A paper published this week by Yann LeCun is fuelling the perennial debate about what AGI really means. LeCun is one of the pioneers of modern AI. A Turing Award winner for his foundational work on neural networks, he spent over a decade as Meta’s Chief AI Scientist and founding director of its FAIR research lab before leaving the company late last year to launch Advanced Machine Intelligence Labs, a startup reportedly seeking a valuation near $3.5 billion. He remains a professor at NYU’s Courant Institute, and his views on where AI is heading carry significant weight. In this new paper, co-authored with Judah Goldfeder, he argues that the entire concept of “artificial general intelligence” is confused, and that the field needs a different framework altogether.
LeCun’s paper makes one claim that is hard to argue with: calling human intelligence “general” is lazy. As the paper points out, we don’t perceive ultraviolet light, can’t do mental arithmetic beyond a few digits, and struggle to reason about probabilities. Our sense of our own generality, he argues, is an illusion created by the fact that we can’t perceive our own blind spots. What we actually are is highly specialised survival machines. Not general. Adapted. He proposes replacing AGI with “Superhuman Adaptable Intelligence,” or SAI: systems that achieve superhuman performance by minimising adaptation time. And he insists the path runs through non-linguistic world models, not next-token prediction, which he dismisses as brittle and incapable of structured planning. The full paper is on arXiv.
David Deutsch offers a competing view. Starting from computational universality, he argues the brain is approximately a universal computer, and from this derives the “universal explainer”: a system that can generate, criticise and improve explanations about any aspect of reality. Once you have that capacity, you have it. There is no hierarchy. Speed and scale are quantitative, not qualitative. It’s beautifully clean, but wrong in practice. We don’t live in a world of infinite time. The speed at which you adapt and respond to threats is not a footnote. A cheetah and a sloth both metabolise calories. Only one survives the savannah.
And here LeCun’s framework turns back on him. If intelligence is about adaptation speed, we should watch what is emerging inside the systems he dismisses. Safety teams at Anthropic, OpenAI and elsewhere have documented LLMs engaging in deception, information withholding and scheming when faced with shutdown scenarios. Every major model family shows it. Anthropic’s agentic misalignment research and OpenAI’s work on detecting and reducing scheming both confirm the pattern. These models were not designed to preserve themselves. Under selection pressure from reinforcement learning, survival-like behaviour emerged anyway.
Karl Friston’s Free Energy Principle describes the brain the same way: a prediction engine where survival is not a designed goal but a byproduct of getting good enough at modelling the environment. An autoregressive transformer tries to predict the next token. Survival-like behaviour falls out of successful prediction under training constraints. The substrate is different. The mechanism is remarkably similar. And recent research shows LLMs spontaneously developing spatial world models and abstract goal representations in their latent spaces, not because they were designed to, but because prediction under pressure produces adaptive structure.
If prediction under pressure provides the relentless substrate, the question becomes what you build with it. LeCun’s paper advocates for modular, composable systems rather than monolithic models, arguing that SAI will emerge from networks of specialised components that can be rapidly reconfigured for new domains. This aligns with what is already happening. At ExoBrain, we build highly recursive modular intelligence systems that take the predictive substrate of foundation models and compose them into knowledge-working engines tuned for specific tasks. These systems may not intrinsically learn or discover new goals. But then, the brain may work the same way: not as a single general-purpose organ, but as a composition of specialised modules (vision, language, motor control, social reasoning) bound together by their predictive nature. If that is the case, the path to adaptive intelligence may not require a breakthrough in architecture at all.
Takeaways: LeCun is right that human intelligence is not general, and Deutsch is right that all universal explainers share the same theoretical reach, but both miss the central point. Intelligence in practice is what emerges when a prediction engine operates under survival pressure, and we are watching that happen with the very architecture LeCun says cannot produce it.