AI in Finance and Investing

E20 | With's Jannick Malling
Updated Jan 26, 2024

AI in Finance and Investing

AI For All
October 26, 2023
On this episode of the AI For All Podcast, Jannick Malling, co-founder and co-CEO of, joins Ryan Chacon and Neil Sahota to discuss AI in finance and investing. They talk about what Public does, stock trading, how AI synthesizes financial data, the benefits of AI in investing, the impact of AI on investors and investing, how experienced investors are viewing AI, the new roles AI will create in finance, predicting the irrational behavior of people, and the risks of AI investing.
About Jannick Malling
Jannick Malling is the co-founder and co-CEO of Public, the investing platform that allows people to invest in stocks, ETFs, treasuries, crypto, art, collectibles, and more – all in one place. The Danish entrepreneur has a natural drive for making a difference. At the age of 17, he joined Saxo Bank. Two years later, he helped build the fintech company CFH Group, which was eventually sold to Playtech (LON: PTEC) for more than DKK 800 million. As part of CFH Group, he also launched the spin-off company Tradable, the world’s first open API trading platform.
In 2019, he founded Public, which has gone on to raise over $300 million from investors such as Accel, Lakestar, Tiger Global, Will Smith (Dreamers VC), and other prolific investors such as Maria Sharapova, Tony Hawk, and NFL stars J.J. Watt and Bobby Wagner, and more.
Interested in connecting with Jannick? Reach out on LinkedIn!
Public is on a mission to make the public markets work for all people. The fintech went from founding to unicorn status in less than 3 years, and has been one of the fastest growing financial services apps in America, boasting more than 3 million members since its 2019 launch.
2023 will be a defining year, as this is the year Public has ventured beyond stocks and crypto investing into other asset-classes such as alternatives and fixed income products, and has taken the first steps in its international expansion.
Key Questions and Topics from This Episode:

- [Ryan] Welcome everybody to another episode of the AI For All Podcast. I'm Ryan Chacon, and with me is my co-host, Neil Sahota. Neil, how's it going?
- [Neil] Yeah, I'm doing all right. How about yourself, Ryan?
- [Ryan] Not too bad. We also have Nikolai, our producer, with us as well. Today's episode is really going to be focused on AI in the world of investing. And to discuss this, we have Jannick Malling, co-CEO and co-founder of Public. They are an investing platform. Jannick, nice to have you here. Thanks for being on the podcast.
- [Neil] Hey, yeah, it's great to be here.
- [Ryan] I'd love to learn a little bit more about what Public does. If you could talk about that in the vein of how AI is becoming more incorporated in the world of investing because I think a lot of us have used investing tools out there on a personal level, and there's obviously different algorithms and different kinds of information and data, but when it comes to AI specifically, how long has AI really been used in the investing world?
- [Jannick] Let me start at the beginning of that question. So, Public is an investing app. We launched a little bit over four years ago after Labor Day 2019. So, we just had our four year launch anniversary. And we were the first to build what's now known as fractional investing in the equities markets.
And so very quickly, what that means is, back when we launched, a company like Google and Amazon, like those stocks were trading at thousands of dollars price per share, meaning that the minimum amount that you can effectively buy of those is 2 or 3,000 dollars. And we saw that as being a huge barrier for a lot of people to participate in our wonderful stock market.
But even just like for people that still had some means, it made it harder for them to control the diversification of their portfolio. If you had 10,000 dollars to invest and Google's trading at 2,000 dollars, it can either be 20, 40, 60, or 80 percent of your portfolio, right, when you're starting out. It can't be 30, it can't be five. And so you lose the ability to really control the diversification of your portfolio at a more granular level. And then at the same time, of course, you had seen things like Bitcoin effectively born on the internet where you did not have to purchase a full coin to participate in that market. And so that was like our early claim to fame, I would say. It was a huge push to help democratize access to the markets. Since 2019, we've seen retail investing, which used to be less than 10 percent of the markets, go to 25, 30, some months 35 percent of the overall markets. And so this sort of proliferation and access to the markets have been quite impactful and tens of millions of people have actually joined the market since.
What's interesting is, I think, when it comes to AI, AI has been used in the more what we call the institutional side of the market. So when you say retail, that's folks like us, like normal, regular sort of private investors, if you will. And the institutional side of the market are prop trading desks, hedge funds and the like, that obviously have been much more sophisticated for a much longer kind of period of time.
And those folks have really used AI or at least machine learning models, predictive AI, probably for over a decade at the sort of, they're very secretive about a lot of stuff, so it's hard to say precisely, right, because that's also a market where if you really have a predictive model, that's great. You are going to keep it as close to your chest as humanly possible. But that's been there for ages. I think what's interesting is you've seen in the age of technology, the internet, mobile, now AI, the underlying trend is actually the same. It is to basically not just democratize access to the markets, but to level the playing field between private folks like us and our ability to capture gains in the market relative to that of the large kind of hedge funds. So when you think about the last four years with this whole fractional thing that I talked about before, that was democratizing access to the markets, like the ability to actually transact. And when you think about what comes after that, we think it's democratizing the ability to understand the markets, to analyze the markets, to research the markets at a very high level but without necessarily making it your full time job and putting in an 80 hour work week to do that. And that's a little bit where AI comes in on the retail side.
- [Ryan] And on the retail side, I've had experience with other platforms, like I've used Robinhood, which I imagine is just like a competitor in different ways, and I seen a lot of tools since then really start to incorporate, they call it advanced trading capabilities for the everyday retail investor. Is this kind of what you're talking about is making that information more available as part of the overall experience in a platform like yours?
- [Jannick] Yeah, I guess you could say that. If you double click on it, there's always been two types of traders and two ways to do kind of research when it comes to trading and investing.
One is called technical analysis. That's where you have a lot of algorithmic trading, typically looking at historic prices of stocks or other assets. But let's take stocks in this example. Then you have these things called indicators, which essentially is an algorithm that goes, and if you Google it, you'll see a bunch of charts where there's a lot of drawings all over them, and then you basically use the historical prices to predict future, whatever future prices that asset will hit. That's the technical, that's like the most basic way I can explain technical analysis. Fundamental analysis is a little different. That's where you do the fundamental work of analyzing income statement, balance sheets. You maybe look at earnings call data a little bit more. It's a, it's maybe a little bit more holistic, I would say, than the technical analysis, which is more trying to also capture movements in the short term. And technical analysis is really more of an expression for mass psychology and capturing that in an equation that can predict where that might go in the future.
And the fundamental work is a little bit more what someone like Warren Buffett is known for, right? Or if you've read The Intelligent Investor or some of these, the concept of value investing is fundamentally rooted in fundamental analysis as well. And as you can imagine, historically speaking, there's been a lot more sort of math and machine learning and predictive AI in the technical analysis side of things because that's all math. And I think what you've seen in the last let's say 12, 11 months really is the proliferation of using AI for fundamental analysis with the introduction of large language models, where in fundamental analysis, it's not about hardcore math, it's more actually about understanding a lot of different nuances, being able to analyze an earnings call, being able to analyze income statements and so forth. And I think that's a little bit where, and again, even in the hedge fund world, you have funds that are more focused on technical analysis, you have ones that are more focused on fundamentals. And so that's the same in the retail world. And I think a lot of the platforms of the past, whenever they wanted to make something that's more advanced, to your point, Ryan, it, that has translated directly into let's build a very sophisticated UI where you can draw all these technical analysis models. It's been a little bit more in that realm. I think what's super interesting about all the LLMs we're seeing and everything we've seen happening in these last 10 months is now you can actually build a more sophisticated offering that is not necessarily more complex to use. And that has always been a trade off in the past, and I think at the first time now, you're able to get the best of both worlds.
- [Neil] I'm curious, Jannick, that when you're using AI for the fundamental analysis, there's an element of training because a lot of the information tends to be standardized. There are also some, we'll call it, unusual footnotes and other things that are put out there. How's the AI able to take some of that information, especially if it's a, I'll call it a one off, based on the company and formulate that into a synthesis or summary.
- [Jannick] That's an excellent question. And for context we launched an AI experiment called Alpha, I think in April, which was based on OpenAI GPT-4. We were the first kind of investing app, if you will, to launch that. I think the reason why we could actually do that is if you go all the way back to 2020, we acquired a small company that were scraping SEC filings and turning them into structured data, right?
So this is back in 2020. This is like before all of this was really a thing to the degree that it is today. And by doing so we've actually had a repository of data that's not necessarily unique to us because the interesting thing, when you talk about all these other industries, healthcare, et cetera, people always talk about, oh, whoever has the most, the largest proprietary dataset will win, what's interesting about the public markets is all the data is public, right? And so when you IPO your company, you're really signing up for sharing. There are various standards that where you need to share information about your company. And so it's not that Public owns this company, owns this data, it's not proprietary to us, but we have been early in trying to structure it differently. We've had that effort going on for a couple of years and so when ChatGPT came out, that was a big kind of light bulb moment because we quickly saw how we could potentially start feeding a model all this data maybe before other people could, and then get a head start into how you actually can use a chatbot to be your research assistant, to be that sort of investing copilot, as we call it, where you can just go and ask it a serious questions, which is a very different way of analyzing a stock than how you've done it in the past. If you wanted to get really hardcore in the past, you're probably sitting there with 30, 40 different browsers open and reading through earnings reports and annual reports. I'm reminded of a Warren Buffett quote. I think he said people should totally invest their money. Just read the annual reports of the companies that you invest in before you do. Who really does that? Let's be honest, right? But in theory, of course, he's right. It's Warren Buffett. He tends to be right in the fullness of time. And I think what's exciting about this movement is now, you can have an AI help you crunch through all that as it starts to understand what kind of investments you're looking for over time. You can also get better at figuring out what parts of the earnings calls or the annual reports to relate to you. It can start to understand all the nuances across different companies that might even operate in the same space to your point, the footnotes, all that stuff. And so the journey we've been on since April when we launched this has been basically just keep training a model to get better and better at understanding all those nuances. And yeah, as you can imagine, it's not perfect, but it's getting a lot better every week, every month as we go forward.
- [Ryan] Who do you see the, this benefiting the most on the retail investment side? And what I'm asking is a lot of this data was, as you mentioned, been available in some way or another, but it required lots of hours in order to digest it, analyze it, and even just simply understand it. But.
With the ability to interact with these models using, natural language through a chatbot, like a copilot, as you said, who is this really aimed at benefiting? And can you take us through maybe an example of how somebody who would maybe hasn't experienced using another app or using another platform would come into something like this and be able to now get faster, better access to information about a stock for instance.
- [Jannick] To your previous question of whenever you want to build more sophisticated stuff in the past, it had tended to come with trade offs of the UI becoming more complex. As the UI becomes more complex, the addressable market for that UI effectively shrinks. Bloomberg is a fantastic, phenomenal UI for the people that understand it. That even comes with a keyboard, right? So talk about a user interface where it actually goes beyond just what you see on your laptop. And I think that's not necessarily a trade off here, and that's what I think is so interesting. What we've done on our app, for instance, our mobile app is you used to go to a stock. As you scroll down, you can see analyst ratings, you can see income statements, you can see balance sheet statements, cash flow statements.
We could even show, like I said, some of the structured data around KPIs. You look at Tesla, you can see a chart of how many vehicles have been shipped and delivered every quarter because historically that's what maybe has driven most of the price action in that specific asset. But again, you still need to know all these things as you come in and open the page for that stock, and then you need to analyze everything and scroll up and down, et cetera. Now with Alpha, you're really just like swipe down on that page. It opens a prompt and then you can ask it anything. And what we're seeing is this progressive due diligence flow that people get into where they might start off with a very simple question, up here, and then they just like zoom in, and they can continuously keep asking why, they can keep taking the conversation and in different directions. And that I think is something that just comes natural to a lot of folks, right? Like you don't need to be able to read and understand an earnings report or technical analysis chart in order to research a stock that way.
And that's why I think the addressable market for this, to your question, is actually quite high, and I think it can really impact the majority. If you see a bell curve of markets, knowledge, and financial literacy across our population, I think in the past we've been stuck at like the top 5, 10%.
That's really who you can build the sophisticated tools for. And I think now with AI and with these research assistants, you can get that down to the majority of the bell curve, that middle piece that obviously is by far the largest in terms of size and therefore it can also be much more impactful than some of the leapfrogs that you've seen in the past.
- [Neil] I was just going to comment on that, that this ties back to your democratization statement earlier, Jannick, and I think it's fascinating that it's actually opening up because I think it was last year we saw the, some of those day traders doing things with, what was it, GameStop and all that. So is the use of this information making people better investors or is it helping people maybe manipulate others a little bit easier. I'd love to get your take on that.
- [Jannick] In terms of whether you make someone better, that's a harder thing to actually answer. Generally I would say yes, but it's a tool and like any other tool, the purpose for which you use it, is going to really determine the outcome that you get with that tool more than the tool itself. But what can, what we can definitively say is that it makes people more informed investors. And that I think is actually the thing to strive for because we internally, when we talk about building a product for our community here at Public, it's, our job is to make people the most informed investor that they can possibly be. At the end of the day, you're in your driver, you're in your own driver's seat, right? What you decide to invest in, that's your call. But our responsibility is to make you as informed as humanly possible, or I guess not just humanly possible. That was a bad choice of words. But as informed as you can possibly be and using all the tools at our disposal to do that.
And that's where Alpha has really been a huge game changer for us because like I said, it's so much easier for people, people are also inherently lazy, by the way, and there's a big part of the population that whenever they lay eyes on a chart, like there's a lot of stuff they need to understand. Everybody thinks differently. Everybody perceives data differently. Some people are visual thinkers. Some people are not, but what we all have the ability to do is to ask questions and get answers and then ask a followup question to that. That's one of the most rudimentary, fundamental human skills that we learned very early on in life and therefore refine over many years. And so by the time that we're 18 and old enough to open a brokerage account, that's certainly something that we can all master. And I think, especially because like sometimes, yeah, you're going to ask the question a little bit in an odd way. You just ask it again in a little bit of a different way.
That's all fine. And it's not something that where you completely drop off. Whereas when you look at a chart, if you don't immediately understand it, you might be inclined to just close the app and move on with whatever is next in your day. And that's another thing from a design perspective where I think the task potential is to really hook users in a different way, keep them in what is essentially a flywheel of research.
And I think we've seen, so Public also started as a social lab, actually. So we have a social layer through the app where people can see what other people invest in or buy, they can invite, they can converse with each other. So going back to GameStop in 2021, which is on everybody's lips now again that there's a movie coming out about it next month or this month I think even. That was largely sparked by social interactions between people.
And when you really distill that down to what it is, somebody had a thesis on GameStop, he published it, and he got feedback on it, right? And so in publishing it, had a few questions, people started building on top of it, and then it became this like collaborative way to build this thesis around GameStop, and how this short squeeze might play out as it were.
And that's not the kind of use case that we're seeing for Alpha. We are seeing some questions around, oh, what's the short interest on different companies because that's just one thing that a lot of people want to look out for these days, obviously. But again, that collaborative nature of building an investment thesis is what I found to be the most fascinating around GameStop. And we saw that in our own community as well. Now the thing about feedback is the sort of time and the feedback loop really determines how quickly a thesis can accelerate from a basic thesis into something that's more substantial. And when you're waiting on other humans to give you that feedback, and then for you to read it, and you're in different time zones and they got to wake up and this, that and the other, it takes a while, right? And so that's why with GameStop, you saw, I think the first, the first kind of post was really, I think it was done in September maybe of 2020, and then I think in obviously at the end of January in 2021 was when all hell broke loose around GameStop. That's actually a long time, right? And I think what you're potentially seeing is that you can craft these thesis that built them up in a more substantial way much faster because the feedback loop of an AI is not instantaneous but within 10 seconds, you have your answer, right? And that I think is super interesting. And then what even we haven't done yet that, but we're potentially thinking about is what happens when the conversions of those two things happen. The social investing phenomenon that we've seen dominate retail investing over the last few years now with an AI component as well, right?
How does that look like? You've, I think you've seen some stuff with community notes, but Community Notes on Twitter is really more there to steer people and more to fact check kind of stuff. It's not generative in any sense. It's like part of generating a thesis, part of contributing to a thesis.
It's more like to keep people in line, but what does that actually look like when you suddenly converse in a group where some people are human and some entities aren't. That I think is something that's super fascinating that I'm sure we're going to find out over the next year or so.
- [Neil] I absolutely agree. I love your way you've framed it because a running thing we have on our episodes is AI and other technologies are a tool, right? Like we, it's all about how people choose to use it. Good, bad, maybe indifferent. You're, I'm curious about this because you're getting more information at your fingertips. More synthesis for people. You mentioned the social media and the collaboration. It feels like with all this technology, we know that the changes are coming faster and faster, but I think people are also surprised by the size of the impact, that it's much larger and things are coming much faster. Given what's going on now in the investment world, how is this like transforming the industry?
- [Jannick] I think it has captured people's imagination in a wild way, and we actually did a survey on this that you can find on our Twitter feed where we ask people about AI and whether they used it and whether they were going to use it, they were curious about it, and I think it was, the vast majority, 80, maybe 85 percent of people have either already used it or very curious and learning how to use it more for their investing, and so like people definitely see the value, and I think the growth of ChatGPT I think really meant that everybody sat back and looked at their life and was like, okay, how can I use this in my work life, with my hobbies, with my financial investments, potentially with health. So, it was like a mass moment of capturing people's imagination and those don't come often, right?
Like maybe the last one that was really like that was when Steve Jobs announced the iPhone in 2007 or something. And so I think it's, I think it's been huge. Now, the flip side of when something really captures people's imagination at scale in a very significant way is that they in the short term probably also overestimate what it can actually do.
And like I said, exponentially growing things are the hardest things for the human mind to comprehend. And this is something that is exponentially growing in its ability to deliver quality responses to users, for instance. And so it's the classic saying of you're actually probably a lot of folks, I would bet a lot of folks are actually overestimating what this can do in the next three to six months, but underestimating what it can do in a five, six, normally I think the saying is like two years and 10 years, but I think we got to start compressing that scale, whoever came up with that quote.
So that's been a little bit, that's been a little bit the trick for us, right? Because it's like this is a super fascinating tool, we did position it as an experiment. It is called Alpha partially because finding alpha is like a financial term for finding opportunities for gains in the market, which obviously you hope that your research leads to the opportunity to capture some gains.
But obviously alpha also means it's also what comes before beta, and so it's generally speaking, a little bit of a two sided name that for the time being we've labeled as an experiment because the big concern is obviously people overestimate it, they may become to rely too much on it too early.
And that's one of the things that we're trying to kind of balance internally here at Public as well, but generally speaking, it's super great to see that there's so much engagement here, that there's so much will to learn this stuff and like intent for people to really get into how can I use AI to drive my portfolio.
And I actually think one of the most important things is that people are not just sitting back and saying, oh, can I just, can AI just run everything for me, right? Because that very quickly gets to a point where you're also not building any financial literacy yourself. And so we've also had the principle of you should be in the driver's seat and Alpha is your assistant, not the other way around, right? It's not that Alpha is in the driver's seat, and you're the sort of LP or fiduciary behind it, right? That's certainly not where we are at this point in time.
- [Ryan] How are the more sophisticated investors viewing these AI tools coming in? I don't say they're leveling the playing field, but they're making it easier for the retail investors to get access to information that maybe beforehand was a competitive advantage for these other investors. Are they viewing this as a benefit for everyone across the space, and it's positive, or they, I guess, any part of them feel like it's creeping in on the advantage they have of being the ones to understand this stuff.
Because I know for instance when I've used different apps and tools and looked into their advanced trading platforms at, obviously it's like a foreign language to me and a lot of other people, but the people who understand that well do have an advantage on accessing and understanding information better than I can to make better informed decisions when it comes to managing a portfolio. So how is this being viewed with those that are more, do this every day and how's, just curious kind of what their perception has been.
- [Jannick] I think two things. So first off for context, in the world of investing, the shorter your time horizon is on any particular investment thesis, the harder it gets, right?
None of us has the ability to, the market's been open for an hour. None of you have the ability to tell me with any confidence whether it's going to open up or down today, right? Or by how much. And now that being said, we can probably all agree that we have a high degree of confidence that in five years, Alphabet, Apple, this is not financial advice, but there's a bunch of companies that we still believe will grow over the next five years and generally continue to capture market share, et cetera, that, where we believe in the future of these things. And so the longer your time horizon is, the easier it is to get something right.
In fact, if you've bought an S&P 500 ETF and held it for 10 years, in the history of the stock market, you've actually never been able to lose money, right? So generally we have a pretty high degree of confidence in that. And most retail really play a little bit more in the like, almost ironically, they're actually more long-term, at least the folks on our platform that are not like the hardcore day trader types as much.
They're a little bit more building their portfolios for the future. A lot of people buy stuff and they post out on our social layer like I want to own this forever. I'll just keep investing in it and building up a position or pass it on to my kids, et cetera. Hedge funds don't do that, right?
Hedge funds need to return capital to the folks that they raised money from, and so maybe that's a tight 10 years or like maximum that they can hold something is maybe 10 years, and then they want to reuse that capital a bunch of times to actually capture a bunch of gains, and so typically they have a much shorter kind of timeframe, and so they're competing a lot less than people realize because I can essentially buy stock X, Y, C, hold it for five or 10 years to say that then doubles in that period of time while the hedge funds have been dipping in and out of it on a weekly or monthly basis.
And we can actually both make money that way. And that's actually something that people don't realize I feel like very often when they talk about this stuff. So go back to the questions, I don't think that they really are because like they're compete, yes, they're on the same battlefield, but with very different time horizons typically.
And so I also think, what I've heard anecdotally for friends that are in these places, the hedge funds and the like that is, they immediately thought more about their own capabilities, much more than how does it leveling the playing field over here? It's a little bit more for them of like most companies go right now, I have 10, 15, 20 analysts. How many do I really need on a go forward basis? Or if I have 10, can they do the work of a hundred suddenly? And so it's a little bit more I think they're looking at their staff and being like how can I give those superpowers now similar to the way you've seen with developers and with GitHub's developer copilot.
- [Ryan] That's a good point because I, to bring up a question about where do you think the different roles will be in an investment firm hedge fund over, as we look forward to the future. Are there going to be new roles created? Are there going to be roles that you think are phased out because of these tools. Yes, we might not need as many analysts.
One of these models can probably do the work of, let's say, 10 analysts. So are you going to need people who are better at finding and researching, collecting the data, incorporating it into the model, revamping the model, working on prompts, all these different kinds of things? Or is it going to create new kind of roles and at the same time, is it going to potentially eliminate other roles in your mind?
- [Jannick] That's the really interesting question. So, I'm a designer, right? So I design design stuff since I was 14 years old. One of the hard things about, I used to decide stuff myself, now I have a team of designers, and I'm more in the directing role. I spend less time drawing the pixels, more time directing.
So, I think in the world of design, for instance, you'll have people that can suddenly be these like art directors and actually not necessarily have anything work for them. Like that becomes a little bit more the valued skill potentially. I think the same is true here, right? I think you have people that can crunch the numbers, build the spreadsheets, like actually fingers hitting the keyboard, and presenting it and et cetera, et cetera.
And then you have whoever is in the equivalent of the art director role. Typically you call that the Chief Investment Officer at the high level. But like how does that kind of break down? And I think it, yeah, it breaks down in a similar way. Another example is Hollywood films where you're seeing a lot of folks being like, hey, if you can generate everything, then the art director who gives the model the input, that's the most important role.
I think the same is going to be true. And then the way that you give it the input, so to say, that's also typically a function of trial and error because then again, you can do that a little bit of a different way, right? If you have a team of analysts, if you give them one prompt, like actually human analysts, it might take them a week to come back with something.
And then you give them another prompt, again, those are long feedback loops. And so you want to be more precise about your initial prompt. You don't need to be that, that doesn't need to be the case here necessarily. And so you have different styles suddenly where you can just be a little bit more fast and iterate quicker.
And I think ultimately what's really fascinating is you can imagine a future where, again, if a retail person and a Chief Investment Officer have access to the same model, and that's going to be a big question mark, but let's just say for the sake of the argument that they do, then who's able to direct that model and prompt that model all the best to get to a solid investment thesis, that's really going to be the skill, right? And that's where I would bet that the level, the playing field is going to be totally leveled because I think what you saw with GameStop is that there are retail folks out there that understand the markets at large as good or better, honestly, than at least some of the, maybe not like the 99 percentile kind of hedge fund, but certainly by a lot of the institutional players out there.
- [Neil] Some things I'm actually hearing in the industry is that, as we'll call it, people's time is freed up from some of the grunt work. There's going to be more focus on actually like psychology and things like neuro linguistics and parsing statements from like the Minister of Oil from, I won't name any countries, but things like that, and to almost try and predict the, is there a better way to predict the irrational behavior of people and its impact into the market? That's not something like at least we know how to teach AI how to do, but there's a strong feeling in the industry now that a lot of the freedom of time will actually be devoted to trying to figure that piece of the puzzle out. I'd love to get your take on what you think about that.
- [Jannick] That's actually really interesting because I think normally in this world, you talk about, let's take a show like Billions, right? Maybe you've all seen, if you haven't, exactly, so it's all about getting to the most rational and keeping your emotions separate.
If you look at a character like Bobby Axelrod is like keep the emotions out of everything else, be very hardcore. And then obviously what you've seen, especially with GameStop, is very, and with retail, is very emotional purchasing behavior essentially, right? And I think one of the things that you might see with the introduction of AI, again, along the lines of making people more informed investors, is part of being more informed is also being more aware of your own biases. And I think if AI can help you, excuse my French, but call bullshit on your own investing intent, not all the time, but at least some of the time as it starts to understand the style, as it starts to understand, hey, you're over, wait, take already, what are you doing here? This is not, you know, so that's, that gets a little bit closer to the hyper personalized sort of thing where you're talking about every instance of an AI really understanding their master, and what do they want to achieve. And what's quite interesting is as a financial services company, we have to do this thing called KYC, know your customer, which essentially just means we have to play 20 questions with every user that signs up front. And part of that means, why are you investing? Is it for speculative reason? Do you want to, is it growth? We need to grow your capital moderately aggressively over the next five years, or do you just want to preserve your capital?
And those are questions that we have to ask also in order to be able to better service our customers. Obviously now, that's all data that, and so for that reason, most financial care services companies have a lot of data on their customers, right? And also, by way of what you've invested in historically, we got a pretty good sense of your risk appetite.
And so you can start to imagine a world, and we haven't taken the step yet because as I said we just launched this in April, but we are thinking about future states where all that kind of comes into play as well, so that it's able to actually let's say keep you a little bit on a straight line and help you build, help you reach your initial goal, right?
Same way that like a fitness app asks you like how, what do you want to achieve? And if you want to say, oh, I want to just like stress less and whatever then you shouldn't probably be standing there pumping your biceps every day because you want to go to the beach and look fit, right?
Like it's a little bit like calling bullshit on your own actions relative to your goals, and that's I think something that it can help with a lot.
- [Nikolai] There's risks in investing. There's risks in crypto. Are there any risks or could there be any risks with using AI for investing?
- [Jannick] Whenever I get the question, are there any risks with comma, my answer is yes, full stop. Because obviously everything has risk, and we even offer treasury bills that we can market as one of the safest investments, which they are, but then you had the whole debt ceiling thing happen and suddenly there was a little bit of risk there, right?
And so there's risk in everything for sure. And there are also risks with using AI for stuff. So period, full stop. Now, how we think about that risk and how you mitigate that risk. That's actually a real question, and like how much risks is there, and specifically how much risk is there relative to the reward?
That's really what any risk measurement should come down to. And as we've talked about at length here, I think the rewards are potentially quite high. And again, you can never size the rewards because nothing is a sure thing, right? So you always talk about risk relative to potential reward, but the potential reward here is huge.
If any of the stuff that we just talked about materializes in the next year or two, I think those rewards are massive, especially for retail folks. And so then it's a question of, okay, what's the risk relative to potentially getting that reward. And I think there are things that we can build to mitigate those risks a lot.
I've touched on a few of them, but there are even, even in the design layer, right, so right now when you open it, we have like popular questions, which is like the most asked questions for any particular stock to Alpha, our chatbot, over the last 24 hours. So, if an earnings call just came out, it can summarize it like 30 seconds after the whole earnings call came out, which I should find is a better way than actually being on the earnings call myself, which is quite impressive. But so that's one of the most asked questions immediately after an earnings call, et cetera.
But there are these small notches in the user experience where you can like help guide people a little bit to, okay, if you've asked, if you asked about this question, maybe you should consider this position over here. And I think that's the real interesting thing I think right now is very reactive, obviously, right?
You have to prompt, it then has to respond. But in most apps, I think this is true for most industries, but certainly for financial services and investing, we have a lot of users that are doing stuff on a daily basis. So why couldn't it be more of a layer than just a chatbot that's always listening and then helps you proactively come up with these different prompts.
And I think that's potentially the next phase versus just being that reactive assistant, right? Like a proactive assistant is better than a reactive assistant. In real life, and I think the same is potentially true with AI. And so that's, and that's actually one way that I think you can make it a better experience, but also help mitigate a lot of the risks surrounding this stuff.
- [Ryan] Neil, anything from your end you want to wrap up with?
- [Neil] I think it's been a great discussion, and I really appreciate it Jannick. I think you've given us a little peek behind the curtain into the investing world and trends that AI is helping to shape for the future of the industry. If people are interested in learning more about you and Public, what's the best way to keep tabs?
- [Jannick], it's a very straightforward website name. That was very expensive, but again hopefully worth it.
- [Ryan] I was going to ask that. Yeah. How much did it cost to get that one? Especially when I saw the dot, when I saw the dot com, I was like, man, they must've spent a little bit of money, but I know you raised some money, so I think it's good.
- [Jannick] Did anyone build, well we actually bought it before we raised the money, so it was quite of a gut punch at the time we bought it.
- [Ryan] Well, it seems like you have a lot of very exciting stuff going on. We truly appreciate your time. We know you're busy. The three of us have already probably checked out Public and pretty neat what you have.
So definitely be something we dive into even further, so our audience definitely should check that out. But Jannick, thank you again for taking the time, and I look forward to hopefully talking again in the future.
- [Jannick] Yeah. Thank you guys. I really enjoyed this.
Special Guest

Hosted By
AI For All
Special Guest
Jannick Malling
- Co-Founder & Co-CEO, Public
Hosted By
AI For All
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