Transcript:

Blaine Mathieu:

Joining me today is Angelo Kastroulis from Ballista, a consulting company and NextGen VC. And he’s going to tell us more about that. And of course, Angelo is also well known for his “Counting Sand” podcast. Thank you for the time, Angelo. We’re going to have some fun today.

Angelo Kastroulis:

Thanks, Blaine. Thanks for having me.

Blaine Mathieu:

Excellent, we really do appreciate it. So why don’t you start by telling us a little bit more about your personal backstory before we find out more about Ballista. How did you get to where you are today.

I’ve been listening to the “Counting Sand” podcast and so heard some of the story actually, but very interesting because obviously you’re a technologist at heart, but you really make the connection between technology, society, science, philosophy in many different areas. And as I’m listening to it, I’m almost reminded of the old Dos Equis commercial, you know, the most interesting man in the world. Well, you even look sort of like the Dos Equis guy, I would say, but you’ve got an amazing background. So tell us, how did you get to where you are today?

Angelo Kastroulis:

Wow. Well, I don’t know if I could live up to that. That’s a big… But you know, you’re right. I have been in technology for many years and I think coming from the trenches, that really gives you a certain perspective and it wasn’t until later on in life that I realized that maybe 20 years into computer science about 20 or 30, even, maybe you get into it. And you realize that there’s a purpose behind all of this information that we’re doing, these computations, all these different things. So I went back to grad school to try to make sense of it, try to figure out this new thing called machine learning and AI, and try to see how can we analyze this for meaningful ways of doing it. I got into the Harvard Data Systems Lab, and that is when I think it kind of opened up to me a little bit where I saw the world a little bit differently.

I got to see the insides of a lot of these commercial databases and how they work, data systems, I should say. And you start to really see that these are not just very huge technical problems, but there’s a reason you’re doing them. And so I think for me, what kind of brought these different things together, you know, the technology side, and it’s almost like a Venn diagram, and that’s what we say about data scientists, right? They’re part mathematician, they’re part computer scientists. And they’re part something else, some kind of domain that you’re interested in.

Blaine Mathieu:

Yes. Yes.

Angelo Kastroulis:

And I think that also applies to just this idea of data systems moving data along. So that’s kind of what gravitated me and I realize that there’s just not a lot of people doing this. And so it really appealed to me.

Blaine Mathieu:

And so what spurred you to actually form Ballista and maybe you can tell us more about what Ballista actually is.

Angelo Kastroulis:

Yeah, sure. Well, it’s one of those things. I think, where you see a need in the market, you see that computation is important to a lot of people, big data, and some of those technologies were starting to really come to fruition. When I was in grad school, my thesis was on actually on my research was also based on using AI inside of a database to try to make data systems faster. And so, you know, that was my thesis. I ended up getting a patent and it kind of made me start thinking of things differently. And then you brought it back to consulting. I was an independent for a long time and brought it back to consulting when you realize what you want to be when you grow up. And people will say, well how, we can’t solve this. There’s no system out there. And you say, well, I can solve this.

Blaine Mathieu:

Right.

Angelo Kastroulis:

Let’s build our own. And I think that’s kind of what spawned to build what is Ballista now I met three awesome partners and we decided to kind of put our forces together and form a company called Ballista.

Blaine Mathieu:

Mm-hmm (affirmative). And what does Ballista do again? Describe the kinds of projects you work on for clients.

Angelo Kastroulis:

Yeah. So Ballista tries to tackle the hardest problems in computer science. And so we’re a consulting firm, part consulting firm, and part VC, and I’ll explain what that part means. But on the consulting side, we’re software engineers, but we’re also data scientists, mathematicians, and we understand domains. And so we have a lot of experience in healthcare and IoT and FinTech. And so we’re trying to kind of bring all the things that we’re learning from these different areas together. For example, one of my biggest customers wanted to build a clinical decision support system. And so we brought our expertise and data systems together to build something to compute quickly. And it’s about massive scale quickly. It’s not just about doing something fast and running on a million machines. Yes. But can you do it on a hundred machines? Right. That’s a whole different problem.

Angelo Kastroulis:

So that’s kind of how we’ve kind of always evolved. Now, the other side of it is I think consulting services is important. It’s got to be part of your DNA. I think you and I were talking about this in the past that think of the life of a computer system or of a business as a genetic algorithm, right. And you are going to do the same things you always do generation after generation, but unless you insert some randomness every once in a while you go do some FinTech work or do some retail work or do some other things, you’re not going to inject any new knowledge into what you’re doing. That’s why we dive a little bit into things like quantum computing, which is most people be thinking that’s crazy. That’s not what you do, but you have to dive into it because the world is going to eventually evolve and you need to be aware and bring those kinds of solutions together. So that’s kind of what we do. We try to always be on the bleeding edge and bringing in, even cutting edge research, research papers from the universities, stuff hot off the press and trying to implement it, that kind of thing.

Blaine Mathieu:

We could go in so many different directions right now, but it sounds to me like the kind of projects you get involved with with clients are the hard ones, the complex ones, the ones where the answer is non obvious. You know, it’s not just, “oh, let’s bring a few developers together. We know exactly what to do. It’s just a matter of time and how many bodies you can put on it.” Instead, you’re solving some of the more complex challenges with technology and computer science today. Can you give us a couple, or an example maybe, of a hard problem you guys have tackled in the past?

Angelo Kastroulis:

Yeah, actually this is one we’re working on currently.

Blaine Mathieu:

Perfect.

Angelo Kastroulis:

It’s really interesting. So, so one problem is that the federal government is very interested in reducing the cost of healthcare. And there are many ways to go about that. One of the ways is just trying to understand if we take some guidelines, how are they followed? What is the outcome and do they affect outcomes for positive, but there also is a cost associated with them and does it affect cost? So that requires computation on a massive scale. These payers have hundreds of millions of patient lives that they’re insuring. And so to be able to report this to the federal government, there’s a lot of data that has to always be crunched. I mean, you’re talking about billions of pieces of data.

Blaine Mathieu:

Yes.

Angelo Kastroulis:

So we started tackling this problem with a few customers and looking at it in the sense of the other solutions out there, they were looking at it like, well, this takes eight hours to execute this computation. And so we said, okay, it shouldn’t take eight hours. What should it take? I said, five minutes. So we built systems that actually compute in 20 seconds. Things that just everybody thinks this is impossible. That’s because we’re not applying the most cutting-edge thought process. We’re trying to kind of do it in a conventional way. And that just doesn’t work.

Blaine Mathieu:

Yeah, it’s not just about applying brute force to a project, but applying intelligence to it. Is it about the application literally applying AI and machine learning to solve these challenges? Is that generally the solution or not always?

Angelo Kastroulis:

Not always. It’s about, I think looking at the value chain and then saying, well, if I change things a little bit, right. Maybe like, if I look at it holistically and I rework the way we think about it, AI has a place. We definitely have expertise in AI in optimization, which we’ve proven that it can beat a human optimizing any day and you can watch it all the time. So yes, that certainly has its place, but sometimes nobody asks the question, “do I even need that? Why does that even exist?” And can we do this a completely different way that eliminates these links in a chain so that we’re more direct to the value, right? The chain is shorter and we can get directly to the value.

Blaine Mathieu:

Really interesting. I want to circle back to the VC thing after. I don’t want to lose that that you brought up, but I want to keep our current thread going for another few minutes. So what really excites you about what’s going on in technology these days? What do you think are the next or the current and maybe next interesting areas of development advancement we’re going to see?

Angelo Kastroulis:

I think there are a lot of interesting things coming. I think AI, thinking about how AI is being used. I think it’s still kind of in the hype cycle a little bit, and I think a lot of organizations don’t know what it is and you see a lot of companies throw AI out there, like we do all this AI and it’s not, you’ll see that.

Blaine Mathieu:

Yep.

Angelo Kastroulis:

But I think we’re maybe coming to the point where we’re realizing that it’s not all about moonshots, that we can use AI to get really huge gains by just eliminating some small problems. So there’s low risk to eliminating, but high reward. I think that is something that excites me about this. This is kind of the thing that we all… It’s intuition that you’d say “of course that’s what we should have done”, but we always fixate on moonshots.

Angelo Kastroulis:

So I think that’s an interesting way, place that this could go. I do feel edge computing is going to be revolutionary in the way that the world views their problems. It is one of those things when I talk about changing the value chain and thinking about it in terms of “well, why not? Why are we even doing that? How can we eliminate this problem?” And I’ll give you an example. For example, if you’re trying to compute data, and this is kind of how we have seen the cloud forever, throw as much as you can up to the cloud, and then we will compute. Throw it into a lake, we’ll compute it later because I don’t know what insights I’m going to get. Well, the lake ends up growing bigger and bigger and bigger. We create new problems for ourselves. We have to clean it and do all kinds of other things. And then all we require now is a more powerful cloud and the cloud keeps growing and it turns on its head, what we wanted to accomplish. And that was let’s reduce costs by putting it in the cloud. Now you go, well, the cloud keeps getting so big that I’m doing so much work in it.

Blaine Mathieu:

Right.

Angelo Kastroulis:

Yeah. And I have lots of thoughts on that. We talk about that a lot on the podcast, this idea of just constantly keep growing it. Well, the question we should be asking is there just a different way to do it? That’s how you’re going to get exponential gains. You’re not going to micro optimize your way to a better cloud. I think that is a big thing that we’re looking. And of course, quantum computing is the other thing that’s been on my mind a lot lately. You know, how can we, again, quantum, everybody thinks of it in terms of moonshots, but I think that there’s starting to emerge as a technology that we can actually start using for optimization problems now that can have some return.

Blaine Mathieu:

So you think actual real world applications of quantum computing could be in the next few years, not few decades away?

Angelo Kastroulis:

I do think so. When you think about things like annealing, they’re very good at optimization, right? Solving for a best path or a best lowest cost, right. They’re really good at that in a lot of ways, many problems could be reduced to that. Now the problem with the moonshot way of things is not every problem will fit in that space. So it becomes very hard to mathematically look at a problem in a quantum way, but there are a lot of problems that I think we can reformulate and reframe. Now in the future, I don’t know that we’ll need to reframe all of our problems, like that will be, it will grow sufficiently that okay, that’s the 10 year horizon. But I think today we could start seeing some problems that would definitely benefit.

Blaine Mathieu:

So you brought up AI machine learning, edge computing. What about IoT? I often see those as being back to the Venn diagram, being a way that these three things can be, or must be very closely related to each other. Thoughts on the evolution and current phase and future of IOT technologies in general?

Angelo Kastroulis:

Yeah. I’m glad you mentioned IoT. It’s one of those things that has found itself into so many areas that didn’t exist before. I’ll give you an example, wearable devices for fitness, for example. Everybody always thought, well, my watch is capturing my blood pressure and it’s got my heart rate. Why do I need to go get another device or go to a doctor to make an appointment? And it was there because a doctor’s responsible for the reading. He can’t trust your watch. He doesn’t know that you’re using it right or that it’s on you properly or whatever. COVID changed it. So now we bought a necessity. We’re forced to look at these devices as first-class citizens. So that’s just an example of how I see IoT now. It’s being elevated to the point where people say, “wow, these things are really useful and everything was okay. We relied on it and everything’s still okay. And it provides so much rich data that I can actually, it can be actionable for me.”

Blaine Mathieu:

Yeah.

Angelo Kastroulis:

And I think that’s what we’re seeing now, that’s in healthcare who has been, I think, very resistant.

Blaine Mathieu:

Mm-hmm.

Angelo Kastroulis:

Right. “Is that FDA approved? We have can’t use that device.” But they were very resistant and now they’re becoming much more open to it, granted out of necessity, but it’s proving a point. I think other industries, which leads to the reason why I believe you have to be involved in various industries. Other industries have been understanding the value of IOT for a really long time. So they were using these devices to try to understand what’s going on at the edge. Bringing that knowledge over to other industries, I think you’re going to see an explosion of IoT, which leads itself back to my original problem that’s going to generate more data. And if we throw it up to the cloud, what have you just done to the cloud?

Blaine Mathieu:

Exactly. Very interesting perspective. And I couldn’t agree more. So let’s change gears. Tell me a little bit about the VC side. So you’re obviously not only consulting with companies helping to use NextGen technologies to create interesting solutions, non-trivial solutions to complex problems, but it sounds like you’re doing some investing in this area as well.

Angelo Kastroulis:

Yeah. So I would say the best way to describe it is that technology cannot live in a vacuum. So you can’t think of it in terms of an organization needs to modernize. So they’ll come to us and say, “how do I modernize my stack?” The real gain is not just from, “I have this project I’ve already designed it”, or “can you help me design it? This is what I want to do.” The real value is one level up where we say “this is the business objective I’m trying to achieve”, and then you evolve in someone in that conversation so they can say, “well, let’s reexamine your value chain.” And that means that technology, there’s an investment in it. And I think people understand when they go to modernization, they’re thinking “I have these aging systems, I need to replace them.”

Angelo Kastroulis:

One piece of advice I’d have is don’t think of the world like that. It’s a constant investment you have to make, because if you’re going to let the system age and then reinvest, you’re going to be so afraid about what’s going to break that you’re going to try to lift it and fit it, and all you’ve done is displaced your problem.

Blaine Mathieu:

Yeah.

Angelo Kastroulis:

So I think that you can’t think about technology in a vacuum. You have to have a sense of business process. You have to have funding to do it. And then of course you also have to think about the sales side of it, right? How do you partner, who should I partner with to be able to deliver my product? So I think that there are those aspects that, we have some unique skills in that area. And that’s why I thought this makes a lot of sense for us to be involved.

Angelo Kastroulis:

And the other side of it is if I were an investor and someone was asking me for technology, money for technology, maybe I’m a little biased because I’m technical, but I would look at it and say, “do I have the confidence that you can pull it off?” It’s so much easier to get some money and then say, well, the money comes with, we’re also going to bring the expertise and we’re going to do it right. And because I have a stake in it, I want to do it right, and I want to do it as quick as possible to get you to market and get out. And so that’s, we’re not here to be part of your journey forever. We want you to get on your feet and get going. That’s the different than just purely consulting. Right?

Blaine Mathieu:

Totally. So since some of this audience likely will be startup CEOs, what kind of companies do you think should be maybe approaching you? What’s at a high level? What are your criteria for what you’re looking for to invest in?

Angelo Kastroulis:

It’s hard to say the criteria we’re looking for. I can tell you what we’ve found to be successful. Organizations that have started in and have some revenue and have figured out that they’ve found their area in the market. You’re not necessarily coming to us and saying, “I got this great idea, is there a market for it?” I think the better approach is to say, “yeah, we have this idea. However, these are the things, the problems we’re seeing with, what we’re running into as an organization. And it might be, we need some funding to grow to the next level. We might, we need some partnership, advice or business strategy or find a CEO, but it could also be, we need to modernize the stack because we can’t get to the next level of growth. We can’t cross the chasm.” If you’re in that area, trying to cross the chasm, that’s probably the best fit.

Blaine Mathieu:

Makes perfect sense. Given your background and focus and all the stuff we talked about, the consulting side of your business. So, perfect. Maybe tell us a little bit more about the “Counting Sand” podcast. What was the thought process behind it? As I mentioned earlier, I’ve listened to most of them now. Really different take on technology and related areas. So what’s going on there?

Angelo Kastroulis:

It’s really, it’s been a great journey, first of all, but you know, what made me decide to even do a podcast? You know, I went through this, maybe everybody does this, I don’t know. I hope every leader does this in any organization. Sit down and think about, do some exercises to determine your core values. And when you do, think about what you’re good at and what you’re not good at in terms of those things. And it’s funny, I sat down and I was doing this exercise and then I stepped away from my life for a moment and said, “what should I be doing if this, given this?” And I chuckled, and I said, “oh I should be a podcaster.” And I said, “but that’s ridiculous”, and I walked away. And then about a month later, I thought, “why is it ridiculous? I shouldn’t be thinking like that.”

Angelo Kastroulis:

And so the idea, I started doing some tests, asked some friends, and kind of tried to put together the right format. I think the thing that I really love about the podcast is you’ll never hear me pitch my company in it. In fact, I only mention it at the end when I say “you can follow me on whatever.” I don’t, because it is not about that. It’s about giving a little bit of knowledge back to the community. And so I think that’s what makes it kind of cool. It’s this mix between research and application. And I’m trying to kind of get everyone to understand, if you don’t be afraid of research, it’s not crazy. The giants have done some great things, you can build stuff on their backs and then we can apply it. It can be done. And I think that’s basically the thing we’re trying to kind of get going.

Blaine Mathieu:

Well, I think you’re accomplishing it, and I highly recommend the folks that listen to this to check it out if they’re not already doing it because I’ve gotten a lot out of it in the last few months since I’ve been listening. Thank you for doing that. So maybe to wrap it up. Yeah. Thank you. Maybe to wrap it up. So any predictions, I guess, or thoughts for 2022 and beyond? You’ve already shared a few, but what have you got?

Angelo Kastroulis:

Yeah. I think if you don’t mind, I’m going to rehash a little bit or go back on in a little more depth of the one you talked about that we talked about. Edge computing. There’s a few drivers here that have been happening for a few decades and they’re catching up with us. And I think especially this year, when you think about, for the longest time, compute has been one of those things that, Moore’s law, and we know it’s coming to an end. Why? Because now they’re talking about the one nanometer process.

Blaine Mathieu:

Right.

Angelo Kastroulis:

You can’t go to zero. So there’s a one nanometer process. That also means that clock speeds have reached their max. So what do we do? We put more cores on a die and then what do we do? We put more machines in a data center.

Angelo Kastroulis:

There’s only so many machines you’re going to be able to pack, right? We can’t make it any smaller. So there’s only so many machines you can pack into these data centers. So you have to think about the world a little differently.

And I think if we, the other implication here is when you’re growing this data center, and I think we’ll talk about this on a podcast, I’m sure, but as you grow this data center, you’re using more and more energy. It’s more and more cost, right? What happens is the cost per CPU now is reversing. And we’ve got to stop that trend. So yes, quantum shifts will change the world in the future, but now you have to think about how do we just eliminate that problem? Why don’t we just not compute inside the big cloud? I don’t mean entirely. I just mean, can we push that closer to the source so that the source is giving us a little cleaner data? And when you think about the source, that means the edge, what’s the edge. It means everything. It means your phone, it means a boat. It means an airplane. It means an IoT device.

Blaine Mathieu:

A Windmill.

Angelo Kastroulis:

Yes. A windmill. Yes, exactly. So if you can push computations down there, you’ve now created orders of magnitude improvement in the cloud. And that’s what I mean, those are not, those are. To get a qualitative change, you have to rethink the value chain. To get a quantitative change, you can micro-optimize. This is not a micro-optimization problem. We need to take it qualitatively to another level.

Blaine Mathieu:

Agreed. A hundred percent, obviously.

Angelo Kastroulis:

I think what’ll be. 2022. I think 2022 is going to be a big year for that.

Blaine Mathieu:

Okay. Yeah. I believe so, too. And it’s interesting, you brought this up because we didn’t talk about this beforehand. I didn’t know what you were going to say, but obviously I fully concur with your assessment. And I think right now we’ve only just begun scratching the surface of what’s possible with processing data closer to the edge, all the way to the far edge and the device edge. We’re just, just beginning to do that. And it will be I think, a pretty radical in a positive way, transformation to how business are able to use compute power. No doubt about it.

Angelo Kastroulis:

I’m going to add one more thing to that. Blaine, I think you’re right. And what you said kind of reminded me of one of the things I care about. AI, right? I think edge computing, we couldn’t have done this. Cause a logical question would be why didn’t we do this 10 years ago?

Blaine Mathieu:

Yeah.

Angelo Kastroulis:

Well, we couldn’t have done it because we were not at the point where the cloud was a problem. And we weren’t at the point where we could have compute feasibly be inside. Like an iPhone is more powerful than the computer that landed the Apollo 11. Yeah. Right.

Blaine Mathieu:

That’s amazing.

Angelo Kastroulis:

You didn’t have that. But now what is really interesting is the next generation of issues is going to be, we have lots and lots of edge nodes. What do you do? How do you optimize them? Now, AI is going to be involved in my opinion. And we say, here’s how you optimize it. You optimize it by making AI handle the millions and billions of nodes that are out there.

Blaine Mathieu:

Yeah. Intelligent optimization of that edge network fundamentally. Right. Okay. Well I think if there’s anybody that has the ability and the team to do that, I think it’s you and your team. So let’s make it happen. I’d love to help you on that.

Angelo Kastroulis:

Absolutely. Thanks, Blaine. I really appreciate that.

Blaine Mathieu:

You bet. Well, I think that wraps it. Again, Angelo, thank you so much for joining us today. It was a great conversation, as I knew it would be.

Angelo Kastroulis:

Of course. I’m happy to be here. Thank you for having me.

Blaine Mathieu:

Yep. And for those that are interested in hearing more of Angelo’s thoughts on these and similar topics, you can connect with him on LinkedIn, of course. And on Twitter, Twitter @AngeloKastr, K A S T R. And also check out ballista.com, two Ls, can’t miss it. And of course you can reach out to me anytime on LinkedIn or at edge@pratexo.com. Thanks again, Angelo.

Angelo Kastroulis:

My pleasure.