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As part of its aggressive campaign to “deconstruct the administrative state” on behalf of the Trump administration, the so-called Department of Government Efficiency controversially outsourced many crucial tasks to artificial intelligence (AI) programs like ChatGPT. In one episode, the program flagged funding for research on plastics industry lobbying and for digitizing audio reels by the Boston Symphony Orchestra while searching through National Endowment for the Humanities (NEH) grants to eliminate those supposedly promoting diversity, equity, and inclusion. Despite such glaring errors, DOGE fully adopted ChatGPT’s recommendations to terminate over 1,400 grants. These errors illustrate two intrinsic problems with AI: hallucination (an AI system producing nonsensical or inaccurate outputs) and sycophancy (an AI system modifying its response to align with users’ views). With AI use likely to become more prevalent within the federal government, existing legal safeguards — including those supplied by administrative law — will need to be updated and adapted to account for these problems.

On June 30, the House Appropriations Financial Services and General Government (FSGG) Subcommittee questioned Russell Vought, Director of the White House Office of Management and Budget (OMB). Given the influential role that Vought has played in the Trump administration, both as a member and in shaping its policy agenda through Project 2025, the hearing provided a critical opportunity for conducting oversight.

Artificial intelligence (AI) is now used widely in the federal government. According to the U.S. Government Accountability Office, use cases of AI in the federal government doubled — and for generative AI, increased nine-fold — from 2023 to 2024. With this growing use of AI, the risk of systematic errors with potentially harmful consequences may increase.

The growing use of AI for such complex governance functions as regulatory decision-making is attracting attention. The practice risks putting two existing bodies of public law—those governing the rulemaking process and procurement, respectively—on a collision course: in particular, rulemaking’s emphasis on transparency and procurement’s emphasis on quick commercial transactions that often do not include robust transparency audits. The impetus for this potential clash is that the architecture of many of the AI systems that the federal government employs are developed by private contractors.