  I present arguments against the hypothesis put forward by Silver, Singh,
Precup, and Sutton (
https://www.sciencedirect.com/science/article/pii/S0004370221000862 ) : reward
maximization is not enough to explain many activities associated with natural
and artificial intelligence including knowledge, learning, perception, social
intelligence, evolution, language, generalisation and imitation. I show such
reductio ad lucrum has its intellectual origins in the political economy of
Homo economicus and substantially overlaps with the radical version of
behaviourism. I show why the reinforcement learning paradigm, despite its
demonstrable usefulness in some practical application, is an incomplete
framework for intelligence -- natural and artificial. Complexities of
intelligent behaviour are not simply second-order complications on top of
reward maximisation. This fact has profound implications for the development of
practically usable, smart, safe and robust artificially intelligent agents.
