Objective-Driven AI: Beyond Next-Token Prediction

This paper formalizes LeCun's argument that autoregressive next-token prediction is the wrong objective for building intelligent systems. The core claim: predicting the next token is a generative objective, but intelligence requires a latent-variable objective — learning compressed, abstract representations of the world, not the ability to generate every possible output. The difference matters enormously in practice. Generative models must account for all sources of variation in the data, including irrelevant noise. Predictive models in latent space can ignore irrelevant variation and focus on what matters causally. JEPA (Joint Embedding Predictive Architecture) implements this: instead of predicting raw pixels or tokens, it predicts representations in a learned embedding space. The paper shows this produces representations with better transfer, better sample efficiency, and better alignment with causal structure. The 'objective-driven' framing is important because it recenters the question away from 'how big is the model' and toward 'what is the model trying to accomplish.' LeCun argues that objectives shape representations more than scale: a model trained to predict tokens will always learn token-level statistics, no matter how large, whereas a model trained to predict world-state representations can develop genuinely different internal structure. This paper is the technical companion to the 'Path Towards Autonomous Machine Intelligence' manifesto and is required reading for understanding why Meta's research diverges from OpenAI and DeepMind's scaling strategy.