Investing in AI Stocks with ROBO Global’s Lisa Chai - 7investing 7investing
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Investing in AI Stocks with ROBO Global’s Lisa Chai

7investing Lead Advisor Steve Symington sits down with ROBO Global Senior Research Analyst Lisa Chai to discuss their long-term oriented pure-play AI strategy, as well as the importance of educating and informing people on where to invest in the world of AI.

December 29, 2020 – By Samantha Bailey

It’s increasingly apparent that artificial intelligence solutions are having a positive impact on nearly every industry. But it’s not always easy to find the very best AI stocks the market has to offer. At the same time, enterprise adoption of AI solutions remains in its earliest stages, leaving a long runway of potentially enormous gains for patient investors who know where to look.

With the recent launch of the ROBO Global Artificial Intelligence Index, a benchmark tracked by the THNQ ETF, ROBO Global aims to “capture the growth of rapidly developing artificial intelligence companies around the globe.”

In the following interview with 7investing Lead Advisor Steve Symington, ROBO Global Senior Research Analyst Lisa Chai discusses their long-term oriented pure-play AI strategy, as well as the importance of educating and informing people on where to invest in the world of AI.

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Lisa and Steve also discuss several underlying trends shaping this nascent investing opportunity, including accelerating adoption of AI, the challenges companies face in finding AI talent, and the state of the autonomous vehicle industry.

Lisa also offers two compelling artificial intelligence stocks she thinks investors should be watching today.

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00:19 – Lisa’s Background and Role at ROBO Global

04:54 – AI Pure Play Strategy

12:24 – Educating Investors on the Importance of AI

18:11 – Lisa’s Autonomous Vehicle White Paper

30:43 – Lisa’s Top AI Stocks


Complete Transcript

Steve Symington  

Hello, everyone. This is Steve Symington, 7investing Lead Advisor. Welcome to our podcast. Today I’m joined by Lisa Chai of ROBO Global. She’s a senior research analyst there. Thank you for joining me, Lisa. Great to have you.

Lisa Chai  

Great to be here.

Steve Symington  

So ROBO Global is a financial services company. That was, in my understanding, the first entity to define robotics and automation as unique and investable class. You also created the first index to track and monitor all those companies on a global basis. You are – I mentioned a minute ago, a senior research analyst there. Would you mind telling us a little bit about yourself your role? What keeps you busy these days?

Lisa Chai  

Yes, of course. Well, I’ve been in Wall Street for over 20 years, covering technology, consumer media, health care, and for various asset classes. (I) worked in the hedge fund industry as well as private equity. And I joined ROBO Global about three years ago. And this is a very interesting role because I get to actually be myself. I have had this passion for technology. I’m a software programmer as well. I got a dual degree in college because I wasn’t sure whether I want to go into finance or computer programming, (so) I decided to do both.

Steve Symington

Uh huh.

Lisa Chai  

I’ve always loved you know, futuristic sci fi movies. I read all of old Wall Street veteran books and fundamental analysis, and I just loved everything about Wall Street investing. I was pretty much a stock, you know, investing junkie. ROBO Global, the platform here allows me to pursue my passion in technology, as well as use my experience of 20 plus years in investing. I joined the team about three years ago. We’re very unique in that we have terrific members in the research team, with by side and research backgrounds, as well as we have nine strategic advisors who have PhDs in computer science, and they’re the sort of the world leaders in robotics and artificial intelligence. So we collaborate very closely in everything from methodology and constructing index strategies, as well as just asking them about what’s new in autonomous driving, what’s new in the drone world. So I’m learning a ton. I mean, I thought I knew a lot about various aspects of different industry groups. But here at ROBO, I really got to understand how everything works in the supply chain of the industrial market. You know, how our cars made from end to end? I learned about driverless forklifts. I actually didn’t know, you know, never even spent time thinking about forklifts ever in my life. But I went to a conference and I saw one I thought that is so amazing. Why didn’t I think of that? Of course there’s a driverless forklift! There’s a huge industry, like an AgTech that I didn’t know a lot about. I remember I was a venture partner for Food Tech. So I kind of understood a little bit about the AgTech world and the food tech world. But I got really deep into actually how AI plays into making sure all the farm and the plants are being watered and there’s so much technology and AI involved in actual the farming and agriculture industry. So that’s interesting, and I’m having a lot of fun, my new role here and I get to meet with founders and thought leaders in companies that are really building these technologies, right?  Building these companies. And that’s sort of the insight that we really want. We want to provide our investors with this type of insight that we’re getting through our network of technology founders and developers. So it’s been it’s been great time. As you know, the markets been very volatile, but it’s been very interesting as well. There’s so much innovation going on right now. So we’re very excited. We launched a new fund, in May of this year, spin off of one of the very important sub sector of the our flagship fund, ROBO. So it’s been it’s been a very, very interesting journey. And thanks for having me.

Steve Symington  

Yes, of course, and you did it right with software engineering and in finance.  I was a software engineer by trade when I left college, but I was a math guy in college and my pivot to finance came a lot later. So kudos for your foresight there. And so on that note, you refer to ROBO Global’s approach as a pure play AI strategy. And you know, that’s something near and dear to my heart. I used to work on old neural networks xand letter analysis software and machine learning. And, you know, now as an investor who relishes finding companies that are utilizing AI to the benefit of their industries, can you tell me more about that AI pure play strategy, and how it’s different from others that are on the market right now?

Lisa Chai  

So when I was talking about earlier about the sub sector that we spun out – we are wondering why the sub sector was performing so well, versus the other sub sectors of the sub sectors of the ROBO index strategy. So when we dug a little deeper we saw really interesting signs of AI moving from kind of an experimental face to commercial application face with very strong use cases. So we spent many months sort of putting together this index strategy and identifying sort of all companies that play a key role in the AI ecosystem. And very much like our culture of having that exposure of the diversification of various market caps and regions, those are very important to us. So none of the companies in the index is more than, let’s say, over 2%. So we’re trying to give up kind of a broader AI ecosystem exposure. At the same time, we have our scoring system that allows us to really be very pure play AI strategy. So think about companies developing AI infrastructure, and those companies are leveraging AI to go after sort of massive revenue opportunities. And many of these companies may not even survive or exist without AI capabilities. So we, we were at first thinking maybe can we even come up with more than 20 companies. And we when we dug a little bit deeper and start measuring companies based on their revenue, purity around AI, technology leadership, market leadership, and also through the cutbacks in R&D investment around building the AI systems. So when we put those factors together, we actually came up with about 70 companies. And we found out the outcome of that was about 70% US and 30%, international in various market caps. It’s not something we decided, hey, you know, we want a larger market cap till. We want to larger tech exposure. It was really just the outcome, because we really want to give a real pure kind of a snapshot of how AI is being played today, and where are the resources and development happening around the world. So we think that’s relative to what we’ve seen from our competitors. I don’t think anyone actually has these type of factors, the type of analysis that we also have to do when we look at market leadership, it has to be number one and number two player. And also technology leadership is very important, because we need to know that we believe that while AI adoption is in the early innings, some of the areas – let’s say in financial services a little bit more mature than other industries. So how is technology been developing? How long have they been building this capability? So we looked through job postings and looking at organizational structure, we looked at Angel list, we looked at many different companies that are requiring and building a portfolio, but they’re not talking about it with investors. So we actually really dug very deep. And we have a very, very big, you know, active research process around that. And these scores are basically kind of managed very frequently, and very live very real time data. But we don’t actively manage the strategy. So we constitute and rebalance every quarter, because we think it’s very important to kind of look at this lens of AI and a long term strategy. Rather than trying to pick couple winners for the next couple quarters. Maybe there are many different funds that could do that. We believe that our core strength is finding companies that could grow over the next 5-10 years. And having a very, let’s say, strong kind of financial position to navigate through tough times. So there are many different factors that we really kind of put into our factoring the scoring system. So we think that teaching cue think score is very important. And we have this internal database, we’ve managed all of the members of the team could take a look and see who has Lisa been talking to. Why did she change these scores? So that process is very, very strong. And we stick to sort of our our core strategy to give that diversification, that exposure of AI.

Steve Symington  

Sure. A few things come to mind right away, I love first your long term view. Our listeners will tell you right away, that they know just how much we appreciate a longer term view, you know, we talked about not putting money to work in the market that someone’s not going to need for at least three years or so. Preferably longer. I can name several companies I’ve owned my own portfolio for more than a decade now. But I love the 5 to 10 year view. And also that interesting thought process when someone starts to do research and and you think, oh, are we going to be able to find 20 companies and you come up with 70. It reminds me of something. I was talking with Chris Mayer, author of 100 baggers a few months ago, and he was saying, you know, the problem becomes  if you can’t find an adequate number of promising investments out there, even in any niche like this, the problem is, I think you’re not looking hard enough. So it’s fantastic that you’re really opening it up, and you’re able to narrow it, you’re able to find so many businesses. And this is a nascent market niche. And I say that with finger quotes, because we both know that AI and robotics and automation have effectively permeated almost every industry already. And it’s hard to call them a niche anymore. But can you speak a little bit to the importance of educating and informing people on where to invest in the world of AI? Like, how do they narrow that down? Why is that important as far as education goes?

Lisa Chai  

Yeah, I think education is very important, because I think there’s been a lot of terminology around artificial intelligence, a lot of buzzwords and hype and smart devices, right? Intelligent insights. Many companies are throwing these terms. But when you look deeper, they’re actually there’s no AI capabilities. They’re just using a partner or third party, right? So it’s very important, I think that have some knowledge and understanding and maybe look at how big is market opportunity, you have to be a believer yourself. You know, we looked at lots of various market data, we also have our own internal data. But you know, if you look at data from McKinsey, and PwC,  they both believe that around $15 trillion in global economic value would be generated from effects of our AI by 2030. You have to believe that every industry is affected by AI. And we’re still in the early innings. Right? So we’re seeing the business models are shifting. And there’s rapid adoption of AI happening today. And there’s gonna be a lot of winners and losers, and how do you identify them? And will you miss it? Are you going to be chasing every single IPO stock that’s going to be coming out? Or can you dig a little deeper and say, maybe there’s some companies that’s been business for a long time, but they’re shifting into AI? We discount them. How about if they’re building they’ve been building a portfolio right around AI capabilities and acquiring AI talent. We found out from various research that we’ve done, the biggest hurdles of AI has been finding talent. There’s a big shortage of talent. And that’s because companies like Google have been the biggest recruiter of AI talent. They’ve really decimated sort of the all the AI scientists coming out from the academia. So the rest of the world, the corporations have to chase this few amount of data scientists coming out. And it’s not also just having a data scientist, you need a AI project manager, you need a software developer engineer, you the entire team to actually have AI capabilities. We’re finding companies are building tools and applications to mainstream AI into your organization into your business process. And maybe a couple years ago, Steve, you might have looked at some of these companies and said, well, you know, I really don’t want these companies that are just using AI or or using API tools. I think now we have realized that there is a talent shortage, and China is catching up. So do we just want to sit back and let everybody else sort of the large tech giants take the lion’s share? Or do we step up at a time like this and say, okay, we need to modernize our IP architecture, we need to, you know, participate in a digital transformation that all consultants are talking about. So how did we do this? So many companies were finding out or having different strategies, but most of the companies don’t have the resources (to) build AI capabilities. So we’re starting to figure out what companies are a little bit ahead of others. So and the work that we’ve done over the last few years, can I show that the 70 companies in our portfolio, while it’s diversified, many of them are mature in terms of having the AI capabilities? So I think it’s really important. So if you want to invest yourself, I think it’s really important to have your own portfolio AI companies and do some, you know, research yourself, you should probably have a couple tech giants that are building the AI ecosystem. Yeah, many of the companies are, you know, I believe, undervalued, if you’re just looking them as a cloud provider, I think they are really building the AI ecosystem further along than anybody else give them credit for. You could also have couple smaller market cap companies that no one’s ever heard of, maybe under followed. But they have one product that they’re doing really well. And they have some AI capabilities around it. And you’ll see that list is going to be consolidation and partnerships. So I think we’re in the early innings. And I think there’s many different approaches. Or you could just rely on professionals and have index funds that are diversified and have done all the work for you. And you just follow along every quarter and see where the rebalance goes. So I think that this is a really interesting and exciting time where you actually have options. You could do your own research, or you can rely on professionals to guide you along this journey of the AI. And I’m a big believer having followed technology changes in revolution over 20 years. This is probably the most exciting time I’ve ever seen where we’re so close to having robots start doing intelligent things that are really useful, because we have these robots that were really good to look at. But they were not very useful, right? They were just at the door of the bank and just saying hello. But now we’re very close to having real smart connect enabled homes, smart city smart cars, and truly truly intelligent, smart, where it’s not just kind of mirroring our human intelligence, but possibly see better than human intelligence, right? Because I don’t think that we’re actually the most intelligent beings in the world. I don’t.

Steve Symington  

That’s a that’s a really interesting point. And I think that’s something that I believe, you know, we’re very close to an inflection point when it comes to computing power, and an actual real AI. And, you know, I spoke with Colin Angle, CEO of iRobot last month, and he was talking about positioning their company AI robot as sort of the central unifying source of spatial awareness in the home and you know, people just want things to work and you know, press a button and, you know, making suggestions about how you can best clean etc. But yeah, I think it’s a really exciting time to be an investor, especially when you’re looking at Tech, and AI and I would like to talk to talk about some specific names that you might be interested in. But first, speaking of specific applications, you sent over a white paper, that was great. It was chock full of fantastic research that you wrote on autonomous vehicle technology. And I’d love to hear your thoughts on the state of that market, and how investors should think about approaching it.

Lisa Chai  

So it took me many months to actually do the white paper. I thought it was going to be quite easy to have some exposure around some of the system within the software side as well sensing technology. So I thought, hey, I know where we are. But what I found doing this report, and I collaborated with several of our strategic advisors who are doing real extensive research in their academic departments, one of our advisors, she runs the AI department at MIT, and a lot of very interesting work over there. And she really helped me understand where they are in helping the technology ball really, in that deep learning face, you know, train the data enough where you could mimic our intelligence. Not just intelligence with emotions having picking up kind of that human cues, and I didn’t really think about it that way. I thought, let’s build a technology. I know who’s got the technology, who’s got the, you know, the systems, got the software, and why can’t we just put it out for pilot test. So where we are today is that we’re in level three, which is not even really semi autonomous. It’s maybe like a quarter of autonomous where we have some capabilities of self driving, but you really need a human driver to navigate. We’re going into level four, in some applications, like on the commercial side, let’s say delivery trucks or wide ride hailing. And then level five is where we really want to be at which is completely fully autonomous, no human intervention involved. That is about 10 years away from our research. And the reason for that is that the the science behind the autonomous system is quite complex. And that’s something that, you know, that surprised me. Aside from regulation around the world, that we still have to go through every state, local countries have different regulations, some countries have even looked at the technology yet. So we have some of those software and hardware to enable fully autonomous, but then we don’t have a regulation. And also, the software itself is having a little bit of problems, right now, it really is having a lot of bias, it needs a lot more training, it needs more trial and error. So it needs a lot of bad data. Sure, we are too worried about accidents to give AI bad data, right” So we have a lot of good data on what good driving should be. But we don’t have enough bad data. And that data means some accidents and some injuries and car crashes. And that’s really not good for any of the companies are involved. And also the national safety, transportation, they have their data on what they want in terms of safety and on the trials. So there’s a lot of complexity around it. But what was really good to know is that we do have the technology, we just need more data, and that’s going to come. So companies like Waymo One, you know, funded by Google has really outpaced everybody else. It’s really hard for anyone to catch up to them at this point. Some of the Chinese players, like Tencent and Baidu, they have their technology as well. So they’re not that far behind, by the way, government backing, so they’re really catching up. And then we have Amazon that made a very large acquisition this summer 1.2 billion for Zoox, and they commented that they’re going to go into the ride hailing service, and not delivery. And then just last week, they said, yes, they’re going to go into delivery trucks, which wasn’t a big surprise to all of us following it. And yesterday, Apple came out with their news – cars. So what’s exciting is that while we believe that true autonomous technology and for you and I to be able to purchase a car and drive fully autonomously won’t happen for these 10 years. But we are preface to say that by next year, we’ll see fully autonomous and vehicles out there, whether it’s a drone type of format, whether it’s a delivery robot that’s on your sidewalk, or whether it’s in, you know, supermarkets, it’s doing a lot more than just scanningmthe shelves. We’re actually going to see a lot more of this happening around us. So the industry is very dynamic. Tech giants have spent billions on it. You got venture capital investing, you know, 10-14 billion in the last 18 months in the space. And the other companies are realizing, wow, we can’t be left behind. So they’re also partnering, investing. So this industry is so dynamic, and things are evolving so quickly that I feel like every week, there’s gonna be some news around the autonomous system and technology. So I think there’s a lot of progress to be made, I think there’s a lot of progress has already been made. I was actually pretty bullish coming out of the report that we have many companies that you could actually have exposure to, you could go through the larger companies like Amazon, Google, an Apple, or, you know, Tencent, or you could go for some of the some of the smaller companies that are just providing maybe the software technology or the voice recognition technology. So there are many different ways, we actually didn’t launch the AI strategy to have autonomous driving as even a subsector. That actually didn’t even occur to us. However, while doing the white paper, we realized we actually had quite a bit of exposure, because we did look at companies that had revenues around AI. We had things that had put investments around AI capability. So the outcome was that we actually had about seven or eight companies that have very strong connections and, you know, key beneficiary of autonomous vehicle technology progressing nicely over the next, you know, several years. So very exciting. And I think if we do this call again, this podcast in the next three months, I bet you there’s gonna be a lot more announcements.

Steve Symington  

Yeah, that’s a lot changed over the last couple of years. I actually had a chance to go down to Southern California a couple years ago and spoke with one of the co founders at GM Cruise and executives at Baidu. And then Waymo, and, they all sort of, you know, a few of them expressed some some skepticis that, for instance, that Tesla would be able to do this without LIDAR effectively and, and but now we have the rewrite and and you know, safety’s another thing I think that’s really improved. The folks down at Baidu specifically, were saying, you know, it’s not enough that we’re 90%, you know, safer than a human driver, it needs to be a factor of 10, before people really learn to accept it, because an accident caused by a robot is unacceptable under any circumstance in people’s minds.

Lisa Chai  

I think, matter of fact, we should really not use the autonomous cars unless it could actually be better than us, right? Because I’m not the best, according to a lot of research out there. Yeah. easily distracted. We’re lazy. And some of us don’t want to drive, and that’s me. So there’s a lot of technologies that’s making sure that AI is going to replace us in terms of the driving capacity, it has to be better than what we can do. I think in some cases, it’s as good as we are. But in many cases, it’s not. And I think you know, longer term, when we do have the Smart City, and I’m very excited about how smart cities gonna look like, for true autonomous world, you need to have traffic lights, right? And you need to have every one of our cars have to be autonomous fleets, autonomous, so they could communicate with each other. Because if I had a my autonomous car, and you didn’t, my car can communicate with yours. Yes. Oh, it’s it really wouldn’t be a very ideal situation. So I think that I think it’s really a vision, a world where, you know, majority of the cars are autonomous, and you got the traffic lights, we have rules and regulation around it. And I think that’s going to be very interesting. I think it could happen the next, you know, 10 to 15 years to have some cities to have some of that smart city grid.

Steve Symington  

Yeah. And I think some of that’s going to take time to collect real world driving examples to for these neural networks to really learn and improve and I remember talking with the folks at Nvidia, and they were saying that how many billions of miles they’ve collected in simulation, but at a certain point You know, then you run into things in the real world that it’s very difficult to simulate. Maxx Chatsko, he’s in Pittsburgh, and he’s talking about the the Pittsburgh left, I think he called it, there’s this strange sequence that they follow there, that the rest of the country doesn’t really. And it’s been messing up autonomous vehicles, because they’re like, how do we handle the Pittsburgh left- the stoplight? And, and yeah, so I think it just takes time. But we will get there. So I’m excited for it. Not just to enable us to be lazy. Because most of us are, especially software engineers, in my mind, we try and find the easiest way to do something in the fewest number line, number of lines of code. But also, you know, for people who are disabled, who can’t, you know, might not be able to drive Actually, my middle son is disabled, and I’m not sure if he’s going to be able to drive. He’s only 10. But this 10 year timeframe sounds pretty good to me, because I’d love to be able to, you know, put him in a vehicle and not have to worry about what’s going to happen, not just from from his capabilities, but I don’t want to put them in an autonomous vehicle that is less than 1,000% safer than I could potentially drive him somewhere. So yeah. So let’s talk about specific companies. I’d love to pivot and give people an idea of kind of how you think about breaking them down. Can you maybe offer a couple or a few of the most interesting names out there individual stocks that you’re kind of keeping an eye on right now?

Lisa Chai  

What’s interesting about the AI index is that we broke it into two classifications, the infrastructure and the application. Our companies are enablers of AI, usually in the semiconductor space, or the hardware networking. But also infrastructure software, where companies like Alteryx, within the technology community, a lot of people know who Alteryx is, they don’t think it’s a household name. It’s a company that are basically they are basically their enabler of AI. They build tools for that mainstreaming of AI that I talked about earlier, which is really important, because enterprises and organizations don’t have resources to have their own AI capabilities. So their software allows data scientists and big data analytic teams to collaborate with each other to discover that data base insights and deploying machine learning codes. This year Alteryx some saw some decelerating growth due to the pandemic, and the AI projects got pushed out, as organizations can prioritize their technology spending for work from home situation. But we believe that, you know, post pandemic, we’re going to go back to the way IT spending has been. Some of the trends that we’ve seen where some of these AI projects were very, very critical. They didn’t lose those seals to competitor, it just got pushed out. It wasn’t even about the budget. So we think that this company, while it’s been around for a while, they’re very well managed. I think that post pandemic, AI practice are very important. We saw massive migration to the cloud this year. And that’s going to cause a lot of problems where okay, all of our data is in the cloud, what do we do now? We need to extract intelligent data out of that help businesses, improve their decision making process, and companies like Alteryx have solutions for that. So that’s one of the companies that we have that I think people should take a look at. On the other side is the application side. These are companies are users of AI and HubSpot is one company that I like to highlight which is a this 15 years ago, this company was founded just as a marketing application software company. They are marketing sales and services platform company with over 86,000 customers and 120 countries. They have developed AI capabilities to help clients execute sales and marketing strategies and thinking think about lead generation and closing deals. So it gives you transparency and visibility and also helps you navigate through the whole sales process, booking the meetings, giving you insight as to how many times your potential customer open that email, and how long he spent time looking at it. So that type of data I think is very valuable they in slowly incorporating AI and machine learning tools throughout entir their platform, and now they’re starting to offer that to their clients. So some of the technology is in house capabilities that they built, and some are required. So we think that here’s a company that has been one of our, you know, good performers this year, 160% year to date. They’re growing 30-40% sales, and we think that they could continue the momentum over the next few years. You know, they are trying to compete in that Salesforce space. Net sales type of platform, but in terms of marketing, they’re actually the probably the best of breed technology platform provider marketing side. So HubSpot is one I think it’s also not a household name. You know, I think you have to have some type of business or commerce to know exactly what HubSpot does. But people that we’ve spoken to users of HubSpot loves the technology and some of the AI that they’re starting to see that’s been incorporated. So I think here’s a company that you’re not too late with it. They they’re just trying to see the revenue growth coming out of the AI capabilities.

Steve Symington

Wonderful. So Alteryx ticker AYXfor anyone wondering HubSpot is HUBS. And, yeah, both very intriguing businesses and for what it’s worth. We agree. Those are really interesting stocks that people should be keeping an eye on. So that’s really fantastic. Thanks for sharing. And I think that about wraps it up. I love ending on those couple names to watch. Thank you so much for for joining me today. Lisa, it was a pleasure.

Lisa Chai  

Thank you. I agree.

Steve Symington  

And and thanks everybody for listening. Again, we are 7investing. I’m Steve Symington, we’re here to empower you to invest in your future.


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