AI Emotions: Open Questions and Next Steps

Recent interpretability research has identified "emotion vectors" in AIs (Sofroniew et al. 2026). These are directions in a model's activations that activate on text that would typically cause an emotion in humans, and that change the model's behavior when steered. We explore what emotion vectors tell us about whether AIs have emotions. We distinguish three hypotheses. On the first, the model merely represents emotions. On the second, the model has states that play the functional role of emotions. On the third, the model has full-fledged conscious emotions. Within the second hypothesis, we distinguish thin functional roles from thick ones. Thin roles are patterns of external behavior, like pro and con attitudes. Thick roles are patterns of information processing: persistence, first-personal structure, fight-or-flight responses, attention control, homeostatic dynamics, and valence with self-direction. We argue that the current evidence supports only very thin roles. We argue that this favors the first hypothesis, where AIs merely represent emotions. We also sketch out some possible next steps for testing these hypotheses further.