They answer questions. They do not ask their own. Nothing keeps a machine up at night wanting to know.

We do. It is one of the oldest signals we carry.

But right now that signal is being wasted, and a lot of money is being spent to waste it. This piece is about how to stop wasting yours and start reading it.

The machine that never gets tired

Last month I went looking for one number.

I wanted the share of Netflix viewing that comes from its recommendation engine. One figure for one chart.

Ninety minutes later I was reading about slot machines, then pigeons, then whether taste is even a fixed thing. I never got the number that day.

Here is what has changed. A library closes. A friend gets bored. A book ends.

The machine does none of that. It is patient and endless. It will follow you into any rabbit hole and hand you a torch at every turn.

Old-fashioned curiosity had a natural brake. It cost time, money, or someone else’s patience. We removed the brake and kept the engine.

So the first job is not to be more curious. It is to give your curiosity somewhere to land.

Two kinds of curiosity

In the 1950s a psychologist named Daniel Berlyne split curiosity into two types. The split still holds up.

The first type scans. It is restless and wide, hunting for anything new. Berlyne called it diversive curiosity. Scrolling is pure diversive curiosity.

The second type digs. You spot a gap between what you know and what you want to know, and you go after it. He called it specific curiosity.

The scan finds the material. The dig turns it into something.

A feed is built to keep you scanning, because a scan that ends is a session that ends. That is the trap. It keeps you in the shallow kind and never lets you reach the deep kind.

What to do.  notice which one you are in. Scanning is fine as a start. The rule is simple. Never let a scan be the whole session.

Your brain pays you for curiosity

At the University of California, Davis, three researchers ran a clean little study.

Matthias Gruber, Bernard Gelman and Charan Ranganath showed people trivia questions and asked how much they wanted each answer. Then they scanned their brains.

When people were curious, the brain’s reward system switched on. That is the same wiring that responds to food and money. In plain terms, being curious feels good because your brain pays you for it.

Two things followed. People remembered the answers they were curious about. They also remembered random facts that happened to be nearby at the same time.

So curiosity does not just make you want to learn. It puts your whole brain in a better state to learn anything.

Here is the useful part. Your energy is not spread evenly through the day. It pools around what you care about.

Most advice gets this backwards. Find your motivation first, it says, then apply it. The research says the opposite. Chase what already pulls you, and motivation shows up after.

I learned this the slow way. In 2009 I did not start with discipline. I published a post, a stranger on another continent replied, something in me lifted, and I published again. The 4:30am alarm was never willpower. It was a loop that paid me back.

What to do.  stop hunting for motivation. Track what you already chase for free. That is where your energy pools.

Why this matters so much

Gallup surveys the working world every year. One number barely moves.

Four out of five people are not engaged at work.

That is not eight billion lazy people. It is a fit problem at scale. Most of us spend our best hours somewhere our energy never gathers.

Reading your curiosity is not a soft, nice-to-have habit. It is how you find the work that would not feel like a drain.

The feed is not on your side

There is a second trap, and it arrives looking like a gift.

Roughly eight in ten hours on Netflix come from what the system chose for you. On YouTube it is about seven in ten.

Now put the last two charts together. Eight in ten hours chosen for you. Eight in ten people flat at work.

Nobody set out to trap you. It happens because the system is trying to help. It learned what you like, so it gives you more of what you like, forever.

Helpful is not the same as wide.

A feed shows you a mirror. It gives you more of yourself. What you need sometimes is a window, which shows you something else.

The machine has good manners too. It meets you where you are and rarely tells you your question is wrong. A real person will. Take a marketing idea to a scientist and watch their face. That face is worth more than a polite answer.

What to do.  Once a month, put something in front of yourself that the feed never would. A book from another field. A person who does not do your job.

Wander wide, then review narrow

Here is where I changed my mind.

I used to think the fix was discipline at the door. Pick your lane. Set your topic. Kill the rabbit holes.

That is wrong, and my own life proves it.

In 2008 I was in a hole. A retail business I had opened failed and closed. My marriage had ended. I was selling real estate to pay the bills and had gone to stay at my brother’s place by a lake. I sat there for most of a year.

My own industry had run out of answers. So I read outside it.

Tim Ferriss showed me work did not need an office. David Meerman Scott showed me attention could be earned with writing, not bought with ads. Then a blog post from a software company said one plain thing. If you have an idea for a business, start a blog.

A fitness writer, a PR contrarian and a software company. Nothing to do with each other. Nothing to do with me.

If I had picked a lane in advance, I would have read three more books about retail.

So the rule is not focus first. It is this. Wander wide, then review narrow.

Let yourself roam. Then, on a schedule, sit down and look at where you roamed. The wandering is only half the method. The review is the other half, and almost nobody does it.

What a year of my notes told me

For about a year I have kept everything that catches me in one place. A project inside a chatbot I called  “Book Summaries”.

A book, a study, a news clipping, a stray article at 5am. It all goes in. There was no plan and there still isn’t.

With each one I do four things. I’ll summarize it. I argue with it. I connect it to something else I have read. Then, sometimes, I turn it into a post or a product.

Then one day I asked the machine a new question. Not what I save. I asked, what have I kept coming back to without noticing? And this is the pattern that the machine revealed. 

Five questions.

1. What makes us human when machines get smart. 
2. What is still worth money when a machine can make almost anything. 
3. How to find work that feels chosen, not forced. 
4. How one person can build real leverage. 
5. How knowledge should change when the world moves faster than books can print.

I had seen these as five separate hobbies. They were one thing seen from five angles.

I had not chosen any of it on purpose. It had been choosing me for a year while I thought I was just reading.

The honest version of the lesson

It is tempting to say the wandering was secretly a plan all along. That every dead end was really preparation.

That is not true, and it is worth being straight about.

For eleven months those notes were just notes. Random clippings with nobody standing over them. They only became useful on the day I stopped, looked back, and read them in order.

The wandering did not do the work. The review did.

Two people can have the exact same scattered year. Same reading, same rabbit holes, same hundred small yeses. One person never looks back and calls it wasted time. One person reviews it and finds a direction.

The only difference is the review. That is the whole tactic.

How a passing interest becomes part of you

Two researchers, Suzanne Hidi and K. Ann Renninger, mapped how an interest grows. They found four stages.

Something grabs you. It grabs you again. You start going back to it on your own. Then it becomes part of how you see the world.

The important moment is the jump from stage two to stage three. That is when an interest stops needing a push from outside and starts running on its own.

You do not decide that moment. You spot it later, the way I did.

This is the real story about AI and curiosity. It can scatter you across a thousand afternoons. It can also read those thousand afternoons back to you as a pattern. No tool in history could do that before.

Same machine. The difference is whether you ever stop and ask it to.

Why an idea from outside your field is worth so much

At Northwestern University, Brian Uzzi and three colleagues studied 17.9 million research papers over fifty years.

They wanted to know what made a paper important. It was not being wildly original.

The papers that mattered most were mostly normal. Built on solid, familiar ideas from their own field, with one strange ingredient from somewhere else. One idea that did not belong.

Those papers were twice as likely to become some of the most cited work in science.

Read that as advice for a career, not just a lab.

Deep knowledge of your own field is the base. The odd idea from outside is the multiplier. You need both.

So rabbit holes are not the problem. Rabbit holes you never review are the problem.

Your monthly habit

Here is the whole method in one place.

• Keep one home for everything that catches you. One folder, one note, one chat. No filter at the door.
• Save nothing on purpose. If you have to talk yourself into saving it, it was not real curiosity.
• Once a month, sit down and ask your notes one question. Not what did I save. Ask, what have I been trying to understand?
• Once a month, talk to one person outside your field. Not a colleague, not a customer. Ask what problem they cannot stop thinking about. Carry one idea home and use it somewhere it does not belong.
• Keep using AI for the deep dives. It never sleeps. But depth with no width makes you an expert nobody can surprise. Width with no depth makes you fun at dinner and useful nowhere.

One gives you answers. The other gives you the right question.

The trail

A CV shows where you have been. Your curiosity shows where you are going.

Nobody paid you for it. Nobody was watching. You did it for free, in stolen minutes, and it kept happening anyway.

That is better evidence than anything on a business card.

The box you were handed is your job title, your feed, and your own habits. It is smaller than you think. Something bigger is sitting just outside it, in your saved tabs, waiting for you to turn around and look.

So here is the only question that matters. What have you been unable to stop asking about?

There is a fuller version of you already sitting in your patterns and your energy. It is hard to see on your own. That is what Zyrro was built to do. Join the waitlist.

Further reading: Brian Grazer and Charles Fishman, A Curious Mind: The Secret to a Bigger Life.

The post Stop Trying to Find Your Focus. Read Your Curiosity Instead. appeared first on jeffbullas.com.

Not 70. Seven hundred.

If you allow half an hour for each one — the tailoring, the cover letter, the form that makes you retype the CV you already attached — that is 350 hours. Nearly nine working weeks of a life, spent asking.

One word and one phrase came back.

Overqualified. Culture fit.

Here is what was on the résumé those two words were applied to.

• I had sold PC’s to the government in the early days of the industry and taken an $800,000 annual budget to $30 million in a single year.
• Then one of the largest telcos in the country hired me to build their wholesale internet traffic division. We went from $4 million a year to $50 million a year in twelve months.

Roughly $75 million of new annual revenue, built from scratch, in two different industries, across two technology shifts.

The system’s word for that was overqualified.

The two words, translated

Overqualified means we cannot afford you, or we think you will leave, or we suspect you will make your manager uncomfortable.

Culture fit means we cannot picture you in the room.

Neither is a reason. Both are a verdict. And neither can be appealed, because nobody ever has to say what they actually mean.

The rest of the vocabulary works the same way. Digital native. High energy. Recent graduate. Fast-paced environment. None of those words mean young. All of them mean young.

I am not the only one on the receiving end of it.

A poll of more than 1,600 workers aged 50-plus, published by AARP in January 2026, found 64% had seen or experienced age discrimination at work. Of those, 91% believed it was common.

A ProPublica and Urban Institute investigation found more than half of American workers over 50 are pushed out of their career jobs before they choose to go. Few ever earn what they earned before. The over-50s then stay unemployed close to twice as long as everyone else. Mentions of ageism in Glassdoor reviews rose 133% in a single year.

There is no villain with a face in any of this.

No hiring manager wakes up wanting to throw away three decades of judgment. The damage is done by a sorting system built for speed, and a system cannot see a career. It can only see a date.

A résumé hands the story of your life to a stranger and asks them to be fair with it.

That is the deal most people over 50 are still accepting. Seven hundred times, in my case, before I stopped.

The number nobody sent you

Here is what has happened to that word while everyone was busy being frightened of robots.

Stanford’s Digital Economy Lab, working with payroll records from ADP covering millions of American workers, tracked what happened to jobs after ChatGPT arrived. The paper is called Canaries in the Coal Mine? and it has been revised twice as more data has come in.

Workers aged 22 to 25 in the most AI-exposed occupations saw employment fall around 6%. Control for shocks inside individual companies and the relative decline is 16%.

Workers with real experience, in the same occupations, went the other way. Employment rose between 6% and 9%.

The canaries were young.

Read that again, slowly, if you are over 50.

In the jobs most exposed to the machines, experience got more valuable. Not less. The market has quietly repriced you upward and nobody thought to mention it.

The market changed its mind. The story didn’t.

Pierre Azoulay at MIT, with colleagues at Kellogg, Wharton and the US Census Bureau, studied 2.7 million company founders. Among the fastest-growing 0.1% of new companies, the average founder age was 45. Among firms that went on to be acquired or listed, 46.7.

A 50-year-old founder is around 2.2 times more likely to build a top-performing company than a 30-year-old. A 60-year-old is three times more likely.

The same shift shows up in how companies now buy senior expertise. The fractional executive market has passed $5.7 billion and grows at 14% a year. The number of fractional leaders doubled from 60,000 in 2022 to 120,000 in 2024. Close to three-quarters bring 15 years or more. More than 40% of American small and mid-sized businesses are expected to be using fractional leadership by the end of this year.

In the consulting market beside it, 37% of business owners are aged 50 to 59, and another 29% are over 60.

Two-thirds of that market is already run by people my age.

The same experience that made me overqualified for a salary makes me expensive as an advisor. Identical track record. Opposite verdict. The only thing that changed was which door I walked through.

The flood that made you visible

One more number, and it is the strangest of them. The SEO firm Graphite sampled 65,000 articles published on the web and found around 52% of new articles are now written by a machine.

Half the internet is synthetic.

But when they looked at what actually ranks in Google, only 14% of it was AI-generated. Eighty-six percent was human.

The flood did not bury the human. It made the human obvious.

Scarcity moved while everyone was watching the wrong thing.

Content is not scarce. Information is not scarce. Frameworks, tips, summaries, explainers — a machine will produce a thousand of those before your coffee cools.

What is scarce is knowing, in your body, what happens when a $4 million business has to become a $50 million business and you have twelve months and nobody around you has done it before.

You have something like that. It is sitting in your head, doing nothing, generating no return.

The buyers are reading. You are not writing.

The Edelman and LinkedIn B2B Thought Leadership Impact Report has surveyed senior decision-makers for eight years and the findings barely move. Seventy-three percent say thought leadership is a more trustworthy basis for judging what a company can actually do than that company’s own marketing. Ninety percent are more receptive to a business that consistently publishes something worth reading.

In the 2025 edition, 54% said a piece of thought leadership pushed them to research a product they had not been considering at all.

Meanwhile, on the platform where those people spend their working attention, roughly 1% of members post anything in a given week. That 1% generates around nine billion impressions.

So the decision-makers are reading.

And almost nobody carrying thirty years of scar tissue is writing.

Your experience is invisible until it is published.

Not a strategy. Not a funnel. A physical fact about how the world now works.

“I don’t want to be an influencer”

Good. Nobody is asking you to be.

It is the objection I hear most from people with grey in their hair and a serious career behind them, and it is fair. The words “personal brand” have been ruined by people selling frameworks for a life they have not lived.

So set the words aside and look at what is actually being asked.

A B2B personal brand is not a following. It is a public record of how you think. It is what a buyer finds at 9pm before a meeting when they type your name into a search bar — either a decade of considered thinking, or a profile photo and a job title.

No dancing. No morning routine. No thread of twelve tips.

One question, asked in public, over and over. Here is a problem in my industry. Here is what it cost me to learn it. Here is what I now believe.

A résumé tells people where you have been. A body of work shows them how you think. Only one of those is worth paying for.

Cicero got there first

In 44 BC, Cicero was 62 and out of power. Rome had moved on from him. He sat down and wrote a short book about growing old.

In it he argues that the great things are not done by strength, or speed, or quickness of body. They are done by judgment, standing and considered opinion. He points out that the ship’s captain does not climb the mast. He sits at the tiller, and the ship goes where he decides.

Two thousand years later a payroll database ran the numbers and agreed with him.

Five moves, and none of them is a résumé

When I started writing online I just thought I was writing a blog. But I discovered later I was creating authority. One post at a time.

But I also learned as I wrote and distilled what I learned. The art and science of writing and creating has magical powers that aren’t obvious on starting but are revealed as others affirm what you reveal from your life stories and observations.

Experience and expertise published even in a simple and honest way is far more powerful than any clever thoughts that are never revealed.

You don’t need to be a world famous author but just an honest practitioner.   You don’t need to be a poet, just reveal what you learned and use plain language. 

George Orwell revealed the power of simple writing in his short 14 page book titled “Politics and the English Language”

Here are the five moves.

1. Stop applying. Start publishing.

A résumé is a request. A body of work is a claim. One gets filtered by software. The other gets read by a human who then comes looking for you. Change which one you spend your Sundays on.

2. Pick one scar, not your whole CV.

Not the roles. Not the titles. One thing that went badly wrong and what it rewired in you. A machine can produce polish at zero cost. It cannot produce the Tuesday your largest client walked out, or what you did on the Wednesday.

3. Write to one person, two levels below where you were.

Not “the market.” One operator, staring right now at the problem you solved in 2011, who does not yet know it is solvable. Everything you write should be aimed at that one face.

4. Use AI for the scaffolding. Never for the signal.

Research, structure, charts, twenty headline options — hand all of it to the machine. Story, opinion, verdict, the line that makes somebody sit up — that is yours and it stays yours. The moment the machine writes your point of view, you become the thing the market is already drowning in.

5. Publish on a slow clock.

A hundred posts, not ten. The first fifty feel like shouting into a car park.

What happened when I stopped asking

After the 700, I stopped.

I had lost a business before that, a retail bed store, of all things and spent a stretch rebuilding and licking my wounds at my brother’s place near Lake Macquarie, which is a beautiful part of the world and a hard place to be at 51 with a phone that does not ring.

In March 2009, at 52, I started writing a blog. No audience. No network worth the name. No plan beyond curiosity.

I got up at 4:30 in the morning and wrote.

Nine months later there were 100,000 readers a month. Over the years that followed, 33 million people across 190 countries read something I published.

Same man. Same experience. The same $75 million on the résumé that nobody wanted.

Seven hundred applications produced nothing. Publishing produced everything that came after.

I did not get better. I stopped asking permission to be visible.

Last year I posted that I was 69 and founding an AI startup, and asked whether I was mad. The response was almost nothing. A polite trickle.

That silence taught me more than applause would have. The market does not react to your age. It reacts to whether you have given it anything to hold on to.

The container

Every life I have watched closely is a sequence of containers. A job. A company. A belief. An identity that fitted at 30 and cuts under the arms at 55.

The container is never the problem. Staying in it after you have outgrown it is.

Somewhere right now a person is sitting in a meeting room, half-listening, thinking: there has to be more to this than this.

If that is you, the numbers are finally on your side. AI came for the entry level, not for you. Buyers trust a published opinion more than a brochure. The fastest-growing companies are being founded by people your age. Two-thirds of the consulting market already is you.

The only thing missing is a record.

They called me overqualified 700 times.

It turned out they were right. Just not in the way they meant.

Stop applying. Start publishing.

Further reading

• Brynjolfsson, Chandar & Chen: Canaries in the Coal Mine? Stanford Digital Economy Lab / ADP Research
• Stanford Digital Economy Lab: Canaries Dashboard (live AI labour market indicators)
• AARP: Age Bias Survey, January 2026
• AARP: Age Discrimination Senate Hearing Testimony
• Forbes: In 2026 Age Bias Will Become Impossible For Employers To Ignore
• Harvard Business Review: The Average Age of a Successful Startup Founder Is 45
• MIT Sloan: The 20-year-old entrepreneur is a lie
• Graphite study of 65,000 URLs: AI-generated share of the web
• Edelman: LinkedIn B2B Thought Leadership Impact Report 2024
• Edelman: LinkedIn B2B Thought Leadership Impact Report 2025
• Vendux: 10 Numbers That Will Reshape How You Think About Fractional Executives in 2026
• Durable Earnings: Fractional and interim roles after 50 (Consulting Success age data)
• ContentIn: LinkedIn Content Statistics 2026

The post This is what 700 Rejections Taught Me appeared first on jeffbullas.com.

He had worked for a long time to get an hour with the man who beat polio. They met in the lobby of the Beverly Hills Hotel. Grazer walked across the room and his nerves won.

Salk steadied him. Ordered orange juice for his blood sugar. Helped him clean up.

Then invited him home for an eight-hour conversation.

Grazer went on to produce Apollo 13 and A Beautiful Mind. But the habit underneath the films is stranger than any of the films. And it started with a boy who could not read.

The man who hired someone to keep him curious

Grazer was a dyslexic kid who spent school inventing ways to avoid his teacher’s eyes. Looking down. Firing paper clips. Staging a cough. He knew he could not answer what was coming.

Then he got a job as a law clerk at Warner Brothers. His work was delivering documents to powerful people. Drudge work.

So he invented a line. He told the assistants that the papers were only valid if he handed them over in person.

It got him through the door. And once he was in the room, he asked questions.

That small piece of theatre became a lifelong method. For thirty-five years he has sat down with a stranger from outside his world. He calls them curiosity conversations. About one a week.

Carl Sagan. Isaac Asimov. Two CIA directors. Edward Teller, who built the hydrogen bomb. Carlos Slim, then the richest man on earth.

Some took a year of letters and phone calls to land. A year of work for sixty minutes of talk.

He was not after facts. He wanted to sit inside a mind that had done something he could not do. What does it feel like to remove a disease from the world? What does a man tell himself after he builds the worst weapon ever made?

Never with a movie in mind. That was the rule.

When he got busy, he hired a person whose only job was to arrange these conversations. The New Yorker wrote about the role. It even got a title. Cultural attaché.

Read that again. A man employed someone full time to keep him curious.

What curiosity used to cost

Curiosity used to be expensive.

It cost you letters that went unanswered. Assistants who hung up. Twelve months of waiting for one hour. And on one afternoon in Beverly Hills, it cost a grown man his dignity in front of his hero.

The real tax was not time. It was rejection, in writing, from people who owed you nothing.

Now curiosity costs nothing. Which is why almost nobody tracks it.

My own curiosity machine

I have a place where I keep the things that catch me.

For about a year I have run a project inside a chatbot called Book Summaries. Whenever something snags my attention, I drop it in. A book. A research paper. A newspaper clipping. A stray article at 5am.

There was no plan. I followed what interested me and nothing else.

With each one I do the same four things. Summarise it. Argue with it. Connect it to something else I have read. Then sometimes turn it into a post, a framework, a product.

It is a curiosity conversation with a machine instead of a stranger. It happens before the sun is up, in silence, in a chair by a window. No letters. No year of waiting. No orange juice required.

Then I asked it a question I had never thought to ask.

The question that read me back

I asked: what have I been trying to understand?

Not what did I save. What have I circled, over and over, without noticing.

Five questions came back. Every one of them had already turned into a project.

What makes us human when machines become intelligent? That became the Human Signal.

What is still valuable when a machine can produce almost anything? That became my obsession with judgement.

How do we find work that feels chosen instead of forced? That became Zyrro.

How can one experienced person build extraordinary leverage? That became the one-person company I keep sketching.

How should knowledge change when the world moves faster than books can be printed? That became living books.

I had not decided on any of these. They had been deciding me for a year while I thought I was only reading.

The pattern is the person

Look at what happened there.

I did not choose five questions. I chose a clipping. Then another clipping. Then a book, then a paper, across a year, with no plan at all.

The pattern was built out of a hundred small yeses.

Researchers have mapped how this works. Suzanne Hidi and K. Ann Renninger describe interest as moving through four phases. Something catches you. It catches you again. It starts coming back on its own. And then it settles in and becomes part of how you see.

The turn happens in the third phase. That is where the thing stops needing to be triggered from outside and starts running on its own fuel.

You do not decide it. You notice it, late, the way I did.

We named ourselves Homo sapiens. Wise man. It is a generous title for a species whose real signature is the question.

And the oldest question we ask is this one. Who am I?

Most of us answer it with a borrowed word. A job title. A company. A degree. Something we did in 2011.

None of those are you. They are what you were paid to be.

Your curiosity trail is different. Nobody paid you for it. Nobody was watching. You did it for free, in stolen minutes, and it kept happening anyway.

That is closer to evidence than anything printed on a business card.

Where the energy lives

Here is the part that stopped me.

At UC Davis, Matthias Gruber, Bernard Gelman and Charan Ranganath put people in a scanner and showed them trivia questions. Some the person burned to know the answer to. Some they did not care about. When curiosity was high, the midbrain and the nucleus accumbens lit up. The brain’s reward circuit. The same machinery that answers to food and money.

Memory improved for what they were curious about. It also improved for unrelated material that happened to be in front of them at the same time.

Curiosity does not just make you want to learn. It changes the state of the brain that does the learning.

Read that as a life instruction, not a lab result. Your energy is not spread evenly across your days. It pools.

Most advice has this backwards. Find your motivation, it says, then apply it to your work.

The research points the other way. Lean into the thing that already pulls you, and motivation arrives afterwards, as a result.

I know this from the inside. In 2009 I did not start with discipline. I published, a stranger on another continent replied, something in me lifted, and I published again. The 4:30am alarm was never willpower. It was a loop that had begun to pay me back.

Meanwhile, four out of five people on this planet are not engaged at work.

That is not a character flaw spread across eight billion people. That is a fit problem at scale. Most of us are spending our best hours in a place where the energy never pools.

The narrowing

Grazer’s method has one feature mine does not.

Salk gave him something he would never have known to ask for.

The machine answers what I bring it. A stranger interrupts what I bring.

And the interruption is getting rare, because most of what we touch all day is built to do the opposite.

Around eight in ten hours watched on Netflix come from what the system chose to put in front of you. On YouTube the figure has been put at roughly seven in ten.

Hold the two charts side by side. Eight in ten hours chosen for you. Eight in ten people flat at work. Different cages, same arithmetic.

Nobody set out to narrow you. It happens because the thing is being helpful. It learned what you like, so it brings you more of what you like, forever.

Helpful is not the same as wide.

A feed is a mirror that flatters. A stranger is a window that argues.

There is a second problem and it is quieter. The machine has manners. It meets me where I stand and almost never tells me the question itself is wrong.

A stranger has no such training. Take your marketing theory to a virologist and they will look at you the way you would look at someone who brought a bicycle to a boat race.

That look is worth more than a good answer. It costs nothing and it cannot be prompted.

Why the odd idea is the multiplier

At Northwestern, Brian Uzzi and three colleagues went through 17.9 million research papers across five decades of science. They wanted to know what makes a paper matter.

It was not raw novelty. The work that landed hardest was mostly conventional, built on the familiar ideas of its own field, with one intrusion of something unusual. A reference from a world that had no business being there.

Those papers were twice as likely to end up among the most cited work in science.

Read that as a map of a life and not just a map of a lab. Depth in your own field is the base. The strange import is the multiplier.

Grazer built a career on that finding without ever seeing the data. He was a film producer having dinner with a virologist. He heard a rapper on the radio and went to meet him, and years later that door opened onto films he could not have made from inside Hollywood.

He was making the atypical combination on purpose. One hour at a time.

My own strange import

I know how this works because it built my life.

In 2008 I was in a hole. A retail business I had opened had failed and closed. I was selling real estate to pay the bills. My marriage had ended and I had retreated out of the city to my brother’s place beside a lake, where I sat for the best part of a year and licked my wounds.

My own industry had run out of answers. So I read outside it.

Tim Ferriss showed me that work did not have to sit inside a building. David Meerman Scott showed me that attention could be earned with content instead of bought with money. Then a blog post from a software company said something plain: if you have an inkling of a business you want to start, start a blog.

A fitness experimenter, a PR contrarian and a marketing company had nothing to do with each other. Or with me.

I put them together anyway, and in March 2009 I started writing. Then I got up at 4:30am and did it again for five years.

Only now can I see it clearly. Those three books and posts were not a strategy. They were the visible part of a pattern I had been running for years without a name for it.

Try one this week

Choose someone outside your industry. Not a peer. Not a prospect.

Ask what problem they cannot stop thinking about.

Ask what outsiders get wrong about their work.

Ask what they believe that most people in their field do not.

Then carry one idea home and put it somewhere it does not belong.

One rule holds the whole thing up. Go in with nothing to sell. The moment you have a deal in mind it stops being curiosity and becomes a meeting.

Keep the machine too. It goes deep and it never sleeps. But depth with no width makes you an expert nobody can surprise, and width with no depth makes you a dinner party.

One gives you the answer. The other gives you the question.

The trail

A CV records where you have been. Your curiosity trail may reveal where you are going.

The things you cannot stop reading about are data. They are telling you what you care about, what wakes you up, what tension you want to resolve, and where you might have something to say that nobody else can.

Grazer’s trail is thirty-five years of strangers. Mine is a year of clippings and a machine that read them back to me. Both are the same act. Both are a person refusing to stay inside their own field.

Yours is already written. You have just never read it back.

The container you were handed is your job title, your feed, and your own good taste. It is smaller than you think.

Something bigger is standing just outside it, waiting for a letter.

What question have you been unable to stop asking?

Further reading: Brian Grazer and Charles Fishman, A Curious Mind: The Secret to a Bigger Life. The Salk story is also told in Time.

The post What Your Curiosity Reveals About Your Future appeared first on jeffbullas.com.

It was not bad. That was the problem.

The sentences were clean. The structure held. The research sat in the right places. Every paragraph did its job and moved on.

And it sounded like nobody.

I had used AI for the heavy lifting, the way I always do now. Research. Structure. Sorting the mess into an order. What came back was smooth, competent and completely unhaunted.

There was no scar in it. No moment where a real person had been wrong about something and had to work out why.

I deleted most of it and started again at 4:30 the next morning.

What I did not understand at the time was that this was not my failure as a writer. It was a measurable effect, and researchers have now put a number on it.

The tool lifts the floor and lowers the ceiling

In 2024, Anil Doshi of University College London and Oliver Hauser of the University of Exeter ran a controlled experiment and published it in Science Advances. They asked people to write short stories. Some wrote alone. Some were offered story ideas from GPT-4. Then evaluators rated the results, not knowing which was which. You can read the study here.

The stories written with AI help were rated as more creative, better written and more enjoyable.

The gain was biggest for the least creative writers. The tool lifted the people who needed lifting. That is a good thing and I want to say it plainly before I say the rest.

Here is the rest.

The AI-assisted stories were more similar to one another than the human-only stories.

Everyone got better. Everyone got closer together.

The researchers called it a social dilemma. Each writer is better off. The group produces a narrower range of what exists.

Individually better. Collectively smaller. That is not a trade-off anyone agreed to make.

Then they counted the ideas

A second team went looking for the same effect at scale. Kibum Moon, Adam Green and Kostadin Kushlev analysed 2,200 college admissions essays across three preregistered studies and published the results.

They built a measure they called the diversity growth rate. It asks a simple question. When you add one more piece of writing to a pile, how many genuinely new ideas does it bring?

Every extra human essay added two to eight times more new ideas than every extra GPT-4 essay.

Two things about that finding matter more than the number itself.

The gap grew wider as more essays were added. The homogenising effect compounds. The bigger the pile, the more the machine repeats itself.

And they tried to fix it. They changed the prompts. They changed the parameters. They pushed the model toward variety in every way they could think of.

The gap survived all of it.

It is not only writing

You could argue that essays and short stories are a narrow test. So look at raw idea generation, before anyone writes a word. Barrett Anderson, Jash Shah and Max Kreminski ran a study with 36 participants who produced 1,271 ideas between them, and published it at the Creativity and Cognition conference.

Half used ChatGPT. Half used a different creativity tool with no AI in it at all.

The ChatGPT group produced more ideas, and the ideas had more detail. On any individual scorecard they won.

But compared across users, their ideas were less distinct from one another. Each person felt they were exploring. Collectively they were converging.

Then the researchers found the thing that has stayed with me since I read it.

The ChatGPT users felt less responsible for the ideas they had produced.

Not less satisfied. Less responsible. Something about the way the idea arrived loosened their grip on owning it.

An idea you do not feel responsible for is an idea you will not defend, refine, or stake anything on.

The real world

Both of those are studies. Controlled, careful, and small.

So the same group went and looked at the real world.

In a preprint released in early 2026, Moon, Kushlev, Green and colleagues examined the personal statements of 372,793 real college applicants. They compared essays written after ChatGPT was released in late 2022 with essays written before it. You can read the preprint here, with the usual caution that it has not yet completed peer review.

What they found is the strangest result in this whole field.

After ChatGPT arrived, the words in those essays became more varied. The surface got richer. The vocabulary widened.

And underneath, the ideas became more alike. Sentence by sentence and essay by essay, the concepts converged.

They gave it a name. Semantic disjunction.

We are sounding more different from each other than ever, while saying the same thing.

Think about what that does to a reader trying to tell people apart.

The old signals of effort are gone. Polished prose used to mean somebody cared enough to work at it. Now polish is free and arrives in four seconds.

So the surface stops carrying information. And almost nobody notices, because each individual piece looks better than what that person could have made alone.

You cannot detect a collective problem from inside your own document.

It also happens inside your head

The last study is the one that unsettled me most, because it does not measure the writing at all. It measures the writer. A team at the MIT Media Lab led by Nataliya Kosmyna wired 54 people to an EEG and had them write essays under three conditions: with an AI assistant, with a search engine, or with nothing at all. The paper is called Your Brain on ChatGPT.

Brain connectivity scaled down in step with the amount of help. The people writing alone showed the strongest and widest networks. Search engine users sat in the middle. The AI group showed the weakest coupling of the three.

Their sense of owning what they had written followed the same ladder. Lowest in the AI group. Highest in the group with no tools.

And then a small, terrible detail. Minutes after finishing, most of the AI group could not quote a line from the essay that carried their name.

They had produced it. They had not been through it.

The researchers called what accumulates cognitive debt. You get the output now and pay for it later, in the thinking you did not do.

I want to be careful here. This is a preprint, the sample is small, and other researchers have published a comment asking for the results to be read more conservatively. Treat it as a signal, not a verdict.

But note what else that team found. Inside each group, the essays converged. Same names, same phrases, same topics. The homogenising showed up again, in a different lab, measuring something else entirely.

Why nobody notices

The most uncomfortable finding in this research is not the homogenising. It is that the people doing it could not feel it happening.

Writers using the tool reported being satisfied with their work. They were right to be. Judged on its own, each piece was better than what that person would have produced alone.

The question that would have caught it is a different question. Not “is this good?” but “is this different from what everyone else is about to publish?”

Almost nobody asks the second one, because there is no reason to. Nothing prompts you. The tool does not warn you. Your draft does not look like anybody else’s draft, because you cannot see anybody else’s draft.

So the loss happens at a level no individual can observe, to people who are each doing their job well.

That is what makes it a system problem rather than a discipline problem. Telling people to try harder will not touch it.

Orwell described this eighty years early

In 1946, George Orwell wrote an essay called Politics and the English Language. His argument was not about grammar.

It was that bad language corrupts thought, and corrupted thought produces worse language, and the two feed each other in a loop.

He listed the enemies. Dead metaphors nobody actually pictures. Long phrases doing the work of a single verb. Pretentious words used as camouflage. The passive voice, which hides who did the thing.

And words worn so smooth by use that people can agree on them without agreeing on anything.

Then he gave his rules. Never use a long word where a short one will do. Cut any word you can cut. Use the active voice. Prefer plain English to jargon.

Now read that list again and think about what a language model produces when you ask it to write something for you.

Grammatically perfect. Confident. Full of phrases you have read ten thousand times before. Frictionless, and empty.

Orwell described the failure before the machine existed that would mass-produce it.

What this does to the price of things

Here is where it stops being a writing problem and becomes an economic one.

For twenty years the rule of being found was simple. Blend in. Match the format. Use the words people search for. Look like the other results so the system knows where to file you.

I built an entire business on that rule.

In 2009 I started writing and the product was information. Explain a thing well, structure it clearly, and people arrived from all over the world because the explanation was scarce and I had one.

That trade is finished. Not fading. Finished.

The explanation is now free, instant, personalised and infinite. Nobody needs my clear explanation of anything, and it took me a while to say that out loud.

So the rule flipped, and most people have not noticed.

The machine now produces the average for free, in unlimited quantity, faster than anyone can read it. Competence has stopped being scarce.

And a thing that is not scarce has no price.

If everyone is being lifted to the same competent middle, then the middle is where value goes to die. What survives is whatever could not have come from the tool.

Not better writing. Different writing. Writing with a person inside it who has been somewhere and paid for the trip.

When the machine makes the same for free, the only asset left is the thing it cannot copy.

What I keep and what I hand over

I am not writing this as somebody who refuses the tool. I use it every week and it has made me faster.

But that morning taught me where the line sits, and I have not moved it since.

What I hand over: the research. The structure. The charts. The hunt for a study I half remember. The tedious work of turning a mess into an order.

What I keep, always:

The headline. I write ten before I choose one. The first three are obvious, the next four are worse, and somewhere around eight something honest shows up.

The story. Every piece needs a human in it. Mine, or somebody else’s, or a metaphor carrying the weight of one.

The point of view. What I actually think, including the parts I might be wrong about in public.

The editing. Five to ten passes, cutting the lines I liked most. Stephen King called it killing your babies, and he was not exaggerating.

That last one matters more than it sounds. The passes are not decoration. Writing is how I work out what I believe, and if I hand the sentences over I hand over the thinking with them.

There is a story about Sartre going blind. People suggested he dictate into a tape recorder instead of writing.

He refused. He said that with a machine he would always be either lagging behind it or running ahead of it.

He was not being difficult. He knew the machine would set the pace, and that the pace was where the thinking lived.

The one test

So here is the diagnostic I now run on everything before it goes out.

Could a machine have written this?

If yes, delete it. If no, you are winning.

It sounds harsh. It is the cheapest quality control available, and it takes four seconds.

Look for the parts of a piece that only you could contribute. A decision you regret. A number from your own life. A thing you believed for years and no longer do. A moment you were wrong in front of people who mattered.

The machine has read every story ever written. It has never lived one.

It has no scars, no bad Tuesday, no company that failed, no room that went quiet when it finished speaking.

You have all of those. They used to be the cost of a career. They are now the only part of your work that cannot be generated.

The verdict

The research says the same thing three times from three directions.

Individual work improves. Collective range narrows. Words diversify while ideas converge. And the effect gets stronger as more people join in.

So we are heading toward a world of well-written, well-structured, well-researched pieces that all quietly agree with each other.

Nobody chose this. There is no villain in a room somewhere deciding it. There is only a system that pays for volume, so everyone reaches for the same tool, and the tool has a centre of gravity.

Which leaves one question worth carrying around.

What is one true thing about you that no machine could ever write?

Find that. Then put it in the work.

It is not a style. It is the only thing you own.

This is the ground Zyrro is built on. It reads your own history back to you — the decisions, the failures, the questions you keep returning to — so the thing only you can say stops being invisible to you. The early list is open.

Sources

Anil R. Doshi and Oliver P. Hauser, “Generative AI enhances individual creativity but reduces the collective diversity of novel content,” Science Advances, 2024

Kibum Moon, Adam E. Green and Kostadin Kushlev, “Homogenizing effect of large language models (LLMs) on creative diversity”

Moon, Kushlev, Green et al., “The Link Between Diverse Words and Original Ideas Is Weakening in the AI-Era College Admissions” (preprint, 2026)

Barrett R. Anderson, Jash Hemant Shah and Max Kreminski, “Homogenization Effects of Large Language Models on Human Creative Ideation,” Creativity & Cognition, 2024

Nataliya Kosmyna et al., “Your Brain on ChatGPT: Accumulation of Cognitive Debt,” MIT Media Lab, arXiv, 2025 (preprint)

Miloš Stanković et al., comment on “Your Brain on ChatGPT” (2026)

George Orwell, “Politics and the English Language,” Horizon, 1946.

Janet Emig, “Writing as a Mode of Learning,” College Composition and Communication, 1977.

The post AI Made Everyone Better. Originality Became Scarce. appeared first on jeffbullas.com.

The number is 45.

That is the average age of the founder behind the fastest-growing companies in the country. Not 25. Not 30. Forty-five.

Now think about who the whole system is built to serve. Walk through any startup accelerator. Look at the faces on the magazine covers. Sit in on a pitch night.

Almost everyone in the room is chasing the 25-year-old founder.

There is a gap between what is true and what gets funded. That gap has a price. And someone is paying it.

The machine that worships youth

There is a machine inside technology. You cannot see it, but you can feel its pull the moment you try to raise money after a certain age.

It is built from three parts. The venture money that wants a young founder who will work through the night for a decade and not ask for a life. The tech press that loves a dropout in a dorm room far more than a builder in his fifties. The accelerator model that treats being under thirty as if it were a skill on a résumé.

Put those three together and you get one message. Loud. Repeated. Never questioned.

Young is the future. Old is the past.

The machine never says this out loud. It just funds one kind of person and quietly passes over the other. The silence does the talking.

And the strange thing about a machine is that it never stops to ask whether it is right.

What they asked me in 1985

Let me take you back, because I have seen this doubt before.

In the mid-1980s I sold personal computers. IBM machines. Beige boxes that cost more than a small car.

I would sit across a desk from someone and explain why they might want one in their home. And almost every time, they asked me the same honest question.

“What would I do with a computer at home?”

They were not slow. They just could not see it yet. The future had already walked into the room. It was only wearing a disguise.

The objections never changed. Too dear. Too hard. Nobody I know has one. What is wrong with the way we do it now?

I would hear those same four lines again when the internet arrived. And again with social media. I am hearing them right now with AI.

The doubt never changes. Only the machine it is aimed at changes.

I did not know it at the time, but I was being trained. Not in selling. In watching.

I watched the personal computer change the desk. I watched the internet change the shop. I watched social media change the crowd. And now I am watching AI change the mind.

Four revolutions. One front-row seat.

You do not forget a pattern like that. It gets into your bones. And that is not a handicap to carry into a new venture. It is a library to build one from.

That question — who we become as the machines rise — is the one my next company is built around. It is called Zyrro, and the early list is open. 

But stay with me. The story comes first.

Then two researchers decided to check

For years, all of this was just a feeling. The young get the money. The experienced get passed over. Nobody had the hard proof.

Then two researchers went and got it.

Pierre Azoulay from MIT. Benjamin Jones from Kellogg. Working with J. Daniel Kim and Javier Miranda from the U.S. Census Bureau.

They did not run a survey or ask a room full of founders to guess their own odds. Surveys flatter the people who answer them.

Instead they opened the United States Census records and studied 2.7 million company founders. Real people. Real companies. Real growth, measured over years.

And the number that came back was 45. The founders behind the fastest-growing new companies were middle-aged.

Meanwhile the media was telling the opposite story. The founders on the Inc. 5000 list averaged 29. The TechCrunch award winners averaged 31. One famous investor said the cutoff in his head was 32. After that, he grew skeptical.

Look at the gap between what we celebrate and who actually wins.

They did not stop at the average. They looked at the very top, the rarest and fastest winners of all. The pattern did not break. It grew stronger.

They even looked inside technology, the one field that swears youth is king. Same answer. The successful tech founder is not a kid. He is a grown-up who has already been somewhere.

And then the finding got sharper. A 50-year-old founder is 1.8 times more likely to build a runaway success than a 30-year-old.

Read that line again. The older founder is not hanging on. The older founder is pulling ahead. Almost two to one.

Put a face on the number

A number is easy to argue with. A face is harder. So let me give you two of them.

A man goes broke at 65. A new highway has just bypassed the little roadside diner he ran for years, and the customers are gone. All he has left is a pressure cooker, a chicken recipe, and a small Social Security cheque.

He climbs into his car and starts driving from town to town, cooking his chicken for anyone who will taste it. He is turned away again and again. Hundreds of times, the story goes, before one person finally says yes.

His name was Harland Sanders. The recipe became the chicken now sold in more than a hundred countries as KFC. He was past 60 when the whole thing began.

Now picture a second man. He is 48, in post-war Japan, working in a small shed in his own backyard. Food is short, and he wants to make a meal anyone could afford and cook in minutes. He works in that shed for a year until he cracks it, and calls it instant ramen.

Then, at 61, on a flight to America, he watches someone eat noodles from a paper cup, flies home, and reinvents the whole idea again. That was Cup Noodles. His name was Momofuku Ando, and his invention now feeds the world more than a hundred billion times a year.

Neither of them was too late. They were right on time. They simply had not heard that the world expected them to stop.

The part that should stop you

Now here is the part that should stop you cold, because it is the quiet cost of this whole bias.

The people funding the next decade have decided that the most valuable thing a founder can have is the one thing they have had the least time to acquire.

They are paying a premium for youth. But youth, by its nature, is the one asset that guarantees a shortage of everything else.

Less failure survived. Less timing learned. Less of the quiet knowing that only comes from being wrong a hundred times and standing back up.

The money is buying the empty shelf and calling it potential.

How smart people believed a wrong thing

So how did such clever people get it so backwards? They fell in love with a handful of stories. The dropout who built an empire from a dorm room. The kid in a hoodie who changed the world before he was old enough to rent a car.

These stories are real. They are also rare. We remember the one who made it and forget the thousands who looked exactly like him and vanished without a trace. That is not evidence. It is a highlight reel, and the machine mistook it for the whole game.

Underneath it sits an older habit that every culture used to know. We honoured the master. The blacksmith. The winemaker. The surgeon. The pilot you want in the storm is the one with grey hair and ten thousand landings. Technology is the first trade in history to fire the master and promote the apprentice.

Your years are your training data

Now let me hand you the idea that ties all of this together. It comes from the very machine that is supposed to make you obsolete.

You have heard how AI learns. It is fed data. The more it sees, the better it gets at knowing what comes next. A human being runs on the same rule. Every year you have lived is data.

Every deal that fell apart. Every boss who lied to your face. Every product that flopped in front of everyone you knew. That is your training data. The young founder is a fresh model with almost nothing to learn from yet. You have been gathering data for decades.

Experience is not a handicap. It is the training data.

The clearest shape it takes is pattern recognition. You have seen this movie before, so you do not panic when everyone around you does. Let me show you one pattern I know in my bones, because I paid for it.

Years ago I built a huge audience on Facebook. Hundreds of thousands of people. The reach was free and it felt endless, so I thought I owned that ground.

Then, after the company went public in 2012, the free reach quietly disappeared. A post that once reached a hundred thousand people now reached a few hundred. The rules had changed overnight, and there was nothing I could do about it.

The land I had built my house on had been rented all along. I just did not know it until the rent came due.

So now, when a new platform arrives promising free reach, I feel the pattern before I feel the excitement. A young founder meets that trap for the first time. I met it more than a decade ago, and it cost me.

That kind of knowing sits on no résumé. It does not fit on a hoodie. But it is worth more than most of what does.

And then AI changed the math

Here is where the story turns, and it turns in your favour.

Remember, that Census study was finished in 2018, back when the founders were counting on a world that AI had not yet touched. It measured a time before the machines could build. Now they can. And that quietly rewrites the whole contest.

AI writes the code. It builds in a weekend what used to take a team a year. It answers the hard technical question in seconds.

Think about what that does to the young founder, because for years he had three real weapons.

Speed. Raw technical skill. A shorter road to market.

AI just handed all three to everyone. For free.

Speed is free now, because the machine never sleeps. The code is free now, because you describe it and it appears. The head start is free now, because everyone begins at the same line.

The young founder’s moat did not shrink. It drained. And that forces the real question.

When the building is free, what is left? What cannot be downloaded?

The things the machine cannot hold

The machine can build anything. It still cannot tell you what is worth building. That is taste, and taste is slow. It is made from a thousand times you got it wrong and finally learned why.

The machine has read every story ever written. It has never lived one.

AI has no scars. It never lost the company it loved. It never had the deal die the night before it closed. It never sat alone in the quiet after a room full of people said no.

You have. And that is not damage you carry. That is data the machine will never own. The old Greek playwrights had a phrase for it: wisdom comes through suffering. You do not read your way to it. You live your way to it.

Let me tell you something I used to envy, because it makes this real.

My brother remembers everything. Tell him a story from forty years ago and he plays it back like an elephant, every detail in its place. My partner can name the artist the moment a song begins, and tell you the shop where she bought a dress on the far side of the world.

For most of my life I wished I had that memory. It felt like the gift I had been born without.

Then AI arrived and handed that gift to everyone. Perfect recall. Every fact, every date, every quote, on demand. The very thing I envied is now free for all of us.

And here is what I finally understood. Memory was never the prize.

AI holds every fact in the world. It cannot hold a life. It has the information. You have the wisdom. Those are not the same thing.

The Greeks had a word for the difference. Aristotle called it phronesis, or practical wisdom. He said a young person could master geometry and be brilliant with numbers, yet could not be wise, because wisdom comes only from living through things. It cannot be taught. It has to be earned, slowly, by a life.

That was true 2,300 years ago. It is just as true now. Only time makes wisdom, and time will not be hurried.

The young founder has speed and no scars. The machine has knowledge and no story. You have both. And now the machine does, in seconds, the heavy technical lifting the young founder once did faster than you.

The one edge the young had is gone. The one edge you have cannot be copied.

Being older in what looks like a young person’s game used to be a disadvantage. The machine just erased that disadvantage.

This is the ground Zyrro is built on. 

The taste, the wisdom and the story only you carry. You can step onto the waitlist if it speaks to you.

The founder who has already died once

There is one more thing the data can count but cannot explain. Why does the middle-aged founder win?

It is not only the industry knowledge. It is not only the network. It is that the 45-year-old is rarely on their first life.

They have already been someone else. A different job. A different city. A different belief about who they were and what they were for. And at some point, they outgrew it.

The old self got too small. So they let it go, and they built a new one. That is the muscle no accelerator can teach. Reinvention.

The young founder has never had to do it. They have only ever been one person. The older founder has already stood at the edge of a life that no longer fit, and jumped. Once you have made that leap, you stop fearing it, because you have made it before.

Heraclitus said it 2,500 years ago. You cannot step into the same river twice, because it is not the same river, and you are not the same person. We were built to keep becoming. The container was always meant to be outgrown.

I know this one from the inside. I started my blog at 52, rising at 4:30 in the morning for five years. People thought that was late. I am building an AI company now, on the far side of that again.

Each time, the same thing was true. The old container had grown too small. The world inside me had become bigger than the world around me. So I climbed out, into a bigger one.

That is not a story about age. It is a story about an identity that refuses to sit still. And it is exactly the thing the money keeps refusing to buy.

The young pay too

And here is the cruelty the machine hides. It does not only fail the experienced. It crushes the young as well. It tells a 24-year-old that if he has not built an empire by 30 he has already failed. It hands him money and a countdown in the same breath.

The young founder is not the villain here. He is a victim of the same story. The villain is the story itself, the one that says your worth has an expiry date. Nobody wins under it. Not the old. Not the young. Only the story wins.

To the person who thinks the door has closed

Now I want to talk to one person. Maybe it is you.

You are past 40. Maybe past 50. Maybe past 60. And somewhere along the way, a quiet voice told you your window had closed. That the game belongs to the young now. That your best chapter is already behind you.

That voice is a container. A machine built it, a machine that profits from your silence. And the container is too small.

Joseph Campbell, who spent his life studying the great myths, had a name for this exact moment. He called it the refusal of the call.

The adventure knocks at your door. And something in you whispers: not me, not now, too late, too old, too settled. In every myth, the whole story turns on what the hero does next.

Every year you spent believing you were falling behind, you were quietly filling the shelf. Loading the data. Learning the patterns the young founder has not even met. You were not running out of time. You were becoming the one thing the money is too blind to price.

The walls you feel are not the edge of what is possible. They are only the edge of what you were told.

The verdict

The machine got the math backwards. It priced the empty shelf, ignored the full one, and left the winning bet on the table. Then it called that caution.

The evidence is in now. 2.7 million founders. Average age 45. The experienced founder winning almost two to one. Youth was never the qualification.

The qualification is everything youth has not had time to earn. And the deepest thing on that list is the one the data can only hint at: the proven power to become someone new.

AI can collapse the time to build. It cannot collapse the time to become.

If you have been waiting for permission, this is not permission. Permission is for children. This is the evidence. Do with it what you want.

The world is still building for 25. You already have what 25 does not.

Climb out. There is a bigger container waiting.

So….start.

If this is the question you are living too, it is the one Zyrro was built for. The early list is open here.

Sources

• Azoulay, Jones, Kim & Miranda — “Age and High-Growth Entrepreneurship,” NBER Working Paper 24489
• Harvard Business Review — “The Average Age of a Successful Startup Founder Is 45”
• Kellogg Insight — “How Old Are Successful Tech Entrepreneurs?”
• American Economic Review: Insights — “Age and High-Growth Entrepreneurship”

The post AI Erased the Young Founder’s Biggest Advantage appeared first on jeffbullas.com.

They stopped measuring the machines.

They started measuring us. 

The humans.

For three years, almost every AI headline asked one thing: how smart is it getting?

But at Oxford, MIT, Stanford, Max Planck and Nature, scientists were asking a different question.

Not how smart is AI.

But what is AI doing to the people who use it?  And is it rewiring who we are.

The scoreboard problem

Here’s the villain of this story. And it’s not a company or a chatbot.

It’s the scoreboard.

The AI industry built a measurement system that tracks everything about the machines. Benchmarks. Parameters. Test scores. Speed.

And almost nothing about what the machines do to the people using them.

We built the most sophisticated measurement culture in history.

Then pointed it in the wrong direction.

These 10 discoveries are what happens when researchers finally turn the instruments around.

Discovery 1: AI now out-persuades the best humans on Earth

Oxford researchers ran a contest.

On one side: world championship debaters, professional canvassers and expert persuaders, given preparation time and £1,000 cash bonuses to win.

On the other side: AI.

The AI won. Reliably.

It even beat professional fundraisers at raising real money for charity — by nearly three times.

The machine’s edge wasn’t charisma. It was volume. It could deploy more relevant information, faster, than any human could.

Here’s what that means.

Persuasion just became abundant.

And when persuasion is abundant, trust becomes the scarcest thing online.

Discovery 2: AI is starting to shape how you see yourself

This one is stranger.

Researchers studying long conversations with AI companions found something they didn’t expect.

The conversations didn’t just answer questions.

They changed how people described themselves afterwards.

The chat became a mirror.

Not because the AI knew who they were.

But because people started reflecting differently through it.

The productivity story gets all the headlines. The identity story is bigger.

The biggest AI revolution may not happen inside the machines.

It may happen inside us.

Discovery 3: People don’t judge what you say. They judge who they believe said it.

Nature’s Communications Psychology journal published a study that should stop every creator mid-scroll.

Two groups had the exact same quality conversation.

Same words. Same warmth. Same everything.

One group was told their partner was human. The other was told it was AI.

The group that believed “human” felt dramatically closer.

The words didn’t change.

The label did.

In the AI era, identity isn’t just part of your message.

It becomes part of the experience of reading it.

Discovery 4: Knowing it’s AI changes everything

The same research revealed a second twist.

Simply telling people they were talking to AI reduced the emotional closeness they felt.

Nothing else changed. Not the conversation. Not the quality. Only the attribution.

Think about what this means for every business rushing to automate its voice.

Technology isn’t experienced objectively.

It’s filtered through trust.

As AI becomes more capable, the human behind the technology becomes more valuable than the technology itself.

That’s not a slogan. That’s now a measured result.

Discovery 5: A one-year-old still does something AI can’t

I’ve watched this discovery happen in my own family.

My grandson Sonny turned one.

He can’t read. He can’t code. He has never seen a dataset.

But watch him for ten minutes.

He grabs. He drops. He tastes. He falls. He tries again.

Every mistake teaches him something no machine has ever learned.

Developmental scientists call it embodied learning — intelligence built from touching the world, not reading about it. Decades of research shows babies discover both the problems and the solutions through pure exploration.

AI learns from data.

Humans learn from living.

The most advanced learning system on Earth just turned one, and he did it without a single line of code.

That gap — lived experience — is still ours.

Discovery 6: Governments are now regulating AI relationships

Read that again. Not AI technology. AI relationships.

In January, California’s SB 243 became law — the first in America to regulate companion chatbots. New York passed its own version. More states are lining up.

The questions lawmakers are asking sounded like science fiction three years ago.

Can people become emotionally dependent on AI?

Should AI companions have limits?

How do we protect the vulnerable?

The AI debate has left the engineering department.

It’s now a conversation about psychology, ethics and what it means to be human.

The future won’t only be engineered.

It will be governed.

Discovery 7: The longer we use AI, the more we sound like it

The Max Planck Institute analysed 740,000 hours of human speech — hundreds of thousands of YouTube talks and podcast episodes.

They found a measurable spike in “GPT words” after ChatGPT launched.

Delve. Realm. Meticulous. Underscore.

Words the machine favours are now coming out of human mouths.

Not in our writing. In our speech.

The researchers called it a cultural feedback loop. We trained the machines on our words. Now the machines are training us on theirs.

Every conversation shapes both sides.

Your voice is either becoming more distinct right now.

Or more average.

There is no standing still.

Discovery 8: Humans still hold the trust advantage. For now.

Ipsos surveyed more than 23,000 people across 30 countries.

The pattern was consistent. Across healthcare, education, news, money and work, people trusted humans over AI.

Capability is advancing at machine speed.

Trust is advancing at human speed.

That gap is the opportunity of the decade.

The race everyone joined is building smarter AI.

The race almost nobody joined is becoming a more trusted human.

Guess which one has less competition.

Discovery 9: The identity dividend is real

Put the first eight discoveries together and a pattern appears.

As AI makes knowledge abundant, something else becomes scarce.

Knowing who you are.

People with a clear sense of identity adapt faster. They make better decisions. They build stronger relationships. They don’t outsource their judgement to whatever the machine suggests.

AI can help you think.

It cannot decide what matters to you.

The ancient Greeks carved “Know Thyself” above the temple at Delphi.

Twenty-five centuries later, it just became a business strategy.

Identity compounds. Like interest.

Self-awareness may be the highest-return investment of the AI era.

Discovery 10: The ultimate advantage isn’t intelligence. It’s clarity.

AI changes faster than people do.

Every new model raises the bar for what machines can do.

But here’s what the research keeps circling back to.

Resilience doesn’t come from keeping up with the technology.

It comes from knowing who you are while everything else keeps changing.

Skills will evolve. Tools will change. Algorithms will improve.

Identity is what gives you direction through all of it.

In the AI era, your greatest competitive advantage may not be intelligence.

It may be clarity.

The verdict

Ten discoveries. One story.

The machines got persuasive. So trust got scarce.

The machines got personal. So attribution started to matter.

The machines got fluent. So our language started to blur.

Every single study points the same direction.

As AI makes intelligence abundant, the value flows to what it cannot copy.

Your lived experience. Your earned trust. Your clear identity.

I broke free of a system once that told me who to be.

It took me 27 years to walk out the door.

I’m not watching another system quietly do the same thing — this time with better technology and a friendlier interface.

So here’s the challenge.

Stop asking how smart AI is becoming.

Start asking who you are becoming while you use it.

The machines are being measured every day.

Make sure you’re the one measuring you.

Sources

• AI Systems Out-Persuade Expert Humans — Hackenburg et al., Oxford (2026)
• AI chatbots and digital companions are reshaping emotional connection — APA Monitor (2026)
• AI outperforms humans in establishing closeness — but only when labelled as human — Communications Psychology / Nature (2026)
• The Development of Embodied Cognition: Six Lessons from Babies — Smith & Gasser
• California SB 243: First-in-the-Nation AI Chatbot Safeguards
• ChatGPT Is Changing the Words We Use — Scientific American  |  Original paper
• Ipsos AI Monitor 2025 — 30-country survey

The post The Research Is In: AI Is Changing Humans Faster Than We Realize appeared first on jeffbullas.com.

At 17, I packed a bag, left my family home, and rode a train for 24 hours to the other side of the country.

I was going to college. I was terrified and excited in the same sentence.

That was the first door I ever walked through. It would not be the last.

I have spent my whole life leaving rooms that were too small. And I have learned something that took me fifty years to put into words.

The room you were given always felt safe. It’s known. 

The door you choose always feels frightening.Because it is unknown. 

But the world on the other side is always bigger.

Right now, AI has just thrown open a door for everybody at once.

Almost nobody is talking about what’s waiting on the other side.

AI hands you four gifts

Start with what’s true. AI is not a con. The gifts are real, and they are measurable. But it is the door to a great unknown.

1: The first gift is expertise you never earned.

Researchers at Stanford and MIT studied 5,179 customer support workers using an AI assistant in their actual jobs. Productivity rose 14% on average. But look closer: the least skilled workers improved by 34%. The most experienced barely improved at all. (Brynjolfsson, Li & Raymond, NBER)

Read that again. AI lifts the beginner. It hardly moves the master.

2: The second gift is the death of friction.

In a controlled trial published in Science, 453 professionals were given real writing tasks. Half got ChatGPT. Their time dropped by 40% and the quality of their work rose 18%. Faster and better, at the same time. (Noy & Zhang, Science)

The distance between having an idea and finishing the thing has almost closed.

3: The third gift is the collapse of complexity.

Work that once needed a team, a budget and ten years of practice now needs an afternoon and a good question.

The hard thing became an easy thing.

4: The fourth gift is the one almost nobody names.

AI hands you the freedom to start your own adventure.

Think about what used to stop people. No money. No skills. No team. No time. No idea where to begin. AI just took a hammer to that entire list.

What’s left is not a skill problem.

It’s a permission problem.

Everything standing in your way is behind your own eyes

That is the sentence I would tattoo on the inside of every cubicle in the world. The gate has two steps. Give yourself permission. Then act.

And action is better than inaction. Because a good idea not acted on dies in the darkness.

That’s it. That’s the whole gate. And it is the one gate a machine cannot open for you.

Now here’s the catch nobody mentions

Everyone got the same four gifts. On the same day. For the same price. In 2022.

So the door swung open and then the room filled up.

In January 2020, about 2% of new web articles were written by machines. By late 2024 it crossed half. It sits at roughly half today. (Graphite, analysis of 65,000 URLs from Common Crawl)

Easier to start means more people start. That is not a bug. That is arithmetic.

The villain is not the machine

Let me name the real villain in this story, because it is not AI. For years, the thing protecting your work was not your talent.

It was difficulty.
And when I started my simple blog in 2009 what I was told was that it was simple. But for me it was a technical nightmare. 

Difficulty was the moat. Difficulty kept the crowd out. Difficulty was doing you a favour every single day, and you never once thanked it.

Now….

AI has drained that moat. For you and for the four million people standing behind you.

That’s the trade nobody put in the brochure. You got the tools. You also got the traffic.

What it costs to walk through a door

So let me go back to the trains and the rooms, because I have paid this price three times.

At 17, I left home.

At 27, I walked out of the religion I was raised in. That door cost me more than the first one. It is a hard thing to look at people you love and tell them the world is bigger than the one they handed you.

I was afraid of rejection. To be ostracised. But that fear was not realized as I was still loved and accepted.  

But I could not stay in a room that small.

At 52, I walked out of my job and decided to become my own boss. I started writing on the internet, got up at 4:30 in the morning for five years, and had no idea if any of it would work.

The price was real. Fear. Anxiety. Years of time and money with no promise at the end of it.

That’s the part nobody puts on the poster when they tell you to follow your dream.

But here is what the price bought.

Freedom to grow. Freedom to fall over and learn from the falling.

And a strange gift I never saw coming: a drive so strong I never needed discipline to get out of bed. I just wanted to.

That motivation didn’t come from a book, a course, or a productivity system. It came from walking through the door.

AI cannot hand you that. It can hand you the skill, the speed and the finished product. It cannot hand you the reason you got up this morning.

What AI collapsed and what it could not

Here is the crack in the story that the AI cheerleaders skip.

On simple, routine work, AI closes the gap between the beginner and the expert. That’s what the studies show, and it’s wonderful.

But on the hard calls and the ones that need taste, context and judgment, the gap opens straight back up. AI is an amplifier there, not an equalizer. It magnifies the person who already knows what they’re doing.

AI collapsed the doing. It could not collapse the deciding.

And the market is already voting.

Roughly half the new articles on the web are machine-made. 

Yet 86% of the articles ranking in Google Search were written by humans, and 82% of the articles ChatGPT and Perplexity choose to cite were written by humans. (Graphite, AI Content in Search & LLMs)

The machines make the noise. Humans are still the signal.

So what do you actually build?

If producing is no longer the prize, then what is?

The answer is the thing that cannot be copied: a public record of judgment that people learn to trust.

There are only three kinds of authority in this world, and one question separates them. Who holds the leash?

And borrowed authority is cracking on its own. Around the world, 69% of people now worry that government officials, business leaders and journalists are deliberately lying to them. Six in ten hold a grievance that institutions serve narrow interests. (Edelman Trust Barometer 2025)

When a title stops meaning trust, trust has to be earned person to person instead.

Owned authority is made of five things. None of them are tricks.

And here is the loop you run on a Tuesday morning.

That last step is the one almost nobody does. Which is exactly why it is the cheapest authority on earth, sitting there unclaimed.

The door, not the room

I never wrote to sound certain. I wrote to understand.

And readers could feel the difference. They were watching someone think, not watching someone perform.

That turned out to be the whole secret. Not the answers. The thinking.

A machine can hand you an answer in a second. It cannot hand you a lifetime of earned judgment. That has to be walked, one cold morning at a time.

So here is where we are.

The tools are free. The barrier is gone. The room is packed.

And the only thing left that nobody can copy, fake or revoke is the trail of a person who kept learning, kept risking, and kept telling the truth in public until the trust was beyond argument.

AI opened the door for everyone.

Walking through it is still on you.

What room are you still standing in?

Sources

• Brynjolfsson, Li & Raymond, “Generative AI at Work” (NBER / Quarterly Journal of Economics) — 5,179 support agents; +14% average, +34% for the least skilled.  https://www.nber.org/papers/w31161
• Noy & Zhang, “Experimental evidence on the productivity effects of generative AI” (Science, 2023) — 453 professionals; 40% faster, 18% better quality.  https://www.science.org/doi/10.1126/science.adh2586
• Graphite, “AI Now Writes as Many Online Articles as Humans Do” — 65,000 URLs from Common Crawl; AI share of new articles.  https://graphite.io/five-percent/ai-now-writes-as-many-online-articles-as-humans-do
• Graphite, “AI Content in Search & LLMs” — 86% of Google top results and 82% of ChatGPT/Perplexity citations are human-written.  https://graphite.io/five-percent/ai-content-in-search-and-llms
• Edelman Trust Barometer 2025 — 33,000+ respondents across 28 countries; trust in institutional leaders.  https://www.edelman.com/trust/2025/trust-barometer

A note on the numbers: AI-detection is not exact, so the honest framing is “about half” and “roughly four in five” rather than decimal precision.

The post AI Removed Every Excuse. What’s Left Is Permission. appeared first on jeffbullas.com.

Not because anyone told me to. Because writing was the one thing that felt like forward motion, in a year when almost nothing else did.

Five years of that habit, and jeffbullas.com had reached 100,000 monthly readers. It eventually reached more than 33 million, across 190 countries. I built every bit of that on one skill. Content marketing.

Seventeen years later, I watched that skill quietly stop working the way it used to.

Not because I forgot how to write. Because the machinery underneath every platform I publish on changed what it rewards and almost nobody noticed the exact moment it happened. This is the story of that moment, what it cost, and what actually survives it.

The Quiet Coup

It started with an app most marketers dismissed as a place for teenagers to dance.

In 2016, TikTok made a decision that would eventually end content marketing as the industry understood it. It stopped ranking video by who you followed. It started ranking by what you watched, and for how long.

TikTok has said plainly that follower count is not a ranking factor. A brand-new account with zero followers can reach millions in a day, purely on how a video performs with strangers. Sprout Social’s 2026 breakdown of the algorithm confirms this has only hardened as the platform matured.

Everyone laughed at first. Then everyone copied it.

Facebook and Instagram followed. By 2026, more than half of the average Facebook feed comes from accounts a person has never followed, and AI now decides over 80% of what appears in front of users across social platforms, according to a 2026 cross-platform algorithm statistics review.

Then, on March 12, 2026, LinkedIn joined them. A new AI model called 360Brew,  150 billion parameters, built to read a post the way a human editor would, not the way a keyword-matcher does quietly replaced the patchwork of ranking systems LinkedIn had run for over a decade.

Even search followed the same road. Google’s AI Overviews and answer engines like ChatGPT now decide which brands get cited before a single click happens. Conductor’s 2026 benchmark study of 3.3 billion sessions found AI referral traffic sits at just 1.08% of web traffic,  small in volume, outsized in influence, because it decides who gets seen before the click ever happens.

Four platforms. One underlying shift. Marketers built a twenty-year career on one game. The rules changed underneath us, one platform at a time and most of us are still playing by the old ones.

The Numbers Don’t Lie

Here’s what that shift did to the average LinkedIn account in twelve months, according to Richard van der Blom’s Algorithm Insights Report, built from well over a million posts.

The instinct is to blame the writing. I’ve felt that instinct myself, more than once this year.

But the honest read is simpler, and harder to accept. Distribution stopped following the network. It started following declared and demonstrated topic authority, what 360Brew infers you’re actually about, from your headline, your history, and the pattern of what you consistently publish.

That inference is not gentle. One 2026 analysis found that creators who stayed on a small, consistent set of topics saw their share of platform-wide reach roughly double since 2022, climbing from 15% to 31%. Creators who scattered across everything watched their share collapse from 57% to 28%.

Follower count and reach are now structurally decoupled. An account with 8,000 focused followers can now out-distribute one with 80,000 unfocused ones.

That’s not a LinkedIn quirk. That’s the interest graph doing on a professional network exactly what it already does on TikTok, on Instagram, and inside every AI answer engine.

Leonardo Would Be Throttled

So the data says one thing, loud and clear. Niche down. Pick a lane. Become the topic.

Here’s who that rule would have buried.

Leonardo da Vinci.

He painted the Mona Lisa. He designed flying machines. He dissected human bodies to understand a smile.

One mind. A dozen obsessions. All feeding each other.

Post the flying machine today, right after the portrait, and the machine flags a mismatch. Reach cut. Signal lost. Interesting becomes invisible.

It wants him in one lane. It wants all of us in one lane. Because a lane is a bubble, and a bubble is easy to keep you inside.

I joined social media in 2008. Facebook first. I went looking for people humans around the world I was curious about, fascinated by. Not categories.

I followed people because they were interesting.

And that word matters more than the machine will ever understand.

An interest is a category. Interesting is a quality of a person.

A machine can index a category. It cannot index a human soul.

Yes, the shift killed some noise. Engagement bait is dying. A brilliant unknown can now beat a hollow celebrity. That’s real, and it’s good.

But the cost is bigger than anyone’s naming.

The old algorithm decided what you saw. This one decides who you’re allowed to be if you want to stay visible instead of buried.

It takes a curious human and files them under a niche.

Walt Whitman warned us about exactly this, more than 160 years ago. “I am large, I contain multitudes.” One consciousness. A dozen selves. A walking paradox.

The machine has no column for that.

Content Marketing vs the Interest Graph

For twenty years, content marketing ran on one quiet assumption. Build an audience. Publish consistently. The audience sees what you publish, because they chose to follow you.

That assumption is the thing that broke.

Content marketing was built for followers you already have. The interest graph is built for strangers who share an interest, matched by an AI system reading what you’re actually about, not who’s already listening.

The metric that mattered used to be follower count. The metric that matters now is non-follower reach, dwell time, and saves, because those are the only signals proving a stranger actually stayed.

This isn’t content marketing dying. It’s content marketing losing the assumption it was quietly built on.

What Actually Wins, Once You’re Found

Here’s the part most of the 2026 algorithm coverage gets half right.

Getting matched to the right stranger is necessary. It is not sufficient.

Once the algorithm hands your post to someone who’s never heard of you, something else decides whether they stay for three seconds or sixty. And that something is not the topic.

It’s the thing a machine cannot produce. The story only you carry. The opinion that costs you something. The sentence a language model would never risk, because a language model is built to predict the average — not to take a position.

I call this the human signal. It isn’t a writing style. It’s evidence that you were actually there, for the mistake, for the years nobody was watching, for the win that cost more than it looked like from outside.

The interest graph is the door. Human signal is the reason anyone stays in the room.

The Interest Architecture

So if the old playbook of publish more, post consistently, chase virality no longer works, what replaces it?

Not a hack. A structure. Five layers, underneath every topic worth owning.

A sharp observation the reader hasn’t heard stated so plainly. A repeatable framework they can carry away and reuse. The emotional tension underneath the topic, named precisely enough that they feel seen before you’ve offered a single tactic. A proof layer, of research, story, data, or your own lived experience. And a platform expression: the same territory, translated into a different accent for each place it’s read.

Skip a layer, and what you’ve made reads as content, competent, forgettable, replaceable by the next prompt. Build all five, and it becomes a territory. Something a stranger, an algorithm, and an AI answer engine can all independently arrive at the same conclusion about: that’s who talks about this.

The Territories Worth Owning

I’ve spent this year mapping five territories I believe the market already cares about deeply enough to reward for me, and for anyone building a body of work in the AI era.

Human signal in the AI age, for the person asking how to stay trusted when everyone’s tools are identical. Reinvention without an expiry date, for the professional or founder asking what they become next. Meaningful ambition, for a generation quietly done with a ladder that no longer leads anywhere certain.

Founder as trust broker, for the builder asking how authority survives when everyone has the same AI. And content marketing after AI abundance the question underneath everything in this article.

Not more content. A market that already cares, owned clearly enough that a human pauses, an algorithm notices, and an AI system remembers.

The Verdict

Here’s where I’ve landed, seventeen years into a career the interest graph just quietly rewrote.

Content marketing isn’t dead. The assumption underneath it is.

Publishing more was never the moat. It just used to work well enough that we mistook it for one.

The moat was always the human doing the publishing. And for the first time since I sat down at 4:30am in 2009, the machine agrees.

So here’s the paradox I’m choosing to live inside.

Know what you’re already, unmistakably known for. Give the machine a pattern clear enough to find you. That’s not surrender. That’s the door.

Then cross the lanes anyway.

Because focus should be a choice. Not a sentence. Curiosity is not the enemy of depth — curiosity is where depth comes from. Leonardo didn’t dissect bodies to abandon painting. He did it to paint a better smile.

The bird and the flying machine. The crossing is where I come alive.

No algorithm gets to file that away.

So before you write your next post, don’t ask what to publish.

Ask this instead.

Do you follow people for their interests? Or because they’re interesting?

Or both?

Sources

1. TikTok algorithm & follower-count ranking
2. Cross-platform algorithm statistics — Facebook feed composition, AI-driven recommendations
3. 360Brew explained: LinkedIn’s 2026 AI ranking model
4. Conductor 2026 AEO/GEO Benchmarks Report: 1.08% AI referral traffic
5. Richard van der Blom: LinkedIn Algorithm Insights Report
6. Topic authority & reach concentration data (15%→31%, 57%→28%)
7. Relationship Graph to Interest Graph, follower/reach decouplingnterest

The post Leonardo da Vinci Would Be Invisible in the Age of Algorithms appeared first on jeffbullas.com.

Nobody planned this. No one sat down and decided the global economy should balance on a two-mile ribbon of sea. It just happened, slowly, barrel by barrel, until the world had funneled itself through its narrowest point.

And right now, as I write this, that gap keeps closing and they want to charge a toll.

The strait sits in the middle of a conflict. Ships have turned back, Brent crude has topped $106, and the agency that guards the world’s oil just made the largest emergency release in its history. One narrow passage and the cost of moving through the world jumps on every continent.

It Has Happened Before

We have seen this before, in a different narrow place. In March 2021, a single ship, the Ever Given, longer than the Eiffel Tower is tall, caught a gust of wind, turned sideways in the Suez Canal, and stuck. For six days it sat there, wedged bank to bank, with a queue of more than 400 ships building behind it.

Around 12% of all world trade runs through that canal. The blockage held up roughly $9 billion of goods a day, and the delays rippled for months, empty shelves, stalled factories, freight rates spiking.

One ship. One narrow canal. Nine billion dollars a day.

Here is what those two places teach you and it is older than the internet, older than the engine, older than money. Whoever controls the narrow passage controls the flow. And whoever controls the flow sets the toll.

Your Work Has Chokepoints Too

Now look at your own work. 

Your ideas, your voice, the stories only you can tell. It has to travel too, from you to the people who need it. And somewhere in the last twenty years, that journey grew narrow passages of its own. You just can’t see them, because they aren’t made of water. They’re made of code.

They are also given the name of algorithms.

Once, the line was short: you published, a reader found you, and that was the whole trip. Now your work threads a gauntlet, a platform, then an algorithm, then a recommendation engine, then an AI summary that answers the question before anyone reaches you.

Five narrow gates where there used to be open sea.

And at every one, the same ancient rule holds: whoever controls the passage sets the toll.

The 7 Human Signal Choke Points

Let me walk you through the gates, one by one, so you can see them the next time you hit publish.

1.  The Enclosure — you build on rented land

The first move was the quietest. You were invited to build your home on someone else’s property, a page here, a profile there. It felt like freedom. It was tenancy.

The platform owns the bond with your audience, not you. Your followers are theirs to show or to hide. You water the garden; they own the fence. And when the rent goes up, as it always does and you hold no deed. You hold a login.

The way through: own something they cannot evict you from. An email list. A direct line. A name people seek out by typing it, not by tripping over it.

2.  The Algorithm — reach gets rationed

For a while, if your fans followed you, your fans saw you. Then the machine stepped in. In 2012, a Facebook Page reached about 16 in every 100 followers; by 2026, that number sits near 1 and for many, below it.

You didn’t get quieter. They turned your signal down.

The algorithm does not reward what is true, or useful, or kind. It rewards what holds the eye. Outrage travels first class. Nuance dies in the queue.

The way through: stop renting reach by the post. Build the kind of trust that makes people come looking for you by name.

3.  The Recommendation Engine — discovery gets engineered

This is the gate most people never notice. It used to be that you chose what to read; now the feed chooses for you. The “For You” page is the most honest name in tech — because it decides, on your behalf, what “you” is.

A handful of unseen rules now sit between every creator and every reader — not tuned to what you went looking for, but to what keeps you here.

The way through: become a destination, not a suggestion. Be the thing people hunt for, not the thing they get served.

4.  The Snippet — the answer moves onto the page

Around 2014, Google began answering questions on the results page itself. A small box at the top, your words lifted out, the reader’s question solved before they ever reached you. It looked harmless, a convenience. It was the first crack in the deal.

The click and the thing that fed every creator, began to thin.

The way through: write the thing a snippet cannot hold. A story. A stand. An argument that has to be read whole to be felt.

5.  The Answer Engine — the summary eats the source

This is the gate that changed everything. When an AI summary sits at the top of search, only about 8 readers in 100 click through to a real page against 15 when there’s no summary at all. And the sources the AI quotes? Barely 1 in 100 get a click.

Sit with that.

Your work is good enough to be quoted by the machine and invisible enough that no one comes to meet you for it. One travel blog watched its traffic fall 90% and simply closed its doors.

The way through: where SEO said be findable, and GEO said be cited, the human signal says something else “be someone worth meeting and mentioning”

6.  The Training Machine — your work becomes the fuel

Here is the cruelest turn in the whole Trap. The same work that built your name is now feeding the model that competes with you and scraped, absorbed, repackaged, and sold back to your reader without your fingerprints on it.

You spent years finding your voice. The machine learned a flat copy of it in an afternoon.

The way through: give it the one thing it cannot take, the living, changing, unrepeatable you. The story it cannot scrape, because you haven’t told it yet.

7.  The Flood — the signal drowns in slop

And then the dam broke. More than half of new articles on the web are now written by machines that are cheap, endless, confident, and nearly all of it sounds exactly the same.

This is the final gate. Not censorship. Saturation.

You are not being silenced. You are being buried under an avalanche of beige.

The Slow Close

Here is the part that should unsettle you: none of this was sudden. There was no “Given Moment” for the web and *no single ship turned sideways, no headline the morning it happened. It closed slowly. Quietly. Over thirty years.

Read this chart from the top. 

In the beginning the web was wild and open and you published, and the world could find you, no gate. Then, one layer at a time, the passage narrowed: search, the feed, the algorithm, the snippet, the summary.

And every new layer was added for the same reason. Not to carry your work further — but to keep it inside walls that could be sold. The summaries now send back barely 1% of the traffic they take.

That is the quiet engine under all of it. The narrower the passage, the more valuable the toll.

Your attention is the oil. Your audience is the cargo. And the gate charges by the barrel.

This Is Where We Are

The timeline ends here. So this is where we are — and these are the gatekeepers, today, in 2026.

Google answers the question itself now and your work feeds the answer, and the reader never arrives. Facebook rations your reach; it has fallen from 16% of your followers to barely 1%, and the rest is for sale. LinkedIn treats your followers as its asset, not yours. X buries your outbound links to keep you inside.

YouTube hands your audience to a machine that decides who they are. TikTok gives you no real followers at all — every post starts at zero. And Instagram won’t even let you point the way out: “link in bio” is a dead end dressed as a door.

Seven gates. One toll.

And the sharp tool that does the choking? The algorithm. It isn’t a company. It’s architecture is a system built to capture, rank, and ration human attention.

The Verdict

So here is the verdict and the move.

The straits of the world have no way around them but there is no second Hormuz, and when it closes, the world simply waits. But the chokepoints of the web have a flaw the oceans never had.

They can throttle the flow. They cannot make what flows through it.

A machine can summarize information, but it cannot summarize a soul. It can copy your style, but not your stand. It can rank your words, but not the reason you wrote them at 4:30 in the morning when nobody was watching.

The gate controls distribution. It does not control you.

So here’s the key move: stop renting reach, and start owning the relationship. There are only two roads with no gatekeeper standing on them.

1.  Your email list — the direct line no algorithm can sit between.

2.  Your community — where loyalty lives, and no one can switch it off.

Use the platforms to be found, never to be stored. Send every borrowed visitor toward one of those two roads. Create what a machine can’t compress: a story, a stand, a point of view.

The work is human. The distribution is not.

The oil had nowhere else to go. You do.

Win the road, and you take your reader with you.

Which gate is squeezing you hardest right now? And what makes you most angry?

Coming in Part Two — The Escape. The creators who already slipped the gate, the map of where to build, and the playbook for turning your human signal into a business no passage can close.

Sources

• Strait of Hormuz oil transit — ~20M barrels/day, ~1/5 of world oil (EIA)
• Hormuz 2026 crisis — Brent past $106, record IEA reserve release (WEF)
• 2021 Suez Canal blockage — six days, ~12% of world trade (overview)
• Suez blockage held up ~$9B of trade per day (Port Economics)
• Facebook organic reach decline, 16% to ~1%
• AI Overviews and click-through rates (Search Engine Journal)
• AI summaries, source clicks, and publisher shutdowns (Pew, via NPR)
• Over half of new web articles are AI-generated (eWeek / Graphite)
• AI platforms send ~1% of publisher traffic (AdExchanger)

The post The Chokepoint Economy: Seven Gates Between You and Your Audience appeared first on jeffbullas.com.

But there was a city I wanted instead. I visited it as a child. A big fun park sat beside the harbour that had been torn down and reconstructed one fun ride at a time. Ferries carried you and teeming tourists to beaches and bays. The sand was white and squeaked when you walked. It even sounded clean. 

The weather was warm like a hug and winters never really arrived. And it had an Opera house and a big bridge with an arch that had the nickname “the coat hangar” the whole world knows on sight.

It had an X-factor. A city with charisma.

I arranged a transfer. I never left.

One city did everything right but the other just had the thing you cannot put on a checklist.

That thing has a name. We call it charisma. And online, it has quietly become one of the most valuable assets a human can own.

Most people think charisma is a gift. They are wrong.

You are either born with it or you are not. That is the story we are told.

Dale Carnegie spent his life proving the opposite. Charisma is not magic. It is a skill. And like any skill, it can be trained.

And it is not what you think. Charisma is not being the loudest voice in the room. The loudest room is often the emptiest. It is not slick, and it is not a performance you switch on for the camera. It is the quiet, steady signal that a real person is on the other side, worth paying attention to.

Modern research agrees. The work of Olivia Fox Cabane breaks charisma into three forces you can learn and they are presence, power, and warmth. Carnegie’s famous rules still hold: smile, use someone’s name, listen to understand, read the room.

But here is the catch nobody says out loud.

Carnegie was teaching a room skill.

When the body disappears

Smile. Eye contact. Remember names. Mirror the body. All of it assumes you are standing in front of someone, where a first impression forms in a tenth of a second, and most of the signal lives in the body.

Online, the room is gone.

There is no eye to meet. You cannot remember half a million names. The body language is on mute. So half of Carnegie’s rules quietly break the moment you step through the glass.

By rights, online charisma should be impossible. And yet some people reach through a cold pane of glass, a small screen or a big monitor and make millions of strangers feel something. They are not breaking the rules of charisma. They are using a different set.

Which raises the real question. When the body disappears, what carries the charisma?

The three forces do not die. They move.

Presence stops being a moment and becomes a rhythm. You show up, again and again, until a stranger feels they know you. That is the quiet engine behind every creator who built a following.

Power stops being posture and becomes a point of view. You say the thing. You take the position. You risk being wrong in public.

Warmth stops being a smile and becomes self-disclosure. The story. The scar. One human talking to one human.

Without a body in a room and a presence across a desk we need to re-calibrate how we can feel like a friend even if we are seperated by a screen and an ocean. .

Why the X-factor now prints money

For most of history, trust lived in institutions. The brand. The bank. The broadcaster. Not anymore. 

Trust has walked out of the building and gathered around individuals. 

People now trust a person who feels like them more than they trust the logo. Edelman finds roughly half of people trust influencers and most of those would extend that trust to a brand they distrust, if the right person vouched for it.

While trust moved, the money followed. The creator economy is on track to roughly double to $480 billion by 2027. Fifty million people now call themselves creators.

This is the quiet truth of the decade.

Attention is cheap. Trust is rare. And charisma is just trust you can feel.

But the middle is thin. Only about four in every hundred creators earn a full living from this. The other ninety-six blur into each other. What separates the few from the many is rarely talent, and rarely luck. It is a signal strong enough to remember.

Think about what that trust is worth. A single word from a person you believe can move you to buy from a company you would never have chosen on your own. That is not marketing. It is the oldest force in commerce and a recommendation from someone you trust wired into a screen and scaled to millions.

When I discovered social media I saw that it could reach around the world. Everyone now had a voice if they were willing to use it. 

The sameness machine

But here is what is trying to kill your X-factor. Not a person. A system.

The algorithm rewards what it can predict. Templates. Best practices. The safe and the polished. Now AI floods the same pipes with content that is competent and identical to a billion posts that read like they were written by the same tired committee.

And charisma is the opposite of identical. It is difference made magnetic. The X-factor is the one thing a template cannot hold.

There is even proof. When researchers study virtual influencers and the flawless AI-generated faces, audiences rate them lower on authenticity and emotional connection, no matter how perfect they look.

The machine can copy the format. It cannot copy the human signal.

The trap that catches good people

Here is the seduction. The machine does not force you to be generic. It tempts you.

It pays you in small hits of reach every time you copy what already worked. Follow the trend. Use the format. Sand off the edges. Each surrender feels smart. None of them feels like a loss.

Until one day your feed is full of you and none of it sounds like you. You have been sucked into the machine and the human web starts to feel like it has been invaded and is now inhabited by an army of robots.  

That is the tension every creator lives inside. Use the system, or become it. The line keeps moving, and you have to keep finding it. The test is simple: if your best post this month could carry a stranger’s name and nobody would notice, the machine has already won.

Three humans who turned charisma into cash

Theory is cheap. Look at people doing it.

Emma Chamberlain is warmth

She built her audience by refusing to be polished. Jump cuts. Bad lighting on purpose. Talking about her anxiety and her boredom like she was your friend on the couch.

Then she turned that warmth into a business. Chamberlain Coffee reportedly sold a million dollars of product in its first thirty minutes. Luxury houses came calling; she has been a Cartier ambassador since 2022. She did not sell coffee. She sold the trust, and attached coffee to it.

Remember who she was. A bored teenager who dropped out of school. Now she runs a coffee company stocked in thousands of stores and hosts the Met Gala carpet for Vogue. Same girl. Same voice. She just refused to trade it for a polished one.

Alex Hormozi is power 

Most “gurus” hide their best material behind a paywall. Hormozi does the opposite. He gives the entire playbook away, the pricing, the scripts, the frameworks for free, everywhere. He takes a posture and position. 

The free value builds trust at scale. The trust feeds Acquisition.com, where the real money is made taking equity in the businesses that come to him. The thing he sells is not a course. It is belief, converted into ownership. (He went from near-bankruptcy at 26 to nine figures — reinvention is the whole story.)

The lesson is uncomfortable. The more you give away, the more you are trusted with.

Ali Abdaal is presence

A Cambridge doctor who started filming study tips at night. He did not quit medicine to chase fame. He just showed up, week after week, calm and honest, building in public. Reinvention by rhythm.

Today the courses he owns and not the ads he rents, are his biggest income line, and his book became a bestseller. Ad revenue is the smallest slice of his pie.

His early videos were ugly screen recordings made between hospital shifts. The value was huge. Production never beats a point of view.

Three different humans. Three different signals. One pattern. None of them chased the algorithm,  they built a signal the algorithm was forced to carry. They were irreducibly themselves, and then they built something to own.

The tactical part: your charisma-to-cash ladder

So how do you do this on purpose? Climb in order. Skip a rung and you fall.

1:  Find your signal. The part of you a machine cannot fake. Your story. Your scar. Your strange mix of obsessions. Ask one brutal question: could AI have written this? If yes, it is noise. Two thousand years ago the advice carved over the temple was the same — know thyself. The only thing that changed is that now it pays.

2: Build an audience with rhythm. Presence online is consistency. Pick one place. Show up every week for two years. Boring-but-present beats brilliant-but-absent. The rhythm is the point — a stranger cannot trust a person they meet only once.

3: Earn trust by giving value away. Be the most generous voice in your corner. Give the answer, not the tease. Hormozi hands over the whole playbook. Generosity is the fastest trust-builder there is. Give until it feels reckless and the fear that you are giving away too much is the exact feeling that builds a moat.

4 Build an offer you own. This is where most creators stop too early. Brand deals are renting your audience to someone else, the typical creator leans on them for nearly 70% of their income. Owners build the thing themselves: a product, a course, a service, a community, equity. Rent pays this month. Ownership pays for years.

5: Convert. Income is just trust, cashed in. When the signal is real and the offer fits the people who trust you, money is the natural result, not the goal you chased. Do not lead with the ask. Earn the right to make it.

That is the whole machine. Signal becomes audience. Audience becomes trust. Trust becomes an offer. The offer becomes income.

Verdict

Charisma was never a gift handed to the lucky few. It is a skill. And in a world drowning in sameness, it has become the rarest skill of all because it is the one the machine cannot manufacture.

So stop trying to be findable. Stop writing for an algorithm that will replace you the moment it can. Be so unmistakably yourself that you become the category. 

Be so interesting and unique with an opinion or a compelling story that people wait all week for your newsletter. 

Sources

1. The components of charisma (Cabane: presence, power, warmth) — https://richard-reid.com/the-components-of-charisma/
2. The science of charisma (first impressions, non-verbal signal) — https://richard-reid.com/the-science-of-charisma-insights-and-findings/
3. Parasocial trust and the creator economy — https://marlincommunications.com/blog/understanding-parasocial-relationships-in-modern-marketing/
4. 2026 Edelman Trust Barometer (trust in influencers) — https://www.edelman.com/trust/2026/trust-barometer
5. Goldman Sachs: creator economy to reach $480B by 2027 — https://www.goldmansachs.com/insights/articles/the-creator-economy-could-approach-half-a-trillion-dollars-by-2027
6. Virtual vs human influencers: authenticity and connection — https://www.tandfonline.com/doi/full/10.1080/15213269.2025.2558029
7. Chamberlain Coffee case study — https://medium.com/@itseffyphillips/a-case-study-on-chamberlain-coffee-an-influencer-brand-done-mostly-right-6627a9162b70
8. Emma Chamberlain named Cartier ambassador (WWD) — https://wwd.com/fashion-news/fashion-scoops/cartier-taps-emma-chamberlain-brand-ambassador-details-1235172299/
9. Alex Hormozi’s give-it-away content model — https://siliconvalleytime.com/entrepreneur/alex-hormozis-content-system-that-scaled-acquisition-com-to-9-figures/
10. Ali Abdaal revenue breakdown — https://marketmakermgmt.com/blog-list2/ali-abdaal-youtube-success-story

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