Research Summary: Practice Makes Perfect: Tacit Knowledge, Generative AI, and Returns to Schooling and Experience

Research indicates that generative AI alters human capital dynamics by substituting machine-supplied codified knowledge for formal schooling while increasing the relative value of practice-based tacit knowledge and judgment. Consequently, returns to traditional schooling may decline as codified knowledge cheapens, whereas returns to experiential learning and practice rise, potentially rotating the workforce experience wage profile.

Why it matters

This shift alters the foundational economic assumptions underpinning organizational talent acquisition, compensation, and human capital valuation. By eroding the premium on codified education while amplifying the value of experiential judgment, institutions must re-evaluate how workforce capabilities are developed and rewarded. Understanding these shifting returns is essential for mitigating structural skill imbalances as AI integration matures.

Key insights

• Generative AI reduces the scarcity and cost of codified knowledge directly at the point of production.
• Judgment developed through practice remains inherently anchored to the human worker rather than automated systems.
• In the short run, AI substitutes for the codified component of worker human capital, dampening the wage premium associated with formal schooling.
• Where codified knowledge and practice-based knowledge complement each other, the economic return to experience increases in domains with rapid experiential learning.
• Technology-specific experience faces risks of obsolescence, altering and rotating the traditional return-to-experience wage curve.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2610.36008