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.
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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?
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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.
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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.
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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.”
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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.
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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.
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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.
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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.
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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:
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​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.
​
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.
​
​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.
​
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.
​
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

“I don’t want to present something that I have to walk back.”

The startup is gearing up for their Series A later this year. The board wants to press forward. The company is performing well.

The fear isn’t a confidence issue. It’s an expectation issue.

When you’re the CEO, you’re usually holding the most uncertainty in the room. The board wants direction. Investors want trajectory. The team wants belief. And you know how many variables are still moving underneath all of it.

Most founders oscillate between two extremes. They either overcommit and spend the next quarter recalibrating expectations. Or they hedge so carefully that conviction thins out and momentum slows.

There’s a third option. It’s what I call:

How to bring integrity to the act of speculation.

Speculation isn’t the problem.

It’s the absence of structure around it. When people can’t tell what’s proven, what’s in motion, and what’s still directional thinking, they collapse it all into one category. That’s when trust erodes.

So we didn’t dial down ambition. We made the architecture of his thinking visible.

Before your next board conversation, separate four things:

• Thesis: What’s changing in the market and why you’re positioned for it

• Assumptions: What’s validated and what’s still being tested

• Principles: What remains true regardless of quarter-to-quarter swings

• Scenarios: Possible paths forward, framed as optionality rather than promises

Notice what this does. It doesn’t eliminate uncertainty. It manages it.

When expectations are structured, stakeholders stop projecting their fears onto your future. Risk is acknowledged without signaling doubt. Optionality is preserved without sacrificing momentum.

You own the narrative.

Execution velocity holds because everyone understands what is fixed and what is variable.

Not because the future is certain.

But because the boundaries are clear.

That’s how you bring integrity to the act of speculation.

Here’s the diagnostic:

When you describe where you’re headed, can someone clearly separate what you know, what you’re testing, and what you’re imagining?

If they can’t, you don’t have a credibility problem.

You have a narrative management problem.

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 do them on a select basis. Let’s talk

The response was immediate. When you see it, you can’t unsee it.

You ship your org chart. Your product mirrors the communication structures that created it. People saw it instantly inside their own teams. In the roadmap. In the seams of the product. In the way decisions actually move.

Which raises a harder question.

What happens when we start layering AI directly into that same communication graph?

AI adds handoffs, not just horsepower

You don’t need to be deep in Claude Code or experimenting with multi-agent workflows to sense the shift.

The broader narrative is everywhere: AI is moving from assistant to actor.

In practical terms, that means systems that draft, analyze, route, trigger, and occasionally chain work together with less supervision. Instead of being a tool you consult, AI increasingly participates in the flow of work itself.
​
And once that happens, each system becomes another node in your communication network. It receives context, produces output, and hands that output to the next node, whether that’s a person or another system. Every additional node creates more handoffs, and every handoff is a place where meaning can get distorted.

I was working recently with a company that had doubled its headcount over a few years.

They had more specialization, better tooling, and more process discipline than ever before. Yet they were shipping at roughly the same pace they had when the team was half the size. It wasn’t intelligence or tools. What had grown fastest was coordination.

Project calls with 15 people where most were listening. Review cycles stacked three layers deep where one might have sufficed. Leadership eventually sent a note explaining that if you were only there to stay informed, recordings and summaries would suffice.

The meetings kept filling up anyway.

Because when people don’t trust that meaning will survive the handoff, they show up to hear it firsthand.

This coordination tax rarely appears as a line item. It shows up as subtle friction, calendar density, and the quiet anxiety that something important might get lost between teams.

Why your team doesn’t get 10x faster

At the individual level, AI can feel transformative.

A strong operator adopts good tooling and their output increases dramatically, sometimes 5x or more. The gains are tangible and hard to ignore. But when you zoom out to the team level, the picture shifts. The organization rarely moves 5x faster. Throughput may improve 10-15% percent. Occasionally more, but seldom in proportion to individual acceleration.

For a while, I couldn’t reconcile that gap.
​
Brooks’ Law helps explain it.
​
Decades ago, the software engineer Fred Brooks observed a pattern: every time you add a person to a team, you slow it down.

That reality doesn’t disappear just because some of the nodes are AI.

Each system can be highly productive on its own, but it still needs context and alignment within the broader workflow. Without a shared foundation, coordination grows quietly in the background, and much of the visible productivity gain gets absorbed into keeping everything coherent.

Most organizations track output improvements carefully. Very few track coordination cost with the same rigor. So AI looks impressive at the individual level, while at the organizational level strategic initiatives still feel slower than they should.

As a leader, you may sense the drag. It just doesn’t show up cleanly on any dashboard.

The hidden work nobody counts

The coordination tax is not abstract.

It’s built from specific, observable dynamics.Outputs that need to be reformatted when they cross a team boundary. Decisions that stall because two groups are using different language for the same priority. Subtle resets that occur when someone says, “That’s not what we meant,” and the workflow loops back to reinterpret assumptions.

If you’ve watched a capable team with good people and modern tools move more slowly than its talent would suggest, you’ve seen this at work. It’s rarely a question of intelligence. More often, it’s a question of coherence.

The teams that consistently move faster tend to share something deeper than tooling. They share language. A common way of framing tradeoffs. A shared understanding of what matters and how decisions are interpreted. An underlying architecture that allows meaning to travel across boundaries without being reconstructed each time.

When that architecture is present, coordination still exists, but it requires less repair.

I’ve seen small teams move with disproportionate speed because everyone could articulate the same priorities without checking in first. When they layered AI on top of that shared interpretive foundation, the outputs were aligned from the beginning.

Less translation. Fewer resets.

The coordination tax didn’t disappear entirely, but it stopped compounding at the same rate.

A quick way to spot your coordination tax

If you’re curious where the friction might be hiding, try mapping a single strategic initiative from end to end.

Count the handoff points, not just between people but between systems, teams, and decision layers. Each transition represents a coordination cost, whether visible or not.

Then ask two questions:

• Which of these handoffs exist because your language isn’t aligned?

• Which exist because the work genuinely requires distinct expertise or systems?

That distinction is often revealing.

In many cases, more handoffs exist than the work itself demands because meaning doesn’t reliably travel across the communication graph. When that happens, people compensate with more meetings, more reviews, and more context sharing.

Remember: AI will amplify whatever structure is already in place.

If interpretation is fragmented, it may amplify confusion and increase the repair work.

If interpretation is aligned, it can amplify clarity and reduce the need for constant translation.

The deeper question isn’t how many agents you deploy. It’s what kind of coherence you’re scaling as your graph grows.
​
P.S. I recently turned Tues into a zero-meeting build day. No calls, no handoffs, no alignment loops. Just deep work. I was so in the groove that I worked until midnight, which I never do. What struck me wasn’t the volume of output. It was how clearly it revealed how much of the other four days are spent maintaining coherence across the team. The tax is easy to miss when you’re inside the graph. It becomes visible the moment you step outside it.

The “AI conversation” has started producing a pattern I can’t ignore.

In nearly every room I’ve been in lately, whether it’s a boardroom, a keynote, or a working session, I’m watching the same thing happen.

Someone brings up AI and the room subtly splits. Not into believers and skeptics.

Into two groups who are both right.

One group – sees momentum. Real applications, real traction, a window that’s opening. The other group – sees the gap. The structured data problem, the regulatory fog, the distance between what is promised and what’s actually currently delivered.

Both are making rational calculations. They’re just standing in different places.

Curve 1: The Lifecycle of Your Narrative

Every narrative has its own lifecycle or s-curve.

Early on, a new narrative acts as a force multiplier. It’s novel, energizing, ahead of the room. People lean in because the idea feels like it’s opening something new. Over time, it matures and stabilizes. It becomes familiar and expected. And eventually, if you’re not careful, it becomes a drag. Familiar. Expected. No longer opening anything.

Where your narrative sits on that arc shapes what it can do.

But that alone doesn’t explain why the same story accelerates one room and slows another.

For that, you have to look at the second curve.

Curve 2: The Hype Cycle of Expectations

Every major technology wave follows a predictable pattern.

The Gartner Group calls it the Hype Cycle.

Excitement spikes. Capital floods in. But quickly the narrative outruns reality. Then disillusionment sets in, not because the technology failed, but because expectations ran ahead of adoption. Over time, substance compounds and the signal stabilizes.

AI is living inside that pattern right now. The excitement is real. So is the fatigue.

That fatigue has a cost. It shows up in skepticism: longer sales cycles, tighter budgets, more pilots, more governance layers, more requests for proof before commitment.

Not because the future isn’t coming. Because belief capacity has shifted.

Where the Curves Collide

A few weeks ago, I spoke to an IT solution provider that works with US government agencies.

It was their annual sales kickoff, with a room of 300 sales and technical consultants.

Everyone in the room knows the future is here.

Quantum risk is real. AI is reshaping the flow of work. The tension wasn’t conviction. It was timing.

Their buyers are living in two horizons at once. Today’s compliance constraints and legacy systems. Tomorrow’s inevitabilities in quantum and AI. Agencies have lived through modernization cycles that overpromised. Large IT transformations that failed to compound. Budget allocations framed as inevitable that did not return velocity.

That history shapes interpretation. It shapes how much future they are willing to buy today.

If you lead only with inevitability, you sound like the last wave. If you lead only with present constraints, you undersell the structural shift underway. The work is helping buyers move along the S-curve while acknowledging where they are on the Hype Cycle. Acknowledging current reality. Naming the inflection point ahead. Giving them language to allocate belief proportionally instead of defensively.

You cannot move people forward until you acknowledge where they’ve been.

When the lifecycle of your narrative and the expectation position of your audience align, velocity increases. When they don’t, friction compounds. Not because the story is weak, but because the posture is miscalibrated.

The Essential Diagnostic

Before your next strategic conversation, ask yourself two questions:
​
First: where is your narrative on its lifecycle?
​
Still a force multiplier. Now in proof mode. Quietly becoming familiar. And where is this audience on the hype cycle? On the upswing. In the trough. Past the noise and rebuilding trust.

Second: what narrative debt are they carrying?
​
Have they absorbed failed pilots, overpromised transformations, budget overruns framed as inevitabilities. That fatigue has a cost. It’s when a board that was ready to approve the rollout asks for another pilot instead.

Most teams map their message. Few map the psychological position of the room.
​
Belief allocation. That’s where velocity is won or lost.

This isn’t really about two curves. It’s about timing, trust, and how much future your audience can absorb right now.

Where are you standing.

And where are they.

Every product is a mirror of the people who built it.

A computer scientist named Melvin Conway noticed this before almost anyone else.

In the late 1960s, while working as a programmer on some of the first large software systems in what would later become Silicon Valley, he identified a pattern others missed. The software teams built consistently mirrored the way those teams communicated.

Not sometimes.

Every time.

He turned that observation into a law.

And that law quietly changed how an entire industry thinks about execution.

I’ve been obsessed with Conway’s Law for years.

It’s the lens I use when I walk into any engagement, whether that’s advising a product org or sitting down with a founder to work through their narrative architecture. Let me break it down, because this is the part most people skip past without fully absorbing.

Conway’s Law: any software or tech product is a direct reflection of the communication structures of the organization that created it.

If your engineering team is organized into three groups that don’t communicate well, your software will have three distinct modules that don’t integrate well. If your product team and your sales team describe what you’re building in different language, your product will feel incoherent to the market. If leadership communicates in abstractions while the frontline operates in specifics, you’ll ship a product with a gap between vision and execution.

The boundaries of your system are held in the boundaries of your communication.

The strengths and limitations of any product reveal the strengths and limitations of the thinking behind it. We design systems that mirror the beliefs, assumptions, and constraints of the very culture that created them. That’s not a metaphor. It’s a law.

The shorthand people use for this is simple: you ship your org chart. Whether you mean to or not.

Three laws. One hidden constraint.

I keep placing Conway’s Law alongside two other laws.

Together, I think they form the real operating framework for anyone building at scale.

The first is the Power Law.
​
This is the math that governs venture capital returns. Across many bets over time, a small number will drive a disproportionate share of total returns, while many contribute in quieter but meaningful ways. The work becomes knowing where your energy can unlock exponential scale, and where steady support is the right move. Power Law dynamics are about asymmetry and compounding. How a few inflection points create outsized impact across an entire system.

The second is Moore’s Law.
​
First observed by Intel co-founder Gordon Moore, it describes how computing power compounds over time, doubling roughly every two years as costs fall. This steady acceleration is the tailwind behind each major technology wave of the last half century. Moore’s Law is about velocity. Not just how fast technology improves, but how quickly the frontier of what’s possible moves forward.

Power Law tells you where the returns are. Moore’s Law tells you when the technology is ready.

But neither one tells you whether you can actually execute.

That’s the gap. Both laws describe external conditions. Market dynamics. Technical capability. Neither accounts for the internal architecture. The human system. The context layer.

And the context layer is what we’re all struggling with right now, especially as everything keeps moving faster. Making sense. Creating meaning. Building shared understanding across teams, across functions, across an organization trying to move at the speed of compute.

The structures you put in place for that...the language, the narrative, the communication architecture. Those are the enabling functions for building anything at scale. Especially at hyper speed.

Conway’s Law is the hidden third law.
​
And right now, it’s the limiting factor on the other two.

Consider what’s happening in AI

OpenAI and Anthropic share closely related origins. A similar talent pool. Comparable starting conditions. The same moment in history. If you look only at Power Law and Moore’s Law, you’d expect broadly similar products.

And yet they feel different.

ChatGPT tends to be prescriptive and declarative. Here’s the answer. Next question. Claude is more reflective and consultative. It thinks out loud, acknowledges uncertainty, and asks clarifying questions.

This isn’t primarily a technology difference. It’s a Conway’s Law difference. The internal communication patterns at each company. What gets rewarded. How decisions get made. What kind of thinking is encouraged. Over time, those signals shape what gets built. Often unintentionally.

Two companies. Similar starting conditions. Meaningfully different products.

Why This Matters More Now

In an AI-first world, Conway’s Law matters more than ever. Not less.

AI systems amplify whatever language you feed it. When internal communication is misaligned, the outputs built on top of it tend to misalign as well. Your strategic docs, operating norms, and SOPs are increasingly treated as structured inputs that AI systems read and build on top of.

Narrative is no longer just how you communicate. It’s part of the architecture. The UX layer that shapes coherence, handoffs, and behavior downstream.

The question isn’t whether you have a narrative architecture. You always have one.

The question is whether it’s intentional, or chasing its tail.

A Simple Coherence Test

Pull up your org chart.

Notice where the boundaries sit between teams. Notice which groups talk regularly and which ones don’t. Now look at how your company shows up. Your product. Your go-to-market motion. Your customer journey.

The seams tend to line up. Disjointed products, inconsistent messaging, and leaky handoffs map to teams that don’t communicate well. Cohesive experiences, clear positioning, and smooth revenue motion map to teams that do.

Once you see it, you can’t unsee it. And once you see it, you can design for it. Not just better products, but better communication structures that make growth, alignment, and execution inevitable.

Run the test and the seams become hard to ignore. If something surprises you, I’m curious to hear what you notice.

Michael

P.S. With two kids under two, it’s hard to miss how much communication happens before language. Conway’s Law starts earlier than we think.

A few years ago, during a Q&A after a talk, someone asked me a question I couldn’t shake:

“Is what you’re talking about similar to Aristotle’s Three Proofs of Rhetoric?”

I’d been sharing my narrative framework See It, Feel It, Believe It - a signature sequence I developed for execs and founders trying to sell a future that didn’t yet have permission to exist. This person in the audience wanted to know if I was copying something a Greek philosopher figured out two thousand years ago.

I hadn’t thought about Aristotle since junior year of college. But the question stayed with me, so I went back and looked.

Ethos. Pathos. Logos.

Oh shit.

The old man

If you’ve forgotten your philosophy class, here’s the quick version: Aristotle identified three modes of persuasion.

• Ethos is credibility: why should I trust you?

• Pathos is emotion: why should I care?

• Logos is reason: why does this make sense?

These are the building blocks of Western rhetoric. They’ve held up for over two millennia.

And somehow, without realizing it, I’d reverse-engineered them.

See It maps to Ethos. Feel It maps to Pathos. Believe It maps to Logos.

The grab bag problem

But here’s where it gets interesting.

Most people treat Aristotle’s three proofs like a grab bag. Pull whichever one fits the situation. Lead with logic here, lean on credibility there, go for emotion when the moment calls for it.

That works fine when you’re communicating something familiar. Something where everyone in the room already agrees on the basic shape of reality.

But what about when you’re not?

What about when you’re dealing with disruption? Ambiguous problem spaces? Exponential change? A paradigm shift that challenges how people see the world?

What about when the thing you’re asking people to believe might be perceived as irrational, illogical, even heretical by current standards?

The grab bag doesn’t work.

The order becomes everything.

Why leading with evidence fails

Here’s what I’ve noticed over years of watching people try to move rooms:

When you lead with Logos (with the data, the conclusions, the proof) you’re asking people to believe something before they have any reason to. You’re imposing a conclusion. And nobody likes having belief imposed on them.

What happens? They push back. They poke holes. They become adversarial (even when your logic is airtight) because you skipped the steps that would let them receive it.

You’re over-rationalizing the narrative to death. You’re giving them Boolean choices before they’ve felt any pull toward where you’re trying to take them.

When you lead with Pathos (with the emotional appeal, the moving story, the personal testimony) you’re zooming in to an n=1. An individual moment that might be powerful but doesn’t set the broader context. It doesn’t give people the 30,000-foot view of where the world is going and why any of this matters at scale.

Without that larger frame, even the most compelling story can feel like an isolated case. Touching, maybe. But not necessarily relevant to me.

So what’s left?

Ethos. But not ethos the way it’s usually taught.

The classical interpretation of ethos is personal credibility - your credentials, your track record, why you’re qualified to speak on this. That matters, but it’s not what I mean by See It.

There’s another definition of ethos: collective motivation. The underlying values and aspirations of a group. What a culture is already moving toward, whether it’s been named or not.

This is what See It does. It names a shift that’s already underway. It captures a collective aspiration. It makes people feel like the world is moving - and that movement creates possibility, not threat.

Once people can see that shift, then they’re ready to zoom in and feel it at a human level. And only after that will they be open to believing your plan for navigating it.

See It. Feel It. Believe It.

Decision gates, where one unlocks the next.

What it looks like in practice

I was working with a VP of data science at a major tech company recently. They lead a team of brilliant data scientists - people who sit on mountains of data and have built models that would make most organizations jealous.

Their frustration? “We know everything, but we can’t communicate it.”

Their team would walk into meetings armed with evidence. Charts. Findings. Conclusions backed by rigorous analysis. And they’d walk out without decisions. Or worse - with decisions that ignored everything they’d presented.

We talked about the sequence. Instead of leading with their findings, what if they started by naming a shift in the industry that everyone in the room could feel but hadn’t articulated? What if they let people sit in that recognition before introducing any data?

They tried it with their team’s next big presentation.

They called me afterward. “They didn’t just approve the recommendation. They asked how fast we could move.”

Same team. Same data. Different sequence.

Why this matters now

Here’s what I keep coming back to.

All of this (Aristotle’s proofs, the sequence, the decision gates) matters more now than it ever has.

And the reason is AI.

We’re living through a moment where the old ways of creating clarity are being stress-tested. AI makes information abundant. It makes data accessible. It can generate arguments, summaries, and analyses faster than any human.

But it can’t do the thing that matters most: help people think about how they think.

That’s meta-cognition.

Rhetoric, at its core, is the deliberate structuring of thought so that meaning can become shared meaning. So that alignment becomes possible. So that a group of people who started in different places can arrive at a common place of understanding.

The challenge with disruption (with ambiguity, with exponential change) is that it’s really hard to find signal.

It’s hard to create coherence when the ground keeps shifting.

This is why the sequence matters. It’s not about control. It’s about coherence.

A control narrative says: here’s what you need to believe, and here’s the data that proves it.

A coherence narrative says: here’s what’s shifting, here’s what it means for us, and here’s how we might navigate it together.

One imposes. The other invites.

Aristotle gave us the ingredients. What I’ve spent twenty years learning is the sequence…and why, in a world that keeps accelerating, getting that sequence right is the difference between a room that leans in and a room that shuts down.

Michael

P.S. The old man was onto something. He just didn’t have to deal with AI.

I was talking with a CEO a few months ago.

He runs a legacy company with decades of history and a strong market position, and after years of effort their digital transformation is finally working. By most external measures, they are winning.

About halfway through our coffee, the energy shifted. He put his mug down and said:

“I finally feel like we’re good at this. I can see the path to victory. But I have this nagging sense that we’re about to get T-boned in five years by AI. Not a collision. An extinction event.”

That line has been stuck in my head.

I hear versions of it all the time. Different industries, different leaders, but the same underlying sensation. The story that got us here worked, sometimes remarkably well, and yet something about the room now feels different.

The question that usually follows is practical.

“How do we fix our messaging?”

But when a leader feels that particular mix of clarity and unease, it is rarely about the words themselves. What they are sensing has much more to do with trajectory than expression.

It is not a messaging problem. It is a curve problem.

Growth has a shape

To understand why a story stops working, you have to look at the shape of growth itself.

Most growth does not fail because it stops working. It fails because it works too well, for too long.

Progress tends to start slowly, then something clicks. Adoption accelerates, results compound, and eventually returns flatten. Not because execution broke, but because the system itself begins to saturate.

That pattern has a shape. It is often described as an S-curve.

Clayton Christensen used the S-curve to explain how technologies take hold. Slow adoption. Rapid acceleration. Eventual maturity.

Specifically disruptive technologies that create a new paradigm and replace the old order.

What matters most isn’t the plateau.

It’s that curves overlap.

Companies get into trouble when they keep refining a mature curve while a new one is quietly forming beneath them.

What took me a long time to appreciate is that this pattern is not limited to technology.

The same curve shows up in products, companies, markets, and even social movements.

Narratives follow it too.

The s-curve of storytelling

Change equals Story. If there is no change, there is no story.

So just like technology, your narrative has a lifecycle. It moves through three distinct phases.

Phase 1: The Exponential Climb (Force Multiplier):
When a narrative is new, it creates disproportionate value. It differentiates you. It surprises people. The story travels without you having to push it. It reframes the world and opens a door to new possibilities. It creates massive lift and acceleration.

Phase 2: The Plateau (Functional Utility):
​Over time, the market absorbs the narrative. It still works, but it no longer amplifies. Competitors sound like you. Buyers understand the category. Growth becomes incremental. You’re spending more energy to get the same result.

Phase 3: The Decline (Drag Co-efficient):
This is the real danger zone. At scale, the math turns against you. What once felt like momentum now feels like resistance. You’re selling into a saturated market with a story calibrated for a smaller world.

You tend to feel this in subtle ways. Meetings start to run longer. Decks get denser.

Growth requires more justification and less imagination. None of this looks like a messaging failure on the surface, but it often reflects narrative drag driven by the law of big numbers.

The “dip in the S-curve” is exactly where the CEO in my story was headed.

Their flagship offering had been in place for almost twenty years. It was safe, timeless, universally accepted…and somewhat static. It described what the company had always done, but it said nothing about where the world was going.

Meanwhile, AI and digital platforms were creating a new S-curve in their industry. And if they kept optimizing the old narrative while that new curve rose beneath them, there wasn’t going to be a graceful transition. There was going to be a collision.

Jumping the curve

The hardest part of leadership is not climbing the curve. It is recognizing when you have to leave it.

Whether you are moving from Product to Platform or from Seed to Series A, you are in a phase shift. The old story no longer creates the lift it once did, and the new one is not fully formed yet.

That in-between period is uncomfortable, and it is also necessary.

Once we separated hype from reality, I could help that CEO make the shift. We stopped describing the functional utility of the past and began articulating the aspirational value of the future.

Lately, I have been asking myself the same question, even when things are going well.

Is the story I am telling oriented toward where we have been, or toward where the world is going? Does our narrative expand the future, or does it quietly constrain it?

When things start to feel heavy, when explanation replaces enrollment, it is often a sign that you are nearing the end of the curve.

The goal is not to be right about the past.

It is to remain relevant for what comes next.

Michael

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 founders & operators do just that. Apply for Q2​​

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

Not blown away. Not deflated. Solid.

Later that day, you hear how someone described you to a colleague. Or you see it paraphrased in a follow-up email. It’s accurate enough. But something’s missing. The sharp edge of what you actually do has been rounded off.

Your work has been translated into safe, generic language.

Nothing was misunderstood. But something important didn’t survive the retelling.

That moment has been sticking with me. Not because it’s rare. Because it’s increasingly normal.

Where differentiation actually slips

Most teams assume differentiation is lost when competitors copy features.

In practice, it usually slips earlier. It slips at the level of translation. When other people explain you in their own default vocabulary, you start to sound interchangeable. Even if the product is strong. Even if the work is real.

This is the quiet erosion no roadmap catches.

I’ve been noticing the same pattern across very different worlds.

Fintech. Healthcare. Enterprise software. Services.

Smart teams. Serious products. Still struggling to stay distinct once they’re not in the room. Not because they lack value. Because the way their value is described doesn’t travel intact.

AI has accelerated this dynamic, but it didn’t invent it. Capabilities converge faster. Features get matched sooner. Which means weak language gets exposed more quickly.

Meaning still moves at human speed. You don’t need to be building an AI company to feel this pressure. You just need to operate in a world where functional differences collapse faster than they used to.

The question most teams don’t ask

Most communication is optimized for clarity in the room.

Slides make sense. Demos land. Questions get answered. What far fewer teams design for is what happens after.

The real question isn’t “Did they understand?”. It’s “What did they repeat?”. This is where language quietly becomes leverage.

I’ve started calling this Semantic Monopoly. Not as a branding exercise. As a description of a state some companies reach. A moment where their words become the default way others describe the problem itself. Not taglines. Not slogans. Phrases that stick because they give people a clean way to think.

When that happens, your story no longer depends on your presence.

It moves without you.

A better way to talk about payment rails

Most payment infrastructure companies lead with rails. Settlement windows. Reconciliation. Compliance.

All accurate. All abstract. All forgettable to anyone who doesn’t live inside payments.

For a startup that I’ve invested in via Veridical Ventures, we’re taking a different approach.

They don’t lead with payment rails anymore.

They lead with a question every business recognizes instantly:

“Where’s my money?”.

That question does a lot of work. It abstracts complex infrastructure without dumbing it down. It turns technical plumbing into a universal anxiety. No glossary required. No explanation needed. The phrase travels on its own.

What changed wasn’t the product.

The product is still sophisticated. What changed is that the problem became speakable. Customers didn’t need to understand ACH. They just needed language for a frustration they already felt. Three-day waits. Lack of visibility. High fraud risk. And worse.

Once that language exists, it starts showing up internally. In emails. In meetings. In justification conversations. At that point, you’re no longer pitching a vendor. You’re naming a problem that now demands a solution.

The real test happens later

The truth shows up quietly about 30 minutes after a meeting.

In the follow-up. The recap. The internal debrief you’re not part of. That’s when you find out whether your language held.

I use a simple check: The 30-minute blind recall test.

Can others explain what you do without looking at their notes? And when they do, are they using your words or their own?

If you hear “They do identify-first, open banking, multi-rail movement in a single API,” the frame didn’t stick.
If you hear “They solve the ‘where’s my money?’ problem,” it did.

This isn’t a marketing metric. It’s a meaning check.

Both statements are true. One has a much higher probability of being repeated. Verbatim.

What stays when you’re not in the room

When teams start paying attention to this, a few things shift:

• Less clever phrasing

• Less feature explanation

• More portable language

• More coherence over time

You stop optimizing for persuasion in the moment and start designing for what survives afterward.

In markets where capabilities converge quickly, meaning still spreads person to person. The advantage isn’t how well you explain yourself. It’s what survives when you’re not there to explain it.

When they talk about you, are they using your words? Or are you playing the game of telephone?

If you’re curious, try the blind recall test after your next important meeting.

Don’t ask for feedback. Just listen for what language comes back to you. That’s the real signal.

Anybody who tells you otherwise is talking nonsense.

To be human is to tell stories.

Lest you think this storytelling hype cycle is just a 2025 thing, there have been many others.

In 2014, the legendary Stefan Sagmeister published a viral video with a similar message:

​“You are NOT a storyteller.”

He argued that unless you are writing novels or making feature films, you are stealing a title you didn’t earn.

Today, I know plenty of out-of-work novelists, filmmakers, and marketers.

Storytelling, meanwhile, has never been more widespread or in demand.

LinkedIn posts…storytelling.
Entrepreneurship…storytelling.
All-Hands…storytelling.
Investor pitch…storytelling.
Job interview…storytelling.

You get the idea.

Leadership is storytelling whether you like the label or not.

Barbarians At the Gates: Insecurity Disguised as Standards

It feels like a defense of quality. But it’s not.

When someone insists you’re “not a storyteller,” it’s rarely about standards.

It’s about fear. Fear that if storytelling isn’t scarce, their position isn’t special.
They’re threatened. Because if everyone is a storyteller, why would anyone need them?

Here is the truth they don’t want you to know:

To be human is to tell stories. Humans are wired for storytelling.

We don’t store facts. We store meaning. Every experience is encoded as a narrative about what it meant. And what it says about us.

To be a storyteller is an identity frame for describing a basic human function.

Everyone does it, like breathing and having sex. Of course, some do it better than others.

But you don’t have to be a “breatharian” or a “sexologist” to be a great communicator.

The Meta-Skill of Our Times

Where the rubber meets the road for your leadership.

Narrative is the #1 competency required to advance to a C-suite or VP role.

What AI has shown us is that “thinking” is the meta-skill of our times.

You cannot think without telling a story. Narrative is how thinking becomes legible to other people. It’s how you structure thinking. It’s how decisions get made. Investing in a startup. Selling the future. Analyzing data.

You cannot think without words. Organizing them. Choosing them. Editing them.

AI can do a lot of that for you.

But the human interpretive layer is the part that makes anything memorable.

This means Storytelling is no longer a “soft skill.”

It is EVERYBODY’S job description.

And the most important skill to differentiate yourself or your business.

I saw this clearly this week with a client. A technical VP at a massive tech company.

Here’s what they told me:

“Our President DMed me out of the blue to say ‘love your recent posts’
People multiple levels above me are quoting my ideas in planning meetings.
Politically, it’s left people confused. My formal authority doesn’t match my growing visibility.
It’s ok for now. Having an oversized impact with plenty of growing headcount and mandate.”

That’s the power of narrative. It resolves the gap between your official title and your true influence. If you have influence that exceeds your box on the org chart, it is because you told a story that stuck in someone’s head.

This is the ultimate power move.

Are You Putting in the Reps?

So, how do you move from “unconscious narrator” to “conscious storyteller”?

You don’t walk into the gym and bench press 300 pounds on day one.

You start with your life.

If you can’t tell a compelling story about what you ate for lunch, you definitely can’t tell a compelling story about your Q3 strategy.

Here’s a quick start inventory:

• What you’re reading: The idea currently rewiring your worldview

• What you’re watching: The character whose dilemma feels uncomfortably familiar

• What you’re eating: Your ability to describe texture, not just calories

• What you’re obsessed with: Where your curiosity goes when no one is paying you

• What you’re wrestling with: The friction you’re actively trying to resolve

• What you’re a paradox about: The two opposing truths you hold at the same time

This is just some of the surface area for how you metabolize your life.

When you get better at sharing the small stories, the big stories that really matter stop feeling like performance and start feeling like truth.

You Have a Choice

You can let the “experts” shame you into silence because you don’t have the same expertise.

You can continue to outsource your voice, waiting for someone else to tell you what you believe.

Or you can just play your own game.

Next time someone tells you that you aren’t a storyteller, tell them:

“That’s your f-ing story, not mine.”*

*(Of course, you should probably find a more polite way of saying it)

Keep telling stories. It’s the only thing you actually control.

Now, it takes work. It takes practice. It takes reps.

So keep breathing. Keep schtoopin’. And keep storytelling.

Storytelling is hard because to master storytelling is to master life itself.

Here’s a hot take from CES on why the future belongs to the storytellers.

This is the work. I’ll see you in the gym.

Michael

P.S. Some weeks you have to use profanity to get your point across.

P.S.S. Three ways I can help when it’s useful:

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 founders & operators do just that. Apply for Q2​​

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