Layoffs make headlines. The explosion of solo businesses doesn’t. Both are the same trend.

Widesizing means AI lets one capable person do what once took a team. The economy isn’t shrinking; it’s fragmenting into smaller, scrappier units.

Large tech companies shed people with the same solemn theater used for royal funerals, and half the internet reacts as if paid work itself just fell off a cliff.

Yes, Block cuts jobs. Oracle cuts jobs. Amazon cuts jobs. Every week another CEO waves some consultancy’s AI slide deck around and calls it their new strategy playbook. Then the alarmists rush in: “Human jobs are finished when corporations need only one-tenth the staff. We need a revolt!”

Slow down, everyone.

Widesizing: The Thing Nobody Talks About While Staring at Layoffs

Headcount inside giant firms is only one place where work shows up. If one company has 100,000 employees, or 1,000 firms have 100 each, or 100,000 solo operators each carry their own little shop, you still have 100,000 people making a living. The shape changes. The payroll department changes. The org chart gets set on fire. Work itself keeps evolving. And let’s not forget that the enterprise itself is a fairly recent invention. The East India Company got its charter in 1600. Before that, humans organized work perfectly well for millennia without an HR department or a Microsoft subscription. The corporation might not last forever either.

That’s why I have little patience for the usual AI doom-chanting. Negativity bias makes people stare at downsizing and completely miss what I’d call widesizing. They see layoffs at the cathedral and ignore the bazaar springing up outside.

Widesizing Is Already Happening

And the bazaar is real. Around the world, millions of new business applications are being filed each year, and much of the jump comes from non-employer firms. Solo businesses. Tiny outfits. One-person agencies. People who used to need a coordinator, analyst, copywriter, junior assistant, and a corporate permission slip can now do the work themselves with software and stubbornness.

That matters.

Widesizing means AI lowers the minimum viable size of a business. A lot of economic activity that once demanded a team can now be handled by one capable person with a laptop, a few subscriptions, and a mild allergy to bureaucracy. Schumpeter would recognize the pattern immediately. Creative destruction is messy because the old machine breaks in public and on the news, while the new machine starts in coffee bars, spare bedrooms, and WhatsApp groups. Nobody writes a headline about someone quietly launching a profitable consultancy from their kitchen table. But multiply that by a few million and you’ve got a trend that dwarfs any single round of layoffs at Meta.

“Widesizing means AI lowers the minimum viable size of a business.”

I’m sympathetic to that world because I’ve lived in it. Actually, no, I’ve failed in it. Repeatedly. I’ve had big ideas, shiny diagrams, brave talk, and the confidence that should come with adult supervision. Some bets worked. Some face-planted so hard they left a crater. (My attempts at building online platforms deserve their own postmortem, which I’ll write once the emotional wounds heal.) So when I say smaller units can win, I’m not selling a fantasy of heroic founders sipping espresso while AI does the laundry. I’m saying the center of gravity is shifting, and widesizing is the word nobody’s using for it yet.

But Let’s Not Get Romantic About It

The cheerful “everyone can become a solopreneur now” crowd skips over the ugly bits. A steady salary bundles income, benefits, training, legal cover, a bit of status, and someone else to absorb economic shocks. Solo work bundles freedom with risk, cash-flow swings, admin sludge, and that charming moment when a client pays three months late while your mortgage remains weirdly punctual.

And AI can punch solopreneurs in the face too.

The Upwork data after ChatGPT’s earlier releases showed freelance writers and designers getting fewer jobs and lower earnings. That’s a real warning. When AI turns entry-level output into a commodity, clients don’t automatically spread the money across thousands of independents. Often they keep the savings and ask one person to do the work of four. Sometimes they do it themselves badly (which is its own small revenge, but cold comfort if you’re the one who lost the gig).

Then there’s the platform trap. Ask the graveyard of “AI wrapper” founders who built neat little products on top of Claude and got screwed when Anthropic announced native features that swallowed their businesses before lunch. Courage didn’t save them. Neither did imagination. Building on someone else’s castle grounds means the landlord can move the fence whenever they feel like it. This is something Joel Spolsky warned about over two decades ago, and it’s still true: don’t build your house on a platform that doesn’t love you back.

And no, some parts of the economy won’t fragment into cheerful little hobbit businesses. Chip fabs, frontier models, biotech labs, cloud infrastructure, defense contracts, power grids. You’re unlikely to go solo with a semiconductor plant. Some layers stay huge because physics, capital, regulation, and plain stubborn reality demand concentration. TSMC isn’t about to be disrupted by some guy in a garage with a 3D printer.

The Barbell Economy, Not the Cliff

So where does that leave us?

We will have a split future, which is less cinematic than “AI will destroy everything” and more useful than “everyone should just learn to code.”

A small number of firms will get bigger because AI rewards scale in data, distribution, and compute. Think barbell strategy applied to the whole economy. Countless tiny firms will also appear because AI slashes coordination costs and expands what one skilled person can deliver. Both things can happen at once. In fact, they already do. The middle is the dangerous place, the comfortable mid-sized company that’s too big to be nimble and too small to compete on raw infrastructure.

That’s the part the doom merchants and the techno-messianists both mangle.

The alarmists talk as if every layoff is a one-way ticket to irrelevance. The evangelists chirp as if everyone can turn a pink slip into a thriving solo practice before the weekend. Rubbish. Some people will get crushed. Some will do better than they ever did inside a large company. A lot will bounce between contracts, products, part-time roles, and experiments while the market sorts out what humans are still best at and what buyers still value.

Nobody knows how this shakes out. Anyone who tells you they do is selling something. (Probably a course.)

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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So What’s the Useful Question?

I wouldn’t ask whether AI will wipe out jobs. That question is too blunt, like asking whether “transportation” will kill the horse. (It did kill the horse-as-transport. It didn’t kill the horse.) I’d ask who gets to shape the next layer of work: giant firms, tiny firms, or people who can switch between both without waiting for permission.

I think that last group has the best shot.

The old deal was simple. Join the enterprise. Climb the ladder. Hope the machine loves you back. The new deal is harsher but more honest. Build a portfolio of skills that don’t evaporate when the next model drops. Own your distribution (your reputation, your network, your ability to find clients without a recruiter). Use AI as a force multiplier where it helps, keep your judgment where it counts, and avoid building businesses that can vanish during somebody else’s product launch keynote.

Widesizing isn’t a utopia. It’s a map of where economic activity goes when AI makes organizations flatter, smaller, and more numerous.

The future of work won’t be decided by pearl-clutching LinkedIn prophets or billionaire CEOs who think every human problem looks better inside a dashboard. It’ll be decided by millions of workers choosing whether to stay ornamental inside bloated organizations or become useful in smaller ones.

“The future of work won’t be decided by pearl-clutching LinkedIn prophets or billionaire CEOs who think every human problem looks better inside a dashboard.”

Some will wait for the wave to hit them.

Others will grab a surfboard.

Jurgen, Solo Chief.

P.S. Are you downsizing, widesizing, or still deciding which board to grab?

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TL;DR: The best AI stack for solopreneurs depends less on which tools you choose and more on how well you know your own strengths and weaknesses. A leader who understands how their brain actually works can match tools to how they think, building what amounts to a mirror of themselves rather than a copy of someone else’s setup.

Is AI working as a prism for you, expanding the reach of your efforts? It should.

Two solopreneurs walk into 2026 with the same three AI tools. Same subscriptions, same access, same tutorials bookmarked. Six months later, one of them is running a smoother operation than they have ever had. The other is drowning in half-configured dashboards, abandoned automations, and a growing suspicion that AI is just expensive noise.

What separates them has nothing to do with the tools.

I didn’t figure that out from reading about it, I figured it out by switching tools myself and watching what actually changed. (I help leaders build exactly this kind of self-aware AI setup in my coaching practice.)

In this post, you’ll learn:

• Why tool-hopping is a symptom, not a strategy

• How I matched three specific tools to how my brain actually works

• The three questions that should determine your AI stack before you sign up for anything new

The Tool-Hopper

You know this person, or… maybe you’ve been this person.

They follow every “my AI workflow” post on Substack and sign up for the new tool the week it launches. Look, their setup is impressive: Claude, ChatGPT, Gemini, Perplexity, Copilot, and three automation platforms, each doing roughly 10% of what it could. It looks great in a screenshot, but in practice, they spend more time configuring workflows than doing the work those workflows were supposed to handle.

Jurgen Appelo nailed this in his piece on technostress and FOBO, the fear of better options. The tool-hopper lives in FOBO. They are always one tool away from the breakthrough, and the breakthrough never comes, because the problem was never the tool.

What I Actually Use

I run four businesses: Leadership in Change (a Substack newsletter on AI leadership), Cozora (an AI education platform with live weekly sessions), Newsletter Compass (a growth tool for newsletter creators), and a coaching and AI build practice at jsalinas.org. My entire AI stack is three tools.

Claude is my thinking partner.

I don’t use it the way most people describe. I don’t ask “what should I do?” I dump seven paragraphs of raw thinking into a conversation and say, “What other approaches exist here? What am I missing? How does this connect to what you already know about my audience?” This works because I’ve invested the time to build the context. I maintain 10 to 12 page skill documents for each of my businesses inside Claude. When I mention Leadership in Change, it pulls the right document automatically and has the full picture: my voice, my audience, my strategy, my constraints. Claude is also my copy editor. I write rough, fast, and big, and Claude helps me refine without flattening.

Gemini generates my images.

Claude creates an on-brand prompt based on my visual guidelines, and I feed that prompt to Gemini for generation. Two tools collaborating on one output, each doing what it does best.

NotebookLM is my research layer.

Here’s the thing: before I go deep into any business decision or article, I load my sources into NotebookLM first and ask questions against those specific documents. This nearly eliminates hallucinations, which matters enormously when the research feeds business decisions, not just blog posts.

Three tools. Each one matched to a specific gap or strength in how my brain works.

Why This Stack Works for Me (and Probably Won’t Work for You)

Here is what I want to be direct about: there is no magic AI stack. Just like there is no magic prompt, no magic business strategy, and no magic Substack note that will make you go viral.

I am not great with details. I think in big connected ideas, and I like to collaborate, to go back and forth, to refine through conversation. Claude is built for exactly that kind of brain. I spent a full year inside ChatGPT before I realized it was the wrong fit for how my brain works. That year wasn’t wasted; it taught me what I actually needed, but it was a year of building in the wrong tool. When I tried Claude, its project system and skill documents matched how I actually think and collaborate. I didn’t switch because Claude was “better.” I switched because Claude was better for me.

The tool-hopper copies someone else’s stack and wonders why the results feel hollow. The self-aware solopreneur builds what I think of as a Mirror Stack, an AI setup that reflects how their specific brain works, not a screenshot of someone else’s workflow.

Three Questions Before Your Next Tool

Before you evaluate another AI tool, before you read another “my perfect workflow” post, answer these honestly:

1. What am I genuinely bad at? Not “what do I wish I were better at.” What actually falls through the cracks, consistently, when nobody is watching?

2. What kind of thinking do I do best? Collaboration? Deep solo focus? Visual processing? Verbal reasoning? Big picture strategy? Granular detail?

3. When I’m doing my best work, what does the process actually look like? Fast and messy, then refined? Slow and methodical from the start? Conversational and iterative?

Your answers should determine your stack. Not a Substack post. Not a YouTube tutorial. Not mine.

If You Only Remember This

• The gap between two solopreneurs using the same AI tools is self-awareness, not software. The tools are commodities. Knowing your own brain is the competitive advantage.

• Go deep into one tool before you touch another. Breadth feels productive but produces shallow, fragile systems. Depth compounds.

• Build a Mirror Stack, not a borrowed one. Match every tool to a specific strength you want to amplify or a specific flaw you need to cover. If you can’t name which one, you don’t need that tool yet.

  

What’s the one thing you’re genuinely bad at that you haven’t matched a tool to yet? I’d love to hear it in the comments.

Joel Salinas is an AI leadership coach and strategist who writes Leadership in Change on Substack and helps leaders build AI-aware practices at jsalinas.org.

The gap between AI tinkerers and everyone else is closing fast. Your window to claim your own galaxy is now.

While most people chat with bots, a few solopreneurs run autonomous AI agents that operate their businesses 24/7. That capability gap is your temporary window to build something competitors can’t replicate. You have no time to lose.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

Subscribe now

Halt!

Stop what you’re doing for a moment.

Actually, I don’t care. Keep scrolling if you want. But 2026 is the kind of moment most people only recognize when it’s already far behind them, and I’d rather not watch you miss it while you’re binge-watching Andor or The Mandalorean.

Right now, a tiny number of solopreneurs and one-person businesses are building their own AI agent orchestration. With real AI agents. Digital assistants that crawl, build, deploy, and run entire business operations 24/7 while their creators walk the dog, watch Skeleton Crew, or cook dinner for friends.

Meanwhile, most people are still asking ChatGPT to write their resumes or generate a slide deck from three bullet points.

That capability gap is temporary. It will close fast. And if you don’t jump to lightspeed now, you’ll spend the rest of your life wondering how you could let such a unique opportunity pass by unnoticed. (As I did by not claiming a hundred domain names in 1995.)

I call this the Hyperdrive Advantage.

My Clunky, Self-Made AI Agent Stack

I’m no expert when it comes to AI agent orchestration. I should probably say that louder. I’M NO EXPERT. But I do spend a lot of time building my own workflows, and I’ve failed at enough of them to have opinions.

For example, I have an automated workflow keeping track of hundreds of Substack publications and their follower and subscriber counts. I also have several agentic workflows where Claude, ChatGPT, Gemini, and Grok act as an LLM council, offering me different perspectives on a research question or a provocative statement before I turn that into an essay. I even have an agent proactively informing me of the upcoming birthdays of the people closest to me.

Most of the struggle with AI orchestration (in my case) is just dealing with all the edge cases. An external service is down for an hour; I run out of tokens mid-workflow; some input is formatted in an unexpected way. Each time, I reroute the power and improve the workflow. It’s slow going, but rewarding. The scenarios are getting more solid every day.

And then there are the problems that don’t get me any further. They just eat up my time. Claude had a bug in its Cowork architecture that messed things up and held me back for several weeks. Make has a few annoying quirks with arrays that waste my time each time I use them. Last weekend, I ran into a wall with Perplexity: the MCP connectors available in Perplexity Computer are not available in Perplexity Chat, which completely defies the point of using Perplexity as an interface to my agentic workflows. There goes another two hours of work leading to nothing (until they fix this)... 🤷🏻‍♂️

It’s the pain of living at the frontier of AI agent orchestration. Only half of everything works. The other part is permanently under construction. 🚧 Every day is a struggle with tokens, time-outs, and the ever-present “Something went wrong.”

But I persist. I do not want to wait until turnkey agentic AI orchestration is available for everyone. That’s exactly why I’m doing this now. My clunky, self-made agentic stack is a temporary project because I aim to exploit the Hyperdrive Advantage.

I am a rebel patching my own Millennium Falcon so I can go on adventures before the rest of the universe arrives in their standard X-wings.

Some early mornings, I stare at yet another failed workflow and wonder if I’m wasting my time. Should I just wait for the polished tools like everyone else? Maybe this whole bespoke-AI-stack thing is just me being stubborn? Then I remember: that feeling of doubt is exactly what makes this moment valuable. If it were easy, everyone would already be doing it, and there’d be no advantage left to claim.

The Original Hyperdrive Maneuver

In 1975, George Lucas wanted visual effects nobody had ever seen. The technology didn’t exist. So he rented a warehouse in Van Nuys (a neighborhood in Los Angeles) for $2,300 a month, hired a ragtag crew of college students, artists, and engineers, and called it Industrial Light and Magic. Everything was hand-built, one-of-a-kind, and constantly breaking. John Dykstra, his effects supervisor, admitted it was “a long shot.”

But Lucas didn’t build ILM to sell special effects. He built it to make Star Wars. The tech was the engine; the franchise was the destination. By the time Hollywood figured out what happened, Lucas owned the characters, the merchandise rights, and a fan base that would follow him for decades. His moat was never the effects shop. It was the galaxy he built with it.

That’s a Hyperdrive Maneuver: build clunky, bespoke technology before anyone else can, and use the head start to lock in something permanent.

Jeff Bezos did the same thing in 1994, building online retail infrastructure when “buying things on the internet” sounded as trustworthy as getting medical advice from a stranger in a parking lot. By the time e-commerce tools became cheap and polished, he already owned the customer data, the logistics, and the habits of millions of buyers. The bespoke tech was the ladder. The moat was everything he built while climbing it.

Right now, we’re at that same moment with agentic AI.

Your Agentic AI Advantage

It seems common knowledge that many people are using AI, but only few go as deep as the technology allows. Most are just chatting with a bot. Nothing more. The number of people running their own AI agents (digital workers that autonomously research, create, publish, and operate) is vanishingly small.

That gap between early adopters and everyone else is your window. Every major technology wave had one: electricity, the internet, smartphones. Each time, the people who moved while the tools were still rough ended up owning systems that latecomers could never quite replicate.

Think of it like evolutionary biology. In ecology, when a new habitat opens up (a volcanic island forms, a forest fire clears a valley, an asteroid wipes out the dinosaurs), the species that colonize first don’t necessarily have the best genes. They have the best timing. They radiate into empty niches and establish themselves before competition even arrives. Biologists call this adaptive radiation. I call it what’s happening right now with agentic AI.

Agentic AI is in its Internet-in-the-1990s phase. Clunky, unreliable, confusing to most people, and wildly powerful for the few who bother to make the modifications themselves.

If you have the grit and savviness to wrestle with today’s messy AI orchestration tools, you can pull an Agentic Hyperdrive Maneuver: launch yourself into economic spaces that barely exist yet and establish yourself before the rest of the world shows up. That’s the Agentic Hyperdrive Advantage.

More Than a First-Mover Advantage

You might be thinking: “Isn’t this just first-mover advantage with a Star Wars theme bolted on top?” Fair question. Wrong answer.

The first-mover advantage has a well-known weakness: the fastest-learner advantage. It doesn’t matter who moves first if a smarter competitor can show up later and out-learn you. Google was nowhere near the first search engine. But they learned faster than AltaVista, Lycos, and all the others combined. The first movers’ head start turned out to be worthless because search engines have no customer lock-in. Users switch in less than ten seconds.

The Hyperdrive Advantage works differently. We’re talking about a technology that gives you access to economic systems that were straight-up inaccessible before. New markets. New services. New ways of operating that weren’t economically viable until you wired up your own agents to do the work. This isn’t about bolting a faster engine onto the same kind of vehicle and hoping nobody catches up.

Google could beat AltaVista because they were competing on the same road. George Lucas couldn’t be beaten that way because he wasn’t selling special effects. He used special effects to sell a galaxy. By the time competitors got their own effects shops, Lucas had already built the franchise, the merchandise empire, and the audience. The system was claimed.

That’s the Hyperdrive Advantage: a first-mover advantage large enough that it gives you first access to a vast new space waiting to be claimed by you. You don’t get a head start by building the best technology. You use improvised technology so you can be the first to plant a flag in unexplored terrain.

This is the crucial takeaway: with the Hyperdrive Maneuver, you don’t make infrastructure your moat. Instead, you use temporary infrastructure to make a moat out of data, reputation, or network. You assume the infrastructure is replaceable.

Here’s where it gets lonely, though. When you’re building your own AI orchestration, there’s no manual. No support team. No one to call when the whole thing falls apart at 3am. You are the single wringable neck for your own duct tape-architecture and for the business you’re trying to build on top of it. That double weight is real. And if you’re already building your own agentic stack, you probably feel it too. (If it helps, know that George Lucas apparently threw up from the stress during the filming of Star Wars. So at least we’re in good company.)

Solo Operators Building Their Hyperdrives

We’re still early. So early that the agentic Lucas and agentic Bezos haven’t revealed themselves yet. In the 1990s, nobody could have pointed at a clear winner of the internet either. But the hyperdrives are being assembled right now, in warehouses and home offices, by people operating in the Outer Rim of their industries.

Aaron Sneed runs a defense-tech company, solo. His secret is “The Council”: about 15 custom AI agents acting as functional departments. Legal, Ops, HR, Marketing, Research. They handle continuous work and escalate only the hard decisions to him. One person operating in a market that normally requires a funded team just to clear compliance.

A photo booth entrepreneur (yes, photo booths) orchestrates over 20 AI agents handling inbound messages, quoting, booking, content editing, marketing, and bookkeeping. A traditionally labor-heavy local service, now scaling across multiple regions with a single operator.

Okay, maybe these examples don’t sound impressive to you. But that’s the point. These aren’t billion-dollar empires—yet. They’re just scrappy little spacecraft. But give them some time.

Meanwhile, I’m building my own hyperdrive right now. Soon, I will point it at some faraway systems. Who knows? Maybe I can claim some little moon or asteroid belt for myself.

Build the Moat Before the Fleet Arrives

Your Hyperdrive Advantage is temporary. I can’t say this enough. The clunky tools you’re wrestling with today will be polished, packaged, and sold to everyone within a couple of years, maybe sooner. Your bespoke AI stack will be outdated. That’s fine, as long as you’ve used the head start to build something that outlasts the tech.

Lucas didn’t cling to his improvised motion-control cameras. He used them to lock in Star Wars. You need to do the same: use your heavily customized agentic hyperdrive to accumulate data, reputation, and relationships before the fleet arrives with their standardized models.

Every client you serve, every experiment you run, every failure you recover from compounds into something latecomers can’t buy. Your prompts get sharper. Your domain knowledge gets deeper. Your name becomes the one people associate with solving a specific problem. Investors call this a compounding economic moat. I call it the whole point.

Your moat might be the 500 clients whose workflows you’ve automated. The proprietary dataset your agents have been quietly building for eighteen months. The reputation as the person who cracked a problem before anyone else even understood the question. It doesn’t matter that you did all of that with a hand-modified, cobbled-together, famously unreliable hyperdrive. You use your clunky tech stack to turn yourself from a rebel into the ruler of your own galaxy far, far away.

Soon, everyone will have their own turnkey, off-the-shelf, X-wing style hyperdrives. Manufacturers will churn out AI orchestration stacks that beat anything you can build yourself. Heck, you might even swap out your own agentic AI orchestration for the new technologies that come onto the market. That’s perfectly fine. You’ve already built your moat. You already rule a system, maybe even an entire galaxy. You took advantage of the Hyperdrive Maneuver.

Your Falcon Is Waiting

And here’s why this moment favors you, the solo operator: while large organizations are stuck in committee meetings debating their AI strategy (and convening sub-committees to evaluate the committee’s recommendations), you’re already patching your Falcon and jumping to lightspeed. No procurement process. No risk assessment framework. No six-month pilot program. You tinker, ship, blow things up, and fix them before their Imperial Senate has even scheduled the next session.

Speed is the solopreneur’s superpower right now. Not capital, not headcount, not brand recognition. Speed. The ability to try something at 10am, watch it fail by noon, fix it by 3pm, and have it running by dinner. Large organizations can’t do this. They have too many layers, too many stakeholders, too many people whose job it is to say, “Let’s discuss this in our upcoming PI planning next month.”

⚠️ And sorry to burst a few bubbles, but Substack subscriptions and self-paced courses on “how to make your own hyperdrive” are not what I mean here. Make-money-online schemes don’t count. Selling hyperdrive manuals and leaving the exploration to the actual rebels does not make you the ruler of your own faraway galaxy. ⚠️

If you’re sitting there thinking, “But I’m not technical enough for this,” remember: neither was George Lucas. He found a handful of film-school dropouts, hobbyist engineers, and other misfits who were crazy enough to try. They figured it out as they went. Like the Millennium Falcon, your AI orchestration stack will be held together with improvisation and determination, not engineering degrees.

The speed gap won’t last long.

Use it wisely.

Jurgen, Solo Chief.

P.S. What does your clunky Millennium Falcon look like right now? Tell me about your stack!

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I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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AI can now do half of the advisory board’s job. The courts want governance boards to do the other half.

Advisory boards are squeezed between AI tools that produce strategic advice cheaply and governance boards absorbing oversight responsibilities. Only contextual judgment survives.

Here’s an uncomfortable question for anyone sitting on an advisory board:

What exactly do you offer that Claude and ChatGPT can’t?

Governance boards just received a legal nudge that feels less like guidance and more like a deadline. The EU’s AI Act (Regulation 2024/1689) requires accountability for high-risk AI systems by August 2026, and regulators are treating that accountability as a board responsibility. The timeline is public and fairly blunt: enforcement for high-risk systems begins on August 2, 2026.

When regulations set a date, boards eventually pay attention.

The advisory board model, meanwhile, is drifting into an awkward middle position. A host of AI models and tools are making some of the board’s traditional contributions cheap and immediate. Meanwhile, governance boards are absorbing the rest because liability travels toward whoever signs the documents. The advisory board isn’t disappearing. But the version many organizations still maintain looks suspiciously like a relic of the past.

Let’s walk through the pressure points.

The Compliance Stampede

Corporate governance has a curious rhythm. Boards move slowly until courts or regulators start writing opinions. That phase has begun for AI.

A 2025 Deloitte Global Survey reported that 66 percent of boards say they have limited or no AI expertise. The number itself isn’t shocking. Boards historically recruit for finance, operations, and regulation. Deep technical literacy rarely makes the shortlist. What changed is the expectation surrounding it.

The oversight principle is gradually expanding toward technological risks. Recent Delaware Court of Chancery rulings have clarified that directors must receive regular briefings on mission-critical technologies. AI is quietly joining that category. The message from the courts is simple: directors cannot claim ignorance about the systems shaping their company’s core operations.

On my side of the Atlantic, regulators went further and put a date on the obligation.

The EU AI Act entered into force on August 1, 2024. High-risk system obligations begin in 2026. Boards now have roughly one governance cycle left to figure out what responsible oversight looks like, and organizations are responding with predictable speed.

Board intelligence platforms are building AI tools directly into governance workflows. Vendors such as Diligent and Nasdaq Boardvantage now advertise automated briefings, document summaries, and preparation tools that reduce director reading time by as much as 60 percent and board preparation time by up to 80 percent.

Anyone who has sat through a board meeting recognizes the irony. The typical board pack runs well beyond 200 pages. AI can now summarize it in minutes. Whether that represents efficiency or an admission that those pages shouldn’t have existed in the first place remains an open question.

Either way, governance boards feel the pressure to develop internal AI competence quickly. Committees are being created. Charters are being updated. Recruiters are searching for directors who understand machine learning well enough to challenge management.

The baseline is shifting.

The Squeeze on Advisory Boards

Advisory boards historically filled a gap that governance boards could not.

Directors carry fiduciary responsibilities. That makes them cautious. Advisory boards had the freedom to think more experimentally. They offered specialized domain insight, industry connections, and candid feedback without legal exposure.

That arrangement worked because two things were scarce: information and perspective.

Both are changing.

General-purpose AI tools now produce credible strategic analysis in seconds. A founder exploring pricing models, market entry, or regulatory exposure can run several scenarios through a capable model before breakfast. The output isn’t always perfect—but neither are board members. Yet the marginal cost approaches zero, and the speed is astounding. The AIs do the job faster, cheaper, and—more often than not—better.

Advisory boards often justify their existence through the “external brain” function. The sparring partner. The person who sees patterns across industries. But ChatGPT, Claude, Gemini, Grok, and Perplexity now perform that role surprisingly well. And one only needs to visit the Product Hunt website to find a legion of AI-driven tools, such as SoloBoard, BoardOS, Foundora, aicofounder, AI debatearena, and Team Debate, eager to offer advice to startups and scale-ups at a mere fraction of the cost of an advisory board member lunch.

There’s even a free download available: Similar to the age-old Delphi Method, the LLM Council pattern is a multi-agent AI architecture where multiple LLMs collaborate like a council to produce more reliable answers through structured deliberation. The pattern enhances reasoning by leveraging diverse AI model perspectives.

Meanwhile, pressure also arrives from the other side. Governance boards are absorbing decisions that once lived in advisory territory. AI risk management, regulatory interpretation, and technical oversight increasingly sit inside fiduciary structures because the courts expect them there.

The accountability disappears upward.

The strategic advice vanishes downward.

The remaining territory for advisory boards becomes narrower by the day: decisions that require judgment shaped by experience, specific industry context, and relationships within and around the organization. That space still exists. It just looks smaller than it used to.

The Signaling Trap

Another dynamic deserves a less polite description. Let’s be honest: many advisory boards function as investor theater.

A skeptical analysis of AI advisory structures would describe them as human validation layers. A startup assembles recognizable names; investors notice the credibility signal, and the board rarely challenges management in any serious way. The advisors charge for their network and reputation, not their experience and insights. Everyone involved understands the arrangement.

The danger appears when executives believe their own marketing. A symbolic advisory board creates the feeling of strategic coverage without the substance of it. Founders assume someone is stress-testing the plan. Investors assume the same. In practice, nobody asks the hard questions. The technology sector is littered with the remains of startups and scale-ups that were guided off a cliff by theater directors.

Large organizations have, with varying success, experimented with external advisory councils to replace internal ethics groups. Governments have assembled high-profile AI advisory boards only to dissolve them months later when political pressure increased. The structures exist. The influence varies dramatically.

Advisory boards were originally intended as places for uncomfortable conversations. When they become branding devices, those conversations disappear.

The Surviving Species

So, what remains valuable about an advisory board?

Contextual judgment.

AI can produce information at extraordinary speed. Governance boards can impose accountability. Neither handles the messy space between them particularly well.

Consider the Techleap “AI Paradox” research covering the Dutch startup environment. The Netherlands has one of Europe’s highest concentrations of AI talent. Yet only 21 percent of its AI startups successfully scale, compared with 81 percent in the United States.

The problem is not information or technical expertise. The gap appears in decisions that connect technology, regulation, financing, and organizational design. Those choices rarely follow a formula. They depend on context that lives inside specific industries and networks.

This is where advisory boards can still matter.

Oxford Law researchers recently proposed an update to the business judgment rule, sometimes called “BJR 2.0.” Their argument is that directors now require cognitive adequacy to understand the technologies influencing corporate decisions. Technological literacy has joined financial literacy as a baseline competence.

Advisory boards operate in the layer above that baseline. They apply experience to situations where the playbook hasn’t stabilized yet. A founder deciding whether to launch an AI product under the EU AI Act. A scaleup balancing regulatory exposure against speed of deployment. An executive team confronting a market that changes every quarter.

AI produces the data. Governance boards enforce accountability. Advisory boards interpret the messy middle.

The organizations that recognize this distinction will keep their advisory boards small, specific, and uncomfortable enough to challenge assumptions. The rest will continue assembling impressive lists of names that rarely challenge directors’ decisions.

AI has reduced the cost of knowledge.

But judgment remains stubbornly human.

Jurgen Appelo, Solo Chief

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References

1. European Commission AI Act Service Desk. Timeline: Implementation of the EU AI Act. Available at: https://ai-act-service-desk.ec.europa.eu/en/ai-act/timeline/timeline-implementation-eu-ai-act (Accessed: April 2026).

2. Deloitte. Governance of AI: A Critical Imperative for Today’s Boards (2nd Edition). 2025. Available at: https://www.deloitte.com/global/en/issues/trust/progress-on-ai-in-the-boardroom-but-room-to-accelerate.html (Accessed: April 2026).

3. Ervin Cohen & Jessup LLP (ECJ Law). Delaware Expands Expectations for Board Oversight of Cybersecurity, by Jeffrey R. Glassman. January 20, 2026. Available at: https://www.ecjlaw.com/ecj-blog/delaware-expands-expectations-for-board-oversight-of-cybersecurity-by-jeffrey-r-glassman (Accessed: April 2026).

4. Nasdaq. AI for Boards: Governance Solutions. Available at: https://www.nasdaq.com/solutions/governance/boardvantage/ai-for-boards (Accessed: April 2026).

5. Techleap. State of Dutch Tech Report 2026. March 15, 2026. Available at: https://techleap.nl/reports/state-of-dutch-tech-report-2026 (Accessed: April 2026).

The future of work isn’t AI taking your job. It’s software updates stealing your morning.

Solo operators pay the full cost of endless tool churn themselves. When vendors move the buttons, your leverage collapses back into hand work.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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I woke up this morning looking at two failed automations and an error log overflowing with warnings. My OpenAI credits maxed out, two scenarios stopped, and one of my automatic imports (which worked flawlessly for months) has now wrecked 2,000 contact records.

Welcome to the future of work.

If you’re a Solo Chief, you know the feeling. You start the day intending to do real work, and by lunch, you’re elbows-deep in tokens, credits, permissions, migrations, hidden settings, broken workflows, vanished buttons, and help pages written for a version of the product that deprecated last week. By dinner, you’ve moved data from A to B, upgraded C to D, reconnected E to F, re-authorized G and H, and reformatted the output twice because some vendor decided your old workflow needed a “better experience.”

People call this learning.

Sometimes, it’s just maintenance with better PR.

And yes, I know the counterargument. Ethan Mollick found that people using AI can finish faster and with better results. I’ve seen that too. LLMs often help me cut through the fog.

Until they don’t.

Because the problem isn’t only that the tasks are hard. The problem is that the ground keeps moving while you’re standing on it. The model says, “Click the menu item on the left,” and the menu item now lives in a floating panel on the right, unless you’re on the new version, unless your account is in the old rollout group, unless Google had one of its little morning moods.

That’s why so much work feels fake-busy. Your effort goes into re-learning surfaces instead of compounding skills. Many jobs will be disrupted in five years, and the half-life of some technical skills has dropped below 2.5 years. You don’t need a consultant to tell you what that means. It means you can finish a course today and toss away your certificate tomorrow.

Then the management crowd arrives with a pep talk. Stay adaptable. Keep experimenting. Be a lifelong learner. All true. Some say the illiterate of the 21st century aren’t those who can’t read and write, but those who can’t learn and relearn. True enough. Also incomplete. For solo operators, all that learning and unlearning has a cost, and you pay all of it yourself.

That’s the part the gurus skip.

Solo Operators Pay the Full Churn Tax

In a company, tool churn can be spread around. One person checks release notes. Another handles migrations. Someone in ops fixes the workflow. Someone in IT resets access. In a one-person business, congratulations, you’re all of them! The same brain that has to sell, deliver, invoice, write, and think also has to play part-time plumber for a leaking pile of half-finished software. A sane person can absorb only so many “small updates” before their brain starts filing a complaint.

And it gets worse at the exact moment you need leverage most. Solo work scales through stable patterns. Templates. Checklists. Automations. Workflows. A reliable sequence you can run without re-deciding everything each time. When a vendor moves the controls, kills an integration, changes the API, or sunsets a feature, your leverage collapses back into hand work. When you spend your executive function deciphering a new interface, it can’t also do your best thinking.

The Lindy Effect as a Survival Strategy

Some people escape the treadmill by refusing to join it. Some of us do very well by keeping a small, boring stack and ignoring shiny new toys. That’s the Lindy Effect doing its magic: the longer a tool has been around, the better chance it’ll still be around next year. Boring tech has better manners.

So the future of work isn’t one thing. It depends on whether you’re building your business on bedrock or quicksand.

But for many Solo Chiefs, the struggle is real because the market now rewards speed while the tooling punishes continuity. We need systems to save time, but the systems themselves keep demanding our time. We need tutorials, but the tutorials expire like supermarket sushi. We need AI to help us catch up, but that same AI sometimes hands us directions to a room that got demolished last night.

Progress Has Two Enemies

That leaves you with an awkward truth. Progress now has two enemies. One is ignorance. The other is churn. Everyone talks about the first one because it sounds noble. Read more. Learn more. Adapt more. No disagreement from me. But the second enemy is nastier because it looks like productivity while it actually steals your day as you’re fighting technological entropy to get your automated workflows back into submission. Everything worked fine last night. You can only pray it all works again before dinner.

It’s easy to confuse motion with progress because the screen is busy and your brain feels hot. I keep telling myself I’m building capability, but often I’m just paying a tax for renting my business on other people’s ever-moving tech stacks.

Welcome to the future of work.

Bring curiosity, yes. Bring grit too. But bring suspicion. If a tool changes faster than your business can benefit from it, you’re not using the tool. The tool is using you.

Back to work. I have 2,000 contact records to fix.

Jurgen, Solo Chief.

P.S. How many hours did your tools steal from you this week? I’d love to hear.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

Subscribe now

Most Claude Code projects are just tents. I’m slowly building a city.

Your overnight agentic hackathon is probably not a viable AI agent architecture.

A few weeks ago, I realized my AI infrastructure sucked.

After a year of experimenting with LLMs, I had ChatGPT, Claude, and Gemini all cross-contaminating each other’s project contexts. My data looked as if it had been distributed by a hand grenade across file systems and cloud services. I used Claude, Cowork, NotebookLM, and Perplexity like a five-year-old uses a box of crayons. And I had workflows and business processes flowing and connecting like noodles in a bowl of ramen someone dropped from the tenth floor.

If I ever wanted to scale my business, I needed to stop treating my architecture like a junkyard.

Does that sound familiar?

The Lasagna Principle: Why Your AI Agent Architecture Needs Layers

Here’s what I’ve learned from years of building software and months of wiring AI agents together: when you’re the only one responsible for the whole stack, sloppy wiring isn’t just a nuisance. It’s a liability you carry alone.

Every solo operator I know who’s tried building an AI agent orchestration stack hits the same wall. They start with one automation. It works. They add a second. Still fine. By the fifteenth or sixteenth, they’re staring at a clump of Christmas tree wiring where a Slack message somehow triggers a scraper that writes to a database that kicks off another workflow that occasionally emails a client the wrong welcome email.

Software engineers addressed this problem decades ago with the N-tier architecture. The idea is old and boring, which is exactly why it works: you separate your system into layers, and each layer only talks to the one directly above or below it. Your interface doesn’t know how you store your data; your data doesn’t care how your workflows run. Each layer minds its own business.

I think of it as lasagna instead of spaghetti. Similar ingredients, completely different structure. And when something breaks (because it always will), you know exactly which layer to open up and poke around in.

For my AI agent orchestration stack, I keep it simple and use just three layers: a UX layer where I interact with the system, a workflow layer where automations orchestrate the work, and a persistence layer where state lives and persists.

The Scenario: One Request Through Three AI Automation Layers

Let me walk you through what happens in each of these three layers when I add a new publication to my Substack tracking system.

UX Layer

Everything starts with a human doing something simple. In this example, I paste the URL of a Substack publication I discovered into a Slack channel. That’s it. I don’t configure anything, don’t fill out a form, don’t open a dashboard. I just drop a link and go back to whatever I was doing. Five seconds.

This is the UX layer: the surface where a person touches the system. It could be a Slack message, a Claude Cowork command, a voice prompt, even a button on a vibe-coded web app. The point is that this layer knows nothing about what happens next. It just accepts input and, eventually, shows output.

I could use Signal or Telegram instead of Slack, of course. In fact, I could use any of these tools to do exactly the same thing. When I’m already in the middle of a session with Claude Code, I can just ask Claude to store the Substack URL. As long as the different tools have access to the same workflow layer underneath, it makes no difference to anyone.

That’s the part that lets me sleep. If Slack goes down or changes its API tomorrow, I lose a doorbell, not the house.

Workflow Layer

Below the surface, a Make scenario wakes up. It catches the Slack message, extracts the URL, calls an Apify scraper to grab the publication’s metadata (name, description, follower count, subscriber stats), and routes the results to the database. If the scraper comes back empty (happens about 2–3% of the time), a fallback kicks in: a Claude Haiku call that reads the raw HTML and extracts what it can.

This is the workflow layer, and it’s the heart of any AI agent orchestration setup. It’s where the logic lives: the if-this-then-that decisions, the error handling, the retries. The workflow layer doesn’t store anything itself, and it doesn’t talk to humans directly. It just orchestrates. Receive a trigger, do some work, pass the result. Its job is connecting layers, calling external services, and sometimes triggering other workflows in sequence.

Instead of Make, you can use an automation tool like n8n or Zapier. The point is that the layer responds to UX signals, interacts with proprietary data and external services, and then returns some feedback to the UX.

Persistence Layer

At the bottom, I use Fibery to receive the data and do what databases do: persist state. A new Publication record gets created along with the scraped metadata. A Snapshot record captures the follower and subscriber counts with a timestamp, so I can track a publication’s growth over time. Several Formula fields automatically compute things like “days since last scrape” and “average weekly growth” from the Snapshot history.

Instead of Fibery, I could use Airtable or Coda to store the data. If the data is less relational and more document-oriented, I might opt for Google Drive or Obsidian. In fact, I use multiple backend tools for data persistence because, as Claude said to me, “Trying to cram everything into one tool is like trying to store wine, cheese, and laundry in the same closet. You can do it, but you probably shouldn’t.”

Here’s what matters: the persistence layer doesn’t know or care that Make or n8n or Zapier sent the data. It could just as easily have come from a Python script or a manual import. The schema is stable regardless of what’s happening in the layers above.

Why AI Agent Orchestration Needs Layers

So why bother with this layering when you could just tell your coding agents to wire Claude Cowork and Notion together and call it a day (or call it a night, in case you were sleeping)?

Because things change. I’ve already swapped my scraping approach twice in three months. First, a direct HTTP call, then Apify, then the Haiku fallback on top. My Fibery schema didn’t notice. Neither did my Slack channel. Each layer absorbed the change without infecting the others.

That’s the real payoff of proper AI agent architecture: you can replace any layer without rebuilding the whole stack. You can swap tools in and out without having to go back to Cursor or Claude Code for a minor change and use a ton of safeguards to ensure that it leaves everything else intact.

On top of that, the feedback of any workflow could end up anywhere. In the example I give here, the Make scenario always sends the feedback to Slack because I’m keeping it simple. But I could just as easily let the workflow notify me through Signal, WhatsApp, or Messenger. The orchestration layer decides. The UX layer just listens.

Side note: I gave Claude access to Fibery and Make so that we’re designing the database schemas and workflow scenarios together. I create stuff and Claude reviews it, or Claude creates it and I tweak the details. All corrections and improvements go back into the Claude skills so Claude gets better at it over time.

Where It Hurts: The Cost of a Layered AI Workflow

I’d be lying if I said it’s all upside. Having more layers means more things that can break independently. Debugging a failure that starts in Slack, passes through Make, and surfaces as a wrong value in Fibery is like playing a game of telephone with robots. It takes longer to set up than a quick-and-dirty single-tool solution. And there’s a constant temptation to over-engineer, to add a fourth or fifth layer “just in case” when what you actually need is to ship something.

I’m building slower than the hammock crowd. But at 8 AM, when something broke and there’s nobody to call, I want to know which layer to open. That’s worth the extra time.

The real question every Solo Chief has to answer for themselves: how much structure is worth the slowdown? I don’t have a formula for that. I only know what my tolerance for 8 AM debugging sessions is, and it’s getting lower every year.

Where I Cheat

Let me be honest here, because maps should include the shortcuts people actually take. The architecture I described is a work in progress. I know how things should work, but that doesn’t mean I always follow my own guidelines. Sometimes, the shortcuts are just too attractive (and mostly harmless).

For example, Fibery offers great visualization and management features on top of its databases. It’s incredibly easy to create tables, workflow boards, reports, and other overviews of the Substack publications I collected there. Following my own advice, I should be designing and building such user interfaces with separate tools and then retrieving and acting on this data through the middle layer. But I’d be giving myself a lot of work to build something that the database tool already offers right out of the box. Why bother building a status change workflow in Make when I can simply update that field directly in Fibery?

For now, I make a conscious choice to cheat. But only as long as I’m the sole user. The single entrepreneur. The Solo Chief. I get special privileges because I give nobody else direct access to the backend. If I ever decide to get others involved in the Substack publications data I’m collecting, I’m going to make sure to build a web app that can only fetch and change the data through the workflow layer, never directly. I might even use Claude Code for that.

From Spaghetti to Lasagna: Building Your AI Agent Stack

Am I happy with the AI agent orchestration architecture I have now?

Not yet. I still have too much spaghetti and too little lasagna. But the direction is clear, and that makes all the difference. Every update I make to my agentic orchestration must satisfy the N-tier architecture. Every new piece I add goes into one of three places: UX layer, workflow layer, or persistence layer.

Now that I have the horizontal architecture fleshed out, I can start separating the agents into their own vertical domains: one agent for Nonfiction Writing, one for Finance & Admin, one for Personal Branding, and so on. Each with their own objectives. Each with their own layers. My agentic organization will look less like a junkyard and more like something I’d trust to run while I’m asleep. I’m slowly building a city while others are just rapidly setting up tents.

I’m a big fan of lasagna, but I rarely turn down a plate of spaghetti. It all comes down to where you want to end up with your business. An unorganized bunch of wires will work fine as long as you’re still experimenting. But for anything more serious and viable, you’ll want to think about giving your AI agent orchestration stack a bit more structure.

Your overnight agentic hackathon is probably not a viable AI agent architecture.

Or, you know, just enjoy the spaghetti. I’m not your mother.

Jurgen, Solo Chief.

P.S. Show us what your architecture looks like. I’m curious!

Both exploration and execution feel productive. That’s why you’re stuck in the wrong one.

Eight concrete signals that tell you when to stop exploring and commit, or when to abandon execution and pivot to something new.

There’s a particular kind of productive-feeling paralysis where you’re working hard, shipping things, and somehow going nowhere. You’re executing, but on the wrong thing. Or you’re exploring, but past the point where exploration is still honest.

For years, I ran a company that was constantly half-exploring, half-executing, feeling vaguely but not entirely productive. I was going wide when I should have gone deep. Or I was executing for too long when I should have pivoted back to experimenting. There was nobody to catch that drift but me, and I was too close to the work to see it.

My energy was perfectly distributed across things that were only half-working: either I was lacking focus or I didn’t have enough unfocus. I wasn’t discovering enough, or I wasn’t delivering enough. (Sometimes both in the same week. Truly a gift.)

The particular hell of exploration versus execution is that both states feel like work. Exploration feels productive. Execution feels productive. The signal that you’re in the wrong phase is subtle, and there’s no one in the next office to notice it before you do.

Do you feel like a pinball perpetually bouncing between discovery and delivery?

Many experts will tell you to balance exploration with execution. Breathe in, breathe out. It feels like very calming advice. It’s also mostly useless to someone whose livelihood depends on getting the call right. Your business is not a Pilates exercise. You’re probably not running your company from a yoga mat. (If you are, congratulations; I’m genuinely envious.)

As single managers, solopreneurs, and one-person businesses, we don’t need balance all the time. We need signals. We need alerts that tell us when to stop exploring and start executing, or when to stop delivery and pivot back to discovery.

Signs You Need to Stop Executing (and Go Wide)

Sometimes, too much focus is the danger. Here’s what it looks like when that’s happening.

The first signal is what we might call the input/output inversion. You’re working harder, but the numbers aren’t moving. Output is happening, but outcomes are not. You’re getting better at executing the wrong thing, which is its own special form of masochism. Effort with too little traction is terrain feedback. The proper response is to stop digging deeper.

The second signal is assumption collapse. Every mission rests on a core premise, some belief about why this idea should work. When that premise turns out to be false, continuing the mission becomes stubbornness dressed up as focus. You can’t pivot within the same box; you need a new box. The answer is to go wide.

A third signal is often more uncomfortable to admit: persistent, recurring curiosity about the same alternatives. You feel there’s another way, but you’re suppressing it. You invested so much in this mission! Stay focused. Ignore the distraction! But if the same adjacent possibility keeps nibbling at your attention for weeks or months, your subconscious is pattern-matching something your deliberate focus is blocking. Occasional curiosity is noise. Recurring curiosity about the same thing is a message.

And then there’s the fourth signal: boredom. I used to think boredom was a weakness, a sign of an unmotivated mind. But evolutionary biologists will tell you that boredom is important. It might have evolved to prevent over-harvesting. Your brain signals boredom not to annoy you, but because the learning curve is flattening. The berries are running out, so move to a different patch. In the exploration versus execution dance, boredom during execution is your body’s way of saying the current vein of ore is depleted.

Signs You Need to Stop Exploring (and Commit)

The opposite trap is equally seductive: exploring long past the point where it’s still honest. Here’s what that looks like.

The first signal is when you can name the constraint. If you can answer in one sentence “what is the single thing limiting everything else right now?”, exploration has become a distraction. Constraints demand execution. The Theory of Constraints is all about this phenomenon: find the bottleneck, exploit the bottleneck. The moment you see it clearly, you’re not exploring anymore. You’re procrastinating.

A second signal is convergent evidence. You’re exploring multiple paths, and they keep pointing to the same place. You talk to five different people, and they all mention the same pain. Three separate experiments produced a similar finding. The terrain is clearly telling you something. At that point, more exploration isn’t learning. It’s avoidance of a conclusion you’re unwilling to face. I’ve been that person, gathering “just one more data point” while the answer stared at me from the whiteboard. It’s not a good look.

The third signal is what I’d call the sufficient clarity test: can you write a falsifiable hypothesis? “I believe X will produce Y for Z people within timeframe T.” If you can state it clearly, you can test it. If you can test it, there’s no longer any reason to keep fooling around. The remaining reluctance is usually fear of failure disguised as intellectual curiosity.

The last signal is a little embarrassing: clarity plus resistance. The next step is obvious, but you find it uncomfortable. When you already know what to do and you’re not doing it, that’s not uncertainty. That’s something else entirely. You should keep exploring if the terrain is genuinely unknown. But you must stop when you know the terrain and you’re just scared of settling down. Resistance is strongest when the work matters most. If your reluctance to commit feels almost physical, you’re probably standing right where you need to dig.

What makes this especially tricky when you’re a Solo Chief: you’re the one generating the signals and the one who has to interpret them. There’s no devil’s advocate in the next chair. Cognitive bias runs unchecked until the business tells you, usually in cash.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month. Subscribe or upgrade.

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The Two Decision Triggers (Pivot Switches)

Here’s the sharpest formulation I’ve found for the when to pivot my business question. Kill execution the moment you learn something that invalidates its core assumption. Not a detail. Not a preference. The assumption itself. If the “why this should work” collapses, stop doing. Start learning. And kill exploration the moment you can write a one-page plan with a goal, a metric, a next action, and the only objection left is “what if it fails?” Fear of failure is not a signal to explore more. It is the signal to commit and move forward.

The pattern many people follow instead (and I’m trying very hard not to look in the mirror here) is to oscillate randomly between exploration and execution, respond to feelings, and call it adaptation. The scenic route has its charms. I could write a book about scenic routes.

Or you can be mindful of the signals and use them to dominate a business terrain faster.

The above applies to every kind of thinking work, but it applies especially to Solo Chiefs, who don’t have an executive team to hide behind when they switch for the wrong reasons. There’s no board to blame. No team to debrief with. The signal-reader is you. So is the one who has to live with the call.

Exploration versus execution. Your job isn’t to balance them as suggested on some corporate wellness poster. Your job is to recognize when to bounce from one to the other. The outcome of your good judgment is the balance that you seek.

Jurgen, Solo Chief.

P.S. Which signal do you tend to ignore the longest? I’d love to hear.

The post “Teamwork Is a Scam“ marked my 100th post on The Solo Chief. And I feel this is just the beginning.

If you’re a paying subscriber, you’ve been keeping this publication alive. You fund the caffeine, the independence, and my habit of telling people the universe is conspiring against them.

I owe you more than a PDF and a thank-you email.

Next week, I…

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Most AI agent content is about building AI agents. That’s not a business, it’s a pyramid scheme.

Here's how I want to become an agent boss: by building and managing AI agents for real value, not meta-content recursion.

It’s 4 AM. I’m staring at the ceiling again, and somewhere in the dark I’m doing the mental arithmetic of how many AI-generated playbooks some author on Substack sold this night while I was busy thinking about something more meaningful—and not getting anything done.

Do you feel a bit anxious when your competitors use Cursor, Codex, Claude Code, or Antigravity to launch one vibe-coded app after the other, funneling market attention toward their throwaway solutions and away from your lovingly handcrafted, far-too-long-in-development product?

Do you sense a Fear of Becoming Obsolete (FOBO) when other solopreneurs have a legion of OpenClaw AI agents iterating rapidly on a dozen business models in parallel while they’re sleeping, and while you only have a smartwatch reporting each morning on your restless night?

Don’t worry. I feel the same.

Sleepless Over the Bestseller Badge

Reading Substack is like doomscrolling through your Instagram feed, noticing that each and every influencer sliding down your screen is better-looking and more successful than you are.

Just look at those leaderboards, bestseller lists, and screenshots of rapidly climbing subscriber stats, embellished with the inevitable humblebrag note, “Thanks to my subscribers. I could not have reached this milestone without you! Here’s what you get when you upgrade. [insert upsell here]”

Swiping as an author across the Substack feed can be an inferiority-inducing anxiety trip. So many others are doing so well! They’re posting and sharing and hustling and playbooking themselves into stardom. It makes you feel ugly. Unpopular. Inadequate. The Quasimodo of creatives.

Few people talk about what it actually feels like to scroll through a Substack leaderboard after midnight when your own subscriber count metric looks like the sun setting over a windless ocean. I’m sure some founders, freelancers, and solopreneurs feel more alone than ever as they measure themselves against leaderboards, whether on Substack or anywhere else.

And yet, no matter how many of those self-congratulatory wins, comments, bestseller badges, and “download-my-framework” posts I see, the only thing I can think is … No.

That’s not how I want to play this game.

The hustle approach doesn’t fit me. (It never did, honestly, but FOBO has a way of making you question your own instincts.)

I Want to Be a Sleepy Agent Boss

But then … what is my approach?

Before we get into that, let me share the thoughts I’m endlessly mulling over while my smart ring is busy recording yet another sleepless night.

I envision a Substack Publishing assistant (let’s call it Lenny) that helps me grow my Substack publication. Lenny does all the work I have no time for, like cross-linking my posts, recommending other authors, scrutinizing the statistics dashboard, and comparing the results with LinkedIn metrics and Google Search Console reports. Lenny is smart enough to share with me only what matters, considering the objectives we’ve agreed upon together. While I’m producing, Lenny is optimizing.

I dream of a Nonfiction Reading assistant (let’s call it Marie) that monitors the English-language nonfiction book market for me, notifying me of new books that seem to satisfy my needs and taste. Marie keeps track of the books I read, the ratings I give them, the ones I didn’t finish, and the backlog I still have. She will sternly push back on my tendency for tsundoku when I’m tempted to order an author’s latest work when their previous two books still sit untouched in my reading queue.

I want a Finance & Admin assistant (let’s call it Warren) to take care of my monthly invoices: collecting receipts from my mailbox, digging them up from the billing catacombs of three dozen online services and digital platforms, and then matching the documents with the transactions on the credit card statement it retrieved from my bank. Warren will also politely hunt down any unpaid invoices and will suggest how to set aside the surplus balance in my bank account—if I ever have any.

I need a Mind & Body assistant (let’s call it Jane) to monitor the streams of health data collected by my smartwatch, smart ring, and the nutrition app I use to track my daily food intake. Jane will follow my exercise regimen, vitamins, minerals, caffeine shots, step count, and sleep stages to give me tips on how to prevent waking up at 3 AM with a brain that keeps generating mental images of unrealized subscriber counts.

I absolutely want various digital assistants helping me invent and build out business ideas that result in actual value generated for customers, not some “exclusive access” to prompt libraries, courses, and AI-generated playbooks targeted at people like me who are unlikely to ever hit the bestseller leaderboard.

At 4 AM, as I turn around yet another time, I dream of a personal staff of AI agents specialized in niche contexts such as Public Speaking, Personal Branding, Board Work, Fiction Reading, Fiction Writing, Travel & Vacations, Friends & Family, Personal Finance, Home & Living, Food & Cooking, and more.

And I need two Chiefs of Staff (let’s call them James and Andy, one for personal matters and the other for professional jobs). They’re familiar with all the specialized assistants and can query them directly about ongoing work, backlogs, commitments, calendar items, and deadlines. When I tell Andy I want to use a dedicated credit card for all my nonfiction book orders, Andy will work with Warren and Marie to make it happen. When I tell James we’re out of milk, James will work with Martha and Jamie to find an alternative for my morning cappuccino.

That’s the dream I have when I lie awake.

I want a personal AI agent architecture. The last conscious thought I have, just before I finally fall asleep at 5 AM, is I want to be an agent boss. One that can actually sleep.

None of that exists, though. So it’s time I started building.

But what I don’t want is to spend two years building an architecture so elegant it belongs in a museum, while everyone else shipped products to actual humans. I’ve made that mistake before. More than once. (Maybe three times. Fine, four.)

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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1) Only in the Cloud, Not on My Computer

I almost hear you hopping up and down excitedly, saying, “That sounds great! You can use Claude Code, OpenClaw, LangChain, CrewAI, …”

Wait, wait, wait! #soundofpullingthebrake

Before anyone helpfully suggests I just fire up OpenClaw or CrewAI and call it a day, here’s the part that actually matters.

First and foremost, I want nothing running on my local computer. Ever. Nada. Zip. Null.

With me frequently traveling for work and moving weekly between homes in Rotterdam and Brussels, the last thing I want is for my digital assistants to work with local files that may or may not be in the right file folder, and may or may not properly synchronize across multiple computers that may or may not actually be turned on.

I’ve already struggled these past few weeks with Claude Cowork getting confused because of out-of-sync skills files, and insisting on a local home folder on my PC that is not the home folder of Google Drive or Dropbox. (Neither can be easily changed.) And copy-pasting local files from one home folder to another is an excellent way of inducing a migraine and another sleepless night.

Let’s not go in that direction. I will happily pay for tokens and credits when it means I don’t have to worry about the infrastructure. I’ve spent enough years of my life juggling with deployment environments to know that the cheapest thing I can buy is to make this someone else’s problem.

All my assistants must live in the cloud.

2) Real Use Cases, No Selling Spades to Gold-Diggers

I want my digital assistants to do real work. Not work about work.

You probably know the category. E-books about writing e-books, podcasts about making podcasts, YouTube channels about how to grow your YouTube channel. We can call this type of business model monetized recursion engines: the endless parade of make-money-online (MMO) schemes offered by meta-content creators.

It’s the equivalent of selling spades to gold-diggers, or tickets to lotteries, or online courses for crypto speculators. Monetizing other people’s desire for fame and money is an all but guaranteed path to profits. (It’s also the business model equivalent of a snake eating its own tail, but I suppose snakes don’t need a Substack leaderboard.)

I don’t want to earn revenue from people who hope to earn revenue. For every successful creator, there are a thousand who can only dream of being similarly successful. If the successful one is successful only because they charge the thousand unsuccessful wannabes for hope and illusion, they don’t have a real business. They run a pyramid scheme.

“I don’t want to earn revenue from people who hope to earn revenue.”

I refuse to write a Substack about how to build AI orchestration around Substack. I want my AI agents to handle real business use cases. The kind where actual value gets exchanged with actual humans for actual reasons.

3) Context Isolation, No Cross-Contamination

One only needs to read a few Substack posts discussing OpenClaw to see the horror stories of AI agents ruining a job, a data repository, or even someone’s reputation, when given unlimited and unconstrained access to someone’s personal computer.

I don’t want Marie to know my credit card numbers, or Warren to have access to my health data, or Jane to see my customer notes. Last time I checked, I counted at least 20 personal and professional contexts and each of them should have its own dedicated AI agent specialized in that and only that. My travel details are irrelevant to the AI agent managing my home improvements. And I don’t see the need for the Personal Finance agent to have access to my online dating profiles.

Think of it like biological cell membranes. Each cell has a clearly defined boundary, lets only the right molecules through, and doesn’t randomly share its DNA with the cell next door. Without that membrane, you don’t get a functioning organism. You get cancer. I want my sleepy agent boss architecture to work the same way: strict context isolation, preventing cross-contamination of contexts.

This will definitely slow me down. But it should also keep me safe.

4) Layered Architecture, Lasagna Instead of Spaghetti

Call me old-fashioned, but I’d rather have lasagna than spaghetti, and I’d rather understand the layers than inherit a bowl of someone else’s noodles.

I’m no fan of the idea of a swarm of AI agents autonomously coding an entire tech stack all by themselves without any insight into how the various layers of the product connect to each other. I’ll be happy to let the assistants help design and build the various parts of my agentic architecture, but I want to stay in control. I want to own the interfaces between the layers.

There’s a lesson from systems theory here: in any complex adaptive system, the interfaces between subsystems are where most of the interesting (and dangerous) behavior emerges. If you don’t own those interfaces, you don’t own your system. You’re just renting it.

“If you don’t own those interfaces, you don’t own your system. You’re just renting it.”

Is that slower? Yes. Is that more responsible? I think so, yes. Will I regret this principle in six months when I’m still debugging layer two while some 22-year-old has shipped fifteen agentic products from a hammock in Bali? Probably. But I’ve watched enough cowboy architectures collapse to know that speed without structure is just expensive failure with better marketing.

5) Multi-Platform, No Vendor Lock-in

Last but not least, I prefer to run a business that’s not intimately tied to one company’s agentic architecture.

It’s hard enough not to rely on Google for everything these days. I have no intention of making things worse by building the entire stack of my agentic workflows through a single supplier. I want MyClaw and Claude Code and AI Studio and Make, just like I use (and pay for) the top five cloud LLMs precisely because I don’t want to be dependent on any single one of them.

Different contexts require different persistence layers, too. For some data, Fibery is perfect. Other things are better stored in Notion, Trello, Google Drive, or Roam Research. Trying to cram everything into one tool is like trying to store wine, cheese, and laundry in the same closet. You can do it, but you probably shouldn’t.

Instead of going all-in with one specific vendor, I want to go deep in my understanding of how not to tie the future of my business to one exclusive technology.

Yes, all these requirements mean I’ll probably be the last Solo Chief on my block to have a fully functioning agent stack. I’ve made peace with that. (Okay, mostly made peace with that. Ask me again at 4 AM tomorrow.)

It’s going to slow me down, but it will also increase viability.

The Future Agent Boss

It’s time to feel less anxious about the future and start building it around ourselves. We should stop comparing us with others who only seem to be more productive and successful, but who are really running monetized content recursion engines and don’t share our strict requirements for a viable business: a cloud-first architecture, context isolation, layered design, and supplier independence.

I haven’t built any of this yet. I’m describing the map I’m drawing, not territory I’ve conquered. Some parts of the map are probably wrong. I’m okay with that. Maps are supposed to get revised. That’s what separates a map from a manifesto.

In the future, I want to tell Taylor (my future Personal Branding agent) to update the photo, tagline, and description across all my social media profiles, once every three months, to reflect the most important thing I’m working on at that time. If Taylor sees a need to employ a specialized agent per platform, I’d probably say that’s a smart move.

In the future, I want to tell Johanna (my future Nonfiction Writing agent) to check my entire archive of articles and make a proposal for turning the strongest posts with the best engagement into a book. Johanna will have to collaborate with Lenny on that because Johanna will only have access to my books, while Lenny will own just my Substack archive.

In the future, I want to tell George (my future Fiction Reading agent) to find me the ten best novels on dystopian parallel universes. George will probably have its own team of subagents for checking which books I’ve already read, reading reviews to match books with my taste, and ordering hardcover copies that won’t break my bank.

And when I have an idea for a new business model that needs a new context, I’ll be working with Andy to see which new professional agent to create or hire.

And I’ll be the sleepy agent boss: someone who builds, delegates to, and manages AI agents to amplify their impact, working smarter, scaling more deliberately, and taking control of their career in the age of AI. Not a hustle-bro with a leaderboard screenshot. Not a meta-content creator selling gold-digging spades. A sleepy agent boss with a map, a lasagna architecture, and a smart ring that records a good night of sleep.

I’m starting to build now. I’ll share what I learn, including the parts where it blows up. Especially those parts, actually. Those tend to be the ones worth reading.

Jurgen, the Solo Chief

P.S. If this reads a bit like me rambling, it’s probably because I just crossed the Atlantic and I am a little sleep-deprived.

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My friend’s employer just lost three clients to a one-person scaleup.

AI isn’t lifting all boats. A barbell is forming and the middle is getting crushed. Here’s how to find your place at either end before the squeeze reaches you.

My friend at a mid-sized company is losing clients to one-person scaleup. What about you? Are you the winner or the loser in the squeeze of the barbell?

With the help of AI, micro-entrepreneurs—tiny teams running small businesses with big-tech tools—are disrupting the corporate landscape, and the stakes are higher than ever. What’s emerging isn’t the “AI lifts all boats” scenario that the tech bros and evangelists promised us. Instead, we’re watching a Barbell Effect take shape: massive winners at both extremes and many businesses getting pulverized in the middle.

The Barbell Economy: Where AI Winners and Losers Are Being Made

Picture the economy as a barbell. On one end, you’ve got the heavyweight champions: massive corporations with near-infinite resources, established customer bases, and the kind of distribution power that makes the average business weep. On the other end, you’ve got the nimble specialists—solo operators and tiny teams who can pivot faster than a squirrel and serve hyper-specific niches with surgical precision.

The middle? That’s where your career goes to die.

Big Tech’s AI Advantage Has Nothing to Do with AI

Despite the breathless coverage of AI startups “disrupting everything,” big tech companies are consolidating power faster than ever. They figured out something that should have been obvious: having the best technology matters less than having unfettered access to customers.

Microsoft and Google didn’t build the first advanced LLM. They let OpenAI do that work. But the mega-corporations have something more valuable: Word, Excel, Google Docs and Sheets sitting on virtually every business computer on the planet. When they add AI features, they don’t need to convince anyone to try a new product. They just add a new button to software people are already using.

Innovative features that were entire startups in 2024—document summarization, meeting transcription, instant translation—are now Tuesday’s software update. A startup might build the most elegant AI writing assistant ever conceived, but if Google adds passable AI writing to Google Docs, most people will use that. It’s already there; it’s free, and it works well enough. What’s the point of paying for Granola when, next week, Gemini does the same out-of-the-box?

Incumbents don’t need to be first or the best. They just need to be good at satisficing: be “good enough” and “early enough” so users don’t look elsewhere.

The Micro-entrepreneur Sweet Spot: Boring, Specific, Profitable

On the other end of the barbell, hyper-targeted solopreneurs and tiny teams are generating money like mini Taylor Swifts. These aren’t companies building “ChatGPT for X.“ They’re solo founders and AI-driven startups solving narrow, often mind-numbingly boring problems most people don’t even know exist.

Maritime insurance compliance. Dental practice billing codes. Predictive maintenance for industrial mixing equipment. These markets are too small for MAANG and the other Big Tech companies to care about, but plenty big enough for someone with domain expertise to build a profitable business or lucrative side hustle.

The magic happens when you combine AI with deep knowledge of how a specific industry actually works. A former insurance adjuster who builds an AI system for processing specialized claims isn’t just building a tool. They’re automating their own former job, which means they understand exactly where the risks and opportunities are.

Sam Altman’s old prediction of “one-person unicorns“ is probably still hyperbolic (running a billion-dollar company involves a lot of contracts and compliance headaches). But the core insight holds. A small team with the right AI tools can deliver output that used to require an army of cubicle workers.

The Middle Market Squeeze: Why Mid-Sized Companies Are Losing

If you’re working at a mid-sized company without a distinctive specialty, you’re getting squeezed from both directions.

From below, AI-powered freelancers and micro-agencies can deliver work at quality levels that were impossible two years ago. A three-person marketing shop using AI tools can produce the same volume as a traditional 20-person agency—at lower prices, with faster turnaround times.

From above, large enterprises are using AI to expand into markets they previously ignored. A global bank can now offer personalized financial advice to small business clients in specific industries, with a tailored offering. This was work that used to belong to regional relationship managers of small to medium-sized businesses.

This squeeze in the middle of the barbell is especially brutal for “body shop” businesses: the companies whose primary value is having warm brains and bodies to perform tasks, like marketing agencies that bill by the hour and software development shops that rent out programmers. When the marginal cost of executing a project approaches zero, charging for time becomes absurd.

The value has to come from somewhere else: network, reputation, strategy, specialization, creative direction. Not everyone can make that transition.

The Hidden Champions Exception: Niche Businesses with Infrastructure Moats

There’s one exception to this squeeze in the middle of the barbell. One type of mid-sized company is actually thriving: what German business scholars call “Hidden Champions.” These companies dominate specific, often obscure global niches.

Think of a company that manufactures specialized valves for rocket fuel systems, or the world’s only producer of equipment for processing cocoa beans at scale. These companies have what I refer to as “infrastructure moats.” Their competitive advantage is tied to real-world processes and knowledge that can’t be replicated by training an AI on Reddit and Wikipedia. Large language models generate bits, not atoms.

These Hidden Champions use AI to supercharge their expertise, not replace it. They build predictive maintenance for specialized equipment, optimize supply chains for unusual materials, or create quality control systems requiring deep manufacturing wisdom.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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Your Career in the Barbell Economy: Three Viable Paths

You know that feeling when you’re scrolling LinkedIn on a Sunday and you see a two-person company advertising a vibe coded product that took your company twelve months to produce? Welcome to the new world of business.

The global reshuffling of careers is already underway. Companies are making decisions right now about which roles to eliminate and which functions to automate.

Three paths remain viable to you:

1. Join a large organization genuinely serious about AI transformation. Focus not merely on adding chatbots to existing processes but rewire entire value streams. You’re betting that platforms will continue to consolidate power.

2. Go deep in a specific, underserved niche. Solo or with a tiny team. Build an AI solution for a market too small for giants but too complex for generalists. This requires risk tolerance and existing domain expertise.

3. Find a mid-sized company with specialized infrastructure knowledge. These are better able to create natural barriers to entry. Such companies need people who can bridge traditional expertise with AI capabilities.

The strategy that’s absolutely not working anymore: competing in the generic middle, offering undifferentiated services that AI can provide faster and cheaper. If this is where you find yourself right now, I suggest you escape while you can!

Jurgen, Solo Chief

P.S. Which path are you on—or trying to escape from?

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Your job isn’t your identity. Your story across all jobs is.

When roles keep shapeshifting, clinging to one title is a single point of failure. Be the admiral of your fleet, not the captain of one ship.

My role has collapsed.

Last Tuesday, I updated the tagline on my LinkedIn profile for the fourth time this year, and I stared at it for ten minutes trying to decide if this one would survive for longer than a month. Few people in my life fully understand what a fleeting role feels like. That’s not a complaint—it’s an observation.

It’s almost as if I’m not sure who I am anymore—a professional identity crisis that I wasn’t expecting at this stage of my career.

Do you feel like that too?

The global market for agile workshops has evaporated. Most conferences and companies have stopped paying for keynote speakers (at least in the agile space). And let’s not discuss the income from royalties on agile books and courseware when most of the world has moved on to YouTube videos, Substack Live sessions, and free self-paced courses.

Are you still a workshop facilitator when there are no workshops to facilitate?

One of my best friends is a copywriter—or maybe I should say he was a copywriter. I know coders and developers who’ve not written a single line of code these past few months. And my bookkeeper saw most of her clients disappear and expects there will soon be no more books to keep.

For most of my career, I knew who I was because of what I did. Author, speaker, entrepreneur. Each role came with its own feedback loop: people told me I was good at what I offered them; I cherished the positive feedback, and that became my identity. It was a comfortable arrangement while it lasted.

Then Covid hit. And the backlash on the Agile Industrial Complex. And then AI started eating the half-life of expertise for breakfast. The knowledge that made me “the agile management and leadership thinker” five years ago is now common sense or just obsolete. The roles keep shapeshifting. The mirrors that used to reflect back a clear professional self are breaking faster than a supply chain through the Strait of Hormuz.

When you derive the coherence of your professional identity from companies, communities, and credentials, you may have a problem.

“Who are you?” is a hard-to-answer question when technological progress is cracking all the surrounding mirrors.

The Ship of Theseus

Imagine you have a sailing ship you maintain continuously to stay operational. You replace one plank, then another, then the mast, then the hull. For how long is it still the same ship? At what point does it become a different ship? And when you’ve renewed every single part, could you reconstruct the original from the discarded pieces? Which one is then the real ship?

For most people, this is just a philosophy puzzle. For Solo Chiefs, it can be an everyday struggle.

Philosophers call this the Ship of Theseus, and many of us are living this question right now. Every month, we learn a new technology. Every week, we reinvent a task. Every day is an opportunity to recognize that the job we thought we had is actually not the same job it was before. We keep swapping out major pieces of our professional selves. New tools, new processes, sometimes a whole new deck or frame.

In the absence of a stable context—a company, a community, a credential—the solo operator’s greatest overhead is the psychological cost of unlearning. You can reframe constant replacement as routine maintenance. That’s a healthy way to think about it. But solo chiefs don’t always get to choose how it feels at 2am when something breaks and there’s nobody to call.

And even the maintenance of the ship ends when changes in the market have rendered the entire ship obsolete.

If that ship was your job, and your identity was that job, then who are you when the ship has retired?

The Professional Identity Crisis

The fundamental reality of the modern economy is that technical expertise has shifted from a career-long asset to a high-depreciation consumable. For the solo operator, professional survival is no longer about defending a specific title. It’s about maintaining a coherent overarching narrative while every underlying skill set is systematically replaced.

A significant number of people are soon at risk of not being able to tell a coherent story about who they are professionally. Does that mean we should just make up a new story? Spin a new tale for our future selves? Write a new tagline and then hope it comes true?

Bah. Not really.

Identity doesn’t come from storytelling alone. It requires doing and experimenting with possible selves first and then narrating afterward. Herminia Ibarra—who actually studied this properly rather than just having opinions about it—found that story follows action, not the reverse. You only know who you are when you can tell a story about what you did. You can’t write the chapter before you’ve lived it.

Not everyone is walking around feeling stressed about their professional identity. That’s worth acknowledging, so I don’t sound like I’m projecting.

A huge portion of professionals are not replacing the planks of their professional selves every month. Some readers have a stable ship. Same role, same license, same environment. The turbulence is in their tools, not their title. A civil engineer doesn’t wake up thinking, “My self is being reassembled like the Ship of Theseus.” They’re still a civil engineer.

Fair enough. This piece isn’t for them.

Others are pretty good at staying anchored even when everything around them is changing. A marketing manager using AI tools may not become a constantly reinvented polymath. They may simply remain “marketing manager,” with technological disruptions absorbing the skill churn underneath. The identity stays. The capability layer shifts.

Good for them, too.

But not all of us find ourselves in that stable environment with the steady company, community, and credentials. Not all of us have a clear job-to-be-done that remains constant despite the underlying technologies shifting every week. For people building careers outside a stable context, professional identity is becoming fluid. The only durable thread is the story that connects the transformations.

And then the question is: do you act your way into a new identity and then tell the story, or do you tell a new story and then force reality to catch up?

If you ask me, what holds it all together is the story we keep telling about ourselves across all the replacements and transitions behind us. The thread connecting the person who failed at one thing to the one now figuring out something completely different. That ongoing narrative creates cohesion when everything else keeps changing.

Even when we switch from one ship to another, we’re still the same captain.

Or better: when we have multiple ships sailing together, we’re still the same admiral.

The Fleet of Theseus

I’ve reinvented myself so many times that the Ship of Theseus feels less like a thought experiment and more like my LinkedIn profile.

But the more I think about it, the more I notice that some things have never changed. I’m still interested in management and leadership. I still work at the intersection of creativity and technology. I’m still the one who loves to teach and tell stories.

The tools, techniques, companies, and communities get swapped in and out. The titles they give me change every year. Yet I love discussing organizational change in the face of disruption now as much as I did thirty years ago. (Whether anyone is paying me for it is a separate question entirely.)

The Ship of Theseus assumes you stick with the ship, that the ship is the only thing you have. But some solo chiefs realize the ship is actually a prison, and the only way to survive is to switch to another ship entirely, leaving one thread of your story behind. For some solo owners, the honest move isn’t a thread of continuity at all—it’s a plot point: the pivot in a larger narrative.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

Subscribe now

For the ultra-prolific solo builder, identity is maybe not even one story but an epic. They aren’t sailing the Ship of Theseus; they’re operating the Fleet of Theseus. Their cohesion doesn’t come from a single narrative thread, but from a repeatable process of rapid experimentation and action, producing multiple parallel storylines in a life-spanning saga.

A static identity is a single point of failure—and that’s a very lonely place for the only person trying to live up to it.

My LinkedIn headline will change ten more times, I’m sure. Yet, the saga continues. The admiral doesn’t need to know which ship is next. They just need to know they’re still the one giving orders.

Jurgen, Solo Chief.

P.S. How many ships are in your fleet right now? I’m curious.

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Everyone saw it. Nobody named it.

Understanding your weird brain matters more than getting a certificate for it.

My classmates were throwing my schoolbag around as if they were playing beach ball until one boy failed to catch it. My bag hit the street, the chocolate milk carton inside it burst open, and I spent the next hour in the changing room using napkins to wipe tears off my face and chocolate milk from my schoolbooks. For the rest of that school year, the brown-stained pages in my books were a daily reminder of my social status in our class.

It was just one of the many humiliations I had to endure as a teenager. The boys called me names, ruined my belongings, and casually threatened me with violence. And it was no secret why they did that.

I was weird.

I had significant problems in social areas because my social intuition was less well-developed than it was for other kids my age. In every break between classes, I walked the streets alone, always trying to evade the ones who were out to taunt or hurt me. When I interacted with others at all, I preferred one-on-one conversations, which felt much less threatening—less overwhelming. It probably didn’t help that, while the boys talked about girls and sex, I could rattle off the entire Dutch Top 40 plus the pop charts of the competing radio stations off the top of my head. Numbers fascinated me more than humans.

At night, I cried myself to sleep, afraid I would never have friends or romantic relationships. Nobody would ever like me.

I had to accept I wasn’t normal.

I’d have to remain solo for the rest of my life.

My outlook didn’t look so bleak a few years earlier at primary school. I was one of the smartest children in our class—unstoppably creative, good at analyzing problems, great at recognizing patterns. My Rubik’s Cube was the love of my life, even though it took me a while to solve it, a pattern that would repeat endlessly throughout my life. One comment from the head teacher stood out in my final report: “Jurgen is slow because of his preciseness.” The intelligence came at a price: my brain needed extra time to match the other smart kids in our class.

I had a Mercedes brain with a Tuk-Tuk engine. Brilliant, but with baggage.

So, I was socially awkward, always on my own, the first at seeing patterns, good at solving puzzles, and precise in everything I did, but also a tad slow in my thinking. It’s funny how those around me saw all the symptoms, but nobody named the problem.

None of that held me back, though.

Socially isolated as I was, my sluggish but determined mind got me through high school with flying colors—8 out of 10 across the board at my final exams and a perfect 10 for mathematics. My 9.45 score in English Language was the highest among 120 fellow graduates. I only left Dutch Language and Literature behind me with a meagre 6 out of 10, because, seriously, who in the world cared? Not me. The ability to focus includes letting go of what was irrelevant.

I was slow and weird, but not stupid.

Everything changed when I went to the Delft University of Technology to study Information and Computer Science. Suddenly, I wasn’t the only freaky person in class anymore. It seemed everyone around me was somewhere on the spectrum from slightly odd to completely bonkers. At the Faculty of Mathematics and Computer Science, analysis and problem-solving was the goal. Preciseness was a virtue. Social awkwardness was the norm. I was literally among like-minded people, and for the first time in my life, I had friends.

I was happy.

Making my way through my studies (slowly but diligently, as always) I used every talent I possessed to compensate for six years of loneliness. I needed to know that I mattered after all. I wanted people to need me. I craved being liked. So I started exploiting my superpowers.

My excellent attention to detail allowed me to be a writer and editor on two yearbook committees. With my hyperfocus, I offered myself as a cartoonist to illustrate the faculty’s official publications. If someone needed marketing posters, they usually called me. When the bar needed coupons, they would always ask me. I probably spent more time in the faculty’s printing room than in any of my computer science classes.

On top of that, I used my talent for specialization to teach myself not only coding, writing, and drawing but also bookkeeping when I became the treasurer of the student society. And because I didn’t like our accounting software, my perfectionism drove me to write a near-flawless bookkeeping program—30,000 lines of code—entirely crafted by me.

For five or six years, I was everyone’s creative machine.

And still, nobody suspected that something was off.

After I finished my studies, my life swung back and forth between triumph and disaster, prosperity and loneliness, when I ventured back into the real world and started doing business with normies—those afflicted with neither my superpowers nor my shortcomings.

Whenever I worked on teams, the people I collaborated with inevitably came to judge my openness and straightforward comments as considerably rude or downright hurtful. My radical frankness and criticism were often a challenge for my coworkers, even by Dutch standards. My first manager said I had the uncanny talent of cutting into other people’s souls, without even being aware of it. I had the dubious talent to bring a few team members to tears with my honest assessment of the quality of their work. And I didn’t understand it at all. I was merely stating the facts. 🤷🏻‍♂️

On the other hand, those who could withstand the lashings of my tongue appreciated my unmatched ability to think outside the box. I had business ideas that nobody else had. I could focus intensely on a limited number of subjects. For thirty years, my mind was like a laser, targeting value and cutting through crap, which helped many colleagues and business partners earn a decent living—some of them hitting a jackpot thanks to me—as long as they forgave me for being reclusive, unreserved, and eccentric.

In my romantic relationships, a few partners likewise had to cope with a long list of peculiar quirks. I have no fondness for small talk, meaning my brain automatically tunes out when someone’s noise-to-signal ratio is too high, and I’d rather spend my time thinking about tomorrow’s puzzles and challenges. I am under-sensitive to sensory stimuli, meaning my mind ignores most environmental cues and changes. I don’t notice someone’s new haircut, do not see they’re wearing different glasses, and remain blissfully unaware that someone redecorated the entire room until they literally point it out to me. If the building is on fire, someone will have to drag me out, because I probably wouldn’t notice. Worst of all (and a quite challenging aspect in any love life, I suppose) my brain seems to show no interest in other people. (There’s emphasis on the word ‘seems’ but, nevertheless, it’s a pretty tough thing having to deal with for someone in a relationship with me.)

In between relationships, there were a few times when I thought it would be less stressful for everyone if I just stayed alone.

Fortunately, that didn’t happen. I found someone who’s been willing to suffer it all for twenty-five years and counting.

For our wedding fifteen years ago, I designed a symbol depicting a dog and a cat, symbolizing the nature of our relationship. I’m the cat, of course. I might travel the world and live countless adventures, but I always, always return. My home is where my spouse is.

I’m loyal.

How could I not be?

It’d be easier to discover an oil well under my house than to find someone else willing to put up with my abnormalities.

Two years ago, as I was pondering my strangeness and unwillingness to conform to the norms of the world, I wondered if maybe I was on the spectrum. I’d never seriously considered myself autistic before. That was certainly because we have a few severe cases of autism in our family, and I could not possibly compare myself to my two aunts—both are lovable as apple pie but also crazy as a doormat. For me, they were my reference cases of what autism meant. Autism implied an inability to cope with the demands of society. Sure, I was weird, but not that weird.

However, autism is hereditary.

When it runs in the family, it can run wide and deep, in many forms and variations.

So I did an online test. And a second one. And a third. (Neurodivergent red flag! 🚩 The fact that I studiously completed three different questionnaires should tell you something about the very traits I was trying to investigate here.) Each of the tests provided the same answer:

“There’s a strong probability that you are autistic.”

I shared the results with my husband, who calmly replied, “Are you surprised? I always suspected you were somewhere on the spectrum.”

Great. Thanks for sharing, sweetheart.

Now, I’ll be transparent. These were three self-assessments, not the result of an official clinical test by a trained mental health professional. But frankly, I have little interest in being awarded an official certificate. What would be the point? It’s unlikely that would give me an enviable three-letter acronym I could add to my LinkedIn profile. I already have what I wanted: understanding.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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The three online tests cleared up a few mysteries I’d never been able to resolve.

The autism assessment explains why I couldn’t pass that speed-reading exam I attempted in my twenties. My autistic brain has difficulty maintaining an overview, which is exactly the skill you need when trying to skim through a dense text while developing an understanding of its message.

Being on the spectrum also explains why I hate escape rooms or solving puzzles with someone standing beside me with a stopwatch. The slower processing of information in my mind prevents me from getting anything done within the allotted time box. And the fear of failure sabotages any attempts at analysis and problem-solving.

Autism also explains why family and friends know they must announce any suggestions for social coffees, lunches, and dinners, preferably twenty-four hours in advance. I have difficulty with unexpected change, and I absolutely hate it when someone’s spontaneous idea completely ruins my plans for the day.

I finally understand.

I’m on the spectrum.

Being on the spectrum saddled me with tremendous opportunities and useful talents, but it also frequently got me into trouble with other humans.

It’s all one package, though.

You can’t have superpowers without the risk of kryptonite.

One of my readers asked me how I’m able to cope with the world. I think it might be the wrong question. The better way to phrase it is, “How can I help the world cope with me and with others like me?”

I’ve already accepted my brain as it is, blessings and curses included—and that’s exactly why I do some of my best work alone, as a Solo Chief. What I can do is let people know what they can expect from me, advantages and dysfunctions included. I’m happy the way I am, and wouldn’t want it any other way.

Acceptance makes everything much easier.

Autism is my superpower and my kryptonite, though I don’t expect anyone to stand in line to make an Avengers movie out of my life. Half of it would show me sitting by myself in a coffee bar, furiously typing away at a keyboard. It’d be a challenge to turn that into a blockbuster film.

Jurgen, Solo Chief

P.S. I couldn’t sell my schoolbooks with those chocolate milk stains in them. But the marks they left on my soul only gave it more color.

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If your value stream starts with a customer order, you’ve already lost.

Most value stream examples begin with a request and end with delivery. Extend yours from first signal to proven impact, or watch competitors eat your lunch.

Last Monday, March 2, at 5 pm, I had a conversation with about the challenges of solopreneurs, lone managers, and other solo chiefs. One of the action items my AI note-taker sent me an hour after the meeting was this one:

⏹️ “Describe your criteria for choosing which AI to use (e.g., integration, skills, personality vs. ambient access) and publish guidance so readers can decide which AI fits their context.”

The algorithm picked up a signal in our conversation.

On Wednesday, around 3 pm, I published the post “Which AI Should I Use?“ with a nice tongue-in-cheek flowchart depicting a decision tree and brief descriptions of fifteen leading AI models. A few hours later, at 6:22 pm, Alex responded:

Exactly two days after our meeting, the reader validated the value of my writing.

From signal to impact: 48 hours.

Common Value Stream Examples

A value stream is a way of describing the work we do to delight our customers. Toyota embraced the concept of value streams decades ago with their Toyota Production System (TPS). Since then, value stream mapping has become a foundational practice in Lean literature, and the Agile community happily adopted the term as well.

I’ve come across several definitions of value streams, and they all boil down to the same thing:

A value stream is the set of actions needed to fulfill a value proposition from a request (order) to realization (delivery).

Here are some often-cited value stream examples:

1. Order-to-Cash (Customer order fulfillment) — From a customer placing an order, through fulfillment and delivery, to receiving payment. This one gets labeled “Order to Cash” or “Order Fulfillment” in most textbooks.

2. Project Request to Completion — From a stakeholder submitting a project or change request, through planning and execution, to delivery and closure.

3. Procure to Pay (Procurement) — From recognizing a need to buy something, through supplier selection and ordering, to receiving goods/services and paying the invoice.

These are perfectly fine value stream examples. But the problem is that they’re too short. We should extend the concept of the value stream in both directions.

Expanding the Value Stream Left: Start with Signal, Not Request

My first issue with the traditional description is that the value stream begins with a request or order from a customer. This is way too late! If you sit around waiting for customers to place an order with your company, you’ll soon be out of business.

In the age of AI, it’s becoming easier than ever to detect trends, intent, and desire long before a customer is even ready to place an order. In fact, while you sit there with your arms folded, waiting for the next request to come in, your competitors are using AI to steer intent and to create desire (toward their product, away from yours) long before the customer even realizes they want something.

When the customer finally understands the value of what the market offers, placing an order should be nothing more than a logical next step in the value stream they’re already in. Without them even knowing it. And you want them flowing through your value stream, not your competitor’s.

If you just wait for customer orders, you’ll soon be out of business.

This means the standard value stream definition is outdated. We should extend the concept to the left to include the customer acquisition funnel all the way to the first signals in the market. The earlier you detect potential value for a customer, the better.

Expanding the Value Stream Right: End with Impact, Not Output

There’s another problem with the traditional value stream definition: it ends too early.

If you’ve been part of the agile community for a while, you’ve probably heard the stories. Product managers treat Scrum teams like feature factories. Product backlogs overflow with pointless user stories. Sprint after sprint of product deployments have never delighted a single user.

The problem is that everyone is so busy doing work and delivering output that nobody has any time left for validating outcomes and impact. Few people in the organization spend their time checking whether the busywork was worth all the trouble. The last column on Scrum and Kanban boards is always called Done. It’s never called Is Everybody Happy?

The last column on Scrum and Kanban boards is always called Done. It’s never called Happy.

Granted, I’m far from the first to point out this pervasive problem in the agile world. Many smart coaches, consultants, writers, and speakers have discussed this phenomenon at length. But few have been able to solve the problem once and for all.

Sadly, we can’t change the entire world (even though I wrote a book about it), but at least in our own work, we can take an important step by always naming the last column of our task boards Happy or Proven to prevent us from fooling ourselves into believing that our work is done. Nothing is called done as long as the user isn’t happy about the impact.

We should extend the standard value stream definition to the right, beyond output, to include the validation process all the way to impact: has anything actually changed for the better?

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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New Value Stream Definition

For several years now, I’ve discussed the importance of extending the concept of the value stream in both directions, and I’m unlikely to change my mind. My preferred alternative interpretation of the value stream is this:

A value stream is the set of actions needed to fulfill a value proposition from a signal (an observed behavior) to impact (a changed behavior).

Fortunately, if you dig a little further, it’s possible to find better value stream examples:

1. Prospect-to-Customer (Sales) — From identifying or attracting a prospect, through qualification and sales activities, to converting them into a paying customer and successfully getting them onboarded. Sometimes called “Prospect to Customer.”

2. Issue to Resolution (Customer service) — From a customer raising a question or problem, through investigation and handling, to confirmed resolution and feedback.

3. Hire-to-Retire (Employee lifecycle) — From identifying the need for a new employee, through recruitment, onboarding, and development, to exit or retirement. This is frequently described as a “Hire to Retire” value stream.

Value Stream Examples for Solo Chiefs

The Solo Chiefs among us might recognize the following examples closer to home:

• Your value stream might start when you hear someone talking about Claude Cowork. It could end with the first participants finishing your brand new eight-hour online course named Claude Cowork for Creatives.

• Your value stream may begin by detecting an increase in the search term “portfolio career” on Google Trends and end with the first five-star ratings on a new book that you just published, titled The Portfolio Career Professional.

• Your value stream could kick off when someone tells you about the struggles of their organization. Many difficult months later, it could end with you wrapping up a successful transformation initiative, with this company recommending you as their preferred consultant.

Value doesn’t start with an order, and it doesn’t end with delivery.

Value begins with a user signal and ends with actual impact.

Jurgen, Solo Chief

P.S. What’s the last column on your task board called—and does it actually prove anyone’s happy?

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Benchmark scores won’t tell you which AI to use. Your personality will.

Fifteen companies actually build their own AI models. Which one fits you depends on risk tolerance, data sovereignty, and whose servers you’ll trust with your recipes.

This week, I sat down with four AI assistants and asked them to explain each other to me. It went about as well as you’d expect.

The AI world has become absurdly crowded.

Every week there’s a new model, a new benchmark, a new press release announcing the dawn of intelligence that will change everything forever. And yet, most of us are wrestling with a much simpler question:

Which AI should I actually use?

I’ll be honest. I had no clue what the real differences were.

Sure, I know the big names. ChatGPT. Claude. Gemini—I use them all the time. But then there’s Mistral, DeepSeek, Qwen, Hunyuan, Kimchi (or Kimi, I always forget) and a growing list of models that sound like they were named by a committee of science fiction fans who couldn’t agree on anything.

So I did what any reasonable person with too many AI subscriptions would do.

I asked four AI assistants to explain the whole thing to me.

I gave ChatGPT, Claude, Gemini, and Perplexity a stack of research prompts and asked them to map the field of companies actually building their own foundation models. (I should probably note that asking AIs to explain the AI ecosystem is a bit like asking your bank to explain why you should trust banks. But here we are.)

After several rounds of synthesis and the occasional disagreement between my AI panel (they argue, by the way, with the passive-aggressive politeness of academics at a conference) we ended up with fifteen companies that actually build their own foundation models.

Then I did what any responsible LinkedIn influencer would do.

I turned it into a slightly unhinged personality test.

Go on. Find out if you’re a Chaos Agent or a Sovereignty Officer. I’ll wait.

The decision tree is tongue-in-cheek, obviously. But there’s a real structure underneath the playfulness: geopolitical choices, business models, deployment preferences, and the very human question of whose AI models you’re willing to trust with your most embarrassing data.

So let’s translate the chaos into something slightly more useful.

Here’s a quick tour of the fifteen model builders shaping the AI world right now.

The Western AI Heavyweights

OpenAI (GPT) is where most people start, and many people stay. GPT powers ChatGPT and is embedded in more enterprise tools, APIs, and workflows than I can count. It’s general-purpose, multimodal, and deeply woven into the fabric of how business software is evolving. If you don’t know where to begin, you’ll probably begin here. It’s the default (which isn’t an insult). Defaults become defaults for a reason.

Anthropic (Claude) was founded by people who left OpenAI because they were worried OpenAI wasn’t being careful enough. Whether you find that reassuring or slightly alarming probably says something about your personality. Claude’s models are particularly good at long-form reasoning, writing, and document analysis. The company talks a lot about AI safety and responsible development, which appeals to organizations that want their AI with a bit more governance and a bit less chaos. The “Careful Overthinker” label on the infographic is affectionate, I promise.

Google (Gemini) benefits from something none of its competitors can match: Google already has you. If your work runs on Gmail, Google Drive, and Google Calendar (and whose doesn’t) then Gemini’s ecosystem integration is genuinely hard to argue with. The flip side is that you’re deepening your dependency on a company that already knows an awful lot about your professional life. A tradeoff worth thinking about.

xAI (Grok) is Elon Musk’s entry into the AI race. It integrates closely with the X platform and has a deliberately more irreverent personality than most corporate AI assistants. Grok attracts a particular type of user: someone who finds the safety guardrails of other models restrictive, who wants real-time access to what the internet is saying right now, and who doesn’t mind a bit of chaos in their tools. The “Chaos Agent” label isn’t entirely a joke.

The European Contenders

Mistral AI is France’s answer to American AI dominance. Their models can run through cloud services or locally on private infrastructure, which matters a lot to developers who don’t want their data leaving their own machines. Technically impressive, genuinely open, and carrying the quiet pride of a startup that’s punching above its weight class. If you like building your own stack and you have a slight allergy to Silicon Valley, Mistral is worth serious attention.

Aleph Alpha is German, which should tell you something. Their focus is on sovereign AI, models designed for governments, regulated industries, and organizations where transparency and data control aren’t optional features. Less flashy than Mistral. Considerably more serious about compliance. If your clients include regulators or you operate in healthcare or finance, Aleph Alpha deserves a spot in your research.

The Open-Model Ecosystem

Meta (Llama) helped ignite the open-weights movement. Meta releases the model weights publicly, which means a massive ecosystem of developers can run them locally, fine-tune them for specific use cases, and build entirely independent tools on top of them. This is very different from the cloud-dependent models above. If you’re technically inclined and want 100% control over where your data goes and who can see it, Llama is the family to explore. Meta’s motives for releasing open weights are, shall we say, not purely altruistic. But you can still benefit from them regardless of what Mark Zuckerberg is thinking.

Cohere (Command / Aya) flies mostly under the radar. Their focus is enterprise AI infrastructure and multilingual models, and their Aya family supports a remarkable range of languages. If your work spans multiple countries and languages, Cohere is worth knowing about. Don’t expect the hype. Do expect reliability.

China’s AI Ecosystem

China has built its own rapidly expanding AI ecosystem, and it’d be a mistake to ignore it. Five major companies are shaping this space.

Baidu (ERNIE) is the institutional backbone of China’s AI strategy, integrated into Baidu’s search, cloud, and enterprise products. Think of it as the OpenAI equivalent for China’s domestic market.

Moonshot AI (Kimi) is a newer startup known for models that handle extremely large documents and long conversations without falling apart. The “AI Upstart” label fits: they’ve got the energy of a company moving fast and making noise.

ByteDance (the company behind TikTok) is building AI capabilities at a scale most people haven’t fully registered yet. Their models power content creation, recommendation systems, and more across platforms with hundreds of millions of users. Whether “The Doomscroll Lord” as a label is funny or terrifying probably depends on your relationship with your phone.

Alibaba (Qwen) focuses on commercial and enterprise applications, integrating deeply into Alibaba’s enormous cloud and commerce ecosystem. If you do business within Alibaba’s world, Qwen makes obvious sense.

Tencent (Hunyuan) connects to WeChat, gaming platforms, and one of the largest consumer ecosystems on the planet. If Alibaba is the enterprise play, Tencent is the consumer and creator play.

DeepSeek grabbed global attention by producing high-performance models at surprisingly low cost. Their approach to efficiency made Western AI labs visibly uncomfortable. Popular among developers and researchers who want serious capability without serious price tags. The “Frugal Genius” label is genuinely accurate.

Zhipu AI (GLM) emerged from academic research at Tsinghua University and represents an important part of China’s AI research ecosystem, less commercially visible than the others, but technically serious.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

Subscribe now

So Which One Should You Use?

Honestly? It depends on things that have nothing to do with benchmark scores.

It depends on whether you care about data sovereignty or just want something that works. It depends on whether you’re building your own tools or using someone else’s. It depends on how you feel about tech billionaires, Chinese servers, and European bureaucracy. (All three camps have legitimate points, by the way.)

The infographic above maps these choices through a series of questions that are (let’s be clear) mostly designed to make you chuckle a bit. But I’d argue that the personality test framing actually gets at something real.

The AI you choose says something about your risk tolerance, your politics, your technical comfort level, and your relationship with control. That’s worth knowing.

Follow the arrows. Find out what kind of AI person you are.

And then, as with all maps: use it to think, not to stop thinking.

Jurgen, Solo Chief

P.S. Which endpoint did you land on? Chaos Agent or Sovereignty Officer?

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Yesterday’s post “Teamwork Is a Scam“ marked my 100th post on The Maverick Mapmaker, which I have now renamed to The Solo Chief.

I could write a grand retrospective. I could publish a numbered list of lessons learned. I could even make a framework about it. Instead, I want to do something I don’t do nearly enough: shut up and listen.

If you’re a paying su…

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Everyone cheers for teamwork. Nobody wants to pay the tax.

You suffer the meetings, slackers, and conflicts. The business pockets the gains. Going solo work might be your better bet.

ATTENTION: I celebrate the 100th post of this newsletter with a rebranding. The Maverick Mapmaker is now The Solo Chief. Different name, same obnoxious attitude!

Some people should not join a team.

At a funeral I attended, the daughter-in-law of the deceased scrambled forward to claim her position as a carrier of the coffin, dismissing concerns from her family that it would likely be too heavy for her. “She’s my mom. I will do her the honor!”

Sure enough, when she took a position at the front, she nearly buckled under the strain of the coffin, nearly crashing it headfirst into the floor. The entire family drew a collective breath while the other five pallbearers scrambled to regain balance, each of them carrying an additional load to compensate for the diminished support at the front. For a few dozen meters, the daughter-in-law was obviously not doing much of the carrying.

I think she should not have volunteered for that team.

A few years later, when it was time to carry my mother to her last resting place, I declined to take a position as a pallbearer. I wasn’t sure if I was up to the task, and the last thing I wanted was for the other volunteers to take up the slack. I had decided my talent lay elsewhere.

I prefer to operate alone.

Why Teamwork Is Overrated: The Challenge

You’ve probably had your own experiences with challenging teamwork.

You may have been on an assignment with a “team” of slackers, loafers, and free riders, perhaps in high school or at university. One “team member” carried zero weight, another had practically no talent, and the third was always sick and disappeared from day one. Who was the one carrying the project toward the finish line? Who was the one earning the highest mark that was then collected equally by the entire team? You, of course.

Or you sat on an agile team that did everything democratically and collectively: shared autonomy, shared empowerment, shared backlog, shared cadence, and shared outcomes. Hell, there was even a shared restroom. But do you remember what never got shared, ever? The end-of-year performance reviews. Nor the salaries. Nor the bonuses. And there was definitely no agile team in sight when colleagues competed for a promotion.

Or maybe you assembled a “team” of designers, artists, editors, builders, translators, and other contributors to work on a book, course, or video series. Everyone was “super excited” to be collaborating with you on this product! They all pitched in and did the jobs you hired them for. But guess who was the only one suffering when the product didn’t sell? When the only ones picking it up were three friends and two former colleagues? You, again. All other “team members” got decently paid and moved on to the next job.

Let’s be honest here. Because there’s no way to say this gently.

Solopreneurs know this. Single founders know this. Most creators know this. And almost every manager in the world is in on the charade. I know it too because I’ve practiced each of these roles for years. Every time I asked people to work as a team, I did that to benefit the organization, not the individuals.

The Benefits of Teamwork (for the Business)

The science of teams is clear: there are plenty of benefits to working as a team. I’m sure you recognize some.

• Teams Are More Productive - Put competent people together and output usually rises. The presence of others sharpens focus, boosts energy on routine tasks, and discourages slacking. Add clear, high-quality communication, and performance climbs faster than endless email threads ever could. (link)

• Teams Are More Innovative - When people think differently, they challenge assumptions and expand options. Cognitive diversity forces deeper processing instead of lazy consensus. If the team stays curious instead of tribal, solutions get smarter and more creative than solo work ever delivers. (link)

• Teams Are More Daring - Shared responsibility lowers the fear of failure. Teams are more willing to test bold ideas when the risks are distributed. That slight psychological safety bump can turn people’s personal “maybe someday” experiments into actual collective innovation initiatives. (link)

• Teams Are More Effective - Strong teams build a mental map of who knows what. That shared directory of expertise speeds up decision-making. When unique information surfaces instead of staying hidden, decisions improve and complexity becomes manageable. It’s the fundamental principle of the division of labor applied to teams. (link)

• Teams Are More Intelligent - The best teams win because of interaction quality, not because one lone genius dominates. Balanced participation, social sensitivity, and a shared understanding of goals create consistent performance across tasks, instead of occasional flashes of brilliance. System performance is in the interactions, not in the parts. (link)

This all sounds marvelous, of course. But do you recognize the issue here? Can you see the devious trick?

All these pluses of teamwork are benefits for the organization, not for the team member. Nobody I worked with ever said, “I’m so happy I’m more productive for the company while being paid exactly the same.” Not a single person I know has said, “I’m so proud our team invented things the company is taking credit for.” And the last time I heard, “I’m really glad I can’t continue my work while waiting for Susan’s expertise,” was … ehm … never.

The Drawbacks of Teamwork (for the Worker)

The science of teams is also clear on the drawbacks of teams, and there are plenty of those as well. Let’s see if some of these minuses make you wince in recognition:

• People Don’t Contribute Equally - Put individuals in a group and individual effort often drops. When contributions aren’t visible, motivation fades. The highest performers compensate for the slack, resentment grows, and team productivity quietly sinks under the comforting blanket of shared responsibility. (link)

• People Point at Each Other - Shared accountability sounds noble until something fails. Then responsibility dissolves into the air. People assume someone else will fix it, standards slip, and quality erodes while meetings multiply to “clarify ownership.” (link)

• People Get into Fights - Diversity can fuel insight, but unmanaged differences easily morph into relationship conflict. And relationship conflict is toxic. Stress rises, trust falls, collaboration stiffens, and performance drops even if the strategy deck still looks impressive. (link)

• Some People Drain Everyone - A single destructive teammate can slash morale, creativity, and output. Negativity spreads faster than enthusiasm. The emotional tax drains everyone, and suddenly half the team is spending more energy coping than contributing. (link)

• Alignment Eats the Work - Two experts can outperform one, but only if coordination costs stay sane. As teams grow, time disappears into syncing calendars, status updates, and alignment rituals. Eventually, communication overhead outweighs the benefit of extra brains. (link)

I’m probably biased, but in my experience, it’s the team members who usually suffer more from these drawbacks than the organization.

Teammates have to compensate for Sally always being absent, Eric never taking responsibility, Maude and Babette trying to bite each other’s heads off, and Steven being an insufferable pain in the neck. And everyone feels exhausted because they spend half of their time on team-building meetings, strategy meetings, planning meetings, check-in meetings, review meetings, reflection meetings, one-on-one meetings, 360 meetings, brainstorm meetings, and other coordination rituals that are needed just to keep the whole dysfunctional mob aligned.

The benefits of teamwork accrue mostly to the organization, while the drawbacks of teamwork are carried by the team members. And while it’s true that the business must also swallow the bitter pill of these same downsides, at least it enjoys enough upside of teamwork (see above) to enjoy a net positive for teamwork. That doesn’t seem to apply to the workers themselves.

Groups versus Teams: What Monkeys Teach Us

“But wait,” I hear some of you objecting, “What about all the benefits of groups? Humans are social animals. We love being part of something bigger.”

Indeed, we are. And we do. The vast majority of monkey species live in groups. But a group isn’t the same as a team. Good luck finding a team of monkeys building a bamboo hut together.

A group is a collection of people. A team is a group with a shared goal.

A group shares space, time, or simply just a label. They might attend the same conference, hang out with the same friends, sit in the same coffee bar, or share the same set of genes. The goals of individual group members can be similar, but they work independently. If one person disappears, the others may weep or shrug, sometimes carry their coffin, and then they move on, with often nothing more than mild disappointment when the departed left their bank account empty.

A team, however, shares a goal that requires interdependence. They structure their work so that teammates rely on each other’s outputs. There’s coordination, shared accountability, and usually some degree of collective identity. If one person disappears, the whole thing starts to wobble and might even collapse. When the team succeeds, all team members share equally in the outcome.

We can have all the benefits of a group without ever needing a team:

• The Group Feels Safe - In many group settings, people can admit mistakes, ask naïve questions, and challenge assumptions without fear of humiliation. That psychological safety fuels learning, faster problem detection, and steady performance improvements over time. I’ve attended local meetups around the world with total strangers where everyone felt safe and respected. All of them were groups, not teams.

• The Group Feels Supportive - When social groups function well, group members can provide practical and emotional support. That social buffer reduces stress, increases happiness, and makes heavy workloads more sustainable than silent solo struggle ever could. But the same applies here: this social support can come from anyone, not necessarily teammates. Your local Weight Watchers or Alcoholics Anonymous groups may offer more emotional support than any product team ever could.

• The Group Feels Empowering - Working alongside others sharpens communication, conflict resolution, and emotional awareness. Working through differences builds interpersonal muscle. Over time, collaboration turns technical contributors into professionals who can influence, align, and lead. Again, any social group can provide this. It’s the reason why training programs, co-working spaces, and professional associations exist. It has nothing to do with teams.

I enjoy socializing with the people of our local homeowners’ association. But I would never consider joining any of them as a team member to collaborate on a project.

The science bears this out.

Teamwork doesn’t exist among monkey species. In the animal kingdom, unrelated monkeys don’t work toward a shared goal unless there is some form of individual payoff. When unrelated monkeys cooperate, scientists classify it as either “mutualism” (getting an immediate shared reward) or “reciprocal altruism” (getting a delayed reward, essentially “I scratch your back, you scratch mine”). The concept of true biological altruism (where an animal works for a shared goal with zero individual benefit) simply doesn’t survive natural selection in primate evolution. They’re fundamentally incapable of selfless teamwork for an abstract cause. Altruistic reciprocity isn’t a thing. (link)

Humans are monkeys. We’re wired for social groups. We’re not wired for selfless teamwork.

So, whenever someone talks about the benefits of teamwork, it’s always smart to be critical and wonder which of those benefits are actually about teams (with a shared goal) and which are just the common benefits of groups (that don’t have a shared goal at all).

The Team Tax: Who Pays for Coordination?

Organizations have every reason to want people to work in teams. And employees have many reasons to socialize in groups while still working alone. The benefits of teamwork accrue mostly to the business, while the pain, stress, and waste of time are mostly suffered by teammates. It’s no surprise that everyone recognizes the sentiment of getting much more done when people are working on their own rather than on a team. My happiest days are those when I have an empty calendar and my only conversations are with my spouse, friends, customers, and the local barista.

Am I suggesting that everyone should resist teamwork? No. But what I am suggesting is that a business should compensate people for the sacrifices they make toward teamwork. For example, a professional earning $75K a year as a lone worker should perhaps earn $150K when expected to work on a team. But that’s usually not what happens, right? Companies move their employees on and off teams as if it doesn’t make a difference to anyone. But it does.

As a professional working on teams, you swing between two extremes: either you waste time on conflict, slackers, and bad apples or you spend significant time on team building, agile rituals, and communication overhead to prevent the conflict, slackers, and bad apples. Either way, you’re paying a tax. The team tax.

Teamwork comes with a tax, and someone should pay the tax.

If you’re a Solo Chief, you’ve already done this math—maybe at 2am, staring at a ceiling, wondering whether the meeting-free calendar and true autonomy you’ve gained is freedom or failure.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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All monkeys know this. The concept of “I scratch your back, you scratch mine” applies universally across all species. Reciprocal altruism is the currency of primates. Everyone with a proposal for collaboration must be able to answer the question, “What’s in it for me?”

In the case of teamwork, the argument “you will enjoy the social bonding of the team” isn’t a valid sales pitch. It’s a really bad value proposition. A professional doesn’t need the stress of teamwork to enjoy the benefits of being a social creature. They might as well go to the local bar. That’s what monkeys do and what social groups are for: all the benefits without the hassle of teamwork.

Managers need a better pitch for teamwork, or else it’s just a scam.

The Agile Teamwork Ideal—and Its Limits

Ever since the emergence of the earliest agile methods and frameworks, the value of teamwork has never been in any doubt. Never debated. It was just obvious.

Preferring to do things alone and refusing to pay the team tax was by many in the community almost seen as heresy. You can’t claim to be agile if you’re doing everything all by yourself, they’d say. “Individuals and interactions” is the first rule of the manifesto! It’s hard to carry a coffin on just one pair of shoulders.

And so we put up with all the endless meetings. We wrote books full of practices to make teamwork manageable. We dutifully nodded with empathy and understanding when Charles had a quarrel with Bill. When all blame got diverted to Chet. And when Raymond was sick (again). And when anyone critically asked, “How does all this overhead benefit me?” we gave them the evil eye and a lecture on agility and altruism.

However, forgive me if I’m mistaken, but “individuals and interactions” doesn’t sound to me as being the same as “team.” I have dozens of interactions with other individuals each day. And yet, I’m not on any team. I collaborate with other Substack writers, but we don’t form a team. I have meetings with readers and clients, but we don’t form a team. I happily advise the M3K brand, but I’m not part of their team. I interact with my fellow participants of ScaleUpNation’s board program, but it’s a fine example of a group, not a team.

I’m all for group interaction. I’m not a big fan of teams.

I’d rather do without the drama.

Why I Prefer Working Alone

You may have guessed that I’m more than a little biased. As a staunch introvert, autistic creator, and solo entrepreneur, I have always preferred sole accountability.

I said no several times when someone suggested to co-write a book. I usually declined when they offered to co-facilitate a workshop. I looked aghast and said, “hell-fucking-no” when a speaker proposed to share the stage with me during a keynote. I do my thing and you do yours (happy to interact, but we don’t share one goal). I don’t even cook as a team. You all stay out of my kitchen, and we socialize when I bring in the dishes.

That’s just me. It’s how I’m wired. I don’t want to pay the team tax.

Your mileage may vary.

I prefer going solo, but I fully understand that many products require teamwork. Many innovations wouldn’t fly without people collaborating and chipping in toward a shared goal. There’s nothing wrong with people joining teams. I’m glad that many of you do! But if they want me to join a team, I would ask, “What’s the win-win? What’s in it for me? How is this great not only for the business but also for my career, reputation, or income? How will you compensate me for paying the team tax?”

If the best answer is, “You’ll be on a great team,” I consider it a scam.

Many creatives, solopreneurs, and lone managers figured this out long before me. They do much better work alone. They’re the only ones responsible for their future. They are the Solo Chiefs of their own work-lives. They are the CXO of Themselves. And in the age of AI, with digital assistants replacing many human teammates, there will be more Solo Chiefs than ever. ChatGPT doesn’t ask for a team-building exercise, Gemini doesn’t punch below its weight, and Claude is never on vacation. One human orchestrating a bunch of machines is the teamwork of the future.

The Solo Chief Challenge

There’s vulnerability in going solo. You may have a social group, but the accountability is all yours. You’re the single wringable neck. If you don’t do it well, nobody will save your ass. ChatGPT, Gemini, and Claude will just look the other way.

So you’d better make it count.

When my mother passed away, I didn’t carry her coffin. I didn’t send the cards. And I didn’t arrange the flowers. My brother, sister, and dad didn’t form a team. We simply divided the responsibilities.

I interviewed family members for their memories of mom and then I sat down all by myself to turn that into one story. The story of our mother. I gave the best eulogy our family had ever heard. It was funny, artistic, emotional. Perfect. It was by me for everyone. That was my contribution to the ceremony and the social gathering, and the family still fondly remembers it even years later.

I could not have done that as a team.

The Solo Chief doesn’t carry coffins. The Solo Chief writes the eulogies.

Jurgen.

P.S. What’s the team tax you’ve been paying—and is anyone compensating you for it?

Does someone you know need this message? Let them know it’s okay to go solo!

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Stop Accepting Fixed Cadences

Technostress is a signal, not a disease. Pick one workflow, make it 10x faster, then move on.

Are you suffering from technostress?

Feeling anxious about the speed of change?

Worried you can’t keep up?

Good. That means you’re paying attention.

You’re not ready for the age of AI if you don’t occasionally think, “My God, last week I was so slow.” When you look back at your earlier professional self and don’t see a snail struggling up the Burj Khalifa, you’re not improving. That vertigo, the cringe of looking back at last month’s version of you, is actually the clearest sign you’re doing something right. The creatives and solopreneurs and fractional leaders who should be worried aren’t the ones with technostress; they’re the ones who feel nothing at all.

So, what to do?

First, don’t panic. You can’t enter every game and expect to win all of them. Olympic athletes don’t panic. They also don’t sign up for every event under the sun (or in the snow). They pick one race and work with a coach to shave off milliseconds. Every single day. They try to accelerate.

I’m no athlete—that would be hilarious—but I try to adopt the same mindset in my professional work. For example, I used to evaluate Substack stats and metrics in my dashboard every three months. I screenshotted 20+ tables and diagrams—Substack doesn’t offer a proper export, which is maddening—and dumped them into ChatGPT. It was slow. It was tedious. And it meant I had no way to respond quickly to trends and changes in my readership. No way to see the immediate effect of something I’d changed or improved.

That had to change.

This week, I discussed with Claude the metrics that actually matter (according to my ten steps in the user journey map). We settled on a baseline and devised a strategy for how to proceed. Being the perfectionist I am, I wanted to jump straight into building automations. But Claude pushed back: “Don’t rush into implementation. Do a few weekly iterations manually first. When we know which metrics actually matter and which to ignore, then we automate.” (That was smart pushback. Smarter than most human colleagues have given me, if I’m honest.) Over the course of a few weeks, I’m speeding up the metrics review process from every three months to every seven days.

That’s what acceleration looks like. (This is also where the Olympic analogy falls apart, because you’ll know an athlete went on quite an impressive amphetamines spree when they run the 5000 meters 10x faster.)

The Accelerometer vs. Fixed Cadences

One thing that’s always bothered me about popular methods and frameworks is how they settle on a fixed cadence, as if it were a law of physics: OKRs (quarterly), SAFe (8–12 weeks), Shape Up (6-weeks). It’s almost as if the authors of such frameworks gave up on continuous acceleration before they even published their works. As if the speed of work were something you decide once and then stop thinking about.

The age of AI calls for a different approach. It calls for the Accelerometer Method.

An accelerometer is a sensor that measures changes in velocity. Most people know it as the thing inside your phone that detects when you tilt the screen or drop it down a toilet. But in this context, I’m using it as a mental model: a persistent awareness of whether your workflows are actually speeding up or just staying in the same range. Using the Accelerometer Method means you treat acceleration itself as the practice. You measure it. You practice it. You don’t settle.

And acceleration shouldn’t feel like panic. A cockroach doesn’t sprint; it adapts. Anxiety comes from trying to speed up everything at once. Real acceleration—in the professional context—means picking one process or value stream and making it tantalizingly faster, then moving to the next. Every week should feel like you’re outpacing yourself, step by step, workflow by workflow. And we’re not talking about 5-10% improvements here. We’re talking 10x. There’s a reason the 10x engineer, 100x engineer, and hyper-productive team were among the buzzwords that surfaced in the AI glossary of 2026.

“Anxiety comes from trying to speed up everything at once. Real acceleration means picking one process and making it tantalizingly faster, then moving to the next.”

From Minutes to Seconds: Workflow Acceleration in Practice

Let me give you a concrete example.

You may have noticed that I like sharing quotes and insights from the articles I read on Substack, LinkedIn, and elsewhere. The practice helps me distill the core takeaway of an article, boost an author whose work I appreciate, and give something back to the community. Win-win-win. I also kept track of which publications I promoted in a large spreadsheet, not only because it’s typical for someone straying slightly across the spectrum, but also because I enjoy keeping an overview of which authors I appreciate most. (Those are the ones I might want to reach out to for collaboration.)

However, my process was a slog: select text, copy, switch apps, paste URL, open spreadsheet, tick a box. Two to three minutes per article, every time. 😣

Not anymore.

I’ve now automated the whole thing with Make and Fibery. I save a text fragment and the article link in one place, and the automation posts every insight to multiple social channels and logs the article in a central database. Total time per article: eight seconds.

From three minutes to eight seconds. My accelerometer is happy.

Accelerometer Thinking Everywhere

I have many more workflows that deserve this kind of treatment besides the weekly statistics review and the article notes: turning ideas from online meetings into drafts for blog posts, collecting insights from books I read and sharing them with readers, matching invoices in my mailbox with transactions on my bank account, collecting health data (sleep, exercise, nutrition) from various apps and discussing it with an AI health coach in a daily review. The list goes on.

In each case, I have zero interest in doing things 5-10% faster. That’s incremental thinking, and it was fine for 2019. I want to go 10x faster or more. I want to go from months to weeks, from days to hours, from minutes to seconds. That’s what a properly calibrated accelerometer should show you.

And without going insane. Without the anxiety.

In fact, the goal is to feel totally relaxed while everything around you is speeding up, including your own adaptation to the environment.

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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So, think about what you can do.

How can you speed up gaining new customer insights from weeks to hours?

Can you condense the time to create mockups and prototypes from hours to minutes?

Can you accelerate the deployment of new versions of your product from minutes to seconds?

The Only Fixed Cadence Is “Faster”

As Solo Chiefs, the last thing we want is technostress. We want a cockroach business, one that survives whatever the future of work throws at it. The age of AI is scary but manageable. We can handle it when we intentionally and persistently speed up the workflows around us and never accept any fixed cadence handed down by someone’s standard method or framework.

If you’re reading this thinking, “I can’t do all of that already,” take a breath. You have a single wringable neck. You can’t accelerate everything at once. Pick the one workflow that’s been bothering you most and make it ten times faster next week. When it’s done, move on to the next.

That’s the Accelerometer Method.

No fixed cadence. No panic. Just persistent, compounding acceleration, one workflow at a time.

Now, if you’ll excuse me, I have a spreadsheet to finish murdering.

Jurgen, Solo Chief.

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P.S. Which one workflow are you going to make 10x faster next week?

In Praise of Stupidity

Mostly, I just embarrassed myself. Occasionally, I struck gold.

One of our national newspapers printed cartoons such as Garfield, Peanuts, and Hägar the Horrible, and I thought, “Hey, I can draw. I could do that too!”

So I sat down one Sunday afternoon, rapidly produced six three-panel cartoons, and wrote a letter to the newspaper offering them my exclusive artwork and suggesting that I make these funnies for them daily—for a very reasonable fee. The newspaper’s editor sent me a kind letter back, politely declining my gracious offer with the comment that “they were not entirely convinced I could sustain a high enough level of quality.”

I was quite disappointed.

Also, I was twelve.

Undeterred by the minor setback, I placed an ad in our town’s local gazette, offering my services as a young creator of comics. I was funny. I could draw. Surely someone local might be interested! And sure enough, I got a call from a person asking me to draw a caricature of their mom—I had no clue how to do such a thing—and then another from a restaurant owner looking for an artist to draw their new pizza menu, a job I didn’t interpret as even remotely comical.

So I gave up on that career.

I’m afraid few twelve-year-olds these days would never try something so delightfully dumb and audacious as I did in the early 80s. Today’s younger generation happily embraces AI to level up their skills, generate their essays, vibe code their web shops, produce their TikTok videos, and get real-time feedback on their creative ideas to prevent making a fool of themselves. Which seems a very Gen-Alpha kind of thing: to not be seen as doing something dumb and naïve. (Clearly, this never bothered me.)

The young ones of today are among the first to discover that Midjourney, Stable Diffusion, and Pose Scanner can help them produce cartoon art of high, sustainable quality, and that ChatGPT and Claude are more than happy to discuss the most sensible and realistic career trajectories for wannabe creatives. And it’s all for free. More importantly, the AIs are unlikely to respond, “If I were you, I’d send this off to your national newspaper.”

And there, I believe, is a problem.

In 2026, we find ourselves snugly surrounded by reason and sensibility. We use AIs to write and evaluate every portfolio deck and media kit we send out. We ask the LLMs to double-check our strategy and every business decision we make. The algorithms eagerly give each of our naïve product ideas a total makeover, complete with a Job-to-Be-Done analysis and an AI-generated pitch deck. The machines are protecting us from our own ignorance. We insulate ourselves from stupidity.

And that could make it harder for creatives, solopreneurs, intrapreneurs, and lone managers—I call them Solo Chiefs—to fail.

Or make it harder to deal with the inevitable failure.

Because remember this when your product launch just flopped: the AI that helped you polish your idea into something “market-ready” has no skin in the game. It doesn’t lose sleep over reputation and accountability. Only you do. You’re the single wringable neck, and the machines only make sure you have fewer excuses for why something doesn’t work.

No matter how much advice we all get from the algorithms, and how much better we all become, the cold hard math of the market dictates that the failure rate remains the same.

90% of products are still crap. AI just paints the crap in prettier colors.

At twenty-four, when I finished my studies as a software engineer, I set my sights on another career. I wanted to launch a Dutch games studio. (This was 1994. There were practically no Dutch game studios around.) I recruited several programmers, two amazing graphics artists, and I convinced a major Dutch animation studio to buy into my vision. I flew to London several times to present my game ideas to UK publishers until one of them actually sent me an unsigned contract worth a million British pounds.

I was ecstatic.

The only thing missing was a signature on the contract from the publisher’s American head office. That signature never came. In hindsight, I suspect someone on the other side of the ocean, with a bit more sense and significantly more experience, could tell that my team had no fucking clue what we were doing. Our game project was all charades, nothing more than make-believe carefully wrapped in pretty mockups. We didn’t even have a working demo.

The publisher averted what undoubtedly would have become a game development fiasco.

The word ignorance didn’t even cover the extent of our stupidity.

Still, I’m proud that we tried.

These days, few people would be as stupid as I had been back then in the 90s. Not in the least because a single person can now practically run an entire game studio by themselves, using AI for art, dialogue, coding, and QA, building polished games in months that would once have required entire teams and several years. And, most critically, without having to beg for a million-pound advance. The failure rates remain the same. It just hurts a little less.

Nowadays, I use AI to double-check every decision I make. I discuss articles, evaluate tech stacks, and dissect business plans. When the stakes are high and the decision is crucial, I even get the AIs to debate with each other before I commit to anything.

Thanks to AI, we Solo Chiefs are able to experiment with countless ideas in rapid feedback cycles. It’s never been cheaper to derisk our strategy and tactics. And it’s never been easier to make ourselves less dumb.

Our digital co-pilots help us write better, draw better, build faster, and think more critically about everything we do. They raise the quality bar for us all. But here’s the price we pay for that convenience: the LLMs also quietly constrain every idea to the boundaries of their training data. We get a polished self-rejection before the real world has a chance to tell us how dumb we really are. They protect us from public embarrassment.

There’s a Zen saying: in the beginner’s mind there are many possibilities, but in the expert’s there are few. A twelve-year-old pitching cartoons to a newspaper has infinite possibilities. A grown-up running the same idea through ChatGPT gets a tactful explanation of why it will never work, complete with a chain of solid reasoning that’s harder to break than the shackles of a sensible career.

We’ve outsourced our stupidity filter to machines that never did anything dumb in the real world. And in doing so, we suppress our ignorance and constrain our creativity to the likeliest successful paths that others have traveled before. Our business models look the same. Our thinking processes look the same. Our pricing tactics look the same. And every startup makes their own variation of Airbnb’s gold standard pitch deck.

Nobody is willing to do anything really stupid.

I’m 56 now. And I’m finding it harder and harder to do dumb things. (Although my late Agility Scales project might be a decent contender.) I sort of regret that.

The most recent example I might offer is traveling a large part of the entire Caribbean—20 countries, 40 islands—in just 60 days. My brain said it was daring. My friends said it was crazy. ChatGPT said exactly nothing because, two years ago, I wasn’t yet in the habit of discussing everything with the machines. Nobody advised against my adventure, which was probably why it was such a dumb and exhilarating experience.

I don’t regret it.

I will also never do something like that again.

That’s the thing about being a Solo Chief. Every stupid decision is yours alone. But so is every wild, unreasonable adventure that nobody can copy from you, and that may never end up in any AI’s training corpus. (I checked. My ignorance from the 80s is invisible to them. Well, until now.)

Sadly, as a Solo Chief, I find myself not doing such gloriously ignorant things anymore. It’s not just because I’m older and (hopefully) a little bit wiser. It’s also because the AIs won’t let me. Each of them will tell me, 24 hours per day, 365 days per year, what works and what doesn’t, based on what they know about what everyone else has already done before me. In a way, I resent that.

There’s something precious in innocent stupidity. In doing something truly dumb. In acting before the AIs tell you enough to talk yourself out of your idea.

In the mid-80s, I became heavily addicted to eurodisco—Modern Talking, Gazebo, Desireless, Sabrina, Bad Boys Blue, the cheesier, the better. I wanted to know all the songs in that genre. In our city library, I found stacks of Yellow Pages from all over Europe. They helped me locate dozens of discotheques and radio stations across the continent, and I sent them all a letter requesting they send me their weekly playlist. My outrageous idea was to aggregate the most popular dance hits across Europe and then send everyone back the results.

You can imagine my disappointment when I received exactly one reply from a radio station in Iceland. For several months, they dutifully sent me their weekly list of popular songs, most of which meant nothing to me. That was far from enough for my grand vision of a pan-European overview.

I was seventeen.

I did the dumbest things when I was young.

Mostly, I just embarrassed myself. Occasionally, I struck gold.

I’m proud of that.

And I kinda miss it now that AI has ruined my ignorance

Jurgen, Solo Chief

Wake Up! For God’s Sake

More output, less impact. Your company wants acceleration, but all you feel is desperation.

You’ve probably seen this.

Your executives are suddenly “all in on AI.” They’ve been to a conference. They’ve nodded at a keynote. They now use the term frontier organization in sentences where it has no business appearing. They don’t really understand the tech, but they absolutely understand the vibe.

So now your team is “empowered” to use a tightly curated set of tools. Microsoft Copilot or some other half-assed platform that nobody in the world is raving about. Maybe an internal chatbot with a heroic name and a dashboard nobody asked for. You’re told this will herald a golden age of productivity!

But what you actually see is productivity theater.

Colleagues send AI note-takers to meetings they don’t attend. The bots generate summaries and task lists that no human truly owns. People auto-reply with polite, fluffy paragraphs that say less than nothing. AI-generated “deep research” reports multiply like TikTok videos. Slide decks bloom and die in shared drives. Spreadsheets appear with impressive formulas but zero decisions attached.

Nobody reads anything, of course. Everyone just summarizes the translations and translates the summaries. They reply to auto-generated requests with auto-generated responses.

And you sit there thinking, “Is this it? Is this the AI revolution?”

Even the executives can feel the hollowness. That’s why the media over the past twelve months were full of headlines such as Futurism’s “Majority of CEOs Alarmed as AI Delivers No Financial Returns,” The Observer’s “Over 80% of Companies Embracing A.I. See No Real Gains Yet, McKinsey Finds,” Harvard Business Review’s “AI-Generated Workslop Is Destroying Productivity,” and yesterday’s Fortune article, “Thousands of CEOs just admitted AI had no impact on employment or productivity.”

Everyone is very busy acting alarmed. Millions invested, benefits unclear. The analysts are warning about “AI workslop” clogging systems like digital cholesterol.

You look around in dread and feel yourself drowning in peak bullshit.

More output, less impact.
More automation, less ownership.

Your company wants acceleration, but all you feel is desperation.

It doesn’t have to be that way.

True Acceleration

You’re not crazy. The problem isn’t that AI doesn’t work. The problem is that your company is sprinkling it on top of broken workflows like parsley on a gas station sandwich.

Because when it’s used properly, the shift isn’t marginal. It’s violent. Entire workflows collapse from months to days, from weeks to hours, from hours to minutes.

Let me show you.

From Months to Minutes: AI Acceleration Across Industries

• In pharma operations, Novartis shrank clinical trial site selection from weeks of analysis to a two-hour meeting. A classic bottleneck just… disappeared. (link)

• In insurance, a large U.S. travel insurer processing 400,000 claims a year moved from three-week turnaround times to minutes, with more than half of claims fully automated. That’s not shaving time. That’s deleting wait queues. (link)

• In law, AI contract review tools cut document review from three hours down to fifteen minutes, with over 90 percent reduction in manual clause reading. Lawyers go from scavenging text to making actual decisions. (link)

• In architecture, Autodesk’s Forma platform helped MBH Architects reduce early-stage schematic design from two weeks to six hours, generating hundreds of building options instead of three. Time collapsed while exploration exploded. (link)

• In construction, Factory_OS uses AI-optimized prefabrication to erect modular housing in roughly two weeks, replacing timelines that used to stretch close to a year. That’s not a tweak. That’s an order-of-magnitude shift in delivery. (link)

• In software, founders using AI-first engineering approaches ship MVPs in six weeks instead of six months. Solo builders use tools like Cursor, Claude Code, and Lovable to create full-stack apps over a weekend that previously required small teams and serious runway. (link)

• In creative production, Artlist used AI video generation to produce cinematic Super Bowl-grade ad concepts. Work that normally demands months, massive crews, and million-dollar budgets was reduced to just five days. (link)

• In healthcare admin, startups like Claimable generate tailored insurance appeal letters in under 30 minutes. What used to be an exhausting multi-hour paperwork battle becomes something you finish before lunch. (link)

• Even solo indie game developers now use AI for art, dialogue, coding, and QA, building polished games in months that once required entire teams and several years. (link)

• And last but not least, in drug discovery, DeepMind built AlphaFold, which predicts protein structures in seconds. What used to take researchers months or years of lab work now runs as computation. That breakthrough was so fundamental it earned several researchers Nobel Prizes. This isn’t productivity theater. This is biology on fast-forward. (link)

Notice the pattern.

These workflows didn’t get “10 percent faster.” They didn’t become “slightly more efficient.” They completely imploded.

The Solo Operator Advantage

Y Combinator (the startup accelerator that funded Airbnb, Stripe, Dropbox, and about a thousand other companies you’ve heard of) has been blunt for years: product MVPs should be buildable “in weeks, not months.” The age of AI can shrink that to days. (link)

Now here’s the part that should make you furious (or hopeful, depending on where you sit). Most of those examples are solo operators, small teams, indie builders. Not Fortune 500 departments with twelve-month timelines and steering committees. The biggest accelerations are coming from people with the least bureaucracy and the most skin in the game. People who can’t afford productivity theater because their rent depends on real impact.

“The biggest accelerations are coming from people with the least bureaucracy and the most skin in the game.”

Sleepwalking Leaders and the AI ROI They’ll Never See

During the 2008 credit crisis, I once had to grab my CEO by the shoulders. Literally. The company was heading straight for a financial wall. Revenue dropping. Runway shrinking. Everyone could feel that things were going badly. And he just… froze. Like a deer in headlights, calmly preparing to sleepwalk the entire business off a cliff through inaction.

I told him, “Wake up, for God’s sake! Our runway is evaporating. We have to act.”

He did.

Later, he thanked me.

I think about that moment a lot when I hear an executive say, “AI isn’t generating returns.”

Of course it isn’t! Not the way you’re doing it.

Look at all the examples where weeks collapsed into hours. Where months turned into five-day sprints. Where entire bottlenecks evaporated because someone decided to redesign the full value stream instead of sprinkling AI on top of whatever mess was already there.

If you treat AI as a note-taking assistant, you’ll get fancier notes. If you treat it as a workflow reinvention engine, you can compress a multi-week process into a single day. You can turn hour-long value streams into minute-long decisions. But that requires imagination. And permission. And the freedom for people to experiment without sending their ideas through three committees and an administrative review board.

So ask yourself honestly: are your colleagues compressing value streams?

Or are they turning AI-generated slide decks into AI-generated task lists?

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

Subscribe now

Wake Up! For God’s Sake

Yesterday, a friend (who shall remain blissfully anonymous) messaged me:

“Our management asked us to brainstorm AI ideas. We have to make a list and submit it to administration so they can maybe implement some of them. My God. I’m wasting my time here.”

That’s not acceleration. That’s desperation offered as a suggestion box.

This is why so many Solo Chiefs exist in the first place. Not because they hated working. Because they couldn’t stomach watching another year evaporate inside a system designed to convert urgency into slide decks and meeting summaries.

If your leadership turns AI into a governance workflow and a brainstorming exercise, they aren’t being cautious. They’re sleeping. And sleeping leaders during a technological shift don’t just cause organizations to stagnate. They walk them off a cliff.

Someone has to shake them awake before they drop into the gorge.

If you still like your job, it has to be you. If you don’t, maybe it’s time to stop drowning in other people’s peak bullshit and move some place where the sleeping leaders can’t take you down with them.

Or become your own solo chief, as I did.

Jurgen, Solo Chief

P.S. Is your company actually compressing workflows—or just generating fancier slide decks?

AI slop creators have no time to think and no defensible moat. We need them anyway.

Slopreneurs flood your feed with AI slop and zero critical thinking. But they’re doing exploratory work the rest of us won’t. Here’s how to survive their content (and learn from it).

You probably know the type.

One day after Claude Cowork came out, they had a YouTube video called “How Claude Cowork Saves Me 10 Hours a Week” and a Substack post titled “If You’re Not Using Claude Cowork, You’re Already Behind.” Not a day goes by without them publishing LinkedIn posts such as, “7 New AI Tools That Feel Illegal To Use,” “Three Tools to Replace Your Entire Team,” and “Top 10 New Tools You’ll Wish You Found Sooner—Number Eight Will Make You Weep with Joy.” Not wanting to miss out on an opportunity, they’d already published an entire course called “Earn Six Figures with Clawdbot” before the tool was renamed to Moltbot and then OpenClaw—three days later.

I call them slopreneurs: entrepreneurs making a living off slop.

slopreneur /slɒprəˈnɜː/ noun informal, derogatory

A person who builds a business model around the rapid, high-volume production of low-quality, derivative content or products — typically leveraging generative AI and other automation shortcuts — prioritizing speed, scale, and short-term profit over originality, craft, or genuine value, without intent to build a sustainable enterprise.

The term slopreneur came to me as I completed my 10K run yesterday morning. Always a great time for creative ideas to ambush you between labored breaths. I thought the term was brilliant. Then it turned out the word was already suggested before. beat me to it three months ago. (Such is the life of a Solo Chief. When you finally have a great idea, you’re probably not the first one. You’re rarely even the second.)

“Their posts fill your feed every day, promising the world in exchange for a comment, creating an avalanche of content optimised to be optimised, always with a distinct angle of self-promotion or quick profit. Emboldened by the latest in AI, it’s the work of a slopreneur. They don’t just inadvertently make slop (hey it’s ok to admit we’ve all inhaled at least once right), they’ve turned it into a whole business model and personality.” - James Rendel, “The Slopreneur”

I say we should pity, warn, and embrace the slopreneur.

The Flaw of the Slopreneur: No Time for Critical Thinking

When your business model depends on being among the first to report on a trend or technology, the last thing you have time for is critical thinking.

That’s why slopreneurs breathlessly broadcast the news that McKinsey has “25,000 AI agents as employees,” completely ignoring that a headcount of AI agents makes no sense whatsoever. One might as well count the number of vibe-coded apps and call them your team of interns.

That’s why slopreneurs stumbled over each other in their haste to report that AI agents on Moltbook were inventing their own religion, completely overlooking the fact that those millions of bots on Moltbook were basically humans trolling the Internet by posing as agents.

That’s why slopreneurs uncritically parrot every press release about ‘AI co-founders,’ ‘algorithmic managers’ and ‘AI board members,’ never stopping to ask how a language model that can be turned off with a switch could possibly have fiduciary duties or legal liability. For Solo Chiefs who actually carry the single wringable neck, this isn’t just sloppy thinking—pun not intended—it’s an insult.

In their hurry to be the first to publish something clickworthy (and hopefully rake in millions of views), slopreneurs have little time for reflection. Almost no time to double-check if something really makes sense. Too litle time for the one thing that’s supposed to separate humans from the language models themselves: critical thinking.

The Risk of the Slopreneur: No Defensible Moat

As a Solo Chief advisor, there’s something else that bugs me. Most slopreneurs have no moat.

Sure, a small selection of influencers is able to attract millions of readers or viewers. They earn a good living with advertising and sponsoring, and maybe some additional revenues with books and courses. There’s no doubt about that.

But how defensible is that business when they have no proprietary data, no network effects, no unique infrastructure? How easily can they retain their views and followers when every competitor can literally offer exactly the same thing? How are they going to build a viable business when all the slop they make is, by definition, cheap perishable waste with a half-life shorter than Sam Altman’s reputation? Slopreneurs must publish their slop faster and faster just to stay in the game. It’s the Red Queen Effect with a content calendar.

I admit, I’ve watched some of the slopreneurs’ videos about n8n, Claude Code, Moltbook, and Antigravity just to familiarize myself with what’s new. There’s certainly value in that. I contributed my share to their ad revenue. But I feel zero loyalty toward any of these creatives. They’re all as replaceable as a pack of baking soda. (Apologies to the baking soda industry, which at least has some brand differentiation.) The slopreneur influencers are all the same to me.

In the age of AI, the question that every Solo Chief must ask is: what is the value that compounds? What becomes worth more and more the longer you stay in business? How are you building your data, network, reputation, or infrastructure in a way that others can’t steal from you? No Solo Chief should envy a million followers. It’s a vanity metric when there’s no moat.

I say this with some empathy. I’ve felt the pull myself—the itch to publish fast when a new tool drops, the fear that silence means irrelevance. Each of us knows the temptation to chase what’s trending instead of what matters. The difference is whether you recognize the trap before it becomes your business model.

The Charm of the Slopreneur: Exploring the AI Frontier

But I don’t want to sound too negative. We must appreciate the value these slopreneur influencers bring.

For example, I watched several videos yesterday about smart rings. I wanted to know the differences between the Oura Gen 4, Ultrahuman Ring Air, and RingConn Gen 2 and what the experiences are of people who’ve been testing them in real life. This means I ended up watching several videos by smart tool reviewers. I appreciated their insights and I wish them well with the few cents of ad revenue my online searches generated.

But here’s the sad truth: I did not subscribe to any of their channels. Their hard-earned knowledge is already a commodity, slurped up by the same LLMs these very same entrepreneurs used to generate their content in the first place. Within seconds after they hit Publish, the value of their knowledge became zero.

And that’s the charm of the slopreneur. Someone needs to do the difficult work of—very briefly—staying two steps ahead of the crowd and the machines. The rest of us can only be pioneers (first followers) and settlers (early majority) when some people volunteer to be explorers. In evolutionary terms, every ecosystem needs its scouts. The bold and daring ones who try the unknown berries first so the rest of us can watch what happens.

Explorers don’t have it easy. Almost nothing is known about the new technological terrain besides the marketing fluff of the vendors, which is most likely full of lies and half-truths—especially so in the AI industry. Their job is to figure out as fast as possible what’s worth our time and money, and then get something out the door and published to inform the horde of pioneers, settlers, and stragglers coming after them.

The slopreneurs are the Marco Polos, James Cooks, and Vasco da Gamas of our time—with better Wi-Fi and fewer cases of scurvy.

It’s hard work, really. No wonder they use AI to speed things up.

If he could, Christopher Columbus would have done the same.

The slopreneur’s real tragedy isn’t the slop. It’s that they’re Solo Chiefs who forgot to ask the crucial question: what survives when the platform goes ‘poof’?

I’m a founder, intrapreneur, and former CIO rethinking governance for the one-person business, navigating sole accountability in the age of intelligent machines—informed by plenty of scar tissue. All posts are free, always. Paying supporters keep it that way (and get a full-color PDF of my book Human Robot Agent plus other monthly extras as a thank-you)—for just one café latte per month.

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My Message for the Slopreneur

Counting the slopreneurs as one rather eccentric subculture among the Solo Chiefs, I want to end this post on a high note.

If you’re a slopreneur: keep doing what you’re doing. The world needs people like you who explore the technological frontiers. Someone’s got to do this hard work. And we’re all better off because of it.

But I have two tips for you.

First, the value of your work increases when you allow yourself a few minutes to actually think. Don’t be just a parrot breathlessly disseminating someone else’s marketing bullshit. You’re not a White House correspondent uncritically swallowing press updates. Your job is to tell the rest of us what’s true and what’s not. That’s where you add your value. The more critical you are, the more likely people will hit Subscribe.

Second, critically assess your defensible moat. What value are you compounding over time? What gets worth more and more with every click and every share of your content? In the age of AI, your posts and videos depreciate faster than an avocado in Paramaribo. To have a viable business, you need something that survives a crisis and the pressure of your competition. Imagine YouTube deleting your account or your LinkedIn profile getting hacked. (It has happened to others.) Will you have lost everything you have? Or would that be just a painful but survivable setback?

The future is only yours when you plan to overcome the worst.

And thanks for the exploratory slop.

Jurgen, Solo Chief

P.S. Be honest—have you ever eaten the unknown berries first, or do you wait and watch?