Alex Khomyakov: Hey everybody and welcome to the Curiosity Code 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, 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? Then we'll start digging into more of the world of HR and hiring.

Owen Dearn: Yeah, lovely. I've spent the last eight years helping payments, fintech, and web3 businesses scale, which has been pretty interesting. I found myself in the last 18 months, particularly with the emergence of stablecoins, well positioned at the interim between TradFi and DeFi, 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, all the way through to traditional incumbents who are looking to adopt a stablecoin or traditional money movement strategy.

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 that you're only as good as your team. I'm really living these questions and that challenge every day when I grow my own company. So looking forward to the conversation. Let's start with just looking at the ecosystem in general, like 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. I think we talked about this the very first time we spoke, but particularly in stablecoins, which is a very hot market right now. Trying to find people with real-world crypto and stablecoin combined with cross-border, like traditional money movement experience, is super hard. There are constant evolving regulatory changes. You know, MiCA in the EU coming into force last year, the Genius Act, the UK finally starting to get their act together around what stablecoins are, and giving some guidelines about how businesses can innovate. I think that's really the biggest thing affecting my core markets. As a result, all businesses are really chasing what is becoming a very small pool of talent globally who have both the compliance, AML, KYC, regulatory sanctions, all of that relevant experience that is so pivotal to businesses being able to release products and serve customers. 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. I've seen 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. Even if I look from the traditional perspective of software development and in fintech. So when you look at your development force, you would start hiring junior, then it will grow to mid-level and then senior. So that's kind of a career evolution. Now, what I'm observing more and more, something that you would hire a junior person for is pretty much done by AI. And you sort of find yourself in a position where you have very senior people slash expensive, and then you have mid-level people that are, you know, they're all right, but you completely, at least where I am, I see that the junior kind of bucket starts to dry off because AI is replacing it. It's interesting because that's what I'm doing myself, but at the same time, there is an opinion from the CEO of Amazon, Matt Garman, that he's saying like placing junior staff with AI is one of the dumbest things he's ever heard. It's because, for exactly the same reason, there's a risk of following our company's future talent pipeline. What's your take on that? How do you experience it from your side?

Owen Dearn: I think what you've said there and your own experiences coupled with what Matt Garman said captures something that I see every day. Yeah, there's a big problem with teams trimming entry-level talent. Let's link that specifically to compliance building on from the first question. Those traditional lower-level first-line defense roles where grads come in or juniors come in and learn transaction monitoring or KYC or whatever it might be—they are increasingly becoming automated. So, there comes a point where the people who are then in the second-line defense teams are essentially managing AI tools, right? As opposed to traditionally, you would have a team of five to ten people doing the grunt work, first-line defense stuff, with the nuance of the regulatory environment in their minds. That is kind of no more. Now, there comes a point where you run into some pretty big problems. If you get rid of entry-level staff to enhance short-term margins, there comes a point where, 12 to 18 months down the line, people come to hire, and they can't promote from within because the bench of talent has gone. There's lots of data out there to support the fact that grad hiring is down considerably, both across big tech and startups. When we as recruiters and talent folks go out to recruit from that pool, we feel the ripple effects over time of the talent not coming through. That does create a problem.

Owen Dearn: I think where you see businesses do this well and kind of mitigate the risk of a dried-up junior talent pool is where they bring in leaner junior teams. A lot of which, by the way, are very comfortable with technology and AI because they've grown up in the emergence of that, right? And they almost then go into wider roles where they're the gatekeeper of the tools. They're finding new use cases to adopt AI in. All of that good stuff, right? I'm not just talking about compliance now, I'm talking about operational efficiency as a whole. So I think, yes, we all know that AI is changing not only hiring but the world of business as we know it. Teams are running much leaner. But I think from a people and progression perspective, that really affects things. It only means that manager-level talent now, as we kind of alluded to earlier, is becoming increasingly difficult to find and increasingly more expensive. That's really what I see. I echo what you're seeing on a daily basis. I see it every single day.

Alex Khomyakov: Yeah, what I'm trying to understand is how do you... So I agree with you that because those young fellows kind of grow up with AI tools, so they know how to use it probably better than anybody else. But at the same time, they're missing core knowledge about the subject. AI is, at least at today's point, it's a great tool, still requires supervision, especially in the compliance domain. You need to know what to look for. What I see is that you still need to have that fundamental knowledge about the subject and learn it and practice it in the workplace. Plus you need that kind of AI supervision management skill. What I find difficult to understand is how you can gain that expertise in the workplace when there is no hands-on work anymore. I don't know, we're probably talking about a near future; it's already happening in some organizations.

Owen Dearn: I think there's a couple of things that stick out to me. One is, you know, some of these entry-level jobs, you learn fast because you do the grunt work, right? You're often being mentored by someone who understands the subject better than you because you're new. In most entry-level jobs, even thinking about my own career, it probably takes you 12 to 18 months to really get up to speed on a subject to the point where you can learn by osmosis from the people around you, you know, just general upskilling that comes with experience. The emergence of AI again relates to my own day job and from speaking to everyone in the industry, the learning curve has become much shorter. I think that the evolution of entry-level jobs should not perhaps be so much the grunt work, but more in terms of like, almost like AI operations type positions where those junior folks own the prompt libraries, they own all of the inputs that train the gen AI in order to manage compliance or customer operations or whatever it might be. As a result, I think entry-level staff will then ramp up quicker on what are quite complex problems because they are experiencing that, but ultimately it's being digested in a more succinct and concise way because the AI is teaching them, right? Just like you and I would go out and scale up on a new knowledge on a new topic, you can do that much quicker these days. I think as long as you have those guardrails in place with the mentorship and management by more senior people who do know the job better than you, then I think that's kind of a nice hybrid of how you can still bring through staff who know what they're talking about and also future-proof the business from a talent perspective.

Owen Dearn: Rather than, you know, us getting three or five years down the line and there's a very hollow talent pyramid where the foundation is no longer there. So I think about how this evolves, I think that's really how I see the best businesses doing it rather than just let's lean into AI and never hire a junior person again.

Alex Khomyakov: So the human factor is still there and it's there to stay. Right. So it's not like we're looking into the future where compliance will be like 90% automated and then you will have just a senior console that will be sort of overlooking certain aspects of it. But still, you're seeing that the human factor is there. What's also interesting is that if we widen the picture a bit, and then we're not only single market and we're looking at all the different combinations of regulations, cultural aspects, different kind of jurisdictional expertise, then I think it's even like another skill is just to program and find the right prompt that will address those kinds of problems and improve it. When I think about that future and that kind of context, I think of compliance people like half AI engineers in a way, you know?

Owen Dearn: Yeah, yeah. Yeah, I agree.

Alex Khomyakov: So it seems like we're moving in the world and we've spoken about development challenges here in management and all different sorts of stuff in previous episodes. But it seems like we're moving overall in the world where depending on the role that you're playing, the percentage of you being an engineer would be 40%, 50%, 60% depending on what you do.

Owen Dearn: I think everyone is kind of becoming a slight AI tool engineer, aren't they? You can run businesses much leaner by upskilling even slightly on the latest tooling. And there are some amazing examples of businesses out there when you think about revenue per headcount, the numbers are off the charts, right? When you zoom out and look at the wider picture, not just talking about compliance and I work across every job type, we've obviously landed on compliance as being a pretty tough one at the moment, but there are naturally more parts of business that are more human-led, you know, sales and commercial teams aren't going anywhere. People do business with people, it is as simple as that. The sales operations process may be streamlined, leveraging AI tools, but ultimately, you only have to go to some of these events that we both go to, Alex, to know that networking is alive and well. People like doing business in person. They like the fact that they can probably free up some more time to do relationship building rather than some of the inputs they may have had to do before. I think if done properly and done responsibly, this can actually enhance other areas of the business rather than hinder them. You look across every job type and AI is changing the landscape. Being the gatekeeper to relevant AI tooling in your given job type is the future, right? I think anyone who isn't upskilling themselves on this is going to be left behind. It's as simple as that.

Alex Khomyakov: Let's talk about the hiring process, like the meat and bones of it. So I've seen many founders in my community and online that are very eager to use AI to accelerate the hiring screening process, sometimes interviewing and all of this, while others are a bit afraid that AI may be biased and it may strip away important aspects of a candidate. So, in your opinion, where do you think AI truly adds the most value in recruitment? And where do we need to draw the line and keep human judgment at the center?

Owen Dearn: I'll break this down into two parts. First, my job. In my job, I've had a much wider bandwidth as a result of being able to lean into technology to help with some of the grunt work around sourcing. You ask anyone who's doing a talent or recruitment role in any sector, and the time vacuum usually, if they have enough business to work, is in the sourcing of candidates. Particularly when you're working globally, you can't dip into the same talent pools every time a role comes up. Leaning into even the most basic of AI is helping with that process and is making me able to deliver quicker. We're talking, you know, two-month timelines to a few weeks as a result of streamlining that initial process. There are lots of tools out there that I've seen businesses and candidates use from your paraforms to your Jack and Jill's, which are gathering quite a lot of pace in the UK. But I still do think that some of the nuances of context within a given domain are missed to the point where even in my own processes, I can't trust it entirely. Whether that is a business, you see a name on someone's resume and you have some context into the business and whether or not they have a good compliance team or a product team or a sales team. Having spoken to some of those people before, you then start to form an opinion of the environment that that person has worked in, and as a result, the type of operator that they might be. I see it more and more that businesses are trying to do a lot of this on their own.

Owen Dearn: I also have work off the back of businesses trying to lean into technology to do this on their own for a few weeks or a few months in some cases. Then being like, okay, we're not getting the results that we expected. As a result, we now need to work with a specialist who knows the space, who can react quickly based on an already existing network that they've built over a period of time. I don't think these platforms are there yet. I think that there are time vacuums, whether you're on the business side trying to hire or you're on my side of the table trying to hire, that are being solved effectively. But much like as I mentioned with sales earlier, I see no world where the human element of hiring goes out the window. From building relationships in person, virtually, whatever that may be, giving people context of how to go to interviews, understanding personality types, talking through lived experiences of working with people before, much like sales, is always going to exist, in my opinion. Some of the grunt work, similar to what we were talking about in compliance, may be taken away.

Alex Khomyakov: So in terms of HR operations, we're looking at a co-pilot kind of setup, not fully automated pipelines where, again, there'll be a single supervisor.

Owen Dearn: Yeah, like AI-enhanced humans.

Alex Khomyakov: Interesting. Basically, we're coming back to the same setup as in compliance, as you said, right? It's kind of better that we're observing the processes we're touching in this conversation.

Owen Dearn: Absolutely, yeah.

Alex Khomyakov: What will be the role of humans in all this connecting and hiring trust, company culture, we see AI automation getting more and more in. We are looking at a co-pilot setup. I'm just trying to think of an even larger picture on a more philosophical scale. I'm looking at Find, your company, a philosophy that emphasizes building strong human relationships. I just want to, because we're getting to the end of this episode, I think we kind of touched base a lot on HR and the struggles in the compliance world and how AI plays in terms of helping but at the same time not hurting and how to balance all of this. So I'd like to draw the line at the end of the conversation as just to see what will be the role of humans in all of this and how to prepare ourselves for that world. What skills would you probably recommend someone work on and focus on to stay at the curve and win that hiring game if somebody is listening and looking for a job? That might help.

Owen Dearn: Yeah, nice. I think there are a couple of things here that spring to mind. So, where can AI help? It's in high ROI, low-risk, maybe high-output type jobs, like in recruitment: sourcing, market mapping, screening, initial screening and data capture, interview operations and scheduling, all that stuff that's like a bit of a time vacuum. Where do humans stay relevant and central to this? Like in any job, it's signal interpretation, we're kind of on that a bit earlier in terms of reading the trajectory of people's careers, reading into the context that you have around businesses, ethics, culture, all that good stuff. Bias control, I think, is also an interesting one as well. I think that actually works two ways. I think firms rushing into AI with weak internal policies around how to structure bias is a problem. The beauty of human beings is that they can take a step back and look at the wider picture rather than going deeper into a given topic that's usually led, when you think about it, by a certain prompt with a given context, right? If that prompt is inaccurate or built out, I see problems coming in there. I like the AI co-pilot thing that we've gotten to here. I see that really as being the future. I fear for a world where we are over-reliant on some of this stuff because there are lots of risks that come with that, both from a talent point of view and from an execution and delivery standpoint. In some of these higher-risk touchpoint areas where business deals are being done, there's regulatory context that needs to come into compliance, whether it's high-level finance operations or whatever that might be, the role for humans I don't think will ever fade. This may create other jobs. I've read some interesting articles in various publications about the creation of new jobs as a result of AI, I don't think we've necessarily seen where they will be yet. We've probably touched on this AI gatekeeper type thing. I think that really just is a console that you add into everyone's role. It would be interesting to see where that goes because I fear that without doing this responsibly, we could limit the role of humans in some of our core areas of business, and that would be a mistake.

Alex Khomyakov: Yeah, it's definitely an interesting evolution of the workforce. Even, you know, I've been talking to my 11-year-old son, and I remember being that age and really wanting to be like somebody, you know, like a doctor or a lawyer. So there was a well-defined spectrum of roles and jobs that you could do. Now, when I talk to young fellows, they're really a bit lost. I was confused by that at the beginning, but then I think I read somewhere that the roles and jobs that our kids will be holding in the future don't exist yet. they're all being created as we evolve in this technology race. All these new roles will be created.

Owen Dearn: Yeah, for sure. I think to touch on the philosophy and maybe an interesting point to explore, I've said this from the start, but I think, ultimately, when you think about my business, it's very human-led. That will endure. I think as ops get automated, trust is still hand-built, right? That comes with time, that comes with connection, that comes with honesty, that comes with all of the traits that we know as humans lead to strong relationships. In my world, what these platforms can't do is give context-rich briefs, why something matters, what good looks like, all of those good things that you see as a future, transparent, honest, open feedback loops, you know, often. I think everyone can relate to a time in their life where they've been looking for a job; they've worked with a recruiter, and unfortunately, there are a lot of them who don't give them feedback. They aren't honest with them. How is anyone ever meant to learn whether they're hired or not if they don't get feedback, right? I think that's really something that I, in myself and my teams, have been steadfast on. That is your superpower. That builds a real trusting, deep relationship with someone when you can do that effectively. You know, this sense of community over, I suppose, transaction maybe, but also automation perhaps. This sense of real-world connections, whether that be in what we're doing here, at big corporate events, roundtables, networking, whatever that could be. I think that's really where the world is going. I see it when I go to some of these things that people still properly crave that.

Alex Khomyakov: Well said, well said. Thanks a lot for your time. That's been a great conversation. Enjoyed it a lot. Thank you.

Owen Dearn: Pleasure.

Alex Khomyakov: And that's it for this episode. Thanks for listening to 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.