Big brains, bigger questions: Top scientists debate AI at Shanghai awards

Top-tier scientists and trailblazers gathered in Shanghai on Saturday for the 2026 World Laureates Forum – a chance to pick each other's brains on future semiconductors, biomedicine, aerospace information, AI, future energy and quantum science.

The eminent crowd includes 32 recipients of international honors like the Nobel Prize, Turing Award, Wolf Prize and Fields Medal, alongside 23 members of the Chinese Academy of Sciences and Chinese Academy of Engineering, 140 senior scientists and 78 budding scientists. 

They huddled around a main forum, six parallel forums, four events and one closing ceremony under the umbrella theme "New Science Paradigm: Break Through for the Next Decade."

The opening ceremony saw the 2026 World Laureates Foundation (WLF) Prize handed out to 11 scientists for contributions to intelligent science or mathematics, life science or medicine, and a dedicated category for young scientists.

Belgian scientist Ingrid Daubechies took home the mathematics award. The life science or medicine award was shared by Yuan Junying, Wang Xiaodong, Shao Feng and Vishva Dixit for clarifying molecular pathways of different forms of programmed cell death. Shao is the first Chinese citizen to win a WLF Prize.

Yuan said her greatest wish is to see her lab's findings translated into clinical therapies, calling it the most meaningful reward. Pursuing big ideas needs practical steps. On that note, Wang said trust is essential in scientific collaboration, and a spot of friendly competition has its merits too. Dixit added a philosophical touch, saying people live because cells can die.

The gongs were just the start. A question wall let attendees post queries ranging from whether a perfectly smooth ball can spin, to whether consciousness can be read.

The future of AI also took center stage. In a keynote address, Fields Medalist Laurent Lafforgue said large language models can already solve some problems human mathematicians have never cracked, but AI cannot shortcut the wisdom that comes from the effort required to actually learn.