While language models understand the world through text, Demis Hassabis argues they lack an intuitive grasp of physics and spatial dynamics. He sees 'world models'—simulations that understand cause and effect in the physical world—as the critical technology needed to advance AI from digital tasks to effective robotics.

A classical, bottom-up simulation of a cell is infeasible, according to John Jumper. He sees the more practical path forward as fusing specialized models like AlphaFold with the broad reasoning of LLMs to create hybrid systems that understand biology.

The next major AI breakthrough will come from applying generative models to complex systems beyond human language, such as biology. By treating biological processes as a unique "language," AI could discover novel therapeutics or research paths, leading to a "Move 37" moment in science.

DE Shaw Research (DESRES) invested heavily in custom silicon for molecular dynamics (MD) to solve protein folding. In contrast, DeepMind's AlphaFold, using ML on experimental data, solved it on commodity hardware. This demonstrates data-driven approaches can be vastly more effective than brute-force simulation for complex scientific problems.

AI is moving beyond simply identifying patterns in existing research papers. It is now able to extrapolate fundamental biological principles, enabling it to understand complex systems from the ground up, like the relationship between atoms, molecules, and proteins.

Applying AI to biology isn't just a big data problem. The training data must be structured for reinforcement learning. This means it must be complete (including negative results) and allow for a feedback loop where AI predictions are tested in the lab, and the results are used to refine the model.

While acknowledging the power of Large Language Models (LLMs) for linear biological data like protein sequences, CZI's strategy recognizes that biological processes are highly multidimensional and non-linear. The organization is focused on developing new types of AI that can accurately model this complexity, moving beyond the one-dimensional, sequential nature of language-based models.

Following the success of AlphaFold in predicting protein structures, Demis Hassabis says DeepMind's next grand challenge is creating a full AI simulation of a working cell. This 'virtual cell' would allow researchers to test hypotheses about drugs and diseases millions of times faster than in a physical lab.

Demis Hassabis suggests a paradigm shift from a physics-centric view (energy/matter) to an information-centric one. In this framework, the universe is fundamentally an information processing system, making AI's role in organizing and understanding information even more profound.