Your best performances will come when you are working in a way that is a full expression of you. The work becomes a natural display of your personality. This is when you not only get better results, but also love the activity — because in doing the craft, you feel alive.

New paper out: AI Must Embrace Specialization via Superhuman Adaptable Intelligence With @JudahGoldfeder, Philippe Wyder, and @ylecun . There is quite a lot of buzz on our paper, so here is my take. Everyone's talking about AGI, but nobody agrees on what it means, and that confusion is actively hurting the field. We surveyed the most prominent definitions and mapped them along two axes: the kind of capability they refer to (learning vs. doing) and the scope (anything, anything important, anything humans can do). The result is a landscape of definitions that don't just disagree, but they're often internally inconsistent. Our starting point is simple: human intelligence is not general. We are specialized creatures, shaped by evolution to excel at a narrow set of tasks critical for survival. We feel general because we can't see our own blind spots. Magnus Carlsen is the greatest human chess player ever, but compared to what's computationally achievable, he's not actually good at chess. That's not a knock on Magnus. It's a statement about the limits of human adaptation, and why anchoring AI's North Star to human-level performance is the wrong move. We propose the term Superhuman Adaptable Intelligence (SAI), or, in other words, intelligence that can learn to exceed humans at anything important we can do and can also tackle tasks entirely outside the human domain. The metric isn't a growing checklist of benchmarks. It's adaptation speed: how fast can a system acquire a new skill? This has concrete implications for how we build. SAI points toward self-supervised learning for acquiring generic knowledge from unlabeled data, and world models for planning and zero-shot transfer. It also pushes back against the current monoculture of autoregressive architectures, because specialization demands architectural diversity, not one paradigm to rule them all. Or as we put it: the AI that folds our proteins should not be the AI that folds our laundry. This paper grew out of a conversation with Yann on our The Information Bottleneck podcast, which led to a public exchange with @elonmusk and @demishassabis on X (not every paper can cite a Twitter feud as source material).