2025-03-04 01:12:42
A week ago, analyst TD Cowen revealed that Microsoft had canceled leases "totalling a couple hundred MWs," with "at least two private data center operators across multiple US markets." The report also details how Microsoft "pulled back on converting negotiated and signed Statement[s] of Qualifications (SQQs)," which it added was "the precursor to a data center lease."
Although the analyst added it was unclear whether Microsoft might convert them in the future, these SQQs converted into leases "close to 100%" of the time. Cancelling them was, therefore, rather unusual.
TD Cowen also added that Microsoft was "re-allocating a considerable portion of [its] projected international spend to the US, which suggests to [TD Cowen] a material slowdown in international leasing."
But one crucial, teeny tiny part of the report was missed by just about everybody, emphasis mine:
As we highlighted in our recent takeaways from PTC [Pacific Telecommunications Council conference], we learned via our channel checks that Microsoft 1) walked away from multiple +100MW deals in multiple markets that were in early/mid-stages of negotiations, 2) let +1GW of LOI's on larger footprint sites expire, and 3) walked away from at least five land parcels that it had under contract in multiple Tier 1 markets.
What TD Cowen is saying is not just that "Microsoft canceled some data centers," but that Microsoft also effectively canceled over a gigawatt of data center operations on top of the previously-reported "multiple +100W megawatt deals." If we add in the land under contract, and the deals that were in-flight, the total capacity likely amounts to even more than that.
For some context, Data Center Dynamics reported that Microsoft had 5GW of data center capacity in April 2024, saying that Microsoft had planned to add 1 gigawatt of capacity by October 2024, and another 1.5 gigawatts of capacity by the first half of 2025. Based on this reporting, one can estimate that Microsoft has somewhere between 6 and 7 gigawatts of capacity at this time.
As a result, based on TD Cowen's analysis, Microsoft has, through a combination of canceled leases, pullbacks on Statements of Qualifications, cancellations of land parcels and deliberate expiration of Letters of Intent, effectively abandoned data center expansion equivalent to over 14% of its current capacity.
Sidebar: Let's explain some terms!
Letter of intent — In this context, it’s a statement that an entity intends to lease/buy land or power for a data center. These can be binding or non-binding. A letter of intent is serious — here's a press release between Microsoft and UAE-backed G42 Group signing a letter of intent with the Kenyan government for a "digital ecosystem initiative" (including a geothermal-powered data center) for example — and walking away from one is not something you do idly.
SOQ — Statement of qualifications. These set the terms and conditions of a lease. While they do not themselves constitute a lease, they convert into signed leases at an almost 100% rate (according to TD Cowen), and are generally used as a signal to the landowner to start construction.
Tier 1 market — Essentially, a market for hyperscale growth, helped by favorable conditions (like power, land, cabling). From what I can tell, there's no fixed list of which cities are T1 and which ones aren't, but they include the obvious candidates, like London, Singapore, etc, as well as Northern Virginia, which is the largest hub of data centers in the world.
Megawatt/Gigawatt — This one is important, but also arguably the most confusing. Data center capacity is measured not by the amount of computations the facility can handle, but rather by power capacity. And that makes sense, because power capacity is directly linked to the capabilities of the facility (with more power capacity allowing for more servers, or more power-hungry chips), and because chips themselves are constantly getting faster and more power efficient. If you measured in terms of computations per second, you’d likely have a number that fluctuates as hardware is upgraded and decommissioned. When you read ‘MW/GW’ in this article, assume that we’re talking about capacity and not power generation, unless said otherwise.
These numbers heavily suggest that Microsoft — the biggest purchaser of NVIDIA's GPUs and, according to TD Cowen, "the most active [data center] lessee of capacity in 2023 and 1H24" — does not believe there is future growth in generative AI, nor does it have faith in (nor does it want responsibility for) the future of OpenAI.
Data center buildouts take about three to six years to complete, and the largest hyperscale facilities can easily cost several billion dollars, meaning that these moves are extremely forward-looking. You don’t build a data center for the demand you have now, but for the demand you expect further down the line. This suggests that Microsoft believes its current infrastructure (and its likely scaled-back plans for expansion) will be sufficient for a movement that CEO Satya Nadella called a "golden age for systems" less than a year ago.
To quote TD Cowen again:
"...the magnitude of both potential data center capacity [Microsoft] walked away from and the decision to pullback on land acquisition (which supports core long term capacity growth) in our view indicates the loss of a major demand signal that Microsoft was originally responding to and that we believed the shift in their appetite for capacity is tied to OpenAI."
To explain here, TD Cowen is effectively saying that Microsoft is responding to a "major demand signal" and said "major demand signal" is saying "you do not need more data centers." Said demand signal that Microsoft was responding to, in TD Cowen's words, is its "appetite for capacity" to provide servers to OpenAI, and it seems that said appetite is waning, and Microsoft no longer wants to build out data centers for OpenAI.
The reason I'm writing in such blunt-force terms is that I want to make it clear that Microsoft is effectively cutting its data center expansion by over a gigawatt of capacity, if not more, and it’s impossible to reconcile these cuts with the expectation that generative AI will be a massive, transformative technological phenomenon.
I believe the reason Microsoft is cutting back is that it does not have the appetite to provide further data center expansion for OpenAI, and it’s having doubts about the future of generative AI as a whole. If Microsoft believed there was a massive opportunity in supporting OpenAI's further growth, or that it had "massive demand" for generative AI services, there would be no reason to cancel capacity, let alone cancel such a significant amount.
As an aside: What I am not saying is that Microsoft has “stopped building data centers.” I’ve said it a few times, but these projects take three to six years to complete and are planned far in advance, and Microsoft does have a few big projects in the works. One planned 324MW Microsoft data center in Atlanta is expected to cost $1.8bn and as far as I know, this deal is still in flight.
However (and this was cited separately by TD Cowen), Microsoft has recently paused construction on parts of its $3.3 billion data center campus in Mount Pleasant, Wisconsin. While Microsoft has tried to reassure locals that the first phase of the project was on course to be completed on time, its justification for delaying the rest was to give Microsoft an opportunity to evaluate “[the project’s scope] scope and recent changes in technology and consider how this might impact the design of [its] facilities."
The same Register article adds that “the review process may include the need to negotiate some building permits, potentially placing another hurdle in the way of the project.” The Register did add that Microsoft said it still expected to complete “one hyperscale data center in Mount Pleasant as originally planned,” though its capacity wasn’t available.
These moves also suggest that Microsoft is walking away from building and training further large frontier models, and from supporting doing so for others. Remember, Microsoft has significantly more insight into the current health and growth of generative AI than any other company. As OpenAI's largest backer and infrastructural partner (and the owners of the server architecture where OpenAI trains its ultra-expensive models, not to mention the largest shareholder in OpenAI), Microsoft can see exactly what is (or isn't) coming down the pike, on top of having a view both into the sales of its own generative AI-powered software (such as Microsoft 365 Copilot) and sales of both model services and cloud compute for other models on Microsoft Azure.
In plain English, Microsoft, which arguably has more data than anybody else about the health of the generative AI industry and its potential for growth, has decided that it needs to dramatically slow down its expansion. Expansion which, to hammer the point home, is absolutely necessary for generative AI to continue evolving and expanding.
Cushman and Wakefield's 2024 Global Data Center Market Comparison gives some chilling context into how significant Microsoft's pullback is. A gigawatt of data center capacity is roughly the entire operational IT load of Tokyo (1.028GW), orLondon (996MW), or the Bay Area (842MW).
And, again, the total figure is likely far higher than a gigawatt. That number only accounts for the LOIs that Microsoft allowed to expire. It doesn’t include everything else, like the two data centers it already killed, or the land parcels it abandoned, or the deals that were in the early-to-mid stages of negotiation.
Microsoft is not simply "walking back some future plans." It is effectively canceling (what it has loudly insisted is) the future. If you think that sounds hyperbolic, consider this: London and Tokyo are respectively the biggest data center markets in Europe and Asia, according to the same Cushman and Wakefield report.
In the event that Microsoft believed there was meaningful growth in generative AI, it wouldn't be canceling anything, or at the very least wouldn't be canceling so much.
Sidenote: Without context, it’s hard to understand how big a 100MW data center is. These are some of the biggest. According to the International Energy Agency, small data centers can consume anywhere between one and five megawatts. These are, for the most part, average-sized facilities. Perhaps not for cloud computing giants, but for other companies, it’s par for the course.
100MW is, by comparison, huge. It’s the equivalent of the annual energy consumption of between 350,000 and 400,000 electric cars. Although there are others that will likely dwarf what we, today, consider to be a large facility. Meta is in the process of constructing a $10bn data center campus in Louisiana, with a proposed 2GW capacity.
Still, whatever way you cut it, a 100MW facility is a big, long-term investment.
Canceling cities-worth of capacity at a time when artificial intelligence is supposedly revolutionizing everything suggests that artificial intelligence isn't really revolutionizing anything.
One other detail in TD Cowen's report really stood out. While there is a pullback in Microsoft's data center leasing, it’s seen a "commensurate rise in demand from Oracle related to The Stargate Project" — a relatively new partnership of "up to $500 billion" to build massive new data centers for AI, led by SoftBank, and OpenAI, with investment from Oracle and MGX, a $100 billion investment fund backed by the United Arab Emirates. OpenAI has committed $19 billion to the Stargate project — money it doesn't have, meaning that part of $25 billion to $40 billion funding round that OpenAI is currently raising will be committed to funding these data centers (unless, as I'll get to later, OpenAI raises more debt).
Leading the round is SoftBank, which is also committing $19 billion, as well as creating a joint venture called SB OpenAI Japan to offer OpenAI's services to the Japanese market, as well as "spending $3 billion annually to use OpenAI's technology across its group businesses" according to the Wall Street Journal.
In simpler terms, SoftBank is investing as much as $25 billion in OpenAI, then spending another $3 billion a year on software that only loses money, and still hallucinates, and shows no sign of getting meaningfully better or more reliable. Whether Softbank actually sees value in OpenAI’s tech, or whether this purchase deal is a subsidy by the backdoor, is open to debate. Given that $3bn is equivalent to OpenAI’s entire revenue from selling premium access to ChatGPT in 2024 — which included some major deals with the likes of PriceWaterhouseCoopers — I’m inclined to believe the latter. Even then, how feasible is it that SoftBank can continue paying?
To get the deal done, Microsoft changed the terms of its exclusive relationship with OpenAI to allow it to work with Oracle to build out further data centers full of the GPUs necessary to power its unprofitable, unsustainable models. The Oracle/Stargate situation was a direct result — according to reporting from The Information — of OpenAI becoming frustrated with Microsoft for not providing it with servers fast enough, including an allotment of 300,000 of NVIDIA's GB200 chips by the end of 2025.
For what it’s worth, The Wall Street Journal reports that Microsoft was getting increasingly frustrated with OpenAI’s constant demands for more compute. The relationship between the two entities had started to fray, with both sides feeling somewhat aggrieved. This, combined with Microsoft's data center pullback, heavily suggests that Microsoft is no longer interested in being OpenAI's infrastructure partner long-term — after all, if it was, it'd fund and support OpenAI’s expansion rather than doing the literal opposite.
Here's a question: if generative AI had so much demand, why is Microsoft canceling data center contracts? Why is Microsoft — Oracle's largest customer as of the end of 2023 — allowing SoftBank and OpenAI to work with Oracle to build the future rather than itself?
As mentioned previously, TD Cowen specifically noted in its report that Microsoft's "shift in appetite for capacity [was] tied to OpenAI," which heavily suggests that Microsoft is, at best, less invested in the future of OpenAI, a statement confirmed by The Information, which adds that Microsoft had been trying to "lessen its reliance on OpenAI technology as they increasingly compete in selling AI products."
At worst, this situation could suggest that Microsoft is actively dumping OpenAI, and is having questions about the fundamentals of the generative AI industry.
In very plain terms, Microsoft, despite its excitement around artificial intelligence and its dogged insistence that it’s the future of technology, has canceled data center leases and over a gigawatt of other datacenter infrastructure. Doing so heavily suggests that Microsoft does not intend to expand its data center operations, or at least, to the extraordinary levels the company initially promised.
While Microsoft has reiterated that it intends to spend $80 billion in capital expenditures on AI in 2025, it's unclear how it intends to do so if it’s pulling back on data center expansion at such a large scale, and the company has provided no tangible explanation or elaboration as to how it might do so. While hardware upgrades could account for some of its capital expenditures, it would be nowhere near the $80bn figure.
Again, hyperscale datacenters aren’t cheap. They’re massive, billion-dollar (or multi-billion) ventures.
Microsoft also added in a comment to CNBC in the same article that it "[continues] to grow at a record pace to meet customer demand."
Quick question: what customer demand?
Microsoft said back in April 2024 that "AI demand was exceeding supply even after a 79% surge in capital expenditures," and CFO Amy Hood said in its next quarterly earnings in July 2024 that "demand remained higher than [Microsoft's] available capacity." On its most recent (January 2025) earnings call, CFO Amy Hood once said "Azure growth included 13 points from AI services, which grew 157% year over year and was ahead of expectations even as demand continued to be higher than our available capacity."
Riddle me this, Batman — why does a company that keeps talking about having demand that exceeds capacity decide to cancel multiple data centers which, collectively, account for a significant chunk of its existing capacity?
Let's see what Microsoft had to say when asked a week or two ago!
“Thanks to the significant investments we have made up to this point, we are well positioned to meet our current and increasing customer demand,” Microsoft’s spokesperson wrote. “Last year alone, we added more capacity than any prior year in history. While we may strategically pace or adjust our infrastructure in some areas, we will continue to grow strongly in all regions. This allows us to invest and allocate resources to growth areas for our future.”
Sounds like Microsoft built too much capacity, and in fact has yet to see the consumer demand to actually reach it! In fact, a couple of weeks ago Microsoft CEO Satya Nadella said in a podcast interview that "one of the things is that there will be [as a result of data center expansion related to AI] overbuild."
Microsoft also in late January said it had “$13 billion in annual recurring revenue” from AI — not profit, and “AI” is not a line item on its earnings, meaning this is all related revenue put into a ball with no context. Either way, this is a piddly amount that works out to about $3.25 billion a quarter. These are mediocre numbers and Microsoft’s data center pullback suggests that they’re not going to improve.
Sidebar: Now, to be clear, Microsoft, according to CNBC, also leases data center capacity through CoreWeave "and other providers," though at that point the reporters stopped being curious enough to ask "how much" or "who the other providers are." Luckily, with the power of research, I found that The Information reported that Microsoft planned to spend "about $10 billion" renting CoreWeave servers between 2023 and 2030, which is not a particularly high amount when you consider that OpenAI burned 5 billion dollars last year on compute alone. And, as I’ve noted in previous articles, most (if not all) of that spending would be massively discounted, meaning it’s significantly more compute than if, say, you spent $5bn with Azure.
From what I can find, as of the end of 2023 CoreWeave had around $2 billion of revenue contracted for 2024, meaning that $10 billion across seven years would be meaningful revenue for the company, but unlikely to even touch the amount of capacity that Microsoft has cancelled.
According to The Information, OpenAI plans to have Stargate handle three quarters of its computing needs by 2030, which heavily suggests that Microsoft canceling so much capacity is, on some level, linked to OpenAI’s future plans. While The Information reports that OpenAI is still forecasting to spend $13 billion in 2025 and as much as $28 billion in 2028 on Microsoft’s cloud compute, in addition to whatever capacity it gets from Stargate or Oracle, one has to wonder how it intends to do so if it needs the capacity Microsoft simply isn’t building. These are two huge numbers. For context, $13 billion is about ten percent of Microsoft’s cloud revenue in the 2024 fiscal year, though it's unclear whether Microsoft counts OpenAI's compute as revenue.
Nevertheless, I have real concerns about whether OpenAI is even capable of expanding further, starting with a fairly obvious one: its only source of money, SoftBank.
Let’s start with a few facts:
Doing some napkin maths, here’s what SoftBank has agreed to:
SoftBank made about $12.14 billion (1.83 Trillion Yen) in revenue in Q4 2024, losing $2.449 billion (369.17 billion Yen), but made a profit of about $7.82 billion (1.18 trillion Yen) in Q3 2024.
Nevertheless, SoftBank has agreed to effectively bankroll OpenAI’s future — agreeing to over $46 billion of investments over the next few years — and does not appear to be able to do so without selling its current holdings in valuable companies like ARM or taking on at least $16 billion of debt this year, representing a 55% increase over its current liabilities.
Worse still, thanks to this agreement, OpenAI’s future — both in its ability to expand its infrastructure (which appears to be entirely contingent on the construction of Stargate) and its ability to raise funding — is entirely dependent on SoftBank, which in turn must borrow money to fund it.
On top of that, OpenAI anticipates it will burn as much as $40 billion a year by 2028, and projects to only turn a profit “by the end of the decade after the buildout of Stargate,” which, I add, is almost entirely dependent on SoftBank, which has to take on debt to fund both OpenAI and the project required to (allegedly, theoretically) make OpenAI profitable.
How the fuck does this work?
OpenAI, a company that spent $9 billion to lose $5 billion in 2024, requires so much money to meet its obligations (both to cover its ruinous, unprofitable, unsustainable operations and the $18bn to $19bn it committed to keep growing) that it has to raise more money than any startup has ever raised in history — $40 billion — with the cast-iron guarantee that it will need more money within a year.
To raise said money, the only benefactor willing to provide it (SoftBank) has to borrow money, and is not able to give OpenAI said money up front in part because it does not have it.
SoftBank — on top of the $30 billion of funding and $3 billion a year in revenue it’s committed to OpenAI itself — also has to cough up $18 billion for Stargate, a data center project that OpenAI will run and SoftBank will take financial responsibility for. That’s $48 billion in cash, and $3 billion in revenue, the latter of which, like all of OpenAI’s offerings, loses the company money.
OpenAI has no path to profitability, guaranteeing it will need more cash, and right now — at the time when it needs the most it’s ever needed — SoftBank, the only company willing to provide it, has proven that it will have to go to great lengths to do so. If OpenAI needs $40 billion in 2025, how much will it need in 2026? $50 billion? $100 billion? Where is that money going to come from?
While SoftBank might be able to do this once, what happens when OpenAI needs more money in 6-to-12 months? SoftBank made about $15 billion of profit in the last year on about $46 billion of revenue. $3 billion is an obscene amount to commit to buying OpenAI’s software annually, especially when some of it is allegedly for access to OpenAI’s barely-functional “Operator” and mediocre “Deep Research” products.
As per my previous pieces, I do not see how OpenAI survives, and SoftBank’s involvement only gives me further concern. While SoftBank could theoretically burn its current holdings to fund OpenAI in perpetuity, its ability to do so is cast into doubt by them having to borrow money from other banks to get both this funding round and Stargate done.
Sidenote: On the subject of Softbank’s holdings, I recommend you look at its most recent Group Report. In particular, go to page 29, which lists the ten largest publicly-traded companies in its Vision Fund portfolio. Note how all of them, without exception, trade at a significant fraction of their peak market cap. If Softbank liquidated its assets — and, I admit, this is a big if, and most likely a “worst case scenario” situation — how big would the losses be?
OpenAI burned $5 billion in 2024, a number it’ll likely double in 2025 (remember, the Information reported that OpenAI was projected to spend $13 billion on compute alone with Microsoft in 2025), and it has no path to profitability. SoftBank has already had to borrow to fund this round, and the fact it had to do so suggests its inability to continue supporting OpenAI without accruing further debt.
OpenAI (as a result of Microsoft’s cuts to data center capacity) now only has one real path to expansion once it runs through whatever remaining buildout Microsoft has planned, and that’s Stargate, a project funded by OpenAI’s contribution (which it’s receiving from SoftBank) and SoftBank, which is having to take loans to meet its share.
How does this work, exactly? How does this continue? Do you see anyone else stepping up to fund this? Who else has got $30 billion to throw at this nonsense? While the answer is “Saudi Arabia,” SoftBank CEO Masayoshi Son recently said that he had “not given [Saudi ruler Mohammed Bin Salman] enough return,” adding that he “still owed him.” Nothing about this suggests that Saudi money will follow SoftBank’s in anywhere near the volume necessary. As for the Emiratis, they’re already involved through the MGX fund, and it’s unclear how much more they’d be willing to commit.
No, really, how does this work?
In my opinion, the OpenAI-SoftBank deal is wildly unsustainable, dependent on SoftBank continuing to both raise debt and funnel money directly into a company that burns billions of dollars a year, and is set to only burn more thanks to the arrival of its latest model.
And if it had a huge breakthrough that would change everything, wouldn’t Microsoft want to make sure they’d built the data center capacity to support it?
About GPT-4.5…
Last week, OpenAI launched GPT 4.5, its latest model that…well…Sam Altman says “is the first model that feels like talking to a thoughtful person.” It is not obvious what it does better, or even really what it does, other than Altman says it is “a different kind of intelligence and there’s a magic to it [he hasn’t] felt before.” This was, in Altman’s words, “the good news.”
The bad news was that, and I quote, GPT 4.5 is “...a giant, expensive model,” adding that OpenAI was “out of GPUs,” but proudly declaring that it’d add “tens of thousands of GPUs the next week” to roll it out to OpenAI’s $20-a-month “plus tier” and that he would be adding “hundreds of thousands [of GPUs]” “soon.”
Excited? Well you shouldn’t be. On top of a vague product-set and indeterminately-high compute costs, GPT 4.5 costs an incredible $75 per million input tokens (prompts and data pushed into a model) and $150 per million output tokens (as in the output it creates), or roughly 3,000% more for input tokens and 1,500% more expensive for output tokens than GPT-4o for results OpenAI co-founder Andrej Karpathy described as “a little bit better and…awesome…but also not exactly in ways that are trivial to point to,” and one developer described 4.5 to ArsTechnica as “a lemon” when comparing its reported performance to its price.
ArsTechnica also described GPT-4.5 as “terrible for coding, relatively speaking,” and other tests showed that the model’s performance was either slightly better or slightly worse across the board, with, according to ArsTechnica one “success” metric being that OpenAI found human evaluators "preferred GPT 4.5’s responses over GPT-4o in about 57% of interactions. So, to be crystal clear, the biggest AI company’s latest model appears to be even more ruinously expensive than its last one while providing modest-at-best improvements and performing worse on several benchmarks than competing models.
Despite these piss-poor results, Sam Altman’s reaction is to bring hundreds of thousands of GPUs online as a means of exposing as many people as possible to his mediocre, ultra-expensive model, and the best that Altman has to offer is that this is “...the first time people have been emailing with such passion asking [OpenAI] to promise to never stop offering a specific model.”
Sidenote: As a reminder, GPT-4.5 was meant to be GPT-5, but (according to the Wall Street Journal) continually failed to make a model that “advanced enough to justify the enormous cost,” with a six-month training run costing around $500 million, and GPT 4.5 requiring multiple runs of different sizes. So, yeah, OpenAI spent hundreds of millions of dollars to make this. Great stuff!
This, by the way, is the company that’s about to raise $40 billion led by a Japanese bank that has to go into debt to fund both OpenAI’s operations and the infrastructure necessary for it to grow any further.
Again, Microsoft is cancelling plans to massively expand its data center capacity right at a time when OpenAI just released its most computationally-demanding model ever. How do you reconcile those two things without concluding either that Microsoft expects GPT-4.5 to be a flop, or that it’s simply unwilling to continue bankrolling OpenAI’s continued growth, or that it’s having doubts about the future of generative AI as a whole?
I have been — and remain — hesitant to “call the bubble bursting,” because bubbles do not burst in neat little events.
Nevertheless, my pale horses I’ve predicted in the past were led by one specific call — that a reduction in capital expenditures by a hyperscaler was a sign that things were collapsing. Microsoft walking away from over a Gigawatt of data center plans — equivalent to as much as 14% of its current data center capacity — is a direct sign that it does not believe the growth is there in generative AI, and thus are not building the infrastructure to support it, and indeed may have overbuilt — something that Microsoft CEO Satya Nadella has directly foreshadowed.
The entirety of the tech industry — and the AI bubble — has been built on the assumption that generative AI was the next big growth vehicle for the tech industry, and if Microsoft, the largest purchaser of NVIDIA GPUs and the most aggressive builder of AI infrastructure, is reducing capacity, it heavily suggests that the growth is not there.
Microsoft has, by the looks of things, effectively given up on further data center expansion. At least, at the breakneck pace it once promised.
AI boosters will reply by saying there’s something I don’t know — that in fact Microsoft has some greater strategy, but answer me this: why is Microsoft canceling over a gigawatt of data center expansion? And, again, this is the most conservative estimate. The realistic number is much, much higher.
Do you think it’s because it expects there to be this dramatic demand for their artificial intelligence services? Do you think it’s reducing supply because of all of the demand?
One might argue that Microsoft’s reduction in capacity buildout is just a sign that OpenAI is moving its compute elsewhere, and while that might be true, here’re some questions to ask:
Microsoft making such a material pullback on data center expansion suggests that the growth in generative AI products, both those run on Microsoft’s servers and those sold as part of Microsoft’s products, do not have the revolutionary growth-trajectory that both CFO Amy Hood and CEO Satya Nadella have been claiming, and this is deeply concerning, while also calling into concern the viability of generative AI as a growth vehicle for any hyperscaler.
If I am correct, Microsoft is walking away from not just the expansion of its current data center operations, but from generative AI writ large. I actually believe it will continue selling this unprofitable, unsustainable software, because the capacity it has right now is more than sufficient to deal with the lack of demand.
It is time for investors and the general public to begin demanding tangible and direct numbers on the revenue and profits related to generative AI, as it is becoming increasingly obvious that the revenues are small and the profits are non-existent. A gigawatt of capacity is huge, and walking away from that much capacity is a direct signal that Microsoft’s long term plans do not include needing a great deal of compute.
One counter could be that it’s waiting for more of the specialized NVIDIA GPUs to arrive, to which the response is “Microsoft still wants to build the capacity so that it has somewhere to put them.” Again, these facilities take anywhere between three and six years to build. Do you really think Blackwell will be delayed that long? NVIDIA committed to a once-a-year cycle for their AI chips after all.
One counter could be that there isn’t the power necessary to power these data centers, and if that’s the case — it isn’t, but let me humour the idea — then the suggestion is that Microsoft is currently changing its entire data center strategy so significantly that it now has to issue over a gigawatt’s worth of statements of intent across the country to different places with…more power?
Another counter is that I’m only talking about leases, and not purchases. In that case, I’ll refer you to this article from CBRE, which provides an elucidating read on how hyperscalers actually invest in data center infrastructure. Leases tend to account for the majority of spending, simply because it’s less risky. A specialist takes care of the tough stuff — like finding a location, buying the land, handling construction — and the hyperscaler isn’t left trying to figure out what to do with the facility when it reaches the end of its useful lifecycle.
Sidenote: I also expect that somebody will chime in and say: “Well, that’s just Microsoft. What about Google, or Amazon?
I’d counter and say that these companies are comparatively less exposed to generative AI than Microsoft. Amazon has invested $8bn in Anthropic, which is a bit less than half Microsoft’s reported investment in OpenAI, which amounted to $14bn as of December. When you consider the discounted Azure rates Microsoft offers to OpenAI, the real number is probably much, much higher.
Google also has a $3bn stake in Anthropic, in addition to its own AI services, like Gemini.
OpenAI, as I also noted in my last newsletter, is pretty much the only real generative AI company with market share and significant revenue — although I, once again, remind you that revenue is not the same as profit. This is true across mobile, web, and likely its APIs, too.
Similarly, nobody has quite pushed generative AI as aggressively as Microsoft, which has introduced it to an overwhelming number of its paid products, hiking prices for consumers as it goes. I suppose you could say that Google has pushed genAI into its workspace products, as well as its search product, but the scale and aggression of Microsoft’s push feels different. That, and it, as mentioned, is the largest purchaser of NVIDIA GPUs, buying nearly twice as many (485,000) as its nearest competitor, Meta, which bought 224,000.
Ultimately, Microsoft has positioned itself at the heart of GenAI, both through its own strategic product decisions, and its partnership with OpenAI. And the fact that it’s now scaling back on the investment required to maintain that momentum is, I believe, significant.
I also recognize that this is kind of a big, juicy steak for someone people call an “AI cynic.”
I have poured over this data repeatedly, and done all I can to find less-convenient conclusions. Letters of Intent are likely the weakest part of the argument — these are serious documents, but not always legally-binding. Neither are SoQs, but as TD Cowen points out, these are treated as the green light to start work on construction, even though a formal lease agreement hasn’t yet been signed. And to be clear, Microsoft let an indeterminate amount of those go too.
Nevertheless, it is incredibly significant that Microsoft is letting so many — the equivalent of as much as 14% of its current data center capacity, at a bare minimum — on top of the “couple hundred” (at least 200) megawatts of data center leases that it’s outright canceled. I do not know why nobody else has done this analysis. I have now read every single piece about the TD Cowen report from every single outlet that covered it. I am astounded by the lack of curiosity as to what “1GW+” means in a report that meaningfully moved markets, as I am equally astounded by the lack of curiosity to contextualize most tech news.
It is as if nobody wants to think about this too hard — that nobody wants to stop the party, to accept what’s been staring us in the face since last year, and when given the most egregious, glaring evidence they must find ways to dismiss it rather than give it the energy it deserves.
Far more resources were dedicated to finding ways to gussy up the releases of Anthropic’s Claude Sonnet 3.7 or OpenAI’s GPT-4.5 than were given to the report from an investment bank’s research wing that the largest spender in generative AI — the largest backer (for now) of OpenAI — is massively reducing its expenditures in the data centers required for the industry (and for OpenAI, a company ostensibly worth at least $157 billion, and which Microsoft owns the largest stake) to expand.
Microsoft’s stake in OpenAI is a bit fuzzy, as OpenAI doesn’t issue traditional equity, and there’s a likelihood it may be diluted as more money comes in. It reportedly owned 49% in 2023. Assuming that’s still the case, are we to believe that Microsoft is willing to strangle an asset worth at least $75bn (several times more than its investment to date) by cancelling a few leases?
How many more alarms do we need to go off before people recognize that something bad is happening? Why is it that tangible, meaningful evidence that we’re in a bubble — and possibly a sign that it has actually popped — is less interesting than the fact that Claude Sonnet 3.7 “can think longer”?
I do not write this newsletter to “be right.” I do not write it to “be a cynic” or “hater.” I write this because I am trying to understand what’s going on, and if I do not do that I will actually go insane. Every time I sit down to write it is because I am trying to understand what it is that’s happening and how I feel about it, and these are the only terms that dictate my newsletter. It just happens that I have stared at the tech industry for too long, and now I can’t look away. Perhaps it is driving me mad, perhaps I am getting smarter, or some combination of the two, but what comes out of it is not driven by wanting to “go viral” or “have a hot take,” because such things suggest that I would write something different if three people read it versus the 57,000 that subscribe.
I am not saying that Microsoft is dying, or making any grandiose claims about what happens next. What I am describing is the material contraction of the largest investor in data centers according to TD Cowen, potentially at a scale that suggests that it has meaningfully reduced its interest in further expansion of data centers writ large.
This is a deeply concerning move, one that suggests that Microsoft does not see enough demand to sustain its current expansions, which has greater ramifications beyond generative AI, because it suggests that there isn’t any other reason for it to expand the means of delivering software. What has Satya Nadella seen? What is Microsoft CFO Amy Hood doing? What is the plan here?
And really, what’s the plan with OpenAI? SoftBank has committed to over $40 billion of costs that it cannot currently afford, taking on as much as $24 billion in debt in the next year to help sustain one more funding round and the construction of data centers for OpenAI, a company that loses money on literally every single customer.
To survive, OpenAI must continue raising more money than any startup has ever raised before, and they are only able to do so from SoftBank, which in turn must take on debt. OpenAI burned $5 billion in 2024, will likely burn $11 billion in 2025, and will continue burning money in perpetuity, and to scale further will require further funding for a data center project funded partially by funding from a company taking on debt to fund them.
And when you put all of this together, all that I can see is a calamity.
Generative AI does not have meaningful mass-market use cases, and while ChatGPT may have 400 million weekly active users, as I described last week, there doesn’t appear to be meaningful consumer adoption outside of ChatGPT, mostly because almost all AI coverage inevitably ends up marketing one company: OpenAI. Argue with me all you want about your personal experiences with ChatGPT, or how you’ve found it personally useful. That doesn’t make it a product with mass-market utility, or enterprise utility, or worth the vast sums of money being ploughed into generative AI.
Worse still, there doesn’t appear to be meaningful revenue. As discussed above, Microsoft claims $13 billion in annual recurring revenue (not profit) on all AI products combined on over $200 billion of capital expenditures since 2023, and no other hyperscaler is willing to break out any AI revenue at all. Not Amazon. Not Meta. Not Google. Nobody.
Where is the growth? Where is all this money going? Why is Microsoft canceling a Gigawatt of data center capacity while telling everybody that it didn’t have enough data centers to handle demand for its AI products?
I suppose there’s one way of looking at it: that Microsoft may currently have a capacity issue, but soon won’t, meaning that further expansion is unnecessary. If that’s the case, it’ll be interesting to see whether their peers follow suit.
Either way, I see nothing that suggests that there’s further growth in generative AI. In fact, I think it’s time for everybody to seriously consider that big tech burned billions of dollars on something that nobody really wanted.
If you read this and scoff — what should I have written about? Anthropic adding a sliding “thinking” bar to a model? GPT-4.5? Who cares! Can you even tell me what it does differently to GPT-4o? Can you explain to me why it matters? Or are you more interested in nakedly-captured imbeciles like Ethan Mollick sweatily oinking about how powerful the latest Large Language Models are to notice the real things happening in the real world with buildings and silicon and actual infrastructure?
Wake the fuck up, everybody! Things are on fire.
I get it. It’s scary to move against the consensus. But people are wondering, right now, why you didn’t write about this, and indeed why you seem more concerned with protecting the current market hype cycle than questioning it.
There are no “phony comforts of being an AI skeptic” — what I am writing about here, as I regularly remind you, is extremely scary stuff.
If I’m right, tech’s only growth story is dead.
2025-02-25 00:42:35
Soundtrack: Mack Glocky - Chasing Cars
Last week, I spent a great deal of time and words framing the generative AI industry as a cynical con where OpenAI's Sam Altman and Anthropic's Dario Amodei have used a compliant media and braindead investors to frame unprofitable, unsustainable, environmentally-damaging and mediocre cloud software as some sort of powerful, futuristic automation.
Yet as I prepared a script for Better Offline (and discussed it with my buddy Kasey, as I often do), I kept coming back to one thought: where's the money?
No, really, where is it? Where is the money that this supposedly revolutionary, world-changing industry is making, and will make?
The answer is simple: I do not believe it exists. Generative AI lacks the basic unit economics, product-market fit, or market penetration associated with any meaningful software boom, and outside of OpenAI, the industry may be pathetically, hopelessly small, all while providing few meaningful business returns and constantly losing money.
I am deeply worried about this industry, and I need you to know why.
Putting aside the hype and bluster, OpenAI — as with all generative AI model developers — loses money on every single prompt and output. Its products do not scale like traditional software, in that the more users it gets, the more expensive its services are to run because its models are so compute-intensive.
For example, ChatGPT having 400 million weekly active users is not the same thing as a traditional app like Instagram or Facebook having that many users. The cost of serving a regular user of an app like Instagram is significantly smaller, because these are, effectively, websites with connecting APIs, images, videos and user interactions. These platforms aren’t innately compute-heavy, at least to the same extent as generative AI, and so you don’t require the same level of infrastructure to support the same amount of people.
Conversely, generative AI requires expensive-to-buy and expensive-to-run GPUs, both for inference and training the models themselves. The GPUs must be run at full tilt for both inference and training models, which shortens their lifespan, while also consuming ungodly amounts of energy. And surrounding that GPU is the rest of the computer, which is usually highly-specced, and thus, expensive.
These models also require endless amounts of training data, supplies of which have been running out for a long time. While synthetic data might bridge some of the gap, at least in situations where there’s a definitive right and wrong answer (like a mathematical problem), there are likely diminishing returns due to the sheer amount of data necessary to make a large language model even larger — data amounting to more than four times the size of the internet.
These companies also must spend hundreds of millions of dollars on salaries to attract and retain AI talent — as much as $1.5 billion a year in OpenAI's case (before stock-based compensation). In 2016, Microsoft claimed that top AI talent could cost as much as an NFL quarterback to hire, and that sum has likely only increased since then, given the generative AI frenzy.
As an aside: One analyst told the Wall Street Journal that companies running generative AI models "could be utilizing half of [their] capital expenditure[s]...because all of these things could break down." As in it’s possible hyperscalers could spend 50% of their capital expenditures replacing broken stuff.
Though these costs are not a burden on OpenAI or Anthropic, they absolutely are on Microsoft, Google and Amazon.
As a result of the costs of running these services, a free user of ChatGPT is a cost burden on OpenAI, as is every free customer of Google's Gemini, Anthropic's Claude, Perplexity, or any other generative AI company.
Said costs are also so severe that even paying customers lose these companies money. Even the most successful company in the business appears to have no way to stop burning money — and as I'll explain, there's only one real company in this industry, OpenAI, and it is most decidedly not a real business.
As a note — I have repeatedly said OpenAI lost $5 billion after revenue in 2024. However, I can no longer in good conscience suggest that it burned “only” $5 billion. It’s time to be honest about these numbers. While it’s fair to say that OpenAI’s “net losses” are $5 billion, it’s time to be clear about what it costs to run this company.
The New York Times reports that OpenAI projects it'll make $11.6 billion in 2025, and assuming that OpenAI burns at the same rate it did in 2024 — spending $2.25 to make $1 — OpenAI is on course to burn over $26 billion in 2025 for a loss of $14.4 billion. Who knows what its actual costs will be, and as a private company (or, more accurately, entity, as for the moment it remains a weird for-profit/nonprofit hybrid) it’s not obligated to disclose its financials. The only information we’ll get will come from leaked documents and dogged reporting, like the excellent work from The New York Times and The Information cited above.
It's also important to note that OpenAI's costs are partially subsidized by its relationship with Microsoft, which provides cloud compute credits for its Azure service, which is also offered to OpenAI at a discount. Or, put another way, it’s like OpenAI got paid with airmiles, but the airline lowered the redemption cost of booking a flight with those airmiles, allowing it to take more flights than another person with the equivalent amount of points. At this point, it isn’t clear if OpenAI is still paying out of the billions of credits it received from Microsoft in 2023 or whether it’s had to start using cold-hard cash.
Until recently, OpenAI exclusively used Microsoft's Azure services to train, host, and run its models, but recent changes to the deal means that OpenAI is now working with Oracle to build out further data centers to do so. The end of the exclusivity agreement is reportedly due to a deterioration of the chummy relationship between OpenAI and Redmond, according to The Wall Street Journal, with the latter allegedly growing tired of OpenAI’s constant demands for more compute, and the former feeling as though Microsoft had failed to live up to its obligations to provide the resources needed for OpenAI to sustain its growth.
It is unclear whether this partnership with Oracle will work in the same way as the Microsoft deal. If not, OpenAI’s operating costs will only go up. Per reporting from The Information, OpenAI pays just over 25% of the cost of Azure’s GPU compute as part of its deal with Microsoft — around $1.30-per-GPU-per-hour versus the regular Azure cost of $3.40 to $4.
OpenAI recently announced that it has 400 million weekly active users.
Weekly Active Users can refer to any seven-day period in a month, meaning that OpenAI can effectively use any spike in traffic to say that it’s “increased its weekly active users,” because it can choose the best seven-day period in a month. This isn’t to say they aren’t “big,” but these numbers are easy to game.
When I asked OpenAI to define what a “weekly active user” was, it responded by pointing me to a tweet by Chief Operating Officer Brad Lightcap that said “ChatGPT recently crossed 400M WAU, we feel very fortunate to serve 5% of the world every week.” It is extremely questionable that it refuses to define this core metric, and without a definition, in my opinion, there is no way to assume anything other than the fact that OpenAI is actively gaming its numbers.
There's likely two reasons it focuses on weekly active users:
The latter point is crucial, because it suggests OpenAI is not doing anywhere near as well as it seems based on the very basic metrics used to measure the success of a software product.
The Information reported on January 31st that OpenAI had 15.5 million monthly paying subscribers, and immediately added that this was a “less than 5% conversion rate” of OpenAI’s weekly active users — a statement that is much like dividing the number 52 by the letter A. This is not an honest or reasonable way to evaluate the success of ChatGPT’s (still unprofitable) software business, because the actual metric would have to be divided paying subscribers by MONTHLY active users, a number that would be considerably higher than 400 million.
Based on data from market intelligence firm Sensor Tower, OpenAI’s ChatGPT app (on Android and iOS) is estimated to have had more than 339 million monthly active users, and based on traffic data from market intelligence company Similarweb, ChatGPT.com had 246 million unique monthly visitors. There’s likely some crossover, with people using both the mobile and web interfaces, though how big that group is remains uncertain.
Though every single person that visits ChatGPT.com might not become a user, it’s safe to assume that ChatGPT’s Monthly Active Users are somewhere in the region of 500-600 million.
That’s good, right? Its actual users are higher than officially claimed? Er, no. First, each user is a financial drain on the company, whether they’re a free or paid user.
It would also suggest a conversion rate of 2.583% from free to paid users on ChatGPT — an astonishingly bad number, one made worse by the fact that every single user of ChatGPT, regardless of whether they pay, loses the company money.
It also feeds into a point I’ve repeatedly made in this newsletter, and in my podcast. Generative AI isn’t that useful. If Generative AI was genuinely this game-changing technology that makes it possible to simplify your life and your work, you’d surely fork over the $20 monthly fee for unlimited access to OpenAI’s more powerful models. I imagine many of those users are, at best, infrequent, opening up ChatGPT out of curiosity or to do basic things, and don’t have anywhere near the same levels of engagement as with any other SaaS app.
While it's quite common for Silicon Valley companies to play fast and loose with metrics, this particular one is deeply concerning, and I hypothesize that OpenAI choosing to go with Weekly versus Monthly Active Users is an intentional attempt to avoid people calculating the conversion rate of its subscription products. As I will continue to repeat, these subscription products lose the company money.
Mea Culpa: My previous piece focused entirely on web traffic to ChatGPT.com, and did not have the data I now have related to app downloads. Nevertheless, it isn't obvious whether OpenAI is being honest about its weekly active users, because it won't even define how it measures them.
In summary, both Operator and Deep Research are expensive products to maintain, are sold through an expensive $200-a-month subscription that (like every other service provided by OpenAI) loses the company money, and due to the low quality of their outputs and actions are likely to increase user engagement to try and get the desired output, incurring further costs for OpenAI.
As it stands, OpenAI has effectively no moat beyond its industrial capacity to train Large Language Models and its presence in the media. It can have as many users as it wants, but it doesn't matter because it loses billions of dollars, and appears to be continuing to follow the money-losing Large Language Model paradigm, guaranteeing it’ll lose billions more.
The Large Language Model paradigm is also yet to produce a successful, mass market product, and no, Large Language Models are not successful or mass market. I know, I know, you're going to say ChatGPT is huge, we've already been through that, but surely, if generative AI was a real industry, there'd be multiple other players with massive customer bases as a result of how revolutionary it was, right?
Right?
Let's look at some estimated numbers from data intelligence firm Sensor Tower (monthly active users on apps) and Similarweb (unique monthly active visitors) for the biggest players in AI in January 2025:
To put this in perspective, the entire combined monthly active users of the Copilot, Claude, Gemini, DeepSeek, and Perplexity apps amount to 66 million, or 19.47% of the entire monthly active users of ChatGPT's mobile app. Web traffic slightly improves things (I say sarcastically), with the 161.6 million unique monthly visitors that visited the websites for Copilot, Claude, Gemini, DeepSeek and Perplexity making up 65.69% of all of the traffic that went to ChatGPT.com.
However, I'd argue that including DeepSeek vastly over-inflates these numbers. It’s an outlier, and it’s also a relatively new company that’s enjoying its moment in the sun, basking in the glow of a post-launch traffic spike, and a flood of favorable media coverage. I imagine that when the dust settles in a few months, we’ll get a more reliable idea of its market share and consistent user base.
Without DeepSeek, the remaining generative AI services made up a total of 39 million monthly active users across their apps, and a grand total of 81.7 million unique monthly web visitors.
Without ChatGPT, it appears that the entire generative AI app market is a little more than half the size of Pokémon Go at its peak, when it had 147 million monthly active users. While one can say I missed a few apps — xAI's Grok, Amazon's Rufus, or Character.ai — there isn't a chance in hell they cover the shortfall.
These numbers aren't simply piss poor, they're a sign that the market for generative AI is incredibly small, and based on the fact that every single one of these apps only loses money, is actively harmful to their respective investors or owners.
I do not think this is a real industry, and I believe that if we pulled the plug on the venture capital aspect tomorrow it would evaporate.
Another counter to my argument is that API calls are a kind of “hidden adoption” — that there is this massive swell of engaged, happy customers using generative AI that aren’t using any of the major apps, and that the connection to these models is the real secret success story.
This isn’t the case.
OpenAI, as I’ve established, is the largest player in generative AI, making more revenue (roughly $4 billion in 2024, though it lost $5 billion after revenue — again, OpenAI lost $9 billion in 2024) than any other private AI company. The closest I can get to an estimate on how many actual developers integrate their applications is a statement from their October 2024 dev day where OpenAI said over three million developers are building apps using its models.
Again, that’s a very fuzzy — and unreliable — metric. I imagine a significant chunk of those developers are hobbyists working on personal projects, or simply playing around with the service out of sheer curiosity, spending a few bucks to write the generative AI equivalent of “Hello World,” and then moving on with their lives. Those developers actually using OpenAI’s APIs in actual commercial projects likely represent a vanishingly small percentage of that three million.
As I’ve discussed in the past, OpenAI’s revenue is heavily weighted toward its subscription business, with licensing access to models like GPT-4o making up less than 30% (around $1 billion) of their its, and subscriptions to their premium products (ChatGPT Plus, Teams, Business, Pro, the newly-released Government plan, etc) making up the majority — around $3 billion in 2024.
My argument is fairly simple. OpenAI is the most well-known player in generative AI, and thus we can extrapolate from it to draw conclusions about the wider industry. In the event that there was a huge, meaningful industry integrating generative AI into distinct products with mass-market consumer adoption, OpenAI’s API business would be doing far, far more revenue.
Let me be a little more specific about why API calls matter.
When a business plugs OpenAI’s models into its apps and a customer triggers a feature that uses it — such as asking the app to summarize an email — OpenAI charges the business both for the prompt (the input) and the result (the output). As a result, where “weekly active users” might be indicative of attention to OpenAI’s products, API calls are far more indicative of consumer and enterprise adoption.
To be clear, I acknowledge that there are a lot — a non-specific amount, but a fair amount — of app developers and companies adopting generative AI. However, judging on the revenue both to OpenAI’s developer-focused business and the lack of any real revenue for any business integrating generative AI, I hypothesize that customers — which include developers integrating OpenAI’s models into both consumer-facing apps and enterprise-focused apps — are not actually using these features that much.
I should also add that OpenAI makes about $200 million a year selling its models through Microsoft, meaning that its API business may be as small as $800 million. Again, this is not profit, it is revenue.
Sidebar: There is, of course, an alternative: that OpenAI is charging way, way less for their models than it should — an argument I made in The Subprime AI Crisis last year — but accepting this argument means that at some point OpenAI will either have to become profitable (it has shown no signs of doing so) or charge the actual cost of operating their unprofitable models.
The Information reported last week that Anthropic has projected (made up) that it will make at least $12 billion in revenue in 2027, despite making $918 million in 2024 and losing $5.6 billion somehow.
Anthropic is currently raising $2 billion at a $60 billion valuation for a business that loses billions of dollars a year with an app install base of 2 million people and a web presence smaller than some niche hobbyist news outlets.
Based on reporting from the Information from two weeks ago, Anthropic made approximately $918 million in 2024 (and lost $5.6 billion), with CNBC reporting that 60-75% of that revenue came from API calls (though that number was from September 2024). In that respect, it’s the reverse of OpenAI — which, itself, points to the relative obscurity of Anthropic and the fact that OpenAI has become accepted as the default consumer entrypoint to generative AI.
This company is not worth $60 billion.
Anthropic has raised $14.7 billion to create an also-ran Large Language Model company that some developers like more than OpenAI, with a competing consumer-facing Large Language Model (Claude) that has an install base of maybe 2% of the five free-to-play games made by Clash of Clans developer Super Cell.
Anthropic, much like OpenAI, has categorically failed to productize its Large Language Model, with the only product it appears to have pushed being Computer Use, a similarly-useless AI model that can sometimes successfully do in minutes what it takes you to do in seconds using a web browser.
Anthropic, like OpenAI, has no moat. While it provides chain-of-thought reasoning in its models, that too has been commoditized by DeepSeek. Its models, again like OpenAI, are unprofitable, unsustainable and heavily-dependent on training data that's either running out or has already run out.
Its CEO is also a sleazy conman who, like Sam Altman, continually promises that his company's AI systems will become powerful and autonomous in a way that they have never shown any possibility of becoming.
Any investor in Anthropic needs to seriously consider what it is they're investing in. Anthropic has, other than iterating on its Large Language Model Claude, shown little fundamental differentiation from the rest of the industry.
Anthropic's business, again like OpenAI, is entirely propped up by venture capital and hyperscaler (Google, Amazon) money, and without it would die almost immediately, because it has only ever lost money.
Its products are both unpopular and commoditized, and it lost $5.6 billion last year! Stop dancing around this fact! Stop it!
Perplexity, a company valued at $9 billion toward the end of 2024, has eight million people a month using its app, with the Financial Times reporting it has a grand total of 15 million monthly active users for its unprofitable search engine. Perplexity, like every generative AI company, only ever loses money, and its product — generative AI-powered search — is so commoditized that it's actually remarkable the company still exists.
Other than a slick design, there is little to be excited about here — and 8 million monthly active users is a pathetic, embarrassing number for a company with the majority of its users on mobile.
Aravind Srivinas is a desperate man with questionable intentions that made a half-hearted offer to merge with TikTok in January and a product that rips off journalists to spit out its mediocre content.
Any investor in Perplexity needs to ask themselves — what is it I'm investing in? An unprofitable search engine? An unprofitable Large Language Model company? A company that has such poor adoption of its product that it was prepared to become the shell corporation for TikTok?
Personally, I'd be concerned about the bullshit numbers it keeps making up. The Information reported that Perplexity said it would make $127 million in 2025, and $656 million in 2026.
How much money did it make in 2024? Just over $56 million! Is it profitable? Hell no!
Its product is commoditized, and it makes less than a quarter of the revenue of the Oakland Athletics in 2024, though its app is marginally more popular.
It's time to stop humoring these companies!
The Wall Street Journal reports that Microsoft intends to spend $93.7 billion on capital expenditures in 2025 — or roughly $8,518 per monthly active user on the Copilot app in January 2025. Those figures, however, may already be out of date with Bloomberg reporting the company is cancelling some leases for AI data centers. If true, it would suggest the company is pulling back from its drunken AI spending binge — although it’s not clear to what extent.
Sidenote: For what it’s worth, Microsoft responded by saying it stands by its original capex plans, although “may strategically pace or adjust [its] infrastructure in some areas.” Take from that what you will, while also noting that a plan isn’t the same as a definitive commitment, and that the company paused construction on a data center in January that was reportedly intended to support OpenAI. It’s also worth noting that as part of these cuts, Microsoft has pulled back from so-called statements of qualifications - the financial rundown and statements that say how they'll intend to pay for the lease (this might also include financing terms) - a document that's a precursor for future data center agreements. In short, they may have pulled out from further data centers they hadn't fully committed to.
Google is currently planning to spend $75 billion on capital expenditures, or roughly $4,167 per monthly active user of the Gemini app in January 2025. Sundar Pichai wants Gemini to be "used by 500 million people before the end of 2025," a number so unrealistic that someone at Google should have been fired, and that someone is Sundar Pichai.
The fact of the matter is that if Google and Microsoft can't make generative AI apps with meaningful consumer penetration, this entire industry is screwed. There really are no optimistic ways to look at these numbers (and yes, I'm repeating from the above):
These are utterly pathetic considering Microsoft and Google's scale, especially given the latter's complete dominance over web search and the ability to funnel customers to Gemini. For millions — perhaps billions — Google is the first page they see when they open a web browser. It should be owning this by now.
47.3 million unique monthly visitors is a lot of people, but considering that Google spent $52.54 billion in capital expenditures in 2024, it's hard to see where the return is, or even see where a return could possibly be.
Google, like most companies, does not break out revenue from AI, though it loves to say stuff like "a strong quarter was driven by our leadership in AI and momentum across the business." As a result of its unwillingness to share hard numbers, all we have to look at are numbers like those I've received from Similarweb and Sensor Tower, and it's fair to suggest that Gemini and its associated products have been a complete flop.
Worse still, it spent $127.54 billion in capital expenditures in 2023 and 2024 combined, with an estimated $75 billion forecast for 2025. What the fuck is going on?
Yes, it is likely making revenue from people running generative AI models on Google Cloud, and yes, it is likely making revenue from forcing AI upon Google Workspace customers. But Google, like every single other generative AI player, is losing money on every single generative AI prompt, and based on these monthly active user numbers, nobody really cares about Gemini.
Actually, I take that back. Some people care about Gemini — not that many, but some! — and it's far more fair to say that nobody cares about Microsoft Copilot despite Microsoft shoving it in every corner of our lives. 11 million monthly active users for its unprofitable, heavily-commoditized Large Language Model app is a joke — as are the 15.6 million monthly active users to its web presence — probably because it does exactly the same shit that every other LLM does.
Microsoft's Copilot app isn't just unpopular, it's irrelevant. For comparison, Microsoft Teams has, according to a post from the end of 2023, over 320 million monthly active users. That’s more than ten times the amount of monthly active users of their Copilot app and the Copilot website combined, and unlike Copilot, Teams actually makes Microsoft money.
Now, I obviously don't have the numbers on the people that accidentally click the Copilot button in Microsoft Office or on Bing.com, but I do know that Microsoft isn't making much money on AI at all. Microsoft reported in its last earnings that it was making "$13 billion of annual revenue" — a projected number based on current contracts — on its "artificial intelligence products."
Now, I've made this point again and again, but revenue is not the same thing as profit, and Microsoft does not have an "artificial intelligence" part of its earnings. These numbers are cherry-picked from across the entire suite of Microsoft products — such as selling Copilot add-ons to its Microsoft 365 enterprise suite (The Information reported in September 2024 that Microsoft had only sold Copilot to around 1% of their 365 customers), selling access to OpenAI's models on Azure (roughly a billion in revenue), and people running their own models on their Microsoft Azure Cloud.
For context, Microsoft made $69.63 billion in revenue in its last quarter. $13 billion of annual revenue (NOT profit) is about $3.25 billion in quarterly revenue off of upwards of $200 billion of capital expenditures since 2023.
The fact that neither Gemini nor Copilot has any meaningful consumer penetration isn't just a joke. It should be sending alarm bells throughout Wall Street. While Microsoft and Google may make money outside of consumer software, both companies have desperately tried to cram Copilot and Gemini down consumers' throats, and they have categorically, unquestionably failed, all while burning billions of dollars to do so.
"BUT ED, WHAT ABOUT GITHUB COPILOT."
According to a report from the Wall Street Journal from October 2023, Microsoft was losing on average more than $20 a month per user on the paid version of Github, with some users costing them more than $80 a month. Microsoft said a year later that Github Copilot had 1.8 million paid customers, which is pretty good, except like all generative AI products, it loses money.
I must repeat that Microsoft will have spent over $200 billion in capital expenditures by the end of 2025. In return, it got 1.8 million paying customers for a product that — like everything else I'm talking about — is heavily-commoditized (basically every LLM can generate code, though some are better than others, by which I mean they all introduce security issues into your code, but some produce stuff that’ll actually compile) and loses Microsoft money even when the user pays.
Am I getting through to you yet? Is it working?
One of the arguments people make is that “AI is everywhere,” but it’s important to remember that the prevalence of AI is not proof of its adoption, but the intent of companies shoving it into everything, and the same goes for “business integrating AI” that are really just mandating people dick around with Copilot or ChatGPT.
No, really, KPMG bought 47,000 Microsoft Copilot subscriptions last year (at a significant discount) “to be familiar with any AI-related questions [its] customers may have. Management consultancy PwC bought 100,000 enterprise subscriptions — becoming OpenAI’s largest customer in the process, as well as its first reseller, and have created their own internal generative AI called ChatPWC that PwC staff absolutely hate.
While you may “see AI everywhere,” integrations of generative AI are indicative of the decision making of the management behind the platforms and the demands of “the market” more than any consumer demand. Enterprise software is more often than not sold in bulk to managers or C-suite executives tasked less with company operations and more with seeming “on the forefront of technology.”
In practical terms, this means there’s a lot of demand to put AI in stuff and some demand to buy stuff with AI on it by enterprises buying software, but little evidence to suggest significant user adoption or usage, I’d argue because Large Language Models do not lend themselves to features that provide meaningful business returns.
To be clear, and to deal with the “erm, actually” responses, I am not saying Large Language Models have no use cases or no customers.
People really do use them for coding, for searching defined libraries of documents, for generating draft materials, for brainstorming, and for summarizing and searching documents. These are useful, but they are not magical.
These are also — and I do not believe there are any use cases that justify this — not a counterbalance for the ruinous financial and environmental costs of generative AI. It is the leaded gasoline of tech, where the boost to engine performance didn’t outweigh the horrific health impacts it inflicted.
When a company uses the term “agent,” they are intentionally trying to be deceitful, because the term “agent” means “autonomous AI that does stuff without you touching it.” The problem with this definition is that everybody has used it to refer to “a chatbot that can do some things while connected to a database,” which is otherwise known as a chatbot.
In OpenAI and Anthropic’s case, “agents” refer to a model that controls your computer and performs tasks based on a prompt. This is closer to “the truth,” other than the fact it’s so unreliable as to be disqualifying, and the tasks it succeeds at (like searching on Tripadvisor) are remarkably simple.
Next time you hear the term “agent,” actually look at what the product does.
Generative AI is probabilistic, and Large Language Models do not “know” anything, because they are guessing what the next part of a particular output would be based on an input. They are not “making decisions.” They are probability machines, which in turn makes them only as reliable as probability can be, and as conscious — no matter how intricate a system may be or how much infrastructure is built — as a pair of dice.
We do not understand how human intelligence works, and as a result it’s laughable to imagine we’d be able to simulate it. Large Language Models do not create “artificial intelligence” — they are the most powerful parrots in the world, trained to respond to stimulus with what they guess is the correct answer.
In simpler terms, imagine if you made a machine that threw a bouncy ball down a hallway, and got really, really good at dialing it in to throw the ball so that it followed a fairly exact trajectory. Would you consider the arm intelligent? How about the ball?
The point I am making is that Large Language Models — a cool concept with some interesting things they can do — have been used as a cynical marketing vehicle to raise money for OpenAI by lying about what they’re capable of doing, starting with calling them “artificial intelligence.”
Revenue is not the same as profit.
I'll say it again — revenue is not the same as profit.
And even then, Google, Amazon and (to an extent Microsoft), the companies making the most investments in AI, do not want to state what that revenue is. I hypothesize the reason that they do not want to disclose it is that it’s pretty god damn small.
It is extremely worrying that so few companies are willing to directly disclose their revenue from selling services that are allegedly revolutionary. Why? Salesforce says it closed “200 AI related deals” in its last earnings. How much money did it make? Why does Google get away with saying it has “growing demand for AI” without clarifying what that means? Is it because nobody is making that much money?
Sidebar: I can find — and I’ve really looked! — one company that appears to be making profit from generative AI. Turing, a consultancy that helps generative AI companies find people to train their models that made $300 million in revenue in 2024 and reached an indeterminate amount of profitability.
While Microsoft may “disclose” it “made $13 billion in AI revenue,” that’s annualized — so projected based on current contracts rather than booked revenue — and does not speak to the specific line items like one would if said line items were not going to make the markets say “hey, what the fuck?”
Put aside whatever fantastical beliefs you may have about the future and tell me, right now, what business use case exists that justifies burning hundreds of billions of dollars, damaging our power grid, hurting our planet, and stealing from millions of people?
Even if you can put troublesome things like “morals” or “the basic principles of finance” aside, can AI evangelists not see that their dream is failing? Can they not see that nothing is really happening? That generative AI, at best, can be kind of cool yet mostly sucks and comes at an unbearable moral, financial and environmental cost? Is any of this really worth it?
And where exactly does this end? Do you truly, gun to your head, your life contingent on the truth leaving your lips, believe that this goes much further than you see today?
Do you not see that this kind of sucks? Do you not see that generative AI runs contrary to the basic tenets of what makes science fiction cool? It doesn’t make humans better, it reduces their work to a stagnant, unremarkable slop in every way it can, and reduces the cognition of those who come to rely on it, and it costs hundreds of billions of dollars and a return to fossil fuels for some reason.
It isn’t working. The users aren’t there. The revenue isn’t there. The best time to stop this was two years ago, and the next best time is as soon as humanly possible.
I have said that generative AI is a group delusion in the past, and I repeat that claim today. What you are seeing in the news is not the “success“ of the artificial intelligence industry, but a runaway narrative created by and sustained by Sam Altman and OpenAI.
What you are watching is not a revolution, but a repetitious public relations campaign for one company that accidentally timed the launch of ChatGPT with a period of deep desperation in big tech, one so profound that it will likely drag half a trillion dollars’ worth of capital expenditures along with it.
This bubble will only burst when either the markets or the hyperscalers accept that they have chased their own tails toward oblivion. There is no justification for any of the capital expenditures related to generative AI — we are approaching the limit of what the transformer-based architecture can do, if we haven’t already reached it. No amount of beating off about test-time compute and connecting Large Language Models to other Large Language Models is going to create a new use case for this technology, and even if it did, it’s unlikely that it ever makes enough money to make it profitable.
I will keep writing this stuff until I’m proven wrong. I do not know why more people aren’t more worried about this. The financials are truly damning, the user numbers so small as to be insignificant, the costs so ruinous that they will likely cost tens of thousands of people their jobs and one of the hyperscalers CEOs their job (although, admittedly, I’m less upset about that), and inflict damage on tech valuations that may rival the dot com boom.
And if the last point feels distant to you, ask yourself: What’s in your retirement savings? That’s right. Google and Microsoft, and hundreds of other companies that will be hurt by the contagion of an AI bubble imploding, just as they were in the 2008 financial crash, when the failure of the banking system trickled down into the wider economy.
I should also not be the person saying this, or at least I should not be the first. These numbers are horrifying, and I have no idea why nobody else is worried. There is no industry here. There is no money. There is no proof that this will ever turn into a real industry, and far more proof that it will cost more money than it will ever make in perpetuity.
OpenAI and Anthropic are not real companies — they are free-riders, living on venture-backed welfare for an indeterminate amount of time because the entire tech industry has agreed to rally around the world’s most unprofitable software. And like any free rider that doesn’t actually produce anything, when the money goes away, they’re fucked.
Seriously, why are investors funding OpenAI? Do they seriously believe it’s necessary to let Sam Altman and OpenAI continue to burn 5 or more billion dollars a year on the off chance he’s able to create something that’s…alive? Profitable? What’s the endpoint here? How many more billions? Where is the fucking money, Sam Altman? Where is the god damn money?
Because generative AI is OpenAI. The consumer adoption of this software has completely failed, and appears to be going nowhere fast. ChatGPT is sustained entirely on deranged, specious hype drummed up by a media industry that thinks it’s more remarkable to write down the last lie that Sam Altman told than say that OpenAI has lost $9 billion dollars in the last year and intends to more than double that number in 2025 for absolutely no reason.
It is time to stop humouring OpenAI, and time to start directly stating that it is a bad business without a meaningful product. The generative AI industry does not exist without OpenAI, and thus this company must justify its existence.
And let’s be abundantly clear: OpenAI cannot exist any further without further venture capital investment. This company has absolutely no path to sustain itself, no moat, and loses so much money that it will need more than $50 billion to continue in its current form.
I don’t know how I’m wrong, and I have sat and thought a great deal about how I might be. I can find no compelling arguments. I don’t know what to do but tell you what I think, and why I think that way, and hope that you, the reader, understand a little bit more about what I think is going on.
I’ll leave you with one thought — and one particular thing that bothers me about generative AI.
Regular people, for the most part, do not seem to want this. While there are occasional people I’ll meet who use ChatGPT to rewrite part of an email, most of the people I meet feel like AI was forced into their lives.
With that in mind, I believe that Apple is radicalizing millions of people against generative AI by forcing them to reckon with the terrible summaries, awful suggested texts and horribly-designed user interface elements of Apple Intelligence.
Something about generative AI has caused the hyperscalers to truly lose it, and the intrusion of generative AI into both Microsoft Office and Google Docs has turned just about everybody I know in the business world against it.
The resentment boiling against this software is profound because the tech industry has become desperate and violative, showing such contempt for their customers that even Apple will force an inferior experience upon them to please the will of the Rot Economy and the growth-at-all-cost mindset of the markets.
Let’s be frank: nobody really needs anything generative AI does. Large Language Models hallucinate too much to be truly reliable, a problem that will require entire new branches of mathematics to solve, and their most common consumer-facing functions like summarizing an article, “practicing for a job interview,” or “write me a business plan” are not really things people need or massively benefit from, even if these things weren’t ruinously expensive or damaging to the environment.
I believe regular people are turning on the tech industry thanks to their frenzied attempts to make us all buy into their latest bad idea.
Yet it isn’t working. Consumers don’t want this shit. They’re intrigued by the idea, then mostly immediately bouncing off of it once they see what it can (or can’t) do. This software is being forced on people at scale by corporations desperate to seem futuristic without any real understanding as to why they need it, and whatever use cases may exist for Large Language Models are dwarfed by how utterly unprofitable this whole fiasco is.
I want you to remember the names Satya Nadella, Tim Cook, Mark Zuckerberg, Sam Altman, Dario Amodei and Sundar Pichai, because they are the reason that this farce began and they must be the ones who are blamed for how it ends.
2025-02-18 02:51:09
It's been just over two years and two months since ChatGPT launched, and in that time we've seen Large Language Models (LLMs) blossom from a novel concept into one of the most craven cons of the 21st century — a cynical bubble inflated by OpenAI CEO Sam Altman built to sell into an economy run by people that have no concept of labor other than their desperation to exploit or replace it.
I realize that Large Language Models like GPT-4o — the model that powers ChatGPT and a bunch of other apps — have use cases, and I'm fucking tired of having to write this sentence. There are people that really like using Large Language Models for coding (even if the code isn't good or makes systems less secure and stable) or get something out of Retrieval-Augmented Generation (RAG)-powered search, or like using one of the various AI companions or journal apps.
I get it. I get that there are people that use LLM-powered software, and I must be clear that anecdotal examples of some people using some software that they kind-of like is not evidence that generative AI is a sustainable or real industry at the trillion-dollar scale that many claim it is.
I am so very bored of having this conversation, so I am now going to write out some counterpoints so that I don't have to say them again.
I KNOW. Stop saying this to me like an Uno reverse! I'm talking about generative AI!
OpenAI and Anthropic are both burning billions of dollars a year, and do not appear to have found a way to stop doing so. The only "proof" that they are going to reverse this trend is The Information saying that "Anthropic's management expects the company to stop burning cash in 2027."
Sidebar: Hey, what is it with Dario Amodei of Anthropic and the year 2027? He said (made up) that "AI could surpass almost all humans at almost everything" "shortly after 2027" in January. He said in one of his stupid and boring blogs that "possibly by 2026 or 2027 (and almost certainly no later than 2030)" that "capabilities of AI systems will be best thought of as akin to an entirely new state populated by highly intelligent people appearing on the global stage—a “country of geniuses in a datacenter." This man is full of shit! Hey, tech media people reading this — your readers hate this shit! Stop printing it! Stop it!
While one could say "the costs will come down," and that appears to be what The Information is claiming, suggesting that "Anthropic said it would reduce its burn rate by "nearly half" in 2025, the actual details are thin on the ground, and there’s no probing of whether that’s even feasible without radically changing its models. Huh? How? Anthropic’s burn increased every single year! So has OpenAI's!
The Information — who I do generally, and genuinely, respect — ran an astonishingly optimistic piece about Anthropic estimating that it'd make $34.5 billion in revenue in 2027 (there's that year again!), the very same year it’d stop burning cash. Its estimates are based on the premise that "leaders expected API revenue to hit $20 billion in 2027," meaning people plugging Anthropic's models into their own products. This is laughable on many levels, chief of which is that OpenAI, which made around twice as much revenue as Anthropic did in 2024, barely made a billion dollars from API calls in the same year.
It's here where I'm going to choose to scream.
Anthropic, according to The Information, generated $908 million in revenue in 2024, and has projected that it will make $2.2 billion in revenue in 2025, and its "base case" — which The Information says would be "the likeliest outcome (???) — is that it will make $12 billion in revenue in 2027.
This is what happens during bubbles! Assets are over-valued based on a combination of vibes and hysteria!
Dario Amodei — much like Sam Altman — is a liar, a crook, a carnival barker and a charlatan, and the things he promises are equal parts ridiculous and offensive. The Information (which needs to do better work actually critiquing these people) justified Amodei and Anthropic's obscene and fantastical revenue targets by citing Amodei's blog, which at no point explains what a "country of geniuses in a datacenter" actually means or what the product might be or what he's going to do to increase revenue by more than thirty billion dollars a year by 2027.
But wait, The Information says it got a little more specific!
Anthropic says its technology could transform office roles such as generating or reviewing legal paperwork and automating software engineering. It cited code repository GitLab and legal search firm LexisNexis as examples of customers. Up-and-coming startups such as Anysphere, which develops the Cursor coding assistant for programmers, are also major buyers of Claude software.
So, just to be abundantly clear, it appears Anthropic's big plan is to "sell people more software to some people, maybe."
Anthropic is currently raising $2 billion at a $60 billion valuation primarily based off of this trumped-up marketing nonsense. Why are we humoring these oafs?
When you put aside the hype and anecdotes, generative AI has languished in the same place, even in my kindest estimations, for several months, though it's really been years. The one "big thing" that they've been able to do is to use "reasoning" to make the Large Language Models "think" (they do not have consciousness, they are not "thinking," this just means using more tokens to answer a particular question and having multiple models check the work), which mostly results in them being a bit more accurate when generating an answer, but at the expense of speed and cost.
This became a little less exciting a month ago when DeepSeek released its open source "r1" model which performed similarly to reasoning products from companies like Google and OpenAI, and while some argue it "built the model to game benchmarks," that is quite literally what every single model developer does. Nevertheless, the idea of "reasoning" being the "killer app" — despite the fact that nobody can really explain why it's such a big deal — is now quite dead.
As a result, the model companies are kind of flailing. In a recent post on Twitter, Sam Altman gave an "updated roadmap for GPT 4.5 and GPT-5" where he described how OpenAI would be "simplifying" its product offerings, saying that GPT-4.5 would be OpenAI's "last non-chain-of-thought model," and that GPT-5 would be "a system that integrates a lot of our technology," including o3, OpenAI's "powerful" and "very expensive" reasoning model, which it...would also no longer release as a standalone model.
To break this down, Altman is describing his next model — GPT 4.5 — as launching in some indeterminate timeframe and doing something probably quite similar to the current GPT 4o model. In the case of GPT-5, it would appear that Altman is saying that it won't be a model at all, but some sort of rat king of different mediocre products, including o3, a product that he would no longer be letting you use.
I guess that's the future of this company, right? OpenAI will release models and uh, well. Uhh.
Uhhhhhhh.
Wait! Wait! OpenAI released a new product! It's called Deep Research, which lets you ask ChatGPT to generate a report by browsing the web. This is almost a cool idea. I sure hope that it doesn't make glaring mistakes and cost a shit-ton of money!
Anyway, let's go to Casey Newton at Platformer for the review:
Generally speaking, the more you already know about something, the more useful I think deep research is. This may be somewhat counterintuitive; perhaps you expected that an AI agent would be well suited to getting you up to speed on an important topic that just landed on your lap at work, for example. In my early tests, the reverse felt true. Deep research excels for drilling deep into subjects you already have some expertise in, letting you probe for specific pieces of information, types of analysis, or ideas that are new to you.
It’s possible that you can make this work better than I did. (I think all of us will get better at prompting these models over time, and presumably the product will improve over time as well.)
Personally, when I ask someone to do research on something, I don't know what the answers will be and rely on the researcher to explain stuff through a process called "research." The idea of going into something knowing about it well enough to make sure the researcher didn't fuck something up is kind of counter to the point of research itself.
Also: "I think all of us will get better at prompting-" Casey, we're paying them! We're paying them for them to do stuff for us!
Nevertheless, I did go and look up one of Casey's examples, specifically one about how the Fediverse could benefit publishers.
Let's do some research!
Despite Newton's fawning praise, the citations in this "deep research" are flimsy at best. The first (and second) citations are from an SEO-bait article about the fediverse from a "news solutions" company called "Twipe" and is used to define "broad cross-platform reach." The next one is from reputable digital advertising outlet Digiday, but it's used to cite how sites like 404 Media and The Verge are "actively exploring the Fediverse to take more control over their referral traffic and onsite audience engagement," which is plagiarised ad-verbatim from the Digiday article.
After that, the next three citations are posts from Hackernews, a web forum started by yCombinator (here's an example). How is this "deep research" exactly?
In fact, this thing isn't well-researched at all. Across the following paragraphs, Deep Research cites the same Digiday article eight times, before going back to citing the same Twipe article again. It also, hilariously, says that federated posts "can simultaneously publish to [a] website and as a toot on federated platforms like Mastodon and Threads," a term that Mastodon retired two years ago.
The next two citations are about Medium's embrace of Mastodon, followed by yet another citation of the Digiday article. Following that, Deep Research cites two different Reddit posts, a company called Interleger moving to the Fediverse, which the report cites several more times, along with yet another forum post, the very same Twipe post several more times, and then the support documentation for social network Bluesky several more times.
I won't go through more of the research paper citation by citation, but you'll be shocked to hear it mostly just cites Twipe and Hackernews and Reddit.
For now, Deep Research is only available on ChatGPT Pro, OpenAI's somehow-unprofitable $200-a-month subscription, though it's apparently coming to ChatGPT Plus in a limited capacity.
Not impressed? Well what if I told you it was very compute-intensive and expensive? Oh, one other detail — the entire thing’s on the very edge of comprehensible.
Here’s a bit under funding models:
"Memberships and Donations: A common monetization approach in the Fediverse (and across the open web) is voluntary support from the audience."
Nobody talks like this! This isn’t how human beings sound! I don’t like reading it! I don’t know how else to say this — there is something deeply unpleasant about how Deep Research reads! It’s uncanny valley, if the denizens of said valley were a bit dense and lazy. It’s quintessential LLM copy — soulless and almost, but not quite, right.
Ewww.
So there you have it folks. OpenAI's next big thing is the ability to generate a report that you would likely not be able to use in any meaningful way anywhere, because while it can browse the web and find things and write a report, it sources things based on what it thinks can confirm its arguments rather than making sure the source material is valid or respectable. This system may have worked if the internet wasn't entirely poisoned by companies trying to get the highest ranking in Google, and if Google had any interest in making sure its results were high quality, which it does not.
I'm sorry, I know I sound like a hater, and perhaps I am, but this shit doesn't impress me even a little. Wow, you created a superficially-impressive research project that's really long and that cites a bunch of shit it found online that it made little attempt to verify? And said report took a while to generate, can only be produced if you pay OpenAI $200 each month, and it cost a bunch of money in compute to make?
Deep Research has the same problem as every other generative AI product. These models don't know anything, and thus everything they do — even "reading" and "browsing" the web — is limited by their training data and probabilistic models that can say "this is an article about a subject" and posit their relevance, but not truly understand their contents. Deep Research repeatedly citing SEO-bait as a primary source proves that these models, even when grinding their gears as hard as humanely possible, are exceedingly mediocre, deeply untrustworthy, and ultimately useless.
Furthermore, nothing about this product moves OpenAI toward profitability. In fact, I think they're doing the opposite. Deep Research uses OpenAI's o3 model which can cost as much as $1,000 a query, and while I imagine these prompts aren't that expensive, they are still significantly more so than a regular query from ChatGPT.
The whole point of hiring a researcher is that you can rely on their research, that they're doing work for you that would otherwise take you hours. Deep Research is the AI slop of academia — low-quality research-slop built for people that don't really care about quality or substance, and it’s not immediately obvious who it’s for.
Surely, if you’re engaged enough to spend $200 on an OpenAI subscription and are aware of Deep Research, you probably know what SEO bait is, and can distinguish between low-quality and high-quality content. If you were presented with a document with such low-quality, repetitive citations, you’d shred it — and, if created by an intern, you’d shred them too. Or, at the very least, give them some stern words of guidance.
Let me put this in very blunt terms: we are more than two years into the generative AI boom and OpenAI's biggest, sexiest products are Deep Research — a product that dares to ask "what if you were able to spend a lot of compute to get a poorly-cited research paper," and Operator, a compute-intensive application that rarely completes a task in minutes that would otherwise have taken you seconds.
As an aside, SoftBank, the perennial money-losers that backed WeWork and WireCard and lost more than $30 billion in the last few years, are trying to invest up to $25 billion in OpenAI.
Everywhere you look, the media is telling you that OpenAI and their ilk are the future, that they're building "advanced artificial intelligence" that can take "human-like actions," but when you look at any of this shit for more than two seconds it's abundantly clear that it absolutely isn't and absolutely can't.
Despite the hype, the marketing, the tens of thousands of media articles, the trillions of dollars in market capitalization, none of this feels real, or at least real enough to sustain this miserable, specious bubble. People like Marc Benioff claiming that "today's CEOs are the last to manage all-human workforces" are doing so to pump up their stocks rather than build anything approaching a real product. These men are constantly lying as a means of sustaining hype, never actually discussing the products they sell in the year 2025, because then they'd have to say "what if a chatbot, a thing you already have, was more expensive?"
The tech industry — and part of our economy — is accelerating at speed into a brick wall, driven by people like Sam Altman, Dario Amodei, Marc Benioff, and Larry Ellison, all men that are incentivized to have you value their companies based on something other than what their businesses actually sell.
We are in the midst of a group delusion — a consequence of an economy ruled by people that do not participate in labor of any kind outside of sending and receiving emails and going to lunches that last several hours — where the people with the money do not understand or care about human beings.
Their narrative is built on a mixture of hysteria, hype, and deeply cynical hope in the hearts of men that dream of automating away jobs that they would never, ever do themselves. Altman uses his digital baba yaga as a means to stoke the hearts of weak-handed and weak-hearted narcissists that would sooner shoot a man dead than lose a dollar, even if it means making their product that much worse. CEOs have the easiest jobs in the world, and no job is easier than Satya Nadella waving to the Microsoft 365 staff and saying “make them put AI in it, pronto” and telling Microsoft CFO Amy Hood that “we must make sure that Bing has generative AI” before jetting off to Davos to yell that he intends to burn more money than ever on GPUs.
Sam Altman believes you are stupid. He believes you are a moron that will slurp up whatever slop he gives you. Deep Research and Operator are both half-products that barely brush against the fabric of their intended purposes, and yet the media screams and applauds him like he's a gifted child that just successfully tied his shoes.
I know, I know, I'm a hater, I'm a pessimist, a cynic, but I need you to fucking listen to me: everything I am describing is unfathomably dangerous, even if you put aside the environmental and financial costs.
Let me ask you a question: what's more likely?
That OpenAI, a company that has only ever burned money, that appears completely incapable of making a truly usable, meaningful product, somehow makes its products profitable, and then somehow creates a truly autonomous artificial intelligence?
Or that OpenAI, a company that has consistently burned billions of dollars, that has never shown any sign of making a profit, that has in two years released a selection of increasingly-questionable and obtuse products, actually runs out of money?
How does this industry actually continue? Do OpenAI and Anthropic continue to raise tens of billions of dollars every six months until they work this out? Do the hyperscalers keep spending hundreds of billions of dollars in capital expenditures for little measurable return?
And fundamentally, when will everybody start accepting that the things that AI companies are saying have absolutely nothing to do with reality? When will the media stop treating every single expensive, stupid, irksome, quasi-useless new product is magical, and start asking these people to show us the fucking future already?
Generative AI is a financial, ecological and social time bomb, and I believe that it's fundamentally damaging the relationship between the tech industry and society, while also shining a glaring, blinding light on the disconnection between the powerful and regular people. The fact that Sam Altman can ship such mediocre software and get more coverage and attention than every meaningful scientific breakthrough of the last five years combined is a sign that our society is sick, our media is broken, and that the tech industry thinks we're all fucking morons.
This entire bubble has been inflated by hype, and by outright lies by people like Sam Altman and Dario Amodei, their lies perpetuated by a tech media that's incapable of writing down what's happening in front of their faces. Altman and Amodei are raising billions and burning our planet based on the idea that their mediocre cloud software products will somehow wake up and automate our entire lives.
The truth is that generative AI is as mediocre as it is destructive, and those pushing it as "the future" that "will change everything" are showing how much contempt they have for the average person. They believe that they can shovel shit into our mouths and tell us it's prime rib, that these half-assed products will change the world and that as a result they need billions of dollars and to damage our power grid.
I know this has been a rant-filled newsletter, but I'm so tired of being told to be excited about this warmed-up dogshit. I'm tired of reading stories about Sam Altman perpetually saying that we're a year away from "everything changing" that exist only to perpetuate the myth that Silicon Valley gives a shit about solving anyone's problems other than finding new growth markets for the tech industry.
I refuse to sit here and pretend that any of this matters. OpenAI and Anthropic are not innovators, and are antithetical to the spirit of Silicon Valley. They are management consultants dressed as founders, cynical con artists raising money for products that will never exist while peddling software that destroys our planet and diverts attention and capital away from things that might solve real problems.
I'm tired of the delusion. I'm tired of being forced to take these men seriously. I'm tired of being told by the media and investors that these men are building the future when the only things they build are mediocre and expensive. There is no joy here, no mystery, no magic, no problems solved, no lives saved, and very few lives changed other than new people added to Forbes' Midas list.
None of this is powerful, or impressive, other than in how big a con it’s become. Look at the products and the actual outputs and tell me — does any of this actually feel like the future? Isn’t it kind of weird that the big, scary threats they’ve made about how AI will take our jobs never seem to translate to an actual product? Isn’t it strange that despite all of their money and power they’re yet to make anything truly useful?
My heart darkens, albeit briefly, when I think of how cynical all of this is. Corporations building products that don't really do much that are being sold on the idea that one day they might, peddled by reporters that want to believe their narratives — and in some cases actively champion them. The damage will be tens of thousands of people fired, long-term environmental and infrastructural chaos, and a profound depression in Silicon Valley that I believe will dwarf the dot-com bust.
And when this all falls apart — and I believe it will — there will be a very public reckoning for the tech industry.
2025-02-11 21:52:52
Soundtrack: Bad Religion — The Resist Stance
A great deal of what I write feels like narrating the end of the world — watching as the growth-at-all-costs, hyper-financialized Rot Economy seemingly tarnishes every corner of our digital lives. My core frustration isn't just how shitty things have gotten, but how said shittiness has become so profitable for so many companies.
Meta made $20.8 billion dollars of profit in its last reported quarterly earnings off the back of products that are bordering on non-functional, Microsoft made $24.11 billion in profit with an increasingly-deteriorating series of productivity products and cloud-based solutions that its customers hate, and Google made $26.5 billion in profit from multiple monopolies and making its core search product worse as a means of increasing the amount of times that people search for stuff.
The business of making our shit worse to increase revenue growth year-over-year is booming. The products you use every day are more confusing and frustrating to use because everything must grow, which means that product decisions are now driven, in many cases, by companies trying to make you do something rather than do something for you, which in turn means that basic product quality — things like "usability" or "functionality" — are secondary considerations.
It’s why your Facebook newsfeed doesn’t show you posts from friends and family, but is happy to bombard you with AI-generated images of weirdly shiny-faced old people celebrating their birthday alone, replete with a heartstring-tugging caption. It’s why whenever you search for something — not just on Google, but anywhere — the keywords you provide aren’t treated as an explicit instruction of something you want to see, but randomly disregarded with no rhyme or reason.
We do not "use" the computer — we negotiate with it to try and make it do the things we want it to do, because the incentives behind modern software development no longer align with the user.
Too often when you open an app you start bargaining with the company behind it — like a popup from Dropbox saying you could save money switching to an annual plan, securing annual recurring revenue and locking you into something it hopes you'll forget. Tech companies have the perseverance and desperate hunger for your money of a timeshare salesman, and they’re not sorry.
And that’s assuming it even loads. We’re all familiar with the tense moment where you open Microsoft Teams and hope that it doesn't crash, or that your audio or video works. We live in a constant state of digital micro-aggressions, and as I wrote last year, it's everywhere — banking apps that now have "helpful assistants" that get in the way of, well, banking, pop-ups during online shopping that promise discounts in exchange for our emails and phone numbers so they can spam us, notifications from apps that are built to push us to interact further rather (like Instagram's "someone just posted a comment on someone else's post" notifications), or the emails we get from Amazon about an order shipping that don't include any of the actual information about the purchase — a product decision allegedly made to stop Google from scraping your emails and selling that info to other parties, which is Amazon's business, not Google's.
Yet my — and I'd imagine your — frustration isn't borne of a hatred of technology, or a dislike of the internet, or a lack of appreciation of what it can do, but the sense that all of this was once better, and that these companies have turned impeding our use of the computer into an incredibly profitable business.
So much of the pushback I get in my work — and the pushback I've seen toward others — is that I "hate" technology, when I'd like argue that my profound disgust is borne of a great love of technology, and a deep awareness of the positive effects it's had on my life. I do not turn on my computer every day wanting to be annoyed, and I don't imagine any of you do either. We're not logging onto whatever social networks we're on because we are ready to be pissed off. If anything, we'd love to be delighted by the people we chose to connect with and the content we consume, and want to simply go about our business without a litany of microaggressions created by growth-desperation and a lack of responsibility toward the user.
Technology has, in many ways, stopped being about "using technology to help people do things," or at the very least "help the user do something that they want to do." Software has, as Marc Andreessen said it would in 2011, eaten the world, and has done so in the nakedly-cynical and usurious way that he wanted it to, prioritizing the invasion of our lives through prioritizing growth — and the collection of as much data as possible on the user — over any particular utility or purpose. Andreessen and his ilk saw (and see) software not as a thing that provides value, but as a means for the tech industry to penetrate and "disrupt" as many industries as possible, pushing legacy providers to "transform themselves into software companies" rather than using software to make their products better, describing Pixar — the studio that made movies like Toy Story and Inside Out that was acquired by Disney in 2006 — as a software company rather than a company that makes something using software.
I realize this sounds like semantics, but let me put it another way: software has, for the tech industry, become far more about extracting economic value than it has in providing it. When the tech industry becomes focused on penetrating markets (to quote Andreessen, "software companies...[taking] over large swathes of the economy") there's little consideration of whether said software is prioritizing the solution to a problem.
Nowhere is this more obvious than the software we use in our professional lives. Microsoft Teams is one of the single worst products I've ever used, because Microsoft's goal isn't to make it easy to have digital meetings, but to make a product good enough and cheap enough to make it easier for your boss to buy the entire Microsoft 365 Suite, even if most of the parts of said suite kind of suck.
Here's another great example: Google Drive. Google Drive is absolutely fucking awful. The people responsible for designing Google Drive's user interface should be made to explain themselves before a judge. Why can’t you sort files by size? Why does it only show image and video thumbnails when viewing a folder in a grid layout? Why, when you attempt to move a file to a folder, are the suggested folders — literally the first window you see — always, without fail, wrong?
The proliferation of software throughout society has been led by the stewards of the Rot Economy, as software — along with its associated managed services — can effectively proliferate infinitely, and can take advantage of how many corporations are run by management consultants (and filled with middle managers) that don't do any real work or have any true connections to the problems they solve.
When your goal is "winning the market," you're not necessarily optimizing for having a great product, or even happy customers. Selling software to a big company doesn't require you to speak to everybody who might use it. You're selling hundreds or thousands of seats (users who might access the product) to management in the gestalt, because let's be honest, your manager or their manager isn't really using any of this stuff, they just want to see that it looks like it works well enough, fits within their budget, and makes them feel good inside. The people leading the charge in the tech industry — Andreessen Horowitz has been one of the biggest and most influential players in Silicon Valley history — have never seen its primary purpose as the creation of value for anybody other than the people selling it.
This manifests in the rest of your daily lives in far simpler ways:
It’s digital tinnitus. It’s the pop-up from a shopping app that you downloaded to make one purchase, or the deceptive notification from Instagram that you have “new views” that doesn’t actually lead anywhere. It is the autoplaying video advertisement on your film review website. It is the repeated request for you to log back into a newspaper website that you logged into yesterday because everyone must pay and nothing must get through. It is the hundredth Black Friday sale you got from a company that you swear you unsubscribed from eight times, and perhaps even did, but there’s no real way to keep track. It’s the third time this year you’ve had to make a new password because another data breach happened and the company didn’t bother to encrypt it.
All of these annoying little examples are inherently hostile toward the user, and they're a direct result of a tech industry oriented around growth driven by the pernicious and aggressive poison of growth-focused software. The Rot Economy has changed the incentives of everything you see and do on the computer — the websites you read that inexplicably recommend laptops that are actively painful to use because of the affiliate revenue they drive to the website (with this intent driven by management rather than the writers themselves), Instagram swapping the location of your notification and message buttons, discounts on stores that require both your email and phone number, social networks that put things in the way of you trying to find the people and things you actually log on to see — all ways in which software is used to extract from, trick you mislead and control you.
Let me give you a current example. Riverside makes arguably the easiest-to-use podcasting software — it works in a browser, it records reliably, it's easy to invite people — but inexplicably moves buttons around every once in a while. It used to be that I could just hit the "plus" button to make a new recording, but now I have to, for no reason, scroll down a list of already-existing studios (which is what they call recordings) that are completely out of order and hit "new studio." On hitting that button, nothing appears to happen because Riverside's new UI wants you to hit "plan," "record," "upload" or "edit," the latter of which allows it to sell its higher-priced subscriptions.
Riverside has changed this interface at least twice in the last year. Another cool thing that it does is if you want to download all of the audio files from an episode, you have to hit "export only," which brings you to an extremely awkward-to-use user interface full of other buttons. For some reason, if you hit the "download cloud" button, it'll download a video of the entire session immediately.
Nothing in Riverside is organized logically. "Projects" are meant to be, I assume, where you can group recordings, except you do not appear to be able to move things into them, and on creating a "Project" you have to create — by scheduling or recording — something inside it as a means of saving it. "Studios" are, it seems, where you record stuff. You can no longer, as a result of recent UI changes, see a full list of studios. You have to scroll through them. A logical way would be to have "projects" that have within them distinct recording sessions, and a drag-and-drop interface.
Riverside is widely respected as "the best" podcast software. I pay for it not because it's good — I regularly find it genuinely, upsettingly annoying to use — but because services like Zencaster and Squadcast are markedly worse. Riverside has also aggressively been upselling customers on its AI services in a way that I find disgusting.
To be quite blunt: I think any podcasting recording company with a bit of money that actually gave a fuck could steamroll basically anyone, including Riverside. As a paying customer, I believe that Riverside needs real competition.
What's frustrating is that it’s actually got the technological side down. Recording audio and video is great. Sadly, due to a combination of business incentives and a complete disconnection from its customer, Riverside has begun to rot. No, I have not reached out to them, because these problems are never actually fixed by people emailing in feedback — they're fixed through public pressure.
It's offensive to me that a company would so thoroughly succeed at beating a technical challenge — being able to record multiple audio sources cleanly and easily with a simple web interface — then wrap it in such awful design. This is the result of a lack of true competition in this industry, or executives disconnected from their users.
If you have other experiences with other software platforms like this, please email me at [email protected]. I'd love to hear them. It's time to hold these companies accountable.
Note, when I say "control" I don't mean that these companies have the ability to subconsciously manipulate you and your desires, so much as they have spent decades finding new ways to gaslight and bully you into doing things they'd like. Everybody knows that Instagram sucks, and it sucks because there's things that you actually want to do on Instagram that Meta has hidden behind hundreds of little user interface changes optimized to increase your time on the app and thus the amount of money you make them.
Sidebar: Have you ever tried to search for something on Instagram? You can’t just type the words that you want to see in a caption. It only allows you to search for certain phrases (and it’s not clear what makes a phrase acceptable or not). You can have two queries, each with the same words but arranged in a slightly different order, and Instagram will let you search for one and not the other.
And the posts you’ll see will contain only some of the keywords (making them irrelevant). The other day I searched for the name of an event with the word “live,” hoping to see whether someone had livestreamed it. Instead, I got hundreds of posts that just had the word “live” in.
Even if you get semi-relevant results, they won’t be organized in any particular coherent fashion. You’ll see content from recent weeks mixed in with stuff from over a decade ago. And you can’t even search for recent posts using a hashtag — Instagram removed that feature a couple of years ago, and no amount of caterwauling from users has persuaded it to reverse course.
Everybody knows that Google Search sucks because it’s optimized to to provide results that make the company more money, but we use it because, well, the web is a huge place and Search, while broken, provides enough of a service that it's useful, to the point that we'll push through the bullshit to get to the thing that we want.
To quote Connor O'Malley, "the computer's bullshit, it's fucking sick, it's not cool anymore, it's not fun anymore...they've changed everything about it, it used to be so cool!"
Google Search was, at one point, extremely cool — something that used to give users a sense of peace and control over an internet that had grown so vast that it was hard to fully grasp, and even once felt like a place you'd go to find a quality result. Facebook was instrumental in me building my life in America when I moved in 2008, both in connecting with people I went to college with at Penn State and operating as a kind of digital address book where people (crazy, I know) used to post updates about their life and pictures of things that they were doing. Once upon a time, Apple's App Store had actual quality standards, both for the apps themselves and the services they sold, which made downloading a new app feel exciting because your first popup wasn't for some sort of monthly subscription product.
I am romanticizing things a little. Capitalism is capitalism, these companies were still worth hundreds of millions or billions of dollars, and evil incentives still existed (see: Microsoft and its historic monopoly over operating systems). Nevertheless, the experience of using hardware and software felt less exploitative, or more simply-put, the stuff we used felt more like they worked.
The reason I'm so onerously explaining this is that I do not believe the majority of people hate technology, but what the technology industry has become in search of growth. In fact, I'd argue that deep down, many people love technology — we love that we can instantly connect to friends using little computers in our pockets, or that we can share photos or videos with effectively anyone with an internet connection. As "one of big tech's angriest critics," I must confess I absolutely love what I can do with the computer, as deep down I'm a brokenhearted romantic that can see, beneath all the slop, growth and bullshit are many, many things I truly, deeply love.
I love that I can write a newsletter and share it with my editor thousands of miles away, and that we can work on an idea or sentence in real-time, despite an entire landmass and an ocean separating us. I love that I can run a business online from anywhere with a stable internet connection, and I love that during work I can also quickly and easily catch up with my friends wherever they are. Beneath the bullshit of Google Search lies the ability to research decades of journalism and academia, and my fury and disgust comes from seeing such a great product get mangled by the incentives of freaks like Prabhakar Raghavan and Sundar Pichai.
Sidebar: None of this is to say that these companies were ever perfect, or even good, or even that they had good intentions, nor is any of this any attempt to cheerlead for them. This is not a shift toward me being more "fair," either, which is often a euphemism for waving away the obvious wrongs of a company or a person out of a misguided sense of politeness.
As much as I may like any given product, these companies are providing a service as a means of making money. I am — as you are — a customer, and the fact that so many of them are making so much more money as they make these products manifestly worse fills my veins full of poison.
The problem is that we, as a society, still act like technology is some distinct thing separate from our real lives, and that in turn “technology” is some sort of hobbyist pursuit. Mainstream media outlets have a technology section, with technology reporters that are hired to cover “the technology industry,” optimizing not for any understanding or experience in using technology, but 30,000 foot view of “what the computer people are doing.”
This may have made more sense 20 years ago — though I’d add that back in 2008 you had multiple national newspapers with technology columnists, and computers were already an integral part of our working and personal lives — but in the year 2025 is a fundamental failure of modern media. Every single person you meet in every single part of your life likely interfaces with technology as much as if not more than they do with other people in the real world, and the technology coverage they read in their newspaper or online doesn’t represent that. It’s why a relatively modest software update for Android or Windows earns vastly more column inches than the fact that Google, a product that we all use, doesn’t really work anymore.
As a result, it’s worth considering that billions of people actually really like what technology does for them, and in turn are extremely frustrated with what technology does to them.
The problem is that modern tech media has become oriented around companies and trends rather than the actual experience of a person living in reality. Generative AI would never have been any kind of “movement” or “industry” if the media had approached it from the perspective of a consumer and said “okay, sure, but what does this actually do?” and the same goes for both the metaverse and cryptocurrency.
Rather than fold their arms and demand our tech overlords prove themselves, the media decided that they would be the ones that would prove it for them, describing ChatGPT as a “revolution” without really expressing why, parroting narratives driven by massive corporations or corporate interests and tutting at those who would disagree. There were multiple other companies doing exactly what GPT-3 did months before ChatGPT launched. It only caught fire because the media insisted it did so. To this day I still can’t find a single journalist who has a cogent explanation as to why ChatGPT is big, other than the fact that lots of people use it.
The problem, I believe, is that the tech media has become poisoned by a mixture of ignorance and cynical optimism where the narratives are driven not by any particular interest or domain expertise, but by whatever they believe the market (or the powerful people they admire) would like it to be.
I know for a fact that the senior editorial staff handling technology at multiple major mainstream publications do not really care about, understand or have any real interest in tech other than a vague attachment to the idea that it’s “important, somehow.” As a result, mainstream tech coverage is focused on market effects (like artificial intelligence, or whatever other “thing” everybody wants to read about) rather than directing coverage from the perspective of “what things are happening to people in real life as a result of technology.”
I also think that the tech media has been infiltrated and controlled by people that want to be famous or associated with famous people. They want them to win. They want a benevolent dictator. They want their products to do well so that they can get the interview with the big-name founder or CEO on stage at a conference. They want access to them for interviews, and they want to make sure they get the first look at their next product release. While one might argue that “people want to hear about AI,” what people want to hear about is largely driven by the narratives the media agrees upon.
The people parroting these narratives — much like the executives they admire — do not find any joy in technology at all, nor do they experience (or care about) the problems that technology might solve for a real person. While I don’t care whether a regular person has any enthusiasm or domain expertise, I believe that anyone working in the tech media should have genuine interest in the technology itself, actual domain expertise, actually fucking use the products they’re writing about and have the ability to say “okay, for a regular person, does this actually fucking matter?”
Because I’d argue that technology really really matters to just about everybody. The things that actually rock about technology — global connectivity, quality-of-life things like Chromecast and Apple Pay, fast and portable laptops that allow us to do things wherever we want to — are things that billions of people enjoy, and in turn billions of people are frustrated and hurt and abused by technology when the harms I described earlier are perpetuated at scale.
The tech media continually acts without context or conscience, or with any kind of appreciation of how much worse things have got. While I understand that it’s hard to break editorial direction at a major newspaper, any coverage of Facebook should, by rights, cover the fact that Facebook is fucking broken and has been for years, and has never made more money than it does today, because that is, in and of itself, extremely horrifying. Any discussion of ChatGPT should add that it lacks any real killer app and burns billions of dollars a year, and, I dunno, discuss how this thing doesn’t really have any real use cases? And never did? Why did we fucking hype this exactly? What is going on?
These, by the way, are the questions I get from readers and listeners every single day. Regular people — people that work outside of the tech industry, teachers, writers, artists, authors, academics, and so on — have been asking what the fuck any of this was since the beginning, and the fact they’re still asking is a damning indictment of the tech media writ large.
Worse still, regular people are also furious at the state of software, and are fully aware that they’re being conned. The tech media continually frames the “growing distrust” of the tech industry as some result of political or social change or a cumulation of scandals, rather than the big, unspoken scandal called “how the tech industry made things worse in the pursuit of growth,” and the greater scandal of exactly how much contempt tech regularly treats their customers with.
And more importantly, regular people feel like they’re being gaslit by the tech media. I am regularly told that people are glad to have *someone* say simple things like “hey the apps you use that feel like they’re fucking with you? They are actually doing that!” with regularity. The feedback I regularly receive is that there are too many articles about technology that seem fundamentally disconnected from reality, or at the very least disconnected from the people at the receiving end of the product.
The modern "Techlash" narrative has been the sole focus on big, meaty problems like Meta's Cambridge Analytica scandal while ignoring the gradual destruction of the products we use every day. In the space of a decade, Google made its ads look near-identical to regular search results, and only a few websites (like Search Engine Land) seemed to take that — and the other changes made to the algorithm of one of the single most important sources of information in the world — seriously.
The fact that I, a part-time blogger with a podcast that runs a PR firm during his day, was the one to uncover and discuss how the ads team made Google Search worse for money nine months after the associated emails were made public is a glaring example of the misalignment of tech media with what actually effects people on a daily basis.
Let me give you another example. NVIDIA — arguably one of the single-most-covered tech companies of the last year — effectively lied about the launch of its RTX 5080 and RTX 5090 graphics cards, doing a "paper launch" where stores like Microcenter received as few as 233 RTX 5090 graphics cards nationwide. While NVIDIA did warn of "stock shortages," it's laughable to even call this a launch — and I'd argue that the tech media has...well...no interest in covering it, despite this being a very, very significant story about how NVIDIA is misleading people about its consumer and prosumer graphics cards (which make up billions of dollars of revenue and a large percentage of NVIDIA's revenue), and does not appear to be able to deliver them on time.
These events hit millions of consumers in a tangible way. NVIDIA, despite all its financial success selling AI chips to companies like Microsoft and Amazon, appears to be spurning one of its core customer bases, and the response from the consumer tech media has been tepid, despite the fact that PC gaming revenue is comparable in size to console gaming ($43.2 billion in 2024 compared to the $51.9 billion that console gaming brought in according to research from NewZoo, with PC gaming growing 4% year-over-year compared to a 1% contraction in console revenue).
Worse still, NVIDIA's 5080 graphics card sucks and "represents how NVIDIA is treating PC gamers in 2025" according to Paul's Hardware, who skewered NVIDIA for slowly reducing the amount of performance gains you'll get out of mid-range cards like the 5080 — a cynical attempt to make it so that anyone looking for a "real" upgrade has to spend $2000 on a 5090, which they also can't seem to find.
This is significant, akin to Apple slowly (over the course of years) reducing the efficiency and performance of the regular iPhone in a hope to juice sales of the iPhone Pro. Which it also kinda did.
Yet the only people taking stories like these seriously appear to be video creators like Paul’s Hardware (1.5 million subscribers) and Gamers Nexus (2.4 million subscribers), who time and time again have taken on stories — like gaming PC builder NZXT creating a “PC rental program” that actively conned consumers with rates worse than a pay day loan company — to protect consumers from active harm as the mainstream media chases their tails about whatever half-broken bullshit OpenAI is slinging this week.
NVIDIA, a company discussed by what feels like every single business and tech outlet, has a documented pattern of misleading and short-changing customers. Why isn’t this everywhere? It’s almost as if the only reason that anyone is talking about NVIDIA is that there’s a herd-mentality in what stories are “important” to the modern media, rather than any kind of relationship to the effects that these companies might have on actual consumers.
The mainstream media — especially when it comes to technology — doesn’t seem capable (or willing) to discuss the real, tangible, obvious problems with the modern tech ecosystem, instead choosing to attack things piecemeal or blandly report “news” with as little context as necessary.
Look, people are pissed off at the tech industry because the tech industry is actively pissing them off. They are getting less value from the products they pay for, and they’re aware that the free products they use are getting worse as a means of making them more profitable. Stories about “distrust in big tech” continually fail to talk about the simplest problems — Facebook sucks, Instagram sucks, our apps suck, everything feels like it’s built to subtly fuck with us, and this is a problem that affects billions of people discussed so rarely that I’m considered “creative” for writing 1000 words about the literal experience of using a shitty laptop.
Sidebar: Let me give you a live fucking example! I just typed “ltieral” instead of literal. Google Docs underlined it in red to suggest there’s a typo, only to say “this word is potentially misspelled. If not misspelled, you can turn off correcting this word using the menu.” When I clicked to see why it wouldn’t suggest a correction, I was sent to this page, which does not explain. However, I was able to click “spelling and grammar check,” which brought up another menu, which says “unknown word ltieral.” I also was not able to exit the spelling and grammar checker popup — the “X” button didn’t work — which meant I had to refresh my browser. It’s unclear why autocorrect no longer properly functions in Google Docs, but I’d measure this problem affects millions of people. COOL!
These problems are everywhere! They’re everywhere, and they are real, meaningful stories, ones that are more important than Anthropic’s Dario Amodei farting into a microphone about how AI will be smarter than humans at some point! Regular people are not pissed off at big tech for any complex multi-faceted series of events that made them furrow their brows with concern — the shit they pay for sucks, the shit they trade their data for sucks, the products are broken or in the process of actively breaking, and when consumers look to these companies they’re told “yeah well, what if we added some generative AI bullshit?”
I’ll give you ANOTHER example.
The App Store is a complete mess. On loading it up, the first ad I receive is for Truth Social, followed by “popular iPhone apps” including Bumble (microtransaction-heavy dating app), Paramount+, Zoom, Max, Amazon Prime Video, and Tinder (another microtransaction-heavy dating app), followed by a microtransaction-heavy mobile game (Madden NFL 25 Mobile Football), followed by another microtransaction-heavy mobile game (Clash of Clans), followed by yet another microtransaction-heavy mobile game (Archero), followed by “Helpful Apps for Every Day,” which included Strava (a fitness app), Letterboxd (a social network for people to review movies), StoryGraph (an app for tracking books you’ve read), Peanut (an app for mothers to connect to each other), some sort of app for "discovering IRL plans near you” (“Pie”) and Partiful, an app for planning parties, immediately followed by an ad for Apple’s own “Apple Invites” app that specifically competes with them.
The next carousel is for “10 Great Dating Apps,” the first of which is OkCupid, a dating app with a 1 out of 5 star rating on TrustPilot, with the first review saying that “everything is designed to force you into paying, but even when you do, you quickly realize it’s not worth it.” OkCupid is owned by the publicly-traded Match group, which also owns three of the other apps on the list (Hinge, Tinder and Plenty of Fish).
The reason I’m agonizingly breaking down these problems is because — much like I wrote about in Never Forgive Them — I believe that the problems of the modern tech industry are far simpler and more pervasive than the media will face. Apple’s App Store — a trillion-dollar marketplace where Apple takes a 30% cut of almost every buck a developer makes — actively promotes and profits off of exploitative free-to-play mobile games that academics believe rob consumers of their right to self-determination and an online dating industry that has adopted these very same ideas to turn romance into, well, its own kind of free-to-play game. The App Store largely promotes apps (and their associated features) from public companies with billions or trillions of dollars in market capitalization, and much like Google Search only functions to bring you results that are convenient for Google, Apple no longer highlights apps based on anything other than a thousand shadowy partnerships and profit incentives.
This is the way that tens if not hundreds of millions of people are introduced to software, and the software they’re introduced to is inherently exploitative. It’s like if every Kroger store sold bread that cost an extra $3 if you wanted to cut it into slices, or bacon that required a subscription to BaconPlus+ if you kept it in the fridge for longer than two days. I’m not even being facetious, this is the actual scale of the actual harms being done against actual consumers by a company with a market capitalization of $3.5 trillion dollars.
When somebody buys a new phone, they are not thinking like me, or you, or someone else deeply aware of the incentives behind these companies. They blindly — because nobody really explains this shit or takes it seriously — download whatever apps they see promoted by Apple. Consumers trust Apple, and as a result trust the companies that Apple chooses to promote, at which point whatever malevolent mechanisms these companies use are more effective because consumers believe that Apple, a company with a multi-trillion dollar market cap, wouldn’t allow nakedly exploitative apps onto their phones. Apple could very easily use its unilateral control over the entire App Store to prevent these companies from existing, or at least choose not to promote them. Instead, it chooses to both ensure and profit from their success by putting them in front of millions of consumers a day.
While microtransactions aren’t inherently evil, when unrestrained they naturally lead to evil outcomes. Modern dating apps effectively require users to buy both a monthly subscription and piecemeal “items” that make your message or profile more prominent in the app. Mobile gaming — an industry that makes tens of billions of dollars of yearly revenue — has become dominated by “free-to-play” games that really require you to spend money to progress, using deceptive psychological techniques to push users into spending money in small amounts that naturally add up to much more than they’d spend on a regular game. I hammer on both of these so hard because they make up the majority of the promoted content on Apple’s App Store.
To be abundantly clear, Apple had (and has) the power to kill any of these industries, or at least vastly limit their harms. Apple controls every single thing that goes on the App Store, and could very easily make dark patterns that manipulate consumers (which are in the majority of subscription apps) against the rules, and penalize apps that predominantly monetize using microtransactions. It could take a stand against companies that combine microtransactions with lootboxes — essentially, in-game content where you don’t know what you’re buying ahead of time, with the content largely determined by chance — which is a way of introducing kids to the horrors of gambling addiction.
You are what you allow, and Apple allows companies to make money by actively abusing and manipulating their customers. It does so both by allowing these companies to make money in this way and actively promoting their apps to users, making tens of billions of dollars a year in the process.
One could argue that it’s the companies choosing to make these decisions, but the scale at which Apple operates means that it’s effectively a kind of government, and any government regulation controls the kinds of products and services that can be offered to a consumer. Apple’s App Store is a kleptocracy where sleazy companies like The Match Group (Hinge, Tinder, Match.com, OkCupid) and SuperCell (Clash of Clans) provide billions of dollars in app store fees by tricking and hurting customers. Apple, through sheer scale, dictates how the economics of apps (and consumer purchasing at large to an extent) operate, and its decision has been to let a thousand poisonous flowers bloom.
This, I’d argue, is one of the largest-scale consumer harms in existence. Apple has perpetuated and profited off of economics that are harmful, manipulative and cruel, and will continue to do so unless meaningful regulation or media pressure makes them do otherwise.
The latter would require the media to actually discuss this problem. I can find no major media outlet that has run anything even close to an evaluation of the state of the modern app store, nor can I find any condemnation of the very obvious harms perpetuated by Apple or Google with their app stores outside of the lawsuit between Epic and Apple, which wasn’t so much about the harms themselves, but the extent to which Apple profited from them.
Similarly, there’s comparatively little coverage of the destruction of Google Search or the horrifying state of Facebook and Instagram. While outlets have had dalliances with the collapse of Search — Charlie Warzel at The Atlantic was earlier than most, myself included — these are usually one-and-done features, a momentary “hmm!” in the slop of breaking news and hot takes, if these stories even happen at all. You might argue that one cannot simply write these stories again and again, to which I say “skill issue.” The destruction of products core to the fabric of society is important and should be in the news constantly, in the same way that news outlets happily report on and discuss crime in modern metropolitan areas.
Companies like Google, Meta, and Apple have been allowed to expand their wealth and influence to the point that they’re effectively nation states, and should be reported on as such.
The manifold ways in which Mark Zuckerberg has manipulated Facebook’s users as a means to express growth to the public markets is a globally-perpetuated act of abuse, yet it remains relatively undiscussed because the media refuses to discuss technology in the way it actually affects people.
The same goes for Apple’s App Store, Google Search, and shit, I’d argue most of the modern internet. How is it not a bigger story that the mobile browsing experience on most websites ranges from “awful” to “impossible to use to the point your browser crashes”?
I think this is the thing that really confuses me — how the fuck is this not being written about? You see it any time you use your phone! It’s everywhere, always, all the time, there are so many examples, yet tech coverage is effectively always “news” or “how to do something on your computer or phone that isn’t obvious” without any acknowledgement that the reason you’re writing this piece is because user interface design is terrible, and you want your website to rank high on Google Search.
And I’d argue that regular people are experiencing the same pain, and they’re so frustrated because they know, beneath the layers of abstraction, of warring incentives and abusive user interface choices, there’s something they want or need.
Shit, I think we all are. The nakedly awful state of modern technology — software in particular — is something that unites us all, and I think a lot of us get a lot more out of the computer than we really want to admit. I’m not just angry at Mark Zuckerberg for turning Facebook into an actively harmful product. I’m angry that he’s done so in a way that took away something that made the internet magical, in the same way I despise Prabhakar Raghavan and Sundar Pichai for doing the same with Google Search. I’m not just angry at one of the many different quarter-page-sized ads that block an article I want to read. I’m angry that one of the coolest things on the internet (access to varied media while sitting on the toilet) is literally obfuscated by the demands of growth.
The internet allows us to do so many things, and what we see today is both a technological marvel and a disgrace to humanity. We, right now, have the ability to talk to somebody thousands of miles away, to send them a photo or video of what we’re doing, to meet people we’d never meet in real life and build meaningful relationships with them.
As a writer, I am able to shoot the shit with my buddy Kasey in Southern California and my editor Matt in Liverpool, England with about the same speed, and as a result write thousands of words of ideas that Matt then edits, all with a few clicks, and then distribute it to 55,000 people with a few more. I can go on Bluesky and shoot the shit with people I know well or who I’ve never met in my life, and have a blast doing so. I can sit in my living room and play a videogame while I stream music to my phone to a big speaker in a few taps, and this technology has become more and more accessible as the years have gone on. We live in a time where technology does really, really cool things that help billions of people. These companies can innovate and they can make our lives better.
The problem is that software may have eaten the world, but growth holds software’s leash. The Rot Economy sits above all things. It is not enough for Apple to make iterations of the iPhone that are better and faster — it must sell more every quarter, and the software sitting on each iPhone must continue to generate more money in perpetuity. The websites you read that have page-wide ads are all run by people that don’t read anything and must see revenue numbers increase, in the same way that The Match Group must find new ways to increase quarterly revenues for their dating apps, even if the way they choose that is “to make using the apps cost more money.” Each of these ideas — a miniature computer that sits in our pocket and gives us access to the world’s information, or an app for falling love — are extremely cool, yet the reckless incentives of growth have poisoned them.
I am, as I have said, a brokenhearted romantic. The internet made me who I am, allowed me to thrive both as a person and a professional, and continues to do so every day, except now I have to fight seemingly every app and service to get them to do what I want. As I’ve said before, I will never forgive these people for what they’ve done to the computer, as I love what the computer has done for me, and hate what the computer now does to other people so that Apple, Google and Meta can increase quarterly revenues.
It’s easy to give into pessimism here, and I’d argue that the better alternative is to be loud and annoying and extremely verbal about the problems you see. Every single website you use has a feedback form, and I encourage you to use it, as I encourage you to complain about these problems on social media, and to regularly say the names of the people who caused these problems to everybody you know. If you’re feeling spicy, perhaps write to your elected officials that you believe the quality of digital products you’re using is getting worse as a means of increasing stock prices, and add that doing so is anti-democratic and anti-competitive, while also actively harmful.
Hell, a lot of these executives have email accounts. Why not let them know how you feel? I’m not saying to be horrible or rude, but you should absolutely look them up and let them know how bad things have become.
Another thing you can do is be less useful. If you use Instagram, use it in a way that generates less engagement. Click through a few stories then drop off the app, don’t use the feed, avoid clicking or staying on any ads, and (as Geoffrey Fowler of the Washington Post recommends) reset your feed regularly. Delete the data that these companies have on you regularly, and any time a company asks you for feedback that isn’t about a customer service rep, skip it or close your browser, as that data is only useful to them. In general, engage with apps less — both in the amount of time you spend on them and the amount you interact with their features — and obsessively read every privacy policy.
These companies make billions off of idle, muscle-memory-based use of their software, so get used to their tricks, and work against them. And if you really don’t use a service, stop using it. I will not, however, judge you for staying. I’m still on Instagram because it’s where a lot of my friends are and I like seeing what they’re up to. Again, I’m not against these products in principle. I just hate what they’ve become.
More importantly, I want you to find solidarity with others against the Rot Economy. Every single person you meet is a victim, every single person you meet faces similar problems to you, and every single person you know is likely angry at email spam, the collapse of social networks and Google, or the abominable state of modern business software.
The reason that these companies have been able to penetrate and poison so many things using software is a combination of lax regulation and a docile societal approach to technology. They want — no, they need — you to feel hopeless, that they are too big, that they can grow forever by doing whatever they want to you, and that there will never be enough negative sentiment to change their ways.
The reality is that these people are extremely vulnerable, and extremely unprepared for any real pushback. Tech executives are poorly-trained, thin-skinned, and have never faced any meaningful negative consumer sentiment, largely because they’ve never faced any real competition. They simply do not believe you will act in a way that doesn’t benefit them, because they’ve done literally everything they can to make it difficult to avoid or leave their systems.
They need you to think that things will always be this bad (or worse), and for you to just sit there and take it rather than screaming in their fucking face that what they’re doing is unacceptable. They want you to give up.
Don't let them destabilise you. Don't let them pump you full of cynicism, of pessimism, of the belief that there is NOTHING good, and thus there can never be anything to aspire to.
Going forward, one of my missions in this newsletter and on my podcast is to give you the language to describe what is being done to you, and the names of those responsible for doing it. I fundamentally believe that anyone can understand the stuff I'm talking about, that the tech behind it is not magic, and that the things being done to you in the name of the growth-at-all-costs Rot Economy and its demands for perpetual growth in engagement metrics and revenue. I want you to understand this stuff so you can make better decisions, but also understand you are the victim of a con where you've been convinced you were “behind the times” when the tech industry just gave up on serving you.
Our economy and the majority of public companies are run by people who don't face any real problems or do any real work, and the tech industry — run by the same people — has oriented itself around building products and services to sell to them. These people do not use their own products, or if they do, they do so in such a distant way that it doesn't really matter if they suck.
It is time to speak about them in plain terms: we are in an era of rot, our markets dominated by a growth-obsessed death cult so powerful that it's just accepted that the only good stocks are those that grow every single quarter. A “good company” isn't one that provides a good service — it's one that provides that service in such a way that they can jack up the prices or upsell customers while also somehow getting more.
If anything the Rot Economy is a global Ponzi scheme where the only companies that succeed are the ones that can continually get more customers or come up with new ones, even if the service or goods provided are bad. It doesn't matter to them that the only thing that grows forever is cancer, and that perpetual growth could very well falter and then crash everything. It's all short-term thinking, all the time.
I want you to start seeing everything through the lens of growth, and I believe everything will make a lot more sense.
And they don't HAVE to do it this way. Success and being a decent — in the moral sense of the word — company are not mutually exclusive.They could have modest 2-5% growth each quarter, they could make good software that people like, they could do all of these things, but they choose not to. They'd rather hurt us, because growth is more important to them than whether our lives fucking suck. They'd rather refuse to maintain or rigorously test their products — especially their software — because investing in customers doesn't grow your customer base as fast as focusing on finding new ones.
These things have been happening for over a decade, and being able to explain it in plain English is important. Having these conversations is important. Talk to your friends and families and coworkers about this stuff, they're all dealing with it too. I don't care if you show them my work — just tell them what's fucking happening.
You cannot change the world on your own, and you may very well go through the world without changing much at all. But in your small way you can, at the very least, contribute to a greater hope and positivity in the bubble around you. The ideas you have — of a fairer, better world, one where people are not vilified for being who they are — are shared by most people. We outnumber them, and by an overwhelming margin.
The demands you make of the world do not have to necessarily seem realistic, but they can be fair. It is not unfair to demand a tech industry exists that is merely worth a few hundred billion dollars while providing a service that largely benefits the world around us. At the very least we can ask for shit that works
Discussing ideas for what a better world might look like is eternal. It is the root of humanity. It is what gives us light in the darkest times and what the darkest people in the world wish to rob us of — not simply hope, but the ingredients of hope, the stuff that builds the foundation that allows us to truly believe.
This isn't to say this is an easy process, nor one without deep, dark moments. But at the very least we can have standards and beliefs in ourselves of what better looks like.
It feels silly to hold up "better software and technology" as such a serious concept, but I think the world as it stands is suffering due to the tolerance we've had for the horrifying condition of modern software, which has now deeply penetrated every part of our lives, in some cases leaving trash lying around that we find ourselves tripping over all the time. Software has, to some extent, truly improved humanity, allowing levels of connection that are truly special, both with those we know and those we barely know.
It has, however, grown without restraint, without true accountability for those who write it and deploy it and (barely) maintain it, or actively and consciously strive to undermine it.
I cannot promise you that I will ever have the solutions to any of these problems, but I can — as you can — say what a better world looks like, and a better world is one where software works for, not against, the people that use it.
There is no harm in liking — or even loving — technology, as liking it allows you to more articulately explain why you fucking hate what they've made of it. Expressing what good looks like — what you love — allows you to cut deeper with your hatred for those who have caused so much harm.
That starts by naming those responsible for poisoning the world with software — Sundar Pichai of Google, Satya Nadella of Microsoft, Tim Cook (and Phil Schiller, who runs the App Store) of Apple, Mark Zuckerberg of Meta, and the other invisible war criminals responsible for the destruction of our digital lives.
They have nothing but their names. The tech industry is woefully unprepared to deal with regular people having the language and understanding of their acts. Crisis PR for tech doesn't know how to deal with real people saying “why did you fuck up your website so badly?” in the thousands or millions.
These people have never, ever dealt with real accountability, or even a real conversation about what they're doing and why they're doing it.
We deserve better, so we should fucking ask for it.
2025-01-30 00:27:45
Soundtrack: The Hives — Hate To Say I Told You So
In the last week or so, but especially over the weekend, the entire generative AI industry has been thrown into chaos.
This won’t be a lengthy, technical write-up — although there will be some inevitable technical complexities, just because the nature of the subject demands it.. Rather, I will address the elephant in the room, namely why the Western tech giants have been caught so flat-footed.
In short, the recent AI bubble (and, in particular, the hundreds of billions of spending behind it) hinged on the idea that we need bigger models, which are both trained and run on bigger and even larger GPUs almost entirely sold by NVIDIA, and are based in bigger and bigger data centers owned by companies like Microsoft and Google. There was an expectation that this would always be the case, and that generative AI would always be energy and compute hungry, and thus, incredibly expensive.
But then, a Chinese artificial intelligence company that few had heard of called DeepSeek came along with multiple models that aren’t merely competitive with OpenAI's, but undercut them in several meaningful ways. DeepSeek’s models are both open source and significantly more efficient — 30 times cheaper to run — and can even be run locally on relatively modest hardware.
As a result, the markets are panicking, because the entire narrative of the AI bubble has been that these models have to be expensive because they're the future, and that's why hyperscalers had to burn $200 billion in capital expenditures for infrastructure to support generative AI companies like OpenAI and Anthropic. The idea that there was another way to do this — that, in fact, we didn't need to spend all that money, had any of the hyperscalers considered a different approach beyond "throw as much money at the problem as possible" — simply wasn’t considered.
And then came an outsider to upend the conventional understanding and, perhaps, dethrone a member of America’s tech royalty — a man who has crafted, if not a cult of personality, then a public image of an unassailable visionary that will lead the vanguard in the biggest technological change since the Internet. I am, of course, talking about Sam Altman.
DeepSeek isn't just an outsider, but a company that emerged as a side project from a tiny, tiny hedge fund — at least, by the standards of hedge funds — and whose founding team have nowhere near the level of fame and celebrity as Altman. Humiliating.
On top of all of that, DeepSeek's biggest, ugliest insult is that its model, DeepSeek R1, is competitive with OpenAI's incredibly expensive o1 "reasoning" model, yet significantly (96%~) cheaper to run, and can even be run locally. Speaking to a few developers I know, one was able to run DeepSeek's R1 model on their 2021 MacBook Pro with an M1 chip. Worse still, DeepSeek’s models are made freely available to use, with the source code published under the MIT license, along with the research on how they were made (although not the training data), which means they can be adapted and used for commercial use without the need for royalties or fees.
By contrast, OpenAI is anything but open, and its last LLM to be released under the MIT license was 2019’s GPT-2.
No, wait. Let me correct that. DeepSeek’s biggest, ugliest secret is that it’s obviously taking aim at every element in OpenAI’s portfolio. As the company was already dominating headlines, it quietly dropped its Janus-Pro-7B image generation and analysis model, which the company says outperforms both StableDiffusion and OpenAI’s DALL-E 3. And, as with its other code, is also freely available to both commercial and personal users alike, whereas OpenAI has largely paywalled Dall-E 3.
It’s a cynical, vulgar version of David and Goliath, where a tech startup backed by a shadowy Chinese hedge fund with $5.5 billion dollars under management is somehow the plucky upstart against the lumbering, lossy, oafish $150 billion startup backed by a public tech company with a market capitalization of $3.2 trillion.
DeepSeek's V3 model — which is comparable (and competitive with) both OpenAI's GPT 4o and Anthropic's Claude Sonnet 3.5 models (which has some reasoning features) — is 53 times cheaper to run when using the company’s own cloud services. And, as noted above, said model is effectively free for anyone to use — locally or in their own cloud instances — and can be taken by any commercial enterprise and turned into a product of their own, should they so desire.
In essence, DeepSeek — and I'll get into its background and the concerns people might have about its Chinese origins — released two models that perform competitively (and even beat) models from both OpenAI and Anthropic, undercut them in price, and made them open, undermining not just the economics of the biggest generative AI companies, but laying bare exactly how they work. That last point is particularly important when it comes to OpenAI's reasoning model, which specifically hid its chain of thought for fear of "unsafe thoughts" that might "manipulate the customer," then muttered under their breath that the actual reason was that it was a "competitive advantage."
And let's be completely clear: OpenAI's literal only competitive advantage against Meta and Anthropic was its "reasoning" models (o1 and o3, which is currently in research preview). Although I mentioned that Anthropic’s Claude Sonnet 3.5 model has some reasoning features, they’re comparatively more rudimentary than those in o1 and o3.
In an AI context, reasoning works by breaking down a prompt into a series of different steps with "considerations" of different approaches — effectively a Large Language Model checking its work as it goes, with no thinking involved, because these models do not "think" or "know" stuff. OpenAI rushed to launch its o1 reasoning model last year because, and I quote Fortune, Sam Altman was "eager to prove to potential investors in the company's latest funding round that OpenAI remains at the forefront of AI development." And, as I noted at the time, it was not particularly reliable, failing to accurately count the number of times the letter ‘r’ appeared in the word “strawberry.”
At this point, it's fairly obvious that OpenAI wasn’t anywhere near the “forefront of AI development,” and now that its competitive advantage is effectively gone, there are genuine doubts about what comes next for the company.
As I'll go into, there are many questionable parts of DeepSeek's story — its funding, what GPUs it has, and how much it actually spent training these models — but what we definitively understand to be true is bad news for OpenAI, and, I would argue, every other large US tech firm that jumped on the generative AI bandwagon in the past few years.
DeepSeek’s models actually exist, they work (at least, by the standards of hallucination-prone LLMs that don’t, at the risk of repeating myself, know anything in the true meaning of the word), they've been independently verified to be competitive in performance, and they are magnitudes cheaper in price than those from both hyperscalers (EG: Google's Gemini, Meta's Llama, Amazon Q, etc.) and from OpenAI and Anthropic.
DeepSeek's models don't require massive new data centers (they run on the GPUs currently used to run services like ChatGPT, and can even work on more austere hardware), nor do they require an endless supply of bigger, faster NVIDIA GPUs every year to progress. The entire AI bubble was inflated based on the premise that these models were simply impossible to build without burning massive amounts of cash, straining the power grid, and blowing past emissions goals, and that these were necessary costs to create "powerful AI."
Obviously, that wasn’t true. Now the markets are asking a very reasonable question: “did we just waste $200 billion?”
First of all, if you want a super deep dive into DeepSeek, I can't recommend VentureBeat's writeup enough. I'll be quoting it liberally, because it deserves the credit for giving a very succinct and well-explained background.
First, some background on how DeepSeek got to where it did. DeepSeek, a 2023 spin-off from Chinese hedge-fund High-Flyer Quant, began by developing AI models for its proprietary chatbot before releasing them for public use. Little is known about the company’s exact approach, but it quickly open sourced its models, and it’s extremely likely that the company built upon the open projects produced by Meta, for example the Llama model, and ML library Pytorch.
To train its models, High-Flyer Quant secured over 10,000 Nvidia GPUs before U.S. export restrictions, and reportedly expanded to 50,000 GPUs through alternative supply routes, despite trade barriers. This pales compared to leading AI labs like OpenAI, Google, and Anthropic, which operate with more than 500,000 GPUs each.
Now, you've likely seen or heard that DeepSeek "trained its latest model for $5.6 million," and I want to be clear that any and all mentions of this number are estimates. In fact, the provenance of the "$5.58 million" number appears to be a citation of a post made by NVIDIA engineer Jim Fan in an article from the South China Morning Post, which links to another article from the South China Morning Post, which simply states that "DeepSeek V3 comes with 671 billion parameters and was trained in around two months at a cost of US$5.58 million" with no additional citations of any kind. As such, take them with a pinch of salt.
While there are some that have estimated the cost (DeepSeek's V3 model was allegedly trained using 2048 NVIDIA h800 GPUs, according to its paper), as Ben Thompson of Stratechery made clear, the "$5.5 million" number only covers the literal training costs of the official training run (and this is made fairly clear in the paper!) of V3, meaning that any costs related to prior research or experiments on how to build the model were left out.
While it's safe to say that DeepSeek's models are cheaper to train, the actual costs — especially as DeepSeek doesn't share its training data, which some might argue means its models are not really open source — are a little harder to guess at. Nevertheless, Thompson (who I, and a great deal of people in the tech industry, deeply respect) lays out in detail how the specific way that DeepSeek describes training its models suggests that it was working around the constrained memory of the NVIDIA GPUs sold to China (where NVIDIA is prevented by US export controls from selling its most capable hardware over fears they’ll help advance the country’s military development):
Here’s the thing: a huge number of the innovations I explained above are about overcoming the lack of memory bandwidth implied in using H800s instead of H100s. Moreover, if you actually did the math on the previous question, you would realize that DeepSeek actually had an excess of computing; that’s because DeepSeek actually programmed 20 of the 132 processing units on each H800 specifically to manage cross-chip communications. This is actually impossible to do in CUDA. DeepSeek engineers had to drop down to PTX, a low-level instruction set for Nvidia GPUs that is basically like assembly language. This is an insane level of optimization that only makes sense using H800s.
DeepSeek's models — V3 and R1 — are more efficient (and as a result cheaper to run), and can be accessed via its API at prices that are astronomically cheaper than OpenAI's. DeepSeek-Chat — running DeepSeek's GPT-4o competitive V3 model — costs $0.07 per 1 million input tokens (as in commands given to the model) and $1.10 per 1 million output tokens (as in the resulting output from the model), a dramatic price drop from the $2.50 per 1 million input tokens and $10 per 1 million output tokens that OpenAI charges for GPT-4o. DeepSeek-Reasoner — its "reasoning" model — costs $0.55 per 1 million input tokens, and $2.19 per 1 million output tokens compared to OpenAI's o1 model, which costs $15 per 1 million input tokens and $60 per 1 million output tokens.
Now, there's a very obvious "but" here. We do not know where DeepSeek is hosting its models, who has access to that data, or where that data is coming from or going. We don't even know who funds DeepSeek, other than that it’s connected to High-Flyer, the hedge fund that it split from in 2023. There are concerns that DeepSeek could be state-funded, and that DeepSeek's low prices are a kind of geopolitical weapon, breaking the back of the generative AI industry in America.
I have no idea whether that’s the case. It’s certainly true that China has long treated AI as a strategic part of its national industrial policy, and is reported to help companies in sectors where it wants to catch up with the Western world. The Made In China 2025 initiative saw a reported hundreds of billions of dollars provided to Chinese firms working in industries like chipmaking, aviation, and yes, artificial intelligence. The extent of the support isn’t exactly transparent, and so it’s not entirely out of the realm of possibility that DeepSeek is the recipient of state aid. The good news is that we're gonna find out fairly quickly. American AI infrastructure company Groq is already bringing DeepSeek's models online, meaning that we'll at the very least get a confirmation of whether these prices are realistic, or heavily-subsidized by whomever it is that backs DeepSeek.
It’s also true that DeepSeek is owned by a hedge fund, which likely isn’t short of cash to pump into the venture.
Aside: Given that OpenAI is the benefactor of millions in cloud compute credits, and gets reduced pricing for Microsoft’s Azure cloud services, it’s a bit tough for them to complain about a rival being subsidized by a larger entity with the ability to absorb the costs of doing business, should that be the case. And yes, I know Microsoft isn’t a state, but with a market cap of $3.2 trillion and quarterly revenues larger than the combined GDPs of some EU and NATO nations, it’s the next best thing.
Whatever concerns there may be about malign Chinese influence are bordering on irrelevant, outside of the low prices offered by DeepSeek itself, and even that is speculative at this point. Once these models are hosted elsewhere, and once DeepSeek's methods (which I'll get to shortly) are recreated (which won't take long), I believe we'll see that these prices are indicative of how cheap these models are to run.
That's a bloody good question, and because I'm me, I have a hypothesis: I do not believe that the companies making foundation models (such as OpenAI and Anthropic) have been incentivized to do more with less, and because their chummy relationships with hyperscalers were focused almost entirely on "make the biggest, most hugest models possible, using the biggest, most hugest chips," and because the absence of profitability didn’t stop them from raising more money, efficiency was never a major problem for them.
Let me put it in simpler terms: imagine living on $1,500 a month, and then imagine how you'd live on $150,000 a month, and you have to, Brewster's Millions style, spend as much of it as you can to complete the mission of "live your life." In the former example, your concern is survival — you have a limited amount of money and must make it go as far as possible, with real sacrifices to be made with every dollar you spend. In the latter, you're incentivized to splurge, to lean into excess, to pursue a vague remit of "living" your life. Your actions are dictated not by any existential threats — or indeed future planning — but by whatever you perceive to be an opportunity to "live."
OpenAI and Anthropic are emblematic of what happens when survival takes a backseat to “living.” They have been incentivized by frothy venture capital and public markets desperate for the next big growth market to build bigger models and sell even bigger dreams, like Dario Amodei of Anthropic saying that your AI "could surpass almost all humans at almost everything" "shortly after 2027." Both OpenAI and Anthropic have effectively lived their existence with the infinite money cheat from The Sims, with both companies bleeding billions of dollars a year after revenue and still operating as if the money will never run out. If they were worried about it, they would have certainly tried to do what DeepSeek has done, except they didn't have to, because both of them had endless cash and access to GPUs from either Microsoft, Amazon or Google.
OpenAI and Anthropic have never been made to sweat, receiving endless amounts of free marketing from a tech and business media happy to print whatever vapid bullshit they spout, raising money at will (Anthropic is currently raising another $2 billion, valuing the company at $60 billion), all off of a narrative of "we need more money than any company has ever needed before because the things we're doing have to cost this much."
Do I think they were aware that there were methods to make their models more efficient? Sure. OpenAI tried (and failed) in 2023 to deliver a more efficient model to Microsoft. I'm sure there are teams at both Anthropic and OpenAI that are specifically dedicated to making things "more efficient." But they didn't have to do it, and so they didn’t.
As I've written before, OpenAI simply burns money, has been allowed to burn money, and up until recently likely would've been allowed to burn even more money, because everybody — all of the American model developers — appeared to agree that the only way to develop Large Language Models was to make the models as big as humanly possible, and work out troublesome stuff like "making them profitable" later, which I presume is when "AGI happens," a thing they are still in the process of defining.
DeepSeek, on the other hand, had to work out a way to make its own Large Language Models within the constraints of the hamstrung NVIDIA chips that can be legally sold to China. While there is a whole cottage industry of selling chips in China using resellers and other parties to get restricted Silicon into the country, as Thompson over at Stratechery explains, the entire way in which DeepSeek went about developing its models suggests that it was working around very specific memory bandwidth constraints (meaning the amount of data that can be fed to and from chips). In essence, doing more with less wasn’t something it chose, but something it had to do.
While it's certainly possible that DeepSeek had unrestrained access to American silicon, the actual work it’s done (which is well-documented in the research paper accompanying the V3 model) heavily suggests it was working within the constraints of lower memory bandwidth. Basically, it wasn’t able to move as much data around the chips, which is a problem because the reason why GPUs are so useful in AI is because they can move a lot of data at the same time and then process it in parallel (running multiple tasks simultaneously). Lower bandwidth means less data moving, which means things like training and inference take longer.
And so, it had to get creative. DeepSeek combined numerous different ways to reduce the amount of the model it loaded into memory at any given time. This included using Mixture of Experts architecture (where models are split into different "experts" that handle different kinds of inputs and outputs — a similar technique to what OpenAI's GPT-4o does) and multi-head latent attention, where DeepSeek compresses the key-value cache (think of it as a place where a Large Language Model writes down everything it's processed so far from an input as it generates) into something called a "latent vector." Essentially, instead of writing down all the information, it just caches what it believes is the most important information.
In simpler terms, DeepSeek's approach breaks the Large Language Model into a series of different experts — specialist parts of the model — to handle specific inputs and outputs, and it’s found a way to take shortcuts with the amount of information it caches without sacrificing performance. Yes, there is a more complex explanation here, but this is so you have a frame of reference.
There's also the training data situation — and another mea culpa. I've previously discussed the concept of model collapse, and how feeding synthetic data (training data created by an AI, rather than a human) to an AI model can end up teaching it bad habits, but it seems that DeepSeek succeeded in training its models using generative data, but specifically for subjects (to quote GeekWire's Jon Turow) "...like mathematics where correctness is unambiguous," and using "...highly efficient reward functions that could identify which new training examples would actually improve the model, avoiding wasted compute on redundant data."
It seems to have worked. Though model collapse may still be a possibility, this approach — extremely precise use of synthetic data — is in line with some of the defenses against model collapse I've heard from LLM developers I've talked to. This is also a situation where we don't know its exact training data, and it doesn’t negate any of the previous points made about model collapse. Synthetic data might work where the output is something that you could figure out on a TI-83 calculator, but when you get into anything a bit more fuzzy (like written text, or anything with an element of analysis) you’ll likely start to encounter unhappy side effects..
There's also some scuttlebutt about where DeepSeek got this data. Ben Thompson at Stratechery suggests that DeepSeek's models are potentially "distilling" other models' outputs — by which. I mean having another model (say, Meta's Llama, or OpenAI's GPT-4o, which is why DeepSeek identified itself as ChatGPT at one point) spit out outputs specifically to train parts of DeepSeek.
Distillation is a means of extracting understanding from another model; you can send inputs to the teacher model and record the outputs, and use that to train the student model. This is how you get models like GPT-4 Turbo from GPT-4. Distillation is easier for a company to do on its own models, because they have full access, but you can still do distillation in a somewhat more unwieldy way via API, or even, if you get creative, via chat clients.
Distillation obviously violates the terms of service of various models, but the only way to stop it is to actually cut off access, via IP banning, rate limiting, etc. It’s assumed to be widespread in terms of model training, and is why there are an ever-increasing number of models converging on GPT-4o quality. This doesn’t mean that we know for a fact that DeepSeek distilled 4o or Claude, but frankly, it would be odd if they didn’t.
OpenAI has reportedly found “evidence” that DeepSeek used OpenAI’s models to train its rivals, according to the Financial Times, although it failed to make any formal allegations, though it did say that using ChatGPT to train a competing model violates its terms of service. David Sacks, the investor and Trump Administration AI and Crypto czar, says “it’s possible” that this occurred, although he failed to provide evidence.
Personally, I genuinely want OpenAI to point a finger at DeepSeek and accuse it of IP theft, purely for the hypocrisy factor. This is a company that exists purely from the wholesale industrial larceny of content produced by individual creators and internet users, and now it’s worried about a rival pilfering its own goods?
Cry more, Altman, you nasty little worm.
As I've written about many, many, many times, the Large Language Models run by companies like OpenAI, Anthropic, Google and Meta are unprofitable and unsustainable, and the transformer-based architecture they run on has peaked. They're running out of training data, and the actual capabilities of these models were peaking as far back as March 2024.
Nevertheless, I had assumed — incorrectly — that there would be no way to make them more efficient, because I had assumed — also incorrectly — that the hyperscalers (along with OpenAI and Anthropic) would be constantly looking for ways to bring down the ruinous costs of their services. After all, OpenAI lost $5 billion (after $3.7 billion in revenue, too!), and Anthropic just under $3 billion in 2024.
What I didn't wager was that, potentially, nobody was trying. My mistake was — if you can believe this — being too generous to the AI companies, assuming that they didn’t pursue efficiency because they couldn’t, and not because they couldn’t be bothered.
You see, the pre-DeepSeek status quo was one where several truths allowed the party to keep going:
Now, I've argued for a while that the latter plan was insane — that there was no path to profitability for these Large Language Models, as I believed there simply wasn't a way to make these models more efficient.
In a way, I was right. The current models developed by both the hyperscalers (Gemini, Llama, et. al) and multi-billion-dollar "startups" like OpenAI and Anthropic are horribly inefficient, I had just made the mistake of assuming that they'd actually tried to make them more efficient.
What we're witnessing is the American tech industry's greatest act of hubris — a monument to the barely-conscious stewards of so-called "innovation," incapable of breaking the kayfabe of "competition" where everybody makes the same products, charges about the same amount, and mostly "innovates" in the same direction.
Somehow nobody — not Google, not Microsoft, not OpenAI, not Meta, not Amazon, not Oracle — thought to try, or was capable of creating something like DeepSeek, which doesn't mean that DeepSeek's team is particularly remarkable, or found anything new, but that for all the talent, trillions of dollars of market capitalization and supposed expertise in America's tech oligarchs, not one bright spark thought to try the things that DeepSeek tried, which appear to be "what if we didn't use as much memory and what if we tried synthetic data."
And because the cost of model development and inference was so astronomical, they never assumed that anyone would try to usurp their position. This is especially bad, considering that China’s focus on AI as a strategic part of its industrial priority was no secret — even if the ways it supported domestic companies was. In the same way that the automotive industry was blindsided by China’s EV manufacturers, the same is now happening to AI.
Fat, happy and lazy, and most of all, oblivious, America's most powerful tech companies sat back and built bigger, messier models powered by sprawling data centers and billions of dollars of NVIDIA GPUs, a bacchanalia of spending that strains our energy grid and depletes our water reserves without, it appears, much consideration of whether an alternative was possible. I refuse to believe that none of these companies could've done this — which means they either chose not to, or were so utterly myopic, so excited to burn so much money and so many parts of the Earth in pursuit of further growth, that they didn't think to try.
This isn't about China — it's so much fucking easier if we let it be about China — it's about how the American tech industry is incurious, lazy, entitled, directionless and irresponsible. OpenAi and Anthropic are the antithesis of Silicon Valley. They are incumbents, public companies wearing startup suits, unwilling to take on real challenges, more focused on optics and marketing than they are on solving problems, even the problems that they themselves created with their large language models.
By making this "about China" we ignore the root of the problem — that the American tech industry is no longer interested in making good software that helps people.
DeepSeek shouldn't be scary to them, because they should've come up with it first. It uses less memory, fewer resources, and uses several quirky workarounds to adapt to the limited compute resources available — all things that you'd previously associate with Silicon Valley, except Silicon Valley's only interest, like the rest of the American tech industry, is The Rot Economy. It cares about growth at all costs, even if said costs were readily mitigable, or if the costs are ultimately self-defeating.
To be clear, if the alternative is that all of these companies simply didn't come up with this idea, that in and of itself is a damning indictment of the valley. Was nobody thinking about this stuff? If they were, why didn't Sam Altman, or Dario Amodei, or Satya Nadella, or anyone else put serious resources into efficiency? Was it because there was no reason to? Was it because there was, if we're honest, no real competition between any of these companies? Did anybody try anything other than throwing as much compute and training data at the model as possible?
It's all so cynical and antithetical to innovation itself. Surely if any of this shit mattered — if generative AI truly was valid and viable in the eyes of these companies — they would have actively worked to do something like DeepSeek.
Don't get me wrong, it appears DeepSeek employed all sorts of weird tricks to make this work, including taking advantage of distinct parts of both CPU and GPU to create a virtual Digital Processing Unit, essentially redefining how data is communicated within the servers running training and inference. It had to do things that a company with unrestrained access to capital and equipment wouldn’t have to do.
Nevertheless, OpenAI and Anthropic both have enough money and hiring power to have tried — and succeeded — in creating a model this efficient and capable of running on older GPUs, except what they actually wanted was more rapacious growth and the chance to build even bigger data centers with even more compute. OpenAI has pledged $19 billion to fund the "Stargate" data center — an amount it is somehow going to raise through further debt and equity raises, despite the fact that it’s likely already in the process of raising another round as we speak just to keep the company afloat.
OpenAI is as much a lazy, cumbersome incumbent as Google or Microsoft, and it’s just as innovative too. The launch of its "Operator" "agent" was a joke — a barely-functional product that is allegedly meant to control your computer and take distinct actions, but doesn't seem to work. Casey Newton, a man so gratingly credulous that it makes me want to scream, of course wrote that it was a "compelling demonstration" that "represented an extraordinary technological achievement" that also somehow was "significantly slower, more frustrating, and more expensive than simply doing any of these tasks yourself."
Casey, of course, had some thoughts about DeepSeek — that there were reasons to be worried, but that "American AI labs [were] still in the lead," saying that DeepSeek was "only optimizing technology that OpenAI and others invented first," before saying that it was "only last week that OpenAI made available to Pro plan users a computer that can use itself," a statement bordering on factually incorrect.
Let's be frank: these companies aren't building shit. OpenAI and Anthropic are both limply throwing around the idea that "agents are possible" in an attempt to raise more money to burn, and after the launch of DeepSeek, I have to wonder what any investor thinks they're investing in.
OpenAI can't simply "add on" DeepSeek to its models, if not just for the optics. It would be a concession. An admittal that it slipped and needs to catch up, and not to its main rival, or to another huge tech firm, but to a company that few, before last weekend, had even heard of. And this, in turn, will make any investor think twice about writing the company a blank check — which, as I’ve said ad nauseum, is potentially fatal, as OpenAI needs to continually raise more money than any startup ever has in history, and it has no path to breaking even.
If OpenAI wants to do its own cheaper, more-efficient model, it’ll likely have to create it from scratch, and while it could do distillation to make it "more OpenAI-like" using OpenAI's own models, that's effectively what DeepSeek already did. Even with OpenAI's much larger team and more powerful hardware, it's hard to see how creating a smaller, more-efficient, and almost-as-powerful version of o1 benefits the company, because said version has, well, already been beaten to market by DeepSeek, and thanks to DeepSeek will almost certainly have a great deal of competition for a product that, to this day, lacks any real killer apps.
And, again, anyone can build on top of what DeepSeek has already built. Where is OpenAI's moat? Where is Anthropic's moat? What are the things that truly make these companies worth $60 or $150 billion? What is the technology they own, or the talent they have that justifies these valuations, because it's hard to argue that their models are particularly valuable anymore.
Celebrity, perhaps? Altman, as discussed previously, is an artful bullshitter, having built a career out of being in the right places, having the right connections, and knowing exactly what to say — especially to a credulous tech media without the spine or inclination to push back on his more fanciful claims. And already, Altman has tried to shrug off DeepSeek’s rise, admitting that while “deepseek's r1 is an impressive model,” particularly when it comes to its efficiency, “[OpenAI] will obviously deliver much better models and also it's legit invigorating to have a new competitor!”
He ended with “look forward to bringing you all AGI and beyond” — something which, I add, has always been close on the horizon in Altman’s world, although curiously has yet to materialize, or even come close to materializing.
Altman is, in essence, the Muhammad Saeed al-Sahhaf of tech — the Saddam-era Iraqi Minister of Information who, as Abrams tanks entered Baghdad and gunfire could be heard in the background, proclaimed an entirely counterfactual world where the coalition forces weren’t merely losing, but American troops were “committing suicide by the hundreds on the gates of Baghdad.” It’s adorable, and yes, it’s also understandable, but nobody should — or could — believe that OpenAI hasn’t just suffered some form of existential wound.
DeepSeek has commoditized the Large Language Model, publishing both the source code and the guide to building your own. Whether or not someone chooses to pay DeepSeek is largely irrelevant — someone else will take what it’s created and build their own, or people will start running their own DeepSeek instances renting GPUs from one of the various cloud computing firms.
While NVIDIA will find other ways to make money — Jensen Huang always does — it's going to be a hard sell for any hyperscaler to justify spending billions more on GPUs to markets that now know that near-identical models can be built for a fraction of the cost with older hardware. Why do you need Blackwell? The narrative of "this is the only way to build powerful models" no longer holds water, and the only other selling point it has is "what if the Chinese do something?"
Well, the Chinese did something, and they've now proven that they can not only compete with American AI companies, but do so in such an effective way that they can effectively crash the market.
It still isn't clear if these models are going to be profitable — as discussed, it's unclear who funds DeepSeek and whether its current pricing is sustainable — but they are likely going to be a damn sight more profitable than anything OpenAI is flogging. After all, OpenAI loses money on every transaction — even its $200-a-month "ChatGPT Pro" subscription. And if OpenAI cuts its prices to compete with DeepSeek, its losses will only deepen.
And as I’ve said above, this is all so deeply cynical, because it’s obvious that none of this was ever about the proliferation of generative AI, or making sure that generative AI was “accessible.”
Putting aside my personal beliefs for a second, it’s fairly obvious why these companies wouldn’t want to create something like DeepSeek — because creating an open source model that uses less resources means that OpenAI, Anthropic and their associated hyperscalers would lose their soft monopoly on Large Language Models.
I’ll explain.
Before DeepSeek, to make a competitive Large Language Model — as in one that you can commercialize — required exceedingly large amounts of capital, and to make larger ones effectively required you to kiss the ring of Microsoft, Google or Amazon. While it isn’t clear what it cost to train OpenAI’s o1 reasoning model, we know that GPT-4o cost around $100 million, and o1, as a more complex model, would likely cost even more.
We also know that OpenAI’s training and inference costs in 2024 were around $7 billion, meaning that either refining current models or building new ones is quite costly.
The mythology of both OpenAI and Anthropic is that these large amounts of capital weren’t just necessary, but the only way to do this. While these companies ostensibly “compete,” neither of them seemed concerned about doing so as actual businesses that made products that were, say, cheaper and more efficient to run, because in doing so they would break the illusion that the only way to create “powerful artificial intelligence” was to hand billions of dollars to one of two companies, and build giant data centers to build even larger language models.
This is artificial intelligence’s Rot Economy — two lumbering companies claiming they’re startups creating a narrative that the only way to “build the future” is to keep growing, to build more data centers, to build larger language models, to consume more training data, with each infusion of capital, GPU purchase and data center buildout creating an infrastructural moat that always leads back to one of a few tech hyperscalers.
OpenAI and Anthropic need the narrative to say “buy more GPUs and build more data centers,” because in doing so they create the conditions of an infrastructural monopoly, because the terms — forget about “building software” that “does stuff” for a second — were implicitly that smaller players can’t enter the market because “the market” is defined as “large language models that cost hundreds of millions of dollars and require access to more compute than any startup could access without the infrastructure of a public tech company.”
Remember, neither of these companies has ever marketed themselves based on the products they actually build. Large Language Models are, in and of themselves, a fairly bland software product, which is why we’re yet to see any killer apps. This isn’t a particularly exciting pitch to investors or the public markets, because there’s no product, innovation or business model to point to, and if they’d actually try and productize it and turn it into a business, it’s quite obvious at this point that there really isn’t a multi-trillion dollar industry for generative AI.
Indeed, look at the response to Microsoft’s strong-arming of co-pilot on Office 365 users, both personal and commercial. Nobody said “wow, this is great.” Lots of people asked “why am I being charged significantly more for a product that I don’t care about?”
OpenAI only makes 27% of its revenue from selling access to its models — around a billion dollars in annual recurring revenue — with the rest ($2.7 billion or so) coming from subscriptions to ChatGPT. If you ignore the hype, OpenAI and Anthropic are deeply boring software businesses with unprofitable, unreliable products prone to hallucinations, and their new products — such as OpenAI’s Sora — cost way too much money to both run and train to get results that, well, suck. Even OpenAI’s push into the federal government, with the release of ChatGPT Gov, is unlikely to reverse its dismal fortunes.
The only thing that OpenAI and Anthropic could do is sell the market a story about a thing it’s yet to build (such as AI that will somehow double human lifespans), and heavily intimate (or outright say) that the only way to build these made-up things was to keep funnelling billions to their companies and, by extension, that hyperscalers would have to keep funnelling billions of dollars to NVIDIA and into building data centers to crunch numbers in the hope that this wonderful, beautiful and entirely fictional world would materialize.
To make this *more* than a deeply boring software business, OpenAI and Anthropic needed models to get larger, and for the story to always be that there was only one way to build the future, that it cost hundreds of billions of dollars, and that only the biggest geniuses (who all happen to work at the same two or three places) were capable of doing it.
Post-DeepSeek, there isn’t really a compelling argument for investing hundreds of billions of capital expenditures in data centers, buying new GPUs, or even pursuing Large Language Models as they currently stand. It’s possible — and DeepSeek, through its research papers, explained in detail how — to build models competitive with both of OpenAI’s leading models, and that’s assuming you don’t simply build on top of the ones DeepSeek released.
It also seriously calls into question what it is you’re paying OpenAI for in its various subscriptions — most of which (other than the $200-a-month “Pro” subscription) have hard limits on how much you can use OpenAI’s most advanced reasoning models.
One thing we do know is that OpenAI and Anthropic will now have to either drop the price of accessing their models, and potentially even the cost of their subscriptions. I’d argue that despite the significant price difference between o1 and DeepSeek’s r1 reasoning model, the real danger to both OpenAI and Anthropic is DeepSeek v3, which competes with GPT-4o.
DeepSeek’s narrative shift isn’t just commoditizing LLMs at large, but commoditizing the most expensive ones run by two monopolists backed by three other monopolists.
Fundamentally, the magic has died. There’s no halo around Sam Altman or Dario Amodei’s head anymore, as their only real argument was “we’re the only ones that can do this,” something that nobody should’ve believed in the first place.
Up until this point, people believed that the reason these models were so expensive was because they had to be, and that we had to build more data centers and buy more silicon because that was just how things were. They believed that “reasoning models” were the future, even if members of the media didn’t really seem to understand what they did or why they mattered, and that as a result they had to be expensive, because OpenAI and their ilk were just so smart, even though it wasn’t obvious what it was that “reasoning” allowed you to do.
Now we’re going to find out, because reasoning is commoditized, along with Large Language Models in general. Funnily enough, the way that DeepSeek may have been trained — using, at least in part, synthetic data — also pushes against the paradigm that these companies even need to use other people’s training data, though their argument, of course, will be that they “need more.”
We also don’t know the environmental effects, because even if it’s cheaper, these models still require expensive, energy-guzzling GPUs to run at full-tilt.
In any case, if I had to guess, the result will be the markets accepting that generative AI isn’t the future. OpenAI and Anthropic no longer have moats to raise capital with. Sure, they could con another couple of billion dollars out of Masayoshi Son and other gormless billionaires, but what’re they offering, exactly? The chance to continue an industry-wide con? The chance to participate in the capitalist death cult? The chance to burn money at a faster rate than WeWork ever did?
Or will this be the time that Microsoft, Amazon and Google drop OpenAI and Anthropic, making their own models based on DeepSeek’s work? What incentive is there for them to keep funding these companies? The hyperscalers hold all the cards — the GPUs and the infrastructure, and in the case of Microsoft, non-revocable licenses that permits it unfettered use and access to OpenAI’s tech — and there’s little stopping them building their own models and dumping GPT and Claude.
As I’ve said before, I believe we’re at peak AI, and now that generative AI has been commoditized, the only thing that OpenAI and Anthropic have left is their ability to innovate, which I’m not sure they’re capable of doing.
And because we sit in the ruins of Silicon Valley, with our biggest “startups” all doing the same thing in the least-efficient way, living at the beck and call of public companies with multi-trillion-dollar market caps, everyone is trying to do the same thing in the same way based on the fantastical marketing nonsense of a succession of directionless rich guys that all want to create America’s Next Top Monopoly.
It’s time to wake up and accept that there was never an “AI arms race,” and that the only reason that hyperscalers built so many data centers and bought so many GPUs because they’re run by people that don’t experience real problems and thus don’t know what problems real people face. Generative AI doesn’t solve any trillion-dollar problems, nor does it create outcomes that are profitable for any particular business.
DeepSeek’s models are cheaper to run, but the real magic trick they pulled is that they showed how utterly replaceable a company like OpenAI (and by extension any Large Language Model company) really is. There really isn’t anything special about any of these companies anymore — they have no moat, their infrastructural advantage is moot, and their hordes of talent irrelevant.
What DeepSeek has proven isn’t just technological, but philosophical. It shows that the scrappy spirit of Silicon Valley builders is dead, replaced by a series of different management consultants that lead teams of engineers to do things based on vibes.
You may ask if all of this means generative AI suddenly gets more prevalent — after all, Satya Nadella of Microsoft quoted Jevons paradox, which posits that when resources are made more efficient their use increases.
Sadly, I hypothesize that something else happens. Right now, I do not believe that there are companies that are stymied by the pricing that OpenAI and their ilk offer, nor do I think there are many companies or use cases that don’t exist because Large Language Models are too expensive. AI companies took up a third of all venture capital funding last year, and on top of that, it’s fairly easy to try reasoning models like o1 and make a proof of concept without having to make an entire operational company. I don’t think anyone has been “on the sidelines” of generative AI due to costs, (and remember, few seemed to be able to come up with a great use case for o1 or other reasoning models), and DeepSeek’s models, while cheaper, don’t have any new functionality.
Chaos Hypothetical! One way in which this entire facade could fall is if Mark Zuckerberg decides that he wants to simply destroy the entire market for Large Language Models. Meta has already formed four separate war rooms to break down how DeepSeek did it, and apparently, to quote The Information, “In pursuing Llama, CEO Mark Zuckerberg wants to commoditize AI models so that the applications that use such models, including Meta’s, generate more money than the sales of the AI models themselves. That could hurt Meta’s AI rivals such as OpenAI and Anthropic, which are on pace to generate billions of dollars in revenue from such sales.”
I could absolutely see Meta releasing its own version of DeepSeek’s models — it has the GPUs and Zuckerberg can never be fired, meaning that if he decided to simply throw billions of dollars into specifically creating his own deep-discounted LLMs to wipe out OpenAI he absolutely could. After all, last Friday Zuckerberg said that Meta would spend between $60 billion and $65 billion in capital expenditures this year — before the DeepSeek situation hit fever pitch — and I imagine the markets would love a more modest proposal that involves Meta offering a ChatGPT-beater simply to fuck over Sam Altman.
As a result, I don’t really see anything changing, beyond the eventual collapse of the API market for companies like Anthropic and OpenAI. Large Language Models (and reasoning models) are niche. The only reason that ChatGPT became such a big deal is because the tech industry has no other growth ideas, and despite the entire tech industry and public markets screaming about it, I can’t think of any major mass-market product that really matters.
ChatGPT is big because “everybody is talking about AI,” and ChatGPT is the big brand in AI. It’s not essential, and it’s only been treated as such because the media (and the markets) ran away with a narrative they barely understood. DeepSeek pierced that narrative because believing it also required you to believe that Sam Altman is a magician, versus an extremely shitty CEO that burned a bunch of money.
Sure, you can argue that “DeepSeek just built on top of software that already existed thanks to OpenAI,” which begs a fairly obvious question: why didn’t OpenAI? And another fairly obvious question: why does it matter?
In any case, the massive expense of running generative models hasn’t been the limiter on their deployment or success — you can blame that on the fact that they, as a piece of technology, are neither artificial intelligence nor capable of providing the kind of meaningful outcomes that would make them the next Smartphone.
It’s all been a con, a painfully-obvious one, one I’ve been screaming about since February 2024, trying to explain that beneath the hype was an industry that provided modest-at-best outcomes rather than resembling any kind of “next big thing.”
Without “reasoning” as its magical new creation, OpenAI has nothing left. “Agents” aren’t coming. “AGI” isn’t coming. It was all flimflam to cover up how mediocre and unreliable the fundament of the supposed “AI revolution” really was.
All of this money, time, energy and talent was wasted thanks to a media industry that fails to hold the powerful to account, and markets run by executives that don’t know much of anything, and it looks like it got broken in two the moment that a few hundred Chinese engineers decided to compete.
It’s utterly sickening.
2025-01-18 03:47:33
In the last week we've seen the emergence of the true Meta — and the true Mark Zuckerberg — as the company ended its fact-checking program, claiming that (and I quote) "fact checkers have been too politically biased and have destroyed more trust than they've created" on both Instagram and Facebook, the latter of which was shown in a study from George Washington University to, by design, "afford antivaccine content producers several means to circumvent the intent of misinformation removal policies." Meta has also killed its diversity, equity and inclusion programs.
Shortly after announcing the policies, Zuckerberg went on the Joe Rogan Experience and had a full-scale pissfit, claiming that corporations are "culturally neutered" and that companies should have both "more masculine energy" and "[have a culture] that celebrates the aggression a bit more," adding that said culture would "[have] its own merits that are really positive." Zuckerberg believes that modern corporate culture has somehow framed masculinity as bad, something he does not attempt to elaborate upon, frame with any kind of evidence (after all, it's Joe Rogan), or really connect to anything besides a sense of directionless grievance.
This means that Meta has now "[gotten rid of] a number of restrictions on topics like immigration, gender identity and gender that are the subject of frequent political discourse and debate," which in practice means that Meta now allows you to say that being gay is a mental illness or describe immigrants as "filth." Casey Newton at Platformer — who I have been deeply critical of (and will be later in this piece!) — has done an excellent job reporting on exactly how horrifying these policies are, revealing how Meta's internal guidelines allow Facebook users to say that trans people are both mentally ill and don't exist (to be clear, if you feel this way, stop reading and take a quick trip to the garage with your car keys), and included one of the most wretched things I've ever read: that Alex Schultz, Meta's CMO, a gay man, "suggested in an internal post that people seeing their queer friends and family members abused on Facebook and Instagram could lead to increased support for LGBTQ rights."
This is the kind of social network that Mark Zuckerberg wants — an unrestrained, unfiltered, unrepentantly toxic and noxiously heteronormative, one untethered by the frustrating norms of "making sure that a social network of billions of people doesn't actively encourage hate of multiple different marginalized groups."
Finally, Mark Zuckerberg can do whatever he wants, as opposed to the past 20 years, where it's hard to argue that he's faced an unrelenting series of punishments. Zuckerberg's net worth recently hit $213 billion, he's running a company with a market capitalization of over $1.5 trillion that he can never be fired from, he owns a 1400-acre compound in Hawaii, and while dealing with all this abject suffering, he was forced to half-heartedly apologize during a senate hearing where he was tortured (translation: made to feel slightly uncomfortable) after only having six years to recover from the last time when nothing happened to him in a senate hearing.
Sarcasm aside, few living people have had it easier than Mark Zuckerberg, a man who has been insulated from consequence, risk, and responsibility for nearly twenty years. The sudden (and warranted) hysteria around these monstrous changes has an air of surprise, framing Meta (and Zuckerberg's) moves as a "MAGA-tilt" to "please Donald Trump," which I believe is a comfortable way to frame a situation that is neither sudden nor surprising.
Mere months ago, the media was fawning over Mark Zuckerberg's new look, desperate to hear about why he's wearing gold chains, declaring that he had "the swagger of a Roman emperor" and that he had (and I quote the Washington Post) transformed himself from "a dorky, democracy-destroying CEO into a dripped-out, jacked AI accelerationist in the eyes of potential Meta recruits." Zuckerberg was, until this last week, being celebrated for the very thing people are upset about right now — flimsy, self-conscious and performative macho bullshit that only signifies strength to weak men and those credulous enough to accept it, which in this case means "almost every major media outlet." The only thing he did differently this time was come out and say it. After all, there was no punishment or judgment for his last macho media cycle, and if anything he proved that many will accept whatever he says in whatever way he does it.
Yet I want to be clear that what you're seeing with Meta — and by extension Zuckerberg — is not a "sudden" move, but the direct result of a man that has never, ever been held in check. It is a fantasy to describe — or even hint — that these changes are the beginning of some sort of unrestrained Meta, rather than part of the intentional destruction of the market leader in growth-at-all-costs Rot Economics.
As I wrote in the middle of last year, Meta has spent years gradually making the experience of using its products worse in pursuit of perpetual growth. When I say "intentionally," I mean that product decisions — such as limiting the information in notifications as a means of making users and heavily promoting clickbait headlines as a means of keeping people on the site longer — have been made for years, at times in broad daylight, that have led to the deterioration of the core experiences of both Facebook and Instagram.
Some are touting Zuckerberg's current move as some sort of master plan to appease Trump and conservatives, suggesting that this is a "MAGA-fication" of these platforms, where conservatives will somehow be given preferential treatment, such as, say, Facebook's recommendation engine promoting dramatically more conservative media than other sources.
Which is something that already fucking happened!
In 2020, journalist Kevin Roose created an automated Twitter account called "Facebook's Top 10," listing the top-performing (as in what posts were shared, viewed and commented on the most) link posts by U.S. Facebook pages on a daily basis, something he was able to do by connecting to Facebook's "CrowdTangle" data analytics tool, a product created for researchers and journalists to understand what was happening on the world's largest social network.
Roose's reporting revealed that Meta's top-performing links regularly skewed toward right wing influencers like Dan Bongino, Ben Shapiro and Sean Hannity, outlets like Fox News, and the page of president-elect Donald Trump. Internally, Meta was freaking out, suggesting to Roose that "engagement" was a misleading measurement of what was popular on Facebook, suggesting the real litmus test being "reach," as in how many people saw it. Roose also reported that internal arguments at Meta led it to suggesting it’d make a Twitter account of its own that had a more "balanced" view based on internal data.
Meta even suggested the obvious answer — sharing reach data — would vindicate its position, only for CrowdTangle's CEO to add that "false and misleading news stories also rose to the top of those lists."
These stories danced around one important detail: that these stories were likely recommended by Facebook's algorithm, which has reliably and repeatedly skewed conservative for many years. A study in The Economist from September 2020 found that the most popular American media outlets on Facebook in a one month period were Breitbart and Fox News, and that both Facebook page engagements and website views heavily skewed conservative.
While one could argue that this might be the will of the users, what a user sees on Facebook is almost entirely algorithmic, and thus it's reasonable to assume that said algorithm was deliberately pushing conservative content.
At this time, Meta's Head of Public Policy was Joel Kaplan, a man whose previous work involved working as George W. Bush's Deputy Chief of Staff for Policy, as well as handling public policy and affairs for Energy Future Holdings, which involved three private equity firms buying Texas power company TXU for $45 billion and immediately steering it into bankruptcy due to the $38.7 billion in debt Energy Future Holdings was forced to take on as a means of acquiring TXU.
Jeff Horwitz reports in his book Broken Code that Kaplan personally intervened when Facebook's health team attempted to remove COVID conspiracy movie Plandemic from its recommendation engine, and Facebook only did so once Roose reported that it was the most-engaged link in a 24 hour period.
Naturally, Meta's choice wasn't to "fix things" or "improve" or "take responsibility." By the end of 2021, Meta had disbanded the team that ran CrowdTangle, and in early 2022, the company had stopped registering new users. In early 2024 — months before the 2024 elections — CrowdTangle was officially shut down, though Facebook Top 10 had stopped working in the middle of 2023.
Meta hasn't "made a right-wing turn." It’s been an active arm of the right wing media for nearly a decade, actively empowering noxious demagogues like Alex Jones, allowing him to evade bans and build massive private online groups on the platform to disseminate content. A report from November 2021 by Media Matters found that Facebook had tweaked its news algorithm in 2021, helping right-leaning news and politics pages to outperform other pages using "sensational and divisive content." Another Media Matters report from 2023 found that conservatives were continually earning more total interactions than left or non-aligned pages between January 1 2020 and December 31 2022, even as the company was actively deprioritizing political content.
A 2024 report from non-profit GLAAD found that Meta had continually allowed widespread anti-trans hate content across Instagram, Facebook, and Threads, with the company either claiming that the content didn't violate its community standards or ignoring reports entirely. While we can — and should — actively decry Meta's disgusting new standards, it's ahistorical to pretend that this was a company that gave a shit about any of this stuff, or took it seriously, or sought to protect marginalized people.
It's a testament to the weak and inconsistent approach that the media has taken to both Meta and Mark Zuckerberg that any of these changes are being framed as anything other than Meta formalizing the quiet reality of its platforms: that whatever gets engagement is justified, even if it's hateful, racist, violent, cruel, or bigoted.
To quote Facebook's then-Vice President Andrew Bosworth in an internal email from 2017, "all the work that [Facebook] does in growth is justified," even if it's bullying, or a terrorist attack, and yes, that's exactly what he said. One might think that Bosworth would be fired for sending this email.
He's now Meta's Chief Technology Officer.
Sidenote: On the subject of bullying, my editor just told me something. His Facebook newsfeed is now filled with random posts from companies celebrating their workers of the month, registry offices (government buildings in the UK where you can get married), and so on. These posts, which are pushed to a random global audience, inevitably attract cruelty. One post from a registry office somewhere in Yorkshire attracted hundreds of comments, with many mocking the appearance of the newly-married couple.
But hey, engagement is engagement, right?
It's time to stop pretending that this company was ever something noble, or good, or well-meaning. Mark Zuckerberg is a repressed coward, as far from "manly" as one can get, because true masculinity is a sense of responsibility for both oneself and others, and finding strength in supporting and uplifting them with sincerity and honesty.
Meta, as an institution, has been rotten for years, making trillions of dollars as it continually makes the service worse, all to force users to spend more time on the site, even if it's because Facebook and Instagram are now engineered to interrupt users' decisionmaking and autonomy with a constant slew of different forms of sponsored and algorithmicly-curated content.
The quality of the experience — something the media has categorically failed in covering — has never been lower on either Facebook or Instagram. I am not sure how anyone writing about this company for the last few years has been able to do so with a straight face. The products suck, they're getting worse, and yet the company has never been more profitable. Facebook is crammed with fake accounts, AI-generated slop, and nonsense groups teeming with boomers posting that they "wish we'd return to a culture of respect" as they're recommended their third racist meme of the day. Instagram is a carousel of screen-filling sponsored and recommended content, and users are constantly battling with these products to actually see the things that they log on to see.
I want to be explicit here: for years, using Facebook has been a nightmare for many, many years.
Sidebar: No, really, I need you to look at this a little harder than you have been.
You log in, immediately see a popup for stories, scroll down and see one post from someone you know, a giant ad, a carousel of "people you may know," a post from a page you don’t follow, a series of recommended "reels" that show a two-second clip on repeat of what you might see so that you have to click through, another ad, three posts from pages you don’t follow, then another ad.
Searching for "Facebook support" leads you to a sponsored post about Facebook "bringing your community together," and then a selection of groups, the first of which is called "Facebook Support" with 18,000 members including a support number (1-800-804-9396) that does not work. The group is full of posts about people having issues with Facebook, with one by an admin called "Oliver Green" telling everyone that this group is where they can "discuss issues and provide assistance and solutions to them." Oliver Green's avatar is actually a picture of writer Oliver Darcy.
One post says "please don't respond to messages from my Facebook, I was hacked," with one responder — "Decker Techfix" — saying "when was it hacked" and asking them to message him now for quick recovery of an account they appear to be posting with.
Another, where a user says "someone hacked my Facebook and changed all password," is responded to by an account called "Ree TechMan," who adds "Inbox me now for help." Another, where someone also says they were hacked, has another account — James Miles — responding saying "message me privately." There are hundreds of interactions like these.
Another group called "Account Hacked" (which has 8500 members and hasn't been updated since late 2023) immediately hits you with a post that says "message me for any hacking services Facebook recovery Instagram recovery lost funds recovery I cloud bypass etc," with a few users responding along with several other scammers offering to help in the same way.
Another group with 6700 members called "Recover an old Facebook account you can't log into" offers another 1800 number that doesn't work. A post from December 5 2023 from a user claiming their account was compromised and their email and password was changed has been responded to 44 times, mostly by scammers attempting to offer account recovery services, but a few times by actual users.
Elsewhere, a group promising to literally send you money on PayPal has 24,000 members and 10+ posts a day. Another, called "Paypal problem solution," offers similarly scammy services if you can't get into Paypal. Another called "Cash App Venmo Paypal Zelle Support" has 5800 members.
This is what Facebook is — a decrepit hole of sponsored content and outright scams. Meta has been an atrocious steward of its product, it has been fucking awful for years, and it's time to wake up.
And, to repeat myself, this has been the case for years. Meta has been gradually (yet aggressively) reducing the quality of the Facebook and Instagram experience with utter disregard for user safety, all while flagrantly ignoring its own supposed quality and safety standards.
It's a comfortable lie to say that Meta has "suddenly" done something, because it gives the media (and society at large) air cover for ignoring a gaping wound in the internet. Two of the world's largest social networks are run — and have been run — with a blatant contempt for the user, misinforming and harming people at scale, making the things they want to see harder to find as they're swamped by an endless stream of sponsored and recommended content that either sells them something or gets them to engage further with the platform with little care whether the engagement is positive, fun or useful.
Casey Newton of Platformer has done an admirable job covering Meta in the last two weeks, but it's important to note that he was cheerfully covering Zuckerberg's "expansive view of the future" as recently as September 25, 2024, happily publishing how "Zuckerberg was back on the offensive," somehow not seeing anything worrying about Zuckerberg's t-shirt referencing Julius Caesar, the historic dictator that perpetuated a genocide in the Gallic wars.
Aside: Personally, I would have said Zuckerberg is more of a Nero type, fiddling (read: chasing AI and the metaverse, and dressing like Kevin Federline) while Rome (read: Facebook and Instagram) burned.
Newton felt it unnecessary to mention the utterly atrocious quality of Facebook while quoting Zuck saying things like "in every generation of technology, there is a competition of ideas for what the future should look like."
Yet the most loathsome thing Newton published was this:
But it left unsaid what seemed to be the larger point, which is that Zuckerberg intends to crush his rivals — particularly Apple — into a fine pulp. His swagger on stage was most evident when discussed the company's next-generation glasses as the likeliest next-generation computing platform, and highlighted the progress that Meta had made so far in overcoming the crushing technological burdens necessary for that to happen.
And it also failed to capture just how personal all this seems to him. Burned by what he has called the 20-year mistake of the company's reaction to the post-2016 tech backlash, and long haunted by criticisms that Meta has been nothing more than a competition-crushing copycat since it released the News Feed, Zuckerberg has never seemed more intent on claiming for himself the mantle of innovator.
This is paper tiger analysis — stenography for the powerful, masked as deep thoughts. This specific paragraph is exactly where Newton could've said something about how worrying Zuckerberg modeling himself on a Roman emperor was. How this company was, despite oinking about how it’s building the future, letting its existing products deteriorate as it deliberately turned the screws to juice engagement. Casey Newton has regularly and reliably turned his back on the truth — that Meta's core products are really quite bad — in favor of pumping up various artificial intelligence products and vague promises about a future that just arrived and fucking sucks.
The reason I am singling Newton out is that it is very, very important to hold the people that helped Zuckerberg succeed accountable, especially as they attempt to hint that they've always been an aggressive advocate for the truth. Newton is fully capable of real journalism — as proven by his recent coverage — but has chosen again and again to simply print whatever Mark Zuckerberg wants to say.
I am also going hard in the paint against Newton because of something else he wrote at the end of last year — "The phony comforts of AI skepticism" — where Newton sloppily stapled together multiple different pieces of marketing collateral to argue that not only was AI the future, but those that critiqued it were doing so in a cynical, corrupt way, singling out independent critic Gary Marcus. I'm not going to go through it in detail — Edward Ongweso Jr. already did so — but I think there are far better comforts for Newton than his ambiguous yet chummy relationship with the powerful.
I also did not like something Newton said at the end of a (paywalled) blog about what he learned from the reaction to his piece about AI skeptics. Newton said, and I quote, that he was "taking detailed notes on all bloggers writing ’financial analyses’ suggesting that OpenAI will go bankrupt soon because it's not profitable yet."
I do not like bullies, and I do not like threats. Suggesting one is "taking detailed notes on bloggers" is an attempt to intimidate people that are seriously evaluating the fact that OpenAI burns $5 billion a year and has no path to profitability. I don't know if this is about me, nor do I particularly care.
I have, however, been taking detailed notes on Casey Newton for quite some time.
While Newton's metaverse interview from 2021 was deeply irresponsible in how much outright nonsense it printed, arguably his most disgraceful was his October 26 2021 piece called "The Facebook Papers' missing piece" — an interview with an anonymous "integrity worker" that attempted to undermine the Wall Street Journal's Facebook Files (that revealed in that "Facebook Inc. knows, in acute detail, that its platforms are riddled with flaws that cause harm, often in ways only the company fully understands"), in a seeming attempt to discredit (by proxy) the bravery of Frances Haugen, the whistleblower that provided the documents that led to this reporting. It's utterly repulsive corporate hand-washing, and it's important context for any criticism he has of Meta going forward, especially if he tries to suggest he's always held it to account.
I think Newton seems a little more comfortable with the powerful's messaging than he should. He's argued that while AI companies have hit a scaling wall, it's actually okay, that NFTs went finally mainstream in 2022, that Clubhouse was the future, that live audio was Zuckerberg's "big bet on creators" in 2021 due to "the shift in power from institutions to individuals" (Facebook shut down its podcast service a year later), that — and I cannot see the full text here, because it's paywalled — "metaverse pessimists were missing something" because "Meta's grand vision [was] already well on its way" in November 2021, that Meta's leadership changed its mind about its name because "Facebook [was] not the future of the company," that Axie Infinity — a crypto/web3 Pokémon clone that has created its own form of indentured servitude in the global south backed by Andreessen Horowitz — was "turning gaming on its head" (the game sucks)...okay, I'll stop.
Casey has also, at times, had dalliances with criticisms — sometimes even of Facebook! No, really! — but it's hard to take them seriously when he writes a piece for The Verge about how "Google plans to win its antitrust trial," printing the legal and marketing opinion of one of the largest companies in the world knowing full-well that the Department of Justice would not get such an opportunity.
In emails revealed in the Department of Justice's antitrust trial against Google Search, Google specifically mentioned having briefed Newton with the intention of, and I quote, "look[ing] for ways to drive headlines on [Google's] own terms." In Newton's defense, this is standard PR language, but it is, within the context of what I'm writing, hard to ignore.
The reason I am so fucking furious at Newton is he is part of the media machine that has helped repeatedly whitewash Mark Zuckerberg and his cronies, running glossy puff-pieces with scumbags like Nick Clegg, and saying things like "the transition away from Facebook’s old friends and family-dominated feeds to Meta’s algorithmic wonderland seems to be proceeding mostly without incident."
That line, I add, was published in 2023, two years after the release of The Facebook Files, which revealed that the company knew its algorithmic timeline had a propensity to push users into increasingly-radical echo chambers. And one year after Amnesty International published a report accusing Facebook’s algorithms of “supercharging the spread of harmful anti-Rohingya content in Myanmar” amidst a genocide that saw an estimated million displaced and tens of thousands massacred.
Newton has spent years using his vast platform to subtly defend companies actively making their products worse, occasionally proving he can be a real journalist (his work on the acquisition of Twitter was genuinely great), only to slip back into the comfortable pajamas of "Are We Being Too Mean To Meta?"
The media — especially people like Newton — are extremely influential over public policy and the overall way that society views the tech industry. As an experienced, knowledgeable journalist, Newton has too regularly chosen to frame "fair and balanced" as "let's make sure the powerful get their say too." As I've said — and will continue to say — Casey Newton is fully capable of doing some of the literal best journalism in tech, such as his coverage of the horrible lives of Facebook moderators, yet has, up until alarmingly recently, chosen to do otherwise.
The cost, by the way, is that the powerful have used Newton and others as mouthpieces to whitewash their contemptuous and half-baked product decisions. I don't know Newton's intentions, nor will I attempt to guess at them. What I will say is that as a result of Casey Newton's work, Mark Zuckerberg and his products have received continual promotion of their ideas and air cover for their failures, directly influencing public opinion in the process.
Worse still, Newton has acted for years like nothing was wrong with the quality of platforms themselves. Had Newton written with clarity and purpose about the erosion of quality in Facebook and Instagram, Zuckerberg would have lost a valuable way to convince the 150,000+ people that read Platformer that things were fine, that the quality was fine, that Meta is an innovative company, and that there was no reason to dislike Mark Zuckerberg.
There was, and there always will be. Putting aside the horrifying person he obviously is, Zuckerberg is a career liar and a charlatan, and has deliberately made the lives of billions of people worse as a means of increasing revenue growth.
And I believe that he's about to inspire a new era of decay, where tech executives, unburdened by their already-flimsy approach to societal norms and customer loyalty, will begin degrading experiences at scale, taking as many liberties as possible.
Meta has fired the starting gun of the Slop Society.
In an interview with the Financial Times from December 2024, Meta's Vice President of Product for Generative AI (Connor Hayes) said that Meta "expect[s] AIs to actually, over time, exist on [Meta's] platforms, kind of in the same way that accounts do," each one having their own bios and profile pictures and the ability to "generate and share content powered by AI on the platform." This came hot off the heels of Zuckerberg saying in a quarterly earnings call that Meta would "...add a whole new category of content, which is AI generated or AI summarized content or kind of existing content pulled together by AI in some way," effectively promising to drop AI slop into feeds already filled full of recommended and sponsored content that gets in the way of you seeing real human beings.
This led to a scandal where users discovered what they believed to be brand new AI profiles. Karen Attiah of the Washington Post wrote a long thread on Bluesky (and a piece in the post itself) about her experience talking to a bot she (fairly!) described as "digital blackface," with Meta receiving massive backlash for bots that would happily admit they were trained by a bunch of white people. It turns out these bots had been around for around a year in various forms but were so unpopular that nobody really noticed until the Financial Times story, leading to Meta deleting the bots, at least for now.
I am 100% sure that these chatbots will return, because it's fairly obvious that Meta intends to fill your feeds with content either entirely generated or summarized by generative AI, be it with fake profiles or content itself. AI-generated slop already dominates the platform, and as discussed earlier, the quality of the platform has fallen into utter disrepair, mostly because Meta's only concern is keeping you on the platform to show you ads or content it’s been paid to show you, even if the reason you're seeing it is because you can't find the stuff you actually want to find.
That's because all roads lead back to the Rot Economy — the growth-at-all-costs mindset that means that the only thing that matters is growth of revenue, which comes from showing as many ads to you as possible. It's almost ridiculous to call us "users" of Facebook at this point. We are the used, the punished, the terrorized, the tricked, the scammed, and the abused, constantly forced to navigate through layers of abstraction between the thing we are allegedly using and the features we'd like to use. It's farcical how little attention has been given to how bad tech products have gotten, and few have decayed as severely as Facebook thanks to its near-monopoly on social media. I, personally, would rather not use Instagram, but there are people I know that I only really speak to there, and I know many people who have the same experience.
As Zuckerberg and his ilk are intimately aware, people really don't have anywhere else to go, which — along with a lack of regulation and a compliant media — has given them permission to adjust their services to increase advertising impressions, which in practice means giving you more reasons to stay on the platforms, which means "put more things in the way of what you want to see" rather than "create more compelling experiences for users."
It's the same thing that happened with Google Search, where the revenue team pushed the search team to make search results worse as a means of increasing the amount of times people searched for things on Google — because a user that finds what they're looking for quickly spends less time on the site looking at ads. It's why the Apple App Store is so chaotically-organized and poorly-curated — Apple makes money on the advertising impressions it gets from you searching — and why so many products on the App Store have expensive and exploitative microtransactions, because Apple makes 30% off of all App Store revenue, even if the app sucks.
And I believe that Zuckerberg loosening community standards and killing fact checking is just the beginning of tech's real era of decay. Trump, and by extension those associated with him, win in part by bulldozing norms and good taste. They do things that most of us agree are bad (such as being noxious and cruel, which I realize is a dilution) and would never do, moving the Overton window (the range of acceptable things in a society) further and further into Hell in the process.
In this case, we've already seen tech's Overton window shift for years — a lack of media coverage of the actual decay of these products and a lack of accountability for tech executives (both in the media and regulation) has given companies the permission to quietly erode their services, and because everybody made things shittier over time, it became accepted practice to punish and trick customers with dark patterns (design choices to intentionally mislead people) to the point that the FTC found last year that the majority of subscription apps and websites use them.
I believe, however, that Zuckerberg is attempting to move it further — to publicly say "we're going to fill your feeds with AI slop" and "we're firing 5% of our workers because they suck" and "we're going to have AI profiles instead of real people on the site you visit to see real people's stuff" and "we don't really give a shit about marginalized people" and "people are too mean to men" knowing, in his position as the CEO of one of the largest tech companies in the world, that people will follow.
Tech has already taken liberties with the digital experience and I believe the Slop Era will be one where experiences will begin to subtly and overtly rot, with companies proudly boasting that they're making "adjustments to user engagement that will provide better business-forward outcomes," which will be code for "make them worse so that we make more money."
I realize that there's an obvious thing I'm not saying: that the Trump administration isn't going to be any kind of regulatory force against big tech. Trump is perhaps the most transactional human being ever to grace the earth. Big tech, knowing this, has donated generously to the Trump inaugural fund. It’s why Trump has completely reversed his position on TikTok, having once wanted to ban the platform, now wants to give it a reprieve because he “won youth by 34 points” and “there are those that say that TikTok has something to do with that.” Tech knows that by kissing the ring, it can do whatever it wants.
Not that previous governments have been effective at curbing the worst excesses of big tech. Outside of the last few years, and specifically the work done by the FCC under Lina Khan, antitrust against big tech has been incredibly weak, and no meaningful consumer protections exist to keep websites like Facebook or Google functional for consumers, or limit how exploitative they can be.
The media has failed to hold them accountable at scale, which has in turn allowed the Overton window to shift on quality, and now that Trump — and the general MAGAfied mindset of "you can say or do whatever you want if you do so confidently or loudly enough" — has risen to power again, so too will an era of outright selfishness and cruelty within the products that consumers use every day, except this time I believe they finally have permission to enter their slop era. I also think that Trump gives them confidence that monopolies like Facebook, Instagram, Microsoft 365 (Microsoft's enterprise monopoly over business productivity software), Google Search, and Google Advertising (remedies will take some time, and the Trump administration will likely limit any punishment it inflicts) will remain unchallenged.
What I'm describing is an era of industrial contempt for the consumer, a continuation of what I described in Never Forgive Them, where big tech decides that they will do whatever they want to customers within the boundaries of the law, but with little consideration of good taste, user happiness, or anything other than growth.
How this manifests in Facebook and Instagram will be fairly obvious. I believe that the already-decrepit state of these platforms will accelerate. Meta will push as much AI slop as it wants, both created by its generative models and their users, and massively ramp up content that riles up users with little regard for the consequences. Instagram will become more exploitative and more volatile. Instagram ads have been steadily getting more problematic, and I think Meta will start taking ads from just about anyone. This will lead to an initial revenue bump, and then, I hypothesize, a steady bleed of users that will take a few quarters to truly emerge.
Elsewhere, we're already seeing the first sign of abusive practices. Both Google and Microsoft are now forcing generative AI features onto customers, with Google grifting business users by increasing the cost of Google Workspace by $2-per-user-per-month along with AI features they don't want, and Microsoft raising the price of consumer Office subscriptions, justifying the move by adding Copilot AI features that, again, customers really don't want. The Information's Jon Victor and Aaron Holmes add that it's yet to be seen what Microsoft does with the corporate customers using Microsoft's 365 productivity suite — adding Copilot costs $30-per-user-per-month — but my hypothesis is it will do exactly the same thing that Google did to its business customers.
I should also be clear that the reason they're doing this is that they're desperate. These companies must express growth every single quarterly earnings or see their stock prices crater, and big tech companies have oriented themselves around growth as a result — meaning that they're really not used to having to compete for customers or make products they like. For over a decade, tech has been rewarded for creating growth opportunities empowered by monopolistic practices, and I'd argue that the cultures that created the products that people remember actually liking are long-dead, their evangelists strangled by the soldiers of the Rot Economy.
You are, more than likely, already seeing the signs that this is happening. The little features on the products you use that feel broken — like when you try and crop an image on iOS and it sometimes doesn't actually crop it, when the "copy link" button on Google Docs doesn't work, when a Google Search gives you a page of forum links that don't answer your question — and I expect things to get worse, possibly in new and incredibly frustrating ways.
I deeply worry that we're going to enter the most irresponsible era of tech yet — not just in the harms that companies allow or perpetuate, but in the full rejection of their stewardship for the products themselves. Our digital lives are already chaotic and poisonous, different incentives warring for our attention, user interfaces corroded by those who believe everything is justified in pursuit of growth. I fear that the subtle little problems you see every day will both multiply and expand, and that the core services we use will break down, because I believe the most powerful in big tech never really gave a shit and no longer believe they have to pretend otherwise.
