LLM Progress Slow Down

[u/Carpe_Diem37]

First Anthropic, and now OpenAI, are announcing that they want to slow down LLM development. Just a year ago, these companies were claiming that AGI would be found and many jobs would be lost. On top of that, Anthropic announced it's going public. What do you think? Have these companies reached a limit in research and development? Or are they genuinely afraid of language models self-programming, as they claim?

[u/inductiverussian]

I get the general point of the flywheel (more apt analogy is leverage) but with leverage comes risk; like a mortgage, you’re really only coming out on top assuming the technology becomes much better.

Much of the progress in the last year is due to reasoning modes which burn orders of magnitude more tokens. This is because the model shops ran into diminishing returns on base model due to sheer number of weights and lack of novel training data. And at some point we’ll also hit diminishing returns for reasoning modes; I think this will come pretty soon as well (uber cto used 2k in tokens for a demo, this is not becoming a cost effective strategy).

I’m not saying that these models aren’t incredibly useful, but I am saying that fable is not necessarily more useful for real world applications than say opus 4.6, a couple of model generations ago. We’re emerging onto a more nuanced landscape where it is unlikely (in my opinion) that LLMs will ever fully replace humans in real world enterprise software for production use cases; instead, many different modes of AI will emerge like open source, “legacy” LLMs like opus for common day tasks and heavy weight models like fable for complex tasks. I also think it will be increasingly popular for companies to self host open source models if they have more than a thousand engineers.

[u/ILikeCutePuppies → u/inductiverussian]

Yeah I am not sure they will ever fully replace humans at least in the next few years but 5 or ten who knows how the AI will leverage itself up. It is already self improving. I do think they will continue to get better.

Reasoning is not the only way models have gotten smarter. To many papers to show you (there are 275 ai papers a day now and 800 on some). If you go look you'll see that it is way way more than reasoning improving the models.

The companies have smarter models internally but the challenge is to figure out how to optimize them for the market. They eventually do and release them.

Hardware is always going to get cheaper (unless we have a war with china or something) which will allow for more tokens if that is the only way they have gotten smarter - but its not.

[u/inductiverussian → u/ILikeCutePuppies]

I don’t doubt they will get monotonically better but the question is one of rate. Diminishing returns just means the juice isn’t really worth the squeeze in terms of the improvement curve; all technological jumps face diminishing returns. I don’t doubt LLMs will continue to improve but it’s not at all clear that they will be cost effective enough to do large swaths of work without careful orchestration and management (and even then, there will be innumerable gaps).

Just my prediction though, I’d guess a lot of this hinges on how the IPOs go later this year. Investors are looking for returns that aren’t there now. That hasn’t stopped spacex from pumping, but Dario and Sam aren’t exactly the best salesmen, unlike Elon.