Summary measures

Overall assessment

Answer: 80

90% CI: (70,90)

Quality scale rating

“On a ‘scale of journals’, what ‘quality of journal’ should this be published in?: Note: 0= lowest/none, 5= highest/best”

Answer: 4

90% CI: (3.5, 5.0)

See here for a more detailed breakdown of the evaluators’ ratings and predictions.

Written report

Manager’s note (David Reinstein): I converted math and Greek letters into latex format, mainly to demonstrate this capacity. I made no other changes.*

Thanks to the Unjournal for their invitation to review “Artificial Intelligence and Economic Growth”. In this essay the authors have three announced goals: Help set an agenda for research on the impact of AI on growth, refine research questions on the subject, and summarise and recontextualize key previous findings with an emphasis on Baumol’s cost disease. This is an ambitious task, but the authors largely succeed!

In the first four sections of the paper, the authors do a wonderful job of outlining a general neoclassical model of automation. They explain how the key parameters of the model determine the impact of automation. They distinguish between two types of economic singularity, and show how the more extreme variety emerges naturally from some parameterizations of their model – something which I believe is an important innovation of this paper (including above Nordhaus (2015) a direct antecedent paper). These models stimulate the reading researcher to ask how these parameters could be estimated, opening a door to applied economists to contribute to the macroeconomic question of growth and AI. After this, section 5 is a bit of a disappointment. It lists several additional economic phenomena that might be caused by AI and automation, and occasionally ties these ideas back to economic growth, but in a less organised way without the assistance of a model. The essay closes with empirical evidence on capital shares and automation, which was adequate for the time empirically, but is somewhat lacking in its interpretation of the data.

Let me start by going into detail about what I liked about the first several sections, including some complementary thoughts it inspired in me. Then I’ll explain what I consider the main factor omitted in these sections: the impact of automation and AI on saving and investment. I’ll close with some thoughts on the limitations of sections 5 and 6, and how they might be improved.

Section 2 of the paper lays out a general, neoclassical, model of automation, drawing on Zeira (1998) and Acemoglu and Restrepo (2016). The key equations are clearly presented. The authors highlight Baumol’s "cost disease" -- the phenomenon that an increase in output of one sector of the economy will make goods in a complementary sector more expensive -- as a key phenomenon to be understood for projecting AI and automation-led growth. '$\rho$' is the parameter in the model that governs how substitutable different goods (for example, automatable and non-automatable ones) are in the economy. When $\rho$ is smaller, the economy is relatively more limited by its scarce labor than it is boosted by automation. It is more likely for interest rates and the capital share to even decline because of greater automation. This effect is exacerbated by capital accumulation over time, in contrast to labor which is inelastically supplied. The way I once heard this phenomenon described is "You can always have more capital per-capita, but you can't have more capita per-capita", and the authors do a good job of explaining this theme from the previous literature.

The authors do a great job of highlighting the importance of “$\rho$” to economic growth. Implicitly the authors are suggesting to applied researchers to go out and measure this elasticity! Between automated and non-automated tasks, or between relatively capital intensive and labor intensive sectors, for example.

The authors explain several special cases of their model, to explain how other parameters balance against each other as well. They focus on the role of “$\beta$”, the share of sectors which are automated. I think the authors are correct in taking a narrative approach to possible paths ‘$\beta$’ can take, rather than following Acemoglu and Restrepo (2016) and trying to endogenize it to the decisions of scientists. It's the right level of detail to stop at, given their more general concerns.

Sections 3 and 4 go farther beyond the current state of the literature, introducing AI as an input to technology production functions and considering versions of an economic singularity. Section 3's formalization is clear, but I might have appreciated a note from the author that other approaches to modelling "AI in the idea production function" might be better -- whereas I think the model in section 2 is more solidly paradigmatic. The key parameter here turns out to be "$\phi$ ", the rate at which knowledge growth is increasing/decreasing in the stock of knowledge.

In section 4, the authors lay out what I think are the best taxonomy of economic singularities I've seen (I think the best alternative that would have been in the literature at the time would have been Nordhaus 2015's). While these are somewhat extreme scenarios, they immediately ground themselves by showing how a type I case is the natural result of the oldest economic model of automation -- the AK growth model. I would make the connection between the AK growth model and the "$\rho=\infty$" (i.e. all goods are perfect substitutes) case of the general model in section 3 more explicit. The authors then show that the key parameter determining whether type-2 singularity is $\phi$. In the simpler model (example 2), $\phi$ being greater than 0 is enough to create an infinite-economic-output singularity. In the third example, the condition is a slightly more complicated function of $\phi$. The section closes with an ok discussion of some more general related concerns regarding an economic singularity, returning again to $\rho$ and the role of 'scarce bottlenecks' in output.

I really appreciated these sections, and feel they do a generally good job at agenda setting for both theorists and applied researchers. For applied researchers, I think the way the paper identifies "$\rho$", "$\phi$", and "$\beta$" as especially important serves as a useful directive towards what they should attempt to measure. What might have made the paper even better is a small table with empirical evidence on these parameters so far, to give the applied researcher inspired by this paper a starting point.

For the theorist, the mind swims with possible extensions to and variations on the approaches presented. Obviously a paper like this can't cover or even suggest every possibility. One might imagine variations of a growth model that allows for "$\rho$” - which can be interpreted as a taste parameter - to be endogenous in some way. In section 5, the authors hint that markups changing over time could be important. They do the same, in referencing Acemoglu and Restrepo (2016) about making "$\beta$" endogenous. Another natural extension makes labor supply endogenous, or might explore an automation —> politics —> growth public choice mechanism. I don't think it's a problem that the authors failed to mention all these possibilities, but some of these I do think are more interesting and directly connect AI and growth than some of the other epiphenomena discussed in section 5 (some of which are less clearly reasoned — for example, isn't it just as plausible to think that AI will increase centralization and superstar firms as it is to decrease it?).

Still I do think that the authors fall down in not focusing more heavily on the role of saving in the model. Throughout the paper, the saving rate in the model is assumed to be constant — a hypothesis that isn't well grounded in either a representative agent model (which achieves a constant interest rate in the long run) or an OLG model (in which saving will be a function of many other considerations). I think this is an important oversight for a document that wants to set the agenda.

I’ll admit I’m a bit of a partisan for this issue, having considered it in (Benzell et al. 2015) and (Benzell et. al. 2022). In the first paper, we show how in OLG models automation technologies can actually lower output and welfare for future generations. The reason is that savings are made by the young out of their labor incomes, for consumption in their retirements. When automation accumulates, the share of income going to young and laboring savers decreases, and the share going to old spenders increases. This reduces the amount which is saved and reinvested. In certain cases, the reduced saving effect is large enough to more than offset the productivity growth effect of automation. The possibility that a new technology could lower long-run output is not admitted for in the authors' model – ruling out certain conceptually coherent scenarios such as the one imagined in Asimov’s “The Caves of Steel” – where highly productive AGIs and automation exists, but a low saving and reinvestment rate by a socialist government keeps society impoverished.

More generally, the exogenous saving framework pursued by the authors doesn't allow for any inter-generational analysis of the impact of automation. On a more practical level, interpreting the decrease in the global interest rate as telling us something about automation (for example, see the recent "cite") needs to account for global demographic and distributional factors that have created a "global saving glut" (cite). In (Benzell et al. 2021), we find that even a rate of automation at 5x the historical rate would fail to overcome this headwind and increase interest rates.

This brings me to the final section of the paper, on the evidence to date on automation and capital shares. Karabarbounis and Neiman (2013) is correctly taken as the starting point, and I think the discussion is ok for the time overall. My main quibble is with the characterization of Autor et. al. (2017) and Barkai (2020). These are presented as 'alternative theories of capital share's increase' but they're more like alternate theories of what $K+N$ are measuring. These papers and Barkai and Benzell (2018) claim it is the profit share of income which is increasing, not the capital share, a theory that is consistent with the microevidence on markups (for example, De Loecker et al. 2020). That has tremendous implications for its interpretation in a model of automation. For example Benzell et al (2022) theorise that the profit share has increased because certain inelastically supplied inputs in the economy are complements to automation and measured as profits. Why do I mention this? Well, because it has dramatic implications for whether the $\rho<1$ or $\rho>1$ case is true: If $\rho \lt 1$ then "capital share" shouldn't be increasing, especially if interest rates and growth are low. On the other hand, $\rho>1$ implies an AK world asymptotically, which also seems unlikely. We think it more likely that $\rho$ is $\lt 1$ , but physical capital's share is actually decreasing, which is how Benzell et al (2022) reconciles this riddle.

Evaluator details

How long have you been in this field?

I started my PhD in Economics in 2012. I became interested in the impact of automation on economic growth shortly after, so about 10 years.

How many proposals and papers have you evaluated?

I have reviewed about 30 papers. I’d say about ⅓ to ½ of these are broadly on the subject of automation.