The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.<p>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.<p>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.
One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow. If Zitron's argument were as solid as he keeps saying it is, you would read it and understand it and see that it is solid. He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument. But no. He jumps. He leaps. He circles back. If the situation were really "Gosh why can't you see it?!"-clear, his explanation of the situation would be clear. It isn't, because it isn't.
This is wishful thinking. AI is getting better exponentially. Anthropic's revenue growth is still growing like crazy and they haven't even released their best model (Mythos) for 4 months now.
Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely misses the incredible ground-level utility that many of us on HN celebrate every day through undeniable, massive productivity gains.<p>At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.
> undeniable, massive productivity gains.<p>Take any stock index, remove AI stocks, what do you see? That's right! Nothing...<p>So where is all the productivity going? Where is the value? Where are the massive unemployment stats or the millions of new startups making big $$$?
Writing about AI, destroying the planet for data centers, there's a lot of money to be made.<p>That being said, AI seems kind of miraculous sometimes.<p>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.<p>And it's not even something to blame individual people for.<p>Driving away from all the other cars to spend a weekend feels like <i>freedom</i>.<p>Using AI to answer a question feels like a "bicycle for the mind".<p>But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt.<p>Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
The environmental impact of answering a question on an obscure topic with ai model is less than an the impact of answering the question with an hour-long google search hunting for references or a drive to the public library.
That's true, and I am not anti-AI. I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.<p>Neither did I want to say that a car is always more wasteful than some alternative.<p>But defaulting to the behemoth is inefficient, unless everyone is driven to do it: then it's in some way reasonable.<p>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.<p>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.<p>People already do that in masses.<p>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.<p>I guess you know the argument.<p>Today, people's thinking atrophies because their LLM is probably right in their summarization of some Wikipedia article, plus 2-3 other random sources.<p>Or so.<p>Using the Wikipedia search function is not expensive.<p>But, I mostly had a bigger picture in mind than what is the cost of inference.
It's like saying if we didn't have cheap commercial flights people would travel by foot anyways and would consume more resources for food &co. than the plane would consume in fuel...<p>80% of generative AI queries wouldn't even exist as google searches.
Vonnegut said in his last living work that the greatest addiction modern people face is the drug of cheap oil.<p>We got addicted to the convenience and overuse, and have started a mass extinction event because of it.<p>The perverse incentives will come for us all.
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...<p>Where did all the stock gains go before AI?<p>FAANG / MAG-7.<p>Was everything from 2012-2020 fake, too?
He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse.<p>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.<p>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.<p>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.<p>The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
I think he doesn't need to understand the technology to point out the books are cooked. a business can sink in either way: the technology flops or the finances flop. he's arguing the /finances/ would flop. he doesn't argue that the /technology/ would flop, only that they can't come up with the money to pay their debters.
There is a piece of this I agree with. That you do not need to be a deep technical expert to notice that a company is burning cash by overcommitting to capex, or relying on heroic revenue projections that may or may not come to pass.<p>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.<p>One part of that is the balance-sheet argument (which already shows insanely good margins). But it also depends on how inference-time compute actually works: routing, batching, kv cache reuse, model segmentation, different latency tiers, etc. Much of those details he's just been straight up wrong about in his writing, so as a result I have to call into question the rest of his reasoning as well (in part to avoid Gell-Mann amnesia).
> that he doesn't really understand how inference works from a technical perspective<p>Could you share what tells about it? I.e. where he was wrong about it?
There's examples both in his writing and also in his appearances on podcasts, interviews, etc.<p>I'll cherry pick a couple:<p>“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]<p>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).<p>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.<p>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.<p>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.<p>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.<p>[1] <a href="https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/" rel="nofollow">https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/</a><p>[2] <a href="https://www.wheresyoured.at/how-does-gpt-5-work/" rel="nofollow">https://www.wheresyoured.at/how-does-gpt-5-work/</a>
Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing.<p><a href="https://unessays.substack.com/p/talk-is-cheap" rel="nofollow">https://unessays.substack.com/p/talk-is-cheap</a>
From an economic perspective productivity is defined as the creation of value isn't it? Then if you "improve productivity" and does not create value in the end you're no improving productivity at all.
It does depend on how you define productivity. But the way it's commonly used is "I'm going faster, personally, with these tools."<p>The thing people I think have a hard time seeing is that "I go faster" does not mean "more features get finished".<p>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.
Also, supposed productivity gains are dubious. I personally experience at best no productivity gains when using LLMs to write code, and sometimes it's an active drain on my productivity. There was that one study a year or so ago showing similar results. People are trying to say the productivity gains are there and undeniable, but that is not true. It is very much a subject of controversy whether AI helps productivity.
I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility.<p>However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.
There is an observational study that was published in March 2026 that followed 4000 teams over 2 years. It shows, in my view, exactly that the productivity gains don't translate into economic value.<p>Here is the report:<p><a href="https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways" rel="nofollow">https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways</a><p>And my commentary:<p><a href="https://unessays.substack.com/p/talk-is-cheap" rel="nofollow">https://unessays.substack.com/p/talk-is-cheap</a>
Its funny, I've noticed the same thing, but did not come to the same conclusion.<p>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:<p>1. Experience some success, which hooks you into relying on AI.<p>2. The AI keeps failing at some task, but you don't want to stop. Keep trying over and over again.<p>3. Run out of tokens and take a break.<p>Now, sometimes 1 doesn't happen. Sometimes 2 doesn't happen. 3 is a certainty though.<p>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.
That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent.<p>EDIT: In fact, parent comment has a link to some numbers.<p>[EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work...<p>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...
He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.<p>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.<p>I find it difficult to take him seriously.
> progress was slowing<p>Do you think it's not slowing? Do I miss anything really important?<p>My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
The most important thing I would point to is Mythos et al and the wave of vulnerabilities that have been discovered in the past couple months. It’s a completely unprecedented event, brought forth almost entirely by improvements in the models themselves.
That said. keep in mind, I’m talking about over the past two years. With Claude code and the capabilities gained since December of last year, there have been incredible gains in the capabilities that are now available. Demand for inference is higher now than it was a year ago, because capability has improved. A specific criticism that I would hold is that claiming that progress with LLMs is slowing, prior to that point, is embarrassingly wrong in my view.
One could argue that the model capability improvements are slowing, and all the improvements were in harnesses. I think that’s a stronger argument, but I have a few problems with it.
1. Utility is utility. Whether that comes from the model or the harness is irrelevant when making claims about utility. I don’t think that’s a useful distinction most of the time, but especially when talking about the technology as a whole.
2. Marginal intelligence gain is different than marginal utility gain. It’s estimated that intelligence grows logarithmically relative to investment. However, the utility of a marginally more intelligent model may grow exponentially, because once behavior crosses a reliability threshold, it unlocks new capabilities.
3. Even on those terms, it’s not clear to me that frontier capabilities are slowing down. With Mythos and its contemporaries, we have been seeing a vast change in the security industry as vulnerabilities are discovered at an unprecedented rate. OpenBSD vulnerabilities, more Firefox vulnerabilities found in a single month than the past two years, critical Linux vulnerabilities. It’s hard for me to look at the effects there, a radical new capabilities baked into the model itself, and see stagnation. A part of the reason it might feel like it’s slowing down is because we plebs don’t have access to the top models.
The maintainer of curl - who has access to mythos - disagrees [0].<p>I think it's dangerous to rely on claims made by people who financially profit from you believing them without checking.<p>[0]: <a href="https://daniel.haxx.se/blog/2026/05/11/mythos-finds-a-curl-vulnerability/" rel="nofollow">https://daniel.haxx.se/blog/2026/05/11/mythos-finds-a-curl-v...</a>
Do you have access to Mythos?
anyone that takes him seriously at this point... I don't want to say very bad words here...
Even if we assume that everything you said holds true, how is that we as a crowd can make viable a service that eats some $300bn annually in infrastructure costs? Where would that money come from? Most tech companies these days are cutting their AI budgets because the per token pricing is killing them.
> undeniable, massive productivity gains.<p>The jury is still out on that.
Yeah they're very much deniable. Raw LOC/hr is much higher, and putting together a MVP, but I've yet to see any evidence that an LLM is capable of doing anything unsupervised, and if you need a human supervising everything it does... why bother having an LLM in the first place?
Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead.<p>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 <i>trillion</i> dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.
I really like some good drama slop that reads like a thriller, it is entertaining.
I don't take any of it THAT serious, but lately with the IPOs that are about to hit the indizes, he has gained a lot of attention.
If you look around the internet, most people publish a negative angle on something and then extrapolate it into some grand conspiracy, which is really captivating.
Its crazy when you enter some echo chamber you never engage with (movies, gaming, art/comics) and they have their own head cannon for why the world is bad and collapsing. It puts your echo chamber into perspective to see the same patterns of argumentation and presentation spin out in a different way
Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).
> through undeniable, massive productivity gains.<p>And where are those? They seem particularly hard to actually observe and only appear in anecdotes.<p>> I'm trying to believe<p>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?
Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire <i>identity</i>. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until <i>something</i> happens that can make him go "aha! I told you guys!!"<p>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.
> undeniable, massive productivity gains.<p>Just because you keep repeating something doesn't make it an undeniable truth.
> undeniable, massive productivity gains<p>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.<p>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.<p>[1] After years of people understanding that LOC is not only a poor productivity metric but also a <i>negative</i> 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.
> Zitron is begging for a collapse at this point<p>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.<p>Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades.<p>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..."
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>have to be roughly twice the size they are today, and then double again basically every year until 2029 or 2030.<p>Anthropic is growing way faster than doubling yearly so don't think this is entirely implausible
Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. <a href="https://www.wheresyoured.at/peakai/" rel="nofollow">https://www.wheresyoured.at/peakai/</a> from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)<p>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.
Not related to AI but, I recently rewatched "The Big Short" and your comment reminded me of it. I can't testify the accuracy of the movie, but for over year, Michael Burry was viewed as in the same manner for shorting the market, while the economy was was in a hype cycle.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).<p>But from the article I linked back in March 2024:<p>"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.<p>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."<p>Since then:<p>- hallucinations are dramatically less of a problem<p>- several mass market use cases have emerged, most notably coding<p>- rate of progress has increased
> several mass market use cases have emerged, most notably coding<p>Most notably? This is not a mass market use case in the way the author is describing. They are asserting that the amount of spend they need to get this off the ground necessitates the entire world coming in on it, and I would say that opinion has aged pretty well. There are a lot of coders, but there are more people scratching their heads as AI is shoved into every part of their lives.
Not the person you are responding to, but here:<p>> 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.<p>We have seen 8 quarters since. Has any of that come to pass?
<a href="https://news.ycombinator.com/item?id=48447549">https://news.ycombinator.com/item?id=48447549</a>
The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.
Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"<p>Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
Why is anti-AI soothing?
Because there are still a <i>huge number</i> of people who would be very relieved if the whole AI thing just went away.
For some of us it is, I suppose as an alternate view to AI booster-ism, particularly if you think the long term effects would be mostly negative.
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He's a Gary Marcus-level contrarian with none of the credentials or contributions to the industry. The "AI bubble" cope narrative is getting stale but will still appeal to luddite autists years after it has ceased to be relevant.
What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:
> Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong<p>So, judge the book by it's cover?<p>> arguing that AI is failing, is a waste of money, is bad, will never work, etc.<p>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?
And its been 3 years of AI boosters telling me that my job as a litigating attorney will not exist in 2 months. Yet here I am, gainfully employed.
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 <i>correct</i>.) 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.<p>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.<p>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.<p>So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.
He's got subscribers. Maybe the attitude is one he's found plays well with them.<p>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.<p>That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.
It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.
> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.<p>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.<p>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.
Zitron is in the business of <i>content creation</i> and not <i>successful predictions</i>. It doesn't matter how many times he (and several others around) will say <i>the end is here</i>, they have to be right only once.<p>BTW, one thing for sure he is right about are the economics, <i>as of today</i> there is no way these massive investments are gone be paid.
I stopped as soon as the popup hit.
As WIRED reported[0], despite constantly writing about how an AI collapse is <i>just about</i> to come, Zitron privately does PR for AI firms on the side. The man is an obvious hack, and it's disappointing that he has become one of the mainstream faces of AI skepticism.<p>[0]: <a href="https://www.wired.com/story/ai-pr-ed-zitron-profile/" rel="nofollow">https://www.wired.com/story/ai-pr-ed-zitron-profile/</a>
I hadn't heard of the TMobile and Brex spend caps, only knew about Uber's because it went viral last week. I expect we'll see more of that now that everyone is paying per token, and it sort of feels like you <i>cannot</i> both have spending caps and require extensive AI usage for performance reviews -- I wonder that will shake out in the end?<p>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.<p>I guess Q3-4 are going to be interesting to see where this all goes.
Buried lede (if the title is the actual promise), the sources don't seem to back the title either. Someone with more patience can correct me if I accidentally missed a bombshell anyway.<p>Edit:<p>> 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.<p>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.
AI has been slowing down relatively, considering its trajectory over the past 20-30 years. For one, even if LLM may have plateaud in terms of intelligence-parameters ratio, research is on-going on new frontiers for ML, including (but not limited to) world models. Other research directions are studying backpropagation and its physical analogies, such as equilibrium of chaotic states.<p>In addition, there's a lot of research on the hardware angle and actual prototypes are already being built such as AI-on-chip Cerebra and Taalas for one.
Anthropic has made $330 billion in compute and chip commitments between Google, Amazon, and Microsoft, another $30 billion with CoreWeave and another $15 billion with SpaceX. To pay for this compute, Anthropic must meet its projected revenue of $174 billion a year by 2029.
Anthropic has raised $95 billion across rounds in February, April (from Google and Amazon), and May. These funds will be insufficient to cover Anthropic’s costs, as will Anthropic’s cash flow, meaning that it will have to raise at least another $200 billion in the next year.<p>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 ?
Ed's argument for why "AI is slowing down" rests on company spending caps, in particular the Uber $1,500/engineer/tool cap.<p>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?<p>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.<p>The TAM of these AI companies just leapt up to $1,500/knowledge-worker/month, how is that "slowing down"?
Maybe in USA in big tech where companies give absurd wages to engineers anyway in some states, that might be acceptable. But to make their ROI they need that (and more) to be spend world wide... no way that is gonna be a budget that is gonna fly in the long term...<p>Companies love to cut costs, and just like they axe employee numbers at will, they will just as well make that kind of budget quickly dissapear the moment they realize they can go a different path for same or better value... Or simply because share holder short-term value demands it...
The Uber $1,500/engineer/month thing is just the <i>first</i> signal we have had of the price companies may be willing to accept. This price will clearly vary wildly across professions, industries and geographies.<p>I think it's a poor number to build an "AI is slowing down" narrative around.
All the top comments are commenting on the author. And now I add this metacommentary. Probably good it was flagged.
There are real issues on the money front. The big AI companies have a financial model that assumes a huge increase in demand in the next year or two. Otherwise the bubble pops.<p><i>"Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees."</i><p>So some of the growth was purchased by underpricing, subsidizing the customers with venture capital. Uber did that, and eventually got out of it by raising prices and squeezing the drivers.<p>The "fuckup" problem is real. LLM-type AI exacts huge costs because it is terrible at reporting "I don't know". When it doesn't know, it generates noise and polishes it.
If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful. You could use small, inexpensive models on small problems, and only use big models when the small models failed. Most customer service bots fit that model.
Needing ever-larger models to fix the noise problem is not cost-effective.
> 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.<p>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.<p>This is breakdown in dialog. If it leads like this then I I don't care how accurate the critical analysis to follow is. I didn't read the rest of the article and don't think anyone else should either out of sheer disdain for this argumentation style.
I find it difficult to separate this piece’s tone from its content. The tone puts me off and makes it hard for me to judge it on its merits, despite some of the arguments seeming sound and well supported.
Given the way tone has been intentionally abused, particularly in this industry, I’ll take a few f bombs and the truth.
Agreed. If the arguments seem sound and well supported, then all we can do is attack the tone.
You can disagree. Sarcastically, or otherwise. But I think you may be reading more into my comment than I put there.<p>I’m not attacking the piece. I’m not saying it’s right. I’m not saying it’s wrong.<p>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 <i>how</i> an argument is made.
Ed's posts are peak preaching to the choir, they're usually factually correct but he is really bad at convincing anyone who doesn't already strongly agree with him.
Have you seen his recent Bloomberg appearance? He's calm, collected, and matter-of-fact -- the complete opposite of how he presents himself on his newsletter and podcasts, but with the same argument. You wouldn't know from listening to him how spicy he usually is.
It's tuned to the audience. Bloomberg was traditionally for people who actually wanted information. People who were fallible and had limited knowledge.<p>Of course that mentality is obsolete. Now we all have infinite access to perfectly correct information via the internet.
I dont really understand the criticism either way.<p>He's in the media business... its in his interest to amp things up.
Perhaps that’s it. I would tend to agree with his position, I think, but don’t appreciate being preached to. Even less so when I agree with what’s being said.
Agreed. I am open to the possibility of the bubble bursting or whatever, but this piece is like 3,000 words and cites everything as evidence the sky is falling. It's just as bad as the pro-AI grifters, just in the other direction.<p>Does the truth normally lie somewhere in the middle of it all?
>Does the truth normally lie somewhere in the middle of it all?<p>Usually does when you decide what constitutes extreme.
Probably. Although I feel more inclined to forgive Ed in this case because it's sort of fighting fire with fire, the insanely hyperbolic and obscenely misleading drivel that's coming out of the most ardent AI boosters is continually unchallenged in the public eye. In a world where we had a more realistic view of AI/ML/LLMs, the limits to its capabilities, and the negative externalities of its widespread adoption in places where it quite frankly does not belong, then I'd be more critical of the Chicken Little sort of writing style
AI companies are racing to win the future of computing.<p>They are possibly in a winner take all death race against each other.<p>The stakes are so high that these cash rich companies cannot afford not to throw everything they have into this.<p>The sunk costs are irrelevant when it’s a question of survival.<p>Whether you hate or love AI computing is being completely reinvented - at the absolute core of this is computers programming computers.<p>Anthropic is winning this race by a country mile right now.<p>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.
It doesn't matter if it's slowing down, pretty much no one has implemented it to its full extent yet. It could stop right now and we'll be finding new implementations a decade from now.<p>Anthropic and Open AI could evaporate tomorrow and we'll still be using the models.<p>The market may collapse, but the people who think AI is going to disappear as a result don't understand what it is.
Whenever I read these kind of articles about AI financials, I'm reminded of identical screeds I read about Uber a few years ago. They were angrily insistent that Uber was a scam company run by criminals and charlatans and could never, ever become profitable or make money for its investors. It was a house of cards that would come crashing down sooner or later, and take everyone's money with it. Now it's 2026. Uber still exists, has revenues of $50bn and is apparently a highly profitable business. I don't know if the original investors have made their money back yet, but Uber certainly hasn't collapsed.<p>Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.
Uber used the classic triple-E philosophy of Microsoft and entered a market that was ripe for disruption -- many cities lacked reliable taxi service entirely, others were cartels that fixed prices. They undercut prices to an extreme degree, subsidized fares, and when it either drove local taxi companies out of business and spurred widespread adoption as the default, it had a captive market and duopoly with Lyft which allowed them to raise fares without losing any market share whatsoever.<p>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.
Absolutely. Even these days, Uber really only has one or two viable competitors. With any 3rd one in a far distant 3rd. Meanwhile, swapping which AI I’m using is as easy as clicking a dropdown. Hardly comparable to a physical car ride.
Yeah, people forget the risk to Uber was real in the early days. If municipalities had enforced their taxi laws, the company would have died and all those millions invested would have been lost (or pivoted into something else).<p>It was only because Uber <i>successfully</i> bulldozed over all regulations that it was able to succeed ... and that was hard to predict before it happened.
His rhetoric is a bit obsessive and frankly biased against AI.<p>That said, I think his voice is useful as a counter to the mainstream opinion.<p>Given the amount of investments, approaching AI from the angle of economics seems correct.<p>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.<p>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.<p>The real cost of AI may or may not be lower than its utility. The bet is that utility is increasing while cost is falling.
I think we need to see Open AI's and/or Anthropic's S1's to really know the state of it all.
every week I see this guy on HN. only forum where ppl still buy this c**
I predict the bubble is going to pop right after the midterm election.
I'm so sick of people who peddle outrage for a living.
"Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology."<p>Bloomberg is interested in what he has to say<p>But not HN commenters
Ed Zitron speaks to a particular type of angry tech conservative. He’s not speaking truth or exposing anything. He’s the soothing voice the tech nerds of yesterday year are yearning for.<p>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.<p>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.<p>I would write against the content of the article but I find it easier and more illuminating to write what he has said before instead. Then it shows how incorrect the guy has been and with what confidence he keeps speaking with.
I'm collecting many kinds of predictions Ed Zitron made so that you can see for yourself whether he has a good track record.<p>-------<p>> 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.<p>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?<p>> 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.<p>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?<p>> I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be,<p>He wrote this in 2024, do you really think we have reached upper limits? Huh?? What I'm using today is <i>significantly</i> more accurate and 2 tiers above what we had.<p>> 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.<p>He says this about AI when we have with all honesty have had industry changing possibilities like agentic coding.<p>> 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.<p>He agrees with the claim that the consumer interest has declined. Since he said this, there was a 9x growth in active users.<p>-----<p><a href="https://www.youtube.com/watch?v=_wStScmT748&t=1s" rel="nofollow">https://www.youtube.com/watch?v=_wStScmT748&t=1s</a><p>"AI Bubble Already Bursting?" (8 months back)<p><a href="https://www.youtube.com/watch?v=T8ByoAt5gCA&t=1s" rel="nofollow">https://www.youtube.com/watch?v=T8ByoAt5gCA&t=1s</a><p>"A.I bubble is bursting with Ed Zitron" (1 year back)<p>He's been constantly crying bubble for years now.<p>-----<p>> AI video won’t get truly fixed just by waiting a year.<p>This is what he had said in 2024, and you just need to compare video from then and now to check whether the predictions came true. Why would anyone trust what this guy has to say?
How’s that meme go? "We are 2/3 years into being 6 months away from AI taking all white collar jobs".<p>The criticism goes both ways. The word "fixed", in Ed terms, can be translated to "become a viable business that justifies the spend".<p>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.
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What's the point of arguing with any of this.<p>It's like someone arguing that cheese isn't real. Yes I can go to the grocery store and take a picture of cheese and show it, but what's the point? They can live in their own world. It doesn't change any of our lives. The world is what it is.
Lol... in this case, cheese imports from China are <i>much</i> cheaper, just not <i>quite</i> as good.<p>And for those who are all "but dur CCP get all ur data" you can use things like AWS Bedrock (at least for earlier versions of Deepseek and Qwen for now) and have more familiar people get all your data. Or buy (at obnoxiously inflated prices) your own HW and not send your data to anyone.
> "but dur CCP get all ur data"<p>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.<p>But, uh, if an LLM is writing 100% of your code you have no actual secret sauce to hide from anyone, so why worry about it.
Perfect for idea people. All the value is in the prompt. Ideas are important, not execution. A decade or two ago, they would have been looking for a technical co-founder.
Yeah, so true. There is no moat to your competitors using the exact same tools and prompts to generate their apps and services. Companies should be hiring/retaining creative thinkers that give them that human edge rather than laying people off under the guise of "improved efficiency"
I think we're going to see a lot of craziness in the future in this regard. Not just "secrets", but hypocrites trying to copyright and patent all the AI outputs. All kinds of rabid attempts at constructing monopolies for every half-baked idea they have tried to utter as a prompt.<p>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.<p>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?<p>My absolute nightmare is that this becomes a "too big to fail" thing and oppressive/fascist governments decide to back full regulatory capture. That instead of letting it unwind, they grant and support enforcement of an increasingly absurd and arbitrary copyright/patent regime to support this monetization scheme.
> What's the point of arguing with any of this.<p>> It's like someone arguing that cheese isn't real<p>I agree with your first statement (any being you) because of your second statement.
Some people seem to see the world only through bubbles. But if you look at human history, despite the ups and downs, we have a trajectory; generally speaking, human-created systems evolve toward ever-increasing complexity, impact, and efficiency.<p>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.<p>Once you see things that way, it is clear that we are not in a bubble; we are in a transition. Yes, there is tons of hype and over-investment, but the demand is real, and so is the impact. Unless you are deep in the tech and have that structural depth, it is easy to dismiss. This is like the invention of the personal computer, but with 100x the impact and speed.
Uhh, citations for all of these claims please.
The only "bubble" with AI is that the initial build out is cyclical, and many of the high flying chip stocks with no software arms (ala Nvidia's CUDA) will come back to Earth. I think anyone that thinks AI is going away or won't have massive impact (though maybe not in the doomsday scenario) are in complete denial.
RTFA; it's not about AI's massive impact or lack thereof ... it's about these businesses not having a viable business model that will sustain them (beyond the next couple years).
I think Zitron's problem is he's equating AI to OpenAI and Anthropic. I'd agree with him that both those businesses are in a dangerous position given how fast they've burnt through cash. However, that's not the entirety of the industry and there are a lot smaller labs doing more for a lot less capital.<p>The business model does appear to be viable for these labs. But that viability comes because they aren't wasting a bunch of R&D money developing worthless products like AI video production.
I admit, I didn't read the whole article; I read a few paragraphs and extrapolated the mindset from which the author operates.<p>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.<p>I can assure you, the people running those companies are smarter than you, me, and the author of this article."
I did. So, I'm confused how does that negate my comment exactly? Your second complete sentence totally is in conflict with your first btw.
What I suspect isn't that AI goes somewhere, but I do think that the cutting edge companies like Anthropic and OpenAI are in a very precarious position. They don't have very much of a moat and the competition has been catching up quick while spending a lot less doing so. IMO, the main thing keeping them alive right now is name recognition.<p>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.<p>That, however, is not the end of AI. Nor will it be the end of Nvidia/micron/etc. It will more just be a localized bubble pop that doesn't eliminate the product from the market.
It is not just about cheaper models; it is about integration with the economy.<p>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.<p>Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
> These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.<p>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.<p>> 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.<p>Certainly the Chinese models appear to be some of the best when it comes to competition, but they aren't the only ones. There are European models and other US based models which all run for cheaper.
I see your point, but having worked as a consultant for a few years, I think most companies will opt to stay once things are stable. Once these systems are functional, nobody wants to touch them.<p>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.<p>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.<p>This is the exact same playbook we saw with social networks. There is a reason why we have only a handful of them dominating globally, and guess what? It's not because of the tech.
> the incentive to change them is incredibly low<p>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.<p>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.<p>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".<p>I get what you are saying if this were a more sticky concrete tech that is harder to move away from. But that's simply not the case for these LLMs. A big selling point they have is that they are super flexible.
We might need to agree to disagree on this one.<p>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.<p>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.<p>Ultimately, a handful of companies are going to provide these core capabilities, just like we have a handful of major cloud providers right now. Why do you think this would change? If anything, the trend toward deep vendor lock-in is even stronger now.
The moat is the infrastructure and lock-in. Similar to AWS or anything else. Small data centers can't compete, and similarly people without massive compute won't be able to either (at least not on the enterprise level.) You might get a few edge models, but for huge businesses they will be using OpenAI and Anthropic (and Google/Microsoft/Amazon, etc).<p>The biggest competitors aren't small models, they are just the traditional players that already have an "in" with enterprises. That I think will start to show its face once this initial round of buildout is complete, which may not be for another 5+ years.
> The biggest competitors aren't small models<p>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.<p>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.<p>It certainly cuts into what exactly these companies can sell in general. For example, if I wanted to integrate AI into a product I'd almost certainly not chose OpenAI or Anthropic. That's because they are simply way too expensive and what they'd give me is a lot less. We've actually ran into just this. We needed a classifier for a lot of records, we picked a free model because, as you can imagine, we didn't need something as good as what OpenAI and Anthopic offered and free works.
I share the same perspective.
> <i>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.</i><p>This is fire erasure<p>/s