What happens when an Armenian woman, born in a collapsing Soviet Union, goes on to head the United Nations Millennium Development Goals and then takes a leap of faith into entrepreneurship? You get Naré Vardanyan, CEO of Ntropy, whose journey we unravel in our latest episode.
Her fascinating trajectory, from her humble beginnings to learning AI on Coursera and launching her own company, is inspiring.
Prepare to be swept away by Naré's bold move to decipher the encrypted world of data privacy and security, especially in the domain of financial services. As we navigate through the tumultuous aftermath of COVID-19, Naré decodes the new dynamics of institutional data usage. Hear her commentary on the need for quality data in the era of open banking and get a glimpse into how industry heavyweights are leveraging data to customize their offerings. Naré takes us through the evolution of language models that are enabling companies to access data more effectively and efficiently.
Finally, Naré explains why she views distribution as an engineering problem and how she grapples with the challenge of prioritizing immediate needs versus future possibilities. Reflecting on her personal journey, she talks about how motherhood has influenced her leadership style and the role of risk-taking in her venture. From discussing her hobbies to the importance of therapy, this is an episode you won't want to miss.
What happens when an Armenian woman, born in a collapsing Soviet Union, goes on to head the United Nations Millennium Development Goals and then takes a leap of faith into entrepreneurship? You get Naré Vardanyan, CEO of Ntropy, whose journey we unravel in our latest episode.
Her fascinating trajectory, from her humble beginnings to learning AI on Coursera and launching her own company, is inspiring.
Prepare to be swept away by Naré's bold move to decipher the encrypted world of data privacy and security, especially in the domain of financial services. As we navigate through the tumultuous aftermath of COVID-19, Naré decodes the new dynamics of institutional data usage. Hear her commentary on the need for quality data in the era of open banking and get a glimpse into how industry heavyweights are leveraging data to customize their offerings. Naré takes us through the evolution of language models that are enabling companies to access data more effectively and efficiently.
Finally, Naré explains why she views distribution as an engineering problem and how she grapples with the challenge of prioritizing immediate needs versus future possibilities. Reflecting on her personal journey, she talks about how motherhood has influenced her leadership style and the role of risk-taking in her venture. From discussing her hobbies to the importance of therapy, this is an episode you won't want to miss.
Bill Cilluffo:
You're listening to the Fintech Thought Leaders podcast from QED Investors. Your deep dive into the world of venture capital and financial services with today's digital disruptors. QED is a global venture capital firm focused on investing in fintech companies all the way from pre-seed to IPO. Fintech Thought Leaders brings together the most talented entrepreneurs tackling today's biggest problems. If you're looking to learn more about what motivates our founders and team members to succeed, you're in the right place. Hello and welcome to the Fintech Thought Leaders podcast. I'm Bill Cilluffo, head of early stage investments at QED Investors. Today on the podcast, I'm thrilled to be joined by Ntropy CEO, Naré Vardanyan. Naré, welcome to the podcast.
Naré Vardanyan:
Hi. Thanks for having me.
Bill Cilluffo:
Well look, we're going to spend a lot more time later on in the podcast diving into Ntropy and what it does, but just to give the listeners some background, I'd love it if you could start by giving us a 60 second version of what Ntropy is and what you guys do.
Naré Vardanyan:
Yeah. For sure. We are a technology company and we have this mission of building language models to understand financial data in any format, context, geography or language source. And the reason for that is if you have the data understandable and clear, you can change the lives of many people and the way they access financial services globally. So that's what Ntropy is focused on the data layer.
Bill Cilluffo:
Perfect. That's super exciting. And I know there's a lot of various applications for this and we'll dive into a bunch of that later. What's interesting is I know you've been at it for a couple of years now, so long before language models became the hot buzzword. I know you're one of the pioneers there, so we'll dive into that story later on. But I'd love to start with some of the early years and some of the background long before you became an entrepreneur. Can you just describe at a high level your path of what led you to become an entrepreneur in the first place?
Naré Vardanyan:
Yes. For sure. I think when I talk about myself, I always say that I end up in situations that are the least statistically likely, whether it's where I was born, my background and then what I end up doing. But that has been the story for a while. I was born and raised in Armenia. It's a small country in the southern caucuses in between Turkey and Iran and Russia. And I was born in a time just right when Soviet Union was about to collapse and a war broke out. It was a territorial conflict between Azerbaijan and Armenia. And all I remember was that we didn't have electricity. My parents made it out as a game because we used to wait for the lights to come on to do all the fun things, and then we tried to treat it as a normal situation. But when I was growing up, it was not how anybody I think in the modern world or in the developed world lives today.
But it led to a lot of curiosity and a lot of exploration because there were little means. So I remember we had a lot of books at home and I used to look into that under candlelight. Yeah. Just spent a lot of time trying to find things to do and ways to entertain myself and to learn about the world outside what was available probably to everybody else. So that was an interesting way to grow up. I would say that when I speak about it, a lot of people say, oh, that probably should have been tough but I think when you're a kid, it's tough on your parents, but you don't really understand what's going on and you treat it as the reality and you find ways to be happy about it and just to consume information in any way possible really.
Bill Cilluffo:
Yeah. That's probably brilliant of your parents actually to almost turn it into a game. That had to take just an incredible amount of fortitude on their part to ... I'm sure you weren't completely shielded from it, but at some level shielding you guys from what was really going on.
Naré Vardanyan:
Yes. And I think the good part is, I remember there was always a big community feeling around it because people would help each other out a lot. In situations like that communities get together. And another thing I remember growing up is everyone, not just myself, probably everyone in my generation had this feeling of things are not how they should be, so we should be the people to change it. Whether it is getting into politics or international relations or doing something entrepreneurial. That was the feeling that everybody grew up with. This is wrong, it needs to be put right, so you need to work very hard to do that. And all of my peers back in the day as well had that mindset. So that was a super interesting experience. And I left the country when I was 17 and I was working for the United Nations for a short period of time. That was my dream actually, to do that. I thought, well, there's this big organization that's going to change the world and I want to be a part of it. That led me out of the country to Geneva and New York City and then London.
Bill Cilluffo:
Wow. That's quite a set of travels. You were working for the UN at 17? Is that right?
Naré Vardanyan:
Yes. I was part of the Young Professionals program. I'm not sure if they have it today, but it was a program that allowed you to work for the United Nations, not as a country representative, which is what most of the paths are, but you could actually have a career in the UN outside of representing your own country. And I was on that path. I was part of ... They were called millennium development goals, and then I guess they didn't meet the deadline, so they turned into sustainable development goals. So I was part of that. The interesting part is that's where I was exposed actually to financial services, mostly because of financial inclusion and the programs the UN was running in developing countries in the Middle East and Latin America and different ways money is accessed around the world and the different experiences that people have with money. So that was my first little look into that.
Bill Cilluffo:
That's great. I know you've talked about before that you also realized that patience is probably required as part of one of these NGO organizations. What did you learn there?
Naré Vardanyan:
Yeah. I have a lot of personal anecdotes about that, but I couldn't understand why do you have to spend so much time on voting on things and then counting those votes and making resolutions. It was all very, very bureaucratic to get anything moved. And I remember there was an earthquake in Haiti and I was very junior, obviously, there were a lot of other people making actual decisions, but I was a part of the group that was deciding on help that was going to be sent and the effort that it took. There were people on the ground that had nothing left basically. Their houses were destroyed. They didn't know where to go, their families. And the effort it was taking on the bureaucratic side to actually send help and all the paperwork and all the meetings, it was just too much. And we used to see recordings of people who were saving dogs and cats of UN officials who had left the country and keeping it for them to come back on the ground and then on the other side, you would figure out who's flying on what plane and et cetera for days. So that was really shocking and disappointing. Especially because I went in with a lot of illusion about what it should be versus what it was.
Bill Cilluffo:
You get a multinational thing where you have no explicit boss. It's all just a number of countries voluntarily coming together. I imagine the entrepreneurial world is a little different.
Naré Vardanyan:
It is.
Bill Cilluffo:
In terms of that aspect. No. That's great. So after the UN and you started going to school, I know you studied international relations and all about helping people if I'm not mistaken here. And now you're into machine learning, which probably also is about helping people eventually, but in a very, very different way. How can you talk about that journey from starting with all about people and now all about data and numbers and how you've thought about that?
Naré Vardanyan:
Like I said, I was very unhappy about the inefficiencies. So at some point I made the decision to get into something that was way more predictable and efficient. But the reality is I was doing a master's at University College London and focused on politics and security and just midway through that, it was the year that DeepMind got acquired by Google. And DeepMind was on campus at UCL. It was basically a group of people who were teaching computers how to play video games. And I knew a lot of people who were involved in that from UCL engineering, and that was just fascinating to me how you can suddenly take human input and create completely automated output and make decisions even though it was in a simulated environment. And I realized that maybe that is not how the world is today, but it's definitely how it's going to be so I wanted to be a part of it.
It was fascinating so then I switched gears and decided to completely focus on data and machine learning. I knew nothing so I started from the basics of Andrew Ng's course that probably everybody knows right now in Coursera and the fundamentals. And I remember because I did maths at school and not in English. So I had to actually Google every single thing to first understand the meaning of what those things are that they're talking about and then say, oh, this is what it means in the maths that I've studied and then put things together. But that was quite a journey. And I wasn't planning on starting a company back then, but I didn't have a visa to stay in the country and I didn't want to go back to Armenia. And the US visa situation was even harder as it is right now too.
Bill Cilluffo:
As it remains.
Naré Vardanyan:
As it remains. Yeah. Nothing's changed on that front. So the only way you could stay is if you registered a business and then attracted some funding. And I thought, well, I guess I have to do that. And that was the first company that I started.
Bill Cilluffo:
That's really impressive. So you started learning AI on Coursera and then started a company to stay in the country. That's awesome. What was that first company that you started?
Naré Vardanyan:
I was still doing some UN related work, but more with UNICEF at the time working with different kids in schools who came from non-English speaking backgrounds, trying to help them get integrated into the school. And when I was doing that, I used to notice how many mental health conditions were hidden under other things that were getting unnoticed. For example, a lot of kids would be expelled because of anger issues, and then you realize that the anger issues are there because they actually don't understand the language because they came to the country late and nobody speaks the language in the family and so on and so forth. So what we wanted to do is we saw that those kids were on their phones all the time, and the idea was is there a way to understand their mental state from the data and how they interact with their phones? That was the hypothesis.
And I had a friend from UCL engineering that I teamed up with, and then we found this group at MIT that were doing real research on understanding bipolar phone data. So we started working on actually commercializing it. Being able to take the input of ... It was a combination of haptics, how fast you type, even how many times you use the space in between words and so on to be able to detect those early signals. And that was the first company. And quite an experience. We actually grew it up to 10 people and raised some funding because I had to do that, otherwise I'd be kicked out of the country.
Bill Cilluffo:
That's a good motivator.
Naré Vardanyan:
Exactly. Yeah. You have no choice. But it was an interesting experience and we were working with the Mercer group because they were building this big platform for employee health tracking and it was being used for that, and then they just acquired the IP because we couldn't push it to market. Apple iOS came up with privacy restrictions, which meant you couldn't just have a key logger that would keep the information of how you're typing and what you're typing and analyze it. And that was a very big problem to get access to the data, so you couldn't actually scale it at that point.
Bill Cilluffo:
How did you go from this company number one ... Fascinating research. Had the chance to sell off the IP. And then what led you to Ntropy?
Naré Vardanyan:
My biggest learning from that was that the most important data in the world is being underutilized because of different reasons. When we started Ntropy, we thought the main reason is privacy and security because that was my experience with health data and specifically using phone data for health purposes. And if you think about the world of data, financial services and the information behind how money moves is probably one of the core sources of information as well to affect people's lives. And I knew from the UN there's a lot of privacy and security limitations there too. So I was thinking, well, there's no better fraud models or fraud is still a huge problem. Underwriting people who don't have proper credit scores or who don't look like what we are used to seeing is still a hard problem. And the reason is because banks don't share data. That was the hypothesis. Why don't they do that?
We know that a couple of companies started as consortiums to solve this issue and there's obviously the networks out there, but at a large scale that data just sits there. It's not shared between large institutions or even smaller ones to be able to do interesting things with it. So the premise of Ntropy was to create a privacy preserving compute environment to be able to process financial services data at scale and make it usable, train machine learning models on it, whether it was for fraud, underwriting or other purposes. And then covid hit. It was 2020 and we started talking to so many different people in the US, in the UK and Europe, and we realized that privacy probably is still a problem, but it's a second order problem because these institutions don't even properly use the data that they already have because of the quality of the data.
As open banking was taking off, which means now this data is accessible not only to the banks themselves, but also to anybody who wants to build products and services on top of it. And in the US with what Plaid had done and Yodlee before Plaid, et cetera, it just meant you had this new generation of companies being built on top of this. We thought the quality of the data is going to become even more important because connectivity eventually is getting commoditized and then you need to be able to do interesting things with the data to make it worth it. So that's what we focused on.
Bill Cilluffo:
No. That makes a lot of sense. I wonder if you can give the listeners maybe a tangible example of the data exists, but it's not always usable, it's not always in a great form. What's a good example of the type of thing that you might do to help a company better access either their own data or someone else's data via Plaid or other sources?
Naré Vardanyan:
Yes. The classic example I think we give to people is if you've looked at your own bank statement, there's probably a lot of transactions that you see that you might take a few minutes to understand what they're about. And the simplest merchant like Amazon appears in loads of different ways, and then there's a long tail of merchants that pop up that are completely unrecognized. And every year we have new payment processors being added new rails, which means this problem becomes more complicated. So if you don't recognize those transactions and they are your transactions and you have all the context, third parties who want to understand you via those transactions, whether to give you a loan or to even authorize a payment for you or recommend you a product, they don't understand you because they don't understand what you're doing and what you're spending your money on. So we take that data and we run bunch of different language models that try to break the transaction down and parse it as a human would. Understand who is the money coming from, who is it going to and what is the context? So if we were to summarize what Ntropy does in a very simple sentence, that's what we do. We take two parties in a money movement, try to understand everything about them and then understand the context. Why is this happening and assign it to the transaction.
Bill Cilluffo:
That's actually a great framing. Almost the humanization of data. If there was a human looking at these transactions, they could probably figure some things out and how can you use these language models to be able to do that in an automated and scaled way? That's fascinating. So when you started the company, probably 99.99% of people in the world had never heard of a language model. People thought of AI as something very different. Now it's clearly in the public, there's been great advances in the technology. How has that affected what you do or has it affected what you do? Has it enhanced it? Has it made it harder? I Now that you've gone from pioneering this technology and uses without much of a light being shined on it, probably most of the clients you tried to talk to didn't really understand what you were doing, and now at least people have a 30 second commercial version understanding of what it is. How has that changed you guys or has it?
Naré Vardanyan:
Yeah. That's a good question. I remember when we just got started, we had this blog post about not being able to do machine learning in house or that super vertical machine learning doesn't make sense. And the whole idea was that if you build a model that is specialized only to do one thing and you constantly fitted the same data from one company, eventually you cap that model. It cannot be as good. It will make the same mistakes and then you have this diminishing returns of the more data you add, it doesn't really do anything to the model. And this was the state of machine learning for a while. There were different methods. A lot of narrow models doing very, very narrow tasks and being really, really good about those tasks. What we were trying to do is to create essentially a brain, like a multipurpose machine that can get ... It's still focused on financial services, but now it can parse data in different formats, data in different languages, from different distributions, from different companies.
And the approach was that the more general it is, the better it is going to get at reasoning and understanding these edge cases that everything else was failing at. And it was super controversial back then. It is the most mainstream thing right now. That's why they're called large models. We still haven't reached the ceiling of how much more can you gain by making the models larger. So the larger the model, the better it is at certain things. And if you look at the different parameters in GPT three, 3.5 and four, you can see how that scale of data that it's trained on and the diversity of the data makes the model better at so many different things. So our approach from being odd and not really accepted became the mainstream. I think that's very interesting. I remember even when we were raising our previous round in VC conversations, everyone was telling me, we don't understand how not making this vertical can make it better. It just does not make any sense. So now it does.
From that perspective, it's exciting to have done that and to have been on the right path that has so much potential. What's even more exciting, I think with the larger models right now, the aperture of the data that we give people access to is way bigger. So when I just first interacted with ChatGPT and GPT four, the first thought I had was well before there were probably a couple million people in the world who can use our API and build models on top of it and then you would have the second and third order effect on the rest of the people. And these are people who have data science or machine learning or some engineering capabilities inside financial services companies. Right now with what large language models allow you to do, you can actually interact with this data in natural language. You can ask questions.
Let's say you're a small business and you're running your company. You could pull up any piece of information about the business, make the model do correlations for you, calculations, predictions and so on without having to even write SQL queries or any line of code. We are at the beginning of this, but that democratization, I think it was very exciting because now the data layer that we're creating is appealing to probably hundreds of millions of people, the knowledge workers everywhere. And that's a big wave for us and it's very exciting. I would say another thing is there's a lot of obviously companies today that are just being created as a very thin layer on top of the bigger models. And because we've been doing this for a while, we know what works and what doesn't and also why that's not very sustainable longer term strategy. So there's a lot of built-in advantages that we have now that can play out in interesting ways because of what's happening.
Bill Cilluffo:
Takes me back 20 years into the past. You talk about the need to leverage these big large models built on all sorts of data and then combining it with your own customized approaches. Several versions of technology ago as Capital One was building credit models, we were always frustrated that we kept paying so much money to Fair Isaac to buy their industry standard models and we kept wanting to beat them. Like, why do we need these models? We've got our own data. Capital One has 40 million customers, we should be able to do better. We really struggled to build models that could compete with taking the Fair Isaac model out. And I think what you're describing is the similar phenomenon, right? Fair Isaac's building it on 300 things and 20 different lending products and all sorts of different data. And so taking their off the shelf model as a standalone wasn't that powerful, but it also never worked to fully take it out of the system. And I think what you're describing is a pretty similar phenomenon.
Naré Vardanyan:
It is. I think that's why there's a graveyard of companies trying to kill FICO, right? I think the concept is similar. I think what is happening right now, it's very hard to understand where the value is going to be created in general. Is it going to be the bigger models that already pre-trained and do new versions and have all this compute in the world that are going to get the best results in everything, whether it's in finance or in law and so on? Or is there a way for anybody else basically in that supply chain to create value? I think a bunch of people are trying to understand that. For Ntropy, I think we're in a great position to be a truly verticalized AI company. And what that means is we have the data, we've seen billions of transactions. And not just seen but labeled and created a source of truth. Because there's a lot of even fintechs that are processing billions of transactions, but there is no source of truth that's been created really in the industry. It just doesn't exist.
We have the team that has the intuition and has been working on these models for a while, and right now this is not public information yet, but we also because of the type of data we've seen and we've labeled, we can basically enable any company to use the power of a larger model at a fraction of the cost of time because we can match the transaction and the skeleton of a transaction to our own cache of what we've already seen and look it up, which means only five or ... We're getting to 1%. That's what we're trying to achieve. Only 1% of the time you'd have to call a larger model. This kills a bunch of privacy issues. It kills the throughput issues and dependencies on the open AI of the world or the combinations of those. And it's also a lot more accessible price wise. So you could bring this intelligence to India and Africa and other places in the world where it's very hard to do now because it's hard to justify the price for a variety of use cases.
Bill Cilluffo:
Yeah. And I guess you're describing, I think one of the exciting things of so many waves of technology. The early wave, it's all sorts of promise, but it's so hard for people to use. And then you keep, in your case, building more and more customized uses of it, the price keeps coming down and you can just see the applicability growing exponentially. So we're at a super exciting time for the technology, I think, and you guys are absolutely at the forefront of it. Let me move to a little bit of reflection on your journey of building Ntropy. Every entrepreneur is faced with hundreds of decisions at any point in time, and some of those go well, and some of those don't go well. As you look back, is there a particular decision that you had to make that when you look back, you said, wow, we really got that right? There was this big fork in the road, we didn't know which way to go and we nailed that decision. And conversely, is there something that's the opposite? That you were faced with something and you, oh, I probably picked the wrong decision and then we had to go back and fix it later and adjust?
Naré Vardanyan:
Yeah. I think when not compromising on the engineering side and what we're doing, so not taking shortcuts and building lookup tables to just get the first few customers, it was a very hard decision back then and for a while I thought it was the wrong decision, but in the long run it helped us build a technology foundation that is very hard to compete with from a technology perspective, and that's a big asset for the business. That was definitely a right call. There are so many wrong ones. And maybe it's cliche and you hear it, but until you fully experience it, you don't understand it. But distribution is everything for a company. And distribution is not as straightforward as many people think when they just get into starting companies. It's an art and science and a combination of it on its own. So it's almost like an engineering problem and you have to approach it as an engineering problem and you need to find different solutions and iterate quickly.
And I think we were very heavy on the technology side of things when we got started and we just had this assumption that, well, if you build great things, they come. But it's more complicated than that. And especially if you're building products that are plugins into third party products and then that's your distribution channel and so on and so forth. So I would say I wish I had dedicated the time to think about this problem way more than the engineering and product side of things when we just got started and that's one of the biggest learnings. Especially in financial services from the experience with QED. There's so many. It's a very unique, detailed and deep area of expertise and there's a lot of things that you need to learn in terms of how these companies work, how to sell to them, who makes the decisions, the compliance aspects of it, and so on and so forth. So it's definitely been a journey and I wish we just dedicated more time into thinking about those problems earlier in the life of the company because you have a lot more flexibility then.
Bill Cilluffo:
No. That makes a lot of sense. I don't think you're alone in that learning, especially given the complexities of the technical problems you're trying to solve. I'm sure that required tremendous focus and not surprising that it's difficult to see the other side. Which actually brings me into my next question. I imagine focus in your particular business is challenging. There's so many things you can do. There's so many clients. You're building what will eventually become a global business, but at some level you need to ship code and you need to ship product and you need to sell customer X today. How do you think about balancing focusing on what you need to do today versus just such a broad array of possibilities that's out there for what you're building?
Naré Vardanyan:
That has changed. So in the beginning we were really focused on the quality of the platform. So we would say we will interact with any customer, any use case, as long as it gives us data. We started with 20,000 transactions that the model was trained on, and no surprise, it was a terrible model. But the promise was that the more transactions it sees, the better it gets. So we would accept any conversation, any call at any cost, just to get more data. And that was the mode in the company for a while. But what's difficult then, it's not even the fact that you are trying to do so many things, but it's also the fact that you're learning less because you're spread so thin. From every customer interaction, there's a certain level of depth you can go through and that's your end, which is not really great if you're building a product that users are eventually going to pay for. So that has changed.
Despite the technology we really focus on problem sets and what problems our customers are solving today. And I think the macro and the market environment forces you to think about which are the use cases where you can add a lot of value, but it's more future looking and not really moving the needle today and where are the areas where you can fundamentally change somebody's business, help them save a lot, or help them actually generate revenue? And we have to think very, very honestly about that and have very honest conversations with customers because that's the only thing that matters, especially in this market if you're building. And it helps you eliminate a bunch of other things that you want to focus on, but maybe are not as important.
Bill Cilluffo:
No. That's great. It's always a challenge. I'm sure that challenge is never going away. I'm not sure many other entrepreneurs face it. As we move into our last segment here, I'd love to focus a little bit on you as a leader and as a person and reflect on that. So let me start with what do you consider your superpower to be? And then conversely, if you were to think about one thing you wish you were better at, what would you point to?
Naré Vardanyan:
The latter, there's many things. But on the superpower, I think I am very, very quick and consuming and parsing information, and that's been something that I have noticed since I was a kid, which means that-
Bill Cilluffo:
Well then you're probably in the right space since that's the company you're trying to build, so that's perfect.
Naré Vardanyan:
Yes. It shows itself in being able to adapt to different situations and environments and people and having the power of observation to get all of those inputs and actually come to a decision. I think that's definitely been a superpower. On the flip side of that, I think sometimes it can be hard to, if you do consume a lot of information, separating noise from signal is also a skill you have to hone and it's taken me some time and I'm still on the journey of being able to do that. And as a founder, it's critical. You can be attracted to loads of different things. You can get a lot of different advice, signal input, feedback, and understanding what matters is important. I'm really focused on improving that and I spend time now weekly just sitting down and thinking about what matters and what doesn't because it's easy to get distracted.
Bill Cilluffo:
Yeah. Well, as a venture capitalist, I can relate. The separating noise from signal is I guess what we try to do all day and I can very-
Naré Vardanyan:
Yeah. That's our job.
Bill Cilluffo:
And some days we feel like we're really good at it and some days maybe less good at it. Especially in environments like 2021 when there was probably a lot more noise than signal, it was easy to get confused. So I can very much relate to that journey. So here's a question. You've obviously described how you grew up in Armenia in a really challenging situation where so many things about the country was breaking much less the financial services piece. You moved a number of times as an immigrant. I think we all know that immigrants into new countries almost always are served poorly by the financial status quo. Any particular learnings from that journey that you've been able to apply as you've either built Ntropy or been an active angel investor or out there in the world? Any big lessons that have struck you?
Naré Vardanyan:
It's fascinating how as you cross the border as a person, you basically stop existing until you build up some history. If you don't exist as an entry in databases and there's no information about you and no trust buildup, then there's very limited amount of things that you can do, especially when it comes to access to capital or resources. Every time I had to move ... And different countries have different rules, but sometimes to even get a sim card, you have a struggle. And then to rent an apartment, you have to have some history of credit. You have to have a bank account. And to have a bank account, you have to have an address, but to have an address, you need a bank account and credit history. So it's a never ending loop of need to be understood as a consumer from things that you've done from your previous history to be able to access all these services and that's changing. I wish it worked quicker and faster, but these things take time. But it is definitely changing.
There are many companies and institutions that are working on this problem. I would say the first and even second wave of fintech we've seen so many companies that have given access to financial services to people who previously would not be able to do that. And reporters these days love to dunk on fintech and say, oh, the business models don't work. Those are just fancy apps or it's never going to work and finance is expensive. These companies won't survive. It's VC craze. But the reality is millions of people could never send money somewhere, have a bank account if it wasn't for these fancy apps or the app store approving them. And it makes a massive change in people's lives. We sometimes might forget it as we are in the weeds trying to make the unit economics work or people working with banking partners or people going through compliance but it's worth it because it does make a huge difference. If you've been on the other side as a consumer you know it firsthand. If you walked into different branches ... And I've had this experience and couldn't open a bank account anywhere, and suddenly you download an app, you take a picture. In seconds, you're approved, and now you can accept money and send money. That's life changing.
Bill Cilluffo:
One of our portfolio companies is Nubank. And I've heard David Vélez speak many, many times about the reason why he started Nubank is because he went to a branch and sat there for six hours as an immigrant from Columbia to Brazil and couldn't do it. And I think what you're describing is something that millions and millions of immigrants all face. My personal version of that was probably the easiest one that exists on the planet, going from the US to Canada. So it probably works much better than most of the rest, and it's still just so frustrating how all this process works. Shifting gears a little bit, we all know that female entrepreneurs are way underfunded in the tech community, probably even more so in the fintech community. I wonder if you can describe a little bit of your journey. I'm sure you've faced countless ridiculous stereotypes. I'm sure you've faced much more of an uphill journey to both get funded and build the company than some of your male peers have. I'd love to give the audience a little bit of a glimpse into some of your perspectives.
Naré Vardanyan:
Thanks for the question. I think it's really important talking about this, and I know a lot of people talk about it, but it's just not enough. And I've had an interesting journey. And the reason I like talking about moving from international relations into machine learning and doing a course or just starting a company because I needed a visa is because often the way we're socialized, I think as females, we are very, very scared of taking risks in the same way as our male counterparts would do just because it's not as accepted or it's not the thing to do, or the downside seems bigger, or maybe because we're also questioned in different ways or the way we're judged out there is different. I would like more people ... I'm sure there are way more knowledgeable, experienced people with a ton of potential who have ideas in the world that just haven't dared to take the step. So I'd like to see that happen more often because it does happen to the other 50% of the population a lot. And out of that, a lot of interesting value is created.
I know there are studies about this, but my personal experience and the most frustrating thing has been always being questioned about the risks of doing something and why it was going to fail versus talking about the upside. As a female, you don't get enough opportunities talking about how big something you're doing can be. I think it's extremely important. You have to be viewed with that lens to be given the opportunity. And as much as the market is a bit different right now and it's healing and things are getting healthier, I think we need to remember that a lot of things wouldn't exist if they didn't make sense in the beginning. Whether it's business model wise or even from a product perspective.
And you need that big thinking and you need to allow people, especially female founders, to think really big and to be able to act on that scale and get access to resources, that still remains a big problem and a massive gap and I would like to see that change. But on the other side, I have to say, and I say this repeatedly, I've had so many people on this journey that were extremely supportive. QED example, we raised over $3 million through covid. Haven't met any of you in person. I was locked up in North Wales. Very rainy.
Bill Cilluffo:
Hey, Naré, if Nigel knew that you were locked up in North Wales, you would've gotten six million, not three million. Come on. You needed to tell us this.
Naré Vardanyan:
That's true. I actually missed out on that. Yeah. I remember Amias asking me, is this your childhood bedroom? And it was like, not really. I'm in North Wales and the only interaction I have is with sheep, but it's beautiful. Honestly, it's one of the most beautiful places out there, but it was very, very secluded, very lonely, especially in covid.
Bill Cilluffo:
For those listeners who don't know, Nigel grew up in Wales and is now a small owner of one of the Wells soccer teams and is one of the more Wales people out there in the world. So it's a great anecdote.
Naré Vardanyan:
Yeah. Now I know why it happened, but that's how we got our first round of funding. And doing something that was very risky could get very big, but also could be nothing, so was given the benefit of the doubt. I remember two days after we closed around, I found out that I was pregnant and that was a whole other experience. But again, I received so much support across the board. So I think sometimes we can get caught up in, oh, it's hard being a female in the industry, et cetera, and not do things, but it's more an internal bias and insecurity and once you step out of it, you do meet a lot of people, resources and opportunities to be able to do things that you want to do.
Bill Cilluffo:
How has motherhood changed your approach as an entrepreneur and a leader?
Naré Vardanyan:
I think it's definitely made me more empathetic. It's made me way more patient as well. I think I was impatient and that was a journey just to become more patient. But the most powerful side of it is just you find resources you never know you had. Time-wise, energy wise, it's just endless resource that you can tap into, which has been new to me and fascinating.
Bill Cilluffo:
I imagine you value sleep in a way that you probably did not before.
Naré Vardanyan:
Yeah.
Bill Cilluffo:
With all of your commitments to the business, to your child, you still need to unwind and find some time for yourself. Every entrepreneur I talk to always struggles with how do they do that. And I know you have such a fascinating collection of hobbies. I know we've talked about your chess playing. I know you've been into art and writing. How do you find time to unwind and find time for yourself in this crazy journey called life?
Naré Vardanyan:
I love painting. I used to do a lot more of it than I do right now. I think one of the things I probably don't have enough time to spend on realistically on any of those hobbies today, but what I have done and I've committed to ... It's not a hobby, but I started therapy last year and that's probably been one of the most important commitments that I've done. Because before it was very hard to get that time in to actually spend on myself and that's been one of the ways that I've been keeping it together and sane through entrepreneurship and everything else. And hobbies wise, I used to do kickboxing and then I broke my toe, so that ended. Right now, just getting time to move my body, whether it is Pilates, yoga, anything really, just to get some movement in the day is really important and I try to do that. I spend way too much time on Midjourney as well, making AI inspired interiors for some reason.
Bill Cilluffo:
While therapy might not be a hobby, there's no question it's great. Investment in yourself. So that definitely qualifies so that's awesome. Well look, we're almost out of time. It's been fantastic getting the chance to chat. I'd love to close with a question I think we ask everyone. Hopefully there's a bunch of young entrepreneurs listening or aspiring entrepreneurs listening. What's one piece of advice that you would give an aspiring entrepreneur?
Naré Vardanyan:
Risk is overrated. The way we think about risk and the society thinks about risk is sometimes too narrow. And I'm sure as a venture capitalist, you get to look at it in different ways too, but I think you should definitely go for things that sometimes seem too risky because the upside is very big and the downside is not as scary. I wish more people took the risk and made the jump to do things that they care about and that they think it should exist in the world because that's how the best stuff is created.
Bill Cilluffo:
That's fantastic advice. I would definitely agree. I think the fear of failure holds so many people back from trying some things. It's wonderful advice, I think Naré. So thank you so much, and it was great seeing you at our annual conference a couple days ago, and congratulations on all that you're building with Ntropy.
Naré Vardanyan:
Thank you. Thanks for having me. I enjoyed the chat.
Bill Cilluffo:
All right. And thanks to all of our listeners. We'll be back soon.
This has been the Fintech Thought Leaders podcast, your window into the world of venture capital and financial services with today's digital disruptors. QED is proud to provide the best fintech advice you can get. To learn more or to read the full show notes from today's episode, check out qedinvestors.com and be sure to also follow QED on Twitter and LinkedIn at QED Investors. Thanks for listening.