Chollet on X: Why Current Scaling Will Not Produce AGI

Chollet argues that current scaling trajectories will not produce AGI because they optimize for a different objective: performance on training distribution tasks. Scaling improves in-distribution performance efficiently, but AGI requires out-of-distribution generalization (what ARC measures) which current training objectives don't directly reward. The thread is not an argument that scaling is useless — it's an argument that scaling alone (same objective, more data, more parameters) cannot bridge the gap to genuine generalization. A different ingredient — possibly something like learned program synthesis, meta-learning, or a fundamentally different training objective — is needed.