In the summer of 2010, Donna Cooper took her family on a day trip to Death Valley. It was a good day. Long, hot, the kind you end tired and know you’ll sleep good. When it was time to head home to Pahrump, she did what we all do…she asked the GPS for the shortest way back.

Donna called the GPS Nell.

Nell said go 550 feet, then turn right. At 550 feet there was a little path. Then a quarter of a mile and turn left. There was nothing there. Just desert, in every direction, in one of the hottest places on the planet.

“She had me running in circles for hours and hours and hours,” Cooper said later.

Three freakin days.

That is how long the Cooper family was lost. A search and rescue helicopter finally spotted them and luckily everyone survived.

The rangers in Death Valley call this “death by GPS” and it actually happens a few times a year. Around the same stretch, a mother and her son followed their GPS down an abandoned mining road and got stuck for five days. She barely lived but this time, the boy did not.

What unsettles the rangers is that the device is almost never wrong. It is too right. It computes the most direct line from where you are to where you want to be, and it sends you straight down a road that stopped existing forty years ago, with total confidence, in a calm voice, the whole way.

Here is the crazy part…

After three days in the desert, Donna Cooper did not lose faith in GPS.

She bought a new one. She named it Rosie.

We talk about trust like it is a decision. Cooper decided to trust Nell. Then she decided to trust Rosie. But that is not what happened, and you know it is not, because you have sat in that passenger seat too.

The trust was not a choice she made. It was a feeling the device created in her.

A confident voice, a clean line on a screen, an arrival time down to the minute.

And the feeling of being guided is so much stronger than the evidence of the road dissolving in front of you that the feeling wins, every time.

The tools that put you in danger are not the ones that fail you. A tool that fails you, you stop trusting. The dangerous ones are the ones that make you feel guided while they walk you somewhere you would never have driven with your own eyes open.

The Guided Feeling

You have had the guided feeling. The draft that appears in eight seconds, so smooth it reads like it was always there. The little clock in your head that says this used to take an hour, and now it takes ten minutes, look at me go!

And the feeling is real.

But what we are all forgetting to ask is whether the feeling is attached to anything.

And the problem is not the skeptics who refuse to touch the stuff. They are fine. They kept their eyes on the road.

The problem is the rest of us.

The experienced ones. The people who use AI well, feel sharp doing it, and have no idea the road stopped existing two turns back. We are producing more than we ever have.

But some of us are getting slower while we do it, and have no idea at all.

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The Clock Disagrees With You

In early 2025, a research group called METR ran the study you would run if you were being honest with yourself. It’s like when you haven’t gone to the dentist in years and keep putting it off because ya just would rather not know.

They took 16 experienced developers. These were people working in their own codebases, projects they had contributed to for an average of five years, some with more than a million lines of code. They gave them 246 real tasks from their actual work.

Then they flipped a coin on each task:

AI allowed, or AI not allowed.

Before they started, the developers predicted AI would make them about 24 percent faster.

After they finished, they reported that it had made them about 20 percent faster.

The screen recordings said they were 19 percent slower.

No, not slower than they hoped. Slower than they were without the tool at all. The thing they reached for to speed up was the thing slowing them down, and the real kicker, the part that should really make ya feel funny, is that even after living through it, they still walked away believing the opposite.

The Confidence Is Wired Backwards

It goes one level deeper.

Microsoft and Carnegie Mellon surveyed 319 knowledge workers about when they actually think critically while using AI. The finding ain’t good, but honestly, makes a ton of sense.

The more you trusted the tool, the less you thought. High confidence in the AI predicted less scrutiny of what it gave you. High confidence in yourself predicted more.

Read that again, because it is doing something quiet and nasty. The better the tool feels, the more it sands down the exact reflex you would need to catch it being wrong.

I heard a story of a founder who knows his numbers like the back of his hand. He ran a board document through AI and forwarded it. A partner caught a figure that was off by a year. He had read it.

He just had not read it in the way he reads everything that does not arrive pre-formatted and “certain”.

The polish bought his trust before his judgment ever showed up to work.

You get in the habit of feeling like it is handled but it costs you the habit of checking, at the very moment checking is the whole job.

I catch myself in this moment all the time. These tools have gotten good. Real good. I will be working late at night and realize I have 3 Codex sessions, 2 Claude Code and a few Chrome browsers humming along.

It’s so much easier to keep tapping “approve” and trusting the AI and man…it feels damn good making that much progress.

But as one of my favorite sayings goes, “the bill always comes in the end.”

The Muscle Is Already Going

This is the one you cannot feel at all, because it is measured underneath feeling.

MIT’s Media Lab wired up 54 people with EEG and had them write essays.

One group used ChatGPT.
One used a search engine.
One used only their own brain (crazy I know).

They watched the brain activity the entire time.

The brain-only group lit up the widest with the most connected networks.
The search group was less.
The ChatGPT group showed the weakest neural connectivity of the three.

The cognitive engagement scaled down in lockstep with how much the tool was doing.

It’s the cognitive version of taking the escalator every day and then being surprised, months later, that the stairs leave you winded.

But here is the most surprising/unsurprising part…

The AI group could not quote their own essays. Sentences they had produced minutes earlier…couldn’t recall them. Because in a real sense they had not written them. They had watched them get written.

The researchers call this cognitive debt.

It feels like nothing and that’s precisely the problem. The bill arrives later (see!), in the one moment you need to think without the tool, and reach for the muscle that’s no longer there.

Why Your Gauge Stopped Working

For your entire career until about two years ago, the feeling of being good and the fact of being good rolled together.

You felt fast because you were fast.
You felt sharp because you had done the reps that morning, that year, that decade.

The feeling was a trustworthy gauge for one boring reason.

There was no way to feel productive without actually being productive. Producing the work was the proof. They were the same event.

But AI has started pulling them apart.

For the first time you can have the complete sensation of expert output with almost none of the underlying work, and your internal gauge has no idea anything changed. It still thinks it is counting reps…but it’s counting vibes. The needle that ran your whole career came loose from the engine, and it still swings, confident as ever, pointing at nothing.

Your Company (Or Your Client) Is Running the Same Thing

Now zoom out to a team, or to a client you advise.

A group ships 30 percent more this quarter. Leadership reads that as a win and raises the number for next quarter. Nobody measures the thing that does not show up in the dashboard:

Whether the people producing the work still understand it well enough to defend it, fix it, or rebuild it when the tool is down.

The organization optimizes the one thing it can see and goes blind to the one thing it cannot.
The startup asks, what can we produce now. The question that would actually protect them is the one nobody asks out loud because it’s not convenient now.

What can we still do when it stops working? Because we all know it will stop working eventually.

A Test Worth Running This Week

So here is something to try.

Pick a task you always hand to AI. The one you have fully outsourced because at this point it would feel stupid not to. Do it once this week without the tool. Time both versions.

Then notice two things.

The clock, which tells you whether you were actually faster.

And the harder one. The parts you could not do as cleanly as you remember being able to.

That second reading is important. The stopwatch measures speed.

The struggle measures the muscle.

You are not doing this to feel bad, or to go back to grinding everything out by hand like a monk. You are doing it to reconnect the gauge. Use AI to move faster through the parts that do not require you. Stay inside the parts that do. Keep your eyes on the road even while ol’ Nellie is talking.

Watch It Run Backwards

Let me show you the reverse happening.

I know a Fractional CMO. Let’s call him Reza. He runs marketing for three companies at once and is sharp, fast, and exactly the kind of person who should be using AI hard. And boy was he. Positioning memos, campaign briefs, board updates, all of it drafted in minutes by asking Claude to write the thing and then cleaning it up.

Then a founder he works with stopped him in a meeting. Pushed on one line of a positioning memo. Asked him why he had recommended going after the mid-market instead of staying upmarket. A simple question. The reasoning should have been right there.

It was not there.

Reza knew the memo said it. He could not reconstruct why it said it, because in a real sense he had not decided it. He had watched it get decided. He recovered, the way we all do, but he drove home knowing exactly what had happened. The gauge had been reading full the whole time the tank was draining.

So he changed one thing. Not the tool. The order.

Now, before Claude writes a word, he opens the first prompt in the pack and it refuses to draft anything. Instead it makes him talk first. He brain-dumps his actual take, messy, into voice mode for ninety seconds. Then the second prompt does not write the memo either. It interrogates him.

“You said mid-market. What changed your mind from the upmarket play you ran last quarter? Name the specific signal. If you cannot name it, say so and we will flag it as an assumption, not a finding.”

Reza answers out loud. The thing that used to be invisible, the actual decision, gets dragged into the light where he has to stand behind it. Only then does the third prompt sharpen what he built, and it flags two lines where his reasoning is still thin and tells him so instead of papering over them.

Same memo with roughly the same words…Maybe ten minutes slower.

“But now it’s mine,” he said. “I could feel the difference in the next meeting. When she pushed, I was already standing there.”

That’s it. Not doing it the hard way. Doing it in the order that keeps you grounded while it happens.

What the Test Can’t Do

You now know the gauge is disconnected. You know which group you are in, the one that drafts and stays inside the thinking, or the one that generates and copies. And you have the one test to feel it for yourself this week.

Here is what the test cannot do.

It cannot rewire your default. The moment a real deadline lands and the tool is sitting right there, helpful and instant, willpower loses. You will reach for the fast version, because feeling fast is the one thing that never feels like a cost. Reconnecting the gauge in your live daily work is not a decision you make once. It is an order of operations you install so the thinking happens whether or not you are disciplined that day.

Your Competence Is Reading This Right Now

And it is already filing objections.

It will feel like:

That study was just developers, my work is different.

It will feel like:

I am the careful one, I always check the output.

And the last one, the quiet one:

I can tell the difference between real speed and the feeling of speed.

That last one is the exact belief those developers were holding while the stopwatch called them liars.

Feeling like you can tell the difference isn’t the same as being able to. And that confidence is the first thing AI takes from you. Not by making you feel worse, but actually by making you feel better.

Every serious practice ever built knows this and builds around it. Sprinters do not trust how fast the race felt. They trust the tape. Musicians record themselves because the playing always feels better than it sounds. The move at the center of mastery, in every field, is the same.

An external instrument, brought in on purpose, because the internal one lies in your favor.

You’ve lived with the gap between feeling good and being good your whole career. The difference now is that something finally learned to sit inside it, speak in a calm voice, and give you directions you’ll actually follow.

Donna Cooper named hers Rosie.

What’s yours called?

-Max

Knowing the gauge is loose won’t reconnect it.

That is what The Sanity Pack is.

Four prompts you paste into Claude or ChatGPT that flip your default from generate-then-copy to think-then-sharpen, the move that kept the MIT study’s second group with their brains on the line.

You get the four prompts, a voice-mode version for thinking out loud the way Reza does, a one-page weekly self-audit to catch yourself drifting, and the full worked example.

Run them on one real task this week. If they don’t catch something you would have shipped and couldn’t defend, reply and I’ll refund you. Keep the prompts anyway.

It’s below.

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Not “a little rusty” and didn’t “quit piano as a kid.” He has said, plainly, that he has no technical ability in music and knows less about the mechanics of recording than almost anyone who has ever done his job.

For long stretches of a session he looks like he’s snoozin’ on a couch with his eyes closed.

But this is the man who co-founded Def Jam out of his NYU dorm room and then spent four decades producing records that don’t feel like they should even be on the same shelf.

• Run-DMC and Slayer

• Red Hot Chili Peppers and Adele

• The Beastie Boys and Johnny Cash

Genres that basically hate each other, all routed through the same guy on the same couch.

Musicians who work with him for the first time tend to ask a version of the same question…

What, exactly, do you do here?

The honest answer doesn’t make them feel any better. He listens, and he tells them the truth about whether it’s any good.

That’s the whole job. He cannot make the sound. He can only tell you, with a confidence that has been right for forty years, when the sound is true and when it’s filler pretending to be a song. The artists can all play. Some of them are the best in the world at it but what they hired was that magic ear.

We treat that as an exception. The genius who “can’t work the gear.”

But there have always been two different roles hiding inside the word “skill,” and we spent about a century thinking they were one.

One job is operating the tool. Fingers on the keys, hands on the board, the craft of making the thing come out.

The second job is knowing what the thing should be. Whether it’s any good. Whether it moves. Whether to keep it or kill it.

Almost everywhere, we bundled those two jobs into one person and called the bundle “skill.” You earned the right to judge the work by first proving you could do the work. The ear came after the hands.

But our guy Rubin skipped the hands. He went straight to the ear. And it turned out the ear was the rare part all along.

Rubin will tell you music is almost incidental to what he actually does. He happens to work in it. Knowing what’s good is the same in any room.

I know, I know. This is kind of an AI newsletter so you can probably guess where this is going.

For most of your life, one sentence kept you out of an entire category of work.

“I can’t code.”

One, maybe two years ago, that sentence meant software was a thing other people made, and you described what you wanted to them, badly, in a doc that cost you thousands.

That barrier has been moved by millions of tiny robots, pre-trained on the best coding practices on the internet.

The operating layer, the syntax, the part where you translate an intention into something a machine will actually run, is the part the robots do now.

You say what you want in plain English and it builds. Not perfectly and not always right on the first try. But it builds, and it never once needs you to know what JSON is.

What it cannot do is the second job.

It cannot tell when its own work has no pulse. It hands you something that it is VERY proud of and…average. And then it waits. It waits for someone to look at the output and say “this is fine!” and fine is the problem.

Rubin puts it better, with guitars. A lot of records get built by stacking the same part over itself, double it, triple it, until you’ve got a “wall of guitars”. And when there’s a wall of guitars, you hear guitar. You don’t hear a person playing guitar. It goes generic.

The personality in the one take where you can hear the fingers catch the strings.

The machine(AI) hands you the wall of guitars. Every time. Full, competent, and nobody in the room.

That someone is you. The job that’s open is the ear.

And the people least likely to notice the job is open are the ones who spent ten years building the exact ear it needs.

Watch how “code is the skill” quietly stopped being true.

The first thing you notice is the swap. You used to need a translator, someone who could take “make it feel premium but not stiff” and turn it into the thing that runs. Now you type “make it feel premium but not stiff” and the translator is built in.

This feels like the technical person still has the edge. They speak the machine’s language natively and you’re working from a phrasebook. So you defer. You keep treating the person who can operate the tool as more valuable than the person who knows what good looks like. That deference is the first cost, and it’s already out of date.

Go one level down. The hard part of building anything was never the typing. I learned this the slow way. The hard part was deciding, precisely enough that the result could be judged, what you actually wanted.

I watched a strategist with no technical background pull better work out of Claude than a developer sitting two seats away. The difference was simple. She could say exactly what “good” meant for an ad in her category, and he couldn’t. He could build whatever she specified. He just had no specification of his own.

When that happens to you, you’ll blame the prompt. You’ll go rewrite it a sixth time. The prompt was never the thing in the way. You hadn’t decided what good was, and no amount of rephrasing decides that for you.

Claude Code Accelerator

None of this is new. Rubin proved it at eighteen. When he started, the only hip-hop records out were made by studio pros who built other kinds of music for a living. They had all the craft. They didn’t know the club. So they made a clean, polished version of a thing they didn’t understand, and it came out fake.

Rubin could barely record. But he’d stood in that club every week and knew exactly what it was supposed to feel like, so he made the real one. The craft was all on the wrong side of the glass. The scarce thing was knowing what good was.

Now the bottom level, the one that's harder to look at directly. Specifying what to build was the floor above. This is the floor beneath it.

Whether what came back is any good, and the nerve to say so when it isn't. When the machine can build anything you describe, that judgment is the only scarce thing left in the room.

Rubin has a word for his actual work.

Reduction.

He coined it at nineteen, on an early LL Cool J record, where he printed “Reduced by Rick Rubin” instead of “Produced by.” Production meant building up. What he was doing was the opposite. Stripping a thing down until only what matters is left, and knowing, in your body, where that line sits. That knowing is the real work. It is the instruction set now. Your taste is the thing that tells the AI what to make, and then tells you whether it made it.

This feels like you’re “just giving feedback.” Like the build is the work and your notes are commentary on the work. It goes the other way now.

The notes are the work. The build is the commodity.

For a hundred years, taste sat on top of craft like a penthouse. You took the elevator up through technique to reach it. That’s why it felt like a luxury, the finishing layer you added once the real skills were in place.

The elevator is free now. Anyone can be at the top floor by the afternoon.

So the penthouse isn’t a luxury anymore. It’s the whole building. We are watching the floor and the ceiling swap places in real time, and the people most exposed are not the ones who never learned to code. They’re the ones whose only edge was that they could.

You can see the same confusion at the company level. Every job post wants someone “proficient with AI tools.” They’re screening for operators, people who can drive the machine.

The thing they actually need is harder to test for and worth far more. Someone who can look at what the machine produced and know, fast and correctly, that it’s wrong.

What matters is whether you can tell when it’s lying to you. Using it is the easy part.

Something to try this week. It takes about ten seconds and it works on any AI output you touch.

The next time AI hands you something and you feel the little reflex to fix it, stop your hands. Don’t fix it yet. Ask yourself one thing.

Could I write down the rule I’m using right now?

You felt the thing was off. That feeling is a rule you already hold, sitting just under language.

“The opening is too eager.”

“It’s explaining the joke.”

“Real people don’t talk in lists.”

When you reach in and pull that rule up into a sentence, you’ve made a piece of your taste visible that was invisible a second ago.

Do that for a week, every time you correct an output, and you’ll notice something uncomfortable and good. You have hundreds of these rules. You’ve had them for years. They’ve just been trapped in your head, leaking out one correction at a time, teaching no one and saving you nothing.

Your taste is reading this right now. It’s the thing that has been deciding, paragraph by paragraph, whether this was worth your time.

You’ve been treating that faculty as the cheap part of your work. The part you’d happily hand off so you could get to the “real” output.

It will feel like the soft skill.
It will feel like the thing you can’t put on an invoice.
It will feel like the part that doesn’t count, because you didn’t sweat for it the way you sweat for the technical stuff.

Rubin lay on that couch for forty years being the most valuable person in rooms full of virtuosos, because he understood the thing they kept forgetting. Anyone in that room could play. He was the only one who could hear.

The machine can play now. All of it. Every instrument, every genre, on demand, for nearly nothing.

The hearing is still yours.

It might be the only part that was ever really yours.

-Max

P.S. Putting that hearing somewhere it can actually work, instead of leaving it trapped in your head leaking out one note at a time, is the thing I built an entire workshop around.

Claude Code Accelerator starts Wednesday. I built it with Luke and Mario, two of the sharpest copywriters and creative strategists I know. Which is to say, two people who already do the thing this whole piece is about for a living.

If you already know what good looks like and you’ve been waiting for permission to build, this is it.

Claude Code Accelerator

Between 1883 and 1929, Andrew Carnegie paid for 1,689 public libraries across the United States.

He’d grown up poor in Pittsburgh. As a teenager working as a messenger boy, he borrowed books from a private library a local colonel opened to working boys on Saturdays. Carnegie never forgot it. When he sold his steel company and became the richest man in the world, he decided to give towns the thing that had changed his life.

Then he attached a strange condition.

Carnegie would pay for the building. The bricks, the shelves, the reading room with the tall windows…

But he wouldn’t pay to run it.

Before he released a dollar, the town had to pass a resolution, a public and binding promise to fund the library every year afterward, usually at ten percent of what the building had cost. The staff, the heat, the new books. Forever.

Towns pushed back. They wanted the gift without the obligation. A library was a fine thing to have on the main street. But Carnegie had seen what happened to libraries a town owned and wouldn’t run. The doors stayed locked five days a week. The shelves thinned. The building turned into a monument to a thing the town didn’t actually use.

So he refused to fund those at all.

Carnegie had worked something out. The check buys a building. Only the town can buy a library. So he made them buy it first, in writing, before he spent a cent.

The Bike in the Corner

Getting a thing and running it…really committed to it…are different jobs.

Your friend buys a Peloton in January. The truck shows up Saturday. Two guys carry it up the stairs, plug it in, set up the screen. The Peloton has officially arrived. By June the bike is in the corner of the bedroom holding a sweatshirt and a phone charger. Your friend still owns a Peloton. Your friend is not a person who rides a Peloton.

Carnegie knew this. He’d seen the locked library. He understood that putting a thing within reach of people and getting those people to actually run it are two different projects, and the second one is the hard one.

The first is a check.

The second is a commitment, and nobody can write it on your behalf.

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Everyone Got One

A few days ago, Google ran the largest distribution of AI agents in history.

At it’s I/O conference it announced that the agent is now the product.

Gemini rebuilt into the search box. Gemini Spark, an assistant that “Give it a task and it works in the background 24/7, even if your phone and laptop are turned off. It operates autonomously, but always under your direction. You choose to turn it on and it's designed to check with you before taking major actions.”. Agentic features reaching the 900 million people who open the Gemini app and the billion more who meet AI inside Search. The keynote framed it plainly.

You have an agent now. Everyone does.

Google built the building. It’s a very good building. I built a smaller one this week. A recap of every I/O announcement, a Omni prompting skill and a few other goodies.

But here’s the question Carnegie would ask before he believed any of the hype…

Who’s going to run it?

I am not talking about the skeptics. The skeptics were never the problem.

I am talking about what happens when everyone gets behind this agentic-wheel without really understanding what it can do?

This is no longer just an LLM chat. It can take actions “on your behalf.” But buyer beware because we are about to have a library with no librarian, and can’t quite tell.

Three Reasons You Can’t Tell

Having the building feels like running the library for three reasons, and each one is harder to see than the last.

1. The agent arrives friendly

   It greets you by name. It offers example prompts. You try one and it does something impressive on the first attempt.

   Feels like: It’s working.

   The cost: Those example prompts came from Google

   None of them came from you. Look at what people are actually sharing online two days after the launch. The clever Gemini Omni trick going around, the one that turns a butterfly into a swarm of fireflies, is Google’s own example, lifted straight from Google’s own prompt guide.

   
   The crowd is copying the manual and posting the manual back at each other. You’re running the company’s demo and calling it your workflow.
   

2. The agent is generic by default

   
   It knows everything about the world and nothing about you. It can write an email. It can’t write the email you’d send to the client who went quiet last month, in the register you use with that account, about the thing only you know happened on the last call.

   Feels like: It’s capable

   The cost: It can do anyone’s work. It can’t quite do yours

   I watched this play out at a marketing agency. The founder couldn’t scope a new project without sitting in the room. Every statement of work and proposal needed his judgment. His team had the exact tools he had, the same subscription, the same models. The tools still couldn’t scope an SOW or a proposal, because scoping those were never generic tasks.

   It was his specific patterns, built over years, living nowhere but his head. I pulled that pattern out of 7 of his past SOWs and proposals and built it into a system. Six hours of his time per project became 30 minutes. The team does this without him now. Nothing about their AI access changed. One task got installed and that was the whole difference.

3. You shrink to fit the tool

   
   This is the one you can’t see without help. You adapt to the generic version. You learn which questions it answers well, and you quietly stop asking the ones it doesn’t.

   A tourist with a translation book can order coffee and find the station. She’s got a few hundred handed-down sentences. She’ll never have the conversation that actually mattered, and after a while she stops reaching for it, because it has trained her to only ask what it can answer.

   Feels like: I’ve adopted AI

   The cost: You adopted the demo. The work that needs you, that runs on your judgment and your context, never gets touched. And you stop noticing it’s missing

The three reasons have been here all along.

The Damage Used to Stay Inside

This used to be a smaller problem.

When AI was a chatbot in a separate tab, running the generic version cost you some speed and some polish. You got a mediocre draft. You fixed it. The damage stayed inside the document.

The agent changes that. Agents act. They send the email. They book the slot. They move the money. They make the call you’d have made, at whatever quality you set them to.

The mediocre version goes out under your name, gets read, gets remembered. By the time you notice the tone was off for that client, the position has already been taken in their head. You can apologize. They can’t unread. Distribution put that in 900 million hands this week. Installation (using the thing) is the only thing left standing between capability and consequence.

The Question Nobody’s Asking

I am the last thing from an AI doomer. If you follow me or have read any of my content you will know I love AI, I think the Gemini and Google updates are freakin awesome.

But the tech is moving SO much fast than most people’s “ai-literacy”. They are being handed a powerful set of tools and instructions, incentives and often work mandates to use it as fast as possible.

This sure doesn’t feel like it will end well…

Picture it inside a business.

A company that rolls the generic version out to 50 people has done something that looks decisive and isn’t. A license on every seat. A leadership team asks whether everyone has access and a training session forgotten in a week.

But here is the sneaky part.

It’s going to sound good. It’s going to look good. It’s going to create PowerPoints for you in minutes that used to take you hours and your leadership is practically begging you to do it.

Now, if that AI hasn’t been loaded and trained on you, your company, your situation, your thinking and your judgement it will give you the same answers it gives everyone else.

Answers that, on the surface, will make you think you will get that next promotion in no time.

Until you try to explain them.

When you have to get up in front of the team and present that fancy pants deck with all those animated charts and realize they are completely shallow.

28 slides that are directionally right but operationally useless (one of my favorites terms). Without the proper context and without people moving from knowing to understanding AI, this will happen over and over again.

So here’s the question that hasn’t been answered.

Sit with it. Write the answer down. Be strict.

If the honest answer is a faster email, a cleaner summary, a decent first draft, then the agent did for you the exact thing it did for 900 million strangers.

If the answer is specific, a draft only your agent could write because it knew your client, a risk it flagged because it had learned your blind spots, a call it got right because it caught what you would have caught, then something is installed. You’ll know, because a stranger couldn’t have gotten it.

Ask it this Friday. Then every Friday for a month. The weeks the answer comes back thin will teach you more than the weeks it’s good.

Twenty Years Across the Table

Financial advisor, 20 years in. Let’s call him Glenn. He had ChatGPT, he had Claude, he had every tool the headlines told him to own. He used them the way the tourist uses the translation book. Summarize this. Clean up that. Distribution, working exactly as distributed.

Then we installed one thing.

Glenn’s edge was never the financial knowledge. Every advisor has that. Glenn’s edge was reading a room. Twenty years across the table from couples had taught him to hear the thing under the thing, the hesitation a client would never say out loud. That skill lived only in Glenn. It had never been written down. It was the reason his clients trusted him, and the reason he couldn’t be in two meetings at once.

We extracted it. The patterns, the tells, the questions he asked himself in the car after a hard meeting. We built it into a system that reviews his call transcripts the way Glenn’s own instinct would.

He uploaded a transcript from a meeting he thought had gone fine.

The system flagged five signals he’d missed. A couple who’d said yes with their words and no with everything else. It told him the close he was planning, a gentle bit of pressure, would walk them straight out the door. It told him to do the opposite. Slow down. Name the hesitation out loud. Ask the question he’d have asked himself if he hadn’t been tired.

Glenn made one call. He followed the deeper read. The couple rescheduled within 20 minutes.

A generic agent could have summarized that transcript for anyone. It couldn’t have caught what Glenn would have caught, because catching it required being Glenn. For one afternoon, it was.

Why You Can’t Just Do This

That recap I created and linked above took an afternoon. It’s free because it should be free. A recap tells you what shipped and changes nothing about whether you can run it. The running was always the scarce part. The running is the only thing worth paying anyone for.

Carnegie wrote his clause into the contract because he knew the building would lie to the town. A library looks finished the day the roof goes on. It isn’t finished. It isn’t even started. It starts the first morning someone unlocks the door, and it has to start again every morning after that.

You understand the difference now.

• Distribution is the building.

• Installation is the librarian.

You can feel which one you’ve been running.

What you can’t do yet is the install itself. It’s genuinely hard, and not in the way you’d guess. Nothing about it is technical. The hard part is that installing your own expertise asks you to be your own interviewer, to pull a pattern out of your head while your head is busy being the expert. You can’t easily extract the thing you’re using to do the extracting.

That’s what The Personal Pattern Protocol is for.

It’s a three-part prompt sequence you run inside Claude, ChatGPT, or Gemini. It does what we did with Glenn, on your own expertise, with no consultant in the room. Glenn’s installation, above, is the worked example. The three prompts and the weekly audit are below.

The Personal Pattern Protocol

Three prompts. The first two are the setup. The third is what keeps the install alive.

The setup takes an afternoon. The audit takes ten minutes, every Friday, forever. Starting this one. The protocol has the same shape as the rest of the article. The setup builds the library. The Friday audit is the running.

Before You Start

Pick your tool. Use whichever AI you already pay for. The interview works the same in Claude, ChatGPT, and Gemini.

Pick your task. Three tests have to pass. Other people rely on you for it. You can describe a recent real instance of it from start to finish. Someone replacing you would do it noticeably worse. If several tasks qualify, pick the one where the “noticeably worse” is most obvious.

Prompt 1: Extract

This prompt interviews you. It will not help you with your work. Its only job is to pull one piece of your judgment out of your head and onto the page.

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You wait. A minute. Two. Five.

There’s no way he’s still down there!

This is the Bajau, Southeast Asian sea nomads who have spent centuries living on boats, visiting land only for supplies, and spending five hours of every working day underwater. The most extreme of them can dive 200 feet on a single breath and stay submerged for thirteen minutes.

And for a long time, nobody could explain how.

In 2018, a Berkeley PhD student named Melissa Ilardo arrived with an ultrasound machine and started doing scans.

The Bajau spleens were 50% larger than those of their non-diving neighbors. She sequenced the DNA and found a specific mutation in the PDE10A gene. The Bajau had evolved a biological scuba tank inside their bodies, an oversized organ that stored extra oxygenated red blood cells and released them during dives.

They’d been carrying it inside them for generations and they had ZERO idea.

You too have a superpower that has been invisible to you your whole life. But you can’t see it because you’re too close to it.

I call this your Cognitive Fingerprint™. The signature patterns underneath your expert work. These are often invisible to you, but your transcripts can bring them to the surface…if someone knows what to look for.

That is exactly what happened with a financial advisor I’d spent weeks building a system for. He had no idea what was inside him or his transcripts, either.

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He Started Typing Something I Never Taught Him

We fed twenty-some client meetings through the analysis. Pulled patterns out. Configured prompts to his specific sequences, decision DNA and even his blindspots.

One morning, he was on a Zoom call with me, poking around and testing things.

Then he stopped clicking and started typing:

“This prospect just ghosted me on our 2nd presentation after I thought we nailed it. Before I send my usual follow-up, can you look at this transcript and see if there were any red flags I was missing?”

That wasn’t in my documentation. That wasn’t part of any setup walkthrough. He just... felt it. I sat there watching him troubleshoot his own sales process with an AI that actually understood how he sells. What happened next was pretty darn incredible.

Eight Times Per Meeting

His system knows things about him that he didn’t know about himself.

• It knows he says “How are you for time? Any questions before we move on?” eight times per meeting. Eight. I showed him the analysis and he laughed out loud. He’d never counted.

• It knows his Bucket Framework. Soon, now, later. And that he runs the same sequence every single time without variation. Time horizon first. Then purpose. Then products. He thought he was improvising. He wasn’t.

• It knows the spending permission reframe. This is the thing that makes him different from every other financial advisor running the same financial playbook.

And most importantly, when it has the full picture, it knows “why” he does all of these things and can give him “obvious” recommendations that he couldn’t see himself.

In the scenario above, AI told my client there were multiple red flags during the first meeting that indicated the couple was nervous and “do not send” his normal follow up email. They probably already feel guilty about not showing up and calling them out would just send them into the hiding.

Based on the recommendation, he pivoted and sent a completely different message than we was planning to.

“They called back an apologized 20 minutes later and were back in the office one week later! Incredible!”

This goes beyond just a one-time, “I need to save this account” situation.

For instance…

When projections show a client leaving $5 million to their kids, most advisors nod approvingly. He catches them by surprise and says “this means you could spend more now.”

His clients aren’t just afraid of running out of money. They’re afraid of dying before they enjoy retirement. Their spouses keep asking them to slow down. Take a trip. Actually live a little before it’s too late.

And then their financial advisor, the person they expect to lecture them about saving more, tells them to spend.

You can watch the tension leave their shoulders. According to him, “I don’t know, that is just what I do.”

But here’s what those patterns look like when you pull them out and document them:

• The “Flexibility Emphasis Pattern” on the left. Five steps he runs every time a client freezes up about making a permanent decision.

• The “Education First Pattern” on the right. His sequence for teaching before asking.

He didn’t write these. I extracted them from his transcripts. He runs them automatically, the same way every time, and had no idea how consistent he was until someone showed him. When he needs to teach his junior advisors his process, the level of sophistication, customization and depth goes way beyond anything he’s done before or was able to generate from just prompting ChatGPT.

The 90% That Never Made It onto Paper

This is why expertise can’t be extracted from what people say about themselves. Books, courses, interviews, biographies. They all capture the explicit stuff. The 10% that made it onto paper.

The other 90% never got documented. It’s sitting in the nervous systems of people who’ve been doing the work for decades.

There is nothing new about the phenomenon. Cognitive scientists have been mapping it for sixty+ years.

• Michael Polanyi called it tacit knowledge

• The Dreyfus brothers tracked how it develops

• Gary Klein watched experts make impossible decisions on instinct

Their conclusion came down to the same thing.

When things look familiar, experts don't deliberate. They execute.

Tacit knowledge (the execution) is the methodology hiding inside your transcripts. The patterns you run without realizing you run them.

But here’s the thing about this elusive little concept…

You can read everything ever written about tacit knowledge and still not have any.

You can’t prompt your way to it.
You can’t even ask the expert to explain it.

Polanyi himself said,“We know more than we can tell.”

You have to earn tacit knowledge yourself. There’s no shortcut.

And even after you’ve earned it, that sucker still refuses to let you know it’s there.

545%

A company wanted to figure out why some of their salespeople closed deals at 2-3x the rate of everyone else. They were using the same product, scripts and in the same market as their colleagues. But they were getting wildly different results.

So they studied them. Thousands of’em.

Almost half the managers and observers had no idea what made their best people the best.

So Cresta pointed AI at the transcripts.

• Not asking anyone what they did.

• Watching what they actually did.

They found four specific micro-behaviors that separated the top performers:

1. Setting expectations

2. Asking discovery questions

3. Handling objections

4. Closing

But the real hero was discovery questions. On average, top performers asked 48% more and on certain types )needs-focused, upsell) the gap widened to 545%.

Five hundred and forty-five percent.

These patterns were invisible to everyone, including the people doing them. The behaviors were so automatic that nobody could see them until AI surfaced them from the transcripts.

The Four Boxes He Checks When a Deal Dies

The financial advisor has his own checklist now.

At the end of each day his transcripts are auto-processed through his AI system, loaded with his Cognitive Fingerprint scanning for his superpowers in action. It’s helping him:

• Scale his expertise without slowing down: Transforming his daily, unconscious habits into a tangible, growing library of training assets without requiring him to spend hours lecturing his team.

• Decode his own “invisible” moves: Pinpointing the exact moment he successfully deployed a technique like “The Value Reframe” or “The Permission Check,” isolating the precise phrasing and timing that made the client say yes.

• Feed his “training engine in a box”: Automatically generating fresh, one-page Advisor Training Cards based on real-world scenarios from that exact day, rather than relying on stale, hypothetical theory.

• Standardize firm-wide excellence: Giving less experienced advisors a daily, updated playbook of recognition triggers, success indicators, and exact quotes they can review before their own meetings tomorrow.

• Audit his own consistency: Providing a daily mirror to ensure he is continually executing his high-scoring patterns—like “The Education First” and “The Spending Shift”—at the highest possible level.

• Protect and codify his intellectual property: Systematically capturing the unique, proven frameworks that drive his firm’s revenue so they become permanent, transferable assets rather than just ideas stuck in his head.

That’s the system surfacing what was inside him from the work he was already doing.

What Would Your AI Tell You?

What would your AI tell you about your last five calls?

Probably something useful. The kind of feedback most AI can give about most conversations in your industry.

Now imagine asking a different question.

“This one didn’t convert. Check it against my methodology. What did I miss?”

That requires something different. A system that knows your patterns. Your sequences. The micro-behaviors you’ve been running for years without realizing how consistent you are.

One reads the transcript. The other reads you.

You can see what he built. What you can’t see is how to build it for yourself.

THE THIRTEEN-MINUTE METHOD

Three prompts that turn your transcripts into a closing edge you can run against any conversation, forever.

The Bajau go down with intent and come back with what they went for. Same idea applied to your transcripts.

How to Use This Kit

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In the late 1990s, if you wanted a job at Microsoft, you sat across from someone who asked you how you would figure out how much Mount Fuji weighed. The riddles weren’t really about Mount Fuji.

They were a proxy for the skill Microsoft thought it was hiring for at the time, which was the ability to reason through a problem you’d never seen before with no resources except your own head.

Google’s interviews famously took the same idea further. Candidates wrote algorithms on a whiteboard while three engineers watched them reason out loud. McKinsey ran case studies. Investment banks ran modeling tests.

Every one of those interview formats was a bet about what the company actually needed from a new hire... encoded as a test you could either pass or fail in a small room with the door closed.

Tests changed when the work changed.

The whiteboard test stuck around for thirty years because writing code by hand while reasoning out loud was a reasonable proxy for the actual job: thinking on your feet while producing. The case study stuck around at McKinsey because the actual job was structured ambiguity. When the work shifted, the tests shifted with it. Sometimes faster than the people taking them realized.

I came across a Sam Altman quote that lands differently once you understand what those tests were really filtering for.

“We basically would like to sit you down with something that would have been impossible for one person to do in two weeks this time last year, and watch them do it in 10 minutes or 20 minutes.”

To compress two weeks of work into twenty minutes, you have to be working alongside AI. The bottleneck shifts to how clearly you can describe what to produce.

OpenAI is changing what they’re testing for. The framing in the press was that they’re “evolving” the process, which is a polite word for what just happened. They’re not testing if you can build something anymore. They’re testing if you can describe what to build clearly enough that AI does it for you. The build follows. The description is what they’re grading.

The company making the most advanced models on earth doesn’t care if you can write the code. They care if you can specify the work.

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OpenAI isn’t the point

I know what you’re thinking.

“Max, why should I care? I have zero interest in working for OpenAI.”

I get it. But you’re missing the point.

OpenAI made the shift explicit. If you produce real work with AI, the same filter is on yours.

For instance…

• If you’re a writer, the test is whether you can describe the piece you’d write so completely that AI can draft it and you only edit.

• If you’re a designer, it’s whether you can describe the layout, the constraints, the brand voice, the user need, in enough specificity that the model produces something you’d actually ship.

• If you’re a consultant, it’s whether you can encode your judgment into a prompt that another person could run without you in the room.

I see this recognition starting to surface, even when people can’t quite name it yet.

Out of all the workshops I run, the most-requested format is the same every time:

“I just want to watch you build live and explain why you’re doing things.”

People who’ve worked with AI long enough know there’s something deeper going on than just better prompts. They want to see how I think.

All the attention is on the machines. Better prompts, better tools, better models. The gap is somewhere else entirely. It’s THE gap that separates the top 5-10% of AI users from everyone else.

This is the new core skill and what people want to watch when I run a workshop.

Directing.

How directing actually works

Film directors hold a vision, communicate it, watch what gets made, and call cut when it misses. With AI, it’s the same skill. Knowing what you want clearly enough to describe it. Recognizing when the output drifted. Closing the gap, in real time. The skill that reads as “soft” until you watch someone good at it produce ten times the output of someone with twice the technical training.

Every director develops a signature over time, the moves they reach for and the ones they skip. You already have one. It’s in every prompt you’ve written. You just haven’t read them as a body of work.

I want to give you a receipt for this, because I am the receipt.

Six months ago I had never written a line of code. I presented at a hackathon. I still haven’t written a line of code. I’ve shipped more than fifty custom Claude skills since then, run an autonomous agent system on a Mac Mini in my living room, and ghostwrite a weekly newsletter for a marketing legend whose voice I extracted into a system. None of that required me to learn syntax. All of it required me to learn how to specify, diagnose, and direct.

The signature got built one directed session at a time.

The clearest version of this lives inside one of my retainer clients. Three people on the team I work with closely, three completely different AI installs.

1. Tom runs sales. He got a system that finds, ranks, and pre-qualifies leads, generates custom outreach, and plugs straight into his email and CRM.

2. David handles client delivery. His install is a Claude Project loaded with messaging for three different avatars, strategy, and deliverables. It’s wired into the Project Management System (PMS), the calendar, and Fireflies, so every meeting he runs gets processed automatically. Tasks land in the PMS. Client context lands in Google Drive. Claude proactively suggests the next step on whatever deliverable is open.

3. Sarah runs hiring. Her system sorts the resume inbox, scores candidates against a company rubric, and generates a personal profile card on each person for the leadership team to review.

The reason all three worked isn’t that I knew Python (I absolutely do not). It’s that I sat with each of them long enough to specify what their work actually was, where the friction lived, and what shape an AI assist needed to take to slot in without breaking what already worked. The build was the easy part. The directing was the whole thing.

It’s probably not a coincidence this is where I am focusing. The two things I spend most of my time on are this (AI implementation) and Cognitive Fingerprint™, which is extracting what makes someone unique and articulating it back to them clearly enough that they can use it.

Both are articulation skills and directly fall under…directing. Which is why the test the new interview is filtering for isn’t a tech skill at all.

What this means for you

You also probably suspect, if you’re honest, that the muscle for the new test is underdeveloped. You can prompt. You’ve used the tools. You’ve watched a few people on YouTube spec out a Claude Code build and thought “I could do that,” and then tried it on something real and noticed the model drifted, you didn’t catch it, you cleaned up the output by hand, and you wondered if you’d actually saved any time.

You’re not missing a tool.
You’re not missing a better model.
You’re missing reps at directing.

Reps come from running real work through repeatable patterns.

Knowing that directing is the skill, and seeing your own directing on the page, are two different things.

The AI Director’s Script is the kit. Bring three real prompts you’ve used recently in AI sessions. The kit walks you through each prompt the way a director reads a draft: for vision, clarity, drift, and cut. Then rewrites them as scripts that work without you in the room. By the end, you’ll have three rewritten prompts and your directing signature named: the move you reach for, the move you skip.

Here’s how it works.

🎬 The AI Director’s Script

Every great film starts with a script. Every great AI session does too. This is yours.

How to use this kit

Bring three real prompts. Use whatever’s actually in your AI history this week. The email draft, the analysis request, the plan you sketched, whatever rough thing you typed last. The rougher, the better.

Run the prompt below in any AI assistant. Claude, ChatGPT, Gemini all work, anything that holds a multi-turn conversation. The session takes 15-20 minutes if you stay honest with it.

Expect to be asked what you actually meant. The kit pushes back on vague answers. That pushback is the directing rep happening in real time. The discomfort is the work. If you breeze through, your prompts were probably already in good shape, and you’ll have the rewrites to prove it. If you get stuck, that’s the gap you came here to find.

You’ll walk away with three things. Three rewrites you can paste into your next AI session immediately. Script notes on each prompt, naming what was thin and what got fixed. And your directing signature: the move you reach for, the move you skip, and the habit change that closes the gap.

What it looks like in practice

For example, let’s run the kit on a simple prompt. Take one almost everyone has typed at some point:

“Write me a LinkedIn post about how AI is changing knowledge work.”

The kit walks you through what that prompt is missing.

• Vision (what you actually wanted): the prompt doesn’t say. “Thoughtful, not cliché” might be in your head, but the model can’t see it. Without vision, you get the AI default. Generic bullet points about productivity gains.

• Clarity (could anyone else have written this prompt?): no audience, no length, no format, no examples of good. A stranger picking up this prompt would produce exactly the bullet-point post you didn’t want.

• Watch (where did the output drift?): the whole output was the drift. You wanted a personal angle. You got “automation of routine tasks” and “new opportunities for growth.”

• Cut (what would have prevented it?): a sentence naming the audience. A sentence naming what the post should NOT do. A sentence about what shape “good” takes.

The rewrite:

“Write a LinkedIn post for marketing peers who use AI daily but don’t think deeply about it. 200 words, first-person, conversational. Open with one specific moment from this week where I noticed AI changing how I work. Skip the productivity gains and automation framing. Something subtler. No bullets, no buzzwords, no ‘in today’s fast-paced world.’ End with a question that invites disagreement in the comments.”

Your directing signature:

You reach for vision well. You know what you don’t want, even when you can’t name what you do want. You skip clarity hardest. Your prompts assume the model knows your audience and your taste. The habit change: name the audience and one anti-pattern at the start of every prompt. One thing the output should NOT do.

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If you’re subscribed to Signal > Noise, surprise, it’s about to be called Cognitive Fingerprint™.

Probably not a big surprise and what this should have been from the start. But like everything, it’s part of the evolution.

But here’s a bit more on the “why.”

Every conversation I see right now treats humans like line items to be replaced.

• Employees as costs

• Headcount as a number to shrink

• AI as the lever

I want to spend my time on what makes you, specifically, you.

The invisible patterns that separate your best work from anyone else’s. The stuff that gets ignored the second someone hands you a generic playbook. AI doesn’t replace that. Pointed at the right thing, it amplifies it.

My goal is to show you how to point it and walk you through the people whose patterns got sharper the second AI showed up.

That’s what CF has always actually been. Now the newsletter has the right name.

Expect a lot more from here and other places. My three partners, , , and , are going to show up in their own unique ways more often too. They’re a big part of why this is working.

Anyway. Dr. Pepper. Keep reading.

— Max

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I was watching baseball this weekend when a commercial came on for Dr Pepper. Instead of getting another snack, I found myself asking Perplexity a question my marketing brain couldn’t get past.

Is Dr Pepper really made with 23 flavors, or is that just a gimmick?

The short answer... yes…but it’s complicated. The formula uses 23 flavor compounds and parent company Keurig Dr Pepper has confirmed it.

But here is where it get’s fun. Nobody outside the company knows what they are.

I’m not talking about the ingredients on the label (carbonated water, corn syrup, caramel color, phosphoric acid, caffeine, sodium benzoate, and a catch-all bucket called “natural and artificial flavors”).

Or any of the viral leaked lists floating around since 2008 with almond and plum and a Bob Hope joke about prune juice (“a drink that moves you!”) That’s all fan fiction. The actual 23 are sealed in a proprietary blend the FDA lets companies hide.

Dr Pepper’s actual recipe is split across two separate Dallas bank vaults. Half in each, like two pieces of a doomsday riddle. 141 years of refusing to show you what’s in the bottle.

And this is where I had to keep digging.

Four brands. Each with a secret vault.

OK, there has to be other companies who have used this trick before, right? Turns out, quite a few:

• Coca-Cola calls their formula “Merchandise 7X,” and they keep it in a vault at the World of Coca-Cola museum in Atlanta. Only two executives know the recipe, and they’re forbidden from flying on the same plane.

  

• KFC splits the 11 herbs and spices across two separate factories. Neither factory has the complete formula. The final mix happens at a third site with 24-hour surveillance.

  

• Chick-fil-A keeps the original sandwich recipe in a literal safe at their Atlanta headquarters. They won’t tell anyone who holds the combination.

But what’s crazy (or so I thought) is that none of them have patented their secrets. Every instinct says you’d want that protection. Patents are how businesses are supposed to guard their inventions.

I kept clicking because something about it felt backward until I read the fine print.

Patents expire after 20 years and require full public disclosure upfront. You show the world your formula in exchange for 20 years of protection, and after that anyone can copy it. Trade secrets, on the other hand, last forever... as long as you never show anyone what’s in the bottle.

The mystery is the asset. Locking it up is the business model.

It all made sense when I found the 1963 Dr Pepper lawsuit. At the time, a federal anti-monopoly rule required major bottlers to carry non-competitive soft drinks in their facilities. If you were Pepsi, you had to bottle anything that wasn’t a cola. Dr Pepper’s lawyers spotted the gap and sued the federal government for the right to be legally classified as a non-cola. “A pepper type drink,” they argued.

They won and because Pepsi had no competing pepper-type drink, the ruling forced Pepsi’s bottling facilities to distribute Dr Pepper nationwide.

A category of one became a legal category of one, which became a national distribution line.

That’s when it stopped being about soda for me.

I work with consultants, coaches and experts for a living. Their whole business is proprietary thinking they’ve built over years of work. So when I saw a 141-year-old soda company still guarding its formula, I had one question... why don’t the experts I work with have a formula to guard in the first place?

• They have the knowledge

• They have decades of experience

• They have something proprietary buried in there somewhere

They’ve just never named it, shaped it, or locked it up.

The soda companies figured out the move in the 1880s. The experts I work with still call it “how I do things.”

I mean, look how powerful this concept is!

141 years after they locked up the recipe, strangers on YouTube are still making free content trying to guess what’s in it.

The vault does the marketing too.

“Take two parts Coke, one part root beer, drink it and tell me that isn’t Dr. Pepper.”

That video is somebody’s spare-time conspiracy theory. Dr Pepper didn’t pay for it.

In 2009, a Tulsa man named Bill Waters was poking through a Texas Panhandle antique store. Under a wooden medicine crate, for $200, he found a tattered ledger from the original Waco drugstore where Dr Pepper was first served in 1885.

Inside, a handwritten recipe titled “D Peppers Pepsin Bitters.” Heritage Auction Galleries put it up for auction expecting $50,000 to $75,000. The AP ran the story. Dr Pepper’s parent company had to issue a corporate statement saying no, that isn’t the formula. The book didn’t sell. The coverage ran anyway.

The vault just keeps generating it.

Now look at yourself.

You’re not a soda. You’re an expert. You sell thinking.

So if you read that story, nod, close the tab, and go back to writing another LinkedIn post about “the top ten ways to do x,”

That’s the problem I want to talk about.

Every expert I know has a vault.

They just don’t know it’s there.

The Legal Pad Test

Early in his career, marketer Todd Brown sat in a coffee shop across from a prospect. A legal pad on the table. Five columns, one per coach. Four rows of criteria. Price. Credentials. Testimonials. Guarantees. Todd was column three, sandwiched between two coaches with nearly identical resumes.

The prospect looked up and said, “You all seem pretty similar. I guess it just comes down to price and who I like best.”

Todd was being priced like canned corn.

He calls his framework the Unique Mechanism Matrix. It plots two axes, uniqueness and substance, across four quadrants.

Commodity Hell is the default address. Nothing unique, no substance behind the differentiation. Price war territory.

Some climb to Commodity Plus. Good at the work, forgettable. The prospect can’t describe what you do that the next person doesn’t.

A dangerous few land in Faux Mechanism. Named something that sounds proprietary. “The 7-Step Blueprint.” “The Accelerator Method.” Press on it and the substance dissolves.

Few reach Unique Mechanism. Unique AND substantive. Category of one. The prospect stops comparing you cell by cell because there’s nothing to compare you to.

The spreadsheet only works when everyone fits in the same columns.

Three Vaults. Two You Can Open.

When you hear “Dr Pepper has 23 flavors” or “Chick-fil-A has a safe,” you translate it as “I need a patent.” You don’t have one. The whole thing feels out of reach.

Todd’s move is to break the mechanism into three types. One isn’t available to you. The other two are wide open.

• Product Mechanism is the what. The ingredient, the formula, the molecule. Dr Pepper’s 23. Coke’s 7X. You can’t have this. Skip it. You’re stealing the move, not the molecule.

• Process Mechanism is the how. The sequence, the method, the protocol. KFC splits the blend across three factories so none has the complete recipe. That’s a Process. The specific way you take a client from point A to point B that no one else is doing in that exact order. Available to you.

• Philosophical Mechanism is the why you. The contrarian position. The belief others in your industry don’t hold. Chick-fil-A closes on Sundays when every other major chain is open. That’s a Philosophy. The thing you argue at dinner that other people in your field don’t argue. Available to you too.

Every expert has both. Few have named either.

The Paper Tiger Test

Before you name anything, a warning.

Naming is the tip. The mechanism lives underneath.

Todd calls the failure mode the Faux Mechanism. A paper tiger that looks real from the front and tears apart the first time someone presses on it.

The test is one question. “Why does your approach actually work?”

• A faux mechanism answers with vibes. “It’s a proprietary system.” “It just works.” “Clients love it.”

• A real mechanism answers with specificity. When we do X in step 2, it surfaces Y the client wasn’t conscious of, which means the work in step 4 lands on actual constraints instead of performed ones.

If you can’t answer “why” with reasons, you don’t have a mechanism. You have a slide deck.

A Name That Dies in a Calendar

The second mistake is a name that dies in obscurity.

Great names pass five filters. Brown gives one rule at the core. A mechanism name should sound like a thing in a textbook, not like sales copy. The other four are how I make sure that one actually holds.

1. It sounds like a thing. Put a ™ after it. Does it look natural, or does it look like you’re dressing up a phrase?

2. It creates curiosity. The person hearing it asks “what’s that?” instead of “cool, thanks.”

3. It’s memorable. One to four words. The person can repeat it at dinner after hearing it once.

4. It signals the mechanism, not the benefit. The name should hint at how it works. The move, the lever, the cause. “Lumbar Reactivation” tells you the muscle group and the action. “Cash Flow Compounding” tells you the engine. “The Back Pain Relief Protocol” tells you the wish, which is product copy. Save the benefit-driven name for the wrapper, not the mechanism inside.

5. It’s differentiated. Doesn’t sound like the 12 other frameworks in your industry.

The named methods I see fail three out of five. “The Growth Accelerator Blueprint.” Sounds proprietary, carries no information, fails the cocktail party test, blends into every other accelerator blueprint out there.

A mechanism without a name dies in someone’s calendar.

Your Move This Week

Not “find your unique mechanism.” Too abstract. I’ve done it. Forty minutes of staring and getting distracted by YouTube.

Instead, pick one specific thing you do with a client that no one else in your industry is doing. One. Be boring about it. Maybe it’s a question you always ask on call three. Maybe it’s a template you hand them before the kickoff. Maybe it’s a specific sequence you use to run a teardown.

Now name the thing. Four words or less and run it through four checks.

• Name. Does it pass the five filters? ™-ready, creates curiosity, 1-4 words, signals the mechanism (not the benefit), different from the 12 other frameworks in your industry.

• Type. Is it a process (the how) or a philosophy (the why you)? Be honest. If it’s “kind of both, a little of each,” it’s probably neither yet.

• Substance. Can you write one paragraph that answers “why does this actually work?” without using the words “experience,” “proven,” or “proprietary”?

• Vault. Can you talk about the concept publicly while keeping the specific sequence private? If the whole thing lives on your sales page, you don’t have a vault. You have a brochure.

If any one of those breaks, you’re not done. Keep working on the thing itself.

If all four hold, you just moved out of column three.

The Gap: The Value Builder

You have the matrix, the three types, the faux tiger test, and the five filters. You have the shape.

Your mechanism is buried in years of client work, call notes, and case studies. The same invisibility that makes it proprietary makes it hard to see alone. The chef can’t taste their own kitchen.

The Vault Builder is three prompts that run this framework on your own material. Feed it your case studies and call transcripts.

• Prompt 1 surfaces 5-7 candidate mechanisms you actually practice (not the ones you claim on your website).

• Prompt 2 runs each candidate through a five-round collapse test so you know which ones hold up under pressure.

• Prompt 3 generates 20 name candidates scored against Todd’s five filters and recommends the top three.

Two formats ship together. Copy-paste prompts you can run in any Claude chat. Or a downloadable Claude skill (vault-builder.skill) hosted at cognitivefingerprint.ai/prompts. Installs once. Runs the whole protocol with a single command. Use whichever fits your setup.

What took me months of Cognitive Fingerprint development, compressed into an afternoon with material you already have.

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Then three things happened in 90 seconds:

• A regional jet missed its speed restriction

• A FedEx heavy requested an early turn

• A Cessna called in on the wrong frequency

The controller didn’t panic. He didn’t freeze. He looked at his radar strips. Moved two of them to the left side of his bay. Keyed the mic. Started sequencing. One instruction at a time. No rush in the voice.

But here’s what he didn’t do.

He didn’t try to hold all twelve aircraft in his head.

Every approach controller at O’Hare manages 20 to 25 aircraft at a time. That’s 857,000 movements a year. One every 37 seconds. The system works because controllers externalize almost everything.

Each aircraft gets a paper strip. Callsign, type, altitude, assigned heading. The strip sits in a physical bay, and when the controller issues an instruction, they mark it. The radar screen shows positions. The strips hold the plan.

The controller’s working memory holds almost nothing. That’s by design.

On January 29, 2025, a controller at Reagan National was working a quieter shift. But the traffic was mixed in a way the system wasn’t built for. An Army helicopter was flying a training route along the Potomac. At the same time, a regional jet was circling to land on a runway whose approach path crossed the helicopter’s route. The two aircraft were on different radio frequencies which meant neither crew could hear the other.

At O’Hare, two separate controllers would have handled this. At Reagan that night, one controller was managing both. Federal investigators found the combined workload caused a loss of situational awareness. The controller lost track of the helicopter. In a disaster that years of warnings should have prevented, sixty-seven people died.

The Reagan controller’s working memory hit a wall. It wasn’t a skills wall or a training wall. It was a biological one, baked into every human brain, unchanged since before we had language to describe it.

Two types of traffic converging on the same airspace, two radio frequencies to monitor, and no second controller to split the load.

His brain was asked to hold what the system should have carried. It couldn’t.

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Before You’ve Done a Single Thing

Your thumb finds your phone before your brain finds the morning. A red notification badge. An email you shouldn’t have opened. A calendar reminder for something you forgot to prepare. This list is already overflowing.

The client request you’ve been avoiding. Something about taxes. Your kid’s school project. The 30 emails you haven’t opened. A promise you made to review someone’s deck. A dentist appointment you keep rescheduling. And a nagging feeling you’re forgetting something important that you can’t quite remember.

Each one of those is an aircraft on your radar. And you’re holding all of them in your head.

And if you are like me there are no strips and no real system.

Just you, staring at the screen, wondering why you feel paralyzed before you’ve done a single thing.

What’s Actually Happening in Your Head

“The Magical Number Seven, Plus or Minus Two” (1956)

George Miller figured out the first piece in 1956. His paper, “The Magical Number Seven, Plus or Minus Two,” pinned down a hard limit. Human working memory holds about seven chunks of information. Not items. Chunks. The ceiling is the same either way.

(Nelson Cowan revised it downward in 2001. His research suggests the real number is closer to four. That’s it. That’s what you have to work with.)

Miller himself was almost apologetic about it. He opened the paper by saying he’d been “persecuted by an integer.” The number is a hard constraint. Like the number of plates a waiter can carry. You can get better at stacking them, but you can’t change the size of your hands or arms or shoulders. (Some of these waiters are insane with how many plates of sizzling fajitas they can stack just on their forearms.)

“On Finished and Unfinished Tasks” (1927)

The second piece came earlier, from a Berlin restaurant in the 1920s. Kurt Lewin, a psychologist, noticed something about a waiter. The man could remember every detail of every open order... who ordered what, which modifications, which table. But the moment the bill was paid, the order vanished from his memory. Gone.

Lewin’s student, Bluma Zeigarnik, turned that observation into an experiment in 1927. She gave people tasks. Interrupted some of them halfway through. Then tested what they remembered. The interrupted tasks were recalled roughly twice as often as the completed ones.

Your brain does not let go of unfinished work. It keeps pinging you over and over like a social media app with those annoying notifications.

Every open loop takes a slot and holds it. Every vague commitment. Every “I should really...”

But oh buddy, it gets worse. The vaguer the task, the harder your brain grips it. “Something about taxes” can’t be chunked. Can’t be filed. It just sits there, taking up space, because your brain doesn’t know what to do with it and refuses to let go until it does.

I watched this happen to a client last month. Brilliant marketing guy about to launch a new campaign. He popped into our Zoom session and I could see the hopelessness on his face right away. He said, “I can’t think straight and I don’t know why.” We spent 11 minutes writing down everything competing for his attention. The list hit 23 items. He stared at it and said, “I thought it was three or four big things.” It wasn’t. It was 23 invisible things. Most of them small. None of them planned. All of them taking a slot.

You’re not carrying 14 tasks. You’re carrying 14 open notifications that your brain can’t swipe away.

So it feels like you’re overwhelmed by how much you have to do. Like the volume is the problem.

In reality, you never had too much to do. You just couldn’t see what you were carrying. The tasks aren’t hard. They’re hiding. And you can’t take action on what you can’t see.

“Consider It Done” (2011)

The third piece came from Roy Baumeister in 2011. His paper was called “Consider It Done.” The title tells you everything.

You don’t have to DO the task to release it from working memory.

You just have to make a plan.

A specific plan. Not “I’ll deal with it later.” A plan with a when and a how. “I’ll check the tax deadline tomorrow at 9 AM on the IRS website.” That’s enough. Baumeister showed that once the brain has a concrete plan, it files the task as handled. The intrusive thoughts stop. The notification clears.

The controller at O’Hare doesn’t remember which planes are descending. The strip remembers. His brain is free to handle what’s happening now.

Your brain is trying to be the strip AND the controller. At the same time. For 14 aircraft.

No wonder you’re anxious before 9 AM.

The People Who Look Calm

The people you know who seem calm under heavy workloads aren’t carrying less than you. They’re not better at tolerating the weight. They’re better at putting it down.

Their working memory is nearly empty. They probably have more to do than you. But nothing is living in their head unplanned. Every open item is visible somewhere. Every item has a next step. Every next step has a slot.

The anxiety doesn’t come from the work. It comes from it flying out of control in your head.

And the most counterintuitive part... the anxiety drops before you do a single task. Baumeister proved it. The moment the items move from your head to a visible, sequenced list, something loosens. Your brain stops clenching around every open item and lets the list hold them instead.

You don’t need to do less. You need a radar.

The Radar

Ten years ago, your scope held fewer aircraft. Now Gloria Mark at UC Irvine finds the average person switches context over 400 times a day. Each switch burns a slot you don’t get back before the next one lands. You’re running 2026 traffic volume on a 1956 cognitive architecture.

The next time the blob hits, try this. One thing. Five minutes.

Open a blank page. Write down every item competing for your attention until the page stops surprising you. Work, personal, half-formed, vague, embarrassing... all of it. Don’t organize. Don’t prioritize. Just get it out of your head and onto something you can see.

Then... before you do a single one... write the next action next to each. Not the project. Not the goal. The action. “Something about taxes” becomes “Google quarterly tax deadlines.” “That thing for Jake” becomes “Text Jake and ask for latest deck.” If you can’t name the action in one sentence, the item is still vague. Sharpen it until your brain can chunk it.

That’s the moment the pressure drops. Not when you finish the list. When your brain believes every item has a plan.

Baumeister proved it. Your brain treats “I know when and how” almost identically to “done.” The strips are in the bay. The radar is on. You can start working now.

One page. One pass. The anxiety has nowhere left to hide.

The Article Is the Proof

Your brain has been running an approach control facility with no external systems this entire time. Every unfinished thought from this morning, every half-read email, every “I should really...” has been sitting in working memory. Taking up one of your four slots. Refusing to leave.

The most anxious people I know aren’t the ones with the most to do. They’re the ones with the least visibility into what they’re carrying. The blob isn’t big. It’s opaque.

Feels like a character flaw. Like everyone else handles this better. Like you should be able to just... manage it.

The cost is that you’ve been blaming yourself for a design problem. Your working memory was never meant to carry your entire life.

• Miller told you in 1956

• Zeigarnik told you in 1927

• Baumeister told you the fix in 2011

The anxiety is a signal. But your system has no radar.

Build the radar. The anxiety has nowhere left to live.

What It Looks Like When the Radar Works

Marcus is 42. Solo leadership coach. Recently separated. Six active clients, keeps dropping balls. He sat down on a Monday morning with coffee feeling paralyzed.

He opened an AI conversation and started talking.

Marcus: I have a client deliverable due Friday, Rachel, she’s waiting on a leadership assessment report. And I need to prep for a call tomorrow with my newest client, James, and I haven’t even looked at his intake form yet. There’s invoicing I’ve been avoiding for like two weeks. And I need to figure out the kids’ schedule for next month because Sandra and I are still fighting about who has them when and I just…I can’t think about that right now but it’s sitting there...

The AI didn’t advise. Didn’t organize. Just said “keep going” and “what else?” When he said “it’s all a blob,” it said “That’s exactly why we’re doing this. Keep going.”

He kept going. Therapist he’s been meaning to find for two months. Conference early bird he keeps missing. Newsletter he skipped last week. Emails he’s afraid to open.

Then it mirrored everything back.

AI: That’s 15 items. They’re all here now. You can stop holding them.

Marcus: Seeing it like that is... weird. It felt like way more in my head.

Then it clarified each item. One question at a time.

AI: The kids’ schedule with Sandra — is there an action on your side?

Marcus: I need to text her with my availability for the next six weeks. I keep not doing it because every conversation turns into a fight. But the action is just sending the text.

AI: The therapist — do you have anyone in mind, or is step one finding names?

Marcus: I could ask my doctor for a referral I guess.

AI: So the action is: text or call your doctor’s office and ask for a referral.

Marcus: That’s so obvious. Why haven’t I done that.

Then it built his day.

Today’s queue:

1. Email scan (~25 min)

2. James call prep (~1 hr)

3. Tom — reschedule text (~5 min)

4. Invoicing (~20 min)

5. Rachel’s report (3–4 hrs) — This is the win.

You have 5 actions today, 7 this week, 2 parked, 2 waiting. Your next move is #1. Open the inbox. Go.

Marcus: I feel weirdly better. Like nothing changed but I can see it now.

Nothing changed. He just stopped holding all of it in his head at once.

Five minutes. Fifteen items. A sequenced plan. The anxiety dropped before he touched the first task.

The Gap

You now know what’s happening in your head. You have the blank page method. And you just watched it work on someone carrying 15 invisible items.

But here’s what I’ve watched happen next. You’ll try the blank page once. Maybe twice. The third time the blob hits... you’ll open the page, write four things, get stuck on the fifth because it’s vague, feel the anxiety spike again, and close the document.

The method works. The follow-through doesn’t.

Because doing this yourself requires you to be your own interviewer, your own clarifier, and your own sequencer... while your working memory is already full. You’re asking the overloaded controller to also be the strip, the bay, and the radar. At the same time.

Marcus didn’t do the blank page. He had a conversation. The AI asked the questions his brain couldn’t form. It held the items his working memory couldn’t carry. It clarified what he was too foggy to sharpen on his own. And it built the queue he didn’t have the bandwidth to sequence.

That’s the difference. The insight is free. The mechanism that runs it while your brain is drowning is what I built next.

The Clarity Protocol is a set of AI prompts you paste into Claude, ChatGPT, or whatever you already use. They do what Marcus experienced... interview you, surface everything you’re carrying, clarify each item into a concrete next action, and hand you a sequenced plan. Three prompts, a voice mode version that does it all in one conversation, and the full walkthrough are below.

Build the radar. The anxiety has nowhere left to live.

The Clarity Protocol

Four prompts. Paste any of them into Claude, ChatGPT, or Gemini. They work the same way Marcus experienced it….

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This article first appeared in the “Beware the Default” newsletter but I really enjoyed writing it and I think there are some good reframes in here that you will enjoy reading.

In 1925, the average American woman spent five and a half hours a week on laundry. She was scrubbing on a washboard. Wringing by hand and hanging everything on a line and praying it didn’t rain.

By the 1950s, the automatic washing machine had reached most American homes. The promise seemed obvious: all that scrubbing and wringing, gone. Free time would appear.

But Ruth Schwartz Cowan studied what actually happened, drawing on time-use research by sociologist Joann Vanek. The finding was not what anyone expected.

By the 1970s, women were spending more time on laundry than before the machines arrived.

Total housework had gone up, not down.

The machines made laundry easier, so people washed clothes more often.

Sheets that used to last a week now lasted three days. Standards for what counted as “clean” quietly recalibrated upward. The stain your grandmother would’ve ignored became the stain your mother couldn’t tolerate.

Cowan called it the “great irony of household technology”. The machines didn’t free anyone.

They moved the bar.

I know, I know…You already see where this is going.

The AI productivity story runs on the same assumption the washing machine did.

Do the same work faster, get the leftover time back. That’s the pitch. That’s the implicit promise every time someone posts a “saved me 4 hours” screenshot.

But the time doesn’t come back. It gets absorbed.

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How the Bar Moves Without You Noticing

Aruna Ranganathan and Xingqi Maggie Ye spent eight months inside a 200-person tech company watching this happen. Their study, published in Harvard Business Review this February, tracked 40 workers who adopted AI tools.

The findings should’ve been a celebration. Workers got faster. WAY faster.

Then three things happened, in order:

First, scope expanded. Workers who finished tasks faster didn’t log off early. They took on more work. The freed-up time just... filled. The same way a new lane on a highway fills with traffic within months like the extra space never even existed.

The capacity didn’t create slack. It created demand.

Nobody sent a memo. The work just appeared. And because it appeared at the exact moment they felt productive, they called it ambition.

It was absorption.

Second, hours extended. Workers started sending prompts and requests after hours. The tool was always available, so the work became always available. The boundary between “work time” and “free time” didn’t blur. It dissolved. Email did this exact thing 20 years ago. Office workers now spend 28% of their week managing email... a technology that was supposed to make communication faster.

But, as you can see (and probably know), the pattern is the same. Lower the friction of doing a thing, and people do more of the thing. Not less. The bar was moving. Everyone called it dedication.

Third, quality dropped. The cognitive load of broader scope plus longer hours plus constant output evaluation added up. The researchers’ conclusion: AI didn’t reduce work. It intensified it. It felt like a mystery. It was the bill coming due.

The gain was the story people told on the way in. What they found was a trap.

Speed up the work -> Absorb the speed into expectations -> Extend the hours to meet the new expectations -> Burn out under the weight of a bar that moved while nobody was watching.

We are watching the washing machine cycle get compressed from decades into months.

The Part That Actually Costs You Something

There’s a layer underneath the burnout that’s harder to measure but worse to lose.

A team led by Elena Hayoung Lee published a study in Nature this March. They gave 269 people tasks to complete with AI assistance. The variable was:

• One group received AI outputs passively (the AI did the work, they reviewed it).

• The other group collaborated actively (working with the AI, shaping the output together).

The passive group finished faster. They also reported lower self-efficacy, lower sense of ownership, and less meaning from the work.

Sit with that for a second. The people who let AI handle it... felt less capable afterward. Not because the AI failed. Because they succeeded without being involved.

The washing machine took away the physical labor of scrubbing. It also took away the thing that made a clean shirt feel earned. The same exchange is happening in your work right now.

You used to write the first draft. Now you edit the AI’s first draft. The output might be better. The feeling of authorship is different. And that difference goes deeper than feeling. It shows up in how you talk about the work, how much you’ll fight for it in a meeting, how deeply you understand what you shipped.

Ownership is a signal of understanding. When it fades, the understanding goes with it.

And honestly, it’s something you have to experience firsthand to really get it. One AI-generated article is easy to dismiss. One delegated task, one social post you didn’t really write. That’s nothing.

The feeling comes later. When it becomes a habit. When you look up one day and realize you feel disconnected from your own work. Not tired. Disconnected. Like the thing you’re producing stopped being yours and just became more noise.

Until I wrote this, I could feel that. I just couldn’t name it.

Dario Amodei, CEO of Anthropic, raised the same question on the Lex Fridman podcast. In a world of increasingly powerful AI, where does meaning come from? His answer wasn’t complicated. Work is a source of deep meaning for most of us, he said, and that won’t change no matter how capable the tools get. The need for meaning doesn’t get automated away.

The Lee study already told you the answer. The people who felt less capable afterward handed the work over and reviewed the output. The ones who kept their sense of ownership stayed inside the thinking as it happened. Shaping it. Treating the AI as a collaborator.

That’s the variable. Stay inside the work or step outside it.

Use AI to move faster through the parts that don’t require you. Stay inside the parts that do.

Why This Time Is Faster

The washing machine took 50 years to fully recalibrate expectations around cleanliness. Email took about 15 to become the thing it was supposed to replace (meetings, memos, phone calls) plus a dozen things it was never meant to be.

AI is doing it in months.

The cycle is the same. The clock is faster. Your organization adopted AI tools sometime in the last year. The expectations have already moved. The “normal output” for a Tuesday has already been redefined by whoever on your team figured out Claude first. And once one person’s output resets the benchmark, everyone else is running to match a bar they didn’t see move.

The Question Worth Sitting With

Before you fill the time AI freed up, ask who decided it needed filling.

Not “how do I use this time productively.” That question has already been colonized by the same expectation inflation that turned the washing machine into more laundry.

Just: who decided?

If the answer is “my calendar filled itself” or “there was more work so I took it” or “I guess I just... did”... you’re living the washing machine paradox. The time was never yours. The bar moved, and you moved with it, and the gap between where you were and where you are now has been papered over by the feeling of being productive.

Productivity is the feeling. Leverage is the question.

The difference between the two is whether you chose what to do with the hour you got back, or whether the hour got taken before you noticed it existed.

Your Organization (Or Client) Is Reading This Too

Your ego will tell you this is a personal discipline problem. That you just need better boundaries, better time management, a system.

But Cowan’s research showed something the self-help version misses. The washing machine didn’t just change individual behavior. It changed what society expected of a household. The standard shifted, and every individual decision happened inside that shifted standard. You couldn’t opt out of “clean” being redefined.

You could only decide how to respond.

The AI version of this is already in motion. The company that saw one team ship 3x faster with AI didn’t celebrate and go home. It asked why the other teams weren’t doing the same. The bar moved for everyone because it moved for someone.

You’re not going to solve this with a morning routine.

The Oldest Trick in the Technology Book

In 1983, when Cowan published her research, the response was predictable. People said it didn’t apply to them. Their washing machine really did save time. They really were spending less effort on laundry than their mothers.

They were wrong, and they were wrong in the exact way that made the data invisible.

The standard moved slowly enough that each incremental shift felt like a personal choice rather than a structural one.

The AI bar is moving faster. Which means you’ll notice it sooner...if you’re looking.

But most people aren’t looking. They’re too busy filling the time they saved.

-Max

This is what we talk about inside Beware the Default. Not productivity tips. The invisible assumptions underneath them. If that’s your kind of room, we’d like to have you.

Sources

• Cowan, R.S. (1983). More Work for Mother: The Ironies of Household Technology from the Open Hearth to the Microwave. Basic Books.

• Ranganathan, A. & Ye, X.M. (2026). “AI Doesn’t Reduce Work—It Intensifies It.” Harvard Business Review, Feb 9, 2026.

• Lee, E.H., Yin, Y., Jia, N., & Wakslak, C.J. (2026). “Relying on AI at work reduces self-efficacy, ownership, and meaning while active collaboration mitigates the effects.” Scientific Reports (Nature), March 15, 2026.

Here’s what happened.

It was the same instructions I’d been using for months but then Claude started overusing tools. Ignoring things I’d told it and sounding like...AI. Took me about four hours of testing to figure out why.

Everyone covered the launch.

• Benchmarks

• Pricing

• Who is better than who

I rarely pay attention to that anymore. It all runs together.

The signal is what nobody wrote about?

The update quietly changed how Claude responds to your instructions, and most of the prompting advice from 2025 now actively makes results worse. I’ve spent the last few weeks rebuilding my own systems around it. Here are seven things that actually matter.

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First, the 30-Second Version of How Claude Actually Works

Every time you send Claude a message, it reads the entire conversation from scratch. Your project instructions. Anthropic’s system prompt. Every message you’ve sent. Every response Claude has given. All of it, every single time.

But here’s the problem with that. Claude pays more attention to recent messages than old ones. Your instructions at the top? They’re like a note you left on the kitchen counter this morning. By message 15, that note is buried under a stack of mail. Claude can still find it. But the conversation happening right now is louder.

This is called recency bias. It’s the single most important thing to understand about working with Claude. Your rules don’t just disappear. They get drowned out.

That part hasn’t changed. What has changed is how Claude responds to the tone and style of those instructions.

1. Stop Shouting

Six months ago, one popular piece of advice was to use emphatic language in your instructions. ALL CAPS for important rules. “YOU MUST always do this.” “CRITICAL: never forget this.” Exclamation marks. Bold. Urgency.

On the old Claude (4.0 through 4.5), this sometimes helped. The model was passive. It needed a push.

Claude 4.6 is different. It takes instructions at face value. It doesn’t need to be shouted at to pay attention. And when you shout anyway, it actually performs worse. Overreacts. Gets twitchy. Starts doing things you didn’t ask for because it’s trying so hard to follow your intense instructions.

Think of it this way.

The old Claude was like a new employee who needed constant reminders.

The new Claude is more like a capable colleague who already wants to do good work.

When you keep shouting reminders at a capable colleague, they start second-guessing everything.

2. It Already Wants to Help (Stop Pushing)

The old Claude was precise but lazy. If you asked it to “review” a document, it would read it and give you some thoughts. But it would rarely fix anything unless you explicitly told it to. People learned to add instructions like “Always take action” and “Default to using tools” and “Go above and beyond.”

Claude 4.6 is proactive. It wants to act. It wants to use tools. It wants to go beyond what you asked.

So what happens when you tell a proactive model to “always take action” and “default to using tools”? It over-acts. It searches the web on every single message. It rewrites paragraphs you wanted it to just review. It uses three tools when the question needed zero.

The fix is counterintuitive. Back off. Remove the pep talks. If you want Claude to use a specific tool, say when:

“Use web search when the question requires current data.”

If you want it to act on something, use the right verb:

“Rewrite this paragraph” instead of “look at this paragraph.”

Verb choice matters more than it used to. “Suggest” means suggest. “Implement” means implement. “Review” means read and comment, not edit. Be precise about the action you want.

3. Claude Remembers You Now

This is the one most people haven’t absorbed yet.

Claude now has a memory system. It learns things about you across conversations: your name, your role, what you’re working on, how you like to communicate, what projects you have going. This memory persists. It updates roughly every 24 hours. If you use Projects, each project gets its own memory.

What does this change practically?

If you’ve been stuffing your project instructions with background information (”I run a consulting company, my clients are mid-market, I focus on AI implementation”), you don’t need to anymore. Memory handles that. Claude already knows.

Your instructions should focus on rules instead. How Claude should behave. What patterns to avoid. What good output looks like. What to do when it’s unsure. The “who I am” context is covered. The “how to act” rules are what need explicit encoding.

You can check what Claude remembers about you in Settings. You can edit it, delete it, or pause it. If you’re in an incognito conversation, memory is off entirely.

4. Your Project Files Might Not Be Loading the Way You Think

If you use Claude Projects and upload reference files (style guides, examples, procedures), here’s something that might explain weird behavior you’ve noticed.

When your project’s files get large enough, Claude stops loading all of them into every conversation. Instead, it searches them, pulling in relevant chunks when it thinks it needs them. This is called RAG (Retrieval Augmented Generation). It happens automatically. Claude doesn’t tell you when it switches.

The problem is when Claude is searching instead of reading everything, it can miss connections between files. If your voice guidelines are in one file and your examples are in another, Claude might find the guidelines but miss the examples. Or vice versa.

There’s also a known issue where this search mode kicks in earlier than it should. Anthropic says it activates when files are very large. In practice, some people see it activate with as few as 13 files regardless of size.

What to do: Keep related information together. If your voice guidelines and your writing examples are connected, put them in the same file. Fewer, more complete files beat many small scattered ones. After adding files, test by asking Claude questions that require pulling from multiple files. If it only answers from one, your files may need consolidating.

5. Claude Decides How Hard to Think

This one is more relevant for people using the Claude API (developers, people building automations). But it affects everyone indirectly.

Previously, you could tell Claude exactly how much thinking effort to put into a response. Set a budget. “Think for 10,000 tokens.” The problem: you had to guess what each task needed. Too low and Claude cut corners. Too high and you wasted time and money.

Claude 4.6 handles this automatically. It looks at your question, assesses the complexity, and decides how deeply to reason. A simple factual question gets quick processing. A multi-step logic problem gets deep analysis. You set the intensity (low, medium, high, or max) and Claude allocates the actual thinking.

For those of you using claude.ai (the web or mobile app), this is already happening. You don’t control it. The model self-calibrates.

The takeaway: If you’ve been adding instructions like “think carefully” or “take your time with this” in your prompts, those phrases are less necessary now. Claude already adjusts its reasoning depth based on what you’re asking.

6. Which Model Are You Even Using?

This is worth a minute because most people don’t think about it.

If you’re on Claude Free or Pro, you’re using Sonnet 4.6. That’s the default. If you’re on Pro, you can switch to Opus 4.6 in the model selector. If you’re on the free plan, you can’t.

The seven changes in this article apply equally to both. The calmer tone, the proactivity, the memory, the file handling. All of it.

But there are a few behavioral differences worth knowing.

• Opus pauses. Sonnet proceeds
  When your prompt is vague or could be interpreted multiple ways, Opus is more likely to stop and ask you what you meant. Sonnet is more likely to pick the most reasonable interpretation and just do it. Neither is wrong. But if you’re doing high-stakes work where assumptions can cause problems (legal drafts, financial analysis, strategy docs), Opus’s habit of flagging ambiguity before acting can save you a rewrite.

• Opus shows its work more
  Sonnet tends to compress its reasoning and give you clean, fast answers. Opus lays out intermediate steps, states its assumptions explicitly, and walks through the logic more visibly. For quick tasks, Sonnet’s approach is better. For complex multi-step problems where you need to verify the thinking, Opus gives you more to check.

• Opus can write longer in a single response
  Opus can produce up to 128,000 tokens in one response. Sonnet caps at 64,000. For most conversations this doesn’t matter. For generating long documents, full reports, or extensive code in a single pass, Opus can finish things that Sonnet would need to break into pieces.

• For 80% of what you do, the difference is invisible
  Following your instructions, writing content, answering questions, coding, researching, analyzing documents. On all of this, Sonnet 4.6 performs within a few percentage points of Opus. In Anthropic’s own testing, developers preferred Sonnet 4.6 over the previous Opus (4.5) 59% of the time.

The simple rule: Start with Sonnet. If you find yourself frequently clarifying ambiguous instructions, or working on problems where you need to see the full chain of reasoning, or generating very long outputs in one shot, try Opus for those specific tasks. Don’t default to the expensive model out of habit.

7. What Still Works (Don’t Throw These Out)

The core techniques for getting reliable output haven’t changed. They’ve been true across every Claude version since 3.0:

• Show examples, not just rules
  Saying “don’t sound like AI” is vague. Showing three sentences that sound like AI and three that don’t gives Claude a clear target. This is the single highest-value thing you can do in any project. It costs 2 minutes and changes everything.

• Repeat your most important rules
  Remember the recency bias from above? Your instructions get quieter over time. If you’re 15 messages into a conversation and quality is slipping, paste your top 3 rules directly into your next message. Takes 10 seconds. Resets the attention to your rules.

• Start fresh when things drift
  If Claude has gone off the rails, starting a new conversation in the same project is the fastest fix. Your project instructions reload at full strength. Memory carries over so you don’t lose context. The conversation clutter is gone.

• Use XML tags for structure
  This sounds technical but it’s simple. If you wrap your instructions in tags like <rules> and </rules>, Claude treats that section as structurally separate from regular text. It pays more attention to tagged content. You don’t need to know XML. Just use the angle brackets.

• Ask Claude to check its own work
  Add a line to your instructions: “After every response, list which rules you followed.” This forces Claude to re-read your rules as part of generating every answer. It keeps the rules in the most recent part of the conversation where they have the most influence.

The One Paragraph Version

Claude 4.6 is more capable and more willing to help than any previous version. The adjustment is surprisingly simple: talk to it like a competent colleague instead of a forgetful intern. Write instructions in plain, calm language. Drop the all-caps urgency. Be precise about what action you want. Let memory carry your background context and save your instructions for actual rules. Show examples of what “good” looks like. And when quality slips 15 messages in, paste your rules into the next message or start a fresh conversation. The architecture rewards clarity, not volume.

Stay Curious,

-Max

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Last week I decided to get serious about a skill gap I’d been ignoring for months.

Not just “learn AI.”

Not only “get better at strategy.”

Something specific enough to understand systems design well enough to explain why most AI implementations fail and what to do instead.

I had a massive reading list with nine books. At my actual reading pace, that’s 4-6 months out (more of a podcast guy these days). And even then, I’d remember maybe 30% of the concepts and almost none of the connections between them.

The thing about a reading list is it feels like a plan. But it’s almost always a deferral.

So I tried something different and used AI to build personalized knowledge files for each book, uploaded them to NotebookLM, and generated overviews I could listen to while walking the dog.

I ended up with three hours of audio. Every concept mapped to

• My actual work

• My actual clients

• My actual problems

And it included personalized translations, built around my specific friction before I ever pressed play (AI is so cool).

But, little did I know, building the files taught me more than listening to them did. By the time I pressed play, the hard cognitive work was already done. The translation was the learning.

Five steps, 2 prompts. Here’s how they work.

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Step 1: Find the Real Skill Gap

Most learning fails before it starts. The gap gets named too broadly to ever close, and a broad gap has no finish line.

“Get better at leadership.” “Learn AI.” “Improve my writing.” Those are categories. You can spend years inside a category and have nothing concrete to show for it.

The way out is specificity. Start with what you’re actually doing, find where it breaks down, and then name what would fix it. Three questions do that work.

1. What’s the work you’re doing right now that matters most? Ground it in reality. Not your goals. Not your roadmap. What you’re actually doing this week.

2. Where does it get stuck, slow, or frustrating? The friction is the diagnostic. “I keep explaining the same thing over and over.” “Every project feels like starting from scratch.” “I can build the thing but I can’t explain why it works.”

3. If you could snap your fingers and be world-class at one thing tomorrow, what would make the biggest difference in that work?

When you name a gap specifically enough, the reading list writes itself.

“Learn AI” produces AI books.

“Understand why the systems I build for clients stop getting used after two weeks” produces books on human-centered design, lean manufacturing, and organizational behavior. A completely different shelf, built around a problem that actually has a solution.

The $1,000 Insight: the name you give your problem is basically a search query. “Get better at AI” returns AI books. None of them will tell you why your clients stop using the systems you build. The more specific you get about what’s actually broken, the more likely you are to find something that actually fixes it.

Step 2: Build Your Reading List

Take your skill gap and give it to Claude with your full context. Your work, where it gets stuck, your background. Ask for 6-10 books, clustered by how they connect to your gap, sequenced by where to start.

One ask that makes the whole thing work…

Ask for books that build the underlying skill, not books about your topic.

I was trying to understand AI adoption failure. The book that moved the needle most was written about lean manufacturing in the 1980s. Same underlying skill, completely different shelf, and I never would have found it searching “AI books.”

The $1,000 Insight: the skill you need and the domain that teaches it best are often completely different things. A book written about lean manufacturing in the 1980s taught me more about AI adoption than anything in the AI section would have. That only happens if you ask Claude for the underlying skill rather than books about the surface problem.

Step 3: Build the Knowledge Files

For each book, ask Claude to build a deep knowledge file (it’s good enough to do this now without the book itself). Every section gets written through the lens of your specific work, your specific friction, your specific clients and problems. The goal is a document that reads like it was written for you, because it was.

Each file should include:

• What the book is really about, framed through your context

• The 6-8 concepts that matter most for your specific situation

• A mapping of those concepts to your actual challenges and projects

• The core framework translated to your domain

• 10 diagnostic questions you could ask yourself or a client

• Key quotes with one-line translations to your context

• How this book connects to the others on your list

Target 3,000-5,000 words per file. Rich enough for at least a 15-20 minute audio overview.

To build the file, you have to articulate your context first. Your work. Your friction. Your specific challenges. That act of articulation is where the real learning happens (the prompt below will help you with this).

By the time you press play on the audio, the hard cognitive work is already done.

Most people read first and try to apply later. This flips the order. You apply first, then listen to reinforce what you already worked out.

The $1,000 Insight: every book contains the same words for every reader. What changes is what you bring to it. When you build the knowledge file, you’re forcing the connection between the book’s ideas and your actual work before you consume a single word. That forced connection is what makes it stick.

Step 4: Upload to NotebookLM and Generate Your Course

NotebookLM is a free Google tool. Go to notebooklm.google.com, create a new notebook, upload your knowledge files as sources, and hit “Generate” on the Audio Overview.

It produces a podcast-style conversation that walks through the material. Because your files are translations rather than generic summaries, the audio references your work, your problems, your language. You can also chat with the notebook directly and ask how a specific framework applies to a specific client situation.

The $1,000 Insight: NotebookLM doesn’t know anything about you. It works entirely with what you give it. Give it generic summaries and you get generic audio. Give it files you built around your own work, your own clients, your own problems, and the audio sounds like someone recorded it specifically for you. The quality of what comes out is a direct reflection of the quality of what goes in.

Note: You have a choice of per-book or combined

Option A: One Audio Per Book (Recommended to Start)

Generate a separate audio overview for each knowledge file. You’ll get 9 episodes, each 15-20 minutes. This works best if you want to listen during walks, commutes, or workouts and absorb one book’s frameworks at a time.

Option B: One Combined Audio (All 9)

Generate a single audio overview with all 9 files loaded. NotebookLM will find the connections between books and weave them together. You’ll get one longer episode (30-45 min) that cross-references concepts across your entire reading list. Better for seeing the big picture, but denser.

My suggestion: Start with Option A to learn each book individually, then generate the combined version after you’ve listened to a few. The combined audio hits differently once you’ve got the individual frameworks loaded.

Choosing Your Format

Start with Deep Dive for each book. It’s the most natural way to absorb the material. Use Brief if you’re short on time and want to triage which books matter most. Debate is surprisingly good for books like “Antifragile” where the ideas provoke strong reactions.

Video Overview is also available with three styles:

Video works especially well for books with spatial concepts (Meadows' system diagrams, Alexander's pattern relationships, Toyota's production flows). If you only generate one video, make it the combined all-9 version on Explainer. It'll map the connections between books visually.

The Key Field: "What Should the AI Hosts Focus On?"

This is where you steer the output. Leave it blank and you'll get a generic overview. Fill it in and the episode becomes yours.

After generating, you can also chat with the notebook directly. Ask things like "How does Meadows' leverage points framework connect to Norman's design principles?" and it'll answer using your personalized context because that's what's in the source files.

Step 5: What You Can Do With What You Just Built

The knowledge files don’t stop being useful after the audio ends.

Each file produces 10 diagnostic questions mapped to your work. Across 9 books, that’s 90 questions. Pull the 15-20 sharpest ones and you have a diagnostic framework you can use with clients, in strategy sessions, or for your own quarterly reviews.

Every concept-to-work mapping is a potential article, talk, or post. The connection is already written. You built it into the file before you started.

What you will start to realize is that the frameworks compound. When you’re evaluating whether to take a project, change an approach, or give a client a recommendation, you’ve got 9 lenses available because you built 9 translations, each mapped to your actual work.

The $1,000 Insight: after you run these prompts, you don’t just have notes from 9 books. You have 90 diagnostic questions you can use with clients. You have concept-to-work mappings that are articles and talks waiting to happen. You have translated frameworks you can actually reach for when you’re in a meeting and need to explain something fast. Think of it as a toolkit you built, not a course you took.

The Meta-Lesson

The skill I was trying to build was systems thinking. The system I built to learn it turned out to be the lesson.

Most learning is passive. Read the book, highlight some lines, forget 90% within a month. This approach is active before it’s passive. You’re translating every concept into your own context before you start consuming it.

The books don’t change. Your context is what makes them useful. Two people running this system on the same 9 books will get completely different outputs (different files, different audio, different toolkits) because they brought different problems to the table.

That’s the whole thing. Two prompts, one afternoon, a walk with the dog (or four in my case).

Below are the two prompts that run this system (and three more to customize the experience)

The first one builds a reading list around the gap you actually have, pulled from domains you’d never think to search. The second turns any book into a knowledge file mapped to your exact clients, your exact problems, and the conversations where you need to sound like the most expensive person in the room.

You will also receive three prompts to customize the audio and video experience even further.

Every book you’ve read and forgotten is a framework you could have been charging for. These prompts fix that, starting with the next one.

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I’ve seen the posts. You’ve seen the posts.

“AI gives you speed, but taste is what separates good from great.”

Very wise....

And very LinkedIn.

But do you notice nobody actually defines it?

Ask them what taste actually means and you get vague responses like:

“I just know.”
”It’s a feeling.”
“You can just tell which output is better.”

So it had me thinking:

What would it actually look like if someone posted this job description?
The role everyone keeps talking about.
The human who sits between the machine and the output.
The one who decides what ships and what gets deleted.

Instead of hypothesizing, I went ahead and wrote one.

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POSITION: Human Architect in the Age of AI

Department: The Area That Still Requires a Pulse

Reports To: Anyone who hasn’t yet been replaced by a chatbot

Salary: Commensurate with your ability to feel existential dread

Context for the Applicant: I was at a dinner party recently when someone asked what I did for a living. “I work with AI,” I said, which is technically true in the same way that saying you “work with wildlife” is true if you spend most of your day preventing your cat from knocking things off counters.

We’re looking for a Human Architect. The title sounds impressive until you realize it’s basically a fancy way of saying “the person who has to sit there and watch a machine do things very quickly and then explain why half of it is wrong.”

The Six Pillars of Your Role (Suffering)

1. ADAPTABILITY Or: Everything You Learned Last Quarter Is Already Obsolete

The tools you master on Monday will be ancient by Thursday. We’re looking for someone who, when told that everything they learned last quarter is now irrelevant, responds not with tears or bargaining, but with the quiet resignation of a person who who spent 15 hours on a presentation and then got told the meeting was moved to next week, 10 minutes before it starts.

2. CURIOSITY Or: The Willingness to Poke Things With Sticks

You must possess the curiosity of a four-year-old at a natural history museum, combined with the stamina of that four-year-old’s exhausted parent who has answered “but why?” thirty-seven consecutive times. The ideal candidate looks at a new AI capability and thinks, “I wonder what would happen if…” and then actually finds out, documenting the results like Sherlock Holmes at a crime scene…

Crouching, squinting, pulling out a magnifying glass, absolutely delighted that nothing makes sense yet.

3. CREATIVITY (TASTE) Or: Knowing the Difference Between Art and a Fever Dream

AI can generate ten thousand ideas before you finish your coffee. The machine’s output is abundant in the way that zucchini from a neighbor’s garden is abundant. Technically impressive, largely unwanted, and requiring you to figure out what to do with it all.

Your job is to have taste. Not in the “I summer in the Hamptons” sense, but in the “I know the difference between something genuinely good and something that merely sounds like it should be good” sense. The AI will hand you a confidently worded paragraph about synergistic value propositions, and you’ll need to recognize it for what it is:

Expensive-sounding gibberish dressed up for a job interview.

4. SYSTEMS THINKING Or: Understanding Why Pulling This Thread Unravels That Sweater

The AI sees trees. Occasionally, it sees forests. It almost never sees that the forest is next to a highway and the trees are about to become a strip mall.

You’ll need to hold multiple moving pieces in your head simultaneously…

Cause, effect, unintended consequence, the thing nobody mentioned in the meeting, and the political landmine buried under the third bullet point. Think of yourself as the person assembling IKEA furniture who actually reads ahead to step seven before discovering that step three was a catastrophic error and the entire bookshelf collapses.

5. COMMUNICATION Or: Explaining Complex Things to Distracted People

You will spend a considerable portion of your time translating to busy, mostly uninterested people. Not languages, but worlds. And it goes both ways.

You’ll explain to the AI what humans actually mean when they say “make it pop.” You’ll explain to the humans what the AI actually did when it,,, “made it pop.” You’ll explain to everyone why the bot’s suggestion to “leverage synergies across the customer journey touchpoints” is just seven words pretending to be a sentence.

But somehow, it works.

6. JUDGMENT Or (and this is my favorite): The Crap-Filter

This...THIS is the job. Everything else is preamble.

AI produces output with the confidence of a golden retriever who has just brought you something from the yard. It might be a ball. It might be a dead bird. It might be your neighbor’s shoe. The AI doesn’t know the difference. The AI is simply delighted to have retrieved something.

Your job is to look at what the machine has fetched and determine whether it’s brilliant insight or a very, very gross dead pigeon. The AI will present you with a statistic that sounds authoritative but was, upon closer inspection, entirely invented. It will offer you a solution that seems clever until you realize it violates three laws and possibly physics. It will write you a paragraph so smooth and professional that you almost miss that it’s saying absolutely nothing at all.

You are the “No” button. You are the pause before the send. You are the human who reads the AI’s confident assertion that the client’s headquarters is in a city that does not exist and says, “Let me just check that.”

The robots provide the speed. You provide the skepticism.

Qualifications

1. A functioning sense of irony

2. The ability to say “that’s not quite right” without using those words

3. Comfort with not knowing what’s happening, even while it’s happening

4. Experience working with systems that are smarter than you in some ways and dumber than a brick in others

5. At least one story about a time AI went horribly wrong...(I got it! Let me let Clawdbot run my Hinge account and see what happens)

To Apply

Send us something that demonstrates you’ve actually read this far and aren’t just an AI scanning for keywords. Bonus points if you include a specific example of a time you caught a confident error before it became someone else’s problem.

The machines are fast. We need you to be right.

What IS the Crap-Filter?

I wrote that on an airplane. Somewhere over Kansas, 35,000 feet up. And when I read it back, it was a fun exercise, but it still didn’t answer my real question...

What IS the Crap-Filter? What IS taste?

“Knowing the difference between art and a fever dream” sounds good. But it’s still the same as “I know it when I see it.”

AI is the average of the internet. It represents good enough but it does not represent real expertise. Every output is a statistical composite of everything that’s ever been written about a topic, smoothed into the most probable next sentence. Polished. Plausible. And completely, reliably, middle of the road.

The model learned what 'good' looks like from an assembly line of thousands of non-expert evaluators, paid by the hour, rating which output sounded better. Its definition of quality was calibrated by people who could tell the difference between “clear and confusing”, but not between “right and convincing”.

Christopher Mims, technology columnist for the Wall Street Journal, described it this way on “The Daily Creative” with Todd Henry.

He’s had AI research tools faithfully reproduce research made by humans, and when he dug into it, the human source was wrong. The AI didn’t hallucinate. It just repeated the consensus with full confidence. No mechanism for knowing the difference.

But Mims also made the point that experts get more out of AI than anyone else, because they know what questions to ask. They push the model past the obvious, past the consensus, down what he called “the long tail” — into the narrow territory where the valuable answers actually live. Without that push, you get the mean.

The machine produces the mean. Expertise lives at the edges. Someone has to feel the distance between the two.

And so, I kept pulling.

What Taste Actually Is

Why can some people look at AI output and immediately know it’s wrong? Not wrong in an obvious way. Wrong in a subtle way. The kind of wrong that sounds right. The kind of wrong that gets past most people.

You know which word “sounds” right because you have years of knowing what right does and does not look like. When AI output is off, it sticks out. Even if you don’t know why right away.

The answer showed up in a field I wasn’t expecting.

Chicken Sexing

In the early 1900s, the Japanese developed a method for determining the sex of day-old chicks. Commercial hatcheries need to separate males from females immediately. Different feeding programs. Different futures.

The problem: newborn chicks look identical. The differences in their anatomy are so subtle that untrained observers get it right about half the time. Coin flip.

Professional chicken sexers can sort a thousand chicks per hour with 98% accuracy. One every three seconds.

When cognitive scientist Richard Horsey asked the experts how they did it, the sexers couldn’t explain. “I just know.” “There was nothing there but I knew it was a cockerel.” They were making correct decisions faster than conscious thought, using criteria they couldn’t articulate.

How did they train new sexers?

The novice would stand next to an expert. Pick up a chick. Make a guess. The expert would say yes or no. No explanation. No theory. Just feedback.

After weeks, the novice would start getting it right. After months, they’d hit 80%. After a year, they’d be in the 90s.

They learned to see something they still couldn’t name.

Researchers call this “perceptual learning.” Years of exposure compressed into pattern recognition so deep it operates below conscious awareness. The sexers weren’t thinking. They were recognizing.

Sommeliers

The master sommelier who identifies a 2018 Domaine de la Romanée-Conti in a blind tasting. Swirl, sniff, sip, name the exact vineyard. It looks like magic.

Studies show that expert sommeliers don’t actually have better noses than novices. Their olfactory sensitivity is roughly the same. The difference is pattern recognition. They’ve built what researchers call “calibrated palates”—thousands of reference points compressed into instant judgment.

When they taste, they’re not doing chemistry. They’re pattern-matching against every wine they’ve ever encountered. The combination of tar, roses, and fierce tannins triggers a recognition cascade. They feel “Barolo” before they can articulate why.

Brain scans confirm it. Sommeliers show different neural activation patterns than casual drinkers. Their brains process wine through memory and recognition circuits, not just sensory ones. The expertise literally rewired how they perceive.

Art Forgery

Wolfgang Beltracchi fooled dozens of experts for decades. He made millions selling fake Expressionist paintings. His technique was flawless. His research was meticulous. He even photographed his wife posing as her grandmother, with his forgeries hanging on the wall, to fake provenance documentation.

What finally caught him wasn’t human intuition. A lab test. One of his “Max Ernst” paintings contained titanium dioxide—a pigment that didn’t exist until after 1920. The painting was supposedly from 1914.

But the experts who got suspicious before the lab results came back couldn’t explain what triggered their doubt. Something felt wrong. The brushwork was technically perfect. The composition was right. The colors matched the period.

Something in the whole didn’t match the parts.

That “something” was pattern recognition firing on mismatches too subtle to articulate. Decades of looking at authentic work, compressed into an instant “this isn’t right” that arrived before the explanation.

The Pattern

This is what taste is.

Taste is pattern recognition compressed into instant judgment. Thousands of decisions made so many times they chunked together. Packed down. Automatic. Invisible even to the person running the patterns.

When someone with taste looks at AI output and says “no, this is wrong,” they’re running every decision they’ve made in that domain against what the machine produced. The output doesn’t match because it can’t match. The machine produced the average. The expert carries the edges. That gap is what they feel before they can name it.

The chicken sexer. The sommelier. The art expert. The consultant who reads a strategy deck and knows instantly that it won’t survive contact with reality.

The mechanism is the same whether you’re sorting chicks or reading a strategy deck.

The Part I Didn’t Expect

I have a methodology called Cognitive Fingerprint. The whole point is extracting unconscious expertise from people who can’t articulate what makes them good. I find the patterns they run automatically. The decisions they make without noticing. The knowledge that became invisible through repetition.

I’ve done this for over fifty people. Coaches. Consultants. Therapists. Executives.

Sitting on that airplane, writing about taste and crap-filters and golden retrievers fetching dead birds, I realized something.

I had been sitting on the answer the whole time.

Chunked expertise can be extracted. Mapped. Made visible.

The philosopher Michael Polanyi spent decades studying how experts think. His conclusion: “We know more than we can tell.” The sommelier can learn to explain what they taste, if you ask the right questions. The chicken sexer’s criteria can be studied and documented, even if they can’t articulate it themselves. The expert who “just knows” can actually show you the pattern, if you know how to look for it.

One client proved it to me. I’d spent the night before analyzing sixteen of her coaching transcripts. When I read back what I’d found, she said: “My heart is pounding. I have chills running up and down my arms.”

Three patterns she’d been running for thirty years. Named for the first time. Her “courage circulation system” — how she harvests courage from one person’s story and transplants it into another’s challenge. Her “two-word read” — how she assesses someone’s emotional state in seconds. Her “confidence reconstruction” — the specific sequence she uses to rebuild someone’s sense of self-worth.

Thirty years of expertise. She’d been trying to explain it her whole career. Visible in an hour.

My own invisible expertise showed up in real time. I’d been studying this problem for years. The answer was already in my head. It just didn’t know it was an answer until I asked a different question.

You Have This Too

There are domains where you make a hundred decisions without noticing. Where you look at something and say “no” before you can explain why. Where your judgment is instant and certain and completely opaque to you.

That’s chunked expertise.

The question isn’t whether you have it. Everyone who’s spent years doing something has it. The question is whether you can see it.

Most people can’t. The better you get, the less visible your own patterns become. The document you wrote in 2019 called “My Process” is still sitting in a folder somewhere. You haven’t opened it in years. It doesn’t match how you actually work anymore. Your expertise outgrew your explanation.

Next time you feel a strong “no” to something, don’t dismiss it as intuition. Don’t override it because you can’t articulate it. Pause. That reaction is data.

Ask yourself:

• What did I see?

• What pattern didn’t match?

• What’s my brain comparing this to?

You might not get the full answer. The chunking happened over years. It won’t unpack in a moment. But you’ll start to see the edges. The shape of what you know without knowing you know it.

That’s the crap-filter. Not a mystical sense. Not a personality type. Just pattern recognition so deep it feels like instinct.

The machines provide the speed. You provide the years.

That’s the part they can’t copy.

-Max

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If you’ve spent years building expertise you can’t fully explain, it’s in there. The patterns exist. They’re running right now.

I extract them. That’s what Cognitive Fingerprint does.

If you want to see what yours looks like: book a conversation.

“How do you come up with so many prompts?”

My answer:

“Let me show you.”

I asked him who his last client was. He said a boutique financial company that sells managed futures. I’d never worked in managed futures. Never studied alternative investments. Didn’t know the industry, the players, or the problems.

Seventy-three minutes later I had a five-layer diagnostic framework, a 20-question intake questionnaire, three chained AI prompts, a branding strategy, a direct-response landing page, and a $297 product called The Consultant’s Offer Engine.

With a lead magnet.
With a guarantee philosophy.
With a name that took five rounds of rejection to surface.

Twenty-five prompts from two chat windows in one afternoon.

I recorded the whole thing. Every prompt, every decision, every timestamp and I’ll share the full build log below….But the build log isn’t why I’m writing this.

I’m writing this because three conversations landed on my desk (sounds way cooler than inbox) this week saying the same thing in different words. Nate Jones. Nathaniel Whittemore. And the third wasn’t a conversation I read. It was one I was inside of, a live build session with a room full of people who’d never watched this happen in real time.

My man needed a product. We had no plan. That session is the build log.

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The Part Everyone Gets Right

Jones (the most prolific, quality AI content creator) put it bluntly:

“This is an art you learn by doing. You do not get to learn to ride a horse by reading a book. You do not get to learn to swim by sitting in a deck chair and watching the ocean. You just got to get in.”

Nathaniel Whittemore, on The AI Daily Brief, went further. He’s been building dozens of live projects despite being, in his words, “completely and utterly non-technical.”

His argument…the capabilities everyone says are coming are already here. Right now. For anyone willing to wrestle with them.

And the line that I loved:

“The foundational mindset shift is to stop looking for tutorials or videos or explainers and fully embrace the idea of AI itself as your learning and build partner.”

They’re both right. The problem I am going to talk about below isn’t them. It’s the ecosystem that heard what they said, wrote it in a carousel, and called it a contribution.

They took the mindset and made it a meme.

The Missing Half

Principles are easy to nod at. Hard to picture.

“Start with vision, not task.” Sure. What does that look like at 4:37 on a Tuesday?

“Push back hard and often.” On what? When? How do you know it’s working?

“Dump first, organize later.” What does “dumping” look like when you’re building something you’ve never built before in an industry you’ve never worked in?

The AI learning conversation is full of good advice and almost completely empty of evidence. Lots of principles. Very few build logs.

So I made one. And then I named each move so you could steal it.

I’m not going to show you all twenty-five moves in sequence. A sequence only makes sense in motion. What I can show you is five moves that changed how the build went, and name them well enough that you recognize them when they show up in your own work.

Below are five of them. The full build log has all twenty-five, with universal prompt templates you can run against any industry.

The Moves

Move 1: The Wide Net

This is how I started my session with Claude…

“What are the top 10 biggest top-of-funnel problems that a boutique financial company has that sells managed futures. Don’t ask me other questions just do your best.”

No strategy, no precision. Dump everything, and sort that sucker out later.

The Universal Version: Pick an industry, ask for the top 10 friction points, and tell the AI not to slow down with clarifying questions. You want volume, not accuracy. You can filter later.

That’s “go wide first, react later” in action. Except you can see it happening. You can see the 10 friction points Claude surfaced. You can see the throughline sentence it added on its own at the end. And you can see me grab that sentence and feed it back in the next move.

Move 2: The Thread Pull

I copied Claude’s own best insight and pasted it back:

“Let’s break this apart into micro problems. Let’s look at this from first principles, what are we really trying to solve here?”

The Universal Version: Find the single best sentence in the AI’s output, quote it back, and ask it to decompose that sentence using first principles. You do the selecting. The AI does the expanding.

Whittemore calls this “AI as mirror.”

Nate Jones would call it directing AI with good knowledge toward an outcome.

Move 3: The Landscape Survey

Before building anything, I stopped and asked:

What are the top frameworks for identifying bottlenecks in any industry, and which fits this situation best?

Claude surfaced Goldratt’s Theory of Constraints, the AARRR funnel, value stream mapping. Then told me why none of them were quite right for this use case, and recommended a hybrid of constraint mapping and a buyer journey diagnostic.

The Universal Version: Before you decide what to build, ask the AI to map the intellectual territory. See what already exists. You’ll either borrow the best parts or learn exactly why you need something different. The choice you make here determines the architecture of everything that follows.

Move 4: The Ambition Check

Claude was ready to build. I pumped the brakes. I hadn’t decided what level of ambition this product should have. A named diagnostic? A scoring system? A category claim? That’s a strategic decision, and it has to happen before the tactical one.

The Universal Version: Before you build anything, ask the AI to show you the spectrum of ambition levels for what you’re creating. Then choose. The choice reshapes everything downstream.

Move 7: The Rough Pass.

Nine words:

“Ok just take a stab at the whole system.”

Nobody talks about this one. Perfectionism kills momentum in AI conversations the same way it kills it everywhere else. I didn’t refine the questionnaire for three rounds. I built all four components in one rough pass so I could see the whole machine, then adjust.

The Universal Version: Ask for the complete rough draft of the entire system before perfecting any single piece. You need to see the whole chain before you can know which link matters most.

Move 14: The Discovered Price.

I said “$297 product” out loud at some point in the session. Not as a decision. More like a placeholder. Claude heard it, built around it, and handed back a full tiered architecture with a lead magnet at the bottom and a $297 core offer at the top. The realization in the room was quieter than a typo story.

We hadn’t decided on a price. We’d been talking for an hour, and nobody had stopped to decide. The price had already been chosen…just not consciously. The architecture that came back was better than anything we would have designed from scratch. The lesson isn’t “make mistakes faster.”

It’s that you’ve already made more decisions than you think. Sometimes you need to see what the AI builds to find out what you actually believe.

Nate Jones:

“You’re actually safer leaning in and going faster than going slower, because slower forces you to constantly think about braking, stopping, and adjusting.”

Move 25: The Rejection Loop

The naming sequence is the part I keep coming back to.

I call it The Rejection Loop in the build log. For five rounds, Claude generated names. I rejected them. Each rejection forced me to articulate what was actually wrong.

“Diagnostic” was wrong because the buyer doesn’t think in diagnostics. “Problem OS” was wrong because they don’t care about problems. They care about having a thing. An offer. Something they can stand behind.

The Principle Underneath the Whole Loop: I hate calling anything a diagnostic or an audit. When you hear those words, it feels like a test. Like homework. Like you have to do something. Put the outcome there instead.

They don’t want the diagnostic. They want what the diagnostic finds. “Offer Engine” isn’t a thing you do, it’s a thing that runs. In the buyer’s mind, that’s a big difference.

“The Consultant’s Offer Engine.” The name came from us but only because the conversation stripped away everything that wasn’t right until the real word surfaced.

In a meeting room, that’s five weeks and a whiteboard covered in sticky notes nobody looks at again.

Nate Jones said there’s no mature state to wait for.

“There’s only a continuously steepening curve, and it’s going to reward folks who can climb early and go fast.”

Whittemore said the capabilities are available now for anyone high-agency enough to work through the rough edges.

The Offer Engine build is what happens when you stop waiting. Zero expertise in the industry in under an hour….

A freakin’ complete product with a business model.

You can sit in the deck chair. Or you can get in the water.

🔒 THE FULL RECONSTRUCTION

I showed you five of the twenty-five moves. The full reconstruction has the rest, and it’s not a document. It’s an interactive site.

Two parts. Two chat windows. Nineteen minutes to build a complete consulting product. Thirty-nine more to turn it into a $297 business.

Part I is the build. Zero domain expertise, nine prompts, a five-layer diagnostic framework, a 20-question intake questionnaire, and a reusable skill that works against any industry. Part II is the business: how the same session produced a two-tier product ecosystem, 10 Phase Accelerators, a community upsell path, and a complete product blueprint.

Every prompt is there, in order, with timestamps. But the prompts aren’t the point. Each one maps to a named pattern with a universal template you can copy directly from the page. My prompts were about managed futures. The templates work on anything.

The site also includes seven meta-principles extracted from the full 73 minutes. The things you only see when you look at all 25 moves together, not one at a time.

Not principles. A reconstruction. With every move named, every template copyable, and every decision explained.

🔒 THE PROBLEM RADAR

The Consultant’s Offer Engine starts with a problem. Before you can build the engine, you need to know what it runs on.

The Problem Radar is a skill that finds the problem worth building around before you build anything. It searches real communities, runs your idea through three filters, and hands you back the exact language the market uses to describe its own pain.

It also tells you when a problem doesn’t pass. Better to know before you build around the wrong one.

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But last Saturday morning, I walked into a hackathon at 9am. A co-working space full of developers already on their laptops setting up GitHub repos, a term I had never heard of a few months ago.

I build growth strategies for businesses and help experts turn what they know into what they sell. I never thought I would be spending my weekend at a coding event, much less participating in one.

My family thought this was quiteeee funny. My brother called it “crossing the threshold into full nerd mode.” My girlfriend gave me the kind of supportive nod you give someone who just signed up for their first 5K and clearly has not trained.

But I showed up anyway…

…and was terrified.

Everyone else looked like they knew what they were doing. They had stickers on their laptops. Specific ones. The kind you earn, apparently.

I sat down with my partner , opened my laptop, opened Claude Code, and just started building.

Hackathons in 2026 are interesting. The coding is the “easy” part. The AI handles a lot of it. What the room was short on was the other stuff...

• What to build

• Why it matters

• Who to build it for

• How to explain it in 60 seconds

By the end of the day, people were coming to our table to ask about the idea, not the code. What we thought they could improve on theirs. How they could bring it to market.

A judge who had built and sold multiple companies walked over, exchanged info, and told us he wanted to get coffee. He said it twice.

Best day I’ve had in months.

I posted from the hackathon while I was still there. I just wanted to tell someone.

That’s when I started noticing I wasn’t the only one walking around with this stupid grin these days.

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The Stupid Grin Is Spreading

runs AJ&Smart, one of the most well-known design agencies in Europe.

He used Claude Code for the first time a few weeks ago...

“It’s been the biggest change to my work ever. Like ever. There’s no comparison in my lifetime. That’s the same feeling you get when you start a business in the beginning. It’s like, oh my god, I can make anything.”

He built a $20,000 marketing funnel in three hours. By himself. For €10.

Andrew Wilkinson, investor and entrepreneur, wrote this publicly...

“For a decade, I was depressed about work. The best part of business is manifesting an idea, seeing a problem, then fixing it. The worst part is trying to herd cats. Claude Code has brought back my fire. If I wake up in the middle of the night, there’s a 50% chance I say ‘Fuck it’ and go downstairs to mainline Claude Code at 4am.”

Lazar Yavanovich is the first official Vibe Coding Engineer at Lovable.

Never written a single line of code. His take... non-technical people actually have an advantage because they don’t know what “shouldn’t” work. He calls it positive delusion. Assume everything is possible until proven impossible.

He built Chrome extensions that engineers said couldn’t be built on the platform. Built desktop apps. Someone on his team prompted their way into generating video inside the tool before it was even a feature. All because they didn’t know it was supposed to be impossible.

“If there wasn’t Lovable, I would have never built anything potentially in my life,” Lazar said. “And I don’t think that would have been a fun life to live.”

, AI strategist, put it simply...

“The friction has now dropped enough that building software has stopped feeling like work and started to feel like play. And play produces very different things.”

Someone built an app that turns pet photos into Renaissance portraits. Your dog as a Baroque duke started as a joke but is doing six figures a month because someone was laughing while they built it.

Then there’s my friend. I’ll keep this vague to protect his privacy. A while back, he built a side project for our group. Just for fun, something useful he wanted to exist. That project caught the eye of a startup. They used it as the foundation for their product. He got equity.

That company just sold for more money than I will ever see in my lifetime.

Is that common? Of course not. But it started as play. It started as “wouldn’t it be cool if this existed.” There was no business plan or pitch deck. Just a guy building something for his friends on a weekend.

Something is happening that nobody’s writing about enough.

Adults are remembering what it feels like to make things.

I spent 18 years in marketing and consulting before I ever touched a builder tool. I had ideas for things I wanted to make. Lots of them. But they lived in a folder and they never got built because the gap between “I wish this existed” and “I made this exist” required skills I didn’t have, or money I didn’t want to spend, or a developer who needed three months and a detailed spec.

That gap closed….Fast. And when it closed, something I didn’t expect happened.

I started having fun. Not productive-fun. Not “this is a good use of my time” fun. The kind where you look up and three hours have passed. The kind where you text your friends at 1 am on a Tuesday. The kind where your family roasts you for going to a hackathon and you don’t care because you’re too busy being excited.

Jonathan said the same thing. Andrew said the same thing. Lazar said the same thing. My friend lived it years ago with a side project that became generational wealth.

The people having the most fun are learning the fastest, building the most interesting things, and stumbling into the best opportunities.

They followed the fun.

Following the Fun

Eight months ago I started playing with AI tools. Building things. Following curiosity wherever it went. I had zero plans to start a company. I just wanted to build a cool thing.

That play turned into a methodology. The methodology turned into a newsletter. The newsletter turned into a business. The business turned into the career I’d been looking for my entire adult life. I help people extract the expertise they can’t see in themselves. I use AI to surface patterns that humans can’t articulate. I help businesses in different industries do the same.

I love every day of it.

None of that was planned. All of it started because I built something for fun and couldn’t stop.

You probably have something like this. An idea. A tool you wish existed. Something your family would roast you for. Something that sounds fun but feels frivolous.

And the good news for you…

The cost of trying just collapsed.

The Playbook

Lazar laid out his path from hobbyist to the first professional vibe coder at Lovable. Simple and worth sharing.

1. Build in Public. Share everything. Every project, every failure. There are no secrets worth keeping.

2. Pick Your Platform. Choose the one that fits how you naturally communicate. Lazar picked LinkedIn because he talks in long-form. X requires a brevity that doesn’t match his cadence. Find yours.

3. Don’t Wait to Be Hired. Lazar was already doing the job before Lovable hired him. The role was recognition of reality, not a career change. “You don’t need a company to hire you. You can hire yourself as a professional vibe coder first.”

4. Send Apps, Not Resumes. Multiple hires at Lovable stood out by building an app that demonstrated their fit instead of sending a traditional application.

5. Go to a Hackathon. Show up knowing nothing. Sit down and start building. See what happens (sounds like a smart guy 😉).

“If you’re scared, just try it,” Lazar said. “It switches from fear to excitement immediately because then you see what’s possible firsthand. You should only be afraid if you’re doing nothing.”

I walked into a hackathon last Saturday knowing nothing. I walked out with new connections, a product idea with real potential, and the kind of energy I haven’t felt since I was a kid making things just to see if I could.

My family was right. Full nerd mode.

I’m keeping it.

Lazar’s Vibe Coding Toolkit

Lazar did a long-form interview where he broke down how he actually builds. The system. The specific rules, documents, and frameworks he uses to go from idea to working product without writing code.

I turned that interview into a complete starter kit. Everything below works without a terminal, without GitHub, and without writing code. If you can paste text into a chat window, you can use all of it.

• The Idea Sharpener is a prompt you paste into any AI tool (Claude, ChatGPT, whatever you use). It walks you through five rounds of questions to turn a rough idea into a clear one-page build brief. You don’t need to know what you’re building yet. You just need to start talking.

• The Framework Reference organizes Lazar’s entire system into a single document... his Five Beliefs, the Parallel Build Method, the PRD documentation system, the Three Wishes Rule for token management, the 4x4 Debugging Framework, and which skills he says actually matter going forward. All directly extracted from the transcript with no embellishment.

• Five Planning Templates (master-plan, implementation-plan, design-guidelines, user-journeys, and tasks) are the exact document structure Lazar uses before he builds anything. Fill these in, or paste them into an AI tool with your Build Brief and let it fill them in for you. Your prompts become two words... “proceed with the next task.” The documentation IS the prompt.

• The Lovable Custom Knowledge File is paste-ready agent rules you drop into Lovable’s Settings panel. It tells the AI how to behave on your project... read docs first, work one task at a time, don’t waste credits on unnecessary changes, and learn from mistakes. Two minutes to set up. Saves you dozens of credits per project.

• The Step-by-Step Guide walks you through the whole process from start to finish... sharpening your idea, filling in your planning docs, setting up Lovable, writing your first prompt, and what to do when things break. No terminal. No GitHub. No code.

The kit also includes a bonus section for readers who want to go deeper with Cursor or Claude Code... same templates, different setup instructions.

Lovable does an incredible job loading up the users with resources but like anything, you got to know about them to use them. I placed my curated resource of Lovable resources and inspiration / resource sites that I am always using below.

🎨 I am also tossing in my “Ultimate Vibe Coding Design Guide” that you can feed any AI as a knowledge file and will help you level up all of your UI.

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Sheryl Sandberg has no idea she does what you’re about to read. Neither did Sam Altman. Neither did Russell Brunson or Rick Rubin.

Turns out, we all have blindspots. No matter how famous you are.

Previous: [Sam Altman] (#1) | [Russell Brunson] (#2) | [Rick Rubin] (#3)

Editor’s note: I’ve been procrastinating on this article for months. But I’m currently skiing in Telluride (and obviously doing a poor job of staying off the computer), and this book was sitting in my Airbnb…It was the sign I needed.

Sheryl Sandberg tells a room full of ambitious, highly accomplished people to stop taking credit for their success.

They furiously take notes.

She tells people going through some of the hardest moments of their lives to “lean into the suck.”

They go out and buy her book.

She tells women to be more aggressive in male-dominated rooms.

Standing ovations.

Sandberg ran Facebook’s business for fourteen years. Before that, Google. Before that, the U.S. Treasury. She wrote Lean In, which sold 4.2 million copies and launched a movement. But credentials alone don’t make advice land. You know people with impressive résumés who get ignored every day. You may have been one of them.

The same advice that triggers eye-rolls from anyone else gets nothing but buy-in from her.

So what in the heck is she doing?

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In 1966, psychologist Jack Brehm documented something he called psychological reactance. When people feel their freedom threatened, even by well-meaning advice, they push back. Not stubbornness. A deep human instinct.

You’ve been there. Standing in front of someone, offering wisdom you earned the hard way, going through precisely what they’re describing. And their brain is screaming:

Who are you to tell me what to do?

They don’t say it out loud. They just nod, smile, and ignore everything you said.

I used to think this was a me problem. Maybe I wasn’t explaining it right. Maybe I needed better examples. Maybe they just weren’t ready.

Then I analyzed years of Sandberg’s interviews and transcripts. What I found made me want to delete half of everything I’d ever written about influence.

She has three moves. Once you see them, you can’t unsee them.

The Pattern Hiding in Plain Sight

I was halfway through a Harvard Business School interview when I saw it. She was explaining why she joined Google, and she attributed the entire decision to Eric Schmidt. Not “I realized” or “I figured out.” Eric told her.

So I started counting how many times she credits someone else for an insight.

Watch any of Sandberg’s interviews. The advice doesn’t come from her.

→ A rabbi told her to lean into the suck
→ Eric Schmidt told her not to be dumb about job criteria
→ Her 10-year-old son said “Mom, they all know what happened to us”
→ Bill Campbell asked if she was done talking about what she used to do

In every one, she’s the channel, not the source.

That’s why it lands. You’re not the student. You’re watching her be the student.

She does it the same way every time.

After compiling my notes I realized Sandberg has three moves. Once you see them, you can’t unsee them. And once you understand why they work, you’ll realize you’ve been triggering resistance your entire career.

Move 1: Disappear

When Sandberg was looking for a job after business school, she had a chart of criteria. Google met none of them. She showed Eric Schmidt the chart and explained the problem.

“He put his hand on my paper and said, ‘Don’t be dumb. If you’re offered a seat on a rocket ship, don’t ask what seat. Just get on.’”

That story has been told thousands of times. It launched a phrase that every recruiter in Silicon Valley now uses. And Sandberg never once claims it as her insight. She attributes it to Eric Schmidt. A specific moment with a specific gesture:

Hand on paper.

She could say “I learned to prioritize growth over job criteria.” It’s the same lesson but you’d forget it by tomorrow. The Eric Schmidt version sticks because you’re not receiving instruction. You’re witnessing a transformation.

When her husband Dave died, she didn’t know how to move forward. Phil Deutch put his arm around her and said:

“Option A is not available. So let’s just kick the shit out of option B.”

That became the title of her book.

She positions herself as a student who got lucky with teachers.

Donald Miller built a $100 million business around this same insight. His StoryBrand framework has one rule:

Your customer is the hero, not your brand. You are the guide. The guide has wisdom and empathy, but the guide is never the main character.

Luke Skywalker is the hero. Yoda is the guide. Yoda doesn’t say “I defeated the Empire.” He says “Do or do not.”

The best documentary filmmakers discovered this decades ago. The observational style, what critics call “fly on the wall,” works because the filmmaker disappears. You forget someone is holding the camera. The subject becomes the story.

Great mentors do it instinctively. Research on effective mentoring found that the best ones “pass on their knowledge through informal conversation and everyday modeling.” They’re listeners who develop your unique voice, not lecturers who impose their own.

They all landed on the same move. The person with authority steps back. The person receiving wisdom becomes the protagonist.

Here’s why your advice doesn’t land.

You’re the hero of your own wisdom. “I developed this framework.” “I figured out the pattern.”

→ You’re asking people to accept that you’re smarter than them
↳ Their brain won’t let them
↳ Reactance kicks in
↳ Arms cross. Eyes glaze

Sandberg asks them to receive what she received. No hierarchy. No threat to autonomy. Just one student passing notes to another.

Move 2: Confess First

Most leaders establish credibility first, then share a failure. Sandberg inverts this.

When discussing her career on Oprah’s Super Soul Sunday, she didn’t open with Google or Facebook. She opened with the void.

“For me, it was a void. It was closing in on me so I couldn’t breathe.”

When talking about joining the tech industry, she starts with not belonging.

“I never wanted to work in business. My dad’s a doctor. My mother was a community organizer. Business was kind of bad in my family.”

When returning to work after Dave’s death, she starts with disaster.

“I came to work the first day. I was there for 4 hours. As far as I could tell, it was total disaster. I fell asleep in a meeting.”

By the time the authority arrives, your guard is down. You’re not being lectured by someone who has it figured out. You’re hearing from someone who struggled with the same things you struggle with.

Therapists figured this out decades ago. Push someone and they push back. Give them room to decide for themselves and they stop fighting you.

Sandberg’s vulnerability looks and feels like weakness. It’s actually strategy. She earns the right to teach you by admitting she needed to be taught.

Here’s why your advice doesn’t land.

You lead with credentials. You establish authority before showing humanity. By the time you share a failure, people have already decided whether to trust you. And guess what that answer usually is?

Move 3: Hand Over the Script

Most advice is abstract. “Be supportive.” “Show empathy.” “Communicate better.”

Words that mean nothing until you’re standing in front of someone who’s crying and you have no idea what to say.

Sandberg gives you the actual sentence. She learned what to say by experiencing what not to say. Every bad platitude she received in grief became a lesson in what actually helps.

• Don’t say “How are you?” Say “How are you today?” The word “today” acknowledges that grief exists on a spectrum. Yesterday was different. Tomorrow might be worse.

• Don’t say “Is there anything I can do?” That question is a burden disguised as an offer. The person in grief can barely remember their own name. They cannot also manage your helpfulness. Just do something. “I’m in the lobby. I brought hamburgers.”

• Don’t say “I.” Say “We.” When Phil had his arm around her and said “let’s kick the shit out of option B,” the “we” mattered as much as the advice. He wasn’t telling her what to do. He was joining her.

I’ve watched that interview three times. The arm around her. The “we.” Every time, I lean forward in my chair. Something about it lands before I can analyze why.

But notice what she did. She gave you the sentence, not the concept.

Scripts remove the last obstacle between knowing and doing. You don’t have to translate the principle into action. The action is right there.

This is uncomfortably practical. Most advice stays safely abstract so you can nod along without changing anything. Sandberg hands you the exact sentence. Now you have no excuse.

There’s research behind this. Psychologists call it Implementation Intentions. People who know exactly what they’ll say are 2-3x more likely to actually do it. Doctors learn exact phrases for delivering bad news. FBI negotiators memorize scripts for de-escalation. Abstract advice requires translation. Scripts are ready to use.

Here’s why your advice doesn’t land.

You tell people what to think. She tells them what to say. You give principles. She gives scripts. Principles require translation. Scripts are ready to use.

What Makes It Stick

There’s one more thing Sandberg does, and it’s the piece that makes everything else stick.

She doesn’t just tell you what to do. She dissolves the guilt you’d feel about doing it.

When she was struggling to feel joy after Dave’s death, she felt guilty dancing at a friend’s bar mitzvah. Her brother-in-law Rob called her. He was crying.

“All Dave ever wanted was for you and your children to be happy. Don’t take that away from him in death.”

Read that again.

Permission to want what she already wanted but couldn’t admit.

Her son did the same thing. “Be yourself.” Three words. Permission to stop performing. He was ten. It took a child to say what the adults had been circling around for months.

Powerful.

“Lean In” itself is permission. Permission to want power. Permission to want success. Permission to stop apologizing for ambition.

Research on psychological reactance backs this up. Adding phrases like “but of course, it’s up to you” dramatically reduces resistance. When you acknowledge someone’s autonomy, their defenses drop.

Most persuasion tries to make you want something new. Sandberg’s persuasion makes you stop apologizing for wanting what you already want. You walk away thinking you made the decision yourself.

That’s why it doesn’t feel like influence. Which is exactly when influence is most powerful.

Now Look in the Mirror

Here’s what connects all of this.

Psychological Reactance is triggered when someone threatens your sense of freedom. The moment you position yourself as the expert with answers, you become a threat. The brain’s response is automatic: resist, dismiss, ignore.

Sandberg never triggers that response because she never positions herself as above you. She’s a fellow student. She struggled. She received wisdom from others. She’s just passing it on.

The channel, not the source.

I had AI analyze those years of interviews looking for a technique. What I found was simpler. She’s just honest about where her wisdom comes from. The rest of us hoard credit like it’s the last croissant.

The patterns aren’t complicated:

• Be the channel, not the source.

• Open with failure, not credentials.

• Give scripts, not principles.

• Grant permission, not instruction.

Here’s what I noticed while writing this: I kept typing “I discovered these patterns.” Then deleting it. Then typing it again.

I didn’t discover anything. Sandberg showed me, the same way she shows everyone. She left it in plain sight for years. I just finally paid attention.

Someone taught you what you know. Probably several someones.

Make sure you remember to mention them.

-Max

Now What?

You just read 2,000 words about influence patterns you’ve been violating your entire career.

You nodded along. You thought “I do that.” Maybe you winced at a few parts.

Here’s what happens next for most people: Nothing.

You’ll send the same pitch tomorrow. You’ll open with credentials. You’ll say “I developed” and “my framework” and “let me explain why.” You’ll watch the prospect’s arms cross. You’ll wonder why Thompson went with someone else.

The 28-year-old with the ChatGPT website will close another deal you should’ve won. Not because he’s better. Because his pitch doesn’t contain the seven words that make people stop listening.

You won’t even know which seven words.

The Zero Resistance Toolkit

That’s the problem with patterns you can’t see. Knowing they exist doesn’t mean you can find them in your own writing.

You can diagnose a client’s business in 30 minutes flat. You can spot their positioning problems, their pricing mistakes, their buried value. You’ve done it a hundred times.

Your own pitch? Blind spot.

Your own bio? Blind spot.

The email you sent yesterday that’s sitting in their inbox, day 4, no response? You have no idea which sentence killed it.

I turned Sandberg’s four moves into prompts that find your blind spots for you.

1. Paste in a pitch. The prompt shows you exactly which phrases trigger resistance, and rewrites them.

2. Paste in an email that got no response. The prompt scores it across four categories of influence failure and flags the three worst sentences.

3. Paste in your bio. The prompt extracts “who taught me” stories you can use instead of listing credentials nobody reads.

4. Paste in the objection you keep hearing. The prompt shows you why your current response makes it worse, and gives you three scripts that dissolve it.

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The playing field would flatten. The little guy would finally have access to the same intellectual horsepower as the big guy. We’d all be geniuses now.

But the gap is widening and three groups are emerging:

1. Domain Experts Using AI

Their output has a different frequency to it. There’s something in the work that you can’t quite name, but you recognize it immediately. They’re pulling ahead of everyone.

2. Domain Experts Not Using AI

They’re doing excellent work, but they can’t match the velocity of group one. It’s like watching someone sprint while you’re power-walking. You’ll get there eventually (and look ridiculous for power-walking). They’ll be three towns over.

3. AI Users Without Domain Expertise

They're getting exposed. They're posting without the nuance that comes from doing the work. When their outputs get pressure tested, they collapse. The first time a client pushes back, it's obvious.

The tool that was supposed to help them is now the thing that gives them away.

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Default Has a Smell

Here’s what happened...

AI gives you the average of the internet.

Technically correct, but it’s typically a “best practice” or the default answer. The kind of thing that sounds right in a meeting but doesn’t actually move anything forward.

And default answers have a smell.

It’s the advice you’d find in a LinkedIn carousel from 2019, complete with the gradient background and the guy pointing at bullet points. It’s not wrong…but indistinguishable from the 47 other posts saying the exact same thing.

Let’s look at some numbers.

The flood is real. Ahrefs analyzed 900,000 new web pages in April 2025 and found that 74% contained AI-generated content. Three out of four new pages. A separate Graphite study found that AI-generated articles now outnumber human-written ones on the open web.

We’re drowning in default.

The novice boost is real. A Stanford and NBER study tracked what happens when workers get access to AI tools. Novices saw a 34% productivity boost. Two months of AI access made them perform like someone with six months of experience.

For some work, that’s a win. If you’re producing templates or first drafts or anything where volume matters more than judgment, the boost is real.

But in knowledge work? Consulting, strategy, anything where clients are paying for your thinking? That 34% boost is a trap. You can produce the output now. You just can’t defend it.

Your nephew who graduated last spring can now produce work that looks, on the surface, like it came from someone with years of experience.

The expert paradox is real.

The same study found the most experienced and highest-skilled workers saw minimal gains. In some cases, quality actually declined when they used AI.

At first glance, that seems backwards.

The issue wasn’t the tool. It was how they used it. Experts who let AI do the thinking lost what made them experts. The ones who used it as a force multiplier - who poured their judgment through it instead of deferring to it - those are the ones pulling ahead.

But what is that “something else” they’re pouring through? What do experts have that doesn’t live in the training data?

The Better You Get, The Less You Can Explain

MIT researchers Stuart and Hubert Dreyfus spent a decade studying how people move from novice to expert.

They mapped five stages:

1. Novice: Follows rigid rules (and panics when the rules don’t apply)

2. Advanced Beginner: Recognizes patterns (and starts to improvise, badly)

3. Competent: Makes plans, sets priorities (and takes responsibility for outcomes)

4. Proficient: Sees situations holistically (and starts trusting their gut)

5. Expert: Operates from intuition (and can’t explain why)

The critical finding buried in their research was the journey to expertise is a journey away from explainability.

The better you get, the less you can articulate what you’re doing.

Their words: “When things proceed normally, experts don’t solve problems. They do what normally works.”

Novices follow the rules. Experts know when to break them.

This is where domain expertise actually lives, in the micro-decisions.

The “except when...” and “yes but in this case...”

The conditional knowledge that signals this person has been in the trenches, not someone who typed a question into a box and copy-pasted the first thing that came back.

AI can give you the rule.

What it cannot give you are the 50,000 to 100,000 domain-specific chunks that experts have accumulated over years. Patterns compressed into instant recognition that fire before conscious thought even kicks in.

Chess masters see the board before they calculate.

Just watch how Magnus Carlsen competes against 10 other players, simultaneously…blind.

“…but Magnus cannot see the boards he’s facing the other way. So he has to keep track of the positions of 320 pieces blind…I just can’t fathom what you’ve just done. It seems like it’s supernatural.”

Surgeons feel when something’s wrong before the monitors catch it.

Firefighters know which way the fire will move before it moves.

Gary Klein studied these people for decades:

• ER doctors

• Firefighters

• Chess grandmasters

• Military commanders

What he found is that experts recognize patterns instead of comparing options. Their first instinct is usually right because they’ve seen this exact situation before, or something close enough, thousands of times.

That recognition is pre-conscious.

It fires before they can explain it.

And AI cannot replicate it because AI is trained on what people wrote down: the explicit knowledge, the stuff that made it onto the page. The 10%.

The other 90% never got documented.

It lives in the nervous system of people who’ve been doing the work. You can’t download it. You can’t prompt your way to it.

You have to earn it the old-fashioned way…

By being confused for years until you’re not.

The 545% Difference Nobody Could See

A company wanted to figure out what made their top sales performers different.

These people were closing deals at 2-3x the rate of everyone else. Same product, same market, same scripts. Wildly different results.

They studied 1,000 of them.

First, they asked the top performers to explain their secret.

The performers couldn’t.

They said things like “I just read the room” and “I go with my gut” and “I don’t know, it just works.”

Then they asked managers and observers to identify what made these people different.

45% of organizations had “no defined or visible insight” into what separated top performers from average ones.

Almost half of the companies studying their own best people had no idea what made them the best.

The expertise was invisible to everyone, including the experts themselves.

Then Cresta ran AI analysis on thousands of conversation transcripts.

Not asking people what they did. Watching what they actually did.

What emerged: The “Magic 4” Behaviors.

Top performers asked 48% more discovery questions and were 545% better at four specific micro-behaviors:

1. Setting expectations

2. Asking discovery questions

3. Handling objections

4. Closing

Not 5% better. Not 50% better. Five hundred and forty-five percent better.

These patterns were operating below conscious awareness.

The performers didn’t know they were doing them.
The managers couldn’t see them.
The trainers couldn’t teach them.

The behaviors were so automatic, so woven into the fabric of how these people worked, that they were completely invisible until AI surfaced them.

This is Polanyi’s Paradox, named after the Nobel laureate who discovered it in 1966:

“We know more than we can tell.”

90% of expertise operates unconsciously.

The more skilled you become, the less you can explain what you’re doing because your knowledge compiles into automatic processes that fire without conscious thought.

Ask an expert how they do what they do, and you’ll get approximately 3,000 versions of “I don’t know, I just see it.”

Years of pattern recognition, compressed into a shrug.

But here’s the thing…AI can detect these patterns in transcripts and behavior, but it cannot transfer them to someone who hasn’t built them through years of repetition.

The Cresta study revealed what the top performers were doing, but it did not suddenly make average performers able to do it.

That’s like showing someone a video of Michael Jordan dunking and expecting them to walk onto the court and do the same thing.

Knowing the pattern exists and having the pattern encoded in your nervous system are two very different things.

What This Actually Means

What happens when everyone has access to the same default answers?

The people without domain expertise produce default content.

And now there’s more default content than ever: 74% of new pages, the average of the internet duplicated at scale, a tsunami of “10 Tips for Better Productivity” and “How to Leverage Synergies” washing over everything.

The experts stand out more now, not less.

A 2025 German study tracking AI’s impact on workers found that “expert workers with deep domain knowledge gain while non-experts often lose.”

The rich get richer.

The people who already knew things now have a force multiplier. The people who were faking it have a magnifying glass pointed at them.

The BCG and Harvard study with 758 consultants found a “jagged technological frontier” where even experts couldn’t always tell which tasks AI could handle and which required human judgment.

The highly skilled workers who thrived were the ones who continued to validate AI outputs and exert what the researchers called “cognitive effort and expert judgment.”

They used AI as a tool without outsourcing their thinking to it.

(There’s a difference between having a sous chef and having someone else eat your dinner.)

So much for democratization.

AI sharpened the contrast between people who actually know things and people who just got better at sounding like they do.

Turns out that’s what happens when everyone gets access to the same default answers.

The Three Paths Forward

If you’re in Group One (experts using AI):

You’re pulling ahead, but the question is whether you can see your own invisible expertise well enough to extract it, package it, and stop being the bottleneck.

Your “Magic 4” behaviors are in there somewhere. You just can’t see them yet because you’re too busy using them.

If you’re in Group Two (experts not using AI):

You can’t keep pace with Group One by staying analog, but your expertise is the asset and AI is just the accelerant.

The years you spent building pattern recognition don’t disappear when you open ChatGPT. They become more valuable as the default content floods in.

Your job is to pour your expertise through the tool.

If you’re in Group Three (AI users without domain expertise):

The tool that was supposed to help you is now the thing that gives you away.

There’s no shortcut to the “except when” knowledge.

There’s only the work, the reps, the years of seeing patterns until they become automatic. AI can speed up some of that learning, but it cannot replace it.

The truth is that you still have to earn the thing that makes the output valuable.

The democratization narrative got the direction wrong.

AI revealed who was standing on solid ground and who was standing on a stack of templates.

The Same Problem Shows Up in Your Offer

The three signals don’t just show up in your content…They also show up in your offer.

• Your proposal

• Your sales page

• Your LinkedIn profile

The thing you send when someone asks “what do you do?”

If that sounds like everyone else in your category, you’re competing on price.

Dan Kennedy built a $100M+ direct response empire asking one question:

“Why should I do business with you versus any and every other option available to me, including doing nothing?”

Most offers fail that test. The expertise is there. It’s just invisible to the person writing the offer.

It surfaces with the right questions.

The Kennedy Interrogation

“Why should I, your perfect prospect, choose to do business with you versus any and every other option available to me, including doing it myself, going with a competitor, or doing nothing at all?”

Kennedy calls himself “The Professor of Harsh Reality.” He doesn’t accept weak answers. Neither should you.

This is a two-part process. First, the interrogation. Then, the rewrite

What You Actually Get

Level 1: The Answers Eight responses about what makes you different. That’s the obvious output.

Level 2: The Patterns Somewhere around question 4 or 5, you’ll notice something. The same theme keeps showing up. The thing you’ve been hiding. The “disadvantage” you apologize for. The opinion you won’t put on your website.

That’s your fingerprint starting to emerge.

Level 3: The Reframe Kennedy’s move is taking what you’ve been apologizing for and turning it into proof you know what you’re doing. “I’m expensive” becomes “You’re paying for mistakes you won’t make.” “I’m slow” becomes “Fast is how you end up redoing it.”

The weakness was the differentiator all along.

Level 4: The Offer That Can’t Be Copied By the end, you’ll have language that only works because of who you specifically are. A competitor can’t steal it because it wouldn’t be true for them.

That’s what makes it defensible.

Part 1: Face the Eight Questions

Paste this prompt into Claude. Answer each question honestly. It will push back when you hedge.

You can stop anytime. Say “done” and it’ll summarize what it learned. But the more questions you answer, the sharper your fingerprint becomes.,,,

Read more

She’d been a client months ago. We did the extraction work. Pulled out her invisible patterns and documented the methodology she couldn’t see in herself.

Then life happened. She got scammed out of a significant amount of money. Her dog of many years got sick and big life changes started stacking up. She’s planning a move to a tiny island where crime is so rare that most people don’t even bother locking their doors.

When she came back, she wasn’t sure the work we’d done would matter anymore.

“I need to take some time to get clarity,” she’d written me a few weeks earlier. “There is definitely a change in direction. I want to follow my flow more. I feel like I am bored with business content.”

Louise thought she was starting over. New industry. New audience. New everything. Done with business content entirely.

She has no intention of retiring.

“Why would I?” she says.

Subscribe now

Someone Discovered Fire

Then she starts talking about TAO.

Subnets. Emissions. Validators. Bitcast mining.

I don’t know what any of this means.

She keeps going.

• Weather forecasting

• Computer vision companies

• 128 businesses on a blockchain

• A coding subnet that’s profitable when Anthropic isn’t

• A weather prediction subnet beating Google’s forecasts

• Code generation “outperforming Claude Code without the billions Anthropic spent”

I’m asking Claude “what is a subnet” in a side window while nodding like I understand.

But here’s the thing…

I’ve never seen her like this.

She’s talking faster. Jumping between ideas. Pulling up dashboards to show me miner rankings. Explaining tokenomics like someone who just discovered fire and needs you to see it.

“It’s the community,” she says. “It’s unreal. Some of the intelligence level of these people. I feel like I’m at home.”

She went from about 100 followers on X in September to over 1,100 by December. She’s ranked 88 on some miner leaderboard I don’t understand. She’s in private invite groups with subnet founders. James Altucher(!) agreed to an interview.

She’s made $375 from two tweets through something called “bitcast mining.” She earned $1283 in two months and “just getting started!”

All of this while processing grief. While planning an international move. While rebuilding from scratch.

“I’ve had a tough two years,” she says. “But this is the most excited I’ve felt about stuff in a long time.”

Familiar Words From an Unfamiliar World

Here’s where it gets fun.

I keep hearing familiar words coming out of her mouth.

“I want to showcase these people and make them look good. That lifts me up.”

“Building patterns across interviews. Cross-sectioning insights. Finding the human side.”

Wait.

That’s the methodology. Pattern extraction. Interview analysis. Making invisible expertise visible. The exact work we did together months ago.

She said she was done with business content. Bored with it. Moving on.

But the methodology underneath? It came with her anyway. She didn’t even notice.

I build her a Claude project while she talks.

• Custom instructions

• Content multiplication maps

• Prompts for pattern extraction

• File structures for organizing insights across her interviews.

I don’t understand a single thing about the domain I’m building for.

The Confusion Was the Proof

Here’s what hit me after we hung up.

I didn’t need to understand Bittensor. Didn’t need to know what validators do or how emissions work.

The methodology doesn’t care about the domain.

Louise isn’t doing something different. She’s doing exactly what she did before. Interview interesting people. Extract their patterns. Document what they can’t see.

Build influence by making others look good.

The container changed. The core didn’t.

My confusion was the proof. If I could build her a working system while understanding nothing, the methodology travels.

It goes wherever you go.

Money Was Chasing Her

Look what actually happened.

She’s not competing with the technical people. She’s the only one doing human-interest pattern extraction in a sea of whitepapers and “incentive mechanism” debates.

“A lot of these subnets are on X talking about technicalities,” she told me. “Bores the pants off people. I show a different perspective.”

She wasn’t chasing money. Money was chasing her.

She wasn’t chasing clients. “I’m not going to rule it out,” she says about coaching. “But I’m not chasing it.”

That’s the part people get backwards. They try to force their methodology into new markets. They build expansion roadmaps. They hire consultants to identify adjacent verticals.

Louise just followed her curiosity and the methodology followed her.

We Get to Play

Near the end of the call, she says something that still sticks with me.

“Isn’t it great? We get to play.”

She just went through two of the hardest years of her life. And she sounds like a kid who just discovered a new game.

“What an age we live in,” she says.

I hung up and realized I was smiling. Like, actually smiling. Her energy was contagious. I didn’t understand half of what she told me, but I understood exactly what I was watching…

Someone coming back to life.

After we hung up, she sent me a rabbit hole.

“Start here: theincentivelayer.com. Then watch this. Then this 10-minute explainer. Then this 60-second one.”

Five links in four minutes. Each one deeper than the last.

I still don’t understand half of it. That’s the point.

Then she texted:

“I haven’t been so fired up as I am after our call in a looooonnnnngggg time…”

Some calls you walk away from feeling lucky you got invited along for the ride. This was one of those (man, I love what I do).

Your Methodology Travels Too

What methodology are you running that you assume only works in your field?

The questions you always ask. The patterns you notice. The thing you do so naturally you’ve stopped calling it a skill.

You probably think it only works in your context.

Louise thought she was leaving everything behind when she got bored with business content. Turns out the methodology underneath was never tied to the content at all. It works on subnet founders the same way it worked on coaching clients.

A sommelier’s palate training works for coffee, olive oil, whiskey. The skill is detecting subtle differences. The domain is just where you point it.

Your methodology is probably the same. You’ve been treating it like it belongs to one industry. But the skill is yours. It goes where you go.

What happens if you point it somewhere unexpected?

You might discover what Louise discovered.

The methodology you’ve been treating as industry-specific has been portable the whole time. You just hadn’t followed your curiosity somewhere unexpected yet.

Go get that TAO, Louise. I don’t know exactly what it is.

But I know I’m lucky I got to watch you find it.

-Max

P.S. Want help uncovering yours?

I do this with people one-on-one. You send me transcripts of your client or coaching calls. Your workshops. Your conversations where you’re in your zone of genius.

Then AI and I go to work. I’ve spent the past year building a system and refining it with 50+ experts to surface the patterns you can’t see because you’re inside them.

You get back a document. And somewhere on page 3, you’ll read a sentence describing what you do and think:

“Wait. I DO do that every time. I just never had words for it.”

That’s the moment.

I guarantee this is unlike any coaching or consulting experience you’ve had. It’s a completely customized experience, where I extract your expertise and then build you a system to teach, scale and monetize it.

You already have everything you need inside of you. You just need someone to pull it out of you.

Book a Call

Or if you want to explore on your own first, here’s a prompt that will get you started…

The Methodology Transplant Finder

Copy this into Claude. It will extract your methodology and show you where it could travel.

What This Prompt Does

Extracts the invisible methodology you’re running. The approach that stays consistent even when the context changes completely. Then shows you where else it could land.

Fifteen minutes. Five specific outputs:

1. Your through-line — one sentence describing what you actually do across every client, every context, every weird situation you’ve been thrown into

2. Your invisible steps — the 3-5 things you do automatically that you’ve stopped recognizing as methodology because they feel like breathing

3. Your diagnostic pattern — what you’re really sniffing for when you assess a new situation (revealed by the three questions you’d ask any new client)

4. Three surprising domains — places your methodology could create value that would make your current colleagues say “wait, what?”

5. One wildcard — the application that feels ridiculous until you realize it might be the biggest opportunity of all

Read more

Instead of your standard “How-To” content, we thought we would up the stakes. Let people submit a problem via a locked “Vault” and when we flip the camera on, it unlocks and we get to solving it.

The problem submitted was:

I lack a reliable, low-friction system that captures and regularly resurfaces each client’s long-term personal context and last-session commitments before calls, so I show up unprepared and risk renewals and family financial stability.

(Note: This was the output of using the 5 Why’s Prompt to Drill help clarify the real proble and why it sounds like AI)

As I read it out loud, every instinct inside of me is screaming the same thing…

Start. Building. Now.

AI is incredible. With a few taps of the keys and hitting enter a few times, you can send it off to do your bidding and it will return an answer that sounds like a senior level consultant worked on it for weeks.

But just because it sounds good doesn’t mean it is good.

So instead I tell my instincts to chill for a second and instead, say: “To me, it’s still not drilled down enough.”

I remove myself from “answer now” mode and notice something I’d never put into words before.

The Layers Underneath

I ask Claude to unbundle the problem from first principles.

What is this person actually trying to accomplish?

Claude comes back with layers…lots of them:

”The surface-level framing is about a missing system - specifically one that captures and resurfaces client context before calls. But there are several layers underneath worth examining.

The Information Problem

There are actually two distinct information challenges bundled together here:

Long-term personal context - This is the accumulated understanding of who this client is, what matters to them, their history, their patterns. It’s the kind of knowledge that builds over months or years of relationship.

Last-session commitments - This is much more tactical. What did we agree they’d do? What did I promise to follow up on? This is accountability tracking.

These require very different capture mechanisms. One is ongoing synthesis, the other is discrete action items.

The Timing Problem

The statement says “before calls” - which raises the question: how far before? Five minutes? The night before? The morning of?

This matters because it determines whether you need a push system (information comes to you) or a pull system (you go get it when you remember to).

The Friction Problem

“Low-friction” appears twice conceptually - in capture AND retrieval. But these often conflict. The more structured and thorough your capture, the more useful your retrieval... but the higher the capture friction.

The Stakes Framing

Here’s where I want to push a bit. The problem escalates quickly from “unprepared” → “risk renewals” → “family financial stability.” That’s a big leap.

Some questions this raises:

Is “showing up unprepared” actually why renewals are at risk? Or is that an assumption? Could renewals be at risk for other reasons that preparation wouldn’t solve?

Is this problem actually happening right now, or is this a fear about what could happen?”

Level 4

The old me would have started building immediately. Hit enter. Watched Claude spin up something impressive.

Shipped it.

Felt productive.

Discovered three days later it solved nothing.

I’ve been burned by that before (be honest, we all have). Hours on workflow looks impressive but just like those n8n workflows your see people promoting on the socials, don’t actually produce anything meaningful.

Quick aside - you mean to tell me you created a workflow that has saved you 100 hours a week and made you 3M+ in profit and you are just going to give it to a stranger on X or LinkedIn because they retweeted it? Social media…

Takes a deep breath, returns to article…

And that’s when I said it out loud:

“It’s almost like you’re trying to solve from Level 4 when Level 1 hasn’t even been discovered yet.”

The problem people bring you is almost never the actual problem.

It’s a symptom bundled with assumptions, wrapped in a solution-shaped request.

AI makes solving feel free. But solving the wrong problem costs you time rebuilding.

Costs you trust when the solution fails.

Costs you the insight you would have found if you’d drilled first.

The better AI gets, the more expensive this mistake becomes. You can build the wrong thing faster and more impressively than ever.

Doctors. Ducks. Blueprints.

Doctors call it differential diagnosis.

Patient comes in with chest pain. A bad doctor treats the symptom.

A good doctor creates a list of every condition that could cause chest pain.

• Heart attack

• Acid reflux

• Pulmonary embolism

• Anxiety

• Burrito from Tuesday

Then systematically rules them out.

If an important potential condition is missed, no method will supply the correct conclusion.

The best treatment in the world won’t help if you’re treating the wrong thing.

Programmers call it rubber duck debugging.

Developer stares at the same six lines of code for forty-five minutes. Finally, they explain it line-by-line to a rubber duck. The act of articulating forces them to examine each assumption. Most of the time, they discover the solution mid-sentence. Not because the duck gives advice. Because the explanation reveals what they’d been taking for granted.

The duck doesn’t solve. The duck forces you to discover Level 1.

Architects won’t draw until they understand how the family actually lives. Their daily routine…time spent in each room. They refuse to build until they’ve drilled past “I want an open floor plan” to “I want to see my kids while I’m cooking.”

It’s the same pattern in a different domain.

The people who are great at building are the ones who refuse to build first.

Your Last Seven Days

Think about your last week with AI.

How many times did you paste something in and hit enter before asking:

• What am I actually trying to accomplish?

• What would success look like?

• What assumptions might be wrong?

If you’re like most people…

Just about every darn time.

This isn’t because you’re lazy. It’s because AI is designed for speed and keeping you engaged. For creating an affinity that makes you want to come back and spend more time on their platform.

You ask, you get.

The dopamine hits before you’ve finished reading the response.

Except the math doesn’t work.

Every half-solved problem boots you back to the beginning. Every misaligned solution demands a do-over. Every impressive-looking output that doesn’t quite fit wastes more time than typing the original prompt.

The thing that makes AI feel fast is the thing that makes your results slow.

After the Drill

After we drilled down on the client prep problem, then we built.

Claude created the Client Prep Engine. A Skill that transforms whatever client inputs exist into pre-call briefs. Transcripts, notes, emails. All processed into something incredibly useful.

But here’s what made it good…

Because we’d drilled first, the Skill knew to ask about timing. Knew to separate long-term context from session commitments. Knew to handle the friction problem explicitly.

None of that would have been there if we’d built from the surface request.

What emerged from the drill-down was a pattern (you know I loveeee my patterns).

These aren’t complicated. But they force you to discover Level 1 before you start solving from Level 4.

You can grab the Client Prep Engine skill.

Or build your own. Let Claude drill before you build.

The old version of you hits enter. The new version asks one question first:

What’s Level 1?

-Max

If you enjoyed this article and this training, we just started a community where our goal is run more experiments and help people think differently about AI.

You can lock in the entry price for life of $27/month and even give it a spin for 7 days to see if ya like it.

Check out Beware The Default Here.

April 13, 1970. Fifty-six hours after launch.

An oxygen tank explodes on Apollo 13. The crew abandons the Command Module and crams into the Lunar Module.

Three men in a tin can designed for two.

The problem hits within hours. The Lunar Module’s carbon dioxide scrubbers are burning out too fast and the Command Module has plenty of spare filters. But those filters are square and the Lunar Module’s filtration system takes round canisters.

Square peg. Round hole. Two hundred thousand miles from Earth…not a Home Depot in sight.

Ed Smylie and his team at Mission Control have twenty-four hours. After that, CO2 levels go toxic and the crew loses consciousness. Then they die.

Here’s the constraint that made it impossible…

They can only use materials already floating around the cabin.

No resupply…no new parts…

Just whatever three astronauts packed for a trip to the moon.

They inventory what’s available.

• Plastic bags from the liquid-cooled garments

• Cardboard ripped from flight manual covers

• Spacesuit hoses

• Gray duct tape

• A sock

A freakin’ sock.

Three astronauts’ lives hanging on a piece of laundry.

In Houston, Smylie’s team cobbles together a prototype using their MacGyver starter kit. They test it in the simulator. It works. CAPCOM Joseph Kerwin radios the instructions to the crew. Step by step. Over the course of an hour, Jack Swigert and Fred Haise assemble it while Jim Lovell floats behind them, watching.

They call it “the mailbox” and CO2 levels start dropping immediately.

145 Studies Said the Same Thing

In 2019, researchers reviewed 145 studies on constraints and creativity. They expected to find that freedom produces better work.

They found the opposite.

The relationship forms an inverted U. Too few constraints and you get paralysis. Too many and you get nothing useful. The sweet spot is in the middle.

They called it the blank canvas problem.

Hand someone a blank canvas and say “paint anything.” They freeze. Hand them three colors and ten minutes. They create.

Constraints aim creativity.

The Apollo 13 engineers didn’t succeed despite the constraints.

They succeeded because of them.

We’re Torching the Script

Zain and I are starting a live show. It’s called The Vault.

The Format:

Working with clients recently, I've discovered I have the most fun and do my best work live. When I am solving real problems in real time for people.

So here's what we are going to do...

You submit a problem. We don’t see it until we turn the camera on.

We pick one and build a solution live using Claude Skills. Thirty minutes. No prep. No safety net.

This sounds reckless…It’s actually strategic.

Most experts spend weeks preparing content designed to look spontaneous. The webinar is scripted. The Q&A is seeded.

The whole production is theater meant to make competence look effortless.

We’re torching the script. The vault opens and we discover what we’re solving at the same moment you do.

This is either going to prove we know what we’re doing or expose that we don’t. Both outcomes are interesting and fun...and to us, that is the point.

Your Constraint

The other constraint is on you.

Before you submit, you run your problem through a 5 Whys Prompt. Three minutes, five questions. You come out the other side knowing what you’re actually trying to solve.

Most people think they know their problem. They don’t. They know the symptom. The real problem is two or three layers down, hiding behind vague language and good intentions.

• “I want to be more productive” is a symptom.

• “I forget to follow up with leads because I have no system and honestly I’m a little embarrassed about it” is a problem we can solve in thirty minutes.

“The 5 Whys” requirement is a filter.

People who do the work show up differently than people who want to be spoon-fed. We’re building for the former. We started a community called Beware the Defaults.

The default is to skip the prompt and submit something vague. Something safe. Something that sounds good in a LinkedIn post. Don’t do that.

Programmable Expertise

One more constraint: Claude Skills.

Every solution we build has to be a Skill. Not a one-off answer. Not a copy-paste prompt you’ll lose in a folder called “AI Stuff” next to seventeen other things you meant to organize. A reusable system you can run again and again.

Skills are the most underrated feature Anthropic shipped. Lots of people still don’t know they exist. The ones who do treat them like fancy prompts. They’re not. They’re programmable expertise.

We’ve been building these privately for awhile and now we’re building them publicly. For problems we’ve never seen. Under time pressure. On camera. In front of people who will absolutely notice if we choke.

The person(s) whose problem gets picked keeps what we build. So does everyone in the community.

Saw Opportunity

There’s a line from that 2019 research that stuck: “Teams that saw opportunity in constraints benefited creatively from them.”

The key phrase is “saw opportunity.”

Constraints don’t automatically make you more creative. You have to choose to treat them as focusing mechanisms instead of obstacles. The teams that resisted constraints performed worse than teams with no constraints at all.

We’re choosing to see opportunity. In the time limit. In the blind selection. In the public accountability. In the possibility that we might fall flat on our faces while strangers watch.

Sunday at 1pm EST, we open the vault.

We have no idea what’s inside.

That’s the point.

Submit your problem:

BewareTheDefaults.com/Vault

The vault locks at 1pm Sunday.

After that, we’re live.

For Paid Subscribers: The Constraint Layer

You’ve probably tried to build an AI workflow before.

You had an idea. “Help me respond to emails faster.” You typed it into Claude or ChatGPT. You got back something that technically answered your question but wasn’t actually useful. So you tried again. Rephrased it. Added more context or tried a different model.

An hour later, you gave up and just wrote the email yourself.

The problem was never the AI. It was the blank canvas.

“Help me with emails” gives the AI nothing to grab onto. No trigger. No constraints. No boundaries. It’s like asking someone to “draw something.” They freeze. Or worse, they draw something mediocre and generic.

What The Constraint Layer does:

It forces you to answer a few questions that turn vague ideas into tight specs. Trigger. Output. Constraints. Context. Ceiling. Audience. Five minutes of thinking that saves hours of frustration.

You come in with “I want to be better at email.” You leave with:

“When I paste a cold inbound email, analyze the sender, check if they match my ICP, draft a reply that books a call if yes, politely declines if no, never use more than three sentences, never use exclamation points, and always end with my Calendly link.”

That’s not a personality setting. That’s a machine.

What’s inside:

• The prompt (works in Claude, ChatGPT, Gemini, or any AI tool)

• Six constraining questions with examples

• Step-by-step instructions for turning your spec into a Claude Skill, ChatGPT Custom Instruction, or saved prompt

• A complete example workflow you can pattern-match from

The Constraint Layer is the sock and duct tape. It’s what focuses everything else.

The Key Insight: Skills Can Be Workflows

Most people treat custom instructions like personality settings:

• “Be concise”

• “Don’t use emojis”

• “Sound professional”

That’s fine. But there’s another level.

A skill can be an entire workflow:

• Triggered by a specific input

• Running a specific sequence of steps

• Stringing together Skills

• Producing a specific output

Example: “When I paste a meeting transcript, extract action items, identify who owns each one, format as a bulleted list with deadlines, and flag anything without a clear owner.”

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I’ve been building and talking with for six months. Mid-call, we decided to say 'screw it' and just hit record. No more talking about it.

Just show people what we've been making

We started by recapping our “meeting” two weeks ago. Four of us met in Atlanta and sat around my dining table for three days straight. (or “little Zain” to help keep it straight) brought a desktop computer. Not a laptop. A desktop. With its own power supply.

Four dogs running around licking our feet. A monitor stacked on pretzel boxes. Twelve-hour brainstorming days. I forgot to eat lunch. Twice.

This was not a polished operation. (We’re proud of this.)

The livestream was us showing what came out of that.

Halfway through, Zain says something that I had to write down.

“I just feel like a kid again. Like you’re outside exploring and doing things you never thought you could do.”

What “Kid Again” Actually Looks Like

His wife has a very successful career but, is by no means, a developer and doesn’t know how to code.

They were in the kitchen making dinner, she had an idea for something that would help her colleagues at work, they sketched it out on the counter, and they just... built it.

A working prototype. In about an hour.

I had a similar thing happen.

All of my stuff is in a million different apps. Three different calendars, Claude, Stripe, Substack, Notion, etc.

So on Friday, I started building my own dashboard that connected all of them together in the EXACT way that I wanted it.

I caught my girlfriend peering over my shoulder (probably…definitely…because I didn’t hear something she said) and her eyes lit up.

We spent the next 2-3 hours building her, her own.

That’s what “feeling like a kid again” actually means. Not productivity. Play.

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The Friction Collapse

Here’s what’s actually happening…

The distance between “I have an idea” and “I have a working thing” evaporated.

Building something used to take weeks. Contractors. Slack threads. Approvals. Waiting for someone to email you back about a font choice. Not to mention that whole “coding” thing.

Now it takes an afternoon. Sometimes a lunch break. Sometimes an hour in the kitchen.

Friction is what makes work feel like work.

Strip it out, and you remember why you got into this in the first place.

The Problem Nobody’s Naming

Most people aren’t feeling this…yet.

And it’s not tools. You have access to the same AI we do. Probably paying for the same subscriptions.

It’s not tutorials either. YouTube has forty-seven thousand of them. Every platform has a course now. Your LinkedIn feed is half AI tips from people who discovered ChatGPT eleven months ago and now have “AI Strategist” in their bio.

The limitation is something else entirely.

Imagination.

You can’t use what you can’t picture.

Most AI frustration isn’t about capability. People don’t know what to ask for because they’ve never seen what’s possible. They’re not stuck on the how.

They’re stuck on the what.

I’ve been there. Staring at a blank prompt window. Cursor blinking like it’s waiting for me to be smart. Knowing this thing is supposed to be powerful, feeling like an idiot for not knowing what to type.

You can’t imagine your way out of an imagination gap.

You need to see it.

So We’re Showing, Not Teaching (well, a little bit of teaching)

The community we’re launching isn’t built like a course. No “complete lesson 3 before unlocking lesson 4” gates. No 45-minute videos that could have been a paragraph.

We’ll have workshops. We’ll teach things. But the energy is different.

• Here’s something we couldn’t stop using this week.

• Here’s an experiment we’re running.

• Here’s what just worked for a client.

• Here’s a new feature that’s going to be huge.

• Here’s why I haven’t slept properly since October.

We’re learning this stuff too. We just want to learn it out loud.

That’s what Beware the Default is.

Beware The Default

What That Looks Like in Practice

, one of our partners, built each of us a personal assistant using Notion’s new AI agents.

You have an idea, message it on Slack, and somehow it just shows up in Notion, packaged and ready to review.

I built a tool called Business Book Prompts.

Blue Ocean Strategy. Jobs To Be Done. StoryBrand.

You’ve read about them. Nodded along. Never applied them.

This tool takes those frameworks and makes them about YOU. It interrogates your specific situation, then spits out a personalized playbook. Scripts you can copy and paste. Checklists. Templates. Even a section that tells you why you might be completely wrong. (Most business books skip that part.)

The point is showing what’s possible when you stop accepting generic outputs.

Both of these tools will be in the community.

The Companion Insight

A few weeks ago I wrote about defaults. The presets you accept without questioning.

That piece was about awareness. Knowing the systems exist.

This is the companion insight.

Questioning defaults is necessary. But not sufficient.

Even if you reject the preset, you still need to see what else is possible. Awareness without imagination just leaves you frustrated. You know something’s wrong but can’t picture what’s right.

The Community Is Live. It’s Called Beware The Default.

Most communities have one person at the center. Maybe two. This one has four of us.

And honestly?

We didn’t start it with a sudden urge to teach. We started it because we had too much “stuff”.

• Workflows we couldn’t stop tinkering with.

• Prompts we kept Slacking each other at 1am.

• Experiments that worked and experiments that caught fire.

All of it sitting on hard drives, cluttering up Notion databases, living rent-free in our heads with nowhere to go.

We needed an outlet. Somewhere to dump everything we’re building and discovering before we lose it. If other people find it useful, great. But this started selfish. Four people who needed a place to put it all.

But it’s turned into a place for people who push past first answers. Who question the invisible systems running their work and their lives.

Who want to feel like a kid again.

JOIN BEWARE THE DEFAULT

But you know I wasn’t going to let you leave empty-handed.

The Adjacent Possible

There’s a concept from Steven Johnson’s book Where Good Ideas Come From called the Adjacent Possible.

At any moment, only certain innovations are reachable. They depend on what already exists. You can’t invent the microwave before electricity. You can’t build YouTube before broadband.

Johnson uses a metaphor I keep thinking about. Picture a house that expands as you explore it. Each room you enter reveals new doors. But you can only access rooms adjacent to where you already stand.

Your expertise is a house you’ve been building for years.

You’ve developed patterns. Approaches. Ways of seeing problems that other people miss. Rooms you’ve lived in so long you forgot you built them.

Here’s the problem: if you can’t see the rooms you’re already in, you definitely can’t see the doors.

This is why experts plateau. They’ve built something valuable and can’t see what it could become. The same invisibility that hides your methodology hides its potential.

Product ideas sitting right next to your current service. Positioning territories that would take you five minutes to claim if you knew they existed. All of them one room away. Obvious to an outsider. Invisible to you.

I built a prompt that maps these adjacent territories.

Here’s how it works:

You describe one thing you do that you’ve never fully articulated. A pattern in how you work. A question you always ask. A move you make that clients comment on but you’ve never bothered to name.

The prompt surfaces 5-7 adjacent territories that expertise could expand into.

Not boring brainstorming. (”Have you considered starting a podcast?”) It’s anchored to your specific patterns. You’re not inventing from scratch.

You’re discovering what’s already one room away.

THE ADJACENT POSSIBLE PROMPT

# Map Your Adjacent Possible

You have patterns you use every day that you've never named. Things clients notice. Questions you always ask. Moves you make without thinking.

This prompt finds the 7 doors sitting right next to that pattern—products, content, positioning angles you haven't seen yet.

---

## Start here:

**The pattern:**
Describe one thing you do in your work that feels automatic to you but that others have noticed or commented on. Don't overthink it—a sentence or two is enough.

**Your context:**
Who do you work with? What do you help them do? (Again, brief is fine.)

---

## What you'll get:

1. **A name for your pattern** — What you're actually doing (not what you think you're doing)

2. **The principle underneath** — Why it works. The belief that makes it valuable.

3. **7 adjacent territories** — Each one shows:
   - What it is (product, content, framework, service)
   - Why it connects to your pattern
   - What makes it valuable
   - One concrete first step

4. **The hidden doors** — Which 2-3 territories you've probably walked past without noticing, and why

5. **Where to start** — The single door to open first, based on your situation

---

The obvious territories confirm you're in the right place.
The third and fourth suggestions are where it gets interesting.

What to expect:

The first few outputs will feel obvious. That’s the point. The obvious ones confirm you’re in the right territory.

The third and fourth suggestions are usually where it gets interesting. Those are the doors you’ve walked past a hundred times without noticing.

Run it on different patterns. You have more than one room in your house.

Zain said he feels like a kid again.

That’s what happens when you can finally see the doors.

The community is live for everyone.

But this prompt is yours now.

Start with one thing you do. See what’s adjacent.

It’s the last week of December. I should be doing something else. Resting. Reading. Pretending to care about college football (my team is awful this year.)

Instead, I’m opening a prompt shop.

(I genuinely can’t help myself.)

If you don’t have kids, or your nieces and nephews live far away, you might not know what Build-A-Bear actually is.

It’s not just a stuffed animal store. It’s an experience.

You pick your bear, you stuff it yourself, you add a heart, you name it. The whole process takes twenty minutes and costs roughly the same as a regular teddy bear from Target…until you get smacked with the upsell outfits, accessories etc.

But the difference is kids don’t throw Build-A-Bear animals in a corner. They sleep with them. They name them. They cry when they lose them on airplanes.

Here’s a quick video that explains it better than I can (and shows why their stock has crushed the market):

The CEO nails it:

“It’s yours. You helped make it.”

There it is…THIS is the real insight.

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Which brings me to the folder on your desktop called 'AI Stuff.'

Most people download prompts the way they buy stuffed animals from Target. They grab something that looks good, use it once, and forget it exists. The prompt sits in a folder called “AI Stuff” next to seventeen other prompts they saved from Twitter threads in 2023.

(Am I the only one who does this? Please tell me I’m not the only one who does this.)

The problem isn’t the prompts. It’s that they weren’t built for you.

• A prompt that assumes you sell to CMOs won’t work if you sell to CFOs.

• A prompt designed for coaches will frustrate consultants.

• A prompt that expects you have transcripts falls apart if you only have notes.

You don’t have an AI problem. You have a specificity problem.

So here’s what I’m doing through New Year’s

You tell me what prompt you need. Be specific. Tell me your situation, what you’ve tried, what “perfect” would look like.

I build it. Custom. For your exact situation.

Then I hand it back with a breakdown of why each part works. The anatomy. The structure. The moves.

This isn’t just getting a fish. You’re learning to fish.

(Or you can just eat the fish. I won’t judge.)

The sneaky part

The act of filling out the request form is already the first lesson.

Most prompts fail because they’re too vague, missing context, or unclear on what success looks like. When you write out what you actually need, why you need it, and what the perfect output would be, you’ve already done the hard thinking.

The custom prompt is the bonus. The clarity is the gift.

So what makes a great request?

• Be specific about your situation. “I’m a consultant who works with healthcare clients” beats “I work in business.”

• Tell me what you’ve tried. If something hasn’t worked, I need to know so I don’t build you the same thing.

• Dream big in the “perfect world” section. Describe exactly what this prompt would do if it worked flawlessly. That’s my target.

How this works (for paid subscribers):

1. You submit a request through the form

2. I build your prompt, custom, for your situation

3. I post it in the community with a full anatomy breakdown (tag you unless you want to stay anonymous)

4. I create a generalized version for the free prompt library

You get the bespoke version. Everyone else gets the template.

Paid subscribers: You get a custom prompt built for your exact situation, plus the full breakdown of why it works.

Free subscribers: You get access to generalized versions of every prompt I build. Same structures, same techniques, adapted for broader use.

You learn. They learn. I get to build prompts during the holidays instead of pretending to enjoy parades.

Win. Win. Win.

The shop is open.

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