  General Purpose Technologies (GPTs) that can be applied in many industries
are an important driver of economic growth and national and regional
competitiveness. In spite of this, the geography of their development and
diffusion has not received significant attention in the literature. We address
this with an analysis of Deep Learning (DL), a core technique in Artificial
Intelligence (AI) increasingly being recognized as the latest GPT. We identify
DL papers in a novel dataset from ArXiv, a popular preprints website, and use
CrunchBase, a technology business directory to measure industrial capabilities
related to it. After showing that DL conforms with the definition of a GPT,
having experienced rapid growth and diffusion into new fields where it has
generated an impact, we describe changes in its geography. Our analysis shows
China's rise in AI rankings and relative decline in several European countries.
We also find that initial volatility in the geography of DL has been followed
by consolidation, suggesting that the window of opportunity for new entrants
might be closing down as new DL research hubs become dominant. Finally, we
study the regional drivers of DL clustering. We find that competitive DL
clusters tend to be based in regions combining research and industrial
activities related to it. This could be because GPT developers and adopters
located close to each other can collaborate and share knowledge more easily,
thus overcoming coordination failures in GPT deployment. Our analysis also
reveals a Chinese comparative advantage in DL after we control for other
explanatory factors, perhaps underscoring the importance of access to data and
supportive policies for the successful development of this complex, `omni-use'
technology.
