AGI and superintelligence scenarios

Recursive AI development change control

Recursive AI development change control is the work of setting and enforcing limits on AI systems that help improve themselves or their successors. AI companies need it so that fast, automated research cycles stay inside boundaries humans have actually agreed.

Demand is still a scenario, not a market. It forms as labs automate more of their own research and the pace of change starts to outrun human review. The hires are tricky because no one has done this job at scale. Existing release and control teams handle bounded changes well. Look for people from release engineering, control evaluation or frontier governance who can say precisely what they would enforce, and what evidence would change their mind.

Scenario 4

Roles in this family

4 roles

ScenarioRecursive Improvement Change-Control ArchitectA Recursive Improvement Change-Control Architect would define enforceable limits on fast, automated changes to AI systems. The role depends on how…ScenarioAutomated AI Research Assurance LeadAn Automated AI Research Assurance Lead would challenge the evidence coming out of heavily automated AI development. The role depends on how AI…ScenarioCapability Acceleration Monitoring SpecialistA Capability Acceleration Monitoring Specialist would watch for evidence that AI systems are improving unusually fast through automated research. It…ScenarioSelf-Modifying System Verification EngineerA Self-Modifying System Verification Engineer would check that stated safety properties hold as AI systems change their own software. The role…

Where the people come from

Potential pathway: release engineering, control evaluation or frontier governance

Not to be confused with. Existing model-release and control functions already cover bounded changes.

Core skills

  • Distributed systems
  • AI control
  • AI governance and assurance
  • Frontier scenario analysis

Where it is used

  • Frontier AI governance
  • Future general-purpose autonomous systems
  • Future superintelligent systems

Depends on a scenario

Rapid automated AI research and recursive change

What AI would need to do
Automated AI research repeatedly improves systems at consequential speed and scale.
What organisations would need to decide
Developers retain controlled release, independent review and enforceable change boundaries.
What would rule it out
Compute, experiment, evaluation or integration bottlenecks prevent rapid compounding gains.

We give no date. The role becomes real if both conditions hold.

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