Neil Thompson is the Director of the FutureTech research project at MIT’s Computer Science and Artificial Intelligence Lab and a Principal Investigator at MIT’s Initiative on the Digital Economy.
Website: https://futuretech.mit.edu/team/neil-thompson
Paper: https://futuretech-site.s3.us-east-2.amazonaws.com/2024-01-18+Beyond_AI_Exposure.pdf
Abstract: 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.
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