There is a comforting dream behind most goal-setting exercises. We sit in a room, open a document, and write down what we want to achieve. Then we break it into priorities, initiatives, milestones, owners, dependencies, risks, mitigation plans, and, if the organization is mature enough, at least three tables that say roughly the same thing in different formats.

For a brief moment, it feels like control. Do A, get B. Do B, get C. If the result does not happen, the explanation is ready: the goal was not clear enough, ownership was not strong enough, the roadmap was not detailed enough, or the alignment meeting did not produce enough alignment. So naturally, we add another layer of detail, because apparently the only thing standing between us and a predictable universe is a better spreadsheet.

I have been thinking about this through the work of Ilya Prigogine, the Nobel Prize-winning chemist and physicist who studied complex systems far from equilibrium. His work on dissipative structures showed something deeply uncomfortable for anyone who enjoys clean planning: in complex systems, order does not simply defeat chaos. Order often emerges from chaos. These systems are not merely complicated machines with missing documentation. They are unstable, adaptive, and open to several possible futures.

At certain points, a small fluctuation can change the system’s overall direction. The past does not mechanically produce the future. The system answers back. This is not a perfect metaphor for business, of course. Organizations are not chemical reactions. Product teams are not molecules, although on some days the difference is less obvious than we would like. But as a way of thinking, it is useful.

Many organizations still set goals as if they operate in a stable, obedient environment. As if the market will politely stay still while the annual plan is executed. As if the user behavior we observed last year will remain largely valid this year. As if the competitive landscape will wait for Q4, when we planned to review the strategy.

Reality is rarely that well-mannered. A competitor appears from a direction no one had on the slide. A technology that was supposed to be “interesting in three years” becomes normal in eight months. A stakeholder changes. A team loses key people. A regulatory risk appears. A user behavior we thought was marginal suddenly becomes mainstream. Or, more quietly, an unexpected conversation opens a much better path than the one we spent months planning.

This is the part where traditional goal-setting often goes wrong. Not because goals are useless. They are not. The problem is not a lack of goals. The problem is pretending that goals give us control over things we can only influence.

A good goal creates direction. It helps people choose. It gives teams a shared language for trade-offs. It says: this is what matters, this is what we are trying to change, and this is the effect we believe is worth pursuing. A bad goal pretends to be a contract with the future. And the future, as usual, did not sign it.

In product work, this distinction matters a lot. A roadmap is often treated as a delivery promise, but in a complex environment it should be closer to a structured hypothesis. We believe these problems are important. We believe these solutions may work. We believe this sequence makes sense based on what we know now. The key phrase is “based on what we know now,” not “based on what we hope will remain true forever.”

The greater the uncertainty, the less useful it is to worship the original plan. Yet this is exactly what many organizations do. When reality becomes less predictable, they respond by demanding more precision from the plan: more milestones, more reporting, more governance, and more status updates explaining why the world has failed to comply with the roadmap. This is not management. This is administrative weather control.

The better question is not “How do we remove uncertainty?” We cannot. The better question is: “Which parts of this system can we influence, and where do we need to stay alert enough to adapt?” That requires a different kind of discipline. Less theatrical certainty and more honest observation. Less obsession with predicting the exact output and more attention to signals. Less punishment for changing course when the evidence changes, and more ability to distinguish between abandoning a goal and updating a hypothesis. This is where many teams struggle. Not because they lack ambition, but because they confuse flexibility with weakness. They think adaptation means giving up. But in reality, adaptation is often the only serious form of execution. Rigid plans look strong in presentations. Adaptive systems survive outside them.

The irony is that the best outcomes in our lives and work often come from things we did not fully plan. A meeting that was not supposed to matter. A side conversation after the official discussion ended. A small experiment no one expected much from. A person who entered the picture late and changed the whole direction. A constraint that forced a better idea. But these outcomes are not pure luck.

Chance opens the door. Preparedness notices it. Competence uses it. This is why the next evolution of goal-setting should not be about abandoning goals and becoming passive observers of chaos. That would be just another fashionable excuse, and we already have enough of them.

The evolution should be about intellectual honesty. We need goals, but we should stop pretending that all goals are control mechanisms. Some are direction markers. Some are bets. Some are learning tools. Some are useful only until the system gives us a better answer.

The mature approach is not to say: “Here is the plan, and reality must follow.” The mature approach is to say: “Here is our direction, our current hypothesis, what we can influence, what we need to watch, and how we will know that the system is telling us something important.” It is less comforting than a beautiful annual plan, but it has one advantage. It is closer to how reality actually works.

A few weeks ago, I attended a workshop on Belbin’s Team Roles. You probably know the concept, or at least the general idea behind it. Every effective team needs different types of contributions. Some people generate ideas, some turn ideas into practical plans, some challenge inertia, some analyze risks, some maintain relationships, some bring deep expertise, and some make sure the work is actually finished rather than becoming another beautifully designed slide in the corporate museum of unfinished intentions.

So far, so good.

Then we came to the part where the idea of a team without a leader appeared. This is where my inner corporate heretic politely raised his hand.

Not because I believe every team needs a boss. Quite the opposite. The best leaders are not always the loudest in the room, and they are definitely not the ones who measure leadership by how many follow-up messages they send after every meeting. A leader is not someone whose main function is to ask, “Any updates?” across five different communication channels. That is not leadership. That is calendar-based anxiety with admin permissions.

But I do believe every serious team needs leadership. There is a difference. A boss controls; a leader enables. A boss wants to be at the center; a leader creates conditions in which other people can do their work. A boss treats the team as a delivery machine; a leader understands that the team is a system of people, roles, expectations, conflicts, priorities, constraints, external pressure, unclear requirements, and the occasional stakeholder who enters the room carrying a “small request” that somehow requires three months of engineering work.

Belbin’s model is useful because it reminds us that teams need different types of contributions. A team made only of idea generators may produce an impressive number of concepts and almost no output. A team made only of implementers may deliver efficiently in the wrong direction. A team made only of evaluators may analyze the project to death. A team made only of relationship-builders may create a safe and friendly environment where nobody wants to ask why the deadline was missed. Balance matters, and the model clearly shows why.

But balance does not eliminate the need for responsibility.

This is where I struggle with the romantic idea of leaderless teams. It often sounds appealing in workshops, especially when everything is theoretical, the coffee is still hot, and no real stakeholder is waiting for an answer by the end of the day. In theory, the team self-organizes. In theory, decisions emerge naturally. In theory, people align around the best argument. In theory, everyone owns the result. Then reality arrives, usually without reading the workshop handout first.

A deadline slips. Two departments want opposite things. A senior stakeholder pushes for a shortcut. A technical risk is discovered late. A conflict within the team stops being a “healthy disagreement” and becomes a polite civil war waged through Jira comments. Suddenly, “shared ownership” starts to look suspiciously like “nobody is actually responsible.”

This is the moment when teams need more than roles. They need someone who can say: “Here is the decision. Here is why. Here is what we will do next. And I own the consequences.” That person does not need to dominate the team. They do not need to have all the answers. In fact, if they think they have all the answers, they are likely the problem. But someone must own the frame.

Someone must protect the team from noise, represent the team to the outside, resolve conflicts when good intentions fall short, and connect internal work to external reality. Someone must ensure that autonomy does not become isolation and that collaboration does not become permanent consensus-seeking. This, to me, is leadership. Not command and control. Not corporate theater. Not “let’s circle back” as a lifestyle. Leadership is the function that enables all other functions to work.

A strong leader enables the Plant to bring ideas without letting the team lose itself in abstract creativity. They help the Implementer turn plans into action without reducing every discussion to a checklist. They let the Monitor Evaluator challenge assumptions without creating a culture of permanent skepticism. They support the Teamworker without making harmony more important than truth. They value the Specialist without allowing expertise to become a private kingdom. Good leadership does not replace team roles. It makes them usable.

This is why the phrase “leaderless team” often feels misleading to me. Many teams that call themselves leaderless still have leadership. They just do not call it that. Someone coordinates. Someone facilitates. Someone exerts influence. Someone breaks deadlocks. Someone represents the group. Someone becomes the person others look to when things get difficult. The title may disappear, but the function remains.

And when the function disappears, the consequences are usually not very modern or progressive. They are just messy.

The strongest teams I have seen do not rely on a leader for every small decision. That would be slow, exhausting, and frankly insulting to capable professionals. Strong teams make many decisions themselves. They challenge each other. They draw on expertise. They do not wait for permission to breathe. But they also know where accountability lies. They know who protects the mission when priorities collide. They know who owns escalation. They know who can make the unpopular decision when all available options are imperfect. They know who carries the responsibility when the beautifully balanced team roles are not enough.

So maybe the real question is not whether teams need a leader. The better question is what kind of leader the team needs. If the answer is “someone who controls every step,” the team is probably not mature enough, or the leader is not. If the answer is “nobody, because we are all adults here,” that may work for a while, especially while things are calm. But when pressure comes, leadership stops being a job title and becomes infrastructure.

And like most infrastructure, nobody appreciates it when it works. They only notice when it is missing.

These days, when a new project lands on our desk, a stakeholder arrives with a difficult question, or a strategy discussion starts going in circles, many of us have developed the same reflex.

Before we call a colleague, before we challenge our own assumptions, and sometimes even before we spend five minutes thinking about the problem ourselves, we open ChatGPT.

• “Can you research this?”

• “Can you validate this idea?”

• “What am I missing?”

• “How would you approach this?”

Sound familiar? It certainly does to me.

To be clear, this is not a criticism. In many ways, it is perfectly rational behavior. We have access to a tool that can summarize information, compare alternatives, challenge assumptions, and produce a reasonably intelligent response in seconds. Refusing to use it would be as strange as refusing to use Google twenty years ago.

What interests me is not the tool itself. It is what this new habit reveals about how our relationship with research, analysis, and decision-making has been changing for years, long before AI arrived.

For most of my career, research was expensive. If you wanted to understand a market, a product, a competitor, or an audience, you had to work for it. You collected reports, interviewed users, studied competitors, reviewed data, argued with colleagues, and slowly pieced together a picture from incomplete information. The challenge was not interpretation. The challenge was access. There were always more questions than answers.

Today, the situation is almost the reverse. Search engines have dramatically lowered the cost of finding information. Social media has multiplied the number of available signals. Analytics platforms have given us dashboards for everything. And now artificial intelligence can summarize, compare, analyze, and explain in seconds what once took hours or days. Access is no longer the bottleneck. Information is no longer scarce. Answers have become abundant.

That sounds like progress, and in many ways it is. We can learn faster, validate assumptions more quickly, and explore topics that would have required enormous effort just a decade ago. Yet abundance creates a different problem. When answers become cheap, we begin to confuse collecting information with understanding it. The ability to retrieve knowledge starts to replace the harder task of deciding what that knowledge actually means.

This distinction matters more than many people realize. Research and thinking are related activities, but they are not the same. Research gathers evidence. Thinking evaluates it. Research identifies patterns. Thinking questions whether those patterns are real. Research helps us understand what happened. Thinking tries to understand why it happened and what might happen next. One produces information. The other produces judgment.

In product management, leadership, consulting, journalism, and strategy, judgment has always been the scarce resource. Information was never the final product. Insight was. Two people can look at the same data and reach completely different conclusions. One sees a trend. The other sees noise. One sees a user problem. The other sees a measurement issue. One discovers an opportunity. The other discovers a risk. The difference is rarely access to information. The difference is interpretation.

The irony is that artificial intelligence did not create this challenge. It merely accelerated a trend already well underway. Long before large language models appeared, many organizations had quietly begun rewarding certainty over curiosity. Executives wanted forecasts. Managers wanted metrics. Recruiters wanted keywords. Schools wanted correct answers. PowerPoint presentations wanted conclusions. Very few systems rewarded anyone for saying, “I don’t know yet, but I think we’re asking the wrong question.”

Over time, uncertainty became something to eliminate as quickly as possible. Open questions were treated as problems waiting to be closed. The professional who sounded certain often appeared more competent than the one still exploring possibilities. Artificial intelligence happens to be exceptionally good at operating in such an environment. It produces answers instantly, presents them confidently, and never hesitates. Most importantly, it never appears uncomfortable with ambiguity.

That is precisely why it is so useful and why it warrants a certain amount of caution. The danger is not that AI makes people less intelligent. The danger is that it can create the impression that analysis has occurred when only information gathering has taken place. A beautifully structured answer can feel like understanding. A polished summary can feel like expertise. A generated recommendation can feel like strategic thinking. Sometimes it is. Sometimes it is simply a very convincing starting point.

Anyone who has spent enough time in large organizations has seen this phenomenon before AI arrived. We have all read reports filled with data yet lacking insight. We have all attended meetings where everyone discussed metrics while no one challenged assumptions. We have all seen strategy decks that answered dozens of questions except the one that actually mattered. The technology changed. Human behavior did not.

This is why I suspect some of the most valuable professional skills are becoming more important, not less. The ability to formulate a good question. The ability to identify hidden assumptions. The ability to recognize contradictions. The ability to sit with uncertainty long enough for a deeper understanding to emerge. These skills become more valuable when answers are abundant, not less.

The future probably belongs neither to those who reject AI nor to those who outsource their thinking to it. It belongs to those who learn to use it without surrendering their judgment. The best professionals will use artificial intelligence the same way previous generations used search engines, calculators, spreadsheets, and databases, as a tool that extends capability, not as a substitute for reasoning.

What remains uniquely valuable is not access to information. Machines are already better at that. It is the ability to decide what deserves attention, what deserves skepticism, and what deserves belief. It is the ability to recognize when a seemingly perfect answer is addressing the wrong question. It is the ability to connect ideas across domains and create something genuinely new.

Ironically, these are not new skills. They were valuable before Google, remained valuable during the age of dashboards and analytics, and will remain valuable long after today’s AI tools are replaced by something faster, smarter, and more impressive.

Technology keeps lowering the cost of finding answers. Human value increasingly comes from knowing which answers matter.

And perhaps even more importantly, from knowing which questions are still worth asking.

That pause between receiving an answer and accepting it as truth may become one of the most valuable professional skills we have left.

Let’s start with a confession.

Most of us have taken part in a strange corporate ritual. Every year, managers and employees gather in meeting rooms or on video calls to set goals. Those goals are carefully aligned with department objectives, which are aligned with company objectives, which are aligned with strategic priorities, which are aligned with another presentation created somewhere several organizational layers above reality. Everyone nods. Everyone agrees. Everyone enters the goals into the system. The process looks rational, structured, and professional. Then the meeting ends, people return to their desks, and something interesting happens: most of them go back to pursuing their own goals.

Not because people are lazy. Not because they are disloyal. Not because they secretly want the company to fail. They do it because they are human. The uncomfortable truth behind many performance management systems is that they rest on an assumption that sounds reasonable but rarely holds up to reality. The assumption is that if company goals are communicated clearly, measured carefully, and tied to incentives strongly, employees will naturally adopt them as their own. In practice, people do not set aside personal ambitions, interests, concerns, and priorities simply because a new quarterly objective appears in a slide deck.

For decades, companies have treated motivation as a relatively simple engineering problem. Increase compensation, improve benefits, add recognition programs, introduce employee-of-the-month awards, and organize team-building activities, and motivation should increase accordingly. The model is attractive because it is easy to understand and even easier to measure. It allows organizations to think about people the same way they think about budgets, processes, and operational metrics. The difficulty is that human beings are considerably more complicated than spreadsheets.

Money matters, of course. Anyone claiming otherwise is usually either very wealthy or trying to persuade someone else to work for less. But money explains far less than organizations often assume. A salary increase can mean security to one employee, status to another, independence to a third, and recognition to a fourth. Meanwhile, a highly curious engineer may value learning opportunities more than a larger bonus. An ambitious product manager may care more about influence and ownership than about another corporate benefit. A senior specialist may willingly sacrifice compensation growth in exchange for greater autonomy. The company believes it is sending a clear motivational signal. The employee receives a message in a language they do not particularly care about.

This is where many performance management systems begin to struggle. The problem is not that employees reject company goals. Rather, employees filter every company goal through a personal lens before deciding how much effort to invest. When leadership announces a strategic initiative, people do not immediately think about shareholder value, organizational efficiency, or market positioning. They ask different questions. Will this help me grow? Will it make my work more interesting? Will it increase my influence? Will it improve my reputation? Will it create opportunities for me? These questions are not selfish. They are completely rational. Every employee, from an intern to a senior executive, translates organizational priorities into personal meaning before deciding how much energy to commit.

What makes this particularly relevant today is the changing nature of work. Many traditional management approaches were developed when organizations primarily coordinated large numbers of people performing standardized tasks. Modern organizations increasingly depend on knowledge workers whose primary tools are judgment, expertise, creativity, and problem-solving. These people often have strong opinions, strong professional identities, and a reasonably accurate understanding of their market value. They have access to remote opportunities, international employers, consulting work, startups, and professional networks that barely existed a generation ago. They are not trapped in a single organization. They choose to remain there.

That shift changes the entire management equation. In the past, companies often relied on compliance. Today, they increasingly depend on commitment. Compliance can be enforced through rules, reporting structures, and oversight. Commitment is different. It appears when people see a meaningful connection between what the organization wants and what they personally want. It cannot be demanded. It cannot be purchased directly. It has to be created.

This is why I have become increasingly skeptical whenever I hear discussions focused exclusively on company goals. The question is not whether company goals matter. Of course they do. The question is whether leaders understand that those goals compete for attention with dozens of other motivations already present in every employee’s mind. Career ambitions, intellectual curiosity, desire for recognition, financial security, family responsibilities, personal values, and simple quality of life all sit at the same table. Company objectives are merely another participant in that conversation.

The irony is that the strongest organizations are often not the ones that demand the highest level of commitment to corporate goals. They are the ones that invest the most effort in understanding what their people actually want. Instead of trying to convince employees to care about business objectives for their own sake, they find ways to align business success with personal success. They create environments where professional growth supports organizational growth, ownership supports accountability, learning supports innovation, and individual ambition contributes to collective outcomes.

That approach sounds almost obvious once stated aloud. Yet many organizations continue to act as though employees should naturally prioritize corporate objectives over their own interests. The reality is exactly the opposite. People will always pursue what matters to them personally. The real challenge of leadership is not fighting that fact. It is ensuring that when people move toward their own goals, they help move the organization forward as well.

Because in the end, people do not come to work every morning to achieve company goals. They come to achieve their own. The best companies simply make those two journeys look like the same road.

A few weeks ago, I caught myself doing something ridiculous.

I was waiting for a meeting to start, casually scrolling through LinkedIn. One person had just become a VP. Another had joined a well-known technology company. Someone else was announcing a successful startup exit. A familiar face was sharing photos from a London conference. Another was celebrating ten years in leadership. Amid all the promotions, milestones, keynote speeches, and carefully written reflections on success, I suddenly felt oddly behind.

The funny part was that nothing in my real life had changed.

I was still the same person I had been fifteen minutes earlier. My experience had not disappeared. My accomplishments had not become any less valuable. My team had not suddenly stopped delivering results. Objectively, everything was exactly the same as it had been before I opened the app. Yet subjectively, something had shifted. The moment I immersed myself in a stream of other people’s achievements, my own career began to feel smaller. Not unsuccessful. Not disappointing. Just somehow less impressive than it had felt before.

That reaction is surprisingly common, and psychology offers a straightforward explanation. More than seventy years ago, psychologist Leon Festinger described social comparison as a fundamental way people evaluate themselves. We rarely assess our success in absolute terms. We assess it relative to others. The challenge is that our brains evolved in environments where “other people” meant family members, neighbors, coworkers, classmates, and a handful of local success stories. Human beings were not designed to compare themselves against thousands of highly curated examples of achievement before their first cup of coffee.

For most of history, comparison occurred within relatively small, stable communities. You knew the successful people around you, and you knew their struggles. You knew who got promoted after years of hard work. You knew who lost a job before finding a better one. You knew who looked successful yet was constantly stressed, and who seemed ordinary yet was quietly thriving. Real life provided context. Success and failure coexisted in the same picture.

Social media has changed the picture entirely.

Instagram is often criticized for fostering unrealistic expectations about beauty, lifestyle, travel, and relationships. LinkedIn does something remarkably similar, but in a professional context. Instead of showcasing perfect vacations and flawless bodies, it highlights promotions, awards, keynote speeches, funding announcements, successful launches, prestigious new jobs, and carefully packaged lessons from triumphant career journeys. The content may look more professional, but the psychological mechanism is almost identical.

What makes LinkedIn particularly effective is that it feels objective. Instagram users generally understand that they are looking at a highlight reel. The platform has become synonymous with curation. LinkedIn, however, presents itself as a professional environment. The stories feel credible, useful, and relevant. We tell ourselves that we are learning about the market, staying informed, or building our network. Yet beneath that rational explanation, our brains are still processing a continuous stream of social status signals.

And status signals are powerful.

When someone your age becomes a Vice President, joins a prestigious company, launches a successful business, or speaks at a major conference, your brain rarely pauses to analyze the full context. It does not calculate the probability of that outcome. It does not consider how many candidates were rejected, how many failures preceded the success, how much luck was involved, or what sacrifices were made along the way. Instead, it performs a much simpler calculation. It notices that someone seems to be moving faster than you and immediately starts measuring the distance between their position and yours.

The irony is that this phenomenon often intensifies rather than diminishes as careers advance. Early in their careers, people compare themselves to colleagues or classmates. Later, they compare themselves to directors, vice presidents, founders, and industry thought leaders. Every promotion resolves one comparison problem and creates another. The benchmark keeps moving. What felt like an ambitious career goal five years ago becomes merely the starting point for the next round of comparison.

I have seen this repeatedly among experienced leaders. People running large teams, managing significant budgets, and shaping important products sometimes feel strangely unsuccessful. Not because they are unsuccessful, but because they spend too much time observing exceptional careers. When your daily feed is filled with executives from Google, Meta, OpenAI, Stripe, or the latest unicorn startup, your sense of what counts as a “normal” career gradually becomes distorted. Statistically rare outcomes start to feel commonplace. Exceptional success begins to look ordinary. Once that happens, perfectly respectable achievements can start to feel inadequate.

The more I think about it, the more I believe social media is changing not only how we evaluate careers but also how we define them.

Many professionals unconsciously design careers that look impressive when summarized in a LinkedIn post. Visibility competes with impact. Public recognition competes with expertise. Activities that attract attention often seem more valuable than those that deliver long-term results. A conference keynote looks more impressive online than two years spent improving an organization’s culture. A startup launch creates a better headline than a decade of patient operational excellence. A personal brand is easier to showcase than deep expertise.

The problem is not that visibility, speaking engagements, or personal branding are bad. They can all be useful and valuable. The problem arises when visibility becomes the scorecard itself. At that point, we stop measuring success by the quality of our work and start measuring it by the quality of the story we tell about our work.

Some of the most effective leaders I have met would barely show up on LinkedIn. They rarely post, do not speak at conferences, and have no interest in building a personal brand. Yet they consistently build strong teams, develop future leaders, solve difficult organizational problems, and deliver meaningful results. Their organizations value them immensely. The algorithm simply does not.

That distinction matters because LinkedIn is not the labor market. It is the showroom of the labor market. It contains valuable information, useful connections, and genuine opportunities, but it also systematically overrepresents success and underrepresents struggle. We see promotions but not disappointments. We see outcomes but not uncertainty. We see victories but rarely the years of ambiguity that preceded them.

Perhaps the healthiest response is not to reject professional social media but to understand what it actually is. A showcase is useful as long as we remember it is just a showcase. The moment we mistake it for reality, comparison becomes inevitable, and dissatisfaction follows close behind.

Careers are lived in the messy, uncertain, unglamorous world of everyday work. They are built on difficult decisions, failed projects, uncomfortable conversations, occasional luck, and long stretches of persistence. None of that makes for particularly engaging content. Most of it never appears in a LinkedIn post.

Yet those invisible parts are where careers are truly made. The algorithm edits them out. Reality does not.

There are two management philosophies that sound absolutely brilliant on LinkedIn: “The manager should not constantly ask when the task will be done” and “The manager has three duties: explain the task, provide resources, and shut up.”

People love these quotes because almost everyone who has worked in a large organization has experienced the opposite. Endless status meetings. Daily “quick syncs” that somehow stretch to an hour. Managers who believe productivity only exists if they can visibly observe it in real time. Entire departments operating like anxious airport security, repeatedly asking whether everyone still has their boarding pass.

So naturally, the fantasy is appealing. Hire smart adults, explain the objective clearly, give them resources, stop interfering, and let competent people do their jobs. In theory, this sounds not only efficient but also civilized. And to be fair, in strong teams with experienced specialists, it often works surprisingly well. Engineers, designers, editors, product people, analysts — most professionals genuinely do better work when they have enough trust, enough context, and enough uninterrupted time to think.

The problem is that these elegant management quotes quietly assume an environment that rarely lasts long. They imagine organizations as calm, rational systems where priorities are stable, communication is clear, dependencies are visible, and everyone shares the same understanding of urgency and success.

Reality is usually less cinematic. Real organizations are noisy systems filled with conflicting incentives, shifting priorities, half-finished transformations, unclear ownership, overloaded specialists, political sensitivities, budget pressures, legacy processes, and stakeholders who confidently approve one direction, only to change their minds three weeks later after a conference, a board meeting, or a conversation with another executive.

And this is exactly where simplistic anti-management philosophy begins to collide with operational reality.

The uncomfortable truth is that organizations do not become chaotic because managers exist. Managers exist because organizations naturally drift toward chaos if no one actively aligns them. The larger and more interconnected the system becomes, the more invisible coordination work is required simply to prevent collisions among teams, priorities, technologies, and people. A surprising amount of management is not about telling people what to do. It is about constantly reducing friction before it becomes visible to everyone else.

The irony is that the best managers often seem “hands-off” precisely because they are quietly intervening behind the scenes. They notice that two departments are heading toward incompatible deadlines long before the conflict erupts publicly. They realize that a “small technical dependency” will eventually delay five projects. They sense when leadership expectations no longer align with delivery reality, even as everyone continues to smile politely in meetings. They absorb pressure, negotiate compromises, clarify ambiguity, and translate vague executive ambition into something an exhausted team can execute without collapsing.

Most of this work is deeply unglamorous, which is probably why online management discussions favor inspirational mythology instead. The internet loves the image of the enlightened leader who empowers everyone through trust, vision, and radical autonomy. It sounds elegant. It also sounds much better than admitting that real management often involves uncomfortable follow-ups, conflict resolution, operational escalation, resource negotiations, damage control, and conversations that nobody wants to have.

Meanwhile, actual management often looks less like philosophy and more like: “Can somebody please confirm which system is considered the source of truth before three teams implement different versions of the same feature?”

Not exactly the kind of sentence people print on conference banners.

At the same time, criticism of micromanagement is completely valid. If a manager spends most of the day asking whether tasks are done, something deeper is usually broken. Either priorities are unclear, ownership is weak, communication is fragmented, the process lacks transparency, or the organization itself operates in a permanent reactive mode. Strong teams should not require constant supervision to function. Mature professionals generally do not need someone metaphorically standing behind them with a stopwatch and a clipboard.

But this is where many fashionable management quotes become misleading. The absence of micromanagement is not the same as the absence of management. A healthy organization does not emerge automatically just because intelligent people were hired. Independence without alignment eventually leads to fragmentation. Total freedom without coordination slowly produces duplicated work, incompatible decisions, missed dependencies, and strategic drift. People start optimizing locally while the organization quietly loses coherence globally.

The real role of management lies somewhere in the uncomfortable middle between control and absence. Good managers are not dictators obsessively monitoring every move. But they are also not passive motivational figures whose entire contribution ends after assigning tasks and distributing inspirational energy. Their job is to build an environment where people can operate independently most of the time while ensuring the broader system still functions coherently under stress, uncertainty, and constant change.

And perhaps the greatest irony of management is this: when it works exceptionally well, people often conclude that management was unnecessary.

Which, from a manager’s perspective, is simultaneously the highest compliment and the most dangerous illusion.

Some management books age badly. You open them 30 years later and find advice that sounds like it was written for a factory overseen by a man smoking next to a forklift.

Philip Crosby’s Quality Is Free, first published in the late 1970s, is strange because half of it feels completely outdated — and the other half feels uncomfortably modern.

The title itself sounds almost absurd today. “Quality is free.” Right. Tell that to a product team drowning in technical debt, a newsroom fixing broken embeds at midnight, or an engineering manager negotiating cloud costs after another emergency rollback.

But Crosby’s point was never that quality costs nothing. His point was that the absence of quality is catastrophically expensive.

That idea has survived surprisingly well.

For years, I have had a slightly provocative theory about work: lazy people often produce higher-quality work on the first attempt.

Not always. There is obviously the destructive kind of laziness too. But experienced, intelligent laziness is different. It hates repetition. It hates fixing the same thing twice. It hates unnecessary motion, unnecessary meetings, unnecessary debugging, unnecessary explanations, unnecessary support tickets.

So these people quietly optimize for permanence.

They write the cleaner process because they do not want to answer the same question fifteen times. They automate the workflow because manual repetition irritates them. They document things because they do not want late-night calls. They think through architecture because they do not want emergency rewrites six months later.

Meanwhile, organizations often reward the opposite behavior: visible urgency, heroic recovery, chaotic multitasking, dramatic late-night fixes, endless firefighting. Entire careers are sometimes built on solving problems that should never have existed in the first place.

Crosby would probably appreciate the irony.

Back in the 1970s, he was mostly talking about manufacturing defects, production lines, warranties, scrap, inspections, and rework. The world was industrial, hierarchical, and obsessed with process control. The enemy was the defective physical product.

Half a century later, the factories became platforms, APIs, apps, recommendation engines, analytics pipelines, AI systems, and distributed cloud infrastructure. But the defects never disappeared. They just changed shape.

Today’s “scrap” is not metal in a warehouse. It is engineering time lost to regressions. It is product managers holding alignment meetings about problems that should never have existed. It is support teams manually correcting workflows. It is editors working around broken CMS logic. It is developers fixing rushed integrations at 2 a.m. because “we’ll clean it up later” somehow became an operating model.

And just like in Crosby’s time, organizations still underestimate the cost of all this invisible chaos.

One of Crosby’s most controversial ideas was “Zero Defects.” Modern people instinctively dislike the phrase because it sounds unrealistic, authoritarian, or detached from reality. In software, especially, people love saying that “bugs are inevitable,” which is true. But Crosby was not really arguing for perfection. He was arguing against normalization.

That distinction matters.

Many organizations quietly build cultures where recurring problems become folklore rather than priorities. Everyone knows the broken workflow. Everyone knows the unreliable report. Everyone knows the feature that randomly fails under load. Entire teams adapt to dysfunction, like people in an old apartment building learning which stair steps creak.

Eventually the workaround becomes more stable than the actual system.

Crosby would probably hate modern startup culture. Not because of technology, but because of the mythology of speed. “Move fast and break things” sounds exciting until you realize someone has to maintain the broken things. Usually, with half the original team gone and the documentation written by archaeologists.

At the same time, some things have changed dramatically since the late 1970s.

Back then, quality management was deeply centralized. Committees, formal programs, structured initiatives, quality departments, slogans on walls. Today, quality is far more embedded in operational systems. CI/CD pipelines, observability, automated testing, SRE practices, feature flags, incident management, telemetry, DevOps culture — much of modern engineering is essentially automated quality management wearing a hoodie instead of a necktie.

Even Crosby’s famous idea that “quality must start at the top” has evolved. Modern organizations increasingly understand that quality is systemic. Leadership still matters enormously, but quality now emerges from incentives, architecture, tooling, communication patterns, deployment practices, staffing realities, and organizational trust.

Ironically, AI is making Crosby’s arguments even more relevant today.

AI dramatically lowers the cost of producing things quickly. But it also lowers the cost of producing bad things quickly. Hallucinated summaries, unreliable automations, synthetic content errors, broken AI workflows, low-quality code generation — the scale of potential rework is becoming enormous.

The old industrial defect is becoming a probabilistic defect.

Which brings us back to Crosby’s original argument from the late 1970s: prevention is cheaper than correction.

Still true.

Not because humans became smarter. Not because technology solved complexity. But because organizations still pay for every shortcut, eventually. The invoice simply arrives later, in a different department, under a different budget line, in a different quarter, explained by a different PowerPoint presentation.

Half a century later, that part aged perfectly.

There is a French expression: esprit de l’escalier — the wit of the staircase.

It captures that deeply annoying moment when you finally think of the perfect response after the conversation is already over. After the meeting ended. After the argument moved on. After you already nodded politely, left the room, entered the elevator, and replayed the entire discussion in your head, like Lt. Columbo quietly returning to the room with “just one more thing.”

Only then does the right answer arrive.

Modern management culture quietly treats this as a weakness. We admire people who respond instantly, speak confidently, dominate discussions, and always appear intellectually armed. The ideal leader in many organizations is someone who can offer immediate opinions on strategy, budgets, hiring, restructuring, AI, cybersecurity, market shifts, and organizational politics. Preferably within the same thirty-minute meeting and without showing visible signs of human cognition.

But after years in product leadership, I increasingly suspect we misunderstand what is actually happening in those moments.

The person with the fastest answer is not necessarily the one doing the best thinking. Very often, they are simply the person most comfortable performing certainty in public.

Organizations inadvertently train people to optimize for reaction speed rather than judgment quality. Meetings become strange little performance arenas where timing matters more than depth. Whoever speaks first often controls the framing of the problem. Whoever sounds most confident creates gravity around their interpretation. Because corporate environments reward momentum, the room starts moving before anyone fully understands whether the direction even makes sense.

Then comes the staircase moment.

You leave the room, and your brain finally exits survival mode. The pressure lifts. You are no longer trying to sound diplomatic, protect relationships, avoid escalation, respect hierarchy, defend your department, manage optics, and simultaneously interpret three contradictory stakeholder expectations disguised as “alignment.” The nervous system calms, and suddenly the real thought appears.

Not the clever comeback. Not the movie-style one-liner everyone imagines afterward. The real insight.

“The roadmap itself is internally contradictory.”

“We are discussing delivery problems because nobody wants to discuss prioritization failures.”

“This initiative was approved because challenging it politically costs more than implementing it poorly.”

“The KPI exists mainly to comfort management, not to measure customer value.”

By then, of course, the meeting is already over. Someone has already summarized the discussion in Teams. Someone else has written “great progress, everyone.” The slide deck has become organizational reality simply because enough people stopped resisting it for forty-five minutes.

Large organizations are full of decisions made just minutes before the smartest idea entered the room.

Ironically, this becomes even more apparent at senior levels. The more political the environment, the less cognitive bandwidth people have for pure thinking. A significant portion of executive communication is emotional risk management disguised as strategic discussion. People constantly calculate consequences: Who supports whom? Which conflict is safe? Which truth is dangerous? Which topic will cause unnecessary escalation? Which sentence may resurface six months later during budgeting season?

No wonder the best insights often arrive later, when the meeting is finally over and the brain can process the conversation without social threat detection running at full capacity.

That is also why some executives suddenly seem remarkably intelligent in follow-up emails sent at 11:43 PM. The staircase finally gave them processing power.

Over time, it became clear that healthier organizations intentionally build mechanisms for delayed thinking. They understand that complex environments rarely yield their best decisions instantly. They allow people to revisit assumptions, clarify concerns, or challenge conclusions after reflection, rather than treating every meeting like a live television debate where hesitation equals incompetence.

Weak organizations interpret this as indecisiveness. Strong organizations recognize it as cognitive maturity.

This matters even more now because AI is making fast answers unbelievably cheap. We are entering a period when immediate responses, summaries, opinions, presentations, and strategic language can be generated almost infinitely. This means the value of leadership shifts elsewhere. The scarce skill is no longer producing quick reactions. The scarce skill is knowing which reactions deserve skepticism, which conclusions were reached too quickly, and which decisions require uncomfortable additional thinking before the organization commits itself to a confident mistake at scale.

Maybe esprit de l’escalier is not really a personal flaw after all.

Maybe it is a warning sign that modern organizations increasingly mistake intellectual theater for actual thought.

And maybe the most dangerous person in the room is not the one who speaks first, but the one whose mind keeps working after everyone else believes the meeting is already over.

There is a famous psychological phenomenon known as King Solomon’s paradox. It captures something both amusing and deeply uncomfortable: people are often remarkably good at giving wise advice to others while being completely unable to apply the same logic to their own lives.

Which, if we are honest, also explains a very large part of modern product management.

Take almost any experienced product leader and place them in another company for half a day. Within minutes, they will identify the core problems with almost surgical precision. They will notice the overloaded roadmap, chaotic prioritization, unclear ownership, endless operational interruptions, a reactive culture disguised as agility, and the complete absence of strategic focus, hidden under layers of dashboards and planning rituals. They will calmly explain that the organization keeps confusing movement with progress. Everyone around the table will nod thoughtfully because the diagnosis sounds intelligent, mature, and painfully accurate. A few hours later, the workshop ends successfully. Someone posts a photo of sticky notes on LinkedIn, and the consultant leaves looking very wise.

Then that same person returns to their own organization and spends the next three months trapped in meetings where seventeen parallel priorities somehow all remain “critical,” even though basic math strongly suggests otherwise.

This is the fascinating part. Most organizational dysfunction is not actually hidden. Mature organizations are often perfectly capable of describing their own problems in extraordinary detail. Everyone already knows that technical debt is growing, priorities shift every week, analytics are incomplete, ownership is unclear, and stakeholders bypass processes whenever pressure mounts. Entire companies openly admit they have too many meetings, too many projects, and too little focus. Yet somehow the organization continues operating in exactly the same way, quarter after quarter, almost like a person repeatedly reading books about healthy sleep at two o’clock in the morning while answering Slack messages.

And this is where Solomon’s paradox becomes highly relevant to product leadership and organizational strategy. Companies are not purely rational systems. They are social systems filled with emotions, politics, insecurities, historical tensions, survival instincts, and power structures — all carefully masked by strategic presentations and modern corporate vocabulary. Outside the organization, product people think analytically. Inside the organization, they think relationally. These are very different modes of thinking, even if we prefer to pretend otherwise.

A product manager advising another company can confidently say that stronger prioritization discipline is needed. That sounds simple, reasonable, and professionally correct. But within their own company, saying “no” to the wrong stakeholder may quietly erode trust, political capital, budget access, visibility, or future career opportunities for years. Suddenly, the obvious strategic decision no longer feels so obvious. The problem is no longer theoretical. It becomes personal.

This is also why consultants often appear smarter than internal leaders. It is not necessarily because they possess deeper wisdom. Distance creates clarity. External advisors are not trapped in the system’s emotional gravity. They do not carry years of unresolved tensions, organizational trauma, reporting-line conflicts, budget fights, executive sensitivities, or memories of failed initiatives that still haunt meeting rooms long after everyone pretends to have moved on. Consultants see systems. Internal leaders experience consequences. These are completely different psychological conditions.

The same paradox becomes visible whenever organizations talk about innovation. Almost every company claims to value experimentation. Innovation sounds exciting in presentations because it conjures an image of a modern, adaptive, forward-looking organization. But the moment experimentation threatens existing ownership structures, established expertise, or comfortable routines, the organization’s enthusiasm shifts dramatically. Suddenly, the “small experiment” requires alignment meetings, business justifications, risk assessments, ROI projections, communication plans, stakeholder reviews, and governance approvals. At some point, the experiment becomes so operationally burdensome that the original purpose quietly disappears beneath the process designed to protect the organization from uncertainty.

AI is making this contradiction even more visible. Nearly every company now says it needs an AI strategy, which often means adding AI-generated summaries to meetings, placing “AI-powered” somewhere on the website, or demonstrating cheerful prototypes during executive presentations. But far fewer organizations are seriously asking the dangerous question beneath all this enthusiasm: what happens if AI actually works? If it truly works, many long-standing organizational assumptions become unstable very quickly. Information flows differently. Expertise is distributed differently. Decision-making speed changes. Entire coordination layers may become unnecessary. Some forms of management become less valuable, while others become critically important.

And organizations instinctively resist changes that redistribute influence or reduce control, even when those changes are strategically logical. So instead of operational transformation, many companies drift toward ceremonial transformation. They adopt the language of change while preserving the structure of the old system almost untouched. Which, honestly, may be one of the most corporate behaviors humanity has ever invented.

The uncomfortable truth behind Solomon’s paradox is that many organizations are not optimized to identify the best decision. They are optimized to maintain stability, preserve relationships, reduce political risk, and avoid internal conflict. The safest roadmap often wins over the smartest. The least controversial initiative often wins over the most strategically important one. Organizations publicly celebrate focus while privately rewarding responsiveness. They praise innovation while quietly punishing visible failure. They speak passionately about long-term strategy and then schedule thirty-minute meetings to discuss transformational change between two operational escalations.

And product managers sit right in the middle of all this, trying to act like rational system designers in environments that are often driven by human psychology, institutional inertia, and low-grade organizational anxiety.

Perhaps this is why experienced product leaders become less ideological over time. Junior PMs often believe every problem has a single correct answer waiting to be uncovered through frameworks and analysis. Senior leaders eventually realize that many organizational decisions cannot be solved mathematically. They are negotiated compromises among incentives, personalities, fears, timing, politics, operational pressure, and the simple reality that companies are made of humans, not diagrams.

That realization does not necessarily make people cynical.

But it usually makes them noticeably quieter when someone confidently says, “We just need better prioritization.”

This episode is a companion to my LinkedIn article.

This podcast is generated using an AI version of my voice from a script written and edited by me.

For years, managers across the technology industry lived with the same permanent anxiety: “We simply do not have enough resources.”

Not enough developers. Not enough designers. Not enough analysts. Not enough time. Not enough budget. Most product managers built their careers negotiating this triangle of constraint, usually with a new spreadsheet template every year.

Then AI arrived and quietly introduced a different question: What if resource constraints were never really the main issue?

Inside organizations, something odd is happening. Teams are producing more output than ever. Prototypes appear overnight. Marketing copy is generated in seconds. Research summaries arrive instantly. Roadmaps are assembled at a pace that would have seemed impossible a year ago. Everyone feels faster, more modern, more efficient, more ‘AI-enabled.’

And yet, many companies still somehow feel strategically stuck.

The uncomfortable part is that AI is making execution dramatically easier, but it is also exposing how weak prioritization was all along.

For decades, most organizations relied on a familiar tool: the four-quadrant prioritization matrix. Quick Wins in one corner: high value, low effort. Big Bets in another: expensive, potentially transformative. Fillers: small, low-impact tasks that keep people busy but rarely move the needle. And then the most dangerous quadrant: Time Wasters: initiatives that consume resources with little to show for it.

Traditionally, this model was mostly about tasks.

But AI changes something fundamental: the matrix is no longer only about tasks. It is now also about human behavior.

Because AI shifts the cost of execution so aggressively that entire categories of work begin moving between quadrants.

Quick Wins get even quicker. And more tempting. A UX tweak that once took weeks of coordination, design, copy, translation, and testing now happens almost instantly. Teams rack up small victories. Metrics nudge upward. Dashboards look healthier. Slack fills with launch announcements. Leadership sees movement and feels reassured.

But there is a subtle trap hidden inside this acceleration.

Organizations can become addicted to visible momentum instead of meaningful progress.

The company starts optimizing dozens of tiny things simply because they are now cheap to optimize. Everyone feels productive as the machine keeps generating visible output. Meanwhile, the harder strategic questions sit quietly in the background, like gym equipment people keep meaning to use.

Big Bets move in the opposite direction. AI lowers the barrier to entry for strategic thinking. Now, every team can generate polished concepts, vision decks, market analyses, technical architectures, customer personas, and future-state scenarios in hours. What once took weeks and real expertise now appears after a single workshop and a few prompts.

This creates a familiar illusion: organizations start confusing the ability to describe strategy with the ability to execute it.

And these are very different skills.

AI is good at helping people imagine futures. It is much less useful for navigating organizational politics, aligning stakeholders, rewriting processes, migrating legacy systems, changing company culture, or surviving budget reviews with finance teams who still expect innovation to be free.

So companies enter a period of strategic inflation. More Big Bets. More transformation initiatives. More AI-first roadmaps. More platform reinventions. More innovative language. More rockets on slides.

Not necessarily more outcomes.

Then there is the evolution of filler work. Historically, these were harmless activities: easy, safe, politically neutral. Small updates. Minor documentation changes. Cosmetic improvements. Tasks that created the feeling of progress without changing much.

AI has turned this category into something almost surreal.

Now organizations can generate filler work at industrial scale.

Summaries of meetings become summaries of summaries. Brainstorms produce AI-generated follow-up brainstorms. Documents generate more documents. Teams create entire ecosystems of polished output with no clear connection to business impact. Somewhere in the system, there is probably already an AI writing weekly reports that are read only by another AI generating executive summaries.

And somehow this does not even sound unrealistic anymore.

But the most dangerous transformation happens in the Time Wasters quadrant.

Historically, bad ideas at least faced natural friction. Terrible projects moved slowly because execution was expensive. Organizations had built-in protection: limited staff, limited time, limited coordination, human exhaustion, technical bottlenecks. Bad initiatives often collapsed under their own weight before they could do real damage.

AI quietly removes many of those friction points.

Now, companies can move in the wrong direction faster, longer, and with more confidence than before.

This is the truly uncomfortable part of the AI conversation that receives far less attention than prompts, copilots, and productivity gains.

AI does not improve judgment. It just amplifies it.

Good prioritization gets more powerful. Bad prioritization gets more expensive.

This leads to a familiar paradox: the teams that look most productive are not always the ones creating the most value. Some teams are becoming very efficient at producing motion without progress. Dashboards look fantastic. Velocity increases. Output explodes. Internal excitement grows.

Meanwhile, the company may be driving straight into a wall at full speed, with beautifully formatted AI-generated documentation explaining why the wall is a growth opportunity.

This is why the real bottleneck in the AI era is not execution capacity. It is the quality of prioritization.

The most valuable people are not the ones producing the most content, writing the most tickets, generating the most slides, or shipping the most visible activity. AI is handling more of that output every day.

The valuable people are the ones who can still calmly ask uncomfortable questions. Why are we doing this? What problem does it actually solve? Does this initiative deserve to exist at all?

And perhaps most importantly: Are we mistaking acceleration for direction?

Ironically, these are not futuristic skills. They are old-fashioned ones: judgment, taste, restraint, strategic clarity. The ability to say no before execution begins, not after resources disappear.

Unfortunately, these skills are much harder to showcase in a LinkedIn post than with screenshots of AI dashboards and words like “transformation”.

But beneath all the excitement, the reality is becoming surprisingly simple: AI boosts execution while humans still choose direction.

And choosing direction was always the hardest part.

There is a familiar contradiction in how we talk about politeness. We grow up hearing that nothing costs so little and is valued so much as politeness. It sounds elegant, civilized, almost universally true. Then people enter actual organizations and discover a different operating system running beneath the official story.

“If you are too polite, people will see you as weak.”

Many professionals do not just believe this as a theory; they have lived it. They have seen polite managers ignored while louder colleagues take over meetings with more confidence than competence. They have watched respectful people get overloaded simply because they are easier to pressure. In meetings, the calmest person is often treated as the least authoritative, while the most reactive is somehow promoted as a strong leader. For all its innovations, corporate life still struggles to distinguish between confidence and aggression.

Politeness does not have a fixed meaning. In stable organizations, it signals professionalism and respect for people’s time. In more political or insecure environments, the same politeness is read as negotiability. A calm manager saying, “Please try to deliver this by Friday,” may be understood by one team as respectful leadership and by another as evidence that deadlines are optional and reality itself remains open for discussion. The sentence does not change. The culture does.

This is how cynicism about politeness takes root. After enough exposure to aggressive systems, people stop trusting that politeness offers any protection. Worse, they start to confuse weak boundaries with politeness itself. These are not the same. The issue is rarely that someone is calm or respectful. The issue is that they cannot hold the line when pressure comes. Being polite is not the same as being unable to say no. One is a skill. The other is surrender dressed up as teamwork.

The strongest leaders I have seen are often quietly polite. Not the scripted politeness of training videos, but a genuine calm and respect. They do not raise their voices or manufacture urgency. They do not dominate meetings just to be seen. Yet they move organizations. They can reject ideas, block projects, and redirect strategy without turning every conversation into a contest. Real authority is quieter than most people expect. The people with the most power are usually the least interested in proving it.

People remain highly sensitive to dominance signals, even in organizations that claim to value collaboration above all else. Underneath the language of agile, psychological safety, and alignment, status displays still drive reactions. Interrupting, speaking with certainty, creating pressure, responding slowly, escalating conflicts. These behaviors still trigger associations with power. The result is one of the ongoing absurdities of modern work: the person with the least emotional control is often seen, at least for a while, as the strongest in the room.

Once you notice this pattern, you’ll see it everywhere. The executive who sends midnight emails is called passionate. The manager who lives in crisis mode is labeled high-performance. The stakeholder who escalates every disagreement is seen as strategic. Meanwhile, the calm person who pauses before speaking risks being seen as lacking urgency or ambition. Especially in places where stress has become a status symbol.

There is a cultural layer here that rarely gets discussed. Some societies link politeness to education and status. Others see toughness as a survival skill. In competitive or unstable environments, people learn early that visible softness attracts pressure. The polite child is interrupted. The polite employee gets extra work. The polite manager is bypassed by louder voices. Over time, people build protective identities around sharpness and controlled aggression. Not because they enjoy it, but because emotional hardness feels safer.

This is how defensive abrasiveness takes hold. After enough time in chaotic systems, people become colder, shorter, more impatient, and a bit hostile just to avoid being seen as weak. You can see this especially in tech, where communication sometimes starts to sound like tired airport security announcements on Slack. Somewhere along the way, professionalism got confused with emotional frostbite.

The irony is that genuinely confident people have little need to perform power. The insecure manager dominates meetings because silence feels risky. The secure manager can sit quietly and still shape the outcome. That is a different kind of authority, and usually a more durable one.

This may be the real lesson behind the contradiction. Politeness without boundaries invites exploitation. Aggression without competence destroys trust. Calm firmness — the ability to stay respectful while holding the line — remains one of the most underestimated forms of authority in professional life.

It is also much harder than just sounding intimidating on Slack.

Most software companies reach a point where everything starts to feel reactive. Tickets accumulate, users get louder, teams get busier. Dashboards show activity. Everyone is busy, but the system is not improving.

And yet, somehow, nothing feels under control.

This is when it becomes clear: what you have is not a support function. It is an Emergency Room. Not as a metaphor, but as an operating model.

Once you recognize the pattern, it is difficult to ignore.

Intake and triage: where everything already starts going wrong

In an actual Emergency Room, nobody treats patients in the order they walked in. A broken finger does not go ahead of a heart attack. That sounds obvious. Until you look at many Customer Care queues.

Tickets come in through forms, email, chat, Slack, and sometimes a phone call from someone senior. Without real triage, everything merges into one stream. The loudest or most recent issue gets attention, or the one with the most senior escalation.

A mature support team behaves more like medical triage than inbox management. It classifies issues by severity, impact, and urgency within minutes. It routes them intentionally. It accepts that some cases will wait. And that this is not negligence, but prioritization.

Most teams do not have a capacity problem. They have a triage problem.

Decisions under uncertainty: guessing, but professionally

In Emergency Rooms, doctors rarely have perfect information. They work with symptoms, fragments, probabilities. They make a call, act, and adjust.

Customer Care operates in the same fog. “The CMS is slow.” Which part? For whom? Since when? No one knows yet. But the clock is already ticking.

Good support agents develop clinical instincts. Pattern recognition. A sense of “this smells like a caching issue” or “this is probably a permissions edge case.” They don’t wait for perfect clarity. They move the case forward while gathering evidence.

Bad systems, on the other hand, demand complete information upfront. They stall. They bounce tickets back to users: “Please provide more details.” Meanwhile, the incident grows quietly in the background.

Hesitation is costly in both settings. In software, we often act as if it is not.

Resource constraints: you don’t have enough, you never will

Emergency Rooms are always constrained — beds, staff, equipment. When a surge hits, something has to give.

Customer Care teams face the same constraints. Agent capacity is limited. Engineering attention is even more limited. When the CMS fails during a peak publishing window, every ticket becomes urgent.

This is when the system shows its actual design. Are there clear incident modes? Can you reassign resources? Do you ignore non-critical requests, or try to handle everything and end up failing across the board?

Most organizations say they have priorities. Few enforce them when it matters.

Escalation pathways: where time is usually lost

In an Emergency Room, escalation is not a failure. It’s the system working as intended. A generalist stabilizes, a specialist intervenes, the case moves forward.

In Customer Care, escalation often becomes a bureaucratic process. Tickets move with missing context. Engineers repeat the same questions. Product teams join too late or too early. Many people are involved, but ownership is unclear.

The difference between a functional and dysfunctional system is not escalation itself, but how it operates. Good escalation brings structure, ownership, and clear expectations. Bad escalation brings frustration.

And frustration, unlike tickets, does not close.

Time sensitivity: SLAs are just a polite version of urgency

In medicine, urgency is obvious. In software, we hide it behind SLAs. “Response within 4 hours.” “Resolution within 24 hours.”

But when your CMS goes down during a major news event, nobody is thinking in SLA language. That is not a “P1 ticket.” That is an emergency.

The issue is not the existence of SLAs. The issue is treating them as a replacement for judgment. Teams optimize for compliance, not for impact. They respond within the window, but do not always solve the problem.

In an Emergency Room, stabilizing the patient matters more than updating the chart. The same principle applies more often than we admit.

Emotional load: you’re not just solving problems

Emergency Room staff deal with fear, panic, and occasionally anger. The technical problem is only part of the job. The emotional state of the patient is the rest.

Customer Care agents live in a softer version of the same reality. Users are frustrated. Editors are blocked. Stakeholders are anxious. And the tone of communication can escalate faster than the issue itself.

Here is the nuance many teams miss: communication is not a wrapper around the solution. It is part of the solution.

A well-timed message can prevent escalation. A poorly handled one can turn a minor issue into a major conflict. The system can be fixed, but the user can still be lost.

Feedback loops: treating symptoms vs fixing the disease

Every Emergency Room generates data. Patterns emerge. Certain injuries spike at certain times. Certain conditions repeat. This informs prevention, staffing, public health.

Customer Care should do the same. Recurring tickets are not “business as usual.” They are signals. Poor UX, fragile features, unclear workflows. They all surface through support long before they appear in product metrics.

Many organizations treat support as an endpoint. Tickets arrive and are resolved, but there is no structured feedback into product. No prioritization based on support data. The same issues quietly return each week.

At that stage, the work is not solving problems. It is managing their recurrence.

From emergency response to preventive care

Here is where the analogy becomes uncomfortable.

A hospital that invests only in its Emergency Room is not a good hospital. It is an overwhelmed one. Real healthcare systems invest heavily in prevention: primary care, early diagnosis, reducing the need for emergencies in the first place.

The same is true for software.

A Customer Care team that is always busy is not a sign of success. It is often a sign that the product is generating problems faster than they can be addressed. Onboarding is unclear, workflows are fragile, and edge cases are now common cases.

Preventive care in a CMS environment looks less dramatic, but more valuable. It is better defaults. Clearer interfaces. Fewer clicks to publish. Smarter error messages. More resilient infrastructure. Thoughtful constraints that prevent users from getting into broken states.

It also means taking support data seriously. Not as anecdotes, but as input into roadmap decisions. If 30% of your tickets come from one feature, that is not a support issue. That is a product decision waiting to happen.

This work is less visible. There is no late-night incident, no dramatic recovery. There are simply fewer emergencies.

Which, ironically, is the real goal.

Most software companies eventually build an Emergency Room. They don’t always admit it. They call it Customer Care, Support, Helpdesk. But the dynamics are the same.

The more interesting question is what happens next.

Do you keep optimizing the Emergency Room for faster triage, better escalation, tighter SLAs? Or do you start asking why so many patients keep showing up in the first place?

When media still had edges

There is a shift in media that most people sense but rarely name. We still talk about reaching audiences, building products, growing traffic, increasing engagement. As if we are still at the center. But if you watch how content actually moves now, the story changes. We are no longer the main interface between information and the user. We are just one layer in a stack, and not the one that matters most.

It was not always this abstract. Media used to have edges. Literal ones. A newspaper stopped at page 32. The news ended at 7:30, whether you were done or not. You bought the whole thing, not a slice. Consumption was limited by design, not just by bandwidth. If someone started reading, they usually finished. Editorial choices shaped the entire experience. Distribution was something you owned and could predict.

Infinite content, finite attention

Then the internet erased those edges. At first, it felt like freedom. No limits on length, frequency, or format. But the trade was clear: infinite content, finite attention. Suddenly, attention, not distribution, became the bottleneck. The rules shifted.

Media stopped competing with other media and started competing with anything that could steal a few seconds of attention. Analytics made this obvious. People did not read the way we hoped. They skimmed, dropped off, clicked away. Audiences did not vanish, but their behavior became fragmented and unpredictable. Control over consumption disappeared, along with the idea that good content would naturally hold attention.

Platforms moved the front door

Platforms did not just speed up the shift. They locked it in. Once users stopped visiting websites and started scrolling feeds, the center of gravity moved. Facebook, YouTube, TikTok did not kill media companies. They just changed their job description. Media stopped being the destination and became a supplier. Distribution was no longer built; it was bargained for.

At first, this looked like a win: massive reach, global audiences, almost no friction. Over time, it turned into dependency. Traffic could spike or vanish for reasons you could not see, because the system was not yours. You were optimizing for a platform that could change the rules overnight. And often did.

Trust shifts from brands to people

Meanwhile, trust was quietly moving. It shifted from institutions to individuals. Audiences responded to a recognizable voice, not a carefully managed brand. One person could build a direct relationship with millions. Something traditional media could rarely match.

Figures like Joe Rogan or Tucker Carlson are not just examples of scale. They show a different kind of connection. Less formal, less filtered, more direct. Media brands did not vanish, but they stopped being the default. They became just another option.

We already adapted. And then the ground shifted again

By the time this shift was clear, the industry had already adapted. It learned to work with algorithms, optimize packaging, and think in terms of channels instead of owned audiences. It accepted that control over distribution was now limited.

And just as that started to feel stable, the ground shifted again.

AI is not just another channel. It is a new kind of interface. It does not deliver your content; it processes, compresses, and rebuilds it. The user does not need to visit your site, scroll a feed, or even pick a source. They ask a question and get an answer. That answer might use your work, but it will not keep your structure, your tone, or your priorities. Tools like NotebookLM show this in a controlled way. Large-scale AI systems take it much further.

From distribution to interpretation

This is where the shift gets deeper. Platforms changed distribution. AI changes interpretation. In the platform era, you could assume that if your content reached the user, it would look the way you made it. In the AI era, that assumption is gone.

Your work becomes input for a system that produces something new. The user interacts with that, not with your original. The relationship is now so indirect it is almost invisible. The pattern is clear: control moves away from the creator, first over distribution, then attention, now meaning.

The paradox of modern media

And yet, one thing has not changed. High-quality journalism still does something none of these layers can replace. It captures reality. It gathers facts, checks them, and builds a coherent account of what happened. Even the best AI systems depend on that layer. They do not create new knowledge. They just reorganize what already exists.

This is the paradox. Media creates the most essential part of the value chain: the raw understanding of reality. But other layers capture more and more of the visibility and value. The better the system gets at repackaging and redistributing, the less visible the original source becomes. Even as it stays essential.

What we are actually building now

In practice, media is not disappearing. Its role is just being rewritten. It is no longer enough to design products for direct consumption. Content now has to survive multiple transformations. It has to move through platforms, adapt to new formats, and be legible to systems that will reinterpret it.

The challenge is not just to make content, but to keep its meaning and integrity as it passes through layers built for other goals. Content is no longer a finished product. It is a component in a system you do not control, for an audience you do not reach, through interfaces you did not choose.

No way back, only forward

It is tempting to think this is a cycle, that media will someday regain control over distribution or rebuild direct relationships at scale. But the direction of change does not support that hope. Each new layer adds abstraction and moves the user further from the source. The original content stays necessary, but it becomes less visible as its own thing.

Journalism will not vanish. The need to understand and explain reality is not going anywhere. But the idea that journalism naturally becomes stable, self-contained media products is already gone. What remains is the core function and the need to make it survive in a system built to reshape and reinterpret everything it touches.

Recently, I heard something that sounded like a punchline. It wasn’t.

In some software companies, developer performance is now measured not just by delivery or quality, but by how many AI tokens they burn each month. The more you prompt, the better your KPI. Tokens as progress.

The first reaction is obvious: why reward people for spending more? Tokens are not free. On the surface, this looks like incentivizing waste.

But look closer, and it gets more interesting. Not more correct, just more revealing. This is less a KPI than a lever to force a behavioral shift.

Activity Is Not Outcome. Even If It’s Measurable

As a KPI, token usage is a clean example of measuring activity instead of outcome. The distinction is not subtle. Tie evaluation to tokens, and you get more prompting, more interaction, more output. What you do not get is better software, faster delivery, or higher quality.

This is the familiar pattern: once a metric becomes a target, it loses its value as a signal. The system optimizes for the number, not the impact. From a distance, it looks like progress. Up close, it is mostly motion.

You are Not Buying Tokens. You are Buying Behavior

Yet companies persist. Which suggests the real goal is elsewhere.

What is really being purchased is not tokens, but a shift in behavior. The aim is to make AI interaction default, not optional. Left alone, most people stick to what they know, underestimate new tools, and avoid the friction of learning something that does not yet feel essential.

So the organization reaches for a blunt tool: remove the choice. Use AI, or fall behind. Eventually, enough people adapt, and the system shifts from experimentation to habit. It is not elegant, but it works. Until it doesn’t.

The Productivity Paradox Nobody Likes to Mention

The catch is that more output does not guarantee better outcomes. Developers can generate more code, product managers more documents, designers more options, journalists more content. At the organizational level, speed and quality do not always improve. Sometimes, they slip.

Output is not outcome. More code means more to review, test, and maintain. More content means more noise. More options mean more decisions. AI rarely removes work; it just moves it, often from creation to validation and correction.

There is also the perception gap. People often feel faster with AI, even when they are not. The interaction creates a sense of acceleration, while hidden costs pile up. This is typical of transition phases: the shape of work changes before the results do.

From Doing the Work to Supervising It

Underneath all this, the roles themselves are shifting. Developers move from writing code to orchestrating it, from direct problem-solving to supervising machine-generated solutions. Product managers shape and evaluate AI outputs. Designers explore wider spaces before narrowing in. Customer care agents supervise more than they compose. Even journalists, in organizations that limit AI in reporting, use it for distribution, summarization, and research.

The pattern is not replacement, but reallocation. New habits, new judgment, and new definitions of good work follow.

Where Forced Adoption Actually Creates Leverage

Forced adoption sometimes creates leverage, but only in the right places. Where work is repetitive, structured, and easy to check, and where mistakes are cheap, AI speeds up iteration. Product managers draft faster, designers explore more options, customer care teams respond more consistently, journalists repackage content across formats. The value is in acceleration, not in replacing judgment.

In these cases, the gain is speed and breadth of exploration, not the outsourcing of decisions. The path to better choices gets shorter, but the choices remain yours.

Where It Quietly Breaks Things

The same approach breaks down in areas that depend on judgment, context, or accountability. Decision-making, prioritization, editorial voice, and complex reasoning do not improve when forced through AI. Here, the tool adds friction. People game the metric, offload thinking they should keep, or spend more time fixing AI output than doing the work themselves.

This is where the original instinct holds: the KPI is wrong. In these areas, mistakes are expensive and quality is hard to measure.

What a More Mature Approach Looks Like

More mature organizations move on. The question shifts from ‘did you use AI’ to ‘did it matter.’ Activity gives way to outcomes, volume to impact. AI becomes just another tool, visible in the workflow only when it adds value.

Judgment returns, including the choice not to use AI when it adds nothing. Experimentation is separated from production. Over time, the organization builds capability, not just usage. The goal shifts from forcing adoption to making effective use unavoidable.

It’s Not Just About Developers

This pattern is not limited to software. The same dynamic plays out in product management, design, customer care, journalism, marketing, analytics. Forced usage speeds adoption where work is structured and execution-heavy. It quietly erodes quality where judgment and context matter.

The real mistake is not force, but lack of precision. Treat every task as equally automatable, and the results will be equally mediocre.

So Was the KPI Stupid?

As a long-term measure, it is hard to defend. As a temporary lever to break inertia, it sometimes works. The problem is when organizations stop at the metric and keep optimizing for tokens, long after the real goal — changing how people work — should have taken over.

In the end, this is not about tokens or even productivity. It is about reshaping habits. And that is always more complex, and more fragile, than any KPI can capture.

For years, product managers have relied on a comforting story. You are the glue. The mini-CEO. The person at the intersection of business, technology, and design. You orchestrate complexity. You make things happen. It sounds important, vaguely strategic, and above all, safe. Safe from reorgs, safe from outsourcing, and certainly safe from automation.

Then AI arrives. No drama, just quiet competence. It writes specs that are good enough. It summarizes research faster than any analyst. It generates ideas on demand. It builds prototypes in hours, not weeks. Suddenly, much of what once justified the role starts to look negotiable.

This is where the discomfort sets in. Not because product management is vanishing, but because some parts were never as essential as we liked to think.

Execution Is Collapsing. Judgment Is Not

One of the more obvious shifts is that execution costs are collapsing. You do not need a full team or multiple cycles to validate a simple idea. A prompt, a tool, and some patience are often enough. The barrier to building has dropped so far that ‘we do not have resources’ is starting to sound less convincing.

Cheaper execution does not make the job simpler. It just changes where the difficulty sits. When it is easy to build ten versions, the real work is deciding which one should exist. When iteration is fast, hesitation is harder to hide. When delivery is not the bottleneck, thinking is.

This is where many product managers start to struggle. Execution was always easier to measure. Judgment is not. You cannot hide behind velocity charts when the real question is whether you are even moving in the right direction.

The Myth of “Human Skills”

The response to this shift is predictable. The industry leans on a familiar story: human skills will save us. Empathy, intuition, emotional intelligence, internal authority. These are framed as the last defense against AI, the things only humans can do.

There is some truth here, but also a fair amount of wishful thinking. Most product roles were not built on deep empathy or rare intuition. They were built on coordination, communication, and structured thinking. Useful, but not uniquely human. And increasingly, not out of reach for machines.

Even intuition, often treated as something mystical, is usually just compressed experience. Pattern recognition built up over time. AI is already good at that. The gap is not as wide as we would like.

The real differentiator is less romantic. It is the willingness to take responsibility for a decision when the data is incomplete, the signals are mixed, and the outcome is uncertain. AI can suggest, simulate, and optimize. It does not carry consequences. Humans still do.

Organizations Are Not Getting Simpler

Another assumption does not survive contact with reality: that AI will make organizations simpler. Fewer people, flatter structures, less friction. It sounds logical. It is mostly wrong.

As execution gets easier, alignment gets harder. More ideas, more experiments, more people who want to contribute. The result is not clarity, but noise. Competing directions, overlapping initiatives, and a growing need to decide what not to pursue.

Stakeholder management does not go away. It gets louder. People still want ownership, recognition, influence. They still bring their biases and ambitions. Faster cycles only amplify this, because there is less time to build consensus and more pressure to commit.

This is where ‘internal authority’ starts to matter, but not in a therapeutic way. It is not about self-reflection for its own sake. It is about being able to hold a position, make a call, and move forward without hiding behind endless validation.

More Tools, More Work, Less Excuses

One thing that gets less attention: AI does not reduce workload. It just moves it around. When writing, analysis, and prototyping get easier, expectations do not stay flat. They expand. You are expected to explore more options, run more experiments, consider more variables, and do it all faster.

The work becomes cognitively lighter at the surface level, but strategically heavier underneath. You are no longer limited by what you can produce, but by what you can meaningfully evaluate. The constraint shifts from output to attention. This creates a different kind of pressure. Not the pressure of scarce resources, but the pressure of too many possibilities. When almost anything can be built, the real skill is deciding what should not be built.lt.

And that is not something you can outsource to a model.

What the Role Actually Becomes

If you strip away the mythology, the future product manager looks less like a mini-CEO and more like something less glamorous and more demanding: a professional decision-maker working under constant uncertainty.

You are not there to produce documents. AI does that faster. You are not there to gather information. AI does that at scale. You are not even there to generate ideas. AI has an endless supply.

You are there to define what matters in a sea of options, to ignore what does not, to make a decision before it feels fully justified, and to change course before failure is obvious. You are there to create direction where none is visible.

That requires clarity of thought, some product taste, and enough psychological stability to operate without constant reassurance. Not because therapy is the future of product management, but because the buffers that once absorbed uncertainty are disappearing.

The Uncomfortable Conclusion

AI will not replace product managers. That is the comforting part.

The less comforting part is what comes next. AI will remove the scaffolding that made many product roles feel more substantial than they were. It will expose who was relying on process rather than thinking, who was coordinating rather than deciding, and who was optimizing for agreement rather than outcomes.

In that sense, AI is not a threat to the role. It is a stress test for it.

If your value was tied to execution, you would feel the pressure. If your value is tied to judgment, you may find yourself with more leverage than before. For the first time in a while, the role starts to look like what it always claimed to be.

Not managing products. But making decisions that shape them.

Most organizations are not struggling with strategy itself. They are struggling with what they think strategy is.

Strategy gets treated as a fixed plan: define it once, align everyone, put it in a deck, and execute. The more stable it looks, the more strategic it feels. Predictability becomes a proxy for rigor.

And then reality happens.

New technologies emerge. User behavior shifts in ways no research predicted. Stakeholders change priorities mid-cycle. Budgets shrink without warning. Entire markets become harder (or suddenly easier) to reach. And quietly, almost politely, the plan stops making sense.

But no one wants to say it out loud.

So the organization keeps moving. Teams deliver against assumptions that no longer hold. Leaders make small adjustments, but the strategy deck stays the same. Roadmaps change, the story does not. The result is not failed execution. It is something quieter: the gap between what the organization says and what it actually does keeps widening.

We call this drift. More often, it is just denial with better branding.

The uncomfortable part is not that the environment moves too fast. It is that we expect strategy to behave like a plan, even when plans expire quickly.

If you treat strategy as something that must remain stable in its details, you are guaranteed to be wrong. The only question is how long it takes you to notice.

A more useful way to think about strategy is to separate two things that are usually mixed together: direction and decisions.

Direction is the part that should remain relatively stable. It is your sense of where you are going and why. It includes your mission, your target audience, your positioning, and the constraints you are not willing to violate. In many cases, these constraints matter more than your ambitions. They define the space within which you are allowed to operate.

Decisions are everything else. What you build, how you distribute it, which technologies you adopt, what you prioritize this quarter, what you drop, what you postpone, what you experiment with. This layer should not be stable. It should change constantly, because it is your interface with reality.

Most organizations blur these layers. They try to make decisions as stable as direction, then wonder why everything gets slower, more rigid, and less connected to reality.

The answer is not to give up on long-term thinking. It is to swap detailed planning for something more resilient: a system that adapts within clear boundaries.

Instead of betting on a detailed future, you set a clear direction, a few guardrails, and a way to judge new opportunities. You do not try to predict what you will build in two years. You define which moves fit, and which do not.

When a new opportunity appears (and it always does), you don’t ask whether it was in the plan. You ask whether it fits the system.

Does it serve the audience we care about? Does it strengthen our positioning or dilute it? Does it respect the constraints we’ve set for ourselves? Does it connect to something we are already trying to achieve, or does it require us to quietly change direction without admitting it?

If it fits, you adapt. If it doesn’t, you say no.

This sounds simple. In practice, it is where most organizations stumble. Not for lack of ideas, but for lack of discipline to say no — especially when the idea is shiny, well-funded, or politically convenient.

Ironically, adapting within boundaries is harder than planning. Planning offers the illusion of control. Adaptation means making uncomfortable choices, over and over.

At this point, someone usually says: “But isn’t this just another way of describing strategy drift?” Not quite.

There are two types of drift. One is unconscious and dangerous. It happens when small decisions accumulate without anyone checking whether they still connect to the original direction. Over time, execution and strategy diverge, and the organization keeps moving — just not in a meaningful direction.

The other type is adaptive. It happens when reality changes, and the organization responds. This is not failure. This is survival.

The difference is not in the movement. It is in the acknowledgment.

If your organization has changed direction but the strategy still claims otherwise, you do not have adaptability. You have fragmentation. Teams work from different assumptions, leaders say something else, and alignment becomes theater.

The fix is not to stop change. It is to make change visible. Even small shifts need to be named and reflected in the story everyone shares. Otherwise, you are running parallel strategies and pretending it is one.

This is where cadence becomes critical.

Not everything moves at the same speed. Direction should be stable, checked now and then. Objectives shift more often. Execution is always in motion. But the real discipline is this: review your assumptions more often than you rewrite them.

A lightweight question is usually enough: has anything changed that would make this no longer true?

If the answer is no, you move on. If the answer is yes, you’ve caught something early — before it turns into months of misaligned work.

And yet, even with all of this in place, many organizations still struggle. Not because the model is wrong, but because something more fundamental is misaligned.

Teams get rewarded for output, not outcomes. Leaders get attention for launching things, not for keeping the system coherent. Stakeholders push for more, rarely for less. In this setup, drift is not a bug. It is the system working as designed.

You can roll out any framework you like. If the incentives stay the same, so will the behavior.

In complex environments — especially those shaped by external constraints, multiple markets, and competing priorities — perfect alignment is not a realistic goal. Trying to achieve it often leads to over-centralization and loss of responsiveness.

A more honest goal is not perfect alignment. It is controlled divergence.

Different teams will optimize for different realities. Different markets will require different tactics. Different constraints will force different trade-offs. That is not a failure of strategy. That is the reality of operating at scale.

What matters is that all of this happens within a shared directional frame. The same “why,” even if the “how” varies significantly.

Strategy, in this sense, is not a plan to execute. It is a system to maintain. Constantly.

It does not eliminate tension between long-term intent and short-term reality. It makes that tension visible, manageable, and, if you are disciplined enough, productive.

The alternative is simpler.

Keep the plan. Ignore the changes. Call the misalignment “execution issues.”

And hope no one notices how far you’ve drifted.

There is a product principle that gets repeated, usually with a half-smile: no one reads anything. Not documentation, not emails, not UI copy, and definitely not news past the headline. It sounds like a joke, but it is really a design constraint. The uncomfortable part is not that it is wrong. It is that it is just accurate enough to shape how we build.

Somewhere along the way, we accepted a contradiction. We produce more content than ever — articles, explainers, threads, summaries — while knowing most of it will not be read. Not in any meaningful way. At best, it gets scanned, skimmed, reduced to a headline and a vague sense of what happened. At worst, it is replaced by a two-line AI summary that sounds confident enough to pass for understanding.

Yet in newsrooms and product meetings, we still talk as if depth is the default. As if the average user is moving patiently from paragraph one to paragraph twelve, collecting nuance, context, and background along the way. It is a comforting fiction. Operationally, it does not hold up.

The so-called Miller Principle, or at least its modern version, forces a different perspective. If no one reads anything, every extra sentence is not just ignored. It becomes a liability. It is a bet that the user will invest attention they do not have. Most of the time, they do not.

Now, place this reality inside news consumption.

We expect users to understand geopolitical nuance, historical context, and conflicting narratives. We expect them to distinguish signal from noise, fact from framing, reporting from opinion. And we package all of this inside a structure that assumes time, focus, and patience. Then we act surprised when the headline becomes the story.

This is not a failure of the audience. It is a mismatch between product design and human behavior.

The uncomfortable implication is that the headline is not just an entry point. For many users, it is the whole experience. Any nuance not present there is not just hidden or delayed. It is lost.

This leaves us with a quiet editorial dilemma. Do we optimize for accuracy or for attention? The industry has been trying to do both and often ends up with neither: headlines too vague to inform, articles too long to be consumed.

Now add AI, and the problem shifts. It is no longer just about attention. It becomes a question of what users actually know.

AI does not just shorten content. It rewrites it into something that feels finished. A clean paragraph, a few confident statements, sometimes even a tidy conclusion. Ambiguity disappears. The user gets an answer, no effort required.

And that is precisely the problem.

When AI simplifies, it does not just remove words. It removes tension and uncertainty. It smooths out the messy edges where real understanding usually lives. What remains is a version of reality that is easier to process, and therefore easier to trust.

We are entering a phase where users are not just skipping the article. They are skipping the existence of the article altogether.

In this world, the role of a news product shifts. It is no longer just about publishing content. It becomes about managing how much of reality survives the process of compression.

This is where things get uncomfortable for product leaders. The trade-offs become harder to ignore.

If you take the ‘no one reads anything’ principle seriously, the answer is not to simplify everything. It is to design for selective depth. Accept that most users will stay at the surface, but make sure the surface is not misleading, and that moving deeper does not feel like a penalty.

Right now, depth often feels like a penalty. Long articles, dense paragraphs, context buried out of sight. The product signals: if you want nuance, you pay with your time. Most users do not take the deal.

The real challenge is not to make users read more. It is to make every extra second of attention disproportionately valuable. The goal is a system where understanding is layered, not hidden behind a wall of text.

But here is the tension. The more efficient the experience, the easier it is to consume a simplified version and move on. Efficiency and understanding do not always align. Sometimes they work against each other.

This leads to a slightly uncomfortable question.

If no one reads anything and AI explains everything, what are we actually optimizing for?

If the answer is speed, we are doing well. If the answer is understanding, we may be designing ourselves into a corner where users feel informed without actually being informed.

That is a much harder problem than low engagement or high bounce rates.

It is a product problem. It is an editorial problem. And increasingly, it is a trust problem.

Jogging did not become mainstream because people suddenly discovered discipline. It became normal because the cost of ignoring physical health became visible, then hard to ignore. Now, not caring about your fitness is the exception, not the rule.

We could see the same shift with mental discipline. The ability to focus, to read deeply, to think without interruption might stop being assumed and start being recognized as rare. And as with physical fitness, what is rare tends to become valuable. The metric just changes from steps to minutes of undistracted attention.

The analogy is not perfect, but it is close enough to make people uneasy. We spent decades learning how to train the body. The brain, meanwhile, was left to fend for itself. Now, as our environment quietly shifts to make focus harder, we are only just noticing the side effects.

We Fixed the Body. We Outsourced the Brain.

Physical fitness did not become a movement because people woke up one day with more willpower. It happened because the downsides of ignoring the body became obvious, then embarrassing. Sedentary living and bad diets stopped being invisible. They became problems with names, metrics, and solutions.

With mental focus, we are still pretending nothing has changed. We act as if attention and depth are default settings, immune to environment. Meanwhile, we keep redesigning our world to make sustained thinking less likely, not more.

Focus did not vanish. We traded it away, bit by bit, for convenience.

Your Brain Eats What You Feed It

Modern content is not just available. It is engineered for frictionless, repeat consumption. Short loops, emotional nudges, infinite feeds. These are not bugs. They are the product.

At this point, the food analogy stops being cute and starts being literal. Ultra-processed food is built to be irresistible. Digital content is built to remove every barrier to consumption. The issue is not occasional indulgence. The issue is what becomes normal.

When most inputs require little effort, the brain adapts. It gets better at skimming, reacting, and switching. It gets worse at staying put, analyzing, or handling complexity. This does not feel like loss. It just feels like the new baseline.

Which is exactly how most long-term shifts begin.

AI as the Perfect Convenience Layer

If that were the whole story, it would be enough. But AI adds a new twist. It does not just distract. It substitutes.

For the first time, we have tools that can handle parts of thinking for us. Structuring ideas, summarizing, drafting, exploring options. These are no longer reserved for people.

This does not make us less capable by itself. It just makes effort optional.

And when effort becomes optional, it usually disappears unless someone chooses to keep it.

The pattern is familiar. First, friction goes away. Then effort. Then the skill itself fades, not because it is lost, but because it is no longer needed.

The Predictable Turn to Regulation

At this stage, the usual response is to look for bigger levers. If the environment is working against us, maybe the answer is to regulate platforms, restrict algorithms, or protect users.

This is a familiar cycle. When a behavior becomes common and starts to hurt, we reach for external controls. Sometimes, especially for younger people, this makes sense.

But attention does not become discipline by decree. You cannot outsource the hard part. No policy will make people pick complexity over convenience every day.

Environment shapes behavior. It does not erase choice.

From Capability to Signal

The next shift is not in policy. It is in what people notice.

Physical fitness used to be a signal. Then it became a baseline. Now, it is often just for show. The skill did not change as much as the story around it.

Mental discipline may take the same path, but in reverse. As distraction becomes standard, focus and deep thinking start to look strange.

And what stands out tends to become valuable. Not because it is new, but because it is rare.

A Different Kind of Cardio

The analogy is not perfect, but the mechanism is simple. Systems adapt to use. Skills that get exercised improve. Skills that do not, fade.

There is no single breaking point. Just a slow change in what feels easy, what feels hard, and what feels worth the effort.

It is not hard to picture a quiet room where people who once tracked steps now sit without devices, working on something slow and difficult. Not out of nostalgia, but out of necessity.

From running clubs to chess clubs. A different kind of cardio.

Most IT organizations have a pattern that is obvious if you look for it, but almost no one says it out loud.

The underdog who improves in public gets the praise, the promotion, the story. The steady high performer, the one who delivers without drama, fades into the background. Trusted, relied on, and eventually taken for granted.

It sounds fair enough. Growth deserves recognition. Effort should matter. Everyone likes a story with an arc. But look closer and it is less about fairness, more about how the system chooses what counts as performance.

Performance systems are not built to weigh underdogs against high performers. They are built to flatten messy, conflicting signals into a single story. And then reward whatever is most visible at the time.

Why this is not a people problem but a system problem

The real issue is not bias in the usual sense. It is structural confusion.

We ask one system to judge output and effort, consistency and improvement, visibility and impact, short-term delivery and long-term thinking. Each pair is a real trade-off. None of them can be averaged away, but most organizations act as if they can be compressed into a single number or a tidy conversation.

In practice, the system just grabs whatever signal is easiest to see at review time. In knowledge work, that signal is almost never the most accurate one.

This is how performance management quietly becomes a storytelling exercise.

Output vs effort: why activity wins over impact

Take output versus effort. In theory, results matter more than activity. In practice, results in software are hard to pin down. Work is collaborative, dependencies are everywhere, and some of the best contributions are the ones you never see: problems that never happen, incidents that never escalate, systems that quietly get more stable.

Faced with all this ambiguity, managers reach for proxies. Responsiveness, visible urgency, number of tickets touched, presence in meetings. These signals are not useless, but they are much easier to spot than the real impact.

So the person who quietly removes a whole class of production issues goes unnoticed, while the person who fights incidents in public channels becomes a hero. The system does not reward chaos on purpose, but it reliably rewards the attention chaos creates.

Consistency vs improvement: why stability becomes invisible

The same distortion shows up with consistency versus improvement. High, stable performance has a strange weakness: it becomes the baseline. Once something is seen as normal, it stops drawing attention. No one tells a story about the thing that just works, every time.

Improvement, on the other hand, is always visible. It has direction, contrast, and emotional pull. Someone who moves from average to good creates a story that is easy to tell and easy to remember.

Over time, this shifts the balance. Organizations start to overweight trajectories and underweight reliability. High performers are not punished, but they stop standing out. Their contribution is assumed, not examined.

Assumption is a fragile kind of recognition.

Visibility vs impact: when performance becomes influence

Visibility versus impact is where the system quietly turns political. Every organization says it values impact, but few invest in making it measurable. What is left is visibility: who speaks, who presents, who shows up at the right moments, who can explain their work well.

Communication matters. But when it replaces evidence, the rules change. Performance reviews become contests of influence, where a clear story can outweigh real contribution.

This is how glue work — mentorship, documentation, reliability, cross-team coordination — either disappears or gets misread. It is critical to the system, but unless someone tracks it on purpose, it loses out to more visible output.

Short-term vs long-term: the trade-off nobody tracks

Short-term delivery versus long-term capability is the tension that usually causes damage later. Shipping features and hitting deadlines create immediate, measurable results. Investing in architecture, reducing technical debt, or mentoring juniors pays off slowly and rarely fits into a single review cycle.

So the system does what it is built to do: it prioritizes what it can measure right now.

Teams get efficient in the short term and fragile in the long term. Learning slows, complexity grows, and eventually delivery starts to slip. When that happens, the usual response is more pressure on delivery, which just repeats the cycle.

Why adding more metrics usually makes it worse

At this point, the instinct is to fix performance by adding more metrics, more dashboards, more reviews. In reality, this usually makes the problem louder, not smaller.

The real issue is that performance systems are asked to do two incompatible jobs at once. They are used for administrative decisions — compensation, promotions, rankings — and at the same time for development, coaching, and growth.

These jobs need different signals and different interpretations. Admin decisions want stability and comparability. Development needs trends, potential, and context. When both are forced into the same box, the system gets inconsistent and easier to game.

Why high performers burn out in “fair” systems

There is another, less comfortable pattern here. High performers get more demand because they are trusted. At the same time, their output becomes the new normal, and recognition fails to keep pace with expectations.

This creates a familiar imbalance: high effort, high responsibility, low visible reward. Over time, this is a reliable path to burnout. Not because people are weak or overcommitted, but because the system slowly disconnects effort from recognition.

What a more coherent system actually looks like

A better approach does not start with new metrics or frameworks. It starts by admitting that performance is multi-dimensional, and that different dimensions belong at different levels.

Some signals make sense at the team or system level: delivery speed, reliability, stability. Others belong to the individual: decision quality, ownership, ability to simplify, contribution to team capability.

Once you separate these, evaluation stops being about squeezing everything into one score. It becomes about building a coherent, evidence-based view of contribution. Invisible work is made visible on purpose. Context is documented, not assumed. Sustainability is treated as part of performance, not an afterthought.

Back to underdogs vs high performers

The tension between underdogs and high performers does not disappear in this kind of system. But it becomes explicit, manageable, and less about who tells the better story.

And that is usually the difference between a performance system that feels political and one that feels fair.