"Recognize when scale alone cannot solve your problem A widespread assumption in AI development is that bigger models trained on more data will eventually solve every remaining limitation. The speaker pushes back sharply: "scale will not solve everything — you need a different kind of architecture." His specific evidence is that moving from correlation to causation, and implementing genuine continual learning, are structural problems that adding parameters cannot address. For anyone allocating research or engineering resources, this is a concrete signal: if your bottleneck is that the system cannot update its knowledge over time or cannot reason about intervention and counterfactuals, throwing more compute at the current architecture is the wrong investment. Source: Vishal Misra, No Priors (Martin Casado), 'How LLMs Actually Work: Bayesian Inference, Causality, and the Path to AGI'"