Economic impacts research at OpenAI

Introducing the OpenAI Economic Research Exchange

OpenAI launches the Economic Research Exchange to study AI’s impact on jobs, productivity, and the economy. Applications are now open for selected research projects.

Introducing the OpenAI Economic Research Exchange

OpenAI launches the Economic Research Exchange to study AI’s impact on jobs, productivity, and the economy. Applications are now open for selected research projects.

Understanding the capabilities, limitations, and societal impact of large language models

OpenAI’s new economic analysis

Analysis provides insights into ChatGPT’s impact on the economy. OpenAI also launches new research collaboration to study AI’s broader effects on the labor market and productivity.

GPTs are GPTs: An early look at the labor market impact potential of large language models

Economic Evaluations of Language Models

arXiv:2607. 19375v1 Announce Type: cross Abstract: Language models perform economically valuable work, yet they are not currently assessed for how well they perform every economically valuable task.

From assistance to execution: How enterprises put AI to work

OpenAI research reveals how enterprises are adopting agentic AI, using ChatGPT and Codex, and how frontier firms are pulling ahead in AI adoption.

Learning to communicate

In this post we’ll outline new OpenAI research in which agents develop their own language.

Accelerating the next phase of AI

OpenAI raises $122 billion in new funding to expand frontier AI globally, invest in next-generation compute, and meet growing demand for ChatGPT, Codex, and enterprise AI.

Measuring the performance of our models on real-world tasks

OpenAI introduces GDPval, a new evaluation that measures model performance on real-world economically valuable tasks across 44 occupations.

OpenAI partners with Scale to provide support for enterprises fine-tuning models

OpenAI’s customers can leverage Scale’s AI expertise to customize our most advanced models.

Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+

We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU languages plus six others. Unlike static or preference-based evaluation, this paradigm is multi-turn, reference-free and programmatically scored, and because the game mechanics are language-agnostic it extends to a new language by localising a fixed set of prompt and word-list files.