Subscribe now

A Tired, but Correct, Narrative.

As you have likely already read from other economic writers, when housing becomes an asset to hold rather than a necessary resource (or a fundamental right, depending on who you ask) we start down a dangerous path towards something between complete societal collapse and a return to an era of Dukes and Barons. While this story isn’t particularly difficult to grasp or misinterpret, it fails to cast any actionable judgement on the financial incentives and political realities present today. At the end of the day, most agree that ownership of housing should be a path to wealth; albeit we completely lost the plot on that wealth creation mechanism.

Housing’s Wealth Creation Mechanism

Today, America’s wealth creation mechanism is so deeply intertwined with debt service, abstract “value creation”, and a gamble on the supply & demand of the local real estate market, that the obvious human benefits for home ownership can’t be part of a productive policy conversation.

Debt Service: To purchase a house in America, whether you see it directly or not, you have to win a bid against the bank. Since mortgages are so widely available, thanks to a friendly looking government guarantee, there is always a bank check available for someone purchasing a home. This pushes valuations up to the point of debt service coverage, despite anything foundational in housing construction or supply & demand factors. This re-enforces the myth that housing prices should only go up, because they must, so long as the capital markets continue compounding themselves. On a macro scale, housing prices track with the availability of debt, which is why we saw such a drastic impact from the 2008 financial crisis. This debt availability has been contained through regulation, but it remains market defining.

Side Note | Covid-Era Fed Policy: What many fail to appreciate about Powell’s decision to rapidly cut and then raise interest rates in the wake of covid, was that the maneuver was designed to tie down the significant influx of federal stimulus into real assets, effectively forcing a ‘reprice’ of assets. These low rates led to immediate asset price inflation, as borrowers could service a significantly larger mortgage pushing up demand, and prices, for real estate. The subsequent rise in interest rates created the opposite effect: the market-clearing price fell, but sellers refused to print it. Equity execution masquerading as a "slowing housing market." This is why you have friends that feel stuck in their massive, but cheap, mortgage.

Abstract “Value Creation”: Buy a house, pay down your mortgage, and retire. This common mantra, under our current financial system, is just as well accepted as “contribute into your S&P 500 401k and wait 20 years”. And in much the same manner, this activity creates an ever-increasing pool of financial assets demanded by an ever-increasing balance sheet.

While it may not be obvious on the surface, paying down your mortgage is mechanically similar to investing in a mortgage bond. Given the fairly safe assumption that there will be another buyer using a mortgage when the home is ultimately sold, paying down your mortgage to save interest is functionally the same as collecting the interest that someone else would pay to own your home.

Since you don’t see the cash flowing out the door anymore it feels like you’ve paid off debt, but you’ve really just bought a derivative that is long on the mortgage market. This is because your alternative to keeping debt outstanding is investing into other cash flowing assets. If we accept that the stable cashflows from a property will always tend to be captured by a mortgage or our mortgage bond derivative, then the real differentiator for this asset class from treasuries is the local market and the appreciation that comes with it.

Gambling on Real Estate: “But Duncan”, you may say, “the real reason to buy a home isn’t to pay down the mortgage, it’s to participate in the appreciation!” and you’d be right. But this unfortunate gamble creates incredibly perverse incentives.

Given that all the stable cashflows from the property can be leveraged, your equity exposure in a property is the portion that either grows by 10x in a decade or collapses your entire net worth. This leaves most people with a very aggressive posture around real estate. While some aim to mitigate this by paying down their mortgage (which again, is just them buying a derivative), the reality is as long as you own a property a portion of your portfolio is highly volatile real estate equity. While it does have an inflation-mitigating upward bias, as more people ‘buy into’ the mortgage market, it is ultimately dictated by factors in the local economy.

This is where the incentives start to break down. Since the main factors in a local RE market are debt service and the balance of supply & demand, the three ways to increase prices are to increase local wages, increase demand for housing (usually follows increasing wages), or most easily constrain supply. This is where the system begins to become perverse.

Side Note | Cash Buyers: All cash buyers are a fairly recent phenomenon that appear to be contrary to this whole thesis. However, I think that we have reached a critical mass where mortgage availability already dictates the baseline price so an eager buyer, with the liquid resources, is still needing to beat out a mortgage borrower. This ultimately just means that these all-cash buyers are still effectively taking the derivative on the mortgage market, with the added equity risk that mortgage-fueled prices will eventually catch up or another all-cash buyer will be around to catch the knife.

Constraint of Housing and the Brewing Economic Crisis

Given the gamble on housing prices, there is an incredibly strong incentive to constrain housing supply through NIMBYism and regulation, an issue that we see every day in California. Ultimately these constraints will push real estate prices to the maximum price that the local labor market can endure. This is especially perverse in a state like California where there is no additional property tax levied on homeowners who enjoy rapid appreciation.

This extraction of economic rents from the local labor market, places a serious burden onto the competitiveness of industry. In the short/medium term, this pressure will result in a labor force that demands higher wages to service the existing stock of businesses, however over time this wage pressure reduces ability to attract capital and compete in the global labor market resulting in firms closing down or moving operations. This creates a stagnant local market where real productive capacity is lost due to an inability to compete globally, creating a negative feedback loop that results in the collapse of housing prices. There are no easy wins for real estate investors when value creation can move anywhere.

At the local level, this is painful (but not a crisis) and leads to a market failure like the one we’ve seen in California: people move from a high-cost place to a lower-cost place. However, on the national stage, and this is a national problem, it creates an impossible challenge for American Industry’s ability to compete as they grapple with ever-increasing wages driven ever higher by a growing housing market crisis. Tariffs may delay the crisis by raising the bar for foreign competition, but they can’t solve the underlying issues or make American exports competitive.

Side Note | What keeps me up at night: When we find ourselves in this housing constrained market, most marginal production ultimately finds its way towards the mortgage bond investors, be it indirectly to those who own their own homes (who have been forced to buy their way into the mortgage market), or the banks, financial institutions, and family offices around the world that hold these bonds. This situation suggests that you have only two options: you can either buy out your landlord, sorry… lender, or a large portion of your income will be diverted into the globalized mortgage bond markets, finding its way into someone’s ever-increasing pile of capital. This rapidly starts to look like an unavoidable privatized tax on the American people.

Why Change is Hard

The easy answer to the crisis is always just to build more housing, loosening the market and putting downward pressure on RE prices. However, this ignores that the system desperately wants to uphold itself for two main reasons. The first is what I’ll call “buying in” and the second is because we don’t use gold anymore.

Buying In: The American Mortgage’s “forced savings mechanism”, that economists love to tout, depends on future mortgage buyers (this is the mortgage derivative that we discussed earlier). This means that generations of homeowners have had their primary retirement vehicle in the idea that they can sell their home to someone who will allow them to liquidate their bet on the mortgage market or rent it out (with rents based off the cost of a mortgage). This means that any downward pressure on real estate prices will destroy many retirees’ “wealth”. While any attempt to bail out the next generation from carrying this burden, will lead to a crisis for existing homeowners.

What’s in a Currency? As many gold bugs won’t let you avoid hearing, the US dollar is not backed by anything physical. Many stop here without realizing that the US dollar is upheld by loan obligations, mainly in the form of US Treasuries or Treasury-derived loans, such as mortgages. This means that any attempt to dial back the mortgage market (~$13.2T USD) would put the currency at risk, which is the most basic explanation for why the 2008 financial crisis resulted in bank bailouts rather than homeowner bailouts. This to say, it is unfortunately not as simple as loan forgiveness to offset the losses for homeowners from new construction.

I don’t plan on selling, so this doesn’t impact me: I wish this were true, however since costs are a function of local property prices, you will feel the financial burden from inflation when property prices climb. Further these rising rents on those who haven’t paid off their home, will cause demand for higher wages and worsening competitiveness for the American economy. No matter how much you try to avoid engaging with the financial system, as long as it dictates taxes, real estate prices, and wages you will have to concern yourself with it.

So, What CAN be done?

Before solutions, let's be honest about what this system already is. Americans pay roughly $600 billion a year in mortgage interest, about what every state and local government in the country collects in property taxes combined. The difference is who collects. Property taxes fund schools and fire departments; mortgage interest funds the bondholders, banks, funds, foreign central banks, and everyone who bought in before you. We got rid of feudal rents just to figure out a way to securitized it. So, the question isn’t whether America should have a housing tax, it’s who should collect it.

This leaves us with a clean but painful solution: Property taxes on land. I know, I know, I hate taxes too. But this appears to be the one lever that can compress housing prices without detonating the system, because it redirects the cash flows instead of destroying them.

Since prices are set by debt service capacity, a bigger tax bill eats into what a buyer can service, compressing prices the same way higher rates do; with the exception that cash flow shifts from private balance sheets (the mortgage bondholders) to the public one. Unlike flooding the market with supply, the repricing is gradual, the money stays in the system, demand for mortgage borrowing shrinks, and the currency is protected thanks to the new tax revenue strengthening Treasury credit without interrupting the flow of dollars.

The pain lands exactly where the “buying in” problem lives: on retirees whose home equity is the retirement plan. So, this only works if the revenue is recycled rather than absorbed: income support for the retirees whose exit we just taxed, construction subsidies so building continues even as prices soften, and credits against mortgage principal. The mortgage credits act as forgiveness in slow motion: household leverage moves onto the public balance sheet at a pace the currency can absorb, instead of all at once in the next crisis.

Side Note | Why Specifically a Land Tax: I recently came across a very compelling argument for land taxes over traditional property taxes. Traditional US property taxes are a tax on the value of the overall property. Build a skyscraper? 1% of $100MM. Leave a vacant lot downtown? 1% of $1MM. This linear cost structure indirectly subsidizes low value uses while pushing the burden onto high-value ones. If we allow developers to earn economies of scale on the cost of land, we’ll find ourselves with an incredibly powerful incentive for right-sized development.

What housing provides, ultimately, is a foundation for wealth. It always has. Improve it, and you capture the value you create. Provide it, and you earn the income of a real service. Live in it, and it becomes the stable ground to learn, connect, and build from. Somewhere along the way, we confused that foundation with a purely financial one: buy a house, pay down your mortgage, and retire. Now the bill for that confusion is coming due, and it leaves us a choice: housing can be one more asset for the global capital markets to intermediate, or it can be the stable ground that the next generation builds its American Dream on. The crisis of the moment proves it can't be both.

Years later I run a finance firm and the feeling from that kitchen hasn’t gone away. Now I at least understand the mechanics to explain it.

Here’s an honest inventory of my own position in this economy. I don’t know my mailman. I don’t know my landlord. I don’t have much hope of ever owning in the neighborhood I live in (and the ticket to hope was building a firm). I park my savings at a bank that lends it to people I don’t know to make money for people I don’t know, and I invest the rest in index funds full of companies I don’t like and, as a customer, often feel abused by. For years I worked a job that didn’t fulfill me, just to pay rent on a drafty building somebody’s parents put up seventy years ago.

It’s no complaint - I’m doing fine, better than most. But it is an observation about structure: nobody in that loop knows me, and I don’t know them. The economy plugs away solving our problems, and requiring us to solve others, without any real sense of accountability other than not running out of cash.

That’s the dissonance of the moment. The scoreboard; stock market, jobs figures, even cash in our savings; says things are fine while our gut says the game is rigged, and I’d argue that the gut is doing the real analysis. It has picked up on something specific: the mechanism that’s supposed to hold everyone in this economy accountable has developed a dead zone at the top.

I call it the Economic Black Hole. To see it we have to start with what money actually is.

The Audit

Cash is an intermediate for trust. It gets created by borrowing (the federal government borrows from the Fed, a business borrows from a bank, you borrow from your uncle) and every one of those loans is a trust judgment with a price attached to it. Interest is the price of doubt (or risk of non-repayment), while inflation is trust growing faster than the problems it's supposed to be solving.

Assets are a different, more real, animal. An asset is the systems, people, and processes, plus the buildings and machines and raw inputs, that directly solve a problem somebody has. “Financial Assets” such as a stock certificate, a deed, an LLC interest are claims on that machinery, not the machinery itself.

Now for the part of capitalism’s grand vision that I’ve actually come to admire. Everyone needs cash to survive and pay rent, groceries, payroll, or debt service. So, every participant in the economy, on a roughly monthly cycle, is forced to sell something (labor, product, inventory, time) at a price someone else sets. That’s the audit. You submit your contribution to the market, the market tells you what it’s worth, and you don’t get to argue, because you have to eat.

The audit comes in three forms:

• Consumption is the one nobody at the bottom escapes: you have to sell to eat, and the market prices you monthly whether you like the verdict or not.

• Investment is the audit you volunteer for: when you convert surplus into assets you’re submitting a judgment about which problems are real, and your returns are the grade.

• Taxation is the public audit, and I’ll say the unfashionable thing and defend it. Cash is trust, sure, but trust denominated in what? In a unit the public maintains. Courts that make a deed enforceable, registries, roads the trucks actually run on, the currency itself. If you accumulate trust in the public’s unit, the public gets to audit the pile on a schedule. That isn’t the state skimming your output so much as the upkeep on the machinery that makes your money mean anything to a stranger. And it happens to be the one audit designed to reach piles the market can no longer touch. Nobody has to enjoy it. Audits keep trust honest, that’s all they’re for.

This audit is the exact reason my firm exists in the form it does. I didn’t have the ability to build the exact thing I wanted on my vision board. It was built this way because we all need cashflow to survive, and the fastest honest route to cashflow was becoming genuinely useful to founders who needed a real financial counterpart. The audit disciplined me into contribution quickly, which is why I still sell my time for money.

The people on that In-N-Out line were passing the same audit I was, every two weeks, without fail. And the system’s implied promise was never “work forever.” It was work, run a surplus on your production of value, convert the surplus into assets, and eventually the assets pass the audit for you. That was the deal.

What broke is symmetrical and opposite at each end: the audit has been made permanent at the bottom and cancelled at the top.

At the bottom, the assets you’re supposed to graduate into (a house, to name the most obvious one, given the scale of cash outflow) have outrun wages so badly that the surplus may never be converted. The audit just repeats, monthly, forever, for people who are contributing the entire time. A wage earner is the most thoroughly audited part of the entire economy, priced by the market every month and taxed by withholding before the paycheck even lands.

At the top, three mechanisms now let accumulated trust skip the audit entirely. Before I name them, a confession.

Subscribe now

The Confession

I funded this firm with a margin loan against a portfolio I built during my years in private equity. Most of my startup capex was time, so I worked night shifts on the early build while the loan carried the initial runway when I went full-time. Mechanically that’s the same move as the most notorious wealth strategy in America: borrow against your assets instead of selling them.

So, I’m not going to tell you this mechanism is the sin. Here’s the actual difference. My loan didn’t exempt me from the audit; it merely delayed it while committing me to march further into it. If I didn’t build something clients would pay for, the position would unwind and the market would hand me its verdict.

On the flip side, as the business grew, I didn't rush to pay the loan down. Instead investing the new cash into more assets, which lowers the borrowing burden without lowering the balance, and gave the bank more collateral and less risk. Structurally that's the same move as buy, borrow, die. The only difference is altitude. My assets aren’t appreciating faster than my life could ever need, so the audit is still there waiting for me at the bottom of the loan. Theirs isn't waiting at all.

Ultimately, below a certain altitude, borrowing against your assets is a bridge back into the audit while above a certain altitude it’s escape velocity. That altitude has a mathematical definition: it’s the point where your assets appreciate faster than your interest plus your cost of living. Past that line the forced sale never comes. Not this year, not in your lifetime, and (we’ll get there) not even at your death.

Below the line you sell to live. Above it, you never have to sell anything at all.

The Three Escape Hatches

1. Never sell

Buy, borrow, die. The largest holders of the most-loved assets don’t sell them. They borrow against them at bank-grade spreads, live and reinvest off the proceeds, and when they die the cost basis steps up and the estate repays the loan without the gain ever being taxed. One structure, and it cancels two audits at once: no forced sale, so the market never renders a verdict, and no realization, so the public audit never comes due.

It’s worth being precise about the mechanics: First, every one of those borrowed dollars is a new deposit created against an asset that never traded. Fresh cash minted against unsold trust. Second, removing the sell pressure of the very largest holders turns asset prices into a ratchet. The first-order effect isn’t the price of eggs, though it leaks in eventually. It’s that the assets everyone below the line is trying to graduate into, a house or a share of the future, only get more expensive relative to the wages that are supposed to buy them.

Alongside this the estate tax quietly along the way: the estate tax was a terminal public audit, the one margin call nobody used to escape, and stepped-up basis cancelled the lifetime of capital gains that came with it alongside a now $30MM hurdle before estate taxes start to kick in for couples.

2. The Intake Fan.

Every month, retirement contributions flow into index funds that buy the biggest holdings automatically. No judgment of contribution, no doubt priced in, just size buying more size. I don’t blame anyone for this, indexing is how normal people escaped paying two-and-twenty for underperformance, and every individual in the chain is behaving rationally inside broken plumbing.

But step back and it’s the investment audit automated out of existence, flows that grade no one’s paper. The aggregate physics are wild too. Research on the Inelastic Markets Hypothesis (Gabaix and Koijen) suggests that inflows into the market create large and permanent price increases. Price stops being a verdict on value creation and instead becomes evidence for more price. The scoreboard feeds itself, and when the market wobbles the state steps in, because the biggest collateral pools are now so systemically important that letting them reprice would shake the financial system itself. The crash, people being forced into liquidation, was the one audit capitalism kept for itself, and it now comes with a standing promise that it won’t be allowed to finish.

3. Own the Inputs.

In one of the rural communities I’ve worked in, a family owns an aggregate mine outside town. Over a few generations the sequence went like this: control the local price of aggregates, win the state construction work that runs on aggregates, roll the profits into owning most of downtown including its operating businesses, become the bank’s favorite borrower (at some point the bank basically stopped pricing doubt, because doubting this family meant doubting the town), then use the credit to buy more productive assets.

They don’t have as much money as God. What they have is economic power: their demand sets local prices, and what they choose to fund sets local priorities. Nobody in this story is a villain and nobody broke a law, you don’t need a billionaire to collapse a local financial market into you. You need a structure where cash flows to you regardless of contribution and nothing can ever force you to sell. Sole ownership of basic inputs, whether that’s oil or aggregates or permitted land or water, does exactly that. Competitive markets in raw materials aren’t an economic nicety; instead they are essential democratic infrastructure. Whoever owns the only mine set the prices and economic direction through what they choose to consume and fund.

The Collapse

So, what do we call this thing? Everyone keeps reaching for “bubble,” but bubble is the wrong word. It stopped being a bubble sometime in the last 15 years when QE was used to fix wobbles.

A bubble is over-extended trust, optimism priced past what the assets underneath can honor, and it resolves the honest way: it pops, prices reset, the audit resumes. A popping bubble is the system’s fever breaking, but there’s a version of this the fever can’t fix.

When a large star dies it explodes, however past a certain mass, the explosion loses to gravity and the core collapses inward into an object with an event horizon, a boundary past which nothing comes back out, not even light. Physics doesn’t end at the horizon, but it does change and the rules that govern everything else stop applying.

That’s what has happened here. A bubble got too big and too structural to be allowed to pop: index-fed, bailout-guaranteed, collateral to the banking system itself. And a bubble that isn’t permitted to pop does the only other thing it can do: it collapses into permanence. Too big to fail was the critical mass.

Earlier I gave the escape altitude a definition, the point where your assets appreciate faster than your interest plus your cost of living. Now I can call it what it is. It’s an event horizon, and it only opens one way. On this side of it the old physics hold: doubt has a price, sales get forced, taxes come due. Past it they don’t. Interest collapses toward zero because doubt has become impossible, the bank can’t doubt the family without doubting the town, and the Fed can’t doubt the collateral pools without doubting the system built on top of them. No sale is ever forced, so no price is ever discovered. Like light, the information doesn’t come back out. No gain is ever realized, so the public audit never arrives, and death doesn’t repatriate anyone because the basis steps up and the loan repays itself.

And the interior of this Economic Black Hole is a vacuum of trust. A vacuum is the absence of pressure, and the pressure that governs everyone else in the economy is the audit: the forced sale, the tax that comes due, the return that grades your judgment. Inside the horizon none of it reaches. Trust just piles up in there, unaudited, forever.

One more piece of astrophysics while we’re here. The brightest objects in the universe aren’t stars. They’re accretion disks, the glow of matter spiraling into a black hole, radiating on the way in. I called the index an intake fan earlier and this is the better name. The next time someone shows you a chart of all-time highs, ask yourself what’s making the light.

Wealth used to be a lagging indicator of problems solved. Inside the vacuum it’s a leading indicator of nothing but itself. That’s the weird feeling. Your gut isn’t confused and it isn’t envious. It has correctly noticed that part of the scoreboard slipped past the horizon and stopped counting.

Orbit

You can’t pop a vacuum and you can’t un-collapse a black hole. There’s no pin. The standing bailout intercepts every crash, and no election repeals compounding. I won’t pretend otherwise, because pretending is how you get hype, and hype is the accretion disk’s house style.

But gravity gives you two positions. You can spiral in, with your wages, rent, deposits, and index flows all drifting across the horizon, your output feeding a mass you’ll never get to audit. Or you can hold an orbit: enough owned mass, moving at enough velocity, to circle without falling - floating independently. In economic terms the mass is the assets that solve your own problems (the roof over your head, the tools of your trade, the firm that feeds you) and the velocity is cashflow that beats what gravity extracts from you in rent, interest, and fees. An orbit isn’t passive. Most average people never stop countering gravity, they just stop losing to it. There’s a whole playbook of orbital mechanics for operators in my next essay.

An orbit isn’t passive, and it isn’t reserved for the rich. The people holding one are mostly ordinary. They never stopped countering gravity, they just stopped losing to it.

And further out there’s a longer project: an economy where inputs stay contested, where credit still carries doubt, where trust has to keep passing all three audits to keep its name.

Somewhere tonight there’s a seventeen-year-old closing a kitchen next to a forty-year-old doing the same job, both passing the audit that never ends, both feeding, through rent and deposits and the index, a mass on the far side of a horizon where the audit never begins.

Close

Everyone is still waiting for the bubble to pop. But unfortunately it may have collapsed. Black holes don’t pop; they pull. You don’t get a vote on the gravity. You get a vote on your trajectory.

If you’re a founder or operator building your orbit — turning cashflow into owned mass — I would like to talk. If you’re a capital allocator trying to figure out what returns mean once price stops being a verdict, happy to discuss. And if you’re still on the line, passing the audit in your business every two weeks and wondering whether orbit is even reachable from where you stand, that is the conversation I most want to have. duncan@saorsapartners.com

Subscribe to Conduit of Value for the next piece: the orbital mechanics playbook — which assets count as mass, how much velocity is enough, and how close you can safely orbit something that big. This piece is a companion to Jobs Are Dead, which argued the market now pays a premium for builders. This one names the gravity every builder is building against.

Subscribe now

One question to leave you with, and I read every reply:

What’s the one asset that would flip your trajectory from spiral to orbit and what’s actually standing between you and it?

Today, June 30, 2026, is the official anniversary of my entrepreneurial story. But Saorsa is really closer to two years old. It started in April 2024, when I started advising a friend’s business. Or maybe in June of 2024 when, I moved back to California with a decision that was certain and an uncertain career path alongside a folder of half-formed ideas, an unbuilt brand, and the sureness that I’d eventually work for myself. So, we can treat “one year” as a convenient fiction.

I’ve spent the past decade doing my best to walk through doors that open onto hallways with more doors. Ever since choosing to study economics over business every room I’ve entered has had more exits than entrances. Right now, I’m standing in the longest hallway I’ve ever seen, with more open doors in front of me than at any point in my career. Many founders to work alongside, problems to solve, a business to scale, or not scale, a Substack following I couldn’t have imagined, and I feel like I’m facing a problem that nobody warns you about. The reward for a good first year isn’t clarity. It’s optionality, and optionality at volume is its own kind of weight. It is exhilarating and faintly paralyzing in the same breath.

To explain where I am, and reflect on the lessons from this past year, I wanted to share my first year as an entrepreneur in enough detail to be useful for the aspiring or the veteran entrepreneurs in my audience.

The leap I tried not to take

Here’s the part of the founder story that usually gets overlooked: I really didn’t want to do it the hard way. (And I’ve come to really appreciate the elegance of employment allowing me to not do everything on my own)

After about 6 months working for a large energy research firm in sales, I was starting to get bored of the corporate life and wanted to start working with companies like I had done back at the investment firm. By early 2025, I started to do the preparation. I’d built a first draft of the website and brand, set up an email, and started networking again in earnest. I sent my first proposal that February. It went nowhere. Then in March, at a solo networking event during what was supposed to be a vacation, I got an offer to join a finance firm — and I took it, fast. I was genuinely excited to get back to working with business owners, and I was hungry for the mentorship I assumed would come from the founder. I told myself: this will solve it. The job would scratch the itch and I could shelve the entrepreneurial ambition.

It didn’t, and I couldn’t. Within about three months it was clear the firm wasn’t for me, and the mentorship I’d hoped for never really materialized. Then the tell arrived from an unlikely place. A prospect I’d pitched back in February — the one who’d passed — rejected the firm’s sales pitch but said, more or less, I’d still go for that proposal you sent me earlier this year.

That was the moment. FP&A and accounting are critical work, but I wasn’t being true to what I was actually capable of. I had a choice: reissue that proposal under someone else’s roof, or under my own. I chose mine and left on good terms — keeping a few billable hours on a contract I was already ramped up on, at friendly pricing, hoping to save the bridge by leaving a friendly arbitrage opportunity for them on my way out the door.

The lesson I’d offer anyone five years from their own leap: the comfortable detour is not a failure, but don’t mistake it for the answer. The job that “solves it” usually just reveals, more expensively and at risk of hurting relationships, what you already knew.

Subscribe now

The luck I won’t take full credit for

I’ll say this plainly: I had a ridiculously lucky first year. I’d braced for several months of negative cashflow. Instead, nearly every probability landed in my favor. Hitting cashflow positive in month 1. Almost every prospect I had at the start became a client. I went from $0 to $165,000 in twelve months with nothing but my cost of living as capex.

But “lucky” still requires setting up the opportunities to get lucky. The dice landed well — after I’d spent a year loading them. I had primed the pipeline through months of networking I wasn’t getting paid for. I had a year of cash on hand, which let me price for relationships instead of for survival. I had near-immediate proof of revenue because I’d already done the unglamorous work of staying in front of the right people.

Consulting is capital-light, which is the massive structural gift here: my biggest input cost was time, and I’d been investing that time before there was any revenue to show for it. So, when people call a fast ramp lucky, I’d push back gently. Luck is what you call preparation when you don’t want to admit how much of the outcome you set up months earlier. The leap looked clean. The runway underneath it took two years to build.

The receipts

A year in, here’s what I can actually point to:

• Four recurring clients, and recurring revenue north of $150,000 in year one from a standing start.

• I helped a client work their way out of trouble with the bank on a revolving line of credit. This is the kind of problem where the work is the difference between a tough year and a closed business.

• I helped another client find a genuinely more effective business model, not just a cleaner spreadsheet, they are now proudly on the verge of cashflow breakeven!

• I signed a lease alongside one of my equity-based partners to open a manufacturing facility, skin in the game, not just advice from the sideline.

But the number I’m proudest of isn’t on that list. It’s retention. Coming into year two, I’m realizing that the easiest client I ever landed is the one I already have. In a business model built on multi-year relationships, the renewal is the whole game, and the fact that the work keeps compounding inside existing accounts is the strongest signal I have that the value is real. But like all stories, it’s not all sunshine and rainbows.

The client I lost

The low point was losing my first retainer client at six months.

I’d started them at too low a price — a mistake of my own desire to win the deal — and their books simply weren’t strong enough to support the work I was trying to do. You cannot build rigorous financial analysis on top of weak accounting; the foundation won’t hold the weight. So, when I proposed bringing in a fractional controller to fix the underlying books and raising my fee to match a scope that kept expanding, the honest answer was that it wasn’t the right time for them. We parted on good terms.

It stung more than the math justified, because my whole model aspires to multi-year relationships, and a six-month churn was nowhere in the plan. But the more I sat with it, the more it read as a standards problem rather than a service failure. I wasn’t willing to keep performing analysis the data couldn’t support, or to undercharge for an expanding scope just to keep a logo. Walking away from work you can’t do well is, eventually, what protects the work you can. I’d rather lose a client at month six than deliver something I don’t believe in for three more years.

The pitch I had completely wrong

If I could mail one page back to myself on day one, it would be about the pitch — because mine was weaker than I thought, and arguably still is.

I came in leaning on trust and breadth, partially biased from my early warm pipeline. I want to add value in your business using finance or I’ll build you a financial model. Both sound reasonable. Both, it turns out, are weak. They ask the founder to take a leap of faith on a vague promise, and they lead with the deliverable instead of the problem.

What actually works best is the opposite. One broad service that solves many problems is a far worse pitch than I can solve your specific problem right now — and if we work well together, the next one too. Founders don’t buy a methodology. They buy relief from the thing keeping them up at night, and then they keep you around for the thing that comes after. I happen to thrive on flexible scope like this, on pointing all of my attention at the single sharpest problem in front of a business. It took me most of a year to realize that the thing I was best at was also the thing that sold. Lead with the problem. The relationship earns you the breadth later. The pitch that wins your first client is rarely the one that wins your tenth.

Share

The hallway ahead

So where does that leave me, twelve months on?

Financially, almost exactly where I started: about the same W-2 paycheck and a similar cushion of cash, which is its own small victory given I expected to burn through a chunk of it. But I’m far more confident in the service I deliver and far more willing to price it at what it’s worth. I have ownership over my work, and the freedom to prioritize it to match my preferences, whether that’s because a friend needs help with a financial model or because a client needs the extra hours this week for an angel investor pitch.

The one thing that’s gotten harder: the low-hanging fruit is picked. My early pipeline was a lightly-dusted set of warm referrals, and I’ve now leveraged most of it. The next year isn’t about proving I can do the work. It’s about building a real sales channel for a business that, until now, has grown mostly on reputation and luck, leaning more into systematic channels and less into relational chance, mainly to find out whether I have a real business here or just a well-paid series of jobs.

But that framing assumes the only answer is “more clients,” and the doors in front of me aren’t all the same kind. Some extend the business I’ve built, this gets at the same repetition process efficiencies and scale that my previous article discussed. I could keep finding clients the way I always have, through aggressive networking and events, or I could lean into digital marketing and confront the question directly: if growth isn’t systematic, is it really a business?

Other doors don’t concentrate the business, instead widening it. Instead of adding clients, I could pour those same hours into a single bet: accelerating growth at the equity-partner client where I already have skin in the game, or backing a friend’s promising SaaS startup, or treating Conduit of Value as more than a calling card and building it into a real media presence with its own revenue. Each of these means trading depth for breadth, and the optionality of many small relationships for the conviction of one.

And then there are the doors that lead out of services entirely, toward deploying capital and the kind of problem that outlasts a client engagement. Starting a membership-based investment club or angel group to underwrite deals and land more capital locally. Raising a small fund to lend to and invest in the small businesses I already understand. Or doing something real in housing development — a genuinely important problem in the community I live in here in San Francisco. These are not the same magnitude of choice as “attend more events.” They’re a different life.

That range is exactly the weight I started this essay describing. The hallway isn’t just crowded. The doors open onto different lives, and you can’t tell from the threshold which rooms are worth the walk. But that is the fun of entrepreneurship, isn’t it.

Which brings me back to the doors. The first year was about getting one open and walking through it. The second is about choosing which of the many doors are worth it. That’s a better problem than the one I had two years ago, staring at a single uncertain door wondering if it would open at all.

I don’t have this hallway figured out yet. But I’ve learned that the work isn’t finding the door. It’s having the nerve to keep walking when every room you enter just reveals more of them. Onward.

SpaceX didn’t get to a reusable rocket by inventing one perfect engine. They built an engine, then built it again, and again — version after version, each slightly cheaper to manufacture and slightly more capable than the last — until the cost curve bent into something the rest of the industry couldn’t touch. No single breakthrough. A thousand small improvements to the same repeated thing. That’s compounding, and almost everyone misunderstands it.

Repetition is the operative word

We’re taught “compounding” as a finance concept: interest earning interest, the chart that hooks up and to the right. True, but it buries the word that actually matters — repetition. Compounding only happens to things you do more than once. The interest compounds because the principal sits there period after period, the same dollar getting another turn.

This applies to much more than just money, in business it appears in systems and process. The return appears as how much better and cheaper you get at a thing each time you do it again.

My one rule since the day I started building my firm is to get 1% better every day. It sounds like a fortune cookie. It also sounds trivial — 1% is nothing, you can’t feel it. Easy to say, easy to aim for, impossible to track. The gain on any single iteration is invisible, so people skip it and go hunting for the visible win: the big swing, the new logo. But a big win is a one-time event, that doesn’t compound when that 1% does. The entire discipline is tolerating invisibility and doing the unglamorous improvement whose payoff you won’t see for fifty iterations.

The value of the second deliverable

Here’s what that looked like for me, stripped down to the spreadsheet work: my first client was an early-stage dirt-bike protection company, and the financial model needed to forecast to cash. Given my time in PE building models for investment, I knew Excel, I knew how to read accounting, and I knew what an operating model was supposed to look like. It still took the better part of two weeks, call it thirty hours glued to the screen, to produce something usable. It was crude, oddly colored, and far too complex. But it forecasted cash, and it worked.

Then I got a second client, a startup turnaround. This is the exact moment compounding either happens or doesn’t. The lazy instinct, the billable hours instinct, is to build another bespoke model from scratch. Instead, I used my lessons from the initial iteration, what worked, what didn’t what was hard to update and spent twenty hours building a template: the three statements wired together, the cashflow dynamics, a hiring build-out, a capitalization framework, and a clean slot to drop the specific business model into. Infrastructure. After that, populating a new client’s model took about eight hours. A twenty-hour investment cut two-thirds off a process I’ll run dozens of times.

That’s the value of the second deliverable framed alongside the trap of the snowflake. If every client gets a hand-built, one-off model, you never compound; you just relearn the same lessons at full price, forever. The gains only show up when you let the first version teach the second.

Why I won’t bill by the hour

This has become the exact reason that I refuse to bill by the hour. Stop and look at the incentive. Under an hourly model, every efficiency gain is a pay cut. The template that makes me three times faster makes me three times poorer. So, the rational hourly consultant never builds the template, they’re paid to stay busy, not to get better, and the entire pricing model is at war with compounding. Linear, un-productized growth, billing more hours to more clients without the incentive to gain efficiency.

Retainers invert it. When you’re paid for an outcome, every minute you save is pure margin, and for the first time you’re aligned with your own improvement instead of penalized for it. This, more than anything, is why so much of professional services stays artisanal and unleveraged: the dominant way it’s priced punishes the exact behavior that would let it compound.

The process improvement loop

Concretely, I run the same loop on a spreadsheet that SpaceX runs on its hardware in process improvement. Five steps, borrowed from SpaceX, in order:

1. Challenge the requirement. For modeling, usually this is false precision. Do we need to know the exact cash balance in October? No. We need the sales target per rep that keeps this hire from bankrupting us. Two different questions, two very different amounts of work. Precision is a cost to interrogate before paying for it.

2. Delete the step. Building a three-statement model from scratch and then analyzing the business is the wrong process. The right one is: learn the business, drop its unique assumptions into the template, read what falls out. The from-scratch build was a step to delete, not optimize.

3. Find the design improvement. My templates hide reference columns to the left and top, flexible cells that let me reshape the balance sheet or P&L for a different business model without rebuilding anything. The improvement that quietly makes the next ten jobs easier.

4. Execute faster. Templated inputs, hotkeys for whoever’s driving the model, closing the gap between a decision and its entry. The unsexy speed work.

5. Automate, Last. Automation comes at the end because you have to automate the correct process, not enshrine a broken one in code. For me that’s become auto pulling the QuickBooks data into the model the moment the month’s books close and creating metrics that provide directional analysis, so we don’t need to dive deep each month. Automate first and you’ve only made the wrong process faster.

How a freelancer becomes a firm

This is how an independent consultant turns into a firm: not one big leap, but a few hundred 1% improvements stacked on top of each other until the practice can do things the freelancer couldn’t. And it only works if you’re building toward something durable. (That’s the other half of this argument, I wrote a companion piece on duration last week.) Chase the next billable hour or next bespoke job and you’ll never spend the twenty hours on the template; the math of compounding needs a horizon long enough for the marginal gains to matter. A firm is just the accumulated infrastructure of every small improvement you were patient enough to make.

Compounding is unglamorous, and that’s the moat. Anyone can have a good idea. Few do the same boring thing 1% better a thousand times in a row, with no applause along the way. The Raptor wasn’t a eureka; it was discipline applied to a repeated process until the numbers bent. So is a good firm.

I’ll happily admit financial modeling is not rocket science. But the method is identical, and that’s the part worth that you can apply in your business. The rocket and the spreadsheet run on the same algorithm. Most people just aren’t patient enough to run it.

If you’re interested in understanding how a financial model can be useful in your business, or just want to discuss process improvement in more depth, I’d be happy to hop on a zoom, email me to continue the conversation: Duncan@saorsapartners.com.

Musk started SpaceX in 2002, back when the American launch industry was a dying business. It burned cash for years on something serious people called delusional, and it didn’t turn cash-flow positive until around 2015. Even now the only part that reliably makes money is Starlink. No public-market investor would have touched that timeline: twenty years to profitability, billions sunk along the way, the promise unproven and the market not existing for most of the route. And yet here we are, begrudgingly clapping at the receipt.

That receipt took twenty years to print. While I’m no particular fan of Mr. Musk, I do find it admirable that he made a decision to invest in duration.

A talent we traded away

Investing in duration means putting capital and work behind something whose payoff is years out, far from certain, and staying in through the hard middle. The middle is long, there’s nothing to show for it, and every instinct, critic, and friend you have is telling you to quit.

We used to be good at this. The railroads, the interstates, Apollo program, Bell Labs, the whole postwar industrial build — none of it would survive a modern earnings call, but the people of the past did all of it anyway. Somewhere we traded that muscle for creation for a less patient one.

You can roughly point to when it started: the Jack Welch era. That’s when the smooth, always-rising earnings line became the actual product a company made, and the business itself became the thing you squeezed to produce it. You managed the stock and the company itself barely mattered. And you can see the toolkit everywhere after that. Buy back the stock instead of building a plant. Sell off key assets for a buck. Bring in the activist investor whose whole job is to make sure you stop spending money on anything that won’t pay off before he sells. And Managers with exclusively short-term incentives.

The other side has a point: short horizons keep people honest. Money should go where it’s most useful right now, not sit forever inside some CEO’s twenty-year vanity project, and plenty of so-called patient investment is just empire-building dressed up as vision. Fine. But we didn’t stop at honest. We made ninety days the only length of time anyone’s allowed to think in. A market that can only see ninety days out has no way to register anything that takes longer than a fiscal year to build, and nobody decided that on purpose; it’s just how the wiring works now.

We’d rather close the mill than improve it

Once you notice it, the ninety-day mind is everywhere, and it usually shows up the same way. Squeezing more out of what already exists than building the thing that lowers costs, improves quality, or otherwise doesn’t yet exist. An extractive economy, to reference my previous work.

The version that frustrates me the most is when we opt for profitability over production. We sooner close a working mill to protect monopoly pricing power before we invest the capital to make that mill competitive to run and allow for induced demand to show its face. Protecting margin is something you can do this quarter and put in the deck. Lowering a cost curve may take ten years and pay off long after whoever approved it has moved on. So, we protect the margin, and then we call it discipline, when really it was just the version of the decision that we could put a number on.

That one instinct explains more than it should. Companies buy back their own stock instead of building anything (In a market where capital is plentiful, mind you). Our political culture chases whatever makes voters happy this cycle and ignores whether the country is still competitive in twenty years — and before anyone reaches for a team to blame, this isn’t a party problem, it’s an incentive problem; short-term comfort wins every election no matter who’s holding the power. Most of the hard things we’re stuck on, we already have the money and the know-how to fix. What we lack is the patience to spend now and collect later, even if it comes long after we’re gone, which is why they stay problems.

Another American mill, closed for business.

American automakers did exactly this to themselves over the last six months, in public.

America has had every advantage, nearly 100 years of Auto dominance. The engineers, the factories, well over a decade of warning this was coming, and enough cash to fund the switch twice over and a rising signal in the form of Tesla’s rise in the early 2010s. However, unfortunately for American investors, building a real EV business is the kind of inconvenience bet that only pays off after years of losing money first: new batteries, new software, supply chains that don’t yet exist, all of it paid for long before the business earns a cent. It was a duration problem, and automakers blinked.

Actually, “blinked” is too kind. They gave up. In a few months Ford, GM, and Stellantis wrote down something like fifty-five billion dollars ($55,000,000,000, or the annual consumption of a small city) on their EV programs, and Ford’s piece alone was the biggest EV write-down in the history of the American car business. They killed the F-150 Lightning, the electric version of their most important vehicle. At the Detroit Auto Show this January the EVs were a softly avoided story, and everyone was back to talking up hybrids and updated gas trucks. They ran back to the trucks that make money this quarter and walked away from the future, because the future was going to cost them ten years of actual investment, actual capital deployment, actual innovation. The good news is: American investors, while fighting to deploy capital into the secondary markets, received nearly $100B in buybacks and distributions from these companies over the past 20 years, lucky us.

That’s the mill, exactly. They pulled out the cash until the mill couldn’t compete and are giving up on improving that mill until it rusts to a halt, all while closing our markets to real competition to protect our sleeping giants from having to make real investments, just because future demands real innovation, real risk, and short-term negative free cash flow.

Now look at who took the other side of that bet, the same ones whose products are banned from competing. China spent more than fifteen years on it, they were patient, and they were ruthless about it. They subsidized the batteries and built out the supply chain. They sat through years of losses and a savage fight among their own companies, and they let a battery maker like BYD slowly turn into one of the best car companies in the world. Chinese auto brands have gone from under three percent of the global market to about eleven, and they’re building plants in Europe now that make western import tariffs close to pointless. Ford’s own CEO has compared this to the arrival of the Model T, and he’s said the competition he’s scared of isn’t the other old carmakers. It’s the Chinese.

Even though their market is in a disarray of creative destruction, we should be worried that the survivors didn’t dodge the pain of the long game. They went straight through the middle of it. Meanwhile, we just walked off the field and took a vacation, paid for by depreciating balance sheets.

The most expensive thing we won’t build

Housing is the one that keeps me up at night. Same disease, working on the problem I care about more than any other, particularly here in California.

It’s a long-horizon problem from every angle. Getting real supply built takes ten or fifteen years once you count the planning and the permitting and the financing and the building itself. And every incentive in the system runs the other way. If you already own a home, more supply is a threat to your biggest asset, so you fight it. If you’re a local official, you get rewarded for killing the unpopular project this year, and nobody ever blames you for the homes that don’t get built a decade out. Nobody anywhere in that chain gets rewarded for the houses that quietly exist in fifteen years.

So, we end up with a disaster slow enough that everyone can watch it happen while nobody does much about it. Not for lack of land or money or workers or knowledge or interest; we have all of that. We just won’t wait. We won’t take the discomfort. It is the clearest case I know of what short-term thinking does to a place: it can take the most important problem you’ve got and make it unsolvable in practice, even while everyone in the picture is behaving rationally in their own ninety-day outlook.

And housing’s just the most obvious version. The same thing quietly guts whole regions — the smaller towns and overlooked markets where somebody patient could build something that lasts, if they were willing to wait around long enough to find out.

Where I try to practice it

I could leave this as an argument about money and markets, but that’s not really how I think about it. For me the long game is as much about business and investing as it is about people.

I bet on people, like I mentioned in my last post: the fastest compounding, untaxable investment I have is my network. Most of the relationships I put real time into will never pay off in a way I could point to on a spreadsheet, and that is fine, because the whole premise is that if you keep showing up for people without keeping score, a decade later the trust you built is the thing quietly routing work and ideas and the occasional out-of-nowhere introduction back in your direction. I’m not trying to get something out of the person in front of me. I’m trying to build a little trust and then leave it alone. It never comes back on a schedule you’d have chosen.

It’s the same bet as the rocket and the cheaper battery, shrunk down to one conversation at a time. You spend now, you get nothing visible back, and the whole thing only makes sense if you plan to still be around in ten years. The move never changes. The only thing that changes is what you’re counting it in.

The few who think in decades

The thing I’d want you to take from all of this: the long game isn’t about being noble. It’s an actual edge, and the reason is embarrassingly simple. Hardly anyone will stay in something long enough to get the payoff, so the patient opportunities just sit there underpriced. Nobody is bidding against you on the stuff that takes ten years.

Which is why I’ve mostly stopped complaining about it. I started using it as a filter instead, a way to find the people I want around me. There’s a small group out there who just don’t run on the ninety-day clock, and they’re easy to miss because they keep quiet about it. You’ve probably met one or two: the founder building for fifteen years with no interest in flipping, the operator who’d fix the mill before stripping it for parts, the person who can drive through a town everyone wrote off and see something there. They don’t make much noise, but they’ll be still standing there, looking like an overnight success, ten years on while the people chasing quarters have already moved to the next thing, and the thing after that.

That’s the group I’m trying to build, and it’s more and more where my own work is heading: economic development aligned investing in the places everyone else skipped, putting patient money into overlooked people and communities with patience for real compounding to take place. The work is entrepreneurship, real industry, maybe housing, and the slow unglamorous build that never photographs well.

If you already think on a longer clock, or you want to and you’re sick of pretending ninety days counts as a plan, then reach out (Duncan@saorsapartners.com). Tell me what you’re building and how far out you’re really looking, and you’ll receive an invite to our Slack - filled with long-duration Operators and Investors. Those are the relationships we care about most: no quick payoff, and ten years of upside.

The receipt takes years to show up. But the decision behind it doesn’t take long at all. You just have to be willing to look stupid for a while, longer than is comfortable, while everyone optimizing for the quarter moves on to the next thing and the one after that. That’s most of the trick. The rest is finding the few people willing to wait it out with you.

A network isn’t just a thing that helps your business. It’s compounds value at every scale you operate at, the relationships you personally hold, the demand a business can sit on top of, and the region a company chooses to plant itself in.

To build on this these we’ll start on the Micro and move towards the Macro.

1. Your Network: Opportunity Surface Area

As a young entrepreneur, I’ve opted to trade cash for trust often, recognizing that the fastest compounding, untaxable investment I have is my network. The reason? I want to maximize my Opportunity Surface Area. (Selfishly, I also enjoy interesting conversations with brilliant people.)

My framework starts with an economic network: a series of interconnected nodes that exchange information, resources, and trust among people and companies. When I say node, I just mean one person or company existing out in the fabric of trust and relationships that underpins trade and business.

Existing networks of trust dictate the likelihood of someone engaging with your product or service. By appearing on the periphery of my network — say, two nodes away — I can rely on the trust in my existing network as a strong signal that engagement is likely to end positively.

But trust decays, usually starting to break down once you get more than two nodes out. So your Opportunity Surface Area is the surface area of the nodes within two connections of you: the people who may have business opportunities, capital, expertise, or a willingness to freely discuss economic opportunity with you or with your first-order connections (who are, in turn, likely to share it with you).

Super Connectors

Here’s a concept I came across a few years back: your friends have more friends than you. The Friendship Paradox, in short, says that people with many friends are connected to many people, which creates a sampling bias — any given friendship is more likely to be with one of these high-connection people.

I call that high-connection person a super connector: someone with a very large network who also benefits from interconnecting it.

A few careers tend to reliable produce them: a good BD-focused banker, the head of a chamber of commerce or similar economic development body, a conference organizer, a community builder. The value of stacking these people into your network is that it drastically expands your Opportunity Surface Area.

Run the math: if the average person has 10 relevant business connections, your Opportunity Surface Area is around 110, your 10 connections, each with 10 of their own. But add just 5 super connectors with, say, 100 relevant connections each, and that number jumps to 615.

Share

That’s still a small network in absolute terms. The point is the density of super connectors. And real networks overlap heavily, which cuts both ways. Two super connectors in the same town and industry probably share 50–80% of the same network. That overlap buys you trust density into any new connection — but it also caps the size of your surface area. The way to fight that is to build isolated networks: connections in different towns or industries, where you capture the full size of your surface area instead of the overlap. The tradeoff is that the depth of your relationship with the initial connection now matters far more.

Coming from the investor business development side, I’ve found that economic developers are some of the most underrated sales superpowers in the world. Build a genuine connection with a few of them and then incentives kick in. Their organizations exist to bring value to their members, so clearly and honestly communicate how you do that and then stay in touch, check in often, and contribute for free when you can (basically be a genuine value add, not purely sales oriented). Then when they hear about a problem that your firm solves, they will think of you first and make the introduction. This is how I’ve gained over half of my client base.

2. Businesses Built on Networks: Aggregating Demand

Scale up one level. Some businesses don’t just use networks; they exist because of, on top of, or alongside one. The obvious examples are Uber and Airbnb — valuable because everyone else uses them — but the same dynamic shows up across industry. The thread is the same as Opportunity Surface Area, just at the firm level: value concentrates where connection concentrates.

Conference organizers. People and businesses in the same industry share the same problems, questions, and concerns. A conference organizer aggregates that industry to increase vendor exposure, create connection, and circulate solutions among similarly focused professionals. It only works because they can aggregate the demand of an industrial network into a critical mass where a business of connection becomes viable.

Community organizations. Same logic. These sit on top of an existing network, deepen engagement, expose the network, and provide an aggregated way to participate in it. My favorite example is a well-run chamber of commerce: it collects memberships from businesses in a community, positions itself as the center point of that network, and from there it provides value by collecting marketing sponsorships, hosting events, and wining grants.

Lobbying firms. For better or worse, the western world — and the US in particular — is increasingly oriented around government intervention: protectionism, subsidy, anti-competitive regulatory hurdles that keep otherwise broken markets stable. A single policy or budget allocation can lift an entire industry, creating opportunity for those willing to head to Washington. It’s rarely economic for one firm to advocate for structural changes in policy or spending on its own — but when the benefit accrues to a whole industry, a membership-based organization can aggregate that demand and sell lobbying as a service.

Here's what these all have in common: each one is a super connector with a corporate structure built around it, an institution whose entire job is keeping a dense network in one place. That gives you two options. The simple one is to get inside a network someone else already built: sponsor the conference, join the chamber, take the membership, and borrow its surface area as your own. The harder one, and one that builds a business of its own, is to find an industry whose demand nobody has organized yet and make yourself its center point.

3. Industrial Clustering: Networks at Regional Scale

The largest scale. Industrial clustering is one of my favorite concepts in economic development: when many similar businesses concentrate in one geography, they create more robust markets for both inputs and outputs, everything from labor to industrial process.

The labor-market version is familiar: California has a hard-to-replace ‘natural resource’ of technical talent, clustered through decades of innovation. That depth extends past engineers to lawyers who’ve papered thousands of Series As, investors who understand moonshot risk, and bankers fluent in venture capital dynamics — a structural advantage for tech, much like New York’s in finance.

But the version I find more instructive is the manufacturing cluster, because it shows how clustering creates new businesses that couldn’t otherwise exist.

When a CNC machine cuts metal, the chips come out covered in coolant and lubricant, which makes them hard to recycle. Cleaning them economically requires a centrifuge or wash station, which requires scale. A single small shop can’t justify that equipment, so it can’t sell its scrap. But put many producers in one area, and a third-party chip-cleaning operation suddenly becomes viable. That business improves everyone’s margins slightly, which improves the whole cluster’s competitiveness. It also lets the cluster capture vertical integration efficiencies while staying fragmented — and fragmented ownership arguably outperforms a paid employee running the same line as an internal cost center.

Repeat that across 10,000 tiny operational improvements, and you eventually have an impassable moat.

Closing

Ultimately, networking is a foundational layer to economic activity. Markets exist because of networks of buyers and sellers, so in business, networks quickly become the thing you see once and then can't stop seeing everywhere. Every commercial relationship, industry organization, and local competitor has some function of network effects underlying them, if you know where to look. If you want to expand your network surface area with other investors, operators, or founders, please send me an email (Duncan@saorsapartners.com), I always enjoy meeting new people!

For more on this topic, I highly recommend The Cold Start Problem by Andrew Chen of A16Z, thanks for the suggestion!

We took that trip last week, ahead of a conference I had down the road. Somewhere between a buttery Chardonnay and a turnaround consultant’s third glass, I worked out what I actually think about the SpaceX IPO.

Surrounded by an artisanal commodity, I realized that the margins running up and down the AI stack, models, compute, data centers, power, and applications, are going to compress, and compress hard, the moment the capital markets start to demand real returns from the consumer side of the market. Not a collapse, more likely an unwind that looks more like inflation than implosion: higher revenues, thinner margins, venture-grade businesses repricing into project finance. The technology is real. The capital structure stacked on top of it is not.

A Lifetime in a Barrel

White Rock Vineyard is a survivor of the 1970s Napa boom — founded in 1977, in the wake of the Judgment of Paris, and still run by the second generation. A decade later, they carved a cellar into the hillside to age their wine. Touring it, I had a flat, unromantic realization: every barrel is an accumulation of hours. Planted, trimmed, picked, crushed, fermented; years of labor sitting on the inventory side of the balance sheet, valued at the cost of the blood, sweat, and tears that went into it. As the wine ages it accumulates nothing but perceived value, while burning off 2% a year as the angel’s share.

That’s the first lesson: wineries are working-capital gluttons. One port producer I met carries as much as ten years of production cost as inventory. Which begs the obvious question, how on earth do you finance that?

Banking on Wine

At the conference later that week I had dinner with a community banker who has spent decades lending to Wine Country. Her answer: industry-specific lines of credit, capitalizing up to 50% of the wholesale price of every gallon of Napa Valley wine. A winery can seriously extend its ability to hold inventory on the bank’s dime.

I couldn’t help but chuckle, realizing my deposits are sitting in someone’s wine cellar, waiting to be enjoyed by a few ex-pizzeria gal pals celebrating their 50th.

“You’re thinking of this place all wrong, as if I had the money back in a safe. The money’s not here. Your money’s in Joe’s cellar, that’s right next to yours, and in the Kennedy cellar, and in Ms. Macklin’s, and a hundred others. You’re lending them the money to make wine, and they’ll pay it back the best they can.” — Napa’s George Bailey, It’s a Full-Bodied Life

Here’s the part to carry out of the cellar: The asset on the balance sheet is also the collateral against the loan that financed it. The price of the wine sets how much you can borrow; the borrowing sets how much wine gets made; the wine that gets made sets the price. The asset price is the borrowing base.

Fermented in a Different Market

The wine industry, as you may have heard, is going through “just a bit of a gully.” Long lead times from planting to fermentation to aging force a winery to forecast demand years out, and that forecast is inherently speculative: the same linear extrapolation that tripped up KTM coming out of Covid has guided most of Napa’s planting decisions, shared one winery turnaround consultant. Layer on seltzers and everything else displacing wine on the shelf, and you get the inventory glut now forcing non-harvests across the Valley.

One Wells Fargo wine banker shared that accumulated inventory has to move, and pushing supply into weakening demand drives the wholesale price down. From there the mechanics turn reflexive. Collateral-coverage requirements force still more product onto the market, prices fall further, lenders’ collateral positions deteriorate, and shrinking balance-sheet capacity pulls capital out of the system. Less credit means less leverage for land, which pulls the bank’s bid out from under asset prices.

And those prices were inflated to begin with, positive forward expectations roughly doubled vineyard farmland over the past decade. That capital arrived as one of two buyers. The fundamental buyer underwrites the vineyard on the cash it throws off. The speculative buyer is everyone else: the trophy purchaser, the family treating the estate as a store of value, the punter betting one of the first two shows up later at a higher price. As the industry strains, cash flows weaken and the fundamental bid recedes; downward pressure that causes the speculators to get spooked and leave. Each exit is a step-function cut to demand for a fixed-supply asset. What makes this case distinct is that the asset price is the borrowing base — inflated values justified more credit, and more capital inflated values. A bubble built on its own collateral, capable of unwinding at remarkable speed.

Now strip away the cellar romance, while a sommelier tastes a decade of decisions in the glass; everyone else tastes wine. For the connoisseur there is excellence. For the rest of the market, the part that actually clears the inventory, it’s just a fermented commodity fighting for market share against every other Friday buzz.

And commodities cycle. It’s the one thing they reliably do: a high price calls in capacity, the glut arrives, the price grinds down to the marginal cost of production, and as it turns out the only cure for low prices is, in fact, low prices. Even though the luxury tier floats above the weather; the commodity tier eats it.

Share

Which is the real bridge to this capital landscape, because AI is commoditizing layer by layer. The models are converging. Compute is fungible by design. Inference is becoming a spot good you arbitrage across providers by the token. Strip away that romance — the AGI vintage notes, the founder’s artisan hand — and most of the stack is a commodity wearing venture multiples. And commodities don’t earn margins through the cycle. They earn the cost of capital.

So, What Does This Have to Do with Rockets?

The SpaceX IPO is astonishing, and I’ll keep my refined palate of unkind adjectives to one: speculative.

For the uninitiated, SpaceX is set to list north of a $1.75 trillion valuation and raise roughly $75 billion, the largest IPO in history. You’d think you were buying a space company. The one segment that reliably throws off cash is Starlink; the launch business reinvests everything it earns. What you’re actually buying, according to their market sizing expectations, aspires to be an AI company — Musk folded xAI (which had already swallowed X/Twitter) into SpaceX earlier this year, and the consolidated losses now flow almost entirely from that AI segment.

SpaceX’s Estimated TAM by Segment from SpaceX S-1 Filing pg. 172.

In the cellar the commodity and the collateral are two different objects: the wine is what cycles, the land is what the loan is written against. The land is scarce, and its scarcity is the floor — when the glut grinds wine down to marginal cost, the asset underneath still holds, and the spiral finds a bottom, even if the wine is sold at a loss. Compute is the opposite on both counts. A data center of GPUs isn’t scarce; it’s reproducible, and they are racing to build more of it. It doesn’t appreciate; it depreciates into obsolescence faster than wine ever evaporates. The thing that played the role of the floor is just another commodity, and a depreciating one at that.

So the same reflexive loop runs, with the floor knocked out. As an equity bet, the ‘collateral’ here isn’t really the hardware; it’s the profit in the chain, and that profit is circular: manufactured by the very inflows it is supposed to justify. Wine pledges a scarce asset while AI pledges its own circularity. One soft quarter on the subsidy side and the loop runs backward: thinner inflows, thinner profit, thinner justification for the inflows. The commodity cycle, with venture investors.

The bundle. SpaceX is the perfect specimen precisely because it looks like the wrong one. It is the least commodity-like company in the entire AI trade — Starlink and launch cadence are about as close to a scarce vineyard as anything in technology. Afterall, while the competitors were shouting “We build datacenters”, Mr. Musk had a stroke of creative genius stapling on “In-Space” to the end of it. Suddenly it's not a server farm, it's a vintage: notes of charred rocket, a metallic finish, and the soft rain that fell days before liftoff. This IPO takes a genuinely scarce asset, and pushes a capex burning commodity-compute core, while pricing the whole bundle on the vineyard story. The vineyard is real, but they’ve piled bagged wine in the cellar.

The margins. All of this justified as the commodity story itself sits on a supply chain whose margins may be a mirage. ASML and Lam at 30%-plus net, TSMC north of 40%, Nvidia nearly 65%, and the hyperscalers somewhere in the 15–35% band; while underneath all of it, demand for models sold to end users at a fraction of the loaded cost to produce, subsidized heavily by the capital inflows received by OpenAI, Anthropic, Google, Meta, and SpaceX. When capital floods a chain like this, the flood shows up as profit at every link. So, the question that should keep an allocator up at night: are these margins a real, or are they just your investment being captured down the chain? Consumption dressed as investment?

Because the end-usage is the whole question, and AI is a substitute good. It competes for the same task execution that human labor performs, which means its demand is ultimately bounded — and priced — against the marginal cost of the labor it replaces (which will reprice in response to its replacement). That’s an enormous ceiling. Global labor is the largest market there is. But it’s a ceiling set by economics, not by narrative, and the moment you wave the substitute away you’re back to the vintner planting on a straight line, right up until his P&L forces the realization that people are drinking White Claws.

How It Unwinds

So what cracks it? Not the technology, the financing. I’m not anti-AI; I’m against the speculative capital structure stacked on top of it. Watch the canary: the first undersubscribed round on the subsidy side. When the subsidy thins, compute has to fight the economics of the labor it replaces on honest terms, and the stack starts grinding toward marginal cost the way wine does.

It rolls downhill, layer by layer. Thinner subsidy means weaker projected demand for compute; weaker demand competes the margin out of the data centers; thinner data-center margins pull the bid out from under the chips; and a softer bid for chips reaches all the way back to the fabs, where the underwriting was written on straight-line growth and fat margins that no longer clear. This competition flips the incentive from scale to efficiency, which strands the least efficient chips (current generation), because they cost too much to run. Each layer reprices from a venture multiple to a project-finance one. Utilities, not unicorns.

The obvious objection is Jevons: make compute cheaper and you simply use more of it, keeping power draw and capex high. I think that’s right, and it’s exactly why I expect inflation rather than collapse. Cheaper, more efficient compute doesn’t kill demand; it lets AI percolate into the processes when it’s actually economic. Volume keeps climbing while margin per unit caves in. That’s the whole shape of the thing: the technology wins, the pool of activity grows, and the people who paid venture prices for utility cash flows are left holding the angel’s share, the value that evaporated from the barrel everyone assumed could only appreciate.

Four conditions. Relationships, who picks up your call. Information, what you know that the model doesn’t. Context, the cycles you’ve already lived through. Resources, the time, talent and treasure, you can deploy on the Tuesday after close. Together they constitute position, and the figure who plays from position has an old name. The Gunslinger.

My piece on this closed on a cliffhanger. Three seats sit at this table — the operator, the allocator, the founder — and the four conditions are worth different things depending on which chair you’re in. The operator is defending a niche from people who suddenly want it. The allocator is watching the thing he charged a fee for turn into a commodity. The founder is learning that the pitch isn’t the market anymore, it’s where he’s standing in it. Same table, three different games, and each player is about to discover the other two have changed.

I’ve sat in two of these seats. I left one last year and bet my next decade on another. This is the piece where I tell you which is which, and what the return of the insider’s game means for each side of the table.

Start with the operator. He’s the one the other two are circling.

On the Operator’s Side of the Table

You’ve spent a decade thinking you were the one without a moat. The bigger shops had the dashboards, the BI team, the McKinsey-trained operator-in-residence. You had the supplier’s quirks, the March buyer, the rep about to burn out — intuition, you called it, because it didn’t fit in a model and you’d half-internalized their framing that things which don’t fit in a model are softer than things that do.

Look again. They had a cost structure you couldn't match and an institutional vocabulary that pulled capital toward their firm. The analytical layer wasn't their moat; it was the toll they collected for translating your moat into language the LPs would fund. You had a knowledge structure they couldn't buy; they had a price tag, and a translation business built on top of it. The price tag just fell off, the translation is free, and what's left on the table is the thing you've been quietly accumulating the whole time, mistaking it for the consolation prize.

The allocators you’ve been talking to (or avoiding) are about to start changing rapidly. For thirty years they won on sourcing alpha and ‘creative financial engineering’ (6.5 turns of leverage is the sort of macaroni art creativity that only a finance guy could be proud of). AI and competition are eating it. Every fund has the same sourcing stack now, every deck is being drafted by a model, and the question they’re quietly asking themselves is “what do we do that justifies our fee?” The honest answer they’re arriving at: what we do with a company after we own a piece of it. Which means their underwriting question is shifting from “is this founder pedigreed” to “does this operator know something we’d take five years to learn?” Your call history, your supplier relationships, and your customer intimacy just became the deal.

At the same time, the founders are coming for your market. The $10 million market within your market that was small for venture and just barely solved by your product is suddenly accessible by two people on an AI cost structure. Your TAM doesn't get attacked head-on; it gets segmented underneath you as a two-person shop carves out the slice serving one specific customer type, then another carves out the next, and the addressable market you've been quoting in your own deck quietly compresses from below. The displaced senior analysts with a thesis and $25,000 are now your competitor — not for your whole business, but for many small slices of it. You have ten years of conditions they don't, but the conditions need to be the ones that can't be replicated, not the ones that were defended by an analytical or operational cost barrier that no longer exists.

You are, for the first time in a decade, the scarce piece on the board, but only for the conditions that can't be replicated. The ones that were defended by an analytical or operational cost barrier are about to be tested in public, and some of what you've been calling defensible was just renting space behind a wall that no longer exists.

The move: separate the two before someone does it for you. Map your conditions, document them, articulate them out loud, and be honest about which ones are durable versus which were defended by cost of analysis or operational complexity. The next person across the table from you, allocator or acquirer or competitor, is going to ask in some form: what am I actually buying access to?

On the Allocator’s Side of the Table

Two things are happening to your edge at the same time, and they’re connected.

Origination is commoditizing. Every fund of meaningful size has the same AI sourcing stack now, the same data providers, the same auto-drafted outbound. The proprietary deal-flow advantage that defined private markets for thirty years is closing fast. Private markets are becoming liquid in a way they weren’t before, not in the public-market sense, but in the sense that the same opportunities are visible to an increasingly large set of buyers at roughly the same time.

When markets become liquid, capital itself becomes a commodity. Your fund’s check looks identical to every other fund’s check. The risk-adjusted return on capital alone converges to the median, because nobody is buying anything anyone else couldn’t have bought. What’s left to differentiate on is execution: what you do with a company after the check clears.

This is the elephant in the conference that nobody is saying out loud yet. The value is migrating to execution: what you actually do with a company after you own a piece of it. To the operating insight you carry, the relationships you bring to bear on a specific problem, the time you’re willing to spend embedded inside the business doing the unglamorous work no model can replicate.

This may very well be a different fund than the one you’ve been running. Different skills, different hires, different incentives, different timeline. The funds that get this right will look closer to patient operating companies than to investment shops. The funds that don’t will report a slowly compressing IRR over the next three vintages and blame macro.

The operators you’ve been backing — or trying to back — are about to have most of the leverage at the table. Their conditions, the things they couldn’t articulate on a deck because it lived in their heads, are becoming the only thing worth paying a premium for. The good operators will figure this out before the average GP does. They’ll start bidding up capital partners based on what those partners can contribute to value creation, not on what the check looks like. The check is the commodity now. Your AUM doesn’t impress them, but your operating bench might.

The founders worth backing in the next decade aren’t going to fit the venture mold, and they don’t want venture money. They want capital that fits the asymmetric edge they have — patient, embedded, willing to be small, willing to look weird on a portfolio page. The allocators best positioned to fund them aren’t the ones with the biggest sourcing engine. They’re the ones who can identify a thesis they couldn’t have produced themselves and back it with capital structured to let it work.

I’m not making this case from the cheap seats. I left origination to work hands-on inside a small book of Owner-Operator businesses, on the bet that capital keeps commoditizing through the 2030s.

The move: stop competing on the layer that’s being commodified. Build the bench that actually creates value once you’re in. Define your edge in terms of execution, not access. Access just got cheap. What you do with it is the only thing that didn’t.

On the Founder’s Side of the Table

The deck you built in 2022, the one with the TAM slide, the team slide, the bottoms-up traction model, is a commodity now. Not in the abstract AI-disrupts-everything sense, but in the specific sense that the analyst on the other side of the table has three of the exact same decks sitting in his trash can from founders who reached the same conclusion you did while chatting with AI. He’s already heard about the market sizing, the competitive map, the customer-interview synthesis. What he hasn’t heard is what you experienced in a meeting during your summer internship that changed how you think about acquiring customers in this market.

What’s scarce is that angle of view, and the allocators who matter are getting better at telling the difference between a founder who has one and a founder reciting a framework about one. The signal isn’t in the deck. It’s in the answer to the follow-up question. The founder with conditions gives you the kind of answer that teaches something the investment memo agent couldn’t have incorporated before the conversation and opens up a second question the analyst couldn’t have anticipated. The founder without conditions gives you the kind of answer that puts the deck back in the pile.

The allocators best positioned to back you spent twenty years underwriting TAM, team, and traction. They’re starting to underwrite something different. Not “is this a big market,” but “what are you standing on that I couldn’t replicate by writing a check to someone else next week.” Build the pitch around the standing. Everyone has the market.

The peer set you should be paying attention to isn’t the other founders in the current batch of YC. It’s the 55-year-old operator who’s built a $7 million niche company over fifteen years in a category adjacent to yours. He knows which of his customers will switch on price and which will switch only when they’re treated badly. He knows the supplier who promises six-week lead times and ships in twelve. He knows the conference where the decisions actually get made, which isn’t the one on your calendar.

The line between “founder” and “operator” was always more porous than the venture press wanted it to be — the asset class needed founders to be a different species so the 2x, 2x, 3x, 3x story worked — and AI cost structure is dissolving the barrier the rest of the way. The operator can now build the software himself over a few Saturdays. You can reach his customers without his fifteen years. Some of these operators will be your customers, some your channel, some your acquirers. Some will decide to expand into your category and beat you there, because they have more conditions than you do and they’ll deploy them faster than you expect. Treat them as peers, because they are peers, even when they don’t look like the press’s idea of one.

The move: figure out which of your conditions are durable and which were just defended by the cost of analysis. The market sizing was defended by cost of analysis. The customer mapping was defended by cost of analysis. The competitive matrix was defended by cost of analysis. All of that is free now. What’s left is what you can see that the model can’t, and what you can access that the analyst can’t. Defend that. Pitch that. The framework is free; the standing isn’t.

Three Updates, One Market

Each of the three seats is updating in isolation right now, which is the part the conference circuit isn’t yet pricing in. The operator is reading pieces like this one and starting to map his conditions. The allocator is quietly redirecting hires from sourcing to operating. The founder is rewriting the pitch around standing instead of market. None of them is coordinating with the other two. All three are responding to the same underlying signal.

What happens next is the part worth watching. When all three sides have updated, the table itself changes. The operator who used to sell to whichever PE firm cleared the auction now sorts on which allocator can actually deploy operating capacity beside him. The allocator who used to compete on check size now competes on the bench he brings. The founder who used to pitch venture now pitches the niche operator with fifteen years of category context, because she’d rather have his rolodex than a Sand Hill Road logo on her cap table.

The deals that get done in that market look weird from outside. Smaller than the venture press covers. More patient than the PE press understands. Stranger than the MBA-track playbook can describe. From inside the room they look like arithmetic — three sides of a table, each holding something the model can’t produce, transacting on terms the analytical era didn’t have language for.

The Era That Isn’t New

The gunslinger metaphor is almost right but not exactly right.

The Old West wasn’t a transitional era between two stable equilibria. It was the absence of institutional cover during a moment when geography expanded faster than regulation. When the institutions caught up, the era ended, and the country moved to a different model.

What’s happening now isn’t that. The model isn’t ending an era. It ended the expensive sophistication moat the era was built on. The market is returning to the equilibrium it lived in for the centuries before McKinsey, before Bloomberg, before the analytical industrial complex priced relationships and context as legacy assets to be modernized away. That equilibrium rewarded conditions, position, and the people who knew things other people didn’t and had the standing to act on what they knew.

I sat in two of these seats. I left origination for the operator’s bench, working shoulder to shoulder with operators and allocators, on the bet that capital keeps commoditizing through the 2030s while operator reps compound into the thing that doesn’t. If the thesis is wrong, I’m wrong with it.

If you’re sitting in one of these seats, thinking through how to build the execution bench the next decade rewards, I’d like to talk. duncan@saorsapartners.com.

Subscribe to Conduit of Value for the ongoing thread. Companion pieces: HumanScale, Jobs Are Dead. Long Live the $10 Million Niche, The Age of the Gunslinger.

Private markets reward people who know things other people don’t. Analysis is mostly a facade: CYA for the LPs and intimidation for the competition. The tower of MBAs looks unbeatable from outside, and from inside the citadel that was part of the marketing. For the founder or operator across the table, the obvious play was to sell rather than compete: these people would outrun you on diligence, outprice you on capital, outhire you on talent. Better to take the check than try to fight. The premium on analysis was real because analysis was expensive and slow since a good research analyst was scarce and a good DCF took a week while a defensible market sizing took two weeks and an expert call you had to pay for.

That world ended sometime in the last eighteen months. It releveled the playing field for operators, founders, and allocators with the right conditions. The same conditions that came with doing their job well over the past 20 years.

Five Decades

The era we’re leaving is about five decades old. KKR was founded in 1976 and the first MBA boom hit shortly after. McKinsey grew from a regional firm into a global consulting franchise across the 1980s and the Bloomberg terminal was launched in 1982. The combination of credentialed analysts, computing tools, and broadly distributed public data made it possible to believe — for the first time in capitalism’s history — that you could simply analyze your way to an edge in private markets.

However, for centuries before that, private market finance ran on conditions.

Nathan Rothschild built a private courier network across the early nineteenth century that systematically gave him news from continental Europe days before London had it. The advantage compounded into one of the great fortunes of the era. JP Morgan personally stopped the Panic of 1907, not because he had a better model of the banking system, but because he could convene the major banks in his library and make their commitments stick. The Medici weren’t better analysts than their banking competitors. They had the better network, both papal and royal, and the franchise was access, not insight.

Even at the peak of this sunsetting era, the people who made the most money still operated on conditions. Warren Buffett bought GEICO because he showed up at the company’s offices on a Saturday in 1951 and a junior employee, Lorimer Davidson, gave him hours of his time. The original KKR deals were sourced through relationships with Drexel and management teams, not through analytical edge over public comparables. The best deals in private equity have consistently been the proprietary ones, sourced through conditions, won before a process formed.

The analytical industrial complex spent five decades selling a story that didn’t entirely match its own results. It just took the model getting cheap enough to expose the gap.

The era we're leaving wasn't the new normal. It was a brief window during which the analytical layer looked like it could substitute for the conditions layer, enabling new institutional scale due to the elegant story of MBAs in towers. It couldn't. It was just expensive enough to look like a moat. What it produced was institutional blandness, that needed to be quietly backfilled by the conditions the analytical layer claimed to have replaced.

The facade is fading as the cost of analysis falls, and the market is going back to the way it always was, an insider’s game.

The Four Conditions

These conditions weren’t dormant the past five decades and they aren’t newly important now; they were always the real work. Even at the peak of the analytical era, the deals that closed were the ones where someone in the room had real conditions on the asset. The model didn’t win the deal, rather: the relationship got you in the room, the context told you what to underwrite, and the analysis was the formality you ran after you’d already decided.

The conditions always mattered, but up until now, they’ve been modeled over. We’re returning to an age where they are all that matter.

Relationships. Who picks up your call. Who returns your text on a Saturday. Who introduces you to the founder before the banker does. Relationships never went away during the analytical era. They quietly kept doing the work while the analytical layer got the credit. The shift now is that the spotlight is gone. Access lives here too: the room you can walk into, the deal you see before it becomes a process, the supplier who quotes you a real number instead of a list price.

Information. What you know that the model doesn’t. The conversation that hasn’t been transcribed. The pattern you saw three jobs ago that nobody else in the room saw. The supplier whisper, the customer churn signal that hasn’t shown up in the data yet, the regulatory rumor from someone two beers in at a conference in Vegas. Information that lives in a person, not a database.

Context. What you’ve already been through. The cycle you survived. The mistake you don’t have to make again because you made it in 2018. The deal that went sideways the way this one is starting to. It’s a library of pattern matches the model can’t borrow. The reason a customer in this niche behaves one way and a customer in the adjacent niche behaves the opposite way, even though the spreadsheet says they should be identical. The model can simulate context. It cannot live in one.

Resources. What you can deploy right now, not in three weeks after committee. Cash, team, attention, time. Capital plus the ability to act fast is a different instrument than capital alone. A check that lands on Tuesday is not the same instrument as a check that lands in February. A team that can do the work alongside management is the most impactful in this new era.

Together they constitute position — where you’re standing on the board. For twenty years the smart money invested in better models. The next decade rewards investing in better positions.

The figure who operated this way before the analytical era arrived had a name. They knew to read the room, knew the terrain, kept his rolodex in his head, moved before consensus formed, and traded on context the institutional players hadn’t seen yet. They were The Gunslinger. Then the railroads came, the federal marshals showed up, the institutions arrived, and the era ended. For about a century.

Prediction Markets Are the Public Proof

The analytical era’s logic is being tested in public right now, and it’s playing out on Polymarket and Kalshi.

I don’t trade these. They’re a casino with a facade of being “better at analysis” bolted on, and I’m not in the business of pricing geopolitical events in two-week windows. But I read them, because they’re the first venue where a generation of analytical assumptions is being stress-tested in front of an audience.

My favorite pitch for prediction markets is the wisdom of crowds: that aggregated expectations from thousands of motivated participants would surface truer signal than expert forecasts. In categories where participants run comparable analysis on comparable data, that’s roughly what happens. The market converges and the prices look efficient.

But in categories where someone has actual information — a relationship inside the campaign, an early read on a regulatory decision, a source in the room — the market is systematically wrong until the asymmetric signal arrives, at which point it snaps. When everyone runs the same model on the same data, common analysis stops being a discovery mechanism. It becomes a coordination mechanism toward the consensus answer.

Being smart the same way everyone else is smart is the definition of having no edge.

In the run-up to the 2024 US presidential election, the consensus was uncertainty. Public polls had Harris and Trump in a statistical tie. Major forecasting models produced odds in the 50-55% range either direction. Polymarket and Kalshi drifted in roughly the same band. The crowd had converged on a single answer: “it’s too close to call”.

Despite this, three weeks before the election, a French former bank trader operating under the name “Théo” hired the polling firm YouGov to run a custom survey across Pennsylvania, Michigan, and Wisconsin. The methodology wasn’t standard. Instead of asking respondents who they intended to vote for, the survey asked them who they thought their neighbors would vote for — a construction designed to bypass social desirability bias, the “shy Trump voter” effect that public pollsters had largely written off as a 2016 anomaly.

When the results came back, Théo described them to the Wall Street Journal as “mind-blowing to the favor of Trump.” He sold most of his liquid assets, scaled his Polymarket positions across eleven anonymous accounts, and ultimately wagered roughly $80 million on Trump — the largest single position in the market’s history. He cleared approximately $85 million on election night.

The structure of the trade is what matters. Théo didn’t beat the market with a better model. Every well-funded participant on Polymarket had access to the same public polls, news, and statistical tools. He beat it with two conditions. Context: he knew the shy-Trump-voter bias the consensus had written off as a 2016 fluke was a live problem, so he knew where the public data was blind. Resources: he was willing to spend his own money to commission a poll that would see into the blind spot. The edge was knowing to source the right private input to run it on.

Now hold that mechanism in your head and look at private markets, because it’s the same one.

A competitive deal process is a prediction market. The banker assembles the data room, every firm runs the same diligence on the same inputs with the same tools, and the valuation that diligence converges on is the consensus price. There’s no ticker, so it may not look like a market. But the clearing bid in a banked auction is the Polymarket line: the answer the shared inputs were always going to produce, rendered three weeks later instead of live. The analytical layer is the consensus price of private markets. It just took a venue with a settlement date to make the mechanism visible.

Which means the edge in private markets is the edge Théo had. Not a better model of the shared inputs — a private input the process never ingested. The proprietary deal seen before the data room exists. The operating partner that has done 2 of these roll ups in the past and knows that market is more seasonal than the materials would imply. The context that tells you the consensus is mispricing the asset. The relationship that gets you the real number instead of the list price. Over the next five years you’re going to watch the Théo dynamic play out across the lower middle market, deal after deal, with gunslingers taking shots that look like luck from outside the room and like arithmetic from inside it, because the position that justified them was never in the data room to begin with.

How to Be a Gunslinger

Most people working in private markets today were trained to be analysts, IB for a couple years, MBA for three, Buy-side after that. Showing up means showing your work: the deck, the memo, the model, the comp set. The seat justifies itself through analytical output.

However, the seat is increasingly rewarding what you do with the company after close: the operating insight, the years of context, the relationships you bring to bear that nobody else can manufacture in a quarter. Four moves to start executing on your conditions:

Take inventory. Most operators and allocators can’t actually list their own conditions. Spend an afternoon writing them down: relationships that pick up your call, information lives in your head, access that other people in your category can’t. What you’ve been through that others haven’t. The list is shorter than you think and more valuable than you’ve been pricing it.

Refuse the consensus trade. If everyone in the room has access to the same model and the model is producing the same answer, you’re not in a gunslinger seat. You’re in the crowd. Pass on those deals. Take the ones where your conditions tell you the consensus is wrong, or where the consensus hasn’t formed yet because the deal hasn’t entered the analytical funnel. This is harder than it sounds. It feels safer to be wrong with the crowd than right alone. Get over it. The analytical era rewarded crowd-rightness. The gunslinger era doesn’t.

Be in the room. Conditions compound through physical and temporal proximity. The deal flow that matters travels through dinners, not data rooms. The information that matters arrives in conversations, not on screens. You can’t build conditions remotely on principle, but you can build them faster in person. Pick the niches and geographies you want to know cold and get embedded. The model will do the analytical work. You do the work that requires a body in a place.

Move when the moment opens. Position is necessary but not sufficient. The gunslinger edge is position plus the ability to fire when the moment opens. Most analytical-era institutions are built to slow that down — committees, processes, diligence cycles, approvals. If you can structure yourself or your firm to act in days rather than weeks when a condition-driven opportunity appears, you’ve built an edge no model can match.

How the Gunslinger Survives

The objection the thesis has to survive is survivorship bias. Théo is a name because he won. The trader who commissioned a similar survey, read it wrong, and lost his stake is a story nobody tells. If you only study the ones who hit, you’ll learn the wrong lesson, and if you can’t tell a gunslinger from a lucky winner, then you’re bound to make the wrong bets.

The answer is that you can tell them apart — but not by looking at the outcome. A win from genuine edge and a win from variance are identical from the outside, and nearly identical from the inside. You can only separate them by looking at the process, before the result lands. The test is whether you could have written down, in advance, exactly why your information was asymmetric and exactly who in the market didn’t have it. Théo could: his neighbor-polling construction bypassed a known, documented bias. That was true on November 4th regardless of what happened on November 5th. The methodology was the condition. The $85 million was the outcome. The gunslinger who confuses the two is already dead.

There are three ways the Gunslinger model fails:

The first death is the phantom edge. You never had a condition. You had a contrarian model and mistook it for information: ran the survey that told you what you wanted to hear, felt out of consensus and assumed that meant you were ahead of it. A contrarian model and a genuine condition produce the identical feeling of seeing what the crowd doesn’t, and identical-looking wins when they pay off. That’s the whole trap. Being wrong alone is not an edge over being wrong with the crowd, it just feels like one. The only defense is to name the asymmetry out loud, in advance, and to notice when you can’t.

The second death is the decayed edge, and it’s the one a thesis about durable conditions has to be honest about since conditions rot quietly. A relationship that’s cooled, a context that’s shifted under you, an information channel that’s gone silent: none of these announce themselves. The most ironic death in this era belongs to the gunslinger who had real conditions, kept trading on them, and never noticed the terrain moved. He isn’t fooling himself and he isn’t over-betting. He’s pricing an expired edge at full value. Conditions are durable, but durable is not permanent, and the work of keeping them live is continuous.

The third death is the over-concentrated edge. Here the condition was real and current — and it still ruined you, because you bet the firm on a single roll. A real edge is an edge in probability, not a guarantee; the dice still roll. The public lesson of Théo — bet everything — is precisely the lesson most likely to put a gunslinger in the ground. He survived. The survivorship bias is that you only hear about the version that did. Size every position so that being wrong is survivable, however strong the read.

Three deaths, one discipline each: name the asymmetry, refresh the conditions, never bet the firm. Notice that only the third is luck, and even the third is mostly sizing. The gunslinger era rewards conditions. It does not forgive the failure to know which kind you actually hold.

The Table is Set

Analysis fell to the floor, the conditions layer it had been quietly standing on came back into view, and the people with real position: relationships, information, access, exposure, context, resources — got the edge handed back to them. The gunslinger isn’t a new figure. He’s the old one, returning to a market that spent fifty years pretending it had outgrown him.

But knowing the era changed is not the same as knowing what to do on Monday. And here the essay has been carrying a quiet oversimplification, because “build your conditions” is not one instruction. It’s three.

The conditions are worth different things depending on which chair you’re sitting in. The operator’s decade of customer intimacy, the allocator’s operating bench, the founder’s read on a market the analysts can’t see from outside — same four conditions, three completely different games. The operator is defending a niche from people who suddenly want it. The allocator is watching the thing he charged a fee for turn into a commodity. The founder is learning that the pitch isn’t the market anymore, it’s where he’s standing in it. Each of them is about to sit down at the same table and discover the other two have changed.

That’s the next piece. Three sides of the table, and what the return of the insider’s game means for each seat, including which one I left, and which one I bet my own next decade on. Next Tuesday.

Subscribe now

If you’re an operator trying to figure out which of your conditions are durable, an allocator trying to build the execution bench the next decade rewards, or a founder building something the model can’t see, I’d like to talk. duncan@saorsapartners.com.

Subscribe to Conduit of Value for the ongoing thread. Companion pieces: HumanScale, Jobs Are Dead. Long Live the $10 Million Niche.

I was sitting in a weekly operations review with a grain trading client (yes, I know — SF strategic finance guy + grain trading is a tale as old as time). The trucking side of the business had quietly gone underwater for the week. When I asked why, the logistics manager explained that they’d run some outside loads for one of their transloading customers. This customer routinely takes a full trucking day of capacity due to delays, but we get paid on volume instead of hours, which is what put the loads underwater. The team’s solution, already half-formed, was to bring on a third-party hourly trucking vendor to absorb the volatility.

I asked a different question: how much would it hurt the P&L to drop them?

The customer generates around $16,000 a year in free cash flow, enough to be impactful, particularly for a low-margin business like commodities. Once you walk through the rest of what comes with them, the picture quickly shifts. Their railcars arrive with ours, competing for yard space, which produces demurrage when prioritization gets confused. The full-day trucking commitment displaces revenue from the grain side, the side that actually produces economies of scale. We realized the operational burden of running outside loads for transloading customers, with its variable volume and non-standard specifications, is a fundamentally different business than the one we’re trying to be the best in the world at.

The real question on the table was never about the $16k. It was: are we a grain trading business, or a transloading business? The answer needs to govern every staffing decision, capital purchase, onboarding standard, and operational priority. The team was building two businesses without picking which one was the real one. The underwater trucking week was the symptom.

We dropped the customer. The conversation happened recently enough that the contract is still winding down — but the math was unambiguous, and the underwater trucking week made the case the team needed. The $16,000 of free cash flow was the cheapest line item the business was carrying.

A calibration note before the framework. I’m writing for the kind of $2–20MM founder whose default is yes: too many initiatives, customers, and product lines; filling their calendar full of work that doesn’t scale their business. If your default runs the other way — if you’re sitting on capital you should be deploying, killing experiments before they have a chance to find product-market fit — the same framework, applied honestly, would tell you to say yes more often. The True Cost of Yes math is calibration-dependent. For most of the founders I work with, the calibration error runs in one direction. If you’re the exception, this article is the wrong medicine.

For the chronic-yes founder, saying no is capital allocation. Every yes consumes a finite stock of operator time, team attention, working capital, and strategic clarity. Treating it as the allocation decision it actually is, rather than as a customer service question, derives more value from your time and builds your business faster.

The True Cost of Yes

The framework I use with partners is simple to state and unforgiving in practice:

The first two are familiar enough that most operators eventually find them. The third is where the damage gets done.

Direct Cost

The line items your accountant already tracks. Variable costs of fulfilling the yes: labor, fuel, materials, hosting, software seats, shipping. In the trucking example, the cost of the driver and fuel for the outside loads.

Most P&Ls show this clearly and most founders calculate it correctly. It’s also the smallest of the three costs for any yes that lives inside an existing business. If the only cost of a deal were variable cost, every deal that priced above variable cost would be worth taking. That math is wrong, which tells you the math is incomplete.

Opportunity Cost

What your scarce resources could have been doing instead. The trucking day given to that outside customer was a day not spent moving grain. The hour the founder spent reviewing the unusual contract terms was an hour not spent on the core sales pipeline. The shop floor capacity allocated to a custom run for one customer was capacity not allocated to your highest-margin standard SKUs.

To price opportunity cost honestly you need two things: a clear sense of what your scarce resource is, and a current price for it. Most $2–20MM businesses have at least three scarce resources, ranked roughly: founder time, key technical or operational staff capacity, and working capital. Trucking businesses add a fourth: rolling stock and yard space. SaaS businesses add a fourth: engineering hours. Lifestyle brands add a fourth: inventory dollars in your best SKUs.

The price for each is the gross margin your scarce resource produces when allocated to your highest-return activity. In the trucking case, an hour of capacity allocated to grain produces gross margin X. An hour allocated to the outside transloading customer produces something lower. The difference is the real opportunity cost of every hour spent on the customer who looked like found money. Almost every time I've run this, the opportunity cost alone is bigger than the FCF. That's before the complexity tax.

Complexity Tax

The hidden one. The cost that doesn’t show up on a P&L line until well after the decision has been made. It has four components — operations are impacted by the first three and ultimately this creates the most expensive tax, strategic ambiguity.

1. Operational mode mismatch. Different customers and product lines run on different cadences, specifications, and tolerances. Mixing variable-volume work alongside fixed-schedule work isn’t just operationally annoying. It forces your team to maintain two mental models for the same task, which doubles the error rate at the seams.

2. System contamination. One non-standard customer means one non-standard process, which means one rule for them and a different rule for everyone else. Multiply across the team and your standards stop being standards.

3. Management overhead. Every yes generates recurring decisions: which crew, which equipment, which pricing tier, which escalation path. The founder who said yes is the one who keeps getting pulled in to adjudicate the exceptions. Multiply across a portfolio of marginal yeses and the founder becomes a switchboard.

4. Strategic ambiguity (what the first three accumulate into). Every yes that lives in a different operating mode quietly raises the question of which business you’re actually running. The grain operation I described had been building two businesses. Three or four of those yeses, accumulated over a few years, and the answer to “what do we do best in the world” gets fuzzy enough that the team starts hedging both. Fuzzy strategy compounds backward.

Run the formula on the trucking customer. Direct cost ate most of the variable revenue, making the contract near break-even before anything else. Opportunity cost on displaced grain margin — based on grain trading unit economics, a trucking day allocated to grain is worth meaningfully more than one allocated to outside loads — added multiples of the FCF in foregone earnings. Operational mode mismatch and management overhead ate hours of the logistics manager’s week. System contamination showed up directly as demurrage on jammed yard space and as delayed grain revenue from prioritization confusion. And the largest piece, the strategic ambiguity, was seen in the fact that the team’s instinct was to add more complexity via a third-party trucking vendor rather than question whether the customer should exist.

Add the three currencies honestly and the $16,000 of free cash flow was the most expensive $16,000 the business had on its books.

Complexity Is Capex of Time

This is the framing I find myself repeating in client meetings until I’m tired of hearing it.

When a founder buys a piece of equipment, they understand it as capital expenditure. They calculate the payback period. They compare against alternatives. They make a deliberate decision because cash is visible and finite.

When that same founder takes on a new customer, a new product line, a new channel partnership, or a new internal initiative, they almost never put it through the same analysis. The cost shows up as time and attention rather than dollars, and time and attention feel more elastic than they actually are. They aren’t. The operator-hours available in a year are a fixed stock. Allocating them is the same decision as allocating dollars, with the same compounding consequences.

Where the value of no actually lives: the highest-return use of operator time in most $2–20MM businesses is compounding the existing core. Optimizing the sales engine you already have. Improving the margin on the SKUs you already sell. Deepening the relationship with the customers who already love you. These are the bets you can size confidently because you already know the unit economics. New bets, by definition, have unknown unit economics, which means you’re paying a learning premium on top of the direct cost.

The right time to say yes to a new initiative is when the existing business is so well-optimized that the marginal hour of operator time produces less return there than it would on the new thing. That bar is rarely met, and almost never met as early as founders convince themselves it is.

Where It Shows Up

The two places where saying no produces the most reliable returns are the wrong customer and the wrong distribution strategy.

Every customer base has a bottom decile that costs more than they pay. Some of the cost is direct. Most of it is complexity tax. The non-standard customer who demands custom terms, custom delivery, custom packaging, or custom support eats into a team’s ability to standardize, which is the only way a small business builds real operating scale. For most clients, the bottom decile nets flat or slightly negative once fully loaded, and the capacity it consumes would produce 2–3x its revenue if reallocated to the top quartile. It may feel like shrinking to drop 10% of your most operationally painful revenue, but that capacity will let you compound the rest of the business more effectively by reallocating it toward expanding or adding simpler customers. What looks like shrinking is reallocation.

I run the same playbook I preach, in a softer form than the trucking client could use. They said no unilaterally. I price work at its true cost and let the buyer decide. Both refuse engagements that don’t compound the core. Theirs is the stronger version. Mine is the weaker version, available to a service business where pricing is the lever I have.

An investment model build for an early-stage company raising venture capital can be a real piece of revenue, but it produces no operational repeatability for the business I’m trying to build, which is a finance partnership that compounds value for owner-operators over multi-year engagements. So I price those engagements high enough that the proposal absorbs the opportunity cost and the complexity tax. A recent $25,000 model build for a very large venture capital raise was rejected at that price. If they were my ICP — an owner-operator with a long view on their business — I could have priced in the long-term value of the relationship. Since it was a one-shot venture raise, it wasn’t worth trying to win the deal. The rejection let me spend the next six weeks on productizing the parts of my practice that actually scale: standard analytics templates, repeatable diligence frameworks, the operational infrastructure for adding the next finance partner. None of that work happens if I take the engagement at half the number, even if I’m making my target rate. The reps I would have gained were the wrong reps for the business I’m building.

The distribution version of the same problem is overseas expansion. The existing brand and product travel, the thinking goes, so why wouldn’t we serve a larger TAM. The unspoken cost is two to four weeks of executive time spent finding distribution partners, meeting regulatory requirements, working through trade barriers, and managing currency exposure. That bandwidth, allocated to the domestic business, would have produced a measurable lift in a known operation. Allocated to international, it produces an uncertain bet on a new operation. The expected return on the domestic bet is almost always higher, especially in the first $5–15MM of revenue where the core is still under-optimized.

Three Patterns I See Constantly

Three patterns I see constantly. Each is a different shape of yes whose true cost — opportunity, complexity, or both — arrives after the decision is made.

“We Should Be Doing This”

Most founders I work with keep a running list of things they “should be doing.” A new channel. A new market, feature, or partnership. The list grows faster than the team can execute against it. Each item, considered individually, sounds reasonable. The list considered as a whole is a slow-motion strategic identity crisis. What the founder is actually saying, when they bring up the list, is that the existing business doesn’t feel like enough. Sometimes that’s a real signal. Most of the time it’s a story problem rather than a business problem, and the answer isn’t to add another initiative to the list. The answer is to articulate what the existing business is, why it’s enough, and why this particular adjacency is the wrong fight to pick this year.

Commission Complexity

The pattern I see in $5–15MM businesses with a real sales team is the founder trying to engineer the perfect commission structure. They want it to track gross margin, factor in product mix, adjust for customer tier, weight for new vs. renewal, and account for territory. The math is elegant. The result is a sales team that spends an hour a week on a personal commission calculator instead of selling. Salespeople are motivated by simple, legible incentives. A commission plan a rep can recompute in their head produces more revenue than one that requires a spreadsheet. The yes here is to internal complexity that feels like good operating practice and functions, in practice, as a tax on the sales engine.

Sunk Cost Survival

The hardest no, by a wide margin, is the one for a product line or customer relationship the business has invested in for years. The founder remembers the early version of it, the team that built it, the customers who came in through it. The fact that it now consumes more in management attention than it produces in contribution margin is hard to see clearly, because the line still throws off some revenue and still has some customers and still feels like part of who the company is. The reframe I use: every quarter you choose not to wind it down is a fresh decision to allocate this quarter’s operator time to it. The test isn’t whether the line was a good decision originally. It’s whether you would start it today knowing what you now know. If the answer is no, the next question is when you stop.

Before your next strategy meeting, spend a week subtracting before spending a quarter adding. Walk the customer list, the product line, the channel list, the open initiative list. Run the True Cost of Yes math on each. Drop what doesn’t compound the core. Pre-allocate the freed capacity to a specific compounding use before anything else can refill it.

Businesses that do this end up smaller in line count, larger in revenue, materially better in margin, and far easier to operate. The founder’s calendar opens up. The strategic question stops being “what do we add?” and starts being “what do we make better?”

That’s a different kind of growth than the one most founders are sold. It is growth. It just doesn’t put you on a panel.

Subscribe now

If you’re somewhere in the middle of a yes you suspect is the wrong yes, I’d be happy to help you think through it: duncan@saorsapartners.com

As requested by the audience, this piece was shorter by design. If you prefer longer articles, or more theoretical pieces, please let me know in the comments below. If this is your first time reading, please subscribe to Conduit of Value to get it in your inbox every week.

Trust isn’t a soft variable. It’s the cheapest, longest-duration form of capital we have. Most of finance has forgotten this, because trust doesn’t underwrite a five-year fund cycle, can’t be marked to market, and doesn’t show up on a balance sheet until you lose it or buy it. In a low-trust environment, that makes trust shelter from extraction — both a moat for anyone who has built it and a market opportunity for the capital that can steward it.

A partner and I needed $100,000 of tooling to scale production. We did not raise it or go to a bank. We went to our supplier, who fronted the risk because we had accumulated the trust that underwrote it.

The next decade will reprice the businesses that have trust, the people who build and steward them, and the capital that knows how to find them. This piece is an argument about where that value is going, whether you are the operator who has spent twenty years quietly accumulating it, the senior employee about to deploy it on your own, or the capital trying to figure out where the next decade’s returns actually live.

How Capital Forgot Trust

For most of economic history, capital had no choice but to run on trust. The Medici lent across borders because their reputation traveled faster than their coin. American Quakers became disproportionately successful in early commerce because their refusal to bargain over price made them the preferred counterparties at a time when haggling was the universal default. Every long-distance merchant carried letters of introduction, because the alternative to trust was not moving capital at all.

The 20th century industrialized this. The Federal Reserve, the SEC, federal deposit insurance, the audit profession, public disclosure rules: institutions emerged that absorbed personal trust and reissued it at scale. A small business in Ohio could borrow from a bank in New York without the bank ever meeting the borrower, because the institutional plumbing certified the trust on both ends. This was the great trick of mid-century American finance, and it worked extraordinarily well for several decades.

Then the institutions that replaced trust slowly began to unwind.

The shift began in 1970, when Milton Friedman published his shareholder primacy essay in the New York Times Magazine, followed by Jensen and Meckling’s agency theory paper in 1976. Both reframed the firm as a contract rather than a community, with managers as agents to be disciplined rather than partners to be trusted. The institutional plumbing that had absorbed trust started engineering it out, in the name of efficiency and redundancy. Pensions were traded for 401(k)s, which moved retirement risk from institution to individual and quietly ended intergenerational trust contracts that had existed for a generation. Quarterly earnings replaced long-term strategy. Auditors got cheaper and ratings agencies got captured. The system kept the language of that replaced trust while removing the substance.

The endpoint of that trajectory is visible in where capital actually sits today. The top ten companies in the S&P 500 now account for roughly 35 to 40 percent of the index by market value, the highest level recorded since at least the early 1970s. The three largest index fund managers, Vanguard, BlackRock, and State Street, collectively own meaningful stakes in nearly every public company in the United States, with their share climbing for two decades. A working-age American with a 401(k) is, in effect, a passive minority owner of a small cluster of megacaps they have never investigated, run by managers they will never meet, with capital deployed by algorithms in which trust is not a variable.

Monopolization is the parallel story on the operating side, commoditizing the business and extracting from brand trust. EssilorLuxottica now owns most of the brand portfolio you encounter at LensCrafters, Sunglass Hut, and Pearle Vision. The customer choosing between Ray-Ban, Oakley, Persol, and Oliver Peoples is choosing between four houses owned by the same landlord. The pattern repeats in industry after industry. When trust stops being the market clearing mechanism, scale replaces it. Whoever owns the scale owns the leverage.

None of this is an accident. It is the mature form of a model that decided forty years ago that trust was a friction to be bypassed. The model worked as long as the institutions doing the removing remained themselves trustworthy. They have not, and the people whose capital is allocated this way are starting to notice that the model is losing the value that underwrote it.

So when I say trust is the cheapest form of capital we have, I mean it narratively and literally. We had it, used it to build institutions to scale it, and then engineered it out of those institutions over the last forty years. The efficiency we pursued cost us the cheaper coordinating mechanism and left us with the more expensive one*.

*A tangent I'll come back to in a future piece: money is mechanically more expensive than trust for at least three reasons. It requires intermediation, each layer of which takes a cut. It carries a liquidity premium, because it can be used to transact more universally. And it requires monitoring and enforcement, because contracts have to be enforced when trust isn't doing the work. If you've worked through this elsewhere or have a fourth reason, I'd like to hear it.

What Trust Does, Mechanically

An early-stage partner of mine and I needed $100,000 of tooling to scale production. We did not raise it or go to a bank. We went to our supplier, paid a deposit that covered their material cost, and amortized the balance over multiple quarters. Both sides deferred cash, betting on the other’s success. The financing was easy because the trust did the underwriting, and the trust had been built years earlier through the small, boring exercise of not screwing each other on small things.

What that arrangement substituted for, in dollar terms, was a $100,000 line of credit at whatever the prevailing rate was, plus a year of relationship-building with a lender, plus the legal and structuring cost of putting the facility together. Conservatively, $15,000 of friction, $10,000 of interest, and a quarter’s worth of operating attention. Trust did the work of $25,000 of friction and three months of management bandwidth. None of it appeared on either company’s books as an asset.

Multiply that by the dozens of small accommodations a healthy supply chain runs on. Net-30 terms that quietly extend to net-60 in a hard month. A partial shipment that goes out before the PO is signed because both sides know the PO is coming. A price that holds through a cost spike because the supplier is playing a longer game. None of these are favors. They are stored productive capacity, accumulated through years of conduct, deployed when needed.

This is what people mean when they call relationships important in business, and the phrase has been thoroughly defanged. It gets used in MBA case studies as a soft variable, when it deserves a line on the balance sheet. The balance sheet has no record of it, even though a balance sheet without it looks much different.

What Underwriting Trust Looks Like

I spent the early years of my career at a Community Development Financial Institution, sourcing deals to deploy capital into small businesses. CDFIs underwrite loans that commercial banks decline. The reason they can do this is not magic. It’s an underwriting model that includes the things commercial banks are structured to ignore: local knowledge, character, the operator’s fifteen-year track record of paying suppliers on the third of every month, and the specific shape of the business in the specific community it serves.

Capital that requires three years of audited financials, liquid collateral worth more than the loan, and a personal guarantee tied to home equity (rebranded on recent bank earnings calls as "a sophisticated AI underwriting model") selects for a narrow band of borrowers. Everyone outside that band either compounds slowly on retained earnings, sells to someone with access to that capital, or does not exist as a business at all. Trust-aware lending widens the universe of opportunity significantly, if you can bear the slower underwriting it requires.

Conventional capital does not just narrow the band of borrowers it will lend to. It also actively strip-mines trust when it acquires businesses that have it. The leveraged buyout pattern is documented enough that I do not need to name companies. The buyer pays a multiple, services the debt by stripping cost, and "monetizes" the trust in the process. The brand thins, suppliers feel the pain as terms tighten, and the senior people leave, the kind of people who made customers say "if Janet retires, we're done." The business survives or it doesn't, but in either case the asset that was actually purchased, twenty years of accumulated trust between a founder and the people around them, is half-gone before the new owners have figured out where the bathrooms are.

I am not making the moralized version of this argument. Private Equity is the right capital for many businesses: highly cyclical companies with too much fixed cost, roll-ups in fragmented industries where consolidation creates real efficiency, underperforming carve-outs that need new ownership to function. The point is narrower. PE is the wrong capital for businesses whose primary asset is trust, because the math of a five-year fund cycle requires you to monetize the trust faster than the trust can be rebuilt.

Both CDFI lending and the LBO strip are capital decisions. One uses trust to underwrite. The other strips trust to amortize debt. The first continues to compound while the other runs out the clock.

Why the Math Just Tilted

Commodity markets have always run on trust. When the product itself is fungible, the only thing differentiating one seller from another is whether the buyer believes the spec, the timeline, and the next delivery. Cargill and Glencore are not in the corn business or the copper business. They are in the counterparty business, and they have been for a century.

White-collar output is now arriving in the same condition. AI is compressing the cost of producing the things that used to be priced as skilled labor: copy, code, decks, analysis, tier-one support. “Jobs Are Dead” argued that a niche product is now reachable for a single operator with $25,000 and a thesis. The corollary, which I want to draw out here, is that as production gets cheaper, the margin migrates from making the thing to whatever isn’t being commoditized. In every market that has been through this transition, what isn’t commoditized is the relationship.

The mechanical version of this is straightforward. When the cost of production falls toward zero, the price of the output falls toward its marginal cost. The supplier’s margin on the work itself disappears. The only remaining margin is on the things AI cannot do: knowing what to build, for whom, on what terms, with what guarantee. The buyer’s question shifts from “can you produce this” to “do I trust you to deliver it on the terms you said.” That second question is where the margin lives. Trust is the only thing that answers it.

Allocators have not priced this in yet. The investable asset in a $5 million specialty manufacturer was never just the equipment. It is the operator’s twenty-year relationship with three distributors who will not switch suppliers, the brand recognition inside a thousand-person enthusiast community, and the quiet fact that the company can ship a custom run in two weeks because the floor staff have done it together for fifteen years. Strip any of those out and the equipment is worth what the auction will pay for it. Keep them and the equipment is the smallest part of the value.

Why the Repricing Goes Deeper

The commoditization of output is the visible part of the repricing. Trust in most major institutions has been declining for two decades, by every credible measure of it. The extractive turn is real. Healthcare bills arrive months after the service, bear no relation to anything the patient experienced, and require a phone tree to dispute. Software subscriptions raise prices annually while shipping product worse than what they replaced. The implicit contract between large institutions and the people who use them has been getting worse on the margin, year after year.

In a low-trust environment, trust-rich enclaves become more valuable, not less. The most visible version is the influencer economy: a single person with a relationship to a hundred-thousand-person audience can monetize that relationship more efficiently than any incumbent media brand, because audiences trust actual people more than they trust institutions. Anyone who has done a home renovation knows the mechanism scales down. The plumber who shows up when he says receives price-insensitive demand in a market full of plumbers who don’t. The lender who has not changed terms on a borrower in a downturn gets first call when the borrower’s friend needs financing. The operator known not to screw his suppliers gets deal flow that competitors paying twice as much will never see.

Trust is shelter from extraction, at every scale. As the broader economy grows more extractive, that shelter gets more valuable to own and more expensive to build. This is not a one-time repricing. It compounds with every year that institutional trust keeps eroding.

Share

Twenty Years of Quiet Trust

The people best positioned for the next decade are the ones who have spent years quietly accumulating trust in some specific corner of the world, and who now have tooling that lets them deploy that trust into a real business without raising venture money to do it.

Owner-operators of $2 to $20 million businesses tend to be these people. Many of them are running companies their parents started, with relationships that go back two generations and customer lists that read like a family tree. The next ten years will either entrench those advantages further or hand them to whoever shows up with a check and a slide deck about synergies. The difference between those two outcomes is whether the right kind of capital reaches them first.

Senior employees who were told their loyalty was their job security are in the same camp. The product person eleven years into the same company, who knows every workflow gap the company will not fix. The operations director with twenty years of vendor relationships that their employer takes for granted. Six years ago they couldn’t justify quitting because the build cost was prohibitive. They have been sitting on an asset that became investable while they weren’t looking.

Some of them will not even need to leave. I know one operator who started as an intern at a manufacturer, stayed twenty-nine years, and bought the founder out for north of $20 million without raising outside capital. The seller financed it almost completely because he had spent three decades watching this person not screw anyone, including him. This is the trust-as-cheap-money thesis in its purest form: a buyer with a limited balance sheet acquiring a company with no formal lender involved, because the relationship was the underwriting.

Allocators with patient horizons are also a beneficiary. Family offices, some endowments, and a small number of credit funds with twenty-year holds are structured for this. Anyone whose capital does not have to clear a five-year IRR hurdle has a structural advantage right now they did not have five years ago, because the assets that compound slowly and steadily are the ones the rest of the market is least equipped to price.

A society grows great when old men plant trees they will not sit under. That line gets quoted at commencement speeches and almost never at investment committees, which is part of the problem I am trying to address. On a five-year horizon, trust looks like a soft variable. On a twenty-year horizon, trust is the variable. Patient capital is not a slogan. It is a math problem, and the math gets clearer the longer the horizon.

Four Signs Your Books Understate Your Business

If you run a business and you are not sure whether this thesis applies to you, here are four tests.

1. You can defer cash. Suppliers extend terms others do not get. Customers pay before they are required to. You have raised working capital from your supply chain or your customer base instead of from a bank, at least once, because both sides preferred it that way.

2. You retain talent below market. Senior people stay through years when they could earn fifteen or twenty percent more elsewhere, because the alternative looks worse on dimensions that don’t appear on a paystub. The accumulated cost savings of not paying market for your best people, compounded across a decade, can run to seven figures. None of it shows up as an asset.

3. You command price above the spec. Customers pay more for the same physical product because they trust you to deliver on time, to honor the warranty, to fix what breaks without arguing about whether it should be covered. The brand is not a logo. It is a contract that does not need to be litigated.

4. Opportunities arrive without being pursued. Sellers bring you businesses before they list. Customers refer their peers without being asked. Suppliers route their best terms to you first. Your pipeline includes a quiet line item that does not appear on any CRM.

If two of these are true, your books understate your business by some meaningful multiple. If all four are true, you are sitting on the kind of asset that wrong-fit capital can destroy in eighteen months and right-fit capital can compound for thirty years. The work, then, is to figure out which kind is reaching you first. If you are working through a problem the four signs framework maps onto, write me at duncan@saorsapartners.com.

Where I’m Placing My Chips

I work with owner-operators because they are running the businesses I think the next twenty years will reprice. The thesis is not an abstract platitude for me. I think most of the value in owner-operated businesses is mispriced, that the mispricing is about to correct, and that the operators sitting on it have between five and ten years before either the right kind of capital reaches them or the wrong kind does. The work I am doing now is mostly with the operators on the inside of that window. The consulting practice is the strategy executing now. A decade or so from now, I might raise a fund to execute on the strategy at scale, however for now I write here because the thinking sharpens when readers tell me where I am wrong.

Trust has been mispriced for forty years. It is starting to reprice. Value will flow to trust-rich businesses faster than it has in a generation. It will also flow to the capital that knows how to find and steward them. I am building toward the second, on the same principles I am asking you to recognize in the first.

An Ask

If you forwarded “Jobs Are Dead,” thank you. Our thesis reached more than 15,000 readers because you sent it.

Share

If you are new here, hit reply and tell me what you are: owner-operator, allocator, builder, writer, something else. I want to know who is in the room before I write what comes next. If the four signs lit up something, write me at duncan@saorsapartners.com. If you know someone living this thesis, forward them the piece. The community gets sharper when it gets more specific.

When did trust last do the work of capital in your life, or capital try to do the work of trust and fail? I read every reply.

That experience reframed how I think about every fundraising conversation since.

Capital is not scarce. Systems that turn capital into durable value are scarce.

The next decade’s wealth building businesses won’t fail for lack of capital, they’ll fail because they pursued the wrong capital, on the wrong terms, ended up funding the wrong question.

This piece is the third in an arc. HumanScale made the case that right-sized businesses are a more reliable wealth-creation vehicle than the venture machine pretends. Jobs Are Dead argued that the $10 million niche just became economically viable for the first time in a generation. This piece is about how to fund those businesses without taking on capital that bends them out of shape.

Capital Is Stored Value Looking for Somewhere Useful to Be

Before getting into where the capital is, it helps to remember what capital actually is. Capital is savings, the form that excess value takes once a person, household, or institution wants to store it for later use. The form of the storage matters because cash under a mattress decays to inflation, a passive index fund pegs returns to whatever the broad market does and nothing more, and real capital — the kind that compounds — is stored productive capacity that gets deployed back into something that creates more value than it consumed.

A worker who saves two weeks of time in a year can use those two weeks to redesign their process and recover four weeks the following year, which is capital working as it’s supposed to work. Capital markets are meant to do this at scale. Most of the time they don’t, because most allocators aren’t actually allocating to value creation but to liquidity, to short-term price movement, and to whatever’s currently in fashion. The thoughtful builder who sees this clearly has an opportunity that the financial industry has structurally underpriced for decades.

Index Funds Solved the Right Problem in 1976 and Became the Wrong Default in 2026

Vanguard’s invention was genuinely democratizing. Before Bogle, ordinary investors were extracted from at every layer by active managers who mostly underperformed, and the index fund offered a sane default that allowed millions of households to participate in market returns without being fleeced. It’s one of the more important financial innovations of the last century.

The problem is that after this became the universal default, trillions of dollars of household and institutional savings shifted towards vehicles whose entire purpose is to track indexes that are increasingly (or often solely designed to be) composed of the same handful of large-cap incumbents. Nearly all transactions in those markets are secondary, meaning the capital is being shuffled between holders rather than directed toward the formation of new productive assets, and the stock market has come to look less like an investment in businesses and more like a liquidity play disguised as a long position on future. None of that is fraudulent or even unreasonable from any individual investor’s perspective, but it’s a poor allocation of capital at the societal level, and it has funneled enormous amounts of stored productive capacity away from the local, community-level deployment where it once lived.

The same capital that used to find its way into the manufacturing operation across town, the new restaurant on Main Street, or the regional service business with a long runway is now globally pooled and managed at scale, which means the community-level deployment of capital has thinned out and the opportunity is sitting there for any builder thoughtful enough to redirect it. The capital around you is greater than you think so long as you can be a steward worth backing.

Most Founders Want One of Three Things

Before the conversation about how to fund a business goes anywhere useful, it helps to be honest about what most founders actually want. Most of the one’s I’ve worked or met at happy hours here in San Francisco fall into one of three camps. The first wants economic freedom, the kind that comes from owning a real thing that produces real cash, and they’re willing to build it themselves over time (guilty as charged :) ). The second wants speed, scale, and the limelight that comes with a venture-backed trajectory, which is a legitimate choice and the one venture capital was designed for. The third wants to change the world toward a specific vision and is willing to subordinate everything else to that goal.

None of these are wrong, but they call for completely different capital strategies, resulting in most of the dysfunction I see in fundraising. Too often a founder is pursuing the first, while running the playbook for the second, and speaking with investors who like the third. Venture math is built for businesses that need to outrun a window, and if your business doesn’t need to outrun a window, venture math will distort it.

The $10 million niche almost never needs to outrun a window and patience is far from a penalty in these markets. A decade of patient building yields more data, more brand, more customer trust, and more of a durable team loyalty than a sprint has ever produced. Frankly that same slow accumulation of value at the community scale has been the more reliable wealth-creation mechanism than the venture lottery for centuries. In many ways that slowness is itself a competitive moat. The reason there’s opportunity in the $10 million niche is that the funding model required to build there is unfashionable, which keeps the supply of competitors thin.

The Founder’s Real Failure Mode

The thing most founders get wrong isn’t that they raise too little or too much. It’s that they raise to extend a hope rather than to validate a system. Capital deployed into a business with no clear test of whether the business actually works, looks more like a weekend in Vegas than an investment

There are three patterns I see most often. The first is funding losses, where the capital is being used to extend runway on an unproven hypothesis and nobody has named what would constitute proof. The second is funding scale prematurely, where the unit economics haven’t yet held but the founder is already trying to grow into them, hoping volume will fix what design did not. The third, and the most expensive, is funding execution before designing the system — where the founder hires the team, builds the product, and ships the launch, only to realize afterward that they can’t articulate which variable they were testing or what the success state looks like. By that point the capital is gone, the team is in motion, and there’s no clean way to learn from any of it.

The diagnosis underneath all three is the same. Founders fall in love with their ideas because they want a market to exist that doesn’t have enough pain behind the problem to support it. The Egg-Mixer 5000 doesn’t need to exist, since nobody is going to pay $40 to mix an egg. The work of system design is what tells you that quickly, before the capital is gone. Most founders fund harder to avoid finding out, and the good ones design experiments that tell them within the quarter.

Build the System First. Capital Runs Through It.

The shift that changes everything is realizing that the asset you’re building isn’t the product, the brand, the team, or the customer list. The asset is the system that turns inputs into value, repeatably, with unit economics that hold. Everything else is a component. Capital is fuel that runs through the system, experiments validate that the system works at small scale, and only after that validation does it make sense to scale capital into it.

This is closer to a scientific method than to anything taught in business school, and it works for the same reason science works. You design the system on paper first, writing down the inputs, the mechanism, the outputs, and the unit economics, along with the assumptions you’re making at each stage. You mark which assumptions are most likely to be wrong and which would kill the business if they were, and then you run the cheapest experiment that can falsify the highest-stakes assumption. You let the data update the system rather than your ego, and you compound learning across experiments until the system is validated end to end. Only then do you scale capital into it.

This sounds rigorous, and it is, but it’s also the work of an afternoon for someone willing to sit down and do it. I’m a fairly lazy guy. I work hard, but if I can build a system that prevents me from burning capital or underperforming, I’m all about it. Systems do for the founder what experimental design does for the scientist — they make sure the work you’re doing is producing usable information rather than just motion, and good system design tells you what to measure, what to ignore, and when to make the next decision.

The most common place the system breaks is at the customer problem. Most early-stage founders don’t really understand the pain they’re solving, who has it, where to find them, and how much it’s worth to them, and without that understanding every other component of the system is built on sand. The unit economics won’t hold because the willingness to pay was guessed at, the marketing channel won’t convert because the audience was generic, and the retention curve won’t compound because the product is solving a problem the customer didn’t actually have at the intensity required to keep paying. Get the customer right and the rest of the system has something to be designed against. Get it wrong, and the rest is decoration.

Sizing the Problem in Dollars, Not Markets

One of the more useful exercises I run with early-stage founders raising capital is reframing their TAM as a dollar-denominated problem rather than a generic market size, because markets are too easy to inflate and a problem is harder to lie about.

Pick a specific group of people, say five thousand strong. Identify a specific pain they all face (real pain, pain they will happily pay to solve), with a specific frequency, at a specific cost. Run the math:

5,000 people × 2 hours per week × $20 per hour × 50 weeks = a $10M problem per year.

Compress that pain from 2 hours per week to 5 minutes per month and you’ve recovered roughly $10M of value annually across that population.

Now your business has a real shape. Capture ten percent of that and you’ve got a real $1MM ARR business with a defensible first toehold. Capture a quarter and you’ve got something serious.

This is the kind of math that actually predicts whether a business deserves capital. Not the addressable market slide, not the bottoms-up forecast, but the pain, the population, and the price they’d pay to make it stop.

Where the Right Capital Actually Lives

Most founders walk into fundraising assuming they need to convince capital to back them, when the reality is closer to the opposite. Going back to those Vegas conference floors, the desperation in the room wasn’t the investment bankers, it was the capital. Family offices are sitting on uninvested allocations, wealthy operators in your community are watching their portfolios drift sideways, and the institutional capital is fighting through three other bidders for every quality opportunity that crosses their desk. The thoughtful builder who shows up with a designed system, a clear experiment, and a fair structure is rare enough to be valuable to all of them.

There are three sources of capital that most $10 million niche businesses should be thinking about, in roughly the order they tend to make sense.

Reinvested Profit

The first option, and the one most founders dismiss too quickly, is profit. Build the business in a way that produces cash from the early days, take less out than the business generates, and let the retained earnings fund the next stage. This is the slowest of the three options and the most certain for the right kind of business. You end up owning all of it. You’re never on someone else’s clock. The system pays for its own scaling, and the act of running profitably from year one forces the system design this whole piece is arguing for, because you simply cannot run unprofitably for long without outside capital subsidizing it.

The mistake most founders make is treating reinvestment as the consolation prize, the boring path you take when you can’t raise. For most $10 million niches it’s the right answer, not the fallback. The math is straightforward. A business that compounds retained earnings at 30% annually over a decade will produce more wealth for the founder than the same business raising twice, giving up most of the equity, to grow at 50% annually for five years. Patience is the cheapest capital that exists, and most builders walk past it because it doesn’t look like growth. It is growth. It just doesn’t put you on a panel.

Bank Debt and SBA Lending

The second option is bank debt and SBA lending, which are real options for asset-backed or strongly cash-flowing operations and worth naming honestly. The caveat is that they’re collateral-heavy and personal-guarantee-laden, and they’re rarely the right shape for the validation phase of a business. They’re a tool for scaling something already proven, not for proving something not yet proven, and a founder who takes on personally-guaranteed debt to test an unvalidated hypothesis is taking the worst kind of risk available — capped upside, uncapped downside, and a personal balance sheet on the line.

Community Capital

The third option, and the one I think that is most underutilized in the country right now, is the capital sitting in your immediate network. Most founders raising at the $250,000 to $1 million level aren’t raising from professional allocators, they’re raising from local angels (who are really just doctors, lawyers, parents, friends, neighbors, and successful operators) in their community. These are the people whose capital used to flow naturally into local businesses before global index investing absorbed most of it, and most of them are mostly invested in public equities not because they prefer it but because no better option has been offered to them.

A group of my friends raised the capital to build a party bus company entirely from their network. They came up with a structure that made sense to their investors — something like 80% of distributions back to investors until 1.5x payback of invested capital, and 10% thereafter. Simple, straightforward, and affordable to paper because their lawyer didn’t have to get creative on the documents. The whole thing was a couple hundred thousand dollars raised from people who knew the founders, understood roughly what they were getting into, and were happy to back something real that they could see operating in their own city. None of that capital was hunting for a venture multiple. It was hunting for a fair return on a business it could understand, run by people it trusted.

The check sizes on this kind of round match the businesses naturally. Five to ten investors at $25,000 to $100,000 each fund a meaningful experiment, twenty investors at the same range fund a meaningful scale-up, and the time horizons match because nobody in the group has an LPA demanding a seven-year exit. The information density matches because these investors actually want to understand what they own, and most importantly the relationship compounds in ways institutional capital never does. These investors become customers, referrers, advisors, and future-round participants, which means the capital is more than just the cash. It’s seeding a real community around the business.

The honest objections to community capital are worth naming. The most common one is that the legal and securities mechanics seem intimidating, and they’re not. Any decent securities lawyer can structure an LLC member-unit raise, a SAFE round, a convertible note, or a small SPV for a low five-figure legal bill, and most of these structures are well-understood templates rather than custom work. The structure is the easy part. The harder part, and the one builders should respect, is the responsibility that comes with raising from people who know you personally. You’re stewarding capital from people who trusted you to do something worth their savings, and the bar for transparency, communication, and integrity is meaningfully higher than it would be with an institutional investor who treats you as one of forty bets in a portfolio (and frankly already priced in your failure after the first reporting cycle). That’s not a downside, it’s a feature, and it’s the stewardship that produces better businesses on the other side. Don’t be a dick about it. Don’t set their money on fire. Don’t lie about how it’s going. Be the kind of operator whose investors send you their friends ten years later. It’s not hard to be a good honest operator.

Lessons from Experience

There are a few lessons that I’ve taken away from sitting in enough of these conversations, on both sides of the table:

1. The right question is never “how do I raise to keep this business going.” The right question is “how do I build a business that generates enough value for the right kind of capital to want a piece of it.” Those are fundamentally different goals that require a different playbook and produce completely different businesses.

2. The system gets designed before the capital comes in. If you can’t draw the business on a single page, with the inputs, the mechanism, the outputs, the unit economics, and the assumptions you’re making, you aren’t ready for outside capital. Take the afternoon to do the work, because the afternoon is the cheapest capital you’ll ever spend.

3. Capital duration should match system maturity. Validation capital for the experiment phase, patient growth capital once the system is proven, and mismatching these is one of the most common ways founders lose control of businesses that were otherwise working.

4. Retained earnings are the most overlooked source of capital available to a builder, and for most $10 million niches they’re the right answer rather than the fallback. The founder who builds profitably from year one ends up owning more of a more durable thing, on a longer timeline, with no clock running against them.

5. Being willing to find out the system doesn’t work is the most underrated trait in a founder. Evolution requires death and rebirth, and the same is true for ideas, products, and ego. A killed experiment on clean data isn’t a failure, it’s the highest return on capital available to a builder, because it tells you exactly where to deploy the next round.

   If you’ve learned something worth sharing, please consider it! This is the best way to support this work!

   Share

What This Looks Like If We Get It Right

The version of the next decade I’d most like to see is the one where more capital flows back toward the local, the patient, and the productive, and away from the abstracted financial machinery that has absorbed most of it. More owner-operated businesses across more communities, building real wealth for more families. Fewer unicorns and more durable five-million-dollar companies that employ thirty people for thirty years and pay back every neighbor who put twenty-five thousand dollars into them at the start. This would be a renaissance of community-level capital relationships that the financial industry doesn’t notice until it’s too late, because the financial industry was built to bypass it. However, it requires a generation of builders who learn that a focus on patience and value creation (run on a system) will rather than speed and exit (run on hope).

That isn’t a fantasy. It’s how most economies were built before the venture narrative captured the imagination of every ambitious twenty-something with a laptop. The mechanics didn’t stop working, but they went out of style since the people doing didn’t need to be in the press releases.

The capital is there. The opportunities are there. The product without a problem doesn’t need to exist, and the builder who finds that out cheaply, who designs the system that validates whatever should exist instead, and raises the right capital from the right people on the right terms, is exactly who the next decade is going to reward. That builder is probably already in your community. Possibly you if you design something worth funding.

If you're a founder trying to build a business worth funding the right way, I'd like to talk. If you're an allocator, including the operators and professionals tired of watching the S&P 500 charts, and you want to be part of an ongoing conversation about how patient capital actually gets deployed, reach out. And if you’re somewhere in the middle, unsure whether to raise at all, that’s the conversation that matters most. duncan@saorsapartners.com

I work with owner-operators of $2-20M businesses on capital strategy, operations, and growth. This is the third piece in an arc with HumanScale and Jobs Are Dead. Subscribe to Conduit of Value for the ongoing thread.

One Question to Leave you with (I reply to everyone): What’s the system you’re trying to design, and what would it cost to test the riskiest assumption inside it?

Leave a comment

A job was always a bundle of tasks sold at a premium. The premium reflected the cost of finding, training, and coordinating a reliable human to execute those tasks. AI has not destroyed work. It has destroyed the premium on generic task execution. Once you see it that way, the layoff cycle stops looking like bad news and starts looking like a preview of how the next decade is going to allocate value.

This piece is an argument about where that value is going and what to do about it, whether you are the person being repriced, the leader watching your org chart melt, or the capital trying to figure out where the next decade’s returns actually live.

The Third Compression

This is the third time in two hundred years that execution has been repriced, and by a wide margin the most aggressive of the three.

The modern school system was adapted by 19th-century industrialists, who first began turning craft into task. The requirement was simple: Factories needed workers who could arrive on time, sit in rows, follow written instructions, move through standardized procedures, and produce reliable output at a predictable cost. The school that prepared them used the same architecture: bells, grades, age-cohort progression, assessment by compliance to a rubric. The purpose was not to produce thinkers or problem-identifiers. The purpose was to produce reliable task-doers and, at the time, that was the right purpose. Industrial production rewarded the output enormously, and the standard of living that came out the other side is part of why we are still running roughly the same curriculum two centuries later.

The second compression came with offshoring. From the 1980s through the 2000s, manufacturing moved first, then back-office white-collar work: IT services, accounting, customer support, legal research. The pattern was consistent. If a job could be clearly documented, it could be done somewhere cheaper. The roles that stayed onshore were the ones that involved judgment, relationship, and original problem definition. The roles that left were the ones that involved execution of someone else’s thinking.

Offshoring should have been the warning. It wasn’t, for a reason worth naming. The jobs that moved were the ones that looked commodity from the start: assembly work, data entry, call centers, low-level coding. The jobs that stayed onshore were harder to export for a specific reason. They ran on accumulated experience applied to repeatable problems: the senior lawyer who had drafted a thousand contracts, the senior analyst who had built a thousand models, the senior consultant who had produced a thousand decks. The apparent moat was twenty years of pattern recognition, and the curriculum doubled down on routing people into exactly those careers. Offshoring appeared to confirm the thesis: commodity execution goes overseas, experienced execution stays home.

AI is that same compression run a third time, without the geographic arbitrage, and it arrives pre-loaded with the pattern recognition that used to require twenty years. Where offshoring could push the cost of a given task down by an order of magnitude, AI is pushing it toward zero. The senior knowledge worker whose value was accumulated experience applied to standard problems, the exact product the system was optimized to produce, is the role now being repriced.

What Died: The Task-Doer, Not the Worker

Two things keep getting conflated in the headlines. “A job” is a transaction. A firm pays a person to reliably complete a bundle of tasks at a known cost. “Work” is the underlying creation of value for a customer. AI came for the margin, not the work.

The task-doer, the role whose economic value came from executing known procedures at a predictable cost, is the role that is vanishing. The roles that survive are the ones that own something. A customer relationship. A piece of judgment. A brand. An audience. Equity. A distribution channel. A craft. None of these can be bundled and sold at a predictable cost, which is exactly why the machine cannot eat them.

If you do not own something, you are being priced. That is true whether you sit in an enterprise sales seat, a junior analyst seat, or a customer success pod. The moment a reliable machine can do your bundle at a tenth of the cost, the price of your bundle collapses toward the machine’s. This is not a forecast, it’s competitive necessity.

The instinct most people have in this moment is to protect the old ladder: upskill, learn prompting, become “AI-augmented,” keep climbing. That is fine advice, and it will buy some people another decade. It does not answer the structural question. The ladder itself is being priced down. Building the ladder is quickly becoming a better bet than climbing it.

Going to Market Has Never Been Cheaper

The story getting all the attention is what is being destroyed. The under-told story is what is being built on the other side.

The same forces compressing white-collar employment are compressing the cost of starting a company. Four inputs have moved at the same time.

Software build. A B2B MVP that cost $500,000 to $2 million to ship in 2020 can be built today, by a small team willing to lean on modern tooling, for a fraction of that. Ship cycles that used to run in quarters now run in weeks. Most of what used to be a funded MVP engineering team is now a motivated operator and a weekend.

Admin and back office. Bookkeeping, legal templates, compliance reviews, contract redlines, CRM setup, tax filings. The entire middle layer that used to require an accountant, a paralegal, and a part-time operator is now a handful of monthly subscriptions and a model that does the reading for you.

Customer service and operations. The same function enterprise software companies are now eliminating thousands of seats to replace is available to a solo operator for less than the cost of a cell phone plan.

Marketing and distribution. Content production, creative iteration, SEO research, customer segmentation, lead qualification. All of it used to live inside agencies charging $10,000 to $30,000 a month. Most of it now runs from a kitchen table.

Pick any category. The input-cost curve has bent. This is not a vibe. It is a line on a chart, and the line is going one direction.

Niche Markets Are Suddenly Viable

Here is the math I want you to leave with.

A $10 million total addressable market, captured at 10 percent share, is $1 million in annual revenue. At 60 percent gross margins, that is $600,000 in gross profit. For a single operator or a small team, that is a life-changing business. For a venture fund with $500 million to deploy, it is a rounding error. For the corporate incumbent with a billion-dollar cost base, it is uneconomical to even staff the meeting to discuss it.

Five years ago, that $10 million TAM was untouchable for everyone. Too small for venture. Too unprofitable for incumbents and too expensive to serve with traditional cost structures. The $10 million niche has been an orphan.

The AI cost curve just adopted it.

A *far* from exhaustive list of what that unlocks:

1. Vertical compliance software for specific regulated trades. Mobile-notary scheduling. Independent insurance-adjuster workflows. Franchise-operator reporting. Each one too small for a big company, too specialized for generic tools, now buildable by two people in a quarter.

2. Micro-manufacturing of discontinued parts. Replacement components for vintage agricultural, marine, or industrial equipment. AI-assisted CAD, domestic CNC, and Shopify-grade distribution make production runs of 50 to 200 units profitable for the first time in a generation. The customer has been waiting for someone to show up.

3. Hyper-specific professional services. R&D tax credit work for indie game studios. IP licensing support for YouTube creators with a real catalog. Bookkeeping for Shopify sellers above $2 million GMV. Each of these was too low-volume to staff the traditional way. Each is workable with a small human team and heavy AI leverage.

4. Occupational sub-niche education. Exam prep for one specific professional license. Training products for a single trade. Certification prep for a specific software platform used by 40,000 people in the country. Production and distribution costs are approaching zero.

Every one of these is a previously unsolved problem getting solved. Not because someone had a new idea. Because the math finally works.

Two Doors, Not One

There are now two doors open for anyone paying attention.

Door A: Build your own. A displaced professional with a thesis and $25,000 can own a real slice of a niche market inside of a year. The ladder climb is being priced down. The build path is open. If you are in the first group of people to walk through that door, you are early, not late.

Door B: Build inside. The same cost curve making solo entrepreneurship viable is providing your best people the ability to build without your infrastructure. If your senior operator can see a $10 million niche and ship a product for $50,000, the only reason they stay is because you have given them a better deal, or partnership, than the one they can now write for themselves. Most companies have not.

This shifts the dynamic for your most self-dependent employees. The old trade was infrastructure dependence, rationalized as loyalty. The new trade is a choice between real partnership and real independence, and you are competing with the latter whether you want to be or not.

The companies that win the next decade will look less like hierarchies and more like platforms for builders. They will make it easier to spin up a new product line, run a small P&L, or launch a sub-brand inside the firm than it is to leave and do it alone.

This shift is bigger than any one company. I have argued elsewhere (see HumanScale) that a more interesting economy is one with more owner-operators, fewer unicorn lottery tickets, and capital that gives a damn about what it builds. The AI cost curve is what makes that argument operational rather than romantic.

Venture math does not fit $1M to $10M ARR durable businesses. However, in this new environment where traditional economies of scale are compressed, those businesses become some of the most durable assets an investor can own. Their moat is speed, specificity, and proximity to the customer, all of which widen as AI commodifies the capabilities once reserved for scale. Direct investment, family-office equity, and community capital are all structured to underwrite that profile. The capital category is opening up at exactly the same moment the builder category is.

A Framework: The Four Levers of Internal Entrepreneurship

Whether you are building solo, leading a company, or allocating capital, this is the test. If all four levers are present, the builder will stay and compound inside the firm. If any one is missing, the builder will leave, and sooner than you think.

1. Mandate. Most people have been trained to execute assignments, not to identify problems. The first job of a good Mandate is to retrain the muscle, provide your team with real problems, owned end to end, with a real P&L or product attached. Not a project, not a committee seat, not a guidebook. Ownership.

2. Margin. Real economic upside tied to the outcome. Equity, phantom equity, profit share, or a meaningful revenue split. Salary plus bonus is not margin.

3. Machinery. AI tools, capital, and distribution access that make a small TAM reachable from inside the firm. If your internal builder has less leverage than a solo founder with a credit card, you have lost before you started.

4. Measurement. An outcome scoreboard everyone agrees on in advance. Revenue, retention, contribution margin. Not activity. Not hours. Not optics.

If you are a builder, ask for the four. If you cannot get them, leave and build your own thing. The cost to do it has never been lower and will not be this high again.

If you are a leader, design the four into your organization. The companies that do this in 2026 and 2027 will look, by 2030, like they compounded talent while everyone else bled it.

If you are capital, the next decade of returns lives in funding builders at both scopes. Inside companies and outside of them. The LP who understands that the $10 Million Niche is newly investable, and that the best operators to back may sit inside existing companies waiting for the four levers, is going to look prescient in five years and obvious in ten.

The New Curriculum

Execution is no longer the goal, and the margin will flow to building. Which means the skills you need to be building now are different from the ones the system trained you for.

For two centuries the skills that compounded were the ones factories and firms needed: showing up on time, following procedures, executing a defined task reliably. Schools taught them and careers rewarded them. And the twenty years of seniority and education just became the machine’s job to do.

What AI cannot do, and what most of us have never been trained to do well, is the other half of work. Identifying problems nobody has defined yet. Seeing markets the incumbents are missing. Building things from nothing. Owning outcomes rather than tasks. Making judgment calls in situations where the rubric does not apply. Developing taste and building the audiences and relationships that compound on their own.

These are the muscles worth training now, and they are not trained by taking another course. They are trained by owning something small that is yours, shipping it, watching what happens, and adjusting. The first product you build will probably not work. Neither will the second. By the fourth, the muscles are different. By the tenth, you are someone who builds rather than someone who executes, and that person is priced very differently.

None of this shows up on a standardized test. That is a problem for the school system to figure out, and it will. For the young professional with decades of career runway ahead, the shift has to start now, in whatever form it can take. A side project, a small business, or a niche product - as long it’s a thing you own and are responsible for. Start small, but start. The cost of starting has never been lower, and the cost of waiting has never been higher.

Subscribe now

Close

The market is done paying a premium for execution. It is starting to pay a premium for building. The job is dead. The builder is not. Whether they build for you, with you, or against you is now an organizational design choice.

If you are a founder or operator trying to install the four levers inside your company before your best people go build on their own, I would like to talk. If you are a capital allocator trying to figure out where the next decade of returns actually lives, happy to discuss. And if you are a builder picking between Door A and Door B, that is the conversation I most want to have. duncan@saorsapartners.com

Subscribe to Conduit of Value for the ongoing thread. This piece is a companion to HumanScale, which made the case for right-sized businesses as a capital thesis. Together they argue for building at a human scale as the default path, not the consolation prize.

One question to leave you with, and I read every reply:

What are you building right now that was not buildable two years ago?

Leave a comment

This wasn’t a hostile split. They’d built something real together, a product-based e-commerce business with genuine traction and a clear path forward, but one partner was ready to move on and the other wanted to keep building. The problem was simple and stubborn: the remaining founder didn’t have the cash to buy him out, and he didn’t have the risk appetite to take on significant traditional debt at that stage. But leaving the departing partner on the cap table wasn’t a real option either. A partner who isn’t building is a problem you carry forever, in decision-making, in future capital conversations, and in your own head at 11pm when something goes sideways.

So we structured a seller’s note. A seller’s note is exactly what it sounds like: instead of writing a check today, the buyer pays the seller over time, directly. The departing partner became a creditor, not an owner. His exit was real and clean. The remaining founder got control of his business without tapping cash he didn’t have. We aligned the repayment schedule to the business’s cash generation so the note wouldn’t choke growth, and we moved on.

Within eighteen months, that same business needed $200,000 in tooling investments to scale a handful of products from proven market fit into full injection molding production. At the same time, a key operational hire was coming on full-time, someone critical to the business running without the founder’s hands on every lever. Cash was going out in multiple directions at once. We had two options: raise equity or get creative.

We modeled both. The payback period on selling through the upgraded units at improved margins was faster than we’d initially assumed. So we built a two-part solution. First, we went to the manufacturer. We offered to cover roughly 25% of the tooling cost as a deposit, enough skin in the game to give them comfort, and amortized the remaining balance over a fixed number of units. This is vendor financing, and it’s one of the most underused tools in e-commerce. The cash outflow per unit stayed within our prior margin profile. We weren’t taking on debt that would strangle us quarter to quarter; we were sharing production risk with a supplier who was already motivated to see us succeed.

Second, to backstop the deposit and give the business a stable base of long-term capital, we raised friends and family money at 10% as interest-only debt, amortizing after year one with an option to extend. The investors took that extension happily. We funded the tooling, the business grew into its new unit economics, and we built something that matters more than most founders realize: a track record of paying back the people who believed in us early. That supplier relationship is now an asset. When we go back to them with a bigger bet, they already know we cover our obligations.

No equity raised. No dilution. No outside investor with opinions about how to run the business. We structured our way to the outcome.

If you’ve been following this series, you know Lever Two was about working capital, how to think about cash conversion cycles, days-based financial discipline, and why growth almost always consumes more cash than founders expect. I noted at the end of that article that not all financing is created equal. This one is where we go deeper on that.

The capital stack isn’t just “debt versus equity.” It’s a full menu of instruments with different costs, different timelines, different flexibility, and different implications for your eventual outcome. Most founders only order off the first page of that menu. Let me show you the whole thing since stacking these instruments correctly enables the balance for your company to thrive.

The Menu: What’s Actually Available

Capital comes in more forms than most people think. Here’s the full range available to a $2–20M e-commerce business, roughly ordered from cheapest to most expensive.

Vendor and manufacturer financing is often the lowest-cost capital you can access, because the provider is a partner with aligned incentives, not a pure lender. When a supplier finances tooling, production runs, or inventory through net terms, deposit-and-amortize structures, or deferred payment arrangements, they’re sharing risk in exchange for your business. The “cost” is real, you’re committing to volumes or timelines, but the stated interest is often near zero. Use this whenever the structure can be made to work.

SBA loans are government-backed term loans that tend to carry higher rates than conventional bank products. That said, they’re specifically designed to finance the kind of risk profile that traditional banks won’t touch: earlier-stage businesses, thinner collateral, non-real-estate assets. For an e-commerce company that can’t pledge a building, an SBA loan is often the most competitive option available for that risk tier. The process is documentation-heavy, but if you qualify, the terms are hard to beat in the context of what you’re asking a lender to underwrite.

Traditional bank loans are available to businesses with strong financials, clean collateral, and predictable cash flow. The rate will be lower than SBA, but the criteria are stricter. Banks want to lend into stability. If your business is post-trough, generating consistent free cash flow, and can pledge real assets, a conventional term loan or commercial mortgage is worth pursuing. Just know that the underwriting process is less forgiving, and the answer is more likely to be no for product-based businesses without hard collateral.

Friends and family debt is exactly what we used above. At rates typically between 8–12%, it’s often cheaper than outside capital, structurally flexible, and patient in a way that institutions rarely are. Structure it properly — written terms, a repayment schedule, honest projections — or don’t do it at all. The relationship risk of informal arrangements is real.

Revolving lines of credit (RLOCs) are powerful when you have accounts receivable to secure against. If you sell to other businesses and carry AR, an RLOC can be a flexible, low-cost working capital tool. For pure DTC inventory companies, though, most banks won’t lend against inventory at favorable terms, which makes this harder to access than founders often expect. If you do carry meaningful AR from wholesale or B2B customers, this can be one of the highest-leverage tools on the list.

Private credit is a relatively new entry for businesses at this stage, and increasingly one of the most interesting. Private credit funds are non-bank lenders — think family offices, specialty finance firms, and institutional credit funds — that write debt checks into businesses that banks either can't or won't finance on flexible terms. Rates typically run 12–18%, which looks expensive next to a bank loan until you read the covenant package. Where a bank might fear growth and require a fixed charge coverage ratio, a clean personal guarantee, and a call provision that kicks in the moment you miss a quarter, a private credit lender is often underwriting the business's growth trajectory and structuring accordingly. For a $5–15M+ e-commerce company opening a new facility, launching a new channel, or carrying a complex inventory cycle, that flexibility can be worth several points of rate. The capital is more expensive. The terms often fit better. Know the difference before you default to the bank just because the rate looks cleaner.

Inventory financing and purchase order financing bridge the gap for product companies who need to fund production before the revenue hits. Costs typically run 12–20% annually. Not cheap, but sometimes the right instrument for a specific moment in a growth cycle.

Revenue-based financing gets marketed aggressively to e-commerce founders. The pitch is no dilution, no fixed payment and you repay as a percentage of revenue. What gets buried is the effective cost. A “factor rate” of 1.3 on a 10-month payback window works out to somewhere between 35–50% annualized. That’s not inherently disqualifying, sometimes expensive capital on a fast payback cycle is the right move, but you need to calculate it honestly before you sign.

Factoring is similar in structure: you sell your receivables at a discount for immediate cash. It’s fast. It’s also among the most expensive capital on this list. I’ve rarely recommended it without a clear refinancing strategy sitting behind it.

Preferred equity is where most founders have the most to learn. Preferred equity investors get paid before common equity holders in a liquidation or exit. In exchange, their upside is typically capped by a preferred return rate, a liquidation preference, or both. A well-structured preferred raise can be extraordinarily efficient for founders: you get patient, growth-oriented capital, and the investor gets a defined return profile that matches their risk appetite. You retain your common equity compounding on the full upside above the preference.

Common equity is the instrument most founders think of first. It’s also, when modeled correctly, the most expensive capital on this list.

Here’s a rough cost-of-capital snapshot:

Instrument               Stated Rate        Eff. Annual Cost         Key Trade-Off
─────────────────────    ───────────────    ─────────────────────    ──────────────────────────────────
Vendor / mfg financing   0–2%               2–5% (opp. cost)         Volume commitment, relationship
SBA loan                 7–9%               8–10%                    Higher rate, finances risk banks won't
Traditional bank loan    6–8%               6–9%                     Lower rate, stricter criteria
Friends & family debt    8–12%              10–13%                   Relationship risk if not structured
RLOC                     Prime + 2–3%       8–11%                    Requires AR; limited for DTC inventory
Inventory / PO finance   14–18%             16–22%                   Asset-specific, short tenor
Revenue-based / RBF      "Factor 1.25"      30–50%                   Fast, no dilution — but run the math
Factoring                "1.5–2% fee"       20–35%+                  Fastest access, highest cost
Preferred equity         12–18% pref        12–20%                   Patient capital, capped investor upside
Common equity            "No cost"          30–50%+ (implicit)       Permanent claim on your future value

Equity is Often Your Most Expensive Capital

When you raise common equity, there’s no monthly payment. No interest rate on the term sheet. It feels like free money. But equity has a cost, it’s just denominated in future value rather than present cash. Every point of ownership you give away today is a permanent claim on every dollar of value you create from this moment forward.

Here’s the math. Say you believe your business will grow from a $2.5M post-money valuation today to a $10M exit in five years. You raise $500K today by giving away 20%.

Implicit cost of common equity
──────────────────────────────────────────────
Equity raised                      $500,000
Stake given                        20%
Projected exit value               $10,000,000
Investor proceeds at exit          $2,000,000
Implicit annual cost               31.9%

You paid 32% annually, permanently, for that capital. Now compare that to a preferred equity structure. Same $500K, but instead of 20% common equity, you issue a 15% preferred instrument that pays out at year five.

Same capital, different structure — founder proceeds at $10M exit
──────────────────────────────────────────────────────────────────────
                              Preferred Structure    Common Equity
──────────────────────────    ───────────────────    ─────────────
Capital raised                $500,000               $500,000
Investor receives at exit     $1,005,679             $2,000,000
Founder proceeds              $8,994,321             $8,000,000
Difference to founder         +$994,321              —

Same capital. Same investor. Same business. One structure costs you nearly $1M more at exit. This is why structuring matters, not just the debt versus equity decision, but within equity the terms shape everything.

And it’s not just the return profile you need to model. It’s the full waterfall: who gets paid first, under what scenario, with what protections. Before my current work in strategic finance, I spent several years at an impact investing firm operating under government-imposed geographic targets that required genuinely creative capital structuring. The most important lesson I took from that work wasn’t about returns. It was about what happens when the stack isn’t right.

There was a company we genuinely liked; strong unit economics, capable management, real market position; but we passed. Not because they were over-leveraged or poorly run. We passed because of structuring. Their existing debt was a series of one-year rolling notes that had, by the founder’s account, “been rolling for years without issue.” Maybe so. But in a downside scenario, those creditors had first priority on assets, ahead of any equity we’d put in. We were being asked to fund the growth while someone else held the downside protection. The question our investment committee kept coming back to: we’re financing this company’s expansion, but to do that, we’d effectively be paying off their existing creditors first, creditors who weren’t willing to participate going forward. That’s a strange risk profile to underwrite.

The founder never understood why we passed. From where he sat, those notes were stable. From where we sat, the structure was wrong. This is why capital stack design isn’t abstract. It shapes who participates in your business, on what terms, and what happens when things don’t go according to plan.

The Finite Pie Fallacy

The most common trap I see founders fall into is treating ownership percentage as a fixed resource to be protected at all costs. The instinct to hold 100% is understandable but it leads to a specific and costly mistake.

If you’ve done the work — built the financial model, pressure-tested your assumptions, validated through your Sales Engine and your margin structure that additional capital will generate a return materially above its cost — then you’ve already established that you can build a bigger pie. The question shifts from “how do I protect my slice?” to “is the arbitrage worth it?”

Say you have an expansion opportunity that generates a 40% IRR. You can fund it by raising capital at a 20% cost. The spread is 20 points, and you have better uses for your existing cash. That is not a hard decision. You raise the capital, capture the spread, and end up with more absolute value, even at a smaller ownership percentage of a larger business, than you would have by doing nothing.

If you’ve proven that you can grow faster with outside capital, and the cost of that capital sits meaningfully below the return it generates, then holding back isn’t discipline. It’s leaving money on the table. Bootstrapping is a legitimate philosophy. But “I want to keep 100% of my company” is not a capital strategy. It’s a preference that should be tested against the actual math of what you’re giving up.

Capital Readiness: What It Actually Means

Most founders think of raising capital as an event: you prepare a pitch deck, take some meetings, something happens. I think about it as a continuous state of readiness, so that when the moment comes, the conversation is about the deal, not about catching up on the basics.

Clean financials is the baseline. GAAP accounting, or at minimum accrual-based books. Not because investors require it (though they do), but because without accrual accounting you genuinely don’t know what your business earned in a given period. Cash accounting tells you what happened to your bank balance. Accrual accounting tells you what your business earned.

Owning your numbers is different from knowing them. Knowing means you can look up last month’s revenue. Owning means you can explain why gross margin was 46% instead of 52%, what drove the variance, and what you’re doing about it. Investors fund operators who understand their business at that level.

A financial model that shows you’re testing assumptions, not just asserting them. Your model should show your drivers — traffic, conversion, AOV, working capital days, payback periods — and your sensitivity to changes in those drivers. Use your gut to design the experiments. Use the data to prove the point. A model you can’t defend is a spreadsheet artifact, not a financial plan.

A clean financial narrative. Owner distributions and personal expenses clearly separated so a reader can see the actual economics of the business. A clear articulation of how every dollar of spend contributes to value creation.

The last piece is what I call leading your own round. When you walk into a capital conversation having already determined what you need to raise, why that number, what structure makes sense, what you’ll pay for it, and what terms you will and won’t accept, you signal something that most investors rarely see: an operator who understands the deal as deeply as they do, or better. Capital is a trust business. Demonstrating that you’ve thought through the risk, the return, and the structure from both sides of the table changes the conversation entirely. You’re not asking for permission. You’re presenting an opportunity.

The Investor You’re Looking For Already Exists

Most founders walk into their first capital conversation with a mental image shaped by Shark Tank, or a PE firm that wants to buy their company, or a VC in a suit who needs you to become a unicorn. That framing eliminates most of the actual universe of available capital.

Capital markets have become extraordinarily specialized. There are investors who write checks specifically into $5–15M product-based e-commerce businesses. There are private credit funds designed for companies at exactly your revenue range. There are family offices that prefer the return profile of a well-structured preferred instrument over traditional fixed income. There is, genuinely, more capital chasing good deals than there are good deals to fund.

What’s in short supply isn’t money. It’s founders who can clearly and correctly articulate why their deal is a good risk-adjusted return. If you know your business inside and out — the IRR at base case, the protection structure in a downside scenario, what makes a lender or investor whole if things go sideways — and you can explain it to someone who invests in that type of deal every day, you will get a meeting. If the deal is what you say it is, you’ll get the capital. The homework is the work. Most founders skip it.

Five Patterns I See Constantly

Not building relationships before you need them. I spend roughly 20% of my time maintaining relationships with investors, private lenders, and bankers — not because I have live deals right now, but because I want them to know what my clients look like and to pick up the phone when a fit comes up. Capital is a trust business. The founder who calls a banker for the first time when they need a commitment in 30 days almost always gets worse terms than the one who’s been having an honest conversation with that banker twice a year.

Thinking the only options are bank debt or selling the company. Friends and family can come in as debt or equity. Suppliers can finance tooling and inventory. Private credit funds operate in every industry. Preferred equity structures offer patient growth capital without surrendering control. The universe of instruments is far wider than most founders ever explore.

Not understanding your own deal before you start looking. Before your first capital conversation, you need to know how much to raise, why that number, what the capital will produce, and what a realistic return looks like for the person providing it. Walking in and asking “how much do you think I should raise?” tells the room everything it needs to know — and not in the way you want.

Raising too much or too little. The founder who wants to raise a large round because it feels like validation often ends up over-diluted and accountable to growth expectations that weren’t their own idea. The founder who raises exactly the cost of one piece of equipment, without modeling installation costs, ramp time, capacity utilization, and the working capital growth that comes with increased volume, often finds themselves raising again in six months from a weaker position. Raise to a clearly defined milestone, with buffer based on your downside scenario.

Optimizing for rate and ignoring terms. I’ve seen founders turn down 12% friends-and-family money with flexible repayment in favor of 8% institutional debt with covenants that nearly broke them during a soft quarter. The rate is one number in a longer conversation. The covenant package, the control provisions, the payment flexibility in a downside scenario — these often matter more than the headline cost. A 14% private credit facility with limited covenants may genuinely beat a 10% bank loan for a business still building toward stable cash flow. Price the full instrument. The terms are half the deal.

The Questions Founders Actually Ask Me

How much should I raise? Model it. Identify the milestone you’re building toward and work backwards to the cash required to get there under your base case, with buffer for variance in your downside scenario. There is no shortcut to this answer. If you can’t define the milestone before your first investor conversation, you’re not ready for that conversation.

My banker has always been happy to lend me money. Why would I consider equity? Because your banker isn’t thinking about what a 36-month operational transition does to your debt service coverage. Banks lend against stability. When you’re ramping a new product line, building out new capacity, or going through any kind of structural change, you’re asking them to lend into uncertainty, and the debt service doesn’t pause because your cash flow is lumpy for three quarters. Patient capital may be worth a higher stated rate precisely because it gives you the flexibility to build without mandatory cash outflows at the moment you least want them. It’s not always about the cost. It’s often about the timing and the structure.

Why does the bank always say no? Usually one of three things. You’re talking to the wrong institution — not every bank wants to lend to every type of business. You’re communicating your risk poorly — they don’t understand your model well enough to feel comfortable with the exposure. Or you don’t understand your own risk well enough to explain it clearly. A good loan package tells the lender the story they need to hear, not to spin it, but to give them the information that makes a yes reasonable. Walk in with messy books and verbal explanations of why things look unusual, and you’ll get a no. Walk in with clean financials, a clear use of proceeds, and a repayment analysis tied to your operating model, and the conversation changes entirely.

Isn’t equity always giving up control? No — and this distinction matters. There’s a difference between economic rights and control rights. You can share in the financial upside of your business with outside investors while retaining full operational authority. A preferred equity structure can provide growth capital without a board seat or meaningful dilution of your voting control. What you want to avoid is selling common equity at an early stage before you’ve demonstrated what the business is worth. That’s when equity gets expensive. A well-structured preferred instrument at the right moment usually isn’t.

When does bootstrapping become a mistake? When you’ve identified a specific opportunity — a hire, an expansion, a product investment — that generates a return on capital materially above what that capital would cost you, and you don’t have the organic cash flow to capture it on your own timeline. Bootstrapping is a discipline, not a doctrine. If your business grows 40% with $500K of outside capital at a 20% cost and 10% without it, you’re leaving real value on the table. The math should make the decision.

I’ve been offered revenue-based financing. Should I take it? Run the effective annual cost first. Take the factor rate, calculate the implied payback timeline, and convert it to an annualized rate. If that number is 35% and you’re growing at 55%, it may be a reasonable bridge as long as you have a clear refinancing plan on the other side. If that number is 35% and your growth rate is 18%, the math is very hard to work. The “no dilution” framing is technically true. The cost is rarely what it’s made to sound like. Run it before you sign.

Have questions of your own?

Email me at Duncan@SaorsaPartners.com

What Comes Next

Lever Seven is about pricing, specifically why it’s the most underleveraged growth tool in most e-commerce businesses, and why a disciplined 5% price increase often does more for your bottom line than a full year of cost initiatives. Everything we’ve covered on margin structure and capital efficiency makes the pricing conversation sharper. That’s where we’re going next.

If this resonated, subscribe to Conduit of Value so you don’t miss it. And if you’re sitting with a capital decision right now — a raise, a refinancing, a partner exit, or just uncertainty about whether your current structure is right for the next phase — reach out (Duncan@saorsapartners.com), this is the work that gets me out of bed every morning.

Not because they were bad at their job. They might have been great — genuinely, we’ll never know. What I know is that they were hired into a role with no defined territory, no quota, no system, and no clear mandate beyond “help us grow.” The founder wanted them to do outbound and handle inbounds and support existing accounts and maybe build out the CRM. The role was designed by accumulation — everything the founder hadn’t gotten to became this person’s job description.

Two years later, the company had a performance problem. Or so they thought. When I pulled back the curtain, what they actually had was a design problem. There was no target on record against which to measure performance. There was no playbook, because one had never been written. The expectations that existed were in the founder’s head and had evolved over time without ever being communicated. The hire wasn’t underperforming. The hire was operating in a vacuum and coming up short against a standard that had been invented retroactively.

Two years. The salary, the benefits, the opportunity cost of a seat filled by someone who couldn’t succeed because success had never been defined. That’s the most expensive kind of hiring mistake, and it’s the most common one.

If you’ve been following this series, you know where I’m going. In Lever One, we built a Sales Engine — predictable revenue requires predictable inputs. In Lever Two, we talked about working capital — growth consumes cash, and you need to understand the timing of that consumption before it consumes you. Both of those articles were about building systems. This one is about the humans inside them, how to decide which ones to bring in, and how to know whether they’re earning their keep.

Hiring is the most emotional decision a founder makes. It’s also, structurally, an investment decision. And like any investment, it has a return profile you can calculate before you make it — if you’re willing to do the math.

The Three Archetypes

Not all hiring decisions are the same. Before you can evaluate whether a hire makes sense, you have to understand what kind of hire you’re actually making. Almost every people decision falls into one of three archetypes, and each one has different financial logic.

The Labor Arbitrage

This is the most straightforward case. The premise is simple: your time has a value, and there is work in your business that can be done by someone whose time costs less. The arbitrage is the spread.

I use an internal reference rate of around $250 an hour — not what I bill clients (I work on retainer), but what an hour of my focused thinking is worth when applied to the highest-leverage problems in a business. That number is a forcing function. When I look at a task list and find work that could be delegated to someone at $25 an hour, the spread is $225. That’s the hourly cost of me doing that work instead. On ten hours a week, that’s over $9,000 a month in shadow cost, real value I’m burning by staying in the weeds.

Most founders have never assigned a number to their own time, which means they have no way of knowing how expensive it is when they spend it badly. The goal isn’t to find a single rate and stick with it — it’s to push that number up over time by continually moving low-value work off your plate and higher-leverage work onto it. A founder who spends their week in $25-an-hour tasks has a $25-an-hour business, regardless of what they’re billing clients.

The priority order matters here. Hire out the biggest spreads first. The $25-an-hour work being done by a $250-an-hour brain is the most expensive inefficiency in any founder-led business. As soon as the economies of scale start to pencil, fix that before you worry about anything else.

A note on internal hires versus external help: labor arbitrage applies to both. A full-time operations coordinator freeing up fifteen hours of your week and a part-time bookkeeper handling your month-end close are both arbitrage decisions. The logic is the same — you’re buying back hours at a rate cheaper than your own.

The Skills Gap

This archetype is different, and founders conflate it with labor arbitrage at their peril. The Skills Gap decision is about capability. The question isn’t “can I do this cheaper?” It’s: how much does it cost us to figure this out ourselves versus bringing in someone who already knows the answer?

The math here is less precise but just as real. How quickly can you build a functioning sales engine with your existing talent versus bringing in a proven sales leader who has built three of them? What’s the value of six months of acceleration? What’s the cost of getting it wrong the first time because you were learning on the job?

The most important version of this decision is the system builder hire. Not someone who executes within an existing system, but someone whose job is to build the system itself — the ops lead who designs the fulfillment workflow from scratch, the head of growth who builds the acquisition engine you don’t currently have. These are Skills Gap hires with compounding returns, because the system they build outlasts them. You’re not buying labor, you’re buying infrastructure. Evaluate it that way.

Where I see founders go wrong is hiring for skills gap reasons while evaluating on arbitrage logic. This happens constantly with marketing agencies. The prevailing instinct isn’t “they’ll save me time” — it’s “they know more than I do, they have better data, they’ve done this before, they’ll produce better outcomes than we would internally.” That’s a legitimate and often correct assessment. But then the founder starts evaluating the relationship on hours delivered per dollar, which is the wrong measurement entirely. If the agency is materially better at the most important function in your business, that premium has an enormous implied return. Getting the strategy right is worth more than the retainer. Getting it wrong — even cheaply — is one of the most expensive mistakes a growing e-commerce brand can make.

The Agent

This is the archetype most founders are still underweighting, and it deserves a direct challenge to a belief that has gone largely unquestioned in small business: that bigger revenue requires a bigger team.

It doesn’t. Not anymore, and increasingly not at all.

A small team of builders and problem-solvers operating with the right systems will routinely outperform a much larger team of task-doers. The founders who understand this are building businesses with ten people that perform like businesses with fifty. The founders who don’t are hiring headcount to do work that software could handle, then wondering why their revenue per employee keeps declining as they scale.

The Agent archetype asks a simple question before any hire: is this human work, or is this computer work wearing a human costume?

The financial logic is yield-based:

Payback Period = Implementation Hours / Hours Saved Per Week

If an automation takes 50 hours to implement and saves one hour a week, your payback is nearly a year. Reasonable, but not exciting. If an automation takes two hours to implement and cuts a 30-minute task down to 15 minutes — and that task runs 25 times a week — you’ve freed up six hours of weekly labor. Payback in days.

The practical framework for applying this before any hire:

First, map the work. Before writing a job description, write out the actual tasks the role will own. Be specific — not “manage operations” but the actual recurring things a person would do on Monday through Friday.

Second, sort by type. Separate the work into three buckets: judgment work (requires human interpretation, context, and decision-making), relationship work (requires human presence and trust), and rules-based work (follows a defined process where the inputs and outputs are predictable). The third bucket is your automation target.

Third, estimate the volume and frequency. Rules-based work that happens once a month is probably fine to leave with a human. Rules-based work that happens dozens of times a week is almost certainly an automation candidate. The yield calculation above tells you whether it’s worth pursuing.

Fourth, ask whether the remaining work justifies a hire. After automation, what’s left? If the answer is ten hours of real human work per week, you probably need a part-time contractor, not a full-time seat.

This isn’t about eliminating people, it’s about not hiring people to do things that shouldn’t be human jobs. The brands building durable competitive advantages right now aren’t the ones with the most headcount. They’re the ones who have figured out which problems require people and which ones require better systems.

Subscribe now

Build the Scorecard Before You Post the Job

Here’s the thing about the salesperson I opened with: the failure mode wasn’t the hire. It was the sequence. The system was supposed to come with the person, or after the person, or emerge from the person’s presence. None of that happened, because that’s not how systems get built.

The single highest-leverage thing you can do before any hire is define what success looks like — specifically, measurably, and time-bound — before the person starts. Not a job description. A success profile. What does a great outcome look like at 30, 60, and 90 days? What’s the metric that tells you this seat is earning its cost? What would have to be true in twelve months for you to call this a good investment?

Most founders can’t answer those questions in advance. They can answer them in retrospect, usually when they’re frustrated and trying to justify a difficult conversation. That inversion is where the cost lives.

Before any hire, work through these four questions:

What does this role produce that didn't exist before?
How will we measure that output?
What's the minimum acceptable output at 90 days?
What does this role need to generate or offset to cover its fully-loaded cost?

That last question deserves emphasis. The fully-loaded cost is always higher than the salary. Add benefits, payroll taxes, equipment, onboarding time, and management overhead, and a $60,000-a-year hire runs closer to $80,000 or more when it’s all in. That’s your hurdle. The role needs to generate that much in incremental value, offset that much in senior team time, or remove that much in operational risk — ideally some combination of all three.

Revenue per employee is the macro version of this test. For e-commerce businesses in the $2-20M range, this number tells you whether your team is structured efficiently relative to the output it’s producing. If revenue per employee is declining as you add headcount, you have a seat design problem — more people, same or lower productivity, usually because roles are poorly scoped and accountability is diffuse.

Share

Hire Ahead or Hire Behind?

Founders ask about timing constantly, and the honest answer comes down to one question: is the system ready to absorb the hire?

Hiring ahead of growth makes sense when the model is scalable and the constraint is capacity, not clarity. If you know that adding X in a given function produces Y in output — and you’re confident in both variables — start looking before the pain gets acute. Recruiting takes time. Onboarding takes time. A hire made in desperation will almost always underperform against a hire made thoughtfully, because the person entering a chaotic system will spend their first ninety days becoming part of the chaos instead of solving it.

The test: if this person showed up tomorrow, do they have a system to operate inside, a target to aim at, and a playbook to learn from? If the answer to any of those is no, you’re not ready to hire. You’re ready to build. The hire comes after.

Hiring behind growth almost always signals that the underlying system isn’t actually ready to scale. You’re adding people to a function that hasn’t been systematized, which multiplies the chaos rather than the output. The salesperson story is a version of this — the founder hired before the infrastructure existed, and paid for it over two years.

The Patterns I See Over and Over

The bad seat is more common than the bad hire. A mountain of responsibilities gets piled onto one person, rationalized as “we need someone scrappy.” The result is a person who can’t do any of it exceptionally well, with no clear KPI to optimize for, evaluated against an ever-shifting standard. Seat design is a skill, and most founders haven’t had to develop it until suddenly they have a team and the design debt is everywhere.

Marketing and agency relationships evaluated on the wrong metric. The logic for bringing in outside marketing expertise is almost always skills-based — they know more, they have better data, they’ve built this before. But founders evaluate the relationship on hours delivered and tasks completed, which is arbitrage logic applied to a skills gap decision. The right question is: are our outcomes better than they would have been? That’s a harder thing to measure but it’s the one that matters.

Hiring a person to fix a system problem. The system will defeat the person every time. Before you add headcount, ask whether the problem you’re solving is a people problem or a design problem. Usually the design work comes first. That said — some hires are explicitly to build the system. A great operations lead hired to design and implement your fulfillment infrastructure isn’t being set up to fail by a missing playbook; they’re being hired to write it. The distinction matters: are you hiring someone to operate a system, or to build one? Both are legitimate. Conflating them is where it goes wrong.

Building a role around a person rather than a need. This is one of the most human mistakes in business, because it comes from a good place. Early employees who were invaluable at one stage often find themselves in roles that have quietly outgrown them, and founders — out of loyalty, gratitude, and genuine affection — reshape the seat to keep the person rather than designing the seat for the current need. The role grows around them and the business pays for it through misaligned accountability and work that doesn’t get done well.

The honest and compassionate path here isn’t to quietly tolerate the mismatch. It’s to have the conversation early, before the frustration accumulates on both sides. Often there’s a version of this that works for everyone — a lateral move to something genuinely suited to what the person does well, a narrower scope that plays to their strengths, or in some cases a direct conversation about what the role needs to become and whether they want to grow into it. What doesn’t work is leaving the seat misaligned out of avoidance. That’s a disservice to the business and, eventually, to the person.

Share

What CEOs Actually Ask Me

“Do I need to hire someone, or am I just overwhelmed?”

Usually both, but the answer matters because they have different solutions. If you’re overwhelmed because you’re doing $25-an-hour work, you need to delegate before you hire. If you’re overwhelmed because genuine demand is exceeding genuine capacity, that’s a different and better problem. Before posting a job, spend a week tracking where your hours actually go. The answer usually tells you whether you need a hire or a better system.

“Should I hire full-time or bring in a contractor?”

Depends on whether the need is ongoing and whether it requires institutional knowledge. Contractors are excellent for skills gap hires, you’re buying expertise for a defined problem or a defined period. Full-time makes sense when the role is core to the operating rhythm and the switching cost of turnover is high. Treat them as genuinely parallel options rather than defaulting to full-time and treating contractors as the consolation prize.

“My hire isn’t performing. What do I do?”

Before anything else, ask whether they have a defined target and a system to work inside. Most performance problems in small businesses are design problems. Redefine the seat before you make a people decision, you’ll often find the problem is structural rather than personal.

“When does it make sense to hire ahead of growth?”

When the system is scalable and the constraint is capacity. If you’re confident that adding a person in a given function will produce a predictable output — and the demand to absorb that output is coming — start the search now. Recruiting and onboarding take longer than founders plan for, and the cost of the seat going unfilled while you’re scrambling to find the right person is real.

“What’s the biggest mistake founders make when hiring?”

Skipping the success profile. Most founders write a job description, post the role, hire the person, and then figure out what success looks like after they’re in the seat. That sequence reliably produces the situation I described at the top of this article — two years of undefined expectations and a performance conversation that shouldn’t have been necessary. Define the target before you start the search. It will change who you hire and how you onboard them.

“What about a fractional executive like a CFO, CMO, COO?”

This is a skills gap decision, full stop. The honest case for fractional isn’t that it’s cheaper, it’s that the depth of thinking you need often doesn’t require forty hours a week of execution. For most businesses in the $2-20M range, a fraction of senior-level thinking applied to the right problems will outperform a full-time hire who’s underutilized and expensive. If you find yourself making hiring and capital decisions without a clear return framework, that’s probably the right moment to bring in someone who does this for a living — not because you can’t figure it out, but because the cost of figuring it out slowly tends to exceed the cost of getting it right quickly.

The Math, Simplified

Before any hire, run this:

Fully-loaded cost = Salary + Benefits + Taxes + Overhead

Shadow cost offset = Hours freed per week x Internal hourly rate x 50
Skills premium = Estimated value of accelerated or improved outcome vs. self-build
Revenue hurdle = What this role must generate or preserve to break even

If (shadow cost offset + skills premium) > fully-loaded cost: make the case

For automation decisions:

Payback weeks = Implementation hours / Hours saved per week

Under 13 weeks: do it now
13-26 weeks: strong case, prioritize accordingly
26-52 weeks: reasonable, schedule it
Over 52 weeks: weigh against strategic value or defer

What’s Coming Next

Lever Six is about the capital stack — how you fund the decisions you’ve been making across this series. We’ve talked about when to hire, how to manage working capital, and how margin structure shapes your options. The next question is where the money comes from to execute on all of it, and what the right kind of capital looks like at each stage of growth.

Subscribe now

If the framework in this article resonated, particularly the accountability-first approach to hiring, subscribe to Conduit of Value for the next one. These come out every two weeks, and the capital stack conversation tends to be the one that changes how founders think about the next phase of growth more than almost anything else.

The first is the founder who built a budget in December, opened the file in March when something felt off, and discovered the model had been gathering dust since January 3rd. The assumptions were vague — some percentage growth on last year, a few expense line items, maybe a note about hiring someone in Q2. No metrics. No drivers. Just a spreadsheet that felt like a commitment at the time and became irrelevant by February.

The second went the other direction entirely. Their model pulls from every data feed in the business: cost per impression, clicks per thousand impressions, website sessions, add-to-cart rate, cart-to-checkout rate, checkout-to-order rate, and seventeen more inputs I had to squint to read. It looked like NASA built it. The problem was that when revenue missed by 12%, nobody could tell you why. The signal was buried under so many inputs that the model produced noise instead of insight.

The third is the most painful. It’s the model built by an accountant — precise, beautiful, and completely disconnected from decisions. Every expense line itemized. Every revenue stream mapped to three decimal places. The kind of artifact that takes eight hours a month to update, generates a 40-tab Excel file, and gets quietly ignored by the fourth month because the founder can feel, viscerally, that it adds no decision value. They’re paying for it and not using it. Which means they’re paying for nothing.

Here’s what all three have in common: none of them forecast cash.

They forecast a P&L. Maybe they tack on a line for debt service. And they call it a day.

That’s not a model. That’s a reporting artifact with a budget column stapled to it.

Every decision in your business is a capital allocation decision. Every single one. When you hire someone, you’re allocating capital. When you run a campaign, you’re allocating capital. When you take on inventory, you’re allocating capital. Even your own time — your labor hours — is capital being deployed somewhere.

If your model doesn’t tell you what cash looks like at the end of the month, three months out, six months out, it cannot support decisions. It can report on them after the fact. That’s a different, considerably less useful thing.

This is Lever Four. We’ve covered the Sales Engine (Lever One) — how revenue is built from CAC, LTV, and predictable acquisition. Then Working Capital (Lever Two) — how cash moves through the operating cycle and how working capital days determine whether growth feeds or starves the business. Then the Margin Machine (Lever Three) — what actually falls to the bottom line after you account for the true cost of a sale, channel by channel, SKU by SKU.

This article wires all three together into a single operating model. One that tells you not just what you earned, but what you can actually do with it.

The Right Number of Assumptions

Before we build anything, let’s talk about what to resist.

I took over a client’s model that tracked every step of their acquisition funnel: cost per impression, clicks per thousand impressions, sessions, add-to-cart rate, cart-to-checkout, checkout-to-order. All legitimate metrics. All valuable in the right context. All completely wrong as primary drivers of a financial model.

The problem isn’t that those metrics don’t matter — they do. The problem is that when you route all of them into a model as live assumptions, the model can no longer tell you what happened. A revenue miss gets absorbed across six variables simultaneously. Was it impressions? Was it add-to-cart? Was it checkout abandonment? You cannot see it. The model generates false precision that buries the signal in noise.

We simplified it to two numbers: Cost per Order and Marketing Spend. That’s it.

From those two inputs you get customers acquired. You already know AOV. From AOV and your gross margin (Lever Three), you know what the order produces before fixed overhead. That’s the model.

The underlying funnel metrics didn’t disappear — they still matter. But they belong in a separate analysis layer, pulled out when you’re forming a hypothesis or diagnosing a specific problem. On a weekly basis, track two numbers to confirm you’re trending in the right direction. On a monthly or quarterly basis, go under the hood to understand what’s driving them and where to focus next.

The financial model that analyzes each funnel step in its own cell isn’t being thorough. It’s making the important things harder to see.

Building the Model: Three Statements, Not One

A model that serves decisions has to be a three-statement model: P&L, balance sheet, and cash flow statement. Not because accounting requires it. Because cash flow is where every decision ultimately lands.

Here’s the minimum viable structure for an e-commerce company:

Revenue layer:

New Customers         = Marketing Spend ÷ CAC
Returning Customers   = Prior Customer Base × Reorder Rate
Total Orders          = New Customers + Returning Customers
Revenue               = Total Orders × AOV

Gross margin layer:

Gross Margin per Order = AOV
                       - Product / Materials Cost     (feeds inventory balance)
                       - Payment Processing            (~2.5–3.5% of AOV)
                       - Shipping Cost
                       ─────────────────────────────
                       = Gross Margin per Order

Total Gross Margin = Gross Margin per Order × Total Orders

This is the structure we laid out in Lever Three. If you haven’t done that SKU-level analysis yet, go back. You need a real number here — otherwise the model will tell you a story that isn’t true. Note that CAC sits in a separate line in your P&L as a marketing expense; it’s not baked into gross margin. Both matter, but they live in different places in the model.

Below the line:

Subtract fixed operating expenses — team, software, rent, anything that doesn’t move proportionally with volume. What remains is EBITDA.

Then get into capitalization. Debt? Model the interest expense, principal payments, and draw activity. Equity investors? Model distributions or capital calls. This is also where the working capital mechanics from Lever Two live — AP days, inventory days, and AR days all flow through the balance sheet, which then informs the cash flow statement and shows you what the bank balance actually looks like at the end of each month.

A few key metrics synthesize all of this into numbers you can track on a regular basis:

• CAC — cost to acquire a new customer

• AOV — average order value (or cart size)

• Gross Margin % — what percentage of revenue survives after product, fulfillment, and payment costs

• New Customer Count — the acquisition volume driver

• Reorder Rate — the retention driver (more on this in Lever Nine)

• Working Capital Days — DIO + DSO − DPO, the cash cycle timing

• Ending Cash Balance — the number everything else feeds

Track those seven metrics and you’re running the business. Everything else is either a sub-driver you pull when you need to diagnose something, or a rounding error you average and move past.

Rolling Forecasts and the Feedback Loop

Here’s what a static annual budget actually does: it gives you a document to feel good about in December and quietly abandon by March.

The shift to a rolling forecast isn’t primarily about mechanics — updating monthly, extending the horizon as you go. It’s about what the conversation becomes.

Instead of “are we on budget?”, the question becomes: were we right about that experiment?

That reframe changes everything. Variance isn’t a performance score anymore. It’s a signal about whether your assumptions were correct. When EBITDA comes in five points below plan because CAC ran 30% hot, that tells you something specific: either there’s a systemic issue in how you’re acquiring customers, or the market shifted and your targeting hasn’t responded. The variance isn’t a failure. It’s a diagnostic.

And if you’re running the business this way — treating each initiative as a hypothesis, collecting the data, operationalizing what worked, and moving on — the model lets you do that faster. Form the hypothesis. Set the assumption in the model. Run the experiment. Check the variance. Take the lesson. Update the assumption or fix the system. That’s the scientific method applied to a business, and the rolling forecast is what makes the feedback loop tight enough to actually be useful. The goal isn’t to run fewer experiments. It’s to fail faster on the ones that don’t work and double down on the ones that do, as quickly as the data collection allows you confidence.

The most common place this loop breaks is at the data input stage. Models built on hourly billing don’t get updated frequently because frequent updates are expensive and the founder stops owning it. It becomes something that happens to them rather than something they use. I build on templated QBO integrations specifically to make actuals fast to load — because speed creates ownership, and ownership creates the habit of actually looking at what the variance is telling you.

When you do look at variance, the question is always: why were we wrong? The honest answer usually points to one of two things — an incorrect assumption about how the business works, or a missing or broken system. CAC running 30% above plan for three consecutive months doesn’t mean your marketing got unlucky. It usually means nothing is reacting to what the market is telling you. That’s more valuable to surface than the number itself. The number is a symptom. The system gap is the diagnosis.

Share

Scenario Planning: Making Capital Allocation Visible

The most underused part of a financial model isn’t the base case. It’s the scenario layer.

A client of mine sold high-ticket products with strong gross margins — the COGS as a percentage of AOV was the same whether the cart came in at $200 or $180. Same product mix, same fulfillment cost ratio. The difference is what happens to that $20. Because CAC is a flat cost per customer — you spent $X to acquire them regardless of what they put in the cart — that $20 incremental AOV isn’t burdened by any additional acquisition cost. It falls almost entirely to the bottom line. We built a scenario around it: what happens if we run out of the specific products that tend to pull cart size toward $200? The answer was ugly enough that inventory management on those SKUs became a hard operational priority. Not because we made a sophisticated argument. Because the model made the downside impossible to dismiss.

The other application I use constantly is headcount decisions, and the model makes them considerably less gut-driven.

Say you’re evaluating a marketing director. The thesis: they improve CAC from $25 to $20. Realistic assumption: two months to learn the business, five more months to execute the improvement fully. You model that timeline against the cash outlay during their ramp, layer in the incremental gross margin once the improvement lands, and check the ending cash balance at every point along the way.

Now extend that logic one level. A lower CAC means more customers acquired per dollar of marketing spend — which at constant AOV directly lifts revenue. If that same marketing director also improves website conversion by 1%, on a $3M business that might be $50–60K in additional gross margin per year, without spending another dollar on acquisition. Those two levers are connected: CAC improvement and conversion improvement are often driven by the same person executing the same strategy. The model lets you quantify both, see the combined cash impact across a realistic ramp timeline, and answer the actual question — not “can we afford to hire?” but “what is this hire worth, over what timeline, and does our cashflow runway support the risk?”

Same logic applies to a sales rep. Revenue per rep is a starting point, not the answer. What’s realistic quota attainment in months one through three versus month six? What does the training burden cost in senior team time during ramp? Does adding this rep require incremental inventory to support the volume? What’s the cash trough from start date to meaningful margin contribution — and does the ending balance stay above your minimum comfort threshold throughout? The model tells you to hire when the expected impact is defined, the cashflow runway absorbs the ramp, and you have the operational signals that the system can actually support the volume.

Operational signal first. Cash confirmation second. In that order.

A Note on Inventory

The financial model should not be driving purchase orders. Inventory needs its own model or at minimum its own logic layer — feeding the financial model’s inventory balance and the AP line, not the other way around.

What matters at the financial model level is the cash impact of your inventory assumptions, and there are two conversations worth having here that most founders never get to.

The first is AP terms. What happens if we extend from 30 days to 60 days with our primary supplier? At scale, that change can unlock six figures of working capital that was sitting invisible inside a default term assumption. The answer from the supplier might be no — but you can offer a small margin improvement as a negotiating chip, effectively treating extended terms as a cheap borrowing mechanism. You can only have that conversation if you’ve already run the scenario and know what the number is worth.

The second is inventory days. Getting from 75 days of inventory on hand to 45 days isn’t just an operations win — it’s a capital release. On a business carrying $500K in inventory, that shift frees roughly $200K in cash that was otherwise sitting on a shelf. The model shows you exactly where that capital goes when it’s unlocked, and whether it’s better deployed back into marketing, used to pay down a line of credit, or held as a cash cushion. That’s not an ops conversation. That’s a capital allocation conversation, and only the model can frame it that way.

Common Patterns I See Constantly

Too many models. I’ve walked into situations where the founder had four separate files: the one the accountant built, the one from last year, the one with the new sales strategy baked in, and the one they actually look at each month. None of them talk to each other. Have one model. Maybe two if you’re tracking a genuinely distinct scenario. More than that and the maintenance burden kills the habit of using any of them.

Percentage-based growth assumptions. “We’ll grow 2% per month” is not a forecast. It’s a wish attached to a spreadsheet. A driver-based model tells you which input was wrong when revenue misses. A percentage-based model just tells you that you missed. Drivers. Always drivers.

Rounding errors masquerading as strategic line items. I’ve seen models that break out every individual SaaS subscription for a company doing $800K in revenue. The marginal $200 of software spend won’t make or break any decision at that stage. Contribution margin and the sales engine will. Take an average for the noise, adjust for lumpy payments billed quarterly or annually, and spend your time on the variables that actually move the needle.

Using historical numbers as assumptions without questioning the system. There’s an implicit belief when founders anchor assumptions to history that prior performance reflects a functioning system. Often it doesn’t. If a sales rep has historically closed $400K per year, that’s data — but it’s not a quota if the CRM is a mess, lead quality has never been analyzed, and there’s no structured process underneath it. Build from first principles. What should this rep produce if the system is right? Use that number to identify the gap between where you are and where you’re going.

Subscribe now

CEO Q&A

“I built a P&L forecast in December. Isn’t that basically the same thing?”

No. A P&L forecast tells you what you expect to earn. A three-statement model tells you what cash you’ll have when a vendor invoice lands, a large inventory order goes out, and a loan payment hits — simultaneously. Those are the moments that create crises, and they’re invisible in a P&L-only view. The P&L is the input. The cash flow statement is the output that actually runs the business.

“My model has thirty inputs. More inputs means more accuracy, right?”

More inputs means more places for the model to be wrong and less ability to explain any individual miss. If revenue comes in light and you have thirty variables, you have thirty possible explanations and no clear answer. The goal is the minimum number of assumptions that explain the maximum amount of variance. For most e-commerce businesses, five to seven driver metrics gets you there. Start there and add complexity only when a specific variable is genuinely moving the needle and you need visibility into why.

“We’ve been growing around 2% per month. Can I just use that?”

You can, but you won’t learn anything from it when it breaks. What drove that 2%? New customers? Higher AOV? Better retention? If you don’t know, you don’t know which lever to pull when growth stalls. Build from drivers and the 2% becomes an output to pressure-test, not an assumption to start from.

“My accountant updates the model. Why do I need to be involved?”

Think about how many ideas are floating in your head right now. New hires, channel experiments, pricing changes, inventory bets — probably a dozen things you want to test. A good model is how you decide which ones are worth your time. You take the initiatives that feel most impactful, run the scenarios, and find out which one actually moves the needle most. Then you execute, measure the variance, and learn something. Then you do it again. That’s the scientific method applied to a business.

The model is most powerful when it’s a live part of how you think — something you’re in regularly, not something that gets delivered to you. A fractional CFO or finance partner can help you build it, keep it current, and pressure-test your assumptions. But the decisions it informs are yours, and the habit of using it has to live with you. If you’re only seeing the output once a month in a summary email, you’re getting reporting. Reporting tells you what happened. The model tells you what to do next — and that only works if you’re in the room when the scenarios are running.

“The model says I can afford another sales rep. When should I pull the trigger?”

Affordability is necessary but not sufficient. The more useful question is: what is the expected return, and does the cash trough during ramp stay above the floor you’re comfortable with? A rep doing $600K at full productivity sounds straightforward — but what’s realistic in months one through three while they’re learning the product? What does senior team time spent on training actually cost in terms of their own output? Does the incremental volume this rep generates require you to carry more inventory, which hits cash before the revenue arrives? And if your quota attainment assumption is aggressive, what does the scenario look like if they hit 70% of it in year one instead? Model the ramp, model the inventory impact, model the cash trough. When the downside scenario still keeps you above your minimum balance and the operational signals say the system can support the volume, move.

“Why does variance analysis matter if I already know the number came in low?”

Because the number is the symptom. Say gross margin came in three points below plan for the second consecutive month. You could note it and move on. Or you could pull the variance and find out that shipping costs spiked because a fulfillment system change created exceptions that nobody caught. That’s not a margin problem — that’s a missing system. The variance pointed you there. Without it, you’re trimming your growth assumptions to account for a problem that could be solved with a process fix. The diagnostic is more valuable than the score.

Share

What Comes Next

The next lever is The People Math — every hire as an investment with a return profile, how to know when demand actually justifies the headcount, and how the operating model we built here makes the hiring decision a calculation instead of a leap of faith.

If you’re not subscribed to Conduit of Value, now is a good time. The series is designed to build — each lever sharpens the ones before it, and this one was the connective tissue.

And if any of this resonated because you recognized your own model in the opening, reach out. I build these for a living, and the first conversation is always free.

duncan@saorsapartners.com

Lever One: The Sales Engine | Lever Two: Working Capital | Lever Three: The Margin Machine

Thirty employees working in a space designed around their craft — real equipment, American-made, every inch of the floor earning its place. A decade of patient building. No outside investment, no debt, no venture deck. Just a founder who came from nothing, had a clear picture of the operation he wanted to build, and spent ten years making every decision in that direction. He onshored his production. He reinvested everything back into the floor. His team tenure is nearly zero turnover. He told me he leaves money on the table regularly — not out of ignorance, but out of philosophy. When everyone around you thrives, you get the best outcomes. He figured that out before most MBAs have been alive.

The business is growing north of 50% a year.

I left wondering why that story feels so surprising, I mean it shouldn’t. It used to be the default.

When I started my firm, I named it Saorsa, a Scottish Gaelic word for freedom. The name was deliberate. I believe the purpose of building a business is to create something that expresses who you are while genuinely serving other people. When you add value, you justify your consumption. The business becomes a form of self-expression that other people can join — your employees, your customers, your community — each participating in something that compounds outward. That’s what the St. George operation was doing. That’s the type of company I set out to work with.

I came to that belief after spending years on the other side of the table.

I worked in private equity and invested in venture capital deals long enough to understand how the machine works, and to respect it. Capital at risk deserves a return, and the system produces genuinely world-changing outcomes. No disagreement there.

But here’s what I kept watching: the shape of venture capital, high-return, fee-intermediated, compressed time horizon, necessitates an extractive growth trajectory whether anyone intends it or not. The incentive isn’t to build the best company. It’s to build the best exit. And when that capital touches a founder who doesn’t know how to speak its language, let alone use it, things go sideways in a very predictable way. Modest, mission-driven companies get swept up in a rate of acceleration that their culture, their team, and their purpose can’t survive. By the time a company reaches a Series B, product-market fit isn’t the question anymore, the question is whether the machine can scale fast enough to justify the capital structure, and the answer is often that it can’t without breaking something important. Companies become the thing they set out to destroy. Nobody in the room is exactly wrong — the founder wanted to grow, the capital wanted a return — but the tangible outcome is one that none of the people building actually wanted.

That’s not a capital problem. It’s a clarity problem.

HumanScale is my attempt to name something I’ve been circling for a while.

Early economies were highly localized. Capital was allocated close to home, by people with long time horizons and real skin in the game. Production was close to consumption. The craftsman knew his customer. The merchant knew his supplier. That proximity created natural constraints that also happened to produce better outcomes: more durable businesses, more resilient communities, more trust in the system. That model of capital — long duration, mission aligned, grounded in relationship rather than return — still exists. It's just not the one LinkedIn celebrates.

We can still build that way, and not because nostalgia is a strategy, but because the underlying economics are sound. A $10 million manufacturing operation in St. George, Utah, run by someone who loves their craft and their customer, is a more stable wealth-creation vehicle than a $50 million venture-backed company grinding toward the next raise. A software business with 10,000 loyal customers and 60% margins is more valuable to the founder, the team, and the surrounding community than the same company stretched thin trying to justify a cap table it didn’t need.

Unchecked growth creates extractive systems. It erodes trust at every layer — with employees, customers, and partners. It converts founders into fundraisers and turns people into headcount. There’s also a subtler cost: it makes the world less interesting to live in, because it produces sameness at scale rather than character at a human level.

The question I keep asking: why can’t we have a few $10 million manufacturing operations, software tools, and niche global lifestyle brands in every community? Why can’t 50 million small business owners do what they love, serve 10,000 customers each, and build real financial independence without ever touching a term sheet? That’s a more interesting society than the one the venture playbook is building.

There’s a threshold where this starts to break, and I’ve felt it in my own work. Somewhere around $25 million in revenue, something shifts. You need HR infrastructure to manage people rather than know them. Decisions get made by process rather than judgment. Systems start requiring people to operate them rather than people being required to build systems. The intimacy that made the thing worth building quietly exits the building. Some businesses genuinely need to be bigger. But most don’t, and most founders scaling past that number are chasing a narrative someone else wrote for them. The ones who do it right compound their culture the same way they compound their capital, on their own terms, at their own rate.

The VC narrative is genuinely brilliant at one thing: capturing young ambition and maximizing its output. And to be clear — opting into that system is a legitimate choice. Running fast can be exhilarating, and when the roulette lands on green it's one of the most powerful wealth-building machines ever designed. The unicorns are real, but they outshine the wreckage beneath them. For every founder who exits at nine figures, there are dozens who spent five years fighting to raise their next round, burning out their team, diluting their mission, and running a sprint they never wanted to run.

The founder in St. George didn’t need that story, he wrote his story on his terms, at his pace, and maintained his vision all the way through.

Share

HumanScale is an argument for a different default. Not against ambition, the founder I visited is one of the most ambitious and brilliant business owners I’ve met. Nor against growth, he’s growing faster than most venture-backed companies in his category. It’s an argument against using capital and scale as a substitute for clarity about what you’re actually building and why. We don't need more capital chasing returns. We need more capital that gives a damn about what it builds.

If you know how much it costs to acquire a customer, how much you make from that customer over time, whether you’re selling something they genuinely love, and whether the business can sustain that love as it grows — you can build something real. Something that lasts. Something that actually produces the freedom you thought you were signing up for when you started.

That’s what I want to work on. That’s why I’m building.

If you’re building and want the ongoing thread — subscribe to Conduit of Value. Future pieces in the HumanScale series will go deeper on specific questions: how to know when you’ve hit your right scale, how to structure for durability rather than exit, how to use capital intentionally without letting it use you.

If you’re a founder somewhere between “this is working” and “I’m not sure it’s working in the right direction” — I’d genuinely enjoy the conversation. Not a sales pitch. Just the kind of conversation that gets me out of bed in the morning. duncan@saorsapartners.com

And if you're a local investor, a community lender, a family office, or simply someone with capital and a longer time horizon than a fund cycle, you're part of this conversation too. The founders I'm describing need patient capital from people who care about the outcome, not just the multiple. If that's you, reach out. The world gets more interesting when money and mission point in the same direction.

His P&L showed profit every month. His cash balance was growing. He had a product people wanted, a growing customer base, and the kind of momentum that makes you feel like you’re finally figuring this thing out. But he couldn’t step away from the business for a week without it stalling. He couldn’t afford to hire anyone meaningful. And every time he tried to model what growth actually looked like, the math got murky in ways he couldn’t explain.

When we sat down together and I asked him how he accounted for his own time, he looked at me like I’d asked him to solve a physics problem.

He hadn’t. Not formally, not anywhere in the P&L. He was shipping product, handling customer service, managing the supply chain, and personally assembling components at a pace that would have cost him $80,000 or more per year to replace. That cost lived nowhere in his books. His gross margin wasn’t real. His profit wasn’t profit. It was wages he’d forgotten to pay himself.

This is the most common margin blind spot I encounter: the business looks healthy until you price the thing that’s holding it together. Once we rebuilt his unit economics with a realistic cost for his time, the picture changed entirely. The margin that looked like 60% was closer to 30%, and that’s before we got to customer acquisition costs, shipping, or discounting.

He wasn’t running a profitable business. He was running an elaborate mechanism that converted his labor into cash, and mistaking the output for a return.

This is the third article in the Levers for Growth series. In Lever One: The Sales Engine, we built the framework for understanding customer acquisition economics: CAC, LTV, SER, and what it actually costs to generate a dollar of revenue. In Lever Two: Working Capital, we looked at how cash moves through a product business and how to stop it from getting trapped in your inventory.

Both of those frameworks depend on one thing being true: that your margin numbers are real.

Most of the time, they aren’t. Not in the way founders think. And that’s what this article is about: how e-commerce profit margins get misread, where contribution margin tells a truer story, and what to do about it.

What Gross Margin Is Actually Telling You

Gross margin is the first number most founders learn to track. It’s the percentage of revenue left after you subtract the direct cost of the product. Simple in theory, chronically misleading in practice.

The formula isn’t complicated:

Gross Margin = (Revenue - COGS) / Revenue

Example:
Revenue:  $100
COGS:     $25 (materials, manufacturing)
Gross Margin: 75%

But here’s what that number doesn’t tell you: how much it actually costs to sell that product and get cash in the bank. Once you add shipping, discounts, returns, customer acquisition, and any labor that didn’t make it into your COGS calculation, the story changes fast.

The framework I use with partners is contribution margin: a cleaner signal for what a product or channel actually contributes to covering your overhead and generating real profit.

Contribution Margin = Revenue - Variable Costs of Selling

Variable Costs include:
  - Product cost (COGS)
  - Discounts and promotions
  - Shipping to customer
  - Returns and restocking
  - Channel fees (Amazon, Shopify, marketplace fees)
  - Direct marketing / customer acquisition cost (CAC)
  - Unattributed labor (founder time, assembly, fulfillment)

Let’s run the full waterfall on a real-looking example — the kind of unit economics I see more often than I’d like:

Revenue:                           $100.00    (100%)
Materials / COGS:                  -$10.00    (10%)
  Reported Gross Margin:            $90.00    (90%)

Unattributed founder labor:        -$40.00    (40%)
Promotional discount:              -$20.00    (20%)
Customer acquisition (digital ads): -$20.00   (20%)
Outbound shipping:                 -$10.00    (10%)

  True Contribution Margin:          $0.00    (0%)

Zero. The business is moving money, but it isn’t making any.

The founder sees 90% gross margin on his P&L and feels good. The cash balance is growing because he’s effectively getting paid, but only in revenue, not in profit. The moment he tries to hire someone to replace his labor, the model falls apart. The moment he stops running ads, revenue disappears. He’s not building equity. He’s treading water in a suit that looks like a life jacket.

This is why gross margin, on its own, is a dangerous number to trust. It measures cost of production. Contribution margin measures cost of selling, this is the number that determines whether your business model works.

The Mindset Shift: You Sell to the Customer, Not the Product

One of the most powerful unlocks I’ve seen with an early-stage product business came not from a pricing change or a cost reduction, but from a reframing of what the business was actually selling.

The company made protective components for dirtbikes — specifically swingarm guards. Their instinct was to expand horizontally: more guard designs, more bike models, broader catalog. More SKUs to serve more customers. Standard growth playbook.

The problem with that instinct, when you look at the contribution margin math, is that it treats each product as its own independent profit event. Acquire a customer for $25, sell them a product with $75 of gross margin, net $50. Repeat.

What we found when we mapped the economics more carefully was that the real opportunity wasn’t in reaching new customers — it was in selling more to the ones they already had. A customer who had already purchased once could be sold a second product, a third, a fourth. The $25 acquisition cost didn’t repeat. The margin on order two was almost entirely additive.

Here’s how the contribution margin evolved across three stages:

Stage 1 — Broad catalog, single SKU per customer:
  Unit cost:       40% of revenue
  Cost to sell:    25% of revenue
  CM:              35%

Stage 2 — KTM-focused pivot, multi-product cart:
  Unit cost:       45% of revenue (new products, supply chain still being optimized)
  Cost to sell:    10% of revenue (CAC amortized across multiple SKUs)
  CM:              45%

Stage 3 — Supply chain dialed in, existing customer base:
  Unit cost:       25% of revenue
  Cost to sell:    10% of revenue
  CM:              65%

The shift from Stage 1 to Stage 3 wasn’t magic. It came from a strategic decision to narrow the focus — double down on one customer type (KTM riders) and build enough product depth to make that customer worth $200 of gross margin instead of $100, while paying the same $25 to acquire them. That’s the economics of a real business, not just a product.

The framing that unlocked this: we’re not selling products. We’re selling to a customer. Once the business accepted that, the growth priorities became obvious. Product development for the existing customer base, not lateral expansion to new markets with smaller addressable audiences and higher acquisition friction.

When you think about it this way, contribution margin by customer becomes as important as contribution margin by SKU. And your Sales Engine, which we built in Lever One, starts to look very different when LTV is expanding not through retention alone, but through wallet share.

What Amazon Is Actually Costing You

Channel economics is where I most often find hidden margin destruction — and the channel that surprises founders most is Amazon.

Amazon is seductive. It offers traffic, trust, and Prime shipping infrastructure you’d never replicate on your own. For many products, it’s legitimately the right channel. But the fee structure is one of the most aggressive in e-commerce, and it’s easy to miss how much it’s actually taking.

Here’s roughly what Amazon FBA costs look like when you stack the full picture:

Revenue (your selling price):          $100.00
Referral fee (avg ~15%):               -$15.00
FBA fulfillment fee:                    -$5.00
Storage fees (varies):                  -$2.00
Inbound placement / misc fees:          -$3.00

Platform fees before product cost:     -$25.00   (25% of revenue)

With advertising (Amazon PPC, common):  -$15.00

Total cost to sell on Amazon:          -$40.00   (40% of revenue)

Those numbers aren’t unusual. Industry benchmarks consistently put total Amazon FBA selling costs — platform fees plus advertising — in the 35-40% range for standard product categories.

Now look at what that does to a business that also sells DTC:

                    DTC (Shopify)       Amazon FBA
Revenue:             $100               $100
Gross Margin:         80%                80%
Post-discount GM:     70%                70%
Cost to sell:         20%                40%
Contribution Margin:  50%                30%

If this business is reporting a blended 40% contribution margin and feeling fine about it, the channel breakdown tells a different story. DTC is generating real profit. Amazon is generating revenue that looks like profit but is quietly consuming cash on every order.

I worked with a client who had Amazon running at roughly 30% of total revenue — a number the team was proud of. When we broke out the channel economics, Amazon was contributing about 5% of total gross margin dollars despite generating nearly a third of the top line. The math was simple: every Amazon order was costing them more to sell than their DTC orders, and the volume amplified the drag.

We killed the channel. Revenue dropped. Margin improved substantially. And the team was able to redirect the energy spent managing FBA inventory, listings, and customer service disputes toward the channel that was actually building the business.

This isn’t an argument against Amazon. It’s an argument for knowing what Amazon is actually costing you before you scale it.

The Accounting Problem No One Talks About

Here’s a practical issue that sits underneath everything else in this article: most founders don’t have the accounting infrastructure to see their margins clearly in the first place.

The most common problem I encounter when onboarding a new partner is cash-basis bookkeeping applied to an inventory-based business. In cash-basis accounting, your cost of goods recognized is based on when you write the check to your supplier — not when the product sells. The result looks like this:

Month 1 (receive inventory, pay supplier):
  Revenue:    $50,000
  COGS:      -$80,000  (paid for 3 months of inventory)
  Gross Margin: -60%   ← meaningless

Month 3 (selling from inventory, no new purchases):
  Revenue:    $70,000
  COGS:       -$5,000  (small reorder)
  Gross Margin: 93%    ← also meaningless

Neither number reflects reality. Blended over three months, the average might look reasonable, but month-to-month you have no idea whether your margins are improving or deteriorating. You can’t make decisions from that signal. You certainly can’t build a reliable financial model from it.

Accrual accounting, specifically matching cost of goods to the revenue it generates when inventory is sold, is the foundation that makes everything else in this series possible. Without it, your contribution margin analysis is guesswork, your channel comparisons are approximate, and your working capital decisions (Lever Two) are based on inventory values that don’t reflect what’s actually moving.

This isn’t an advanced CFO problem. It’s step one. And if your accounting setup isn’t there yet, the single highest-leverage thing you can do for your business right now is fix it. If you’re not sure where to start, reach out and I’m happy to connect you with an e-commerce accounting firm that can get you set up properly —duncan@saorsapartners.com.

Common Patterns I See in $2-20M E-Commerce Businesses

The markup mentality. Most founders I work with were taught to price as a multiple of cost: 4x, 5x, 10x. The problem with markup-based pricing is that the math obscures what actually matters. A 5x markup means your COGS are 20% of revenue. But does that account for the returns rate? The channel fees? The discount you’re running on the site? Thinking in gross margin terms, cost as a percentage of revenue, keeps the math honest as your cost structure evolves and is the standard that ties to your financial model and inventory valuation. The moment you make a pricing decision using a markup multiple, you’ve disconnected from the number your P&L actually reports.

All channels are not created equal. This one takes founders by surprise more than almost anything else. They’ve been running DTC and Amazon side by side, assuming the diversification is healthy. When we break out the cost of selling by channel, there’s almost always a meaningful gap: one channel is generating real contribution margin, another is generating revenue that barely covers its own acquisition cost. The response I hear most often is some version of “but Amazon is 30% of our revenue.” That’s the wrong number to anchor on. Revenue without margin isn’t an asset.

Marketing spend treated as fixed overhead. I see this constantly: a monthly marketing budget described as “that’s just what we spend.” $4,000 a month on digital ads, loosely attributed to “brand awareness,” with no clear connection to orders generated. The fix is simple. Break it into an effective cost-per-order by channel and stack that against your contribution margin to determine whether that spend is actually profitable. It’s not complicated. It’s just that nobody’s done it. Once you have that number, every marketing conversation changes.

Disorganized or cash-basis accounting. Already covered above, but worth repeating: if you can’t see your COGS on an accrual basis by SKU, you are operating on intuition, not information. The founders who understand their inventory by counting pallets and checking the bank balance are not wrong to do so — but that visibility ends at “do we have product” and “do we have cash.” It tells you nothing about margin.

CEO Q&A

My gross margin looks strong — 70% blended. Should I be worried?

Maybe. The first question I’d ask back is: what’s in your COGS calculation? If you’re not accruing inventory on a per-unit-sold basis, that 70% might be an artifact of your billing cycle, not your actual economics. The second question is whether you’ve priced your own time. The third is whether that 70% is consistent across channels, SKUs, and customer segments, or whether it’s a blend that’s hiding something weaker underneath.

I’m thinking about cutting a product that isn’t hitting our markup target. Is that the right call?

Maybe not. I was in a leadership meeting where a CEO was about to discontinue a product because it couldn’t hit a 10x markup target. The product was at 7.5x. When we translated that into gross margin terms, the difference was about 3 percentage points, genuinely not material to the business. The decision was about to be made on the wrong metric entirely. Before cutting a SKU, run the actual contribution margin waterfall. Markup multiples are a rough heuristic, not a profitability verdict.

We have a product that drives most of our revenue. How do I know if it’s actually our best product?

Revenue rank and contribution margin rank are often different lists. A high-volume SKU with a steep promotional discount, a meaningful return rate, and significant fulfillment complexity can consume far more of your time and margin than its revenue share suggests. Run the full cost-of-selling waterfall on your top three products by revenue and see whether the ranking holds. Often it doesn’t.

All channels feel important. How do I decide where to focus?

Calculate the contribution margin on each channel independently. Stack up revenue, then subtract COGS, channel fees, platform costs, and the direct marketing spend attributable to that channel. What’s left is the contribution. Then ask: if I doubled the volume in each channel, which one would create the most value? Usually the answer becomes obvious, and it’s rarely the channel generating the most revenue.

I don’t have time to build fancy financial models. What’s the minimum I should track?

Five numbers: contribution margin by channel, cost-per-order by marketing channel, days of inventory on hand by SKU, cash balance, and outstanding payables. That’s not a financial model. That’s a Monday morning scorecard. If you’re tracking those five things weekly with any discipline, you’ll catch problems before they compound. We’ll go deeper on this in Lever Eight.

If you want help building the next layer — connecting those five numbers into a forecast you can actually run decisions through — that’s the work I do with partners. Reach out at duncan@saorsapartners.com.

When is a discount strategy actually worth it?

When it either clears aged inventory that would otherwise cost you storage or write-down, or when the LTV of the acquired customer is measurably higher than the contribution you gave up. A 20% discount on a product with a 30% contribution margin means you’re essentially paying the customer to take your product. That can make sense as a new-customer acquisition mechanism if you have strong retention data showing repeat orders. It almost never makes sense as a permanent site-wide discount, which is effectively a permanent price reduction you’ve normalized in your customers’ expectations.

My accountant says our margins look fine. Why are you telling me to re-examine them?

Your accountant is probably doing their job correctly within the scope they’ve been given. Most small business accountants are set up for tax compliance, not operational decision-making. The questions I’m raising, contribution by channel, unattributed labor in COGS, accrual-basis inventory matching, are operational accounting questions that require a layer of analysis beyond a standard monthly close. It’s not a criticism of your accountant. It’s a scope question and if your books are set up to handle tax obligations and produce a P&L, that’s necessary but not sufficient for the kind of decisions this series is asking you to make.

What’s Coming in Lever Four

Once you can see your margins clearly, by SKU, by channel, by customer type, the next question is obvious: what’s this business going to look like in six months? In twelve?

Lever Four is about forecasting. Not the fantasy spreadsheet you built in January that hasn’t been touched since. A driver-based model that starts with the inputs your Sales Engine generates (traffic, conversion, AOV, reorder rate) and flows them through your margin structure, cashflow dynamics, and working capital cycle to produce something you can actually make investment decisions from.

We’ll also get into why the goal of a financial model isn’t to be right. It’s to be less wrong, faster. And how your margin structure, the framework we’ve covered here, changes your working capital assumptions in ways most founders don’t see coming.

Subscribe now

Duncan Young is the founder of Saorsa Growth Partners, a fractional CFO firm serving e-commerce and inventory-based businesses doing $2-20M in revenue.

I was brought in to figure out what had gone wrong. On the surface, the numbers told a familiar story: revenue was growing, margins were stable, and the team was working hard. But the RLOC balance had quietly crept up by $3 million, and nobody could clearly explain why.

The answer wasn’t hiding in the P&L. It was hiding in the timing.

When we broke the business down into days rather than dollars, the picture became obvious. Inventory holding periods had drifted longer. The team had gotten generous with vendors, paying early out of habit rather than strategy. And a handful of customers had been slow-rolling their receivables for months with no consequences. Each of those shifts, individually minor, had compounded into a cash crisis.

We built a scorecard. We started managing AR collections with urgency. We leaned into our payables terms instead of leaving money on the table. And we interrogated how much inventory we actually needed on hand, measured in days of throughput, not dollar value sitting in the warehouse. Within a few months, the line was back in compliance and the bank was comfortable again.

That experience is where I cut my teeth on working capital management, and honestly, it makes e-commerce feel like a walk in the park. But the principles are identical. Whether you’re moving grain by the railcar or shipping DTC orders from a 3PL, the mechanics of cash conversion will either fund your growth or quietly strangle it.

This is the second article in my Levers for Growth series. In Lever One: The Sales Engine, we built the framework for understanding how dollars flow into revenue and how to make that process predictable. If you recall, one of the red flags I flagged was inventory becoming the limiting factor on growth, and I touched on the importance of working capital dynamics when evaluating whether your AOV exceeds your Order Acquisition Cost.

This article picks up right there. Because even the best Sales Engine in the world won’t save you if your cash is trapped in a warehouse.

The Cash Conversion Cycle: Your Business in Three Numbers

The Cash Conversion Cycle (CCC) is the single most useful framework for understanding how cash moves through a product business. It measures the number of days between when you pay for inventory and when you collect cash from selling it.

The formula is straightforward:

CCC = Days Inventory Outstanding (DIO) + Days Sales Outstanding (DSO) - Days Payables Outstanding (DPO)

DIO = How long inventory sits before it sells
DSO = How long it takes to collect payment after a sale
DPO = How long you take to pay your suppliers

A shorter CCC means your cash cycles back to you faster. A longer CCC means more of your equity is locked up in operations, unavailable for growth, marketing, hiring, or anything else.

For most e-commerce businesses, DSO is relatively low (credit card payments settle in 1-3 days for DTC), which means the real battleground is DIO and DPO. How long is your cash sitting in product, and how effectively are you using your vendor payment terms?

Here’s where it gets practical. Let’s say your business looks like this:

DIO: 90 days (inventory sits for 3 months on average)
DSO: 3 days (DTC, credit card settlement)
DPO: 15 days (you pay vendors quickly)

CCC = 90 + 3 - 15 = 78 days

Your cash is locked up for 78 days on every cycle. 
On a business doing $5MM in annual revenue, that's roughly 
$1.07MM of working capital tied up at any given time.

Now imagine you tighten DIO by 15 days and stretch DPO by 10 days:

DIO: 75 days
DSO: 3 days  
DPO: 25 days

CCC = 75 + 3 - 25 = 53 days

Same business, same revenue, but now you've freed up 
roughly $340K in working capital. That's cash you can 
deploy into your Sales Engine, invest in tooling, or 
simply sleep better at night.

Those aren’t theoretical numbers. That kind of swing is exactly what I’ve seen with partners after we start managing working capital with intention.

The Mindset Shift: Think in Days, Not Dollars

Here’s the thing that surprises most founders when we first sit down together. They’ll tell me, “We have $400K in inventory,” and feel good about it. Or bad about it. But they don’t really know, because the dollar figure alone is meaningless without context.

$400K in inventory could be perfectly healthy or dangerously bloated. It depends entirely on how fast that inventory moves.

When I work with e-commerce partners, one of the first things we do is reframe every working capital line item from dollars into days:

Days of Inventory on Hand (DIO): Not “how much inventory do we have” but “how many days of sales does our current inventory cover?”

Days Sales Outstanding (DSO): Not “how much are customers owed” but “how many days does it take to collect after a sale?”

Days Payables Outstanding (DPO): Not “how much do we owe vendors” but “how many days of breathing room are we getting from our supplier terms?”

The shift to days does two things. First, it makes the numbers actionable. “We have 147 days of inventory” is a decision. “We have $400K of inventory” is just a fact. Second, and this is the part that consistently lights a fire under founders, it connects directly to cash. When I build day-based assumptions into a financial model and we start playing with scenarios, founders can see in real time how a 5 to 10 day swing in any of these figures frees up (or consumes) significant cash. That moment, where DIO goes from an abstract metric to “oh, that’s $50K I could be spending on ads,” is when the mindset shifts.

The SKU-Level Trap

The average Days of Inventory number can also be deeply misleading at an aggregate level, especially in e-commerce where you’re managing dozens or hundreds of SKUs.

I’ve worked with companies sourcing overseas where the blended DIO looked like 180 days. Not great, but the founder shrugged and said, “That’s just how importing works.” When we broke it down by SKU, the picture was completely different. Some products had 45 days of stock and were regularly stocking out, costing the business sales and ad efficiency every time they went dark. Other SKUs were sitting at 400+ days, essentially dead capital that had been ordered “just in case” and was now gathering dust.

Aggregate DIO told one story. SKU-level DIO told the real one.

This is where forecasting demand and framing order decisions around days of inventory rather than “let’s make sure we don’t run out” becomes critical. The conservative instinct to over-order is understandable, but it has a real cost. One partner running about $5MM in revenue was carrying roughly $300K in excess inventory driven almost entirely by this mindset. At their growth rate of 50% year over year, that $300K could have been invested in marketing, improving EBITDA, or paying down debt. Instead, it was sitting on pallets.

Domestic Manufacturing: A Working Capital Play Hiding in Plain Sight

Most founders evaluate domestic vs. overseas manufacturing purely on unit cost. And on that single dimension, overseas almost always wins. I’ve seen differentials as stark as this:

Overseas: 2,000 unit MOQ | $2/unit | 20-week lead time
Domestic: 200 unit MOQ | $10/unit | 4-week lead time

At face value, overseas is 5x cheaper per unit. 
But let's look at what each option actually costs in working capital terms.

The overseas order ties up $4,000 in product cost, but you won’t see that inventory for nearly five months. Factor in ocean freight, customs, potential delays, and the capital is locked for even longer. And because of the high MOQ, you’re committing to 2,000 units whether the product sells or not. If it’s a new SKU, that’s a meaningful bet.

The domestic order ties up $2,000 in product cost and arrives in a month. You can test, iterate, reorder, and respond to demand signals in near real-time. Yes, the per-unit cost is higher, but the capital is cycling faster and the risk per order is dramatically lower.

Here’s what I’ve seen work well in practice. For proven, high-velocity SKUs where demand is predictable, overseas manufacturing can make sense because you can forecast the volume and absorb the lead time. But for new product launches, seasonal tests, or anything where demand is uncertain, domestic manufacturing is often the smarter working capital decision even though the margin looks worse on paper.

There are other advantages that don’t show up in a unit cost comparison. Domestic suppliers tend to offer more flexible payment terms because there’s more inherent trust in the relationship. I’ve seen onshore partners finance tooling and offer friendlier deposit structures on both tooling and inventory orders, things that overseas suppliers almost never do at the same scale. One of my partners was able to get tooling financed by their domestic manufacturer specifically because the relationship was built on transparency and consistent ordering patterns. That tooling investment unlocked their ability to scale production rapidly without deploying their own equity.

The takeaway isn’t “always go domestic.” It’s that the unit cost comparison misses the full picture. When you factor in MOQ risk, lead time, cash cycle speed, and the flexibility to redirect capital toward growth, domestic manufacturing often pencils out far better than the spreadsheet initially suggests.

Financing Working Capital: Where Should Your Capital Live?

This is the conversation where I most often have to push back on founders’ instincts. The natural reaction, especially for bootstrapped operators, is “I don’t want to take on debt.” And I get it. Debt feels like risk. But here’s the reframe I always come back to:

Every dollar of equity you have sitting in inventory is a dollar that is not being invested in growth.

If your Sales Engine (which we built in Lever One) is generating a 20% return on marketing spend, and your inventory is just sitting there generating a 0% return until it sells, you have a capital allocation problem. The question isn’t whether debt is good or bad. The question is: where does each dollar in your business generate the highest return?

Let me put numbers on it:

Scenario A: Self-Fund Inventory
You have $100K in available cash.
You use it to purchase inventory.
That inventory generates revenue over 90 days.
Your equity earns the margin on that inventory, call it 10-15% 
annualized after accounting for the time value.

Scenario B: Finance Inventory, Deploy Equity to Growth
You finance the same $100K in inventory at 8-12% annual cost.
You deploy your $100K into your Sales Engine.
Based on your proven SER of 2.5x, that $100K generates 
$250K in new gross margin.
Net of the financing cost ($8-12K), you're dramatically ahead.

The math almost always favors financing working capital on proven products and deploying equity toward growth, assuming your Sales Engine is dialed in. This is why Lever One comes first in this series. You need predictable unit economics before this strategy makes sense.

Share

What Financing Actually Looks Like

The most common tools I see e-commerce companies use:

Revolving Lines of Credit (RLOC): The gold standard for working capital financing. You draw when you need to purchase inventory and pay down as revenue comes in. RLOCs can be painful to secure from traditional banks, but a lot of that friction comes from the founder not having the financial visibility to communicate the risk profile to the lender. When you walk into a bank with a clear CCC analysis, a demand forecast, and a scorecard showing your Days metrics trending in the right direction, the conversation changes.

Revenue-Based Lending: Faster to secure than an RLOC and common in e-commerce (Shopify Capital, Clearco, etc.). The cost is higher, but for short inventory cycles, it can be a useful bridge. Just make sure you understand the true effective rate and don’t layer multiple advances.

Vendor Terms: Arguably the most underutilized form of financing in small business. Net 30, Net 45, Net 60 terms from your suppliers are interest-free working capital. Yet I regularly see founders paying invoices on receipt out of habit or a desire to be “good partners.” Being a good partner means paying on time, within your agreed terms. Using the full window isn’t disrespectful, it’s intelligent cash management. It directly improves your DPO and shortens your CCC.

Factoring: I’ll be honest, factoring is generally my least favorite option. The fees are steep and the structure can create dependency. But in specific situations, particularly B2B e-commerce with slow-paying wholesale customers, it can unlock cash that’s otherwise trapped in receivables. If your DSO is 45+ days because your retail partners pay slowly, factoring that AR to redeploy into inventory or marketing can make sense as a tactical tool.

The bottom line: once you have a proven, systematic product line, equity shouldn’t be funding your inventory. A good capital partner, whether that’s a bank, a lender, or even well-structured vendor terms, frees your equity to do what it does best: compound growth.

Common Patterns I See in E-Commerce Companies

As a Fractional CFO for e-commerce businesses across a range of sizes and categories, certain working capital mistakes show up again and again. These aren’t edge cases. They’re the norm for companies in the $2-20MM range that haven’t yet built financial discipline around their cash cycle.

1. Ordering to “Not Run Out” Instead of Forecasting Demand

This is the most common pattern by far. The founder has been burned by a stockout before, maybe they lost momentum on a bestseller or had to pause ads while waiting for inventory, so now they over-order everything as insurance. The intention is rational, but the execution is expensive. When we actually run the numbers and frame orders in terms of days of inventory rather than “just get more,” the excess becomes obvious. That $300K of extra inventory I mentioned earlier? That came directly from this instinct.

2. Ignoring AP as a Cash Management Tool

Some founders pay every invoice the day it arrives. Others stretch payables until vendors are calling to complain. Both extremes hurt. Paying early means you’re financing your suppliers’ working capital instead of your own. Paying late damages relationships and can cost you favorable terms. The sweet spot is simple: understand your terms, use them fully, and negotiate for better ones as your volume grows. Every day you add to DPO is a day of free financing.

3. Treating Inventory as One Number

As I covered in the SKU-level section, aggregate inventory metrics hide massive imbalances. The companies that manage working capital well are the ones that can tell you, by SKU, how many days of stock they’re holding and whether that number is appropriate for the velocity of that product. The companies that don’t manage it well say “we have $X in inventory” and leave it at that.

4. Not Connecting the Sales Engine to Inventory Planning

This ties back directly to Lever One. If your Sales Engine can predictably generate demand, your inventory planning should be driven by that engine, not by gut feel. When ad spend goes up, inventory needs to be there. When you’re testing a new channel, inventory commitment should match the test budget, not a full-scale launch. The Sales Engine tells you how much demand you’re creating. Working capital management tells you how to fund it without running out of cash.

5. Avoiding the Financing Conversation

Too many bootstrapped founders view all debt as dangerous. As a result, they self-fund inventory with equity that could be generating multiples in growth. The opportunity cost is real and often invisible because the founder never runs the comparison. If your Sales Engine produces a 2.5x SER and your inventory financing costs 10%, you’re leaving enormous value on the table by using equity for working capital.

Subscribe now

CEO Q&A: The Questions I Actually Get Asked

Rather than a typical FAQ, these are the real questions I hear from bootstrapped e-commerce CEOs in the $2-20MM range, and the honest answers I give.

“How do I know how much inventory to order? It always feels like guessing.”

It feels that way because it probably is. Most founders I work with are ordering based on a combination of gut feel, fear of stockouts, and round numbers. The fix starts with converting your inventory position into Days of Inventory by SKU. Once you can see that your top seller has 30 days of stock while a slow mover has 300+ days, the ordering decision becomes much clearer. Pair that with a demand forecast from your Sales Engine, even a simple one, and you move from guessing to planning. It’ll never be perfect, but “roughly right” beats “confidently wrong” every time.

“My bank won’t give me a line of credit. What am I doing wrong?”

Usually, it’s not that the business doesn’t qualify. It’s that the business can’t clearly communicate its risk profile. Banks want to see that you understand your cash cycle, that you have a plan for how you’ll draw and repay the line, and that there’s a system behind your growth. If you walk in with a CCC breakdown, a scorecard showing your Days metrics, and a revenue model that connects spend to output, you’re speaking their language. A lot of the friction with lenders comes from a lack of financial infrastructure, not a lack of creditworthiness.

“Should I be worried that my margins are lower with domestic manufacturing?”

Lower per-unit margins are real, but they’re only one piece of the equation. If domestic sourcing cuts your lead time from 20 weeks to 4 weeks and drops your MOQ by 10x, you’re cycling cash faster, testing products cheaper, and carrying less risk per order. I’ve watched founders agonize over a $8 per-unit margin difference while ignoring the fact that overseas ordering had $200K of their equity locked in a container ship for five months. The margin matters. But so does what your capital could be doing if it weren’t trapped in transit.

“We’re growing fast but always feel cash-strapped. What gives?”

Growth consumes cash. This is the part that surprises founders who are doing everything right on the P&L. Revenue is up, margins are healthy, but the bank account keeps feeling tight. The answer is almost always that your CCC is expanding alongside your revenue. You’re buying more inventory to support more sales, but the cash from those sales doesn’t arrive fast enough to fund the next cycle. This is where financing working capital becomes essential. Growth is not a cash flow problem to solve. It’s a capital allocation problem to manage.

“I don’t want to take on debt. Why can’t I just fund everything myself?”

You can, and plenty of founders do. But here’s what I’d ask you to consider: every dollar sitting in inventory is earning you the margin on that product whenever it eventually sells. If your Sales Engine is generating $2.50 in gross margin for every $1 you invest, and your inventory is generating maybe $0.15 in margin per dollar per cycle, where do you want your equity? The aversion to debt is understandable, but the opportunity cost of self-funding inventory is very real, especially when working capital financing is available at rates that are a fraction of your growth returns.

“How do I get started if I have none of this in place today?”

Start with three numbers: your average DIO, DSO, and DPO. You can calculate these from your existing financial statements or even from your Shopify/ERP data. Then calculate your CCC. That single number will tell you how long your cash is locked up per cycle, and it gives you a baseline. From there, pick the biggest lever. For most e-commerce companies, that’s DIO, because inventory is where the most cash gets trapped. Break it out by SKU, identify the outliers, and start making ordering decisions based on days rather than dollars. You don’t need a perfect system on day one. You need visibility.

What Comes Next

A well-tuned Sales Engine tells you how to create demand. Working capital management tells you how to fund it without choking on your own growth. Together, they form the foundation of a business that compounds rather than one that constantly scrambles.

In the next installment of Levers for Growth, we’ll move up the strategic ladder and tackle a topic that ties everything together: how to think about profitability, margin structure, and the financial model that turns your operating business into a wealth-building machine.

If you’re running an e-commerce business and any of this hits close to home, whether it’s inventory stress, cash flow tightness despite healthy growth, or simply wanting a partner who can help you see the numbers clearly, this is exactly the kind of work I do. Reach out anytime.

This article marks the start of my Small Business Growth Series: Levers for Growth. The goal is to give founders and operators practical frameworks and playbooks for building businesses that compound: businesses that support your family, your team, your community, and ultimately your financial future.

Why Build a Sustainable Sales Engine?

Imagine the familiar scene: you’re at the Courtyard Marriott bar after another trade show, entering business cards into your laptop with an Old Fashioned in hand. You’ve spent $2,000 on conference fees, $2,500 on travel, $1,500 on marketing materials, and lost several days of fulfillment work. In return, you estimate about $20,000 in potential revenue, realistically closer to $15,000 once everything shakes out. After all that effort, you’ve bought yourself roughly one more month of runway, along with a few inevitable late nights.

Now consider a different version of the same investment decision. Instead of spending nearly $6,000 and several days of labor to hope for $15,000 in eventual revenue, you invest $10,000 across your sales team, advertising, and conferences, and reliably see $35,000 of revenue hit your P&L. The same intent, to generate business, but with predictable and data-backed outcomes. That is what a Sales Engine creates: a consistent, measurable relationship between the dollars you put in and the revenue you can expect to get out.

It gives you the ability to throttle growth up or down as operations evolve. It helps you forecast inventory requirements with confidence. Most importantly, it aligns financial strategy with growth strategy so the business becomes more stable, more scalable, and far less chaotic.

A well-built Sales Engine keeps you from sprinting off the cliff like Wile E. Coyote. Instead, you see the terrain ahead, plan your path, and grow with intention.

Key Concepts

Borrowing from my friends in Venture Capital, experts in “growth-at-all-costs”, there are a few terms that don’t leave the walls of Silicon Valley nearly often enough: CAC, LTV, AOV/ACV, and SER. Finance loves its gate-keeping language, but these metrics are foundational if you want to understand the economics of a scalable Sales Engine.

Let’s break down each concept and how it shows up in real decision-making.

Customer Acquisition Cost (CAC)

This is the average cost of acquiring a new customer, that is the total amount spent on marketing and sales efforts (directed at new customers) relative to the number of customers acquired.

I also often look at Order Acquisition Cost (OAC) in tandem with CAC. OAC measures the cost to acquire any order (including repeat customers), which tends to matter more for e-commerce clients where returning customers meaningfully lower blended acquisition costs.

CAC = (Total Sales & Marketing Cost)/(Number of Customers Acquired)

For example: A company that acquired 1000 customers last year and spent $50,000 on Sales & Marketing has a CAC of 50.

Pro Tip: CAC becomes dramatically more meaningful when you break it down by channel. Your “blended CAC” may look healthy, but individual channels often hide inefficiencies. Tracking CAC by channel (paid social, paid search, referrals, partnerships, outbound, etc.) helps you double down on profitable acquisition paths and shut off the ones silently burning cash.

Lifetime Value of Customer (LTV)

LTV measures the total revenue you expect from an average customer over the full duration of your relationship with them. It captures not just the first purchase, but the entire economic contribution a customer makes over time. For many businesses, especially e-commerce, LTV is the financial backbone of predictable growth. It tells you how aggressively you can acquire customers, how long it takes to recover acquisition costs, and how much working capital you can confidently deploy.

The key insight is that customers rarely behave in a single transaction. They rebuy, upgrade, reorder consumables, respond to promotions, join loyalty programs, or develop purchasing habits that compound their value. A properly measured LTV reflects these patterns and gives you a realistic picture of what a customer is actually worth, not just what they spend on day one.

LTV = Sum of All Revenue from the average Customer 

For example: Our Average customer spends $500 per order and orders every 6 months for an average of 2 years. This means we have an LTV of $2,000.

* Pro Tip: For product companies, evaluating LTV on a gross margin basis often tells a more accurate story. Using the example above:

Revenue LTV = $2,000
At 75% gross margin → $1,500 true margin LTV

This is the number that actually funds growth.

Average Order Value (AOV) / Average Contract Value (ACV)

AOV (common in D2C) and ACV (common in B2B) represent the average dollar amount a customer spends per purchase or per contracted period. These metrics help you understand customer behavior, revenue composition, and how efficiently you can afford to acquire customers..

AOV = (Total Revenue) / (Number of Orders)
ACV = (Total Contract Value) / (Number of Contracts)

For example: An ecommerce store generating $200,000 from 2,500 orders has an AOV of $80. A B2B startup closing $500,000 across 10 annual contracts has an ACV of $50,000.

* Pro Tip: When AOV or ACV is low, CAC must also stay low or you’ll burn cash fast. Increasing AOV/ACV (through bundling, upsells, pricing strategy, or contract expansion) is typically one of the highest-leverage ways to improve unit economics.

Sales Efficiency Ratio (SER)

SER deserves far more airtime outside of venture-backed startups. It measures how efficiently your Sales & Marketing spend converts into new gross margin, not revenue.

SER gives you a clear read on whether your growth model scales.

It’s also worth noting that I treat SER as a holistic efficiency metric (including salaries and overhead), while CAC is more marginal and often excludes fixed costs.

SER = (New Gross Margin Generated) / (Sales & Marketing Spend)

For example: If your company spent $100,000 on Sales & Marketing in Q1 and generated $250,000 in new gross margin during the same period, your SER is 2.5x. In other words, every $1 spent produced $2.50 in new gross margin. 2-3x is healthy, if you're well above that it means that you're under leveraging your sales engine, and if you're below it means you need to drive more value or improve sales efficiency.

* Pro Tip: Investors love SER because it provides a quick read on the scalability of your growth engine. A ratio above 3.0 generally signals efficient growth; below 2.0 usually means you’re spending more to acquire gross margin than it's initially worth. Strong companies improve SER over time as their brand, product, and go-to-market motion mature.

Venture Math: Investing in Growth

As I often preach to my partners, as a small business owner, you are an investor first and an operator second. After all, you’ve bet your life on the company, sometimes mortgaging your house, and certainly sacrificing the most scare resource of all, time. Given this, it’s crucial that you understand the math that growth-oriented investors are running to protect their investment while maximizing returns on capital.

Depending on your line of business, a few comparisons should drive your decision making:

LTV < CAC: If the cost to acquire a customer is lower than the lifetime value of that customer, you have a fundamental business problem. This is likely from Product Market Fit limitations but also may be a function of poor efficiency. When counting these, it’s important to be really thoughtful of the true costs.

For example: If you’re making $300 per customer and it only cost you $50 in Ads to acquire that customer, it may seem value accretive, however if you’re spending 15 hours per week creating Instagram content, the true economics may be much less favorable. 

LTV > CAC: If your customers are worth significantly more than it costs you to acquire the customer, you should continuously invest in acquiring new customers since you expect to recover this value in the long run. One area that often goes overlooked is the situation where the first order is not profitable, but the lifetime relationship is.

For example: It costs $100 to acquire a customer and only make $80 of gross margin on that customer. Often between your brand and email channels the company has a great retention/retargeting strategy where it costs only $5 per additional order. 

Order 1 | Gross Margin = $80 | Cost to Acquire = $100 | Total Gain/Loss = -$20
Order 2 | Gross Margin = $80 | Cost to Acquire = $5   | Total Gain/Loss = +$55

This is the power of understanding your LTV; by understanding customer behavior you can invest in growth that isn't immediately obvious.

AOV/ACV > Order Acquisition Cost: Assuming a reasonable working capital dynamic, if your Average Order Value/Average Contract Value is greater than your single Order Acquisition Cost (OAC), you can reasonably continue to spend to drive more growth up until your OAC = AOV. While there is nuance here with Gross margin, this gives you a lever to grasp at the economies of scale that come with higher volumes to drive higher profitability.

For example: Let’s say it costs you $40 in advertising and sales effort to acquire a single order (your OAC), and the Average Order Value (AOV) for that customer is $75. Even before layering in retention or repeat purchase behavior, you are generating more revenue per order than you are spending to acquire that order. As long as gross margin is healthy (say 60–70%), you have room to scale this channel.

AOV: $75 | Gross Margin (70%): $52.50 | OAC: $40
Net Contribution per Order: $52.50 – $40 = $12.50

This means every incremental dollar you put into this acquisition channel is producing a positive contribution today, not just in the lifetime value model. You can safely continue spending up to the point where your OAC climbs toward your AOV. As volume increases, ad efficiency often improves, vendor costs decline, and operational scale increases—further widening that positive gap.

This dynamic is a core engine behind profitable scaling: when AOV comfortably exceeds OAC, growth becomes a financial decision rather than a leap of faith.

SER > 2: If your Sales Efficiency Ratio is consistently greater than 2, you are generating at least $2 in new gross margin for every $1 you spend on Sales & Marketing. This is the moment when growth-oriented investors start smiling. A strong SER indicates that your sales engine is both efficient and scalable, meaning you have room to safely dial up spending to increase revenue without jeopardizing profitability.

A high SER often means that your brand is resonating in the market, your sales team is effective, and your retention or expansion motions are functioning properly. In this environment, strategic investment into sales channels, people, technology, or performance marketing often produces asymmetric returns.

For example:
Your company spends $50,000 this quarter and generates $130,000 in new gross margin. Your SER is:

SER = 130,000 / 50,000 = 2.6

This suggests you’re leaving profitable opportunities on the table. You could likely increase your Sales & Marketing spend to accelerate growth, as long as operational capacity (production, customer support, fulfillment) can absorb the increase.

If your SER rises materially above 3–4x, it may actually be a sign that you’re under-investing in growth. Your sales engine is working, but you’re not feeding it enough volume to maximize its output.

Case Study: Turning Data Into a Scalable Sales Engine

One of my favorite examples of building a true Sales Engine comes from a client I’ve worked with over the past year, a product company with strong organic traction but no reliable way to forecast revenue or justify meaningful investment in growth. Like many founders, they were hustling their way to each next month, unable to confidently answer the question: If I spend $X on sales and marketing, what do I get back?

When we started, the numbers looked like this:

• AOV: $150

• Gross Margin: 50% (Transitioning to 75% with investments in lower unit costs)

• CAC: $50

• LTV: $1,800

• Order Acquisition Cost (OAC): $30 blended across new and returning customers

These are strong fundamentals, but they were hidden beneath a lack of visibility. So, we built the visibility.

Collecting the Data

The first step in building a Sales Engine is simple, but not easy: you must know what is actually happening inside your business. For this client, we started by running a cohort analysis, which is just a fancy way of saying we grouped customers by the month (or channel) they first purchased and tracked their behavior over time. Cohorts reveal patterns that topline revenue can’t: repeat purchase cycles, contribution by customer segment, and whether retention is improving or degrading.

Alongside that, we:

• Implemented proper attribution tracking so we knew which channels were driving sales and at what cost.

• Built a weekly scorecard with CAC, AOV, gross margin, SER, and retention indicators.

• Measured OAC separately from CAC to monitor the blended efficiency of returning vs. new customers.

• Ran controlled experiments with ad spend based on hypotheses we developed from the data.

A Sales Engine isn’t magic, it’s disciplined testing, consistent measurement, and removing the guesswork. It’s truly just Business as a Science.

Optimizing the Revenue Model

As we collected cleaner data, we spotted opportunities. For instance, the client’s AOV had room to grow. By introducing incentives like free shipping at a $200 cart threshold, we nudged the AOV from $150 to $185, a massive lift to our cashflow, especially at scale.

At the same time, we saw that LTV, which was already strong at $1,800, could be significantly improved as margin expanded. When the company invested in tooling and transitioned to better manufacturing processes, we watched margins rise from 50% to 75%, instantly increasing the financial leverage of every customer acquired.

Using the Engine to Unlock Capital Investment

Here’s where things get fun. Once the Sales Engine was in place, meaning we had reliable, data-backed unit economics, we were able to ratchet up ad spend confidently. We could now show, with data:

• If we spend $1, we get $4-$5 in gross margin.

• Here’s the range of outcomes based on historical variance.

• Here’s how long it takes for a cohort of customers to repay its acquisition cost.

That predictability allowed us to do something many small businesses struggle with: secure financing for tooling and manufacturing investments.

Because we could demonstrate consistent return on spend and forecast revenue with meaningful accuracy, our manufacturing partner was willing to support large CapEx for injection molding tooling since we were executing a predictable model.

And because we knew how much revenue we could generate per advertising dollar, we were able to purposefully toggle spend, move inventory faster, shorten the sales cycle, and proactively manage inventory rather than react to it.

Scaling With Eyes Wide Open

Today, this partner’s growth isn’t guesswork. It’s a system. As we scale, we’re constantly testing assumptions using the same methodology I outline in another article, “The risk is in the darkness, not the leap.”

In other words, risk doesn’t come from moving quickly, it comes making decisions blindly. By bringing data into the light, we built a growth strategy the founder can trust because it’s measurable.

Implementation Tactics: How to Build Your Own Sales Engine

A Sales Engine is built through clear data, simple modeling, and steady iteration. The goal is reliable predictability. You want to reach a point where you can say, “If I spend $X, I can expect $Y in return,” and have the numbers to support the decision.

Below is the framework I use with partners to create that level of visibility and control.

Step 1: Gather Clean, Decision-Ready Data

Most small businesses struggle with forecasting because they don’t have enough clarity on what is driving results. Before you optimize anything, you need accurate information about customer behavior and channel performance.

Start with the fundamentals:

• Cohort Analysis: Group customers by the month (or channel) of acquisition and track how they behave over time. This reveals retention trends, purchase frequency, and the true value of each customer segment.

• Channel-Level Attribution: Understand which channels are producing customers and at what cost. Guessing here leads to wasted spend. I religiously track my introductions and touchpoints in HubSpot for B2B sales and strongly advocate to my e-commerce clients to improve attribution infrastructure with software like Triple Whale.

• Weekly Scorecard: Track CAC, AOV, gross margin, SER, OAC, and retention indicators every week. Consistent reporting creates focus and accountability. The name of the game is trends and rapid feedback to action, rather than absolutely perfection.

• Segmentation: Separate new and returning customers to understand blended efficiency and long-term contribution.

This foundation allows you to treat your business as a working system rather than a series of disconnected activities.

Step 2: Build a Simple Revenue Model

Your revenue model should translate inputs into outputs in a way that helps you make real decisions. As I’ve personally had to learn with time, complexity doesn’t make it better however simplicity does.

Focus on a few core relationships:

• Spend → Traffic

• Traffic → Orders

• Orders → Gross Margin

Add reasonable sensitivity ranges to understand how changes in CAC, conversion rate, or AOV affect the model.

Finally, incorporate cohort repayment timelines, which tell you how long it takes for customers to recover their acquisition cost. This becomes critical when planning cash needs. If it turns out to be the very first order, stress test the model to see what you could reasonably push CAC to before it breaks your LTV, this will give you the guardrails to be more aggressive with your experimentation.

A functional model gives you confidence in your growth decisions and establishes a baseline for testing.

Step 3: Calibrate With Controlled Tests

Once you know your baseline economics, begin testing scenarios for your sales and marketing inputs. The goal is to validate or refine your assumptions.

A practical approach is to:

• Increase S&M spend by 10% to 20% at a time.

• Track CAC, AOV, and OAC weekly to see the impact.

• Compare forecasted results to actuals.

• Adjust assumptions based on the patterns you see.

I love visual charts for this process, for example, here was an early visualization that helped us form an initial hypothesis for higher daily Ad spend. The weekly data (which I couldn’t find unfortunately) shows an even stronger correlation.

This calibration phase builds trust in the numbers. You begin to understand the relationship between dollars invested and dollars returned.

Step 4: Allocate Capital Based on Performance

When your numbers stabilize, you can begin allocating more capital toward the parts of your engine that consistently generate returns. This is where the business shifts from reactive to strategic.

Key actions include:

• Increasing investment in channels that produce healthy SER and contribution margins.

• Reducing or eliminating spend in channels with poor returns.

• Using predictable results to negotiate better manufacturing terms, secure financing, or justify larger CapEx investments.

Predictability strengthens your position with partners and allows for more intentional growth.

Step 5: Turn the Engine into a Weekly Discipline

Like any system, a Sales Engine must be maintained. The goal is to keep the system aligned as conditions change.

Embed the following into your rhythm:

• Weekly performance reviews

• Monthly forecasting updates

• Quarterly assumption resets

• Continuous testing of channels, offers, pricing, and creative

This keeps your growth strategy grounded in data rather than intuition and ensures the engine stays healthy as you scale.

Red Flags That Signal Your Sales Engine Needs Attention

A strong Sales Engine will give you consistency, predictability, and confidence. When it starts drifting, the early warning signs are usually clear if you know where to look. These are the red flags that matter most, the ones that almost always indicate something in the system needs attention.

1. CAC Rising Faster Than LTV

If your Customer Acquisition Cost grows faster than your Lifetime Value of Customer, the gap that funds your growth disappears. This is one of the earliest signs that a channel is losing efficiency, the offer has gone stale, or the market is shifting. When the spread tightens for more than a few weeks, pause and re-evaluate the core drivers of demand.

2. SER Dropping Below 2 for More Than a Short Period

Your Sales Efficiency Ratio reflects the health of your growth engine. A SER consistently below 2 means you are spending too much to generate gross margin and are likely consuming cash rather than creating it. This signals the need to reassess channel mix, creative fatigue, margin structure, or customer quality.

3. Forecasts Consistently Miss Reality

If forecasts are regularly off by 20–30 percent or more, one of your assumptions is out of date. This could be CAC instability, AOV shifts, a change in repeat purchase behavior, or inaccurate attribution. Consistent misses erode confidence and make planning difficult, especially around inventory, staffing, and cash management.

4. Margin Decline Without Operational Change

When margins shrink despite stable production costs, the cause is almost always higher discount pressure, smaller order sizes, or an acquisition strategy that is attracting lower-value customers. This red flag is easy to overlook because revenue may still appear healthy, but the business becomes less resilient with each period.

5. Inventory Becomes the Limiting Factor

When you routinely run out of inventory or consistently over-order, the Sales Engine isn’t aligned with operations. Either demand is not being forecasted accurately, or spend is being adjusted without visibility into production timelines. This misalignment creates unnecessary cash strain and limits growth potential.

The Entrepreneur→ Investor Mindset Shift

Building a Sales Engine is not just a financial exercise. It requires a shift in how you think about growth, risk, and decision-making. Most entrepreneurs start by relying on instinct and hustle, which is the most necessary thing in the earliest stages, but instinct has limits. Systems scale; intuition alone does not.

The mindset shift is moving from reacting to results toward engineering them.

When you begin treating Sales and Marketing as investments rather than expenses, you gain a new level of control. Each dollar becomes a strategic tool you deploy with intention, supported by data and clear expectations. Instead of asking, “Can we afford to spend this?” you start asking, “What return should we expect if we spend this?”

This shift also reduces the emotional weight that so often comes with running a small business. Forecasting becomes clearer. Inventory planning becomes manageable. Capital conversations become easier to navigate because they are backed by evidence, not optimism. You don’t eliminate uncertainty, but you dramatically reduce the areas where it can hide.

A well-built Sales Engine gives founders something they rarely talk about but deeply need: breathing room. It creates the capacity to think strategically, invest in people, and build a healthier and more resilient company rather than living month to month. Now that you are systematically selling, it’s time to start managing inventory more effectively.

If you’re working to build this level of predictability in your business, or you’re unsure where the gaps in your current engine may be, this is the kind of work I support entrepreneurs with every day. You can follow along and go deeper in future articles by subscribing to Conduit of Value, and if you ever want to explore what this looks like inside your business, feel free to reach out.