The next leap in biotech moves beyond applying AI to existing data. CZI pioneers a model where 'frontier biology' and 'frontier AI' are developed in tandem. Experiments are now designed specifically to generate novel data that will ground and improve future AI models, creating a virtuous feedback loop.

Tackling monumental challenges, like creating a biologic effective against 800+ HIV variants, is not a single-shot success. It requires multiple iterations on an advanced engineering platform. Each cycle of design, measurement, and learning progressively refines the molecule, making previously impossible therapeutic goals achievable.

While AI promises to design therapeutics computationally, it doesn't eliminate the need for physical lab work. Even if future models require no training data, their predicted outputs must be experimentally validated. This ensures a continuous, inescapable cycle where high-throughput data generation remains critical for progress.

AI models are trained on large lab-generated datasets. The models then simulate biology and make predictions, which are validated back in the lab. This feedback loop accelerates discovery by replacing random experimental "walks" with a more direct computational route, making research faster and more efficient.

The ultimate goal isn't just modeling specific systems (like protein folding), but automating the entire scientific method. This involves AI generating hypotheses, choosing experiments, analyzing results, and updating a 'world model' of a domain, creating a continuous loop of discovery.

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.

Contrary to the idea that AI will make physical experiments obsolete, its real power is predictive. AI can virtually iterate through many potential experiments to identify which ones are most likely to succeed, thus optimizing resource allocation and drastically reducing failure rates in the lab.

Current LLMs fail at science because they lack the ability to iterate. True scientific inquiry is a loop: form a hypothesis, conduct an experiment, analyze the result (even if incorrect), and refine. AI needs this same iterative capability with the real world to make genuine discoveries.

The founder of AI and robotics firm Medra argues that scientific progress is not limited by a lack of ideas or AI-generated hypotheses. Instead, the critical constraint is the physical capacity to test these ideas and generate high-quality data to train better AI models.