Digital versions of labor and capital can be reproduced much more cheaply than their traditional forms. This increases the supply and reduces the marginal cost of both labor and capital. What then, if anything, is becoming scarcer? We posit a third factor, ‘genius’, that cannot be duplicated by digital technologies. Our approach resolves several macroeconomic puzzles. Over the last several decades, both real median wages and the real interest rate have been stagnant or falling in the United States and the World. Furthermore, shares of income paid to labor and capital (properly measured) have also decreased. And despite dramatic advances in digital technologies, the growth rate of measured output has not increased. No competitive neoclassical two-factor model can reconcile these trends. We show that when increasingly digitized capital and labor are sufficiently complementary to inelastically supplied genius, innovation augmenting either of the first two factors can decrease wages and interest rates in the short and long run. Growth is increasingly constrained by the scarce input, not labor or capital. We discuss microfoundations for genius, with a focus on the increasing importance of superstar labor. We also consider consequences for government policy and scale sustainability.
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Why then, if emerging technologies are so impressive, are interest rates so low, wage growth so slow and investment rates so flat? And why is total factor productivity growth so lukewarm? To resolve this paradox, we propose a model of aggregate production with three inputs. The third factor corresponds to a bottleneck which prevents firms from making full use of digital abundance. Bottlenecks are ubiquitous in economics. This paper is typed on a computer that is over 1000 times faster than those of the past, but our typing is still limited by our interface with the keyboard.
An assembly line that doubles the output, speed or precision of 1, 2 or 99 out of 100 of processes will still be limited by that line’s weakest link. In other words, no matter how much we increase the other inputs, if an inelastically supplied complement remains scarce, it will be the gating factor for growth.
Our model can explain why ordinary labor and ordinary capital haven’t captured the gains from digitization, while a few superstars have earned immense fortunes. Their contributions, whether due to genius or luck, are both indispensable and impossible to digitize. This puts them in a position to capture the gains from digitization.
In our digital economy technology advances rapidly, but humans and their institutions change slowly. Institutional, managerial, technological, and political constraints become bottlenecks (Brynjolfsson et al., 2017). Before a firm can make use of AI decision making, its leaders need to make costly and time-consuming investments in quantifying its business processes; before it can scale rapidly using web services it needs figure out how to codify its systems in software. Therefore, digital advances benefit neither unexceptional labor nor standard capital, at least insofar as they can be replicated digitally (Brynjolfsson et al., 2014). The invisible hand instead favors those who are a scarce complement to these factors.
The inputs in our model are traditional capital and labor and a relatively inelastic complement we dub ‘genius’ or G. When G is relatively abundant, the economy approximates a two-factor one. But as G becomes relatively scarce, it becomes a bottleneck for output and captures an increasing share of national income. We show that when traditional inputs are sufficiently complementary to G, innovations in automation technology can reduce both labor’s share of income and the interest rate.
This theory fits what we know about the limitations of digital technologies, including cutting-edge AI. While general artificial intelligence might someday lead to an economic singularity, contemporary AI technologies have clear limitations, making humans indispensable for many essential tasks. Agrawal et al. (2018a) and Agrawal et al. (2018c) observe that AI is good at prediction tasks, but struggles with judgment – often a close complement. Brynjolfsson et al. (2018) create a rubric for assessing which tasks are suitable for machine learning and use it to evaluate the content of over 18,000 tasks described in O-Net. They find that while the new technology delivers super-human performance for some tasks, it is ineffective for many others. In particular, despite their many strengths, existing computer systems weak or ineffective at tasks that involve significant creativity or large-scale problem solving. Even tasks amenable to automation may require large organizational investments before business processes can be automated.
The only essential feature of G in our model is that it is inelastically supplied, because, in part, it is not subject to digitization. For concreteness, our primary interpretation for G is superstar individuals. They may be exceptionally gifted with the ability to come up with an exciting new idea, sort through bad ideas for a diamond in the rough,3 or effectively manage a business. If these good ideas are owned by and accumulate within firms, they correspond to a kind of alienable genius.
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Many have the sense that intangible assets and superstar workers are more abundant than ever. Perhaps the most surprising thing then about our result is that these factors are increasingly scarce. We contend that this is due to confusion between the value and importance of these inputs, which are increasing, and their relative abundance, which is decreasing.
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