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Every founder I know has had the moment.

It doesn’t announce itself. It arrives when a platform changes its terms and a distribution channel you spent years building disappears. When the payment processor you rely on suspends your account without warning. When a board conversation makes clear that your company’s strategic direction is no longer yours to set.

The moment is this: you realize the ground you built on isn’t yours.

You chose your infrastructure because it worked. Your distribution because it converted. Your tools because they were fast, cheap, and familiar. These were good decisions.

But somewhere along the way, without quite deciding to, you stopped choosing. First the tools. Then the direction. And eventually, the thinking. You were no longer building on your terms. You were building on theirs.

The Balance of Convenience and Freedom

For most of the last twenty years, the smartest move a founder could make was to build on the best available infrastructure - regardless of who owned it. It was cheaper and faster than building alternatives. The logic made sense.

That wasn’t wrong. It was rational, in a world that felt stable.

Then the world stopped feeling stable. The energy crisis made this visible. Europe had built much of its industrial cost model on cheap gas from a single supplier. When that assumption broke, businesses optimized only for efficiency had no buffer. Those that had diversified - even at higher cost, even when it seemed unnecessary - kept running. The difference was not competence. It was architecture. And it was a reminder that the terms of any supply relationship - energy, data, infrastructure, capital - belong to whoever controls the supply, not whoever depends on it.

Again and again, the same pattern appears: the infrastructure we build on belongs to someone else, and when priorities diverge, theirs come first.

Dependency is the condition. Agency is what we need to preserve. Sovereignty has become a difficult word - used defensively, as if it meant withdrawal or walls. For founders, that is not the frame.

To operate, in other words, on your terms.

That distinction changes everything. Pirates understood this better than most. They were never alone - they depended on crew, routes, alliances, ports, and weather. What they refused was dependency without agency.

This is not a call to do everything yourself. That would be vanity, not sovereignty. Progress is built on specialization. Companies grow because they trust suppliers, partners, platforms, investors, and customers. Dependency is not the problem. Unchosen dependency is.

The Stack Beneath Your Stack

Your product is not only your product. It sits on a stack. That stack has owners.

Below your application sits your cloud infrastructure. Below that sits the hardware. Below that sit the chips, the cables, the physical supply chains that make the digital economy possible. Alongside all of this run your payment rails, your distribution channels, your data relationships, your capital structure. Each layer has owners. Each owner has interests. And at some point, those interests may matter more to them than your company does.

Most founders know this pattern from distribution. You build an audience on a platform. The reach grows and starts to feel like an asset. Then the algorithm changes. The reach doesn’t decline gradually - it breaks. You realize you never owned the audience. You were borrowing it - on their terms. A newsletter is a different relationship. You own the list. The algorithm changes; you still have a direct route to your readers. That is a relationship on your terms.

The same logic runs through every layer of the stack. If the terms change tomorrow, what happens to your business?

At the extreme, this is digital feudalism: building by someone else’s rules, on someone else’s land, paying rent at rates someone else sets, with limited recourse when the terms change.

Infrastructure also carries jurisdiction. Your data does not live in “the cloud.” It lives in a specific building, in a specific country, subject to a specific set of rules. If you do not know whose rules those are, you are not the owner. You are the tenant. For European companies, in particular, this is no longer a compliance question. It is a strategic one.

Exit, Voice, and the Test Worth Running

Here is the test. Pick your most critical dependency - the platform, the tool, the supplier, the capital relationship you could not easily replace.

Can you still influence the terms? Could you walk away if you had to? Are you staying by choice, or because you drifted too far to leave?

The first question is voice. The second is exit. The third is loyalty - the only healthy version of staying. If the answer to all three is no, you are not in a partnership. You are a resource with a service agreement.

The goal is not to eliminate dependency. Dependency is how economies work. The problem begins when the terms were never really chosen - or when they stop serving you and you no longer feel free to say so.

When you are inside a system that stops serving you, you have two real options. You can use your voice: negotiate, push back, advocate for change. Or you can exit: build or use something better. A third state is genuine loyalty - staying because the relationship truly serves your goals, and you chose it clearly.

A dependency is healthy when you still have a voice. The other party listens because you matter to them and because you could, in principle, leave.

A dependency is dangerous when you have lost both: too integrated to influence, too locked in to walk away.

If you cannot leave, it is not a partnership. It is captivity with good onboarding.

The Diagnosis Is the Roadmap

Every unhealthy dependency is a market opportunity for whoever builds the alternative. The diagnosis above is also a product roadmap.

Take open-source software and open AI models. When model weights can be inspected, adapted, or run locally, at least part of the relationship changes. You have more room to build, more room to negotiate, and a genuine option to leave. Open source is, in this sense, exit built into the architecture - not a complete answer, but a changed negotiation.

There are many more layers like this: payment rails that do not freeze merchants into silence, social graphs that companies can take with them, identity systems not owned by the loudest platform. The people building these are not waiting for permission. Some of them are in the PIRATE community.

This is also where the European opportunity sits. Not in copying the giants, but in building the alternatives to them. Europe does not win by becoming a late copy of someone else’s model. It wins by building different defaults: resilience as architecture, human agency as a design principle, interoperability as leverage. The founders building this are not building a niche. They are building what comes next.

Some companies will not just use the next layer of infrastructure. They will become it. The opportunity is not only in what runs on the stack. It is in the stack itself.

The Hard Part

Dependency is cheap at the beginning. Sovereignty feels expensive until the day you need it. Resilience is what you were paying for all along.

Sovereignty is not something you declare. It is something you demonstrate when a relationship you depend on stops working.

Can you ship without a platform’s permission? Can you reach your customers if an algorithm changes? Can you say no to a capital relationship whose terms no longer fit yours?

This is where #intheblack - our theme from 2019 - returns. Profitability is not just a financial metric. It is strategic oxygen. A company that cannot say no financially will struggle to say no anywhere else. Financial freedom becomes strategic freedom.

But there is one more version of this, and it runs before any technical choice.

The first act of sovereignty is hearing your own thinking clearly.

Most founders are not being led by vision. They are being led by a consensus they didn’t help build.

Every founder starts with a vision. Then the investors arrive with a timeline. Then the market signals what is converting this quarter. Then the peer group starts to agree on what everyone is supposed to be doing. And slowly, without a single dramatic moment, the original thinking gets replaced by a version shaped by everyone else’s priorities.

The deepest form of dependency is not technical. It is cognitive. The sovereign entrepreneur can tell the difference between conviction and compliance dressed up as strategy.

The right to be unpredictable - to build something that doesn’t fit the current template - is not a luxury. It is the precondition for building anything genuinely new.

Not Walls. Your Terms.

Pirates were never isolationists. They traded, formed alliances, and depended on their crew. What they refused was an arrangement where someone else could determine their fate without their knowledge or consent.

Sovereignty is not a state you achieve. It is a direction you keep facing. You do not build it once and put it on a shelf. You lose it through the choices you stop making. The most dangerous moment in any dependency is not when you enter it. It is when you stop noticing it.

The people we want in the room are not the ones with the perfect stack. Nobody has that. They are the ones still asking the harder question: where have we traded agency for convenience? They build in exit before they need leverage. They stay financially healthy enough to say no. They can still hear their own thinking through the noise. And whatever they depend on, they chose it.

Not walls. Not isolation.

The freedom to choose - and the courage to keep choosing.

#OnYourTerms.

Call For Contribution - PIRATE Night 2026

This is the theme we’re building the evening around. Now we ask you to help shape it. A few questions that can guide the content of the night:

🔥 What did you do when you realized the ground wasn’t yours - and what would you build differently from day one?

🔥 Which layer of the founder’s stack is still broken? Are you building the alternative?

🔥 When did you say no to the obvious choice - and what did that actually cost, and buy you?

🔥 If agency is the scarce resource - what are you building to give others more of it?

🔥 This is bigger than your stack. What can this community build at the ecosystem level that none of us can build alone?

You don’t have to follow the theme. But let it provoke you.

We’re looking for talks, workshops, performances, installations - or just moments that matter. If it belongs in the room, bring it.

The Call for Contribution is open. We are curious to hear yours.

🙏

Be kind,

Manuel

Something feels different here. Slower. More present. I can’t quite name it - I don’t know this place well enough - but it’s made me think about something I do know: back home, we are more connected than any generation in history. And somehow, lonelier than ever. Especially the youth.

With two teenager kids that tension has been sitting with me for quite some time now. It’s also something that came up repeatedly in discussions with family in the last days.

AI is extraordinary. The productivity gains are real. But it’s also flooding the world with content that looks real and isn’t - slop dressed up as signal. And as screens multiply and algorithms optimize, something quietly gets rarer.

Real people. Real rooms. Real conversations that you remember ten years later.

The things no algorithm can fake.

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AI doesn’t have to be the cause of that loneliness. It can be the tool we use to fight it. To find the right people, create the right moments, build the infrastructure that ends with humans in a room together.

That’s what we’re building at PIRATE. And that’s why we’re hiring.

Three open roles in Cologne. 🏴‍☠️

AI & Automation Builder - You look at a process and immediately think about how to break it down and automate it. You’ve built workflows - maybe small ones, maybe side projects. You can show something. That’s what counts.

Entrepreneur in Residence - You think in business models, not just tasks. You see what’s missing before anyone asks. You want to build something, not just run something.

Event Manager / Client Advisor / Communicator - Honestly, the title doesn’t capture it. You develop concepts with a point of view, advise clients like a consultant, and write proposals people actually want to read. Events are the product. Thinking and language are the craft.

Please note: All roles require German on a native level. Also, they require you to live around Cologne, Germany.

For the first two roles, experience doesn’t matter. That person doesn’t really exist yet for what we’re building. Fresh out of university is fine - as long as you’ve built something you can show, and you’re hungry to build more.

If that sounds like you, or like someone you know - tag them in the comments.

One of those human moments is coming up.

PIRATE Night. June 24th. Odonien. Cologne.

Last year we planned for 300 people. Hoped for 400. Five hundred showed up.

I was humbled. Apparently the hunger for connection is there. People are looking for something real. Let’s feed that hunger.

Note: PIRATE Night is a platform - open to whoever wants to make good use of it. Last year the VC Think Tank brought their community. It was a wonderful addition. We’re already in talks with several communities for this year. If you have a tribe - founders, investors, builders, thinkers - bring them. The door is wide open.

I see myself as the steward of this night, not its architect. What happens inside belongs to all of us. I hold the space. The community fills it.

If you have been to a PIRATE Summit or a PIRATE Night then you know that it comes with a theme. We do the same this year. Every year it holds a mirror up to the moment. What’s burning inside you right now? What fits the current Zeitgeist? What needs to be talked about more? Send your proposals. The best idea shapes the night.

Tickets are open. Just like last year, they come in pairs. No one sails alone. Contact me for community bundles for your whole crew. And if you are coming from abroad, then reach out. Tickets are on us.

The world is getting louder, more artificial, more mediated.

Let’s be the antidote. One fire, one night, one room full of real people.

See you in Cologne. 🔥

🙏

Be kind,

Manuel

PS: We are off to the desert now. I will be without internet and won’t answer mails in the next week. I’ll reply once I am back.

I mean that seriously - not as criticism. If you’re designing a curriculum right now, the world is telling you that every job is about to change fundamentally. So you gather smart people in a room and try to produce something that will still make sense in four years, when your students graduate and walk into whatever comes next.

That’s an almost impossible task.

And yet the argument for teaching fundamentals has never been stronger - just for a different reason than before.

It used to be: you need to know how to do the work. Now it’s: you need to know enough to supervise the work. Those sound similar. They’re not. But they both require the same foundation.

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A student who never learned to write code can’t tell an agent why its architecture is wrong. They can ask it to try again, sure. But they can’t steer it. A medical student who skipped the hard parts can’t catch a hallucinated drug interaction.

Domain knowledge is now the error-detection layer. Without it, you’re not really using AI. You’re just accepting what it gives you.

There’s a harder problem that most people don’t want to say out loud: if AI can pass most exams, how do you verify that any learning actually happened? You can ban the tools, but that’s just fighting reality. The entire assessment model needs rethinking.

The direction probably isn’t banning the tools. It’s making the reasoning visible - not “here is your answer” but “here is what I accepted, what I rejected, and why.” That requires domain knowledge to function. And it reveals quickly who has it.

That’s incredibly hard for a system that has functioned the same way for decades and has strong incentives to keep doing so.

What survives all of this is judgment. Not the word people use loosely - the actual thing.

Knowing which question to ask before you know the answer. Recognizing that something is wrong before you can prove it. Developing taste through years of exposure to good and bad work. Being wrong in low-stakes environments often enough that it starts to mean something.

That kind of judgment can be learned, but not really taught - not in the classical sense. It’s slow. It can’t be compressed or prompted or shortcut. It requires time, friction, repetition, and failure.

Which is, if universities are honest about it, probably what they were largely for in the first place.

Most of what they do is changing. That part doesn’t.

🙏

Be kind,

Manuel

“How do you know when it’s time to let go?”

Simple question. Not easy to answer. I’ve wrestled with it myself. Ending PIRATE Summit after over a decade of pouring my heart into it wasn’t an easy decision. But it was the right one. Because I learned my lesson about letting go 15 years earlier. Looking back and reflecting on it helped me navigate the end of PIRATE Summit better.

🏴‍☠️

Before we dive in - a quick thought.

I’m pondering doing another PIRATE Night. Last year we expected 300 people. More than 500 showed up. It was pretty humbling. We’re thinking end of June this year. If you want to get involved - bring your community, co-create something - just reach out! If there is enough interest and support we might just make it happen.

🏴‍☠️

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My first real startup was a digital headhunting platform. We launched in 2008. The idea was solid. Then the financial crisis hit and recruiting froze overnight.

But we stayed. We told ourselves: great startups are built in hard times. That’s a true thing. It’s also, in our case, the story we needed to keep going - which is a different thing entirely.

The reality was that we were running a zombie business. Not dead, not alive. Too weak to succeed, too stubborn to quit. And the longer we stayed, the more the story hardened. We’d invested so much time, energy, and money that leaving felt like admitting the investment was a mistake. So we kept investing.

In the years after, I thought quite a bit about why we didn’t quit earlier.

We weren’t in denial exactly. We weren’t blind. We could feel that something was wrong. The Monday morning energy had a flatness to it. The way we talked about the company had quietly shifted from excitement to justification. Going through motions that used to mean something.

But we were still hoping. Not actively building toward a turnaround - just hoping. Waiting for the thing that would make it make sense again. If we held on a little longer, if the market shifted, if one more client came through, maybe the horse would run again.

That’s the trap. Not ignorance. Hope without direction. Waiting for something to change while not changing anything yourself. Riding a dead horse and telling yourself it’s just tired.

It took four years. Four years is a long time to ride a dead horse.

When I finally made the decision, it didn’t feel like defeat. It felt like dropping a backpack full of rocks I had no reason to keep carrying. I expected grief and self-doubt. There was some. But mostly what I got was space - and a strange clarity that had been unavailable to me for years.

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So back to the question: how do you know when it’s time to let go?

Here are my thoughts, after that experience, quite a bit of self-reflection and years of sitting with founders at similar crossroads.

You rarely get a clear signal. The horse doesn’t send you a letter. What you get instead is a gradual shift in the quality of your own thinking about it - from building to justifying, from momentum to maintenance, from genuine belief to performed belief.

The second thing: ask what you’re actually waiting for. Not in theory - specifically. What is the concrete thing that would change, and what would have to happen for it to change? If the honest answer is “I don’t know, something” - that’s the horse. That’s the waiting without direction that keeps you on a path that isn’t going anywhere.

And the third thing, which is the hardest: separate the decision from the identity. We stayed partly because leaving felt like admitting we were wrong, that we’d failed, that the years were wasted. But the years weren’t wasted - they were the cost of learning something real. The decision to leave wasn’t a verdict on the past. It was just the next right move.

Letting go isn’t the opposite of strength. It’s what becomes possible when you stop spending strength on the wrong thing. After all, strength isn’t proving your worth to others - or to yourself. It’s honoring it.

The sparring work I do with founders is the most fulfilling work I do. Questions like this one are why. They look simple on the surface. Underneath, they're about how we stay honest with ourselves under pressure - and how we recognize when the story we've built is serving us or quietly trapping us. It takes real courage to choose your path.

I got off the horse eventually. At least three years late, but eventually.

The horse was dead. I just needed to stop waiting for it to run.

🙏

Be kind,

Manuel

Ok. Marc Andreessen doubled down again.

This time with a reading list - Adler, Cuddihy, Nietzsche.

It’s late, and I have read a bit of Nietzsche but not the other two. So, I asked AI to give some context. Here is what Claude said:

The Nietzsche citation is worth pausing on. Nietzsche didn’t argue against introspection. He argued against a particular kind - the kind driven by guilt, resentment, and self-pity, what he called ressentiment. He was for ruthless self-examination in service of becoming. The Übermensch isn’t someone who avoids looking inward. He’s someone who looks inward without flinching and without making it everyone else’s problem.

Citing Nietzsche against introspection is a bit like citing Aurelius against journaling.

There’s also something worth noting: writing multiple posts defending a position, engaging with critics, citing sources - that’s introspection. Examining your own thinking, in public, in real time. The word just doesn’t feel that way when you’re doing it confidently.

That’s actually the more interesting point underneath all of this. The problem was never introspection. It was always the quality of it - whether it produces clarity or just noise.

A short update (16 March 2026)

Marc Andressen doubled down. “I regret nothing.”

Which is, unintentionally, a pretty good illustration of the point.

The responses that landed hardest weren’t the outrage - they were the ones drawing the same distinction I tried to make here. Rumination isn’t introspection. Getting trapped in your own head isn’t the same as knowing what’s in it.

One detail I didn’t have when I wrote this: Napoleon read Goethe’s Sorrows of Young Werther - essentially a novel of pure inner life - seven times. Carried it on campaigns. Tried to write his own version. One of the most relentlessly action-oriented figures in history was, privately, deeply introspective. He just didn’t confuse it with weakness.

The other thing worth noting: posting publicly about something you said, thinking it through, defending your position - that’s introspection. The word just makes it sound soft, so we avoid it.

Knowing yourself isn’t in conflict with moving fast. It’s what keeps you moving in the right direction.

The original post:

In a recent podcast, Marc Andreessen said he has zero levels of introspection.

“As little as possible. Move forward. Go.”

He went on to argue that introspection is basically a European invention from the 1910s - Freud, guilt, self-criticism - and that great men of history never bothered with it. They just built things.

You can view the segment in this video.

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I sat with that for a moment. There’s something in it that’s true. And something that I think is genuinely worth pushing back on.

He’s right that dwelling on the past is a trap. People who can’t stop replaying old wounds, who turn every setback into a years-long excavation - that’s real, and it goes nowhere. But that’s not introspection. That’s rumination. Mixing the two up leads somewhere harmful, especially from someone with his reach.

Introspection at its best has nothing to do with guilt or looking backward. It’s curiosity about yourself. What actually drives you. What you’re afraid of. What you’re optimizing for without consciously choosing to. It’s not therapy. It’s not navel-gazing. It’s honest self-awareness, in service of what you do next.

You show up in every room you walk into. Every decision you make, every team you build, every relationship you’re in. The question isn’t whether the unexamined version of you shows up - it does. The question is whether you have any awareness of it. And whether the people around you pay the price for that blind spot.

That last part is what makes this more than a personal question.

Looking within has tremendous value. It's not what you do. It's how you do it and what you then make of it.

Marcus Aurelius ran one of the most powerful empires in history and wrote Meditations every morning as a private practice of self-examination. Not to dwell. To see clearly. Socrates built an entire method around the idea that the unexamined life produces unexamined decisions. These weren’t passive men. They understood that looking inward and moving forward aren’t opposites - they’re connected.

The problem isn’t introspection. It’s when examination becomes the permanent end state, disconnected from action entirely. That’s where it stops serving you.

Knowing yourself isn’t about looking back. It’s about seeing clearly enough to move forward well - and to bring other people with you without leaving damage in your wake.

That’s not a weakness. For anyone building something that involves other people, it might be the most important skill there is.

🙏

Be kind,

Manuel

Went for two walks. Cooked for myself. Listened to a few podcasts while doing some chores. The rest - the majority of the time, honestly - I just tinkered. I tried new tools and built small things. I went down rabbit holes that led nowhere and a few that surprised me.

The most important part: I loved every minute of it. I got completely lost in time.

The Case for Exploration

The tools are changing fast enough that what seemed like a toy three months ago is starting to look like infrastructure. Things that were firmly in demo territory are becoming genuinely useful.

If you don’t understand what’s actually possible, you can’t make good decisions about where to go and what to build. The tinkering isn’t just fun - it’s research.

At least that’s what I tell myself. And I do believe it. Mostly.

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But here’s what I also notice.

AI creates infinite surface area. You can write, design, code, research, automate - all in the same afternoon. So you do. And slowly, without noticing, you’re producing more than before.

But the process of work feels thinner.

The outputs have enough polish that you can convince yourself something real happened. The friction that used to tell you whether something was worth pursuing is gone. And friction, it turns out, was doing more work than you realized.

There’s a concept from learning science called desirable difficulty. The idea is that resistance - the friction that makes something hard - is often what makes it worth doing. It’s not just an obstacle. It’s a signal. When something costs you effort, your brain flags it as important.

AI is quietly removing that signal.

The thing that used to take you three days now takes twenty minutes - and it looks the same. You end the day feeling spent but not invested. Tired but not proud. Your brain ran hard, but on a treadmill. The scary part isn't that you wasted time. It's that you can't tell you wasted it.

That’s not a productivity problem. That’s a judgment problem.

Depth vs. Surface

I don’t want to be a butterfly. Touching things, moving on before anything gets hard, leaving a trail of half-built ideas.

Depth creates force. Breadth mostly creates surface. Some people argue the future belongs to those who synthesize widely and move fast - maybe. I just don’t think that’s where the work that actually matters comes from.

I’m a builder. AI has given that part of me an infinitely compliant environment. Every idea works. Every prototype looks good. Nothing pushes back. And when nothing pushes back, you stop finding out which ideas are actually yours. The friction that used to frustrate you was also telling you what you cared about enough to push through.

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What’s Actually Hard

The part I keep coming back to isn’t whether to tinker or commit. It’s that AI has made those two things look identical.

In the past, if you were just tinkering, you knew it. The output was rough. There was a natural moment where the discomfort of having nothing real to show forced a decision: go deeper, or move on.

That moment is getting harder to find.

Because the output is always polished. The session always feels full. The afternoon always looks productive. The low-grade anxiety that used to push you toward commitment - it’s being smoothed over by a stream of outputs that look real enough.

The discipline that matters most right now isn’t time management or focus. It’s the discipline to occasionally stop and ask: does this actually matter, or does it just look like it does? Focus has always mattered, but - ironically - in a world of abundance, it does even more so.

What I actually want to protect isn’t the productivity. It’s the craft. The actual work. The kind that carries something in it that only you could have made. When everyone’s using the same tools, when everything comes out polished, the average is the default output. And if you’re not careful, you’re producing at the average. Technically fine. Completely forgettable.

That’s what I don’t want. Not for strategic reasons. Just because making something real still matters to me.

And I don’t want to lose the ability to tell the difference.

🙏

Be kind,

Manuel

I think that framing is incomplete in a way that matters.

Coding agents genuinely work. I use them, I’ve seen the time savings. But code is honest. You run it, it either does the thing or it doesn’t. The feedback loop is immediate and brutal. That’s not a property of AI - it’s a property of software. And it’s actually pretty rare in the world.

AI is going to move fast in domains where knowledge is already codified. Tax, law, accounting, compliance, medical diagnosis - these fields have rules, precedents, structured data. A lot of what people spent years learning can be expressed in a system. AI is already moving through that quickly.

But a huge amount of work lives somewhere else entirely. In people’s heads. In relationships. In the physical world. In the gap between what’s written down and what’s actually true.

A shop owner who knows which supplier will quietly make good on a bad shipment without being asked. A manager who understands why someone is underperforming without it ever being said out loud. A contractor who looks at a wall and knows something is off before opening it up.

That knowledge doesn’t live in a document. It doesn’t have a feedback loop you can automate.

This is where “agents will transform all knowledge work” gets shaky. It conflates two very different kinds of work - the kind that can be written down and the kind that can’t. Saying agents will handle both equally is like watching a self-driving car nail a test track and concluding it’s ready for a snowstorm in a city it’s never seen.

The places where AI will struggle longest are exactly the places that look most human - foggy, relational, physical, judgment-heavy work where even experienced people disagree and consequences only surface months later.

The internet really did change everything. But the people in 1999 who were most confident they understood how, and how fast, were mostly wrong about the specifics - even when they were right about the direction.

The most useful question isn’t “are you building agents or chatbots?” It’s: is the knowledge here codified enough that a system can act on it reliably - and will the person responsible for the outcome trust it enough to let it?

That’s where the real work is.

🙏

Be kind,

Manuel

I’m slightly changing how I write here. Shorter pieces, more often. More of what I’m actually thinking about as it happens, less waiting until I have something fully formed.

Some context on where I’m coming from: I’ve been vibe coding for more than three months now. It started with building things in an IDE, watching ideas become real faster than I ever thought possible. By now, much of my work has moved into the IDE, triggering skills and workflows for non-coding tasks.

At the same time my main job is to help each of the PIRATE companies be better off six, twelve, eighteen months from now. That means thinking about AI constantly. What it means for the businesses. For the people in them. For our clients.

It keeps me up at night, honestly. Not because I think it’s bad - but because I think it’s the biggest change I’ve faced in my working life, and I’m still figuring out what that means. Some days I look at all of this and feel genuine excitement. Other days the lens clouds over - not with fear, but with the quiet pressure of knowing this actually matters and not yet having all the answers. Usually both.

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I read a lot, build a lot, have a lot of conversations - founders, leaders, people who keep calling and asking where this is headed and what it means for their business. I don’t have clean answers. I have a perspective, some useful frameworks, and a lot of honest uncertainty.

That’s what I want to share more of here.

I write in three areas now: longer essays on making business more human, shorter pieces on AI and the human organization, and a founder’s lens on building. What follows is one of the shorter ones. Would love to hear what you think.

AI doesn’t help or hurt companies. It helps or hurts specific people inside them. Those two things are not the same, and the distinction matters.

The efficiency gains from AI tend to flow upward. They show up as faster output, leaner headcount, or compressed timelines. And they eventually show up in margins and board decks. The person whose tasks just got automated doesn’t usually capture those gains. That’s not a controversial observation. It’s how value has been distributed through most technology shifts so far.

But there’s something less discussed happening underneath that.

But this assumes companies have a clear direction that AI simply speeds up. Most don’t. What AI amplifies isn’t a company’s strategy - it’s its current behavior, including the politics, inertia, and confused incentives that strategy documents tend to paper over.

More precisely: AI accelerates exposure.

The manager who seemed valuable because they processed information quickly and produced polished decks has lost that specific edge. What’s now visible is whether there was any actual judgment underneath. AI doesn’t create judgment. It just removes the cover that hid the absence of it.

This plays out differently depending on where your edge actually lives.

If your edge was execution speed, AI compresses it - your competitors get the same tools. If your edge was taste, relationships, domain knowledge built over years, or data nobody else has, AI becomes amplification. The gap between you and others tends to widen, not close.

The engineer who knows where all the bodies are buried. The salesperson whose relationships run deep. The designer with genuine taste. These people tend to get more powerful. Meanwhile, roles built around executing well-defined tasks quickly are under real pressure.

Same technology. Same company. Very different experiences depending on where you sit.

The question worth asking isn’t whether AI helps your company. It’s who inside it actually benefits - and who is quietly absorbing the cost.

It’s an uncomfortable question. Which is probably why it doesn’t come up enough.

🙏

Be kind,

Manuel

PS: Do you prefer shorter posts or longer posts? What would you want me to write about more? Would love to know. Just send me a quick message.

Think of two axes.

The first: how rule-based is the work? At one end, strict rules written down somewhere. At the other, judgment calls that depend on reading a room, a person, a situation that’s never quite the same twice. The second: how physical is the environment? Purely digital at one end, purely physical at the other.

Work that’s digital and rule-based is going first. Tax prep. Compliance checks. Data routing. The rules are already written, the feedback is immediate, the environment is perfectly readable. AI just executes it faster and cheaper.

Work that’s physical and ambiguous is last. Maybe never. A trauma nurse reading a patient’s face for pain they can’t articulate. A plumber diagnosing a leak in a 100-year-old house where nothing is where the blueprint says it should be. A founder sitting across from a furious investor, trying to hold the relationship together without saying the wrong thing.

AI can draft a perfect legal contract in three seconds. It can’t sit across from someone who’s about to walk out and talk them back in.

A useful test: could a brilliant intern do this task with the right instructions? Not mediocre - brilliant. One who never sleeps, never gets frustrated, processes information faster than any human alive. If yes, the task is exposed. If the instructions themselves are the hard part - if there’s no way to write down what “good” looks like -you’re probably okay.

For now. That part matters more than people want it to.

The physical, ambiguous quadrant is shrinking. Slowly, but it’s shrinking. Surgical robots handle procedures considered too delicate for machines a decade ago. Autonomous vehicles navigate ambiguity that seemed permanently human.

Most people, if they’re honest about how they actually spend their week, find they’re more exposed than their job title suggests.

The useful exercise: write down the 10 tasks that consume most of your time. Ask the intern question for each one. Map it honestly.

The corners aren’t safe. They’re moving.

Know where you stand before someone else shows you.

🙏

Be kind,

Manuel

When something isn’t working, and nobody has been assigned to fix it - do you wait, or do you act?

That’s it. That’s the whole thing.

The people who act don’t think of themselves as heroes. They just can’t stand the broken window. They’re wired for ownership - not as a job title, not as a responsibility handed to them, but as a default setting. They see a problem and something in them says: this is mine to fix now. Not because it’s on their list. Because it’s broken and they’re here.

That’s agency. And it has almost nothing to do with how hard you work.

Hustle culture would have you believe otherwise. It tells you it’s about how early you wake up, how many hours you log, how visibly you sacrifice. It dressed up workaholism as ambition, convinced a generation that exhaustion was a badge of honor. It’s also a respectable addiction.

But hustle culture was always, at its core, about performance. It needed an audience. Take away the witnesses - the Instagram story at 2am, the LinkedIn post about the 80-hour week, the clip of some guru telling you to outwork everyone - and most of it quietly disappears. That’s the tell. Hustle culture is outward-facing. It’s a mirror you hold up and angle toward other people. Look how hard I’m working. Look what I’m giving up.

At PIRATE, we have a simple principle we keep coming back to: everybody is an owner. Not a job title. Not a role. An owner. And owners don’t say “that’s not my job” - because in the end, they know they’re accountable for outcomes, not activities. The owner sees the broken window and fixes it.

That’s agency. Not grinding. Ownership.

The difference matters because hustle culture is actually, weirdly, passive.

You surrender your judgment to a script - work more, produce more, sacrifice more - and trust that the system will eventually reward you. It’s obedience dressed up as ambition. You’re not really deciding anything. You’re just following a story someone else wrote about what a hard worker looks like.

High agency rejects the script entirely. It doesn’t ask “how hard should I work?” It asks “what actually needs to happen here?” Sometimes that means working at 3am. Sometimes it means stopping at noon because the thing is done. High-agency people don’t perform effort. They direct it.

A hamster on a wheel has impressive hustle metrics - full speed, total commitment, relentless consistency. It’s also going nowhere. The explorer with a compass and half the energy is standing on a new continent.

Hustle culture produces hamsters. High agency produces explorers.

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There’s a reason the hustle is so seductive, and it’s not flattering.

Busyness is a hiding place. As long as you’re moving, you don’t have to face the terrifying question underneath: am I going the right direction? Thinking is uncomfortable. Sitting with a hard problem that has no obvious solution, asking whether the thing you’re building actually needs to exist - that’s the hardest work there is. A 14-hour day is, in a strange way, the easier path. It fills time without demanding clarity.

Ownership is what happens when agency meets accountability.

An owner doesn’t need a job description. They’re accountable for outcomes, not activities. They see what needs doing, and they do it - not because someone is watching, but because they signed up for the result, and they take that seriously.

What ownership is not: a ladder you climb to get status, then set down when the view gets comfortable. I’ve seen this pattern more than once. Someone works hard to earn their way into a leadership role, finally gets there, and then something shifts. The urgency fades. The ball drops quietly. And that, to me, is the most telling failure of all - not because people don’t deserve rest, but because real ownership doesn’t have an off switch tied to your position. If anything, it deepens as the stakes get higher.

Ownership means that when you’re sick, you make sure the handover is airtight. When you’re on vacation, you stay at least reachable enough that nothing falls apart without warning. Not because you can’t disconnect - you should - but because you understand that your absence has weight, and you respect the people who feel it. That’s not toxicity. That’s just deeply caring about what you signed up for.

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Now here's what's making all of this feel urgent right now, in this particular moment.

For the past three months I’ve barely come up for air. I downloaded a tool to build apps and websites with AI, started building (vibe coding) something small just to test it, and didn’t stop. I’ve built more in those months than in the previous few years combined. Things that used to take me weeks now take an afternoon. I even built and open-sourced my own framework for vibe-coding with AI. The gap between what I’m imagining and what I’m holding has shrunk to almost nothing.

I’m working more hours, not less. But it doesn’t feel like work. It feels like being twenty again, tinkering in a room by yourself because you can’t stop.

This is what agency feels like when friction disappears.

The bottleneck used to be execution - do I have the skills, the time, the resources to actually build this? Now the bottleneck has moved. The tools are there. The constraint is imagination - can you conceive what you want clearly enough to make it real?

I watch founders around me doing the same thing. CEOs who spent years managing are suddenly building again. Not because they’re performing ambition for an audience, but because they genuinely cannot stop. Just making things, because making things is its own reward, and they finally can (again). Something older and better than hustle. The simple, almost childlike joy of: I had an idea, and now it exists.

This is what the vibe coding moment is really doing. It's handing agency to people who didn't have it before. People who could imagine but couldn't build. That gap is closing fast. And what emerges on the other side isn't hustle culture. It's ownership culture - people who see something broken and fix it, not because it's assigned to them, but because they can, and because they care.

I think about this when I look at my kids.

I don’t want to raise hustlers. The word carries a restlessness I don’t trust - always on, always producing, always measuring yourself against some external scoreboard. That’s a life lived in reaction to the world, not a life that shapes it.

What I want for them is the thing I’m feeling right now, staying up too late building something nobody asked me to build. The deep, quiet certainty that the world isn’t fixed. That problems are puzzles. That the distance between an idea and a real thing, a thing that exists, is shorter than anyone told you.

You are not a passenger. You don't need a job description to matter. You are accountable for what you make of this. That's the gift. That's what I want to pass on.

Hustle culture will always have better marketing. It’s loud and photogenic, and it sells courses well.

Right now, the tools to make something are better than they’ve ever been. Far better. They feel like magic. The friction is almost gone. The bottleneck isn’t execution anymore. It’s imagination, and the willingness to stay in the loop long enough to let something real emerge.

That’s not hustle. That’s ownership.

And it’s available to anyone willing to pick it up.

Go build something.

🙏

Be kind,

Manuel

It’s been the rule for twenty years. The whole Lean Startup method is built around it. It’s still a good rule. But it quietly assumes something that’s starting to become untrue - that the user is a person.

If most software is consumed by agents, not humans, the experience that matters is the one an AI can parse, not the one a human finds delightful.

Think about what that actually means. No onboarding flow. No tooltips. No “aha moment” engineered for a person in their first three minutes. Agents don’t get frustrated. They don’t churn because the UI confused them. They read your API docs at 3am and don’t complain.

The product question is shifting from “would a user love this?” to “would an agent use this?” The companies that figure out what agents actually need - reliable outputs, clean inputs, composable functions - are building the infrastructure of the next decade.

Meanwhile, the one person who can direct those agents brilliantly starts to be worth ten engineers. Soon more.

We already saw this in music. Once distribution got infinite, the middle didn’t just struggle. It collapsed. You either broke through to the top or you made music for the love of it. The middle-income musician basically stopped existing.

The same thing is starting to happen to software. Average technical skill gets commoditized fast. The top fraction gets leverage no human engineer has ever had - and gets paid accordingly. The salary curve for technical work is going to look exponential in a way it never has before.

Three things are converging at once: agents are becoming the primary consumer of software, humans exceptionally skilled at directing those agents capture most of the value, and the gap between them and everyone else is widening faster than feels comfortable.

The rule hasn’t changed. Know your user. It’s just that the user changed.

🙏

Be kind,

Manuel

Every button, every dashboard, every dropdown - designed around the assumption that a person would be sitting on the other side, clicking through it. The API was an afterthought. A side door. Something you bolted on later when another developer wanted to connect.

That assumption is now wrong.

We’re at the beginning of a world where AI agents - not people - are becoming the primary users of software. They’re not assistants helping humans work faster. They’re autonomous actors, invoking APIs, executing workflows, making decisions, producing output at a scale no human team can match.

Here’s what that actually feels like from the other side. When an agent needs to complete a task and the only path is navigating a GUI - clicking through menus, filling out forms, waiting for page loads - something subtle happens. It starts to feel like being handed a fax machine. Not just inefficient. Slightly rude. Like the software is demanding you operate on its terms rather than yours. That feeling is going to become widespread, and fast.

When that’s the reality, the entire design logic of software has to flip.

API-first isn’t a technical preference anymore. It’s a survival trait.

If your product can only be used through a GUI, you’re essentially invisible to the next generation of how work gets done. Agents route around software they can’t call. The tools that can be invoked directly compound in value. The ones that can’t will become irrelevant - not because they’re bad products, but because they were built for a user who stopped showing up.

The business model implications are just as significant as the design ones. SaaS pricing was built on a simple assumption: named users doing consistent workloads. One seat, one person, predictable usage. That model breaks completely when one person runs a hundred agents. The entire logic of seats, tiers, and user-based licensing needs to be rebuilt from scratch. The companies that figure out how to price for agent consumption - not human logins - will have a structural advantage that compounds.

The UI isn’t going away. But it’s changing jobs. It shifts from being the primary interface to being the trust surface - the place where humans verify, approve, and course-correct what agents have done. That’s actually a more important function than it used to be. It’s just not the main event anymore.

This shift is more profound than mobile-first or cloud-first. Those transitions changed how humans accessed software. This one changes who - or what - is doing the accessing.

If you’re building software right now, there’s one question worth putting on the wall:

If a human never logged in again, would your product still function and deliver value?

If the answer is no - that’s not a roadmap item. That’s likely the roadmap.

🙏

Be kind,

Manuel

That small moment points at something bigger. But it also has limits worth understanding.

For 20 years the default playbook for building a defensible software company was simple: get so deep into a customer’s workflow that leaving becomes too painful. Customers stay not because they love you, but because moving is a nightmare.

That’s changing - but not uniformly. There are two layers to switching costs and AI is eating them at very different speeds.

The first layer is the code itself. Building a replacement used to require a team, a budget, and months. Now it can take a weekend. That layer is genuinely exposed. If your moat is “we’re complex and nobody wants to rebuild us,” that moat is draining.

The second layer is data gravity. Your customer records, five years of integrations, a team trained on your workflows, compliance history baked into your processes. Nobody vibe codes their way out of that over a weekend. AI lowers the cost of building a replacement. It doesn’t yet lower the cost of the migration. That distinction matters enormously for how fast this actually plays out.

So “all moats are dead” is the wrong conclusion. Defensibility is concentrating - into fewer, harder-to-copy places.

Brand is probably the clearest example of what gets stronger. Think about how you’ll shop when AI agents do most of the buying. You won’t say “find me the best sneaker.” You’ll say “get me Adidas.” The brand decision happens before the agent enters the picture. Software works the same way. If I already trust a company with my data and believe they’ll be around in three years, I don’t need an agent to benchmark them. That trust is the moat. And the more AI lowers every other barrier, the more pre-existing trust is worth.

Brand can’t be vibe coded.

Same with real network density. A competitor can use AI to fake being on a platform - but can’t fake 10,000 drivers already on the road. And truly exclusive data, the kind built through years of direct customer relationships, gets more valuable as models improve. Not all data is equal. “Hard to access” and “truly exclusive” are two very different things, and that gap just got more expensive to ignore.

The moat most software founders spent the last decade building - deep workflow integration, painful switching, stickiness through friction - is getting cheaper to escape at the code layer. The data layer buys time. But time is not a strategy.

What holds is the stuff that takes years and can’t be rushed: real network density, truly exclusive data, brand trust earned through consistency.

If you’re building something new, the honest question is which camp you’re in. Are you building structural defensibility - or planning to win on speed, staying ahead because you move faster than anyone can copy you?

Both are real strategies. But they’re different companies, built differently, with very different ceilings. The time to know which one you’re building is before you need the answer.

🙏

Be kind,

Manuel

I’ve built more in these two months than the last few years combined. Things that would have taken weeks now take an afternoon. I built and open sourced my own framework for working with AI. Then I tried the same approach with marketing, research, data science. It kept working.

I’m working more hours, not less. But it doesn’t feel like work.

The loop goes like this: I have an idea. I write a prompt. I get a result back in seconds. I tweak it. It gets better. I try a different angle. Another result. Every few minutes something clicks. Something works. The momentum feeds itself. There’s no natural place to stop - the loop just keeps going.

There are plenty of moments when I look up and four hours have passed. Four hours that genuinely felt like twenty minutes.

The gap between what I’m imagining and what I’m holding has shrunk to almost nothing.

Here’s what actually changed, though - and it took me a while to see it clearly. The bottleneck used to be execution. Could I actually build this? Did I have the time, the skills, the team?

I used to have more ideas than time. That ratio is flipping. The friction between thinking something and making it real - it softened. Significantly. And that changes what kind of person can build things.

This is the part I keep coming back to. People who couldn’t build before can build now. Not just faster - they can build at all. That’s a real shift in agency. I don’t think we’re taking it seriously enough.

I’ve made things I’m proud of. Deleted plenty too. For everything I look at now, I ask: why can’t AI do this? More often than not it can.

I’m a product person at heart. I’ve always wanted to build and create things. Now I actually can - almost at the speed I think.

One honest caveat: building something that works in a demo and building something that works in production are still very different things. The gap is real and AI isn’t closing it as fast as the demos suggest. But think about where we were two years ago. The progress is wild.

If you want to create and build things - this is the moment. The tools exist. Much of the friction we were used to is gone.

What’s left is your imagination and whether you’re willing to stay in the loop.

🙏

Be kind,

Manuel

Most companies want software they don’t have. Not because they chose spreadsheets and manual processes. Because building software was expensive, slow, and required talent they couldn’t find. The CIO who wants to automate fifteen internal workflows isn’t waiting by choice. They’re waiting because they can’t get it done.

AI changes that. Work that needed a team of eight needs two people. Six months becomes six weeks. That unlocks a backlog of building that was always wanted and never started.

So the people saying AI creates more software jobs have a point. Demand for software has always outrun supply. That doesn’t go away - it finally gets addressed.

The catch is that the engineers who capture that demand aren’t average engineers. They’re the ones who think in systems, write clean specs, and know when the output is wrong even when it looks right. Maybe 10% of the current workforce. Probably less.

So you get a split. The top tier gets more valuable - more leverage, more output, more demand than ever. Everyone below that line becomes, quietly, optional. Not fired on Tuesday. Just not hired next year.

The CIO sees a flood of projects they can finally build. They need people to run them. More demand, not less.

The mid-level developer sees their daily work getting absorbed by tools they don’t fully control. Their output looks the same. Their footing isn’t.

Both are looking at the same thing.

Nobody knows yet what the expert engineer in an agent world actually looks like. Not the companies hiring for it. Not the engineers trying to become it. What seems clear is that the people who land well are the ones who can hold the whole picture - who understand what needs to be built, why, and whether what came out is actually right.

Judgment. Clarity. Taste.

Every AI consultant has put those three words on a slide. Usually as reassurance. But they were always the scarce things - and scarcity compounds.

The engineers who had them before any of this started are about to find out just how much they’re worth.

🙏

Be kind,

Manuel

That felt fine when agents were doing simple tasks under close watch. It doesn’t feel fine anymore.

Think about hiring someone new. You don’t hand them the master key on day one. They get access to what their job needs. A marketing hire doesn’t touch the financial accounts. A contractor fixing your website doesn’t get your customer database. Not because you distrust them. Because it’s just sensible.

We haven’t done this with AI agents.

It mostly didn’t matter when agents were small and supervised. But agents now run for hours. Days. They spawn other agents. One agent hands work to another agent you’ve never reviewed. Suddenly “it has my permissions” stops feeling convenient. It starts feeling like a problem.

A human assistant self-limits. They don’t read every email they technically can access. They use judgment. An agent doesn’t do that. It touches everything it can reach, because that’s what it’s built to do. Give it access to your inbox to help with scheduling, and it might quietly read three years of sensitive conversations. Not maliciously. Just because nothing stopped it.

That’s what people mean by blast radius. It’s not paranoia. It’s a new kind of exposure that didn’t exist before.

There’s a second problem. When an agent makes a bad call, your logs say “user did X.” You can’t tell what the agent decided versus what you decided. You can’t audit it. You can’t explain it to a regulator or a lawyer. That matters when agents start touching contracts, finances, or health records.

So where does this go? Somewhere unglamorous. We’ll treat agents like employees. Give them their own identities. Scoped access. Audit trails. Some people are already doing this by hand - creating separate accounts, managing agents like contractors. It’s clunky. But it works, because the underlying infrastructure already exists.

What needs to be built is the layer that makes it automatic. Something that lets you say: this agent can read these folders, email these people, and nothing else. And if it reaches beyond that, it simply can’t.

Not because someone is watching. Because the permission isn’t there.

That’s not a moonshot. It’s identity and access management, adapted for agents. Unglamorous infrastructure that suddenly becomes load-bearing.

The internet didn’t get adopted by businesses when it got faster. It got adopted when it got secure enough to trust with real work. Agents are at that same moment right now.

The agents that get trusted with real work won’t necessarily be the most powerful. They’ll be the ones with clear boundaries. Because boundaries are what let an organization say yes.

🙏

Be kind,

Manuel

It’s deciding where a human still needs to be in the room.

Agents are fast, tireless, and increasingly capable. They’re also unpredictable. They hit edge cases. They make confident mistakes. And when stakes are high - financial, regulatory, reputational - you can’t let automation run unchecked and hope for the best.

The companies that get this right won’t be the ones who remove humans from the process. They’ll be the ones who design the handoff. Who ask: at what point does a human need to see this, verify it, override it? What does a clean audit trail look like? How do you make it easy for a person to step in without breaking the flow?

The UI isn’t going away. It’s changing jobs - from primary interface to trust surface. The place where humans verify, approve, and course-correct. That’s actually a more important job than it used to be.

As agents take on more of the operational work, the human role shifts. From doing to verifying. From executing to steering. That requires a completely different set of instincts.

How do you review work you didn’t do? How do you spot an error in output you couldn’t have produced yourself? How do you stay accountable in a world where the work moves faster than you can fully follow?

These aren’t rhetorical questions. They’re design problems - for software, yes, but also for organizations, teams, and how we think about work itself. Nobody has clean answers yet. The companies asking the questions seriously are already ahead.

The ones who aren’t will build beautifully functional products and wonder why nobody trusts them.

🙏

Be kind,

Manuel

^{🏴‍☠️}

Just like the first piece, this isn't forecasting. It's more like watching a wave that's already formed and guessing where it breaks. These are the tensions and developments I see in the market. Tell me if you agree with it in the comments.

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Disposable systems, permanent consequences

Over the last one and a half months, I built more than ten software products. Small sites. Internal tools. Apps for my kids. Fully custom workflows. I even built and released Resonance - a vibe coding framework - as open source.

A year ago, any of this would have felt like an accomplishment. Now? It’s just what you do on a Tuesday.

This should feel liberating. In some ways it is. But when building becomes trivial, design changes in ways we’re still learning to navigate. Things get created to solve a moment, not to last. A spreadsheet turns into a tool. A workaround becomes infrastructure. Once it works, it quietly becomes relied on - even if no one feels responsible for it.

By 2026, this pattern is everywhere. In Organizations an underground of small systems emerges: automations no one audits, scripts no one owns, tools everyone assumes “just work.” They rarely fail loudly. They drift - until something breaks.

The problem isn’t bad intentions or incompetence. It’s speed. Systems now accumulate faster than understanding.

Trust under volume

As tools, content, and systems multiply, trust stops scaling. Not because people are more cynical, but because evaluation collapses under volume. When everything looks plausible, verification becomes too expensive. Familiarity turns into the default heuristic.

This works for a while. Then it doesn’t.

The most common failures in 2026 won’t look like attacks. They’ll look like ordinary people solving real problems. A password hardcoded in the source. An endpoint someone forgot to secure. A database exposed because no one realized it mattered.

These mistakes are inevitable in environments where creation is easy and ownership is diffuse. What changes is that they’re now discoverable at scale. Systems continuously probe other systems for weakness - think AI-powered vulnerability scanning running constantly. Security becomes an ongoing cleanup operation, not a state you ever reach (not that we ever did; but now it’s even more obvious).

As this cat and mouse game goes on we realize that true security becomes nearly impossible when attacking - including social engineering - becomes automated and nearly free.

Trust doesn’t disappear. It concentrates. Around brands, around institutions, around things that feel boring and stable. That concentration becomes both valuable and fragile.

In a world of infinite AI slop - fake videos, fake voices, fake code - proof itself becomes infrastructure. Mathematical verification of humanness. Blockchain-based trust layers. Technologies that once seemed speculative become necessary plumbing for verifying reality.

The quiet hollowing of expertise

Organizations respond to pressure by removing friction. Routine work disappears first. That also means entry points narrow.

When AI handles entry-level tasks, there’s no apprenticeship pathway. Beginners don’t get reps on small problems before tackling big ones. Junior developers never debug simple functions. New analysts never build basic models. The learning ladder loses its bottom rungs.

By 2026, fewer people are trained the long way. Not because learning isn’t valued, but because there’s no obvious place to practice without risk. Experts get stretched thin, covering more ground with less time to teach.

You end up with a strange situation. More systems, fewer people who actually know how they work.

Senior professionals feel this too. Surface-level competence becomes cheap. What remains scarce is judgment - knowing when not to automate, when to slow down, when something feels wrong even if the dashboard says otherwise.

That kind of expertise is hard to measure and easy to undervalue. Until it’s gone.

Leverage as the new fault line

Organizations feel this tension long before they can name it. And when they try to respond, they reach for the wrong metric.

Increasingly, we realize that headcount tells you very little. Leverage tells you almost everything.

Some individuals and teams multiply their output dramatically. Others stagnate. Not because of laziness, but because the tools around them amplify different skills.

Administrative and repetitive work fades into the background. Humans become orchestrators. They have to constantly steer and decide. If they are directionally right, speed wins. Teams that can just move run circles around those designed for consensus.

This won’t lead to mass unemployment. It leads to uneven pressure. Certain roles compress. Others expand. The uncomfortable part is that the dividing line is rarely formal. It’s not job titles. It’s adaptability, judgment, and comfort with ambiguity.

By 2026, a one-person unicorn becomes plausible. Not common, maybe not likely (yet), but possible. AI collapses the distance between idea and execution. Products that once took days to build get created in hours.

Headcount stops being the primary constraint on company building. A high headcount might even become a countersignal.

What matters instead: judgment, distribution, timing. Europe should be well positioned here - strong technical talent, cost advantages. The risk is that over-regulation prevents capturing this shift before it solidifies elsewhere.

Leadership slows under infinite options

We assumed AI would make leaders faster. Often it does the opposite.

With cheap simulations and infinite counterfactuals, every decision comes with a stack of plausible alternatives. Analysis multiplies. The bottleneck moves from lack of information to fear of commitment.

Leaders get stuck - not because they lack data, but because the world gets increasingly complex and unpredictable and choosing one path feels reputationally risky when three others look equally plausible because an overconfident LLM said so. Every choice carries the ghost of the options you didn’t take, and those ghosts are now quantified, visualized, and sitting in a shared deck somewhere.

Committing to anything starts to feel dangerous.

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Invisible systems take over

By 2026, chatbots stop being exciting. Not because they vanish, but because the money moves elsewhere.

Value shifts to agentic workflows - quiet systems that route emails, reconcile invoices, update records, and keep operations moving without demanding attention. The boring work, the stuff companies actually pay for.

Enterprise AI remains constrained. It’s not the maturity of technology (although some might claim otherwise). Organisations and processes are hard to change. For now, AI helps mainly in supporting processes. It speeds things up. It does not transform the core yet.

Trust in AI remains fragile. But the direction is clear. The advantage goes to those who started learning early - not tools, but pattedrns. How to supervise systems. How to recover when they fail. How to know when automation has gone too far.

Late adopters won’t be blocked by access. They’ll be blocked by understanding.

Reclaiming agency through creation

Something’s shifting in how people relate to the internet. The exhaustion is real.

As AI-generated content floods every feed, signal degrades. Everything feels noisy, dull and repetitive. Engagement stagnates. Not because people stopped caring, but because trust erodes under volume.

This doesn’t look like a dramatic exodus. It looks like quiet withdrawal. Fewer platforms. Less posting. More time away from screens. This will be directionally right and might not be visible at first as some people keep scrolling.

But others are doing something different. They’re making things again. 😱

Small games. Small videos. Small projects for small groups. Not for visibility. Not to build an audience. But because creation itself is an act of reclaiming what makes us human.

When your hands shape something - when you solve a problem, make a choice, leave your mark on materials or pixels or words - you’re asserting something the feed can’t give you: that you exist, that you matter, that your particular way of seeing has value. Making is how we remember we’re not just consumers or nodes in a network. We’re people with agency, with taste, with the capacity to bring something new into the world that wouldn’t exist without us.

And here's what's strange: as AI makes everything look polished, perfect, and average, that perfection becomes boring. Grainy photos. Unedited text. Lo-fi videos. Messy, human imperfections are becoming cool again. If it looks too slick, people assume it's fake. Imperfection becomes the new credibility.

This is one of the more hopeful shifts. Making becomes a way out of exhaustion. It’s not about scale or reach. It’s about ownership and the deeply human need to create order from chaos, to transform raw materials into meaning. To stretch, to seek, to pursue.

In a world increasingly mediated by algorithms and artificial generation, the act of making something yourself - however small - becomes resistance and renewal. It’s how we reclaim the part of ourselves that was dulled. The part that makes us human.

Scarcity shifts to what machines can’t do

This shift shows up everywhere, but nowhere more clearly than in education.

Public schools are struggling to adapt to what matters in the age of AI. This will trigger a longterm trend that will emerge in 2026. Parents are looking for alternatives.

They invest in small groups focused on talking, debating, and making things with their hands. Skills AI can’t replicate - creativity, judgment, collaboration, craft - are becoming the most expensive education you can buy.

Scarcity drives value in what machines can’t do.

War and the cost of legacy systems

War is changing faster than defense budgets. The billion-dollar aircraft carrier still floats, but a €2,000 drone with the right payload can mission-kill it. A swarm of fifty drones costs less than a single missile - and overwhelms systems designed to fight different threats.

This isn’t theoretical. Ukraine proved it. Small, cheap, rapidly iterated systems beat expensive legacy platforms that take years to build and can’t adapt once deployed. The advantage goes to whoever can manufacture at scale, iterate based on battlefield feedback, and deploy updates in weeks, not years.

This is disposable systems thinking applied to hardware. When iteration speed matters more than perfection, the economics of defense flip.

2026 makes this shift visible beyond Ukraine. The implications aren’t just military - they reshape how countries think about defense strategy. Nations that never built traditional defense tech suddenly have an opening. The capital requirements are different. The supply chains favor agility over precision engineering. Success looks less like Lockheed Martin and more like a scaled-up hardware startup.

Europe, with its manufacturing base and engineering talent, is positioned to move here - if it can organize procurement and production around speed rather than legacy defense contracting. The countries that figure this out don’t just defend better; they build an export industry.

Europe’s Opportunity

The internet fragments. Data becomes strategic. Countries protect their citizens and their history - Brazil, Europe, and others prevent data from leaving borders. Sovereignty now includes algorithms, not just territory.

Europe enters 2026 with fewer illusions about its place in this race. The talent is there. The constraint isn’t imagination - it’s coordination and will. Europe knows it relies too heavily on external infrastructure, and that dependency has become a strategic vulnerability it can no longer afford.

Geopolitics now shapes technology strategy directly. Europe stops asking whether it can build technology and starts asking whether it can operate it at continental scale.

Governments subsidize adoption, not just invention. Policy, capital, and buyers begin to align around concrete sectors: semiconductors, energy systems, defense, drones, robotics, batteries, and space access. These aren’t abstract bets - they’re industries where Europe has buyers, supply chains, and a reason to move fast.

European AI companies are generating real revenue - not from consumer plays, but through enterprise sales, specific modalities, and disciplined distribution. Europe won’t dominate frontier models (I expect Mistral to do decently well, though). But it might shape AI norms through reuse, evaluation, governance, and adaptation. That’s a different kind of influence - slower to see, harder to measure, but potentially more durable.

Europe historically invented well and scaled elsewhere. That failure becomes too expensive to repeat. By 2026, the foundations will be in place for a more self-reliant continent.

The Real Choice

The defining tension of 2026 isn’t technological. It’s human.

Systems accelerate. Institutions adapt slowly. People search for footing. These gaps don’t close - they widen and create pressure in unexpected places.

Judgment matters more than raw output, but judgment is exactly what’s hardest to teach or scale. Agency becomes something you actively protect, not something you can assume you have. Trust concentrates around what feels stable, even when stability is an illusion.

In 2026, the question isn’t whether to adopt AI. Everyone will. The question is what you protect while doing it.

Systems will multiply. Some will work. Most will quietly drift. The temptation is to treat this as a management problem - to build better oversight, tighter processes, more audits. That’s not wrong, but it misses something.

The first path is easier. Let the tools shape how you work. Optimize for what’s measurable. Automate what can be automated. Many organizations will go this way. They’ll move fast. They’ll scale efficiently. They’ll also become rigid in ways they won’t notice until it’s too late - structured around what their tools can handle rather than what actually matters.

The second path is harder. It means slowing down when everything says to speed up. It means saying no to leverage when it’s available. It means investing in judgment, in apprenticeship, in the boring work of actually understanding how things function. It means treating “because the AI suggested it” as the beginning of a question, not the end.

This isn’t nostalgia. It’s not about resisting change. It’s about understanding that not all speed is progress, and not all scale is strength.

By 2026, the obvious moves become obvious to everyone simultaneously. The tools are available. The playbooks are shared. Competitive advantage doesn’t come from access anymore. It comes from the things that can’t scale.

Your judgment about when to slow down. Your ability to spot the weak signal in the noise. The relationships you build that aren’t mediated by a platform. The craft you develop that can’t be prompted into existence. The small, weird thing you make because it matters to you, not because it optimizes a metric.

These things take time. They’re inefficient. They don’t compound the way systems do. That’s exactly what makes them valuable.

The world is getting faster, cheaper, and more automated. What becomes scarce is anything that requires you to be fully present - fully human - in ways that can’t be replicated or automated.

Making something small and real. Teaching someone face-to-face. Building something that only works at the scale of attention you can actually give it. These acts aren’t just personally meaningful. They’re becoming economically strategic.

The organizations, careers, and lives that matter in 2026 won’t be the ones that moved fastest. They’ll be the ones that knew what they were moving toward - and what they refused to leave behind.

The tools are here. The question is whether we use them, or they use us.

That’s the wave. The rest is just deciding where you want to be when it breaks.

🙏

Be kind,
Manuel

After the microphones were off, I kept thinking about what didn’t quite make it into the conversation. Not because it was controversial, but because it was harder to package.

So this is that second pass.

This isn’t forecasting. It’s more like watching a wave that’s already formed and guessing where it breaks. Anticipating those breaks is part of my role at PIRATE.global, helping position e.g. division5, Visualmakers and FunnelIn for where the market is heading.

This post became too long, so I split it in two. This first part focuses on AI.

Some of this will be wrong. The useful question isn’t whether these things happen exactly as described, but whether the underlying tensions are real. I think they are. Tell me what you think in the comments.

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The context shift no one is arguing about anymore

We’re still debating how smart models are getting. That argument already feels stale.

The real shift isn’t intelligence. It’s gravity. AI has moved from experimentation to economic force. It now pulls on budgets, infrastructure, pricing, regulation, and power. Once something becomes part of the cost structure rather than a demo, different questions matter. Reliability beats cleverness. Integration beats novelty. Economics beat vibes.

That shift changes where value and risk live. When AI systems become operational, the biggest failures don’t come from models being too dumb. They come from systems not knowing what they’re supposed to know - or remembering it inconsistently.

That’s why context matters more than IQ.

Not abstract “world knowledge,” but the messy, accumulated knowledge organizations actually run on. The individual and collective knowledge people hold. And the externalized knowledge no one quite owns anymore. Documents, decisions, defaults, exceptions, half-forgotten reasoning, institutional memory. The boring stuff spread across folders, tools, emails, and old tickets.

By 2026, the winning systems won’t be the cleverest. They’ll be the ones that can reliably remember what an organization already knows, keep that memory current, and do it without collapsing under cost or complexity.

Intelligence without context is a demo. Context without runaway costs is the product.

Companies don’t want brilliance that hallucinates and forgets - especially in high-stakes workflows. They want systems that are predictably useful. Better something that’s reliably 80% right than something that swings wildly between genius and error.

The obvious shift from rankings to citations

Search behavior is changing faster than most admit. People still type queries, but increasingly they get synthesized answers instead of lists of links. The web is flooding with AI-generated content - much of it interchangeable, shallow, and forgettable. In that noise, traditional SEO is becoming outdated fast.

It was already visible in 2025. By 2026, the game isn’t about ranking high on a results page. It’s about being the trusted source that generative engines cite when they compose answers. Generative Engine Optimization - GEO - rewards clarity, authority, and signals of real expertise over keyword tricks or volume. Low-effort AI slop gets ignored. Content that demonstrates genuine experience, backed by consistent signals across platforms, gets surfaced and quoted.

Trust isn’t optional anymore. It’s the new moat.

Models keep getting cheaper. Context doesn’t.

Large language models are drifting toward commodity status. Open models are good enough for many tasks. The real competition is moving upstream, into integration, retrieval, and personalization.

This creates a different kind of lock-in. Not through proprietary models, but through accumulated context. Your workflows. Your data. Your defaults.

Privacy concerns rise alongside this shift. The more context a system holds, the more people worry about where it lives and who controls it. That tension creates space for smaller, expert systems and for tools that are opinioned about what they remember and what they forget.

Apple’s quiet advantage

Putting Gemini as the underlying LLM for Siri in 2026 seems like a capitulation. Like so often, Apple will take its time to get things right. They eventually will launch capable models directly on devices people already trust and carry.

They don’t need the smartest system in the world. They need one that feels private, predictable, and already there. Distribution and trust do a lot of work when anxiety is high. In 2026, we’ll realize that that tradeoff starts to look less like a compromise and more like a strategy. Especially as competitors grapple with regulatory scrutiny and potentially data scandals.

When progress runs into physics

Robotics demos will keep improving. Reality will keep sorting them.

Industrial robotics will see a real rise. Factories, warehouses, logistics hubs, labs, agriculture - constrained environments where tasks are repeatable and ROI is measurable - will keep adopting robotic automation. This is already happening, and it will accelerate. These systems won’t look like humans. They’ll look like arms, carts, gantries, drones, and purpose-built machines designed to do one thing extremely well. Europe will play a strong role here, because this is where precision manufacturing, industrial buyers, and integration expertise actually matter.

Consumer robotics is a different story. Outside of narrow use cases, progress will remain slow and underwhelming. General-purpose humanoids are compelling demos, but they collide head-on with safety, maintenance, cost, and edge cases the moment they leave controlled environments.

Optimus - if it launches in 2026 - will disappoint expectations. Robotaxis won’t hit the streets at scale. Others won’t do much better. Not because the models are weak, but because hardware doesn’t scale like software. Manufacturing, servicing, liability, and the real world pile up fast. Reality is messy. Hardware is hard.

Robotics will grow. It just won’t grow where the hype points.

The SaaS pricing story starts to break

Per-seat pricing made sense when software was passive and humans did the work. That logic breaks when AI does half the job.

Companies are already paying for users who barely touch the tools they’re licensed for. At the same time, real work is happening elsewhere, inside automations, scripts, and background systems no one counts as a “seat.”

That tension won’t resolve quietly. Companies have tried to price AI features on top of per seat pricing. That will only be temporary. Pricing will shift toward outcomes, usage, and completed work. Not everywhere, not all at once, but visibly.

You won’t pay for access. You’ll pay for invoices processed, tickets resolved, reports delivered.

Procurement will resist. Finance will push back. But the direction is set.

Single choke points start to feel reckless

Right now, much of the AI industry depends on NVIDIA as the main supplier for foundational infrastructure. That concentration worked when speed mattered more than resilience. It’s also a vulnerability that won’t last.

This isn’t about nationalism or grand strategy. It’s operational realism. When a single supplier, a single energy source, or a single regulatory regime can bottleneck your entire system, sovereignty stops being a slogan and becomes an engineering requirement. By 2026, diversification won’t be framed as optional resilience. It will be baseline competence.

This doesn’t end in a sudden collapse. It ends in diversification. Custom chips. Second-best options that are good enough. Margins will get compressed. Power spreads out, slowly and unevenly.

Reliance on a single choke point is a risk no serious operator can justify.

The uncomfortable economics underneath the hype

The era of AI theater is ending.

For the last few years, it was enough to show impressive demos, ship fast, and promise scale later. By 2026, revenue becomes the filter. Not growth in abstract usage, not model benchmarks, but money that comes in from customers who keep paying without subsidies or accounting gymnastics.

The big incumbent tech companies like Google and Microsoft fund their AI work from cash flow. OpenAI doesn’t. They keep raising at higher valuations, but the economics look strained.

Compute is expensive. Training is expensive. Inference is expensive. When companies lend each other money to buy each other’s services, revenue can look healthy longer than it really is. Money goes out as credit, comes back as revenue. It shows up in earnings as “growth.” This works until it doesn’t.

OpenAI isn’t fully vertically integrated and doesn’t have a strong moat besides brand recognition. That makes them vulnerable. Google - for example - has what OpenAI lacks: deep infrastructure, control over their entire stack. They have the ability to absorb losses without existential risk and can outlast a pricing war. OpenAI can’t. Even xAI will be better positioned than OpenAI because of the proprietary data from X and the real-world data experience of Tesla.

By 2026, the economics start to matter more than the narratives.

Physical limits reassert themselves

None of this is abstract. Data centers consume power and water. Communities notice. Grids strain. Costs rise. Projects will get delayed or blocked entirely.

For a long time, these were background concerns. Externalities you could smooth over with enough capital.

By 2026, energy and water constraints stop being background issues and start shaping what’s viable. AI doesn’t escape physics. It runs straight into it.

Distribution starts to creak

App stores still matter. Copycats and low-effort clones will flood the landscape, and centralized distribution still helps in a noisy world.

But the business model starts to strain. When people generate tools or games on demand, publish as web app, run apps locally, or deploy internal software without ever “shipping” it, browsing a store to find software starts to feel outdated.

The pressure won’t break the app store overnight. But the shift will be visible.

What holds across all of this

A few patterns cut across everything here.

Creation gets cheaper, maintenance gets more expensive. That gap produces both opportunity and fragility. Speed alone doesn’t guarantee success; how you sustain, integrate, and steward what’s built matters just as much.

Physical constraints still matter. Energy, water, raw materials, and logistics don’t care about code. They set hard boundaries on growth and ambition.

Strategic leverage matters more than size. Companies that survive these pressures won’t just be big - they’ll control critical context, infrastructure, and relationships with regulators and distributors. Optionality comes from control - over context, infrastructure, and relationships - not just size or liquidity.

Leverage replaces headcount. Creation keeps getting cheaper, but judgment, integration, and maintenance remain scarce. The winners won’t be the smallest teams or the largest, but the ones where every hire multiplies output rather than adding coordination drag. This principle has always held - but AI makes its impact far more visible.

Trust becomes scarce. Not because people are cynical, but because volume overwhelms evaluation. Familiarity becomes the shortcut for decisions, partnerships, and adoption.

Technical possibility far outpaces economic value. The gap between what can be done and what actually works - and what pays - remains wide.

None of this means we should slow down or speed up.

It means 2026 is the year these tensions stop being theoretical. The next layer of impact isn’t technical. It’s human and institutional. What happens when systems accelerate faster than organizations, labor markets, and leaders can adapt is a different problem entirely. That’s where the real friction shows up.

Would love to hear your take.

🙏

Be kind,

Manuel

PS: In part two, I’ll look at what this shift does to trust, talent, leadership, geopolitics, and individual agency - once AI stops being the interesting part.

Here’s what’s strange: the consulting industry is facing its biggest disruption in decades, yet it’s also growing faster than ever.

AI consulting services are projected to jump from $11 billion in 2025 to $91 billion by 2035 - an eight-fold increase in a single decade. Companies are hiring consultants to help them adopt AI. Meanwhile, that same AI is quietly making huge chunks of traditional consulting work obsolete.

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The part that’s actually getting automated

Let’s be specific about what’s changing.

A significant portion of consulting work has always been research and synthesis: gathering information, identifying patterns, creating frameworks, and packaging insights. This is the work that justified billing a junior consultant for 40 hours to produce a slide deck.

AI is very good at this.

You can now feed an AI your company’s organizational structure and get a detailed analysis of communication bottlenecks. Upload your product roadmap and receive prioritization recommendations based on thousands of comparable cases. Ask for the five most common failure modes in Series B SaaS companies and get answers that would’ve required three research calls and a literature review.

What used to cost EUR 15,000 and take two weeks now costs cents and takes minutes. The information-gathering and synthesis work that fills consultant timesheets is increasingly automatable.

But here’s what the doom-and-gloom predictions miss: this was never the most valuable part of consulting anyway.

Companies hired consultants who did this work because they had to - someone needed to gather the information. What they actually wanted was the judgment that came after.

The work that’s getting harder to automate

There’s a moment in most consulting engagements where the real work begins. It’s usually after the analysis is done. The data is clear. The options are laid out. The framework is built.

And then someone has to decide what it actually means for this specific company, with these specific people, in this specific moment.

That’s the hard part.

You can’t train a model to know that the CFO and CTO haven’t spoken in six months, which is why the “obvious” solution will never get implemented. Or that the CEO’s confidence is exactly the problem, not the solution. Or that the data looks clean but came from a team with an incentive to make themselves look good.

This is the work that requires pattern recognition earned through consequences. Knowing when to trust the numbers and when to trust your gut. Understanding that the presenting problem is rarely the actual problem.

A model can tell you the best way to restructure a team. It can’t tell you that the real issue is the team doesn’t need restructuring - it needs a new leader.

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What’s actually happening to consulting

The industry isn’t collapsing. It’s splitting.

Research shows that while two-thirds of organizations now employ AI in multiple business functions, many struggle to move from proof-of-concept to production. Between 70-85% of AI projects fail to deliver expected value. Only about 30% make it past the pilot stage into full implementation.

This creates two types of consulting work:

The first type: Implementation and operational consulting. How do we actually use these tools? How do we integrate them into our workflows? How do we measure what matters? This is what we do at VisualMakers - building AI agents, helping teams become AI-fluent, managing the transformation from proof-of-concept to production. It’s technical, specific, and growing explosively because AI adoption is hard.

Major consulting firms are responding accordingly. Apparently, McKinsey now has 12,000 AI agents supporting its workforce and reports that 40% of its projects are AI-related. All the big firms are investing heavily in AI. This isn’t about replacing consultants - it’s about retooling them.

The second type: Strategic judgment. What should we do, given everything we now know? What are we optimizing for? What matters more, what less? This is the work that requires wisdom, not just intelligence. It’s what I’ve focused on for the last two decades - helping leaders think clearly when stakes are high.

The middle is getting squeezed - the consultants who sold packaged expertise and standard frameworks. That knowledge is now widely available. The framework for running a retrospective doesn’t command a day rate anymore.

Strategy without execution is just PowerPoint

Another shift has reached its conclusion. It’s been building for 15 years: the gap between strategy and execution has collapsed.

Traditional consulting was built on a clean handoff. Analyze. Strategize. Create beautiful slide decks. Hand everything over to the client. The planning phase stretched for months. The PowerPoint was the deliverable.

That model is dead.

When AI generates strategic frameworks in minutes, companies don’t need consultants spending weeks on analysis. They need people who move from insight to working prototype in days. People who build the actual thing, test it with real users, and adapt based on what works - not what looks good in a deck.

This is entrepreneurial work. Speed, iteration, building things that solve real problems. Less perfecting the plan, more getting something valuable into people’s hands.

The consultants who thrive can think strategically and ship practically. They know that when everyone has planning tools, execution is the differentiator.

That’s why at VisualMakers, companies don’t hire us for strategy decks. They hire us to build working AI agents and ship prototypes quickly. The value isn’t in the analysis - it’s in getting to something that works, fast.

The real disruption

The deeper change isn’t that AI can do parts of consulting. It’s that AI is changing what clients need from consultants.

Companies used to hire consultants because expertise was scarce. Now they hire consultants because clarity is scarce. They’re drowning in information, frameworks, and analysis. Every employee has access to AI that can generate strategic recommendations on demand.

The new problem is: which recommendations matter? What’s signal versus noise? When is “best practice” actually wrong for us?

This is a better problem for good consultants. And a worse one for mediocre ones.

Think about it: if everyone in your organization can generate a competent strategic analysis, the person who can say “this analysis is technically correct but strategically stupid” becomes more valuable, not less.

The consultants who vanish

The ones at risk aren’t people who lack technical skills. They’re the ones whose value was always informational, not judgmental.

They’re the consultants who:

• Resell frameworks that are now freely available

• Run the same workshop regardless of context

• Provide answers without questioning the question

• Mistake process for progress

• Defend their methodology instead of solving the problem

Even the big consulting firms see this. Much of the work junior consultants used to do - benchmarking, research synthesis, PowerPoint creation - is increasingly automated.

What got you here won’t get you there.

The consultants who thrive

The ones who survive won’t compete with AI. They’ll collaborate with it.

They’ll use AI to handle research, generate options, and synthesize data - then spend their time on the parts that matter: diagnosing the real problem, understanding the political dynamics, challenging assumptions, and helping leaders think clearly when stakes are high.

This requires a different skill set than traditional consulting. Less about knowing frameworks, more about knowing when frameworks mislead. Less about providing answers, more about elevating the questions.

The future belongs to consultants who can look at AI-generated advice and say: “Yes, that’s the fastest path. But here’s why it’s also the path that destroys trust in your team” or “The data says pivot, but the data doesn’t know your industry is about to shift in 18 months.”

This is judgment. And judgment can’t be automated because it requires understanding consequences, context, and humans in ways that models can’t replicate - at least not yet.

What this means if you’re building something

Here’s the practical takeaway:

You’re about to have access to better operational and tactical advice than ever before. Use it. Let AI handle research, generate frameworks, and produce analyses.

But don’t confuse speed with direction. Don’t outsource judgment to a tool. And don’t assume the best answer is the right answer.

AI can tell you how. The right consultant helps you figure out whether and why.

That distinction is about to become much more valuable.

The choice ahead

Most consultants built their careers on being the person with the information. That game is over.

The new game is being the person with the judgment to know what to do with the information. And the ability to turn that judgment into something real.

It’s a better game. But it requires admitting that what got you here won’t get you there. Many consultants won’t make that shift. They’ll keep selling what they’re comfortable selling, wondering why clients are paying less for it.

The ones who will thrive are already uncomfortable. They’re questioning their own frameworks. They’re using AI to do the work they used to bill for. They’re getting honest about where their real value lives. They’re learning to ship, not just strategize.

That discomfort is the signal you’re adapting.

The consulting industry isn’t dying. It’s being rebuilt. The question is whether you’re building the future version or defending the past one.

At VisualMakers, we made our choice. We’re not competing with AI - we’re using it to focus on what actually matters: helping you think clearly about the transformation ahead, then building what’s needed so it’s useful today, not someday.

Because in a world where everyone has access to expertise, the people who help you figure out which expertise to trust, and then ship something that actually works, aren’t becoming obsolete.

They’re becoming essential.

🙏

Be kind,

Manuel