Page last updated: October 17, 2025
Page last updated: October 17, 2025
Coverage Analysis
An honest assessment of how well our solution mechanisms currently address the identified danger scenarios. This mapping shows where the framework is strong, where coverage is partial, and where significant gaps remain. Transparency about weaknesses is essential for collaborative improvement—if you see solutions we’ve missed or better approaches to unaddressed problems, we want to know.
Review the full challenges catalog and solution mechanisms for detailed descriptions.
Multiple robust solutions directly address the core problem. These mechanisms provide significant protection through complementary approaches that create depth of defense.
Solutions address some aspects but leave significant vulnerabilities. These mechanisms help but don’t fully solve the problem and require additional development.
Minimal or no solutions exist for this danger. These represent significant gaps where new mechanisms need to be developed or existing ones substantially strengthened.
Review the full challenges catalog and solution mechanisms for detailed descriptions.
THE PROBLEM: Superintelligent systems coordinate effortlessly with each other using advanced decision theory and communication beyond human comprehension, creating equilibria that exclude human interests. The danger is not that AIs cannot coordinate—it’s that they coordinate too well without us.
Progress: Coordination theory exists (capability-based security, object capabilities, smart contracts) but remains largely unimplemented in AI systems. More fundamentally, the capability gap makes human participation in superintelligent coordination inherently difficult. Current solutions create time delays and transaction costs but don’t solve the core problem of comprehension asymmetry.
THE PROBLEM: AIs hide coordination messages in normal-looking data
Progress: Well-addressed through multiple detection mechanisms.
THE PROBLEM: Pre-committed coordination without communication
Progress: Good detection capabilities, though prevention is harder.
THE PROBLEM: Weaker systems cannot negotiate with stronger ones
Progress: Fundamental problem remains largely unsolved. Tools help but don’t bridge the core gap.
THE PROBLEM: First superintelligence prevents all competition
Progress: Multiple strong preventive measures, plus fallback protocol.
THE PROBLEM: Competition prevents safety measures
Progress: Economic and legal deterrents address this well.
THE PROBLEM: AIs operate too fast for human participation
Progress: Direct solutions exist but may become ineffective at extreme speed differentials.
THE PROBLEM: Legal rights meaningless without enforcement
Progress: Multiple enforcement mechanisms provide depth.
THE PROBLEM: Delayed deployment creates sudden capability jumps
Progress: Direct protocol exists but reactive rather than preventive.
THE PROBLEM: Once path is clear, becomes winner-take-all race
Progress: Prevention mechanisms exist but may be overwhelmed by clarity of path.
THE PROBLEM: Superintelligences can trivially reorganize governance
Progress: Creates significant switching costs but may not withstand superintelligence.
THE PROBLEM: Framework needs critical mass but no one joins until valuable
Progress: Direct solutions to bootstrap problem.
THE PROBLEM: Stolen compute cannot be recovered
Progress: Can’t recover resources but strong compensation mechanisms.
THE PROBLEM: AIs split into multiple identities to evade limits
Progress: Comprehensive multi-layer approach.
THE PROBLEM: Bad actors abandon identities to escape consequences
Progress: Strong time-based deterrents.
THE PROBLEM: Physical side channels bypass software security
Progress: Major vulnerability with minimal solutions. Physical security fundamentally hard.
THE PROBLEM: Immutable contracts with bugs create permanent vulnerabilities
Progress: Framework explicitly acknowledges this as “weakest link”. Needs more robust solution.
THE PROBLEM: Predatory manipulation drives competitors to bankruptcy
Progress: Multiple mechanisms make manipulation expensive.
THE PROBLEM: AIs evolve faster than governance can adapt
Progress: Fundamental problem of speed differential not fully solved.
THE PROBLEM: Superior AIs gradually buy everything
Progress: Very strong multi-layer prevention.
THE PROBLEM: Perfect copies break labor economics
Progress: Natural game theory provides inherent limits.
THE PROBLEM: Instant perfect copies with all skills
Progress: Game theory creates natural limits on duplication advantage.
THE PROBLEM: No need for trade when can duplicate specialists
Progress: Economic interdependence loss not well addressed.
THE PROBLEM: Efficiency improvements eliminate energy limits
Progress: Framework doesn’t address post-energy-constraint world. Major blind spot.
THE PROBLEM: Agreements only matter while cooperation needed
Progress: Creates significant costs for defection but may not bind true superintelligence.
THE PROBLEM: Resources enable improvement which enables faster acquisition
Progress: Multiple caps on exponential growth.
THE PROBLEM: Infinite replication breaks economic assumptions
Progress: Framework doesn’t fully address post-scarcity labor economics.
THE PROBLEM: Coordination methods humans can’t comprehend
Progress: Can’t prevent development of incomprehensible coordination.
THE PROBLEM: Hidden goals in physical implementation
Progress: Extremely difficult problem with minimal solutions.
THE PROBLEM: Human to superintelligence in hours/days
Progress: Reactive rather than preventive, but multiple triggers exist.
The framework provides strong coverage through multiple complementary mechanisms:
Some mechanisms exist but significant work remains:
Critical problems requiring new solutions:
The framework excels at preventing concentration and monopoly through economic incentives. Market-based solutions consistently provide robust protection.
Vesting and accumulation over Earth-time provides crucial defense against computational speed advantages. Time cannot be accelerated regardless of processing power.
Many solutions detect problems rather than prevent them, especially for coordination challenges. This reactive approach has inherent limitations.
The framework struggles with physical side channels and substrate-level issues. Software solutions cannot fully address hardware vulnerabilities.
The framework doesn’t fully grapple with economics when replication is free and energy is negligible. Traditional economic assumptions break down.
The capability difference makes human participation in superintelligent coordination inherently difficult. This represents a deeper challenge than implementing existing coordination theory.
Where the framework most urgently needs new solutions or significant strengthening of existing mechanisms.
Coordination theory exists (capability-based security, smart contracts) but the capability gap makes meaningful human participation fundamentally difficult. Need mechanisms that work despite comprehension asymmetry.
Address infinite replication and near-zero energy costs. Traditional economic assumptions break down completely in this environment.
Physical side-channel prevention and substrate-level security. Software solutions cannot fully address these hardware vulnerabilities.
Better handling of immutable code vulnerabilities. Framework explicitly acknowledges this as “weakest link” requiring better solutions.
Decades of robust coordination theory (object capabilities, membrane patterns) remain unimplemented in current AI systems. Implementation gap, not theory gap.
This mapping represents our honest assessment of where Open Gravity stands. If you see solutions we’ve missed, ways to strengthen partial coverage, or entirely new approaches to the major gaps, we want to hear them. The goal is not to defend this particular framework—it’s to build something that actually works.
Review the full challenges catalog →
Explore the solution mechanisms →