Cecilia Jona-Lasinio Keynote - 9th Annual IP Data & Research Conference

Value of the Information & Strength of the Argument

The value of this presentation lies in its novel methodological approach to integrating AI into a comprehensive intangible investment framework, enabling cross-country comparisons and empirical estimation. The speaker provides concrete evidence from the U.S. growth accounting exercise, showing how AI-related software capital contributes to productivity gains. The argumentation is well-structured: it starts with descriptive statistics on intangibles, then introduces theoretical concepts (GPT and IMI) to motivate the AI measurement, and finally supports the claims with data. The use of the McKinsey survey to illustrate AI’s impact across business functions adds practical relevance. However, the presentation is dense and assumes familiarity with growth theory, making it more suitable for specialists.

Scientific Rigor, Source Quality, Title Accuracy

The presentation draws from established research by Carol Corrado and co-authors, a forthcoming paper presented at the 2026 AEA meeting, and a McKinsey survey. These sources are credible and directly cited. The methodological transparency is commendable, including careful decomposition of assets and explicit treatment of official statistics gaps. The title ‘The Hidden Engine of Growth’ aptly captures the central thesis that intangibles (and AI as a component) are underappreciated drivers of economic prosperity. There is no visible sponsor segment, and the talk remains purely academic. The speaker openly acknowledges limitations (e.g., reliance on official data for up to 2023) and the need for further research. Overall, the rigor is high and the title accurately reflects the content.

The title accurately reflects the content: the speaker discusses intangible assets, AI, and their role in global economic growth throughout the keynote.

8/10

The presentation is based on rigorous academic research, including peer-reviewed frameworks and partnerships with WIPO and international institutions. Data sources are clearly cited, and methodological choices are transparent.

Markers derived by PSI from the transcript: the creator did not define chapters.

• 02:00Introduction of the intangible asset taxonomy based on Corrado et al. (2005), listing assets like software, R&D, branding, and organizational capital.
• 05:00Discussion on the gap in official statistics: over 60% of intangible assets are not captured, including organizational capital and training.
• 10:00Global trends in intangible investment shares of GDP, highlighting heterogeneity across countries and the average 12.8% share in 2025.
• 15:00Anecdote about resilience of intangibles during crises, using quarterly data and the COVID-19 pandemic as an example.
• 20:00Transition to the AI part: definition of AI as a general-purpose technology and an innovation in the method of invention.
• 25:00Presentation of the growth accounting exercise for the U.S., decomposing labor productivity growth and quantifying software capital contribution.

• Corrado, C., Hulten, C., & Sichel, D. (2005). Intangible Capital and Economic Growth — Historical taxonomy and measurement framework for intangible assets.
• Jona-Lasinio, C., Corrado, C., Bontadini, F., & Ascal, J. (2026). Paper presented at the AEA annual meeting on AI and productivity — Joint work linking AI capital to productivity growth, with empirical evidence from the U.S.
• McKinsey survey on generative AI and business functions — Survey showing that AI has the highest impact on intangible-intensive business functions such as marketing, software engineering, and R&D.
• Alan (2020) paper on knowledge production and AI — Conceptual diagram illustrating the shift from handcrafted knowledge to machine learning-driven knowledge production.