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Quantifying disruption in the age of AI: An AI-based approach to evaluating startup innovation and investment potential

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  • Imerman, Michael B.
  • Fabozzi, Frank J.

Abstract

Clayton Christensen's disruptive innovation theory highlights how technologies transform industries, but quantifying disruption remains challenging. This paper introduces Disruptor Detective, an AI-driven tool that evaluates companies using seven criteria of disruptive innovation, integrating financial data and natural language processing. Given the limited data available about private companies and early-stage startups, this approach provides an objective framework for investors to assess their disruptive potential. We analyze 12 generative AI firms, including OpenAI and Hugging Face, to demonstrate the tool in action. The tool revealed varied disruption scores, with firms developing open-source models, such as Hugging Face, exhibiting the most substantial disruptive potential. In contrast, firms focused on specialized applications show more incremental innovation. Additionally, disruption scores exhibit a positive, albeit modest, correlation with financial metrics such as valuation and revenue. The findings provide a scalable, data-driven approach to evaluating disruptive innovation, bridging the gap between qualitative theory and quantitative assessment in venture capital.

Suggested Citation

  • Imerman, Michael B. & Fabozzi, Frank J., 2025. "Quantifying disruption in the age of AI: An AI-based approach to evaluating startup innovation and investment potential," Finance Research Letters, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:finlet:v:82:y:2025:i:c:s1544612325007500
    DOI: 10.1016/j.frl.2025.107491
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