Shane Legg

Sources#

• From AGI to ASI

Summary#

Shane Legg is a co-founder of DeepMind and a long-standing theorist of machine intelligence. With Marcus Hutter (his PhD supervisor) he formalized the Legg–Hutter score — a definition of intelligence as an agent's expected performance averaged over all complexity-weighted computable tasks (Legg & Hutter 2007a; Legg's 2008 thesis Machine Super Intelligence). In this corpus he is the senior (final) author of the "From AGI to ASI" report (June 2026), the DeepMind document that opens the theory-of-superintelligence cluster in this wiki.

Role in the corpus#

Legg's intellectual fingerprint is on the report's foundational move: grounding informal AGI/ASI talk in the smooth Legg–Hutter intelligence continuum, so the authors can avoid sharp capability thresholds and instead reason about the gap between AGI and ASI. His framing — that there is a measurable continuum with Universal AI/AIXI as its incomputable endpoint — is what lets the report bound ASI from above with theory while extrapolating from below with today's systems.

Connections#

• Universal AI (AIXI) — co-originator of the universal intelligence measure that AIXI maximizes
• Artificial Superintelligence (ASI) — the continuum framing that makes the report's coarse AGI/ASI definitions workable is his
• Marcus Hutter — co-author of the Legg–Hutter score; the two anchor the Universal AI framework together
• Google DeepMind — co-founder; the lab behind the report
• AGI-to-ASI Pathways — senior author of the report that lays out the four pathways

Open Questions#

• The report assumes alignment is "solved to a sufficient degree" to focus on trajectories — how does Legg's AGI-timelines optimism square with that scoping choice?

Sources#

• From AGI to ASI — final author; Legg (2008), Legg & Hutter (2007a) cited as the basis for the intelligence measure