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
A lot of the most interesting work in legal tech seemed to be coming from people who were newer to the field, earlier in their careers, or just not as widely known yet as
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
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
Let me explain why that is a principle, not a preference, and
We pay AI tools to do the hard work, like the synthesis, the heavy lifting, and the cognitive labor we do not have time for. What we often get instead is a tool that produces a decent first draft and then hands the real work back to us.
Not just the hard work. The administrative
