IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2511.06545.html

Vibecoding and Digital Entrepreneurship

Author

Listed:
  • Ruiqing Cao
  • Abhishek Bhatia

Abstract

As generative artificial intelligence (GenAI) automates coding tasks and expands access to technical resources, this paper examines how GenAI-enabled coding automation, colloquially known as "vibecoding," affects digital entrepreneurial entry and venture performance. We exploit ex-ante variation in ventures' exposure to vibecoding based on the product characteristics of their initial launches and estimate difference-in-differences models around the diffusion of GenAI coding tools. Vibecoding increases first-time launches and shortens time to launch, but economically viable entry rises only where vibecoding augments, rather than fully automates, product development. In these partially exposed product segments, viable entry increases by 11%, driven entirely by ventures founded by individuals with STEM education or work experience, especially those whose most recent employment was outside middle management. Among ventures launched before GenAI became widely accessible, performance gains similarly concentrate among partially exposed ventures with engineering-intensive initial teams. Together, these results suggest that GenAI-enabled coding automation does not eliminate the value of technical expertise. Instead, vibecoding creates the greatest value when it complements internal engineering capabilities, allowing ventures to delegate lower-level coding tasks to GenAI while shifting human effort toward higher-level problem solving and dynamic adaptation.

Suggested Citation

  • Ruiqing Cao & Abhishek Bhatia, 2025. "Vibecoding and Digital Entrepreneurship," Papers 2511.06545, arXiv.org, revised May 2026.
  • Handle: RePEc:arx:papers:2511.06545
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2511.06545
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ewens, Michael & Nanda, Ramana & Rhodes-Kropf, Matthew, 2018. "Cost of experimentation and the evolution of venture capital," Journal of Financial Economics, Elsevier, vol. 128(3), pages 422-442.
    2. Sebastian Krakowski & Johannes Luger & Sebastian Raisch, 2023. "Artificial intelligence and the changing sources of competitive advantage," Strategic Management Journal, Wiley Blackwell, vol. 44(6), pages 1425-1452, June.
    3. Gianluigi Giustiziero & Tobias Kretschmer & Deepak Somaya & Brian Wu, 2023. "Hyperspecialization and hyperscaling: A resource‐based theory of the digital firm," Strategic Management Journal, Wiley Blackwell, vol. 44(6), pages 1391-1424, June.
    4. Bronwyn H. Hall, 2004. "Innovation and Diffusion," NBER Working Papers 10212, National Bureau of Economic Research, Inc.
    5. Fabrizio Dell'Acqua & Charles Ayoubi & Hila Lifshitz & Raffaella Sadun & Ethan Mollick & Lilach Mollick & Yi Han & Jeff Goldman & Hari Nair & Stewart Taub & Karim Lakhani, 2025. "The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise," NBER Working Papers 33641, National Bureau of Economic Research, Inc.
    6. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    7. Dushnitsky, Gary & Stroube, Bryan K., 2021. "Low-code entrepreneurship: Shopify and the alternative path to growth," Journal of Business Venturing Insights, Elsevier, vol. 16(C).
    8. Anil R. Doshi & J. Jason Bell & Emil Mirzayev & Bart S. Vanneste, 2025. "Generative artificial intelligence and evaluating strategic decisions," Strategic Management Journal, Wiley Blackwell, vol. 46(3), pages 583-610, March.
    9. Goldfarb, Avi & Taska, Bledi & Teodoridis, Florenta, 2023. "Could machine learning be a general purpose technology? A comparison of emerging technologies using data from online job postings," Research Policy, Elsevier, vol. 52(1).
    10. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    11. Sinan Aral & Erik Brynjolfsson & Lynn Wu, 2012. "Three-Way Complementarities: Performance Pay, Human Resource Analytics, and Information Technology," Management Science, INFORMS, vol. 58(5), pages 913-931, May.
    12. Ruiqing Cao & Rembrand Koning & Ramana Nanda, 2024. "Sampling Bias in Entrepreneurial Experiments," Management Science, INFORMS, vol. 70(10), pages 7283-7307, October.
    13. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue nov.
    14. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    15. Jean-Michel Dalle & Matthijs den Besten & Carlo Menon, 2017. "Using Crunchbase for economic and managerial research," OECD Science, Technology and Industry Working Papers 2017/08, OECD Publishing.
    16. Lynn Wu & Lorin Hitt & Bowen Lou, 2020. "Data Analytics, Innovation, and Firm Productivity," Management Science, INFORMS, vol. 66(5), pages 2017-2039, May.
    17. Timothy F. Bresnahan & Erik Brynjolfsson & Lorin M. Hitt, 2002. "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(1), pages 339-376.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jay Dixon & Bryan Hong & Lynn Wu, 2021. "The Robot Revolution: Managerial and Employment Consequences for Firms," Management Science, INFORMS, vol. 67(9), pages 5586-5605, September.
    2. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 375-415, March.
    3. Kristina McElheran & Mu-Jeung Yang & Zachary Kroff & Erik Brynjolfsson, 2025. "The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)," Working Papers 25-27, Center for Economic Studies, U.S. Census Bureau.
    4. Prasanna B. Tambe, 2026. "Reskilling the Workforce for AI: Domain Expertise and Algorithmic Literacy," Management Science, INFORMS, vol. 72(1), pages 515-537, January.
    5. Erik Brynjolfsson & Wang Jin & Kristina McElheran, 2021. "The power of prediction: predictive analytics, workplace complements, and business performance," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 217-239, October.
    6. Nico Voigtlaender, 2009. "Many Sectors Meet More Skills: Intersectoral Linkages and the Skill Bias of Technology," 2009 Meeting Papers 1136, Society for Economic Dynamics.
    7. Alex Chernoff & Gabriela Galassi, 2023. "Digitalization: Labour Markets," Discussion Papers 2023-16, Bank of Canada.
    8. Gavin Wang & Lynn Wu, 2025. "Artificial Intelligence, Lean Startup Method, and Product Innovations," Papers 2506.16334, arXiv.org, revised Aug 2025.
    9. Johannes Lehmann & Michael Beckmann, 2024. "Digital technologies and performance incentives: Evidence from businesses in the Swiss economy," Papers 2412.12780, arXiv.org.
    10. Torrent-Sellens, Joan, 2024. "Digital transition, data-and-tasks crowd-based economy, and the shared social progress: Unveiling a new political economy from a European perspective," Technology in Society, Elsevier, vol. 79(C).
    11. Xiaoning Wang & Lynn Wu, 2026. "Artificial Intelligence, Lean Startup Method, and Product Innovations," Management Science, INFORMS, vol. 72(1), pages 756-782, January.
    12. Peng Huang & Marco Ceccagnoli & Chris Forman & D.J. Wu, 2022. "IT Knowledge Spillovers, Absorptive Capacity, and Productivity: Evidence from Enterprise Software," Information Systems Research, INFORMS, vol. 33(3), pages 908-934, September.
    13. Tyna Eloundou & Sam Manning & Pamela Mishkin & Daniel Rock, 2023. "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models," Papers 2303.10130, arXiv.org, revised Aug 2023.
    14. Brad N. Greenwood & Kartik K. Ganju & Corey M. Angst, 2019. "How Does the Implementation of Enterprise Information Systems Affect a Professional’s Mobility? An Empirical Study," Information Systems Research, INFORMS, vol. 30(2), pages 563-594, June.
    15. Nicholas Bloom & Raffaella Sadun & John Van Reenen, 2010. "Recent Advances in the Empirics of Organizational Economics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 105-137, September.
    16. Lynn Wu & Bowen Lou & Lorin Hitt, 2019. "Data Analytics Supports Decentralized Innovation," Management Science, INFORMS, vol. 65(10), pages 4863-4877, October.
    17. Hilal Atasoy & Rajiv D. Banker & Paul A. Pavlou, 2021. "Information Technology Skills and Labor Market Outcomes for Workers," Information Systems Research, INFORMS, vol. 32(2), pages 437-461, June.
    18. Nicholas Bloom & Luis Garicano & Raffaella Sadun & John Van Reenen, 2014. "The Distinct Effects of Information Technology and Communication Technology on Firm Organization," Management Science, INFORMS, vol. 60(12), pages 2859-2885, December.
    19. Sam Ruiqing Cao & Marco Iansiti, 2022. "Organizational Barriers to Transforming Large Finance Corporations: Cloud Adoption and the Importance of Technological Architecture," CESifo Working Paper Series 10142, CESifo.
    20. Jonas Hjort & Jonas Poulsen, 2019. "The Arrival of Fast Internet and Employment in Africa," American Economic Review, American Economic Association, vol. 109(3), pages 1032-1079, March.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2511.06545. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.