The “How” and “Why”
Our approach to researching the economics of AI
OpenAI’s Economic Research team spends most of our time writing reports and papers, analyzing data and sharing our insights with the world. But we also get many questions about how we choose what work to do in the first place. In this post, we explain how we prioritize across research topics and develop techniques to answer some of the most important economic questions of our time.
Let’s start with what we believe.
We believe strongly that AI is transformational. Our research and work by our colleagues at OpenAI shows that AI is already:
Performing economically valuable work at a level as good or better than trained professionals
The technology is real, advancing quickly, and is on a trajectory to become one of the most important technologies of this century.
AI’s rapid capability growth trajectory means that measurement is critical to understanding its current and future economic effects. One dynamic that makes AI harder to measure compared to previous technologies is that every subsequent model improvement expands use cases and advances capabilities in new ways that are difficult to capture using traditional measurement tools.
AI is infinitely customizable and, as capabilities advance, each user’s experience becomes more distinct. We aim to help the public understand and react to these changes. This is why we emphasize the importance of building measurement infrastructure and sharing data with policymakers and researchers. Unlike in past economic transformations (imagine being an economist during the Industrial Revolution!), we have a wealth of data that tells us what is happening in real time. That means it is our job to think critically and carefully about interpreting that data and translating it into insights for a broad audience.
Humility and curiosity are key. Although we leverage some of the most interesting data in the world to explore these questions, we will not always arrive at definitive answers. There is a rapidly growing community of scholars studying the economics of AI and new results emerge every day. We can’t expect to be the only voice on these important topics. Instead, we should contribute to the broader research conversation and enable other economists to engage in these questions.
We believe in collaboration. Whether with government organizations, like the U.S. Department of Labor, or university researchers and institutions like the World Bank and the Partnership for AI, collaboration is key. We need perspectives from academics, multilateral organizations, governments, and the private sector to get this right. No one entity has the tools and resources to tackle these questions alone. We must work together. Incorporating these perspectives helps us keep people at the center of the AI transition and learn what kinds of information are most useful to the world.
We understand the difficulty of the task before us. Accurate economic measurement and forecasting have always been difficult. These efforts are even more challenging now that we stand at the precipice of a major technological shift. Our role is to proactively measure and inform, but not overclaim. Based on capabilities and adoption patterns, we are doing our part to measure the following phenomena:
AI’s impact on the labor market, including changes in work, layoffs, and growth: Using our data, we examine jobs, workers, skills, new work, mobility, growth, and realistic scenarios for expanded human agency in an AI economy. Alex and Caroline are addressing these topics in their Work at the Frontier Series and my colleagues Kevin and Sonny are exploring these topics in their work on entrepreneurship.
Changes in how organizations and firms change to adopt and adapt to AI: How organizations adopt AI, where value is created or blocked, and how tools like agents reshape workflows, productivity, and entrepreneurship. Work like B2B Signals from my colleagues G and Neel show these changes are unfolding in the enterprise data.
Understanding how changes in the technology itself can create economic transformations: In this area, we explore frontier questions about research acceleration, science and innovation, recursive self-improvement, and the distribution of economic power. One example is this paper on agentic AI’s impact on work that Drew, David, Chris, Sonny, Alex, and Ronnie recently released in collaboration with our colleagues across OAI.
We are a small team within an AI lab, so we will not be able to generate every answer or serve as the sole source of truth. We aim to provide useful information that helps people around the world make better choices for themselves and their communities.
Taken together, we aim to make the ongoing transition to the AI economy more legible: to understand where AI is creating opportunity, where it may create disruption, and what workers, businesses, and policymakers may need to respond. This is important work and there is a lot more to do.
– The OpenAI Economic Research Team