Abstract
Generative AI has captured the imagination of scientists, businesses, and policymakers alike. From drafting essays and coding software to generating creative content and solving complex equations, these systems increasingly appear capable of tasks once thought to require human intelligence. Headlines tout AI as a potential revolution in research, predicting breakthroughs in medicine, physics, and beyond. Yet much of the excitement rests on speculation. While generative AI can produce impressive outputs, its capabilities in domains that demand creativity, intuition, and original reasoning remain unclear. Can a machine truly discover something new, or is it fundamentally limited to reorganising existing knowledge? This is not just a technical question – it strikes at the heart of what it means to reason, to be creative, and to push the boundaries of human understanding. To explore this question, my co-author Shibo Li (Kelley School of Business, Indiana University Bloomington) and I conducted a study to test ChatGPT-4's ability to function as a scientist in the molecular genetics field. We wanted to see whether a generative AI system could independently navigate the scientific process – generating hypotheses, designing experiments, interpreting results, and revising ideas – without being explicitly guided.