4Responsible.AI · Comparison Matrix

Human Coding vs AGI vs RSI

Three trajectories of capability development — one designed around human capability and agency, two oriented toward machine capability — compared dimension by dimension SRC-4R-01.

Dimension	Human Coding	AGI	Recursive Self-Improvement (RSI)
Primary focus	Designing human–AI work	Expanding general AI capability	AI improving its own capabilities
Central actor	Human within a human–AI system	Increasingly general AI	Self-improving AI system
Direction of development	Propel humans + bound AI	Broaden AI capability	Compound AI capability
Core question	How should humans and AI work together?	What increasingly general intellectual work can AI perform?	How can AI improve the system that produces its own intelligence/capability?
What gets “coded”	Work, workflows, roles, interactions, authority and learning	Machine capabilities across domains	AI architecture/process/capabilities
Coordination	Determines the right combination of human, AI, agent, data and knowledge	AI may coordinate across increasingly broad domains	Improved systems may increasingly coordinate their own resources
Orchestration	Humans intentionally design interactions, sequences, triggers and loops	AI increasingly orchestrates complex cognitive activity	AI may modify/improve its own orchestration
Delegation	Explicitly bounds what humans delegate to AI	Increasing capability expands what could be delegated	Increasing capability can continually expand the delegation frontier
Encoding	Intentionally preserves human judgment, learning, knowledge and skills while capturing reusable intelligence	AI encodes increasingly generalizable capabilities	Improvements become inputs to subsequent improvement cycles
Human judgment	Something intentionally exercised and developed	Can increasingly be augmented or replicated in some domains	Potentially displaced if capability growth becomes the dominant objective
Human learning	Design objective	Possible benefit, but not inherent requirement	Not necessarily an objective
AI learning	Bounded by organizational purpose, authority and policy	Increasingly broad	Potentially recursive and compounding
Human agency	Explicit preservation objective	Depends on deployment/governance	Could decline if human authority does not evolve with AI capability
Human role	Leader, judge, orchestrator, delegator, learner and domain expert	Collaborator, user, governor and/or delegator	Potentially supervisor/governor of increasingly autonomous improvement
AI role	Bounded capability within intentionally designed work	General cognitive capability	Capability that participates in improving itself
Success measure	Better outcomes and stronger human capability	Breadth/depth of AI performance	Rate, quality and sustainability of capability improvement
Primary risk	Poorly coded work can create dependency or ineffective human–AI interaction	Capability may exceed organizational preparedness/governance	Improvement may outrun meaningful human understanding or control
Bounding mechanism	CONDITIONS + CODE + delegation boundaries + preservation requirements	Governance, alignment, access and deployment controls	Strong technical/governance constraints become especially important
Propelling mechanism	Practice, judgment, learning velocity, cognitive capacity and coevolution	AI augmentation can increase human capability	Primarily propels machine capability unless intentionally connected to human development
Desired trajectory	Human ↑ + AI ↑ while Human Agency is preserved	AI ↑↑	AI ↑ → AI improves AI → ↑↑↑

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Source of record for every factual claim on this page: SRC-4R-01 — the 4Responsible.AI source infographic (source-document-id 4R-AI-1787261779629), Carlton L. Robinson.