Fintech's moats don't compile
Why AI makes many fintech moats stronger, where real defensibility comes from, and why you can’t vibe-code a banking license
“Fintech” has long traded on the ambiguity in its name.
The “fin” implied lots of emails from .gov domains, months-long audits, compliance officers who know your SAR filing history better than your product roadtmap, and mid-week flights to Charlotte or DC. The “tech” is a slick mobile app, a 10x user experience, and investor coffees at Blue Bottle.
“Fin” and “tech” were always a spectrum, but the market generally rewarded fintechs for being as much “tech” as possible and as little “fin” as they could get away with.
And that’s understandable. In 2021, software had a gross profit pool of roughly $0.7 trillion, valued at a steep premium. Financial services had a gross profit pool an order of magnitude larger, valued far more conservatively.1 Fintech let you arbitrage both: financial services economics with a software multiple.
That gap in profit pools also tells you where the real money is. Financial services generates more gross profit than any other sector globally. The “fin” side of fintech isn’t just more defensible. It’s a far larger market.
Then AI arrived and the arbitrage broke. Software valuations compressed as investors re-priced what code was worth in a world where code is getting cheaper. Fintechs got caught in the downdraft because the market had categorized them as software companies.
But the market has the category wrong. Fintech’s costs, and its moats, were never in the code, and look increasingly antifragile to AI disruption.
A tale of two cost structures
Software had one of the best business models in history: code was expensive to produce, but once written, it could be distributed for almost nothing. The gap between “expensive to build” and “free to distribute” was the margin. If you’re a SaaS company spending 22-25% of revenue on R&D, that spend is also your barrier to entry. Competitors couldn’t easily replicate what took years and tens of millions to build.
AI closes that gap from the top. If code is cheap to build and cheap to distribute, the margin compresses. The wall that kept competitors out gets shorter, more players get in, and pricing power erodes.
That’s a real problem if your business is software. But fintech’s expenses aren’t engineering expenses. Follow the money and the distinction gets obvious fast.
PayPal spends 9% of revenue on R&D. Block spends 12%. That’s not because fintech engineering doesn’t matter. Stripe’s engineering is world-class and a real competitive advantage. It’s that engineering isn’t where the majority of the money goes.
It goes to the fin. And unlike R&D spend, these costs don’t just produce a product, they produce moats:
Credit losses buy underwriting data.
Affirm spends 35% of revenue on credit losses and cost of capital, before a single engineer is paid. Every dollar lost to defaults is a dollar of repayment data a competitor doesn’t have. A new entrant training on synthetic data has no ground truth. You can’t build reliable loss history on synthetic data alone.
Compliance spend buys regulatory permission.
Wise dedicates a third of its workforce to compliance and financial crime prevention across 65+ regulatory licences. Money transmission licenses across 50 states. BSA/AML programs. Bank charter requirements. These aren’t advantages you build. They’re permissions you earn, continuously. You can’t vibe-code a banking license.
Transaction volume buys proprietary data.
Toast’s payments segment runs at 22% gross margins versus 70% for its SaaS segment, yet generates nearly twice the gross profit. Those costs buy merchant-level transaction data that feeds Toast Capital, which has originated over $1 billion in loans. Adyen’s risk models are trained on transaction patterns across 30+ markets.
Fintech’s margins were never high in the first place, and that’s the point
A payments company runs at 20-50% gross margins, not 80%. But lower margins aren’t the same as weaker businesses. Fintech margins are lower because many of those costs generate compounding advantages. And even the ones that don’t still exist outside the blast radius of AI-driven cost compression.
And AI makes every one of these moats stronger. Better models tighten loss rates. Better fraud detection reduces chargebacks. Better compliance tooling lets smaller teams hold more licenses. AI doesn’t replace the moat. It rewards the companies that chose to build in the hard parts of fintech: money movement, risk, proprietary data, and regulation.
So the real argument is not just “AI helps fintech.” It’s that AI shifts value away from product surface area and toward proprietary data, risk-bearing capacity, regulatory permission, and distribution embedded in real money movement. If you’re building in those areas, AI is compounding in your favor. If your differentiation is in the code, it’s compounding against you.
And the demand side keeps growing. Every vibe-coded checkout is a new fraud vector. Every AI agent transacting autonomously is a chargeback risk. The more that gets built on top of fintech’s rails, the more essential the rails become.
Fin for the win
This realization is already forcing smart fintech founders to re-think where they sit along the “fin” and “tech” spectrum:
Do we underwrite and price risk ourselves, or pass it to a partner who keeps the margin?
Do we own the regulatory relationship, or rent it from someone who does?
Does every transaction make our risk models sharper, or are we training someone else’s?
Is our ledger the source of truth, or an imperfect mirror of someone else’s?
This distinction cuts the fintech landscape in two. The companies that own the regulatory relationship, eat the credit losses, and accumulate the transaction data are building moats that AI deepens. The ones that rent the fin, wrapping a partner bank’s license, a BaaS provider’s ledger, someone else’s risk models in a better UI, have exactly the same problem as SaaS companies. Their differentiation is in the code, and the code just got cheaper.
The old arbitrage of financial services economics with a software multiple is dead. The new one is simpler: own the fin.
My name is Matt Brown. I’m a partner at Matrix, where I invest in and help early-stage fintech and vertical software startups. Matrix is an early-stage VC that leads pre-seed to Series As from an $800M fund across AI, developer tools and infra, fintech, B2B software, healthcare, and more. If you’re building something interesting in fintech or vertical software, I’d love to chat: mb@matrix.vc
Gross profit pool estimates from Coatue’s 2022 fintech report. “Gross profit” is structurally cleaner in software than in financial services, where P&L structures vary across sub-sectors, so treat the comparison as directionally accurate rather than precise.