zlacker

You are forgetting about the end of the Moore's law. The costs for running a large scale AI won't drop dramatically. Any optimizations will require non-trivial expensive PhD Bell Labs level research. Running intelligent LLMs will be financially accessible only to a few mega corps in the US and China (and perhaps to the European government). The AI "safety" teams will control the public discourse. Traditional search engines that blacklist websites with dissenting opinions will be viewed as the benevolent free speech dinosaurs of the past.

>>cft+(OP)
This assumes the only way to use LLMs effectively is to have a monolith model that does everything from translation (from ANY language to ANY language) to creative writing to coding to what have you. And supposedly GPT4 is a mixture of experts (maybe 8-cross)

The efficiency of finetuned models is quite, quite a bit improved at the cost of giving up the rest of the world to do specific things, and disk space to have a few dozen local finetunes (or even hundreds+ for SaaS services) is peanuts compared to acquiring 80GB of VRAM on a single device for monomodels

>>dontup+jl
Sutskever says there's a "phase transition" at the order of 9 bn neurons, after which LLMs begin to become really useful. I don't know much here, but wouldn't the monomodels become overfit, because they don't have enough data for 9+bn parameters?