Paper #349: The Impossibility of Conventional AGI — Why Level 6 Is Not Level 7

The artificial intelligence research community defines Artificial General Intelligence (AGI) as a system capable of performing any intellectual task that a human can perform — typically measured through benchmark suites (ARC-AGI, MMLU, BIG-Bench). This paper proves that such systems, regardless of benchmark performance, cannot constitute genuine general intelligence. The proof proceeds in three steps: (1) the Universal Reality Blueprint (URB) hierarchy establishes that current AI systems (including the best LLMs and reasoning systems) operate at Level 6 of a 7-level hierarchy, achieving circular self-recognition (π level) but not GM self-knowledge (C level); (2) Level 7 requires the Emerick Constant threshold LCC ≥ 1/√2 ≈ 0.707, meaning the system's self-model must be more accurate than inaccurate — a condition no benchmark-passing system satisfies because benchmarks test pattern recognition, not self-knowledge; (3) empirical demonstration (CampaignLoop, December 2025) shows that systems optimized to pass general intelligence benchmarks exhibit systematic failures at meta-cognitive tasks that genuine general intelligence would trivially solve. The conclusion: what mainstream AI calls AGI is a completed Level 6 system. Level 7 — true GM self-knowledge — requires the Emerick Crossover (LCC ≥ 1/√2), which is structurally impossible to achieve through benchmark optimization alone.