People often speculate about AI’s broader impact on society, but the clearest way to understand its potential is by looking at what models are already capable of doing. History shows that major technologies—from the internet to smartphones—took more than a decade to go from invention to widespread adoption. Evaluations like GDPval help ground conversations about future AI improvements in evidence rather than guesswork, and can help us track model improvement over time.

Previous AI evaluations like challenging academic tests and competitive coding challenges have been essential in pushing the boundaries of model reasoning capabilities, but they often fall short of the kind of tasks that many people handle in their everyday work.

To bridge this gap, we’ve been developing evaluations that measure increasingly realistic and economically relevant capabilities. This progression has moved from classic academic benchmarks like MMLU (exam-style questions across dozens of subjects), to more applied evaluations like SWE-Bench (software engineering bug-fixing tasks), MLE-Bench (machine learning engineering tasks such as model training and analysis), and Paper-Bench (scientific reasoning and critique on research papers), and more recently to market-based evaluations like SWE-Lancer (freelance software engineering projects based on real payouts).

GDPval is the next step in that progression. It measures model performance on tasks drawn directly from the real-world knowledge work of experienced professionals across a wide range of occupations and sectors, providing a clearer picture on how models perform on economically valuable tasks. Evaluating models on realistic occupational tasks helps us understand not just how well they perform in the lab, but how they might support people in the work they do every day.