Research preview. Independent expert review is pending. The worked analyses are hypothetical.
Method and corrections Interactive field model
Inspect the system around the AI system. The model is organized by the clauses of the definition: societies, the four objects, the benefits and harmful patterns governance aims at, and the conditions it races against. Apply a cross-cutting test, narrow by lifecycle stage or level, and inspect how definitions, claims, relationships and sources travel together.
Apply a cross-cutting test Whole model Power Legitimacy Distribution Adaptability
Narrow by lifecycle stage or level Lifecycle stage Any stage Research Data collection Model development Evaluation Procurement Deployment Monitoring Incident response Retirement Level Any level Community and organisational use Corporate Sectoral National Regional International Loading interactive controls. Concept links remain available. Field model · whole model
Groups follow the clauses of the definition; they are not a sequence or causal chain. Actual relationships appear in the inspector.
Jump to selected entity Societies …how societies should shape…
actor AI developer or provider actor Deploying organization actor Affected people actor Civil society and independent researchers Incentives …the incentives…
incentive Deployment pressure incentive Competitive race dynamics incentive Liability exposure incentive Procurement leverage
Institutions …institutions…
institution Regulator or public authority institution Internal safety and governance team institution Standards body institution International institution
Infrastructures …infrastructures…
infrastructure Evaluation capacity infrastructure Compute infrastructure Data, language resources, and data labor infrastructure Energy, water, and minerals infrastructure Data work and content moderation Safeguards …and safeguards around AI…
safeguard Risk management process safeguard Impact assessment safeguard Traceability safeguard Audit record safeguard Meaningful human oversight safeguard Supervision and regulatory review safeguard Pre-deployment evaluation and red teaming safeguard Incident reporting and post-deployment monitoring safeguard Complaint and appeal routes safeguard Review and sunset provisions safeguard Compute controls Benefits and harmful patterns …so that its benefits are broadly shared and harmful patterns are addressed…
principle Accountability principle Safety principle Broadly shared benefit pattern Contestable decision pattern Concentration of power pattern Systematically unreliable provenance pattern Over-reliance and deskilling pattern Sycophancy and engagement-driven agreement pattern Homogenisation and loss of authorship Choices and conditions …while they are still choices rather than conditions.
condition Lock-in condition Governance lock-in condition Reversibility window
Condition · Choices and conditions established
Lock-in The state in which technical dependencies, market structures, or harmful practices have become embedded conditions that are costly or impossible to reverse, often through increasing returns to early adoption.
What has already hardened, and what is about to?
Tests Adaptability Power
Lifecycle Deployment Retirement Recorded relationships Read each statement from its first entity to its second. Status describes our mapping, not certainty of cause.
Reversibility window — closes into → Lock-in
Mapping: asserted
The Collingridge dilemma and increasing-returns adoption describe the same transition: the period in which change is cheap ends as arrangements accumulate adopters and dependents.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Deployment pressure — accelerates → Lock-in
Mapping: hypothesized
Pressure to scale quickly shortens the window in which a technology or vendor can still be swapped, because early adoption compounds.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Claims and evidence · 3 source-assertion Confidence: high Contestability: low
Early in a technology's development change is easy but its consequences are poorly understood; once the consequences are clear, change has become costly and slow.
Our reading: Source-level citation pending passage review against the 1980 text. The dilemma is widely restated; this record cites the original.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
source-assertion Confidence: high Contestability: medium
Under increasing returns to adoption, small early events can lock a market into one technology, including an inferior one, making later reversal costly.
Our reading: Source-level citation pending passage review. Whether historical lock-in to inferior standards was as common as these papers suggest is itself debated in economics; the mechanism is not.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
source-assertion Confidence: high Contestability: low
US federal AI contracts must give agencies the right to keep monitoring system performance and should limit vendor lock-in, so purchasing terms carry safeguards into the market.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
M-25-22: Driving Efficient Acquisition of Artificial Intelligence in Government (opens in a new tab) Section 4(d), ongoing testing and monitoring; Section 4(c)(ii), vendor lock-in
AI contracts must let the buying agency keep monitoring and evaluating the system’s performance, and should include terms such as knowledge transfer that reduce the risk of vendor lock-in.
Scope note: Rescinds and replaces M-24-18. Applies to federal executive agencies; excludes national security systems.
Passage checked 2026-10-09 · omb-m-25-22-monitoring