The Experience Gap: AI Adoption in the United States and Germany
Presenter: Anoushka Kunder
Faculty Sponsor: Brenda K. Bushouse
School: UMass Amherst
Research Area: Artificial Intelligence
Session: Poster Session 1, 10:30 AM - 11:15 AM, Auditorium, A61
ABSTRACT
Artificial Intelligence is rapidly transforming the technology sector, generating
widespread concern over the widening inequality gap for junior level developers, who reap little
to no measurable productivity gains from AI and are most vulnerable to job displacement. This
thesis examines how the United States can promote a policy framework to support displaced
workers due to AI. This study employs a mixed method research design. The quantitative
analysis aims to establish the scope of AI driven inequality between junior level developers
compared to senior level developers by drawing on longitudinal software developer productivity
and AI adoption data (Daniotti et al., 2026) as well as data on government investment in work
training programs (OECD). The quantitative analysis measures (1) AI adoption rates among
software developers, (2) AI productivity benefits between junior and senior level workers, and
(3) government spending responses for worker training after AI implementation in the
workforce. The qualitative portion of the study is a comparative policy analysis of the United
States and Germany, aiming to assess differences in the quality of the support institutional
systems provide for displaced workers. The qualitative literature review examines the Workforce
Innovation and Opportunity Act, the Registered Apprenticeship Program, union coverage, works
councils, and dual vocational education and training systems in both countries grounded in
Varieties of Capitalism Theory (Hall and Soskice, 2001). This study, overall, aims to assess
whether the AI driven inequality for junior level tech workers in the United States can be
mitigated through institutional design.
