Artificial Superintelligence (ASI)

Sources#

From AGI to ASI

Summary#

The DeepMind "From AGI to ASI" report (Genewein, Hutter, Legg et al., June 2026) deliberately uses coarse, qualitative characterizations rather than sharp definitions, grounded in the smooth Legg–Hutter intelligence continuum:

AGI — human-level artificial general intelligence: roughly median individual human performance on most cognitive tasks ("Competent AGI" in Morris et al. 2024). The first AGI will already be superhuman on many tasks while not yet general enough.
ASI — artificial general superintelligence: superhuman across virtually all tasks and domains of human interest. The report sets the bar high — ASI exceeds what large, well-coordinated collectives of human experts (≈ tens of thousands of experts working for ~10 years) can achieve, on virtually all tasks. Narrow superhuman systems (AlphaFold, AlphaGo) are explicitly ruled out — ASI is general.
Universal AI (UAI) — the incomputable theoretical limit (AIXI); ASI approximates it from below.

This page is the hub for the cluster: the definitional anchor that the pathways, frictions, limits, and dynamics pages orbit.

Why coarse definitions, not thresholds#

Because the Legg–Hutter score is a continuum, the report doesn't need a precise AGI/ASI cutoff — only a large gap between them, under which pathways and implications can be discussed. The authors add several clarifying remarks:

Relative-to-humans is a moving target (Remark IV): humans armed with better tools/education get more capable, so "human-level" drifts. Taken to the extreme, a human could "reach ASI on any task" by first building ASI — clearly against the spirit. So AGI is pinned to today's median human.
Capability profiles are jagged (Remark III): the score may be smooth in compute, but concrete systems are jagged vs. human level — AI progress is non-uniform (Jagged Intelligence (Ghosts, Not Animals)).
A single ASI may be a collective of millions of instances acting in parallel; to dodge individual-vs-collective hairsplitting, the bar is set at exceeding large expert collectives (see Multi-Agent Collective Intelligence).

Neither omniscient nor omnipotent#

A central corrective theme: exceeding human intelligence by a large margin does not imply omnipotence. ASI is bound by hard physical, complexity-theoretic, and logical limits — some precisely characterizable via the AIXI framework (e.g. maximal data efficiency). It is not guaranteed to cure ageing, build Dyson spheres, reshape matter with nanobots, upload brains, or restore the pre-industrial climate. See Fundamental Limits of ASI for the full catalogue, and note these hard limits may leave substantial slack above practical ASI limits.

What makes digital ASI alien#

The single most distinctive fact about AI: we know its full algorithmic description (its code). This implies substrate independence, lossless replication of source and memory state, and arbitrary speed-up/pause/copy — a set of Advantages of Digital Intelligence that widen the human–AI gap as compute grows. Consequently, human-intelligence intuitions often break down for advanced AI, and ASI "societies" may be radically un-human (Borg-like homogeneous super-collectives, market-like specialist ecologies, or Hutter's compute-tethered virtual worlds).

Will progress stall exactly at human level?#

The report's headline judgment (low confidence): it is implausible that AI progress stalls exactly at human level. Even if individual-model progress plateaus, collective capability can keep rising by running many AGI instances (Multi-Agent Collective Intelligence). For progress to halt at human level, several of the frictions would have to be hard blockers simultaneously. More likely: either AI plateaus before AGI, or goes from AGI to (weak) ASI relatively smoothly — unless recursive self-improvement makes the transition rapid, which "cannot be ruled out."

Connections#

Universal AI (AIXI) — the formal upper bound; ASI is the practical region approaching it; supplies the continuum that lets these definitions stay coarse
AGI-to-ASI Pathways — the four technological routes from AGI to ASI and the frictions along them
Fundamental Limits of ASI — the hard physical/complexity/logical bounds that keep ASI finite
Advantages of Digital Intelligence — the substrate properties that make the human→ASI gap widen with compute
Multi-Agent Collective Intelligence — the "ASI as a collective of instances" reading
Intelligence Explosion Dynamics — whether the AGI→ASI transition is smooth or explosive
Jagged Intelligence (Ghosts, Not Animals) — Remark III: concrete capability profiles are jagged even if the score is smooth
Research Taste as the Human Bottleneck — what (if anything) stays human as systems cross into ASI

Open Questions#

Can we even recognize ASI? We lack benchmarks for general superhuman performance (only narrow ones like chess), and the tasks must be abstract/open-ended enough to reveal it.
Is the jaggedness of capabilities a fundamental theoretical property, or an artifact of comparing against human performance? (Open question 6d in the report.)
Where does practical ASI plateau relative to the hard limits — how much slack is there?

Sources#

From AGI to ASI — Section 3 ("Characterizing Artificial Superintelligence"); informal AGI/ASI/UAI definitions and Remarks I–V