Why Scale Will Not Solve AGI | Vishal Misra - The a16z Show

In this discussion, computer scientist Vishal Misra explores the underlying mathematical mechanics of Large Language Models (LLMs), moving from early empirical observations to formal proofs that these models function as Bayesian inference engines. He outlines the current limitations of AI and the specific architectural shifts required to achieve Artificial General Intelligence (AGI). ### The Matrix Abstraction and Early Discoveries The foundation of Misra’s work is a mathematical model that views an LLM as a gigantic, sparse matrix. In this abstraction, every row corresponds to a possible prompt, and every column represents a probability distribution over the model's vocabulary (approximately 50,000 tokens for models like GPT-3). When a user provides a prompt, the LLM approximates a row in this matrix to predict the next token. Misra’s interest in this began in 2020 when he used GPT-3 to solve a complex natural language querying problem for a cricket database. He designed a Domain Specific Language (DSL) that GPT-3 had never seen during its training. By providing a few examples—a process known as "in-context learning"—the model learned to translate English queries into this new DSL in real-time. This observation led Misra to hypothesize that LLMs are not merely "stochastic parrots" but are performing Bayesian updating: starting with a prior belief and updating their posterior probability distribution as new evidence (the prompt) is presented. ### The Bayesian Wind Tunnel To move beyond empirical observation, Misra and his colleagues at Columbia University developed the "Bayesian Wind Tunnel." This is a controlled experimental environment where they test various AI architectures (Transformers, Mamba, LSTMs, and MLPs) against tasks that are combinatorially impossible to memorize. Because the tasks are mathematically tractable, the researchers can analytically calculate what the "perfect" Bayesian posterior should be. The results were definitive: the Transformer architecture matched the precise Bayesian posterior distribution to an accuracy of $10^{-3}$ bits. This proved that Transformers are mathematically performing Bayesian inference. Other architectures showed varying degrees of success: Mamba performed reasonably well, LSTMs only partially succeeded, and MLPs failed entirely. This suggests that the ability to perform Bayesian updating is a function of the Transformer architecture itself, rather than just the data it is trained on. ### Human Cognition vs. LLM Processing Despite their mathematical precision, Misra identifies several fundamental differences between LLMs and human intelligence. 1. **Objective Functions:** A human’s primary objective is survival and reproduction, which drives our learning and behavior. An LLM’s objective is solely to minimize the error in predicting the next token. Misra dismisses claims of LLM consciousness, arguing they are "grains of silicon doing matrix multiplication" without an inner monologue or existential drive. 2. **Plasticity:** Humans exhibit lifelong plasticity; our synapses change as we learn. In contrast, an LLM’s weights are frozen after the training phase. While they can perform Bayesian inference during a conversation (in-context learning), they "forget" everything once the context window is cleared. 3. **Shannon Entropy vs. Kolmogorov Complexity:** LLMs currently operate in the realm of Shannon entropy, which focuses on correlations and pattern matching. Human intelligence, however, excels at Kolmogorov complexity—finding the shortest possible "program" or causal model to explain a phenomenon. ### The Einstein Test and the Path to AGI Misra proposes the "Einstein Test" as a high bar for AGI. He suggests training an LLM on physics data available only up to 1910 and seeing if it can derive the Theory of Relativity. He argues current LLMs would fail because they are bound by "data gravity." They correlate the majority of existing data (Newtonian mechanics) and treat anomalies as noise. To derive Relativity, Einstein had to reject existing axioms and create a new "manifold"—a shorter, more elegant representation of the universe. This requires moving from association (correlation) to intervention and counterfactuals (causation). While LLMs are excellent at the "Shannon part" of intelligence—processing vast amounts of data to find connections—they lack a causal model that allows for true simulation and the creation of new conceptual frameworks. ### Future Directions The discussion concludes with a roadmap for the next generation of AI research. Misra argues that simply scaling models with more data and compute will not lead to AGI. Instead, research must focus on two parallel tracks: * **Continual Learning:** Developing mechanisms for true plasticity that allow models to retain new information without "catastrophic forgetting" or the need for constant retraining. * **Causal Modeling:** Moving the architecture from correlation to causation, potentially utilizing Judea Pearl’s "do-calculus" and causal hierarchy. By solving these architectural challenges, AI may eventually move beyond being a sophisticated reflection of its training data to becoming a system capable of genuine discovery and simulation.