Have you ever felt like you're running a race, but nobody can agree on where the finish line is?
That's pretty much what's happening in the world of AI right now, according to one of its godfathers, Yann LeCun. He and his team just published a paper that basically throws a bucket of cold water on the industry's biggest buzzword: AGI, or Artificial General Intelligence.
Their argument is simple but powerful: What if we're all optimizing for a goal that we can't even properly define? It’s a thought that really stops you in your tracks. For years, AGI has been this mythical endpoint, but if you ask ten different experts what it means, you’ll probably get ten different answers. LeCun’s team argues that this makes it a pretty weak target for serious research.
So, they're proposing we change the finish line entirely. Let's break down what they're saying, because it could genuinely shift how we think about the future of AI.
First Off, Is Human Intelligence Really "General"?
Before we even get to the "A" in AGI, LeCun’s paper pokes a hole in the "G" for "General." We tend to use human intelligence as the gold standard for what a "general" intelligence should look like. But is it, really?
Think about it. We humans are fantastic at the things we needed to be fantastic at to survive. We can see, walk, talk, plan a trip to the grocery store, and figure out social situations. We seem "general" because we're judging from inside our own little biological bubble.
But step outside that bubble, and our "general" intelligence looks a lot more specialized. You can't calculate massive prime numbers in your head, but your phone can. You can't see in infrared or predict protein folding with perfect accuracy. In many, many domains, machines are already superhuman.
The point isn't to knock ourselves down a peg. It's that our intelligence is more adaptable within our specific context than it is truly universal. And that distinction is a really big deal.
The Messy, Confusing World of AGI Definitions
If our own intelligence isn't a great template for "general," then what are we even aiming for with AGI?
This is where things get messy. LeCun and his co-authors point out that nobody in the industry or academia has a stable, agreed-upon definition.
- Some people say AGI is an AI that can do anything a human can do.
- Others define it by its economic usefulness—can it do most jobs?
- Still others focus on its ability to reason or learn new things on its own.
These aren't the same thing! An AI that’s economically useful might not be able to have a deep philosophical conversation. An AI that can do every human task might not be good at tasks outside our experience. It's like trying to build a vehicle without knowing if it's supposed to be a car, a boat, or a plane. You end up with a weak design that doesn't excel at anything.
A Better Goal: Meet Superhuman Adaptable Intelligence (SAI)
So, if AGI is a fuzzy target, what should we aim for instead? The paper introduces a new concept: Superhuman Adaptable Intelligence, or SAI.
This is more than just a new acronym. It’s a fundamental shift in thinking. Here’s the core idea:
An SAI is an intelligence that can learn to become superhuman at any task a human can do, while also being able to learn useful tasks that are completely outside the human skill set.
See the difference? It’s subtle but crucial. It’s not about a system that already knows everything. It’s about a system that can learn anything—and learn it well. The focus moves from a static inventory of skills to the dynamic process of learning.
Why Learning Speed Is the New Superpower
This reframes the entire problem in a much more practical, engineering-friendly way. Instead of trying to check off a massive, ever-growing list of "things an AI should do," we should measure something else: adaptation speed.
How quickly can an AI acquire a new skill? How fast can it master a new domain when it encounters one?
This feels so much more intuitive. Think about the smartest people you know. It's not just about the facts they have memorized; it's about how quickly they can pick up a new instrument, understand a new scientific concept, or learn the rules of a new game. Their intelligence is defined by their ability to adapt.
LeCun’s paper argues that this should be our North Star for AI. Adaptability, not some vague notion of "generality," is what we should be building toward.
Specialization Isn't a Bug, It's a Feature
Here’s another big idea from the paper that I love: the notion that we should stop chasing a single, monolithic AI model that does everything perfectly.
In the real world, high performance comes from specialization. You don't ask your heart surgeon to also file your taxes and fix your car. Excellence requires focus. Why would we expect AI to be any different?
The paper suggests that future intelligent systems will likely be a collection of specialized models and components that work together. It’s not about one AI to rule them all. It’s about having a diverse, hierarchical system where different parts are good at different things. This just feels more grounded in reality.
So, How Do We Get There?
If fast adaptation is the goal, how do we build systems that can do it? The paper points to a couple of key areas.
1. Self-Supervised Learning is Key
If you want an AI to learn quickly, you can't always rely on giving it perfectly labeled data for every single task. There just isn't enough of it in the world, and creating it is slow and expensive.
This is why the paper champions self-supervised learning (SSL). This is a way for models to learn the underlying structure of the world directly from raw data—like video or audio—without needing a human to label everything. It's more like how a baby learns, by observing and interacting with the world. SSL allows a model to build a foundational understanding that it can then use to adapt to new tasks much, much faster.
2. We Need Better "World Models"
The paper also argues that just predicting the next word in a sentence or the next pixel in an image isn't enough to create truly adaptable intelligence.
To really thrive, especially in the physical world, an AI needs an internal "model" of how the world works—a sort of intuitive physics engine. This world model allows the AI to understand cause and effect, to simulate potential outcomes of its actions, and to plan ahead. This is what enables it to adapt to a new situation with just a few tries (or even zero!), because it can "imagine" what might happen.
A Warning Against Sticking to What We Know
Finally, the paper offers a gentle but firm critique of the current AI scene. Right now, autoregressive Large Language Models (LLMs) are dominating everything. They’re incredibly powerful, but they’re not the only architecture out there.
LeCun and his team warn against this "architectural monoculture." By focusing so heavily on one type of model, we risk getting stuck in a local maximum and missing out on other, potentially more powerful, approaches. They point out that today's LLMs have known weaknesses, like their tendency to make stuff up or lose track during long conversations.
Their message isn't that LLMs are bad. It's that we should keep exploring. We should be building and testing all kinds of different ideas, because the final answer for truly intelligent systems probably hasn't been invented yet.
And honestly, that’s an exciting thought. It means the biggest discoveries are still ahead of us. By redefining the finish line from the fuzzy goal of AGI to the much clearer target of SAI, we might just get there a whole lot faster.
