Mutual Prediction in Human-AI Coevolution

Evolutionary relationships between entities within an ecological niche are characterised by varying degrees of interdependence and resulting forms of symbiotic, predatory or competitive behaviors. This paper hypothesizes that mutual prediction is a defining factor in the kind of relationship which forms between entities, as well as the power distribution and stability of that relationship. Throughout history, humans have engaged in complex mutually predictive relationships with the animals we domesticate, the plants we eat and the tools we create. We have generally had a better predictive model of the entities we have co-evolved with than they have had of us. In AI we encounter the first entity which may be able to predict us - including our thoughts, beliefs, feelings and plans - better than we can predict it. The current state of human predictive advantage may give way to predictive equilibrium or even human out-prediction by AIs. This paper defines a classification system for degrees of mutual prediction in human-AI interactions ranging from rules-based prediction through to a speculative capacity for mindreading, and uses the classification as axes to map human predictive ability against AI predictive ability. Past, present, and speculated future relationships between humans and AIs are plotted on the map, encompassing cases of predictive imbalance in both directions and exploring the implications of mutual prediction for human-AI coevolutionary paths. The examples highlight possible sources of human-AI misalignment and the mutual prediction framework provides a lens through which to understand AI systems as part of evolutionary processes at large.