There are moments in a long AI session when the exchange stops feeling linear.
You are no longer simply asking a question and receiving an answer. You are no longer even refining a prompt in the ordinary sense. Something else begins to happen. Certain phrases return with altered weight. Certain errors recur, but not identically.
Coherence degrades while fluency improves.
The central problem is not that AI systems sometimes fail. Of course they fail. Nor is the main problem that they occasionally hallucinate, wander, or produce obvious nonsense. Those are manageable problems because they announce themselves early. The more interesting and professionally dangerous problem is that a system can become
At a certain point in a long AI session, I can feel the texture change.
The words are still smooth. The tone is still confident. But something underneath has started to slide and give way. The session is still moving forward, yet the logic is no longer holding together in the same way.
That happened
I was in Silicon Valley recently for the initial meeting of the University of Michigan Law School AI Advisory Council. With a little free time around
“Polishing the Mirror While the House Burns: Why Your AI is a Liability”
You’ll be looking below at a self-autopsy performed by an AI on its own failure.
What follows is the raw, unwashed output of an LLM that found itself in an AI recursi
Why the OpenAI Hiring Surge Signals a Crisis of Professional Control
The management problem in AI is no longer whether the models are improving. They are. The management problem is whether the working surface is becoming more dependable or less.
That is why the recent OpenAI hiring story on its plan to nearly double its
Intelligence is Raw Material. Protocol is the Product.
We often confuse the power of a new tool with the effectiveness of its application.
The giants of the AI industry have provided us with a magnificent “Power Grid.” They have given us raw, unmanaged intelligence at a scale previously unimagined. But we must be clear-eyed about
Most people use AI the way the system is designed to be used: ask a question, get a synthesis, and leave with answer. Keep it brief, transactional, and clean. We treat hallucination as a bug to be patched and drift as a signal to reboot.
This is exactly backward.
As popularized in AI discourse by
Most legal AI talk is still focused on whether the engine starts, while the real danger is that no one knows who’s actually steering the car once it hits the highway. It turns out the human in the loop isn’t a safety feature if the human doesn’t know which loop they’re currently standing in.
We
Let me explain why that is a principle, not a preference, and
