ANIRBAN MAHANTI | From 7investing

Computer networking, data science, and machine learning are at the heart of enterprise software. Anirban Mahanti has serious form in the technology space. Just check out his bio below. This is a far-reaching discussion on technology and looking for opportunities from innovation.

Anirban Mahanti is a lead advisor for 7investing. Before 7Investing, Anirban spent 5-plus years at The Motley Fool in various roles, including as the Director of Research and the founding lead advisor of the stock-picking newsletter Extreme Opportunities.

Anirban is an Information Technology expert by training. Before transitioning to becoming a full-time investor in 2015, Anirban held various roles at NICTA (now known as Data61 following the organization’s merger with CSIRO), a leading Australian Information Communications Technology research center. Before NICTA, Anirban was an Assistant Professor at the University of Calgary in Canada and then at the Indian Institute of Technology Delhi.

In his research career, Anirban has invented new technologies, co-authored over 70 peer-reviewed research papers, supervised post-doctoral fellows, Ph.D. and MSc students, and consulted for the industry. Anirban’s technical expertise is at the intersection of computer networking, data science, and machine learning.

Given Anirban’s background, it should be no surprise that his favorite investing ideas are at the bleeding edge of innovation, especially in the enterprise software space. He’s a firm believer in following top-flight research, which often is years ahead in identifying where the puck will be in the future. For context, Anirban’s doctoral dissertation was on scalable video streaming systems, back in 2003, well before the first large-scale streaming systems went online. His team was also one of the first to publish a study on YouTube traffic characteristics back in 2006. And he was applying machine learning (ML) and artificial intelligence (AI) to computer networking problems (SPAM detection, network traffic identification) in 2005, about a decade before ML and AI caught the fancy of mainstream media.

Anirban received a Ph.D. and MSc in Computer Science from the University of Saskatchewan, Canada, and a BE in Computer Science and Engineering from Birla Institute of Technology, India. He lives in Sydney, Australia, with his wife and daughter, although he considers himself a global citizen with living and work experience spanning continents.

TRANSCRIPT FOLLOWS AFTER THIS BRIEF MESSAGE

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EPISODE TRANSCRIPT

Chloe (1s):

Stocks for Beginners, Phil Muscatello and Fin Pods are authorized reps of Money Sherpa. The information in this podcast is general in nature and doesn't take into account your personal situation.

Anirban (13s):

That's, so one of the things that I've discovered is that you know, some of the great companies, they tend to control more and more of their tech stack and that's almost an underrated advantage. Nobody really thinks of the advantages Apple's chip design, for example, gives them, but there nobody I mean like you know that's not a general talk, right? People are not going you know they got great skills in silicon, but it's the great skills in silicon, which is driving the Mac adoption for example. Or You know these crazy things that they do with Vision Pro. Why does nobody else have the input that you can get using your hands and eyes?

Anirban (52s):

It's again, crazy new input design. That's because you can marry the hardware with the software, with the human computer interaction. That's just a crazy level of primary technology invention capability.

Phil (1m 4s):

Hi, and welcome back to Stocks for Beginners. I'm Phil Muscatello. Computer networking Data science and machine learning are at the heart of enterprise software. I'm happy today to be able to talk to someone who has serious form in this space. Hello and Anirban How are you Phil? Very good. And you are also a major Tesla fan as well, aren't you?

Anirban (1m 23s):

I am a Tesla fan, yeah, I've got You know Tesla car and I've got Tesla batteries and got some Tesla shares as well. So yes, I am, I'm You know. I've been on the Tesla bandwagon since 2013.

Phil (1m 36s):

Anirban Mahanti is a lead advisor for 7investing before 7investing. Anirban spent over five years at the Motley Fool. Most recently as their director of research at the Motley Fool. He was also the founding lead advisor of the stock picking newsletter. Extreme Opportunities. He has an academic record that's way too long to go into here, but you can browse at your leisure in the blog post. So coming from academia to investing, how did that go? What was that like? And it's I mean it's a pretty big transition for many people to take that kind of step. It

Anirban (2m 8s):

Is, but You know, like I knew when, when I was in academia or doing research or doing applied industrial research sort of thing, still talking with companies, consulting with companies, You know, going to conferences, seeing what companies and academic folks are doing at the same time also investing, right? So I, I was across investing for a long time and then sort an opportunity presented and I thought, well, it might be cool to do this sort of You know on a full-time basis. You know I've been in academia for a long time, so it sort of meant like, well it's You know, a change of pace, a change of You, know a new challenge. And it was great. It's like any change that is, I guess 360 turn from what you are normally doing, it is definitely difficult in the beginning, right?

Anirban (2m 56s):

Because You know what You know as as an investor. You know when you're investing your money because you're typically, if you're investing when you're following somebody else's advice or You know you're looking online, You know you're following the websites. You know CNBC and whatnot, or reading the Wall Street Journal, there's some input that you're getting there, but You know, You know suffering of reference changes when you become part and parcel of that crowd and you have to now produce content that makes sense and you have to produce information that might be useful to whoever is gonna read it and things like that. So it was, it was a big transition and you sort of started at the bottom of the ladder, so to speak. You know, so I started as an analyst and sort of worked my way up over time.

Anirban (3m 36s):

But yeah, it was, it was an interesting journey and definitely enjoyed it. And then it was a different change pace for me. So yeah, I still dabble a little bit. You know I maintained or at least started dabbling back in research, working with students at, at universities and working with colleagues I've known. So it's just to keep in touch with what's going on. So yeah, I try to dabble in, to put into the spectrum.

Phil (3m 59s):

What was it like when you first made the transition? Was there anything about the way that you were thinking about companies that changed from when you were looking at them from an academic point of view to an investing point of view?

Anirban (4m 12s):

So when you were an academic, most of the time you don't really think about, like as an academic, you're not thinking about investments per se. You think about technologies and you're thinking about ideas and you think about, basically one of the big things increasing in the computing world is novel ideas or basically new ideas, right? That You know, sort of You know or solving a problem. So you, you start think from the point of view, okay, You know, here's the problem and what's a solution to it or what's a better solution, a better way of doing the same thing You know, it could be something like, no, today you're streaming videos. Can you make it more efficient? As an example, You know you look at what generative AI is doing today, for example, You know, can you use generative You know people wanna compose emails. That's a normal task.

Anirban (4m 52s):

People want to write blogs. That's a normal task. How can you make that task better, simpler, more engaging, more entertaining, more informative, right? That's sort You know where the technology comes. So yeah, so you're the the, the technologies are working on some of the underlying tools and methods and, and things like that and trying to sort of advance the, my biggest takeaway sort of initially though was you, when I sort of started looking as sort of the full-time investor, my biggest takeaway was there's sort of a gap between where the technology is and when sort of it hits mainstream. There's always a lag and sometimes you can exploit that lag if you understand, but the end, here's the thing, academics and researchers work on hundreds of different things all the time.

Anirban (5m 36s):

Not everything becomes practical, right? And, and this specific example to give, for example, You know my PhD dissertation was on video streaming technologies. The surface technologies I invented or worked on, the greater they were You know even they were probably to be the best you could use, but they relied on something called the broadcast cu on the internet called multicast. Now multicast never really became a a reality, but streaming did become a reality in different ways, right? So sometimes the best technology, the best ideas may not necessarily get commercialized. That's the thing to realize. But you can sort of see, try that are materializing and you can sort of try to capitalize some of them.

Anirban (6m 17s):

That's sort of the, i I think the edge from knowing the tech that you can get. But you have to realize that not, again, not every company in that space, it's very hard to sort of go in and look at from the time of say You know streaming videos. If you think about it, there's so many different companies, right? And if you invest in 20 a hundred of them, the biggest success is the one that people know of. People don't remember all the others that didn't actually succeed in a big way. So that's something to again, bear in mind, right? Another example would be You know if you think about what's happening, for example, today in this generative AI space, right? One of the core pieces of the work, which is known as Attention, right? Attention. There was a paper called Attention Is Already that came from Google, right?

Anirban (7m 1s):

So Google is left behind, so to speak, in many ways because the, the tech for the generative modules and so on was brought mainstream by a different company, right? Open ai. So I mean those are sort of the things, but at least You know people who are in the field would rehab realized, okay, this is a transformative technology then You know. So they came up with a tension on your need. They came up with Transformers, You know in say in in Google DeepMind and things like that. And then you'd sort of follow the research and you'd say, okay, there's You know Meta is doing this and they are doing that. And so, so you'd sort of see a trend and you'd realize, okay, maybe this trend is going to shape in some way where computing is headed.

Anirban (7m 41s):

But the, I think the hardest thing is that then to turn that into an investing idea, I think it's very hard. It's hard by definition because You know, again, there'll be so many different startups in that space. Which one do you wanna back?

Phil (7m 52s):

And that's part of the thing about investing is it's so easy to get excited by stories, especially when you're first beginning. You think, oh this is such a great idea, it's gotta make a lot of money, but this is actually something that you've gotta be really careful about.

Anirban (8m 7s):

That is true. Yeah. So stories are, I I mean stories. I at least told the, the stories tend to move Stocks as well, right? And, and a good story is important. I guess a part of the job as CEO is to be a good storyteller because being a good storyteller has multiple advantages. One of them being You know it works as a recruiting tool, works as a sales tool, it works for pitching to raise money, right? So a, a good CEO or You know leader at a company has to be a good storyteller. It's kinda important. It is important. At the same time you have to sort of distinguish yourself directly said that You know which stories I don know result in something big. There is, I, I don't think there's, see the the thing there is that there's no, at least in my view, there's no formula to identify that story is good because no one has a hundred percent track record of identifying that story is good.

Anirban (8m 59s):

Sometimes you can see I guess the way this You know my sort of rough model would be I see what I like then I look around me what people like, right? So sort the Peter Lynch sort of model where if people are using something there's, there must be some interest, there's some reason behind the interest and You know you look at the You know and then you can decide to whether you wanna bracket yourself in sort of the early adopter part or the late adopter or You know the early majority, whichever You know in the itself, you think with the NER hype cycle or or adoption cycles, then you sort of have to figure out, well where is it inside the adoption curve? So I also look at You know, is it technology interesting to me? Does it solve a problem?

Anirban (9m 40s):

And You know, do I think other people are using it are likely to use it? Do they think it's interesting? And that's sort that guides what I think it's gonna happen. But Are, they gonna example would be there a lot of counter examples here. So counter example would be Apple makes a smartphone, the iPhone one could say that well all the other phone manufacturers of that time, be it Motorola and Nokia and everybody else, well they should also be able to transition and build this and in fact they should be very good at it because they're the ones who own the market in terms of the cell phones, right? And they have the distribution for it. So then the question becomes why wouldn't that happen and why do you think Apple would continue? That's where I think You know if you have an answer for that, you can use that as a basis for making your, I guess your thesis.

Anirban (10m 25s):

And in most cases I think that the difficulty for I guess sort of old to new is, and the new touches so many different parts of the technology stack so to speak, right? So an iPhone is not just manufacturing it and making it look good, it touches design like manufacturing. So you now need a design aspect. You need to have material science, engineering, you need to know human computer interaction and just the design aspects of it. How does one interact with it then, okay, to make all of this thing work, you'll need to have skills in the software, but then not everything's about software because the software actually is tightly integrated with the hardware.

Anirban (11m 7s):

So you need to have underlying hardware skills. You know what would you put on device, what would you wanna put off device, what's gonna be on the secure enclave? Sometimes what you'd find is that, oh, there is no hardware tech that I can buy off the shelf. So then you probably have to design it, which is why for example, apple as a company designs its own chips, right? It just allows them to have their own roadmap and not be dependent on somebody else's roadmap when it comes to, and they You know they're the the largest chip designer in the world. So those skills that sort of go from design to, or the hardware design to actually manufacturing it, to having You know the OS layer. Then to have the chip layer and then to have the control for the chips and You know the software that writes in assembly code for example, or machine language code, all of that is basically the tech stack, right?

Anirban (11m 59s):

And, and a company that doesn't have all of those skills or those companies that typically in, in the old world, the the usual method of doing approach to doing these things would be to say, You know what? I am good at putting together this, but I need That that and that. So I'm gonna just outsource those two experts. Those experts. Then in turn actually service. So you might find that You know there's a company out maybe in Germany that is also servicing Nokia and Motorola and Samsung and somebody, everybody else. So everybody else basically gets the same thing and the IP is probably owned by this company out in Germany. And if you wanna make a change, well the change has to be made by the IP owner. That's the usual model versus the this vertical integration where you own more and more and more of the key components of the tech.

Anirban (12m 43s):

That's a different model. And I think a lot of competitive advantages come from ordering sort of the key technology elements. I think That that that's so far of the things that I've discovered is that You know some of the, some of the great companies, they tend to control more and more of their tech stack and that's almost an underrated advantage. Nobody really thinks of the advantages. Apple's chip design for example gives the, but nobody I mean like You know that's not a general talk, right? People are not going, oh, You know they got great skills in silicon, but it's the great skills in silicon, which is driving the Mac adoption for example. Or You know these crazy things that they do with Vision Pro, right?

Anirban (13m 25s):

Why does nobody else have the input that you can get using your hands and eyes? It's again, crazy new input design. That's because you can marry the hardware with the software, with the human computer interaction. That's just a crazy level of primary technology invention capability.

Phil (13m 43s):

You made a little reference in that answer to the hype cycle. How do you see the hype cycle? Have you got a description of the hype cycle? I think it'd be great for beginner investors to be aware that there is a cycle in this space.

Anirban (13m 55s):

Yeah, like there's, there's a definition. I don't have You know. So there's a, Gartner has a hype cycle definition basically has this form. The way we think about it is that You know things start low, then it becomes You know the sort of the, the noise becomes exponentially increasing to heat sort the peak and then sort of it starts fading out and fading out and then you sort You know you hit this phase of You know despondency, nobody cares about that tech anymore. But then some real stuff eventually starts again, You know from the depth of despair start tick. That's the problem I have with, it's very difficult. Like I think not everything fits in that sort of pattern is is like you should, like for example, You know you could, I remember the 3D printing trace, there was a time when everybody thought that from shoes to you know tables to whatever else, we're just gonna 3D print it.

Anirban (14m 50s):

We all have custom stuff and You know it was a high pipe pipe then sort of oh, it fizzled out, fizzled, fizzled, fizzled out, completely gone. But the next part didn't really happen where it didn't really take off I mean if you wanted to say that You know 3D printing is used in many different parts, but it's not mainstream. So the hype site also of made in the hype made look like it's not the mainstream, right? Same thing with the AI and the general technologies that we're talking about now, Are, they gonna more actually to go because You know there's a lot of consumer touchpoints, but is it don don't know.

Phil (15m 25s):

So getting back to AI and Google's role in it, I heard on another podcast the, the other day that someone was analyzing it and his thesis was that while Google developed the technology initially they held back on it because it, they felt, well they felt that Google felt that it was a direct competitor to their business model of search. Do you see it that way?

Anirban (15m 50s):

Well it's so hard to know, like, so you can make a case for it in the sense that if the search results result in a definitive answer that sort of takes away the benefit of having links, right? Or their You know applicable links

Phil (16m 4s):

And the, and the ad model as well

Anirban (16m 6s):

And they add therefore the ad model, right? But here's the thing, it could be certain elements of their business, You know, or certain divisions or certain parts of the business You know look at this and say, oh You know it's gonna disrupt our, but most good companies like it disrupt our existing business work. But most companies are firmly aware that especially the tech land, that if you don't disrupt yourself or your business model, somebody else is gonna disrupt your business model, right? So an example I I I might be speculating here. First of all, I think Apple is slowly but steadily disrupting the app store model right now. Because if you look at it, the next generation devices like such as the watch has an attitude it's not really very successful on, in fact, all the things that are really successful are con are primarily controlled by Apple, right?

Anirban (16m 53s):

And it makes we see what type of success they're looking to do with the next generations of the spatial comput as an example. But if we sort of think about Google's case, I, again, I can make an argument either way, but if you look at what typical use case would be for generative ai, like from a search point of view, if, if I wanna know You know, can you please explain to me what quantum mechanics means or can you please explain to me what conduction is? Well even today, if you, if you put that query into Google, it basically would surface either a Wikipedia page as You know and part of it and then ask you to click through it, right?

Anirban (17m 34s):

Actually, there's no ad revenue there. Most of the ad revenue though, like you think comes from things like what, like You know people are searching, can I get a sneaker of this type or You know I'm looking for that air Jordan that looks like this. Can I get a ticket for Taylor Swift concept You know where and all of those things. And then those result and clicks, I guess there's a simple way for them to embed You know the generative ai like something like Google's bar for example with such, because you could, for things that are very straightforward, you want to give definitive answers or you wanna be co You know corresponding with people you correspond and you give them the answer for stuff that is elsewhere, you can definitely have the sidebar of ads or you could even make ads somehow flow through the conversation that you're having with the ads.

Anirban (18m 22s):

I think it's possible. Personally, my view is that don don't think they thought it's a trick to their business model. I think they can very much have a business model around using generative AI and have search and actually leverage generative AI in say Google work suite, right? It's actually, they, they've now now made You know if you, if you had a personal Google user, you can enable the degenerative AI on Google Docs, right? So you can just You know select and say rephrase this, do that and, and all of those things. So they are making it available. I, I think gonna be extremely powerful and for businesses you can charge for that. I don't think that's the reason my primary thinking is that I, I think they were conservative because there's a lot of what's known as hallucination, right?

Anirban (19m 9s):

So hallucination basically rules the AI thinks it knows, but actually doesn't know. Well that's You know basically makes up rubbish. For example, there's an a, there's been a criminal trial where the judge has found that all the citations and stuff was made up because a person has been practicing law for a long time, basically just relied on generative AI technology to write stuff up and didn't bother to crosscheck with a reliable database as to whether or not those cases actually do exist. And so I think they knew that the, the problem of hallucination, we also knew about the problem that there's a lot of biases that can be built into because of generative AI and depends on who is training these.

Anirban (19m 52s):

And You know there's reinforcement learning in the sense that You know you have human, you have human feedback involved. So you type something You know you ask for something, it gives you something response, those responses and are then graded by human graders, right? So how do you take care of the biases that they are there among those humans? And or you can ask the the response, the the person who receives the response to actually rate it. How do you actually take that? All of those things are I think, raise interesting questions. So I think Google just probably thought, and this is don don't have the way of knowing, but my guess is that there are others just a bit premature and you'll get more bang for the buck by readying it, right?

Anirban (20m 33s):

And, but I think just open AI and Microsoft forced their hand and, and therefore they had to just rush through it and be part and parcel of this You know gig. But that's my interpretation. So part

Phil (20m 47s):

And parcel of investing in the technology sector is that a lot of these companies that are starting up have a lot of potential to profit, but sometimes not even any revenue yet. How do you value companies in this space and at That that end if, if you really, if you wanna get in on the bleeding edge of where the technology is heading,

Anirban (21m 5s):

Companies that are not profitable I think are typically so like You know, so I, I collaborate with a colleague who has got a small early stage venture venture fund and You know, so they just sort of bat ideas and talk about ideas and then and You know and some of those malians will look nose bleed at early stage companies, but, but You know the thing, those companies that have got revenue, they might be growing month on month at least in the early stages. You know revenue might be growing a hundred percent plus month over month, right? Not year over year. So they would have some crazy multiple but You know the crazy multiple would be off like You know a hundred thousand dollars revenue or something like that. So small amount of dollars if you don't have any revenue, like really as a concept that's really, really early stage BC land I mean it's a bit of dark throwing going on users.

Anirban (21m 51s):

There's a lot of, I think You know from experience. You, you would have a feel of this sort of work. So you You know and, and the other thing that I think people do not realize but or maybe do realize, I don't know a lot of companies that are successful today in the public markets, if you couldn't raise the history back in time as startups, they probably started as something else. They probably You know they did a pivot at some point. You know I was reading about Roku for example, and, and the the Roku founder and then a startup with a team You know one of our Tivo equivalent companies, Tivo competitors You know that sold it to someone worked for a few years as, or a few months, I'm not sure exactly at Netflix as a VP of tech, I think looking at building a networks play then came and did this and then You know initially was focused on something else and did the pivot.

Anirban (22m 44s):

So pivot is quite common, right? So things change and, and we also just have a survival bias that we, we see those companies that have pivoted, pivot and pivoted and that's survived and become something else for you realize. So there's a lot of myth, but I think if you spoke to VC people, a lot of them basically invest their funds on how they feel about the founders, right? So they, they're investing in people and ideas both and the people that ideas are very tightly coupled. So I think that's what a lot of that early stage investing is all about is people and ideas investing in people that you like, you feel that they have You know they're willing to take on the challenge and and and build a team. So I think that's what happened in the very early stages.

Phil (23m 27s):

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Phil (24m 19s):

Okay, well let's start looking into some companies that you've written about recently and they're not startup, they're pretty well established companies. The first one is Dlocal and the NASDAQ code is DLO. Tell us about this company. What does it do? What do you find interesting about it?

Anirban (24m 36s):

So this one, it's a controversial company in the sense that it's a pretty active short going on. So short means investors have borrowed shares from other people that own a piece of that business and actually sold it. And the thesis is that the, the stock prices are go down so they can buy when the price is low and give it back, give the shares back to people that they boarded from. There's an active short from Marty Waters that claims that there's a fraud here. So why would you like a business that somebody thinks is a fraud? Well, so at a high level this is, this is a business out of Uruguay so Latin American business that is focused on payments, right?

Anirban (25m 20s):

And it does, so two things. It allows payments to be processed for a company or an enterprise and it also processes payments for an enterprise. So That, that sounds a bit confusing, but the way I would, I would explain that is suppose you are Amazon or suppose you're Nike and you sell stuff online in different countries, you want to be able to take payments from customers. So you basically need a payment processor and if you want a payment processor that works really well in emerging economies, then you could go to Dlocal. So that's one example. But also could be that Nike has say a small presence in some country that's CI Chile and just making it up.

Anirban (26m 6s):

These, these are just examples and it wants to pay people that work for it. Our contractors for example, like for Nike, how do you pay You know you could set up your bank account and do everything or you could work with the local which could do the payments for you. So, so one of the things that's called paying out to your contractors and employees and things like that. So you for example, a ride sharing business, you have you're, you are receiving money from customers but you're also paying out to people who are actually driving. So that's payout to people who are contractors pay in is when you are receiving payments from customers. So what I like about it is I see now can I caveat this because You know up in many that in front in the financial services industry and it sometimes it's very difficult to actually support it.

Anirban (26m 51s):

But these companies, you look, look at the list of logos that it, it services that's some these, these are companies are the best of the best. You know Salesforce, Meta, I believe Nike, Spotify, these guys are using them. So I see the enlist of customers that are A grade B, I see them serving those markets that are generally not served by the big payments companies like Adden or PayPal and things like that. And C I think if you look at sort of the demographic movement, think about countries like India and so on, these have quite a lot of young population and a large population, right?

Anirban (27m 31s):

So a lot of emerging, emerging economies together have a huge population that're all trying to sort of You know, become You know rise from lower middle class, upper upper middle class and You know raise a standard living. So there's a huge opportunity there, right? So servicing that market is an interesting opportunity to sort of get exposure there and

Phil (27m 50s):

Presumably in some of these economies, their local currency and their local economies aren't able to support this kind of entrepreneurial activity of these young people as well and inflation that might get in the way of whatever they're transacting in.

Anirban (28m 4s):

Yeah, so often I think the biggest issue is that the payments rail in emerging markets are completely different in many ways from the payment rails in developed markets. Which means that the companies that work in developed markets are not necessarily in the best position to serve the developing markets. An example might be like credit cards for example are very common in You know US, Europe and Australia, New Zealand, the credit cards are not that common if you go to Brazil, right? So they You know or if you go to India, they use You know things like UPI, which is a universal payment identifier. So people pay using their phone using a U UPI and these people might not even have a bank account. So how do you handle those payments sort of integrate and make it work for these other large enterprises, war blue business in these markets.

Anirban (28m 51s):

I think that's, it's a very interesting problem to solve. And, and then so I think the solving interesting problem then when I look at the reported numbers, the revenues are growing strongly, the total processing volume is growing strongly, it's a profitable company. And then so the venture fund private equity investors that took it public, I've, I've forgotten the name now, I think it's General Ventures gb, yeah. So they still known as significant share stake in the business and have been buying stock through this sort of downturn. The stock price management had been buying stock as well. So again, none of that is the proof that You know things are fine.

Anirban (29m 32s):

But I do see You know on sort a risk adjusted basis, I find that, well it's an interesting market. the the reporting numbers are great, the clients that they're serving are a rate. So it ticks a lot of those boxes for me. And again, investing to me at least personally and I personally own share. So it's sort of risk reward basis. I think there are risks here, but I guess if I had a basket of opportunities way I would describe this, if I had a basket of say 30 odd opportunities that looked exactly like this, I would swing at because I shake on balance, it'll work out. That's how I look at it.

Phil (30m 7s):

And of course there's not a recommendation to buy by any means. Do your own research.

Anirban (30m 12s):

Yes, always. Yes. People should always do their own research and You know, You know if somebody talks about something, I think it's an interesting way to, for people to surface an idea but then you need to have a look and know whether it's right for you and stuff like that.

Phil (30m 25s):

So it seemed to be that it was about a year ago that cybersecurity was a theme that many people and many investors were, were talking about. But it, it seems to be on the other end of the hype cycle at the moment. And so, but you're interested in Zscaler as another company? Sorry, what's the code there? I didn't write down what the code was.

Anirban (30m 42s):

ZS.

Phil (30m 43s):

Zs, okay. So tell us about Zscaler, what do they do? Well obviously cybersecurity and what do you find interesting about them?

Anirban (30m 49s):

So I guess cybersecurity in general, I'm interested in for a couple reasons like You know, they might be on the other side, other end of the hype cycle, but don don't think the number of attacks that companies and government infrastructure have been experiencing have decreased. They You know it continues unabated that is state sponsored cyber attacks. And as you move more and more things to the cloud or You know basically You know you put your health records online and you want everything to be automated. Well there's just, you need to have them secure. And the other thing I like really about this is companies could decide to stall their IT digital transformation projects.

Anirban (31m 31s):

Can a company in good conscious actually pause their cybersecurity projects? I don't think so because I mean no you, if you are dealing with customer data or you have a lot of employees, you, you want to ensure and should be ensuring that they are protected and covered and safe to, to it seems like it's a bit You know it is affected by the cycle but shouldn't be as affected by the economic cycle is another reason I like it. And then of course I think there's a number of things happening in that sort of space, so, so the key thing here is this company provides a number of different tools. So if you want to access the internet as an employee of a company which uses Zscaler, then you want to make sure that the traffic that's going from your device to the internet is safe and the traffic that's coming back from the internet to your device is also safe.

Anirban (32m 22s):

You also wanna ensure that when you're accessing company infrastructure that is also safe. In the past accessing company infrastructure meant that you basically vpn. So use a virtual private network and the idea was that at least from a tech point of view, the company's infrastructure would sit in a couple of different offices. There'd be in You, know some large buildings and then you would build a perimeter around the fence so to speak. So you have your building and you protect it using your fence and the fence is supposed to keep the bad people in outside, right? So people could go to the office and then work from inside so that inside the property and, and that's fine. Or your vpn, which is a private way of entering the property.

Anirban (33m 3s):

Once you're inside then you're regarded as safe, like you have That that you have a special key. The problem is That that kind of not longer works because infrastructure is no longer housed per se in a fenced property because it may to be in the cloud somewhere. Somewhere something is sitting in the Microsoft cloud, something some somewhere else. How do you ensure everything is fenced? So to protect these assets that a company has infrastructure assets that are on the cloud for example, you would also meet some tech that's another tech that these guys provide. Then the third thing to realize is inspecting traffic that's going back and forth is really difficult because I remember everything is encrypted including the hackers communications. So would also be encrypted, right?

Anirban (33m 43s):

So you're basically looking, looking at encrypted comms and try to do basically deep packet inspections. How do you do that? So they, these guys basically have built what I would say a distributed cloud. So they have single of machines spread across the globe, which basically can sit in the middle, sort of inspect the traffic at scale, including encrypted traffic and ensure that You know traffic is going from that device to sort of your device to the phone or from your phone to their cloud. Then somewhere else on the internet. All of that happens in a safe manner and happens quick without introducing significant delays. So I, I think a pretty big technical challenge to address.

Anirban (34m 23s):

So I think they do a good job there. And so the growth story tells us that You know the signing lots and lots of customers.

Phil (34m 30s):

I believe that there are a few competitors in this space. Do you see Zscaler as having some kind of competitive advantage?

Anirban (34m 38s):

There are a number of companies in this space. So the biggest competitors I would say are companies that what I would say say sell hardware solutions that provide perimeter style protection. So they basically help build these parameters. So these are firewall boxes and things like that. Now a lot of those companies are trying to move to a cloud. So that's one thing. So I guess the biggest competitor in many ways is legacy technology. And you just spent millions building an infrastructure to protect your infrastructure, then it's very hard to rip it off. So the the existing company which has sold you that infrastructure probably has a leg up in the, in its way to come and say, okay, You know we can now make it cloud compatible by doing this, this and this.

Anirban (35m 21s):

So that's one way to think about that. Now, as a Palo Alto network used to be software a, I used to call it a legacy company, but now I think Palo Alto Networks has built a lot of cloud tech into its products. And again, they're doing this You know sort of hybrid You know you have on-premises cybersecurity, you have cloud and you sort of marry the two together. So that's an interesting way of looking at it because that's probably it. Trans helps you transition. So that's one. But otherwise CloudFlare competes with them. So CloudFlare, although primarily being sort of a network performance business has sort of tried to reposition themselves as a cybersecurity business as well because they have a similar sort of proxy network that they've built now they've built that.

Anirban (36m 4s):

So these computer infrastructures spread across the internet to speed up delivery of webpages. That's primarily their reason for building it. But you could not think that You know you have this infrastructure capability, you can now try to use that to make You know security solutions as well. They built it to have specialized routing between their devices so that You know you can have fast routing and things like that, content delivery and so on. So they sort of, they are trying to compete as well in that space. But there's a of complementary things that this company also does. It complements, for example, with other companies like say Okta, which provides single sign on tech, it collaborates with a company such as CrowdStrike, which provides the best way sort think about CrowdStrike, we will say that this is the company that provides modern virus protection, right?

Anirban (36m 55s):

So it's You know, real time virus protection on the end devices. So it's a lot of these companies collaborate, a lot of these companies compete and that just I think boils out the fact that this is a very large market and a lot of different angles. True.

Phil (37m 9s):

Yeah, that's what I can, that's what I can hear in your description of it, that there's so many subtleties and nuances involved in how the approaches and the solutions that are being utilized. That's

Anirban (37m 18s):

Right. And but, but that's partly because You know the, just the cybersecurity as a whole, but it's like, I don't think there's one size fits all type of approach here plus different You know it's a large company, small company, whether you have got You know your own infrastructure, everything is in the cloud, it's hybrid. Do you have part of your workforces? Bring your own devices. So many different ways to look at is how do you handle password? What solutions do you have for You know a password management, what service You know policies do you have? What type of data do you handle? All of these things I think introduced different specificities and those specificities result in require different relations.

Phil (37m 57s):

Okay, Anirban. So just to finish off, what sort of advice would you give a new investor, someone who's just woken up to today and they've started thinking about investing and that the stock market is going to be something for them? Just give us one little tip that you would suggest for them.

Anirban (38m 12s):

Nick You know once you read broadly, right? Being informed is the starting point for everything, right? So You know, let's You know read, don't blindly follow what other people say. Read, be informed, build a trusted circle of people that you can listen to hear from and things like that. So I think that's the biggest thing and that's I think generally true for most things is just broad knowledge development I think is just helpful.

Phil (38m 41s):

So Anirban, just give us a little bit of information about seven investing and why listeners might be interested in utilizing the services.

Anirban (38m 49s):

Yeah, so, so certain investing is a the US-based company that focuses on surfacing stock ideas, seven different ideas. Each month there's a research report that the companies, each company that has been surfaced and there's a scorecard that tracks these companies, that company updates that are provided by for the companies that have been put forward and the You know one of the seven ideas each one, it doesn't provide, I guess things like You know positioning, sizing and things like that. It just gives people ideas as to what these are some of the companies that the advisors find interesting and why with the full detailed report. That's what the company primarily does.

Phil (39m 28s):

If listeners are interested in finding out more, go to seven investing.com and if you wanna sign up with the promo code Stocks for Beginners, all lowercase one word, you'll get a 33% discount on an annual plan. Anirban Mahanti, thank you very much for joining me today.

Anirban (39m 42s):

Thanks you for having me. For

Chloe (39m 44s):

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