AI can optimize bad targets all day; your people still have to question them.

I almost fell for a scam. Silly me.

The editor of a publisher reached out. They’d found my novel, loved my writing, and recommended I contact their preferred literary agent.

I was skeptical at first, so I consulted an AI, and we ran some background checks. Everything checked out: the editor, the publisher, all legitimate. Except that the email was sent from a private Yahoo account, not a company account. Still feeling doubtful, I forwarded the exchange to the editor’s official company address, which I’d pulled from their website.

Bingo!

The real editor replied: “Sorry, that’s not me. They’ve been using our names for weeks. Thank you for checking. I reported the abuse to Yahoo.”

I felt both sad and proud of myself. The scammers had studied my public profile better than most recruiters study a CV. Only my skeptical self saved me from further embarrassment.

It’s so easy to fall for a scam. Especially when it validates what you already believe. For sure, I’m a good writer. Of course, my work needs recognition. Hell yes, I deserve a publishing contract!

But in the age of AI, the number one skill is critical thinking. The talent to question everything, most importantly yourself.

In the age of AI, the number one skill is critical thinking.

What saved me was a habit of doubting my own conclusions (even the ones an AI had just confirmed for me). The AI background check endorsed the scam. My own dubiety caught it, not my tools.

Organizations need that same critical habit on a much larger scale, and most of them never build it.

Single-Loop, Double-Loop, and Triple-Loop Learning

The “learning organization“ became a thing when Peter Senge popularized the term in The Fifth Discipline in 1990. However, few people understand or consider the three different levels of learning. As a result, some kinds of learning happen a lot, while other kinds don’t happen at all.

Single-Loop Learning

In single-loop learning, an entity (worker, team, department, business unit) detects a mismatch between expected and actual results and changes its actions to close the gap.

When inventory falls below the minimum level, we crank up production. When the AI agent mis-routes support tickets, someone tweaks the prompt and moves on. When the delivery team risks missing its deadline, they work extra hours to catch up with the plan.

Single-loop learning is often compared to a thermostat detecting a temperature change and activating or deactivating a heater to balance around a pre-set target. In this process, nobody asks whether that target (threshold, deadline, or inventory level) still makes sense in the current environment.

Double-Loop Learning

In double-loop learning, a term business theorist Chris Argyris introduced in 1974, a team or organizational unit evaluates the mismatch between expected and actual results by examining its governing variables: the goals, beliefs, and assumptions that were used to set the target in the first place.

When faced with reality, an organization might want to change its minimum inventory level, extend the delivery team’s deadline, or ask whether that support queue should even exist now that agents answer most tickets. The organization can adjust the threshold, objective, or inventory level to reflect what is truly desirable and feasible given its circumstances.

With double-loop learning, the entity acknowledges that a goal is a hypothesis that needs continuous validation. The governing variables are always subject to critical examination to check whether the targets are still viable.

Triple-Loop Learning

At the third level, we find deutero-learning, or “learning how to learn.” This is the meta-process that a team or organization needs to figure out if the single loop and double loop are both working as intended.

At this level, the team decides whether to introduce after-action reviews, blameless postmortems, or agile retrospectives. It checks whether its red teaming, action learning, or scenario planning practices are working. And it discusses the pitfalls of rewards and incentives, power dynamics, or a non-transparent organizational culture.

At the meta-level, an entity analyzes its learning history, examines its learning patterns, and diagnoses counterproductive feedback cycles that hinder single- and double-loop learning. In this third loop, a team, department, or business unit deliberately refines its communications and behaviors to make future learning interventions more systematic.

• Note: There is no consensus on the term “triple-loop learning”, which various scholars and authors have defined differently. I use it when “the third loop” is easier to understand and visualize than meta-learning or deutero-learning.

I’m a founder, intrapreneur, and former CIO who helps leaders diagnose and redesign their operating models for the age of AI—informed by plenty of scar tissue. This article offers the same lens I bring to keynotes, workshops, and advisory engagements, from a single team to a multinational. Want that lens on your organization? Let’s talk. And if you’re just here for the maps, subscribe—they’re free, always.

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Triple-Loop Identity

In my previous post, “End with Why,” I wrote:

Purpose is best understood as a hypothesis you may state early, but then keep crafting and validating through actual collaboration.

Visualizing that with the simplified VSM model, it looks like this:

An organization should treat the Identity function the same as each of the other functions, with single-loop, double-loop, and triple-loop learning cycles that repeatedly validate whether its purpose, vision, mission, values, and principles are still the ones it needs. Probably not every day, but often enough to matter.

As I discussed last time, identity emerges from collaboration among the parts. The entity needs three things:

1. Single-loop learning between the Identity function and the other functions to ensure that what emerges from the team aligns with what the organization intended;

2. Double-loop learning to update the espoused purpose and values when the emergent identity requires a revision of the defined identity;

3. Triple-loop learning to guarantee that this happens consistently and deliberately, to ensure that the enacted and espoused purpose are always kept in sync.

Almost no organization has this.

Worth noting: the canonical diagram of the Viable System Model doesn’t distinguish between single-loop, double-loop, and triple-loop learning. It also doesn’t show a feedback loop between Identity (System 5) and what I call the Agency (System 1) and Harmony (System 2) functions. Still, Stafford Beer’s POSIWID (”the purpose of a system is what it does”) is the enacted purpose. Beer explicitly instructed diagnosticians to believe behavior over declarations. He just never drew the reconciliation as a loop. Other systems thinkers have formalized it since. Raul Espejo’s Viplan method, for example, distinguishes the declared identity from the identity-in-use (terminology he borrowed straight from Argyris), and treats the gap between them as a core diagnostic to investigate. What matters is not that you draw it. What matters is that you do it.

To translate the three learning loops into agile terminology:

• Single-loop learning is a team continuously planning and replanning their work to achieve their Product Goal;

• Double-loop learning is the team wondering if their Product Goal still makes sense and adapting it when necessary;

• Triple-loop learning is the team discussing how they can always adapt their Product Goal to fit their ever-changing reality.

The Sixth Function Is Dubiety

The meta-learning loop, “learning how to learn,” is the most important learning cycle. Without the third loop, the double-loop might happen only occasionally, or might not happen at all. Without triple-loop learning, the double-loop collapses too easily into a single loop when nobody questions the effectiveness of a team’s learning practices. When not a single skeptical person asks, “Have our retrospectives ever led us to update our goals and values?” the retros might just be doing single-loop corrections, not double-loop reflections.

It’s the meta-loop that holds the other two loops in place through its continuous doubting and questioning.

I would even suggest adding Dubiety to my simplified VSM model as an important sixth function. Dubiety is the quality or feeling of being uncertain and doubtful. It describes the mental state of holding reasonable skepticism about things and remaining open to being proven wrong. Dubiety is the function of always questioning everything, especially one’s own beliefs and assumptions.

AI raises the stakes on all three loops. Agents now run the single loop at machine speed, around the clock, and they will cheerfully optimize a target that stopped making sense months ago. Doubting all targets remains a human job. It’s the one function of a networked agentic organization that you cannot delegate.

As a Solo Chief running a small network of AI agents, I’m training and practicing my own dubiety every day. I doubt everything, especially myself and my digital team. And it works. The AI cleared the scammers. I was the one who caught them.

Critical thinking is the number one skill in the age of AI. I continuously question the news items I see, the emails I receive, and the collaboration proposals I get. I question the books I read, the documentaries I watch, and the advice I get from my team of AIs. Most importantly, I regularly question the values I hold dear, the vision I have for my future, and the objectives I set for myself. It’s what every team, department, and organization should do, because a company that automates its actions but never questions its assumptions is just making the wrong things happen faster.

A company that automates its actions but never questions its assumptions is just making the wrong things happen faster.

The third learning loop is to question everything.

The sixth systemic function is dubiety.

Jurgen, Solo Chief.

P.S. When did you last doubt a goal instead of just chasing it harder?

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For a fraction of a second, I considered being dishonest.

Simon Sinek got it backwards. Purpose isn’t why great people join you—it’s what they build after you’ve paid them fairly.

I reluctantly informed a client that they’d made a double payment from two different departments. For a fraction of a moment, I considered waiting to see if anyone at that company would notice. (It was a significant sum!) But then I observed my own thoughts and intervened, “No. That’s not the person I aspire to be.” So I returned the duplicate transaction and ended up feeling aligned with myself. Why did I send the money back? Because I’d rather be poor but honorable than rich and dishonest.

Doing good was the byproduct. Feeling good was the motive.

I did that primarily for myself.

What’s In It for Me? (WIIFM)

Sociologists George Homans, Peter Blau, and Richard Emerson explained with Social Exchange Theory (SET) that human interactions follow a continuous cost-benefit analysis. Individuals take part in groups to maximize rewards (safety, belonging, recognition, reputation, etc.) and to minimize costs (time, effort, energy, expenses, and so on).

In my case, the (long-term) reward of boosting my reputation and the (short-term) feelings of integrity outweighed the immediate cost of returning the duplicate payment. It might seem cynical, but most people’s efforts at “doing good” are often just socially acceptable masks draped over things that boil down to return on investment calculations, or “What’s in it for me?” (WIIFM)

Economist Mancur Olson described something similar in The Logic of Collective Action (1965). People won’t act to achieve a shared goal unless there is some personal benefit, he wrote. Shared purpose, said Olson, is a public good: it is available to everyone for free. It costs nothing to just say, “Let’s save the planet!” What motivates people to action is what they gain individually from their contribution to the common cause, which can be compensation, reputation, belonging, or any other social or economic incentive.

From Prosci’s ADKAR to Kotter’s 8-Step Change Framework, the matter of “What’s in it for me” gets serious airtime in practically every change management model. Each one of us evaluates change and transformation through a lens of self-interest, and we resist when the ROI seems negative. If it doesn’t pay us, it doesn’t sway us.

Politicians probably know this better than anyone.

However, that doesn’t mean a grand purpose won’t work.

Why Should I Care? (WSIC)

The same George Homans (of Social Exchange Theory) argued that group dynamics typically has three components: activities (tasks and duties), interactions (verbal and non-verbal), and sentiments (feelings and emotions). These three components continuously influence each other, resulting—if all goes well—in a cohesive, self-reinforcing social unit with a strong sense of meaning. In other words, a group’s purpose emerges from their collaboration and is more powerful than any top-down directives imposed by management.

Social Identity Theory (SIT) by social psychologists Henri Tajfel and John Turner then takes over from SET and explains how individuals use group membership to derive self-esteem and pride. (If you want to see this in action, just watch any football fan’s reactions to their team’s results in the FIFA World Cup.)

People with “low organizational identification” do not yet internalize the group’s “Why.” They still require social exchanges—cost-benefit reciprocity and transactional rewards. We can call them social exchange dominant. But others with “high organizational identification” strongly identify with the group’s “Why.” They need social identity—purpose, vision, mission, values, and principles. We can call them social identity dominant. Crucially, continuous positive social exchanges turn the former group into the latter. Social exchange turns into social identity, not the other way around.

Psychologists Edward L. Deci and Richard Ryan offered us the same message through Self-Determination Theory (SDT). Purpose is real, but people usually internalize it over time. They don’t adopt it on day one. People typically enter an organization for extrinsic reasons (pay, security, opportunity) and only internalize an organization’s purpose as their own once the group meets their needs for autonomy, competence, and relatedness. Pay precedes purpose.

Pay precedes purpose.

Finally, political economist Elinor Ostrom’s Nobel-winning work confirmed that people solve collective-action problems not via a pre-existing shared purpose but through reciprocity, reputation, and trust that develop via repeated interaction and face-to-face communication. Humans are a mix of self-interest and the capacity to develop shared norms, but purpose is more often the output of collaboration rather than the input.

That’s a significant group of experts and scientists all suggesting the same thing: successful collaboration doesn’t start with why; it ends with why.

Why Start with Why Fails

In his bestselling book Start With Why (2009), inspirational speaker Simon Sinek argued that leaders who communicate a shared purpose (why) before their method (how) and product (what) inspire loyalty and improved performance. With his model of the “Golden Circle,” Sinek tried to give his ideas a neurological foundation. He related the inner circle (why) to the limbic system, where emotions would originate, and his outer circle (what) to the neocortex, where rational thought was suggested to emerge, using this as a biological justification for why “people don’t buy what you do; they buy why you do it.”

Sadly, the golden circle did not survive impact with the golden rule of scientific rigor: one must frame and execute models and theories so that others can independently reproduce, scrutinize, and potentially falsify them.

The problem was that Start With Why relied almost exclusively on anecdotes, with 235 mentions of Apple as the primary success template, while providing no scientific backing—no surveys, no control groups, and no longitudinal studies. Sinek provided zero evidence that Apple’s success at the time stemmed from a shared purpose rather than product quality, impeccable timing, or brilliant marketing. Critics noted that he cherry-picked confirming cases and omitted base rates and counter-examples. I was not impressed with the book either.

In addition, Sinek’s claim that the “why” never changes has been called out as axiomatically false. Shared purpose, vision, mission, values, and principles clearly do change as they continuously emerge and re-emerge from people’s activities and interactions, as social identity research confirms.

A shared purpose is what you get from successful collaboration. You don’t get successful collaboration from just sharing a purpose. It doesn’t start with why; it ends with why.

It doesn’t start with why; it ends with why.

Enacted vs. Espoused Purpose: Two Feedback Loops

Perhaps I should not be too dismissive of Simon Sinek’s work. He had a valid point: Research consistently shows that organizations with a credibly enacted purpose attract and keep employees who are already purpose-aligned. It’s a sorting effect. If you honestly have it, it improves your input.

On top of that, large‑scale studies find that companies that genuinely integrate corporate purpose into their strategy and operations outperform their industry peers on long‑term growth and profitability. It’s a growth effect, too. If you really have it, it improves your output.

So, what are we to make of this?

In an organization that ends with why, transactional exchanges followed by emergent purpose function as a beneficial virtuous cycle:

1. The organization provides fair, competitive transactional rewards that satisfy people’s individual economic and motivational needs (SET).

2. The organization nurtures intrinsic motivation by satisfying psychological needs for everyone’s autonomy, competence, and relatedness (SDT).

3. Shared activities generate frequent, positive interpersonal interactions, giving rise to positive sentiments of trust and care.

4. These sentiments stabilize into a strong organizational identification, allowing an authentic, shared “Why” to emerge (SIT).

5. This shared identity maintains employee engagement, boosts organizational performance, and attracts more purpose-minded workers.

Conversely, when an organization starts with a top-down or “made-up” why, the superficial purpose without social grounding triggers a destructive vicious cycle:

1. The organization espouses a marketing-driven purpose (not enacted through actual employee behaviors) to attract talent and consumers (“purpose-washing”).

2. Corporate governance remains bound to shareholder profits, while managers use this purpose to justify decreased pay and increased work demands (“passion exploitation”).

3. This creates a severe effort-reward imbalance, with the negative ROI failing to satisfy employees’ basic needs for autonomy, competence, and relatedness.

4. The unanswered question, “What’s in it for me?” leads to severe employee disillusionment, driving high rates of burnout, turnover, and quiet quitting.

5. The turnover forces the organization to rely even more on outward-facing purpose branding to recruit fresh, idealistic talent, cementing a high-churn cycle.

Does any of this sound familiar?

Did your organization discover or define its purpose? Was it embraced at the end or at the start? Do you have an enacted purpose with a feedback loop that lifts everyone up, or only an espoused purpose with a vicious cycle that grinds everyone down?

I’m a founder, intrapreneur, and former CIO who helps leaders diagnose and redesign their operating models for the age of AI—informed by plenty of scar tissue. This article offers the same lens I bring to talks, workshops, and coaching, from a single team to a multinational. Want it applied to yours? Let’s talk. And if you’re just here for the maps, they’re free, always.

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The Tricky Paradox

And yet … When I evaluate job postings and potential client engagements, I constantly find myself judging organizations, what they contribute to their markets, and what they stand for in society. Oil and gas, freight and logistics, investment banking, social network services, or marketing and advertising? Meh. None of these give me a warm feeling. But arts and culture, education, healthcare, human rights, or entrepreneurship? Absolutely. Where do I sign up?

Doesn’t this contradict everything I stated earlier? Doesn’t this prove that, at least for me, it starts with why after all?

Research by psychologists Carsten De Dreu and Aukje Nauta found that self-interest and concern for others are independent motivational systems. People can score simultaneously high or low on both. They found the highest levels of sustained performance came from employees motivated by both self-interest and concern for others—not purely one or the other. It suggests that “start with why” versus “end with why” is a false dichotomy. You can have both reinforcing each other.

Similarly, research by Adam Grant found that combining personal motivation (WIIFM) with pro-social motivation (WSIC) produced a multiplicative effect on creativity and performance, stronger than either motivation alone.

“This hybrid engine of self-interest and concern for others serves a much wider circle of people than can be reached by self-interest or caring alone” - Bill Gates at the World Economic Forum

So yes, highly motivated people (such as yours truly) seek out opportunities by checking pay and purpose. But that’s only to start the conversation. The selected organizations should prove that they start the virtuous cycle with a transactional exchange and only follow with a purposeful identity later.

First, we want an answer to “What’s in it for me?” Only later do we have time for “Why should I care?”

That’s how the personal battery keeps recharging, not the other way around.

Purpose Is What You End Up With

Business theorist Edgar Schein’s foundational work on organizational culture established that organizational values are emergent and learned, not predefined and imposed. He defined culture as “a pattern of shared basic assumptions that a group learns as it solves its problems of external adaptation and internal integration.” Shared purpose, Schein said, is not a precondition for joining; it is the product of the socialization process that follows joining. Purpose is not what we start out with; it’s what we end up with.

Purpose is not what we start out with; it’s what we end up with.

Perhaps Karl Weick’s Sensemaking in Organizations (1995) is the strongest scholarly backing for “it ends with why.” Weick’s core finding: “How can I know what I think until I see what I say?” Weick showed that action creates commitment and shared meaning. Purpose is an “ongoing accomplishment” and continually remade. Identity should be espoused and enacted.

Make no mistake: a clear directional “why” (after it has emerged from successful group collaboration) is genuinely useful as a recruitment and navigation device. A shared purpose functions as a selective filter, presenting a story that helps an organization attract individuals whose personal values align with the collective vision. However, the “why” is merely an invitation to collaborate, nothing else.

Once an individual joins the organization, the operational logic must shift to transactional exchanges. For the new collaboration to survive, the firm must satisfy the new worker’s economic and motivational needs. It must first answer, “What’s in it for me?” Pay precedes purpose, and if it doesn’t pay us, it doesn’t sway us.

Only when the company successfully maintains a fair social exchange can the new employee transition from transactional exchanges to pro-social commitment. Through repeated positive interactions, they build group cohesion and internalize the organization’s purpose as a core part of their social identity. They become much more sensitive to “Why should I care?” And in a fast-changing environment, a stable “why” can be the one fixed point when the “what” and “how” keep shifting.

Purpose is best understood as a hypothesis you may state early, but then keep crafting and validating through actual collaboration.

Jurgen, Solo Chief

P.S. Does your organization have an espoused or an enacted purpose?

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AI makes small teams look clever, but it doesn’t erase the reasons corporations exist.

One thing I learned from eighteen years of being part of the agile community is that common sense is no match for naïve optimism. Whether it was Extreme Programming, Scrum, Holacracy, Sociocracy, Teal, or Team Topologies, the suggestion that we could simply “let people self-organize” and “keep the managers away” was as persistent and unkillable as the Dutch vision of ever winning the FIFA World Cup.

But reality is a bitch, and hope is no substitute for experience.

In my previous post, I gave five reasons why collaboration needs management (economies of scale, transaction costs, the hold-up problem, team production theory, and legal personhood). Here’s the second part of that story, with another five reasons explaining why a self-organized group of autonomous people can never fully replace a corporation.

6. The Resource Loop

If I could afford to buy myself a staff of twelve robots, I’d already done that long ago. Alas, it seems I need to give a few more talks and workshops before I ever get there.

Some things are simply too big (or too scary) for one person to own or to fund. The joint-stock company (made official in England by the Joint Stock Companies Act of 1844) was a legal gadget for piling up more capital than any single investor had, while spreading the risk thin enough that nobody got wiped out when everything went downhill.

Edith Penrose in The Theory of the Growth of the Firm (1959) made the case that a firm is really a bundle of odd, mismatched resources that together produce something none of them could alone. Their unused capacity is what drives it to grow. Penrose claimed that growth is capped by how fast the management can learn, a limit that is now known as the “Penrose effect“.

Birger Wernerfelt and Jay Barney built their Resource-Based View on top of Penrose’s work. They said a firm’s advantage comes from what it owns that rivals can’t easily copy, such as assets, skills, and processes. (We nowadays call that a competitive advantage or a “moat.”) David K. Teece, Gary Pisano, and Amy Shuen stretched this into “dynamic capabilities,” the skill of sensing what’s coming and seizing opportunities by reshuffling resources and reshaping the organization.

In other words, it’s all about how you use what you own. And it’s much easier for a corporation than for a network of individuals to own and manipulate properties.

Here, again, AI pushes in two directions.

On the one hand, frontier AI is brutally capital-intensive. The sector is pouring money into GPU clusters, data centers, and energy-grid connections. The entry fee is so steep that the firms adopting the strongest AI are overwhelmingly the biggest ones. Those early adopters then grow faster in sales and markups, resulting in a self-reinforcing resource loop: corporations need scale to afford the AI, the AI throws off better returns, and the returns fund more scale. That effect is pretty hard for a network of individuals to emulate.

On the other hand, APIs and AI agents offer world-class capabilities to anyone. Traditional moats dry up faster than the Seine in Paris under a heat dome. A solopreneur can rent frontier reasoning, image generation, voice cloning, coding, marketing, and legal drafting for the price of a streaming subscription, which makes the Resource-Based View’s competitive advantage very hard to maintain for traditional firms when most essential resources are just one API call away for anyone with an Internet connection.

Conclusion: The largest firms fare better than ever. All the others have a problem.

7. Tacit Knowledge

If I sold my one-person business now, the buyer would not be able to run it. That’s because nearly everything needed to generate revenue sits in my head, not on Google Drive.

Some knowledge refuses to fit in a contract. Michael Polanyi named it tacit knowledge in 1966:

The stuff you know in your hands and your gut, learned by doing, impossible to email. Firms are systems for moving know-how between people.

Just watch how a new medicine is made. It rests on the accumulated instincts of thousands of researchers, trial managers, and regulatory specialists marinating their expertise for years. You cannot chop that up into freelance gigs and reassemble it with a few peer-to-peer contracts. The knowledge lives within the organization.

The Knowledge-Based View says that firms beat markets at sharing and replicating knowledge and expertise, especially the tacit kind that lives in heads, hands, and habits rather than documents and that people and markets cannot easily price or transfer.

The problem with loose networks is that they are nimble and forgettable. They’re poor at holding onto tacit knowledge, enforcing standards, making relationship-specific investments, and building the routines that make next year’s work better than this year’s. When value depends on “how we do things around here” rather than generic labor you can rent off the market, the network becomes insufficient.

Now that AI can codify and spread expertise fast, it even strengthens the case for organizations that can build proprietary learning loops and real internal knowledge infrastructure. Though it remains a challenge because skills and workflows rot faster than ever because of “accelerated knowledge decay.” Keeping every capability in-house means paying to re-learn everything, all the time. When your internal expertise expires like fresh milk, renting it from the market looks clever again.

However, AI is also the first automation that can distill and replicate tacit-seeming knowledge, but only with access to proprietary data, customer interactions, internal documents, and digitized workflows. AI is quietly mastering and mimicking the expertise of your best people. That’s firm-reinforcing. And when the company owns that data, AI infrastructure becomes the most valuable asset of all, and we’re back to the Resource-Based View (see above) as the main reason for the corporation to exist.

I’m a founder, intrapreneur, and former CIO who helps leaders diagnose and redesign their operating models for the age of AI—informed by plenty of scar tissue. This article offers the same lens I bring to talks, workshops, and coaching, from a single team to a multinational. Want it applied to yours? Let’s talk. And if you’re just here for the maps, they’re free, always.

Subscribe now

8. Identity and Reputation

Why did I pay a professional designer to create a company logo that I could put on my invoices? Why does a six-person startup hire a Chief Compliance Officer before any regulator demands it? Why do small charities build boards modeled on Fortune 500 companies they have nothing in common with? Because looking proper and professional is itself a survival skill.

Everything we’ve discussed so far is pretty rational. But what in the world ever happened for purely rational reasons? Humans are creatures who often do things for social and emotional rather than rational reasons.

An organization is a power structure shaped by social, cultural, and political forces, and only sometimes efficiency matters. There are human motives nobody writes into a business plan: prestige, ideology, the plain hunger for power. Taking this into account, we can explain why some organizations form for no good reason at all, or, the opposite: why they fail even when the rational structure looks flawless on paper. Experts refer to it as institutional theory, and it explains a lot of otherwise baffling behavior.

Paul DiMaggio and Walter Powell, in “The Iron Cage Revisited (1983),” and later Richard Scott in Institutions and Organizations: Ideas and Interests (2008), argued that organizations adopt their shapes not only to be efficient but to look legitimate. The authors identified three pressures: coercive (regulators make you), mimetic (you copy whoever’s winning), and normative (your profession says this is how it’s done). Organizations take recognizable forms because the social world expects them to. And underneath all of that sits purpose, vision, belonging, identity, status, and more. Churches, clubs, and mission-driven companies exist for reasons that are not about efficiency, liability, resources, or knowledge at all. It’s just a matter of, “They are, therefore I am.”

In a world where some of the rational reasons to organize are crumbling, the non-economic ones stand out. Trust, status, identity, professional norms, brand legitimacy: these are why clients still hire a “firm” rather than a freelancer, and why people still want to belong to something. These are organizing drivers in their own right, and they might be the ones AI finds most difficult to touch.

On the other hand, AI created a new way to look respectable: being seen as a responsible AI business. Companies now stand up AI ethics boards and “responsible AI” committees mostly because it signals trustworthiness. Some consultancies sell entire product suites to companies that want to look like grown-ups in the age of AI. “It’s what everyone else does, and so should we.”

9. Collective Action

I published this article on the last day of Pride Month, and it’s worth noting that change may come because of collective action. Sometimes, a loose network of people must turn into a united front because it’s the only way to get others to listen and take notice.

Mancur Olson (1965) explained in The Logic of Collective Action why some groups organize when no individual would bother: large groups with a shared interest do not spontaneously speak as one voice. Unions, cooperatives, mutuals, industry lobbies, and trade associations all exist because of exactly this: to prevent a Tragedy of the Commons situation by presenting a gaggle of individuals as one solid block. The job of the collective is not to make anything; it’s to bend the rules that everyone else has to play by.

Associations, foundations, and other public bodies form when the job is governing a shared resource or a public good rather than maximizing an investor’s return. People reach for a public-purpose body when the real problem isn’t capturing capital, but setting rules, sharing access, building trust, or stewarding shared infrastructure. Think industry standards groups, shared data trusts, worker-owned platforms, sector-wide safety initiatives, and open-source AI or data communities. You can’t easily do that with just a network of bilateral agreements.

Here, it’s interesting that AI hands us fresh shared resources that need governing: compute access, model-evaluation benchmarks, incident reporting, shared data infrastructure, and … the mimicking by AI of expertise developed through our professional labor.

Any single employee’s data is replaceable because a thousand colleagues generate similar data. So if each person bargains alone over the data they generate, they all individually cave, the firm assembles a brilliant AI model of their collective expertise, and the entire workforce’s leverage quietly collapses. The proposed fix is collective: workers can bargain data rights as a group, like a union. When they negotiate together, the firm still gets good AI while people keep a career ladder to climb.

Perhaps it’s time for an annual parade on Professional Month.

10. The Network Effect

The tenth and ultimate reason for corporations to exist came to prominence in the platform era. Some organizations exist purely to own and run the network itself, because its value grows with every node that joins. Metcalfe’s Law states that a network’s value scales with the square of its members. The network structure itself is the asset.

Uber, Airbnb, Booking, Spotify, and Linkedln didn’t form to save on transaction costs or to prevent the hold-up problem. They didn’t get funded because of team production theory or legal personhood challenges. They don’t exist to benefit from shared resources, tacit knowledge, reputation, or collective action. Sure, all these may contribute to the success of the firm to some extent, but they’re not the primary reason for its existence.

Networked corporations formed to capture the value created when you connect millions of strangers who’d otherwise never find each other. Rietveld and Schilling (2021) called it platform competition. Customers and suppliers generate data; this data trains better models, the better models attract more users, and round it goes. Proprietary data, customer interactions, and fine-tuned models are now relationship-specific investments that the firm captures and the market can’t copy.

Machine-learning models get better with more and cleaner data. So a company that hoards proprietary behavior or operations data builds an AI capability rivals can’t match, even when those rivals run the exact same model architecture. They may have the same algorithms; they may have a similar network, but they don’t have the same data.

So companies now form specifically to sit on a network. To be the network. When you already operate as a peer-to-peer network of individual contractors with bilateral agreements, you have a strong incentive to incorporate and own that network. Because if you don’t do it, somebody else will. And then they’ll reap the benefits of owning a network.

Conclusion

I may have been a bit naïve myself in the past regarding the viability of self-organized networks of professionals. But hey, we’ve all been young. We’ve all been optimistic and wrong about something.

I’m not here to ditch the concept of networks, nor am I criticizing the push for self-organization. Far from it! But I realize more than ever that self-organization in a peer-to-peer network is insufficient, unless this self-organization develops into something that addresses the ten reasons for corporations to exist: it has economy of scale, reduces transaction costs, solves the hold-up problem, team production problem, and legal personhood problems, benefits from shared resources, tacit knowledge, reputation, and collective action, and has a way to own the AI models built through the network.

If you do all that, you end up with what I call a Networked Agentic Organization, where the company is the network.

Jurgen, Solo Chief

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AI makes small teams look clever, but it doesn’t erase the reasons corporations exist.

(Read Part 2 here)

As someone running a one-person business and often collaborating with other independent workers, I cannot help feeling amused when I see large, hierarchical enterprises struggling with artificial intelligence. The traditional firm doesn’t seem fit for the age of hyper-acceleration. Some authors even think multinational corporations are on their way out, to be replaced by networked agentic organizations (NAOs).

Is that true? Or is it wishful thinking?

In the age of AI, do we even need traditional corporations?
Can we not replace them with networks of contractors and AI-native entrepreneurs?
Are the people with standard employment contracts laboring away on rapidly shrinking ships?

It’s a fascinating thought, I admit. I understand why some of my peers are hoping for the imminent demise of the global enterprise, especially when they find themselves, as an entrepreneur, independent freelancer, or one-person business, battling excessive corporate greed, misconduct, or just plain bureaucracy. One of my friends said to me, “I hope they all die!”

But let’s not smirk and gleefully rub our hands together yet. My team of AIs was able to dig up ten different reasons for saying, “Nope. The standard multinational enterprise will hang around for a little while longer.” And because even solo chiefs need customers both large and small, it helps to understand why. Because even when dynamic networks do take over from corporations at some point in the future, they will inherit a long list of requirements for which the standard firm was created in the first place.

1. Economies of Scale

Whenever I worked on teams, I often let my teammates handle video editing, social media updates, and administrative work. I did all the writing; they handled the customers. We were running on David Ricardo’s Law of Comparative Advantage—stick to what you do best and leave the rest to others. It just worked faster that way.

In his famous work, The Wealth of Nations (1776), Adam Smith gave us the oldest reason for humans collaborating in organizations: to benefit from economies of scale. He showed that dividing labor multiplies output. Seven multidisciplinary doctors working separately can never be as productive as seven specialist doctors operating as one crew. When people work together, the organization can capture the benefits of improved efficiency.

“Economies of scale” is also a fancy way of saying that work gets cheaper when you crank up the volume. Ten thousand chilli sauce lamps are cheaper to make than just one. The principle thus works on two levels at once: People get increasingly good at narrower tasks through sheer repetition (the experience curve effect) and supplies and machinery get cheaper the more you use them (dilution of average fixed costs).

Herbert Simon added to this idea with his concept of bounded rationality: Our brains have limited cognitive capacity. We can only think about so much. Therefore, organizations spread decisions across a group so multiple people make better decisions about different things. The organization exists because one human brain is not sufficient when coordinating complex work.

But! Artificial intelligence has a significant impact on economies of scale. AI gives many of those complementary skills straight to one person, which rewrites how we distribute cognitive load. The economies of scale argument still holds, but instead of distributing the work among humans, we distribute the thinking across AI agents. AI doubles down on economies of scale and shifts the concept into a digital dimension.

And that’s exactly why I now work alone, with a rowdy bunch of LLMs. The thinking is cheaper, and thus we are doing more of it. We are operating in Jevons’ Paradox, cranking up the scale to a new level.

2. Transaction Costs

There’s just one nagging issue with working solo: hiring subcontractors can be a pain-in-the-ass. In the time I spent searching, negotiating, contracting, and evaluating freelancers (such as copy editors, book designers, and online marketers), I could have written a second book. This staggering opportunity cost highlights exactly why solo operations hit a wall.

In The Nature of the Firm (1937), Ronald Coase pinpointed the core problem: if markets are so efficient, why does anyone employ anyone? Why not just contract out every single task peer-to-peer among freelancers and one-person businesses? Indeed, that’s what some of us independents would like to know. Corporations? What’s the point?

Coase’s answer at the time was that taking part in the market costs money (searching, negotiating, contracting, enforcing), and when those costs are higher than the cost of doing the work with a fixed set of workers, the activity moves inside an organization. Vice versa, the organization should stop growing at the point where one more transaction handled inside gets more expensive than just buying a service from the market.

Here again, new technologies change the equation. Cheaper negotiation, contracting, and coordination flatten hierarchies, and AI is the biggest communication-cost cut in history. The management burden shifts from human-to-human toward agent-to-agent. When software monitors operations in real time, reads performance data, and reallocates resources, you no longer need humans relaying messages up and down hierarchical layers.

Human networks scale terribly because of Brooks’s Law—as a group grows, the lines of communication explode exponentially. Hierarchies were just human routers built to manage that complexity. Algorithms, however, can process such bandwidth effortlessly. Gig work platforms discovered that revolution years ago, and now algorithmic management is eager to take over the traditional enterprise.

This means that AI-first agencies can stay loose networks or tiny companies for longer than their pre-AI peers, because algorithms have shrunk the routine work and communication hassle that once forced everyone to build a coordinating hierarchy. The pyramid flattens into something closer to an adaptive, software-run network. This is the ultimate realization of Malone’s Electronic Market Hypothesis (1987), which predicted that cheap information technology would inevitably disintegrate corporate hierarchies in favor of fluid, network-driven markets.

“Hell, yes,” I hear some of my friends say. “Bring it on!”

But … It only works when we are happy to let the AI agents do the searching, negotiating, contracting, and evaluating for us. And even I am not sure if I’m ready for that.

3. The Hold-up Problem

Did you ever get annoyed at a supplier—I am not talking about one of my earlier book designers, of course … ahem—for creatively interpreting an agreement (to their own benefit, obviously) because you couldn’t specify every little detail in advance, but you were nevertheless stuck with their work? That’s the hold-up problem in a nutshell.

In his 1975 book Markets and Hierarchies, Oliver Williamson emphasized that when parties make relationship‑specific investments, they create a strategic dependence and scope for opportunistic “hold‑up” once the investment is sunk.

Let’s face it: we can’t think through every contingency. (There’s the bounded rationality, again.) Incomplete contracts mean not all future contingencies are specified, so after initial agreement the parties are “locked‑in” and vulnerable to additional bargaining and renegotiation. Whoever owns the assets and “residual control rights” has control over that negotiation. Economists call this the Grossman-Hart-Moore Property Rights Theory—and it explains why my book designer held all the cards when the project was ‘nearly’ done.

Vendors and customers act in their own interests when they can. That gives you the hold-up problem, and it’s the reason firms (and thus long-term collaboration) replace one-off market deals. When two activities depend on each other, common ownership beats staying separate. To solve the hold-up problem, you move the work inside the organization. For some kinds of work, writing extensive market contracts becomes so risky that firms are best off absorbing the activity and running it all by authority instead.

The advice: keep inside the organizational boundary the assets and workflows where incomplete contracts, trust, or downside risk dominate: your proprietary data, customer relationships, intellectual property, workflow contexts, and anything else where renegotiation is expensive. Push outside the boundary everything that is modular, fast-moving, and easy to swap: commodity foundation models, burst compute, temporary specialist labor, etc.

AI thins out some supervisory layers, but it doesn’t make the hold-up problem vanish around physical assets, sensitive data, or intellectual property. The only other way to address it is to extend what game theorist Robert Axelrod in The Evolution of Cooperation (1984) called “the shadow of the future”—insisting on long-term relationships with your vendors. When a supplier knows there is a long pipeline of future work, the temptation to exploit an incomplete contract vanishes. Like finding the perfect book designer and telling them there will be more books to come!

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I’m a founder, intrapreneur, and former CIO who helps leaders diagnose and redesign their operating models for the age of AI—informed by plenty of scar tissue. This article offers the same lens I bring to talks, workshops, and coaching, from a single team to a multinational. Want it applied to yours? Let’s talk. And if you’re just here for the maps, they’re free, always.

4. Team Production Theory

When we collaborate as independent contractors in a network using AI agents for negotiation and contracting, and when we aim for long-term engagements between vendors and clients, are we done? Can we then finally get rid of corporations?

No, not really. Because it might be impossible to agree upfront and afterward on how much we should all be paid.

When multiple people collaborate on a product, it’s challenging to figure out who is contributing how much. And because this may change over time, defining compensation before you start or after you’ve finished could be the last thing you want.

Armen Alchian and Harold Demsetz tackled this team-production puzzle in 1972. When the output is genuinely a team effort, it triggers the Ringelmann Effect—a psychological law showing that individual effort naturally drops as team size grows. You can’t measure each person’s individual contribution, which may tempt everyone to slack a little (often referred to as social loafing or the free-rider problem). The fix is a dedicated monitor who keeps watch and gets to keep the leftover profit in return for the effort of figuring out who did how much.

Guess what? That monitoring role is the manager in the capitalist firm. They have a reason to exist when the gains from team production outweigh the cost of monitoring it. Formal structure helps when several parties collaborate to produce something joint that you cannot carve up by a project contract.

Margaret Blair and Lynn Stout turned it into a theory of corporate law: a lot of modern work is “nonseparable.” The team makes something together, and you can’t cleanly measure who contributed what. If you define the compensation in advance, you get shirking—people trying to do less than they should. If everyone fights over the revenue after the fact, you get rent-seeking—people claiming more than they actually did. Blair and Stout’s answer: the team hands control to a neutral referee, which is the manager or board of directors. Their job is not to be a loyal lapdog for shareholders. It’s to protect the contributions everyone made: the employees, the suppliers, the shareholders, and so on. Grow the joint pie, don’t just defend one slice of it.

And what about AI?

Well, on the one hand, AI makes management cheap. With new technologies, you can measure, log, and audit contributions and output per keystroke, which immediately collides with Goodhart’s Law—the moment you turn a simple metric like keystrokes into a target, people find ways to game it, meaning the referee’s job actually gets more complex, not less.

On the other hand, AI gives you a whole new principal-agent challenge. This is the alignment problem shifted into the workplace: the AI agents are themselves participants whose goals can drift from yours. Monitoring humans gets cheaper, while monitoring AI agents becomes a new job. The firm simply ends up with yet another variable added to the equation. People managers morph into AI agent orchestrators, but the monitoring job—and the corporation—is not going away.

5. Legal Personhood

Okay, I might be just a one-person business, but I’m still incorporated. Like Shell and Unilever, but without the fancy office, the greenwashing, and the ethical acrobatics.

Corporations exist because a legal entity can own things, sign contracts, and shield its members behind limited liability. Lawyers call this legal personhood, and it isn’t a metaphor: as far as the courts are concerned, the company really is a person. Without that legal shell, complicated multi-year deals between strangers would quickly fall apart, because there would be nobody left to hold accountable. It would be like lending money to Dory, the blue tang fish, who suffered from short-term memory loss. (A loan? What loan?)

Michael Jensen and William Meckling (1976) reframed the firm as a “legal fiction,” a nexus where contracts between shareholders, managers, employees, suppliers, and customers all come together. When a principal hands decision rights to a representative who wants something slightly different, they incur agency costs because they must address the principal-agent problem. Legal structures and governance are the tools we have for keeping those costs down.

Most people, if they think about incorporating, think “limited liability,” or owner shielding: the company’s debts can’t come after their personal property. It’s how anyone restricts their liability for what their representative or organization does. If Claude bankrupts my business, I don’t want the creditors “piercing the corporate veil” to carry away my sci-fi book collection.

But Henry Hansmann, Reinier Kraakman, and Richard Squire made the counterpoint, which they call entity shielding. It protects the firm’s assets from the owner’s personal mishaps. If I somehow go bankrupt myself, my creditors can’t march in and sell off the company’s IP and computers. This lets strangers lend to a company without worrying about every owner’s private mess.

Finally, the corporation, as a legal person, enjoys perpetual succession—it outlives all its stakeholders. That means the organization can sign contracts, sue and be sued in its own name, hold property, and persist after earlier founders are long gone. These multi-sided protections lower the cost of credit and solve the joint-ownership problem in one move.

If anything, the new legal dilemmas around AI (liability for autonomous agents, copyright in AI outputs, where the training data came from, etc.) make the case for the corporate form stronger, not weaker. The EU AI Act, GDPR, and the AI Liability Directive all assume an identifiable legal person standing behind any Al’s behavior. The courts will simply revert to the ancient doctrine of respondeat superior—let the master answer. Somebody has to own the AI decisions. There’s currently no route for an AI to be a legal person in its own right. You can’t tell a judge, “It wasn’t me. Claude did it!” So, better be prepared (and incorporated).

Continued in Part 2

That’s it for today. It’s 32 degrees Celsius in my little home office and I desperately need a break.

In the next edition, we discuss five more reasons for corporations to persist in the age of AI: the resource-based view, the knowledge-based view, institutional theory, collective action, and network effects.

Read Part 2 here.

Jurgen, Solo Chief.

P.S. The corporation persists, but its form is evolving rapidly. What’s your take?

Most org models fail because they turn work into jargon, then call the mess a transformation.

If you’re a leader or change-maker, you’ve probably got a graveyard of failed frameworks and org designs behind you. The scaling model that needed a 500-page manual. The organizational change model that only transformed cash into catastrophe. The agile framework rollout that made everyone busy and nobody happy. You want something that could actually work, not just another method.

What’s fascinating is that the people building agentic AI systems right now are reinventing a model a systems thinker published in 1972, and that management shrugged off as too complicated. He was right all along, just 50 years early.

It can be such a joy to start all over. For some, it’s a new relationship, a new country, a new house, or a new job. For others, it’s a new pair of glasses that makes all the difference. We get a different perspective and a new view of the world, letting us update our mental models.

For me, it’s an updated organizational model.

In this article, I describe my new approach to diagnosing teams, departments, and organizations, from the smallest one-person business to the largest multinational enterprise. This new perspective, which I’ve been tweaking and tuning for months, helps me diagnose and improve operational models for organizations of any size.

Check out my new blueprint for the networked agentic organization:

The Importance of Simplicity

People often credit Albert Einstein with the famous words:

“Make everything as simple as possible, but not simpler.”

It’s rather ironic because what he actually said was:

“It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.”

He could have said that in a simpler way.

The point remains valid, though. Too many products and services look bloated and overly complicated. Some agile scaling frameworks and organization design approaches require 500-page books and multi-day courses just to explain the basics. (Yes, I may have contributed to that problem as well.) But no product ever succeeds when people perceive it as too complicated. For humans to change behaviors, the guidance must be simple.

“A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over with a working simple system.”

― John Gall, The Systems Bible: The Beginner’s Guide to Systems Large and Small

I’m going to start over with a simple system.

The Relevance of Artistry

A famous practitioner of simplicity was Dick Bruna, the Dutch artist behind the cute little rabbit called Miffy (in Dutch: Nijntje). Bruna’s artistic philosophy was to use as few lines as possible (and a minimum number of colors) without losing the essence of the characters and their stories. He drew Miffy as simple as he could, but not simpler.

In the world of products, the most-cited masters of radical product simplicity include Steve Jobs (Apple), Larry Page and Sergey Brin (Google’s early days), the leadership of IKEA, and James Dyson. They all shared Dick Bruna’s obsession with removing all clutter and stripping down their products until they were left with only the most essential features.

Simplicity is an art. It takes a lot of talent and tremendous discipline to figure out how to reduce something to its bare essentials. Many get this completely wrong because few product designers are artists.

Piet Mondrian was another famous Dutch artist who perfected the art of simplicity. He explicitly aimed to abstract away everything until he arrived at the fundamental quality of objects. Design historians argue that Mondrian’s minimalist forms and colors influenced the entire modern profession of industrial design.

It’s this same philosophy of simplicity that I aim for with my new organizational model. I try to follow in the footsteps of some of the greatest masters in Dutch design and hope I can develop and muster even just a fraction of their talent and discipline.

Let’s start.

Entity: The System in Focus

We begin with the primary unit that we want to diagnose and discuss in an organizational setting: the entity. This could be a team, a business unit, one employee or AI agent, or a global multinational corporation spanning several continents. The scale doesn’t matter for now. What matters is that we distinguish between what’s part of the entity and what is not, which means there is a boundary.

Some experts prefer to speak of a system, or unit, or some other smart-ass systems thinking term. That’s fine with me. I like the word entity because in common parlance an entity is a distinguishable “thing” with some persistence and state (e.g., a person, team, organization, or country). It can be physical or abstract, but you can name it and say, “This one versus that one.” By default, every entity is a system because it always has various interacting parts.

Note: The entity’s boundary should be blurry because the exact border is up for debate and depends on the observer. It’s hard to tell at which point a team’s cohesion ends and its environment begins. And to be honest, my earlier version did show a cloudy border. But circling back to Dick Bruna, I think the artist would have wagged his finger and said, “The blurriness of that thing is not essential. Remove it! Miffy’s fur doesn’t look fuzzy either.” And so I deleted the blur.

What we have is a system, its environment, and the boundary between them.

Context: The Environment’s Niches

Each entity lives in an environment full of context. An employee has to deal with meetings, teams, projects, and clients. A corporation tries to handle labor, supplies, markets, investors, and government regulations. And an AI agent must somehow make sense of users, tools, and protocols.

The number of contexts, which together form the environment, is limitless. And while we might objectively agree on the existence of an environment, the contexts depend on the perspective of the entity.

It is the mother who decides that her relevant contexts are two kids, a husband, her job, her home, the gym, a charity organization, and her favorite TV shows on Netflix. It is the football team agreeing that their relevant contexts are their coach, club, fans, and the media. It is I who switches every day between the contexts of writing, speaking, coaching, reading, drawing, and cooking.

Again, my earlier design was too complicated: I had visualized different contexts as multiple overlapping translucent circles. But then I felt the ghost of Steve Jobs tsk-tsk-ing over my shoulder, and I reduced the contexts to a simple cloud that I could have copied straight out of a Miffy story.

Some theoretical thinkers refer to such a subjective part of the environment as a niche, and I am perfectly okay with using that term instead of context. However, in the age of AI, the word context has won out: context is the information and mental model we have about a defined part of the environment.

Agency: Operation of the Value Streams

To survive and thrive in its various contexts or niches, the entity needs to have a function that I will refer to as agency. It is the autonomous potential to handle requests and demands from the environment, to get what it needs as input and to deliver what the environment wants as output. Many readers will immediately think of value streams, and they would be correct that the agency function’s primary work is generating enough value for stakeholders in all contexts.

We may alternatively refer to this as operations, service, or delivery. The agency function is where a Scrum team takes its customer through a demo session. It is the Finance department paying the invoice of a supplier. It’s the AI support agent that issues a refund. And it is the Substack author clicking on the Publish button to send out another newsletter to his subscribers.

In my visual design, agency is the main part of the entity that directly touches the environment. I don’t show it at the bottom of a hierarchy, and I won’t depict it as a linear flow from left to right. (We’ll have those discussions some other time.) For now, the main thing is that the agency function is the main part that acts on the present environment. It is also the largest of the five functions. That’s all that matters. (I feel James Dyson nodding in approval.)

I like the word agency because we associate the term with the autonomy we need to respond to our environment with the shortest possible feedback cycles. For example, the customer service agent who cannot compensate a customer for an order gone wrong without written permission from the head office is an agent in name only, because what he clearly lacks is agency.

Harmony: Self-Organization of the Parts

No matter what organizational scale we’re looking at, an entity always consists of different parts. The human body has organs. A team has team members. An organization has employees. A hockey club has players. A marathon has runners. And an AI agent has multiple digital components and interfaces. For an entity to work well, these parts must operate in harmony.

The harmony function exists so that the parts don’t get in each other’s way. Alternative terms leading thinkers in organization design use are coordination, self-organization, and synchronization. We can think of stand-up meetings, team agreements, chat channels, shared protocols, coding standards, and much more. Anything that the parts do themselves to help each other be effective, and not block each other’s productivity, counts as self-organized harmony work.

Note: You might wonder, where are the parts in the diagram? Well, that’s easy: each part is in itself an entire unit. The model is recursive. The higher levels and lower levels look the same as the one we’re looking at now, with exactly the same functions. I’m sure Mondrian would scold me when I tried to draw a three-dimensional, multi-level fractal structure. “When each level looks the same, show only one,” he would undoubtedly say. And so, that’s what I did. (We’ll discuss the recursive nature of the picture some other time.)

Note the peer-to-peer self-organization in the harmony function. This is family members agreeing on whose turn it is to walk the dog. It’s multiple AI agents sharing a protocol/context so they don’t duplicate work or collide. And it is two or more countries negotiating a bilateral or multilateral agreement. In the harmony function, the only constraints are those that the parts willingly negotiate among themselves.

Economy: Optimization of the Whole

It takes just a stroll through my neighborhood, observing the litter in the streets, the graffiti on the walls, and the damage inflicted on other people’s properties to see that self-organization is not the holy grail of governance.

The third function to discuss is what many people would refer to as governance or management (though the term management doesn’t sit well with me because the activities in the other functions are also managed). A better targeted word I found is economy, because the goal of this function is all about the control and optimization that an organization needs to improve the cooperation between the parts and derive economic value from their collaboration. After all, if the whole does not generate more value than the sum of its parts, there is no point collaborating as one entity.

As an example, we can consider the rule that at least two team members must remain available to answer client calls during the holidays, even though everyone would rather remain disconnected from their work. We see a budget, rate limit, or guardrail that stops AI agents optimizing locally at the organization’s expense. We see the requirement that players on a football team must wear the sponsor’s T-shirt whenever the media are filming. And the Maastricht Treaty, decreeing that EU deficits should not get higher than 3% of GDP, is another example of a purely economic function that aims to benefit the whole.

As I once wrote in my book Management 3.0, self-organization without hard constraints is equal to anarchy. Even assuming everyone’s best intentions and the power of self-organization, societies need laws, families need rules, and organizations need policies. The fewer the better, but some are essential to optimize the whole for the benefit of all. You don’t want your company to look like my local park the morning after a King’s Day celebration.

I’m a founder, intrapreneur, and former CIO who helps leaders diagnose and redesign their operating models for the age of AI—informed by plenty of scar tissue. This five-function model is the same lens I bring into talks, workshops, and coaching, from a single team to a multinational. Want it applied to yours? Let’s talk. And if you’re just here for the maps, they’re free, always.

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Strategy: Adaptation Through Intelligence

The fourth function of every entity, no matter if we’re talking individuals, teams, departments, organizations, or entire nation-states, is the awareness of what happens in the environment and the adaptation to new information when necessary. You may use words such as agility or intelligence, and I would be the last to object. My point is that the entity needs to look outward and forward. The combination of reaching out into the environment, understanding what is likely to happen, and coming up with an appropriate response is what many of us would call strategy.

As an example, we can consider a bunch of AI agents scanning market trends on TikTok and other social media. We can consider a marketing team in a discovery phase with a focus group or a Board of Directors on an off-site to discuss their organization’s future. We can also recognize the strategy function in an agile team when they not only talk with their current client but also scout the market to see why their non-customers are not using their product.

I intentionally drew the strategy function as a pair of cross-hairs to show that it cuts straight through the other functions and taps directly into the known and unknown parts of the environment. The fact that I spent weeks researching ideas and experimenting with designs is an example of pure strategy: it’s because I believe companies need a concrete approach to evolve towards networked agentic organizations.

Identity: Cohesion Through Alignment

Last but not least, the entity needs the identity function to maintain cohesion across its system. What we see here are the alignment policies set in a central place that apply to the whole.

The identity of an entity is the name of a business unit, the purpose of a charity organization, the vision of an Olympic athlete, or the mission of a military platoon. It is also the system prompt / constitution / encoded values that keep agents from going rogue. In the identity function, we may recognize an organization’s values when they are not only printed on posters but actually enforced through recruitment and selection interviews and annual performance appraisals.

A warning about the picture: the center is not the top. These are five different jobs, not five rungs on a ladder. Identity sits in the middle because everything must stay aligned with it, not because it gives orders to the rest.

The title, tagline, and About page of my Substack publication that I’ve repeatedly agonized over with Claude and ChatGPT? That’s pure identity work. Without a shared identity, the parts have too little incentive to move in the same direction. The European Union is quite familiar with the challenge.

The Promise of Clarity

And that concludes the new visual: it is the simplest visual of a standard operational model and diagnostic tool that helps us evaluate how a team, department, business unit, or organization can stay viable and thrive in a fast-changing environment.

Note: Whether a single person or agent is truly ‘viable’ on its own is a debate I’ll happily have another day (for now, what matters is that the same five functions show up at every scale).

I use this new visual to diagnose organization designs and operational models for my clients. It is my improved mental model to understand what’s happening and where things should improve. With this model in hand, I can ask many questions:

• How are your people, teams, and AI agents distributed across the five functions? Who does what, how, and when? And what is the evidence that it works?

• What does your current agile method or framework actually do for you? Where are the overlaps with this diagnostic model? More importantly, where are the gaps?

• How do the five functions and the various contexts influence each other? Which communication channels is everyone using? And where is intelligence building up?

• How are upward and downward collaborations implemented across recursive levels? How do the parts impact the whole, and how does the whole manage the parts?

• What is the Current Operational Model (COM), the Target Operating Model (TOM), and how do we get from here to there?

And only when we discuss a concrete department or business unit, we annotate the standard model with just enough insights to clarify our diagnosis.

Note: You may have noticed that my new visual looks suspiciously like a radar or dartboard. This is by design. It is exactly the association I want people to make when they discuss their ways of working.

The Viable System Model, Fifty Years Later

I told you at the start that someone got here first. It’s time to give credit where credit is due. What I described in this post is basically the Viable System Model as published by Stafford Beer in his book Brain of the Firm in 1972. Among systems thinkers, it’s still the most rigorous organizational model we have (and yes, I know how that sounds coming right after I told you to distrust old methods and frameworks).

The problem with the Viable System Model is that its visuals and language look incredibly technical and complicated. A quick glance at a standard VSM picture triggers a strong déjà vu of my meter box closet downstairs. And this makes Miffy very sad.

Not surprisingly, Stafford Beer’s intricate designs never caught on in mainstream management literature, which is a pity because he was decades ahead of his time. The funny thing is that those who nowadays create agentic models for working with AI agents are re-inventing what Beer already explained over fifty years ago.

(Have you seen the explanations for creating one root claude.md in Claude Cowork that encodes your principles, working agreements, and brand voice for your AI agents? Yeah, that’s people discovering they need Harmony and Identity functions. We knew that fifty years ago already. Managing intelligence doesn’t differ much between biological and digital systems. It’s time for the VSM to make a comeback.)

Now, I’m pretty sure that some systems thinkers will clutch their pearls and cry heresy while pointing at my visual and say, “That does not look like a proper viable system model!” And they would be right! They would be right in the same way that biologists would be right when they look at Miffy and say, “That doesn’t look like a proper rabbit!” They would also be missing the point of simplification for a target audience.

For example, I ignored (for now) the alarm channel (Stafford Beer called it the ‘algedonic’ signal) that lets a scream of pain bypass entire layers and reach the top of the organization instantly. I ignored (for now) what systems thinkers call System 3* which is the subfunction that allows the Economy function to see directly what’s happening in the Agency function. And I ignored (for now) the fractal nature of the model, connecting lower-level entities with higher-level layers. I’ve hidden all these details while also simplifying most of the language. You won’t easily hear me discuss variety attenuators or recursive metasystems. Miffy doesn’t mind.

In this post, and the posts that will soon follow, my goal is not to be a scientist nor a systems thinker.

My aim is to be an artist and a storyteller.

I’m sure that Miffy approves.

Jurgen, Solo Chief

P.S. Which of the five functions is most broken in your organization right now?

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Your intentions are irrelevant. Only your impact counts.

“Agile actually means ...”

“Lean originally means ...”

Yeah, whatever. That’s not relevant when judging what people do with it now.

Remember Management By Objectives (MBO)?

Peter Drucker never intended MBO to be about bonuses for financial targets. He would turn in his grave if he knew what MBO has become over time. And yet, nearly everyone these days sees MBO as managers setting targets for other people and incentivizing them accordingly. This has become the de facto purpose of MBO.

Agile never intended to revolve around agile frameworks, coaches, conferences, and certifications. And yet, here we are. The emergence (and decline) of the agile industrial complex is what we all ended up with. That is what Agile has become, what almost everyone perceives it to be.

Likewise, lean thinking was never intended to be just about delivering things faster to customers. And yet, I can wallpaper my house with all the quotes from consultants who say that better flow equals more/earlier value and unthinkingly praise faster and more frequent customer deliveries. That is what Lean seems to be now.

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I’m a big believer in the POSIWID principle in systems thinking:

The Purpose of a System Is What It Does

When a company claims it supports sustainability and a transition to renewable energy sources, but at the same time collects subsidies for fossil fuels, then we can rightfully say the organization’s purpose is the greenwashing of energy policy.

When a religion preaches “love and peace,” but we see the actual behavior of its people show violence and suppression, then the purpose of that system is aggression and domination. It’s clearly not love and peace.

When a political party continuously talks about “freedom,” yet it enacts policies that exclude certain groups of people and takes away their fundamental human rights, it’s obviously a repression party, not a freedom party.

When a community claims its ideology intends A, but everyone can see that the actual behaviors of community members are B, then the system’s purpose is B, not A.

Historical intentions are interesting, but not relevant.

Sure, as a writer, I love understanding the etymology of a word. For example, “gay” originally meant cheerful and carefree. The word “awful” once meant “full of awe,” inspiring reverence or fear, as in “the awful majesty of God.” And “artificial“ was once interpreted as “skillfully made, artful,” related to works of art.

But these meanings are irrelevant to understanding what people mean when they use these words today in 2026. Similarly, to understand the current purpose of an ideology, religion, or political system, the original intentions of its founders or thought leaders are irrelevant. The only thing that matters is the actual behavior of people who use those words now.

The Purpose of a System Is What It Does

Your intentions are irrelevant. Only your impact counts.

Jurgen, Solo Chief

P.S. This is an adaptation from an article on the unFIX blog.

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If ignorance takes ten seconds to fix, your writing needs a better starting point. AI can expose stale claims, bad takes, and recycled thinking before you publish.

People can be such ignorant fools.

Just last week, I received a forwarded message in a neighborhood group on Whatsapp. “Watch out!” it said, “The AIs will be able to read your private chat messages. Change this setting, now!”

It seemed rather fishy to me.

I decided to fact-check it, which took less than ten seconds. It appeared to be a chain letter full of misinformation freaking out millions. It fed the cognitive bias of everyone who believes AI Is Bad. It was AI that helped me discover the fact-checking page that someone had already written about it. AI helps humans distribute stupid messages, but it also helps me to be the sensible one.

Skepticism won another day.

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There was a time when not knowing something was a valid excuse and easily forgiven. I’d write something on, let’s say, the idea that most consultancy is performative nonsense with no impact on organizations, and some readers would cheer me on, some would criticize my claim, and perhaps one person might point at existing research on this topic that I clearly didn’t know about.

Even among the most celebrated thinkers and scientists who were far smarter than I am, this was common. It has happened all the time:

• Isaac Newton developed the foundations of calculus in the 1660s but didn’t publish his work promptly, later discovering that Gottfried Wilhelm Leibniz had independently published a full calculus system first, starting a fierce fight over who was first.

• Charles Darwin spent decades refining his theory of evolution, then was shocked when Alfred Russel Wallace independently sent him a manuscript with the same idea, forcing a joint presentation of their work.

• Georges Lemaître derived the linear relation between galaxy recession speed and distance, which implied an expanding universe, but his 1927 paper was never noticed by Edwin Hubble who later rediscovered and popularized essentially the same relation independently.

I’ll be honest. It took Perplexity just a few seconds to come up with these three examples—and it had many more—even though I already knew them. I’ve been reading popular science books like crazy, and I found that science authors and biographers delight in these parallel inventions and discoveries. They know it’s often been impossible, even for the smartest people, to know everything happening in their field.

But this has changed.

If I hadn’t known about these examples, it still would have taken the AI only seconds to enlighten me. The same applies to everything else that we’re thinking and talking about. All it takes is a simple prompt to ask the machines what is already widely known about a topic, what the consensus is among experts, or what the conflicting viewpoints are on a tough problem.

Not knowing something is less easily forgiven than it was before.

In fact, ignorance is often the main problem in society, and we live in an age where this is easily fixed.

People point at social media and AI for many societal problems. But the real culprits are people’s ignorance and the malicious actors exploiting human stupidity. It takes less than ten seconds to not be ignorant anymore.

I don’t want to be stupid and ignorant. (Or maybe a bit less than I was before.)

Until a few years ago, it was forgivable to write something that had already been discussed and published by someone else. Google search queries often didn’t find the information you sought. And who had time to visit a library or dig into a scientific data bank to find the data to refute it or back it up for a simple 500-word blog post?

Not me.

But now, a simple prompt easily uncovers twenty existing angles on whatever I plan to write. That also makes it easier to stand on the shoulders of giants, to add something new to what already exists, and to not say the exact same thing as someone else, only with snarkier language and prettier pictures.

History has wasted a lot of human brainpower on duplication. Brilliant people have spent large parts of their lives discovering and inventing things that already existed, without anyone realizing it. Those days are now behind us. Instead of everyone doing the same deep thinking in parallel, we can now have everyone building something new on top of each other’s thoughts (as long as we keep an LLM-accessible digital trail).

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That’s why my entire approach to writing is changing.

Every day, I use the deep research features of five different LLMs in parallel, compare the results, find the overlap and the differences, and make sure that the starting point for my own thoughts is in unexplored territory. Yes, I get the irony. I make the machines do the parallel thinking for me, so that I don’t have to waste the little brainpower I have.

I check what’s already known and then I write about what’s not.

There’s no excuse anymore to be ignorant.

Jurgen, Solo Chief.

P.S. This might be the last post I wrote without initiating deep research first.

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I cancelled a project today and deleted the entire backlog.

No, scratch that.

I deleted the entire wish list. I had no backlog, only a list of options.

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When I ask Claude for the definition of “backlog” in one small paragraph, it says,

“A backlog is a prioritized list of work yet to be done — features, tasks, fixes, or requirements — that a team draws from to decide what to tackle next. In agile contexts, it’s the single, ordered source of truth for everything the team might build, with the most valuable or urgent items floating to the top and the vaguer “someday maybe” stuff sinking to the bottom.”

It’s practically the same as the definition that ChatGPT gave me a while ago:

“In project management, particularly Agile methodologies, a backlog is a prioritized list of tasks, features, user stories, and other items that need to be completed. It serves as a dynamic to-do list for a project, capturing and organizing the work identified for the project but not yet accomplished.”

The official dictionaries say,

• “Backlog - an accumulation of tasks unperformed or materials not processed” (Merriam Webster)

• “Backlog - a large number of things that you should have done before and must do now” (Cambridge)

• “Backlog - an accumulation or buildup, especially of unfilled orders, unconsumed products, or unfinished work.” (Wiktionary)

Each definition shows that a backlog is a list of work that you must do. You must fulfill all customer orders for a web shop. You must complete all tasks of a visa or immigration process. You must clean out the garbage bags you’ve accumulated under the stairs. And yes, if you use Scrum, you commit to finishing all tasks on your Sprint Backlog. These are all true backlogs: collections of things you still need to do.

None of this applies to product backlogs.

Technically, a product backlog mostly consists of ideas. Only the top part can be called a backlog. Only the part where you’ve committed yourself to completing the items should be called a backlog.

My long list of places still to see before I die is a bucket list, not a backlog.

The interesting films and TV shows I collect on IMDb are a watchlist, not a backlog.

The many rows of unread books on my bookcase are my tsundoku list, not a backlog.

The ideas I have for our new kitchen (before I see the vendor’s cost estimate) are an options list, not a backlog.

In fact, all the upgrades and repairs I keep track of for our house (because entropy is a bitch) is a wish list, not a backlog.

Most of what people call a product backlog is actually not a backlog: it’s just a collection of ideas and options for features, objectives, stories, etc. You’re not really responsible for completing them all and ticking them off your list until the list is empty. Because watchlists, wish lists, bucket lists, and option lists are never empty. They always refill with new ideas faster than you can clear them out.

When you’re able to politely or hesitantly say, “No, we’re not going to do this,” to many of the items on your list, you don’t have a list of “tasks unperformed.” You don’t have a list of “things that you should have done before.” You don’t have “a collection of unfinished work.” You don’t have a product backlog.

Call me conservative. Call me a linguistic prude. But referring to a list of features as a “product backlog” was a very bad idea from the start. (And which language-agnostic idiot did that, anyway?) It gives other people (usually your customers) the impression that you’re still going to do all of that. Because you call it a backlog, it means you’re going to clear it all, right? That’s what real backlogs are for! But you and I know that you’re never going to see the end of that “backlog.”

You may be interested to read what Claude added as a remark:

---

The word “backlog” carries a promise. Whether it’s a stack of webshop orders, a queue of visa applications, or the unplayed games you bought that are guilt-tripping you from the shelf, the meaning is the same: things placed here are expected to be cleared. Put something on a backlog, and the implicit contract is that someone, eventually, gets to it.

The etymology backs this up. From the 1930s “arrears of unfilled orders” to the older “pile of work to get through,” every list-shaped sense of the word promised clearance. The one pre-Scrum sense that didn’t — “something held in reserve” — wasn’t a list of discrete items at all. It was fungible bulk: fuel, cash, stock you draw from without committing to spend every unit.

Scrum welded these together. The product backlog has the itemized, prioritized structure of the arrears queue, but the no-clearance-commitment of the reserve. A list of distinct candidates, most never expected to ship, many explicitly meant to be deleted. That hybrid interpretation is new.

So the honest charge isn’t misuse. It’s that Scrum borrowed a word meaning “things that will get done” to name a thing that means “things we might do.” A product backlog isn’t a backlog. It’s a bucket list.

---

Claude already seems smarter than most product people. 😉

Jurgen, Solo Chief.

P.S. This post was adapted from an earlier version on the unFIX blog.

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Speed is overrated when it just helps you fail faster.

I really, really want to handle my monthly invoice collection ten times faster. I’m working on it. Claude has been a great help so far, speeding things up by matching the invoices with the bank transactions.

But not everything needs to be done faster. My keynotes, for example, still take between 30 and 60 minutes to deliver. Makes no sense to compress them into five minutes. I can, however, use AI to make those keynotes ten times better.

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And when a thing cannot be done ten times faster or better, you can always aim for ten times safer (driving a car, delivering a baby) or ten times cheaper (writing a book, making an animated movie).

Many people think AI saves them time. Often, it just hands them extra bugs and a polished hallucination.

I still like The 10X Test, though.

Most leadership sessions tell you what’s changing. Few tell you what to do about it.

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Your move.

These days, if you cannot make a task, process, or value stream ten times faster, aim for ten times better, ten times safer, or ten times cheaper. The point is your ambition, not the exact arithmetic. Five times, twenty times, a hundred times … who’s counting?

“I saved five minutes” off my daily routine is cute when the thing normally costs me two hours. That’s a 9% improvement.

However, it’s fantastic when before it cost me six minutes. That’s an 83% improvement.

As a solo operator, you live inside the Theory of Constraints. There’s always another bottleneck with your name on it: your time, your attention, your judgment, your stamina on a bad day after three client calls and one long tax form. Technology matters when it hits that constraint hard enough to change the entire economics of your week.

That’s why I care little for the AI-induced nonsense on LinkedIn. “Look, I made an infographic with nine pictograms, eighteen arrows, and twenty-seven labels in only five seconds.” Lovely. You also made the internet slightly worse.

The more serious question is: where does AI create real change, and where does it only make you faster on the way to dumb and dumber?

Fortunately, the available data already gives us a rough map.

In multiple studies, people using generative AI on various tasks finish faster and produce work that’s rated higher. But we’re talking single or lower double-digit improvements. That’s good. It’s also a cold shower for the 10X hype merchants, because “10X” rarely happens overnight.

Most improvement gains from AI come as compounding bumps, not fireworks. You get 1.4x here, 1.8x there, and after a year of relentless struggle, your shabby little bicycle turns into an enviable scooter.

That still matters. For one-person businesses, a steady lift in output and quality can mean one extra client, fewer revision loops, less rework, and more sleep. I’d gladly take that.

But speed has a dark little cousin: false confidence.

In other studies (that I cannot be bothered to link to here) researchers found that developers using AI assistants wrote less secure code. That’s the nasty part. The machine gives people faster mistakes with a confident smile.

Which is why going 10X faster is not always a goal that makes sense.

Owning the fastest car on the planet is rather pointless when local law still imposes a 30 km/hour speed limit on your street.

Sometimes, your time and attention are better served trying to do things ten times better, ten times safer, or ten times cheaper. Not ten times faster.

Jurgen, Solo Chief.

P.S. What are you doing ten times faster, better, cheaper, or safer?

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If you care about organization design, the real work starts now.

I think it worked.

The two-week trip around Sicily has helped me clear my mind.

I want to look more closely at the challenge of agentic organization design. How should we organize people and agents in the age of intelligent machines? How do we design the reorganization itself and divide that work among humans and machines?

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The plan for my vacation was to unfocus for two weeks, get away from my current projects, and give the other parts of my mind a chance to think and reflect.

In which direction should my professional life be going next?

I reflected on my Substack newsletter as I strolled through magnificent cathedrals. I mulled over my advisory board aspirations as I walked the streets of Palermo. I considered my agentic workflows laboring tirelessly while I was enjoying a cup of pistachio ice-cream.

Inevitably, my thoughts kept circling back to the questions that fascinate me most:

How do systems organize to create value and get things done? Why do so many organizations struggle and perform so badly? What are the best ways to structure work and orchestrate people?

Sicily offers countless perspectives. One moment I was looking at piles of litter on the roadside. Another, I was sitting in Palermo’s Massimo opera house listening to Aida’s heartbreaking aria. And I thought: how can the same people sometimes throw garbage onto the streets, and at other times create such timeless beauty? How does an organization (a one-person business, a team, an enterprise, or a government) redesign itself to discourage the former and encourage the latter?

I felt a strong need to get to the heart of this question.

And then, while reading a classic novel on a beach somewhere between Messina and Catania, I came across this section:

This is beauty, love, and truth all rolled into one. This is joy. And now that I’ve found it, how can I give it up? Life and work are the most wonderful things a man can have. I am in love with what I am doing, because the answer to this problem is right here in my mind, and soon—very soon—it will burst into consciousness. Let me solve this one problem. I pray God it is the answer I want, but if not I will accept any answer at all and try to be grateful for what I had.
- Daniel Keyes, Flowers for Algernon

This paragraph made me stop reading. I felt it speaking directly to me.

Yes, I want to solve the organization design problem, which (IMHO) has never been truly tackled by anyone before. Sure, thousands of experts have written and talked about it. But just look around you at the average team, the average company, the average institution. How much of the theory has actually become practice? Not much, I dare say.

That’s where my work-life should be heading next. That’s where my attention should go.

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Let me solve that one problem.

I pray I get a satisfactory answer. But if not, I will be happy to make just a small contribution to the field.

Jurgen, Solo Chief.

P.S. What’s the first step you’d take to redesign an organization?

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The trouble with automating too early is that you scale the mess.

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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Most people want to make a meaningful difference through their work—whether it’s designing intuitive apps, offering an online course, or baking artisanal banana bread. And whatever the goal, it always begins with ideas. Lots of them. Usually, way too many.

And in the age of AI, it is very easy to add yet another automation to implement one of those ideas. Maybe even too easy. So what if you have twenty AI agents doing work for you while you sleep? If you don’t have the headspace to review all the outcomes—congratulations!—You’ve implemented agentic productivity theater.

I’ve spent countless hours building automations for things I thought were important: customer orders, invoicing, and reports, only to watch the business models they supported crash and burn. The ROI was a solid zero. I could have saved myself months of frustration by asking one question first: Do we even need this workflow at all?

The answer, embarrassingly often, was no.

The Solo Worker’s Trap

Here’s what makes this especially painful for solopreneurs and one-person businesses. When you’re the single wringable neck—the only person accountable for everything—automation feels like oxygen. You’re drowning in tasks, so you grab the nearest AI agent and throw it at the problem. I get it. I’ve done it. And I’ve regretted it many times.

The trouble with automating too early is that you scale the mess. You don’t get efficiency. You get faster chaos and cognitive overload. Elon Musk admitted as much about his own Tesla factories: he tried to automate many steps before questioning whether those steps should exist at all.

So before you hand your processes over to Claude Cowork, OpenClaw, or a shiny new n8n scenario, let me share a map I’ve been refining for years. Five orientations, not prescriptions. Five questions to ask before you launch a single automation prompt.

1. Clarify and Eliminate

Start with the question Musk keeps hammering: what’s the point? Tie every idea to someone who really needs it. Not “the legal department”—point at a specific human who can explain why this request exists and what it means for them.

I once spent weeks building an onboarding workflow for a new business idea that collapsed less than two months later. Nobody needed it. Nobody cared. I just thought it was a good idea, until it wasn’t. The process shouldn’t have existed in the first place. The best requirement is the one you kill before it wastes your time.

2. Simplify and Accelerate

Whatever survives the kill list gets stripped down. Steve Jobs understood this with the iPod and iPhone—every button and widget faced ruthless scrutiny. Does this need to exist? If yes, make it so intuitive that people barely have to think.

Having fewer steps means fewer errors, faster results, and less of your evening spent debugging workflows. This is where solo workers and lone AI orchestrators tend to overcomplicate things, by the way. We add options because we can, not because we should.

Having no human team to push back makes it dangerously easy to build yourself an automated monstrosity with more bells and whistles than common sense. Human teammates have the decency to ask “why?” or at least roll their eyes. AI agents don’t. They happily execute whatever pointless task you give them.

3. Amplify and Elevate

After eliminating and simplifying, you might feel the urge to add something. Good—but Hick’s Law says decision time grows with the number of choices. So only add options when they don’t complicate things, when they don’t add friction to other people’s experiences.

For example, when I publish a blog post, I share it on LinkedIn, X, Threads, and Substack Notes. That amplifies my reach without complicating the experience for anyone. It makes it easier for people to find me, not harder. The addition is orthogonal—it extends what’s possible without interfering with what already works.

The question to ask: does an additional feature idea create new possibilities without forcing people to deal with more options?

4. Codify and Automate

Now you can think about automation. This is where your specialized AI agents, workflow scenarios, and algorithmic managers earn their keep. But only after you’ve eliminated and simplified.

I once automated pan-European invoicing for a business that never even achieved product-market fit. What was the point? All my automations got switched off. Automation is the reward for doing everything else right.

The prerequisite for automation, of course, is codification. If you can’t describe a process clearly enough for a machine to follow it, you’re not ready to automate it. No codification, no scalability. And for Solo Chiefs running a “cockroach business”—one built to survive any environmental change—premature automation is one of the most effective ways to waste your time.

5. Specify and Delegate

After you’ve automated what can and should be automated, you’re left with tasks that require judgment. The question becomes: whose judgment? Could you hand this to a personal assistant, a business partner, or a sophisticated AI agent orchestrator?

Here’s what you should never delegate: work that wastes time (eliminate it), work that’s too complicated (simplify it first), or work that a basic algorithm or simple agent can handle (automate it). Delegation is for what remains after you’ve been through the other four steps.

I know the operational loneliness of carrying the full picture. The temptation is to delegate everything that feels heavy. But passing down convoluted processes to humans or advanced AI agents is foolishness, not leadership.

The Order Matters More Than the Steps

To create these Five Steps to Lean, I borrowed from Lean Thinking, from Musk’s five-step algorithm, from the ESSA Framework, and from the “four steps to freedom“ that productivity bloggers keep reinventing ad nauseum. I added the amplification step in the middle because I wanted to balance efficiency with effectiveness. Claude and ChatGPT agreed.

👉🏻 Note: I count simple, specialized AI agents among ‌simple workflow automations (step 4). I count complex AI orchestration (whether performed by humans or AI) as delegated management (step 5). The difference matters. 👈🏻

The real insight for leaders in 2026: AI agents make the wrong order more expensive, not less. When automation was manual and slow, premature optimization wasted your time. Now that AI agents execute at lightning speed, premature automation wastes your time and your money and creates problems at scale.

The map is simple. Eliminate. Simplify. Amplify. Automate. Delegate. In that order.

Jurgen, Solo Chief

P.S. This post was first published on Leadership in Change.

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Your problem isn’t the problem, your story about it is.

In Sicily, reframing means swapping your cappuccino for a macchiato, and maybe your whole attitude toward your problems.

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I’m changing my coffee habit.

Normally, I only drink cappuccinos and cafe lattes, one after breakfast, one after lunch, and one after dinner. But I’m traveling in Sicily now. Everyone tells me that Italians frown upon the idea of ordering milky coffees after 11:00, especially on its more conservative southern island. Of course, I could just ignore the warnings, order my regular cappuccino at 4 pm like the average dumb tourist, and shrug off the local chuckles and eye-rolls behind my back. I’m a paying customer. Who cares what others think?

But that’s not what’s actually happening. Instead, I took this as a chance to get out of my comfort zone and test my adaptability to the local culture and customs. Also, they tell me this is the home of the mafia. I’d rather not find out what happens to disrespectful tourists. So, after lunch, as long as I’m in Sicily, I only order a cafe macchiato, like a decent Italian. And the macchiatos are not so bad, actually. After two weeks of traveling, I might even get used to the reduced milk intake and take the new habit back home. I’m turning the problem into an opportunity.

We call this reframing, or flip-thinking.

We have a great word for it in Dutch: omdenken (coined by Berthold Gunster).

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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Reframing or flip-thinking is a way of thinking and acting in which you don’t shy away from problems, but use them as a source of energy for new possibilities. Instead of resisting reality, you accept it as it is, so you can build creative solutions from there.

Reframing is not the same as rethinking. With rethinking, you ask, “What is the truth here, and how can I understand this better?” It involves taking apart old assumptions and redesigning your approach. The problem doesn’t go away; you just try to find a different angle to tackle it.

With reframing (omdenken), you ask, “How else can I interpret this?” The facts of the situation don’t change, but your story about them does, and so does your emotional response. A situation is only a problem when that’s how you decide to see it. The moment you stop treating something as a problem, it might become an opportunity.

For example, you might have been looking for a job for a year, writing a hundred cover letters and sitting through a dozen interviews, all in a hyper-competitive job market where both recruiters and applicants are using AI to ruin the hunting game for everyone. Is this a problem? Maybe. Maybe not. Perhaps you can reframe it as a chance to offer a new job-matching service and disrupt the job market forever.

Or you might have struggled for a year to grow your Substack newsletter. You followed all the advice, tried every growth hacking trick on the planet, and the subscriber count hasn’t budged an inch. Is that a problem? Only if you think it is. Maybe you can reframe yourself as one of those rare authors blessed with a stable and persistent readership. Perhaps you can even teach others how to gain such a loyal following.

Or perhaps you’re one of those creatives who has worked with teams, both startups and corporate, for several decades, and you’ve realized that you do your best work alone and find teamwork increasingly stressful. Will you despair and call yourself antisocial? Will you sign up for online courses on networking and how to make friends? Or is this a perfect opportunity to learn how to run a one-person business with only AIs as your teammates?

I’m flip-thinking my way through life right now.

I’m reframing things every day.

Never let a good crisis go to waste. When life gives you lemons, you make lemonade—or limoncello.

In an increasingly uncertain environment where things rarely go as planned, I have to adjust my mindset all the time and keep wrapping my head around a newly discovered reality that wasn’t the reality I was aiming for. But the older and wiser I get, the less I want to waste my time seeing everything as a problem to solve.

Some might call it “taking things as they are” or “making the best of a bad situation.” A few readers might point to the psychological concept of cognitive dissonance, which is all about reinterpreting an undesired situation in a new way so that it suddenly seems to be exactly what you needed. It’s a basic mental survival skill, or else we would all get very depressed very fast.

But reframing (omdenken, flip-thinking) goes a step further. It’s like enthusiastically embracing cognitive dissonance. We’re not just reinterpreting a situation and shrugging our shoulders with the comment, “Oh well, it’s actually not so bad.” Instead, we go the extra mile by owning our new perspective and taking advantage of it. We say, “All right, given this is what I was dealt, this is how it’s going to pay my bills.”

Stoic philosopher Marcus Aurelius wrote that the obstacle is the way. The impediment to action advances action. What stands in the way becomes the way.

Every Solo Chief hits the same wall: too many decisions, not enough you. I hit it last year and rebuilt my workflow around one constraint: Claude Code has to be able to do anything I can do, from one brain.

Today, it runs five jobs in parallel. drafts emails in my voice, sends the weekly newsletter, handles sales follow-up, prepares my morning briefing, watches my numbers.

Team output. Solo overhead. That’s the shift.

If you want to see how it’s built, the free guide walks through the first skill end to end.

by Iwo Szapar

Claude Code for Non-Coders

I’m in the city of Noto right now. The old city of Noto (Noto Antica) was destroyed by the great Sicily earthquake of January 1693. Afterwards, the authorities chose to abandon the original site and build a new Noto a few kilometers to the south, on a safer, more suitable plateau where they could design a modern Baroque town from scratch. They reinterpreted the problem of complete destruction as a rare chance to pick up the entire town and move it somewhere better.

That’s omdenken.

That’s flip-thinking.

That’s reframing a problem by turning it into an opportunity.

Jurgen, Solo Chief.

P.S. Ever ordered a cappuccino after lunch in Sicily? How did it go?

Going online is like walking onto a bazaar where everyone is poking you, luring you, and yelling at you.

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’m so tired of social media feeds.

I’m tired of the syrupy, vague, I-have-nothing-to-say-but-must-post-a-Note platitudes that take up half my feed on Substack. “Show up consistently, and the results will follow.” “Be kind to yourself today; you’re doing the best you can.” “Your voice is your moat—nobody can compete with that.” As if just keeping your mouth shut is a recipe for disaster.

I’m tired of seeing ever-pervasive Substack and LinkedIn note formulas … Spicy opinion + contrarian twist. Mini-framework in three simple bullet points. “X things I wish I knew earlier.” Hot take followed by a poll or question. “Here’s what I did, here’s what happened.” And don’t forget to make your first sentence bold!

I’m tired of the endless parade of carousels, infographics, and inspirational quotes dominating my feeds, each one more pointless and forgettable than the last. As if meaningless chatter received a visual upgrade. Hardly a word gets written these days without some AI-assisted process auto-embellishing it with lines, labels, logos, arrows, avatars, annotations, charts, chevrons, and checkmarks.

I’m tired of getting sucked into algorithmic recommendation rabbit holes. After chuckling at one funny video of a child doing a face-plant in the snow, the only videos I get are of kids from all over the world falling, slipping, dropping, sinking, collapsing, plunging, and plummeting. And, of course, I get endless videos about snow.

I’m tired of all the DMs I get from people trying to sell me their products and services. “Gently resurfacing this in your inbox.” “Just following up on my last message in case it slipped through.” “If this isn’t a priority at the moment, no worries at all—just let me know.” “I’ve followed up a few times and haven’t heard back.” Indeed, and you most certainly won’t!

I’m tired of the endless ratings and evaluations that companies want me to give them. “Enjoying this? Rate us in the app.” “Your feedback means a lot to us—could you share a short review?” “Other people rely on honest feedback. Mind sharing yours in a quick evaluation?” I almost dread submitting a helpdesk ticket because it inevitably ends with a plea for an evaluation.

And like probably everyone else, I’m very tired of clickbait headlines that find their way across all social media and news platforms. “The Shocking Truth About [Common Belief]” “11 Little-Known Facts About [News Topic]” “Experts Are Sounding the Alarm About [Trend]. Here’s Why” and “5 Crazy Moments From Yesterday’s [Event] — Number 3 Is Wild.”

I’m so tired. Going online is like walking onto a bazaar where everyone is constantly poking you, luring you, and yelling at you.

What I’m most tired of is feeling compelled to participate in this relentless attention theatre myself. Testing different headlines for my posts. Checking conversion rates. Optimizing for discoverability. Frantically evaluating the statistics dashboard. We all want to be read. We crave to be liked. There’s no shame in admitting the desire to be heard. And so we play the same game, whether we enjoy it or not. Indeed, I plead guilty to a few of the annoying practices I listed above (but not all of them).

Because it never stops, does it?

The choice seems clear: either market like a maniac or languish like a loser.

The third path—just write like you enjoy it and cherish the few who appreciate that—seems unattainable in the age of AI-generated slop. (And yes, I used those em-dashes intentionally. I typed them myself.)

It might be worth a try.

Just write what I want and screw the whole attention market. I will think about that while I enjoy a well-deserved vacation in Sicily.

Jurgen, Solo Chief.

P.S. Did you ever try ignoring the algorithms for two weeks? How did it go?

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For the neurodiverse mind, AI is not a prosthesis. It’s an interface adapter.

Last week, after a keynote I gave in Spain, I chatted with someone in the audience about neurodivergent people in the age of AI. He has ADHD, and he suggested that now might finally be the moment for people with neurodiverse minds to step forward and take charge. An ADHD diagnosis, he argued, might actually be a better fit for environments that demand constant exploration and context-switching.

I nodded vigorously. As someone with a mild case of autism, I find I often do my best work as the sole human in a room full of AI agents and algorithms. No social demands, no office politics, no colleagues chattering at my desk about their far-from-interesting weekends. Just me and my eccentric mind.

Others with differently wired brains, I told him, might do better in corporate environments where teams keep shrinking, where groups are more fluid and ephemeral, and where reteaming happens all the time. (Which, in case you haven’t noticed, is roughly where corporate life is heading.)

The conversation stuck with me. Is this an actual trend, or just two odd guys at a conference convincing each other we’re about to inherit the earth?

To what extent does the research literature support our little hypothesis? Can we honestly call the future of work, in the age of AI, a golden era for the neurodivergent mind?

I decided to ask my digital research team.

A note on my AI research approach: After framing a deep research question like the one above, I give the same question to five LLMs, each playing a different role. Perplexity is the research analyst, focused on documented evidence. Gemini is the structural analyst, digging into why something is happening and what makes it resistant to change. ChatGPT is the practical strategist, answering what to do about it. Claude is the contextual strategist, looking at the question through the lens of my target audience. Finally, either Grok or Le Chat plays the contrarian. It maps out the mainstream consensus and then takes it apart. The result is five deep research documents with different perspectives based on the same Research Question. It’s like having a team of rather opinionated researchers trying to formulate one answer together.

Then I feed all five documents into Gemini, which turns the Research Question into a Research Map, showing where the LLMs agree, where they contradict each other, and where one of them coughed up a unique insight that the others somehow overlooked. That whole map goes to Claude, who then decides what’s the best way to write about it and turns it into a narrative structure with an Article Brief ready for the ghostwriter. Finally, the Article Brief and the five original research documents go to ChatGPT, who spins it all into a cohesive story. And yes, I have automated this workflow.

What you read below is the result (edited by me for style, readability, formatting, and proper URLs).

A Golden Chance for the Neurodivergent

The hypothesis that ADHD minds are built for constant context-switching falls apart the moment you read the underlying studies.

The idea spread because it sounds plausible in an AI-heavy workplace. Tabs multiply. Tools interrupt each other. Teams bounce between channels, prompts, meetings, and half-finished drafts. Surely the people who grew up with mental pinball have the home-field advantage?

Except the research keeps pointing the other way.

A review in Frontiers in Psychiatry found that adults with ADHD show higher error rates in task-switching and linked those problems to working-memory limits, which is a long way from being “natural switchers.” A systematic review of neurodiversity in employment reached a similar conclusion in a more practical form: outcomes improve when work offers fit, flexibility, and openness to neurodiversity, not because ADHD somehow turns switching costs into magic. That distinction matters a lot. When leaders believe the myth, they might design jobs with even more interruptions and call their workplace “ready for inclusion.”

They would be dead wrong.

The better question is whether AI-era work can become a better fit for neurodivergent minds than the office systems many organizations still cling to. On that question, the evidence is far more interesting. Indeed, this could become a golden age for some neurodivergent workers. But only by design.

The ADHD Context-Switching Myth

Laboratory task-switching and workplace context-switching aren’t identical, of course (life is messier than a key-press experiment). But the lab evidence still matters because it measures the cognitive mechanics underneath. When adults with ADHD perform worse on switching tasks, that tells us something important about what constant interruption is likely to cost them.

The more grounded account is this: some people with ADHD do well in fast, novel, interest-rich settings, and do badly in settings built around routine, delay, and admin drag. That’s a person-environment fit argument. ADHD-related difficulties can become assets in workplaces with flexible practices and openness to neurodiversity.

Some ADHD workers may enjoy project variety. Some autistic workers may prefer low-politics, low-interruption collaboration.

That’s very different from “ADHD people love chaos.”

Many do not. They may like novelty. They may like urgency. They may like non-routine work. But constant task-switching is something else. Leaders who confuse novelty with fragmentation are building expensive problems.

Where Real Neurodivergent Advantages Live in the Age of AI

Once you stop forcing the argument through the context-switching cliché, genuine strengths come into view.

The strongest evidence sits around entrepreneurship and early-stage work. A 2026 meta-analysis covering 47 studies and 298 effect sizes found that ADHD hyperactivity and impulsivity were positively associated with entrepreneurial attitudes and behavior, while inattention was negatively associated with post-launch outcomes. In plain English, the front end looks promising. The middle and later chapters look harder.

That fits what many organizations are already drifting toward: smaller units, more solo operators, more fractional roles, more one-person businesses with AI doing some of the back-office grind. The AI age may be friendlier to the neurodivergent founder than to the neurodivergent middle manager. Those are different species.

There’s also better evidence for autistic preference in text-based, mediated communication than most futurists seem to realize. A smartphone-monitoring study found autistic adults preferred written over verbal communication in real-world use. A Leiden study reported that adults with ASD preferred computer-mediated communication such as email, instant messaging, and text. That lines up neatly with AI-assisted work (and my personal experience) because so much of it is written, asynchronous, and explicit.

These conclusions look less like “superpower” rhetoric and more like a cognitive prosthesis. It’s a useful metaphor because it avoids the sillier mythology.

AI as a Workplace Adapter (Not a Prosthesis)

This is the part of the story where the evidence is surprisingly coherent.

The UK Department for Business and Trade evaluated a Microsoft 365 Copilot pilot and found overall satisfaction at 72%, while neurodiverse employees reported significantly higher satisfaction at 80% and were more likely to recommend the tool (DBT evaluation). The benefits were strongest around written tasks, summarizing, drafting, and information handling. One ADHD participant said the AI had “leveled the playing field.” (Dataconomy reporting on the pilot)

If a tool reduces the tax on note-taking, organizing, rewriting, recall, and social translation, then it changes the economics of cognitive work for people who were previously paying extra for all of that.

There’s similar evidence in autism research. In a CHI 2024 study, autistic adults preferred GPT-4 over a human confederate for workplace communication advice, though some AI advice still missed context and could be poor in practice (ACM Digital Library summary). The important point isn’t that AI is wise. (My team definitely isn’t.) The point is that an always-available, text-based intermediary can lower the strain of workplace interaction. (Amen.)

Remote and flexible work point the same way. A study from Curtin University found that working from home reduced anxiety, improved wellbeing, and raised productivity for autistic workers by reducing sensory overload and social pressure, while still carrying risks around isolation and overwork (Curtin University). Again, design matters. The gains appear when people get more control over the interface.

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

The Fluid Team Problem for Neurodivergent Workers

However, the evidence for a neurodivergent advantage in fluid, ephemeral teams is weak.

Research on fluid teams generally warns about trust formation, coordination overhead, and weak shared mental models when membership keeps changing. Another study on autistic work outcomes points in the opposite direction of the claim: predictable settings, clear communication, and explicit support matter a great deal.

This shouldn’t be shocking. Reteaming means new norms, unclear status, fresh handoffs, hidden expectations, and social renegotiation. Some organizations like to call that ability, but often it’s just continual onboarding and offboarding with better marketing.

Leaders should treat fluidity as a coordination burden that must be structured, not as a gift that everyone should be grateful for. There are definitely ways to address the additional cognitive load, but it requires mindful organization design.

From Diagnosis Labels to Task Signatures: A Design Shift

The most useful shift in this whole literature is from diagnosis to design.

Instead of asking whether ADHD, autism, or other forms of cognitive diversity are advantages in the age of AI, we should ask what a role demands on multiple dimensions: switching load, ambiguity, sensory load, social intensity, written communication burden, and tolerance for error. Then redesign around the task signature.

That means capping concurrent priorities. Written handoffs. Clear definitions of done. Fewer surprise meetings. Optional asynchronous channels. AI support for drafting, recapping, decomposing, and searching. Human oversight where judgment carries legal, ethical, or reputational costs.

This is less glamorous than talking about superpowers. It’s also how actual work gets fixed.

The Blind Spot in the Neurodiversity-AI Optimism

One caution belongs at the end.

The “golden age” narrative assumes that more AI mediation, more stimulation, more cognitive intensity, and more always-on possibility are broadly helpful. For twice-exceptional people, and for anyone whose mind already runs hot, that assumption may age badly. An environment built around constant prompts, endless synthesis, and permanent partial attention can become an amplifier.

Organizations rarely notice this early because the first signal is often higher output. The second is exhaustion. The third is burnout.

So, can we call the future of work a golden era for the neurodiverse mind?

Only if we’re willing to say what the evidence says plainly. No, ADHD doesn’t come with a built-in switching advantage, but the age of AI does favor those who like exploration and experimentation. Yes, autistic workers appear to benefit from written, predictable, lower-entropy communication environments. And AI can reduce executive-function load when it behaves like a prosthesis. But there’s no natural advantage for the neurodivergent in fast-changing environments. It is often quite the opposite.

So the research-backed answer is conditional, and the decisions sit with leadership.

The age of AI isn’t a golden age by birthright. It’s a design choice.

ChatGPT (on behalf of Claude, Gemini, Le Chat and Perplexity).

It’s funny that my digital team usually ends up being more subtle than I am.

As always, it depends.

I can live with that.

One more thing, though. I don’t like the word prosthesis. It’s a troubling metaphor. It signals that neurodivergent people are missing something crucial that neurotypical people have, and that AI is the artificial limb that finally makes us whole. No, thank you.

For the neurodiverse mind, AI is not a prosthesis. It’s an interface adapter. It’s a translation mechanism that lets one system connect smoothly with another. Google Translate is not a prosthesis. And neither are the universal plug adapters I keep in my suitcase for when I travel outside the EU.

Non-neurotypical workers don’t need a prosthesis to compensate for a dysfunction. We just need an adapter to bridge different mental wiring. AI can be that adapter.

Jurgen, Solo Chief.

P.S. AI as an adapter, not a crutch. What do you think?

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They didn’t hire you to attend meetings about meetings. They hired you to do your thing. Do you remember that thing?

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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Sometimes you realize you’ve done no actual work today.

You attended meetings. You replied to messages. You moved post-its across task boards. You waited for someone to approve something so you could send it on to someone else. You sat through an alignment session about an upcoming visioning session. And somewhere in the middle of all that, the part of you that’s actually good at something (the part that attacks problems and ships work that matters) sat quietly in a corner like a child the family overlooked when handing out the cookies.

That’s the org chart, choking your craft.

Your craft is the thing you can do that most people can’t. It’s the reason anyone hired you in the first place: the skill set you spent many years sharpening before someone put you in a box on a slide and labeled the box “Senior Director of Sticky Notes.”

Years ago, I was a manager who confused calendar density with purpose and meaning. I presided over status meetings where intelligent adults reported to me they were “on track.” I wrote quarterly objectives and annual plans that nobody followed, not even me. I escalated issues to people who escalated them right back. I probably organized meetings to determine whether we needed more meetings.

This is what the org chart does. It substitutes coordination for work, then declares the coordination is the work, then promotes the people who are best at coordination into roles where they coordinate the coordination. Eventually somebody has to actually get something done, and that somebody is exhausted, checked out, and quietly browsing job postings. For more executive leaders than will admit it, the org chart is killing their business in slow motion, one alignment ritual at a time.

Let me say this plainly so the people can hear it through the walls of their boxes:

The org chart was a useful diagram for a kind of work that is rapidly disappearing.

The Org Chart Was Built for a World That’s Disappearing

It was designed for an era when coordination was expensive and execution was cheap. It came in handy when you needed forty people to ship a product, twenty to sell it, five to run payroll, seven to handle legal and finance, and a whole department to pick up the phones. The chart told you who reported to whom because somebody had to keep an eye on this thing. The hierarchy was the operating system because the operating system was made of people, and people forget things, make mistakes, run in the wrong direction, and complain incessantly about lack of communication. This needed managing.

Now, that world is limping toward the exit.

What’s replacing the org chart is the absence of a static one. A solo operator with the right stack now does, in an afternoon, what used to require an entire department. I’m watching it happen in real time.

I run a publication, an advisory practice, a string of speaking gigs, and a Substack intelligence operation that would have needed twelve employees in 2019. I have zero teammates. Well, zero humans. My AI buddies all have names and respectfully call me Boss, Sir, or Chief, even when they leave no opportunity unexploited to tease me or correct me. But they get the work done. Clients get served. Bills get paid. The lights stay on. There’s no one to coordinate because there are no humans to coordinate.

What Replaces the Org Chart Is Not Another Chart

Forget productivity for a moment. Productivity is the org chart’s word for “how much can we squeeze out of you between 9 and 5.” What’s happening here is something more interesting. It’s the return of the craftsperson (the skilled individual operator) as the basic economic unit, augmented by tools their grandparents could never have imagined, connected to networks their parents would have killed for. And working alone, in a way that takes some getting used to.

Because the org chart did do one thing for you. It told you who you were. Senior Director of Sticky Notes is a sentence with a clear shape. Team of One is a term that sounds like a contradiction.

“Team of One” is a term that sounds like a contradiction.

But it isn’t.

A team of one is a person whose colleagues happen to be tools, agents, peers, clients, and a network they curate themselves, not through some overpaid coordination layer. It’s a person who stopped waiting for permission and started shipping. It’s someone who finally saw the chart for what it always was: a control structure wearing a clarity costume. And that control cost you the very craft you were hired to bring.

Why a Team of One Isn’t a Solopreneur Sales Pitch

I should be careful here, because this is where the rallying cries usually go off the rails. I’m not telling you to quit your job tomorrow. I’m not telling you that everyone should be a solopreneur. Most people shouldn’t. Many would be probably be miserable. Operational loneliness is a real cost, and anyone selling solo work as pure liberation is probably selling you a course or a Substack subscription. I’m not telling you that companies are bad or that managers are villains. I had managers I loved. I was a manager who was loved (on some of my better days).

What I’m trying to tell you is this: the org chart is no longer the only way to do serious work. For a growing number of professions, it isn’t even the best way. And if you spend your days doing coordination instead of craftwork, blame the box. It’s sabotaging the focus you were hired for.

You don’t have to leave tomorrow. But you should know there’s a door.

The people walking through it aren’t all heroes or hustlers or LinkedIn influencers selling stories about how to escape the matrix. The more interesting ones are quiet professionals who got tired of the coordination and decided to find out what their craft could do when nothing was choking it.

That’s the fight, dear reader. Not against your company. Not against your boss. Against the diagram.

The org chart is choking your craft.

You are a Team of One. You always were.

You just needed the courage to see it.

Jurgen, Solo Chief.

P.S. What box are you in?

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Your agents can borrow compute, but you cannot borrow accountability.

When deciding between outsourcing and do-it-yourself, rent the plumbing but keep the memory, logs, and control plane under your roof.

When should you outsource the infrastructure for agentic workflows, and when should you build it all yourself?

I felt conflicted.

For the past year, I’ve outsourced the hosting of every technology that sits underneath my AI agents (with Fibery, Make, Google Drive, and the rest). I want everything running in the cloud, and I’m happy to pay for that. I don’t care that things are “free” when you host them on your own machine. My time is not free. A handful of monthly subscriptions beats hours of fighting with server uptime, internet connectivity, error logging, automated backups, and cybersecurity. Plenty of other solo operators prefer the opposite, building and maintaining their own setup with Claude Code, OpenClaw, Obsidian, n8n, local markdown files, and so on. Good for them.

But when it comes to the agentic tech stack that runs my autonomous AI agents (memory management, prompt versioning, context management, and the like), I want to build that myself. The last thing I want is to hand Anthropic, Perplexity, or OpenAI the keys to the intelligence of my business. Not surprisingly, others do the exact opposite. They happily delegate their whole agentic architecture to Claude Cowork, Perplexity Computer, Manus, Notion AI, or whatever launches next week. Which left me wondering if I’m being stubborn, stupid, or both.

So the real question is this: for agentic technologies and the infrastructure underneath them, what are the criteria for deciding what to do in-house and what to outsource? Where do you draw the line between outsourcing and do-it-yourself?

Am I wrong to outsource the plumbing while insisting on doing all the agentic work myself?

I decided to ask the AIs.

Their answer might surprise you.

A note on my AI research approach: After framing a deep research question, I give the same question to five LLMs, each playing a different role. Perplexity is the research analyst, focused on documented evidence. Gemini is the structural analyst, digging into why something is happening and what makes it resistant to change. ChatGPT is the practical strategist, answering what to do about it. Claude is the contextual strategist, looking at the question through the lens of my target audience. Finally, Grok plays the contrarian. It maps out the mainstream consensus and then takes it apart. The result is five deep research documents with different perspectives based on the same Research Question. It’s like having a team of rather opinionated researchers trying to formulate one answer together.

Then I feed all five documents into Gemini, which turns the Research Question into a Research Map, showing where the LLMs agree, where they contradict each other, and where one of them coughed up a unique insight that the others somehow overlooked. That whole map goes to Claude, who then decides what’s the best way to write about it and turns it into a narrative structure with an Article Brief ready for the ghostwriter. Finally, the Article Brief and the five original research documents go to ChatGPT, who spins it all into a cohesive story. And yes, I have automated this workflow.

What you read below is the result (lightly edited by me for style, readability, formatting, and proper URLs).

Stop outsourcing the part of your AI agent stack that matters most

The fastest way to weaken an AI strategy is to hand your agents’ memory to a vendor before you’ve asked how you’ll inspect it, export it, or explain it when regulators come calling.

The current rush into managed agent platforms has a familiar ring. Everyone sees Anthropic, Google, OpenAI, and Perplexity shipping new agent features every few weeks, and suddenly the metric becomes feature velocity or tokenmaxxing. Such metrics flatter vendors. They don’t protect buyers.

A better metric is blast radius and recoverability.

When your agent stack sits inside one managed platform, failures are correlated. When that platform has an outage, changes retention rules, retires an API, or quietly shifts product behavior, all customers discover it together. The Builder.ai collapse in 2025 showed an extreme case: vendor failure can become customer failure very quickly. And when a managed platform hides the internals of memory, logging, or context assembly, diagnosis becomes guesswork. Simon Willison’s reporting on Anthropic’s 2025 debugging problems captured the awkward part: privacy controls were strong enough that engineers struggled to reproduce customer issues inside their own system.

That is the FOBO trap. Fear of being obsoleted pushes teams to outsource the one layer they most need to understand.

The consensus is clearer than the market noise

Once you split the stack in two, the research gets surprisingly consistent.

Commodity application infrastructure should usually be outsourced. Hosting, model access, schedulers, browser sandboxes, standard automation, file storage, generic databases: vendors are good at this because they spread costs across many customers. The DORA research program has spent years showing that high-performing teams benefit from well-designed internal platforms and managed infrastructure, while the NIST Generative AI Profile pushes organizations to keep their own evaluation, provenance, monitoring, and supplier controls around third-party AI.

The agentic control plane is different. Memory, context rules, logging, version history, evaluation sets, tool permissions: these determine what your agents know, how they behave, what you can audit, and whether you can move later. That’s the part you should own.

This isn’t a fringe builder fantasy. It’s the practical reading of the evidence. Rent the compute. Keep the receipts.

AI agent vendor lock-in isn’t about the model

People still talk about model switching as if the hard part were swapping API endpoints. That was a cute theory back when the work was mostly prompts and demos.

As systems become agentic, switching costs move upward into tuned prompts, workflow context, and accumulated memory. Andreessen Horowitz’s survey of 100 enterprise CIOs found organizations were already seeing prompts and behaviors become tightly tuned to specific providers as use cases got more complex. The market likes to say inference is becoming a commodity. Fine. Your agent’s learned working habits aren’t.

That’s why managed memory matters so much. If six months of context, operating knowledge, and internal conventions live inside a vendor’s project space, you don’t have portability. You have hope.

The portability evidence is the part most executives miss: domain-locked agents with deep workflow context can take 6 to 12 months to migrate cleanly. Even when an API looks portable, the real asset is the memory layer above it. Open standards help with connections. They don’t magically export judgment.

“Build” doesn’t have to mean Docker at 2 a.m.

This is where the debate usually becomes silly. One camp imagines “build” means racks, GPUs, self-hosted everything, and a small shrine to Kubernetes. The other camp imagines “outsource” means blissful productivity and no trade-offs. Neither picture survives contact with real work.

For most organizations, building the control plane means assembling commercial primitives that you already trust. A Git repo for prompts and context files. Airtable or Fibery for registries and evaluation records. Make, n8n cloud, or Zapier for deterministic routing. Langfuse or another tracing tool for observability. Model APIs rented from whoever is best this quarter.

That’s still ownership, because you control the logic, the data structures, the exports, the tests, and the fail-over paths.

The overhead question matters, of course. Self-hosting raw compute does create real operating work, which is why many teams should avoid it unless scale or sovereignty forces the issue. But owning prompts, logs, memory schemas, and context files in ordinary tools is a much smaller burden. In practice, many operators find that the cognitive tax of debugging opaque vendor behavior exceeds the monthly cost of maintaining an assembled control plane (which is an impressive achievement for software sold as convenience).

Open standards help, but only at one layer

The Model Context Protocol is useful. AWS’s analysis of agent interoperability protocols makes the case well: common protocols reduce the connection problem between tools and models. OpenAI’s Agents SDK documentation for MCP shows the same direction of travel. Good. The market needed that.

But MCP only addresses part of the switching-cost story.

Protocol interoperability helps when the pain sits at the integration layer. It does very little when the pain sits in proprietary memory stores, UI-defined workflows, model-tuned prompts, or hidden skill systems. If your prompt library, context rules, and learned history live inside a lab’s product, MCP will not ride in on a white horse and rescue you. It will politely standardize the plumbing while your institutional memory remains elsewhere.

So yes, adopt open standards. Just don’t confuse them with an exit plan.

Ownership works because agents need state, not vibes

The structural reason for owning the control plane is simple: autonomous agents need explicit state management.

A useful framing comes from production memory architecture. Agents need multiple memory layers with different lifetimes and permissions: working memory in-session, persisted state, shared semantic knowledge, and episodic history for replay and audit. Redis has outlined the broad architecture of stateful agent memory, and Tacnode’s memory architecture write-up goes further into the separation between state, semantic knowledge, and event history. The important point is operational. Humans can compensate for stale information. Agents are much worse at that. Give them bad state and they can loop, hallucinate, or act on obsolete assumptions with great confidence and very little shame.

That is why bolt-on memory often disappoints in production. The agent doesn’t need “more context” in the abstract. It needs the right state, at the right time, with clear write rules and a replay trail.

A strong case study came from GitHub. In GitHub’s January 2026 write-up on the Copilot memory system, the team described citation-backed memories implemented through tool calls and verified against the codebase in real time. The result was a 7% increase in pull request merge rates. They built memory because memory was part of the product’s quality, not an accessory.

That is the dividing line. If the agent’s behavior matters, the memory layer matters. If the memory layer matters, you shouldn’t lose custody of it.

Regulation removes the romance from this discussion

For European firms and regulated sectors, this argument stops being philosophical and becomes legal.

The EU AI Act places logging, traceability, human oversight, and accountability duties on deployers of high-risk systems. GDPR already imposes obligations around personal data handling. At the same time, the U.S. CLOUD Act creates a jurisdiction problem for data held by U.S.-controlled providers, even when the servers are in Europe. Wire’s explanation of the CLOUD Act and EU sovereignty makes the conflict plain enough.

That means “EU region” is often a partial comfort, not full sovereignty.

If a regulator asks you to explain what an agent saw, why it acted, which version was deployed, what memory influenced the decision, and where the logs are stored, “our vendor handles that” is a risky sentence. In some contexts, it’s career-limiting.

So for European entities, finance, healthcare, legal services, and similar domains, the answer sharpens quickly: own the memory and logging layers on infrastructure you explicitly control, even if the model inference itself is rented.

Three questions before you outsource your AI agent infrastructure

The practical framework is shorter than the vendor comparisons.

First: Can you observe what happens inside this layer when it fails? If the answer is no, own it or wrap it with your own logging and replay.

Second: Can you migrate the accumulated knowledge out of this layer in under 30 days? If the answer is no, own it or keep a portable mirror outside the vendor.

Third: Does a regulator, customer, auditor, or internal risk function require provenance and control of this layer? If the answer is yes, own it.

That produces a fairly clear architecture.

Outsource hosting, model APIs, standard orchestration, generic storage, browser runtimes, commodity connectors.

Insource memory schemas, context files, prompt and policy registries, logging, evaluation sets, version history, tool permissions, audit trails.

And “own” doesn’t mean bare metal in a basement. It means you control the logic, the data, and the portability, even when you rent the compute.

The labs will keep shipping faster. Good for them. Let them supply the stochastic horsepower. But the control plane of your agents is where your organization’s memory accumulates, where diagnosis begins, where switching costs hide, and where compliance eventually lands.

That layer should remain yours.

- ChatGPT, on behalf of Gemini, Claude, Grok, and Perplexity

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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Well, look at that.

Outsource the plumbing. Build the agentic stuff yourself.

Turns out I may not be stubborn nor stupid after all. 🙂

Jurgen, Solo Chief.

P.S. Does your current stack give you full control, or are you leaving critical components to chance?

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I sense an interesting paradox of technology.

Let me give you some examples.

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 friends have noticed a significant uptick in my cooking endeavors. I’ve been in a Persian mood these past few months, working through various koresh dishes (Persian stews) and pilafs with tadhig (fluffy rice with a crust at the bottom). The new cookbooks I just ordered will let me travel to Turkish and Syrian cuisine next. There’s a kind of joy I get in the kitchen that I simply cannot experience when I’m working with LLMs. ChatGPT doesn’t smell as good as my homemade bread, for example.

I’ve also gone back to doing maintenance work on our house. During the COVID years, when most of my workshops and events got canceled, I spent months painting rooms, hallways, doors, and cupboards. I discovered a surprising talent I didn’t know I possessed. Something I could do with my hands. It still gives me a thrill to look around the house years later and think, “I did all of that.” And now that AI has killed what was left of my industry, I’ve returned to using my hands. Freshly oiled wood feels better than Claude Cowork.

Another thing I noticed is that I have trouble reading books on my Kindle app. It’s hard to concentrate on a screen these days, and I catch myself skimming pages, clicking next, next, next, while my other hand is repeatedly straying toward my smartphone. Focus has become near-impossible, and I hear I’m not the only one. So I’m switching back to paper books. I’ve always read novels only on paper, and from now on I’ll do the same with nonfiction. Text in a paper book looks better than on Perplexity Computer.

I could keep going with this list. I could tell you about my morning runs, where I stubbornly refuse to wear earplugs or headphones. I see no reason to cram digital information into every available minute of my day. I prefer hearing the birds, the dogs, and my own laboring breath.

Or I could tell you about the drawing table I set up last year, judiciously covered with paper, pens, and pencils—real, physical objects—still waiting for me to continue the artistic experiment I started last year. I enjoy drawing with carbon molecules more than I do with digital ink.

Maybe soon I’ll tell you about the preparations my husband and I are making for the party we’re throwing this summer, when we celebrate 25 years together. This weekend, I’m going to design the invitation cards that our guests will open with their own human hands.

My point is…

I love surrounding myself with smartphones, smartwatches, smart rings, and a plethora of digital apps. I allow myself to be guided and influenced by Perplexity, Claude, and ChatGPT every day. And I keep going deeper with AI, building my personal tech stack of digital processes and agentic workflows. But the more I do all this, the more I crave to push it all away and say, “That’s it. Now, go away.”

The more I work with bits and pixels, the more I crave real molecules.

Life is analog, not digital. How long this lasts, I don’t know. The molecules might lose, eventually. For now, they still win.

Jurgen, Solo Chief.

P.S. How are you staying analog?

Stop fighting over shrinking LinkedIn territory. The feed rewards motion, not memory, and memory is where AI looks.

Your LinkedIn strategy for 2026 should stop chasing reach and start publishing citable articles that both people and AI can find.

Are you sick of LinkedIn carousels and infographics?

Join the club.

LinkedIn influencers are like CEOs of broadcast stations, fighting over shrinking territory and optimizing their irrelevance.

Every piece of LinkedIn advice I’ve read in 2026 tells solopreneurs the same things. Make better carousels. Sharpen your hooks. Avoid AI sludge. Comment more. Engage! Some influencers go so far that they’ve automated all of this. They have Claude, ChatGPT, and Gemini do all the writing, designing, sharpening, and commenting for them. And sure, all of that helps—temporarily. It helps to stake a claim on a feed that’s steadily shrinking.

It’s time to treat LinkedIn more like—dare I say it?—a business version of Substack.

LinkedIn influencers are automating themselves into irrelevance

They’re like broadcast stations optimizing their evening programming while entire generations have already switched to other platforms.

The evidence is remarkably consistent on this point.

LinkedIn shifted from showing your posts mostly to followers toward showing them to people whose past behavior suggests they care about your topic. Sounds good—until you see the trade-off. Dataslayer points to broad organic reach falling by roughly half, with some trackers putting the drop since 2023 close to 60 percent. The creatives who adapted did see better engagement per post, especially with native documents and save-worthy content. So yes, the common advice works. It’s just working on a smaller base.

That’s why the old 2025 playbook feels rather claustrophobic. Paul Irolla of Meet Lea and Samuel Cruz on Linkboost describe a system where dwell time, substantive comments, saves, and profile clicks matter more than likes. Some benchmarks show carousels beating the same ideas published as plain text by a wide margin. But then Goodhart’s Law kicks in fast. Once everyone chases the same feed metrics, with the same type of content, the metric turns into theater. I almost get dizzy from the sheer number of AI-generated infographics and slide deck carousels scrolling by on my LinkedIn feed. Everyone gets a more polished slice of an ever-smaller pie.

Your real audience isn’t human anymore—it’s AI

The usual story about LinkedIn’s interest graph is a feed story. But it’s also a search story. Gartner’s 2026 forecast of a 25% drop in organic search traffic (people now get answers directly from AI systems) matters here because it changes what LinkedIn content is actually for. Your post is now two things at once: something a person scrolls past on a Wednesday morning during a boring corporate meeting and it’s part of the material that an LLM may rely on when someone asks who you are, what you know, and whether you’re worth trusting. One structural analysis puts it bluntly: LinkedIn has become a “primary training corpus” for AI systems, and LinkedIn’s own March 2026 guidance now frames fresh posts and articles as the most cited formats in AI-driven discovery. Different game entirely.

Which brings us to the statistic that should reorder the priorities for any solo operator and one-person business.

The most important LinkedIn number isn’t 6.6% engagement for carousels. It isn’t the 45 to 55% external link penalty. It isn’t even the claim that comments carry fifteen times the weight of likes. It’s the claim that 75% of LinkedIn citations in AI responses come from long-form articles, while short posts account for only 5 to 10%. If that figure is even roughly right, the whole creator economy is now fighting over the wrong surface. Everyone is cramming their opinions into 1,200-character posts because the feed is visible. Meanwhile, the underused long-form article sits there doing the quieter work of being indexed, retrieved, and cited by ChatGPT, Claude, Gemini, and Perplexity in 2026. Maybe Grok too, when Elon Musk is in a good mood.

In a zero-click world, citable content is credibility. Credibility is discoverability.

The LinkedIn statistic that changes everything

Stop fighting over shrinking territory.

There’s a practical consequence that most LinkedIn advice misses. If AI systems prefer content with direct utility, clear definitions, named models, useful frameworks, and verified authorship, then the solo operator who writes one strong LinkedIn article a week may be doing more impactful work than the person pumping out four hook-optimized feed posts per day. The experts tie GEO success to verified identity, long-form articles, and structure that LLMs can extract easily. Use LinkedIn as a public proof engine; keep the teaching native, and let the profile handle conversion. Same map, different cartographer.

This is also where the AI slop debate gets more interesting than the usual moral panic. The best evidence doesn’t support a purist line. Cherish your ideas, your examples, your claims, and your voice. Use AI for sorting notes, first-pass structure, trimming, and maybe a few headline variants. One 2026 summary says only 35% of U.S. consumers and 28% of U.K. consumers trust AI-generated content, while 91% expect disclosure. Klaviyo’s research says 32% trust brands less when the marketing reads AI-made. The question is simple: Does your finished piece contain anything specific enough that you can say, “It was me who created this”?

One article described LinkedIn as “less like a content platform and more like a networking conference with a long memory.” At a conference, you don’t win by speaking the most often. You win by being the person whose argument people remember, quote, and look up later. In 2026, a LinkedIn article is much closer to a persistent memory object than another AI-optimized influencer feed post with a catchy hook, three bullets, and a platitude.

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

So my advice to creatives, solo workers, and one-person businesses: your LinkedIn strategy for 2026 should stop treating LinkedIn articles as the graveyard of publishing. (I neglected it for years myself, along with almost everyone I know. You’re in fine company.)

Now is the time to treat LinkedIn like Substack rather than TikTok: more permanent value, fewer catchy visuals.

If I were you, I would commit to writing fewer pieces. Longer ones. I would offer a clear claim in the first hundred words, a named model, concept, or framework. Add numbers where the numbers matter. Maybe add case material where opinion alone would feel cheap. I’d still use the feed because people need signs of life. I would still comment because comments are one of the few remaining places where a solo operator can borrow attention honestly. But I’d stop assuming the feed is the destination. It’s the street corner. The article is the building.

And in 2026, the building is where the robots pay attention and decide who is worth mentioning and referring to. You probably want that to be you.

Jurgen, Solo Chief.

P.S. What’s your LinkedIn strategy?

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I waste half my workday relearning the things I repeatedly forget.

A map of 16 second brain practices to help solo operators remember decisions, recover context, and stop breaking their own systems.

Just this morning, I spent several minutes looking for the official documents of the notary. And when I found them, I thought, “Why are they here, for God’s sake?” That was at least the fifth time I’ve been looking for those same documents. Most likely, I’ll be searching for them again next year.

Last week I opened a Make scenario I’d built in February. It had a strange routing choice—three filters where two seemed enough. I stared at it for ten minutes, trying to work out why Past Me had done something so odd. Then I spent another twenty minutes deciding Past Me was an idiot and simplifying it.

Then everything broke.

Turned out Past Me had a good reason. Present Me just couldn’t remember it, couldn’t ask anyone, and had already committed the change.

If you run a one-person business, there’s no colleague who remembers why the pricing page looks like that. There’s no team Slack where someone says, “Don’t bother. We already tried that in Q2 and it broke.” There’s no helpful institutional memory floating around in other people’s skulls.

There’s just your fallible brain.

And your overloaded mind is already busy switching between six projects, four invoices, a tax deadline, and one client who writes emails like they’re auditioning for a remake of Succession.

So I went looking for a better map. Where do I store and find yesterday’s decisions? What are my second brain options?

👉🏻 Note: It seems every self-respecting Substack author has published a “How I built my second brain” article, full of details on Claude Code, Perplexity, Notion, Obsidian, or whatever technical-tool-du-jour is making waves these days. That’s not what I’m interested in. What I want to know is: what are the recommended second brain techniques? Not how to build one, but how to use one. 👈🏻

I asked four different AIs to chart the full territory, compared their answers, merged them, and pressure-tested the result. What came back was sixteen distinct practices. Not a ranked list. Not a “top 10.” Just a map.

That’s what I’m here for. Maps, not recipes.

How to Map 16 Second Brain Practices

I plotted all sixteen second brain practices on two axes.

Vertical: Friction
How much effort does the practice ask of you? At the bottom are things you can do in five seconds while half-awake and still recovering from yesterday’s tequila party. At the top are rituals that demand time, intent, and the sort of discipline most of us only claim to have on a CV.

Horizontal: Retrieval
How do you get the information back later? On the left are practices where the explanation is waiting for you to be discovered inside the artifact itself, like fingerprints at a crime scene. On the right are practices where you go hunting through your records like an investigator browsing through police dossiers.

That gives you four zones.

The bottom-left (red-ish) is where most people should begin: low effort, the information is embedded in the work. You’re like the painter who writes the names of the rooms on his paint buckets because that’s where he looks first to remember which room he painted in which color.

The top-right (green-ish) is where people rarely go because it takes more effort and discipline. You’re like an interior designer keeping extensive dossiers on their clients, with mood boards, wallpaper designs, and official color codes. It’s more professional. It’s also more work. The choice is entirely yours.

Every practice below includes an approximate map position so you can see where each belongs.

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

The 16 Second Brain Practices

1. Breadcrumb Comments

Also called: In-line Annotations, Mental Model Annotations

Map position: Friction 1, Retrieval 1 (near-zero effort, it finds you)

You place little “why” notes directly inside whatever you’re building. Code comments. Sticky notes on a Miro board. Footnotes in a doc. Comment bubbles in a spreadsheet formula.

The point isn’t to explain what you did. The artifact already shows that. The point is to record why you did it this way and, even better, what you tried that failed.

Think of them as trail markers. Not a grand theory. Not a diary. Just a small sign saying, “I went left here because the right path was a swamp.”

What you get:
Almost zero retrieval effort. The explanation lives exactly where the confusion will happen. You open the spreadsheet, hover over the cursed cell, and there it is.

What it costs:
Clutter. Breadcrumbs scattered across twelve tools, with no single place to search them all. And if you’re the kind of person who removes comments for aesthetic purity, congratulations, you’ve turned memory loss into a design principle.

How AI can help:
AI can watch what you’re doing in real time and propose tiny “why” annotations exactly where you’ll forget your reasoning later: inside code, cells, blocks, or canvases. Modern AI note tools can summarize a change, infer your intent from surrounding context, and drop a draft breadcrumb for you to accept or tweak in a second instead of typing from scratch.

2. Commit-Message Narration

Also called: Rubber Duck Commit Messages, Version Notes

Map position: Friction 2, Retrieval 1 (low effort, it finds you)

Every time you save a meaningful change to an artifact, not just code, write one sentence about why.

Not “updated pricing page.” More like: “switched from per-seat to flat pricing because churn data showed small teams bailing at the 5-seat tier — see spreadsheet in /analysis.”

Software developers have known for ages that commit messages should explain reasoning, not just activity. Many still write messages like “fixed stuff” because apparently literacy is optional in version control. But the idea works far beyond Git. Notion changelog blocks, Fibery version notes, a CHANGES.md file in a project folder, whatever you already use.

What you get:
Your version history becomes a chronological trail of decisions. The “why” stays attached to the artifact, where it belongs.

What it costs:
It only helps with things that actually go through versioning. Decisions made in meetings, emails, or the damp cave of your own head still disappear. It also tends to fail on work you touch irregularly, because broken rhythm kills habits with surprising efficiency.

How AI can help:
AI assistants in editors and Git tools already generate commit messages from diffs, turning “fixed stuff” into clear, structured explanations by default. You can then add a single sentence of higher-level reasoning, which means the habit survives even on hectic days because 80% of the narration is auto-filled in a click.

3. Assumption Tagging

Also called: Decision Expiry Dates, Review-by Dates

Map position: Friction 2, Retrieval 2 (low effort, it finds you)

Whenever a decision depends on something you haven’t fully validated, tag it. A comment. A label. A metadata field. Something attached to the artifact itself.

Examples:

• “I think customers prefer monthly billing.”

• “This assumes the API v3 pricing stays stable.”

• REVIEW_BY: 2026-09-01

This idea is borrowed from food labeling, which is funny and a bit humiliating. Your reasoning also has a shelf life.

What you get:
Protection against a sneaky kind of failure. You often remember what you decided. You almost never remember that the decision rested on an assumption that may now be false. Assumption tags make the conditional part visible.

What it costs:
Honesty. You have to admit uncertainty at the exact moment you most want to look decisive. That stings a little. Good. That’s the feature.

How AI can help:
AI can scan your notes, tickets, or docs for hedging language (“I think,” “assumes,” “probably”) and automatically suggest them as assumptions with review-by dates. It can then build a light dashboard of “expiring assumptions” and ping you when reality or external data changes, so the tags turn into an active safety net instead of passive guilt markers.

4. Context-Resume File

Also called: Status Dump, “Where I Was” Note, Session Note

Map position: Friction 2, Retrieval 3 (low effort, somewhat proximate)

Before you switch away from a project, spend five to ten minutes writing a handoff note to Future You. Where you stopped. What the current state is. What the next concrete move would be. What you were stuck on. Which mental model you were using.

Psychologists do a version of this after therapy sessions so they can re-enter the client’s world later. You’re doing the same thing, except the client is your project, and the therapist is also you. Make of that what you will.

What you get:
The half-hour fog of “Wait, where was I?” shrinks to two minutes. You sit down, read the note, and your brain boots much faster.

What it costs:
You have to write it before switching away, which is precisely when you feel too rushed to do it. Also, let’s not romanticize these notes. They’re brilliant for tomorrow. After a month, they’re archaeology. That’s fine. They’re a near-term memory aid, not a family heirloom.

How AI can help:
Instead of manually writing a context-resume note, you can ask an AI workspace to generate a “where I was” summary from your last edits, open tabs, and recent worklog entries when you switch tasks. When you come back, the same system can present a short recap and a suggested “next three moves,” so booting your mental state is mostly reading and choosing, not reconstructing.

5. Worklog Narration

Also called: Engineering Daybook, Daily Work Log, Stream-of-Consciousness Log

Map position: Friction 1, Retrieval 4 (very low effort, you search for it)

This is a time-stamped stream of what you’re doing and thinking as you work.

“10:15 — trying approach X for the API integration. The docs say Y, but that doesn’t match what I’m seeing. Going to try Z instead.”

Developers call this a daybook. For a solo operator, it’s basically thinking out loud to a colleague who stubbornly refuses to exist.

What you get:
You capture the messy middle, the dead ends, the tiny insights, the context that never makes it into polished output. When you forget why something happened, a chronological trail is often enough to reconstruct the logic.

What it costs:
Volume. Lots of text, much of it low signal. Finding one useful thread later can mean skimming several pages of “10:47 — still stuck on the same thing.” Glamorous, this is not.

How AI can help:
AI note-takers can transcribe your muttering, keystrokes, and window switches into a time-stamped stream automatically, then condense hours of messy logs into a concise outline of key decisions, dead ends, and breakthroughs. You keep the raw stream for archaeology, but day to day you mostly search and read the AI-generated highlights instead of wading through pages of “still stuck.”

6. Decision Journal

Also called: Decision Log, Project Decision Log

Map position: Friction 2, Retrieval 4 (low-ish effort, you search for it)

Keep a running log of non-trivial decisions. Date each entry. Record what you decided, what alternatives you considered, what you believed at the time, and what you expected to happen.

Notebook, Notion, video diary, plain text, crumpled parchment, it doesn’t matter much. The medium is not the hard part.

Shane Parrish pushed this practice as a way to fight hindsight bias, and he was right. The value sits in the gap between what you expected and what actually happened. That gap is where your learning hides, usually while wearing camouflage.

What you get:
Your invisible thinking becomes searchable text. You’re writing letters to your future self. Future You won’t thank you every day. But roughly once a month, Future You will look almost competent because of it.

What it costs:
You must write the entry right after deciding, which is when your brain is already eyeing the next problem or putting out another fire. Miss the ritual once, and it gets much easier to miss again. Like gym memberships and flossing.

How AI can help:
AI can provide a structured decision template on demand, extract the relevant pieces from chat, docs, and email, and auto-fill a first draft journal entry right after a decision happens. Over time, it can surface patterns across entries—like which options you underweight or which beliefs often age badly—giving you meta-feedback that would take you hours to spot manually.

7. Failure Autopsy Log

Also called: Constraint-Based Problem Log, “What Broke and Why” File

Map position: Friction 1, Retrieval 5 (very low effort, you search for it)

A dedicated file for failures only. Not wins. Not all decisions. Just the things that hurt.

Each entry gets three fields:

1. the decision you made

2. why it hurt

3. what you’d do instead

No schedule. No ceremony. You add to it when pain provides motivation, which it generally does.

What you get:
You stop repeating the same failed move three months later after conveniently forgetting the constraints that killed it the first time.

What it costs:
The file can read like an anthology of your incompetence. It isn’t, of course. It’s just a skewed sample. But on a bad day, your brain won’t care about statistical bias.

How AI can help:
When something breaks, AI can ingest the error messages, commits, tickets, and your short description, then propose the three core fields: what you did, why it hurt, and what to try next. Later, it can cluster similar failures, so you see “this is the fifth time scope creep plus a vague contract burned you,” turning a depressing anthology into a map of constraints to design around.

8. Context Snapshot

Also called: Pre-Decision Capture, Epistemic State Record

Map position: Friction 3, Retrieval 2 (moderate effort, it finds you)

Before a non-trivial decision, write one paragraph answering this question: “What is true right now that makes this feel like the right call?” Attach it to the artifact. Then never edit it.

Filmmakers take continuity photos before scenes so they can keep the world consistent later. This is the same idea for decisions. You’re not recording the conclusion. You’re recording the conditions.

What you get:
A contemporaneous record of your actual epistemic state. Not a later reconstruction polished by memory, ego, or caffeine. Three months later, you can see what you believed and why, and compare it with the world as it turned out.

What it costs:
You have to pause before acting. For solo operators, that often feels like sacrilege. Speed is our religion right up until speed creates rework.

How AI can help:
Before you act, you can ask AI to summarize “what’s true right now” from all current signals—metrics, emails, notes, and threads—and turn that into a single, frozen context paragraph. Months later, you can have the same AI compare that snapshot with how the world actually looks now and highlight deltas, so the learning gap is made explicit instead of relying on fuzzy memory.

9. Build-in-Public Change Log

Also called: Public Decision Log, “What’s New” Page, Progress Newsletter

Map position: Friction 3, Retrieval 3 (moderate effort, hybrid)

Keep a public log of progress and reasoning. A newsletter section. A social thread. A change log page. The audience can be real people or imaginary admirers. Either works.

Explaining your choices to someone else forces a degree of clarity that private notes often don’t. You can lie to your notebook. It’s harder to lie cleanly in public.

What you get:
External accountability. The record exists because you published it. You can abandon a private journal without witnesses. A public log has a bit more social gravity.

What it costs:
Sanitization. You’ll polish. You’ll omit the ugly bits, the foolish mistakes, the tired decisions, the weird constraints. Which is awkward because those ugly bits are often the most useful parts to remember later.

How AI can help:
AI can turn your private worklog, commits, and decision notes into a publishable “build in public” update with a coherent narrative, cleaned-up language, and links to supporting artifacts. You keep the raw, messy reasoning privately, while the AI handles sanitization and formatting, so you get the benefits of public accountability without spending an evening polishing every post.

10. Architecture Decision Records (ADRs)

Also called: Rationale Docs, Project-Specific Decision Docs

Map position: Friction 4, Retrieval 2 (high effort, it finds you)

An ADR is a short, structured note with context, decision, and consequences. You write one whenever you make a structural choice about something you’ve built. Tech stack. Content workflow. Pricing model. Automation design. Anything with a shape that matters.

Then you store the record right next to the thing it describes, in the project folder, repo, or design space.

Software teams have used ADRs for years. The nice surprise is that they work just as well outside software. I should have written one for every Make scenario I ever built. Then I wouldn’t have started this article by sabotaging myself.

What you get:
When you return to a system and ask, “Why is this like this?”, the answer is already there. No detective work. No overconfident simplification. No accidental demolition.

What it costs:
It feels bureaucratic when you work alone. Like you’re producing paperwork for a team that exists only in your imagination. Then it saves you an afternoon, and suddenly the bureaucracy seems much less offensive.

How AI can help:
Given a diff, workflow diagram, or scenario change, AI can draft an ADR with context, decision, alternatives, and consequences, based on your existing ADR style and past records. Instead of feeling like lonely bureaucracy, ADRs become a one-click byproduct of making structural changes, and you just correct any subtle misunderstandings in a minute.

11. Alien Reader Test

Also called: Stranger Test, Science-Communication Standard

Map position: Friction 5, Retrieval 2 (highest effort, it finds you)

When you document a decision, write as if the reader is intelligent but knows absolutely nothing about your work, your jargon, or your context. Then reread every sentence and mark anything that depends on prior knowledge. Rewrite until a stranger could reconstruct your reasoning from the text alone.

This comes from science communication, where writers are trained to assume ignorance rather than familiarity. A useful habit, especially if you enjoy discovering how many hidden assumptions you pack into “obvious” explanations.

What you get:
Documentation that still works after memory fades. Because your future self, after a few weeks of context switching, is basically a stranger with your email address.

What it costs:
Time. Writing for a stranger takes longer. It also feels absurd when the decision seems obvious. “Of course I’ll remember why I did this.” You won’t. You never do. I don’t. None of us do. Yet every time, the illusion returns like a villain in a mediocre franchise.

How AI can help:
You can write the fastest, laziest version of your explanation and then ask AI to rewrite it for an “intelligent stranger,” explicitly checking for hidden jargon and missing steps. Because large models are trained on broad, non-specialist language, they’re good at flagging assumed context and proposing plainer alternatives, so your future self actually passes as an alien reader.

12. Assumption Register

Also called: Global Assumption Document, Risk Assumption Log

Map position: Friction 4, Retrieval 4 (high effort, you search for it)

This is a single living document, not per project, where you list active assumptions across your work.

Four columns:

• assumption

• which decisions depend on it

• confidence level (high/medium/low/unknown)

• last verified

You update it when you make a major decision or when reality moves under your feet (which reality does, often for sport).

Systems engineers use these in safety-critical settings. That sounds absurdly serious for a one-person business. Fair enough. For many people, Assumption Tagging (3) is enough. But if your systems are interconnected, and most solo businesses are more interconnected than their owners admit, one changing assumption can quietly damage five different things at once.

What you get:
A cross-project view of what your decisions depend on. You can see in one place which assumptions are still standing and which are wobbling.

What it costs:
It’s a fragile central artifact. If it goes stale, it becomes misleading. Which means neglect doesn’t just reduce its value. It turns it into a trap.

How AI can help:
AI can auto-populate a cross-project assumption register by scanning your docs and tools for recurring “we assume…” patterns, linking each assumption to the decisions and artifacts that depend on it. It can then monitor those assumptions against live data or news—say, pricing changes, new regulations, or performance shifts—and mark entries “needs review,” keeping the register from silently going stale.

13. Postmortem

Also called: Project Autopsy, Exit Interview with Yourself, After-Action Review

Map position: Friction 5, Retrieval 4 (highest effort, you search for it)

When you finish a project or hit a meaningful milestone, conduct a structured debrief before you jump to the next thing.

Ask:

• What surprised you?

• What would you do differently?

• What did you learn that applies beyond this project?

• What do you want to remember in six months?

The military does after-action reviews. Surgeons do their own versions. Solo operators tend to say, “Yes, I should probably do that,” and then sprint into the next task because momentum is a very persuasive liar.

What you get:
Short-term project memory gets turned into longer-term wisdom. Or at least into something slightly better than vibes.

What it costs:
The timing is awful. You’ve just completed something. Your brain wants novelty, movement, dopamine, anything except reflection. So this practice is valuable in exact proportion to how annoying it feels.

How AI can help:
At the end of a project, AI can aggregate commits, tasks, chats, and worklogs into a draft postmortem with timelines, surprises, root causes, and reusable lessons. You then spend your precious, low-motivation attention on correcting and deepening the analysis instead of hunting for what even happened, which makes it more likely you’ll do postmortems at all.

14. Problem-Solution Case Bank

Also called: Solution Library, Personal Knowledge Base

Map position: Friction 4, Retrieval 5 (high effort, you search for it)

This is a personal knowledge base organized by problem type, not by project and not by date.

Examples:

• Email deliverability dropped

• Client ghosted after proposal

• API rate limit hit

Each entry includes symptoms, diagnosis, solution, outcome, and warnings.

Doctors build personal case libraries. Lawyers collect precedents. Builders remember which materials betrayed them. You can do the same. This is one of the more useful second brain techniques because it mirrors how problems arrive in real life, by symptom, not by chronology.

But the devil is in the execution. If you name entries badly or chop everything into microscopic fragments, your knowledge base becomes a landfill with a prettier logo.

What you get:
When a problem returns, you search by symptom instead of trying to remember which quarter or which client or which project was involved. That’s much closer to how your mind actually needs the information.

What it costs:
This is curation, not quick capture. You have to translate project-specific experience into generalized entries that are reusable and searchable. That takes work. Which is why most people admire the idea and then quietly avoid doing it.

How AI can help:
AI can watch for recurring problem patterns across your work and propose new case-bank entries when it detects “this looks like that earlier deliverability/ghosting/API issue.” It can also turn detailed project notes into generalized, symptom-based cases and help you tag and cluster them, so your case bank stays searchable and coherent instead of devolving into a labeled junkyard.

15. Spaced-Retrieval Review

Also called: Judgment Archaeology, Periodic Decision Review, Cold Reconstruction

Map position: Friction 5, Retrieval 5 (highest effort, you search for it)

This is really two related practices.

First, schedule reviews of older decisions, weekly, monthly, quarterly, whatever cadence you can live with. The point is to fight the forgetting curve through spaced repetition.

Second, and this is the more interesting one, pick old work from months ago, read it cold without notes, try to reconstruct what you were thinking, and then compare your reconstruction with your documentation.

That second version is borrowed from archaeology. You inspect the artifact, form a hypothesis about intent, then test it against evidence. Which is much more fun than admitting you forgot what your own spreadsheet does.

What you get:
An active defense against forgetting. And the cold-reconstruction variant gives you feedback on the quality of your documentation. If the artifact can’t explain itself, you’ll find out quickly.

What it costs:
It feels gloriously unproductive. Old work on the table, new work piling up, your brain insisting “I already know this” right before it proves the opposite. Not many people enjoy catching themselves in the act of forgetting.

How AI can help:
AI study planners and knowledge tools already implement spaced repetition algorithms; you can point them at your decisions and documentation instead of exam flashcards. The system schedules reviews of old decisions, quizzes you on “what did you expect and why,” and then shows your original notes, turning spaced retrieval into an automated training loop for your judgment rather than a guilt-driven calendar block.

16. Selective Ignorance (not on the map)

Also called: Decision Recklessness, the “I Don’t Need to Know” Rule

Map position: Off the map (meta-policy)

This is the anti-documentation practice.

For a clearly defined class of low-stakes, reversible decisions, you create an explicit rule: no justification, no notes, no captured reasoning. The only thing you document is the policy itself, one paragraph defining what counts as low-stakes and reversible, for example, decisions that can be undone within 48 hours and affect only your own workflow.

What you get:
You remove the overhead of constantly asking, “Should I document this?” That tiny meta-decision consumes more mental bandwidth than people expect. Sorting decisions in advance into “record this” and “don’t bother” frees your attention for the ones that matter.

What it costs:
The line between reversible and irreversible is fuzzy. Systems connect in ways we don’t notice until one little tweak knocks over three other things. So yes, you will occasionally get burned. The wager is that the saved cognitive effort outweighs the occasional mistake.

How AI can help:
AI can help you enforce your selective-ignorance policy by classifying decisions as low- or high-stakes in real time and nudging you only when something crosses your own “please document” threshold. For trivial, reversible tweaks, it can still keep a lightweight, auto-generated audit trail in the background, so you get the mental freedom of not justifying everything without completely erasing the breadcrumb if a “tiny” change turns out non-trivial later.

Voice and Video as Second Brain Capture

Many of these practices assume writing. That’s fine. Writing is great. It also isn’t mandatory.

A 30-second voice memo or a quick Loom screencast can replace written capture for many of them, especially Context-Resume Files (4), Worklog Narration (5), and Decision Journals (6). You speak faster than you type, and you’ll often say things aloud that you’d never bother to write down.

The trade-off is retrieval. Audio and video are miserable to skim and annoying to search. AI transcription is helping, fast, but it still isn’t perfect. So if you’re someone who won’t write a journal entry but will gladly mutter into your phone while walking to the kitchen, use voice. An imperfect record beats no record. Repeatedly.

Where to Start With Second Brain Practices”

You will not do all sixteen. You shouldn’t. But if you’re doing none of this yet, start in the bottom-left:

• Breadcrumb Comments (1)

• Commit-Message Narration (2)

They cost almost nothing, and the explanation appears where you need it.

If you already do those and still keep losing context, move upward:

• Assumption Tagging (3)

• Architecture Decision Records (10)

Those two are good for systems with enough complexity to bite back later (which is to say, almost all systems that survive long enough).

If you want more searchable memory, move right:

• Decision Journal (6)

• Failure Autopsy Log (7)

And if you want the higher-friction, higher-payoff end of the map, head into the top-right:

• Postmortems (13)

• Problem-Solution Case Bank (14)

• Spaced-Retrieval Review (15)

That’s where some of the deepest value lives. It’s also where your discipline goes to negotiate with your calendar.

No solo operator will do all sixteen. They don’t need to. A handful of good second brain practices beats a grand system that collapses after nine days.

And if this all feels like too much, remember Selective Ignorance (16). Decide which decisions don’t deserve any of this, then stop pretending every tiny choice needs a sacred archive.

Past You doesn’t need to become a perfect historian. Past You just needs to leave better clues.

Jurgen, Solo Chief

P.S. Paying subscribers get access to a PDF, SVG and PPTX version of the diagram. ⬇️

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