The Five Thousand Minutes Problem.
The short version: a case for AI etiquette. When something took three minutes to generate and takes twenty to read, the sender has spent the reader’s time, not their own — and the backlash lands on everyone doing serious AI-assisted work. It is different when someone has built a solid system, vetted its output and made sure it is worth your time. Two norms to start with: earn the time you ask of your reader, and show how you used AI. And when you review AI-built work, improve the factory, not the car.
01Five thousand minutes
I spend a lot of time on stages and panels these days.
People often ask how I got so comfortable up there, and assume I love the spotlight. I’m not an extrovert. It comes down to practice.
The spark came from a book: Scott Berkun’s Confessions of a Public Speaker. One idea in it stayed with me. When a hundred people listen to you for an hour, that’s a hundred hours of their lives. Do the same sum for ten minutes in front of five hundred people and you are asking them for five thousand minutes. You had better be confident in yourself, in your ability and in what you will bring. If you don’t think it’s worth putting serious preparation in to earn those five thousand minutes, don’t do it.
That doesn’t mean five thousand minutes of preparation for every talk. It means being mindful of the time you ask from the people on the other end, whether you are presenting, sending a document or posting something. That balance is exactly what has gone wrong in much of AI-generated content. Someone creates a huge amount of material with AI in three minutes and expects the receiver to spend twenty minutes going through it. That asymmetry is the source of most of the frustration.
It is different when someone has built a solid, high-quality system that uses AI to give you insights, has vetted the quality of what it produces, and has made sure it is worth your time. The output may take seconds to generate, but the effort went into the system and the checking. The balance holds.
Time is half of it. The other half is intent. A painter can cover a canvas in a single black square, or a musician can write a very simple song, and the work can carry more artistic value than a beautiful picture that took ten times as long. The value lies in the person and the intent behind it. Out of everything they could have made, they chose this one thing and took a risk on it. That choice is what makes it real.
The same holds for AI. Out of everything you could have spent your time on and everything you could have said, you worked with AI and chose to deliver this specific thing. The reader should be able to see that choice.
02Two kinds of AI users
AI has split us into two groups, and the split runs through both the people who create content and the people who read it.
On one side are the early adopters who have built proper systems. They have frameworks, guardrails, curated context, data governance and testing. Their output is shaped by thinking, calculation and a deliberate strategic framework. They tailor a message to its audience because the system underneath was designed to do that.
On the other side is everything else. Our organisations, our media and even our music are filling up with low-substance content. It wastes people’s time and annoys readers. It also does damage beyond itself, because it creates a problem for everyone producing high-quality AI-assisted strategy, analysis, music, video and art. When most of what arrives is noise, the signal gets ignored along with it.
What I value, and what I don’t.
What I value most in my colleagues is their connection to customers and the market, and how sharp they are about both. AI and information systems still capture that poorly. A recording of a meeting is not the meeting. The texture of the room, the hesitation before an answer and the thing a client almost said are all in the people who were there.
I also value the people who see patterns, connect dots, run the numerical work and the lift studies, and shape strategy from it. That work is changing. Increasingly, I want to reward the people who build the right bridges at an architectural level, between systems, datasets, trends and market events. I value someone who has set up AI properly far more than someone who has drawn a nice framework on a slide. A strategist who gives AI the right guardrails and context, picks the right themes at the right moment, and helps others move with those insights can produce and land strategy at a pace we have never seen before.
What I do not value is something else. It’s the person with little substance, little context and little time spent with customers, who uses AI to answer their own questions and worries, generates a document and sends it to me as something I now need to respond to.
Then there’s the faux pas, and the risk it creates: people decide AI-generated work is not worth their time, and to be fair, that reaction has grounds. Most of us have received something that took the sender three minutes to produce and will take us twenty to work through.
Worse is the feeling of being tricked: receiving something that was never meant to be real, presented as if it were. Examples include life advice on raising your children, getting healthier or balancing your life, produced after ten seconds of prompting and handed over as a five-minute read. There are the fabricated “real life” viral videos. And in our own world, there’s the person who did not do the homework, did not have the customer meeting and did not gather client insight, yet uses AI to build something large around a moment that was in fact very small.
These are poor uses of AI. They are disruptive, and they are a breach of etiquette.
The backlash brings its own risk. People start hunting for signs of AI in everything they read, and some refuse to read it at all. That is a catch-22, and it drives a negative spiral.
It also punishes exactly the wrong group: the people using AI to do things they could never do before. They’re connecting strategy in new ways, running analysis they never had time for, and bringing deep or niche insight to the table. If we treat all AI-assisted work the same way, we lose the best of it.
03Two norms, and the factory
I propose two simple norms to start with.
- Earn the time you ask of your reader. If a document will take someone twenty minutes, you should know why it is worth those twenty minutes. That effort doesn’t have to go into every document by hand. It can sit in the system you built, the context you gathered and the quality you vetted. What doesn’t work is three minutes of generating for twenty minutes of reading.
- Show how you used AI. Say briefly how you set it up and how it helped you reach your insight or conclusion: the data you used, how it was cleared and governed, and how you tested the output. That tells the reader a deliberate setup sits behind it, and that it deserves to be taken seriously.
The reader’s side: discuss the factory, not the car.
Etiquette runs both ways. Working with AI-generated documents, structures and strategies calls for a different kind of collaboration. The aim shifts from changing individual sentences or boxes to improving, together, the system that produced them.
Take quality control on a strategy deck. When an agent reviews it, I still read it myself; the agent lets me be far more meticulous than I could ever be alone in the time I have. I can check every calculation and every number, however large the deck is. That takes more than one clever prompt: a set of scripts running in the background traces the figures used across the slides. I’ve built a way of reviewing a deck through eight different lenses and stakeholder views, covering layout and formatting, usability, the numbers, business language and strategic drift, among others. These are basic checks, but they raise the baseline enormously.
So when I sit down with a strategist, the conversation is about how to use that agent and what its input and output tell us about improving the deck. Points of agreement or disagreement on individual slides can still be handled afterwards.
The best way I can describe it: we stop debating the car and start working on the factory. You build a better car by building a better factory, rather than reworking one car at a time. It takes more effort up front, and it feels less direct than just making the edit. But the conversation shifts from the outcome to the process that can recreate that outcome again and again.
I learned this from composing music and setting up studios. When a sound is off, you don’t patch that one recording. You fix the signal path at its source. Fix it in the production line, not in post-production.
04A first draft
These two rules are a first draft of an etiquette we will need to write together, as creators and as readers, as the tools and our skill with them keep improving.
If we get it right, the people doing new work with AI will get the attention they’ve earned, and the rest will improve because of it.
Where the numbers came from.
In the spirit of rule two: the argument is mine. The text was drafted and edited with an AI model, a coding agent set it into this page, and the four illustrations were generated on a local Flux model, then chosen by me.
A hundred people listening for an hour is a hundred hours of their time.
Scott Berkun, Confessions of a Public Speaker (O’Reilly, 2009). The passage, as recorded in Derek Sivers’ book notes: “When 100 people are listening to you for an hour, that’s 100 hours of people’s time devoted to what you have to say.” The ten-minutes-times-five-hundred version is my own arithmetic on the same idea.
“Three minutes to produce, twenty to work through” is an author observation from my own inbox, not a measured average.
If any claim here is mis-cited or out of date, mail me at rt.nl/contact and I'll fix or retract.