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Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely mis

by dkobia 3mo ago

Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely misses the incredible ground-level utility that many of us on HN celebrate every day through undeniable, massive productivity gains. At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.

ndnjdjdkdjd 3mo ago
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oudlys 3mo ago
Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing. https://unessays.substack.com/p/talk-is-cheap https://unessays.substack.com/p/talk-is-cheap
- bigstrat2003 3mo ago
  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.
  - asdfasgasdgasdg 3mo ago
    I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility. However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.
    - oudlys 3mo ago
      There is an observational study that was published in March 2026 that followed 4000 teams over 2 years. It shows, in my view, exactly that the productivity gains don't translate into economic value. Here is the report: https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways And my commentary: https://unessays.substack.com/p/talk-is-cheap https://unessays.substack.com/p/talk-is-cheap
      - asdfasgasdgasdg 3mo ago
        If it was published in March 2026, even if the data was collected up to the day the study was published, 7/8ths of it would fail my “within the last six months” test. But I am looking forward to the results of future studies on this topic!
        oudlys 3mo ago
        I get wanting to wait for more data. And thinking that LLMs have improved enough that this will change. My view is that it's not really about how good the models are - it's about how we're using them. Understanding what you've built is an important part of value creation, and LLMs eliminate that.
    - dminik 3mo ago
      Its funny, I've noticed the same thing, but did not come to the same conclusion. I currently don't have work access to Claude Code, but most of my teammates do. Watching from the outside, the cycle seems to look like this: 1. Experience some success, which hooks you into relying on AI. 2. The AI keeps failing at some task, but you don't want to stop. Keep trying over and over again. 3. Run out of tokens and take a break. Now, sometimes 1 doesn't happen. Sometimes 2 doesn't happen. 3 is a certainty though. Now, if you told me that the productivity gain from 1 is enough to offset the loss from 2 and 3, I could believe you. But I also wouldn't be surprised if it didn't.
      - chillacy 3mo ago
- nyeah 3mo ago
  That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent. EDIT: In fact, parent comment has a link to some numbers. [EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
  - oudlys 3mo ago
    I've posted numbers that indicate that productivity is becoming decoupled from value delivery. If you follow the link in my comment it reviews a pretty robust study of 4000 teams over 2 years. There is no product throughput increase.
    - nyeah 3mo ago
      Interesting data, thanks.
    - d33d 3mo ago
      Yep. Code acceleration is great, but.... something precedes that. Vision and strategy re. expansion of offerings and businesses. Once a firm reaches maturity in what it offers and is only touching the edges - this code acceleration is literally useless when you factor in all of the trade-offs. This is a good thing - it means fat and slow incumbents are sitting ducks to be out-witted by creative and imaginative founders, which is healthy for a well-functioning economy. Now the economics of existing frontier models are not sustainable - its looking like a mix of the airline (supersonic vs subsonic) and EV industry with China in the background providing decent offerings at much lower prices.
      - oudlys 3mo ago
        I think its worse than that. I admit that if a small team or an individual uses an LLM, it's likely they can create value faster. I think as soon as you don't own the responsibility for the defects you generate with an LLM, their use starts to destroy value. Regardless of product maturity. This is what I think the data says. https://unessays.substack.com/p/talk-is-cheap https://unessays.substack.com/p/talk-is-cheap
- amatheus 3mo ago
  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.
  - oudlys 3mo ago
    It does depend on how you define productivity. But the way it's commonly used is "I'm going faster, personally, with these tools." The thing people I think have a hard time seeing is that "I go faster" does not mean "more features get finished". It's a scale issue, and one scale is better than the other. People only pay for finished features, they do not pay for how much code you emit.
    - fl4regun 3mo ago
      economists define productivity as gdp per hour worked. Like a lot of other economic measurements, its mostly a bogus number people use as an argument on why their politics are better than someone elses politics. You can have an efficient business located in a poor country making the same product and same quality as that same business in a rich country, the rich country will be more "productive" because local cost of goods is higher there (i.e. a restaurant in NYC is more "productive" than a restaurant in bangladesh).
      - oudlys 3mo ago
        Sure. But that's not, in my view, how most people use the word productivity when describing LLM use. In my field - operations - productivity is usually described as some rate of production for a specific asset. 100 widgets / machine / hour - for example. "My productivity is 3 PRs / day with the LLM as opposed to 1 PR per every three days". That's how I think people are thinking about it. My point is that's not the same thing as value. I.e. what people will pay for.
        fl4regun 3mo ago
        You're correct, I just wanted to add that there is another definition that you may see used online, and it is very specific, and it's important to be aware it's NOT exactly the same thing most normal people mean when they say "productivity".
- dzhiurgis 3mo ago
  That report doesn't match what faros.ai conclude which is mostly a paywalled report.
  - oudlys 3mo ago
    The report was not paywalled for me. It just required a work email. Which is totally fair from my perspective. Faros is providing a ton of value with the report. People do deserve to get paid - even if in collected emails. You're right my analysis is at variance to what Faros.ai says. I think they interpret their data trying to rescue utility for the dominant patterns of LLM use. But I think to anyone who is experienced with process improvement or queuing theory, their interpretation is clearly weak. Rework is a huge problem in queue systems, and they mostly just elide the throughput impact of an 860% increase in code churn coupled to a massive spike in bugs. Obviously draw your own conclusions. But I don't think because I disagree with the interpretation of the people who originated the data makes me wrong.
selectodude 3mo ago
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- tomhow 3mo ago
  Please don't comment like this on HN. We can disagree with people whilst still affording people basic decency; it's a condition of participating here. If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.
gdcbe 3mo ago
I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work... It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...
- cogman10 3mo ago
  Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost. Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost. That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
  - remich 3mo ago
    Because it doesn't. Not for the tasks where using Opus instead of a lower tier model is appropriate, at any rate. Benchmarks show this, as do revealed preferences of actual users. To believe that Qwen is as capable as Opus at 1/20 the cost you have to believe that every person who does not make the choice to use Qwen over Opus for a given task is some mix of ignorant or delusional. This is certainly an opinion you can hold about other engineers, but it's definitely a questionable one at best.
    - cogman10 3mo ago
      The benchmarks between the two are close and the engineers that have used both (like myself) can attest that the differences aren't so wide as you might believe. I'd say that yes, ignorance plays a role here because a decent number of engineers are looking strictly at the benchmarks and choosing Opus just for that reason. But I'd also say that a major factor for Opus use is because Opus is being purchased for the engineers by their employers. They don't get to pick which models they are using.
enraged_camel 3mo ago
Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!" This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.
- nozzlegear 3mo ago
  > This, combined with his extreme ignorance, Extreme ignorance?
  - dannersy 3mo ago
    I can see how folks find Zitron shrill, but I wouldn't dare say he is ignorant. The man seems to be bending over backwards to do actual journalism when most outlets take CEOs at their word and promote a market that goes irrationally up despite evidence. Case and point, the situation in Iran is such a mess, yet every time Trump says there is a deal of some sort, the market rallies. It has been months now of this game.
- dannersy 3mo ago
  As a listener, I have not heard him predict collapse once. Maybe I got to him late, but so far as I know, he claims that the industry as it exists is unsustainable. You may think that is him dodging the logical progression of "collapse" but I think so far he has been pretty correct on the unsustainable part, the signs are there. If anyone could predict such things they'd be a billionaire many times over. In the end I think he will be more or less correct, the timing and how long investors can prop it up is the much harder part to predict.
AlexandrB 3mo ago
> undeniable, massive productivity gains How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication. I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think. [1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.
elorant 3mo ago
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.
- aspenmartin 3mo ago
  Cite a real source for that last bit, I don’t think that is true. Also the budgets should be cut the spend at some places goes beyond any reasonable amount. The strategy there is to hook everything in and find the right processes, then cut the rest. Things then get better and better with each model release. The way you make a viable service that eats 300bn annually is to have enough demand to service that. Anthropic underbought compute. That tells you something.
  - elorant 3mo ago
    https://www.theverge.com/tech/930447/microsoft-claude-code-discontinued-notepad https://www.theverge.com/tech/930447/microsoft-claude-code-d... https://finance.yahoo.com/sectors/technology/articles/ai-binge-companies-balk-soaring-014735313.html https://finance.yahoo.com/sectors/technology/articles/ai-bin... https://blog.pragmaticengineer.com/the-pulse-token-spend-breaks-budgets-what-next/ https://blog.pragmaticengineer.com/the-pulse-token-spend-bre...
  - jazzcomputer 3mo ago
    When you say "Things then get better and better with each model release." How far behind are models that can be run locally, and do you expect that this will be widespread?
    - aspenmartin 3mo ago
      https://epoch.ai/data-insights/consumer-gpu-model-gap https://epoch.ai/data-insights/consumer-gpu-model-gap I think over the years local models have fairly consistently been ~7 months behind frontier performance. Local models are hugely important but I don’t see the calculus changing. I can imagine it’s certainly the case for many tasks that there will be diminishing gains for performance improvements or reliability pass some threshold, in which case you don’t need frontier performance and you can certainly use local models or at least cheaper tiers of proprietary models if local is too much of a hassle. Plus of course use cases where local is necessary or the pros of having local models or on device models outweighs that of frontier. Maybe things will change though, I would assume through basically government subsidies from China etc, to undercut existing frontier labs, but you can always spend more (better data more compute etc) for better performance and that I can imagine will always have a selling point.
alexashka 3mo ago
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- vanuatu 3mo ago
  i don't think this comment contributes much to the discussion. can you elaborate more than saying "no"?
  - deleted 3mo ago
    [deleted]
mawadev 3mo ago
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
frisbee6152 3mo ago
He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024. The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either. I find it difficult to take him seriously.
- bdangubic 3mo ago
  anyone that takes him seriously at this point... I don't want to say very bad words here...
- solomatov 3mo ago
  > progress was slowing Do you think it's not slowing? Do I miss anything really important? My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
  - frisbee6152 3mo ago
    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.
freejazz 3mo ago
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).
- remich 3mo ago
  Perhaps they aren't, but not currently viable !== always unviable.
  - 48terry 3mo ago
    Just 5 more years and $500 billion more, bro. We're still so early.
  - freejazz 3mo ago
    And?
  - amlib 3mo ago
    Is it really worth it to cause a global economical collapse and harm society well-being to an unimaginable degree just to find out if it is viable? Why cant it naturally grow and prove it's worth?
righthand 3mo ago
[flagged]
dist-epoch 3mo ago
> Zitron is begging for a collapse at this point No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job. Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades. Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."
spmurrayzzz 3mo ago
He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse. I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place. My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit. But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time. The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
- solomatov 3mo ago
  > that he doesn't really understand how inference works from a technical perspective Could you share what tells about it? I.e. where he was wrong about it?
  - spmurrayzzz 3mo ago
    There's examples both in his writing and also in his appearances on podcasts, interviews, etc. I'll cherry pick a couple: “When these new models ‘reason,’ they break a user’s input and break into component parts, then run inference on each one of those parts.” [1] This is not at all how test-time compute works. At best, this is a very loose metaphor that he may have used out of convenience. This might sound a bit pedantic to point out, but this is a very basic thing that he's getting wrong (presumably at least, again it could be that he just used a poor metaphor). A less pedantic example would be his claims related to gpt-5/chatgpt auto-routing. He argued that having a router means OpenAI can no longer cache static prompts, because the user prompt has to come before the hidden instructions [2]. This is just not at all how this works at inference-time. There is no evidence that the standard approach of system>developer>user instruction hierarchy has changed, the public API and caching docs maintain this. But even more broadly, it suggests he is reasoning about kv/prefix caching at the wrong level of abstraction. It's true that conventional prefix caching does require a stable prefix, so yes, if you literally put variable user content before the static prompt, you would destroy the cacheability of that static prompt. But that is exactly why inference systems are designed to preserve reusable prefixes where possible (via checkpointing or similar), and why serving systems care so much about prefix caching. This is also a big part of how disaggregated prefill/decode infra works where cache-aware routing is critical. His argument treats a bad prompt layout as if it were a necessary consequence of routing, rather than an avoidable implementation choice. A router can read the user request, decide which model path to use, and then construct a normal downstream model call with stable static instructions first and user content later. Treating that as impossible implies a fundamental architectural misunderstanding. [1] https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ [2] https://www.wheresyoured.at/how-does-gpt-5-work/ https://www.wheresyoured.at/how-does-gpt-5-work/
cm277 3mo ago
Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead. The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.
- aspenmartin 3mo ago
  I don’t get this. We already have an insane demand. And yes exactly, this is primarily just with coding agents, but are you aware of what’s coming down the pipeline? It’s not hard to be you just have to find a decent way to keep up with literature. * robotics (need to close data gap and release first viable product to get a data flywheel) * conversational ai (no one is ready for this and we’re getting closer and closer to natural speech. The quality still isn’t good enough but it’ll be soon). * other agentic use cases, openclaw adoption was crazy and that had a ton of barriers to entry * ai products, like the one OpenAI is working on with Johnny Ive Anyone thinking it’s unreasonable to hit whatever revenue requirements is just not that aware of what’s happening. Not to mention were capacity constrained already!! This is barely speculation at this point.
  - sterlind 3mo ago
    I don't think the issue with robotics is a data gap. maybe somewhat, but the real issues are that: - RL is extraordinarily sample-inefficient. - distribution shift/catastrophic forgetting aren't solved. only off-policy learning with giant decorrelated batches works. - the breakout success of transformers as an architecture doesn't neatly translate to robot motion policy models. the field is missing fundamental breakthroughs. I also find it very interesting that conversational AI has taken this long. where are the models with good turn-taking? passive listening? the ability not to respond in paragraphs? has Anthropic simply not gotten around to it?
themafia 3mo ago
> through undeniable, massive productivity gains. And where are those? They seem particularly hard to actually observe and only appear in anecdotes. > I'm trying to believe For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?
PedroBatista 3mo ago
> undeniable, massive productivity gains. The jury is still out on that.
- deaton 3mo ago
  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?
  - aspenmartin 3mo ago
    Because it can perform much faster? Monitoring allows you to multitask more effectively. I would also disagree that you can’t one shot anything…claims like this are weak and I have enough counter examples in my own life that it’s trivially false. The question is more: can it one shot the right things with a low enough failure rate for it to be a good replacement. It’s hard to figure that out a priori.
toasty228 3mo ago
> undeniable, massive productivity gains. Take any stock index, remove AI stocks, what do you see? That's right! Nothing... So where is all the productivity going? Where is the value? Where are the massive unemployment stats or the millions of new startups making big $$$?
- moritzwarhier 3mo ago
  Writing about AI, destroying the planet for data centers, there's a lot of money to be made. That being said, AI seems kind of miraculous sometimes. Similar to cars. So enticing that we make everything else in the world worse in order to maximize the profit, make it indispensable, subsidize it, and make the dependency on it irreversible. And it's not even something to blame individual people for. Driving away from all the other cars to spend a weekend feels like freedom. Using AI to answer a question feels like a "bicycle for the mind". But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt. Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
  - MSFT_Edging 3mo ago
    Vonnegut said in his last living work that the greatest addiction modern people face is the drug of cheap oil. We got addicted to the convenience and overuse, and have started a mass extinction event because of it. The perverse incentives will come for us all.
    - moritzwarhier 3mo ago
      It is exactly this thought, in the form of this sentence, that could replace almost all of my comments in this thread. It feels depressing, but I think the same. When thinking about the larger world, it becomes increasingly hard to ignore. And of course it is not new. There were "doomers" already in the midst of the 20th century, but it doesn't mean that they were wrong.
bakugo 3mo ago
> undeniable, massive productivity gains. Just because you keep repeating something doesn't make it an undeniable truth.
ath3nd 3mo ago
[dead]
lbrito 3mo ago
>undeniable, massive productivity gains. How can something so undeniable have zero scientific evidence? Are there any large peer reviewed or meta studies confirming your claim?
- aspenmartin 3mo ago
  It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model. I think the surest sign of productivity gains is the sheer volume of adoption. If you look beyond headlines, adoption is just incredible. Of course adoption does not necessarily point to productivity gains, but if this was some sort of FOMO or smoke and mirrors you would not see this much retention and this feverish a pace of adoption. You would not see a large segment of the profession using coding agents exclusively. All of these companies track productivity, again with imperfect proxies, yet everything points to a pretty consistent picture. Same with benchmarks, again a lot of crappy benchmarks but a lot of high quality ones too and a very diverse collection of tasks and capabilities they probe.
  - 48terry 3mo ago
    Your second paragraph appears to be 3 different instances of saying "X does not necessarily point to productivity gains... but in the case of AI, X definitely means productivity" without really saying why that is true or why other explanations do not fit. Adoption meaning productivity supposes there are no other dominant factors for the AI push nor AI retention. It is possible for practices to be picked up or continued in spite of causing productivity DROPS. What studies have suggested are factors that make for productive work environments and what is actually enforced in the workplace are different things.
    - aspenmartin 3mo ago
      It’s 3 different weak but complimentary proxies. We form beliefs from imperfect evidence and I find these fairly convincing when it’s hard to find any hard evidence of no productivity and exactly the scenario you would expect under the hypothesis that we do see productivity gains. None of this is supposed to be unassailable. I would challenge then if you disagree what the evidence you have for this is? Adoption implying at least some significant productivity gains doesn’t contradict there being other factors. You’re seeing entire companies reshaped. The argument is this is all for show or CEOs are in some sort of idiot class? “It is possible for practices to be picked up or continued in spite of causing productivity drops” well of course. I just find that incredibly far away from Occam’s razor. My point is: we have lots of evidence that’s highly consistent with real productivity gains, and I don’t see many pieces of evidence to the contrary.
demorro 3mo ago
They are absolutely deniable. Huge swathes of people deny them.
dofm 3mo ago
He has recently made the very good point that actually, the FAANG companies are struggling to put any ROI numbers on that incredible ground-level utility. Uber, for example, is so unclear there is any ROI, they are cutting their exposure pretty radically. He points out that one single Anthropic customer — a payments provider — accidentally had to pay Anthropic $500M for one month of token spend. That is half what Apple is reportedly paying Google for the supply side of their entire consumer AI strategy.
- squidsoup 3mo ago
  It doesn't matter under Capitalist Realism, the banks were bailed out, the AI companies will be bailed out, and you will pay for it. There is no alternative.
  - HerbManic 3mo ago
    I'm not sure if they would be bailed out. The government tends to help with bank bailouts as they are essentially the hemoglobin of the economy, I see this being more like the dot-com bubble were they will just let it fall and have the bigger more entrenched player pickup the scraps for cents on the dollar.
    - dofm 3mo ago
      According to NOTUS, Altman himself seems to have sold Trump on the idea that the USA should own a chunk of OpenAI, and despite their potential IPO, OpenAI have recently followed up on the idea. Owning a chunk is pretty directly how more than one country injected confidence into their at-risk banks; it’s certainly how it was done in the UK.
Leynos 3mo ago
I quite like my mechanical spider from Wild Wild West and the coffee it makes with a 50% success rate

by dkobia 3mo ago

ndnjdjdkdjd 3mo ago

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oudlys 3mo ago

Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing. https://unessays.substack.com/p/talk-is-cheap https://unessays.substack.com/p/talk-is-cheap

bigstrat2003 3mo ago
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.
- asdfasgasdgasdg 3mo ago
  I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility. However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.
  - oudlys 3mo ago
    There is an observational study that was published in March 2026 that followed 4000 teams over 2 years. It shows, in my view, exactly that the productivity gains don't translate into economic value. Here is the report: https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways And my commentary: https://unessays.substack.com/p/talk-is-cheap https://unessays.substack.com/p/talk-is-cheap
    - asdfasgasdgasdg 3mo ago
      If it was published in March 2026, even if the data was collected up to the day the study was published, 7/8ths of it would fail my “within the last six months” test. But I am looking forward to the results of future studies on this topic!
      - oudlys 3mo ago
        I get wanting to wait for more data. And thinking that LLMs have improved enough that this will change. My view is that it's not really about how good the models are - it's about how we're using them. Understanding what you've built is an important part of value creation, and LLMs eliminate that.
  - dminik 3mo ago
    Its funny, I've noticed the same thing, but did not come to the same conclusion. I currently don't have work access to Claude Code, but most of my teammates do. Watching from the outside, the cycle seems to look like this: 1. Experience some success, which hooks you into relying on AI. 2. The AI keeps failing at some task, but you don't want to stop. Keep trying over and over again. 3. Run out of tokens and take a break. Now, sometimes 1 doesn't happen. Sometimes 2 doesn't happen. 3 is a certainty though. Now, if you told me that the productivity gain from 1 is enough to offset the loss from 2 and 3, I could believe you. But I also wouldn't be surprised if it didn't.
    - chillacy 3mo ago
nyeah 3mo ago
That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent. EDIT: In fact, parent comment has a link to some numbers. [EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
- oudlys 3mo ago
  I've posted numbers that indicate that productivity is becoming decoupled from value delivery. If you follow the link in my comment it reviews a pretty robust study of 4000 teams over 2 years. There is no product throughput increase.
  - nyeah 3mo ago
    Interesting data, thanks.
  - d33d 3mo ago
    Yep. Code acceleration is great, but.... something precedes that. Vision and strategy re. expansion of offerings and businesses. Once a firm reaches maturity in what it offers and is only touching the edges - this code acceleration is literally useless when you factor in all of the trade-offs. This is a good thing - it means fat and slow incumbents are sitting ducks to be out-witted by creative and imaginative founders, which is healthy for a well-functioning economy. Now the economics of existing frontier models are not sustainable - its looking like a mix of the airline (supersonic vs subsonic) and EV industry with China in the background providing decent offerings at much lower prices.
    - oudlys 3mo ago
      I think its worse than that. I admit that if a small team or an individual uses an LLM, it's likely they can create value faster. I think as soon as you don't own the responsibility for the defects you generate with an LLM, their use starts to destroy value. Regardless of product maturity. This is what I think the data says. https://unessays.substack.com/p/talk-is-cheap https://unessays.substack.com/p/talk-is-cheap
amatheus 3mo ago
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.
- oudlys 3mo ago
  It does depend on how you define productivity. But the way it's commonly used is "I'm going faster, personally, with these tools." The thing people I think have a hard time seeing is that "I go faster" does not mean "more features get finished". It's a scale issue, and one scale is better than the other. People only pay for finished features, they do not pay for how much code you emit.
  - fl4regun 3mo ago
    economists define productivity as gdp per hour worked. Like a lot of other economic measurements, its mostly a bogus number people use as an argument on why their politics are better than someone elses politics. You can have an efficient business located in a poor country making the same product and same quality as that same business in a rich country, the rich country will be more "productive" because local cost of goods is higher there (i.e. a restaurant in NYC is more "productive" than a restaurant in bangladesh).
    - oudlys 3mo ago
      Sure. But that's not, in my view, how most people use the word productivity when describing LLM use. In my field - operations - productivity is usually described as some rate of production for a specific asset. 100 widgets / machine / hour - for example. "My productivity is 3 PRs / day with the LLM as opposed to 1 PR per every three days". That's how I think people are thinking about it. My point is that's not the same thing as value. I.e. what people will pay for.
      - fl4regun 3mo ago
        You're correct, I just wanted to add that there is another definition that you may see used online, and it is very specific, and it's important to be aware it's NOT exactly the same thing most normal people mean when they say "productivity".
dzhiurgis 3mo ago
That report doesn't match what faros.ai conclude which is mostly a paywalled report.
- oudlys 3mo ago
  The report was not paywalled for me. It just required a work email. Which is totally fair from my perspective. Faros is providing a ton of value with the report. People do deserve to get paid - even if in collected emails. You're right my analysis is at variance to what Faros.ai says. I think they interpret their data trying to rescue utility for the dominant patterns of LLM use. But I think to anyone who is experienced with process improvement or queuing theory, their interpretation is clearly weak. Rework is a huge problem in queue systems, and they mostly just elide the throughput impact of an 860% increase in code churn coupled to a massive spike in bugs. Obviously draw your own conclusions. But I don't think because I disagree with the interpretation of the people who originated the data makes me wrong.

selectodude 3mo ago

[flagged]

tomhow 3mo ago
Please don't comment like this on HN. We can disagree with people whilst still affording people basic decency; it's a condition of participating here. If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.

gdcbe 3mo ago

I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work... It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...

cogman10 3mo ago
Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost. Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost. That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
- remich 3mo ago
  Because it doesn't. Not for the tasks where using Opus instead of a lower tier model is appropriate, at any rate. Benchmarks show this, as do revealed preferences of actual users. To believe that Qwen is as capable as Opus at 1/20 the cost you have to believe that every person who does not make the choice to use Qwen over Opus for a given task is some mix of ignorant or delusional. This is certainly an opinion you can hold about other engineers, but it's definitely a questionable one at best.
  - cogman10 3mo ago
    The benchmarks between the two are close and the engineers that have used both (like myself) can attest that the differences aren't so wide as you might believe. I'd say that yes, ignorance plays a role here because a decent number of engineers are looking strictly at the benchmarks and choosing Opus just for that reason. But I'd also say that a major factor for Opus use is because Opus is being purchased for the engineers by their employers. They don't get to pick which models they are using.

enraged_camel 3mo ago

Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!" This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.

nozzlegear 3mo ago
> This, combined with his extreme ignorance, Extreme ignorance?
- dannersy 3mo ago
  I can see how folks find Zitron shrill, but I wouldn't dare say he is ignorant. The man seems to be bending over backwards to do actual journalism when most outlets take CEOs at their word and promote a market that goes irrationally up despite evidence. Case and point, the situation in Iran is such a mess, yet every time Trump says there is a deal of some sort, the market rallies. It has been months now of this game.
dannersy 3mo ago
As a listener, I have not heard him predict collapse once. Maybe I got to him late, but so far as I know, he claims that the industry as it exists is unsustainable. You may think that is him dodging the logical progression of "collapse" but I think so far he has been pretty correct on the unsustainable part, the signs are there. If anyone could predict such things they'd be a billionaire many times over. In the end I think he will be more or less correct, the timing and how long investors can prop it up is the much harder part to predict.

AlexandrB 3mo ago

> undeniable, massive productivity gains How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication. I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think. [1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.

elorant 3mo ago

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.

aspenmartin 3mo ago
Cite a real source for that last bit, I don’t think that is true. Also the budgets should be cut the spend at some places goes beyond any reasonable amount. The strategy there is to hook everything in and find the right processes, then cut the rest. Things then get better and better with each model release. The way you make a viable service that eats 300bn annually is to have enough demand to service that. Anthropic underbought compute. That tells you something.
- elorant 3mo ago
  https://www.theverge.com/tech/930447/microsoft-claude-code-discontinued-notepad https://www.theverge.com/tech/930447/microsoft-claude-code-d... https://finance.yahoo.com/sectors/technology/articles/ai-binge-companies-balk-soaring-014735313.html https://finance.yahoo.com/sectors/technology/articles/ai-bin... https://blog.pragmaticengineer.com/the-pulse-token-spend-breaks-budgets-what-next/ https://blog.pragmaticengineer.com/the-pulse-token-spend-bre...
- jazzcomputer 3mo ago
  When you say "Things then get better and better with each model release." How far behind are models that can be run locally, and do you expect that this will be widespread?
  - aspenmartin 3mo ago
    https://epoch.ai/data-insights/consumer-gpu-model-gap https://epoch.ai/data-insights/consumer-gpu-model-gap I think over the years local models have fairly consistently been ~7 months behind frontier performance. Local models are hugely important but I don’t see the calculus changing. I can imagine it’s certainly the case for many tasks that there will be diminishing gains for performance improvements or reliability pass some threshold, in which case you don’t need frontier performance and you can certainly use local models or at least cheaper tiers of proprietary models if local is too much of a hassle. Plus of course use cases where local is necessary or the pros of having local models or on device models outweighs that of frontier. Maybe things will change though, I would assume through basically government subsidies from China etc, to undercut existing frontier labs, but you can always spend more (better data more compute etc) for better performance and that I can imagine will always have a selling point.

alexashka 3mo ago

[flagged]

vanuatu 3mo ago
i don't think this comment contributes much to the discussion. can you elaborate more than saying "no"?
- deleted 3mo ago
  [deleted]

mawadev 3mo ago

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

frisbee6152 3mo ago

He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024. The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either. I find it difficult to take him seriously.

bdangubic 3mo ago
anyone that takes him seriously at this point... I don't want to say very bad words here...
solomatov 3mo ago
> progress was slowing Do you think it's not slowing? Do I miss anything really important? My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
- frisbee6152 3mo ago
  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.

freejazz 3mo ago

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).

remich 3mo ago
Perhaps they aren't, but not currently viable !== always unviable.
- 48terry 3mo ago
  Just 5 more years and $500 billion more, bro. We're still so early.
- freejazz 3mo ago
  And?
- amlib 3mo ago
  Is it really worth it to cause a global economical collapse and harm society well-being to an unimaginable degree just to find out if it is viable? Why cant it naturally grow and prove it's worth?

righthand 3mo ago

[flagged]

dist-epoch 3mo ago

> Zitron is begging for a collapse at this point No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job. Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades. Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."

spmurrayzzz 3mo ago

He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse. I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place. My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit. But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time. The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.

solomatov 3mo ago
> that he doesn't really understand how inference works from a technical perspective Could you share what tells about it? I.e. where he was wrong about it?
- spmurrayzzz 3mo ago
  There's examples both in his writing and also in his appearances on podcasts, interviews, etc. I'll cherry pick a couple: “When these new models ‘reason,’ they break a user’s input and break into component parts, then run inference on each one of those parts.” [1] This is not at all how test-time compute works. At best, this is a very loose metaphor that he may have used out of convenience. This might sound a bit pedantic to point out, but this is a very basic thing that he's getting wrong (presumably at least, again it could be that he just used a poor metaphor). A less pedantic example would be his claims related to gpt-5/chatgpt auto-routing. He argued that having a router means OpenAI can no longer cache static prompts, because the user prompt has to come before the hidden instructions [2]. This is just not at all how this works at inference-time. There is no evidence that the standard approach of system>developer>user instruction hierarchy has changed, the public API and caching docs maintain this. But even more broadly, it suggests he is reasoning about kv/prefix caching at the wrong level of abstraction. It's true that conventional prefix caching does require a stable prefix, so yes, if you literally put variable user content before the static prompt, you would destroy the cacheability of that static prompt. But that is exactly why inference systems are designed to preserve reusable prefixes where possible (via checkpointing or similar), and why serving systems care so much about prefix caching. This is also a big part of how disaggregated prefill/decode infra works where cache-aware routing is critical. His argument treats a bad prompt layout as if it were a necessary consequence of routing, rather than an avoidable implementation choice. A router can read the user request, decide which model path to use, and then construct a normal downstream model call with stable static instructions first and user content later. Treating that as impossible implies a fundamental architectural misunderstanding. [1] https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ [2] https://www.wheresyoured.at/how-does-gpt-5-work/ https://www.wheresyoured.at/how-does-gpt-5-work/

cm277 3mo ago

Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead. The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.

aspenmartin 3mo ago
I don’t get this. We already have an insane demand. And yes exactly, this is primarily just with coding agents, but are you aware of what’s coming down the pipeline? It’s not hard to be you just have to find a decent way to keep up with literature. * robotics (need to close data gap and release first viable product to get a data flywheel) * conversational ai (no one is ready for this and we’re getting closer and closer to natural speech. The quality still isn’t good enough but it’ll be soon). * other agentic use cases, openclaw adoption was crazy and that had a ton of barriers to entry * ai products, like the one OpenAI is working on with Johnny Ive Anyone thinking it’s unreasonable to hit whatever revenue requirements is just not that aware of what’s happening. Not to mention were capacity constrained already!! This is barely speculation at this point.
- sterlind 3mo ago
  I don't think the issue with robotics is a data gap. maybe somewhat, but the real issues are that: - RL is extraordinarily sample-inefficient. - distribution shift/catastrophic forgetting aren't solved. only off-policy learning with giant decorrelated batches works. - the breakout success of transformers as an architecture doesn't neatly translate to robot motion policy models. the field is missing fundamental breakthroughs. I also find it very interesting that conversational AI has taken this long. where are the models with good turn-taking? passive listening? the ability not to respond in paragraphs? has Anthropic simply not gotten around to it?

themafia 3mo ago

> through undeniable, massive productivity gains. And where are those? They seem particularly hard to actually observe and only appear in anecdotes. > I'm trying to believe For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?

PedroBatista 3mo ago

> undeniable, massive productivity gains. The jury is still out on that.

deaton 3mo ago
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?
- aspenmartin 3mo ago
  Because it can perform much faster? Monitoring allows you to multitask more effectively. I would also disagree that you can’t one shot anything…claims like this are weak and I have enough counter examples in my own life that it’s trivially false. The question is more: can it one shot the right things with a low enough failure rate for it to be a good replacement. It’s hard to figure that out a priori.

toasty228 3mo ago

> undeniable, massive productivity gains. Take any stock index, remove AI stocks, what do you see? That's right! Nothing... So where is all the productivity going? Where is the value? Where are the massive unemployment stats or the millions of new startups making big $$$?

moritzwarhier 3mo ago
Writing about AI, destroying the planet for data centers, there's a lot of money to be made. That being said, AI seems kind of miraculous sometimes. Similar to cars. So enticing that we make everything else in the world worse in order to maximize the profit, make it indispensable, subsidize it, and make the dependency on it irreversible. And it's not even something to blame individual people for. Driving away from all the other cars to spend a weekend feels like freedom. Using AI to answer a question feels like a "bicycle for the mind". But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt. Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
- MSFT_Edging 3mo ago
  Vonnegut said in his last living work that the greatest addiction modern people face is the drug of cheap oil. We got addicted to the convenience and overuse, and have started a mass extinction event because of it. The perverse incentives will come for us all.
  - moritzwarhier 3mo ago
    It is exactly this thought, in the form of this sentence, that could replace almost all of my comments in this thread. It feels depressing, but I think the same. When thinking about the larger world, it becomes increasingly hard to ignore. And of course it is not new. There were "doomers" already in the midst of the 20th century, but it doesn't mean that they were wrong.

bakugo 3mo ago

> undeniable, massive productivity gains. Just because you keep repeating something doesn't make it an undeniable truth.

ath3nd 3mo ago

[dead]

lbrito 3mo ago

>undeniable, massive productivity gains. How can something so undeniable have zero scientific evidence? Are there any large peer reviewed or meta studies confirming your claim?

aspenmartin 3mo ago
It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model. I think the surest sign of productivity gains is the sheer volume of adoption. If you look beyond headlines, adoption is just incredible. Of course adoption does not necessarily point to productivity gains, but if this was some sort of FOMO or smoke and mirrors you would not see this much retention and this feverish a pace of adoption. You would not see a large segment of the profession using coding agents exclusively. All of these companies track productivity, again with imperfect proxies, yet everything points to a pretty consistent picture. Same with benchmarks, again a lot of crappy benchmarks but a lot of high quality ones too and a very diverse collection of tasks and capabilities they probe.
- 48terry 3mo ago
  Your second paragraph appears to be 3 different instances of saying "X does not necessarily point to productivity gains... but in the case of AI, X definitely means productivity" without really saying why that is true or why other explanations do not fit. Adoption meaning productivity supposes there are no other dominant factors for the AI push nor AI retention. It is possible for practices to be picked up or continued in spite of causing productivity DROPS. What studies have suggested are factors that make for productive work environments and what is actually enforced in the workplace are different things.
  - aspenmartin 3mo ago
    It’s 3 different weak but complimentary proxies. We form beliefs from imperfect evidence and I find these fairly convincing when it’s hard to find any hard evidence of no productivity and exactly the scenario you would expect under the hypothesis that we do see productivity gains. None of this is supposed to be unassailable. I would challenge then if you disagree what the evidence you have for this is? Adoption implying at least some significant productivity gains doesn’t contradict there being other factors. You’re seeing entire companies reshaped. The argument is this is all for show or CEOs are in some sort of idiot class? “It is possible for practices to be picked up or continued in spite of causing productivity drops” well of course. I just find that incredibly far away from Occam’s razor. My point is: we have lots of evidence that’s highly consistent with real productivity gains, and I don’t see many pieces of evidence to the contrary.

demorro 3mo ago

They are absolutely deniable. Huge swathes of people deny them.

dofm 3mo ago

He has recently made the very good point that actually, the FAANG companies are struggling to put any ROI numbers on that incredible ground-level utility. Uber, for example, is so unclear there is any ROI, they are cutting their exposure pretty radically. He points out that one single Anthropic customer — a payments provider — accidentally had to pay Anthropic $500M for one month of token spend. That is half what Apple is reportedly paying Google for the supply side of their entire consumer AI strategy.

squidsoup 3mo ago
It doesn't matter under Capitalist Realism, the banks were bailed out, the AI companies will be bailed out, and you will pay for it. There is no alternative.
- HerbManic 3mo ago
  I'm not sure if they would be bailed out. The government tends to help with bank bailouts as they are essentially the hemoglobin of the economy, I see this being more like the dot-com bubble were they will just let it fall and have the bigger more entrenched player pickup the scraps for cents on the dollar.
  - dofm 3mo ago
    According to NOTUS, Altman himself seems to have sold Trump on the idea that the USA should own a chunk of OpenAI, and despite their potential IPO, OpenAI have recently followed up on the idea. Owning a chunk is pretty directly how more than one country injected confidence into their at-risk banks; it’s certainly how it was done in the UK.

Leynos 3mo ago

I quite like my mechanical spider from Wild Wild West and the coffee it makes with a 50% success rate