Will AI take my job?

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1. 1

   Follow the money: A startup-based measure of AI exposure across occupations, industries, and regions.

   PNAS nexus (Fenoaltea et al.)Published Jun 23, 2026Checked Oct 10, 2026

   “Our findings indicate that even though white-collar high-skilled occupations are theoretically highly exposed, they are heterogeneously targeted by AI startups. Roles involving routine organizational tasks, such as data analysis and office management, show significant exposure, while occupations involving tasks that are tied to ethical or high-stakes considerations-such as judges or surgeons-present lower AISE scores, despite technical feasibility for automation. Our approach challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead societal desirability and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as the technical feasibility of AI applications. This framework provides a forward-looking tool for policymakers to monitor the evolving impact of AI and navigate a fast changing labor market landscape.”

2. 2

   Automation technologies and their impact on employment: A review, synthesis and future research agenda

   Technological Forecasting and Social Change (Filippi et al.)Published Mar 10, 2023Checked Oct 10, 2026

   “This paper aims to review prior studies investigating how automation technologies affect employment. Our structured systematic review resulted in 102 publications recovered from Web of Science, Scopus and hand searching. The literature investigating how automation technologies affect employment is extremely complex and detailed, given that the impact of automation is evaluated at different levels of analysis (i.e., global, international, continental, country, regional, labour market, industry, firm, occupational, worker, and work activities) by adopting alternative methods (i.e., estimating the probability of automation or the net impact of employment) and, for some levels of analysis, the impact of each specific type of automation technology is evaluated. Moreover, the results are often inconsistent and inconclusive since only few clear results emerge and the impact of automation technologies is unclear for many levels of analysis. Research gaps and future research agenda are identified and discussed based on previous evidence.”

3. 3

   Occupational Vulnerability to AI-Driven Change: The Role of Skill Composition, Task Structure, and Psychosocial Buffers.

   American journal of industrial medicine (Welithotage & Nowrouzi-Kia)Published Sep 6, 2026Checked Oct 10, 2026

   “AI Exposure was quantified using the Artificial Intelligence Occupation Exposure (AIOE) score, and the Psychosocial Buffer Index (PBI) captured protective job features relevant to occupational health. Principal Component Analysis (PCA) was used to examine the relationships among AI Exposure, PBI, and occupational skill composition.ResultsFive distinct occupational clusters emerged, systematically differentiating themselves in both AI exposure and buffering capacity. High AI exposure clusters varied in PBI, suggesting that structural job features can mitigate automation-related vulnerability, while low-exposure roles with limited buffering may still face occupational stress. PCA results revealed that AI exposure aligns with cognitive intensity, whereas PBI shows independent dispersion across the skill space.ConclusionsThese findings highlight that AI-related occupational risk is multi-dimensional and indicates the relation between automation potential and the job structure. This framework provides actionable insights for targeted interventions, including retraining, workflow redesign, and collaborative AI integration to support worker well-being in an AI-driven labor market.”

4. 4

   The risks and bottlenecks to automation in employment in Argentina. New impacts on the occupational structure in selected economic sectors.

   Frontiers in sociology (Chávez & Rodríguez)Published Mar 19, 2026Checked Oct 10, 2026

   “The analysis covered sectors with different levels of technological integration-high (e.g., software), medium (e.g., food), and low (e.g., textiles). An automation risk index was constructed based on job skills and tasks. The results indicate that professionals, scientists, managers, and technicians exhibit a lower risk of automation, while elementary and industrial occupations face a higher risk. Social and creative intelligence were identified as 'bottlenecks' in the face of automation, an aspect that we have emphasised in this analysis. The software and pharmaceutical sectors are more protected, unlike the textile and hotel industries. In addition, the use of Artificial Intelligence (AI) is more prevalent in lower-risk occupations, mainly for complex and skilled tasks, complementing human work. In conclusion, the study emphasises the need to understand these changes in order to comprehend and predict the future of the labor market. Skills involving emotional and creative intelligence offer robust protection against automation, although emerging generative AI could begin to impact tasks previously considered secure.”

5. 5

   Artificial Intelligence and Jobs: Evidence from Online Vacancies

   Journal of Labor Economics (Acemoğlu et al.)Published Apr 1, 2022Checked Oct 10, 2026

   “We study the impact of artificial intelligence (AI) on labor markets using establishment-level data on the near universe of online vacancies in the United States from 2010 onward. There is rapid growth in AI-related vacancies over 2010–18 that is driven by establishments whose workers engage in tasks compatible with AI’s current capabilities. As these AI-exposed establishments adopt AI, they simultaneously reduce hiring in non-AI positions and change the skill requirements of remaining postings. While visible at the establishment level, the aggregate impacts of AI-labor substitution on employment and wage growth in more exposed occupations and industries is currently too small to be detectable.”

6. 6

   Artificial intelligence and jobs: evidence from US commuting zones

   Economic Policy (Bonfiglioli et al.)Published Nov 22, 2024Checked Oct 10, 2026

   “We study the effect of Artificial Intelligence (AI) on employment across US commuting zones (CZs) over the period 2000–2020. A simple model shows that AI can automate jobs or complement workers, and illustrates how to estimate its effect by exploiting variation in a novel measure of local exposure to AI: job growth in AI-related professions built from detailed occupational data. Using a shift-share instrument that combines industry-level AI adoption with local industry employment, we estimate robust negative effects of AI exposure on employment across CZs and time. We find that AI’s impact is different from other capital and technologies, and that it works through services more than manufacturing. Moreover, the employment effect is especially negative for low-skill and production workers, while it turns positive for workers at the top of the wage distribution and for those in STEM occupations. These results are consistent with the view that AI has contributed to the automation of jobs and to widen inequality.”

7. 7

   Artificial intelligence, hiring and employment: job postings evidence from Sweden

   Applied Economics Letters (Engberg et al.)Published May 6, 2025Checked Oct 10, 2026

   “This paper investigates the impact of artificial intelligence (AI) on hiring and employment, using the universe of job postings published by the Swedish Public Employment Service from 2014 to 2022 and full-population administrative data for Sweden. We exploit a detailed measure of AI exposure according to occupational content and find that establishments exposed to AI are more likely to hire AI workers. Survey data further indicate that AI exposure aligns with greater use of AI services. Importantly, rather than displacing non-AI workers, AI exposure is positively associated with increased hiring for both AI and non-AI roles. In the absence of substantial productivity gains that might account for this increase, we interpret the positive link between AI exposure and non-AI hiring as evidence that establishments are using AI to augment existing roles and expand task capabilities, rather than to replace non-AI workers.”

8. 8

   Automation, workers’ skills and job satisfaction

   PLoS ONE (Schwabe & Castellacci)Published Nov 30, 2020Checked Oct 10, 2026

   “This paper studies the extent to which automation affects workers' job satisfaction, and whether this effect differs for high- versus low-skilled workers. The empirical analysis uses microdata for several thousand workers in Norway from the Working Life Barometer survey for the period 2016-2019, combined with information on the introduction of industrial robots in Norway from the International Federation of Robotics. Our identification strategy exploits variation in the pace of introduction of industrial robots in Norwegian regions and industries since 2007 to instrument workers' fear of replacement. The results indicate that automation in industrial firms in recent years have induced 40% of the workers that are currently in employment to fear that their work might be replaced by a smart machine in the future. Such fear of future replacement does negatively affect workers' job satisfaction at present. This negative effect is driven by low-skilled workers, which are those carrying out routine-based tasks, and who are therefore more exposed to the risks of automation.”