AI Exposure Scores: what they measure, what they miss, and what comes next

2026-06-22

AI Summary

Existing static AI exposure metrics fall short of practical policy needs due to their lack of dynamism, geographic generalizability, and flexibility in task definition, further exacerbated by insufficient synergy between research and policymaking. This study systematically critiques the limitations of the “GPTs are GPTs” exposure score introduced in 2023 and, for the first time, identifies and integrates five key improvement pathways: dynamic indicator construction, methodological integration, expansion of task frameworks, worker-centered measurement approaches, and the adoption of high-quality data. Drawing on a literature review and a policy analysis framework, the paper proposes an ex post evaluation mechanism tailored to uncertainty and a strategy for reimagining policy visions, thereby shifting the policy paradigm from prediction to preparedness. It further advocates bridging the academic–practitioner gap through participatory research, robust data infrastructure, and policy-oriented scholarly communication.

Abstract

A set of exposure scores calculated in 2023 has become a central empirical input to the future of work debate. Produced by Eloundou et al. (2023) and referred to here as the GPTs are GPTs scores, they define exposure as the share of occupational tasks a large language model can assist with. This work is a genuine methodological contribution, but as the scores travel from the time and place they were produced, the limitations the authors named do not always travel with them. Two gaps have widened as a result. The first is structural, between what static exposure scores measure and what policy questions actually require. Taking the diffusion of these scores as a case study, we show how their temporal, geographic, and ontological limitations compound in policy-facing analyses, and we survey five families of research responding to these limits: dynamic and benchmark-based measures, ensemble methods, task-framework extensions, worker-centered metrics, and adoption and usage data. The second gap is the one we argue needs more attention: the coordination between researchers and policymakers. The policy-relevant work which ask who is harmed, who benefits, how, and when, continues to reference the static GPTs are GPTs scores without engagement with the methodological updates that would let these questions be answered more reliably. We then ask what additional steps towards navigating uncertainty remain: ex-post frameworks and the deliberate, political work of reimagining what futures are worthy of building towards are. Closing the research-policy gap is a shared task: policymakers must widen their evidence base, engage workers as epistemic partners, and shift from prediction to preparedness; researchers must build data infrastructure, adopt participatory methods, and write with policymakers in mind. Better measurement matters, but it will not close the second gap alone.

Problem

• AI exposure scores
• future of work
• policy-relevant research
• measurement limitations
• research-policy gap

Innovation

• AI exposure scores
• future of work
• policy-relevant metrics
• dynamic measurement
• worker-centered evaluation