Pawel Brodzinski on Leadership in Technology

Whatever it takes to lead a team, build a product, and run a business

Tag: organizational culture

What LEGO Can Teach Us about Autonomy and Engagement
Last time, I built the connection between distributed autonomy (or lack thereof) and engagement (or lack thereof). Admittedly, I drew from different sources, and one could question some claims or connections I made.
- I mix engagement and motivation, and they are not simple substitutes for one another.
- Gallup’s State of the Global Workplace has faced some criticism for not actually asking about engagement, but instead inferring it (by the way, this book explains the methodology behind the research).
- We all have anecdotal stories about limited-autonomy environments where engagement doesn’t seem to be a problem.
So how is it, really? Do we really feel more engaged when we have more control over the work we do?

I have the privilege of running the course on progressive organizations in all sorts of settings, from MBA programs, through postgraduate studies, to professional training. As a part of the course, I designed a little experiment to run with all those different crowds. Over the years and across contexts, it keeps telling the same story.

What Can LEGO Teach Us About Autonomy?

The experiment is fairly simple. I get a group of people to build a relatively simple LEGO set. Twice.

The Managed Build

The first run is well-organized. We pick one team member as a manager, who starts by assigning tasks to the rest of the team. A typical team member’s job would be to:
- Be responsible for a specific type or color of pieces.
- Build a particular part of the model.
- Etc.
Over the years, I experimented with how much freedom a group’s manager has in organizing work. It doesn’t seem to matter. What’s important here is that the whole work organization is designed by—and, to a degree, enforced by—a single person.

Then they get to build a catamaran. With instructions. Displayed on a screen. With me controlling the pace. Actually, it’s they who control the pace. I “flip” the page once the last team is ready.

Eventually, all the teams build perfect catamarans. Up to specs. There are some subtle challenges in the process, but that goes beyond the context of autonomy versus engagement.

The Self-Organized Build

The second run is different. There aren’t managers anymore. There is no task assignment pre-building. The whole instruction is: “Self-organize.”

There is no instruction either. The only thing a group gets is the picture of a hydroplane they’re building.

People have all the freedom to organize their work. Sometimes they do plan. Much more often, they don’t. A creative and messy process commences. Inevitably, it’s all louder and more chaotic than the first run. On average, it’s a bit longer, too.

Eventually, I get my hydroplanes. Some of them perfect. Others not so. However, I’m yet to receive one that differs from the picture in anything other than minor details.

The Lesson

While there are many facets to this experiment, the big lesson is about engagement. After each run, I ask everyone individually to assess their engagement during the task on a scale from 1 to 5:
1. Very low
2. Rather low
3. Neither low nor high
4. Rather high
5. Very high
The underlying hypothesis is, of course, that the second run, the one where people have more autonomy, yields better engagement.

Across all the teams that have ever participated in the exercise, the current running averages are:
- 3.24 for the managed build
- 3.94 for the self-organized build
There wasn’t a single experiment in which teams were less engaged in the second run (though in one case the results were close—0.14 difference).

In other words, I’m yet to see a group of people who would be less engaged in a creative LEGO build when they were given more autonomy.

Some Experiment Caveats

One important aspect of the experiment design is that the models are relatively simple, while I organize people in groups of 4 or 5. As a result, there are too many hands for the task. It is so by design. It’s an environment where it’s relatively easy for people to disconnect, should they choose to.

Also, it’s LEGO. For some people, it will be inherently engaging no matter what. They tend to take an active part in the first run, disregarding their assigned role.

Those two aspects of the game create an environment in which people use the full scale when assessing their engagement. I’ve only had one group that hasn’t used 1s at all. Possibly too many AFOLs in the room.

The pace of flipping the instruction pages in the managed build tends to be a minor source of frustration for faster teams. Again, that’s by design. It’s just another dimension of limited autonomy. After all, with real work, we have all sorts of interdependencies.

A side note: Interestingly, it’s not always the same team that is the slowest throughout the whole run. It’s a classic case of a shifting bottleneck.

Distributed Autonomy Is a Crucial Prerequisite for Engagement

My working hypothesis is that the main reason behind appalling engagement levels is limited autonomy. The theory suggests as much.

Source: Gallup’s State of the Global Workplace

The LEGO experiment is a neat way to confirm that in practice. With a simple change of giving people more autonomy, the declared engagement goes up by more than 20%.

The observable behaviors are different, too. The managed build generates way less energy, fewer discussions in teams, less movement across the room. If you saw randomized silent movies (no audio) from the respective experiment runs, it would be obvious which is which.

Distributed autonomy—being able to decide how we work—is an absolutely crucial aspect of our workplaces. And a prerequisite for high motivation and engagement.

This is part of a short series of essays on autonomy and how it relates to other aspects of the modern workplace. Published so far:
I’m writing these posts by hand. Like an animal.
웃https://okhuman.com/NbZHoQ
January 28, 2026
Limited Autonomy Is the Main Reason for Low Engagement Levels
I like the following quote from Gallup’s State of the Workplace. It’s from the 2023 report, but it didn’t lose any relevance.

After dropping in 2020 during the pandemic, employee engagement is on the rise again, reaching a record-high 23%.

Yup, it reached a record high in 2022, stayed there in 2023, and dropped again in 2024.

Source: Gallup’s State of the Global Workplace

We had COVID-related uncertainty to blame for the drop last time. This time it’s AI-related uncertainty. Here’s a thing, though. We discuss marginal changes. A per cent here, a per cent there.

The big lesson remains the same. Engagement levels in the modern workplace are appalling.

If it were a football team (a soccer team for my American readers), it would be as if 2 players tried to win, 7 just moved around without much engagement, and 2 more tried to score an own goal. If you’d rather take a basketball metaphor, you get one baller who tries to win, 3 who fake defense, and one who keeps turning the ball over to the other team.

The only hope of actually winning is that the other team is about as disengaged as yours.

These are realities we have lived with in the past decade. Before that, it was even worse.

Autonomy, Mastery, Purpose

So why is the engagement so low? I like Dan Pink’s answer. In his classic book Drive (and no less classic TED talk: The puzzle of motivation), he points 3 prerequisites for high motivation.
- Autonomy. The ability to decide about important aspects of the work we’re doing.
- Mastery. Being able to work according to our own aspirational quality standards and get better at what we do.
- Purpose. Having a shared goal with a broader team or group, we collaborate with.
Remove either, and you remove the conditions for engagement. Since we have a motivation gap, at least one part of the trio must be the culprit.

Purpose tends to be relatively individual for companies. You can probably instantly think of organizations that are purposeless (take any that have “increasing value for shareholders” painted all over the place) as well as those that are purposeful.

Mastery is trickier. However, in the context of knowledge work, I see a one-way correlation between autonomy and mastery. If you can make all relevant decisions about how you work, you very likely can work according to the aspirational standards you set for yourself. If you have autonomy, you can have mastery, too. The vice versa is not necessarily true.

So yes, when I have to explain Gallup’s results, I blame autonomy, or rather, lack thereof.

Hierarchy Discourages Autonomy Distribution

In a modern corporation, we perceive hierarchy as the only possible organizational paradigm. Hierarchy here is understood as a decision-making power distribution structure. The higher up you are in a hierarchy, the more (and more important) decisions you can make.

Sadly, that very structure discourages us from distributing autonomy to lower levels. If I hypothetically allowed my team to make the decisions assigned to me, inevitably, I’ll face a situation where someone makes a decision I disagree with. Then I face two choices, both bad.

I can stick with the decision that goes against my experience, intuition, and better judgment. However, since it was mine to make, I’ll be responsible for its outcomes. If my experience, intuition, and judgment were any good, I would pay the consequences of a mistake, even though I knew it was a wrong call in the first place. Psychologically, it’s a tall order.

The other option is to change the decision. In one swift move, I fix the decision and show my team that they didn’t have any autonomy in the first place. They could “make” decisions only as long as these were decisions I would have made anyway. If that sounds like a kick in the teeth, it’s because it is.

Hierarchy discourages managers from distributing autonomy. Add to that how prevalent this organizational model is, and we have an answer to why engagement in the modern workplace sucks big time.

The Writing Is On the Wall

No matter which vantage point we choose, we see the same picture.
- We cheer appalling engagement levels only because they’re slightly better than they were.
- We listen to Dan Pink’s rants with awe, then go back to the same old solutions that never worked.
- We applaud stories of bold leaders who challenged the status quo with stunning results, and rationalize them, saying, “It would have never worked in my company.”
I’m curious, how well your current “solutions” work? If we believe Gallup data, there isn’t much to brag about. On the one side, we have unquestioned dogma, which we have followed for more than a century. On the other, we have science. In such cases, I tend to pick team science.

This is Dan Pink again:

“This is one of the most robust findings in social science, and also one of the most ignored.”

“There is a mismatch between what science knows and what business does.”

The writing is all over the wall. And it will only get more pronounced as we surrender parts of our autonomy to AI agents. Let’s not expect fundamental changes in our motivation levels.

Unless we start treating the autonomy gap seriously, that is.

This is part of a short series of essays on autonomy and how it relates to other aspects of the modern workplace. Published so far:
This post has been human-created: 웃https://okhuman.com/g8lX5w
January 22, 2026
We Will Not Trust Autonomous AI Agents Anytime Soon
OpenAI and Stripe announced what they call the Agentic Commerce Protocol (ACP for short). The idea behind it is to enable AI agents to make purchases autonomously.

It’s not hard to guess that the response from smartass merchants would come almost immediately.

As much fun as we can make of those attempts to make a quick buck, the whole situation is way more interesting if we look beyond the technical and security aspects.

Shallow Perception of Autonomous AI Agents

What drew popular interest to the Stripe & OpenAI announcement was an intended outcome and its edge cases. “The AI agent will now be able to make purchases on our behalf.”
- What if it makes a bad purchase?
- How would it react to black hat players trying to trick it?
- What guardrails will we have when we deploy it?
All these questions are intriguing, but I think we can generalize them to a game of cat and mouse. Rogue players will prey on models’ deficiencies (either design flaws or naive implementations) while AI companies will patch the issues. Inevitably, the good folks will be playing the catch-up game here.

I’m not overly optimistic about the accumulated outcome of those games. So far, we haven’t yet seen a model whose guardrails haven’t been overcome in days (or hours).

However, unless one is a black hat hacker or plans to release their credit-card-wielding AI bots out in the wild soon, these concerns are only mildly interesting. That is, unless we look at it from an organizational culture point of view.

“Autonomous” Is the Clue in Autonomous AI Agents

When we see the phrase “Autonomous AI Agent,” we tend to focus on the AI part or the agent part. But the actual culprit is autonomy.

Autonomy in the context of organizational culture is a theme in my writing and teaching. I go as far as to argue that distributing autonomy throughout all organizational levels is a crucial management transformation of the 21st century.

And yet we can’t consider autonomy as a standalone concept. I often refer to a model of codependencies that we need to introduce to increase autonomy levels in an organization.

The least we need to have in place before we introduce autonomy are:
- Transparency. We can’t let people make decisions without relevant data to inform them, or decisions will be plain wrong.
- Technical excellence. Independence in acting requires the capabilities to perform these acts competently.
- Alignment. Unless we align everyone’s efforts, more autonomy only means more pull toward opposing directions.
- Explicit boundaries. We need to understand the limits within which we can act autonomously. Otherwise, we’d be both overwhelmed with possibilities and petrified by potential consequences.
- Care. Without intrinsic, genuine care about the outcomes of our decisions and actions, it’s just flailing around (and without purpose, let me add).
Remove either, and autonomy won’t deliver the outcomes you expect. Interestingly, when we consider autonomy from the vantage point of AI agents rather than organizational culture, the view is not that different.

Limitations of AI Agents

We can look at how autonomous agents would fare against our list of autonomy prerequisites.

Transparency

Transparency is a concept external to an agent, be it a team member or an AI bot. The question is about how much transparency the system around the agent can provide. In the case of AI, one part is available data, and the other part is context engineering. The latter is crucial for an AI agent to understand how to prioritize its actions.

With some prompt-engineering-fu, taking care of this part shouldn’t be much of a problem.

Technical Excellence

We overwhelmingly focus on AI’s technical excellence. The discourse is about AI capabilities, and we invest effort into improving the reliability of technical solutions. While we shouldn’t expect hallucinations and weird errors to go away entirely, we don’t strive for perfection. In the vast majority of applications, good enough is, well, enough.

Alignment

Alignment is where things become tricky. With AI, it falls to context engineering. In theory, we give an AI agent enough context of what we want and what we value, and it acts accordingly. If only.

The problem with alignment is that it relies on abstract concepts and a lot of implicit and/or tacit knowledge. When we say we want company revenues to grow twice, we implicitly understand that we don’t plan to break the law to get there.

That is, unless you’re Volkswagen. Or Wells Fargo. Or… Anyway, you get the point. We play within a broad body of knowledge of social norms, laws, and rules. No boss routinely adds “And, oh by the way, don’t break a law while you’re on it!” when they assign a task to their subordinates.

AI agents would need all those details spoon-fed to them as the context. That’s an impossible task by itself. We simply don’t consciously realize all the norms we follow. Thus, we can’t code them.

And even if we could, AI will still fail the alignment test. The models in their current state, by design, don’t have a world model. They can’t.

Alignment, in turn, is all about having a world model and a lens through which we filter it. It’s all about determining whether new situations, opportunities, and options fit the abstract desired outcome.

Thus, that’s where AI models, as they currently stand, will consistently fall short.

Explicit Boundaries

Explicit boundaries are all about AI guardrails. It will be a never-ending game of cat and mouse between people deploying their autonomous AI agents and villains trying to break bots’ safety measures and trick them into doing something stupid.

It will be both about overcoming guardrails and exploiting imprecisions in the context given to the agents. There won’t be a shortage of scam stories, but that part is at least manageable for AI vendors.

Care

If there’s an autonomy prerequisite that AI agents are truly ill-suited to, it’s care.

AI doesn’t have a concept of what care, agency, accountability, or responsibility are. Literally, it couldn’t care less whether an outcome of its actions is advantageous or not, helpful or harmful, expected or random.

If I act carelessly at work, I won’t have that job much longer. AI? Nah. Whatever. Even the famous story about the Anthropic model blackmailing an engineer to avoid being turned off is not an actual signal of the model caring for itself. These are just echoes of what people would do if they were to be “turned off”.

AI Autonomy Deficit

We can make an AI agent act autonomously. By the same token, we can tell people in an organization to do whatever the hell they want. However, if we do that in isolation, we shouldn’t expect any sensible outcome. In neither of the cases.

If we consider how far we can extend autonomy to an AI agent from a sociotechnical perspective, we don’t look at an overly rosy picture.

There are fundamental limitations in how far we can ensure an AI agent’s alignment. And we can’t make them care. As a result, we can’t expect them to act reasonably on our behalf in a broad context.

It absolutely doesn’t limit specific and narrow applications where autonomy will be limited by design. Ideally, those limitations will not be internal AI-agent guardrails but externally controlled constraints.

Think of handing an AI agent your credit card to buy office supplies, but setting a very modest limit on the card, so that the model doesn’t go rogue and buy a new printer instead of a toner cartridge.

It almost feels like handing our kids pocket money. It’s small enough that if they spend it in, well, not necessarily the wisest way, it’s still OK.

Pocket-money-level commercial AI agents don’t really sound like the revolution we’ve been promised.

Trust as Proxy Measure of Autonomy

We can consider the combination of transparency, technical excellence, alignment, explicit boundaries, and care as prerequisites for autonomy.

They are, however, equally indispensable elements of trust. We could then consider trust as our measuring stick. The more we trust any given solution, the more autonomously we’ll allow it to act.

I don’t expect people to trust commercial AI agents to great extent any time soon. It’s not because an AI agent buying groceries is an intrinsically bad idea, especially for those of us who don’t fancy that part of our lives.

It’s because we don’t necessarily trust such solutions. Issues with alignment and care explain both why this is the case and why those problems won’t go away anytime soon.

Meanwhile, do expect some hilarious stories about AI agents being tricked into doing patently stupid things, and some people losing significant money over that.
October 16, 2025
Fundamental Flaw of Hustle Culture
It’s all over the news. AI companies force their engineers to permanent crunch mode. Expectation for working long hours is like a badge of honor in Lovable job ads. Google defined a 60-hour work week (at the office) as a productivity sweet spot.

But in the spirit of one-upmanship, everyone was beaten by Scott Wu, Cognition CEO. He announced 6-day work at the office, 80-hour weeks as the new norm.

“We don’t believe in work-life balance—building the future of software engineering is a mission we all care so deeply about that we couldn’t possibly separate the two”
Scott Wu, Cognition CEO

You see? All it takes to suck twice as many hours from every engineer is to stop believing in work-life balance. Voila!

Why All the Hustle?

The visible reasons for all that hustle are obvious. Everyone understands that, at the end of the day, there will only be a very few winners of the AI race.

They will get rich. Everyone else will go bust.

To make things worse, the bubble has been pumped to its limits. If you want to get a prediction that AGI is just around the corner, there’s no shortage of optimists.

However, notably, after GPT-5’s lackluster premiere, Sam Altman mentioned that AGI is not a very useful term. Whoa! That’s new! One would wonder what might have triggered such a twist in the official messaging.

Anyway, seemingly, the rest of the AI crowd is yet to catch up. The extreme hustle culture they install in their companies clearly suggests that they believe AGI is around the corner.

Otherwise, how would we explain 60/70/80-hour workweeks?

I mean, these are smart people. They do realize such work is not sustainable, right? Right?

Cynicism

OK, I’m not naive. There’s a ton of cynicism behind the hustle culture. The top leaders do it because everyone else does it, too. So they can get away with it. And people fall for this trap.

Given all the hype, it’s easy to promise mountains of gold to everyone. If. You. Hustle. Just. A. Little. Bit. More.

People will rationalize it by asking themselves a question: Am I fine coping with that toil for a couple of years and then walk away with $10M?

Seems like an acceptable tradeoff, doesn’t it? CEOs of AI companies prey on that.

However, I believe that they know the correct question should be: Am I fine shortening my life for 1-2 years because of the toil when someone dangles $10M in front of me?

The answers to these questions might be different. But if you expect prominent AI figures suggesting such an alternative vantage point, well, don’t hold your breath.

They will cynically exploit the opportunity even if it improves their odds of succeeding only marginally. After all, everyone else is doing the same.

The Cost of Extreme Hustle Culture

What’s fascinating is that it’s a herd behavior. No one seems to stop and validate whether hustle culture even works. Not even companies historically known to be data-driven, like Google.

It’s as if a simple linear approximation was all they could conceive: twice as many hours, twice as much work done.

Any team lead with even meager experience would disagree. It’s kinda obvious that the last hour of continuous work would be less productive than the first, when we’ve been well-rested.

So, how about adding a few more hours each day? And then replacing one rest day with another workday?

If you need to spell it out for you, here it is. It means more mistakes, more rework, more context switching tax. And even more toil. Which generates rework of the rework. A vicious cycle.

At some point, and rather quickly, each additional hour has diminishing returns. Then, at some point, each additional hour has a negative return, i.e., it decreases the total output delivered.

If you wonder why Henry Ford introduced a 5-day, 40-hour workweek in 1926, while keeping a 6-day pay, it’s not because he was an altruist. He wanted better overall productivity. And, surprise, surprise, he got what he wanted.

Economics of Crunch Mode

Sure, a factory floor in 1926 is an entirely different environment from an engineering office a century later. Yet Ford’s was hardly the only such experiment.

Across many examples, it’s extremely hard to find any argument that supports the hustle culture.

“We have omitted from this list countless other studies that have shown [dcreased productivity] across the board in a great number of fields. Furthermore, although they may exist, we have not been able to find any studies showing that extended overtime (i.e., more than 50 hours of work per week for months on end) yielded higher total output in any field.”

Note, it’s about total output, not output per hour.

Now, when dozens of research papers from different contexts tell the same thing, I tend to listen. So when it comes to the most recent trend for crunch mode in AI startups, there are two potential explanations.
1. Extreme hustle culture and extended crunch don’t work. Thus, AI startups are harming themselves.
2. AI startups are so completely different that they operate under a different set of rules.
Because they surely employ human beings similar to you and me.

At a risk of oversimplifying matters, these companies do software engineering. A fancy and cutting-edge flavor, I give them that, but software engineering nonetheless. They are not that different.

Well, put two and two together.

Data-Driven? Data-Driven My Arse

If either of them, celebrity CEOs, had actually looked at the data, they might have realized that they’re harming their businesses.

Of course, they’re harming their people, too. Yet I wouldn’t expect enough empathy or reflection from Sam Altmans of this world to make it a viable point in a discussion.

If they want cutting-edge and speed, they’d be better off going against the tide and sticking to healthy work conditions. Ultimately, these companies have no shortage of investment money, and if AGI is, indeed, just months ahead, they could burn through some of those dollars by hiring more.

Even more so, given that raising funds for these startups is easier than ever. These days, you don’t even need to tell what you’re working on, let alone release anything, to get billions. That is, given that you properly market your idea as AI.

That is true, of course, only unless AGI is not even remotely close and the AI startups CEOs know it all along (but won’t say, as then it would be harder to attract investors’ dollars).

Extended Crunch Mode Story

There are industries known for crunch mode (I’m looking at you, game dev), and there’s no shortage of stories about how extended hustle was behind well-known disasters.

I had a chance to listen to a creative director from CD Projekt RED speaking about their engineering culture just weeks before the launch of Cyberpunk 2077. During Q&A, inevitably, he was asked whether they would release on an announced date (which had already been moved a couple of times).

“There’s no other option,” was his answer.

We know how it ended. “Buggy as hell” was the reviewers’ consensus. The game was pulled from sale on PlayStation. And shareholders filed a class action lawsuit over the share price drop. A hell of a launch party, if you ask me.

CD Projekt RED has extended crunch mode to thank for all that fun stuff. In an interesting twist, after they dropped the hustle and started working in a more sustainable way, they were able to recover from the initial disaster.

Unsustainability of Hustle Culture

The camel’s back is already broken, but I’ll add one more straw anyway.

People will burn out working under such a regime. Some of them will last months, some quarters, some may even last years. But break they will.

Again, I don’t expect empathy from the celebrity CEOs, but the consideration of their bottom lines is what they’re paid for, isn’t it? So, what’s the cost of replacing an expert engineer specialized in AI? Given the outrageous poaching offers we see, it’s absurdly high.

And I don’t even mention all the time lost before a company manages to hire a replacement. Yes, precisely the time that seems to be precious enough to make CEOs force their engineering teams to toil for 6 days and 80 hours a week.

It. Is. Not. Sustainable.

Never has been. Never will be.

If similar topics are interesting, I cover anything related to early-stage product development (and, inevitably, AI) on the Pre-Pre-Seed Substack.
August 14, 2025
Care Matters, or How To Distribute Autonomy and Not Break Things in the Process
At Lunar Logic, we have no formal managers, and anyone can make any decision. This introduction is typically enough to pique people’s curiosity (or, rather, trigger their disbelief).

One of the most interesting aspects of such an organizational culture is the salary system.

Since we all can decide about salaries—ours and our colleagues—it naturally follows that we know the whole payroll. Oh my, can that trigger a flame war.

Transparent Salaries

I wrote about our experiments with open salaries at Lunar in the past. At least one of those posts got hot on Hacker News—my “beloved” place for respectful discussions.

As you may guess, not all remarks were supportive.

My favorite, though?

IT WILL FAIL. Salaries are not open for a reason. It is against human nature.

No. Can’t do. Because it is “against human nature.” Sorry, Lunar, I guess. You’re doomed.

On a more serious note, many comments mentioned that transparent salaries may/will piss people off.

The thing they missed was that transparency and autonomy must always move together. You can’t just pin the payroll to a wall near a water cooler. It will, indeed, trigger only frustration.

By the same token, you can’t let people decide about salaries if they don’t know who earns what. What kind of decisions would you end up with?

So, whatever the system, it has to enable salary transparency and give people influence over who earns what.

Cautionary Tale

Several years back, I had an opportunity to consult for a company that was doing open salaries. Their problem? Selfishness.

In their system, everyone could periodically decide on their raise (within limits). However, each time after the round of raises, the company went into the red. All the profits they were making—and more—went to increased salaries.

The following months were spent recovering from the situation and regaining profitability, only to repeat the cycle again next time.

Their education efforts had only a marginal effect. Some were convinced, but seeing how colleagues aimed for the maximum possible raise, people yielded to the trend.

The cycle has perpetuated.

So what did go wrong? After all, they followed the rulebook. They merged autonomy with transparency. And not only with salaries. The company’s profit and loss statements were transparent, too.

It’s just people didn’t care.

Care

Over the years, when I spoke about distributed autonomy, I struggled to nail down one aspect of it. When we get people involved in decision-making, we want them to feel the responsibility for the outcomes of their decisions.

The problem is that we have a diverse interpretation of the word. I once was on the sidelines of a discussion about responsibility versus accountability. People were arguing about which one was intrinsic and which was extrinsic.

As the only non-native English speaker in the room, I checked the dictionary definitions. Funny thing, both sides were wrong.

Still, I’d rather go with how people understand the term (living language) rather than with dictionary definitions.

So, what I mean when I refer to being responsible for the outcomes of one’s decisions is this intrinsic feeling.

I can’t make someone feel responsible/accountable for the outcomes of their call. At most, I can express my expectations and trigger appropriate consequences.

To dodge the semantic discussion altogether, I picked the word agency instead.

The only problem is that it translates awfully to my native Polish. Frustrated, I started a chat with my friend, and he was like, “Isn’t the thing you describe just care?”

He nailed it.

Care strongly suggests intrinsic motivation, and “caring for decision’s outcomes” is a perfect frame.

How Do You Get People to Care?

The story of the company with self-set salaries—and many comments in the Hacker News thread—shows a lack of care for their organizations.

“As far as I get my fat raise, I don’t care if the company goes under.”

So, how do you change such perspectives?

Care, not unlike trust, is a two-way relationship. If one side doesn’t care for the other, it shouldn’t expect anything else in return. And similarly to trust, one builds care in small steps.

Imagine what would happen if Amazon adopted open salaries for its warehouse workers. Would you expect them to have any restraint? I didn’t think so. But then, all Amazon shows these people is how it doesn’t give a damn about them.

And that can’t be changed in one quick move, with Jeff Bezos giving a pep talk about making Amazon “Earth’s best employer” (yup, he did that).

First, it’s the facts, not words, that count. Second, it would be a hell of a leap for any company, let alone a behemoth employing way more than a million people.

As I’m writing this, I realize that taking care of people’s well-being is a prerequisite for them to care about the company. And that, in turn, is required in order to distribute autonomy.

The Role of Care

The trigger to write this post was a conversation earlier today. We’re organizing a company off-site, and I was asked for my take on paying for something from the company’s pocket.

Unsurprisingly, the frame of the question was, “Can we spend 250 EUR on something?”

Now, a little bit of context may help here. Last year was brutal for us business-wise. Many people make some concessions to keep us afloat. Given all that, my personal take was that if I had 250 EUR to spend, I’d rather spend it differently.

But that wasn’t my answer.

My answer was:
- Everybody knows our P&L
- Everybody knows the invoices we issued last month
- Everybody knows the costs we have to cover this month
- Everybody knows the broader context, including people’s concessions
- We have autonomy
- Go ahead, make your decision
In the end, we’re doing a potluck-style collection.

Sure, it was just a 250 EUR decision. That’s a canonical case of a decision that can not sink a company. But the end of that story is exactly the reason why I’m not worried about putting in the hands of our people decisions that are worth a hundredfold or thousandfold.

We’ve never gone under because we’ve given ourselves too many selfish raises. Even if we could. The answer to why it is so lies in how we deal with those small-scale things.

After all, care is as much a prerequisite for distributed autonomy as alignment is.

This is the third part of a short series of essays on autonomy and alignment. Published so far:
June 5, 2025
The Role of Alignment
In the first part of this series, I focused on why autonomy in a workplace is a critical ingredient if we want to stay relevant. Not only is it a response to the nature of everyday work, with the increasing significance of remote work and the rise of AI, but it is also an emergent outcome of the large-scale evolution of the economy.

However, if there is a universal warning that should be attached to the advice suggesting decentralizing control, it should be the following.

It’s never as simple as “give people more autonomy.” The way people act in a decentralized system depends on a broader culture, which one should consider before giving everyone more power.

Purpose

One common theme in the discourse on organizational culture is purpose. A shared theme that people and teams aspire to change into reality.

By the way, when considering joining any company, I recommend asking about their purpose. In fact, I’d ask different people this very question and see whether their answers are aligned.

“Making more money” is not a purpose. It’s a tactic. Ditto “increasing value for shareholders.” If you want to send a man to the moon, that’s a great purpose. But it doesn’t have to be that big. I’m a fan of honest aspirations like “creating a healthy workplace that sustains a few dozen employees and their loved ones,” too.

Aside from its strategic role, or impact on motivation, purpose has a role in the discussion about autonomy. It is the force that encourages alignment of all the efforts happening in an organization.

Misalignment

Imagine a company guided by the “making more money” aspiration. People would naturally see different, sometimes contradicting, ways of generating revenues. They’d be pulling in different directions.

Using a physical metaphor, we could consider a circle as the whole organization and arrows within as different individuals pursuing different goals.

All those forces combined would create some momentum. The company would be slowly moving wherever the push is strongest.

What would happen if we gave people more autonomy in such a setup? It is the equivalent of giving every individual more influence over the whole company. Each force vector would become stronger.

Now, everyone has better leverage, but the combined effect on the organizational momentum is marginal. The reason is obvious. It’s all the contradicting priorities. People try to push in different directions.

Alignment

In contrast, we can start in exactly the same situation. However, instead of pursuing the agenda of distributed autonomy, we’ll begin with an attempt to sync up everyone’s efforts.

It would mean getting more arrows to point in a similar direction. I don’t expect a perfect alignment. Every individual has their own goals, which would never be matched perfectly with an organization’s goals. But we can get closer.

The basic tool we have is the purpose. Once it’s clear to everyone what that is, two things will happen. Some people will adopt it and adjust their actions to help achieve it. It’s as if they redirected their vector more toward the desired direction.

Others will figure that they’d rather keep pushing where they have before. For them, it would be clear they wouldn’t get much support. The odds are they’ll leave soon. If our HR does even a half-decent job, whoever comes in their place would be better aligned with the purpose.

One way or the other, we’d get more people rowing in (roughly) the same direction.

That itself changes the organizational momentum significantly. Not only did we remove the opposing force, but we also added a supporting one.

If we follow up with increasing autonomy in this setup now, we will maximize the gains.

Again, everyone has bigger leverage, but thanks to synchronized efforts, the impact is so much more significant.

Alignment First

One could argue that we can achieve the same outcome independently of the order of changes. After all, if we refocused everyone’s efforts after increasing autonomy, the end game would look the same.

In theory, yes. In practice, achieving alignment in such a manner is much less likely and more difficult.

Each vector is a representation of somebody’s drive. The stronger it is, the harder it is to redirect it significantly. Think of the arrows as if they had weight proportional to the force they represent. With bigger weights, it simply requires more effort to align the vectors.

In some cases, alignment will be impossible altogether. We extend our individual expectations to the whole organization. It’s like saying, “I want to pursue this agenda, and thus, I want my company to enable that.” While we would rarely, if ever, express it with these exact words, that’s a prevalent theme in conversations happening around job changes (exit and job interviews alike).

Bigger arrows tend to break before we can realign them to a significantly different direction.

Alignment versus Autonomy

There’s a fantastic depiction of the relationship between autonomy and alignment proposed by Stephen Bungay in The Art of Action.

He plots a two-dimensional plane with our culprits defining the axes.

In an environment with low autonomy and alignment, we won’t see much action. People will neither feel empowered, nor they would have a sense of clarity. You can expect a lot of confusion and minimal tangible outcomes.

If we stick to low autonomy but increase alignment, we will have clarity about the goals. However, the actions will still be carefully managed and controlled. It would be a typical micromanagement environment. Not the most inspiring workplace in my book.

On the opposite end, there’s a low-alignment, high-autonomy environment. There will be a lot going on in such an organization. The problem is that much of that effort will be misdirected. Some of it may be actively counterproductive.

Finally, we have our most desired quadrant with high alignment and autonomy. That’s where we have clarity about the goals, and people act without waiting for permission. Their actions, thus, will be both targeted and effective.

Interestingly enough, Stephen Bungay doesn’t stop by showing what we should expect in each type of environment. He also suggests the best path from the bottom left to the upper right corner.

Unsurprisingly, this path leads through increasing alignment first and only then distributing more autonomy.

I can personally attest it’s a good way, as we did the opposite at Lunar. The price we paid for neglecting alignment was steep. There was a load of interpersonal conflicts, which became a borderline tribal war, and 20% of the company left in the aftermath. Show me a leader who’d willingly drive their company there.

Big Picture

If there were only one big-picture suggestion, I’d couple with my strong encouragement to make distributed autonomy a central piece of organizational culture, it would be about alignment.

Decentralizing control means everyone gets more power over a company and everything it does. That may only get us promising results if everyone rows (roughly) in a similar direction.

We won’t get that unless we explicitly work on alignment. Or are extremely lucky.

I tend not to rely on the latter.

This is the second part of a short series of essays on autonomy and alignment. Published so far:
May 19, 2025
Pivotal Role of Distributed Autonomy
I’m a massive fan of distributed autonomy. I believe that, in principle, giving people more autonomy at work is the largest organizational challenge the modern workplace faces.

Yes, the news of the day is either remote/hybrid work or the impact of AI on everyday jobs. Reinventing the organizational structures of a 21st-century corporation doesn’t belong to a broad discourse.

From both perspectives, however, distributed autonomy plays a pivotal role.

Autonomy in Remote Work

With remote work, the dependency is straightforward. Much of the work has moved from the office—where it could be physically supervised by a manager—to homes, where supervision is significantly limited.

The manager’s control is limited to the outcomes but not the actions that lead to them. For example, I can observe whether my engineers deliver features or add code to the codebase, but I don’t see when, how, and how much time they spend on activities that lead to “new features.”

Sure, some organizations would turn to digital tools to control employees’ activities. Guess what. It doesn’t work. Well, it does, but not the way they intend. Here’s what this kind of monitoring does to people:
- It reduces job satisfaction.
- It increases stress.
- It reduces productivity.
- It increases counterproductive work behaviors.
One hell of a slam dunk, really.

It’s not only the lack of control, though. It’s also the availability of help. For the vast majority of organizations, remote work creates additional communication barriers.

My leader no longer sits at the next desk. I can’t see whether it’s a good moment to interrupt them. Sure, I can drop them a DM on Slack, but they may not answer instantly. So, whenever I face one of those micro-decisions that I might have naturally delegated to my leader in the past, I may call a shot myself. It feels more convenient.

What has just happened here was me grabbing a little bit more authority. I might have had it all the time, but I didn’t use it because it was easier to ask the leader. Now, the path of least resistance is making decisions myself.

Multiply that by everyone in an organization, and suddenly, we have more distributed autonomy.

The choice is between embracing and strengthening the change or resisting it. In the latter case, well, we tax ourselves on every single front, from productivity to employees’ mental health. Not really a choice, is it?

Autonomy and AI

The emergence of AI creates another shift in the nature of work. We get a relatively powerful co-pilot that can help us with many tasks that would be difficult or arduous in the past.

Back then, we might have turned to the experts for help. Or drop the task altogether if it was non-essential.

The experts would give us a suggestion, and we’d accept it as the decision. If we abandoned the task, there would be no decision to make whatsoever.

But now, with our AI co-pilot, we have new capabilities at our fingertips. Yet it wouldn’t make any decision for us. Again, the path of least resistance is to grab some of that power, make a call, and move on.

As an example, it’s often a challenge to dig up a relevant source to link in my writing. I often remember a research paper or article covering a useful reference. But its topic or author’s name? Beats me.

Googling it was always a struggle, so I either turned to a human expert friend or gave up.

But now? LLMs are pretty decent in digging up relevant options. Still, the work of reviewing suggested sources and choosing a valuable one is on me. I now face a decision that I earlier deferred to an expert or dodged entirely.

More autonomy again.

Adhocracy

The changes coming from different directions align with a broader evolution of the nature of work. Julian Birkinshaw, in his book Fast/Forward, provides a neat big picture.

Over the past century or so, the world has evolved from the industrial, through the information, to the post-information era. Each step changes the rules of the game.

A hundred years ago, scaling was the biggest challenge, and the effective use of resources was advantageous. Thus, bureaucracy was a winning strategy.

In the second half of the 20th century, we saw the increasing value of information, and its accessibility and effective use gave us an edge. Thus, meritocracy was gaining ground.

Now, information is ubiquitous. In fact, with the help of LLMs, we can easily generate as much of it as we want. The world becomes less about who knows what. It’s about who can act upon (incomplete) data in a fast and effective manner. Thus, ad-hoc action gives an advantage.

Birkinshaw coins the term adhocracy to describe this new mode of operation.

A side note: one important part of the model is that all three modes of operation coexist. However, an organization will defer to the default mode whenever it faces uncertainty. We can’t expect a bureaucratic, hierarchical behemoth to act in an adhocratic way routinely.

The coexistence of all modes will naturally create tension. The decision can’t be made at the same time made by:
- a manager with the most positional power
- an expert with the best data and most expertise
- a line professional involved in the task hands-on
If we want to embrace adhocracy, which Birkinshaw argues is a prerequisite for organizational survival, we necessarily must move authority down the hierarchy.

We need to distribute more autonomy. Again.

Common Part

It’s not a surprise. When you go through the stories of companies successfully embracing non-orthodox management models, autonomy would be one shared part of all.

Be it a turnaround story in David Marquet’s, unsurprisingly titled, Turn the Ship Around, or Michael Abrashoff’s It’s Your Ship, pushing autonomy down the hierarchy was crucial.

And the fact that the military context would pop up so frequently in this discussion shouldn’t be a surprise. Decentralizing control was a pivotal part of the revolution of the 19th-century Prussian army. Its victory streak forced other armies to follow suit.

Yes, the corporate world, despite all its inspirations from the military lingo, takes its sweet time to adopt the truly important inventions. And yes, our views of the military tend to be rooted more in Hollywood movies than in the actual realities of these gargantuan organizations.

I often mention that we’d see more distributed autonomy in late 19th-century armies of the West than in many 21st-century corporations.

We’ll arrive at the same conclusion if we stick to management theory. Take Holacracy, Sociocracy, Teal, or whatever generates the most buzz these days. The cornerstone of each of those will be autonomy. It may go into how we design roles (Holacracy), get to the principle list (self-government in Teal), or define the decision-making process (consent in Sociocracy). But it’s always there.

When you think of it, it’s only natural. For hundreds of thousands of years, homo sapiens lived in small tribes of hunter-gatherers that were egalitarian and had very little to no hierarchy.

Even when our species started evolving into bigger societies, adopting a strong hierarchy wasn’t given and was only one of the possible ways of coordination.

I’d speculate that we are genetically predisposed to autonomy.

Reinventing Autonomy

Wherever we look, we seem to be reinventing the role of distributed autonomy. It’s critical to succeed on a battlefield. Staying relevant in business increasingly requires its presence. It sneaks along with the changes in the nature of work. We know it’s a prerequisite for engagement and motivation.

Nothing should be easier than embracing the change and giving people more power.

Sadly, it’s not the case. Power is a privilege. And as with every privilege, those in power will not give up easily. The good old bureaucracy will fight back.

More importantly still, even if we have the means to overcome the resistance, the challenge is not as easy as “Let’s just give people more autonomy.”

We need to take care of other things before we embark on this journey. But that’s the topic for another post.

This is the first part of a short series of essays on autonomy and alignment. The following part(s) will be published on the blog and linked here during the next weeks.
May 7, 2025
Levels of Slack Time
I learned the meaning of slack time years back in the context of Kanban. Once we start managing workflow for effectiveness, we necessarily create these moments when we want to stop the work. Otherwise, we’d overload the bottleneck.

When we design the slack time in the system, the natural question is, what’s next? How do we use it?

The Use of Slack Time

My first natural, although with the benefit of hindsight simple, answer was to optimize the teamwork.

There’s always a colleague who would appreciate help. There’s always a stage of the process that no one loves (code review, anyone?). There’s always an improvement task that just never makes it the top priority (like paying technical debt back).

Then, there’s a whole another context. Individual. Slack time creates a perfect opportunity to invest in oneself. Catch up with what’s new with technology (with the AI race, it seems like there’s something to catch up with almost every week). Learn something outside of the immediate context of the project. Sharpen one’s saw.

But then, when we start considering the value of slack time in a more generic context, we realize that it goes way beyond the team level.

Think of a fire station. Their whole system is designed around slack time. We don’t try to optimize the work for fire brigades for utilization. It would mean that we want firefighters fighting fires all the time.

Thus, we’d either have an excess of fires or firefighters moonlighting as arsonists.

Cross-Team Slack

We can then abstract away the idea of slack time and consider how it applies in different contexts.

The most basic context would be individual. Slack time helps me to get better.

Going just a bit further, we have the team level. Slack time helps the team to become more effective. We achieve that either by coordinating around the ongoing tasks (short-term help) or working on improving the long-term performance (continuous improvements, paying technical debt back, etc.).

So far, it’s all the basics. But what if we take another step into a broader context?

Then we end up in cross-team/cross-project land. We start considering coordination problems. We look at interdependencies. We analyze an entirely different layer of work design.

As much as we have a bottleneck within a development team (let’s say it’s testing), we will have one if we consider a cross-team initiative (let’s say it’s passing the security acceptance).

Once we understand this high-level picture, we can use slack time to help the entire initiative become more effective. It’s likely that one team being able to deliver more features or deliver them more predictably will not move the needle of the whole product by a millimeter.

The actual bottleneck may be in the design, or release, or even grand product-related decisions.

That’s where cross-team slack time can be redirected. It would mean we’re asking the question: what’s the best way to help when I see the big picture?

Organizational Improvements

Can we go further up, though? Once we consider individual, team, and cross-team levels, is there more?

But of course, there is.

Once we strip the context from the product/project work, we look at the bare bones of any company—its organizational design. And there’s always plenty to do around these parts.

At this level, we wouldn’t ask questions about improving my project/product. We’d look for ways to make the whole company improve.

Think of any novel initiatives. Of course, “novel” is in the context of this company; I don’t expect us to routinely do world-changing stuff. Starting a self-help group. Running a new product experiment. Looking for fellow folks to challenge existing management paradigms. Opportunities are almost endless here.

The more extravagant you become with the ideas, however, the less likely you’d be allowed to implement them. And here comes our culprit.

Role of Autonomy

We established different levels of slack time:
- Individual
- Team level
- Cross-team
- Organizational
The further down that list you go, the bigger the potential impact of the changes. But most people wouldn’t even consider going beyond the team level. Why?

Simplest answer? They don’t have enough autonomy.

The degree to which we can impact our organization depends on our sphere of influence. And that is strongly correlated with how distributed autonomy is in any organization.

It’s not binary, of course. It’s not that I can or cannot make a decision in a cross-team context. Sometimes, even when I can’t call the shots, I can influence the decision-maker.

But if I don’t have autonomy on a team level, I shouldn’t expect to have influence on a cross-team level. Thus, we can use the autonomy distribution as our measuring stick.

Limited Autonomy, Limited Impact

Whenever I teach that stuff during workshops and university courses, I ask people a series of questions to assess how much their organizations distribute autonomy.

The questions are separated into groups, roughly referring to the three levels mentioned above: team, cross-team, and organization. The outcome is the same every single time.

We see a lot of autonomy on a team level. Teams can freely decide about many things. I think we have Agile to thank for that.

But then, it breaks. Once we get to cross-team, the perceived autonomy goes from “significant” to “barely there.” And it won’t be a surprise that when we touch the organizational level, it’s non-existent.

In such an environment, we shouldn’t expect to get that much out of slack time.

So, the next step after introducing slack time to our systems is to get more autonomy in it as well.
April 2, 2025
(Non-)Challenges of Distributed Decision-Making

An internet discussion (yeah, I know, quite a bad idea for a trigger) inspired me to share some of the uncommon things we do at Lunar when it comes to decision-making.

In short, as Lunar, anyone can make any decision as long as they go through an advisory process. The latter means consulting with people with expertise on the topic and those affected by a decision.

Very few edge cases (like letting people go) have a somewhat different process, but the vast majority of calls follow the pattern described above.

So how come people don’t get extravagant and give themselves hefty raises, go for super-fancy events, buy tons of gadgets, etc.?

Care

There are a few prerequisites to distributing autonomy that I could spend hours talking about. In fact, I’m doing exactly that during my course (called Progressive Organizations) at a local university. Anyway, for this consideration, the key prerequisite is care.

When I say care is needed when we give people the power to make (any) decisions, it means that they need to feel responsible for the outcomes of their calls. Whatever happens, good or bad, they won’t be like, “Meh. Whatever.”

They will care.

That is enough to avoid an obvious extravaganza. After all, if we can predict something might be, well, not very wise or cause controversy, we’d think twice before putting our reputation at stake.

Hard decisions

It’s easy to make an obvious call. Let’s organize a company offsite! We’ve been doing it to great success for a decade, so it’s kinda no-brainer, isn’t it?

But when it comes to tough choices, believe me, people don’t queue up to pick up the responsibility. It’s where it falls to the usual suspects: people who you’d consider leaders.

And sensibly so. After all, these are people who are equipped with experience, knowledge, and intuition for such situations. They’ve been doing it for years. That’s one of the reasons we keep them around.

Also, when in doubt about whether going for this fancy conference abroad is extravagant or not, the leaders would use past experiences and provide some context.

“Why wouldn’t you consider a more local event instead? Here’s one we’ve sent people to, and they’ve been happy.”

“Have you considered how everyone might treat these trips if we treat such an escapade norm?”

And suddenly, no one really wants to push for that.

Learning the culture

I love one challenge I often get when I talk about radical autonomy. “What stops people from giving themselves a hefty raise?”

That’s the best part. Nothing. And they still don’t do it.

When you join a new group–any new group–two things happen. First, you influence the group. You provide a new behavior, perspective, thoughts, needs, etc. However, the bigger the group, the smaller your influence. After all, you’re but one person.

More importantly, though, the group influences you, too. Whatever is the norm in how they behave, what they do, what is accepted and what is not, strongly influences how you act. That’s obvious. We want to belong.

The very same thing works when anyone joins an organization. No one on their first day (or week or month) attempts to reinvent how things are done here. We wait and orient ourselves. We observe and learn norms.

With decision-making, it means considering how, when, and what kind of decisions they make. What triggers controversy, and what goes as expected.

So, if a healthy norm is that we try to keep our payroll fair, no one in a blatant way violates the norm. It would be too high of a price to pay in social credit.

Not making decisions

OK, but that whole thing means that we departed from the idea that every decision has a designated decision-maker. My team leader accepts my time off requests, my director gives me a raise, and a VP greenlights strategic efforts. We’re no longer there. It’s like anyone who wants to act acts.

And if no one wants to act… Then what?

Ultimately, there are the most mundane or unpleasant decisions that no one would fancy. Show a person who actually likes to let people go because of economic reasons, and I’ll show you a psychopath.

Normally, we’d have a designated person who is responsible for those tough calls, but hey, we gave up on that idea.

We do, however, have a person who serves as a safety net. In Lunar case, it’s me. I’d do anything that no one else is willing to do (and yes, that’s why I throw rotten food from a fridge in our cantina). Part of that burden is making the toughest decisions.

Think of it not as a designated decision-maker but rather as a fallback decision-maker.

Is it enough?

Would that be all that needs to work in order to distribute autonomy? Especially when we talk about the most radical way of doing it (remember, anyone can make any decision).

Surely not.

And I’m happy to be challenged. We most likely have a good answer to that. We have been using this system for 12 years, and it’s doing just fine.

If I learned anything during that time, the most difficult parts are really not the ones people think. And the gain from everyone’s involvement and care is immense.

February 20, 2025
Choosing Partners: Make or Break

There’s been an interesting long-term study on graduates. Those who prioritized wealth and financial success in survey responses were significantly more likely to achieve it 20 years later. It seems almost intuitive, doesn’t it? Their choices, actions, and focus were likely geared toward accumulating wealth and maximizing financial gains.

This phenomenon isn’t confined to individual career paths. I’ve observed a similar pattern across organizations within our industry. It’s quite telling to look at the trajectory of companies over a decade or more.

Some organizations have had a laser focus on growth, and subsequently, they’ve experienced remarkable expansion. Others have chased public recognition and local fame, their representatives becoming well-known figures in relevant communities. Still others have invested heavily in technological excellence, attracting top-tier engineers.

The intriguing part is this: often, you could predict these outcomes simply by understanding the priorities of the company’s leadership.

Why? Because leadership’s influence on an organization is immense. Their personal aspirations become the organization’s goals. Their decisions, consciously or subconsciously, are often optimized to serve their own individual objectives. It’s no surprise, then, that an organization’s achievements frequently mirror the priorities of its leadership – much like the undergraduates in the study.

This observation, by the way, also translates to the prevalent issue of short-term thinking in the corporate world. After all, the top leaders are typically manipulated to “increase value for their shareholders” with hefty rewards. But that’s a discussion for another time.

Acknowledging the above, here’s a valuable tip: before partnering with any company, try to understand what truly motivates its top leaders. Not what they say officially on their website or in the materials they send you.

Find who’s on the very top of the organization and do a little bit of research. Heck, ask AI to do it for you if you can’t get a report from someone working directly with them. And if you can get them to talk to you, it’s a home run.

Ideally, get them to articulate their priorities, values, and beliefs. That should be easy, especially when dealing with a smaller organization. This insight will be a telltale of how your collaboration will look like.

You’ll gain a picture of how you, your company, your product, and your goals fit into their agenda. That fit should be a critical factor when choosing a technical partner, business collaborator, or, really, anyone in whom you want to invest loads of money.

Looking back on my 13 years at Lunar Logic, every long-term collaboration we’ve forged serves as a testament to this principle. Our (often unspoken) priorities were consistently aligned with those of our clients and partners.

And if there’s one reason to explain our record 17-year-long (and counting) collaboration with one client, it’s precisely this match. By the way, not only is it the longest gig in the history of Lunar, but also, hands down, the best client we could have dreamt of.

Coming back to Lunar’s focus, it’s never been on rapid growth, widespread fame, or even undisputed technical superiority.

We’ve been this crazy company that decided to experiment with radical organizational culture. We have no managers. Anyone can make any decision (in a structured way, of course). By many accounts, we are rebels.

Does it work, though?

I bet Michael, who has collaborated with us for 17 years, would confirm. And he’d be far from the lone example on this account.

People stay at Lunar for over twice as long as the norm in our niche suggests. So, it’s another voice that we do something right.

But walking the talk, if you want to get an accurate picture of whether there’s a match, let me know. In the spirit of transparency, which is one of our values, I’m happy to share a lot about Lunar.

Then, it’s anyone’s choice whether we are a good match or not.

February 10, 2025