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Starting Up AI

Author

Listed:
  • Emin Dinlersoz
  • Can Dogan
  • Nikolas Zolas

Abstract

Using comprehensive administrative data on business applications over the period 2004-2023, we study emerging business ideas for developing AI technologies or producing goods or services that use, integrate, or rely on AI. The annual number of new AI business applications is stable between 2004 and 2012 but begins to rise after 2012, and increases faster from 2016 onward into the pandemic, with a large, discrete jump in 2023. The distribution of AI business applications is highly uneven across states and sectors. AI business applications have a higher likelihood of becoming employer startups and higher expected initial employment compared to other business applications. Moreover, controlling for application characteristics, employer businesses originating from AI business applications exhibit higher employment, revenue, payroll, average pay per employee, and labor share, but have similar labor productivity and lower survival rate, compared to those originating from other business applications. While these early patterns may change as the diffusion of AI progresses, the rapid rise in AI business applications, combined with their generally higher rate of transition to employers and better performance in some post-transition outcomes, suggests a small but growing contribution from these applications to business dynamism.

Suggested Citation

  • Emin Dinlersoz & Can Dogan & Nikolas Zolas, 2024. "Starting Up AI," Working Papers 24-09, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:24-09
    as

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    File URL: https://www2.census.gov/library/working-papers/2024/adrm/ces/CES-WP-24-09.pdf
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    References listed on IDEAS

    as
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