Modernizing how the federal government permits critical infrastructure is essential to building a faster, safer, and more competitive U.S. economy. From energy projects and advanced manufacturing to transportation and water systems, permitting determines how quickly promising ideas become real-world investments. Yet today, environmental and technical reviews often take years, which slows innovation, increases costs, and delays the benefits these projects deliver to communities.

That’s why OpenAI has partnered with the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) and its PermitAI™⁠(opens in a new window) team to evaluate whether coding agents can help effectively accelerate federal permitting work. PermitAI, an initiative funded by the Department of Energy’s Office of Policy, and OpenAI worked together with 19 subject matter experts on the National Environmental Policy Act review process to design a benchmark (called DraftNEPABench) for assessing how well AI models perform on tasks relating to NEPA workflows such as drafting environmental impact statements.

Across a representative set of drafting tasks spanning NEPA document sections from 18 federal agencies, 19 experts found that generalized coding agents have the potential to speed up NEPA document drafting work by as much as 1 to 5 hours per subsection—up to roughly 15% reduction in drafting time—signaling a meaningful step forward in how AI can support complex government workflows.

Federal permitting is a complex and document-heavy process in government. Reviews often require reading hundreds of pages of technical reports, cross-checking information across multiple sources, and drafting detailed analyses that must meet regulatory requirements.

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