As machine learning systems become more prominent in hypothesis generation, simulation, and interpretation, the role of scientists will not disappear — it will evolve. Our ability to be creative about what data we generate, how we go about it, and what it enables, will become an increasingly central part of how we contribute to discovery.

This is not a loss. It’s an opportunity. It’s a chance — for both scientists and funders — to reclaim the architectural layer of science as a thoughtful, creative, and essential realm of experimentation and discovery. And it’s a place where human judgment still matters — perhaps more than ever.

We need funding mechanisms that enable sustained, coordinated design sprints across interconnected entities with different areas of expertise.