What if the AI industry is optimizing for a goal that cannot be clearly defined or reliably measured? That is the central argument of a new paper by Yann LeCun, and his team, which claims that Artificial General Intelligence has become an overloaded term used in inconsistent ways across academia and industry. The research team argued that because AGI lacks a stable operational definition, it has become a weak scientific target for evaluating progress or guiding research.

Why Human Intelligence Is Not Truly ‘General‘

The research team in the paper starts by challenging a common assumption behind many AGI discussions: that human intelligence is a meaningful template for’ ‘general’ intelligence. The research team argue that humans only appear general because we evaluate intelligence from inside the task distribution shaped by human biology and survival. We are good at the kinds of tasks that mattered for our existence, such as perception, motor control, planning, and social reasoning. But outside that range, human ability is limited, and in many cases machines already outperform us. The research paper’s point is not that humans are narrow in every sense, but that human intelligence is better understood as specialized and adaptable rather than general in any universal sense.

The Problem With Human-Centered AGI Definitions

That distinction matters because many AGI definitions quietly inherit a human-centered benchmark. The research team argues there is no real consensus on what AGI means across academia or industry. Some definitions focus on doing everything a human can do. Others focus on economic usefulness, broad task competence, open-ended reasoning, or the ability to learn. These are not equivalent definitions, and they do not produce one clean evaluation target. The research team therefore argue that existing AGI definitions are insufficient because they are often ambiguous, difficult to assess, or not truly general once examined closely.

The Shift From AGI to SAI

The research paper’s alternative is Superhuman Adaptable Intelligence, or SAI. It defines SAI as intelligence that can adapt to exceed humans at any task humans can do, while also adapting to useful tasks outside the human domain. That is a subtle but important shift. Instead of asking whether a system already matches humans across a fixed checklist of tasks, the research team asks how quickly the system can learn something new and how broadly it can continue adapting. In this framework, the key metric is adaptation speed: the speed with which an agent acquires new skills and learns new tasks.

Why Adaptation Speed Matters More Than Static Benchmarks

This reframes the problem in a more engineering-friendly way. A benchmark based on a growing catalog of tasks becomes messy fast; the space of possible skills is effectively unbounded. The research team argued that evaluating intelligence as a static inventory of competencies is the wrong abstraction. What matters more is whether a system can specialize rapidly when it encounters a new domain, new objective, or new environment. That is why the research paper treats adaptability, rather than generality, as the better North Star.

Specialization as a Feature, Not a Failure

A second major claim in the research paper is that AI progress should not be framed as a march toward one universal model that does everything equally well. The research team argued that specialization is not a weakness of intelligence but a practical route to high performance. Humans themselves are not a counterexample; they are part of the evidence. The research paper suggests that future AI systems will likely need internal specialization, hierarchy, and diversity across models and modalities rather than a single monolithic system. In plain terms, the research paper argues that one model should not be expected to master all domains with equal efficiency just because current marketing language likes the word ‘general.’

Why the Research Paper Points to Self-Supervised Learning?

From there, the research paper connects SAI to self-supervised learning. The logic is straightforward. If the goal is fast adaptation across a very large task space, then relying only on supervised learning becomes limiting because supervised methods assume access to large, reliable labeled datasets. In real settings, that assumption often fails. The research team argues that self-supervised learning is a promising pathway because it can exploit structure in raw data and has already driven strong results across domains. Importantly, they do not claim that SAI requires one specific architecture. They present self-supervised learning as a promising route, not a final architectural answer.

World Models and the Limits of Surface-Level Prediction

The research paper also argues that strong adaptation likely benefits from world models. Here the research team move away from the idea that token-level or pixel-level prediction alone is enough for robust intelligence in the physical world. They argue that what matters is learning compact representations that capture system dynamics. In that view, a world model supports simulation and planning, which in turn support zero-shot and few-shot adaptation. The research paper points to latent prediction architectures such as JEPA, Dreamer 4, and Genie 2 as examples of the kind of direction the field should explore, while again stating that SAI does not dictate a single architecture.

A Warning Against Architectural Monoculture

The research team also criticize the current level of architectural homogeneity in advanced AI. They note that autoregressive LLMs and LMMs dominate the ‘general’ AI landscape in part because shared tooling and benchmarks create momentum. But the research paper argues that this concentration narrows the search space and can slow progress. It further claims that autoregressive systems have well-known weaknesses, including error accumulation over long horizons, which makes long-horizon interaction brittle. Their broader point is not that current large models are useless. It is that the field should avoid treating one successful paradigm as the final template for intelligence.

Key Takeaways

• The research paper argues AGI is not a precise scientific target: According to the research team, AGI is used inconsistently across academia and industry, making it difficult to define, measure, or use as a stable research goal.
• Human intelligence should not be treated as the definition of ‘general’ intelligence: The research paper argues humans appear general only within the task space shaped by biology and survival, but outside that range, human capability is limited.
• The research team propose Superhuman Adaptable Intelligence (SAI) as a better target: SAI is defined around the ability to adapt beyond human performance on human tasks and also learn useful tasks outside the human domain.
• Adaptation speed is more important than static benchmark breadth: Instead of asking whether a system already knows many tasks, the research paper focuses on how quickly it can acquire new skills and adapt to new environments.
• The research paper favors specialization, self-supervised learning, and world models over one monolithic path to intelligence: The research team argued that future AI systems will likely need internal specialization and strong world modeling, rather than assuming one universal architecture will solve everything.

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