TechTalk: A Venture Capital Firm’s Journey to Transform Underserved Markets

Kathy Chiu: 

Thank You, Fritz. This is great. 

Fritz Spencer: 

Awesome. Now, I gave you a bit of an intro, but could you also give us a brief summary of your career journey and what led you to co-found DeepWork Capital? 

Kathy Chiu: 

I'm glad to. So, I started my career, I was trained at MIT as an electrical engineer, specializing in something called Digital Signal Processing, which is the math part of electrical engineering. But because it was mostly the math part, I very quickly slid over to the dark side and became a quant jock in an economics and finance consulting firm where I was doing things like derivative pricing, valuation work, and that brought me to business school. I went to Stanford for my MBA and became a product manager. 

Now, when you're a product manager, you can work with big and little companies alike. And it was when I was working at a startup that ultimately exited to HP, which was a big computer company. Got me to get to know a lot of venture capitalists that were working with a startup. And eventually when somebody started a fund, I was hired as their first technology associate. And so, that brought me into the business. And fast-forward many, many years later, I came to Orlando for some other reasons and realized there was an untapped opportunity. And so, my two co-founders and I started DeepWork Capital to invest in what we believe is an under-invested but very, very high potential market. 

Fritz Spencer: 

Great. Thanks for sharing. And as an accountant, being the math person, it seems right up my alley, so we have that in common. So, let's hop into DeepWork. Can you share the core thesis or criteria behind your investment strategy and how that differentiates you from other venture capital? 

Kathy Chiu: 

Sure. Well, I will say this. As an investor, number one rule, it doesn't matter if you're selling chickens or technology, it's buy low, sell high. And so, the buy low part is tough because if you think about it, venture capital mostly, most people would think about, there's even a TV show called Silicon Valley right. People think about hot markets like that. Or if it's in life sciences, people are thinking about Boston. Where because there are such a huge venture capital community, there's a lot of capital chasing very few deals. And sometimes deals will get hot and cold for reasons beyond what the founders can really control. 

And so, what we're thinking is in some of these under-invested markets, sometimes you'll see a lot of very solid businesses, very solid founders. And they're maybe going about things in a slightly more pragmatic and step-by-step fashion. But because they don't happen to be in some of those hot markets, capital is a challenge for them. And so, that seemed like a win-win opportunity between investors and these founders. And it was with that we founded DeepWork Capital to test out this thesis. 

And we had some early successes which allowed ourselves to be successful and continue to raise funds and grow ourselves. But in the meantime, we were also able to find a lot of very solid businesses that take a more kind of pragmatic approach and proving out milestone after milestone before they jump for the big valuation. And I think that is a very healthy thing for both the investors and the companies that were being invested. So, if you ask what our core thesis is, I would say it is working with founders that are pragmatic and have a lot of grit, and also have a very realistic understanding of what steps are required before they can reach for that big, big dream instead of trying to leapfrog a lot of things because you really do have to get there. Meaning things happen because you do them, not because you think about them. 

Fritz Spencer: 

Got it. And so, is there a criteria behind that? I understand the founder mentality that you're looking for. What about for the actual product or service? Is there a criteria behind what you guys will specifically invest in? 

Kathy Chiu: 

Absolutely. So, I would say on the high level, I would call ourselves a generalist fund. Meaning we're not focused on one very narrow niche of the market. And that's by design. Because to be honest, technology trends come and go. If today you say you're an AI fund, 10 years down the road, that may just be a underlying technology everybody uses. So, that's not really a thesis. So, we don't define ourselves that way. However, there are certain businesses that are more VC friendly. They just make more sense for a VC investment. What does a VC look for? Well, usually you're looking for... you're taking on quite a bit of risks, so you're also looking for things with great potential. 

And so, there absolutely can be, let's say if you started a franchise of laundromats. That may be a very nice business and you can absolutely grow it healthfully and maybe even get very big. However, it's not really VC fundable. So, what we are looking for is usually something with a special factor, the X factor that allows it to have that kind of a hockey stick trajectory. And so, this usually tends to be technology with a very big market, or it could be in life sciences where this drug cures cancer. Of course the market's all going to want it. So, we're looking for those hypo businesses. 

Fritz Spencer: 

Great. And speaking of tech trends, which you touched on a little bit, I'd love to know more about what emerging tech trends are you most excited about? And how do you think that they might shape the future of that industry? 

Kathy Chiu: 

So, I think if you ask that to just about anybody, the word AI will come out a lot of people's mouths. But the truth is what does that mean? And what that means is that at different stages, you go back, say a decade or two ago, when people say AI, they're still talking about what is the model really going to spit out something that does hallucinate? Well, what I would say is now, I wouldn't even call it trend, it's really just the status of things. What we're now looking at is that this whole topic of AI is finally going from, "Oh, my God, look at the cool things it's doing," to, "Well, actually I have problem and that's the only way to solve it." 

So, it's becoming at a point of that technology trajectory where it was going from being pushed by people who has the technology and thought it was so cool and really want to push it into the market, to a pool where the market's like, "Oh, neat, I've been trying to solve this problem for the last two or three decades. And finally, something can bring about a solution." And I think what you're seeing is that. 

So, today when you're looking at AI startups, and especially for some of these smaller companies, we're not talking about the IBMs and the Googles of the world. Those big boys are fighting in a different stratosphere. The startups are now figuring out how can I use this as an application? So, let's go, since we're talking about deep tech. And that's one thing that DeepWork actually focused on quite a bit. So, in the world of deep tech, let's say life sciences, what people are talking about, you probably have seen a lot of articles talking about how, oh, in the space of drug discovery, they're going to use AI to come up with all these hypothetical molecules and figure out all the way how they're going to interact with each other and eliminate the need for all the wet testing, which is not anytime soon. 

Because even if it were going to work that way, I think our regulators will never say, "Oh, yeah, feel free to use this new methodology and the next step will be testing it in humans." And suppose you kill someone that will never be okay. However, what people are missing is that there's actually a lot of other industries and verticals that use similar methodology in testing their product. And for example, a lot of material scientists. And this could be any sensor makers or people who have done physics testing requirements, AI is making those computations so much more possible. And let me explain why. Because as long as you're modeling, what AI really is, a statistical way of viewing the world. So, instead of a deterministic way where you know exactly how things work and you're building it to this complexity that no one can ever solve, not to mention you don't even know what some of the parameters are, but AI means is okay, as much parameter as you can identify. But there's always going to be missing ones. 

And what it's doing is to say, "Hey, we accept that there's some other forces that work here." So, the output is going to tell you how those forces on an aggregate level is going to impact the output. So, that's what AI really does. And this is allowing us to conduct some of these very complicated multi-physics experiments, and material science and chemistry. And all of that is now becoming a tool versus just a dream. And I think that's, if you want to call it an emergent trend, I think it's hardly a trend. It's just how things are today. And so, when we invest, we'll keep that in mind. Does it mean that we're necessarily investing in those tools? Not necessarily, but does it mean that every company we invest in, we say, "Okay, what tools are available to your industry? And are you good at using it more so than your competitors?" Just so we're not investing in a dinosaur, right? 

Fritz Spencer: 

Got it. Yeah, AI is certainly the hot topic of this year, and it probably will be for the next coming years, and especially in the way that you've described it, adapting it into multiple different use cases across all verticals. And that leads me, you walked me right into the next question, which is about the tech that your portfolio companies are building. Could you give me an example of what some of those technologies are and how they're exciting to you guys? 

Kathy Chiu: 

So, this is like you're asking me to say, which one is your favorite children? And that's a very hard thing to say. But I will tell one called Canary. And Canary is a company that came out of University of Florida. What they do is nose machine interface. So, people talk about, oh, brand machine interface, but this is nose and machine. So, you go to the airport and if ever they have a like a drug bust that they're trying to do, they have all these dogs walking around, and so they tell everybody walk by one after another, and then the dogs there trying to sniff out the drugs. Those dogs cost a lot to train. Not to mention dogs only live so long and they're also dogs. You put a hot dog next to them, they're off chasing the hot dogs. So, there's a lot of factors that you have to combat. 

But here's the worst part. You have this dog and it's brilliant. It can smell all kinds of things, but you only taught it to bark when there's cocaine. Well, maybe it smells something like fentanyl or something worse, and it doesn't know to tell you about it because you never told it. And so, that's the training requirement and also the missed opportunity. What if you're able to somehow read the dog's mind in terms of what it's smelling and forget a dog. Dogs are expensive. How about a rat? Can you get a rat to do that? Because guess what? Rats also smells really well. A rat can smell all kinds of stuff. That's why they can get into just about anything. 

So, Canary actually does just that. They took a rat and they mounted these sensors, think about almost like an electrical grid onto the olfactory bulb. And what happened is that when the rat is in an environment, you're smelling all kinds of smells and turned out all those smells will light up different parts of that olfactory bulb and send electrical signals into your brain. Now, the rat's brain hasn't been taught to say, oh, associate this smell with a word or bark, because the rat cannot be taught to bark even worse than dogs. However, those light up patterns are absolutely there. 

So, you can imagine a situation where the scientists can use AI to decode what some of these patterns are. And one rat can smell all kinds of smells simultaneously because that's what the real world is. And that's what Canary does. And so, they actually already have some of these prototype rats that are smelling, and you can see the light up and you can see them detecting different smells through this rat who has not been trend and does not have to be trend. And all they ever have to do is send in these almost like cyborg of a rat into the environment and all kinds of stuff will come out. So, that's an example of something that is really amazing that is telling you what the future holds. 

Fritz Spencer: 

Super interesting. I'm definitely expecting to see the TSA with rats on leashes next year. 

Kathy Chiu: 

They stay in a cage, actually. They don't need to even be out. They can just live in their little cage. 

Fritz Spencer: 

Well, thanks for sharing a little bit about one of your favorite children. My dad always said he had a favorite. He just said he wouldn't tell us which one. And I think you should always use that. So, let's move on to maybe a few more of your portfolio companies. What is the key problem that you are working to solve both for and with your portfolio companies? What's the value that you guys are adding to their deck? 

Kathy Chiu: 

Well, I would say for almost every portfolio company, even the most terrific ones, we're almost always working with them on solving the financing problem. Because if you think about it, we solve that problem at the moment we invest, but then we also need to solve that problem by attracting later future round investors. And a lot goes into that. Obviously, there's our own network. Sure, we know a lot about other people who could follow us or co-invest with us, and that's important. That helps. But just as important is understanding the market metrics, because every industry gets measured differently. And so, if you understand what a company will be valued on, and as you're thinking about building the business and the milestone you must hit in order to attract the next round of capital, that's a very helpful strategic exercise. So, we actually do help people do that. 

We also, at the end of the day, our portfolio companies, they only raise so many deals in their lifetime, any one entrepreneur, because most of the time you're spent building the business. We, on the other hand, are seeing across a portfolio. So, to us, I mean we're the deals are us people. And so, we absolutely are able to be helpful when a strategic transaction is happening. We've had portfolio companies that leaned on us when they signed that very first licensing, or that very first major customer deal or something that will change the trajectory of the company for good or bad. We are usually there to help really be that sounding board and advisor on things that entrepreneurs may not see in volume, but we do. 

Fritz Spencer: 

Got it. So, you have a lot of professional experience, personal experience, product management experience, engineering experience. Does your background influence the way that you assist these startups? I know you have subject matter experts that you guys assign, so can you tell me a little bit more about that and how it all pertains to deep tech? 

Kathy Chiu: 

Sure, especially deep tech. Deep tech has a tendency to be a science, a cool science that's looking for a market. Because sometimes you go, "Wow, look at what it does in the lab," but then what good is it? And usually the typical failure mode is, I mean, if the entrepreneur is like, "I don't know," then usually he won't go start a company. The bigger failure mode is to say it can do everything. If it's a drug as a panacea, if it's a material science, oh, my god, it can fly, it can swim, it can do all kinds of stuff. But the real important thing is to identify that you can go and prove its use because it's very important. The first step is always the hardest. 

And I think my product management background and some of my other co-founders also have technical backgrounds, really just put us in a mode where we are very rigorous in how we see products value. You have to really articulate the use case and not just what it does, but also what does that mean then to your users and to your customers. And because they always have alternatives. People always say, "I have no competitor. No one's doing this thing just the way I am." Well, but it doesn't matter. What matters is the customer had this problem and there's so many different ways to solve it. You actually have to be the best way in order to get that business. 

Fritz Spencer: 

Got it. That's so great. Thanks for sharing so much about DeepWork Capital. I want to thank you again, Kathy, for taking the time to have this conversation with me today. It was an absolute pleasure. 

Kathy Chiu: 

Yeah, thank you so much. I had a lot of fun as well. Thank you 

Fritz Spencer: 

Always. And as a side note, I hear you're also dipping your toe into the comedic area? 

Kathy Chiu: 

I've been found out. Yeah. Just for kicks, I do stand-up comedy for fun. 

Fritz Spencer: 

That's amazing. People always tell me I should have been a comedian, but I ended up being an accountant, so I don't know. 

Kathy Chiu: 

Oh, my goodness. 

Fritz Spencer: 

I don't know how you figured it out before me, but maybe I'll have to see how, check that out. 

Kathy Chiu: 

You might just have to change your career. I don't know, Fritz. You seem like a really funny guy. 

Fritz Spencer: 

Thanks so much for saying that. And a special thanks also to our listeners for tuning into TechTalk, the entrepreneurs and innovators who turn to EisnerAmper for audit, tax, advisory and outsourcing solutions to help propel their business forward. Again, subscribe to EisnerAmper podcasts to listen to more TechTalk episodes or visit eisneramper.com for more tech news that you can use. 

Transcribed by Rev.com