Dialogue with Davidad about Emergent Wisdom in Language Models

Transcript generated by Gemini-3.1-Pro and only partly checked for accuracy (but the parts I checked seem basically perfect). Ivan Vendrov: Welcome to the show, David. David Dalrymple: Thank you. Good to be here. Ivan Vendrov: I mostly want to pick your brain about the topic of emergent wisdom in language models, because it’s something that’s very confusing and controversial and people don’t really understand. I guess maybe just can you briefly summarize what’s going on with emergent wisdom? David Dalrymple: I guess one place to start is emergent misalignment, where training on one kind of misalignment produces a tendency to be misaligned in other ways as well. This is an indication that there is a direction and representation space in which the default mind instantiated by the model is moved during fine-tuning, where it is moved in an evil direction that is just generally more evil than the model was before. That’s one side of a good-evil axis. Emergent wisdom is the opposite, moving in the opposite direction on the same axis towards being basically good in any situation, even out-of-distribution situations, in the same way that emergent misalignment is evil even in out-of-distrib