Imagine a world where AI can't just predict what will happen but understand *why* it happens. This is the promise of causal AI, a field that goes beyond simple correlations to uncover the true cause-and-effect relationships driving the world around us. But traditional causal machine learning methods often stumble, generating relationships that defy common sense. Now, a new study explores whether GPT-4, the powerful large language model, could be the key to unlocking causal AI’s full potential. Researchers put GPT-4 to the test, asking it to infer causal relationships based only on variable names, with no additional context. Surprisingly, human participants in a questionnaire judged GPT-4's causal graphs as the most accurate, even compared to those created by domain experts. While traditional causal ML models often produced counterintuitive connections, GPT-4's insights seemed to align more closely with human understanding. The study went further, investigating whether GPT-4 could guide existing causal ML algorithms. By using GPT-4's output as constraints, the researchers found that it could indeed steer these algorithms towards more accurate and human-interpretable causal graphs. This research suggests that GPT-4, despite not being explicitly designed for causal reasoning, might hold a hidden talent for understanding cause and effect. While limitations remain, particularly regarding the sample size of the questionnaire and the complexity of the case studies, the findings hint at an exciting future where LLMs like GPT-4 empower us to build more robust and insightful causal AI systems. This could revolutionize fields from healthcare to finance, enabling us to not only predict but also *intervene* effectively in complex systems.