Custodes Futurifor the advancement of humanity

Technological factor, barrier 7 of 7

Automation and job displacement

Acemoglu and Restrepo (2020) studied industrial robots in U.S. labor markets.

Evidence

  • Employment and wages. One more robot per thousand workers reduced the employment-to-population ratio by about 0.2 percentage points and wages by about 0.42%.
  • Who is affected. Effects were concentrated among workers in routine manual jobs.

Why it matters: Technology can raise overall productivity while displacing specific workers. Without retraining and support, displaced workers face the economic scarcity described in the economic factor. Newer research on artificial intelligence is still emerging, and its effects are not yet established.

Proposed and experimental methods

Methods that are proposed, under trial, approved in some places, or tried and then failed. Each shows a stage label and an evidence rating. A stage label shows how far a method has progressed, not whether it works. The stage labels are explained on the technological factor page.

  • Sector-focused job training (Large trial, A). Programs screen applicants, then combine occupational and soft-skills training with support services for jobs in specific higher-paying industries. A review of randomized evaluations found earnings gains of 12% to 34% that persisted after training, driven mainly by placing workers in higher-paying industries and occupations rather than by raising employment rates (Katz et al., 2022). The programs studied served low-wage workers in general, not specifically workers displaced by automation.
  • Using AI to augment workers (Early trial, B for productivity, C for jobs). Among 5,179 customer support agents, access to a generative AI assistant raised issues resolved per hour by 14% on average and 34% for novice and low-skilled workers, with minimal effect for the most experienced (Brynjolfsson, Li, and Raymond, 2025). In an online experiment with 453 professionals, ChatGPT cut writing time by 40% and raised quality by 18% (Noy and Zhang, 2023). But linked Danish survey and administrative data showed precise null effects of chatbot adoption on earnings and hours, ruling out effects larger than 2% two years after ChatGPT's launch (Humlum and Vestergaard, 2025).
  • Wage insurance for displaced workers (Approved but not scaled, B). Workers who take a lower-paying new job receive part of the pay difference. Using an age-based eligibility cutoff in the U.S. Trade Adjustment Assistance program, eligibility raised short-run employment and long-run cumulative earnings, mostly through shorter periods without work, and the authors conclude the program pays for itself under conservative assumptions (Hyman, Kovak, and Leive, 2024).
  • Job guarantee for the long-term unemployed (Early trial, B). This builds on the guaranteed income entry in "Learned helplessness and low sense of control" in the psychological factors. From October 2020 to March 2024, every long-term unemployed resident of Gramatneusiedl, Austria (the town of the 1930s Marienthal unemployment study) was offered a job. Long-term unemployment nearly disappeared, participants gained income, economic security, social recognition, and well-being with no effects on physical health, and the net public cost was about €237 per participant per month (Kasy and Lehner, 2026). The randomized part included only 62 people in one town.
  • Taxing robots during the transition (Theoretical, C). In a model with automation and workers choosing skills, the authors find that robots should be taxed while current routine workers who cannot easily switch to non-routine jobs remain in the workforce, and that the optimal robot tax falls to zero once they retire (Guerreiro, Rebelo, and Teles, 2022). No country has adopted such a tax.

Sources cited on this page

  1. Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244. DOI B Moderate
  2. Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889-942. link B Moderate
  3. Guerreiro, J., Rebelo, S., & Teles, P. (2022). Should robots be taxed? The Review of Economic Studies, 89(1), 279-311. link C Limited
  4. Humlum, A., & Vestergaard, E. (2025). Still waters, rapid currents: Early labor market transformation under generative AI (NBER Working Paper No. 33777). National Bureau of Economic Research. link B Moderate
  5. Hyman, B. G., Kovak, B. K., & Leive, A. (2024). Wage insurance for displaced workers (NBER Working Paper No. 32464). National Bureau of Economic Research. link B Moderate
  6. Kasy, M., & Lehner, L. (2026). Employing the unemployed of Marienthal: Evaluation of a guaranteed job program. American Economic Journal: Economic Policy (forthcoming). link B Moderate
  7. Katz, L. F., Roth, J., Hendra, R., & Schaberg, K. (2022). Why do sectoral employment programs work? Lessons from WorkAdvance. Journal of Labor Economics, 40(S1), 249-291. link A Strong
  8. Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. link B Moderate: C for job outcomes

Every source for this factor is listed on the technological factor page.