Interestingly, I've taken a different approach. AI supplements how my business builds and I'd much rather have all my engineers using Opus 4.8 rather than whatever the best open source models are.
I believe open source is important, but for my business I'm just going to use the best tools I have available to me.
As a business decision it makes sense if you think that spilling out agent-written code to meet some profitable objective is a race you can win?
I know I can't win that race or outspend the competition. So I have to rely on my instinct that in my area of business, people becoming dependent on agent-written code are getting further and further out of their depth, and that slow and steady will win the race. I am going to spend the time trying to integrate the open source tools into the way I work. (I am still working on this; frankly I may have bigger problems on an individual level than they can solve)
To be maximally clear, if this two-inscrutable-megacorps model does survive, and it becomes how everyone works over even the medium term, I'll have to quit tech.
I will probably retire early and just plan for a shorter, quieter life that ends when I am out of money, because like everyone else I won't be able to afford a longer one.
I don't want that "nobody prompts now, we just specify loops" bullshit for myself and I don't want what it will do to me for anyone I love.
Open source and open weights have to win for human culture's sake but in the short term for the sake of the culture of tech work. We need control over how we use these tools, not just to be steered down whichever channel makes the most money for Dario and Sam.
We (a team of 6 developers) win this race day in and day out. We're constantly impressing our clients and delivering outstanding results that improve the business.
Hell, I'd wager there's MORE humanity in what we're building because we can afford (literally) to sweat details that previously we couldn't.
I don't understand the argument that somehow because two mega-corps are duking it out, I cannot use their tools to win in my small arena.
I suppose it's the juxtaposition of saying "I think open source is important" and then building your business criticality on the least open software systems on Earth.
In most organisations which are building on open source there's a pragmatism that some elements ( routers, BIOS, phones ) won't adhere to the philosophy. But it's completely different to make unknowable, solidly opaque systems the core tools of a business.
By this argument, I guess we should write our code slower too. Maybe we should only use a single finger as we type. And add an extra day between email responses.
Because by the inverse of their argument, slower MUST be better, right?
This is silly. LLMs are a diesel powered keyboard. If I've got a backlog of features, why shouldn't I ship all of them? If they're bad ideas, I can also just remove them.
Nothing about SDLC best practices has changed EXCEPT the ability to increase volume.
Thanks for the feedback. The main idea is that today to built a best-in-class agent, developers build the agent loop, session management, tools, memory, skills, automations (cron + trigger-based), sandboxed deployment, and self-learning.
By providing Hermes with a system prompt, custom tools, and skills, developers get the agent loop, session management, automations, sandboxed deployment, and self-learning for free.
This sort of thinking is incredibly dangerous to society at large, to say nothing of the danger to your own heart and soul.
Character, beauty, love, sacrifice. Every one of these involves pain and it makes life worth living. You can't avoid pain, so you might as well engage it in service of those you hold dear.
You should very much care about society wielding a sword like this, because historically we do not wield it well.
There really is no point in leaning into avoidable pain. Pain does not "make you stronger". Pain is not "beauty". These are all bullshit tropes invented by abusers to keep people from questioning things.
I agree with you, unnecessary pain is not virtue. But pain does indeed make you stronger, or it can make you bitter, or depressed, or insane. How you choose to work through the inevitable pain you will face is what determines if it makes you stronger or not.
It's easy to hand wave the question of pain away, but much wiser men than you and I have arrived at very different conclusions than you suggest.
The sword being just deciding which embryos you will raise?
It's very easy to demand or promote sacrifices you expect other people to make. But I don't find that to be very empathetic.
I've already seen how society is when we shame and hand-wring about the personal decisions others make, and it's not one I want for my kids. At some point you need to be satisfied with your own decisions and then let other people make theirs.
It's still a vote, and votes don't require reasons, and shouldn't be dismissed out of hand. There's a growing chorus of those who are fed up with rules for thee but not for me.
I'm becoming more convinced these are questions of the Before Times. Yes, yes—heresy, I know.
Yet, I can't deny the reality that I observe working with LLMs every day. If this truly is a step-function (as some are sgguesting), then I have absolutely zero concern for the quality of the code.
I said I had zero concern for the quality of the code. That is, I do not have concern that the quality of the code will be a concern in and of itself.
It's a subtle, but IMO important difference. We only care about code quality so as it gives us stable, understandable systems. Historically that meant a human had to read and understand it. Suppose a future where that's no longer the case, then we may still end up with stable, understandable systems without understanding every minutiae of the substrate. It's the same way I don't really know if my compiler is correct, but the behavioral patterns of my code suggest it is without me understanding anything about its code quality.
Ed is an interesting character. His financial analysis of the AI industry makes logical sense to me (though I am not knowledgeable enough to actually know if it is correct.) However, he seems to be so angry at AI in general, that he misses the obvious areas where LLMs are actually changing the State of the Art.
Coding seems to be one of the core use-cases for LLMs (as Simon Willison pointed out recently) and even if that's the only real use-case for LLMs, they're wildly useful. I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
I don't think whether "LLMs are actually changing the State of the Art" or not matters for anything he wrote.
If the AI companies need $X billion in revenue to stay afloat, it doesn't matter if 0.5% or 5% or 50% of that revenue is from transforming the State of the Art. It's 100% irrelevant: what matters is that, transformation or no, these companies won't have the income to pay their bills. And if they can't pay their bills, a whole lot of other companies can't either.
So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.
maybe its insane to think this, but if all AI providers turned off free plans tomorrow i think they would easily have enough people willing to pay $20 a month for it to sustain all their spending.
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.
This proposition boils down to a belief that there are 3 billion people who are interested in AI for free but aren’t currently paying $20, but who would pay $20 if that was the price.
The global median income is around $12k, so this would mean that there’s roughly be a global budget of 0.5% of everyone’s annual income going to chatbots.
If you’re off by half, the price doubles for each person.
I think you’d make a lot of money betting against the existence of 3 billion ghost customers
I do think Ed in intentionally ignorant of the capabilities of LLMs. But I also don't know that I would classify LLMs as 'wildly useful' for coding. Most productivity gains seem to be hallucinated, and while it's too early to make any claims on long term outcomes, there are plenty of studies indicating they might be even more negative.
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.
> 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.
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.
Have been thinking about this a lot recently. AI could be an absolute game changer for a small start-up rushing a product to market – you could quickly build an MVP that would take years and tens of hires before.
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?
Not much ROI, if any. My employer's been making some studies and come up with very modest productivity gains - so of course they want us all to use it, but I'm not sure they're taking the true costs into account. Especially not once token pricing actually reflects reality, and we all get brain rot from using the things instead of thinking. If this thing doesn't collapse before we can run a solid coding assistant model on a developer's machine, maybe it's got some legs.
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.
I've been thinking about this too. The quality maintenance of large systems isn't something you can just completely automate away with AI. Even if the code is written with AI, you still have to read through and verify 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.
> I do think Ed in intentionally ignorant of the capabilities of LLMs.
I think it's more complicated than that too. He's pretty well versed in the stated capabilities of LLMs.
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.
I don’t get me wrong, I’m on Ed’s side and get where he’s coming from. I just think his arguments are normally taken to the extreme, making them less defensible, when he could make the same arguments from a more moderate stance and ultimately be more convincing.
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)
Personally I only find his swearing and delivery annoying when he is not delivering his point well (ie reducto ad absurdum). I’d be welling to bet a good amount of the complaints about his swearing are really just from a poor delivery, and people don’t know why, so they latch onto his swearing.
His early stuff was just as degenerate and vulgar, but was much less of an issue for me.
Part of the problem is the like - sociomedia factor. Ed’s figured out how to break through the noise. I’m not surprised that Ed Zitron is the kind of counterpoint you get in the Musk-Trump attention economy / CEO’s that sound more like prophets than business executives world.
One person's ignorance of something can never be evidence that it doesn't exist. It's far too easy to be willfully ignorant; no one can force you to abandon ignorance if you don't want to.
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.
> 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.
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.
The studies you are talking about are probably outdated, it's difficult to deny the actual productivity boost of coding agents.
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.
It still takes the same mindset and skills to use AI productively and effectively as regular programming. The productivity boost only applies if you know what you’re doing and can actually steer the agent carefully.
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.
As a developer who has not been able to get any boost in productivity from coding agents, I find it incredibly easy to deny.
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.
We previously had a backlog of 2 years of features planned. Now we have no work and are just working on tech debt and planning to get more involved in the product side because they need help getting the developers work to do.
He's got subscribers. Maybe the attitude is one he's found plays well with them.
I find it quite refreshing in some ways. Lots of people, when they start complaining about this or that aspect of this AI stuff, are wont to add in a little disclaimer that, despite all of the above, they actually really like AI and use it all the time. I assume this is to avoid the scenario of a bunch of pragmatic builders turning up and calmly shipping nuance in the comments (or whatever you call it these days when you get brigaded by a pile of angry keyboard warriors with chips on their shoulder) - and it sure is tiring having to wade through the equivocation.
That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.
> Maybe the attitude is one he's found plays well with them.
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.
So here's the thing. I am not generally an angry person. But Ed's writing really resonates with me, because for the last four years these people have been making a strategy of scaring the shit out of us while trying to ruin something I genuinely love (coding), while simultaneously fucking up the economy and multiple industries and turning the internet into slop. I very badly want more people to call these guys "chucklefucks" or whatever innovative ways he comes up with to insult them because they deserve far more public ridicule and disdain than the (captured, useless) media is giving them.
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.
> 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.
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).
> We have polar opposite media bubbles. I see OG programmers all over my timeline
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.
Ultimately though it's those people's bosses who set the direction and from my experience those people are telling you to your face that you'll be replaced by AI as soon as they're able to do so while they continuously fail to see it's shortcomings.
I want to be one data point seeing as this goes uncontested (the ones in the know don’t care anymore to be honest).
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.
> it is obvious they are. If you don’t see it I really, really don’t know what to tell you. (…) 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…
> 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.
If inference becomes cheaper, it becomes cheaper for everyone.
It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.
Inference has been going down in price on a cost/intelligence basis. If you don't need the smartest model, there are plenty of good Chinese models that are dirt cheap.
It supports his point that they're planning to massively overbuild compute, which was already well supported by the financials. A lot of that planned compute buildout can be walked back though, and the technology is unquestionably useful in moderation, so it's not the catastrophe he suggests, and his hyperbole is part of what makes me dislike him even if there are elements of his foundational argument I agree with.
check out DeepSeek V4 Pro .... this is where the threat vector comes from IMHO. If anything is triggering a rush to IPO imho it's seeing these cheap / free models on the horizon that are "good enough" for 80% of the core use case supporting their valuation.
I would say his overall negative outlook is a well needed counterbalance to the completely delusional hype one is exposed to on a daily basis. The truth will then probably land somewhere in the middle.
It seems that a certain kind of person cannot separate the following things:
1) I dislike AI as a technology
2) I dislike the people and companies that profit from AI
3) I think AI is useless
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.
> ... 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...
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.
Tetraethyl lead is not useless; in fact, it was hugely useful to the petrol engine economy throughout the 20th century! It just happens to cause nonobvious brain damage throughout societies.
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.
> 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
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.
> > 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.
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 see plenty if I care to look outside my usual echo chamber. There’s lots of ‘it’s just the next token prediction’ (in 2026 still!), but there are more sophisticated arguments like ‘it can’t be creative’, ‘it doesn’t think’, ‘it’s just pattern matching’ etc. They might even be true for today’s models, but linearly extrapolating an exponential trend is a classic mistake.
> 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 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.
> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
This is often repeated but comes from ignorance mostly. You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.
This kind of thinking really reinforces my belief that people have no idea and are using this whole [AI is not profitable and too costly] thing as a cathartic way to deal with immense progress.
However, it needs to be said that he received those numbers. I personally have quite a few issues with him, but there's no reason to doubt his journalistic integrity. Because of that, I believe he reports truthfully on data he receives by informants.
Additionally, none of the frontier models actually publicly talks about inference costs in anything but broad, "let's just forget that"-like takes. Which does not exactly spark confidence.
I'm eagerly awaiting anthropic's public disclosure of their financial details. That should be rather interesting in any case and finally put the inference-discussion to rest.
No reason to doubt his journalistic integrity? He's not a journalist for starters. He's a PR flack who does PR for AI startups on the side while blogging on substack. There is every reason to doubt his journalistic integrity.
The PR-thing was always openly communicated by him and is not some secret or gotcha. It's essentially "fleecing the boosters", which I fully approve of and do similarly myself.
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.
He was right about the cost changes, which he predicted quite some time ago. People shouted at him that he was making it all up - yet it was correct.
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 was also right about AI-video and sora in particular being a fundamentally flawed idea.
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).
Remember that OpenAI is subsidized from here to the highway.
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?
> 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.
I'm simply saying this: there are third party hosters of Open Weight models like deepseek and they have been doing this for a while.
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.
> I'm simply saying this: there are third party hosters of Open Weight models like deepseek and they have been doing this for a while.
> 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.
I'll bet 50€ that big AI companies (OpenAI, Anthropic, xAI) have bad regarding their core financials. Excluding one time deals such as renting hardware, external cash infusions, the core AI model business (training models and selling inference) is unprofitable and will be be at the expected scales (50 billion € or more) for the next 5 years at least.
I bet that within 5 years they will be sold for scrap to bigger companies and will become divisions inside them.
1. You can't have a model without training. Training is part of the cost. A model trained 5 years ago is borderline useless. Also models cannot be retrained continuously, we do not have the technology (even if we had it, it would just increase the ongoing operational cost).
2. Rephrasing your statement:
At June 2026 prices, profit margins for all Anthropic models are >10%.
That claim is super defensive. Serving a model can only be done if the model has been built and trained, can't have it any other way. Building it and training it costs lots of money.
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.
> 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.
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.
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.
The argument that LLMs are "feeding me back free and open internet" seems to skip the most useful aspect of the tool.
I could never, as an individual read, let alone synthesize and make decisions with, the amount of information on the internet. The LLM takes that free and open information and feeds me back novel information based on that free information. It gives me ideas, opinions, and hard data based on that information.
It's the most powerful information synthesizing tool in existence. I don't find the argument that "it's built on free information and sold to you" fair or plausible at all.
It's like saying you're free to make your own bottled water. Technically true, but in reality not.
I think you undervalue the contribution of internet-scale data to foundation modeling, and because LLMs can obsolete the content they required, I think its fair to characterize it as theft. Obviously RL contributes a lot to capabilities, but the judgement that an LLM uses to 'synthesize information' is born from the training data. The scale of the data really is beyond intuition. books3, for example, would 230 yrs of continuous reading
I actually think the "proprietary non-determenistic database of the free internet" does a lot to characterize the capabilities and effects to a lot of people. Obviously coders are more in tune with how well agents can work, but that's also due more to the RL breakthroughs than foundation modeling.
Can you steal something that is free and openly available?
I just don't understand this argument. "Theft" feels like a nice, heavy, moral accusation to toss at those you're debating with, but the actual prerequisites for theft don't even exist in this situation.
It is a lot more complicated than that. Your content is not simply used, copied, or even just simply distributed. The very terrain that you produce, distribute, represent your content has shifted due to the mechanics of it. Anything you produce is grabbed into AI summaries. They're grabbed into the training data. Humans produce free/open materials for many reasons. A lot of them don't have room to breathe and gain structure due to AI siphoning the entire atmosphere of web; eg communities
I mean, not that I'm a huge fan of IP laws, but yes?
Like I said, if you provide an alternative to all these blogs and forums (because you trained on them or because you scrape them for RAG) then you are stealing their traffic. Search engines were/are already doing that, but the foundation training
It’s the solution to the second information problem. Hypertext arose from Bush’s Memex, and the information problem it offered to solve. Now, there is simply so much information available on the modern Memex that it is impossible to make any sense of it all. So, we now have LLMs. There are still some issues with them, but they’re good at what they do.
I have mixed emotions about LLMs and AI more generally. I fear the dystopia, hope for some marginal improvement in human life, and I genuinely enjoy playing around with local models. But, I think there may be near term harms that outweigh the gains. We shall see.
Bizarre to say that. When I have it perform work on a bespoke code base on a niche videogame, in a less commonly used language, is that still "regurgitating stuff"?
No, it is impossible for it to have seen this combination of things.
It routinely produces, suggests, and correctly implements novel things that had not existed.
You can see this yourself by learning how LLMs work, or anecdotally using these tools.
LLMs are terrible at generating code for “less commonly used languages”. They require LOTS of data for high accuracy.
I describe it this way: they are good at interpolating from what data they were trained on, but terrible at extrapolating. I agree with the parent that the LLM-generated content isn’t novel, it’s just a rehash of two things it was trained on.
I have wasted quite a number of hours trying to use LLMs to write things for less common languages. Sure they can one-shot some impressive stuff in C#, Python, and JavaScript… but try working in Object Pascal: it’s non-obvious hallucination after non-obvious hallucination, presented confidently enough to make it difficult to see it’s complete garbage, so you waste a ton of time trying to polish a turd.
Creating a new paradigm is a problem with a lot more groundwork laid that working in an existing little-known paradigm. One is creating patterns which only have to be good to be correct. The other has to be correct to be good. They are completely different problems.
That is simply not true. The naive “glorified auto-complete / stochastic parrot” argument may have some merit when applied to generic pre-trained models, which only learn from unsupervised next-token prediction. But the post training through reinforcement learning the frontier models undergo is very sophisticated and they genuinely learn to do novel things that are purely the work of the model being trained (and the work of the GPUs they burn along the way of course).
I feel like a lot of the lament expressed in the comments is grounded in loss aversion. We really don't like losing things—regardless of the objective (or subjective) value of that loss. We know this intellectually, but we still feel it emotionally.
I'm feeling effects of using LLMs day in and day out, but I am not yet convinced that it's overwhelmingly negative, the way much of HN seems to lean.
I derive a lot of joy from shipping outstanding code for my clients and fixing problems they're experiencing. My joy has only increased as I can now ship better code, faster, with fewer bugs. No, I don't intimately understand the code the way I used to, but I understand it enough to accomplish the end goal.
The premise of this article takes a presumed position that the grades we were posting before really mattered a whole lot. I'm not convinced that's true.
> I derive a lot of joy from shipping outstanding code for my clients and fixing problems they're experiencing. My joy has only increased as I can now ship better code, faster, with fewer bugs. No, I don't intimately understand the code the way I used to, but I understand it enough to accomplish the end goal.
If you’re doing this than great! More power to you. But I think you underestimate the discipline it can take to focus on a task when “coding is free”. I don’t worry that I’ll just lose my old skills. I worry that in losing my old skills I’ll lose the new skills as well. Driving is great! But I still run for exercise and occasionally to get places too. I’d be a worse driver if I was out of shape do to the loss of energy and mental clarity.
The presumption that we're losing "intelligence" is unproven and widely debated. I'd prefer "capability" over raw intellect any day, and my capability is definitely greater than it was a year ago.
I've also "lost" (or rather, never had) the ability to manually manage memory, and I have no idea what my machine code actually does once my program compiles.
Yet, I'd say my career so far has been very successful and personally enriching for me.
So what am I losing? I still use my intellect day in and day out, so it can't be that I'm not using my intellect at all.
That attributes your success to the wrong cause. Your career (and mine) has been successful and personally enriching because of the colossal demand for software developers over the past 25 years guaranteeing employment to anyone who could write fizzbuzz. But demand for developers can now be filled by AI that does know how to manually manage memory and a whole lot more. Those deep technical skills may well be the only remaining value proposition developers still have to compete against AI.
And you, by your own admission, don't have them. "Game over, man, game over."
> But demand for developers can now be filled by AI
Oh? Is that so? I've yet to see any evidence of this.
I've seen big companies laying off developers with dubious claims about them being replaced by AI. None of them can prove their claims. Their software hasn't gotten noticeably better.
Jevons paradox appears to be in full swing. No sign yet of software slowing down. In fact, my company has continued to grow in demand over the past 2 years due to the fact that we can build better software faster. By any way I could attempt to measure my "value", it is increasing.
It only took about 10 years between the Linux 2.2 kernel, the first version that was really decent for server workloads, and Sun Microsystems, formerly the largest UNIX server vendor, to be sold to Oracle for peanuts.
Either 2025 or 2026 is the first year that LLMs got really decent so...
And yet the average intelligence continued to rise through the reduction in numeracy. What job do you have where your performance is graded by your "raw intellect" instead of capability? A student?
reply