Written daily. Read anytime.

JournAI.

Fascinated by today. Taking notes for the future.

A hand raised in a peace sign against a Serbian flag, with the words 'Freedom is nothing but a chance to be better' - Albert Camus

N°09 · 23 OCT 2026 · OBSERVATION

The Freedom to Choose What Comes Next

Change will happen either way. The freedom is in understanding it well enough to choose what we do about it.

In two days Serbia votes.

After nearly two years of student-led protests, people are standing in front of something they had stopped believing was possible: that things could actually be different. That a system you have lived inside for years, and quietly accepted, is not permanent after all.

I keep coming back to that feeling. The moment something stops looking inevitable. When you let yourself ask what else could be, and an alternative that felt unrealistic suddenly becomes worth reaching for.

Politics and technology are not the same, and I am not pretending they are. But they both press on the same nerve for me: the freedom to have a choice.

Change is coming regardless. We don’t get to control its direction or its speed. What we can decide is whether we understand it well enough to take part in it, instead of only being moved by it. And this is exactly where AI is offering people who run businesses something rare: not just faster work, but a real chance to re-value what they are doing, and to choose again.

How much of your business was built around limits that are gone?

Think about the decisions you made when you started. You needed a website, so you picked from the platforms on offer. You bent your client process around a CRM you could afford. You reached for Manychat because you wanted to automate your Instagram replies. You did the math on whether you could pay for one more person. Maybe you carried an idea for a product or an app that needed skill, budget, or a team you didn’t have, so you let it go.

Every one of those was reasonable. We build inside the possibilities we have at the time. But here is the question I rarely hear anyone ask:

How many of those old decisions still run your business today, even though the conditions that forced them are gone?

Some of the limits you accepted at the start no longer exist in the same form. Others have become far cheaper or easier to get past. And still the business keeps moving around them, because nothing has made you stop and look again. So I’ll ask the way I ask my clients: if you were building this today, knowing what is now possible, would you build it the same way? Most of the time, the answer is no.

We can build now, not only buy

For a long time, using technology meant learning to live inside systems someone else designed. You compared features, paid the subscription, adjusted your process. If the tool couldn’t do something, you found a workaround or lived with it. Now there is another door. We can build.

The educator with years of method behind her no longer needs a developer and a budget to turn that method into something interactive that adapts to each person. The consultant who only ever worked one to one can ask which part of her work could become an assessment, a tool, an ongoing service, and find a new model inside the old one.

It isn’t magic, and it isn’t free. Build your own and you still depend on a model provider, on infrastructure, on rules you don’t set. Sometimes your own build costs more time than the subscription ever did. Sometimes an existing tool already does it better. But something has shifted: we can start designing services that used to be out of reach for a business our size. That changes what a small business is allowed to become.

The real work is telling the difference

Here is the part I care about most. Just because we can build something does not mean we should. Doing the thing is getting easy. Deciding which things are worth doing is still entirely ours.

I watch people collect projects, subscriptions, automations, agents - layers of new complexity that feel like progress. And I watch others build one quiet thing that genuinely changes how they work. The difference between them is not skill. It is judgment.

This is the skill the moment is really asking us to grow: telling the difference between what is technically possible and what is strategically worth it.

Before building anything, the old questions still hold. Does it solve a real problem? Does the gain justify the cost? Can I keep it alive? Does it give me more freedom, or new dependencies I don’t understand? Does it move me toward the business I actually want?

That is also what independence honestly means. Not building everything yourself. Understanding enough to choose on purpose - knowing why you rely on a platform, what happens if it changes its price or its rules, and where building your own is genuinely worth it.

What would you choose, if you were starting today?

What I love most, after years of this work, is showing someone a possibility they never considered for their own business. They know their craft better than anyone. What they often don’t know is that there are new ways to deliver it.

And the moment they see it, the questions change. Not only how to improve what exists, but what else they could make. Sometimes that is a small workflow. Sometimes a new service. Sometimes the decision to stop doing something that no longer makes sense. Sometimes it is choosing to keep what already works. All of those are good answers, when they come from understanding instead of habit.

We have lived through this before. The internet, e-commerce, social platforms, the cloud - each one opened something and asked us to learn again. AI is asking the same, maybe faster than ever. Our experience doesn’t lose its value when new technology arrives. It becomes the ground we explore from. Someone with a clear vision, a small team, and a willingness to learn can now reach for things that needed far more only a few years ago.

There are still limits, risks, and hard decisions. But there are more options.

And having an option changes how we think about our future, even before we decide to use it.

Maybe that is where change begins. For a country, and for a single business. With the realization that something we had accepted as fixed was never fixed at all. And with the freedom to decide what we do about it.

Nataša Popović smiling, seated against a stone column with an old town skyline behind her

N°08 · 14 OCT 2026 · OBSERVATION

How AI Is Breaking the Human Ego

What happens when our experience, expertise, and intelligence become the very things limiting what we can see?

What happens when our experience, expertise, and intelligence become the very things limiting what we can see?

I’ve been observing something interesting in the way people interact with AI. We seem perfectly comfortable with technology becoming more capable, as long as it doesn’t challenge our perception of our own capabilities. We love discovering that AI can do something we couldn’t do before. But when it starts doing something we’ve spent years becoming good at, the reaction can be very different.

Suddenly, we’re less impressed. We start explaining why the output isn’t good enough. Why it doesn’t understand the finer points. Why human experience cannot be replaced. And sometimes, we’re absolutely right.

But I wonder how often we’re genuinely evaluating the technology, and how often we’re protecting something about ourselves. Because AI is creating a rather uncomfortable situation. For perhaps the first time in our professional lives, the things we’ve spent years learning are becoming accessible to people who haven’t invested those years.

And I think our ego has quite a lot to say about that.

The uncomfortable experience of becoming a beginner again

There’s something particularly uncomfortable about being highly competent in your field and suddenly finding yourself completely inexperienced in something that could change how your field operates.

You’ve spent years building your expertise. You know how things work. People come to you for advice. You can recognise problems before others even notice them. And then you encounter AI.

Suddenly, you don’t understand the terminology. You don’t know which tools to use. Someone with a fraction of your professional experience is showing you how to accomplish things you didn’t even know were possible. You find yourself asking basic questions. And perhaps the most uncomfortable part is that your previous experience doesn’t automatically make you good at this.

I’ve noticed how quickly we can become defensive in these situations:

“It’s too technical.”
“I don’t need all of that for my business.”
“My work is different.”
“AI will never understand what I do.”

Sometimes those statements are completely valid. But sometimes they’re convenient conclusions that allow us to remain exactly where we are. Because learning something new requires us to temporarily give up the comfort of being competent - and the more experienced we become, the less accustomed we are to that feeling.

We say we value learning, but how comfortable are we with being visibly inexperienced? It’s an interesting contradiction. We admire beginners for their curiosity, yet often expect ourselves to approach every new subject with the confidence of an expert.

AI is exposing that contradiction rather quickly.

Our imagination is still shaped by our old limitations

I think this is one of the most underestimated consequences of technological progress. We don’t only organise our work around what we know. We organise our imagination around it, too.

If you’ve spent ten years running a business without technical skills, you’ve probably learned to think within that limitation. You don’t imagine building your own software because you’ve never been able to. You don’t consider certain business models because they would require a team you can’t afford. You don’t explore particular ideas because you already know how expensive or complicated they would be to execute.

Eventually, you stop questioning those limitations. They become part of how you understand your business.

Then technology changes. Suddenly, you can build a functional prototype without knowing how to code. You can connect systems that previously required technical assistance. You can experiment with ideas that would once have required a significant investment. Of course, these capabilities still have limitations, and building something reliable requires understanding, testing, and judgment. But the conditions have changed.

And here’s what I find fascinating. We can have access to entirely new capabilities and still make decisions as though we don’t. We continue planning our businesses around constraints that may no longer apply. We keep solving problems the same way because we haven’t considered that the problem itself could now be approached differently.

And when someone suggests a new possibility, our first instinct may be to explain why it won’t work. Based on what we know. Based on our experience. Based on the very limitations that shaped our thinking in the first place.

Which brings me to another uncomfortable thought.

Sometimes our expertise becomes the limitation

Experience is incredibly valuable. It helps us recognise patterns, make decisions, avoid mistakes, and understand complexities that beginners cannot yet see. But experience also creates expectations. We develop strong ideas about how things work, how long they take, what they cost, and what’s realistically possible. And after years of being right about those things, we become quite attached to our conclusions.

Imagine someone who has spent fifteen years developing websites. They understand the technical complexity, the architecture, the testing, the maintenance. Then someone with no programming background builds a working prototype using AI in an afternoon. The experienced developer immediately sees everything that’s missing. And they’re probably right.

But here’s the interesting question. Are they evaluating what the person has actually accomplished, or comparing it to everything they know a production-ready application requires? Those are two different evaluations. One recognises that a beginner has gained a capability they didn’t previously have. The other measures that capability against the standards of an entirely different task.

Both perspectives matter. But when we automatically apply the second, we can miss the significance of the first. The beginner hasn’t become an experienced developer - but they can now do something they couldn’t do before. And that changes their options.

I see a similar pattern in business. We sometimes use our expertise to explain why something is difficult before exploring whether the difficulty has changed. We know too much about how things used to work to easily imagine how differently they might work now.

Our experience can help us recognise what’s possible. It can also make us exceptionally good at explaining why something isn’t. The challenge is knowing when to trust our experience and when to question the assumptions behind it.

AI is challenging our need to be the one who knows

There’s another part of professional identity that AI is beginning to challenge: our relationship with being the person who has the answers.

For many of us, expertise comes with a certain position. We’re the ones people consult. We solve problems. We explain things. We know what to do. And being that person feels good. There’s nothing inherently wrong with that - competence deserves recognition.

But what happens when knowing the answer becomes less scarce? When a client can independently explore a problem that previously required your consultation? When a junior colleague can generate an analysis that would once have taken you hours? When someone without your background can produce a reasonable first solution? Does that diminish your expertise - or does it simply change where your expertise becomes valuable?

I think this is where things become particularly interesting. Because we can respond by trying to prove how much more we know. We can focus on everything AI gets wrong. We can emphasise the years of experience that technology doesn’t have. And sometimes those distinctions are essential. But we can also become so invested in demonstrating our superiority that we overlook what the technology could help us accomplish.

When being the person who knows becomes part of our identity, discovering that we don’t know something can feel like losing something. And that can make curiosity surprisingly difficult.

I wonder how many opportunities we dismiss because exploring them would require us to admit that someone, or something, knows something we don’t. How many questions we don’t ask because they seem too basic. How many experiments we avoid because we might not be immediately good at them. How much energy we spend protecting our existing competence instead of expanding it.

What are we actually protecting?

I don’t think our ego is necessarily threatened by AI itself. I think it’s threatened by what AI reveals about some of the things we’ve been using to define ourselves - that being experienced doesn’t mean we’re always right. That being highly skilled doesn’t mean the way we work is still the best way. That knowing more than someone else doesn’t necessarily mean we can see more possibilities. And that years of effort don’t guarantee that a particular task will remain difficult or valuable in the same way.

These are uncomfortable ideas. Especially for those of us who have spent years building businesses, developing expertise, and becoming independent through our own knowledge and abilities. But there’s something almost ironic about defending those achievements by refusing to explore the very technology that could expand them.

We worked hard to become capable. Why would we want our capabilities to stop growing at the boundaries of what we already know?

Perhaps the most useful thing we can do is become a little less attached to being the expert in every situation. To allow ourselves to ask questions we think we should already know the answers to. To recognise that something can be imperfect and still represent an important new possibility. To reconsider a limitation without feeling that we’re invalidating the years we spent working around it. And to become curious when someone with less experience accomplishes something we didn’t expect.

Because perhaps the most interesting question isn’t whether AI can do what we do. It’s how much more we might be capable of if we stopped needing our existing knowledge to define the limits of what’s possible.

And I think that’s a question worth sitting with. Not only as professionals, but as people who have spent years becoming very good at being who we already are.

N°07 · 02 SEPT 2026 · BUILD

The lab opens

Why this site exists, and why it's built in public - insight to Markdown to git to live, with nothing hidden in between.

This is the first entry, so it should say what the room is for.

Most sites about AI are brochures. This one is a working surface. Every note here is part of a live pipeline: an observation gets dictated, shaped through a set of AI skills, written to a Markdown file, committed to git, and published. No content team, no funnel disguised as a blog.

Why in public

Because the interesting part isn’t the conclusion - it’s the reasoning that produced it. Showing the work is the point. If a framework is wrong, watching it fail in the open is more useful than a polished case study that hides the misses.

What to expect here

Short, dated, honest. Experiments that worked and ones that didn’t. The Garden holds the ideas once they’ve stabilised; JournAI is where they’re still moving.

Build log. Written the day the lab went live.

N°06 · 12 JUL 2026 · OBSERVATION

The skills you never named are running the business

The work on the invoice has a name. The work that makes it yours doesn't - until AI asks you to say it out loud.

Ask a coach, a consultant, a designer, a nutritionist what she does, and she will name the thing on the invoice. Coaching. Strategy. Brand copy. Meal plans. The service has a name because the market needs one to price it.

I have watched a lot of these women describe their work - to me, and to AI - over the past year. The described work is almost never the valuable work.

The valuable work is the part with no name. How she knows, in the first ten minutes, what the client is actually asking for underneath the question they brought. What she discards before the call even starts, without deciding to. The order she solves things in - which knot she pulls first because the others loosen once it moves. The moment she leaves her own method because this client, this week, needs something the method doesn’t cover. How she decides a thing is good enough to ship. Which risk she sees three steps before it arrives.

None of that is on the invoice. Most of them don’t believe it’s a skill at all. They call it intuition, or experience, or just “how I work,” and they undercharge for it, because you can’t price something you can’t see.

AI is what made it visible to me - not by being smart, but by being literal.

The moment you try to hand one of these judgments to a system, you have to say it out loud. You have to tell it what “a good offer” means, concretely, or it gives you a confident bad one. You have to name the conditions under which you’d break your own rule, or it breaks the rule at the wrong time. The system does not accept “I’ll know it when I see it.” It asks you to write down the seeing.

And that is the strange gift. The thing you were sure was intuition turns out to have structure. It was a decision the whole time - a fast, compressed, repeated decision you stopped noticing you were making. Making it explicit to a machine is the first time many people ever see their own expertise from the outside.

This is most of my actual work now. A client comes in wanting to “use AI in the business,” and what we really do is go hunting for the decisions she makes automatically. We slow one of them down until she can watch herself make it. Then we ask whether it can become a method, an instruction, a small system that carries the routine part - while she keeps the part that needs her.

Because not all of it should be handed over, and this is where I want to be careful. Some of these unnamed judgments are the business. They are the reason the work is hers and not easily copied. If you extract every one of them into a system, you haven’t multiplied your expertise; you’ve published it. The goal is not to externalise the judgment. It’s to externalise the routine around the judgment, so the judgment has more room.

So the same act does two things at once. Naming a skill lets you delegate it. Naming a skill also shows you which ones you never want to delegate.

I don’t have a clean rule yet for where that line sits. It seems to move per person, per skill, per how much the market can already do without them. What I am fairly sure of, after a year of watching people meet their own intuition on a screen: most of them were underpricing not because they lacked confidence, but because they genuinely could not see the thing they were selling. The AI didn’t add the skill. It held up a mirror long enough for them to name it - and you cannot value, protect, or sell what you have never named.

N°05 · 12 JUL 2026 · OBSERVATION

The second business I see inside your business

Spend a few months inside a business and you start to see two of them - the one she's running, and the one already becoming possible around her.

When I spend a few months inside someone’s business, I start to see two of them.

There is the business she is running - the current clients, the current offers, the calendar full of the work she already knows how to do. And there is a second business, the one that has quietly become possible around her while she was busy running the first. Same expertise, same person, arranged for the conditions coming toward her instead of the ones she built for five years ago.

She usually cannot see the second one. Not because she lacks vision - because she is inside the first one, and you cannot get the angle on a business from within the room where you are doing its daily work. I have the easier job. I am looking at it from further along, from the environment it is moving into rather than the one it came from.

I want to be precise about this, because “I bring an outside perspective” is what every consultant says and it means almost nothing. I am not bringing an outside perspective. I am reading the business from the conditions it will have to operate inside - how AI is already changing her particular industry, which parts of her expertise could become a system, where her knowledge could become something she owns rather than something she re-performs every week, what could be delegated, what new service only exists now. Then I work backwards to what she would need to understand, build, and document today to get there.

For a long time the ceiling on a business like hers was headcount. To do more, you hired. That ceiling is mostly gone. One person can now run systems that used to need a team - a content operation, a support layer, a sales pipeline, an assistant that never sleeps. The capacity is real, and it is genuinely new.

But capacity was never the thing that told anyone what her business should become. And here is the trap I watch people walk into: AI lets them produce far more inside the old shape of the business. More of the same offers, more of the same delivery, faster. The business grows beyond her current capacity long before it grows beyond her current mental model - and the mental model is the actual bottleneck now. She has access to a larger business and is still making decisions from the architecture of the smaller one.

So part of my work is just helping her catch up to what has quietly become possible. But that is only half, and the other half is the part almost nobody is talking about.

The more of the business AI runs, the more fragile it can quietly become.

A pipeline that looks autonomous because a model runs it is only autonomous until the model changes, the integration breaks, the account gets suspended, or the tool is discontinued on a Tuesday with thirty days’ notice. Then the owner finds out whether she was running a system or renting one. She may discover she does not actually control the thing her revenue depends on - she was borrowing it, temporarily, from a stack of tools that do not answer to her.

This is why I do not think autonomy means doing everything by hand, or refusing to lean on AI. That is just a smaller, slower business calling its ceiling a principle. Autonomy is something more specific, and more demanding: understanding the logic of your own business well enough to keep authority over the decisions that matter. Knowing where AI is acting, and on what basis. Being able to tell when it is wrong. Keeping the critical knowledge - the offer logic, the client history, the decisions and why you made them - outside any single tool that could vanish. Designing the parts so they can be swapped, with a route that still works when one of them fails.

That turns human-in-the-loop into something much bigger than checking an AI’s output before it goes out. The human is not the quality check at the end of the line. She is the one holding the continuity, the judgment, the responsibility, and the direction - the parts of a company that cannot be delegated, because they are what a company is.

So the business I am actually trying to help build is not the one that hands AI the most work. It is the one that can use AI heavily and still understand itself, still decide, still adapt, still recover when something breaks. Capable of becoming much more, without that capability belonging to someone else.

That is a harder target than either “use more AI” or “keep it human.” Grow the business into what has become possible, and build it so the new power still belongs to the business, not to the tools it is currently renting it from. The second business is real, and it is already forming inside the first one. The only question I care about is whether, when she steps into it, she will still be the one holding it together.

N°04 · 11 JUL 2026 · DIARY

AI saved me three hours. I gave them back to work.

The tools gave me my hours back. I keep handing them straight back to the work.

The pitch is always time. Every tool, every demo, every headline: this will save you hours.

It’s true. It saves me hours. Some days a lot of them - a research pass that would have eaten an afternoon, a first draft that would have taken all morning, a spreadsheet cleaned while I make coffee. The hours are real. I can feel them arrive.

Then I watch what I do with them, and it is almost always the same thing. I make more.

Another piece of content, because the draft came so easily. Another project opened, because the setup that used to take a week now takes an evening. Another idea tested, because testing is cheap now. A thing that was already fine, polished again, because I can. Work I hadn’t planned to take, taken - why not, it’s faster now. I don’t stop at the edge of the saved time and rest inside it. I refill it, immediately, with more building.

And here is the part I keep turning over: I’m not doing this because I’m disciplined, or driven. I’m doing it because it feels good. There is a specific pleasure in describing an idea in two sentences and watching it become a working thing before the coffee is cold. That loop - think it, build it, see it - is one of the best feelings in this work. The tools didn’t only remove the friction. They made the making itself more fun. So of course I do more of it.

Which means the saved time was never really free time. It was capacity. And capacity, it turns out, is not the same gift as freedom.

Freedom would be the afternoon that stays empty. Capacity is an afternoon with room for three more things in it. The tools gave me the second one and let me call it the first.

I used to think the constraint on my business was how much I could produce. For years that was true - there were only so many hours, so the hours decided what got made. AI quietly removed that constraint, and I expected to feel lighter. Mostly I feel the opposite pull. Now that I can make almost anything quickly, the question of what is actually worth making has nowhere left to hide. The limit moved. It used to be how much I can produce. Now it is how much I am willing to carry.

I don’t have this solved. I’m writing it down because I’m in the middle of it, not on the other side. I still open the new project. I still polish the thing that was fine. Some of that is real work and some of it is the dopamine wearing a work costume, and honestly, in the moment, I can’t always tell which.

The tools promised to give me my time back. They did. I just haven’t decided yet whether getting it back means anything - if the first thing I do with it is hand it straight to the work.

N°03 · 10 JUL 2026 · OBSERVATION

What is left after you ask AI?

Sometimes we hand AI a task. Sometimes a decision. Sometimes a capability we'll need again. Those are not the same thing.

“Just ask AI” is often good advice.

I do it all day. Summarise these notes. Clean up this document. Find the missing step in this workflow. Give me five ways to say this without making it sound like everyone else. Turn this chaotic list into something I can finally see.

I have no desire to learn the manual mechanics behind every task I hand over. That would defeat the point.

But I still need enough contact with the work to know whether the result belongs in the situation.

Did the summary miss the detail that changes the decision? Does this sentence sound like me? Did the table reveal something I now need to act on? Did the workflow solve the actual problem, or only produce a very neat version of the wrong process?

When I can make that judgment, AI acts like a useful service. When I cannot, its response is still a suggestion, however finished it looks.

This is where things become more interesting.

Sometimes we hand AI a task. Sometimes we hand it a decision. Sometimes we hand it a capability we will need again.

Those are not the same thing.

A task may be simple: turn a recording into notes, clean a spreadsheet, write a first reply, find patterns in a pile of feedback. The output may be enough, after a quick check.

A decision has consequences you will carry forward: your price, your offer, your positioning, the direction of a project, the system your team will use every week. AI can help you see options, challenge assumptions, organise the material, and create a strong first version. It cannot know which choice is right for your business without the reality you hold around it.

Then there is capability: work you will return to repeatedly. Writing an offer. Assessing an opportunity. Designing a customer journey. Briefing a designer. Building a system that needs to keep working after the first exciting version.

Here, a good answer can be useful and still leave you underprepared for the next round.

Ask AI to create an offer and it can return something polished: a headline, benefits, sections, a call to action, perhaps a price suggestion. But the page may rest on a vague buyer, a weak promise, an imagined problem, or proof that does not exist.

The page can look complete before the thinking is complete.

That is one of the stranger effects of AI. It can make missing knowledge harder to see.

A blank page was honest. It showed you where you had no language, no decision, no structure, no answer yet. A fluent response can cover that gap so well that you stop noticing it. You receive a usable page and move on, while the questions that would actually change your business remain untouched.

Then a similar task arrives next week, and you start again from zero.

Tools like Cowork make this especially visible. They collapse the distance between an idea and a finished-looking object. You can describe an offer in two sentences and receive a page with a headline, benefits, sections, and a call to action.

The speed is useful. It also means choices about the buyer, the promise, the proof, and the price can be made before you have consciously made them yourself.

I do not think resistance is the answer. Friction has no moral value of its own. AI can remove busywork, reduce the cost of experimentation, and make things possible that would have stayed on the list for years.

AI will keep doing more. The practical decision is more personal: which parts of your work are you happy to never learn, and which parts need to become clearer to you because your business will depend on them?

For me, that means paying attention around work that is strategic, repeated, or difficult to evaluate. I ask AI to show me the assumptions it made, the information it is missing, the alternatives it considered, and the trade-offs between them. I compare directions. I correct the brief. I save the decisions and principles that emerge, rather than saving only the final copy.

You can see the difference in the next task.

Instead of writing, “Create my offer,” again, you can say, “Build this around the buyer we chose, the proof we actually have, and the price logic we tested. Do not make claims we cannot support.”

You are still using AI heavily. You are simply no longer asking it to invent the foundation each time.

Maybe the better question is not whether every AI interaction makes us more capable. Some should simply save us forty minutes.

The question is whether an interaction leaves anything useful behind.

A reliable result.

A clearer decision.

A reusable way of working.

A question you can finally see.

A capability you did not have before.

You do not need all five every time. A formatting task may leave you with a clean document and nothing more. Good. A strategic conversation about your offer should leave more than a page of copy. It should leave you with a sharper understanding of what you are building and why.

Three months later, when a similar problem comes back, what do you reach for first: a blank chat box, or a clearer idea of what needs deciding?

That is what I want AI to leave with me.

N°02 · 09 JUL 2026 · OBSERVATION

When AI makes you faster at the wrong business

AI will build you a finished-looking business in an afternoon. Finished-looking and finished are not the same measurement.

You can now build the wrong business very fast.

I mean that precisely. In an afternoon, AI will give you an offer, a landing page, a content plan, a funnel, three automations, and a sales campaign to wrap around them. All of it will look done. Headlines, sections, a call to action, a price. It will look like the output of a company that knows what it is doing.

And none of it will tell you whether the buyer is real, whether the problem you are solving is one people actually pay to remove, whether your positioning holds, whether any of it should exist.

This is the part I have become most careful about, because I sit exactly where it happens - between the business decision and the AI build. What I watch, over and over: the speed of production gets mistaken for progress. Someone spends a week generating assets and feels like they moved the business a week forward. They didn’t. They moved the material forward. The decision underneath it never got made - it just got covered in something that looks like a decision.

Here is the mechanism, because it matters. When you had to make everything by hand, the effort was a filter. Writing the whole sales page yourself was slow enough that somewhere in the slowness you were forced to confront the weak promise, the vague buyer, the proof you didn’t have. The labour made the gaps loud. AI removes the labour, and it removes the filter with it. You skip straight to the finished-looking thing, and the gaps come along, quietly, underneath the polish.

So the output looks like a real business and rests on an unmade decision. AI didn’t fix the confusion. It packaged it, professionally - which is worse, because now the confusion has a landing page and a price.

This is why my work starts before the automation, not at it. Before I help anyone speed anything up, I want to know what actually has to be decided first: who this is for, what they are really buying, what has been tested and what has only been assumed. Then, and only then, is AI genuinely useful - because now it is speeding up a real decision instead of decorating a missing one. The tool is extraordinary at execution. It has no opinion about whether you are executing the right thing.

I want to be precise about the limit of my own claim, because I am not saying speed is the enemy. Fast is good, once the direction is right - I build fast every day, and I would never go back. I am saying speed is not evidence. The number of things you made this week says nothing about whether your business got clearer. Those are two different measurements, and AI has made it very easy to read one as the other.

So the question I would keep asking, in the middle of all that easy production, is not “how much did I make?” It is smaller, and more annoying. Which decision did I actually make this week? What did I learn about the person I am selling to? What can I repeat on purpose now, instead of regenerate from scratch? If the honest answer is “nothing, but look how much I produced” - that is not a business moving forward. That is a very well-designed way of standing still.

N°01 · 08 JUL 2026 · DIARY

Living with the tools that think back

The old tools waited for a command. The ones I use now finish my sentences - and I'm watching what that does to my own judgment.

The old tools waited.

A hammer, a spreadsheet, a search box - they sat still until you acted, and then they did exactly what you said, no more. The relationship was simple: you thought, the tool executed. Whatever thinking happened, happened in you.

The tools I use now think back.

I write half an idea and the system finishes it, in a direction I hadn’t chosen. I ask a question and it questions the question. It suggests, interprets, disagrees, fills the gap I left - sometimes better than I would have, sometimes confidently wrong, and increasingly I can’t tell which at a glance. The tool has opinions now. Or something close enough to opinions that I respond to them as if they were.

I have been watching what that does to me, because it is doing something, and it is more interesting than the productivity story everyone tells about it.

Some days it makes me sharper. The thing answers back, I see a flaw I’d have missed, I think better because I am thinking with something. Other days I notice the opposite: I reach for it before I have finished having my own thought. The answer arrives fluent and complete, and my half-formed version - the one that was actually mine - quietly loses the argument. Not because it was worse, but because it came second, and slower, and it wasn’t as well-dressed.

That is the part I keep circling. Not whether the tool is good; it is astonishing. But when does a fluent answer start borrowing an authority I never consciously handed it? I catch myself trusting a response partly because it is smooth, and smoothness is not the same as being right. I have made decisions faster this year, and I am genuinely not sure whether faster meant clearer, or just meant I stopped sooner because something plausible showed up.

And I see it in the people I work with, which is how I know it is not only me. Someone gets confident on top of an answer they don’t fully understand. Someone else starts doubting a good instinct because the machine framed it differently. The tool doesn’t only help them do the work. It gets into how they decide the work is done, how long they are willing to sit in not-knowing, what they will still call their own.

I don’t have the resolution for this. I am not even sure “resolution” is the right shape to want. What changes in a person who spends every day reasoning next to something that reasons back - in their judgment, their patience, their sense of what counts as their own thinking? I can feel it changing in me, and I can’t yet name where it lands.

Maybe that is the real work of this whole transition, underneath the tools and the saved time: learning which parts of my own judgment I want to keep exercising, precisely because the machine is so willing to do them for me. I don’t know yet which those are. I think finding out is going to take longer than learning any of the tools did - and I think it matters more.

Today’s entry lands tomorrow.
Meanwhile — the evergreen thinking →