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…
AI is slowing down
221–230 of 820 posts
Re: AI is slowing down
#222Earlier quoted context omitted.
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…
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 clos…
- 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?
Re: AI is slowing down
#223Earlier quoted context omitted.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda). But from the article I linked back in March 2024: "Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models…
No they aren't. The models still hallucinate just like they always did. You cannot trust them, ever, to get something right.
> several mass market use cases have emerged, most notably coding
They aren't really useful for coding based upon the above. Since you can't trust them, you have to carefully review everything they make, which in turn destroys any productivity they could've given you.
> rate of progress has increased
I have yet to see any progress. Opus 4.8 that you get today is no more effective than GPT-3.5 was. Much less would I agree that the rate of progress has increased. Only hype has increased, but there has yet to be a drop of substance.
Re: AI is slowing down
#224I find it nuts that I can use Claude Code for $20pm - I imagine that won't last forever but have to say it is great value for money. So when I see monthly budgets in the thousands for developers at some larger companies, I'm curious to learn how they are managing to spend that kind of figure: how much code/documentation are they feeding into their prompts, are they using agent orchestration systems to make the code f…
As for how to spend that much -- not that hard, to be honest. Just give it a lot of context and some relatively open-ended problem and it will easily eat through tons of tokens.
I have $200 subscription for Codex and it is crazy what it can do in terms of debugging. I have a pretty complex Electron setup with some native code linked via Node addons, a few App Extensions and it can easily read the source code to see how the builder works internally (e.g. if your end Info.plist is not correct), debug the xcodebuild output to see at which step something is not linked correctly (like after XCode major version bump), etc.
It is not a silver bullet but if you are not the one paying for it, there is no downside to throw a problem at it and see if it can come up with a fix.
> And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
I am curious about that myself. I have a good machine now (Macbook Pro M5 Pro with 48GB memory), so I'll give it a try; I don't have high expectations so if it is actually helpful would be very neat.
Re: AI is slowing down
#225Earlier quoted context omitted.
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 al…
I agree with your message but not sure about the conclusion. Cars themselves are commodified luxury available (in the US pretty much required) to everyone, and they do need to be subsidized, both in terms of infrastructure and the lifestyle they require. But with AI what is the exact price? My understanding is that R&D is extremely expensive, but running non-SOTA models is not that bad. We are getting pretty close to…
First, because I initially failed to answer your more closed questions (this paragraph is edited in):
> We are getting pretty close to models which can be useful locally in many applications. Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
I don't think there's a way around making the best of AI capabilities with minimum price and maximum control, and I'd agree this is met by on-prem data centers, just not in a rationally targeted way.
Back to my original comment:
Because it (my conclusion) was not so clear, and maybe I just wanted to highlight some observations without delivering a real argument for or against things [, I thank you for your open question].
The utility/leverage aspect for AI seems more esoteric than the one for cars because, apart from Chatbots, it's more hidden.
And also, similar to cars (or many other phenomena of industrialization), yes, my first vague point was the subsidization of infrastructure. But also, the power gap: that's something not only associated with AI or cars, but with a lot of technologies we all hold dear: sewage, powerline, logistics, etc etc.
What reminds me of cars in the current AI frenzy is the fixation on cementing infrastructure. And also, I think, a lot more people agree on, for example, some kind of universal right to, for example, clean water.
But all of industrialization confronts people with questions of efficiency, inequality, and collective support.
Most people would, for example, support a right to get a minimum amount of clean water when you are living and working in a tradionally inhabited space (if you're on the social-darwinist side) or at least not harming society (if you're more of a social democrat).
And, similar to the buildup of car infrastructure, and the procurement of resources, space etc for maximum building, giant data centers can obstruct people in buying drinking water. Or walking outside (AI obstructs traditional methods of online collaboration).
Re: AI is slowing down
#226Earlier quoted context omitted.
The maintainer of curl - who has access to mythos - disagrees [0]. I think it's dangerous to rely on claims made by people who financially profit from you believing them without checking. [0]: https://daniel.haxx.se/blog/2026/05/11/mythos-finds-a-curl-v...
That blog post is very clear about the maintainer having no access to Mythos.
Re: AI is slowing down
#227Earlier quoted context omitted.
>undeniable, massive productivity gains. How can something so undeniable have zero scientific evidence? Are there any large peer reviewed or meta studies confirming your claim?
It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model. I think the surest sign of productivity gains is the sheer volume of adoptio…
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.
Re: AI is slowing down
#228I have found agentic coding to be extremely useful for a bunch of small, middleware, very focused bits of software for small businesses:
* A company had a very specific scheduling need, they needed to move about 8-15 staff around with a bunch of different shifts, and have custom reports on who was working how many hours, and have the employees get a nice clean email summarizing their schedule
* A manager wanted a very simple "let me send a text to add a to-do to the group list" need
* A sales team of 3 wanted to be able to type pricing of raw goods into their phone, have it compared to other market sources, and have it text the other 2 salespeople and their manager when they were out in the field
All of these were coded with Codex in about 4 hours with further refinements over the next week of back-and-forth with the people using the tools.
I suppose yes we could have found some custom middleware solutions that did similar things, but it's nice to be able to make a web page or tiny mobile app that just does EXACTLY what the person wants.
It's hard to do that and then listen to someone who says it's all just garbage.
Re: AI is slowing down
#229Earlier quoted context omitted.
Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).
Perhaps they aren't, but not currently viable !== always unviable.
Re: AI is slowing down
#230Earlier quoted context omitted.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda). But from the article I linked back in March 2024: "Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models…
> - hallucinations are dramatically less of a problem
Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
> - several mass market use cases have emerged, most notably coding
Coding seems to be the only one, but there are still a lot of open questions about how the market can sustain the costs, and that's without considering the market dynamics that could emerge once costs are lowered enough that open source models start to become an attractive option.
> - rate of progress has increased
Debatable.