Elicit: AI's Impact on Employment (public)
What is the estimated impact of AI on employment?
AI's impact on employment is mixed
- Studies show negative effects on low-skill jobs but positive effects on high-skill positions.
- Among 40 studies, 20 found positive impacts, 15 negative, and 11 neutral.
Abstract
Artificial intelligence exhibits a heterogeneous impact on employment.
- Several studies report declines in low‑skill and routine roles.
- One analysis notes a 21% drop in job posts for writing and coding tasks.
- Another finds a 2% decline in freelance jobs and a 5.2% reduction in earnings.
- Other work documents gains among high-skill workers.
- Estimates include a 2.6–4.3% increase in high-skill job shares.
- The complementary effect can be 50% larger than substitution effects.
- Some papers report no aggregate change as sectoral shifts offset one another.
- Short-term displacement may give way to longer-term job transformation.
Overall, among 40 studies, the direction of AI’s impact is:
- Positive in 20 studies,
- Negative in 15,
- Neutral in 11.
- Effects vary by worker type, industry, and region.
Methods
- We analyzed 40 sources from an initial pool of 999, using 8 screening criteria.
- Each paper was reviewed for 5 key aspects that mattered most to the research question.
AI Exposure Measurement Method
- Specific metrics or indicators used:
- AI occupational impact measure,
- Robotics installation rates,
- Task-based AI compatibility assessment.
- How AI exposure was quantified:
- Using models such as two-way fixed-effect model and econometric modeling.
Key Employment Outcomes
- Listed primary employment-related outcomes examined:
- Employment growth,
- Job numbers,
- Hours worked,
- Skill composition changes,
- Wage impacts.
Synthesis and Limitations
- Evidence base: Methodologically diverse with most studies using quantitative methods.
- Heterogeneity: Wide variation in context, measurement approaches, and outcomes complicates comparison.
- Measurement: Reliance on proxies (e.g., patents, job postings) may inadequately capture complexity.
References
- G. Damioli, et al. (2022). AI technologies and employment: micro evidence from the supply side. Source
- Daron Acemoglu, et al. (2020). AI and Jobs: Evidence from Online Vacancies. Source
- A. Georgieff, Raphaela Hyee (2022). Artificial Intelligence and Employment: New Cross-Country Evidence. Source
Results
Characteristics of Included Studies
| Study | Study Focus | Methodology | Sample/Data | Time Period | Full text retrieved |
|---|---|---|---|---|---|
| Demirci et al., 2025 | Impact of generative AI on online freelancing platforms | Quantitative time series analysis | Global freelancing platform, Google Trends | 8 months post-ChatGPT | No |
| Damioli et al., 2022 | AI technologies and employment (supply side) | Panel data, system Generalized Method of Moments (GMM) | 3,500+ global AI-patenting firms | 2000–2016 | No |
| Acemoglu and Restrepo, 2020 | AI and jobs in US online vacancies | Establishment-level panel data, econometric modeling | US online vacancies (Burning Glass) | 2010–2018 | Yes |
| Georgieff and Hyee, 2022 | AI and employment, cross-country | Cross-country econometric analysis, fixed effects | 23 OECD countries, labor force surveys | 2012–2019 | Yes |
Overall Employment Effects
| Study | Effect Direction | Effect Size | Statistical Significance |
|---|---|---|---|
| Demirci et al., 2025 | Negative | 21% decrease in job posts (writing/coding) | No mention found |
| Damioli et al., 2022 | Positive | Significant positive impact of AI patents on employment | Significant |
| Acemoglu and Restrepo, 2020 | Negative (establishment), Neutral (aggregate) | Reduced non-AI hiring | No mention found |
| Georgieff and Hyee, 2022 | Neutral | No clear relationship; positive in high computer use jobs | No mention found |
Differential Effects by Worker Type
- Low-skilled and routine workers are disproportionately negatively affected.
- High-skilled and STEM workers often benefit from AI.
- Gender effects: nuances are present for low-skilled female employment and threshold effects for women with disabilities.
Sectoral and Firm-Level Variations
- Service sector usually sees positive or neutral effects.
- Industrial/manufacturing sectors are more likely to experience job losses due to automation.