> But language models offer a new way of thinking about these complex systems. They show us that meaningful patterns can emerge from vast networks of relationships, without requiring reduction to simple mathematical laws. They offer the prospect of a fundamental paradigm shift in the way science is done. AI language models reveal that many truths in complex domains resist reduction, shifting science toward prediction and rich context over universal laws. ## Prediction over explanation In this new paradigm, the focus shifts from seeking simple, general, universal explanations to making accurate predictions. Rather than trying to isolate single causes, we embrace the complex web of interconnected factors that influence outcomes. Language models demonstrate that highly accurate predictions can emerge from statistical patterns across vast datasets, even when we can't trace exact causal chains. This approach may be particularly valuable in fields like psychology, where traditional reductionist methods have often fallen short. ## Data & context The goal isn't just to amass data, but to create the kind of comprehensive, nuanced datasets that allow AI models to capture the intricate web of relationships that characterize real-world systems. With access to data trusts, academic and independent researchers could train models that solve real world problems through more accurate predictions. -- https://every.to/chain-of-thought/seeing-science-like-a-language-model