An investor’s perspective — TFN

As synthetic intelligence strikes from experimentation to infrastructure, buyers have gotten much more selective about what qualifies as a very AI-native firm. In 2026, the hole between startups that merely combine AI and people constructed completely round it’s widening quick, particularly in extremely regulated sectors like finance.

Dmitry Volkov, serial entrepreneur and investor, has been observing this shift from the entrance row. An early investor in OpenAI, Revolut, and Patreon, Volkov has deployed over $500M throughout greater than 20 ventures and is now backing what he sees as the following logical evolution of fintech: AI-first banking.

Via his new enterprise, Molit.ai, Volkov is backing the event of a European financial institution designed from the bottom up round synthetic intelligence not as a characteristic, however as its working system. We spoke with Volkov about what constructing AI startups seems like heading into 2026, how investor expectations have modified, and why he believes banking is prepared for a full architectural reset.

From an investor’s perspective, what basically modifications when constructing an AI startup in 2026 in comparison with even three or 4 years in the past?

One of many greatest modifications is how buyers have a look at knowledge. A couple of years in the past, sheer quantity was typically handled as a moat. From what I’ve seen, that assumption now not holds. What issues now could be whether or not the info is proprietary, legally unique, and generated via actual product utilization. Aggregated or scraped knowledge is much much less defensible.

Timing has additionally modified. Buyers are now not affected person about monetisation. There may be an expectation that founders perceive early on how income will likely be generated. That forces groups to slim their scope and be very exact about the issue they’re fixing. Broad, open-ended AI ambitions are a lot tougher to justify right now.

Competitors has intensified as nicely. Constructing fashions is extra accessible than ever, which implies differentiation more and more comes from product execution. The strongest groups I see are deeply product-driven. They deal with fixing concrete consumer issues slightly than constructing general-purpose fashions with out a clear software.

You’re an early investor in firms like OpenAI, Revolut, and Patreon. What frequent patterns do you now recognise in startups that efficiently scale in an AI-first world?

Probably the most constant sample is focus. Revolut labored as a result of it stripped banking all the way down to what customers truly wanted and rebuilt the expertise round that. Patreon succeeded as a result of it addressed a really particular downside creators had been dealing with and did so in a approach that aligned incentives on each side.

One other sample is readability round monetisation. The businesses that scale nicely don’t postpone income discussions. They design enterprise fashions that work early, which supplies them flexibility later. That self-discipline tends to separate firms that develop steadily from those who stay caught in experimentation.

Molit.ai is positioned as a financial institution rebuilt from zero with AI at its core. What satisfied you that banking was prepared for such a radical architectural reset?

I’m satisfied this will’t be fastened by bolting AI onto legacy programs. From what I’ve seen, banks are already too constrained by how they had been initially constructed. Their architectures had been designed for a really completely different period, and people constraints present up in every single place.

Neobanks proved that banking is now not about branches or paper contracts. At this level, banking is a expertise and product self-discipline. AI has turn into a useful resource that firms merely can’t compete with out. If drugs, advertising and marketing, cybersecurity, and media are all being reshaped by AI, it might be unusual to imagine banking is by some means exempt.

That’s precisely why we’re approaching this in a different way from day one. Molit.ai treats the financial institution itself as a technology-native system, the place intelligence is embedded into the core structure slightly than layered on prime.

Conventional fintech focuses on including extra options, whereas Molit.ai frames banking as a day by day partnership with AI. How does this shift change consumer behaviour and long-term buyer loyalty?

I believe characteristic rely is commonly overrated. What truly issues is how providers are delivered. Most monetary merchandise power customers to navigate complexity that exists for inside causes, not consumer ones.

AI permits banking to occur on demand, with far much less friction. An actual partnership implies belief and relevance. When a system understands who a consumer is, what they do, and what they sometimes want, interactions turn into easier and extra well timed.

Over time, that modifications how folks relate to monetary providers. Banking stops being one thing you handle often and turns into one thing that matches naturally into day by day workflows. That shift tends to provide stronger long-term loyalty than any single characteristic ever might.

Regulation and belief are main obstacles in monetary providers. How does an AI-first banking mannequin deal with compliance, safety, and transparency with out counting on heavy human intervention?

Being AI-first doesn’t imply eradicating people from the method. It means making human decision-making simpler. AI permits deeper investigations, stronger sample recognition, and clearer documentation.

When designed correctly, these programs are sometimes extra clear than conventional ones. Selections are based mostly on broader and extra constant info, which improves auditability and accountability. In my opinion, this results in stronger compliance outcomes, not weaker ones.

You’ve stated that in most banks, AI acts as a barrier between the client and actual assist. How did that perception form Molit.ai’s product and interface design?

Many banks deal with buyer help as a price middle. Their AI programs are designed to deflect requests, not resolve them. They act extra like filters than assistants.

We took the other strategy. Help is constructed into each interplay. The system is designed to grasp the client’s historical past, preferences, and context in order that assistance is related and well timed. As a substitute of forcing customers to adapt to the system, the system adapts to them.

Many founders nonetheless deal with AI as a characteristic slightly than a basis. How do you consider whether or not an organization is actually AI-native or simply retrofitting intelligence onto legacy programs?

One clear sign is whether or not the system repeatedly learns from actual utilization. If buyer interactions enhance the product over time, that’s often an indication of an AI-native structure.

If AI is just layered on prime of static workflows, with out influencing core logic, it’s virtually at all times a retrofit. In actually AI-native firms, intelligence is inseparable from the product itself.

Waiting for 2026 and past, what recommendation would you give founders constructing AI-first startups right now, particularly these aiming to show complicated infrastructure, like banking or finance, into life-style merchandise?

Founders should be very clear in regards to the issues they’re fixing and the folks they’re fixing them for. On the similar time, they should construct programs able to adapting to issues that don’t but exist.

AI ought to perform as infrastructure, not as a characteristic. And there should be a transparent path to monetisation. Irrespective of how superior the expertise is, my expertise exhibits me that sustainable development nonetheless depends upon understanding who pays, why they pay, and the way that scales.