value into the ecosystem and everything to kind of replicate the approach that David Swensen took to the asset class. And that’s where we are now, we actually made it work. What are those unique angles? I think that sort of ties this together. You have the F1 background, you’re European. NR: So the unique angle, of course I have the F1 platform, which is a really unique advantage to be able to meet people from the VC ecosystem, make friendships, get insights. Appear on this podcast. NR: (laughing) I’m very, very lucky in that sense. But that’s something you seem to think about very strategically. Like, “This is an advantage that I have, I’m going to exploit this and push this”. Is this part of the thesis up front, particularly once you started? NR: Well, first of all, I really enjoy welcoming this incredible community to my sport, it’s amazing for me to be able to showcase my sport in a way. So this is where you did better from Drive to Survive in the end, because even if you sort of missed that era, now suddenly everyone’s interested in F1. NR: Oh yeah, definitely, I would not be here today if it wasn’t for Drive to Survive because that’s what has really engaged the whole tech community in my sport. It’s lovely to be able to invite people, bring them up close, show them what my sport is about, and see how excited everybody is and to share that with them is really amazing, so I enjoy that. And it’s a great opportunity to, as I said, build friendships and get insights, but then also to add value. How does that start? First of all, of course, curating the group that I invite. I invite the founder and then I invite the CIO of a big company and they then actually have a very valuable exchange. The CIO happens to be looking for the product that the founder is building, the founder obviously needs to go to market, so there’s a great way for me to build connections, and that’s how you start adding value. And beyond that, what we do is also we bring U.S. innovation to the German large corporates, we help with that. So Germany is your specific focus in particular in Europe. NR: Because I’m German, and because of my history and everything, I’m very well connected in Germany to all the C-levels in the large corporates. Does this even go back to like not just growing up in Germany, but also working for Mercedes, being the driver who’s interacting with all this? NR: Yeah, of course. All these large caps have been sponsors in F1, they’re all in the paddock, so I know them very well, and they’re all in desperate need of transformation now. Of course, there’s AI, there’s sustainability, there’s all these points and they’re not exactly the fastest, the German companies. They’re a little bit — many of them are real legacy businesses, who are not necessarily known to being the most brave when it comes to adopting new innovation and things like that. And are these generally like just regular companies, like manufacturer companies, things like that? NR: It goes all the way to the car manufacturers, whether it’s BMW or Mercedes and we have found a unique positioning where we’re able to support, just selectively, with bringing their attention to a couple of products that are just being built in the US in the startup ecosystem, whether it was vibe coding or it’s even legal tech, all these different things, and we can bring their attention to some of these innovations and really add value by creating these connections. So this is one of the secret sauces to Rosberg Ventures and to adding value, which works very well, and we’re hosting dinners with some of the C-levels and inviting some of the startups, etc., and it works very well. So you recently announced a new fund, $200 million assets under management . How did you grow your network on the asset side? Is that mostly then German money that’s coming back to the U.S. and you’re completing the cycle there? NR: Mainly German, so it’s German capital because the Europeans really lack connectivity, I realized that the Europeans lack access to U.S. venture capital and they know of the importance and the value that’s being created there, but they don’t have the access and they really kind of miss the boat on that, so it’s not too easy to convince them that, “Hey let’s join forces and partner up here, and let’s invest in the best opportunities in the U.S.”. So that’s been working very well and my way to raise or to convince these families is really going via the principle who I may know from F1 or whatever and then I say — I don’t even say too much like what i’m building because you don’t want to sell straight away — it’s more like, “Hey can you introduce me to your family office? I would love to just have a conversation with them”, and then the introduction, and I speak to them, I explain what we’re doing, and it’s just an obvious one. We’re kind of indexing the top 10 VC funds in the U.S., and also the top 10 growth stage companies, startups in the U.S., and indexing those and it’s kind of a no-brainer then, that’s how we’ve been able to raise capital very, very quickly. That makes sense. So everyone sees the opportunity, it’s not clear to get the capital in, you go in first sort of as like a seed investor with your own money, and that sort of starts that virtuous cycle, and that makes sense and then they get access to the German market in the long run. You’re bringing a unique angle and it’s just all about deal flow, I think it’s pretty compelling. Why is it so hard to do business in Europe ? Has everyone just given up on having a big startup ecosystem there and, “Let’s just get our money into the U.S.”? NR: So you mean the startup ecosystem in Europe? NR: There are flashes of real hope at the moment. Vibe coding was pioneered in Europe, the vibe coding for prosumers, that’s Lovable out of Sweden, and there’s many other examples. I mean, ElevenLabs, the global leader in voice AI, European, and many, many more examples. So there is flashes of real hope. But of course, we lack the breadth in the whole ecosystem and that’s as a result of a few things. It’s a bit of a chicken-and-egg. One, of course, it’s much harder to scale in Europe because of the geographical limitations, it’s so hard to go from Germany to France, different language, different regulatory framework, it’s just a huge friction there in the go-to market, so that’s one challenge. And then historically also, there’s been quite a lag in the distributions and liquidity in that asset class in Europe and so therefore, funding is not as ample as in the U.S. So it’s kind of a chicken-and-egg there also. But I think Europe is really working on trying to introduce one regulatory framework across the entire Europe, across all countries for startups, so that’s in the plan, so a lot is happening, and let’s see if Europe can develop more and more such promising companies. How have you managed this shift? You started out sort of a fund of funds sort of model, then you mentioned you’re doing more direct investing. Is that just a natural evolution of getting more access, having more assets under management? Or what was that explicit goal and strategy that you were seeking to pursue? NR: Well I think the holy grail in venture capital is is to invest directly in the startups and the fund of fund was the natural starting point from an asset class point of view, also from from copying and being inspired by what Yale did, and then from there the fund of fund is like a Trojan horse because it gets you positioned well into into the market where you see everything and then it really helps to identify which are the breakout startups, which are the most promising with the generational founders. So it really helps to create a short list and also to create those connections and to build those opportunities to actually invest directly in the startups. We met in San Francisco a couple of months ago, you had just met with Dreamer , I actually met with them the next day, they launched and were immediately acquired by Meta , was that your first exit of a direct investment? NR: So this is an important point that I don’t just like try and support the companies that I’ve backed. So in this case, this was the CTO of Stripe, the ex-CTO of Stripe, who was my friend, David Singleton , he built this together with Hugo [Barra] , who used to have a senior role at Facebook. Yep, I knew him when he was at Xiaomi , he was at Google, he was at Meta, he’s been all over the place. NR: Everywhere, it’s an incredibly promising founding team, and so I was just trying to support. And they happened to say that Stratechery, that they were the biggest fans in the world of you and Stratechery, so I was like, “Okay, well, that’s easy, I just met Ben yesterday, so I can make the connection there”. Yeah, it’s a pity how that went — I mean, pity because also from our point of view, I was so excited about that product, actually, it was vibe coding AI agents. Yep, it’s very compelling. I was looking forward to writing about it, they got snapped up before I could even get there. NR: I was looking forward to really using it at scale, but, yeah, now it’s bought by Meta and let’s see what Meta does with it, but it will certainly be, I’m sure, very promising what they build with that. As you’ve made this transition and levered up into tech and going from fund of funds to direct investment, it’s a time of great upheaval in tech , given AI. Theoretically, this should mean more startup opportunities. On the other hand, the frontier lab models might just eat everything. How are you thinking about that as an investor? Is it like, “I’m finally getting to the stage where I can get into startups, and now I’m not sure that I want to”? Or are you optimistic? NR: I’m very optimistic. I’m very optimistic because AI, the value creation within this wave of AI is going to be something like we’ve never seen before, and I do think there’s a lot of opportunities beyond just the frontier labs to capture market share, create new markets. But at the same time, you do need to be careful because we see the legal tech. Legal tech is a really big new market that’s being created there with a leader like Harvey and Legora , the two leaders, and then now Anthropic came out with a product which kind of starts to threaten their position a little bit. And Anthropic has been doing that for every sector, it feels like almost, so that is a little bit of a concern. It does feel like a safer place at the moment to be invested in frontier labs and neo labs, that does seem the more safe place to be. But nevertheless, I think there’s like, for example, Elevan Labs, voice AI, it’s very defensible what they’re building. They are a frontier lab themselves, by the way, because they build their own models. But still, voice probably is going to commoditize, the research, as in many cases and there it’s then going to be about the platform, distribution, products. And there, ElevenLabs is doing an excellent job. So it does look at the moment like they’re going to be able to really win and sustain any potential threat from these frontier labs so there are examples where beyond the frontier labs, many, many examples where they can be success stories, so it’s an exciting time. You mentioned platform and distribution, and this sort of seems to be a theme: you’ve thought about the F1 reputation and background, “I can leverage that, I know these sort of companies, I can leverage that”, you saw YouTube early on, you were on that, you’re here on this interview. Is that why you still do Sky Sports? Everyone’s favorite commentator , is that you love to commentate, does that keep Nico Rosberg sort of front and center? NR: You’re right. I do enjoy staying connected with the sport, but there’s the second reason that it’s really helpful for me to stay kind of relevant and it does help me also with relevance, even in the tech ecosystem. Because, of course, if then some people enjoy watching me and things like that, it’s easier to connect with them in future, even in the tech ecosystem. So that is twofold. We talked before, you were born with sort of steering wheel in your crib, in some respects, a advantageous background. But what I see as an overall theme is pretty consistently you identifying and leveraging your advantages and like what we just articulated is a good example. So now you’re in the investing world, totally separate, but figuring out what you have, how to work with it and build towards that. Is that the overarching sort of theme that you see in your life? What still drives you, is it that bit about being a little bit insecure and wanting to prove yourself and being super competitive? Is that just like you can’t turn that off and that’s what that’s why you’re still here? NR: I’m a super extreme competitor, I need to compete, I want to win, and I have now chosen venture capital as my space to try and win more and more in future. And I think, yeah, this is what I’m carrying over from the sport. I was very methodical about how do I get that win, in sports, every detail. I worked on every single detail possible to put all the pieces together to be the best that I could be and to get to that win eventually and I think that’s something that I’m now replicating in the world of venture capital, trying to optimize for everything and put everything together to be able to win more and more. How do you think about that with your kids, just out of curiosity? Your daughter sort of popped into the background on the call here. NR: So with my kids, because I went through such an extreme intensity in my sporting career, I, with them, am more focused on well-being rather than pushing them towards some success. But at the same time, you just credited your massive drive and competitiveness with your success. NR: Exactly, yeah, but wellbeing and happiness is what I put at a higher level for my kids and that doesn’t necessarily have to be success. So I’m very eager to push to try and help them discover their real passions, and we’re getting there. So my daughter, I put her in a go-kart two weeks ago, she drove slower than I could walk, so I could walk faster, and she ended up crying, so I hope she doesn’t listen to this one day, but I don’t see which one it is either, so we’re fine because I have two daughters. So it was clear that this is not her passion, and then we will never go again. But I can see that her passion is music, guitar, singing and so there I do nudge her towards more lessons, guitar lessons, drum lessons, without overdoing it, because I see that that’s her natural passion, you know? So that’s the approach I’m taking, but definitely really focused on happiness and well-being. So you mentioned you’re on holiday in Ibiza. I understand you have an ice cream shop there , is that right? NR: So yeah, with my wife, because she’s an interior designer, so she’s super creative and for some reason, we both of us, we love ice cream and we’ve been coming to Ibiza all our life, and there’s never been a nice ice cream place. So just as a hobby, we just said, “Hey, why don’t we open one ourselves?” — our friend, our common friend, he likes to make ice cream, so we do that, and it’s become a huge business. We have now a chain here in Ibiza, and very successful, and it’s the number one ice cream place. So Ben, next time you’re in Ibiza, ice cream is on us. (laughing) Sounds like a deal. You have an interesting life in terms of you learn five languages growing up, you have parents from different countries. Obviously, as part of being an F1 driver, you’re all over the world. You’re doing this connection between Germany in particular and Silicon Valley. Do you feel like, you talk about eras and riding them and starting and beginning in terms of F1 — do you feel that era, you’re like the pinnacle of like globalized civilization? Do you feel that that is an era that is going to persist past you, or do you feel that sort of cracking and changing? NR: This is related to the sport or? Just in general, just given you are like an international man of mystery, although maybe not that mysterious, but it’s like your superpower is connecting and linking all these disparate pieces together and seeing the ability to sort of build through them. And I’m wondering, is that something, an opportunity, that you think is going to persist given the way the world is going? NR: Well, I’m very optimistic in that sense, I’m very optimistic. And I see a long road ahead. And I think it’s an amazing time for venture capital now, it’s incredible, a time that we’ve never seen something like that before, the speed of innovation, and there may be my F1 speed also helps me, it doesn’t scare me at the moment because I’m used to driving 220 miles an hour. So maybe I’m one of the only people in the world where I’m not getting scared by the speed of innovation that we’re seeing in the startup ecosystem, because I’m quite used to speed. You actually focused a lot on e-mobility and electric vehicles. I do have to ask you, how are you feeling about the current F1 regulations , this 50-50 split? A lot of complaints that driver’s skills being taken away. What’s your view? NR: I saw a message from Toto actually recently, and he said, the F1 driver job might be the very last place that AI is going to endanger that job. Because it’s very, very hard for AI to try and replicate what we are doing in that racing car at the edge of physics. But has it been diminished a little bit if you’re going around a curve or you’re on a straight and your car’s just slowing down on its own? NR: No, I understand, F1 has tried to stay technologically relevant so they have gone full hybrid which is one of the most efficient powertrains in the world, the way they’ve done it, but of course yeah it’s a little bit to the detriment of racing on the edge, because now they’re going through a high speed corner towards the end of the straight and they actually downshift on the straight after the corner which is unheard of in the sport. But to be honest I’m quite easygoing about that because I like to really focus on just, “Is the racing exciting?”, “Is there good battles?”, “Is it unpredictable?”, “Is there rivalries?”, and as long as that’s happening, I think all fans will kind of forget about these regulations and will just enjoy the sport once again and be super excited. I think the season is shaping up really nicely. We have this super underdog, this 19-year-old who was really having a struggle last year, who suddenly has come to life and is showing his real talent and is dominating the championship so far, 19 years old, he’s still like a child, it’s incredible, Kimi Antonelli , Italian guy, driving for Mercedes. So it’s so exciting to see him in front and now everybody else trying to catch up to him, I think it’s great. You are associated with Mercedes, they are doing very well, I am a Kimi fan, my kids got a picture with him last year, so he’s by default who we’re cheering, for sure. But who do you cheer for in F1? NR: I do cheer for Kimi as well now because he used to be my driver in go-karting as well, so I know him since he’s 12 years old, and he is a generational talent of the level of [Max] Verstappen, Hamilton. His talent is exceptional and he’s so humble and authentic and nice guy also, so you can only cheer for him. It’s such a challenge that he’s facing, being a driver of the Mercedes team, leading the championship all of a sudden, an incredible challenge, and I can so relate because I was in that position and it’s so hard. It is so hard what he’s getting himself into now for the rest of the year. I’ve been writing him also and I said, just without telling him what he should do, I just told him like what I did and what worked for me, I’ve been writing him. And one thing, for example, was just really take it race by race, don’t think about the end of the season, don’t think about championship, just race by race, try and optimize for the next race, go in to win, and that’s it and then the rest will just see how it goes. Are you surprised it’s been a decade and Lewis [Hamilton] is still in F1? NR: I am quite surprised, because that’s a long time, and we weren’t exactly young at the time. So when I stopped 10 years ago, he was already almost 32 and he’s still going now, which is incredible and huge respect, respect for him to keep going, keep grinding, keep the motivation. Still seems as motivated as ever, driving really well again this year, he’ll definitely win some races this year, I think he’ll win some, so he’s doing really well. And every win that Lewis gets is another notch on your belt, right? NR: (laughing) That’s a little bit of an egotistical view to it, which sometimes I do think about. Yes, the better my success looks, which is nice, yeah. You won one, you beat Lewis. It’s a championship, if you’re going to win one, that’s about as good as it gets. But, hey, you didn’t stop there, it’s super impressive what you built, very interesting to learn more and I look forward in 10 years when Nico Rosberg is the champion VC investor. What is it, the Midas list ? Are you gunning for number one? NR: Yeah, sure, Midas List, that’s gonna be a hard one, but those kind of targets, at some point, yes. Nico Rosberg, great to talk to you. NR: Thank you very much. This Daily Update Interview is also available as a podcast. To receive it in your podcast player, visit Stratechery . The Daily Update is intended for a single recipient, but occasional forwarding is totally fine! If you would like to order multiple subscriptions for your team with a group discount (minimum 5), please contact me directly. Thanks for being a supporter, and have a great day!

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AI Is Really Weird

If you like this piece and want to support my independent reporting and analysis, why not subscribe to my premium newsletter? It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I just put out a massive Hater’s Guide To The SaaSpocalypse , as well as last week’s deep dive into How AI Isn't Too Big To Fail .  Subscribing helps directly support my free work, and premium subscribers don’t see this ad in their inbox. I can’t get over how weird the AI bubble has become. Hyperscalers are planning to spend over $600 billion on data center construction and GPUs predominantly bought from NVIDIA, the largest company on the stock market, all to power generative AI, a technology that’s so powerful that none of them will discuss how much it’s making them, or what it is we’re all meant to be so excited.  To make matters weirder , Microsoft, a company that spent $37.5 billion in capital expenditures in its last quarter on AI , recently updated the terms and conditions of its LLM-powered “Copilot” service to say that it was “for entertainment purposes only,” discussing a product that apparently has 15 million users as part of enterprise Microsoft 365 subscriptions , and is sold to both local and national governments overseas , including the US federal government . That’s so weird! What’re you doing Microsoft? What do you mean it’s for entertainment purposes? You’re building massive data centers to drive this!  Well, okay, you’re building them at some point. As I discussed a few weeks ago, despite everybody talking about the hundreds of gigawatts of data centers being built “to power AI,” only 5GW are actually “under construction,” with “under construction” meaning anything from “we’ve got some scaffolding up” to “we’re about to hand over the keys to the customer.”  But isn’t it weird we’re even building those data centers to begin with? Why? What is it that AI does that makes it so essential — or, rather, entertaining — that we keep funding and building these things? Every day we hear about “the power of AI,” we’re beaten over the head with scary propaganda saying “AI will take our jobs,” but nobody can really explain — outside of outright falsehoods about “AI replacing all software engineers” — what it is that makes any of this worthy of taking up any oxygen let alone essential or a justification for so many billions of dollars of investment. Instead of providing an actual answer of some sort , AI boosters respond by saying it’s “just like the dot com bubble” — another weird thing to do considering 168,000 people lost their jobs as the NASDAQ dropped by 80% in two years , and only 16% of the world even used the internet , and those that did in America had an average internet speed of 50 kilobits per second ( and only 52% of them had access in 2000 anyway ). Conversely, to quote myself: And with that incredibly easy access , only 3% of households pay for AI . Boosters will again use this talking point to say that “we’re in the early days,” but that’s only true if you think that “early days” means “people aren’t really using it yet.”  Yet the “early days” argument is inherently deceptive. While the Large Language Model hype cycle might have only begun in 2022, the entirety of the media and markets have focused their attention on AI, along with hundreds of billions of dollars of venture capital and nearly a trillion dollars of hyperscale capex investment . AI progress isn’t hampered by a lack of access, talent, resources, novel approaches, or industry buy-in, but by a single-minded focus on Large Language Models, a technology that has been so obviously-limited from the very beginning that Gary Marcus was able to call it in 2022 .  Saying it’s “the early days” also doesn’t really make sense when faced with the rotten and incredibly unprofitable economics of AI. The early days of the internet were not unprofitable due to the underlying technology of serving websites , but the incredibly shitty businesses that people were building. Pets.com spent $400 per customer in customer acquisition costs , millions of dollars on advertising, and had hundreds of employees for a business with a little over $600,000 in quarterly revenue — and as a result, nothing about its failure was about “the early days of the internet” at all, as was the case with Kozmo, or any number of other dot com flameouts.  Similarly, internet infrastructure companies like Winstar collapsed because they tried to grow too fast and signed stupid deals rather than anything about the underlying technology’s flaws. For example, in 1998, Lucent Technologies signed its largest deal — a $2 billion “equipment and finance agreement” — with telecommunications company Winstar , which promised to bring in “$100 million in new business over the next five years” and build a giant wireless broadband network, along with expanding Winstar’s optical networking. Eager math-heads in the audience will be able to see the issue of borrowing $2 billion to make $100 million over five years, as will eager news-heads laugh at WIRED magazine in 1999 saying that Winstar’s “small white dish antennas…[heralded] a new era and new mind-set in telecommunications.” Winstar died two years later because its business was built to grow at a rate that its underlying product couldn’t support . In the end, microwave internet (high-speed internet delivered via radio waves) has become an $8 billion-a-year industry , despite everybody’s excitement. In any case, anytime that somebody tells you that we’re in “the early days of AI” has either been conned or is in the process of conning you, as they’re using it to deflect from issues of efficacy or underlying economic weakness.  In fact, that’s a great place to go next. Probably the weirdest thing about this entire era is how nobody wants to talk about the fact that AI isn’t actually doing very much, and that AI agents are just chatbots plugged into an API. Per Redpoint Ventures’ Reflections on the State of the Software and AI Market , “the agent maturity curve is still early, but the TAM implications are enormous,” with agents able to “...run discretely for minutes, [and] execute end-to-end tasks with some oversight.” What tasks, exactly? Who knows! Truly, nobody seems able to say. To paraphrase Steven Levy at WIRED , 2025 was meant to be the year of AI agents, but turned out to be the year of talking about AI agents. Agents were/are meant to be autonomous pieces of software that go off and do distinct tasks. In reality, it’s kind of hard to say what those tasks are. “AI agent” now refers to literally anything anybody wants it to, but ultimately means “chatbot that has access to some systems.”  The New York Times’ Ezra Klein recently talked to the entity currently inhabiting former journalist and Anthropic co-founder Jack Clark recently about “how fast AI agents would rip through the economy,” but despite speaking for over an hour, the closest we got was “it wrote up a predator-prey simulation (a complex-sounding but extremely-common kind of webgame that Anthropic likely ingested through its training material )” and “chatbots that talk to each other about tasks,” and if you think I’m kidding, this is how he described it: Anyway, this is all bad, because multiple papers have now shown that, and I quote, agents are “...incapable of carrying out computational and agentic tasks beyond a certain complexity,” with Futurism adding that said complexity was pretty low . The word “agent” is meant to make you think of powerful autonomous systems that carry out complex and minute tasks, when in reality it’s…a chatbot. It’s always a fucking chatbot. It might be a chatbot with API access or a chatbot that generates a plan that another chatbot looks at and says something about, but it’s still chatbots talking to chatbots. When you strip away the puffery, nobody seems to actually talk about what AI does.  Let’s take a look at CNBC’s piece on Goldman Sachs’ supposed contract with Anthropic to build “autonomous systems for time-intensive, high-volume back-office work”: …okay, but like, what does it do? Right, brilliant. Great. Love it. What tasks? What is the thing you’re paying for? Okay, great, we have two things it might do in the future , and that’s “employee surveillance” (?) and making pitchbooks. The upshot is that, with the help of the agents in development, clients will be onboarded faster and issues with trade reconciliation or other accounting matters will be solved faster, Argenti said. Onboarding? Chatbot. “Issues with trade reconciliation”? Chatbot connected to a knowledge base, like we’ve had for years but worse and more expensive. Oh, and “other accounting matters” will be solved faster, always with the future tense with these guys. How about Anthropic and outsourcing body shop giant InfoSys’ “AI agents for telecommunications and other regulated industries ”? Let’s go through the list of tasks and say what they mean, my comments in bold: How about OpenAI’s “Frontier” platform for businesses to “ build, deploy and manage AI agents that do real work” ?  Shared context? Chatbot. Onboarding? Chatbot. Hands-on learning with feedback? Chatbot. Clear permissions and boundaries? Chatbot setting. Let’s check out the diagram! Uhuh. Great. What real-world tasks? Uhhh.  Reason over data? Chatbot. “Complex tasks”? No idea, it doesn’t say. “Working with files”? Doesn’t say how it works with files, but I’d bet it can analyze, summarize and create charts based on them that may or may not have errors in them, and based on my experience of trying to get these things to make charts (as a test, I’d never use them in my actual work), it doesn’t seem to be able to do that. “Evaluation and optimization loops”? Unclear, because we have no idea what the tasks are. What are the agents planning, acting, or executing on? Again, no idea.  Yet the media continues to perpetuate the myth of some sort of present or future “agentic AI” that will destroy all employment. A few weeks ago, CNBC mindlessly repeated that ServiceNow CEO Bill McDermott believed that agents would send college grad unemployment over 30% . NowAssist , ServiceNow’s AI platform, is capable of — you guessed it! — summarization, conversational exchanges, content creation, code generation and search, a fucking chatbot just like the other chatbots.  A few weeks ago, The New York Times wrote about how “AI agents are fun, useful, but [not to] give them your credit card,” saying that they can “do more than just chat…they can edit files, send emails, book trips and cause trouble”: Sure sounds like you connected a chatbot to your email there Mr. Heyneman.  Let’s go through these: Yes, you can string together chatbots with various APIs and have the chatbot be able to activate certain systems. You could also do the same with a button you bought on Etsy connected to your computer via USB if you really wanted to. The ability to connect something to something else does not mean that anything useful happens at the end, and LLMs are extremely bad at the kind of deterministic actions that define the modern knowledge economy, especially when choosing to do them based on their interpretation of human language. AI agents do not, as sold, actually exist. Every “AI agent” you read about is a chatbot talking to another chatbot connected to an API and a system of record, and the reason that you haven’t heard about their incredible achievements is because AI agents are, for the most part, fundamentally broken.  Even OpenClaw, which CNBC confusingly called a “ ChatGPT moment ,” is just a series of chatbots with the added functionality of requiring root access to your computer and access to your files and emails. Let’s see how CNBC described it back in February :  Hmmm interesting. I wonder if they say what that means: Reading this, you might be fooled into believing that OpenClaw can actually do any of this stuff correctly, and you’d be wrong! OpenClaw is doing the same chatbot bullshit, just in a much-more-expensive and much-more convoluted way, requiring either a well-secured private space or an expensive Mac Mini to run multiple AI services and do, well, a bunch of shit very poorly. The same goes for things like Perplexity’s “Computer,” which it describes as “an independent digital worker that completes and workflows for you,” which means, I shit you not, that it can search, generate stuff (words, code, images), and integrate with Gmail, Outlook, Github, Slack, and Notion, places where it can also drop stuff it’s generated. Yes, all of this is dressed up with fancy terms like “persistent memory across sessions” (a document the chatbot reads and information it can access) with “authenticated integrations” (connections via API that basically any software can have). But in reality, it’s just further compute-intensive ways of trying to fit a square peg in a round hole, by which I mean having a hallucination-prone chatbot do actual work. The only reason Jensen Huang is talking about OpenClaw is that there’s nothing else for Jensen Huang to talk about: That’s wild, man. That’s completely wild. What’re you talking about? What can NemoClaw or OpenClaw or whatever-the-fuck actually do? What is the actual output? That’s so fucking weird! I can already hear the haters in my head screaming “ but Ed, coding models! ” and I’m kind of sick of talking about them, because nobody can actually tell me what I’m meant to be amazed or surprised by.  To be clear, LLMs can absolutely write code, and can absolutely create software, but neither of those mean that the code is good, stable or secure, or that the same can be said of the software they create. They do not have ideas, nor do they create unique concepts — everything they create is based on training data fed to it that was first scraped from Stack Overflow, Github and whatever code repositories Anthropic, OpenAI, and Google have been able to get their hands on.  It’s unclear what the actual economic or productivity effects are, other than an abundance of new code that’s making running companies harder. Per The New York Times :  As I wrote a few weeks ago , LLMs are good at writing a lot of code , not good code, and the more people you allow to use them, the more code you’re going to generate, which means the more time you’re either going to need to review that code, or the more vulnerabilities you’re going to create as a result. Worse still, hyperscalers like Meta and Amazon are allowing non-technical people to ship code themselves, which is creating a crisis throughout the tech industry.  Worse still , LLMs allow shitty software engineers that would otherwise be isolated by their incompetence to feign enough intelligence to get by, leading to them actively lowering the quality of code being shipped. Per the Times: The Times also notes that because LLM coding works better on a device rather than a web interface, “...engineers are downloading their entire company’s code to their laptops, creating a security risk if the laptop goes missing.”  Speaking frankly, it appears that LLMs can write code, and create some software, but without any guarantee that said code will compile, run, be secure, performant, or easy to read and maintain. For an experienced and ethical software engineer, LLMs can likely speed them up somewhat , though not in a way that appears to be documented in any academic sense, other than it makes them slower .  And I think it’s fair to ask what any of this actually means. What’s the advantage of having an LLM write all of your code? Are you shipping faster? Is the code better? Are there many more features being shipped? What is the actual thing you can point at that has materially changed for the better?  Software engineers don’t seem happier, nor do they seem to be paid more, nor do they seem to be being replaced by AI, nor do we have any examples of truly vibe coded software companies shipping incredible, beloved products.  In fact, I can’t think of a new piece of software I’ve used in the last few years that actually impressed me outside of Flighty . Where’s the beef? What am I meant to be looking at? What’re you shipping that’s so impressive? Why should I give a shit? Isn’t it weird that we’re even having this conversation? Shouldn’t it be obvious by now? This week, economist Paul Kedrosky told me on the latest episode of my show Better Offline that AI is “...nowhere to be seen yet in any really meaningful productivity data anywhere,” and only appears in the non-residential fixed investments side of America’s GDP, at (and I quote again) “...levels we last saw with the railroad build out or with rural electrification.” That’s so fucking weird! NVIDIA is the largest company on the US stock market and has sold hundreds of billions of dollars of GPUs in the last few years, with many of them sold to the Magnificent Seven, who are building massive data centers and reopening nuclear power plants to power them, and every single one of them is losing money doing so, with revenues so putrid they refuse to talk about them!   And all that to make…what, Gemini? To power ChatGPT and Claude? What does any of this actually do that makes any of those costs actually matter? And as I’ve discussed above, what, literally, does this software do that makes any of this worth it?   Ask the average AI booster — or even member of the media — and they’ll say something about “lots of code being written by AI,” or “novel discoveries” (unrelated to LLMs) or “LLMs finding new materials ( based on an economics paper with faked data )” or “people doing research,” or, of course, “that these are the fastest-growing companies of all time.” That “growth” is only possible because all of the companies in question heavily subsidize their products , spending $3 to $15 for every dollar of revenue. Even then, only OpenAI and Anthropic seem to be able to make “billions of dollars of revenue,” a statement that I put in quotes because however many billions there might be is up for discussion. Back in November 2025 , I reported that OpenAI had made — based on its revenue share with Microsoft — $4.329 billion between January and September 2025, despite The Information reporting that it had made $4.3 billion in the first half of the year based on disclosures to shareholders .  While a few outlets wrote it up, my reporting has been outright ignored by the rest of the media. I was not reached out to by or otherwise acknowledged by any other outlets, and every outlet has continued to repeat that OpenAI “made $13 billion in 2025,” despite that being very unlikely given that it would have required it to have made $8 billion in a single quarter. While I understand why — I’m an independent, after all — these numbers directly contradict existing reporting, which, if I was a reporter, would give me a great deal of concern about the validity of my reporting and the sources that had provided it.  Similarly, when Anthropic’s CFO said in a sworn affidavit that it had only made $5 billion in its entire existence , nobody seemed particularly bothered, despite reports saying it had made $4.5 billion in 2025 , and multiple “annualized revenue” reports — including Anthropic’s own — that added up to over $6.6 billion .  Though I cannot say for certain, both of these situations suggest that Anthropic and OpenAI are misleading their investors, the media and the general public. If I were a reporter who had written about Anthropic or OpenAI’s revenues previously, I would be concerned that I had published something that wasn’t true, and even if I was certain that I was correct, I would have to consider the existence of information that ran counter to my own. I would be concerned that Anthropic or OpenAI had lied to me, or that they were lying to someone else, and work diligently to try and find out what happened. I would, at the very least, publish that there was conflicting information. The S-1 will give us the truth, I guess.  Let’s talk for a moment about margins , because they’re very important to measuring the length of a business.  Back in February in my Hater’s Guide To Anthropic, I raised concerns that Dario Amodei was using a different way to calculate margins than other companies do .  Amodei told the FT in December 2024 that he didn’t think profitability was based on how much you spent versus how much you made: He then did the same thing in an interview with John Collison in August 2025 : Almost exactly six months later on February 13, 2026’s appearance on the Dwarkesh Podcast, Dario would once again try and discuss profitability in terms other than “making more money than you’ve spent”: The above quote has been used repeatedly to suggest that Anthropic has 50% gross margins and is “profitable,” which is extremely weird in and of itself as that’s not what Dario Amodei said at all. Based on The Information’s reporting from earlier in the year , Anthropic’s “gross margin” was 38%.” Yet things have become even more confusing thanks to reporting from Eric Newcomer, who ( in reporting on an investor presentation by Coatue from January ) revealed that Anthropic’s gross margin was “45% in the quarter ended Sep-25,” with the crucial note that — and I quote — “Non-GAAP gross margins [are] calculated by Anthropic management…[are] unaudited, company-provided, and may not be comparable to other companies.” This means that however Anthropic calculates its margins are not based on Generally Accepted Accounting Principles , which means that the real margins probably suck ass , because Anthropic loses billions of dollars a year, just like OpenAI. Yet one seemingly-innocent line in there gives me even more pause: “Model payback improving significantly as revenue scales faster than R&D training costs.” This directly matches with Dario Amodei’s bizarre idea that “...If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million, and then it made $200 million of revenue.” Yes, I know it’s a “stylized fact” or whatever, but that’s what he said, and I think that their IPO might have a rude surprise in the form of a non-EBITDA margin calculation that makes even the most-ardent booster see red. This week, The Wall Street Journal published a piece about OpenAI and Anthropic's finances that included one of the most-offensive lines in tech media history: Two thoughts: As I said a few months ago about training costs: The Journal also adds that both Anthropic and OpenAI are showing investors two versions of their earnings — one with training costs, and one without — without adding the commentary that this is extremely deceptive or, at the very least, extremely unusual. The more I think about it the more frustrated I get. Having two sets of earnings is extremely dodgy! Especially when the difference between them is billions of dollars. This should be immediately concerning to every financial journalist, the reddest of red flags, the biggest sign that something weird is happening… …but because this is the AI industry, the Journal runs propaganda instead: That “fast-growing” part is only possible because both Anthropic and OpenAI subsidize the compute of their subscribers , allowing them to burn $3 to $15 for every dollar of subscription revenue. And no, this is nothing like Uber or Amazon , that’s a silly comparison, click that link and read what I said and then never bring it up again. I realize my suspicion around Anthropic’s growth has become something of a meme at this point, but I’m sorry, something is up here. Let’s line it all up: Anthropic was making $9 billion in annualized revenue at the end of 2025, or approximately $750 million in a 30-day period. Per Newcomer , as of December 2025, this is how Anthropic’s revenue breaks down: Per The Information , Anthropic also sells its models through Microsoft, Google and Amazon, and for whatever reason reports all of the revenue from their sales as its own and then takes out whatever cut it gives them as a sales and marketing expense: The Information also adds that “...about 50% of Anthropic’s gross profits on selling its AI via Amazon has gone to Amazon,” and that “...Google typically takes a cut of somewhere between 20% and 30% of net revenue, after subtracting infrastructure costs.”  The problem here is that we don’t know what the actual amounts of revenue are that come from Amazon or Google (or Microsoft, for that matter, which started selling Anthropic’s models late last year), which makes it difficult to parse how much of a cut they’re getting. That being said, Google ( per DataCenterDynamics/The Information ) typically takes a cut of 20% to 30% of net revenue after subtracting the costs of serving the models . Nevertheless, something is up with Anthropic’s revenue story.  Let’s humour Anthropic for a second and say that what it’s saying is completely true: it went from making $750 million in monthly revenue in January to $2.5 billion in monthly revenue in April 2026. That’s remarkable growth, made even more remarkable by the fact that — based on its December breakdown — most of it appears to have come from API sales. That leap from $750 million to $1.16 billion between December and February feels, while ridiculous , not entirely impossible , but the further ratchet up to $2.5 billion is fucking weird! But let’s try and work it out.  On February 5 2026, Anthropic launched Opus 4.6 , followed by Claude Sonnet 4.6 on February 17 2026.  Based on OpenRouter token burn rates , Opus 4.5 was burning around 370 billion tokens a week. Immediately on release, Opus 4.6 started burning way, way more tokens — 524 billion in its first week, then 643 billion, then 634 billion, then 771 billion, then 822 billion, then 976 billion, eventually going over a trillion tokens burned in the final week of March.  In the weeks approaching its successor’s launch, Sonnet 4.5 burned between 500 billion and 770 billion tokens. A week after launch, 4.6 burned 636 billion tokens, then 680 billion, then 890 billion, and, by about a month in, it had burned over a trillion tokens in a single week.  Reports across Reddit suggest that these new models burn far more tokens than their predecessors with questionable levels of improvement.  The sudden burst in token burn across OpenRouter doesn’t suggest a bunch of people suddenly decided to connect to Anthropic and other services’ models , but that the model themselves had started to burn nearly twice the amount of tokens to do the same tasks. At this point, I estimate Anthropic’s revenue split to be more in the region of 75% API and 25% subscriptions, based on its supposed $2.5 billion in annualized revenue (out of $14 billion, so a little under 18%) in February coming from “Claude Code” (read: subscribers to Claude, there’s no “Claude Code” subscription).  If that’s the case, I truly have no idea how it could’ve possibly accelerated so aggressively, and as I’ve mentioned before , there is no way to reconcile having made $5 billion in lifetime revenue as of March 9, 2026, having $14 billion in annualized revenue on February 12 2026, and having $4.5 billion in revenue for the year 2025. Things get more confusing when you hear how Anthropic calculates its annualized revenues, per The Information : So, Anthropic is annualizing based on the last four weeks of API revenue times 13, a number that’s extremely easy to manipulate using, say, launches of new products. In simpler terms, Anthropic is cherry-picking four-week windows of API spend — ones that are pumped by big announcements and new model releases — and annualizing them. The one million token context window is a big deal, too, having been raised from 200,000 tokens in previous models. With Opus and Sonnet 4.6, Anthropic lets users use up to one million tokens of context, which means that both models can now carry a very, very large conversation history, one that includes every single output, file, or, well, anything that was generated as a result of using the model via the API. This leads to context bloat that absolutely rinses your token budget.   To explain, the context window is the information that the model can consider at once. With 4.6, Anthropic by default allows you to load in one million tokens’ worth of information at once, which means that every single prompt or action you take has the model load one million tokens’ worth of information at once unless you actively “trim” the window through context editing .  Let’s say you’re trying to work out a billing bug in a codebase via whatever interface you’re using to code with LLMs. You load in a 350,000 token codebase, a system prompt (IE: “you are a talented software engineer,” here’s an example ), a few support tickets, and a bunch of word-heavy logs to try and fix it. On your first turn (question), you ask it to find the bug, and you send all of that information through. It spits out an answer, and then you ask it how to fix the bug…but “asking it to fix the bug” also re-sends everything, including the codebase, tickets and logs. As a result, you’re burning hundreds of thousands of tokens with every single prompt. Although this is a simplified example, it’s the case across basically any coding product, such as Claude Code or Cursor. While Cursor uses codebase indexing to selectively fetch pieces of the codebase without constantly loading it into the context window, one developer using Claude inside of Cursor watched a single tool call burn 800,000 tokens by pulling an entire database into the context window , and I imagine others have run into similar problems. To be clear, Anthropic charges at a per-million-token rate of $5 per million input and $25 per million output, which means that those casually YOLOing entire codebases into context are burning shit tons of cash (or, in the case of subscribers, hitting their rate limits faster). if Anthropic actually made $2.5 billion in a month — we’ll find out when it files its S-1! — it likely came not from genuine growth or a surge of adoption, but in its existing products suddenly costing a shit ton more because of how they’re engineered.  The other possibility is the nebulous form of “enterprise deals” that Anthropic allegedly has, and the theory that they somehow clustered in this three-month-long period, but that just feels too convenient.   If 70% of Anthropic’s revenue is truly from API calls, this would suggest: I don’t see much evidence of Anthropic creating custom integrations that actually matter, or — and fuck have I looked! — any real examples of businesses “doing stuff with Claude” other than making announcements about vague partnerships.  There’s also one other option: that Silicon Valley is effectively subsidizing Anthropic through an industry-wide token-burning psychosis. And based on some recent news, there’s a chance that’s the case. As I discussed a few weeks ago, Silicon Valley has a “tokenmaxxing” problem , where engineers are encouraged by their companies to burn as many tokens as possible, at times by their peers, and at others by their companies. The most egregious — and honestly, worrying! — version of this came from The Information’s recent story about Meta employees competing on an internal leaderboard to see who can burn the most tokens, deliberately increasing the size of their prompts and the amount of concurrent sessions ( along with unfettered and dangerous OpenClaw usage ) to do so:   The Information reports that the dashboard, called “Claudeonomics” (despite said dashboard covering other models from OpenAI, Google, and xAI), has sparked competition within Meta, with users burning a remarkable 60 trillion tokens in the space of a month, with one individual averaging around 281 billion tokens, which The Information remarks could cost millions of dollars. Meta’s company-mandated psychosis also gives achievements for particular things like using multiple models or high utilization of the cache. Here’s one very worrying anecdote: One poster on Twitter says that there are people at Meta running loops burning tokens to rise up the leaderboards, and that Meta’s managers also measure lines of code as a success metric.  The Information says that, considering Anthropic’s current pricing for its models, that 60 trillion tokens could be as much as $900 million in the space of a month, though adds that this assumes that every token being burned was on Claude Opus 4.6 (at $15 per 1 million tokens).  I personally think this maths is a bit fucked, because it assumes that A) everybody is only using Claude Opus, B) that none of that token burn runs through the cache (which it obviously does, and the cache charges 50%, as pointed out by OpenCode co-founder Dax Radd ), and C) that Meta is entirely using the API (versus paying for a $200-a-month Claude Max subscription for each user).  Digging in further, it appears that a few years ago Meta created an internal coding tool called CodeCompose , though a source at Meta tells me that developers use VSCode and an assistant called Devmate connected to models from Anthropic, OpenAI and xAI. One engineer on Reddit — albeit an anonymous one! — had some commentary on the subject: If we assume that Meta is an enterprise customer paying API rates for its tokens, it’s reasonable to assume — at even a low $5-per-million average — that it’s spending $300 million or more a month on API calls. As Radd also added, there’s likely a discount involved. He suggested 20%, which I agree with. Even if it’s $300 million, that’s still fucking insane. That’s still over three billion dollars a year. If this is what’s actually happening, and this is what’s contributing to Anthropic’s growth, this is not a sustainable business model, which is par for the course for Anthropic, a company that has only lost billions of dollars. Encouraging workers to burn as many tokens as possible is incredibly irresponsible and antithetical to good business or software engineering. Writing great software is, in many cases, an exercise in efficiency and nuance , building something that runs well, is accessible and readable by future engineers working on it, and ideally uses as few resources as it can. TokenMaxxing runs contrary to basically all good business and software practices, encouraging waste for the sake of waste, and resulting in little measurable productivity benefits or, in the case of Meta, anything user-facing that actually seems to have improved. Venture capitalist Nick Davidov mentioned yesterday that sources at Google Cloud “started seeing billions of tokens per minute from Meta, which might now be as big as a quarter of all the token spend in Anthropic.” While I can’t verify this information ( and Davidoff famously deleted his photos using Claude Cowork while attempting to reorganize his wife’s desktop ), if that’s the case, Meta is a load-bearing pillar of Anthropic’s revenue — and, just as importantly, a large chunk of Anthropic’s revenue flows through Google Cloud , which means A) that Anthropic’s revenue truly hinges on Google selling its models, and B) that said revenue is heavily-inflated by the fact that Anthropic books revenue without cutting out Google’s 20%+ revenue share. In any case, TokenMaxxing is not real demand, but an economic form of AI psychosis. There is no rational reason to tell somebody to deliberately burn more resources without a defined output or outcome other than increasing how much of the resource is being used. I have confirmed with a source at that there is no actual metric or tracking of any return on investment involved in token burn at Meta, meaning that TokenMaxxing’s only purpose is to burn more tokens to go higher on a leaderboard, and is already creating bad habits across a company that already has decaying products and leadership. To make matters worse, TokenMaxxing also teaches people to use Large Language Models poorly. While I think LLMs are massively-overrated and have their outcomes and potential massively overstated, anyone I know who actually uses them for coding generally has habits built around making sure token burn isn’t too ridiculous, and various ways to both do things faster without LLMs and ways to be intentional with the models you use for particular tasks. TokenMaxxing literally encourages you to do the opposite — to use whatever you want in whatever way you want to spend as much money as possible to do whatever you want because the only thing that matters is burning more tokens. Furthermore, TokenMaxxing is exactly the kind of revenue that disappears first. Zuckerberg has reorganized his AI team four or five times already, and massively shifted Meta’s focus multiple times in the last five years, proving that at the very least he’ll move on a whim depending on external forces. After laying off tens of thousands of people in the last few years , Meta has shown it’s fully capable of dumping entire business lines or groups with a moment’s notice, and while moving on from AI might be embarrassing , that would suggest that Mark Zuckerberg experiences shame or any kind of emotion other than anger. This is the kind of revenue that a business needs to treat with extreme caution, and if Meta is truly spending $300 million or more a month on tokens, Anthropic’s annualized revenues are aggressively and irresponsibly inflated to the point that they can’t be taken seriously, especially if said revenue travels through Google Cloud, which takes another 20% off the top at the very least.  Though the term is pretty new, the practice of encouraging your engineers to use AI as much as humanly possible is an industry-wide phenomena, especially across hyperscalers like Amazon, Microsoft and Google, all of whom until recently directly have pushed their workers to use models with few restraints. Shopify and other large companies are encouraging their workers to reflexively rely on AI, with performance reviews that include stats around your token burn and other nebulous “AI metrics” that don’t seem to connect to actual productivity. I’m also hearing — though I’ve yet to be able to confirm it — that Anthropic and other model providers are forcing enterprise clients to start using the API directly rather than paying for monthly subscriptions.  Combined with mandates to “use as much AI as possible,” this naturally increases the cost of having software engineers, which — and I say this not wanting anyone to lose their jobs — does the literal opposite of replacing workers with AI. Instead, organizations are arbitrarily raising the cost of doing business without any real reason.  Because we’re still in the AI hype cycle, this kind of wasteful spending is both tolerated and encouraged, and the second that financial conditions worsen or stock prices drop due to increasing operating expenses, these same companies will cut back on API spend, which will overwhelmingly crush Anthropic’s glowing revenues. I think it’s also worth asking at this point what is is we’re actually fucking doing.   We’re building — theoretically — hundreds of gigawatts of data centers, feeding hundreds of billions of dollars to NVIDIA to buy GPUs, all to build capacity for demand that doesn’t appear to exist, with only around $65 billion of revenue (not profit) for the entire generative AI industry in 2025 , with much of that flowing from two companies (Anthropic and OpenAI) making money by offering their models to unprofitable AI startups that cannot survive without endless venture capital, which is also the case for both AI labs. Said data centers make up 90% of NVIDIA’s revenue, which means that 8% or so of the S&P 500’s value comes from a company that makes money selling hardware to people that immediately lose money on installing it. That’s very weird! Even if you’re an AI booster, surely you want to know the truth , right?  The most-prominent companies in the AI industry — Anthropic and OpenAI — burn billions of dollars a year, have margins that get worse over time , and absolutely no path to profitability, yet the majority of the media act as if this is a problem that they will fix, even going as far as to make up rationalizations as to how they’ll fix it, focusing on big revenue numbers that wilt under scrutiny. That’s extremely weird, and only made weirder by members of the media who seem to think it’s their job to defend AI companies ’ bizarre and brittle businesses. It’s weird that the media’s default approach to AI has, for the most part, been to accept everything that the companies say, no matter how nonsensical it might be. I mean, come on! It’s fucking weird that OpenAI plans to burn $121 billion in the next two years on compute for training its models , and that the media’s response is to say that somehow it will break even in 2030, even though there’s no actual explanation anywhere as to how that might happen other than vague statements about “efficiency.” That’s weird! It’s really, really weird! It’s also weird that we’re still having a debate about “the power of AI” and “what agents might do in the future” based on fantastical thoughts about “agents on the internet ” that do not exist, cannot exist, and will never exist, and it’s fucking weird that executives and members of the media keep acting as if that’s the case. It’s also weird that people discussing agents don’t seem to want to discuss that OpenAI’s Operator Agent does not work , that AI browsers are fundamentally broken , or that agentic AI does not do anything that people discuss. In fact, that’s one of the weirdest parts of the whole AI bubble: the possibility of something existing is enough for the media to cover it as if it exists, and a product saying that it will do something is enough for the media to believe it does it. It’s weird that somebody saying they will spend money is enough to make the media believe that something is actually happening , even if the company in question — say, Anthropic — literally can’t afford to pay for it . It’s also weird how many outright lies are taking place, and how little the media seems to want to talk about them. Stargate was a lie! The whole time it was a lie! That time that Sam Altman and Masayoshi Son and Larry Ellison stood up at the white house and talked about a $500 billion infrastructure project was a lie! They never formed the entity ! That’s so weird! Hey, while I have you, isn’t it weird that OpenAI spent hundreds of millions of dollars to buy tech podcast TBPN “to help with comms and marketing”? It’s even weirder considering that TBPN was already a booster for OpenAI!  It’s also weird that a lot of AI data center projects don’t seem to actually exist, such as Nscale’s project to make “one of the most powerful AI computing centres ever” that is literally a pile of scaffolding , and that despite that announcement the company was able to raise $2 billion in funding . It’s also weird that we’re all having to pretend that any of this matters. The revenues are terrible, Large Language Models are yet to provide any meaningful productivity improvements, and the only reason that they’ve been able to get as far as they have is a compliant media and a venture capital environment borne of a lack of anything else to invest in .  Coding LLMs are popular only because of their massive subsidies and corporate encouragement, and in the end will be seen as a useful-yet-incremental and way too expensive way to make the easy things easier and the harder things harder, all while filling codebases full of masses of unintentional, bloated code. If everybody was forced to pay their actual costs for LLM coding, I do not believe for a second that we’d have anywhere near the amount of mewling, submissive and desperate press around these models.  The AI bubble has every big, flashing warning sign you could ask for. Every company loses money. Seemingly every AI data center is behind schedule, and the vast majority of them aren’t even under construction . OpenAI’s CFO does not believe that it’s ready to go public in 2026 , and Sam Altman’s reaction has been to have her report to somebody else other than him, the CEO. Both OpenAI and Anthropic’s margins are worse than they projected. Every AI startup has to raise hundreds of millions of dollars, and their products are so weak that they can only make millions of dollars of revenue after subsidizing the underlying cost of goods to the point of mass unprofitability .   And it’s really weird that the mainstream media has a diametric view — that all of this is totally permissible under the auspices of hypergrowth, that these companies will simply grow larger, that they will somehow become profitable in a way that nobody can actually describe, that demand for AI data centers will exist despite there being no signs of that happening. I get it. Living in my world is weird in and of itself. If you think like I do, you have to see every announcement by Anthropic or OpenAI as suspicious — which should be the default position of every journalist, but I digress — and any promise of spending billions of dollars as impossible without infinite resources. At the end of this era, I think we’re all going to have to have a conversation about the innate credulity of the business and tech media, and how often that was co-opted to help the rich get richer. Until then, can we at least admit how weird this all is? Telecommunications: AI agents will help carriers modernize network operations, simplify customer lifecycle management, and improve service delivery—bringing intelligent automation to one of the most operationally complex and regulated industries in the world. Meaningless. Automation of what?  Financial services: AI agents will help firms detect and assess risk faster, automate compliance reporting, and deliver more personalized customer interactions, such as tailoring financial advice based on a client's full account history and market conditions. Chatbot! “More-personalized interactions” are a chatbot with a connection to a knowledge system, as is any kind of “tailored financial advice.” Compliance reporting? Summarizing or pulling documents from places, much like any LLM can do, other than the fact that it’ll likely get shit wrong, which is bad for compliance. Manufacturing and engineering: Claude will help accelerate product design and simulation, reducing R&D timelines and enabling engineers to test more iterations before production. I assume this refers to people using Claude Code to do coding, which is what it does. Software development: Teams will use Claude Code to write, test, and debug code, helping developers move faster from design to production. Claude Code. Enterprise operations: Claude Cowork will help teams automate routine work like document summarization, status reporting, and review cycles. Literally a chatbot that deleted every single one of a guy’s photos when he asked it to organize his wife’s desktop . “Gather information” — search tool, part of chatbots for years. “Write reports” — generative AI’s most basic feature, with no details on quality. “Edit files” — to do what exactly? Chatbot feature. “Send and receive messages through email and text” — generating and reading text, connected to an email account.  “Delegate work” — what work? No need to get specific!  Are you fucking kidding me? If you simply remove billions of dollars in costs, OpenAI is profitable! Why do you think these companies are going to break even anytime soon? You have absolutely no basis for doing so other than leaks from the company!  Anthropic said on February 12, 2026 it had hit $14 billion in annualized revenue . This would work out to roughly $1.16 billion in a 30-day period, let’s assume from January 11 2026 to February 11 2026. Anthropic’s CFO said it had made “exceeding $5 billion” in lifetime revenue on March 9 2026. On March 3, 2026 Dario Amodei said it had hit $19 billion in annualized revenue.  This would work out to $1.58 billion in a 30-day period. Let’s assume this is for the period from February 2 2026 to March 2 2026. On April 6, 2026, Anthropic said it had hit $30 billion in annualized revenue . This works out to about $2.5 billion in a 30-day period. Let’s assume that said period is March 6 2026 to April 6 2026. Anthropic’s $14 billion in annualized revenue from February 16, 2026 includes both the launch of Claude Opus 4.6 , as well as the height of the OpenClaw hype cycle where people were burning hundreds of dollars of tokens a day .  This announcement also included the launch of Anthropic’s 1 million token context window in Beta for Opus 4.6 Anthropic’s $19 billion in annualized revenue from March 3, 2026 included both the launch of Claude Opus 4.6 and Claude Sonnet 4.6 . This period includes around half of the January 16 to February 16 2026 window from the previous $14 billion annualized number, and the launch of the beta of the 1 million token context window for Sonnet 4.6. To be clear, the betas required you to explicitly turn on the 1 million token context window, and had higher pricing around long context. Anthropic’s $30 billion in annualized revenue from April 6 2026 included two weeks’ worth of massive token burn from the launches of Sonnet and Opus 4.6. This includes a few days of the previous window (March 3 to April 5). This also included the general availability of the 1-million token context window , enabling it by default, billed at the standard pricing. Massive new customers that are making payments up front, which makes this far from “recurring” revenue. Massive new customers are spending tons of money immediately, burning hundreds of millions of dollars a month in tokens, and paying Anthropic handsomely for them.

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Kev Quirk 1 months ago

I Hate Insurance!

So yesterday I received an email from Admiral , our insurance provider, where we have a combined policy for both our cars and our home. Last year this cost £1,426.00 , but this year the renewal had gone up by a huge 33%, to £1,897.93 broken down as follows: Even at last year's price this was a shit tonne of money, so I started shopping around and here's what I ended up with: These policies have at least the same cover as Admiral. In some cases, better. I knew it would be cheaper shopping around, but I didn't think it would be nearly half. So, I called Admiral to see what they could do for me, considering I've been a loyal customer for 7 years. They knocked £167,83 (8.8%) off the policy for me, bringing the revised total to £1,730.10. Nice to see that long-term customers are rewarded with the best price! 🤷🏻‍♂️ So I obviously went with the much cheaper option and renewed with 3 different companies. It's a pain, as I'll now need to renew 3 policies at the same time every year, but if it means saving this much money, I'm happy to do it. Next year I'll get a multi-quote from Admiral to see if they're competitive. Something tells me they will be, as with most things these days, getting new customers is more important than retaining existing ones. Unfortunately having car and home insurance is a necessary evil in today's world, but I'm glad I was able to make it a little more palatable by saving myself over £700! If your insurance is up for renewal, don't just blindly renew - shop around as there's some serious savings to be had. Thanks for reading this post via RSS. RSS is ace, and so are you. ❤️ You can reply to this post by email , or leave a comment . Wife's car - £339.34 My car - £455.68 Our home (building & contents) - £1,102.91 Wife's car - £300.17 My car - £402.22 Our home (building and contents) - £533.52 Total: £1056.86 (44% reduction!)

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News: OpenAI CFO Doesn't Believe Company Ready For IPO, Unsure Revenue Will Support Commitments

News out of The Information's Anissa Gardizy and Amir Efrati over the weekend - OpenAI CFO Sarah Friar has apparently clashed with CEO Sam Altman over timing around OpenAI's IPO, emphasis mine: I cannot express how strange this is. Generally a CFO and CEO are in lock-step over IPO timing, or at the very least the CFO has an iron grip on the actual timing because, well, CEOs love to go public and the CFO generally exists to curb their instincts. Nevertheless, Clammy Sam Altman has clearly sidelined Friar, and as of August last year, the CFO of OpenAI doesn't report to the CEO . In fact, the person Friar reports to ( Fiji Simo ) just took a medical leave of absence: It is extremely peculiar to not have the Chief Financial Officer report to the Chief Executive Officer , but remember folks, this is OpenAI, the world's least-normal company! Anyway, all of this seemed really weird, so I asked investor, writer and economist Paul Kedrosky for his thoughts: Very cool! Paul is also a guest on this week's episode of my podcast Better Offline , by the way. Out at 12AM ET Tuesday. Anyway, The Information's piece also adds another fun detail - that OpenAI's margins were even worse than expected in 2025: Riddle me this, Batman! If your AI company always has to buy extra compute to meet demand, and said extra compute always makes margins worse, doesn't that mean that your company will either always be unprofitable or die because it buys too much compute? Say, that reminds me of something Anthropic CEO Dario Amodei said to Dwarkesh Patel earlier in the year ... It is extremely strange that the CFO of a company doesn't report to the CEO of a company, and even more strange that the CFO is directly saying "we are not ready for IPO" as its CEO jams his foot on the accelerator. It's clear that both OpenAI and Anthropic are rushing toward a public offering so that their CEOs can cash out, and that their underlying economics are equal parts problematic and worrying. Though I am entirely guessing here, I imagine Friar sees something within OpenAi's finances that give her pause. An S-1 - one of the filings a company makes before going public - is an audited document, and I imagine the whimsical mathematics that OpenAI engages in - such as, per The Wall Street Journal , calculating profitability without training compute - might not match up with what actual financiers crave. If you like this piece and want to support my independent reporting and analysis, why not subscribe to my premium newsletter? It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of  NVIDIA ,  Anthropic and OpenAI’s finances , and  the AI bubble writ large . I just put out  a massive Hater’s Guide To The SaaSpocalypse , as well as last week’s deep dive into How AI Isn't Too Big To Fail . Supporting my premium supports my free newsletter. OpenAI CFO Sarah Friar has, per The Information, said that OpenAI is not ready to go public in 2026, in part because of the "risks from its spending commitments" and not being sure whether the company's revenue growth would support its spending commitments. Friar (CFO) no longer reports to Sam Altman (CEO) and hasn't done so since August 2025. OpenAI's margins were lower in 2025 "...due to the company having to buy more expensive compute at the last minute."

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Rik Huijzer 1 months ago

COVID vs Oil Crisis

COVID declared a pandemic on 11/3 (11*3=33) 2020. On 11/3 2026, the IEA wrote _"The IEA Secretariat will provide further details of how this collective action will be implemented in due course. It will also continue to closely monitor global oil and gas markets and to provide recommendations to Member governments, as needed."_

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The Subprime AI Crisis Is Here

Hi! If you like this piece and want to support my independent reporting and analysis, why not subscribe to my premium newsletter? It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I just put out a massive Hater’s Guide To The SaaSpocalypse , as well as last week’s deep dive into how the majority of data centers aren’t getting built and the overall AI industry is depressingly small . Supporting my premium supports my free newsletter, and premium subscribers don't get this ad. Soundtrack: Metallica — …And Justice For All   Bear with me, readers. I need to do a little historical foreshadowing to fully explain what’s going on. In the run-up to the great financial crisis, unscrupulous lenders issued around 1.9 million subprime loans , with many of them being adjustable rate mortgages (ARMs) with variable rates that, after a two-or-three-year-long introductory period , would adjust every twelve months, per CBS News in July 2006 : At the time, 18% of homeowners had adjustable-rate mortgages, which also made up more than 25% of new mortgages in the first quarter of 2006, with (at the time) over $330 billion of mortgages expected to adjust upwards. Things were grimmer beneath the surface. A question on JustAnswer from 2009 showed a homeowner that was about to lose their house after being conned into a negative amortization loan — a mortgage where payments didn’t actually cover the interest, meaning that each month the balance increased . Dodgy lenders were given bonuses for selling more mortgages, whether or not the person on the other end was capable of paying, and by November 2007 , around two million homeowners held $600 billion of ARMs.  Yet the myth of the subprime mortgage crisis was that it was caused entirely by low income borrowers. Per Duke’s Manuel Adelino : Despite The Big Short’s dramatic “stripper with six properties” scene made for a vivid demonstration of the subprime problem, the reality was that everybody got taken in by teaser rate mortgages, driving up the value of properties based on a housing market that was only made possible by mortgages that were expressly built to hide the real costs as interest rates and borrower payments rose every six to 36months. I’ll add that near-prime mortgages — for borrowers with just-below-prime credit scores — were also growing, with over 1.1 million of them in 2005, when they represented nearly 32% of all loans. Many people who bought houses that they couldn’t afford did so based on a poor understanding of the terms of their mortgage, thinking that the value of housing would continue to climb as it had for over a hundred years , and/or the belief that they’d easily be able to refinance the loans. Even as things deteriorated toward the middle of the 2000s, people came up with rationalizations as to why things would work out, such as Anthony Downs of The Brookings Institution, who in October 2007 said the following in a piece called “Credit Crisis: The Sky is not Falling”: Brookings also added that “...the vast majority of subprime mortgages are likely to remain fully paid up as long as unemployment remains as low as it is now in the U.S. economy.” At the time, US unemployment was 4.7% , but a year later it was at 6.5%, and would peak at 10% in October 2009.   In an article from the December 2004 issue of Economic Policy Review , Jonathan McCarthy and Richard W. Peach argued that there was “little basis” for concerns about housing prices, with “home prices essentially moving in line with increases in family income and declines in nominal mortgage interest rates,” and hand-waved any concerns based on vague statements about “demand”: From the outside, this made it appear that the value of housing was exponential, and that the “pent-up demand” for homes necessitated a massive boom in construction, one that peaked in January 2006 with 2.27 million new homes built . A year later, this number collapsed to 1.084 million, and in January 2009, only 490,000 new homes had been built in America, the lowest it had been in history.  Denial rates for mortgages declined drastically ( along with the increase in things like 40-year or 50-year mortgages ), which meant that suddenly anybody was able to get a house, which made it only seem logical to build more housing. Low interest rates before 2006 allowed consumers to take on mountains of new credit card debt, rising to as high as 20% of household incomes in 2007 , to the point that by the 2000s, credit card companies were making more money from credit card lending than the fees from people using the credit cards, with $65 billion of the $95 billion of the credit card industry’s revenue coming from interest on debt, with lending-related penalty fees and cash advance fees contributing another $12.4 billion, per Philadelphia Fed Economist Lukasz Drozd. While the precise order of events is a little more complex, the general gist of the subprime mortgage crisis was straightforward: easily-available money allowed massive amounts of people — many of whom couldn’t afford to buy these houses outside of the easy money that funded the bubble — to enter the housing market, which in turn made it much easier to sell a house for a much higher price, which inflated the value of housing.  People made decisions based on fundamentally-flawed information. In January 2004, the Bush administration declared that America’s economy was on the path to recovery , with small businesses creating the majority of new jobs and the stock market booming. Debt was readily-available across the board, with commercial and industrial loans spiking along with consumer debt ( including a worrying growth in subprime auto loans ). The good times were rolling, as long as you didn’t think about it too hard. But, as I said, the chain of events was simple: it was easy to borrow money to buy a house, which meant lots of people were buying houses, which meant that the value of a house seemed higher than it was outside of the easy money era. Easily-available money put lots of cash into the economy, which led to higher prices, which led to inflation, which forced the federal reserve to raise interest rates 17 times in the space of two years , which made it harder to get any kind of loan, which made it harder to get a mortgage, which made it harder to sell a house, which made people sell houses for cheaper, which lowered the value of houses, which made it harder to refinance the bad loans, which meant people foreclosed on their homes, which in turn lowered the value of housing, all as demand for housing dropped because nobody was able to buy housing. The underlying problems were, ultimately, the illusion of value and mobility. Those borrowing at the time believed they had invested in something with a consistent (and consistently-growing) value — a house — and would always have easy access to credit (via credit cards and loans), as before-tax family income had never been higher . In the beginning of 2007, delinquencies on consumer and business loans climbed, abandoned housing developments grew , and a US economy dependent on the housing bubble (per Paul Krugman’s “ That Hissing Sound ” from August 2005) began to stumble. By November 2009 , 23% of US consumer mortgages were underwater (meaning they were worth less than their loans). The housing bubble was created through easily-available debt, insane valuations based on debt-fueled speculation, do-nothing regulators ( like eventual Fed Chair Ben Bernanke, who said in October 2005 that there was no housing bubble ) and consumers being sold an impossible, unsustainable dream by people financially incentivized to make them rationalize the irrational, and believe that nothing bad will ever happen. In February 2005 , 40% ($19 billion) of IndyMac Bancorp’s mortgage originations in a single quarter came from a “Pay-Option ARM,” which started with a 1% teaser rate which jumped in a few short months to 4% or more, with frequent adjustments. Washington Mutual CEO Kerry Killinger said in 2003 that he wanted WaMu to be “ the Wal-Mart of banking ,” and did so by using (to quote the New York Times) “relaxed standards,” including issuing a mortgage to a mariachi singer who claimed a six-figure income and verified it using a single photo of himself.  By the time it collapsed in September 2008, WaMu had over $52.9 billion in ARMs and $16.05 billion in subprime mortgage loans .  Had Washington Mutual and the many banks making dodgy ARM and subprime loans underwritten loans based on the actual creditworthiness of their applicants, there wouldn’t have been a housing bubble, because many of these borrowers would’ve been unable to pay their mortgages, and thus wouldn’t have been deemed creditworthy, and thus no apparent housing demand would’ve grown.  In very simple terms, the “demand” for housing was inflated by a deceitfully-priced product that undersold its actual costs, and through that deceit millions of people were misled into believing said product was viable. Did you work out where this is going yet? In September 2024 , I raised my first concerns about a Subprime AI Crisis: This theory is important, and thus I’m going to give it a lot of time and love to break it down.  That starts with the parties involved, and how the economics involved get worse over time, returning to my theory of “ AI’s chain of pain , and the hierarchy of how the actual AI economy works. The AI industry has done a great job in obfuscating exactly how brittle its economics really are, and as a result, I need to explain both how money is raised , money is deployed, and where the economics begin to break down. Generally, AI is funded from only a few places:: Some things to keep note of: This is a crucial point, so stay with me.  AI models work by charging a per-million token rate for inputs (things you feed in) and outputs, which are either the things that the model outputs (such as an image, text or code), or the “ chain of thought reasoning ” many models rely upon now, where they take an input, generate a plan (which is an “output”) and then do stuff based on said plan. AI startups, for the most part, do not have their own models, and thus must pay OpenAI or Anthropic (or other providers to a much lesser extent) to build services using them.   When you pay for access to an AI startup’s service — which, of course, includes OpenAI and Anthropic — you do so for a monthly fee, such as $20, $100 or $200-a-month in the case of Anthropic’s Claude , Perplexity’s $20 or $200-a-month plan , or OpenAI’s $8, $20, or $200-a-month subscriptions . In some enterprise use cases, you’re given “credits” for certain units of work, such as how Lovable allows users “100 monthly credits” in its $25-a-month subscription , as well as $25 (until the end of Q1 2026) of cloud hosting, with rollovers of credits between months. When you use these services, the company in question then pays for access to the AI models in question, either at a per-million-token rate to an AI lab, or (in the case of Anthropic and OpenAI) whatever cloud provider is renting them the GPUs to run the models. A token is basically ¾ of a word. As a user, you do not experience token burn, just the process of inputs and outputs. AI labs obfuscate the cost of services by using “tokens” or “messages” or 5-hour-rate limits with percentage gauges, and you, as the user, do not really know how much any of it costs. On the back end, AI startups are annihilating cash, with up until recently Anthropic allowing you to burn upwards of $8 in compute for every dollar of your subscription . OpenAI allows you to do the same, though it’s hard to gauge by how much. This is where the economic problem has begun. When the AI bubble started, venture capitalists flooded AI startups with cash, encouraging them to create hypergrowth businesses using, for the most part, monthly subscription costs that didn’t come close to covering the costs. As a result, many AI companies have experienced rapid growth selling a product that can only exist with infinite resources.  The problem is fairly simple: providing AI services is very expensive, and costs can vary wildly depending on the customer, input and output, the latter of which can change dramatically depending on the prompt and the model itself. A coding model relies heavily on chain-of-thought reasoning, which means that despite the cost of tokens coming down (which does not mean the price of providing them has decreased, it’s a marketing move ), models are using far, far more tokens, increasing costs across the board . And consumers crave new models. They demand them. A service that doesn’t provide access to a new model cannot compete with those that do, and because the costs of models have been mostly hidden from users, the expectation is always the newest models provided at the same price. As a result, there really isn’t any way that these services make sense at a monthly rate, and every single AI company loses incredible amounts of money, all while failing to make that much revenue in the first place.  For example, Harvey is an AI tool for lawyers that just raised $200 million at an $11 billion valuation , all while having an astonishingly small $190 million in ARR, or $15.8 million a month. It raised another $160 million in December 2025 , after raising $300 million in June 2025 , after raising $300 million in February 2025 .  Cursor is an AI coding tool that raised $160 million in 2024 (As of December 2024, it had $48 million ARR, or around $4 million of monthly revenue), $900 million ($500 million ARR/$41.6 million) in June 2025, and $2.3 billion in November 2025 ($1 billion ARR/$83 million). As of March 2, 2026, Cursor was at $2 billion annualized revenue, or $166 million in monthly revenue.  I’ll get to Cursor in a little bit, because it’s crucial to the Subprime AI Crisis. The Subprime AI Crisis is what happens when somebody actually needs to start making money, or, put another way, stop losing quite so much, revealing how every link in the chain was funded based on questionable assumptions and deadly short-term thinking.  Here’s the order of events as I see them. The entire generative AI industry is based on unprofitable, unsustainable economics, rationalized and funded by venture capitalists and bankers speculating on the theoretical value of Large Language Model-based services. This naturally incentivized developers to price their subscriptions at rates that attracted users rather than reflecting the actual economics of the services. Venture capitalists are also part of the subprime AI crisis, sitting on “billions of dollars” of AI companies that lose hundreds of millions of dollars, their companies built on top of AI models owned by OpenAI and Anthropic with little differentiation and no path to profitability. Nobody is going public! Nobody is getting acquired! As I discussed back in AI Is A Money Trap , there really is no liquidity mechanism for the billions of dollars sunk into most AI companies. Going public also reveals the ugly financial condition of these startups. MiniMax, for example, made a pathetic $79 million in revenue in 2025, and somehow lost $250.9 million in the process . Much like the houses in the great financial crisis, AI startups only retain their value as long as there is a market, or at least the perception that these companies could theoretically go public or be acquired. It only takes one failed exit or firesale to break the illusion.  At least you can live in a house. Every AI company will be a problem child that burns money on inference, bereft of intellectual property thanks to their dependence on OpenAI and Anthropic. What use is Perplexity without an eternal subsidy? The value of having Aravind Srivinas sitting around your office all day? I’d rather start my car in the garage.  “Fast-growing” AI companies only grew because they were allowed to burn as much money as they wanted selling services that are entirely unsustainable, raising more venture capital money with every burst of user growth, which they use to aggressively market to new users and grow further to raise another bump of venture capital. As a result, AI labs and AI startups have created negative habits with their users in two ways: To grow their user bases as fast as possible, AI startups (and AI labs) allowed their users to burn incredible amounts of tokens, I assume because they believed at some point things would become profitable or they’d always have access to easy venture capital. This created an entire industry of AI startups that disconnected their users from the raw economics of the product, creating a race to the bottom where every single AI startup must have every AI model and every AI feature and do every AI thing, all at an incredible cost that only ever seems to increase. Another fun feature is that just about every product gives some sort of “free” access period for new (and expensive!) models, like when Cursor had a free access period for GPT 5’s launch . It’s unclear who shoulders the burden here, but somebody is paying those costs. In any case, nowhere are the subsidies higher than those of Anthropic and OpenAI, who use their tens of billions of dollars of funding to allow users to burn anywhere from $3 to $13 per every dollar of subscription revenue to outpace their competition.  The Subprime AI Crisis is when the largest parties are finally forced to reckon with their rotten economics, and the downstream consequences that follow.  As I reported in July 2025 , starting in June last year, both OpenAI and Anthropic launched “priority service tiers,” jacking up the price on their enterprise customers (who pay for model access via their API to provide models in their software) for guaranteed uptime and less throttling of their services while also requiring an up-front (3-12 month) guarantee of token throughput.  Anthropic’s changes immediately increased the costs on AI startups like Lovable, Replit, Augment Code, and Anthropic’s largest customer, Cursor, which was forced to dramatically change its pricing from a per-request model to a bizarre pricing model where you pay model pricing with a 20% fee , but also receive A) at least as much as you pay in your subscription fee in tokens and B) “generous included usage” of Cursor’s Composer model: What’s crazy is that even with this pricing, Cursor still gives away 16 cents for every dollar on its $60-a-month plan and $1 for every dollar on its $200-a-month plan, and that’s before “generous usage” of other models. I’ll also add that Anthropic has already turned the screws on its subscription customers too, adding weekly limits to Claude subscribers on July 28, 2025 , a few weeks after quietly tightening other limits . Over the next few months, just about every AI startup had to institute some form of austerity. Replit shifted to something called “effort-based” pricing in June 2025, and then launched something called “Agent 3” in September 2025 that burned through users’ limits even faster — and, to be clear, Replit’s pricing gives you your subscription price in credits every single month on top of the cloud hosting necessary to get them online , meaning that a $20-a-month subscriber likely burns at least $25 a month, and Replit remains unprofitable.  Coding platform Augment Code was forced to change its pricing in October 2025 on a per-message basis, which meant that any message you sent cost the same amount no matter how complex the required response. In one case, a user spent $15,000 in tokens on a $250-a-month plan. Since then, Augment Code has moved to a confusing “credit” based model where they claim you use about 293 credits per Claude Sonnet 4.5 task, and users absolutely hate it because Augment Code was too cowardly to charge users based on the actual model pricing, because doing so would scare them away. Now Augment Code is planning to remove its auto-complete and next edit features , claiming that their global usage was in decline and saying that developers “...are no longer working primarily at the level of individual lines of code; instead, they are orchestrating fleets of agents across tasks.”  Elsewhere, Notion bumped its Business Plan from $15 to $20-a-month per user thanks to its new “AI features,” which I imagine sucked for previous business subscribers who didn’t want “AI agents” or any of that crap but did want things like Single Sign On and Premium Integrations. The result? Profit margins dropped by 10% . Great job everybody! In February 2026, Perplexity users noticed that rate limits had been aggressively trimmed from even its January 2026 limits , with $20-a-month subscribers now limited to arbitrary “average use weekly limits” on searches, and “monthly limits” on research queries ( that one user worked out dropped them from 600 deep research queries a month to 20 ), down from 300+ searches a day and generous deep research limits.  Price hikes and product changes are likely to accelerate in the next few months as things get desperate. But now for a quick intermission… I have been training with with Nik Suresh, author of I Will Fucking Piledrive You If You Mention AI Again , and while I’m kidding , I want to be clear that if you don’t stop bringing up Uber and AWS as examples of why AI will work out I may react poorly as I’m fucking tired of this point because it’s stupid and wrong. I will put you in the embrace of God, I swear.  The AI bubble and its representative companies do not and have never represented the buildout of Amazon Web Services or the growth and burnrate of Uber. If you are still saying this you are wrong, ignorant and potentially a big fucking liar.  As I discussed about a month ago , Amazon Web Services cost around $52 billion (adjusted for inflation!) between 2003 (when it was first used internally) through two years after it hit profitability (2017). OpenAI raised $42 billion last year. Anthropic raised $30 billion in February. You are full of shit if you keep saying this.  As I discussed a few weeks ago , Uber’s economics are absolutely nothing like generative AI. Uber did not have capex, and burned those billions on R&D and marketing (making it more similar to Groupon in the end): Here’re some other myths I’m tired of hearing about: Yet the most obvious one that I hear is the funniest: that Anthropic and OpenAI can just raise their prices! As both OpenAI and Anthropic aggressively stumble toward their respective attempts to take their awful businesses public, both are making moves to try and become “respectable businesses,” by which I mean “businesses that still lose billions of dollars but in less-annoying ways.” Last week, OpenAI killed Sora — both the app and the model — along with a $1 billion investment from Disney, with the Wall Street Journal reporting it was burning a million dollars a day , but Forbes estimating the number was closer to $15 million . OpenAI will frame this as part of its "refocus" on a “Superapp” ( per the WSJ ) that combines ChatGPT, coding app codex, and its dangerously shit browser into one rat king of LLM toys that nobody can work out a real business model for. All of this is part of a supposed internal effort to “ prioritize coding and business customers ” that we’ve heard some version of for months. Meanwhile, OpenAI’s attempts to bring advertising to its users have been a little embarrassing, with a two-month-long trial involving “less than 20%” of ChatGPT users resulting in “$100 million in annualized revenue,” better known as about $8.3 million in a month from what was meant to be a business line that brought in “low billions” in 2026 according to the Financial Times . Timing confusingly with this “refocus” is OpenAI’s plan to nearly double its workforce from 4,500 to 8,000 people by the end of 2026 . In fact, writing all this down makes it feel like OpenAI doesn’t really have much of a focus beyond “buy more stuff” and saying “superapp!” every six months. Hey, whatever happened to OpenAI’s plan to be “the interface to the internet” that Alex Heath reported would happen by the first half of 2025 ? Did that happen? Did I miss it? In any case, OpenAI’s other strategy is to absolutely jam the gas pedal on its Codex coding product — for example, one user I found was able to burn $2,192 in tokens on a $200-a-month ChatGPT plan , and another was able to burn $1,461 in three days on the same subscription.  Meanwhile, Anthropic has been in the midst of a months-long rugpull following an all-out media campaign through December and January, pushing Claude Code on tech and business reporters who don’t bother to think too hard about things, per my Hater’s Guide to Anthropic : On February 18, 2026,  Anthropic started banning anybody who used multiple Claude Max accounts , something that had never been an issue before it needed everybody to talk about Claude Code non-stop. The same day, Anthropic “ cleared up ” its Claude Code policies, saying that you can’t connect your Claude account to external services, meaning that all of those people who have been spinning up OpenClaw instances and buying $10,000 worth of Mac Minis are going to find that they’re suddenly having to pay for their API calls.  Around a month later, Anthropic would start a two-week-long 2x-rate limit promotion for off-peak usage that ended on March 27, 2026. A day before on March 26 2026, Anthropic would announce that it was starting “peak hours,”  with Claude users maxing out their sessions faster between the hours of 5am and 11PM pacific time Monday to Friday, with a spokesperson limply adding that “efficiency wins” will “offset this” and only “7% of users will hit the limits.” All of this was sold as a result of “managing the growing demand for Claude.” If I’m honest, this might be Anthropic’s most-egregious swindle yet. By pumping off-peak usage and then immediately cutting it just before introducing peak hours , Anthropic further muddies the water of how much actual access you get to their products. Peak hours appear to have become aggressively restricted, and I imagine off peak feels…something like the regular peak hours used to. Users almost immediately started hitting limits regardless of what time or day they were using it. One user on the $100-a-month Max plan complained about hitting 61% of his session limit after four prompts (which cost $10.26 in tokens). Another said that they hit 63% of their rate limit on their $200-a-month plan in the space of a day, and another hit 95% after 20 minutes of using their Max plan (I’m gonna guess $100-a-month). This person hit their Max limit after “ two or three things .” This one vowed to cancel their $200-a-month subscription after hitting their weekly limit in the space of a day, saying that they (and I’m going off of a translation, so forgive me) “expected a premium experience for $200, and what they got was constant limit stress.” This guy is scared to use Claude Code because of the limits . This guy blew 28% of his limits in less than an hour . This guy “can’t even do basic work on a 20x Max plan.” This guy hit his limits “in a few prompts” on Anthropic’s $20-a-month Pro plan, and the same prompts would have (apparently) consumed 5% of the limits “normally” (I assume last week), and while Thariq from Anthropic assured him that this was abnormal , he didn’t bother to respond to this guy in the thread who said he ran out of usage on the Max plan in 15 minutes . While Anthropic Technical Staff Member Lydia Hallie posted that Anthropic was “aware people are hitting usage limits in Claude Code way faster than expected” and that some investigation was taking place, it’s hard to imagine that Anthropic had no idea that these limits were so severe or that any of this was a surprise.  Naturally, OpenAI had already reset limits on its Codex coding model the second that these reports begun , claiming that they “wanted people to experiment with the magnificent plugins they launched” rather than saying something more-truthful like “we’re lowering limits so that the hogs braying with anger at Anthropic start paying OpenAI instead.” While an eager Redditor claimed that these rate limits were a result of a cache bug on Claude Code , Anthropic quickly said that this wasn’t the reason , nor did they say anything about there being a reason or that anything was wrong.   Meanwhile, users are complaining about the reduced quality of outputs from its Claude Opus 4.6 model , with some saying it acts like cheaper models , and another noting that it might be because of Anthropic’s upcoming Mythos model , which was leaked when Fortune mysteriously somehow discovered an openly-accessible “data cache” that included 3000 assets but somehow no actual information about the model other than it would be a “step change” and its cybersecurity powers were too much to release at once , the tech equivalent of deliberately dropping a magnum condom out of your wallet in front of a woman, or Dril ’s “I was just buying ear medication for my sick uncle…who’s a model by the way” post. I’m gonna be honest I just don’t give a shit about Mythos or Capybara or any blatant leaks intended to spook cybersecurity stocks , especially as these models are also meant to be much more compute-intensive, and thus, vastly more expensive to run.  How will that work with these rate limits, exactly?  I think there’re a few ways this goes: I wager that this is just the first of a few major belt-tightening operations from both Anthropic and OpenAI as they desperately shoulder-barge each other to file the world’s worst S-1. Both companies lose billions of dollars, both companies have no path to profitability, and both companies sell products — both to consumers and businesses — that simply do not work when users are forced to pay something approaching a sustainable cost.  Even with these egregious limits, a user I previously linked to was allowed to burn $10 in tokens in four prompts on a $100-a-month plan. Even in the world of Amodei’s Stylized Facts, that would still be $5 of prompts every 5 hours, which over the course of a month will absolutely be over $100.  Yet the sheer fury of Anthropic’s customers only proves the fundamental weakness of Anthropic’s business model, and the impossibility of ever finding any kind of profitability. And the AI industry has nobody to blame but itself. While it’s really easy to make fun of people obsessed with LLMs, I want to be clear that Anthropic and OpenAI are inherently abusive companies that have built businesses on theft, deception and exploitation. Anybody who’s spent more than a few minutes in one of the many AI Subreddits has read story after story of models mysteriously “becoming dumb,” or rate limits that seem to expand and contract at random. Even the concept of “rate limits” only serves to further deceive the customer. Outside of intentionally asking the model, users are entirely unaware of their “token burn,” or at the very least have built habits around rate limits that, as of right now, are entirely different to even a month ago. A user who bought a $200-a-month Claude Pro subscription in December 2025 , a mere three months later, now very likely cannot do the same things they did on Claude Code when they decided to subscribe, and those who use these subscriptions for their day jobs are now having to sit on their hands waiting for the rate limits to pass, and have no clarity into whether they’ll be able to work at the same rate they did even a month ago, let alone when they subscribed.  All of this is a direct result of Anthropic, OpenAI, and other AI startups intentionally deceiving customers through obtuse pricing so that people would subscribe believing that the product would continue providing the same value, and I’d argue that annual subscriptions to these services amount to, if not fraud, a level of consumer deception that deserves legal action and regulatory involvement. To be clear, no AI company should have ever sold a monthly subscription, as there was never a point at which the economics made sense. Yet had these companies actually charged their real costs, nobody would have bothered with AI, because even with these highly-subsidized subscriptions, AI still hasn’t delivered meaningful productivity benefits, other than a legion of people who email me saying “it’s changed my life as a programmer!” without explaining to me what that means or why it matters or what the actual result is at the end.  Isn’t it kind of weird that we have these LLM subscriptions to products that arbitrarily become less-accessible or less-performant in a way that’s impossible to really measure, and labs never seem to address? We don’t know the actual rate limits on Claude (other than via CCusage or Shellac’s research ), or ChatGPT, or any of these products by design , because if we did, it would be blatantly obvious how unsustainable and ridiculous these products were.  And the magical part about Large Language Models is that your most engaged customers are also your most-expensive, and the more-intensive the work, the more expensive the outputs become.  If you’re about to say “well they’ll just raise the prices,” perhaps you should check Twitter or Reddit, and notice that Anthropic’s customers are screaming like they’re being stung to death by bees because of new rate limits that only let them burn $10 of compute in five hours. Do you think these people would be comfortable with a $130-a-month, $1,300-a-month or $2,500-a-month subscription? One that performs the same way (if not worse) as their $20, $100 or $200-a-month subscription did? Or do you think they’ll do Aaron Sorkin speeches about Anthropic’s greed and immediately jump to ChatGPT in the hopes that the exact same thing doesn’t happen a few months later?  Much as homeowners were assured that they’d simply be able to refinance their homes before the adjustable rates hit, AI fans repeatedly switch subscriptions to whichever provider is currently offering the best deal, in some cases paying for multiple subscriptions under the explicit knowledge that rate limits existed and would become increasingly-punishing. Based on the reactions of their users, I don’t really see how the AI labs — or AI startups, for that matter — fix this problem.  On one hand, AI subscribers are acting like babies, crying that their product won’t let them use $2500 of tokens for $200. This was an obvious con, a blatant subsidy, and a party that wouldn’t last forever.  On the other, AI labs and AI startups have never, ever acted with any degree of honesty or clarity with regards to their costs, instead choosing to add “exciting” new features that often burn more tokens without charging the end user more, which sounds nice until you remember that things cost money and money is not unlimited. The very foundation of every AI startup is economically broken. The majority of them sell some sort of “deep research” report feature that costs several dollars to generate at a time, and many sell some form of expensive coding or “computer use” product, tool-based web search features, and many other products that exist to keep a user engaged while burning tokens, all without explaining to the user “yeah, we’re spending way more than we make off of you, this is an introductory rate.” This intentional, blatant and industry-wide deception set the terms for the Subprime AI Crisis. By selling AI services at $20 or $50 or even $200-a-month, AI startups and labs created the terms for their own destruction, with users trained for years to expect relatively unlimited access sold at a flat rate for a service powered by Large Language Models that burn tokens at arbitrary rates based on their inference of the user’s prompt, making costs near-impossible to moderate.   And when these companies make changes to slightly bring costs under control, their users act with revulsion, because rate limits aren’t price increases, but direct changes to the functionality of the product. Imagine if a subscription to a car service was $200-a-month, and let you go 50 miles, or 25 miles, or 100 miles, or 4 miles, or 12 miles depending on the day, and never at any point told you how many miles you had left beyond a percentage-based rate limit. To make matters worse, sometimes the car would arbitrarily take a different route, driving you five miles in the opposite direction, or decide to park on the side of the curb, charging you for every mile.  This is the reality of using an AI product in the year of our lord 2026. A Claude Code or OpenAI Codex user cannot with any clarity say that in three months their current workload or workflow will be possible based on their current subscription. Somebody buying an annual subscription to any AI product is immediately sacrificing themselves to the whims of startup CEOs that intentionally decided to deceive users for years as a means of juicing growth.  And when these limits decay, does it eventually make the ways in which some of these users work with Claude Code impossible? At what point do these rate limit shifts start changing how reliable the experience is and how much one can get done in a day? What use is a tool that gets more unreliable to access and expensive over time? Even if this week’s rate limits are an overcorrection, one has to imagine they resemble the future of Anthropic’s products, and are indicative of a larger pattern of decay in the value of its subscriptions.   I’m going to be as blunt as possible: every bit of AI demand — and barely $65 billion of it existed in 2025 — that exists only exists due to subsidies, and if these companies were to charge a sustainable rate, said demand would evaporate. There is no righting this ship. There is no pricing that makes sense that customers will pay at scale, nor is there a magical technological breakthrough waiting in the wings that will reduce costs. Vera Rubin will not save AI, nor will some sort of “too big to fail” scenario, because “too big to fail” was based on the fact that banks would have stopped providing dollars to people and insurance companies would have  stopped issuing insurance. Despite NVIDIA’s load-bearing valuation and the constant discussion of companies like OpenAI and Anthropic, their actual economic footprint is quite small in comparison to the trillions of dollars of CDOs and trillion plus dollars of mortgages involved in the great financial crisis. The death of the AI industry would be cataclysmic to venture capitalists, bring about the end of the hypergrowth era for the Magnificent Seven, and may very well kill Oracle, but — seriously — that is nothing in comparison to the scale of the Great Financial Crisis. This isn’t me minimizing the chaos to follow, but trying to express how thoroughly fucked everything was in 2008.  On Friday I’m going to get into this more in the premium. This wasn’t an intentional ad, I just realized as I wrote that sentence that that was what I have to do.  Anyway, I’ll close with a grim thought. What’s funny about the comparison to the subprime mortgage crisis is that there are, in all honesty, multiple different versions of the Stripper With Five Houses from The Big Short: All of these entities are acting based on a misplaced belief that the world will cater to them, and that nothing will ever change. While there might be different levels of cynicism — people that know there’re subsidies but assume they’ll be fine once they arrive, or people like Sam Altman that are already rich and don’t give a shit — I think everybody in the AI industry has deluded themselves into believing they have the mandate of Heaven.    Back in August 2024 , I named several pale horses of the AIpocalypse, and after absolutely fucking nailing the call two years early on OpenAI’s “big, stupid magic trick” of launching Sora to the public , I think it’s time to update them: Anyway, thanks for reading this piece. Data centers raise debt from either banks, private credit, private equity or “business development companies,” non-banking entities that borrow money from banks to lend to risky companies. In an analysis of 26 prominent data center deals, I found ( back in December 2025 ) several names — Blue Owl, MUFG (Mitsubishi), Goldman Sachs, JP Morgan Chase, Morgan Stanley, SMBC (Sumitomo Mitsui) and Deutsche Bank — that come up regularly.  AI Labs (and AI startups) raise funding from venture capitalists (EG: Dragoneer (Anthropic, OpenAI, Perplexity) and Founders Fund (Anthropic, OpenAI)), hyperscalers (Google, Amazon, NVIDIA, Microsoft, all of whom have now invested in both OpenAI and Anthropic), sovereign wealth funds (GIC, Singapore’s sovereign wealth fund, invested in Anthropic), and even banks providing lines of credit , as they did for both Anthropic and OpenAI .  Many of the big names in data center development (who I believe have all, in some way, backed CoreWeave) funded those lines of credit, including Morgan Stanley, SMBC, JPMorgan and MUFG. Those common names are points of failure, in particular SMBC and MUFG, two large Japanese banks that have aggressively loaned to just about every part of the AI economy. This pairs badly with the fact that the Japanese government is considering interest rate hikes thanks to the continuing chaos in the Middle East , which will make debt more expensive. Venture capitalists are funded by limited partners (EG: pension funds, investment banks and wealthy individuals), and the venture capital industry is facing an historic liquidity crisis (IE: they can’t raise money and their investments aren’t selling), which means that it cannot sustain the AI industry forever. NVIDIA (and other hardware sellers to a much lesser extent) sells GPUs and the associated hardware to data center developers and hyperscalers . At around $42 million a megawatt between GPUs, data center and power construction, these data centers are almost entirely paid using debt. This is the only link in the chain that is really profitable. Data center developers rent their GPUs to AI labs and hyperscalers. Developers, who raised $178.5 billion in debt in the US alone last year , must borrow heavily to fund buildouts, and due to many of these projects being run by either brand or relatively new developers, debt costs are higher.  As a result, based on my premium data center model , many data center projects are unprofitable even with a paying customer , and that’s assuming they even get built. To make matters worse, as I discussed last week, only 5GW of data center capacity out of over 200GW announced is actually under construction globally , which means many of these loans are currently on interest-only payments. All evidence points to GPU compute either being a low or negative-margin business. CoreWeave — the largest, best-funded and NVIDIA-backed AI compute provider — had an operating margin of -6% and net loss margin of -29% in 2025 .  CoreWeave’s largest customers are Microsoft, OpenAI and NVIDIA, which means that it should, in theory, be getting the best rate around. Hyperscalers like Google, Meta, Amazon, and Microsoft, who both rent GPUs from data center providers and rent GPUs to AI labs (as well as offering API access to some AI labs’ models — Google and Amazon sell Anthropic’s, Microsoft sells OpenAI’s models, and both it’s own models and other models like xAI’s Grok ). Hyperscalers steadfastly refuse to talk about their AI revenues, and do not break out costs. I would also put Oracle in this bucket. AI labs rent GPUs from either hyperscalers or data center companies to either train models or run inference (creating the outputs of models), sell access to models via their API, and offer subscription services to both consumer and business customers. Important detail: in almost every case, an AI lab must make an up front commitment, likely with a prepayment, to secure future capacity. This means that AI labs are often having to pony up massive amounts of up-front capital on top of their incredibly high ongoing costs. Anthropic has made $5 billion in revenue and spent $10 billion on compute to date , and had to raise another $30 billion in February 2026 after raising about $16.5 billion in 2025 alone. Through September 2025, OpenAI made $4.3 billion in revenue and spent $8.67 billion on inference alone . Neither of these companies have a path to profitability. AI startups buy access to models via AI labs’ API, building services that have “AI features” powered by said models, paid on a per-million token basis (for input tokens (user-fed data) and output tokens (model outputs)). Every single AI startup is unprofitable , and every AI startup functions by offering a service powered by AI models provided by AI labs. In every case that I’ve found, these providers always offer far more in token burn than the cost of their subscriptions. Consumers and businesses pay for monthly subscriptions or, in some cases, API access to models. Customers paying for AI services in most cases pay for a monthly service, such as Anthropic’s Claude Pro or Max or Perplexity Pro/Max, running from $20 a month to $200 a month.  These subscriptions for the most part mask the amount of tokens that you are actually burning as a customer, but in every single case that I’ve found, that amount is always in excess of the subscription cost. Cursor has, at this point, raised $3.36 billion, and turned it into, at best, about a billion dollars of revenue, and that’s assuming it linearly grew between periods versus (more likely) having up and down months. As AI labs grow, their costs increase dramatically, both in their immediate compute costs and the demands from GPU providers for up-front cash to secure future compute allocation.  In parallel, as AI startups grow, they burn more money per customer, which increases their dependence on venture capital. As this happens, AI labs are facing both a cash and compute crush, which means they have to start either controlling the amount of compute customers use or make more money from serving it. AI labs are thus forced to raise prices on AI startups, either through tolls (priority processing) or raw cost increases. Another important detail: one of the ways that AI labs raise prices isn’t even through “making things more expensive,” but selling access to models that burn more tokens. Think of this as the variable rate mortgage of the Subprime AI Crisis.  As AI labs raise prices on their AI startup clients, these startups are forced to reduce the quality of their services and/or increase their costs after years of getting their customers used to a significantly-cheaper or better service, which makes their products less attractive, leading to customer churn.  Worse still, these customers are used to using subscriptions from Anthropic and OpenAI with remarkable rate limits that are impossible for even a well-capitalized AI startup to compete with, which means that these changes only slow the rate of burn rather than making these companies profitable. As a result, these AI startups are more dependent on venture capital. While OpenAI and Anthropic are pretty happy on the top of the food chain, they are also dependent on the existence of AI startups for revenue for their models, which means that while these price changes increase the amount of revenue they get in the short term, they invariably push AI startups toward cheaper open source models and death. AI labs have, this entire time, been massively subsidizing their own products. Per Forbes , AI coding platform Cursor has faced numerous problems competing with Anthropic, who it claims at one point let users burn $5000 a month in tokens on a $200-a-month subscription, which reflects my own reporting from last year . Cursor also claims in the same article that its enterprise customers are profitable, but I call bullshit considering the multiple enterprise customers who have reached out to tell me they can burn $2 or $3 for every $1 of subscription.  The problem is that a subsidy is always a losing proposition, which means that at some point Anthropic and OpenAI will have to massively reduce the amount of tokens that people use on their accounts. As I’ll get to later, this infuriates users and sends them running for the doors. At some point, the cost of doing business with Anthropic and OpenAI will kill AI startups, as there is no point at which any of them become sustainable, which will in turn kill the revenue from selling access to their models. At some point, users will be forced to burn tokens at a rate that actually matches their subscription costs, which will reduce the value of the product, which will in turn reduce the amount of subscribers they will have. And at some point, Anthropic and OpenAI will be left with a bunch of compute reservations they’ve made that they don’t need and can’t afford due to miss-timed growth projections. As Dario Amodei said back in February, there’s no hedge on Earth that could stop Anthropic from going bankrupt if it buys too much compute . As the two largest customers of AI compute — there really isn’t even a distant third outside of xAI and hyperscalers, the latter of which are predominantly standing up OpenAI and Anthropic (or in Meta’s case a bunch of unprofitable LLM bullshit) — who’s going to pay for all of those data centers? Fucking Aquaman ? Users are inherently trained to expect a service that they pay for on a monthly basis, and their experience of said service is entirely separated from “token burn,” making it impractical to impossible to get them to use models directly, or to apply rate limits. The longer a user has used the service, the more their habits orient around an “unlimited” or “partially limited” service, which means your only options are to raise prices or apply rate limits, with the only justification for either of them being “new models” (which are more expensive) or “we’re unable to afford to run our company,” which the user doesn’t give a shit about. They’re profitable on inference - no they are not! There is no proof of this statement anywhere! What’s your source here? Sam Altman saying it in August 2025 ? Dario Amodei saying he had gross margins of 50%? That was a “stylized fact” that he specifically said wasn’t about Anthropic , not that you care! What else have you got for me here? SemiAnalysis’ InferenceX ? Gun to your head, explain to me how that’s the case. Oh you’ve heard the companies do “batch processing”? Why is all that “batch processing” not making them profitable?  I swear to god if you say any shit about how these companies would be “profitable without training” I’m going to scream. No! AI training costs are not going away. They are an inherent part of running these companies, and are not capex . They are operating expenses . AI is being funded by the largest companies in the world with the most healthy balance sheets- I will obliterate you with the 100-Type Guanyin Bodhisattva ! Microsoft is the only remaining hyperscaler that is funding the AI buildout without debt, and none of them will talk about AI revenues. This point is trotted out by imbeciles to try and say “this is nothing like the dot com bubble,” which I fundamentally agree with — it’s worse! It’s weirder! It’s a bigger waste! And they collectively need $2 trillion in brand new AI revenues by 2030 for any of it to make sense! The cost of AI services is going down because the token prices are going down - you are a silly person! You do not actually understand anything! The cost of tokens is not the same as the cost of serving tokens! OpenAI cut the cost of its o3 reasoning model by 80% a few weeks after the release of Claude Opus 4 . Do you think that happened because of magical price reductions on the ops side? If so, I wish to study your brain. It’s the gym model they want people to subscribe and not use it it’s the gym model it’s the gym model- TZZZZT, whoops, looks like you got tazed. Anthropic will announce that it’s “fixed the bug” (IE: eased rate limits it intentionally set) and apologize to the community, prolonging the inevitable. Rate limits will continue to decay over time, just at a slower pace.  Anthropic keeps the limits where they are, and we hit a new normal that makes everybody really mad. The AI companies that only have customers because they spend $3 to $10 for every dollar of revenue. The venture capitalists that are ultra-rich on paper, heavily leveraging their firms in companies like Harvey (worth “$11 billion”) and Cursor (worth “$29.3 billion”) that burn hundreds of millions or billions of dollars and are now both too large to sell to another company and too shitty a company to take public. The AI labs that have built massive businesses on selling heavily-subsidized subscriptions to customers who don’t want to pay for them and API calls to AI startups that can only pay them if infinite resources exist. The AI data center companies that, thanks to readily-available debt, have started 200GW of projects (and only started building 5GW of them) for AI demand that doesn’t exist, entirely based on the theoretical sense that maybe it will in the future. Oracle, who is building hundreds of billions of dollars of data centers for OpenAI (which needs infinite resources to be able to pay its compute costs), is taking on equally-large amounts of debt, all because it assumes that nothing bad will ever happen. The customers of AI startups that are building lifestyles, identities and workflows around them believing that we’re “just at the beginning” on top of unsustainable AI subscriptions. Any further price increases or service degradations from Anthropic and OpenAI are a sign that they’re running low on cash. Any reduction in capex from big tech is a sign that the AI bubble is bursting, as NVIDIA’s continued growth only comes from Microsoft, Google, Amazon, Meta, Oracle and other large companies buying tens of billions of dollars of servers from Taiwanese ODMs like Foxconn and Quanta. Any further price increases or service degradations from AI startups , such as Cursor, Perplexity, Harvey, Lovable or Replit. These are all token-intensive venture-hogs that burn $4 or $5 for every $1 of revenue. Any discussion of layoffs at AI companies . The collapse of a data center deal that has yet to commence construction. The collapse of a data center already in construction, but before it’s finished. The collapse of an already-constructed data center. CoreWeave or any major data center player having trouble or failing to raise debt. We’ve already seen the beginnings of this with CoreWeave’s issues raising for its Lancaster PA data center . The Further Collapse of Stargate Abilene: If anything happens to the construction of OpenAI’s flagship data center (being built by Oracle) in Abilene Texas, you know shit is getting bad. Any problems or delays with OpenAI or Anthropic going public: both of these companies are the financial equivalent of Chernobyl, so I can only imagine it’ll take some talented accountants to get them in any shape where investors without lead poisoning actually want to get involved. Any problems with Blue Owl as an ongoing concern: Blue Owl is the loosest lender in the AI bubble, and if it falls behind on their loans or has issues with its limited partners, that’s a bad sign too. Any problems with SoftBank : SoftBank was somehow able to raise $40 billion in debt (payable in a year ) to fund its chunk of OpenAI’s pseudo-$110 billion round, running over its promised 25% ratio of loans to the value of its assets. This puts SoftBank in a very precarious position. ARM’s stock tanking: A great deal of SoftBank’s wealth comes from its investment in ARM, including a $15 billion margin loan based on its stock. If ARM drops below $80, things are going to get hairy for Masayoshi Son. Any issues with NVIDIA’s customers’ ability to pay: If NVIDIA’s customers don’t reliably pay it, things will look bad come earnings season. NVIDIA misses on earnings: This is an obvious one, but I think the markets will crap their pants if NVIDIA misses on earnings estimates.

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HeyDingus 1 months ago

I’m returning my Studio Display XDR and buying another one

Sooo… I did a thing. I couldn’t help but be slightly dissatisfied by the clarity of my Studio Display XDR ’ s nano-texture display. It just made everything look a little less than Retina-quality. And for this price, I don’t want to have lingering regrets each time I use it. So, I ordered a second non-nano-texture version, banking on Apple’s generous return policy . It came in today. I set it up about 30 minutes ago. I put the two displays side by side and… it’s no question. The nano-texture is going back. Showing the same content on each display, at the same brightness level, I can absolutely see the fuzziness introduced by the “ matte” display. It’s not that nano-texture is all bad. I love how it looks when the display is dark — there are zero reflections. 1 But the point is to enjoy it while the display is on . Without nano-texture, everything is as crisp as I had hoped. I tend to lean toward the display when I’m concentrating, and even close up, the display is razor sharp. I technically have until April 9th to send back the nano-texture XDR , but, honestly, I think I’m going to package it up tonight. Well… maybe tomorrow. I might as well enjoy having 10k pixels of display at my disposal while I can. If I hold onto the original display until the last day that I can send it back, I will have had it for 24 days. That’s a full 10 extra days beyond the stated 14-day return period. It’s possible that I could have squeezed in even a few more days by initiating the return today, the 14th day after it was delivered, instead of the 11th. With that in mind, one could get nearly a month of use for testing and comparison of Apple’s products, with the ability to return it (free shipping both ways) for a full refund. That’s serious commitment to customer satisfaction, and one area where Apple’s standards haven’t slipped. To boot, by paying with Apple Card’s Monthly Installments (which allow you to pay for an item over 12 months with 0% interest), I’ve only been charged $287.92 for the nano-texture display, and $263.92 for the regular one. I think that was just the taxes for each one. To be sure, it’s a privileged position I’m in to be able to do these shenanigans, but there’s a lot to be said for how easy Apple has made it to purchase even it’s most expensive products with very little risk. If I were in an environment with light sources behind me, my decision might be very different. I think there’s definitely a place for this non-reflective display — it’s just not in my home office. ↩︎ HeyDingus is a blog by Jarrod Blundy about technology, the great outdoors, and other musings. If you like what you see — the blog posts , shortcuts , wallpapers , scripts , or anything — please consider leaving a tip , checking out my store , or just sharing my work. Your support is much appreciated! I’m always happy to hear from you on social , or by good ol' email . If I were in an environment with light sources behind me, my decision might be very different. I think there’s definitely a place for this non-reflective display — it’s just not in my home office. ↩︎

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iDiallo 1 months ago

How we get radicalized in America

Be healthy, be young, fall ill. You have a great job of course, you have insurance. It would be ok if the worst thing about health insurance in America was it is hard to navigate. No! The actual problem is that your insurance is incentivized not to cover you at your most vulnerable moment. You pay them every month. That's money that goes from your paycheck, into their pockets. Now if they cover you, that's money that leaves their pocket, and go into your treatment. There are two ways they can make money. 1. You continue paying every month, and never fall ill. 2. You fall ill, and they deny you care. Only the second option is an active option. Health Insurance is a scam that we have normalized in the United States. It helps no one, it makes healthcare unaffordable, and you have to fight tooth and nail to get any sort of care. When Luigi was in the headlines, and news anchors were asking how such a young man can get radicalized, I shook my head. In America, it is our tradition to get 2 jobs. It is our tradition to live paycheck to paycheck. And it is our tradition to get radicalized the moment we get sick. When you get sick, the healthcare industry tries to charge much as they can get away with and the insurance industry tries to deny as much as it can.

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The AI Industry Is Lying To You

Hi! If you like this piece and want to support my independent reporting and analysis, why not subscribe to my premium newsletter? It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I just put out a massive Hater’s Guide To The SaaSpocalypse , as well as the Hater’s Guide to Adobe . It helps support free newsletters like these! The entire AI bubble is built on a vague sense of inevitability — that if everybody just believes hard enough that none of this can ever, ever go wrong that at some point all of the very obvious problems will just go away. Sadly, one cannot beat physics. Last week, economist Paul Kedrosky put out an excellent piece centered around a chart that showed new data center capacity additions (as in additions to the pipeline, not brought online ) halved in the fourth quarter of 2025 (per data from Wood Mackenzie ):   Wood Mackenzie’s report framed it in harsh terms: As I said above, this refers only to capacity that’s been announced rather than stuff that’s actually been brought online , and Kedrosky missed arguably the craziest chart — that of the 241GW of disclosed data center capacity, only 33% of it is actually under active development: The report also adds that the majority of committed power (58%) is for “wires-only utilities,” which means the utility provider is only responsible for getting power to the facility, not generating the power itself, which is a big problem when you’re building entire campuses made up of power-hungry AI servers.  WoodMac also adds that PJM, one of the largest utility providers in America, “...remains in trouble, with utility large load commitments three times as large as the accredited capacity in PJM’s risked generation queue,” which is a complex way of saying “it doesn’t have enough power.”  This means that fifty eight god damn percent of data centers need to work out their own power somehow. WoodMac also adds there is around $948 billion in capex being spent in totality on US-based data centers, but capex growth decelerated for the first time since 2023 . Kedrosky adds: Let’s simplify: The term you’re looking for there is data center absorption, which is (to quote Data Center Dynamics) “...the net growth in occupied, revenue-producing IT load,” which grew in America’s primary markets from 1.8GW in new capacity in 2024 to 2.5GW of new capacity in 2025 according to CBRE .   The problem is, this number doesn’t actually express newly-turned-on data centers. Somebody expanding a project to take on another 50MW still counts as “new absorption.”  Things get more confusing when you add in other reports. Avison Young’s reports about data center absorption found 700MW of new capacity in Q1 2025 , 1.173GW in Q2 , a little over 1.5GW in Q3 and 2.033GW in Q4 (I cannot find its Q3 report anywhere), for a total of 5.44GW, entirely in “colocation,” meaning buildings built to be leased to others. Yet there’s another problem with that methodology: these are facilities that have been “delivered” or have a “committed tenant.” “Delivered” could mean “the facility has been turned over to the client, but it’s literally a powered shell (a warehouse) waiting for installation,” or it could mean “the client is up and running.” A “committed tenant” could mean anything from “we’ve signed a contract and we’re raising funds” (such as is the case with Nebius raising money off of a Meta contract to build data centers at some point in the future ). We can get a little closer by using the definitions from DataCenterHawk (from whichAvison Young gets its data), which defines absorption as follows :  That’s great! Except Avison Young has chosen to define absorption in an entirely different way — that a data center (in whatever state of construction it’s in) has been leased, or “delivered,” which means “a fully ready-to-go data center” or “an empty warehouse with power in it.”  CBRE, on the other hand, defines absorption as “net growth in occupied, revenue-producing IT load,” and is inclusive of hyperscaler data centers. Its report also includes smaller markets like Charlotte, Seattle and Minneapolis, adding a further 216MW in absorption of actual new, existing, revenue-generating capacity. So that’s about 2.716GW of actual, new data centers brought online. It doesn’t include areas like Southern Virginia or Columbus, Ohio — two massive hotspots from Avison Young’s report — and I cannot find a single bit of actual evidence of significant revenue-generating, turned-on, real data center capacity being stood up at scale. DataCenterMap shows 134 data centers in Columbus , but as of August 2025, the Columbus area had around 506MW in total according to the Columbus Dispatch, though Cushman and Wakefield claimed in February 2026 that it had 1.8GW . Things get even more confusing when you read that Cushman and Wakefield estimates that around 4GW of new colocation supply was “delivered” in 2025, a term it does not define in its actual report, and for whatever reason lacks absorption numbers. Its H1 2025 report , however, includes absorption numbers that add up to around 1.95GW of capacity…without defining absorption, leaving us in exactly the same problem we have with Avison Young.  Nevertheless, based on these data points, I’m comfortable estimating that North American data center absorption — as the IT load of data centers actually turned on and in operation — was at around 3GW for 2025 , which would work out to about 3.9GW of total power. And that number is a fucking disaster. Earlier in the year, TD Cowen’s Jerome Darling told me that GPUs and their associated hardware cost about $30 million a megawatt. 3GW of IT load (as in the GPUs and their associated gear’s power draw) works out to around $90 billion of NVIDIA GPUs and the associated hardware, which would be covered under NVIDIA’s “data center” revenue segment: America makes up about 69.2% of NVIDIA’s revenue, or around $149.6 billion in FY2026 (which runs, annoyingly, from February 2025 to January 2026). NVIDIA’s overall data center segment revenue was $195.7 billion, which puts America’s data center purchases at around $135 billion, leaving around $44 billion of GPUs and associated technology uninstalled. With the acceleration of NVIDIA’s GPU sales, it now takes about 6 months to install and operationalize a single quarter’s worth of sales. Because these are Blackwell (and I imagine some of the new next generation Vera Rubin) GPUs, they are more than likely going to new builds thanks to their greater power and cooling requirements, and while some could in theory be going to old builds retrofitted to fit them, NVIDIA’s increasingly-centralized (as in focused on a few very large customers) revenue heavily suggests the presence of large resellers like Dell or Supermicro (which I’ll get to in a bit) or the Taiwanese ODMs like Foxconn and Quanta who manufacture massive amounts of servers for hyperscaler buildouts.  I should also add that it’s commonplace for hyperscalers to buy the GPUs for their colocation partners to install, which is why Nebius and Nscale and other partners never raise more than a few billion dollars to cover construction costs.  It’s becoming very obvious that data center construction is dramatically slower than NVIDIA’s GPU sales, which continue to accelerate dramatically every single quarter. Even if you think AI is the biggest most hugest and most special boy: what’s the fucking point of buying these things two to four years in advance? Jensen Huang is announcing a new GPU every year!  By the time they actually get all the Blackwells in Vera Rubin will be two years old! And by the time we install those Vera Rubins, some other new GPU will be beating it!  Before we go any further, I want to be clear how difficult it is to answer the question “how long does a data center take to build?”. You can’t really say “[time] per megawatt” because things become ever-more complicated with every 100MW or so. As I’ll get into, it’s taken Stargate Abilene two years to hit 200MW of power . Not IT load. Power .  Anyway, the question of “how much data center capacity came online?” is pretty annoying too.  Sightline ’s research — which estimated that “almost 6GW of [global data center power] capacity came online last year” — found that while 16GW of capacity was slated to come online in 2026 across 140 projects, only 5GW is currently under construction, and somehow doesn’t say that “maybe everybody is lying about timelines.” Sightline believes that half of 2026’s supposed data center pipeline may never materialize, with 11GW of capacity in the “announced” stage with “...no visible construction progress despite typical build timelines of 12-18 months.” “Under construction” also can mean anything from “ a single steel beam ” to “nearly finished.” These numbers also are based on 5GW of capacity , meaning about 3.84GW of IT load, or about $111.5 billion in GPUs and associated gear, or roughly 57.5% of NVIDIA’s FY2026 revenue that’s actually getting built. Sightline (and basically everyone else) argues that there’s a power bottleneck holding back data center development, and Camus explains that the biggest problem is a lack of transmission capacity (the amount of power that can be moved) and power generation (creating the power itself):  Camus adds that America also isn’t really prepared to add this much power at once: Nevertheless, I also think there’s another more-obvious reason: it takes way longer to build a data center than anybody is letting on, as evidenced by the fact that we only added 3GW or so of actual capacity in America in 2025. NVIDIA is selling GPUs years into the future, and its ability to grow, or even just maintain its current revenues, depends wholly on its ability to convince people that this is somehow rational. Let me give you an example. OpenAI and Oracle’s Stargate Abilene data center project was first announced in July 2024 as a 200MW data center . In October 2024, the joint venture between Crusoe, Blue Owl and Primary Digital Infrastructure raised $3.4 billion , with the 200MW of capacity due to be delivered “in 2025.” A mid-2025 presentation from land developer Lancium said it would have “1.2GW online by YE2025.” In a May 2025 announcement , Crusoe, Blue Owl, and Primary Digital Infrastructure announced the creation of a $15 billion joint vehicle, and said that Abilene would now be 8 buildings, with the first two buildings being energized by the “first half of 2025,” and that the rest would be “energized by mid-2026.” Each building would have 50,000 GPUs, and the total IT load is meant to be 880MW or so, with a total power draw of 1.2GW.  I’m not interested in discussing OpenAI not taking the supposedly-planned extensions to Abilene because it never existed and was never going to happen .  In December 2025, Oracle stated that it had “delivered” 96,000 GPUs , and in February, Oracle was still only referring to two buildings , likely because that’s all that’s been finished. My sources in Abilene tell me that Building Three is nearly done, but…this thing is meant to be turned on in mid-2026. Developer Mortensen claims the entire project will be completed by October 2026 , which it obviously, blatantly won’t. I hate to speak in conspiratorial terms, but this feels like a blatant coverup with the active participation of the press. CNBC reported in September 2025 that “ the first data center in $500 billion Stargate project is open in Texas ,” referring to a data center with an eighth of its IT load operational as “online” and “up and running,” with Crusoe adding two weeks later that it was “live,” “up and running” and “continuing to progress rapidly,” all so that readers and viewers would think “wow, Stargate Abilene is up and running” despite it being months if not years behind schedule. At its current rate of construction, Stargate Abilene will be fully built sometime in late 2027. Oracle’s Port Washington Data Center, as of March 6 2026, consisted of a single steel beam . Stargate Shackelford Texas broke ground on December 15 2025 , and as of December 2025, construction barely appears to have begun in Stargate New Mexico . Meta’s 1GW data center campus in Indiana only started construction in February 2026 .  And, despite Microsoft trying to mislead everybody that its Wisconsin data center had ‘arrived” and “been built,” looking even an inch deeper suggests very little has actually come online” — and, considering the first data center was $3.3 billion ( remember: $14 million a megawatt just for construction), I imagine Microsoft has successfully brought online about 235MW of power for Fairwater. What Microsoft wants you to think is it brought online gigawatts of power (always referred to in the future tense), because Microsoft, like everybody else, is building data centers at a glacial pace, because construction takes forever, even if you have the power, which nobody does! The concept of a hundred-megawatt data center is barely a few years old, and I cannot actually find a built, in-service gigawatt data center of any kind, just vague promises about theoretical Stargate campuses built for OpenAI, a company that cannot afford to pay its bills.  Everybody keeps yammering on about “what if data centers don’t have power” when they should be thinking about whether data centers are actually getting built. Microsoft proudly boasted in September 2025 about its intent to build “the UK’s largest supercomputer” in Loughton, England with Nscale, and as of March 2026, it’s literally a scaffolding yard full of pylons and scrap metal . Stargate Abilene has been stuck at two buildings for upwards of six months.  Here’s what’s actually happening: data center deals are being funded by eager private credit gargoyles that don’t know shit about fuck. These deals are announced, usually by overly-eager reporters that don’t bother to check whether the previous data centers ever got built, as massive “multi-gigawatt deals,” and then nobody follows up to check whether anything actually happened.  All that anybody needs to fund one of these projects is an eager-enough financier and a connection to NVIDIA. All Nebius had to do to raise $3.75 billion in debt was to sign a deal with Meta for data center capacity that doesn’t exist and will likely take three to four years to build (it’s never happening). Nebius has yet to finish its Vineland, New Jersey data center for Microsoft , which was meant to be “ at 100MW ” by the end of 2025, but appears to have only had 50MW (the first phase) available as of February 2026 .  I’m just gonna come out and say it: I think a lot of these data center deals are trash, will never get built, and thus will never get paid. The tech industry has taken advantage of an understandable lack of knowledge about construction or power timelines in the media to pump out endless stories about “data center capacity in progress” as a means of obfuscating an ever-growing scandal: that hundreds of billions of NVIDIA GPUs got sold to go in projects that may never be built. These things aren’t getting built, or if they’re getting built, it’s taking way, way longer than expected, which means that interest on that debt is piling up. The longer it takes, the less rational it becomes to buy further NVIDIA GPUs — after all, if data centers are taking anywhere from 18 months to three years to build, why would you be buying more of them? Where are you going to put them, Jensen? This also seriously brings into question the appetite that private credit and other financiers have for funding these projects, because much of the economic potential comes from the idea that these projects get built and have stable tenants. Furthermore, if the supply of AI compute is a bottleneck, this suggests that when (or if) that bottleneck is ever cleared, there will suddenly be a massive supply glut, lowering the overall value of the data centers in progress…which are, by the way, all filled with Blackwell GPUs, which will be two or three-years-old by the time the data centers are finally turned on. That’s before you get to the fact that the ruinous debt behind AI data centers makes them all remarkably unprofitable , or that their customers are AI startups that lose hundreds of millions or billions of dollars a year , or that NVIDIA is the largest company on the stock market, and said valuation is a result of a data center construction boom that appears to be decelerating and even if it wasn’t operating at a glacial pace compared to NVIDIA’s sales . Not to sound unprofessional or nothing, but what the fuck is going on? We have 241GW of “planned” capacity in America, of which only 79.5GW of which is “under active development,” but when you dig deeper, only 5GW of capacity is actually under construction?   The entire AI bubble is a god damn mirage. Every single “multi-gigawatt” data center you hear about is a pipedream, little more than a few contracts and some guys with their hands on their hips saying “brother we’re gonna be so fuckin’ rich!” as they siphon money from private credit — and, by extension, you, because where does private credit get its capital from? That’s right. A lot comes from pension funds and insurance companies. Here’s the reality: data centers take forever. Every hyperscaler and neocloud talking about “contracted compute” or “planned capacity” may as well be telling you about their planned dinners with The Grinch and Godot. The insanity of the AI buildout will be seen as one of the largest wastes of capital of all time ( to paraphrase JustDario ), and I anticipate that the majority of the data center deals you’re reading about simply never get built. The fact that there’s so much data about data center construction and so little data about completed construction suggests that those preparing the reports are in on the con. I give credit to CBRE, Sightline and Wood Mackenzie for having the courage to even lightly push back on the narrative, even if they do so by obfuscating terms like “capacity” or “power” in ways that reporters and other analysts are sure to misinterpret. Hundreds of billions of dollars have been sunk into buying GPUs, in some cases years in advance, to put into data centers that are being built at a rate that means that NVIDIA’s 2025 and 2026 revenues will take until 2028 to 2029 to actually operationalize, and that’s making the big assumption that any of it actually gets built. I think it’s also fair to ask where the money is actually going. 2025’s $178.5 billion in US-based data center deals doesn’t appear to be resulting in any immediate (or even future) benefit to anybody involved. I also wonder whether the demand actually exists to make any of this worthwhile, or what people are actually paying for this compute.  If we assume 3GW of IT load capacity was brought online in America, that should (theoretically) mean tens of billions of dollars of revenue thanks to the “insatiable demand for AI” — except nobody appears to be showing massive amounts of revenue from these data centers.  Applied Digital only had $144 million in revenue in FY2025 (and lost $231 million making it). CoreWeave, which claimed to have “ 850MW of active power (or around 653MW of IT load)” at the end of 2025 (up from 420MW in Q1 FY2025 , or 323MW of IT load), made $5.13 billion of revenue (and lost $1.2 billion before tax ) in FY2025 .  Nebius? $228 million, for a loss of $122.9 million on 170MW of active power (or around 130MW of IT load). Iren lost $155.4 million on $184.7 million last quarter , and that’s with a release of deferred tax liabilities of $182.5 million. Equinix made about $9.2 billion in revenue in its last fiscal year , and while it made a profit , it’s unclear how much of that came from its large and already-existent data center portfolio , though it’s likely a lot considering Equinix is boasting about its “multi-megawatt” data center plans with no discussion of its actual capacity . And, of course, Google, Amazon, and Microsoft refuse to break out their AI revenues. Based on my reporting from last year , OpenAI spent about $8.67 billion on Azure through September 2025, and Anthropic around $2.66 billion in the same period on Amazon Web Services . As the two largest consumers of AI compute, this heavily suggests that the actual demand for AI services is pretty weak, and mostly taken up by a few companies (or hyperscalers running their own services.)  At some point reality will set in and spending on NVIDIA GPUs will have to decline. It’s truly insane how much has been invested so many years in the future, and it’s remarkable that nobody else seems this concerned. Simple questions like “where are the GPUs going?” and “how many actual GPUs have been installed?” are left unanswered as article after article gets written about massive, multi-billion dollar compute deals for data centers that won’t be built before, at this rate, 2030.  And I’d argue it’s convenient to blame this solely on power issues, when the reality is clearly based on construction timelines that never made any sense to begin with. If it was just a power issue, more data centers would be near or at the finish line, waiting for power to be turned on. Instead, well-known projects like Stargate Abilene are built at a glacial pace as eager reporters claim that a quarter of the buildings being functional nearly a year after they were meant to be turned on is some sort of achievement. Then there’s the very, very obvious scandal that NVIDIA, the largest company on the stock market, is making hundreds of billions of dollars of revenue on chips that aren’t being installed. It’s fucking strange, and I simply do not understand how it keeps beating and raising expectations every quarter given the fact that the majority of its customers are likely going to be able to use their current purchases in the next decade. Assuming that Vera Rubin actually ships in 2026, it’s reasonable to believe that people will be installing these things well into 2028, if not further, and that’s assuming everything doesn’t collapse by then. Why would you bother? What’s the point, especially if you’re sitting on a pile of Blackwell GPUs?  Why are we doing any of this?  Last week also featured a truly bonkers story about Supermicro, a reseller of GPUs used by CoreWeave and Crusoe, where co-founder Wally Liaw and several other co-conspirators were arrested for selling hundreds of millions of dollars of NVIDIA GPUs to China , with the intent to sell billions more.  Liaw, one of Supermicro’s co-founders, previously resigned in a 2018 accounting scandal where Supermicro couldn’t file its annual reports, only to be (per Hindenburg Research’s excellent report ) rehired in 2021 as a consultant , and restored to the board in 2023, per a filed 8K .  Mere days before his arrest, Liaw was parading around NVIDIA’s GTC conference , pouring unnamed liquids in ice luges and standing two people away from NVIDIA CEO Jensen Huang. Liaw was also seen congratulating the CEO of Lambda on its new CFO appointment on LinkedIn , as well as shaking hands (along with Supermicro CEO Charles Liang, who has not been arrested or indicted) with Crusoe (the company building OpenAI’s Abilene data center) CEO Chase Lochmiller .  Supermicro isn’t named in the indictment for reasons I imagine are perfectly normal and not related to keeping the AI party going . Nevertheless, Liaw and his co-conspirators are accused of shipping hundreds of millions of dollars’ worth of NVIDIA GPUs to China through a web of counterparties and brokers, with over $510 million of them shipped between April and mid-May 2025. While the indictment isn’t specific as to the breakdown, it confirms that some Blackwell GPUs made it to China, and I’d wager quite a few. The mainstream media has already stopped thinking about this story, despite Supermicro being a huge reseller of NVIDIA gear, contributing billions of dollars of revenue, with at least $500 million of that apparently going to China. The fact that Supermicro wasn’t specifically named in the case is enough to erase the entire tale from their minds, along with any wonder about how NVIDIA, and specifically Jensen Huang, didn’t know. This also isn’t even close to the only time this has happened. Late last year, Bloomberg reported on Singapore-based Megaspeed — a (to quote Bloomberg) “once-obscure spinoff of a Chinese gaming enterprise [that] evolved into the single largest Southeast Asian buyer of NVIDIA chips” — and highlighted odd signs that suggest it might be operating as a front for China.  As a neocloud, Megaspeed rents out AI compute capacity like CoreWeave, and while NVIDIA (and Megaspeed) both deny any of their GPUs are going to China, Megaspeed, to quote Bloomberg, has “something of a Chinese corporate twin”: Bloomberg reported that Megaspeed imported goods “worth more than a thousand times its cash balance in 2023,” with two-thirds of its imports being NVIDIA products. The investigation got weirder when Bloomberg tried to track down specific circuit boards that NVIDIA had told the US government were in specific sites: Things get weirder throughout the article, with a Chinese company called “Shanghai Shuoyao” having a near-identical website and investor deck (as mentioned) to Megaspeed, with several of the “computing clusters under construction” actually being in China.  Things get a lot weirder as Bloomberg digs in, including a woman called “Huang” that may or may not be both the CEO of Megaspeed and an associated company called “Shanghai Hexi,” which is also owned by the Yangtze River Delta project… who was also photographed sitting next to Jensen Huang at an event in Taipei in 2024. While all of this is extremely weird and suspicious, I must be clear there is no declarative answer as to what’s going on, other than that NVIDIA GPUs are absolutely making it to China, somehow. I also think that it would be really tough for Jensen Huang to not know about it, or for billions of dollars of GPUs to be somewhere without NVIDIA’s knowledge.  Anyway, Supermicro CEO Charles Liang has yet to comment on Wally Liaw or his alleged co-conspirators, other than a statement from the company that says that their acts were “ a contravention of the Company’s policies and compliance controls .”  Jensen Huang does not appear to have been asked if he knew anything about this — not Megaspeed, not Supermicro, or really any challenging question of any kind for the last few years of his life.  Huang did, however, say back in May 2025 that there was “no evidence of any AI chip diversion,’ and that the countries in question “monitor themselves very carefully.”  For legal reasons I am going to speak very carefully: I cannot say that Jensen is wrong, or lying, but I think it’s incredible, remarkable even, that he had no idea that any of this was going on. Really? Hundreds of millions if not billions of dollars of GPUs are making it to China — as reported by The Information in December 2025 — and Jensen Huang had no idea? I find that highly unlikely, though I obviously can’t say for sure. In the event that NVIDIA had knowledge — which I am not saying it did, of course — this is a huge scandal that, for the most part, nobody has bothered to keep an eye on outside of a few brave souls at The Information and Bloomberg who give a shit about the truth. Has anybody bothered to ask Jensen about this? People talk to him on camera all the time.  I’ll also add that I am shocked that so many people are just shrugging and moving on from Supermicro, which is a major supplier of two of the major neoclouds (Crusoe and CoreWeave) and one of the minors (Lambda, which they also rents cloud capacity to). The idea that a company had no idea that several percentage points of its revenue were flowing directly to China via one of its co-founders is an utter joke. I hope we eventually find out the truth. Nevertheless, this kind of underhanded bullshit is a sign of desperation on the part of just about everybody involved. So, I want to explain something very clearly for you, because it’s important you understand how fucked up shit has become: hyperscalers are forcing everybody in their companies to use AI tools as much as possible, tying compensation and performance use to token burn, and actively encouraging non-technical people to vibe-code features that actually reach production.  In practice, this means that everybody is being expected to dick around with AI tools all day, with the expectation that you burn massive amounts of tokens and, in the case of designers working in some companies, actively code features without ever knowing a line of code.  “How do I know the last part? Because a trusted source told me — and I’ll leave it at that” One might be forgiven for thinking this means that AI has taken a leap in efficacy, but the actual outcomes are a labyrinth of half-functional internal dashboards that measure random user data or convert files, spending hours to save minutes of time at some theoretical point. While non-technical workers aren’t necessarily allowed to ship directly to production, their horrifying pseudo-software, coded without any real understanding of anything, is expected to be “fixed” by actual software engineers who are also expected to do their jobs. These tools also allow near-incompetent Business Idiot software engineers to do far more damage than they might have in the past. LLM use is relatively-unrestrained (and actively incentivized) in at least one hyperscaler, with just about anybody allowed to spin up their own OpenClaw “AI agent” (read: series of LLMs that allegedly can do stuff with your inbox or Slack for no clear benefit, other than their ability to delete all of your emails ). In Meta’s case , this ended up causing a severe security breach: According to The Information, Meta systems storing large amounts of company and user-related data were accessible to engineers who didn’t have permission to see them, and was marked a sec-1 incident, the second highest level of severity on an internal scale that Meta uses to rank security incidents.  The incident follows multiple problems caused at Amazon by its Kiro and Q LLMs. I quote Business Insider ’s Eugene Kim:  Despite the furious (and exhausting) marketing campaign around “the power of AI code,” I believe that these events are just the beginning of the true consequences of AI coding tools: the slow destruction of the tech industry’s software stack.  LLMs allow even the most incompetent dullard to do an impression of a software engineer, by which I mean you can tell it “make me software that does this” or “look at this code and fix it” and said LLM will spend the entire time saying “you got this” and “that’s a great solution.”  The problem is that while LLMs can write “all” code, that doesn’t mean the code is good, or that somebody can read the code and understand its intention (as these models do not think), or that having a lot of code is a good thing both in the present and in the future of any company built using generative code.  LLM-based code is often verbose, and rarely aligns with in-house coding guidelines and standards, guaranteeing that it’ll take far longer to chew through, which naturally means that those burdened with reviewing it will either skim-read it or feed it into another LLM to work out what the hell to do. Worse still, LLM use is also entirely directionless. Why is anybody at Meta using an OpenClaw? What is the actual thing that OpenClaw does, other than burn an absolute fuck-ton of tokens?  Think about this very, very simply for a second: you have given every engineer in the company the explicit remit to write all their code using LLMs, and incentivized them to do so by making sure their LLM use is tracked. You have now massively increased both the operating costs of the company (through token burn costs) and the volume of code being created.  To be explicit, allowing an LLM to write all of your code means that you are no longer developing code, nor are you learning how to develop code, nor are you going to become a better software engineer as a result. This means that, across almost every major tech company, software engineers are being incentivized to stop learning how to write software or solve software architecture issues .   If you are just a person looking at code, you are only as good as the code the model makes, and as Mo Bitar recently discussed, these models are built to galvanize you, glaze you, and tell you that you’re remarkable as you barely glance at globs of overwritten code that, even if it functions, eventually grows to a whole built with no intention or purpose other than what the model generated from your prompt.  Things only get worse when you add in the fact that hyperscalers like Meta and Amazon love to lay off thousands of people at a time, which makes it even harder to work out why something was built in the way it was built, which is even harder when an LLM that lacks any thoughts or intentions builds it. Entire chunks of multi-trillion dollar market cap companies are being written with these things, prompted by engineers (and non-engineers!) who may or may not be at the company in a month or a year to explain what prompts they used.  We’re already seeing the consequences! Amazon lost hundreds of thousands of orders! Meta had a major security breach! The foundations of these companies are being rotted away through millions of lines of slop-code that, at best, occasionally gets the nod from somebody who has “software engineer” on their resume, and these people keep being fired too, raising the likelihood that somebody who knows what’s going on or why something is built a certain way will be able to stop something bad from happening.  Remember: Google, Amazon, Microsoft, and Meta all hold vast troves of personal information, intimate conversations, serious legal documents, financial information, in some cases even social security numbers, and all four of them along with a worrying chunk of the tech industry are actively encouraging their software engineers to stop giving a fuck about software.   Oh, you’re so much faster with AI code? What does that actually mean? What have you built? Do you understand how it works? Did you look at the code before it shipped, or did you assume that it was fine because it didn’t break?  This is creating a kind of biblical plague within software engineering — an entire tech industry built on reams of unmanageable and unintentional code pushed by executives and managers that don’t do any real work. LLMs allow the incompetent to feign competence and the unproductive to produce work-adjacent materials borne of a loathing for labor and craftsmanship, and lean into the worst habits of the dullards that rule Silicon Valley. All the Valley knows is growth , and “more” is regularly conflated with “valuable.” The New York Times’ Kevin Roose — in a shocking attempt at journalism — recently wrote a piece celebrating the competition within Silicon Valley to burn more and more tokens using AI models : Roose explains that both Meta and OpenAI have internal leaderboards that show how many tokens you’ve used, with one software engineer in Stockholm spending “more than his salary in tokens,” though Roose adds that his company pays for them. Roose describes a truly sick culture, one where OpenAI gives awards to those who spend a lot of money on their tokens , adding that he spoke with several tech workers who were spending thousands of dollars a day on tokens “for what amount to bragging rights.” Roose also added one more insane detail: that one person found a loophole in Claude’s $20-a-month using a piece of software made by Figma that allowed them to burn $70,000 in tokens . Despite all of this burn, Roose struggled to find anybody who was able to explain what they were doing beyond “maintaining large, complex pieces of software using coding agents running in parallel,” but managed to actually find one particularly useful bit of information — that all of this might be performative: I do give Roose one point for wondering if “...any of these tokenmaxxers [were] producing anything good, or whether they [were] merely spinning their wheels churning out useless code in an attempt to look busy.” Good job Kevin.  That being said, I find this story horrifying, and veering dangerously close to the actions of drug addicts and cult followers. Throughout this story in one of the world’s largest newspapers, Roose fails to find a single “tokenmaxxer” making something that they can actually describe, which has largely been my experience of evaluating anyone who talks nonstop about the power of “agentic coding.”  These people are sick, and are participating in a vile, poisonous culture based on needless expenses and endless consumption.  Companies incentivizing the amount of tokens you burn are actively creating a culture that trades excess for productivity, and incentivizing destructive tendencies built around constantly having to find stuff to do rather than do things with intention.  They are guaranteeing that their software will be poorly-written and maintained, all in the pursuit of “doing more AI” for no reason other than that everybody else appears to be doing so. Anybody who actually works knows that the most productive-seeming people are often also the most-useless, as they’re doing things to seem productive rather than producing anything of note. A great example of this is a recent Business Insider interview with a person who got laid off from Amazon after learning “AI” and “vibe coding,” and how surprised they were that these supposed skills didn’t make them safer from layoffs: To be clear, this person is a victim . They were pressured by Amazon to take up useless skills and build useless things in an expensive and inefficient way, and ended up losing their job despite taking up tools they didn’t like under duress.  This person was, at one point, actively part of building an internal Amazon site using AI, and had to “learn to vibe code with a lot of trial and error” and the help of a colleague. Was this a good use of her time? Was this a good use of her colleague’s time? No! In fact, across all of these goddamn AI coding hype-beast Twitter accounts and endless proclamations about the incredible power of AI agents, I can find very few accounts of something happening other than someone saying “yeah I’m more productive I guess.”  I am certain that at some point in the near future a major big tech service is going to break in a way that isn’t immediately fixable as a result of thousands of people building software with AI coding tools, a problem compounded by the dual brain drain forces of layoffs and a culture that actively empowers people to look busy rather than actually produce useful things. What else would you expect? You’re giving people a number that they can increase to seem better at their job, what do you think they’re going to do, try and be efficient? Or use these things as much as humanly possible, even if there really isn’t a reason to? I haven’t even gotten to how expensive all of this must be, in part because it’s hard to fully comprehend.  But what I do know is that big tech is setting itself up for crisis after crisis, especially when Anthropic and OpenAI stop subsidizing their models to the tune of allowing people to spend $2500 or more on a $200-a-month subscription .  What happens to the people who are dependent on these models? What happens to the people who forgot how to do their jobs because they decided to let AI write all of their code? Will they even be able to do their jobs anymore?   Large Language Models are creating Silicon Valley Habsburgs — workers that are intellectually trapped at whatever point they started leaning on these models that were subsidized to the point that their bosses encouraged them to use them as much as humanly possible. While they might be able to claw their way back into the workforce, a software engineer that’s only really used LLMs for anything longer than a few months will have to relearn the basic habits of their job, and find that their skills were limited to whatever the last training run for whatever model they last used was.  I’m sure there are software engineers using these models ethically, who read all the code, who have complete industry over it and use it as a means of handling very specific units of work that they have complete industry over. I’m also sure that there are some that are just asking it to do stuff, glancing at the code and shipping it. It’s impossible to measure how many of each camp there are, but hearing Spotify’s CEO say that its top developers are basically not writing code anymore makes me deeply worried, because this shit isn’t replacing software engineering at all — it’s mindlessly removing friction and putting the burden of “good” or “right” on a user that it’s intentionally gassing up. Ultimately, this entire era is a test of a person’s ability to understand and appreciate friction.  Friction can be a very good thing. When I don’t understand something, I make an effort to do so, and the moment it clicks is magical. In the last three years I’ve had to teach myself a great deal about finance, accountancy, and the greater technology industry, and there have been so many moments where I’ve walked away from the page frustrated, stewed in self-doubt that I’d never understand something. I also have the luxury of time, and sadly, many software engineers face increasingly-deranged deadlines set by bosses that don’t understand a single fucking thing, let alone what LLMs are capable of or what responsible software engineering is. The push from above to use these models because they can “write code faster than a human” is a disastrous conflation of “fast” and “good,” all because of flimsy myths peddled by venture capitalists and the media about “LLMs being able to write all code.” Generative code is a digital ecological disaster, one that will take years to repair thanks to company remits to write as much code as fast as possible.  Every single person responsible must be held accountable, especially for the calamities to come as lazily-managed software companies see the consequences of building their software on sand.  In the end, everything about AI is built on lies.  Hundreds of gigawatts of data centers in development equate to 5GW of actual data centers in construction.  Hundreds of billions of dollars of GPU sales are mostly sitting waiting for somewhere to go. Anthropic’s constant flow of “annualized” revenues ended up equating to literally $5 billion in revenue in four years , on $25 billion or more in salaries and compute. Despite all of those data centers supposedly being built, nobody appears to be making a profit on renting out AI compute. AI’s supposed ability to “write all code” really means that every major software company is filling their codebases with slop while massively increasing their operating expenses. Software engineers aren’t being replaced — they’re being laid off because the software that’s meant to replace them is too expensive, while in practice not replacing anybody at all. Looking even an inch beneath the surface of this industry makes it blatantly obvious that we’re witnessing one of the greatest corporate failures in history. The smug, condescending army of AI boosters exists to make you look away from the harsh truth — AI makes very little revenue, lacks tangible productivity benefits, and seems to, at scale, actively harm the productivity and efficacy of the workers that are being forced to use it. Every executive forcing their workers to use AI is a ghoul and a dullard, one that doesn’t understand what actual work looks like, likely because they’re a lazy, self-involved prick.  Every person I talk to at a big tech firm is depressed, nagged endlessly to “get on board with AI,” to ship more, to do more, all without any real definition of what “more” means or what it contributes to the greater whole, all while constantly worrying about being laid off thanks to the truly noxious cultures that are growing around these services. AI is actively poisonous to the future of the tech industry. It’s expensive, unproductive, actively damaging to the learning and efficacy of its users, depriving them of the opportunities to learn and grow, stunting them to the point that they know less and do less because all they do is prompt. Those that celebrate it are ignorant or craven, captured or crooked, or desperate to be the person to herald the next era, even if that era sucks, even if that era is inherently illogical, even if that era is fucking impossible when you think about it for more than two seconds. And in the end, AI is a test of your introspection. Can you tell when you truly understand something? Can you tell why you believe in something, other than that somebody told you you should, or made you feel bad for believing otherwise? Do you actually want to know stuff, or just have the ability to call up information when necessary?  How much joy do you get out of becoming a better person?If you can’t answer that question with certainty, maybe you should just use an LLM, as you don’t really give a shit about anything. And in the end, you’re exactly the mark built for an AI industry that can’t sell itself without spinning lies about what it can (or theoretically could) do.  Only 33% of announced US data centers are actually being built, with the rest in vague levels of “planning.” That’s about 79.53GW of power, or 61GW of IT load. “Active development” also refers to anything that is (and I quote) “...under development or construction,” meaning “we’ve got the land and we’re still working out what to do with it. This is pretty obvious when you do the maths. 61GW of IT load would be hundreds of thousands of NVIDIA GB200 NVL72 racks — over a trillion dollars of GPUs at $3 million per 72-GPU rack — and based on the fact there were only $178.5 billion in data center debt deals last year , I don’t think many of these are actually being built right now. Even if they were, there’s not enough power for them to turn on. NVIDIA claims it will sell $1 trillion of GPUs between 2025 and 2027 , and as I calculated previously , it sells about 1.6GW (in IT load terms, as in how much power just the GPUs draw) of GPUs every quarter, which would require at least 1.95GW of power just to run, when you include all the associated gear and the challenges of physically getting power. None of this data talks about data centers actually coming online.

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DHH 1 months ago

Denmark desperately needs more inequality

The Danish election is tomorrow. One of the central themes in the incumbent campaign has been a proposed wealth tax. The fig leaf for this proposal was "smaller classrooms in the early grades", but that quickly fell off, and the debate centered on "inequality". And it's true that inequality is a problem in Denmark: There's not nearly enough! I know that sounds sacrilegious. Even most of the business-friendly press and parties in Denmark dance around this topic. Which makes political sense because the word "inequality" leads most people to think of poverty and destitution. But that's not the reality in the little kingdom that could. Denmark has an enormous state apparatus (half of GDP and a third of all workers!) that offers equal access to everything from health care to education and a million programs in between. It could surely be slimmed and trimmed, but on the whole, it works remarkably well. The average Dane is incredibly well cared for by any international standard (high-trust society, hurray!). By those same standards, it's the 8th most equal country in the world on income, as measured by the Gini coefficient (0.28). But this is where the numbers start spellbinding the debate. Because the Danish Gini coefficient perversely "degrades" if new businesses succeed, as any time successful founders and high-paid employees earning incomes above the median "worsen" inequality.  This is obviously nonsense. When the pie gets bigger, it gets better for all, as long as nobody is robbed of their existing slice.  Denmark should clearly want new successful businesses! It should love to see founders reap big rewards when the risks pay off. It should celebrate early employees making fortunes on stock grants. But all too often, it just doesn't. Just to put it on a pin: Danes hate flashy cars with a passion that stretches back much further than the current green excuses. But buying a $300,000 Ferrari in Denmark is one of the most patriotic things you can possibly do! You'll end up paying almost three times the price for the privilege, and sending 2/3s of that to the treasury in taxes. Truly a contribution to the common cause worthy of admiration, not scorn!  But because the debate around inequality is anchored in a fixed-pie paradigm, scorn is all you're likely to get. Anyone who does well in Denmark is immediately suspected of having succeeded at the expense of others. Probably through some form of nefarious exploitation, even if we can't prove what?! There is a core national politics of grievance and envy. But, however human that may be, the future progress and prosperity of the country depends on rejecting this zero-sum delusional dogma. The Danish economy is currently doing well compared to the rest of the EU, but it's dangerously dependent on a handful of vintage corporations pulling the bulk of the load. This simply has to change if the Danes wish to retain their high standards of living going forward. No corporation lasts forever. Novo Nordisk was Europe's most valuable company at the start of last year, now it's worth half that, and is out of the top ten. And who knows what the closing of the Hormuz Strait will do to Maersk. These two companies alone represent roughly a quarter of all Denmark's exports! Meanwhile, new business formation just hit an all-time low. And only a tiny portion of the big employers in Denmark were created in the last thirty years. And thus, almost all the wealth that funds the highly-prized welfare state is coming from really old companies. Many of them over a hundred years old. This is wonderful in many ways. The Danes should be rightfully proud to host Maersk (1904), Novo (1923), Vestas (1945), Lego (1932), and other international heavy-weights. But it can't rely on this aging corporate vintage to forever bear fruit for tomorrow. Tomorrow needs to be tended to by planting new seeds. New companies. New growth. New capital. And that's just not going to happen if the Danish state declares itself at war with capital formation or accumulation. It should be so lucky to have more rich people, with more capital, and the talent to deploy it toward a better, shared future (or spend it on heavily-taxed Ferraris!). The ballot boxes open tomorrow morning. It's predicted to be a close one. Fingers crossed for a prosperous choice.

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