tonii141

I just see a lot of people who’ve put money in the LLM basket and get scared by any reasonable comment about why LLMs aren’t almighty AGIs and may never be. Or maybe they are just dumb, idk.

I’m not demanding anything, I’m pointing out that performance tends to degrade as context scales, which follows from current LLM architectures as autoregressive models.

In that sense, Yann was right.

a) That "no-tools" win depends on prompt orchestration which can still be categorized as tooling.

b) Next-token training doesn’t magically grant inner long-horizon planners..

c) Long context ≠ robust at any length. Degradation with scale remains.

Not moving goalposts, just keeping terms precise.

a) Still true: vanilla LLMs can’t do math, they pattern-match unless you bolt on tools.

b) Still true: next-token prediction isn’t planning.

c) Still true: error accumulation is mitigated, not eliminated. Long-context quality still relies on retrieval, checks, and verifiers.

Yann’s claims were about LLMs as LLMs. With tooling, you can work around limits, but the core point stands.

Not true. AI has been around far longer than modern LLMs and has performed well in non-generative areas, often with orders of magnitude fewer parameters.

What do you mean by "they add instability"?

I agree, but maybe there is no need for billions of neurons to be simulated right away. Artificial neural networks were pretty small at the time.

I have been involved in research focused on ML control for some time, and believe me, I would love to see an AI model capable of controlling arbitrary systems at different operating points or in different environments. However, it is simply not feasible yet. This AI drone is no different, especially because reinforcement learning was used to train the model, which is generally not practical for real-world systems due to disturbance variables and the continuous need for adaptability.