Abstract: This paper draws upon the evolutionary concepts of technological relatedness
and knowledge complexity to enhance our understanding of the long-term
evolution of Artificial Intelligence (AI). We reveal corresponding patterns in
the emergence of AI - globally and in the context of specific geographies of
the US, Japan, South Korea, and China. We argue that AI emergence is associated
with increasing related variety due to knowledge commonalities as well as
increasing complexity. We use patent-based indicators for the period between
1974-2018 to analyse the evolution of AI's global technological space, to
identify its technological core as well as changes to its overall relatedness
and knowledge complexity. At the national level, we also measure countries'
overall specialisations against AI-specific ones. At the global level, we find
increasing overall relatedness and complexity of AI. However, for the
technological core of AI, which has been stable over time, we find decreasing
related variety and increasing complexity. This evidence points out that AI
innovations related to core technologies are becoming increasingly distinct
from each other. At the country level, we find that the US and Japan have been
increasing the overall relatedness of their innovations. The opposite is the
case for China and South Korea, which we associate with the fact that these
countries are overall less technologically developed than the US and Japan.
Finally, we observe a stable increasing overall complexity for all countries
apart from China, which we explain by the focus of this country in technologies
not strongly linked to AI.