The DTC edge isn't best-in-class marketing anymore. It's unit economics that can absorb the worst.

"It used to be that the person with the best marketing performance was winning. These days it's the person who can afford the worst marketing performance and still be profitable."

That's Sam Lewkowict, co-founder of Black Wolf, a men's grooming brand he's built since 2018. He's watched the DTC playbook get written, losing money all the tim, and rewritten twice. And his read on where the competitive moat actually lives right now is worth sitting with.

The brands winning paid acquisition today aren't necessarily the ones with the best creative, the best targeting, or the sharpest ROAS. They're the ones whose business model can absorb a bad number. That's a structural shift, and if you're still optimizing your way to a 3.0 ROAS as the end goal, you may be competing on the wrong dimension entirely.

How we got here: two wrong answers

The DTC industry has swung between two bad extremes, and most operators have lived through both.

The first era was scale-at-all-costs. Cheap capital, ad arbitrage, and the promise that LTV would eventually justify any CAC. Brands got funded on vibes and ad arbitrage, not real demand. The underlying products often hadn't cleared a true PMF bar. They were being scaled through ad spend, not pulled through by repeat customers.

Then the correction came, and it overcorrected. Suddenly the orthodoxy flipped: you have to be profitable on the first purchase. First-order unit economics became the metric investors and operators alike obsessed over.

Sam's take on that second phase is sharp: "Everyone looked at like tech and said just spend endless amount of money. A lot of things got funded that didn't have true product market fit and were simply just being scaled through... ads, arbitrage. And then everyone reflects like, oh, that's all bad. You have to be super profitable on the first purchase. You have like these crazy unit economics. And that was never realistic."

Neither frame is right. The first ignores product reality. The second ignores how repeat-purchase businesses actually compound.

What the smarter operators figured out

The brands that are scaling fast right now aren't optimizing for first-purchase margin. They're optimizing for CAC:LTV tolerance.

Sam knows operators running at sub-0.9 ROAS deliberately, because they've built a model that can absorb it. On paper, that looks broken. In practice, it's a moat. If your product has genuine repeat-purchase behavior baked in, a sub-1.0 blended ROAS on new customer acquisition is something you can afford and your competitors can't match.

Sam frames the PMF question as the prerequisite. Without true demand, you can't build a model that absorbs bad first-purchase economics because there's no second and third purchase to absorb it with. He draws on his earlier time in B2B to explain how CPG operators tend to be sloppier about clearing that bar: "Product market fit is real and I feel like people didn't look at CPG the way they should have. Like in tech. Like product market is a real milestone. Right. Like it's an actual bar you have to clear. I feel like people can lie to themselves and say they have product market fit in CPG when they don't."

That clarity matters. The unit economics tolerance model only works if the LTV is real. Organic repeat, not retention email heroics.

When the model clicks, the growth is different

Here's what changes when a brand actually cracks this: the pace of scaling becomes qualitatively different.

Sam describes how the operators who've figured this out evaluate PMF: "Now it seems like people are honing in on understanding what is product market fit. And you can see it. It's typically measuring your cat versus ltv. First purchase is like whatever, you know."

And what happens once they've found it is a different kind of growth entirely. As Sam puts it: "When you hit it people are going raising a ton of money and scaling into it really aggressively because they know they have it. There's a couple guys I know who have hit product market fit and the, the speed at which they scale is insane."

Sam backs this up with a concrete data point: he knows brands scaling north of eight figures a month within their first year. Not a decade of brand equity, not a category with legacy demand. That kind of growth rate isn't achievable through incremental optimization. It comes from a model that lets you pour capital into paid acquisition because the back-end can absorb the front-end loss.

This is the asymmetry the angle is pointing at. Two brands competing in the same category, same CPMs, same targeting. One needs a 2.5 ROAS to stay afloat. The other built a model where 0.85 is fine. The second brand wins the auction every time, scales faster, and the first brand can't figure out why their numbers don't work anymore.

What this means for how you should think about your stack

The implication isn't "ignore first-purchase economics." It's that the question you should be asking has shifted.

The old question: "What do I need to do to improve my ROAS?"

The new question: "What would my business need to look like structurally for a sub-1.0 ROAS to be fine?"

That second question points you at product, subscription or repeat-purchase mechanics, AOV, and the actual cohort behavior of your customers, not your ad account. It's a harder question, but it's the one worth spending time on.

Three things worth checking before you optimize your next campaign

Measure your real repeat-purchase rate by cohort, not blended. If your blended retention looks acceptable but your 90-day cohort repeat rate is soft, the LTV tolerance model isn't available to you yet regardless of what you do in the ad account.
Run the scenario at 0.9 ROAS. What does your P&L look like if your blended ROAS drops to 0.9 and stays there for 90 days? If that's a business-ending scenario, the question isn't how to improve ROAS, it's how to build the model that makes 0.9 survivable.
Audit whether you actually have PMF or just ad-driven demand. Pull back spend meaningfully for 30 days and watch what organic and direct does. If demand collapses with the ads, the LTV math you're running may be optimistic.