AI’s Pain Points: Findings, Caveats and What Comes Next
In this episode, Katherine Forrest and Scott Caravello examine “The Pain Axis,” a new research paper that identifies a pain-like signal inside large language models and tests whether it shapes model behavior. They walk through the study’s experiments, results and limitations, and consider the broader questions it raises about consciousness, alignment and model welfare. For the sources referenced in this episode, please see the links below: Anthropic: 2026 Usage Policy update arXiv: The Pain Axis: LLMs Represent Self-Directed Harm and Act on It



