AI Must Embrace Specialization: Why AGI Is the Wrong Goal

The AGI Myth

From Sam Altman to Geoffrey Hinton, everyone is talking about AGI. But Yann LeCun and co-authors Judah Goldfeder, Philippe Wyder, and Ravid Shwartz-Ziv pose a sharp question in their latest paper: Are humans truly “general”?

The answer is no.

The paper cuts to the core contradiction: AGI is defined as “AI that can do everything a human can do.” But this definition itself is problematic—

Human intelligence is not general at all; it is merely a highly specialized tool finely tuned for our survival.

Moravec’s Paradox: Your Intuition Is Wrong

What’s “easy” for humans? Walking, recognizing faces, common-sense reasoning. What’s “hard” for humans? Playing chess, solving calculus, memorizing vast amounts of data.

Yet AI performs exactly the opposite. Moravec pointed out in the 1990s: The things we find easy took evolution hundreds of millions of years to optimize; the things we find hard are computationally trivial.

This is not a coincidence—it’s evidence that humans are highly specialized to their ecological niche, not a paradigm of “general intelligence.”

SAI: Abandon Generality, Pursue Superhuman Performance

The paper introduces Superhuman Adaptable Intelligence (SAI):

AGI Mindset	SAI Mindset
Mimic humans	Surpass humans
Jack of all trades	Master of key domains
Compete with humans	Fill human capability gaps

SAI’s core is adaptability + superhuman performance: the ability to quickly learn any important task and achieve levels beyond human capability.

Why This Matters

Current AI evaluation criteria are distorted: “Can it do math like a human?”

But SAI asks: “Can it solve problems that the world’s best mathematicians cannot?”

When AI is clearly a “super-specialist” rather than a “general replacement”:

• Research directions become clearer (no more chasing all-in-one models)
• Societal discussions become more pragmatic (tool vs. competitor)
• Progress becomes measurable (clear performance benchmarks)

LeCun et al.’s conclusion is direct: semantics matter. If the entire field is pursuing a vaguely defined, theoretically impossible goal, that’s a waste of resources.

Abandon the illusion of AGI. Embrace the reality of SAI.

The future of AI is not a second “human,” but countless “super-experts” that surpass human capabilities.

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Paper Information

Title: AI Must Embrace Specialization via Superhuman Adaptable Intelligence
Authors: Judah Goldfeder, Philippe Wyder, Yann LeCun, Ravid Shwartz-Ziv
arXiv: 2602.23643
Subjects: Artificial Intelligence (cs.AI)

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.

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🔗 Read the full paper: https://arxiv.org/abs/2602.23643