According to @ch402, once computation is modeled, traders must question whether the modeled system truly performs the same as the original, highlighting non-equivalence risk when relying on replicated AI systems for signals, backtests, or execution logic; source: @ch402, X, Aug 8, 2025. For crypto and algorithmic trading, this underscores model risk management needs where divergence between a proxy model and its reference can impair execution quality, PnL attribution, and robustness of AI-driven strategies; source: @ch402, X, Aug 8, 2025.