The Diagnostic You Already Ran
An Ode to (Valuable) Meetings
Almost everything that ruins a meeting is decided before anyone walks into the room.
TL;DR: In January 2023, Shopify deleted every recurring meeting with more than two people, and the instructive part was watching which ones people fought to get back. A meeting is a symptom: the damage was done days earlier, by someone dragging a block onto a calendar. Overstuffed invite lists are what an organization that writes nothing down looks like. If your AI avatar could attend without loss, that is a document with catering. The undelivered action item costs more than the silent attendee; it books the next meeting. Judge your AI by the meetings it prevents, then cancel one and watch.
Continue reading “An Ode to (Valuable) Meetings”This Time, AI Doesn’t Have an Alibi
The work AI is absorbing now is the work we offshored twenty years ago, for the same reason. The advantage won’t go to whoever swaps a human for a machine. It goes to whoever redesigns the work.
AI had an alibi for the vanishing entry-level job until 2025. It won’t have one for what comes next. The work most exposed to it in the short term is the one we spent the 2000s offshoring, from call centers to entry-level coding to the back office, because both waves seek the same thing: how much of a job is written down and how little of it is judgment. That work already left once. The mistake now would be to automate it in place, swapping a machine for a person and changing nothing else. The larger prize, including for the economies that first took this work, belongs to whoever is willing to rethink how it gets done at all.
Continue reading “This Time, AI Doesn’t Have an Alibi”The SaaS Bill Just Split Into Two Meters
One meter counts how many people log in. The other counts everything the agents they authorized won’t stop doing, and the surviving architecture stacks both.
Your next software renewal will likely carry two prices, not one. One line still counts the people who log in. The new line counts everything the agents they turned loose did while nobody was watching a screen. Salesforce already reports it publicly: Agentforce revenue up 205% year over year, with the seat line holding right beside it. This is a stacking model, not a swap. It changes three things at once: what you sell, what you buy, and how fast a software bill you thought was fixed can run away from you.
Continue reading “The SaaS Bill Just Split Into Two Meters”Every AI Answer Is a Bet Dressed as a Fact
Leadership was always about judgment under uncertainty. Now the machine is uncertain, too, with every AI answer arriving as a single number in a confident voice. Re-attaching the odds is the skill LLMs quietly made critical.
Your AI sounds equally sure whether it is right or guessing. That flat confidence is not a quirk; the same training that makes a model agreeable also makes it overconfident. Leadership has always meant deciding before all the facts are in, and the tool used to be the one certain thing on the desk. It just stopped being certain. This is the fourth cognitive lens: reading every answer as one drawn from a distribution, and supplying the calibration that the machine cannot.
Continue reading “Every AI Answer Is a Bet Dressed as a Fact”Your AI Agent Will Never Question the Premise
It was built to reply with the “best” existing answer. The one move it can’t make is the one now worth the most.
Ask an agent to price your product, and it hands you a polished version of what your competitors already do. That’s not a bug; it’s the objective working perfectly. A model is a machine for the most likely next thing, which means it drifts toward the average answer, the one everyone else’s agent is also producing. The expensive human move runs the other way. SpaceX did it with a rocket and found that 98% of the cost was due to inherited habit. Here’s why that move is now the edge, and why a faster agent makes skipping it more dangerous, not less.
Continue reading “Your AI Agent Will Never Question the Premise”When the Parts Got Cheap, the Connections Got Expensive
AI discounted all traditional knowledge work. The value didn’t vanish. It moved to the one place a model still can’t reach, and most leaders aren’t looking there.
AI has made the components of leadership work nearly free: analysis, draft, model, first-pass decision. The value didn’t evaporate. It relocated to the one place a model still can’t reach: the connections between the parts. That shift is why systems thinking, long filed under “nice-to-have,” is now the highest-return skill a leader owns. A nine-second corporate catastrophe this spring shows what it costs to keep watching the parts instead.
This is the second article in a short series on three cognitive lenses for thinking under pressure: tension, connection, and reduction.
Continue reading “When the Parts Got Cheap, the Connections Got Expensive”Your Hardest Problems Aren’t Problems
The ones that keep coming back to the same meeting were never yours to solve. In the age of AI agents, tensions move to the core of the job.
A company handed two-thirds of its customer service to AI, shed the equivalent of 700 support agents, and called it solved. A year later, the CEO said the words every leader dreads: We went too far. It was not an AI mistake. It was a category mistake, the same one that breaks reorgs, rewrites, and roadmaps. Many of your hardest calls were never problems with answers. These were tensions to be managed, and AI agents just turned spotting the difference into a survival skill.
This is the first article in a short series on three cognitive lenses for thinking under pressure: tension, connection, and reduction.
Continue reading “Your Hardest Problems Aren’t Problems”AI Has an Alibi
Everyone blames it for the vanishing entry-level job. The timeline says otherwise.
The entry-level job is disappearing, and the headline has already named the culprit: AI. Here is the problem with the case. The sharp decline set in around late 2022, and AI was nowhere near good enough to replace anyone until late 2025. A cause cannot arrive three years after its effect. So who actually did it? The honest answer is a lineup, and the most interesting suspect never makes the headline: a feedback loop nobody had on a budget line.
Continue reading “AI Has an Alibi”The most durable thing in your software isn’t in the code.
