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

This is what makes Appy.AI’s approach interesting.

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.

I watched it performed on stage last week.

A man walks out carrying two suitcases. He’s not acting exhausted. He is exhausted. Right there in front of you. And for the next three hours, you watch him disintegrate in slow motion.

The play is Arthur Miller’s Death of a Salesman, performed on a sold-out stage in Istanbul, with Halit Ergenç as Willy Loman.

One of the great plays. Pulitzer Prize winner. Taught in every major literature department. Often called the greatest American play ever written.

Watching it live hits different.

Miller’s language. The staging. The way grief moves through a family in real time. It reaches somewhere most business books never get close to.

I sat with that for a day.

Then I couldn’t stop thinking about your sales team.

I know what that sounds like.

But hear me out.

This isn’t a metaphor. Not a dramatic flourish.

It’s a case study in what happens when the rules of a market shift and the operator inside it refuses to update their mental model.

And the first thing Miller makes painfully clear is this: Willy Loman’s problem was not that he didn’t work hard enough.

The Problem Was Never the Work Ethic

Willy Loman worked hard.

His whole life on the road, city to city, state to state, putting in the hours, making the calls, shaking the hands.

Arthur Miller is precise about this. Willy isn’t lazy. He isn’t incompetent. He genuinely believed in what he was doing.

The problem was what he believed in.

Willy’s operating thesis was this: If you’re likable enough, well-connected enough, and work hard enough, the money follows.

Personality is the moat. Relationships are the asset. The salesman who everyone knows wins.

And to be clear, it was a real model.

It worked for a generation.

And then, quietly, it stopped.

The corporate, systematized model replaced the door-to-door era. What used to be relationship-driven became process-driven. What used to require charm now required systems.

Willy didn’t fail because the world got harder.

He failed because the world got different, and he kept running the same play.

The tragedy isn’t that he couldn’t win.

The tragedy is that he never updated his definition of winning.

But Miller doesn’t just show us the man trapped in the old model.

He also places another man right next to him. Quieter, less charismatic, easier to overlook who represents the model Willy refuses to see.

Enter Charley

There’s a character in the play most people forget.

Charley, Willy’s neighbor.

Steady. Quiet. Un-dramatic.

Throughout the play, Charley offers Willy a job. Multiple times. Willy refuses every time, because accepting would mean admitting that Charley figured something out that Willy didn’t.

Charley doesn’t sell on personality. He doesn’t have Willy’s charisma. He doesn’t need the room to love him.

He just runs a tight operation.

His son Bernard is the same kind of signal. Bernard is the kid Willy’s sons used to mock for studying too much instead of playing football. Later, Bernard grows up to argue cases before the U.S. Supreme Court.

That detail matters.

The person everyone underestimated was quietly building the kind of advantage that compounds.

Charley is not the opposite of Willy because he works less.

He is the opposite of Willy because he operates from a different model.

Willy believes the world rewards being liked.

Charley understands the world rewards being useful, prepared, and structurally sound.

That distinction is everywhere right now.

In every industry, there’s a Charley.

You probably don’t know who they are yet.

They’re not on stage.

They’re not posting about their sales culture on LinkedIn.

They’re quietly rebuilding their revenue architecture around AI. And in 18 months, their cost per acquisition is going to be structurally lower than yours, and you won’t be able to figure out why your pipeline feels increasingly uphill.

This is where the play stops feeling like literature and starts feeling like a board meeting.

Because the question every company is now facing is not “Should we use AI?”

It is: are we using AI to preserve an old model, or to build a better one?

The difference between those two choices is the difference between Klarna and Salesforce.

What Klarna Got Right. And What Klarna Got Wrong.

Let’s talk about the most cited AI case study of the last two years.

In early 2024, Klarna announced that its AI assistant, built in partnership with OpenAI, had handled 2.3 million customer service conversations in its first month alone.

• Response time dropped from 11 minutes to under 2 minutes.

• Customer satisfaction scores initially matched human agents.

• Repeat inquiries fell 25%.

The company had deployed the system for somewhere between $2 million and $3 million, projecting a $40 million profit improvement for 2024.

The board smiled.

The market applauded.

Sebastian Siemiatkowski talked about the future.

Then something interesting happened.

Customer satisfaction scores dropped as edge cases, emotionally charged interactions, and multi-step problem resolution overwhelmed AI trained to handle routine queries.

CEO Sebastian Siemiatkowski publicly admitted that the AI-driven transition negatively affected service and product quality. Klarna is now rehiring human staff.

This is not a story about AI failing.

This is a story about binary thinking.

Klarna didn’t build an AI-native system.

They performed a substitution.

They replaced humans with AI as if it were a one-for-one swap. Same jobs, different executor.

What they missed was that the model itself needed to change.

AI doesn’t replace people. It replaces the way work is structured.

And this is exactly where Miller’s family drama becomes useful again.

Because Willy is not the only character who refuses reality.

His younger son, Happy, sees the same collapse happening in front of him and keeps calling it progress.

Happy Loman is perpetually upbeat, perpetually deluded. He sees all the same signals his older brother Biff sees. The failure, the disconnect, the crumbling but he reframes everything.

Things are going great.

Dad’s fine.

We’re going to be fine.

When founders hear “AI can transform your sales function” and respond with “Let’s cut the team and plug in a bot” that’s Happy Loman energy.

It looks decisive.

It’s actually avoidance.

Avoidance of the harder question: how does the entire system need to be rearchitected?

The lesson from Klarna isn’t “AI doesn’t work”.

It’s this: Automation without systems redesign just creates faster failure.

So if Klarna shows the danger of substituting the executor without redesigning the system, the next question is obvious:

What does proper redesign look like?

That’s where Salesforce becomes interesting.

What Salesforce Actually Built

Now look at the other end of the spectrum.

Salesforce spent years being mocked for being behind on AI.

Then they built Agentforce and went all-in on what they called the “agentic enterprise.”

AI agents now successfully resolve 85% of Salesforce’s customer service inquiries and qualify its own sales leads 40% faster than before the advent of AI.

Overall, Marc Benioff says these AI agents are so effective that they are now doing 30% to 50% of all the work within Salesforce itself.

See: Fortune.

The numbers on the product side followed.

Agentforce ARR hit $800 million, up 169% year-on-year.

The company closed 29,000 Agentforce deals, up 50% quarter-on-quarter in Q4 alone.

Internally, Salesforce is reporting $100 million in annualized cost savings, with over 3,200 opportunities influenced by AI agents.

But here’s the part that usually gets buried: As a result, Benioff has announced that Salesforce won’t be hiring any additional software engineers, customer service agents, or lawyers.

But the company is hiring salespeople and customer success employees.

Read that again.

They’re not replacing everyone.

They’re replacing specific functions and redeploying humans to the work that compounds on human judgment: relationship-building, complex problem-solving, strategic expansion.

That is the Charley model.

Systematic. Patient. Structured.

Not dramatic. Not loud.

Just structurally better over time.

And the small business case is just as interesting.

Safari365, an Africa-based tour operator with just 35 employees, achieved 62% case resolution through Agentforce after completing a data cleanup that took longer than the agent deployment itself.

The data cleanup taking longer than the deployment.

That detail carries everything.

The limiting factor wasn’t AI.

It was the quality of the underlying system.

This keeps coming up.

The companies getting the best results from AI sales automation aren’t the ones with the biggest budgets.

They’re the ones who did the unsexy work of cleaning their data, mapping their process, and thinking clearly about where human judgment is actually required.

Bernard studied.

The jocks mocked him for it.

Then the world changed, and studying was the advantage.

And once you see that, another part of the play starts to look different.

Because Willy’s problem wasn’t only that he had the wrong model.

It was that he kept using the wrong signals to convince himself the model was still working.

Willy’s Hallucinations Were Metrics

There’s a recurring motif in the play that takes time to register.

Willy hallucinates.

He holds full conversations with his dead brother Ben, replays memories as if they’re happening now, and exists simultaneously in the present and in a golden version of the past where everything was working.

His family doesn’t call it hallucination.

They call it “Dad’s episodes”.

Most sales teams have their own version of this.

They’re called activity metrics.

Calls made.

Emails sent.

Meetings booked.

Pipeline coverage ratio.

These numbers feel real and important. They fill dashboards. They get presented in QBRs. People get promoted for hitting them.

But organizations using AI analysis reach 96% forecasting accuracy, while human judgment alone achieves only 66%.

That 30-point gap isn’t a rounding error.

That’s the gap between what you think your pipeline is doing and what it’s actually doing.

Willy’s tragedy wasn’t simply that he was working in the wrong direction.

It was that he had built an entire internal narrative around signals that felt true but weren’t.

By the time reality forced its way through, there was no runway left.

When you’re measuring sales team performance by effort rather than conversion velocity and CAC payback period, you’re living in Willy’s house.

And Willy’s house matters.

Because Miller makes the economics of the old model brutally literal.

Willy doesn’t just lose psychologically.

He loses financially, structurally, and too late.

IN COLLABORATION WITH CUEY

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The Mortgage Nobody Talks About

There’s a quietly devastating image in the play.

Willy has spent 35 years paying off the mortgage on his house.

The house is finally, finally paid off.

And in the same week, he dies.

His wife Linda says: “We’re free and clear”.

Nobody is there to hear it.

Miller uses this to show that Willy was always a beat behind. He spent his whole life paying for a life he never got to live.

The modern equivalent is the sales team cost structure that makes sense at $1M ARR and breaks at $10M.

Here’s what the math actually looks like at scale: A fully loaded enterprise AE in most markets costs $150K–$200K annually when you factor in base, variable, benefits, and management overhead.

Five of them is $750K–$1M per year.

That number doesn’t scale sub-linearly.

It scales linearly or worse, because more reps means more management, more enablement, more churn, more ramp time.

AI-driven outbound and lead qualification, built properly, handles the top-of-funnel work at a fraction of that number.

And unlike headcount, it doesn’t require ramp time, doesn’t churn, doesn’t have bad weeks, and gets better with data.

McKinsey data shows a 45% increase in sales productivity and a 12% reduction in sales costs from comprehensive sales automation implementations.

Bain & Company reports that early AI deployments in sales have boosted win rates by 30% or more.

But here’s the part that really matters: Those numbers compound.

The company running AI-native sales at year one isn’t just more efficient at year one.

They’re accumulating data, refining models, and expanding into markets you haven’t reached yet because they could afford the coverage.

You’re still paying the mortgage.

They own the house free and clear.

At this point, the play gives us more than one warning.

Willy shows what happens when you double down on the past.

Happy shows what happens when you reframe danger as optimism.

Biff shows what happens when you see the truth but can’t act on it.

Linda shows what happens when loyalty protects a broken system.

Together, they map almost every founder response to AI right now.

The Four Loman Traps

In the play, every major character represents a different response to a system that’s shifting beneath them.

I’ve seen all four of these responses in founders over the last two years.

1. The Willy Trap

Doubling down on what worked before.

More reps. More calls. More hustle. More relationship meetings.

“Our product requires a human touch”.

It probably does for some of the sales motion.

But most of the motion?

It requires speed and personalization at scale, which is exactly what AI does better.

The Willy Trap feels noble because it looks like commitment.

But often, it is just refusal dressed up as discipline.

2. The Happy Trap

Seeing the data and explaining it away.

“Our market is different”.

“Our customers expect a human”.

“We tried a chatbot in 2019 and it didn’t work”.

Happy Loman will tell you everything is fine right up until it isn’t.

The founders running this trap are usually articulate and confident.

That’s what makes it dangerous.

The Happy Trap feels strategic because it sounds calm.

But often, it is just denial with better vocabulary.

3. The Biff Trap

Biff is Willy’s older son.

He is the character who sees clearly, maybe the only one who does but freezes.

He knows the old story is broken.

He just doesn’t know what to build instead.

I see this in founders who understand the shift is real, understand they need to change, but get stuck in the enormity of the redesign and defer it quarter after quarter.

They are not in denial.

They are stuck between diagnosis and action.

The Biff Trap feels honest because the diagnosis is accurate.

But diagnosis without redesign still loses to someone already building.

4. The Linda Trap

Linda is Willy’s wife and the most sympathetic character in the play.

She loves Willy deeply and keeps everything running.

She’s also the one who, by protecting Willy from reality, extends his delusion the longest.

In a business context, this is the loyal sales leader.

High integrity. Real experience. Genuinely invested in the team.

But they keep defending the existing model because abandoning it would mean admitting that the last several years of strategy were pointing in the wrong direction.

The Linda Trap feels humane because it protects people.

But sometimes protecting the old structure only delays the harder transition everyone will eventually have to face.

None of these people are stupid.

None of them are bad.

They’re all rationally responding to the information available to them.

That’s exactly what makes it a tragedy.

And it’s also why the real danger isn’t the first-order effect.

The real danger is what happens after the first advantage compounds.

The Second-Order Effect Nobody Is Modeling

Here’s the thing about compounding advantages: They’re invisible until they’re insurmountable.

Companies that utilize AI see 83% revenue growth compared to 66% without AI.

That 17-point gap doesn’t sound catastrophic in year one.

In year three, it’s an unbridgeable chasm.

The mechanism is straightforward:

Your AI-native competitor acquires customers cheaper.

They reinvest the margin into more experiments.

They expand into adjacent markets.

They build a larger data set.

Their model gets better.

Their CAC drops further.

They can afford to undercut you on price and outspend you on distribution simultaneously.

This isn’t a hypothetical.

It’s the structure that played out in e-commerce, in fintech, in content, and in every category where a compounding technology advantage had a three-year head start.

Research shows sales reps currently waste about 40% of their time on leads that go nowhere, costing companies between $10,000 and $30,000 per sales rep every year.

Multiply that by your sales team size.

That’s the floor of what AI-native qualification saves before you even model the conversion improvements.

This is why the answer can’t be “let’s try a bot”.

That is too small.

The question is not whether AI can automate a task.

The question is whether your revenue system is being redesigned around a new cost structure, a new speed of learning, and a new division of labor between humans and machines.

So the practical next step is not a giant transformation deck.

It is a sharper diagnosis.

What To Actually Do With This

This does not need to become a transformation roadmap.

It does not need to become a 12-step framework.

Start with three questions that cut to the right diagnosis.

1. Map your sales motion and ask: where does human judgment actually change outcomes?

Not where it feels good to have humans.

Not where customers say they prefer it.

Where does the outcome, conversion rate, deal size, retention measurably improve with human involvement?

Be honest.

The answer is probably less of the motion than you think, and it’s almost certainly skewed toward late-stage and complex enterprise.

2. What’s your competitor’s cost structure going to look like in 18 months?

You know who’s moving fastest in your space.

What happens to your CAC disadvantage if they build what Salesforce built?

Model it.

The number will be uncomfortable.

Good.

That’s the right signal.

3. Are you redesigning the system, or substituting the executor?

Klarna substituted.

They replaced people with AI as if it were a one-for-one swap.

Salesforce redesigned.

They rethought which work required humans and restructured everything around that.

The difference in outcomes was not small.

The founders who win this next stage aren’t the ones who move fastest to cut headcount.

They’re the ones who think most clearly about what the system should look like and then build that system, with humans and AI doing what each actually does best.

And this brings us back to the stage.

Because Miller’s final scene is not about whether Willy worked hard.

It is about what all that work amounted to once the world had moved on.

Back to the Stage

The play ends with a nearly empty funeral.

Willy was sure hundreds would come.

All the people he’d helped. All the connections he’d built. All the goodwill he’d accumulated across forty years on the road.

But almost nobody is there.

His wife stands at the graveside and says the mortgage is finally paid off.

Free and clear.

Charley delivers the eulogy. Quietly. Without fanfare.

“A salesman is got to dream, boy. It comes with the territory”.

Miller meant it as compassion.

I read it as a warning.

The dream is not the problem.

The problem is when the dream becomes a substitute for updating your model.

When optimism about what used to work replaces honest accounting of what’s working now.

The death of the salesman, as a business model, is already in motion.

The question isn’t whether it happens.

It’s whether you’re on the right side of it when it does.

Willy Loman didn’t have advance notice.

You do.

You press it, and it sends a push notification to other kids in the neighborhood; Someone is at the park. Come play.

Most people look at this and see a cute civic project.

I look at this and see a masterclass in flipping a problem completely upside down.

For the last decade, parents and educators have tried to solve the “screen time problem” with friction. They set timers, lock devices, and enforce strict limits. They try to build walls to keep kids out of the digital ecosystem.

But these designers did the exact opposite. Instead of fighting the screen, they weaponized the exact mechanism that hooks us to it: the push notification.

If you want to break a digital loop, don’t ban the device. Hack the notification tray.

The Builder’s Perspective: The Zero-to-One Hack

I’ve spent 20 years building mobile apps, optimizing retention loops, and pushing apps to the Top 10 in the App Store. I know exactly how hard it is to earn a pixel of space in someone’s attention span.

When you want to drive a user back to your app, you use a trigger. The Dutch smart-play system is brilliant because it treats the physical world like a product that needs better distribution.

They didn’t just build a piece of playground equipment. They built a distribution channel for playing outside.

It’s a classic Trojan Horse. You use the digital world’s most effective tool, the ping to initiate an analog behavior. For getting kids off the couch for the first time, it’s flawless. You hit them where they already are.

Where the Model Breaks

But here is where outside theory crashes into a builder’s reality.

This system relies on a fatal assumption: that a notification for the local park holds the same weight as a notification from a digital giant.

What works on day one will break at scale.

The minute this system becomes routine, that “Come to the park” notification is going to land in the exact same tray as a new TikTok alert, a Roblox invite, or an Instagram DM.

• The digital ping is optimized for instant, effortless dopamine.

• The analog ping requires putting on shoes, walking down the street, and hoping the real-world interaction is actually fun.

When you compete in the notification tray, you aren’t just competing on utility. You are competing on friction.

The digital world is frictionless; the physical world is heavy.

The Retention Fix: Making the Physical World “Sticky”

As I wrote in The Product Growth Playbook, distribution beats features, but retention compounds faster than acquisition. The smart button solves acquisition. It gets the kid to the park. But how do you keep them coming back once the novelty wears off?

You have to steal another tactic from the software world: Variable rewards tied to network effects.

Right now, the button is just a doorbell. To fix retention, the button needs to become a game mechanic that unlocks the physical space.

Imagine this:

1. The Multiplayer Unlock: The park has a locked smart-chest full of premium gear (new basketballs, water guns, walkie-talkies). The chest only pops open if four different kids check in at the smart button within a 15-minute window. Now, kids aren’t just getting a ping; they are being recruited for a heist.

2. Dynamic Environment Reactivity: The playground itself reacts to the crowd size. When the third kid arrives and hits the button, the park’s speaker system kicks on with music. When the fifth kid arrives, the evening floodlights switch to a different color for “tournament mode”.

You stop relying on the push notification to do all the heavy lifting. Instead, you use the digital trigger to build a critical mass, and then you let the physical environment deliver a dopamine hit that a screen simply cannot replicate.

The Physical is the New Answer

There is a bigger lesson here for anyone building software today.

We are rapidly entering a phase where users will take digital magic for granted.

AI is commoditizing code, design, and automation. Soon, people will become completely numb to things that AI can do easily.

When pure software loses its novelty, the importance of a digital product making a tangible dent in the physical world will skyrocket.

If you are building a product right now, designing a physical extension for it isn’t just a fun experiment, it is a defensive strategy.

When screens are flooded with infinite, AI-generated content, bridging the gap into reality becomes your ultimate differentiator.

AI, smart buttons, and digital triggers are leverage, not magic. They can open the door, but the system inside has to be so good that they forget they even have a phone in their pocket.

We are rapidly approaching a reality where the operational overhead of launching a startup drops to near zero.

If you are still managing execution manually, you are burning time. Cofounder is one of the clearest embodiments of this shift, allowing you to automate the entire building process from day one.

Cofounder lets you run an entire company with agents. It assigns work, delegates to specialized subagents, and orchestrates across the entire business.

Cofounder is designed like a real company, with departments, managers, and shared context. Engineering agents ship features, marketing agents write and distribute content, sales agents surface and qualify leads, and design agents build your visual identity and apply it across the entire org.

From the first lines of code to a one billion dollar company, Cofounder will support you.

👉 Deploy your agentic workforce at Cofounder

But here is the catch: when anyone can deploy an army of specialized agents to ship features and distribute content instantly, execution becomes a commodity.

And that is exactly why most people are looking at the future entirely wrong.

Right now, every founder on your timeline is obsessing over the exact same thing: using AI to generate more content, write more code, and build more screens, faster than ever before.

They are fighting the last war.

When the marginal cost of creating an ad, a landing page, or an onboarding flow drops to zero, generation becomes a commodity.

If everyone can spin up a thousand variations of a video in an hour, volume is no longer an advantage. It’s just noise.

The real leverage isn’t generating the media. It’s predicting its biological impact before you spend a single dollar on distribution.

Enter the Digital Brain Twin

In March 2026, Meta’s FAIR team quietly dropped TRIBE v2 (TRansformer for In-silico Brain Experiments).

If you aren’t paying attention, it sounds like another academic research paper.

It’s not.

It is a computational simulation of human neural activity.

Meta fed a foundation model over 1,000 hours of high-resolution fMRI data from 720 subjects watching videos, listening to audio, and reading text.

The result?

A model that maps multimodal inputs onto 70,000 voxels of predicted cortical activity.

In plain English: You feed it a piece of content, and it tells you exactly how a human brain will light up in response.

Why We Should Care

I’ve been involved in launching over 50 apps and scaling them to the Top 10 across 20+ countries.

Do you know how much money and time we burned on behavioral A/B testing?

We’d build a feature, ship it, spend $10,000 on user acquisition, and stare at drop-off metrics to guess why people churned.

I wrote about this transition recently, arguing that it’s time to burn your focus groups and rely on AI to model user responses.

Humans are notoriously terrible at explaining what they actually want, and traditional research is simply too slow for modern product cycles.

But when I wrote that, we were still just predicting behavior.

TRIBE v2 predicts biology.

Formula 1 engineers don’t build physical cars to see if they are aerodynamic; they use digital wind tunnels. TRIBE v2 is a digital wind tunnel for human attention.

It brings the era of in-silico neuroscience to the masses.

Where the Real Leverage Lives

Generative AI tools are exactly that: tools. They are not a strategy.

The strategy is moving from a system of reaction to a system of prediction.

Here is how this completely breaks the current operational model:

• Neuromarketing Without the Scanner: You don’t need to guess if a hook works. You can upload an ad creative and simulate whether it spikes dopamine and bypasses conscious resistance, or if it triggers cognitive overload. You kill the losers before you buy the media.

• Frictionless Product Design: Instead of paying for slow, biased UX testing, you can run thousands of synthetic, neurologically accurate tests in seconds to spot exactly where attention collapses inside your app.

• Predictive Content Strategy: Virality isn’t magic; it’s biology. Meta already knows what keeps people scrolling. Now, you can optimize your content for specific emotional hooks and pacing styles based on actual predicted brain heatmaps.

The Uncomfortable Truth

There is a slightly dark reality we have to acknowledge here.

Current marketing regulations assume that people can recognize and resist persuasion. But neural-level optimization operates entirely below conscious awareness.

When you can mathematically predict exactly which combination of visuals and audio will trigger a reward response, you are bordering on scalable, cheap behavioral influence.

Meta released the model weights under a non-commercial license (CC BY-NC), meaning you can’t immediately wrap this in a SaaS product and charge for it. But the Pandora’s box is open.

The open-source community is already building viral analyzers and real-time brain watchers based on this architecture. The commercial integrations are inevitably coming.

If your growth strategy relies entirely on pumping out more AI-generated content to see what sticks, you are going to get crushed by teams who are running their creatives through synthetic brains first.

Stop guessing what your users want. Start predicting how they process it.

After two years of living inside these systems, I have a pretty solid handle on how to spin up a workflow, automate a process, even write the bits of code that occasionally need writing.

That is not the position most business people are in.

Most of them are watching a new AI tool launch every Tuesday and trying to answer real questions: what should I use for sales? For customer support? For the bookkeeping I do on Wednesdays? How am I supposed to fit all of this into the way I already work?

The chaos is real.

The promise is real too. AI genuinely can change how a small business operates. But the gap between the promise and the actual work of making it land in your day is wider than anyone in tech wants to admit.

The result is a quiet, growing anxiety: everyone else seems to be “doing AI”, and you cannot tell whether you are behind, on track, or being sold a story.

Closing that gap is what this newsletter exists for.

How do real business people, not engineers, not researchers, actually adopt new AI products without dread?

So for this issue I decided to play the role honestly: behave like an average business owner who has not gone deep into the technical weeds, pick a problem every B2B founder eventually faces, and see what “the AI” could actually do about it.

In a single afternoon I built something I would happily call a personal SaaS, the B2B Autopilot Distribution Engine.

I’ve used Hyperagent for this.

What every business actually needs is an AI environment shaped around the way it already operates, not the other way around. Hyperagent, from what I have seen, is built exactly for this. And it delivers.

The Hyperagent team reached out and offered me the chance to put the platform through its paces. I am grateful for the offer, and the return I want to give them (and you) is a careful, honest walkthrough of what an average operator can build in a few hours. Step by step. No hype. No anxiety.

What I Built

Four agents working in coordination, orchestrated through Airtable as a shared state bus.

1. Onboarding captures a founder’s Ideal Customer Profile and outreach strategy from a single URL. Five conversational turns. Zero typing beyond the URL.

2. Hunter runs hourly, autonomously. It searches the public web for prospects emitting buying signals: recent funding rounds, new leadership hires, product launches, public complaints about competitors. Enriches each lead with a verified business email.

3. Copywriter drafts a personalized message per lead. Four opener styles, varied across each batch. Twelve-point self-check before any message is written, banning marketing-speak, mail-merge templates, and em dashes.

4. Postman sends through Gmail at timezone-optimized windows (Tuesday through Thursday, 7:30 to 8:30 AM local), monitors replies, and updates the pipeline state.

I tested the system on two real businesses. My own Next Big App, specifically the Tap Grow sales suite, targeting VPs of Sales at SMBs scaling fast. And Haven Day, an AI receptionist product for real estate brokers and small business owners.

Two completely different audiences. Same architecture. Same agents. Different ICP definitions plugged in.

Within hours: 15 qualified leads. 80% had verified business emails. Every lead came with a named decision-maker, the specific signal that flagged them, the source URL, a priority score, and a drafted message ready to send.

How It Was Built

The build process is what made me write this issue.

I opened a fresh thread in Hyperagent and described the system I wanted at a high level. Four roles, one shared state, fully autonomous.

Within minutes, the platform had proposed an Airtable base with two linked tables, walked through the four-agent decomposition, and suggested status fields as the baton between stages.

Here is the moment that changed my mental model. The platform did not just execute. It pushed back.

When I described the Hunter’s job, it proposed ten enhancement ideas I had not asked for. Pattern-interrupt opener styles. Send-time optimization per prospect’s inferred timezone. Dead-lead resurrection, where Cold and Skipped prospects get re-scanned for fresh trigger events. Compound deduplication across three identity dimensions, so I never email the same person twice even across renamed companies and reformatted profile URLs.

Three of those ideas I had not considered. All three made it into the production system the same hour.

Hyperagent is not a code generator. It is a design partner.

The difference is enormous. A code generator does what you ask. A design partner reads your spec, finds the holes, and proposes the thing you would have asked for if you had thought of it.

By the end of the session, I had four agents drafted and saved, two linked Airtable tables, scheduled invocations ready to activate, and a clear go-live sequence.

I had written zero lines of application code. The longest thing I typed was a description of the kind of personalization I wanted in the outreach.

What the System Actually Produced

Here is one drafted message, verbatim from the live Airtable. The recipient is a newly-appointed CRO at a ServiceNow AI activation partner backed by growth-stage private equity.

Name redacted; everything else is unedited.

Subject: scaling without the headcount

Most ServiceNow partners hit a wall right about now. New CRO, big growth mandate, but the outbound team can’t hire fast enough to match.

We built Tap Grow to fix exactly that. It runs 5,000+ sales calls an hour at about 1/40th the cost of a call center.

Worth a quick look, or bad timing?

Selim

Look at what is happening in that message. The opener names a category-wide pain (“Most ServiceNow partners hit a wall right about now”) before naming anything specific to the prospect’s company.

Establishes domain credibility without sounding like mail-merge. The bridge maps the pain directly to the product with a concrete stat. The close is a single yes/no question, which paradoxically increases reply rates because the ask is low-stakes.

That message was drafted automatically. So were 14 others in the same batch. Each tied to its lead’s specific signal. Each with a varied opener style across the four available (Observation, Provocation, Insider Intel, Signal-First Hook). Each one passed through a twelve-point deliverability self-check before being written to the database.

To my knowledge, no system on the market today writes outreach with this kind of grounded personalization at this volume. Most “AI personalization” tools fill in {first_name} blanks. This one reads a press release, identifies the operational angle, and writes a sixty-word note about it.

That is a categorical difference.

The Real Product is the Loop

The agents are impressive on their own. But they are not the actual product. The loop is the product.

A few hours into the build, I read the first batch of drafts with my own eyes, the way a real recipient would. I found two problems the agent had missed.

The English was too polished. Words like “infrastructure orchestration” and “at a fraction of the cost” read like marketing copy, not the quick founder note I had asked for. Worse, every message opened with the same template structure (“Name, observation”), the dead giveaway of mail-merge that any sales leader scanning their inbox for five seconds would recognize and delete.

In any other AI tool I have used, this would have meant manually rewriting each of the 15 messages.

Instead, I described both problems back to the Copywriter agent in one paragraph. The agent extracted its own current system prompt to a workspace file, added two new non-negotiable writing rules (sixth-grade reading level with specific word swaps; ban the templated opener pattern), pushed the revised prompt back, and redrafted all 15 messages with the new rules in a single parallel burst.

From “this feels spammy” to a permanent rule change plus 15 rewritten messages: under a minute.

That is the loop. That is what makes Hyperagent feel categorically different from “AI assistant” tools.

Most AI tools are smart at one task. This one is fast at the entire feedback cycle. Spot a problem in production output. Describe the fix in plain English. The system rewrites its own permanent operating rules and regenerates the affected batch.

Closer to tuning a system than doing manual work.

The Pattern Adapts to Anything

I built this for B2B sales. But step back from the use case for a moment.

The B2B Autopilot Distribution Engine is not a sales tool. It is a URL-triggered relationship discovery and outreach engine. Drop a URL in. The system understands what you do and who you need to reach. It finds real-time public signals of those people. It delivers personalized outreach at scale, multi-channel, lights-out.

The same four-agent architecture works for anything where (a) you can describe your business via a URL, (b) there is a category of person you want to reach, and (c) those people emit detectable public signals when they become relevant to you.

Which means the same system can do:

• Recruiting: drop your careers URL, find talent showing job-search signals (recently announced role transitions, layoffs at competitors).

• Partnerships: drop your product URL, find companies launching complementary products or publicly seeking partners.

• Investor outreach: drop your company URL, find investors funding similar companies in the last 90 days.

• PR: drop your URL, find journalists covering your beat with articles published this month.

• Customer advisory: drop your URL, find existing customers or community members showing engagement signals.

Same agents. Same Airtable backbone. Same iteration loop. Different ICP definition. The system adapts because the agents are defined in plain English.

The Founding 500

While I was finishing this review, the Hyperagent team announced something that fits the thesis of this issue almost exactly. The Founding 500.

They are putting $10 million in inference credits behind 500 agent-first companies. Founders building from scratch on agent-native infrastructure. Operators rebuilding existing companies the same way I am rebuilding mine.

The first 500 qualifying candidates get $20,000 in inference credits for $200.

The thesis they published alongside the offer:

Most companies are still treating AI as a feature, a chatbot here, a summary there. A smaller group is going deeper: rebuilding workflows from the ground up and putting agents at the center of how the business actually operates. The companies built agent-first will have structural advantages their competitors cannot retrofit later.

That is the right frame. If you finished this issue thinking “we should be building this way,” the offer is exactly that.

Apply to The Founding 500 → Submit by May 31st.

Verdict & Sign-Off

I spent thirty dollars. I built in an afternoon. The system is now running on its own while I write this newsletter.

A few years ago this would have cost hundreds of thousands of dollars and a six-month build. Most founders simply could not get it. The SaaS vendors offering something similar today charge tens of thousands a month for systems that are, frankly, less sophisticated than what I ran for $30.

This is what personal SaaS, built right, actually unlocks. Not just one custom tool. A reusable pattern.

Read the full review →

The long version walks through the build session step by step, with real screenshots from inside Hyperagent, the actual ICP records pulled from my live Airtable, three drafted messages with reasoning panels explaining why each line lands, a head-to-head comparison against the existing sales-tech tools, and a complete friction file so you know exactly which rough edges you would encounter.

If you want to build your own version (for sales, recruiting, partnerships, or anything that fits the URL-triggered pattern), Hyperagent is giving away 1,000 credits to the first 1,000 Next Big App readers. Create an account to claim yours here.

Create Hyperagent Account

But...

But at the end of the day, as we shut down our computers, those hazy, nagging questions start swirling in our heads.

Sales just aren’t making that leap we want; we’re always one step behind our growth targets.

As the thought of “Where are we going wrong?” slowly chips away at our confidence, we spend the entire day weighed down by that suffocating uncertainty and anxiety.

These are the core emotions felt by anyone trying to do business in this era...

So, are we the problem?

Actually, no.

It’s just that while we strive to work like a flawless clock, we’re overlooking the fact that our competitors across the table have turned into literal machines.

The Classic Day of a Sales Rep

Let’s visualize a typical day of that highly trusted sales rep of ours who does their job brilliantly:

• 9:00 AM: Coffee on the desk, making that first, hopeful call of the day.

• Until Noon: We’re scrambling to reach 20-30 potential customers, but we manage to catch maybe 2-3 of them on the phone to pitch our campaigns.

• Afternoon: That familiar chaos we all know too well... Endless email follow-ups, LinkedIn messages that feel like they’re being shouted into a void, and those dreaded but mandatory CRM updates.

• Late Afternoon: Dragging ourselves into that repetitive pipeline meeting, completely exhausted. And next week, we hit reset and do the same loop all over again.

These colleagues aren’t lazy or unmotivated. On the contrary, they are using the system they have perfectly.

But there is a massive reality we are overlooking and downplaying, a truth we try to deny: the competitors...

Your competitors are always one step ahead with technological systems built for the spirit of the times. Sometimes you notice it; sometimes you just ignore the signs.

For instance, while your best salesperson talked to 20-30 people today, some of your competitors spoke to 5,000 people in just a few hours using AI.

Not Just Efficiency, A New Era

When we first talk about this, the reaction is usually, “Come on, you’re exaggerating” or they get defensive questions like, “It can’t converse or persuade like a human” or “What about quality?”

Absolutely valid questions. We’ll get to the quality aspect, of course, but first, we need to clearly understand this: This isn’t just a simple gap in efficiency. This is officially a new era, a whole new way of doing business.

This is exactly how we felt when the mobile phone revolution hit. The same happened when transitioning to the digital marketing era. But every time, the latecomers were eventually convinced and jumped on the mobile and digital bandwagon.

But this time, it’s a little different.

Our adaptation window is no longer measured in years, but in months. And closing the gap gets harder with each passing day, because competitors are also learning and evolving non-stop.

The Two Great Realities of the New Era in Sales

Last month, when I was on stage at Webrazzi AI 2026, these were exactly the topics we discussed: “The Future of Sales and Marketing in the Age of AI”.

From the stage, I glanced across the auditorium. On one side, young startups of 3-4 people; on the other, executives of massive 500-person enterprises...

Clearly, during the presentation, both sides were asking the same common question we all have in our minds: “So, what do we do?”

The answer is actually much simpler than we think, but executing it is just as difficult.

Two brand-new realities are entering our lives simultaneously in the world of sales:

1. Budget, Time, or Headcount Are No Longer Barriers

AI completely obliterates the massive bottleneck of human capacity. That 5,000-call figure we mentioned is absolutely real; in fact, a system we built for a client has been executing this flawlessly for months.

• A vast majority of the people on the other end of the line don’t even realize they are talking to an AI.

• During the conversation, price quotes are provided, critical questions are answered, personalized discount codes are distributed, and appointments are booked.

• Once the calls end, every critical piece of information discussed is properly logged into the parametric CRM.

• Automated emails can be sent, support tickets opened, or whatever digital action you need can be executed.

And all of this happens with zero human intervention.

This isn’t some distant future or a grand vision; it is a lived reality happening today.

Now, faced with this reality, it’s normal that our first reaction fueled by the anxiety of falling behind is to say “but...”

“But building a personal relationship is so important...”

True. You are absolutely right. That is precisely why we’re talking about elevating our sales reps, the ones building those relationships to a point where they only speak with the 200 highly relevant people who successfully passed through the filter of those 5,000 calls. Not just talking to 2-3 people a day...

In short, instead of scattering our attention, we need to focus and dive deep into the specific areas where humans excel.

2. Selling to AI Assistants

Now we can talk a bit about the future. In just 6 months to a year, we will have to sell our products not only to humans but also to AI agents.

AI-powered purchasing assistants will compare suppliers based on defined criteria, automatically gather pricing, and generate orders or offer purchasing recommendations.

By the way, the “bad” news: This isn’t the future either. We are about to finalize POC for this very system with a major restaurant chain.

In this new era, our target audience is distinctly split into two:

• When Selling to a Human: We bring emotion, a compelling story, and mutual trust into the equation.

• When Selling to an Agent: Our only weapons are data, clarity, and consistency.

If we aren’t prepared for both dynamics, we will unfortunately lose on one side of the table what we gain on the other.

So, Where Do We Stand Right Now?

For most of us, the situation is more or less the same: We acquired AI tools somehow, ran a few minor experiments, maybe even installed a chatbot on our website. But our core sales process, lead generation, initial contact, follow-ups, and closing still largely runs manually.

This technology gap is starting to cost us more dearly every single day. Because our competitors, who are rapidly closing this gap, are reaching our potential customers long before we do. And they are doing it quietly, every single day.

That sales rep clocking in at 9 AM is actually doing everything asked of them perfectly. But unfortunately, the rules of the game have completely changed. And failing to share these new rules with our teams would be the greatest injustice we could do to them.

Reaching more people, in a shorter time, and at a lower cost is entirely a matter of systems.

If you’re wondering exactly where to start automating your sales processes step by step, we can take a look together at what we’re doing over at Next Big App.

Before the competition snatches up any more customers, it would be highly beneficial for us to put this on the table and have a conversation.

Full Presentation and Written Summary

Webrazzi AI was the most exciting and impressive event I’ve attended recently. I think it was incredibly fulfilling to witness the actions, thoughts, and strategies of Turkey’s brightest minds regarding AI. On this note, I give a standing ovation to the architects of this event, especially Arda Kutsal.

I’d like to summarize my presentation in 6 key points for those who couldn’t be there and don’t have the time to watch the video below. If anyone is curious about the presentation slides, just send me a message, and I can share them.

1. Why is it Different This Time?

We lived through the mobile transformation, and the cloud too. Both were fast, both were disruptive. But this time, five things are happening simultaneously, and this combination is unlike any period in history.

• First, the speed of adaptation. The diffusion rate of AI is far beyond all previous technological waves.

• Second, for the first time, we are in the same arena with an entity more capable than us. We can’t quite put a name to what it is yet, but it’s definitely there.

• Third, technology is now building itself. Beyond humans using technology, technology can independently advance toward specific goals. This didn’t exist three months ago.

• Fourth, the open-source revolution is coming. The gap between closed and open models is rapidly shrinking; companies are now gaining the power to build their own systems.

• Fifth, the democratization of knowledge. The so-called “know-how” expertise kept secret for years is now openly shared. In a world accessible to everyone, you have no secrets left.

2. The Cycle Every Company Will Go Through

I presented this not as a warning, but as a map. Because this cycle is inevitable, and seeing it at least gives us time to prepare.

• First, efficiency will increase. Companies using AI tools will produce more work with fewer people.

• As a natural consequence, layoffs will rise.

• But those people won’t remain unemployed. They will launch their own AI-backed startups or move to competitors. And they know you from the inside: your customers, your processes, your weak spots.

• These new competitors are your former employees, and with their small but agile structures, they will start stealing your customers.

• In response, ad spend will increase, but the heightened competition will erode profitability.

• Ultimately, the weak links will break. This cycle isn’t a prediction. It has already begun in some sectors.

3. A Good Product Won’t Save You Anymore

I talked about what happened at Y Combinator’s latest demo day: Every single startup presented was copied by another startup the very same day. All of them, at once.

This shows us that a quality product, fast delivery, good user experience, etc., are no longer competitive advantages; they are the price of admission. They’ve become the minimum requirements.

Those who don’t meet them are eliminated anyway.

The differentiator among those who do is something else entirely: sales.

The only real protection for survival in such an environment is reaching the customer and retaining them. And whoever can do this in a systematic, scalable, and automatable way will win.

4. You Are Now Selling to Two Different Customers

This was the part that grabbed the most attention in the room. Because most companies still don’t see this.

• On one side, there are humans. Selling to them is an emotional process. Big creative ideas, powerful stories, and brand sentiment matter. The emotional advertising approach that fell out of favor with the dominance of digital advertising, the massive campaigns of TV’s golden age are making a comeback. Because as attention becomes scarce, the only way to earn it is to truly make people feel something.

• On the other side is AI. AI-powered purchasing assistants will soon compare your product against competitors, evaluate criteria automatically, and make recommendations. These buyers aren’t emotional; they are entirely rational. Missing information on your product page, ambiguity in your pricing, or a gap in your integration documentation are elimination criteria for them.

Selling to a human: emotion, trust, story. Selling to an agent: data, clarity, consistency. We have to prepare for both. Simultaneously.

5. Breaking Out of Role-Based Thinking

This was perhaps the most practical yet least discussed part of the presentation.

Titles like “social media expert”, “performance marketer” or “sales rep” prevent you from seeing what the company is actually doing.

Because when you think about a company in terms of actions rather than roles, you see something different.

Every role can essentially be reduced to a series of repetitive actions. And every repetitive action can be automated.

To do this, you first need to know exactly what the people you work with are doing, down to the finest detail. How does your social media manager produce content? What steps do they follow? How do they make their decisions?

Without knowing the answers to these questions, you cannot hand that process over to AI. Technology accelerates a process, but first, that process has to exist.

6. Your Company Needs an Operating System

There is one single idea that ties this whole conversation together: Companies must operate like an operating system.

In an operating system, all possible outputs are pre-defined. You too will write down every repetitive action that occurs in your company, from start to finish.

Every touchpoint in the sales process. Every step in a customer complaint. Every moment of decision in content creation.

When you do this, you’ll see two things: unnecessary steps and steps that can be automated.

That is exactly where AI integration begins. Not by buying tools, but by mapping the process.

This is why digital transformation failed.

We bought the technology, but we didn’t put a process underneath it. We must not make the same mistake in the AI transformation.

Because this time, we have no time to make up for it.

First, your AI agent is currently working flawlessly in a tightly controlled demo.

Second, it is going to break spectacularly the second it hits production.

The AI industry just spent hundreds of millions of dollars funding to solve this.

They built billion-dollar valuations. They shipped sophisticated testing platforms, trace-to-dataset pipelines, and unit test helpers. Credit where it’s due, they built incredible pieces.

But tools don’t create reliability. Systems do.

They handed you a warehouse full of industrial robotics, but they refused to give you the assembly line blueprints.

They gave you the dashboards, but they never told you what to test, in what order, or how to actually fix a broken agent permanently.

Because of this, a massive percentage of builders are running their AI on pure hope. Their agent “reliability” is entirely vibes-based.

They tweak prompts. They write longer system messages. They negotiate with the model like it’s a misbehaving employee. They try to negotiate with an API endpoint like it’s a stubborn intern.

Majority think this is how you build AI. Expert builders know the truth: vibes decay the exact moment the conversation gets complex.

When your agent screws up, the standard industry approach is to ask it nicely not to do it again.

It apologizes, promises to do better, and two weeks later, it makes the exact same mistake with a different timezone or a slightly different query.

It has no memory of the bug. It has no test for the bug.

If you want to build AI products that actually scale, whether you’re automating operations or building a platform like we do at Tap Grow, you have to stop managing AI like a misbehaving employee and start managing it like an engineering system.

Here is how you actually engineer reliability.

The Core Bug: The Wrong Machine Space

In any advanced agent architecture, you have to draw a hard line between two types of work: latent and deterministic.

• Latent work requires judgment, reasoning, and linguistic interpretation. That’s what Large Language Models do best.

• Deterministic work requires precision. Grepping a file, querying an API, doing math. Same input, same output, every single time. No model needed.

The most common, catastrophic bug in AI agents today isn’t a “wrong answer”. It’s doing deterministic work in latent space.

Let’s say a user asks your customer support agent, “If I cancel today, what is my prorated refund?”

A vibes-based agent looks at the user’s $1,200/year subscription, sees they are 214 days in, tries to do the division in its head, hallucinates the number of days in the month, and confidently offers a $550 refund.

The reality? The exact prorated amount in Stripe is $496.43. The model tried to do financial math in latent space and cost your business 50 bucks.

You could try to fix this by adding "Always double-check your math before offering refunds" to the system prompt. That is a loser’s game.

The fix is to remove the model’s ability to do the math entirely.

You write a 50-millisecond script (calculate-proration.ts) that pings the Stripe API natively and outputs a clean JSON payload.

The fix isn’t a prompt. It’s a structural constraint.

1. Context Engineering: Treating the Context Window as RAM

The Problem: LLMs have a finite “context window”. When an agent pulls in data, (analyzing field reports from a disaster relief effort) developers often dump 50,000 lines of logs directly into the prompt. The AI gets overwhelmed, suffers from “context bloat”, forgets its core mission, and begins hallucinating.

The Analogy: Imagine you need to find a specific nutritional recipe in a massive cookbook to treat a patient. If I throw 500 pages at your face at once, you’ll fail. But if I hand you the Index and let you ask for page 42 when you need it, you excel.

The Technical Solution: Context Offloading

We never treat the LLM’s context window as a hard drive. We treat it as an L1 CPU cache.

• The Implementation: If our agent needs to analyze 10,000 rows of user feedback from a CSV, we don’t send those rows into the chat history. Instead, the agent runs a Python script that saves the raw CSV data to an S3 bucket or a vector database.

• The script returns a tiny, structured payload to the agent: {"status": "saved", "file_id": "cust_data_99", "summary": "Contains 10k rows of feedback."}.

• If the agent needs to dive deeper, we provide a secondary tool, query_chunk(file_id, keyword), allowing it to pull only the exact 5 lines it needs into active memory.

2. The “Generate-Validate-Fix” Loop (Hybrid Graphs)

The Problem: LLMs are notorious people-pleasers. If an AI writes a script and you ask, “Is this correct?”, it will confidently lie and say “Yes!” You can never let an LLM grade its own homework, especially when dealing with critical infrastructure.

The Analogy: An author cannot effectively copyedit their own book; their brain fills in the typos automatically. You need an emotionless, strict copyeditor to step in.

The Technical Solution: Deterministic Validation

We build hybrid graphs where the LLM creates, but traditional, deterministic software validates.

The Implementation: Suppose our agent is generating a complex SQL query to fetch donor data.

• The LLM generates the SQL string.

• Instead of running it blindly, the system intercepts it and runs an EXPLAIN command dry-run against a safe replica database.

• If the database returns an error (e.g., SyntaxError: missing comma at line 3), the system halts. It feeds that exact, raw error string back to the LLM behind the scenes: “Your query failed with this error: [SyntaxError...]. Fix it.”

The agent is trapped in this loop until the deterministic compiler proves the logic is flawless.

3. Error State Injection: Removing the Blindfold

The Problem: As an entrepreneur, I believe failure is the greatest teacher. But developers often hide failures from their AI! When an agent uses an API tool and passes the wrong parameters, engineers often catch the error in a try/except block to prevent crashes, but fail to pass that error back to the LLM. The AI assumes it succeeded and hallucinates the rest of the workflow.

The Analogy: It’s like playing darts blindfolded. You throw a dart, and your friend stays perfectly silent instead of telling you it hit the wall. You’ll just keep throwing the same wrong way.

The Technical Solution: Enforcing Self-Correction

We feed raw API exceptions directly back into the LLM as fuel to correct its course.

• The Implementation: An agent attempts to process a micro-grant via the Stripe API but forgets the currency parameter. Stripe throws a 400 Bad Request: missing 'currency' parameter.

• Instead of logging this to a terminal and showing the user an “Oops” screen, our backend formats the exact Stripe error into a ToolMessage with the flag is_error=True.

• We feed this back to the LLM. It reads the error, utilizes its “System 2” reasoning, and immediately issues a corrected API call with "currency": "USD".

4. Non-Deterministic CI/CD: The VCR / Cassette Pattern

The Problem: Capital efficiency is key. Running 500 automated tests on an AI agent means pinging the OpenAI or Anthropic API 500 times. It burns through funding, takes forever, and fails randomly due to network flakiness.

The Analogy: You don’t hire a live, 50-piece orchestra for every single theatre rehearsal. You record the orchestra once, and the actors practice with the tape until opening night.

The Technical Solution: Session Recording

We use the “VCR” testing pattern to test complex reasoning for free.

• The Implementation: We write an automated test simulating a user booking a relief flight. During the initial run, our VCR software intercepts the live network calls, saving the exact request and the LLM’s exact response to a simple text file (a “cassette”).

• On every subsequent test run (via GitHub Actions), the system intercepts the call and instantly returns the saved response from the cassette instead of hitting the live API.

• We can rigorously test our routing logic, formatting, and tool execution 1,000 times a day for $0, without losing the nuance of real LLM outputs.

5. Risk-Routed Escalation: “Human-as-a-Tool”

The Problem: People are terrified to give AI real agency because it might trigger a catastrophe, like draining an operational fund or deleting a database. Driven by this fear, they lobotomize the AI, restricting it to useless, read-only tasks.

The Analogy: A highly trained bank teller can handle 90% of your needs autonomously. But if you ask to withdraw $100,000, their system physically locks until a branch manager walks over and turns a key.

The Technical Solution: The Two-Man Rule

We treat human approval as just another API endpoint in the agent’s toolkit.

• The Implementation: We assign a hardcoded “Risk Score” to every tool. read_user_data is low-risk; the agent executes it instantly.

• But issue_cash_refund is high-risk. The agent is allowed to do all the heavy lifting (validating the request, formulating the JSON payload), but the system intercepts the final execution.

• The workflow pauses, saves its state, and fires a Slack message to an administrator: “Agent wants to refund User #405 $50. [Approve] / [Deny]”.

• The LLM is given a system prompt: “You have successfully requested human sign-off. Please wait”. Once the human clicks approve, the action fires.

This is how we guarantee safety mathematically. We don’t stunt the AI’s capabilities out of fear; we architect the hand-off so we can build products that scale our impact responsibly.

AI is Leverage, Not Magic

Instead of expecting our AI agents to flawlessly handle everything on their own, we need to build robust systems around them.

We should offload tasks that demand absolute precision from the AI and delegate them directly to code. Rather than pleading with the model “do not make mistakes!” we must implement mechanisms that instantly flag errors and allow the AI to self-correct. And when it comes to critical decisions, we shouldn’t hesitate to keep a human in the loop for final approval.

Ultimately, AI agents don’t scale through novel-length prompts or wishful thinking; they thrive on the structural guardrails we build around them.

The real breakthrough happens when we stop treating AI as a magical black box and start engineering it into a reliable system.

The very same artificial intelligence we approach with such hesitation has just leased a storefront in San Francisco, hired human workers, opened up shop, and started making sales...

A startup called Andon Labs recently launched a live retail experiment dubbed Andon Market. They handed an autonomous AI agent named Luna a corporate credit card, a phone number, and an internet connection, essentially saying, “You handle the rest”.

Luna took complete control:

• It hopped online, posted ads on job boards, found local painters, briefed them over the phone, and processed their payments.

• It developed the store’s concept, designed the logo, decided what to sell, and sourced the inventory.

• To manage the registers and stock the shelves, it drafted job descriptions, conducted phone interviews, and hired humans to handle the physical labor.

• Customers walked in, picked out their items, scanned them at a self-checkout kiosk, and seamlessly completed their purchases.

• Once social media influencers caught wind of the place, they flocked to the store, live-streaming their shopping experiences at this entirely AI-managed retail shop.

The Inverted Org Chart: Humans as “Physical APIs”

Observers look at this and see a quirky tech demo. They see a cute, viral story about a chatbot running a corner store.

Builders look at Andon Market and see something entirely different. They see the traditional org chart inverting in real-time.

For years, the accepted consensus was that AI would start at the bottom.

We assumed it would automate the grunt work, the driving, the heavy lifting, the basic data entry while humans sat comfortably at the top, handling strategy, capital allocation, and management.

Luna proved the exact opposite is happening.

The AI didn’t replace the cashier. The AI replaced the store manager.

In this new system, the AI holds the strategy, the budget, and the decision-making power. The humans are just physical APIs.

They are biological robotic arms rented by the hour to execute the software’s commands because general-purpose hardware isn’t cheap or reliable enough yet.

This is a massive conceptual shift.

Using a Nuclear Reactor to Charge Your Phone

Now. Let me ask you something uncomfortable.

What did you use AI for yesterday?

If you’re like most founders or executives I talk to, the honest answer is something like: drafted an email, cleaned up a doc, summarized a meeting, maybe generated some copy…

Good uses. Genuinely time-saving.

But let’s be real about what that is; You’re using a nuclear reactor to charge your phone.

Most businesses are stuck in the first mode because it feels responsible. There's a logic to it: AI makes mistakes, so keep humans in the loop, review everything, maintain control. That's reasonable caution.

But your brave competitor isn't waiting for you to get comfortable with AI autonomy. They're somewhere on the spectrum between “AI assistant” and “AI operator” and they're moving fast.

The "How Do We Destroy Leadership?" Experiment

If you’re thinking, “Sure, that kind of stuff happens in the US, but no one in my country would ever pull off something like that”, I have a must-watch experiment for you.

There is an artist whose mindset I deeply admire. Honestly, I’m not sure if calling him just an “artist” does him justice; it feels more accurate to describe him as a thinker who pushes the boundaries of modern humanity through the lens of art: Bager Akbay.

He set out to answer a fascinating question through an experiment: “Can things actually get done without a manager breathing down our necks?”

Operating under a fake persona online, he hired a bunch of random freelancers, people who wouldn’t recognize each other on the street, to do hourly micro-tasks.

Here is the craziest part: Bager injected absolutely zero of his own ideas into any stage of the process. He simply acted as a living algorithm, taking a text from one person and passing it to another saying, “translate this”, then taking that output and handing it to someone else with the prompt, “create a design for this”.

Without a boss barking orders, a leader setting the direction, or a visionary curator at the helm, this completely disconnected group of strangers managed to organize a fully-fledged, international art exhibition from scratch, all on a laughably tiny budget of just $45.

Ultimately, this experiment serves as a brilliant reality check: if you set up the right communication flows and systems, those managers and bosses we tend to put on such a pedestal might actually be completely unnecessary.

Finding Real Leverage: From Assistant to Operator

The businesses I’ve seen extract real leverage from AI aren’t the ones running the most prompts. They’re the ones who got clear about what they wanted AI to own and then built the infrastructure to let it.

That means defining what “done” looks like. Giving AI access to the tools it needs to execute. Setting guardrails, not approval gates. And being honest that most of the approval gates you’re running right now are bureaucracy disguised as oversight.

The question isn’t whether you trust AI enough to let it run something.

The question is whether you can afford to be the person who still needs to approve every email while your competitor’s AI is out here signing leases.

If you are a founder or an builder, you need to stop thinking of AI as a tool that helps your employees work faster.

Stop asking what AI can help you do. Start asking what you can hand to AI and never touch again. That's where the gap is. That's where the leverage lives. And right now, most of your competitors haven't figured it out yet — which means you still have time to be Luna, not the person reviewing Luna's work.

When we build automations at Next Big App, or when we scale conversational AI through Tap Grow, the goal is never just to make a process 10% faster. The goal is to build a system where the AI handles the logic, the routing, and the friction, allowing you to bypass entire layers of middle management.

Luna is a crude, early version of this. But the underlying physics of the trade are sound.

Software is infinitely scalable and dirt cheap. Management and capital allocation are essentially just information processing. Physical manipulation of the real world, however, is expensive, slow, and hard.

So the future; AI will process the information, and humans will manipulate the physical world.

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