Three gaps — continual learning, long reasoning, memory — and why they decide whether agents ship safely.

Prologue

A few days ago (April 29), Demis Hassabis — CEO of Google DeepMind and 2024 Nobel laureate in Chemistry — appeared on the podcast Agents, AGI & The Next Big Scientific Breakthrough . He predicted that AGI (artificial general intelligence) could arrive around 2030, and outlined several critical weaknesses in today’s AI.

Hassabis spent much of the time on one question: What is today’s AI still missing?

Continual learning: unlike humans, it cannot keep learning for life and constantly renew what it knows.

Long-term reasoning: very weak on long logic chains and multi-step planning.

Real memory: not just a context window, but structured, indexable long-term memory.

Hassabis describes today’s models as exhibiting “jagged intelligence” — he contrasts solving IMO-level problems with still making elementary mistakes when a question is rephrased: strong peaks next to brittle failures.

The…