Y Combinator

Demis Hassabis argues current AI methods are part of the AGI path, but continual learning, memory, long-term reasoning, and consistency remain unsolved. He expects AGI around 2030, making agents, distilled models, multimodal Gemini, and open Gemma important. He highlights AlphaFold’s scientific impact, future virtual cell modeling, and urges founders to pursue interdisciplinary deep tech with clear objectives, strong data or simulators. True creativity and novel scientific hypothesis generation remain open challenges.

In this episode, Kuan Vuong of Physical Intelligence explores why robotics may be reaching a true inflection point. He explains how recent advances in foundation models, cross-embodiment learning, and real-world deployment are making robots more adaptable, more useful, and faster to build than before. Using examples like laundry folding and warehouse packaging, the conversation shows how robotics is shifting from a difficult, highly customized engineering problem into a more scalable platform opportunity. Overall, it is a clear and forward-looking discussion about why the cost of building in robotics is falling, what mixed autonomy looks like in practice, and how this could unlock a new wave of robotics startups.

This episode explores how Billion2One grew from a bold scientific idea into one of the fastest-scaling molecular diagnostics companies in the U.S. Founders Oguzhan Atay and David Tsao share how they built a breakthrough blood-based testing platform that can detect extremely rare DNA signals with remarkable precision, first in prenatal screening and now in cancer diagnostics. The conversation traces their journey from two PhD students with limited resources to a public company processing hundreds of thousands of tests each year, while also looking ahead to their bigger ambition: building ultrasensitive blood tests that could one day detect cancer at its earliest, most treatable stage.