AI is slowing down
wheresyoured.at
wheresyoured.at
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.
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.
everyone is still fighting for market share so they are giving stuff away, but that doesnt mean people wouldnt be willing to pay for it if it wasnt free.
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.
Kind of a self-fulfilling prophecy.
What's not a problem, by the way. That's why people always recommend content creators to be themselves. If they try to be somebody else, they find their public is already busy following other people.
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.
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.
I'll gladly tell my customers all the most glorious stuff about AI and big tech while spending a significant chunk of the money they pay me on supporting AI-/tech-counterculture, such as doctorow, zitron and quite a few other writers, journalists and activists.
It's the old "you live in a society" counter-point against anti-capitalist activism. Needing to make ends meet does not imply that your points or principles are meaningless, it just implies that you have no interest in being homeless and that way losing your chance to actually change things.
So that's fine to me. But: I stated it for a reason, because I know others don't agree. I, personally, consider him trustworthy. You do not, and that's fine. I suspect we both await anthropic's Z.1, which will be able to settle a big chunk of the debate.
If he is right, the numbers will show it.
He was also right about AI-video and sora in particular being a fundamentally flawed idea.
He was also right about the dangers and problems with the general inaccuracy of LLMs and people relying on it.
Also about the expected triggering of ROI-checking in companies, such as Uber is doing now. His prediction is, ROI is negative. And I'm awaiting the society's consensus on that.
The general direction seems correct to me. He's not a technical guy and does not have the knowledge to critique models on a factual basis. I do wish he'd just focus on the stuff he _does_ know about, which is the financial side of things.
He is a much needed counterweight to the unhealthy hype going around, imho.
He specifically predicted that AI videos have plateaued in 2024 which is egregiously wrong.
> He was also right about the dangers and problems with the general inaccuracy of LLMs and people relying on it.
He specifically predicted that accuracy won't increase but accuracy has increased over the time significantly to the point where you can't get it to say anything inaccurate using the reasoning models.
> Also about the expected triggering of ROI-checking in companies, such as Uber is doing now. His prediction is, ROI is negative. And I'm awaiting the society's consensus on that.
The whole Uber skepticism is a good point because all of those people were wrong and Uber is profitable now.
You didn't address my other criticisms - he claimed that revenue would drop in 2024 and it skyrocketed. He claimed that users weren't interested in ChatGPT but now it has a billion users (6x jump).
1. What data?
2. Intuitions = vibes.
Vibes are bad when used against you, but good when used in your favor.
Come on :-)))
But if you don't believe me, lets have a bet based on what the IPO filings show?
A better way to model this, since you seem interested is the following:
How much would it cost you to start such a service for, say, 10k users?
Any other internet service has had virtually Zero cost, $0. Google, Facebook, youtube, Wikipedia, you name it. They all went into the dumpster to pick up a thrown away desktop computer, and they could serve up towards 100k if not a million users.
How much would it cost you to serve, say, 10k simultaneous users with a SOTA model? And if you wanted to go cash positive after a year, how much would each user have to pay?
My post has this same argument - we have multiple third party companies running open weight models. They are obviously not subsidised. And people are willing to pay for it. And these models are as good as the SOTA models from last year. So this kinda proves my point that SOTA is sustainable.
What hardware is needed, how much of it, cooling, and what does it all cost you?
Or are you saying I can take my old desktop and serve Deepseek v3.2 to 10k users simultaneously and it would cost me about $1 per megatoken?
Obviously they are not subsidised, do you disagree? If you agree, they have a way to price it at a point that people wanna pay for it and also they aren't losing money.
So there's nothing inherent about inference that makes it too costly or whatever.
> Obviously they are not subsidised, do you disagree? If you agree, they have a way to price it at a point that people wanna pay for it and also they aren't losing money.
> So there's nothing inherent about inference that makes it too costly or whatever.
Do we have audited GAAP financial data for any of these companies? If we don't, all these are... vibes, man.
But isn't it okay to suggest that random 3rd party hosting companies are ... not losing money? Why?
I bet that within 5 years they will be sold for scrap to bigger companies and will become divisions inside them.
Statement: margins API prices of all models are greater than 10% in Anthropic.
2. Rephrasing your statement:
At June 2026 prices, profit margins for all Anthropic models are >10%.
Even so, fine, I'll take that bet. Anthropic inference prices are still marginally subsdizided. Once they're public they will hike their API prices several times over the next 24 months. Even that might not save them, because when we take all their expenses into account, they will probably need to raise prices 2-3x compared to their June 2026 prices.
Ok lets state the bet like this: 5 months after their IPO, it will be clear that their API prices still have greater than 10% margin.
It said: upfront investment: $3M to $6M.
Customers should pay $25k per month.
Checks out
>The "$25k per month" figure is almost certainly the result of ChatGPT making assumptions, not a fact derived from any known business model.
https://chatgpt.com/share/6a28193b-6ec0-8333-a1af-d07e8d89ef...
Your whole calculation is also ridiculous - I think you assumed what revenue per month is required to pay off hardware within a year? Why would I use hardware within a year?
I would suggest consulting with ChatGPT and coming up with a better and more coherent argument.
Minimum upfront: about $15M
Comfortable upfront: $20M–$25M
Monthly revenue needed: $900k–$1.5M
Required price per 10k individual customers: $99–$149/month
API-equivalent output price: usually $8–$20/M output tokens, unless utilization is very high.
I try to do some napkin math of what it takes to start a company serving an LLM to customers. I thought maybe 10,000 users sounded like a reasonable number.
I'd like also to compare it to traditional Internet companies, e.g. Twitter. I'd guess Twitter with 10k users would cost me literally a dumpster dive, so essentially no cost.
If I were to start an LLM company, what would my initial investment look like?
Turns out around 15+ million dollars assuming I would get 10,000 users willing to pay 200$/month.
I just don't see how the numbers you show add up.
"Chinese models are dirt cheap" isn't going to do anything good for the return the investments in OpenAI and Anthropic demand.
These are three completely separate positions to have. You can think AI is incredibly useful and also dislike it because it will, for example, reduce your relative status in society. You can love the tech but think that Sam Altman is a dishonest person, etc. But for some reason, most anti-AI commentators feel compelled to present all three arguments.
Which is even sillier when you think about it, because if it's useless, then you really shouldn't care: the markets will eventually find out that it's useless, and everything will go back to normal, and the people you don't like will have lost money, so there's no point in being outraged. Of course, I don't really believe that they think it's useless. I do think they're worried about what it'll do to their prestige, though, and they're just hoping beyond hope that somehow everyone will one day "wake up" and share their belief that LLMs are just "stochastic parrots" with no utility, despite the fact that people are using them every day and can watch in real time as they improve.
Except that in the process of the markets finding out, things will not go back to normal if everyone's retirement is tied to the market. And in the process of finding out, things will not go back to normal if the hype cycle disrupts traditional hiring/firing decisions.
If it's as bad as some of us believe, then when it falls apart, a lot of people get hurt as collateral damage.
The market eventually found out about Bear Stearns, but a lot of innocent people lost their homes in the process.
People who are against AI don’t care if it’s useless, they care it’s harmful. And you can’t systematically cause harm then say “oops, our bad” and have everything return to how it was with a snap of the fingers. The consequences of harm don’t go away when the source does.
> I do think they're worried about what it'll do to their prestige
Why must this always be the argument? It was the same with cryptocurrencies and NFTs, there is a specific type of proponent who always accuses critics of secretly being pro the technology but publicly against it due to some ulterior motive. Most people aren’t selfish lying rat bastards who think like that.
> Why must this always be the argument? It was the same with cryptocurrencies and NFTs, there is a specific type of proponent who always accuses critics of secretly being pro the technology but publicly against it due to some ulterior motive. Most people aren’t selfish lying rat bastards who think like that.
Meanwhile, the prestige to be gained/lost from supporting/doubting the big mainstream thing is immense, and the incentives are actually in completely the opposite direction...
Anyway, on that topic The Line Goes Up video covers the arguments about prestige far more extensively and far more elaborately than I ever could: https://www.youtube.com/watch?v=YQ_xWvX1n9g
But it's very much not the doubters who are worrying about prestige in crypto and NFTs, and probably not with AI either.
i think you are wrong
And in that period where the markets are irrational people are losing their jobs, hardware is being priced out of consumer markets and the rich are trying to embed themselves so hard that we get to pay for it when the market corrects itself. I think your take is highly indicative that you live in a shrinking bubble unaffected by those things.
In some ways things that are both useful and harmful are the hardest to deal with. And this isn't just "prestige", it's the already-decaying post-truth infosphere and the already-overheating CO2 levels in the atmosphere.
AI is useful in cases where you can automatically catch errors. Programming is uniquely suitable for this, because we have already got all our machinery of type systems and CI tests to catch human errors. How useful it is in other cases depends on how cheap it is to catch errors and how much they cost - and whether the cost of errors is inflicted on other people.
So far the data for productivity in coding is.. sus. The productivity gains outside of toy projects are mostly anecdotal and it's hard to tell if those accounts are even real humans or just astroturfing and bots. Almost every programmer I know personally has a pretty measured opinion on where these things are useful and where they're not. The breathless hype seems mostly from non coders.
They are not only useful it is obvious they are. If you don’t see it I really, really don’t know what to tell you. You can tell yourself I am bot or shill or whatever if that helps you sleep but .. just trying to help out another dev here. Wake the F up.
Your whole post is dismissive and insulting and has zero arguments. No one is helped by that, no one is going to change their mind with that, you’re only making the divide more pronounced. You’re being actively unhelpful.
> You can tell yourself I am bot or shill or whatever
I mean, your account was obviously created specifically to post that comment…
We have polar opposite media bubbles. I see OG programmers all over my timeline either grieving the "end of software engineering" (a la Ryan Dahl) or extolling "automatic programming" (a la antirez).
The person you’re replying to, in the bit you quoted, said specifically:
> Almost every programmer I know personally
People you know personally are not a “media bubble”. They are, to borrow your expression, polar opposites. It’s people you can speak with candidly and trust versus bits of text without the full context.
There are definitely use cases for LLMs in coding. And at times, they can be wildly useful. But I feel like the industry atm wildly overestimates their broader/long term utility.
Anecdotally, I have not seen an explosion in quality/bespoke software since LLMs. In fact I've noticed the opposite to quite the extreme. Not only are new products worse in quality, but the quality of existing products is falling off a cliff.
The fact that he isn't a deeply involved technical developer who knows the ins and outs and nuances of using LLM tools is the point, because the stated capabilities of LLMs are that they are trivial to use, extremely powerful, and getting so much better every month that you personally can replace developers without even trying as a completely non-technical person with basic writing skills.
Given the hype and extreme claims being made, the fact that he remains ignorant and gets practically no use out of LLMs immediately disproves those statements. The counterargument boiling down to "you're using it wrong" is actually just a further indictment of Sam Altman and his like, because it shouldn't be possible to use LLMs wrong!
The rest, well, the hype needs to die before anyone can make sane estimates of what LLM tech can do for us in various fields. Right now it's all a complete mess.
His arguments, albeit valid, can often sound like reductos ad absurdums the way he presents them.
One of the worst things about LLM writing is how it makes big promises of what it can prove in some piece of writing, and then never really follows up on that, or has specifics that go all the way towards the original, grandiose statement.
And frankly, Zitron is guilty of that pattern of writing too, or of relying on some unstated "baseline" knowledge which is clear from his other writing but not in the specific piece.
So, basically yeah, agreeing about the ad absurdum thing.
(I will note, the tone, the swearing, etc. really doesn't matter nearly as much as these problems, and everyone instead obsessing about the swearing and personality is really boring)
His early stuff was just as degenerate and vulgar, but was much less of an issue for me.
On the other hand, the hype of "Sam Altman and his like" being plainly exaggerated doesn't mean there's nothing at all behind it. It's plain to see there's something important about LLM capabilities. I don't even use them myself, as emotionally I find them entirely repugnant, and I can still see that.
We need to wait to get the whole story about LLMs, but we don't need to wait to confidently reject both extremes of opinion about them.
I'm not talking about the quantity of code produced, but about actual user needs that are now resolved that would not have been before.
The main productivity gain will not come from existing software engineer, but from people that couldn't code at all before but are now able to do things by themselves. We are still very early.
I’m a solopreneur, if I could lighten my load I would. However I have yet to save time using coding agents, with the exception of “I made this change to my model file, update all model to match the new format.” Which is cool, but maybe 0.01% of my job, and took a 1 hour task down to 10 minutes.
Is it? Can you produce any evidence for such a productivity boost?
Vibecoding hits a glass ceiling very quickly and this will not be solved incrementally. Besides, if the agent could work autonomously to that degree then it would no longer need any prompting at all and we’re living in a very different world. On the other hand that would make the debt actually meaningless, so I guess that is 'a' solution.
Very little new or ground-breaking (I struggle to think of things AI has produced that aren't themselves just more AI), but various previously-stable sites and services breaking.
This is the big one. It's clear that AI can generate huge volumes of code by KLOC. It is not clear that spending a lot of money tokenmaxxing will eventually result in increased real revenue for software businesses, and eventually even an MBA has to look at a "money in vs money out" chart.
But how much ROI is there for large businesses with established products and huge development teams burning through tokens making subtle tweaks that can’t be directly tied to revenue?
That doesn't seem very likely.
The legacy of LLMs will live on in various models doing various specialist things (they seem like a really good progression on speech synthesis for example) but the current edifice will come crashing down and if we're very very very lucky they won't take the global economy with it.
Even though that is still faster than regularly writing code, I end up losing that nuanced knowledge that I get from going through documentation and writing it out by hand - actually doing the work. I just don't see it actually replacing developers unless managers are willing to produce MORE code with the SAME level of quality.
If inference becomes cheaper, it becomes cheaper for everyone.
> 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
We sure do need a reset.
The reset is prone to happen by other means.
…assuming the AI pace of progress slows down enough that there are still meaningful white collar jobs to have.
(also I suspect the anti-globalization discourse will get even more pointed)
Perhaps LLM's (or something better) will develop to be more efficient and quickly become something most people run on local hardware. Perhaps fad-obsessed management types will move onto the next big thing and AI will start being used more judiciously. Perhaps society will set sane regulatory limits that shape the direction AI is going in, from models that take jobs people want to models that, given the right hardware, can do the jobs few want.
Anthropic and OpenAI don't have to succeed for AI to succeed. If they turn out to be a bubble that bursts and torches a lot of investors, it might actually be a fundamentally good thing for everybody else.
If this current building spree ends in massive solar and other power generation being overbuilt and cutting energy costs, we've had a really good outcome.
If you look at SpaceX plans and ambitions, they hope to deploy massive compute to orbit (multiple Terrawatts, hundreds of thousands of sats). If their ambitions even slightly materialise it would make ground based compute pale in comparison.
Whether or not they succeed in their plans is beside the point - the point is they know that terrestrial electric infra can't sustain the growth they need
> The radiator surface area problem also scales uncomfortably. At 838 watts per square meter, rejecting 1 megawatt of waste heat requires roughly 1,200 square meters of radiator. Deploying that much surface area on a satellite is a structural engineering challenge that gets harder with every order of magnitude. The ISS solar arrays span about 2,500 square meters total.
So even a 2MW data centre in space requires a cooling array rivalling the international space station. Starcloud launched a single H100 in November and they were unable to run it 24/7 due to heat buildup.
Even with novel solutions to make heat transfer to the fins more efficient, like phase-change liquids, the limiting factor is that the vacuum of space is a tremendous insulator.
https://thecoolingreport.com/intel/starcloud-orbital-data-ce...
https://satnews.com/2026/03/17/the-physics-wall-orbiting-dat...
> Whether or not they succeed in their plans is beside the point
No, I think that does matter eventually? Maybe for the IPO value?
> In an interview, McCalip says his initial rough calculations a few years ago suggested that data centers in space would cost in the range of 7 to 10 times more, per gigawatt of capacity, than their terrestrial counterparts. “It just wasn’t practical,” he says. “Not even close.” But when Elon Musk began publicly backing the idea, McCalip revisited the numbers using publicly available information about Starlink’s and Tesla’s technologies and capabilities.
Nobody who suggests the idea has ever presented a model that is even remotely close to reality
I can't believe the people who actually listen to this guy can tie their own shoelaces. And somehow he became the richest person on earth. Humans are irredeemable.
Tbh I think it’s already happening in real terms, but the CPIs aren’t fully showing it yet.
Why though? This isn't a bank run situation. Sure, it's unwise to bet against "US President does something stupid", but the rationale is missing.
The oil situation remains very concerning, and you can't print oil.
There’s lots of precedent for this over the past decades, both in the U.S. and over the pond.
Unless you are not company and you don't have some "enterprise deal". And you need an enterprise deal, as 1) it guarantees that your (and your customers) data will not be sold to someone else 2) you are scared that your competitor will have such deal and become much more productive.
This is what we have now. What will be the future?
Well, soon, if you want something like financial advice or medical advice or job search/CV polishing you will be told, that your $20/$200 is not covering that, you need to purchase additional model to have that. Will you do that? It depends how much you are desperate to get medical advice or find a job.
Anthropic Mythos is an example. Soon, if you are programmer and you will ask AI Agent to spot a bugs, AI Agent will tell you that you need to buy extra model for this. Same with performance analysis, same with the design using tool X, Y or Z.
This is pretty scary, as it will put our well-being, productivity in the hands of few corps. It will be event worst that Google Search monopoly we used to have (until AI chats broke this, replacing Google Monopoly with a few other vendors monopoly).
Can this be prevented? Surely. Hopefully we will have capable open models and consumer-level hardware will catch up. But I think this is the place where governments should step in, invest into alternative models which will be at least comparable with flagships.
Chinese models shows that this is doable, DeepSeek is worst than Chat GPT/Claude/Gemini, but not that much and is clearly better than Grok (which is a huge disappointment for me). I guess India would join this game (especially with nationalist like Modi as the leader).
Europe could join this game, the problem is it kills its capabilities with high energy prices and inability to come out with some reasonable, well financed solution. So the only thing EU was able to come up with is some set of regulations that are blocking fast AI development in Europe...
There is French Mistral, but it is French, it is under-financed, it is only-French, as France would not like to lose control over it.
Germany have totally different strategy, they invest into manufacturing oriented AI, what makes a lot of sense, but does not help with the dangers we are facing.
The rest of the Europe is just too poor to spend billions on AI.
There is still time to buckle up for Europe, but given the course of events, stupidity of Brussels elites who does not see the storm coming I am not optimistic.
Quoting Modi is a joke which one cannot even remotely relate w.r.t to AI and don't even feel sorry for saying that since that man blabbers on every stage about non-sensical / non-existent stuff! Watching his videos is the best timepass one can have!
Having said that, the infra and mindset is definitely not there in India to even remotely to innovate or compete in AI race!
Academia is a huge BS where every other person is a backstabber!
A lot of talent is there for sure, but all wants to work for some company or another since there is absolutely zero support for entrepreneurs . No real innovation.
All copy cats as they have proven with mobile and robotics. Just copying or masking Chinese products with the local brand names and reselling. That's all they are good at and that's the irony.
So far, nothing has come from that country which is a real innovation or ground breaking. The day it happens probably one can consider that they are good.
But otherwise, they are good at selling / reselling and scamming the world and nothing else! They cannot produce anything or whatever they produce is taken control by a handful of big corporates from the western region. That's a narcistic corporate monopoly!
Extremely bad tax structure, endless corruption and useless and unqualified ministers occupying worst portfolios, people are really struggling to survive!
Where will they innovate or compete in the global AI race?
Everyone at every level just want to scam and make money to surive that's all!
Period.
Either that he's HN mods's favorites or we're experiencing a special case of xkcd 1053[0]: there are always ten thousand people haven't realized how wrong Ed Zitron is.
At this point I don't think even Ed himself believes what he writes. It's just fan service for subscribers. And that is totally reasonable if we view blogging as a business.
On Reddit, if 500 people like a submission and 500 people dislike it, then it'll end up with 0 points and fail to reach /r/all
But on HN, the same content will end up at 500 points since only the people who like it can affect its rank (unless it dies after getting mass flagged).
The end result is that HN's system favors "hot takes" while Reddit's system favors "preaching to the choir".
The two systems have their pros and cons, but personally I dislike being unable to downvote articles that are full of nonsense.
It's not common, but every so often one of these submissions will be flagged by the time I get around to clicking the notification.
What do you mean "clearly"? Do you know his engagement numbers? Or you just find the content engaging?
I don't wacth/read his content at all because I find AI-doomers really boring and tiresome.
80k * $7/month * 12 months/year = $6.7M/year
[1] https://www.theguardian.com/technology/2026/jan/19/ed-zitron...
from Zitron's own website [0]:
> I have 84,000 subscribers and a 55-60% open rate, as well as an 8-11% clickthrough rate.
if the 84k number was all paid subscribers, then the "55-60% open rate" would mean that ~40% of his paying audience doesn't bother to read the thing they're paying for, which does not add up.
also, that's in the "Can I Advertise On Your Newsletter?" section. if there were an even higher number of non-paying subscribers, he'd have an obvious incentive to mention that, because the total number of eyeballs is what a potential advertiser cares about.
If I start a climate crisis cult, doesn't mean my predictions are wrong
That said, I just wanted to point out the grift he is doing. He’s making money by telling some people what they want to hear.
If AI invents a cure for cancer, he will tell you it’s still useless because it didn’t invent immortality.
— https://www.theargumentmag.com/p/ais-biggest-critic-has-lost...
Here[0] is a fun selection of excerpts from his July 2024 post "How Does OpenAI Survive?"[1]
"I see no signs that the transformer-based architecture can do significantly more than it currently does."
[0]: https://xcancel.com/pathsnotchosen/status/206360940100129633...
- tell lies to ones who want to hear lies -> riches
- tell truth to ones who want to hear truth -> modesty
- tell truth to ones who want to hear lies -> bankruptcy
p.s. read it on a blog name I dont remember :(
I agree with the general thesis that AI is a bubble and there's a lot of over/mal-investment, but that hasn't stopped me from riding the wave and making a considerable amount of money investing in AI-related stocks.
If I was more ambitious, I'd write a $70/year newsletter ranting about how AI is a massive bubble that could collapse at any moment to make money on both sides.
Personally, I see both parties making predictions.
Meanwhile, I still must labour. How many more yachts are needed before the earth becomes the utopia they keep selling everyone on? How many more communities must have their rights trampled against their expressed will?
Or do I need to scan my retinas and provide Alt-man a DNA sample for a handful of empty crypto currency first?
https://www.wheresyoured.at/subprimeai/
He is extremely vague in his predictions and timelines, and yet still manages to miss.
His predictions timing sucks but he’s right in pointing out the insane numbers involved.
I think AI is a wonderful tool but I also think that there are many wonderful tools that if we bet our entire economy on would result in a catastrophe.
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.
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.
I don't think people will be doing business with the labs directly. "Enterprise AI" will be distilled down into purpose built products, with the model just basically being a generic commodity, and nearly irrelevant to the enterprises buying whatever these products are much like how I don't care if whatever SaaS was built in React, Vue, or some other framework as long as it works.
Ironically, for as much shit as they get about Copilot, Microsoft I think has the right idea for the long game they just suck at execution. Copilot is the tool, integrated into the rest of their enterprise stack, it doesn't care what the model is behind the scenes (they already offer you the ability to choose between different models).
That doesn't really bode well for the labs and their trillion dollar IPOs, because they are effectively reduced down to being a developer framework.
Even within the Fortune 5 of the US if be surprised if any of them are paying more than $1B annually currently in total.
And then you can take the parent context into account. If they can just equip users with a slightly more expensive Mac and call their Dell rep to order a few thousand DGX Spark to handle the rest... Why would they risk their trade secrets and intimate details flowing into models that may or may not be trustworthy long term?
Most large enterprise have been burned by SaaS over the years in some way. I can't imagine there aren't architects in the large organizations that are truly weighing how to effectively use AI. And beyond that we're seeing more and more progress in SLMs and orchestration agents which become easier to run at scale on-prem.
AWS billing.
Probably the same reason the Gemini app is still well behind ChatGPT in consumer usage and adoption despite being preinstalled on android phones worldwide ? Why are people using GPT on Windows. There's even a copilot button on new keyboards!
Or maybe its the same reason Microsoft Edge is not the most popular Windows browser ? Maybe its the same reason Instagram threads did not even dent Tiktok ?
You are asking the question the wrong way around. People use and like what they like and have a strong preference to continue doing so.
This is just human behaviour. You don't need mind blowing moat. You begin to have problems only when:
- Users are constantly using your product unsatisifed.
- There's a competitor(s) with a significantly better offering that people are talking about.
Will Apple's offering be providing any meaningful/significant benefit over just using GPT ? If not, don't expect any miracles.
Judging by the announcements today about its integration into the OSes? They are offering useful things ChatGPT cannot offer unless they write an "everything app".
One can (maybe should) make the argument that this is the browser monopoly again, but given that the USA has seemingly no intention of ever litigating that question again even if the EU does, there are clearly features here that OpenAI is effectively locked out of offering.
Open AI can match it but at what price?
Well, it can mean a lot of things, which is why Apple outlined plenty of specific use-cases and details of what they meant by "integrating into the OS" here.
Why do I care if AI is integrated into my OS when I can choose my preferred AI and it can use the OS directly?
The other day I wrote up some notes for a presentation. Opened Google Slides, clicked the gemini button, pasted the notes in and asked it to make the slides. Nope; gemini can only modify a single slide at a time in Google Slides.
Pasted the same notes into claude, it wrote a pptx file, I imported that into Slides, job done.
Being integrated into the product doesn't always mean a better result.
Not being integrated can be an advantage because it gives you the freedom to think outside the box.
Meanwhile an AI engineer embedded into an incumbent slide app team has to ask permission and get cross functional alignment for every little feature. And deal with neckbeard tech leads lecturing them on what the right architecture is
If AI use via Apple represents 10% of the total that vaguely implies that the total AI market is worth around $10 billion per year (which admittedly seems a bit low), and if it is just 1% (which also seems low) then we get $100 billion per year upper-end estimate.
Which just is not enough to justify the current valuations of AI companies.
This is a bizarre way to try and estimate market size.
Maybe Apple’s AI represents 100% of the market! Maybe it represents 0.000001%! We can make up any numbers we want!
Anthropic subscriptions alone already dwarf your $10 billion number.
OpenAI's commitment to privacy is absymal relative to the sensitivity of the data people are dumping onto the platform. The CEO also has a reputation for being untrustworthy.
The biggest threat to ChatGPT's moat may be a brilliant marketing campaign by Apple that really gets people thinking about what platform they want to be upload their secrets to.
But not because anybody asked for that. It is part of the force feeding so execs can make this exact argument, and pretend that demand is there.
I've been using Kagi Assistant for my AI needs, and have to say, Siri will probably replace it in the fall. The question will be, will I still want to keep Kagi for search, or will this new Siri get me where I need to be on all fronts? I need to start paying more attention to how often I actually use the search results vs just the AI summary.
There are things I didn't see Apple show and I wonder how Siri will handle it. One example would be basic coding. They mentioned LLMs in Xcode and Siri with the Shortcuts app and Safari Extensions, but I just had Kagi write up a webpage as a means to display a bunch of data it gave me. Gemini could also do this, so maybe it's not a problem for Siri, but it remains to be seen. There is also a question of what the experience will be like. ChatGPT, for example, handles writing up this code is a much nicer way than Kagi Assistant. Kagi feels more like the results I would have had from ChatGPT a couple years ago where it just dumps out the code in a block and any change is an entirely new code dump, meanwhile ChatGPT goes into a coding interface with a live editor. Going to Xcode feels like overkill, Siri will probably be not enough... so that's a gap in the market Apple may not serve. I assume there will be several things like this. The prosumer level of AI usage, if you will.
I could see the same thing being useful for the ultimate output of a lot of chats. For example, they showed Siri comparing specs for few different products. I used an LLM to do this once as well, but it was comparing 12 things with about 50 attributes. The table was fine, but what was better was asking for a webpage that let me click on the attribute rows I cared about so it could total up each column, which allowed me to easily rank them and better make a decision.
Once it can make html files, it’s a small step to have Siri throw it into iCloud, and make it web accessible. This would be more of a feature than something it would just do, but I could see this being used in the same way Google talked about making dynamic widgets to help explain concepts within Google Search. That’s dynamic coding with an LLM as well, even if people don’t know it. Apple wouldn’t even need to show the code, they could just save it directly to a file and open Safari. That’s essentially what their extension builder will do… write some JavaScript and load it into Safari.
I'm not saying that this is what really happens. I'm saying that believing a CEO is as foolish and as grounded in reality as believing Ed Zitron.
I don't, and that's the point, isn't it?
It's the keys to a substantial chunk of the kingdom for $1B a year. Literally they are getting, for a very small price, the right to distill their own models from Gemini.
Is there money in this for someone with a data centre? Possibly. Is there money in it for NVIDIA? Possibly.
But either way, that's not OpenAI or Anthropic, is it?
Here is a different interpretation: Apple bought the rights to distill and use a smaller version of one unspecified model in the Gemini family (there are many such models).
The distillation will be carried out at Google's data centres so that the original weights never leave Google premises.
For this to be keys to be kingdom it would need to cover all current and future models and would need to be very permissive with regards to distillation parameters and allowed uses of the distilled model.
I expect the reality to be somewhere between these two extremes.
gobby ... British rabble-rouser. "Gob" is the Dick van Dyke approved word for mouth.
Would you care to wager on that?
Because I would gladly take the other side at even odds.
> consumer AI is worth, to Anthropic
Anthropic does not really care about consumer AI. I expect consumer is where their least profitably customers are.
My primary expectation is that Apple will mostly increase usage of AI by general consumers. To me, this reads like Instagram adding stories. Did it stop Snapchat's growth? Sure. But I would be cautious about claiming it will take too many users away from OpenAI. I think it will be a fairly different product offering.
If you're paying to use ChatGPT right now, you might be using it for hobby coding, projects, or image generation. If you're paying a lot for ChatGPT, you're almost certainly using it for personal programming projects.
The $100/month (and up) subscribers aren't going to churn because of this, and I would be extremely surprised if the $20/month users do in any meaningful way.
I don't gamble. Though you might not be alone taking the bet:
https://www.notus.org/technology/trump-blindsided-ai-compani...
"OpenAI CEO Sam Altman pitched the idea of turning over shares in his company to Trump in early 2025 and discussed the matter again with senior officials in recent weeks"
If you’re only giving even odds you’re not very confident in openAI at all. $15 billion is peanuts.
It's a steal at even odds
It's still positive EV at much worse odds but not worth my time
2. You can’t know whether it’s worth your time or not since you don’t know how much the wager was for.
If you’re 99% confident, 10:1 odds with a 10x larger bet is still a steal. My guess is your confidence is somewhere just north of 50%.
I just don't see how being the "premium" provider really works if much cheaper models are basically good enough.
I actively hate it when it brings in some nonsense it thinks it knows about me. I told it my income once in an attempt to use it to find the perfect rewards credit card mix. Now anytime I try to get it to search for a deal it brings up some nonsense about “as a high income individual you don’t worry about saving $X, you care more about reliability, so you don’t need to look for the lowest cost” or something similar.
I have iterated through different option configurations to reach a level of 'customization' that more or less conforms to my own use case, and this does include opting out of any and all lasting memory between instances and across chat sessions; and adds a selection of single initialization prompts which shape the chatbot's behavior to my requirements for that session's objective. these trim most if not all af the sycophantic interactions, reduce outputs to the specific formats and contours as defined and omits any of the 'explanations of the underlying reasons behind...' which is just noise. This also has enabled some pretty useful results without ever spending a dime on a paid account: the premium behavior presented to 'potential customers' as a lure continues to work for me, and for iteration across instances and accounts is possible with machine-ready yaml context file when a single sessions hits the 90% wall : one emit and ingest cycle rotation across account profiles in firefox and i pick right up with a fresh limit.
Bouncing between ChatGPT and Claude, and between models for discrete subsets of larger tasks has really been impactful for my particular needs; but as i am not working in regions of knowledge that are beyond my own expertise and because I require the model to limit responses to very specific parameters, the logic space for unchecked hallucinations is low (but not zero).
The most useful project results for me have been in developing an air-gapped private menagerie of multi-domain models which uses an operating structure not dissimilar to OpenMythos; but then my background includes HPC environment development for NUMA, unikernels, MPI and bare metal hypervisor design - so getting a design plan and functional code without requiring a team of programmers and months of time in order to even start using models under my control which have zero public facing risk for the projects i'm working on is a much better place to spend limited budget on. Last gen hardware in the V100 class is perfectly capable of running and delivering the physics calculation optimizations as required and I would rather buy and/or install solar+storage to supply the electricity for token generation than rent the same from any of the frontier models AND trust that "don't train and learn from me" preferences are and continue to be followed.
If your use-case is a a 'lifestyle shopping assistant' then just turning off customization might be sufficient to stop it from telling you how to live your best life.
Web SaaS gonna end up being seen as another failed play at slim clients and entirely centralized sources of pay to play access to eyeballs; more AOL-ification of networks
Honestly, I don't think I need anything from AI at all. It's a convenience but it doesn't really enable anything I wasn't doing before. That's probably their biggest problem. The biggest thing is it enables non-coders to write code, but it's very debatable that that's a good thing excluding personal projects
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.
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.
* (Yes, I'm being sardonic, but if you did bother to click them, then I'm legitimately interested in your answer.)
- His own objectivity - he consistently throws shade (rightfully) at the pro-AI side being financially 'required' to hold a certain world view, but is completely blind to his own claim to fame effecting him similarly.
- He consistently claims AI can't be made to work, and tries to prove this by calculating with the bubble prices. Its like saying tulips could never be profitable in the middle of the mania because ships were too expensive as proven by their current price to use for shipping tulips.
Add in the semi regular instance downplaying AI's usefulness contradicting my own experience and I mostly dont bother reading him anymore.
Its not like I'll be surprised that shit hits the fan, and he's not going to call the 'when' any better than wallstreetbets or an octopus.
Most hugely transformational technologies in the past also resulted in giant bubbles that burst, because investors piled into lots of companies in the hope that their particular company would win out. Railroads, automobiles, telecommunications networks, the Internet, etc. etc. were all hugely important, transformational technologies that all caused giant bubbles that burst.
But Ed Zitron seems hellbent on saying AI is a nothing burger, and that's why the bubble will burst. But the latter doesn't necessarily follow from the former, and indeed the examples I gave show that the exact opposite is often true.
I believe that the AI bubble will burst precisely because it is such a transformational technology. AI may not live up to the ways its biggest cultists like to shout ("Feel the AGI flow through you!!!"), but similarly in the .com boom/bust there was tons of nonsense about how we'd do absolutely everything online, we were in a new "eyeball economy", whatever that meant, yada yada, yet I'd argue that in some ways the Internet was actually a bigger impact than originally envisioned, just not necessarily in the way that late 90s boosters envisioned it.
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.
When an author is this relentless in pushing you to sign up, there is good reason to suspect that financial motives are unduly driving an agenda.
I counted 8 such instances:
1. In the sidebar
2. At the top of the article
3. Popup in the middle of the screen after just a couple of scrolls into the body
4. Several paragraphs into the article
5. At the bottom of the article
6. At the bottom of the page under the comments section
7. Popup at the bottom of the screen after scrolling to the end of the body
8. (My personal favorite) Click the "user" icon in the bottom-right corner, which you'd normally expect to open an AI chat bot these days, and (surprise) you're prompted to sign up for a paid subscription
This sort of behavior just completely tanks any and all credibility this person may have.
This sort of behavior completely tanks any and all credibility this commentator may have.
Correction: You may like them because you think they convey information. But without any sort of vetting process, the internet has become a cesspool of "news" or "general knowledge" places that ended up quite successful, but which are essentially just a contest of who is most confident when talking about topics and who can present bullshit in the most engaging way. You can see the peak of this on the JRE podcast. Anyone with actual expertise in a subject would be able to call out many of the guests, but since the host knows nothing about most of their fields he just gives them a platform to spread their opinions as facts. And millions of people who also don't know better will accept them without question.
(And he's not Gen Z anyway is he; he's among the older millennials. He's appropriating it for muckraking purposes.)
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?
He is preaching to the choir, if you already hate AI you will love the article, if you don't hate AI already you will find the article insufferable.
Would an angry Pythagoras' theorem be wrong, simply by virtue of his anger?
The square of the hypotenuse — you multiply it by its bleedin' self — is LITERALLY equal to the sum of the squares of the other two sides. Again. Multiply each of the other sides BY THEMSELVES, and then add them together. It's that bleedin' simple Jim Cramer could do it. That number is the same as the square of the hypotenuse, no matter what idiot CEOs think.
So too, Zitron?
I'm neither (or both, if you want - I can hate the direction its taking humanity while not hating my usage of it or opportunities it brings), and I definitely did not find his writing to be either lovable or insufferable.
I enjoyed reading it in a "smells-like-BOFH-but-in-finance" type of way.
Some of his manner reminds me also of Max Keiser's "The Truth About Markets" which I credit with telling me about the subprime crisis a couple of years before it happened, even though people thought he was insane… because he kind of is.
If you're writing in an attempt to convince people of something, isn't how you deliver the message of critical importance?
This is basic Sales 101. The way you sell (products, services, ideas, etc.) is directly related to how successful you are.
That doesn't make him wrong.
He's selling a paid newsletter, so at least one of his motivations is to make money. His target subscribers are certainly people who lean towards his viewpoint but he still needs to do some convincing because the market of people who are open if not warm to his thesis is much bigger than the market of people who already share his thesis.
> That doesn't make him wrong.
I think it's way too early for anyone pontificating about AI, the economics of AI, etc. to be declared "wrong" or "right". This is going to take years, if not decades, to play out.
But I would never consider subscribing to this newsletter and the biggest reason is the writing style/tone. I find it unpleasant.
So even when people agree with you, you can lose them by being abrasive, unpleasant, unlikable.
That's exactly what the first (titled) section does?
What turned me off though was this paragraph:
> 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.
That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.
Really tried to read through this all the way, but man I'm just not in love with this guy. I feel like the frustration is clouding his judgement. This line is another one with a fact that isn't really grounded:
> so, you know, they only need to grow by 496% by the end of 2029!
Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.
Idk, not really sure what my point is here. There are just so many facts and numbers quoted in here... It's a bit exhausting to refute a piece like this, when parts are genuinely correct, and parts are maybe subconciously exaggerated due to some emotional leaking into the argument.
I didn’t read it that way. I see a lot of value in it.
I just don’t see us justifying the amount of infrastructure being built or current valuations. Or in the unlikely event that we do, the societal upheaval is going to take away the ability to monetize it meaningfully.
OpenAI and Anthropic may make it through. But that is different from saying valuations are justified or that all this infrastructure will pay off.
How else would you read the above statement? He's just preaching to his own choir IMO.
My take: like any gold rush, a lot of dumb ideas will get backed and they will all fail. And then we'll keep the ones that worked. SSND. Good luck picking the winners a priori.
The problem is, when there is so much overinvestment, everything gets wrecked. In the aftermath of the dotcom boom there was at least a bedrock of fiber and still useful equipment to build upon amid the rubble. This time we are going so much further; also many of the durable assets are misplaced bets and the depreciating ones will depreciate more steeply.
But confounding this, K80s and V100s are still offered by cloud providers 13 and 9 years after their releases and academia still loves their GTX 1080 Pascals in their desktops. At companies, the beancounters take a computation and find the best architecture !/$ for that calculation. It does not need to be brand new shiny. It's Nvidia's job to make that case, not them. But anyway, the real data is right there. And those old GPUs demonstrate the dark fiber is already in place (and it's not so dark or they'd pull their racks).
AI is the special case. New GPU generations are the only way to access HW implementations of last year's research on precision modes and matrix math. If that slows down, that would be the first real bellwether of a slowdown. It hasn't happened yet. I'm a little surprised myself, but I also think coding agents are the vanguard of general design agents and that's going to hit a lot of industries at once. So as long as the next generation of GPU halves the price of tokens and doubles throughput (or better), the demand for tokens will continue to rise IMO.
What I don't think is that AI can come for anyone's job successfully no matter what the C-suite sorts insist.
In summary, if you're a bear, you can point to the depreciation cycle and scream the sky is falling. And if you're a bull you can point to GPUs staying in production for a very long time despite the depreciation. Guess we have to wait for 2030.
Try 1-2: https://www.tomshardware.com/pc-components/gpus/datacenter-g...
But let's run with Deep Layer(tm)'s hot take from 2024, GPUs all die in 2 years, no exceptions*. Poor guy just spent $400,000 on a DGX with 8 B200s, each of those B200s generates a piddly ~$3,000 in profit monthly spewing tokens, netting $576K in 2 years, that's a pathetic 20% annual return. Oh no... Won't someone call Michael Burry!
*Never mind the 3-year warranty or any extended service contract, that GPU is D E D and you're S O L.
If token prices fall a bunch then it may not even be worth leaving on, depending on your facility’s relative power, cooling costs.
If we push far into oversupply eventually a bunch of firms building this infrastructure are going to lose out.
Chinese providers realized that LLMs have peaked and have started trying to reduce the price per token. Deepseek pro v4 can easily add tests to my complicated code and costs cents for a million tokens.
I can ask Claude or ChatGPT architecture questions and then use Deepseek for the rest.
How are these businesses going to pay to price of energy and GPU depreciation again?
The real challenge IMO is whether enterprise will want to run the models on-site for 100% security and privacy, but even then, what stops Anthropic from offering such an option on-prem or in the cloud?
China's available AI coding agent subscription slots are apparently gone by 9:30 every morning: https://hellochinatech.com/p/china-ai-coding-boom-economics-...
But you run with this Anthropic will die because it will run out of electrons narrative. At least it's creative.
Do your assumptions - " if you look at the trajectory " - factor in a slowing economy, a slowing growth in quality improvements in the tech, and/or the asymptote of market saturation for punters happy to stump up more than $50 a month?
Not saying this would be good (qualitatively) or even good business in any sense, but we’ve already seen companies willing to sacrifice headcount to cover CAPEX for these models.
In a consumption-driven economy, businesses need consumers. Any gains from these layoffs would be short term at best.
That's the kind of claim that requires and asterix, and things like this are what feeds into the AI propaganda machine.
That is an anualized revenue, which are projected numbers and not "real numbers".
E.g. When Anthropic stated $1B ARR (an extrapolated value) what they were actually reporting is $(1/12)B Monthly revenue. If it helps their current monthly revenue is 47 times that, for a grand total of $(47/12)B per month in revenue.
People like you would be why I put "(titled)" in the reply.
> That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.
The premise is that AI is significantly more expensive than current subscription & token fees. Within that framing, yes basically all AI users are getting conned. Tricked into redesigning their workflow around an unaffordable technology, in the hopes there will be too much sunk cost and they'll just eat a thousands-a-month fee.
> Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.
"Doesn't guarantee it will continue to grow" is an understatement.
Let's take a generous assumption of the average subscription; $1000/month/seat. This will be quite a bit higher than pretty much everything but hardcore software dev, we'll re-do the math with $200 in a moment. Let's also grab Ed's $60B figure for both Anthropic/OpenAI, as it's more generous.
That's 30 million subscribers for Anthropic, 30 million for OpenAI, 60 million total.
They need to 5x. So 240 million extra subscriptions.
... Are there 240 million people left on the planet who can afford $1000/month?? (Either directly, or their employer) This kind of scaling is already hitting the limits of people on the planet. That sounds ridiculous for "240 million people" against 8 billion, but remember that $1000/month is a lot of money and a lot of jobs just do not benefit from AI. 2/3rds of employment in the US is stuff that happens in the physical world. Claude won't restock shelves, manufacture goods, construct buildings, cook food, or wipe geriatric asses.
Go again with $200/month. While this monthly fee is much more palatable, the sub-count inflates to 300 million subs needing to grow to 1.5 billion. They'd need to sell a sub to everyone in Europe and North America.
(And while there's loads of people in Africa and Asia, most of those are low income. You're not getting expensive AI subscriptions out of them or their employers either. China's obviously not gonna buy US AI, India has a GDP-per-capita of $250/month.)
Yep. Every man, woman and child, and even then provided we include Russia, Mexico, Cuba, Haiti etc, and, out of desperation to get to 1.5 billion, Turkey, which is in Europe a little.
This statement cleanly encapsulates the entire problem with all of the frontier models' companies' pre-IPO numbers.
They have something-something "new technology" and we don't know anything about how the market is going to settle on the ethics, the utility, the human capacity opportunity cost impacts of not training and/or mis-educating an entire cohort of intern-engineers for a few seasons to a generation, the full environmental costs of hardware and operations necessary for the training each new larger model, ... and we cant even quantify the unknown-unknowns - the risks we cannot forsee.
To predict market revenues for the next few years based on the curves, that they self report without external disclosure of the underlying numbers, is just like expecting your 2 yr old to continue growing at the same pace in the future and in the past - laughable. Good thing it was just water not coffee and it didnt quite come "out my nose" :- ) Thank you kind stranger!
Where are those numbers from?
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.
I am still to see a solid counter to what he brings up there.
Hype cycles never last forever, but that doesn't mean all the value has been tapped by any means. The fact that modern GPUs can solve ridiculously complex high dimensional functions is a superpower in every possible field of research.
These articles are lengthy but, to my understanding, Ed's idea is...
* AI companies have committed to purchasing X amount of compute
* Data centers are being constructed to meet this demand, they'll need to charge amount Y
* AI companies do not have sufficient revenue to pay amount Y
IMHO this isn't surprising, personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls. This doesn't seem like a big enough market for the huge dollar amounts I'm seeing thrown around.
I'm curious why you think Ed is so far off the mark on this. To me, it seems like we are headed for a big correction on the whole AI thing.
• He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.
• All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.
• He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.
Agreed, but I'd argue that Ed doesn't have much else to work with. I'd like to see journalists take this tack and start asking these executives to either back up their statements or back down from them. They should be held accountable for their statements.
Even if we dial down these numbers by a magnitude they are still insanely large and the AI companies do not seem to be making enough money to balance things out.
> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.
I agree that older hardware from Nvidia doesn't become worthless when Nvidia releases new, more powerful hardware. I have to point out that it certainly loses a great deal of value and that's not nothing.
> He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.
Is that really the case? I mean, I read about the build out of these data centers being delayed all of the time. I read this last week and it seems roughly in line with Ed's ravings:
> A JPMorgan analysis last month found that more than 60% of data-center capacity planned for completion in 2027 isn’t yet under construction, and another 7% is delayed.[0]
[0]: https://www.msn.com/en-us/news/technology/america-s-data-cen...
Just like Michael Burry kept comparing NVDA to CSCO and now he doesn't do so anymore now that NVDA's P/E is ~31 and CSCO's is ~41. Funny that.
I am the OP and I totally agree with you on this one point. In fact the progress being made by open weights models strongly suggests that some of this hardware has much more of a life.
The overarching point he makes about incomplete data centres is that the current offering is running successfully on that very incomplete capacity, right?
What he is saying is that he cannot believe the demand exists to fill any of the unbuilt stuff, but much of it is still commitments that are going to have to be paid for, unless they can be backed out. He points to Nadella essentially confirming there will be overcapacity.
He also makes an interesting point that people tend to think "I can't get a GPU right now" means "there is intense, live demand for GPUs in data centres" when in fact the reason you can't get one is buy-and-hold. Including much of that new replacement hardware: it is being bought even the old stuff would (let us stipulate will) do the job.
I think he (or someone who interviewed him) recently said it reminded them less of the dot com boom and more of the Chinese real estate bubble.
I don't know to what extent we can say the current offering is running successfully. Anthropic have had visible capacity constraints for 18 months now with lots of throttling and quota capping going on. Those are good signs that demand does exceed supply at the current price point.
Additionally, Mythos has not launched publicly and one reason seems to be that it's too slow/expensive to make widely available, i.e. is capacity constrained.
But supply/demand is always in equilibrium, in some sense. So you could argue that it's currently balanced, or would be if priced correctly. That tells you little about future demand though.
FWIW on capacity constraints, my gut instinct is that like every other startup these AI companies are are really only now beginning to do the serious efficiency work, because they had money and resources to throw at scaling without it; never optimise too early is pretty much a startup mantra.
I think all the labs have done a lot of efficiency work for a long time, tbh. You can see the evidence in their papers, open source releases and product design choices like model routers. They know they need to reduce their cost base a lot to become profitable.
This is alarmingly obvious whenever he talks out of his depth about things like how companies actually use AI and reason about business decisions.
https://www.tomshardware.com/pc-components/gpus/datacenter-g...
He mixes estimated capex spend by like 3 different sources with actually commitments by the LLM providers.
He talks about how crazy it would be for ai providers to double revenue every year. But openai is doubling every 9 months and anthropic is doubling every 3.
It's obvious if AI consumption stops growing today those companies are in trouble, and if AI consumption keeps growing at current rates they'll be more than fine.
Most people expect growth rate to slow, just no one knows by how much. This will determine if there is an over build out or not.
That seems like a giant paucity of imagination. I can easily name a lot of areas where AI is already having a large impact and it's not hard to imagine the impact growing:
1. Customer service. Yes, we all like to laugh at the silly chatbot mistakes, linked list reversals and Instagram oopsies, but a lot of companies are putting a lot of effort (and spend) into AI for customer service.
2. The legal profession is already spending a lot on AI, and it will only grow. Again, we all like to read about hallucinated case citations, but those are solvable problems (honestly I felt they were more human problems than tech problems to begin with) and there are so many areas in research and document summarization that AI is really good at.
3. Radiology. There are lots of arguments over whether AI will "replace radiologists", but that's besides the point. The largest radiology groups in the country already use AI software to check for specific missed diagnoses, and the expected spend on AI will grow, a lot.
4. Enterprise knowledge management. Services like Glean are popular and growing.
I can easily go on.
The real question is what situations are the flagship, larger models useful in and will that produce enough demand.
Now ofc it can be said that they haven’t implemented it properly but at some point it needs to be considered that why isn’t no one figuring it out?
But, I also think he has missed the mark on a fair few things in terms of out comes. He may be proven right yet in terms of the general shape of things for some parts of the industry but also will have some big misses.
My general take away usually comes down to, places like OpenAI, Anthropic and Oracle have gone in a little to hard to fast and it may hurt them long term as they struggle to make it work in terms of economics. not that they can't just it will be difficult. But places like Microsoft, Google, Meta, Apple, Amazon; they have a very long runway to endure the growing pains and make it through to a long term business that no longer burns cash.
Exactly what the AI evangelists are doing.
Who makes consumer devices? Google
Who makes operating systems? Google
Who makes browsers? Google
Who makes the world’s most popular websites? Google
By the time 90% of average internet users get to chatgpt.com or whatever, they already went through several Google chokepoints, each layer is one more place Google can answer their questions.
And that’s not even getting into the chips, the data centers, the data, the talent, the consumer apps, the enterprise apps, the cloud platform, the brand, and of course the biggest cash printing machine in human history.
You would honestly have to be insane to bet against G.
But it isn't like this hasn't been the long-running strategy for Google as well - provide more results on search so that people don't go to the site with ads, provide paid product results for shopping, to offer more services to keep people providing personal/behavioral queues to Google and more opportunities for ad placement.
If anything, AI turned up the heat such that the frog noticed what temperature the pot was. But that doesn't really put them in a better position to execute than Google.
Unless you are confident that Google will be antitrust forced to weakening their hold in some of those markets. A crack is all you need for the water to rush in and widen the crack.
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.
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.
Here is the report:
https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways
And my commentary:
My view is that it's not really about how good the models are - it's about how we're using them. Understanding what you've built is an important part of value creation, and LLMs eliminate that.
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.
Even if you take for granted that AI is as good as the best people say in writing code. And Ive spent a lot of time generating codes, I won’t disagree - Then the question becomes - does this change your daily incentives such that you reach for code as the solution to your problems rather than something else (coordinating with your colleagues? Product management? Planning and Design?
So from a holistic perspective, I think intentionally limiting your own AI usage is the best approach for maximum long-term productivity.
But what if the problem you’re trying to solve is the altogether too often problem of like getting teams that are dependent on you to upgrade the library they use. And what if the library is a breaking change, and last year they upgraded to the library on your advice and it broke production and now they’re suss and want to accept all changes, and integrating that library change isn’t in their critical path so they’re just not going to spend time on it, even if you submit the MR them. Even if you show them their tests pass after the change.
Importantly to the above, you probably need more devs to do more of the above in parallel. You don’t hire devs to write more code, you hire more devs to carry on the mental load of a broader scope of work. Even in the before times, so much code got stuck at the integration step.
But because all that is hard, instead you go and codegen to fix an obscure bug that sure makes a few customers happy, but no one thought was a limiting factor for paying your company more money.
It’s not that I don’t think AI can help, I think it’s a prerequisite for the job and everyone should use it. It’s more that I think in the grand scheme of things, people will bias towards using it for tasks that aren’t in the critical path - refactors, tech debt, bug smashing, tool building; and I think it could really help devex and that’s good.
But I think people are bad at knowing the difference between “my job feels a bit easier” or “I’m more productive” and “this task had an impact on the bottom line” and when you extrapolate that out to a whole engineering org, that’s where the productivity statistics get lost.
I’ll addd one data point to this is like this thread itself. So many people on AI skepticism threads point to their own subjective experience as evidence we’re not in a bubble, and sort of ignore the entire concept of economics. I’m not saying we’re in 100% in a bubble, but subjective experience isn’t great evidence of it.
And this is just sort of one of the factors, what about the increased cost and mental load of supporting more software? What about junior engineers who feel pressured to ship work but don’t actually learn the software engineering? What about lost context from not intimately understanding your software?
Although if this theory is true — that AI helps with coding but coding is not the friction point in organizations with multiple humans, even that should allow faster iteration by allowing one human to do more coding therefore reducing the size of teams required to make some programs. You should see good acceleration in solo shops too.
I’m a platform engineer. The primary failure mode for platform engineers is building tools people don’t want. AI doesn’t really make that easier. Or it can but it can also make it harder by making it easier to chase down ideas that you don’t get traction on. And I think that - net balanced across the organization is probably why productivity gains get sort of averaged out.
For sure I think solo devs who have a system are seeing gains, as long as you can I think have the discipline to have a process that includes feedback and learning and your not just feeding off of dopamine hits of one shotting features but yeah. I mean for solo devs the code was never really the limiting factor, it was product-market fit and marketing.
So solo devs who have a system may be laughing themselves all the way to the bank, but we may not see a lot of net new solo devs.
But if code is cheap now then it’s sort of inherently devalued. 2 8 person startups can probably relatively easily find a dev with AI experience to rocket ship their code generation, which means the basic skills of talking to customers, change management, and building the right thing become even more valuable.
Even solo devs I wonder - almost every post-mortem of a failed company goes “I wish we had spent more time talking to customers and less time writing code”
Again if you can get the discipline right, maybe as a solo devs you can get more work done faster and spend more time with your family. That’s incredibly valuable!
But if you go and add a big new feature, or a second product - unless your community is primed for constant growth(no man’s sky is one community where more more more seems good) you’re just growing the surface area where all the other skills are more necessary.
I think this is right. They are much better applied as editors than authors, IMO.
The key thing is stay in control of your output. i.e. understand it thoroguhly. I think you let the LLM make decisions you don't really understand, you're increasing the likelihood of introducing defects that are expensive to address.
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.
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.
This is what I think the data says.
I actually think this is precisely the reason LLMs can't be the basis for a technological revolution. Because it's only one way.
Like, if you have a compiler, and it has a bug. You can discover if that bug is influencing your code execution and patch it. You can go both up and down the stack.
With LLMs, there is no way to patch it's translation function. You have to rely on it to forward process.
I don't think there is any way to avoid us understanding our tech stacks.
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.
I think I just disagree about the power of the LLM to deliver revolutionary innovation. That's something you do. Not the machine.
And, pretty soon on your journey to scale, the LLM becomes a hinderance rather than a help.
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.
In my field - operations - productivity is usually described as some rate of production for a specific asset. 100 widgets / machine / hour - for example.
"My productivity is 3 PRs / day with the LLM as opposed to 1 PR per every three days". That's how I think people are thinking about it.
My point is that's not the same thing as value. I.e. what people will pay for.
“This random part of the code is slow, I used an LLM to generate a PR that speeds it up.”
Okay, you optimized the part that’s not a bottleneck, sped up nothing and cost the company $100 in tokens. Good job?
"If an LLM builds a feature, and no one uses it, did it make value?"
You're right my analysis is at variance to what Faros.ai says. I think they interpret their data trying to rescue utility for the dominant patterns of LLM use.
But I think to anyone who is experienced with process improvement or queuing theory, their interpretation is clearly weak. Rework is a huge problem in queue systems, and they mostly just elide the throughput impact of an 860% increase in code churn coupled to a massive spike in bugs.
Obviously draw your own conclusions. But I don't think because I disagree with the interpretation of the people who originated the data makes me wrong.
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.
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...
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.
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.
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.
Extreme ignorance?
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.
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.
How far behind are models that can be run locally, and do you expect that this will be widespread?
I think over the years local models have fairly consistently been ~7 months behind frontier performance. Local models are hugely important but I don’t see the calculus changing. I can imagine it’s certainly the case for many tasks that there will be diminishing gains for performance improvements or reliability pass some threshold, in which case you don’t need frontier performance and you can certainly use local models or at least cheaper tiers of proprietary models if local is too much of a hassle. Plus of course use cases where local is necessary or the pros of having local models or on device models outweighs that of frontier.
Maybe things will change though, I would assume through basically government subsidies from China etc, to undercut existing frontier labs, but you can always spend more (better data more compute etc) for better performance and that I can imagine will always have a selling point.
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.
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.
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...
And presumably the GP thought that saying the maintainer had access to Mythos made it a more compelling argument. Otherwise why even mention it?
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.
Have a muck about with what Qwen 3.6 or Gemma 4 can do and you'll see. I mean this as an illustration but Qwen just isn't as far behind as I expected, and compared to the data centre hardware it will run on a potato.
The frontier models are losing their undeniable edge over that which is unmetered.
And even putting aside my optimism for the smaller open weights models, there's a huge amount of scope for the larger, hosted open weights models that are only just behind the cutting edge and which cost, what, 1/25th of the price on opencode go, openrouter etc.
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.
Old WSB saying: The market can remain irrational for (far) longer than one can remain solvent.
And unfortunately, a lot of the market on the "buyer" side has been acting irrationally. When you see CEOs telling their employees that they don't care about token cost, only about "how much AI do you use" because that is what the stock market wants to hear - that's when you know we're all getting cooked, the question is how long it takes until the bubble bursts.
Why cant it naturally grow and prove it's worth?
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..."
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.
Could you share what tells about it? I.e. where he was wrong about it?
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/
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).
Also, if there is significant gains from caching, then like.. what are even doing here? Inputting something and then reading cached pieces of text based on their similarity to the input? Kinda like a search engine?
The newest biggest model can still matter even if you do not run every prompt through it. You'll always have some task where even small amounts of loss are unacceptable and thus you need to make sure frontier intelligence is used for it.
On the router point, yes, routing has some overhead. But the router does not need to run the biggest model to decide which model to use. We've been using tiny classifiers for recommendation engines for ages now, usually on CPU. If routing saves you from sending a large fraction of traffic to the expensive reasoning model, the routing overhead can easily be worth it.
> Also, if there is significant gains from caching, then like.. what are even doing here? Inputting something and then reading cached pieces of text based on their similarity to the input? Kinda like a search engine?
The caching I'm talking about is explicitly the attention/kv cache, so its not input similarity retrieval (that would be more like what you'd use in a RAG/IR system). Prompt caching is generally about reusing already-computed attention scores for repeated prompt prefixes. The idea being you don't recompute the same static system prompt, tool definitions, schemas, long shared context, or repeated boilerplate every time. In more sophisticated systems, you usually store multiple checkpoints so that a small prompt change doesn't result in all-or-nothing hit/miss scenario.
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.
Sure you can. Just because there is a non-zero amount of margin pressure from the lower tier inference providers does not imply that revenue forecasts ought to be poor. Jevon's Paradox gets oversold in this current cycle, but I do think it's a relevant lens to view this through given how much demand has outpaced capacity.
The argument is that customers learning to spend less per task can be good for the viability of the market (really the total demand) even if it is bad for naive revenue-per-token assumptions. If a workflow goes from economically stupid to economically viable because you route 80% of it to cheaper models and reserve frontier models for the hard cases, that can expand total usage and improve cost per useful outcome.
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.
* 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.
- 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?
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.
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?
The jury is still out on that.
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 $$$?
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.
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.
It feels depressing, but I think the same. When thinking about the larger world, it becomes increasingly hard to ignore. And of course it is not new.
There were "doomers" already in the midst of the 20th century, but it doesn't mean that they were wrong.
AI, Crypto, car-centric urban planning, cardboard suburbs in a barren field with bright green grass.
You tell someone that their selfish choice to drive a pickup truck only to haul something maybe one time a year, they shit their pants. They can't stand it, they start personal attacks and ask "what about you??". They seem to know it's wasteful, damaging, etc but they deserve it for some reason.
80% of generative AI queries wouldn't even exist as google searches.
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.
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?
If we want to get really pedantic, every generated token is the answer to the query: what's the next most probable token in this sequence of tokens?
When I post this http request containing this reply, you could say my machine is querying the server to ask "what did you do with the message I just gave you", but then query stops having any useful semantic value to distinguish it from "request"
Regardless, this is tangential. I don't disagree that a lot of LLM use is not in pursuit of knowledge, but enough of it is for me to think that preferring LLMs not to exist is a hard position to defend, at least without making the case for existential doom.
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 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.
Oh my god, no. I also want the benefits of automobiles! They are strictly more capable than, say, trains. That's where I would derail the discussion completely when going into details, but no, I am not against cars as a technology.
Apart from all the ethical and social arguments (logistics, ambulances, the elderly, etc etc). But that's not where I wanted to go.
I was making a leap here simply because of the whole complex around prisoner's, dilemma, the commons, state economy, and so forth.
Since at least ~100yrs ago, I guess cars and streets as the primary mode of transportation have also "won the vote" / are what the majority wants, so it's also an interesting analogy for diminishing returns maybe.
Building out more car infrastructure is certainly not controversial where there is absolutely none but there are commercial or residential buildings.
Anyway, lots of associations are worth considering here IMO. The ultimate limiting capacity here, when disregarding all environmental or health concerns, is simply space and the positive externalities (cities etc) around existing infrastructure.
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
> we make everything else in the world worse in order to maximize the profit
> destroying the planet for data centers
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!
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?
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).
Where did all the stock gains go before AI?
FAANG / MAG-7.
Was everything from 2012-2020 fake, too?
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...
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
https://insights.som.yale.edu/insights/this-is-how-the-ai-bu...
> AI-related stocks have accounted for 75% of S&P 500 returns, 80% of earnings growth and 90% of capital spending growth since ChatGPT launched in November 2022.
If all these false practices can pull revenue out of nothing, why doesn’t every company do it? How come AI companies seem to be able to pull off financial magic that no other company can match?
All your analyses still ignore the revenue point.
Then why can't anyone point at actual numbers? The best we get is "look: line go up" while pointing at either the companies selling the AI shovels or the companies selling $1 of tokens for 50ct.
When cars replaced horses we didn't have to twist the numbers to understand the benefits. When emails replaced mails we didn't have to do 6 hours of mental gymnastics to see the increased productivity. Heck my grandma could tell the benefits of computers when the town hall she worked for finally discontinued typewriters
They're spending hundreds of billions if not trillions, and have nothing to show for it besides like 5 stocks pumping like shit coins. On top of the the drawbacks are massive and very visible...
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
Parent comment:
> Now remove the companies selling the AI shovels: https://pbs.twimg....
From your linked image, "excluding AI stocks" is "+16%" (the figure with AI stocks is far higher).
Your sole source says +16% excluding AI - in what kind of market is +16% “nothing”?
It's nothing because it happens all the time, it's not statically relevant, like not at all: https://www.macrotrends.net/2526/sp-500-historical-annual-re...
This forum is full of techies with very strong opinions about their toys but 0 economical, political or historical education, and it shows
And even more so since inflation was 2-3%, not considered high, during most of that period.
I mean, do you know what the value of those stocks would be if AI didn't exist. Maybe they would be much more negative. Maybe we would be in a recession. Without a control this type of analysis is meaningless.
And that is even assuming that AI productivity gains are happening now instead of 5-10 years from now.
Infrastructure doesn't produce value overnight. How long did it take the Interstate System to provide measurable value? I asked Gemini. Supposedly increased national productivity by 25% over 39 years[1]. But if you drove on a newly finished interstate in 1959, you saw the same cars just moving a lot faster.
That's what we're seeing right now. People can produce an incredible amount of stuff really quickly with AI. Is it directly connected to measurable productivity across the entire economy? No, because, realizing a mass productivity increase from infrastructure takes time.
[1] - https://www.richmondfed.org/publications/research/econ_focus...
Just because you keep repeating something doesn't make it an undeniable truth.
How can something so undeniable have zero scientific evidence? Are there any large peer reviewed or meta studies confirming your claim?
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.
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.
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.
I agree that there are incentives to waste tokens but this is strategic: they spend now for people to build and explore, then they keep the things that work and drop the things that don’t.
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.
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.
Owning a chunk is pretty directly how more than one country injected confidence into their at-risk banks; it’s certainly how it was done in the UK.
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"?
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...
I think it's a poor number to build an "AI is slowing down" narrative around.
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.
Maybe you could use your $1,500 quota to ask AI for better arguments?
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.
The more signals the better! What cap did your company pick, and what geography / kind of industry are you in?
The cap is moderately above the high subscription tiers, and managements/the executives were clearly extremely concerned about how expensive it would be if we all or even mostly came close to hitting it. I heard that they originally wanted to go lower but the developers in the pilot program blew past their planned limit very quickly.
As for the company, its almost entirely B2B SaaS (I think it has some offerings that are used by consumers, but they're mostly/entirely paid for by another business on behalf of their customers), and they have developers all over the world, although the headquarters and biggest group of developers is in silicon valley (my office is in the midwest).
And as you have written on your blog it's a soft cap that can be exceeded with justification.
My company provides employees with API keys and soft limits, but as soon as you approach ~$400/month they ask that you get a Claude/Codex Max subscription instead. Curious if it's not the same case at Uber.
While this seems to be allowed because the current ToS don't seem to explicitly forbid it, I'd be surprised if this loophole stayed open for long... Why would they even distinguish between business and (much cheaper) individual plans if companies can work around it by telling employees to just pay for the latter themselves?
Another way you could take it is, avg Uber salary is what $300k/yr? Does Uber think LLMs make their engineers at most .5% more productive?
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.
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.
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?
Far more interested in dialog and characters developed by a writer - simulation is boring
It entirely depends on the situation. Background NPCs that just have conversations among themselves would be a great use of LLMs to make the world feel more immersive. Obviously you never want to directly engage the player with LLM generated writing.
I have deployed LLM-based NPCs in production for a reasonably popular game (100k DAU), so this is news to me.
And we’re only about a year into the agentic coding era portion of that era.
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
So? Something can be bad for society while improving your individual lot in life.
The negatives: Culture is destroyed, truth is destroyed, democracy is destroyed.
I guess its a wash at the end of the day.
Just because those things happened in Dune doesn't mean they'll come true. It should be obvious what massive potential the thinking machines have for humanity as a whole.
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.
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
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.
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.
Every facet of the field is being pushed on and advanced at the same time.
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.
> - 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.
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.
And it got me thinking, they sell these AIs as assistants, but it couldn't even look up a passage from a book. This is basic, elementary stuff, it should get it right. I would have fired this assistant right away if it were a person. Not only did it get it totally wrong, it came to me with utmost confidence that this is the quote from the book. Unreliable assistants? That's the product they're trying to sell? Get out of here with that trash. I can't trust it.
Nevertheless, it all misses the point if we get to AI post-scarcity utopia. But thats a big if.
The only way they make their money back is if everyone pays them tons of money for it.
> 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?
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?
It is literally judging the book by it's author, which is an extremely rationale judgement to make.
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.
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.
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.
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.
What I remember from that time period is people predicting that we were in a tech bubble driven by social media, that obviously Facebook and LinkedIn were overvalued because social media was a trivial fad, and so on. Example article pulled at random:
https://theconversation.com/linkedin-is-floating-on-air-or-i...
And yet there was no bubble, these companies did fine and Meta became a financial Godzilla.
Anyone remember this video?
https://www.youtube.com/watch?v=I6IQ_FOCE6I
How many of the logos that scroll by there still exist?
Ironically I feel like it captures the spirit of the then-coming 2010s boom more than the climate in 2007, though some of the language it's using is decidedly pre-mobile and more "web 2.0"-ish.
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.
So the claim is the cost isn't coming down enough to make it make sense for a lot of uses in the long term. When I hear that next to the most wild claims, some by influential people, that the entire white collar workforce is going to be replaced very shortly, it's a bit of a useful reality check.
If I had someone giving me free access to cranes and excavators, I'd be raving about how easy it was to build houses now. But tomorrow when I have to pay full price for them, I'm going to be making very different calculations about return on investment.
The question we need to be asking is "what is the likely full-price cost we'll have to pay for these tools, and is that cost likely to be worth paying?"
What Ed's pointing to is that the full-price cost will have to cover the capital expenditures that have been invested, or the companies which risked that capital will go bust. That gives us a floor for what the full price cost will be, and that floor seems higher than the value being offered by the tools.
Is reading comprehension really this bad nowadays?
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).
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.
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
When asked whether it will be possible to fix Sora's videos after they've been generated, Murati said "eventually," and then couched that by saying "that's what we're trying to figure out...how to use this technology as a tool that people can edit and create with." She promised that there would "eventually" be "more steerability, control and accuracy...and reflecting of intent of what you want."
You'll "eventually" be able to add audio to Sora videos, and when asked when Sora's generative videos will be available to the public, she once again said "eventually," and when pushed said that Sora's launch would "definitely be this year, but could be a few months."
Murati, living in a world of "eventuallies," provided no technical insights, no specifics, and very few details.
Please speculate on why OpenAI wouldn't just leave it up (whether or not they were able to improve it).
I think a much more reasoned critique of AI is that of Tyler Cowen, whose argument is basically that most processes aren't constrained by lack of intelligence but by organizational and social factors which mean for AI to be useful you have to redesign organizations and work to take advantage of what AI is good at. Since most organizations are fairly bureaucratic that takes a while, especially in the large industries that are the most economically important.
Ed's criticism of the large AI companies seems particularly misguided to me since they are the ones actually advancing the technology and seem to have real moats given their access to large amounts of training data from their users. I don't see any possible future in which 5 or 10 years from now there is less AI than we have now and I would expect usage to be much higher.
As an example of obvious wrong things, I remember a tweet of his where he was mocking people talking about agents and agentic coding. He was kind of saying that he was going crazy as agents weren't a thing really and people talking about them like they were real. Something like "agents?! what agents?! these guys hear themselves?!". The answers were full of hundreds of people patiently explaining how they were actually _using_ agents. This wasn't in 2023, it was a couple of months ago.
He just has an audience and an engagement target. His objective is clicks, not informing.
I think the most compelling part of the article is that these numbers point to a situation where the level of investment required seems unsustainably high by plain dollars.
You don’t really have to agree with the author to see how it plays out. OpenAI and SpaceX and Anthropic need to go public this year to avoid running out of money. There’s no more private money, not enough to fund them. The IPO is the last funding round.
They can continue growing extremely quickly and AI can still be highly useful and maybe be transformative, but still not have the money to fund that growth.
That part he wrote about an AI company gone bust canceling their Oracle contract made Oracle feel like a Nortel analogy to me. If they have a sudden lapse with a big chunk of their customers they are writing down triple digit billions of dollars.
I do have other sources of information and I probably agree in general that AI companies are doing pretty shady financial shenanigans. I even think it's possible that openai is in real trouble. But I don't extrapolate that into "AI is useless", which is what he does.
It’s great that he cites a lot of sources but some of them aren’t great, like the Microsoft story about canceling their Claude spend. I think that particular story isn’t much of an indicator of anything, and it might not even be true.
But the financial part…this guy isn’t the only person out there sounding the alarm about the math not mathing.
There is a financial argument and capability argument.
In this case, he doesn't make the claim one follows from the other.
But he's linking out to sources
Someone else separately linked Ed’s 2024 claims [1] that:
A. AI revenue had about already maxed out.
B. AI's output accuracy was already about as high as it would ever be
C. AI users were already declining or was as high as it would ever be.
So we have 3 2024 claims about whether AI was already the biggest/best it would ever be, and whether AI usage was even already shrinking. All ended up being the opposite of true.
Have you looked at whether Ed’s previous claims that went against popular AI views and are testable ended up being true or not?
Does it matter whether an author’s claims like that are true or not for whether you will continue consuming them?
If straightforward claims like the above are so easily disprovable, what makes you believe that he isn't cherry-picking other stats in order to spread misinformation or disinformation, as the individual stats he points to might even be completely true, but if they are cherry-picked, they may be more misleading than elucidative?
If someone has a multi-year history of frequently spreading false claims, should we trust their predictions about future events more than other sources?
Publications love a doom and gloom rant, which is why he seems to have built an entire career on hysterical anti-ai screeds. It doesn’t mean that he’s right though.
The problem is when untrusted authors take positions, then it circulates widely, then people discredit the author and by proxy the position, when the position could be correct.
The article has a number of emotional appeals in it. Something more focused on raw numbers would foster more curious discussion.
IF I WROTE AN ENTIRE BLOG POST IN ALL CAPS ABOUT HOW AI IS LITERALLY SATAN PEOPLE WOULD JUST THINK I AM A CRAZY PERSON
If that's his message, why is he going on about ecconomic sustainability? Whether or not you have a coherent business model has nothing to do with how morally corrupt you are.
Ultimately i agree with the GP post, the article reads like something preaching to the choir. If you already agree it seems natural. If you don't agree it looks like an incoherent rant that is not particularly convincing.
Normally a grift involves tricking someone. The AI situation seems more like a bunch of investors knowingly investing in something very speculative. If they lose their money, while that is the nature of speculative investments.
> The AI situation seems more like a bunch of investors knowingly investing in something very speculative. If they lose their money, while that is the nature of speculative investments.
This is a bit reductive, especially as the government’s role in shaping American life is reduced and the influence of extremely high wealth individuals impacts every citizen. We’re somewhat losing our democratic rights when Congress does not answer to our interests, and the extremely high wealth individuals directly (FoxNews) or indirectly (X, Meta incentives) manufacture consent.
The whole theory of low taxes and light regulations is that high wealth individuals will be incentivized to “create jobs”, like modern Lords of the Manor. When those who essentially steer the direction of the economy take actions that are highly speculative seemingly only to enrich themselves and concentrate wealth further despite public outcry … well, maybe there isn’t a word that elegantly describes that situation, but “grift” is as close as I can muster.
Are they actively lying to people in concrete ways? Not just, we think this is going to be big, when that is questionable, but do they say AI can do X right now, when objectively it cannot even remotely do X under any circumstance? Lying is what makes the difference.
> well, maybe there isn’t a word that elegantly describes that situation, but “grift” is as close as I can muster.
I think the word you are looking for is capitalism.
Rich people put their money where they think it will generate more money. The economy uses that to determine what to focus on. Sometimes rich people are wrong and are punished by losing a bunch of money. That is how the system is supposed to work. It is far from perfect but traditionally has done better than the alternatives.
> I think the word you are looking for is capitalism.
“It” may be a consequence of some countries’ and cultures’ attempt at implementing capitalist policies, but I don’t think that’s anyone’s working definition of capitalism.
“It” being a runaway concentration of wealth being held by few individuals who are not as well-informed and smart as they think they are, exploiting economies of scale and highly financialized systems.
> That is how the system is supposed to work.
The system is supposed to work in the way that we design it to work. Massive speculative bubbles where every rich yahoo is simply looking for an “exit strategy” is far from ideal.
You’re far too forgiving, covering your eyes while the industrialists rob us blind. There’s a culture of grift and exploitation running rampant in the US at the moment, and this sort of apologia only serves to exacerbate.
Its literally the dictionary definition.
> Misleading investors is pretty much the greatest talent of all of history’s greatest capitalists - were they all grifters? Is it the extent of the lying that matters?
Some are. Plenty are in the grey.
But there is a big difference between objectively lying like in theranos, and hyping things up questionably. There is a reason why Theranos people are in jail, while many a hype man walk free.
> anyone’s working definition of capitalism.
How about the dictionary definition of capitalism. Private individuals owning and controlling the means of production and subjecting to their whims is as fundamental to capitalisim as you can get.
Maybe there is an exception for monopolies, but i dont think any of the AI companies are monopolies.
> held by few individuals who are not as well-informed and smart as they think they are
I mean, seems to be working out pretty well for them. Can't argue with success.
> The system is supposed to work in the way that we design it to work
Key word being "we". As in society. Not you personally.
> There’s a culture of grift and exploitation running rampant in the US at the moment, and this sort of apologia only serves to exacerbate.
People making heated diatribes instead of cold logical arguments serve to exacerbate it.
Your arguments aren’t nearly as logical as you think they are, they merely tell the rest of us the blind spots your world view is plagued by.
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.
This is nothing new for Zitron. The last article of his I saw:
> I also severely doubt that Anthropic managed to make the cost of running its services profitable in the space of six months.
> [Per The Information in January], Anthropic missed on its gross margin projections, saying that its inference costs were 23% higher than the company had anticipated.
> How did Anthropic, which faced a massive influx of new business to the point that Anthropic was forced to buy more compute from Elon Musk, magically become profitable? Other than that discount, of course.
If you follow the link to The Information, you’ll see that it’s a paid article with the headline “Anthropic Lowers Gross Margin Projection as Revenue Skyrockets”. But what happens when you actually read the article?
> Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025, according to two people with knowledge of its financials. That margin was 10 percentage points lower than its earlier optimistic expectations, though it’s still a big improvement from the year before.
So, according to Zitron’s own source, Anthropic are actually earning 40% gross profit margin on inference, and that is a dramatic jump upwards! This totally contradicts his position that it’s an implausible “swindle” for Anthropic to claim profitability. He’s counting on the fact that most of his readers don’t subscribe to The Information and will only see the headline, or that they will just see a citation and trust that it backs him up without checking.
Does the truth normally lie somewhere in the middle of it all?
Usually does when you decide what constitutes extreme.
"Some people say the Sun sets at East, other people say it sets at West. The truth, of course, is certainly on the middle."
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.
"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."
Ed Zitron. Mar 18, 2024
He's in the media business... its in his interest to amp things up.
Of course that mentality is obsolete. Now we all have infinite access to perfectly correct information via the internet.
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.
Apple themselves have said there is usage limits, with a subscription upgrade for more usage. So clearly AI Labs are directly competing on that front, it's just a normal default/chosen decision. Considering there are defaults and still successful competitors (eg. safari v chrome), there's no reason to think that competition can't handle this too.
Edit: I want to add that Google is also probably willing to give the model away at a discount to its true value in exchange for guaranteeing that their primary competition (who has tons of cash) won’t have an economic incentive to enter the foundation model training arms race.
Most users who actually want these features for anything more serious than summarization and style updates will probably find value in a modest subscription or ad-supported tier of higher quality models, even if just for occasional usage. Apple can provide this, but once you're comparing features, for many Gemini/Claude/ChatGPT may be a better fit.
Oh, and I think there is an unfortunate but real risk that once again, apple totally over-promises here, and their AI models that they ship end up being pretty poor, and that drives users further into subscriptions.
OK, that I would concede is a possibility. Though Gemini is clearly capable, and the (alleged) story is that they have licensed a one-trillion parameter form of Gemini. I don't think they are making the same mistake.
ETA: I also concede they could make a different mistake ;-)
Specifically for image generation. They haven't indicated you have limits for Siri interactions.
Start at 1:07:00 in their announcement video. Craig is absolutely talking about "Apple Intelligence" as a whole in this segment.
Pragmatically, of course they'd need to add metering to any cloud available APIs that rely on large models. There's no way they will eat the cost of serving unlimited access to a cloud LLM to end users if they won't eat the cost of an image generation model.
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.
> If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year
Ok let me read the thing so I can make up my mind… start scrolling down and get slapped by some subscribe pop up.
That’s where I decided to just cut my losses and go do something else.
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?
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.
The best solution, from an efficiency point of view, is to use smaller models on datacenters, requiring much less of them.
MacBooks have a lot of memory and a lot of FLOPs. They mostly sit unused all day. Yes, the excess energy use will be higher than a GPU in a datacenter doing the same work, but you have to generate an absurd amount of tokens before the dollar-efficiency catches up with the MacBook.
Obviously, you can agree with one thing and not with other. And he can be right in one thing and not the other. But, if you listen to what he is saying, he is absolutely saying the technology is not significant.
He is very very clear and open on what he thinks about usefulness of ai. He is not saying "it isn't as great as the hype". He is saying "it is useless". He is simply not the centrist kind of guy when it comes to AI usefulness.
AI in general is not slowing down. New smarter / bigger models popping up, get blocked, new open source models exceed commercial https://x.com/prz_chojecki/status/2065741640635990128?s=20. All is good here.
AI adoption is nowhere near the current capabilities of AI. I think its because of the lack of expertise at companies. Everyone has an opinion but in fact nobody knows how to work with AI. There is neither AI dedicated positions at companies nor AI transformation leaders on the market.
What companies can do in this case? Install Claude Code and wait how things change over time.
Anthropic is growing way faster than doubling yearly so don't think this is entirely implausible
That’s really the only way the math works - outright displacement of labour
Now that you can get Gemini, operated by Apple (with the Apple privacy features that come along with that), why would you ever consider going Android/Pixel (outside of running GrapheneOS, but I'm talking regular consumers here)?
Google isn't even making anything on the deal with Apple. They pay $20B/year to be the default search engine. This is Apple just giving a $1B a year discount to that to be able to license Gemini.
If you're in the Google ecosystem like Gmail and Calendar, it is exceptionally refreshing to be able to use an assistant that uses that ecosystem, instead of iOS requiring you to use Mail or its own Calendar app.
I don't think there's any real gap between Pixel and iPhone on the things that matter: UX jank, battery life, camera. Even the messaging issue in the US has closed with encrytped RCS support between them launching. So now it's just an ecosystem question, which might be why Gemini is mentioned so much with Pixel.
But given how dependent OpenAI are on Samsung, it's hard to believe they will see a radically better deal in material terms.
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.
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 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.
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."
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.
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.
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 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 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.
This is fire erasure
/s
The internet was a bubble: you could make a web page and sell it for millions because next year it was going to be worth billions. And then internet grew up.
AI is technology that's still beginning to find its place to settle. It's far from mature and that's perfectly fine. We'll have reached a reasonable plateau once the technology and the related stack stops changing every month and instead develops incrementally and boringly over the span of few years. That's like internet in the 2008-2010, and many investors will have a collection of new burn marks by that time.
Not only financially there's an unsustainable push for AI by the zealots du jour who are more often than not managers rather than engineers. AI is championed most ruthlessly as a silver bullet revolution by people who least grasp the limitations of AI. It'll take some time to figure out the dreamed-up proceeds won't be there, and "then what?".
I predict that the real bottlenecks of development will re-emerge as soon as the limitations of AI will manifest out of the hype. They bottlenecks are human-based, in development processes and in human interactions. A large part of development is trying to understand what we want and what we need and you can't offload that to AI.
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.
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?
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.
>If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful
Anthropic's interpretability research explored this topic a bit in 2025. Apparently, the signal is extractable:
It turns out that, in Claude, refusal to answer is the default behavior: we find a circuit that is "on" by default and that causes the model to state that it has insufficient information to answer any given question. However, when the model is asked about something it knows well—say, the basketball player Michael Jordan—a competing feature representing "known entities" activates and inhibits this default circuit (see also this recent paper for related findings). This allows Claude to answer the question when it knows the answer. In contrast, when asked about an unknown entity ("Michael Batkin"), it declines to answer.
By intervening in the model and activating the "known answer" features (or inhibiting the "unknown name" or "can’t answer" features), we’re able to cause the model to hallucinate (quite consistently!) that Michael Batkin plays chess.
Sometimes, this sort of “misfire” of the “known answer” circuit happens naturally, without us intervening, resulting in a hallucination. In our paper, we show that such misfires can occur when Claude recognizes a name but doesn't know anything else about that person. In cases like this, the “known entity” feature might still activate, and then suppress the default "don't know" feature—in this case incorrectly. Once the model has decided that it needs to answer the question, it proceeds to confabulate: to generate a plausible—but unfortunately untrue—response.
[1] https://transformer-circuits.pub/2025/attribution-graphs/met...
This is the part that concerns me the most, AI companies will start bidding on increasingly contentious contracts out of desperation. In practice, this means building services that facilitate killing to anyone they're not legally prevented from doing business with... and even then, they may be able to do business with them still through intermediaries.
Some are still steadily increasing prices.
A 1TB NVME drive - a good one - cost about $70. Now it costs anywhere from $150 for shit-tier drives to $300+ for the higher end stuff that used to cost $100-120.
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.
For example,
Maybe we'll get there, maybe not. These days I only hear of datacenter investments.
I really love LLMs for debugging and rubber ducking, but I kinda want to write all my code.
LLMs tend to have a hard time understanding composition.
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.
How's that?
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 ?
Impressive.
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 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.
Also, if Anthropic or OpenAI fail in their revenue requirements I believe they would be absorbed by the guys with the money printing machines (Google, Microsoft and Meta).
> No matter how horny or flaccid you are
These analogies are great.
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.
-------
> 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.
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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?
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.
Any benchmark on AI shows that it gets better linearly. So his prediction was correct. There is no exponential AI improvement anywhere.
?? https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
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.
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.
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.
But it's famous for having collapsed after their IPO. It took 4 years to get back at the same nominal valuation (not inflation corrected), and after all the 2020s inflation it is still at 2x the initial price.
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.
So it's okay for everyone's who's due to retire in the next 13 years to have their 401k or equivalent wiped out when the correction happens?
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.
That seems doable. Next generation architectures and the models they produce are accelerating progress. More capable with less data and compute, which ironically will drive more demand, aka Jevon's paradox.
> If you are someone in the executive team of any major tech company, know that your employees are, for the most part, completely and utterly miserable.
I agree this is a problem. Adopting too eagerly and too early, and not listening to feedback from the people who are using these tools is a recipe for disaster.
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.
I don't know much about the economics side; TFA gives a barrage of stats that seem to make a compelling case for bubblehood. OTOH, the claims about the utility of LLMs being unmeasurable are weak (the same criticism applies to hiring programmers, or indeed most office workers) and the metal spider straw man is frankly embarrassing to anybody who has actually used recent frontier agents for programming and seen what they can do.
Bloomberg is interested in what he has to say
But not HN commenters
Yes! Email me at ed@ezpr.com. I have an extremely high bar both for advertisers and the cost of advertising on here - I have 84,000 subscribers and a 55-60% open rate, as well as an 8-11% clickthrough rate.
I do not do any kind of outcome-based advertisement (IE: X number of people click through and you pay me Y), so any kind of agreement would effectively be a sponsorship. I have an engaged reader base and you will have to pay to get in front of them, as I also do not need advertising to support this newsletter."
Maybe the "AI" companies could pay for sponsorship
Would he take the money and run their ads
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.
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.
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.
> It's like someone arguing that cheese isn't real
I agree with your first statement (any being you) because of your second statement.
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.