Can Current AI Match (or Outmatch) Professionals in Economically Valuable Tasks? - A Demonstration Utilizing OpenAI’s GDPval Benchmark
Saahir Vazirani
Mentored by Jesse Gilbert
Working report from the SPAR program. May not reflect the authors' current views.
Abstract
This project demonstrates current AI capability for the audiences of nonprofits, civil society organizations, worker advocacy groups, and professional associations—and secondarily among policymakers who interpret these signals into regulation or economic policy. I adapt GDPval, a benchmark measuring AI performance on economically valuable real-world tasks, into an interactive display navigable by constituency or profession (e.g., financial managers). The research question is whether seeing present-day, task-level capabilities within one’s own field meaningfully increases support for responsible AI strategies such as equitable deployment expectations, public-interest AI infrastructure investment, and workforce adaptation planning. Early prototyping and GDPval’s documented findings suggest that profession-aligned displays make AI capability more tangible for civil society and provide policymakers with a clearer grounding for economic transition and AI safety considerations.