2026-04-25 00:40:45
Soundtrack — Brass Against — Karma Police
It was January 21, 2025. Per The Information, Larry Ellison, CEO of Oracle, had just flown to Washington DC from Florida, and had to borrow a coat “...so he wouldn’t freeze during an interview he did on the White House lawn, according to two people who were involved in the event.” He was there to announce a very big — some might even say huge — new project standing next to SoftBank CEO Masayoshi Son and OpenAI CEO Sam Altman.
“Together, these world-leading technology giants are announcing the formation of Stargate, so put that name down in your books, because I think you’re gonna hear a lot about it in the future. A new American company that will invest $500 billion at least in AI infrastructure in the United States and very, very quickly, moving very rapidly, creating over 100,000 American jobs almost immediately,” said President Donald Trump.
After he was done, Ellison stepped to the podium. “The data centers are actually under construction, the first of them are under construction in Texas. Each building’s a half a million square feet, there are ten buildings currently being built, but that will expand to 20.”
Following Ellison, SoftBank’s Masayoshi Son added that Stargate would “...immediately start deploying $100 billion dollars, with the goal of making $500 billion dollars within [the] next four years, within your town!” turning to Donald Trump with his hands extended. It was unclear what town he was referring to.
Altman added that it would be “an exciting project” and that “...we’ll be able to do all the wonderful things that these guys talked about, but the fact that we get to do this in the United States is I think wonderful,” though it’s unclear what “the wonderful things” or “this” refers to.
It’s been 15 months, and Stargate LLC has never been formed. SoftBank and OpenAI have contributed no capital to the project, other than SoftBank’s own acquisition of a former electric vehicle manufacturing plant in Lordstown, Ohio that it intends to turn into a data center parts manufacturing plant with Foxconn, which is best known for effectively abandoning a $10 billion factory in Wisconsin back in 2021. Oh, and Project Freebird, a SoftBank-built project that exists to funnel money to its subsidiary SB Energy, though I can’t imagine how SoftBank actually funds it.
No government money was ever involved, no funding ever left anyone’s bank account, no "initiative" ever existed, and OpenAI, Oracle and SoftBank have, in my opinion, conspired to mislead the general public about the existence and validity of a project for marketing purposes.
The “data centers actually under construction” referred to a 1.2GW project in Abilene Texas that had been under construction since the middle of 2024, and had originally been earmarked by Elon Musk and xAI, except Musk pulled out because he felt that Oracle was moving too slow. While Ellison said that there were ten buildings under construction with plans to expand to twenty, only eight were actually being built (each holding around 50,000 GB200 GPUs across NVL72 racks), with the extension up in the air until March 2026, when Microsoft agreed to lease 700MW — so another seven buildings — that were meant to go to OpenAI. These buildings will not make Oracle any money, as Oracle is, despite spending so much money, leasing whatever land it uses from Crusoe.
Sidenote: Previously-unknown information from the Wall Street Journal published this week shows that the reason why Microsoft ended up buying the additional capacity at Abilene was because lenders were uncomfortable with providing additional funding to provide compute that was ultimately destined to go to Oracle.
As far as those eight buildings go, only two are actually online and generating revenue, though sources with direct knowledge of Oracle’s infrastructure have informed me that work is still being done on both buildings despite CNBC reporting that they were “operational” in September 2025.
Let’s break this down. Based on a presentation by landowner Lancium from May 2025, the Stargate Abilene campus was meant to have 1.2GW of AI data centers online by year-end 2025.
Based on reporting from DatacenterDynamics, the first 200MW of power was meant to be energized “in 2025.” As time dragged on, occupancy was meant to begin in the first half of 2025, had “potential to reach 1GW by 2025,” complete all 1.2GW of capacity by mid-2026, be energized by mid-2026, have 64,000 GPUs by the end of 2026, as of September 30, 2025 had “two buildings live,” and as of December 12, 2025, Oracle co-CEO Clay Magouyurk said that Abilene was “on track” with “more than 96,000 NVIDIA Grace Blackwell GB200 delivered,” otherwise known as two buildings’ worth of GPUs.
Four months later on April 22, 2026, Oracle tweeted that “...in Abilene, 200MW is already operational, and delivery of the eight-building campus remains on schedule.” It is unclear if that’s 200MW of critical IT capacity or the total available power at the Abilene campus, and in any case, this is only enough power for two buildings, which means that Oracle is most decidedly not “on schedule.”
Sources familiar with Oracle infrastructure have confirmed that while construction has finished on building three, barely any actual tech has been installed. It also appears that while construction has begun on a power plant of some sort, it’s unclear whether it’s the 360.5MW gas power plant or 1GW substation. In any case, Abilene needs both to turn on the GPUs, if they ever get installed.
Abilene is, for the most part, the only part of the Stargate project that’s anywhere near complete.
I say that because the other data centers — Shackelford, Texas, Port Washington, Wisconsin, Doña Ana County, New Mexico, Saline, Michigan, and Milam County, Texas — are patches of land with a few steel beams, if that. To be explicit, every single Stargate data center is funded by Oracle and its respective financial backers.
Oracle is taking on a massive amount of debt to build these data centers, working with a labyrinthine network of financiers and construction partners to pull together the capacity necessary to get paid for its five-year-long $300 billion compute deal with OpenAI.
Oracle has also, per Bloomberg, deliberately raised money using “project financing” loans that are repaid using the projected cashflow, allowing it to keep the massive amount of debt off of its balance sheet. This is remarkable — and offensive! — because it’s borrowing over $38 billion to fund construction of its Wisconsin and Shackelford data centers (the largest debt deal of its kind on record) and said debt will now effectively not exist despite its massive drag on Oracle’s cashflow, which sat at negative $24.7 billion in its last quarterly earnings.
Based on estimates ($30 million in critical IT and $14 million in construction per megawatt) from TD Cowen’s Jerome Darling, the total cost of Oracle’s 7.1GW of data center capacity will be somewhere in the region of $340 billion to build.
All of these data centers are being built for a single tenant — OpenAI — which expects, per The Information, to lose over $167 billion (assuming it hits annual revenues of over $100 billion) by the end of 2028, and as a result does not actually have the money to pay Oracle for its compute on an ongoing basis.
In addition to its commitments to Oracle, OpenAI has also made commitments to spend $138 billion on Amazon over eight years, $250 billion on Microsoft Azure over an unspecific period, $20 billion with Cerebras over three years, $22.4 billion with CoreWeave over five years, and a non-specific amount with Google Cloud.
All of this is happening as Oracle’s core businesses plateau, even after Oracle reshuffled them in Q3 FY25 to represent Cloud, Software, Hardware and Services segments, the latter three of which have barely moved in the last 9 months as low-to-negative-margin cloud compute revenue grows.
In other words, Oracle’s only growth comes from a segment requiring hundreds of billions of dollars of compute.
To make matters worse, every single one of these data centers is behind schedule. Stargate Abilene was meant to be done at the beginning, middle, and now the end of this year, yet sources tell me there’s no way it’s finished before April 2027.
Bloomberg also reported late last year that Oracle had delayed several data centers from 2027 to 2028, but here in reality, every other Stargate data center is somewhere between a patch of dirt, a single steel beam, multiple steel beams, or less than half of a shell of a single building. Considering it’s taken two years for Stargate Abilene to build two buildings, I don’t see how it’s possible that these are built before the beginning of 2029.
And at that point, where exactly will we be in the AI bubble? What GPUs will be available? What other kinds of silicon will exist? What will the demand be for AI compute?
I don’t think that OpenAI exists for that long, and even if it does, it will have to raise at least $200 billion in the space of three years to possibly keep up with its commitments.
I’m surprised that nobody (outside of JustDario, at least) has raised the seriousness of this situation.
Stargate, as it stands, will kill Oracle, outside of OpenAI becoming the literal most-profitable and highest-revenue-generating company of all time within the next two years. Even then, by the time that Abilene is built, its 450,000 GB200 GPUs will be two-years-old, and entirely obsolete far before its debts are repaid. A similar fate awaits whatever GPUs are put in the other Stargate data centers.
Today’s newsletter is a thorough review and analysis of the ruinous excess of Stargate, a name that only really means “data centers being built for OpenAI in the hopes that OpenAI will pay for them.” Oracle is mortgaging its entire future on their construction, and even if it gets paid, I see no way that the cashflow from OpenAI’s compute spend can recover the cost before its GPU capex is rendered obsolete, let alone whether it can cover the debt associated with the buildout.
I’m Larry Ellison — Welcome To Jackass.
Welcome to the end of Oracle, or Sell The Compute To Who, Larry? Fucking Aquaman?
2026-04-23 01:24:17
Documents viewed by Where’s Your Ed At shed additional light on Microsoft’s transition to token-based billing for GitHub Copilot, as the company grapples with spiraling costs of AI compute.
As reported on Monday (and as announced soon after by Microsoft), the company has taken the step to suspend new sign-ups for individual and student accounts, has removed Anthropic’s Opus models from the cheapest $10-a-month plan, and plans to further tighten usage limits.
According to the documents, the announcement for token-based billing will be tomorrow (4/23), with changes to GitHub Copilot rolling out at the beginning of June.
Explainer: At present, GitHub Copilot users have a certain amount of “requests” — interactions where you ask the model to do something, with Pro ($10-a-month) accounts getting 300 a month, and Pro+ ($39-a-month) getting 1500. More-expensive models use more requests, cheaper ones use less (I’ll explain in a bit).
Moving to “token-based billing” means that instead of using “requests,” GitHub Copilot users will pay for the actual cost of tokens. For example, Claude Opus 4.7 costs $5 per million input tokens (stuff you feed in) and $25 per million output tokens (stuff the model outputs, including tokens for chain-of-thought reasoning.)
Users will pay a monthly subscription to access GitHub Copilot, and receive a certain allotment of AI tokens based on their subscription level. Organizations paying for GitHub Copilot will have “pooled” AI credits, meaning that tokens are shared across the entire organization.
For an initial promotional period running from June, July and August, GitHub Copilot Business Customers will pay $19 per-user-per-month and receive $30 of pooled AI credits, and Copilot Enterprise customers will pay $39 per-user-per-month and receive $70 of pooled AI credits. Afterward, users will receive either $19 or $39 of tokens depending on their subscription level.
While the documents refer to moving “all” GitHub Copilot users to token-based billing, it’s unclear at this time how Microsoft will be handling individual Pro or Pro+ subscribers.
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2026-04-22 06:44:29
The following exists as a record of what happened previously, please see above for the full story.
In developing news, Anthropic appears to have removed access to AI coding tool Claude Code from its $20-a-month "Pro" accounts. This is likely another cost-cutting move that follows a recent change (per The Information) that forced enterprise users to pay on a per-million-token based rate rather than having rate limits that were, based on researchers' findings, often much higher than the cost of the subscription.
Update: Anthropic's Amol Avasare claims that it is "...running a small test on ~2% of new prosumer signups. Existing Pro and Max subscribers aren't affected." This does not really make sense given the fact that all support documents and the Claude website reflect that Pro users do not have access to Claude Code.
I am waiting for further comment.
Previously, users were able to access Claude using their Pro subscriptions via a command-line interface and both the web and desktop Claude apps. Users were, instead of paying on a per-million-token basis, allowed to use their subscription to access Claude Code, but will likely now have to pay for API access.
Anthropic's Claude Code support documents (as recently as this April 10th archived page) previously read "Using Claude Code with your Pro or Max plan." The page now reads "Using Claude Code with your Max plan."
Pricing on Anthropic's website reflects the removal of Claude Code on both mobile and desktop.
Some Pro users report that they are still able to access Claude Code via the web app and Command-Line Interface.
It is unclear at this time whether this change is retroactive or for new Pro subscribers, or whether Anthropic intends to entirely remove access to Claude Code (without paying for API tokens) from every Pro customer.
I have requested a comment from Anthropic, and will update this piece when I receive it, or if Anthropic confirms this move otherwise.
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It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large. I recently put out the timely and important Hater’s Guide To The SaaSpocalypse, another on How AI Isn't Too Big To Fail, a deep (17,500 word) Hater’s Guide To OpenAI, and just last week put out the massive Hater’s Guide To Private Credit.
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2026-04-22 00:28:59
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Soundtrack — Megadeth — Hangar 18 (Eb Tuning)
For the best part of four years I’ve been wrapped up in writing these massive, sprawling narratives about the AI bubble and the tech industry at large. I still intend to write them, but today I’m going to do what I do best — explaining all the odd shit that’s happening in the tech industry and explaining why it’s concerning to me.
And because I love a good bit, I’m tying these stories to my pale horses of the AIpocalypse — signs that things are beginning to unwind in the most annoying bubble in history.
Anyway, considering that the newsletter and the podcast are now my main form of income, I’m going to be experimenting with the formats across the free and premium to keep things interesting and varied.
Pale Horse: Any further price increases or service degradations from Anthropic and OpenAI are a sign that they’re running low on cash.
Let’s start with a fairly direct statement: Anthropic should stop taking on new customers until it works out its capacity issues.
So, generally any service — Netflix, for example — you use with any regularity has the “four nines” of availability, meaning that it’s up 99.99% of the time. Once a company grows beyond a certain scale, having four 9s is considered standard business practice…
…unless you’re Anthropic!
As of writing this sentence, Anthropic’s availability for its Claude Chatbot has 98.79% uptime, its platform/console is at 99.14%, its API is at 99.09%, and Claude Code is at 99.25% for the last 90 days.
Let me put this into context. When you have 99.99% uptime, a service is only down for a minute (and 0.48 of a second) each week. If you’re hitting 98.79% uptime, as with the Claude chatbot, your downtime jumps to two hours, one minute, and 58 seconds.
Or, put another way, 98.79% uptime equates to nearly four-and-a-half days in a calendar year where the service is unavailable.
More-astonishingly, Claude for Government sits at 99.91%. Government services are generally expected to be four 9s minimum, or 5 (99.999%) for more important systems underlying things like emergency services.
This is a company that recently raised $30 billion dollars and gets talked about like somebody’s gifted child, yet Anthropic’s services seem to have constant uptime issues linked to a lack of capacity.
Since mid-February, outages for systems across Anthropic have become so common that some of its enterprise clients are switching to other AI model players.
David Hsu, founder and CEO of software development platform Retool, said he prefers to use Anthropic’s Opus 4.6 model to power his company’s AI agent tool because he believes it is the best model for enterprise. He recently changed to OpenAI’s model to power his company’s agent. “Anthropic has just been going down all the time,” he said.
The reliability of core services on the internet is often measured in nines. Four nines means 99.99% of uptime—a typical percentage that a software company commits to customers. As of April 8, Anthropic’s Claude API had a 98.95% uptime rate in the last 90 days.
Yet Anthropic’s problems go far further than simple downtime (as I discussed last week), leading to (deliberately or otherwise) severe performance issues with Opus 4.6:
One of the most detailed public complaints originated as a GitHub issue filed by Stella Laurenzo on April 2, 2026, whose LinkedIn profile identifies her as Senior Director in AMD’s AI group.
In that post, Laurenzo wrote that Claude Code had regressed to the point that it could not be trusted for complex engineering work, then backed that claim with a sprawling analysis of 6,852 Claude Code session files, 17,871 thinking blocks and 234,760 tool calls.
The complaint argued that, starting in February, Claude’s estimated reasoning depth fell sharply while signs of poorer performance rose alongside it, including more premature stopping, more “simplest fix” behavior, more reasoning loops, and a measurable shift from research-first behavior to edit-first behavior.
While Anthropic claims that it doesn’t degrade models to better serve demand, that doesn’t really square with the many, many users complaining about the problem. Anthropic’s response has, for the most part, been to pretend like nothing is wrong, with a spokesperson waving off Carl Franzen of VentureBeat (who has a great article on the situation here) by pointing him to two different Twitter posts, neither of which actually explain what’s going on.
Things only got worse with last week’s launch of Opus 4.7, which appears to have worse performance and burn more tokens.
One Reddit post titled, "Claude Opus 4.7 is a serious regression, not an upgrade," has 2,300 upvotes. An X user's suggestion that Opus 4.7 wasn't really an improvement over Opus 4.6 got 14,000 likes. In one informal but popular test of AI intelligence, Opus 4.7 appears to say that there were two Ps in "strawberry." Another user screenshot shows it saying that it didn't cross reference because it was "being lazy." Some Redditors found that Opus 4.7 was rewriting their résumés with new schools and last names. Multiple X users posited that Opus 4.7 had simply gotten dumber.
Some X users have suggested the culprit is the AI model's reasoning times. Anthropic says the new "adaptive reasoning" function lets the model decide when to think for longer or shorter periods. One user wrote that they couldn't "get Opus 4.7 to think." Another wrote that it "nerfs performance."
"Not accurate," Anthropic's Boris Cherny, the creator of Claude Code, responded. "Adaptive thinking lets the model decide when to think, which performs better."
I think it’s deeply bizarre that a huge company allegedly worth hundreds of billions of dollars A) can’t seem to keep its services online with any level of consistency, B) appears to be making its products worse, and C) refuses to actually address or discuss the problem. Users have been complaining about Claude models getting “dumber” going back as far as 2024, each time faced with a tepid gaslighting from a company with a CEO that loves to talk about his AI products wiping out half of white collar labor.
Some might frame this as Anthropic having “insatiable demand for its products,” but what I see is a terrible business with awful infrastructure run in an unethical way. It is blatantly, alarmingly obvious that Anthropic cannot afford to provide a stable and reliable service to its customers, and its plans to expand capacity appear to be signing deals with Broadcom that will come online “starting in 2027,” near-theoretical capacity with Hut8, which does not appear to have ever built an AI data center, and also with CoreWeave, a company that is yet to build the full capacity for its 2025 deals with OpenAI and only has around 850MW of “active power capacity” — so around 653MW of actual compute capacity — as of the end of 2025, up from 360MW of power at end of 2024.
Remember: data centers take forever to build, and there’s only a limited amount of global capacity, most of which is taken up by Microsoft, Google, Amazon, Meta and OpenAI, with the first three of those already providing capacity to both Anthropic and OpenAI.
We’re likely hitting the absolute physical limits of available AI compute capacity, if we haven’t already done so, and even if other data centers are coming online, is the plan to just hand them over to OpenAI or Anthropic in perpetuity?
It’s also unclear what the goal of that additional capacity might be, as I discussed last week:
Yet it’s unclear whether “more capacity” means that things will be cheaper, or better, or just a way of Anthropic scaling an increasingly-shittier experience.
To explain, when an AI lab like Anthropic or OpenAI “hits capacity limits,” it doesn’t mean that they start turning away business or stop accepting subscribers, but that current (and new) subscribers will face randomized downtime and model issues, along with increasingly-punishing rate limits.
Neither company is facing a financial shortfall as a result of being unable to provide their services (rather, they’re facing financial shortfalls because they’re providing their services to customers), and the only ones paying that price because of these “capacity limits” are the customers.
What’s the goal, exactly? Providing a better experience to its current customers? Securing enough capacity to keep adding customers? Securing enough capacity to support larger models like Mythos? When, exactly, does Anthropic hit equilibrium, and what does that look like?
There’s also the issue of cost.
Anthropic is currently losing billions of dollars a year offering a service with amateurish availability and oscillating quality, and continues to accept new subscribers, meaning that capacity issues are not affecting its growth. As a result, adding more capacity simply makes the product work better for a much higher cost.
Anthropic’s growth story is a sham built on selling subscriptions that let users burn anywhere from $8 to $13.50 for every dollar of subscription revenue and providing a brittle, inconsistent service, made possible only through a near-infinite stream of venture capital money and infrastructure providers footing the bill for data center construction.
Put another way, Anthropic doesn’t have to play by the rules. Venture capital funding allows it to massively subsidize its services. The endless, breathless support from the media runs cover for the deterioration of its services. A lack of any true regulation of tech, let alone AI, means that it can rugpull its customers with varying rate limits whenever it feels like.
If Anthropic were forced to charge its actual costs — and no, I don’t believe its API is profitable no matter how many people misread Dario Amodei’s interview — its growth would quickly fall apart as customers faced the real costs of AI (which I’ll get to in a bit). If Anthropic was forced to provide a stable service, it would have to stop accepting new customers or massively increase its inference costs.
Anthropic is a con, and said con is only made possible through endless, specious hype. Everybody who blindly applauded everything this company did is a mark.
Congratulations to all the current winners of the “Fell For It Again Award.” Per the Financial Times:
Anthropic has said it will hold off on a wider release of the model until it is reassured that it is safe and cannot be abused by bad actors. The company also has a finite amount of computing power and has suffered outages in recent weeks.
Multiple people with knowledge of the matter suggested Anthropic was holding back from a wider release until it could reliably serve the model to customers.
So, yeah, anyone in the media who bought the line of shit from Dario Amodei that this was “too dangerous to release” is a mark. Cal Newport has an excellent piece debunking the hype, but my general feeling is that if Mythos was so powerful, how did Claude Code’s source code leak?
Did… Anthropic not bother to use its super-powerful Mythos model to check? Or did it not find anything? Either way, very embarrassing for all involved.
Pale Horse: data center collapses, misc.
As I’ve discussed in the past, only 5GW of AI compute capacity is currently under construction worldwide (based on research from Sightline Climate), with “under construction” meaning everything from a scaffolding yard with a fence (as is the case with Nscale’s Loughton-based data center) to a building nearing handoff to the client.
I reached out to Sightline to get some clarity, and they told me that of the 114GW of capacity due to come online by the end of 2028, only 15.2GW is under construction, including the 5GW due in 2026.
That’s…very bad.
It gets worse when you realize that the majority of that construction is for two companies:
Sidenote: I’ll also add that Anthropic has agreed to spend $100 billion on Amazon Web Services over the next decade as part of its $5 billion (with “up to $20 billion” more in the future, and no, there’s no more details than that) investment deal with Amazon, with Anthropic apparently securing 5GW of capacity and bringing “nearly 1GW of Trainium2 and 3 capacity online by the end of the year,” which I do not believe, but whatever.These deals shouldn’t be legal.
So, to summarize, at least 4.6GW of the 15.2GW of data center capacity under construction is for OpenAI, with at least another 4GW of that reserved for Anthropic through partners like Microsoft, Google and Amazon. In truth, the number could be much higher.
This is a fundamentally insane situation. OpenAI and Anthropic both burn billions of dollars a year, with The Information reporting that Anthropic expects to burn at least $11 billion and OpenAI $25 billion in 2026. The only way that these companies can continue to exist is by raising endless venture capital funding or, assuming they make it to IPO, endless debt offerings or at-the-market stock sales.
It’s also very concerning that only such a small percentage of announced compute capacity is being built, especially when you run the numbers against NVIDIA’s actual sales.
Last year, Jerome Darling of TD Cowen estimated that it cost around $30 million per megawatt in critical IT (GPUs, servers, storage, and so on) and $12 million to $14 million per megawatt to build a data center, making critical IT around 68% (at the higher end of construction) of the total cost-per-megawatt.
Now, to be clear, those gigawatt and megawatt numbers for data centers refer to the power rather than critical IT, and if we take an average PUE (power usage efficiency, a measurement of how efficient a data center’s power is) of 1.35, we get 11.2GW of critical IT hardware, with the majority (I’d say 90%) being GPUs, bringing us down to around 10.1GW of GPUs.
If we then cut that up into GB200 or GB300 NVL72 racks with a power draw of around 140KW, that’s around 71,429 racks’ worth of hardware at an average of $4 million each, which gives us around $285.7 billion in revenue for NVIDIA.
NVIDIA claims it had a combined $500 billion in orders between 2025 and 2026, and $1 trillion of sales through 2027, and it’s unclear where any of those orders are meant to go other than a warehouse in Taiwan.
At this point, I think it’s fair to ask why anyone is buying more GPUs, as there’s nowhere to fucking put them. Every beat-and-raise earnings from NVIDIA is now deeply suspicious.
New Pale Horse: Any and all signs that companies are facing the economic realities of AI, including any complaints around or adaptations to deal with the increasing costs of AI.
Last week, a report from Goldman Sachs revealed that (and I quote) “...companies are overrunning their initial budgets for inference by orders of magnitude (we heard one industry datapoint on inference costs in engineering now approaching about 10% of headcount cost, but could be on track to be on par with headcounts costs in the next several quarters based on current trajectories.”
To simplify, this means that some companies are spending as much as 10% of the cost of their employees on generative AI services, all without appearing to provide any stability, quality or efficiency gains, or (not that I want this) justification to lay people off.
The Information’s Laura Bratton also reported last week that Uber had managed to blow through its entire AI budget for the year a few months into 2026:
Uber’s surging use of AI coding tools, particularly Anthropic’s Claude Code, has maxed out its full year AI budget just a few months into 2026, according to chief technology officer Praveen Neppalli Naga.
“I'm back to the drawing board because the budget I thought I would need is blown away already,” Neppalli Naga said in an interview.
…
He wouldn’t disclose exact figures of the company’s software budget or what it spends on AI coding tools. Uber’s research and development expenses, which typically reflect companies’ costs of developing new AI products, rose 9% to $3.4 billion in 2025 from the previous year, and the firm said in a recent securities filing it expects that cost will continue rising on an absolute dollar basis.
Uber’s CTO also added that about “...11% of real, live updates to the code in its backend systems are being written by AI agents primarily built with Claude Code, up from just a fraction of a percent three months ago.” Anyone who has ever used Uber’s app in the last year can see how well that’s going, especially if they’ve had to file any kind of support ticket.
Honestly, I find this all completely fucking insane. The whole sales pitch for generative AI is that it’s meant to be this magical, efficiency-driving panacea, yet whenever you ask somebody about it the answer is either “yeah, we’re writing all the code with it!” without any described benefits or “it costs so much fucking money, man.”
Let’s get practical about these economics, and use Spotify as an example because its CEO proudly said that its “top engineers” are barely writing code anymore, though to be clear, the Goldman Sachs example didn’t specifically name any one company.
For the sake of argument, let’s say that the company has 3000 engineers — one of its sites claims it has 2700, but I’ve seen reports as high as 3500. Let’s also assume, based on the Spotify Blind (an anonymous social media site for tech workers), that these engineers make a median salary of 192,000 a year.
In the event that Spotify spent 10% of its engineering headcount (around $576 million) on AI inference, it would be spending roughly $57.6 million, or approximately 4.1% of its $1.393 billion in Research and Development costs from its FY2025 annual report. Eager math-doers in the audience will note that 100% of headcount would be nearly half of the R&D budget, or around a quarter of its $2.2 billion in net income for the year.
Now, to be clear, these numbers likely already include some AI inference spend, but I’m just trying to illustrate the sheer scale of the cost.
While this is great for Anthropic (and to a lesser extent OpenAI), I don’t see how it works out for any of its customers. A flat 10% bump on the cost of software engineering is the direct opposite of what AI was meant to do, and in the event that costs continue to rise, I’m not sure how anybody justifies the expense much further.
And we’re going to find out fairly quickly, because the world of token subsidies is going away.
Pale Horse: Any further price increases or service degradations from AI startups, and yes, that’s what I’d call GitHub Copilot, in the sense that it loses hundreds of millions of dollars and makes fuck-all revenue.
As I reported yesterday, internal documents have revealed that Microsoft plans to temporarily suspend individual account signups to its GitHub Copilot coding product, tighten rate limits across the board, remove Opus models from its $10-a-month Pro subscription, and transition from requests (single interactions with GitHub Copilot) towards token-based billing some time later this year, with Microsoft confirming some of these details (but not token-based billing) in a blog post.
This is a significant move, driven by (per my own reporting) Microsoft’s week-over-week costs of running GitHub Copilot nearly doubling since January.
An aside/explainer: if you’re confused as to what “token-based billing” means, know that the vast majority of AI services currently subsidize their subscriptions, using another measure (such as “requests” or “rate limits”) to meter out how much a user can use the service. Nevertheless, these services still burn tokens at whatever rate that it costs to pay for them — for example, $5 per million input and $25 per million output for Opus 4.7, as I mentioned previously — meaning that the company almost always loses money unless a person doesn’t use the subscription very much.
Companies did this to grow their subscriber numbers, and I think they assumed things would get cheaper somehow. Great job, everyone!
The move to token-based billing will see GitHub users charged based on their usage of the platform, and how many tokens their prompts consume — and thus, how much compute they use. It’s unclear at this time when this will begin, but it significantly changes the value of the product.
I’ll also say that the fact that Microsoft has stopped signing up new paid GitHub Copilot subscriptions entirely is one of the most shocking moves in the history of software. I’ve literally never seen a company do this outside of products it intended to kill entirely, and that’s likely because — per my source — it intends to move paid customers over to token-based-billing, though it’s unclear what these tiers would look like, as the $10-a-month and $39-a-month subscriptions are mostly differentiated based on the amount of requests you can use.
What’s remarkable about this story is that Microsoft is one of the few players capable of bankrolling AI in perpetuity, with over $20 billion a quarter in profits since the middle of 2023.
Its decision to start cutting costs around AI suggests that said costs have become unbearable — The Information reported back in January that it was on pace to spend $500 million a year with Anthropic alone, and if that amount has doubled, it likely means that Microsoft is spending upwards of ten times its GitHub Copilot revenue, as I can report today that at the end of 2025, GitHub Copilot was at around $1.08 billion, with the majority of that revenue coming from its CoPilot Business and Enterprise subscriptions.
The Information also reported a few weeks ago that GitHub had recently seen a surge of outages attributed to “spiking traffic as well as its effort to move its applications from its own servers to Microsoft’s Azure cloud”:
“Since January, every month, every week almost now has some new peak stat for the highest [usage] rate ever,” [GitHub COO Kyle] Daigle said. He attributed the growth to “both agents and humans,” and also noted that the rise of AI coding tools has led to a rise in humans without deep coding knowledge starting to use GitHub’s platform more.
“Agents” in this case could refer to just about anything — OpenAI’s Codex, Anthropic’s Claude Code, or even people plugging in the wasteful, questionably-useful OpenClaw to their GitHub Copilot account, and if that’s what happened, it’s very likely behind the move to Token-Based Billing and rate limits.
In any case, if Microsoft’s making this move, it means that CFO Amy Hood — the woman behind last year’s pullback on data center construction — has decided that the subsidy party is over. Though Microsoft is yet to formally announce the move to Token-Based Billing, I imagine it’ll be sometime this week that it rips off the bandage.
Two weeks ago, Anthropic did the same with its enterprise customers, shifting them to a flat $20-a-seat fee and otherwise charging the per-token rate for whatever models they wanted to use.
I’m making the call that by the end of 2026, a majority of AI services will move some or all of their customers to token-based billing as they reckon with the true costs of running AI models.
I kept things simple today both to give myself a bit of a break and because these were stories I felt needed telling.
Nevertheless, I do have to remark on how ridiculous everything has become.
Everywhere you turn, somebody is talking about “agents” in a way that doesn’t remotely match with reality, like Aaron Levie’s epic screeds about how “AI agents make it so every other company on the planet starts to create software for bringing automation to their workflows in a way that would be either infeasible technically or unaffordable economically,” a statement that may as well be about fucking unicorns and manticores as far as its connections to reality.
I feel bad picking on Aaron, as he doesn’t seem like a bad guy. He is, however, increasingly-indicative of the hysterical brainrot of executive AI hysteria, where the only way to discuss the industry is in vaguely futuristic-sounding terms about “agents” and “inference” and “tokens as a commodity,” all with the intent of obfuscating the ugly, simple truth: that generative AI is deeply unprofitable, doesn’t seem to provide tangible productivity benefits, and appears to only lose both the business and the customer money.
Though my arguments might be verbose, they’re ultimately pretty simple: AI does not provide even an iota of the benefits — economic or otherwise — to justify its ruinous costs. Every new story that runs about cost-cutting or horrible burnrates increasingly validates my position, and for the most part, boosters respond by saying “well LOOK at how BIG the REVENUES are.”
It isn’t! AI revenues are dogshit. They’re awful. They’re pathetic. The entire industry — including OpenAI and Anthropic’s theoretical revenues of $13.1 billion and $4.5 billion — hit around $65 billion last year, and that includes the revenues from providing compute generated by neoclouds like CoreWeave and hyperscalers like Microsoft.
I’m also just gonna come out and say it: I think the AI startups are misleading their investors and the general public about their revenues. My reporting from last year had OpenAI’s revenues at somewhere in the region of $4.3 billion in the first three quarters of 2025, and Anthropic CFO Krishna Rao said in an an affidavit that the company had made revenue “exceeding” (sigh) $5 billion through March 9, 2026, which does not make sense when you add up all the annualized revenue figures reported about this company.
Cursor is also reportedly at $6 billion in annualized revenue (or around $500 million a month) and “gross margin positive” — which I also doubt given that it had to raise over $3 billion last year and is apparently raising another $2 billion this year.
Even if said numbers were real, the majority of OpenAI, Cursor and Anthropic’s revenues come from subsidized software subscriptions. Things have gotten so dire that even Deidre Bosa of CNBC agrees with me that AI demand is inflated by token-maxxing and subsidized services.
Otherwise, everybody else is making single or double-digit millions of dollars and losing hundreds of millions of dollars to get there. And per founder Scott Stevenson, overstating annualized revenues is extremely common, with AI startups booking “three-year-long” enterprise deals with the first year discounted and a twelve-month out:
The reason many AI startups are crushing revenue records is because they are using a dishonest metric
The biggest funds in the world are supporting this and misleading journalists for PR coverage.
The setup: Company signs 3-year enterprise deals. Year 1 is discounted (say $1M), Year 2 steps up ($2M), Year 3 is full price ($3M).
They report $3M as “ARR” — even though they’re only collecting $1M right now.
The worst part: The customer has an opt-out option at 12 months! It’s not actually a 3 year contract.
While it’s hard to say how widespread this potential act of fraud might be, Stevenson estimates that more than 50% of enterprise AI startups are using “contracted ARR” to pump their values. One (honest) founder responded to Stevenson saying that his company has $350,000 in contracted ARR but only $42,000 of ARR, adding that “next year is gonna be awesome though,” which I don’t think will be the case for what appears to be a chatbot for finding investors.
This industry’s future is predicated entirely on the existence of infinite resources, and most AI companies are effectively front-ends for models owned by Anthropic and OpenAI, two other companies that rely on infinite resources to run their services and fund their infrastructure.
And at the top of the pile sits NVIDIA, the largest company on the stock market, which is selling more GPUs than can be possibly installed, and very few people seem to notice or care.
I’m talking about hundreds of billions of dollars of GPUs sitting in warehouses that aren’t being installed, with it taking six months to install a single quarter’s worth of GPU sales. The assumption, based on every financial publication I’ve read, appears to be “it will keep selling GPUs forever, and it will all be so great.”
Where are you going to put them, Jensen? Where do the fucking GPUs go? There isn’t enough capacity under construction! If, in fact, NVIDIA is actually selling as many GPUs as it says, it’s likely taking liberties with “transfers of ownership” where NVIDIA marks a product as “sold” to somebody that has yet to actually take it on.
Sidenote: There’re already signs that GPUs are beginning to pile up.
You see, when a hyperscaler buys an AI server, what actually happens is an ODM — original design manufacturer — buys the GPUs from NVIDIA, builds the server, and then ships it to the data center, which, to be clear, is all above board and normal. These ODMs also book the entire value of the NVIDIA GPU as revenue, which is why revenues for companies like Foxconn, Wystron and Quanta Computing have all spiked during the AI bubble.
Oh, right, the signs. Per Quanta Computing’s fourth quarter financial results, inventory — as in stuff that’s sitting waiting to go somewhere — has spiked from $10.54 billion in Q3 2025 to $16.3 billion 2025, and nearly doubled year-over-year ($8.33 billion) as gross profit dropped from 7.9% in Q4 2024 to 7% Q4 2025. While this isn’t an across-the-board problem (Wistron’s inventories dropped quarter-over-quarter, for example), Taiwanese ODMs are going to be one of the first places to watch for inventory accumulation.
In any case, I keep coming back to the word “hysteria,” because it’s hard to find another word to describe this hype cycle. The way that the media, the markets, analysts, executives, and venture capitalists discuss AI is totally divorced from reality, discussing “agents” in terms that don’t match with reality and AI data centers in terms of “gigawatts” that are entirely fucking theoretical, all with a terrifying certainty that makes me wonder what it is I’m missing.
But every sign points to me being right, and if I’m right at the scale I think I’m right, I think we’re about to have a legitimacy crisis in investing and mainstream media, because regular people are keenly aware that something isn’t right, in many cases, it’s because they’re able to count.
2026-04-21 01:11:58
Note: Microsoft has now confirmed some of these details in a blog post.
Leaked internal documents viewed by Where’s Your Ed At reveal that Microsoft intends to pause new signups for the student and paid individual tiers of AI coding product GitHub Copilot, tighter rate limits, and eventually move users to “token-based billing,” charging them based on what the actual cost of their token burn really is.
Explainer: At present, GitHub Copilot users have a certain amount of “requests” — interactions where you ask the model to do something, with Pro ($10-a-month) accounts getting 300 a month, and Pro+ ($39-a-month) getting 1500. More-expensive models use more requests, cheaper ones use less (I’ll explain in a bit).
Moving to “token-based billing” would mean that instead of using “requests,” GitHub Copilot users would pay for the actual cost of tokens. For example, Claude Opus 4.7 costs $5 per million input tokens (stuff you feed in) and $25 per million output tokens (stuff the model outputs, including tokens for chain-of-thought reasoning.
The document says that although token-based billing has been a top priority for Microsoft, it became more urgent in recent months, with the week-over-week cost 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.
This is a significant move, reflecting the significant cost of running models on any AI product. Much like Anthropic, OpenAI, Cursor, and every other AI company, Microsoft has been subsidizing the cost of compute, allowing users to burn way, way more in tokens than their subscriptions cost.
The party appears to be ending for subsidized AI products, with Microsoft’s upcoming move following Anthropic’s (per The Information) recent changes shifting enterprise users to token-based billing as a means of reducing its costs.
GitHub Copilot currently has two tiers for individual developers — a $10-per-month package called GitHub Copilot Pro, and a $39-a-month subscription called GitHub Copilot Pro+.
According to the leaked documents, both of these tiers will be impacted by the shutdown, as will the GitHub Copilot Student product, which is included within the free GitHub Education package.
According to the documents, Microsoft also intends to tighten rate limits on some Copilot Business and Enterprise plans, as well as on individual plans, where limits have already been squeezed, and plans to suspend trials of paid individual plans as it attempts to “fight abuse.”
Although Microsoft has regularly tweaked the rate limits for individual GitHub Copilot accounts, most recently at the start of April, the document notes that these changes weren’t enough, and that more rate limits changes are to come in the next few weeks.
As part of this cost-cutting exercise, Microsoft intends to remove Anthropic’s Opus family of AI models from the $10-per-month GitHub Copilot Pro package altogether.
Microsoft most recently retired Opus 4.6 Fast at the start of April for GitHub Copilot Pro+ users, although this decision was framed as a way to “further improve service reliability” and “[streamline] our model offerings and focusing resources on the models our users use the most.”
Other Opus models — namely Opus 4.6 and Opus 4.5 — will be removed from the GitHub Copilot Pro+ tier in the coming weeks, as Microsoft transitions to Anthropic’s latest Opus 4.7 model.
The move towards Opus 4.7 will likely see GitHub Copilot Pro+ users reach their usage limits faster.
Microsoft is offering a 7.5x request multiplier until April 30 — although it’s unclear what the multiplier will be after this date. This might sound like a good thing, but it actually means that each request using Opus 4.7 is actually 7.5 of them. Redditors immediately worked that out and are a little bit worried.
Premium request multipliers allow GitHub to reflect the cost of compute for different models. LLMs that require the most compute will have higher premium request multipliers compared to those that are comparatively more lightweight.
For example, the GPT-5.4 Mini model has a premium request multiplier of 0.33 — meaning that every prompt is treated as one-third of a premium request — whereas the now-retired Claude Opus 4.6 Fast had a 30x multiplier, meaning each request was treated as thirty of them.
The standard version of Claude Opus 4.6 has a premium request multiplier of three — meaning that, even with the promotional pricing, Claude Opus 4.7 is around 250% more expensive to use.
The announcements for all of these changes are scheduled to take place throughout the week.
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2026-04-18 00:57:30
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.
Sidenote: while there are some strict “private credit” firms — such as software specialist Hercules Capital — many of the “private credit” firms I’ll discuss are really asset managers. These asset managers create and raise specialist private credit funds that either extend debt directly to a party (such as Apollo’s involvement in xAI’s $5.4 billion compute deal), or as part of a leveraged buyout, where a private equity firm buys another company and raises the debt using the company’s own assets and cashflow as collateral, putting the debt on the company’s balance sheet.
The eager, aggressive growth of private credit has even led it to start targeting individual investors, per the Financial Times:
Last year, a retired doctor in France’s southern region of Provence received a brochure in the mail from his bank touting a new investment opportunity.
A New York asset manager called Blackstone was offering the 77-year-old the chance to invest €25,000 into its flagship private debt fund. The former doctor called his son to ask: had he ever heard of Blackstone, or private debt?
His son Mathieu Chabran, co-founder of alternative investment group Tikehau Capital, had indeed heard of the powerful pioneer of private markets. But he was floored to discover that a company with $1tn in assets, which has minted over half a dozen billionaires, was seeking new business from novice investors such as his father.
The FT also neatly summarizes the problem of having regular investors involving themselves in the world of private credit:
He believes people like his father do not fully understand the risks of investing in funds that are harder to sell out of but which offer the opportunity to invest in private loans, property deals and corporate takeovers, with the allure of high returns.
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).
Sidenote: When you read about a “private credit fund,” it’s often a fund owned by an asset manager. For example, Blackstone recently raised “Blackstone Capital Opportunities Fund V,” a $10 billion “opportunistic” credit fund that incorporates as a special purpose vehicle that holds and invests the capital, and eventually sends out disbursements. Investors include New York State’s Common Retirement Fund ($250 million), Texas’ Municipal Retirement System ($200 million), and Louisiana Teachers’ Retirement System ($125 million), per Private Debt Investor.
Funds tend to have a life-cycle of somewhere between five and 10 years, which only really works if everybody keeps paying their loans.
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:
Blue Owl tried to merge a private fund (OBDC II, which allowed quarterly payouts) into another, publicly-traded fund (OBDC), but OBDC II’s value (as judged by Blue Owl itself) was 20% lower than that of OBDC, all to try and hide what are clearly problems with the economics of the fund itself. The FT has a great story about it.
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):
Before 2018, Software As A Service (SaaS) companies had had an incredible run of growth, and it appeared basically any industry could have a massive hypergrowth SaaS company, at least in theory. As a result, venture capital and private equity has spent years piling into SaaS companies, because they all had very straightforward growth stories and replicable, reliable, and recurring revenue streams.
Between 2018 and 2022, 30% to 40% of private equity deals (as I’ll talk about later) were in software companies, with firms taking on debt to buy them and then lending them money in the hopes that they’d all become the next Salesforce, even if none of them will. Even VC remains SaaS-obsessed — for example, about 33% of venture funding went into SaaS in Q3 2025, per Carta.
The Zero Interest Rate Policy (ZIRP) era drove private equity into fits of SaaS madness, with SaaS PE acquisitions hitting $250bn in 2021. Too much easy access to debt and too many Business Idiots believing that every single software company would grow in perpetuity led to the accumulation of some of the most-overvalued software companies in history.
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:
The Blue Owl Credit Income Corp. fund said that 11.6% of its portfolio consisted of loans to “internet software and services” companies at the end of the fourth quarter. The Journal found its software exposure to be around 21%.
The Blackstone Private Credit Fund, known as Bcred, reported 25.7% in software at the end of the third quarter, while the Journal found roughly 33% exposure.
Ares Capital Corp. reported 23.8% in “software and services” at the end of the fourth quarter, while the Journal found nearly 30% exposure.
The Apollo Debt Solutions fund reported 13.6% in software in the fourth quarter, while the Journal found a roughly 16% exposure.
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.
