China: $12.4 billion.

That’s a 23x difference.

Now here’s the more interesting number.

By March 2026, Stanford’s AI Index found that the performance gap between the leading U.S. and Chinese AI models had narrowed to just 2.7%. The two countries had already traded places at the top several times since early 2025.

Read those two numbers together.

23x the private investment.

2.7% the model-performance gap.

That should make a lot of executives in developed markets slightly uncomfortable.

Because there is still a deeply embedded assumption in the U.S. and Europe that goes something like this:

Historically, that was mostly true.

With AI, I’m not sure it will stay true for very long.

And if you run a company in New York, London, Berlin or Paris, assuming it will is becoming dangerous.

The Lead is Real, The Moat is Not

Let me be clear.

The United States has an enormous structural advantage in AI.

Capital. Chips. Research universities. Talent. Cloud infrastructure. Frontier labs. Venture capital. Customers willing to pay.

I’m not arguing that these advantages disappeared overnight.

I’m arguing something different.

The technology produced by those advantages is becoming globally accessible at an astonishing speed.

A breakthrough used to take years to diffuse.

Now it can take months.

Sometimes weeks.

Look at DeepSeek.

You don’t need to move to China, negotiate a technology-transfer agreement or build your own data center to use a Chinese reasoning model.

AWS made DeepSeek available as a fully managed model on Amazon Bedrock. Microsoft offers DeepSeek models through Azure AI Foundry. Google added DeepSeek to Vertex AI Model Garden.

Think about how strange that would have sounded a few years ago.

Three of America’s largest technology companies are providing infrastructure through which their customers can deploy Chinese AI models.

That’s not a criticism.

It makes perfect business sense.

But it tells you something important about where competitive advantage is moving.

The model itself is becoming a component.

And components get commoditized.

Open Models Have Changed the Speed of Catch-up

One of the most interesting AI charts I’ve seen recently isn’t about funding.

It’s about usage.

OpenRouter analyzed more than 450 trillion tokens processed on its platform between January and mid-June 2026.

At the beginning of the year, DeepSeek represented roughly 9% of token volume.

By June, it was around 18%.

More interestingly, Chinese models collectively surpassed American models in token share on OpenRouter in early June.

Now, OpenRouter is one platform. I wouldn’t confuse its traffic with global AI market share.

But directionally, it tells us something important.

Capability travels.

Fast.

And buyers increasingly have the ability to choose between models based on performance, price, latency and suitability rather than where the model was created.

This changes the economics of catching up.

A company in Istanbul, Bangalore, São Paulo or Jakarta no longer needs to reproduce hundreds of billions of dollars of AI infrastructure before it can benefit from the output of that infrastructure.

• It can rent the intelligence.

• Use an open model.

• Fine-tune it.

• Build on top of it.

• Combine several models.

• And increasingly, swap one for another when the economics change.

That last part matters more than it sounds.

Because if your competitive advantage is:

“We have access to the best AI.”

You may not have much of a competitive advantage.

Your competitor may have access to something almost as good next quarter for a fraction of the cost.

Your Company Does Not Automatically Inherit Silicon Valley’s Lead

This is where I think developed-market companies risk becoming complacent.

The adoption statistics look fantastic.

Stanford’s 2026 AI Index says 88% of surveyed organizations were using AI in at least one business function in 2025.

Sounds like the transition is well underway.

It is.

But using AI and reorganizing a company around AI are two completely different things.

McKinsey found that nearly 90% of organizations were at least experimenting with AI, while only 7% said they had scaled it across the enterprise.

That gap is the whole game.

Giving everyone ChatGPT, Claude or Copilot is not AI transformation. It’s access.

Your employees using AI to rewrite emails faster is useful. It is not a moat.

The hard part starts when you ask:

• Which workflow changes?

• Which decision changes?

• Which cost changes?

• Which customer experience changes?

• Which revenue number changes?

• And what can we now do that was economically impossible two years ago?

That is where organizations slow down.

Committees appear. Governance meetings appear. Twenty-seven AI pilots appear.

Everyone has a strategy deck. Nobody can tell you what happened to gross margin. And the latest McKinsey research makes the difference quite visible.

Among its AI “high performers”, nearly three-quarters report fundamentally redesigning workflows because of AI.

Among everyone else?

About one-quarter.

That’s not a tooling difference. It’s an operating-system difference.

The winners aren’t simply using AI more. They’re changing the machine around the AI.

Your Dangerous Competitor May Not Look Dangerous Yet

For decades, a mid-sized company in a developing economy had disadvantages almost everywhere.

Less capital. Smaller talent pools. Worse infrastructure. Less access to technology. More expensive knowledge. Smaller domestic markets.

If you were sitting inside a large American or European company, you could reasonably assume many competitors elsewhere were years behind.

AI doesn’t eliminate those disadvantages. But it attacks several of them at the same time.

• A ten-person export company can now perform market research that once required consultants.

• A small sales team can personalize communication across hundreds or thousands of accounts.

• A customer-service operation can handle dramatically more conversations without scaling headcount linearly.

• A developer who has never worked with a particular framework can become productive in it much faster.

• A local company can translate, localize, analyze, code, research and create at a level that previously required much more specialized talent.

This is why I think we make a mistake when we talk about AI only as a productivity technology.

Productivity is the obvious part.

The bigger shift is that AI makes capabilities affordable that many companies previously couldn’t afford at all.

And that disproportionately benefits the company that was constrained in the first place.

Imagine you already have 200 analysts. Making every analyst 20% more productive is valuable.

Now imagine your competitor could never afford an analyst team in the first place.

Suddenly, three people with the right AI systems can do a meaningful part of what previously required twenty.

Those two outcomes are not economically equivalent. One is optimization.

The other is capability creation. And capability creation is how gaps close much faster than incumbents expect.

The Real Moat is Becoming Learning Speed

There’s another reason waiting is expensive.

Companies often talk about AI adoption as if it were something they could postpone and then catch up on later.

• “We’ll let the technology mature”

• “We’ll see which vendors win”

• “We’ll implement once the use cases are clearer”

It sounds rational.

The problem is that your competitors aren’t only accumulating software while you wait. They’re accumulating experience.

Suppose a competitor starts redesigning its sales operation around AI today.

Over the next two years, it learns:

• Which leads should be contacted first.

• Which tasks should stay human.

• Which responses can be automated.

• Where hallucinations create risk.

• What data the system needs.

• How customers react.

• How salespeople actually use it.

• Where automation hurts conversion.

• Where it improves conversion.

• And how the workflow should change again.

After two years, you can buy the same model.

You cannot buy those two years of organizational learning.

This is the part I think many boards underestimate.

The compounding asset is not the AI. It’s the feedback loop around the AI.

The model can be copied. The API can be replaced.

The workflow knowledge accumulated through thousands of real customer interactions is much harder to reproduce.

That’s where the moat moves.

Don’t Start with an AI Strategy

This is also why I’m skeptical when someone tells me their company is “working on an AI strategy”.

AI is too horizontal for that to be a useful starting point.

I would start somewhere much less exciting: Which business number do you want to move?

Revenue? Conversion? Churn? Customer response time? Cost per ticket? Time to create a proposal? Time from lead to meeting? Customers served per employee?

Pick one.

Then find the workflow constraining that number. Then redesign that workflow using what AI makes possible today. Measure before. Change the system. Measure after.

Did the number move?

If yes, keep going. If not, kill it and attack another bottleneck.

You don’t need an 80-page transformation plan to begin. You need one economically meaningful constraint.

AI is leverage.

Leverage is only useful when you apply it to the right point.

The Comfortable Era is Ending

The West is not about to stop producing the world’s most important AI technology.

That’s not my point.

Something subtler is happening.

Producing the technology and capturing the economic advantage from the technology are becoming two different things.

We’ve seen versions of this story before.

Invention happens in one place. Manufacturing advantage emerges somewhere else. Distribution gets captured somewhere else again.

Eventually, the company or country that created the breakthrough discovers that creation alone did not guarantee value capture.

AI may compress that cycle dramatically. The Stanford numbers are a useful warning.

The U.S. can outspend China more than 23 to 1 in private AI investment while the performance gap between its leading models shrinks to a few percentage points.

Meanwhile, open models and global cloud infrastructure can put advanced capabilities into the hands of companies almost anywhere.

So if you run a company in a developed market, I wouldn’t spend too much time congratulating yourself for being geographically close to the AI revolution.

Your company does not automatically inherit Silicon Valley’s lead.

You still have to earn it.

The question is no longer: Who invented the technology?

Increasingly, it is: Who reorganized around it fastest?

Because the technology advantage you think separates you from the rest of the world is becoming cheaper, more open and more portable every month.

And if someone with fewer resources learns to use it faster than you do, don’t be surprised when they stop looking like the company that was supposed to be behind.

Deadpool. Free Guy. The Proposal…

The sarcastic Canadian guy who has somehow turned playing slightly different versions of Ryan Reynolds into a very successful Hollywood career.

Fair enough.

But while most of us were watching his movies, Reynolds was quietly building a second career.

And it may be even more interesting than the first.

He became an entrepreneur.

Then a marketer.

Then the co-founder of a creative company.

He bought pieces of companies most people had barely heard of, plugged his own attention and creativity into them, and helped turn them into unusually valuable brands.

• Aviation Gin was later included in a transaction worth up to $610 million.

• Mint Mobile became part of a $1.35 billion acquisition by T-Mobile.

But Reynolds wasn’t simply the celebrity appearing at the end of somebody else’s advertising process.

He was an owner.

He shaped the creative.

He helped write the ads.

He understood the audience.

He had his own distribution.

And, crucially, he could move before a normal company had finished scheduling the meeting about whether it should move.

The interesting part is how he got there.

Because Ryan Reynolds did not learn marketing at business school.

He learned it because Hollywood kept telling him no.

Before the Gin, There Was Deadpool

Reynolds spent roughly a decade trying to get Deadpool made.

It wasn’t an easy sell.

Deadpool was violent, foul-mouthed, self-aware, R-rated and constantly broke the fourth wall.

In other words, not exactly the safest foundation for a giant studio superhero franchise.

Reynolds kept pushing anyway.

Eventually, he and the team were allowed to create a few minutes of test footage showing what their version of the character might actually look like.

Then the footage sat there.

The movie still wasn’t getting made.

Until July 2014, when something strange happened.

The test footage appeared on the internet.

Fans went crazy.

Suddenly the studio wasn’t evaluating an abstract pitch anymore.

There was visible evidence of demand.

For more than a decade, Reynolds joked about who might have leaked the footage.

Then, in 2025, he finally admitted it.

He did.

Reynolds said he had listened to his instinct that people would be interested, even though he knew leaking it was the wrong thing to do.

Whatever you think of the ethics of the leak, it reveals something fascinating about how his brain works.

He couldn’t win the argument inside the conference room.

So he changed the environment outside the conference room.

He didn’t convince the gatekeeper.

He created enough demand that the gatekeeper had to pay attention.

That may have been Reynolds’ first great marketing lesson.

And he hadn’t even started a marketing company yet.

The Movie That Accidentally Created a Marketer

Eventually, Deadpool got made.

But compared with the giant superhero productions around it, the team had relatively few resources.

Reynolds later described the experience as having to make every dollar feel like ten.

So they replaced spectacle with character.

Then they applied the same thinking to marketing.

Reynolds took the Deadpool suit from the set and started shooting inexpensive promotional material with George Dewey, who would later become his partner at Maximum Effort. Reynolds has described this period as the origin of his move into marketing.

Instead of making only conventional trailers, they treated advertising as entertainment.

Deadpool appeared on Tinder.

The violent superhero movie was advertised as if it were a Valentine’s Day romance.

There were ridiculous billboards.

Public-service announcements.

Cheap internet videos.

Reynolds moved almost seamlessly between actor, character and advertiser.

And people voluntarily shared the marketing.

That was the breakthrough.

Marketing didn’t have to interrupt the entertainment.

Marketing could be the entertainment.

The lack of resources had forced Reynolds and Dewey to discover a different way of creating attention.

And it worked.

Deadpool became, at the time, the highest-grossing R-rated movie ever.

But Reynolds walked away with something arguably more valuable than another successful movie.

He had learned how to manufacture attention.

Then He Realized Attention Was an Asset

This is where the story becomes much more interesting than “celebrity starts a side business”.

Most celebrities already possess enormous amounts of attention.

But historically, they have monetized it in a very simple way.

• A company pays the celebrity.

• The celebrity appears in an advertisement.

• Maybe they make an Instagram post.

• The celebrity gets a cheque.

• The campaign ends.

• The company owns the product, the customer relationship and most of the long-term upside.

Reynolds gradually arrived at a more interesting model.

Don’t just rent your attention to brands.

Own the asset your attention makes more valuable.

In 2018, he bought an ownership stake in Aviation Gin.

Even the origin story feels strangely Reynolds-like.

He discovered Aviation while drinking Negronis in Vancouver. He kept ordering the same drink, realized the thing he particularly liked was the gin, and eventually found his way into the company.

Once involved, Aviation said Reynolds would play an active role in the business and oversee creative direction; Reynolds also talked about wanting to participate in marketing, sales and distribution.

Think about what had changed.

If Reynolds made a brilliant commercial for somebody else’s gin, he could get paid for a commercial.

If Reynolds made millions of people care about his gin, he could increase the value of an asset he owned.

Marketing was no longer simply a service.

It became a mechanism for equity creation.

Around the same period, Reynolds and Dewey formalized what they had learned during Deadpool into Maximum Effort.

Reynolds later described the company as something that happened almost by accident: they had developed a way of marketing Deadpool, then needed to market Aviation, and suddenly found themselves running a marketing business.

Now the machine was coming together.

Reynolds had:

• Attention.

• Creative ability.

• Distribution.

• A marketing company.

• And equity in the thing being marketed.

That combination is much more powerful than celebrity endorsement.

And it reveals something Reynolds understood unusually early.

His Real Asset Wasn’t Fame. It Was Distribution.

There is an obvious objection to this entire story.

Of course Ryan Reynolds could do this. He’s Ryan Reynolds.

Correct.

Reynolds started with an unfair advantage almost no entrepreneur has.

Millions of people already knew his face.

Entertainment media followed what he did.

A single post from his account could reach more people than many companies could reach with enormous advertising budgets.

Pretending otherwise would make the whole story useless.

But Reynolds understood something about fame that many famous people don’t.

Fame is not merely attention. Fame is distribution.

And distribution is an asset.

Most celebrities rent that distribution out.

Reynolds connected his distribution to things he owned.

Now a viral joke didn’t merely earn likes.

It could introduce millions of people to Aviation Gin.

A clever video could generate awareness for Mint Mobile.

A cultural moment could become media without buying that media.

His personality became part of the companies’ distribution infrastructure.

That is a much smarter model.

And it doesn’t only apply to celebrities.

Because although you probably don’t have Ryan Reynolds’ distribution... you can build your own version of it.

You Don’t Need 50 Million Followers

Maybe 3,000 people read your newsletter. Maybe 12,000 people follow you on LinkedIn. Maybe 40,000 people watch your YouTube channel. Maybe 600 people genuinely trust what you recommend.

It isn’t Ryan Reynolds scale.

That misses the point.

The important question is:

If the answer is yes, you own some distribution.

And once you own distribution, something fundamental changes.

• When you launch a new product, you don’t begin at zero.

• When you have an idea, there is already someone listening.

• When you learn something useful, you already know where to publish it.

• When you build your second product, the audience from your first product can come with you.

You are carrying distribution with you.

This is one of the most valuable assets a modern entrepreneur can build.

Reynolds figured it out at celebrity scale.

A generation of entrepreneurs is beginning to figure it out at individual scale.

But Reynolds added another ingredient.

Speed.

The Ad That Had a 36-Hour Expiration Date

At the end of 2019, Peloton released a Christmas commercial called The Gift That Gives Back.

The premise was simple.

A husband gives his wife a Peloton bike.

She documents a year of workouts and later shows him how much the gift changed her life.

The internet had... other interpretations.

People found the commercial awkward, dystopian, sexist, controlling or some combination of the four.

Memes exploded.

Hot takes followed.

The actress at the center of the commercial, Monica Ruiz, suddenly became known as the “Peloton Wife”.

Most brands would have watched the internet drama from the sidelines.

Reynolds saw an opportunity.

His team began trying to find Ruiz within hours.

They eventually reached her.

Then they wrote an Aviation Gin commercial.

Shot it.

Edited it.

And released it.

According to Reynolds: The whole thing took roughly 36 hours.

In the ad, Ruiz sits at a bar between two friends looking like she’s just escaped something.

Three martinis sit on the table. One friend tells her she’s safe. They toast to “new beginnings”.

Then Reynolds publishes the commercial with four words:

Exercise bike not included.

The internet loved it.

But I think most people learned the wrong lesson from the campaign.

They saw the joke. Reynolds saw the window.

Reynolds Himself Has Now Formalized the Idea

There is a nice twist to this story.

Fastvertising has moved from an eccentric Ryan Reynolds marketing trick to something people are seriously studying.

In the January–February 2026 issue of Harvard Business Review, Reynolds coauthored “Marketing at the Speed of Culture” with Harvard Business School professor Ayelet Israeli, Leonard Schlesinger and Matt Higgins.

Their argument is not simply “be funny quickly”.

Successful fastvertising requires organizational conditions that allow people to respond quickly: empowered teams, streamlined governance, relevance over unnecessary production polish, and enough human judgment to know when a cultural moment should or absolutely should not become advertising.

They also make an important point about AI: generative tools can accelerate production, but human judgment remains critical.

Which gets us to the real lesson.

Humor Wasn’t Scarce. Speed Was.

Thousands of people made funny Peloton jokes that week.

Humor was abundant. Execution speed was scarce.

The Aviation ad worked because the joke arrived while the joke still mattered.

That’s a very different way to think about cultural marketing.

Attention has a half-life.

A cultural moment does not maintain the same value forever.

At hour one, people discover it.

At hour twelve, people share it.

At hour thirty-six, everybody has an opinion.

A week later, your beautifully produced brand response can already feel like someone explaining an old meme.

Modern marketers obsess over: CAC. CPM. CTR. ROAS. Conversion rate.

I think they should start measuring another number.

TTP: Time to Publish.

How much time passes between: “We should do something with this” and published.

That number rarely appears on a marketing dashboard. It should.

Because in a world where attention moves quickly: Latency is a cost.

Founder-Led Marketing’s Hidden Advantage

Founder-led marketing is usually explained with one word: Authenticity.

People trust people more than corporations.

A founder has a real voice. Customers like seeing the person behind the product. All true.

But I think authenticity is only half of the advantage.

The other half is much more mechanical. Decision compression.

Think about the traditional process.

• Someone notices an opportunity.

• Someone writes a brief.

• A manager reviews it.

• Brand reviews it.

• An agency gets involved.

• The agency produces concepts.

• Marketing chooses one.

• Legal reviews it.

• Management asks for a safer version.

• Someone schedules another meeting.

• Then eventually somebody publishes it.

Every handoff adds latency.

Now compare that with a founder who possesses taste, authority and proximity to the customer.

Idea. Decision. Publish.

Sometimes the entire loop occurs inside one person’s head. That is extraordinarily powerful.

Not because founders are automatically smarter. Many aren’t.

But when ownership + taste + authority sit close together, companies can move at a completely different speed.

Reynolds wasn’t simply the face of Aviation.

He was part of a low-latency decision system.

Then He Ran the Experiment Again

Aviation could be dismissed as a special case.

Celebrity. Alcohol. Funny ads.

Fine.

Then Reynolds bought an ownership stake in Mint Mobile in November 2019.

Discount wireless plans are considerably less sexy than gin.

Which makes Mint a much more interesting experiment.

The playbook remained remarkably similar.

Reynolds appeared directly in the advertising.

The production was often deliberately inexpensive.

The writing was self-aware.

The company made jokes about being a company trying to sell you something.

One Mint campaign proudly centered on a commercial that cost just $500.

Another had ChatGPT write the commercial. But perhaps the purest demonstration of the system happened in 2022.

Comedian Dave Foley tweeted Reynolds asking whether, if he switched to Mint Mobile, he could appear in one of the company’s ads.

Reynolds accepted.

George Dewey called Mint while heading to the set.

The concept was aligned in minutes.

The ad was written on the move.

Shot. Edited. Published.

From tweet to finished commercial: six hours.

Six hours.

There are companies that need six hours to decide who should attend the kickoff meeting.

The Acquisitions Matter. But Not for the Reason You Think

Eventually, the numbers became enormous.

In 2020, Diageo agreed to acquire Aviation Gin and Davos Brands in a transaction worth up to $610 million: $335 million upfront plus as much as $275 million tied to future performance.

Important detail: That number did not represent Aviation Gin alone.

The acquisition also included other brands in the Davos Brands portfolio. Reynolds retained an ongoing interest in Aviation.

Likewise, T-Mobile announced in 2023 that it would pay up to $1.35 billion for Ka’ena Corporation.

Again, important detail: That was not Mint Mobile alone.

Ka’ena also included Ultra Mobile and Plum.

The transaction closed on May 1, 2024.

So the lazy version of this story would be:

Ryan Reynolds made funny commercials and sold two companies for nearly $2 billion.

That’s a nice LinkedIn fairy tale.

It isn’t accurate. Product mattered. Pricing mattered. Distribution mattered. Teams mattered. Market timing mattered.

But the marketing capability clearly mattered too.

When T-Mobile completed the Ka’ena acquisition, it specifically pointed to Mint’s unusually strong direct-to-consumer marketing capabilities as one of the things that had fueled its success.

That’s the more interesting story.

Reynolds repeatedly connected: ownership + attention + creative control + distribution + speed.

And that combination created an unusually efficient growth machine.

Your Approval Process Is Part of Your Media Budget

Now imagine two brands notice exactly the same cultural opportunity.

Brand A can publish tonight.

Brand B needs nine days. Brand B may have: The better agency. Better designers. More research. A larger budget. A 140-page brand guideline.

It doesn’t matter.

Brand A owns the moment.

Which leads to an uncomfortable idea: Your approval process is effectively part of your customer acquisition cost.

We just don’t account for it that way. We measure campaigns that run. We measure campaigns that fail.

We do not measure brilliant campaigns that never existed because Legal could only join the meeting next Tuesday.

Nobody’s dashboard says:

Cultural moments lost because Steve was on vacation: 17.

But the cost is real.

Someone else captured the attention. You paid for safety with distribution.

There Is a Point Where Professionalism Becomes Latency

Companies add processes for sensible reasons; Consistency. Quality. Compliance. Legal protection. Risk management. Brand safety.

Initially, the processes help.

Then another stakeholder joins. Another approval. Another recurring meeting. Another document.

Eventually something strange happens.

Everyone contributes to the decision. Nobody can make the decision.

The work becomes more professional. The organization becomes less capable of acting. And the incentives push companies naturally in this direction.

A visible mistake has an owner. A missed opportunity usually doesn’t.

Nobody gets fired because an advertisement that never existed failed to go viral. So companies accumulate defensive processes.

Every individual layer feels rational. Together they create massive latency. And research suggests that slow does not necessarily mean better.

McKinsey found that only 48% of surveyed managers said their organizations consistently made decisions quickly, while just 37% said decisions were both high-quality and fast.

More surprisingly, organizations that made decisions quickly were around twice as likely to report high-quality decisions as slow decision-makers.

A much broader meta-analysis published online in 2024 examined 127 studies and 239 effect sizes and found that decision speed, implementation speed and competitive-response speed were all positively related to firm performance overall.

The lesson isn’t: Make every decision quickly.

The lesson is: Stop making reversible decisions travel through systems designed for irreversible ones.

A billion-dollar acquisition deserves friction.

A topical Instagram video probably doesn’t need six executive approvals.

AI Just Moved the Bottleneck

This is where the Reynolds story becomes much more important in 2026.

For decades, production itself was expensive.

Research took time. Writing took time. Design took time. Editing took time. Video took a lot of time.

Companies built enormous organizations around those constraints.

Then generative AI arrived.

In a study involving 758 Boston Consulting Group consultants, people using GPT-4 on tasks within the model’s capabilities completed work more than 25% faster, completed over 12% more tasks, and produced work rated more than 40% higher in quality.

Another study covering more than 5,000 customer-support agents found an average productivity improvement of roughly 14% after workers received generative-AI assistance.

And those are relatively early examples.

Today, a tiny team can research markets, write copy, produce images, generate voice, edit video, create landing pages and iterate on dozens of concepts at speeds that would have sounded absurd a few years ago.

Fantastic.

Now imagine this workflow:

• Research: 5 minutes

• Concepts: 10 minutes

• Copy: 30 seconds

• Images: 3 minutes

• Video: 20 minutes

• Management approval: 11 days

What exactly did we accelerate?

Nothing meaningful.

We optimized the part of the system that was no longer the bottleneck.

The bottleneck moved. From production to permission.

Companies are going to spend millions making employees five times faster with AI... and then feed their output into the same organizational architecture designed when producing a campaign took three months.

It’s like installing a Formula 1 engine and driving it through Istanbul traffic at 6 p.m.

Technically faster. Practically stationary.

Reynolds Now Has a Name for It: Fastvertising

There is a nice closing loop to this story.

What began with a frustrated actor trying to get Deadpool made eventually became an actual management concept.

In the January–February 2026 issue of Harvard Business Review, Ryan Reynolds co-authored an article with Harvard Business School professors Ayelet Israeli and Leonard Schlesinger and investor Matt Higgins.

The title: “Marketing at the Speed of Culture”

The concept they describe is fastvertising: rapid-response advertising designed to participate in cultural conversations before those conversations move on.

In other words, Reynolds’ accidental education became a system.

• Deadpool taught him constraints.

• Constraints taught him creativity.

• Creativity taught him how to create attention.

• Attention became distribution.

• Distribution became more valuable when attached to ownership.

• Ownership compressed decision-making.

• Compressed decisions enabled speed.

• And speed became an advantage.

Which brings us to the part of this story I think entrepreneurs should pay much more attention to.

The Ryan Reynolds Model Is Coming for the One-Person Company

Strip away Hollywood. Strip away the celebrity. Strip away 50 million followers.

What remains?

A surprisingly simple machine:

• Build attention.

• Own the relationship with the audience.

• Own the thing you sell.

• Create the marketing yourself.

• Distribute directly.

• Move quickly.

Twenty years ago, assembling that machine as one person was extremely difficult.

You needed a designer. A copywriter. A developer. A video editor. A media buyer. Maybe an agency.

And distribution usually had to be purchased.

Today?

One person can build an audience on LinkedIn. Publish a newsletter. Run a YouTube channel. Build software. Generate creative. Produce video. Write a landing page. Analyze customer feedback. Automate support. Sell globally.

And increasingly, AI can sit behind almost every one of those activities.

This is why Reynolds is such an unexpectedly useful model for solo founders.

Obviously you don’t have his audience. You don’t need his audience.

You need the smallest viable version of his machine.

Maybe your Maximum Effort is you plus five AI tools.

Maybe your Hollywood audience is 10,000 newsletter subscribers in an extremely specific industry.

Maybe your Aviation Gin is a $29 SaaS product.

The scale is different. The architecture isn’t.

And this may become one of the biggest entrepreneurial shifts created by AI.

For decades, a company had an enormous structural advantage over an individual.

A company could coordinate many specialized people. AI begins to compress those people into capabilities.

Which means something strange happens.

The individual starts acquiring some of the advantages of the organization... without necessarily acquiring all of its bureaucracy.

That is a very powerful combination.

The One-Person Company Has One Advantage Big Companies Can’t Easily Buy

People usually think small companies lose because they lack resources. Sometimes they do.

But smallness contains an advantage too. Distance. Or rather, the lack of it.

• Distance between customer and founder.

• Distance between insight and decision.

• Distance between decision and execution.

• Distance between execution and distribution.

A giant company can buy the best AI models. It can hire the best consultants. It can spend $20 million on transformation.

What it cannot instantly buy is an organizational architecture with zero friction.

A solo founder can hear something from a customer at 9:00 a.m., change the product at 10:00, record a video at 11:00 and tell the entire audience about it before lunch.

No steering committee. No alignment meeting. No deck explaining why the deck should exist.

That speed used to be limited by what one human could physically produce.

AI is removing that limitation.

Which makes smallness more interesting than it used to be.

Don’t Copy Ryan Reynolds’ Jokes

The wrong takeaway from all of this is: We need funnier marketing.

Maybe you do. But that’s the surface.

Don’t copy Reynolds’ jokes. Copy the architecture that allowed the jokes to exist.

Ask:

• How many people must approve a reversible marketing decision?

• Who actually owns the voice of the company?

• Can the person closest to an opportunity act on it?

• Do the people creating your marketing have direct access to customer reality?

• Which decisions genuinely carry major brand risk?

• Which decisions are slow simply because they inherited a process?

• How long does it take your company to go from: “That’s interesting.” to: Published.

And for founders, ask an even more important set of questions:

• Am I building an audience or merely buying traffic?

• Do I own the relationship with that audience?

• Do I own the products my distribution makes valuable?

• Can I move from insight to execution without waiting for somebody else?

Because those four questions describe a very different kind of business.

The Scarce Resource Is Moving

For years, companies competed on their ability to produce.

Who had the best agency? The biggest creative department? The largest media budget? The best production capabilities?

AI is making production abundant. Soon almost everybody will be able to generate: 100 ideas. 100 pieces of copy. 100 images. 100 videos. 100 landing pages.

So what becomes scarce?

• Taste. Knowing which of the 100 ideas matters.

• Judgment. Knowing which moment deserves a response.

• Trust. Having an audience that actually wants to hear from you.

• Distribution. Being able to reach that audience without renting access every time.

• And perhaps most surprisingly: Permission.

The ability to actually do the thing while it still matters.

That is why the Ryan Reynolds story isn’t really about Ryan Reynolds.

It’s about a larger shift in how companies can be built.

Reynolds happened to start with the ultimate unfair advantage: fame.

But what made that fame economically interesting was that he figured out the rest of the system.

He turned attention into distribution. Distribution into ownership. Ownership into speed. And speed into an organizational advantage.

AI is now giving ordinary entrepreneurs tools that make a smaller version of that machine possible.

So don’t try to become Ryan Reynolds. Build your own version of his operating system.

Own an audience. Own what you sell. Own your creative process. And shorten the distance between an idea and the world.

Because your next competitive advantage may not be another AI tool.

It may not be another agency. It may not be another $500,000 campaign. It may simply be this: fewer people between the idea and Publish.

Sounds ridiculous today, doesn’t it? Imagine calling someone for help with a video game. Just Google it. Ask a friend. Start the game over…

But there’s something in that phone call that billions of dollars of AI investment still haven’t quite managed to recreate:

The moment the customer needs help, there’s someone on the other end who actually understands them.

The Nintendo employee doesn’t read from a script.

• “Which quest are you on?”

• “How many heart containers do you have?”

• “Where exactly are you stuck?”

They understand the context. Diagnose the problem. Give the right answer.

What’s remarkable is that the kind of one-to-one attention a gamer could get 40 years ago is still considered a luxury at many large companies today.

We’re Using Technology to Solve the Wrong Problem

Whenever companies encounter a new technology, they tend to jump to the same question:

“How much money can this save us?”

Fewer people in the call center. Fewer tickets. Shorter calls. Higher automation rates.

Then the technology gets placed between the company and the customer.

IVR. Forms. Chatbots…

And now, AI.

Too often, the goal isn’t to serve the customer better. It’s to delay the moment they reach a real person for as long as possible.

I think that’s a fundamental mistake in how we think about technology.

Technology shouldn’t be used to reduce human connection.

It should be used to multiply it.

The right question isn’t:

“How can we do this with fewer people?”

It’s:

The first is a cost-cutting strategy.

The second is a competitive strategy.

Great Service No Longer Has to Be a Scaling Problem

Until now, truly personalized service has been expensive.

Your most valuable customers get a dedicated relationship manager. A private banking customer calls and someone already knows who they are. A VIP customer doesn’t wait in line. They don’t have to explain their history again. They speak to someone who already understands what they need.

Giving that experience to millions of customers simply wasn’t economically realistic.

So companies rationed attention.

At the top: one-to-one service.

At the bottom: a queue.

One of the biggest things AI changes, in my opinion, is exactly this:

The economics of one-to-one service.

Built properly, an AI system doesn’t just answer questions.

• It knows who the customer is.

• It knows what they’ve done before.

• It knows what they bought.

• It knows where they are in the journey.

• It knows what problems they’ve had in the past.

And it builds the current conversation on top of all that context.

So instead of giving the customer a bot that starts from zero every single time, you give them an assistant that actually knows them.

And you can do that for thousands, potentially millions of people at the same time.

You no longer need to hire thousands of additional employees to make that possible.

But you do need to put AI in the right place.

Customer Service Is Actually Part of Sales

On an org chart, sales, customer service, retention, and cross-sell all live in different boxes.

Customers don’t think in boxes.

They’re just trying to do something.

• “Is this product right for me?”

• “Which one should I choose?”

• “How do I set this up?”

• “Why didn’t this work?”

• “What am I supposed to do now?”

• “Is there a better option for me?”

The company may label some of these questions “sales”.

Others “customer support”.

Others “customer success”.

From the customer’s perspective, they’re all the same question:

Are you going to help me?

And every moment in which you help them has a commercial consequence.

• Help them make a decision and conversion goes up.

• Help them get more value from the product and retention improves.

• Solve their problem quickly and churn goes down.

• Understand what they actually need and the right upsell opportunity appears naturally.

That’s why treating customer service purely as a cost center feels increasingly strange to me.

What you actually have is an always-on growth channel.

The Biggest Opportunity Isn’t in Big Problems. It’s in Small Moments.

We usually think about customer experience when something goes wrong.

A complaint comes in. A ticket gets opened. Someone calls the support line.

But a lot of customer loss doesn’t happen during dramatic moments.

It happens through tiny moments of friction.

They don’t understand something. They can’t make a choice. They don’t want to spend another two minutes figuring it out. They can’t find an answer. They open another tab…

And then they never come back.

In many of these moments, the company doesn’t even know the customer needed help.

This is where conversational AI that can speak naturally across the phone, website, or mobile app becomes especially powerful.

It doesn’t have to be a system that simply responds when something goes wrong.

It can become a layer that stays with the customer throughout their journey.

It can step in at exactly the moment help is needed.

And it can make that help deeply personal.

This Is the Biggest Difference We See in Enterprise Projects

At Next Big App, our work with large organizations in Türkiye keeps revealing the same divide.

When AI is treated as little more than a smarter FAQ bot, the outcome is limited.

Call volume drops a little. Costs come down a little. But the customer experience doesn’t fundamentally change.

Once you bring the customer’s history, transaction context, current stage, and intent into the conversation, something very different happens.

The system stops merely answering questions.

It starts helping. It makes the customer feel looked after.

Let me give you a real example.

At one large company we work with, customers who need support fill out a form on the website. It could be a complaint, a suggestion, a request, anything.

What happens next looks roughly like this:

• The form lands in an internal system and waits for an employee to categorize it. Manually.

• Then it gets assigned first to a department and then to an individual employee. Again, manually.

• That employee has to find time in the middle of an already busy day to get back to the customer. Usually by phone.

• They listen to the customer, understand the issue, and genuinely try to help.

• After the call, they take whatever internal actions are necessary: call another employee, update a system, fix whatever caused the problem, and so on.

• Then they call the customer again with an update. If the customer is happy, great. If not, the whole cycle begins again.

• Finally, the employee has to document everything. They sit down and write a long email summarizing the entire case from beginning to end.

You can see what’s happening here.

This is the kind of process only a company that genuinely cares about its customers would be willing to take on.

The amount of effort being spent to make one customer happy is genuinely worth applauding.

So what did we do for them?

We built a system that uses AI to understand the request, the context, and the relevant information from a much larger pool of company data.

It can automatically call the customer, listen to the entire issue, resolve it immediately when possible, or generate a report and route the case to the right teams automatically.

The goal isn’t just efficiency.

It’s to improve the customer’s experience while also making much better use of employees’ time.

At the beginning, the concern was predictable:

“What if the AI makes a mistake while talking to a customer on the phone?”

Over time, that changed into something closer to:

“The AI is actually more patient, more knowledgeable, and better at finding solutions than I am when I’m trying to squeeze these calls into the middle of a hectic day.”

Now both customers and employees are happier.

That, in a nutshell, is how we think about conversational AI.

Not as “replace the human with a bot.”

Almost the opposite.

We see it as a way to take the kind of high-quality attention you could previously afford to give only a small number of customers — and make it available to everyone.

So that every customer, at any moment, can feel like there’s someone beside them who already knows who they are.

To me, that’s the real promise of AI in customer experience.

Same Technology. Two Completely Different Outcomes.

You can have the same model. The same integrations. The same data…

But the objective you give the system changes everything.

If your goal is:

“Let’s cut call center costs by 20%.”

You’ll probably end up putting a slightly smarter barrier between the customer and your company.

If your goal is:

“Let’s help every customer as well as the best employee in our company would.”

You’ll design a completely different experience.

In one, technology gets between you and the customer.

In the other, it brings you closer.

Over the next few years, I don’t think competitive advantage will come from answering the question:

“Which companies are using AI?”

Everyone will be using it.

The real divide will be somewhere else:

Between companies that use AI to replace human connection, and companies that use AI to scale it.

That Nintendo player was only trying to get the White Sword.

But what they actually received was something more valuable:

The feeling that, at the exact moment they needed help, someone was there who genuinely wanted to help them.

Today, we have the technology to give that feeling to millions of people at once.

Using it only to cut costs would mean choosing the smallest part of the opportunity.

Then something strange happened.

People started watching television shows made of 45-second to two-minute episodes.

Not clips.

Not trailers.

Actual serialized dramas.

They’re called microdramas, and the numbers are becoming difficult to dismiss.

The U.S. audience more than doubled last year, from 26 million to 66 million monthly active users. The category is expected to generate around $1.5 billion in U.S. revenue this year and nearly $2 billion next year.

And here is the number I find much more interesting.

ReelShort, one of the largest microdrama apps, recently reached roughly 38 minutes of daily mobile viewing per user.

That’s more daily mobile viewing time than Netflix, Amazon Prime Video or Disney+ on mobile.

Think about the contradiction.

The episodes are two minutes long.

The viewing time, in aggregate, can be nearly 40.

Maybe our attention spans aren’t the problem. Maybe our calendars are.

We Keep Blaming Attention

“People have no attention span anymore” has become one of those explanations that feels true because we repeat it so often.

Nobody reads. Nobody watches long videos. Nobody finishes anything. Everything needs to be shorter.

But microdramas suggest something more interesting.

People apparently have enough attention to spend 38 minutes watching melodramatic stories about billionaires, forbidden romances and improbable family secrets.

What they may not have is a clean, predictable 38-minute block.

That is a very different problem.

A 45-minute Netflix episode asks you to make a commitment before you start.

A two-minute microdrama asks almost nothing.

You can watch one while waiting for coffee. Three in an Uber. Two before the meeting starts. Seven while your kid is finishing practice. Another four in bed.

By the end of the day, you may have consumed half an hour of story without ever deciding to “watch TV”.

That distinction matters.

The scarce resource may no longer be attention.

It may be uninterrupted attention.

Time is Becoming Granular

For most of the industrial era, products were designed around relatively large blocks of time.

• A newspaper expected a morning ritual.

• Television expected a 30- or 60-minute slot.

• A movie expected two hours.

• A bank expected you to visit a branch.

• Enterprise software often still assumes you will sit down, open the application, understand where you are and complete a workflow.

The internet removed some of those constraints.

Mobile removed even more.

But many products kept the old assumption hidden inside the new interface:

That assumption is getting expensive.

Microdramas reverse it.

They don’t ask the user to create a viewing session. They fit themselves into the cracks that already exist. That is why calling them “short-form Netflix” misses the point.

The important innovation isn’t the duration.

It’s the minimum viable unit of time required to receive value.

That unit has collapsed.

Gaming Figured This Out Years Ago

Microdramas are not the first product category built around fragmented time.

Hyper-casual mobile games made essentially the same bet years earlier.

The best of them require almost no setup, almost no learning curve and almost no commitment.

Open the app. Tap. Swipe. Avoid the obstacle. Try again…

AppsFlyer describes hyper-casual games as simple, mechanic-driven products designed for broad accessibility, with some puzzles intended to be completed in a minute or less. The category became a major force in mobile downloads precisely because the commitment required to start playing was so low.

The business model evolved. Hyper-casual increasingly blended into hybrid-casual as studios looked for stronger retention and monetization.

But the behavioral insight underneath it was already there. You didn’t need to convince someone: “I am now going to play a video game.”

You only needed a spare 45 seconds.

Standing in line. Waiting for someone. Sitting in the back of a taxi. Pretending to listen on a conference call. We’ve all been there.

The game could enter time that previously wasn’t considered usable gaming time.

That is a bigger innovation than making games shorter.

It created inventory out of fragments.

Microdramas are now doing something remarkably similar to television.

And I suspect many other categories will follow.

This Changes How I Think About Product Design

There is a metric I think more companies should care about: Time to useful completion.

Not time spent. Not session length. Not even time to value in the conventional onboarding sense.

I mean:

Five years ago, maybe your customer could reliably give you 20 minutes.

Today, perhaps they can still give you 20 minutes in total.

But you get it as: 4 minutes + 90 seconds + 6 minutes + 3 minutes + 5 minutes.

Mathematically similar. Operationally completely different.

A product designed for the first world can fail badly in the second.

This is especially important because companies frequently interpret the failure incorrectly.

• Users abandon a workflow, so we assume the interface is confusing.

• They don’t complete onboarding, so we simplify the copy.

• They stop using a dashboard, so we add notifications.

• They don’t watch the training, so we make the video more entertaining.

Sometimes those are the right fixes.

But sometimes the product is simply asking for a unit of time the customer no longer has.

You are optimizing the experience inside the session.

The real problem is the requirement to have the session at all.

The Microdrama People Understood This

A traditional television episode needs setup.

Characters enter. Context develops. Tension builds.

A microdrama doesn’t have that luxury.

You arrive late to the party and someone is already sleeping with the billionaire.

Reuters describes these series as fast-paced and built around immediate hooks and cliffhangers; some run for 50 to 75 tiny episodes.

It sounds ridiculous until you realize how carefully the product matches the environment.

• Every episode creates a small unit of completion.

• Every cliffhanger creates a restart point.

• Every restart requires almost zero context reconstruction.

The content is not simply shorter. The architecture is interruption-tolerant.

That is the useful idea. A lot of software isn’t.

Stop halfway through a complex enterprise workflow and come back tomorrow.

Where were you? What had you already checked? Why did you make that selection? Which tab had the information you needed?

Before you can continue the work, your brain has to reload the save file.

You reconstruct the state. Reopen the tabs. Remember the decisions. Rebuild the mental model.

And the more complex the workflow, the heavier that reload becomes.

Some products don’t lose users because the task is difficult. They lose them because resuming the task is difficult.

That distinction is easy to miss.

AI Makes This More Interesting, Not Less

The obvious AI product strategy right now is to make existing tasks faster.

Take a 20-minute workflow and make it 12. Then eight. Then five…

Useful.

But there is a larger opportunity.

What if the workflow no longer needed to be a session?

Instead of helping me operate the software faster, the product could hold the context while I disappear.

• I do one small thing.

• The system continues.

• It asks me for a decision only when necessary.

• I answer from my phone in 20 seconds.

• It continues again.

• I return later without reconstructing everything that happened.

That is not just automation. That is a different relationship with time.

The software absorbs fragmentation so the human doesn’t have to.

This is where AI becomes particularly powerful. Not because it can generate another paragraph or summarize another document. Because it can maintain context across gaps that humans find expensive.

The product remembers. The workflow persists. The user comes and goes.

This is Also Why Some “Engagement” Metrics are Becoming Dangerous

If you run a SaaS company, you may instinctively want customers spending more time inside your product.

More sessions. Longer sessions. More screens visited.

For some products, that makes sense.

For others, it may increasingly be evidence that you are consuming a scarce customer resource.

Imagine two products that create the same business outcome.

• Product A needs a focused 40-minute session every Tuesday.

• Product B needs four two-minute decisions spread across the week.

Which one is easier to keep using when the company gets busy?

Which one survives a chaotic month?

Which one does the executive actually adopt?

Product B might show dramatically less “engagement”. And have dramatically better retention.

That is the kind of metric inversion worth paying attention to.

Locally, less time in product looks worse. Systemically, requiring less uninterrupted time may make the product far harder to remove.

The Question I Would Ask

Look at one important workflow in your business.

Don’t ask:

How do we make this faster?

Ask:

How much uninterrupted attention does this workflow require from the customer?

Then make the question more uncomfortable:

What would we build if we could only ask for 90 seconds at a time?

You may end up with a shorter workflow.

But you may also end up with a completely different product.

Different notifications. Different state management. Different defaults. Different automation. Different pricing. Different definition of engagement.

That is what new constraints do.

They don’t merely change features. They change architecture.

Hyper-casual games looked trivial.

Microdramas look like disposable soap operas shot vertically for a phone.

It would be easy to dismiss both as evidence that people can no longer concentrate.

I think that gets the lesson almost exactly backwards.

People still have time. They still have attention.

They can apparently spend 38 minutes a day finding out whether the billionaire finally marries the woman he pretended not to love.

What they increasingly lack is the ability or willingness to hand one product a large, uninterrupted piece of their day.

The next generation of products won’t only save people time. They’ll stop requiring time to arrive in one piece.

They had been waiting more than two years for it.

The concert is scheduled to end in 2640.

Yes, 2640.

In Halberstadt, Germany, an organ has been performing John Cage’s ORGAN²/ASLSP — As Slow as Possible since September 5, 2001.

The full performance is designed to last 639 years. On August 5, 2026, organizers added one pipe to the organ, changing the sound from seven notes to eight.

After almost 25 years, the concert is roughly 4% complete.

Even better: the performance began with silence.

For the first 17 months, the piece produced no audible chord at all. That wasn’t a technical problem. That was the performance.

Imagine presenting those results at your next quarterly review.

“We’ve been working on this for 17 months.”

“And what have we produced?”

“Nothing you can hear.”

Good luck with the next job search…

The Problem isn’t Always the Metric

Business culture has become very good at measurement.

Weekly active users. CAC. Conversion. Revenue per employee. Churn. Sales velocity. Tickets closed. Features shipped.

Usually, when the numbers disappoint us, we question the work.

Sometimes we should question the clock.

Because every system has its own natural timescale.

• A paid acquisition campaign can tell you something within days.

• A pricing change might take weeks.

• A sales hire can take months before reaching full productivity.

• A new positioning strategy might look worse before it looks better because the old message was optimized for the wrong customers.

• A retention improvement can appear almost trivial in the first month and enormous several years later.

The metric can be perfectly accurate while the conclusion is completely wrong.

You’re just looking too early.

Management Has a Sampling-Rate Problem

I’ve seen this pattern many times.

A company makes a change designed to improve the system over six months.

Three weeks later, somebody opens a dashboard.

Nothing dramatic has happened.

So we change something else.

Then something else.

By month three, we’re no longer measuring the original decision. We’re measuring the accumulated noise created by our impatience.

We call this being data-driven.

Sometimes it is just highly instrumented anxiety.

The faster we can measure things, the stronger the temptation to intervene.

That’s an important distinction.

The frequency at which you can observe a system is not necessarily the frequency at which you should manage it.

Imagine checking the temperature of an oven every second and changing the dial every time it moves by half a degree.

More data wouldn’t make you a better cook.

It would prevent the oven from stabilizing.

Companies do the equivalent constantly.

AI is Going to Make This Worse

AI is dramatically shortening the time between idea and action.

You can rewrite the onboarding today. Generate 20 ad variants this afternoon. Change sales messaging tomorrow morning. Build a feature prototype before lunch. Analyze customer feedback in minutes…

This is mostly good.

But faster execution creates a new temptation: faster judgment.

Those are not the same thing.

If an experiment that once took six weeks to launch can now be launched in two days, it doesn’t necessarily mean the customer behavior you’re trying to understand suddenly reveals itself 21 times faster.

The production cycle got shorter.

The feedback loop may not have.

That mismatch matters.

When execution becomes cheap, organizations can easily become addicted to changing things.

And constant change can destroy the very signal they’re trying to observe.

Some Progress is Invisible at First

The John Cage concert is useful precisely because it takes our normal intuition about progress and breaks it.

For 17 months, silence was progress.

For years at a time, the same chord can be progress.

On August 5, adding one pipe was progress.

The next chord change is scheduled for October 5, 2027.

Viewed hourly, almost nothing happens.

Viewed across decades, the piece is moving exactly as intended.

The system hasn’t changed.

Only your observation window has.

Businesses have versions of this everywhere.

A customer-success process may create very little visible revenue this quarter while quietly changing next year’s renewal curve.

Hiring someone excellent can initially reduce team output because good people ask uncomfortable questions before improving the system.

Removing low-quality customers can make revenue growth look weaker while making the company healthier.

Refactoring infrastructure can produce weeks of apparently zero customer value while removing a constraint that would have become expensive at 10x scale.

This isn’t an argument for patience as a virtue.

“Just give it more time” is one of the oldest excuses for bad strategy.

It’s an argument for something more precise:

Decide the clock before you look at the result.

Match the Measurement Window to the Mechanism

Before starting an initiative, there are three different questions worth separating.

• What should move?

• How much should it move?

• And when should we reasonably expect to see it?

Companies are usually reasonably good at the first two.

The third one is strangely neglected.

We define a success metric without defining its natural feedback period.

Then management cadence fills the vacuum.

• If leadership meets weekly, everything starts feeling like a weekly problem.

• If investors care about quarters, every investment starts getting evaluated quarterly.

• If the dashboard refreshes every morning, yesterday suddenly feels meaningful.

The clock silently becomes part of the strategy.

And that’s dangerous.

Because the wrong clock can make a smart decision look stupid.

It can also make a bad decision look good.

Some problems take years to appear.

A brand can be weakened slowly while short-term conversion stays healthy.

Technical debt can accumulate while shipping velocity initially improves.

A team can burn out while quarterly targets continue being hit.

Just as measuring too early creates false negatives, measuring too narrowly can create false positives.

Time changes what you can see.

The Question I’d Ask

When something in your company appears to be underperforming, don’t immediately ask:

“Should we change it?”

Ask:

Not how often the dashboard refreshes. Not when the next board meeting happens. Not when the quarter ends.

How long does cause need to become effect?

Sometimes the answer will be three days.

Sometimes three months.

Occasionally three years.

And apparently, sometimes 639.

The people running the John Cage concert have one enormous advantage over the rest of us: Nobody can pretend the project should be judged by next quarter.

In business, the mismatch is harder to see.

Which is why we often kill good systems not because they failed, but because our clock ran out before their feedback loop did.

Others become part of internet culture.

Nearly a year after American Eagle’s Sydney Sweeney campaign first launched, people are still debating it. Not because the ad was technically brilliant. Not because everyone loved it. Quite the opposite.

It started with a simple wordplay.

“Sydney Sweeney has great jeans.”

The ad played on the words genes and jeans, with Sweeney saying, “My genes are blue.”

Within hours, that joke became something much bigger.

Some critics argued it echoed ideas about eugenics, racial superiority, and outdated beauty standards.

Others dismissed the backlash entirely, insisting it was just a clever denim commercial and another example of internet outrage.

Politicians joined the debate.

Marketing experts turned it into a case study.

Millions of people who had no intention of buying jeans suddenly had an opinion about American Eagle.

The company’s stock even climbed as the conversation spread.

The internet kept the campaign alive long after American Eagle stopped paying to promote it.

Whether people loved it or hated it became almost irrelevant.

The conversation itself became the product.

This summer, American Eagle brought Sydney Sweeney back for another campaign.

That decision is more interesting than the campaign itself.

Companies rarely repeat creative.

They repeat outcomes.

American Eagle wasn’t betting on a celebrity.

It was betting on something far more valuable:

A conversation that refused to die.

Most people are asking the wrong question.

“Was it a good campaign?”

The better question is:

That distinction changes almost everything.

The Internet Changed the Rules

For decades, marketing was about broadcasting messages.

• You bought attention.

• You delivered a story.

• You hoped people remembered it.

The internet no longer works that way.

Today, the most valuable campaigns aren’t consumed.

They’re extended.

Every quote tweet, reaction video, podcast discussion, LinkedIn post, and group chat becomes free distribution.

The audience doesn’t just receive the message.

They become part of the media plan.

The internet has quietly changed the unit of marketing.

It used to be impressions.

Now it’s participation.

You’ve probably noticed this yourself.

The most thoughtful thing you’ve ever written on X or LinkedIn often disappears without a trace.

You spend hours putting together original insights, practical frameworks, or hard-earned lessons.

A few likes.

A handful of comments.

Then it’s gone.

But post something that divides people, even slightly and suddenly the algorithm comes alive.

The comments don’t stop.

People argue.

Others jump in to defend one side or the other.

The post keeps resurfacing for days.

That’s because social platforms don’t optimize for agreement. They optimize for interaction.

And nothing generates interaction more reliably than a conversation people feel compelled to join.

Distribution is no longer something companies own. It’s something audiences decide.

AI Is Changing the Economics

This is where AI enters the picture.

Most people think AI’s biggest impact is making it cheaper to build products.

That’s true.

But it’s only half the story.

The bigger shift is what happens after creation becomes cheap.

• When everyone can build faster...

• When everyone can generate content...

• When everyone can launch products...

• Creation stops being the bottleneck.

• Distribution becomes the bottleneck.

AI isn’t just commoditizing software.

It’s commoditizing creation itself.

And whenever something becomes abundant, something else becomes scarce.

In this case, what’s becoming scarce is attention.

Not purchased attention. Earned attention.

The kind people voluntarily pass to someone else.

The New Competitive Advantage

The companies that win over the next decade won’t necessarily build the best products.

They’ll build the products that generate the strongest self-reinforcing conversations.

Because every conversation creates more awareness.

• More awareness creates more users.

• More users create more stories.

• More stories create more conversations.

That’s a flywheel.

And flywheels are much harder to compete with than features.

Look at the companies that have defined the last decade.

Figma didn’t become valuable because it made design files.

It became valuable because it changed how teams think together.

People shared workflows.

Templates.

Ideas.

Not because Figma asked them to.

Because using Figma naturally created conversations.

Notion followed the same pattern.

People didn’t recommend it because it stored documents better.

They recommended it because it became part of their identity.

Their dashboards.

Their second brains.

Their productivity systems.

The product became content.

The content became distribution.

Distribution became growth.

ChatGPT followed the same pattern.

Yes, it had an incredible model.

But dozens of companies have built incredible models.

Very few became household names.

People shared prompts.

Screenshots.

Unexpected answers.

Funny mistakes.

Creative use cases.

The product didn’t just solve problems.

It generated stories worth telling.

That’s a very different kind of advantage.

The Question Every Founder Should Ask

This is where many founders still get stuck.

They think distribution begins after the product is finished.

In reality, distribution begins while the product is being designed.

Not with:

“How do we explain this?”

But with:

“What will make someone tell another person about this?”

No advertising budget can manufacture that moment at scale.

Only products and ideas that naturally create conversations can.

This doesn’t mean manufacturing outrage. Or designing fake controversy.

Artificial polarization is obvious. And it rarely lasts.

The lesson isn’t to build controversial products.

The lesson is to build products that are inherently conversational.

So instead of asking:

“How do we explain our product better?”

Ask:

“What conversation does our product make inevitable?”

Because in a world where AI makes creation cheaper every month, creating will no longer be the hard part.

Being talked about will.

And that may become the most valuable competitive advantage of the AI era.

Sold out.

I checked the next day.

Sold out again.

Then the day after that.

Still sold out.

I honestly can’t remember the last time a film I decided to see on a whim was completely booked for three or four days straight.

Isn’t that a little strange at a time when so many people keep telling us cinema is dead?

We have enormous screens at home. We have Netflix. We have Disney+. We can pause a film, rewind it, watch it from bed, and glance at our phones whenever we lose interest.

Humanity has reached the technological point where we can watch Odysseus’s ten-year journey home without ever leaving the sofa.

And yet people are still fighting for seats to see a film in a cinema.

And not only in Istanbul.

Thousands of people have reportedly travelled from across Europe to Prague just to see the film in true 70mm IMAX.

One family drove for three days from Norway. By the end of July, the Prague cinema had welcomed more than 22,000 viewers, running four or five screenings a day, including midnight showings.

The reason this particular cinema could do that is almost absurd.

While everyone else was switching to digital, it never got around to replacing its old projector.

That “laziness” eventually made it one of only 41 cinemas in the world capable of showing the film in true 70mm IMAX.

The technology its competitors abandoned became its competitive advantage.

Of course, keeping an old projector in your basement is not enough to persuade people to drive for three days.

Someone has to make that projector meaningful again.

That person was Christopher Nolan.

Nolan’s Real Product Isn’t the Film

The Odyssey is the first feature film shot entirely with IMAX film cameras.

That is easy to say.

It is much harder to do.

IMAX cameras are not merely enormous. They are also extremely loud. In the past, filmmakers generally used them for large action sequences, then switched to smaller cameras for quiet dialogue scenes.

Nolan refused to accept that compromise.

To shoot the entire film in the same format, he worked with IMAX to develop a custom enclosure that reduced the camera’s noise. Together, the camera and its soundproof housing became a colossal piece of machinery weighing roughly 180 kilograms.

The film stock also had to be changed about every three minutes.

A normal producer would look at all this and ask:

“Is any of this really necessary?”

Nolan’s answer is almost a summary of his entire career:

“Yes.”

But this insistence is not simply a technical fetish.

Nolan loves scale.

He loves explosions, oceans, wars, outer space, atomic bombs, collapsing cities, and time bending in on itself.

But those things are not what make his films truly interesting.

Nolan searches for enormous emotional contradictions inside enormous stories.

He does not use IMAX only to enlarge the world.

He uses it to enlarge the fracture inside a human being.

The Small Knot Inside the Epic

Most of us already know at least something about the world of The Odyssey.

The Trojan Horse. The Cyclops. The Sirens. The gods. A great warrior trying to make his way home.

But the centre of the story is not merely a collection of mythical creatures and naval battles.

It is a man who cannot escape the consequences of his own intelligence.

Odysseus is known above all for his cunning. He wins through strategy rather than brute strength. The Trojan Horse is the most famous example.

He turns an object people believe to be a gift into a machine of death.

It is a brilliant tactic.

It is also a deception that pushes ethical boundaries to their limit.

His intelligence wins the war.

But it does not end the story.

On the contrary, it begins the real one.

To me, Odysseus’s journey home is not only a struggle against gods and monsters. It is a long reckoning with his own decisions, his pride, his desires, and the consequences of the intelligence he chose to use.

That structure is perfectly suited to Nolan.

There was a similar man at the centre of Oppenheimer.

A man intelligent enough to solve one of the most complex scientific problems in history, yet unprepared to live with what that solution would do to the world.

Building the atomic bomb was a technical achievement.

Living with that achievement was a human tragedy.

This is exactly the territory Nolan keeps returning to:

The distance between the triumph of intelligence and the defeat of the human being.

The difference between an idea that works and an idea that is right.

The way winning a war changes the person who won it.

The vast stories, images, and budgets exist for this reason.

Not to conceal the emotional conflict, but to magnify it until it becomes impossible to ignore.

Why Do People Still Go to the Cinema?

At this point, it becomes easier to understand why the old projector in Prague suddenly became so valuable.

The projector alone is not an advantage.

The IMAX camera alone is not an advantage.

Homer’s ancient story alone is not an advantage.

Even Nolan’s name is not enough on its own.

The success comes from the way all these elements reinforce one another.

Nolan took an old story that everyone thought they already knew and connected it to the inner contradictions human beings have never managed to escape.

To tell that story, he chose a technology so difficult that most directors would never dare to use it for an entire film.

Rather than accept the limitations of the technology, he had the camera redesigned.

The small number of true IMAX projectors left in the world then became part of the film’s distribution system.

Scarcity transformed a screening into an event.

People travelled not merely to consume the content, but to become part of the experience.

Remove any link from this chain and the result changes.

If the film had been shot in an ordinary format, the projector in Prague would matter less.

If the story were nothing more than a visual spectacle, the film would become a three-hour technical demonstration.

If the projectors had not been preserved, there would be nowhere to show Nolan’s images in the form he intended.

And if the emotional centre failed, people might praise the image quality but forget the film a few days later.

This is why it is not enough to say that preserving old technology creates a competitive advantage.

A more accurate statement would be: An abandoned capability becomes a competitive advantage when it is connected to a powerful new human need.

Netflix Didn’t Kill Cinema

Netflix became exceptionally good at performing one particular function of cinema:

Making content easy to access.

Going to the cinema is inconvenient.

You have to choose a specific time. Sit in traffic. Find somewhere to park.

The person next to you may chew popcorn like an industrial crushing machine.

Watching at home is easier than all of that.

So if cinemas try to compete with Netflix on convenience, they will lose.

But the purpose of cinema is not merely to move an image onto a screen.

It had started to feel that way because the industry spent years making the experience increasingly ordinary.

The same auditoriums. The same small screens. The same digital images. The same popcorn.

Cinemas had already started turning themselves into Netflix before Netflix arrived.

Netflix simply performed that job better at home.

Nolan is moving in the opposite direction.

He has not made cinema easier.

He has made it even more difficult.

He does not tell the audience, “You can watch this film anywhere.”

He tells them, “If you want to truly see this film, you need to come to the right place.”

According to conventional business logic, that sounds like a terrible distribution network.

Fewer venues. More expensive infrastructure. More difficult operations. Limited capacity.

But as this example shows, reducing friction is not always the solution to a distribution problem.

Sometimes, when friction becomes part of the product, it strengthens the power of the distribution network rather than weakening it.

When you create an experience people will want to talk about once they reach it, the journey and the difficulty do not reduce its value.

They prove it.

This Is How We Should Look at “Dead” Businesses

In business, we love announcing that certain categories are dead.

Physical retail is dead.

Sales calls are dead.

Newsletters are dead.

Human customer service is dead.

Cinema is dead.

But perhaps the category itself is not dead.

Perhaps only the version that can be easily replaced has died.

An ordinary cinema experience can be replaced by a large television at home.

A screening that makes people travel to another country cannot be replaced so easily.

• A generic sales call can be replaced by an automated email. But understanding a customer’s business better than they understand it themselves is much harder to replace.

• A newsletter written like a press release can be produced by AI in seconds. But an original insight drawn from twenty years of experience, one that genuinely changes the reader’s decision is not so easily replaced.

• Standard customer service can be automated with a bot. But human judgement capable of understanding an angry, complicated customer and saving the relationship is much harder to replace.

I do not think the greatest opportunities of the future will emerge only in entirely new categories.

They will also emerge in the parts of supposedly “dead” categories that digitalisation abandoned, even though human beings still need them.

The goal is not to bring the old business back exactly as it was.

Imitating the 1980s is not a strategy for selling vinyl records.

Keeping an old projector operational is not a strategy by itself either.

The real question is:

Ritual?

Trust?

Belonging?

Craftsmanship?

Excitement?

The feeling of experiencing something together?

Once you find that value, an old capability that everyone else abandoned can become meaningful again.

Not Everything Old Is a Future Opportunity

There is an obvious danger here: nostalgia.

Something does not become valuable merely because it existed in the past.

Nor does difficulty make something valuable.

Doing a job manually does not automatically make it better than automating it.

Nolan’s success does not come from romanticising old technology.

It comes from understanding what that technology can still do uniquely well.

He then creates modern demand around that capability.

That distinction matters.

Successful revivals do not recreate the past exactly as it was.

They connect a unique capability from the past to an unresolved human need in the present.

The cinema in Prague preserved the projector.

Nolan gave it a purpose again.

Homer provided the story.

IMAX provided the scale.

Nolan placed human conflict at the centre.

And the audience contributed its time, its money, and sometimes a three-day journey.

Competitive advantage, as you can see, rarely comes from one brilliant decision.

It comes from a chain of decisions that reinforce one another.

So the next time I encounter a business that people say is “finished,” I no longer want to ask only:

“Would anyone still use this?”

There is a better question:

Because cinema did not die.

The kind of cinema people could more easily watch at home did.

That distinction will always matter.

Not because they weren’t looking hard enough.

Because they weren’t looking the right way.

That changed.

Using the Atacama Large Millimeter / submillimeter Array, researchers detected glycolaldehyde, a simple sugar and one of the molecular building blocks of life, in the gas surrounding a young star system.

The molecules had almost certainly been there long before we noticed them.

The universe didn’t suddenly become sweeter.

Our instruments and more importantly, our ability to interpret weak, noisy signals became better.

The Signal Was Always There

That’s how many of history’s biggest discoveries actually happen.

• Penicillin wasn’t invented. It emerged from a contaminated experiment that Alexander Fleming could easily have discarded.

• The cosmic microwave background wasn’t discovered because scientists were actively searching for evidence of the Big Bang. Arno Penzias and Robert Wilson were trying to eliminate what they thought was annoying interference from a radio antenna.

• John Snow didn’t need to collect an entirely new body of data to identify the source of London’s cholera outbreak. He plotted existing deaths on a map and revealed a pattern no one had seen before.

The data already existed.

The breakthrough came from seeing it differently.

Every breakthrough begins with the realization that the signal was always there.

We had simply mistaken it for noise.

Most Companies Don’t Have a Data Problem

This is also why I think most companies misunderstand AI.

They believe AI creates value by generating new content.

I think its biggest opportunity is somewhere else entirely.

AI is becoming humanity’s best tool for finding patterns that have been hiding in plain sight all along.

Most executives don’t have a data problem.

They have a perception problem.

Every company I meet tells me some version of the same story:

“We need more data.”

Then I ask a simple question:

“What data are you not collecting today that would completely change your business?”

Usually, the room goes quiet.

Because deep down, they already know the answer.

Almost none.

Companies are drowning in information.

• Customer support tickets.

• Sales call recordings.

• CRM notes.

• Emails.

• Slack conversations.

• Meeting transcripts.

• Product usage logs.

• Feedback forms.

• Invoices.

The average company has accumulated years of institutional knowledge.

It just exists as disconnected fragments.

The problem isn’t scarcity.

It’s interpretation.

Productivity Is Not the Real Prize

For years, AI has been marketed as a content machine.

Write emails. Generate proposals. Create presentations. Summarize meetings…

Those are useful capabilities. But they are not transformational. They are productivity gains.

The real shift happens when AI stops producing outputs and starts discovering relationships.

Imagine an AI that listens to every lost sales call from the past three years.

Not to summarize them.

But to uncover the objections that consistently appear six weeks before customers churn.

Or an AI that connects support tickets with feature usage and reveals that your highest-value customers aren’t leaving because of missing features.

They are leaving because onboarding created the wrong expectations months earlier.

No employee could realistically connect all those dots.

Not because people are incapable.

Because the volume exceeds human cognition.

That is where AI becomes more than a faster assistant.

It becomes an instrument of discovery.

The Best Companies Will Ask Better Questions

That’s why I believe the next competitive advantage won’t belong to companies with the biggest models.

Or even the most data.

It will belong to companies that ask better questions.

Not:

“What can AI generate for us?”

But:

“What has our business been trying to tell us for years that we’ve never been able to hear?”

That is a fundamentally different mindset.

One treats AI as a faster employee.

The other treats AI as a new scientific instrument.

History suggests the second group usually wins.

The telescope didn’t create new stars.

The microscope didn’t create new bacteria.

The map didn’t create the cholera pattern.

And AI won’t create the most valuable insights inside your business.

It will reveal the ones that have been quietly waiting there all along.

Your next breakthrough is probably already in the building. It’s buried inside yesterday’s noise.

At the World Cup, you have to give it back. The obvious reason is that the game needs to continue. The less obvious reason is that what you are holding is no longer just a football.

Inside Trionda, the official ball of the 2026 World Cup, is a motion sensor that records data 500 times per second. It tracks the ball’s movement, helps officials identify precise touches and sends information to the VAR system in real time. It even needs to be charged before a match.

Catching one in the stands is less like catching a souvenir and more like briefly taking possession of a very round computer.

During England’s quarter-final against Norway, that computer became the most controversial witness in football.

Norwegian goalkeeper Ørjan Nyland launched the ball high up the pitch. It climbed into the air, seemed to hang there for a moment, then dropped so abruptly that Norway’s players and staff became convinced it had struck one of the cables holding the camera system above the field.

Seconds later, Jude Bellingham scored England’s equaliser.

The rule was straightforward. If the ball had touched the cable, play should have stopped and everything that followed would have been irrelevant. Nyland protested immediately. Norway’s staff pointed toward the wire, the replays looked unusual, and manager Ståle Solbakken remained convinced that the ball had changed direction in mid-air.

But the most important witness was not the referee, the goalkeeper or even the Spidercam footage.

It was the ball itself.

FIFA examined the data from the sensor inside it, what it calls the ball’s “heartbeat”. A cable impact should have created an additional spike while the ball was airborne. According to FIFA, no such spike appeared, so there was no evidence of contact.

The goal stood.

The decision was settled. The argument was not.

Many England supporters and neutral observers accepted the sensor data as more reliable than human perception. Others watched the ball’s sudden movement and reached the opposite conclusion: they trusted what they believed they had seen with their own eyes.

The sensor had data. Norway had eyes.

Neither side thought the other had presented enough evidence.

That is what makes this story interesting. Not whether the cable was definitely touched, or whether millions of football supporters need an emergency lesson in the Magnus effect. The more useful lesson is that a system can produce an answer without producing trust.

Accuracy and trust are different products.

A machine may reach the correct conclusion and still fail to persuade people. That is because we do not trust technology simply because it gives us an answer. We trust it when we understand how the answer was reached, what evidence was used and whether the conclusion can be challenged.

Good technology should hide its operational complexity. It should not hide its reasoning.

AI is About to Learn the Same Lesson

Most companies currently have the opposite problem. Their AI systems are highly visible while the work is being done and strangely opaque when something goes wrong.

An employee chooses a tool, opens another tab, explains the context again and uploads the same files. They refine the prompt, inspect the result, move the output into another system and perhaps ask a second AI to improve what the first one produced.

Then they check the final work themselves, because nobody wants to discover a hallucinated number halfway through a board meeting.

We bought AI to reduce people’s workload. Instead, we created a new operating role: manager of artificial employees who require constant supervision.

One tool writes. Another researches. Another analyses spreadsheets. Another creates presentations. A fifth promises to automate the first four. Each product looks useful in isolation, but together they form a small digital organisation with no chief of staff.

The employee still has to break down the objective, select the right system, transfer the context, monitor the outputs and assemble the final result.

We automated production. We did not automate coordination.

This coordination cost rarely appears in a software budget. It appears in interruptions, repeated explanations, fragmented attention and the small decisions employees make every time they wonder which tool they are supposed to use.

One AI application may save ten minutes. Fifteen disconnected applications can create an entirely new management layer.

The obvious solution is to make the complexity disappear: one interface, one AI coworker and a collection of specialists working behind it. But simply hiding everything inside a black box creates a different problem.

The first time the system makes a questionable decision, the user will want to know what it did, which information it used and why it chose that particular path. They will also need some way to inspect the work, correct the result and remain responsible for the final decision.

A black box feels magical until the first controversial call.

The right standard for an AI coworker is therefore not that it never makes a mistake. Human coworkers fail that test before lunch.

A more useful standard is whether it can reduce the amount of coordination you perform without reducing your ability to understand what happened.

The work should become less visible.

The accountability should not.

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One AI Coworker, Fifteen Specialists Behind It

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Instead of giving employees another collection of separate AI tools to coordinate, Appy gives them one AI business partner called Violet. Behind Violet is a team of 15 specialists that can help with work such as marketing strategy, project management, financial modelling and presentation creation.

You describe the outcome you need in plain English. Violet determines which specialists are relevant, coordinates the work and returns with the result.

More importantly, this happens inside Slack or Microsoft Teams, the places where teams already communicate. There is no new dashboard employees have to remember to check, no code to write and no complicated workflow builder to maintain.

That does not mean people should blindly accept whatever an AI coworker produces. It means they can delegate the coordination without surrendering their judgment.

The aim is not invisible magic.

It is less management, fewer handoffs and a clearer path from request to result.

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The AI industry remains obsessed with capability. Which model reasons better? Which agent can browse the web? Which tool can create the most impressive presentation?

Those questions matter, but they are not the questions most employees feel during the working day.

Employees feel the management burden. They notice how many systems they need to open, how often they must explain the same task and how much of the final output they still have to assemble themselves. They also notice when an automated system expects trust without offering enough evidence to deserve it.

The World Cup ball offers a useful model because the footballer does not have to operate the technology. The player simply kicks the ball. The sensor carries the context, sends the information to the relevant system and produces evidence when the moment requires it.

That is good product design.

But the controversy also showed that producing data is not the same as producing confidence. When the decision feels wrong, people want to inspect the evidence rather than being told that the machine has spoken.

That is trust design.

The winning AI products will not necessarily be the ones with the longest feature lists or the most specialists. They will be the products that remove the most coordination from the working day while making their actions easier to understand.

The World Cup ball did not ask the player to become a sensor operator.

AI should not ask every employee to become an AI manager.

Good technology should disappear from the workflow, not from accountability.

They see large companies hiring Chief AI Officers, building internal AI teams, running innovation workshops, buying enterprise platforms, and creating 73-slide transformation decks with words like “enablement”, “alignment”, “governance”, and “future-ready”.

Which is usually corporate language for:

Small businesses look at this and think AI is probably not for them yet.

• Too complex.

• Too expensive.

• Too technical.

• Too early.

I think that’s completely wrong.

The AI opportunity for small businesses is not building an AI department.

It is building one workflow that never sleeps.

And this distinction matters more than it sounds.

Because most businesses don’t lose money because they lack strategy decks.

They lose money in much uglier, more boring places.

• A lead comes in and nobody calls fast enough.

• A customer asks a question and waits too long.

• A cart is abandoned and nobody follows up.

• A sales call contains a buying signal and nobody notices.

• A customer says, “Your competitor is cheaper” and that sentence dies inside a call recording forever.

This is where money leaks.

Not in the cinematic business moments.

Not in the strategy offsite.

Not in the “we need to become an AI-first company” slide.

In the broken loops.

And this is where AI becomes useful.

Not as magic.

Not as a toy.

Not as another subscription your team tries for two weeks and then silently abandons.

As a worker inside a workflow.

That distinction is everything.

AI Looks Expensive When You Copy How Large Companies Adopt It

Small businesses should not copy large companies.

Small businesses have a different advantage: speed.

• No committees.

• No internal politics.

• No 18-month transformation project.

No one needs to schedule a meeting to decide whether another meeting is needed.

A small business can do something much better:

• Find one painful workflow.

• Put AI inside it.

• Measure the result.

• Improve the loop.

• Then move to the next one.

That is not an AI department.

That is leverage.

Where Small Businesses Actually Lose Money

Most businesses do not lose money because they lack ambition.

They lose money because important things happen only sometimes.

• Sometimes new leads get called quickly.

• Sometimes missed calls get returned.

• Sometimes abandoned carts get followed up.

• Sometimes customer feedback gets reviewed.

• Sometimes the CRM gets updated.

• Sometimes old customers get reactivated.

• Sometimes sales calls are analyzed.

• Sometimes the team notices a pattern before it becomes a problem.

“Sometimes” is expensive.

And the smaller the business, the more dangerous “sometimes” becomes.

Because small businesses usually do not have extra people sitting around waiting to catch what falls through the cracks.

• The founder is busy.

• The sales team is busy.

• The support person is busy.

• The agency is busy.

Everyone is busy.

And when everyone is busy, the boring but valuable work gets delayed.

• Follow-up.

• Reminders.

• Call summaries.

• Lead qualification.

• Customer check-ins.

• Objection tagging.

• Review analysis.

• Reactivation.

None of this sounds exciting.

That is exactly why it matters.

Boring work compounds when it is done consistently.

Boring work leaks money when it depends on human memory.

The First AI Employee Will Be Boring

I don’t think the first AI employee for most small businesses will be a strategist.

• It will not be a genius analyst.

• It will not be a brand visionary.

• It will not sit in a digital corner wearing a tiny Patagonia vest and telling you to “rethink the customer journey”.

The first AI employee will probably be much more boring.

• The person who answers the phone.

• The person who calls back leads.

• The person who follows up after a form submission.

• The person who reminds customers.

• The person who asks for feedback.

• The person who summarizes calls.

• The person who updates the CRM.

• The person who notices when ten customers complain about the same thing.

Very glamorous, I know.

But this is how leverage usually looks in real life.

Less Iron Man suit.

More “we stopped losing leads because someone now replies in 30 seconds”.

Your first AI employee should not be impressive.

It should be useful.

The Tool Is Not the Strategy

A lot of companies will waste a lot of money on AI over the next few years.

Not because AI is weak.

Because their thinking is weak.

• They will subscribe to one tool for writing.

• One tool for meetings.

• One tool for research.

• One tool for images.

• One tool for automation.

• One tool for customer support.

• One tool someone saw on LinkedIn and bought before lunch.

Then they will wonder why nothing changed.

A long AI tool list is not a strategy.

It is usually a very expensive junk drawer.

The tool is not the strategy.

The workflow is the strategy.

AI becomes valuable when it makes something important happen every time.

Not when it sounds smart.

Not when it generates a clever paragraph.

Not when it creates a cute image of a dog wearing sunglasses on a skateboard.

Although, to be fair, that has its place in civilization.

AI becomes valuable when it changes the operating rhythm of the business.

The Never-Sleeping Workflow

Here is the concept I think every small business should understand:

A Never-Sleeping Workflow is a business process where AI makes sure an important action happens every time, not just when your team remembers to do it.

That is the unlock.

Not more tools.

Not more dashboards.

Not more “AI experiments”.

One workflow that never sleeps.

For example:

• Every new lead gets called within 60 seconds.

• Every missed call gets an automatic callback.

• Every abandoned cart gets a voice follow-up.

• Every old customer gets reactivated after 60 days.

• Every sales call gets summarized and scored.

• Every customer objection gets classified.

• Every negative review triggers a recovery flow.

• Every support conversation becomes product insight.

• Every qualified buyer gets routed to the right person.

• Every repeated complaint becomes visible before it becomes expensive.

This is not science fiction.

This is not a 2032 thing.

This is the practical frontier of AI right now.

Small.

Specific.

Boring enough to be real.

Valuable enough to matter.

The E-Commerce Example

Take a simple e-commerce business.

They spend money on ads. People visit the site. Some add products to cart. Many don’t finish checkout.

Classic problem.

The usual response is predictable:

Spend more on ads. Send more emails. Add another pop-up. Change the discount. Redesign the checkout. Blame the agency.

Sometimes those things help.

But often the business is missing something simpler.

• What if the customer just needed a nudge?

• What if they had a question?

• What if they were comparing options?

• What if they were ready to buy but got distracted?

Now imagine an AI voice agent that calls selected abandoned cart users within minutes.

Not tomorrow.

Not when someone has time.

Not after a spreadsheet is exported, forgotten, reopened, assigned, and half-followed.

Immediately.

• It calls.

• It understands the customer.

• It answers questions.

• It offers help.

• It sends a payment link.

• It books a callback if needed.

• It updates the CRM.

• It summarizes what happened.

Then it runs again tomorrow.

That is not “using an AI tool”.

That is installing a workflow that never sleeps.

Big difference.

The Second-Order Effect

At first, you think AI saves time.

Then you realize it changes what your business can afford to pay attention to.

That is the real shift.

Before AI, many businesses ignored most customer conversations because listening to all of them was impossible.

Before AI, many old leads were never followed up because the economics did not work.

Before AI, many sales calls were never analyzed because managers could only listen to a tiny sample.

Before AI, many small tasks stayed manual because automation was too expensive, too technical, or too annoying.

Now the math changes.

And when the math changes, strategy changes.

AI does not just make existing work cheaper.

It makes previously unreasonable work reasonable.

• Calling every abandoned cart customer used to sound ridiculous. Now it can be a workflow.

• Analyzing every customer conversation used to be impossible. Now it can be a dashboard.

• Creating personalized follow-up for every lead used to require a team. Now it can be a system.

This is where small businesses can build an unfair advantage.

Not by pretending to be large companies.

But by using AI to do the consistent operational work that large companies are often too slow, political, or overcomplicated to implement.

What AI Exposes

There is one uncomfortable part.

When you put AI inside a workflow, it does not only automate the work.

It exposes the business.

• If your sales process is vague, AI will expose it.

• If your lead qualification is messy, AI will expose it.

• If your CRM is chaos with a login screen, AI will expose it.

• If your follow-up depends on someone remembering something, AI will expose it.

• If your offer is unclear, AI will expose it.

• If your team does not agree on what a good customer looks like, AI will expose it.

This is uncomfortable.

But useful.

Because most businesses do not only need automation.

They need clarity. AI forces clarity. It forces you to define the script.

• The trigger.

• The handoff.

• The success metric.

• The fallback.

• The next action.

• The owner.

• The outcome.

This is why “just add AI” rarely works.

If the workflow is broken, AI will not magically fix it.

It will show you exactly where it breaks.

That is not a bug.

That is the work.

The 5-Step AI Workflow Test

If you want to use AI in a way that actually matters, don’t start with tools.

Start with this test.

1. Find the leak

Where does money, attention, speed, or customer intent disappear?

Examples:

• New leads are not contacted fast enough.

• Missed calls are not returned.

• Abandoned carts are ignored.

• Customer complaints are not analyzed.

• Sales calls are not reviewed.

• Old customers are not reactivated.

The leak should be specific.

“Improve sales” is not specific.

“Call every qualified lead within 60 seconds” is specific.

2. Define the repeated action

What should happen every time, but currently happens only sometimes?

This is the most important question.

Because “sometimes” is where AI can create leverage.

If the answer depends on someone remembering, checking, copying, pasting, listening, tagging, or following up manually, you probably found something.

3. Remove human memory from the loop

Human judgment is valuable.

Human memory is overrated.

Do not build critical business processes around someone remembering to check a spreadsheet.

Or follow up tomorrow.

Or listen to a call later.

Or update the CRM after lunch.

That is not a process.

That is a hope with a calendar invite.

4. Put AI inside the workflow

Now decide what the AI worker should actually do.

Should it call? Answer? Summarize? Classify? Route? Remind? Score? Draft? Trigger? Update?

The goal is not to make AI look smart.

The goal is to make the workflow run.

5. Measure one business outcome

Do not measure vibes.

Measure one outcome.

Revenue recovered. Calls answered. Leads converted. Response time reduced. Manual hours saved. Churn reduced. Customer complaints detected. Sales objections classified.

If you cannot connect the workflow to a business outcome, you may still have something interesting.

But you probably do not have leverage yet.

The Practical Starting Point

So where should a small business start?

Not with:

“How do we use AI?”

That question is too broad.

It creates tool shopping, internal confusion, and long conversations that somehow end with someone suggesting a Notion template.

Start with this instead:

That answer is your first AI employee.

Maybe it is a follow-up agent. Maybe it is a missed-call recovery agent. Maybe it is an abandoned-cart voice agent. Maybe it is a customer feedback analyst. Maybe it is a review mining workflow. Maybe it is a sales call scoring system.

It does not need to be impressive.

It needs to work.

At Next Big App, this is how we think about building AI products.

Not more tools for the sake of tools.

Practical AI workflows that help businesses recover revenue, reduce manual work, and understand customers faster.

Because AI is not the advantage by itself.

The advantage is what AI makes happen consistently.

• Even when your team is busy.

• Even when it is after hours.

• Even when the lead came in at the wrong moment.

• Even when nobody feels like updating the spreadsheet.

• Even when the customer signal is buried inside the 847th call recording of the month.

That is the real promise.

Not an AI department.

Not another dashboard.

Not a bigger tool stack.

One workflow that never sleeps.

Start there.

He has four championships. Four MVPs. Every scoring record that matters. A son in the league. A body of work so complete that nobody would blink if he spent his last contract collecting a paycheck and a farewell tour.

He just told the Lakers he’s not coming back.

Not because he’s chasing a bigger check.

Not because LA benched him or disrespected him.

Because, by most reports, he wants to know the organization around him is still built to win, and he’s not convinced anymore.

So at an age where literally every incentive points toward comfort, he’s choosing the harder path. Free agency. A new system. Possibly a reunion with Steph Curry and the Warriors, walking into someone else’s locker room at 41 to chase one more real shot.

I want to talk about why that decision is more instructive than anything he’s ever done on a basketball court.

The Default Setting Is Coast

Here’s something I’ve watched play out over and over, in sports and in business: the moment someone has “made it”, their default setting flips from compete to protect.

Founders do it after the first real exit.

Executives do it after the corner office.

Operators do it the moment the business finally stops being an emergency every week.

It’s not laziness. It’s not even really comfort-seeking on purpose.

It’s quieter than that. You stop asking “is this still the best environment for me to keep getting better in” and start asking “is this still fine”.

Fine becomes the bar. And fine is a very low bar to clear once you’ve already won.

The tradeoff nobody wants to admit is this: the years right after you’ve proven yourself are usually the years you’re most likely to start declining, because that’s exactly when you stop demanding the environment prove itself back to you.

The companies that avoid this trap are the ones everyone remembers, because they’re rare.

• Reed Hastings didn’t wait for DVD rentals to die before he killed them himself, he redirected Netflix into streaming while the DVD business was still printing cash and still the undisputed market leader.

• Steve Jobs did the same thing with the iPod, a product generating obscene margins with zero real competition, by building the iPhone knowing full well it would cannibalize his own cash cow.

Neither man waited for the market to force the decision. They ran the audit early, while everyone else in the room was still enjoying the view.

LeBron inverted that.

What Inversion Actually Looks Like

Inversion thinking isn’t a productivity hack. It’s a discipline: instead of asking “what does winning look like”, you ask “what does losing look like, and am I already doing it”.

Losing, for someone in LeBron’s position, doesn’t look like a bad season. It looks like staying somewhere out of familiarity.

It looks like optimizing for the easy version of the story, the retirement tour, the jersey retirement, the soft landing, instead of the accurate version of the story, which is: am I still in the best possible system to compete for what I actually came here to do.

Reports say he spent last year monitoring whether the Lakers were building a genuine contender around him, even while he was still under contract.

That’s not entitlement. That’s someone refusing to let inertia make the decision for him.

Most people, at 41, with his resume, never run that audit. They let the calendar and the comfort of “this is just where I am now” make the call.

He ran the audit anyway. And the answer he got was no.

There’s an old story from Intel that captures this exact move.

By the mid-80s, Intel was still known as the memory chip company, even as Japanese competitors were quietly eating that business alive.

Andy Grove turned to his co-founder Gordon Moore and asked him a question that later became legendary: if the board fired us both and brought in a new CEO, what would that person do?

Moore didn’t hesitate. He said the new guy would get Intel out of memory chips.

So Grove and Moore walked out of the room, and walked back in as if they were that new CEO, and made the call themselves.

That single mental trick, firing yourself in your head and asking what a fresh set of eyes would decide, is precisely what LeBron ran on his own career.

He didn’t wait for a front office to tell him the Lakers weren’t built to win.

He fired himself as the loyal incumbent and asked what a player with no sentimental attachment to the jersey would do.

Why This Matters If You Run Anything

If you’re a founder or an operator, here’s the uncomfortable mirror.

You built something. Maybe it’s working. Maybe it’s more than working, it’s genuinely good, and you’ve earned the right to relax a little.

That’s exactly the moment the coast-default kicks in, and it’s exactly the moment you should be running LeBron’s audit on your own situation instead.

Is the team around you still built to win the next version of the game, or just the version you already won?

Is the system still sharpening you, or are you just occupying a familiar seat in it?

Are you optimizing for the comfortable story you get to tell later, or the accurate one about whether you’re still getting better?

Nobody forces this audit on you once you’ve had success. That’s the trap.

Early on, the market forces honesty on you constantly, you either grow or you die.

Later, you can coast for years on reputation before anyone notices the decline, including you.

I’ve watched a version of this play out with founders after their first real exit. The wire hits, the title softens into “advisor” or “board member”, and suddenly the same person who used to obsess over every metric is coasting on a comfortable retainer.

Nothing is technically wrong. It’s just quiet. It’s fine. And fine, again, is the trap.

The founders I respect most are the ones who take the exit money and immediately go put themselves back in a room with real risk, a new startup, an angel check with actual downside, anything that puts them back in a system that has to prove itself to them again.

Jeff Bezos used to tell Amazon the same thing in different words.

• Day one is intensity.

• Day two is stasis, followed by irrelevance, followed by a painful decline, followed by death.

His whole point was that day two doesn’t announce itself. It arrives disguised as comfort. Which is exactly why the mental move matters more than the calendar age.

LeBron isn’t behaving like a 41-year-old future Hall of Famer collecting a farewell tour. He’s behaving like it’s day one.

LeBron just showed 41-year-olds, and honestly everyone else, what it looks like to refuse that trap on purpose, in public, with everything to lose reputationally if it doesn’t work out.

The Sharp Takeaway

Winning doesn’t earn you the right to stop competing for your own environment. It just raises the price of staying somewhere that no longer deserves you.

The people who stay great longest aren’t the ones who coast on what they’ve already built.

They’re the ones who keep asking, at every level of success, whether the system around them is still worth staying in and they leave the moment the honest answer is no.

It may simply be creating a mystery.

More specifically, sparking a debate around whether your product, your content, or even the people behind it are real humans or “AI”.

Many of you have probably seen the recent viral wave around Mebrure’s song “Değilim”.

She hid her face, used a digital avatar, and suddenly everyone in the comments started arguing about whether the whole thing was completely AI-generated.

And what happened?

With zero ad budget, she generated millions of organic impressions in a very short time.

In her official statements, the artist clearly said that she is not AI. She said the lyrics, composition, and voice all belong entirely to her.

But that was almost beside the point.

Today, when every feed is full of content that looks and sounds the same, radical transparency does not always create attention. Sometimes, leaving a little uncertainty does.

When you introduce just enough ambiguity, people start debating in the comments. And that is exactly the kind of engagement signal algorithms love.

So these days, whether AI is actually involved or not can become a reason for virality.

You can probably think of many global examples where brands, artists, or products did not explain everything perfectly on day one. Instead, they left some room for speculation, curiosity, and public interpretation.

How something spreads is often bigger than the thing itself.

Maybe it is time we started treating curiosity not as an accident, but as a deliberate part of our growth strategy.

To boost subscriber retention, Netflix consistently experiments with new entertainment formats.

We saw this early on with Bandersnatch, their choose-your-own-adventure interactive episode, though it didn’t trigger a massive wave of follow-ups.

While the platform currently hosts a library of casual mobile games, Unhinged marks an ambitious shift toward a genuinely immersive horror experience.

Video:

Here’s how it works: you scan a QR code on your TV screen, and your smartphone instantly transforms into the character’s flashlight.

Wherever you point your hand in real life, the beam of light on the TV moves in perfect synchronization.

When your character receives an in-game phone call, your actual phone starts vibrating; the caller’s voice comes directly from your handset, while the eerie ambient noise continues to blast from the TV.

Instead of forcing players to buy a $1,000 VR headset to feel true suspense, Netflix achieved brilliant horror atmosphere through clever sensory synchronization.

They are seamlessly blurring the line between the digital world and physical reality.

As product builders, this is a great reminder that breakthrough innovation doesn’t always require inventing entirely new technology from scratch. Instead, it’s about how creatively we can bridge the tools and habits users already have. We should all take a page out of this design playbook.

The rig detects the incoming pigeon, takes aim, and —bzzzt— blasts it with water.

Video:

Honestly, this might be the clearest lesson in software and business development I’ve seen in a long time.

Think about it: no major brand is ever going to mass-produce a device for that hyper-specific, niche problem on a single balcony. But the friction this guy faced was so real and frustrating that he took matters into his own hands and built his own solution.

I believe this is exactly the crisis companies are facing in the software world today.

We hit a bottleneck in our workflow that is entirely unique to us. To fix it, we go out and buy a massive, clunky, off-the-shelf software license. We end up using barely 5% of its features while forcing our team to mold themselves into their rigid frameworks.

But there’s a flip side to the coin. Recognizing this need and dumping the job on your internal team with a casual, “Hey, let’s just build a quick automation here” is a massive trap.

You inevitably end up with amateur, fragile systems that collapse at the slightest breeze.

Hyper-specific problems require professional engineering. Just like you wouldn’t ask your accountant to pick up a welding torch to build a balcony rig, you shouldn’t expect your in-house team to become AI systems engineers overnight.

The real way forward is to stop forcing your business processes into cookie-cutter software. Instead, partner with “digital mechanics” who can custom-design and scale these digital rigs specifically for you.

We need to leverage the power of AI and technology the right way. Keep your team focused on their core competencies, and leave the bespoke AI systems to the experts.

He was already getting far more attention than the industry average across almost every social media channel.

Now, he is back with a new reality show format.

The show is called “Scale or Fail”.

Eight entrepreneurs compete for a $100,000 prize.

When someone who has clearly cracked the code of social media starts blending business with entertainment to drive even more sales, I think it is fair to say that organic attention in B2B has entered a completely new phase.

The picture is becoming very clear:

Organic content marketing is going through serious hyperinflation.

Thanks to AI, accessing information and “creating content” have become incredibly easy.

Which means the era of standing out by simply sharing raw knowledge is probably over.

Here is the trailer:

If ten articles net you 10,000 visitors, then a hundred articles must net you 100,000.

That linear assumption is dead.

In 2026, scaling content velocity doesn’t build authority; it just burns cash.

Google has fundamentally shifted from a search engine that refers traffic to an Answer Engine driven by AI Overviews (SGE).

When zero-click searches are the default baseline, running a traditional, high-volume content factory breaks completely.

The algorithm no longer rewards the sheer mass of words you publish. It rewards Information Gain and Machine-Readable Structure.

If your growth playbook is still built on mass-producing synthesized articles that repackage existing web insights, your organic distribution is about to scale to zero.

Here is the reality of how Google’s modern backend operates—and how we build LLM-ready architecture at Next Big App to survive it.

The RAG Filter: Why “More” is No Longer “Better”

When Google’s SGE generates an overview on the SERP, it relies on a RAG (Retrieval-Augmented Generation) framework.

It doesn’t just guess the next word; it pulls data from trusted index sources, extracts specific text chunks, and synthesizes them into a direct answer.

During this retrieval phase, Google applies a strict algorithmic filter: Information Gain.

If your article merely summarizes the same concepts already sitting in the top 10 search results, your Information Gain score is flat zero.

The RAG mechanism doesn’t need an eleventh version of the same opinion. It discards your page before it ever reaches the AI overview window.

To win a citation in an AI-driven search world, your content strategy must shift away from volume and lean heavily into two non-negotiable pillars:

• Proprietary Data: First-party surveys, internal platform metrics, and original case studies that cannot be scraped or simulated elsewhere.

• Unique Methodologies: Proprietary frameworks and named intellectual property unique to your leadership team, explicitly fed to the crawler via Quotation schema.

From Keywords to Entities: The Semantic Blueprint

Outsiders think SEO is a copywriting problem. Operators know it’s a database architecture problem.

Traditional content factories focus on keyword density. SGE, powered by Gemini and advanced LLM variants, completely ignores keyword stuffing.

First, we need to understand one thing very clearly: search engines now crawl the internet for a single reason, to answer one question:

It reads text to identify entities, unique, identifiable concepts (companies, products, specific methodologies) and maps how they relate to one another in its global Knowledge Graph.

If your brand and the specific solutions you offer are not explicitly modeled as interconnected “Nodes”, the AI cannot map your authority.

At Next Big App, we map our content silos using a graph database framework like Neo4j or ArcadeDB before we ever publish a word. This allows us to pre-simulate exactly how an AI engine will interpret our brand ecosystem.

A strong content strategy should not start with isolated keywords. It should start with a clear knowledge structure that helps search engines and AI systems understand your brand’s role in the market.

The core flow looks like this:

• Your company develops a proprietary framework: This gives your brand a unique point of view instead of making it sound like every other company in the category.

• The framework addresses a specific customer pain point: The content should clearly explain the problem your audience is struggling with, why it matters, and why existing approaches are not enough.

• The customer pain point connects naturally to a target solution: Your product or service should appear as the logical resolution to the problem, not as a forced sales pitch.

• This creates a semantic chain: Company → Framework → Pain Point → Solution

This matters because AI search systems do not only look for keyword matches. They look for clear relationships between entities, concepts, problems, and answers. When your content repeatedly explains your framework, applies it to real customer problems, and connects it to a concrete solution, your brand becomes easier to understand, cite, and recommend.

In practice, this means you are not just publishing articles for traffic. You are building a recognizable authority map around your company. Over time, your brand becomes associated with a specific way of solving a specific problem.

When your site architecture mirrors this clean, conflict-free semantic web, Google’s crawlers can instantly parse your contextual relevance.

Automating the Structural Pipeline

Mapping your internal data structure is useless if the search engine can’t ingest it instantly.

You need to translate your content universe into the native language of AI crawlers: JSON-LD (Schema Markup).

Writing this code manually is an operational bottleneck. Instead, we use LLM-powered automation pipelines via API integrations to handle it dynamically at scale.

The Workflow:

1. Extraction: The moment a highly structured, data-rich article is published, a Python script sends the raw text to an LLM via API.

2. Entity Recognition: The model executes Named Entity Recognition (NER) to isolate the core concepts, internal experts, and product relationships.

3. Semantic Ingestion: The LLM generates a hyper-detailed, nested JSON-LD payload, mapping your internal entities directly to authoritative nodes like Wikipedia or Wikidata using sameAs tags.

4. Dynamic Injection: The backend automatically injects this schema payload into the page header.

JSON

{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "headline": "Overcoming the Volume Illusion",
  "about": [
    {
      "@type": "Thing",
      "name": "Knowledge Graph",
      "sameAs": "https://en.wikipedia.org/wiki/Knowledge_Graph"
    }
  ],
  "mentions": [
    {
      "@type": "SoftwareApplication",
      "name": "Neo4j",
      "sameAs": "https://en.wikipedia.org/wiki/Neo4j"
    }
  ]
}

Designing Content to Be “Chunked”

The final piece of the architectural puzzle requires an uncomfortable operational sacrifice. To be cited by an AI engine, your content must be deliberately formatted to be easily dissected and served directly on the SERP without a click.

You must trade vanity blog traffic for downstream model authority. LLMs process data in distinct vector windows (chunks). If your layout consists of dense, unstructured walls of prose, the RAG mechanism misses your core insights.

• Hierarchical H-Tags: Format your H2 and H3 headers directly as explicit questions or razor-sharp, definitive statements.

• TL;DR Definition Blocks: Place a highly concise, 2-to-3 sentence summary block at the absolute top of every major section. This gives the RAG loop a clean, pre-optimized text chunk to clip and cite directly inside the AI Overview.

The Strategic Realignment

The era of scaling mediocre words to capture search traffic is over.

In the age of Generative Engine Optimization, your primary metric isn’t search volume. It’s model mindshare.

Stop funding content factories that repeat what the internet already knows. Start building a hyper-structured, data-forward architecture that proves your brand is the definitive authority on the topic.

If the model doesn’t recognize your structure, the market won’t recognize your business.

Not because they’re addicted to social media.

Because it’s an algorithmic fight for survival.

They’ve found this method to grab an order the second it appears on the screen.

That’s how insane the competition has become.

The better question is: “What kind of work requires what level of intelligence?”

Too many companies try to pick one model and use it for everything. They either overspend on simple tasks or make basic workflows unnecessarily slow.

The reality is this:

Open-source models are now very close to paid models. GLM 5.2 for harder tasks. Gemma 4 for simpler ones.

I use both through Ollama Cloud, and I’d recommend looking into this kind of setup.

I also wouldn’t pay too much attention to exaggerated claims like “Claude Mythos is so far ahead that governments had to ban it”. GPT 5.5 is excellent too. But the gaps are no longer as dramatic as the marketing makes them sound.

For GLM 5.2 benchmarks:

When building your AI operating system, the key is to layer models by task difficulty:

• Analytics, coding, complex reasoning: stronger model

• Classification, summarization, repetitive work: smaller model

• Routine automation: lightest possible model

Costs drop immediately. Speed goes up. The system becomes more flexible.

Don’t get trapped in the “America builds the best model, so just pay more to do better work” narrative.

Intelligence is becoming democratized.

Companies should design their AI stack accordingly.

What it does is not simply “digitize the score”.

It removes the friction.

It works from your watch. One tap for your point. Double tap for your opponent’s. Your phone stays safely outside the court, showing the score. The speaker gives you motivating voice announcements.

Made a mistake? Press and hold.

The player’s attention is no longer on the scoreboard.

It’s back in the game.

This is how good products are born.

You take a repeated pain, turn it into a system, and solve it so quietly that the user barely notices the product doing the work.

The real product here is not scorekeeping.

It’s giving attention back to the game.

And honestly, when building products, I think that’s what we should focus on more than the feature itself.

Out of sheer reflex, the tech press spent days obsessing over new AI features, interface face-lifts, icon shadows, and the predictable “no revolution this year” commentary.

While competitors seemed to roll out mind-blowing AI breakthroughs daily, the prevailing narrative was that Apple was casually coasting along, focusing on mere cosmetics.

Regular readers of this newsletter might remember we made the exact same observation two years ago during WWDC 2024. But I believe the real story lies elsewhere.

The real story is hidden in the subtle lines of code, the quiet signals sent to developers, and Apple’s classic, long-game strategic playbook.

Let’s take a closer look at this clue and the strategic chess move masquerading as a “defeat”.

The Foldable iPhone is Coming

Following the anti-climactic conclusion of WWDC, developers digging into the iOS 27 developer beta spotted explicit strings within the system code: fold state and angle degrees.

These aren’t just random technical remnants. They are clear signals that Apple is already bending its ecosystem to fit a device it hasn’t even named yet.

What’s even more critical is the unmistakable message Apple delivered to developers right after the keynotes:

“Stop designing apps based on fixed physical device dimensions. Start preparing for dynamic interfaces and flexible screen sizes.”

Translation?

The foldable iPhone is no longer a rumor. It’s actually happening. Apple isn’t prepping the market before launching the product; it’s prepping the ecosystem.

The wheels are already in motion for what will likely be a 4.5 mm ultra-thin foldable iPhone Ultra, commanding a price tag north of $2,000 when it hits shelves.

We are witnessing a classic Apple masterclass. Before unveiling a brand-new product category, they align developers, suppliers, the operating system, and consumer expectations. Then, right when everyone thinks “Apple is too late”, they step onto the stage and rewrite the rules of the game.

This is exactly what is playing out with the foldable iPhone.

Those tracking the breadcrumbs are already visualizing the device.

In fact, dropping an incredibly thin phone (iPhone Air) out of nowhere was merely the groundwork for this massive transition.

For anyone curious about what this phone will look like down to the finest detail, you can check out the video below.

Did Apple Fail the AI Race?

To an outside observer, Apple might look like it’s lagging behind Anthropic, DeepSeek, Nvidia, Google, or OpenAI in the AI race. But that’s a superficial view.

Apple is calmly watching its competitors burn billions of dollars training massive models every two months, only to throw them away.

This explains a moment from WWDC that left many baffled: Apple took Google (its arch-rival) that spent billions developing lightweight, on-device AI models and essentially introduced it as a mere supplier, singing its praises.

Why?

Because Apple will simply use Google’s LLM in its upcoming models. Shocking, right?

But you have to understand the game Apple is playing. Their goal isn’t to build the absolute best AI model. Their strategy is straightforward:

“The best models will eventually become cheaper, smaller, and integrate directly into devices. My job is to control the most valuable real estate they run on, the strongest distribution network, and the most seamless user experience.”

While others burn billions manufacturing the engines, Apple controls the garage, the road, the steering wheel, and the driver’s seat.

That’s why asking why Apple entered the foldable market so late is the wrong question.

The right question is: Why is Apple spending billions of dollars just to eliminate that tiny crease in the middle of the screen?

Because Apple understands the fatal flaw that traps almost every founder, product manager, and SaaS company in the tech world: People don’t buy what they need; they buy what they desire.

The Valley of Death Between “Want” and “Need”

You’re probably familiar with Y Combinator’s famous motto, which has been repeated like gospel in the tech world for years: “Make something people want.”

Notice the wording.

They don’t say, “Make something people need”.

They say, “Make something people want”.

The vast majority of startups get trapped in the canyon between those two words. Because needs are rational, but desires are psychological.

• Need lives in an Excel spreadsheet. Want shows up on a credit card statement.

• Need makes you say, “That makes sense”. Want makes you say, “I want this right now”.

• Need wins compliments. Want drives sales.

What a consumer or a business actually needs is rarely what makes them pull out their credit card.

As a founder, you pitch “time saved”, “cost reduction”, “efficiency”, “dashboards”, and “automation”.

The customer nods politely.

Then, they turn around and buy the product that makes them feel more powerful, smarter, more modern, safer, or more exclusive.

Humans like to think they make rational purchasing decisions, but in reality, they just rationalize emotional ones.

In 2026, nobody rationally needs a foldable iPhone. Current iPhones already do everything exceptionally well. Calls, videos, emails, meetings, gaming, payments, and AI processing.

But this isn’t about need. It’s about desire.

• People want to unfold a screen and see absolutely zero imperfections.

• They want to hold an object that feels like it’s from the future.

• They want to experience that engineering marvel that makes them wonder, “How is this so thin, so flawless, and so stunning?”

That’s why the crease isn’t just a technical problem. The crease is a scratch on the face of desire.

While competitors have spent years selling foldables with a visible groove down the center, Apple has spent billions to eradicate it. Because Apple knows:

• People don’t want more features; they want fewer hesitations.

• They don’t want a bigger screen; they want a more flawless feel.

• They don’t want a more powerful processor; they want to feel like they belong to an exclusive future.

The Coldplay Paradox of Product Development

For a founder, one of the most painful moments is pouring your heart into a product, adding genuinely useful features, making the user’s life easier, cutting costs, accelerating workflows, building dashboards, reports, automations, integrations, and AI capabilities... and then hitting the market only to be met with crickets.

This is the product development version of the famous irony in Coldplay’s Fix You:

You pour everything into it, but it falls flat.

Why?

Because you gave the user what they needed, but failed to give them what they wanted to feel.

Users don’t walk around asking, “What do I need?” They ask, “Who will I become if I use this?”

That’s why the success of the iPhone Ultra won’t just hinge on the raw power of the A20 chip, split-screen multitasking, battery life, or AI capabilities. Those matter. But the trigger for the actual purchase will likely be much simpler: opening the screen and seeing no crease.

Because that seamlessness isn’t just physical perfection; it’s a status symbol. An engineering fetish. Psychological comfort. That “Apple nailed it again” feeling.

Good products solve problems. Great products solve the unspoken emotions within the customer.

Apple’s Real Leverage: Timing Over Being First

For years, the Android world has wrestled with the chaos of optimizing for thousands of screen sizes, resolutions, and aspect ratios.

The exact same thing happened with foldables.

The hardware arrived, but the user experience lagged behind. The screens grew, but the apps couldn’t scale. The devices folded, but the ecosystem didn’t bend with them.

This is where Apple differentiates itself.

They align the software ecosystem long before dropping the hardware.

When they tell developers to stop thinking in fixed dimensions, it’s not just technical advice; they are shifting the rules of engagement before even stepping onto the field.

This is Apple’s ultimate leverage.

Apple is rarely first. But when they arrive, the user says, “Yes, this is exactly how it was supposed to be.”

• The MP3 player didn’t start with Apple.

• The smartphone didn’t start with Apple.

• The tablet didn’t start with Apple.

• The smartwatch didn’t start with Apple.

• The foldable phone won’t start with Apple either.

But history shows us that being first is one thing, while capturing what people actually want is another.

Most startups burn out trying to be pioneers, while Apple waits for the right psychological threshold.

Sometimes, winning a market isn’t about having the biggest tech breakthrough. It’s about removing the smallest hesitation.

The Real Lesson for Founders

The most critical takeaway here for founders isn’t the foldable iPhone. The real question is: What “crease” are you ignoring in your own product?

• What is that tiny friction point breaking your customer’s purchasing decision?

• At what point during a demo does a user think, “This is nice, but...”?

• Where do they believe in the product but hesitate to commit?

• What sale are you losing not because a feature is missing, but because an emotion is absent?

Too many founders look for answers in the wrong places:

• More features.

• More powerful tech.

• More detailed dashboards.

• Longer pitches.

• More rational benefits.

• Larger AI models.

• More complex demos.

Yet sometimes, closing the deal doesn’t require more. It requires something smoother. Something that builds more trust. Something clearer, easier to explain, or something that triggers that exact feeling: “Yes, this is exactly what I was looking for.”

If you’re building startup, you need to find your customer’s psychological crease. If you’re selling a service, you need to uncover that invisible hesitation at the point of purchase. If you’re building an AI product, stop pitching “our model is better” and start figuring out which fear you are alleviating.

Because customers rarely buy your technology; they buy the new identity that the technology gives them.

Final Thoughts

People appreciate rational utility, but they open their wallets for psychological comfort, status, trust, desire, and a sense of flawless execution.

While competitors race to train the largest models, stack features, and burn through astronomical budgets, we need to focus on a different metric: Where is the crease in the customer’s mind?

Find that line, and obsess over erasing it. Because at the end of the day, markets aren’t won by the most powerful technology. They are won by whoever eliminates the customer’s smallest hesitation with the greatest elegance.