Scenario Stress Lab
A structured tool for policy students and think-tank researchers to pressure-test their own AI policy ideas against the scenario logic used in Plan A and AI 2027, surfacing gaps and unintended consequences.
AI policy students, junior researchers and civic-tech fellows drafting governance proposals
- Guided worksheet that walks you through the same five-stage scenario scrutiny the Plan A authors use on their own work
- Adversarial prompt library that asks "what actor sabotages this?" and "what incentive breaks this?" for every claim
- Side-by-side diff against Plan A, AI 2027 and four other published scenarios to flag where your proposal diverges
- Exportable one-pager for sharing proposals with supervisors or mailing lists
The Plan A authors explicitly call out that most AI policy proposals collapse under scenario scrutiny and that almost nobody applies this lens to their own work, which is a clear tooling gap.
Plan A's authors explicitly state 'most AI policy proposals fall apart under scenario scrutiny' and that this lens is 'rare in AI policy,' and the source HN thread drew 109 points/74 comments; institutional AI policy orgs (IAPS) and Harvard foresight courses confirm a real audience drafting governance proposals.AI 2040: Plan A ↗Institute for AI Policy and Strategy ↗
Existing tools (IBM, Microsoft, AI Policy Desk, AI4Policy paper) cover governance OF deployed AI systems — none specifically stress-test user's own governance PROPOSALS against scenario logic like AI 2027/Plan A; Plan A authors themselves confirm the niche is unfilled.AI 2040: Plan A ↗AI Governance for Small Teams: The Complete Guide (2026) ↗
Target users (policy students, junior researchers, civic-tech fellows) are notoriously low-budget; think tanks are grant-funded not SaaS-buying; direct willingness-to-pay is weak unless positioned as an institutional/fellowship license — below-average monetization.Scenario Planning and Simulation for Think Tanks ↗AI 2040: Plan A ↗
AI governance debate is structurally a multi-year issue and scenario-based policy analysis (intelligence agencies, climate bodies) is a durable methodology; however the specific Plan A/AI 2027 framing has a 2-3 year peak window before being absorbed into standard practice.AI4Policy: AI-Enabled Scenario Planning for Policy-Making in the Age of AI ↗
Pure software build: an LLM-prompt pipeline that ingests a proposal, applies the AI 2027/Plan A scenario rubrics (race ending, slowdown ending, Plan B/C/D/S branches), and outputs a gap analysis — trivially buildable as a thin web app with no external data dependencies.AI 2040: Plan A ↗