AGI =
A system that can do most of the cognitive things typical humans can do across many different tasks (not just one narrow domain). Think spectrum, not on/off.
Minimal AGI vs Full AGI vs ASI
Minimal AGI: broadly human-typical competence across most cognitive tasks.
Full AGI: covers the full range of human capability, including rare peaks.
ASI: broadly beyond the best humans.
Goal:
Stop arguing about the word “AGI.” Start naming the variables that actually matter:
Capability
(how strong + how broad) Note: capability is not one number.
Capability has two axes:
1 Performance (depth): how well it performs compared to humans.
2 Generality (breadth): how many different tasks it can do well (including unfamiliar ones).
Capability ladder
L1 Emerging: broad but inconsistent.
L2 Competent: human-typical across most tasks.
L3 Expert: top-decile performance across many tasks.
L4 Exceptional: near-top human broadly.
L5 Superhuman: beyond all humans broadly.
Jagged intelligence (uneven capability)
Systems can be brilliant in some areas and brittle in others. Peaks create over-trust. Assume jaggedness unless proven otherwise.
Autonomy (how much it’s allowed to act)
Autonomy (the real “risk throttle”)
Autonomy is not intelligence. It’s permission-to-act
A0 No AI: humans do it (chosen for learning, assessment, or safety).
A1 Assistant: AI suggests; human executes.
A2 Co-pilot: AI drafts; human approves major steps.
A3 Delegate: AI executes bounded tasks with limits + logs.
A4 Operator: AI runs workflows; human handles exceptions.
A5 Agent: AI pursues goals over time with broad tool access (highest risk).
Impact (How Widely it’s deployed)
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Benchmarks that matter
Ignore single “AGI achieved” stunts. Prefer ecologically valid test suites (messy instructions, long-horizon tasks, tool use, uncertainty) plus adversarial red-teaming (“try to break it”). -
System 2 safety (deliberative safety)
For high-stakes actions, require slow reasoning (alternatives, consequences, uncertainty), not “first impulse” outputs. Useful, but not sufficient—systems can rationalize. -
Interpretability and intent
Interpretability helps answer: “Is it trying to do what we asked, or just looking compliant?” Combine with audits, monitoring, and rollback. -
Alignment under pluralism
“Human values” differ across cultures and domains. Alignment is partly a governance problem: who sets defaults, what rights are protected, how local norms apply. -
No-AI zones (sanctuaries)
Some contexts should remain intentionally human: education/assessment, certain civic processes, safety-critical procedures, and “authentic human” spaces.
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