Feature 1.2 · Static comparison
AI vs AGI vs ASI
Eight capability dimensions in plain language. The violet ASI column describes a hypothetical tier — notice the Hypotheticalmarker, not just the color.
| Capability | AIToday's systems · Established | AGIHuman-level breadth · Conceptual threshold | ASI HypotheticalSuperintelligence · Speculative, not observed |
|---|---|---|---|
| Scope of tasksHow many different kinds of tasks the system can handle. | Narrow: excels at specific trained tasks (e.g. transcribe speech, label images, draft text). | Broad: handles any cognitive task a skilled human could, including unfamiliar ones. | Beyond-human breadth: tackles problem classes humans cannot frame, across every domain at once. |
| Learning & adaptationHow the system picks up new skills and adjusts to change. | Learns from large training runs; adapting to something new usually means retraining. | Learns new skills from a few examples or instructions, like a person starting a new role. | Rewrites its own learning methods; improvement compounds without human-designed training. |
| Reasoning & problem-solvingHow the system works through hard, unfamiliar problems. | Strong pattern-matching within its training area; fragile on truly novel problems. | Reasons step by step through unfamiliar problems and checks its own work. | Finds shortcuts and proofs humans miss; reasons over timescales and data no human could hold. |
| Knowledge transferWhether skill in one area helps in a completely different area. | Little transfer: a chess model cannot drive a car; skills stay siloed. | Transfers ideas across domains — physics intuition helps with an engineering design. | Instant cross-domain synthesis: every new insight upgrades every capability at once. |
| Autonomy & planningHow long and independently the system can pursue a goal. | Short tasks with supervision; needs prompts, checks, and course-correction. | Plans and executes multi-step, multi-day goals with limited supervision. | Sets and pursues its own long-horizon research programs, coordinating millions of sub-tasks. |
| Tool use & agencyHow the system acts on the world beyond producing text or scores. | Uses tools only when a human wires them up (APIs, plugins, scripts). | Discovers and combines tools on its own to finish a job, asking for help when stuck. | Builds its own tools and infrastructure, then orchestrates whole agent ecosystems. |
| Creativity & noveltyWhether the system produces genuinely new ideas, not remixes. | Recombines training patterns; outputs feel novel but stay near what it has seen. | Produces ideas a skilled expert would call original and useful. | Opens whole new fields — the equivalent of inventing calculus, repeatedly. |
| Self-improvementWhether the system can upgrade its own intelligence. | Cannot redesign itself; improvements come from human researchers. | Improves its own workflows and skills, but its core architecture still needs humans. | Recursively redesigns its own architecture — each generation builds a smarter next one. |
Reading this table correctly: AI describes systems that exist today. AGI is a conceptual threshold nobody has verifiably crossed. ASI is entirely speculative — included so you can reason about it, not because it has been observed.