Presented at the NBER Economics of AI conference
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. Klinger, Mateos-Garcia, and Stathoulopoulos address this with an analysis of Deep Learning (DL), a core technique in Artificial Intelligence (AI) increasingly being recognized as the latest GPT. They 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, the researchers describe changes in its geography. Their analysis shows China’s rise in AI rankings and relative decline in several European countries. Klinger, Mateos-Garcia, and Stathoulopoulos 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, the researchers study the regional drivers of DL clustering. They 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. Their analysis also reveals a Chinese comparative advantage in DL after they 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.