Reward is Enough

This 2021 AI journal paper by David Silver and Demis Hassabis is the theoretical underpinning of DeepMind's research program. The central hypothesis: all aspects of intelligence — perception, language, knowledge, memory, cognition, imagination, planning, and social intelligence — can be understood as arising from a single objective: the maximization of a cumulative reward signal. If this is true, then a sufficiently powerful reward-maximizing agent, operating in a sufficiently rich environment, would develop all the capabilities we associate with general intelligence as instrumental subgoals of reward maximization. The paper argues this is more than speculation: reinforcement learning has already produced agents with sophisticated perceptual, language, and planning capabilities — not because these were programmed in, but because they were useful for maximizing reward. The paper does not claim that arbitrary environments will produce general intelligence — the richness of the environment matters enormously. But it argues that there is no fundamental limitation preventing reward maximization from being sufficient for AGI. This contrasts sharply with approaches that try to engineer specific cognitive capabilities from scratch (like symbolic AI or cognitive architectures).