There are advancements that do not follow s curves - consider for instance total data transmitted over all networks, or financial derivatives volumes.

I think a better question for AI is “is it more like a network effect, liquidity effect, or a biological/physical effect”?

It can be S curve (and it almost surely is), but on every chart you can plot, you don't see even of an inkling of the bend yet.

This could be right for the current architecture of LLMs, but you can come up with specialized large language models that can more efficiently use tokens for a specific subset of problems by encoding the information differently (https://www.nature.com/articles/d41586-024-03214-7).

So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve the quality of the output while reducing the amount of transformers needed for decoding and encoding IO and for internal reasoning.

There are also difference inference methods, like autoregressive and diffusion, and maybe others we haven't discovered yet.

You combine those variables, along with the internal disposition of layers, parameter size and the actual dataset, and you have such a large search space for different models that no one can reliably tell if LLM performance is going to flatline or continue to improve exponentially.

It’s more of a guess if you don’t know about things like scaling laws and RL with verification. The onus of “we’re going to saturate” anytime soon is on that claim because every measurement points to that not being true.

He said "will stop anytime soon". He didn't say forever.

Great. You see a shape in graphs. And that shape tells you that _at some unknown point in the future_ progress will slow (but likely not stop).

Now back to the point, what reason do you have to believe progress will stop soon? If you have no reason, then it sounds like you agree with OP.

Which makes the patronizing sarcasm all that much more nauseating.