Economic impact of AI

I’m still reading through this brilliant and thoughtful post by Tom Cunningham, but it’s so thoughtfully written.

1. There is no standard definition of machine intelligence. There have been many attempts to give a definition or a metric of machine intelligence but most have been unsatisfactory. The lack of a common language makes work in this field difficult, but it’s also a big opportunity.

2. There is no standard model of AI’s economic impact. Economists have been using a wide range of assumptions to model AI’s impact, there is no standard framework. There seems to me an opportunity for ambitious economists to propose deep models of AI’s impact. A promising line would concentrate on AI’s ability to find low-dimensional representations of the world.

3. GDP will be a poor proxy for AI’s impact. AI’s benefits are likely to elude GDP for two reasons: (1) it will reduce the necessity for exchange (and GDP measures exchange); (2) it will lower the labor required for services, and the value-added from services are typically imputed from the wage-bill.

4. Transformative AI will raise the relative value of resources, and possibly lower the value of labor. If computers can do all human work then there will still be scarcity in natural resources (land, energy, minerals). Because humans require resources to do work (energy, land), demand for human labor will fall, creating a gap between land-rich and land-poor.

5. AI will likely have a discontinuous impact on science and technology. Many existing models treat computers as substitutes for humans in the R&D process, but there is reason to expect AI to have a qualitatively different effect on scientific progress.