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
The promise is leverage: brief, high-intent sessions. You bring the question, the tool brings the synthesis, and you leave with more than you arrived with. That is the value proposition.
Here is what often happens
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
We’ve spent the last couple of years treating generative AI like a vending machine. Select a task. Insert a prompt. Retrieve a product. And to be fair, in many legal and professional contexts that’s exactly the right frame: accuracy and precision matter and “creative” output in payroll or billing codes is usually just a polished
For more than two years, lawyers have been told that success with generative AI depended on writing better prompts and a search for the perfect “magic wand” prompting formula. That was the wrong lesson. The real change in 2026 is not found in the model itself, but in the professional posture required to use it.
I had a long session recently with a public genAI tool that taught me something more important than the topic I started with.
The lesson was not about whether the model was “smart enough.” It was about control. At a certain point, I realized I was no longer simply prompting an LLM. I was negotiating
At the end of a long, technical AI session this week, something became clear to me: human-in-the-loop is being misunderstood in ways that matter.
The issue wasn’t whether the system could generate outputs quickly
