Co-founder of DeepMind and its Chief AGI Scientist. See Google DeepMind.
The Legg-Hutter measure
Legg's doctoral work with Marcus Hutter produced a formal definition of machine intelligence as "the average performance of an agent across all computable tasks," weighted so that simpler tasks (lower Kolmogorov complexity) count for more. The measure is not computable in practice; its purpose is to supply a well-defined limit against which informal notions of general intelligence can be positioned, and it subsumes many of those informal notions as special cases.
Its policy relevance is that it makes intelligence a continuum rather than a threshold. DeepMind's From AGI to ASI (June 2026), on which Legg is a senior author, uses it for exactly this purpose: because the measure is smooth, "we do not need to very precisely define the Legg-Hutter score threshold of AGI and ASI," only to establish that a significant gap separates them. The report's third definitional tier, Universal AI, is the theoretical maximum of the measure, formalized through the AIXI agent of Legg's co-author Hutter.
Positions
Legg has been associated with the view that AGI is a concrete engineering target on a decadal rather than indefinite horizon, and that its arrival should be planned for rather than debated in the abstract. From AGI to ASI reflects that posture in its structure: it brackets the question of when AGI arrives and asks what follows, treating the four pathways it maps — scaling, algorithmic paradigm shifts, recursive improvement, and multi-agent collectives — as parallel possibilities rather than competing predictions, and treating the impact of the frictions along them as open research questions rather than settled parameters (From AGI to ASI (Google DeepMind, June 2026)).
Relationships
- related: Google DeepMind — co-founder and Chief AGI Scientist
- related: From AGI to ASI (Google DeepMind, June 2026) — senior author
- related: Superintelligence, AGI Timelines, Demis Hassabis