What Fintech Founders Get Wrong About “AI-Native” Development

Mal, an Abu Dhabi fintech startup, closed a $230 million seed round in January 2026, the largest seed round in Middle East and Africa history, to build what it describes as the world’s first AI-native Islamic digital bank. By May, it had secured in-principle approval from the UAE Central Bank. Most of the coverage that followed focused on the funding and the founder’s ambition.

Forbes actually went looking for the substance behind the label, and contributor Zennon Kapron flagged that the detail on how Mal will actually use AI stays rudimentary, with the service description in its own funding announcement reading fairly generic. That’s not a story about one bank failing to deliver; it’s too early to say that. It’s the clearest recent example of the default pattern in fintech right now.

This isn’t really about Mal specifically. “AI-native” has become the pitch deck’s favorite adjective across the category, and most of the time, nobody checks whether the word is describing the architecture or describing the ambition.

I’ve sat in enough of these conversations now, with founders, with the engineers actually building the thing, with investors trying to evaluate it, that I can usually spot the gap before the founder can. It’s rarely dishonesty. It’s a founder who genuinely believes the label applies, because nobody has made them defend it yet.

It’s also worth saying this isn’t the first time a legitimate technical term got hollowed out through overuse. The same thing happened to “cloud-native” a decade ago, and more recently to “zero trust.” A real architectural distinction gets stamped onto every pitch deck until it stops meaning anything specific.

A recent Forbes Councils piece on this exact inflation cycle in cybersecurity put it well: virtually every startup in its category now claims the “native” label, and the claim usually collapses the moment you ask which specific thing, the people, the process, or the technology, is actually native, because each of those carries different evidence.

What the term is supposed to mean

CRV’s framing is the cleanest one I’ve come across, and it draws a hard line. AI-native means the product was built on AI from the ground up, to the point that pulling the intelligence layer out would leave the company without a product that functions in any meaningful way. CRV calls this the removal test: strip the AI out, and if the core value survives, you’re AI-enabled; if nothing useful remains, you’re AI-native. Decisions, workflows, and outputs run through AI systems by default, not as an enhancement layered on top of a rules-based core.

Everything that doesn’t clear that bar is AI-enabled. Often genuinely good software, just not the same category. The distinction sounds academic until you look at what’s riding on it. PitchBook has pegged the early-stage valuation premium for startups with AI genuinely built into the core product at 242% over peers without it. That gap is why every founder wants the label, and it’s exactly why investors, and increasingly regulators, have started testing for it directly instead of taking it at face value.
Where founders actually go wrong

In my experience, it’s rarely one dramatic decision. It’s three smaller ones that compound.

They treat it as a pitch, not an architecture decision. Mal isn’t unusual in this respect; it’s a well-funded, well-covered example of a broader habit. “AI-native” gets written into the deck before it’s built into the system, and by the time the product ships, the claim has outrun the codebase. Nobody set out to overstate anything. The positioning just moved faster than the engineering did.
They chase AI-native speed and an underweight compliance architecture. This is the one I watch founders miss most often, and Sky9 Capital’s read on 2026 fintech funding backs it up with real numbers, not just anecdotes. Investors who’ve watched fintech companies fail at the licensing stage are now evaluating regulatory clarity at the earliest meetings, and compliance architecture is the signal most early-stage founders leave weakest. Sky9 goes as far as calling the AI-native versus AI-bolted-on distinction a standard diligence question in 2026, now, not an edge case. Speed to an AI-native pitch and speed to a defensible compliance posture pull in different directions, and founders consistently over-invest in the first at the expense of the second.
They confuse using AI tools with being built on AI. Shipping fast with Cursor or an AI coding agent doesn’t make the product AI-native; it makes the build process AI-assisted. Those are different claims. A traditional risk-scoring system that a team wrote with AI’s help is still a traditional risk-scoring system.
The same Forbes Councils piece makes this point sharply about developers themselves: someone who builds software with an AI coding assistant isn’t an “AI-native developer,” they’re a developer using AI tools, and the distinction matters because in most organizations, code review still happens, testing frameworks still apply, and approval processes still gate releases. That’s an AI-assisted process, not an AI-native one. The architecture question is whether AI sits inside the decision loop the product runs on, not whether AI touched the repo.

The debt this creates.

I think of this as positioning debt: the gap between what the pitch claims about your architecture and what the architecture can actually demonstrate under inspection.

Like any debt, it’s invisible until someone calls it. A casual conversation doesn’t test it. A technical due diligence call does. A regulator asking how a specific decision got made does. A competitor with a more precise claim, sitting next to yours in the same funding round, does.
Positioning debt doesn’t cost you anything the day you write the pitch deck. It costs you the day someone with the right question is in the room, and by then, the fix isn’t a better slide, it’s months of re-architecture you didn’t budget for.
What the founders getting it right do differently.

The companies drawing a real distinction right now aren’t the ones using the term most confidently. They’re the ones who can answer a specific, boring question without flinching: which decision in your product would break if you removed the AI layer, and what would you fall back to? It’s the same removal test CRV uses, just asked out loud in a room instead of applied quietly in a diligence memo.

If the honest answer is “most of the product still works fine,” you’re AI-enabled, and that’s a legitimate thing to be, as long as you say so. If the honest answer is “the product doesn’t function,” you’re closer to AI-native, and you can say that with evidence behind it instead of ambition.
The founders getting this right also build the compliance case alongside the AI case, not after it. A credit decision engine that’s genuinely AI-native and can produce an audit trail for how it reached a specific outcome is a stronger pitch than the same engine with a better-written deck. It’s also the harder thing to fake.
I saw this play out firsthand on a cross-border payments build I worked on for a client in Lithuania. Compliance went into the same build as payments and onboarding from day one, not added as a phase afterward, and that foundation is still supporting more than 30,000 users on it. Nothing about that was glamorous. It was mostly the unglamorous work of deciding, before a line of product code shipped, which compliance checks lived inside the architecture and which lived in a review queue. That decision is invisible to a user and invisible to most of a pitch deck. It’s the first thing a regulator or an auditor goes looking for.
The Reframe

“AI-native” was never supposed to be a marketing word. It described a specific architectural choice with real tradeoffs, and it still does, for the founders willing to build to the definition instead of borrowing the label. The pattern with Mal, with most of the fintech pitch decks I read this year, isn’t fraud. It’s a term that got adopted faster than the discipline needed to earn it, in fintech specifically, because the cost of getting caught is so much higher than in most software categories.

If someone removed the AI layer from your product tomorrow, what would actually break? Drop your thoughts below, I’d genuinely like to know how different people answer that for their own product.
If you want to talk through where your own product’s positioning and architecture actually line up, or where they don’t yet, message me on LinkedIn.