The Future of AI Research: LeCun's 2025 Roadmap for the Next Decade

LeCun's forward-looking statement on where AI research needs to go in the next decade — delivered as a keynote at NeurIPS 2025 and expanded into a blog post. The roadmap identifies five research priorities: (1) Scalable world model learning from multimodal data (video, audio, language, and sensorimotor signals) without manual annotation; (2) Hierarchical planning systems that can decompose long-horizon goals into achievable subgoals using a learned world model; (3) Sample-efficient few-shot adaptation — human-level performance on novel tasks from very few examples, analogous to ARC-AGI; (4) Grounded language understanding — models that associate language with real-world referents through multimodal learning, not just statistical patterns in text; (5) Safe exploration — AI agents that can learn from interacting with real environments without requiring unrestricted access to potentially dangerous action spaces. LeCun explicitly frames this as a 10-year roadmap, not a 2-year product timeline. He's deliberately separating the research agenda from commercial deployment pressures, arguing that the field's current problem is too much focus on near-term product development at the expense of foundational research that will enable the next generation of AI. The roadmap is the clearest statement of where Meta AI Research is investing and why, and it positions JEPA and world models as the foundational bet underlying all five priorities.