Forecasting AI-Era Productivity: The Intellectually Converged Human Framework and a Missing Cognitive Mediator in Production Function Theory
Why AI investments fail without developing workers' ability to use them
Massive spending on artificial intelligence hasn't delivered expected productivity gains because companies deploy AI without first building workers' capacity to actually use it effectively. A new framework shows that the match between AI availability and what researchers call "convergence capacity"—a combination of practical understanding, self-awareness, flexible thinking, and ability to connect ideas—accounts for 86% of productivity differences across wealthy nations, compared to just 31% for AI deployment alone.
Countries and companies are pouring billions into AI tools that sit underutilized because workers lack the cognitive skills to integrate them into their jobs. South Korea exemplifies the problem: despite strong workforce education and significant AI investment, low convergence capacity means minimal actual productivity gain. The framework suggests that before buying more AI, organizations need to invest in training that builds workers' ability to learn across domains, think flexibly, and adapt—a shift that could unlock trillions in stranded AI value currently going unrealized.