> **来源：[研报客](https://pc.yanbaoke.cn)** # GLOBAL ECONOMICS ANALYST # How Concerned Should We Be About a Job Apocalypse? ■ AI can potentially automate tasks that account for $25\%$ of all work hours in the US. This significant exposure has raised concerns around widespread and permanent job loss, sparking fears of a “job apocalyptic” or “humans going the way of horses.” We expect that the AI transition will lead to a meaningful amount of labor displacement. Our baseline forecast for a $15\%$ AI-driven labor productivity uplift and the historical relationship between technologically driven productivity gains and job loss implies that $6 - 7\%$ of jobs will be displaced over the adoption period. Risks are skewed toward greater displacement if AI proves more labor displacing than prior technologies. While increased AI-driven displacement may create significant real income losses and economic headwinds for affected workers, AI will also create new jobs. AI infrastructure investment is already boosting employment. Productivity gains increase capacity and lower costs, providing an employment tailwind in sectors where demand is not saturated. And technological change is a main driver of long-run job growth via the creation of new occupations—only $40\%$ of workers today are employed in occupations that existed 85 years ago—suggesting that AI will create new roles even as it renders others obsolete. - There are several channels through which technological change can create new kinds of jobs. First, it can create new occupations directly. More than 6mn workers are currently employed in computer-related occupations that did not exist 30-40 years ago, while another 8-9mn are employed in roles enabled by the gig economy, e-commerce, content creation, or video games. Second, it can create new occupations by enabling increased specialization. Third, it can indirectly boost discretionary demand in occupations that emerge due to income gains, even if these roles were already technologically feasible. For example, 1mn workers today are employed in pet care, nail salons, educational support/tutoring, and athletic coaching occupations that emerged over the last 30 years. Even under our optimistic assessment that AI will create new jobs, we anticipate an increase in frictional unemployment as workers transition to new jobs. Under our baseline assumption for an orderly, 10-year firm-level adoption period we anticipate a peak gross unemployment rate increase of a bit more than $\frac{1}{2}$ pp. And although we see upside unemployment rate risks if AI adoption is more # Joseph Briggs +1(212)902-2163 joseph.briggs@gs.com Goldman Sachs & Co. LLC # Sarah Dong +1(212)357-9741 | sarah.dong@gs.com Goldman Sachs & Co. LLC frontloaded or results in more labor displacement, we remain skeptical that unemployment rate increases will be permanent as long as human labor maintains a competitive advantage in certain aspects of production. # How Concerned Should We Be About a Job Apocalypse? We have extensively argued that generative artificial intelligence (AI) will provide significant boosts to labor productivity and economic growth, primarily from its ability to automate a large share of work tasks. Our baseline estimate—which uses the Department of Labor's O*NET database to estimate the share of work tasks that can potentially be automated following the full adoption of generative AI—is that AI can potentially replace $25\%$ of all work hours in the economy (over and above normal structural change), with higher exposure among administrative, legal, and computer occupations and lower exposure for manual jobs (Exhibit 1). Exhibit 1: AI Could Automate $25\%$ of All Work Tasks in the US Source: Haver Analytics, Goldman Sachs Global Investment Research Our estimates and the broad consensus that AI opens new automation possibilities have raised concerns that AI could lead to widespread and permanent job losses creating a "job apocalyptic". These concerns echo prior predictions from Nobel Prize-winning economist Wassily Leontief that "humans could go the way of horses" due to technological change—a reference to the sharp decline in the horse population used in production shown in the first half of the 1900s (Exhibit 2)—that have reemerged in the current debate around the labor market impacts of AI. Exhibit 2: Some Commentators Fear that Humans Could "Go the Way of Horses" as Production Inputs Source: Goldman Sachs Global Investment Research # Labor Displacement Is Likely... Given emerging evidence that AI (when appropriately deployed) can deliver significant efficiency gains, recent corporate commentary suggesting that AI automation raises the bar for hiring for new roles, and survey responses from our GS banking team pointing to AI-driven headcount reductions among large US corporates over the next 1-3 years, we anticipate that widespread adoption of AI will lead to meaningful labor displacement. Combining our baseline forecast for a $15\%$ AI-driven labor productivity uplift with the historical relationship between technological-driven productivity gains and job loss (left chart, Exhibit 3) implies that $6 - 7\%$ of jobs could be displaced as AI is widely adopted. This estimate corresponds to AI fully automating work in the $20 - 30\%$ most exposed occupations (ranked by the AI-displacement risk scores that we developed last year). If AI proves more labor-displacing than historical technologies, the overall displacement rate would be higher (right chart, Exhibit 3). Exhibit 3: Some Labor Displacement Likely, Historical Evidence Suggests $6 - 7\%$ of Workers Will Be Temporarily Displaced Source: Data compiled by Goldman Sachs Global Investment Research # ... But Al Will Also Create New Jobs While AI-driven displacement could create significant real income losses and economic headwinds for affected workers, history strongly suggests that AI will also create new jobs. This can occur through several channels. Most immediately, the infrastructure buildup necessary to facilitate the AI transition is providing a near-term employment boost. As shown in Exhibit 4, job growth for electricians, HVAC installers, commercial building contractors, and utility construction workers has outpaced both broader construction and economy-wide job gains since the Chat-GPT moment in late 2022. While this impulse may not be entirely attributable to AI nor sustainable over the longer-run, job growth in "data center exposed" occupations has outperformed the rest of construction sector by almost 200k over the past 3 years. Exhibit 4: Al Infrastructure Investment Is Providing a Near-Term Boost to Employment Source: Goldman Sachs Global Investment Research Over the longer-run, AI-driven productivity gains may directly boost labor demand for some existing services if increased capacity leads to market expansion (see Bessen 2018 for a detailed analysis of this dynamic historically). As shown in Exhibit 5, productivity gains in cotton, steel, and motor vehicle production drove job growth over extended periods as technology-driven capacity increases and cost declines created new demand for these products. Only after demand was saturated did further increases in productivity lead to job losses (although losses were admittedly severe in these industries once this occurred). In the current context, this dynamic suggests that employment in highly-exposed but fast-growing industries (e.g., healthcare) could actually experience an AI hiring tailwind. Exhibit 5: Employment Often Rises as Productivity-Driven Cost Declines Expand Markets, but Then Falls as Demand Becomes Saturated Source: Goldman Sachs Global Investment Research Most importantly, technological change has been a main driver of employment growth via the creation of new occupations. Exhibit 6 summarizes recent research from David Autor and coauthors that shows how important this force has been both over the long-run. Using data from the Census Bureau's Alphabetical Indexes of Industries and Occupations, they find that $60\%$ of workers today are employed in occupations that did not exist in 1940. This means that over $85\%$ of the job gains over the last 85 years have accrued to new occupations, most of which were created by technology. Looking at this data over the recent period, we similarly find that job growth has outperformed the most since 2000 in categories where new occupations have disproportionately been created. We similarly find that job growth has outperformed the most over this period in categories where new occupations have disproportionately been created. Exhibit 6: Technological Creation of New Occupations Has Been a Main Driver of Job Growth, Both Over the Long-Run and More Recently Source: Goldman Sachs Global Investment Research # How Technology Creates New Occupations If the results above are a guide to future structural change, it means that even a large AI-related displacement of workers can be absorbed elsewhere if given enough time, particularly via the creation of new work opportunities and occupations. While it is hard to say exactly where "new AI occupations" will emerge, history provides guidance on how technology creates new occupations. First, technology creates new occupations that are directly made possible by technological progress. Exhibit 7 illustrates this dynamic over the last 30-40 years. The left chart shows that more than 6mn workers (almost $4\%$ of total employment) are employed in computer-related occupations—including 2mn as software engineers, 700k as support specialists, 644k as IT systems managers, 599k as IT systems analysts, and 319k as computer programmers—that did not exist in the pre-computer era. The right chart considers new industries that have emerged due to digital technology and the internet but that are less directly tied to computers. Compiling employment estimates from our US economic team's recent research on the gig economy (4mn), the BEA's estimates of e-commerce employment (3.3mn),<sup>1</sup> our equity analyst's recent research on the creator economy (750k), and US International Trade Administration's sizing of the US video game sector (350k), we estimate that 8-9mn jobs have been created in these industries. While not exhaustive, these estimates highlight that the economic transformation delivered by computer and digital technologies has created millions of new jobs, even while rendering others obsolete. Exhibit 7: Technology Has Directly Created Millions of New Jobs over the Last 30-40 Years Source: Goldman Sachs Global Investment Research Second, technology can create new occupations and jobs by enabling increased specialization. While this dynamic is relevant for most industries, it is most visible in the healthcare sector. While the fundamental objective of healthcare has remained relatively constant, technological advancement and improved scientific understanding of the human body have enabled nurses and doctors to shift from more "generalist" occupations that focus on a broad set of treatments to more "specialist" occupations that focus on specific conditions. Using the historical Alphabetical Indexes of Industries and Occupations lists—which have classified US Census respondents into roughly 30,000 occupational categories since 1850—we record how many distinct healthcare occupations were included in each decennial census from 1920-2020. Exhibit 8 shows that the number of distinct healthcare occupations has grown from 100-200 in the first half of the twentieth century to over 1000 in the 2020 Census. This increased specialization has in turn led to improvements in demand for health services, thereby contributing to job growth in the sector. Exhibit 8: Technology Can Create New and More Specialized Occupations Within an Occupation Category, as Has Been the Case for Healthcare Over the Last 100 Years Source: Census Bureau, Goldman Sachs Global Investment Research Third, technological change can indirectly boost discretionary demand for new occupations that were always technologically feasible but emerge as increases in aggregate income and demand and spare economic capacity spur innovation. Exhibit 9 illustrates this dynamic in the US since 1990. The last thirty years have seen rapid employment growth for workers in service-sector occupations like pet care, professional athletes/coaches/umpires, educational support/tutors, and nail technicians that did not previously exist. While technological progress is not the only factor driving the increases in income, demand and innovation that led these new personal services to emerge, it was a major contributor. Exhibit 9: Increased Economic Income, Capacity, and Demand Can Lead to Creation of New Industries and Jobs That Were Always Technologically Feasible Source: Goldman Sachs Global Investment Research Assuming these dynamics repeat, job gains in new occupations that are either directly related to AI, or that emerge indirectly as AI facilitates increased specialization or boosts aggregate demand for discretionary services, should help offset AI-driven job losses. # AI's Impact on Unemployment While we are optimistic that AI will create new jobs that absorb displaced workers, finding a job takes time, particularly if workers need to reskill to transition to new industries. As a result, we expect a notable increase in "frictional" unemployment that raises the unemployment rate even under our optimistic view regarding AI's labor market impacts. How much could the unemployment rate rise as displaced workers are transitioning to new roles? Under our baseline assumptions that 1) the adoption timeline is 10 years at the firm level (with 6-year intra-firm adoption lags), 2) $6 - 7\%$ of workers are displaced during this transition period, and 3) most workers take less than a year to find a new job (more specifically, we assume that most workers find new jobs at a rate consistent with the unemployment rate impulse shown in Exhibit 3), we estimate a peak gross unemployment rate increase of around 0.6pp (corresponding to a 1mn increase in unemployed workers; Exhibit 10). That said, the actual increase in the unemployment rate would likely be somewhat lower if the Fed moves to lower rates to offset the weaker labor market. However, both the displacement rate and the transition period are uncertain, and we cannot rule out larger increases in the unemployment rate. If AI-driven labor displacement turns out to be twice as large as our baseline (corresponding to a scenario where AI fully automates work tasks in the $50\%$ most exposed occupations) or AI adoption occurs twice as quickly (i.e., over a 5-year period with 3-year intra-firm adoption lags), the peak unemployment rate impact would double to 1.2pp (2mn unemployed workers). And in a scenario where AI displacement both is larger and occurs more quickly, the peak unemployment rate impact would rise to 2.4pp (4.1mn unemployed workers). We see further upside risks from "congestion externalities"—where a sharp rise in unemployment leads to a less efficient matching process—or if AI-related job losses front-run AI-related productivity gains. Exhibit 10: Our Baseline Assumptions Imply a Roughly $\frac{1}{2}$ pp Boost to the Unemployment Rate Due to Frictional Unemployment, with Upside Risks if Adoption Occurs More Quickly or AI Causes More Displacement Source: Goldman Sachs Global Investment Research Although the unemployment rate increases due to AI could be significant, the historical evidence that technology-driven job losses in one sector of the economy are compensated by job gains elsewhere strongly suggests that they will only be temporary. While historical patterns do not always repeat, as long as human labor maintains a competitive advantage in certain aspects of production, we expect that this time will not be different. # Joseph Briggs # Sarah Dong # The Global Economics Team # Jan Hatzius +1(212)902-0394 jan.hatzius@gs.com Goldman Sachs & Co. LLC # Joseph Briggs +1(212)902-2163 joseph. briggs@gs.com Goldman Sachs & Co. LLC # Sarah Dong +1(212)357-9741 sarah.dong@gs.com Goldman Sachs & Co. 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