Central to science has been the notion that our theories are supported and proven by testing them against the real world. But in many fields, from medical research to climate science and AI, theories are increasingly not tested against reality but against simulations that generate predictions of how reality will behave. Critics however argue that these predictions often depend on assumptions that embed perspectives and biases. Even celebrated advances, like AlphaFold’s protein mapping or the 2024 Nobel Prize in Chemistry for AI-driven drug design, raise the question of whether we are discovering reality or just reproducing the outlooks of the models themselves. And its not only about research, with systems as complex as the climate projections from simulations influence the policy of entire nations, even the world. 

Are these simulations genuine windows onto reality, or reflections of the beliefs and biases built into them?  Do we risk mistaking simulations for the truth, eroding public trust, and undermining the core methodology of science?  Or are simulations a vital shortcut to speed research and drive new and powerful theories?