Thanks for your perspective from somebody working on the field. I still wonder though: what would the results be if we'd just use a richer dataset + more parameters? Would it be really that different results? (except costs, as MoE def helps with that)

MoE: I assume some people just specialize in working with routing as with that, as by reducing the amount of params and just using a subset, you end up making it less costly. So, AI researchers are only working on optimizations on getting this better?

Same question on Reasoning, so AI researchers are working mostly on optimizations on top of it, like CoT and so on, like mini-optimizations.

So basically, they work on those micro-optimizations, put them together and see a % improvement in a benchmark?

I'm sure this is probably awesome for languages, which if I'm not mistaken, it was the use-case initially used on "All you need is attention" and the entire LLM revolution.

But this seems to be a very clear path to be "taking the car to the carwash by foot" for a long time, isn't it?

It feels like we'll keep "taking the car to the carwash by foot" until somebody optimizes for that prompt, or some pre-training done, and then there'll be another prompt that will show that the AI has real trouble with very basic real-world reasoning and imagination.

Isn't it the case, or do you see any kind of research that could take us from that plateau full of micro-optimizations that get us a few cm higher to the peak?