Brynjolfsson et al. (2025) compare employment trends for workers in more versus less AI-exposed occupations, using the task exposure measures from Eloundou et al. (2023) and payroll data from ADP. Johnston and Makridis (2025) do a similar task-based analysis using US administrative data, but they aggregate treatment to the industry level. Hui et al. (2024) study how freelance jobs on Upwork responded to the release of ChatGPT and advanced image generation tools, comparing workers in directly affected categories to those in unaffected categories before and after each tool's release date. Hampole et al. (2025) instrument for firm-level AI adoption using historical university hiring networks: firms that historically recruited from universities whose graduates later entered AI-related roles faced lower adoption costs.
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The value is not in the verification workforce. It is in what verified delivery enables. Consider a company delivering ML kernels for new hardware. Today, months go to testing and qualification. When AI writes the kernel and proves it correct in one pass, that timeline collapses to hours. A provably correct hardware design delivered in weeks rather than the year it currently takes changes the economics of an entire industry.