Cross-functional alignment isn’t a new idea. Matrix organizations, cross-functional teams, dotted-line reporting, alignment meetings, shared OKRs. Organization and management experts have been working on these problems for decades. Every management book tells you to break down silos. Every leadership seminar teaches you to align teams around shared goals.

And yet problems tend to persist. Not because leaders are bad at alignment. Because bad alignment is a symptom for many types coordination failures. Bad alignment is a symptom, it is not a sufficient diagnosis.

The Same Problem, Three Different Causes

A mid-market SaaS company noticed that their enterprise product launches kept failing. Product, sales, and customer success were supposed to work together on every launch. They had cross-functional meetings. They had shared timelines. They had executive sponsorship. Leadership kept saying “we need better alignment.” They tried everything the leadership playbook recommends.

Nothing worked. Launches kept missing the mark. Leadership concluded they had a culture problem.

They didn’t. They had three different problems that looked identical from the outside but required completely different solutions.

Problem one was a design problem. Product was measured on time to get new features out. Sales was measured on deals closed. Customer success was measured on ticket resolution time. Each team was individually incentivized to optimize for their own metrics. When a launch required product to slow down feature work, sales to delay pipeline activity, and customer success to invest in pre-launch preparation, each team faced a reasonable reason not to cooperate. The shared timeline existed on paper. The incentives each team was evaluated on pointed in three different directions.

Better leadership wouldn’t fix this. Better meetings wouldn’t fix this. The system was designed so that cooperation was a sacrifice. The fix was redesigning the economics. It was creating shared launch metrics that all three teams were evaluated on jointly. This made cooperation the rational choice rather than the self-sacrificing one.

Problem two was a connection problem. Customer success had detailed knowledge from support conversations about which features customers actually used and which they ignored. This knowledge would have been invaluable for product prioritization and sales positioning. But it lived in support tickets and team conversations. It never reached product planning or sales in a usable form. Product built features based on their roadmap. Sales pitched based on their assumptions. Customer success watched customers struggle with launches that didn’t reflect how they actually used the product.

Better leadership wouldn’t fix this either. The knowledge existed. It just didn’t flow. The fix was transfering that knowledge. Ensuring that customer success insights reached product planning before the roadmap was set and reached sales enablement before the pitch was built. Not through another reporting template that would strip out context. Through someone who understood all three functions well enough to translate between them.

Problem three was a protection problem. The company had a small team exploring a new approach to customer onboarding. The work was promising but early. It didn’t fit the standard launch process. It couldn’t show revenue impact yet. Every quarter, the team had to justify their existence. They had to compete in reviews against existing departments showing clear metrics. After two quarters of “no measurable results,” leadership cut the cord. They reassigned their resources to proven optimization work.

Better leadership wouldn’t fix this. The measurement system was doing exactly what it was designed to do. It allocated resources toward measurable returns. The fix was protecting the new innovative work. They needed different evaluation criteria for experimental initiatives. They needed someone to translate what the team had learned into language leadership could evaluate. They needed time and space for the work to mature before being judged by metrics designed for a different type of activity.

From the outside, all three problems looked like “silos” and “lack of alignment.” The standard leadership response—more meetings, more communication, more vision speeches—addressed none of the actual causes. Each problem had a different economic factor underneath it and required a different fix.

Why Economic Thinking Changes the Diagnosis

This is the counterintuitive conclusion that runs through this entire series. Economics makes you a better leader because it gives you specific, diagnosable explanations for problems that leadership intuition often treats as generic.

Consider what each economic insight actually provides.

Transaction cost economics (Coase, Williamson) explains that cooperation has real costs. Things like information costs, negotiation costs, enforcement costs. These costs often increase as organizations grow. When a leader sees departments failing to collaborate, the intuitive response is “we need to communicate better.” The economist instead asks: what are the actual friction costs preventing this collaboration? Can we redesign the system to reduce them? The leadership response leads to more meetings. The second leads to structural changes like shared budgets, joint metrics, different authority structures. Solutions that make collaboration rational rather than relying on goodwill.

The knowledge problem (Hayek) explains that the information needed for good decisions is spread throughout the organization. Often it’s found in forms that resist centralization. When a leader sees bad decisions being made, the intuitive response is “we need better data” or “we need more reporting.” The economist instead might ask: is the relevant knowledge even able to be quantified? Is the reporting process itself destroying the context that makes it valuable? The leadership response leads to more dashboards. The second leads to ensuring decisions get made where the knowledge lives or investing in people who can translate between local knowledge and central decision-making without flattening it.

Measurement economics (Goodhart, Muller, McCloskey) explains that measurement systems create systematic bias toward the quantifiable over the valuable. When a leader sees innovation dying, the intuitive response is “we need to prioritize innovation” or “we need an innovation budget.” The economist might ask: is the measurement system making innovation invisible and punishing the people who pursue it? The leadership response leads to a speech. The second leads to fundamentally different evaluation frameworks for different types of work. It leads to portfolio evaluation for exploration, different time horizons for different functions, or space for work that can’t justify itself on a quarterly dashboard.

In each case, the economic thinking doesn’t replace leadership judgment. It sharpens it. It takes a vague sense that “something isn’t working” and gives you a specific mechanism to investigate and a specific intervention to try.

The Diagnosis Determines the Intervention

This is what separates the design, connect, protect framework from generic leadership advice. Each diagnosis leads to a different action.

If you diagnose a design problem and try to solve it with better knowledge flow, nothing changes. The incentives are still misaligned. People now have better information about an opportunity they’re still punished for pursuing.

If you diagnose a connection problem and try to solve it with new metrics, nothing changes. The knowledge still doesn’t reach the right people. You’ve redesigned the incentives around outcomes that teams don’t have the information to achieve.

If you diagnose a protection problem and try to solve it with better knowledge transfer, nothing changes. The measurement system still kills the work. Everyone now understands the opportunity and still watches it die on the dashboard.

The interventions aren’t interchangeable because the underlying economics forces are different. Misaligned incentives, knowledge that doesn’t flow, and measurement bias are three distinct problems that share the same symptom: coordination failure. Treating them all as “alignment issues” is like treating every fever with the same medicine.

Sometimes a fever means you have the flu. Other times a fever means you have heat stroke. You don’t treat them the same way.

Why the Wrong Fix Makes Things Worse

This isn’t just about wasted effort. Applying the wrong intervention to a coordination problem can actively make things worse.

I learned this firsthand at Lyft. Our vehicle service centers offered maintenance and repair for drivers’ vehicles. Most maintenance and repair operations run on a traditional model: one technician per car, start to finish. Leadership wanted to improve throughput and hit profitability targets, so they tried to redesign this. They decided to shift to an assembly line model. In this case multiple technicians worked on vehicles simultaneously, each handling their specialty.

The workflow change made operational sense. But it broke the compensation system. Under the old model (that every competing repair garage used), individual technicians were evaluated on their own output. They were compensated for cars completed, quality of work, and speed. Performance bonuses were straightforward. Under the assembly line model, no single technician owned a vehicle’s outcome. Individual performance metrics stopped making sense. So compensation shifted to team-based bonuses.

Now leadership had a new problem. Some technicians felt they were carrying others. High performers who had thrived under individual incentives felt punished by team-based compensation. Their contribution (and compensation) was averaged with less productive teammates. The workflow improvement that was supposed to increase throughput actually decreased morale. It created retention risk among the best technicians. It made recruitment efforts difficult. All this together threatened both the quality and profitability the design intended to improve.

Fixing the workflow without simultaneously redesigning the incentive structure didn’t move us closer to the goal. It moved us sideways into a different set of problems. The two systems were interconnected in ways that meant optimizing one in isolation created new distortions in the other.

Sometimes Second Best is Totally Fine

This is a pattern economists have studied extensively. In a complex system with many less than ideal situations, removing one doesn’t automatically move you closer to the best outcome. Sometimes it moves you further away. This can happen because the remaining issues interact with the change in ways you didn’t anticipate.

If your main problem is misaligned incentives and you invest in better knowledge flow without fixing the incentives, you may make things worse. People now have better information about an opportunity they’re still punished for pursuing. The frustration increases. The coordination doesn’t.

If your main problem is measurement bias and you redesign incentives without addressing the measurement system, people are now incentivized to pursue work that the evaluation system will still destroy. They’ll try, fail to show results on the dashboard, and learn not to try again.

This is why diagnosis has to come before intervention. This is why the Design, Connect, Protect distinction matters practically, not just conceptually. The three failure modes interact. Getting one right while the other two remain broken can produce outcomes worse than leaving them all broken. You can end up creating new problems between the fixed and unfixed parts of the system.

It’s also why harmonizer thinking requires economic reasoning rather than leadership instinct. Instinct says “fix what you can see.” Economic reasoning says “understand the system well enough to know which limit is actually the problem. Then know what happens downstream when you change it.”

That difference in approach is often the difference between a leader who makes things better and a leader who makes things different but no less broken.

What This Means for How Leaders Develop

Most leadership development focuses on communication, vision, empathy, and decision-making under pressure. These matter. But they’re general capabilities applied to every situation the same way.

Economic thinking acks as an important diagnostic. A leader who understands transaction costs sees coordination failures differently than one who doesn’t. They ask different questions. They investigate different mechanisms. They design different solutions. Not because they’re smarter, but because they have a framework that distinguishes between problems that look identical on the surface.

This is what we mean by harmonizer thinking. It’s not a specific role to hire for. It’s a way of seeing organizational problems that most leaders haven’t been trained to see. Business education teaches optimization and motivation. They often don’t teach the economics of coordination, knowledge, and measurement. These are central to understanding and explaining why organizations can break down as they grow.

The leads us to a counterintuitive implication. The subject most likely to help you become a better organizational leader isn’t always leadership studies.

It’s often times economics.

Not the economics of GDP and interest rates. The economics of how people coordinate. Why that coordination breaks down. And what specific mechanisms cause specific failures.

Every growing company faces transaction cost problems, knowledge distribution problems, and measurement bias problems. Having names for these, and the ability to tell them apart, gives you an advantage that no amount of general leadership advice provides.

The Bottom Line

Cross-functional coordination isn’t new. Every organization has been trying to break down silos and align teams for decades. What’s been missing isn’t “alignment.” It’s better diagnosis of the causes of the breakdown.

The Design, Connect, Protect framework provides that diagnosis.

Design problems need structural solutions. New rules, new metrics, new incentive systems.

Connection problems need knowledge transfer. Ensuring the right information reaches the right decisions in forms that preserve context.

Protection problems need advocacy. Different evaluation frameworks for different types of work, with space for the unmeasurable.

These aren’t leadership platitudes.

They’re diagnostic categories grounded in specific economic realities. They’re built on transaction costs, knowledge distribution, and measurement bias. Each leads to a different intervention because each addresses a different cause of the same symptom.

Economics gives leaders something that leadership advice alone cannot. It gives leaders the ability to see why coordination is failing, not just that it’s failing. That alone can be the difference between a leader who keeps calling meetings and one who actually fixes the system.

Not every cost or benefit stays between the buyer and seller. When our actions spill over onto others, understanding why it happens matters more than assuming someone needs to step in.

The Neighbor’s Bonfire

Your neighbor loves weekend bonfires. He invites friends over, lights up the fire pit, and has a great time. He bought the wood. He’s on his own property. The transaction between him and the firewood seller was completely voluntary. Everyone involved agreed to the deal.

But the smoke drifts into your yard. Your kids can’t play outside. Your laundry on the line smells like a campfire. You didn’t agree to any of this.

That smoke is what economists call an externality. Externalities are costs (or benefits) that land on someone who wasn’t part of the original exchange. Your neighbor isn’t doing anything wrong in his own mind. He made a voluntary purchase and is using his property. But the full cost of his bonfire isn’t falling on him. Part of it is falling on you.

This is one of the most common reasons people say markets “fail.” And it’s worth understanding carefully, because how you think about this problem shapes how you think about solving it.

What Externalities Are

Externalities come in two forms.

Negative externalities are costs that fall on someone outside the transaction. A factory that pollutes a river imposes costs on the people downstream. A loud bar imposes costs on the residents next door. The producer benefits, the customer benefits, but a third party pays a price they never agreed to.

Positive externalities are benefits that spill over to people who didn’t pay for them. A neighbor who maintains a beautiful garden might raise your property value. A beekeeper’s bees pollinate nearby farms. The teenager who decides to wear deodorant for the first time (everyone can relate here). The buyer and seller both benefit…and so do others who had nothing to do with the exchange.

In both cases, the price of the purchase or exchange doesn’t capture the full benefits and costs across all direct and indirect parties.

The Usual Response, and Its Trade-Offs

When people encounter externalities, the instinct is to call for a rule. Tax the polluter to make up for the damage. Subsidize the beekeeper so they can do more good. Pass a law about bonfires. Detention to every smelly teen!

Sometimes that works. But it always comes with its own costs. Regulations can be captured by the people they’re supposed to restrain. Taxes require someone to measure the damage accurately, which is harder than it sounds. Subsidies can encourage more of some thing than the amount people actually want. And every intervention introduces new incentives that produce their own unintended consequences.

The question isn’t whether externalities are real. They are. The question is whether the solution creates fewer problems than the problem itself.

What Ownership Makes Possible

There’s another way economists think about this. Many externalities exist not because markets failed, but because property rights are unclear or incomplete.

If the polluted river is owned by someone, the factory can’t pollute it without consequence. In that case the owner will demand compensation to offset cleaning costs or take the factory to court. If your neighbor’s smoke crosses onto your property, that’s a dispute that clear property rules can help resolve. When ownership is defined and enforceable, people can negotiate directly. The person causing the cost and the person bearing it can find a solution that works for both of them. Often times they can do this without anyone else getting involved.

This isn’t always straightforward in every case. Some problems are widely spread out. You can’t easily negotiate with a million car drivers about air quality. But the principle still matters: the clearer the ownership, the fewer the spillovers go unaccounted. Many of the externalities we blame on “market failure” start with failures to define who owns what.

When Markets Solve It Themselves

Markets also develop their own solutions to spillover problems when they’re allowed to.

Insurance companies price risk in ways that incentivize safer behavior and decrease external costs they have to cover. Neighborhood associations create shared rules for shared spaces. Industry groups develop standards that go beyond what any regulation requires, because their credibility depends on it.

These aren’t perfect. But they have something government solutions often lack: built-in feedback. When they stop working, people stop paying for them. That self-correction is valuable.

The Bottom Line

Not every cost stays between buyer and seller. That’s real, and it matters. But recognizing the problem and knowing how to solve it are two different things. The best responses tend to start with clear property rights, rely on the people closest to the problem, and stay humble about unintended consequences. Externalities are a reason to think carefully, not a reason to assume that every spillover requires a new rule.

Voluntary exchange creates value because both sides choose to participate. When decisions are made on our behalf, even with good intentions, we lose the information and feedback that help resources go where they’re needed most.

The Search for a Better Park

Let’s say a few years ago, a neighborhood needed a new park. Everyone agreed the old one was in bad shape. But how to fix it became a different question entirely.

The city council proposed a $4 million renovation funded by a tax increase. The plan included a splash pad. A performance stage. A new redesigned walking trail. The mock-ups looked great.

But some residents wanted something simpler. They just wanted the old playground fixed and to have some better lighting. Others would have preferred the $4 million to go toward road repairs. A few small business owners pointed out that the tax increase would eat into the cash flow they’d been putting toward hiring.

The park got built. It looked beautiful. The splash pad was popular in the summer. But the performance stage sat mostly empty. Meanwhile, the roads didn’t get fixed. Some of the business owners delayed their hires. And more than a few residents felt they’d paid for someone else’s priorities.

Was the park a waste? Not exactly. Some people genuinely valued it. But the process revealed something important: when one group makes spending decisions for everyone, some people end up paying for things they wouldn’t have chosen, and the things they would have chosen don’t get done.

That trade-off is real. And it’s worth understanding clearly.

Why Voluntary Exchange Works So Well

Throughout this series, we’ve built up a picture of how markets coordinate millions of people without anyone being in charge. A key ingredient to all that is voluntary choice.

Every time you buy something, you’re saying: “I’d rather have this than keep my money.” Every time a business sells something, it’s saying: “I’d rather have the revenue than the product.” Both sides expect to benefit. If they didn’t, the exchange wouldn’t happen.

This process generates enormous amounts of information. Prices tell producers what people want. Profits tell them they’re getting it right. Losses tell them to adjust. The whole system runs on feedback that no one has to design or manage.

What Changes When Choices Are Made For Us

Government currently plays a role in the economy that some people may take for granted. Courts enforce contracts. Laws protect property. Public infrastructure can connect communities. Whether the government is the only ways to provide these institutions is a deeper question, but for now, they’re the foundation that makes voluntary exchange possible.

But government also makes spending decisions that go beyond this foundation. And when it does, the economics change.

Tax-funded programs replace individual choice with collective decision-making. That’s not automatically bad, but it does mean we lose a critical feedback loop. When this happens there are no prices telling officials whether the park was worth more than the road repair. There’s no profit-and-loss signal telling them the performance stage was a poor use of funds. The information that would normally guide resources toward their best use simply isn’t there.

This matters because:

• Without market signals, officials have to guess what people value, and different people value very different things.

• Without profit and loss, programs that aren’t working don’t automatically get corrected. A private business that builds something nobody wants goes under. A government program that is unsuccessful can get a bigger budget next year.

Without individual choice, the people paying for a decision and the people making it aren’t always the same people. This changes the incentives.

The Unseen Side of Every Public Decision

This doesn’t mean government programs never help anyone. They often do. But every dollar spent publicly is a dollar that was taken from someone who would have spent it differently. The community center gets built, but the business doesn’t hire. The subsidy supports one industry, but consumers pay higher prices. The may tariff protect one set of jobs, but likely raises costs.

These aren’t arguments against government. They’re arguments for taking trade-offs as seriously as we’ve taken them throughout this entire series.

The question that matters is asking whether a specific action creates more value than what it displaces. And without the feedback that voluntary exchange provides, that question is genuinely hard, if not impossible, to answer.

The Bottom Line

Voluntary exchange works because both sides choose to participate, and the signals it generates help resources flow toward their best use. When decisions are made collectively, we lose that feedback. This means even well-intentioned programs can direct resources away from where people would have sent them. Understanding the difference between chosen exchange and directed spending helps us tell whether any given intervention is helping or quietly making things harder.

Throughout this series, we’ve explored why coordination is expensive, how knowledge resists centralization, why measurement can kill innovation, and how structure enables or prevents complex work. But there’s an underlying tension beneath each of these seperate ideas that none of them fully resolves.

The systems that make operators and refiners effective (standardized processes, clear metrics, hierarchy) are the same systems that kill creator work. And the freedom that creators need threatens the reliability that operators and refiners depend on. This isn’t a tension you can end or assume away. It’s one you have to learn to use rather than be constrained by.

Two Companies, One Shift

When cloud computing emerged as an alternative to on-site data centers, two enterprise software companies with similar market positions faced the same choice.

Company A (optimization wins). Their on-site business was highly profitable. $500M in annual revenue, 40% margins, customers on predictable multi-year contracts. Moving to cloud would cannibalize this revenue stream. The entire organization was optimized for on-premise success.

When product development proposed a cloud initiative, every function had rational objections. Sales: cloud deals are smaller and compensation will drop. Operations: we don’t have cloud capabilities and building them will distract from our profitable core. Customer success: our existing customers are happy with on-premise and don’t want to migrate. Finance: cloud will hurt current profitability because we’ll recognize revenue slower.

Everyone was right from their individual perspective. The cloud initiative got delayed, scaled back, and eventually killed. Company A continued optimizing their on-premise business. They focused on better processes, enhanced features, more efficient operations.

They optimized themselves right into irrelevance. As the market shifted to cloud over five years, revenue declined 60%. They were eventually acquired at a fraction of their peak valuation.

Company B (managed creative destruction). Same initial position. Same profitable on-premise business that cloud would cannibalize. But the executive team recognized the core dilemma: if we don’t destroy our own business, someone else will.

They created a separate cloud division with fundamentally different economics. Different success metrics—customer acquisition and platform stability, not profitability. A five-year runway to reach profitability while the on-premise business funded the transition. A separate budget that didn’t compete with on-premise optimization. And a dedication to bridging the two divisions. They needed a way to ensure the cloud team learned from on-site customer relationships without on-site blocking cloud’s progress.

The result: Company B managed the transition while maintaining the core business. Cloud revenue eventually exceeded on-site. They maintained market leadership while competitors optimized themselves into decline.

The difference. Both companies understood cloud was important. Both had the talent. Both had customer relationships to leverage. The difference was managing the economic conflict between optimization and innovation. Company A let optimization win because that’s where the immediate rewards were for every individual team. Company B restructured the economics so both were viable at the same time.

Why Innovation Creates Value by Destroying It

Joseph Schumpeter, a highly influential economist from the early 20th Century, had a fundamental insight called creative destruction1. It wasn’t just that innovation and optimization conflict. It was that innovation creates value precisely by destroying existing value.

A new technology makes existing skills irrelevant. A new business model undermines existing competitive advantages. A new product makes existing products less valuable. The value creation happens through the displacement itself. Digital cameras didn’t add to film photography. They destroyed it. Cloud computing didn’t supplement on-site infrastructure. It displaced it.

Inside organizations, this creates an inherent conflict. Your operators and refiners have optimized their current processes, relationships, skills, systems. These represent real economic value built through years of accumulated learning. Your creators identify opportunities that likely will make those valuable creations less valuable or even obsolete.

The same organization must both protect value it’s created and destroy value it’s created. These aren’t just different activities. They’re opposing forces where one’s gain feels like the other’s loss. It only feels this way, though, if we approach our work with an expectation that it should never change.

Most organizations resolve this by choosing preservation over destruction. They protect existing value. They default to what is certain and measurable at the expense of creating new value, which is uncertain and difficult to measure. This is rational for every individual and team inside the system. The problem is it’s often catastrophic for the organization as a whole.

Why Capabilities Become Constraints

Economists Richard Nelson and Sidney Winter showed that organizational skills can become barriers to change.2 Organizations function through routines. They create established patterns for how teams qualify leads. They create consistent approached to designing products. They have a predictable way to handle exceptions, divide resources, and coordinate across teams.

These routines are genuine economic assets. They encode valuable learning from experience. They enable coordination without constant negotiation. They create the consistency that customers depend on. They allow the specialization that operators and refiners need to do their jobs well.

But the same routines that enable efficient execution create barriers to innovation. The better you get at your current approach, the harder it becomes to adopt a different one. Company A was exceptional at on-site software because they’d refined those routines over a decade. That same expertise made them terrible at cloud. It wasn’t because their people were incapable. They needed different routines than they’d developed. Economists call this a “competency trap.” Capabilities become constraints.

People whose expertise is embedded in specific routines resist innovations that would require different routines. This isn’t stubbornness. It’s rational economic self-interest. If your value comes from mastering certain routines, innovations that make those routines obsolete can be threatening.

When a creator identifies an opportunity, they’re implicitly saying: the routines you’ve spent years developing need to change or become obsolete. Even if the creator is right, they’re threatening value that operators and refiners have created. So creator insights get rejected. They aren’t turned down for being wrong. They’re turned down because they threaten existing organizational capital.

The Organizational Immune System

The resistance to innovation that most organizations experience isn’t about closed-mindedness. It emerges from individually rational economic behavior that produces collectively destructive outcomes.

The operator’s logic: “My job is to maintain stability. Innovation creates disruption that threatens the metrics I’m evaluated on. My bonus depends on uptime, and changes can reduce it.”

The refiner’s logic: “Resources spent on innovation are resources not spent on the optimization I’m measured on. My promotion depends on efficiency results.”

The middle manager’s logic: “Innovation might destroy my division’s revenue. My career depends on my quarterly performance.”

The executive’s logic: “R&D investments reduce near-term earnings and stock price. I may not be here when they pay off.”

Every one of these ideas is individually rational. Taken together, they create an organization that rejects beneficial innovation. Think of it like a biological immune system. An immune system doesn’t evaluate whether a foreign body is helpful or harmful. It identifies anything unfamiliar and attacks it. That’s exactly what’s happening here. Innovation is unfamiliar work that disrupts established routines. It threatens existing metrics and redirects resources away from proven activities. The organization’s incentive structure identifies it as a threat and mobilizes against it. The same self-preserving logic that makes immune systems effective in the first place ends up stopping beneficial, new work.

This is why alignment speeches don’t work. Telling people “we need to innovate” doesn’t change their economic incentives. They understand that innovation matters. It’s intuitive. But it’s still irrational for them as individuals given how they’re actually measured and rewarded. The CEO’s speech changes nothing about the economic logic they face every day.

Company A’s leaders were all individually rational. Sales protecting their commission structure. Operations protecting their expertise. Finance protecting current profitability. They were all protecting their trees, while the forest slowly died.

Why Portfolio Thinking Matters

The economic problem isn’t that organizations evaluate innovation projects badly. It’s that they evaluate them using individual project logic when portfolio logic applies.

Individual project evaluation works for optimization. Each initiative either generates positive returns or doesn’t. You can measure results within quarters. Kill what doesn’t work. Expand what does.

Innovation requires portfolio evaluation. Most individual experiments fail. But the portfolio succeeds if you maintain enough experiments to capture the rare dramatic successes while learning from everything else. If a creator team runs ten experiments and eight fail, one produces modest results, and one opens a $50M market that’s an extraordinarily successful year. But individual measurement reports an 80% failure rate.

You can kill innovation by using individual project logic on work that only makes sense as a portfolio. Each “failed” experiment becomes evidence to cut the program. But the “failures” were generating learning that made the successes possible.

This connects to the measurement problem from “Why Metrics Kill Innovation.” It’s easy to measure individual project failure. It’s hard to measure portfolio learning and option value. Organizations tend to underweight innovation because the costs are immediate and visible while the benefits are distant and hard to quantify.

Financial economists understand that some investments create “option value.” This can be thought of as the ability but not obligation to go after future opportunities. Innovation investments work the same way. Company B’s cloud experiments had three types of value.

1. Direct revenue from cloud products

2. Learning that improved their business overall

3. The option to pursue new opportunities as markets evolved.

Traditional ROI analysis captures only the first. Portfolio and option value thinking captures all three. This is why creator work is essential even when it doesn’t generate immediate measurable returns. Creator work creates flexibility that the organization can turn to when conditions change. Organizations that underinvest in creator work are destroying option value. They sacrifice future adaptability and resilience for current optimization.

The Exploration-Exploitation Tradeoff

Organizational theorist James March formalized this tension.3

• Exploitation (what we’ve been calling optimization) refines existing capabilities within known parameters. Returns are quick, certain, and measurable.

• Exploration (what we’ve been calling innovation) searches for new capabilities in unknown domains. Returns are slow, uncertain, and distant.

The bias toward exploitation is structural, not cultural. Managers get evaluated on time horizons shorter than exploration payoffs. Annual performance reviews punish exploration “failures” before successes can emerge. Boards get nervous about exploration spending without visible returns. Investors pressure for near-term results.

But organizations need to optimize over their existence, which, hopefully, is decades or longer. The people making resource use decisions have 3-5 year time horizons. Exploration investments that would pay off in year seven often may not benefit the manager who made them. Rational managers favor exploitation. The result is underinvestment in exploration even when more investment would be optimal for the long-term.

March had a key insight. The right balance depends on how quickly your environment changes. Stable environments need less exploration. Unstable environments need much more. Most organizations get this backwards. They explore aggressively when young and small, then reduce exploration as they mature and have more the resources to fund it. Companies gain capacity for investment exactly when their systems evolve to prevent it.

How Harmonizer Thinking Changes the Economics

The solution isn’t to convince people to act against their economic interests. It’s to restructure the economics so that supporting innovation becomes individually rational.

This is the same principle we’ve seen before in this series applied to the innovation-optimization tension.

• In “The Cost of Working Together,” harmonizer thinking meant building new rules and structures that made coordination the rational choice.

• In “What Headquarters Can’t See,” it meant brokering knowledge between people who had it and people who needed it.

• In “Why Metrics Can Kill Innovation,” it meant protecting valuable work from measurement systems that would destroy it.

• In “Why Structure Determines Strategy,” it meant bridging separated functions so organizations got specialization without fragmentation.

Here, it means doing all of these together to manage creative destruction.

In Company B someone thinking this way might have:

• Created shared success metrics. The cloud team’s progress contributed to everyone’s evaluation. This made cloud support individually rewarding rather than individually threatening.

• Maintained separate budgets. so cloud “failure” on profitability metrics didn’t hurt on-premise teams.

• Made sure the cloud team learned from on-site customer relationships.

• Made sure the on-site team didn’t block experimentation.

• Translated between different evaluation frameworks so leadership could assess both divisions on appropriate terms.

When the individual economic logic aligns with organizational needs, the immune system becomes an adaptive rather than a barrier. People still act in their economic self-interest. But their self-interest now includes supporting innovation rather than blocking it. You don’t change human nature. You change the economics that guides rational human behavior.

The Bottom Line

Schumpeter’s creative destruction explains why the success patterns from Growth Isn’t One Sided are so difficult to sustain. Innovation creates value by destroying existing value. Inside organizations, this means the same company must both protect what it’s built and destroy what it’s built. These are opposing forces.

Organizations understandably default to optimization. Those returns are near-term and measurable. Individuals are evaluated on shorter time horizons. But the company needs longer term thinking. Routines make companies good at what they already do. But, they make them bad at doing things differently. Incentive structures almost always reward exploitation over exploration.

Understanding this as an economics problem enables systematic solutions. Portfolio evaluation that judges innovation on collective learning rather than individual project success. Structural protection that prevents optimization from crowding out innovation. Different measurement approaches for different types of work. And harmonization that aligns individual incentives with organizational needs rather than trying to convince people to act against their interests.

The competitive advantage goes to organizations that can manage creative destruction internally rather than waiting for external pressure to force their hand. They optimize what works today while discovering what will work tomorrow. They exploit and explore simultaneously.

Doing this requires every principle this series has covered. You have to match coordination efforts to the type of work being done. You have to make sure knowledge reaches the right decisions. You have to measure what matters without destroying what you can’t measure. You have to design structures that enable different types of work to coexist. And you have to manage the tension between preserving value and creating it. Not by choosing one over the other, but by building systems where both can happen at the same time.

None of this is easy. But it’s a lot harder if you don’t recognize the problem in the first place. Most organizations experiencing creative destruction don’t know that’s what’s happening. They think they have a motivation problem, or a culture problem, or a leadership problem. They have an economics problem. And economics problems have economics solutions.

A successful and growing economy depends more on good rules than wise overseers. Clear, consistent institutions allow people to cooperate, plan, and adapt — even when no one is in charge.

The Game Works Well When the Rules Are Clear

Most of the time, when we think of what makes an economy work, we picture active players: entrepreneurs, workers, investors, consumers. But behind every decision they make is something easy to miss: the rules of the game.

Rules shape actions. In soccer, you can’t use your hands. In chess, each piece moves a certain way. The same is true in the economy. The rules, whether written or unwritten, determine what kinds of decisions are possible, encouraged, or punished.

Economists call these rules “institutions.”

Institutions aren’t necessarily buildings or organizations. They’re the invisible skeleton that holds the system together. Think of things like:

• Property rights

• Contract enforcement

• Banking laws

• Trust in the currency

• Cultural norms of fairness or reputation

When institutions work well, the economy just feels like it works. People trade, invest, and build for the future. When institutions break down, even smart people with the best intentions struggle to get anything done.

Why Institutions Matter

Imagine two cities. In one, there are clear rules, honest courts, and stable money. In the other, property can be seized without warning, contracts are never written down, and money loses value overnight.

In which society would you rather:

Start a business?

Save for retirement?

Lend money to a friend?

In the first society, you can plan. In the second, you just try to survive.

That’s the power of institutions. They reduce uncertainty. They help strangers cooperate rather than conflict with each other. They allow value to be stored over time. And they give people the confidence to try new things. That’s what makes them foundational to long-run prosperity.

Good Rules Matter Most

People often think the solution to economic problems is to elect the “right leader.” Someone who will fix everything with wisdom and fairness.

But no matter how smart a ruler is, they face the same problem we all do: limited knowledge and self-interest. No matter how great they are, they aren’t an all-knowing angel. Even well-meaning leaders can’t replace the information embedded in millions of scattered decisions.

That’s why good rules are better than relying on only good rulers.

Rules don’t have to be perfect, but they certainly can be better or worse. Ideally the rules need to be clear, consistent, and applied the same way no matter who, when, or where an issue may come up. When they are applied consistently and in a predictable way, people can take chances, make deals, and adjust as circumstances change.

Institutions in Everyday Life

You experience institutions every day without even noticing:

When you swipe your card at a store and it goes through? That’s a system of financial and legal trust.

When you sign a lease or work contract and expect it to be honored? That’s contract enforcement.

When you buy from a stranger on the internet and expect the product to arrive? That’s a mix of reputation systems, third-party guarantees, and social norms.

None of this works because someone planned it all. It works because people built, maintained, and followed institutions. These institutions almost always evolve over time, rather than being invented overnight.

The Cost of Weak Institutions

What happens when institutions are weak, inconsistent, or corrupt?

• Bribes might replace rules. Decisions go to the connected, not the capable.

• Investment likely dries up. No one builds for the long term if tomorrow is uncertain.

• Talent can run away. Entrepreneurs and skilled workers go where their efforts are protected.

• Wealth stagnates. Not because people don’t want to try, but because the system doesn’t reward good decisions.

Weak institutions are a tax on human progress. They punish those who want to play by the rules by changing the rules halfway through.

Institutions Are Built, Not Given

Strong institutions don’t appear by magic. They’re shaped by history, culture, technology, and incentives. Some evolve informally—like trust. Others require formal systems—like courts.

The key insight? We must be humble about how much we can engineer institutions from scratch. Like language or markets, they often emerge through trial, error, and iteration.

But once in place, they’re incredibly powerful. They allow free people to cooperate without being controlled by having a predictable means of addressing conflict.

The Bottom Line

A healthy economy depends on good rules that sometimes simply work quietly in the background. These rules shape the incentives and expectations that guide human action, trade, and trust. Markets don’t need to be managed, but they do need to be made possible. And that starts with the right rules.

Without prices that reflect reality, no one, not even the smartest, most informed planner, can know how best to use scarce resources.

How Do We Decide What to Make and How Much of It?

Imagine trying to bake and sell bread without knowing what flour costs. Or deciding to build a bridge without knowing whether steel or concrete is cheaper. These situations sound far-fetched, but they are more common in history than people realize, and they lie at the core of what some economists call the calculation problem.

Economic calculation is how producers make decisions. It’s how they answer basic but essential questions:

• What should we produce?

• How should we produce it?

• How much should we produce?

To answer these, producers rely on prices. Prices reflect information about scarcity, alternatives, and consumer wants. Prices condense millions of choices into a simple signal that anyone can use.

The Information Hidden in Every Price

Every price tells a story you could never learn any other way. The price of copper reflects:

• How much copper exists in known mines

• How difficult it is to extract and refine

• What industries need copper right now

• What substitutes are available

• What people expect copper prices to be tomorrow

No single person knows all this. But the price captures it all. When copper gets more expensive, construction companies may start using more plastic pipes. Electronics manufacturers may look for alternatives.

Why Markets Do This Better Than Government Planners

In markets, prices emerge from voluntary exchange. Buyers and sellers interact, each using their own knowledge, wants, and limits. The result? Prices that reflect reality, even if no one understands all the pieces.

In economies where governments own inputs like raw materials, machinery, or labor, there are no real prices. Without private ownership and competition, there’s no true exchange. Planners are left with no alternative other than to guess at values, set quotas, and hope for the best. This is where things go wrong.

Without market prices, which rely on property rights and voluntary exchange:

• Resources don’t flow where they’re most needed.

• Producers can’t tell whether they’re creating value or wasting effort.

• Innovation stalls, because no one gets a clear signal about what’s working.

A Tale of Two Bakeries

Let’s say you own a bakery. In a market:

You buy flour based on demand. If it surges, you bake more. If it drops, you bake less. While producing, you track the price inputs like flour, butter, electricity, and wages. If flour prices spike, you might switch to recipes that use less flour, or you might try to charge more, if customers are willing and able to pay.

Now imagine you run a bakery under a system without prices:

The government gives the same amount of flour to everyone, both bakers and non-bakers. You don’t know what flour costs, or how scarce it is, or if someone else needs it more. If you produce too much, it goes to waste. If too little, customers’ needs aren’t met. When flour becomes scarce, you have no way to know until you run out or it stops coming.

That’s the calculation problem. It’s not a math error, it’s a knowledge error. Without prices, there’s no way to know what choices make sense.

The Soviet Steel Mill Problem

Here’s a real-world example of what happens without prices. In the Soviet Union, the government set production targets for steel mills to meet. They said something like “produce 1,000 tons of steel goods.” Sounds reasonable, maybe? But without market prices to guide them, the mills had no way to know what kind of steel goods consumers needed.

So, they made what seemed easiest. They made thousands of thick, heavy sheets and enormous nails. These met their weight quotas but the products weren’t useful for much (what are you going to do with a 20 lb nail?). Meanwhile, other producers needed thin steel for machinery and small nails for roofing. The mills produced millions of tons of steel goods nobody needed and the producers were compensated for making things no one needed.

Why? Because tons of steel isn’t the same as the right tons of steel. Without prices to signal what consumers valued, there was no way for the mills to know the difference, or really to care.

When Prices Lie, Decisions Fail

Even in markets, problems arise when prices get don’t reflect reality. Suppose the government pays some of the cost of corn production. This allows producers to get away with cheaper prices. Suddenly:

• Food companies use more corn syrup instead of sugar

• Farmers plant corn instead of vegetables

• Ethanol producers use corn for fuel instead of food

These decisions make sense based on the fake, low corn price. But they’re actually wasteful. We end up using corn for things that aren’t worth its real cost.

Why It Matters

Prices must come from voluntary exchanges not by decree. The more people that own and exchange goods means more information is reflected within prices. Without ownership and exchange, prices don’t exist. If prices don’t exist, no one can figure out if what they are producing is helpful or wasteful.

The Bottom Line

Without prices, planning is guessing. With prices, it’s calculation. And calculation is what keeps the economy moving forward.

Throughout this series we’ve explored why coordination becomes expensive (”The Cost of Working Together”), how knowledge distribution affects decisions (”What Headquarters Can’t See”), why measurement systems create systematic biases (”Why Metrics Can Kill Innovation”), and why a fundamental job of organizational leadership is designing rules that make cooperation rational rather than hoping for motivated compliance (”Bad Rules Beat Good People”). But all of these insights need organizational structure to actually work.

You can understand the idea that operators, refiners, and creators need different systems. But, if your structure forces them into uniform processes, that understanding doesn’t help.

You can recognize that some decisions need decentralization. But, if your structure centralizes authority, recognition doesn’t matter.

You can design thoughtful measurement systems and well-aligned incentives. But, if your structure makes coordination impossible, none of it matters.

Structure is where institutional design becomes real. It’s how the rules of the game actually get implemented. Get it wrong and success becomes near impossible no matter how good your strategy, people, or resources.

Two Companies, Same Strategy, Different Structures

Two SaaS companies pursued identical strategies: maintain a profitable core product while building a next-generation platform. Both understood they needed different systems for each. Both had smart leadership. Different structures produced radically different results.

Company A used a single product team with all work integrated within it. The same engineering team worked on both core product and the new platform. They used the same prioritization process for maintenance of the current system and innovation for the new product. They had a unified roadmap. They split resources. They had common success metrics.

The logic: “We’re one company with one strategy. Integration enables sharing best practices and maximizes resource efficiency.”

What actually happened: In Q1, the new platform got 40% of engineering resources as planned. By Q2, critical bugs in the core product pulled resources away “just temporarily.” A major customer threatened to cancel their contract unless they got specific core product features. Project management prioritized measurable core product work. New platform resources dropped to almost nothing and became an afterthought.

This pattern repeated for months. Important new platform work was repeatedly deprioritized for urgent core product needs. Engineering productivity dropped. The platform shipped eighteen months late. By then, a competitor had captured the market. Company A was eventually acquired at a disappointing valuation.

Company B split the work into separated divisions. The Core Product Team got 70% of resources and focused on optimization of the current system that was generating revenue and clients. They had dedicated engineering. They planned in quarters. They focused on profitability and customer satisfaction. The New Platform Team got 30% of resources and focused on innovation. They had their own dedicated engineering. Planned in annual sprints. And their metrics focused on technical milestones and customer validation.

Company B assigned someone to bridge the two divisions. Their role was to think across the boundary rather than within each side. They made sure core product insights informed platform design. They prevented the platform from rebuilding what already existed. They managed resource allocation. And they translated between different evaluation frameworks. This was harmonizer thinking in practice. They didn’t create a new department. They simply made a deliberate commitment to having someone focus on the connection between the two rather than the success of either one alone.

What actually happened: When the core product hit critical bugs, the core product team solved them without pulling resources. When a major customer requested features, the core team delivered without affecting the platform. The team member thinking like a harmonizer made sure customer insights informed new platform strategy without creating resource conflicts.

After eighteen months: the core product achieved profitability improvements. Meanwhile, the new platform began customer pilots on schedule. Engineering was productive because teams had focused work. The company launched its platform on time, created competitive differentiation, and was eventually acquired at a premium valuation.

The difference wasn’t strategy. Or talent. Or total resources. It was structure. Company A’s integrated structure created conflicts that favored optimization over innovation. Every resource decision became zero-sum. Urgent measurable work always won. Company B’s separated structure enabled both at the same time. Different evaluation systems meant they weren’t competing. Physical separation prevented resource conflicts.

The Fundamental Trade-off: Specialization vs. Coordination

Every org structure solves the same economic problem that Adam Smith identified centuries ago. Specialization creates value but requires coordination. Coordination can become more expensive as specialization increases.

From earlier in this series, we know operators need reliability and clear processes. Refiners need systematic improvement and analytical tools. Creators need exploration and failure tolerance. When these functions get brought together, conflicts can emerge in terms of priorities, resources, and evaluations.

Operators demand stability that can prevent creator experimentation. Creator uncertainty can threaten refiner’s goals to precise system improvement. Urgent needs from operators can consume creator resources. Measurable refiner and operator work dominates unmeasurable creator work. Separation lets each function optimize for its own requirements without interference.

The thing is, business problems don’t care about your org chart. Separation can create its own costs when problems span across different teams. Separate teams often end up solving similar problems independently. Knowledge developed in one team doesn’t reach others. Different teams make contradictory decisions. Opportunities that require cross-functional work die because nobody owns the coordination.

The org design question is always the same: do the benefits of specialization exceed the costs of coordination? This is Coase’s question from “The Cost of Working Together” applied to structure. Separate when specialization benefits exceed coordination costs. Integrate when coordination benefits exceed specialization costs.

When to Separate

Separate functions when their requirements are fundamentally incompatible. The strongest case for separation shows up when:

• Time horizons conflict (optimization evaluated quarterly, innovation over years)

• Risk profiles conflict (optimization minimizes failure, innovation requires it)

• Resource needs conflict (optimization is predictable, innovation is lumpy and uncertain)

• Success metrics conflict (optimization measured on efficiency, innovation on learning)

When these conflicts exist inside a single company, optimization usually gets prioritized above all else. Not because managers don’t value innovation. Because the rules of the combined system (shared metrics, unified resource allocation, common evaluation frameworks) make optimization individually rational and innovation individually costly.

This is the measurement bias from “Why Metrics Can Kill Innovation” and the design failure from “Bad Rules Beat Good People” put together. The system isn’t broken. It’s working exactly as designed. It’s just designed for one type of work.

Effective separation means dedicated teams, dedicated budgets, different evaluation frameworks, and different processes. But separation without coordination is just fragmentation. This is where harmonizer thinking applies. Thinking in this way bridges separated functions so the organization gets specialization benefits without paying the full cost of lost coordination.

When to Integrate

Integration makes sense when coordination benefits exceed specialization costs. This happens when:

• Functions need constant communication. Like customer success and sales, where handoffs occur daily and success depends on a shared customer understanding.

• When specialization costs are low. Like different product lines in similar markets that share capabilities and can be evaluated on similar metrics.

• When consistency matters more than adaptation. Like brand and marketing, where fragmentation confuses customers and duplication wastes resources.

Even when you integrate, you still need to acknowledge different needs inside the shared structure. Create sub-teams with specialized focus. Use time separation—innovation sprints alternating with reliability sprints. Design evaluation systems that support both optimization and exploration. Protect time and budget for innovation even within an integrated structure. Integration works when you can manage conflicts without a systematic bias toward one type of work.

When Hierarchy Can Help and When It Fails

Hierarchy is the default structure in large businesses for a reason. It reduces real transaction costs. Authority replaces negotiation. A boss decides instead of parties bargaining. It creates clear accountability. It enables standardized processes. These benefits explain why every growing company reaches for hierarchy first.

But hierarchy fails predictably when knowledge is distributed. From “What Headquarters Can’t See,” we know that hierarchy centralizes decisions while valuable knowledge is often local. Information gets filtered climbing the chain. Context gets lost in reporting. Decisions are made without the knowledge needed to make them well. The larger the organization, the more severe this failure becomes.

Hierarchy can work when decisions depend overwhelmingly on systematic knowledge. These are times when consistency matters more than local adaptation. They also apply when speed of decision matters more than quality of local information. Hierarchy fails when decisions depend on local, tacit, time-sensitive knowledge. These are times when local adaptation creates more value than consistency, and when innovation requires the kind of risk-taking that hierarchical control tends to prevent.

Beyond Hierarchy

When hierarchy fails, organizations need different coordination mechanisms. Oliver Williamson, who extended Coase’s work, argued that the choice between governance mechanisms should match the economic properties of the activity being governed. Routine, well-specified work fits hierarchical control. But the most valuable cross-functional work is often too complex for hierarchy and too uncertain for market mechanisms. It needs something else.

That something else is what Elinor Ostrom’s work pointed toward in “Bad Rules Beat Good People.” Cross-functional opportunities inside organizations look a lot like the shared-resource problems she studied. No single department owns them. Success requires multi-party cooperation. Neither hierarchy nor internal markets can coordinate them on their own. The five rules we drew from her research are exactly what harmonizer thinking works to bring inside organizations. As a reminder these rules were

1. Make cooperation individually profitable

2. Let affected parties design their own processes

3. Match rules to context

4. Use graduated consequences

5. Back the system with legitimate authority

The shared budgets, joint metrics, and reputation systems we discussed in “The Cost of Working Together” are those principles made operational. In practice, this means most conflicts get resolved at the working level. They use pre-agreed frameworks rather than escalating everything up the chain. People thinking like harmonizers, who look across boundaries rather than up through them, make this possible by creating agreements before disputes become crises. But it only works if leadership supports these efforts rather than overriding them. When executives arbitrarily override cross-functional decisions, people quickly learn that the real authority is elsewhere. The coordination system collapses.

Structural Requirements for Each Function

Given everything this series has covered, here’s how structure can enable the three types of work.

Operators need decentralized authority with process standards. Operator work depends on local, time-sensitive knowledge. Centralizing operational decisions kills effectiveness because the knowledge doesn’t survive the reporting chain. By the time central decision-makers review operational issues, circumstances have already changed. Give operators authority at the point of customer interaction. Let them use local knowledge without escalation. Provide them with process standards that enable coordination, knowledge sharing across operators, and centralized resources for common needs. The operator principle: autonomy within boundaries.

Refiners need centralized analysis with distributed implementation. Refiner work benefits from systematic analysis across contexts. Data patterns that only appear in aggregate or optimization opportunities that need to compare performance across locations. Centralize analytical capabilities, data access, and knowledge management. But distribute implementation authority so local teams can adapt improvements to their context. Pure centralization loses local knowledge and creates resistance. Pure decentralization loses systematic analysis and creates duplication. The refiner principle: centralized learning, distributed execution.

Creators need protected separation with strategic alignment. Creator work needs protection from optimization pressure. Dedicated teams that don’t compete for operational resources. Different budget processes. Different evaluation criteria. But creators can’t be isolated completely or they lose connection to customer reality and organizational capabilities. Strategic alignment and coordination with the rest of the organization, maintained through harmonizer thinking, keeps creator work grounded without subjecting it to the measurement and resource dynamics that would kill it. The creator principle: separation with connection.

Aligning Incentives With Structure

Structure enables different types of work. But incentives determine what actually happens. If structure separates innovation and optimization but incentives reward only measurable short-term results, structure fails. As we discussed in “Bad Rules Beat Good People,” compensation is one lever among many. Decision rights, information flows, authority structures, and evaluation frameworks all shape behavior independently of pay. Structure has to align all of these, not just the compensation plan.

The most visible misalignment is incentive time horizons. Most incentive systems reward measurable outcomes on short time horizons. This works for operator and refiner work. It destroys creator work. If everyone is compensated the same way, everyone optimizes the same way. That means optimizing for what’s measurable now rather than what’s valuable later.

The solution is matching incentive time horizons to the economics of the work.

• Operators create value through reliability and quality on short cycles. Quarterly bonuses tied to operational performance make sense here. They reward what operators actually produce.

• Refiners create value through systematic improvement on medium cycles. Annual bonuses tied to learning and capability building match how their value materializes.

• Creators create value through exploration and option creation on long cycles. Long-term equity that vests over multiple years aligns their incentives with the time horizon of their actual contribution.

On top of role-specific incentives, shared participation in overall company success, equity or profit sharing, keeps specialization from becoming selfishness. Everyone benefits when the whole organization succeeds. This creates natural pressure toward collaboration even when functions are structurally separated.

The same logic applies to cross-functional initiatives. When a project needs creator exploration, refiner optimization, and operator delivery, give it a dedicated budget and shared success metrics. All participating functions get rewarded based on the project’s outcome. When collaboration is profitable for everyone involved, it stands a better chance at happening naturally. When only one function benefits, it requires forcing. This predictabily creates resistance and produces poor results.

Looking Ahead: Creative Destruction Inside the Firm

Understanding organizational design as an economic problem explains how to build structures that enable operators, refiners, and creators to work together. But there’s a tension we’ve been circling throughout this series that structure alone can’t resolve.

The systems that make operators and refiners effective (standardized processes, clear metrics, hierarchical coordination) are the same systems that kill creator work.

The freedom that creators need (uncertainty tolerance, failure acceptance, long time horizons) threatens the reliability that operators and refiners depend on.

Structure can separate these functions, but the underlying tension between optimization and innovation remains.

Next, we’ll explore Schumpeter’s creative destruction as an internal organizational challenge. How do you build organizations that can destroy their own successful approaches when the market demands it? How do you keep the structures that enabled past success from preventing future adaptation? And what role does harmonizer thinking play in managing the most fundamental organizational tension: the need to follow through on what works and explore what might work next at the same time?

The Bottom Line

Organizational structure is about economic trade-offs between specialization and coordination. Every structural choice represents a decision about which costs to accept.

Separation creates specialization but requires coordination. Integration enables coordination but limits specialization. Hierarchy enables quick decisions but limits local knowledge use. Decentralization uses local knowledge but creates consistency challenges. You can’t eliminate these trade-offs. You can only choose which ones to accept deliberately.

The practical implications follow from the economics of the situation and the roles involved. Separate innovation from optimization when their requirements systematically conflict. In a combined structure, measurable urgent work tends to almost always beat unmeasurable important work. Bring functions together when coordination benefits exceed specialization costs. This is helpful when constant communication, shared knowledge, and consistency matter more than specialized focus. Match structure to the work. Hierarchy for decisions requiring systematic knowledge. Peer coordination for cross-functional challenges. Decentralized authority for work that depends on local knowledge.

Structure alone doesn’t determine success. But the wrong structure makes success near impossible, and more dependent on luck than we might like. The right structure can better enable operators, refiners, and creators to all succeed at the same time which is what long-term success actually requires.

Property rights make exchange, investment, and cooperation possible. Without them, markets can’t function and progress stalls.

The Garden You Won’t Plant

Imagine you rent a small house with a bare backyard. You’d love to plant a garden, but your lease is month-to-month and your landlord has been talking about selling the property.

So you don’t plant anything. Why invest weeks of work and money if someone else might benefit from it, or worse, tear it all out next month?

That hesitation isn’t laziness. It’s rational. You’re responding to the fact that you don’t have a secure claim on the outcome of your effort. Without confidence that what you build will remain yours, you won’t build.

Now scale that feeling up to an entire economy, and you start to see why property rights matter so much.

What Property Rights Actually Are

Property rights are the rules that define who can use something, benefit from it, and transfer it to someone else. They apply to physical things like land, tools, inventory, and to intangible things like ideas, contracts, and creative work. Property rights are essential for things that are rivalrous (one person’s use of a thing reduces another person’s use) and excludable (owners are able to prevent other’s from using it).

But property rights aren’t just about legal ownership. They’re a bundle of expectations:

• Can I use this resource the way I see fit?

• Will I benefit from improving it?

• Can I sell it, trade it, or give it away?

• Will someone protect my claim if it’s challenged?

When these expectations are clear and reliable, people invest, plan, and cooperate. When they aren’t, people protect what they have rather than building something new.

Why Everything Else Depends on This

Throughout this series, we’ve talked about prices, trade, saving, investment, and entrepreneurship. Every one of these depends on property rights functioning in the background.

You can only sell something if it’s yours to sell. A buyer can only pay for something if the money they’re offering belongs to them.

Trade requires transferability. If you can’t transfer what’s yours to someone who values it more, trade doesn’t happen. Without trade, we lose the gains from specialization, comparative advantage, and cooperation that make prosperity possible.

Investment requires security. It is rare for anyone to put money into a business, a piece of land, or someone’s education if the returns can be taken or the rules can change without warning. The carpenter who saves to buy power tools in order to produce more only does so because he expects to keep the benefit of that investment.

Entrepreneurship requires the freedom to try. Starting a business means rearranging resources in a new way. That requires the freedom to obtain and use property based on your own judgment about what might work.

Remove any of these and the entire system we’ve been describing slows down or stops.

The Tragedy of No Ownership

When nobody owns a resource, nobody takes care of it.

Consider a public park with no maintenance budget and no one assigned to look after it. Trash builds up. Equipment breaks. People stop visiting. This can happened because no one had the incentive or the authority to maintain it.

The same logic applies to fisheries where no one owns the fish, forests where no one owns the trees, and aquifers where no one owns the water. When everyone can take but no one is responsible, resources can get used up faster than they can recover.

This isn’t because people are greedy. It’s because the incentives point in the wrong direction. Ownership aligns the person using the resource with the long-term consequences of how they use it. Without that alignment, short-term thinking wins every time.

Ownership Doesn’t Require a Government Deed

Property rights often start informally. Families establish norms about shared spaces. Communities develop customs about water use or grazing land. Online platforms create reputation systems that function like property protections for digital sellers.

Formal legal systems can strengthen and extend these arrangements. But the instinct to define “what’s mine” and “what’s yours” shows up wherever people cooperate and has throughout history. It’s not a government invention. It’s a foundation of human coordination and governments can either support or undermine that foundation.

The Bottom Line

Property rights make the rest of economics work. They give people the confidence to invest, the ability to trade, the incentive to maintain what they have, and the freedom to try something new. When property is secure, people think long-term. When it isn’t, they think about survival.

No one person has all the knowledge in the world. The best systems are those that help us use what we do know to make better decisions, even if we don’t know everything.

Who Knows What We Should Do?

Every day, people make decisions based on things only they know. How long the commute is that day, whether their kid is getting sick, how much their neighbor might pay for a used bike. These tiny pieces of knowledge may not seem like much, but they’re everywhere. And they matter.

The central challenge in economics isn’t just scarcity, it’s figuring out how to make the best use of all this spread out knowledge. The real question isn’t, “What should we do?” It’s, “Who knows enough to decide what should be done?”

What Is The Knowledge Problem?

No single person or authority can know enough to make good decisions for everyone else. This is a very simplified version of what’s known as the knowledge problem.

Knowledge is spread out. It lives in the minds of millions of individuals, each with access to their own slice of information. This knowledge is often based on time, place, habits, and situations no outsider could ever fully grasp. Because of this, we all know some things, but no one knows all of it.

Different Kinds of Knowledge

To see why this matters, it helps to distinguish between different types of knowledge:

• General Knowledge: Broad principles like how engines work or that too much pesticide can kill plants.

• Local Knowledge: Information specific to a particular time and place. Something like knowing that customers in your neighborhood prefer different groceries when it’s hot out.

• Tacit Knowledge: Skills and insights you have but can’t easily explain. Like knowing when bread dough feels “right,” or sensing when a customer is about to buy.

All three types matter. But local and tacit knowledge tend to be the most valuable, and the hardest for others to access.

The Wal-Mart Example

During Hurricane Katrina, Wal-Mart’s disaster response for getting supplies to the region outperformed government agencies. While FEMA waited for damage reports and approval processes, local Wal-Mart managers got to work. Among other actions, these managers verified which roads were passable by going and inspecting them. This allowed them to route deliveries correctly. This information didn’t exist in any government database. It took on the ground, second-to-second data.

Wal-Mart had supplies arriving before FEMA had even finished its assessment. They achieved this because they had systems that trusted and enabled people with local knowledge to make local decisions.

Why Local Knowledge Beats Central Plans

This is why local decision-makers can repeatedly outperform distant authorities. They see changes as they happen. They understand local trade-offs. And they know details that never make it into official reports.

For example:

• A store manager likely understands the buying habits of local customers better than the company’s CEO. If a retail chain mandates the same winter inventory across the U.S., stores in hot and cold climates might end up with the same amount and types of coats. The result? Unsold goods in one state and empty shelves in another.

• A farmer knows the specific conditions of their land (soil quality, weather, and pest risks) better than a policymaker miles away.

• A teacher knows which students learn better through visuals versus through discussion. This knowledge doesn’t show up in standardized test data.

Making decisions from too far away means making decisions without being able to see what truly matters.

When General Knowledge Isn’t Enough

While general knowledge is important, it has limits. General principles are certainly helpful, but they must be applied with care at the local level.

For example, McDonald’s has general guidelines for their service. But how they put those guidelines in place differs across the globe. In some markets they may not serve beef, or they may offer table service. No matter what, the general principle of fast, consistent food service stays the same. But knowing the right way to apply the knowledge changes.

How Markets Solve the Knowledge Problem

Markets don’t solve this problem by giving one person more knowledge. They solve it by not needing to.

Prices, profits, and losses carry information. They do all this without anyone having to centrally process, verify, and approve every data point and fact or opinion.

When a restaurant owner sees that fish prices have jumped, she doesn’t need to understand if global fishing regulations are to blame. She just knows fish is expensive today. Maybe that means tonight’s special should be chicken instead.

The beauty of markets is that they bring together all this spread-out knowledge without requiring anyone to collect it.

The Bottom Line

No one knows everything, but everyone knows something. Good systems don’t concentrate decision-making at the top. They build feedback loops that let each person act on what they know best. When people are free to use their knowledge, outcomes across the economy improve.

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.