AGI Is Overhyped — And Nobody Can Explain Why It’s Impossible. CMA Can

Everyone in AI seems to be working with the same definition of AGI: an AI that can do what humans do. But if you look closely, most people already know this isn’t really achievable — not in any meaningful sense.

So what’s actually going on?

It’s hype. Massive, deliberate hype.

Think of it like an emcee at a festival screaming “This is going to be the most insane night of your LIVES!” — while backstage, the crew is desperately trying to make sure the sound system doesn’t die. The gap between the pitch and the reality is just… understood. By everyone. Quietly.

The AI industry works the same way. The actual goal is something like “close enough to human” — but what gets said publicly is “AI will do everything humans can do.” Two very different claims.

And here’s what bothers me: the serious academics who push back and say “AGI is impossible” — when you actually press them, they can’t explain why. They just say “it obviously can’t.” That’s not an argument. That’s intuition wearing a lab coat.

CMA does not stop at intuition. It gives a structural reason.

The argument: AI lacks internal constraint and dependency. Human cognition shouldn’t be defined by content — what we know, what we process — but by processing conditions. The way cognition terminates, what it terminates against, is what makes it human. AI has no internal fixed points. It has no dependency structure that generates genuine processing boundaries.

So AGI, defined as “AI doing what humans do,” is structurally impossible. Not probably hard. Not currently out of reach. Impossible at the architectural level.

Now, you might be thinking: if this framework is that significant, why isn’t it everywhere?

Fair question. Here’s the problem.

Academia is siloed. Deeply, stubbornly siloed.

Cognition as a field necessarily touches philosophy, cognitive science, neuroscience, AI, psychology, sociology, behavioral and evolutionary biology — and more. But each of these disciplines has its own vocabulary, its own conceptual defaults, its own way of carving up the same ideas.

The same word means different things across fields. “Constraint” in philosophy is normative. In cognitive science it’s computational. In neuroscience it’s structural. In AI it’s technical. CMA’s concept of internal constraint and dependency cuts across all of them — but it doesn’t slot neatly into any single field’s language.

This isn’t a translation problem. It’s a re-description problem. To be legible to each field, the same structural claim has to be rebuilt in that field’s own terms. That takes time. That takes targeted work.

It’s the same dynamic you see in human social crossing — same culture, same education level, same basic vocabulary, and somehow people still talk past each other. The concepts don’t map. The assumptions don’t align.

Getting CMA to register across disciplinary lines is that problem, at scale.

Annoying.

But that’s the work.