As AI technologies continue to evolve and diffuse throughout the economy, understanding their value and impact on innovation has become increasingly important. Human decision-making plays a crucial role in the development and implementation of AI, with significant implications for stakeholders across industries, from investors and firms to policymakers and society at large.
In their working paper, “The Value of AI Innovations”, Terrence Tianshuo Shi, along with Wilbur X. Chen and Suraj Srinivasan, members of D^3’s Digital Value Lab, examine the value of AI innovations through a detailed analysis of AI patent data. As the latest in a line of transformative technologies, AI has shown a distinct capacity to impact innovation, commercialization, and industry specialization. Investors and firms are beginning to appreciate the growing significance of AI innovations, which exhibit value premiums and generate knowledge spillovers across sectors.
Key Insight: AI Innovations Generate Higher Value Premiums
“Our analysis shows that AI patents are roughly 9% more valuable compared to non-AI patents.” [1]
This value premium is driven largely by investor interest in the higher forward citation rates of AI patents, which indicate greater potential for future technological development. The premium also tends to increase in industries and firms where occupational tasks are well-suited to AI. As AI continues to evolve, this growing market interest suggests that firms investing in AI are positioning themselves for more valuable long-term outcomes.
Key Insight: The Increasing Importance of Knowledge Spillovers
“We find that AI innovations exhibit substantially more forward citations compared to other innovations, and the potential of follow-on innovation in these innovations are valued more.” [2]
AI technologies offer a significant advantage through their ability to generate knowledge spillovers across various industries. This cross-industry impact enhances AI’s overall value, as innovations in one sector often benefit others. Policies that promote open access to AI technologies, such as the open-sourcing of TensorFlow, amplify the positive effects of these spillovers. As AI moves beyond its initial focus in the tech industry, it is also becoming increasingly critical in non-tech sectors like retail, finance, and transportation, where its potential to drive innovation is being realized.
Key Insight: AI Patents and Profitability
“AI patents facilitate a relatively fast development of new products and services, or reductions in production costs, leading to higher profit margins in the 2-3 years after the patenting of AI technologies.” [3]
Firms that invest in AI patents often experience a significant boost in profit margins. The delayed but impactful profitability gains highlight the long-term strategic advantage of AI investments. Unlike other short-term technologies, AI serves as a critical driver for sustained financial performance and market competitiveness, particularly as firms refine AI’s applications to their specific needs.
Key Insight: Policy’s Role in Unlocking AI’s Value
“The open sourcing of TensorFlow are associated with a… 2% increase in the value premium of AI innovation in application sectors.” [4]
Public policies and corporate decisions that facilitate the sharing of AI innovations have had a profound impact on the value of AI technologies. The American Inventor Protection Act (AIPA) and Google’s decision to open-source TensorFlow are two examples of such actions. These initiatives accelerated the disclosure and public use of AI innovations, enabling faster knowledge spillovers and further innovation. The impact of these policies has been particularly noticeable in non-tech industries, where open access to AI tools has empowered firms to adopt and refine AI applications that align with their specific industry challenges.
Why This Matters
For business professionals and executives, the insights from The Value of AI Innovations provide a roadmap for harnessing the transformative power of AI. Investing in AI technologies not only ensures a competitive edge but also creates long-term value through profitability, innovation spillovers, and industry-specific applications. As AI continues to evolve, firms that position themselves to adopt and integrate AI innovations will see substantial growth and gain a strategic advantage in an increasingly digital economy.
References
[1] Wilbur X. Chen, Terrence Tianshuo Shi, and Suraj Srinivasan, “The Value of AI Innovations” Harvard Business School Working Paper 24-069 (May, 2024): 1-60, 2.
[2] Chen, Tianshuo Shi, and Srinivasan, “The Value of AI Innovations”, 5.
[3] Chen, Tianshuo Shi, and Srinivasan, “The Value of AI Innovations”, 20.
[4] Chen, Tianshuo Shi, and Srinivasan, “The Value of AI Innovations”, 29.
Meet the Authors
Wilbur X. Chen is an Assistant Professor in Accounting at Hong Kong University of Science and Technology and a faculty affiliate in D^3’s Digital Value Lab. He graduated from Harvard University with a Ph.D. in Business Administration, from the Chinese University of Hong Kong with a M.Phil. in Accountancy, and from McGill University with a B.Sc. in Physics. His research interests are in capital markets, corporate governance, equity valuations and the economics of digitization.
Terrence Tianshuo Shi is a doctoral student at Harvard Business School. He was awarded the John M. Olin Research Fellowship in Law and Economics at Harvard Law School and named a Deloitte Foundation Doctoral Fellow for 2024. Prior to joining the doctoral program, Terrence earned a Bachelor’s degree in Economics from Tsinghua University in 2018 and worked as an equity market quantitative researcher at XY Investments.
Suraj Srinivasan is the Philip J. Stomberg Professor of Business Administration, Unit Head, Accounting and Management and faculty director of D^3’s Digital Value Lab. He holds a PhD in Business Administration from Harvard Business School. Professor Srinivasan’s expertise spans three research domains – data science and artificial intelligence, corporate governance and boards of directors, and financial reporting and risk management.