For four decades Costco paid above-market wages, held margins thin, capped the hot dog at $1.50, and built its model around member trust rather than member extraction.

In the early 2000s, these policies created friction with Wall Street analysts, starting with Bill Dreher at Deutsche Bank. Dreher said that “From the perspective of investors, Costco’s benefits are overly generous,” and “It’s better to be an employee or a customer than a shareholder”. If Costco would only treat its employees and members worse, there was so much shareholder value to be unlocked.

Costco’s founder Sinegal dismissed the pressure to cut employee benefits or raise prices for short-term gains. In a 2003 Fortune interview, he said: “We think when you take care of your customer and your employees, your shareholders are going to be rewarded in the long run. And I’m one of them [the shareholders]; I care about the stock price. But we’re not going to do something for the sake of one quarter that’s going to destroy the fabric of our company and what we stand for.”

There’s usually no way to adjudicate this kind of debate; the counterfactual doesn’t exist. In this case, it does.

Sam’s Club ran the Dreher playbook. It does less than half of Costco’s same-store sales and has half the profit per employee.

Wall Street analysts have updated their views on Costco. The doctrine they were applying has not. The specific case was absorbed as an exception. The doctrine that called Costco’s policies value leakage is more entrenched than ever. Buybacks are at record highs. Executive comp is more stock-price-tied than in 2005. Layoff-driven margin expansion is the standard playbook. Why didn’t the lesson take?

The usual explanations for why the lesson didn’t take, that analysts are short-sighted, that quarterly pressure distorts judgment, that finance culture rewards aggression, all point at behavior. The source of that behavior is structural. Measurement regimes are often treated as neutral, but neutrality and objectivity are different properties. An instrument can be perfectly objective about what it measures while being biased in what it has chosen to measure. The real power of any measurement regime lies not in the accuracy of its measurements but in the selection of what gets measured at all and in what, by exclusion, becomes invisible. The lesson didn’t take because the instruments the doctrine runs on can’t see the thing Costco was doing.

The Invisible Asset

What the instruments couldn’t see was trust capital: the value built through credibility, reliability, and consistency between a company and its stakeholders (employees, customers, and partners). Trust capital has three properties. Trust compounds by repeatedly making and meeting commitments, so it is built slowly. Like all other forms of trust, it can be destroyed much more quickly than it is built. Finally, it is structurally invisible to the measurement regime firms report against; accounting rules do not permit its recognition as it accrues.

This invisibility is the heart of the category error Wall Street analysts continue to make today. Wall Street analysts lean on quarterly earnings and discounted cash flow models to value firms. These instruments are built on accounting inputs, and accounting rules forbid recognizing trust capital as it accrues. No amount of tuning the model, longer forecast periods, different discount rates, or more generous terminal assumptions changes this. The asset is invisible to the inputs, so it’s invisible to the outputs. This is why analysts see the inputs to value creation as deductions from it.

The accounting profession isn’t completely blind to trust capital. When a firm is purchased for more than its book value, double-entry accounting requires the excess to land somewhere. That somewhere is goodwill. Aggregate goodwill on S&P 500 balance sheets is roughly $4 trillion, three times the cash those same firms hold. The accounting framework’s refusal to recognize trust capital during its accrual reflects a structural limit of the framework itself.

Goodwill can show up in a balance sheet only when a company is acquired, though it reflects an asset that was accruing the entire time. When brand reputation takes a hit, goodwill is marked down on the balance sheet. It can never be marked up. It’s an asset that defies the laws of physics; something that can be acquired, and destroyed, but never created.

The Ideology in the Machinery

This measurement regime is the machinery shareholder primacy runs on. When the doctrine says “maximize shareholder value,” what that means in practice is maximize what the instruments can see. Trust capital cannot appear in the inputs, so it cannot appear in the outputs. A firm building it looks like a firm wasting money. A firm depleting it looks like a firm unlocking value. The doctrine isn’t making a philosophical mistake about stakeholder primacy. It’s running on measurements that systematically misrepresent what firms are doing.

Shareholder primacy is the doctrine that a corporation’s purpose is to maximize returns for its shareholders. It licenses everything from aggressive buybacks to layoffs timed for earnings calls to the wholesale dismantling of research divisions that won’t pay back within the horizon an analyst model can see. It is treated, inside boardrooms and business schools alike, as the settled answer to the question of the purpose that firms exist to serve.

It has become a cultural norm, but it is not enshrined in American corporate law or case law. No statute requires directors to maximize shareholder value. The law gives directors and officers wide latitude, requiring only that they act in good faith, on an informed basis, and in the company’s best interests. Shareholder primacy is treated as a law of nature, but It’s an ideology, and like most ideologies its origin is traceable.

The modern version starts with Milton Friedman.

Before The Translation

When Friedman published “The Social Responsibility of Business Is to Increase Its Profits” in 1970, it landed amid a specific corporate pathology. ITT, under Harold Geneen, had grown from a telecom company into a $17 billion conglomerate (roughly $135 billion today) sprawling across Sheraton Hotels, Wonder Bread, Hartford Insurance, and dozens of other unrelated businesses.

Reporting requirements at the time aggregated results at the firm level, not the business unit, which meant outsiders couldn’t see which divisions generated value and which were being subsidized. The conglomerate form let managers cross-subsidize weak units with cash from strong ones, hide capital misallocation behind organizational complexity, and direct retained earnings toward empire-building rather than productive use. Friedman’s doctrine was a response to this. He saw managers operating with effectively no accountability to anyone, and argued that returning the firm’s purpose to a single, measurable objective would restore the discipline that scale had eroded.

His argument is structured around agency, not extraction. Managers, in Friedman’s framing, are agents of multiple principals: stockholders, customers, and employees. Taking from one to fund the manager’s personal priorities is, in his words, spending their money. The doctrine’s modern operationalization ( cutting wages to lift margins and raising prices to hit earnings targets) substitutes executive judgment for principals’ consent. By Friedman’s own standard, this is the violation his argument was meant to prevent.

Friedman explicitly endorsed trust capital. In the same essay, he described community investment as something that “may make it easier to attract desirable employees... reduce the wage bill or lessen losses from pilferage” and called the resulting goodwill “a by-product of expenditures that are entirely justified on [the firm’s] own self-interest.” He understood that relational investment produces returns. He saw no contradiction between his doctrine and a firm building trust capital.

The distortion came later. When the doctrine got operationalized through quarterly earnings and DCF models, the part of Friedman’s argument that depended on long-term relational investment became unmeasurable. What survived was the part the instruments could see.

Drawing Down The Account

Dreher’s criticism of Costco presupposed a fixed pool of money. The question in his mind was one of distribution; the generosity toward customers and employees could only come at shareholders’ expense. This is the firm as a redistribution problem. There’s a fixed pool of value and the question is how to divide it. Shareholders want more, stakeholders want more, and management strikes the balance. And because management’s primary compensation is stock, the balance tilts in one direction.

This frame is missing time. A fixed pool exists only in a given moment. Firms operate across years and decades, and the value they produce in year ten reflects the relationships they invested in during years one through nine. Costco’s brand is estimated to be worth $146 billion, about a third of its market cap. All of this value accrued after its founding. The redistribution frame can’t represent the process that produced it.

The firm is a nexus of long-term relationships, and the health of those relationships is shareholder value properly understood. This is the view Friedman articulated and Costco’s founders operationalized.

The analyst’s playbook is mathematically correct on its own terms. Cut wages, OpEx shrinks, earnings rise. Raise prices, revenue expands, earnings rise. What the math can’t see is that the savings are coming from somewhere: not from another P&L line, but from the trust capital the firm has spent decades building.

Trust capital depletion has the structure of drawing on a savings account that doesn’t appear on your statements. Decades of kept commitments, fair treatment, and restraint from extraction, all accruing to a balance the books don’t reflect. To people who only see the P&L, the withdrawals feel like found money because nothing on the books shows what’s leaving. The depletion only shows up later, in eroded customer loyalty, in higher employee turnover, through the slow unwinding of the relationships that were producing the returns in the first place.

Calling this zero-sum is too generous. The Sam’s Club playbook didn’t preserve the total, it shrank it. Same business model, different strategic choices, half the same-store sales and half the profit per employee. The strategies the analyst recommended weren’t redistributing the pie. They were shrinking it while transferring a larger share of the smaller pie to shareholders. This isn’t zero-sum capitalism. It’s negative-sum capitalism dressed in zero-sum logic.

Competing Realities

The debate isn’t shareholders versus stakeholders. It’s durable capitalism versus extractive capitalism, and it exists on a different axis.

The durable quadrants form a coalition. Loyal customers, founder-operators, and pension funds all benefit when the firm continues to keep its commitments. Their interests compound together. The extractive constituencies are aligned in their intent, but are not a coalition. Extraction is a competition for finite spoils, and every group that drains value leaves less for the next extractor.

Once you see the coalitions, the original framing falls apart. The shareholder/stakeholder dichotomy assumed two coherent groups with opposed interests, with management in the middle balancing claims. The interests of durable shareholders and durable stakeholders run together. The opposition the framing insisted on was an artifact of looking at the firm at a single moment, with the time dimension stripped out. On any reasonable horizon, durable shareholders and durable stakeholders want the same thing: a firm that keeps its commitments and compounds the relationships that produce its value.

The Self-Fulfilling Doctrine

Friedman’s argument depended on things the system could not measure: multiple principals with competing claims, long-horizon investment, and the accumulation of trust.

When that argument was operationalized through quarterly earnings, discounted cash flow models, and stock-tied compensation, those elements did not survive translation. What could be measured became what counted, and what could not be measured was treated as if it did not exist.

What remained was the portion of the doctrine that fit the machinery: shareholder value maximization, expressed through quarterly earnings and stock price. The metrics began as compression, a way to make a complex objective legible to a system that needed numbers. Compression gave way to substitution as a signal of value creation became the target of optimization.

Over time, the map did not merely misrepresent the territory. It became the territory.

The substitution holds because the territory is not independent of the people operating on it. Firms run by operators who treat the firm as a redistribution problem become redistribution problems. Trust does not accrue because no one is investing in it. Durable coalitions cannot form because no one is offering the credible long-term commitments that would make them possible. The world becomes the world the map describes.

Sinegal believed the firm was something different and acted accordingly. Costco is the result.

Durable and extractive capitalism aren’t just competing views. They’re competing realities. The system will produce whichever reality its operators assume.

Subscribe now

A newly minted Chief Revenue Officer inherited a few existing teams into their newly created org. Marketing, merchandising, pricing. These previously disjointed teams would finally be together around the same table. Expectations were running high, but there was one problem. These teams had long been in conflict, and simply putting them around the same table did not , by itself, resolve the issue.

Most of the meetings had a similar vibe. Pricing and merchandising teams attempted to delineate responsibilities and get things in writing, and the marketing team refused to play along. Routine work fell through the cracks, and the team consistently found themselves struggling with their core responsibility of running the promo calendar.

When the CRO saw the distrust between the teams and the looming crisis this would cause, they sprang into action. They scheduled a series of team all-hands and ran, or attempted to run, sharing exercises. They encouraged teammates, with varying degrees of force, to be vulnerable with each other, thinking that shared vulnerability was the path to building the trust that the team was lacking.

These sessions were characterized by terse statements, awkward silences, and more than a few people looking visibly upset. While there were moments of vulnerability, distrust within the team was moving in the wrong direction. The CRO eventually asked one of their lieutenants what they thought was going on. The lieutenant responded by saying, “The head of the marketing team hasn’t made or kept a commitment in over three years. I don’t know if they’ve ever responded to a calendar invite. You’re asking me to reveal something of myself to someone I wouldn’t trust to make a ham sandwich, let alone do their job.”

The CRO’s first full quarter on the job became their last. After a few of these sessions, the team had tuned them out. The company had the biggest revenue miss in its history, and the CRO was out of the job in less than six months. Of the dozens of people in their org, every one of them departed the company over the next year.

Dysfunctions of The Five Dysfunctions

The CRO in our story correctly identified signs that their team didn’t trust one another. People were unwilling to ask for help and never revealed weakness. Meetings were filled with people who didn’t seem like they wanted to be there, and missed meetings reached a point where it became difficult to function. So why did this leader attempt to address this problem in the way they did?

They knew what behaviors were associated with high-functioning teams, and they attempted to right the ship by imposing these behaviors on their team. They started, and ended, with the base of the pyramid: trust. The complete model sounds like this:

Trust is foundational, and comes through vulnerability. With trust established, people begin to speak up and debate ideas, and are more likely to commit to decisions. Commitments allow teammates to hold each other accountable. These behaviors create a whole that is greater than the sum of the parts, a motivated team focused on collective goals.

If this model sounds familiar, that is because it is codified in one of the most popular business books ever written: Patrick Lencioni’s The Five Dysfunctions of a Team. It is still considered required reading among many executive teams. Lecioni’s elegant, intuitive ladder is responsible for The Five Dysfunction’s enduring success.

This elegance and intuitiveness is also what makes the model dangerous. It’s hard to argue against the list of traits and behaviors of effective teams. Of course trust is important. The question is not whether trust matters, but what causes it.

Trust Is a Verb

Trust as a building block of high-performing teams fits our intuition and is well-documented in research. Stephen Covey dedicated an entire book to the overwhelming body of evidence in The Speed of Trust. With trust as sine qua non to high-performing teams, we need to know two things: what exactly is it, and how do people establish it?

We get our answer from Mayer, Davis, and Schoorman’s research into organizational psychology. They define trust as willingness to be vulnerable with another person. The definition of trust has vulnerability as the outcome, not as an intervention to create trust. When vulnerability is disconnected from behavior, it tends to feel inauthentic and can erode trust.

These authors have also documented how trust is created: it comes from the perception that the other party is able, benevolent, and acts with integrity. Said another way, people trust you when they believe you have the competence to do the thing, they believe you care about their interests at least somewhat, and they believe your actions match your promises.

This finding gives us a trust-through-action model of trust building. Commitment becomes the base of the pyramid, because commitment provides the opportunity to demonstrate both competence and integrity. Behavioral integrity is established by consistently keeping commitments. As employees experience consistent support and kept promises over time, they respond reciprocally with loyalty and discretionary effort.

Meeting commitments might seem like an embarrassingly low bar, but literature on this topic suggests few organizations ever clear it. Jim Dethmer, who has spent decades coaching executive teams, gives us a model for high-performance organizations: people keep their commitments 90% of the time, and get ahead of and repair the 10% they cannot keep. This contrasts with his finding that people keep just 30 to 40% of their commitments at work.

Bad Apples, Bad Barrels

Keeping commitments is impactful and simple to describe. So why is it so rare? Doty and Kouchaki’s Commitments, Disrupted gives an answer that few leaders expect.

When people fail to keep commitments, the first impulse is to diagnose it as a failure of character. Doty and Kouchaki call this explanation “bad apples”. Of the four sources of commitment drift, this is the only one that involves failures of character. The other three are systemic. They contrast “bad apples” with “bad barrels”. These are systems where incentives and cultural pressures push people to renege on promises. “Weak commitments” are promises that had no hope of being realized because they were unrealistic or unclear at their inception. Finally “disconnected dots” are failures of coordination where people own parts of promises without visibility into the whole.

Their research shows that teams consistently rate themselves as reliable while viewing other teams as unreliable. This symmetry is the signature of a systems problem. It’s also proof that most people make the same mistake that Lecioni’s model makes: they assume failures are the fault of people rather than the barrels they are living in.

The marketing leader in the story at the start of this essay who never kept commitments may have been a bad apple. Doty and Kouchaki ask another question: what type of system allowed this person to be successful despite letting down their colleagues for years? It could only be a bad barrel.

This reframe is critical because it offers at least the possibility of discovering problems that are not rooted in personnel. This approach is also, not incidentally, much more demanding of leadership. Of our four sources of commitment drift, leadership has a role in all of them. Leaders are directly responsible for designing the systems their employees occupy; every bad barrel is a failure of leadership, as are systems where people cannot connect the dots across functions. Weak commitments and bad apples are reflections of culture and standard-setting.

Sixth’s Time’s a Charm

After the CRO’s team had the biggest miss in company history, the CEO shouted down the team and said “I know someone knew this was going to happen and didn’t speak up.” They were right; a team member told their peers it would be a historic miss weeks before the event. They had already spoken up five times to no end. There would not be a sixth.

If commitment is the currency of trust, voice is where that trust gets spent.

Trust helps create the conditions where people are willing to speak up to surface problems, challenge assumptions, and ask uncomfortable questions. Voice is the organizational behavior that trust unlocks, and voice is the heart of high-performing organizations.

In Albert Hirschman’s Exit, Voice, and Loyalty, he describes the conditions for people choosing to exercise their voice. The probability of success x the value of the potential outcome must be greater than the personal cost of exercising their voice. Trust acts on both sides of the equation, lowering the perceived cost and increasing the probability of success. Absent these conditions, people will opt for silence and eventually, exit.

Voice is how organizations learn, adapt, and change course. The most powerful moments I’ve experienced and written about started when someone broke through an intractable problem by reframing it. Without voice, problems can go unsolved for years because nobody has the safety or the standing to challenge the status quo.

Voice is professional vulnerability. It’s the willingness to risk being wrong in public, to challenge a decision, and to surface information that people don’t want to hear. It requires trust, but a specific kind of trust: the belief that the system will treat your contribution as signal rather than punish it as dissent. This trust comes from behavioral reliability, not through intimate knowledge of your co-worker’s life. Close friendships are a near inevitability of teams that work well together, but this is a byproduct rather than an input.

Scoring the Scoreboard

I worked with a business that knew it cost them 5x more to win back a churned customer than it cost to prevent them from churning. Yet, an audit revealed that the company was spending next to nothing on retention. A bit of digging revealed that the retention marketing team received credit for winbacks. but not for retention. They responded by deliberately let customers churn so they could win them back, costing the company millions of dollars.

A team with high trust where voice is valued and where commitment is kept is a formidable force, but this alone is insufficient. What we have is a scalar, and what we need is a vector. Trust and voice determine how strongly a team performs: the magnitude. Direction comes from accountability: knowing where effort should go.

Accountability, like trust and productive conflict, does not exist for its own sake. It exists to help generate an outcome on behalf of the organization.

The retention marketing team hit the exact target they were given. Hitting targets is an execution problem, and people are good at execution. What most people in execution roles are not good at is surfacing when they’ve been given the wrong target. Knowing whether the target is right requires a vantage point that most people in the organization don’t have, and surfacing a concern about a target is an act of voice that carries significant risk; you’re telling your boss they’ve made a mistake.

What this highlights is how accountability flows in both directions. Leadership is responsible for choosing the right targets and aligning the incentives of teams and people such that they work towards the best collective outcomes. Once leadership has held up its end, people with formal authority can then set expectations for their reports, measures against them, and act on the results.

Accountability is best thought of as flowing only vertically. Lateral accountability requires peers to absorb the social cost of confrontation without any of the formal authority that makes confrontation worth the cost.

Choosing the right targets, aligning incentives, ensuring teams aren’t working at cross-purposes, these are commitments leadership makes to the organization. When they get it wrong, it’s not the team that failed. It’s the system. And the team knows it, which is why poorly designed accountability erodes trust rather than reinforcing it.

With the addition of accountability and outcomes, we now have a fully realized pyramid of high-performance teams. Commitment serves as the foundation layer. If people can’t rely on each other to do what they said they’d do, nothing above it matters. When commitments are kept consistently, teammates learn to trust each other. Trust sets the stage for productive conflict. Conflict requires a functioning voice channel, venues where speaking up isn’t futile, where P(success) is high enough to justify the cost.

Where the first three layers of the pyramid create highly functional teams, accountability to outcomes focuses their efforts to the right ends. Accountability only flows vertically because it follows authority. Results follow whatever scoreboard leadership actually built. If politics has a higher ROI than value creation, people will play politics, and they’re right to.

A Sip of Beer

One of Stafford Beer’s gifts to the world is POSIWID: The purpose of the system is what it does. One of the reasons Beer emphasized viewing systems as black boxes that should be understood through their inputs and their outputs is that anyone who peers inside the box risks assuming outputs match inhabitants’ intents. If a system regularly punishes candor and rewards politics, that is what the culture becomes.

One of the most common category errors in business writing is when authors locate organizational failures in the character of people. Many of the most popular books, like The Five Dysfunctions, Amp It Up, and Radical Candor share this problem. Moralizing failure by framing it as individual deficiency in courage, standards, discipline, or character risks blinding leaders to systems and incentives problems.

High-performance teams don’t emerge from exhortation. They are made when leaders design systems where good people can meet high standards without being punished in the process.

The Gradient

The operations team was in disarray. A handful of customer orders were getting corrupted every day. Some were fulfilled twice, others not at all, and customer service was feeling it.

The Vice President of Engineering convened his team to find a fix. After a week of investigation, they traced the corruption to an upstream system owned by the shop team. The proposed solution was to detect and repair the errors as they happened. Then the estimate came back: three months of engineering work.

The sheer level of effort and risk of this plan led an engineering manager to ask a simple question. If the problem was upstream, why not ask the shop team to fix it at the source?

The VP didn’t hesitate. “I didn’t ask because they never do anything we want.”

The engineering manager paused. “Have you ever asked them what they want?”

The room went quiet. People looked around, unsure how to react. The VP seemed genuinely puzzled. After a long moment, it was clear that no answer was forthcoming.

“Pause the project,” the engineering manager said. “Give me a week.”

He spent the next day talking with people on the shop team to understand their goals. It didn’t take long to find a lever: fixing the fulfillment issue would help them hit one of their own OKRs. He took the proposal to their leader, framed in their language.

The shop team resolved the problem in three days.

Three days instead of three months.

What the engineering manager did was simple, at least conceptually. They asked what the other team was trying to accomplish, then positioned their request so it traveled in the same direction.

Think of every team as sitting on a slope defined by their goals, metrics, and incentives. Requests that align with that slope travel downhill: easy to say yes to because they help the team get where they’re already going. Requests that cut against the slope require the other team to push uphill: spending effort on something that doesn’t advance their goals, or actively competes with them.

The VP’s three-month fix wasn’t avoiding the slope. It was pushing straight uphill and everyone could see it. The engineering manager’s move was to read the slope and find the path where the request traveled downhill. Same problem, wildly different cost.

Subscribe now

The Theory of Mind You Already Have

Every team already has a theory of mind about the other teams around them.

It may not be written down and it may never have been examined. However, it must exist, because without a theory of why other people behave the way they do, coordinated action would be impossible.

“They never do what we want” is a theory of mind. It’s also a dead end. It models the other team as difficult, selfish, and adversarial. It explains their behavior through character. Once that story takes hold, collaboration failures become self-fulfilling: don’t bother asking, just work around them.

The gradient lens is a more useful theory. It’s the same behavior with a different explanation. They’re refusing because the request is uphill for them. It’s a more accurate interpretation that opens up moves that blame forecloses.

When you see the system compelling behavior, you stop directing anger at the humans inside it. Not because anger is ungenerous, but because it’s useless. Where do you go from “they’re difficult”? You work around them, escalate, or give up. The adversarial frame forecloses every move except force. “The system makes this uphill for them” opens a question: is there a path that’s downhill?

People behave rationally within their systems. When their behavior looks irrational, you don’t understand the system yet.

This is where compassion and effectiveness turn out to be the same thing.

The Progression

Once you see gradients, new moves become available that offer progressively more leverage:

Navigate: Position your requests downhill. Understand what the other team is optimizing for and frame your ask so it travels in the same direction. This is what the engineering manager did. Same request, different framing, three days instead of three months.

Design: Identify initiatives that naturally align teams. Instead of clever positioning on each request, design work so cooperation is the path of least resistance. When the gradients point the same direction, coordination becomes straightforward.

Reshape: Recognize when the incentive gradient, yours or theirs, is driving the wrong behavior. Then do something about it. This is the most difficult move because it requires admitting that the system you’re part of might be the problem. It’s also the highest leverage because you’re no longer navigating the slope; you’re reshaping it.

The diagnostic question at every level is the same: before you design an intervention, ask which way is downhill for the people you need to move. This is a bedrock requirement for understanding the system well enough to choose actions likely to succeed.

A Slingshot to Heaven

If this is so straightforward, why don’t more people do it?

The VP wasn’t trying to avoid the slope. Three months of engineering effort was evidence they were pushing a boulder up the Matterhorn, but that felt like the responsible move. It was a plan fully within their control. It didn’t require depending on a team that “never does anything we want.”

The engineering manager’s move looked riskier: pause everything, give me a week, let me go talk to people. One path looked like engineering rigor. The other looked like a long shot.

This brings us to the deeper problem: complex systems are counterintuitive. People reach for simple, intuitive fixes because they’re easy to understand and create the immediate sensation of forward movement, but these fixes are almost always fragile or counterproductive.

Donella Meadows spent her career studying where interventions actually work. Her insight was that there’s a hierarchy of leverage and most people never look past the lower rungs.

At the bottom are parameters: the numbers we adjust. Headcount, budgets, targets, OKRs. Easy to change, rarely transformative.

Above those are feedback loops: what gets rewarded and punished. These shape behavior over time regardless of what anyone intends. A team that gets celebrated for shipping features will ship more features, whether or not those features solve problems.

Higher still are goals: the why underneath—what the team believes they exist to accomplish. Not the metrics that proxy for success, but the underlying outcome the system is trying to produce. The engineering manager operated here. He didn’t change what the shop team was rewarded for. He understood what they believed they existed to accomplish and aligned their request with it.

At the top are paradigms: the patterns of thought that shape what goals seem legitimate, what gets measured, what questions are even askable. The paradigm underneath “they never do anything we want” might be something like: cross-functional work is zero-sum, or other teams’ motivations are unknowable, so don’t bother trying. These assumptions are so deep they become the air you breathe.

Most organizations spend most of their energy at the parameter level. Not because leaders are unsophisticated, but because parameters are visible. You can point at them, measure them, take credit for changing them. The higher levels require understanding dynamics that play out over longer time horizons and across organizational boundaries.

Deeper, system-level fixes feel riskier even though they’re the interventions that drive stable, significant improvement. They provoke resistance and often worsen symptoms short-term before things improve. Much like pulling a slingshot backward, you have to briefly move farther from the target to generate the force that reaches it.

The Independence Bargain

The structures that make teams efficient day-to-day also cause cross-functional capability to atrophy.

Small, autonomous teams have become a bedrock organizational principle. Amazon’s two-pizza teams, Airbnb’s EPD teams, and similar structures integrate functions into compact, end-to-end units, curtailing alignment problems and enabling rapid iteration. Communication overhead scales with the square of team size. Small teams are a direct response to this reality.

These structures work precisely because they minimize the need for cross-team coordination. Minimizing a need, however, is not the same as eliminating it. Teams that rarely need to coordinate risk losing the ability to do so effectively when it matters.

By the time the operations team faced their fulfillment crisis, autonomous operation had become so habitual that no one had practice in understanding how another team saw the world. “They never do anything we want” was less a fact about the shop team and more a foreseeable outcome of an organization optimized for independence.

Behnam Tabrizi’s research found that 75% of cross-functional teams are dysfunctional, driven by systemic organizational problems rather than problems internal to the teams.

When facing cross-functional problems, people reach for the tools at hand like steering committees, RACI matrices, and weekly syncs. These tools share a common assumption: coordination is a compliance problem to be managed through structure. They fail because they treat alignment as a process problem while ignoring the underlying issue: teams don’t have a model of each other’s reality.

I Want to Break Free

If this pattern is both costly and well-known, why does it persist?

The VP’s “they never do anything we want” wasn’t learned from careful empirical study. It was the residue of a few failed interactions that felt explanatory enough to stop asking questions. Once that story hardens, it stops being a hypothesis and starts being doctrine, and doctrine changes behavior. People stop learning because certain questions no longer seem reasonable to ask.

This is why the engineering manager’s move was disorienting. Not because it was actually risky; three days of talking to people is cheap. It was disorienting because it broke the frame. If you’ve accepted that cross-functional work is a battle of wills, “go ask what they want” isn’t just unusual; it’s folly.

The irony is that, in retrospect, the “safe” path was objectively far riskier. Three months of engineering work meant more resources, more complexity, and more ways to fail. This approach also leaves the underlying dynamic intact to produce the next crisis. It felt safe because it was familiar.

The scarcity trap isn’t really about time or resources. Higher-level interventions like designing around incentives feel risky because they are never tried. They are never tried because they feel risky. It’s about the feeling of risk being uncalibrated from actual risk, and the miscalibration becomes self-reinforcing.

Organizations caught in this loop can’t think their way out. The only way to recalibrate is to act: to generate evidence that the “risky” path is actually more reliable than the familiar one. The engineering manager didn’t argue that understanding the shop team’s incentives would work. He asked for a week and demonstrated it.

The only way to break a self-reinforcing story is to stop talking and run the experiment.

Shaping the Gradient

Everything so far has been about reading existing gradients. Understanding what other teams are optimizing for and positioning requests accordingly is a skill anyone can practice, regardless of their level.

Senior leaders have a unique opportunity and responsibility because they are empowered to reshape incentive gradients rather than just navigating them. Every goal a leader sets, every metric they choose, and every behavior they reward or punish shapes the slope that everyone navigates. They determine which requests will travel downhill by default and which will require people to push uphill.

The VP in our story wasn’t the one who created the misaligned incentives between teams, but someone was responsible. Someone decided what each team would be measured on, and either didn’t consider or didn’t prioritize how those metrics would interact at the boundaries. The three-month workaround and the three-day fix were both downstream consequences of that original design choice.

The highest-leverage work available to leadership is designing systems where good outcomes are the path of least resistance rather than requiring heroic effort or clever positioning every time.

The question isn’t whether you’re shaping gradients. If you’re a leader, you already are. The question is whether you’re doing it intentionally.

The Trade

Autonomous teams are necessary because coordination overhead is real. You cannot run a company at scale without them.

But cross-functional capability requires teams to carry models of each other’s reality, to invest attention in things that aren’t their core job. That’s inherently less autonomous. The moment you ask teams to understand each other’s incentive gradients, you’re asking them to do something that autonomous operation trained them not to do.

These two things are in genuine tension. There is no organizational design that resolves it.

The only thing you can do with an unresolvable tension is manage it.

Someone has to remember the other teams are real. Not structurally; psychologically. Teams stop carrying models of each other’s incentives because they don’t need them most of the time. When the need arises, they reach for the adversarial frame because it’s all they have.

Cross-functional work has to happen often enough to stay fresh. Capability decays without use. When the rare crisis arrives, everyone is rusty and, under pressure, interprets that rust as evidence of bad intent.

Someone must own the boundary, not just the team. A human, not a committee. Cross-functional problems die because they fall between charters. When no one owns the seams, everyone is locally rational and globally stuck.

Leaders must treat misalignment as a design flaw, not a people flaw. If misalignment gets blamed on personalities, nothing upstream changes. If it’s treated as signal, leaders get permission to reshape the system.

Organizations don’t fail at cross-functional work because they lack the right structure. They fail because they forget that autonomy is a trade, not a free lunch.

Autonomy buys speed, focus, and accountability. What it sells off is shared understanding. Incentive gradients don’t disappear. They just become invisible until a crisis forces teams to collide.

The work, then, isn’t to eliminate the tension. It’s to remember it exists. To notice when teams have stopped carrying models of each other’s reality. To treat misalignment as a signal, not stubbornness. To recognize that coordination, like any capability, decays without use.

The gradients are always there. Someone is always walking downhill.

The only real question is whether the system is designed so they’re all walking in roughly the same direction, or whether alignment depends on the rare appearance of someone willing to stop, look around, and ask what the terrain actually looks like.

In August 2025, Coinbase CEO Brian Armstrong posted a directive in the engineering Slack channel to onboard AI tools that week. He held a meeting that Saturday and fired everyone who had not complied. Absent from this story is what Coinbase did to help its engineers adapt, because they did nothing. Nothing was the only option because the capacity to help didn’t exist.

This isn’t a story about one company’s ruthlessness. It’s the endpoint of a fifty-year transformation that stripped corporations of their ability to absorb shocks on behalf of their workforce. Understanding how that capacity was destroyed is the first step toward rebuilding it and, for the first time in two generations, the means to rebuild it exist.

Subscribe now

Unraveling the Grand Bargain

For most of the postwar era, the corporation stood between its workers and the volatility of the market. Capital provided stable employment and goods to workers and their families. Workers, in turn, provided employers their full productive effort.

In 1950, the United Auto Workers and General Motors signed the Treaty of Detroit. GM guaranteed long-term contracts, pensions, health insurance, and wages tied to inflation; in exchange, the UAW gave up the right to strike over certain workplace controls and guaranteed uninterrupted production. This became the blueprint for the American manifestation of postwar stakeholder capitalism.

This arrangement went largely unchallenged until 1970, when economist Milton Friedman published The Social Responsibility of Business Is to Increase Its Profits in the New York Times. This article, now better known as the Friedman Doctrine, gave intellectual legitimacy to the idea that firms exist for the sole purpose of maximizing shareholder value. He explicitly refuted the idea that firms owe their employees or the public anything beyond operating within the bounds of the law.

Friedman argued that corporate obligations to workers and society were not just economically inefficient, but an immoral theft of shareholder wealth. The institution that had functioned as a shock absorber for its workforce was redefined as waste. As this doctrine took hold, companies began to strip out their capacity to absorb shocks in the name of higher efficiency and higher profits.

What followed was five decades of increased profitability through higher efficiency, but this efficiency came at a cost. One of the most fundamental principles in systems theory, engineering, and economics is the efficiency-resilience trade-off. The exact mechanisms that make a system efficient are the same mechanisms that make it fragile.

The first wave was breaking the taboo around mass layoffs. Until the 1970s, mass layoffs were seen as a failure of management and a last resort to save a dying company. Wall Street’s newfound interest in quarterly profits and takeover threats from private equity turned downsizing into a routine strategic tool. Jack Welch popularized the use of mass layoffs to boost stock prices even when companies were highly profitable. The workforce became the first place firms looked to absorb economic pain, rather than the last.

Once the taboo of firing direct employees was broken, the next logical step to maximize shareholder value was to stop hiring them in the first place. The 1990s marked the start of a wave of outsourcing, offshoring, and subcontracting as firms focused more and more on their “core competencies”. Turning fixed costs into variable contracts shielded firms from long-term obligations. When a macroeconomic shock hits, the firm doesn’t even have to announce layoffs; it simply declines to renew vendor contracts, instantly transferring the economic pain down the supply chain.

From the 2010s onward, algorithmic management and the gig economy achieved the ultimate, frictionless realization of the Friedman doctrine. Zero-hour contracts and just-in-time scheduling software shifted toward paying labor only for the exact minutes they are generating revenue. This marked the total removal of the firm from the labor equation. There is zero slack or paid downtime, so the system is perfectly efficient for capital. The worker absorbs 100% of the friction, idle time, and economic variance.

Decades of research on occupational health, most notably the Whitehall studies tracking British civil servants, have shown that the single strongest predictor of mortality is how much control someone has over their work. Case and Deaton’s studies on deaths of despair, the spike in mortality from suicide, drug overdoses, and alcohol-related liver disease among working-class Americans, documented what happens when the Whitehall finding scales to an entire class. The people most affected had lost control not just over their daily tasks but over their life trajectory. The economic variance that corporations shed had to go somewhere. It went into their bodies.

Colliding with a Vacuum

The fifty-year trend was abstract for white-collar workers because the hollowing out was happening around them. Their institutions were degraded but their work itself was safe. They could feel the loss of stability, the thinning of management, and the erosion of tenure, but the fundamental threat that the work might not need a human belonged to someone else. Factory workers, call center employees, retail clerks. Not them.

AI changes that. For the first time, the hollowing out meets an existential threat to the work itself, and it hits the population that’s been living inside degraded institutions without realizing how degraded they’ve become. They’re about to find out, because the institution that’s supposed to help them navigate this transition has already been stripped of the capacity to do so.

A friend recently worked on an AI rollout with a client and two things stood out. First, AI anxiety was real across their teams. People were experiencing real disruption to the way they work and felt uncertain about their futures. Second, this corporation was cutting costs to pay for more AI, pulling even more adaptive capacity out of the system at a time when its employees were asking, even begging for help adopting these tools.

Even more extreme versions of this are already playing out. Eric Vaughan, the CEO of IgniteTech, laid off nearly 80% of his staff when they did not adopt AI on his desired timeline. When asked about it, Vaughan said he’d do it again given the chance.

This is the Friedman doctrine reaching the perfect moral inversion of stakeholder capitalism. Absent from all of these stories is what the company did to help people adapt. Were the tools ready? Was the transition resourced? Was anxiety addressed? Was the pace of change survivable? The capacity to help people adapt, to resource transitions, and to manage the rate of change was gutted long before the arrival of AI.

When you strip an organization of its middle management, its slack, and its forward-planning capacity, the organization loses the ability to adapt to major changes in its environment.

Because the organization cannot adapt, it demands that the individual adapt instantly and perfectly. IgniteTech and Coinbase blamed their workers for a problem created by their lack of institutional capacity. This is the system operating exactly as designed.

The catastrophic strategic error these companies and others like them are making is confusing a chronic environmental shift with an acute operational problem. Demanding immediate adoption “or else” is a crisis playbook. This playbook worked in the past because disruptions were discrete events: market crashes, technological breakthroughs, new competitive threats. But AI is not an acute crisis; it is a continuous acceleration of environmental change.

Emptying the Well

The model Coinbase and IgniteTech have adopted assumes that chewing through employees via burnout and forced attrition is sustainable. Sustainability assumes there’s a sufficient supply of qualified replacements, the cost of turnover is low relative to the cost of retention, and the knowledge required to do the work is transferable enough that new hires reach productivity quickly.

AI changes at least two of those conditions. First, the knowledge required to be effective is increasingly context-dependent. The developer who’s been working with your codebase and your AI toolchain for a year isn’t interchangeable with one you hire off the street, because effectiveness with AI tools compounds with familiarity with the system, the domain, and the specific ways the tools interact with your architecture. The replacement hire who is burned out from their last job and behind on your specific stack starts the adaptation cycle over from zero while the technology continues to change underneath them. Progress resets while the company eats onboarding costs.

Second, the rate of change means the adaptation cycle never completes. In the old model, you could chew through people because each new hire was adapting to a relatively stable environment. Learn the job, do the job, get burned out, get replaced. The replacement walks into roughly the same job. When the job itself is being redefined continuously, each replacement walks into a different job than the one the previous person burned out doing. The onboarding cost rises with every cycle. At some point the math breaks.

Third, the chew-through model destroys institutional learning. Every person who leaves takes with them whatever they figured out about how to work effectively in the new environment. The organization never accumulates knowledge about its own adaptation process. It’s perpetually starting over. The firms that retain people through the transition will compound their learning. The firms that churn will keep paying the same tuition over and over without ever graduating.

What Grows Back

The Friedman doctrine didn’t just change who captures economic value. It changed the information environment of the firm by degrading every mechanism by which organizations perceive, interpret, and respond to reality. Not as a side effect, but as a direct consequence of treating everything that isn’t immediately productive as waste. And now AI arrives, demanding the fastest organizational adaptation in history, and finds institutions that have spent fifty years destroying their own capacity to adapt.

Taking full advantage of AI requires reimagining workflows entirely, and in its ultimate form, reimagining the job itself. The reward is a tremendous boost in productivity, as much as a full order of magnitude in output. We don’t need to guess whether an AI surplus is coming; people on the leading edge of the curve are already realizing it.

AI doesn’t just increase output, it changes the texture of the workday. AI eliminates the most routine tasks first: building a deck, writing tests, clearing up a spreadsheet. From a productivity standpoint, this is a win. These tasks served another purpose: cognitive rest. Periods of lower-intensity work give the brain time to reset between high-judgment decisions. The Friedman era stripped every institutional shock absorber until one remained, hidden inside the texture of the work itself. With this stripped away, what remains is a workday of orchestration, judgment, and synthesis, with no recovery periods built in. Output per hour rises, but cognitive load rises with it.

People on the leading edge of AI adoption are already experiencing this. Yegge describes the profound exhaustion that follows sustained agentic work, a phenomenon widespread enough to have earned its own name: the “AI Vampire”.

This cognitive tax will land on a workforce already experiencing AI anxiety. When the productivity surplus hits the mainstream, the hollowed out institution will act on muscle memory. Leadership’s instinct will be to treat 10x productivity gains as an opportunity to fire 90% of their people. Organizations that capture the entire surplus while offering nothing to help their workers recover will find themselves mining for fool’s gold. Workers will simply burn out and take the tacit knowledge required to execute successfully with them.

The coming AI surplus creates the first structural opportunity in over half a century to begin to rebuild the capacity of the firm. To survive the chronic disruption AI brings, firms cannot simply cut headcount. They must use the economic gains to intentionally fund the biological and institutional recovery of their workforce. The firms that thrive will be the ones that use the productivity surplus to buy back the slack.

There are three interventions that will enable firms and their people to thrive in this new reality. These interventions build on each other, and are sequenced so each one creates the capacity for the next.

Building a Buffer

The most fundamental problem employees face is an overwhelming information environment. At most firms adopting AI, every individual employee is doing their own sensemaking about AI: scanning Reddit, reading blog posts, trying tools on their own time, attempting to distinguish between hype and substance with no institutional support. That’s an enormous duplication of effort across the workforce, and most people are bad at it because filtering signal from noise in a rapidly evolving technological landscape is a specialized skill. It’s unreasonable to expect a frontend developer or a product manager to also be an effective technology analyst in their spare time.

The role of the institution is to step back in and do the heavy lifting of sensemaking and pacing. This becomes the organization’s outer shock absorber. This role is to stand at the boundary and absorb the raw chaos of everything that is changing, filter the signal from the noise, and introduce information at a survivable, sequenced rate. Some companies are hiring Chief AI Officers to fill this function, while others might embed it in an existing role or build a small dedicated team.

A small team and AI augmentation can monitor the landscape, filter signal from noise, tailor guidance to different roles, and push curated updates to the workforce. The function itself is a demonstration that AI amplifies human judgment rather than replacing it. This team must be the most fluent AI users in the company because their credibility depends on it. This is an internal product team with the workforce as their customer, understanding what different groups need, building feedback loops, and iterating on what works. Every recommendation they make carries implicit proof: we tried this, here’s what works, here’s what doesn’t, here’s what you can ignore.

This intervention provides the what; to succeed, it must be paired with the how.

Buying Back the Slack

The next intervention institutions can make is building learning hours into the workday. Even with curated information, adaptation takes time and cognitive resources. When that time competes with production, production always wins because production is measured and learning isn’t. Boxing off learning hours within the workday does two things: it gives people the actual time to adapt, and it sends a signal that the organization considers adaptation part of the job rather than a personal responsibility to be handled after hours. That signal matters as much as the hours themselves, because it’s the organization saying “we own this problem, not you.”

This time must be paired with institutional sensemaking. Unstructured hours where people are expected to simply “go figure it out” just relocates the same sensemaking burden from nights and weekends to the workday without actually reducing it. Learning hours become the delivery mechanism for the curation. Without curation, learning time can turn into doom scrolling Reddit’s AI subs. Without dedicated time, even the best curated content goes unread due to the demands of production. These two interventions are load-bearing for each other.

The framing of this time is critical to making it sustainable. Learning hours are vulnerable to the pressures of immediate, visible costs (hours not producing) getting prioritized over diffuse, long-term benefits (workforce adaptability). When AI-enabled workers can produce in four hours what used to take eight, learning hours aren’t additional cost, they’re a reinvestment of surplus that already exists. The reframe makes cutting learning hours equivalent to demanding more than full output, which is harder to justify than cutting a “perk.”

Curation and protected time address the challenge of learning in an ever-changing environment. They don’t address the deeper vulnerability: The roles people occupy are themselves becoming unstable.

When the Walls Come Down

Bell Labs originated the title “Member of the Technical Staff” to emphasize collective expertise over hierarchy. Companies like OpenAI and NVIDIA have revived and modernized the role in recent years to solve a different problem: roles are collapsing because AI is making the boundaries between them obsolete. MTS job postings show enormous scope, merging previously distinct roles into high-output generalists.

Companies like Perplexity originated the “product engineer” role, which collapses engineering, product management, and design. Typical projects have one or two people on them. Taking their podcast as an example: it’s built by one person end to end, a brand designer who also does audio engineering, ElevenLabs integration, scripting, and research. That’s one person spanning what would traditionally be PM, design, engineering, and content and shipping a product that people actually use.

These roles existed as separate disciplines because execution in each domain required specialized skill and sheer effort. As AI drops those costs, the walls come down. When a PM can go from a customer story to a working prototype without an engineer, and an engineer can define requirements and test them with users without a PM, this is the path of least resistance rather than a power grab. But when your identity is tied to a domain of work, someone else entering that domain feels like an incursion. The resulting conflicts look like turf wars and ego, but they’re structural. The boundaries that kept the peace no longer exist.

This change poses a significant threat to identity. Roles like product manager, designer, and engineer were historically tied to methods, and AI is coming for every method simultaneously. As roles collapse into one another, the labels people use to identify themselves lose their meaning.

Builder roles address this challenge by tying identity to an outcome: being someone who makes things to solve problems. The methods are explicitly expected to evolve, and the tools are fluid by definition. AI shifts from threat to identity (“Will AI replace me?”) to just another tool in the builder’s kit (“How do I build better with AI?”). This is a durable, stable identity because building always needs to happen.

The builder role doesn’t eliminate expertise. People will still go deep. The difference is that depth becomes a capability you bring to building rather than a boundary that defines what you’re permitted to work on. The specialist says “that’s not my job.” The builder with a specialty says “I’m strongest in this area, but I can contribute everywhere.” The knowledge doesn’t change. The relationship between the person and the work changes.

Changing roles dissolves professional identities that people spent years constructing, and that entire career paths, hiring pipelines, compensation structures, and status hierarchies are built around. This is a significant cost. It’s not a coincidence that builder roles are found at young companies that grew up in this new reality and thus never had to pay the transition cost. Companies will transition despite the cost because the cost is one-time and permanently raises resilience. Established companies will face the transition cost whether they manage it deliberately or not. The question isn’t whether to pay the cost but whether to pay it on your terms through intentional consolidation or pay it chaotically through territorial conflict, confusion, and attrition.

These same costs are the reason that this change can only happen after the first two changes we’ve discussed. The Chief AI Officer function and the learning hours can be implemented relatively quickly because these changes relieve employee stress, so the workforce’s capacity does not govern the speed of adaptation. Role consolidation is a longer-term structural change that becomes possible after the other interventions have freed up enough capacity and relieved enough stress to make the transition tolerable.

The Work of a Generation

Designing new systems and rebuilding the capacity of a firm to absorb shocks is not the work of a single essay or a single quarterly off-site. This is the work of a generation.

Whether it’s creating an institutional buffer, protecting time, or redefining roles, it will take a few tries to get this right. And we must get it right. The alternative is continuing to run an obsolete, 50-year-old playbook that is already resulting in institutional collapse and deaths of despair. The surplus is coming, the need is acute, and the only question is whether leaders will use it to rebuild or to repeat the cycle of extraction that brought us here.

For the first time in two generations, the economic incentives of the firm and the needs of its workers are aligned. For fifty years, firms pushed more and more variance onto labor, relentlessly stripping away resilience and making employees absorb the shock in the name of efficiency. The AI surplus gives us the capital to finally buy back our slack. If we step up to the challenge, we won’t just survive the disruption. We will build institutions that are fundamentally stronger, deeply adaptable, and in harmony with the humans who power them.

In 2018, Microsoft faced a crisis: surveys showed that entire groups of high-performing employees in their Surface and xBox divisions were miserable. These employees had specialized skills and would be hard to replace if they started quitting. Microsoft was finally making headway in hardware and the wrong employees leaving risked undoing their progress.

The head of the hardware division began to focus attention on the problem. He and his staff explored what they believed to be the most likely culprits: overdemanding managers or employees having to work odd hours to support their global supply chain. None of these explanations panned out. They turned to Microsoft’s organizational analytics team to conduct a study… on employee misery.

The analytics team hit many dead ends before finally breaking through while they were crunching the numbers on meetings. Employees were spending an average of 27 hours a week in meetings, which was typical for Microsoft at the time. People in other divisions were quite happy despite this meeting load, so it wasn’t the sheer hours.

They discovered it was not a question of the number and length of meetings, but how many people were in the room. The most miserable employees were attending meetings with 10, 20, even 30 people in the room. In these meetings, two or three people would take up all of their time while the rest of the room listened passively.

Little progress was made during these meetings, and these meetings ate up the time necessary to do focused work. Nights and weekends were the only times free from meetings, and this became the “real” workday. The high performers Microsoft was most at risk of losing were the ones who had restructured their lives around the problem.

The Physics of Coordination

Twenty-seven hours a week in meetings sounds extreme until you look at the data on how most organizations actually spend their time. Across industries, roughly 60% of knowledge workers’ time goes to coordination activities, leaving just 40% for everything else. As bad as 40% sounds, it overstates the time available for real work. Microsoft’s own telemetry shows employees are interrupted every two minutes by meetings, emails, or notifications. Research on sustained concentration suggests that deep work requires at least 15 uninterrupted minutes to begin and much longer to produce anything of value. In most organizations, the calendar makes this arithmetically impossible during working hours. How did this become the default?

The load-bearing assumption is that coordination costs scale combinatorially with team size, and this necessitates meetings. The math behind it is simple: coordination costs scale with the square of headcount (n * (n - 1) / 2). Thus, the enormous amount of time spent in meetings at scaled organizations is a mathematical inevitability, the cost of reaching scale.

This mathematical truth does not survive first contact with the reality of work.

I was an early engineering hire at an online consignment marketplace in 2013. The company grew, and the engineering team grew with it. By the time the team had eight engineers under one manager, the standups reached a point where I actively tried to avoid listening to what my colleagues were working on. The information was irrelevant to my work, and I didn’t want it taking up headspace. My wife regularly encouraged me to call in sick so I could get work done without putting in another weekend. Was I coordinating with the other 7 engineers in that meeting? Were they coordinating with me? Were the 30 people in meetings together at Microsoft coordinating with each other?

The formula assumes a complete graph, with every member of a team coordinating with every other member. The reality of communication at work looks more like a sparse graph, with few nodes connecting. All employees connect to the manager (hopefully), but there are far fewer work-related connections to each other. This means most of the time attributed to coordination isn’t coordination at all.

An 8-person team has in theory 28 communication paths. Most of those 28 paths carry no useful information, but the organizational machinery acts as if all of them matter. The waste isn’t in the combinatorics; it’s in treating a sparse graph as if it were fully connected.

If people aren’t coordinating in these meetings, what is going on?

What Coordination?

Luna and Renninger give us a clean taxonomy of possible meeting outcomes in The Leader Lab. Meetings can only do three things: share information, generate ideas, or make decisions. Only the last two require people to be in the same room at the same time, and only the last one moves the business forward.

Informational meetings present a serious problem because employees broadly understand that status meetings are useless. A survey conducted by Clarizen in 2015 showed that almost half of employees would rather do any other unpleasant activity, including watching paint dry, rather than attend another status meeting. Yet organizations spend the majority of their time in them anyway. A McKinsey study on time management revealed that executives in the average organization spend 10% of their time in decision meetings and over 50% of their time in status meetings. At the highest performing organizations, this time allocation is inverted.

Everyone from junior employees to executives hate status meetings and studies confirm the instinct. It’s possible to simply delete most of them from the calendar and share the information async. Why do these patterns persist? Asked another way, what forces are so powerful that they demand these meetings in the face of both lived experience and ample data? The answer is that meetings aren’t just consuming time. They’re serving a purpose that has nothing to do with coordination.

The Status Economy

Decades of research in psychology have converged on three needs that are non-negotiable in human functioning. People need to feel effective and masterful in their environment. They need agency over their own behavior rather than being pawns of external pressure. And they need meaningful connections with other people. Psychologists call these competence, autonomy, and relatedness. Deci and Ryan named this Self-Determination Theory. When these needs are satisfied on an ongoing basis, people develop and function effectively and experience wellness. When these needs are unmet, people find it distressing and become less effective.

These aren’t nice-to-haves. They are needs, and people will seek to have those needs met one way or another. The structure of the workday is so dominated by meetings that it leaves employees with very little choice in how their needs are met. When we map the most common meeting pathologies, they tie back to meeting a fundamental need.

Competence. When deep work is impossible because time is fragmented, it becomes difficult to demonstrate competence through output. What’s left? Performance. Polished status updates, impressive decks, and the articulate summary of what your team accomplished. The meeting becomes the venue where competence is displayed because the workday offers no other stage.

Autonomy. When someone’s calendar is controlled by other people’s meeting invitations, autonomy is scarce. Meetings offer a perverse form of it: calling a meeting asserts control over other people’s time. The 60% ad hoc meeting rate is evidence in plain sight. These are unscheduled interruptions that the caller initiates; an act of agency in a system that otherwise offers very little. Inviting someone to your meeting is one of the few unilateral actions available in most corporate environments.

Relatedness. When teams are too large for every relationship to have depth, and when work is fragmented enough that genuine collaboration is rare, meetings become the primary social venue. Being in the room becomes a proxy for belonging. Being excluded from a meeting triggers the same threat response as social exclusion, because in a meeting-dominated workday, it functionally is social exclusion. This explains why meeting audiences tend to balloon. People fight to be included not because they’ll contribute, but because exclusion signals they don’t belong. Many documented human needs (power, approval, achievement) are ultimately driven by the need to belong.

Microsoft’s study confirms the pattern. Large meetings were the key driver of misery, but they didn’t make everyone miserable. They were effective at meeting the needs of those who controlled them.

Imagine a 20 person meeting where three people dominate the discussion. It will almost inevitably be the most senior people in the room doing the talking. In doing so, all of their needs are met. They demonstrate competence by speaking in front of the group, experience autonomy by directing the conversation, and experience relatedness by being at the center of a social interaction. The remaining 17 people are passive, lack agency over their time and the discussion itself, and their presence is interchangeable. Meetings don’t simply fail to meet the needs of most workers, they actively thwart meeting those needs.

Whose needs are met boils down to power. Senior people call meetings, set the agenda for those meetings, and do most of the speaking. Junior people attend and optionally listen. The meeting-industrial complex is a system where people with power meet their psychological needs by consuming the time and attention of people without power. This isn’t a conspiracy. It’s just what happens when meetings are the primary venue for need-fulfillment and access to that venue is determined by hierarchy.

This also explains why the meeting-industrial complex is so resistant to change. The people with the authority to dismantle it are the same people whose needs it serves. The VP who spends their day in back-to-back meetings feels productive. The engineers on their team who can’t get four uninterrupted hours to build features and design systems are the ones suffering, but they don’t control the calendar.

I once worked with a CEO who said “asking your reports about their needs is the dumbest fucking thing in the world because the point of power is that I get what I want and I don’t have to give a shit about what you want.” Few senior leaders would ever say this out loud, but the meeting-industrial complex operates as if it were policy. The difference is that this CEO was conscious of the dynamic. In most organizations, the same asymmetry exists but it’s as invisible as the air everyone breathes. That invisibility makes it harder to address. A problem you can name is a problem you can fix. A problem that feels like the natural order of things just perpetuates itself.

The natural order is not an inevitability. It has been broken before, and the results are striking.

Escape Velocity

After processing the results of Microsoft’s misery study, the leader of the hardware division realized that the problem started with them. They began a sustained effort to reduce the number of meetings and block off focus time on calendars. Microsoft rigorously tracked off-hours work so they could intervene quickly and right the ship where the intended changes were not taking hold. Work-life balance was soon restored and the dreaded exodus never materialized.

Microsoft’s outcome was not a one-off. A 2022 study published in the MIT Sloan Management Review found that reducing the number of company meetings by 40% led to a 71% increase in employee productivity. Shopify purged 12,000 recurring meetings in 2023. Meeting time dropped by 33%, they added 150 FTEs worth of recovered hours, and it became one of the most popular changes in Shopify’s history. Dropbox’s Virtual First initiative limited meetings to debate and decisionmaking. This initiative both improved productivity and became a major retention driver for its employees.

Bain conducted a study on 300 large companies globally. Companies in the top quartile of performance in their industries lose 50% less time to organizational drag, and their employees are 40% more productive. The study found that top quartiles started with the same talent mix as their peers but produced dramatically more output.

Across these examples, the performance gains weren’t just about recovered hours. They were about recovered autonomy, competence, and connection. In other words, their needs were now being met through work rather than meetings.

These companies didn’t achieve these results by asking people to schedule fewer meetings. Structural changes underpinned every success.

When we read the examples, patterns begin to emerge. Shopify deleted the meetings from the calendar rather than asking people to opt out. Dropbox built an entirely new operating model around async-first. Microsoft tracked off-hours work and intervened when the old patterns reasserted themselves. The systems were redesigned so that the old behavior was made difficult.

In organizations where the changes stuck, we also see cultures where status comes from output rather than presence. This shift allows people to sustainably meet their needs through productive means rather than unproductive ones (meetings).

The meeting trap is like gravity, an ever-present force acting in one direction. Shopify imposed a set of constraints after the purge that meant many “default” syncs literally had no valid slot in the operating cadence. Microsoft had to track off-hours work because the old patterns kept creeping back. Extreme measures like these evidence the strength of the force they are trying to overcome. What would it take to change the laws of organizational physics that make unproductive meetings so pervasive?

The answer starts with the unit of organization itself: the team.

The One-Pizza Team

Amazon recognized that team independence is necessary for velocity, but larger teams incur more drag. Amazon balanced these priorities by organizing around the smallest units that could still function autonomously: two-pizza teams (~8 people). This was the right answer in its day; the nature of engineering work made it impossible to shrink the teams further. But this structure leaves significant room for improvement.

The reason traces to an effect Maximilien Ringelmann discovered while studying rope-pulling. Per-person output drops as teams scale, and the effects are significant. By the time a team has 8 people, their combined output is half of their potential. Research following his initial study has shown us this result is common to every context where it has been tested. When the first study was published, the question was one of cause. Was it a coordination problem, or something else? It was later proven that the main driver is “social loafing”. When teams are larger, people feel less responsible for the outcome and don’t try as hard.

AI agents change this equation by reducing execution cost. Claude Code was built in a week using a small team, and this is representative of where AI is going. It’s not the same work 10x faster, it’s work that was previously impossible for small teams becoming possible. Leaders at AI companies like Imbue and Sundial are already organizing around three-person teams as execution capacity expands. The effects compound: AI agents expand what each person can accomplish, enabling smaller teams. Smaller teams are independently more productive per person: more ownership, less diffusion of responsibility, every contribution visible. The AI multiplier and the small-team multiplier stack. The implications for the meeting-industrial complex are profound.

Under what circumstances would a three person team sitting together hold a formal meeting? Context is naturally shared in small groups, ownership is obvious, and decisions can be made immediately. Competence, autonomy, and relatedness are built into the work itself. It’s difficult to imagine an unproductive meeting, let alone a scenario where such meetings dominate the calendar.

When execution capacity is effectively unlimited, when three people with AI can build what used to take twenty, the scarce resource shifts from “can we build it?” to “should we build it?” and “are we building the right thing?” The constraint moves from execution to sensemaking. This will have significant implications for how organizations are structured.

As small teams close to the work become more self-sufficient, the need for resource allocation and day to day management shrinks. Management shifts to ensure coherence, that teams are tracking roughly in the same direction and solving problems that matter. Flatter structures become possible as the work that justified the hierarchy is absorbed by the teams.

Structure alone isn’t enough. The deeper question is what the organization rewards.

When status comes from visibility, presence, and convening power, meetings become the primary path to feeling competent, autonomous, and connected. The meeting industrial complex is dismantled not by demanding fewer meetings, but by changing where meaning and status come from.

The organizations that escape the trap do so by providing better ways for employees to meet their needs. Competence comes from what you shipped, autonomy comes from genuine ownership, and belonging comes from being on a team where your presence matters. This is a choice leaders must make. When the organization provides better channels for fulfillment, the meetings disappear on their own.

Share

Zombie projects are the default state of most organizations.

Quitting failing efforts is hard enough for individuals. Institutions make it worse. Organizational structure doesn’t just fail to correct for this problem; it amplifies it, compounds it, and in some cases makes it recursive: once a bad bet exists, the safest move for insiders is to keep it alive.

There is no better example of how this plays out than Sony’s VAIO.

The Promise of Lilac Boxes

The PC market in the 1990s was characterized by commodification and price wars. PCs were highly modular and used open standards by the early 1990s, making hardware differentiation difficult beyond minor tweaks.

It was in this highly competitive market that Sony launched the VAIO line of PCs in 1996. PCs during this era were referred to as “beige boxes”, indistinct in both form and function. Sony saw an opportunity to fill a gap in the market: Offer computers that combined beautiful aesthetics with strong A/V features, consistent with Sony’s legacy of designing stylish, high-end consumer electronics.

The first lilac-and-grey models debuted to significant fanfare. The colorful aesthetics and sleek forms were completely unique for the era, predating the first iMac by two years. Steve Jobs admired VAIO so much that he (unsuccessfully) pitched porting OS X to Sony. The early models sold well. Customers saw the innovation as worth the premium. VAIO proved its thesis, inspiring other companies to follow suit. That early success also meant Sony had something emotionally and politically hard to walk away from later.

Success was short-lived. By the early 2000s, plunging PC prices opened a gap with Sony’s models that consumers could no longer justify. Then came the trends that VAIO missed: the ultrabook, the tablet explosion, and ecosystem integration that made Apple’s walled garden a feature rather than a limitation.

After over a decade of mounting losses, Sony finally threw in the towel when they sold the brand in 2014, taking a $1.1B hit in that year alone.

The Institutional Trap

In Quit, Annie Duke catalogs the cognitive bugs that make individuals hold on too long: loss aversion, identity attachment, and escalation of commitment. These are individual pathologies. Institutions make them worse.

Inside a firm, people aren’t optimizing for organizational value. It’s something closer to: don’t get fired, don’t lose headcount, don’t lose status or narrative control. Keeping a failing project alive is often the rational move. The longer it lives, the more constituents it builds, and the more ambiguity about whether it could turn around protects its sponsors.

Profitable divisions deepen the trap. During VAIO’s long decline, PlayStation printed money. Sony as a whole remained profitable in many years, which meant VAIO’s losses never posed an existential threat. Where an individual feels losses directly, an institution can redistribute them, defer them, hide them.

This is why zombie divisions aren’t anomalies; they’re what game theory would call an equilibrium. Given how careers, budgets, and hiring rewards work, no single actor can safely be the one to pull the plug.

These dynamics explain how the story we’re about to share was possible.

The Lifeline

Sony introduced MiniDisc in 1992 as a replacement for the cassette tape, finding early success in Japan and, to a lesser extent, Europe. By the early 2000s, the format was under existential pressure. PCs with CD burners had become ubiquitous, the media was cheaper, and MP3s had reached widespread adoption. The iPod launched in 2001.

It was at this moment, with MiniDisc’s decline already evident, that Sony’s Consumer Electronics division treated VAIO PCs as a lifeline.

This is a Sony VAIO laptop from 2002. The top bay is a DVD-ROM drive. The bottom bay, taking up half the modular space in a chassis where every cubic centimeter is contested, is a MiniDisc drive.

Photo credit: reddit u/alwaus

Sony adapted MiniDisc to store data and pitched it as a replacement for the 3.5” floppy. MD drives appeared in VAIO desktops starting in 2000 and laptops by 2002. This wasn’t a peripheral you could plug in; Sony re-engineered VAIO bays with custom circuit boards and proprietary connectors. This diverted R&D, manufacturing capacity, and marketing to bundle a struggling format into a product line already fighting for survival. Resources went to MiniDisc integration instead of features that differentiated VAIO.

The cost extended beyond the hardware itself. VAIO engineering cycles became tied to MiniDisc firmware, media compatibility, and software integration, slowing development at precisely the moment when agility against Dell and HP mattered most. By the time MD drives appeared in laptops, ZIP drives were already widespread and the iPod was eating the world. None of the imagined use cases materialized.

Sony kept including MiniDisc drives until the mid-2000s.

Zombies All the Way Down

The inclusion of MiniDisc in VAIO PCs shows us how failures to quit can compound, and how one zombie can nest inside another.

MiniDisc was already dying when Sony embedded it in VAIO. Finding a new use case allowed the division to craft a new narrative about the technology’s viability, while saddling VAIO with dependencies that made it harder to compete. The poor sales of expensive, MD-equipped models accelerated VAIO’s decline from promising newcomer to zombie in its own right. The cycle became self-perpetuating: each failure created the conditions for the next.

How did this happen? How does a dying format get designed into a computer chassis without someone asking whether it makes sense?

The answer lies in how Sony was organized.

Warring Fiefdoms

At COMDEX 1999, Sony introduced three competing digital music players from three different divisions, each running incompatible proprietary technology. The divisions did not share information or collaborate; they saw each other as competitors, not colleagues.

This sounds like dysfunction but it was by design. Sony operated as 25 autonomous units, each with its own P&L. They were quasi-independent companies under the Sony brand, and they treated each other accordingly.

Gillian Tett’s The Silo Effect documents how Sony’s divisions operated as warring fiefdoms. The silo structure explains how MiniDisc integration happened: the MD division had its own budget, its own leadership, its own survival imperatives. Embedding MD into VAIO wasn’t a company-wide strategic decision; it was one silo colonizing another to stay alive.

Silos don’t just create inefficiency; they create constituencies. And constituencies fight for survival in ways that diverge from what’s good for the institution. The MD division needed to justify its existence; embedding in VAIO gave it a new narrative regardless of whether that narrative served Sony. Without cross-functional scrutiny, no forum existed to ask whether the integration made sense. Coordination problems became political problems. In a unified structure, “should we put MD drives in laptops?” is an engineering question. In a silo structure, it’s a question of which division wins.

Given the incentives, every action the MD division took was rational for the people in that division, and harmful for the company as a whole.

In 2005, Sony acknowledged the silo problem explicitly as a cause for poor financial results. They also announced a restructure of the company specifically to “eliminate the business silos that had prevented us from focusing resources on our most competitive champion products.”

It was too late for VAIO.

Without deliberate design, institutional incentives make timely quitting all but impossible. But some companies have treated quitting as a first-class design problem, and their stories look very different from Sony’s. The most successful companies show us that a combination of systems and culture can counteract these forces consistently and without heroics.

Building an Offramp

Companies that quit in a timely fashion do so by making quitting routine. Systems and culture, working together, make this sustainable. Systems ensure quit decisions are made regularly, objectively, and consistently. Culture ensures quitting is safe, creating the conditions for the systems to survive.

Amazon shows us what the culture looks like.

Amazon’s launch of the Fire Phone was seen as a spectacular failure in its era. Amazon lost $170M, including $83M in unsold inventory. More interesting than failure itself is how Amazon pulled the plug in just three months, and how they acted in the wake of the shutdown.

When asked about the Fire Phone failure in 2016, Jeff Bezos said: “If you think that’s a big failure, we’re working on much bigger failures right now—and I am not kidding. Some of them are going to make the Fire Phone look like a tiny little blip.”

He then explained the underlying logic: “You need to be making big, noticeable failures. The size of your mistakes needs to grow along with the company. If it doesn’t, you’re not going to be inventing at scale that can actually move the needle.”

The review decision sat with executives above the group that created the Fire Phone—the exact separation of ownership and evaluation that Sony’s silo structure prevented.The executive who launched the Fire Phone remained a respected figure and went on to lead other divisions. Amazon lowered the cost of failure by showing that failures were both expected and were not career ending. This culture allowed Amazon to shut down the Fire Phone so quickly.

While culture makes quitting safe, it is systems that ensure it happens.

Danaher became one of the most successful industrial conglomerates by building an operating system designed, in part, to prune failing bets before they became a drag on company performance.

Two design choices matter most:

Centralized capital allocation. Danaher conducts all investment through a single capital allocation process, making it harder for dying bets to hide. This separates the quit decision from the people with the most to lose.

Explicit quit criteria set in advance. Long before Annie Duke described kill criteria, Danaher had implemented it at an institutional level. All divisions were subjected to the same explicit criteria, and divisions no longer meeting them were candidates for exit.

Danaher’s structure assumes some bets won’t work and builds the exit path into the operating model. Recent divestitures show this discipline in action. Since 2016, Danaher has spun off Fortive (industrial businesses), Envista (dental), and Veralto (water/packaging). These businesses were profitable but growing slower than Danaher’s portfolio targets. Rather than let them consume capital and management attention, Danaher spun them out while they still commanded high valuations.

Amazon built a culture where failure doesn’t end careers. Danaher built a system where exit criteria are explicit and centralized. Together, they illustrate what deliberate design looks like: quitting becomes safe and routine. Sony had neither cultural permission to fail nor structural mechanisms to force the question. Zombie divisions filled the vacuum.

Every institutional incentive pushes toward keeping failing bets alive. This is why zombies are the default rather than an aberration. Without deliberate design, organizations don’t get a clean exit. They get a dying format designed into a struggling product line: one zombie infecting another, accelerating the decline of both.

In Silicon Valley, Netflix is well known for its distinctive management philosophy. The Netflix culture deck, later expanded into No Rules Rules by Erin Meyer and Reed Hastings, is still required reading at many startups. It describes a freedom and responsibility culture that replaces much of the process that defines corporate life with high talent density, radical candor, and broad employee autonomy.

Netflix’s success is beyond question, which makes one detail especially interesting: for many years, Netflix did not everywhere follow its own (no) rules. During the DVD era, the company operated a nationwide network of warehouses that functioned under an entirely different operating system. Warehouse employees worked inside strict, highly specified processes governing nearly every moment of their shifts. I’ve spoken with the people who designed those systems, and they’ll tell you the same thing: rigid processes and clear hierarchies were essential to delivering a reliable customer experience at scale.

The leaders at Netflix understood something that many organizations miss. While the danger of too much process is real, process has a legitimate and sometimes indispensable role, depending on context. This essay explores that tension. It’s an attempt at a practical theory of process: what purpose process actually serves, when adding it makes sense, and why removing process later is orders of magnitude harder than adding it in the first place.

Subscribe now

What Process Actually Does

Process exists for one reason: to suppress variance.

Every procedure, review, and checklist narrows the range of possible outcomes. Adding requirements for code reviews and test coverage limits the types of bugs that can reach production. Checklists ensure minimum standards are met every time.

Described this way, suppressing variance sounds positive. If you asked most employees if they want unpredictable outcomes, who would say yes? There is, however, a catch: outperformance is also variance.

The exceptional outcome lives in the same distribution tails as the failures that process is built to prevent. When we compress the range of outcomes, we don’t just eliminate disasters, we also eliminate breakthroughs.

Organizations fail to understand this cost because the effects are asymmetric. New processes have an immediate, visible benefit: fewer errors. Innovation happens over longer time periods, so the cost (fewer innovations), takes longer to be felt. The errors being addressed are concrete and linked to a process. The innovations lost are at best theoretical; they never existed, so they leave no trace back to the process that killed them.

The Ratchet Effect

Processes accumulate because the incentives for adding and removing are fundamentally asymmetric.

New processes have a natural champion: the person making the proposal. This champion can directly link the proposed process to some acute issue, whether it’s a recent error or a known organizational problem. As long as the process has a story that makes it seem prudent, this process is likely to become part of the organization, with credit being amassed by the champion.

Removing process has no natural champion. The beneficiaries are hypothetical future employees who would have shipped faster, thought more creatively, taken more initiative. They’re not in the room. They might not even work here yet. The victory condition for removal is diffuse and slow to materialize: maybe things get slightly faster, maybe innovation picks up over time. Hard to measure. Hard to take credit for. Process removal is also likely to run into political challenges. Process is often a means of control for people in the organization, and those people are likely to feel threatened by process removal when it would mean giving up control.

There’s also a career incentive asymmetry. The person who adds process and prevents a visible error looks prudent. The person who removes process and enables an error, even if that same removal also enabled three successful innovations, looks reckless. Diffuse organizational benefit doesn’t show up in performance reviews. Specific, attributable failures do.

This triggers a doom loop. Once the organization begins to treat judgment as a liability, talent begins to self-select out. Rule-followers come to dominate the employee base and talent density drops, “proving” the company needs more process. This is why Netflix places such a premium on talent density; less talent is both the cause, and effect, of heavy-handed process.

This is what drives the ratchet effect: once added, processes become nearly impossible to remove. The incentives in organizations heavily bias towards process accretion. The cumulative effect is that organizations become progressively more constrained, despite nobody seeking that outcome.

The ratchet usually turns one direction, but it operates on territory, not systems as a whole. What looks like freedom in the postwar period (nuclear plants in three years, polonium in cereal box prizes) wasn’t a result of deregulation. It was unregulated territory, a new frontier where the harms were not just unrealized but unforeseen. Process is scar tissue. No scars yet, no process.

This is why paradigm shifts appear to reset the clock. The technological revolutions of the early 20th century outran existing processes, which were built for an agrarian and early industrial economy. The new economy grew up in the gaps. By the 1970s, slowing technological growth allowed process to catch up to the frontier and fully enclose the territory.

Risks of Removal, Real and Perceived

While processes often address some acute issue, the people who advocated for them move on and the thinking that drove them is quickly forgotten. This results in a sort of organizational Chesterton’s fence: processes that are enforced rigorously, with no understanding of what problem the process was intended to solve.

With the purpose and reasoning behind a process lost to time, removal of that process now invokes a powerful psychological bias: the fear of the unknown. This creates circumstances where the perceived risk of removal far outpaces the actual risk.

This is loss aversion applied to organizational design. It’s why even well-intentioned process audits tend to produce modest results. The gravitational pull toward keeping things as they are is immense.

Everything above might suggest that process is almost universally bad. It isn’t. The problem lies not in process itself, but in process applied without regard to context. There are domains where suppressing variance is exactly correct, where the cost of error so dramatically exceeds the cost of rigidity that heavy process becomes a competitive advantage.

When Variance Is The Enemy

Netflix’s warehouse operations weren’t an exception to their philosophy; they were evidence that the leadership understood the need to right-size processes in a given context. The challenge is knowing when variance is your enemy and when it’s your only path forward.

Consider aviation and surgery. These are fields where a single mistake can have deadly consequences. The checklist revolution in both domains, documented extensively by Atul Gawande and others, has saved countless lives. The purpose of process in high-stakes domains is not to replace human judgment, but to ensure judgment is spent only where it adds value. By making the routine reliable, they reduce the need for constant improvisation and ensure that when deviation is required, it happens deliberately and from a stable base.

Innovation in aviation and surgery still happens, but it is quarantined to labs, simulations, and tightly controlled pilots. This is systems hygiene: funneling innovation to contexts that can absorb variance without catastrophe. Slower learning cycles are appropriate when mistakes are irreversible, public trust is fragile, and error costs dominate opportunity costs.

Intel’s famous “Copy Exactly!” methodology offers another example. When I was in graduate school for industrial engineering, we examined how Intel went to extraordinary lengths to replicate their manufacturing processes across facilities, matching equipment layouts down to the centimeter and even the orientation of buildings relative to the earth’s magnetic field. This seems absurd until you understand semiconductor manufacturing. Yield loss compounds relentlessly. A tiny variation in one step cascades into defect rates that destroy profitability. In this context, rigid process becomes a source of competitive advantage.

This suggests a general principle: Organizations should suppress variance in proportion to the cost of error, and nowhere else.

The first half of this law explains Netflix’s warehouses, Intel’s fabs, and the surgical checklist. The second half is where most organizations fail.

Most process failures stem from suppressing variance where error costs don’t justify it. This happens in two distinct ways:

The process is a self-referential loop: It creates the conditions that mandate its own existence. The solution is to recognize the loop and eliminate it.

One-size-fits-all governance: Process appropriate for one context gets applied uniformly across contexts with different error costs. The solution is structural separation.

Let’s take each in turn.

Breaking the Loop

We’ve established that unwarranted process is detrimental to long-term company health. We’ve also established that process in some circumstances isn’t only justified, but advantageous. How can a company looking to escape bureaucratic hell discern when they are cutting fat and when they are cutting muscle?

The strongest tell is that processes are self-referential; they create the conditions that justify their existence. For example, a defensive hiring process:

1. Make hiring extremely rigorous and consensus-driven to avoid bad hires

2. Hiring slows

3. Slow hiring compounds the pain of being short-staffed

4. Which makes managers reluctant to exit underperformers

5. Which “proves” you need rigorous hiring to avoid bad hires

When you notice a process that feels impossible to remove because of problems the process itself created, you’ve found a self-referential loop. These are high-leverage points in any organization not because they’re easy to break, but because this is where intervention compounds rather than just improving one thing.

Innovating When You Have Something to Defend

The second categorical process error, one-size-fits-all governance, is a challenge every company that reaches scale must face. The company now has much to defend: significant revenue, an established customer base, and a growing reputation. Many of the employees who made their careers on the company’s growth will be eager to protect what they have built. As expectations rise, so does the cost of making errors. It is perfectly reasonable for companies in this position to want to flatten some variance.

When multiple groups operate under shared governance (e.g., same parent company, division, or compliance function), organizations default to uniform processes to ensure consistency, auditability, and risk mitigation. Risk contagion is when the highest-risk unit’s controls become the de facto minimum standard for everyone under shared governance. This prevents fragmentation where siloed processes create enterprise vulnerabilities, but it sacrifices speed and effectiveness in the exact groups you need to sustain innovation.

If risk contagion pulls everything toward the highest-process context, the only way to preserve variance where it’s needed is to create enough distance that the contagion can’t spread. This naturally leads to the question: how much separation is enough?

Separation of Mandate vs. Separation of Means

You can have a different mission while still being subject to the same processes. True independence requires different means: different hiring, different accountability, different timelines. Separation isn’t one choice, but a series of choices along a spectrum: same division → separate division → subsidiary → external entity.

Greater levels of separation offer greater degrees of process freedom and a longer half-life of that freedom. The strategic question is: how much separation do we need for this particular mission? A six-month sprint to prototype something might survive with divisional separation. A multi-year effort to build a new business line probably cannot; the half-life is too short for the timeline.

“Startup culture with big company resources” is the lie organizations tell themselves to avoid real separation. They want the benefits of variance without paying the cost of actual independence. The resources come with strings: reporting requirements, budget cycles, HR policies, performance reviews calibrated to the parent’s norms. You can’t declare a different culture while remaining tethered to systems that enforce the old one.

Low levels of separation are harder to maintain because jealousy creates political pressure to close the gap, especially when the group subject to fewer rules begins to find success. Everyone sees who gets autonomy and who doesn’t. The people outside the protected space resent it. Political pressure builds to revoke the special treatment, killing innovation. This is exactly what happened to Saturn under General Motors. It started with genuine independence: different labor agreements, different dealer model, and different manufacturing. Saturn’s success built resentment from other divisions that became rancorous. GM leadership starved it of resources to keep the peace, leading to its destruction.

Persistent pressure from the parent organization explains the evolution of Alphabet’s X division, which develops “radical innovations”. Their strategy for incubating new technologies has evolved from independent units, to independent subsidiaries, to fully external entities.

The progression tells a story:

• Internal units: Minimal separation, antibodies win quickly.

• Alphabet subsidiaries: Legal and organizational separation, but still sharing infrastructure, HR systems, cultural gravity.

• Fully external entities: True independence, own board, own incentives, can develop genuinely different operating norms.

Over time, Alphabet’s X recognized that you cannot indefinitely fight human nature within a shared institution. Structural separation is necessary. Not just a different mandate, but different means: different hiring, different accountability, and different timelines. The progression from internal units to subsidiaries to fully external entities is evidence of how much distance it actually takes.

Fighting the Ratchet

The ratchet will keep turning unless someone actively fights it. Breaking loops requires someone with enough authority to absorb the political cost. Maintaining separation requires someone with enough altitude to protect the air gap.

Only senior leaders have the positional authority to do this. Everyone else is operating inside the local incentives that favor process accumulation. The person who removes process and enables an error is vulnerable; the person who keeps process and perpetuates sluggishness is safe. Only executives have enough standing to absorb the political cost of removal and enough time horizon to see the benefits materialize.

What does this look like in practice?

Executive attention and urgency. British retailer Julian Richer offers a model for how the process ratchet can reverse outside of a crisis. He created a “Cut the Crap Committee“ to roll back process at a time when Richer Sounds was performing well. Process usually reverses only during crisis because that’s when the issue naturally becomes urgent. What Richer understood is that leaders have the power to create urgency by focusing sustained attention on problems that would otherwise stay invisible. Richer didn’t wait for a crisis to grant him permission. He gave himself permission.

Pruning tied to scale. Every process is calibrated to a moment in time: a particular scale, technology, and competitive landscape. When headcount or revenue crosses a major threshold, old processes don’t automatically become wrong, but many do. Tie mandatory process audits to scaling milestones: each significant growth threshold triggers review. The question isn’t “is this process working?” but “is this process still earning its cost at our current scale?”

Error budgets for overhead. Borrowed from site reliability engineering: define an acceptable threshold for process burden, say, approval cycle times or percentage of time spent on administrative work. If you’re consistently under budget, you’re probably over-controlled. Threshold breaches freeze new process additions until existing ones are pruned. This makes the tradeoff quantitative rather than political.

None of these work as one-time initiatives. The ratchet turns continuously; fighting it requires continuous effort. Ninety-day pushes can build habits, but without sustained executive attention, entropy reasserts itself.

These tactics matter because the alternative, waiting for a crisis to force elaboration, is how most institutions fail.

Process Kudzu

Most people who have joined a small company that succeeded and went on to become a much larger company can share a similar story: The early days are defined by utter chaos and relentless speed in the pursuit of building something customers want. After the company finds success, process starts to creep in. The changes are almost imperceptible at first. Then one day, they wake up and realize it now takes six weeks to execute on something that used to take six days.

This arc is predictable:

Startup: No process because there’s nothing to protect. Speed is the only advantage. You ship, sell, and market without asking permission.

Growth: Customers arrive. Revenue becomes meaningful. Then something goes wrong: a bug, a PR disaster, a compliance failure. The question gets asked: how do we make sure this never happens again? Organic structure begins to take shape, and informal processes arise to avoid the worst outcomes.

Formalization: As a company reaches maturity, informal structures and processes are replaced by explicit hierarchies, policies, and procedures. Extensive red tape introduced during this phase triggers a bureaucratic crisis, with process requirements so stringent that the business loses the ability to function effectively

Elaboration: Attention shifts from the harms process prevents to the harms process creates. The organization takes a critical eye towards process and begins to prune, returning the organization’s ability to adapt and innovate.

This pattern is so common that an entire field, organizational life-cycle theory, has emerged to study it. The mystery lies in the last stage. Most organizations rarely reach elaboration and remain stuck in bureaucratic crises for years, even decades. The ratchet keeps turning until the capacity to move is gone.

We can see the same arc play out with nation-states:

Startup: The United States was a startup country, and westward expansion was its growth strategy. Manifest destiny pushed settlers into territory with minimal federal presence, ad hoc justice, and rampant land grabs. The Wild West wasn’t a policy choice; there was little to protect and no capacity to enforce process.

Growth: The frontier officially closed in 1890 when the Census declared no more contiguous unsettled land. Population concentrated in cities as railroads and industry scaled. Harms became impossible to ignore: Standard Oil’s monopoly, meat-packing horrors exposed by muckrakers, 146 workers killed in the Triangle Shirtwaist fire. The Progressive Era asked a question that would echo for decades: how do we make sure this never happens again? The regulatory state began with the FDA, the FTC, antitrust enforcement, and labor laws.

Formalization: By the 1970s, a new generation of harms demanded attention: smog-choked cities, polluted rivers, destroyed ecosystems. Laws like the National Environmental Policy Act (NEPA) built an apparatus designed to slow down building so abuses could be seen and stopped. The proceduralism driven by laws ushered in a crisis of red tape, slowing down all building indiscriminately. Wind farms that would replace coal plants spend over a decade in permitting.

Elaboration: In the late 1970s, regulations became tangible villains amidst the Winter of Discontent in the UK, gas lines in the U.S., and empty shelves. Think tanks focused attention on postwar interventions as the cause. Airline deregulation in the U.S. was the first domino to fall in the wave leading to the Thatcher/Reagan era, when deregulation was extended to trucking, energy, finance, and labor.

Whether these reversals were wise policy is fiercely contested and beyond the scope of this essay. The point is narrower: this is a rare case when the ratchet actually turned backward, and they share a common structure.

Wartime poses an existential threat to survival. Two world wars concentrated executive authority and created a “just get it done” culture that overrode peacetime proceduralism. Processes didn’t disappear; they were suspended by war powers.

Economic crises create an existential threat to political leadership. Stagflation didn’t threaten Britain’s or America’s survival, but it threatened the governing consensus. Thatcher-Reagan had permission to deregulate because the status quo was politically untenable.

Corporate near-death is the existential equivalent to wartime. Apple was 90 days from bankruptcy at the start of Jobs’s second tenure. IBM faced similarly dire circumstances when Lou Gerstner took over as CEO. The board grants emergency latitude (wartime powers) because the alternative is liquidation.

In all of these examples, existential threat provided the permission structure for reversing the ratchet. Nations have learned to formalize the exception for wartime. Economic crises and corporate near-death still rely on improvised permission; someone has to recognize the threat and act before the formal structures catch up.

This is why most organizations never reach elaboration: the threat has to be visible and credible enough to override the local incentives. Without a crisis, only sustained leadership attention can create the permission structure to reverse the ratchet.

Conclusion

Process is a tool for suppressing variance. It has legitimate, sometimes essential applications. Organizations handling money, lives, or safety-critical systems should suppress variance heavily. The cost of error justifies the cost of rigidity.

Process has a natural tendency to expand beyond its useful boundaries. The mechanism is seductive: each addition makes sense in isolation, prevents a visible harm, and carries only invisible costs. The ratchet turns. Turning it back requires fighting psychology, organizational incentives, and the accumulated weight of well-intentioned decisions.

The leaders who understand this recognize that process management is not administrative overhead; it’s strategic work. Dialing in the level right for each context and actively fighting accumulation is how organizations maintain the capacity for both reliability and innovation.

Most organizations fail at this. They add process and never remove it. They let the ratchet turn until the organization is optimized for preventing yesterday’s mistakes at the cost of discovering tomorrow’s opportunities.

The pattern is predictable, and the mechanism is understood. The question is whether leaders will treat it as their responsibility.

Systems that fear variance eventually fear learning, and systems that fear learning eventually stop adapting. The rest is just a matter of time.

Anthropic recently revealed that almost all of the code enhancing Claude is now written by Claude itself, under human guidance. This self-bootstrapping isn’t unique to Anthropic. Every major AI lab is racing toward the same threshold: systems that outperform even Nobel-caliber humans across every cognitive task.

Most think tank and academic work on AI displacement rests on two backward-looking claims: technology usually automates tasks rather than whole jobs, and new labor-intensive sectors always emerge to absorb the displaced. Both made sense when manufacturing and services rose alongside automation, when capital and labor were geographically coupled, and when winner-take-all dynamics were weaker.

Those guardrails no longer hold. Generative AI plus instant global distribution means cognitive automation can scale across millions of roles simultaneously, without creating new mass-employment industries to offset the losses.

This essay models what happens when AI doesn’t just augment but substitutes, gradually catching and then surpassing human cognitive labor across the economy. We can understand the logic through a simple game between firms, where locally rational choices add up to macroeconomic self-destruction. Call it The Game That Ate Itself.

Rules of the game

We can understand how AI‑driven automation plays out by treating it as a simple game between firms.

The game has two competing firms that split a market between them. Both are rational, strategic decision‑makers trying to maximize long‑run profits. Crucially, total demand for their products depends on aggregate consumer income.

Each cell in the payoff matrix represents the net present value of the firm’s future profits; a payoff of zero means the firm has effectively exited the market.

Each quarter, every firm chooses between two actions: keep operating under the current cost structure, or invest in and deploy more automation. At any point in time, their choices produce a payoff matrix like this:

Both firms start in the “don’t automate” cell. From there the four outcomes line up like a classic prisoner’s dilemma:

• Reward (R): If neither automates, they enjoy stable demand and profits.

• Temptation (T): If I automate and you don’t, my costs drop, I undercut you, and I take the market.

• Sucker (S): If you automate and I don’t, I get wiped out.

• Punishment (P): If we both automate, neither gains advantage; we just reset the game with lower costs and no extra share.

This isn’t a one‑shot decision; it repeats every quarter. Each period, both firms decide whether to introduce more automation. In a textbook iterated prisoner’s dilemma, the canonical “good” strategy is tit‑for‑tat: start by cooperating, match your rival’s last move, and punish defection but forgive quickly. On paper, that ought to stabilize the market. Here, it doesn’t.

The usual stabilizing mechanism completely breaks down here because playing the game eats the board. Every round of automation reduces the amount of labor needed for the same output. Lower labor income erodes demand, reducing the size of the market in all future rounds.

In formal terms, the per‑round payoff matrix is a function of a hidden state variable, aggregate demand, that declines whenever firms defect by automating. The more rationally they play, the faster they saw off the branch they’re sitting on.

Now scale from two firms to thousands across the whole economy. The right move for any given firm in any given quarter is to automate a bit more. Even if every executive fully understands that these locally rational choices add up to macroeconomic destruction, none of them has a credible way to coordinate restraint. The iterated nature of the game is what makes it a trap rather than an obviously suicidal choice: each round only erodes a little demand, but refusing to play in any round is indistinguishable from choosing to die.

Under these rules, the Nash equilibrium is not a stable prosperity but a slow‑motion economic catastrophe. Most firms and households are either wiped out or pushed to the margins. Given these dynamics, what would it take to change the outcome?

Remaking the game

The payoff structure of the automation game leads straight toward economic self‑destruction. Once we accept that the Nash equilibrium is bad, the only real question is how to change the game. All serious proposals fall into some mix of coordination or rule‑changing. What follows is a survey of those levers and whether they actually touch the mechanism.

Taxing and redistributing the automation dividend shifts some surplus from AI‑intensive firms (or capital returns) into income floors or wage subsidies. In principle, this keeps demand alive even as direct employment shrinks.

The obvious obstacle is jurisdictional arbitrage: capital moves faster than tax regimes. Any serious version would need OECD‑style minimum coordination, but faster, broader, and with actual teeth rather than polite statements.

Shorter standard workweeks try to share the remaining human‑necessary work across more people by cutting hours (for example, a four‑day week). Proponents argue this preserves employment, dignity, and broad consumption while still cashing in productivity gains. Making that stick requires strict labor law, which re‑opens the jurisdictional arbitrage problem: work flows to regions with weaker protections. Countries can end up racing to the bottom on labor standards the way Ireland raced to the bottom on taxes.

The deeper problem is that it assumes there is still meaningful human work to distribute. If AI is genuinely substitutive across most cognitive tasks, the policy shifts from sharing real work to inventing make‑work to preserve the employment‑to‑consumption loop. It may have value as a transitional measure, but it papers over a structural break rather than addressing it.

Job‑transition guarantees scale something like Trade‑Adjustment Assistance into the automation era: if your role is automated, you get guaranteed income and funded retraining into sectors where humans remain bottlenecks. Conceptually, it mirrors shorter workweeks; both only work if there is still enough non‑automatable work somewhere in the economy.

Variants like Just Transition Framework try to push the cost of retraining onto the firms that cause the displacement. That sounds fair until you remember the competitive landscape: any burden that falls only on incumbents and not on AI‑native firms just accelerates the incumbents’ collapse. AI‑native firms have no legacy workforce to retrain, so the rule turns into a competitive moat for exactly the firms driving the disruption.

Stronger labor regimes try to give workers more say in how AI is deployed internally, pushing toward augmentation rather than outright replacement. In the extreme, this collapses into a neo‑Luddite technology ban. Instead of changing the payoff structure, it effectively cancels the game by making automation itself off‑limits.

As with job‑transition guarantees, it is almost impossible to design this without disproportionately punishing incumbents. The more fundamental problem is that the game is multi‑level: the domestic labor market sits inside the game of remaining a sovereign nation. If you take seriously arguments that whoever controls AI controls everyone else, unilateral restraint starts to look less like virtue and more like a suicide pact with your own citizens.

Completing the circuit

Taken seriously, all of the labor‑law‑first options fail on their own terms. That leaves redistribution not as an ideological preference but as the only lever that actually touches the mechanism.

Thomas Piketty’s Capital in the Twenty‑First Century argues that when the return on capital exceeds the growth rate of the economy (the famous r > g), wealth concentrates. The larger the gap between r and g, the faster that concentration happens. Piketty is describing a world of factories and land.

When capital returns come from owning the substitute for human cognition itself, that divergence accelerates catastrophically. The asset that is appreciating (AI capital) is the same asset that is destroying the underlying source of labor income. Returns to capital not only outpace growth; they actively crush the labor share that used to anchor demand.

The standard redistribution debate is so fraught because it centers on deservedness: do people have a right to income they didn’t “earn”? That framing invites endless moral combat and lets opponents anchor on welfare dependency and the dignity of work. In the context of mass automation, redistribution is not about whether displaced workers deserve to eat; it is about whether the firms that displaced them still want customers. Ethics give way to plumbing.

The companies building the most powerful automation in history are, directly or indirectly, entirely dependent on the consumer economy that automation is eroding. If we follow the logic to its endpoint: a few AI companies automate everything, capture all of the surplus, and then… sell to whom? Each other? We end up with a handful of firms sitting on infinitely productive capital but have no one to monetize it against. If you’re looking for a reason to be hopeful, this is it. The companies that most need someone to pull the redistribution lever are the ones building the trap.

The winning team

Chet Booker was the third-generation owner of a manufacturing business outside of Lexington, Kentucky. They produced precision fasteners used in the aviation industry. The last ten years were characterized by intense competition from overseas suppliers. The workforce was down 20% from its peak, and Chet had invested nearly all of the company’s profits over the past five years in automation to help them compete. Despite these improvements, pricing pressure was intense and margins were slim.

His fortunes seemed to change after the new administration came into power. Sweeping tariffs raised the cost of overseas competitors’ parts by 40%. He raised prices 30% and still found himself running three shifts to keep up with demand.

He had a domestic competitor in California. When a friend with connections to the administration suggested that a modest donation and some public support might help Chet’s chances with defense contracts, he understood what was being offered. He made the donation and posted a photo with an official. Within six months, he was the preferred supplier for two major defense contractors while his California rival found their contracts mysteriously delayed, their applications lost, their inspections multiplied. Chet told himself they probably deserved it; they’d been dismissive of him at an industry conference once, called his operation “quaint.” He didn’t feel bad. He started thinking about a second factory, maybe passing something bigger to his son than what his father had passed to him.

Less than two years later, order volume was drying up. The second factory never happened. Chet thought he was going to hand the business over to his son; instead he was looking at a firesale to KKR. What happened?

A familiar arc

There have been moments in history when stories like Chet’s could be found in abundance: Spain in the 1940s, Argentina in the 1950s, Germany in the 1930s. The setting changes, but the arc is the same. A regime promises to secure national strength by closing the borders to foreign competition. Businesses that align with the regime flourish initially, but the good times are short-lived. The economy inevitably begins to rot, and in the end, no industry or company is spared.

The term for this policy is autarky, which is a state of national economic self-sufficiency. The goal of autarky is for a country to produce all goods and services it needs internally, minimizing reliance on international trade. Autarky is sold as strength through independence from foreign supply chains, protection from external shocks, and a high degree of self-reliance. Given the stated goals, autarky may sound reasonable in the abstract. To understand why it is so harmful in practice, we turn our attention to Francoist Spain.

The Spanish trap

Spain instituted autarky after the Nationalists won the Civil War in 1939. Tariffs, quotas, licenses, exchange controls, and outright bans gave Spain the thickest borders in Europe, all but eliminating trade.

The results were catastrophic. Agriculture had been Spain’s greatest strength. Exports of oranges, olive oil, wine, and sardines more than offset its grain imports. Spain had been an importer of tractors and fertilizer. When these critical inputs dried up under autarky, production collapsed. The policy caused a famine that killed 200,000 people. Black markets for grain became commonplace.

Industry fared no better. Self-imposed isolation forced Spanish factories to produce their own steel, coal, and basic consumer goods instead of importing cheaper, better inputs and specializing in higher-value work. State-controlled syndicates monopolized entire sectors, blocking private expansion. Favored firms thrived on government contracts, but absent competitive pressure, they stagnated. The incentive was to secure the permit, not to run an efficient plant. Firms optimized for quotas and political connections rather than productivity.

Spain was neutral in World War II. While Germany, France, and Britain saw their cities bombed and their industrial bases destroyed, Spain’s infrastructure remained largely intact. They should have had a decisive advantage in the postwar years. Instead, by the early 1950s, Spain’s GDP had fallen to just 40% of the Western European average. The countries rebuilding from rubble were outperforming a nation that had seen far less destruction and had a six year head start.

The gap was entirely self-inflicted. When Spain finally reopened to foreign investment and trade under the 1959 Stabilization Plan, the protected industries collapsed. Textile and consumer-goods factories that had expanded behind tariff walls turned out to be small-scale, obsolete, and unable to compete. Decades of protection had made them dependent, and their products were inferior and overpriced.

What followed was the “Spanish Miracle”, a period of rapid growth that quickly narrowed the gap with the rest of Western Europe. The turnaround proved what the previous two decades had cost. The stagnation wasn’t bad luck or external circumstances; it was autarky.

Trading prosperity away

We know, before a country closes its borders, roughly what it will sacrifice by forgoing specialization and trade. Economists estimated that trade barriers alone cost Spain 20% of a year’s consumption. The economic principles that predict this (comparative advantage and gains from trade) were established in the early 19th century, so the harm was eminently foreseeable. This raises a question: If autarky is so reliably destructive, why do regimes keep reaching for it?

The answer lies in the regimes that have implemented this policy: Peronist Argentina, Francoist Spain, Nazi Germany, and Apartheid South Africa.

Autarky is a tool for regimes that trade economic prosperity for ideological purity and control. Authoritarian regimes require centralized control. Autarky delivers it by eliminating the external forces that could threaten the regime: competition, foreign capital, international pressure, and the flow of information that accompanies trade.

When trade is choked off, the government becomes the primary buyer (or the gatekeeper to the only buyers), the sole allocator of permits, the gatekeeper to survival. Every firm’s fortunes now depend on political favor. This dependency breeds corruption, but this corruption is a feature, rather than a bug, in this system.

Once the rules change, businesses face a simple choice: participate or die. The firm that refuses to make the donation, to cultivate the connection, to play the game just loses to competitors who will. There is no neutral ground. The regime doesn’t need to coerce loyalty; it has structured the economy so that loyalty is the price of staying in business.

This is why autarky and authoritarianism travel together. Closed borders create economic dependency. Corruption converts that dependency into control. The firms that think they’re winning are actually being absorbed into a system they can no longer exit.

Eighteen months later

This brings us back to Chet, eighteen months after the photo with the official.

Chet’s political patron publicly disagreed with a cabinet official and soon found himself under indictment. Chet’s photo with him seemed to resurface every time Chet was close to landing a significant government contract.

The following year, the DoD shifted its priority to favor “national champions” (RTX/Lockheed Martin). Chet’s small-supplier status was revoked as “non-strategic”, and his government contracts dried up entirely.

He pivoted toward commercial aviation, only to find global demand buckling under counter-tariffs. Boeing’s international sales collapsed, and selling into Airbus became commercially impossible.

The large loans Chet took out to expand production under the promise of protection and guaranteed contracts made it impossible to ride out the downturn. By the time KKR came calling, Chet had run out of options. He sold for pennies on the dollar.

The factory tripled production the next month.

The operations team at an online marketplace saw its warehouses rapidly filling up, facing the urgent reality of racks running out of space. Overcapacity was already slowing down operations and on its way tobecoming a full-blown crisis. The team could see that inbound far outpaced sales, and their diagnosis was that the company was listing too much. In response they slowed down listings.

Ops averted the immediate crisis but the problems were only beginning. Growth panicked when revenue fell; leadership decided growth mattered more so growth won the day, putting the problem back on ops. Ops started scrapping inventory to free space. Now finance raised the alarm as costs piled up, but operations won the day this time. This pattern of conflicting teams and the problem bouncing from function to function persisted for two years.

Finally, the product team built an outlet section which offered volume discounts to move excess inventory. The numbers improved and the product team celebrated having finally solved the problem. People moved on and the question of why outlet had been created faded into memory.

Three years later, the company had gone public. They were burning cash at a furious pace with no clear path to profitability. The question that unlocked this problem came not from senior leaders or the teams that had been in conflict about oversupply, but from a seasoned employee who had worked across most of the functions of the company. In an organization where functions worked independently, this individual’s cross-functional visibility was an accident of tenure rather than a design choice.

The reframe was that inventory wasn’t one flow, it was many, and those flows were systematically mismatched: Cheaper apparel and smaller sizes were in perpetual oversupply and this comprised everything selling through the outlet. Once characterized, the problem wasn’t “how do we sell this?” It was “why are we accepting this?” The answer rippled back through the entire system - supply, ops, finance, marketing. Supply and demand finally matched and the company was profitable a year later. Outlet shut down because there was no oversupply left to sell. This reframing didn’t just fix the financials; it corrected a failure of sensemaking.

At each stage the diagnosis made sense. Ops really was out of space. Growth really was watching revenue fall. Product correctly identified price as the most powerful lever under their control. If you had been in the room, you likely would have supported every one of these decisions. None of them were negligent, yet none of them addressed the problem that mattered.

Subscribe now

Known, Unknown, and Invisible

Most people in industry have stories like this, where intelligent, well-intentioned people on different teams disagree about the appropriate course of action, and leadership find themselves forcing alignment. A problem solved in one part of the company has unforeseen consequences elsewhere. Symptoms mask problems that are only revealed fully in times of crisis.

One of the most useful ways I’ve found to think about this comes from Leadership Without Easy Answers by Ron Heifetz, who spent his career studying how capable people repeatedly fail in leadership roles.

Type 1: Known problems with known solutions. Heifetz calls problems “technical” when they can be solved by experts with existing knowledge. Think of a surgeon performing a known procedure (e.g. heart surgery), or an enterprise upgrading a software suite.

Type 2: Known problems with unknown solutions. Classic examples include climate change and aging; common business-flavored examples are “our best people keep leaving”, or how to approach technical debt. You can name and measure the problem, and agree on what the problem is, but the path to resolution is uncertain and requires experimentation and discovery rather than a known playbook.

Type 3: Unknown problem and unknown solution. These are sensemaking problems, characterized by seeing symptoms but being unable to locate the cause. Reframing is required to make the problem visible and solvable. The example at the start of this chapter is one such example. Before the reframe: “we have too much inventory” / “we’re not acquiring the right customers” / “our prices are too high.” After the reframe: “we’re accepting supply that doesn’t match demand.”

One of the most common problems Heifetz discusses is people attempting to apply technical fixes when the problem requires adaptation. He defines this as addressing deep-seated conflicts that require learning, mindset shifts, and people doing the work rather than expert fixes. Heifetz was concerned with political economy, but companies often fall prey to the temptation of a quick fix.

The most consequential mistakes are when Type 3 problems are mischaracterized as Type 1 or Type 2. When a problem is assumed to be known when it is not, companies can, and often do, spend enormous resources attempting to solve the wrong problem. The even greater danger is that these solutions mask symptoms, removing urgency and making the remaining signal harder to see.

This is exactly what happened in our example at the start of the chapter. It was three years from outlet to resolution because the symptoms were masked. Over $100 million in losses stemmed not from failed execution, but from solving the wrong problem.

If we return to the opening story, the conflict between ops and growth was, at the time, interpreted as a priority dispute. Ops saw a capacity crisis and growth saw a revenue crisis. This view of the conflict required picking a winner. Picking a winner ended a conversation that had not yet produced understanding.The reality was not two different problems, but two teams seeing different sides of a single problem that neither one of them could name.

What are the signs that we’ve mischaracterized a problem and need to broaden our perspective rather than doubling down? And how can we avoid mischaracterizing problems in the first place?

The Clarion Call of Execution

When solutions fail to solve the problem at hand, a common trap senior leaders often fall into is assuming these failures are rooted in execution. “Failure to understand and execute the strategy” assumes three things are true. First, the problem has been correctly identified. Second, the strategy encodes a valid causal model. Third, disagreement means people didn’t get the memo.

When the problem is Type 1 or Type 2, all of these assumptions might hold. When the problem is Type 3, none of these assumptions hold.

The execution diagnosis is attractive because it preserves authority. Accountability flows in one direction. It also avoids reopening the problem and feels actionable (”execute harder”). A Type 3 diagnosis is uncomfortable because it implies that we don’t yet know what’s wrong, and disagreement is informative. Leadership must hold uncertainty longer, and strategy must remain provisional. Ramping up pressure on execution narrows thinking precisely when solving the problem requires zooming out.

Most leadership selection systems reward decisiveness, confidence, narrative coherence, and speed of convergence. They do not reward holding competing hypotheses, public uncertainty, reversing decisions, and updating beliefs in public. This is why misdiagnosis persists even when signals are present. The signals exist, but the frame makes them hard to see.

Signals in the Noise

There are a few common tells that a problem has been mischaracterized.

The first is when problems are solved locally and produce unexpected second-order effects, often surfacing elsewhere in the organization after the solution is applied. A common trap is assuming that the locus of pain is the locus of cause. This is understandable: the team experiencing the symptoms has the strongest incentive to act, and local intervention is often the fastest way to relieve pressure. In complex systems, treating symptoms locally frequently shifts the burden rather than resolves it, creating new problems upstream or downstream.

A second tell is that solutions are abundant, but explanations are not. When a team cannot agree on why something is happening, yet has no shortage of ideas for what to do, it is almost certainly operating in an unknown-problem regime. This pattern is usually easy to recognize: lots of ideas, lots of initiatives, lots of motion, and no shared causal story. Activity substitutes for understanding.

A third tell is when learning fails to compound. You keep acting, but your understanding doesn’t deepen. Action should generate signal, but signal only becomes learning when it can be interpreted. When the frame is wrong, each action stands alone, epistemically isolated from the last.

These signals are useful diagnostics, but they share an important limitation: they are usually visible through a pattern of failure. By the time they appear, the organization has often already paid a significant price.

The Signal That Arrives First

The single strongest signal that you are facing a Type 3 problem, and the only one that is present before failure occurs, is misalignment between teams. Persistent misalignment is the organization telling you that it does not yet understand the problem it’s trying to solve. In this context, misalignment can show up in two ways.

The first is the familiar one: teams disagree on priorities. The second is subtler: symptoms become visible through deteriorating metrics, and teams cannot agree on what problem they believe they are facing.

Misalignment is treated as noise when in many cases it is distributed signal. Each group has a perspective driven by locally true observations of the system as it intersects with their incentives, mental models, and lived experience. Much like the parable of the blind men and the elephant, no one has a complete picture, but the failure is one of synthesis, not of politics, poor communication, or competing egos.

Performing this synthesis requires a perspective spanning many parts of complex businesses, so this responsibility necessarily falls to senior leadership. This duty is not one of omniscience, but rather of asking the right questions and keeping the process of discovery going long enough for the problem to reveal itself. When teams are in conflict right now, you can ask: is this a priority conflict (we agree on the problem, we’re fighting over resources) or is this a diagnostic conflict (we’re looking at the same situation and seeing different problems)?

Structure also plays a role in problem identification. In our example, the company had no senior leader with dominion over all of the core functions and those functions operated in silos. No single leader had all of the information, so the problem was only revealed through a fortuitous set of circumstances. Structures where senior leaders have a span of control and span of authority that allows them to both have the perspective necessary and to act on it have an advantage.

Before converging on a solution you have to explore the problem by actively seeking perspectives that don’t match, pressure-testing hypotheses, and treating disagreement as information. Only after you’ve converged on what the problem actually is do you diverge again on possible solutions. Two expansions, two contractions.

The discipline is in holding the first diamond open long enough. The pressure to skip to solutions is immense. It feels like progress, creates a sense of certainty, and for leadership, externalizes the problem because it shifts the need to one of execution.

This puts leaders in a genuine bind. The same instincts that made them successful, like move fast, project confidence, and drive alignment, become liabilities when the problem is unknown. Slowing down looks like weakness and holding uncertainty looks like indecision. Asking “what if we’re solving the wrong problem?” looks like undermining your own strategy.

One way to handle this conundrum is the executive delegating the power and responsibility of problem discovery to their Chief of Staff. This solves a number of problems. The CoS pressure-tests hypotheses and seeks disconfirming evidence in a role where this behavior is appropriate and expected. Then the executive gets to do what executives do: converge, decide, and act. The decisive behavior is in service of solving the right problem.

This is also politically safer. The CoS can ask the dumb questions, surface the uncomfortable possibilities, float the “what if we’re solving the wrong problem?” question without it looking like the executive is wavering. The exploration happens in a protected space.

The specifics matter less than the principle: When incentives stand in the way of getting the best outcome, the system must be redesigned so this is no longer the case.

The leaders who navigate Type 3 problems well tend to separate two things: confidence in the process of discovery from confidence in any particular answer. They can say ‘we’re going to figure this out’ while also saying ‘we don’t yet know what this is.’ That’s a hard stance to hold, but it’s the only honest one when you’re facing the unknown.

At a system level, we look to processes that counterbalance these instincts. The double diamond does this by making problem discovery an explicit phase with its own discipline: you cannot move to solutions until you’ve pressure-tested competing hypotheses about what the problem actually is. The process can be lightweight. It only requires the discipline to ask, before any solution is proposed: have we converged on the problem, or just on our discomfort with not having an answer?

What Leaders Uniquely Do

When misalignment shows up, the work is to first decode it. Misalignment is rarely the failure leaders think it is; more often, it’s the last honest signal the system has before learning shuts down. Organizations are comfortable diagnosing failures of coordination. They are not comfortable diagnosing failures of knowing.

The most fundamental reframe leaders can make is understanding that problem identification is the job. Leaders don’t solve problems themselves; they have teams who are closer to the work and should be much more capable of solving the problem once it is known. What leaders uniquely do is ensure the organization is solving the right problem. When that fails, no amount of execution excellence can compensate. Execution is how you solve problems. Identification is how you solve the right ones.

The truth-identification machine

The ultimate aspiration of a product team is to discover what is true about customers, markets, incentives, behavior, and business value. Everything else, planning, structure, rituals, frameworks, is just scaffolding around this core purpose.

Most product teams say they want to be data-driven. Very few have the discipline to seek out data that proves them wrong.

Subscribe now

Falsification as the discipline

Karl Popper (1902–1994) was one of the 20th century’s most influential philosophers of science. His big idea was deceptively simple: Science doesn’t prove things; it disproves things. Knowledge grows by eliminating wrong ideas, not by conclusively verifying right ones.

Popper in brief:

1. He believed you can never truly “prove” a theory, you can only fail to disprove it so far.

2. He thought the defining feature of a scientific theory is that it can be falsified. A real theory makes predictions that could turn out to be wrong.

3. He thought progress comes from the cycle of conjecture (idea) → refutation (test).

If this sounds like the build-measure-learn loop, it’s not — at least as most teams practice it. Build-measure-learn is a process; falsification is a discipline. The difference is that falsification requires you to articulate in advance what evidence would cause you to abandon your hypothesis. Without that, the risk is shipping, then pattern-matching your way back to what you already believed.

This isn’t just a process gap; it’s a psychological one. Popper understood something deeper — that people naturally default to confirmation, not falsification. We look for evidence that flatters our beliefs and ignore anything that threatens them. Popper saw falsification as a discipline you must deliberately choose because it cuts directly against instinct, ego, and social identity.

The advantage falsification brings beyond identifying the truth is that this mindset increases velocity and decreases time to value. A sharp understanding of what would disprove your hypothesis allows you to design and sequence tests that grant you that knowledge as quickly as possible. Failing tests narrow the realm of possibility, allowing progressively better hypotheses until the underlying truth comes into view.

This concept applies to mental models as much as it does product launches. Every mental model is a set of claims that need to be pressure-tested. Better mental models create the foundation for better hypothesis generation. Product management is hypotheses all the way down.

Thus, you want a team full of people asking themselves “how can I prove myself wrong?”

The sociology of resistance

Falsification is conceptually straightforward, but deeply countercultural. Adopting a falsification mindset challenges instincts, incentives, and identity. Product managers don’t have a theory problem; they have a sociology problem.

Two of the greatest challenges are psychological: confirmation bias and the drive for certainty.

Humans are wired to seek out information that confirms our ideas and avoid information that threatens them. We gravitate to evidence that flatters our initial assumptions because it stabilizes our identity and simplifies our world. We are also profoundly certainty-seeking creatures. Uncertainty registers in the brain as a threat, so we instinctively paper over ambiguity rather than expose it. Falsification threatens both drives.

The very act of attempting to disprove a belief forces you to admit you don’t already know the answer. And if you follow the practice honestly, you will disprove your hypotheses with some regularity — publicly demonstrating that you didn’t know the answer. For many people, this is psychologically destabilizing. It feels like status loss.

The organizational dynamics are even more hostile.

Cultures that reward overconfidence often mistake intellectual humility for weakness. People are extremely status-loss avoidant, so they converge on whatever beliefs feel safest to hold in public, not the beliefs most likely to be true. In cultures where product launches that do not deliver their intended outcome are treated as failures rather than experiments, truth-seeking becomes painful and politically dangerous. The rational move in these environments is to avoid disconfirming evidence, to shield your ideas from reality, and to run ornamental tests — experiments designed to look rigorous while avoiding real risk — instead of consequential ones.

Some organizations go further and create explicit incentive structures that are incompatible with truth-seeking. The purest example is the feature factory, where the goal is not discovering what works but delivering what the executive team already believes will work, regardless of evidence. Even more sophisticated organizations can fall into the same trap through heavyweight process cultures. OKRs are a prime example. Every team knows the score that demonstrates the appropriate level of ambition. People are incredibly creative when the goal is hitting a number or satisfying a process, so creative that the number or the process becomes the target itself. In these cases, the epistemic function of the work evaporates. Falsification dies because the game is no longer about discovering truth; it’s about performing alignment.

This is why falsification must be deliberately protected. Left alone, people default to identity-protection, fear avoidance, and target-chasing. Truth-seeking requires a system that rewards curiosity, rapid updating, and intellectual honesty, and that treats “being wrong” as progress rather than a loss of status.

The following sections describe the organizational practices required for truth-seeking, and the container leaders must build to make those practices not just possible but inevitable.

Action as the path through uncertainty

I once faced a decision with enormous upside and daunting uncertainty. I had identified a change to a company’s offering that, if successful, would have a $100M+ payoff within 2 years. The new offering hinged on people having some flexibility in their schedules. Implementing this change also came with significant risk — it would cost nearly $1M to implement, and would have taken years to unwind if it underperformed, all while racking up millions in program costs. A classic one-way door product launch.

The upside was so high that the CEO viewed finding a path forward as critical, but other executives were skeptical that incentives could shift behavior to the point where the program would break even. The instinct was to analyze more, but I noticed that further analysis was just rearranging the same information. Forward motion ceased. Analysis could not resolve the core uncertainty: Would customers actually shift?

We broke the impasse by shifting our thinking. Instead of asking “what would need to be true?”, we asked “what evidence would cause us to abandon our efforts?”. Once we asked the question that way, the kill condition became clear: if fewer than 3% of visits would shift, the program would not be viable. This became the hypothesis to test. From there, we sought the lowest lift, lowest commitment intervention that could provide this data. We arrived at setting up a test using gift cards to incentivize customers to shift behavior. In 2 weeks, and at 2% of the cost of a full rollout, we had our answer: 6% of visits shifted, and the full program was greenlit.

We took a high-stakes, irreversible decision, and found a simple, low-cost, reversible action hiding within it.

We can think of decisions as fitting into three categories:

Type 1: Irreversible, high-consequence decisions
These are decisions that meaningfully shape the state of the system and cannot be undone without enormous cost. Amazon launching Prime is a canonical example. Major contractual commitments that change the structure of a business is another example.

These decisions deserve rigor, alignment, and careful sequencing.

Type 2: Reversible, limited-blast-radius decisions
These decisions are operational and recoverable. You can revert them quickly if needed. Changes to checkout flows, pricing tests, and personalization algorithms all qualify.

Most product decisions are Type 2. These should be fast.

Type 3: Information decisions (epistemic actions)
These are not decisions about the business — they are decisions about how to learn. They are taken not because we believe the action is inherently correct, but because acting is the only way to collapse uncertainty.
They have three defining characteristics:

1. The purpose is not to improve the system; it is to reveal truth about the system.

2. They are reversible by design because their purpose is not impact, but information.

3. They are the only decision type designed to increase the quality of future decisions.

Examples:

• A two-week elasticity test to determine whether lowering price increases revenue or simply erodes margin.

• A targeted incentive experiment to learn whether customers will shift their behavior.

• A falsification test designed to break a core assumption. For example, testing whether “power users” actually drive referrals or merely correlate with them.

A Type 3 decision is not even a bet; it is a probe.

Analysis paralysis has different causes depending on the type of decision you’re facing.

For Type 2 decisions held to Type 1 standards, it’s a calibration problem. The fix is simply recognition: this is reversible, move faster.

For actual Type 1 decisions, the fix is decomposition. You’re not lowering your standards; you’re finding the Type 3 actions that rule out bad outcomes before committing. The mistake is treating the Type 1 decision as atomic when it’s actually a bundle of uncertainties, some of which can be resolved cheaply.

How much certainty is enough? A useful heuristic: decisions should be made with between 40% and 70% of the needed information. At less than 40%, you’re just guessing. Above 70% and you’ve waited too long.

For Type 2 decisions, 40% is enough. You’re not seeking certainty; you’re seeking velocity. Modern product development makes most changes cheap to ship and quick to reverse, so the cost of delay almost always exceeds the cost of being wrong.

For Type 1 decisions, 70% is the target, but the question isn’t “how do I analyze my way there?”, it’s “what Type 3 action gets me there fastest?”

The critical mental shift is going from treating uncertainty as a signal you cannot yet act to recognizing it as the signal that you must act. Analysis gives you deduced information: conclusions drawn from existing knowledge. Action gives you induced information: new data that didn’t exist until you moved. This is why action resolves ambiguity more efficiently than analysis. You can’t update your beliefs until you have something to update them from.

A container for truth-seeking

Everything above describes how a PM should think. This mindset is necessary, but it isn’t self-sustaining. Certain environments amplify truth-seeking; others extinguish it. Whether these behaviors take root depends far less on individual PMs than on the incentives and environment leaders design. The first environment leaders must design is their own — how they think about uncertainty, risk, and the portfolio as a whole.

This approach might feel more uncertain at the portfolio level; more bets, more individual failures. It’s best viewed as a certainty-for-certainty trade. You’re giving up the feeling of confidence in any single bet for confidence that your decision-making system works. The PM trades certainty-on-this-decision for velocity. The leader trades certainty-on-any-one-bet for confidence that the portfolio will perform. Same structure, different altitude.

This reframe is what unlocks everything else. If you’re managing the distribution of outcomes rather than each outcome, you can afford to let your PMs move faster, tolerate more individual misses, and treat negative results as information rather than failures.

This reframing naturally raises the question of the people themselves: who thrives in a truth-seeking environment and who struggles.

We’ve laid out a set of traits you want in your product managers: a high tolerance for ambiguity, paired with a high drive for epistemic clarity. A bias toward decisive action to test hypotheses. A falsification mindset to identify dead ends quickly.

Many people will find this bar difficult to meet. The tools themselves (articulating hypotheses, defining decision criteria, practicing falsification) are teachable. The real challenge is putting them into practice. That requires a particular temperament: willingness to be wrong, willingness to update, the ability to tolerate ambiguity without stress, and the instinct to remain curious rather than defensive when launches don’t deliver.

This mindset runs counter to basic psychological drives. Ambiguity is uncomfortable for most people. They identify with their ideas, so disproving an idea can feel like a threat to the self. And above all else, people associate being wrong with status loss — not internally, but socially.

You can teach skills, but temperament is much harder. Identity is nearly unteachable. So what does that imply for you as a product executive?

Your job is to create an environment where the people already wired for this mode of thinking can reveal themselves. Most people will not rewire their identities, but they will absolutely change their behavior when the system rewards certain modes of thought and penalizes others. The goal at the executive level isn’t to force people to think like you, it’s to define the rules of the game.

If a culture rewards overconfidence, bravado, rhetorical dominance, and post-hoc justification, then the people who thrive will be the ones wired for those incentives. Truth-seeking evaporates because the environment punishes it.

If a culture rewards explicit hypotheses, rapid updating, intellectual honesty, and speaking in probabilities instead of absolutes, then people wired for truth-seeking will surface. Those who aren’t will plateau or self-select out.

Only senior leaders can set these incentives.

What a truth-seeking culture needs is surprisingly simple. Leaders must reward good reasoning, not “being right.” Praise someone for updating quickly, not for winning a debate. Make public falsification safe. People need to hear leaders say, “I was wrong; we learned something important.” Treat action as information gathering, not as risk-taking. The first question after a launch should be: “What did that teach us?” Treat analysis as incomplete without movement. Ask: “What data can we generate?” not “What else can we model?” Make hypotheses explicit and transparent.

And above all else, drive the social cost of updating beliefs toward zero. You cannot have truth-seeking if people fear the humiliation of being wrong.

You don’t create truth-seekers; you create the conditions that let truth-seeking flourish and make its alternatives untenable.

Amp It Up, written by Frank Slootman (former Snowflake CEO, former ServiceNow CEO), became a bestseller shortly after it was published in 2022. Its very strong reviews (4.5 stars on Amazon at time of writing) reflect a book widely loved by executives. It is written to appeal to leaders under pressure, and offers them a decisive, clarifying, energizing path forward. It is easy to understand why this would be appealing.

This is a review of what Amp It Up licenses when read uncritically.

What the Book Argues

In Amp It Up, Slootman makes a straightforward argument: most organizations operate at a fraction of their potential, and leaders can dramatically accelerate performance by raising intensity across every dimension.

The core thesis is that “amping it up” means increasing pace, sharpening focus, and raising standards, simultaneously and relentlessly. Slootman asserts that mediocrity is the default state of organizations, and leaders must actively fight entropy by refusing to accept “good enough.”

The book advances four core arguments:

On pace: Most companies move too slowly because they confuse activity with progress. Slootman advocates compressing timelines aggressively. If someone says something will take a year, ask why it can’t be done in a quarter. Speed itself becomes a competitive advantage and cultural value.

On focus: Organizations dissipate energy across too many priorities. Slootman pushes for ruthless prioritization—identifying the one or two things that actually matter and saying no to everything else. He’s skeptical of consensus-driven decision-making, which he sees as producing lowest-common-denominator outcomes.

On standards: Leaders must be intolerant of underperformance and willing to make difficult personnel decisions quickly. The cost of inaction outweighs the cost of fixing a bad hire. Companies should engage in continuous “topgrading” - systematically upgrading talent.

On alignment: Strategy means nothing without execution alignment. Everyone needs to understand not just what they’re doing but why, with clear metrics and accountability.

The book is unapologetically intense. Slootman doesn’t soften his message for palatability. It’s essentially a manifesto for operational urgency over comfort.

Where Slootman is Right

Before criticizing Amp It Up, it’s worth acknowledging why it resonates, and why, in some contexts, it’s directionally correct.

Slootman’s core insight is that intensity is often underused as a leadership tool. Leaders hedge. They tolerate “good enough” instead of reaching for “great”. They allow timelines to stretch, standards to soften, and accountability to blur. Over time, that hesitation compounds into cultural rot. By the time leaders act, the organization has already normalized underperformance.

In companies where the problem really is complacency — where direction is broadly correct but execution energy has faded — raising urgency can be both clarifying and effective. Tight timelines force prioritization. High standards expose weak assumptions. Pressure reveals where the system is brittle and who can rise to the moment.

Seen this way, Amp It Up is a reaction against managerial passivity. It’s a call for leaders to stop negotiating with mediocrity and start acting like outcomes matter, because they do.

Diagnosis Assumed

The book assumes the hardest work of leadership, understanding what’s actually wrong, has already been done. Execution energy is the only missing variable. That assumption is rarely true and when it isn’t, urgency becomes actively harmful.

This is dangerous because when the problem at hand is misunderstood, urgency amplifies error. Execution pressure can easily collapse learning. Power bypasses evidence, and dissent gets interpreted as softness rather than a signal to introspect.

When faced with a significant challenge, every genuinely great leader begins by holding up a mirror.

Their self-interrogation asks “How have my actions contributed to this problem?” and “What signals might I be missing?”. Leaders do this not because they assume guilt, but because they understand that power creates asymmetric blind spots, and only they can correct for them. Amp It Up never asks leaders to hold up a mirror; it asks them to hold a stopwatch.

Catnip for Executives

Amp It Up is such catnip for senior leaders because it offers a simple, convenient story: If the company isn’t performing, it’s because the people doing the work aren’t intense enough.

The book feels good because it:

• Flatters leaders’ self-image as high-standards truth-tellers

• Frames underperformance as a failure of will, not understanding

• Turns urgency into a virtue independent of correctness

It offers a single-cause explanation for complex failure. That’s intoxicating to anyone under pressure, especially startup CEOs, because it collapses ambiguity into a simple lever: push harder.

This framing does three very convenient things:

1. Transfers all responsibility downward

2. Frames dissent as a character flaw

3. Redefines leadership as enforcement, not inquiry

The most revealing thing about this book is what it asks of leaders: almost nothing. Demand more, tolerate less. It does not ask leaders to interrogate their own models & perceptions, seek disconfirming evidence, or ask whether urgency is masking ignorance.

I’ve now seen multiple organizations where leaders, convinced intensity was the missing ingredient, bypassed evidence, overrode constraints, and used authority to force speed — only to discover too late that what they’d accelerated was the wrong thing. When leaders refuse to ask whether they might be wrong, urgency creates fragility rather than excellence.

The irony is that urgency doctrine is most seductive to leaders who are farthest from the work. A leader who is deeply embedded in the work rarely needs a book to remind them that speed matters; they feel it already.

A Brief Note on War

Amp It Up frequently borrows the language of war to describe business: enemies, campaigns, intensity, total commitment. I understand the rhetorical appeal, but business is not war, and pretending otherwise has consequences.

I was in Iraq during the surge. I know what urgency feels like when the stakes are actually life and death.

That experience left me with a deep skepticism of leaders who invoke wartime urgency without wartime stakes. War metaphors do conceptual work in this book. They normalize collateral damage, frame dissent as disloyalty, justify urgency at the expense of deliberation, and treat losses as acceptable if the “mission” continues.

That framing is deeply dangerous in a business context because the moral stakes are fundamentally different, but the human cost is still real. This brings us to the one respect where the analogy holds perfectly: the people who pay the price are almost never the ones making the call.

Accountability Flows Both Ways

Critics might conclude I’m arguing against accountability, urgency, or high standards. I’m not.

I’ve spent most of my career in environments where accountability was non-negotiable: missed numbers had consequences, bad decisions were visible, and outcomes mattered. I’ve seen what happens when leaders hesitate too long, tolerate drift, or confuse empathy with permissiveness. That failure mode is real — and damaging.

The question is not whether accountability matters.

The question is who it applies to first.

In any organization, accountability that flows in only one direction is not accountability at all. It’s pressure. Pressure without learning doesn’t produce excellence; it produces fear, gaming, and brittle execution.

True accountability starts with the people who have the most power and the least signal. Leaders are farther from the work by definition. Their models are necessarily more abstract. That makes them more likely, not less, to be wrong.

This is why every great leader I’ve worked with begins accountability by holding up a mirror.

They ask:

• What assumptions am I making?

• What evidence would tell me I’m wrong?

Only after that work do they turn urgency outward.

The Limits of a Phase-Specific Playbook

Slootman’s background is helpful context for interpreting the issues in this book. He arrived at ServiceNow in 2011, when the company was already growing rapidly with proven enterprise demand. The product worked, the market existed, but the machine needed tuning, not rethinking.

Snowflake is a similar story. He joined in 2019 after the core data warehousing product had demonstrated clear differentiation. The cloud data market was exploding. His job was to capture a wave already forming, not to figure out if there was a wave.

In both cases, he was brought in specifically because the strategic diagnosis was settled. The board had already decided “we have something, we need to scale it faster.” He’s essentially a specialist in a particular phase of company evolution: post-validation, pre-scale-ceiling.

This context doesn’t diminish what Slootman accomplished. Execution at that speed and scale is genuinely hard. If you’re a leader in that situation—proven model, clear market, execution clearly the bottleneck—this book may serve you well. It does, however, reframe the book’s applicability. He’s written a manual for a specific situation and marketed it as a general theory of leadership.

Urgency is a tool, not a theory of leadership. Leadership begins and ends with curiosity. Without it, intensity is little more than coercion.

At a DTC brand I worked with, the average new customer placed 2.5 orders in their first year. This statistic came up constantly — board decks, roadmaps, acquisition modeling — because it seemed to reveal something essential about “the typical customer.” It wasn’t treated as a mathematical artifact; it was treated as identity. It shaped marketing spend, retention strategy, and even product features.

Another well-known fact was that more than half of new customers churned after their first order. The breakthrough came only when we combined these two truths. If over half of customers placed exactly one order, yet the average was 2.5, then the remaining customers must place far more than 2.5 orders to pull the mean upward.

When we removed the one-and-dones, the picture snapped into focus: the customers who stayed placed over six orders in their first year.

The distribution wasn’t centered at all; it was bimodal. And the “average buyer” the company had been building around for years did not exist; it was a mathematical hallucination. That hallucination drove the wrong LTV model, the wrong acquisition strategy, the wrong retention messaging, the wrong product priorities, and, most dangerously, the wrong mental picture of who the customer even was.

There were many possible contributors to this fractured pattern: targeting failure, onboarding failure, experience failure, positioning failure, expectation failure. The most important insight was not which one was at fault — it was that the average concealed the very structure of the system.

Once we de-averaged the data, the “typical customer” vanished, and a missing segment emerged. The business stopped optimizing for a fictional 2.5-order persona and started asking a question that transformed the business: Why can’t we retain a customer who should naturally buy two or three times a year?

Subscribe now

Seeing Like a Company

The problem we just described isn’t a data science problem, a product maturity problem, or even a business problem; it’s a recurring structural error in how humans manage complexity at scale. Smart people have been making this mistake for over 200 years across every imaginable institution, starting with the first modern states. The pervasiveness of this problem hints that the cause is not stupidity; it’s necessity.

We continue to use averages because they solve the problem of legibility. James C. Scott describes this challenge in Seeing Like a State. Reality is messy and complex. The only way it can be understood, recorded, and controlled from a central location is by standardizing and simplifying this reality. Innovations imposed from above like standardized surnames, cadastral maps, censuses, and fixed property boundaries made societies legible to their governments, enabling the first modern states to emerge. Scott shows us:

• Compression is unavoidable. Legibility is the price of operating at scale.

• Information loss is not neutral. What you erase shapes what you can see.

• Some losses are fatal. Flatten the wrong structure and you lose the ability to act.

Legibility has produced policy failures and even disasters. When Tanzania forcibly relocated millions into planned villages, officials optimized for what they could see from the capital: neat grids and standardized plots. In the process, they destroyed the local knowledge that had made the land productive. The history of central planning is littered with five-year plans optimized for legible abstractions that bore no resemblance to ground truth. The pattern is always the same: the map becomes more real than the territory, and the territory suffers. Legibility is a tool, and this tool can become a worldview. The abstraction is mistaken for reality, making it impossible to understand and act upon the system.

Modern corporations face similar challenges. It is impossible for a company to understand its business, plan, and execute using the messy reality of individual customers, orders, and items sold. We often turn a jagged, heterogeneous, multi-modal distribution of human behavior into single numbers: the average order count, the average user, the average session, the average funnel.

The metric becomes an object of reasoning. Once that happens, strategies are built around the abstraction, incentives are optimized around the abstraction, and evidence that does not fit the pattern is dismissed as noise. Averages are dangerous not because they are wrong, but because they are so easy to reason with.

Legibility is required to operate at scale. The simplifications that produce legibility must preserve relevant pieces of the structure of the underlying reality to enable understanding and effective control. Averages erase that structure.

Heterogeneity in Practice

Averages are an extreme. They homogenize populations, eliminating noise, but losing signal in the process. If we view the other extreme, pure heterogeneity, the picture is even worse. If every customer or order is treated as fully unique, there are no patterns, no learning, no generalization, no leverage, and therefore no scalability. The solution lies in viewing heterogeneity as a discipline rather than a principle.

The skill is discerning which distinctions matter and which can be safely discarded, with the goal of having the fewest categories that let us understand and control the system in question. A categorization is justified only if it increases our ability to explain, predict, or intervene in the system. All segmentation is descriptive, but it is only worth the cognitive cost if it is actionable.

We can approach a new system through the lens of information gain — how much our understanding and ability to act improves with the next incremental feature. We can consider a list of the many ways we can segment customer and order data while keeping this test in mind: Does knowing that someone belongs to this segment change what I would do? Adding segments in order of maximum information gain until we reach diminishing returns provides the minimum abstraction that preserves the structure necessary for understanding and control.

How to read the chart:

• At the left extreme, everything is collapsed into a single average.
  Noise is minimal, but most decision-useful signal has been lost: we can observe change, but not identify what is driving it or how to intervene.

• As categorization becomes more granular, signal increases rapidly while noise grows slowly.
  Breaking the average reveals structure in the system, enabling explanation, prediction, and targeted action.

• Beyond a point, additional granularity fragments the system.
  Sample sizes shrink, interactions explode, and noise grows faster than signal, reducing, rather than improving our ability to reason and act.

There are a few archetypal mistakes that violate the information-gain rule. Convenience segmentation optimizes for ease of creation rather than usefulness. These segments are often inherited from infrastructure (channel, acquisition source, product category) or inherited from vendors (mirroring what the provider sold you), or inherited from history (decisions someone made years ago that nobody revisited).

This last failure mode is especially common because the right abstraction is only right at a moment in time. Young companies with low volumes need coarse groupings to learn anything. As companies scale, finer distinctions become meaningful. As product lines & customer bases evolve, new groupings become necessary to understand, predict, and drive performance.

Vanity segments are those that feel sophisticated but don’t differentiate behavior. “Millennials” or “premium customers” may sound strategic while lacking the predictive power to change decisions. Performative segments exist to satisfy a stakeholder or a decision already made. The “need to understand our Gen Z customer” may follow a decision to launch a TikTok campaign, not the other way around.

It’s critical to get segmentation right because segmentation’s costs aren’t just analytical, they’re organizational. More categories means more metrics, more metrics beget more owners, more owners require more coordination, and more coordination slows decision making. Heterogeneity can reveal structure, but cannot tell you what to value nor how to make these tradeoffs.

Thus, we arrive at a set of organizational problems. First and foremost, who holds the model? Who holds the line when proposed segments do not aid decisionmaking? Who revisits structure periodically and questions if the segmentation developed two years ago is still highly effective today? What roles do people at different levels in the organization, from individual contributors to executives, play in building and maintaining this structure?

The Compression Bargain

Different levels within organizations face vastly different information environments. This necessitates a division of sensemaking responsibilities.

Front-line operators, PMs, analysts, and domain experts live in distributions. They see the tails, the modes, and the instabilities. They understand why the average moved, and surface causal structure, not just metrics. People at this level can afford to zoom in to see more detail with a narrower field of view. This is the only way they can interpret what they are seeing and make informed decisions about what to do next.

Senior leaders live in a world of averages out of necessity. Leaders constantly wrestle with the challenge of information overload. For them, averages are a critical form of compression that they rely on to understand the business. If leaders tried to live in raw distributions, they would fail at their job. An average, however, tells you nothing about what is moving the average.

Middle managers translate between these two realities, giving them the most challenging job in the organization. They take inputs from the front-line teams and decide what information to compress, what to preserve, and what to leave out in their communications with senior leadership. Middle managers are incentivized to make information legible to senior leaders, giving them a structural bias towards overflattening.

Trust is the bridge between these realities that allows the system to flex when it needs to. Epistemic trust — trust that someone will interrupt the compression layer only when it matters, and that doing so will not be punished.

It is incumbent on middle management to understand the information environment senior leaders face. This means surfacing compressed, even highly compressed information most of the time, while developing exceptional judgment around when compression is likely to lead to poor resource allocation decisions at the senior leadership level. It also requires skilled communication to explain why additional detail is warranted at this time, for this topic, otherwise the detail is perceived as academic or overcomplicated.

Senior leadership’s half of this contract is allowing middle managers to build trust and credibility specific to information sharing. Trusting someone to surface important exceptions requires actually engaging when they do. If a middle manager raises “the average is hiding something” and gets dismissed or deprioritized, they will quickly learn not to raise it again. The senior leader’s job isn’t just to grant trust; it’s to honor the bet when someone spends their credibility. This means treating the interruption as signal, not noise, even when it’s inconvenient. When this contract breaks, organizations don’t just make bad decisions; they stop learning.

Building a Trust Economy

Creating an environment with the trust necessary to surface the right information at the right time takes intentional effort because the incentives are asymmetric. Raising an exception is costly. It burns credibility, risks being seen as academic, and slows decisions. It can look like poor judgment in hindsight. Ignoring exceptions is cheap. Nothing breaks immediately, accountability is diffused, and failure is frequently delayed and deniable. The truth fails to surface not because it is unknown, but because surfacing it is irrational under default conditions.

Middle management must learn how to spend credibility well, and this comes through rare, disciplined escalation. In practice, this looks like bringing both the compressed signal and, when appropriate, the minimum required additional depth for understanding. These exceptions should be framed as decision-relevant rather than intellectually interesting, and explicit about why the average is unsafe this time. The meta-signal is that you understand senior leadership’s need for compression, and you’re interrupting it intentionally.

Senior leadership must understand how trust is earned and destroyed. Trust is built by what happens after someone takes a risk. In practice, this means delaying judgment and asking clarifying questions instead of deflecting when someone surfaces a greater level of detail. Leaders can reinforce the behavior by closing the loop later (e.g. “You were right to flag that”) and not punishing false positives when the reasoning was sound. Inconsistency kills trust. If sometimes exceptions are welcomed, and sometimes dismissed, without a clear pattern, middle managers can’t learn when it’s safe to speak.

This trust must be built in advance of moments of crisis when averages are inappropriate because you can only spend the trust you have already accumulated. In practice, this means that leadership must show curiosity before the stakes are high, and middle managers must demonstrate good management before escalation matters. Smaller, low risk interruptions are practice reps for when it matters.

These reps can come through rituals and norms. A few practices worth considering:

Unpack an average in regular meetings. Make this part of a quarterly or monthly business review. The average should be unpacked with the intent of understanding how the component parts drove decisions this period. This framing makes the exercise useful rather than academic. This builds not only the muscle of looking, but also a shared understanding of the power of this practice. Leaders come to expect to see distributions and learn to ask what’s underneath when an average moves. The practice serves both sides.

Extend postmortems to epistemic failures. Blameless postmortems are already utilized in many organizations and are well-suited here. Postmortems frequently focus on execution failures. Here, we extend them to epistemic failures — when we had the data but compressed it too far, or chose a metric that hid the problem. Keeping postmortems blameless, by asking what we missed rather than who missed it, keeps the focus on calibration rather than accountability.

Name when you’re spending credibility. This is the most cultural and hardest to formalize. Shared language (e.g. “I’m spending credibility here”) lets everyone know they’re raising something outside of the usual flow. This differentiates routine updates from genuine exceptions and tells senior leaders that this is the moment to engage. This works only if it’s rare and honored when used.

The key to all of these practices is that they are embedded in existing structures, so they don’t require additional calendar space or new political capital to establish. Adapt these to your organization with this principle in mind.

The hard part isn’t knowing what to do; it’s making it safe to do it. Organizations don’t fail when averages lie; they fail when no one is authorized to say so.

Subscribe now

Why I’m writing this

I spent eleven years at ThredUp, eventually running a $280 million P&L and helping lead the company from significant losses to profitability. Along the way, I did what a lot of ambitious operators do: I read the books.

Good to Great. The Lean Startup. Thinking, Fast and Slow. I could quote them chapter and verse. So could plenty of the leaders I worked with. And yet, the same strategic missteps kept happening.

Not because people were careless or unintelligent. And not because we hadn’t found the “right” framework yet. We had frameworks coming out of our ears. The issue was something more subtle and, once you see it, hard to unsee.

Over time, I started noticing a pattern. Smart people would reach for solutions before they could articulate what problem they were solving. One part of the business would “fix” something and another part would absorb the fallout. The organization would thrash: progress in one corner, regress in another. Everyone felt it, but it remained oddly difficult to name what was going on.

What I eventually came to believe is this: we are largely products of the systems we inhabit.

Systems shape what gets rewarded and what gets punished. They shape what information travels and what gets buried. They shape who feels safe telling the truth, and who learns to stay quiet. Most importantly, they shape the types of problems we can even see, let alone solve.

This is the persistent gap between theory and practice I’ve seen in every organization I’ve worked in or advised. Leaders read books about what successful companies do, but those books rarely examine the underlying structures that make certain actions possible in some contexts and impossible in others. They tell you what to do without helping you understand why you can’t do it.

Seeing the System is my attempt to close that gap.

This is the home for my long-form essays on how systems shape outcomes, and how leaders can act on those systems to create better results for their businesses, their employees, and themselves.

I draw on epistemology, sociology, and philosophy because those fields have spent centuries developing tools for exactly this: understanding the forces that shape human behavior while remaining largely invisible to the people inside them. Business writing has mostly ignored that tradition. I think that’s a mistake.

What to expect

I’ll publish long-form essays twice a month—the kind of pieces that take time to develop and reward careful reading. In between, I’ll share shorter observations roughly once a week as they occur to me: patterns I’m seeing in my consulting work, ideas I’m wrestling with, questions I don’t have answers to yet.

I’m launching with both free and paid tiers. For now, free subscribers will get access to everything. As the community grows, I’ll think more carefully about what lives where, but my bias is toward accessibility.

Who this is for

This is for people who want to go beyond frameworks to level up how they think and exercise judgment.

How we perceive what’s happening. How we reason under uncertainty. How organizations adapt, update, and refine their worldview. How incentives, status, and institutional physics shape decisions, even when everyone involved is trying to do the right thing.

I’m not here to tell you what successful companies do. I’m here to help you see what’s actually happening in yours.

Welcome to Seeing the System.