The faster AI automation spreads through the economy, the more profound its potential impacts, both positive (improved productivity) and negative (worker displacement). The previous literature on "AI Exposure" cannot predict this pace of automation since it attempts to measure an overall potential for AI to affect an area, not the technical feasibility and economic attractiveness of building such systems. In this article, we present a new type of AI task automation model that is end-to-end, estimating: the level of technical performance needed to do a task, the characteristics of an AI system capable of that performance, and the economic choice of whether to build and deploy such a system. The result is a first estimate of which tasks are technically feasible and economically attractive to automate - and which are not. We focus on computer vision, where cost modeling is more developed. We find that at today's costs U.S. businesses would choose not to automate most vision tasks that have "AI Exposure," and that only 23% of worker wages being paid for vision tasks would be attractive to automate. This slower roll-out of AI can be accelerated if costs falls rapidly or if it is deployed via AI-as-a-service platforms that have greater scale than individual firms, both of which we quantify. Overall, our findings suggest that AI job displacement will be substantial, but also gradual – and therefore there is room for policy and retraining to mitigate unemployment impacts.

As university involvement in technology transfer and entrepreneurship has increased, concerns over the patenting and licensing of scientific discoveries have grown. This paper examines the effect that the licensing of academic patents has on journal citations to academic publications covering the same scientific research. We analyze data on invention disclosures, patents, and licenses from the University of California, a leading U.S. academic patenter and licensor, between 1997 and 2007. We also develop a novel "inventor–based" maximum–likelihood matching technique to automate and generalize Murray's (2002) "patent–paper pairs" methodology. We use this methodology to identify the scientific publications associated with University of California patents and licenses. Based on a "difference–in–differences" analysis, we find that within our sample of patented academic discoveries, citations to licensed patent–linked publications are higher in the three years after the license, although this difference is not statistically significant. But when we disaggregate our sample into (a) patented discoveries that are likely to be used as "research tools" by other researchers (based on the presence of material transfer agreements (MTAs) that cover them) and (b) patented discoveries not covered by MTAs, we find that citations to publications linked to the licensed patents in the latter subset to be not only higher for publications linked to licensed patents than for unlicensed patents, but that this difference is statistically significant. In contrast, licensing of patented discoveries that are also research tools is associated with a reduction in citations to papers linked to these research advances, raising the possibility that licensing may restrict the flow of inputs to "follow–on" scientific research.

The ability to edit genes with CRISPR has, in a few short years, been transformative to genetics, generating follow-on science such as drought-resistant crops and mosquitos that cannot carry malaria, and is widely expected to win a Nobel prize. The rush of scientists to embrace CRISPR in its early days provides an important and data-rich environment in which to study how tacit information is codified and transferred for others to build upon. In particular, the introduction of CRISPR allows us to explore whether embedding the associated knowledge into an easy-to-distribute tool (a plasmid) and making it available through a biological resource center (Addgene) solves the "tacit information" problem and the physical localization that accompanies it. We show that codification into a scientific tool does solve the problem of access; scientists of equivalent caliber experiment with CRISPR in equal measure regardless of where in the U.S. they are. However, scientists across different geographies have unequal success in converting that experimentation into published science, suggesting that some tacit information problems persist. The remaining tacit information seems to be driven by expertise, with geographies specialized in mammalian CRISPR helping to create publishable science on mammals and geographies specialized in bacterial CRISPR helping to create publishable science on bacteria. Collectively, our case study of the earliest days of CRISPR speaks to the tacit information challenges that are, and are not, solved by distributing embedded materials.