# VC10X - Why Infrastructure Will Win the Fintech Cycle - TX Zhuo, GP, Fika Ventures

An episode of [VC10X - Investing, Venture Capital, Asset Management, Family Office, Wealth Management](https://invisiblenetwork.ai/podcast/1632806986), 26 May 2026, 34 min.

Audio: https://anchor.fm/s/a453ddd4/podcast/play/120549931/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2026-4-26%2F424899820-44100-2-720bbfa578ba8.mp3

TX Zhuo is General Partner at Fika Ventures, an early-stage venture firm with over $500 million in AUM backing B2B companies across vertical AI, fintech, commerce enablement, and healthcare in North America. Before becoming a VC, TX bootstrapped a company to a successful exit without raising venture capital — an experience that permanently shaped how he evaluates founders and business models today. In this episode, TX breaks down why he believes infrastructure will win this fintech cycle over consumer apps, what separates a venture-scale AI company from an AI feature, and why the best vertical AI companies are ones where ripping out the platform would feel like open heart surgery. He also shares the three mistakes from the last fintech boom that investors and founders still haven't fully absorbed, and what the financial system looks like in ten years when AI agents, real-time settlement, and hyper-personalized products converge. If you're a founder building in fintech or vertical AI, or an investor trying to separate durable businesses from the noise, this one is worth your full attention. ⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.com We talk about - • Why infrastructure will win the current fintech cycle over consumer-facing apps • What separates a venture-scale AI company from an AI feature • The three biggest mistakes from the last fintech boom that investors and founders still haven't fully internalized • How Fika identifies boring industries ready for a massive software company • What the financial system looks like in ten years as AI agents, real-time settlement, and hyper-personalized products converge Links: Fika Ventures - https://www.fika.vc/ Connect with TX Zhuo - https://www.linkedin.com/in/tianxiangzhuo/ Connect with Prashant: https://linkedin.com/in/choubeysahab Subscribe to VC10X newsletter - https://vc10x.beehiiv.com Subscribe on YouTube - https://youtube.com/@VC10X Subscribe on Apple Podcasts - https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986 Subscribe on Spotify - https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ VC10X website - https://vc10x.com Timestamps: (00:00) - Preview (00:30) - Introduction to the Guest and Episode Topics (02:13) - Tx's Journey: From a Bootstrapped Founder to a VC (03:51) - The Value of Having a VC on Your Side (05:10) - The Importance of "Earned Insights" in Founders (06:17) - Investing in "Boring" Industries with Low Software Penetration (07:22) - What Makes an AI Product a Venture-Scale Business (08:52) - When Embedded FinTech Becomes a Real Business Model (10:01) - The Upsides and Risks of AI in FinTech (12:35) - Why Infrastructure Will Win This FinTech Cycle (14:00) - Key Mistakes from the Last FinTech Boom (16:09) - Which FinTech Categories Will Command Premium Valuations (18:42) - Building Defensibility in Vertical AI Companies (20:30) - Signs That a "Boring" Industry Is Ready for Disruption (22:16) - Sources of Differentiation When AI Commoditizes Intelligence (24:36) - The Future of Finance: AI, Blockchain, and Payments Converge (27:46) - The Secret to Fika Ventures' Success and Fundraising (30:01) - Choosing Between a Great Founder and a Great Market (30:21) - What Tx Would Do Differently if Starting Fika Today (31:53) - Rapid Fire Round Begins (33:33) - Conclusion #VC10X #VentureCapital #Fintech #VerticalAI #FikaVentures #EarlyStageVC #B2BStartups #AIStartups #StartupPodcast #FounderAdvice

## Transcript

[0:00] What separates an AI feature from a venture scale AI company?
[0:04] I think for us a venture scale AI company owns its own data
[0:08] loop. Do you think the next big
[0:11] fintech company would actually be consumer facing or the
[0:14] infrastructure companies defending the system underneath?
[0:17] My strong bias here is like I think infrastructure will win
[0:20] this cycle. What mistakes from the last
[0:23] fintech cycle do you think investors and founders still
[0:26] haven't fully internalized? I think it's burning cash to
[0:28] acquire customers with no loyalty.
[0:32] Hey. Everyone, this is prashant.
[0:33] And I'll be host for the VC10X podcast.
[0:35] And today we have DX Zuo with us.
[0:37] DX is General Partner at FICA Ventures, a firm with over $500
[0:41] million in AUM focused on early stage B2B startups across
[0:45] vertical AI, fintech, commerce, enablement, and healthcare in
[0:49] North America. What makes TX's perspective
[0:52] genuinely different is that before he became AVC, he was a
[0:55] founder who bootstrapped the company all the way to a
[0:57] successful exit without raising a single dollar.
[1:00] Venture capital. That experience shapes
[1:03] everything about how he backs founders today.
[1:05] In this conversation, we get into what FICA looks for in
[1:09] early stage companies, Why TX believes infrastructure will win
[1:12] this fintech cycle over consumer apps, what it actually takes for
[1:16] an AI product to be a venture scale business and not just a
[1:19] feature, and where the financial system is headed.
[1:22] As AI. Blockchain and modern payment
[1:24] rails converge without wasting any time.
[1:27] Let's dive straight in. This episode is brought to you.
[1:29] By Podcast Enix, my podcasting agency for VCs.
[1:34] We help VCs start and run branded podcast that can serve
[1:37] as their entire content engine. Turn the conversations you're
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[1:49] Don't just build another VC firm, build a firm that can
[1:52] stand out to LP's and one that founders can brag about having
[1:56] you on their cap table. Check out the services at
[1:59] podcast ten-x.com. I'll put the link in the
[2:02] description below. Now enjoy the episode.
[2:05] Hey TX, we'll have you on the VC10X podcast.
[2:07] How are you doing? I'm doing well.
[2:09] Prashant, thanks so much for having me.
[2:11] Very excited to have this conversation today.
[2:14] Absolutely. Likewise.
[2:15] And to start things off, let's start with your story.
[2:18] You were a founder who scaled and sold the business without
[2:21] raising venture capital. How does that shape the way you
[2:24] judge seat state founders today? And you're thinking that is sort
[2:29] of the right for that and are the things that you didn't take
[2:31] along from that experience? Yeah, Prashant it.
[2:35] It's funny that I'm AVC now and kind of ironic because I
[2:38] actually did try to raise venture capital when I started
[2:43] my company, but because I was a new founder, didn't have the
[2:47] relevant experience. So it's just harder for me to
[2:49] raise capital at that point in time.
[2:52] So I ended up bootstrapping the company, but ended up being one
[2:55] of the kind of most interesting kind of experiences in my life.
[2:59] And I think it's really kind of shaped my perspective of how I
[3:02] look at founders today. I know that there are many
[3:06] different permutations to build a successful company.
[3:09] But I think for me, I think it makes me bias towards founders
[3:13] who deeply understand unit economics from from day one and
[3:18] know that what the capital that they raised, they're not using
[3:21] it for kind of growth at all cost narratives.
[3:23] There's actually a real use for this capital.
[3:25] So I I think that steadfast focus to kind of business model
[3:30] resilience and capital efficiency, something that has
[3:32] kind of been built into my system of the years.
[3:36] Absolutely. That's quite interesting.
[3:38] And do you think you didn't raise at that point just because
[3:41] you were a newbie sort of and didn't have those connections?
[3:44] Or if you were to do it again, would you go and raise mental
[3:48] capital this time around? Or would you still try to do it
[3:50] bootstrapped? Yeah, that's, that's a great
[3:54] question. I, I, I think hindsight is all
[3:56] obviously 2020, but I think looking back, I probably would
[3:59] want to raise kind of venture capital and I'll, I'll give you
[4:03] a quick anecdote on on why that's the case.
[4:05] I feel that throughout the journey we didn't have great
[4:08] advice on how to run the company because it was just my Co
[4:11] founder myself running it for the most part.
[4:13] And when it came to selling the company, both of us had an offer
[4:18] that we kind of knew where the offer would stand and both of us
[4:22] did role plays for 2-3 weeks leading up to the offer cycle.
[4:26] This is the minimum offer that we would accept.
[4:28] But the moment we walked into a room and the acquirer gave us a
[4:31] number, we both said yes at the same time.
[4:33] This is unrehearsed, but I think that that shows you kind of the
[4:37] lack of sophistication that we had.
[4:39] So I would say like me 20 years ago would definitely have taken
[4:43] venture capital money. Me today, maybe that's a
[4:45] different question, but I, I think net, net is is a huge
[4:48] positive to have a VC on your side.
[4:50] So I would probably do it. Absolutely.
[4:53] That's quite interesting. And FICA has been a venture
[4:57] capital firm that's backing these companies early on, right.
[5:00] So when you back companies, what's the lens that you use,
[5:04] how you're looking at it? You mentioned you're in
[5:05] economics and all those sort of things.
[5:08] Anything beyond that that you would like to mention?
[5:11] Yeah, I I think something that always stands out to us is we,
[5:15] we call it earned insights. Has the founders spend enough
[5:18] time in a category of problem space to see something that
[5:22] others can't see? I think for us it's, it's
[5:25] probably paramount that either a founder has lived the problem
[5:29] themselves. Like the founders of build UPS
[5:31] is a great example. Both of them came from other
[5:34] project management or commercial construction.
[5:36] So we felt that the product that they were building really speaks
[5:40] to the experiences that they've had while someone who comes to
[5:43] us, it's like, hey, I've, I've done the kind of 100 interviews
[5:46] with my ICP and we think that hey, this is a real problem and
[5:50] This is why we have the right solution.
[5:53] So I feel like those deep insights that really cover kind
[5:56] of the domain specific problems of what really stand out to us.
[6:00] Absolutely. And you've been invested in
[6:02] companies like build UPS and a pipe is another big name in your
[6:05] portfolio and you are like one of the first, if not the first
[6:09] investor in these companies. So when you meet a company that
[6:12] looks too early or too weird for most investors, what signals
[6:16] make you lean in? Yeah.
[6:19] I think for us sometimes the Tam is not that obvious to a lot of
[6:24] other VCs. And I think when that happens
[6:27] then it, it's very easy for everyone to invest, right.
[6:29] I, I think we, we try to identify similar patterns.
[6:32] I think we like industries where we call it massive GDP
[6:36] contribution, but embarrassing low software penetration.
[6:40] So if you think of construction, insurance, food services,
[6:44] logistics, these are industries that we always gravitate
[6:47] towards. And I think we lean in what we
[6:50] see. Early customer pool, I think we
[6:52] don't need to see a lot of revenue, but their two or three
[6:54] kind of design partners or the customers were genuinely
[6:58] desperate for the product that these founders are building.
[7:01] That, I think gives us a lot of confidence that they're solving
[7:05] a real and kind of very relevant problem.
[7:08] Absolutely, and in BDB software, many AI startups are promising
[7:12] efficiency, but efficiency alone may not create durable
[7:16] companies. What separates an AI feature
[7:19] from a winter scale AI company for you?
[7:23] Yeah, I think the, the first question you probably have to
[7:26] ask is kind of AI the product or it's AIA feature inside someone
[7:31] else's product. So I, I think for us a venture
[7:34] scale AI company owns its own data loop.
[7:38] So I think the more customers that the company on board, the
[7:41] smarter it gets and it creates kind of proprietary insights
[7:44] that I think it can extract value from.
[7:47] So it might start, for example, it might start being a workflow
[7:51] company then with workflow against a lot of customer data,
[7:55] then now it can move into procurement, it can move into
[7:58] payments. And I, I think if the other
[8:02] it's, it's obviously hard to predict because these kind of
[8:04] foundational model companies are moving very, very quickly.
[8:07] But I, I think if you look kind of two years out and you think
[8:10] that this is something a foundational model company would
[8:14] potentially build or be able to release.
[8:16] And then that's something we typically back away from.
[8:19] So I think we we test the durability of kind of what the
[8:23] company is building. That's pretty important to us.
[8:26] And can this company become the dominant company who is
[8:30] collecting data insights that allow it's it to either release
[8:35] new products or get to a skill where it's very hard for its
[8:38] competitors to disrupt? Absolutely, and a lot of SAS
[8:42] companies are now adding payments, lending, insurance or
[8:46] financial products. When does embedded fintech
[8:49] become a real business model? What's just an extra revenue
[8:52] line? Yeah, I think the litmus test is
[8:55] does the embedded financial product improve the core
[8:58] software experience or improve the end users experience?
[9:03] I think so in some cases, some examples would be a software
[9:08] platform that helps, maybe we'll use build UPS as another as an
[9:12] example. Again, I think build UPS primary
[9:15] objective is to help a lot of contractors do their job better.
[9:19] So it started off with kind of organizational schedules,
[9:23] dispatch, I think create better internal workflows and then
[9:28] handling invoices and payments was a natural extension.
[9:31] This is something that is a cost Center for a lot of contractors.
[9:35] So by building up a piece of embedded fintech or embedded
[9:40] payments that allows you to take away some of these core pain
[9:44] points for the end customer. That's when I I feel it becomes
[9:47] a real business model rather than just a revenue line.
[9:50] Yeah, absolutely, completely makes sense.
[9:52] And you said AI is a double H squad for fintech.
[9:56] Where do you think AI creates the biggest upside and where
[9:59] does it create the most dangerous new risk?
[10:03] Yeah. I think right now, I think we're
[10:05] still in the first few innings of what AI is doing to fintech.
[10:09] So this is obviously a perspective from this current
[10:12] point in time. But I think over the years or
[10:15] even months this will change quickly.
[10:17] But for me, I, I see a lot of, I think historically kind of
[10:21] manual workflows be improved a lot by AI.
[10:24] So we think about underwriting now.
[10:27] I think with AI this ability to take a lot of more data signal.
[10:30] So whether it's consumer, whether it's business
[10:33] underwriting or insurance are the right thing.
[10:36] I feel that we're getting too much better decision making.
[10:40] The other is risk assessment. I think if you look at kind of
[10:43] even vendor management in fintech used to be a static kind
[10:47] of assessment of vendor risk. Now I think with AI, we can
[10:52] obviously improve that where you're doing continuous
[10:55] monitoring. And then I think the third
[10:58] biggest one that we're all seeing in real time is the
[11:00] customer experience. I I think with AI agents, we're
[11:04] now able to interface with the customer, Taylor, kind of our
[11:10] responses to them and even handle kind of real time
[11:13] customer concerns and questions in real time.
[11:15] So think about financial advice, thinks about tax preparation.
[11:19] These are all activities that now can be done kind of in real
[11:24] time with the end customer. So that's pretty exciting to me.
[11:27] On the risk side, I think we see AI powered fraud, the
[11:33] proliferating kind of the fintech ecosystem.
[11:37] So all all the way from in the past we used to be able to use
[11:41] voice or facial recognition to identify someone.
[11:44] That's now all kind of gone away.
[11:47] So I think it's creating a lot of challenges with the ability
[11:52] to kind of redirect fraud or or really kind of validate
[11:58] someone's identity. So I think that's creating a lot
[12:01] of new issues where it's also placing a lot of strain on the
[12:05] current incumbent fintechs because dealing with these
[12:08] issues used to be a color like 2-3 percent of their time.
[12:10] Now it says in the double digits in terms of where their focus
[12:14] areas are. So based on that, we are
[12:18] entering a new era where we need new and faster tools to deal
[12:22] with these AI challenges. Absolutely.
[12:24] I think very well said. And do you think with that the
[12:29] next big Fintech company would actually be consumer facing or
[12:33] the infrastructure companies defending the system underneath?
[12:36] Wow, that's a, that's a very big question.
[12:38] And, and maybe this is my humble opinion.
[12:40] I, I guess I could be very wrong because if you look back across
[12:43] the last 10 years, all the most successful fintech companies
[12:47] have been consumer fintech companies.
[12:49] But my strong bias here is like, I think infrastructure will win
[12:52] this cycle. I think that's real value
[12:56] creation opportunity now and the kind of picks and shovels layer.
[13:01] I think we, we talk about identity verification, we talk
[13:04] about fraud prevention, we talk about payment orchestration.
[13:09] I, I think there will be 10 pole companies in each sub vertical
[13:13] of Fintech that are going to be powered by AI and they will form
[13:20] kind of the backbone for a lot of these AI native experiences.
[13:23] So think about AI native financial advisors, think about
[13:27] autonomous finance. I think ensuring that we are
[13:30] building the right backbones, pulling the right data,
[13:33] surfacing the right level of custom insights, then you can
[13:37] overlay it with the kind of UI layer.
[13:39] I think that that to me is where a lot of the value is going to
[13:44] be created. So short answer, going back, I
[13:46] feel like the infrastructure layer kind of wins in this
[13:48] cycle. Got it and fintech went through
[13:52] a huge boom and then a reset. What mistakes from the last
[13:56] fintech cycle do you think investors and founders still
[13:59] haven't fully internalized? Yeah, I, I think maybe they're
[14:03] like 3 or 4 that stand out to me.
[14:06] I think the first one is confusing distribution with
[14:09] defensibility. I think a lot of fintechs in the
[14:12] last cycle felt that hey, oh, we are able to build a partnership
[14:16] with either like in a traditional Fintech player or
[14:20] we've built out kind of these few distribution channels.
[14:24] Maybe we're very good at kind of Facebook marketing, but I think
[14:28] that is always going to be disrupted pretty quickly.
[14:32] I think within fintech, we call them kind of promiscuous bias.
[14:37] We're always going to optimize for the best fintech
[14:39] partnership. So I feel that's a short term
[14:42] mode and something that will not create kind of long term value.
[14:46] The the second one is I think revenue quality.
[14:50] A lot of fintechs had bloated revenue numbers where they were
[14:54] quoting kind of interchange revenue or maybe float based
[14:58] revenue or I would say that their business were predicated
[15:02] on kind of the zero rate environment.
[15:04] And I think once the margin change, I think the quality of
[15:07] revenue went away very quickly. So for me it's like
[15:11] understanding that the business model is not susceptible to
[15:15] these kind of short or medium term macro fluctuations.
[15:18] That's very important. And then the third is I, I, I
[15:23] joke and again I'm I'm guilty as charged because we've been
[15:26] exposed to a couple of these companies.
[15:28] I think it's burning cash to acquire customers with no
[15:30] loyalty. So as you get to scale, you're
[15:34] really transitioning from the early adopters to kind of the
[15:39] mass customer base and the LTV to CAC in many cases didn't
[15:43] quite work. I think customers were either
[15:46] inundated with several offerings that were pretty similar or they
[15:50] would start to churn once a better offer came to the table.
[15:53] So I think those were the top three mistakes that I personally
[15:57] saw in the last cycle. Absolutely.
[15:59] Yeah. That's very helpful.
[16:01] And what kind of inter companies do you think will come on
[16:03] premium valuations again and which categories may never
[16:07] recover the old multiples? Yeah, yeah.
[16:11] Well, that's, that's a very bold prediction, but that that I
[16:13] think this is a fun one. I think going back to my
[16:17] previous comment, I think infrastructure companies I think
[16:21] will come in very high kind of valuations, especially those
[16:25] servicing kind of large critical kind of sub sectors.
[16:28] Think about lending, insurance. I think the second is vertical
[16:33] AI companies embedded in mission critical revenue generating
[16:38] workflow. So I think I'll use insurance as
[16:41] an example. If you're really helping kind of
[16:44] the frontline brokers, wholesalers and maybe even
[16:48] carriers kind of identify revenue opportunities and really
[16:52] helping them kind of streamline the cycle towards kind of
[16:56] closing deals or binding policies.
[16:58] I feel like these kind of companies that are embedded in
[17:01] mission critical workflows that kind of expand throughout the
[17:04] organization. So that's an important second
[17:07] piece. It's not a siloed product.
[17:08] It kind of extracts kind of information for all different
[17:11] aspects of your operation. I feel those are going to be
[17:15] kind of very interesting companies that will command kind
[17:18] of high valuation setting companies that might not
[17:22] recover. I would say lending platforms
[17:27] might be pretty difficult, especially targeting kind of SMB
[17:31] businesses, I would say. And lending, I would also
[17:37] include, I'd say like payday lenders.
[17:40] I would say this, this has become a very saturated category
[17:44] where there's a lot of marketing and acquisition spend, but it,
[17:49] it's hard to kind of yield the returns that they're looking
[17:52] for. The, the, the second one.
[17:55] I, I feel like there's also a consolidation which among neo
[18:00] banks. So I think there's just so many
[18:02] neo banks that can exist in my opinion.
[18:05] So you could have maybe a very interesting kind of consumer
[18:10] facing out, but ultimately consumers are looking for
[18:13] financial value. So what what are you providing?
[18:16] Are you providing kind of differentiated underlying
[18:19] financial products? And to me, I think a lot of that
[18:22] is now becoming extremely similar.
[18:24] So this is probably going to be a pretty commoditized category.
[18:29] Absolutely quite interesting and like you mentioned, vertical
[18:32] size has emerged as one of the most attractive categories and
[18:36] venture. What makes vertical SAS company
[18:38] truly defensible once AI makes software easier to build?
[18:42] Yeah. And then and, and Prashan, I,
[18:44] I've been told by my own investors that I need to call it
[18:48] vertical AI. Now I can't call it vertical SAS
[18:50] that it was. So that's, that's the joke.
[18:52] But I, I think we, we think about it in a couple ways,
[18:55] right? We think about workflow depth of
[18:58] these vertical AI companies. I think you're not just a tool,
[19:01] you're the really the operating system for how the business runs
[19:05] day-to-day. Like what I talked about, you're
[19:07] covering every single aspect of the business.
[19:09] And I think ripping the platform out will be akin to kind of open
[19:13] heart surgery. That's what we think will be
[19:15] very defensible. And I think there's also the
[19:18] data mode effects. I think the more customers on
[19:20] the platform, the better benchmarking pricing
[19:23] intelligence you can offer. I think that's very interesting.
[19:26] And then I think that's also the ecosystem lock in, I think in
[19:29] this integrations with industry specific tools, accounting
[19:34] platforms, supply chains, I think every integration you
[19:39] build is in another mode that you've created.
[19:43] And I, I would say the last thing will be we at figure like
[19:47] the target, we call it the physical economy.
[19:50] So a lot of industries where there's still a lot of human
[19:54] interactions to get the job done.
[19:56] So think of manufacturing, think of construction.
[19:59] I think we are building AI for these verticals.
[20:02] That's an aspect of leveraging software in the field.
[20:05] And capturing kind of real time data.
[20:08] And if you are the kind of software front that allows you
[20:11] to do that, then it's much more difficult to disrupt your
[20:14] position over time. Absolutely.
[20:16] I completely agree. And build UPS is a great example
[20:21] of software for an industry that was historically in the SOAP,
[20:24] right? How do you evaluate whether a
[20:26] boring industry is actually ready for a massive software
[20:30] company? Yeah, I think we always like
[20:33] boring industries that figure. So this is kind of our bread and
[20:36] butter. So I think signals we see that
[20:39] an industry is ready. A lot of times we see a
[20:42] fragmented market which is pretty large.
[20:44] So a lot of companies still running on spreadsheets paper
[20:48] kind of legacy on Prem software and there isn't a consolidation
[20:52] towards a single kind of software vendor.
[20:56] I think this the second is either regulatory or compliance
[20:59] burden that suddenly creates the need to adopt kind of more
[21:02] efficient software platforms. The third is like a a
[21:06] generational shift of people kind of in charge of running
[21:11] these businesses or being the front line producers.
[21:14] I think they give you one example is like the average
[21:17] insurance broker is probably in their late 50s.
[21:20] So I think we see a new wave coming of, of people taking over
[21:24] that category. So this is a very kind of, I'd
[21:27] say like a good sign for us to, to prove that, hey, there might
[21:32] be a new generation that's really more willing to embrace
[21:35] these modern tools. And then I, I think that the,
[21:38] the final thing we would say it's like really rising labor
[21:42] costs and slim margins getting, forcing an industry to adopt
[21:47] technology to compete. So I think these are the signs
[21:50] where we feel that software can really enter boring industry and
[21:54] become a massive company. Absolutely.
[21:57] We have talked about AIA lot and AI could make financial advice,
[22:01] tax prep, compliance and back office finance far more cheaper,
[22:05] right? But if everyone gets access to
[22:08] powerful tools, where does differentiation move?
[22:12] Distribution trust, data, workflow or regulation?
[22:17] Yeah, I think, I think to be honest, it's, it's a, it's a bit
[22:20] of everything. I, I know AII think what maybe
[22:25] we'll start with workflows. So I think AI does commoditize
[22:28] the kind of intelligence part of it, but I think not the
[22:31] integration layer and not the coordination between different
[22:35] groups of people. So I think if you are deeply
[22:38] embedded within the workflow, that's still a core
[22:40] differentiation. And I think AI needs
[22:44] information, right. We need to feed AI with right
[22:48] kind of accurate information. So I think if you are the data
[22:51] pipes and you're actually building and helping AI get
[22:55] better, that's also a very important note within an
[22:58] industry. And if we talk about kind of
[23:01] these legacy, I'd say like physical economy companies or
[23:04] even financial services, I think a lot of these buyers are
[23:10] hesitant to kind of hand over the keys to an ad right away.
[23:14] I think they want to trust companies where they work with
[23:17] for a long time. So I feel like this trust and
[23:20] credibility over time is still pretty important.
[23:24] And maybe maybe distribution is is 1 where we feel like, as I
[23:29] mentioned above, could be more easily disrupted.
[23:32] I think people are more willing to explore kind of new
[23:36] partnership channels. So among the few, I feel like,
[23:39] hey, this is maybe the less sticky in my kind of humble
[23:41] opinion. And and I think regulation and
[23:45] compliance does create an interesting mode as well.
[23:47] I think if you build kind of compliance into your core
[23:51] product, I think you become an interesting, you build an
[23:55] interesting barrier that's expensive and time consuming for
[23:58] con competitors to replicate. So I think all these you
[24:02] mentioned are definitely very interesting kind of AI
[24:04] differentiators. It's it's usually a combination
[24:06] of both, all of them that create a a lasting kind of
[24:10] differentiated company. Yeah.
[24:13] So I I think companies who want to do this should embrace kind
[24:17] of these different kind of angles to creating value.
[24:22] Yeah, absolutely. And with AI coming in, looking
[24:25] out 10 years, what do you think financial system looks like if
[24:29] AI, blockchain and modern payment infrastructure actually
[24:32] converge? What changes meaningfully and
[24:35] what probably stays the same? Yeah, I think what changes
[24:39] meaningfully, it's like I think real time settlement becomes the
[24:43] normal, not the exception. I think we now still have our
[24:46] 2-3, the ACH that will feel very outdated or like I think sending
[24:50] wise overseas that will become instantaneous over time.
[24:54] I think AI agents will handle the majority of routine
[24:57] financial transactions. So think about paying bills,
[25:00] optimizing cash flow, managing investments all autonomously.
[25:04] And then I, I think 1 interesting caveat to notice
[25:08] that I think there'll be a lot of agent to agent communication.
[25:12] So you and I probably in five to 10 years will be represented by
[25:15] our own personal agent that's going to interact with the
[25:18] financial institution. So a lot of, I think fintechs
[25:21] need to build towards kind of that agent to agent interaction
[25:24] will and I, I think now financial products become hyper
[25:29] personalized over time. I think right now you see ads,
[25:32] it's like, hey, do you want to take out a $10,000 loan for X
[25:36] amount APR? I think in three to five years,
[25:39] you see a very specific number to you like the number to TX
[25:42] might be $9400. The number to Prashant will be
[25:45] 5300. And that's where AI becomes very
[25:49] tailored not only to the product offering, but the underwriting
[25:52] that they do with each individual.
[25:55] I think what will stay the same, I think because of a lot of
[25:59] these investments, there's, there's going to be even more
[26:02] protection around the end consumer to make sure that the
[26:07] understanding kind of what they're actually buying or what
[26:11] they're actually getting exposed to, especially we're going to be
[26:13] represented by our own agents. So I'd say like AI will make
[26:16] regulation and compliance even more important.
[26:21] I think the the next part is I think for us, we as consumers
[26:26] are going to be maybe slightly more skeptical on what products
[26:30] are being offered to us. So I would think that trust and
[26:33] relationships don't matter. I don't think an individual is
[26:37] going to move kind of all the investments overnight to a new
[26:40] organization. I think that kind of long
[26:43] standing relationship and credibility are also going to be
[26:46] important. And I, I, I think in, in this
[26:50] world, it doesn't mean that with AI can all the traditional risk
[26:54] go away. And I think we talked about kind
[26:56] of fraud risk. We, we talk about identity risk.
[27:01] So all of these kind of new challenges are going to come
[27:04] into play. And we as kind of consumers need
[27:06] to be informed of those risk. And I think on the on the
[27:10] regulatory and business side, I think there needs to be even
[27:13] more attention paid to kind of our consumer can be exposed.
[27:19] Yeah, absolutely. You were insightful and you've
[27:22] been running FICA for so many years and it has over $500
[27:26] million in AUM. What do you think has been the
[27:29] secret behind the success? What helps you raise those
[27:32] subsequent funds? Is it the underlying portfolio
[27:34] companies or is it the returns that you've generated for your
[27:38] previous LP's? Or is it just the relationships
[27:41] you have built with these LP's that keeps compounding over time
[27:45] and subsequent funds? Yeah.
[27:47] I think we obviously feel very humbled that we continue to get
[27:53] kind of great support from my old piece and are able to do
[27:56] what we love. But I think it comes down to a
[27:59] few things. Obviously, performance matters.
[28:01] I don't think there's any way you can escape from that
[28:04] conversation at any point. But I think more importantly is
[28:08] being accountable for what you would say you would do.
[28:12] I think for us, I think we we set out kind of with every new
[28:15] fund we raise kind of goals that we want to get to, whether it's
[28:19] ownership targets in every deal, whether it's being clear like,
[28:22] hey, these are the verticals we want to double down on and these
[28:25] are investments we're going to avoid.
[28:27] And I think for the most part, we've been very consistent in
[28:31] sticking to our promises. So I think that visibility and
[28:34] transparency is something that our investors like to see that
[28:37] has enabled us to continue kind of building trust with them.
[28:42] Obviously there there think that if if I were to do start figure
[28:46] from scratch, that will consider doing a bit differently.
[28:49] I think you always take pride in being contrarian, but I think in
[28:53] the moment, it's obviously very difficult when everyone is
[28:57] telling you not to do a deal. Like every reference call you do
[29:00] is like, hey, pointing towards there being additional concerns
[29:04] or questions about this as well. But I think the moments we've
[29:07] done the best at figures like when we've invested in founders
[29:10] who weren't really central casting or we invested in
[29:13] categories where others people thought it'll be too slow, too
[29:17] niche or too early to disrupt, that's when we did our best
[29:21] work. So continuing to lean into that.
[29:23] And then I think over time, what we've done is like we've been
[29:27] even more concentrated with our position.
[29:30] So I think we call it fewer bets, hire conviction.
[29:34] When we have conviction in the company, we don't rely on 3rd
[29:37] party, even if we're the only ones in the cap table, that's
[29:39] perfectly fine. So I think that's something that
[29:42] we want to lean into more and more over time.
[29:45] Absolutely. And I have a either or question
[29:47] for you. So let's say there is a great
[29:49] founder building in a not so good category, right.
[29:53] And on the other side, there is a great category, but not so
[29:58] great founder building that. Which one would you pick?
[30:03] Great founders always more important, but we need the
[30:05] founder to be open minded to pivot.
[30:08] Yeah, that makes sense. Now to my last question before
[30:11] we do the rapid fire around. If you're starting fecal
[30:13] ventures from scratch today, knowing what you know about AI,
[30:18] Fintech and vertical SAS, what would you do differently?
[30:22] Yeah. For me, I would double down even
[30:25] further on these physical economy verticals.
[30:29] I think really built even more relationships.
[30:32] It's becoming clear that if you look at vertical AI, the
[30:35] opportunity is bigger. These industries that we talked
[30:37] about, I, I think the, the other thing we would optimize on is we
[30:42] build a great network or extended community.
[30:46] And this I think spans across kind of all different functions
[30:50] within the organization for, for me, if I think we're do FICA,
[30:54] again, I think even honing in on those relationships and really
[30:58] focus on kind of who are going to be buyers of software over
[31:02] the next 5 to 10 years. I think that will be kind of
[31:04] even more interesting for us and we definitely can adopt A AI and
[31:12] I'd say like technology internally within Feek over the
[31:15] last kind of 1 1/2 years. I wish I embraced that a bit
[31:19] earlier. So to give you some examples of
[31:21] a company comes to us and wants an introduction to something on
[31:25] network that's automatically keyed up so we can automatically
[31:29] kind of find candidates for companies.
[31:32] If a company comes to a, say, I want to find a senior director
[31:35] of marketing, we can surface 15 candidates right away.
[31:38] But this is just I, I think a sliver of what we could
[31:41] potentially do. So as much as we preach kind of
[31:45] efficiency to our founders, this is something that I wish I know
[31:48] we're still making good strides by which we focus on even
[31:51] earlier on the life cycle or FIFA.
[31:54] Absolutely. That's quite interesting.
[31:56] Now we'll do the rapid fire on wherein I'll ask you 6 quick
[32:00] questions about the investing you're doing at FICA and you
[32:03] have to give 6 quick answers. Sounds good.
[32:05] Yeah. So the first one is what are the
[32:07] sectors and regions you invest in?
[32:11] B2B companies in general that's been across vertical AI,
[32:14] fintech, commerce, enablement, healthcare and then all our
[32:19] companies are North American based, so US plus Canada.
[32:24] What's the typical stage of investment for you?
[32:28] We call it pre Series A, So we would invest both of the pre
[32:32] seed and seed level and then we're perfectly fine being the
[32:35] first institutional. Check.
[32:37] Awesome. And do you lead rounds?
[32:40] A preference to lead most of the time, but I think we're very
[32:44] open to Co leading when it's it's a great investment we don't
[32:47] want to miss out on or we've known the Co investor for a long
[32:50] time. What's the typical check size
[32:54] you put in? These days it's 1 to 5 million
[32:58] as initial checks, kind of with meaningful follow on results for
[33:02] winners. Amazing.
[33:04] Where can founders get in touch in case there's a direct way?
[33:08] Yeah, TX at FICA VC. My e-mail is short and sweet.
[33:13] Or just message via LinkedIn. Amazing.
[33:16] Last one where you can listen follow.
[33:18] I guess I'll be LinkedIn. LinkedIn, we, we, we write a lot
[33:23] on Medium. So follow us on Medium.
[33:25] I think we have at least 10 posts a year.
[33:28] And then we're also fairly active on kind of Twitter or X
[33:32] these days here. Awesome.
[33:34] I'll make sure to put those links in the show notes below.
[33:37] So I'll let's just get there easily.
[33:39] Thanks so much for doing this with us, CX.
[33:41] It's been a lovely conversation and I wish you a happy
[33:43] investing. Thank you.
[33:45] It's my pleasure, Prashant. Thanks for having me.

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