Economic Impacts Research at OpenAI

Call for expressions of interest to study the economic impacts of Codex.

4 minute read

Core to our mission of ensuring that artificial general intelligence benefits all of humanity is understanding the economic impacts that our models have on individuals and society as a whole. Developing tools to rigorously measure the economic impacts of our models is essential to making smarter development and deployment decisions and critical to informing public policy options that maximize human prosperity and minimize the risk of economic harms from AI. Our ability to generate high quality evidence to inform these decisions will be greatly enhanced by developing a range of productive research partnerships, and we firmly believe that AI developers need to support external researchers undertaking this work, rather than exclusively conducting research in-house.

Under this premise, you can see our first public research agenda on these topics. This describes our preliminary priorities for research on the economic impacts of code generation models broadly. Today, we are excited to complement this research agenda with concrete action to facilitate improved measurement of the economic impacts of our models. We are launching a call for expressions of interest from researchers interested in evaluating the economic impact of Codex—our AI system that translates natural language to code. If you are a PhD level researcher (including current doctoral students) interested in collaborating on this research, we would encourage you to fill out the expression of interest form.

Read Research Agenda

Importance of Studying Economic Impacts

As an AI research and deployment company, OpenAI recognizes that our decisions around AI system design and deployment can influence economic impacts and the distribution of economic benefits from advances in AI. Despite remarkable technological progress over the past several decades, gains in economic prosperity have not been widely distributed. In the US, trends in both income and wealth inequality over the last forty years demonstrate a worrying pace of economic divergence and uneven access to opportunity. While recent evidence suggests that there is little immediate risk of widespread technological unemployment due to AI, it is clear that the labor market impacts of increasingly advanced AI will vary widely across different types of workers. Unemployment shocks, even if transitory, have been shown to have widespread negative effects on individual wellbeing, and increasing economic inequality may amplify societal cleavages.

We are eager to support and conduct research that has the potential to impact decision-making on three axes:

  1. AI deployment policies
  2. AI system design decisions
  3. Evidence that public policymakers can draw on.

While we don’t anticipate that the current capabilities of Codex could threaten large-scale economic disruption, future capabilities of code generation and other large language model applications could. We need to engage in research about the economic impact of our models today in order to be positioned to assess the safety of developing and releasing more capable systems in the future. Codex provides a tractable opportunity to establish the foundation for this research going forward.

External Research Collaborators

As an external research collaborator, you would be connected (via OpenAI) to firms that are currently using Codex models or that plan to in the future. You would have the opportunity to work with OpenAI and these firms to implement research projects focused on empirically measuring the impact of Codex on outcomes like worker and firm productivity, labor demand, and skill development. Where necessary and when possible, OpenAI would help facilitate data access to enable impactful research and would provide academic access to Codex and future models. OpenAI will also provide research management resources to external researchers, and researchers would have the freedom to publish their results independently or as co-authors with collaborators at OpenAI. Finally, we intend to facilitate discussions between external researchers, AI developers, AI-adopting firms, and workers in various industries that have been affected by advances in AI in an effort to widen the range of perspectives that can shape the path of AI development and deployment.

If you are a researcher considering submitting an expression of interest, please fill out this form. Additionally, consider emailing us your questions at to learn more about our goals for economic impacts research and how you can be involved.

If you are a company or user of Codex models and want to learn how you can contribute to this work moving forward, please fill out this form.

Submission Process

If you would like to submit an expression of interest to be a Research Collaborator please use this form.

Submit collaborator interest

We are currently seeking submissions from PhD-level researchers, including current doctoral students. When evaluating expressions of interest, we will assess your background and experience, clarity of motivation to collaborate with OpenAI, and both the clarity and decision-relevance of your research interests related to the economic impact of Codex.

If you are a company or user of Codex models and want to learn how you can contribute to this work moving forward, please fill out this form.

Learn how to contribute

We are in the process of connecting researchers with firms that are best equipped to support particular research interests. If you’re interested in learning more about how your organization can support or sponsor research on economic impacts of AI systems, please contact us here.

Additional Information

If you have any questions about the submission forms or the call for expressions of interest, please contact us at


Thanks to Steven Adler, Lama Ahmad, Stephanie Bell, Miles Brundage, Katya Klinova, Gretchen Krueger, Jade Leung, Anna Makanju, Katie Mayer, Richard Ngo, Cullen O’Keefe, Girish Sastry, Sarah Shoker, and Natalie Staudacher for feedback on drafts of this document. Thanks to Michelle Alexopoulos, Sarah Bana, Alex Bartik, Erik Brynjolfsson, Tim de Stefano, Avi Goldfarb, Marlène Koffi, Mina Lee, Zanele Munyikwa, Mark Muro, Frank Nagle, Maria del Rio-Chanona, Daniel Rock, Anna Salomons, and Ben Weidmann for helpful discussions on potential avenues for research on the economic impacts of code generation models.

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