AI 读懂了所有规则，公司却要重新理解自己

TL;DR - An essay arguing that when an AI agent correctly reads all of a company's conflicting rules (refund policy, accounting state, sales promise, risk-control threshold), it exposes organizational contradictions rather than knowledge gaps — forcing firms to re-specify their own authority, priorities, and accountability structures.

• AI shifts error structure, not error existence: it can reduce lookup/omission errors, surfaces pre-existing latent conflicts, adds new failure modes (fabrication, misread context, overconfidence), and amplifies bad definitions/metrics at software speed; shared models also correlate previously independent judgments.
• The right baseline is the post-AI human-machine system vs. today's real people/processes — measured on error severity, detectability, reversibility, correlated blast radius, who bears the cost, and business outcome, not just average accuracy.
• Data alone is insufficient: an "enterprise ontology" must encode object states, permitted actions, evidence thresholds, who executes/approves/grants exceptions, reversibility, and completion criteria, plus which value gets protected when commitments conflict.
• History is evidence, not authorization — logs record only the chosen branch (no counterfactuals); the proposed remedy is "decision memory" (evidence, rejected options, uncertainty, actual outcomes), with rollout staged: advisory-only first (tracking why humans reject suggestions), then low-risk reversible actions automated.
• Cites Brynjolfsson/Li/Raymond (5,179 agents, ~14% resolutions/hour, larger gains for novices), Dell'Acqua's "jagged frontier" (758 workers, worse accuracy past the capability boundary), Dietvorst's algorithm aversion, and a 776-professional experiment where AI-assisted individuals approached two-person team performance.