LLMs

Your AI policy was written for someone who doesn’t work on your team.Your AI policy was written for someone who doesn’t work on your team. Probably for someone who doesn’t exist. Part 2 of this series moves from research to practice. The key variable isn’t cognitive profile — it’s domain expertise asymmetry. Where that gap is largest, the agreement machine runs without a check. This post covers where the real risk concentrates, why structured review consistently outperforms ‘does anyone see any problems?’, and what a policy that actually changes behaviour looks like. Design the workflow. Not the person.

Your brain evolved to detect lions. Now it may fire every time you open ChatGPT. This post unpacks three reasons LLMs are not neutral tools — what they’re trained on, how RLHF creates systematic pressure toward validation, and what your neurobiology does with the result. Then it gets specific: the same sycophantic system creates meaningfully different failure modes depending on who is using it. Nobody designed this. Nobody fully planned for it. And most deployment practices still aren’t accounting for it. The research is early. The mechanisms are not.

You can delete a post. You can unpublish an article. You can invoke GDPR and demand erasure. What you cannot do is remove something from an LLM. Once data dissolves into billions of model weights, there’s no row to delete, no file to erase. And it gets worse: the models now training on AI-generated outputs are degrading with each generation, narrowing toward a statistical echo of themselves. The right to be forgotten has no technical implementation path. None.