Future progress in biology requires moving beyond static models. The new paradigm involves an AI that reasons over hypotheses, prioritizes experiments, learns from the empirical outcomes, and updates its internal world model. This creates a scalable, closed-loop system for scientific discovery.

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.

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.

Just as biology deciphers the complex systems created by evolution, mechanistic interpretability seeks to understand the "how" inside neural networks. Instead of treating models as black boxes, it examines their internal parameters and activations to reverse-engineer how they work, moving beyond just measuring their external behavior.

Unlike classic theories based on simple equations, large AI models represent a new kind of scientific object. Rather than being mere predictive tools, they could be a novel form of explanation that we must learn to manipulate through new operations like distillation and merging, much like Mathematica made massive equations workable.

Demis Hassabis argues that machine learning is the ideal framework for understanding biological systems. Unlike physics, which is elegantly described by mathematics, biology's messy, data-rich nature with many weak correlations is perfectly suited for ML to model and decipher.

Dr. Fei-Fei Li cites the deduction of DNA's double-helix structure as a prime example of a cognitive leap that required deep spatial and geometric reasoning—a feat impossible with language alone. This illustrates that future AI systems will need world-modeling capabilities to achieve similar breakthroughs and augment human scientific discovery.

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.

Afeyan proposes that AI's emergence forces us to broaden our definition of intelligence beyond humans. By viewing nature—from cells to ecosystems—as intelligent systems capable of adaptation and anticipation, we can move beyond reductionist biology to unlock profound new understandings of disease.