AGI Hits a Structural Wall – A Billion-Dollar Problem
This paper formally defines where current AGI hits a structural wall — not a technical one. 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.…
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This paper formally defines where current AGI hits a structural wall — not a technical one. 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.
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