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🔍 Ex-Goldman Sachs Banker Builds AI That Outperforms Nvidia, Google & OpenAI | Meet the 30-under-30 founder who raised $6.6M and secured 90+ global clients in under 2 years. His AI is transforming how hedge funds invest billions—could this change the future of finance forever?

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In this revealing conversation, Jonathan Nguyen sits down with Hojun Choi, co-founder and CEO of LinqAlpha, to explore how AI is revolutionising the investment landscape for hedge funds and financial institutions. Discover how LinqAlpha's hyper-automation platform is enabling analysts to function at superhuman levels, processing vast amounts of unstructured data and extracting meaningful insights that would traditionally take countless hours of manual work. From raising impressive seed funding to landing major clients like Samsung Financial Network and KPMG, Hojun shares candid insights about building a truly global AI company.

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Transcript

Jonathan Nguyen

00:00 - 01:17

I asked Hojun Choi to tell us how the hell he managed to raise $6.6 million in funding and secure Samsung Financial Network, KPMG, and hedge funds across Asia and the US as clients. in less than two years. He's co-founder and CEO of LinqAlpha, an AI startup that outperformed Nvidia, Google, and OpenAI in Search Precision last year, and already has over 90 global clients. So did his past experience as a Goldman Sachs banker help him close those deals? Well, that's what we're about to find out. A candid conversation about what it takes to build a truly global AI company. Welcome back to another episode of the Unsensible Podcast, where we interview various founders and startups, particularly ones that look like they're in a Korean band. And joining me from a Forbes article I saw online recently is one such startup, LinqAlpha, and I've got Hojun Choi. Hojun, give us the pitch in 30 seconds. 

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Hojun Choi

01:17 - 01:46

Thanks, Jonathan. My name is Hojun. I'm co-founder and CEO of LinqAlpha. We're an AI startup built by Goldman Sachs alumni and MIT PhDs to revolutionize finance using Gen AI. Last year, we outperformed NVIDIA, Google, and OpenAI in AI search precision. And based on that, we started building a finance specific SaaS for hedge funds, asset managers, and investment banks. Currently, we have more than 90 global clients. We're trying to build a future AI analyst for these firms. 

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Jonathan Nguyen

01:46 - 02:10

Awesome. That amount of traction in a seed state startup is pretty amazing. You guys are in Forbes 30 Under 30. Yeah. I saw quite a number of write-ups on you guys already, and I noticed that you did a 6.6 million raise. The company has been around since 2022-ish, but you've pivoted several times. Tell me a little bit more about this origin story of yours. 

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Hojun Choi

02:10 - 03:21

I started my career in finance, so it was natural to me that we're going to start building a finance-specific SaaS. But we wanted to start by having a solid technical foundation, which was why we first built an AI search engine. And when you think about finance, there is a massive volume, so both structured and unstructured data, meaning Word, PDF, Excel spreadsheet, PPT, audio. To build any type of workflow processing these type of data, you have to have a very good semantic vector search. And so that's what we initially built. After proving that we're able to build such a good search engine, we were able to raise $6.6 million. And then we really started to build a hyper automation platform for hedge funds and asset managers. We were able to gain a lot of traction because from the very beginning, we tried to find solid design partners who are able to work with us to automate some of the key parts of the workflow. And this is something that's characteristic of a lot of AI agent startups. in the sense that we don't just tackle a point solution to build for our clients. We actually try to automate many of the workflow throughout their day. And that's how we actually gained traction over the past one year. 

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Jonathan Nguyen

03:21 - 04:06

 Yeah, it's really interesting that, okay, so the company started, you guys are all from Korea, is that correct? and Korean startups like you don't really hear a lot about them you know in terms of like Korean startups trying to exit Korea and establish big operations outside of Korea I can think of you know obviously not startups nowadays but you know, Nava and Kakao, all these social media applications. A lot of them started in Korea, but they didn't really go outside of Asia. What was the impetus for you guys to look outside Korea and think, hey, there's a bigger pond that we want to play in and we think we can play in it? 

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Hojun Choi

04:06 - 04:59

I think one of the limitations of building a business just within Asia is it's a very consumer focused market. And the way I think about it is for SaaS business to grow in terms of market size, you need to have a more mature cloud penetration and an understanding of software and clients who are willing to pay for software. And I think many of the Asian countries haven't gotten there yet. Versus when you look at the U.S., software industry has actually been around for a long time. Clearly, there was much more opportunity there. But at the same time, I think it was also because I come from a finance background. And whether you use Bloomberg in New York or you use Bloomberg in Seoul, Hong Kong, it's the same interface. So much of the software is actually region culture agnostic, which is, I guess, something that's different from most of the consumer. which might need to localize much more. And that also gave us an edge in globalizing in terms of our expansion. 

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Jonathan Nguyen

04:59 - 05:06

So did you just wake up, you know, one day and say, you know what, I want to start an AI company. What's the story there? 

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Hojun Choi

05:06 - 05:53

Yeah, so I'm a bit of a third culture kid. So I was born in Italy. And I was raised when I was young in this small town called Changyu near the southern port city of Busan. There were only like 10,000, 20,000 people living there. And then I lived in Seattle and Shanghai. I graduated from an international school, came back to Korea for college, but went to Hong Kong for my first job at Goldman Sachs. And I came back to Korea for private equity, and now I'm running a business in the US. And so, you know, like global was always something that I wanted to pursue. Essentially, I'm living my dream right now because I get to actually meet all these different clients in both the US, Asia, and Europe, and actually pitching at a global level, along with other peers who have graduated from Y Combinator or Techstar. 

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Jonathan Nguyen

05:53 - 06:24

Yeah, tell me a bit more about that journey because a lot of founders, one of the things I hear a lot from founders we work with and founders we interview on this pod is it's really effing hard being a founder, particularly in that grind before you get  your first check. Was it a grind or were you able to secure friends and family funding? How did you guys deal with that phase before you kind of got it? 

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Hojun Choi

06:24 - 08:34

Yeah. So I think, you know, there were some pivots, especially before Gen AI actually came about. For instance, I was actually working on something completely different from called litigation funding, where it's actually pretty prevalent in the US, but in Asia, it's pretty new, where we would fund legal costs for a plaintiff. And then if you lose, I don't get anything back. But if you win, I get three, four X, the initial principle. And it's because structured finance was something that I used to do back in Goldman Sachs. So I wanted to do something similar with legal claims. And that went on okay. And we were able, I was actually investing in around like 40, 50 different types of litigation. I literally called each plaintiff explaining what litigation funding is. And it sounds a little shady, right? Like, why are you funding my legal costs? Why should I be paying you 3, 4x the initial principle? But eventually, like it was starting to kick off, you know, and we saw some traction. It's just that it wasn't exponential. And that's when there was this legal AI startup in the U.S. called Harvey, one of the first few startups that OpenAI actually invested in. And I had the conviction that AI is going to have a huge synergy with all of the knowledge workers. And obviously I was in legal, so I called up my current co-founder and then told him, OK, let's build something in legal AI first. And we started building, actually sold to a few big law firms in Korea with that legal AI research tool. But then again, like the SaaS market was pretty constrained in terms of size. And we just thought there was so much more opportunity outside of Asia, which was why we decided to kind of pivot to really build a finance software for global audience. And so there was a bit of a journey there. I think in general, you know, like, versus when I was in a big organization, startup has always been something that the type of stress that I've had doing a startup is much more manageable versus the type of stress that I would get working in a big organization because I need to have full control over the direction the strategy, the vision, as long as I'm aligned with that, I'm okay handling a lot of pressure and grind. I've been running startups for like three, four years now. And I think, you know, this is going to be my career for the rest of my life. 

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Jonathan Nguyen

08:34 - 09:00

It's interesting you say that because one thing I find, I mean, I have co-founders and there is definitely a personality type that is more or less suited to startup than others. What you're describing is you kind of thrive on it through most of the times. I'm sure there are tough times, but most of the times you thrive on it because there's a sense of control of your destiny. Is that what you're saying? 

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Hojun Choi

09:01 - 10:01

Yeah. And also, you know, it's almost I think it's almost by fate. So I often liken this to like Greek tragedy where, you know, there's a protagonist, he knows he's walking down the road with demise, but he can't stop himself. Like, I just have to do it, right? It's not about optimizing the results or getting things right. I just feel like I have to do it. And in some ways, I think a lot of the CIOs or founders of hedge funds. are similar. When you generate returns, you think, okay, like it's the same money, there's no name tag to it. But you know, I recall watching this interview of the CEO of Muddy Waters, that's a short seller. And you know, the anchor was asking, why do you short sell people hate you, you know, you get sued. And then I think he said something like, you know, it's a personality flaw. It's the only way I would be investing for the next like decades. And I think it's exactly the same for me, I have to build this AI automation tool. And when I actually had that ChatGPT moment, I felt like, you know, this is it. Like it was almost like I had a vision. And so that's why I'm so engrossed in kind of building the startup. 

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Jonathan Nguyen

10:02 - 10:30

Let's talk a little bit about the startup because I saw you guys at Hong Kong FinTech Week you know, in amongst the sea of booths and things that, you know, I go to all these conferences, I listen to all the pitches. And when I saw you guys, I was like, okay, these guys have a value proposition. Because it's actually a lot of startups do not have a very clear value proposition. I saw you guys, these guys know where they're going. Unpack it for us. Like, what is it? What is it you do? And who do you do it for?  

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Hojun Choi

10:30 - 12:01

It really began because I... I used to work 80 to 100 hours at Goldman Sachs, so I know how much data there is out there. And there's been an explosion of both structured and unstructured data. These independent research shops, obviously all the investment banks producing reports, And then there's all these news articles. And nowadays, Jensen Huang, he comes out on podcasts to talk about Blackwell. And so investors have to process all these data. And humans only have so much memory and attention span. And so we really wanted to use Gen AI to unleash the full human potential. And to do that, you have to have a really good... hyper automation platform. By that, what I mean is not just like ChatGPT, where you ask a question and get a response, but actually being able to have AI to search and sift through millions of documents to bring you the most relevant parts of text, and then synthesizing those information to produce meaningful reports that would have taken you a couple of hours to do. And then also, just beyond productivity, helping you go beyond, say, like confirmation biases, You can have AI agents working as your devil's advocate, where it would kind of suggest any counter arguments against the biases that you might have. So it's really almost like a thought or discussion partner as well. And so that's the kind of agent that we want to build. And we've been working closely with a lot of these hedge funds, CIOs and investment banks to build something else. that could make impact from day one on top of existing kind of foundational models. 

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Jonathan Nguyen

12:01 - 12:10

Okay, take me through a workflow. One of your clients signs on the dotted line. They become a client. What do they get? What are they looking at? 

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Hojun Choi

12:10 - 14:21

Yeah. So let's start with like screening for stocks, right? Because there's like a bunch of stocks in the world. You have to decide which one you're going to invest in. And maybe you have an idea. Like there's been a lot of talks around doge or tariffs. Maybe you want to look for semiconductor companies with a market cap of 100 to 200 billion dollars that have high exposure to, say, like Chinese experts, right? That may be impacted by tariffs. Now, things like 100 billion, 200 billion market cap, these are structured criteria. You can always use the filters on Bloomberg or FactSet or CapIQ to search for those kind of screens. But what you couldn't do before is using natural language to search for themes, right? Like tariff storage and whatnot. And that's something that Gen AI can enable on top of existing screener, for instance. And now you have a smaller kind of a short list of stocks that you want to analyze further. But the problem is many of these stocks happen to be ones that investment banks don't cover. So there's no existing research report, in which case we can use our primary agent to generate a comprehensive primer of what the company does, analyzing risks, challenges, market opportunities within just five minutes, right? Otherwise, you would have had to kind of go through all the filings, 10 years of history. You can do it in just one to five minutes as well. And once you've decided on which company to invest in, and you've made that investment, you also have to monitor the portfolio, right? You can have an alert agent screen through all the transcripts, news, filings, and then give you an alert, maybe if there is kind of a significant mention, right? Think about like Nvidia share price at the end of January. After DeepSeek became a huge thing, investors, you know, kind of were into panic. But actually, there were a lot of conversations already on Twitter by the end of December when the model was actually released that they had only spent $6 million. Although this is controversial now, investors could have read that way before January. But actually, it was because of a lot of latency and information that they were not able to process that at the right time. And so even portfolio monitoring can be enhanced with January. From beginning to the end, it's a end-to-end automation of all these workflows relating to investing. And that's how you're actually automated. 

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Jonathan Nguyen

14:21 - 14:42

Do you see many competitors in this space? I mean, I visit all of these conferences and I see a lot of people trying to do something very similar. They're all taking a little bit different of a different angle or a different approach on it. Who are the kind of competitors that you're seeing and what kind of value props do you see where you're differentiated? 

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Hojun Choi

14:42 - 16:20

Yeah. Yeah. So I think there are some larger players in the market. For instance, there's a startup called Hebbia that started out by focusing on private equity funds and due diligence for M&A deals. There's also Rogo that's been working on a lot of the finance related tasks for especially like bigger investment banks. I think for us, real focus is on hyper verticalization. actually understanding a lot of the tasks that analysts and portfolio managers have to do and reflecting that into our pre-configured workflow. I think that's important because when you think about LLMs and a lot of startups get steamrolled by newer models, The only data that they don't have are things that are going on inside of analysts mind, these processes that don't leave a trace on the web. And so what we try to do is really focus on working with hedge funds to get our workflow to the level of precision where, you know, these analysts can deploy them from day one to see the impact and enhance their productivity. And we haven't been trying to expand across, say, like private equity, like all these different types of clients, because we thought it's better to focus sharply, specifically on public equities. And so I think our workflows are much more specific. And we actually now have more than 10 workflows. And as we build these workflows, you know, have 40, 50, 100 of them, we expect our AI reasoning layer to curate the right type of workflow for the clients. That's really going to be the moat that, you know, neither these relatively horizontal finance players nor the AGI players can really penetrate. 

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Jonathan Nguyen

16:20 - 16:40

So how have you found... the process of doing BD at the moment? Are you going off, you know, a list that are net, are there networks that, you know, you've been part of? Are you going door to door? How is it, how easy has it been for you to, you know build this kind of pipeline? 

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Hojun Choi

16:40 - 18:17

Yeah, so the first 100 users were my friends, ex-colleagues, my friends of friends. And then we really started to kind of strategize in terms of, you know, taking a systematic approach to go to market. So we were fortunate enough to find some advisors, industry veterans who are kind of aligned with our vision. Also, I think just in general, compared to last year, a lot of the bigger enterprises, banks, asset managers are keen to kind of work with startups. Partly because they spent the past 12 months building a product only to realize that they became obsolete the moment it was released within the organization. I think the pace of evolution of these AI models is so fast, it doesn't make sense for a lot of enterprises to take on this role. We can take on this risk because our organization is lean and we think about AI 24-7. You think about the past month, there were, I think, more than four models released from XAI, Anthropic, DeepSeek. And we were able to test them right away and deploy them at the right nodes within our workflows to kind of automate the processes. And so I think demonstrating that we have a lean kind of a strategy really convinces these bigger organizations to work with. And this actually involves a lot of cold mailing as well. So I've been reaching out to hundreds of different, say, head of research, engineering, tech, to really convince them that it's worth their time to meet me because I've met more than 90 organizations. I know. how the industry is kind of evolving, adopting Gen AI. And so sharing that industry trend and alongside that, just introducing our product has been an effective way of kind of a market. 

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Jonathan Nguyen

18:17 - 18:31

Yeah, they say being a CEO is a glamorous job, but there's a lot of cold calls. There's a lot of all the jobs that no one else wants to do yeah 

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Hojun Choi

18:31 - 18:50

exactly it's almost like you know you get used to getting rejections it's like you're constantly reaching out but you know you don't get too ahead of yourself even if the conversation goes well you just keep calm and carry on as long as the chances aren't zero you keep your chances up by you know swinging as many times as you can when 

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Jonathan Nguyen

18:50 - 19:19

When we talk to our clients one of the key things that we primarily concern ourselves with. How are you going to sell this stuff? Technology is great, guys. You're all PhDs. I know you're all smart, but how are you going to sell this stuff? And SaaS, cold outreach is such a core part of of the next 100, right? So you got your first 100 users. Great. The next 100 users looks nothing like your first 100. 

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Hojun Choi

19:19 - 20:00

Yeah, exactly. And I think, especially in the context of finance, it's also important not to feel too much like a vendor, but you have to have insights that you can share with the client as well. Because, you know, like, These finance guys, they're approached by a hundred different vendors out there selling alternative data, SaaS, automation platforms. And so you really have to have a differentiated message. And I think the initial kind of grind helped me to get that insight. And now I'm able to kind of share some of that. And a lot of the senior leaders are interested in what we've experienced or observed in the market. So I think it's turned out to be a pretty kind of like a fruitful process looking back. 

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Jonathan Nguyen

20:00 - 20:11

Have there been any insights derived from Linq that you're aware of that's driven a multi-million dollar, billion dollar trade? 

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Hojun Choi

20:11 - 20:52

Yeah, so I think right now it's actually less compared to like quant days, right? Where AI used to kind of point out the alpha signal. I think a lot of the fundamental investing strategy is more about just analytics and insight extraction. And so it's not like that we directly recommend the trade, but it's more that we make sure that you don't lose out on the betas. And I think a lot of Gen AI's fundamental kind of value add is making sure that everyone can play, perform at at least the average or above average, whatever resources you have. And so in some sense, it's really democratizing these institutional investment opportunities. 

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Jonathan Nguyen

20:53 - 21:19

Have you, in your data set, let's say you mentioned Jensen Huang, he does a lot of podcasts. Sometimes he does podcasts that are not necessarily even to the traditional tech or, you know, investment AI crowd. I saw him on a podcast called "Huge if true". It's a very consumer tech type YouTube channel. Would you guys pick up signals from those types of podcasts? 

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Hojun Choi

21:19 - 22:46

Yeah. So I think there's two things, right? One is sentiment analysis component. Now, before the age of Gen AI, sentiment analysis was pretty primitive in some sense. You relied on frequency of words or PCA, whereas now you have context aware sentiment analysis. and you're able to kind of score different types of themes and quantify them for whether it be like quant trading or fundamental analysis. So LLM definitely has enhanced these type of tools to pick up new signals. And when you think about it, it's also becoming multimodal. So looking at videos, I think soon you'll be able to kind of analyze time series, how people's expression is different from different points in time. and maybe you blink a few more times than you used to, and then AI is going to pick that up. Definitely there is a better kind of more efficient way of processing these type of data. And the second thing is just following the narrative. So if you've attended like say like 100 podcasts over the past few months, maybe your narrative has slightly changed and that reflects your view of the industry or the product. And now what's great about Gen AI is it's able to kind of contextually analyze how things have become different in terms of the messaging and whatnot. It really understands human speech. And so I think that's a way that a lot of the fundamental analysts are trying to analyze, let's say like 10 years worth of transcripts. And so like these two aspects are really, I think, game changing in terms of like investment methodology. 

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Jonathan Nguyen

22:46 - 22:49

What does it look like if you guys succeed? 

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Hojun Choi

22:50 - 24:01

Yeah, it's a great question. So I think our vision is you know, say you're an analyst or PM who's looking to set up your own shop and you have investors that are willing to put money on. And instead of hiring all these research associates, you're able to basically work at a superhuman level with the support of an army of AI agents. And so I think there's going to be lower and lower hurdle for you to begin your own thing. And I think it's true for many of the knowledge workers, different industries, even legal, when you think about it, the big law firms, they used to have strength because when you go into a large scale M&A deal, you have to like sift through hundreds of say like these agreements. And you have to have a lot of juniors doing that. Now, if you're a capable partner, you can just use AI to analyze and extract key information from different classes and compare them and whatnot. And so in some sense, it's a bit of a decentralization of all these capabilities. And you're able to be able to reach a superhuman level of analysis, memory, attention span with the help of AI. 

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Jonathan Nguyen

24:02 - 24:13

In your ideal world, I mean, you did the job of a, you were at Goldman as an analyst. You probably did a lot of this research. Does the technology replace the people? 

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Hojun Choi

24:13 - 25:16

Yeah, that's a great question. So the way I think about it is it could replace junior jobs or roles, but people will work at a more enhanced or elevated, I think, capacity rate. So right now with a lot of investment banks, it's almost like an apprenticeship. You join the firm, you spend the first one or two years learning how to build LBO models, learning how to conduct research, draft investment memorandums. And now with the help of AI, you're able to perform at an associate level without going through that, you know, all that 100 hour work to learn the knowledge of a capable analyst. And so maybe endless jobs that we know today will disappear, but people will have more things to do. And I think it's also human nature. You always try to maximize your productivity. There's always something more to do when you have more productivity. So I'm not sure if it would actually replace people, but I think it will definitely replace these type of roles at a junior level. 

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Jonathan Nguyen

25:16 - 25:53

Yeah, I think I agree with you. I think what we've seen every single time and a truth that you can rely on is that shareholders will still demand quarter on quarter growth. And so if those analyst jobs disappear... The growth demands will still be there. We've seen the role of prompt engineer appear, for example, right? In your experience, when someone deploys Linq, does it replace a role or do you find that then the hedge fund still needs to have an analyst? to actually steer the software? 

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Hojun Choi

25:53 - 27:27

Yeah. So I think right now we're increasingly seeing, you know, junior roles also becoming somewhat of a manager role in the sense that even like software engineers, you manage an AI to code better as opposed to, you know, coding everything from, scratch. And I think it's similar with a lot of the hedge funds as well. So a lot of the ground, like foundational work would be done, drafted by AI, and then analysts can kind of refine it. Now, having said that, because AI is getting better at an exponential speed, eventually, I think there's going to be less and less manual repetitive tasks, obviously. And so it becomes a question of, can people or junior analysts value add or create meaningful insight with the additional time that they have. And I think I can pretty confidently say that, you know, like all these hedge fund analysts, they're brilliant people and they have very sharp minds. And so I think given the same 60, 70 hour, right, they will always find something new to do and new insights to differentiate themselves and to find contrarian views. And so I don't think it's going to be replacing people per se, but it will definitely pressure a lot of analysts that are not using the tool to find ways to enhance productivity. Because if all your peers are just getting better, faster, smarter, right, then you're going to lose out if you're not using the tool. So I think the best strategy is for every analyst to embrace the change. and actually find ways to generate more insights on top of what AI is providing to you. 

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Jonathan Nguyen

27:27 - 27:42

Do you, in the recent weeks, have you sensed a different set of questions coming from your clients based on the tariffs on, tariffs off and the almost vertical direction of the VIX? 

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Hojun Choi

27:42 - 28:51

Yeah, so I think one of the most often asked questions are, why is the share price moving like this? And then, you know, you're able to have a string of questions answered with AI, like, you know, what are the latest developments with, say, like tariffs, Doge, and especially during earnings season, right? Like you have to look at a couple hundred stocks to understand the sentiment. And I think because of the macro uncertainty, there's been a lot of heightened interest in the recent consumer and retail conferences and industrial conferences. to see what these companies are saying about consumer confidence and demand and macro. And so we actually tried to opportunistically release more of the content summarizing much of these transcripts. And they've actually garnered a couple of thousand views. It's only simple summaries. It surprises me how even the simplest of features can really generate so much impact with AI. We've done a quick kind of analysis of thematic development around the tariffs and Doge. And that's also received a lot of positive feedback. But definitely, I think these are kind of like the turbulent times where I think Gen AI can really support investors. 

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Jonathan Nguyen

28:51 - 29:01

Switching gears a little bit. So a 6.6 million seed round is basically unheard of in Asia. It's very rare. How were you guys able to do that? 

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Hojun Choi

29:01 - 30:13

Yeah, to be honest, I think a large part of it was because we had a very strong team. And my co-founder, he finished his PhD degree in MIT in just two years. And he's an engineering genius. And it's also, you know, we've been in the startup scene for some time. I initially ran a legal tech startup before starting this one. My co-founder worked on all these different types of like insurance, blockchain related projects before really focusing on finance specificity. And so having that experience, I think also convinced investors that we're not being naive and we can handle and manage the capital efficiently. And even now, when there's a lot of bubble in the market, a lot of our peers are trying to raise even more. to aggressively spend on things that may or may not help in terms of revenue or growth, where we're trying to be more cautious and making sure that we're making the right bets, right, as opposed to just aggressively spending on like marketing and whatnot. And so I think, we have a very good relationship with our investors, they have very strong trust in us. And that I think is going to help with the subsequent rounds as well. well. 

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Jonathan Nguyen

30:13 - 30:18

How long was the first fundraising efforts taking you? 

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Hojun Choi

30:18 - 30:39

So it took a couple of months and we had a conversation ongoing, partly because, you know, like, for instance, some of the investors would introduce other investors as well. And so generally, I think there was a bit of a consensus that they're going to invest together. And fortunately, it was more smooth than most of the other funding rounds. 

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Jonathan Nguyen

30:39 - 30:48

What's next for you? Crunchbase is saying that they've got a 67% probability these guys are going to raise again in the next 18 months. 

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Hojun Choi

30:50 - 31:20

Yeah, it's very true. We're actually targeting to fundraise before the end of this year, partly because we've seen some traction with larger investment banks. And we're pretty confident that we'll be able to prove to a lot of the investors that our platform is quite sticky and investors with decades of experience are starting to adopt AI. And so there's behavioral changes as well. And so by, I think, summer or autumn, I think we'll probably start our fundraising efforts, hopefully close by the end of the year. 

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Jonathan Nguyen

31:20 - 31:31

Yeah, the sales cycles that are between hedge funds and big investment banks are very different. Are you prepared to walk into that hornet's nest? 

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Hojun Choi

31:31 - 32:21

Yeah, so actually, we recently signed with a global investment bank and it It took around five months to really go through all the compliance and legal review. I think it's just part of being in finance. But at the same time, I think buy side investors are much more lean in terms of Gen AI adoption. And so we try to take, you know, like an approach where we manage the flow deals with leaner single CIO funds and try to close the deals more quickly. while in parallel continuing our conversation with larger enterprises and asset managers, investment banks. And they're always keen to hear the new workflows that we're building with single CIO funds as well. And so just demonstrating the velocity helps in both ways. And so hopefully we'll keep that running and we'll have a few more elephants that we hunt down by the end of the year. 

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Jonathan Nguyen

32:21 - 32:39

What would you say to a 22-year-old that is soon to graduate, just graduated, and they're looking at their career path in finance and the idea of becoming a founder, setting up their own startup. What advice would you give them? 

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Hojun Choi

32:39 - 33:14

Yeah, I think when you're 22, there's so much uncertainty ahead. It's almost like a startup, right? And I think my general advice is when there is so much uncertainty, taking an action is a better insight than anything else. like reading books, meeting people, it could help incrementally. But at the end of the day, you have to take an action quickly. And if you don't like it, you can always pivot. So rather than just kind of gauging or like, you know, comparing different options, I think just to be able to tackling at first actually helps the most when it comes to building your career. 

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Jonathan Nguyen

33:14 - 33:16

Any lessons learned that you'd like to share? 

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Hojun Choi

33:16 - 34:17

To be honest, everyone has a different personality. For me, I was actually more of the conservative type and I thought of my career more as a linear optimization back in college. So I wanted to work in the most competitive job possible because given the same amount of time, I always wanted to have a steeper learning curve than my friends. But it turned out, you know, life isn't as linear and you can't optimize it and there's no answer to it. And I only came to realize that, you know, maybe finance is not my thing. Four years after doing finance and yet, you know, Starting with my first venture, I was able to adapt pretty quickly and I have no regrets at all. I'm taking a detour or not optimizing my career for a particular objective. So I think maybe plan less and execute more because, you know, Whatever you do, there's no fixed answer. Just believe in yourself because you are your answer. So that's what I would say for any kind of college kids who are somewhat uncertain and are too cautious to take a step. 

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Jonathan Nguyen

34:17 - 34:25

I've got one more question for you. It's the hardest question. 10 years from now, where do you see all of this going? Where are we going to end up? 

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Hojun Choi

34:25 - 35:14

Yeah, that's a great question. I'm in between an optimist and a pessimist. So I think much of the population that are not as passionate about work might end up just not working. And in fact, back in the days, if you want to accomplish anything, you had to collaborate, cooperate with other people. But now, you know, with the advancements in robotics and AI, and as Sam Altman said, you know, you might as well build a one-person unicorn startup. And then it comes down to a question of, you know, what kind of life do you want to really live? It's going to be more about finding your own value set. That's going to be important. And for me, I think I'll continue to try to build something. Even if AI is better than me at building certain things, I'll still try to find that niche where I can be differentiated. And I think that's my value. 

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Jonathan Nguyen

35:14 - 35:25

I think what you're getting at there, you're kind of hinting at a belief that... that we're going to get to general intelligence in the next 10 years. 

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Hojun Choi

35:25 - 36:11

- Yeah, I mean, it depends on, I guess, how people define general intelligence. And as many people have pointed out, it also requires a lot of the physical world to be incorporated into AI to really get to, I think, the ultimate general intelligence. But even within the next two to three years, we expect a lot of the AI software to be able to replace you know, jobs, white collar jobs. And so within 10 years, I'm pretty certain that, you know, there's going to be an even greater advancement with humanoids and, you know, AI actually working like humans, thinking like humans, experiencing the physical world like humans. So I guess that is going to be something that I actually look forward to experiencing very soon. 

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Jonathan Nguyen

36:11 - 36:30

Well, Hojun, thank you very much for joining us and sharing your thoughts. There's a lot there to take away from that, especially that final thought. And I'm looking forward to checking back with you in a year's time to see where you guys have ended up. I'm really excited to have had this conversation. So see you again in a year. 

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Hojun Choi

36:30 - 36:33

Thank you so much. This was really fun. See you again in a year. 

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