I spent 20 years running consulting engagements at Fortune 500 companies. Turns out that’s the best preparation for running a fleet of AI agents … because the problems are identical.
Every meal planning app treats cooking as the hard problem and shopping as a logistics detail. They have it backwards. Cooking is mostly solved. Shopping is the last mile.
Every real org has the same topology: principal, role-holder, specialists. Staff AI maps onto it, node for node, and the cost collapse shows up in the deliverables that were always just human-handoff overhead.
Two frames for what AI is doing to work. The tool frame makes tools smarter. The staff frame makes roles unnecessary. Those aren’t the same product, the same company, or the same industry.
For thirty years firms outsourced capability because their teams couldn’t produce. AI collapses the production gap. What’s revealed underneath is what was there all along.
User Acceptance Testing is supposed to be users + acceptance + testing. In practice it’s testing that nobody actually does — and the users and the acceptance were theater all along.
On roles, fleets, and the Hegelian reversal waiting at the end of the AI transition. The sequel to Knowledge Work Was Never Work and Apps Are Irrelevant.
I spent months building a meal planning app. This weekend I replaced it with two emails, a spreadsheet, and an AI model — and realized the stage I was racing toward wasn’t the destination.
Knowledge work was always coordination between humans who couldn’t share state directly. The artifacts were never the work. They were the overhead — and AI just made the overhead optional.