Implementation Roadmap

Implementation Roadmap

Why This Handles the Critical Period (2025-2030)

Timeline Assumptions

• 2025-2027: AI reaches human-level capability in most cognitive domains
• 2027-2030: Potential for recursive self-improvement, but not yet realized
• Post-2030: Either we have safe AI that can help with harder alignment, or we’re in trouble

The modular approach is specifically designed for this window. Here’s why it might be sufficient:

1. Prevents catastrophic failures during development Each iteration is safe even if not perfectly aligned. You get warning shots, not silent failures.

2. Improves as capability increases Better models → better forecasting → better navigation. The system gets safer as it gets smarter (assuming no scheming).

3. Enables recursive improvement Safe human-level AI can help design better modular architectures, improve forecasting, red-team its own security. You bootstrap safety research itself.

4. Buys time for harder problems If we can maintain safety through 2030, we have capable AI assistants to help solve superintelligence alignment. We don’t need the perfect solution now.

The Key Bet

The Key Bet

The architecture bets on: Preventing scheming during the human-level period is tractable, and forecasting quality scales with capability.

If both hold, you get a window where:

• AI is useful enough to help with alignment research
• Dangerous enough to be existential risk if uncontrolled
• But controllable through decomposition + honest coordination + forecasting

That might be enough.

Development Phases

gantt
    title Implementation Roadmap
    dateFormat  YYYY
    section Research
    Empirical validation    :2025, 2026
    Adversarial testing     :2026, 2028
    section Engineering
    Prototype implementation :2025, 2026
    Tooling & frameworks     :2026, 2028
    section Deployment
    Low-stakes deployment    :2026, 2027
    Medium-stakes deployment :2027, 2028

2025-2026 (Near-term)

• Empirical validation of key assumptions
• Prototype implementation (at least one domain)
• Benchmark performance vs monolithic models
• Develop anti-scheming architectural patterns

2026-2028 (Medium-term)

• Scale to more complex systems
• Adversarial testing and red-teaming
• Deployment in medium-stakes applications
• Policy work: propose standards for high-risk AI deployment

Success Criterion

Tip

By 2028, demonstrate that modular architecture + honest agency + forecasting enables transformatively useful AI without catastrophic failures, with empirical evidence this scales to human-level capability.

What’s Needed

Empirical Research

• Scheming propensity in narrow vs broad contexts
• Does decomposition actually reduce scheming? (test monolithic vs modular)
• Forecasting capability for self-prediction with calibration tracking
• Performance benchmarks: modular vs monolithic (what’s the cost?)
• Do fine-tuned narrow models behave more predictably than frontier models?
• Does temperature=0 (least surprise) prevent covert signaling?
• Byzantine coordinator voting: how many coordinators needed?
• Red-team testing: adversarial attacks on architectural patterns

Engineering

• Prototype implementations proving feasibility (research assistant, code generation)
• Open-source frameworks for modular AI development
• Tooling for: decomposition boundary analysis, budget allocation, monitoring/alerting
• Anti-scheming architectural patterns codified and reusable
• Fine-tuning pipelines for creating 10+ task-specific models
• Formal verification tools for critical coordination logic
• Tripwire and circuit breaker implementations

Policy and Standards

• Proposed safety requirements for high-stakes AI deployment
• Evaluation methodology for modular systems (how to assess safety?)
• Industry coordination on: minimum safety standards, shared red-teaming, incident reporting
• Regulatory frameworks: mandating modularity for critical applications?
• International standards: what should cross-border deployment require?

Theory

• Formal models of compositional safety (how do safety properties compose?)
• Optimal decomposition theory (where to draw boundaries?)
• Bounds on forecasting accuracy requirements (how calibrated is “good enough”?)
• Analysis of economic incentives (why would companies adopt this?)
• Information-theoretic limits on coordination through bottlenecks
• Game-theoretic analysis of Byzantine coordinator voting