AI adoption is loud, but much of it still feels like extra work

AI adoption is finally loud, but a lot of it still feels like extra work, not extra leverage, because most of us are paying a rework tax on generic output, then pretending that is the future of work.

The part nobody wants to name is that models execute instructions, and most of our operating logic still lives in people’s heads, not in writing.

I wrote a short, sharp take on the specification problem, starting from what we learned embedding an agent inside a real performance workflow, then moving through Whitney, Nonaka, and Taleb, with one point worth repeating.

If you want the durable advantage from AI, the binding constraint is not the model, it is whether you can specify how the organization actually runs, and keep that specification current while everything changes around it.