Energy-Based Models: A Foundation for the Next Generation of AI

LeCun's tutorial and framework for energy-based models (EBMs) is one of the most underappreciated contributions in modern deep learning. EBMs define a scalar energy function over configurations of variables — low energy means compatible/likely, high energy means incompatible/unlikely. The model doesn't need to produce a probability distribution over all possible outputs; it just needs to assign low energy to correct answers and high energy to wrong ones. This is more general than maximum likelihood training (which forces models to assign probabilities to everything, including things that should be impossible) and more stable than adversarial training (GANs). LeCun argues EBMs are the natural framework for JEPA and world models because a world model doesn't need to predict everything that could happen — it needs to assign low energy to states of the world that are consistent with observed data and constraints. This connects to his argument against autoregressive models: LLMs use maximum likelihood, which forces them to put probability mass everywhere, leading to confabulation. An EBM-style world model can be 'skeptical' — assigning very high energy (low plausibility) to claims that contradict observed world structure. The tutorial is long (70 slides + notes) but represents LeCun's clearest technical statement of why the next generation of AI needs EBMs or JEPA-style objectives rather than generative objectives.