  Since its emergence around 2010, deep learning has rapidly become the most
important technique in Artificial Intelligence (AI), producing an array of
scientific firsts in areas as diverse as protein folding, drug discovery,
integrated chip design, and weather prediction. As more scientists and
engineers adopt deep learning, it is important to consider what effect
widespread deployment would have on scientific progress and, ultimately,
economic growth. We assess this impact by estimating the idea production
function for AI in two computer vision tasks that are considered key test-beds
for deep learning and show that AI idea production is notably more
capital-intensive than traditional R&D. Because increasing the
capital-intensity of R&D accelerates the investments that make scientists and
engineers more productive, our work suggests that AI-augmented R&D has the
potential to speed up technological change and economic growth.
