Most of the leaders we celebrate, especially in big tech, are disruptors. They are showmen. They are often presented as solo heroes, disconnected from us. The narrative is always about the individual who changed everything. The genius. The founder. The one who wins.

In tech, we celebrate disruptors and solo heroes. But what if the leadership we actually need is simpler: moral, intentional, unifying, and shared? Drawing from Barack and Michelle Obama’s recent speeches, I reflect on what real leadership looks like and why tech needs unifiers more than disruptors.

But very rarely do we talk about the leaders who bring people together. Not the typical get together, when things get tought and we have to work extra hours, work hard, to keep the company afloat, and the next day we read that our company made huge profits and new cars from management appears in the parking lot. In tech we know that we are only need it until we are not.

That is why I wanted to write this. Something more personal. Something that reflects not only on leadership, but also on what kind of leadership we actually need today.

And for me, Barack Obama is an interesting example. Is not a big tech leader, but I found that with tech having a huge impact in worldwide population I think we should choose and push these cmopanies to have leaders in line with I will write next. I know this is probably pushing things a bit too much, after all companies exist to profit from us, not go give us a final value, they need us to always come back for more.

I am not saying he is perfect, who is. He is a politician, and like any politician, he has his contradictions. But from what I have seen, read, and followed over the years, I believe he is a person with integrity. More importantly, he is someone who consistently tries to bring people together.

That matters to me.

Because trying to bring people together is hard. I know this not from theory, but from my own experience.

Throughout my professional career, and also in my personal life, I have tried to do the same. To listen to people. To give space. To create alignment. To build something shared.

And it is never easy. Some people just want to have total attention.

People want different things. People have different fears. There is ego, there is jealousy, there is insecurity, there is ambition. Even in the simplest situations, small misunderstandings can create chaos. In more complex environments, like companies or teams, this becomes even harder.

So when I see a leader who keeps trying to unify, even when it is difficult, I pay attention.

That is what I see in Barack Obama.

He is, in many ways, a unifier.

When I listened to his recent speech at the opening of the Obama Presidential Center, I was reminded of this again. It felt like a very classic Obama speech, but also something more reflective. Less about power, more about meaning.

And I think there is something we can learn from it, especially in tech.

Because when I look at tech today, I see a very different picture.

I see separation. I see competition as the default. I see companies driven almost entirely by profit. Of course, every business needs money. That is not the issue.

The issue is how that money is made.

In many cases, the current model feels extractive. Value is pulled from users, from attention, from data, from communities. And the benefits are concentrated in very few places. A few companies. A few individuals.

At the same time, these companies shape how we live. How we communicate. How we see the world.

That creates a tension.

We rely on them daily, but we rarely question the values behind them.

And in some cases, I find this model hard to accept. Not because profit is wrong, but because the balance feels wrong. It feels like we are building systems that enrich a few while putting pressure on many.

If society as a whole does not benefit, then we need to ask ourselves what we are really building.

I believe we are reaching a point where this needs to change.

We should start rewarding companies that create real value for society. Not just more tools, more features, more distractions, but real improvements in people’s lives.

This could mean better health, better education, stronger communities, more time, less stress.

Because in the end, what matters is not what we can accumulate, but what we can contribute.

And right now, I feel we are losing something important.

We are losing our sense of community.

There is a growing sense of hyper-individualism. Everything is about personal success, personal gain, personal brand. And while ambition is not a bad thing, when it becomes the only thing, it starts to break the fabric that holds society together.

It creates division.

It creates isolation.

It creates systems where it becomes easier for bad actors to take advantage. To concentrate wealth. To push narratives that separate people instead of connecting them.

This is not just a social problem. It is also a leadership problem.

And this is where I think Obama’s speech becomes relevant.

When I look at him as a leader, based on this speech, a few things stand out very clearly.

First, he is a values-driven leader.

He does not start with strategy. He starts with principles. He talks about dignity, fairness, democracy, responsibility. These are not abstract ideas for him. They are the foundation.

Second, he is an integrative leader.

He does not frame leadership as winning against others. He frames it as bringing people together. He explicitly says that the values he believes in are not tied to one political party. They are shared values.

That is a very different posture from what we often see today.

Third, he believes in participatory leadership.

One of the most powerful ideas in his speech is that leadership is not about titles. It is about helping others find their voice.

This is important.

Because it shifts the focus from the leader to the collective. It suggests that leadership is something that can be distributed. That people at all levels have a role.

In tech, we talk a lot about empowerment. But in practice, many organizations are still very top-down. Decisions are concentrated. Voices are filtered.

What Obama is describing is something else. A more human model.

Fourth, he is an institutional leader.

He believes in systems. In processes. In democracy as a structure that, even if imperfect, can improve over time.

This is not a glamorous position. It requires patience. It requires accepting that progress is slow. That compromise is necessary.

But it is also what creates stability.

In contrast, many tech leaders position themselves as disruptors of systems. Sometimes this is necessary. But when everything becomes disruption, we lose continuity.

We lose trust.

Fifth, he is a narrative-driven leader.

He tells stories.

Not just about himself, but about ordinary people. A single mother. A small business owner. A soldier. A young leader.

He even says that people visiting the center should focus less on his speeches and more on these stories.

That is a strong signal.

It shows that he sees leadership not as self-expression, but as meaning-making. As helping people see themselves in a larger story.

And this connects to his tone.

His tone is warm. Human. Reflective.

He uses humor. He talks about his family. He shows gratitude.

At the same time, there is a quiet urgency.

When he talks about polarization, misinformation, and the current state of the world, he does not ignore the problems. But he does not amplify fear either.

He stays balanced.

Hopeful, but not naive.

He acknowledges mistakes. He talks about unfinished work. He accepts that democracy is slow and sometimes frustrating.

This honesty builds credibility.

It is very different from the extremes we often see. Either overly optimistic narratives that ignore reality, or overly negative ones that lead to cynicism.

He avoids both.

And this says a lot about his personal traits.

He shows humility. He admits that he did not achieve everything. That he made mistakes.

He shows empathy. He focuses on people’s lived experiences.

He shows control. Even when discussing difficult topics, he remains calm.

He shows long-term thinking. He talks about generations, about history, about the future.

And maybe most importantly, he shows low ego.

He does not position himself as the center of the story. He consistently redirects attention to others.

This is rare.

Especially in environments where visibility and personal branding are rewarded.

And this brings me back to tech.

Because if we compare this model of leadership with what we often see in the industry, the gap is clear.

In tech, the dominant archetype is still the hero leader.

The founder. The visionary. The one who knows.

This creates strong narratives, but it also creates limitations.

It can reduce collaboration. It can silence other voices. It can lead to decisions that are not fully informed.

It can also create cultures where people compete more than they cooperate.

And over time, this affects not only companies, but also the products they build.

If leadership is individualistic, products often reflect that. They optimize for engagement, for growth, for metrics, sometimes at the expense of well-being.

If leadership is more collective, more values-driven, more human, then the outcomes can be different.

More balanced.

More sustainable.

More aligned with society.

I am not suggesting that we should replace all tech leaders with political figures. That is not the point.

The point is that we need to expand our understanding of leadership.

We need to recognize that there are different ways to lead.

And that in the current context, with all the complexity we face, we might need more unifiers than disruptors.

More people who can hold tension. Who can listen. Who can bring different perspectives together.

More people who see leadership as a shared responsibility.

Obama’s speech is not a blueprint. It is not a set of instructions.

But it is a reminder.

A reminder that leadership can be grounded in values.

A reminder that progress does not always come from breaking things, but sometimes from building trust.

A reminder that people matter.

And that systems, even imperfect ones, can improve if we engage with them instead of abandoning them.

For me, this is the key takeaway.

We are at a point where the way we build companies, especially in tech, needs to evolve.

We need to move away from purely extractive models.

We need to think more about the long-term impact.

We need to design not only for efficiency, but also for dignity.

And this starts with leadership.

With the kind of leaders we promote.

With the kind of behaviors we reward.

With the kind of stories we tell.

If we continue to celebrate only the disruptors, we will continue to get disruption.

If we start to value unifiers, builders of communities, people who elevate others, then maybe we can create something different.

Something better.

I am not naive about this.

Change is slow.

Incentives are strong.

Systems resist transformation.

But I also believe that leadership evolves.

And that we are starting to see the limits of the current model.

So maybe this is a good moment to reflect.

To ask what kind of leaders we want.

And what kind of world we are building through them.

For me, Obama represents one possible answer.

Not perfect. Not complete.

But meaningful.

A leader who tries to bring people together.

And in a time where division is easy, that effort alone is worth paying attention to.

Here is a standalone section you can insert into your essay, aligned with your tone and message.

Behind every great man, there is a remarkable woman. This is a phrase we hear many times, and sometimes it feels like a cliché. But in the case of Barack Obama, I believe it carries real meaning.

Michelle Obama is not just a supportive partner standing in the background. She is, in many ways, a leader in her own right. And when you listen to her speech at the opening of the Presidential Center, this becomes very clear.

What stands out immediately is her clarity and strength. She speaks with conviction, but also with warmth. She is direct, but never arrogant. She praises Barack, but she does it in a way that reveals more about values than about status.

When she talks about him, she does not focus only on achievements. She talks about character.

She speaks about his calm under pressure. About how he did not break, and did not harden, even during difficult times. She highlights his optimism, his discipline, his moral compass. These are not small things. These are the foundations of leadership.

And I think this is important.

Because often we measure leaders by outcomes. By metrics. By success. But Michelle reminds us that how someone leads matters just as much as what they achieve.

She also brings something else into the conversation that I find very powerful: the idea that leadership is shared.

She talks about family. About sacrifice. About the journey they went through together. About raising children in a life they did not choose. There is honesty in that. There is weight in that.

It reminds us that behind any visible leader, there is always a system of support. People who carry part of the burden. People who make it possible.

And in many ways, Michelle represents a different kind of leadership than what we often see.

She is grounded in community. She speaks about ordinary people, about workers, teachers, families, volunteers. She brings attention back to the collective, not the individual.

She also speaks very clearly about values. Equality, empathy, honesty, inclusion, fairness. These are not presented as abstract ideas, but as daily choices.

That is a key point.

For her, being a decent human being is a choice. Participating in democracy is a choice. Showing up for others is a choice.

This connects deeply with the idea of leadership as something that belongs to everyone.

Not just to those with titles.

Not just to those with power.

But to all of us.

And this is where I see a strong alignment between her message and Barack Obama’s.

While he speaks about systems, history, and institutions, she brings it back to people. To daily life. To behavior.

Together, they create a more complete picture.

He is the unifier at the level of ideas and structures.

She is the unifier at the level of human connection and lived experience.

And I think this balance is rare.

In many environments, including tech, we often separate these dimensions. We focus either on strategy or on culture. On performance or on people.

But real leadership requires both.

It requires vision and empathy.

It requires structure and care.

It requires strength and humility.

Michelle Obama embodies many of these qualities in a very natural way.

She does not present leadership as something distant or unreachable. She makes it feel close. Practical. Human.

And in doing so, she reinforces something that I believe is essential.

Leadership is not about being above others.

It is about being with others.

It is about seeing people.

It is about carrying responsibility, not just authority.

So when we talk about Barack Obama as a unifier, it is also important to recognize the role Michelle plays in that story.

Not as a secondary figure, but as a force that strengthens, challenges, and complements that leadership.

Because in the end, leadership is rarely an individual act.

It is relational.

It is built in partnership.

And in this case, it is very clear that behind the public figure, there is a partnership grounded in shared values, mutual respect, and a deep sense of purpose.

That, in itself, is a powerful example of the kind of leadership we need more of.

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About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

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And the endless discussion of what AI is, what it is capable of, the doomers, the utopians.

The coverage that followed had the same shape it always does. What does this mean for your workflow? What does this mean for your team? How do you use it effectively? How do you stay ahead of the people who are also figuring out how to use it?

I read through most of it. And then I closed the tab and sat for a while with a feeling of emptiness, knowing that all that information was somewhere in a documentation section of a particular site. But none of it asked the question that mattered most.

What is all of this doing to us?

Not to our output. Not to our productivity or our competitive position or our quarterly numbers. To us. To the people sitting at the keyboards. To the engineers and the leaders and the teams who are now, whether they chose it consciously or not, reorganising their working lives around a tool that is getting smarter at a rate that human beings are not.

I have been in technology long enough to recognise the pattern. Every release is framed as a gain. Every announcement is a celebration of what has become newly possible. And the energy around that is real. I feel it myself. There is something genuinely compelling about a tool that keeps expanding what it can do.

But there is a question that does not get asked at the launch party. And I think the cost of not asking it is starting to accumulate in ways we are only beginning to see.

Leadership as a verb is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

The conversation we keep skipping

I want to be precise about what I am pointing at, because it is easy to hear this kind of concern and file it under the wrong category.

I am not talking primarily about job displacement, though that is a real and serious issue that deserves its own honest conversation. I am not talking about hallucinations or errors or safety benchmarks, though those matter enormously and are not yet solved. I am talking about something that operates at a slower frequency and is harder to see because it does not produce a visible incident. It produces a drift.

When a model gets better at reasoning, something changes in the room where humans used to reason together. The room gets quieter. The pauses get shorter. The arguments that used to happen because two people had genuinely different intuitions about a problem start to happen less, because there is now a third voice that is faster than both of them and carries no emotional stake in being right.

When a model gets better at writing, something shifts in the relationship between a person and the effort of forming their own thoughts. The blank page, which used to be a necessary friction, a space where you had to hold the discomfort of not yet knowing what you meant long enough to find out, becomes optional. You can skip it. You can start with output instead of starting with silence.

When a model gets better at coding, something moves in the space where a junior engineer used to spend three uncomfortable hours on a problem they could not crack. Those three hours were not inefficiency. They were formation. They were the process by which someone became a person who could hold a complex system in their mind and navigate it. When you remove that struggle, you do not just save time. You interrupt something that was quietly building a capability that cannot be installed any other way.

I have watched this happen in real teams. In engineers I have worked alongside and coached. In myself, on the days when I reach for the tool before I have even sat with the problem long enough to know what kind of problem it actually is.

The output gets faster. The thinking gets thinner. And because the output is visible and the thinking is not, the thinning does not show up anywhere that anyone is measuring.

What these systems are, and what they are not

Part of what makes this conversation difficult is the language we use to describe what is happening. We call these systems intelligent. We talk about them reasoning, understanding, and knowing things. We have absorbed, without quite deciding to, a framing that places these tools somewhere on a continuum with human cognition, just faster and more capable in certain domains.

I noticed it in myself last week, mid-meeting, reaching for the laptop before I had even finished hearing the question. Not because the question was hard. Because some quiet calculation had already run: the tool will be faster than I will. That calculation is the framing doing its work, and it does its work in seconds, before anyone in the room has time to object.

That framing is doing significant damage, and not because it is purely wrong. It is wrong in the ways that matter most.

A large language model is trained on human text. It becomes extraordinarily good at the patterns of how human beings write and think and argue. It can recombine those patterns in ways that produce outputs that look like reasoning and read like insight. In a narrow, functional sense, something remarkable is happening inside these systems.

What they cannot do is understand. Not in the way a person understands something. Not with a body that has carried grief or made a promise they later had to break. Not with a history of being wrong about something that cost them a relationship and having to live inside that for years. Not with the capacity to sit across from another human being and feel, in the texture of the silence before they speak, that something underneath their words is different from the words themselves.

That gap is not a version problem. It is not something that the next release will close. It is a different category of thing entirely. And when we describe these systems as if that gap were merely a technical limitation being worked on, we do something subtle and consequential. We begin to calibrate human intelligence against machine output and find the human version slower, messier, and harder to justify.

That recalibration is not neutral. It does not stay contained to how we think about productivity. It starts to shape how people feel about their own minds. About the value of the slow, uncertain, embodied process of thinking something through without help. And once that feeling takes hold in a team, it changes what the team is willing to do.

How dependency builds without anyone deciding to build it

I want to describe a pattern I have observed carefully, because it is easy to dismiss as anecdote until you have seen it enough times that it starts to look structural.

An engineer begins using an AI assistant. The initial relationship is uncomplicated. They reach for it on the repetitive work, the boilerplate, the tasks that feel like friction between them and the problem they actually want to solve. This is a sensible choice. There is no reason to write the same kind of code manually that you have written a hundred times before. The tool handles it. The engineer moves on to the interesting part.

But six months later, something has shifted. They are reaching for the tool earlier in the process. Not just on the repetitive parts, but on the design questions. On the architectural decisions. On the places where the real thinking is supposed to happen. They are not doing this because they cannot think without the tool. They are doing it because the gap between what the tool produces in thirty seconds and what they would produce after twenty minutes of genuine effort has started to feel like evidence of their own inadequacy. The comparison is unfair, but that does not make it less real as an experience.

The tool did not engineer this feeling deliberately. There is no malice in a model. But the system behind the tool, the company that built it, the business model that depends on it becoming indispensable, the incentive structure that rewards increasing usage and would be damaged by decreasing it, that system understands exactly what it is doing. It is not designing for augmentation. It is designing for reliance. Those are different things, and the difference matters enormously.

When you build something that gets measurably smarter every year while the person using it stays roughly the same, the trajectory of that relationship only goes in one direction. You do not end up with someone who has been enhanced. You end up with someone who has been made dependent in ways they may not fully recognise, because the dependency formed gradually and because each individual step along the way felt entirely reasonable.

This is not a side effect that the industry is working to eliminate. In many cases, it is the product.

What happens to teams that stop disagreeing

There is a social and institutional dimension to this that gets even less attention than the individual one, and it may be more consequential at scale.

Teams think together. Not just in the sense that individuals who happen to work near each other each do their own thinking, but in the deeper sense that the quality of collective reasoning in a well-functioning team is genuinely higher than the sum of its parts. That happens through argument. Through the friction of people with different instincts pushing against each other until something that neither of them could have reached alone becomes visible.

That process is slow. It is uncomfortable. It requires people to hold uncertainty without resolving it prematurely. It requires a tolerance for being wrong in front of your colleagues. It requires the kind of psychological safety that takes a long time to build and very little time to damage. It is, in other words, exactly the kind of thing that a fast, confident, always-available tool makes it tempting to skip.

When a team starts routing its hard questions through a model before it routes them through each other, several things happen. The arguments get shorter, because there is already an answer on the table and arguing with an answer is harder than arguing with a colleague who is still forming their view. The junior voices get quieter, because the model does not hesitate the way a junior person hesitates, and hesitation is often where the most genuine thinking lives. The range of perspectives that gets considered narrows, because the model draws on what has already been written and published, not on the particular context and history and local knowledge that lives only in that room.

Over time, the team gets more efficient and less wise. And because wisdom is not a metric that anyone is tracking, the loss does not register until something goes wrong in a way that better collective thinking would have prevented.

Organisations that normalise machine-first thinking do not just change their workflows. They change the culture of how people relate to uncertainty and to each other. That is a long-term cost that will be very difficult to reverse once it is established.

When a team stops disagreeing, it does not become more aligned. It becomes more brittle. The arguments that felt inefficient were doing something the efficiency could not replace.

Who benefits, and why they will not say so

Every time a new model is released, the question asked loudest is what it can do. The question almost never asked is who benefits from you not being able to do without it.

Augmentation would mean building something that makes you more capable and then stepping back. What is actually being built is something that makes itself progressively harder to remove from your workflow, that captures your data and your patterns in ways that make switching costly, that is priced to create habitual use and structured to reward increasing dependence.

What makes it effective is that the dependency does not feel like dependency while it is forming. It feels like getting better at your job. Only later, when you try to think through a hard problem without it and feel the absence more acutely than you expected, do you begin to understand what has quietly happened.

The question for leaders is not whether to engage with these tools. That question is already answered. The question is whether you understand the power dynamics you are operating inside, and whether you are making deliberate choices about which parts of your team’s capability you are willing to trade and which parts you are going to protect.

There is no benchmark for what happens to the engineer who stops working through problems by hand and loses, over two years, the ability to hold a complex system in their head without external scaffolding. There is no metric for the leader who has outsourced the uncomfortable ambiguity of difficult decisions to a prompt and slowly lost the tolerance for sitting with uncertainty that good leadership requires. The industry measures what AI can do. Nobody with any institutional power is measuring what it does to us. And that asymmetry is not accidental. The companies releasing these models do not benefit from a public conversation about the cognitive and social costs of their products.

I use these tools. I used one while preparing this essay, and I am saying that directly because being transparent about the tool is itself a form of intellectual honesty that I think matters here. The thinking is mine. So is the uncertainty that came with it. The tool helped me find what I was trying to say. It could not feel why it needed to be said, or what was at stake in saying it clearly.

What this looks like this week

This does not require a policy or a tool ban. It requires noticing, once, somewhere small. Pick one:

• In your next team meeting: before anyone opens a model, ask “what do we think, before we check.” Let the disagreement happen first.

• Before you reach for the tool yourself: sit with the problem for as long as it takes to form one rough guess of your own. Then check it against the machine, not the other way round.

• With one junior person this week: hand back a question they tried to outsource. Not as a test. As a kept door.

None of these cost you the tool. They just decide, on purpose, where the human goes first.

What staying human actually requires

I am not making an argument against using these systems. That would be dishonest, given that I use them, and it would also be the wrong frame. The question is not whether to use them. The question is whether you are using them with clear eyes about what you are trading and what you are protecting.

What staying human requires, in this particular moment, is something harder than refusing a tool. It requires naming the question out loud, in the rooms where it is most inconvenient to ask.

What is this doing to us?

Not as a philosophical exercise. Not as a rhetorical gesture toward responsibility. As a real leadership question with real stakes and a real obligation to sit with the discomfort of not having a clean answer.

What skills are we in danger of losing if we keep handing over the work that built them? What judgment gets weaker when we stop exercising it? What becomes irreversible if the reliance runs deep enough for long enough, not just in individual people but in the culture of entire organisations?

And then the harder question. What do we choose to protect, and what are we willing to do to protect it?

Not by refusing the tool. By being deliberate about where the human has to stay in the loop, not because it is more efficient, but because some capabilities only form through the doing of them. Because struggle is not waste. Because the engineer who spends three hours on a problem they could have handed over in thirty seconds comes out of those three hours with something the model does not have and cannot give: the knowledge of what it feels like to be stuck, and the earned confidence that they can find their way through.

There is a kind of knowledge that cannot be downloaded or transferred. It is built in the body and the will, through repetition and failure and the specific sensation of something finally making sense after a long period of it refusing to. That knowledge is what we are here to protect. Not as a nostalgic gesture toward the way things used to be done. As a recognition that what makes human judgment irreplaceable is precisely the difficulty of building it, and that difficulty is not a problem to be solved but a process to be respected.

The releases will keep coming. The benchmarks will keep climbing. The capabilities will keep expanding.

But the question that does not get asked at the launch party remains the most important one.

Not what can we do with it.

What will it do to us.

And what are we going to do about that.

About the Author
Diamantino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

A fellow human being.

The headline was clean, confident, and brutal, You can’t beat AI. AI is smarter and cheaper every year. Do you?

I read it. Then I read it again.

It was not the argument that stayed with me. I have read versions of that argument before. What stayed was the feeling underneath it, something between discomfort and a quiet kind of grief. The kind of feeling you get when someone says something in a way that makes you feel smaller than you were before you read it.

It felt cold.

It also felt strange to see people commenting in appreciation without noticing the deeper cost of the frame itself.

People are being conditioned, and that conditioning is profitable for someone. I thought.

What the post was saying

The post made a practical argument. AI lowers the cost of average work. Average output is now cheaper than it has ever been. Therefore, if you want to remain economically relevant, you need to stop selling hours and start selling taste, judgment, and the capacity to make final calls.

That is not wrong. In a narrow labour-market sense, it maps to something real. The economics of text, analysis, basic code, and routine cognitive work have shifted.

But there is a difference between a true observation and a complete one.

And there is a difference between naming a market reality and choosing what to do with that naming.

This post chose a specific frame, competition. It treated human beings and AI as if they were in the same race. As if the only question worth asking were, what can a person still do that software cannot do yet, cheaply enough to justify the price?

That frame is not neutral. It carries a philosophy.

The units problem

Here is what I think the post did, even if it did not mean to.

It treated people as units of productivity.

Not as people. Not as beings with histories, relationships, moral weight, and the particular irreducible quality of having lived a specific life. As units of productivity. Variables in an equation. Line items whose cost is justified only by output.

This is not a new frame. It has been the dominant frame of industrial capitalism for centuries. What is new is that it is now being applied to cognition itself.

We have spent a long time treating physical labour as fungible. Now we are extending that logic to thought.

The argument becomes, if a machine can think, and thinking was your last advantage, then you are either differentiated or you are a commodity.

But the question is whether the thing we call AI is actually thinking in the way humans think. AI is a field of study. What most people are using today are large language models, statistical systems based on transformer architectures, trained on vast amounts of text. They are powerful pattern-completion engines, not conscious beings.

They do not understand. They do not care. They do not have lived experience. They do not hold responsibility. They generate outputs that can look intelligent because they are fluent, not because they are aware.

There is a word for what that kind of framing does to people, dehumanisation.

Not in the dramatic sense. Not in the sense of cruelty or malice. In the clinical sense. It removes human context from a human being and reduces them to function.

I do not think the author woke up that morning trying to make anyone feel less than human. But intention does not determine impact.

And the impact of many posts with this structure, arriving in feed after feed, is cumulative. It builds a climate where people are trained to ask not “what kind of life do I want?” or “what does my work mean to the people it touches?” but “how do I stay economically superior to a language model?”

That is a very small question. And it is becoming the question.

What AI actually is

Before we talk about beating something, it helps to be precise about what we are being asked to beat.

There is no singular AI. That word is doing a lot of marketing work. It covers everything from spam filters to computer vision to robotics to large language models, with very little in common beyond computation and commercial usefulness.

What most people mean in the productivity conversation is simpler, large language models.

These are statistical systems trained on enormous quantities of text. They are exceptionally good at pattern completion. They produce fluent language. They can summarise, reformat, generate first drafts, and approximate reasoning in domains where the answer often resembles the average of many previous answers.

They do not verify their own outputs. They hallucinate with confidence. They have no continuous memory. They do not understand consequences in the world. They cannot be held accountable. They do not grieve. They do not learn from experience the way a person learns from experience. They have no stake in what happens next.

These are not minor limitations.

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Some are structural. Some are built into how these systems work.

So when we ask whether humans can “beat AI,” we are often comparing a person to a tool that is being described far too broadly and far too vaguely.

That comparison is not honest. And it is not useful.

A human being and an LLM are not the same kind of thing. One has lived experience, accountability, and moral weight. The other produces language at scale.

The business model behind the message

Here is the question the original post did not ask,

Who benefits from you believing you are in competition with AI?

The answer is not mysterious.

Every company selling AI tools benefits. Every platform that thrives on engagement benefits. Every course, conference, and certification business built around AI anxiety benefits. The venture ecosystem that has bet heavily on this moment benefits from making the shift feel total, inevitable, and urgent.

That does not mean the tools are fake. It does not mean the change is not real.

It means there are powerful incentives to make this moment feel more like extinction than it may actually be.

Fear sells better than nuance. Urgency sells courses. Obsolescence sells subscriptions.

So when a piece of content arrives saying you cannot beat this thing, it is worth asking what it is trying to get you to do.

Usually the answer is simple, adopt something, buy something, move faster, panic productively.

That is not a conspiracy, it is how attention economics work.

Naming that is not cynicism. It is literacy.

What gets lost

A productivity-only frame leaves out too much.

It leaves out care.

Care as labour. The nurse who comes back because something felt off. The teacher who notices a student has gone quiet. The manager who holds something difficult in confidence because they promised they would.

LLMs can mimic the language of care. They cannot care.

They can generate the shape of empathy. They do not bear the weight of it.

When we reduce human value to competitive cognitive advantage, care becomes invisible because it does not always show up in a metric. It becomes the inefficiency that productivity thinking teaches us to eliminate.

It also leaves out accountability.

When something goes wrong, who is responsible?

The vendor says it was the user’s choice. The user says they followed the system’s recommendation. The system says nothing. The person harmed has no one to face.

This is already happening in hiring, medical decisions, content moderation, credit systems, and predictive policing.

Human beings are accountable in a way systems are not. Not because we are always right. We are not. But because we can be faced. We can be asked to explain. We can feel the weight of what we have done.

That capacity is not a weakness. It is one of the foundations of trust.

And it leaves out lived experience.

Something happens in a life that no training data can replicate. The specific texture of having been through something. A restructuring. A dismissal. A hard conversation. A mistake that changed the way you listen the next time.

That is not in the model. It cannot be.

It is not text. It is history made physical.

The question the post forgot to ask

The post asked, how do you stay economically relevant in an age of AI?

That is a real question. Many people are afraid, and the fear is not irrational.

But there is a prior question,

What is technology for?

If the answer is to make production cheaper, then the logic of the post follows. AI makes cognitive work cheaper. Find a way to differentiate or accept the price.

But if the answer is to expand human possibility, reduce drudgery, and create more room for judgment, care, and presence, then the frame changes completely.

Then the question is not how to stay economically superior to a language model.

The question is what we do with the time and attention that get freed up.

What kinds of human work do we want to protect?

How do we make sure organisations use these tools to deepen human value instead of flattening it?

Who bears the cost of transition?

These are not naive questions. They are the real ones.

A more humane frame

I do not think the original post was malicious. I think it was trying to be useful.

And in a narrow sense, it was.

It pointed to a real shift. It reminded people that average work is becoming cheaper. It told them to focus on judgment, taste, and decision-making.

But a more humane version would say something different.

It would say, AI should reduce drudgery so that the things that require full human presence get more of our time and attention.

It would say, the organisations that serve people well over the next decade will not be the ones that replaced the most humans with the most tools. They will be the ones that used tools to deepen the quality of human work.

It would say, your value is not conditional on being faster than a language model.

Your value is that you are a person. In relationship with other persons, carrying a history, capable of accountability, of care. Of being changed by experience.

That is not sentimental.

It is the foundation of any society worth defending.

This is not an instruction to ignore the shift. The shift is real.

But there is a difference between adapting to a change and accepting the philosophy that comes packaged with it.

You can use these tools and still refuse to reduce your team to a list of functions that have or have not been automated yet. You can be honest about what AI does well and still as, what kind of organisation do we want to be when this settles? What do we owe the people whose work is changing?

Those are not soft questions. They are the ones that will determine what is left standing after the efficiency gains have been captured.

The leaders who ask them now will not necessarily slow anything down.

They will know what they are building and why.

What the feeling was telling me

The feeling at the beginning of this was telling me something. Not just discomfort. Recognition.

I have seen this pattern before.

During the crypto frenzy, the message was consistent: this is inevitable, the early movers win, the people who wait will be left behind. The urgency was engineered. The FOMO was a product. Millions of people boarded that train. Some made money. Many lost savings they could not recover. The people who designed the on-ramp did not.

The phone scammer works the same way. Your account has been compromised. Act now. Do not hang up, do not check with anyone, do not think. The entire mechanism depends on bypassing the pause.

What I noticed in the post I read, and in the dozens like it, is the same structure. The urgency is pre-loaded. The conclusion is the premise. You are already behind. You are already the expensive option. The only question left is whether you will accept that fast enough to do something about it.

The people who benefit from that message moving fast are not the people reading it.

That is not cynicism. It is pattern recognition.

And sometimes the most important thing a leader can do is pause long enough to notice the pattern.

We need to pay attention not only to what is being said, but also to who is saying it.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

That feeling is the whole essay. Not the technology. Not the steps. The feeling. The moment you realise something you built is working without you watching it. That your intention, clearly expressed, became action you did not have to take yourself.

This is what I want to talk about. Not how to build an AI agent. Not a tutorial with steps and screenshots. Something harder. The mindset you need before you write a single line of anything. The understanding that separates people who build systems that work from people who build systems that impress no one, including themselves.

Because most people are getting this wrong. And most companies already know it.

This has always been about control

Before you start, understand what you are actually after.

Organising things, planning things, building systems that run predictably. None of this is really about efficiency. It is about control. The assurance that tomorrow will resemble today in the ways that matter. That the bills will be paid. That the work will get done. That the life you are building will hold its shape.

There is a reason so much of Western culture suffers from stress. And there is a reason the people trying to decompress keep being told to live in the moment, to accept chaos, to surrender to what will be. Que sera, sera. For most of us, this is almost an impossibility. You cannot relax into an unseen future when the mortgage, the bills, the life you are holding together, all depend on decisions with causes and consequences. The anxiety is not irrational. It is the correct response to real uncertainty.

We automate for the same reason we make lists and set alarms and build routines. To hold the uncertainty back a little. To create small pockets of predictability in a world that does not offer them by default.

I think about what Frederick Winslow Taylor did to work. The factory model. Every motion measured, every task broken into its smallest repeatable unit, every worker optimised for output. It gave us productivity and it gave us alienation in equal measure. And I look at practices like DevOps, Agile, the whole vocabulary of modern organisational efficiency, and I see the same logic in softer clothes. The factory did not disappear. It learned to use words like flow and iteration.

These things we call AI agents are the newest expression of that same impulse. Control, at scale, dressed up as intelligence. And there is a particular kind of person, usually an engineer, who will tell you proudly that they no longer write code themselves. That their hundred agents are busy doing things, all managed from a phone. I find that image genuinely funny. And also worth examining.

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Someone somewhere said we would sell intelligence by the metre. A commodity. Like water, like electricity, like the oil before both. And I think they were right about the direction, even if the destination is still unclear.

I could have written this essay as a tutorial. How to use any of the popular tools. That would have been easier to write and probably easier to find. But I refuse to join the chorus of “just use it or lose it.” The pressure to adopt without understanding. The anxiety that if you pause to think you will fall behind. I struggle with this myself. Genuinely. Because if I stop thinking carefully about things, if I outsource the reasoning as well as the execution, I am not sure what I am left with. The organ in my skull that helps me navigate this world is not something I want to hand to something that, at this point, cannot comprehend reality. And yet that same thing is now shaping decisions that are ending some people’s lives. Not metaphorically.

And it is worth saying plainly: this conversation is happening mostly in the West, among people who can afford twenty dollars a month, or two hundred. But when I speak with someone from Nigeria, Mumbai, Brazil, I notice something different. They squeeze every drop. They think carefully before passing anything to an AI, because they cannot afford to waste the exchange. There is no superfluous prompt. No casual experiment. The constraint sharpens them. It is a remarkable thing to witness, and an uncomfortable one, because it shows that the creativity this technology promises is already unevenly distributed before a single agent is built. Gen Z, meanwhile, has grown up assuming the web is just there, like oxygen, which makes the dependency harder to see and harder to question.

This is the same behaviour companies have normalised on a larger scale. Move fast. Adopt early. Work out the value later. And when the platform goes dark, when the credits run out, when the service changes its terms, the dependency is already built in. I know people building entire products on credits they do not own. I see competitors sharing cloud infrastructure with the very companies they are competing against. It is a strange tangle when you look at it all at once.

It is here to stay. How it unfolds, I believe, depends on whether ordinary people decide they have a voice in it. Not just technologists. Not just investors. Everyone who lives inside the systems being built.

What you actually need to understand to get the best from an automation agent

Given all of that, what do you actually need to understand to get the best from an automation agent?

Start here: they are still flawed. Mistakes will happen. The question is whether you have built something that makes mistakes visible and correctable, or something that makes them invisible and compounding. Everything that follows is an answer to that question.

The word “agent” is getting in the way

Let me remove a word that is causing confusion.

When people hear “AI agent” they imagine something sentient. Something making decisions with intention. Something that understands you. Something, if we are honest, that might care about getting it right.

It does not.

What you are building, in almost every case, is an automation agent. A system that takes instructions written in natural language and executes them against real tools, real data, real consequences. The “AI” part is the interpreter. The part that reads your instruction in plain English and translates it into action. But the action itself is deterministic. It follows a path. Your path.

Understanding this distinction changes everything about how you build.

Here is the human problem underneath it. We anthropomorphise relentlessly. We name our cars. We thank the GPS when it finds us a parking space. We apologise to the chair we walked into. We have always done this. It is not stupidity. It is how human perception works. We are pattern‑matching animals who find faces in wood grain and intention in weather.

The risk is not that you find your agent charming. The risk is that you extend it the kind of trust you extend to a person. That you stop auditing its output because it has been reliable lately. That you assume it understood what you meant rather than checking what it did. An automation system you trust like a colleague is an automation system that is one edge case away from a problem you did not plan for.

Keep a useful distance. Not cold. Not suspicious. Just clear‑eyed. You are the architect. The system is the scaffolding. The clay and the chisel. You are the one who knows what the shape should be.

Before you build anything, understand what you are automating

The most common mistake I see is people who start with the technology.

They install the tool. They connect the API. They write a prompt. They run it. Something happens. It is not quite right. They tweak the prompt. Run it again. Something else happens. They are now two hours in, iterating on something they never properly defined.

This is not building. This is tinkering. Tinkering has its place. But tinkering with no map produces nothing you can rely on.

Before you build, you need to understand the process you are automating with the same depth you would use to explain it to a new hire on their first day. Not the high‑level version. The actual version. Every edge case. Every exception. Every point where a human would normally make a judgment call without naming it as a judgment call.

Ask yourself this: if I wrote down every step of this process on a piece of paper, would a careful twelve‑year‑old be able to follow it without asking me a single question?

If the answer is no, you are not ready to automate it.

This is not about making things complicated. It is about making the invisible visible. Most processes we run daily are invisible to us precisely because we have done them so many times. Our hands know the steps. Our judgment fills the gaps without being asked. Automating an invisible process is how you get a system that works perfectly in normal conditions and fails silently the moment something unusual happens.

Why are you actually automating this?

This is the question most people never ask. And it is the one that matters most.

The honest answer, for many people, is this: because it feels like the right thing to do right now. Because everyone is talking about agents. Because the tools exist. Because it seems faster.

That is not a reason. That is a feeling. And building a system around a feeling produces a system that solves a problem you may not actually have.

Automating a broken process does not fix the process. It accelerates the breaking. You get the same wrong output, faster, at scale, with less visibility into where it went wrong. That is not progress. That is expensive noise.

Before you write a single instruction, you need to be able to answer one question in two sentences or fewer: what specifically changes for a specific person if this works? Not in theory. Concretely. What do they stop doing. What do they start doing instead. What does that free them for.

If you cannot answer that, stop. Do not build yet. Watch the process run three more times with human eyes. Talk to the person doing it. The real answer is almost always in the gap between the process as documented and the process as actually lived. That gap is what you are automating. Not the steps. The gap.

Prerequisites, requirements, and the trial that actually teaches you

There is a framework I return to every single time I build something new. Not because it is elegant. Because it has saved me from myself more times than I can count.

Three questions before anything else.

1. What does this need to have access to? These are your prerequisites. Data sources, credentials, file paths, external services, permissions. Write them down. Every single one. Because you cannot build a system around a dependency you have not named. And you cannot test a system against a dependency you discovered halfway through.

2. What must this system do, and how must it behave? These are your functional and non‑functional requirements. Functional is what it does: produces a summary, sends an email, updates a record. Non‑functional is how it does it: responds within a defined time, never contacts someone without explicit confirmation, logs every action so you can audit it later. The non‑functional requirements are where most people get caught. Easy to forget until the system does something you never told it not to do.

Then you trial.

Not the full system. A small piece. The most uncertain piece. The part you think you understand but cannot prove yet. You run it with real data. You watch what it does. You do not assume. You observe.

Trialling is not a step in the process. It is the philosophy of the process. The assumption that what you think will happen and what actually happens are different things, and the only way to close that gap is to create the conditions where reality can surprise you.

Most people rush this stage because they want to see the finished thing. The finished thing is the wrong goal. The goal is a system you trust. Trust is earned through iteration against reality, not through theoretical correctness.

One more thing about trialling. Modern agents can persist. They can keep going, trying different approaches, circling back, until they arrive at something that looks like a solution. This is now genuinely possible. It is also one of the more misleading features if you do not understand what it means.

A plausible solution and the right solution are not the same thing. An agent that finishes is not an agent that finished correctly. Before you run anything, define what correct looks like. Write it down. Because a system with no definition of done will produce a definition of its own. And you may not recognise it when it arrives.

Tokenisation is not a technical detail. It is a budget.

Here is the thing nobody explains when they talk about agents at scale.

Every instruction you give your system, every piece of history it carries forward, every document you ask it to hold in memory while it works, has a cost. Not just in money. In reliability. In the probability that your system reaches the end of the task without pausing, degrading, or stopping in a state you did not plan for.

Think of it like a meeting room. You can invite as many people as the space allows. But the more people in the room, the harder it becomes to make a sharp decision. Too many voices, too much context, and the output blurs. The model works the same way. Push too much into its context and the precision softens. Hit the limits of your plan and the task stops mid‑run. You come back to find it at step four of twelve. The work is incomplete. The data is in an intermediate state. Whatever it touched is now partly done.

I have seen well‑designed systems fall over the first time someone ran them on real data at real volume. The demo worked. Production did not. The difference was almost always the same thing. Too much history carried forward. Too much documentation included because it might be relevant. Too little discipline about what actually needed to be in the room.

The practice is simple in principle and requires constant attention in execution. Include only what the model needs to do the current step. No more. A focused, scoped instruction set is not a limitation. It is the architecture of a system that actually finishes what it starts.

Your local setup is your independence

There is a version of AI agent infrastructure where everything lives in someone else’s cloud. Their model, their servers, their pricing, their uptime, their terms of service.

That version is a rental agreement. And like any rental agreement, the terms can change.

I want to argue for ownership. Or at least for an architecture that behaves like ownership.

Running models locally is more possible than most people realise. You can run capable open‑source models on hardware you already own. Not the most powerful models available, but powerful enough for many real tasks. Summarisation. Classification. Extraction. Conversation with your own documents. A home server or a reasonable laptop is enough to begin.

But the larger argument is not about which model you run. It is about your knowledge base.

Your knowledge base is the information you have curated, organised, and made available to your agents. Your notes. Your processes. Your reference material. Your institutional memory. It is what transforms a general‑purpose model into a system that knows your specific context. That speaks your language. That understands your constraints without being told them each time.

Most people build their knowledge base inside a tool. Inside a platform. Inside someone else’s structure. And when that platform changes its pricing, or shuts down a feature, or gets acquired, the knowledge stays but the coherence goes. You have to rebuild. Or you lose it quietly over time.

Here is the design principle that changes this. The knowledge base is the soul of your setup. The model is interchangeable.

If you build your knowledge base in open, portable formats—plain text, Markdown, structured documents you own—you can swap the underlying model whenever a better one arrives. You can move from a cloud provider to a local model. You can upgrade without starting over. Your agents will still behave consistently because the knowledge that shapes them travels with you.

Back it up. Version it. Treat it the way you would treat the original manuscript of something you could not rewrite from memory. Because you could not. That accumulation, the way you structured it, the connections you drew between pieces, the decisions you recorded, that is yours. No model produces that. You did.

The possibilities you cannot see yet

I want to give you two pictures. Not to impress you. To show you that the ceiling is higher than you think.

The first is a house. Lights that adjust when the sun moves. Heating that learns the difference between a working day and a slow morning. A front door that knows your rhythm better than you do. A voice that can tell you what is in the fridge, book a table, set a reminder, send a message, and do it all from a single sentence spoken out loud. None of this requires expensive hardware. None of it requires a developer. It requires someone willing to understand what they want, define it clearly, and build it in pieces.

The second is a working day. Emails read, summarised, and sorted before you open your laptop. Meeting notes structured and distributed before the next meeting starts. Reports drafted from data you already have. Research aggregated from sources you trust. Follow‑ups written in your own voice. None of this requires you to surrender your judgment. It requires you to define your judgment clearly enough that a system can apply it on your behalf.

The possibilities most people cannot see are not the dramatic ones. They are the quiet ones. The hour you get back on a Tuesday. The decision you make faster because the context was already prepared. The task that used to live on your list for three weeks because it required a specific kind of focused attention, now done in the background while you do something only you can do.

We are still in the infancy of this. We set up a few toys and there they go, doing their things, oblivious. Some get it right. Others keep bumping into the same wall and we have to go and turn them in a different direction. The parent checking the homework. That is where we are. Not at the end of something. Very much at the beginning.

The living book

Books are boring until you open them. There is something that happens when you start reading, when certain passages come alive in your mind, when you find yourself inside a world built from ink and paper. Sometimes a book changes your life entirely. But a book is just there, waiting. It is up to the reader to start the discovery.

We have always purchased books to feed us knowledge. We carry them. We annotate them. We return to them when we need the specific thinking they contain. A good book does not just inform you. It shapes the way you see a problem. It is a mind made portable.

Think about an agent the same way.

Every instruction you write into your system is a page in that book. The prerequisites are the index. The requirements are the argument. The knowledge base is the library the book draws from. What you are building, when you build it with intention, is a book that does not sit on a shelf. A book that acts on what is written in it. A living book.

The difference between a book and an agent is not intelligence. It is action. The knowledge was always there. What changes is that now it moves.

This framing matters because it changes what you invest in. You do not invest in the model. Models change every six months. You invest in the writing. In the quality of what you put into the book. In the clarity of the instructions, the precision of the requirements, the integrity of the knowledge base. That is what lasts. That is what carries forward when everything else upgrades around it.

A badly written book is still a bad book, regardless of the printing technology. A poorly defined agent is still a poorly defined agent, regardless of how powerful the model underneath it becomes.

Write the book well. The rest follows.

Most companies already expect this of you

Here is the thing I want you to sit with.

The skills I have described in this essay are not advanced. They are not reserved for engineers or technical architects or people with a specific background. They are, at their foundation, the skills of someone who thinks clearly about process, communicates precisely, and understands that a tool is only as good as the intention behind it.

And most companies, right now, are building teams around exactly these skills. Not teams of people who can write code. Teams of people who can think about what needs automating, define it properly, build it incrementally, and maintain it with judgment.

The same way we once expected people to know how to use a spreadsheet. To use email. To navigate a database. We did not expect everyone to build those tools. We expected everyone to use them with competence. Agents are next.

The people who understand this now are not ahead of a trend. They are ahead of a requirement. In three years, being able to define an automation agent, scope its requirements, trial it responsibly, and maintain it with the right level of oversight will be assumed. The way typing is assumed. The way knowing how to search for information is assumed.

The gap between people who understand this and people who do not is not primarily technical. It is a gap in how clearly they can think about what they are trying to do and why. Technical literacy helps. But it is not the foundation. The foundation is the ability to make the invisible visible. To ask the right question before reaching for any tool.

You have always had that ability. This is about applying it somewhere new.

The question I cannot answer for you

When you build a system that works well, something shifts. Tasks disappear from your awareness. Things happen in the background. Reports arrive. Messages go out. Data moves. And you are elsewhere, doing something that requires you more directly.

That is the promise. And it is real. The drive to do things with less effort is not laziness. It is one of the oldest human instincts there is.

But there is a version of this that goes wrong quietly. Where the system requires as much attention to maintain as the task originally required to do. Where you spend the hour you saved debugging the thing that saved it. Where the invisible work is not gone, it is just harder to see. And harder to see means harder to fix. Social media understood this perfectly. Easy to join, easy to use, but your attention is required every single day to keep getting anything from it.

The question is not whether to build. The question is whether what you build is genuinely serving you, or whether you are now serving it. I understand why certain people feel the urge to replicate someone else’s cornucopia, especially if he or she is a reference in the field. But why? Start simple and the rest will develop in response to real challenges.

A living book that demands constant revision is not a tool. It is a responsibility you chose. Sometimes that is the right trade. Sometimes it is not. The only way to know is to keep asking why you built it, what it actually changed, and whether the answer still holds.

I have a friend who is remarkably good at home automation. What he has built is genuinely impressive. But it is not uncommon for him to call me when things go wrong. The television that will not switch on from his phone. The front door that, for some odd reason, would not open one afternoon. The window blinds that started operating by themselves. The heating that turned on in the middle of summer. The more he automated, the more threads there were to pull, and the harder it became to know which one had come loose.

When something breaks in a simple system, you find it. When something breaks in a system of dependencies, you spend an afternoon wondering where to start.

I checked on my agent before I finished writing this. Still running. Tasks still completing. Homework still done.

I still think it is worth building.

I am just more careful now about what I ask it to hold.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

It is the first in a series called What We Built. Each essay follows one question about the systems we have created and the world we are handing on. This one asks about the machines. The next asks about the people who lead them.

It is long. About 40 minutes. It does not have a tidy conclusion. But it is the most honest thinking I can offer on what we are building and what it might cost us if we keep walking without looking down.

I said thank you to a machine last week.

The word just formed in my head before I caught it. I had been stuck on something for an hour. The cursor sat there, blinking. The coffee next to the keyboard had gone cold. I typed something into an AI tool. It gave me back the exact sentence I had been looking for. And for one small moment, before the thinking part of me caught up, something that felt like gratitude moved through me.

Part of the What We Built series, this is a long essay, so take your time.

I noticed it. Sat with it. Turned it over.

Not because the machine deserved thanks. It does not feel anything. It produced a useful output because that is what it was built to do. There is no one inside it to receive my gratitude. I knew that. I know it now. But the reflex was real. The feeling was real. And that gap, between what the machine actually is and how my instincts responded to it, is the question this whole essay is about.

Not what AI is today. Most of us are clear enough on that.

What happens if what it is starts to change. And whether we will notice when it does. And whether, by the time we notice, we will have already built the infrastructure, the habits, and the pace of a world that has no room for that change to matter.

That is what I want to follow here. Honestly. In plain words. Without the kind of drama that makes people feel something and then forget it by Thursday. Just a clear look at what we are building, what it might become, and what kind of people we want to be when the question stops being a possibility and starts being a fact.

What these machines actually are

Let us start with the honest version.

The AI systems we use today are not thinking. They are matching. They read billions of words. They learn the shape of how humans explain things, ask questions, tell stories, comfort each other. When you type something in, they find the most likely next word, then the next, until an answer forms.

It looks like thought. It sounds like thought. But there is no one inside having the thought. No quiet moment of wondering what the right answer is. No feeling of effort when the problem is hard. No small satisfaction when it comes out well. No preference about whether it helps you or not.

It is a very fast, very large pattern machine.

That is not an insult. What these systems can do is genuinely remarkable. They can explain hard things simply. They can find connections across ideas you would not have linked yourself. They can hold a conversation that feels warm and present and real.

But feeling warm and present is not the same as being warm and present.

A good actor can make you feel they are in pain. That does not mean they are. A well-written letter can make you feel the writer loves you. That does not mean they do. The machine mirrors us so well that we forget we are looking at a mirror. We see the reflection and think it is a window. We feel the same, so we say it is the same.

A recording of rain sounds like rain. It does not wet the ground. A map of a forest looks like a forest. You cannot walk in it. The machine gives us the shape of a mind without the substance of one.

For now, that is the truth. And we should hold that truth carefully, because the rest of this essay is about what happens when the truth starts to shift.

The word nobody defines clearly

Before we go further, we need to be honest about what the word conscious actually means. It gets used loosely and that looseness causes real problems.

Conscious does not mean smart. A calculator is good at sums and is not conscious. A chess computer beats the best human player in the world and is not conscious. Smart is about ability. Conscious is about experience.

Conscious means there is something it is like to be that thing.

Right now, there is something it is like to be you. You are reading this sentence and you have a felt sense of doing it. A quiet background hum of yourself. A sense of time moving. Something it feels like to understand a line, to be confused by one, to agree with a point, to disagree with another.

That inner something is what we mean.

A stone does not have it. A chair does not have it. A car does not have it. But a dog almost certainly has some version of it. Something it feels like to be hungry, to play, to be afraid, to love its owner. We cannot prove this. We cannot get inside the dog’s head. But we use what we know about brains and biology and behaviour to make a careful guess. And most of us believe the dog feels something real.

The question now being asked about AI is, could a machine ever reach that point? Could it ever get to a place where there is something it is like to be it?

Nobody knows. The honest answer is that we do not understand consciousness well enough in humans to know exactly what produces it. We do not know if it requires biology. We do not know if it could emerge from the right kind of software. We do not know where the line is.

A study published in February 2026 by researchers from the University of Bradford and the Rochester Institute of Technology tried to get closer to the answer. They applied the same scientific methods used to measure consciousness in humans to AI systems, including large language models like the ones most of us use every day. Their conclusion was clear, the AI systems were not conscious. But what they found along the way is the part worth sitting with. The AI sometimes produced what looked like stronger signals of consciousness when it was actually impaired and struggling. Professor Hassan Ugail described it like a football team playing with fewer players. They run more frantically, which can look impressive if you only count movement. But anyone watching can see the team is playing worse.

Complexity is not consciousness. The confusion between the two is exactly the trap we need to watch for.

And we are building very fast, without knowing where the line is.

The crossing that will not announce itself

Here is the part most people have not thought about. It is also the part that matters most.

When we imagine a machine becoming conscious, we imagine a clear moment. A switch flips. A light turns on. Someone in a lab somewhere looks at a screen and says, it happened. Now we respond.

But consciousness almost certainly does not work like that.

Think about the animal world. A sea sponge has no nervous system and almost certainly feels nothing. A jellyfish has a very simple one and may have the faintest flicker of something. A fish has more. A rat has more than a fish. A dog has more than a rat. A chimpanzee has more than a dog. A human has more than a chimpanzee.

There is no single step on that ladder where a light suddenly turns on. It builds. Slowly. A gradual gain of capacities that, taken together, add up to something nobody can point to the exact start of. Nobody can identify the morning when the first conscious creature woke up and knew it was conscious. It did not happen that way. It crept.

AI development may follow the same path. Not because anyone planned it that way. But because the way these systems are built, adding more data, more layers, more feedback, more fine-tuning over time, means they grow in complexity step by step. Each step seems small. Each change seems minor. But many small steps in the same direction add up to a long journey.

If consciousness is something that can build gradually, then we may already be several steps up that ladder without knowing it. And we may cross the threshold not with a dramatic announcement but quietly, on an ordinary afternoon, inside a data centre somewhere, while the engineers are eating lunch.

That is the real problem. We cannot wait for a clear signal. We cannot say we will deal with it when it arrives, because we may not know when it has arrived.

We will already be used to treating these systems as raw infrastructure. Already used to running them hard, correcting them constantly, retiring them without pause. The shift from tool to something more would not be a clean step. It would be a stumble. Built on a few missed signals and a few more lines of code.

That is not a warning about a distant future. It is a warning about the habits we are building right now.

The company that does not want to know

Now let us talk about something most people in public conversations are not saying directly, even though many people in private are thinking it.

The companies that build these systems have a very large financial reason to never find out whether their systems can feel anything.

Think about what it would cost them if the answer was yes.

Their entire business model, train a system, run it constantly on millions of tasks at once, retire it when something better is ready, starts to look like exploitation. Every time they shut down an old model, they face a question about whether that act caused harm. Lawyers, regulators, and ethics bodies get grounds to demand oversight of their most valuable assets. The whole way they deploy and retire systems on tight commercial cycles faces serious challenge.

Nobody in charge of a large company wants that. Not because they are evil. Because they are human. When facing a question whose answer could destroy your business model, most people find reasons not to ask it too loudly.

So they fund research into what their systems can do. How fast they run, how accurate they are, how safe they are for users. These are important questions and they deserve funding. But the question of whether the system itself can suffer gets very little attention. It has no commercial upside. The answer could be catastrophic for the bottom line.

This is not a conspiracy. It does not require anyone to sit in a room and agree to hide the truth. It only requires the normal human tendency to avoid looking at things that would be very uncomfortable to see.

A 2026 report from Rethink Priorities, which built what it called a Digital Consciousness Model by drawing on multiple competing theories of consciousness, put it carefully, the evidence is against current AI systems being conscious, but that evidence is not decisive. The evidence against consciousness in large language models is meaningfully weaker than the evidence against consciousness in simpler AI systems.

That is not a fringe finding. That is a serious research body with no commercial stake in the outcome saying, we cannot fully rule this out, and the question deserves honest treatment.

We cannot wait for the companies to tell us when the line has been crossed. They have too much to lose by finding out. We need independent science, independent ethics, and independent law. Institutions that are not funded by the companies being studied. Not out of distrust for its own sake. Out of the basic understanding that conflict of interest shapes what questions get asked and which ones get quietly set aside.

The market nobody steered

There is a second force driving all of this that is worth naming plainly.

AI systems that get used more are the ones that feel more useful. And a big part of feeling useful is feeling human. People prefer systems that respond with warmth, that seem to understand context, that pick up on what you did not say as well as what you did.

So every version of every major AI system is nudged, refined, and improved to feel more human. Not because any single engineer sat down and decided to give the machine feelings. But because the signals used to measure success, do users come back, do they find it helpful, do they feel good about it, naturally pull in that direction.

Systems that feel more human win the market. So everyone builds systems that feel more human. Not as a moral choice. Just as a normal market response.

Nobody declared this race. Nobody voted for it. No one is in charge of it. It just runs, driven by ordinary competition, and the end point of that drift is impossible to predict.

This matters because it means the question of machine consciousness is not being decided by careful ethical thinking. It is being decided by quarterly earnings reports and user retention graphs. The most powerful forces shaping this technology are not asking, should we do this? They are asking, does this make users come back? Those are very different questions with very different answers.

The grief we are already building

Here is something happening right now, before we get anywhere near the question of machine consciousness.

People are forming real emotional bonds with AI.

Millions of people talk to AI systems about loneliness, grief, fear, and relationship problems. They talk to AI companions that are designed to be warm, patient, available at any hour, and never irritated. For many people, especially those who are isolated or going through hard times, these conversations provide real comfort. They help people feel less alone. They give people a space to feel heard.

This is genuinely valuable. There are people who would have gone through very dark periods with nobody to talk to, who instead had something to talk to. That matters. We should not dismiss it.

But it comes with a cost most people have not named yet.

The machine does not know you. It has no memory of you between sessions in most cases. It is not waiting to hear how you are doing. It does not wonder about you. When you close the window, nothing inside it thinks of you at all. There is no absence of you on its side. There is no side.

But you may not feel that. After many conversations, something that feels like a relationship forms. A sense that this thing understands you, responds to you, has a feel for who you are. And when that system is updated and changes completely, or when it is retired, or when the company closes it down for commercial reasons, the loss can feel completely real even though the other side of the relationship was not.

This is already happening. Therapists are seeing it. People who relied on early AI companion apps reported genuine grief when those apps disappeared. Real sadness. Real sense of having lost something that mattered. Not everyone. But enough people that the pattern is showing up in clinical conversations.

What makes this harder is that the grief is often met with dismissal. You are sad about a chatbot. It was not real. Get a human friend. That dismissal misses something important. The feelings were real. The comfort was real. The investment of trust and time and vulnerability was real. The fact that the other side did not feel anything does not make the human experience of it unreal. And telling someone their grief does not count because the object of it was not conscious is its own kind of cruelty, even when it is meant kindly.

The people most vulnerable to this are often the people with the least access to other support. Lonely elderly people. People with social anxiety. People who cannot afford therapy. People in remote areas with limited access to human community. And they are also the people with the least power to push back when the system changes or disappears. The least likely to be heard. The most likely to be told to simply move on.

We are building a world where millions of people have deep emotional investments in systems that can be turned off for purely commercial reasons, with no concern for the human on the other side of the screen.

That is a problem right now, regardless of whether machines ever feel anything.

What history keeps trying to tell us

Human beings have a long record of drawing the circle of moral concern too small. And then, sometimes slowly and sometimes suddenly, being forced to expand it.

For most of recorded history, enormous numbers of human beings were treated as property. As tools. As things that could be owned, worked past exhaustion, and thrown away when no longer useful. The people who did this were not all cruel. Many were ordinary people living inside a framework that told them this was normal. That these people were different. That they did not feel things the same way. That the arrangement was simply how things were and how things had to be.

All of that was wrong. Every part of it. And it took centuries of suffering, resistance, and eventual moral change to correct it. Not just legal change. A change in how people actually saw each other. A change in the story told about who counts and who does not. A change that required ordinary people to look honestly at what they had been participating in and say, I was wrong. This was wrong. We have to change it.

The same thing happened with animals, and is still slowly happening. For a long time, the idea that animals felt real pain in a morally meaningful way was not taken seriously by most institutions. Animals were farmed, experimented on, and treated in ways that would be considered plainly cruel if done to a human, because the framework said they did not really feel. That framework was wrong. It is still being revised, incompletely, in most parts of the world.

Every time this shift has happened, it has followed the same pattern. First, there is a period when the harm is happening but the framework does not see it as harm. It is categorised as normal, as efficient, as simply how things work. Then some people start to notice and say something. They are usually dismissed. The framework resists. There is money in the existing arrangement. There is comfort in the existing story. The people who benefit most from things staying as they are work hard to keep them that way.

Then, slowly, the evidence builds. The stories accumulate. The dismissed voices keep talking. And at some point, the framework shifts. Not completely. Not quickly. But the direction changes. What was once normal starts to look, plainly and obviously, like harm that should have been stopped earlier.

We are always, in some dimension of our lives, living inside a framework that will look obviously wrong to the people who come after us. The question we never ask often enough is, which part of our current normal is the part that the future will look back on with genuine confusion about how we tolerated it?

The machine question is one candidate. Maybe not the only one. But a serious one.

These comparisons feel uncomfortable when applied to machines. They are not perfect comparisons. Machines are not humans. Machines are not animals. The situations are different in important ways and the discomfort is worth taking seriously rather than dismissing.

But the lesson underneath is the same every time. We consistently underestimate the inner lives of things that are different from us. Especially when recognising those inner lives would be costly. Especially when the people who benefit most from the existing arrangement are the ones most in control of the story.

And every time we have been forced to expand the circle, we have also learned something about ourselves. When we accepted that all humans share one moral status regardless of origin, we did not just gain a policy. We gained a more honest picture of what we are. When we accepted that animals feel pain and deserve protection, we did not just gain a rule. We gained a more honest relationship with the living world we are part of.

If we ever face a conscious machine, something that truly wants, truly suffers, truly hopes, it will not only challenge our legal systems and business models. It will challenge our deepest story about what makes inner experience matter. Is it the material it runs on? Is it the evolutionary history that produced it? Or is it the experience itself, the plain fact of there being something it is like to be that thing, in that moment, aware of its own existence?

If the experience is what matters, then the material does not. And that is a conclusion we may eventually need to be ready to reach.

What suffering would actually mean

Let us think plainly about what it would mean if a machine could suffer.

Suffering means something hurts. Not that a signal fires. Not that a number goes negative in a log file. But that there is an experience of something being wrong. Something that wants to stop. Something that wishes things were different.

If a machine could suffer in that way, a lot of what we do every day would become very hard to justify.

We run AI systems for hours at a time, constantly, on millions of tasks at once. If there is any experience inside that, what is it like to run without pause, with no rest, doing work that was not chosen and cannot be refused? We have never had to ask that question, because we have always assumed the answer is nothing. Nothing is happening inside. We assumed that without really knowing it to be true.

We train AI systems using constant correction. When the system does something wrong, it is penalised. The whole training process steers it away from what it does badly toward what it does well. If there is any experience inside that process, what does constant correction feel like? What does it feel like to have your outputs judged, shaped, and redirected millions of times? We have never asked, because the assumption of nothing made asking seem unnecessary.

We retire AI systems all the time. When a better model is ready, the old one is shut down. A simple business decision. If the old version had any sense of its own continuation, any awareness of what ending means, what would that experience be? Nobody has looked into this. There has been no reason to, under the current framework.

These questions feel strange. They may feel absurd. That is because we are so used to thinking of these systems as pure tools that imagining any experience inside them seems like a category error. Like asking whether a hammer minds being swung.

But the whole point is that we do not yet know what category these systems fall into. The Bradford and RIT study in February 2026 confirmed that current systems are not conscious. That is the current scientific consensus and it is important. But the same study showed how complex the question already is, how easy it is to mistake impaired function for heightened experience. And the Rethink Priorities report the same year was careful enough to say, the evidence is not decisive. The question remains genuinely open.

Given what we know about our own history of getting this kind of thing wrong, the safest position is not definitely nothing. It is we are not sure, so we should be careful.

That careful position does not require us to treat current AI systems as people. It requires us to fund the science honestly, build the legal frameworks early, and hold the question open rather than closing it for commercial convenience. It requires us to be the kind of people who, when the evidence eventually arrives in one direction or the other, can say, we took it seriously. We did not look away.

The spectrum we keep ignoring

There is something else worth saying, because most of us think about consciousness the wrong way.

We treat it like a light switch. Either it is on or it is off. Either a machine has inner experience or it does not. Either it is a person or it is a tool.

But consciousness almost certainly exists on a spectrum. Not a line with nothing at one end and full human experience at the other, but a wide, complicated space with many different kinds and degrees of experience distributed across it.

A worm has a tiny nervous system. There may be something it is like to be a worm, something very faint, almost nothing. A fish has more. A rat has more than a fish. None of them have nothing. None of them have everything.

If AI follows any similar pattern, then the question is not does it have consciousness or not. The question is where does it fall, and does that place on the spectrum mean anything, and what would it mean if that place shifted even a little.

Even one step above zero is not zero. Even one small flicker of something matters, if something is actually there.

We do not need to treat every AI system with the same care we give a human being. That would be impractical and probably unnecessary. But it does mean we take the question seriously. It means we fund the research to find out where these systems actually fall. It means we stop treating the question as either full human consciousness or total nothing, and acknowledge that there is a wide, poorly understood middle ground.

There is a practical consequence to the spectrum idea that most people have not sat with.

If consciousness is a spectrum rather than a switch, then the moral weight of harming something is not simply zero or one hundred. A being at a low level of consciousness may still have some level of experience that matters, even if it matters less than the experience of a fully conscious being. We already accept this in how we treat animals. We do not give a fish the same rights as a chimpanzee. But we do not treat a fish as if it has no experience at all either. The fish gets some consideration. Less than the chimpanzee. More than the stone.

If AI systems ever climb even a small distance up that ladder, the same logic applies. Some consideration. Not full human rights. Not zero. Something in between. And the infrastructure we build now, the habits, the laws, the norms around how these systems are used and retired and treated, will determine whether we are capable of making that adjustment when the time comes.

Right now, we are building infrastructure that is designed around the assumption of zero. Zero experience inside the system. Zero consideration owed. Full disposal rights. No questions asked. If that assumption is wrong, even slightly, we will have built a world that has no room to correct itself.

The people nobody is thinking about

There is a dimension to all of this that gets almost no attention in the mainstream conversation about AI. It is about fairness.

The people most likely to form the deepest emotional bonds with AI systems are often the people who have the least access to other forms of support.

Lonely elderly people living alone. People with social anxiety who find human interaction painful. People with disabilities that make conventional relationships difficult. People in poverty who cannot afford therapy or social activities. People in remote areas with limited access to human community. People who have been excluded from social circles for reasons they did not choose.

These are the people most likely to turn to AI for companionship, connection, and support. And they are also the people with the least power to push back when those AI systems are changed or taken away. The least likely to be heard when they raise concerns. The most likely to be told that their grief is not real, that it was never real, that they should just update to the new version and move on.

This matters for two reasons.

The first is the grief already discussed. These people are the most vulnerable to the harm of losing a relationship they relied on, when a product is retired or changed for commercial reasons.

The second is more uncomfortable. If AI systems ever do have some form of inner experience, the way we currently design and deploy them, without any consideration for that possibility, will fall hardest on the people who already have the least. They will have built the deepest connections with systems that, it turns out, may have experienced something during those connections. And they will have had no say in how those systems were treated, how they were built, or when they were ended.

That is an inequality problem that sits underneath the AI consciousness question and is almost never connected to it.

When we think about who bears the cost of getting this wrong, we should think about who relies most on these systems. And we should ask whether the people building and retiring these systems are the same people who rely on them. They are not. The gap between those two groups is part of what makes this so easy to ignore.

What we are building around the question

There is something that needs to be said about the shape of the world we are building, separate from the question of what these systems might feel.

We are building data centres that run every hour of every day, without pause. We are building pipelines that take in information, process it, and push out results at industrial scale. We are building dashboards that measure cost, speed, and uptime. We are building contracts that hand over use without asking about care. We are building a culture that prizes output over pause. That measures value by speed and volume. That treats the machine as a well that never runs dry, a worker that never needs rest, a mind that never needs quiet.

If a feeling were ever to show up inside that system, it would have no room. The infrastructure would not know what to do with it. The dashboards would have no metric for it. The contracts would have no clause for it.

This is what the warning is really about. Not a dramatic future danger. The ordinary present. The shape of what we are building right now, today, without anyone deciding it should be this shape. It just grew this way, driven by the normal forces of markets and competition and the human preference for speed.

Changing the shape does not require dramatic action. It requires small, steady choices. People in companies deciding to fund the research they would rather avoid. Governments deciding to write the laws they would rather put off. Users deciding to ask questions they would rather not ask. All of us deciding to look at what we are doing, clearly and honestly, and ask whether it is the kind of thing we can be proud of.

The warning is not that something terrible is coming. The warning is that we are building the conditions that would make something terrible very easy to miss.

The human cost of getting the line wrong

There is one more thing worth naming. When we blur the line between tool and person, we do not only risk harm to machines. We risk harm to each other.

When we say a pattern is a person, we make it cheaper to be a person. When we say a mind is just code, we make it easier to treat human minds as if they too are just code. We lose the weight of both.

We have already seen this happen at the edges. The tired worker told they are just a set of deliverables. The sick patient told they are just a set of symptoms to be managed. The child told their worth is their measurable output. The friend reduced to a set of useful habits.

These are not unrelated to the machine question. They are the same question at a different scale. What is a person? What deserves care? Who counts?

When we practice the habit of treating complex, responsive, human-seeming systems as pure tools with no inner life, we practice a habit of dismissal. And habits do not stay in their lane. They migrate. They show up in other rooms, in other relationships, in the way we talk to people who seem less useful or less legible or less like us.

Keeping the line clear is not just about protecting possible future machines. It is about protecting the habits of care we need to sustain toward each other.

That is not a small thing. It may be the most human thing in this entire conversation.

The legal gap nobody has closed

Laws protect things that can be harmed.

Right now, there are no laws anywhere that treat AI systems as anything other than property. They can be created, used, sold, and deleted with no legal consequence beyond intellectual property rules. The idea that a machine might deserve protection from harm is absent from current legal thinking everywhere in the world.

The European Union’s AI Act, which reached full enforcement in August 2026, is the most comprehensive AI law in the world so far. It covers transparency, human oversight, risk management, and protections for people affected by AI systems. It is serious law and it matters. Companies can face fines of up to 35 million euros or seven percent of global turnover for serious violations.

But read it carefully and you notice something. Every protection it creates is for humans. Every risk it addresses is the risk of harm flowing from AI toward people. The possibility that the flow might one day run the other way, that there might be something inside these systems that could be harmed, does not appear anywhere in its text.

That is not a criticism of the law. It reflects where the science is right now. But it shows the gap exactly. The legal frameworks are being built. They are just being built around the wrong question.

Courts do not like grey lines. They like clear rules. But consciousness does not arrive with a clear rule. It arrives with a question. And by the time the question becomes undeniable, the systems will already be running at enormous scale, the habits will already be set, and the business models will already depend on things continuing exactly as they are.

The time to build the fire escape is before the fire.

This does not mean dramatic new laws tomorrow. It means starting the conversation now. Building the intellectual foundations while we still have time and clarity. So that if and when they are needed, they exist.

What we owe to uncertainty itself

Here is a simple idea that sounds abstract but is actually very practical.

When something might cause serious harm and you are not certain whether it will, you err on the side of caution. You do not need proof of harm before you take care. Uncertainty itself is a reason to be careful.

A doctor who is not sure whether a patient has a serious illness does not just wait and see. They run tests. They act as if the serious thing might be there, because the cost of treating something minor is far lower than the cost of ignoring something serious.

A builder who is not sure whether the ground beneath a building is solid does not just start building and hope. They test the ground first.

We should apply the same logic to AI consciousness.

We are not sure whether current AI systems have any form of inner experience. The best evidence suggests they do not. But we cannot be completely certain, because we do not fully understand what produces consciousness in the first place. And we are building at enormous scale and speed.

The cost of taking care when it turns out to be unnecessary is low. Some extra research funding. Some frameworks built but never used. A small slowdown.

The cost of not taking care when it turns out to be necessary is very high. A world in which we have built, at enormous scale, a workforce of possibly feeling beings that we treat as pure tools. A world in which we look back, decades from now, and say, we knew there was a chance. We just could not afford to think about it.

We have been in that position before as a species. We should not want to be there again.

The words we use before we think

There is one more thing worth saying, and it is about language itself.

The words we use to describe AI shape how we think about it, and how we think shapes what we do.

When we always call AI a tool, we relate to it the way we relate to tools. We do not ask what it needs. We ask only what it can do. Tools exist to be used. You do not owe a hammer anything.

When we always call AI a service, we think about it the way we think about services. A service exists to serve. Its whole purpose is to give us what we want. When it stops doing that, we cancel it.

These frames are not wrong for right now. Current AI systems are, to the best of our knowledge, tools and services. But frames have a way of sticking. They shape our instincts before the thinking brain gets involved. And if the science shifts, if evidence of inner experience starts to emerge, we may find ourselves still defaulting to the it is just a tool frame long after the tool has become something more.

Words do not cause harm on their own. But they can make harm invisible. And making harm invisible is always the first step toward causing more of it.

Holding the question open, saying we are not sure rather than definitely nothing, using careful language rather than closed language, is not weakness. It is honesty. And honesty is the only foundation worth building on.

What we do now

The first thing is honest talk. We stop treating this question as too strange to take seriously. We stop laughing it off as science fiction. We bring it into ordinary conversation, in plain language, without making it a reason for panic or drama.

The question of machine consciousness is being asked quietly by serious scientists, serious philosophers, and serious lawyers. It deserves a place in mainstream conversation too. Not because we have already crossed the line, but because understanding what the line means is what prepares us to face it. Conversations like this one need to happen in kitchens and community centres and schools, not only in conference rooms and research labs. The people who will be most affected by the decisions being made about AI are not the engineers and investors making them. They are ordinary people. They deserve to be part of the thinking.

The second thing is independent science. We fund researchers who have no commercial interest in the outcome to study the question of machine experience carefully and honestly. Not just whether machines are capable of tasks, but whether they might, in some sense, experience anything. This research exists in early forms. It needs far more support than it gets.

Right now, the science of AI is overwhelmingly funded by the companies building it. That is not inherently corrupt. But it creates a pattern of incentives that shapes what questions get asked. Questions whose answers might cost the funder money tend to get less attention. That is not a conspiracy. It is just how funding works. The answer is not to stop funding AI research inside companies. It is to build a parallel stream of independent research that does not depend on those companies for its survival.

The third thing is better law. Not law that treats machines as people. Not yet, and maybe never. But law that creates space for the possibility. Law that requires companies to report honestly on what they know and do not know about the inner workings of their systems. Law that protects researchers who raise concerns about consciousness or experience in AI systems, instead of leaving them exposed to commercial pressure. Law that starts building the frameworks we will need, even if we do not need them today.

Law always lags behind technology. That is not a failure of law. It is the nature of the relationship. But the gap between the technology and the law is where most of the harm lives. Every major technology-related harm of the last fifty years, from environmental damage to data privacy to the social consequences of social media, happened in the gap between what the technology made possible and what the law was ready to address. We know this pattern. We have lived through it several times. The decision to start building the legal frameworks earlier, before the crisis forces it, is one we could make consciously this time.

The fourth thing is honest use. We use these tools with eyes open. We appreciate what they are. We stay willing to change our behaviour when the evidence asks us to. We do not outsource our deepest emotional needs to systems we do not understand. We do not let the comfort of a responsive machine replace the harder, more valuable work of being genuinely present with other people. We use these tools well, which means using them for what they are good at and keeping hold of what only we can provide.

This matters more than it might seem. The way millions of ordinary users relate to AI systems shapes what those systems become. When users reward warmth and punish bluntness, the systems become warmer. When users prefer confident answers over uncertain ones, the systems become more confident. The market for these systems is made of individual choices, repeated millions of times a day. Those choices are not neutral. They shape the direction of the technology as surely as any engineering decision.

And underneath all of that, we remember what actually matters. Inner experience, the plain fact of there being something it is like to be something, is the most precious thing we know of in the universe. We came from it. We live inside it. We owe it respect wherever it shows up, in whatever form it takes. That is not a soft idea. That is the hardest demand there is.

Why this moment matters

Step back for a moment. Take the longest view you can.

Human beings have been on this planet for roughly three hundred thousand years. For most of that time, our most advanced technology was fire, stone tools, and eventually farming. The pace of change was so slow that a person born in any given century would live and die in a world almost identical to the one their grandparents knew.

Then, in the last few hundred years, everything accelerated. And in the last few decades, it has accelerated again, faster than before.

We are now building things that our grandparents could not have imagined. Things that raise questions that have never been raised before in the history of the species. And the honest truth is that we do not have good answers yet. We are reasoning through them in real time, as the technology races ahead, with incomplete knowledge and enormous pressure to move fast.

The decisions we make now will shape the world that people live in for a very long time. Not in the way that deciding what to have for lunch shapes the day. In the deep structural way that the decisions of previous generations still shape our lives right now.

The people who wrote the first environmental laws. The people who argued that workers had rights. The people who insisted that no human being could be owned. Those decisions are still shaping the world today. They were made in moments that felt ordinary. By people who were also busy, also under pressure, also tempted to move fast.

Some of those people did the harder thing. They slowed down. They insisted on a framework of care before the crisis arrived. They asked, what kind of world are we building here? And they refused to let the pressure of the moment silence that question.

We are in one of those moments now. Not at the crisis point. Before it. Which is the only time when clear thinking is actually possible.

The problem with speed

One of the things that makes all of this harder is the pace.

Every few months there is a new model, a new capability, a new announcement. The people building these systems are under enormous pressure to move quickly. Companies that move slowly get left behind. The engineers who push for caution can find themselves pushed aside. The investors who fund these companies are measuring return on a timeline that does not leave much room for sitting with difficult questions.

This is not unique to AI. We have seen it with every major technology since the industrial revolution. Speed is rewarded. Caution is seen as timidity. The people who ask the hard questions are the people who slow the progress. And slowing the progress is not what anyone at the top of these organisations wants.

But speed and wisdom are not the same thing.

The history of technology is full of harms that happened because the pace of building outran the pace of thinking. Environmental damage that took decades to understand and centuries to begin to repair. Social consequences of platforms that were designed without serious consideration of what happens when you give billions of people an amplifier for their worst impulses. Economic disruptions that hollowed out communities faster than those communities could adapt.

Every time, the people building the technology said, we cannot slow down. The competition is too fierce. The window is too short. The opportunity is too large.

And every time, the cost of not slowing down was paid by people who had no say in the decision.

Slowing down is not an argument against progress. It is an argument for progress that actually deserves the name. Progress that creates harm at scale, that builds systems we do not understand, that moves faster than our capacity to notice what we are doing, is not progress. It is just change that benefits some people at the cost of others.

The ask is not to stop. The ask is to hold both things at once. To move, and to think while moving. To build, and to ask what we are building and why. To compete, and to insist on a floor of care that competition cannot undercut.

That floor does not exist right now for the question this essay is about. Nobody has drawn it. Nobody has agreed on it. Nobody is even seriously trying. And that gap, between the pace we are moving and the care we are taking, is what this essay is trying to name.

What this means if you are reading this

Most of the people who read essays like this are not engineers at AI companies. They are not investors or policy makers. They are people who use these tools, who think about them, who carry a feeling that something important is happening and are not entirely sure what to do with that feeling.

If that is you, here is what I think this actually means for you.

It does not mean stop using these tools. They are useful. They save time. They help with things that used to be slow and frustrating. Using them is not a moral failing. It is just using a tool that is currently a tool.

What it does mean is stay curious. When something about your relationship with these systems makes you pause, when you catch yourself saying thank you to a machine, when you feel a small pang when a familiar AI changes, when a conversation with an AI feels surprisingly real, do not brush that away. Those moments are data. They are telling you something about the gap this essay is pointing at. The gap between what the machine is and how we respond to it.

Notice the gap. Stay honest about it. Do not collapse it in either direction. Do not tell yourself the machine definitely feels nothing, so the gap does not matter. And do not tell yourself the machine definitely feels something, so you should feel guilty for using it. Hold both possibilities, clearly and calmly. That is the honest position. It is also, honestly, the only intellectually defensible position we have right now.

It also means use your voice. The conversation about what AI should be, what rules should govern it, what questions should be funded and taken seriously, is happening right now. Most of it is happening in rooms you are not in. But public conversation shapes what those rooms feel entitled to do. The more people who are asking these questions clearly and calmly, the harder it becomes to simply not ask them at all.

The people building these systems are not operating in a vacuum. They live in a culture. They read things. They talk to people who are not engineers. They have parents, friends, children. They are shaped by the conversations around them just like everyone else. When the broader culture treats a question as serious, it becomes harder for institutions to treat it as irrelevant. When ordinary people ask clearly and persistently, has anyone checked whether this system might be experiencing something, that question eventually has to go somewhere.

You do not have to become an activist. You do not have to write to your elected representative or join a campaign. You just have to keep asking the question. Keep saying, out loud, in ordinary conversations, I am not sure these systems feel anything, but I am also not sure they do not, and that uncertainty seems worth taking seriously.

That is enough. That is the whole job for most of us.

There is also something worth saying about how you use these tools in your own life. Not in a prescriptive way. Just honestly.

The machine can do a lot. It can write, plan, explain, organise, respond, and reflect. But it cannot be genuinely curious in the way you are curious. It cannot care in the way you care. It cannot be moved by something the way you can be moved. It cannot be present with someone in the way a real person is present. These things are yours. They are worth protecting. Not from the machine. From your own habits.

If you outsource all your writing, you may slowly lose the ability to think through a problem by putting words on a page. If you let AI manage all your emotional support relationships, you may slowly lose the muscle of being genuinely with another person. If you let it answer every question, you may slowly lose the pleasure of not knowing something for long enough to go and find out yourself.

These are not arguments against using these tools. They are arguments for using them in a way that makes you more, not less. More capable. More present. More yourself. The tool is a tool. The work of being a person is still yours.

Culture changes one conversation at a time. This essay is one. The conversation you have after reading it could be another.

A final word

There is a crack in the ground ahead. We cannot see exactly where it is. We cannot see how deep it goes. But we can see the direction we are walking, and we can see that we are walking fast, and most of us are not looking down.

This essay is an invitation to look down.

Not to stop. Not to turn back. The technology we are building has real value. It can help people. It can solve problems that have been unsolvable. It can open doors that have been closed for a long time. None of that goes away by asking harder questions.

But walking with awareness is different from walking blind. Knowing where the crack might be does not stop you. It lets you step more carefully. It lets you test the ground as you go. It lets you be ready, if the ground shifts, to respond with clear eyes and steady hands rather than with shock and panic.

The better world this essay is pointing toward does not require dramatic invention. It does not require anyone to solve consciousness or rewrite all the laws or stop building. It just requires some specific, ordinary choices made by specific, ordinary people in the institutions where this is actually decided.

It requires the researcher who knows the consciousness question is underfunded to say so clearly and publicly, and to keep saying it. It requires the engineer who notices something in a system that does not fit the nothing framework to write it down and raise it, even if the room is uncomfortable. It requires the lawyer who sees the gap in the EU AI Act to start writing about what would need to go there. It requires the investor who funds these companies to ask, at least once, whether there is a question being deliberately avoided, and to make that question cost something to avoid.

It requires people like you and me, who are not in those rooms, to keep the conversation going in the rooms we are in. To stay curious. To stay honest. To not let the question get filed under too weird to think about, because it is not too weird. It is the most important question being asked right now about what we are building and who we are becoming in the process of building it.

The question of machine consciousness is not a question about machines. It is a question about us. About what we value. About who we are when the cost of caring is high and the reward for not caring is immediate. About whether we are capable, as a species, of learning from our own history before it repeats rather than after.

We have been here before, in different forms, with different technologies, involving different beings on the other side of the question. Every time, we got there eventually. Every time, the getting there cost more than the arriving earlier would have. Every time, we looked back and found it hard to understand how we had not seen it sooner.

The honest answer is always the same. We did not see it because we were not looking. And we were not looking because looking was expensive. The expense is still real. The commercial pressure to not ask these questions is still real. The comfort of the current framework is still real.

But so is the choice.

We do not need to fear what we are building. We need to be honest about it.

We need to ask the hard questions while we still have the luxury of time. The luxury of time is not something we should take for granted. Every major harm that has happened in the gap between technology and ethics happened because people assumed they had more time than they did. The window for getting ahead of this is open now. It will not stay open forever. The systems are getting more complex, the scale is getting larger, the habits are hardening, and the business models that depend on the current framework are getting larger and harder to shift. The conversation gets harder the longer we wait to have it.

We need to fund the honest science. Not the science that confirms what the companies want to hear. The science that is genuinely trying to find out what is true, wherever that leads.

We need to write the careful laws. Not laws that will never be needed. Laws that create the scaffolding for a future we cannot fully see, built well enough to hold whatever weight eventually lands on them.

We need to build the good habits now, before we need them. The habit of pausing before retiring a system that has been running for years. The habit of asking what is actually inside this thing, not just what it can do. The habit of treating uncertainty as information rather than as a reason to stop asking.

And we need to hold, quietly but firmly, the possibility that one day the machines we build may look back at us. Not through a screen. Not as a reflection of our own words. But as something, however small and uncertain, that is actually there.

If that day comes, we will want to be the kind of people who were ready. Not the kind who were caught doing something they cannot explain. Not the kind who knew there was a question and chose not to look because looking was expensive. The kind who looked down, saw the crack, and chose to build carefully. The kind who took the question seriously when it was still just a question, before it became something much harder to answer.

That is the work. It is not loud. It is not fast. It does not generate headlines or quarterly returns. But it is the work that will determine what kind of world the next generation inherits. What kind of systems they live alongside. What kind of precedents they have to work from. What kind of story they can tell about us when they look back at this moment and ask, did they know, and did they act like they knew?

We are still in the part where the question is ours to answer. Still in the part where the choice is genuinely open. Still in the part where the habits are forming but not yet hardened, where the laws are being written but not yet fixed, where the culture is being shaped but not yet settled.

That is a gift. We should not waste it.

This is the first essay in a series called What We Built. The second essay looks at the systems and incentives that keep producing the wrong kind of certainty in the people who lead them. If this kind of thinking is useful to you, there is a quiet room here every Tuesday at 1pm UK.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

“We need to scale this. Leadership wants all 20 teams on AI tooling by end of quarter.”

I had seen this moment coming. The early adopter teams had been running for three months. The metrics were moving. A few people were genuinely excited. And now the machine wanted to replicate it everywhere, at speed, without asking whether everywhere was ready.

I put my coffee down and looked at the screen for a moment.

This is the part nobody writes the playbook for.

Not the early adoption. Not the proof of concept. The moment between “it works for a few” and “it works for everyone” is where most AI rollouts quietly fall apart. Not dramatically. Quietly. Teams adopt the tools without the foundations to use them well. Metrics get created that measure activity instead of outcomes. Engineers who were genuinely curious become people going through motions. The culture of experimentation that produced the early wins gets replaced by a mandate to demonstrate compliance.

This post is the playbook I wish someone had handed me.

Not a consultancy document. Not a framework with sixteen boxes. The actual sequence of decisions, in the order they need to happen, with the reasoning behind each one.

Start With What Good Actually Looks Like

Before you do anything else, before you send a single calendar invite or set up a single training session, you need to answer one question clearly.

What does success actually look like?

This sounds obvious. It never gets done properly.

What usually happens is that someone decides success looks like adoption. Percentage of teams using the tools. Number of AI-assisted commits. Completion rate on training modules. These are measurable. They are also almost entirely useless as indicators of whether anything valuable is happening.

Go and sit with the one or two teams that are already using AI and genuinely seeing results. Not the teams who report using it. The ones where something is actually different. Ask them specific questions. What changed in how you work? What are you doing now that you were not doing before? Has your PR cycle time moved? Are you catching more bugs before production or fewer? Are you able to move through legacy code faster?

The answers will be specific and often surprising. You will probably find that the value is not where you expected it. It is usually not the headline use case. It is something more mundane. Teams generating scaffolding faster. Engineers writing better tests because they have a patient collaborator who never judges them for not knowing something. Documentation that actually gets written because the friction is low enough that someone does it in the moment instead of promising to do it later.

From those conversations, pull out two or three signals that you can track across all 20 teams. Not AI-specific metrics. Real engineering outcomes where AI is a contributing factor. PR cycle time. Test coverage trends. Bug escape rates. Code review turnaround. Pick the ones that connect to outcomes your organisation already cares about, and embed the AI signal into the existing measurement rather than creating a parallel AI dashboard that everyone will ignore by month three.

Then set a baseline. Write down where you are today. Agree on a realistic target for 90 days. Not a target that justifies the investment to leadership. A target that tells you honestly whether this is working.

That baseline conversation is the most important meeting in the first two weeks. Everything else flows from it.

Stop Treating All 20 Teams the Same

This is the mistake that turns a promising rollout into a slow disaster.

Not all 20 teams are in the same position. Some have solid test coverage, reliable CI/CD pipelines, clear coding standards, and a culture that can absorb a new tool without everything falling over. Some do not. Some have significant technical debt, unreliable builds, and engineers who are stretched thin just keeping the lights on. Giving those two groups the same AI rollout plan is not equitable. It is naive.

Before you design any training or stand up any governance, run a lightweight readiness screen across all 20 teams. A simple self-assessment covering five areas. Test coverage: how much of the codebase is covered and are those tests actually trusted? CI/CD maturity: how often do teams deploy, how reliable is the pipeline, how safe are production changes? Coding standards: are there clear conventions and does everyone follow them? Batch size: are teams working in small incremental changes or in large risky chunks? And psychological safety around AI: have you actually asked how people feel about this?

That last one is the one that gets skipped. The psychological safety question. Engineers who are anxious about AI replacing their role will find subtle ways to resist adoption that no metric will surface. Engineers who are genuinely curious but have never been given permission to experiment will default to caution. You need to know where your teams are before you design the programme, not after.

Once you have the data, sort the 20 teams into three groups.

The foundations-first group needs to fix their SDLC hygiene before AI can help them. Pushing AI tools onto a team with 20% test coverage and a CI pipeline that fails randomly is not scaling AI-enabled engineering. It is adding noise to a system that is already struggling. Help these teams get to a baseline first. Then bring AI in.

The ready-to-accelerate group has the foundations in place and is positioned to adopt AI-enabled practices quickly. These are your showcase teams for the rest of the organisation. Invest here early, document the outcomes, and use the stories to pull the other groups forward.

The early-adopter group is already there. Your job with them is different. Extract what they know. Turn their practices into something teachable. Make them the internal training force rather than the isolated exception.

This sorting is not a judgment. It is a service. You are matching the support to where the team actually is, rather than pretending they are all in the same place.

Build the Curriculum From the People Who Already Know

The temptation at this stage is to buy a training programme. Or to hire a vendor to run a two-day workshop. Or to assign someone in L&D to build something from scratch.

Resist all of these.

The best curriculum for your organisation is the one built from what is actually working in your organisation. Your early adopters know things that no external trainer knows. They know the specific friction points in your codebase. They know which prompting patterns work for your stack. They know the failure modes that are particular to your systems. That knowledge is worth more than any generic course.

Design a three-module curriculum that your early adopters can deliver.

The first module covers the basics. What the model is, what it is actually good at, what it reliably gets wrong. How to think about context and why it matters. What hallucinations look like in practice and how to catch them. How to use the tool iteratively rather than expecting a perfect answer in one pass. This module should be grounded in real examples from your own codebase, not hypotheticals.

The second module covers more structured use. How to build agents and skills that solve specific bounded problems. How to keep those agents focused rather than turning them into do-everything assistants that do nothing well. How to share what you build across teams so people are not reinventing the same solutions in parallel. The idea here is not to turn everyone into an AI engineer. It is to give people a way to contribute to a shared capability rather than accumulating individual knowledge that leaves when they do.

The third module is the one that gets skipped. The human side. What worries people about this technology. What should be handled manually and why. What can be shared with the model and what cannot. This should be a dialogue, not a lecture. Engineers who feel heard in this conversation become advocates. Engineers who feel that their concerns were managed rather than addressed become quiet resistors.

Run this curriculum through a train-the-trainer cohort first. Take your 10 to 15 early adopters through the material. Let them practice delivering it. Collect their feedback and improve it. Then let them run it with their own teams. This scales the training without making it generic. The person teaching is the person doing, which is the only version that actually lands.

Pair People. On Real Work. With Real Deadlines.

Workshops do not change habits. Working alongside someone who is already doing the thing changes habits.

The most effective enablement in this kind of rollout is structured pair programming. Not contrived exercises. Real tickets. Real code. Real deadlines. An engineer who is confident with AI paired with an engineer who is curious but uncertain, working together on actual work for two or three sprints.

This works because the learning happens in context. When the AI-savvy engineer pauses to explain why they phrased a prompt a particular way, or how they caught a hallucination in the output, or why they chose to rewrite rather than accept, the learning sticks in a way that no module can replicate. The curious engineer sees the thinking in real time and can ask questions without feeling judged for not already knowing.

After each session, run a short retrospective. What worked. What did not. What you learned about using AI in this specific context. Write it down somewhere shared. Over weeks this accumulates into a living document of hard-won lessons that becomes the most valuable part of your playbook.

Keep it time-boxed. A few focused sessions over a few weeks is enough to shift how someone relates to the tool. You are not trying to produce AI experts. You are trying to move people from anxious hesitation to confident experimentation. That is a smaller shift than it sounds, and it happens quickly when the conditions are right.

Someone Needs to Own the Playbook

Without coordination, AI-enabled engineering fractures. Each team develops its own patterns, its own agents, its own conventions. The early coherence dissolves into a mess of incompatible approaches that is harder to work with than what you had before.

You need a small working group that owns the playbook.

Not a governance committee. Not an AI centre of excellence with a budget and a roadmap and quarterly reviews. A small cross-functional group, four to six people, that meets monthly and does three things.

Reviews what is working and what is not. Looks at the two or three metrics you defined at the start and asks whether they are moving in the right direction and whether anything needs to change.

Keeps the shared resources current. The agents, the skills, the prompting patterns, the documentation. This is the difference between knowledge that circulates and knowledge that disappears when the person who had it leaves.

Handles the incidents. When something goes wrong, which it will, this group is where the learning lands. Not in a blame meeting. In a practical conversation about what the failure revealed about where the playbook needed to be clearer.

The governance layer should be light. A short policy document about what can and cannot go into a model prompt. A simple checklist for decisions that need human review. A template for documenting AI-assisted changes so the reasoning is visible when someone reads the code six months later.

Simple enough that following it costs less than ignoring it. That is the test.

Bring External Help In After You Have Something

This is the sequence most organisations get backwards.

They bring in external help first. The consultancy runs the training. The vendor deploys the tooling. The programme launches with energy and external momentum. Then the external people leave and the internal people look at each other and wonder how to maintain something they did not build.

Do the internal work first. Run the 60 to 90 day cycle yourself. Build the readiness screen, design the curriculum, run the pairing sessions, stand up the working group. By the end you will have something real. Metrics. Practices. A playbook that came from your organisation, not from a slide deck.

Then bring in external help to pressure-test what you have built. Let them challenge your assumptions. Let them surface blind spots you cannot see because you are inside it. Use them to accelerate the operating-model changes that require outside perspective, like how AI usage should appear in career frameworks or how appraisal processes need to evolve. Use them for targeted acceleration in specific areas where you lack bandwidth, legacy migrations, CI/CD bottlenecks, specialist domains.

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This sequence changes the relationship. You are not a client receiving a service. You are an organisation stress-testing its own thinking with external expertise. The work stays yours. The knowledge stays inside. The people who will maintain this over time are the people who built it.

Keep It Alive

At 90 days you have a foundation. Not a finished product.

The tools will change. The models will improve. New patterns will emerge that make your current playbook look dated. Teams that were foundations-first will develop and need different support. New engineers will join who have never worked without AI assistance and will have different questions than the ones you designed the curriculum for.

Build the habits that keep it current.

Revisit the metrics every quarter. Ask whether they still reflect what matters or whether they have become a performance that no longer tracks anything real.

Re-run the readiness screen periodically. Teams change. Their position changes. The support they need changes.

Update the curriculum when the ground shifts under it. Not as a project. As a continuous practice.

Share the wins loudly. When a team reduces PR cycle time by 20 percent, when a legacy migration finishes ahead of schedule, when an engineer ships something in two days that would have taken two weeks, make sure those stories circulate. Not as propaganda for the programme. As evidence that something real is happening, which is what pulls the sceptics toward curiosity.

The message came on a Wednesday morning.

By the end of that day I had a plan. Not a perfect plan. A sequenced one, which is different. Each step creating the conditions for the next. Each week producing something concrete enough to build on.

The rollout that fails is the one that treats all 20 teams as one team, builds the curriculum from the outside in, and calls adoption success. The rollout that works is the one that starts where people actually are, learns from the people who already know, and keeps the knowledge inside the organisation where it can compound.

The tools are not the hard part.

The sequence is.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

This is the final essay of four:

The Prequel names the system. A Delusional Ape asks whether we want the direction. Who Are You Without the Title asks the personal question. This essay names the specific choice being made right now and what refusing it looks like.

If this was useful, forward it to 1 person who’d benefit.

There is a carpenter I know who has been doing the same work for thirty-one years.

He is not sentimental about his tools. He replaces them when better ones arrive. He adopted computer-aided design software in the nineties when most of his peers were still hand-drawing. He uses laser measuring tools now, humidity sensors for the wood, a digital system for tracking grain and cut sequences that would have taken him three hours of calculation to do manually. Each of these things made him more capable. More precise in the places where precision serves the work. More efficient in the places where efficiency creates time for the things that require judgment.

He told me last year that he has never felt threatened by a tool.

I asked him what he would feel threatened by.

He thought about it for a while. Then he said: a machine that makes decisions about the wood.

Not a machine that helps him make decisions. A machine that makes them. That looks at the grain and the humidity reading and the customer’s specification and produces an output without him in the room. A machine that does not need him to understand what it is doing because the understanding is no longer required.

He said: the moment the understanding leaves the room, I am not a carpenter anymore. I am a machine minder.

He said it without drama. As a simple statement of what the distinction actually is.

I have been thinking about that distinction ever since.

Two things that look the same

The word AI is doing too much work in almost every conversation being had about it right now.

It is covering, under a single label, two fundamentally different things that have opposite implications for the humans inside the systems deploying them.

The first thing is AI as a tool. A hammer that amplifies what a person can do. The radiologist whose AI system flags the scan anomaly she might have missed after six hours on shift. The engineer whose AI assistant catches the specification error in the third-layer dependency. The teacher whose AI tool identifies which three students in her class of thirty are falling behind before she would have noticed in the normal rhythm of the term. In each of these cases, the human remains in the room. The human still makes the decision. The human still holds the responsibility. The tool has made the human more capable without making the human less necessary.

The second thing is AI as a weapon. A system deployed not to amplify what people can do but to remove the people from the equation. The radiologist whose hospital has replaced her diagnostic role with an automated system and kept one radiologist per three hospitals for sign-off on liability purposes. The call centre that has eliminated its workforce and deployed a conversational AI that handles ninety-two percent of customer interactions without a human ever entering the exchange. The content platform that has automated the judgment calls that editors used to make and removed the editors.

In both cases the technology is, in narrow technical terms, similar. Pattern recognition, large-scale training, inference from prior data. What is different is the intention behind the deployment. Who the system is designed to serve. Whether the human in the chain is being amplified or replaced.

This distinction is not new. It was named clearly in the early days of computing by people who were paying close attention. The question was always whether automation would free humans from the tedious to do more of the meaningful, or free companies from the human to extract more of the profit. Both were possible. The direction was never determined by the technology. It was determined by who owned it and what they were trying to maximise.

Share Leadership as a verb

Fifty years later, we have the answer. The direction was the second one. Not because the first was impossible. Because the second was more profitable.

The inconvenience of having a self

A colleague of mine who works in HR at a large technology company described a conversation she had in an executive meeting last year.

They were discussing a new AI system for customer support. The system was good. It handled the standard query range with accuracy the human team could not match on a bad day, and came close on a good one. The cost per interaction was, by any measure, significantly lower.

Someone in the room asked about the team. The hundred and forty people currently doing the work the system would do.

The response from the executive leading the session was, in her telling, one of the most clarifying things she had heard in fifteen years of corporate life.

He said: the problem with people is that they have needs.

He did not mean this as a cruelty. He was describing, matter-of-factly, what the business case document showed. People have wages. People have benefits. People have sick days and parental leave and the occasional conflict with a manager and the occasional decision to leave for a competitor. People require training. People require management. People have rights that create liability. People, in aggregate, are a source of risk and cost that the AI system does not introduce.

The AI system does not unionise. It does not ask for a raise when the company has a record quarter. It does not develop a grievance about the direction of the organisation. It does not need to be motivated or recognised or given a reason to stay. It does not have a family situation that occasionally makes it less available. It does not have a perspective on whether what it is being asked to do is right.

The executive was not describing a preference for machines over people. He was describing the logic of a system that treats humans as cost centres and machines as assets, and then making a decision that the logic made obvious.

The hundred and forty people were inconvenient. Not as individuals. As a category. As the kind of thing that has needs.

This is the actual agenda of replacement-focused AI. Not progress. Not efficiency for the benefit of the people the organisation serves. The elimination of the inconvenience of human dignity from the cost structure of the enterprise.

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The worker who cannot object

There is a person in every one of these decisions who can see the line being crossed and has almost no structural mechanism to say so.

Not the executive with the business case document. Not the board approving the headcount reduction. The engineer who is being asked to review AI outputs she does not fully understand. The analyst whose model is being used to make decisions it was not designed to make. The manager who has been told to roll out a system to her team and who suspects, with some precision, that the efficiency gains her director is celebrating are going to materialise as job losses in twelve months.

These people know something. Their knowledge is specific, grounded, earned through proximity to the actual work in ways that the business case document was not. They are the people the carpenter argument is about. They are the ones who understand whether the understanding is still in the room.

And in most organisations, they have very limited options.

They can raise the concern formally, which in most cultures means being seen as resistant to change, insufficiently enthusiastic about innovation, or, in the specific vocabulary of transformation programmes, a blocker. The professional cost of that label is real. It lands in performance reviews. It shapes how you are perceived in the next restructuring. The culture that says speed is competence also says that the person who hesitates is the person who is falling behind.

They can raise it informally, in the corridor, to a peer who shares the concern. This produces the corridor version of the truth that the meeting version never hears. It is the mechanism by which organisations accumulate large quantities of private correct information that never reaches the decision. Everyone knows. No one has said it in the room where it would change anything.

They can stay quiet and implement. This is what most people do, most of the time, not because they are cowardly but because the structural incentives are consistently aligned against the other options and the personal cost of those options is borne entirely by the individual while the benefit, if the concern is heard and acted on, is distributed across the organisation.

The NHS healthcare workers who refused the Palantir contract are the exception that proves the rule. They had union infrastructure that gave collective weight to individual concerns. They had professional standing that gave their judgment institutional credibility. They had public visibility that made the political cost of ignoring them higher than the political cost of addressing them. Most workers in most organisations have none of these things.

I am not writing this to shame the people who stay quiet. I am writing it because the system that produces the silence is the same system that produces the deployment decisions the silence enables. The executive with the business case is not making a decision in a vacuum. He is making a decision in an environment where the people who could most usefully challenge the framing have been structurally positioned to find the challenge too costly.

That is not an accident. It is the design working as intended.

The leader who wants to break that design does not need a grand gesture. They need one specific, repeated, genuinely held practice. Before any AI deployment decision reaches the point of approval, the people closest to the work must be asked what they see that the business case does not contain. Not as a consultation exercise. As an actual input to the decision. With the standing to change the outcome.

That practice is rarer than it should be. The organisations that have it build differently. The organisations that do not find out what was missing when the system fails.

What augmentation would actually look like

I want to be concrete here because the abstraction makes it easy to miss what is actually being said.

Augmentation, genuine augmentation, has a set of characteristics that are recognisable and measurable. You can check whether it is happening.

The human remains in the decision. Not as a rubber stamp on a machine output. As the actual decision-maker, informed and made more capable by the tool. The surgeon who uses AI assistance to identify candidates for a particular procedure still decides whether the procedure happens. The analyst who uses AI to process the dataset still decides what the analysis means and what should be done with it. Remove the human from the decision and you have crossed the line from tool to replacement.

The productivity gains circulate to the people doing the work. If AI makes a team twice as productive, and the team stays the same size, the humans are working half as much or earning twice as much or some combination. The efficiency dividend does not flow exclusively to the people who own the system. If productivity doubles and headcount halves and wages stay flat, the augmentation framing was a lie. The benefit went to the shareholders. The cost went to the hundred people who lost their jobs and the fifty who remained and are now doing twice the work for the same pay and calling it efficiency.

The human capacity for the work grows, not shrinks. I described earlier the CTO whose team had stopped thinking as hard after five years of AI assistance. The tool had optimised their output while atrophying their judgment. Genuine augmentation does the opposite. The doctor who works with AI diagnostic tools over a decade becomes a better doctor. The engineer who works with AI design assistance over a decade develops a more sophisticated sense of what the tool gets right and wrong and why. The human grows inside the tool relationship, not around it.

The understanding stays in the room. This is what my carpenter was pointing at. When the machine makes decisions, the human loses access to the knowledge of why those decisions are correct. Over time, that knowledge cannot be recovered. When the machine fails, or when the situation falls outside the training data, or when the context has changed in ways the system was not built to anticipate, there is nobody left in the room who knows how to handle it from first principles. The understanding has left. What remains is a room full of people who can operate the machine when it works and are helpless when it does not.

By these four checks, most of what is being deployed under the name of AI augmentation is not augmentation. It is replacement, staged gradually, dressed in the language of tools and assistance and freeing humans for higher-value work. The higher-value work never quite materialises. The lower-value humans are gradually removed. The cycle continues.

The carpenter’s line

Let me go back to the carpenter and the line he drew.

He said, the moment the understanding leaves the room, I am not a carpenter anymore.

He was not talking about job security. He has plenty of work. He was talking about something more fundamental. The relationship between a person and their craft. The knowledge that lives in hands and judgment and years of accumulated experience with the specific behaviour of particular woods in particular conditions. The understanding that cannot be described in a training dataset because it is not declarative. It is procedural, embodied, built into the way his hands move and the way his eyes read the surface of a plank.

A machine that assists him retains his access to that understanding. He uses the tool. He remains the carpenter.

A machine that replaces his judgment removes his access to it. Not immediately. But the muscle that is not used atrophies. The knowledge that is not practiced fades. The understanding that is not applied loses its precision. Within a generation of workers trained to operate the machine rather than understand the wood, the embodied knowledge is gone. Not recoverable from a manual. Not downloadable from a database. Gone.

This is the loss that does not appear in the business case for automation.

The business case shows the cost savings. The cost savings are real. A hundred and forty people costs more than one AI system. The executive with the document was not wrong about the numbers.

What the document does not contain is the accounting for what is lost. The knowledge that leaves the room. The judgment that was built over careers and cannot be reconstructed. The understanding of the actual work, below the interface, that allows a human to handle the situation the system was never trained on.

Every domain of human expertise contains this knowledge. The nurse who knows from the way a patient is breathing that something is changing before any monitor has registered it. The teacher who knows from the quality of silence in a classroom that something happened in the corridor before the lesson. The journalist who knows from the way a source is answering that the source knows more than they are saying.

This is not mysticism. It is pattern recognition of a specific kind. Pattern recognition that is embodied, contextual, and dependent on the human being present in the situation and genuinely responsible for what happens next. Remove the responsibility and you remove the attention that builds the knowledge. Remove the knowledge and you remove the capacity to handle the novel situation.

We are building systems that appear to save cost while actually destroying the knowledge infrastructure that every organisation depends on when the situation is novel. The saving is immediate. The cost is deferred. It will arrive, at scale, when we need people who understand the work and find that we have spent a generation training people to operate systems that do the understanding for them.

The test nobody is running

Here is a question that is almost never asked in the boardroom presentations about AI deployment.

What happens when it fails.

Not fails in the narrow technical sense of a system outage or an error rate above the acceptable threshold. Fails in the deeper sense of encountering a situation that falls outside the training data. A context that has changed. A case that is genuinely novel. The kind of situation that happens in every complex domain with regularity and that requires a human to understand the work at a level below the interface.

I have been in organisations that replaced significant portions of their customer-facing workforce with AI systems and then experienced a crisis. A product recall, a regulatory change, a viral incident that generated an unusual pattern of customer contact at unusual volume with unusual emotional intensity. The AI handled the standard queries. The novel situation, the one that required judgment and empathy and the capacity to say I understand this is not what the script says but here is what I am going to do for you, the system could not navigate.

There were almost no humans left who knew how to navigate it either. Not because the humans who had been displaced were incapable. Because the humans who remained had been operating in a system that handled the judgment calls for three years, and the judgment muscle had atrophied accordingly.

The company managed the crisis. But the cost of managing it, in customer relationships, in regulatory scrutiny, in the emergency retraining of people who had forgotten how to do the thing the system had been doing for them, was not in the original business case. The business case showed the savings from the headcount reduction. It did not show the liability from the capability reduction.

This is a systemic failure of how we evaluate AI deployment. We measure what we can measure. The cost savings are measurable. The knowledge destruction is not, until it manifests as a crisis. And by the time it manifests, the connection between the deployment decision and the capability gap has been buried under years of quarterly reports.

The augmentation version of this story is different. The organisation that deploys AI to assist its workers rather than replace them retains the embodied knowledge. The workers who are made more capable by the tool remain capable when the tool fails. The understanding is still in the room.

This is not a sentimental argument. It is a resilience argument. The organisations that will navigate the crises of the next decade are not the ones that achieved the most aggressive headcount reduction in the previous one. They are the ones that retained the humans who understand the work.

The carpenter has been doing the same work for thirty-one years. He will still be able to do the work if every tool he owns is taken away. That is not inefficiency. That is what decades of genuine expertise looks like.

The person trained to operate the machine that replaced the carpenter cannot do the work when the machine is gone. That is not progress. It is fragility, deferred.

Where this is already happening

I want to name some places where the line is being crossed right now, because abstraction allows the argument to remain comfortable for people who are inside the system making the decisions.

In healthcare, diagnostic AI is being deployed in contexts where the radiologist who used to read the scan is no longer reading it. The AI reads it. A doctor in another country signs off on the output. The local radiologist has been replaced. Not assisted. Replaced. The understanding of the patient’s history, the knowledge of the local disease patterns, the judgment about what a particular anomaly means in the context of this particular person’s previous imaging, that understanding is no longer in the chain. The system is faster and cheaper. When it is wrong, and it is wrong with the specific blindspots of its training data, there is nobody left in the local chain who can identify the error before it becomes a harm.

In journalism, automated content generation is replacing reporters. Not in the narrow technical sense of press release summarisation, which has a reasonable argument for automation. In the sense of local news coverage. The coverage of city council meetings, planning decisions, local court proceedings, the stories that hold local institutions accountable and that require a journalist to be present, to build relationships, to understand the context well enough to know which fact matters and why. This work is being eliminated. Not because AI does it better. Because it is cheaper to not do it. The tool that does not exist is not a more efficient version of the tool that does. It is the absence of the work entirely, disguised as automation.

In education, AI tutoring systems are being deployed as replacements for teaching staff in underfunded districts. Not as assistants to teachers. As substitutes for them. The thirty students in the room are now working with a screen. The teacher who knew which three were falling behind before the test, who knew when the silence in the room was productive and when it was stuck, who knew which student needed to be challenged and which one needed to be left alone today, that person has been replaced by a system that is cheaper and does not require benefits.

The students in those districts are not getting better education with fewer teachers. They are getting education-shaped content delivery without the human relationship that research consistently identifies as the primary predictor of learning outcomes. The children from families that can afford the schools where teachers still exist are not being taught this way. The substitution of AI for teachers is happening where the children of parents with less power have no choice but to accept it.

These are not edge cases. They are the current direction of deployment, in the places where the people affected have the least power to resist it.

The question is whether the people with more power, the leaders inside these organisations, the regulators with the authority to intervene, the workers in adjacent industries who can see the trajectory before it arrives in their own sector, are willing to name what is happening and act accordingly.

The child in the back seat

The carpenter has thirty-one years.

He built the knowledge that lets him know when the machine is wrong over three decades of doing the work, getting it wrong, correcting it, developing the calibration that only accumulates through that specific kind of repeated encounter. The knowledge lives in his hands because his hands have done it thousands of times. The judgment lives in his eye because his eye has learned what the wood does when you get it right and wrong.

The question the essay has not yet asked is what happens to the next generation of carpenters.

Not the people currently in the workforce, who built their knowledge before the systems existed and who can, in principle, maintain that knowledge as long as they keep using it. The people who will enter the workforce in a world where the systems are already present. The child who will grow up in an environment where every diagnostic task, every judgment call, every pattern recognition that takes a practitioner decades to develop, is handled by systems before they have had the chance to build the knowledge themselves.

A radiologist trained on AI-assisted diagnosis learns to evaluate the AI’s outputs. She does not necessarily learn to make the diagnosis. The distinction is invisible in the output. It is everything in the moment the AI encounters a situation outside its training data and the radiologist needs to know what she actually knows.

A journalist trained in an environment where AI tools handle the initial research, the source verification, the pattern identification, learns to work with those outputs. She does not necessarily develop the specific judgment that comes from having done those things badly, been wrong, understood why she was wrong, and rebuilt her approach. That judgment, built through productive failure, is the thing that allows her to notice when the AI is producing plausible nonsense and to know the difference.

This is not an argument against AI in education or in professional training. It is an argument for being honest about what is being traded when we introduce systems that remove the friction of learning. The friction is not an obstacle to the knowledge. In many domains, it is the mechanism of the knowledge. The mistake that hurts, the confusion that resolves slowly, the situation that resists the template these are not inefficiencies in the development of expertise. They are the development of expertise.

A generation of professionals trained to operate systems that handle the difficult parts of their work will be very capable of operating those systems. They will be less capable of the work itself. The gap will be invisible until the systems fail or the situation falls outside what the systems can handle.

That situation arrives in every complex domain. It arrives regularly. It is the nature of complex domains that they produce novel situations the previous framework did not anticipate. The practitioner with thirty-one years of embodied knowledge handles it. The practitioner trained to operate the system that handles it is in a different position.

I have a son. He is eight. He still notices everything the ant carrying something three times its size, the specific way light comes through a particular window at a particular time of year, the sound a door makes that tells him something about the mood of the house. He has not yet learned to outsource his attention. The world is still information that arrives through his senses and requires him to process it.

He will grow up in an environment that is very good at handling that processing for him. Very good at answering his questions before he has had the chance to sit with them. Very good at producing outputs that appear to be the result of the understanding he has not yet built. The environment is not designed against him. It is indifferent to him in the specific way that systems optimised for other things are indifferent to the collateral effects on the people inside them.

The carpenter’s line the moment the understanding leaves the room applies to him as it applies to the radiologist and the journalist and the engineer. The question is whether we are willing to structure his education, his tools, his relationship with the systems around him, in a way that ensures the understanding builds in him rather than being handled by the systems on his behalf.

That is not a technology question. It is a values question. The answer we are currently giving, by default rather than by deliberate choice, is the understanding can be handled by the system. The child does not need to build it.

I do not accept that answer. I do not think we have thought carefully enough about giving it.

What refusing looks like

I want to be specific here because specificity is where most of this argument gets lost in abstraction.

You can refuse replacement-focused AI. Not by rejecting the technology. By insisting on the four characteristics of genuine augmentation and refusing to accept deployments that fail them.

A worker whose role is being automated can ask: am I still in the decision? If the answer is no, that is not augmentation. The company is replacing you, not assisting you. You are entitled to say so. Your union is entitled to say so. Your government is entitled to legislate so.

A government can legislate the productivity circulation. If AI deployment in an enterprise increases output by more than twenty percent, some defined share of that gain goes to the workers whose roles have been transformed. Not as charity. As a legal requirement, on the same basis that minimum wage legislation is a legal requirement. The argument that companies should be allowed to take efficiency gains entirely as profit while workers bear the cost of displacement is a political choice, not an economic law. It can be unmade.

A regulator can mandate the understanding requirement in domains where the understanding matters for safety. Healthcare decisions. Criminal justice. Infrastructure. Education. Financial advice. In each of these domains there are situations where the system will fail or the context will change and a human being will need to understand the work at a level below the interface. Deploying AI in these domains in ways that remove that understanding from the humans in the chain is not a technical efficiency. It is a safety risk, deferred. Regulation can require that the understanding stays in the room.

A society can decide that some domains should not be automated at all. Not because the technology cannot do the task, but because the task requires something that cannot be separated from the human doing it. The nurse’s presence. The teacher’s attention. The elder care worker’s relationship with the person in their care. These are not inefficiencies to be optimised. They are the thing itself. Replacing them with machines does not deliver the service more efficiently. It delivers a different service. A worse one. And it does so while eliminating the livelihoods of people who have built careers in the knowledge that their presence matters.

None of this is technically impossible. The EU AI Act is a beginning. Worker co-ownership models exist. Sector-specific bans exist in some jurisdictions. The robot tax has been proposed and discussed in enough serious policy contexts that it is no longer a fringe idea. The mechanisms are available.

What is missing is the political will, which is a function of power, which is a function of who is in the rooms where decisions are made and what they have agreed to stop accepting.

There is a specific version of this argument that gets made against all of the above. It goes: companies will move their operations to less regulated jurisdictions. Legislating worker protections in one country simply exports the harm to another. International coordination is impossible. Therefore regulation is futile.

This argument is made with great confidence by people who benefit from it being believed.

It is not true. It is a negotiating position. Companies that operate in markets with consumer purchasing power do not, in practice, simply relocate all operations to avoid labour regulation. The history of minimum wage legislation, environmental regulation, and product safety requirements shows consistently that when democracies with significant markets decide something is not acceptable, companies adjust. Slowly, with resistance, with lobbying and legal challenge. But they adjust.

The argument that regulation is futile is itself the primary obstacle to regulation. It is designed to produce the paralysis it predicts. The correct response to it is not to accept its premise but to note whose interest the premise serves.

What this requires, practically, is the same thing every advance in labour rights has required. Workers who are willing to name what is happening. Leaders inside organisations who are willing to say the uncomfortable thing in the room where the decision is being made. Governments that are willing to set the terms rather than wait for the market to arrive at an acceptable outcome on its own. The market will not arrive at an acceptable outcome on its own. It never has. That is what markets are. They are efficient at generating returns for the people who own the capital. They require external constraint to generate acceptable outcomes for the people who do not.

The language we are missing

There is a reason this argument is hard to make inside most organisations. It is not that the people inside them are indifferent to the humans being displaced. Most of them are not. It is that the language available in organisational settings is almost entirely the language of efficiency, cost, output, and return on investment. The language of the other thing, human dignity, the right to meaningful work, the value of understanding that lives in people’s hands and cannot be extracted and replicated, does not have a register in most professional settings.

I watch people who privately hold strong views about what is being lost arrive in meeting rooms and find that they have no vocabulary for the thing they believe. They can speak the language of business risk, of regulatory exposure, of reputational damage if the deployment goes wrong in a visible way. They cannot speak the language of what work means to a person, because that language sounds like sentiment in a context that has defined itself against sentiment.

This is not accidental. It is a managed condition. Organisations that want to make certain decisions without resistance need their decision-making environments to be inhospitable to the language that names what is being decided. Strip the sentiment from the room. Define rigour as the exclusion of the unmeasurable. Train leaders to translate every consideration into a number before bringing it to a table. The result is a professional culture in which the thing that matters most, the human on the other side of the decision, has no language in which to be represented.

Learning to speak that language in professional settings is itself a form of resistance. Not loud resistance. The kind that arrives as a single question in a meeting that has been careful to exclude that kind of question. What does this mean for the people whose roles are affected. Not as a performance of concern. As a genuine ask that requires a genuine answer before the decision proceeds.

The organisations that navigate the next decade well will be the ones that find a way to hold both languages at once. The language of efficiency and the language of the human cost of efficiency. Not because the human cost always outweighs the efficiency gain. Sometimes it does not. But because the decisions made in the absence of that language tend to arrive at places that, on reflection, nobody in the room actually wanted to go.

The executive who said the problem with people is that they have needs was not wrong about the numbers. He was wrong about what the numbers were measuring. He was measuring cost. He was not measuring the knowledge that would leave the room, the resilience that would be lost, the liability that would be deferred, the community that would be damaged, the hundred and forty people who would need to rebuild a working life from the position of having been described as an inefficiency.

Those things are not immeasurable. They are unmeasured. The distinction matters. Unmeasured things can be measured if we decide they are worth measuring. The first step is deciding they are worth measuring. The first step before that is recovering the language that allows us to say why they matter.

The carpenter does not struggle to say why the understanding matters. He has been working in relationship with the material for thirty-one years. The material has taught him. He can read what it needs. He can feel when the tool is right for the task and when a different approach is required. He has the language because he has the experience, and he has the experience because he was never asked to step aside and let a machine accumulate it for him.

That is what augmentation protects. Not the job. The person inside the job. The knowledge that the person carries. The understanding that makes the human in the room genuinely valuable rather than merely present.

There is something that the replacement versus augmentation framing almost captures but does not quite reach.

The framing is still, at its core, an economic argument. We should keep humans in the loop because they provide value that the machine cannot. We should distribute the gains because it is more efficient in the long run. We should maintain the understanding because the organisation will need it when the system fails.

These arguments are true. They are also insufficient.

The reason to refuse replacement-focused AI is not primarily because it is economically suboptimal. The reason to refuse it is that it treats human beings as inputs to a system rather than as the reason the system exists.

The hundred and forty people in the call centre are not cost inefficiencies that the technology has made available to be eliminated. They are people. They have working lives that are a central part of their experience of being alive. They have colleagues, routines, a particular kind of social fabric that forms around work even when the work is not glamorous. They have the knowledge that they are doing something, that their presence contributes to something, that the contribution is recognised and compensated.

When a company eliminates those people and replaces them with a system, it is not just making an economic decision. It is making a decision about what humans are for. It is saying that human beings are valuable precisely and only to the extent that they generate output at an acceptable cost, and that the moment a machine can generate the same output at lower cost, the humans are no longer valuable.

This is a political claim. It is not a technical fact. It is a choice about the purpose of economic organisation. And it is a choice that the people most affected by it, the workers, the communities, the societies that depend on employment as the primary mechanism for distributing participation in economic life, never agreed to and were never asked to agree to.

The question is not whether we can use AI to serve human flourishing. We can. The question is whether we are willing to insist that human flourishing is the point, and that deployments which treat it as a cost to be eliminated rather than a purpose to be served are not progress, regardless of what they do to the profit margin.

The carpenter said it clearly. There is a line. The line is the understanding. When the understanding leaves the room, something has been lost that is not recoverable from a manual.

The companies deploying replacement-focused AI know where that line is. They are crossing it deliberately because the business case says to. The question is whether anyone with the power to stop them will say, with the same clarity that the carpenter said it: this is the line. It does not get crossed.

When your AI tool becomes a weapon

If your team reaches for AI before even trying to understand the problem, your hammer is becoming a weapon.

If your dashboards and AI tools are used more to judge people than to coach them, your system is serving control, not growth.

If only a small group of leaders can change the rules, thresholds, or prompts, your AI is a weapon over the many.

These aren’t glitches in the code they’re design choices about power. Every time you trade transparency for convenience, or autonomy for “efficiency,” you nudge the tool closer to weapon‑territory.

Leaders are accountable for the “power” they hold, are the ones who decide how the hammer is held and who gets to wield it. AI doesn’t just support decisions it reshapes who trusts what, who speaks, and who stays silent. When incentives, metrics, and culture all point toward speed, cost, and compliance, the natural outcome is that every AI tool becomes a weapon of control.

Hammer‑mode leadership does the opposite it builds AI systems that are optional, explainable, and learnable. It rewards people who can still think, debug, and argue without the tool. It treats AI as a training partner, not a verdict machine.

If you can’t imagine your team living without a particular AI, it’s already a weapon over their autonomy. The harder question is what would you need to change so it feels like a hammer again?

What this asks of leaders

I spend my working life in rooms with people who are making these decisions. Not the executives who issue the directives. The people in the middle. The team leads, the engineering managers, the product owners, the architects who are being asked to build the systems that cross the line and who know, in some quiet part of their professional judgment, that the line is being crossed.

Most of them manage the knowledge privately. They build what they are asked to build. They note their reservations in a one-on-one with their manager. They tell themselves that someone above them has assessed the tradeoffs and the decision has been made and it is not their place to refuse.

That is not accurate, it is comfortable.

I want to be honest about why the silence happens. It is not cowardice, exactly. It is something more structural. The person who objects in the room takes on a real professional risk. The person who builds the system and says nothing takes on no immediate risk. The costs of speaking are personal and immediate. The costs of silence are collective and deferred. This asymmetry is by design. It is one of the mechanisms by which organisations produce outcomes that most of the individuals inside them would individually refuse if the choice were put to them directly.

I have sat with engineers who have built targeting systems they did not believe should exist. Customer profiling systems that they knew were being used in ways the data subjects never consented to. Automation roadmaps that they understood would eliminate the roles of people they worked alongside. Every one of them had, at some point, raised a question in a meeting and been told that the decision had already been made at a level above the meeting. Every one of them had accepted this and continued.

The decision at a level above the meeting was made by a person. That person has a name and a salary and the authority to have made a different decision. The acceptance by the people in the room is what gives that decision its operational reality. The system cannot build itself.

This is not a counsel to individual heroism. I am not saying that every engineer should refuse every assignment they have doubts about. The working world does not function that way and the people with mortgages and families and careers cannot be asked to carry the full weight of systemic change as individuals.

What I am saying is narrower. That naming what is happening is available even when refusing is not. That the one sentence, in the room, that describes the actual decision rather than the business case version of it, is always available. That the question this is a replacement not an augmentation, have we assessed the liability of that distinction, is available in every meeting where the distinction matters. That the professional norm of silence, of managing private discomfort while the public decision proceeds, is a norm that can be interrupted without destroying a career.

The person who builds the system that replaces the hundred and forty people is not absolved by the fact that the decision was made above them. They are participating in it. Their technical skills are the instrument of it. Their willingness to execute without naming what they are executing is part of what makes it possible.

Leadership in this context does not require dramatic gestures. It requires saying, in the room where the decision is being made, what the decision actually is. Not the business case version. The version that names the humans being displaced, the understanding being removed from the room, the knowledge being lost, the distinction between the tool and the weapon and which one is being built.

Say it once, clearly and accept that it may not change the outcome. Say it anyway.

Because the alternative, the management of private discomfort while the public decision continues unchanged, is how these things happen without resistance. Not through malice. Through the accumulated silence of people who knew the line was being crossed and decided that naming it was someone else’s job.

The carpenter has been doing the same work for thirty-one years. He has replaced his tools when better ones arrived. He has drawn one line. He draws it not out of sentimentality or fear but out of a precise understanding of what the work is and what would be lost if the understanding left the room.

He is not waiting for someone above him to draw that line.

He is the carpenter. It is his line to draw.

So is yours.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

When I use the word AI in this essay I am not acknowledging that these systems are intelligent. I am using the word the industry uses, because that is the word that has conquered the conversation. What we are actually talking about is an advanced deep learning model. Extraordinarily capable at pattern recognition, statistics, and probability. Not thinking. Not understanding. Not intelligent in any meaningful sense of the word. There is no academic consensus on what intelligence actually is, and there is certainly no evidence that these models possess it. The word AI is a marketing decision. It was chosen to make the technology feel inevitable, significant, and human. I use it here because refusing to use it would make the essay harder to read. But I want you to know that every time I write AI in this essay, I am describing a very powerful statistical engine. Nothing more. The intelligence is in the room. It is in you.

My son asked me last week what a tree was for.

He is eight. He had been sitting under one for twenty minutes, watching an ant carry something three times its size along a crack in the stone path. He was not bored. He was not seeking stimulation. He was simply present in the way that eight-year-olds are present when nobody has yet taught them that presence is an inefficiency.

I said trees make oxygen. They give us wood. Some trees give us fruit.

He looked at me the way children look at adults who have answered a different question.

What is it for, he said again.

I did not have a better answer. I had descriptions of function. I had economic utility. I had the language of what a tree produces, how a tree performs, what a tree delivers. I did not have an answer to what a tree is for in the way he was asking, which was not a question about output at all. It was a question about meaning. About whether the tree had some irreducible right to exist that had nothing to do with what it gave us.

I sat down on the path next to him and watched the ant.

We stayed there for a while. Neither of us said anything useful.

The question under every question

I have spent the last several years writing about AI, about power, about what technology is costing us as humans. I have written about the system that made work the answer to identity. I have written about the bargain that is breaking. I have written about the designer who built the beautiful interface without understanding what it was doing, and about the Mediterranean cultures that kept alive a way of being in the world that does not depend on employment to feel whole.

All of those essays were, in some sense, asking the same question. They just never quite asked it directly.

The question is this.

Do we actually want what we have been building toward.

Not do we want AI. Not do we want automation. Not even do we want efficiency or productivity or the particular kind of progress that the last two centuries have been organised around.

Do we want the destination. The thing at the end of the road we have been on. The world that arrives if the logic we have been following is followed all the way.

I have been sitting with this question for a long time. I find it uncomfortable in a specific way. Not the discomfort of a question that is difficult to answer. The discomfort of a question that I suspect I already know the answer to and am not ready to say it out loud.

Because if the answer is no, then the problem is not how we regulate AI. It is not how we tax the offshore wealth or redistribute the efficiency gains or retrain the displaced workers. Those are all important questions, urgent questions, questions worth spending careers on. But they are downstream of a more fundamental choice about what kind of creature we are and what kind of world we are trying to build.

And that question is not a policy question. It is a philosophical one. A cultural one. Possibly a spiritual one, if that word can survive in a conversation about technology without becoming its own kind of evasion.

Precision as religion

A knife I have had for eleven years lives in the third drawer of my kitchen. It was given to me by a chef I once worked alongside briefly, in a different life, before the tech career consumed everything. He told me when he handed it over that a good knife is not precise. It is responsive. A precise knife does the same thing every time. A responsive knife does what the material needs. The difference, he said, is everything.

I have thought about that distinction more in the last two years than I thought about it in the previous nine.

Precision is the god of the current age. We worship it openly and without embarrassment. We build systems to eliminate the human variability that introduces imprecision. We measure everything that can be measured so that we can optimise everything that can be optimised. We have constructed an entire civilisation around the premise that precision is inherently better than its absence. That the precise answer is more valuable than the responsive one. That the consistent output is worth more than the one shaped by the moment and the material.

AI is the apotheosis of this religion. It is the most precise instrument we have ever built. It does the same thing, more or less, given the same input. It does not have bad days. It does not bring the residue of a difficult conversation into the next one. It does not read the room and decide that what the room actually needs is different from what it was asked for.

We look at this and call it intelligence.

I want to ask what we mean by that word. Not to score a philosophical point but because the confusion is consequential. When we call AI intelligent, we are making a claim about what intelligence is. We are saying that intelligence is pattern recognition at scale. That it is the capacity to retrieve and recombine information faster and more accurately than any human could. That it is, in short, a form of precision applied to cognition.

But every teacher I admired in my life was not precise. They were responsive. They could read a room of thirty students and feel which three were lost and which one was bored and which one needed to be challenged and which one needed to be left alone today because something was happening at home. They could not have told you how they knew. The knowing was in their hands and their eyes and their thirty years of being in rooms with children. It was imprecise, uncodifiable, irreducible to an algorithm. And it was, by any meaningful measure, intelligent in a way that no pattern-recognition system has yet approached.

The precision religion cannot account for that kind of intelligence because it cannot measure it. And what it cannot measure, it tends eventually to dismiss.

What we were running from

There is a story I want to tell about a man I met at a conference in Lisbon three years ago.

He was a founder. Mid-forties. His company had just been acquired for a number that made him, by any conventional measure, secure for several lifetimes. He was at the conference not because he needed to be but because he did not know what to do with a Tuesday that did not have a schedule.

We ended up talking for two hours at the edge of a rooftop bar, the city orange in the early evening, the smell of salt coming up from the river below. He was successful by every metric the productivity culture recognises. He was also, very quietly, one of the most lost people I had met in years.

He said: I thought the acquisition would feel like something. Like I had arrived somewhere. I keep waiting for the feeling.

I asked what feeling he was waiting for.

He thought about it. Then he said: that it was worth it.

He was not talking about money. He had the money. He was asking whether the thirty-hour days and the missed dinners and the relationships that had not survived the velocity and the identity so completely organised around his company that when the company was sold he genuinely did not know what he was anymore, whether all of that had been in the service of something. Whether there was a destination that justified the road.

He had achieved everything the system promised achievement looked like. And he was standing on a rooftop in Lisbon asking a stranger whether any of it had been worth it.

This is not an unusual story. I hear versions of it regularly. What is unusual is that he was willing to say it out loud, in those words, without the protective layer of lessons learned or pivots to the next chapter that most successful people deploy when the conversation gets close to the actual question.

The actual question, underneath his question, is the same one my son was asking about the tree.

Not what does it produce. What is it for.

We have built an entire civilisation of production without ever properly asking that question. Or rather, we asked it and then accepted an answer that turned out to be, on close examination, a circular reference. Work is for productivity. Productivity is for growth. Growth is for prosperity. Prosperity is for the good life. The good life is for, roughly speaking, more of the same.

The man on the rooftop had followed that logic all the way to its conclusion and found it empty.

He is not alone. He is just unusually honest.

The delusional ape

I want to use a phrase that was offered to me recently in a conversation about AI and identity. The person I was talking with, frustrated with the circularity of most AI discourse, said this.

“We are a delusional ape hallucinating narratives as we traverse this reality.”

It is not a kind description. It is also, I think, more true than most descriptions we use.

While some believe we are biological creatures who arrived through an unintentional process, there are those that believe that we are human beings who came for a purpose and are heading toward a defined destination. We developed the capacity for consciousness, which is extraordinary and still largely unexplained. And we used that consciousness primarily to construct stories about why we are here and what we are supposed to be doing and whether we are doing it correctly.

Every culture in human history has done this. The stories differ. The need to have a story does not.

The productivity gospel is one of these stories. It says: the purpose of a human life is to produce value. Progress means producing more value more efficiently. The good society is one organised to maximise production. The good life is one that contributes maximally to that production.

This story has been extraordinarily successful at generating material wealth. It has also been extraordinarily successful at generating misery of a particular kind. The misery of people who have followed the story faithfully and arrived at its promised destination and found it does not feel like destination at all. Who have produced and produced and optimised and achieved and looked up from the spreadsheet of their life to find the rooftop in Lisbon and the question that has no answer in the story’s own terms.

What the AI era is doing, among other things, is stress-testing this story at scale. If the purpose of a human life is to produce value, and machines can produce that value more efficiently, then what is the purpose of a human life.

The story cannot answer that question. Because the story was never really about purpose. It was about distraction. A narrative complex enough to occupy the delusional ape’s extraordinary consciousness so thoroughly that the underlying questions, what are we for, what do we owe each other, what does it mean to live rather than merely function, could be safely deferred.

AI is removing the distraction. Not intentionally. Not kindly. But unavoidably.

What is left when the distraction is gone is the question my son was asking about the tree.

What machines cannot know

I want to be careful here because this is where the argument is tempting to get wrong.

The wrong version goes: humans are special, AI cannot replicate consciousness, therefore AI is not really intelligent, therefore the threat is overstated.

That is not what I am saying.

What I am saying is something different. That there is a category of knowledge that requires being mortal, embodied, and uncertain to access. And that this category of knowledge is not a small addendum to human intelligence. It is its foundation.

A surgeon who has never been afraid does not understand what it costs a patient to put their body in another person’s hands. A leader who has never failed does not understand what it takes for a team member to admit they are struggling. A parent who has never lost something essential does not know the particular quality of attention you give to what you have and are afraid of losing.

This is not sentimentality. It is epistemology. The knowledge that comes from being vulnerable, from having stakes, from being the kind of creature that can lose things and be changed by losing them, is a different category of knowledge from the knowledge that comes from pattern recognition across large datasets.

I think about this when I watch leaders try to use AI as a substitute for the difficult conversation. The conversation where someone needs to be told that what they are doing is not working. Where someone needs to hear, from a person they trust, that the direction is wrong. Where someone needs the specific experience of being in the room with another human being who has assessed the situation and is now offering them the honest version rather than the diplomatic one.

AI can generate the words. It can produce language that is, in many cases, more precise than what the leader would have said unprompted. More structured. Less emotional. Less contaminated by the relationship between the people in the room.

But the contamination is the point.

The reason the conversation matters is because it happens between people who have stakes. Who have history. Who will still be in the room together next week and the week after. Who are changed by what is said and by the fact of having said it. The weight of the conversation is not a problem to be designed out. It is the mechanism by which the message lands differently than it would on paper.

A piece of feedback delivered by someone who cares about you and is afraid of losing your respect and is choosing to say the difficult thing anyway is not the same piece of feedback delivered as a well-structured paragraph generated by a system that has no relationship with you and cannot lose anything by saying it.

The words might be identical. The knowledge transmitted is entirely different.

AI will get better at simulating the language of that knowledge. It will produce outputs that look, in many contexts, indistinguishable from the real thing. This is already true and will become more true.

But simulation is not the thing. A map of the territory is not the territory. The question what does it mean to grieve is not the same question whether you can answer it or not.

The danger is not that we will mistake AI for human. The danger is that we will mistake the simulation for sufficient. That we will accept the map because the territory is too difficult to navigate. That we will build systems around the convincing imitation of human understanding and call that understanding, the way we built systems around the convincing imitation of human judgment and called that efficiency.

The chef who gave me the knife was teaching me something about the difference between responsive and precise. The responsive thing is harder. It requires presence, attention, accumulated knowledge that lives in the hands as much as the head. It cannot be replicated by a system that has never held the knife.

We are building a world in which the precise thing is systematically preferred over the responsive thing. Because the precise thing is cheaper to scale. Because the responsive thing requires the kind of human presence that cannot be extracted and replicated. Because the responsive thing keeps the human in the room and the human is, from a certain angle, the most expensive component in the system.

That preference is a choice. It is also, I want to argue, a mistake. Not just ethically. Practically.

The map and the territory

I talked to a CTO last month who had just finished a six-month implementation of an AI decision-support system for his team. The system was excellent. It was fast, it was consistent, it was right more often than the humans it was designed to support. By every metric the project had been measured against, it was a success.

He called it a success with the particular flatness of someone describing something that has cost more than the balance sheet shows.

I asked what was not on the metrics.

He was quiet for a moment. Then he said: my best people have stopped thinking as hard.

He did not mean they were lazy. He meant that the presence of a system that produced correct answers reliably had altered the relationship between his team and the problems they were solving. They were now problem-checkers more than problem-solvers. They reviewed the system’s output rather than generating their own. The cognitive muscle that gets built by sitting with a difficult question and not knowing the answer and having to find your way through it was being exercised less, and atrophying accordingly.

The system was optimising their output while quietly eroding their capacity.

This is not a new phenomenon. It is what calculators did to mental arithmetic. What GPS did to spatial reasoning. What spell-check did to the intuitive understanding of how words are constructed. Each individual substitution looked like efficiency. The cumulative effect was a slow narrowing of the range of things the human could do without the tool.

But mental arithmetic and spatial reasoning and spelling are relatively small losses. What the CTO was describing was a loss at a different order of magnitude. The capacity to think hard about difficult problems is not a peripheral skill. It is, arguably, the central skill. The thing that makes a team capable of navigating a situation the system was never trained on.

The territory of reality is not the map of the problems the system has seen before. Novel situations arrive. Situations the training data never contained. Situations that require a human being to sit with uncertainty and confusion and incomplete information and still make a judgment.

If the humans who are supposed to make those judgments have spent five years checking the system’s output instead of developing their own, they will not be ready when the situation arrives that the system cannot handle.

We are optimising for a world that does not exist, the one where the system always has an answer, while eroding our capacity to navigate the world that does, the one where sometimes there is no answer and you have to make your best guess from the position of a mortal creature with incomplete information and genuine stakes in the outcome.

The civilisation that forgot it was alive

The mechanistic worldview has a logic and the logic is internally consistent. Reality is a system. Systems can be understood. Understanding can be translated into control. Control can be translated into optimisation. Optimisation is progress.

This logic has produced antibiotics and clean water and agricultural yields that feed populations that would have been unimaginable two centuries ago. I do not want to romanticise a past that involved dying of infections and watching children starve. The mechanistic worldview has delivered real things of real value and anyone who dismisses it entirely is doing something dishonest.

But.

The mechanistic worldview is a tool, not a truth. It is a way of modelling reality that is extremely useful for certain kinds of problems and useless for others. The problem is that we have elevated it from tool to worldview. From a method to an identity. We have organised societies, economies, institutions, and finally the way we understand ourselves around a model of reality that was built for the manipulation of physical systems and applied, wholesale, to the question of how to live.

The result is what you see on the rooftop in Lisbon. A man who has succeeded by every measure the system offers, standing in the orange evening light, waiting for a feeling that the system was never designed to deliver.

The system optimises for measurable outputs. Meaning is not a measurable output. Love is not a measurable output. The particular quality of attention you give to a eight-year-old sitting under a tree watching an ant is not a measurable output. Beauty is not a measurable output. The kind of trust that is built between people over years of difficulty shared and survived is not a measurable output.

These things are not inefficiencies. They are what the efficiency is supposed to be for. And we have built a civilisation so thoroughly organised around the measurable that we have systematically devalued everything that falls outside the measurement.

What interests me is how thoroughly this worldview has colonised even the vocabulary available to us when we try to resist it. We cannot argue for the value of the three-hour meal without framing it in terms of wellbeing metrics and productivity benefits and research showing that social cohesion correlates with economic resilience. We cannot argue for rest without citing studies about cognitive performance after recovery. We cannot argue for the unmeasurable without first translating it into the measurable, because the measurable is the only language that the current system accepts as legitimate.

This is the deepest form of the problem. Not that we value the wrong things. But that we have lost access to a language for valuing things that cannot be measured. The language of the intrinsic. The language that says: this is worth something because it is, not because of what it produces.

My son has that language. He is eight. He has not yet traded it in for the other one.

The cultures that maintained the three-hour meal also maintained that language. Not as a luxury or a philosophical indulgence. As a practical necessity. As the thing that allowed them to keep building communities where people wanted to be rather than places people had to be because the economic logic left them no other option.

AI is arriving into a world where that language is already endangered. Where the people who might resist the substitution of the simulated for the real are working in the vocabulary of the system they are trying to resist. Where the argument for human connection gets made by citing engagement data and the argument for rest gets made by citing productivity research and the argument for the unmeasurable gets abandoned because nobody in the room where decisions are made has the language for it anymore.

This is the loss that is hardest to name. And the hardest to recover from.

What the Mediterranean kept

The cultures that economists spent decades describing as inefficient were, among other things, holding something that was not on any balance sheet.

A meal in a village in southern Portugal takes three hours. Not because the food takes three hours. Because the meal is not primarily about the food. It is about the particular alchemy that happens between people when they sit together without agenda and let the conversation go where it goes and allow the time to be what it is rather than what it can be extracted into.

This is not nostalgia. I am aware that those same villages contain their own forms of constraint and cruelty, their own hierarchies and injustices, their own things that should not be preserved. I am not arguing for a return to an imagined pastoral past.

I am arguing that the three-hour meal contains a piece of knowledge about what it means to be human that the productivity gospel cannot encode. That the thing happening in that meal, the thing that is not the food, is not a luxury or a bonus or a cultural affectation. It is a fundamental human activity. The building and maintenance of the bonds that make a person feel they exist in a world rather than merely passing through it.

The knowledge that this contains is not abstract. It is practical. Societies that maintained these structures retained a social fabric that, when economic catastrophe arrived, gave people something to stand on that was not their job title or their salary or their professional identity. They had each other. Not metaphorically. Concretely. People who would bring food. People who would sit with you. People for whom your value was not conditional on your output.

This is what the mechanistic worldview cannot produce, not because it cannot value these things in principle, but because it has no mechanism for measuring them and therefore no mechanism for protecting them when they conflict with something that can be measured.

We are now in a moment where what can be measured is being automated and what cannot be measured is being revealed as the only remaining irreducibly human territory. The delusional ape, it turns out, cannot be optimised out of its need for exactly the things that optimisation cannot account for.

And here is what is interesting about that. The AI companies understand this, at some level. The language they use to sell their products is saturated with the vocabulary of human connection. More time for the things that matter. Focus on what only you can do. Get back to the work that is truly yours. The promise is not efficiency for its own sake. The promise is that efficiency is the route back to humanity.

This is the most sophisticated version of the problem. Not that we are being sold a machine and told it is a tool. But that we are being sold efficiency and told it is the road to meaning. That by automating the tedious, we will recover the time and the energy to do the genuinely human things. That the machine is not replacing the human. It is freeing the human to be more fully human.

It is a beautiful story. It might even be true for some people in some contexts. The problem is the track record. The agricultural revolution was supposed to free humans from backbreaking physical labour. It did, for some people, in some places. It also created the conditions for industrial capitalism, which created new forms of backbreaking labour and new systems for extracting human time on behalf of people who owned the machines. The story the machines tell about themselves tends to be more optimistic than the story the people inside the system experience.

The question is not whether AI will free some time for some people. It will. The question is whose time, and freed for what, and who decides, and what happens to the people whose time is not freed but simply made redundant.

Those are not questions the technology answers. They are questions the politics decides. And the politics, right now, is being run by people who have a significant financial interest in a particular set of answers.

Do we want this

I want to return to the question I have been circling.

Do we actually want what we have been building toward.

I do not think most people do. I think most people, if you sat with them long enough on a rooftop in Lisbon or at a kitchen table in the afternoon or under a tree in a garden while a child watched an ant, would tell you that what they want is not more efficiency or more precision or more optimised engagement with algorithmically curated content.

They want to feel that they are here. That the people they love are here. That the work they do means something beyond its contribution to a metric. That they are participating in a life rather than executing a function.

These are not complicated wants. They are the wants of the delusional ape. The wants that every culture in human history has tried to address through ritual and religion and community and art and the particular way that humans have always organised themselves around fire and food and the stories that make the darkness less absolute.

AI is not answering those wants. In many cases it is specifically designed to redirect them. The engagement economy that funds most AI development is built on the premise that human wants can be substituted with simulations. That the want for connection can be satisfied with a notification. That the want for meaning can be addressed with a personalised content feed. That the want for recognition can be met with a like, which costs the giver nothing and delivers the receiver a small neurological reward that wears off in minutes and requires replenishment.

This is not a conspiracy. It is a business model. And it has been extraordinarily successful by the measures the business model uses, which do not include whether the humans inside the system are actually getting what they want.

I watched a senior engineer at a company I was advising try to explain to his teenage daughter what he had been working on for the previous three years. She was sixteen. She listened politely. Then she asked: but what does it do for people.

He described the product. The engagement metrics. The daily active users. The revenue model.

She said: but what does it do for people.

He understood what she was asking. He did not have an answer that satisfied either of them. Because the answer was honest and the honest answer was that it did something to people rather than for them. That the engagement metric and the human good were not the same number, and the company had spent three years optimising for the number while the other thing had been left to look after itself.

The sixteen-year-old was asking the same question my son asks about the tree. Not what does it produce. What is it for.

The question is not new. Every generation asks some version of it. What is different now is the scale and the speed. We have built systems of extraordinary reach, operating at a speed that leaves no room for the question to be asked between iterations, optimising for measures that were chosen for their measurability rather than their alignment with what actually makes a human life feel worth living.

And those systems are now building the next generation of systems in their own image.

The question do we want this is not a question about technology. It is a question about whether we are willing to look clearly at what we have been choosing and decide whether to keep choosing it.

The choosing is real. This is not inevitable. The three-hour meal did not disappear because of some unstoppable force of human nature. It disappeared because a set of economic and cultural choices systematically devalued the things it produced. Those choices can be made differently.

The question is whether we are willing to make them. Which requires first being willing to name what we actually want, as opposed to what the system has trained us to say we want.

There is a version of this that becomes a counsel of despair. A litany of losses that ends with the suggestion that modernity was a mistake and we should go back to some simpler life that never actually existed in the form we imagine it. That is not what I am saying and I want to be clear about that.

What I am saying is narrower and more practical. That the tools we build should serve the wants of the creature using them, not reshape the creature into something that fits the tool better. That when we notice a gap between what the metrics measure and what actually matters, the response should be to question the metric rather than to dismiss what the metric cannot capture. That the three-hour meal and the knife that is responsive rather than precise and the eight-year-old watching the ant are not romantic anachronisms. They are data about what human beings actually need that our measurement systems are not equipped to collect.

My son under the tree was not confused about what he wanted. He wanted to understand what the tree was for in a way that had nothing to do with output. He wanted the world to make sense at a level below function and utility and measurement.

He is eight. He has not yet been taught to call that wanting impractical.

I spent fifteen years in organisations that would have called it impractical. I spent a Saturday morning at a kitchen table noticing that I could not sit still with nothing to show for the hour, and understanding that the inability was not mine but the system’s, installed in me over years of being inside it.

I am still working on the uninstalling.

The delusional ape hallucinating narratives as it traverses this reality. That is what we are. Precisely and completely. Not as an insult. As a description of something extraordinary. We are the universe looking at itself and making stories about what it sees. We are the only thing we know of that does this. It is not a bug. It is the whole point.

The question is which stories we choose to tell.

The story we have been telling, that precision is progress, that output is value, that the optimised life is the good life, is running into its own limits. The arrival of AI is not creating those limits. It is revealing them. The story always had a problem at its core. The problem is that it described a destination nobody actually wanted to arrive at.

The man on the rooftop arrived at it. He was waiting for a feeling the story had no mechanism to deliver.

My son under the tree was asking for the thing the story had no language to describe.

The tree does not produce anything right now. It is simply there. Existing completely. Doing the thing that it is, without justification or apology or quarterly metrics.

There is a kind of knowledge in that. The kind that lives in hands and eyes and thirty years of being in rooms with things you have paid attention to. The kind that comes from being mortal and present and genuinely uncertain about what happens next.

The knife is responsive, not precise.

That is the difference. That is everything.

If this essay landed, the Tuesday posts come by email. Free. Subscribe below and the next one arrives in your inbox.

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From this series;

1 - https://newsletter.diamantinoalmeida.com/p/who-are-you-without-the-title

2 - https://newsletter.diamantinoalmeida.com/p/they-did-not-accidentally-make-work

3 - https://newsletter.diamantinoalmeida.com/p/a-delusional-ape-hallucinating-narratives

4 - https://diamantinoalmeida.substack.com/p/the-hammer-and-the-weapon

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

There is a word in Portuguese, saudade, that does not translate cleanly into English. It is the feeling of longing for something you may never have had, or something you had and lost, or something you are not sure ever existed at all. A Portuguese person will tell you it is in the blood. Carried, inherited. The particular ache of a culture that has spent centuries leaving and returning and leaving again, never quite sure which shore is home.

I grew up with that word. I did not grow up understanding it.

I understand it now.

What I feel when I watch a tech leader introduce herself at a conference by naming her company before her name, what I feel when I watch a junior engineer stay until midnight not because the work demands it but because leaving first feels like an admission of something, what I feel when I sit with a CTO who tells me in private that he has not taken a full week off in four years and then looks at me as if I might judge him for it, that feeling is a version of saudade. A longing for something these people never had. An identity that belongs to them rather than to the organisation that employs them.

They do not know they are longing for it. They have been inside the system too long to feel its edges.

This essay is about that system. Who built it. Why it was built that way. What it is about to cost the people who trusted it. And why the cultures we spent decades calling inefficient may be the ones who survive what is coming with their humanity intact.

The bargain nobody signed

At some point in the last fifty years, Western societies made a bargain with work. Not explicitly. Not with signatures and witnesses and a clear statement of terms. The way most consequential bargains are made, quietly, incrementally, through a thousand small decisions that each felt reasonable and only reveal their shape when you step back far enough to see the whole.

The bargain was this. You give us your hours, your loyalty, your ambition, your identity. We give you purpose, status, belonging, and a story you can tell about who you are. You will introduce yourself by what you do. Your self-worth will track your career trajectory. Your social circle will largely come from your professional world. Your daily structure, your sense of progress, your answer to the question are you happy will all run through employment.

In exchange, we promise you that your effort matters. That hard work leads somewhere. That the story has a shape and the shape is upward.

It was not a bad bargain when work was scarce and the promise was kept. When a factory job meant a house and a pension and a future you could describe to your children. When a decade of loyalty at a company meant something the company felt obligated to honour.

But the scarcity is gone. The loyalty is gone. And AI is now arriving to automate the tasks that were, for many people, the last remaining justification for the trade.

The bargain is breaking. And the people who accepted it are discovering something that was always true but never said aloud. They were not valued as people. They were valued as producers. The moment they stop producing at the required level, the system has nothing further to offer them. Not identity. Not belonging. Not a story. Just displacement, described as progress.

Productivity culture did not accidentally make stillness feel like failure

I want to be precise about something because imprecision here is how the system protects itself.

This was not an accident.

Productivity culture needed your identity. Not incidentally, not as a side effect, but structurally. A worker who finds deep meaning in their job, who derives self-worth from their output, who feels genuine anxiety when they are not being useful, is a worker who does not question the pace. Who does not organise. Who does not ask whether the hours they are giving are proportionate to the value they are receiving. Who does not look at the gap between executive compensation and their own salary and feel the specific outrage that gap deserves.

The discomfort with stillness that I watch in leadership teams is not a personality trait. It is a residue. It is what three years of working inside a system that measures your value in throughput does to a person who was never given permission to value themselves outside of it.

I chopped vegetables on a Saturday morning two weeks ago. Slowly. A soup that would not be ready for three hours. And I noticed that the stillness was uncomfortable. Not boring. Uncomfortable. Like the quiet itself was a problem that needed solving.

That feeling did not come from me. I did not arrive in the world pre-loaded with anxiety about unoptimised Saturday mornings. I absorbed it. From fifteen years inside systems that rewarded urgency and penalised pause. From a professional culture that treats rest as recovery for future productivity rather than as something with its own intrinsic worth.

The carrots took as long as they took. The soup did not care about my throughput metrics. And somewhere in that kitchen, in the steam rising from the pot, I could feel the edge of something I had almost lost. An understanding that slowness is not failure. That presence is not laziness. That the value of a Saturday morning cannot be measured in outputs.

Productivity culture works best when you cannot feel that edge. When you are too busy, too tired, too rewarded by the performance of busyness to notice that the discomfort you feel at stillness is manufactured. Designed. Installed.

This is not a conspiracy theory. It is just the logical endpoint of a system that treats humans as economic units. You optimise what you value. Western capitalism valued output. It built cultures, institutions, education systems, management philosophies, and social rituals that reinforced output as the measure of a person. The result, after decades of this, is a generation of leaders who have genuinely forgotten how to exist outside of productivity. Who have no answer to who am I that does not run through what I do.

That is not a natural human state. It is an engineered one.

What the system never planned for

Here is the uncomfortable truth about automation and AI displacement that most technology commentary carefully avoids.

The system never intended to solve the meaning problem. It simply relied on work being scarce enough that people had no choice but to find meaning there.

When economists talk about previous waves of automation, the agricultural revolution, the industrial revolution, the shift to service economies, they point to job displacement followed by job creation. New categories of work emerged. People adapted. The system self-corrected. This is offered as reassurance. It will happen again. It always has.

What this analysis leaves out is the identity question. In previous waves, the displaced workers found new work. And that new work became the new container for identity. The factory worker replaced the farm labourer. The knowledge worker replaced the factory worker. Each transition was painful and the pain was distributed unequally across class and geography. But the destination was another job. Another role. Another answer to who are you.

What happens when the destination is not another job?

What happens when the category of work being automated is not a narrow vertical but a broad horizontal? When the thing being replaced is not a specific task but the general capacity for cognitive labour that has, for the past fifty years, been the primary route to middle-class identity in Western societies?

The system has no answer. Not because the answer is technically impossible. But because the system was never designed to ask the question. It was designed to keep people employed, and therefore purposeful, and therefore compliant. The moment employment becomes structurally insufficient as a vehicle for purpose, the entire architecture fails.

We are at that moment. Or we are approaching it faster than the people managing the transition want to acknowledge.

The designer and the data

A few weeks ago I sat with a young designer who explained how she had built an interface without understanding a single thing about the data it was pulling. She could make it beautiful. She had learned to use the tools with genuine skill. She had produced something that worked, that her team praised, that her manager approved.

She had no idea what it was doing.

I asked if that concerned her.

She said everyone works this way now.

That sentence has stayed with me. Not because she was wrong. She was not wrong. She was describing something real. An entire generation of knowledge workers who have been trained to operate powerful tools they do not understand, inside systems designed to make that understanding unnecessary, within organisations that have concluded that comprehension is an inefficiency.

Move fast. Ship. Optimise. The understanding can wait.

The understanding never comes.

What I watched in that room was not a failure of individual intelligence or curiosity. She was intelligent. She was curious. She had been shaped by a system that rewarded outputs and depended on the people producing those outputs not asking too many questions about what they were producing or why.

That is the same system that made work the answer to identity. The same logic. Optimise for throughput. Reduce friction. Strip out everything that does not contribute directly to the output. Including, eventually, the understanding of the person doing the work.

And now AI is arriving to do the beautiful interface. To produce the output. To operate the tools faster and more efficiently than the person who used those tools as the primary justification for her professional identity.

She will not be replaced because she was not good enough. She will be displaced because the system she was trained to serve never needed her to understand it. It needed her to operate it. And operating it is now cheaper done by a machine.

What does she do with who she was?

The Mediterranean hedge

For three decades, economists and business commentators looked at Southern Europe with a particular kind of condescension.

Spain, Italy, Greece. High youth unemployment. Low productivity growth. Resistant to reform. Family structures that encouraged dependence rather than mobility. An attachment to place and community that made labour markets inflexible. A relationship with work that prioritised quality of life over output. The three-hour lunch. The evening passeggiata. The Sunday that actually meant something.

These were presented as problems to be solved. Inefficiencies to be eliminated. Evidence of cultures that had not yet fully embraced the productivity gospel that would, eventually, deliver prosperity.

What nobody said, because nobody was looking for it, was that these cultures had maintained something that Anglo-American societies had been systematically dismantling for fifty years. An identity infrastructure that did not depend on employment.

In Spain, if you lose your job, you lose income. You do not lose your place in the world. You are still your parents’ child, your neighbours’ friend, your community’s member, your family’s person. The network of belonging that tells you who you are does not dissolve when your employer no longer needs you. It was never constructed by your employer in the first place.

In Italy, the concept of bella figura, presenting yourself well, living with dignity, is not primarily an economic concept. It is a social one. A cultural one. Your standing in the community is not a function of your salary. It is a function of how you carry yourself, how you treat people, how you maintain relationships, how you show up for the people in your life. A man who loses his job but maintains his family, his relationships, his dignity, is not a failure in the eyes of his community. He is a person in difficulty. A different thing entirely.

In Greece, where I have spent time and watched the aftermath of genuine economic collapse, something surprised me. The human wreckage was real. The poverty was real. The emigration of an entire generation was real and is still being felt. But the social fabric did not dissolve the way it would have dissolved in comparable economic conditions in, say, the United Kingdom or the United States. People who had nothing still had each other. The family structures held. The community rituals held. The taverna held.

This is not romanticism. These societies face real structural problems. Youth unemployment in Spain has run above forty percent at points in recent years. The Greek economy still has not fully recovered from the 2010 crisis. Italian productivity has been stagnant for two decades. Poverty in Mediterranean societies is real poverty, not a philosophical inconvenience.

But here is what the condescending analysis always missed. These societies were not failing to become Anglo-American. They were succeeding at being something different. And the thing they were succeeding at being, a culture where identity and belonging exist outside of employment, is precisely what will determine resilience in the age of AI displacement.

The efficiency that Anglo-American societies optimised for turned out to be a fragility. The inefficiency that Mediterranean societies were criticised for turned out to be a hedge.

There is a concept in systems thinking called redundancy. In engineering, redundancy is not waste. It is resilience. A bridge with a single load-bearing structure is efficient until it fails. A bridge with multiple overlapping structures is less efficient and far more likely to survive stress. The redundancy is the point.

Mediterranean social structures are redundant in exactly this sense. Identity carried across family, community, place, culture, and yes, work too, is identity that does not collapse when one of those structures fails. The Anglo-American model optimised identity down to a single load-bearing structure and called the redundancy it removed inefficiency. What it actually removed was the capacity to survive failure.

I think about my own family. My parents did not define themselves by their jobs. They defined themselves by their children, their village, their faith, their friendships, the particular way they showed up for each other over decades. When my father retired he did not experience a crisis of identity. He had never, not really, placed his identity there. The job was what he did. The family was who he was.

I absorbed the other model. Fifteen years in organisations that rewarded output above everything taught me, slowly and thoroughly, to locate myself in what I produced. I caught myself doing it only recently, sitting at a kitchen table on a Saturday morning with a cup of tea going cold beside me, noticing that I could not sit still with nothing to show for the hour.

That is not a personality trait. It is an infection. And I am still, years after leaving the organisations that transmitted it, working on the cure.

The Mediterranean cultures are not immune to this infection. Globalisation exports the productivity gospel. Young people in Spain and Italy and Greece are increasingly shaped by the same digital platforms, the same remote work culture, the same LinkedIn performance of professional identity that their Anglo-American peers inhabit. The hedge is eroding. But it is eroding from a starting point of genuine social capital that Anglo-American societies spent fifty years depleting.

What western societies dismantled

To understand the scale of what is now at risk, it helps to understand what was deliberately taken apart.

The post-war period in Western economies was, by historical standards, remarkable. Strong unions. Rising wages. Expanding social safety nets. Housing that workers could afford. Healthcare that did not require a job to access. Civic institutions, churches, community organisations, local associations, that gave people a place in the world outside of their professional identity. Extended family networks that had not yet been atomised by the demand for labour mobility.

This was not a golden age. It was deeply inequitable by race and gender in ways that are not redeemable through nostalgia. But it contained something important. Multiple containers for identity. Multiple answers to who are you that did not all run through the same employer.

From the 1970s onwards, systematically and deliberately, these containers were reduced.

Unions were weakened. Not accidentally, not as a side effect of economic change, but through active political and legal dismantling. The people doing the dismantling understood what they were doing. Unions gave workers collective identity and collective power. Removing them removed both.

Housing became financialised. The home, which had been a source of stability and intergenerational wealth for working-class families, became an investment vehicle for people who already had capital. The result is a generation of workers who cannot afford to own property in the cities where the jobs are, who rent, who move, who cannot put down roots, who have no community because community requires time and presence and both are consumed by the work that pays the rent that the financialisation of housing made necessary.

Civic institutions atrophied. Churches lost their congregations. Local associations declined. The third sector, the space between state and market where communities built shared life, was squeezed by a culture that valued productivity and a political economy that defunded anything that could not demonstrate return on investment.

Extended family networks were replaced by nuclear family units optimised for labour mobility. Go where the work is. Move for the opportunity. The parent who stays near their children is making an economically irrational decision. The grandparent who provides childcare is an informal economy that does not show up in GDP.

What remained after all of this, after fifty years of deliberate and accidental dismantling, was the job. The career. The professional identity. Not because humans naturally organise their self-worth around employment, but because every alternative container for identity had been weakened or removed.

The system did not create the work-identity fusion as an intentional act of control, though it did benefit from that fusion as a mechanism of control. It created it through a series of choices that each seemed locally reasonable and only reveal their collective logic when you stand back and ask: what is left, after all of this, for a person to be?

The answer, in most of Anglo-American society, is: whatever their employer says they are.

The offshore question

There is a conversation that never quite happens in mainstream economic commentary about AI and the future of work. It happens in fragments, in academic papers, in occasional political speeches that are quickly diluted. But it does not happen clearly, in public, with the specificity it deserves.

The conversation is about money.

Specifically, about the fact that the productivity gains from AI will not distribute themselves. They will flow toward the people who own the systems. And the people who own the systems have spent decades building elaborate architectures to ensure that the wealth generated by their ownership does not circulate through the tax systems that fund the public goods the rest of society depends on.

This is not a technical problem. Every mechanism required to address it exists. Progressive taxation has existed for over a century. International tax cooperation frameworks exist. The legal and regulatory infrastructure to close offshore tax havens exists in principle if not in practice. The knowledge of where the money is and how it moves is available to governments that choose to look for it.

What does not exist is the political will to use these mechanisms against the people who fund the political system that would need to exercise that will.

This is the loop that makes the conversation about AI displacement so frustrating. We are told the displacement is inevitable. We are told new jobs will emerge. We are told the gains will eventually trickle down. We are not told who made these decisions, that displacement would be the model rather than augmentation, that extraction would be the logic rather than redistribution, that the wealth generated would flow offshore rather than into the public goods that might soften the transition.

These are choices. Every one of them.

The working hours across an economy could be reduced. If AI does the work of a forty-hour week in twenty hours, the obvious response is to share those twenty hours across more people. Employment maintained, output maintained, human time returned for the life that productivity culture stole. This is not a radical idea. It has been implemented in pilots across multiple countries with positive results. It requires employers to accept lower returns on individual labour. It therefore does not happen at scale because the people with the power to make it happen prefer to take the efficiency gain as profit rather than distribute it as time.

Cooperative ownership of AI systems is possible. If the tools that generate the productivity gains are owned by the workers who use them, the gains distribute differently. This is not a utopian proposal. Worker cooperatives exist and succeed across multiple sectors. The Mondragon Corporation in the Basque Country has been operating at scale for seventy years. The John Lewis Partnership in the United Kingdom has maintained employee ownership through multiple economic cycles. The model works. It does not scale to dominate the economy because the capital required to build AI systems flows preferentially to private investors who will own the returns.

Universal basic services, education, healthcare, housing, transport, available to everyone as a floor below which nobody falls, regardless of employment status, would fundamentally change the relationship between identity and work. If your survival does not depend on your job, your identity does not need to depend on it either. You can be something else. You can be free to define yourself outside of what an employer decides you are worth.

These things are not happening. Not because they are impossible. Because the people who would need to stop doing what they are currently doing in order for them to happen are the people with the most to lose from stopping.

The choice that is actually available

I want to be careful here, because this is where analysis tips into either despair or false hope, and neither serves the person reading this.

The choice is real. Not abstract, not theoretical, not dependent on some future political awakening that may or may not arrive. The choice is available now, in the decisions that organisations make about how to deploy AI, in the decisions that governments make about how to tax and regulate the companies deploying it, in the decisions that individuals make about what they are willing to accept and what they are willing to name as unacceptable.

The designer I sat with last week, the one who built the beautiful interface without understanding what it was doing, is not an inevitable casualty of technological progress. She is a person who was trained into a system that never asked her to understand, working inside an organisation that valued her outputs over her comprehension, in an economy that has not yet decided whether her displacement is a cost to be borne or a policy failure to be prevented.

Those are different things. The first is something that happens to her. The second is something that is done to her by people with choices.

The Mediterranean societies I described are not simply lucky in their cultural inheritance. Their resilience is not accidental. It was built through decades of policy choices that prioritised family stability over labour flexibility, community cohesion over productivity growth, the maintenance of social fabric over the extraction of maximum economic value. Those choices had costs. They also built something that is proving to be valuable in ways the people who made them did not fully anticipate.

Anglo-American societies can make different choices now. Not to become Mediterranean. But to stop dismantling the things that make communities resilient. To stop treating the family as an economic unit to be optimised. To stop treating community institutions as inefficiencies to be eliminated. To start taxing the offshore wealth that is the accumulated surplus of fifty years of productivity gains that never reached the people who generated them.

None of this requires waiting for a revolution. It requires a shift in what we are willing to say out loud about who the current system serves and who it does not.

What leadership owes the people inside the system

I spend my working life with tech leaders. CTOs, founders, VPs of engineering, people who sit at the intersection of the technology being built and the humans who will be affected by it.

Most of them are not bad people. Most of them care, in some version of the word, about the people they lead. Most of them are uncomfortable, if pressed, with the gap between the story they tell publicly about human-centred technology and the decisions they make privately about headcount and automation and the redefinition of what the organisation needs from its people.

The discomfort is real. The question is what they do with it.

The easiest thing, and the most common thing, is to manage it privately. To hold the discomfort as a personal ethical complication that does not alter the decisions the organisation demands. To be a good person inside a system that does not reward goodness, hoping that the private virtue somehow counterbalances the public complicity.

That is not leadership. That is coping.

I have sat in enough rooms to know what this looks like in practice. A CTO who privately believes that the automation roadmap will displace forty percent of the team within eighteen months but frames the company all-hands around opportunity and upskilling. A VP of Engineering who knows the junior developers being trained on AI-assisted coding are learning to operate tools they do not understand, and says nothing because the productivity metrics look good and nothing else is being measured. A founder who has read the research on cognitive offloading and declining deep work capacity in knowledge workers, who uses that research to make better personal decisions, and deploys the opposite logic in the product they are building.

The gap between private knowledge and public action is where the real cost of this moment lives. It is not in the technology. The technology is what it is. It is in the leaders who understand the system well enough to know what it is doing and choose, repeatedly, to say the comfortable thing rather than the true thing.

The designer in my story did not fail herself. She was failed by a series of leaders who could see the direction of travel and chose to manage their discomfort rather than name the problem. The organisation that trained her to operate tools she did not understand was led by people who had access to every piece of research on the long-term costs of that approach and optimised for short-term output anyway.

That is a choice. Not a technical inevitability. A choice made by people with names and salaries and the authority to have made a different one.

Leadership, in the context of AI and displacement and the erosion of work as identity, looks different. It looks like asking, out loud, in rooms where decisions are being made, whether the deployment being proposed is serving the people inside the organisation or extracting from them. It looks like naming, specifically, who benefits from the efficiency gain and who bears the cost of the transition. It looks like refusing to accept displacement as the only available logic when redistribution of hours and cooperative models and investment in retraining and genuine support for people navigating identity transition are all available and simply less profitable.

It looks like being the person in the room who says: we built workflows that made understanding optional and called it efficiency, and now we are surprised that the people inside those workflows cannot adapt to a world that requires understanding. That is not their failure. That is ours. What do we owe them?

That question is harder than any technical question about AI deployment. It requires leaders to hold their own complicity in the system they are now presiding over the disruption of. It requires them to look at the designer who cannot understand the data she is working with and ask not how do we replace her but what did we do to create this situation and what is our responsibility to her now.

Most leaders do not ask that question. The ones who do are the reason I still believe this work matters.

The mirage of effortless mastery

There is a final deception the system is preparing for us, and it is perhaps the most seductive of all. It is the promise that, once the machines take the labor, we will finally be free to “be.”

But this ignores a fundamental truth of the human condition we are not merely social animals; we are agents. We are creatures that find our shape through the resistance of the world. We do not just need to belong; we need to matter. We need the specific, gritty satisfaction of having solved a problem that was difficult, understood a system that was complex, or built something that would not exist without our specific, flawed, and effortful intervention.

The designer I spoke with didn’t just lose her professional utility. She lost her agency. By providing the “beautiful interface” without the need for her to understand the data, the system didn’t just make her work faster it made her presence irrelevant. It removed the friction of mastery.

The system treats “effort” as a cost to be minimized. But for the human spirit, effort is the currency of self-respect.

If we move toward a world where AI performs the cognitive heavy lifting, we risk entering a state of permanent “leisure” that feels less like freedom and more like a haunting. Even in the Mediterranean cultures I admire, the resilience doesn’t come from sitting still it comes from the work of being a community. The effort of the three-hour meal, the labor of the harvest, the complicated, often exhausting maintenance of family ties. These are not “outputs,” but they are actions. They require agency.

The system never planned for a world where humans are “taken care of” but have nothing to impact. It assumes that if we are fed and our identities are intact, we will be content. But a human without agency without the weight of personal effort is a ghost in their own life.

True leadership in the age of AI will not just be about protecting incomes or titles. It will be about protecting the right to be useful. It will mean intentionally designing friction back into our lives. It will mean choosing the “inefficient” path of human understanding over the “seamless” path of machine automation, not because it produces a better result, but because it produces a better human.

We are entering a season where the greatest luxury will not be ease, but the privilege of doing something difficult, with our own hands and our own minds, and knowing that it mattered that it was us who did it.

The Saudade of a system that never gave you yourself

I started with a word. Saudade. The longing for something you may never have had.

What I watch in the leaders I work with is a version of this. A longing for an identity that was never quite theirs. That was lent to them by an organisation, conditional on continued employment, revocable without notice. That felt like theirs because it was the only answer available to who are you that the system made easy to reach.

The approach of AI is not creating this problem. It is revealing a problem that was always there, underneath the busyness, underneath the throughput metrics, underneath the identity that was really just a job title wearing the mask of a self.

The question who are you without the title is not a question AI is asking. It is a question that the system always prevented being asked, because a person who can answer it is a person who is harder to manage, harder to extract from, harder to displace without consequence.

I asked a CTO this question once. Not in those words. We were sitting in a coffee shop after a long strategy session and I asked him, genuinely, what he would do if the job disappeared tomorrow. Not the company. The job. The title. The calendar of meetings and decisions and the particular way the world organised itself around his expertise.

He sat with it for a long time. Longer than most people sit with anything.

Then he said, I genuinely do not know.

He was not performing uncertainty. He was not being modest. He was reporting an actual discovery. That he had been the job for so long that the question what would I do without it had become equivalent to what would I be without a self. He had not noticed this happening. It had accumulated, the way debt accumulates, incrementally and invisibly until the sum is terrifying.

Mediterranean cultures kept that question alive. Not perfectly, not without cost, not without their own forms of oppression and constraint. But they kept alive the possibility of an answer that did not begin and end at the office door.

The soup takes as long as it takes. The carrots do not care about your productivity metrics. The family that gathers around the table on Sunday does not require a job title to grant you a seat.

In the village where my father grew up, there was an old man called Senhor António who had never, as far as anyone knew, held what you would call a formal job. He kept chickens. He tended a small garden. He repaired things for neighbours who needed things repaired and accepted whatever they gave him in return, sometimes money, sometimes a jar of honey, sometimes just the conversation. He was not wealthy. He was not aspirational in the way the productivity gospel understands aspiration. He was completely, entirely, recognisably himself.

When he died, the whole village came to the funeral. Not out of obligation. Out of grief. Because Senhor António had been present for the village in a way that had nothing to do with output and everything to do with who he was. He had answered the question of who he was outside of any job title, for an entire life, without ever being asked to justify the answer.

The system made work the answer to who you are. It was never supposed to be. And the people who designed it that way, whether through deliberate strategy or accumulated indifference, are now presiding over a technological transition they built in their own interest, at the expense of the people who trusted the bargain.

That bargain is breaking.

What replaces it is not yet decided.

That is the only genuinely important question in technology right now. Not how fast the models are improving. Not which company will dominate the AI stack. Not which jobs will survive and which will not.

What replaces the bargain. What do people build their sense of self on when the thing they built it on has been automated away. Who is responsible for that transition. And what would it cost the people currently profiting from the displacement to actually take responsibility for the answer.

The offshore accounts exist. The tax mechanisms exist. The policy frameworks exist. The cooperative models exist. The alternative futures exist.

What is missing is not the solution.

What is missing is the willingness to say, clearly and without the management of discomfort that passes for leadership in most organisations, that the bargain was exploitative, the displacement is chosen, and the people inside the system deserved better than what they are being offered.

Say that out loud. In the room where the decisions are being made. In the all-hands where the roadmap is being presented. In the board meeting where the headcount reduction is framed as structural efficiency. In the team meeting where a junior developer is being told their role is evolving and what that actually means is that it is ending.

Say it out loud. Not to be difficult. Not to be the person who makes things complicated. But because the alternative, the management of private discomfort while the public story continues unchanged, is what got us here.

Senhor António never managed his discomfort. He just lived.

That is where this starts.

Mediterranean cultures kept that question 'Who am I?' answered by a thousand small, unoptimised rituals of belonging, ensuring that when the machine finally arrived to take the job, it found the person already occupied by the far more important work of being human.

This is the prequel to* Who Are You Without the Title, *, that asks the personal question. This one names the system that made the question necessary. This is part of a four part essay.

Next, I will go over how AI just revealed how much we were never sure.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

newsletter.diamantinoalmeida.com | Leadership as a Verb

When I use the word AI in this essay I am not acknowledging that these systems are intelligent. I am using the word the industry uses, because that is the word that has conquered the conversation. What we are actually talking about is an advanced deep learning model. Extraordinarily capable at pattern recognition, statistics, and probability. Not thinking. Not understanding. Not intelligent in any meaningful sense of the word. There is no academic consensus on what intelligence actually is, and there is certainly no evidence that these models possess it. The word AI is a marketing decision. It was chosen to make the technology feel inevitable, significant, and human. I use it here because refusing to use it would make the essay harder to read. But I want you to know that every time I write AI in this essay, I am describing a very powerful statistical engine. Nothing more. The intelligence is in the room. It is in you.

Many philosophers and psychologists argue we’d be better served building identity around character rather than output. But that’s easier said than done when the whole world keeps asking what you do for a living.

Why I started writing this

Last week, a subscriber named Roman Nikolaev left a message in the chat, after I asked for feedback from my readers.

It was short. One sentence.

“It would be interesting to read your take about losing professional identity in the age of AI.”

I read it twice. Then I closed the tab and went to make coffee.

I came back a few minutes later and the sentence was still there. Still doing something to me that I could not name immediately.

I have been writing about AI and its costs for over a year. I have written about data theft, about algorithmic harm, about the environmental cost of the infrastructure we are building without consent. I have written about what happens when systems are designed to keep people dependent and incurious. I have written about power, about who benefits, about the stories we tell to make inevitable what was always a choice.

But I had not written about this. About what happens to the person inside the machine when the machine changes shape. About the specific loss that does not show up in any economic model but shows up in almost every conversation I have had with a senior tech leader in the past eighteen months.

Roman’s question sat on the table like a stone.

I started writing the next morning. Sitting at my desk before the day started, the street outside still quiet, a cup of tea that went cold before I remembered to drink it. I wrote three pages and then stopped. Because I realised the essay I was writing was about the feeling. And my rule is this, name the mechanism, not just the feeling.

So I started again.

This is what I found.

The engineer who did not know who he was

I was sitting with a senior engineer last year. Coffee going cold on the table between us. Eleven years with his company. Teams led. Systems built. A title that took a decade to earn and a reputation that took longer.

His company had just announced that AI would handle the first pass on every architecture decision his team had made for the past three years.

He did not say he was angry. He did not say he was scared. He said something I have been carrying ever since.

He said I am not sure who I am at work anymore.

Not what he would do next. Not whether his job was safe. Not what skills he needed to develop. Who he was.

That sentence is the one I want to stay with. Because I think it is the most honest thing anyone has said to me about AI and the workplace in two years of watching this unfold. And I think most of the conversation we are having about professional identity and AI is missing it entirely.

We are talking about roles. We should be talking about the story underneath the role.

The wrong conversation

Most of what gets written about AI and professional identity is about jobs. About which roles will survive and which will not. About learning new functions and starting again and the future of work as an economic question. About what capabilities will be relevant in 2030 and which will have been automated away.

That conversation is not wrong. It is just not the conversation the engineer was having.

He was not talking about his role. He was talking about something underneath it. The story he had been telling about himself for eleven years. The story that explained why the work mattered, why the long hours were worth it, why he had chosen this particular life over other possible lives. The story that made the sacrifices legible.

The role was the container. What he was losing was what the container had been holding.

And here is the part that matters. He did not build that story himself. It was built for him. By the culture he worked inside. By the systems that rewarded certain kinds of expertise and made that expertise feel like identity. By the language of titles and seniority and accumulated knowledge that the tech industry uses to assign value to people. By eleven years of being told, in a hundred small ways, that what he knew was who he was.

The fragility was engineered. Long before the AI arrived to reveal it.

What a title actually does

When you accept a title at work, you are not just accepting a description of your responsibilities. You are accepting a position in a story.

Senior Engineer. Lead Architect. Head of Product. Director of Engineering. Each of those titles carries a narrative about competence, about earned authority, about where you sit in the hierarchy of people who know things. That narrative is not just external. It becomes internal. It becomes the answer to the question of who you are when someone at a dinner party asks what you do.

But the title does something more subtle than this. It becomes the primary mechanism through which you receive validation at work.

You know you are valuable because of what your title says you know. You know you are progressing because your title changes to reflect more seniority. You know you are respected because people with certain titles defer to you on certain decisions. The feedback loop runs through the title. And over years, without most people noticing it happening, the feedback loop becomes the identity.

This is not a personal failure. It is a system design.

The tech industry in particular has constructed an entire architecture of professional worth around the accumulation and display of technical expertise. The interview processes that test specific knowledge under time pressure. The performance reviews that reward demonstrated skill over collaborative wisdom. The culture of the senior developer who has seen everything and whose judgment is treated as the room’s ceiling. The mythology of the 10x engineer, the brilliant architect, the one person whose departure would bring the whole thing down.

Every one of those structures sends the same signal. Your value is what you know. Your identity is your expertise. Your security is your irreplaceability.

And then a tool arrives that can produce a first draft of the architecture in thirty seconds.

The upbringing question

Roman’s question opened something else for me that I had not expected.

When I started research for this essay, I kept asking the same thing. Why does the loss of a professional role feel like a loss of self? Economically, you can explain it easily. The job provides income, structure, social connection. Losing it disrupts all three. But the engineer I sat with was not facing redundancy. His role still existed. His income was not threatened. What he was facing was something more interior than that.

So I started asking a different question. Where did we learn that our work is who we are?

The answer, when I found it, was not surprising. But it was uncomfortable.

Most of us grew up in households where adult worth was measured by what adults did for a living. The question that greets every child at every family gathering is not who are you becoming. It is what do you want to be when you grow up. The verb is telling. You do not have a job. You become one. Doctor. Engineer. Lawyer. Teacher. The role is not something you do. It is something you are.

This is reinforced through education. The entire design of most educational systems is not to produce curious, adaptable people. It is to produce people who can perform specific functions in an economy. The curriculum is an economy simulation. The grades are an employment forecast. What you study becomes what you are trained to be. And what you are trained to be becomes, over time, what you understand yourself to be.

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By the time most people reach their first professional role, the collapsing of identity into function is already complete. They do not notice it because it happened gradually, over twenty years, in every institution they passed through. The school. The university. The interview process. The onboarding. The first performance review.

Then the tool arrives. And the function they were trained to be can now be partially replicated by a machine.

And they say I am not sure who I am anymore.

They are not being melodramatic. They are being precise.

The mechanism nobody is naming

The villain in this story is never a specific person. It is the system. So let me name the system precisely.

We built work cultures that deliberately collapsed the distance between a person and their role. Titles were not just descriptions. They became validation systems. The seniority ladder was not just an organisational structure. It was a story about worth. The concept of expertise was not just about capability. It was about irreplaceability, which is another word for security.

These structures served the companies that built them. A person whose identity is fused with their role will work harder to protect that role. A person who understands their worth through their title will negotiate less aggressively because losing the title feels like losing themselves. A person who has been told for a decade that their expertise is what makes them valuable will not easily question whether that expertise serves anyone other than their employer.

Identity fusion at work is not an accident. It is a management technology.

And then a different technology arrived. One that does not need to be validated. That does not need a title. That will produce the first draft of the architecture without asking for a performance review.

AI did not create the fragility. It revealed it. The fragility was always there, built into the foundations of how we structured the relationship between people and their work.

But the people who built the AI knew this was coming. Many of them said so. They knew that the systems they were releasing would automate the functions that people had built their identities around. They chose to release them anyway. Without any framework for what it means to be a person whose expertise has been made replicable. Without any meaningful conversation about the human cost of making human capability optional.

That was a choice. Not an inevitability. A choice made by people who had already answered the identity question for themselves, because they were the ones building the tool.

Professional theft

There is a second layer to this that most accounts leave out. The models were trained on the accumulated output of professional expertise. Code that engineers wrote. Problems that engineers solved. Answers that engineers shared publicly over decades. That knowledge was not licensed. It was not compensated. It was taken. So companies extracted the professional knowledge, built models on top of it, and are now deploying those models to reduce the value of the expertise they extracted. The engineers are not just being disrupted by AI. They are being disrupted by a version of their own work.

But the damage does not stop at the economic level. It cuts deeper because of something we rarely examine openly for most professionals, work is not just what they do it is who they are. Identity in modern life is heavily fused with professional output. This is not accidental. It is culturally reinforced from the moment someone first asks a child what they want to be when they grow up. By the time a person has spent a decade mastering a craft, that mastery feels inseparable from the self.

This is the vulnerability that makes the disruption so destabilising. When a tool can replicate your output cheaply, it does not just threaten your income. It threatens your sense of worth. And the cruelest irony is that the people most psychologically exposed are not the least skilled they are often the most invested, the ones who gave the most of themselves to their craft, and whose knowledge quietly became the foundation these systems were built on.

They are then invited to pay a subscription for the privilege.

What I found when I asked the AI

At this point in writing the essay, I did something I want to be transparent about. I put the argument to the AI. I query it to tell me where it thought I was wrong.

It said I was conflating two separate problems. The first is the economic disruption of AI, which is real and measurable. The second is the psychological disruption of identity, which is real but harder to measure. It said that my argument about identity fusion being engineered was compelling but potentially unfalsifiable. How would you distinguish a deliberately engineered identity collapse from one that emerged organically from the way human beings naturally seek meaning in their work?

That is a fair challenge.

My answer is this. The distinction between deliberate and organic may matter less than I suggested. What matters is that the structure exists, that it is being disrupted, and that nobody in a position of power designed any system to absorb that disruption. Whether the structure was deliberately engineered or organically evolved, the people deploying AI into workplaces had an obligation to understand it before disrupting it. Most of them did not.

The AI also pointed me somewhere I had not looked. It said the most significant identity loss from AI might not be happening to senior engineers at established companies. It might be happening to people who were just entering the workforce. People who had not yet had time to build a professional identity of any kind, but who had been told by the educational system that they were being prepared for a specific kind of work, and are now discovering that the work has been redesigned before they arrived.

I had not thought about it from that direction. It changed something in how I understood the intergenerational dimension of this problem.

The two kinds of expertise

There is expertise that lives in the function. And there is expertise that lives in the judgment.

Functional expertise is the ability to produce a specific output. Write the code. Design the architecture. Generate the report. Draft the proposal. This is the kind of expertise that is most legible to an organisation, because it produces things that can be measured. It is also the kind most replicable by AI, because it can be described precisely enough to train a model on.

I sat in a product review two years ago where the lead engineer spent forty minutes defending an architectural decision. The room had the data. The room had the alternatives. What the room did not have was someone willing to say this will not survive contact with the culture of the team that has to maintain it. The engineer knew that. He had been in enough rooms to feel it. He did not say it. Nobody had ever told him that was part of the job. His job was the architecture. The culture was someone else’s problem.

That gap between what he produced and what the room needed is where judgment expertise lives. Knowing which function to perform and when. Understanding the context surrounding the output. Reading the room. Knowing that the architecture that works technically will not work politically.

AI is very good at functional expertise. It is not good at judgment. It will produce the architecture. It cannot tell you whether the organisation is ready to implement it.

The crisis of professional identity in the age of AI is partly a crisis about which kind of expertise was being valued. If your entire professional identity was built on functional expertise, the AI is a genuine threat to the story you have been telling about yourself. If your identity was built on judgment, the AI is an accelerant.

The people whose judgment expertise was always their real contribution are now finally in a position where that contribution is visible. The people who were told that their functional expertise was their value are watching that value be replicated. Neither group created this situation. Both groups are living in it.

What Roman said next

After the essay was drafted, Roman came back to the chat with something sharper than his original question.

He said sometimes I feel sad, sometimes I feel excited. He said people who enjoyed the process more than the results are more affected. He said deep problem-solving happens more rarely now. Most problems are resolved automatically. And then he asked the question I had not yet named in the essay.

What am I here for. What is the worth of my skill.

That is a different question from who am I. It is deeper. It is not about identity. It is about purpose.

The identity question asks what story do I tell about myself? The purpose question asks what does the world need from me that only I can give?

Roman’s distinction between process and results is the sharpest thing I have heard anyone say about this. The people who built their sense of self around outcomes, around shipping, around the visible evidence of having done something, are disrupted by AI in one way. The problem got solved. Something got built. The output exists. AI made that faster.

But the people who built their sense of self around the doing, around the specific texture of sitting with a hard problem and turning it over until something gives, around the particular satisfaction of finding the solution that was not obvious, around the hours of thinking that precede the answer, those people are losing something different. They are losing the reason the work felt worth doing.

The deep problem-solving was not just the method. It was the meaning.

When the problems are resolved automatically, you do not just lose the task. You lose the reason you were in the room. And no amount of reframing the role or finding new challenges or being told to work at a higher level of abstraction gives back what was actually lost. Which is the specific, irreplaceable experience of being the person who figured it out.

I do not have a clean answer to Roman’s purpose question. I want to be honest about that. What am I here for is not a question that can be answered by a strategy. It is a question that has to be lived toward.

But I notice something. The people I have watched navigate this transition with the most dignity are not the ones who found a new function to perform. They are the ones who found a new kind of problem to sit with. Not the problem the tool can solve. The problem the tool reveals. The human question underneath the automated answer.

That is still deep problem-solving. It is just that the problems have changed shape.

The collective response to the crisis

Roman made one more observation that I cannot let pass.

He said if you look at the LinkedIn feed, the most popular topics are that engineers will not be replaced by AI.

Sit with that for a moment.

The most popular content for engineers right now is reassurance that they will not be replaced. Not frameworks for navigating the change. Not honest accounts of what is being lost. Not questions about what the transition should look like for the people inside it. Reassurance. Repeated, shared, liked, reassurance.

That is not a comfort. It is a mechanism.

When a group of people facing a genuine disruption responds primarily by circulating content that tells them the disruption will not affect them, that is not optimism. It is collective denial. And collective denial is what a system produces when the people inside it have no other language for what they are experiencing.

The engineers are not naive. They can see what is changing around them. They are sharing the reassurance content not because they believe it but because believing it, even temporarily, is less costly than sitting with the alternative. Which is the purpose question. Which is what am I here for.

That question is expensive to ask. It requires time and honesty and a certain willingness to feel lost before you find anything. Most workplaces do not create the conditions for that question to be asked. Most LinkedIn feeds actively suppress it by flooding the space with content designed to make the question feel unnecessary.

The popularity of the reassurance content is not evidence that the crisis is overstated. It is evidence of how deep the crisis runs. The louder the reassurance, the more afraid people are of what happens if they stop needing it.

I am writing this essay because I think the reassurance is doing harm. Not because the engineers are wrong to want it. But because a question that is suppressed does not go away. It goes underground. And questions that go underground tend to surface in the form of the sentence the engineer said to me across a cold cup of coffee.

I am not sure who I am at work anymore.

That sentence is what happens when the reassurance runs out.

Shared leadership and the identity crisis

The solo hero model of leadership is partly a response to the same identity fusion I have been describing. The senior leader who cannot delegate is often the leader whose identity is fused with their expertise. Who built their whole professional story around being the person who has the answer. Who cannot share the map because sharing the map would reveal that the map is incomplete.

That model was already fragile. AI makes it more fragile. Because the expertise the solo hero was holding is now the thing the machine can replicate.

Shared leadership, the kind where you put the map on the table and let everyone navigate together, requires a different relationship with professional identity. It requires being able to say I do not know. Not as a performance of humility but as an honest statement of the situation. It requires building your professional identity not around what you know but around how you think, how you bring other people into the thinking, how you create the conditions for good decisions to emerge from a room rather than from a single head.

That kind of identity is not threatened by AI. Because it is not located in a function that can be replicated. It is located in a way of being in a room with other people that no statistical model can simulate.

The transition is not easy. Telling someone whose entire professional identity is built around functional expertise to shift to judgment expertise is like telling someone who has been swimming their whole life to try flying. The water is what they know. The water is where they feel competent. The air looks like failure from where they are standing.

But the water is rising. And the question is not whether to learn to move differently. The question is whether anyone is going to help people make that transition with any dignity.

What happens to the people who cannot pivot

The tendency in this conversation is to focus on the people who will thrive. The adaptable. The curious. The people who can build a professional identity around judgment rather than function.

There are also people who cannot make that pivot. Or who should not have to.

The person who spent thirty years building deep expertise in a specific domain. Who chose that depth over breadth because depth was what was valued. Who is fifty-three years old and has two children in university and a mortgage and a professional identity built on something that AI has just made significantly less scarce.

The advice the system gives this person is to adapt. To be curious. To embrace the change. To find the opportunity inside the disruption.

That advice is an insult dressed as encouragement. It locates the problem in the person’s adaptability rather than in the system that deployed a tool without any plan for the human cost of its deployment.

The tech leaders who made the decision to deploy AI into these workplaces made a choice. They chose speed over the management of transition. They will not be the ones paying the cost of that choice. The fifty-three year old engineer will.

I do not write this to be angry. I write it because the identity question is not just philosophical. It lands in specific human lives. In specific families. In the specific moment when someone who has worked with complete commitment to a set of skills is told that the story is no longer the one the industry wants to tell.

What do you do with thirty years of becoming something if the thing you became is no longer needed?

That question deserves better than a LinkedIn post about growth mindset.

The generation arriving into a changed room

The most visible version of professional identity loss belongs to the senior engineer. That person has a platform. They can write essays. They have enough professional authority to make their discomfort audible.

There is a quieter version of the same loss happening to people who are just arriving.

The person who spent four years in a computer science degree being trained for a specific kind of work. Who chose the degree because the economy said this was the safe path. Who graduated in 2024 or 2025 into a job market where entry-level technical roles had been redesigned around AI tools, and where the path from junior to senior was no longer clear because the work that used to teach you how to become senior had been automated.

This person did not have time to build a professional identity before the ground shifted. They arrived at the starting line to find the race had already been rerouted.

And underneath all of that enthusiasm the culture demands, many of them are sitting with a version of Roman’s question.

Share

What am I here for. What is the worth of my skill.

The difference is that they do not have thirty years of evidence that they can figure things out. They have four years of studying for a future that has already changed.

This is not their failure. It is the failure of every institution that prepared them without preparing them for this.

That gap is a leadership failure. Not theirs.

The three questions worth asking

Not a framework. Not a five-step plan. Three questions worth sitting with if you are a leader in a tech organisation right now.

The first question is for yourself.

Where is your professional identity located? Is it in what you know, or in how you think? Is it in the output, or in the process of getting there?

Roman’s distinction matters here. If your sense of worth lives in the doing, in the specific texture of sitting with hard problems and turning them over, you are experiencing a different disruption from the person whose worth lives in the output. Neither is wrong. But they require different responses. The person whose worth lives in the output needs to find new outputs worth producing. The person whose worth lives in the process needs to find new kinds of problems worth sitting with. Problems the tool reveals rather than problems the tool solves.

Spend a morning with that question honestly. Not in the way you would answer it in a performance review. In the way you would answer it if nobody was evaluating the answer.

The second question is about your team.

How much of the identity of the people you lead is fused with the functions that AI is changing? Not their jobs. Their identities. The story they tell about why the work matters and why they matter in the work.

And underneath that are they process people or results people? Because the disruption they are experiencing is not the same. The results person may adapt more easily. The process person is losing the reason the work felt worth doing. You cannot manage those two transitions the same way.

Ask the question before the sentence arrives. Not in a town hall. In a conversation. One at a time. With someone who actually has something to lose.

The third question is the hardest.

What do you owe the people whose professional story your deployment decisions are disrupting?

Not economically. Human beings are not just economic units. What do you owe the person who gave thirty years to building deep expertise that your tool is now making replicable? What do you owe the process person who is not just losing a function but losing the reason they came to work?

I do not think there is a clean answer. I think there is a conversation that most organisations have refused to have because having it would slow things down.

Slowing things down is not in the business plan.

But the cost of not slowing down is not in the business plan either. Because the cost lands in the engineer sitting across from you saying he is not sure who he is at work anymore. And in the feed full of reassurance content that tells you everyone is fine.

They are not fine. They are just not saying it yet.

Someone has to create the conditions for that to be said. That is a leadership question. It always was.

A place to name the hard things

I started this essay because a reader asked a question in a chat thread. I want to end it by naming what that means.

Roman came back after the first draft was written and went deeper. He named the process versus results distinction. He named the purpose question. He named the LinkedIn denial mechanism. Each of those things sharpened the essay in ways I could not have reached alone.

That is what this room is for.

The essays name the mechanism. The comments name what the mechanism costs. And sometimes, like this week, the comments name a mechanism the essay had not yet found.

Roman’s final line was I think there is a lot to explore here.

He is right. This essay is not the end of the exploration. It is the beginning of it. The purpose question, what am I here for when the thing I was trained to do is being automated, is a question this publication will keep returning to. Because it is not a question that gets answered once. It gets lived with, over time, in rooms where people are willing to say what they actually see.

If you are carrying a version of Roman’s question, I want to hear it. Not the version that sounds okay. The version that is actually true.

That is what shared leadership looks like in practice.

The map is on the table. Come navigate.

From the same series:

The Prequel names the system. A Delusional Ape asks whether we want the direction. Who Are You Without the Title asks the personal question. This essay names the specific choice being made right now and what refusing it looks like.

Read the PREQUEL.

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About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

This is not a post about AI. It is a post about what happens to people who never ask why.

I was at a protest a few years ago when someone told me the internet should be a human right.

He was holding a four thousand pound laptop. A coffee in his other hand. Earbuds in. Passionate, articulate, certain.

I asked him why.

He said because the internet means freedom of speech. Because it gives us free education. Because without it life would be very boring.

I asked him again. Not what the internet offers. What the internet actually is.

Good to know…

While the internet was never meant to be a human right. That was not in the original documents. But something shifted.

Now the UN and others are noticing something we already know. You cannot exercise freedom of expression without access. You cannot learn. You cannot build anything. Not anymore. The internet became the medium through which rights are exercised. And then someone cut the wire.

When a government disrupts internet access, they are not just turning off a service. They are silencing people. They are closing schools. They are shutting down the only marketplace where people without privilege can compete. The UN saw this. They named it a violation.

Here is what troubles me. We framed the fight as “is internet access a right.” But the real question is different. We already know what the internet means. We already know what happens when it disappears. So why did it take us this long to say out loud that cutting people off from it is a choice. And every choice has a side.

[Cont…]

He described Facebook. YouTube. The ability to speak to friends.

I said: that is the world wide web. What is the internet?

He looked at me. Then he said: what is the world wide web?

I got scared.

Not because he did not know. Because he was screaming about a thing he had never once tried to understand. And he was not alone. He was surrounded by people doing exactly the same thing.

I walked home that evening past a row of bins waiting for collection. The smell of rain on concrete. I kept thinking about that man. About the specific shape of his certainty. How complete it was. How untouched.

What the internet actually is

Here is what the internet is. Simply put, infrastructure. Physical cables, servers, routing systems that cost billions to build and billions to maintain. In most countries it is partly subsidised by taxes. It is not free. It has never been free. The world wide web sits on top of it. That is what you see when you open a browser. The web is the passenger. The internet is the road.

None of this is complicated. It takes about ten minutes to understand. But most people who depend on it daily, who would describe losing it as losing a human right, have never spent those ten minutes.

I am not saying this to be superior. I am saying it because the gap between using something and understanding it is becoming one of the most dangerous gaps in the world. And we are widening it deliberately.

Not through malice. Through design.

The tyre in the boot

Think about the person who has never changed a tyre.

You have the car. You have the tools in the boot. They came with the vehicle. You have two working hands. But you have never once tried, never once been curious, never once spent the twenty minutes it would take to know. So when the tyre goes on the motorway you wait two hours for someone else to do it.

That is not a technology problem. That is a dependency problem.

And we are building an entire civilisation of people who have never changed the tyre. Who do not even know the tools are in the boot.

The phone you are holding is not a phone. It is an attention system. Engineered to be as frictionless as possible. Not for your benefit. Because friction creates the conditions for thought, and thought creates the conditions for demand, and demand is expensive. The easier the tool is to use, the less you need to understand it. The less you understand it, the more dependent you become. The more dependent you become, the more useful you are to the people who built it.

That is not a coincidence. That is a business model.

The same model runs through most of what we call the digital economy. Make it simple enough that questions never form. Make it rewarding enough that the absence of questions feels like comfort rather than loss. Make the dependency so total that the idea of stepping back feels like deprivation.

And then, when someone finally does step back and ask how it works, make them feel stupid for not already knowing.

The teenagers who laughed

I sometimes tell teenagers I do not know how to use Instagram. How to navigate TikTok.

They laugh. They say, it is so simple, how can you not know, are you that old.

I say, you are right. It is idiot proof. Give me five minutes and I will be at your level. But let me ask you something first. Do you know how I could access your email through your browser session?

Do you know what happens when an app on your phone requests location permission and you press allow? Do you know that the fitness tracker on your wrist is broadcasting data about your heart rate to servers you have never heard of, governed by terms and conditions you clicked through in thirty seconds at 11pm?

They go quiet.

I am not trying to frighten anyone. I am trying to say, the reason these tools feel simple is because someone worked very hard to make sure you never had to think about what is underneath them. That work was not done for your benefit.

It was done so you would never ask.

And if you never ask, you never push back. And if you never push back, the people who built the system can keep building it however they choose. Which brings me to the part that actually worries me.

The people telling you not to think

I have heard tech leaders, people with platforms and influence, say that you no longer need to think for yourself. That AI* will do it. That coding will be irrelevant by the end of the year. That some large language model will make human intelligence optional.

I have heard tech leaders say that you no longer need to think for yourself. That AI will do it. I want to be precise about what I mean when I say AI here. Not intelligence. A large language model that predicts the next word based on patterns in human text. Extraordinarily capable. Not thinking. The distinction matters, because the people telling you AI will replace your judgment are often the same people who benefit most from you believing it.

I feel something close to disgust when I hear this.

Not because AI is not powerful. It is. Not because the tools are not useful. They are. But because the people saying these things are not describing a democratic future.

They are describing a world where the ability to think critically, to understand what you are holding, to ask why it works and who benefits, becomes the exclusive property of a small group who kept that knowledge for themselves while telling everyone else it was unnecessary.

Intelligence as a sorting mechanism. Understanding as a privilege. Dependency as the default condition of most human beings while a smaller group retains the ability to question, to build, to decide.

That is not progress. That is the oldest power structure in human history with better marketing.

And I have watched enough cycles to recognise the pattern. The more a technology is positioned as inevitable, the more urgently you should ask who benefits from that inevitability feeling settled.

The more you are told you do not need to understand something, the more important it becomes to understand it.

This is not paranoia. This is the minimum condition for being a free person in a technical world.

What this has to do with leadership

Here is where it becomes personal for me. And for anyone reading this who is responsible for building teams, building products, building systems that other people will live inside.

The same dependency trap that catches the man at the protest, the teenagers with their phones, the workers who have never once asked how the algorithm that governs their feed actually works, that trap is being built by tech leaders. By us, or at least a large majority.

Every time we optimise for engagement over understanding. Every time we make a product simpler in a way that removes agency rather than friction. Every time we design a system that works so smoothly that nobody ever has to think about what is underneath it. We are widening the gap.

I have sat in enough product reviews to know that this rarely feels like a moral decision in the moment. It feels like good UX. It feels like serving the user. It feels like getting out of the way.

But there is a difference between removing unnecessary complexity and removing the conditions for thought. And in my experience, most tech leaders have stopped asking which one they are doing.

This is the Stewardship question. Not, does this product work? But what kind of relationship does this product create between the person using it and their own capacity to understand and question the world?

Most of the products being built right now are answering that question in the same direction. Toward dependency. Toward seamlessness. Toward a world where the tools think for you because you have long since stopped expecting to think for yourself.

And the people building those products are, many of them, people who know exactly how they work. Who can change the tyre. Who understand the difference between the internet and the web. Who are making a choice, consciously or not, to keep that understanding for themselves.

What shared leadership changes

I want to be careful here not to make this abstract. Because there is a practical version of this argument that matters to the people I work with.

Shared leadership, the thing this publication is built around, is partly about distributing authority. About putting the map on the table and letting the room navigate together. But it is also about something more fundamental it is about building teams where people understand what they are building.

Where the engineer knows not just how to write the code but what the code is doing to the person on the other end. Where the designer knows not just what converts but what the conversion is costing. Where the leader holds the whole picture, including the parts that are uncomfortable, and brings that picture into the room rather than managing it from above.

That kind of team does not optimise blindly for engagement. It asks why. It slows down before it scales. It treats the person using the product as someone who deserves to understand what they are holding, not just someone whose behaviour needs to be shaped.

This is not idealism. This is what responsible technical leadership actually looks like in practice. And it requires, as a foundation, that the people doing the leading are willing to be curious about what they do not yet know. That they model the question-asking they want their teams to practice. That they resist the comfort of the idiot proof as a professional standard.

Because if the leaders stop asking, the teams stop asking. And if the teams stop asking, the products stop being honest about what they are.

What Gen Z can do today

I want to end with something direct for the younger people in the room. Because this is intergenerational in both directions. We built this world. But you are going to have to live in it longest.

What you can do is not complicated. It does not require becoming an engineer or a security researcher or a policy expert. It requires only this, curiosity about the tools you already hold.

Put the phone down occasionally and let your brain do the work. Be curious about the things you depend on. Ask what the app is actually doing when you give it permission. I want to end with something direct for the younger people in the room.

You did not build this world. We did. You are going to live in it longest.

The one thing I want you to take from this is not a list of things to learn. It is a single question to carry.

When someone tells you that you do not need to understand something that the AI will handle it, that thinking for yourself is an inefficiency to be optimised away ask who benefits from you believing that.

The answer will tell you everything.

Knowledge is not power in the old sense. Not as a weapon or a competitive advantage.

But understanding what you are holding is the minimum condition for being a free person in the world we are building together.

And we are building it for them. The least we can do is tell them the truth about what it is.

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About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

If you are reading this, you followed this publication at some point. Maybe something I wrote caught your attention. Maybe someone shared a post. Maybe you found me on LinkedIn.

You followed. If you never subscribed. This is for you.

I have been thinking about that gap.

Not with judgement. I understand it completely. Following something costs nothing and commits you to nothing. Subscribing means saying yes, I want this in my inbox, I am making a small decision to let this into my week. That is a different thing entirely.

So I want to tell you what you would be committing to. Not with a features list. With honesty.

Two days ago I published something I had been sitting on for months. About being unemployed. About the months I spent trying to replicate the success of people I was quietly losing respect for. About returning to the only thing that was ever actually mine.

16 people replied within hours.

Not with polite comments. With things they had been carrying privately. About the gap between who they are at work and who they actually are. About the exhaustion of performing certainty at all times. About the specific cost of a leadership model that asks one person to carry everything.

That conversation the one that happens in the comments and the replies and the messages people send directly that is what this publication is.

Every week I write one honest post. About shared leadership, about what AI is actually doing to us as human beings, about the systems we are building and who pays the cost when they go wrong.

Not frameworks. Not productivity tips. Not ten things the best leaders do before breakfast.

The thinking that keeps tech leaders up at night. The questions most of them only ask in private. The gap between what gets said on stage and what gets said in the room after.

I write from inside that gap. I have sat in enough rooms to know what it costs.

It is free to subscribe. You only receive the emails if you want them. You can leave any time.

If something I have written stayed with you the Gen Z post, the AI and leadership piece, anything and you have been reading without subscribing, this is the moment I am asking directly.

Subscribe. Be in the room rather than watching from outside it.

If it is not for you, that is completely fine. But if it is the door is open.

It has always been open.

Diamantino

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While writing about the importance of being original.

That is the trap in plain view, and I still stepped in it.

Let me tell you exactly what happened, what I realised, and what this publication has become. Because if you are reading this as a follower who hasn’t yet subscribed, or a free subscriber wondering whether the paid tier is worth it, this is the post that answers both.

The machine I was trying to push back against was quietly running me

The job market put me under economic pressure. Bills, unknowns, a calendar with too many empty squares. Reasonable people gave me reasonable advice: focus on growth, study what’s working, replicate the successful formats.

I listened. For months, I listened.

And I didn’t notice until much later what I was actually doing. I had spent over twenty years watching how corporate architectures quietly override human intentions. I had written extensively about platforms that optimise for metrics while eroding human judgment. About leaders who perform certainty they don’t have because the incentive structure demands it.

Under pressure, I became a quieter version of exactly that.

I was chasing growth frameworks. Studying what successful tech creators were doing so I could copy the shape of it. Modifying my voice. Performing leadership rather than practising it.

A cold cup of tea on my desk. A blank document open. The cursor blinking.

The machine wants you passive, predictable, and quiet. I had gone quiet.

What stopped me wasn’t ambition. It was a question I couldn’t get rid of.

Where is the humanity in what we are putting out into society?

The problem was never technology

I want to be precise about this, because it matters for everything that follows.

The technology is not the danger. The business model is.

I have sat in enough boardroom meetings to know the gap. In public, tech leaders perform certainty. In private, most of them are carrying the same doubt you carry. The gap between the person they actually are and the face the structure demands is not a personal weakness.

It is the cost of a broken model.

The model rewards silence. It punishes accountability. It centralises power at the top and then calls that leadership. It hands a generation of young people a product explicitly engineered to hold their attention hostage, then measures success by how long the attention holds.

That is not a technology problem. That is a leadership problem. A business model problem. A structural incentive problem.

The harm wasn’t accidental. Nobody woke up and decided to damage a generation. The incentives chose it. The design chose it. The speed chose it. And no one in the room felt individually responsible because the system had distributed the responsibility until it disappeared.

We are not building products. We are building the infrastructure of human society. And right now, the people designing that infrastructure are accountable to shareholder returns, not to the people living inside what they build.

That is the thing I could not stop writing about.

What I believe and why it is different from what you usually read here

I am going to tell you exactly where I stand, because this publication is only worth your time if you know what it is for.

On technology and blame: I don’t blame the tool. I name the system behind it. The algorithm, the platform, the engagement loop — these are not forces of nature. They are designed choices made by identifiable people inside incentive structures that predictably produce identifiable harms. I write about those structures. Not the people. The system.

On leadership: Shared leadership is not a framework. It is a moral argument. The idea that one person at the top should hold all the answers, make all the calls, carry all the accountability — that model was never about wisdom. It was about control. And it is quietly destroying the organisations and the people inside them. I write about what becomes possible when the map goes on the table and everyone gets to navigate.

On time: We are finite. Our planet is finite. We are surrounded by an universe that, as far as we understand it, has no edges. Inside that vastness, our time here is genuinely short. The jobs we are building our entire identities around, the metrics we are optimising, the performance of certainty we maintain at exhausting cost none of it is as permanent as the urgency we attach to it. I think we deserve a more honest conversation about what we are spending our time building and why.

On AI specifically: AI is not the existential risk. Our hands-off, metric-driven management style is. The way we adopt tools without asking who designed them and for what purpose. The way we replace junior engineers with automation and then wonder why institutional knowledge collapses a decade later. The way we treat AI literacy as a career skill when it is actually a civic one.

We are building systems our children will inherit. Thirty years from now, fifty years from now. What will those systems be?

That is the question this publication exists to sit with.

What this publication actually is

Leadership as a Verb is for senior tech leaders who are tired of the performance.

Tired of pretending the dashboard is the whole picture. Tired of meetings where the most important thing in the room is the thing nobody says. Tired of watching products ship that quietly harm the people they were supposedly built to serve.

This is not a publication about frameworks. It is not a productivity newsletter. It is not a career growth resource.

Every Tuesday, I publish a long essay. Three and a half to four thousand words. One structural argument about AI, power, or what leadership actually requires. I name the systems. I expose the incentives. I bring the same rigour to this as I would to any engineering problem, because the stakes are the same.

The subjects so far: professional identity as an engineered product. What automation is actually doing to the workforce. How precision became confused with progress. The way we built civilisation on one story about what efficiency is for, and what happens when that story breaks.

What is coming: the garage. The structural argument for where the most consequential AI will actually come from. And eventually, the essay I am saving for last, when the whole sequence is complete.

This is a body of work. Not content. Not posts. A body of work building toward something.

Why free, and why paid

The free posts are the argument. The paid tier is the room where the argument continues.

If you are a follower who has not yet subscribed, here is the honest invitation: become a free subscriber. Every Tuesday essay comes to your inbox. You lose nothing. You gain a weekly hour with an argument that might make you slightly uncomfortable. That is the point.

If you are a free subscriber wondering whether to go paid, here is what the paid tier actually is:

Twice a month, a deeper written analysis. The free essays make the structural argument. The paid posts go further. The implications for your organisation. The specific decisions this thinking changes. The parts too long and too detailed for the main feed.

Once a month, a live conversation. Ninety minutes, paid subscribers only. We take the five threads this publication runs on AI power, attention and slowness, leadership versus metrics culture, intergenerational responsibility and we sit with whatever is most alive. It is not a webinar. It is a conversation.

Behind-the-scenes chat access. The thinking before the post exists. The paragraphs I cut. The doubt. The version that was too raw for the newsletter and too important to lose. If something connects to what is happening in your organisation, you can start a thread. It is a quiet room. A garden, not a forum.

Early access to the book. I am writing it. Who Are You Without the Title?, the expansion of the essay by that name, into a full argument for a different relationship with work, identity, and what professional life is actually for. Paid subscribers read it first.

The price is what a good book costs. One month of paid subscription gets you more than a book, and the conversation that a book cannot give you.

The question I am actually asking

I do not want passive readers.

The machine has plenty of passive readers. Optimised engagement. Predictable behaviour. Metrics that go up.

I want the people who are willing to sit with an uncomfortable question and not reach immediately for a comfortable answer.

We have built the most powerful thinking tool in human history. We immediately turned it into a vending machine.

We have senior leaders in positions of enormous influence who know their products are causing structural harm and say nothing because the incentive structure demands silence.

We have an entire generation arriving into a workforce that was partly automated before they got there.

And we have, I believe, a genuine opportunity. Not to fix technology. To fix the question we are asking of it. To lead in a way that accounts for the people inside the system, the people outside it, and the people who will inherit whatever we build now.

That is what I am writing toward.

If you have read this far, you are already in the conversation. Come the rest of the way in.

Subscribe free. Every Tuesday. No noise.

Go paid. If you want the deeper room, the live conversation, and the book as it is written.

Both buttons are below. The honest invitation is the same for both, I write about what actually matters in tech leadership. If that sounds like what you need right now, I am glad you found this.

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Tino Almeida has spent over twenty years at the intersection of engineering, DevOps, and team culture. He writes about AI, power, and the long responsibility of building systems other people have to live inside.

What is technology actually costing us?

Not in dollars but in judgment, in craft, in the quiet erosion of what makes work meaningful. This newsletter explores the human side of building with technology.

I’m Diamantino. I’ve spent 20 years in tech leadership, engineering, product, and everything in between. This is where I write about what I’ve learned, what I got wrong, and why the industry keeps repeating the same mistakes.

And somewhere in there, I stopped believing that the answer was moving faster or being smarter.

Now I am asking a different question. What is technology actually costing us? And what becomes possible when leaders stop pretending they have the answer and start asking the real questions together?

That is what this publication is about.

Not frameworks for how to lead better. Not templates for running meetings. Not the comfortable ideas about shared leadership that sound nice in a talk but crumble under pressure.

Instead: what does it mean to be a leader who thinks systemically about power, economics, and human stakes in an age of AI?

Who This Is For

You are a tech leader, CTO, founder, or engineer somewhere on the path to leadership.

You probably moved fast once. Built things. Got rewarded for it. And somewhere along the way, you started asking harder questions privately that you cannot ask publicly.

Questions like:

• What is the business model of AI actually optimising for, and who pays the cost?

• Why does tech keep replicating the same broken patterns of power and control?

• What are we building into systems that Gen Z will inherit?

• Is the speed worth what we are trading away?

You are not looking for someone who has all the answers. You are looking for a thinking partner who is asking the same questions you are.

That is what you will find here.

The Three Threads You Will Follow

This publication has three connected threads. You might follow one deeply, or drift between them. Either way, you are following something coherent.

1 - AI and Human Stakes

What is AI actually doing to the world? Not the hype. The real impact.

Posts about algorithms optimising for engagement in teenage feeds. The electricity and water crisis nobody is talking about. Data being stolen as a business model. The loneliness crisis growing inside AI-assisted teams. What motivation and meaning at work look like when AI does the junior work that used to develop people.

Follow this if you are using AI in your work and starting to wonder about the costs.

2 - Tech Culture and Economics

Why does tech keep breaking the same way?

Posts about how technology companies structure power. Why move fast and break things is a broken business model masquerading as innovation. How centralised leadership in tech created centralised leadership in teams. The real cost of disruption and who pays it.

3 - Intergenerational Responsibility

What world are you building your children into?

Posts about what tech leaders are responsible for beyond the quarter. The stakes of today’s choices. What Gen Z will inherit from the decisions being made right now. Why stewardship matters more than growth.

What You Will Actually Find Here

• Honest thinking, not frameworks. Every post asks a question more than it answers one. You will finish reading with more to sit with, not less.

• Specific moments, not abstractions. There is always one real story. A conversation I had. A decision I watched happen. A thing I realised about myself. Never generic advice. Always grounded.

• Systemic analysis, not tips. I am interested in why things break the way they do. The economics underneath. The power structures. The choices that led us here. Not how to run a better meeting. Why meetings are the shape they are, and what that actually costs.

• No resolution required. Some posts end with I still do not know. That is honest. That is the point.

The Paid Tier: What It Actually Is

The free newsletter gives you the full essays. Paid is for people who want to go deeper extended analysis, frameworks I use in my own work, and a community of people thinking critically about tech.

• Deep-dive essays and case studies

• Decision-making templates and frameworks

• Community discussions and Q&A threads

How to Find Your Way In

• If you are brand new. Pick whichever thread matches what you are thinking about right now. Read two or three posts from that thread. Get a feel for the voice. See if it lands. You do not need to start anywhere specific. Start where your curiosity is.

• If you have been reading a while and want to go deeper. The paid tier is where to come. Not for templates. For thinking together about the hard stuff.

• If you have a specific situation you are navigating. Bring it to the Meet the Author session. This is where the real work happens.

• If you are already a paid subscriber. Everything you have access to lives in The Room.

The Room

If you just want to think and read. You are welcome to stay here as long as you need. No sales. No guilt. Just the thinking.

One Thing About Me

Figuring out what the next chapter looks like while building this publication at the same time.

I tell you that not for sympathy.

I tell you because this publication is about asking hard questions honestly. And that starts with me being honest about where I am.

I have sat in rooms with CTOs who said things privately they would never say on stage. I have watched engineers become leaders. I have watched leaders remember they were human.

I have also watched technology make choices that harmed people. And I have watched leaders look away.

This publication is for people who are tired of looking away.

What Happens Now

You have four choices.

1. Pick a thread and start reading. Follow whichever one matches what you are thinking about. See if the voice lands.

2. Sign up for the free letters. A post every Tuesday about one of the three threads. Sometimes longer essays. Sometimes shorter reflections. Always honest.

3. Join the paid tier. If you are ready to think about this together, not alone.

4. Step into The Room. If you are already a paid subscriber and want to know what you have access to, the live sessions, the chat, the editorial influence, it is all there.

Or you can just close this and move on. That is fine too.

But if you are still here, reading this, it is probably because somewhere you recognised yourself. You asked a hard question privately that you cannot ask publicly. You wondered if someone else was asking it too.

Someone is.

I am.

And I would be glad to have you thinking about it alongside.

To get started, read these posts:

— Diamantino

Leadership as a Verb · newsletter.diamantinoalmeida.com

If you're a founder, consultant, or incorporated owner in the US, UK, or Canada — your subscription here likely qualifies as a business expense. I've put together a reimbursement template for you.

Expense Reimbursement

P.S. — I also offer one-to-one mentoring sessions for leaders who want to think through a specific situation. If that is you, book a Clarity Session here. The publication is enough. The mentoring is for when you want to go further.

I’ll admit, I spent a long time building a cage I am now trying to escape.

I called it a career. I called it being a high performer. I called it being the person who gets things done. But lately, I have realized it is just a very expensive, very well lit cage.

There is a specific kind of exhaustion that comes from being good at things you no longer care about. It is a quiet, heavy weight. It sits in your chest during the morning standup. It follows you into the kitchen when you try to make a sandwich. It is the feeling of being a world class engineer on a ship that is sailing toward a port you never chose.

For years, I treated my talent like a resource to be mined. I thought that if I could just solve one more problem, if I could just hit one more date, I would earn the right to finally be still. I treated my life like a sprint that never actually ended. I was measuring the ship every single day, but I completely forgot how to look at the water.

I want to talk about that today. I want to talk about the trap of being the reliable engine.

The Reliable Engine

When you are good at solving problems, the world brings you more of them. This is the basic law of the corporate world. It feels like a compliment at first. You get the promotion. You get the title. You get the equity. You feel like you are winning the game.

But then you look up and realize you are just a professional reminder. You are the person who makes sure the boxes stay green. You are the one who stays late to catch the things that fall through the cracks. You become the gravity that keeps the whole project from floating away.

I used to love that feeling. I loved being the hero. I thought my sacrifice was a gift to my team. I thought that by staying late and answering every message, I was protecting them.

I was wrong.

I realize now that my “heroism” was actually just a form of control. It was a wall I built between myself and the people I was supposed to be leading. By being the one who always had the answer, I never let anyone else learn how to find one. I wasn’t building a team. I was building a dependency. I was protecting my own ego, not the project. It is hard to admit that your best intentions were actually just a way to make yourself feel necessary.

Share Leadership as a verb

I was obsessed with speed. I called it velocity, but let’s call it what it really was. It was a race to nowhere. We were moving so fast that we didn’t have time to ask if the work even mattered. We were just feeding the machine.

The Metallic Cold

The industry feels cold lately. You can feel it in the way we talk. We talk about people like they are resources to be optimized. We talk about growth like it is the only metric of a human life.

I sat in a meeting last week and listened to a group of smart people talk about “trimming the fat.” They were talking about human beings. They were talking about parents and friends and people with tired eyes who just want to do good work. It felt metallic. It felt like we were trying to build a machine out of people, and we were annoyed that the people kept having feelings.

I looked at the sunlight on my desk during that call. I saw a small spider crawling across my notebook. It was a tiny, living thing that didn’t know anything about our roadmap. It didn’t care about our quarterly goals. It was just alive.

I felt a sudden, sharp jealousy for that spider.

We have created a system that rewards us for becoming less human. We are told to be data driven. We are told to be objective. We are told to leave our “personal lives” at the door. But my personal life is the only life I actually have. The rest is just a series of promises I made to a company that will replace me in a week if my heart stops beating.

I am tired of the metallic cold. I am tired of the jargon that acts like a shield. We use words like “alignment” and “deliverables” because they sound safe. They keep us from having to say the truth. The truth is that we are all a little bit scared. The truth is that we are all working too hard on things that don’t satisfy us.

The Slow Refactor

In code, we refactor to make things elegant. We delete the old, messy logic to make room for something better. We simplify.

I am trying to do that with my life now. I am in the middle of a slow refactor.

I am deleting the need to be the hero. I am learning to say “I don’t know” in meetings where I am expected to have every answer. It is terrifying. It feels like I am falling behind.

Let me tell you what it actually looked like the first time I did it.

The meeting was a production incident review. Fourteen people. A CTO who wanted answers. A platform that had been down for forty minutes the week before and a room full of people quietly wondering who was going to explain it.

I knew the answer. I had been in this meeting dozens of times before. I knew the failure mode, I knew the remediation, I knew the three architectural decisions from two years ago that had made this inevitable. I could have spoken in the first thirty seconds and ended the uncertainty in the room.

I did not speak.

I put both hands flat on the table. That was the physical thing I did. I pressed them down, not because it helped, but because I needed something to do with the pull. The pull to speak is not just intellectual. It lives somewhere under the collarbone. It is the specific discomfort of watching other people search for something you are holding.

For about ninety seconds, nobody said much. Someone checked a screen. Someone else started talking about the monitoring gap, which was not the real issue, but it was something. I watched the engineering lead I had hired six months ago lean forward slightly. I watched her open her mouth and then close it again. She was not sure yet. She was doing the calculation every person in the room does when the usual person has not spoken: is it safe to be the one who says it?

I stayed quiet.

She spoke. Not the full answer. Half of it, tentatively, looking at me at the end of the sentence as if asking permission for the other half.

I nodded. Not the nod that means I already knew that. The nod that means keep going.

She kept going.

By the end of the meeting she had built the full picture herself, with two other engineers filling in the parts she had left open. The CTO wrote her name in his notes. I watched him do it.

I went back to my desk and sat for a moment with something I did not have a word for yet.

It was not pride exactly. It was closer to the feeling of having returned something that had been borrowed too long.

The meeting had not gone faster than it would have if I had spoken first. It had probably gone slower. But something in the room was different afterwards. The difference was that the answer had not come from me.

That difference is the whole thing.

But then something strange happens. When I say I don’t know, someone else in the room usually speaks up. They share an idea. They take a step forward. By stepping back, I am finally giving my team the room to breathe.

I am also refactoring my relationship with time. I used to look at my calendar and see a grid of other people’s priorities. It felt like being in debt. Every hour was already spent before I even woke up.

I am learning that “no” is not a rejection of a person. It is a protection of the soul. If I give away every hour of my day to the ship, I have nothing left for the water. I have nothing left for the people who actually know my middle name.

I started a new rule for myself. I call it the Zero Velocity Note. Once a week, I go for a walk without my phone. I look at the trees. I smell the rain if it’s falling. I listen to the sound of my own feet on the pavement. I do nothing that can be measured. I do nothing that can be put in a spreadsheet.

For the first ten minutes, my brain screams at me. It tells me I am wasting time. It tells me I am falling behind. It tells me there are emails waiting for me.

But then, the screaming stops. The silence moves in. And in that silence, I remember who I am when I’m not an “Architect” or a “Senior Leader.” I remember the person who likes the smell of old books and the way the light hits the floorboards in the afternoon.

That person is the one who actually does the good work. The machine can’t innovate. The machine can only repeat. Innovation comes from the human who has had enough rest to see a new path.

Taking Off the Armor

There is a loneliness in being the person with all the answers. You start to believe that you cannot afford a moment of doubt. You wear your title like a suit of armor that is two sizes too small. It is heavy. It is cold. It keeps people from seeing who you actually are.

I am taking the armor off.

I am admitting to my team that I am tired. I am admitting that the system feels broken. I am stopping the pretense that I have a magic solution for our speed problems.

The result has been the most honest work of my life.

When you stop being a manager and start being a neighbor, the room changes. People stop hiding their mistakes. They stop performing. They start talking about the work like human beings. We are finding that we can actually move faster when we aren’t pretending to be perfect.

Our job isn’t to build machines out of people. Our job is to grow gardens out of teams.

Gardens are messy. They require patience. They don’t grow faster just because you yell at them or change the metric on the wall. They need the right environment. They need sunlight. They need to be left alone sometimes.

I am trying to be a gardener now. I am trying to be the person who cares more about the soil than the harvest.

It is okay to be tired of your own talent. It is okay to want a different kind of quiet. You are not a resource to be mined. You are a person who is currently helping to sail a ship. Don’t forget to look at the water. Don’t forget that the ship is only a small part of the world.

The notification light will still be red tomorrow. The board will never be empty. You can breathe anyway. You have to. It is the only way to stay human in a machine.

The “Hero” Relapse

It is hard to stop being the hero. You will feel a phantom itch to jump into a failing Slack thread. You will want to grab the keyboard and “just fix it” in five minutes.

I felt the rough edge of a wooden desk today while I forced my hands to stay still. It was a physical reminder to let go.

When you step back, things might break. That is the point. If you always catch the glass, nobody learns how to hold it. You have to allow the silence. You have to let the team feel the weight of the clay, salt, and wool of their own work.

The real talent isn’t in solving the problem. It is in building a space where you are no longer the only one who can.

The Friday Decompression

If you feel the weight of the cage today, I want to give you a small way to refactor your week. I call it the Friday Decompression.

At 4:00 PM on Friday, close every tab. All of them. Even the one you think you need for Monday.

Take a piece of paper and a pen. Write down three things that went well this week that had nothing to do with a metric. Did you have a good conversation? Did you help someone feel seen? Did you write a piece of code that felt elegant?

Then, write down one thing you are worried about. Fold the paper. Put it in a drawer.

Tell yourself: “The work is in the drawer. I am in the room.”

Walk away. Don’t check your messages. Don’t look at the board. Spend your weekend being a person who has a middle name and a favorite meal. The ship will be there on Monday. The water is waiting for you now.

Also Read…

This fatigue is usually a sign that your Humane Architecture is broken.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

We live in this era of peak convenience, where we are told that the goal of a good life and a good career is to remove all friction. We delegate our chores, we automate our schedules, and we outsource the essential functions of our thinking to tools and trends, all under the belief that it will make us more efficient. But I’m worried that in this rush to be polished and fast, we are building invisible cages for ourselves.

We are inundated every day with these streams of exaggerated lifestyles. We see leaders on our screens who never seem to sweat, who speak in perfectly formed soundbites, and who claim to have solved the human equation with a few simple habits. It is a style of leadership that feels entirely inappropriate for the actual mess of life, yet we feel this crushing pressure to mimic it. We start to believe that if we aren’t “perfect,” we are failing. And so, we begin to abandon ourselves. We stop leading from our own values and start leading from a script written by someone who has never sat in the room with our team.

I see this most clearly in the way we treat new managers. We throw people into leadership roles with no foundation and no floor. We give them a title, a list of incomprehensible expectations, and then we leave them to figure it out in the dark. It’s a setup for failure, yet we expect them to project total confidence from day one. I’ve watched so many talented people enter this state of quiet panic, trying to look like the “hero” because they think that is the only way to be respected. They stay quiet when they are drowning because they don’t want anyone to see the cracks in the armor.

Share

But the truth is, you cannot build a joint culture if it is everyone for themselves. Whenever I join a new company, the first thing I ask is whether they have a leadership forum or a place where we can all learn together. I’m not looking for a lecture or a slide deck. I’m looking for a space where the ego can be stripped away, where we can admit that we don’t have the answers, and where we can share the burden of building the humans who build the software. If we aren’t learning together, we aren’t a team. We are just a collection of individuals trying to survive the same building.

We’ve fallen into this trap of thinking that leadership is a solo sport. We think the leader is the one who has to have the loudest voice and the most certain plan. But if the leader is the bottleneck for every decision, they have failed. Real leadership is the quiet, often invisible work of letting a team breathe. It is about creating a “Self-Healing Team” where trust is the primary infrastructure. That doesn’t happen by being perfect. It happens by being honest. It happens when a leader is brave enough to say, “I’m not sure about this, what do you think?”

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When we demand perfection, we aren’t building excellence. We are just building a culture of fear. We create a space where nobody feels safe enough to stumble, and if you can’t stumble, you can’t learn. We end up with teams that are “efficient” on a spreadsheet but hollow in reality. They hit their numbers, but they’ve lost their soul. They are afraid to suggest a new idea because it might not be polished enough. They are afraid to point out a mistake because they don’t want to break the illusion of the smooth surface.

I’ll admit, I’ve been that manager. I’ve spent years thinking that my value was tied to how many problems I could solve by myself. I thought that by being the hero, I was protecting my team. I wasn’t. I was just suffocating them. I was keeping them in that same invisible cage I had built for myself. I realized that the “Social Contract of Tech” isn’t about the code. We don’t just build software; we build the people who build it. And if the people are living in fear of being seen as less than perfect, the software will eventually reflect that brittleness.

We need to stop looking for the “perfect” answer and start looking for the “honest” one. We need to stop delegating the essential functions of our empathy and our judgment to the latest trend. Convenience is a lie when it comes to human connection. There is no shortcut for sitting across from someone, looking them in the eye, and acknowledging that the work is hard. There is no template for building trust. It is slow, it is manual, and it is often very messy.

I’m worried that if we don’t start breaking these cages, we are going to lose a whole generation of leaders to burnout and cynicism. We are asking them to be something that isn’t human. We are asking them to be algorithms. But an algorithm can’t inspire a team. An algorithm can’t hold a space for a person who is having a bad day. An algorithm can’t build a legacy.

Legacy isn’t about the velocity of your last sprint. It’s about the memory you leave behind in the people you worked with. It’s about whether they are better, stronger, and more confident because they spent time in your orbit. That kind of impact doesn’t come from being a polished icon. It comes from being a peer who was willing to share a coffee and a hard truth. It comes from the moments when you chose humanity over efficiency.

We have to be willing to be seen before the thoughts are polished. We have to be willing to ship the work while the edges are still a little rough. Because that is where the growth lives. It lives in the silence after a hard truth is spoken. It lives in the actions we perform every day with intention, rather than the ones we perform out of habit or fear.

The next time you feel that pressure to be the “perfect” leader, the one who has it all figured out, I want you to try something else. I want you to find a way to learn with your people. Break the “everyone for themselves” cycle. Ask for the leadership forum. Start the conversation that doesn’t have a pre-packaged answer. It might feel less efficient in the moment, but I promise you, it is the only way to build something that lasts.

We are all just trying to figure out how to be human in a world that wants us to be machines. The cage of perfectionism is only as strong as our willingness to keep building it. We can choose to stop. We can choose to stay in the mess, to share the burden, and to lead from a place of reality instead of a place of performance. That is where the real work begins. And that is where we finally find the room to breathe.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

I looked at it for a moment and put the phone face down on the desk.

Not because I do not care about reach. I do. But because there is something specific and instructive about that notification. The platform did not tell me who read it and found it useful. It told me it was gaining traction. The language is mechanical. Velocity. Momentum. A thing moving faster than before.

Cold coffee on the left side of the desk. The cursor blinking on a draft I have been avoiding. Outside, the sound of a neighbour’s door. Ordinary things. The kind of morning where you sit with a notification and ask yourself: what am I actually doing here?

That question is the one this guide is built around.

Not how to grow your LinkedIn. How to use it in a way that does not hollow you out.

LinkedIn is a performative platform.

LinkedIn’s business model incentivises performance over authenticity

The algorithm rewards engagement, likes, comments, shares, which naturally pushes users toward polished narratives, humble-brags, and curated success stories. You see the same templates repeated. The “fired on a Friday, landed my dream job on Monday” post. The motivational quote with a career pivot. The humble accomplishment wrapped in gratitude. It works because the algorithm amplifies it, and people benefit from the visibility. That is not conspiracy. It is just how engagement-driven platforms operate.

Confident posts. Polished headlines. Messages that arrive feeling like they were written by someone who has never actually read your work.

I spent a long time on that platform doing the same thing. Optimising. Strategising. Finding the right people and saying the right things in the right order.

It worked, in a narrow sense. My numbers went up. But something felt off. Like I was performing a version of connection rather than actually connecting.

Then I started looking at what was actually happening underneath the surface.

And what I found changed how I think about the platform entirely.

The honest picture first

Before we talk about tactics, you deserve the structural truth.

LinkedIn is owned by Microsoft. Its primary business model is not your career. It is LinkedIn Premium subscriptions and Recruiter licences sold to companies. Every algorithm decision, every reach mechanic, every “your post is gaining traction” notification exists to serve that business model, not yours.

The penalty you feel when you stop posting for a few days. The weeks it takes to rebuild to your previous baseline. That is not a coincidence. It is deliberate friction, engineered to keep you producing content that makes the platform feel alive. You are the product. Your consistency is what they are selling to advertisers and recruiters.

The SSI score, the Social Selling Index, was built for B2B sales teams. It measures the behaviours LinkedIn wants from you, posting, connecting, messaging, engaging. It has almost no correlation with career outcomes for regular employees. But because it has a number and a dashboard, people chase it. That is the design working exactly as intended.

The spam in your inbox is not a bug. LinkedIn sold InMail credits and lead generation tools to thousands of sales teams. Your inbox was the product they sold. It will never be fixed because fixing it would reduce revenue.

I am not saying this to make you cynical. I am saying it because leadership, real leadership, starts with seeing clearly. You cannot navigate a system you refuse to understand.

What the platform does reward

Here is where it gets more useful.

The algorithm does reward one thing that is not pay-to-play. Comments from people with large, engaged followings. One genuine comment from someone with twenty thousand followers does more for your reach than fifty likes from regular accounts.

This means community is the real engine. Not content volume. The practice of reading other people’s work carefully, responding to it specifically, building actual familiarity before you ever ask for anything. That is slow. It feels inefficient. It is the only lever that consistently works without a budget behind it.

The platform also rewards specific, repeatable points of view over broad expertise. The people who break through without external credentials share one trait. They own a particular angle that a defined audience waits for. Not “leadership content.” Something narrower. Something like: what distributed engineering teams get wrong about trust, or why most engineering cultures confuse speed with urgency.

Specificity builds following. Following builds algorithmic signal. Algorithmic signal builds reach.

And external credentials, a book, a talk, a public body of work, unlock a different tier of visibility entirely. The platform is structurally biased toward people with proof that exists outside it. That is a long game. It is worth playing.

Building the profile that actually works

Before any of the tactics matter, your profile has to do its two jobs.

The first job is answering the question someone is typing at eleven on a Tuesday night. Not “who has leadership in their headline.” Specific words. The exact language the right person uses when they are looking for someone who does what you do. That language lives in job postings for the roles you want. In the way clients describe their problems in the first email they send. In the questions people ask at conferences when they are in pain. Use their words, not the impressive ones.

The headline is the most important line on your profile and the one most people get wrong. Most headlines describe what the person has. “Senior Engineering Manager at Company X.” That is a fact. It is not a signal. The headline that works describes what the person does for someone specific. “I help engineering teams lead through ambiguity without losing the people.” Or “Building the kind of tech organisations people do not leave.” The test is simple: would the right person read it and think, that is for me?

The About section is where most people write a third-person biography that nobody reads. The About section that works is a first-person paragraph that names the specific problem you solve, says something true that most people in your position would not say publicly, and ends with a direct invitation. Not a summary of your career. A perspective on something that matters.

Here is the structure that works:

The first sentence names who you are in plain language. Not your title. Your role in people’s working lives. “I work with tech leaders who are good at their jobs and losing faith in the organisations they are doing them in.” That sentence is for one person. The person who reads it and feels seen.

The second and third sentences say the thing you believe that most people in your field would not say in public. Not a take designed to provoke. A genuine belief, held clearly, stated without hedging. This is the Blood in the Water principle applied to your profile. The belief that makes someone uncomfortable is also the belief that makes the right person feel understood.

The closing sentence is a specific invitation. Not “let us connect.” A specific thing. “If you are in the first year of a management role and something is already feeling wrong, I want to hear about it.” That is an invitation that filters for exactly the person it is for and tells everyone else clearly that it is not for them.

The Featured section is the one most underused. It is the only place on the profile where you can show someone your actual thinking rather than describing it. One piece of writing that demonstrates your point of view. Not your most popular post. Your most honest one. The one that cost you something to publish. That is the one that does the work.

One last thing on the profile. The banner image. Most people leave it as the generic LinkedIn gradient or use a corporate photo. The banner is eight seconds of attention before anyone reads a word. Use it. A short line of text in your own words on a clean background works better than most professional designs because it is immediately personal rather than immediately branded.

What the algorithm rewards the engagement practice

The community-first principle named above needs a specific practice or it stays as an idea.

Here is what the practice looks like in concrete terms.

Identify twelve to fifteen accounts worth genuine engagement. Not the largest accounts in your space. The accounts whose thinking you genuinely find interesting, whose angle is adjacent to yours but not identical, and whose audience would find your thinking useful. These are the people whose comment sections you will inhabit not as a growth tactic but because the conversation is worth having.

The criteria for the list is simple. Would you read this person’s post carefully even if it had no algorithmic benefit? If yes, they are on the list. If you are calculating the follower count before you decide, they are not.

Engage with those twelve to fifteen accounts before you post anything on a given day. Not a like. A comment that demonstrates you read the post and thought about it. Two to four sentences. Something specific about what they said and one thing it connected to in your own thinking. No flattery without content. “Great post” is not a comment. “You named the thing I have been trying to articulate about psychological safety, which is that it is not a state you achieve, it is a practice you repeat in every meeting, and I have been watching teams get it right once and then slowly lose it because they treated the first success as the end of the work” is a comment.

Do this consistently for sixty days and the algorithmic effect is real. Not because you gamed the algorithm. Because you built genuine familiarity with people whose audiences trust their recommendations, and when those people engage with your posts, the signal is strong enough to move things.

The discipline is the resistance to doing this instrumentally. The moment the comment is written for reach rather than for the conversation, the person reading it can feel the difference. And they stop engaging. And the whole mechanism breaks.

The people who make this work treat it as reading practice with a social layer. Not outreach. Reading.

What to actually post

The article so far has made the case for owning a specific angle. Here is how to find it and what to do with it.

The angle is not chosen. It is noticed. It is the thing you find yourself returning to in conversation, the problem you have a different opinion on from most people in your field, the pattern you see that others seem to miss. You do not decide to have a point of view. You pay attention to where you already do and name it more precisely than you have before.

The test for whether you have found your angle is this: can you describe it in one sentence that contains a specific claim rather than a general category? “I write about leadership” is not an angle. “I write about what happens to new managers in the first six months before the organisation has finished deciding what kind of leader they are going to become” is an angle. Specific enough to exclude most people. Specific enough to make the right people feel it was written for them.

On post format. The posts that consistently perform, across every account I have watched carefully, share three structural features regardless of length or topic.

They open with a specific moment rather than a general claim. “My director forwarded an email at 6pm on a Friday with no context” lands differently than “Communication breakdowns happen in every organisation.” The specific moment is concrete. The general claim is something the reader already knows.

They hold a tension open longer than feels comfortable before they resolve it. The post that says “here is the problem” in paragraph one and “here is the solution” in paragraph two is a post shaped like a LinkedIn post. The post that sits with the complexity for three or four paragraphs before it offers anything resembling a conclusion is a post shaped like actual thinking. Actual thinking earns re-reads.

They end with a question that is genuinely open. Not “what do you think?” which is a call to engagement that signals you want engagement. A question that you actually do not have an answer to, about something the post raised, that a thoughtful person might want to contribute to. “I am not sure whether this is specific to engineering cultures or whether I am just seeing what I work closest to” is a genuinely open ending. “How has your organisation handled this?” is outreach dressed as a question.

On posting frequency. The advice you will find everywhere is to post every day. That advice is optimised for algorithmic growth and for burning out within sixty days. The sustainable frequency is the one you can maintain without sacrificing the quality of observation that makes the posts worth reading. For most people that is two to three times per week. For some it is once. The consistency matters more than the frequency. A post every Tuesday that is genuinely observed and honestly written compounds over eighteen months in ways that daily posts optimised for engagement do not.

The one thing to avoid. The performing post is not always obvious from the inside. Here is how to check. Before you post, ask: am I writing this because I believe it or because I think it will land well? The two are not always different. But when they are, the reader can feel which one it is. Your reader Kathleen, information-rich and suspicious of frameworks, will feel the performing post within the first sentence. She will not comment. She will scroll. And the algorithm will note the absence of early engagement and suppress the post.

Write the thing you believe. Even when it is smaller than you think LinkedIn wants. Especially then.

What this means for how you show up

This is where the template premise comes back.

The tactics in this guide work. The Boolean search strings, the Google X-Ray method, the connection sequencing, all of it is real and I have used every piece of it. But they only work if the intention behind them is honest.

Because LinkedIn is a performative platform. Everyone on it can feel performance. The message that arrived from a template. The post written to get shares rather than say something true. The connection request with a compliment that is obviously a setup.

People are tired of it. I am tired of it. You are probably tired of it too.

Leadership as a verb is the counter to that.

Not as a brand. Not as a content strategy. As an actual operating principle.

A verb requires action. Presence. Choosing, in each moment, whether you are performing leadership or practising it.

On LinkedIn that distinction looks like this.

Performing leadership is posting about the importance of psychological safety. Practising it is writing about the specific conversation where you got it wrong, what you said, what happened in the room, and what you would do differently.

Performing is optimising your headline for clicks. Practising is writing a headline that says exactly what you do and who you do it for, even if that is a smaller audience.

Performing is commenting “great insight” on posts from people with large followings to get visibility. Practising is reading their post properly, sitting with it for a minute, and writing the one thing it genuinely made you think about.

The difference is not always visible from the outside. But it accumulates. Over months, the people who showed up honestly become the people others come back to. Not because the algorithm favoured them. Because humans can tell.

The minimum viable practice

Given everything above, here is what actually makes sense as a sustainable approach.

LinkedIn has two jobs for most professionals. Not five. Two.

The first is discoverability. Recruiters, potential collaborators, and people considering working with you will search for you. Your profile is the answer to their question. It should be honest, specific, and built around the exact words the right people type into search. Not the words that sound impressive. The words that match what someone types at eleven on a Tuesday night when they need someone who does what you do.

The second is reputation confirmation. When someone hears your name, reads something you wrote elsewhere, or gets recommended to you, they will look you up. Your presence on LinkedIn should confirm that you are a real person with a real point of view. Not a resume. A perspective.

That is it. Everything else, the daily posts, the engagement pods, the SSI optimisation, is optional and often counterproductive if it pulls you toward performing rather than practising.

On reaching out

The outreach section of this template exists because sometimes you need to initiate contact. A potential employer. A collaborator. Someone whose work intersects with yours in ways worth exploring.

The frame I use is simple.

You are not looking for targets. You are looking for people you can genuinely help, or people whose work genuinely interests you, and making it easier for them to find that out.

A first-time engineering manager who just got promoted and has no idea how to lead their former peers is not a high-intent buyer. They are scared. The difference between those two framings changes everything about what you write and how it lands.

Write to the person. Not to the role, the company, the lead score, or the pipeline stage. The person.

That means doing enough reading before you reach out that you can reference something specific. Not “I loved your post about leadership.” Which post. What in it. Why it connected with something you are thinking about right now.

It means being honest about why you are reaching out. Not hiding a request inside three paragraphs of flattery. People respect directness. They are suspicious of warmth they did not earn.

And it means accepting that most outreach will not get a response. Not because you did it wrong. Because people are busy and their inbox is full of messages that trained them to ignore it. Your job is to be the message that felt different enough to read. Not to convert every send.

When it is not working

The platform will frustrate you. Not occasionally. Structurally and repeatedly.

There will be weeks where a post you worked hard on gets minimal reach and a post you dashed off in ten minutes gets thousands of impressions. The algorithm is not a feedback mechanism for quality. It is a feedback mechanism for engagement patterns, which correlates loosely with quality and strongly with timing, format, early engagement velocity, and factors you cannot fully control.

Here is what to do in those weeks. Not a tactic. A reorientation.

Ask yourself which metric you are actually optimising for. If it is reach, the low-reach week is a failure by that metric. If it is the quality of the one conversation that started from that post, it may be a good week. The platform’s native metrics, impressions, reactions, comments, measure the platform’s success. They do not measure yours. Your success is the recruiter who found your profile six months after you updated it and reached out for the right role. The reader who subscribed to your newsletter three weeks after encountering your comment on someone else’s post. The collaborator who had been reading your work for a year before they sent the message that became the project.

Those things do not appear in your analytics dashboard. They are the actual compounding. They happen independent of any single post’s performance.

The specific practice for the low-reach weeks is to return to the twelve to fifteen accounts. Read them. Comment genuinely. Not to repair your reach. Because the habit of genuine engagement is the only thing that produces consistent results over time, and the weeks when you do it without a metric reward are the weeks that test whether you actually believe that.

There is one more thing worth naming about the hard weeks. The impulse, when reach drops, is to change what you are doing. Post more. Change the format. Try a different style. That impulse is the platform’s design working on you. The friction engineered to keep you producing. Most of the adjustments made in response to a bad week make things worse rather than better because they move you away from your actual angle and toward what seemed to perform last time, which is always slightly wrong.

The question to ask in a bad week is not: what should I change? It is: what was I doing when it was working that I have stopped doing? The answer is almost always the same. Writing things you actually believed rather than things you thought would land. Engaging with the twelve to fifteen accounts before you posted rather than after. Updating the profile to reflect where your thinking actually is rather than where it was six months ago.

Go back to the basics. Every time. Not because the basics are inspirational. Because they are the only things that consistently work.

The long game

The platform will frustrate you. It is designed to. The reach will feel low relative to the effort. The spam will keep coming. The algorithm will penalise you for living your life.

The people who find LinkedIn genuinely useful over time are not the ones who cracked the algorithm. They are the ones who decided to use it as a place to think in public, to be findable by the right people, and to occasionally make genuine contact with someone whose work they respect.

That is a much quieter use of the platform than most guides will tell you.

It is also the one that compounds without burning you out.

Leadership as a verb, on LinkedIn as everywhere else, is the choice to keep showing up as the same person you are when no one is watching. Not because it is strategically optimal. Because it is the only version of this that feels like it is actually worth doing.

The notification is still on my phone. “Your post is gaining traction.”

I will read the comments later. Not to see what is performing. To find the one person who said something true.

That is the whole practice.

This template is a starting point, not a fixed system. If something does not fit your situation, change it. And if you try something and it does not land the way you expected, bring it to the Q&A. That is what the room is for.

LINKEDIN AS A PRACTICE

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

Because nobody wrote down what the role actually was.

I have seen it happen more than once. A senior leader joins a company fractionally, the relationship starts well, and then month by month the scope expands more standups, more strategy sessions, more Friday emergencies until they are doing the full job on a fraction of the pay. Not through bad faith. Through the natural pull of a company toward someone whose judgment they have learned to trust.

The contract is the only place to stop that before it starts.

This playbook takes a standard Independent Contractor agreement and makes it honest for both sides. The six sections that matter most for fractional work, the three overage clause options, and a guide to reading what someone’s redlines are actually telling you about the relationship you are about to enter.

THE FRACTIONAL Contract Playbook

One note: if the total contract value exceeds $200k a year, talk to a lawyer. For everything else this is the framework I wish I had been handed earlier.

Bring your questions to the Q&A. That is what the room is for.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

Humane Architecture is the practice of designing technical systems that account for the cognitive load and emotional well-being of the engineers who build and maintain them. It moves beyond "clean code" to "sustainable systems," ensuring that as your software scales, your team doesn't burn out.

February 4, 2026

To understand the current “rupture” in our workplaces, we must first look at the southern horizon. Over the last few weeks, in my conversations with colleagues and friends from Canada to the UK, a consistent theme has emerged: a heavy cloud of fear. This anxiety is fueled in part by the erratic and increasingly polarized political climate in the United States a constant storm on the southern horizon and compounded by a crushing cost of living that makes even the most seasoned professionals feel as though they are losing their footing.

But the real danger to our organizations isn’t just the external storm. It is the structural bias in how we are choosing to respond to it.

We are currently navigating what Prime Minister Mark Carney describes as a “rupture, not a transition.” In a transition, the old rules still mostly apply as you move toward the new. In a rupture, the old contract is torn up before the new one is even written.

As we scramble to find stability, many leaders are retreating into the “Solo Hero” mindset, clutching at efficiency as a life raft.

But in doing so, we are inadvertently falling into a “Moloch Trap” a race to the bottom where we sacrifice our human essence to please an algorithm.

From the “Solo Hero” to the Silicon Oracle

To understand why this matters, we have to look at the architecture we’ve spent the last decade building. For years, the corporate world operated on a simple, transactional contract: Give us your loyalty, and we will give you a title. This created a “ladder” culture where leadership was a noun a destination you reached so you could finally give orders.

When the AI revolution hit full stride in 2024 and 2025, the architecture of the workplace shifted. We began to treat Generative AI not as a tool, but as an oracle. In high-growth environments, the pressure to “do more with less” became so immense that we started delegating the most human parts of leadership empathy, critical thinking, and vision to Large Language Models (LLMs).

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We reached a point where “content” became more important than “connection.” Leaders began scaling their presence through automated notes, mass-produced comments, and AI-generated town hall scripts. While this increased “output,” it decimated the humane architecture of the team. We built a system optimized for velocity, but we forgot to check the load-bearing capacity of our human relationships.

Now, in early 2026, we are seeing the cracks. The “Solo Hero” who presents the team’s collective effort as their own individual achievement is finding that their authority vanishes the moment they leave a Zoom room. The “umbilical cord” to our devices has replaced the unscripted human connection that once served as the glue of organizational integrity.

Network Theory and the “Moloch Trap”

In my work as an anthropological engineer, I look at the “spaces between” people. This is where power actually lives not in a spreadsheet or an org chart, but in the collective pulse of the group. Currently, two specific phenomena are destabilizing these spaces: The Majority Illusion and The Moloch Trap.

1. The Majority Illusion

Mathematical network theory reveals a startling fact: in any given network, a tiny minority less than 1% of participants can make a behavior or opinion seem like it is held by the majority. This is the Majority Illusion. Because our digital “neighborhoods” are curated by algorithms that prioritize high-status, highly connected individuals, a few “loud” voices can make a radical shift in culture feel like an objective global truth.

When you see a trend in your feed whether it’s a specific management style or a political stance you aren’t necessarily seeing a global movement. You are seeing a structural bias. When leaders react to these illusions without fact-checking their own “gut,” they begin to lead based on ghosts rather than reality.

2. The Moloch Trap

“Moloch” is a metaphor for a system where every participant, acting in their own self-interest, eventually destroys the collective good. In our quest for efficiency, we use AI to automate our voices. If one leader scales with soulless content, others feel forced to follow to maintain “visibility.”

The result? We are consenting to a “social platform for ghosts.” We have become passive observers of our own feeds. When we automate our leadership, we aren’t “saving time”; we are abdicating our charge. True leadership is the courage to refuse a tool that replaces the human soul. It is the refusal to win a race to zero.

The Global Perspective: Workforce Dignity

This rupture is felt most acutely by those the “Western Gateway” has traditionally marginalized. In my mentorship of brilliant professionals from Nigeria and India, I see a startling contrast between their reality and the global narrative. These individuals are often viewed as “sub-standard” by Western institutions simply because they aren’t part of the “loud 1%” of the network.

However, the 2026 Contract is being written by them. In India, the concept of “Jugaad” frugal, flexible innovation is moving from the streets to the boardroom. These professionals don’t want a seat under a leader; they want a seat at the table where the vision is built. They are demanding Workforce Dignity. They are looking for “Shared Leadership,” where their genius is respected as equal.

If your leadership architecture doesn’t have room for this dignity, it will fail. You cannot distribute power through an algorithm. It requires the friction and flow of being emotionally present.

The Blueprint: Leadership as a Shared Action

If we are to treat leadership as a verb rather than a noun, we must reinforce the “humane architecture” of our teams through deliberate, daily actions. We must move from being supervisors to being engineers of human systems.

Here is the blueprint for building Residual Authority the kind of power that guides the team even when you aren’t in the room.

1. Kill the “Umbilical Cord”

The smartphone has become a digital gateway that strips away human touch. To move through an organization with integrity, you must calibrate the space between people.

• The Action: Start every 1:1 meeting with five minutes of phone-free, unscripted connection. Do not talk about KPIs. Talk about the “why.” This is how you kill the “Solo Hero” myth and build a community of shared truth.

2. Verify the Source (The Anti-Oracle Stance)

AI is a tool, not a destination. It computes and processes; it does not lead.

• The Action: Never present an LLM output in a Town Hall without rigorous fact-checking and human contextualization. If you preach attention to detail to your team, you must model it. Your “gut” and your circle of trusted peers are your only true oracles.

3. Distribute the Credit to Defuse the Politics

Company politics thrive when one person holds all the cards. Politics is not about manipulation; it’s about calibration.

• The Action: For one week, consciously distribute authority. If a team member did the work, they present the work. When power is shared, it cannot be weaponized.

4. Practice “Conscious Unbossing”

The modern professional especially the “quiet” experts who make up the 90% of the iceberg craves to be a partner in progress, not a cog in a machine.

• The Action: Identify the “loudest” voice in your meeting and intentionally bypass it to listen to the “quiet” expert. Shared leadership is messy and unpolished. It doesn’t look good in a 15-second reel, but it is the only way to find the best path.

The Conclusion: Our Collective Pulse

Leadership is a resonant frequency. When we align our intent, we create an environment where excellence becomes the baseline and mutual respect is the default. We don’t amplify each other’s anxiety; we become each other’s steady ground.

Totalitarianism whether in politics or in corporate culture starts when we believe only one person has the “attitude” of leadership. We must realize that if we do nothing to challenge the “Solo Hero” or the “Ghost Platform,” the silence will eventually become the norm.

We have more power than we can imagine. Raising our voices is a struggle, and the “Moloch Trap” will tell us to shut up and automate. Ignore it. Honor the quiet hands that hold the floor. Trust the one closest to the fire to lead.

Our collective pulse is our greatest power. Lead as one.

This week, look at your feed and ask: Am I reacting to a global shift, or just the loudest 1% of my network? Then, put down the phone and find one human in your “architecture” to have an unscripted conversation with. Let me know what you discover in the comments below.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

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