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.