Vishal Misra
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- a16z47 min
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.
- a16z51 min
Will LLMs Get Us To AGI?
Martin and Vishal define Artificial General Intelligence as the capacity to generate entirely new scientific paradigms rather than merely interpolating within existing data manifolds, a capability they argue current Large Language Models lack despite their sophisticated Bayesian reasoning. They detail a formal Matrix Abstraction Model explaining how in-context learning functions as evidence-based posterior updates, while simultaneously critiquing the industry's reliance on prompt engineering and empirical scaling as insufficient for achieving recursive self-improvement or true innovation. The discussion concludes that a fundamental architectural leap beyond probability-based transformers is necessary to transition from generating "confident nonsense" to producing outputs that fall completely outside training distributions.