As synthetic intelligence strikes from experimentation to infrastructure, buyers have gotten way more selective about what qualifies as a really 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.
By means of 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 function, however as its working system. We spoke with Volkov about what constructing AI startups appears 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. Just a few years in the past, sheer quantity was usually 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 by way of actual product utilization. Aggregated or scraped knowledge is much much less defensible.
Timing has additionally modified. Traders are now not affected person about monetisation. There’s an expectation that founders perceive early on how income can 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 at present.
Competitors has intensified as nicely. Constructing fashions is extra accessible than ever, which suggests differentiation more and more comes from product execution. The strongest groups I see are deeply product-driven. They deal with fixing concrete consumer issues quite than constructing general-purpose fashions and not using a clear utility.
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 right down to what customers really wanted and rebuilt the expertise round that. Patreon succeeded as a result of it addressed a really particular drawback creators have been going through and did so in a manner 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 may’t be fastened by bolting AI onto legacy methods. From what I’ve seen, banks are already too constrained by how they have been initially constructed. Their architectures have been designed for a really completely different period, and people constraints present up in all places.
Neobanks proved that banking is now not about branches or paper contracts. At this level, banking is a know-how and product self-discipline. AI has develop into a useful resource that firms merely can’t compete with out. If medication, advertising and marketing, cybersecurity, and media are all being reshaped by AI, it might be unusual to imagine banking is someway 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 quite than layered on high.
Conventional fintech focuses on including extra options, whereas Molit.ai frames banking as a every day partnership with AI. How does this shift change consumer behaviour and long-term buyer loyalty?
I believe function depend is usually overrated. What really issues is how providers are delivered. Most monetary merchandise pressure customers to navigate complexity that exists for inner 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 usually want, interactions develop into easier and extra well timed.
Over time, that modifications how folks relate to monetary providers. Banking stops being one thing you handle sometimes and turns into one thing that matches naturally into every day workflows. That shift tends to provide stronger long-term loyalty than any single function ever may.
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 allows deeper investigations, stronger sample recognition, and clearer documentation.
When designed correctly, these methods are sometimes extra clear than conventional ones. Selections are primarily based 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 shopper and actual assist. How did that perception form Molit.ai’s product and interface design?
Many banks deal with buyer help as a price heart. Their AI methods are designed to deflect requests, not resolve them. They act extra like filters than assistants.
We took the alternative method. Help is constructed into each interplay. The system is designed to know the shopper’s historical past, preferences, and context in order that assistance is related and well timed. As an alternative of forcing customers to adapt to the system, the system adapts to them.
Many founders nonetheless deal with AI as a function quite than a basis. How do you consider whether or not an organization is actually AI-native or simply retrofitting intelligence onto legacy methods?
One clear sign is whether or not the system constantly learns from actual utilization. If buyer interactions enhance the product over time, that’s normally an indication of an AI-native structure.
If AI is just layered on high of static workflows, with out influencing core logic, it’s virtually all the time a retrofit. In really AI-native firms, intelligence is inseparable from the product itself.
Looking forward to 2026 and past, what recommendation would you give founders constructing AI-first startups at present, particularly these aiming to show advanced infrastructure, like banking or finance, into life-style merchandise?
Founders have to be very clear concerning the issues they’re fixing and the folks they’re fixing them for. On the similar time, they should construct methods able to adapting to issues that don’t but exist.
AI ought to operate as infrastructure, not as a function. And there should be a transparent path to monetisation. Irrespective of how superior the know-how is, my expertise reveals me that sustainable progress nonetheless will depend on understanding who pays, why they pay, and the way that scales.