Air Street Capital: AI industry remains strong despite academic brain drain, tech nationalization

London-based venture capital firm Air Street Capital today published the State of AI Report 2020, its third annual survey canvassing research, talent, industrial, and political trends in the field of AI. Coauthored by University College London visiting professor Ian Hogarth and AI investor Nathan Benaich, the report aims to highlight technological breakthroughs and areas of commercial application for AI as well as the regulation of AI, its economic implications, and emerging geopolitical issues.

Among other findings, this year’s report implies AI remains mostly closed source, harming accountability and reproducibility, while corporate-driven academic “brain drain” appears to be impacting entrepreneurship. Self-driving cars are in the Precambrian stages. And political leaders are beginning to question whether acquisitions of AI startups should be scrutinized or outright blocked.

AI research

According to Air Street Capital’s report, only 15% of AI research papers publish their code, and there’s been little improvement on the metric since mid-2016. Based on data from the website Papers With Code, which highlights trending research and the code to implement it, code availability has actually decreased from above 20% in December 2019. “For the biggest tech companies, their code is usually intertwined with proprietary scaling infrastructure that can’t be released,” the coauthors of the report concluded. “This points to the centralization of AI talent and compute as a huge problem.”

This development perhaps isn’t surprising, given that professors are departing universities for corporations at an accelerating rate. The report points out that Google, DeepMind, Amazon, and Microsoft hired 52 tenured and tenure-track professors from U.S. colleges between 2004 and 2018 and that Carnegie Mellon, the University of Washington, and Berkeley lost 38 professors during the same period. To put that in perspective, no AI professor left in 2004, whereas in 2018, 41 AI professors resigned.

The report’s coauthors believe this has