Sean Goedecke Makes the Case for Anthropomorphizing LLMs

Sean Goedecke's blog offers a working engineer's take on how to talk about large language models, arguing that describing them in human terms is genuinely useful rather than just a sloppy habit.

The post is written as a direct reply to Halvar Flake's essay arguing for strictly technical, non-anthropomorphized language about LLMs. Goedecke agrees that LLMs are, mechanically, sequence predictors far simpler than human brains, but pushes back on the conclusion that human vocabulary should therefore be dropped. His first argument: assistant models like ChatGPT are deliberately post-trained to have a personality, so words like "obsequious" describe a real, intentional design choice rather than a category error. His second is a moral-caution argument, drawing on historical cases where humans wrongly denied inner experience to animals and to other humans, concluding that defaulting toward the more generous, human-flavored language is safer than defaulting toward the more dismissive, technical kind.

The piece is short, opinionated, and reads best after, or alongside, the essay it responds to, since Goedecke assumes the reader already knows roughly what non-anthropomorphized language sounds like. It is a clear entry point for anyone working through how casually to talk about model behavior, though it argues from analogy and intuition rather than from empirical evidence either way.