Leif Abraham of Public, founder and chief executive officer, discusses agentic artificial intelligence, brokerage user experience and the future of automated investing in a conversation recorded for NYSE Wired FinTech Exchange. Abraham explains Public's mission to modernize brokerage services for investors seeking to compound capital and describes product strategy, agent-driven automation and risk management features; they emphasize architectural decisions that separate artificial intelligence reasoning from deterministic trade execution to enable auditable user-approved agent workflows.
theCUBE Research frames the conversation, with hosts Gemma Allen, John Furrier and Dave Vellante guiding a focused discussion on user experience, security, go-to-market and roadmap priorities for retail and high net worth users. Analysts highlight that focusing on the top quartile investor, building product completeness and data integrations and leveraging Automated Customer Account Transfer Service ACAT transfers and superior user experience increases customer retention and makes AI automation appropriate for real assets.
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Leif Abraham, Public
Leif Abraham of Public, founder and chief executive officer, discusses agentic artificial intelligence, brokerage user experience and the future of automated investing in a conversation recorded for NYSE Wired FinTech Exchange. Abraham explains Public's mission to modernize brokerage services for investors seeking to compound capital and describes product strategy, agent-driven automation and risk management features; they emphasize architectural decisions that separate artificial intelligence reasoning from deterministic trade execution to enable auditable user-approved agent workflows.
theCUBE Research frames the conversation, with hosts Gemma Allen, John Furrier and Dave Vellante guiding a focused discussion on user experience, security, go-to-market and roadmap priorities for retail and high net worth users. Analysts highlight that focusing on the top quartile investor, building product completeness and data integrations and leveraging Automated Customer Account Transfer Service ACAT transfers and superior user experience increases customer retention and makes AI automation appropriate for real assets.
>> Palo Alto Studio Connection, Silicon Valley and Wall Street.
Leif Abraham
>> I'm John Furrier, co-host of theCUBE here with Dave Vellante, my co-host.
Gemma Allen
>> Welcome to theCUBE studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired, FinTech Exchange. And today we're having a conversation with a company that started by making investing more accessible. Now it's planning the next revolution. It's making the investor less involved with agents that can research, monitor, and actually execute trades. So is the future of the brokerage a better app or an AI that invests for you, that is what we're going to unpack today. Leif Abraham, welcome to NYSE Wired.
Leif Abraham
>> Thanks for having me.
Gemma Allen
>> So you started Public in 2019. A lot has happened in these last 7 years. Maybe help me understand the original thesis of this company and how things have evolved for you. Break it down.
Leif Abraham
>> Yeah, if you look at the investing brokerage space in the US, you have the incumbents who are fantastic businesses. But if you log into your Schwab account or so, no offense, you will feel like you're looking at a tax form. And then if you look at the more new brokers that evolved throughout the last decade, they were very heavily focused on speculation. So what we really saw is that the next generation Schwab didn't truly exist. And that is really what we sort of set out to build. And so our focus truly being on more of that top quartile in the US, right? Like people that have the means to compound in the markets, who have money left over at the end of the month to really put it to work. And so on. And so people who are serious about their investing, you can say.
Gemma Allen
>> So if we think about the world of 2019, it kind of in some way— well, I guess some folks were using Robinhood and these kind of more retail investor savvy apps. Right. But it still felt as though the institutional side of investment and those kind of more high-risk, high-reward plays felt somewhat of an isolated class, right? Are you saying or do you believe that that has kind of fundamentally changed? Like, do you think that there is opportunity for what is a traditional retail investor who has somewhat more disposable income than a 22-year-old who's just playing around with an index fund or a savings account, has more opportunity now to earn the way, you know, folks who have been super, super leverage-heavy have been able to earn over the last while?
Leif Abraham
>> For retail investors, I would put it two ways. Number one, yes, I think age plays a role. If you're younger, you have more time to gain. You're also young, you might get your first exposure to the market. And so you might be more risk-on than maybe some other people. And you'll make some risky bets and maybe dabble in crypto and all that kind of stuff. that's fine. I do also say that though, there is an aspect of just wealth profile of a person. Right. So if you don't actually have the assets that give you the ability to compound and if you compound an average of 10% a year in the markets, if you do that from a base that is $1,000, like it will take a long time to show real effects, obviously. And so I think there's also just a wealth aspect there. So we generally see that people that just don't necessarily have the means to compound in the markets and also are just a little bit more open for more speculative bets that have higher risk and higher return because of that limitation as well, which we think is a tricky situation. But also why you've seen many of these more mainstream platforms kind of lean so heavily on the speculative side, because if you design for mainstream America, that happens to be how you monetize people, etc., etc. So it's a little bit of a tricky dance. Hence also, again, why it's important for us to focus on that top quartile, because we believe if you're also building for people that have the means to kind of compound in the markets, you make different design choices, you offer different products,
Gemma Allen
>> etc.So I don't want to harp too much on this, but I want to make sure I understand the kind of user audience profile. Somebody who's not big enough to have a family office. But it's not your typical retail investor. There's that kind of middle ground there.
Leif Abraham
>> Yes.
Gemma Allen
>> In somewhat of an untapped market. Okay. So you said something interesting there at the beginning. You said you log into these brokerage accounts and they look like a tax form. I can attest. I agree with you. Right. They can be pretty painful. Are you competing on UX, on agility? What are you truly trying to compete on in terms of, first of all, grabbing their attention, right, and making this relatively seamless, but also ensuring that there is or there is not really kind of a fully defensible technical play to this business longer term?
Leif Abraham
>> For us, it's always twofold. There's things that people expect and there's things that we have to invent that we believe they would want to engage with and get value from. And things that people expect is like people want to have certain account types. People have an expectation of what Schwab might offer in terms of asset classes and so on. And so if we want to acquire that customer, we have to be able to offer these things. And that's like the baseline, so to say, just like product completeness and so on. Now for Public specifically, it's— we're in the business of kind of inventing the future in the space. And that means we— it's our responsibility to really think throughhow does someone interact with an investing platform in the age of AI, for example. Right. And where the space is going. And I think that is also what creates a lot of product intrigue of why someone would want to try us out versus sticking with theirkind of old school investing platform.
Gemma Allen
>> So help me understand the true agentic features on this platform. How autonomous is the experience? Like, where do you draw the line in terms of what an agent can and can't do for you? It can execute a trade based on what? Is it based on your own individual kind of guesstimate or research? Help me understand it a little bit more.
Leif Abraham
>> Yeah, so the high-level baseline is on Public, you can automate anything with an AI agent. So any action you could take within a brokerage account, you can automate with AI agents on Public. Obviously, most people use this for trading strategies, but it could also be things like risk management, right? So what we see, for example, a lot is that options traders have a very specific kind of criteria of when they actually want to exit their positions. They can have an AI agent just actively running at all times that will monitor all the options positions whenever they have a contract open to essentially just automatically follow those instructions and close those positions out and so on. Right. Another thing is there was a strategy that went viral the other day on X where it was if you bought the close and sold the open of Micron every day in the last X years, you would have had hundreds of thousands of percent of return or something. And that kind of backtest chart just went completely viral and whatnot. That actually became now one of the most replicated strategies that, you know, like of Agents on Public that users have actually implemented, for example. And then they run that as one specific trading strategy and so on. And so you can really automate anything that's there. And I think what's very interesting around that is that it helps people move up in their sophistication level because you might have an inkling of an idea. And the AI can help you figure it out. It's like your sparring partner and get to something that's more fully formed and then that can automatically run your portfolio and so on. And so that we find super interesting.
Gemma Allen
>> So talk me through what level of research and intel the AI can do for you, right? Is it like an AlphaSense type of level or is it something a little bit more top line level? Like if you were to say, hey, you know, Oracle stock is suffering a bit right now. Oracle is an interesting company. Give me a theory on where you think it's going to go in the next 3 years and then you can execute a trade based on that. Like, is that a fair use case or are there some limitations there?
Leif Abraham
>> Obviously, it will not necessarily forecast for you and try to make predictions of the future. What you would think about it much more is that, yes, you can do research on the current data and information that is out there. Obviously, it's probably got plugged into all market data you can expect that should be plugged into, including asset-specific things like options chain data, etc., etc. So it's plugged into everything essentially you can think of, whether you can find within a brokerage account or find with other company KPIs and so on. And that is the kind of input in a way. But then as it goes into the future, you should think about it much more as like strategy, principle criteria based, you know, and, you know, the buy the close, sell the open is a good idea. You know, for example, it's a good example there. And so it's much more about that. It will not help you predict the future. If it could, I would not make it available. I would secretly keep it for myself and run a hedge fund. But that's really not the focus there.
Gemma Allen
>> Well, let's just stay on that jovial topic for a second because you mentioned some of the virality that's happened on TikTok. We also had a period where people thought, I can buy a Mac mini and I can run Ollama and that can just invest for me and I'm going to be rich in 5 years. Right. Those theories were definitely circling around TikTok and all sorts of platforms too. Whether or not you're ever going to let— folks clearly have, but you want to let a Mac mini and Ollama log into your Fidelity account for you or not is a pretty risky bet, right? But in terms of the industry broadly, there is certainly this viewpoint that, yeah, we're going to democratize the absolute bejesus out of investment and everyone's going to have a way in which they can get rich. Where do you think the line is drawn, though? What are your thoughts on the broader sentiment in the industry right now?
Leif Abraham
>> So first off, I think what you're describing is true. You could connect through what's called an MCP, which is kind of like a wrapper around the API of a brokerage and so on. And you could connect a lot of different brokerages to your Claude or OpenAI or ChatGPT, OpenAI. Or whatever, right? You can do that and that's fine. You can run something. There's some risk with that, right? So I would put that still in the experimentation camp. Hence also why you see online people creating separate accounts for it and not really letting that loose on their real life savings necessarily, because at the end of the day, you're giving up control to truly an LLM, its own free thinking, its own reasoning, through the updates that Anthropic might make tomorrow on the model that you have no control over and so on. And so what we've done with AI agents in Public is number one, actually integrate them into the platform. So it happens within the security of your authenticated brokerage account. Right. You're not connecting it to some third party. Right. Because giving your API keys, your MCP to another third party basically means you're giving your password to your brokerage account to another piece of software. Right. And so there are some risks with that as well. And so the other thing that we've done is we've sort of separated that reasoning that the AI is doing and the trade execution. And so how that looks like is you can chat with the agent on Public. It will help you create your AI agent and your strategy, for example, and it will help you finesse it. It will ask you follow-up questions to get to a more, concrete outcome. Once it has a fully formed idea of what you want to create, it essentially creates that agent, suggests it for you. It shows you a description of it, like an actual visualization of that workflow, and then you swipe to approve it. But what happens in the background in that moment is that now that agent is basically being written in actual code. Think about it as it's now being set into stone and it sits there and it cannot be edited. It can be edited, but it will not self-edit itself or whatever. It doesn't have free thinking anymore, like no free reasoning at that point. So it's a deterministic workflow that then executes so you can trust it. Because now it will only do what you told it to do. You had full control over it. You verify that the workflow and the strategy is exactly what you wanted it to do. And therefore what AI agents at Public can do is something that has much more security to it. And so therefore it becomes something that people actually use with their real life savings, their real money. Like we had someone come in a few months ago who literally fired their wealth managers at UBS. Moved $50 million, 5-0, into a Public account and now has AI agents running covered call strategies in their portfolio.
Gemma Allen
>> Wow.
Leif Abraham
>> And that is happening right now with real money, . And so there's a big difference between the architectural decision that we've made with the AI agents from Public to take it out of this experimentation, kind of my weekend project. I tinker with some AI agents and clown around into something that you can use with real money and that has safeguards built in that you can trust.
Gemma Allen
>> So talk about the go-to-market. How did you find that individual? How did that individual find you? And interestingly, with brokerages, as is the case with banks, there's a lot of data to say that people don't move banks because they're just too lazy, right? that is often one of the core reasons that people stay with the same bank or the same— a lot of decisions we make, we make when we're 18, and it just seems like so much hassle to wake up tomorrow and say, you know what, I'm moving to Chase, right? What's happening in the brokerage space right now? Talk to me about the GTM. Who are you targeting and how?
Leif Abraham
>> So first off, I completely agree assets are sticky, which is also a reason why obviously we love this business because it also means for us assets are sticky. Now, obviously in the brokerage space it's even easier because you have ACAT transfers, like account transfers. You can literally move your account from a Schwab over to a Public tomorrow. Many people are doing that. Everyone should. But, and so that's a little bit more simplified there. And so it's really much more, we have to give people enough reasons to press those buttons and to do that. And in most cases, what we see is people sign up, they deposit some money, they have a great experience, and then they consolidate accounts with us. And that is the usual kind of sequence. And for us, really, most of our customer acquisition comes from word of mouth, which again, therefore, is driven by us doing a good job at giving people great service, but also just being the people in the space that truly innovate and make sure that the platform does not look like a tax form. And actually, it's a delightful, awesome experience that kind of inspires people of a lot of other things they can do and help to make money at the end of the day, of course.
Gemma Allen
>> So the roadmap, the future product roadmap, talk me through what are you building towards? Like you mentioned that you're not necessarily providing intel, right? But is that a feature that you think will be on the roadmap 3 years from now? Are we going to have a miniature Bloomberg terminal for retail investors where they can get access to really good live data through one brokerage account? Because I know I sure as hell would sign up for that one. Right. Like what? How are you thinking about it competitively from a product deployment perspective?
Leif Abraham
>> I think what you describe is already happening. And yes, there might be certain bells and whistles to a Bloomberg Terminal that is not in a fully borderline free brokerage or retail, software product yet. But I think that is generally just a matter of time. I think data specifically, it gets very quickly kind of commoditized and it's really much more about the software and the experience you build around that data. And I think Agents is a great example of that, of the power an agent has is very much also dependent on the data and tools it has access to. And so for us, for example, whenever we launch a new feature right now, the Agentic layer just sits on top of that as well. And so with everything else we're launching, it might be an account type, it might be an asset class, we're launching certain prediction markets now, for example, those things will just only add more capabilities for the AI agents as well, which makes them more powerful, which makes them more sticky, which makes them more intriguing, and so hopefully more successful as well, right, in what they can do for you. And so that just keeps kind of growing and growing. So I think it's much more about the experience you build around those pieces than just access to the piece of data in the first place.
Gemma Allen
>> Leif Abraham, thank you so much for joining us at NYSE Wired. Fascinating product. Wish you guys all the best.
Leif Abraham
>> Yeah, thanks for having me.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is FinTech Exchange, one of our programs with NYSE Wired. Thanks for watching.
>> Palo Alto Studio Connection, Silicon Valley and Wall Street.
Leif Abraham
>> I'm John Furrier, co-host of theCUBE here with Dave Vellante, my co-host.
Gemma Allen
>> Welcome to theCUBE studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired, FinTech Exchange. And today we're having a conversation with a company that started by making investing more accessible. Now it's planning the next revolution. It's making the investor less involved with agents that can research, monitor, and actually execute trades. So is the future of the brokerage a better app or an AI that invests for you, that is what we're going to unpack today. Leif Abraham, welcome to NYSE Wired.
Leif Abraham
>> Thanks for having me.
Gemma Allen
>> So you started Public in 2019. A lot has happened in these last 7 years. Maybe help me understand the original thesis of this company and how things have evolved for you. Break it down.
Leif Abraham
>> Yeah, if you look at the investing brokerage space in the US, you have the incumbents who are fantastic businesses. But if you log into your Schwab account or so, no offense, you will feel like you're looking at a tax form. And then if you look at the more new brokers that evolved throughout the last decade, they were very heavily focused on speculation. So what we really saw is that the next generation Schwab didn't truly exist. And that is really what we sort of set out to build. And so our focus truly being on more of that top quartile in the US, right? Like people that have the means to compound in the markets, who have money left over at the end of the month to really put it to work. And so on. And so people who are serious about their investing, you can say.
Gemma Allen
>> So if we think about the world of 2019, it kind of in some way— well, I guess some folks were using Robinhood and these kind of more retail investor savvy apps. Right. But it still felt as though the institutional side of investment and those kind of more high-risk, high-reward plays felt somewhat of an isolated class, right? Are you saying or do you believe that that has kind of fundamentally changed? Like, do you think that there is opportunity for what is a traditional retail investor who has somewhat more disposable income than a 22-year-old who's just playing around with an index fund or a savings account, has more opportunity now to earn the way, you know, folks who have been super, super leverage-heavy have been able to earn over the last while?
Leif Abraham
>> For retail investors, I would put it two ways. Number one, yes, I think age plays a role. If you're younger, you have more time to gain. You're also young, you might get your first exposure to the market. And so you might be more risk-on than maybe some other people. And you'll make some risky bets and maybe dabble in crypto and all that kind of stuff. that's fine. I do also say that though, there is an aspect of just wealth profile of a person. Right. So if you don't actually have the assets that give you the ability to compound and if you compound an average of 10% a year in the markets, if you do that from a base that is $1,000, like it will take a long time to show real effects, obviously. And so I think there's also just a wealth aspect there. So we generally see that people that just don't necessarily have the means to compound in the markets and also are just a little bit more open for more speculative bets that have higher risk and higher return because of that limitation as well, which we think is a tricky situation. But also why you've seen many of these more mainstream platforms kind of lean so heavily on the speculative side, because if you design for mainstream America, that happens to be how you monetize people, etc., etc. So it's a little bit of a tricky dance. Hence also, again, why it's important for us to focus on that top quartile, because we believe if you're also building for people that have the means to kind of compound in the markets, you make different design choices, you offer different products,
Gemma Allen
>> etc.So I don't want to harp too much on this, but I want to make sure I understand the kind of user audience profile. Somebody who's not big enough to have a family office. But it's not your typical retail investor. There's that kind of middle ground there.
Leif Abraham
>> Yes.
Gemma Allen
>> In somewhat of an untapped market. Okay. So you said something interesting there at the beginning. You said you log into these brokerage accounts and they look like a tax form. I can attest. I agree with you. Right. They can be pretty painful. Are you competing on UX, on agility? What are you truly trying to compete on in terms of, first of all, grabbing their attention, right, and making this relatively seamless, but also ensuring that there is or there is not really kind of a fully defensible technical play to this business longer term?
Leif Abraham
>> For us, it's always twofold. There's things that people expect and there's things that we have to invent that we believe they would want to engage with and get value from. And things that people expect is like people want to have certain account types. People have an expectation of what Schwab might offer in terms of asset classes and so on. And so if we want to acquire that customer, we have to be able to offer these things. And that's like the baseline, so to say, just like product completeness and so on. Now for Public specifically, it's— we're in the business of kind of inventing the future in the space. And that means we— it's our responsibility to really think throughhow does someone interact with an investing platform in the age of AI, for example. Right. And where the space is going. And I think that is also what creates a lot of product intrigue of why someone would want to try us out versus sticking with theirkind of old school investing platform.
Gemma Allen
>> So help me understand the true agentic features on this platform. How autonomous is the experience? Like, where do you draw the line in terms of what an agent can and can't do for you? It can execute a trade based on what? Is it based on your own individual kind of guesstimate or research? Help me understand it a little bit more.
Leif Abraham
>> Yeah, so the high-level baseline is on Public, you can automate anything with an AI agent. So any action you could take within a brokerage account, you can automate with AI agents on Public. Obviously, most people use this for trading strategies, but it could also be things like risk management, right? So what we see, for example, a lot is that options traders have a very specific kind of criteria of when they actually want to exit their positions. They can have an AI agent just actively running at all times that will monitor all the options positions whenever they have a contract open to essentially just automatically follow those instructions and close those positions out and so on. Right. Another thing is there was a strategy that went viral the other day on X where it was if you bought the close and sold the open of Micron every day in the last X years, you would have had hundreds of thousands of percent of return or something. And that kind of backtest chart just went completely viral and whatnot. That actually became now one of the most replicated strategies that, you know, like of Agents on Public that users have actually implemented, for example. And then they run that as one specific trading strategy and so on. And so you can really automate anything that's there. And I think what's very interesting around that is that it helps people move up in their sophistication level because you might have an inkling of an idea. And the AI can help you figure it out. It's like your sparring partner and get to something that's more fully formed and then that can automatically run your portfolio and so on. And so that we find super interesting.
Gemma Allen
>> So talk me through what level of research and intel the AI can do for you, right? Is it like an AlphaSense type of level or is it something a little bit more top line level? Like if you were to say, hey, you know, Oracle stock is suffering a bit right now. Oracle is an interesting company. Give me a theory on where you think it's going to go in the next 3 years and then you can execute a trade based on that. Like, is that a fair use case or are there some limitations there?
Leif Abraham
>> Obviously, it will not necessarily forecast for you and try to make predictions of the future. What you would think about it much more is that, yes, you can do research on the current data and information that is out there. Obviously, it's probably got plugged into all market data you can expect that should be plugged into, including asset-specific things like options chain data, etc., etc. So it's plugged into everything essentially you can think of, whether you can find within a brokerage account or find with other company KPIs and so on. And that is the kind of input in a way. But then as it goes into the future, you should think about it much more as like strategy, principle criteria based, you know, and, you know, the buy the close, sell the open is a good idea. You know, for example, it's a good example there. And so it's much more about that. It will not help you predict the future. If it could, I would not make it available. I would secretly keep it for myself and run a hedge fund. But that's really not the focus there.
Gemma Allen
>> Well, let's just stay on that jovial topic for a second because you mentioned some of the virality that's happened on TikTok. We also had a period where people thought, I can buy a Mac mini and I can run Ollama and that can just invest for me and I'm going to be rich in 5 years. Right. Those theories were definitely circling around TikTok and all sorts of platforms too. Whether or not you're ever going to let— folks clearly have, but you want to let a Mac mini and Ollama log into your Fidelity account for you or not is a pretty risky bet, right? But in terms of the industry broadly, there is certainly this viewpoint that, yeah, we're going to democratize the absolute bejesus out of investment and everyone's going to have a way in which they can get rich. Where do you think the line is drawn, though? What are your thoughts on the broader sentiment in the industry right now?
Leif Abraham
>> So first off, I think what you're describing is true. You could connect through what's called an MCP, which is kind of like a wrapper around the API of a brokerage and so on. And you could connect a lot of different brokerages to your Claude or OpenAI or ChatGPT, OpenAI. Or whatever, right? You can do that and that's fine. You can run something. There's some risk with that, right? So I would put that still in the experimentation camp. Hence also why you see online people creating separate accounts for it and not really letting that loose on their real life savings necessarily, because at the end of the day, you're giving up control to truly an LLM, its own free thinking, its own reasoning, through the updates that Anthropic might make tomorrow on the model that you have no control over and so on. And so what we've done with AI agents in Public is number one, actually integrate them into the platform. So it happens within the security of your authenticated brokerage account. Right. You're not connecting it to some third party. Right. Because giving your API keys, your MCP to another third party basically means you're giving your password to your brokerage account to another piece of software. Right. And so there are some risks with that as well. And so the other thing that we've done is we've sort of separated that reasoning that the AI is doing and the trade execution. And so how that looks like is you can chat with the agent on Public. It will help you create your AI agent and your strategy, for example, and it will help you finesse it. It will ask you follow-up questions to get to a more, concrete outcome. Once it has a fully formed idea of what you want to create, it essentially creates that agent, suggests it for you. It shows you a description of it, like an actual visualization of that workflow, and then you swipe to approve it. But what happens in the background in that moment is that now that agent is basically being written in actual code. Think about it as it's now being set into stone and it sits there and it cannot be edited. It can be edited, but it will not self-edit itself or whatever. It doesn't have free thinking anymore, like no free reasoning at that point. So it's a deterministic workflow that then executes so you can trust it. Because now it will only do what you told it to do. You had full control over it. You verify that the workflow and the strategy is exactly what you wanted it to do. And therefore what AI agents at Public can do is something that has much more security to it. And so therefore it becomes something that people actually use with their real life savings, their real money. Like we had someone come in a few months ago who literally fired their wealth managers at UBS. Moved $50 million, 5-0, into a Public account and now has AI agents running covered call strategies in their portfolio.
Gemma Allen
>> Wow.
Leif Abraham
>> And that is happening right now with real money, . And so there's a big difference between the architectural decision that we've made with the AI agents from Public to take it out of this experimentation, kind of my weekend project. I tinker with some AI agents and clown around into something that you can use with real money and that has safeguards built in that you can trust.
Gemma Allen
>> So talk about the go-to-market. How did you find that individual? How did that individual find you? And interestingly, with brokerages, as is the case with banks, there's a lot of data to say that people don't move banks because they're just too lazy, right? that is often one of the core reasons that people stay with the same bank or the same— a lot of decisions we make, we make when we're 18, and it just seems like so much hassle to wake up tomorrow and say, you know what, I'm moving to Chase, right? What's happening in the brokerage space right now? Talk to me about the GTM. Who are you targeting and how?
Leif Abraham
>> So first off, I completely agree assets are sticky, which is also a reason why obviously we love this business because it also means for us assets are sticky. Now, obviously in the brokerage space it's even easier because you have ACAT transfers, like account transfers. You can literally move your account from a Schwab over to a Public tomorrow. Many people are doing that. Everyone should. But, and so that's a little bit more simplified there. And so it's really much more, we have to give people enough reasons to press those buttons and to do that. And in most cases, what we see is people sign up, they deposit some money, they have a great experience, and then they consolidate accounts with us. And that is the usual kind of sequence. And for us, really, most of our customer acquisition comes from word of mouth, which again, therefore, is driven by us doing a good job at giving people great service, but also just being the people in the space that truly innovate and make sure that the platform does not look like a tax form. And actually, it's a delightful, awesome experience that kind of inspires people of a lot of other things they can do and help to make money at the end of the day, of course.
Gemma Allen
>> So the roadmap, the future product roadmap, talk me through what are you building towards? Like you mentioned that you're not necessarily providing intel, right? But is that a feature that you think will be on the roadmap 3 years from now? Are we going to have a miniature Bloomberg terminal for retail investors where they can get access to really good live data through one brokerage account? Because I know I sure as hell would sign up for that one. Right. Like what? How are you thinking about it competitively from a product deployment perspective?
Leif Abraham
>> I think what you describe is already happening. And yes, there might be certain bells and whistles to a Bloomberg Terminal that is not in a fully borderline free brokerage or retail, software product yet. But I think that is generally just a matter of time. I think data specifically, it gets very quickly kind of commoditized and it's really much more about the software and the experience you build around that data. And I think Agents is a great example of that, of the power an agent has is very much also dependent on the data and tools it has access to. And so for us, for example, whenever we launch a new feature right now, the Agentic layer just sits on top of that as well. And so with everything else we're launching, it might be an account type, it might be an asset class, we're launching certain prediction markets now, for example, those things will just only add more capabilities for the AI agents as well, which makes them more powerful, which makes them more sticky, which makes them more intriguing, and so hopefully more successful as well, right, in what they can do for you. And so that just keeps kind of growing and growing. So I think it's much more about the experience you build around those pieces than just access to the piece of data in the first place.
Gemma Allen
>> Leif Abraham, thank you so much for joining us at NYSE Wired. Fascinating product. Wish you guys all the best.
Leif Abraham
>> Yeah, thanks for having me.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is FinTech Exchange, one of our programs with NYSE Wired. Thanks for watching.
Gemma Allen