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AI and Coder Employment: Compiling the Evidence

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Abstract

We evaluate whether LLMs have had any discernible impact on the aggregate labor market so far. We focus on occupations that are computer programming-intensive, motivated by data showing that coding is one of the most LLM-exposed tasks. Linking O*NET to CPS we find that aggregate employment of coders has decelerated sharply since the introduction of ChatGPT. Using a novel control variable for industry-level shocks we show that the deceleration is not attributable to the exposure of coders to slowing industries, suggesting instead that coders experienced an occupation-specific shock around the introduction of ChatGPT. Coder employment has continued to grow in recent years, though much more slowly than it did pre-2022. We validate the industry-level control variable by examining historical examples of occupations that experienced either occupation-specific or industry-level shocks. We also provide statistics on the agreement rates between different measures of AI exposure.

Suggested Citation

  • Leland D. Crane & Paul E. Soto, 2026. "AI and Coder Employment: Compiling the Evidence," Finance and Economics Discussion Series 2026-018, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:102997
    DOI: 10.17016/FEDS.2026.018
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    References listed on IDEAS

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    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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