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📖Harvard Study: AI Adoption Linked to Expanding Workloads Research published in Harvard Business Review found that AI tools introduced at a U.S. technology company did not reduce employee workloads over an eight-month period. Instead, approximately 200 employees who independently adopted AI took on broader responsibilities, worked longer hours, and increased multitasking. The study combined behavioral tracking with more than 40 in-depth interviews. Employees reported that AI made unfamiliar tasks feel manageable, encouraging them to operate beyond their formal roles. The research also identified boundary erosion between work and personal time, with staff submitting prompts after hours or during breaks. Engineers noted additional time spent reviewing and coaching colleagues on AI-assisted code, as requests for “vibe-coding” support accumulated. The findings indicate that productivity gains from AI adoption may coincide with role expansion, longer working time, and shifting workplace expectations, raising implications for labor governance, workload regulation, and organizational oversight of AI deployment. #AIandLaw#FutureOfWork#AIGovernance#WorkplaceAI

📖AI Adoption and the Erosion of Skill Formation A new paper, “How AI Impacts Skill Formation,” by Judy Hanwen Shen and Alex Tamkin, concludes that aggressive workplace deployment of AI may undermine professional development when workers are no longer cognitively engaged in their tasks. The findings indicate that reliance on AI can substitute for learning-by-doing, the mechanism through which expertise traditionally accumulates inside organizations. The paper highlights a structural risk for early-career professionals. Under time pressure and organizational demands, junior employees may default to AI tools to complete assignments quickly, bypassing the skill-building processes that would normally prepare them for higher-responsibility roles. This creates a pipeline problem: future experts may never fully form. A further implication is operational. As companies shift toward AI-generated outputs with human oversight, workers whose skills were weakened by prior AI reliance may lack the capacity to validate or debug the systems they supervise. The research frames AI not only as a productivity technology, but as a force that can reconfigure the long-term competence base of the workforce. #AIRegulation#FutureOfWork#Skills#AIGovernance#WorkplaceAI#AIEthics