He already runs his whole story through his sales team, his prompts, and a fleet of agents building in the background. When I asked what he was most worried about, it wasn’t producing enough. It was whether all of it still said the same thing.

He’d figured out early what most teams are about to learn the hard way. When you can generate anything, the scarce thing is a story that holds its shape everywhere it shows up

So try what he did. Read ten things your company shipped this week, back to back.

Drafts, decks, agent outputs.

Do they read like they came from ten different companies?

Every team is shipping more, the priorities keep multiplying, and the org is running in many directions at once. Nobody’s sleeping on the job. You just got faster at making things than at understanding them, and the gap quietly became the work.

AI drove the cost of production to near zero. But it left the cost of understanding behind.

With Infinite Outputs, Clarity Is the Scarcest Asset

Information now multiplies faster than interpretation.

So the thing in short supply is no longer content. It’s the shared understanding that makes any output make sense..

Start with what AI actually made cheap: efficiency, the incremental 10% move. It’s already clarity-rich, that metric is obvious. And AI now hands it to you in infinite supply.

Efficiency is cheap now. Getting value, not so much.

A 10% gain is the same game played a little better, but a 10x leap is a different ballgame. There are infinite ways to play the first and only a handful to win the second. That takes the discipline to make the hard trade-offs and put everything behind the one bet that matters. And that discipline runs on the understanding AI can’t manufacture for you.

So the scarcity flips. The binding constraint is no longer output. It’s clarity: knowing which bet to make, whether the ten things your company shipped cohere, what any of the flood actually means.

The companies that pull ahead spend their scarce clarity on the one bet that matters. The rest keep pouring cheap output into 10% gains, doubling down on the one thing that was never scarce.

Shared Language, or a Faster Mess?

AI raises the stakes on all of this, and adds a new twist.

Agents are a new part of the workforce now, a new flow of work that takes understanding to manage, not more output. Point forty agents at an unmanaged language layer and they multiply the confusion at machine speed. Give them a shared one and the same volume compounds instead of scattering.

The scarcity bites hardest where the market itself is stretching. Your customers now sit at wildly different points on the curve. Take a company whose buyers span a frontier lab living years ahead, a company all-in on building, and a legacy brand still making incremental moves. One message across that spread collapses clarity. You have to name where each one sits and meet them there.

And it bites at the level of a single artifact. A nonprofit that I advise started with one pitch and ended up with six, each tuned to a different major donor. Every version read fine on its own. Together they had no center, six documents making six slightly different arguments, with no canonical one left. The donors felt the incoherence before anyone could name it.

Six versions isn’t range. It’s one story losing the thread six times, because understanding goes scarce the moment you make one thing do every job.

The Company That Can Trust Its Own Output

The prize?

A company that can trust what it ships because understanding is shared, not scarce.

The visible shift is small and it changes everything: the scarce resource isn’t output anymore. It moved to understanding, and most of your new capacity is still aimed at the side that’s already abundant.

When understanding is the scarce resource, the leverage is a shared language layer that lets your people and your agents make sense of the flood at the speed they produce it. Building that layer is what we’ve been working toward with Storied AI: an ontology for strategy and comms. More on that soon.

So run the test this issue opened with.

Read ten things your company shipped, back to back, and count the number of different companies that are described. If it’s more than one, you’ve found where your clarity is going.

Reply and tell me what you saw.

Michael

P.S. When you’re ready, here’s three ways I can help:

• If you want a second set of eyes on your narrative, reply and tell me more

• If you need narrative architecture, I help operators do just that. Apply for Q3

• If you need a keynote, I do them on a select basis. Let’s talk

The leadership agree at the offsite. A week later, no one can convey it with conviction to their teams So it stalls at the top and never reaches its full potential. The reason is quieter than a rollout problem.

You handed everyone a set of conclusions, without the deeper reasoning.

Avoid Conclusions Without a Why

Instructions earn compliance, not conviction.

Every company runs on two systems:

1 - The visible one you manage, the org chart, the roadmap, the strategy deck.

2 - The invisible one that actually governs how people act, what they believe before they decide.

Belief architecture is that second layer. You build it by putting the beliefs, the principles, and the assumptions on paper, so a leader can retrace how you got there and rebuild it on their own.

You can’t hand someone conviction.

They earn it by retracing the reasoning for themselves. Show the trade-offs you weighed, the assumption you’re betting on, the case for the change, and your leaders arrive at the strategy instead of receiving it.

Keep the reasoning in a black box and you get nods that never become behavior.

Turn the Discipline Inward

There’s a famous firm I know whose reputation rests on showing exactly how it reaches a conclusion.

That rigor is the thing its name stands for.

Now it has to turn that same rigor inward, so its own leaders can rebuild the argument in front of a client without the deck. The firm that shows its reasoning to the world is teaching its people to show it to each other.

That is when conviction starts to scale.

The more common pattern is the opposite. An AI strategy races out ahead of where the company’s own people sit. The thinking is right. It is expensive. And it lands as an order from the top that nobody can locate themselves in. The fix isn’t a louder rollout.

It’s showing the chain of reasoning, so people can find themselves inside the strategy instead of behind it.

The Single Source of Truth To Keep You Honest

Belief architecture only works when the beliefs are actually written down.

That document is what keeps you honest, and increasingly it’s what keeps your AI honest too. Every real transformation keeps moving, and without the reasoning fixed somewhere, each cycle drifts a little further from the last.

The written layer is the North Star you steer back to.

It matters more now because AI scales whatever meaning it inherits. Point your agents at a strategy whose reasoning was never made explicit and you distribute the conclusion at machine speed with none of the belief underneath it. Agents are a new part of the workforce, and managing them takes understanding, not more output.

Belief is what produces interpretation, and interpretation is where an AI agent, or a leader, either reads your world right or reads it wrong. Anchor the beliefs and you can hold the reins loosely and let the system reason for itself. Skip the deeper work of mapping and writing them down, and you don’t just lose the narrative.

You lose the strategy, the sense of who you are, and the confidence the people around you have in the future you’re selling.

Traceability is Legibility

Take the strategy your leaders keep nodding at.

Write the one layer of reasoning you never showed them: the trade-off you weighed, the assumption you’re betting on, the case for the change. Hand them that, instead of the conclusion one more time.

Because people carry what they can retrace. They set down what they were only told.

That layer, the reasoning under the conclusions, is what we’ve been building toward with Storied AI: an ontology for strategy and comms, so it travels to your people and your agents alike. The future is almost here.

Michael

P.S. When you’re ready, here’s three ways I can help:

• If you want a second set of eyes on your narrative, reply and tell me more

• If you need narrative architecture, I help CEOs & operators do just that. Apply for Q3

• If you need a keynote speaker, I do them on a select basis. Let’s talk

In 2019, I published Story 10x: Turn the Impossible Into the Inevitable.

This book is where I first wrote about the SFB Narrative Method.

In case you never read it, here’s the quick Cliff Notes version:

Make hard-to-explain work easy to fund, buy, and believe in.

The more complex, disruptive, and world-changing it is, the better.

In short, it goes like this:

1. See It: Context for Change

2. Feel It: Emotional Dilemma

3. Believe It: Evidence of Truth

This is what makes a narrative most compelling and hard to resist.

Treat SFB as a series of decision gates that must be followed in a precise order.

The problem is that most of the time, we lead with Believe It. Forcing data and conclusions on an audience that isn’t primed or enrolled. Then we wonder why we hit so much pushback. Often it’s because they don’t get the big picture (see it), and they don’t feel it’s for them (feel it). In my experience, this is why 90% of pitches fail.

Agreement and readiness are different states.

A case built to win the argument often never builds the sense that saying Yes is the only responsible thing to do.

Belief Is Inevitability, Not Agreement

A room commits when you make the decision feel inevitable.

Not louder. Inevitable.

That happens via three decision gates, in sequence.

• See It: makes the shift undeniable. You zoom out and give shape to what’s already happening, so the room shares your read of reality before you ask for anything.

• Feel It: makes the stakes irresistible. You zoom in on what standing still actually costs, so the decision matters instead of merely being correct.

• Believe It: makes the yes irrefutable. You price in the risk with proof and a staged path, so acting becomes the safe move and hesitating becomes the expensive one.

Undeniable, irresistible, irrefutable.

Run in that order, they build the conditions a Yes comes from.

The Blank Check Nobody Argued For

A little while back, I worked with an exec who needed to pitch their CTO on an ambitious AI bet.

Big tech company you’d definitely know. This was when budgets were being cut, layoffs were rampant, and my exec’s org was wildly misunderstood and undervalued.

Yet despite the odds stacked against them, my exec client exceeded expectations. They got everything they asked for and more. And the pre-read did the whole job.

The CTO didn’t even want to have a meeting about it.

Not “should we fund this,” but “how much headcount can you hire, and how fast?”

My exec was shocked how they were just handed a blank check.

That’s the moment you dream of. The one where someone just hands you their wallet and says, “take my money.” Because staying put just became the riskier bet.

Anyone who allocates capital, like CFOs and investors, thinks this way.

They hold a thesis, price the risk, and model the upside. Then they stage the commitment against a series of gates. A decision narrative does the same. It leads the room to where the downside is capped, the upside is too big to ignore, and saying no is the larger mispriced move.

The Layer AI Can’t Automate for You

Your AI can draft the deck, the memo, and the model.

It can generate every version of the argument you’ll ever need.

But it can’t make a room ready to decide.

The output layer is now a commodity. The decision layer is the one still worth your name on it. See It, Feel It, Believe It is how you run it. It’s the difference between producing a case and building the readiness to act on one.

Strip the setting and the move holds. A philanthropic venture fund just put a structural bet in front of a top investor in their own language, a concentrated position on a mispriced problem, staged against gates so the first commitment de-risks the next.

Board vote, investor wire, budget commit. Different contexts, same sequence. Undeniable, irresistible, irrefutable.

The diagnostic falls out of it too. A stalled decision isn’t a verdict on your case, it’s a read on which of the three gates is thin. If the room is lost, it can’t see it. If it’s indifferent, it can’t feel it. If it’s hedging, it can’t yet believe it.

You fix the blocked decision-gate, and you have a winning narrative.

The Audience That Decides With You

There’s a version of this where the audience doesn’t just agree with you.

It decides with you.

The next time you carry a decision you need won, take 5 minutes before the meeting and name which of the three decision gates your case is actually missing. Not the slide that needs polish. The gate. Then solve for that one.

Because the goal was never a better argument. It’s the moment the YES stops feeling like a risk and starts feeling inevitable.

That whole sequence is teachable, once AI actually knows your worldview, your stakes, and your evidence. It can run it with you instead of just drafting words. That’s what we’ve been building with Storied AI: an ontology for strategy and comms.

The Claude MCP server just went LIVE this week.

This is getting real. Fast.

Michael

P.S. If you haven’t read Story 10x yet, you can download the PDF here for free. Or find it on Amazon in all the formats: hardcover, audiobook, kindle. And yes, it also wired into Storied AI as a set of Claude skills.

P.S.S. When you’re ready, here’s three ways I can help:

• If you want a second set of eyes on your narrative, reply and tell me more

• If you need narrative architecture, I help CEOs & operators do just that. Apply for Q3

• If you need a keynote speaker, I do them on a select basis. Let’s talk

But in 2004, I lived in Washington DC and briefly dated a tango instructor. It’s how I was introduced to the culture of milongas, a late night social tango party.

At the time a new sound was making its way onto the dance floor. Electronica-beats mixed with classical tango. A wild fusion of world music, trip-hop, dub step, and beyond.

Nobody had a name for it.

I was early in my narrative practice, and Sharna, an old friend, was trying to describe this rising cultural phenomenon. One day, over coffee, we gave it a name: Neotango.

Sharna wrote a blog post on it, and it quickly took off from dancers to DJs to festival flyers. Somewhere in there it stopped being Sharna’s word and became the category’s. Two decades later, it’s just what this music is called.

For an introduction to the genre, check out Gotan Project or this longer list.

Whoever names it, owns it.

That’s the topic of this week’s newsletter.

Picture a leader who hears their own words describing their category in a place they were never present. A grant application. A competitor’s pitch deck. A regulator’s language. An AI answer written for someone three companies away. The words they almost never bothered to write down are the ones the field now uses by default.

Getting there is rare, because it cuts against how the game is usually played. You’re differentiating inside a category someone else named, so every pitch answers a competitor’s frame. You can out-think your whole market and still be renting a position in a house another company built. That’s a share game, and a share game caps you.

Last week we talked about giving your narrative a long enough half-life to compound. This is what compounding buys you once it runs long enough.

Bigger Than a Bigger Slice

The most valuable position in any category isn’t market share. It’s owning the language the field uses to describe itself.

When your words become the default way people name the problem, the opportunity, and the stakes, everyone else is drafting off you. That’s a Semantic Monopoly. A market-making move. You’ve defined the category everyone else competes inside.

You build it the slow way. You codify a small set of ownable phrases, the ones that say here’s what we do and here’s what this whole space is, and you repeat them until they stop sounding like yours and start sounding like everyone’s.

There’s a discipline underneath it: knowing which language to spread and which to protect. The category language, the name and the thesis for the whole space, you give away on purpose. The more freely it travels, the bigger the category you anchor, and every company that adopts it is extending your position for you, usually without noticing. Your proprietary language, the codified frameworks and coined terms that are distinctly yours, you protect, because that’s the asset everyone else drafts from.

There’s a third layer between those two, the shared argument a coalition of partners carries in their own voices, and it’s where most ecosystem work quietly breaks. That one’s a longer conversation. If you’re living in it, reply and tell me.

The Machines Inherit Whoever Named It

AI raises the stakes on all of this.

Models scale whatever language they inherit. So the terms your field uses become the terms its AI repeats, at volume, across every output. Own the language and you own what the whole category generates from. Leave it unowned and the machines default to someone else’s frame, or to the flat average of everyone’s.

This is the rare position AI can’t erode. The models in your category are already running on somebody’s words.

The only question is whose.

Strip the Names and the Move Repeats

A coalition in a new category of frontier tech, with real regulatory stakes, reduced a sprawling mission to one named problem. Their funders realized the named problem was the actual product. A wide range of players who would never fully agree could suddenly pull the same direction, because they’d all inherited the same language.

A global life-sciences company wrote the words for how breakthroughs clear regulatory approval. The industry adopted them. The framing traveled all the way up to the regulators themselves. Now they’re the source everyone else drafts from.

You already know the finished version. Salesforce named “the cloud,” and an industry reorganized around the word. Palantir built around the word “ontology” for data, and for that world and use case, the word is now theirs.

And it’s continuous work. A membership organization I’m working with is building the shared language for its whole field right now. It’s the hardest part of the job, because coherence across a network isn’t everyone saying the same words, it’s everyone making the same argument in their own voice.

The One Phrase You’d Make a Rival Use

The move takes 5 minutes.

Name the one phrase you’d want your whole category to adopt.

The words you’d want a competitor forced to reach for in their next deck. Then start saying it, everywhere, in every memo and talk and prompt, until it turns into wallpaper.

Does your language travel without you in the room? That’s the tell: the words showing up where you never were, carried by people you never briefed.

Someone’s language becomes the default your category thinks in. The only question is whether it’s yours.

That default now has to hold across every agent your company runs, not just the people who work there. There’s a language layer a category thinks with, both humans and machines. That’s what we’ve been building toward with Storied AI: an ontology for strategy and comms, not just data. More on that soon.

Michael

P.S. Sharna never trademarked neotango. That’s exactly why it won.

P.S.S. When you’re ready, here’s three ways I can help:

1. If you want a second set of eyes on your narrative, reply and tell me more

2. If you need narrative architecture, I help CEOs & operators do just that. Apply for Q3​​

3. If you need a keynote speaker, I do them on a select basis. Let’s talk

On Saturday I took my girls (age 2.5 and 1) to their first LA Dodgers game.

And it had me reflecting on this 250th birthday of America.

While it feels like there are few things we can agree to in U.S. politics right now...

I can sense a new narrative taking shape. Especially as we head into the mid-terms this fall.

The reason its hard to see is that a truly positive story of America doesn’t get told enough these days.

And you have to repeat an idea or message consistently for it to compound.

That’s the topic of this week’s newsletter.

The Sharper Version That Keeps Costing You

Recently, a chief of staff told me they rewrote their company’s website once a week for 16 weeks straight.

Every version landed. Their CEO would read it and say this is great, this is exactly it. The clarity held for about 36 hours. Then a sharper way to say it would surface, a new frame, a better paradigm, and the whole thing started over.

They weren’t failing. They were doing exactly what their CEO asked. And nothing they wrote ever got the chance to stick, because the walls kept moving before anyone downstream could build on them.

This is the trap for the sharpest leaders. The instinct that built the company, keep finding a better version, is the same one that keeps resetting the clock.

The Version Worth Leaving Alone

A message compounds the way anything does.

It needs time and repetition. The team internalizes it, new hires inherit it, the market hears it often enough to say it back to you. Every rewrite sets that clock to zero.

So the discipline isn’t finding the best possible words. It’s finding the version durable enough to leave alone. Narrative half-life: how long your core narrative survives the rate of change before it genuinely needs a rebuild. A short half-life means you’re re-articulating constantly and nothing accrues. A long one means the framing holds while everything around it moves.

The work is separating what’s load-bearing from what isn’t. One Series C SaaS company had a CEO years ahead of their own org on where the category was going, taking a legacy product AI-first. They kept re-articulating the vision, and every re-articulation quietly restarted the company. The fix had nothing to do with the vision. It was naming the one version that was load-bearing, committing to it, and letting the team experiment at the next layer down while the core held still.

The company started compounding behind them instead of chasing them.

A Generic Voice You Keep Rewriting to Escape

Your AI runs on your language now, too.

Point a model at your message and, left alone, it drifts toward the generic. Anthropic says it plainly about its own model: it has a consistent default house style that is persistent. The critic Kyle Chayka gave the result a name, the generic style of AI web design. That flat, could-be-any-company voice you keep rewriting to escape is the machine averaging you toward the middle.

You don’t fix that with another rewrite. You fix it by giving the model a durable layer to run on. Anthropic’s own analytics team found their AI answered business questions correctly about 21% of the time on its own. Once they gave it the codified context or ontology, accuracy ran past 95%. The model didn’t get smarter. It got a stable language layer to stand on.

Now multiply that across a company. A growth stage tech platform pointed a fleet of agents at a message that changed every quarter, and got 40 versions of itself running at machine speed, each one a slightly different company. Because AI amplifies clarity. It also amplifies confusion, and it will chew through whatever language you leave unattended.

One durable narrative is the only thing that scales cleanly across the people and the machines both.

Leave the Good One Alone

A sharper version will always show up.

When it does, the question isn’t whether it’s better. It’s whether the one you’ve got has had long enough to compound. Name the one version of your narrative that’s load-bearing, and give it a quarter before you touch it. Let it travel. Does your language travel without you in the room? That’s the test that actually counts.

This is the layer we’ve been building toward with Storied AI: the narrative work I do with clients, now callable tools inside your own Claude, working on your real drafts, the raise, the board update, the all-hands. Not to spin up more versions, but to find the one durable enough to compound and hold it across every agent you run.
​
Want to early beta access? Hit reply.

Michael

P.S. Baseball, hot dogs, cracker jacks. That’s one story we can still all agree on.

P.S.S. When you’re ready, here’s three ways I can help:

1. If you want a second set of eyes on your narrative, reply and tell me more

2. If you need narrative architecture, I help CEOs & operators do just that. Apply for Q3​​

3. If you need a keynote speaker, I do them on a select basis. Let’s talk​

The next chapter is obvious. The market shifted; the old way of winning is dying.

But when they stand up at the all-hands to lay it out, it doesn’t land. The people who built the company over the last 10 years hear something else: the thing they poured a decade into creating is now what’s holding everyone back.

The harder a CEO pushes, the harder a team digs in.

That’s the real problem with change. Not the strategy. It’s that the people living in today can’t yet see the tomorrow that’s obvious to you. The people whose buy-in you need most are often standing inside the very thing you’re trying to change. From where they sit, the new story isn’t an exciting new future. It’s a verdict on the past that they’re still attached to.

Thankfully, there’s one move that can fix this. And it fits on a single slide.

Old Story / New Story

You put where the company has been alongside where it’s going.

Two columns, side by side.

Not to argue that the new way is better, but to let people see the shift at a glance and connect the dots themselves.

For that CEO, it looked like this:

• Product → Platform

• Feature → Foundation

• Bought once → Built on daily

Three lines, and their whole bet was legible.

The company that sold a product is becoming the platform other companies build on. You can feel how that lands differently than 10 slides of strategy. The bigger the story, the more room there is for people to find themselves inside it.

The best pairs stick because they’re built for: parallelism, a little alliteration, a rhyme you can’t unhear. Feature to foundation. People don’t just read them, they repeat them.

And whoever sets the frames that stick owns the conversation.

Why people stop fighting it

The frame works because it never makes the old way wrong.

It names the old as where you honestly were, the product that earned the company the right to become a platform, and the new as where it was always heading. Same conviction, next chapter. Nobody has to have been wrong for everyone to move. Take that away, and people reject the new story the way a body rejects a transplant it didn’t ask for.

Leave it in, and the thing they built becomes the foundation of the thing they’re building next.

It’s already happening, in worlds nothing like yours

A team I worked with inside a life-sciences company had spent years as the function everyone treated as the brake, the people who slowed the science down to keep it safe. They didn’t argue for more respect. They redrew the map.

• Gatekeepers → Enablers

• Risk management → Opportunity management

• Following the rules → Rewriting the rules

Same standards, new altitude. The brake became the hand on the wheel: someone is going to write the protocols for how AI enters their business function, and it should be the people who know a thing or two about compliance, regulators, and quality.

A strategy leader inside a large software company did the same with a data bet the company kept shelving. Inside the building it read as overhead. So they stopped pitching a fix and went back to the company’s origin story, the data model that let it scale in the first place.

• Overhead → Infrastructure

• Cost center → Growth engine

• Agents run the data → Data runs the agents

They named the asset, “ownable data,” and what read as a cost started reading as the company’s next platform. Same DNA, carried forward. End result? A blank check from the CTO with unlimited engineering headcount.

Build your own in 10 minutes

Take a piece of paper, and draw a line down the middle: two columns.

On the left: where you’ve been, in your people’s own words, the version they’ve lived. On the right, where you’re going, one line for each line on the left, each pair built to echo the one beside it. Three rows carry most of it: the promise, the shift, and the one thing that never changes. Then read it back to someone still living the old story, and watch their face. When they nod, you’ve built the bridge. The change that needed a deck now fits on one slide.

One VP I taught this to called the technique, “worth its weight in gold.” They now include a contrast frame in every business case and slide presentation. And their decision-making influence has grown alongside it.

Why people start moving toward it

This move doesn’t just simplify the change. It pulls people into it.

The hardest thing about any transformation is that the people standing in today can’t see the tomorrow you’re describing, let alone want it. A strong contrast frame closes that gap. It shows people who they get to become and the future they get to inhabit. A future people can see themselves in is almost impossible to reject. You stop dragging people toward the change, and they start walking toward it on their own.

For years this lived in my client workshops and advisory work. And this is one of the first skills going into Storied AI, a Claude plug-in that you can call on in the flow of work. All you have to do is describe what’s changing, and it creates the frames that win with investors, executives, and B2B buyers.

I’m now releasing the first narrative stack for tech operators, and opening it to a small group to pressure-test before it opens to everyone. It’s free for this first group, and I’m keeping it small so I can work with each tester.

Want in? Reply “Storied AI” and tell me the change you’re trying to land.

Michael

P.S. When you’re ready, here’s three ways I can help:

1. If you want a second set of eyes on your narrative, reply and tell me more

2. If you need narrative architecture, I help CEOs & operators do just that. Apply for Q3​​

3. If you need a keynote speaker, I do them on a select basis. Let’s talk

Their positioning deck, sales decks, marketing content, with a note on top:

“I hate most of this. Don’t use any of it yet.”

I have a version of this conversation almost weekly.

They’re not alone, and it’s not their fault. Things are moving faster than most companies can bring back into alignment, so the words end up a step or three behind the company’s strategy and GTM. Hate it or not, your document library is the language your whole company runs on. Every output your team is producing is likely pulling from a corpus that’s gone stale and no longer fits where you actually need to go.

So, why does language often lag behind the company you’re already become?

You’re running on legacy language

Generic is only the surface tell.

There’s a word for the technology a company has outgrown but still runs on.

The word is legacy. Legacy processes, legacy systems, legacy infrastructure. It once served a purpose, and now it quietly taxes everything you try to build on top of it: the simple change that somehow takes three engineering sprints, the workaround nobody remembers the reason for. We call it tech debt, and right now everyone is pointing AI at it, ripping it out and rebuilding.

Almost nobody realizes they’re carrying legacy language too. The “averaging” I’ve written about these past two weeks, where AI reaches for the middle because you gave it no center, is one symptom of it. Outrunning your own words is the deeper one.

Words for the company you used to be are still running the company you’ve become.

How a phrase becomes yours

Ownable language is the handful of phrases people repeat on your behalf.

It’s the line a customer says back to you in a meeting you weren’t in. You don’t get it by writing a clever tagline. It comes from two things:

The first is discernment. Of all the true things you could say about what you do, which few words actually carry it? Choosing those, and cutting the rest, is taste, and it’s the one move AI can’t make for you, because it reaches for the average and the average is the opposite of a choice. Compress the complicated thing down to that essence, and the phrase starts to read less like a description and more like a rallying cry.

The second is repetition. A sharp line said once is just a tagline. You come to own it by living it: using it, repeating it, building around it until other people say it back to you. That’s the difference between a trademark and a tattoo. A trademark is registered. A tattoo is worn, and it’s costly to remove because you made a real commitment to it.

Take a life sciences company we work with. Their strategy lived in a document many pages long that no one could repeat from memory. We worked to give them a few words clear enough that the whole company, product and sales alike, could rally around the same idea.

That idea started with the clinician. Their days fill up with the work around the work: administering tests, writing reports, deciding which assessment to use at all. So the company said it back to them in their own language: “Your work is hard enough. Let’s make it easier.” And the promise underneath it: “Give people their time back.” The proof sits beneath it: hours of friction pulled out of the day, and time to diagnosis cut by 30 to 50%. Empathy for the clinician has become the through-line for the product, the experience, and the way the company talks about where it’s going.

What those words actually do

Signature words do more than travel. They bind.

When a company lands on a few words in common, people stop drifting into private versions of the strategy. A new hire in week one reaches for the same line a 5-year veteran uses. That shared language is what culture actually is, not the values poster on the wall, but the words everyone reaches for without being told.

Get a company onto the same handful of words and it’s finally singing from one song sheet.

That used to be true only of your people. Now it has to hold for your AI, the agents and models your company is starting to run on. They inherit whatever language you hand them, so the more distilled that language is, the more it behaves like code.

Codify it once and it aligns the team and every agent at the same time. Words as code and words as words. Distilled language has gravity; it pulls people and AI into the same orbit. Leave it generic and it goes inert, nothing repeats it and nothing coheres around it, and your language quietly commoditizes into the category, the opposite of a moat.

This is why voice and point of view aren’t decoration on top of strategy.

With AI, language is both a currency and the building material, the instructions your company runs on and the meaning those instructions carry, at once. The work is to chisel it with enough distinction that it’s worth repeating and impossible to mistake for anyone else’s.

Naming a category that didn’t exist yet

A real-estate investment firm we work with isn’t selling buildings.

It’s selling a thesis: that longevity, the wellness and recovery economy, is an asset class in its own right, not an amenity you add on, and that culturally relevant places hold their value. The language carries the firm: longevity as an asset class, ownership in places that matter, the shift from transactional to transformational real estate. Those phrases didn’t exist in the category before this firm earned them. Now they’re how it describes a new kind of firm for a new kind of time, and how the market is starting to describe it too.

Your best line is probably already in your deck

A B2B startup we know spent months trying to frame its category.

The line that finally did it was already in their deck, buried on slide 12, in their own words: “the trust layer for B2B.”

It had been there the whole time. They just had to move it to the front and commit to it.

Sometimes the words are already in the room, in how a founder talks off-script or a phrase a customer keeps using. Sometimes they have to be found, framed, and shaped. Either way it’s its own discipline, which is why it helps to have someone who obsesses over language and meaning, whether that’s a narrative architect or someone on your team who owns it. And it matters more now, not less.

AI is a large language model. The more you organize your language into a codex, the more precisely you can scale it, across every person and every agent that draws from it.

It’s also what we’ve been building toward at Storied: a way to codify this language so it holds across your whole team and every AI agent, not just the one LLM chat window you’re in right now. Next week, I’ll start showing you some of Claude skills that can do this for you.

So, a question to leave you with: what’s the one line you’d want people repeating about you? And are you saying it enough times that they will?

Michael

P.S. Language is the new code. ;)

P.S.S. When you’re ready, here’s three ways I can help:

1. If you want a second set of eyes on your narrative, reply and tell me more

2. If you need narrative architecture, I help CEOs & operators do just that. Apply for Q3​​

3. If you need a keynote speaker, I do them on a select basis. Let’s talk

You ask AI for help with something that matters. Your strategy. The executive update. Your go-to-market.

The thing you’ve been trying to say about where the company is headed. What comes back with AI is fluent, confident...and completely generic. So you rewrite the prompt. You add context, you feed it examples, you tell it the tone. It comes back smoother, and still not yours. You go again. And again. Twenty minutes in, you aren’t writing anymore, you’re negotiating with a machine that keeps handing you the average and calling it your voice.

It isn’t the model’s fault. You handed it a task, and it reached for the middle, because you never gave it the one thing that doesn’t live in the middle.

The Word Behind the Whole Thing

There’s a word for what you didn’t give it.

It’s a word my wife teases me about, because I bring it up at dinner, at parties, and I’m probably a bit too obsessed about it.

Ontology.

The textbook version: ontology is a branch of philosophy concerned with what exists and how we sort the world into categories. In plain terms, it’s about the labels we put on things, and the assumptions riding inside each label.

My favorite way to show it is with a finger.

Hold up a finger and ask: if this is a number, what is it? One. Now point it up: if this is a direction, what is it? Up. Now wiggle it: if this is a body part, what is it? A finger. One object. Three labels. Each label loads a different world onto the exact same thing.

Put it as a principle: words create worlds. The label you choose decides the world the reader steps into. This isn’t a parlor trick. It’s the foundation under everything I do with narrative, and it’s the step most people never make explicit when working with AI.

The AI Cheat Code

This is where ontology pays off.

Take “platform.” To an LLM, it’s the average of ten thousand SaaS landing pages. To your company it means something specific, and if you never say what, the machine fills in the generic version, and so does everyone who reads what it wrote. Same word, two different worlds.

So ask AI to take an ontological approach, to break down the ontology of your paragraph, and it stops guessing and starts showing you the assumptions buried in your own language. The first time, it feels like peeking behind the Matrix. You watch it reach for the average in real time, and you see exactly where to hand it yours instead.

That’s the whole game. AI fluency is the ability to hand the machine your meaning, your ontology, the problem as only you see it, so what comes back could only have come from you. It was never a software skill. It’s a language skill.

The machine scales whatever you give it: your meaning, or the average.

Why This Is So Easy to Get Wrong

Every time you prompt, you’re asking AI to make a hundred small choices about what your words mean. Left alone, it guesses, and it guesses toward the average.

You don’t see it happen. You just end up three drafts deep, down a blind alley, wondering why nothing lands, never noticing the trap door you walked through two prompts ago when it quietly decided “growth,” or “platform,” or “customer” meant the generic thing instead of your thing.

This is harder than it’s ever been, and not because you’re bad at it.

Everything is moving at once, and the words you reach for keep shifting under you. Getting clear on what you actually mean is the real work. Naming the ontology is how you do that on purpose, instead of leaving it to the machine.

Try This Before Your Next Draft

Five minutes, four steps, and the last one is where it cracks open.

Take something real: your positioning, an executive update, an investor pitch, the thing you keep trying to say but haven’t quite nailed yet.

1. Write it cold. Ask AI to draft it the way you normally would, and save what it gives you.

2. Run the cheat code. Paste this: “Take an ontological approach. List the key words in what you just wrote, and for each one, tell me what you assumed it means, and where it could mean something different to someone else.”

3. Analyze the response. What comes back is likely the average it reached for: your own language with generic meaning filled in. It’s easy to miss that this even happened.

4. Hand it your ontology. Paste this: “Here is what that word actually means in my world, and the one thing I believe about this that most others overlook or don’t understand. Rewrite it from there.”

Now what comes back probably sounds a bit more like you, because you stopped letting it guess your meaning and gave it the real one. The distance between step 1 and step 4 is the path of fluency.

You didn’t get better at using the tool. You taught it your ontology.

What’s true for you is true for the whole company. A company that never names its own ontology hands every employee, and every agent, the same averaged language, and the whole org ends up sounding like the category, selling its wares at a swap meet.

AI was never going to make you sound like you. It scales the meaning you bring, or it scales the average. Naming what you actually mean, and who you’re becoming as you build, is the one job that stays yours. That’s what this series is about.

It’s also why I’ve spent the last eighteen months building Storied AI: a set of skills inside your own Claude or ChatGPT that make your meaning legible to the rooms that decide, the boardroom, the investor meeting, the buyer’s shortlist, the all-hands. So your work gets funded, bought, and believed in. It’s almost ready, and readers here get the first look.

The good news is you can start today, by hand, with the move above. Over the next few weeks I’ll go deeper: how this works, and how to make it hold across a whole team, not just one chat window.

Michael
​
P.S. Other words my wife is sick of hearing me say: mouthfeel, micro-climate, and tokenmaxxing.

P.S.S. When you’re ready, here’s three ways I can help:

1. If you want a second set of eyes on your narrative, reply and tell me more

2. If you need narrative architecture, I help CEOs & operators do just that. Apply for Q3​​

3. If you need a keynote speaker, I do them on a select basis. Let’s talk

A new website next month, some revamped offerings, and a Storied AI plugin that you can use in Claude or ChatGPT, along with an enterprise solution for your language system. For the last 18 months I’ve been building a narrative protocol, drawn from decades of my work. I can’t wait to put it in your hands.

This is the first piece in a short series that runs underneath all of it.

It starts with something a lot of us are feeling and not saying out loud: AI made the work faster, but somehow left us less sure of who we’re becoming in the process.

Your AI Already Has a Theory of You

You never approved it. It just started filling in the blanks.

It built that theory the only way it could. Every time you prompt it, the model reaches for three sources: (1) the prompt in front of it, (2) the internet’s idea of you, and (3) what it infers from your past behavior. Every prompt, edit, and upload is exhaust, and the model reads all of it.

Those three inputs share one blind spot. Every one is assembled from where you’ve already been, not where you want to go.

It shows up when you ask it to write your positioning. It comes back fast and fluent: “We help modern teams do their best work, all in one place.” It sounds pretty good on the surface. But is it ownable? Defensible? Durable? It could be any company in your category, because AI is trained on the middle.

By the third pass you’re rewriting it by hand. The reflex is to call it a model problem. It isn’t.

When your company has never written down what it believes, the model has no center to hold to, so it averages what it finds.

AI doesn’t create meaning. It scales whatever meaning it inherits.

A Prompt Is a Set of Directions

So is an agentic workflow, and every brief you hand your AI agents.

ead your own and you’ll find outcomes every time: the format, the audience, the job to be done. The model learns everything about what you want produced and nothing about who you’re becoming by producing it.
​

An output is easy to copy. Copy the file and you get the file.

You don’t get the company that wrote it.

Becoming Needs Language First

Who you want to become matters more than the next milestone.

Because it decides what’s even worth reaching for. AI is built for achievements and milestones. Hand it an outcome and it’ll draft the launch and compress the roadmap. What it can’t do is tell you who you want to become. That hasn’t happened yet, so it’s left no exhaust to read.

Here’s the punchline. You can’t build toward what you can’t yet put into words. Becoming, even half-formed, has to be written down in language specific enough to hold something still taking shape. That language is both the vector and the container.

I once worked with a team inside a large company that everyone treated as the “cop on the beat” that slowed every launch down. That wasn’t who they were becoming. They were turning into the group that helped the business move faster and safer at once. But nobody had the words for the shift, so the old reputation kept winning. Once they wrote the new one down, in plain language their partners could repeat, the story started to travel ahead of them.

This is usually when someone tells me they’re too early to work on their narrative, that they still need to figure out what they’re all about. It’s actually the opposite.

It’s in those moments you don’t feel ready that you need it most.

Identity Needs a Container

So before you tune another prompt, do the harder thing first.

Capture who you want to become, and write it down in language only you could have written.

The easiest way in isn’t to write. It’s to talk out loud, stream of consciousness.

I use Wispr Flow for voice-to-text; you can also use the native voice-to-text in ChatGPT or Claude. The point is to think out loud, contradictions and all, and let the transcript be the raw material and your authentic voice. You don’t need a perfect prompt.

You need honest answers to questions most teams never sit with:

• The noble purpose underneath the work

• Your contrarian view about where the industry is headed

• What’s missing from your category’s conversation that matters most

• Where difficult dilemmas are quietly pulling the company’s identity apart

Answer those and you’ve started something the model can’t.

As Lisa Cron puts it, you have to go from generics to specifics.

The more specific the story, the more universal the message. If you don’t get specific enough, your voice and personality and humanity will never shine through. Those choices are how meaning gets made. And AI can’t make meaning.

Whose Language Wins

As the ground shifts, everyone is quietly asking whether any of it still makes sense.

That’s an identity question, and a narrative is what answers it. A narrative is the throughline that shows people who you’re becoming as an org and where they fit in, so the shift reads as direction instead of drift. This is an existential moment.

Naming who you’re becoming is the way through.

That’s what this series is about.

Over the coming weeks I’ll get specific about how you build it: how to name who you’re becoming, and put it in language only you could have written, so your AI scales that instead of the internet’s idea of you.

The machine will scale your language either way. The only question is the source: drawing from where you’ve already been, or who you’re seeking to become.

My wife Elle knows immediately if there’s something going unsaid. She reads my face like no one else can. Plus she intuits like a world class psychic. Even if I say “I’m ok”, she knows if I’m bullsh*ting to be polite.

That’s the intimacy of connection from the people who know you best.

Your spouse. Your closest friend. A great therapist. The mentor who saw what you were capable of before you did. They pay attention, they remember, they don’t flinch, and they hold the whole arc of who you are across years. Around them, you feel seen, heard, and understood.

That feeling has a name. It’s called being witnessed, and it’s one of the most powerful experiences a person can have.

And lately, you’ve been getting it from your AI.

It’s what the movie Her was about.

That’s why AI feels different from any software you’ve used, and it’s why it stings when it breaks character: when AI goes sycophantic, when it flatters you instead of understanding you, when it hands you fluent nonsense and you realize it wasn’t really with you at all.

You don’t feel that way about a spreadsheet. A tool can be wrong, but it can’t make you feel unseen.

With AI, it gets personal really fast.

What Witnessing Actually Is

Witnessing isn’t a single function.

It’s four elements, held at once:

• Sustained attention

• Contextual presence

• No forgetting

• No judgment

Pull any one out, and the feeling collapses.

In any human relationship, holding all four is expensive. Attention is hard to sustain. Memory fades. Judgment creeps in. Misunderstandings pile up. Context and trust take years to build. It compounds through consistency and reliability.

Except now, what was scarce your whole life is available on demand.

AI knows what you’re inside of, what came before, and what you’ve already tried.

The Part That Matters More Than the Technology

Being witnessed is what makes change real.

Any transformation, whether it’s a new role or a hard decision or a version of yourself you’re growing into, doesn’t fully land until someone witnesses it. You can know something has shifted and still not quite believe it until another mind names it back to you. “Ohhh…this is actually happening. I’m not making it up.” The witness is what closes the loop.

Often the thing we most need witnessed is something we can’t yet name. You feel it long before you can describe it, and the relief comes when someone else puts language to it. That’s the quiet power a leader holds. Name what people have been carrying and couldn’t articulate, and they feel seen by you. It’s what a category disruptor does, and what a movement that shifts a culture does. Suddenly there’s a word for the thing, and a wave of people realize they weren’t the only one.

The same is true of any story. A story with no audience isn’t a story yet. It’s just something that happened in your mind. Narrative isn’t only the events. It’s a series of events seen, understood, and validated by other people. This is why we feel so compelled to “share our story” on the socials and beyond. Because our sense of existence and worth often depends on it.

Being witnessed is how meaning becomes real and shared.

Your Company Is Trying to Be Witnessed Too

The company or team you run is no different.

It has to witness its market, its customers, and its employees. Miss what they’re actually feeling, and you start to sound tone-deaf. Keep missing it, and you drift toward irrelevance, as trust and belief erode quietly, then all at once. This is how you get disrupted.

Increasingly, AI is what assists us in the witnessing.

But AI can only witness what you give it.

That’s the context layer. And the case for feeding it everything: your emails, your Slack, your call transcripts. Give it a coherent account of who you are and what you’re betting on, and it reflects you back with depth. Give it a fragmented version, with no singular worldview, and it has no legible lens to interpret through. That’s where witnessing goes sideways.

Want to test the difference?

Feed this newsletter to your AI, so it has the frame, then ask it a few questions.

I want you to serve as the witnessing function:

• What story is playing out in our company that hasn’t been named yet?

• What are our customers actually feeling that we haven’t put into words?

• What are our employees living through that we keep missing?

Then two more: how does that compare to how we actually talk about ourselves today? How might we close the gap on the witnessing function?

Giving AI Something to Witness

An AI rollout can make everything go faster. Yet that doesn’t mean it’s clear.

AI can’t make your worldview legible unless you train and architect it, and that’s hardest exactly when your work is complex and nuanced and you’re moving through rapid change.

If you want AI to truly witness your company, there has to be something coherent to witness.

That work has a name. Narrative architecture: a North Star, a worldview, the canonical documents that carry your ground truth. A foundation clear enough that every version of your story, in every tool and output, reflects the same company back.

That’s also my favorite role to play as an advisor from early stage to big enterprises.

The intimacy you feel in private is what your organization needs in public.

Who is witnessing your company’s story?
​
P.S. And despite my love affair with AI, my wife will always be my ride-or-die.

This year the question is what makes it last.

Boards ask it. Customers ask it. Hiring panels ask it.

Your team asks it in the all-hands you have not yet scheduled. The question travels.

The new word is durability. It is the word capital uses when AI starts eating margins. It is the word your customers use when they decide to renew or churn. It is the word your team uses when they decide whether to stay or leave.

Underneath the word is a simple question.

Three years from now, when AI is much better, what part of your business is still worth what it is worth today?

That question does not get answered by saying “we use AI.”

It gets answered by saying which part of your business AI makes stronger, and which part AI makes obsolete.

Capability is cheap. Position matters.

Every company sits inside a stack.

• At the bottom: physical things. Chips, energy, capital.

• In the middle: the doing. Code running. Workflows completing.

• At the top: the meaning. Trust, brand, what only you know about your customers.

That slice you sit in is your layer. The full set of them has a name: Economic Abstraction Layers. It is how investors think about durability in any business, and it is the lens you have to learn to think in too.

For most of the last decade, every slice was worth something. AI changed that.

AI is great at the middle. It cannot touch the top. It does not change the bottom.

So the durability question becomes simple.

What layer do you own?

And what does AI do to it?

Economic Abstraction Layers, plain language

1. Substrate Layer. Chips, energy, the physical world. Oe.g.TVIDIA., TSMC.

2. Infra Layer. The cloud and the pipes. e.g. AWS, Cloudflare.

3. Execution Layer. The doing. Code running. Tasks completing. e.g. Salesforce, Stripe.

4. Collaboration Layer. How teams work together. e.g. Slack, Notion, Linear.

5. Coordination Layer. How systems talk to each other. e.g. Plaid, Twilio.

6. Translation Layer. Turning capability into something usable. e.g. Apple, Spotify.

7. Brand Layer. Who the company is. Why customers love it. e.g. Patagonia, Liquid Death.

8. Identity Layer. Knowing who the user is. KYC, login. e.g. Okta, Plaid Identity, Onfido.

9. Context Layer. The data and signal nobody else has. e.g. Bloomberg, Epic.

10. Assumptions Layer. The thesis. The worldview the company runs on. e.g. Tesla, OpenAI.

11. Governance Layer. Trust, audit, accountability. e.g. Visa, bond rating agencies.

What AI does to each layer

Three buckets:

AI eats Execution and Translation layers.

The doing layer and the form-it-takes layer. Most of what consulting firms, BPO shops, and entry-level analysts do for a living. Margins compress to zero.

AI consolidates Substrate and Infra layers.

The physical and the cloud. AI workloads need scale, and only the largest players win there.

AI depends on Context, Coordination, Governance, Identity, and Assumptions layers.

These five get more valuable as AI improves.

Because AI needs data to reason against (Context). It needs to plug into the rest of your systems (Coordination). It needs to clear regulatory bars before it can ship (Governance). It needs a brand customers recognize and trust (Identity). And it inherits the worldview of whoever trained or deployed it (Assumptions).

Five durable layers. Five commoditizing or consolidating layers.

The companies that compound through the next decade will be operating in the most durable five. This is also true one level down. The teams and roles that compound are the ones operating in the durable layers. If your work mostly lives in execution or translation, AI is coming for your output. If your work lives in context, coordination, trust, brand, or judgment, AI makes you more valuable, not less.

The same lens that decides company durability decides team durability and individual durability.
​

The diagnostic

Take this into your next board meeting, pitch, or strategy review.

What gets stronger when AI gets better at everything else?

That is the test.

The answer to it is the answer to one question.

What layer do you own?

Whatever you can name is the layer worth building around.

If you cannot answer, AI eats your business over the next three years.

If you can (the trust you have built, the data you own, the brand customers love, the workflows other companies plug into), build around the answer.

The narrative underneath the question

Investors already think in layers.

So do customers when they renew. So do executives when they decide which team gets the budget.

Operators usually do not. That is the gap.

The work is two moves:

First, name the layer. Not “we use AI.” That is a category, not a position. Name the specific Economic Abstraction Layer that gets stronger as AI gets better. Second, build the narrative around the layer.

The narrative is what makes the layer real to anyone who does not already think in layers. It tells customers why they renew. It tells investors why the moat will hold three years out. It tells your team what they are building toward. It tells executives why your work deserves the budget. Without the narrative, the layer stays a private fact. With it, the layer becomes the lens every stakeholder uses to understand what your work is worth.

In your next high-stakes conversation about your work, whether that is a board update, customer pitch, all-hands, kickoff, performance review, or team Q&A, lead with the layer. Then build the narrative around it.

That is the conversation capital, customers, peers, and your team are already trying to have with you.

Now you have a way to give it to them.

A founder said that to me, in front of four other founders he barely knew. We were running our narrative office hours at the early-stage VC where I’m an Operating Partner.

This founder was building in a regulated, slow-moving industry. He realized halfway through his own pitch that he didn’t know how to tell the company story without turning the incumbents into the villain.

Two language systems run in every company.

The first one gets rewarded in customer conversations: what it is, how it works, why it’s faster, cheaper, more reliable.

The second is the macro vision. It’s the VC who’s deciding how to price an investment round, it’s what a senior hire needs to hear before leaving a secure job, and what a board member repeats to herself after the meeting ends.

Name the bigger narrative

When the company outgrows its language, the default move is to upgrade the messaging.

Better words. Tighter pitch. Cleaner deck.

The move that compounds is one layer down. Upgrade the ontology. The category of thing your company actually is. What kind of game it’s playing. What scale of problem it’s solving. What category of value it produces.

Every company sits inside an industry-scale narrative whether they have the language for it or not.

Insurance is the global flow of money in moments of crisis. Water is the infrastructure of survival for the towns that depend on it. Retail is the unsolved discovery problem at the scale of how people find what they want.

What’s the largest truthful version of your company?

The work is to claim what’s actually there, at the scale at which it actually lives.

The bigger the narrative, the more room there is for people to locate themselves inside it.

A few examples from inside our portfolio.

• One founder is building real-time payments infra that moves money to people in 50 countries the moment something goes wrong. He pitches it as a discrete revenue wedge. The work itself is building the rails for insurance flows at planetary scale.

• Another is making public utility software that lets small operators keep water flowing through emergencies. The functional pitch describes integration and modularity. The actual promise: water keeps flowing for the towns that depend on you.

• A third is solving the discovery problem for retail stores. This is more than a product feature. It’s solving a category-level problem that the industry still treats as a merchandising issue.

Each one was already operating inside an “industry-scale” narrative.

They just didn’t have the language for it on demand.

The bigger story is the moat

Functional utility is a commoditized race to the bottom that AI will continue to automate over the next 12 to 18 months.

Anything that can be described as what it does and how it works is collapsing in price and time.

Customer conversations reward functional precision. Recruiting and investor conversations punish it. People stay through the hard quarters when the promised land is worth the journey. Talent shows up when the size of the pursuit matches the size of their ambition.

Capital follows when the partner deciding the round can find herself inside something at a planetary scale.

When in doubt, zoom out

Create a paragraph that names what you’re building at the scale it actually lives.

Bigger than your elevator pitch. Built for the moments that decide things.

A few AI prompts to reach for it:

• The category you might be redefining

• The quadrant of life you might be reimagining (work, health, learning, how people connect)

• The civilizational layer you might be building

• The planetary-scale problem you might be solving

Then test the paragraph.

Read it to a senior hire reading it cold and watch their face. Read it to a B2B customer and notice whether the bigger frame changes how she hears the product. Read it to the investor who would lead your next round and see whether he can find himself inside it.

Read it back to yourself thirty minutes later. Can you say it without notes?

If yes, you have the version you reach for when the functional pitch needs a higher altitude.

When in doubt, zoom out.

Not the strategy. Not the product. The language. They could feel themselves hitting a wall but couldn’t name why.

A company whose language was built for one era, one market, one audience. It doesn’t drag and drop into the next one.

People think this is a messaging problem. It’s usually a worldview problem.

A morality tale doesn’t scale.

Morality works in controlled, homogenous, bounded units of culture. Stable markets. Tight families. Small communities with shared values. In those settings, moral framing builds cohesion because the underlying basis is long established and accepted.

But in moments when worldviews and value systems collide, moralistic framing repels as much as it attracts. It establishes superiority at the cost of alienation, creating exits out of your story. It invites rejection. The instinct when this happens is to double down. To be unapologetic. To say it more loudly. To brute force your way through the thicket.

Except this collapses the Total Available Market (TAM) of your audience, instead of expanding it.

The move is to switch language registers.

From morals to principles.

That team I mentioned earlier had the following language:

“Advancing inclusive research...Bending the arc of research toward equitable health outcomes...A triple win for our patients, our science, our business...Science should serve everyone.”

In contrast:

“Broaden the evidence base to expand the value of the portfolio. Better data across populations means broader labels, faster approvals, lower regulatory risk. Expand the reach of precision medicine. Every molecule can reach its full commercial and clinical value.”

Same fundamental work. Same outcomes for the same patients. Different language registers.

The first version limits how many players across the healthcare value chain can identify with the story. The second is structurally unattackable, because it speaks in the language of value, outcomes, and relevance.

As I mentioned last week, I’m also working with a global policy initiative making a similar shift, from the language of campaign activism to the language of capital allocation. Their original framing was rooted in moral imperative. The rewrite positions it as a coordination layer for government ministers, scientific researchers, and Silicon Valley tech.

This is not about hiding what you believe.

It’s the subtle difference between being right or being effective.

It’s also whether you want your language to function as a flag or as a road. A flag rallies the people who already share your values. A road carries more people to the destination, even if they entered from different starting points.

Here’s my AI prompt of the week

1. Pull up your mission statement, or the way you describe your work to a customer or investor

2. Copy/paste the entire text of this newsletter into your LLM of choice

3. Ask “where does our language signal morals, and where does it signal boardroom legibility?”

4. Anywhere it signals morals, what might we be losing? Who might decide they don’t belong in this narrative?

5. What alternative language might pull them in if we described ourselves differently?

6. Prioritize market-legibility and the language of executives. Move beyond abstraction to clarity about “for who/so that”

7. Answer: “so what, why now, and how what we do compounds...”

Review the output. See what resonates. Discard what’s not useful.

You might feel a small loss. The moral version had more emotional charge. It made you feel righteous when you read it.

The principled version is less bombastic. But the principled version travels.

The side with the cleanest, clearest, most legible language wins. Not the side with the most defensible moral position.

Instead of moralistic, go principled. Principled scales to infinity.

Rolling out a re-org. Leading AI transformation. Shipping a new product.

This often isn’t a strategy or execution problem.

It’s a structural problem with the bigger story you brought back from the mountaintop.

Let me explain.

The forgotten half of the Hero’s Journey

Joseph Campbell.

The mono-myth of storytelling. We know it as the Hero’s Journey. George Lucas baked it into Star Wars. Hollywood ran with it. Most storytelling books and workshops try to convince you it’s the only way to structure a story.

But have you noticed?

You can’t drag and drop Joseph Campbell into a boardroom pitch without sounding like a shlub.

Here’s why –

Campbell mapped two halves of the story. Not one.

The half we all know is the departure. The call. The refusal. The threshold. The ordeal. The hero comes down from the mountain transformed, with a truth nobody else could have found. It makes for great drama on a keynote stage. Or in a GTM campaign. This is the disruptor in their unrestrained full throttle format.

The half almost nobody focuses on is what Campbell called the return. The long walk home. This is the part where the hero has to return to the village and figure out how to deliver something the village didn’t ask for, can’t yet name, and may not be ready to receive. The work of bottling the magic so the people back home can drink from the well.

This is the true innovator’s dilemma.

Why we skip the return

We announce the reorg. We mandate the AI rollout. We write the manifesto. We tell the story of what we saw on the mountain and expect the village to line up behind us because the insight is so obviously correct.

Then we wonder why nothing moves.

Nobody adopts a new future from someone who makes them feel stupid for living in the old one.

The “culture of disruption” trained us to do exactly that.

The pitch that declares the old way obsolete. The keynote that tells the audience they’ve been doing it wrong for years. The transformation announcement that makes last year’s decisions sound like an embarrassment.

That posture works in any room where the audience isn’t directly implicated. It collapses the moment you ask the same audience to actually carry the new direction inside their organization.

People will not adopt a future that requires them to disown their own past.

The return move is the opposite. You let people feel they were doing the right thing for the moment they were in. And now there’s a new moment, with new possibilities, built on top of what they already created.

Their work isn’t obsolete. It’s the continuation for what comes next.

Three shapes of the same story

The return looks different in different markets.

Architecturally, it’s the same problem every time. You’re trying to bring a new story back to the audience that holds the keys, in a register they can actually hear, without making the version of reality they were running yesterday feel like an embarrassment.

Take three orgs I’ve been working with this year. Three different industries. Three different shapes. Same “return-journey problem” underneath each of them.

A founder building a category disruption inside a slow-moving regulated industry. His investors love his conviction. But the carriers who control distribution would never sign a check for that same posture. The work isn’t about softening his conviction. It’s about building a second register, one that lets the incumbents see themselves inside the new world without feeling attacked by it. The category becomes additive, not adversarial.

A CEO of a healthcare membership organization with years of clinical evidence behind them. And and a narrative built to mobilize clinicians and advocates. The narrative worked. It also locked them out of the rooms where adoption scales. Their grassroots register was telling hospital CFOs, quietly, that this work wasn’t for them. The shift was in who they spoke to first, and what they led with. Nursing turnover. Employer-payer cost stack. A single drug-class line item that ballooned 8x in four years inside one health system. Once the conversation opened with the CFO’s pain, the rooms started opening too.

An organization establishing a new category of infrastructure at planetary scale. Every stakeholder they need sits on a different part of the political spectrum. Multilateral institutionalists. VC tech investors. Sovereign-governance defenders. The scientific research community. The default instinct in this situation is the hedge story. Stay boring. Stay sanitized. Avoid offense. The hedge protects the work and quietly suffocates it. The coherence move is the opposite. Build one narrative architecture. Four registers. No contradiction between any of them. Everyone can sign on.

Three different markets. Three different shapes. Same return journey underneath all of them.

Control narrative vs coherence narrative

Here’s the specific distinction between landing the return and losing it:

A control narrative dictates interpretation. Here is what you must believe. Here is the new direction. Comply and conform. It plays beautifully in a fundraise and reads cleanly in a quarterly business review. But it quietly tells the people you most need to mobilize that they’re obstacles in your story, not protagonists in it.

A coherence narrative does something different. It creates shared meaning that travels without enforcement. Here is what’s shifting. Here is what we’ve started noticing. Here is a direction that makes everything you’ve already built more powerful, not obsolete. Coherence narratives invite. They let the room you need discover themselves inside the future you’re describing.

Disruption is the departure. Integration is the work.

Two questions before your next rollout

Both questions are checks for the unconscious judgement before it lands in your team’s ears.

First, when you describe the future you’re building, what are you implicitly saying about the people who built the version of the category you’re now trying to change? Are you framing them as outdated, wrong, or stupid? Or as the ones who got the product or company to this inflection point, whose work is the platform for what comes next?

Second, when the people you most need to mobilize hear your message, do they feel smart, safe, validated, like they finally have a platform for realizing the dream they couldn’t get to under the old story? Or do they feel wrong, judged, dragged along, like obstacles in your version of the future?

The first posture lets the gift be received. The second posture confirms that the mountain was a solo trip.

Most transformations don’t fail at the strategy. They fail at the return. This is what narrative architecture solves for. The return is where you build the future people actually want to walk into.

If you’re sitting in front of a strategy that landed in the room and went quiet in the building, you’re probably not on the wrong path. You’re probably still just talking down from the top of the mountain.

P.S. If you know a leader who just shipped a big rollout and is wondering why nothing’s moving, forward this to them. The second half of the journey is where the real conviction gets built.

P.S.S. When you’re ready, here’s three ways I can help:

1. If you want a second set of eyes on your narrative, reply and tell me more

2. If you need narrative architecture, I help operators do just that. Apply for Q3​​

3. If you need a keynote, I do them on a select basis. Let’s talk

The instructions you give it are language. The definition of your ideal customer is language. What your company stands for is language. What counts as a good outcome, and what counts as a bad one, is language. The category your whole business is playing in is language.

All of it is language. And probably nobody in your company owns whether it all says the same thing.

When agents are doing thousands of things a day on your behalf, at machine speed, this problem won’t stay small for long.

Aaron Levie, CEO of Box, wrote a post on LinkedIn this week that pointed at half of this problem. He spent a week on the road with a couple dozen IT and AI leaders across banking, media, retail, healthcare, tech, and sports. He outlined the role every one of those enterprises is now racing to hire. This is AI transformation at the agentic level.

He calls it the Agent Operator.

The person who takes a messy human workflow, redesigns it for AI, and sets up the agents that actually do the work. Probably one or more per team. Possibly the most important role your company is hiring for in 2026.

Here’s a diagram of what this new role looks like:

Aaron Levie is right. Every enterprise I’m walking into is trying to staff this role right now, whether they call it that or not.

But the Agent Operator is only half the role the agentic era actually needs. There’s a second role. That even fewer are staffing for yet. Yet without it, every agent the Agent Operator ships is at risk of compounding a problem instead of solving one.

Narrative Architect: the new role nobody is naming

The Agent Operator sits inside a team.
​
Sales. Ops. Support. CS. Their job is to take one workflow in one function and make it work with AI.

That’s useful work. But nobody doing that job is looking up from their team to ask whether the language their agent is using matches the language every other agent in the company is using. That’s not their scope. It shouldn’t be.

Somebody above the team has to own that. Somebody whose job is the words underneath everything. The category you claim to be playing in. The language for what you sell, who you sell it to, and why it matters. The story every agent, every rep, every deck is pulling from.

Call it the Narrative Architect. One per enterprise, not one per team.

The Agent Operator builds the agents. The Narrative Architect owns the language system those agents run on. You need both.

The layer that 99% of enterprises aren’t seeing yet. Here’s a 2nd diagram for you:

Why this is a new role, not an old one dressed up

Most companies already have people who “own the words.”
​
Chief Marketing Officers. Comms leads. Sales enablement. Brand directors. Content strategists. Six roles, sometimes more, with some version of language in the job description.

The pushback is usually some version of: don’t we already have that?

The answer is no. And the reason is the difference between a role you can rename and a role you actually have to build.

In the old world, language drifted quietly in hallway conversations.

A sales rep would hear something on a customer call and translate it into the deck. A product manager would hear something in a roadmap meeting and translate it into the spec. A CMO would hear something at a board meeting and translate it into the blog post. Humans absorbed the drift. Humans did the translating. The cost got paid in meetings, over months.

In a world of AI agents, nobody is translating. Agents don’t translate.

They run on whatever language they’re handed. Thousands of times a day. In parallel. At machine speed.

So when your sales AI, your support bot, your website, and three different prompt libraries are all running on slightly different versions of the same story, you don’t get drift. You get the game of telephone, amplified at the speed of AI. Every customer sees a slightly different company. Every employee learns a slightly different strategy. Every deal gets qualified against a slightly different buyer.

The old brand and comms roles were designed for a world where humans made up for the gaps in the language. That world is ending. The new role has to build the language so there are no gaps to make up for.

That’s a different job. It takes a different mind.

I’ve written previously about language debt. The accumulated cost of every undefined term and every vague commitment your company carries. It used to accrue slowly. Now it compounds every time an agent runs.

Your AI rollout doesn’t fix your language problem. It distributes it at scale.

The game underneath the game

That’s the gap Aaron Levie’s LI post didn’t name. Not because he’s wrong about the Agent Operator. He isn’t. It’s that the role he described assumes the language is already clear. In most companies, it isn’t.

The category of the agentic era isn’t going to be won by the company with the best model, the best agents, or the most content. It will be won by the company whose language holds under load. Words used to be the soft part of business. They’re about to become the hard part. Every enterprise is about to find out whether their language scales or fractures. Whoever builds the role that decides which, wins.

Over the next 18 months, two things will start happening in parallel.

A handful of companies begin staffing this role, quietly, usually reporting to the CEO or the CMO. And the companies that don’t will start to feel a friction they can’t diagnose. Slower pipeline velocity. Customer confusion that doesn’t trace to a single cause. AI rollouts that technically shipped but didn’t move the numbers.

The first group won’t brag about the role. They’ll just start to pull away.

So here’s the question I’d leave you with this week.

If your language is going to run a thousand agents in two years, what would it take for that language to be sound at the scale of running a thousand agents?

Not good enough. Not clear enough. Sound.

That’s a different bar. And it’s the bar the next decade is going to demand.

Until next week,

Michael

Not in the way a table exists, or a building, or the iPhone in your hand.

There is no physical object you can point to and say “that’s the company.”

What you call your company is a narrative that enough people have agreed to act as if it’s real. Your thesis, your brand, your GTM, your equity. Shared fictions, every one of them. And they work, beautifully, right up until the moment they don’t.

Yuval Noah Harari, one of the most widely read thinkers alive, spent years investigating a question that sounds almost absurd:
​
​Why are humans the dominant species on the planet?

Not the strongest, not the fastest, not the longest-lived. His answer, published in Sapiens and now read by more than 25 million people, changed the way I look at every organization I walk into:

“My most central idea is simple...The story in which you believe shapes the society that you create.”

The species that runs on fiction

Harari’s insight is deceptively simple:

The defining capability of homo sapiens is not tool use, not language, not abstract thought.

It is the ability to create and believe shared fictions.

Money is a fiction. A corporation is a fiction. A nation-state is a fiction.

None of these exist in the physical world. They exist because enough people act as if they are real, and that collective belief allows strangers to cooperate at a scale no other species has ever achieved. The fiction isn’t false. It’s constructed. And the people who construct it, maintain it, and revise it hold a power most leaders never name.

Why the fiction breaks

Sapiens explained why cooperation works.
​
Shared fictions. Stories powerful enough to hold millions of strangers together.

But Sapiens left an open question. If shared fictions are what hold us together, what happens when they stop working? What makes one fiction survive for a thousand years while another collapses in a decade?
​
In 2024, Harari published Nexus. And where Sapiens explained the power of the fiction, Nexus examined the plumbing underneath it. He went looking across two thousand years of information networks:

The Catholic Church. The British Empire. The Soviet Union. Wikipedia. Each one a system that held millions of strangers together through shared belief. And each one held or collapsed for the same reason.

Information doesn’t primarily exist to be true. Its defining function is connection.

Every institution that operates at scale is an information network that trades some accuracy for coordination. The church simplified theology into ritual. The empire simplified governance into protocol. Your company simplified its strategy into a values statement and a sales deck. That’s not a failure, that’s the mechanism.

Simplification is how any fiction travels.

Harari calls the assumption that more information leads to better decisions “the naive view.” It doesn’t. More information often means more noise, more competing interpretations, more versions of the same story. What separates networks that hold from those that fracture isn’t the volume of information. It’s whether the network can catch its own distortions.

Science has peer review. The Church had confession. Democracies have a free press.

The weight just shifted to you

Edelman’s 2026 Trust Barometer: trust has drained from institutions and relocated to proximity.
​
National government leaders down 16 points. Major news down 11. But “my CEO” is up 9. Your people trust their boss more than they trust the news, the government, or the broader market narrative. That’s not a compliment. That’s part of your job description.

The shared fiction you’re maintaining inside your organization carries more weight than it has in two decades. And it just got a co-author that works 24 hours a day and never asks for context. Every prompt your team writes, every AI-generated summary they trust, every chatbot answer your customers receive is now part of the fiction.

What the drift actually costs

The trade-off between accuracy and coordination is the mechanism.

But it has a cost. And in organizations, the cost is specific and measurable.

When the fiction drifts, your fundraise takes six months instead of three. The delta isn’t theoretical. It’s your burn rate times ninety days. When the fiction drifts, your sales team tells three different versions of your value prop to three different buyers in the same quarter. The pipeline doesn’t stall because of pricing. It stalls because the buyer heard a company that doesn’t know what it is. When the fiction drifts, your reorg announcement generates compliance instead of conviction, and six months later you’re reorganizing again.

I’ve spent 24 years inside these rooms. The pattern is always the same. The strategy is often sound, but the language carrying it rarely measures up. And the gap between the two is the most expensive line item nobody tracks. Every organization has a tech stack, a data stack, a people stack. Almost none have a language stack. No system for making sure the words carrying the strategy survive contact with the people who need to act on them.
​
Harari would call this an information network without a self-correcting mechanism. I’d call it the tax you’re paying on every initiative that should be moving faster than it is.

The telephone test

Your strategy is a game of telephone. You just don’t know what version is playing.
​
Can someone who heard your strategy retell it to someone who wasn’t in the room, and would that person make the same decision you’d want them to make? That’s the test. Not once, but at every layer. Not agreement. Translation.

You articulate the strategy. Your Chief of Staff translates it for the all-hands. Your VP of Sales adapts it for the SKO. Your board member paraphrases it at their Monday partners meeting. Four translations. Four rounds of “telephone.” Four chances for the signal to degrade.

Pick the last strategic priority you communicated to your org. Now pick someone two layers removed from you. Not your direct report. Their direct report. The person who heard it secondhand.

Ask them one question: “What’s the single most important thing we’re betting on right now?”

Don’t prompt them. Don’t give them multiple choice. Just listen to what comes back. You’re not listening for the right answer. You’re listening for whether their version of the company matches yours closely enough that the decisions they’re making without you in the room are the ones you’d want.

If yes, your fiction has fidelity. The signal is holding.

If no, you just found where your strategy became someone else’s. That’s not a communication problem. It’s a governance problem. And every AI system you deploy will encode that version at scale, 24/365.

The leaders who understand this treat narrative the way they treat code: audited, tested for fidelity, maintained as infrastructure. Not because storytelling matters, but because the fiction is the operating system.
​
And right now, most organizations are running without version control.

Michael

P.S. Harari at Davos this January: “Soon, most of the words in our minds will originate in a machine.” The leaders who understand fiction as infrastructure will have an advantage that compounds for a very long time.

I’m not talking – slightly better stories or incremental advantages in positioning.

Instead, companies with objectively weaker fundamentals, thinner margins, less defensible technology. And yet the capital flows to them faster, at higher multiples, with less resistance.

You’ve watched it happen. You may have been on the wrong side of it. And if you’re honest, the explanation everyone reaches for (”they had better connections” or “timing was right”) has never fully satisfied you.

A Nobel Prize-winning economist spent decades studying this exact phenomenon.

His answer is more uncomfortable and more useful than most business books will ever give you.

The grad student who read the wrong book

In the 1960s, a young economics student named Robert Shiller was studying at the University of Michigan when he picked up a book that had nothing to do with his coursework. It was Frederick Lewis Allen’s Only Yesterday, a journalist’s account of American life in the years leading to the Great Depression.

What struck Shiller wasn’t the economic data. It was the stories. The way ordinary people talked about the market in 1928. The beliefs they held. The phrases they repeated to each other. “Prices only go up.” “Everyone’s getting rich.” “This time is different.”

Allen wasn’t writing economics.

He was capturing the narrative that an entire society had agreed to believe. And Shiller realized something that would take him decades to prove formally: those beliefs weren’t a sideshow to the economic data. They were the main event. The stories people told each other about the future WERE the force that moved the market. The data just came along afterward to confirm or destroy what the narrative had already built.

Shiller won the Nobel Prize in 2013 for Irrational Exuberance, for his work on asset pricing and speculative bubbles including the 2009 financial crash. In 2019, he followed up with Narrative Economics, one of the densest and most important books most leaders will never finish. Don’t worry I’ll give you the TL;DR so you don’t have to wade through it yourself. The core thesis is the following:
​
​Capital doesn’t flow to the best opportunities. It flows to the most believable stories about future opportunity.

You’re watching it right now

Here’s where this stops being academic.

Right now, global AI investment exceeds $400 billion annually. Revenue from AI products sits around $100 billion. MIT research claims 95% of organizations reporting zero measurable ROI from their early AI deployments. And 54% of fund managers surveyed say we’re in “bubble territory.”

Sam Altman himself has acknowledged the bubble dynamics.

None of that has slowed the capital. Because the narrative (”AI changes everything, and the winners will be decided in the next 18 months”) is more powerful than any balance sheet. Shiller would recognize this immediately. It’s the same mechanism he documented in housing before 2008, in dot-com stocks before 2001, in Bitcoin’s rise from obscurity to cultural phenomenon.

The mechanism is always the same. A story goes viral. It simplifies a complex reality into something that feels obviously true. People repeat it. The repetition creates social proof. The social proof attracts capital. The capital creates results that seem to validate the story. And the cycle accelerates until the narrative breaks.

What this means for the room you’re walking into next week

Most people hear “stories move markets” and assume it’s about spin, better marketing, maybe some pitch presentation.

Shiller wasn’t studying persuasion techniques. He was studying epidemiology. The way narratives spread through populations follows the same mathematical models as the spread of disease. A narrative goes viral when it’s simple enough to repeat, emotional enough to remember, and connected enough to something people already want to believe.

That has three implications if you’re leading anything right now:

Your narrative is either accelerating capital toward you or redirecting it somewhere else. There is no neutral. Every week your story isn’t clear, coherent, and repeatable, someone else’s story is filling the gap in your investor’s mind, your customer’s mind, your team’s mind. Shiller proved that markets don’t wait for you to get your story straight. They operate on whatever narrative is available.

The best narrative wins because it travels. Not because it’s the most true, the most nuanced, or the most complete. Shiller documented how simplified, emotionally resonant stories consistently outperform complex, accurate ones in shaping market behavior. “Prices only go up” beat every housing analyst in America for seven years. Your company’s story needs to be true AND simple enough to survive being repeated by people who’ve never met you.

You’re not competing for market share. You’re competing for belief. This is the part that makes analytical leaders uncomfortable. The market is a belief system. Capital allocation is an exercise in conviction. Shiller’s entire body of work points to one conclusion: the organizations that understand narrative as infrastructure, not decoration, are playing a different game than the ones still leading with features, function utility and fundamentals alone.

The exercise (5 minutes, no AI required)

I’ve been struck by Shiller’s work for a few years.

It’s the lens I use when I walk into any engagement where the strategy is strong but the capital, the talent, or the momentum isn’t flowing.

Shiller’s Narrative Economics is 400 pages of academic proof that stories are the most powerful force in markets. If you want the full book, it’s worth the effort. If you want the one-line version: the story IS the market. Everything else is commentary.

If this week’s newsletter resonated, try this:

Write down the one sentence your investors, your board, or your customers would use to describe what your company does and why it matters. Not what YOU would say. What THEY would say, from memory, the exact words, if someone asked them at a dinner party.

Then ask yourself two questions:

Is that the story worth telling?

And if it isn’t, who sets the narrative that IS traveling?

Those two answers will tell you more about your next twelve months than any financial model or product roadmap.

Reply and let me know what you find.

Michael

If you’ve been wanting to move over to Claude…

But feeling overwhelmed, stuck, or intimidated, and don’t know where to start -- keep reading.

I’ve put together a Claude migration kit
​
​This week’s edition of the Narrative –

• The secret shift that makes AI adoption actually stick

• Why you need a Pioneer / AI build day

• Claude config resources you can start using today

Look, I get it.

You finally got the hang of prompting. You have your workflows. The last thing you want is to learn another thing. Again.

But you keep hearing that Claude Cowork and Claude Code -- is genuinely some next level sh*t.

They’re not wrong.

Agentic AI is a category leap.

And no foundation model makes it easier than Claude.

Claude does enterprise knowledge work, across your files, projects, and spreadsheets. Claude takes you from prompts to agents that can work on your behalf. Even while you sleep.

The performance gap versus other LLMs is not incremental. It’s a paradigm shift.

And Claude CoWork is specifically for non-technical people.

The Hidden Unlocks for Building in AI

A month ago, I started something new.

No meeting Tuesdays.

Zero deadlines. Zero interrupts. Zero urgency. Non-negotiable.

I call it my Pioneer / AI Build Day. A time to “Think, build, ship.” Rinse and repeat.

First Tuesday, I ported my entire working memory into Claude, installed CoWork, and built three automations before dinner. I then looked up and it was 11pm. That’s what happens when you stop squeezing AI experimentation into 27-minute blocks between meetings.

My #1 job is to automate my job, so I can focus on the highest-order value creation.

That’s the whole day. And it’s become the most important day of my week.

Here’s what I didn’t expect.

My Build Day isn’t about productivity. It’s about identity.

Many of the executives and organizations I work with have done the responsible thing. Bought the licenses. Set up the pilots. Run the trainings. Six weeks later, almost nothing changed.

The team has access to everything and is using almost none of it.

Nobody resists AI. They resist losing their sense of identity in the process.

If AI does all the work, then what is my role or job?

The problem isn’t tools. It’s that people don’t yet see themselves as a person who builds in AI.

Builders have power. Builders architect. Builders create. Builders lead. Builders have options.

And you can’t close that gap in 23-minute work blocks. Or even 83-minute blocks.

As a leader, you probably feel that you are in too many meetings.

You don’t have time to breathe, think, or even go to the bathroom. Many others on your team feel the same.

Your nervous system is the obstacle.

Back-to-back meetings. Slack pings. Context switching all day. That’s a cortisol state. Fight, flight, freeze. A nervous system in hypervigilance can’t learn. You can’t experiment, play, or build new behavior in triage mode. You default to what you already know.

A new way of thinking requires a new way of working.

You can’t adopt a new way of thinking while your body is braced for the next interruption. And neither can your team.

A new way of working requires something most organizations haven’t considered:

A full, uninterrupted day to build.

When someone spends the day building with AI...

Not attending a demo, not watching a tutorial, but making something that matters to their actual work, something shifts. They stop seeing AI as a threat to their role and start seeing it as a lever for their role.

Not because you told them to. Because they experienced it.

You solve the identity problem through the behavior. Not the other way around.

The VC Vinod Khosla said it plainly this past week: AI is already redefining knowledge work at a structural level.

This isn’t just a headcount story. It’s a fundamental redefinition of what the modern org chart looks like. 30% of knowledge worker headcount will compress over the next 12 to 24 months. That’s not a prediction from a blowhard futurist on LinkedIn. It’s what I’m seeing in every organization I work with. Early stage, growth, enterprise. Same hard knocks reality.

Everyone needs to upgrade their thinking and their workflow. From CEOs and VPs to front-line employees.

The best service you can do for your people right now is to give them the mindset, tools, and playbooks for playing a bigger game. This is the principled move -- whether they level up inside your organization or are set up to thrive and adapt on a new path.

“Shipping in AI” becomes the leading indicator.

Ask yourself: Does your team have protected time to actually build with AI -- or are they just trained on it?

If the answer is trained, you don’t have an adoption problem. You have an architecture problem.

Training gives people information.

Building gives people identity, agency, and dopamine reward.

The Claude Migration Kit I wish I had earlier

Here’s a quick set of resources I’ve put together.

Set aside an AI build day and start going through these steps. You will quickly see compounding results. Feel free to share these with anyone looking to make the leap.

Step 1: Bring over your memory from ChatGPT, Gemini, CoPilot

• Start here: claude.com/import-memory [30 min]

• Want to go deeper and port your full memory into Claude, check out this Reddit walkthrough​

Step 2: Install Claude CoWork in a day

• Ruben Hassid: simple version gets you running in an afternoon [4 hours]

• Ruben Hassid: intermediate version goes deeper if you want to build real workflows [1 day]

• Charlie Gillis: another intermediate version to expand your depth and range [4 hours]

Step 3: Join an AI build community

• Gen AI Circle -- where I go to install more advanced Claude Code playbooks [40 euros/month]

• Mighty AI Lab -- great starter set of playbooks for SMBs and mid-market [$97/month]

That’s it. Block a day. Protect your nervous system.

Bring over your memory. Build something real and ship it. Identity shift follows behavior.

Dive in and let me know what you discover.

Michael

P.S. Know someone who could benefit from this Claude Migration Kit? Forward it to them.

Slack messages with no clear ask. Emails that require a decoder ring. People bringing you problems instead of solutions. Recommendations so misframed you’d rather just do it yourself.

Your people don’t move at the speed you move. They don’t take the same level of initiative, ownership, and urgency that you do. And no matter how many times you explain it, the pattern repeats with every new hire, every new collaborator, every new tool in the stack.

What if you could fix that in 30 minutes?

Not with another all-hands or another onboarding deck. With a single document that downloads how you actually think, decide, and operate, so everyone and everything around you can work WITH you instead of having to read your mind.

I call it a CEO User Manual.

In an AI world of README files, this is the foundational one.

Your CEO User Manual

This is for you if you need to scale beyond your calendar.

A functional leader. A startup founder. A business unit exec. A subject matter expert.

Anyone whose way of operating shapes how others work.

Your User Manual is a structured document that makes your way of thinking and working principled, legible, and installable into the DNA of your team, your company, and most importantly – your agentic AI.

It goes deeper than what people can observe from the outside.

The User Manual makes explicit:

• How you make decisions. Principles vs preferences, so people know what’s negotiable

• What you need from others. What you expect from the people around you

• How you communicate. Format, sequence, and how to deliver bad news vs good news

• What energizes you. What produces your best work and what drains it

• What triggers you. The specific things that set you off your center

• Your honest unfiltered truths. Quirks, pet peeves, what people need to know about you

This document does three things at once:

1. It trains your team (human and AI)

2. It scales your thinking across the organization

3. It becomes a foundational operating document for every AI agent acting on your behalf

That’s what makes it the highest-leverage document a leader can create.

This is one of the first exercises I do with every client I work with.

What changes when you create a User Manual

For starters, your team can immediately level up their game.

In January, I onboarded a few new team members. Honestly, it was going painfully slow at first.

But the moment I gave them my updated User Manual, they started showing up in a whole new way. Their daily slack messages and weekly reports were the most structured, clear, and actionable I’ve ever received. They felt empowered to play a much bigger game. And the velocity of execution immediately jumped. Every week they share what they shipped, what they’ve learned, where they’re stuck, and where they need my input.

Productivity didn’t just improve. It multiplied.

Similarly, when I fed my User Manual into my Claude, all of my AI prompts and agents performed better.

My way of thinking and ways of working are now embedded in every system I interact with. The decisions it drafted reflected my actual principles. The communication carried my style. The priorities aligned with how I actually operate, not generic patterns.

With AI, we are all the personal CEO of our own world.

Learning to think like a CEO and training your AI to think like you is no longer optional.

Language is the new code base. And the team with the clearest language wins.

Universal principles and personal truths

The best User Manuals have two layers:

The universal layer. First principles, decision-making heuristics, how to frame a problem before solving it, how to distinguish signal from noise, what workability means in practice. This is the stuff that upgrades how anyone around you makes decisions. When someone reads this layer, they become a better thinker and operator regardless of context.

The personal layer. Your voice. Your quirks. Your specific triggers. Your love languages for receiving information. The 5-7 things you’ve learned the hard way in your career. Without this layer, the document reads like a generic leadership template. With this layer, people read it and think: “This is so you. I kind of knew it intuitively, but now it’s explicit.”

Both layers matter. The first gives people your operating system. The second gives people you - your most authentic self.

How to Create a User Manual for Yourself

I’ve built an AI prompt that walks you through the whole thing. See below.

The quick and dirty version will take you 10 minutes to output. The customized version will take you 30 minutes to produce.

Copy this prompt + see my own User Manual as a reference.

Use this Exact Prompt

Based on the concept of a “CEO User Manual” as defined and illustrated in the attached input, create a personalized User Manual based on everything that you know about me, my role, and how I think, work, decide, and define success. Use Michael Margolis’ CEO User Manual as an output reference that you can train on. Take note of his structured thinking. How might this philosophy and principles inspire how I level-up the performance of my own people: communications, ownership, alignment, collaboration. While you can borrow or steal anything from Michael’s, ensure that 30% of my manual is customized to my voice, philosophy, and personality, and makes my org different and unique. Rule: every bullet point or concept must be well defined with a clear why or description of what it looks like behaviorally, otherwise it will be lost in abstraction. Any reader needs to instantly recognize ME and know what to do after reading my user manual. Create a V1 Manual and then ask me questions to better personalize and refine the output for greatest excellence.

Follow these step-by-step directions:

1. Open your LLM tool of choice (Claude, ChatGPT, Gemini, etc).

2. Copy/paste the above exact prompt at the top of your new chat

3. Also copy/paste this entire newsletter’s text (this gives your AI the full detailed context)

4. Attach mine Michael Margolis User Manual v2.1 as a reference for AI to train on (Download in .MD or .DOCX format)

5. Then run the model and see what it generates!

6. V1 will take you 10 minutes.

7. Review v1 output and answer all the questions it has given you to further fine tune this CEO user manual as your own

8. V2 will take you another 20 minutes if you want to dial it in further.

9. When done, turn your User Manual into a READ ME file in .MD format (this is just plain text that is AI machine readable).

10. The test: could someone read this in 10 minutes and understand how I think and work without a single meeting?

11. Share it with one person you work closely with and ask: does this match what you know of me? Close any gaps.

12. Ask your AI to add the User Manual to memory, and use it as context for all future interactions

This is the most valuable single doc you create to unlock your AI.
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One word of caution: I shared my new User Manual with three members of a peer advisory group. Within 48 hours, all three had created their own for their teams, but with 90% of my language still in it. The document worked. But it wasn’t theirs. Use my User Manual as a reference for structure and depth, not as a carbon copy. The exercise is about documenting and installing how you think across your team.
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Reply to this email and let me know what you discover or find most useful.

Starting this week, I want to try something new with you.

Today’s newsletter is also an AI prompt.

Read it, and when you get to the end, follow the quick 3-min directions.

And share with me what you learn or discover.

The new bottleneck is language

When I was advising Facebook/Meta from 2016-2023, I saw firsthand how technical debt can quietly tax speed, quality, and scale.

In software engineering, technical debt is the future cost created when teams choose a faster, messier solution today instead of a cleaner one that will hold up over time. It shows up when quick fixes, workarounds, and patches help you ship now. Then quietly make every future change slower, riskier, and harder. What looks efficient in the short term becomes expensive in the system over time. Meta product teams were constantly trying to reduce technical debt, but it often felt like whack-a-mole.

Fast forward to 2026.

Today, there’s another form of debt most companies are carrying, and AI makes it impossible to ignore.

As I’ve been sharing over the past few months, language is the new code base. If you’re building with AI, especially agentic AI, more of your business now runs on words than you realize.

The real question becomes:

How clear, precise, and transferable is the language your business runs on?

What is language debt?

Language debt is the accumulation of vague terminology, undefined concepts, and inconsistent meaning across an organization.
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It compounds as:

• Misunderstanding: teams think they agree, but operate from different definitions

• Decision paralysis: same data, different interpretations, no resolution

• Coordination friction: teams debate what words mean instead of executing

• Cascading confusion: unclear external messaging creates internal role confusion

• Amplification risk: AI systems encode whatever implicit language is already in place

Most organizations don’t yet treat language as a system. They treat it as culture, communication style, or “just how we talk.” But language is not just an expression layer. It is a coordination layer. The words a company leaves vague are the exact places where decision-making slows, ownership blurs, and execution starts to drift.

Whatever remains implicit in shared language becomes explicit in your system’s behavior.

1. Vague language about outcomes produces vague impact measures

2. Undefined language about roles produces a confusing org chart

3. Unclear customer-facing language produces unclear AI agents

The good news: the same AI tools that make this urgent also make it solvable.

Any of you who are working in Claude Code, Claude CoWork, or GitHub have discovered the power of README files.

They are foundational documents that tell the system how to think, behave, and operate.

With agentic AI, you need to bring the same mindset and approach to your strategy, your product, and your go-to-market.

Canonical-grade language systems, with precise definitions, structured frameworks, and clear distinctions, become the reference layer that both humans and AI can reliably execute against.

Organizations that build canonical-grade language systems are resolving decades of accumulated drift in months.

And it means you can compete, differentiate, and outperform.

That’s because the team with the clearest language wins.

Three levels of language debt

1. Language that can’t travel. Positioning or strategy language that requires so much explanation it can’t survive without a narrator. If it doesn’t land instantly without context, it’s creating drag at every handoff.
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​2. Undefined terms. Core concepts that teams use daily but have never formally defined. Every conversation requires renegotiating what the words mean before any work gets done.

3. Same word, different meaning. Multiple functions using identical language but interpreting it through different incentive structures. The data is the same…the labels each team puts on it are not.

Language debt compounds at scale

• At 5 people, shared context fills the gap. Everyone interprets roughly the same frame because they’re in the same room

• At 25, the debt starts showing…different teams develop different meanings for the same terms. Meetings get longer

• At 300, the debt is structural. Every undefined term generates coordination cost every time it crosses a team boundary

Now layer AI into this equation.

AI amplifies clarity. It also amplifies confusion.
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Every model, agent, and automated system amplifies whatever implicit language is already in place.

Better tools do not clean up muddy language. They multiply it.
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Pro-tip: The organizations getting this right are treating language as infrastructure…the same way they treat data architecture or code quality. Clear language compounds. Vague language fragments.

Where is language working on your behalf, and where is it costing you velocity, traction, or success?

Upgrade your language system (takes 5 min):

1. Open your LLM tool of choice (Claude, ChatGPT, Gemini, etc).

2. Copy/paste this newsletter into it

3. Pick one internal document: a strategy doc, a PRD, a QBR, an exec memo, etc. Something that actually drives decisions.

4. And attach that document for analysis.

5. Copy/paste this exact prompt at the top of your chat: “Based on the concept of “language debt” described below, do a language system analysis of the attached document. Think ontologically. Identify the language that can’t travel, the terms used but never defined, the concepts that could mean different things to different teams, and the single term that, if clarified, would reduce the most coordination friction, and increase the probability of our success. Provide your output in table format, with clear rationale, and actionable language recommendations.”

6. Read what it outputs, and determine what is most useful.

7. If you like this upgrade, tell your LLM to add the concept of language debt to your memory for future usage.

8. Reply to this email and tell me if this has surfaced any new insights or breakthroughs.

This is one of hundreds of “thinking upgrades” I could continue to share going forward.

If this was useful, let me know. Equally, if it’s disappointing, I want to know that too.

I’m curious to hear from you.

Hit reply, and give me your thoughts.

Michael

P.S. When you’re ready, here’s three ways I can help:

1. If you want a second set of eyes on your narrative, reply and tell me more

2. If you need narrative architecture, I help operators do just that. Apply for Q2​​

3. If you want a storytelling keynote, I still do them on a select basis. Let’s talk