LeCun on Scaling Laws: Why More Parameters Won't Get You to AGI

LeCun's most direct engagement with scaling law proponents (Gwern, Kaplan, Hoffman, and implicitly OpenAI's research direction). The scaling laws finding — that LLM performance improves predictably with compute, data, and parameters — is real and empirically solid. LeCun's argument is not that scaling laws don't hold, but that they hold within a regime that doesn't lead to intelligence. A key analogy he uses repeatedly: building a taller building won't get you to the moon. You can keep scaling next-token prediction, and performance on NLP benchmarks will keep improving, but you won't get a system that has a world model, plans under uncertainty, or reasons causally — because the objective doesn't incentivize those capabilities. The implication for AI labs: the bet on scaling is a bet that intelligence emerges as an epiphenomenon of next-token prediction at sufficient scale. LeCun thinks this is a fundamentally wrong hypothesis, and that we will need architectural innovation (JEPA, world models, modular architectures) rather than just more compute. He notes that the companies who have bet most heavily on scaling have strong incentive to believe scaling works, which should make us appropriately skeptical of their assessments. This post generated significant pushback from scaling-law proponents and remains one of the most substantive public technical debates in contemporary AI.