My research examines the transformative effects of artificial intelligence and automation on labor markets, educational choices, and firm behavior. Through innovative experimental methods and large-scale empirical analyses, I investigate how individuals and organizations adapt to technological disruption, with particular emphasis on the economic valuation of human capital and educational investments.

Individual and Firm Economics of Automation and AI

My work in this area explores how artificial intelligence reshapes career decisions, labor market dynamics, and organizational training strategies. The emergence of generative AI (GenAI) tools, such as ChatGPT, has fundamentally altered the automation landscape, now threatening occupations previously considered immune to technological substitution.

My job market paper, co-authored with Martina Viarengo and Thea Zöllner, investigates how the next generation of workers values the integration of GenAI into their future occupations. Through a discrete-choice experiment with a representative sample of over 7,000 Swiss adolescents on the verge of entering the labor market, we examine their preferences for working with GenAI. Our findings reveal a general aversion to GenAI usage, which is particularly strong among females. However, this attitude is nuanced: adolescents welcome GenAI collaboration when it is not associated with increased automation risk and when overall usage levels are relatively low. Crucially, the availability of continuous education opportunities significantly increases the willingness to work with GenAI for both genders.

Complementing the job market paper’s labor supply focus, I am currently examining the demand side for young workers in an ongoing project, studying how training firms adjust apprenticeship demand in anticipation of automation. Together with Claudio Schilter, we embedded a vignette experiment in a survey of more than 2,800 training‑firm recruiters to elicit planned adjustments across scenarios varying automation intensity and timing. We find sizable intended apprenticeship position cuts with higher automation intensity and sooner implementation. Effects are strongest in routine and highly AI-exposed occupations, as well as among large, capital-intensive firms. A higher retention share of trained apprentices mitigates the intensity of responses, while profit motives heighten sensitivity to earlier timing.

Exploiting ChatGPT’s surprise launch in November 2022, in a project with Daniel Goller and Stefan C. Wolter, we deliver the first causal evidence that GenAI redirects occupational choices. A difference-in-discontinuity design applied to 45 million Swiss apprenticeship-search queries reveals an 8 % and persistent drop in the supply of apprentices, strongest in occupations with a large share of cognitive tasks, high demands on language skills, and those previously considered “safe” from automation. Alongside evidence of declining applicant quality in Switzerland’s most popular—and highly GenAI-exposed—commercial apprenticeship, our findings suggest that teenagers' perceptions of GenAI risks, as mirrored in their occupational choices, closely align with those of labor market experts.

In another project with Maria A. Cattaneo and Stefan C. Wolter, we demonstrate that individuals place substantial monetary value on reducing automation risk. In a discrete-choice experiment with nearly 6,000 Swiss participants, we find that respondents would accept salary reductions of almost 20% of the median annual wage to reduce their automation risk by 10 percentage points. This finding reveals the profound anxiety surrounding AI-driven job displacement and suggests that automation concerns significantly influence career decisions. Notably, willingness to pay for risk reduction increases with higher baseline automation risk levels, contrary to patterns observed in other risk contexts.

My ongoing research on students as AI users examines how the next generation of workers engages with artificial intelligence technologies across school and home contexts. This study, based on survey data from 5,000 Swiss students aged 8-18, reveals significant contextual differences in AI usage patterns, ranging from passive homework assistance to interactive, collaborative problem-solving. The study identifies distinct AI-user typologies predicted by digital infrastructure, sociodemographic factors, and teacher practices, highlighting how individual resources and institutional settings shape AI adoption. These findings have important implications for understanding educational equity and developing AI literacy in an increasingly automated economy.

My analysis of routine job dynamics in Switzerland from 1992 to 2018 provides crucial context for understanding current AI disruptions. Contrary to expectations, I find that the decline of routine employment resulted primarily from reduced inflow rates rather than increased outflows, suggesting that Switzerland's labor market response to digitalization differs markedly from patterns observed in the United States.

Valuation of Tertiary Education

My second research focus examines how individuals value educational credentials in an era of rapid technological change. This work challenges conventional assumptions about education as an intrinsic good, instead revealing that individuals approach educational decisions through explicit cost-benefit calculations.

Through a discrete-choice experiment, in collaboration with Maria A. Cattaneo and Stefan C. Wolter, we investigate preferences for different educational pathways when controlling for wages, hierarchical status, and automation risk. Our findings suggest that respondents primarily view higher education as an economic investment, rather than pursuing it for intrinsic fulfillment. This economic rationality extends to incomplete degrees, where my collaboration with Andrea Diem and Stefan C. Wolter reveals that university dropouts without job-relevant majors face similar labor market outcomes to those who never enrolled, while alternative ways of human-capital accumulation, such as traineeships, are significantly preferred.

The intersection of AI and educational choice represents a particularly promising avenue for future research. As GenAI transforms skill requirements across occupations, understanding how students weigh AI usage capabilities alongside traditional occupational attributes becomes crucial for career guidance and policy formation.

Future Research Directions

My research agenda will continue to explore the dynamic relationship between technological advancements and human capital formation. Through rigorous empirical methods, particularly choice experiments and natural experiment designs, I aim to provide evidence-based insights for individuals navigating career decisions, firms developing human capital strategies, and policymakers addressing the societal implications of automation and AI.

My research program contributes to our understanding of how technological progress reshapes economic behavior, while providing practical guidance for stakeholders across the education-to-work transition in the dawning era of artificial intelligence.