AGI Hits a Structural Wall – A Billion-Dollar Problem

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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 its

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Website: philpapers.org/rec/SCHTAB-13Open