On July 8, 2026, the SCOR Foundation hosted a webinar titled: AI Bubble and its Three Dragon-Kings: Systemic Risks, Challenges, and Opportunities for Reinsurance.

This webinar was led by Didier Sornette, Chair Professor and founding Co-Dean of the Institute of Risk Analysis, Prediction and Management (Risks-X) at the Southern University of Science and Technology (SUSTech) in Shenzhen. A member of the SCOR Foundation's Scientific Board, Didier Sornette is also Professor of Finance at the Swiss Finance Institute and Professor Emeritus at the Swiss Federal Institute of Technology in Zurich (ETH Zurich), as well as being a Fellow of the American Association for the Advancement of Science, and a member of the Swiss Academy of Engineering Sciences and the Academia Europaea.

The webinar explored today’s artificial intelligence which displays the characteristics of a social bubble, as defined by Professor Sornette to describe the Apollo program, the mapping of the human genome, and all major technological breakthroughs. Yet, as with railways, the Internet, or clean technologies, a bubble may end in a dragon-king crash, while also generating a positive dragon-king over the medium term: new capabilities, cognitive productivity, scientific automation, and lasting infrastructure.

For reinsurance, the central challenge is twofold: opportunities in modeling, prevention, and efficiency, but also systemic risks such as cyber avalanches, uncontrolled autonomous actions, loss of human expertise, and the vulnerability of critical infrastructure.

The ultimate ethical question remains: what purpose should AI serve?
 

Didier Sornette is internationally recognized for his pioneering work on complex systems and extreme risks. He introduced the Dragon-King theory of extreme events and developed key frameworks including the endogenous-exogenous approach to complex system organization, Quantum Decision Theory, and the log-periodic power law singularity (LPPLS) model for diagnosing and forecasting systemic transitions such as financial bubbles and crashes. His research combines rigorous data-driven statistical analysis with nonlinear dynamical modeling to understand, predict, and mitigate crises across disciplines, including finance, geophysics, engineering, energy systems, cyber-security, health, and social dynamics. 

He has published over 800 peer-reviewed scientific papers and 19 books, with more than 62,000 citations and an h-index of 122.