alt Hacker NewsThis is quite a short sighted analysis. I do think the valuations are quite and they would need to meet the reality, but don’t think there’s gonna be a crash or we’d ever go back to pre-AI era. It would more or less would be a correction to valuations.
The future of AI would be on-device models which are as powerful as current frontier models and also I can imagine companies have their own deployments of inference of open weighted models for most of the use cases and use the frontier models for extremely niche or higher intelligence tasks.
As an example I use Claude code heavily for every day development and Opus 4.8 was already good enough for my use cases and never used Fable. Also note that I use AI as a tool to help with my work and I do not offload everything I have to do to AI in a single prompt
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> [local, but for] extremely niche or higher intelligence tasks
Access to a centralized copy of the web/literature/media, scraped and indexed/integrated, seems another non-local center of gravity. Versus a local model's last minute reaching out to "manually" search and browse.
Also mass parallelism for large ensembles. Perhaps unless/until we get those local models as 10k+ tok/s chips. Local can follow frontier because frontier is still sort of "expensive rack local". If STOA becomes massive burst-parallel ensembles, that following may get harder.
> but don’t think there’s gonna be a crash […]. It would more or less would be a correction to valuations. [...] The future of AI would be on-device models which are as powerful as current frontier models […]
That’s the crash… that’s pretty much exactly Ed Zitron’s thesis
> As an example I use Claude code heavily for every day development and Opus 4.8 was already good enough for my use cases and never used Fable.
I think the flaw in this logic is thinking about how AI is currently used only. Yes, Opus is good enough for the task you are asking it to do, but that doesn't mean that is all you will ever need.
As AI gets better and better, it will open up new use cases that require the better performance.
The money being poured into AI infrastructure means there is a market for new ways of doing things that take 1/1000 of the power or are 1000x faster, or both.
And there are many such moonshot startups.
AI on GPUs is an efficient as gaming on CPUs.
All that math where perfect precision is not required means that you can’t tell do things in different ways.
Why is on-device AI the future? What is your reasoning behind this? Look at the proportion of things we compute on someone else’s computer relative to what we compute on our own device. Why would this change for LLMs?
How's it "quite short sighted"? Are you saying the math _does_ make sense? if so, how?
The validity and permanence of a technology has literally nothing to do with how irresponsible people have been while placing speculative bets on it.
In this case, it’s really irresponsible.
Thats great but this is like when 3g came out. Sure it was the future, but it was half baked, impersonal, expensive, unreliable and required a culture shift to be adopted.
Onboard decent LLM performance thats _power efficient_ is at least two/three hardware generations away. (assuming linear performance)
but, the valuations, with debt trade and private credit obscuring exposure is a recipe for disaster.
Seriously, you should try Fable. It picks up on subtleties that Opus misses.
> I do think the valuations are quite and they would need to meet the reality, but don’t think there’s gonna be a crash or we’d ever go back to pre-AI era. It would more or less would be a correction to valuations.
Something to consider: would your description also apply to the dot-com boom of the late 1990s? The internet was real, the ideas for internet business were real, and we were not going to the previous reality. But the valuations weren't quite right and a "correction" happened at some point.
When people talk about AI crash, that's what they mean. Not that AI is a hoax, but that the correction could be quite violent and have effects on the broader economy.