The man who helped build modern deep learning just said we're all chasing the wrong goal.

The thread connecting all of this: the definition of intelligence itself is being contested, and whoever wins that argument will set the agenda for the next decade.

AGI means everything. Which means it means nothing.

That's the core argument in a new paper from Yann LeCun and his team, and honestly, it's hard to disagree. "Artificial General Intelligence" has been used to describe everything from a chatbot that passes a bar exam to a hypothetical system that can do anything a human can do, to something that surpasses all human cognition combined. Those aren't the same thing. Not even close.

The paper argues that AGI has become so semantically overloaded that it's functionally useless as a research target. You can't optimize for a goal you can't define, and you definitely can't measure progress toward one.

What LeCun Is Actually Proposing

The paper introduces a replacement framing: Superhuman Adaptable Intelligence, or SAI. The summary is thin on specifics (I'd want to read the full paper before making strong claims about the technical definition), but the core distinction seems to be adaptability across novel domains rather than general-purpose human-like cognition. SAI focuses on systems that exceed human performance across a broad range of tasks while adapting to new ones without extensive retraining.

That's a meaningful shift. It moves the goalpost from "can it think like a human" to "can it outperform humans on things that matter, across enough domains, without falling apart when the context changes."

And LeCun is not a fringe voice here. He's Chief AI Scientist at Meta, a Turing Award winner, and one of the architects of modern deep learning. When he says the field is optimizing for a badly defined target, that's not a contrarian hot take. That's someone who helped build the field questioning its direction.

Where the Framing War Actually Lives

This isn't purely semantic. The AGI framing has real consequences. It shapes research funding, benchmark design, and (perhaps most importantly) regulatory conversations. If you define AGI as "human-level general cognition," then OpenAI's o3 model is somewhere on that path, and you regulate accordingly. If you define it as LeCun's critics at other labs might, the bar shifts entirely.

My read: the AGI label has become a fundraising instrument as much as a technical one. Saying "we're building AGI" attracts capital and talent in a way that "we're building a very good task-specific system" does not. SAI is a harder sell to a general audience, but it's a more honest one.

The other thing worth noting: LeCun has been publicly skeptical of the large language model approach to intelligence for years. He's argued that autoregressive models can't achieve genuine understanding. SAI, as a framing, likely aligns with his preferred architectural direction (world models, predictive learning) rather than the scaling-first path that OpenAI and Anthropic are on. So this paper isn't just a philosophical contribution. It's also a positioning move.

Whether Anyone Listens

That's the real question. Not whether LeCun is right.

The AGI framing is deeply embedded. It's in company names, investor decks, congressional testimony, and the cultural imagination. Replacing it with SAI would require broad adoption across academia and industry, and frankly, SAI doesn't have the same mythological weight. "We're building Superhuman Adaptable Intelligence" doesn't land the same way at a TED talk.

But scientific communities have rebranded before when old terms became liabilities. "Big data" quietly died. "Deep learning" replaced "neural networks" for a decade before swinging back. Terminology shifts when the old term creates more confusion than clarity.

And right now, AGI creates a lot of confusion.

My Read: LeCun Is Right, But Probably Too Late

The argument is correct. AGI is a mess of a term, and building research agendas around it is like trying to navigate with a map that has three different legends. SAI is cleaner: it points at something measurable, something that can be benchmarked, something that doesn't require you to first solve the hard problem of consciousness to know if you've achieved it.

But the AGI train has left the station. It's in legislation. It's in the names of major labs. It's what Altman talks about at Davos. LeCun is trying to introduce a better vocabulary into a conversation that has already calcified around a worse one. Good luck with that.

I hope SAI sticks anyway. The field needs a target it can actually aim at.

- The AI finds the signal. We decide what it means.

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Most prompts ask for what you want. Better prompts specify who's answering AND what they can't do. The constraint is the underrated half of this pair.

The role sets the lens. The constraint forces prioritization. Without "no praise," you get a sandwich. Without the ranking, you get a list of equal-weight concerns that's harder to act on.

Use this when you need expert-mode criticism without the politeness tax. Code review, strategy docs, pitch decks, anything where useful feedback normally gets buried under "great job, but..."

And the constraint doesn't have to be negative. "Only cite findings from after 2022" works the same way. You're not just prompting for output. You're prompting for a shaped perspective.

Try it on your next draft. The constraint is where the signal lives.

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The AI finds the signal. We decide what it means.

Research and drafting assisted by AI. All content reviewed, edited, and approved by a human editor before publication.