One Takeaway

Statistics like GDP are helpful summaries, but they offer limited explanations of how the economy is doing. Without a crystal ball to predict the future, it’s better to focus on principles than managing statistics.

The Number Everyone Talks About

Maybe last night’s news told you that GDP is expected to grow this next quarter. Good news…right?

Maybe. GDP adds up everything we spend. This includes whether we’re building a new business or replacing a flooded basement. Both count the same in GDP stats. More spending sometimes is progress and other times only looks like progress. Not all spending makes us better off.

That doesn’t mean GDP is useless. It means it’s a summary, not a story. And summaries need someone who knows how to read them.

The same is true for every economic stat you hear on the news. Once you know what questions to ask, you’ll be able to understand them better than most people do.

What the Numbers Can Miss

Take unemployment. The headline might say 4%. That sounds healthy. But behind that number are six different ways the government measures unemployment. The most narrow measures only long-term job seekers. The most broad includes part-time workers who want full-time jobs and people who’ve stopped looking entirely. Depending on which measure you use, the story can change.

A falling unemployment rate could mean new jobs are being created. Or it could mean people gave up looking. The number alone doesn’t tell you which.

Or inflation. The news says 3%. But your rent went up 10% and your grocery bill climbed 5%. Meanwhile, the price of your TV dropped. The official number averages all of that together. Your lived experience of inflation and the reported number can feel like they’re describing two different economies.

These numbers aren’t wrong. They’re often just incomplete. And that’s an important difference. The problem isn’t the stats themselves. It’s treating them like the full picture when they’re really a sketch.

The Right Questions Change Explanations

You don’t need a degree in economics or data analysis to understand these data points. You simply need to start with questions that most people don’t think of when they hear a headline number.

When you hear GDP grew: Ask whether that growth came from productive investment or from spending that replaced something lost.

When you hear unemployment fell: Ask what’s happening underneath. Are people finding work they want? Or are they settling, or giving up?

When you hear inflation is under control: Ask in what way? If you’re a retiree on a fixed income and food and medical costs are climbing, a low headline number doesn’t describe your reality.

The numbers are summaries. The human decisions and actions behind them are the full picture.

Why This Matters Beyond Your Living Room

These aren’t just questions for you to ask while watching the news. They’re the same questions policymakers should be asking. The trouble is they most often aren’t.

When a government designs a program to “reduce unemployment,” it’s targeting a number. But if that number can fall for reasons that have nothing to do with people finding good work, then hitting the target doesn’t mean they solved the problem. When a central bank promises to “control inflation,” it’s managing an average. But if that average hides the fact that housing and food costs are surging while electronics get cheaper, the policy might look successful on paper while families feel squeezed.

This is one reason we can’t simply make the economy do what we want. The tools we use to measure success are helpful, but are blunter than we think. When we build policies around moving a number, we risk improving the scoreboard without improving the game.

GDP growth is great, but not if it comes at a loss to lives, liberty, and livelihood.

Why Predictions Fail But Principles Don’t

It’s tempting to think that better numbers would lead to better predictions. If we just measured more precisely, we could see what’s coming. But the economy is made up of millions of people making plans, changing their minds, and responding to each other in real time. That’s not a system that lends itself to perfect forecasting.

Economic statistics can help us spot broad trends over time, compare approaches in similar situations, and identify problems that need attention. These are genuinely useful things. But they can’t tell us what caused what without deeper analysis. They also can’t predict what comes next.

That’s why principles matter more than predictions. Understanding how prices work, why people cooperate, and what drives growth helps you adapt to whatever comes next. A headline number can’t do that. How you think about that number can.

The Bottom Line

Economic statistics are tools, not answers. They can sketch the outline of what’s happening, but they can’t paint the full picture. The best economic thinking doesn’t try to predict what the numbers will say next. It gives you the principles to understand what the numbers mean and what they miss.

Every article in this series has circled the same observation. Organizations fail at coordination not simply because people are selfish or lazy ,but because the systems they operate within can unintentionally make cooperation irrational. Misaligned incentives punish collaboration (”The Cost of Working Together”). Reporting structures strip out the knowledge that matters (”What Headquarters Can’t See“). Measurement systems destroy the most valuable but least measurable work (”Why Metrics Kill Innovation“).

The common thread is that in each case, the people weren’t the problem. The rules were.

Economist James Buchanan won the Nobel Prize in part for applying this insight to political institutions. His core question wasn’t “how do we get better politicians?” It was “how do we design rules so that self-interested political actors produce outcomes that serve the public interest?” He argued that focusing on the character of the people inside a system is a losing strategy. Focusing on the rules of the system itself is where lasting improvement comes from.

The same logic applies inside every organization. And almost nobody applies it there.

The Motivation Trap

Most management approaches treat coordination failure as a motivation problem. Teams aren’t collaborating? Inspire them. Departments are siloed? Build culture. Innovation is dying? Bring in a speaker to talk about the importance of risk-taking.

All this assumes that if people cared enough, they’d cooperate. The default solution is to make them care more. Hire only those who bleed ping, or black, or whatever color is in your brand toolkit. This can work for a bit, but it’s unsustainable.

This approach has a fundamental flaw. It requires everyone to be the best version of themselves at all times. On the days they love the company, they go above and beyond. They bridge gaps between departments. They flag problems that aren’t their responsibility. They pursue opportunities that don’t fit their metrics. On the days they’re tired, frustrated, or simply focused on their own deliverables, the coordination fails.

As Steven Kerr explains in his famous article “On the Folly Of Rewarding A and Hoping for B”, this turns the organization into “a fortunate bystander” rather than an active force shaping behavior. Some people will be generous with their time and attention regardless of incentives. Some will bridge cross-functional gaps out of personal commitment. But the organization isn’t causing these behaviors. It’s just getting lucky when they happen.

Kerr’s insight cuts to the core: “By altering the reward system the organization escapes the necessity of selecting only desirable people or of trying to alter undesirable ones... where such reinforcement exists, no one needs goodness (Kerr pg. 782).”

That last phrase is vital. A well-designed system doesn’t need everyone to be selfless. It needs the rules to make cooperation individually beneficial. The goal is for people to do the right thing for the organization on their best days and their worst days, because the right thing for the organization is also the right thing for them.

Design Problems, Not Motivation Problems

Buchanan’s contribution was reframing political dysfunction from a people problem to a rules problem. Bad outcomes don’t always come from bad people (to be clear, bad people are a problem too). They instead can come from rules that make bad outcomes individually rational.

Inside organizations, the same reframing transforms how you approach every persistent coordination failure.

The motivation framing: “Our teams aren’t collaborating on cross-functional problems. We need to build a culture of collaboration. Let’s do an offsite. Let’s bring in a facilitator. Let’s have the leadership team set the example and model the behavior we want to see.”

The design framing: “Our teams aren’t collaborating on cross-functional problems because each team is measured on independent metrics that make collaboration a sacrifice. Product loses momentum. Sales loses pipeline time. Customer success loses ticket resolution speed. The system punishes collaboration. Let’s redesign the incentives so that success is profitable for every team involved.”

The first approach asks people to act against their incentives. It works briefly. Offsites generate enthusiasm, facilitators create temporary alignment. But when you get back in front of your computer it fades as people return to the daily reality of how they’re actually measured and rewarded.

The second approach changes the daily reality. It doesn’t require sustained enthusiasm or cultural transformation. It requires getting the rules right, then letting self-interest do the work that motivation can’t sustain.

This is what Buchanan meant by focusing on the rules of the game rather than the players. You don’t need better people. You need better rules.

What Good Rules Look Like

Buchanan’s work gives us an important principle: focus on the rules of the game rather than the character of the players. But principles need evidence. How do you actually design rules that produce voluntary cooperation? For that, we turn to Elinor Ostrom.

Ostrom won the Nobel Prize for studying a problem economists had largely given up on. She researched how communities manage shared resources without either markets or top-down control. Conventional theory predicted that fisheries, forests, and irrigation systems held in common would be overused and destroyed. This is famously known as the “tragedy of the commons.” Ostrom went and looked at actual communities around the world and found something different. Many of them had developed rules that produced voluntary cooperation for generations without external enforcement.

What made these systems work wasn’t better people. It was better rules. Ostrom identified specific design principles that successful self-governing communities shared. These principles can translate directly to the cross-functional coordination challenges inside organizations. Combined with Buchanan’s insights, her research points to five rules for designing systems where cooperation becomes the default choice.

Rules must make cooperation profitable for individuals, not just the organization. It’s not enough for collaboration to be “good for the company.” Each worker needs to see personal benefit. Shared success metrics where all participating functions receive bonuses based on collective outcomes. Joint budget authority where teams must agree on allocation, creating natural negotiation that reveals real priorities. Career advancement paths that reward cross-functional contribution, not just siloed performance.

People who live under the rules should design them. Ostrom’s research showed that systems imposed from the top fail far more often than those designed by the affected parties. When teams co-design their collaboration processes, they build in features that work for their context. When leadership imposes frameworks, they create compliance without commitment. The difference is a focus on information rather than buy-in. The people doing the work know which rules would actually help and which would just add complexity.

Rules must match the type of work. One-size-fits-all solutions create unnecessary friction. Operator coordination needs formal standards and clear processes. Creator coordination needs lightweight check-ins and experimental flexibility. Refiner coordination needs structured improvement cycles with room for iteration. Applying operator rules to creator work kills innovation. Applying creator rules to operator work creates chaos. Good system design is specific to context.

Rules must have graduated consequences. Ostrom also found that successful rules start with mild consequences for non-cooperation. Things like peer feedback and reputation effects are helpful steps before escalating to formal consequences. This keeps enforcement costs low and maintains relationships. Most conflicts can get corrected informally when the rules are well-designed. Heavy-handed enforcement from the start signals distrust and creates resistance.

Rules must be supported by legitimate authority. When leadership respects cross-functional decisions made through these processes, those processes work. When they override them arbitrarily, people learn that the real authority is elsewhere. The coordination system collapses. The rules only function if the organization genuinely commits to them.

Why This Isn’t Just “Better Incentive Design”

You might read this and think: this is just about aligning incentives. HR and compensation teams already work on this.

It’s deeper than that. Compensation is one lever. Buchanan’s insight is about the entire system. Things like decision rights, information flows, authority, evaluation frameworks, resource allocation processes, career paths all shape behavior independently of compensation.

Consider the persistent cross-functional problems in your organization. The ones that survive every reorg and every new initiative. These problems persist not because your compensation structure is wrong (though it might be). They persist because the full system of rules makes those problems nobody’s rational priority to solve. Rules like: Who has authority? Who has information? Who gets evaluated on what? Who allocates resources? Who resolves disputes?

This is the gap that your organization may be currently filling with hope. Hoping that someone will take ownership of cross-functional problems that don’t appear in anyone’s metrics. Hoping that teams will collaborate despite incentives that point in different directions. Hoping that people will flag problems that aren’t their responsibility because they care about the company.

Some will. On some days. But you’re banking on people being the ideal version of themselves to address gaps and misalignments as they come up. That’s not sustainable. You need systems where people do their job well on the days they love the company and on the days they feel differently. The success of your company depends on building rules where people aren’t assumed to be the ideal version of a worker. You need systems where cooperation happens because it’s rational, not because it’s virtuous.

The Connection Across This Series

This idea to design rules for voluntary cooperation rather than hoping for motivated compliance is the economic logic underneath every concept in this series.

Transaction costs might be high because the rules make cooperation expensive. Redesign the rules (shared budgets, joint metrics, cross-functional authority) and cooperation becomes cheaper.

Knowledge doesn’t flow because the rules don’t make sharing rational. If the customer success manager’s insight about a struggling account doesn’t connect to any incentive or evaluation they face, why would they invest time translating it for product development? Redesign the rules so that knowledge sharing is rewarded, and information flows.

Measurement kills innovation because the rules evaluate all work the same way. Redesign the rules (portfolio evaluation for creators, reliability metrics for operators, improvement metrics for refiners) and different types of work can coexist.

In every case, the intervention isn’t motivation. It’s design. Thinking like a harmonizer to design new rules and structures that make cooperation rational is Buchanan’s economic insights applied inside the firm. You can’t rely on inspiration to make people cooperate. You need to build systems where cooperation is the obvious choice.

The Bottom Line

Your organization’s cross-functional problems aren’t motivation problems. They’re economic problems. The people inside your organization are responding rationally to the rules, incentives, and structures they operate within. When those rules make cooperation a sacrifice, people won’t cooperate no matter how many offsites you run or team speeches you give.

Buchanan’s insight from political economy applies directly here. Focus on the rules of the game as much if not more than you focus on hiring. Design systems where self-interested behavior produces collective benefit. Make cooperation profitable for individuals, not just desirable for the organization. Build rules that work when people are at their best and when they’re not.

Where such systems exist, no one needs to rely on goodness alone. They just need rational self-interest, which is the one thing you can always count on.

One Takeaway

The economy runs on signals, not switches. The more we try to control outcomes from the top down, the more we distort the very signals that help people make good decisions from the ground up.

Messing With Signals Messes With the System

If you could push a button to raise your income, lower unemployment, or make your money worth more, wouldn’t that be nice?

That’s the promise a lot of economic policy tries to sell. Twist a few knobs, set some rates, pass the right bill and poof the economy will do what we want.

But the truth is, the economy isn’t a machine with levers and dials. It’s a system of people. And every person is acting, choosing, reacting, and adapting based on the signals around them.

Change those signals, and you change the choices.

This means the tools some choose to use to “manage the economy” (interest rates, inflation targets, stimulus packages) aren’t neutral. They shape behavior. And when used poorly, they mislead the very people the economy depends on.

Coordination Without a Conductor

Markets work not because anyone is in charge, but because everyone is adjusting to everyone else.

That’s the real beauty of systems without central control. You don’t need a master plan. You need clear signals. Prices tell us where things are scarce. Interest rates tell us whether people are saving or spending. Profits and losses tell us whether we’re creating value or wasting resources.

These aren’t just numbers. They’re information.

They help us answer essential questions: Should I invest now or wait? Should I hire more people or cut back? Should I move to this city, change careers, buy a home?

What Happens When The Signals Are Wrong?

In the early 2000s, families across the U.S. looked at the numbers and made what seemed like a smart decision. Interest rates were low. Housing values seemed to never stop climbing. Their monthly payment on a new home would barely be more than rent. Every signal said: buy now.

So they did. And so did millions of others.

But those signals were misleading. Interest rates weren’t low because Americans were saving more. They were low because the Federal Reserve had pushed them there. Housing prices weren’t climbing because of real demand. They were climbing because cheap credit flooded the market with buyers who couldn’t actually afford what they were purchasing.

When rates adjusted and the credit dried up, homes lost significant amounts of their value. Monthly payments jumped. Owners got stuck owing more than the house was worth.

These families didn’t make bad decisions. They made reasonable decisions based on bad information. That’s exactly what distorted signals do. They turn good judgment into bad outcomes across millions of people at once.

Why Some Try Anyway

So why do we keep trying to manage the system from the top?

Because it’s tempting. Big problems seem to call for big solutions. And aggregates like GDP, inflation, and unemployment give the illusion of control. They turn a messy, dynamic system into a scoreboard. And if you think you can move the score by adjusting a few settings, why not try?

But the scoreboard isn’t the game. When you focus too much on moving the numbers, you forget about the players. You forget about the incentives, the trade-offs, the limits, the local knowledge. You forget the stuff that actually makes the economy work.

Good Rules Beat Good Intentions

This doesn’t mean we throw up our hands and do nothing. But it means we focus on what can work.

We don’t need a better pilot. We need a better autopilot. That means:

• Clear, stable rules people can rely on.

• Honest money that holds its value over time.

• Prices that reflect reality, not someone’s best guess.

• Freedom to adjust, innovate, fail, and try again.

When we focus on those things, the system works surprisingly well and better than any other alternative. Not because we control it. But instead because we’ve stopped trying to.

The Bottom Line

The economy is made up of people, not equations. Every attempt to manage it from the top down risks distorting the signals people rely on to make good decisions. It’s true that you can’t manage something if you can’t measure it. But it’s also true that just because you can measure something does not automatically mean it needs to be managed.

One Takeaway

Entrepreneurs move the economy forward. They don’t follow a map. But they do spot new paths no one else may see and take a chance to walk them.

The Entrepreneur’s Role

Economies don’t grow on autopilot. Someone has to take the leap. Someone must start the business, test the product, build the thing that doesn’t yet exist. That someone is the entrepreneur.

Entrepreneurs are not just business owners. They’re decision-makers. Chance-takers. Problem-solvers. They coordinate people, resources, and ideas to create something new in a world that offers no guarantees.

More than anything, they act. And in doing so, they shape the future.

Entrepreneurs Are Alert

Some people walk past an empty lot and see weeds. Others see a future coffee shop.

That’s alertness. It’s the ability to notice what’s missing, what could be better, or what’s about to change. Entrepreneurs are constantly scanning the world for unmet needs or underused opportunities.

• A growing neighborhood with no childcare provider.

• A product that’s good, but could be great with one tweak.

• A process that’s clunky, and ripe for a better way.

This kind of awareness isn’t luck. It’s practiced, intentional, and essential. Entrepreneurs add value by offering something better, faster, cheaper, or completely new.

Entrepreneurs Use Judgment

Once an opportunity is spotted, there’s a second hurdle: action.

Entrepreneurs act based on judgment, not certainty. They use incomplete information to make bets about the future. And they do it in real time before they know what the “right” answer is.

Judgment is what separates talkers from doers. Anyone can say, “That should exist.” The entrepreneur is the one who builds it. If it works, they profit. If it doesn’t, they lose.

Either way, the outcome helps everyone learn what brings value to customers, and, importantly, what doesn’t.

Entrepreneurs Throw Out the Old

Progress doesn’t come without tradeoffs. New ideas can sweep in and replace old ones. That’s what some economists call creative destruction.

Streaming services replace cable. Smartphones replace landlines. Ride-share apps replace taxis. These shifts are disruptive, but they also make life better.

• Consumers win with better, cheaper, more convenient options.

• Resources move from outdated industries to new opportunities.

• Entrepreneurs benefit by leading the change.

Creative destruction can be painful in the short term. Jobs change. Companies close. But long-term prosperity depends on this cycle of renewal.

Entrepreneurship is a Function, Not a Title

You don’t need a business card that says “founder” to be entrepreneurial. What matters is the types of actions you make in the economy.

Entrepreneurs:

• Use the price system to guide their decisions.

• Turn abstract knowledge into real-world solutions.

• Create value by thinking differently and acting boldly.

They don’t wait for permission. They act when others hesitate.

Why This Matters

Entrepreneurs keep the economy from standing still. Without them, we wouldn’t have innovation, job creation, or economic growth.

But none of this is guaranteed. Entrepreneurship requires a system that rewards experimentation and allows failure. That means we need:

• Market prices that reflect reality

• Freedom to try (and fail)

• Absence of barriers that discourage starting a business or that slow down innovation

The Bottom Line

Entrepreneurs shape the market and are the sources of growth for the economy. They discover what’s possible and create new ways to serve others. Their alertness, judgment, and willingness to destroy the old to make way for the new are what keep our economy dynamic, resilient, and alive.

In Growth Isn’t One Sided, we saw that creators need different measurement approaches than operators and refiners. In What Headquarters Can’t See we noted how the knowledge needed for good decisions often resists centralization. But there’s a related problem: that same knowledge often resists measurement. And when something can’t be measured, it becomes invisible to management systems. This means it often gets pushed aside.

This isn’t a problem due to bad metrics or poor implementation. It’s about the fundamental fact that measurement systems can create biases no matter how well-designed the metrics are.

Two Teams, Same Metrics, Different Outcomes

A fast-growing fintech company launched two new initiatives with two different teams. Both teams reported to the same Executive. Both were measured using the same framework that had made the company data-driven and successful.

The Payments Team. Mission: reduce payment processing costs. Key Metrics: monthly cost per transaction, processing success rate, quarterly cost savings. All clear, quantifiable, and attributable to their work.

The team performed great. A/B tests on processing algorithms showed a 3% cost reduction. Other changes led to a statistically significant 0.2% improvement in success rates. Infrastructure changes saved $400K per quarter. Every experiment had clear success metrics and rapid feedback.

After 18 months: $4M in measured, attributed cost savings. The team ended up expanding. The manager got promoted. Leadership used the team as a perfect example of how innovation should work.

The New Market Team. Mission: identify new market opportunities for financial services. Key Metrics: customer acquisition cost, market penetration, quarterly revenue from new initiatives.

Quarter 1: Explored changes to finance for healthcare. No revenue. High customer acquisition costs from experimental pricing. Zero market penetration. Metrics: all red.

Quarter 2: Pivoted to SMB lending based on partnership feedback. Minimal revenue from a break-even pilot. Acquisition costs looked terrible. Market penetration unmeasurable because the market itself was still being defined. Metrics: still red.

Quarter 3: Discovered an opportunity in contractor payroll. Partnership conversations promising but no contracts signed. No revenue to report.

Quarter 4: Team disbanded. Resources reallocated to “proven” optimization work like the Payments Team.

One year later: a competitor launched a contractor payroll service that became a $500M revenue line. The opportunity the New Market Team had identified in Quarter 3 was real. The measurement system killed it before value could materialize.

What happened? The Payments Team was doing (important) refiner work. They were improving existing systems where outcomes are measurable and attribution is clear. The metrics captured their value perfectly.

The New Market team was doing creator work. They were exploring uncertainty where outcomes take time and attribution can be ambiguous. The same types of metrics made their valuable work look like failure.

The metrics weren’t bad. Revenue, acquisition cost, and penetration are perfectly reasonable things to track. The problem is more fundamental. Valuable exploration generates unmeasurable or negative metrics in the short term. Measurable work tends to be optimization of things you already understand. As a result traditional measurement systems can’t distinguish between “failing” and “learning.”

Why Measurement Systems Break Down

Economist Charles Goodhart identified a problem that affects all measurement systems. When a measure becomes a target, it ceases to be a good measure.

The management pattern is predictable. You identify a metric that correlates with something valuable. You set it as a target and reward people for improving it. Eventually, people find ways to improve the metric that don’t improve the underlying value. The metric stops measuring what it was supposed to measure.

This isn’t about bad people gaming systems. It’s about rational behavior under constraints. When a customer satisfaction target is set at 4.5 out of 5, support teams learn to survey only happy customers. They resolve tickets quickly without solving problems. They can even coach customers on how to respond. These actions lead to the score going up. Actual satisfaction, measured by something different like retention and referrals, goes down. This is often referred to as “Metric-hacking.”

When you measure engineers on lines of code written, they write duplicative code. When you measure sales teams on quarterly revenue, they may close deals with unsustainable discounts. When you measure teams on number of experiments, trivial changes get labeled “experiments.” In each case, the metric improves while the thing you intended to measure gets worse.

Psychologist Donald Campbell identified a related effect. He found that the more important a metric becomes for decisions, the faster it corrupts. High-stakes metrics create strong incentives for manipulation. The definition gets negotiated. The measurement gets gamed. The metric becomes meaningless while appearing objective. This is why measurement systems can degrade over time. The very act of using them for high-stakes decisions creates pressure to corrupt them.

Management scholar Jerry Muller argues in his book, The Tyranny of Metrics, that this reveals a fundamental error. Often times leaders end up believing that measurement replaces judgment. In reality, measurement demands more judgment, not less. Judgment about what to measure. Judgment about how to interpret what you find. Judgment about when the numbers are being gamed. Organizations that eliminate judgment in favor of pure measurement make systematically worse decisions. By doing this they remove the interpretation layer that makes measurements meaningful.

These effects combine into what you might call the “Weight of the Measurable.” Some activities produce clear, quantifiable metrics quickly (like operator and refiner work). Other activities produce ambiguous, delayed, or unmeasurable outcomes (like creator work). Budget and headcount flow toward measurable activities following a gravitational-like pull. They’re easier to evaluate and justify. Unmeasurable but valuable activities get starved as a consequence.

This happens because measurable work has lower transaction and tracking costs. It’s easier to evaluate performance objectively. Feedback is faster. Attribution is clearer. There’s less political negotiation over resources. Managers favor it because it’s easier to justify even if it’s not as valuable.

Innovation is most often unmeasurable in the short term. Exploration doesn’t produce revenue yet. Early experiments often fail. Attribution can be ambiguous. Outcomes are delayed by quarters or years, not months.

Optimization is measurable. Improvements produce clear metrics. Tests show results quickly. Attribution is direct. Outcomes appear this month.

The result: measurement-driven organizations underinvest in innovation while overinvesting in optimization. Not because managers don’t value innovation. Because measurement systems make optimization visible and innovation invisible.

Obsessed with the Observable

Our world has developed what I can only describe as “a fetish for more data.” Data is helpful, but it is not all-knowing. You cannot allow your company to operate under the delusion that numbers remove the need for interpretation and judgment. You cannot become obsessed with the observable.

This is a version of what economist Friedrich Hayek called “scientism.” This is based on the belief that methods which work well in engineering work equally well for understanding human systems. In What Headquarters Can’t See we explored how local, tacit knowledge resists centralization. The measurement version of the same problem is that local, tacit knowledge also resists quantification. The customer success manager’s sense that an account is at risk, the operator’s judgment about a failing process, the creator’s instinct about an emerging market don’t become more real when you put a number on them. They simply become more convincing. Often the attempt to quantify them strips out the context that made them valuable in the first place.

Valuable knowledge that leads to action often resists centralization as well as quantification. If you make all your decisions based on dashboards, but some of the most valuable information can never be contained by a dashboard, then you’re missing out on the most vital knowledge for your businesses’ success.

The “Stat Sig” Trap

Economists Deirdre McCloskey and Stephen Ziliak argue in The Cult of Statistical Significance that confusing statistical significance with economic (or business) significance is one of the most expensive errors in modern decision-making. The same confusion plays out inside organizations every day.

Statistical significance (”stat. sig.”) means the observed difference is unlikely due to random chance. Business significance means the difference matters enough to change your decision. These are completely different questions. Organizations often treat them as identical.

An A/B test with a million users finds that changing a button color increases conversion by 0.02%. Stat. sig. at p < 0.001. Business significance: the improvement costs more to implement than it generates. Statistical standards say ship it. Business judgment says ignore it.

A pilot program in three cities shows a 25% increase in customer lifetime value. Not stat. sig. at p = 0.15, maybe because the sample is small. Business significance: if real, a 25% LTV increase is transformational. Statistical standards say kill it. Business judgment says expand the test.

Organizations trained to worship stat. sig. findings can kill valuable innovations while focusing on trivial optimizations. The most valuable innovations often start with weak signals in small samples. They start with conversations with a handful of customers. Or even experiments with limited users. These end up beginning as outliers rather than patterns. These are not good situations to find stat. sig. results. Mandating statistical significance for all decisions eliminates the exploration needed for innovation.

Type 1 vs Type 2

This connects to a deeper bias that deserves its own attention. Organizations tend to be better at avoiding visible mistakes than avoiding invisible ones. In statistical terms, there are two kinds of errors. A false positive (type 1 error): funding something that fails. A false negative (type 2 error): killing something that would have succeeded.

These errors are not treated equally. False positives are visible. Everyone knows you funded that failed project. There’s a name attached. There’s a post-mortem. False negatives are invisible. Nobody knows the initiative you killed would have been a $500M revenue line. There’s no post-mortem for the road not taken.

Innovation requires accepting some false positives to avoid false negatives. You have to fund experiments that don’t pan out to not kill experiments that would have transformed the business. But measurement-driven organizations set high bars for funding. They’re quick to kill a project for underperformance. They must show strong evidence before scaling. Every one of these rules optimizes against visible failure. None of them protect against invisible missed opportunity.

The contractor payroll team from our opening example was a false negative. The measurement system couldn’t distinguish between “this isn’t working” and “this hasn’t worked yet.” So it killed a real opportunity to avoid a visible failure.

Different Functions Need Different Measurement

Management scholar Steven Kerr identified the core dysfunction (On the Folly of Rewarding A, and Hoping for B). He found that organizations hope for long-term growth, innovation, and strategic positioning. But they reward quarterly results, measurable efficiency, and short-term wins. The unmeasurable things they hope for get neglected while the measurable things they can track get optimized.

This plays out differently across the three functions.

Measuring operator work. Operator work can be measured, but the wrong metrics destroy its value. Measuring pure efficiency (such as volume divided by time) drives out quality and judgment. Measuring short-term costs can drive out reliability investments. Measuring individual output can drive out collaborative problem-solving.

Better operator metrics focus on system reliability, customer outcomes, and problem resolution rather than speed. They use longer time horizons such as quarterly and annual rather than daily and weekly. They measure team performance rather than just individual output. The goal is enough measurement for accountability without so much that it drives out the context that makes operator work valuable.

Measuring refiner work. Refiner work is partially measurable, but measuring only immediate efficiency gains drives out capability building. Measuring only cost reduction drives out quality improvements. Measuring only successful experiments drives out the necessary failures that generate learning.

Better refiner metrics focus on rate of improvement, knowledge creation, and process capability over time. Better metrics take a portfolio view. They measure suites of improvements rather than individual projects. They value learning even from experiments that didn’t produce the expected result. The goal is measuring improvement and learning while avoiding pressure for immediate gains. Immediate pressure can stifle experimentation.

Measuring creator work. Creator work resists measurement almost entirely in the short term. Measuring short-term revenue stops exploration before it can generate revenue. Measuring success rate of experiments drives out necessary risk-taking. Requiring stat. sig. eliminates small-sample learning.

Better creator metrics focus on rate of experimentation, quality of learning, and the value of new options that get created. What future opportunities did this enable that didn’t exist before? These better metrics look at the entire portfolio rather than individual experiments. They use long time horizons. They explicitly accept that most individual experiments will fail. But, the portfolio can succeed even when most of its components don’t.

Consider what this means in practice. If a creator team runs ten experiments in a year, and eight fail, one produces modest results, and one opens a new market worth $50M—that’s an extraordinarily successful year. But a measurement system that evaluates experiments individually reports an 80% failure rate. The team looks terrible on paper while creating enormous value. Portfolio evaluation sees the $50M opportunity. Individual measurement sees eight failures.

The critical point: for creator work, measuring success or failure of individual experiments can be actively harmful. The right question isn’t “did this experiment work?” It’s “is our portfolio of experiments generating knowledge and creating options faster than it costs?”

Harmonizer Thinking as Measurement Translation

To review, In The Cost of Working Together we described harmonizer thinking as building new rules and systems inside the organization. In What Headquarters Can’t See we described it as knowledge brokering. It translates between local and central understanding. Now we can see a third dimension of its value. Harmonizer thinking protects valuable work from measurement systems that would kill it.

This isn’t a separate function. It’s the same way of thinking applied to the measurement problem. The person who designs shared metrics across functions also needs to ensure those metrics don’t destroy creator work. The person who translates local knowledge for central decision-makers also needs to advocate for that knowledge when the dashboard tells a different story.

In practice, this means several things.

Arguing for different metrics for different work. When leadership wants a uniform scorecard across all teams, harmonizer thinking makes the case that applying revenue targets to an exploration team is like grading a research lab on manufacturing output. It doesn’t argue against measurement. It argues for measurement that matches the work. Like reliability metrics for operators, learning metrics for refiners, portfolio metrics for creators.

Translating unmeasurable value into language leadership can act on. One team may have spent three quarters “failing” by every metric on the dashboard. But, they may have built relationships, identified a market, and developed knowledge that no competitor has. That value is real but invisible to the measurement system. Harmonizer thinking makes it visible. It doesn’t invent metrics to justify the work. It connects the dots on what the team has learned and what options that learning creates.

Protecting experimentation from premature judgment. Measurement systems want to evaluate quickly. Innovation needs time to develop. Harmonizer thinking creates space between these two pressures. It advocates for longer evaluation windows. It builds portfolio-level assessments. It helps create patience to distinguish between “this isn’t working” and “this hasn’t worked yet.”

This is perhaps the most immediately valuable thing harmonizer thinking does. Building systems and brokering knowledge are important but they address chronic problems. Protecting valuable work from measurement bias addresses an acute one. Somewhere in your organization right now, a team, or an IC, is doing work that could transform the business. Your measurement system could be telling you they’re failing. Without someone who can see the difference, you’ll make the rational decision to cut them. But when you do you may never know what you lost.

Measurement and the Knowledge Problem

Valuable knowledge is often local, tacit, and context-specific. Now we can see how the measurement problem compounds this.

The knowledge that matters most for good decisions often resists measurement. The customer success manager’s sense that an account is at risk. The operator’s judgment that a process is about to fail. The creator’s insight about an emerging opportunity. These are exactly the kinds of knowledge that drive good decisions. They’re also exactly the kinds that measurement systems can’t capture.

Organizations build decision-making systems around measurable, systematic knowledge while ignoring valuable local knowledge. Aggregated customer data is measurable, so it gets favored. A customer success manager’s tacit sense that something is wrong is unmeasurable, so it gets ignored. This happens even when it’s more accurate than the dashboard.

This is why uniform measurement across all functions can be damaging. Management wants “objective” metrics for everyone. But operator value often lives in reliability and judgment that resist quantification. Refiner value lives in systematic improvement that’s partially measurable but delayed. Creator value lives in option creation that’s largely unmeasurable. A uniform system measures what it can. But that means it measures most refiner and operator work well, and creator work not at all.

Looking Ahead: The Rules Underneath the Metrics

Understanding measurement economics explains why metrics can kill innovation. But measurement is only one part of a larger system that shapes behavior inside your organization.

Metrics are rules. But so are decision rights, budget processes, career paths, and evaluation frameworks. All these shape what people do independently of what leadership says it values. When any of these rules make cooperation a sacrifice rather than a rational choice, no amount of measurement redesign will fix the coordination failure.

Next, we’ll explore why persistent cross-functional problems aren’t motivation problems, they’re design problems. We’ll see why economist James Buchanan’s insights about institutional rules apply directly inside organizations. Why hoping for good employees is not a sustainable strategy. And why designing systems where cooperation is individually rational produces better outcomes than any speech from leadership ever could.

The Bottom Line

Measurement economics explains a systematic problem that affects every growing organization. What gets measured gets managed. What can’t be measured gets eliminated. Even when the unmeasurable may be more valuable than the measurable.

Goodhart’s Law means metrics stop being good measures when they become targets. Campbell’s Law means the more important a metric, the faster it corrupts. The obsession of the observable means the weight of easily quantified work crowds out valuable work that resists quantification. And the fear of visible failure exceeds the fear of invisible missed opportunity.

These biases combine to kill innovation in your business.

The answer isn’t eliminating measurement. It’s matching measurement to work type. Operators need reliability and quality metrics, not just efficiency. Refiners need learning and improvement metrics, not just cost reduction. Creators need exploration and option value metrics, not revenue targets. And for uncertain work, measure portfolios rather than individual experiments. Focus on judging learning value rather than success rates.

The competitive advantage goes to organizations that can measure what matters without measuring everything. That can hold people accountable without killing valuable uncertainty. That can use data to guide decisions without worshipping statistical significance at the expense of business judgment.

One Takeaway

Markets work because people try things. Sometimes they win, sometimes they lose. But, every outcome reveals what’s valuable and what isn’t.

Embracing the Unknown

Every decision we make about the future involves uncertainty. You can plan, prepare, and guess, but you’ll never know exactly what will happen next. That’s true in life, and it’s especially true in economics. Entrepreneurs embrace the unknown by trying new ideas. Some may think what entrepreneurs do is risky. But, risk isn’t quite the right word. A better word in this case is uncertainty.

Economists make an important distinction here:

• Risk is when you can measure the odds. Like flipping a coin or rolling dice.

• Uncertainty is when there are no set odds to measure. Like starting a new business, entering a new market, or launching a product.

In the real world, most meaningful decisions involve uncertainty, not measurable probabilities.

Profit and Loss: The Market’s Scorecard

Every time you act in a market, whether as a business owner, a worker, or a consumer, you’re placing a bet on what you think is most valuable. That bet might pay off (profit), or it might not (loss). Either way, you learn something.

• Profit means you created value. Your product or service met a real need.

• Loss means your resources could have been used better elsewhere. You took a shot and it didn’t work.

It’s not personal. It’s a feedback loop. Profits reward good guesses. Losses expose bad ones.

This is how the economy figures out what works and people learn how to produce things others value.

Uncertainty Makes Entrepreneurship Possible

Entrepreneurs are the people who step into the unknown. They make bets no one else is willing or able to make. They look at the world, spot a gap, and try to fill it—without any guarantee of success.

This is what makes them essential:

• They coordinate resources without being told.

• They take chances with their own time and money.

• They discover what consumers want before anyone else does.

Without uncertainty, there would be no opportunity. If everything were known in advance, there would be no need for entrepreneurs at all.

A Simple Example

You buy a food truck and plan to sell gourmet grilled cheese sandwiches. You think people will love them. You find a great location. You test your recipes. You print menus. You start selling.

• If people line up, you earn profits, and maybe expand to a second truck.

• If the response is lukewarm, you learn. Maybe the price is too high. Maybe the neighborhood isn’t a good fit. Maybe grilled cheese isn’t as exciting as you thought.

Either way, the market has given you feedback. Profit means keep going. Loss means pivot or stop.

You’ve gained knowledge that helps you, and others, make better decisions next time.

Why This Matters

Uncertainty, profit, and loss aren’t just quirks of markets. They’re essential for markets to work.

• They reveal what people value.

• They encourage new thinking.

• They prevent continued waste by signaling when something isn’t working.

When we remove profit and loss from a system we lose that feedback. And when people don’t face uncertainty, they stop learning.

The Market as a Discovery Process

Think of the economy as a giant experiment. Millions of people try things every day. Some succeed. Some fail. But every outcome teaches us something.

• Profits don’t just enrich entrepreneurs, they show what should be done.

• Losses don’t just hurt, they show what shouldn’t be done.

Over time, this process moves resources toward more valuable uses. That’s how progress happens.

The Bottom Line

The real world is uncertain. Markets turn that uncertainty into information. Every decision, win or lose, helps us understand what people want and how to serve them better. The best thing we can do is pay attention to the signals, and keep trying.

A restaurant. An office park. A strip mall. Someone looked at a piece of land and said, “This is what should go here.” Then they spent real money making it happen.

That bet doesn’t just involve the land. It involves everything built on it. Kitchen equipment. Office wiring. Parking configurations. Loading docks. Utility connections. Layout, fixtures, and signage. All of it shaped for one specific use.

This is something economists call capital heterogeneity. Capital isn’t some shape-shifting blob that can become a different tool by simply rearranging itself. A dollar becomes a deep fryer or a server room or a loading bay. Once it’s committed, it takes a specific shape. And the more specific the shape, the harder it is to repurpose.

Converting a restaurant into office space means ripping out thousands of dollars of specialized equipment. Reconfiguring the floor plan. Adding different utilities. None of that investment transfers cleanly. Some of it is simply lost.

This is a normal cost of economic life. Markets change. Demand shifts. Buildings outlive their original purpose. When that happens, someone has to bear the cost of reshaping capital to fit its next-best use.

The question is whether we make that process easier or harder.

The Role Zoning Plays

Most people understand the basics of zoning. You can’t build a factory next to a school. You can’t put a nightclub in a residential neighborhood.

But zoning doesn’t just separate incompatible uses. It locks every parcel of land into a specific regulatory category. Each category comes with its own rules: what you can build, how tall, how dense, how much parking, what the building can be used for.

Change the use? You need a variance. Or a full rezoning. That means submitting applications, public hearings, reviews, and approvals.

Here’s the important part: zoning increases capital heterogeneity artificially.

Without zoning, a property owner who sees demand shifting can begin adapting. A dying strip mall near a hospital corridor could start transitioning to medical offices. An old warehouse near a growing downtown could become residential lofts. The conversion is still expensive, but the owner can start moving immediately rather than having to check some boxes that have nothing to do with market demand.

With zoning, that same owner has to pause. The strip mall is zoned C-1, neighborhood commercial. Medical offices are a different category. The warehouse is zoned for industrial use. Residential is a different category.

Even if the owner has the capital, the vision, and the market demand, they still need permission.

Dead Time

Here’s what the permission process often looks like.

Step one: check the zoning. Discover your intended use doesn’t match. Step two: file for a variance or rezoning. Pay fees. Wait. Step three: planning commission review. Staff reports. Traffic studies. Environmental assessments. More fees. More waiting. Step four: public hearing. Neighbors raise concerns about parking, traffic, and neighborhood character. Step five: compromise. Scale back the plan. Accept conditions.

If everything goes well, you might break ground eighteen months later.

If the application is denied, start over.

All of this is dead time. Capital sitting idle. The building stays vacant while the market opportunity slowly disappears. Carrying costs pile up. The property generates no value for the owner, no tax revenue for the community, and no service for the people who need it.

The economic cost of zoning isn’t just the fees and the studies. It’s the delay. It’s the capital that can’t move to where it’s needed.

Why This Matters More Than You Think

Every community faces market shifts. Remote work empties office buildings. E-commerce kills retail strips. Neighborhoods age and their needs change.

Adaptation is how economies stay healthy. Property owners see new opportunities, repurpose buildings, and direct capital toward higher-value uses. This process is messy and imperfect, but it works only as long as people are free to respond to what they see.

Zoning interrupts this process. It adds a layer of permission between recognizing an opportunity and acting on it. And that layer has real costs that go far beyond paperwork.

When adaptation is slow, you get stranded capital. Vacant buildings. Declining property values. Investment that can’t flow to where it would do the most good. The community ends up losing twice. Once from the obsolete use that no longer serves anyone. And again from the new use that never gets built.

Some cities have recognized this. Houston has no formal zoning code. Land uses mix naturally, and buildings adapt quickly when conditions change. Tokyo allows mixed use by right. Property owners respond to demand without waiting for permission. Even cities with traditional zoning, like Minneapolis, have started loosening restrictions to make adaptation easier.

These approaches aren’t perfect. No policy is. But they reflect a key principle: markets change faster than regulators can anticipate. When you lock land use into rigid categories, you’re betting that current uses will remain optimal indefinitely.

That bet always loses in the long run.

The Framework

Here’s the way to think about this whenever zoning comes up in your community.

Every regulation that restricts how property can be used raises the cost of adaptation. Every restriction beyond what the market and property owners determine is needed for safety is a bet against change. It’s a bet that today’s use categories will still make sense in ten, twenty, or forty years.

The question communities need to ask is “how much adaptation cost are we willing to accept?” Because every rule that makes it harder to repurpose a building or change the existing use of scarce land is a rule that makes your community a little more brittle, a little less able to respond when conditions shift.

Capital is already hard to repurpose. That’s the nature of investment. We don’t need to make it harder.

One Takeaway

Interest rates are the reward we get for delaying consumption now, and the price we pay to borrow from the future.

The Trade-off Between Now and Later

Every choice about saving or spending reflects a personal trade-off between the present and the future. This trade off is known as time preference. This might seem like a topic for psychology, but the idea of time preference can shape entire economies.

When lots of people have low time preference (they’re willing to wait), there’s more funding available for long-term projects. When most people have high time preference (they want things now), money becomes scarce for investment, and economic progress slows down.

Why Savings Are the Bridge

Savings are essential because they’re the bridge between consumption and production. Think of savings not as money sitting idle, but as resources being redirected from immediate use to future productivity.

Without savings, there’s less available resources in the economy to be used for investments. This means there’s less funds available to build factories, launch new ideas, or develop long-term projects. Imagine a business that spends every dollar the day it comes in. There’s no room to invest in something bigger tomorrow because everything’s being consumed today.

How Savings Turn Into Growth

When people save, their money doesn’t just sit idle. It enters the economy through:

• Deposits in banks

• Purchases of stocks and bonds

• Contributions to retirement or investment accounts

These savings become the fuel for investment. With more deposits, banks can lend more money to businesses. Businesses then use the money to build, expand, and innovate.

But here’s the key: this process takes time. Investment is delayed consumption. It requires confidence that the resources we set aside today will create something more valuable tomorrow.

Interest Rates: The Signal That Coordinates Time

Interest rates are how the economy balances time preferences and investment opportunities. They’re both the price borrowers pay for money and the reward savers receive for waiting.

• When savings are plentiful, market interest rates tend to fall. Borrowing becomes easier and less costly. This encourages businesses to invest in longer-term, more complex projects.

• When savings are scarce, market interest rates tend to rise. Loans become expensive. Only the most promising long-term projects can justify the cost.

Think of interest rates as signals that help coordinate decisions across time. They tell entrepreneurs whether enough people are saving today to support the demand they expect tomorrow for their new project. Interest rates can also can signal back to savers that entrepreneurs want to work on big plans for the future and it may be worth saving now for future gain.

Why Consumption Alone Can’t Drive Growth

You’ll often hear that consumers spending more is the path to economic growth. But while consumer spending creates activity, it doesn’t create productive capacity. Buying new clothes doesn’t increase output. Building the machines that make clothes faster and better does.

• Spending without saving generates short-term activity.

• Saving and investing builds long-term ability to produce more.

Lasting growth comes from increasing our ability to produce more efficiently. That means building tools, equipment, and infrastructure. These are all essential make more goods and services in the future.

A Simple Example

Picture a carpenter who wants to grow their business:

• With only hand tools, they can build one piece of furniture a day.

• If they save enough to buy power tools, they can triple their output, and maybe hire help.

That doesn’t just help the carpenter. It helps their customers, their suppliers, and the broader economy. All because they used savings to invest.

The same principle scales up. Households across the country save. Banks channel those savings to businesses. Those businesses use the money to build factories, develop new technologies, and expand operations. That’s how economies grow over time.

The Danger of Skipping the Wait

Some economic thinking treats savings as a problem to be solved. Some claim that consumption should be “stimulated” at all costs. But short-term consumption can’t replace the long-term benefits of savings. Without saved resources, we can’t build the systems and capital goods that improve living standards.

A household that spends everything it earns lives paycheck to paycheck. So does an economy.

The Bottom Line

Growth takes time, patience, and genuine savings. Interest rates coordinate individual time preferences with entrepreneurial opportunities. If we want to produce more, innovate more, and live better—not just now but in the future—we need systems that accurately reflect how much people are willing to wait. Every dollar saved and invested is a choice to build something bigger in the future, and interest rates help ensure those choices align with real opportunities.

One Takeaway: All the knowledge needed to run your business doesn’t exist in any one place. And it can’t be centralized without destroying its value. Understanding the knowledge problem explains when to centralize decisions, when to decentralize authority, and why the answer changes as you scale.

In Growth Isn’t One Sided, we saw that operators, refiners, and creators need different systems to function effectively. In The Cost of Working Together we explored why transaction costs make internal coordination difficult. But there’s a deeper challenge underneath underlying these points: the knowledge problem.

As companies grow from 10 to 100 to 1,000 people, total knowledge in the organization increases dramatically. But the ability of any central decision-maker to access and use that knowledge decreases. Small companies tend to be able to run on founder instincts. Founders try to know everything happening in the business. When it’s smaller it’s much easier to get a full picture of the business. But, this changes with large companies. Large companies need to embrace decentralized decision-making. No executive can possibly know all the information needed for good decisions.

But decentralization creates its own problems. Coordination failures. Inconsistent choices. Local (i.e. close-to-the-problem) optimization that can hurt the whole. How do you balance centralized strategy with distributed knowledge? The answers come from Nobel-prize winning economist Friedrich A. Hayek’s insights on the role and types of knowledge in our world.

Two Product Teams, Same Company, Different Decisions

A fast-growing SaaS company had two product teams. Both reported to the same Chief Product Officer. Both followed the same development process. Both served enterprise customers. They faced similar decisions about feature prioritization.

The Marketing Automation Team (centralized decision-making). Every feature request went through a central prioritization committee. Product managers gathered customer feedback, analyzed usage data, and submitted business cases. The CPO, VP of Product, and Head of Engineering met bi-weekly to review submissions and set priorities.

The process was rigorous. Standardized ROI frameworks. Data-driven impact analysis. Strategic alignment scoring. Time and budget allocation optimization.

When customers in the healthcare vertical requested HIPAA compliance features, the local PM documented the requirements. Development cost: 3 engineering-quarters. Projected revenue: $2M from healthcare customers.

The committee reviewed it against other requests. The ROI looked marginal. Three quarters of development for $2M didn’t clear their hurdle rate. Healthcare was only 15% of the market.

The features got deprioritized. The local PM told frustrated customers it wasn’t on the roadmap.

Six months later, three major healthcare customers churned to a competitor who had built HIPAA compliance. The lost revenue: $8M annually. The centralized committee hadn’t known that healthcare customers chose specifically based on compliance. The ROI analysis wasn’t right to factor this in. It also didn’t factor in the cost of losing these reference customers. Or that losing them could damage future healthcare sales. It also wasn’t helpful in figuring out if a competitor was actively targeting this vertical.

The Sales Enablement Team (decentralized decision-making). The local PM had authority to make most feature decisions without central approval. They were simply held to a quarterly development budget. She sat in with the sales team. She heard customer conversations daily, and understood competitive dynamics in real-time.

When enterprise customers started asking about Salesforce integration, she didn’t need to write a business case. She heard the pattern directly from customers. “We love your product, but we can’t buy without Salesforce integration.” She was already aware of the competitive landscape because it came up in client conversations. Two competitors had basic integration, one was building deep integration.

She allocated 1.5 engineering-quarters from her team’s budget. The business case wasn’t obvious from central metrics. Integration would impact maybe 30% of prospects. But she knew from her front-line context that the 30% asking represented 70% of potential revenue. She knew that lack of integration was a deal-killer, not a nice-to-have. She knew that the competitor’s deep integration was 6 months out, so they had a window to act.

The integration shipped. Enterprise sales increased 40% quarter-over-quarter. She made a better decision than any central committee could have because she had access to knowledge that didn’t exist in centralized data systems.

The difference. Both teams had smart PMs, good processes, and access to the same company resources. The difference was in who had authority to make decisions and whether they could access the knowledge that mattered.

This is Hayek’s knowledge problem. The knowledge needed for good decisions is spread throughout the organization in forms that can’t be fully combined. Solve it well and you make better decisions faster. Solve it poorly and growth itself can undermine your decision quality.

Knowledge Exists in Two Different Forms

Hayek won the Nobel Prize in Economics partly for explaining a problem that most management theory ignores.

Hayek recognized that the knowledge needed to run a complex system (like an economy) doesn’t exist in any centralized location and can’t be effectively centralized. The Soviet Union’s central planners failed not because they were stupid. They failed because the knowledge needed to run an economy doesn’t exist in a form that central planners can access and use.

The same problem affects every organization that grows beyond the point where one person can know everything.

Local knowledge is specific to context. In this case local means “close-to-the-problem.” This can mean close in proximity (if your business operates across many geographic markets). This can also simply mean closest to the opportunity or breakdown point. The sales rep knows the customer in front of them is evaluating competitors right now. The operator knows this specific machine makes a noise before it fails. The customer success manager knows an account’s satisfaction is down without looking at a dashboard. This knowledge is tacit, time-sensitive, and held by people close to the situation. It gets lost when transmitted through formal reporting systems.

Systematic knowledge is general across contexts. Optimization algorithms. Strategic frameworks. Process improvements that work in many locations. Performance patterns that reveal themselves in aggregated data. This knowledge is explicit, relatively stable over time, and can be enhanced by central analysis.

Decisions that depend on systematic knowledge benefit from centralization. Resource allocation across regions. Process standardization. Strategic positioning. Capital investment. Centralizing these enables optimization based on existing historical patterns.

Decisions that depend on local knowledge benefit from decentralization. Feature prioritization. Sales approach adaptation. Operational problem-solving. Customer relationship management. Decentralizing these enables speed, context, and adaptation.

Your company needs both. Centralize too much and you make decisions without the knowledge that makes them good. Decentralize too much and you lose coordination.

Why Centralization Destroys Local Knowledge

The temptation to centralize is strong. Centralization promises consistency, optimization, reduced duplication, and clear accountability. But the process of centralizing information destroys much of its value.

Reporting filters out context. When the healthcare PM submitted her business case, here’s what happened to her knowledge. She knew healthcare customers were strategically important despite being only 15% of the market. She knew they were price-insensitive if you solved compliance. She knew the competitive window was closing. She knew the technical risk was lower than it appeared.

What made it into the ROI analysis: $2M projected revenue. 3 engineering-quarters. 15% market segment. Comparison to features with “better” ROI. Everything that couldn’t be quantified, everything about timing and competitive dynamics, everything she understood tacitly got filtered out. Not because the committee was incompetent. Because her knowledge didn’t survive the reporting process’ standard format.

Information arrives too late. Local knowledge is often time-sensitive. The value of knowing a customer is evaluating competitors right now is high. The value of knowing they were evaluating competitors last quarter is near zero. Central decision-making has latency. It requires time to gather, format, queue, deliberate, and communicate back.

Aggregation loses what matters. Your customer satisfaction score across all customers might be 4.2 out of 5. That’s fine for a board presentation. But the customer success manager knows Enterprise Customer A is at 3.5 and dropping, while Healthcare Customer C is at 2.5 and likely to churn. The aggregate says everything is fine. The granular knowledge reveals problems that need immediate action.

When Decentralization Creates Problems

Hayek explains why centralization fails. But total decentralization doesn’t always work in a business context either.

Local teams optimizing for their own metrics without coordination can destroy business value. Sales might promise custom features product hasn’t committed to. Product ships capabilities sales hasn’t been trained to sell. Customer success gives discounts that wreck unit economics. Each team is locally rational. Together they lose money.

Multiple teams can end up solving the same problem independently without knowing it. Engineering builds redundant capabilities. Operations reinvents processes that exist elsewhere. Customers get inconsistent experiences across regions. Pricing varies without strategic reason.

The question isn’t centralize or decentralize. It’s systematically matching decision authority to where the relevant knowledge actually lives.

One quick note, decentralization has more practical limits in a business than in an economy. This is due to the fact that individuals within businesses are focused around a single common end/goal. This is not true for an economy where many people are focused on their own, varying ends/goals.

The Knowledge Version of Coase’s Question

In “The Cost of Working Together” we discussed Coase’s insight: firms exist because internal coordination is sometimes cheaper than market coordination. The same logic applies to knowledge and decisions.

Some good rules of thumb for businesses on when to centralize decisions are:

• When the knowledge it requires is systematic.

• When consistency across the organization creates more value than local adaptation.

• When the delay of central decision-making won’t destroy time-sensitive value.

Generally speaking, capital allocation (with exceptions), broad strategy, brand standards, and core process design fit the bill. These benefit from central analysis.

Some good rules of thumb for businesses on when to decentralize decision are:

• When the knowledge it requires is primarily local.

• When adaptation to context creates more value than consistency.

• When time-sensitivity makes central approval too slow.

Operational problem-solving, customer relationships, local market tactics, and feature prioritization for specific segments are good examples of things that benefit from, or absolutely need, decentralization. These need authority close to the knowledge.

For any major decision, the practical test is three questions.

1. What knowledge does this decision actually need?

2. Where does that knowledge live?

3. What gets lost if you force that knowledge through a central reporting process before it can influence the decision?

This isn’t a one-time design choice. As companies grow, the answers change. Decisions that should be centralized at 50 people might need decentralization at 500. Decisions that should be centralized in stable markets might need decentralization during times of rapid market change. The knowledge problem is a moving target in your organization.

Different Functions Need Different Knowledge Access

The operators, refiners, and creators framework maps directly onto different knowledge requirements.

Operators need local knowledge access. Operator work depends on knowledge of particular circumstances, times, and places. This customer has this problem right now. This process is breaking down in this specific way. Centralizing operator decisions can destroy effectiveness. The knowledge that makes operators valuable doesn’t often survive centralization. By the time central decision-makers review operational issues, circumstances may have changed. To deal with this, it’s best to decentralize operational decisions while centralizing strategic direction and process standards.

Refiners need systematic knowledge access. Refiner work depends on systematic knowledge that benefits from centralization. Process improvements that apply across contexts. Data patterns that only appear in aggregated analysis. Optimization opportunities that compare performance across the organization. A single location’s data might not reveal patterns that appear across all locations. The best way to deal with this is to centralize refiner analysis and system-wide improvement while maintaining connections to local operational knowledge. Usually it’s best to have refiners both at the HQ and local level. This allows both deep (vertical, in market) focus as well as broad (horizontal, across market) focus.

Creators need hybrid knowledge access. Creator work depends on both. They need local, external knowledge about emerging opportunities and customer needs. They have to have access to internal knowledge about strategic positioning and constraints. Pure centralization is too slow to capture opportunities. Pure decentralization can waste resources on uncoordinated experiments. The goal here is to decentralize identifying opportunities and initial experiments while centralizing strategic direction and resource allocation.

Harmonizer Thinking as Knowledge Brokering

In “The Cost of Working Together” we explained how harmonizer thinking solves transaction cost problems by building new internal rules and structures. We described it as a form of institutional entrepreneurship—redesigning the rules of the game. Now we can see another dimension of its value. Harmonizer thinking also solves the knowledge problem by brokering between centralized and decentralized knowledge.

This is a different kind of work than what we discussed in the previous article. There, harmonizer thinking built structures—shared budgets, joint metrics, reputation systems. Here, it’s doing something more fluid. It’s about moving knowledge between people who have it and people who need it, translating between different functional languages along the way.

Translating local knowledge for central decision-makers. The customer success manager knows an account is at risk, but that knowledge lives in support conversations. These in-person conversations reveal things like tone shifts, response delays, and questions that signal the customer is evaluating alternatives. A dashboard just shows a satisfaction score. Someone thinking like a harmonizer converts the tacit understanding into something central leadership can act on. The challenge is doing this without flattening it into a number that strips out the context.

Translating systematic knowledge for local decision-makers. Strategy documents and company-wide priorities exist. But, local teams often don’t know how those priorities apply to their specific context. Harmonizer thinking connects strategic direction to local decisions. It explains not just what the company is trying to do but why it matters for this particular team’s work. It helps frontline workers see how their local knowledge can serve the broader goal.

Identifying which decisions are better centralized versus decentralized. This might be the most valuable function. Most organizations default to one approach based on culture rather than the knowledge needs of specific decisions. Harmonizer thinking recognizes when local knowledge is essential versus when systematic analysis should be the standard. It knows when time-sensitivity requires local authority versus when coordination creates more value. It makes the organizational design adaptive rather than fixed. In short, it helps you know when to calculate and when to judge.

Why Harmonizer Thinking Can’t Be Replaced by Information Systems

This might cause you to pause and ask an important question. If the problem is knowledge distribution, can’t we just build systems to capture local knowledge?

Not quite. Here’s why.

Much local knowledge is tacit. It can’t be fully explained to someone else. The customer success manager’s sense that a customer is about to churn, based on changes in communication patterns, doesn’t fit in a CRM field.

Local knowledge is often too context-specific to generalize. You can’t aggregate insights like “this customer needs this feature because of their specific business model.” This is especially true across hundreds of customers.

Time-sensitive knowledge can become obsolete before reporting systems process it. The competitive intelligence that “this customer is talking to our competitor now” has immediate value. It’s worthless in next week’s quarterly review.

The valuable context gets filtered out in standardized reporting. Everything that makes local knowledge useful for decisions can’t be captured in the forms and templates that information systems need.

Harmonizer thinking solves this not by capturing all local knowledge centrally. Instead it ensures decisions get made at the level where the relevant knowledge exists. It brokers between centralized and decentralized decision-making so that each type of decision gets made where the knowledge needed for good decisions is actually available.

This is especially relevant right now. Many scale-ups are trying to solve coordination problems by buying software. They’re focused on better dashboards, more sophisticated analytics, AI-powered insights. These tools are valuable for systematic knowledge. They are, at their core, incapable of replacing the local, tacit knowledge that harmonizer thinking brokers. Investing in information systems without investing in knowledge brokering solves half the problem. But, it ignores the harder half.

Warning Signs You’ve Got the Balance Wrong

Signs you’ve over-centralized.

• Decisions take weeks when they should take days. Good opportunities die waiting for approval.

• Local teams work around formal processes to get things done.

• Central decision-makers don’t have context for good decisions.

• Innovation slows because everything needs central approval.

Signs you’ve over-decentralized.

• Different parts of the organization make contradictory decisions or resources get wasted on duplicate efforts.

• Customers get inconsistent experiences.

• Local teams optimize for their own metrics at the company’s expense.

• Strategic initiatives fail because local teams don’t align.

The Knowledge Problem Changes With Scale

What works at 50 people often fails at 500. The knowledge problem evolves as your company does.

Start-up phase (10-50ish people). Founders have access to most if not all relevant knowledge. Small enough that everyone knows what’s happening. Informal communication keeps everyone aligned. Centralized decision-making works because knowledge is naturally concentrated. But there’s a clear transition signal that pops up. This happens when founders can’t keep up with all decisions. Important information stops reaching decision-makers on it’s own.

Scale-up phase (50-500 people). Knowledge becomes distributed. There’s too many people, too many processes, or too many customers for founders to know everything. Specialization concentrates expertise in teams. Informal communication breaks down. This is where most coordination failures first appear. This isn’t because people stop caring or the culture is lost. It happens because the knowledge needed for good decisions no longer reaches the people making them. Channels need to be more formalized.

Mixed centralization and decentralization becomes necessary. Some decisions must decentralize. Operators need authority to respond to local conditions. Some must stay central. Things like strategy and resource allocation need systematic analysis.

Harmonizer thinking emerges as the knowledge brokering function here. It translates between local and central knowledge in both directions. The goal is to build systems that enable centralized strategy and decentralized execution without creating either bureaucratic friction or uncoordinated chaos.

Grow-up phase (500+ people). Knowledge is highly distributed and specialized. No executive can know even a fraction of what the organization knows. Most operational decisions must be local. Central planning focuses on resource allocation and strategic boundaries. Harmonizer thinking becomes essential at this scale. Culture and principles guide decentralized decisions where direct oversight can’t reach. The primary challenge becomes maintaining strategic coherence across decentralized decision-making while preserving and highlighting the local knowledge that makes decisions good.

Looking Ahead: When Measurement Becomes the Problem

Understanding the knowledge problem helps provide an answer on when to centralize versus decentralize. But there’s a related challenge that affects both. We often try to measure what we can quantify. We are told to manage to metrics. But what happens when the most important knowledge can’t be measured and management prioritizes only what’s measurable?

Operators facing pressure to hit metrics might ignore unmeasurable local knowledge that would improve outcomes. Refiners optimizing for efficiency might destroy unmeasured effectiveness. Creators pursuing innovation might abandon valuable uncertainty because it doesn’t produce results on the timeline that performance evaluation systems demand.

Next, we’ll explore why metrics can kill innovation. How measurement systems create bias toward the quantifiable rather than the important. And how to design measurement systems that support the three functions rather than undermining them.

The Bottom Line

Hayek’s knowledge problem explains why organizational design gets harder as you scale. The knowledge needed for good decisions becomes increasingly distributed in forms that resist centralization.

Small companies can centralize because founders access most knowledge through direct experience. Growing companies must decentralize because the knowledge needed for good decisions exists in specialized teams. This tacit understanding can’t be centralized without destroying it.

But decentralization can create internal coordination problems. The challenge isn’t choosing between centralization and decentralization. It’s matching decision authority to knowledge and problem location. Operators need local authority because operational knowledge is local and time-sensitive. Refiners need systematic knowledge access because optimization requires aggregated data. Creators need hybrid authority because opportunity identification is local but resource allocation should be systematic.

The competitive advantage goes to organizations that solve the knowledge problem well. It goes to organizations that make decisions based on the knowledge that actually matters. They use it where it exists without creating either centralized friction or decentralized chaos.

One Takeaway

Capital goods aren’t interchangeable. Growth depends not just on how much capital we have, but whether we’re using the right tools for the job.

What Are Capital Goods?

Capital goods are tools, machines, buildings, and other resources businesses use to produce goods and services for creation, not for consumption. You don’t eat a hammer or drive a tractor for fun (usually). These goods are used to make other goods.

They’re the tools that help us turn raw materials into value. But not all tools are created equal.

Capital Isn’t One Big Blob

One of the biggest mistakes people make in thinking about capital is assuming it’s all identical. That more of it is always better, or that any kind of capital can be thrown at any problem.

But economics teaches us the opposite. Capital is diverse. One tool can’t always substitute for another. Hammers aren’t screwdrivers. A lawn mower can’t replace a computer chip. A tractor isn’t going to help you build a bridge like a crane would.

Capital matters when it fits the task at hand.

Why This Matters

The specific nature of capital has serious implications:

• Not all capital is useful everywhere. Having lots of one kind of machine or tool doesn’t help if the task requires something else entirely.

• Bad matches can waste resources. If capital is invested in the wrong tools, in the wrong industries, or in the wrong places, it ends up sitting idle. That’s not growth. It’s stagnation.

• Misallocated capital can do more harm than good. Overbuilding in one area while underinvesting in another doesn’t lead to balance. It leads to bottlenecks, shortages, and lost opportunity.

Capital only becomes productive when it aligns with the specific demands of a project. That’s why the price system and real-time feedback are so critical. They guide decisions about where and how to invest.

A Kitchen Example

Let’s say you have $3,000 to equip your kitchen. If you spend it all on blenders, you’re going to have a tough time making a steak dinner. The value of your kitchen isn’t just in the dollar amount of your tools. It’s in whether you have the right tools to make what you want to make.

The same is true in an economy. An economy full of misfit capital—outdated machinery, misplaced infrastructure, or tech that no one needs—isn’t positioned for growth. What matters is fitness for purpose, not just quantity.

The Role of Markets in Matching Capital

Markets, when left to operate freely, solve this alignment problem better than “experts” making decisions on behalf of others ever could.

• Prices help communicate which capital is valuable and which is not.

• Profit and loss guide businesses toward productive uses and away from wasteful ones.

• Entrepreneurs constantly experiment, adjust, and use resources based on feedback.

Without these signals, and the process of trial and error, we’re left guessing. When we guess wrong, entire industries or economies can drift off course.

Why “Capital Stock” Can Be Misleading

You might hear phrases like “we need to invest in our capital stock” as if capital were just a big pile of stuff. But this mindset glosses over what really matters: how well that capital fits current production needs.

A government might build dozens of factories in remote regions. But without skilled workers, transportation networks, or demand for the goods, those factories won’t generate value. They’ll collect dust and waste resources that could have been used elsewhere.

The Bottom Line

Capital is not just a number. It’s a network of tools, built with purpose, and used with care. If we want long-term growth, we need more than just investment. We need investment in the right tools in the right places, guided by the right signals.

One Takeaway

The more time and steps we invest in creating value, the more productive and prosperous we become. If we’re willing to wait.

Why Can’t We Magically Have All We Want?

It’s easy to take finished goods for granted. A sandwich. A car. A smartphone. But behind every product you consume is a long chain of choices, tools, and steps that took place before you ever saw it. Production isn’t a straight line. It’s a complex web of processes that takes time to fully come together.

Some economists call this the structure of production. And while it might sound abstract, it’s central to understanding how the economy works and why building wealth requires patience and planning.

From Raw Materials to Reality

Not all goods are the same. Some are made to be consumed immediately (like food or clothing). Others exist to help create those consumables (like tractors, factory equipment, or software systems). Economists refer to these as:

• Lower-order goods: Consumer goods used directly (like meals or phones).

• Higher-order goods: Capital goods and raw materials used to produce something else (like machines or lumber).

The more complex and productive an economy becomes, the more layers of higher-order goods exist behind the scenes. Your morning coffee requires beans, roasting equipment, packaging machinery, transportation trucks, and retail systems. Each step in the chain builds toward that brewed cup.

Roundabout Isn’t a Detour

Production can work in roundabout ways. That might sound inefficient, but it’s actually a strength. Taking the long way, by building tools or infrastructure first, can make future production easier, faster, and better.

Imagine two paths across a city:

• One is a narrow road you can use right now.

• The other is a highway that will take months to build.

The road gets you there today. But the highway, once built, gets everyone there faster for years to come. That’s the power of roundabout production. It redirects effort now to multiply progress later.

Time Transforms What’s Possible

The structure of production isn’t just a technical process, it’s a temporal one. Each step in production unfolds over time. And as time passes:

• New technologies emerge.

• Consumer preferences shift.

• Resource costs change.

• And producers revise their actions based on all this.

This makes flexibility a critical part of success. Producers must adapt based on what they learn over time, not just what they assumed at the start.

The Foundation: Saved Resources

Longer production processes require resources up front. You need time, money, labor, and materials before any finished goods can exist. Someone has to forgo immediate consumption to make this possible.

Every capital good (every tractor, crane, or 3D printer) exists because someone delayed gratification and chose to invest in future productivity instead of present enjoyment.

Relatable Example

Think of a dam. Building it is expensive, slow, and complex. It takes years before anyone benefits. But once complete, it generates clean electricity for decades. That one investment keeps homes lit, factories running, and cities growing. Without the patience to plan, save, and build it, none of those benefits happen.

The same logic applies everywhere, from building software to launching new supply chains. The longer and more thoughtful the production process, the more abundant and generally more affordable the final goods become.

Why This Matters

If we only focus on what we want right now, we’ll never build what we need for the future. Growth requires us to think past immediate consumption:

• Complex production takes time to organize and execute properly.

• Quality improvements come from trial and error processes that can’t be rushed.

• Innovation emerges from experimentation that may not pay off immediately.

Roundabout production isn’t wasteful. It’s how we multiply output and create prosperity that lasts. The more sophisticated our production processes become, the more wealth we can create for everyone.

The Bottom Line

The structure of production shows us that great things take time. The economy grows not just through instant consumption, but through patient investment, flexible planning, and the slow, deliberate building of capacity. The more steps we take before reaching the finish line, the more valuable, and plentiful, the finish line can become.

Often, the most valuable business opportunities need coordination between different types of work. In these situations operators, refiners, and creators must work together. These challenges don’t fit into any single department’s responsibilities. These “Bill problems” tend to be very persistent. Solving them requires cooperation that your organization may unintentionally disincentivize.

This raises important design questions. Why do departments struggle to work together even when cooperation would clearly benefit the business? What makes internal coordination harder as companies grow? And how does harmonizer thinking create economic value beyond just “improving teamwork”?

The answers come from Nobel Prize-winning economist Ronald Coase. One of Coase’s most important insights were about transaction costs. Transaction costs are the cost of the hidden friction that comes from working with others. But understanding the problem is only half the story. The other half is understanding what harmonizer thinking actually builds to address these costs—new internal rules that make coordination possible where it wasn’t before. It doesn’t just reduce the cost of working together. It creates the conditions that allow the company to execute strategies that competitors cannot.

Two Companies, Same Opportunity, Different Outcomes

Two mid-market SaaS companies—both around 200 employees, both Series B—noticed the same thing in their customer data. Large clients were churning because the product didn’t integrate their CRM workflows they already relied on. Both companies had the engineering talent to build the integration. Both had sales teams hearing the same request on every enterprise call. The opportunity was obvious and urgent. Their largest contract renewals were six months out. Without the integration, those accounts were likely to leave as well.

Company A (traditional coordination). Product and sales spent three months negotiating what the integration should look like. Sales wanted a features that would close the deals they were losing. Product wanted a design that would scale beyond CRM to other enterprise tools. Engineering flagged that the sales-driven spec would create technical debt. They claimed this would slow development for the next two years. Each team escalated their case to the CEO, who split the difference in a way that satisfied nobody.

The project got approved, but with fragmented ownership. Product owned the technical spec. Sales owned the go-to-market. Customer success owned onboarding. They each reported into different VPs with different priorities. Engineering built the integration based on product’s spec. But, they didn’t hear directly from customers about how they’d actually use it. Sales kept promising features on calls that weren’t in the spec. Customer success learned about the new integration the week before launch. They had no documentation, no training, and no input into the user experience.

The integration shipped five months late. By then, two of their three largest accounts had already signed with a competitor. The accounts that stayed found the new product clunky. It technically worked, but it didn’t match their actual workflows. This happened because nobody with direct customer knowledge had real authority in the design process.

Company B (harmonizer thinking in action). Someone at Company B recognized this wasn’t simply a product problem, a sales problem, or a customer success problem. Instead it was a coordination problem. It required new rules and structures to solve. They didn’t just schedule a cross-functional meeting. They built a different way of operating.

They created a team with shared budget authority across product, sales, and customer success. This wasn’t a committee that met weekly to argue. It was a team with a pooled budget that had to agree on how to spend it. That single change meant every function had to reveal what they actually valued. It kept them from simply lobbying for their priorities.

They established joint success metrics. Success wasn’t “did the integration ship” or “number of deals closed.” Instead they aligned first on contract renewal rate for existing enterprise accounts. Customer success brought direct insight from support conversations into design sessions. It wasn’t some snazzy PowerPoint summary, but instead as an active voice in the room with authority over design decisions. Sales participated in technical scoping so they understood what they could and couldn’t promise. Engineering heard directly from customer feedback. This meant they understood what “works” actually meant in practice. They didn’t focus on building a product. They focused on building a product that actually was helpful.

The integration launched three months after kickoff ahead of schedule. The design reflected actual customer workflows. This happened because the people with that knowledge had decision-making authority. They weren’t passive advisors. Enterprise renewal rate hit 94%. Then, sales started closing new enterprise accounts. They were able to shift their focus and show an integration built by people who understood real workflows.

Company A’s coordination costs did more than slow them down. The months of internal negotiation. The misaligned incentives. The information that never reached the people who needed it. Leaders focused on empire building rather than value generation. Those costs consumed the opportunity. By the time they shipped, the market had moved.

But notice what Company B actually did. They did more than make things move faster. They built new rules and structures that changed the alignment and focus of the work. Things like shared budget, joint metrics, cross-functional authority changed the rules of how they normally operate in their day to day. The value wasn’t making people “play nicer.” It was creating systems where coordination became the rational choice rather than a sacrifice.

Why Internal Coordination Becomes More Expensive as Companies Grow

Coase won the Nobel Prize for asking two big questions that explain why coordination challenges become more severe at scale.

1. If markets coordinate millions of strangers efficiently through price signals, why do firms exist at all? Because sometimes internal coordination is cheaper than market coordination. It’s more efficient to hire employees than negotiate separate contracts for every task. Internal teams share information more easily than external contractors. Long-term employment enables trust that one-time market transactions can’t provide.
   
   

2. If firms are better than markets, why doesn’t everything happen inside one giant company? Because internal coordination has hidden costs that increase as organizations grow. These transaction costs eventually exceed the benefits of internal coordination. This is exactly the pattern we saw in Growth Isn’t One Sided when growing companies start moving slowly despite having more resources.

(These two points are bit of an oversimplification, but they’re useful for our current use)

The Fundamental Problem: No Internal Price Signals

Markets coordinate through prices. When demand increases, prices tend to rise. This sends a signal to producers to increase supply. No meetings required. No approval processes. No internal politics. Prices combine all the distributed information about supply and demand into a single number. That number guides decision-making.

Coase showed that sometimes authority within a firm is cheaper than prices for coordination. But this creates a new problem. Without prices, how do you know what internal activities are actually valuable?

In a market, if marketing needs engineering support, they’d pay a price that reflected the opportunity cost of the engineers. If the feature is worth $500K to marketing but engineering time costs $200K, the transaction happens. If engineering time costs $800K, marketing declines. Prices reveal relative value. Inside a firm, marketing and engineering argue in meetings about priorities. Unfortunately, there’s no true price mechanism to reveal relative value. The decision ends up hinging on political influence rather than economic value.

As organizations grow so too do the substitutes for prices. More meetings, approval layers, negotiation cycles, and budget processes. These all become more expensive. More functions specialize. More hierarchy deepens. More competing priorities. More information gets lost between decision-makers and front-line knowledge.

This is one dimension of the economic function harmonizer thinking serves. The goal is to create coordination mechanisms that approximate what prices do. Working across teams and priorities to reveal relative value, align incentives, and pull together distributed information—without formal pricing systems that would likely create their own problems.

Companies exist at the boundary where internal transaction costs equal external market costs. Harmonizer thinking can extend this boundary—not just by making existing coordination cheaper, but by designing new internal rules and systems that make coordination possible where it previously wasn’t. The first dimension makes the firm more efficient. The second makes the firm more effective.

The Hidden Costs of Internal Coordination

Every time departments need to collaborate, they face costs that don’t appear on income statements. While the costs are implicit, they have an explicit impact on effectiveness.

Information costs multiply. How long does it take to figure out who owns the customer onboarding process? Which team has authority to approve market experiments? In a 20-person company, you know who does what. In a 200-person company, finding the right person requires asking around. In a 2,000-person company, entire directories exist just to solve this search problem. People spend time searching for information and expertise in an ever changing closed environment.

Negotiation costs expand. Marketing wants engineering resources for new features. Sales wants operations to customize processes for key accounts. Customer service wants product changes. Engineering wants to reduce technical debt. All are valuable. None has a price that reveals relative value. So, organizations hold endless meetings. They create priority maps. They escalate to executives. And after all this still can make poor allocation decisions. Time ends up spent negotiating internal resource allocation rather than creating value. (Economists call this “bargaining costs.”)

Enforcement costs emerge. In markets, contracts and reputation create accountability. Inside firms, internal commitments often lack formal enforcement. When marketing promises product features, how do you ensure engineering can deliver? Internal accountability relies on informal relationships and goodwill. These mechanisms can work great in small organizations where everyone knows everyone. But, they quickly break down as organizations grow. Relationships become more distant. Individual reputation effects matter less in larger groups.

The “Bill Problem” as Transaction Cost Failure

Bill identifies a cross-functional problem that would create significant business value. He realizes improving customer onboarding needs to be improved. Solving it requires cooperation from teams that don’t have customer onboarding in their performance metrics. Product gets measured on feature rollout. Sales gets measured on deals closed. Customer success gets measured on support ticket resolution. None gets measured on onboarding quality.

Without pricing mechanisms, cooperation becomes irrational from each department’s perspective. Product loses time rolling out new features by helping with onboarding. Sales loses time that could close more deals. Customer success takes time from support tickets. Each is incentivized to optimize for their individual metrics. As a result, the problem goes unfixed and the business loses value.

Traditional solutions struggle to solve this. Appeals to “teamwork” ignore the reality that people respond to incentives, not intentions. Formal processes add bureaucratic costs without solving the incentive problem. Often organizations create unclear ownership that increases transaction costs rather than reducing them.

This is where harmonizer thinking becomes valuable. Instead of relying on misaligned incentives, harmonizer thinking focuses on system changes so collaboration serves individual interests. Tools like:

• Shared project budgets where all functions have authority

• Cross-departmental success metrics tied to joint outcomes

• Resource-sharing arrangements that benefit all participants

• Reputation systems that reward collaborative behavior.

As a result, coordination becomes attractive rather than punishing.

This is not to say companies should avoid managing specialized silos using traditional approaches. There is still value there especially for scalable problems. This approach is important when opportunities and situations arise that require more than a single specialist team solve the problem. You need the right approaches for the right problems.

How Harmonizer Thinking Creates Market-Like Systems

Without prices, you need alternatives to figure out what’s valuable and align incentives. Harmonizer thinking solves this by designing new internal rules and structures.

Creating “shadow prices” through budget authority. Rather than forcing departments to negotiate every idea, you can create project budgets with shared control. Marketing and engineering both have authority over a $2M feature development budget. Neither can unilaterally spend it. They must agree on allocation. This creates a pseudo-market. Each side reveals how valuable they think specific initiatives are by how much budget authority they’re willing to commit. Team members could receive bonuses based on the amount of budget they conserved and the revenue they generate. Marketing won’t push for low-value features because that consumes budget they control. Engineering won’t reject valuable features because that wastes shared resources.

Building “reputation markets” for internal services. Track which teams are helpful versus get in the way on cross-functional work. Create a quarterly “collaboration index” that shows which teams respond quickly and contribute to solving difficult problems. Teams with high scores get priority access to experiment resources. This creates economic incentives for cooperation. Future cooperation becomes the value that motivates current cooperation. This helps approximate what repeated market relationships create.

Designing “profit-sharing” for shared outcomes. When operators, refiners, and creators need to collaborate, structure success so each function benefits. A new product launch requires operator reliability, refiner optimization, and creator innovation. Rather than separate success metrics, they share a revenue target. Everyone gets bonuses based on net-new revenue regardless of individual functional contributions. This eliminates the incentive to focus on individual metrics at the expense of collective success.

Creating information aggregation systems. Synthesize distributed knowledge into actionable formats. Operations knows customers are struggling with a specific workflow. Product needs explicit requirements. Marketing needs business cases. Someone thinking like a harmonizer translates between these different focus areas. They make sure distributed information actually influences decisions. This approximates what price signals do in markets.

Harmonizer Thinking as Institutional Entrepreneurship

Notice what these mechanisms represent. Shadow prices, reputation markets, profit-sharing structures, information aggregation systems. These aren’t project management tools. They’re new rules and structures built inside the organization. Harmonizer thinking isn’t about running the existing system more efficiently. It’s about designing new systems that change the rules of the game based on the fact that the market is changing.

This connects directly to what we discussed previously (here and more in depth here) about the distinction between management and entrepreneurship. Israel Kirzner described the entrepreneur as someone who notices misalignments. These are essentially gaps between how resources are currently arranged and how they could be arranged to create more value. Harmonizer thinking does this inside the firm. It notices that the current rules and structures make cross-functional cooperation irrational. Then it builds new ones where cooperation becomes the rational choice.

This is the critical distinction between harmonizer thinking and project management. A project manager serves an important function by coordinating within existing structures. They schedule meetings, track deliverables, manage timelines. Harmonizer thinking redesigns the structures themselves. When a project manager sees departments failing to cooperate, they escalate to leadership or add process. When someone thinking like a harmonizer sees departments conflicting, they ask: what would make cooperation the default? Then they build the solution.

Project management reduces friction within the current system. Harmonizer thinking creates a new system where the friction doesn’t exist in the first place.

This is why harmonizer thinking creates capabilities that competitors can’t easily replicate. You can copy a project management methodology. You can’t copy the accumulated knowledge of how to redesign systems so that silos naturally reinforce each other’s work. That knowledge is embedded in relationships and organizational norms. These take time to evolve and can’t be transferred through a best practices document.

How Different Work Types Affect Transaction Costs

The operators, refiners, and creators framework maps directly onto different transaction cost patterns.

Operator coordination involves standardized processes and predictable outcomes. Formal mechanisms work well here. Clear procedures, defined responsibilities, and systematic communication keep transaction costs low. Requirements are well-specified. Success criteria are measurable. You know what good looks like.

Refiner coordination sits in the middle. It requires systematic analysis combined with operational knowledge. Structured improvement processes, data sharing, and performance measurement can manage these costs. The work is more complex than operations but still follows recognizable patterns.

Creator coordination is where traditional mechanisms break down. Uncertain outcomes and experimental approaches generate high transaction costs under standard processes. Innovation needs flexible coordination that can handle changing requirements. Bureaucratic overhead not only slows experimentation down. It kills it.

The harmonizer advantage is providing different coordination mechanisms for different types of work. Instead of one approach that works well for operators but stifles creators, or one that gives creators freedom but leaves operators without structure, harmonizer thinking designs each to fit. The organization achieves lower total transaction costs than competitors who apply one-size-fits-all methods.

Why Some Coordination Problems Cost More Than Others

Oliver Williamson, another Nobel-winning economist, extended Coase’s insights. He identified a key factor in coordination difficulty that he called asset specificity. This refers to how specialized and non-transferable certain capabilities or knowledge become.

When knowledge becomes highly specialized, it creates bottlenecks. Customer service develops deep understanding of specific problems. But, product development can’t easily access it. Regional sales teams understand local dynamics. But, they don’t transfer to other regions. Engineering builds expertise with technologies. But, other teams can’t use their systems. Creator functions develop insights about emerging opportunities. But, operator functions can’t immediately act on them.

This specialization creates value. It also traps knowledge. The customer service rep who has handled thousands of conversations has insight into product pain points. But translating that knowledge for product developers requires time, context, and bridging different functional languages. Those translation costs often exceed the value of any single insight. Organizations become less intelligent collectively even as individual teams become more capable.

Routine activities work well with traditional management systems. When knowledge is standardized and transferable, tools like SLAs, defined processes, and clear reporting structures keep coordination costs low. The knowledge moves easily between parties.

Everything else is where harmonizer thinking creates the most value. Some of this work is genuinely uncertain, like innovation projects where you can’t write an OKR specifying what the solution looks like before you’ve discovered it. Some of it is simply complex, requiring multiple functions to coordinate around problems that don’t fit any single team’s reporting structure. What these activities share is that predefined targets and standard processes can’t handle them. They need ongoing relationships with understood expectations but flexible execution. Not business as usual. Not top-down oversight. A more robust approach built for the activities where most cross-functional opportunities live.

Measuring the Value of Harmonizer Thinking

Traditional metrics like meeting hours, approval rounds, and email volume capture coordination effort. They don’t capture opportunity cost. Transaction cost economics points us to better measures.

• Coordination time. Time from problem identification to solution implementation. If cross-functional initiatives drop from 6 months to 2 months, that’s 4 months of opportunity captured sooner. Plus resources freed for the next problem.

• Resource reallocation. Percentage of time shifted from coordination to production. If engineering drops from 30% to 10% of time in cross-functional negotiations, that’s 20% more capacity for actual development.

• Opportunity capture rate. Percentage of identified cross-functional opportunities actually pursued. Moving from 20% to 60% means 3x more opportunities converted to real initiatives.

• Coordination efficiency. Value created per unit of coordination effort. Dropping from 500 to 100 person-hours of coordination per $1M value created is a 5x improvement.

The key insight: stop measuring how much coordination is happening. Measure how much value creation is happening relative to the effort spent coordinating. The economic value of harmonizer thinking is in opportunity cost reduction. Resources that would have been consumed by overhead instead create customer value, develop capabilities, or capture market opportunities.

Why This Creates Competitive Advantage

Coase’s insights suggest companies should bring activities in-house when internal coordination costs are lower than market alternatives. Harmonizer thinking extends this boundary. It can change the economics of firm size.

Traditional economic theory says coordination costs eventually exceed the benefits of scale. This is why conglomerates often trade at discounts. The costs of managing diverse businesses outweigh the synergies. Harmonizer thinking can shift this dynamic.

Traditional scaling sees coordination costs grow faster than headcount. A 10-person company coordinates easily. A 100-person company manages with some overhead. A 1,000-person company can drown in process.

Scaling with harmonizer thinking sees coordination costs grow slower. Four mechanisms drive this.

1. Reusable coordination systems means the 10th cross-functional project likely costs less than the 1st because the systems already exist.

2. Built up relationship capital means trust from early collaborations reduces negotiation costs later. Teams that have worked together coordinate faster next time.

3. Improved information gathering means better knowledge of where expertise lives, reducing search costs even as the organization grows.

4. Shared success frameworks can be re-used for new initiatives at much lower cost than the original design.

Companies without harmonizer thinking face decreasing returns to scale. They either stay small, accept high coordination costs, or outsource the hard stuff. Each option limits them. Companies that apply harmonizer thinking have better odds at facing increasing returns to scale. They grow larger while maintaining coordination efficiency. They execute strategic complexity that competitors can’t match.

Think about what this looks like in practice. Apple coordinates hardware, software, and services internally while competitors manage these through market relationships. Uber and Lyft’s city-level teams coordinated with central platform development faster than competitors who either centralized everything or fully decentralized. Amazon coordinates across different work types faster than competitors who silo functions.

This advantage is sustainable. Competitors can copy your products, marketing, and pricing. They struggle to copy your coordination efficiency. It depends on accumulated relationship capital, evolved ways of working, and embedded practices that take time to build. You can’t transfer them through a best practices document.

Looking Ahead: Where Does the Knowledge Live?

Understanding how harmonizer thinking reduces transaction costs is essential for executing complex strategies. But it raises a deeper question. How do you know what to coordinate?

The market-like mechanisms we’ve discussed—shadow prices, reputation systems, shared success metrics—all assume that the relevant information can be gathered and acted on. But as companies grow, the knowledge needed for good decisions becomes increasingly distributed. The sales rep knows something engineering doesn’t. The customer success manager sees patterns that product can’t access. The regional operator understands local dynamics that headquarters has never encountered.

This is the knowledge problem. Small companies can run on founder instincts because founders know everything happening in the business. Large companies can’t—and the process of centralizing information often destroys the very context that makes it valuable.

Next, we’ll explore why centralized decision-making fails as you grow, when to centralize versus decentralize authority, and how harmonizer thinking serves as a knowledge broker between the people who have information and the people who need it.

The Bottom Line

Transaction cost economics explains why coordination challenges get worse as companies grow. Coase showed that organizations exist where internal coordination costs equal external market costs. As firms grow, internal costs rise. More meetings. More approvals. More negotiations. More monitoring.

But harmonizer thinking does more than make coordination cheaper. It serves a dual function.

First, it reduces transaction costs by creating mechanisms that approximate what markets do. Shadow prices reveal relative value. Shared success metrics align incentives. Information aggregation systems synthesize distributed knowledge. Reputation systems enable cooperation.

Second, and more importantly, it redesigns the internal rules of the game. It builds new operating structures that make it possible for operators, refiners, and creators to reinforce each other’s work rather than compete for resources.

The competitive advantage goes to organizations that understand harmonizer thinking not as coordination management, but as system building. The economic value isn’t found in making collaboration cost less. It’s making the firm capable of strategies that competitors with inferior systems cannot execute.

One Takeaway

Long-term prosperity begins with production, not consumption, because nothing can be consumed that wasn’t first produced.

We Can’t Consume What We Don’t Create

It’s common to hear people say that spending drives the economy. You’ll hear that “consumption makes up 70% of GDP,” or that economic growth depends on getting people to spend more. But this flips the order of things. You can’t consume what hasn’t first been produced. And you can’t grow an economy by focusing only on spending.

At the root of every sandwich, car, or concert ticket is one thing: production. Someone created something valuable, and only then can someone else consume it.

Production Comes First

Production means using resources (time, effort, tools, materials) to make something that others want. Whether it’s plucking an apple from a tree, designing a smartphone, or teaching a class, production is what makes consumption possible.

Even in simple societies, no one can eat unless they first gather, hunt, or grow. Nothing gets consumed unless someone first creates, collects, or contributes something. In that sense, every act of consumption depends on someone else’s prior act of production.

Consumption Feels Good, But It’s Not the Only Goal

Consumption is necessary. It is necessary for our needs and it satisfies our wants. But if we consume everything we produce, there’s nothing left to invest in tools, skills, or systems that help us produce more in the future. In this case, growth slows and options shrink.

Think of it like this: if a farmer eats all the grain they harvest and saves none to plant next season, their future harvest disappears.

How Societies Grow

What really fuels economic growth is the ability and willingness to:

• Save some resources rather than consume them all,

• Invest those savings into productive tools and processes, and

• Build capital goods, like machines, that make future production easier and more efficient.

This shift, focusing less on consumer goods and more on capital goods, can transform living standards over time.

An Everyday Example

Take two communities. In the first, people work hard and spend everything they earn. In the second, people still enjoy life, but they also save a portion of their income and use it to invest in better tools, education, and infrastructure.

After a few years, the second community:

• Produces more with less effort

• Has higher wealth

• Offers more job opportunities and

• Enjoys greater stability

Why? Because they didn’t just work and spend. They produced and reinvested.

Time Preferences: Now vs. Later

One of the key drivers behind all of this is what economists call time preference: how much you value things now versus in the future.

• High time preference means spending now, focusing on short-term enjoyment.

• Low time preference means delaying gratification and saving today so you can build something better for tomorrow.

Individuals with lower time preferences tend to invest, build skills, and grow businesses. Societies with lower time preferences tend to develop stronger economies.

Consumption Isn’t Bad, but It’s Not Enough

None of this means consumption is bad. It’s the reward for productive effort and often lead to an enjoyable life. The simple point is, consumption is not the cause of growth. If we focus only on what people want today, we miss the chance to build a better tomorrow.

All work and no play isn’t the goal. But all play and no work isn’t either.

The Bottom Line

Economic growth is based on production not spending. Creating, saving, and investing are what lay the groundwork for future prosperity. It’s true in households, businesses, and entire economies. When we focus on producing and saving value, rather than just consuming it, we build the tools, skills, and systems that make life better for everyone.

One Takeaway

Money makes trade possible, but it isn’t wealth. Money is a tool that helps us exchange what we’ve created for what others have made.

What is Money, Really?

Money touches nearly every part of our lives, yet few people stop to ask: What is money? At its core, money is just a tool. It’s a commonly accepted medium of exchange. It allows us to trade easily, efficiently, and with nearly everyone else.

Without money, every transaction would require a direct swap: “I’ll give you this if you give me that.” Direct swaps like this are called barter. While barter can work in small, simple communities, it breaks down quickly in more complex settings.

Why Barter Breaks Down

Imagine you’re a dairy farmer and need eggs. Your neighbor raises chickens, but they don’t want milk. They want oranges. Now you have to find someone with oranges who also wants milk. This is called the double coincidence of wants, and it’s incredibly inefficient. It would be better if both of you always had what the other wanted.

Money solves this problem. You sell your milk for money, and then use that money to buy eggs. Everyone can now trade more easily, even if they don’t directly want what the other person spends their life making. That’s what makes money powerful: it connects people with different needs in a seamless, scalable way.

What Makes Something “Good” Money?

Not everything can function as money. To be widely accepted, a good form of money should be:

• Uniform: One unit should be the same as any other.

• Portable: Easy to carry and transfer.

• Divisible: You can split it into smaller units without losing value.

• Recognizable: People can easily identify and trust it.

• Durable: It doesn’t wear out quickly.

• Hard to Fake: It needs to be difficult to counterfeit.

Historically, gold, silver, and even shells have served this role. Today, we use government-issued currency.

Money Isn’t Wealth—It’s a Claim on Wealth

Money itself doesn’t feed you, house you, or keep you warm. It’s not valuable because of what it is, but because of what it lets you do. Money is a claim on real goods and services that other people have produced that you can now access through trade.

This is why the world can’t print its way to prosperity. More dollars don’t mean there’s magically more goods to satisfy people’s wants. If everyone suddenly had twice as much money, but the number of homes, clothes, and food stayed the same, prices would just rise to meet the new money supply. You’d be no better off. You’d have more zeroes in your bank account, but the same slices of bread in your pantry.

Why Stability Matters

A stable money supply allows people to save, plan, and invest with confidence. When governments or central banks alter or influence the value of money by printing too much of it, or by pushing interest rates too high or low, they can distort the signals that guide our decisions. These distortions can lead to wasted resources, failed investments, and long-term harm to the economy.

Think of it this way:

• Money is like a measuring stick. If it keeps changing length, no one can build anything that lasts.

• Money is like language. If the meaning of words changes constantly, communication falls apart.

The same is true for trade and production.

A Final Thought on Money

Most economic activity today depends on the reliability of money. It enables everything from grocery shopping to international trade. But it’s easy to forget: money works only because people trust it, and because it’s tied to the value people create.

Printing more doesn’t make us richer. Producing more things people want to trade for is what actually builds wealth.

The Bottom Line

Money is one of the greatest tools for human cooperation ever invented. It lets us trade without needing to barter, save without wasting, and coordinate without confusion. But it only works when it’s stable and trusted. Remember: money is a claim on value—not value itself. True wealth comes not from printing more, but from creating more of what other people value.

Scalable solutions work for some business challenges while others need non-scalable approaches. Companies that try to scale everything can miss opportunities that need nuance. Companies that customize everything struggle to achieve efficiency.

This creates a critical challenge for growing organizations.

• How do you tell which problems will need optimization and those that need adaptation?

• Why do sophisticated analyses sometimes produce worse results than simple rules of thumb?

• How can you build an organization that excels at both scalable and non-scalable problem-solving?

Economics can help here. For decades economics has taught that not all information is the same. Knowing this can help you match your problem-solving method to your information environment.

Two Ways to Solve a Similar Problem

When Starbucks expands to a new US city, they use a highly analytical, scalable approach. They use things like demographic analysis, traffic pattern studies, and real estate algorithms. All with the goal of understanding foot traffic and sales potential. They optimized their site selection model has across thousands of locations. More data makes the model better. Each new store provides information that improves results for the next one.

When Starbucks entered China, the same approach failed at the beginning. They lost money for the first nine years. Their model did not capture things like tea culture, differing social patterns, or competitor dynamics.

They couldn’t just apply their proven formula. They needed local market experimentation. They needed partnerships with regional operators who understood context. They needed to adapt the Starbucks concept itself to different cultural preferences. This was a fundamentally non-scalable approach that required judgment rather than optimization.

So. What’s the difference between these two situations?

It’s not that China is more complex than the US.

It wasn’t the case that Starbucks lacked analytical capabilities.

The difference is the signal-to-noise ratio in the information available for decision-making.

For US expansion: thousands of previous locations provided high-signal data about what works. Statistical patterns were reliable. More analysis genuinely helped.

For China entry: zero comparable precedents meant low-signal information. Historical patterns from different contexts provided noise, not signal. More analysis of US data wouldn’t have helped because the underlying relationships were different in China.

Understanding when you have signal versus noise is under-rated and mis-understood. Being aware of this allows you to match your approach correctly. It helps you know when it’s more appropriate to use calculation or judgment to make decisions.

Companies that are able to make this distinction scale effectively. Whereas those that rely on sophisticated analysis to problems that need judgment stall.

Signal, Noise, and Decision-Making

Businesses encounter two fundamentally different types of information: Signal and Noise.

Signal represents reliable patterns that predict future outcomes with reasonable accuracy. Seasonal demand cycles, established customer preferences, and price change data provide signal. Signal allows analytical approaches to be effective.

When you have signal, the calculation approaches from When to Calculate and When to Judge help optimize decisions. This is because past patterns do a reliable job of predicting future performance.

Noise represents random variation that appears meaningful but doesn’t predict anything useful. One-off events, shifting competition, changing customers, and new regulations don’t provide reliable guidance for future decisions.

When you’re dealing with noise, judgment approaches help you deal with uncertainty. In these cases, optimization approaches can actually make performance worse.

Why Signal-to-Noise Ratios Determine Scalability

The signal-to-noise distinction is a question of statistical inference under sampling constraints.

Consider two market entry decisions:

Entering your 50th US city. You have 49 previous market entries to learn from. Even if each market is unique, you have enough data to identify patterns that generalize. Which demographic indicators predict success? What store formats work in different density areas? How do local competitors typically respond?

With 49 data points, you can distinguish patterns from randomness. If your analysis says “cities with X demographic profile succeed 80% of the time,” that’s statistically meaningful. You have high signal relative to noise.

Entering your first international market. You have zero comparable samples. Even if you analyze the new market, you can’t distinguish which differences matter from which are random. Is the difference in coffee consumption patterns meaningful or misleading? Will competitive dynamics mirror your home market or differ?

With zero comparable samples, you cannot distinguish signal from noise. Any pattern you think you see might be coincidence.

Scale creates sample size. Sample size enables reliable statistical inference. Statistical inference justifies optimization.

When you’re doing something for the first time (or the first few times), you do not have high signal. You don’t have enough samples to distinguish patterns from noise. More sophisticated analysis doesn’t help. The limitation is statistical, not analytical.

The scalable/non-scalable distinction maps directly onto signal versus noise.

Scalable problems have strong signal-to-noise ratios. Supply chain optimization, pricing in mature markets, and process improvement all involve patterns. These patterns persist across contexts and time periods.

The operator and refiner functions excel at scalable problems. Their systematic approaches can identify and improve reliable relationships.

Non-scalable problems have low signal-to-noise ratios. New market entry, innovation, and differentiation all have unique contexts and characteristics. Analytical approaches can’t capture these effectively. Reliable statistical inference is impossible in these situations. This means judgment and experimentation are the only rational approaches for these problems.

The creator function excels at non-scalable problems. Their experimental approaches can discover what works without requiring predictable patterns.

Most scale-ups waste resources by treating noise like signal. They are quick to apply optimization approaches to situations that need adaptation. This happens even when they don’t yet have the sample size for reliable patterns to emerge.

How Problems Move From Non-Scalable to Scalable (And Back)

The scalable/non-scalable distinction isn’t permanent. Problems can evolve as you gain experience and as conditions change.

You are on a learning (and data) curve. Your first market entry is non-scalable. You don’t have enough data to optimize, so you rely on judgment. You try different approaches, see what works, and learn from failures.

But each new market entry provides data points. By your 10th entry, patterns begin to emerge. Maybe by your 50th entry, you have enough signal to start optimizing. You can identify which variables matter and build models that predict performance.

Experimentation converts unknowns into data. That data enables analysis. That analysis enables optimization.

But conditions can change. When Uber’s market entry playbook worked for US cities, they had a scalable approach. When they entered India, or Brazil, or China, existing patterns didn’t apply. Local dynamics, regulations, and competition differed fundamentally.

The problem became non-scalable again. Some (not all) signal they’d accumulated in the US became noise in other countries. They needed new experimentation to generate new market-specific signal before they could optimize.

Things like technological shifts, regulatory disruptions, or innovations can invalidate built up signal. When underlying relationships change, historical data stops predicting future performance. Scalable problems become non-scalable again. You then have to accumulate new signal under the new conditions.

This leads to an important strategic point. You will have to invest in experimentation and judgment when building signal. This is true whenever you are early in a new domain or entering unfamiliar territory. The goal is to learn, not optimize. Success metrics should focus on how much and how well you’re learning per action.

You then can shift to optimization as signal accumulates (scaling a proven model). The goal becomes extracting the most value from identified patterns. Success metrics should focus on improvement.

But, you must be ready to shift back to experimentation. Conditions can, and, do change. Accumulated signal can become old news. In these cases, organizations that keep optimizing based on outdated patterns only become efficient at one thing. Doing things that no longer matter.

This explains why set-in-stone organizational designs fail. Companies optimized for scaling proven models struggle when disruption makes their models obsolete. They’ve built systems for exploiting signal, but they’ve lost the capability for generating new signal. When their accumulated knowledge becomes obsolete, they can’t adapt.

Two Types of Knowledge, Two Types of Problems

Philosopher Michael Polanyi’s insights are essential here. Polanyi identified the difference between explicit and tacit knowledge. This distinction also maps perfectly to scalable versus non-scalable business challenges.

Explicit knowledge can be written down, transmitted, and analyzed quantitatively. Things like financial data, documentation, market research, and operational metrics represent explicit knowledge. All these are able to travel well through organizations. This type of knowledge supports scalable approaches. You can add it up, compare it, and optimize it across different contexts.

Tacit knowledge cannot be fully explained or said out loud. But, it does guides effective judgment. Things like sentiment, market timing, competitive intuition, and operational “feel” represent tacit knowledge. This knowledge exists in the experience and understanding of people doing the work. This knowledge supports non-scalable approaches. It provides context and insight that you can’t capture in formal analysis, but often determines success or failure.

Another, more descriptive, term for this is “embodied knowledge.” This exists in how people recognize patterns. It’s about how we make judgments rather than in documentable facts and relationships.

Why Tacit Knowledge Matters More for Non-Scalable Problems

Explicit knowledge captures patterns that can generalize (scalable). Tacit knowledge captures patterns that don’t (non-scalable).

When you document a sales process, you’re converting tacit knowledge to explicit knowledge. This works when the pattern is stable enough to generalize across contexts. You can write down:

“Ask these questions in this order. Address these common objections this way. Close using this approach.”

But when each sales situation is unique, documentation often fails. The patterns that predict success might include subtle customer cues, timing, or context. Things like:

• Do they lean forward or back when discussing price?,

• When to push versus when to give space

• How does this deal fit their broader company politics?

These are things experienced salespeople recognize but can’t fully articulate. You learn these things by doing not by studying or analyzing in a formal sense.

This is why scaling sales through documentation and training has limits. You can document the generalizable patterns. But you can’t document the contextual patterns. At least not all of them. And even if you could, it would likely be too expensive or take too much time.

As companies grow, they get better at managing explicit knowledge. They build up improved data systems and analytical muscle. They build dashboards, create documentation, establish best practices, and create training programs.

But they often lose access to tacit knowledge. Distance ends up growing between decision-makers and front-line experience. The VP of Sales has never met most customers. The product team doesn’t observe how features actually get used. Strategic decisions get made only based on reports and metrics (explicit knowledge). The tacit understanding of context gets filtered out because it doesn’t fit in a standardized report.

This creates organizations that can analyze scalable problems well. But, they struggle with non-scalable challenges. They’re excellent at optimizing what can be documented and measured. But, they’ve lost the capability to recognize and respond to what matters in specific contexts.

Building Organizations That Preserve Both:

1. Codify what generalizes. Convert tacit knowledge into processes where patterns are stable enough to document. When you’ve identified approaches that work consistently, capture them in systems that let you scale the pattern.

2. Preserve access to tacit knowledge. Keep decision-making authority with people who have contextual insight where patterns don’t generalize. When success depends on context-specific factors, ensure those with direct experience make decisions. Don’t rely on only distant analysis.

The mistake most scaling companies make is trying to convert all tacit knowledge to explicit knowledge. They create comprehensive documentation, detailed processes, and centralized decision-making based on data systems. These are fine and can be helpful. But, just because these approaches can lead to success, it doesn’t mean they’re always appropriate.

The solution isn’t to avoid documentation and systematization. It’s about first recognizing which knowledge can be effectively centralized (high-signal, generalizable patterns). And second, recognizing which needs to remain decentralized (low-signal, context-specific patterns).

Why Optimizing Based on Historical Data Can Make Performance Worse

Economist Robert Lucas won the Nobel Prize in 1995. He won, in part, for identifying a problem that explains why data-driven strategies may fail at scale. In simple terms, the “Lucas Critique” states that statistical relationships change when you act on them. (This may be an oversimplification, but it is helpful in this context.)

The Lucas Critique in Business Contexts

Example 1. Let’s say your customer acquisition funnel shows that email campaigns convert at 5%. You build a model predicting that doubling email volume will double conversions. But when you put it in place, conversion rates drop to 2%. Customer behavior changed in response to higher email volume. People who used to read your emails now mark them as spam or unsubscribe.

The historical relationship (5% conversion) was true when emails were occasional. It broke down when your optimization changed the conditions.

Example 2. Now, let’s say you have a pricing analysis that uses historical price sensitivity data. It shows that a 10% price increase would improve profitability with minimal market share loss. But when you implement it, you lose market share faster than expected. Your competitors didn’t follow your lead. They held prices steady and gained your price-sensitive customers.

The historical relationship (low price sensitivity) was true when competitors’ prices moved together. It broke down when your action changed competitive dynamics.

Your historical data reflects an equilibrium where certain factors were constant. The statistical relationships you observe depend on that equilibrium persisting. When you change the system by acting on the data, you can change the equilibrium. This can then make all the historical relationships invalid.

In scalable problems, the system is large enough that your actions don’t change the equilibrium. Optimizing your supply chain doesn’t change supplier behavior industry-wide. Improving your operational processes doesn’t trigger competitive responses. Those are internal to your organization.

In non-scalable problems, your actions change the system. Entering a new market changes competitive dynamics. Changing your pricing changes customer expectations. Implementing your growth strategy changes what your competitors do.

Analyses based on historical data assume the equilibrium stays constant. This works for scalable problems where you’re small relative to the system. But, in some cases your actions will change the system. This then requires you to rely on adaptation rather than optimized plans.

Scale-ups are vulnerable to this. As companies grow from small to significant players, they cross a critical threshold. They become large enough that their actions change market balance. The Lucas Critique starts applying to decisions where it didn’t before.

When you’re a small startup entering a market, competitors don’t change their strategies in response to you. You can test approaches. You can optimize based on results. Your actions don’t trigger responses that invalidate your learnings.

When you’re an established scale-up entering a market, competitors notice. They respond. Your presence changes the competitive landscape. Historical patterns stop predicting future results. Your actions shift the equilibrium.

This is why sophisticated data-driven approaches that worked great during early growth can fail at scale. It’s not that the analysis or your analysts got worse. It’s that your company became important enough that the Lucas Critique applies. Your tweaks now change the system. You end up optimizing to a moving rather than stable target.

Diagnosing Scalable vs. Non-Scalable Problems in Your Organization

Signs you’re dealing with scalable problems:

• Historical data predicts future performance with reasonable accuracy

• Optimization efforts produce measurable improvements

• Best practices from other organizations apply with minimal adaptation

Signs you’re dealing with non-scalable problems:

• Each situation has unique characteristics that matter for success

• Historical patterns don’t predict current performance

• Local knowledge creates advantages that central analysis misses

Signs you’re applying the wrong approach:

• Sophisticated analysis produces recommendations nobody believes will work (treating noise as signal)

• Local innovations fail when scaled to other situations (trying to scale what’s non-scalable)

• Experiments applied to problems with analytical solutions (defaulting to judgment when calculation works)

Building Dual-Method Organizations

There are a few specific approaches to help build organizations that excel at both scalable and non-scalable problems. (We discussed these in Growth Isn’t One Sided and here)

Use separate but connected systems for different problem types.

• Scalable problems benefit from standardized analytical processes, centralized expertise, and systematic optimization approaches.

• Non-scalable problems benefit from decentralized decision-making, local knowledge access, and experimental learning processes.

The key is creating connection between these silos. This connection let insights flow between both systems without forcing them to use the same approaches.

• Operators and refiners need analytical systems that maximize efficiency.

• Creators need experimental systems that maximize learning.

• Harmonizer thinking ensures both systems inform each other without undermining each other.

Use portfolio thinking to balance resources.

• You need to think through scalable and non-scalable approaches from a combined sense, not an individual sense.

• Take into account your industry characteristics, competitive environment, and organizational stage.

• Match your resource allocation to your signal-to-noise environment.

Create information systems that distinguish between explicit knowledge and tacit knowledge.

• Use dashboards, documentation, training programs, best practice libraries for explicit knowledge. This enables scaling what can be scaled.

• Preserve access to tacit knowledge through networks, local decision-making, and direct customer access. This enables adaptation where scaling doesn’t work.

• Don’t try to convert all tacit knowledge into explicit knowledge. Recognize that some knowledge loses its value when codified.

Use measurement systems that account for different logic of scalable versus non-scalable work.

• Measure scalable work on efficiency gains and improvement. Success is optimization. Find ways to do the same thing better, faster, cheaper.

• Measure Non-scalable work should on learning, adaptation, and option creation. Success is discovery and knowledge-sharing. You want to identify what works in this specific context then generate knowledge for future application.

• Using scalable metrics for non-scalable work kills the experimentation you need. Using non-scalable metrics for scalable work prevents the optimization you should achieve.

The Harmonizer Advantage

Harmonizer thinking becomes essential when organizations need to coordinate between approaches.

Harmonizer thinking excels at distinguishing between different problems. It recognizes what needs optimization versus adaptation. It can tell when analysis is appropriate versus when judgment works better.

It helps translate between explicit knowledge systems (scalable) and tacit knowledge networks (non-scalable). This enables organizations to capture the benefits of both without letting either dominate.

It builds portfolio approaches to resource use that invest in both types of problems, based on the types of information available in specific challenges. Organizations often default to one approach or the other. More often than not they default to optimization-heavy approaches because they’re easier to justify. But just because you can justify a choice does not mean it’s correct. Harmonizer thinking maintains the legitimacy of both approaches by demonstrating when each creates value.

Harmonizer thinking knows when to stop analyzing and start experimenting. It recognizes that in high-noise situations, learning by doing provides better insight than learning by analysis. This prevents the “analysis paralysis” that kills the ability to adapt in fast-changing markets. It also prevents experimenting too much. Constant experiments can lead to wasted resources. Sometimes analysis is the best approach.

The Coordination Challenge

Understanding which problems are scalable versus non-scalable is foundational for effective organizational design. But most valuable opportunities require both approaches working together.

You need operators running scalable processes efficiently. Creators discovering non-scalable opportunities. Refiners optimizing the transition from discovery to scale.

These functions need to coordinate without undermining each other. Operators can’t force creators to adopt standardized processes for discovery work. Creators can’t prevent operators from optimizing proven approaches. Refiners need access to both creator insights (what’s possible) and operator data (what’s working).

But internal coordination faces hidden costs that grow as organizations scale. These costs don’t always explicitly appear on income statements but they drain effectiveness. Next week, we’ll explore why this coordination is so difficult. At its core, harmonizer thinking isn't just about "improving communication." It's about solving transaction cost problems that make internal cooperation more expensive than it needs to be.

The Bottom Line

Economics explains why the distinction between scalable versus non-scalable is so critical in business.

Scalable problems with reliable signal rely on systematic optimization and analytical approaches. You can use calculation in these situations because you have enough samples to distinguish patterns from noise. More data helps. Historical patterns predict future performance. Optimization creates real value.

Non-scalable problems with high noise need adaptation and judgment-based approaches. You can’t use reliable calculation because you don’t have enough samples to distinguish signal from noise. More analysis of existing data doesn’t help in these situations. You need different information, not more of the same information. Experimentation generates the signal that analysis requires.

Understanding this distinction enables better resource allocation. You stop wasting analytical resources on uncertain problems. But, you also ensure predictable problems get the optimization attention they deserve.

The first step is being able to identify which problems are scalable versus non-scalable. Step two is applying the right problem-solving approach rather than defaulting to one method for all challenges.

This isn’t about a fight between data and intuition. It’s about using economics to match your decision-making method to your information environment. Use calculation where you have signal. Use judgment where you have noise. Then, build systems that preserve both and apply each where it creates the most value.

One Takeaway

Voluntary exchange works because people value things differently. Those differences create opportunities for everyone to be better off.

What Makes a Trade Worth It?

When you hand over $5 for a sandwich, it’s not because someone tricked you. It’s because in that moment, you value the sandwich more than the $5 in your pocket. And the person behind the counter? They value the $5 more than the sandwich they just made. You both walk away feeling like you got the better deal.

That’s the magic of voluntary exchange. It’s not a win-lose scenario. It’s a win-win. Both sides willingly agree to trade based on their own judgment of what’s worth more to them.

This idea isn’t just for sandwiches and cash. It’s the foundation of how people cooperate, specialize, and build wealth together.

Why Trade Benefits Both Parties

To an outsider, some trades might look unfair. Maybe you sold an old television for $50 that once cost you $300. Or someone swapped a rare collectible for less cash than you’d expect. But here’s the thing: value is personal. If both people said “yes” to the trade, that means both expected to gain something from it.

Nobody is forced. Nobody is tricked. Both sides act on what matters most to them at the time. Even if someone later regrets the trade, that doesn’t mean the trade was unjust. It means people sometimes learn after the fact.

In economics, we don’t judge trades based on hindsight. We focus on the moment of choice, where each person believes they’re better off.

Why Trade Creates Wealth (Even Without Making More Stuff)

Voluntary trade doesn’t always create new goods, but it does create more value. That’s because:

• It moves goods into the hands of those who value them most.

• It frees people to specialize and focus on what they do best.

• It turns competition into cooperation, aligning incentives.

A farmer with extra vegetables can trade for carpentry. A musician can trade a performance for equipment repairs. Each person gets more value from the trade than they could create on their own. And society benefits from the overall increase in satisfaction.

A Simple Example: The Garage Sale

Imagine selling a dusty old bike at your garage sale for $100. To you, it’s just taking up space. To the buyer, it’s a sweet deal and a new ride. You each gain something you value more than what you gave up. No new bike was built, but both of you are better off than before the trade.

That’s wealth creation through voluntary exchange.

The Salesperson’s Secret

Good salespeople understand that trade isn’t about persuasion. It’s about value alignment. A smart seller doesn’t try to convince you that an item is “worth” something in the abstract. They ask what you care about, and then show how the product meets your needs.

When buyers and sellers understand each other’s goals, they can make deals where both walk away happy.

Why It Matters

Voluntary exchange is how people cooperate without needing to agree on everything. You don’t need to like what someone else likes. You just need to want what they’re offering more than what you’re giving up. That simple truth powers economies, builds businesses, and helps people solve problems together.

The Bottom Line

Every voluntary trade is a quiet act of cooperation. It’s how we align different values to create mutual gain. We don’t need to force anyone. We just need to listen, offer, and agree. And when that happens, everyone walks away better off. Even if they value things differently.

One Takeaway

Order doesn’t require control. Instead, order requires people to be able to pursue their goals, respond to prices, and adjust to others.

We Don’t Need a Master Plan

Think about your last cup of coffee. Did someone carefully design every step? Was there some overseer planting the beans, roasting them, shipping them, and stocking the shelves, all just for you?

Of course not. And yet, somehow, it all came together. That’s the power of spontaneous order: an outcome that looks planned but isn’t. It happens not because someone is in charge, but because individuals (each with their own goals) cooperate through markets without even realizing it.

No one needs to know the entire process. A coffee farmer doesn’t know who you are. The barista doesn’t know who harvested the beans. But each person in that chain does their part because they see an opportunity to benefit. The result is your morning caffeine fix.

What Is Spontaneous Order?

Spontaneous order is what happens when

• People act in their own interest.

• Prices guide behavior.

• Exchange brings people together.

Without needing top-down direction, people are able to adapt, experiment, and respond to what others do. Through trial and error (and the price system) we get what feels like magic. People work together and solve incredibly complex problems without someone overseeing everything.

When it comes to your morning coffee, the farmer grows beans to earn a living, not as a personal favor to you. The shipping company responds to rising demand by adjusting routes and prices. And finally, you buy a bag of beans or a cup of coffee because it smells great and fits your budget.

Nobody sees the whole system. Yet everyone contributes to its success.

Why Spontaneous Order Matters

Spontaneous order scales. No one person could manage the decisions needed to supply coffee (or anything else) to millions of people every day. Spontaneous order handles complexity far better than a room full of experts ever could.

It adapts. Markets (the combined millions of actions taken by individuals every day) shift when something changes. This can be for any number of reasons such as a drought in Brazil or a new flavor trend in Los Angeles. The system adjusts without anyone needing permission to make a change.

It surprises us. Some of the most powerful innovations—like language, open-source software, or even the internet—weren’t designed from the top down. They evolved from the bottom up, through trial, error, and cooperation.

An Example

Take the global supply chain for a smartphone. Rare earth minerals from Africa. Computer chips from Taiwan. Screens from South Korea. Assembly in China. Logistics by air, sea, and truck. Millions of decisions made by people who don’t know each other and likely never speak to each other. And yet, somehow the phone arrives in your pocket.

This kind of order can’t be built by blueprint. It emerges from incentives, signals, and voluntary choices.

Why It Works in More Than Just Markets

Spontaneous order can apply to all social behavior. You can find it in organizations, teams, and even communities. When people are given the freedom to respond to available knowledge, you get:

• More innovation. People closest to problems find the best solutions.

• More ownership. When people shape the systems they use, they care more.

• More agility. Things move faster when decisions don’t require constant approval.

Good managers in organizations know when to guide and when to get out of the way. They recognize the benefits of spontaneous order even if they don’t know what to call it.

Planned vs. Unplanned

Planning isn’t always bad. For simple, repeatable tasks, it can be powerful and necessary. But the more complex and dynamic the system, the more you should trust in spontaneous order. Rigid plans can’t keep up with real-time information, shifting preferences, or unpredictable events.

Markets solve these challenges not through control, but through countless small, individual adjustments made by regular people acting on what they know and what they want.

The Bottom Line

The economy is a living process. It is messy, creative, adaptive, and out of any one person’s control. Because of this it produces unintended benefits. When people are free to go after their goals, respond to price signals, and trade with one another, remarkable things happen. Not because anyone planned it, but because no one had to.

One Takeaway: Growth Isn’t One Sided established that managers and entrepreneurs think differently. But how do you know when a problem requires calculation versus judgment? Luckily, economists have done a lot to help provide some ideas on this. Their research and ideas can explain why many companies trying to scale can get this balance wrong.

The Big Picture. Businesses can benefit both managerial thinking (optimization) and entrepreneurial thinking (innovation). Companies that excel at both outperform those that choose one or the other.

This point raises critical questions for companies trying to scale.

• How do you know when to calculate versus when to judge?

• Why do organizations default to one approach even when it’s failing?

• How can you build systems that apply the right approach to the right challenges?

Economists have spent decades studying the fundamental difference between risk and uncertainty. Their ideas can help explain why managers and entrepreneurs operate from different directions. But they also can help us understand when each approach may work best. And why mixing them up destroys both efficiency and innovation.

The Two Approaches to the Same Challenge

Consider how Netflix approached two growth problems that required completely different methods. One growth challenge was how to get their current users to watch more programs. The other growth problem was how to gain new users in new regions. To increase current user usage, they turned to recommendation algorithms. To gain new users they had to take a different approach.

For recommendation algorithms, Netflix could use calculation. They had millions of user ratings. They had data on viewing patterns and completion rates. They ran A/B test results to get reliable signals about what content to suggest. More data usually improved the system. They optimized piece by piece. They used statistical models to predict what users would enjoy. They based their models on viewing history and similarity to other users. They also refined these models and made updates to make them better over time.

For international market entry, Netflix had to use judgment instead of calculation. Would Korean audiences respond like American audiences? How would local competitors react? What content would work in markets with different cultural contexts? What regulatory challenges would emerge? No amount of US data answered these questions. They had to experiment, adapt, and learn through actual market presence.

Same company, same leadership, same commitment to being data-driven. Yet each action took different approaches. Why?

Because the information needed for each problem was different. Understanding this separates companies that grow effectively from those that optimize into irrelevance.

Two Different Decision-Making Environments

Frank Knight and other economists have explained the difference between risk and uncertainty. Their work boils down to the fact that our actions, including all our business decisions, fall into one of two categories: risk or uncertainty

Risk-based problems have measurable probabilities and known variables. You can calculate optimal inventory levels based on things like demand patterns, lead times, and storage costs. You can optimize pricing because you have data on competitor prices and customer price sensitivity. Supply chain efficiency. Operational processes. Resource allocation. These questions and decisions fit within established systems. They generally all involve stable relationships that analytical approaches can identify and improve.

Think of operating under risk like a well-shuffled deck of cards. You don’t know which card comes next, but you know the probability distribution exactly.

Uncertainty-based problems involve unknown possibilities and unmeasurable variables. You can’t calculate whether customers will want a product that doesn’t exist yet. You can’t optimize for market segments you haven’t discovered. You can’t measure the success of innovations before they’re tested. New market entry. Competitive differentiation. Product development. These all involve changing relationships where historical data provides limited guidance.

Think of operating under uncertainty like taking a card from a well shuffled deck of cards. The problem is you don’t know how many cards there are. You don’t know whether the cards are playing cards, Pokemon cards, or business cards. There’s no probability to calculate. The outcomes themselves are being discovered through your actions.

Why This Difference Is About the Nature of Knowledge, Not More Information

The distinction between risk and uncertainty isn’t about how much data you have. It’s about whether probability theory can even apply to your situation.

When you’re optimizing inventory, more data helps. Demand patterns tend to be a product of stable underlying relationships. Customer behavior, seasonal patterns, and supply chain dynamics create variation. Because of this, statistical analysis can capture and leverage.

When you’re deciding whether to enter a new market or develop a new product, more historical data doesn’t help in the same way. You’re operating in new territory. You can’t predict the future through analysis of past patterns. The relevant relationships don’t yet exist.

No amount of data about horse-drawn carriage demand would have told you the market size for automobiles. No analysis of letter-writing patterns would have predicted email adoption curves. No study of video rental behavior fully predicted streaming service success.

This is why even sophisticated companies with massive data capabilities still need judgment. The limitation isn’t technological or informational. It’s fundamental to the nature of creating new knowledge and markets. When you’re creating new product categories you’re actively bringing new realities into existence. These don’t have probability distributions yet.

Many economists (such as Frank Knight, Israel Kirzner, Armen Alchian, Peter Klein, and Nicolai Foss) emphasize this point. Entrepreneurship is more than better decision-making under uncertainty. It’s a completely different activity than optimization under risk. Entrepreneurs do more than divide resources efficiently. They take action and discover new possibilities that weren’t knowable in advance.

Most businesses need both types of decision-making, but in different rates depending on the situation.

Why Organizations Default to Calculation (And Why This Kills Growth)

Most companies try to apply analytical approaches to everything. They do this because calculation feels more professional and defensible. (Also, people HATE uncertainty). Data-driven decisions sound smarter than judgment-based decisions. Spreadsheet projections look more serious than experimental budgets. Optimization metrics appear more scientific than adaptation strategies.

There are good reasons for this bias. Calculation is easier to justify to boards and investors. It’s easier to scale through organizational systems. It’s also easier to “run the numbers.” Business schools teach analytical frameworks. Consulting firms sell optimization methodologies. Performance reviews reward measurable outcomes.

But this preference for calculation creates hidden costs that compound as organizations grow.

1. Missed opportunities accumulate. The opportunities that demand judgment get overlooked. This happens because you can’t measure them with existing tools. Economists call this opportunity cost; the value of the road not taken. Using analytical resources on problems that don’t have calculable answers leads to waste. It also leads to missing out on the judgment-based opportunities you could have pursued instead.

2. Bureaucratic costs multiply. Requiring ROI projections for experimental projects kills experimentation. The doesn’t happen because the projects are bad. It happens because genuine uncertainty means ROI projections are simple fiction. Demanding data justification for market adaptation slows response to changing conditions. The time spent analyzing often exceeds the window for effective action. Bureaucratic processes optimized for control can create “trained incapacity.” Playbooks can kill adaptation.

3. Competitive disadvantage grows. While you’re analyzing market data, adaptive competitors are testing solutions with customers. While you’re optimizing existing processes, they’re discovering new possibilities. Their advancements could make your optimized processes irrelevant. Companies that over-rely on calculation excel at static efficiency. They are great at optimizing the now. But, they can do this at the expense of dynamic efficiency (changing under new situations).

When Calculation Works, When Judgment Works

Clear patterns emerge for when each approach creates the most value:

Use calculation for stable processes with measurable relationships. Operator work (maintaining inventory, optimizing staffing, etc.) responds well to analytical approaches. You have historical data, known variables, and measurable outcomes. More data helps. Statistical analysis reveals patterns. Optimization creates value.

Use judgment for uncertain markets and novel situations. Creator work (identifying market opportunities and adapting to competitive changes) requires entrepreneurial thinking. In these situations you’re working with incomplete information and shifting variables. The goal isn’t optimization but discovery of what’s possible and valuable. Israel Kirzner called this “entrepreneurial alertness.” This alertness has to do with the ability to notice opportunities that aren’t yet reflected in current market prices or metrics. As Peter Klein and Nicolai Foss point out, this “alertness” is based on judgment and the decision to act. The opportunities themselves are being created by discovery.

Use systematic improvement for optimization within known systems. Refiner work to an extent combines both approaches. Refiners use analytical tools to identify improvement opportunities. They also apply some level of judgment to rank which improvements are worth pursuing as well as how to best bring them to life.

The Rideshare Example: When Each Approach Creates Value (And When It Destroys Value)

Uber and Lyft’s expansion provides clear examples of both approaches working well. They also highlight the costs of applying the wrong approach.

When Calculation Worked. Uber’s surge pricing algorithm usually works great because it operates in more of a calculable/risk environment. They have tons of historical supply and demand data. This allows them to make predictions about how riders and drivers may respond to price changes under normal conditions. The calculation approach enabled dynamic optimization that would be impossible through judgment alone.

When Calculation Failed. The same approach can fail when applied to situations out of the norm. The centralized algorithm does poorly when calculating surge pricing during unprecedented events. During events like festivals or weather emergencies rider behavior doesn’t follow normal patterns. Letting the algorithms run wild has led to some serious PR disasters in the past. 5x surge pricing might be economically optimal during rush hour. But it may be socially catastrophic when people are trying to evacuate in an emergency.

Rideshare companies ended up adopting manual processes to cap price increases in these times. As such, they moved to using judgment-based interventions for unusual situations. This illustrates a critical point. Using calculation for uncertainty problems can produce worse outcomes than using judgment-based rules. If the algorithm gets this wrong, it can create disaster that human judgment would have avoided.

When Judgment Enabled Market Entry. Uber and Lyfts market entry decisions required judgment from the start. Local teams discovered that Las Vegas tourists had different needs than San Francisco commuters. That college towns required different supply strategies than business districts. That regulations varied in ways that affected success far more than simple demographic data suggested.

Calculation helped optimize operations within markets they understood. But judgment was required to discover how to succeed in markets with different characteristics. The most successful local teams combined both. Teams used calculation for operational planning. And the relied on judgment for local strategy (that is, if central teams allowed it).

The Information Challenge in Growing Organizations

As organizations scale, they encounter a version of what Nobel-winning economist Friedrich Hayek called “the knowledge problem.” No single person has perfect knowledge. No one has all the knowledge needed for every decision a scaling business needs to make. This knowledge is distributed across teams and people. As a result it is difficult, if not impossible, to centralize all this knowledge.

Calculation approaches assume you can combine relevant knowledge and information into analytical tools. But much of the knowledge needed for effective decision-making exists in local contexts. In the minds of those closest to the problem. It can’t be easily transmitted through formal reporting systems.

Hayek’s had a key insight. Prices help send the knowledge of millions of people around the world. Prices allow markets to coordinate efforts across millions of people. As a result, central planners don’t need to know everything for the world to run smoothly. Inside organizations, you don’t have price signals. Because of this, you need other mechanisms to handle distributed knowledge.

Judgment approaches assume decision-makers have access to relevant local knowledge and context. But growing organizations create distance between decision-makers and front-line information. This makes judgment-based decisions less reliable because they’re based on incomplete knowledge, determined by people too far away from the problem.

This means organizations must match decision-making authority to information access. People with the best information about specific situations should have authority to make decisions about those situations. This should be the case whether those decisions need calculation or judgment.

This is why Uber and Lyft empowered local teams with decision authority. These teams had contextual knowledge that headquarters couldn’t access through data systems. It’s why Netflix empowers regional content teams to greenlight local productions.

What About AI?

Artificial intelligence can improve calculation capabilities while highlighting the continued importance of judgment.

AI excels at pattern recognition and optimization in data-rich, stable environments. These are exactly the conditions where calculation approaches work best. AI can handle things like supply chain optimization, pricing analysis, and fraud detection. Generally AI will likely be more effective than human analysis in these cases. Modern machine learning attempts to automate the discovery of patterns in data. It accomplishes this goal very well. But not all patterns are causal or meaningful.

AI struggles with novel situations and contextual judgment. These situations rely on understanding of values, culture, and unprecedented circumstances. Market entry decisions, innovation strategy, and organizational design still need human judgment. These situations involve factors that you can not reduce to historical patterns.

AI systems trained on past data don’t develop alertness to future possibilities that don’t yet exist in the training data. They optimize based on patterns. They don’t discover novel opportunities that break existing patterns.

Companies that use AI to automate calculation tasks can free up human capacity. This allows them to focus on judgment-intensive challenges where context creates the most value. But this only works if organizations understand which problems demand calculation versus judgment. If you use AI to automate the wrong things, you can wind up making mistakes faster and at greater scale.

Why Dual-Method Organizations Are Hard (And Worth Building)

Successful organizations need both calculation and judgment. More important, they need to know how to use them for the right problems at the right times. This fitness of “approach to situation” is based on the information environment of different decisions. But building these organizations is difficult.

The challenge is, calculation and judgment rely on incompatible evaluation systems.

Calculation demands measurable metrics, short feedback loops, and clear success criteria. You can gauge analytical decisions quickly. Outcomes are observable and counterfactuals are estimable.

Judgment requires tolerance for failure, long time horizons, and portfolio-based evaluation. You can’t assess individual entrepreneurial decisions in isolation. Many judgment-based initiatives fail. Value comes from the distribution of outcomes across repeated attempts.

Most organizations solve this tension by choosing one approach and pushing the other into a corner. Mature companies choose calculation, creating sophisticated systems that kill innovative capacity. Startups choose judgment, maintaining flexibility but possibly limiting optimization.

A possible solution is to focus on “ambidexterity.” This means separate out the calculation and judgment activities, but coordinate between them.

On one hand you can do this by having different evaluation systems for different teams. Operators measured on efficiency, creators measured on discovery, refiners using hybrid approaches. This is why the operators/refiners/creators distinction matters. It creates organizational space for dual methods to coexist.

Or you can use different evaluation periods. You could use quarterly reviews for operational metrics. This would give calculation approaches the short feedback loops they need. You could then use annual or longer periods for innovation initiatives. This would give judgment approaches the time they need to see results or learnings. Many effective organizations have dual budget processes to do this. They usually manage operational budgets quarterly with tight ROI requirements. For innovation budgets, they stick to annual evaluations with a focus on learning-based outcomes.

Coordination requires people or leaders who can translate between calculation and judgment logics. This helps the organization make decisions that balance both ideas rather than letting one dominate.

This coordination is where harmonizer thinking becomes essential. Harmonizer thinking solves the fundamental problem of dual-method organizations. It assesses when to use calculation and when to use judgment. It helps prevent either approach from taking over areas where it doesn't apply.

Without this coordination function, organizations either:

1. Let calculation logic dominate everything, killing judgment-based work, or

2. Let judgment logic dominate everything, undermining analytical based efficiency.

A Practical Test: Is This a Calculation or Judgment Problem?

When facing a specific business decision, ask these questions:

1. Can I identify the probability of different possible outcomes?

   • Yes, based on historical patterns → Calculation likely works

   • No, this is a novel situation → Judgment likely needed

2. Would running an experiment give me more useful information than more analysis?

   • No, analysis would clarify the situation → Calculation likely works

   • Yes, only market testing will reveal the answer → Judgment likely needed

3. Is the situation novel enough that historical patterns may not apply?

   • No, past patterns should hold → Calculation likely works

   • Yes, new circumstances → Judgment likely needed

4. Can I wait for enough data to make the decision analytically?

   • Yes, without significant opportunity cost → Consider calculation

   • No, market conditions need faster action → Use judgment

5. Are the key variables measurable and stable?

   • Yes, clear metrics exist and relationships are consistent → Calculation likely works

   • No, important factors resist quantification → Judgment likely needed

If 3+ questions point toward judgment, treat it as an uncertainty problem. Use experimental approaches and tolerate failure (within bounds). Judge on learning rather than immediate results. Give decision-making authority to people with contextual knowledge.

If 3+ point toward calculation, treat it as a risk problem. Use analytical approaches and optimize. Assess on measurable outcomes, and centralize decision-making where you can combine information.

If they’re split, you’re dealing with a mixed problem. This will requires using both approaches either in sequence or in parallel. If in sequence, use judgment to discover possibilities and then turn to calculation to operate. If in parallel, use judgment for the novel aspects and calculation for established aspects.

Building Organizational Systems That Apply the Right Approach

Create different evaluation systems for management and entrepreneurship roles. Using management metrics for entrepreneurship kills experimentation. Using entrepreneurship metrics for management creates operational chaos.

Establish different time horizons for different types of decisions. Calculation-based decisions can have shorter feedback loops because results are measurable quickly. Judgment-based decisions need longer evaluation periods. Discovery takes time and requires portfolio thinking. A quarterly review cycle works well for operational optimization. It can fails for innovation initiatives where learning compounds over years.

Design different information flows for different decision types. Calculation decisions need aggregated data and systematic analysis. Judgment decisions need access to local information and knowledge that often can’t be centralized.

Match coordination mechanisms to decision types. Standardized processes work well for calculation-based decisions. In these situations, consistency and optimization are the goal. Experimental processes work better for judgment-based decisions. In these situations adaptation and discovery are the goal.

The Competitive Advantage of Getting This Right

Companies that master the calculation-judgment distinction gain competitive advantages that compound over time:

• Less waste because you invest resources appropriately.

• Faster decision-making by using the right approach rather than universal processes.

• Better adaptation to changing market conditions. You can quickly shift to judgment-based approaches when calculation becomes inadequate

• More effective innovation because experimental work gets tolerance for failure that discovery requires. Operational work gets the systematic improvement focus that efficiency requires

• Enhanced learning because you’re applying the right method to extract value from experience

Where We’re Going Next

Understanding when to calculate versus judge is foundational for growing organizations. But there’s a related distinction that scaling companies struggle with. Some problems respond to systematic, scalable approaches, while others need adaptive, non-scalable thinking.

Next week, we’ll explore how information economics explains this distinction. The most valuable opportunities often exist in the non-scalable category. It just happens that most measurement systems happen to ignore these options.

The Bottom Line

Economists over the last century have provided insights that help explain when to rely on calculation versus judgment hat apply directly to the management versus entrepreneurship distinction from Growth Isn’t One Sided.

Frank Knight’s risk versus uncertainty framework provides the foundation. Calculation works when you’re operating under risk with knowable probability distributions. Judgment works when you’re operating under uncertainty where the future is new and unknowable.

But the distinction is deeper than having more or less data. It’s based on the nature of knowledge in our world (a.k.a epistemological, for the philosophy nerds out there). Probability theory either applies to your situation or it doesn’t. No amount of analysis converts genuine uncertainty into calculable risk.

Companies that match their decision-making methods to their information excel at both efficiency and adaptation. Those that apply calculation logic across every problem miss opportunities that need judgment. Those that rely only on judgment miss efficiencies that calculation could provide.

The competitive advantage goes to organizations that apply the right approach to the right problems. The alternative is to defaulting to one method for all decisions. To do this well, organizations must develop some ambidexterity. They must separate calculation and judgment activities while maintaining coordination between them.

This requires harmonizer thinking; the ability to translate between these different approaches and shift between both rather than letting one dominate areas where it doesn't apply.

Understanding the economics underlying these ideas helps you make better individual decisions. It also helps you design organizational systems that enable both optimization and discovery. You need both to create advantages as markets evolve.

One Takeaway

Even if you’re great at everything, you’re better off focusing on what you do best, and letting others do the rest.

Why Not Just Do It All Yourself?

You may be the most capable person on your team, in your family, or at your company. But being the best at everything doesn’t mean you should do everything. Economists call this idea comparative advantage.

It’s one of the most powerful ideas in economics. It’s all about how we work with each other. Comparative advantage shows that even if one person is better at everything, two people working together can still achieve more by focusing on what each does relatively better.

A Simple Example

Imagine you and your kid are tackling two garden tasks: pulling weeds and planting rose bushes. Neither of you wants to spend more time than necessary on these chores. You’re faster at both. But, that doesn’t mean you should do both.

Let’s say you can pull 25 weeds in the same time it takes your kid to pull 12. You also can plant 5 rose bushes in the same time your kid plants 4.

If you spend one hour planting roses, you give up the chance to pull 25 weeds. Your kid only gives up 12 weeds in the same situation. That’s a smaller sacrifice. So while you’re better at both tasks, it costs you more (in terms of weeds) to plant roses than it costs your kid.

This means your time is relatively better spent pulling weeds, and your kid’s time is relatively better spent planting roses. If each of you specializes based on comparative advantage and then cooperates, you get more chores done in the same amount of time.

Why It Works

Comparative advantage isn’t about being the best. It’s about what you give up when you choose to do one task instead of another (your opportunity cost). If your opportunity cost is higher, it makes sense to let someone else take the lead. This is true even if you could technically do it faster.

This idea shows up everywhere.

• A CEO may have a knack for graphic design, but they’ll likely create more value focusing on strategy and hiring others to design.

• A brilliant surgeon could also be a good administrator, but they save more lives by spending time in the operating room and hiring an office manager.

• Tom Brady may have been great at taping up ankles before a game, but the he probably scored more points because he spent time warming up instead and left the taping to the trainers.

Even someone less skilled overall still has something valuable to contribute when they focus on where their trade-offs are smallest.

The Big Idea Behind Cooperation

Comparative advantage is one of the secrets behind why trade is so beneficial. If we all tried to do everything ourselves, we’d be less productive, more stressed, and far less wealthy.

Instead:

• We specialize in what we’re relatively good at.

• We exchange what we produce for what others produce.

• We all end up better off, even if some of us could do both jobs on our own.

In short, we create more value together than we ever could alone.

The Bottom Line

You don’t have to be the best at everything to be valuable, and trying to do it all yourself is usually a bad idea. Comparative advantage shows us that productivity and progress come from smart trade-offs, focused effort, and mutual work. We do better by doing less—together.

One Takeaway

Focusing on what we do best lets us do less and achieve more, because no one can be good at everything, but together we can create almost anything.

Why Can’t We Just Do Everything Ourselves?

Imagine if you had to grow your own food, make your own clothes, build your own house, and generate your own electricity. Life would be exhausting…and short. Luckily, we don’t live in that world. We live in a world of specialization, where each of us does a few things really well and trades for the rest.

This is about more than simply convenience. It’s the foundation of modern life. Specialization is what makes high living standards, innovation, and cooperating with others around the world possible. And it all starts with a simple reality: we each have limited time, energy, resources, and knowledge. Which means while we may be able to do a lot, we can’t do everything.

The Power of Specialization

Specialization happens when people focus on doing one type of work. This allows them to build expertise, learn to make less mistakes, and produce more than they personally need. From there, trade naturally follows.

• A baker bakes bread not just to feed themselves, but for others to eat too.

• A carpenter makes furniture not just for their home, but for customers’ homes too.

• A software engineer writes code not to solve every problem in their life, but to serve millions of users.

Each of these people depends on others for their housing, transportation, clothing, and tools. And yet, they’re better off than if they tried to do it all themselves.

Why Dividing Labor Works

Breaking big tasks into smaller roles, which economists call the division of labor, allows groups, companies, and societies to grow far beyond what any individual could achieve alone.

This works because:

• Repetition builds skill. Doing one task over and over leads to mastery.

• Time is used more efficiently. Less switching between tasks means more focus.

• Trade connects people. Everyone can rely on others for what they don’t produce.

Whether you’re on a factory line, a film crew, or a startup team, dividing tasks and trusting others to do their part gets better results in less time.

The Dinner Party Principle

Picture three friends cooking dinner. If everyone tries to do everything—chop, stir, sauté, plate—it’s chaos. But if one chops, another cooks, and the third sets the table, dinner is ready fast and (hopefully) stress-free.

Now imagine this scaled up to a city, a country, or the world. The same principle holds: millions of people do the work they do best, and trust others to do the rest. That’s how airplanes are built, hospitals run, and your morning coffee ends up in your hand.

More Than Just Productivity

Specialization without a doubt boosts output. But in doing so, it improves quality of life. It leads to:

• Higher living standards: We get access to better goods at lower costs.

• More time for leisure: Productivity frees up time for rest, play, and learning.

• More opportunity: By trading what we do best, we can earn more and reach more people.

It’s not about doing everything. It’s about doing the right things, and letting others do theirs.

The Bottom Line

We don’t thrive by doing everything. We thrive by doing what we do best and trading for the rest. Specialization and the division of labor unlock more wealth, more growth, and more cooperation. These ideas allow individuals to live better lives by doing less, not more. They also lead to increasing trust that someone, somewhere, is willing to fill in the gaps of what else we need.