In 2024, Demis Hassabis and John Jumper of Google DeepMind were awarded part of the Nobel in chemistry for their neural network AlphaFold, which predicts the three-dimensional structures of proteins by learning from thousands of experimentally measured shapes. AlphaFold seemed to have solved this problem, and Hassabis and his team called it “the template for how AI can accelerate all of science to digital speed.” A wave of startups building foundation models for biology, chemistry, and materials discovery raised billions of dollars. However, the article states that AlphaFold may not be the best template for science's metamorphosis because the conditions that produced it are rare. The primary condition for its success was the Protein Data Bank, a data set of roughly 170,000 experimentally validated protein structures, which took 53 years of international scientific cooperation and roughly $21 billion worth of experimental work to assemble. Even in fields with requisite cohesion and resources, another barrier is the scientific impossibility of generating comparable data. Instead, the acceleration of science will come about thanks to another approach: AI agents.