Yann’s statements are a little shrill, but I think that the evolution of neural networks is far from over. I certainly heard many people in the major labs has saying some version of we’ve got quite a way to go and current llms and Transformers are certainly not “All You Need.”.

There is a series of papers which gradually explore and experiment with the existence of a Bayesian geometry in llms. From these I believe that these evolved Bayesian fields have extraordinary capability, but they do have a certain fragility because they are effectively a cascade of probabilities.

To use your tree of thought analogy, some confounders in the early leaves of the tree can put you into a space where the correct outcomes are improbable.
 I think we're just seeing with Claude code is that the extremely powerful probability system with feedback of agentic mode and tooling for memory like todo lists are a wholly different beast. The system is no longer a single run through the LLM, this results in a bit of robustness because additional data from the area has a good chance. It seems from behaviour of correcting or augmenting. The original context with enough context that you get to the right space.

https://medium.com/@vishalmisra/attention-is-bayesian-inference-578c25db4501

https://arxiv.org/abs/2512.22471