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AI Must Embrace Specialization via Superhuman Adaptable Intelligence
AI Must Embrace Specialization via Superhuman Adaptable Intelligence
Judah Goldfeder Philippe Wyder Yann LeCun Ravid Shwartz-Ziv
Abstract
Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact definition. One common definition of AGI is an AI that can do everything a human can do, but are humans truly general? In this paper, we address what's wrong with our conception of AGI, and why, even in its most coherent formulation, it is a flawed concept to describe the future of AI. We explore whether the most widely accepted definitions are plausible, useful, and truly general. We argue that AI must embrace specialization, rather than strive for generality, and in its specialization strive for superhuman performance, and introduce Superhuman Adaptable Intelligence (SAI). SAI is defined as intelligence that can learn to exceed humans at anything important that we can do, and that can fill in the skill gaps where humans are incapable. We then lay out how SAI can help hone a discussion around AI that was blurred by an overloaded definition of AGI, and extrapolate the implications of using it as a guide for the future.
One-sentence Summary
Arguing that the concept of AGI is flawed, the authors advocate for AI to specialize toward superhuman performance, introducing Superhuman Adaptable Intelligence (SAI), defined as intelligence that learns to exceed humans at any important task and fills skill gaps where humans are incapable, to refocus AI discourse and guide future development.
Key Contributions
- This work introduces Superhuman Adaptable Intelligence (SAI), defined as intelligence that can learn to exceed humans at any important task they can perform and fill skill gaps where humans are incapable, recasting the goal of AI away from ill-defined human-level generality.
- The paper argues that progress toward SAI demands specialization and architectural diversity, proposing that systems composed of specialized modules, predictive world models, and self-supervised latent prediction (as in Dreamer 4, Genie 2, or JEPA) provide a more effective path than monolithic autoregressive models.
- It reorients evaluation away from static human-centric benchmarks and toward measurable adaptation dynamics—the speed and efficiency of skill acquisition under realistic resource constraints—as a more concrete North Star.
Introduction
The pursuit of Artificial General Intelligence (AGI) shapes much of today’s AI narrative, but the term remains overloaded with inconsistent definitions, fueling polarized debate and muddying research direction. Prior notions of AGI typically treat human intelligence as the benchmark of generality, overlooking evidence that humans are specialized adapters rather than universal problem solvers; definitions that aim for true generality clash with the No Free Lunch theorem, and human-centric yardsticks ignore valuable tasks outside human ability. The authors argue that this flawed framing obscures progress, and they propose Superhuman Adaptable Intelligence (SAI) as a clearer North Star: an intelligence measured by how rapidly it can adapt to exceed human performance on any important task, whether or not humans can do it. This shift foregrounds specialization, self-supervised learning, and modular world models over the quest for a single generalist system.