One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow. If Zitron's argument were as solid as he keeps saying it is, you would read it and understand it and see that it is solid. He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument. But no. He jumps. He leaps. He circles back. If the situation were really "Gosh why can't you see it?!"-clear, his explanation of the situation would be clear. It isn't, because it isn't.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
That's exactly what the first (titled) section does?
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
Which of the hyperlinks provided at the beginning sounded like what you wanted, and after you clicked it how did it disappoint you?
The information you are describing is stuff I would not expect anybody to repeatedly duplicate across periodic blog-posts.
Agreed. Phrases like "journalists are currently gooning over OpenAI and Anthropic" really put me off. It's a poor attempt at modern muckraking; cheeky yet offering little substance.
I didn't say it was. I'm just observing that his muckraking style is part of a very long British pundit tradition. Americans have never liked it — Intel got very upset about The Register's coverage of "the Itanic".
(And he's not Gen Z anyway is he; he's among the older millennials. He's appropriating it for muckraking purposes.)
Sure, but does that vibe invalidate the argument? What an odd time the middle of an argument is to be clutching pearls and worrying about prose quality.
Style and vibes notwithstanding, is there anything in your view that wrong with the argument itself? Could a better or more polite writer have convinced you with the same shape of logic?
I particularly enjoy reading big banners asking me to pay for a newsletter subscription if I "liked" the content. Not if I found it interesting. Not if it actually provided any value whatsoever to me. No, you just have to "like" it. In other words, it is meant to be written in an engaging way and perhaps reinforce your believes like an echo chamber or even stir up certain strong emotions. Not to convey information. So, thanks, but no. I'm sure this opinion blog is very well written, but I don't think it is more well founded than anything else in this sea of opinions that sports a bigger garbage patch than the Pacific Ocean.
Arguments have smells but rigour demands you investigate further. Zitron's smelly prose is, ironically, just the kind of stylistic distraction that AI can help condition; the further irony is that he will one day seem to have been right, for a year or two.
The money is indeed losing its mind over AI, and Zitron is a stopped clock. A correction is coming but the tool isn't going anywhere.
It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.
Today Apple launched its revamped AI offering. Judging by several reports, Apple pays Google a mere billion dollars a year to operate it.
Consumer revenue is only a smallish share of the puzzle, but still:
If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide? Why would you pay for ChatGPT, or even tolerate its inevitably increasingly desperate ad placements?
Assume Google will have similar tools in their phones, and Google search will continue to have the offering it does.
In short, where is the evidence that once Apple's tech exists, consumer AI is worth, to Anthropic or OpenAI, anything noticeably more than that $1B a year?
Maybe OpenAI strikes a deal to put something in Samsung phones. Let's say Samsung is ten times as desperate as Apple (which is how it looks, often). Still only $10B a year?
2026 consumer revenue projections from OpenAI are pitched at $14-15 billion, apparently. If they get that, it's the only year they will get that, because by late this year, everyone with an iPhone will have something useful built in.
Ed Zitron is a mouthy British rabble-rouser, but I think he is probably mostly on the money.
Right but OpenAI are for real making that prediction about their consumer revenue, which seems decidedly ambitious (considering that they are making nothing from their current phone placement). And they have said that they expect it to be quite a large share!
"OpenAI projects revenue will be divided nearly equally between its consumer and enterprise business units by 2030"
That it is so absurdly ambitious and so likely to run up against reality strikes me as really indicative of the quality of the envelopes these calculations are being sketched on.
Lots of dismissive comments ITT, very few tackling the substance of the article.
> AI Cannot Afford To Slow Down — It Needs $3 Trillion Or More In Revenue By End Of 2030 To Sustain Its Existence
Is this true?
With the total 2024 wages being 11.7 trillion USD [0], and nonfarm payrolls totaling 158,000 in the same year [1], it's an order of magnitude higher than my back of the napkin guesses I've made that AI needs to take or create 1/20 jobs minimum to break even.
If I thought there were some actual small cabal of people running the global economy, this is almost like a novel: massive amounts of money entered the economy starting in 2008 and 2020-2023, the rich became insanely wealthy. Their wealth is now all tied up in the 2020s version of the railroad/fiber, we're going to essentially erase trillions of dollars from the global economy and reset.
There's no "reset" required, other than resetting what people understand about money and finance, and having them realise that net worth is a stupid thing to use to excuse their revolutionary bloodlust.
Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely misses the incredible ground-level utility that many of us on HN celebrate every day through undeniable, massive productivity gains.
At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.
Writing about AI, destroying the planet for data centers, there's a lot of money to be made.
That being said, AI seems kind of miraculous sometimes.
Similar to cars. So enticing that we make everything else in the world worse in order to maximize the profit, make it indispensable, subsidize it, and make the dependency on it irreversible.
And it's not even something to blame individual people for.
Driving away from all the other cars to spend a weekend feels like freedom.
Using AI to answer a question feels like a "bicycle for the mind".
But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt.
Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
The environmental impact of answering a question on an obscure topic with ai model is less than an the impact of answering the question with an hour-long google search hunting for references or a drive to the public library.
That's true, and I am not anti-AI. I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.
Neither did I want to say that a car is always more wasteful than some alternative.
But defaulting to the behemoth is inefficient, unless everyone is driven to do it: then it's in some way reasonable.
By adding "corrupt" and "dependent", as well as the economic terms, I wanted to offer a broader critique and create an analogy, not just talk about energy usage on its own.
What I had in mind was: it's easier to go many places that are a mile or less from me, by car. Because everything is obstructed by cars. And I'm atrophied by lack of movement.
Best would be to drive somewhere to move/walk.
People already do that in masses.
And doing shopping by car, because everything else seems unbearable, also takes away your time, apart from wasting energy compared to more, smaller shops that would be reachable by foot, bycicle etc.
I guess you know the argument.
Today, people's thinking atrophies because their LLM is probably right in their summarization of some Wikipedia article, plus 2-3 other random sources.
Or so.
Using the Wikipedia search function is not expensive.
But, I mostly had a bigger picture in mind than what is the cost of inference.
I think it's a good analogy in many ways, and personally I think car-centric society has a lot of flaws. I think the ease that AI brings to tasks may erode mental capabilities in the same way cars have eroded our collective physical health.* That said, it doesn't seem to me that we would be better off without cars altogether, despite all the related issues.
I am concerned about the environmental impacts that AI poses, but they don't seem to me to be so catastrophic. Solar and battery tech has made enormous leaps in the past couple decades, and we will need to pivot to clean energy future irrespective of AI.
*This said, I have become gradually more alarmed over the past decade at the lack of epistemological rigor in the general public, as made apparent through the rise of social media. I don't know that AI becoming a truth-seeking crutch for people wouldn't be more good than bad.
> I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.
Hand wringing about AI datacenter's environmental impact is well and good. We should keep the data centers accountable for their consumption and waste.
I just wish the same people had been upset the last 20 years with poor water resource management in a lot of areas (the west US especially) with urban, ranching and farming development.
It's like saying if we didn't have cheap commercial flights people would travel by foot anyways and would consume more resources for food &co. than the plane would consume in fuel...
80% of generative AI queries wouldn't even exist as google searches.
To be clear, your position here is that insurmountable barriers to information is the preferable state of the world?
One claim of the parent comment was that AI is ineffective. For the purpose of finding answers to questions, it is more resource-efficient than the alternatives, and, to your point, capable of answering questions that were impossible to answer via other means before. In what way is that ineffective?
I do plenty of AI queries, both pragmatic ones and some for entertainment: witnessing talktotransformer was mind-bending already at the time! And since then, I've tried frontier models, local, coding agents, and use plenty of them on the regular.
I awe at the capabilites of generative AI.
I also enjoy sitting in or driving a car.
I did not want to make a moral argument, unless you consider each and every form of utilitarianism as moralism.
I agree with your message but not sure about the conclusion. Cars themselves are commodified luxury available (in the US pretty much required) to everyone, and they do need to be subsidized, both in terms of infrastructure and the lifestyle they require.
But with AI what is the exact price? My understanding is that R&D is extremely expensive, but running non-SOTA models is not that bad. We are getting pretty close to models which can be useful locally in many applications.
Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
Thanks for asking an open question about my point.
First, because I initially failed to answer your more closed questions (this paragraph is edited in):
> We are getting pretty close to models which can be useful locally in many applications.
Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
I don't think there's a way around making the best of AI capabilities with minimum price and maximum control, and I'd agree this is met by on-prem data centers, just not in a rationally targeted way.
Back to my original comment:
Because it (my conclusion) was not so clear, and maybe I just wanted to highlight some observations without delivering a real argument for or against things [, I thank you for your open question].
The utility/leverage aspect for AI seems more esoteric than the one for cars because, apart from Chatbots, it's more hidden.
And also, similar to cars (or many other phenomena of industrialization), yes, my first vague point was the subsidization of infrastructure. But also, the power gap: that's something not only associated with AI or cars, but with a lot of technologies we all hold dear: sewage, powerline, logistics, etc etc.
What reminds me of cars in the current AI frenzy is the fixation on cementing infrastructure. And also, I think, a lot more people agree on, for example, some kind of universal right to, for example, clean water.
But all of industrialization confronts people with questions of efficiency, inequality, and collective support.
Most people would, for example, support a right to get a minimum amount of clean water when you are living and working in a tradionally inhabited space (if you're on the social-darwinist side) or at least not harming society (if you're more of a social democrat).
And, similar to the buildup of car infrastructure, and the procurement of resources, space etc for maximum building, giant data centers can obstruct people in buying drinking water. Or walking outside (AI obstructs traditional methods of online collaboration).
The original point of the stock market was to fund gigantic society-level projects (like railroads). Modern VC has replaced some of that at smaller scales but not all of it at the largest scales. So this could just be the stock market performing the function it was designed to perform -- helping fund something transformative on a societal level.
Not sure what your point is. Stock markets are based on money going into securities based on estimated future value. Even if AI were doubling productivity at a non-AI company, there is more leverage to that money going into an AI company.
The question is, is AI leading to massive productivity gains in companies that implement it? AI productivity gains take time to diffuse, but so far companies in the S&P 500 are seeing very high growth. YOY earnings growth rate for the S&P 500 is 21.7%
https://advantage.factset.com/hubfs/Website/Resources%20Sect...
My point is that they're selling us Skynet and the end of employment as we now it, things that we shouldn't even have to measure to perceive the results of, yet no one is able to measure any of it
Pointing a finger at nvidia, google, and the other few companies stuck in circular investment schemes that shouldn't even be legal and saying "OOGA BOOGA line go UP, UP GOOD!" doesn't count in my book
He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse.
I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place.
My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit.
But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time.
The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
I think he doesn't need to understand the technology to point out the books are cooked. a business can sink in either way: the technology flops or the finances flop. he's arguing the /finances/ would flop. he doesn't argue that the /technology/ would flop, only that they can't come up with the money to pay their debters.
There is a piece of this I agree with. That you do not need to be a deep technical expert to notice that a company is burning cash by overcommitting to capex, or relying on heroic revenue projections that may or may not come to pass.
But that is not the full argument he is making. If the claim is that the labs will not be able to pay their creditors because inference is structurally incapable of becoming profitable, then he absolutely needs to be right about the technical economics of inference.
One part of that is the balance-sheet argument (which already shows insanely good margins). But it also depends on how inference-time compute actually works: routing, batching, kv cache reuse, model segmentation, different latency tiers, etc. Much of those details he's just been straight up wrong about in his writing, so as a result I have to call into question the rest of his reasoning as well (in part to avoid Gell-Mann amnesia).
> That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
But does it also not mean that they will make less money given that there is already brutal competition for that lower tier from openrouter, Deepseek, Amazon, etc.?
You can't on the one hand say "customers are beginning to understand they can spend less" and on the other hand suggest that this is good for forecasts of revenue.
There's examples both in his writing and also in his appearances on podcasts, interviews, etc.
I'll cherry pick a couple:
“When these new models ‘reason,’ they break a user’s input and break into component parts, then run inference on each one of those parts.” [1]
This is not at all how test-time compute works. At best, this is a very loose metaphor that he may have used out of convenience. This might sound a bit pedantic to point out, but this is a very basic thing that he's getting wrong (presumably at least, again it could be that he just used a poor metaphor).
A less pedantic example would be his claims related to gpt-5/chatgpt auto-routing. He argued that having a router means OpenAI can no longer cache static prompts, because the user prompt has to come before the hidden instructions [2]. This is just not at all how this works at inference-time. There is no evidence that the standard approach of system>developer>user instruction hierarchy has changed, the public API and caching docs maintain this.
But even more broadly, it suggests he is reasoning about kv/prefix caching at the wrong level of abstraction. It's true that conventional prefix caching does require a stable prefix, so yes, if you literally put variable user content before the static prompt, you would destroy the cacheability of that static prompt.
But that is exactly why inference systems are designed to preserve reusable prefixes where possible (via checkpointing or similar), and why serving systems care so much about prefix caching. This is also a big part of how disaggregated prefill/decode infra works where cache-aware routing is critical. His argument treats a bad prompt layout as if it were a necessary consequence of routing, rather than an avoidable implementation choice.
A router can read the user request, decide which model path to use, and then construct a normal downstream model call with stable static instructions first and user content later. Treating that as impossible implies a fundamental architectural misunderstanding.
Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing.
From an economic perspective productivity is defined as the creation of value isn't it? Then if you "improve productivity" and does not create value in the end you're no improving productivity at all.
Also, supposed productivity gains are dubious. I personally experience at best no productivity gains when using LLMs to write code, and sometimes it's an active drain on my productivity. There was that one study a year or so ago showing similar results. People are trying to say the productivity gains are there and undeniable, but that is not true. It is very much a subject of controversy whether AI helps productivity.
I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility.
However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.
There is an observational study that was published in March 2026 that followed 4000 teams over 2 years. It shows, in my view, exactly that the productivity gains don't translate into economic value.
If it was published in March 2026, even if the data was collected up to the day the study was published, 7/8ths of it would fail my “within the last six months” test. But I am looking forward to the results of future studies on this topic!
Its funny, I've noticed the same thing, but did not come to the same conclusion.
I currently don't have work access to Claude Code, but most of my teammates do. Watching from the outside, the cycle seems to look like this:
1. Experience some success, which hooks you into relying on AI.
2. The AI keeps failing at some task, but you don't want to stop. Keep trying over and over again.
3. Run out of tokens and take a break.
Now, sometimes 1 doesn't happen. Sometimes 2 doesn't happen. 3 is a certainty though.
Now, if you told me that the productivity gain from 1 is enough to offset the loss from 2 and 3, I could believe you. But I also wouldn't be surprised if it didn't.
As I work with Claude more and gain a feel for its capabilities, I tend to run into 2 far less often, as I'll decompose my messages more for the current model limitations. The threshold also changes each release.
That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent.
EDIT: In fact, parent comment has a link to some numbers.
[EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
I've posted numbers that indicate that productivity is becoming decoupled from value delivery. If you follow the link in my comment it reviews a pretty robust study of 4000 teams over 2 years. There is no product throughput increase.
Code acceleration is great, but.... something precedes that. Vision and strategy re. expansion of offerings and businesses. Once a firm reaches maturity in what it offers and is only touching the edges - this code acceleration is literally useless when you factor in all of the trade-offs.
This is a good thing - it means fat and slow incumbents are sitting ducks to be out-witted by creative and imaginative founders, which is healthy for a well-functioning economy.
Now the economics of existing frontier models are not sustainable - its looking like a mix of the airline (supersonic vs subsonic) and EV industry with China in the background providing decent offerings at much lower prices.
I admit that if a small team or an individual uses an LLM, it's likely they can create value faster.
I think as soon as you don't own the responsibility for the defects you generate with an LLM, their use starts to destroy value. Regardless of product maturity.
If you are producing something that delivers a far better experience, irrespective of what's under the hood (see Claude Code et al), you will decimate an incumbent who is trying to use LLMs in the context of incrementally improving a mature product.
LLMs are suited for the development of revolutionary innovation, not incremental.
Yeah this part scares me a little. I imagine it scares everyone who is more than a couple of years out of school. I hear that "the solution to LLM tech debt is more LLM." That might be true, but it might not be.
It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model.
I think the surest sign of productivity gains is the sheer volume of adoption. If you look beyond headlines, adoption is just incredible. Of course adoption does not necessarily point to productivity gains, but if this was some sort of FOMO or smoke and mirrors you would not see this much retention and this feverish a pace of adoption. You would not see a large segment of the profession using coding agents exclusively. All of these companies track productivity, again with imperfect proxies, yet everything points to a pretty consistent picture. Same with benchmarks, again a lot of crappy benchmarks but a lot of high quality ones too and a very diverse collection of tasks and capabilities they probe.
Your second paragraph appears to be 3 different instances of saying "X does not necessarily point to productivity gains... but in the case of AI, X definitely means productivity" without really saying why that is true or why other explanations do not fit.
Adoption meaning productivity supposes there are no other dominant factors for the AI push nor AI retention. It is possible for practices to be picked up or continued in spite of causing productivity DROPS. What studies have suggested are factors that make for productive work environments and what is actually enforced in the workplace are different things.
It’s 3 different weak but complimentary proxies. We form beliefs from imperfect evidence and I find these fairly convincing when it’s hard to find any hard evidence of no productivity and exactly the scenario you would expect under the hypothesis that we do see productivity gains. None of this is supposed to be unassailable. I would challenge then if you disagree what the evidence you have for this is?
Adoption implying at least some significant productivity gains doesn’t contradict there being other factors. You’re seeing entire companies reshaped. The argument is this is all for show or CEOs are in some sort of idiot class?
“It is possible for practices to be picked up or continued in spite of causing productivity drops” well of course. I just find that incredibly far away from Occam’s razor.
My point is: we have lots of evidence that’s highly consistent with real productivity gains, and I don’t see many pieces of evidence to the contrary.
He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.
The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either.
Progress is slowing, in an important way. Have a muck about with what Qwen 3.6 or Gemma 4 can do and you'll see.
The frontier models are losing their undeniable edge over that which is unmetered. Qwen just isn't all that far behind and compared to the data centre hardware it will run on a potato.
Commodification is coming, and with it slimmer profit margins; it's hard to see them making anywhere near the kind of money they need to in a commodified market.
Do you think it's not slowing? Do I miss anything really important?
My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
The most important thing I would point to is Mythos et al and the wave of vulnerabilities that have been discovered in the past couple months. It’s a completely unprecedented event, brought forth almost entirely by improvements in the models themselves.
That said. keep in mind, I’m talking about over the past two years. With Claude code and the capabilities gained since December of last year, there have been incredible gains in the capabilities that are now available. Demand for inference is higher now than it was a year ago, because capability has improved. A specific criticism that I would hold is that claiming that progress with LLMs is slowing, prior to that point, is embarrassingly wrong in my view.
One could argue that the model capability improvements are slowing, and all the improvements were in harnesses. I think that’s a stronger argument, but I have a few problems with it.
1. Utility is utility. Whether that comes from the model or the harness is irrelevant when making claims about utility. I don’t think that’s a useful distinction most of the time, but especially when talking about the technology as a whole.
2. Marginal intelligence gain is different than marginal utility gain. It’s estimated that intelligence grows logarithmically relative to investment. However, the utility of a marginally more intelligent model may grow exponentially, because once behavior crosses a reliability threshold, it unlocks new capabilities.
3. Even on those terms, it’s not clear to me that frontier capabilities are slowing down. With Mythos and its contemporaries, we have been seeing a vast change in the security industry as vulnerabilities are discovered at an unprecedented rate. OpenBSD vulnerabilities, more Firefox vulnerabilities found in a single month than the past two years, critical Linux vulnerabilities. It’s hard for me to look at the effects there, a radical new capabilities baked into the model itself, and see stagnation. A part of the reason it might feel like it’s slowing down is because we plebs don’t have access to the top models.
The article says in the second section that the author did not have access to Mythos. I think it’s dangerous to rely on claims made by others without even bothering to read them first, let alone check.
It found hundreds of vulnerabilities in Firefox, according to Mozilla: how does Mozilla benefit? It found a 27 year old vulnerability in OpenBSD. How do they benefit from that? Is that made up? Are the maintainers of those codebases lying for the benefit of Anthropic’s IPO? Is copy fail a fabrication by big AI? The 12 OpenSSL vulnerabilities found in January?
Im not sure whose claims you think I’m relying on. I trust Firefox that they’re not overstating the number of CVES they’ve found. Same for OpenSSL. The OpenBSD folks definitely don’t seem like the types. I’ve not known Linux to fabricate CVEs either. I think my sources are fine.
He has recently made the very good point that actually, the FAANG companies are struggling to put any ROI numbers on that incredible ground-level utility.
Uber, for example, is so unclear there is any ROI, they are cutting their exposure pretty radically.
He points out that one single Anthropic customer — a payments provider — accidentally had to pay Anthropic $500M for one month of token spend.
That is half what Apple is reportedly paying Google for the supply side of their entire consumer AI strategy.
I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work...
It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...
Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost.
Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost.
That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
Because it doesn't. Not for the tasks where using Opus instead of a lower tier model is appropriate, at any rate. Benchmarks show this, as do revealed preferences of actual users. To believe that Qwen is as capable as Opus at 1/20 the cost you have to believe that every person who does not make the choice to use Qwen over Opus for a given task is some mix of ignorant or delusional. This is certainly an opinion you can hold about other engineers, but it's definitely a questionable one at best.
The benchmarks between the two are close and the engineers that have used both (like myself) can attest that the differences aren't so wide as you might believe.
I'd say that yes, ignorance plays a role here because a decent number of engineers are looking strictly at the benchmarks and choosing Opus just for that reason.
But I'd also say that a major factor for Opus use is because Opus is being purchased for the engineers by their employers. They don't get to pick which models they are using.
Yeah they're very much deniable. Raw LOC/hr is much higher, and putting together a MVP, but I've yet to see any evidence that an LLM is capable of doing anything unsupervised, and if you need a human supervising everything it does... why bother having an LLM in the first place?
Because it can perform much faster? Monitoring allows you to multitask more effectively. I would also disagree that you can’t one shot anything…claims like this are weak and I have enough counter examples in my own life that it’s trivially false. The question is more: can it one shot the right things with a low enough failure rate for it to be a good replacement. It’s hard to figure that out a priori.
Even if we assume that everything you said holds true, how is that we as a crowd can make viable a service that eats some $300bn annually in infrastructure costs? Where would that money come from? Most tech companies these days are cutting their AI budgets because the per token pricing is killing them.
Cite a real source for that last bit, I don’t think that is true. Also the budgets should be cut the spend at some places goes beyond any reasonable amount. The strategy there is to hook everything in and find the right processes, then cut the rest. Things then get better and better with each model release.
The way you make a viable service that eats 300bn annually is to have enough demand to service that. Anthropic underbought compute. That tells you something.
Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead.
The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.
I don’t get this. We already have an insane demand. And yes exactly, this is primarily just with coding agents, but are you aware of what’s coming down the pipeline? It’s not hard to be you just have to find a decent way to keep up with literature.
* robotics (need to close data gap and release first viable product to get a data flywheel)
* conversational ai (no one is ready for this and we’re getting closer and closer to natural speech. The quality still isn’t good enough but it’ll be soon).
* other agentic use cases, openclaw adoption was crazy and that had a ton of barriers to entry
* ai products, like the one OpenAI is working on with Johnny Ive
Anyone thinking it’s unreasonable to hit whatever revenue requirements is just not that aware of what’s happening. Not to mention were capacity constrained already!! This is barely speculation at this point.
I don't think the issue with robotics is a data gap. maybe somewhat, but the real issues are that:
- RL is extraordinarily sample-inefficient.
- distribution shift/catastrophic forgetting aren't solved. only off-policy learning with giant decorrelated batches works.
- the breakout success of transformers as an architecture doesn't neatly translate to robot motion policy models.
the field is missing fundamental breakthroughs.
I also find it very interesting that conversational AI has taken this long. where are the models with good turn-taking? passive listening? the ability not to respond in paragraphs? has Anthropic simply not gotten around to it?
All of these points are great. The first one motivates world models which lots of labs work on. Not many people tend to understand the strategic value of those “open world” or interactive generation models: its robotics and planning. But also like you say you’re right, there are complicated problems to solve and it’s not totally clear the timeline. But where there’s data and compute, there’s a way.
For conversational AI these labs do have lots of things to do lol but you’re right; it likely also requires some architectural improvements but you see the infancy: look at the llama4 speech duplex model. Very unimpressive yet all of the components are there. Just a matter of pushing on them, licensing and commissioning better data, etc. takes time and compute is stretched thin.
Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).
And where are those? They seem particularly hard to actually observe and only appear in anecdotes.
> I'm trying to believe
For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?
I really like some good drama slop that reads like a thriller, it is entertaining.
I don't take any of it THAT serious, but lately with the IPOs that are about to hit the indizes, he has gained a lot of attention.
If you look around the internet, most people publish a negative angle on something and then extrapolate it into some grand conspiracy, which is really captivating.
Its crazy when you enter some echo chamber you never engage with (movies, gaming, art/comics) and they have their own head cannon for why the world is bad and collapsing. It puts your echo chamber into perspective to see the same patterns of argumentation and presentation spin out in a different way
No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job.
Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades.
Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."
How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication.
I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think.
[1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.
Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!"
This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.
Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. https://www.wheresyoured.at/peakai/ from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)
Predicting the timing of such a thing is notoriously difficult. I don't think being wrong about timing 2 years ago means there won't be a correction.
I'm also reminded of all the HN posts from 2007-2009 that predicted that the adoption of social networking would be a terrible thing for privacy, that it would destroy society, that people would lose their jobs over crazy shit they said on the Internet, that it would lead to the decline of trust and in-person interactions, that people would forget how to socialize, etc.
They were right about all of that but it took 15-20 years and the companies involved grew 100x in that timefold, eventually reaching trillion-dollar valuations that would've seemed insane in 2007.
There is a tremendous amount of money to be made in destroying society.
Not related to AI but, I recently rewatched "The Big Short" and your comment reminded me of it. I can't testify the accuracy of the movie, but for over year, Michael Burry was viewed as in the same manner for shorting the market, while the economy was was in a hype cycle.
I'm open-minded to arguments about AI being a financial bubble and a bad business.
I'm not open-minded to arguments about utility, given that I personally witnessed LLMs evolve from interesting but useless toys to insanely helpful tools I use every day.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).
But from the article I linked back in March 2024:
"Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models' capabilities will continue a rapacious pace of progress that has unquestionably slowed, with OpenAI admitting that GPT-4 may be worse on some tasks.
As I've written before, hallucinations are a feature not a bug. These models do not "know" anything. They are mathematical behemoths generating a best guess based on training data and labeling, and thus do not "know" what you are asking it to do. You simply cannot fix them. Hallucinations are not going away."
Since then:
- hallucinations are dramatically less of a problem
- several mass market use cases have emerged, most notably coding
I think the points you raise are reasonable signals to consider, but I don't think they show the author being "consistently wrong". The overall thesis still remains plausible even though we have seen LLMs continue to improve.
> - hallucinations are dramatically less of a problem
Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
> - several mass market use cases have emerged, most notably coding
Coding seems to be the only one, but there are still a lot of open questions about how the market can sustain the costs, and that's without considering the market dynamics that could emerge once costs are lowered enough that open source models start to become an attractive option.
> Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
Another important consideration: Hallucinations getting less common/severe but not (as-good-as) solved makes them worse.
LLMs used to very obviously get things wrong. And people wouldn't trust them. Now they're good enough that people blindly trust them.
Now people just directly PR AI output with little to no manual review. We even have clowns calling for the complete abolition of directly human-authored code.
Whatever gains were had in better AI code output over the past two years I lose in having to review much more thoroughly.
Has rate of progress increased? How does one measure that? Genuinely curious - would be very interesting to map out the "effectiveness" of each AI model vs how long it took to train/release.
From my perspective, the model gains are mostly incremental now and a lot of the gains are just from things like improving the agent harnesses. I could be wrong though.
> hallucinations are dramatically less of a problem
No they aren't. The models still hallucinate just like they always did. You cannot trust them, ever, to get something right.
> several mass market use cases have emerged, most notably coding
They aren't really useful for coding based upon the above. Since you can't trust them, you have to carefully review everything they make, which in turn destroys any productivity they could've given you.
> rate of progress has increased
I have yet to see any progress. Opus 4.8 that you get today is no more effective than GPT-3.5 was. Much less would I agree that the rate of progress has increased. Only hype has increased, but there has yet to be a drop of substance.
> several mass market use cases have emerged, most notably coding
Most notably? This is not a mass market use case in the way the author is describing. They are asserting that the amount of spend they need to get this off the ground necessitates the entire world coming in on it, and I would say that opinion has aged pretty well. There are a lot of coders, but there are more people scratching their heads as AI is shoved into every part of their lives.
> I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley.
We have seen 8 quarters since. Has any of that come to pass?
if you can't predict when it will pop then you should really not predict anything. I can also predict that Google will pop. I won't tell you when but I'll tell you that it will. I'll remain thoroughly unfalsifiable and I'll keep pushing the dates.
The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.
What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:
Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
They ain’t profitable yet. Most of the model maker’s will be gone soon. It’s unsustainable unless you’re Google who has other income coming in to support their hobby, and the Chinese model makers are spending a fraction to be six months behind and many of them will be there for the long-term because they have backup support (government) who is in the race for the long-term.
One other thing that’s working against the model makers is the hardware is getting better and the models are getting smaller and more capable. I don’t think we’re going back to the mainframe days. Local will be the endgame.
Is Ed right? Probably because in the end it’s unsustainable the companies left will be the companies that have income coming from somewhere else and there’s one large tech company that isn’t even participating in the boondoggle unless you count $1 billion dollars a year as participating ultimately there is no moat in AI model making.
Nvidia and Microsoft trying to introduce another Arm processor in a laptop of all things won’t change the tide either.
That's the exact opposite of rational. It is, in fact, a formal logical fallacy (ad hominem). His argument can be correct even if he himself is not typically correct.
On the surface, that's quite fair. However, there's one problem: it is much easier to make statements than to verify them, and that asymmetry is part of why the internet has been slowly eroding society.
It's useful/necessary to use past writing/arguments from an author to say whether they should actually receive any further critical evaluation, or be dismissed. We shouldn't say definitively "they're always wrong, so they're wrong now". However, it's reasonable to say: the author has a demonstrated lack of credibility, so we can probably assume they're wrong here, particularly if they have been wrong in this domain so many times before. Or if they happen to be correct, it's probably not strongly demonstrated by their work.
> which is an extremely rationale judgement to make.
So it's "rational" to take bias into reading? Why even read? If you know what you think and refuse to accept new information then what purpose is there in consuming anything?
You should just read the comments and get a warm fuzzy that the crowd, for the time being, agrees with your intentionally static ideology.
Comments like these obviously hope they can sway the crowd before they can take an unbiased reading of the article. If the author is that wrong then the crowd here should be able to discover that on their own. If the author convinces the crowd then I'd think you'd want to present a better argument than "well, he was wrong _before_." Post hoc, ergo propter hoc, in action.
He's a Gary Marcus-level contrarian with none of the credentials or contributions to the industry. The "AI bubble" cope narrative is getting stale but will still appeal to luddite autists years after it has ceased to be relevant.
The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
We are only five or six years into the leap LLMs represent. For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up. It took so long not because of the physics of radio waves required time to mature and improve, but because everything else needed to profit from it did require time.
To me, LLMs are not so much AI as it is a building block. Radiowaves maybe, or the equivalent of transistors. We are already seeing that it's possible to chain LLMs into agents. Currently, price is a strict limiting factor for coding and agents.It's probably fine-ish if all you want is Claude Code or Codex, but there are many other possible compositions of LLMs that most people don't dare to experiment with. For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost. Were prices of inference hardware go down and inference algorithms keep improving, I'm convinced (and afraid) we would see things very difficult to imagine today.
> For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost.
Hah, I'm actually working on just this problem.
Cost isn't the issue. There are only so many coherent (in context) responses and scenarios, that you don't need an LLM to generate text in the game, in real time. Instead, you can have LLMs build a vast corpus of "atoms" (dialog messages, fragments, cues, etc.) that can be stringed together in a deterministic way in response to player input. These can also be pre-screened and subjected to various tests prior to implementation.
To a player interacting in the game, a system like this would seem functionally indistinguishable from generated text within the game's designed interaction envelope. And it has huge advantages: Although it can expose seams if the player breaks character and decides to probe it, it won't be exploitable the way an LLM would be.
> The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
Worthless statement. Wow, you suspect something can make things better, worse, or both? That's a keen insight there.
> For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up.
We are still so early.
I mean, we have advertised them in multiple super bowls, have companies that basically own tech news (incredulous journalists will repeat any stupid insane shit a CEO wants to say), that say they're valued at over a trillion dollars and nobody with the power to argue those finances seems willing to do anything but agree. We have built hundreds and hundreds of acres of data centers (and made deals for data centers that are never going to happen) that demand *billions* per month. They are devouring all the silicon to where people are visibly seeing the price of hardware double, triple, more in price. Work places insist on employees using AI (then pulled back because it turns out this stuff costs money and it's not fun anymore when it's not subsidized).
But we just need more time, more eyes, more people looking at it.
Ed is an interesting character. His financial analysis of the AI industry makes logical sense to me (though I am not knowledgeable enough to actually know if it is correct.) However, he seems to be so angry at AI in general, that he misses the obvious areas where LLMs are actually changing the State of the Art.
Coding seems to be one of the core use-cases for LLMs (as Simon Willison pointed out recently) and even if that's the only real use-case for LLMs, they're wildly useful. I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
I don't think whether "LLMs are actually changing the State of the Art" or not matters for anything he wrote.
If the AI companies need $X billion in revenue to stay afloat, it doesn't matter if 0.5% or 5% or 50% of that revenue is from transforming the State of the Art. It's 100% irrelevant: what matters is that, transformation or no, these companies won't have the income to pay their bills. And if they can't pay their bills, a whole lot of other companies can't either.
So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.
He's got subscribers. Maybe the attitude is one he's found plays well with them.
I find it quite refreshing in some ways. Lots of people, when they start complaining about this or that aspect of this AI stuff, are wont to add in a little disclaimer that, despite all of the above, they actually really like AI and use it all the time. I assume this is to avoid the scenario of a bunch of pragmatic builders turning up and calmly shipping nuance in the comments (or whatever you call it these days when you get brigaded by a pile of angry keyboard warriors with chips on their shoulder) - and it sure is tiring having to wade through the equivocation.
That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.
It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.
> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
This is often repeated but comes from ignorance mostly. You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.
This kind of thinking really reinforces my belief that people have no idea and are using this whole [AI is not profitable and too costly] thing as a cathartic way to deal with immense progress.
However, it needs to be said that he received those numbers. I personally have quite a few issues with him, but there's no reason to doubt his journalistic integrity. Because of that, I believe he reports truthfully on data he receives by informants.
Additionally, none of the frontier models actually publicly talks about inference costs in anything but broad, "let's just forget that"-like takes. Which does not exactly spark confidence.
I'm eagerly awaiting anthropic's public disclosure of their financial details. That should be rather interesting in any case and finally put the inference-discussion to rest.
No reason to doubt his journalistic integrity? He's not a journalist for starters. He's a PR flack who does PR for AI startups on the side while blogging on substack. There is every reason to doubt his journalistic integrity.
Zitron is in the business of content creation and not successful predictions. It doesn't matter how many times he (and several others around) will say the end is here, they have to be right only once.
BTW, one thing for sure he is right about are the economics, as of today there is no way these massive investments are gone be paid.
This is wishful thinking. AI is still getting better rapidly. Anthropic's revenue is still growing at an unprecedented rate and they haven't even released their best model (Mythos) for 4 months now.
The chart seems logical to me. Most problems are solved in the app space. New apps don't have to be the new facebook. They just need to be useful for the right audience (even a small one). It's like you have meat and bread in the supermarket, and add all other stuff you dont really need. Will be bought, but not as much as meat and bread, right?
This rests on a lot of assumptions that the published figures for "planned" datacentres, "committed" AI spend, etc. are irreversible. I suspect that at least some of it is possible to back out of.
Although I see huge utility in AI, I think he is right in terms of overspending and overenthusiastic build out. Because of for example what Apple is doing by putting an extremely efficient model with task adapters right onto phones.
Also because we now have a massive demonstration that vastly more efficient hardware is desperately needed.
Similarly other effective efforts towards on-device AI like Nvidia RTX Spark PCs and 2bit quants of strong models like DS4.
So inevitably, significant investment will be going into vastly more efficient CIM efforts like Mythic AI and new FeFET devices etc. in order to make human-level and beyond AI at scale feasible. There is so much demand for this and the power requirements of current hardware are so excessive, it seems unlikely that the data center build-outs will be able to recoup their costs before the more efficient paradigms make it out of the lab and start scaling.
Ed's argument for why "AI is slowing down" rests on company spending caps, in particular the Uber $1,500/engineer/tool cap.
I interpret the exact same evidence in the opposite direction. A year ago the idea that a company would spend $1,500/month/employee on AI tooling felt absurd, what could people possible want to do with AI that would cost that much?
Then coding agents (and, increasingly, general purpose agents) happened and suddenly companies are having to set limits because otherwise the demand from their employees is too high.
The TAM of these AI companies just leapt up to $1,500/knowledge-worker/month, how is that "slowing down"?
Maybe in USA in big tech where companies give absurd wages to engineers anyway in some states, that might be acceptable. But to make their ROI they need that (and more) to be spend world wide... no way that is gonna be a budget that is gonna fly in the long term...
Companies love to cut costs, and just like they axe employee numbers at will, they will just as well make that kind of budget quickly dissapear the moment they realize they can go a different path for same or better value... Or simply because share holder short-term value demands it...
The Uber $1,500/engineer/month thing is just the first signal we have had of the price companies may be willing to accept. This price will clearly vary wildly across professions, industries and geographies.
I think it's a poor number to build an "AI is slowing down" narrative around.
The problem is that $1500/engineer/month would be a pretty modest amount of demand for labs. OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending. On so many levels of the industry this growth is now priced in. You don't think so?
>OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending.
No they're not. In reality, actual 'explosive uncapped growth of unlimited agentic token spending' will result in valuations several times more than a 'mere' $1T.
It's also not $1,500 per month per engineer. It's that per month per engineer per tool. Which means it could easily be at least $3,000 (Claude Code and Cursor) or $4,500 if Codex was also an option on top of those two.
And as you have written on your blog it's a soft cap that can be exceeded with justification.
I find it nuts that I can use Claude Code for $20pm - I imagine that won't last forever but have to say it is great value for money.
So when I see monthly budgets in the thousands for developers at some larger companies, I'm curious to learn how they are managing to spend that kind of figure: how much code/documentation are they feeding into their prompts, are they using agent orchestration systems to make the code factory run 24/7, and how much value is coming out the other end versus before?
And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
Those are the kind of questions I'd love to ask - I just wonder how much stuff is truly cutting edge and how much might be wasteful?
Developers at big companies need to pay per token, they don't have subscription available. So in case you use that, you likely spend way more than $20 in tokens.
As for how to spend that much -- not that hard, to be honest. Just give it a lot of context and some relatively open-ended problem and it will easily eat through tons of tokens.
I have $200 subscription for Codex and it is crazy what it can do in terms of debugging. I have a pretty complex Electron setup with some native code linked via Node addons, a few App Extensions and it can easily read the source code to see how the builder works internally (e.g. if your end Info.plist is not correct), debug the xcodebuild output to see at which step something is not linked correctly (like after XCode major version bump), etc.
It is not a silver bullet but if you are not the one paying for it, there is no downside to throw a problem at it and see if it can come up with a fix.
> And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
I am curious about that myself. I have a good machine now (Macbook Pro M5 Pro with 48GB memory), so I'll give it a try; I don't have high expectations so if it is actually helpful would be very neat.
As a tangent, I don’t understand where and why meta fits into the AI race. They did not get any mind share (consumers) from the llms so far, granted they started the open source side to this but the Chinese companies produce far better models and have essentially become the default for on device set up.
They have ai glasses and integration into instagram and facebook as the other avenues. I don’t see ai glasses as compelling yet, and don’t know how much more ad revenue or user engagement they can squeeze out with llms baked into the IG of FB flows. They are spending a lot and not seeing any returns. Am I wrong in being pessimistic about meta with AI?
Buried lede (if the title is the actual promise), the sources don't seem to back the title either. Someone with more patience can correct me if I accidentally missed a bombshell anyway.
Edit:
> If you’re wondering what the story is, [...] I expect it to be out in the next two weeks [...] I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.
Ok, this takes clickbait to new lows. The headline is trying to sell the teaser here, with very limited meat in the middle of the sandwich.
I find it difficult to separate this piece’s tone from its content. The tone puts me off and makes it hard for me to judge it on its merits, despite some of the arguments seeming sound and well supported.
You can disagree. Sarcastically, or otherwise. But I think you may be reading more into my comment than I put there.
I’m not attacking the piece. I’m not saying it’s right. I’m not saying it’s wrong.
What I’m saying is, the tone made it hard for me to judge the arguments fairly, despite finding some of them convincing. And as much as I dislike it, persuasion does partly depend on how an argument is made.
Ed's posts are peak preaching to the choir, they're usually factually correct but he is really bad at convincing anyone who doesn't already strongly agree with him.
Have you seen his recent Bloomberg appearance? He's calm, collected, and matter-of-fact -- the complete opposite of how he presents himself on his newsletter and podcasts, but with the same argument. You wouldn't know from listening to him how spicy he usually is.
It's tuned to the audience. Bloomberg was traditionally for people who actually wanted information. People who were fallible and had limited knowledge.
Of course that mentality is obsolete. Now we all have infinite access to perfectly correct information via the internet.
Perhaps that’s it. I would tend to agree with his position, I think, but don’t appreciate being preached to. Even less so when I agree with what’s being said.
Agreed. I am open to the possibility of the bubble bursting or whatever, but this piece is like 3,000 words and cites everything as evidence the sky is falling. It's just as bad as the pro-AI grifters, just in the other direction.
Does the truth normally lie somewhere in the middle of it all?
Probably. Although I feel more inclined to forgive Ed in this case because it's sort of fighting fire with fire, the insanely hyperbolic and obscenely misleading drivel that's coming out of the most ardent AI boosters is continually unchallenged in the public eye. In a world where we had a more realistic view of AI/ML/LLMs, the limits to its capabilities, and the negative externalities of its widespread adoption in places where it quite frankly does not belong, then I'd be more critical of the Chicken Little sort of writing style
I hadn't heard of the TMobile and Brex spend caps, only knew about Uber's because it went viral last week. I expect we'll see more of that now that everyone is paying per token, and it sort of feels like you cannot both have spending caps and require extensive AI usage for performance reviews -- I wonder that will shake out in the end?
Anecdotally, $dayJob consumes Anthropic models via Azure subscriptions which lend themselves pretty neatly to the spending dashboards Ed mentions are missing from Anthropic themselves, and finance seems ok with the current usage, but there's no real hard incentives internally for AI usage either.
I guess Q3-4 are going to be interesting to see where this all goes.
Given how I can manage and develop a huge production code base with an incredibly small team - and the rest of the industry apparently is not able to do it - I deem that we are still in the very early days.
There are real issues on the money front. The big AI companies have a financial model that assumes a huge increase in demand in the next year or two. Otherwise the bubble pops.
"Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees."
So some of the growth was purchased by underpricing, subsidizing the customers with venture capital. Uber did that, and eventually got out of it by raising prices and squeezing the drivers.
The "fuckup" problem is real. LLM-type AI exacts huge costs because it is terrible at reporting "I don't know". When it doesn't know, it generates noise and polishes it.
If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful. You could use small, inexpensive models on small problems, and only use big models when the small models failed. Most customer service bots fit that model.
Needing ever-larger models to fix the noise problem is not cost-effective.
Isn’t the thing that an LLM never knows (at all)? It just guesses words based on the context and previous words and often gets lucky.
It isn’t thinking or knowing and then expressing the resulting understanding but just spitting out contextual words and hoping it reaches a conclusion or ending of some sort.
As WIRED reported[0], despite constantly writing about how an AI collapse is just about to come, Zitron privately does PR for AI firms on the side. The man is an obvious hack, and it's disappointing that he has become one of the mainstream faces of AI skepticism.
AI has been slowing down relatively, considering its trajectory over the past 20-30 years. For one, even if LLM may have plateaud in terms of intelligence-parameters ratio, research is on-going on new frontiers for ML, including (but not limited to) world models. Other research directions are studying backpropagation and its physical analogies, such as equilibrium of chaotic states.
In addition, there's a lot of research on the hardware angle and actual prototypes are already being built such as AI-on-chip Cerebra and Taalas for one.
Anthropic has made $330 billion in compute and chip commitments between Google, Amazon, and Microsoft, another $30 billion with CoreWeave and another $15 billion with SpaceX. To pay for this compute, Anthropic must meet its projected revenue of $174 billion a year by 2029.
Anthropic has raised $95 billion across rounds in February, April (from Google and Amazon), and May. These funds will be insufficient to cover Anthropic’s costs, as will Anthropic’s cash flow, meaning that it will have to raise at least another $200 billion in the next year.
How people take this seriously?
Anthropic is at 45B ARR
S-1 shows inference margin climbed to 70% (obviously could drop)
So where that 200B number is coming from ?
I guess my ears kind of turn off when you say "it's all slop, none of the apps are good, and it's a failure because no one has used AI to make the next Salesforce".
I have found agentic coding to be extremely useful for a bunch of small, middleware, very focused bits of software for small businesses:
* A company had a very specific scheduling need, they needed to move about 8-15 staff around with a bunch of different shifts, and have custom reports on who was working how many hours, and have the employees get a nice clean email summarizing their schedule
* A manager wanted a very simple "let me send a text to add a to-do to the group list" need
* A sales team of 3 wanted to be able to type pricing of raw goods into their phone, have it compared to other market sources, and have it text the other 2 salespeople and their manager when they were out in the field
All of these were coded with Codex in about 4 hours with further refinements over the next week of back-and-forth with the people using the tools.
I suppose yes we could have found some custom middleware solutions that did similar things, but it's nice to be able to make a web page or tiny mobile app that just does EXACTLY what the person wants.
It's hard to do that and then listen to someone who says it's all just garbage.
His rhetoric is a bit obsessive and frankly biased against AI.
That said, I think his voice is useful as a counter to the mainstream opinion.
Given the amount of investments, approaching AI from the angle of economics seems correct.
We all have some level of personal experience using AI/LLMs, both chatbots and coding tools, and I personally enjoy using them, but I am sure this experience is relevant in this discussion.
I also enjoy luxury hotels, gourmet food, jet skis and helicopters, but this is not something I indulge in often because of the cost-utility ratio.
The real cost of AI may or may not be lower than its utility. The bet is that utility is increasing while cost is falling.
I think it's time to distinguish between what frontier AI companies need regarding AI, and what will happen with AI if these companies don't get everything they need. Probably there's a bit more to this. Much of the technology is available via open source already and there's a growing ecosystem of AI tech that isn't really dependent on anything else than the hardware infrastructure needed to run it.
A good analogy might be networking companies and infrastructure companies during the dot com bubble. It devalued a lot of companies but the internet stayed. A lot of dot com companies didn't make it. Much of the infrastructure investment did not go to waste, however. Nor did a the technology go away.
I think it will be the same with data centers, related infrastructure, GPU hardware, algorithms, OSS components, etc. for AI companies. More companies need that stuff than is currently available. The ones that don't make it will have a lot of assets that they can pass on to the one that still have a chance. I don't think a lot of that stuff will get decommissioned or will be underutilized. It might get a little hair cut in value though. And like during the dot com bubble, some companies actually survived and did quite well. Especially those in the business of selling shovels during a gold rush.
After the inevitable consolidation that follows the next logical stages in the hype cycle, I don't think AI will go away. It might be a bit of a bloodbath for some silicon valley investors that placed the wrong bets in the last few years. But that's the price of doing business over there. That doesn't mean it's all bad. And the smarter ones probably spread their risk enough that they still might come out looking alright.
And like with the dot com bubble, many financial types have no clue what is happening and are running around like headless chickens. Which is why they ended up sinking a lot of money in exactly the wrong things. You'd hope they would have learned something.
But articles like this suggest that that might be too much to hope. They still don't really get how technology tends to not stagnate and might continue to deliver potential for performance and cost optimization. The current level of investment is only unsustainable if that doesn't happen and nothing else changes. I don't think those kind of closed world assumptions are a safe bet at all.
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
Who writes like this? When you lead with "everyone who doesn't agree with me is a lying cheat coward imbecile" I think we should just turn the volume down on you to zero.
This is breakdown in dialog. If it leads like this then I I don't care how accurate the critical analysis to follow is. I didn't read the rest of the article and don't think anyone else should either out of sheer disdain for this argumentation style.
I don't think anybody actually believes that the current investment is going to yield returns that they are projecting. Neither did people back in Dotcom or Railways or any other hype/bubbles. Yet these technology did transform and the returns came to fruition.
Internet continued to thrive and grow even after the stock market came and went, it took 13 years to roughly nasdaq to recover but the explosion of GDP from internet has been largely decoupled from the previous bubble boom and bust.
If you use the stock market as a yard stick to project new revolutionary technology we shouldn't have had trains, internet. In fact internet should've stopped with the bust of Nasdaq and everybody would've moved back to using paper but we didn't it gave rise to the next wave of economic output powered by this new tech.
The handwringing tone of the article is off-putting.
Ed is confused between whether AI is useful, and whether the current level of funding and valuations are sustainable. The following statements can both be true:
1. AI is already quite useful and will continue to be so. This is true even if AGI doesn’t happen.
2. The funding and valuations of many AI companies are too far ahead of their skis, and will probably roll back. Some may fail entirely.
About the “where’s the productivity in AI?” question: I think it’s entirely possible that the primary benefit of AI will not be top-line growth but reduced costs (through reduced human labor). Companies will need to reduce prices to prevent losing market share to existing or new competitors, meaning that GDP may not increase, but costs will.
He may be bombastic but Zitron is right about the AI problem. These companies do hemorrhage cash, and have no viable plan to even become solvent. It may not be a scam but it sure looks like one. The problem it poses for the economy... is just as he says.
Whenever I read these kind of articles about AI financials, I'm reminded of identical screeds I read about Uber a few years ago. They were angrily insistent that Uber was a scam company run by criminals and charlatans and could never, ever become profitable or make money for its investors. It was a house of cards that would come crashing down sooner or later, and take everyone's money with it. Now it's 2026. Uber still exists, has revenues of $50bn and is apparently a highly profitable business. I don't know if the original investors have made their money back yet, but Uber certainly hasn't collapsed.
Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.
Uber used the classic triple-E philosophy of Microsoft and entered a market that was ripe for disruption -- many cities lacked reliable taxi service entirely, others were cartels that fixed prices. They undercut prices to an extreme degree, subsidized fares, and when it either drove local taxi companies out of business and spurred widespread adoption as the default, it had a captive market and duopoly with Lyft which allowed them to raise fares without losing any market share whatsoever.
It's a pretty classic business strategy, and not directly comparable to any of the AI companies. There's a reason people compare the current situation to the dotcom era and not Uber. Also, don't take Uber as an example of a slam-dunk VC success story and leave it at that -- plenty of dumb ideas get pitched and funded and go bankrupt for every Uber.
Yeah, people forget the risk to Uber was real in the early days. If municipalities had enforced their taxi laws, the company would have died and all those millions invested would have been lost (or pivoted into something else).
It was only because Uber successfully bulldozed over all regulations that it was able to succeed ... and that was hard to predict before it happened.
Absolutely. Even these days, Uber really only has one or two viable competitors. With any 3rd one in a far distant 3rd. Meanwhile, swapping which AI I’m using is as easy as clicking a dropdown. Hardly comparable to a physical car ride.
It doesn't matter if it's slowing down, pretty much no one has implemented it to its full extent yet. It could stop right now and we'll be finding new implementations a decade from now.
Anthropic and Open AI could evaporate tomorrow and we'll still be using the models.
The market may collapse, but the people who think AI is going to disappear as a result don't understand what it is.
AI companies are racing to win the future of computing.
They are possibly in a winner take all death race against each other.
The stakes are so high that these cash rich companies cannot afford not to throw everything they have into this.
The sunk costs are irrelevant when it’s a question of survival.
Whether you hate or love AI computing is being completely reinvented - at the absolute core of this is computers programming computers.
Anthropic is winning this race by a country mile right now.
This is such an important future bet for these companies that the trillions must be spent because there’s no future or a greatly diminished future for some of them unless they have ownership of the technology.
"Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology."
Well there are a lot of commenters so presumably some interest. I just had a look at the Bloomberg bit https://youtu.be/zbKDmkJPVvI and didn't see sweaty boosters rattled, just Ed doing his usual spiel - they are loss making and so it's all a big con. Which is kind of unproven on the big con bit.
Ed Zitron speaks to a particular type of angry tech conservative. He’s not speaking truth or exposing anything. He’s the soothing voice the tech nerds of yesterday year are yearning for.
The angry polemic that goes on and on and on with cuss words used liberally is just meant to evoke emotion and cathartic resolution to the type of people mentioned above. Not truth.
The thing is, there are a lot of people that find comfort in what he’s writing - primarily because it’s a coping mechanism against how quickly things are moving and a way to deal with being left behind. When you spend time, years, building institutional knowledge and making a whole identity out of it, you obviously will feel bad with the threat of it being commoditised.
I would write against the content of the article but I find it easier and more illuminating to write what he has said before instead. Then it shows how incorrect the guy has been and with what confidence he keeps speaking with.
I'm collecting many kinds of predictions Ed Zitron made so that you can see for yourself whether he has a good track record.
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> While complex, generative AI is a technology that probabilistically generates answers, and has no "intelligence." It is inherently limited by its architecture, and in turn can only get "better" in a linear fashion. I see no signs that the transformer-based architecture can do significantly more than it currently does.
He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?
> Furthermore, I hypothesize a race to the bottom in generative AI will significantly hamper OpenAI's ability to expand revenue, compounded by the fact that we're approaching the limits of transformer-based architecture.
He wrote this in 2024 and since then Anthropic's revenue increased by 160x to $40 B dollars a year and OpenAI's increased by 6x. Do you think this person gets predictions right still?
> I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be,
He wrote this in 2024, do you really think we have reached upper limits? Huh?? What I'm using today is significantly more accurate and 2 tiers above what we had.
> And if there are true industry-changing possibilities waiting for us on the other side, I am yet to hear them outside of the fan fiction of Silicon Valley hucksters.
He says this about AI when we have with all honesty have had industry changing possibilities like agentic coding.
> There are indications that consumers have also lost interest. As pointed out by Alex Kantrowitz’ Big Technology newsletter, traffic to ChatGPT on both mobile and web has started to stagnate, if not decline. In January 2024, ChatGPT had 1.6 billion visits — 11% below the all-time peak of 1.8 billion. This makes it only modestly more popular than Bing, which had 1.3 billion unique visits during that period. On the mobile front, ChatGPT has an estimated 6.3 million US users — or 1.7 times less than the total of new Snapchat users added during Q4 2023.
He agrees with the claim that the consumer interest has declined. Since he said this, there was a 9x growth in active users.
"A.I bubble is bursting with Ed Zitron" (1 year back)
He's been constantly crying bubble for years now.
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> AI video won’t get truly fixed just by waiting a year.
This is what he had said in 2024, and you just need to compare video from then and now to check whether the predictions came true. Why would anyone trust what this guy has to say?
How’s that meme go? "We are 2/3 years into being 6 months away from AI taking all white collar jobs".
The criticism goes both ways. The word "fixed", in Ed terms, can be translated to "become a viable business that justifies the spend".
In regards to AI video, I think the fact that Sora is no long around is an indicator. And there is seemingly no real appetite for AI video outside of memes, jokes, and misinformation, probably indicates that the prediction around AI video has come true.
This website can't run if this sort of rhetoric is accepted: "they told lies so we can tell lies".
Frankly this is anti-social and should not be tolerated here.
>In regards to AI video, I think the fact that Sora is no long around is an indicator. And there is seemingly no real appetite for AI video outside of memes, jokes, and misinformation, probably indicates that the prediction around AI video has come true.
His point was about the performance and accuracy and not about the community/market. He was wrong.
It's like someone arguing that cheese isn't real. Yes I can go to the grocery store and take a picture of cheese and show it, but what's the point? They can live in their own world. It doesn't change any of our lives. The world is what it is.
Lol... in this case, cheese imports from China are much cheaper, just not quite as good.
And for those who are all "but dur CCP get all ur data" you can use things like AWS Bedrock (at least for earlier versions of Deepseek and Qwen for now) and have more familiar people get all your data. Or buy (at obnoxiously inflated prices) your own HW and not send your data to anyone.
The funniest part of this is that people are often talking about how LLMs are now writing 100% of their code, then also saying that they don't want to expose their code to foreign government exfiltration by using foreign models.
But, uh, if an LLM is writing 100% of your code you have no actual secret sauce to hide from anyone, so why worry about it.
Perfect for idea people. All the value is in the prompt. Ideas are important, not execution. A decade or two ago, they would have been looking for a technical co-founder.
Yeah, so true. There is no moat to your competitors using the exact same tools and prompts to generate their apps and services. Companies should be hiring/retaining creative thinkers that give them that human edge rather than laying people off under the guise of "improved efficiency"
I think we're going to see a lot of craziness in the future in this regard. Not just "secrets", but hypocrites trying to copyright and patent all the AI outputs. All kinds of rabid attempts at constructing monopolies for every half-baked idea they have tried to utter as a prompt.
Meanwhile, like I think you suggest, I would assume everyone can generate similar outputs themselves. The idea that you can claim priority on your dream prompt and lock up the market on prompt responses sounds delusional to me. It's not novel invention when you're spit-balling at the same level of abstraction as every fantasy/scifi writer who ever was.
So I also have doubts about the sustainable business model. How long will it take for this fantasy to unravel, as people discover they cannot monetize their AI outputs as much as they dreamed, and in turn cannot afford to pay the AI services they use?
My absolute nightmare is that this becomes a "too big to fail" thing and oppressive/fascist governments decide to back full regulatory capture. That instead of letting it unwind, they grant and support enforcement of an increasingly absurd and arbitrary copyright/patent regime to support this monetization scheme.
Some people seem to see the world only through bubbles. But if you look at human history, despite the ups and downs, we have a trajectory; generally speaking, human-created systems evolve toward ever-increasing complexity, impact, and efficiency.
The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today. To have tools that can understand, manipulate, and produce it is a massive leap forward.
Once you see things that way, it is clear that we are not in a bubble; we are in a transition. Yes, there is tons of hype and over-investment, but the demand is real, and so is the impact. Unless you are deep in the tech and have that structural depth, it is easy to dismiss. This is like the invention of the personal computer, but with 100x the impact and speed.
The only "bubble" with AI is that the initial build out is cyclical, and many of the high flying chip stocks with no software arms (ala Nvidia's CUDA) will come back to Earth. I think anyone that thinks AI is going away or won't have massive impact (though maybe not in the doomsday scenario) are in complete denial.
RTFA; it's not about AI's massive impact or lack thereof ... it's about these businesses not having a viable business model that will sustain them (beyond the next couple years).
I think Zitron's problem is he's equating AI to OpenAI and Anthropic. I'd agree with him that both those businesses are in a dangerous position given how fast they've burnt through cash. However, that's not the entirety of the industry and there are a lot smaller labs doing more for a lot less capital.
The business model does appear to be viable for these labs. But that viability comes because they aren't wasting a bunch of R&D money developing worthless products like AI video production.
I admit, I didn't read the whole article; I read a few paragraphs and extrapolated the mindset from which the author operates.
Regarding your comment about the business model—the people in Silicon Valley are not stupid. They know the playbook; we've seen it with social networks. The issue isn't the business model itself; it's that these companies need to dominate the market, and the big players are competing for that on a global scale. It's the exact same playbook that played out in financial systems and social networks, and now it's happening with AI. Once these technologies are deeply integrated into enterprises and the global economy, these players will dominate the market for decades to come.
I can assure you, the people running those companies are smarter than you, me, and the author of this article."
What I suspect isn't that AI goes somewhere, but I do think that the cutting edge companies like Anthropic and OpenAI are in a very precarious position. They don't have very much of a moat and the competition has been catching up quick while spending a lot less doing so. IMO, the main thing keeping them alive right now is name recognition.
If I were to make a prediction, it's that ultimately these cheaper models are going end up eating their lunch. I don't think they'll make back the money they've invested and once that reality hits investors, those two companies are sunk.
That, however, is not the end of AI. Nor will it be the end of Nvidia/micron/etc. It will more just be a localized bubble pop that doesn't eliminate the product from the market.
It is not just about cheaper models; it is about integration with the economy.
These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
> These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Much like the electric grid, what we are seeing is a convergence on standard APIs. For example, most of these cheaper models are hosted using APIs compatible with OpenAI. It's not a matter of rewiring your electric plug to work with a different socket standard, instead it's just the process of plugging it into a new socket.
> Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
Certainly the Chinese models appear to be some of the best when it comes to competition, but they aren't the only ones. There are European models and other US based models which all run for cheaper.
I see your point, but having worked as a consultant for a few years, I think most companies will opt to stay once things are stable. Once these systems are functional, nobody wants to touch them.
I remember one government project where we wanted to migrate a system from COBOL to a modern stack. The requirement was for the UI to stay exactly the same as the old green terminal; the evaluation criterion was pixel-perfect proximity to the original. We literally had to build terminals using web tech.
These models are not the same as each other. Once they are integrated and working, the incentive to change them is incredibly low. So really, the race is about who can integrate deeper, wider, and faster over the next couple of years—that is what will determine the long-term winners.
This is the exact same playbook we saw with social networks. There is a reason why we have only a handful of them dominating globally, and guess what? It's not because of the tech.
There is no incentive to rewrite working software in COBOL to something else. You don't really change the people cost of maintaining that code all that much and you incur a huge rewrite cost.
AI is different, it's an ongoing cost to the company. If that cost raises aggressively, you can bet companies will race to eliminate it, no matter how integrated it is. Companies can and do do this all the time.
And the models are close, not the same, but close. That's what matters in LLM stuff in general. If a model is capable of doing the same work for less, it will be chosen. Especially since the switch over cost is often on the level of "point the tool at this URL instead of that URL".
I get what you are saying if this were a more sticky concrete tech that is harder to move away from. But that's simply not the case for these LLMs. A big selling point they have is that they are super flexible.
I don't think the transition will be as simple as just flipping a URL. There is an entire legal and technical infrastructure being built around these models and their integration. I think you underestimate an organization's resistance to change once things actually work, as well as the sheer complexity of making that shift.
I also expect pressure will eventually drive the cost of running these models down. Power plants are being built, more capable chips are being produced, and a big chunk of the capital right now is being used to scale the physical infrastructure—the data centers and energy grid. Once that stabilizes, these companies will have positive cash flows. Again, it's highly similar to what we saw with the expansion of social networks, just with more aggressive and widespread adoption.
Ultimately, a handful of companies are going to provide these core capabilities, just like we have a handful of major cloud providers right now. Why do you think this would change? If anything, the trend toward deep vendor lock-in is even stronger now.
The moat is the infrastructure and lock-in. Similar to AWS or anything else. Small data centers can't compete, and similarly people without massive compute won't be able to either (at least not on the enterprise level.) You might get a few edge models, but for huge businesses they will be using OpenAI and Anthropic (and Google/Microsoft/Amazon, etc).
The biggest competitors aren't small models, they are just the traditional players that already have an "in" with enterprises. That I think will start to show its face once this initial round of buildout is complete, which may not be for another 5+ years.
I disagree. Mainly because those small models are exactly what erode away the moat of needing a giant data center. Those smaller models have been proving themselves to not be far of from the SOTA models.
As OpenAI and Anthropic look to raise their prices, businesses will be much more compelled to looking at cheaper models. And if the narrative is "do the same as you did with OpenAI at 1/20th the cost" that's going to sell to a lot of businesses.
It certainly cuts into what exactly these companies can sell in general. For example, if I wanted to integrate AI into a product I'd almost certainly not chose OpenAI or Anthropic. That's because they are simply way too expensive and what they'd give me is a lot less. We've actually ran into just this. We needed a classifier for a lot of records, we picked a free model because, as you can imagine, we didn't need something as good as what OpenAI and Anthopic offered and free works.
> The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today.
One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow. If Zitron's argument were as solid as he keeps saying it is, you would read it and understand it and see that it is solid. He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument. But no. He jumps. He leaps. He circles back. If the situation were really "Gosh why can't you see it?!"-clear, his explanation of the situation would be clear. It isn't, because it isn't.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
That's exactly what the first (titled) section does?
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
Which of the hyperlinks provided at the beginning sounded like what you wanted, and after you clicked it how did it disappoint you?
The information you are describing is stuff I would not expect anybody to repeatedly duplicate across periodic blog-posts.
Agreed. Phrases like "journalists are currently gooning over OpenAI and Anthropic" really put me off. It's a poor attempt at modern muckraking; cheeky yet offering little substance.
He's just a Brit, writing in a style we write in. Sweary, comical, red-top. The Register did it for years.
I don't think you know what "gooning" means. It's edgy Gen Z slang and has nothing to do with being British.
I didn't say it was. I'm just observing that his muckraking style is part of a very long British pundit tradition. Americans have never liked it — Intel got very upset about The Register's coverage of "the Itanic".
(And he's not Gen Z anyway is he; he's among the older millennials. He's appropriating it for muckraking purposes.)
Sure, but does that vibe invalidate the argument? What an odd time the middle of an argument is to be clutching pearls and worrying about prose quality.
Style and vibes notwithstanding, is there anything in your view that wrong with the argument itself? Could a better or more polite writer have convinced you with the same shape of logic?
I responded to a comment about the prose. Why are you not calling out that one instead?
I particularly enjoy reading big banners asking me to pay for a newsletter subscription if I "liked" the content. Not if I found it interesting. Not if it actually provided any value whatsoever to me. No, you just have to "like" it. In other words, it is meant to be written in an engaging way and perhaps reinforce your believes like an echo chamber or even stir up certain strong emotions. Not to convey information. So, thanks, but no. I'm sure this opinion blog is very well written, but I don't think it is more well founded than anything else in this sea of opinions that sports a bigger garbage patch than the Pacific Ocean.
A big chunk of text asked for support on the basis of the article. I hadn’t read the article.
I scrolled down a bit to read. A popup took up my screen, asking me to subscribe, having read essentially nothing at this point.
I just left. Life is too short.
His arguments on the numbers of AI are actually pretty solid.
I am still to see a solid counter to what he brings up there.
Arguments have smells but rigour demands you investigate further. Zitron's smelly prose is, ironically, just the kind of stylistic distraction that AI can help condition; the further irony is that he will one day seem to have been right, for a year or two.
The money is indeed losing its mind over AI, and Zitron is a stopped clock. A correction is coming but the tool isn't going anywhere.
It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.
Today Apple launched its revamped AI offering. Judging by several reports, Apple pays Google a mere billion dollars a year to operate it.
Consumer revenue is only a smallish share of the puzzle, but still:
If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide? Why would you pay for ChatGPT, or even tolerate its inevitably increasingly desperate ad placements?
Assume Google will have similar tools in their phones, and Google search will continue to have the offering it does.
In short, where is the evidence that once Apple's tech exists, consumer AI is worth, to Anthropic or OpenAI, anything noticeably more than that $1B a year?
Maybe OpenAI strikes a deal to put something in Samsung phones. Let's say Samsung is ten times as desperate as Apple (which is how it looks, often). Still only $10B a year?
2026 consumer revenue projections from OpenAI are pitched at $14-15 billion, apparently. If they get that, it's the only year they will get that, because by late this year, everyone with an iPhone will have something useful built in.
Ed Zitron is a mouthy British rabble-rouser, but I think he is probably mostly on the money.
I expect that a lot of the money will be in Enterprise AI.
Right but OpenAI are for real making that prediction about their consumer revenue, which seems decidedly ambitious (considering that they are making nothing from their current phone placement). And they have said that they expect it to be quite a large share!
https://mlq.ai/news/openai-projects-over-280-billion-revenue...
"OpenAI projects revenue will be divided nearly equally between its consumer and enterprise business units by 2030"
That it is so absurdly ambitious and so likely to run up against reality strikes me as really indicative of the quality of the envelopes these calculations are being sketched on.
Lots of dismissive comments ITT, very few tackling the substance of the article.
> AI Cannot Afford To Slow Down — It Needs $3 Trillion Or More In Revenue By End Of 2030 To Sustain Its Existence
Is this true? With the total 2024 wages being 11.7 trillion USD [0], and nonfarm payrolls totaling 158,000 in the same year [1], it's an order of magnitude higher than my back of the napkin guesses I've made that AI needs to take or create 1/20 jobs minimum to break even.
[0] https://fred.stlouisfed.org/series/BA06RC1A027NBEA [1] https://fred.stlouisfed.org/series/PAYEMS
If I thought there were some actual small cabal of people running the global economy, this is almost like a novel: massive amounts of money entered the economy starting in 2008 and 2020-2023, the rich became insanely wealthy. Their wealth is now all tied up in the 2020s version of the railroad/fiber, we're going to essentially erase trillions of dollars from the global economy and reset.
We sure do need a reset.
There's no "reset" required, other than resetting what people understand about money and finance, and having them realise that net worth is a stupid thing to use to excuse their revolutionary bloodlust.
Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely misses the incredible ground-level utility that many of us on HN celebrate every day through undeniable, massive productivity gains.
At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.
> undeniable, massive productivity gains.
Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
So where is all the productivity going? Where is the value? Where are the massive unemployment stats or the millions of new startups making big $$$?
Writing about AI, destroying the planet for data centers, there's a lot of money to be made.
That being said, AI seems kind of miraculous sometimes.
Similar to cars. So enticing that we make everything else in the world worse in order to maximize the profit, make it indispensable, subsidize it, and make the dependency on it irreversible.
And it's not even something to blame individual people for.
Driving away from all the other cars to spend a weekend feels like freedom.
Using AI to answer a question feels like a "bicycle for the mind".
But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt.
Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
The environmental impact of answering a question on an obscure topic with ai model is less than an the impact of answering the question with an hour-long google search hunting for references or a drive to the public library.
That's true, and I am not anti-AI. I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.
Neither did I want to say that a car is always more wasteful than some alternative.
But defaulting to the behemoth is inefficient, unless everyone is driven to do it: then it's in some way reasonable.
By adding "corrupt" and "dependent", as well as the economic terms, I wanted to offer a broader critique and create an analogy, not just talk about energy usage on its own.
What I had in mind was: it's easier to go many places that are a mile or less from me, by car. Because everything is obstructed by cars. And I'm atrophied by lack of movement. Best would be to drive somewhere to move/walk.
People already do that in masses.
And doing shopping by car, because everything else seems unbearable, also takes away your time, apart from wasting energy compared to more, smaller shops that would be reachable by foot, bycicle etc.
I guess you know the argument.
Today, people's thinking atrophies because their LLM is probably right in their summarization of some Wikipedia article, plus 2-3 other random sources.
Or so.
Using the Wikipedia search function is not expensive.
But, I mostly had a bigger picture in mind than what is the cost of inference.
I think it's a good analogy in many ways, and personally I think car-centric society has a lot of flaws. I think the ease that AI brings to tasks may erode mental capabilities in the same way cars have eroded our collective physical health.* That said, it doesn't seem to me that we would be better off without cars altogether, despite all the related issues.
I am concerned about the environmental impacts that AI poses, but they don't seem to me to be so catastrophic. Solar and battery tech has made enormous leaps in the past couple decades, and we will need to pivot to clean energy future irrespective of AI.
*This said, I have become gradually more alarmed over the past decade at the lack of epistemological rigor in the general public, as made apparent through the rise of social media. I don't know that AI becoming a truth-seeking crutch for people wouldn't be more good than bad.
> I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.
Hand wringing about AI datacenter's environmental impact is well and good. We should keep the data centers accountable for their consumption and waste.
I just wish the same people had been upset the last 20 years with poor water resource management in a lot of areas (the west US especially) with urban, ranching and farming development.
> That's true, and I am not anti-AI.
Me neither!
It's like saying if we didn't have cheap commercial flights people would travel by foot anyways and would consume more resources for food &co. than the plane would consume in fuel...
80% of generative AI queries wouldn't even exist as google searches.
To be clear, your position here is that insurmountable barriers to information is the preferable state of the world?
One claim of the parent comment was that AI is ineffective. For the purpose of finding answers to questions, it is more resource-efficient than the alternatives, and, to your point, capable of answering questions that were impossible to answer via other means before. In what way is that ineffective?
I do plenty of AI queries, both pragmatic ones and some for entertainment: witnessing talktotransformer was mind-bending already at the time! And since then, I've tried frontier models, local, coding agents, and use plenty of them on the regular.
I awe at the capabilites of generative AI.
I also enjoy sitting in or driving a car.
I did not want to make a moral argument, unless you consider each and every form of utilitarianism as moralism.
That might be true, but at least I started asking way more questions since we’ve had competent LLMs.
Vonnegut said in his last living work that the greatest addiction modern people face is the drug of cheap oil.
We got addicted to the convenience and overuse, and have started a mass extinction event because of it.
The perverse incentives will come for us all.
I agree with your message but not sure about the conclusion. Cars themselves are commodified luxury available (in the US pretty much required) to everyone, and they do need to be subsidized, both in terms of infrastructure and the lifestyle they require.
But with AI what is the exact price? My understanding is that R&D is extremely expensive, but running non-SOTA models is not that bad. We are getting pretty close to models which can be useful locally in many applications.
Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
Thanks for asking an open question about my point.
First, because I initially failed to answer your more closed questions (this paragraph is edited in):
> We are getting pretty close to models which can be useful locally in many applications. Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
I don't think there's a way around making the best of AI capabilities with minimum price and maximum control, and I'd agree this is met by on-prem data centers, just not in a rationally targeted way.
Back to my original comment:
Because it (my conclusion) was not so clear, and maybe I just wanted to highlight some observations without delivering a real argument for or against things [, I thank you for your open question].
The utility/leverage aspect for AI seems more esoteric than the one for cars because, apart from Chatbots, it's more hidden.
And also, similar to cars (or many other phenomena of industrialization), yes, my first vague point was the subsidization of infrastructure. But also, the power gap: that's something not only associated with AI or cars, but with a lot of technologies we all hold dear: sewage, powerline, logistics, etc etc.
What reminds me of cars in the current AI frenzy is the fixation on cementing infrastructure. And also, I think, a lot more people agree on, for example, some kind of universal right to, for example, clean water.
But all of industrialization confronts people with questions of efficiency, inequality, and collective support.
Most people would, for example, support a right to get a minimum amount of clean water when you are living and working in a tradionally inhabited space (if you're on the social-darwinist side) or at least not harming society (if you're more of a social democrat).
And, similar to the buildup of car infrastructure, and the procurement of resources, space etc for maximum building, giant data centers can obstruct people in buying drinking water. Or walking outside (AI obstructs traditional methods of online collaboration).
The original point of the stock market was to fund gigantic society-level projects (like railroads). Modern VC has replaced some of that at smaller scales but not all of it at the largest scales. So this could just be the stock market performing the function it was designed to perform -- helping fund something transformative on a societal level.
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
Where did all the stock gains go before AI?
FAANG / MAG-7.
Was everything from 2012-2020 fake, too?
They went from ~9% of the sp500 to ~35% over your timeframe...
Not sure what your point is. Stock markets are based on money going into securities based on estimated future value. Even if AI were doubling productivity at a non-AI company, there is more leverage to that money going into an AI company.
The question is, is AI leading to massive productivity gains in companies that implement it? AI productivity gains take time to diffuse, but so far companies in the S&P 500 are seeing very high growth. YOY earnings growth rate for the S&P 500 is 21.7% https://advantage.factset.com/hubfs/Website/Resources%20Sect...
> YOY earnings growth rate for the S&P 500 is 21.7%
Now remove the companies selling the AI shovels: https://pbs.twimg.com/media/HIAjbZxacAARHwD.png
> Not sure what your point is.
My point is that they're selling us Skynet and the end of employment as we now it, things that we shouldn't even have to measure to perceive the results of, yet no one is able to measure any of it
Pointing a finger at nvidia, google, and the other few companies stuck in circular investment schemes that shouldn't even be legal and saying "OOGA BOOGA line go UP, UP GOOD!" doesn't count in my book
Is the image you provided depicting revenue, or stock value? My point is about revenue.
Revenues don't matter when you sell a dollar for 50ct and half of the deals are circular anyways
So you're claiming that the revenue growth of the S&P 500 over the last few years is largely due to "selling dollars for 50ct" and circular deals?
He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse.
I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place.
My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit.
But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time.
The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
I think he doesn't need to understand the technology to point out the books are cooked. a business can sink in either way: the technology flops or the finances flop. he's arguing the /finances/ would flop. he doesn't argue that the /technology/ would flop, only that they can't come up with the money to pay their debters.
There is a piece of this I agree with. That you do not need to be a deep technical expert to notice that a company is burning cash by overcommitting to capex, or relying on heroic revenue projections that may or may not come to pass.
But that is not the full argument he is making. If the claim is that the labs will not be able to pay their creditors because inference is structurally incapable of becoming profitable, then he absolutely needs to be right about the technical economics of inference.
One part of that is the balance-sheet argument (which already shows insanely good margins). But it also depends on how inference-time compute actually works: routing, batching, kv cache reuse, model segmentation, different latency tiers, etc. Much of those details he's just been straight up wrong about in his writing, so as a result I have to call into question the rest of his reasoning as well (in part to avoid Gell-Mann amnesia).
> That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
But does it also not mean that they will make less money given that there is already brutal competition for that lower tier from openrouter, Deepseek, Amazon, etc.?
You can't on the one hand say "customers are beginning to understand they can spend less" and on the other hand suggest that this is good for forecasts of revenue.
> that he doesn't really understand how inference works from a technical perspective
Could you share what tells about it? I.e. where he was wrong about it?
There's examples both in his writing and also in his appearances on podcasts, interviews, etc.
I'll cherry pick a couple:
“When these new models ‘reason,’ they break a user’s input and break into component parts, then run inference on each one of those parts.” [1]
This is not at all how test-time compute works. At best, this is a very loose metaphor that he may have used out of convenience. This might sound a bit pedantic to point out, but this is a very basic thing that he's getting wrong (presumably at least, again it could be that he just used a poor metaphor).
A less pedantic example would be his claims related to gpt-5/chatgpt auto-routing. He argued that having a router means OpenAI can no longer cache static prompts, because the user prompt has to come before the hidden instructions [2]. This is just not at all how this works at inference-time. There is no evidence that the standard approach of system>developer>user instruction hierarchy has changed, the public API and caching docs maintain this.
But even more broadly, it suggests he is reasoning about kv/prefix caching at the wrong level of abstraction. It's true that conventional prefix caching does require a stable prefix, so yes, if you literally put variable user content before the static prompt, you would destroy the cacheability of that static prompt.
But that is exactly why inference systems are designed to preserve reusable prefixes where possible (via checkpointing or similar), and why serving systems care so much about prefix caching. This is also a big part of how disaggregated prefill/decode infra works where cache-aware routing is critical. His argument treats a bad prompt layout as if it were a necessary consequence of routing, rather than an avoidable implementation choice.
A router can read the user request, decide which model path to use, and then construct a normal downstream model call with stable static instructions first and user content later. Treating that as impossible implies a fundamental architectural misunderstanding.
[1] https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/
[2] https://www.wheresyoured.at/how-does-gpt-5-work/
Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing.
https://unessays.substack.com/p/talk-is-cheap
From an economic perspective productivity is defined as the creation of value isn't it? Then if you "improve productivity" and does not create value in the end you're no improving productivity at all.
It does depend on how you define productivity. But the way it's commonly used is "I'm going faster, personally, with these tools."
The thing people I think have a hard time seeing is that "I go faster" does not mean "more features get finished".
It's a scale issue, and one scale is better than the other. People only pay for finished features, they do not pay for how much code you emit.
Productivity is defined revenue per worker hour. And we know worker hours are going down as there are fewer workers with the layoffs.
Also, supposed productivity gains are dubious. I personally experience at best no productivity gains when using LLMs to write code, and sometimes it's an active drain on my productivity. There was that one study a year or so ago showing similar results. People are trying to say the productivity gains are there and undeniable, but that is not true. It is very much a subject of controversy whether AI helps productivity.
I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility.
However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.
There is an observational study that was published in March 2026 that followed 4000 teams over 2 years. It shows, in my view, exactly that the productivity gains don't translate into economic value.
Here is the report:
https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways
And my commentary:
https://unessays.substack.com/p/talk-is-cheap
If it was published in March 2026, even if the data was collected up to the day the study was published, 7/8ths of it would fail my “within the last six months” test. But I am looking forward to the results of future studies on this topic!
Its funny, I've noticed the same thing, but did not come to the same conclusion.
I currently don't have work access to Claude Code, but most of my teammates do. Watching from the outside, the cycle seems to look like this:
1. Experience some success, which hooks you into relying on AI.
2. The AI keeps failing at some task, but you don't want to stop. Keep trying over and over again.
3. Run out of tokens and take a break.
Now, sometimes 1 doesn't happen. Sometimes 2 doesn't happen. 3 is a certainty though.
Now, if you told me that the productivity gain from 1 is enough to offset the loss from 2 and 3, I could believe you. But I also wouldn't be surprised if it didn't.
As I work with Claude more and gain a feel for its capabilities, I tend to run into 2 far less often, as I'll decompose my messages more for the current model limitations. The threshold also changes each release.
That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent.
EDIT: In fact, parent comment has a link to some numbers.
[EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
I've posted numbers that indicate that productivity is becoming decoupled from value delivery. If you follow the link in my comment it reviews a pretty robust study of 4000 teams over 2 years. There is no product throughput increase.
Yep.
Code acceleration is great, but.... something precedes that. Vision and strategy re. expansion of offerings and businesses. Once a firm reaches maturity in what it offers and is only touching the edges - this code acceleration is literally useless when you factor in all of the trade-offs.
This is a good thing - it means fat and slow incumbents are sitting ducks to be out-witted by creative and imaginative founders, which is healthy for a well-functioning economy.
Now the economics of existing frontier models are not sustainable - its looking like a mix of the airline (supersonic vs subsonic) and EV industry with China in the background providing decent offerings at much lower prices.
I think its worse than that.
I admit that if a small team or an individual uses an LLM, it's likely they can create value faster.
I think as soon as you don't own the responsibility for the defects you generate with an LLM, their use starts to destroy value. Regardless of product maturity.
This is what I think the data says.
https://unessays.substack.com/p/talk-is-cheap
You're not really getting it.
If you are producing something that delivers a far better experience, irrespective of what's under the hood (see Claude Code et al), you will decimate an incumbent who is trying to use LLMs in the context of incrementally improving a mature product.
LLMs are suited for the development of revolutionary innovation, not incremental.
Yeah this part scares me a little. I imagine it scares everyone who is more than a couple of years out of school. I hear that "the solution to LLM tech debt is more LLM." That might be true, but it might not be.
Interesting data, thanks.
>undeniable, massive productivity gains.
How can something so undeniable have zero scientific evidence? Are there any large peer reviewed or meta studies confirming your claim?
It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model.
I think the surest sign of productivity gains is the sheer volume of adoption. If you look beyond headlines, adoption is just incredible. Of course adoption does not necessarily point to productivity gains, but if this was some sort of FOMO or smoke and mirrors you would not see this much retention and this feverish a pace of adoption. You would not see a large segment of the profession using coding agents exclusively. All of these companies track productivity, again with imperfect proxies, yet everything points to a pretty consistent picture. Same with benchmarks, again a lot of crappy benchmarks but a lot of high quality ones too and a very diverse collection of tasks and capabilities they probe.
Your second paragraph appears to be 3 different instances of saying "X does not necessarily point to productivity gains... but in the case of AI, X definitely means productivity" without really saying why that is true or why other explanations do not fit.
Adoption meaning productivity supposes there are no other dominant factors for the AI push nor AI retention. It is possible for practices to be picked up or continued in spite of causing productivity DROPS. What studies have suggested are factors that make for productive work environments and what is actually enforced in the workplace are different things.
It’s 3 different weak but complimentary proxies. We form beliefs from imperfect evidence and I find these fairly convincing when it’s hard to find any hard evidence of no productivity and exactly the scenario you would expect under the hypothesis that we do see productivity gains. None of this is supposed to be unassailable. I would challenge then if you disagree what the evidence you have for this is?
Adoption implying at least some significant productivity gains doesn’t contradict there being other factors. You’re seeing entire companies reshaped. The argument is this is all for show or CEOs are in some sort of idiot class?
“It is possible for practices to be picked up or continued in spite of causing productivity drops” well of course. I just find that incredibly far away from Occam’s razor.
My point is: we have lots of evidence that’s highly consistent with real productivity gains, and I don’t see many pieces of evidence to the contrary.
Because even in a field like software engineering where the output of our work is save in version control, measuring baseline productivity is hard.
LoC: people argue it’s not what’s important
PRs/day: same as LoC
Getting projects done faster: oh but what about the quality.
Solve the technical problems and actually be more productive, the social systems build around the old way of doing things will hole you back.
Finish a PR in 10 minutes doesn’t matter if you’re waiting days for a human review.
He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.
The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either.
I find it difficult to take him seriously.
Progress is slowing, in an important way. Have a muck about with what Qwen 3.6 or Gemma 4 can do and you'll see.
The frontier models are losing their undeniable edge over that which is unmetered. Qwen just isn't all that far behind and compared to the data centre hardware it will run on a potato.
Commodification is coming, and with it slimmer profit margins; it's hard to see them making anywhere near the kind of money they need to in a commodified market.
> progress was slowing
Do you think it's not slowing? Do I miss anything really important?
My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
The most important thing I would point to is Mythos et al and the wave of vulnerabilities that have been discovered in the past couple months. It’s a completely unprecedented event, brought forth almost entirely by improvements in the models themselves. That said. keep in mind, I’m talking about over the past two years. With Claude code and the capabilities gained since December of last year, there have been incredible gains in the capabilities that are now available. Demand for inference is higher now than it was a year ago, because capability has improved. A specific criticism that I would hold is that claiming that progress with LLMs is slowing, prior to that point, is embarrassingly wrong in my view. One could argue that the model capability improvements are slowing, and all the improvements were in harnesses. I think that’s a stronger argument, but I have a few problems with it. 1. Utility is utility. Whether that comes from the model or the harness is irrelevant when making claims about utility. I don’t think that’s a useful distinction most of the time, but especially when talking about the technology as a whole. 2. Marginal intelligence gain is different than marginal utility gain. It’s estimated that intelligence grows logarithmically relative to investment. However, the utility of a marginally more intelligent model may grow exponentially, because once behavior crosses a reliability threshold, it unlocks new capabilities. 3. Even on those terms, it’s not clear to me that frontier capabilities are slowing down. With Mythos and its contemporaries, we have been seeing a vast change in the security industry as vulnerabilities are discovered at an unprecedented rate. OpenBSD vulnerabilities, more Firefox vulnerabilities found in a single month than the past two years, critical Linux vulnerabilities. It’s hard for me to look at the effects there, a radical new capabilities baked into the model itself, and see stagnation. A part of the reason it might feel like it’s slowing down is because we plebs don’t have access to the top models.
The maintainer of curl - who has access to mythos - disagrees [0].
I think it's dangerous to rely on claims made by people who financially profit from you believing them without checking.
[0]: https://daniel.haxx.se/blog/2026/05/11/mythos-finds-a-curl-v...
The article says in the second section that the author did not have access to Mythos. I think it’s dangerous to rely on claims made by others without even bothering to read them first, let alone check.
It found hundreds of vulnerabilities in Firefox, according to Mozilla: how does Mozilla benefit? It found a 27 year old vulnerability in OpenBSD. How do they benefit from that? Is that made up? Are the maintainers of those codebases lying for the benefit of Anthropic’s IPO? Is copy fail a fabrication by big AI? The 12 OpenSSL vulnerabilities found in January?
https://venturebeat.com/security/mythos-detection-ceiling-se... https://www.wired.com/story/mozilla-used-anthropics-mythos-t... https://cyberscoop.com/copy-fail-linux-vulnerability-artific... https://www.schneier.com/blog/archives/2026/02/ai-found-twel...
Im not sure whose claims you think I’m relying on. I trust Firefox that they’re not overstating the number of CVES they’ve found. Same for OpenSSL. The OpenBSD folks definitely don’t seem like the types. I’ve not known Linux to fabricate CVEs either. I think my sources are fine.
That blog post is very clear about the maintainer having no access to Mythos.
Does that matter that somebody else ran it for him?
Do you have access to Mythos?
Nope. Just watching the volume and severity of CVEs coming through since it’s been running. It’s been a busy few months.
anyone that takes him seriously at this point... I don't want to say very bad words here...
He has recently made the very good point that actually, the FAANG companies are struggling to put any ROI numbers on that incredible ground-level utility.
Uber, for example, is so unclear there is any ROI, they are cutting their exposure pretty radically.
He points out that one single Anthropic customer — a payments provider — accidentally had to pay Anthropic $500M for one month of token spend.
That is half what Apple is reportedly paying Google for the supply side of their entire consumer AI strategy.
I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work...
It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...
Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost.
Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost.
That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
Because it doesn't. Not for the tasks where using Opus instead of a lower tier model is appropriate, at any rate. Benchmarks show this, as do revealed preferences of actual users. To believe that Qwen is as capable as Opus at 1/20 the cost you have to believe that every person who does not make the choice to use Qwen over Opus for a given task is some mix of ignorant or delusional. This is certainly an opinion you can hold about other engineers, but it's definitely a questionable one at best.
The benchmarks between the two are close and the engineers that have used both (like myself) can attest that the differences aren't so wide as you might believe.
I'd say that yes, ignorance plays a role here because a decent number of engineers are looking strictly at the benchmarks and choosing Opus just for that reason.
But I'd also say that a major factor for Opus use is because Opus is being purchased for the engineers by their employers. They don't get to pick which models they are using.
They are absolutely deniable. Huge swathes of people deny them.
> undeniable, massive productivity gains.
The jury is still out on that.
Yeah they're very much deniable. Raw LOC/hr is much higher, and putting together a MVP, but I've yet to see any evidence that an LLM is capable of doing anything unsupervised, and if you need a human supervising everything it does... why bother having an LLM in the first place?
Because it can perform much faster? Monitoring allows you to multitask more effectively. I would also disagree that you can’t one shot anything…claims like this are weak and I have enough counter examples in my own life that it’s trivially false. The question is more: can it one shot the right things with a low enough failure rate for it to be a good replacement. It’s hard to figure that out a priori.
Even if we assume that everything you said holds true, how is that we as a crowd can make viable a service that eats some $300bn annually in infrastructure costs? Where would that money come from? Most tech companies these days are cutting their AI budgets because the per token pricing is killing them.
Cite a real source for that last bit, I don’t think that is true. Also the budgets should be cut the spend at some places goes beyond any reasonable amount. The strategy there is to hook everything in and find the right processes, then cut the rest. Things then get better and better with each model release.
The way you make a viable service that eats 300bn annually is to have enough demand to service that. Anthropic underbought compute. That tells you something.
Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead.
The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.
I don’t get this. We already have an insane demand. And yes exactly, this is primarily just with coding agents, but are you aware of what’s coming down the pipeline? It’s not hard to be you just have to find a decent way to keep up with literature.
* robotics (need to close data gap and release first viable product to get a data flywheel)
* conversational ai (no one is ready for this and we’re getting closer and closer to natural speech. The quality still isn’t good enough but it’ll be soon).
* other agentic use cases, openclaw adoption was crazy and that had a ton of barriers to entry
* ai products, like the one OpenAI is working on with Johnny Ive
Anyone thinking it’s unreasonable to hit whatever revenue requirements is just not that aware of what’s happening. Not to mention were capacity constrained already!! This is barely speculation at this point.
I don't think the issue with robotics is a data gap. maybe somewhat, but the real issues are that:
- RL is extraordinarily sample-inefficient.
- distribution shift/catastrophic forgetting aren't solved. only off-policy learning with giant decorrelated batches works.
- the breakout success of transformers as an architecture doesn't neatly translate to robot motion policy models.
the field is missing fundamental breakthroughs.
I also find it very interesting that conversational AI has taken this long. where are the models with good turn-taking? passive listening? the ability not to respond in paragraphs? has Anthropic simply not gotten around to it?
All of these points are great. The first one motivates world models which lots of labs work on. Not many people tend to understand the strategic value of those “open world” or interactive generation models: its robotics and planning. But also like you say you’re right, there are complicated problems to solve and it’s not totally clear the timeline. But where there’s data and compute, there’s a way.
For conversational AI these labs do have lots of things to do lol but you’re right; it likely also requires some architectural improvements but you see the infancy: look at the llama4 speech duplex model. Very unimpressive yet all of the components are there. Just a matter of pushing on them, licensing and commissioning better data, etc. takes time and compute is stretched thin.
Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).
Perhaps they aren't, but not currently viable !== always unviable.
Just 5 more years and $500 billion more, bro. We're still so early.
And?
> through undeniable, massive productivity gains.
And where are those? They seem particularly hard to actually observe and only appear in anecdotes.
> I'm trying to believe
For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?
I really like some good drama slop that reads like a thriller, it is entertaining. I don't take any of it THAT serious, but lately with the IPOs that are about to hit the indizes, he has gained a lot of attention. If you look around the internet, most people publish a negative angle on something and then extrapolate it into some grand conspiracy, which is really captivating. Its crazy when you enter some echo chamber you never engage with (movies, gaming, art/comics) and they have their own head cannon for why the world is bad and collapsing. It puts your echo chamber into perspective to see the same patterns of argumentation and presentation spin out in a different way
> undeniable, massive productivity gains.
Just because you keep repeating something doesn't make it an undeniable truth.
> Zitron is begging for a collapse at this point
No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job.
Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades.
Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."
> undeniable, massive productivity gains
How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication.
I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think.
[1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.
Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!"
This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.
> This, combined with his extreme ignorance,
Extreme ignorance?
Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. https://www.wheresyoured.at/peakai/ from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)
Predicting the timing of such a thing is notoriously difficult. I don't think being wrong about timing 2 years ago means there won't be a correction.
I'm also reminded of all the HN posts from 2007-2009 that predicted that the adoption of social networking would be a terrible thing for privacy, that it would destroy society, that people would lose their jobs over crazy shit they said on the Internet, that it would lead to the decline of trust and in-person interactions, that people would forget how to socialize, etc.
They were right about all of that but it took 15-20 years and the companies involved grew 100x in that timefold, eventually reaching trillion-dollar valuations that would've seemed insane in 2007.
There is a tremendous amount of money to be made in destroying society.
> Predicting the timing of such a thing is notoriously difficult.
So, it stands to reason that it wasn't a prediction, but a lucky guess (unless the alleged predictor has a history of correct predictions).
Not related to AI but, I recently rewatched "The Big Short" and your comment reminded me of it. I can't testify the accuracy of the movie, but for over year, Michael Burry was viewed as in the same manner for shorting the market, while the economy was was in a hype cycle.
Burry of course has famously predicted 40 of the last 5 crashes, so maybe not the best example.
I'm open-minded to arguments about AI being a financial bubble and a bad business.
I'm not open-minded to arguments about utility, given that I personally witnessed LLMs evolve from interesting but useless toys to insanely helpful tools I use every day.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).
But from the article I linked back in March 2024:
"Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models' capabilities will continue a rapacious pace of progress that has unquestionably slowed, with OpenAI admitting that GPT-4 may be worse on some tasks.
As I've written before, hallucinations are a feature not a bug. These models do not "know" anything. They are mathematical behemoths generating a best guess based on training data and labeling, and thus do not "know" what you are asking it to do. You simply cannot fix them. Hallucinations are not going away."
Since then:
- hallucinations are dramatically less of a problem
- several mass market use cases have emerged, most notably coding
- rate of progress has increased
I think the points you raise are reasonable signals to consider, but I don't think they show the author being "consistently wrong". The overall thesis still remains plausible even though we have seen LLMs continue to improve.
> - hallucinations are dramatically less of a problem
Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
> - several mass market use cases have emerged, most notably coding
Coding seems to be the only one, but there are still a lot of open questions about how the market can sustain the costs, and that's without considering the market dynamics that could emerge once costs are lowered enough that open source models start to become an attractive option.
> - rate of progress has increased
Debatable.
> Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
Another important consideration: Hallucinations getting less common/severe but not (as-good-as) solved makes them worse.
LLMs used to very obviously get things wrong. And people wouldn't trust them. Now they're good enough that people blindly trust them.
Now people just directly PR AI output with little to no manual review. We even have clowns calling for the complete abolition of directly human-authored code.
Whatever gains were had in better AI code output over the past two years I lose in having to review much more thoroughly.
Has rate of progress increased? How does one measure that? Genuinely curious - would be very interesting to map out the "effectiveness" of each AI model vs how long it took to train/release.
From my perspective, the model gains are mostly incremental now and a lot of the gains are just from things like improving the agent harnesses. I could be wrong though.
On the front page right now is the newest announcement from Xiaomi serving large model at over 1,000 tok/s on standard server gpus.
Every facet of the field is being pushed on and advanced at the same time.
> hallucinations are dramatically less of a problem
No they aren't. The models still hallucinate just like they always did. You cannot trust them, ever, to get something right.
> several mass market use cases have emerged, most notably coding
They aren't really useful for coding based upon the above. Since you can't trust them, you have to carefully review everything they make, which in turn destroys any productivity they could've given you.
> rate of progress has increased
I have yet to see any progress. Opus 4.8 that you get today is no more effective than GPT-3.5 was. Much less would I agree that the rate of progress has increased. Only hype has increased, but there has yet to be a drop of substance.
> several mass market use cases have emerged, most notably coding
Most notably? This is not a mass market use case in the way the author is describing. They are asserting that the amount of spend they need to get this off the ground necessitates the entire world coming in on it, and I would say that opinion has aged pretty well. There are a lot of coders, but there are more people scratching their heads as AI is shoved into every part of their lives.
Not the person you are responding to, but here:
> I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley.
We have seen 8 quarters since. Has any of that come to pass?
Even if you see a real bubble or catastrophy in the making, predicting when it will pop is a fools game.
if you can't predict when it will pop then you should really not predict anything. I can also predict that Google will pop. I won't tell you when but I'll tell you that it will. I'll remain thoroughly unfalsifiable and I'll keep pushing the dates.
https://news.ycombinator.com/item?id=48447549
The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.
I highly recommend folks read Wired's profile on him: https://www.wired.com/story/ai-pr-ed-zitron-profile/
Tim Lee also pointed out that when Ed has posted details on some of his analysis, they have had some....oddities: https://x.com/binarybits/status/2034377838883700953
What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:
Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
They ain’t profitable yet. Most of the model maker’s will be gone soon. It’s unsustainable unless you’re Google who has other income coming in to support their hobby, and the Chinese model makers are spending a fraction to be six months behind and many of them will be there for the long-term because they have backup support (government) who is in the race for the long-term.
One other thing that’s working against the model makers is the hardware is getting better and the models are getting smaller and more capable. I don’t think we’re going back to the mainframe days. Local will be the endgame.
Is Ed right? Probably because in the end it’s unsustainable the companies left will be the companies that have income coming from somewhere else and there’s one large tech company that isn’t even participating in the boondoggle unless you count $1 billion dollars a year as participating ultimately there is no moat in AI model making.
Nvidia and Microsoft trying to introduce another Arm processor in a laptop of all things won’t change the tide either.
Why is anti-AI soothing?
Because there are still a huge number of people who would be very relieved if the whole AI thing just went away.
For some of us it is, I suppose as an alternate view to AI booster-ism, particularly if you think the long term effects would be mostly negative.
He also does PR for AI companies and only really acknowledges this in interviews. As far as I know he never discloses it in his rants.
And its been 3 years of AI boosters telling me that my job as a litigating attorney will not exist in 2 months. Yet here I am, gainfully employed.
> Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong
So, judge the book by it's cover?
> arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Then the opposite should be easy to prove. AI is succeeding, is efficient, is universally good, and is working everywhere it's tried. Are those true?
> So, judge the book by it's cover?
It is literally judging the book by it's author, which is an extremely rationale judgement to make.
That's the exact opposite of rational. It is, in fact, a formal logical fallacy (ad hominem). His argument can be correct even if he himself is not typically correct.
On the surface, that's quite fair. However, there's one problem: it is much easier to make statements than to verify them, and that asymmetry is part of why the internet has been slowly eroding society.
It's useful/necessary to use past writing/arguments from an author to say whether they should actually receive any further critical evaluation, or be dismissed. We shouldn't say definitively "they're always wrong, so they're wrong now". However, it's reasonable to say: the author has a demonstrated lack of credibility, so we can probably assume they're wrong here, particularly if they have been wrong in this domain so many times before. Or if they happen to be correct, it's probably not strongly demonstrated by their work.
> It is literally judging the book by it's author
How is that better?
> which is an extremely rationale judgement to make.
So it's "rational" to take bias into reading? Why even read? If you know what you think and refuse to accept new information then what purpose is there in consuming anything?
You should just read the comments and get a warm fuzzy that the crowd, for the time being, agrees with your intentionally static ideology.
Comments like these obviously hope they can sway the crowd before they can take an unbiased reading of the article. If the author is that wrong then the crowd here should be able to discover that on their own. If the author convinces the crowd then I'd think you'd want to present a better argument than "well, he was wrong _before_." Post hoc, ergo propter hoc, in action.
He's a Gary Marcus-level contrarian with none of the credentials or contributions to the industry. The "AI bubble" cope narrative is getting stale but will still appeal to luddite autists years after it has ceased to be relevant.
The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
We are only five or six years into the leap LLMs represent. For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up. It took so long not because of the physics of radio waves required time to mature and improve, but because everything else needed to profit from it did require time.
To me, LLMs are not so much AI as it is a building block. Radiowaves maybe, or the equivalent of transistors. We are already seeing that it's possible to chain LLMs into agents. Currently, price is a strict limiting factor for coding and agents.It's probably fine-ish if all you want is Claude Code or Codex, but there are many other possible compositions of LLMs that most people don't dare to experiment with. For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost. Were prices of inference hardware go down and inference algorithms keep improving, I'm convinced (and afraid) we would see things very difficult to imagine today.
> For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost.
Hah, I'm actually working on just this problem.
Cost isn't the issue. There are only so many coherent (in context) responses and scenarios, that you don't need an LLM to generate text in the game, in real time. Instead, you can have LLMs build a vast corpus of "atoms" (dialog messages, fragments, cues, etc.) that can be stringed together in a deterministic way in response to player input. These can also be pre-screened and subjected to various tests prior to implementation.
To a player interacting in the game, a system like this would seem functionally indistinguishable from generated text within the game's designed interaction envelope. And it has huge advantages: Although it can expose seams if the player breaks character and decides to probe it, it won't be exploitable the way an LLM would be.
> The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
Worthless statement. Wow, you suspect something can make things better, worse, or both? That's a keen insight there.
> For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up.
We are still so early.
I mean, we have advertised them in multiple super bowls, have companies that basically own tech news (incredulous journalists will repeat any stupid insane shit a CEO wants to say), that say they're valued at over a trillion dollars and nobody with the power to argue those finances seems willing to do anything but agree. We have built hundreds and hundreds of acres of data centers (and made deals for data centers that are never going to happen) that demand *billions* per month. They are devouring all the silicon to where people are visibly seeing the price of hardware double, triple, more in price. Work places insist on employees using AI (then pulled back because it turns out this stuff costs money and it's not fun anymore when it's not subsidized).
But we just need more time, more eyes, more people looking at it.
Where in the radio wave timeline did this happen?
Ed is an interesting character. His financial analysis of the AI industry makes logical sense to me (though I am not knowledgeable enough to actually know if it is correct.) However, he seems to be so angry at AI in general, that he misses the obvious areas where LLMs are actually changing the State of the Art.
Coding seems to be one of the core use-cases for LLMs (as Simon Willison pointed out recently) and even if that's the only real use-case for LLMs, they're wildly useful. I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
I don't think whether "LLMs are actually changing the State of the Art" or not matters for anything he wrote.
If the AI companies need $X billion in revenue to stay afloat, it doesn't matter if 0.5% or 5% or 50% of that revenue is from transforming the State of the Art. It's 100% irrelevant: what matters is that, transformation or no, these companies won't have the income to pay their bills. And if they can't pay their bills, a whole lot of other companies can't either.
So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.
He's got subscribers. Maybe the attitude is one he's found plays well with them.
I find it quite refreshing in some ways. Lots of people, when they start complaining about this or that aspect of this AI stuff, are wont to add in a little disclaimer that, despite all of the above, they actually really like AI and use it all the time. I assume this is to avoid the scenario of a bunch of pragmatic builders turning up and calmly shipping nuance in the comments (or whatever you call it these days when you get brigaded by a pile of angry keyboard warriors with chips on their shoulder) - and it sure is tiring having to wade through the equivocation.
That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.
It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.
> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
This is often repeated but comes from ignorance mostly. You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.
This kind of thinking really reinforces my belief that people have no idea and are using this whole [AI is not profitable and too costly] thing as a cathartic way to deal with immense progress.
We know that inference cost is very significant, as he shows for example in this piece.
https://www.wheresyoured.at/oai_docs/
However, it needs to be said that he received those numbers. I personally have quite a few issues with him, but there's no reason to doubt his journalistic integrity. Because of that, I believe he reports truthfully on data he receives by informants.
Additionally, none of the frontier models actually publicly talks about inference costs in anything but broad, "let's just forget that"-like takes. Which does not exactly spark confidence.
I'm eagerly awaiting anthropic's public disclosure of their financial details. That should be rather interesting in any case and finally put the inference-discussion to rest.
No reason to doubt his journalistic integrity? He's not a journalist for starters. He's a PR flack who does PR for AI startups on the side while blogging on substack. There is every reason to doubt his journalistic integrity.
Zitron is in the business of content creation and not successful predictions. It doesn't matter how many times he (and several others around) will say the end is here, they have to be right only once.
BTW, one thing for sure he is right about are the economics, as of today there is no way these massive investments are gone be paid.
For the purposes of content creation they don't even have to be right once
This is wishful thinking. AI is still getting better rapidly. Anthropic's revenue is still growing at an unprecedented rate and they haven't even released their best model (Mythos) for 4 months now.
The chart seems logical to me. Most problems are solved in the app space. New apps don't have to be the new facebook. They just need to be useful for the right audience (even a small one). It's like you have meat and bread in the supermarket, and add all other stuff you dont really need. Will be bought, but not as much as meat and bread, right?
This rests on a lot of assumptions that the published figures for "planned" datacentres, "committed" AI spend, etc. are irreversible. I suspect that at least some of it is possible to back out of.
Although I see huge utility in AI, I think he is right in terms of overspending and overenthusiastic build out. Because of for example what Apple is doing by putting an extremely efficient model with task adapters right onto phones.
Also because we now have a massive demonstration that vastly more efficient hardware is desperately needed.
Similarly other effective efforts towards on-device AI like Nvidia RTX Spark PCs and 2bit quants of strong models like DS4.
So inevitably, significant investment will be going into vastly more efficient CIM efforts like Mythic AI and new FeFET devices etc. in order to make human-level and beyond AI at scale feasible. There is so much demand for this and the power requirements of current hardware are so excessive, it seems unlikely that the data center build-outs will be able to recoup their costs before the more efficient paradigms make it out of the lab and start scaling.
>have to be roughly twice the size they are today, and then double again basically every year until 2029 or 2030.
Anthropic is growing way faster than doubling yearly so don't think this is entirely implausible
Ed's argument for why "AI is slowing down" rests on company spending caps, in particular the Uber $1,500/engineer/tool cap.
I interpret the exact same evidence in the opposite direction. A year ago the idea that a company would spend $1,500/month/employee on AI tooling felt absurd, what could people possible want to do with AI that would cost that much?
Then coding agents (and, increasingly, general purpose agents) happened and suddenly companies are having to set limits because otherwise the demand from their employees is too high.
The TAM of these AI companies just leapt up to $1,500/knowledge-worker/month, how is that "slowing down"?
Maybe in USA in big tech where companies give absurd wages to engineers anyway in some states, that might be acceptable. But to make their ROI they need that (and more) to be spend world wide... no way that is gonna be a budget that is gonna fly in the long term...
Companies love to cut costs, and just like they axe employee numbers at will, they will just as well make that kind of budget quickly dissapear the moment they realize they can go a different path for same or better value... Or simply because share holder short-term value demands it...
The Uber $1,500/engineer/month thing is just the first signal we have had of the price companies may be willing to accept. This price will clearly vary wildly across professions, industries and geographies.
I think it's a poor number to build an "AI is slowing down" narrative around.
The problem is that $1500/engineer/month would be a pretty modest amount of demand for labs. OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending. On so many levels of the industry this growth is now priced in. You don't think so?
I don't have a particularly great answer to that question - I'm not enough of a financial analysis to have confidence in an opinion.
I do however think that shouting "look, Uber capped pricing at $1500/engineer/month hence AI is slowing down" is a questionable position to take.
>OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending.
No they're not. In reality, actual 'explosive uncapped growth of unlimited agentic token spending' will result in valuations several times more than a 'mere' $1T.
It's also not $1,500 per month per engineer. It's that per month per engineer per tool. Which means it could easily be at least $3,000 (Claude Code and Cursor) or $4,500 if Codex was also an option on top of those two.
And as you have written on your blog it's a soft cap that can be exceeded with justification.
I find it nuts that I can use Claude Code for $20pm - I imagine that won't last forever but have to say it is great value for money.
So when I see monthly budgets in the thousands for developers at some larger companies, I'm curious to learn how they are managing to spend that kind of figure: how much code/documentation are they feeding into their prompts, are they using agent orchestration systems to make the code factory run 24/7, and how much value is coming out the other end versus before?
And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
Those are the kind of questions I'd love to ask - I just wonder how much stuff is truly cutting edge and how much might be wasteful?
Developers at big companies need to pay per token, they don't have subscription available. So in case you use that, you likely spend way more than $20 in tokens.
As for how to spend that much -- not that hard, to be honest. Just give it a lot of context and some relatively open-ended problem and it will easily eat through tons of tokens.
I have $200 subscription for Codex and it is crazy what it can do in terms of debugging. I have a pretty complex Electron setup with some native code linked via Node addons, a few App Extensions and it can easily read the source code to see how the builder works internally (e.g. if your end Info.plist is not correct), debug the xcodebuild output to see at which step something is not linked correctly (like after XCode major version bump), etc.
It is not a silver bullet but if you are not the one paying for it, there is no downside to throw a problem at it and see if it can come up with a fix.
> And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
I am curious about that myself. I have a good machine now (Macbook Pro M5 Pro with 48GB memory), so I'll give it a try; I don't have high expectations so if it is actually helpful would be very neat.
As a tangent, I don’t understand where and why meta fits into the AI race. They did not get any mind share (consumers) from the llms so far, granted they started the open source side to this but the Chinese companies produce far better models and have essentially become the default for on device set up.
They have ai glasses and integration into instagram and facebook as the other avenues. I don’t see ai glasses as compelling yet, and don’t know how much more ad revenue or user engagement they can squeeze out with llms baked into the IG of FB flows. They are spending a lot and not seeing any returns. Am I wrong in being pessimistic about meta with AI?
All the top comments are commenting on the author. And now I add this metacommentary. Probably good it was flagged.
Buried lede (if the title is the actual promise), the sources don't seem to back the title either. Someone with more patience can correct me if I accidentally missed a bombshell anyway.
Edit:
> If you’re wondering what the story is, [...] I expect it to be out in the next two weeks [...] I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.
Ok, this takes clickbait to new lows. The headline is trying to sell the teaser here, with very limited meat in the middle of the sandwich.
Given this, his righteous anger towards craven boosters and grifters is pretty funny. Pot calling the kettle black.
I find it difficult to separate this piece’s tone from its content. The tone puts me off and makes it hard for me to judge it on its merits, despite some of the arguments seeming sound and well supported.
Given the way tone has been intentionally abused, particularly in this industry, I’ll take a few f bombs and the truth.
>I’ll take a few f bombs and the truth.
Don't want to ruin it but go read some old posts from the author about AI, the tone is the same and he is very much wrong.
Agreed. If the arguments seem sound and well supported, then all we can do is attack the tone.
You can disagree. Sarcastically, or otherwise. But I think you may be reading more into my comment than I put there.
I’m not attacking the piece. I’m not saying it’s right. I’m not saying it’s wrong.
What I’m saying is, the tone made it hard for me to judge the arguments fairly, despite finding some of them convincing. And as much as I dislike it, persuasion does partly depend on how an argument is made.
Thanks, it's very clear what you're saying.
Ed's posts are peak preaching to the choir, they're usually factually correct but he is really bad at convincing anyone who doesn't already strongly agree with him.
Have you seen his recent Bloomberg appearance? He's calm, collected, and matter-of-fact -- the complete opposite of how he presents himself on his newsletter and podcasts, but with the same argument. You wouldn't know from listening to him how spicy he usually is.
It's tuned to the audience. Bloomberg was traditionally for people who actually wanted information. People who were fallible and had limited knowledge.
Of course that mentality is obsolete. Now we all have infinite access to perfectly correct information via the internet.
wow someone tell the philosophers this guy has figured out the knowledge problem!
I dont really understand the criticism either way.
He's in the media business... its in his interest to amp things up.
Yes, of course.
Perhaps that’s it. I would tend to agree with his position, I think, but don’t appreciate being preached to. Even less so when I agree with what’s being said.
Agreed. I am open to the possibility of the bubble bursting or whatever, but this piece is like 3,000 words and cites everything as evidence the sky is falling. It's just as bad as the pro-AI grifters, just in the other direction.
Does the truth normally lie somewhere in the middle of it all?
>Does the truth normally lie somewhere in the middle of it all?
Usually does when you decide what constitutes extreme.
Probably. Although I feel more inclined to forgive Ed in this case because it's sort of fighting fire with fire, the insanely hyperbolic and obscenely misleading drivel that's coming out of the most ardent AI boosters is continually unchallenged in the public eye. In a world where we had a more realistic view of AI/ML/LLMs, the limits to its capabilities, and the negative externalities of its widespread adoption in places where it quite frankly does not belong, then I'd be more critical of the Chicken Little sort of writing style
I think we need to see Open AI's and/or Anthropic's S1's to really know the state of it all.
Totally agree, remember WeWork's S1 and the fall that followed. Don't think it's the same case, but it'll clarify a lot of things
Funny I just read an article on how it was actually speeding up.
I hadn't heard of the TMobile and Brex spend caps, only knew about Uber's because it went viral last week. I expect we'll see more of that now that everyone is paying per token, and it sort of feels like you cannot both have spending caps and require extensive AI usage for performance reviews -- I wonder that will shake out in the end?
Anecdotally, $dayJob consumes Anthropic models via Azure subscriptions which lend themselves pretty neatly to the spending dashboards Ed mentions are missing from Anthropic themselves, and finance seems ok with the current usage, but there's no real hard incentives internally for AI usage either.
I guess Q3-4 are going to be interesting to see where this all goes.
Given how I can manage and develop a huge production code base with an incredibly small team - and the rest of the industry apparently is not able to do it - I deem that we are still in the very early days.
There are real issues on the money front. The big AI companies have a financial model that assumes a huge increase in demand in the next year or two. Otherwise the bubble pops.
"Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees."
So some of the growth was purchased by underpricing, subsidizing the customers with venture capital. Uber did that, and eventually got out of it by raising prices and squeezing the drivers.
The "fuckup" problem is real. LLM-type AI exacts huge costs because it is terrible at reporting "I don't know". When it doesn't know, it generates noise and polishes it. If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful. You could use small, inexpensive models on small problems, and only use big models when the small models failed. Most customer service bots fit that model. Needing ever-larger models to fix the noise problem is not cost-effective.
Isn’t the thing that an LLM never knows (at all)? It just guesses words based on the context and previous words and often gets lucky.
It isn’t thinking or knowing and then expressing the resulting understanding but just spitting out contextual words and hoping it reaches a conclusion or ending of some sort.
As WIRED reported[0], despite constantly writing about how an AI collapse is just about to come, Zitron privately does PR for AI firms on the side. The man is an obvious hack, and it's disappointing that he has become one of the mainstream faces of AI skepticism.
[0]: https://www.wired.com/story/ai-pr-ed-zitron-profile/
AI has been slowing down relatively, considering its trajectory over the past 20-30 years. For one, even if LLM may have plateaud in terms of intelligence-parameters ratio, research is on-going on new frontiers for ML, including (but not limited to) world models. Other research directions are studying backpropagation and its physical analogies, such as equilibrium of chaotic states.
In addition, there's a lot of research on the hardware angle and actual prototypes are already being built such as AI-on-chip Cerebra and Taalas for one.
Anthropic has made $330 billion in compute and chip commitments between Google, Amazon, and Microsoft, another $30 billion with CoreWeave and another $15 billion with SpaceX. To pay for this compute, Anthropic must meet its projected revenue of $174 billion a year by 2029. Anthropic has raised $95 billion across rounds in February, April (from Google and Amazon), and May. These funds will be insufficient to cover Anthropic’s costs, as will Anthropic’s cash flow, meaning that it will have to raise at least another $200 billion in the next year.
How people take this seriously? Anthropic is at 45B ARR S-1 shows inference margin climbed to 70% (obviously could drop) So where that 200B number is coming from ?
Anthropic's S-1 is not public yet
I guess my ears kind of turn off when you say "it's all slop, none of the apps are good, and it's a failure because no one has used AI to make the next Salesforce".
I have found agentic coding to be extremely useful for a bunch of small, middleware, very focused bits of software for small businesses:
* A company had a very specific scheduling need, they needed to move about 8-15 staff around with a bunch of different shifts, and have custom reports on who was working how many hours, and have the employees get a nice clean email summarizing their schedule
* A manager wanted a very simple "let me send a text to add a to-do to the group list" need
* A sales team of 3 wanted to be able to type pricing of raw goods into their phone, have it compared to other market sources, and have it text the other 2 salespeople and their manager when they were out in the field
All of these were coded with Codex in about 4 hours with further refinements over the next week of back-and-forth with the people using the tools.
I suppose yes we could have found some custom middleware solutions that did similar things, but it's nice to be able to make a web page or tiny mobile app that just does EXACTLY what the person wants.
It's hard to do that and then listen to someone who says it's all just garbage.
His rhetoric is a bit obsessive and frankly biased against AI.
That said, I think his voice is useful as a counter to the mainstream opinion.
Given the amount of investments, approaching AI from the angle of economics seems correct.
We all have some level of personal experience using AI/LLMs, both chatbots and coding tools, and I personally enjoy using them, but I am sure this experience is relevant in this discussion.
I also enjoy luxury hotels, gourmet food, jet skis and helicopters, but this is not something I indulge in often because of the cost-utility ratio.
The real cost of AI may or may not be lower than its utility. The bet is that utility is increasing while cost is falling.
if you think AI is slowing down, you may not be smart enough to tell the difference anymore.
every week I see this guy on HN. only forum where ppl still buy this c**
The top twenty comments are negative about Ed. I think maybe HN just likes being skeptical.
I think it's time to distinguish between what frontier AI companies need regarding AI, and what will happen with AI if these companies don't get everything they need. Probably there's a bit more to this. Much of the technology is available via open source already and there's a growing ecosystem of AI tech that isn't really dependent on anything else than the hardware infrastructure needed to run it.
A good analogy might be networking companies and infrastructure companies during the dot com bubble. It devalued a lot of companies but the internet stayed. A lot of dot com companies didn't make it. Much of the infrastructure investment did not go to waste, however. Nor did a the technology go away.
I think it will be the same with data centers, related infrastructure, GPU hardware, algorithms, OSS components, etc. for AI companies. More companies need that stuff than is currently available. The ones that don't make it will have a lot of assets that they can pass on to the one that still have a chance. I don't think a lot of that stuff will get decommissioned or will be underutilized. It might get a little hair cut in value though. And like during the dot com bubble, some companies actually survived and did quite well. Especially those in the business of selling shovels during a gold rush.
After the inevitable consolidation that follows the next logical stages in the hype cycle, I don't think AI will go away. It might be a bit of a bloodbath for some silicon valley investors that placed the wrong bets in the last few years. But that's the price of doing business over there. That doesn't mean it's all bad. And the smarter ones probably spread their risk enough that they still might come out looking alright.
And like with the dot com bubble, many financial types have no clue what is happening and are running around like headless chickens. Which is why they ended up sinking a lot of money in exactly the wrong things. You'd hope they would have learned something.
But articles like this suggest that that might be too much to hope. They still don't really get how technology tends to not stagnate and might continue to deliver potential for performance and cost optimization. The current level of investment is only unsustainable if that doesn't happen and nothing else changes. I don't think those kind of closed world assumptions are a safe bet at all.
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
Who writes like this? When you lead with "everyone who doesn't agree with me is a lying cheat coward imbecile" I think we should just turn the volume down on you to zero.
This is breakdown in dialog. If it leads like this then I I don't care how accurate the critical analysis to follow is. I didn't read the rest of the article and don't think anyone else should either out of sheer disdain for this argumentation style.
I don't think anybody actually believes that the current investment is going to yield returns that they are projecting. Neither did people back in Dotcom or Railways or any other hype/bubbles. Yet these technology did transform and the returns came to fruition.
Internet continued to thrive and grow even after the stock market came and went, it took 13 years to roughly nasdaq to recover but the explosion of GDP from internet has been largely decoupled from the previous bubble boom and bust.
If you use the stock market as a yard stick to project new revolutionary technology we shouldn't have had trains, internet. In fact internet should've stopped with the bust of Nasdaq and everybody would've moved back to using paper but we didn't it gave rise to the next wave of economic output powered by this new tech.
I don't see AI to be any different.
The handwringing tone of the article is off-putting.
Ed is confused between whether AI is useful, and whether the current level of funding and valuations are sustainable. The following statements can both be true:
1. AI is already quite useful and will continue to be so. This is true even if AGI doesn’t happen.
2. The funding and valuations of many AI companies are too far ahead of their skis, and will probably roll back. Some may fail entirely.
About the “where’s the productivity in AI?” question: I think it’s entirely possible that the primary benefit of AI will not be top-line growth but reduced costs (through reduced human labor). Companies will need to reduce prices to prevent losing market share to existing or new competitors, meaning that GDP may not increase, but costs will.
Bbut.. Elon said we are all going to be billionaires
He may be bombastic but Zitron is right about the AI problem. These companies do hemorrhage cash, and have no viable plan to even become solvent. It may not be a scam but it sure looks like one. The problem it poses for the economy... is just as he says.
I stopped as soon as the popup hit.
Whenever I read these kind of articles about AI financials, I'm reminded of identical screeds I read about Uber a few years ago. They were angrily insistent that Uber was a scam company run by criminals and charlatans and could never, ever become profitable or make money for its investors. It was a house of cards that would come crashing down sooner or later, and take everyone's money with it. Now it's 2026. Uber still exists, has revenues of $50bn and is apparently a highly profitable business. I don't know if the original investors have made their money back yet, but Uber certainly hasn't collapsed.
Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.
Uber used the classic triple-E philosophy of Microsoft and entered a market that was ripe for disruption -- many cities lacked reliable taxi service entirely, others were cartels that fixed prices. They undercut prices to an extreme degree, subsidized fares, and when it either drove local taxi companies out of business and spurred widespread adoption as the default, it had a captive market and duopoly with Lyft which allowed them to raise fares without losing any market share whatsoever.
It's a pretty classic business strategy, and not directly comparable to any of the AI companies. There's a reason people compare the current situation to the dotcom era and not Uber. Also, don't take Uber as an example of a slam-dunk VC success story and leave it at that -- plenty of dumb ideas get pitched and funded and go bankrupt for every Uber.
Yeah, people forget the risk to Uber was real in the early days. If municipalities had enforced their taxi laws, the company would have died and all those millions invested would have been lost (or pivoted into something else).
It was only because Uber successfully bulldozed over all regulations that it was able to succeed ... and that was hard to predict before it happened.
Absolutely. Even these days, Uber really only has one or two viable competitors. With any 3rd one in a far distant 3rd. Meanwhile, swapping which AI I’m using is as easy as clicking a dropdown. Hardly comparable to a physical car ride.
It doesn't matter if it's slowing down, pretty much no one has implemented it to its full extent yet. It could stop right now and we'll be finding new implementations a decade from now.
Anthropic and Open AI could evaporate tomorrow and we'll still be using the models.
The market may collapse, but the people who think AI is going to disappear as a result don't understand what it is.
I predict the bubble is going to pop right after the midterm election.
Concur.
AI companies are racing to win the future of computing.
They are possibly in a winner take all death race against each other.
The stakes are so high that these cash rich companies cannot afford not to throw everything they have into this.
The sunk costs are irrelevant when it’s a question of survival.
Whether you hate or love AI computing is being completely reinvented - at the absolute core of this is computers programming computers.
Anthropic is winning this race by a country mile right now.
This is such an important future bet for these companies that the trillions must be spent because there’s no future or a greatly diminished future for some of them unless they have ownership of the technology.
"Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology."
Bloomberg is interested in what he has to say
But not HN commenters
Well there are a lot of commenters so presumably some interest. I just had a look at the Bloomberg bit https://youtu.be/zbKDmkJPVvI and didn't see sweaty boosters rattled, just Ed doing his usual spiel - they are loss making and so it's all a big con. Which is kind of unproven on the big con bit.
I'm so sick of people who peddle outrage for a living.
Ed Zitron speaks to a particular type of angry tech conservative. He’s not speaking truth or exposing anything. He’s the soothing voice the tech nerds of yesterday year are yearning for.
The angry polemic that goes on and on and on with cuss words used liberally is just meant to evoke emotion and cathartic resolution to the type of people mentioned above. Not truth.
The thing is, there are a lot of people that find comfort in what he’s writing - primarily because it’s a coping mechanism against how quickly things are moving and a way to deal with being left behind. When you spend time, years, building institutional knowledge and making a whole identity out of it, you obviously will feel bad with the threat of it being commoditised.
I would write against the content of the article but I find it easier and more illuminating to write what he has said before instead. Then it shows how incorrect the guy has been and with what confidence he keeps speaking with.
I'm collecting many kinds of predictions Ed Zitron made so that you can see for yourself whether he has a good track record.
-------
> While complex, generative AI is a technology that probabilistically generates answers, and has no "intelligence." It is inherently limited by its architecture, and in turn can only get "better" in a linear fashion. I see no signs that the transformer-based architecture can do significantly more than it currently does.
He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?
> Furthermore, I hypothesize a race to the bottom in generative AI will significantly hamper OpenAI's ability to expand revenue, compounded by the fact that we're approaching the limits of transformer-based architecture.
He wrote this in 2024 and since then Anthropic's revenue increased by 160x to $40 B dollars a year and OpenAI's increased by 6x. Do you think this person gets predictions right still?
> I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be,
He wrote this in 2024, do you really think we have reached upper limits? Huh?? What I'm using today is significantly more accurate and 2 tiers above what we had.
> And if there are true industry-changing possibilities waiting for us on the other side, I am yet to hear them outside of the fan fiction of Silicon Valley hucksters.
He says this about AI when we have with all honesty have had industry changing possibilities like agentic coding.
> There are indications that consumers have also lost interest. As pointed out by Alex Kantrowitz’ Big Technology newsletter, traffic to ChatGPT on both mobile and web has started to stagnate, if not decline. In January 2024, ChatGPT had 1.6 billion visits — 11% below the all-time peak of 1.8 billion. This makes it only modestly more popular than Bing, which had 1.3 billion unique visits during that period. On the mobile front, ChatGPT has an estimated 6.3 million US users — or 1.7 times less than the total of new Snapchat users added during Q4 2023.
He agrees with the claim that the consumer interest has declined. Since he said this, there was a 9x growth in active users.
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https://www.youtube.com/watch?v=_wStScmT748&t=1s
"AI Bubble Already Bursting?" (8 months back)
https://www.youtube.com/watch?v=T8ByoAt5gCA&t=1s
"A.I bubble is bursting with Ed Zitron" (1 year back)
He's been constantly crying bubble for years now.
-----
> AI video won’t get truly fixed just by waiting a year.
This is what he had said in 2024, and you just need to compare video from then and now to check whether the predictions came true. Why would anyone trust what this guy has to say?
How’s that meme go? "We are 2/3 years into being 6 months away from AI taking all white collar jobs".
The criticism goes both ways. The word "fixed", in Ed terms, can be translated to "become a viable business that justifies the spend".
In regards to AI video, I think the fact that Sora is no long around is an indicator. And there is seemingly no real appetite for AI video outside of memes, jokes, and misinformation, probably indicates that the prediction around AI video has come true.
This website can't run if this sort of rhetoric is accepted: "they told lies so we can tell lies".
Frankly this is anti-social and should not be tolerated here.
>In regards to AI video, I think the fact that Sora is no long around is an indicator. And there is seemingly no real appetite for AI video outside of memes, jokes, and misinformation, probably indicates that the prediction around AI video has come true.
His point was about the performance and accuracy and not about the community/market. He was wrong.
What's the point of arguing with any of this.
It's like someone arguing that cheese isn't real. Yes I can go to the grocery store and take a picture of cheese and show it, but what's the point? They can live in their own world. It doesn't change any of our lives. The world is what it is.
Lol... in this case, cheese imports from China are much cheaper, just not quite as good.
And for those who are all "but dur CCP get all ur data" you can use things like AWS Bedrock (at least for earlier versions of Deepseek and Qwen for now) and have more familiar people get all your data. Or buy (at obnoxiously inflated prices) your own HW and not send your data to anyone.
> "but dur CCP get all ur data"
The funniest part of this is that people are often talking about how LLMs are now writing 100% of their code, then also saying that they don't want to expose their code to foreign government exfiltration by using foreign models.
But, uh, if an LLM is writing 100% of your code you have no actual secret sauce to hide from anyone, so why worry about it.
Perfect for idea people. All the value is in the prompt. Ideas are important, not execution. A decade or two ago, they would have been looking for a technical co-founder.
Yeah, so true. There is no moat to your competitors using the exact same tools and prompts to generate their apps and services. Companies should be hiring/retaining creative thinkers that give them that human edge rather than laying people off under the guise of "improved efficiency"
I think we're going to see a lot of craziness in the future in this regard. Not just "secrets", but hypocrites trying to copyright and patent all the AI outputs. All kinds of rabid attempts at constructing monopolies for every half-baked idea they have tried to utter as a prompt.
Meanwhile, like I think you suggest, I would assume everyone can generate similar outputs themselves. The idea that you can claim priority on your dream prompt and lock up the market on prompt responses sounds delusional to me. It's not novel invention when you're spit-balling at the same level of abstraction as every fantasy/scifi writer who ever was.
So I also have doubts about the sustainable business model. How long will it take for this fantasy to unravel, as people discover they cannot monetize their AI outputs as much as they dreamed, and in turn cannot afford to pay the AI services they use?
My absolute nightmare is that this becomes a "too big to fail" thing and oppressive/fascist governments decide to back full regulatory capture. That instead of letting it unwind, they grant and support enforcement of an increasingly absurd and arbitrary copyright/patent regime to support this monetization scheme.
> What's the point of arguing with any of this.
> It's like someone arguing that cheese isn't real
I agree with your first statement (any being you) because of your second statement.
Some people seem to see the world only through bubbles. But if you look at human history, despite the ups and downs, we have a trajectory; generally speaking, human-created systems evolve toward ever-increasing complexity, impact, and efficiency.
The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today. To have tools that can understand, manipulate, and produce it is a massive leap forward.
Once you see things that way, it is clear that we are not in a bubble; we are in a transition. Yes, there is tons of hype and over-investment, but the demand is real, and so is the impact. Unless you are deep in the tech and have that structural depth, it is easy to dismiss. This is like the invention of the personal computer, but with 100x the impact and speed.
Uhh, citations for all of these claims please.
Download the tools and use them along with your head
You need citations for humanity shared history?
The only "bubble" with AI is that the initial build out is cyclical, and many of the high flying chip stocks with no software arms (ala Nvidia's CUDA) will come back to Earth. I think anyone that thinks AI is going away or won't have massive impact (though maybe not in the doomsday scenario) are in complete denial.
RTFA; it's not about AI's massive impact or lack thereof ... it's about these businesses not having a viable business model that will sustain them (beyond the next couple years).
I think Zitron's problem is he's equating AI to OpenAI and Anthropic. I'd agree with him that both those businesses are in a dangerous position given how fast they've burnt through cash. However, that's not the entirety of the industry and there are a lot smaller labs doing more for a lot less capital.
The business model does appear to be viable for these labs. But that viability comes because they aren't wasting a bunch of R&D money developing worthless products like AI video production.
I admit, I didn't read the whole article; I read a few paragraphs and extrapolated the mindset from which the author operates.
Regarding your comment about the business model—the people in Silicon Valley are not stupid. They know the playbook; we've seen it with social networks. The issue isn't the business model itself; it's that these companies need to dominate the market, and the big players are competing for that on a global scale. It's the exact same playbook that played out in financial systems and social networks, and now it's happening with AI. Once these technologies are deeply integrated into enterprises and the global economy, these players will dominate the market for decades to come.
I can assure you, the people running those companies are smarter than you, me, and the author of this article."
I did. So, I'm confused how does that negate my comment exactly? Your second complete sentence totally is in conflict with your first btw.
What I suspect isn't that AI goes somewhere, but I do think that the cutting edge companies like Anthropic and OpenAI are in a very precarious position. They don't have very much of a moat and the competition has been catching up quick while spending a lot less doing so. IMO, the main thing keeping them alive right now is name recognition.
If I were to make a prediction, it's that ultimately these cheaper models are going end up eating their lunch. I don't think they'll make back the money they've invested and once that reality hits investors, those two companies are sunk.
That, however, is not the end of AI. Nor will it be the end of Nvidia/micron/etc. It will more just be a localized bubble pop that doesn't eliminate the product from the market.
It is not just about cheaper models; it is about integration with the economy.
These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
> These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Much like the electric grid, what we are seeing is a convergence on standard APIs. For example, most of these cheaper models are hosted using APIs compatible with OpenAI. It's not a matter of rewiring your electric plug to work with a different socket standard, instead it's just the process of plugging it into a new socket.
> Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
Certainly the Chinese models appear to be some of the best when it comes to competition, but they aren't the only ones. There are European models and other US based models which all run for cheaper.
I see your point, but having worked as a consultant for a few years, I think most companies will opt to stay once things are stable. Once these systems are functional, nobody wants to touch them.
I remember one government project where we wanted to migrate a system from COBOL to a modern stack. The requirement was for the UI to stay exactly the same as the old green terminal; the evaluation criterion was pixel-perfect proximity to the original. We literally had to build terminals using web tech.
These models are not the same as each other. Once they are integrated and working, the incentive to change them is incredibly low. So really, the race is about who can integrate deeper, wider, and faster over the next couple of years—that is what will determine the long-term winners.
This is the exact same playbook we saw with social networks. There is a reason why we have only a handful of them dominating globally, and guess what? It's not because of the tech.
> the incentive to change them is incredibly low
There is no incentive to rewrite working software in COBOL to something else. You don't really change the people cost of maintaining that code all that much and you incur a huge rewrite cost.
AI is different, it's an ongoing cost to the company. If that cost raises aggressively, you can bet companies will race to eliminate it, no matter how integrated it is. Companies can and do do this all the time.
And the models are close, not the same, but close. That's what matters in LLM stuff in general. If a model is capable of doing the same work for less, it will be chosen. Especially since the switch over cost is often on the level of "point the tool at this URL instead of that URL".
I get what you are saying if this were a more sticky concrete tech that is harder to move away from. But that's simply not the case for these LLMs. A big selling point they have is that they are super flexible.
We might need to agree to disagree on this one.
I don't think the transition will be as simple as just flipping a URL. There is an entire legal and technical infrastructure being built around these models and their integration. I think you underestimate an organization's resistance to change once things actually work, as well as the sheer complexity of making that shift.
I also expect pressure will eventually drive the cost of running these models down. Power plants are being built, more capable chips are being produced, and a big chunk of the capital right now is being used to scale the physical infrastructure—the data centers and energy grid. Once that stabilizes, these companies will have positive cash flows. Again, it's highly similar to what we saw with the expansion of social networks, just with more aggressive and widespread adoption.
Ultimately, a handful of companies are going to provide these core capabilities, just like we have a handful of major cloud providers right now. Why do you think this would change? If anything, the trend toward deep vendor lock-in is even stronger now.
The moat is the infrastructure and lock-in. Similar to AWS or anything else. Small data centers can't compete, and similarly people without massive compute won't be able to either (at least not on the enterprise level.) You might get a few edge models, but for huge businesses they will be using OpenAI and Anthropic (and Google/Microsoft/Amazon, etc).
The biggest competitors aren't small models, they are just the traditional players that already have an "in" with enterprises. That I think will start to show its face once this initial round of buildout is complete, which may not be for another 5+ years.
> The biggest competitors aren't small models
I disagree. Mainly because those small models are exactly what erode away the moat of needing a giant data center. Those smaller models have been proving themselves to not be far of from the SOTA models.
As OpenAI and Anthropic look to raise their prices, businesses will be much more compelled to looking at cheaper models. And if the narrative is "do the same as you did with OpenAI at 1/20th the cost" that's going to sell to a lot of businesses.
It certainly cuts into what exactly these companies can sell in general. For example, if I wanted to integrate AI into a product I'd almost certainly not chose OpenAI or Anthropic. That's because they are simply way too expensive and what they'd give me is a lot less. We've actually ran into just this. We needed a classifier for a lot of records, we picked a free model because, as you can imagine, we didn't need something as good as what OpenAI and Anthopic offered and free works.
I share the same perspective.
> The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today.
This is fire erasure
/s
Agreed haha! our beloved fire.