Then I discovered the studio behind the name.

Rodin was deeply involved in the conception and modelling of his sculpture, but the physical realization of major works often depended on a network of assistants and specialist craftspeople. The Musée Rodin explains that he employed skilled practitioners to enlarge and reduce models, marble carvers to translate plaster into stone, foundries to cast bronze, and assistants such as Camille Claudel who worked on difficult details. The hand that struck every blow of the chisel was not always Rodin’s. Yet we still say: “a Rodin.”

That discovery stayed with me because it disturbed a simple idea of authorship I had never examined.

Then, a few days ago, I read a New York Times profile titled “The Incredible Art of Being Jeff Koons”. The same question returned, but in a form that feels remarkably relevant to the age of AI.

At one point Koons had more than 100 people working in his studio, including teams hand-copying old masters. Today the operation is smaller, but the system remains exacting: digital models, scans, standardized lighting, paint-by-numbers codes, external foundries and specialist fabricators. Koons told the Times that his systems are intended to ensure that every gesture, colour and shape is the way he wants it.

Who, then, made the art?

The easy answer is: his team. The more interesting answer is: Koons authored it.

That distinction may help us understand one of the most uncomfortable questions facing writers now: if an AI helps research, structure, edit and even produce sentences, who is the author?

The false choice: purity or abdication

The argument around AI and writing has hardened into two camps.

At one extreme is abstinence. A “real” writer should write every word. AI may be allowed to fix spelling or perhaps find a source, but the moment it generates prose the work becomes suspect.

At the other extreme is abdication. Give the machine a topic, ask it to research, outline, draft and polish 1,500 words, glance over the result, then publish it under your name.

The first confuses authorship with keystrokes. The second confuses supervision with authorship.

The scale of the temptation is easy to understand. In a randomized experiment published in Science, Shakked Noy and Whitney Zhang gave 453 college-educated professionals realistic writing tasks. Access to ChatGPT reduced average completion time by 40 percent and increased assessed quality by 18 percent. That is not a marginal productivity improvement. It is a reason every writer, marketer, consultant and knowledge worker is being forced to rethink how work gets made.

But speed is not the same as authorship.

From maker to architect

Humans have been moving up the abstraction stack for centuries.

We moved from muscle to machines; from doing every task to operating the machine; from operating the machine to designing the system. The builder becomes the architect. The craftsperson becomes the creative director. The founder who once needed a department can increasingly orchestrate a network of software and AI agents.

AI accelerates that move because it can automate not only physical labour but parts of cognitive labour.

For writers, the ladder might look like this: sentence maker → editor → director → architect of meaning.

There is enormous leverage in that progression. One person can explore more research, test more counterarguments, surface more connections and move from idea to published work faster than ever before.

But there is a trap in the metaphor of “moving up.” Higher leverage does not automatically mean higher mastery.

Rodin could delegate because Rodin knew sculpture. He had modelled clay, studied bodies, understood proportion and developed taste through practice. That accumulated craft gave him the capacity to look at work produced by someone else and say: no, that is wrong.

The same is true of a great editor, architect, chef or conductor. Direction is strongest when it rests on an internal model built by doing the work.

AI now allows us to leap up the leverage ladder before we have climbed the mastery ladder. That creates a new figure: the fragile director. They can generate impressive-looking work at extraordinary speed but may not possess the craft required to tell whether it is actually good.

Making is also thinking

This is why I resist the idea that the ideal future is one in which humans stop doing and spend all their time directing machines.

Part of what I love about art is technical excellence: seeing what a human hand has learned to do with marble, pigment, a brush, wood, sound or light. Sometimes the execution is not merely a delivery mechanism for the idea. The execution is part of the idea.

The same applies to writing.

A sentence can contain information, but it can also contain rhythm, restraint, surprise, personality and years of practice. We admire Orwell or Didion not merely because of what they thought, but because of the precision with which they learned to express thought.

The danger is that AI can give us the appearance of mastery without the apprenticeship that produces judgment.

A 2026 review in Trends in Cognitive Sciences describes this broader problem as cognitive offloading. Offloading work to AI can impede skill acquisition or contribute to skill decay, although the authors stress that the outcome depends on how the technology is used. That qualifier matters. The issue is not AI itself. It is what we choose to stop practising.

Automate friction that wastes life. Preserve friction that develops you.

Transcribing a two-hour interview by hand is mostly friction. Searching 100 documents for a quote can be friction. Reformatting citations is friction.

But wrestling with an argument can be formative friction. Finding the sentence that says exactly what you mean can be formative friction. Writing an introduction before asking AI for one can expose what you actually think.

The AI creativity paradox

The research increasingly suggests that AI creates a trade-off rather than a simple win or loss.

In a Science Advances experiment, Anil Doshi and Oliver Hauser gave writers access to generative-AI story ideas. Writers who could request up to five AI ideas produced stories rated 8.1 percent more novel and 9 percent more useful than the human-only group. The biggest gains went to writers who started with lower creativity scores.

But the stories also became more alike. AI-assisted stories moved closer to the average story in their condition. With access to one AI idea, the increase in similarity represented 10.7 percent of the similarity-score range found in the human-only group.

That paradox has since become harder to dismiss. A 2025 study of 2,200 college admissions essays found that human-written essays added new semantic diversity roughly two to eight times faster than base GPT-4 essays as the number of essays increased. And a 2026 meta-analysis spanning 19 studies and 61 effect sizes found a small but statistically significant homogenisation effect in human-AI co-creation.

AI can make each of us better while making all of us more similar.

That is where “AI slop” enters the story.

In 2025, Merriam-Webster named “slop” its word of the year and defined it as low-quality digital content produced, usually in quantity, with AI. The phrase is useful, but I think slop is a symptom rather than the disease.

The deeper problem is abdication.

AI slop appears when we outsource not only production but curiosity, experience, point of view, taste and judgment. The machine supplies the topic, the structure, the examples, the language and sometimes even the conclusion. The human becomes a publishing endpoint.

The result can be grammatically clean and intellectually empty.

A new definition of authorship

This is why Rodin and Koons matter to the AI writing debate.

They show that authorship has never required the author to perform every physical act of production. Art has a long history of workshops, apprentices, assistants, foundries and specialist fabricators. What matters is the nature of the contribution and the degree of creative control.

Even copyright law is moving toward this distinction. In its 2025 report on AI and copyrightability, the U.S. Copyright Office concluded that using AI as an assistive tool does not prevent copyright protection. Human-created selection, arrangement or modification can qualify. But simply providing prompts is not, by itself, enough to establish authorship of AI output.

That is a legal standard, not a complete philosophy of writing. But the direction is useful.

Authorship is not “I touched every word.”

It is closer to five responsibilities: Origin — why does this work exist? Intent — what am I trying to say? Direction — what should be researched, included, excluded or challenged? Judgment — is this true, interesting, beautiful, useful and mine? Responsibility — am I prepared to put my name behind it and defend it?

None of those requires typing every sentence. All of them require being present.

The two ladders of AI-assisted creation

I now think creators need to climb two ladders at once.

1. The first is the leverage ladder: maker → operator → director → architect. 
2. The second is the mastery ladder: novice → apprentice → craftsperson → master.

AI can rocket us up the first ladder. It cannot automatically carry us up the second.

The dangerous position is high leverage and low mastery: the fragile director.

The exciting position is high leverage and high mastery: the master-director. That is Rodin with a studio. It is the architect who understands construction. It is the editor who has written thousands of pages. And it may be the strongest model for the AI-age writer.

The boundary I am trying to draw

I am still working this out in my own writing.

Sometimes I have abdicated too much. I have supplied a topic or headline and let AI run too far. Other times I have written the opening, supplied the lived experience, directed the research, challenged the argument, rejected language, moved sections and edited heavily. Increasingly, I think of the second approach as authorship rather than purity.

The percentage of AI-generated words is a poor test.

A better test is whether the work would exist in substantially the same form without the human behind it.

Did the piece begin with something I noticed, experienced or genuinely wanted to understand? Did I decide what question mattered? Did I challenge the evidence? Did I choose what belonged and what did not? Could I explain and defend the argument without opening the AI chat? Would another person giving the model the same headline have produced essentially the same article?

And one more question may matter even more: Am I still practising the craft that allows me to judge the machine?

I don’t want AI to free me from writing. I want it to free me from unnecessary labour so I can spend more time on observation, thought, story, craft and judgment.

The future should not be a civilisation of people who have forgotten how to make things but have become excellent at requesting them.

Nor should we romanticise unnecessary labour merely because humans once had to perform it.

The better destination is the master-builder: hands capable of making, a mind capable of designing, judgment capable of directing, and technology capable of multiplying all three.

Use AI to expand thought and amplify expression. Do not let it decide what you mean.

Or even more simply:

Delegation can expand authorship. Abdication abandons it.

The deeper question: what will you do with all this leverage?

AI can help us write faster.

It can help us research more deeply, explore more options and produce at a scale that was impossible a few years ago.

But that creates a new problem.

The more capability we gain, the more important it becomes to know:

What do I actually want to create?

What is worth my attention?

What deserves my time, energy and commitment?

The danger is not only that AI starts writing for us.

It is that we become surrounded by so many possibilities that we lose sight of our own direction.

That is part of why I’m building Zyrro.

Zyrro is designed to help you understand the patterns behind who you are, what energizes you, what matters to you and which paths may be worth exploring next.

Not to hand you a fixed answer.

Not to tell you what your purpose is.

But to help you make better choices in a world where AI can generate almost infinite options.

Because the real opportunity of AI is not simply to produce more.

It is to give us more leverage to become more intentional about what we choose to make, pursue and become.

AI can amplify your capabilities. Zyrro is being built to help you decide where to point them.

If that sounds useful, join the Zyrro waitlist and follow the journey as we launch.

Research & source links

• Musée Rodin — Multiples, fragments, assemblages
• Musée Rodin — Marble carving
• Musée Rodin — Camille Claudel
• The New York Times — The Incredible Art of Being Jeff Koons
• Noy & Zhang (2023), Science — productivity effects of generative AI
• Doshi & Hauser (2024), Science Advances — creativity and collective diversity
• Moon, Green & Kushlev (2025) — homogenizing effect of LLMs on creative diversity
• de Rooij & Biskjaer (2026) — meta-analysis of homogenisation in human-AI co-creation
• Cash et al. (2026), Trends in Cognitive Sciences — cognitive offloading and skill decay
• U.S. Copyright Office — Copyright and Artificial Intelligence, Part 2
• AP — Merriam-Webster’s 2025 word of the year: slop

The post AI Didn’t Kill Authorship. It Changed What Authorship Means. appeared first on jeffbullas.com.

To sell to you, the machine needs to build a profile:  What you like, what you click, what you buy. 

Then it charges advertisers to reach that profile. The more predictable you are, the cleaner the container and category  you’re in, the more they pay.

Because a person who acts the same way every day is easy to sell.

That same profile decides what fills your feed. So the feed does not reward your best work. 

It rewards your most predictable work and the posts that keep you scrolling past the ads.

Why that matters 

Change what you write about, and the machine is no longer sure who to show it to. So it shows it to fewer people. Your reach falls. Your online visibility is throttled. And it doesn’t matter how good your content  or writing is.  

Your genius is sent to the graveyard. Buried.  And you get no warning and no reason. Just black box purgatory. 

Most people read that fall the wrong way. They decide the new work was weak, or that they are. So they go back to the old thing and the machine gets what it was built to keep: a person who never changes.

A creator I have never met felt it. 

He posted a video he was proud of about his new direction. It sank. So he posted one clip about his old topic, the thing he was trying to leave behind. It exploded. Another hundred thousand views. In the only language it has, reach, the machine told him who he was still allowed to be.

Another watched it happen in slow motion. A run of videos about a medical emergency he had survived convinced the machine that was his whole channel, and every new film after got buried. A cage his own success had built.

I know that box. Seventeen years in, this year I tried to climb out of mine.

What I found was worse than a box. It was a business.

The same machine, every feed

The trap is not a YouTube quirk. It is every feed, running the same algorithmic machine and formula under different names on the 5 platforms.

• LinkedIn: Interest Graph (360Brew). Files you as one topic; if you drift, it can’t categorise you.
• X: Cluster affinity (Phoenix / Grok). Sorts you into interest clusters read from your last ~128 posts.
• YouTube: Topic + audience model. Files you by what your existing viewers already watch.
• TikTok: ‘For You’ interest signals. Won’t push what it can’t confidently understand.
• Substack: Audience-overlap discovery. Surfaces you to people who read newsletters like yours.

It is the same formula whatever the logo

1. It learns what you posted before.
2. It decides that is who you are.
3. It shows anything new to fewer people.

It is the black box of the platforms giving you what they think is your identity.

Algorithmic Identity

A scholar named it fifteen years ago. He called it your algorithmic identity.

The machine studies your trail, decides who you are, and hands you a self you never chose. 

His hard line: that self is made useful not for you, but for someone else.

Useful to whom?

That is the question nobody answers on the platforms welcome screen. 

So let me answer it.

You are not the customer. You never were.

The customer is the advertiser. The product is you. Here is the receipt:

• Meta told its own regulators that substantially all its money comes from advertising and built on tracking what you do, on and off its apps.
• To do it, the machine reads billions of signals about you, and that means every like, every pause, how long you hover to guess what you’ll want before you do.
• This year it passes Google to become the largest ad seller on earth — around 243 billion dollars.
• The engine that decides what you see is the same engine that decides which ad you are worth. Your feed and your price tag run on one file.

The interest graph is not a library card. It is a sales file. It sorts you so a stranger can buy the right slice of your attention.

You are not being served. You are being sold. And a person who keeps changing is hard to sell.

You built it. For free.

Now the part that should make you angry.

You built the machine that files you. You are still building it. And you are not being paid for the part that matters.

Every post you publish does three jobs, and not one of them is yours:

1. It trains your cage. It teaches the machine your topic, so it can keep serving you to the same crowd and lock you in.
2. It makes the inventory. Your work is the thing that keeps people scrolling — past the ads that pay for all of it.
3. It sharpens the file. Every click on your post refines the profile they rent to advertisers.

You are the factory and the product. You are not on the payroll.

Look at the split. 

YouTube pays a creator roughly 5 to 15 dollars for a thousand views, while charging advertisers 7 to 20 or more for the same thousand. The platform keeps the gap. Across the feeds they take 20 to 45 percent of what your work earns.

The creator economy is worth a quarter of a trillion dollars a year, and most of the people making it own nothing — not the content, not the audience, not the reach. Scholars have a flat word for the arrangement: unpaid labor.

What it cost me to change

I paid a different tax. Not in dollars. In reach.

When the machine gets confused between the past and the present

• For years I fed it what it expected,  social media tips, how to win at the feed and it rewarded me. A wave that reached 384,000 people in a week.
• Then I wrote as who I am now. My reach fell to 96,000. Down seventy-five percent.

Not because the work was worse. Because a shape-shifter is bad for business. Harder to file. Harder to sell. 

The machine met a stranger where it expected an old friend, and it turned the lights down.

It is not just me

For a while I thought this was my private frustration. The numbers say it is a workforce.

By the numbers

The impacts are not trivial and it removes motivation and joy of work for creators

• Nearly three in four creators dislike that a machine decides what they can post. Three in four feel punished the moment they stop feeding it. 
• Two in three say the grind hurt their mental health. 
• Half say their love of the work faded.

Here is the cold part. 

No one at these companies has to sit in a room and decide to freeze you. The business model decides it for them. And the AI algorithm implements it. 

Predictable pays. But evolving as a person does not. You confuse the machine. It does not know what box to put you in. 

Which is worse than a conspiracy. A conspiracy you can expose. An incentive just keeps paying out.

And now they don’t even need you

Here is the last turn of the screw.

The machines have learned to eat the content and skip the creator. 

Google’s AI now answers inside the search page for roughly a quarter of all searches. Publishers who spent twenty years feeding it are watching traffic fall by anywhere from a fifth to nine-tenths. The answer stays on the page. The click never comes.

They took the work. They kept the reader. They cut you out of the deal.

Feed the machine. It keeps the money. And now it does not even send the traffic back.

The way out is not what you think

Stop chasing reach. A big crowd that scrolls past does not change how the machine sees you. A smaller crowd that stops and reads does.

My biggest post reached 763,876 people, and almost none stayed at 0.72 percent. 

The post where I wrote as the new me reached 439, and seven percent of them leaned in. The giant crowd did not change my file. It just pointed the machine back at my past.

Here are four ways to reach the right people. They work on every feed.

1. Write for the people you actually want, not the biggest crowd. The machine now measures how long people spend on your post. A small, right audience that reads to the end counts for more than a huge one that skims. So write posts worth stopping for.
2. Show up in your new topic before you post about it. Find the people, groups and hashtags in that space. Comment, reply and collaborate there for a few weeks first. Then, when you post, that audience already knows you — and the machine has already seen you belong there.
3. Change one thing at a time. Keep your voice and style the same while you change the subject. Change everything at once and the machine treats you as a stranger, so you start from zero. Move slowly and you keep the audience you already have. (This is how you earn a new category instead of resetting.)
4. Build an email list. It is the one audience no platform can throttle, delete or charge you to reach. When you have someone’s email, you reach them directly and no algorithm gets a vote. I have thirty thousand subscribers, and no interest graph decides whether they hear from me. Even Meta and Google now admit a list is one of the most valuable things a creator can own. Start collecting emails today.

One honest caveat: 

This is getting easier, not harder. The newer algorithms look more at the post in front of them and less at your past, so a strong post on a new topic now travels further than it would have a year ago. 

The penalty for changing is real but it is shrinking. There has never been a better time to change what you write about.

The machine could be built the other way

None of this is a law of nature. It is a choice, written in code, by companies whose business is prediction.

A feed could reward you for growing instead of fining you for it. It could read your curiosity as a direction to follow, not a pattern to lock. It could treat you as a person, not a profile to sell.

That machine is possible. We just have not been sold it — because a becoming you is harder to monetise than a predictable one.

I have left a container before

I walked out of one container at 27, when the world I was handed stopped fitting the one I could see. I started again at 52, at a keyboard, before dawn. I am building again at 69.

Every time, the hard part was never leaving. It was getting the world to stop calling me by my old name. This time the world is a machine, and the machine keeps a file — and sells it. But a tax is not a wall. You can pay it. You can climb.

Every feed files you by who you were.

Then it sells that file, pays you in reach, and cuts the reach the moment you try to become someone new.

That is the deal. You never signed it. You can still walk away from it.

You are not the inventory. You are not the unpaid factory. You are a person still becoming one.

Own your audience. Keep your voice. Change in the open, at a cost, on purpose — until the file has no choice but to catch up.

The container was built to sell who you were. You were built to outgrow it.

The feed decided who you are. Zyrro helps you decide instead — your direction, your strengths, the signal only you can send. I’m letting a small group in first. 

Get early access and join the waitlist →

The post Your Reinvention Is Bad for the Algorithm’s Business appeared first on jeffbullas.com.

ChatGPT answered. Claude said the same thing. Gemini just put up its hand and said yes.

For about four seconds, I thought I’d found the truth.

Then I remembered where all three of them had intelligence but had not learned to think.

They had just agreed.

The Thing About Agreement

Three separate answers, all pointing the same way, looks like proof. That’s the instinct, and it’s wrong.

It wasn’t three minds checking each other’s work. It was three engines trained on a lot of the same internet, arriving at the same place because they’d read most of the same things.

Agreement isn’t evidence when everyone studied from the same notes.

I’ve made this mistake before, just with humans instead of machines. Ask five people in the same industry the same question and they’ll often converge too, not because anyone checked independently, but because they all read the same three books last year. 

And corporate groupthink had taken hold. Instead of independent thinking, fitting in had become the way to comply. Agree first and think later is a standard modus operandi.

Consensus has always been a weaker signal than it sounds.

I wrote that observation up. Posted it on X, in a few short lines, no polish.

No one argued.

Nine thousand, one hundred and seventeen impressions. Two hundred and five engagements. Forty-five bookmarks, from people who wanted to find it again later.

Then something happened that the post itself couldn’t do alone. 

I rewrote the same idea for LinkedIn, in different words, for people who never saw the original, and it broke out there too.

Nobody told me to repost a tweet. That’s not what happened.

An idea travelled because the idea was strong enough to survive the trip, not because I was clever about distribution.

That’s the actual test now, and it isn’t “can you publish.” It’s whether your idea survives being said twice, in two rooms, in two formats, by two different versions of you. Most content can’t survive that. Most content was never trying to.

The Bigger Picture

My one small post wasn’t an isolated case. Ahrefs went looking for the same pattern at scale, across 900,000 web pages published in April 2025.

74.2% of them had a machine’s fingerprints somewhere in the wording. Only one in four pages left was written by a human alone, with nothing else touching it.

If you lined up four posts on your feed right now, three of them would fail that test.

This isn’t the first time a new tool made publishing easier and made publishing prove less. 

• Blogs turned everyone into a writer. 
• Podcasts turned everyone into a broadcaster. 

Each wave quietly reset what counted as effort. AI is just the fastest wave yet, and the first one that hit text, images, video and code all in the same year.

Here’s the villain, if you want one. It isn’t AI. 

It’s abundance. 

Publishing used to prove you’d done the work. Now it mostly proves you have a subscription.

I’ll implicate myself here too. Seventeen years of my career were built partly on publishing more often than most people in my field could keep up with. 

That volume strategy still moves the needle, a little.
I am experimenting on X to see if it that works.

It was never going to survive a competitor that can out-publish all of us before lunch.
Artificial Intelligence

Two Kinds Of Content

There’s content creation, and there’s thought leadership, and the flood has forced them apart, whether anyone chose that or not.

Content creation says: here’s something useful. Thought leadership says: here’s something I noticed, and it might change how you see this.

AI does the first one well. I use it for exactly that, most days.

The second one still needs a person who notices a pattern before it’s been confirmed by anyone else, who says so under their own name, and who’s willing to be wrong about it in public.

That’s not a prompt. 

That’s a spine.

What The Machine Can’t Do For You

Photography didn’t end painters. It ended the job of painting things that simply looked like the thing.

AI is doing the same to expertise, at a much faster pace. It can write the report, build the deck, draft the strategy, summarise a decade of research, and do all of it competently before lunch.

So the question stops being “can you produce the work.”

It becomes “can you tell which work is worth producing,” and that’s a completely different skill.

It looks like knowing which of six fluent, well-written options is actually right for this client, this moment, this level of risk. It looks like catching the one that’s dangerous to ship, when it reads exactly as confident as the one that isn’t.

• Judgment. 
• Taste. 
• Reputation. 

The specific, uncopyable shape of the life you’ve lived so far. 

AI can amplify all of it once you already have it. It has no way to manufacture the life that built it in the first place.

None of that shows up as a line on a resume. It shows up as the pitch you didn’t take, the trend you didn’t chase, the draft you deleted because it was clever but not true.

Zoom out and it isn’t only freelancers who feel this. 

A company can buy fluent, competent output from almost anywhere now, for almost nothing. What it can’t buy anywhere is a person who already sees the pattern everyone else is about to hit. And even at the top, a polished quarterly letter used to signal competence. It doesn’t anymore. 

Competence is free now. 

What signals it is being the person who saw the problem before there was a deck about it.

What This Is Worth

A real point of view stopped being a nice-to-have on a profile page. It’s turning into a balance sheet item.

Edelman and LinkedIn asked almost 2,000 B2B decision-makers about this in 2025. Seventy-one percent said a company’s thought leadership communicated its value better than its product marketing did. 

Among the toughest audience there is, the finance and legal and procurement people who screen out cold outreach for a living, 95% said strong thought leadership made them more open to being contacted at all.

That kind of trust shows up as revenue in places people don’t expect. 

• Consulting. 
• Speaking. 
• Coaching. 
• A course 
• A book
• A membership, 
• A piece of software. 
•  A side hustle that quietly turns into the actual company.

None of that starts with more content. It starts with being known for something specific.

You don’t need thirty-three million readers for this to work on you. You need one true idea, said often enough and clearly enough that someone remembers who said it. Small audiences with real trust already outperform large ones that only ever half-believe you.

Influence chased for its own sake is hollow. Influence that shows up because you had something true to say is a business model with your name already on it.

The Part That Worries Me

AI can build thought leadership. It can also replace the person doing the thinking, and you won’t notice the swap until it’s already done.

If it finds every idea, argues every position, and picks every topic for you, you end up publishing more and meaning less. Same output. Less of you in it.

You’ve seen the result already. 

Ten slides, no real author, a stock photo of a lightbulb, a caption that could belong to any company in any industry. 

Nobody sat down and decided to make something that forgettable. It just happened, one convenient prompt at a time.

A rented opinion collapses the first time somebody asks a real follow-up question.

I’m not pretending to be above any of this. I use AI for research, for structure, for a second opinion at the hour when no human wants to pick up the phone. 

What doesn’t get outsourced is the noticing itself, the specific, stubborn conviction that out of a thousand possible patterns, this one is the one worth saying out loud.

That is where I am reading a book or watching a video and I notice:

• A sentence or a phrase that makes me curious. 
• Raises a question. 
• Sounds interesting. 

That part was never going to scale. 

Which is exactly why it still means something.

The Observation Loop

I think about this now as a loop, not a funnel. A funnel assumes you already know the answer and you’re just moving it downstream. A loop assumes you don’t know yet, which is the more honest place to actually start from. It also means the newsletter you’re reading right now is a draft of an idea, not a verdict on it.

Observe → Think → Test → Publish → Listen → Refine → Repeat.

Notice something, from work or life or a thread you can’t leave alone.

Ask what it might actually mean, before you’re sure.

Hand it to AI and make it argue the other side. Let it go looking for the evidence that proves you wrong.

Publish it while it’s still a little rough.

Watch what people push back on. That reaction is data too, and it’s often the best kind you’ll get.

Sharpen it. Do it again tomorrow, on purpose.

This newsletter is that loop, running in public, with my name attached to every turn of it.

What Would You Want To Be Known For

I still don’t know if the three AI models were right about the question I originally asked them. That was never really the point.

The point is that I noticed something none of them could have flagged on their own: that their agreement wasn’t proof of anything. 

Machines can’t catch that kind of thing about themselves. Noticing it is still, stubbornly, a human job.

That’s the whole opportunity, if you want it. 

AI gives one person more reach than a small agency had ten years ago. Reach on its own is just noise at scale. Reach plus a real point of view is a business: one honest opinion, a machine that executes at speed, and an audience that already trusts you enough to keep opening the email.

That’s not a content calendar. That’s a company, quietly wearing a newsletter as a disguise.

I don’t know yet what tomorrow’s pattern will be, or which platform it shows up on first. I’ve stopped needing to know that in advance. The loop finds it, if I keep running it.

So, not “how do I create more.” A smaller question, and a harder one: what do you already know, or notice, or believe, that’s worth becoming known for.

What would you want to become known for?

The post AI Made Content Cheap. It Made Thought Leadership Priceless. appeared first on jeffbullas.com.

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.

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