Rudradeb Mitra – an AI Engineer turned tech Entrepreneur, Founder at Omdena, a platform for building AI for the real-world through global collaboration. He published 10 research papers on AI and he’s the author of the Creating Value With Artificial Intelligence book that you can find on Amazon: https://www.amazon.com/Creating-Value-Artificial-Intelligence-Engineering-ebook/dp/B07H68JLBR
Ciprian Borodescu: I’m here with Rudradeb Mitra, an AI engineer turned tech entrepreneur, founder at Omdena – a platform for building AI for the real world through global collaboration. He published 10 research papers on AI, he’s the author of the Creating Value With Artificial Intelligence book that you can find on Amazon. I’m super excited, and it’s an honor to have you on this podcast. Thank you so much for being here.
Rudradeb Mitra: Thanks a lot for having me, Ciprian, in your podcast. I’m happy to be here, too.
Ciprian Borodescu: After reading your book, I know now that we’re both big fans of Start With Why by Simon Sinek. And so, let’s start with that. Do you remember when you came across that book and the impact it had on you? What was your life before and after reading it?
Rudradeb Mitra: Well, interestingly, I actually didn’t read his book. I think I came across this through a video and it didn’t have a huge impact on me, to be very honest. It’s something that I have always been like that, you know, I have always in my life, right from my childhood to university, the choices that I had made in my life, I always was questioning why I should do something. So, I don’t think it has a huge impact. But it just helped me to connect the dots. So sometimes you follow certain things that just come to you, like in my case, you know, intuitively it was natural for me to do that although the society around me would say something else. Right? So they would say that “Oh, no, you have to follow this pattern. Even if you don’t understand, you don’t have to ask the questions. You just have to do this.” And I always have that. So when I heard from him, it just helped me to connect the dots, get the theory behind the practice, if I say that way.
Ciprian Borodescu: Yeah. You probably saw the TEDx Talk, right?
Rudradeb Mitra: Yeah, most likely that one, or maybe he had also… Not only TED Talks, but he also had some, like, you know, his own videos where he’s talking. I don’t remember which one. But I actually wrote an article about that, also, many years ago – I think three years ago about entrepreneurship. And I think that I wrote in that article, why do I want to work with people who do not want to work hard? And there also I referred to him in saying that working hard is something that we feel as an effort. And I think that when we really understand why we are doing something, it doesn’t feel like an effort. It gives a purpose, it comes naturally.
Ciprian Borodescu: It becomes effortless. Yeah.
Rudradeb Mitra: Yeah. And that’s something I talk a lot about – effortless living and building things. So I think that, to me, it was natural. It made sense completely.
Ciprian Borodescu: Yeah. You know that he also published another book recently, The Infinite Game?
Rudradeb Mitra: I don’t know, actually, I will be happy to read it. These days, unfortunately, I don’t read a lot of books, which has been for the last six, eight months, so I haven’t been following who is writing what, but I’d be happy to check that out. Thanks for that.
Ciprian Borodescu: Yeah, absolutely. That’s also on my wish list. That’s why I mentioned it. Cool. So, you have over 10 years of experience in the AI field. And some of our listeners didn’t even hear about AI until a few years back. Walk us through your journey from being a student, a researcher and software engineer, mentor and advisor for various startups to founding Omdena. How did that come to be? And how did you make the leap?
Rudradeb Mitra: It’s an interesting question, and thanks for asking this question. There are two parts to the question. The first part of the question is how did I come across or hear about AI? So, it happened in 2002, actually, so it’s not 10 years ago, it’s 18 years ago, and I always loved mathematics. AI has been always there, right? At that time, AI I remember was a lot around chess programs and around AI planning algorithms and puzzle-solving. So I loved that part. And so, that’s how my first experience with AI came. I published a paper way back in 2002. I got then invited to come to Germany, work in RoboCup soccer-playing robots, in 2003, planning algorithms. So I think at that time, for me, machine learning was not that big. Actually, machine learning, I think recently became that big. I think that AI was to be more the traditional AI, right? Planning expert system. So there’s a lot of other algorithms. So, it just happened because I love mathematics, I love puzzle solving. And under this graduate transition is also quite interesting because the research comes naturally because I love doing research or learning something new and going into a specific topic. And publishing the paper was also like, somehow it happened, it was not like I was trying to publish any paper, it was just like I did some work and someone said, “Oh, you should write about it.” But then I started my Ph.D. at a very young age, in 2004 and 2005. But then I left the University at that time, also after a year. And I think that what I realized was that building solutions or technically advanced solutions and actually selling it and applying for the real world are two different things.
Ciprian Borodescu: Yeah, that’s a good point.
Rudradeb Mitra: And that’s something that made me perhaps move the transition more from a researcher, then to become an entrepreneur because I always thought that the most important thing of technology is when we can apply that in the real world for the benefit of the society. And this was always in my mind, it was not like it happened a few years ago. It was always what I was looking for. And in terms of mentor and advisor, it’s also, again, I mean, I don’t know how much you know about my philosophy of life, and I always think my philosophy of life is don’t plan life. I speak a lot that we create our self-driven goals or our own self goals and we try to follow those goals without really knowing whether these goals are good for us or not. Instead, in turn, what I do is I kind of just do what I can do today the best and then let the Universe set the goals – so, whatever it leads me to. So I never had a plan and never had any grand vision of doing anything like this, but it just naturally progressed one after another. The same with being a mentor and advisor. I remember it happened because I wrote an article, then I was invited to give a talk, and in the talk, there was one person from Google for startups, and she comes to me and then she says, “Do you want to be part of the mentor network?” It just happens naturally, perhaps. And then the same with founding Omdena. Like, I think that while being a speaker, and while mentoring, I saw the talent out there, I saw there’s so much of great talent who don’t get those opportunities. I think the opportunities are somehow unfortunately limited to only the top tier, you know, often in Harvard and Stanford – which might have been true 10-15 years ago, because knowledge was centered in those places. But now knowledge is everywhere. So, that came naturally to me that we should be able to leverage this huge talent out there of people who are well educated, who have access to knowledge, and who are very smart and intelligent and very driven to solve great problems in the world. To me, that just seems very natural and that’s why I thought that “Hey, we should build something like that.” So yeah, because you asked how did I make the leap? I don’t think there is any leap. I think it’s just a natural progress and incremental slow by slow movement. It does look like a leap when I look back in the last 15-17-20 years, but every year, every month is not a leap. It’s just slowly moving in one form or another.
Ciprian Borodescu: That’s very interesting. And I like the fact that you mentioned the life philosophy. In the past year or so, I started reading the stoic philosophy, and one of the things that it’s at the core of the stoic philosophy is the fact that in life there are certain things that are in your control, but most of the things are out of your control. And of course, there are those that you can influence or that are partially in your control. So, I totally agree with your life philosophy. In a sense, you can have your goals and stuff, but you know, like this pandemic showed, life can throw a curveball at you. And what do you do then?
Rudradeb Mitra: Absolutely. But not only that. I actually think that the best things come in life when you don’t make a plan and that has been my experience. I mean, if someone would have told me five years ago, “Make your perfect plan for the next five years”, I would have never made such a great plan as life has shown me because five years ago, I would have never thought that I could be all of the things that I am today. So, we only can plan things that are incremental. We don’t normally plan very disruptive and that’s the beauty, that if you don’t plan and let the universe show you the plan, maybe there are things that are much more interesting. So it’s not only that we cannot control it, I would go further and say we should not try to control it because then perhaps life shows the best things.
Ciprian Borodescu: Absolutely. Absolutely. And you mentioned technical talent or talent in general available in different hotspots over the world. And sometimes I wonder how can AI startups succeed with limited access to funding and talent? What are the levers that we can pull to push through and reach a point where AI is no longer considered elitist? It seems that there is a huge gap, basically, between AI tech giants and the rest of us. How do you see this developing?
Rudradeb Mitra: Absolutely. I don’t think there’s an agenda behind it, but maybe they definitely benefit from having this kind of knowledge gap, or at least perceiving there is a knowledge gap, right? That the tech giants should have all the knowledge and the rest of us should kind of use the technologies that they build. In fact, not only tech giants. I was in a panel discussion a couple of weeks ago, invited by the guys at Facebook, and I was in this talk with some professors from different top universities, including Harvard and MIT and I felt that there was still a notion among people that they know better than the rest, and we will build solutions that others will use. And I think that that mindset is still there and I think it’s fine. I mean, that’s their thought process, and how they want to do it, and many people believe that. But I’ve always believed that the best way to do something or to change something is to do it, right? Rather than talk about it. You know, we can go and talk that talk, but no one will believe that. So that’s what, I think hopefully, Omdena is kind of showing to the world that, look, to build even sophisticated models, you don’t need this top talent, you don’t need a lot of money, you don’t need a lot of funds or talent from these universities from the top places. You can actually leverage the wisdom of the crowd and the people and build very sophisticated models.
Ciprian Borodescu: Yeah. Basically just start small, iterate, and see where that leads.
Rudradeb Mitra: Absolutely. And I think the tech giants do have a place to play and they should play the role of providing the infrastructure, which I appreciate. I think that they should provide the AWS and we couldn’t have built what we are building without AWS credits or Google Colab and TensorFlow. And I think that that’s what the giants do, but I don’t think that tech giants or universities should build products for us because I think they have their own role, and research has its own role, and technology and project development have their own role. But product building is to be left for the people because I think that the people who face the problem are the best suited to build the products for themselves.
Ciprian Borodescu: Yeah. So, in your book, Creating Value With Artificial Intelligence, you’re talking about a very good framework for identifying use cases and problems that can be solved through intelligent use of data. In fact, it’s a set of features that make the collaboration between humans and machines work. Can you dive a bit deeper into it and give a few examples?
Rudradeb Mitra: Absolutely. So, I think the framework of what I’m talking about is to start with the why, start with the problem, right? So why do you want to build the solution? And a lot of companies, a lot of startups say, “We want to use AI.” I mean, that’s the wrong place to start. So first, you have to start with, “Okay, is there a problem that is worth solving using machine learning or our technology? Or in general technology, but also AI and machine learning? And how do I identify the right problem?” And I say that, first of all, identify problems that have a huge human error or the existing error in those ways you’re solving is quite high, for various reasons. And then see if there are patterns. Once you have these things that you can see they are repeat patterns, then how is currently the problem solved? There are potential repeat patterns, and there are high errors, then go and look out for the data to solve those problems. Because a lot of times, again, companies make this mistake that they first start with the data. They say, “Okay, we have this data. What can we do with this data?” And I always say that’s the wrong approach because then you are limiting yourself to the data that you only have, rather than starting from the problem which might require other data that you may not have, but if you look at only the data you have, then you will ignore the data out there, which may be publicly available, and you can get that data anyways. So, I think the framework for me is very simple: start with the problem, identify if there are patterns, and then look for the data that you have. And then, of course, once you select the right problem, you know what data to collect or what data you have, then go and overcome the challenge with data. That’s basically how I would say you should go and look for the solutions.
Ciprian Borodescu: It’s interesting that you’re saying that. So, based on my experience, I’ve seen that there are companies that have a lot of data, just as you mentioned, but they don’t know how to do AI. On the other extreme, there are companies that, well, they want to do some sort of AI, whatever, not necessarily thinking of a problem, but they do not have the data. And I think there needs to be some sort of common strategy or a strategy to unify data collection, figuring out, like you said, the problem that you need to start with, at the end of the day, with or without the data, with or without AI.
Rudradeb Mitra: Exactly.
Ciprian Borodescu: Sometimes we have customers and we say like, “Guys, what you’re trying to solve here doesn’t require AI. It’s just a simple statistic. You have that in Google Analytics, or whatever. And sure, you can pay us tons of money, but that’s not what we want to do. Right? I mean, it’s not efficient for you.”
Rudradeb Mitra: Yeah, I think honesty is very important in the tech world. I find that, unfortunately, many people are dishonest and they’re just saying, “Oh, we’ll use this technology and machine learning or whatever” and you spend a lot of money. And it is relatively easy to earn money, unfortunately, with these buzzwords, but I always would say, don’t use AI or machine learning unless you really have to use it. So, avoid using it as much as you can, until you think, “Okay, this makes complete sense.” It’s the same logic as when we talk about building startups. I always say, don’t go and build startups unless you think you really have to build a startup. So that’s what I would always say, and I do agree with you that there are companies who have data, there are companies who don’t have data, but I think what I have seen over the last year and a half, and you said there’s a ton of publicly available data also, and even if you don’t have a primary source data, you can use secondary data sources to derive insights for the data that you are missing, right? There are different ways to overcome the data challenges like insufficient data, or even data you don’t have, then you use other forms of data. So that’s why I always say, don’t start with the data because there are ways to get over the data. But unless you are solving the right problem, even if you build a solution, you will fail because that solution may not be adopted. Like, as you know, 87% of AI solutions are never making it to production and one of the reasons is perhaps that the companies are solving problems that they don’t need to solve using AI.
Ciprian Borodescu: And you know what happens. I mean, from a digitalization point of view, I think it’s almost as we jumped from 2020 to 2030 in a matter of months due to this pandemic. And personally, I’m still trying to figure out the implications of all of that. On one hand, if we take retail, for example, clearly more and more businesses have today an online strategy versus a year ago; at the same time, more and more consumers are buying online. And so, long term, these trends, I guess, will hold. However, I can’t help but wonder who are the real winners here? And what can we learn from all of this, from this forced pivot, let’s call it, that took a lot of businesses by surprise, that it took a pandemic of this magnitude to force companies to prioritize digital transformation? And now, AI is part of the digital transformation but I think it’s the next step, right? And I’m wondering, do we need to go through a similar painful experience for companies and people to seriously consider adopting and embracing AI? Did we really learn our lesson here?
Rudradeb Mitra: It’s really hard for me to answer this question. You know, it’s a very philosophical question and I do not know how to answer it.
Ciprian Borodescu: It is, it is.
Rudradeb Mitra: I think it is natural in us. If I just try to answer philosophically in the way that change takes time, it doesn’t happen so quickly and so fast until we are forced to change, right? So the pandemics and government policies are some of the top-down structures of change. Or otherwise, that’s why innovation happens – that’s why startups exist. I think that, in some way, the fact that companies like Uber and Airbnb appeared is because the traditional travel industry and traditional taxi industry didn’t manage the change. So, I do not necessarily think that the lack of change is always bad because if the lack of change would represent other opportunities for startups to come and embrace it and build those kinds of solutions that people will use. I think that’s just the natural cycle now. So for me, it’s okay.
Ciprian Borodescu: No pain, no gain, right?
Rudradeb Mitra: Exactly. If a bank says, “Oh, we don’t want to change, we want to just use the way it is. We are very slow to change.” Okay, that’s their choice. And they may cease to exist in 10-15 years and that would drive a lot of FinTech companies and startups coming up and taking the opportunity. So I think that just part of the thing. I personally am not frustrated – I work with a lot of banks – if the bank says, “Okay, we don’t want to change.” Okay, that’s your choice. And I think that’s good because then it creates opportunities for startups, right? If all these big companies were so fast at change, and they were actually changing, I think that will not create any opportunities for startups.
Ciprian Borodescu: Yeah, you have a point there, yeah. That’s a good point, actually. Alright, can you give us a few examples of projects and products that you’ve built? And what were the biggest challenges, both from a technical point of view and business? And how did you overcome them?
Rudradeb Mitra: That’s, again, a very good question. I mean, I was thinking what would be the best answer to this. I think as a startup, I think that of course, for example, Omdena would be one of the biggest challenges I have experienced over the last one year and three months, because people loved the idea right in the beginning, but there was hardly anyone who believed it could work. Can you imagine people who have never met, live in different parts of the world – and not the top tier, not necessarily from all these top-tier universities – coming together voluntarily and building something very meaningful and useful and sophisticated, in a two-month period? I mean, that in itself was an idea – and still is – quite disruptive, but in some ways, unbelievable. So for me, that was one of the biggest challenges: How could you make this work? And I think there are still many things to overcome, but one of the ways I think that I did overcome was believing in people.
Ciprian Borodescu: That’s such an important point. I love that. I love that.
Rudradeb Mitra: Yeah. Because what I’ve seen is the power of the people. You know, there’s no doubt in my mind. I mean, all these people who are coming together and doing something I’ve known all of them – some people more, and some people less – but I personally tried to call and talk to them. And I think that this belief that people can actually do something, and that perhaps made me overcome all of these challenges. I mean, that would be one of the things. In terms of other projects, I think there’s quite some startups I’ve built. Of course, every startup had its own challenges and its own issues. But I think, one core thing if I have to take from all of the learnings that I have learned in my last 18 years of building startups, and also mentoring – and that’s another thing when I do mentoring is I learn a lot and meet these awesome entrepreneurs, is basically believe in people. I think that’s the best way to overcome, is to pick the right people, work with the right people, select the right people, surround yourself with the right people, and then you can perhaps overcome any challenges.
Ciprian Borodescu: Yeah, exactly, with the right team. Yeah. So, I know you’ve worked on a lot of AI products and you’re mentioning quite a few in your book. I was wondering, maybe you can give an example of that kind of AI product?
Rudradeb Mitra: Sure. I mean, the book was, of course, written a couple of years ago, right? And since then, I’ve also worked on another 24-25 projects, because of Omdena. So let me take one example that I preferred in the book and I can refer to one in Omdena. I think the biggest challenge in AI is always adoption. How do you build solutions that people will trust, that people are willing to adopt, right? So that’s the challenge I was overcoming. So there was this app that we had built to track driving data, how people are driving and collecting data from the driven data? But again, driving data is very private, and how do you make people share the data? Right? How will you do that? So I think what we have understood – and that’s something I also applied in almost every startup – was that we have to connect to the intrinsic and extrinsic motivations of people. And once we understand what people want or what motivates people intrinsically – so in our case was, you know, we wanted to make roads safer. So, we kind of stressed on that aspect in the beginning. We said, “Hey, we are trying to make roads safer. We already tried to reduce road accidents.” So you connect this intrinsic motivation. And then connect it to the extrinsic motivation of giving something back in return that’s tangible, something they can use, so they can get something as a reward. So, we kind of gamified the system and that helped us to overcome the data challenges, because most AI challenges I think, are not basically necessarily technical challenges. They are often the data challenges, right? So that was one of the ways that I understood that how do you motivate people to do that? I wrote an article, it was “10 Tips for Successful Adoption of Machine Learning.” It was actually one of the trendings in Hacker News also. And there, I talked about this intrinsic – extrinsic motivation. I just thought we’re educating people, like trying to educate people on what data to use. So these are some of the ways that I think overcome their challenges. Another project that I can talk about, which was a bit more technical in overcoming data challenges was in Istanbul, we were working with Impact Hub, and our goal was to build a route planning algorithm to go from point A to point B when an earthquake strikes. And we didn’t know exactly what to really look at, what do we mean by actually route planning after an earthquake happens? Because that was one of the biggest concerns of businesses in Istanbul, as I understand that, if an earthquake happens, the first thing that people will think is how can I reach to my nearest people, right?
Ciprian Borodescu: Yeah.
Rudradeb Mitra: And through various interactions, while building this collaborative way of building the solution, we realized that the density of an area or let’s say, a road, how the width of a road might indicate whether the road will be functional after an earthquake or not. So okay, we can build the route planning algorithm, which should take into account the width of the road – unlike Google, which uses, for example, the shortest path or the shortest time, which is based on the traffic at a given point, which may not be working after an earthquake. And then we had to collect the data with the width of the road and we looked at OpenStreetMap and we immediately saw that the data there was incomplete, it was not correct. So using the width of the road, to actually using OpenStreetMap kind of place, we used satellite images. So we scanned through the satellite the building tops, and then we analyzed the distance between the different building tops. So by that, we not only included the open area in terms of the width of the road, but also the small parks and some open spaces that also will be opened during and after an earthquake. And that is much more accurate than just if you have taken into account the width of the road.
Ciprian Borodescu: That’s such an elegant solution. Nice.
Rudradeb Mitra: Yeah, and that was the way we did it. And I can give you a few more examples but the idea there is to show that we should start with the problem, and then look at innovative ways to find the data. Like, if we have just looked at the OpenStreetMap data, we would’ve built an incorrect model. So that’s one of the nice examples to show how we, for example, overcame the challenge.
Ciprian Borodescu: I think one of the learnings for me personally out of this episode is the fact that, you know, if you start from the data, then it’s kind of like thinking inside the box. And I like your approach. Of course, start with the problem, but not necessarily adding data as the next step. Just consider the problem and see where you can actually collect data. If you have it, then that’s fine. If you need some more, then strategize around that. So, that, for me personally, it’s a really, really good point.
Rudradeb Mitra: Absolutely. And just to add one more comment, if you also start with your own data, then you might add biases, and then you will also not think about the bias aspect of that data.
Ciprian Borodescu: Yeah. I just wanted to ask you what are three actionable takeaways you’d want entrepreneurs or executives to remember after reading your book? And when thinking of investing in developing intelligent products? And if you could just distill the book into three actionable takeaways.
Rudradeb Mitra: I think that three action points to me would be, first of all, what I said before, identify the right problem – and in the book, I do talk about what are the ways to identify the right problem. The second is, the data is out there, which I also say that, you know, think out of the box – I agree with you, think out of the box – the data is out there. Once you have the right problem, find ways that you could gather the data and think about how could you overcome the challenges with the data. And the third point is that it’s about the people. It’s very important when you’re building the solutions, to involve the stakeholders and the users as much as you can, rather than just engineers sitting in a room and kind of in silo and building the solution. So that’s the third part. Even big companies like Amazon and Microsoft had failed and that’s where they ended up building solutions that were, you know, somewhat sexist and racist. So, that’s very important. Identify the right problem, get the data that is out there – think ways to get the data – and involve the people, the stakeholders and the users.
Ciprian Borodescu: Yeah, yeah. I love the point about the people. And I also, on that point, want to pick your brain on the diversity topic. Where do you see things headed? Personally, I feel like there are more and more women involved in STEM – at least this is what we are seeing here in Eastern Europe, which is amazing – however, it still feels that we need to be intentional about it, and be on the lookout for women that are interested in a tech career. And based on your experience, how the ecosystem feels in the UK or Asia versus Eastern Europe, in terms of the diversity of the AI talent?
Rudradeb Mitra: Thanks for asking this question. I do think it’s quite important to have diversity and I think diversity is not just about gender, or race, or skin color. I actually wrote a post perhaps a week ago, saying that, “I think a room of all white male, but from all over the world is more diverse than a room with people from different genders, race, color, but all from San Francisco or Silicon Valley.”
Ciprian Borodescu: That’s such a good point. Interesting. Such an interesting perspective.
Rudradeb Mitra: Yeah. And it’s important for us to understand that diversity is not just a token diversity, like, get some people from here, here, here. Okay, now we are diverse. And I think the importance of diversity is diversity that we call cognitive diversity, right? Which is thought process diversity, different opinions, and ideas. So that’s very important to talk about. And, of course, I will talk about the part of the women because it’s absolutely important to have women. I’m not saying that you can’t just have males but this part also kind of happens organically. Like, at Omdena, when we built this team of people collaborating and organizing, we never tried to do a token diversity. When we selected the team, I’m not looking for X amount of women, X amount of people from this country. I never do that. I just select the best people that I think are the best people for this project. And, to my surprise – I wrote this post also like a month or two months ago – that I thought okay, let me just calculate what exactly is the number of women out there or what number of people in different countries are out there? 35 to 40% were women.
Ciprian Borodescu: Nice, okay.
Rudradeb Mitra: Again, I stress this very much, it was not done by design. And I am happy that we didn’t do that by design. It’s not that I said, “Okay, I need 1/3 women, so let’s go and find 1/3 women.” What I’ve seen is that if you give equal opportunities – and what online education is doing is giving equal opportunities – then diversity comes organically automatically. You don’t have to do something to be diverse, right? It’s like, we have 30% from Africa, 30% of Asia, Eastern Europe, from all over the world – we’re actually quite diverse. But again, we don’t put that effort to make it diverse. It just organically happens. So once we create an equal opportunity, diversity comes naturally.
Rudradeb Mitra: The key is, how do you create equal opportunity, right? And education on equal opportunity is important. So yes, women have been traditionally I think, a bit lagging behind because, in many countries and many cultures, they didn’t get equal opportunities. So, I think that that’s the way to solve the diversity if we are serious about it. Not token diversity, but cognitive diversity is creating equal opportunities in the world, and make education and knowledge accessible to everyone, and I think that internet and online courses have really done well. In fact, a lot of the women I was trying to understand who are a lot of these women, they are, of course, students, but a lot of these women are in their mid-career, actually, the mid-30s, and 40s, who are part of Omdena. And in fact, in our core team, Karen and Laura, they’re also from the same group of people who have like 15-20 years of experience in other sectors, and they want to change and move to another sector, like for example, machine learning and AI. Unfortunately, the world is very unfair to them. And not only females, but I’m also talking about males and females, right? Both groups.
Ciprian Borodescu: Yeah.
Rudradeb Mitra: Because we kind of have this notion of someone has to be a student and then they enter the career and keep staying in the career for the rest of their life. Changing careers is something that people do not take very, I don’t know, very easily, and it’s not so easy to do that. So, what online education has done is it has given access to a lot of these people to kind of take a break and rethink and try to do something else in their life. And we have to give the opportunity to those people also rather than just not restrict to say, “Okay, we need 20-year-olds or 25-year-olds only. And I think that ageism is another big issue that most companies and look at most startups, they are mostly hiring people in their early 20s, and things like that. So I think that’s a way to build diversity and age is another very important factor in diversity, actually.
Ciprian Borodescu: Yeah, those are very, very good points. Thank you for that. So, clearly, AI is hyped these days. And I would like to invite you to give our listeners a framework or kind of like a set of questions that can help them identify those companies or startups that are indeed doing AI versus those that just play around with some keywords on a landing page. I know for sure that, for example, in the marktech space there is a lot of that happening. And I met clients and potential leads and customers that are questioning us, for example. How can we know that what you’re doing there is actually AI? Because you’re asking for a lot of money; for example, you’re asking for a big budget or whatever. And I’m wondering what your thoughts are.
Rudradeb Mitra: I think that that is true – what you said – a lot of people are using buzzwords. In some cases – and sometimes if I look back – even I have been actually guilty of that. In one of our projects, we didn’t really use AI. We were using data science, and we still put the word AI ourselves. When we were doing the demo, we used AI to solve this kind of problem, energy, whatever. So I think that one is that happens and I know that very well that happens. And I think that if we want to know how to identify whether something is an AI or not, or just data science, and just data analysis, I think the only way to do that is to just dig deeper and try to understand what they are going to build, what is the solution they are building and what are the algorithms they are using. I do not know just by looking at the landing page or just to marketing material anyone can make an analysis whether they are or they aren’t using any kind of deep learning or machine learning or even other traditional AI algorithms.
Ciprian Borodescu: And at the end of the day, you know, for the business decision-maker, I guess it doesn’t really matter. I mean, those nuances between data science, statistics, deep learning – I mean, those are terms that don’t say much to a business decision-maker.
Rudradeb Mitra: Exactly. And it doesn’t matter, exactly as you said, why use it? I understand that some of the companies are using this word to increase the payment or to increase the bill. But that is a red flag also for me. If they say, “I need to charge you X amount because I use AI”, that’s a red flag.
Ciprian Borodescu: It’s interesting because this question comes, well, pretty rare from customers but it comes a lot from investors, especially if you’re in the startup world and you want to raise money, right? They begin to question you, and you know this, “Is this really AI? Are we investing in the right technology here?” And stuff like that. So probably, this is what triggered a lot of startups and a lot of small companies to hype these terms, I guess.
Rudradeb Mitra: Yeah, I mean, investors – and I know this is a podcast, but I don’t mind saying that I have not very high respect for most of the investors. There are very, very few smart investors, right?
Ciprian Borodescu: That really understand what’s happening behind the app. Yeah.
Rudradeb Mitra: That really understand what’s happening in the world. Forget about technology. Just what’s happening in the world. And in fact I, again, wrote a post about it a month ago, saying I’m glad that average investors don’t understand what we are doing. And I think that’s truly a compliment for me that we are doing something really great. When an average investor says “What we are doing?” That’s good because an average investor looks for this steady, standard formula. Basically, they look at… Often in Europe, and in Asia – the US is a bit different but in Europe, they copy X idea in a market while doing Z. It’s just like, you know, they need to do the simple mathematical formulation. Okay, you copy x idea, which works in this country? And it’s just ridiculous to have this thing.
Ciprian Borodescu: Yeah. On some levels, I understand the name of the game and the game they are trying to play. It’s ruthless, it’s high risk, and they kind of need to do that.
Rudradeb Mitra: Absolutely.
Ciprian Borodescu: The best of them, they take high risks and they are innovative in thinking and they can see the opportunities, but they are like, what? 5% of the VCs? Something like that.
Rudradeb Mitra: But, also, this comes to the same as how we started this podcast. You must start with why. I think it also comes at, why an investor is investing?
Ciprian Borodescu: Oh, yeah.
Rudradeb Mitra: Most investors are fund managers, they have never built a company, they are never going to build a company, they don’t understand that you’re building a company. So they are just fund managers, they just want to maximize profits, they just want to maximize the army. That’s their motivation. That’s why they are doing it. So that’s why they’re reducing the risk by doing something else. But other investors who aren’t doing that why because they want to really invest in and be part of something truly innovative, truly something changing the world, making a proper impact in the world.
Ciprian Borodescu: Absolutely.
Rudradeb Mitra: There you cannot define an early-stage startup with a simple sheet. You know, an Excel sheet will never represent… So I think that, again, it comes down to why they are doing what they’re doing. But I think it’s not only for investors, but it’s also for startups and a lot of entrepreneurs are doing it for making money and then they are also driven by this quick money, and how do you make money rather than change? So I think it’s just how it is, you know. There are some people who are driven by something else, others are driven by something else. But what I think where entrepreneurs make a mistake – because I’ve mentored a lot, and I was actually mentoring one startup from Belarus, and he asked me this question that “My investors want to look at these metrics. How can I show that?” And I always will say that you don’t build a company for an investor. And that’s a mistake a lot of startups end up doing that they just go from one raising investment to the next raising investment to the third raising investment, focusing purely on return of investment for the investors or catering to their needs and their demands, rather than actually listening to their users and customers often. So I think that we definitely need to make a shift, which I think hopefully will happen after COVID. But in general, the focus is not just about how do we maximize the return of investment, but more maximizing the impact that we’re creating in the world – and that, I think, should be how we should be gauging and looking at investors and also startups.
Ciprian Borodescu: Yeah, that’s such a good point, and thank you for mentioning that. So what’s your plan for the rest of the year? And what are the growth opportunities for your company as we move out of this pandemic, hopefully, next year?
Rudradeb Mitra: Yeah. So, I also mentioned during the podcast that I don’t have self-created goals and kind of plans as such. I don’t plan actually.
Ciprian Borodescu: So your plan is not to have a plan. That’s a good plan.
Rudradeb Mitra: Yeah, let it show. But it’s also very important to have a vision. So planning, I don’t plan. But having a vision and moving towards a vision is very important because that drives you every single day to put that effort and the work. And, you know, not effort, but the hard work, the time that you want to put into something that makes you feel fulfilled, and you want to be part of it.
Ciprian Borodescu: Basically, having a purpose, having a reason, having kind of like a sense of direction, right?
Rudradeb Mitra: Exactly. That’s very important. So I have to have that sense, where I want to be or what makes me do every day what I’m doing, but I really don’t have a plan for this year. But we have been quite like, you know, in the last three months, we have grown quite well, decently well, I think. We have been also extremely lucky to be covered by magazines like Forbes, but also NASA highlighted us. So I think that, again, it happened completely organically and I didn’t put any effort into those things as such. And I think that that will perhaps also will be reflected in the growth for the rest of the year. We’ll see how that works out.
Ciprian Borodescu: Awesome, awesome. Rudradeb, it was a pleasure to have you, and thank you so much for sharing your wisdom with me and with us. I learned a lot from this episode. How can people reach out to you for ideas and comments?
Rudradeb Mitra: I mean, the best would be LinkedIn. I’m quite active on LinkedIn, so I think that’s the only social media that I use. So, feel free to add me on LinkedIn.
Ciprian Borodescu: Awesome. Thank you so much.
Rudradeb Mitra: Thanks a lot, again. I enjoyed the conversation.