Economic growth under transformative AI
Philip Trammell (Global Priorities Institute, Oxford University) and Anton Korinek (University of Virginia, NBER and CEPR)
GPI Working Paper No. 8-2020, published in the National Bureau of Economic Research Working Paper series and forthcoming in the Annual Review of Economics
Industrialized countries have long seen relatively stable growth in output per capita and a stable labor share. AI may be transformative, in the sense that it may break one or both of these stylized facts. This review outlines the ways this may happen by placing several strands of the literature on AI and growth within a common framework. We first evaluate models in which AI increases output production, for example via increases in capital's substitutability for labor or task automation, capturing the notion that AI will let capital “self-replicate”. This typically speeds up growth and lowers the labor share. We then consider models in which AI increases knowledge production, capturing the notion that AI will let capital “self-improve”, speeding growth further. Taken as a whole, the literature suggests that sufficiently advanced AI is likely to deliver both effects.
Other working papers
Meaning, medicine and merit – Andreas Mogensen (Global Priorities Institute, Oxford University)
Given the inevitability of scarcity, should public institutions ration healthcare resources so as to prioritize those who contribute more to society? Intuitively, we may feel that this would be somehow inegalitarian. I argue that the egalitarian objection to prioritizing treatment on the basis of patients’ usefulness to others is best thought…
Against Anti-Fanaticism – Christian Tarsney (Population Wellbeing Initiative, University of Texas at Austin)
Should you be willing to forego any sure good for a tiny probability of a vastly greater good? Fanatics say you should, anti-fanatics say you should not. Anti-fanaticism has great intuitive appeal. But, I argue, these intuitions are untenable, because satisfying them in their full generality is incompatible with three very plausible principles: acyclicity, a minimal dominance principle, and the principle that any outcome can be made better or worse. This argument against anti-fanaticism can be…
AI alignment vs AI ethical treatment: Ten challenges – Adam Bradley (Lingnan University) and Bradford Saad (Global Priorities Institute, University of Oxford)
A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching…