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
How effective is (more) money? Randomizing unconditional cash transfer amounts in the US – Ania Jaroszewicz (University of California San Diego), Oliver P. Hauser (University of Exeter), Jon M. Jachimowicz (Harvard Business School) and Julian Jamison (University of Oxford and University of Exeter)
We randomized 5,243 Americans in poverty to receive a one-time unconditional cash transfer (UCT) of $2,000 (two months’ worth of total household income for the median participant), $500 (half a month’s income), or nothing. We measured the effects of the UCTs on participants’ financial well-being, psychological well-being, cognitive capacity, and physical health through surveys administered one week, six weeks, and 15 weeks later. While bank data show that both UCTs increased expenditures, we find no evidence that…
Will AI Avoid Exploitation? – Adam Bales (Global Priorities Institute, University of Oxford)
A simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its failure is instructive…
Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Rethink Priorities), Jojo Lee and Victor Yaneng Wang (Global Priorities Institute, University of Oxford)
The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. …