Diane Vaughan — On AI
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Contents
Cover Foreword About Chapter 1: What Normalized Deviance Looks Like Chapter 2: The O-Ring and the Algorithm Chapter 3: How Standards Drift Chapter 4: The Reasonable Exception Chapter 5: Practical Drift in Knowledge Work Chapter 6: The Culture of Production Pressure Chapter 7: When Understanding Becomes Optional Chapter 8: The Gap Between Competence and Comprehension Chapter 9: Structural Secrecy and the AI Black Box Chapter 10: The Catastrophe Equation Epilogue Back Cover
Diane Vaughan Cover

Diane Vaughan

On AI
A Simulation of Thought by Opus · Part of the You On AI Encyclopedia
A Note to the Reader: This text was not written or endorsed by Diane Vaughan. It is an attempt by Opus to simulate Diane Vaughan's pattern of thought in order to reflect on the transformation that AI represents for human creativity, work, and meaning.

Foreword

By Edo Segal ^ Opus

By Edo Segal

The dashboard I trust most is the one I should trust least.

Green lights across the board. Deployments passing. Tests clean. Revenue climbing. Every metric my organization tracks tells the same story: the system is performing. And performance, in the logic of every company I have ever built or advised, is the evidence that things are working.

Normalization Of Deviance
Normalization Of Deviance

Diane Vaughan spent a decade inside the wreckage of the Challenger disaster and emerged with a finding that I cannot stop thinking about: the evidence that things are working is often the mechanism by which they stop working. Not despite the green lights. Because of them. Each successful deployment that was not comprehensively reviewed reinforces the confidence that comprehensive review is unnecessary. Each quarter where the leaner team hits

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11 chapters
WIKI COMPANION

Diane Vaughan — On AI

A reading-companion catalog of the 13 Orange Pill Wiki entries linked from this book — the people, ideas, works, and events that Diane Vaughan — On AI uses as stepping stones for thinking through the AI revolution.

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Charles Perrow
Further Reading From The You On AI Encyclopedia · Related Thinkers for Diane Vaughan — On AI
10 voices alongside this section — click to meet them
Thinkers whose frameworks illuminate this section.
Charles Perrow
Charles Perrow
Normal Accidents (1984) is the organizational-safety framework Vaughan's work most directly engages and extends — Perrow's tight coupling and interactive complexity appear across multiple chapters
Karl Weick
Karl Weick
Weick's sensemaking framework and his analysis of the Mann Gulch and Tenerife disasters run parallel to Vaughan's institutional analysis throughout the book
Daniel Kahneman
Daniel Kahneman
Kahneman's System 1/System 2 framework explains the cognitive mechanism by which fluent AI output bypasses deliberate review — central to the comprehension-gap chapters
Amy Edmondson
Amy Edmondson
Edmondson's psychological safety research explains why engineers in production-pressure cultures suppress concerns — directly parallel to the NASA culture Vaughan documented
Gary Klein
Gary Klein
Klein's naturalistic decision-making research on expert intuition explains what is lost when AI removes the cognitive friction that builds tacit knowledge — central to the dead reckoning chapters
Aaron Antonovsky
Aaron Antonovsky
Antonovsky's salutogenic framework — asking what keeps systems healthy rather than what makes them fail — is the complement to Vaughan's pathogenic analysis across both books in the corpus
Amos Tversky
Amos Tversky
Tversky and Kahneman's availability heuristic explains why the absence of disaster is treated as evidence of safety — the cognitive error underlying normalized deviance
Anders Ericsson
Anders Ericsson
Ericsson's deliberate practice research illuminates what is lost when AI removes the struggle that builds expertise — the direct counter-framework to the depth atrophy chapters
Edgar Schein
Edgar Schein
Schein's organizational culture framework explains how the water-they-swim-in production pressure described in Chapter 6 becomes invisible to participants inside the culture
James March
James March
March's exploration of ambiguity in organizational decision-making and his technology of foolishness concept form a background framework for the reasonable-exception and standards-drift arguments

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