Authority Delegates. Accountability Doesn’t.
Directionally Correct, Specifically Utterly Wrong
The errors in a generated research report are not spread evenly. They cluster where the decision is, and the rule you already use to catch them is aimed at the wrong variable.
TL;DR: A generated research report is right only if every link in it holds, so accuracy falls away as the steps pile up. In one benchmark’s public filings, the same models score 83% on answering what a document says and 35% on answering what follows from it. Verifying the recent and the obscure misses it; decisions rest on public material. The mode that most resembles diligence fabricates the most citations. Mark every claim retrieved or composed, and re-source only the composed ones before money moves.
Continue reading “Directionally Correct, Specifically Utterly Wrong”An Ode to Electronic Communication (Now That Words Are Free)
Writing got free, reading did not, which makes every message you send a withdrawal from somebody else’s day.
TL;DR: AI made writing free and left reading as expensive as it always was, so every inflated message moves work from the person sending it to the person receiving it. The meter is bolted to the side that got cheaper, and nothing counts what lands on the other. The padding a model adds is billed twice, once in tokens and once in attention. Five daily AI users voting together now spot machine prose 299 times out of 300. Change what you ask the tool for: cut the draft in half every time. Send nothing that came back on the first prompt.
Continue reading “An Ode to Electronic Communication (Now That Words Are Free)”The Diagnostic You Already Ran
How much of your work an agent can take depends on how completely you can specify it, and you are paying for the gap on every single run.
TL;DR: Everyone has the same models, and nobody has the same space around them, which is where the real variable sits. The framework for measuring that gap ran in a management journal back in 2005, written for an entirely different purpose. Two questions decide it: can you codify the work, can you measure the result. What ruled work out then, untransferable team knowledge, is exactly what you can now supply. A colleague is onboarded once; an agent without memory is onboarded every time. Specify once, where the agent reads by default; unclear thinking now runs a meter.
Continue reading “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”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 blank canvas and the seasoned eye.
Ten years ago, streaming a movie over Starlink at 30,000 feet would have sounded like fantasy. We are about to be just as wrong about AI, and the people who get the next decade right will not be the ones you expect.
We are reliably bad at imagining ten years out, and we miss in one direction: we underestimate. AI is the next thing we are underestimating. The instinct in most rooms is that the young will lead and everyone else will catch up. That instinct is wrong twice over, and the answer to why is older than any of the tools.
Continue reading “The blank canvas and the seasoned eye.”Where you work stopped mattering. When your AI resets started.
The remote-versus-office debate has aged out; now, shifts run on an AI token clock you do not control.
Your AI tool reset now shapes your calendar. Claude meters in 5-hour windows, ChatGPT in 3. A heavy user on a Max plan can use an entire window in an hour, then wait four. This pacing splits your day into four shifts, making your meeting culture count against subscription tokens. Below is a proposed schedule for a modern knowledge worker, with three actions to try this week.
Continue reading “Where you work stopped mattering. When your AI resets started.”You signed it. You own it. That is the only test that matters.
The argument over what counts as cheating is the wrong argument. Here is the one worth having, and the three questions that settle it.
A friend asked me last week whether it was cheating to have ChatGPT clean up his English before he sent a client memo. He is fluent, not native; the model fixes a stray preposition, tightens a sentence, lifts the register half a notch. He has been doing this for two years. He has never asked the question out loud before.
I asked him whether he uses Grammarly. He laughed. Of course, he uses Grammarly. Everyone uses Grammarly. Grammarly is not cheating; Grammarly is hygiene.
That is the entire debate, in two minutes.
Continue reading “You signed it. You own it. That is the only test that matters.”