Goldman-Sachs flows neg- AI augments, and aggregate demand is upwards. Prefer the actual study to overhyped news articles.

Collectively, our estimates suggest that a large share of employment and work is at least partially exposed to automation by AI, raising the prospect of significant labor savings. To assess the robustness of our estimates, we compare our baseline US estimate to a wider range of scenarios, including those in which AI can perform more or less difficult tasks than we assume in our baseline, and in which we relax our assumption that AI cannot assist with jobs which are primarily outdoors or physical (i.e., a scenario in which AI is complementary with robotics and existing machinery). Our scenario analysis suggests that the ultimate share of work exposed to automation could range from 15-35% (Exhibit 7, left panel), a range which is consistent with—but on the conservative side of—existing estimates in the literature (Exhibit 7, right panel). Our relatively conservative baseline primarily reflects our narrower focus on the impact of generative AI, in contrast to other studies which sometimes consider a wider range of related technologies (including robotics) that increase the scope for automation. Although the impact of AI on the labor market is likely to be significant, most jobs and industries are only partially exposed to automation and are thus more likely to be complemented rather than substituted by AI. In Exhibit 8, we assume for illustration that jobs for which at least 50% of importance- and complexity-weighted tasks are exposed to automation are likely to be substituted by AI, while jobs with an exposure of 10-49% are more likely to be complemented, and jobs with a 0-9% exposure are unlikely to be impacted. In our baseline, these assumptions would be consistent with 7% of current US employment being substituted by AI, 63% being complemented, and 30% being unaffected, though the ultimate effects will depend on how labor demand and occupational workloads evolve in response to partial labor savings in the majority of occupations. The large share of employment exposed to automation from generative AI raises the potential for a boom in labor productivity that significantly increases global output. There are two main channels through which AI-driven automation could raise global GDP. First, most workers are employed in occupations that are partially-exposed to AI automation and, following AI adoption, will likely apply at least some of their freed-up capacity toward productive activities that increase output. Academic studies confirm that workers at early-adopting firms experience higher labor productivity growth following AI adoption, with estimates generally implying a 2-3pp/year boost (Exhibit 9). While differences in the capability of generative AI relative to earlier vintages make it hard to extrapolate these results forward, they clearly suggest that generative AI can drive an economically significant increase in productivity. Second, we anticipate that many workers that are displaced by AI automation will eventually become reemployed—and therefore boost total output—in new occupations that emerge either directly from AI adoption or in response to the higher level of aggregate and labor demand generated by the productivity boost from non-displaced workers. Both of these channels have plenty of historical precedent. For example, information technology innovations introduced new occupations like webpage designers, software developers, and digital marketing professionals, but also increased aggregate income and indirectly drove demand for service sector workers in industries like health care, education, and food services. To demonstrate how technological innovation that initially displaces workers drives employment growth over a long horizon, in Exhibit 10 we show results from a recent study by economist David Autor and coauthors.5 Using Census data, they find that 60% of workers today are employed in occupations that did not exist in 1940, implying that over 85% of employment growth over the last 80 years is explained by the technology-driven creation of new positions. Exhibit 11 leverages a different academic study by economists Daren Acemoglu and Pascual Restrepo that decomposes changes in labor demand into contributions from productivity growth and technology-driven worker displacement and reemployment (among other factors) to show how the drivers of labor demand change over time.6 Technological change displaced workers and created new employment opportunities at roughly the same rate for the first half of the post-war period, but has displaced workers at a faster pace than it has created new opportunities since the 1980s. These results suggest that the direct effects of generative AI on labor demand could be negative in the near-term if AI affects the labor market in a manner similar to earlier advances in information technology, although the effects on labor productivity growth would still be positive. The combination of significant labor cost savings, new job creation, and a productivity boost for non-displaced workers raises the possibility of a labor productivity boom like those that followed the emergence of earlier general-purpose technologies like the electric motor and personal computer. These past experiences offer two key lessons. First, the timing of a labor productivity boom is hard to predict, but in both cases started about 20 years after the technological breakthrough, at a point when roughly half of US businesses had adopted the technology (Exhibit 12, left panel). Second, in both of these instances, labor productivity growth rose by around 1.5pp/year in the 10 years after the productivity boom started, suggesting that the labor productivity gains can be quite substantial (Exhibit 12, right panel). To estimate the boost to labor productivity in the US from widespread adoption of generative AI, in Exhibit 13 we sum up the implied labor productivity effects from direct labor cost savings, a productivity boost for non-displaced workers, and a composition effect from the reemployment of displaced workers in new positions. Our baseline analysis incorporates our key findings from above, including that about 7% of workers are fully displaced but that most are able to find new employment in only slightly less productive positions, that partially exposed workers experience a boost in productivity consistent with existing estimates (Exhibit 9), and that effects are realized over a 10-year period that starts around the time when roughly half of businesses have adopted generative AI. Under these assumptions we estimate that widespread adoption of generative AI could raise overall labor productivity growth by around 1.5pp/year (vs. a recent 1.5% average growth pace), roughly the same-sized boost that followed the emergence of prior transformative technologies like the electric motor and personal computer. This estimated boost to labor productivity growth is both admittedly quite large and highly uncertain. Exhibit 13 therefore also considers other plausible scenarios and shows that the boost to US productivity growth could easily range from 0.3-3.0pp depending on the difficulty level of tasks generative AI can perform, how many jobs are ultimately automated, and the speed of adoption: