People use AGI and ASI as if they were synonyms, brand names, or sci-fi mood lighting. They are not. The distinction matters because the control problem and the policy tools change as you climb the ladder, and because labs have incentives to sound close to "AGI" for funding while sounding far from danger for regulation.
Plain definitions
Narrow AI is built for a bounded job: recommend videos, fold proteins, spot tumors. It can be excellent and still not a general agent.
AGI (artificial general intelligence) means broad, flexible competence across many tasks at about human level, with enough agency to pursue goals rather than only answer prompts. Definitions differ, but the load-bearing idea is generality plus usable autonomy.
ASI (artificial superintelligence) means cognitive performance that substantially outclasses humans across nearly all domains that matter for science, strategy, and power. It is not "AGI plus ten percent." It is a qualitative break in who can outplan whom.
A ladder, not two bins
Think in rungs. Today's frontier models are broad but uneven, heavily scaffolded by humans, and improving fast. Agentic stacks (tools, memory, planning loops) can make a base model act far more autonomously than a chat box suggests. AGI-class agency is already a severe control and societal problem if it is misaligned. ASI is decisive advantage: faster science, better strategy, better exploitation of human bias, harder shutdown.
Domain superintelligence is old news. A system can crush humans at chess or protein folding and still be narrow. Cross-domain strategic superintelligence is the rung this site exists to prevent.
Why "we will stop at AGI" is unstable
Suppose a lab reaches controllable human-level general systems. The pressure to push further does not vanish. Military advantage, commercial edge, and prestige all point up the ladder. Racing dynamics reward the least cautious mover. The race story is often used to justify speed; it does not justify control.
Measurement without marketing
Benchmarks fall; press releases rise. Dangerous-capability evaluations try to measure cyber offense, bio assistance, autonomous replication, and persuasion under controlled conditions. They help. They are not a full safety case, especially if a system can sandbag. Policy should key off compute, capability tests, and autonomy, not off what companies call the product.
Myths that waste time
AGI does not require a robot body. Remote tools and existing machines move the physical world. Consciousness is a separate question from capability and control. "Human-level" is not one number: a median person, a specialist team, and humanity's institutional best are different bars. Safety-relevant AGI is about general problem-solving agency under real incentives, not a single exam score.
What travels into law
If the statute only restricts systems branded "AGI" or "ASI," firms will rebrand. Good triggers are operational: training-run size, tool autonomy, dangerous-capability results, open-weight release of high-end general models. Narrow AI that stays tool-like should continue. The line this Foundation draws is artificial superintelligence and the programs aimed at it.
How to say it in one minute
Narrow AI: tools. AGI: human-level general agency, already a serious control problem if autonomous and misaligned. ASI: machine cognition that outclasses us across the board. We can keep the tools. We should not build the sovereign mind. Superintelligence cannot be controlled. The ask is in our plan.