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The role of data for AI startup growth

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  • Bessen, James
  • Impink, Stephen Michael
  • Reichensperger, Lydia
  • Seamans, Robert

Abstract

Artificial intelligence (AI)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find a positive correlation between having proprietary training data and obtaining future venture capital funding. Moreover, this correlation is greater for startups in markets where data is a major advantage and for startups using more sophisticated algorithms, such as neural networks and ensemble learning.

Suggested Citation

  • Bessen, James & Impink, Stephen Michael & Reichensperger, Lydia & Seamans, Robert, 2022. "The role of data for AI startup growth," Research Policy, Elsevier, vol. 51(5).
    • Handle: RePEc:eee:respol:v:51:y:2022:i:5:s0048733322000415
    DOI: 10.1016/j.respol.2022.104513
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    as
    1. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2021. "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 333-372, January.
    2. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
    3. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    4. Garrett A. Johnson & Scott K. Shriver & Shaoyin Du, 2020. "Consumer Privacy Choice in Online Advertising: Who Opts Out and at What Cost to Industry?," Marketing Science, INFORMS, vol. 39(1), pages 33-51, January.
    5. David J. TEECE, 2008. "Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy," World Scientific Book Chapters, in: The Transfer And Licensing Of Know-How And Intellectual Property Understanding the Multinational Enterprise in the Modern World, chapter 5, pages 67-87, World Scientific Publishing Co. Pte. Ltd..
    6. Jasjit Singh & Ajay Agrawal, 2011. "Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires," Management Science, INFORMS, vol. 57(1), pages 129-150, January.
    7. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    8. Jason Furman & Robert Seamans, 2019. "AI and the Economy," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 161-191.
    9. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    10. Nanda, Ramana & Samila, Sampsa & Sorenson, Olav, 2020. "The persistent effect of initial success: Evidence from venture capital," Journal of Financial Economics, Elsevier, vol. 137(1), pages 231-248.
    11. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    12. John Gibson, 2019. "Are You Estimating the Right Thing? An Editor Reflects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 41(3), pages 329-350.
    13. Maryam Farboodi & Laura Veldkamp, 2021. "A Model of the Data Economy," NBER Working Papers 28427, National Bureau of Economic Research, Inc.
    14. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    15. Catherine Tucker, 2019. "Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(4), pages 683-694, June.
    16. Hal R Varian, 2014. "Beyond Big Data," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 49(1), pages 27-31, January.
    17. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    18. Ramana Nanda, 2016. "Financing high-potential entrepreneurship," IZA World of Labor, Institute of Labor Economics (IZA), pages 252-252, April.
    19. Lesley Chiou & Catherine Tucker, 2017. "Content aggregation by platforms: The case of the news media," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 26(4), pages 782-805, December.
    20. Maria Savona, 2019. "The Value of Data:Towards a Framework to Redistribute It," SPRU Working Paper Series 2019-21, SPRU - Science Policy Research Unit, University of Sussex Business School.
    21. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    22. Frank Nagle, 2018. "Learning by Contributing: Gaining Competitive Advantage Through Contribution to Crowdsourced Public Goods," Organization Science, INFORMS, vol. 29(4), pages 569-587, August.
    23. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    24. William R. Kerr & Ramana Nanda, 2009. "Financing Constraints and Entrepreneurship," Harvard Business School Working Papers 10-013, Harvard Business School.
    25. Jentzsch, Nicola, 2016. "State-of-the-Art of the Economics of Cyber-Security and Privacy," EconStor Research Reports 126223, ZBW - Leibniz Information Centre for Economics.
    26. Erik Brynjolfsson & Lorin M. Hitt, 2000. "Beyond Computation: Information Technology, Organizational Transformation and Business Performance," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 23-48, Fall.
    27. Bo Cowgill & Fabrizio Dell'Acqua & Samuel Deng & Daniel Hsu & Nakul Verma & Augustin Chaintreau, 2020. "Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics," Papers 2012.02394, arXiv.org.
    28. James Bessen & Maarten Goos & Anna Salomons & Wiljan van den Berge, 2020. "Firm-Level Automation: Evidence from the Netherlands," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 389-393, May.
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    Cited by:

    1. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia S. Foster & Nikolas Zolas, 2023. "AI Adoption in America: Who, What, and Where," NBER Working Papers 31788, National Bureau of Economic Research, Inc.
    2. Igna, Ioana & Venturini, Francesco, 2023. "The determinants of AI innovation across European firms," Research Policy, Elsevier, vol. 52(2).
    3. Nam, Jinyoung & Kim, Junghwan & Jung, Yoonhyuk, 2023. "Understandings of the AI business ecosystem in South Korea: AI startups' perspective," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 278005, International Telecommunications Society (ITS).
    4. Alessandra Colombelli & Elettra D’Amico & Emilio Paolucci, 2023. "When computer science is not enough: universities knowledge specializations behind artificial intelligence startups in Italy," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1599-1627, October.
    5. ZHU Chen & MOTOHASHI Kazuyuki, 2024. "The Fundraising of AI Startups: Evidence from web data," Discussion papers 24021, Research Institute of Economy, Trade and Industry (RIETI).
    6. Christian Peukert & Margaritha Windisch, 2023. "The Economics of Copyright in the Digital Age," CESifo Working Paper Series 10687, CESifo.
    7. Flavio Calvino & Luca Fontanelli, 2023. "Artificial intelligence, complementary assets and productivity: evidence from French firms," LEM Papers Series 2023/35, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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    More about this item

    Keywords

    Artificial intelligence; Competition; Data; Algorithms; Venture capital;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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