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So what I'm reading is that they're either making a gentleman's agreement to let Chinese labs run circles around them or the easy gains are over and they're now running into a wall (lemmy.ml)

submitted 1 month ago by yogthos@lemmy.ml to c/technology@lemmy.ml

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[–] MalReynolds@slrpnk.net 7 points 1 month ago (1 children)

So a repeat of the crypto crash for graphics cards when ASICs ate their lunch. Mind you, that's only for inference (although a super fast QWEN 3.8 would meet a lot of peoples needs).

The argument for datacentres is for training the models, but then they'll need to prove that they haven't hit a diminishing returns wall, which will be hard if, as seems likely, they have. Also the Chinese have been doing it in a cave, with a box of scraps (figuratively), and gotten at least 90+% as good results.

Seems like the recent advances have been in the frameworks, which don't need no stinking (literally if fossil fueled) datacentres.

[–] yogthos@lemmy.ml 7 points 1 month ago

Right, I'd argue that China proves you don't need massive data centers for training. And yeah, I think something like Qwen 3.8 is more than enough for tasks most people do. There are a lot of tricks you can do as well with the harness, where there's a lot of attention is shifting now. And it's a lot cheaper and faster to develop better harnesses than train new models. I expect we'll start seeing a shift towards neurosymbolic systems before long where the LLM acts as a stochastic component within a symbolic logic engine.