Скачать или смотреть Adaptive Human Capital: Workforce Measurement Beyond W-2s in the AI Era

In this research-driven episode from Di Tran University – College of Humanization, we examine how workforce development, credential validation, and labor measurement must evolve in an AI-accelerated economy.

Traditional workforce systems rely heavily on W-2 wage data, enrollment counts, and short-term placement metrics—frameworks that increasingly fail to capture licensed self-employment, micro-enterprise ownership, and human-touch labor that resists automation. Drawing on federal guidance, audit history, and empirical labor data, this study introduces a Full-Stack Workforce Development Model and a Proof-of-Work framework that moves beyond static certificates toward verifiable, real-world performance.

This episode explores:

Why large portions of the workforce are invisible in W-2 data

How AI is displacing routine cognitive labor while elevating human-touch industries

The role of digital portfolios, licensure, and public work artifacts as auditable proof of work

Policy-safe approaches for recognizing non-W-2 outcomes during government audits

The future of workforce accountability, dignity of work, and human resilience in an AI economy

This is not advocacy or a funding proposal. It is public-interest research intended to support policymakers, workforce boards, educators, and practitioners navigating the next era of labor measurement and human capital development.