I’ve been thinking about this post for days, which is a great sign, and in particular I think there’s a deep truth in the following:
Indeed, my guess is that people’s utility in the goods available today does have an upper asymptote, that new goods in the future could raise our utility above that bound, and that this cycle has been played out many times already.
I realize this is tangential to your point about GDP measurement, but I think Uzawa’s theorem probably set growth theory back by decades. By axiomatizing that technical change is labor-augmenting, we became unable to speak coherently about automation, something that is only changing recently. I think there is so much more we can understand about technical change that we don’t yet. My best guess of the nature of technological progress is as follows:
In the long run, capital and labor are gross substitutes, and basically all technological change in existing goods is capital-augmenting (-> labor-replacing by the gross substitutes assumption).
However, we constantly create new goods that have a high labor share of costs (e.g. the services transition). These goods keep increasing as a share of the economy and cause an increase in wages.
This idea is given some empirical support by Hubmer 2022 and theoretical clarity by Jones and Liu 2024, but it’s still just a conjecture. So I think the really important question about AI is whether the tons of new products it will enable will themselves be labor-intensive or capital-intensive. If the new products are capital-intensive, breaking with historical trend, then I expect that the phenomenon you describe (good 2′s productivity doesn’t grow) will not happen.
As for the prediction—fair enough. Just to clarify though, I’m worried that the example makes it look like we need growth in the new good(s) to get this weird slow GDP growth result, but that’s not true. In case that’s the impression you got, this example illustrates how we can have superexponential growth in every good but (arbitrarily slow) exponential growth in GDP.
I’ve been thinking about this post for days, which is a great sign, and in particular I think there’s a deep truth in the following:
I realize this is tangential to your point about GDP measurement, but I think Uzawa’s theorem probably set growth theory back by decades. By axiomatizing that technical change is labor-augmenting, we became unable to speak coherently about automation, something that is only changing recently. I think there is so much more we can understand about technical change that we don’t yet. My best guess of the nature of technological progress is as follows:
In the long run, capital and labor are gross substitutes, and basically all technological change in existing goods is capital-augmenting (-> labor-replacing by the gross substitutes assumption).
However, we constantly create new goods that have a high labor share of costs (e.g. the services transition). These goods keep increasing as a share of the economy and cause an increase in wages.
This idea is given some empirical support by Hubmer 2022 and theoretical clarity by Jones and Liu 2024, but it’s still just a conjecture. So I think the really important question about AI is whether the tons of new products it will enable will themselves be labor-intensive or capital-intensive. If the new products are capital-intensive, breaking with historical trend, then I expect that the phenomenon you describe (good 2′s productivity doesn’t grow) will not happen.
Great to hear, thanks!
As for the prediction—fair enough. Just to clarify though, I’m worried that the example makes it look like we need growth in the new good(s) to get this weird slow GDP growth result, but that’s not true. In case that’s the impression you got, this example illustrates how we can have superexponential growth in every good but (arbitrarily slow) exponential growth in GDP.