A new paper from Brookings’ Center on Regulation and Markets that studies the costs and benefits of implementing artificial intelligence systems, and to what extend costly customization is needed to make AI investments worthwhile.
MIT FutureTech Research Scientist Martin Fleming, MIT Postdoctoral Associate Wensu Li, and MIT FutureTech Director Neil C. Thompson focus on how an enormous amount of customization will be needed for AI to move from a few generalist systems to the myriad of specialized systems needed for use throughout the economy. Whether such customization can be justified depends on the performance needs of companies deploying AI systems, as well as the ability of technology providers to achieve greater scale.
To examine AI adoption and job displacement, the authors developed a cost framework for computer vision, one of the most developed areas of AI. The framework showed that labor automation should happen in two phrases:
- Initially, there is significant disruption as automation occurs for tasks that are already economically attractive to automate.
- In the second phase, AI rollout slows, as new tasks wait for either business model innovation or large AI cost decreases to overcome their initial economic unattractiveness.
“In the years immediately ahead, large scale deployment will be most attractive, favoring deployment in large organizations with high-wage workers with many workers performing the same vision tasks,” the authors write, adding that, over a longer time horizon, “cost decreases and platformization will make automation more attractive for firms with low-wage tasks and fewer workers engaged in vision tasks.”
The authors also suggest a handful of policy implications:
- Regulatory action will be needed to guard against the rise of natural monopolies, as has been experienced in social networks, search, and e-commerce.
- Substantial employment and worker retraining programs will be needed as the initial wave of AI automation replaces many tasks done by humans.
- Labor market data and measurement programs will be needed to track the impact of AI implementation.
- Direct support of academic research is needed to ensure AI research and development priorities address the public interest and advancement of knowledge.
Read the full paper HERE.
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