[2602.23643] AI Must Embrace Specialization via Superhuman Adaptable Intelligence

-->

Computer Science > Artificial Intelligence

arXiv:2602.23643 (cs)

[Submitted on 27 Feb 2026]

Title:AI Must Embrace Specialization via Superhuman Adaptable Intelligence

Authors:Judah Goldfeder, Philippe Wyder, Yann LeCun, Ravid Shwartz Ziv<br>View a PDF of the paper titled AI Must Embrace Specialization via Superhuman Adaptable Intelligence, by Judah Goldfeder and 3 other authors

View PDF<br>HTML (experimental)

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.

Subjects:

Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2602.23643 [cs.AI]

(or<br>arXiv:2602.23643v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2602.23643

Focus to learn more

arXiv-issued DOI via DataCite

Submission history<br>From: Judah Goldfeder [view email]<br>[v1]<br>Fri, 27 Feb 2026 03:26:21 UTC (429 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled AI Must Embrace Specialization via Superhuman Adaptable Intelligence, by Judah Goldfeder and 3 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context: cs.AI

next >

new<br>recent<br>| 2026-02

Change to browse by:

cs

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)