Challenge + goals

There has been a massive shift in federal policy to invest in critical and emerging technologies. AI is one of those technologies, and there is intense interest from all sectors in developing data and evidence to inform those investments. The challenge that we face is that of measurement. AI, like many technologies, is neither a well-defined scientific field nor an industry. We believe our current scientific and industrial classifications need to be rethought. Workshop participants have been invited both because of their expertise in data, measurement, and evidence and because of their ability to contribute to substantive debate.

The workshop will examine and build on new approaches, beginning with AI. Stanford’s AI Index is a widely cited benchmark. Other approaches have been developed. One is to combine UMETRICS data with workforce data which has been highlighted in Julia Lane’s industry of ideas approach. Others have been highlighted in Erik Brynjolfsson’s work as well as a previous 2019 workshop hosted by the AI Index. Yet others have come from the AEI’s Workforce Futures Initiative.

Our goal is to produce a possible roadmap that would identify an empirically implementable, dynamic, and flexible approach to characterize critical and emerging industries, with AI as the initial case study. Ideally, the approach would allow for the measurement of the effect of exogenous or policy shocks on new technologies, and their effects on economic activity and workforce outcomes.