Having started a company, it is only natural for our friends and acquaintances to ask us how much we pay. Our honest answer tends to raise eyebrows: significantly above “market,” for almost any role. Not only for a handful of so-called 10x positions, but across the board where it matters. We say this without bravado and with full awareness of who we compete against for talent: top trading firms, Big Tech, frontier-AI labs, and the best independent builders.
The justification isn’t vibes; it’s arithmetic and game theory. In a positive-sum, fast-growing market where the pie is compounding, “overpaying” for the ingredients that accelerate compounding is not extravagance-it’s strategy.
The Equity Equation
Start with the equity equation (popularized by Paul Graham), because it gives us a clean ruler for every trade. Whenever you give up a fraction n of your company, whether by selling equity for cash or granting equity to a hire, the deal only makes sense if it increases your expected outcome by more than 1/(1−n). Give up 7% and you need the company’s expected value to rise by roughly 7.5% to break even. Give up 30% and you need about 43% improvement. It’s the same logic whether the counterparty is an investor, a distribution partner, or an employee. The question is always: does this trade make what we keep worth more than what we started with?
This is why paying above market is often cheaper than it looks. Suppose our post-money valuation is $20M and to land a particular engineer we must offer $250k more (present-valued) than “market.” That’s effectively 1.25% dilution (250k ÷ 20M). The equity equation says it is fine to do it if we believe this hire raises our expected outcome by more than ~1.27% (since 1/(1−0.0125) ≈ 1.0127).
For a truly great engineer working on core systems, that’s a low hurdle. If we conservatively account for salary plus overhead over time by multiplying by ~1.5× (a useful rule of thumb for fast-growing startups), the “premium” becomes $375k, or 1.875% dilution, and the break-even improvement is still only ~1.91%. In a business where shipping a quarter earlier can shift the trajectory by multiples, paying that premium is rational.
If a person increases the company’s expected outcome by a factor i, the break-even share n equals (i−1)/i. Believe a candidate raises our trajectory 20% (i = 1.2)? Break-even equity is ~16.7%. That doesn’t mean we grant that exact number; salary, overhead, risk preferences, and a margin of “profit” for the company all factor in; but it does mean that we have plenty of room to pay for someone truly excellent over their career.
Cash = Equity
Now for a point that often gets lost in compensation debates: cash and equity are the same pool seen through different windows. If we raise $X from investors to fund salaries, we’ve sold n = X / post-money of the company. That is dilution, indistinguishable in substance from granting equity directly to employees. In that sense, paying cash is paying equity in disguise.
The right mix of cash and equity for an employee is therefore a function of two things: the employee’s risk preference and the financing cost of cash (i.e., the price at which we can sell equity to investors). A practical implication follows: runway planning is an aggregate forecast of team preferences over cash versus equity. When we say we’re raising N months of runway, we are implicitly guessing the portfolio mix our future team will want; we are intermediating between employees (who value cash today) and investors (who value ownership) in one common currency: the company’s equity.
This perspective changes how we structure offers. If an exceptional candidate prefers more cash today, we can often finance that preference by selling a bit more equity to investors rather than by cutting the candidate’s equity to the bone. The cost doesn’t vanish, someone must bear it, but we would rather spread more of it to outside capital than tax the upside of the builders who create the value. Aligning incentives beats nickel-and-diming. And because we price every stretch against the same 1/(1−n) yardstick, we know when a premium is accretive versus when it isn’t.
Why be willing to step above market, even against giants? Because we recruit on the right tail of the distribution, where the returns are power-law distributed. Trading firms and AI Researchers pay so much precisely because they understand that a few people shift outcomes nonlinearly and that shipping months earlier compounds. We share that view. If “winning” a candidate requires stepping over a median-market peg, we’ll do it, provided the equity equation clears. In practice, when the role is on the critical path for product, distribution, or research, it almost always does.
The Alternative Theory of Unions
Paul Graham’s alternative theory of unions gives the broader macro-intuition. In the mid-twentieth century, manufacturing was a growth market. Labor was the scarce input to scale, so companies rationally “overpaid” for it to protect throughput. The moment growth slowed and competition turned zero-sum, the same firms fixated on cuts. Seen this way, the “era of overpay” was not moral exceptionalism; it was a growth company’s rational response to a binding constraint. The analogy for startups today is obvious: in our domain, the scarce inputs are extraordinary talent and compute. If they accelerate compounding, you pay what clears the market.
This is also why cost obsession is typical in stagnant industries. In positive-sum mode, the organization optimizes for outcomes: shipping, learning, iteration speed. In zero-sum mode, it optimizes internal transfer prices: headcount freezes, approval gates, salary haircuts. Discipline is necessary in any mode, but discipline without growth is just austerity. If we ever find ourselves spending more energy slicing a fixed pie than enlarging it, the problem isn’t that compensation is too high; it’s that we’ve drifted off the frontier. The cure for zero-sum dynamics is not across-the-board cost cutting—it’s rediscovering the next positive-sum curve.
Of course, paying above market isn’t a license to suspend judgment. It changes what we optimize. We start with a very high bar for ability and fit, because “overpaying” mediocrely is the fastest way to set money on fire. We reserve true premiums for roles that move near-term compounding: core product, go-to-market leverage, research breakthroughs, critical infrastructure. We make expectations legible, tie fair compensation to realized shifts in expected outcome, and keep a clean cash-equity choice architecture so candidates can express their risk preferences without accidentally taxing their upside. And we decide fast: translate the premium into implied dilution, run the 1/(1−n) test, and either lean in or pass.
Fair Compensation
What if we’re wrong sometimes? We will be. The goal isn’t perfection; it’s positive expected value over a portfolio of hires. Returns on talent are power-law distributed: a small number of outliers will pay for many merely good decisions. Our job is to increase the probability and magnitude of those outliers through better selection, faster onboarding, and environments where leverage can express itself. If the expected value of the portfolio comfortably exceeds the sum of the premiums, we did it right.
All of this loops back to the opening claim: we choose to pay significantly above market because we are playing a positive-sum game in which speed compounds. Cash and equity are two wrappers for the same currency—ownership—and we will finance the mix that best matches the person and the moment. The equity equation is our compass and our brake; it keeps generosity from becoming self-harm and keeps frugality from becoming self-sabotage. When we talk about “overpaying,” what we really mean is paying for acceleration at a fair price that makes what we keep worth more than what we started with.
In a positive-sum race, you don’t win by clipping coupons. You win by compounding faster. That is why we overpay.
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