Caltech professor Frances Arnold developed her Nobel-winning "directed evolution" method out of desperation. Realizing her biochemistry knowledge was limited compared to peers using "rational design," she embraced a high-volume, random approach that let the experiment, not her intellect, find the solution.

The success of iterative design processes hinges entirely on the metric being measured. An enzyme evolved for temperature stability won't necessarily remove clothing stains unless stain removal is the specific property being screened for. This highlights the critical importance of defining the right success metric from the start.

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.

Conventional innovation starts with a well-defined problem. Afeyan argues this is limiting. A more powerful approach is to search for new value pools by exploring problems and potential solutions in parallel, allowing for unexpected discoveries that problem-first thinking would miss.

Professor Susan Athey highlights that the school's most significant academic breakthroughs, like Nobel Prize-winning work in market design, originated not from abstract theorizing but from engaging directly with industry challenges. This connection to real-world problems created a feedback loop that led to cutting-edge, field-defining theoretical research.

The traditional method of engineering enzymes by making precise, knowledge-based changes (“rational design”) is largely ineffective. Publication bias hides the vast number of failures, creating a false impression of success while cruder, high-volume methods like directed evolution prove superior.

With directed evolution, scientists find a mutated enzyme that works without knowing why. Even with the "answer"—the exact genetic changes—the complexity of protein interactions makes it incredibly difficult to reverse-engineer the underlying mechanism. The solution often precedes the understanding.

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.

Nubar Afeyan argues that companies should pursue two innovation tracks. Continuous innovation should build from the present forward. Breakthroughs, however, require envisioning a future state without a clear path and working backward to identify the necessary enabling steps.