Hallucinations are a daily reality in the AI and LLM tools many of us use. In this episode of the Professor Insight Podcast, we explore new research from OpenAI and Georgia Tech titled “Why Language Models Hallucinate.” The findings shed light on why large language models often produce confident but false statements, and why this problem persists even in the most advanced systems. Listeners will discover how hallucinations begin during pretraining, why they survive post-training, and how current benchmarks actually encourage models to guess instead of admit uncertainty. We’ll walk through real examples, the statistical roots of the issue, and the socio-technical traps created by the way we evaluate AI today. The episode also highlights the bold proposal from researchers: to redesign scoring systems so that honesty is rewarded, not punished. This conversation matters because hallucinations aren’t just harmless quirks. They can shape trust, decision-making, and even safety in classrooms, businesses, and healthcare systems. By unpacking the causes and potential fixes, this episode offers listeners a clearer understanding of how we might steer AI toward becoming not just more capable, but more trustworthy.