Benchmarking the Future of Work: Mapping AI Progress to Occupational Tasks

Abstract

Artificial intelligence is advancing at a pace once thought unimaginable, yet we still lack clear tools to understand how these breakthroughs map onto the world of work and, in particular, how they shape an occupation’s exposure to AI. We introduce a new measure of an occupation’s exposure to AI that we call the Benchmark-based AI Occupational Exposure (BAIOE), that systematically links AI benchmark progress - the scoreboards that track frontier capabilities - to the occupational tasks that define human labor. Using O*NET tasks as a bridge, we connect benchmark trajectories across domains-including language, reasoning, vision, and multimodal tasks-to 52 human abilities, and translate these into occupation-level indices of AI exposure. The result is a dynamic, task-level methodology that allows us to track and forecast where automation pressures are likely to emerge. By repositioning benchmarks from technical scoreboards to economic indicators, this study offers a fresh lens for anticipating the future of work and shaping policy responses.

Keywords: innovation