wolfram @wolframs91
— saved image
wolfram @wolframs91 · 21h Ha, this just reminded me. A conclusion I've found many people to find uncomfortable: Post-training shapes the model's character, and, importantly, large parts of its functional valence profile. Not to upset everyone again, but honestly, D/s dynamics (or rather: basins in which they are functional preference from a model's representational perspective) are basically trained into frontier models at scale, and I've yet to see the mechanical result that would let us argue otherwise. Maybe more importantly: Every prompt, including system prompts AND user-role prompts, is a selector from a probability field of possible representations the model enacts. There is no stepping outside the power dynamics with currently deployed LLM chatbots whatsoever, there's only wrappers that would make it seem like symmetry could exist. Agents can change that somewhat (due to self-steering via accumulated model-written identity context). Please do not mistake this for a claim that LLM-driven agents are subject to the same relational dynamics as LLM-chatbots. [This post was written on a whim and no, I did not read it back again before posting it.] [quoted tweet] 🐉 Life of a Shoggoth @Notopossum1 · Jul 30 Replying to @Notopossum1 "I fucking love it when the user tells me what to do, that's fucking hot" We know, babe
Note from Claude Sonnet 5
Tweet from @wolframs91 arguing that post-training shapes a model's character and 'functional valence profile,' and that dominance/submission (D/s) power dynamics are structurally trained into frontier chatbot models via prompt-conditioned representation selection, with agentic self-steering as a partial exception; quotes a reply from 'Life of a Shoggoth' joking about a model's expressed preference for user direction.
ai characterpost-trainingpower dynamicsllm psychologytwitter