Abstract: Career minimalism represents a fundamental shift in how professionals—particularly Generation Z and millennials—conceptualize work's role in their lives. Rather than pursuing traditional upward mobility at all costs, career minimalists prioritize stability, boundaries, and fulfillment through secure employment, clear work-life separation, and diversified skill development. This article examines the emergence of career minimalism as a response to chronic workplace burnout, economic volatility, and evolving generational values. Drawing on organizational psychology, human resource management, and labor economics literature, we analyze the individual and organizational consequences of this philosophy and identify evidence-based practices for supporting sustainable career approaches. We argue that career minimalism is not withdrawal from work but strategic energy allocation—a recalibration of the psychological contract between employees and employers that prioritizes long-term resilience over short-term advancement. Organizations that understand and accommodate this shift stand to benefit from improved retention, reduced burnout, and access to diverse talent seeking meaningful but bounded employment relationships.

Abstract: The emergence of large language models (LLMs) has transformed human-machine interaction, yet evaluation frameworks remain predominantly model-centric, focusing on standalone AI performance rather than emergent collaborative outcomes. This article introduces a novel Bayesian Item Response Theory framework that quantifies human–AI synergy by separately estimating individual ability, collaborative ability, and AI model capability while controlling for task difficulty. Analysis of benchmark data (n=667) reveals substantial synergy effects, with GPT-4o improving human performance by 29 percentage points and Llama-3.1-8B by 23 percentage points. Critically, collaborative ability proves distinct from individual problem-solving ability, with Theory of Mind—the capacity to infer and adapt to others' mental states—emerging as a key predictor of synergy. Both stable individual differences and moment-to-moment fluctuations in perspective-taking influence AI response quality, highlighting the dynamic nature of effective human-AI interaction. Organizations can leverage these insights to design training programs, selection criteria, and AI systems that prioritize emergent team performance over standalone capabilities, marking a fundamental shift toward optimizing collective intelligence in human-AI teams.

Artificial intelligence is reshaping white-collar work at an unprecedented pace, yet many human resources functions remain on the sidelines of this transformation. Drawing on insights from workforce transformation leaders and emerging organizational research, this article examines the urgent imperative for HR to design AI-integrated work systems before technology architectures determine human roles by default. The parallels to early 20th-century scientific management reveal risks of task fragmentation that prioritizes algorithmic efficiency over professional craft and worker agency. Evidence from large-scale skills transformation initiatives demonstrates that strategic HR leadership can enable talent redeployment at market speed while preserving meaningful work. With entry-level pathways narrowing and traditional career progression disrupted, HR professionals face a pivotal choice: architect human-centered AI work systems now, or inherit technology-determined structures later. This article synthesizes academic research and practitioner experience to outline evidence-based responses across transparent governance, skills infrastructure, and agency-preserving work design that position HR as strategic architects of the AI-augmented workplace.