> But I think there's also the bitter lesson to be learned here: many times people say LLMs won't do well on a task, they are often surprised either immediately or a few months later.

Heh. This is very true. I think perhaps the thing I'm most amazed by is that simple next-token prediction seems to work unreasonably well for a great many tasks.

I just don't know how well that will scale into more complex tasks. With simple next-token prediction there is little mechanism for the model to iterate or to revise or refine as it goes.

There have been some experiments with things like speculative generation (where multiple branches are evaluated in parallel) to give a bit of a lookahead effect and help avoid the LLM locking itself into dead-ends, but they don't seem super popular overall -- people just prefer to increase the power and accuracy of the base model and keep chugging forward.

I can't help feeling like a fundamental shift something more akin to a diffusion-based approach would be helpful for such things. I just want some sort of mechanism where the model can "think" longer about harder problems. If you present a simple chess board to an LLM or a complex board to an LLM and ask it to generate the next move, it always responds in the same amount of time. That alone should tell us that LLMs are not intelligent, and they are not "thinking", and they will be insufficient for this going forward.

I believe Yann LeCun is right -- simply scaling LLMs is not going to get us to AGI. We need a fundamental structural shift to something new, but until we stop seeing such insane advancements in the quality of generation with LLMs (looking at you, Claude!!), I don't think we will move beyond. We have to get bored with LLMs first.