Understanding market concentration in the AI supply chain
Artificial intelligence is more than just the applications we use, such as chatbots and predictive algorithms. Beneath the surface of AI lies a technology stack that is increasingly concentrated at each layer. (Click on the graphic below.)
Government regulation can address the concentration in this complex supply chain to promote innovation, competition, and fairness. Key considerations for policymakers include:
- Subsidizing semiconductor production facilities to diversify supply chains and reduce reliance on a concentrated industry
- Building public cloud services to provide competitive alternatives and set fair pricing baselines, as well as set clear interconnection and interoperability standards to help reduce switching costs and foster greater competition
- Addressing vertical integration through nondiscrimination rules that ensure equal terms for access to proprietary AI models and hardware
- Ensuring that vertically integrated applications compete fairly with their counterparts and do not benefit from favorable terms for accessing models, data, and computational resources
The AI Technology Stack
Tejas N. Narechania is a professor of law at the University of California, Berkeley, School of Law and a faculty co-director of the Berkeley Center for Law & Technology. His scholarly focus is on the institutions of technology law and policy, among other subjects. Before joining Berkeley Law, Narechania clerked for Justice Stephen G. Breyer of the U.S. Supreme Court and for Judge Diane P. Wood of the U.S. Court of Appeals for the Seventh Circuit. He also has advised the Federal Communications Commission on network neutrality matters and served as special counsel at the FCC from 2012–2013.
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