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Why Scale Will Not Solve AGI | Vishal Misra - The a16z Show
Researchers have mathematically validated that Large Language Models function as "Bayesian wind tunnels," where in-context learning precisely updates token probability distributions in real-time rather than relying solely on statistical correlation. Despite demonstrating this capability through the open-sourced "TokenProbe" tool and reproducing results across transformer architectures, current models remain fundamentally limited by their frozen weights and inability to perform causal reasoning or discard established axioms. Bridging the gap toward Artificial General Intelligence therefore requires a new architectural approach to implement true continual learning and move from association to simulation, as identified in recent work comparing LLM behavior to Judea Pearl's causal hierarchy.