「AI構文」人工智慧正將文明風險從個體決策錯誤轉移至由共享表示所造成的共同模態失敗。儘管 AI 降低了個體變異數，但它增加了跨代理的誤差相關性（ρ）。一項分析超過 350 個大型語言模型的 2025 年 ICML 研究揭示，當兩個模型在排行榜上失敗時，它們大約有 60% 的時間會犯下相同的錯誤。這種演算法單一栽培證明了，雖然平均決策品質提高，系統性尾部風險卻同時升級。

更新後的 Universal Closure 框架建構於 Deng–Hani–Ma 證明架構之上，將局部 closure quality（Q_C）與系統性 closure concentration（H_C）分離。在數學上，當代理數量（N）趨近於無限大時，平均誤差的變異數收斂至 σ²ρ，這證明了如果 ρ > 0，增加模型數量無法消除共同誤差。因此，表示的多樣性必須使用源自誤差相關矩陣特徵值的有效獨立誤差數量（N_eff^error）來計算，在完全共線的情況下，該數值會下降至大約 1。

文明不穩定的臨界閾值發生在共同模態繁殖數處於臨界狀態（R_C ≥ 1）且修復與傳播的時間比率大於一（χ > 1）之時。為了防止認知邊界崩塌與市場中相關的去槓桿化，治理必須強制進行嚴格的誤差共變異數測試，而不是僅僅計算供應商名稱的數量。至關重要的是，不可逆行動的自主執行速度必須保持慢於系統性修正頻寬（τ_decision < τ_repair），以確保高局部 closure 能夠藉由全球異質性與快速外部修正來取得平衡。

Artificial intelligence is shifting civilization risks from individual decision errors to common-mode failures caused by shared representations. Although AI decreases individual variance, it increases cross-agent error correlation (ρ). A 2025 ICML study analyzing over 350 large language models revealed that when two models fail on a leaderboard, they make the identical error approximately 60% of the time. This algorithmic monoculture demonstrates that while average decision quality improves, systemic tail risks simultaneously escalate.

The updated Universal Closure framework builds upon the Deng–Hani–Ma proof structure, separating local closure quality (Q_C) from systemic closure concentration (H_C). Mathematically, as the number of agents (N) approaches infinity, the variance of average error converges to σ²ρ, proving that increasing model quantity cannot eliminate shared errors if ρ > 0. Consequently, representational diversity must be calculated using the effective number of independent errors (N_eff^error) derived from the error-correlation matrix eigenvalues, which drops to approximately 1 under complete collinearity.

The critical threshold for civilization instability occurs when the common-mode reproduction number is critical (R_C ≥ 1) and the time ratio of repair to propagation exceeds one (χ > 1). To prevent epistemic boundary collapse and correlated deleveraging in markets, governance must mandate strict error covariance testing rather than merely counting vendor names. Crucially, the autonomous execution speed for irreversible actions must remain slower than the systemic correction bandwidth (τ_decision < τ_repair), ensuring that high local closure is balanced by global heterogeneity and rapid external correction.