Alex Khomyakov: Hey everybody and welcome to the CuriosityCode podcast. I'm your host, Alex. Today I'm joined by Owen Dearn, a strategist at Find who's helping high-growth fintechs and stablecoin leaders build teams that thrive under regulatory pressure. Together we'll be exploring the future of fintech and Web3 hiring and how talent, compliance, and AI are reshaping the way companies scale. Welcome to the show.
Owen Dearn: Nice to see you, Alex.
Alex Khomyakov: Let's start with a couple of words about you, Owen. Where are you at the moment? What are you working on? And then we'll start digging into more of the world of HR and hiring.
Owen Dearn: Yeah, lovely. So I've spent the last eight years helping payments, fintech, and Web3 businesses scale, which has been pretty interesting. In the last 18 months or so, particularly with the emergence of stablecoins, I've found myself well-positioned at the interim between traditional finance and decentralized finance, particularly from a network perspective. A year ago, I founded Find, which helps global fintech payments and Web3 businesses hire talent. I work with a blend of early-stage high-growth startups, VC or PE backed, through to traditional incumbents who are looking to adopt stablecoin or traditional money movement strategies.
Alex Khomyakov: Great. Yeah, the subject of talent and making the right hiring decisions and overall HR is really close to me in day-to-day operations because what I've learned over the years of running a business is you're only as good as your team. So I'm really living these questions and challenges every day as I grow my company. Looking forward to the conversation. Let's start by looking at the ecosystem in fintech. In your opinion, what's the single biggest talent gap founders are wrestling with and what's driving it?
Owen Dearn: I think the biggest thing right now is compliance and risk talent. We spoke about this the first time we talked, but particularly in stablecoins, which is clearly a very hot market right now, trying to find people with real-world crypto and stablecoin experience combined with cross-border traditional money movement experience is super hard. Constantly evolving regulatory changes, like the MECA in the EU coming into force last year, the Genius Act in the UK, are giving guidelines about how businesses can innovate. That's really the biggest thing affecting my core markets. As a result, all businesses are chasing what is becoming a very small pool of global talent with the necessary compliance, AML, KYC, and regulatory experience pivotal to businesses being able to release products and serve customers. So I think that's the real bottleneck right now.
Alex Khomyakov: What do you think about the role of AI and how it plays out in the current industry evolution, specifically in compliance? Let's talk about that because as you identified, that's the gap. As a software development company, over the past few years, we've built a few solutions that focus on compliance AI automation. So I've seen the evolution in terms of how people use the systems and how they help to scale. But at the same time, that impacts the type of talent you need in place.
Owen Dearn: Yep.
Alex Khomyakov: What's interesting is that you would typically see a career progression from junior to mid-level and then senior. But now what I'm observing is that something you would hire a junior person for is pretty much done by AI. So we're in a position where we have very senior, expensive people, and mid-level people are alright, but the junior bucket is starting to dry up because AI is replacing it. It's interesting because that's what I'm observing myself, but there's opinion from the CEO of Amazon, Matt Garman, that replacing junior staff with AI is one of the dumbest things he's ever heard. There's a risk of hollowing out the company's future talent pipeline. What's your take on that?
Owen Dearn: I think what you've said there and your experiences, coupled with what Matt Garman said, capture something I see every day. Yes, there's a big problem with teams trimming entry-level talent. Let's link that to compliance from the first question. Those traditional lower-level first-line defense roles where grads come in and learn transaction monitoring or KYC are increasingly becoming automated. So people in second-line defense teams are just managing AI tools, whereas traditionally, you would have a team of five to ten doing the first-line defense work. Now that's no more. You run into problems when you get rid of entry-level staff to enhance short-term margins. After 12-18 months, people come to hire and they can't promote from within because the bench of talent is gone. Data supports that grad hiring is significantly down across sectors. We feel the ripple effects when recruiting from a diminished pool. Where businesses mitigate the risk is by bringing leaner junior teams comfortable with technology and AI. These juniors then go into wider roles as gatekeepers of the tools, finding new use cases for AI, enhancing operational efficiency. I think AI is changing hiring and overall business, but from a people perspective, manager-level talent is becoming more difficult to find and more expensive. That's what I see every day.
Alex Khomyakov: I agree that young people grow up with AI tools, so they probably know how to use them better than anyone else. But they're missing core knowledge about the subject. AI still requires supervision, especially in compliance; you need to know what to look for. So you still need to learn and practice at the workplace and manage AI. What I find difficult is understanding how you can gain that expertise when there is no hands-on work anymore. We're probably talking about the near future. It's already happening in some organizations.
Owen Dearn: A couple of things stick out. Entry-level jobs teach you fast because you do the grunt work, mentored by someone more knowledgeable. In most entry-level jobs, it takes 12-18 months to get up to speed. The learning curve has become shorter with AI. Entry-level jobs should be more like AI operations positions where juniors own prompt libraries and train the AI to manage compliance or operations. As a result, they ramp up quicker on complex problems because the AI is teaching them. With guardrails of mentorship and management by senior people, you get a nice hybrid of bringing through knowledgeable staff and future-proofing the business from a talent perspective. Otherwise, we risk a hollow talent pyramid without a foundation. That's how I see the best businesses doing it instead of leaning into AI and never hiring a junior again.
Alex Khomyakov: So the human factor is still there and is here to stay. We're not looking at a future where compliance is 90% automated with a senior console overseeing aspects. The human factor remains. If we widen the picture to all different combinations of regulations, cultural aspects, and jurisdictional expertise, it's another skill to program and find the right prompt to address those issues and improve. When I think about that future, I think of compliance people like half AI engineers.
Owen Dearn: Yeah, I agree. Everyone's becoming a slight AI tool engineer. Businesses run much leaner by upskilling slightly on the latest tools. When you zoom out and look at the wider picture, not just compliance, I work across every job type. We've landed on compliance as particularly tough, but there are naturally more human-led parts of business, like sales and commercial teams. People do business with people. Sales operations may be streamlined with AI, but ultimately, people enjoy in-person business. Events and networking show alive and well. I think if done properly and responsibly, AI can enhance other business areas.
Alex Khomyakov: Let's talk about the hiring process, the meat and bones of it. I've seen many founders eager to use AI to accelerate the hiring screening process and interviewing, while others fear it may be biased and strip away candidate aspects. Where does AI add the most value in recruitment? And where do we need to draw the line and keep human judgment central?
Owen Dearn: I’ll break this into two parts: first, my job. I've had a much wider bandwidth by leaning into technology for sourcing grunt work. The time vacuum in sourcing candidates used to be months, but now it's weeks. Many tools out there, but some nuances of context within a given domain are missed. Even in my processes, I can't trust AI entirely. When you know something about a business's compliance or product team, you form an opinion on the operator they might be. I see businesses trying to do a lot on their own and not getting the results expected. It requires a specialist with a network to react quickly. These platforms aren't there yet, solving time vacuums effectively, but I don’t see a world where the human element of hiring goes away. Building relationships in person or virtually, giving people context about interviews and career trajectory will always exist, similar to sales. Grunt work in recruitment might be automated.
Alex Khomyakov: So in HR operations, we're looking at a co-pilot setup, not fully automated pipelines with just one supervisor.
Owen Dearn: Yeah, like AI-enhanced humans.
Alex Khomyakov: Interesting. So we’re coming back to the same setup as in compliance, observing processes we've touched on in this conversation.
Owen Dearn: Absolutely, yeah.
Alex Khomyakov: What will be the role of the human in connecting and hiring trust, company culture, with AI and automation increasing? We’re looking at a co-pilot setup. I'm thinking on a philosophical scale, looking at Find’s philosophy emphasizing strong human relationships.
Owen Dearn: I think there's a few things. Where can AI help? High ROI, low risk, high input jobs, like sourcing, market mapping, initial screening, data capture, interview operations, and scheduling—those time vacuums. Where do humans stay relevant? Signal interpretation, reading career trajectory and business culture, bias control, firms with weak AI policies risk bias. Human beings can step back and look at a wider picture. I like the AI co-pilot concept for the future. I fear over-reliance on AI because of risks, both from a talent point of view and execution and delivery. In higher-risk, touchpoint areas like deals, compliance, and high-level ops, human roles endure. This may create other jobs, new roles emerge because of AI. Thus, responsible development of AI must integrate human participation in core business areas, which is crucial to avoid negative impacts.
Alex Khomyakov: Yeah, it's definitely an interesting evolution of the workforce. Talking with my 11-year-old son, I remember desiring to become someone like a doctor or a lawyer. It's a well-defined career spectrum. Talking to young fellows now, they seem a bit lost. I was confused at first, but then I read that future jobs don’t exist yet, they’re created as technology evolves, creating new roles.
Owen Dearn: Yeah, for sure. Touching on our philosophy, I think my business is very human-led, which will endure. As operations automate, trust remains handcrafted over time through connections, honesty, and strong relationships. Platforms can't provide context-rich briefs or transparency like humans can. Everyone can relate to job searches where recruiters don't give feedback. How does one learn without feedback? My team and I are steadfast on that—honest, constructive feedback builds a deep, trusting relationship.
Alex Khomyakov: Well said. Thanks a lot for your time. This has been a great conversation. Enjoyed it a lot. Thank you.
Owen Dearn: Pleasure.
Alex Khomyakov: That's it for this episode. Thanks for listening till the end. Don't forget to subscribe to the channel on YouTube, hit the like button, and stay tuned for the next episodes. Bye-bye.
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