A Framework for Increasing Competition and Diffusion in Artificial Intelligence
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This study was originally posted at American Action Forum.
A Framework for Increasing Competition and Diffusion in Artificial Intelligence
Artificial Intelligence (A.I.) is developing rapidly, and countries from around the globe are beginning to articulate national strategies for handling the political ramifications. With A.I. powering innovations such as driverless cars, autonomous drones, full-sequence genetic analytics, and powerful voice-assistant technology, the future certainly looks full of potential. Unsettled questions, however, about who will reap these benefits and when they will be achieved leave storm clouds on the political horizon.
Amid questions of industrial concentration and economic inequality on one side, and concerns about lagging U.S. productivity and the slow pace of A.I. diffusion on the other, there is an underexamined overlap that connects these questions to the same set of policies: high barriers to entry due to supply-side constraints.
There are significant barriers to entry in A.I. development and application, many of which stem directly from government policies. These barriers have inadvertently boosted the market power of incumbent firms and in reducing them, we may enable new firms to compete better, while also removing some of the bottlenecks that slow down research and integration of A.I. systems across the entire economy.
Read the full study here.
