It shows that no amount of scaling, reinforcement learning, or recursive optimization will break through three deep epistemological and formal constraints:

1. Semantic Closure — An AI system cannot generate outputs that require meaning beyond its internal frame.

2. Non-Computability of Frame Innovation — New cognitive structures cannot be computed from within an existing one.

3. Statistical Breakdown in Open Worlds — Probabilistic inference collapses in environments with heavy-tailed uncertainty.

These aren’t limitations of today’s models. They’re structural boundaries inherent to algorithmic cognition itself — mathematical, logical, epistemological.

But this isn’t a rejection of AI. It’s a clear definition of the boundary condition that must be faced — and, potentially, designed around.

If AGI fails at this wall, the opportunity isn’t over — it’s just starting. For anyone serious about cognition, this is the real frontier.

Full paper:

https://philpapers.org/rec/SCHTAB-13

Open to critique, challenge, or counterproofs.