AI for Growth: Lessons from Leaders Justin Roopnarine

Megan Driscoll: Welcome to AI for Growth Lessons from Leaders. Today I have with me Justin Rubnerin. I am super excited about this conversation. This is a, I'm having someone on who I have never had someone in this industry.

So Justin, tell us a little bit about who you are and what you do, and we'll get started. Justin Roopnarine: Casha, thanks, Megan. So I'm a partner at Limitless Capital. We're essentially a fund that focuses on the global equity space.

And as a partner right now, my main responsibilities are, of course, scaling our operations, refining our investment thesis, and of course facilitating capitalist reductions. Thanks for having me on. Megan Driscoll: Awesome. Yeah, you're welcome.

Thanks for coming. ⁓ I do think that the financial industry is ⁓ an interesting space to talk about how AI is sort of changing that. So maybe we could just start at like a kind of a micro level, like how are investment companies like your firm utilizing AI and sort of implementing that into your business operations? Justin Roopnarine: Yeah, of course.

So, ironically enough, since ⁓ let's say twenty twenty one was the heyday of all these, you know, recent LMs and whatnot, ⁓ before, right? You know, traditional methods it's there's quite a lot of data in the finance industry as you might imagine, you know, from ⁓ decan data down to seconds, minutes, even smaller than that, ⁓ EPS E because all these reports everywhere. Long story short, there is quite a bit information. And it takes time and a lot of money and resources to sort through all of that.

You know what AI has allowed us to do, at least with the recent LM trend and, you know, other similar I guess tools in that vein, we can organize our information a lot more quickly and gain insights that would have taken a lot long to discover. So for example, you know, it just Yes, we're looking back at company reports or other just data ⁓ from, I don't know, five, ten years ago. It's hard, you know, to just determine, hey, why why did this jump up all of a sudden, so to say, or why did this all of a sudden drop?

What happened? What was the volatility looking like? You know, of course we had a programs that we still use to this day that kind of, you know, sort through all that data and find those spikes or troughs or whatnot. But with AI we kind of d you know, gleam some more insights, more information.

All right, we've determined when these spikes were. Volatility all of a sudden jumped, but what was the major news event, right? All right, AI, can you help me just search for major news events around that time? And more often than not, it's able to just help us gain or glean more insights from just information that we might have not we might have missed before.

And is interesting in that aspect. It's basically a very helpful tool that has the entire knowledge of the internet alongside it, that we can kind of pair it along with data. Megan Driscoll: Yeah. It it it actually is kind of amazing what it can do for your industry, just in terms of like you just talked about like the trends and whatnot.

⁓ because I do feel like, you know, ⁓ a common term we use in human resources is past is past behavior is a predictor of future behavior. So like an interviewing technique is to get people to talk about how they've handled a situation in the past, because that will inform you as to how someone will handle a Justin Roopnarine: Exactly. Megan Driscoll: ⁓ future situation. And I think that the that the at least the stock market is very similar to that, right?

Like the we we have these things happen and we don't often learn from them, but then they just sort of repeat themselves over and over again. And so it's how can you, ⁓ I guess more importantly, understand and predict when something is gonna go down or up based on trends from the past. It seems like it should be getting easier and easier to do that with A AI. Do you think that's true that we'll be able to predict those trends more easily or ⁓ Justin Roopnarine: Mm-hmm.

Megan Driscoll: Do you think it's just not there yet? Justin Roopnarine: Well, yeah. Possibly. I mean I hate to give you the I don't know at this time, but let let's explore both angles, right?

Yeah, just first and front foremost, you know, of course we are firm, you know, past performance is not indicative of future results. ⁓ same thing kind of goes with stocks. You know, hey, we can kind of learn from the past how they moved. You know, if we saw this pattern in the past, maybe it's an indication of what it might do in the future, you know, albeit that.

But long story short, ⁓ you know, AI is kind of the best when it helps us amplify our judgment, not when it replaces our accountability. We're not gonna say, all right, because We saw this pattern in the past. At this date, at this time, it's gonna do the exact same thing this time around. Is it a good indication or predictor?

Yes, to a certain extent, but does it guarantee it will do the exact same thing? No. Will it ever come to a day where, you know, hey, if we see this exact same series of events or maybe similar events happening, that it could give us some sort of judgment about the future? Perhaps.

Could we make trades or hedges against that that might you know either protect our investors or whatnot or our positions or potentially seek to gain from those? Yes, but you know, AI on messy data essentially creates some confusion. So one, make sure the data's clean. Make sure what we're predicting, you know, all s makes sense.

Long story short, does our investment thesis or does our trade idea does our hedge make sense? ⁓ and then from there. Sure, let's explore. Assuming, you know, we see this same pattern in the past.

Let's predict that's gonna do the exact same thing now. Fair prediction, right? Alright. Let's do it.

Let's put on a trade for that, but also put on another hedge. So i to answer your question, I think I'm I'm gone a little bit too far, but it's useful. For judging or you know, making decisions from previously occurring patterns. You know, when we see wa pattern recognition at the end of the day.

And when we see a similar pattern happening now in the current or potentially in the future, we can identify that quickly and pot and make a position such that all right, we've seen this in the past, maybe you can predict what will happen in the future, but if we're completely wrong for whatever reason, we're hedged in that aspect. Does that help or make sense? Okay. Megan Driscoll: Yeah, yeah, it does.

⁓ you know, I wonder, ⁓ you know, as it relates to to this in general, like I wonder if there's gonna be like ⁓ a Claude fund. And you know, this is purely ⁓ what Claude tells you to do. So this is Claude's prediction based on past performance of the stock market of where you should invest. Is like that possible?

Is that something that like could happen? It would be so interesting to see like w Clawed against some of the top investment firms and like who wins over five years, you know? Justin Roopnarine: No, I completely agree. That would be that would be something.

I'm sure they'll probably do that one day too. Just compare all the leading LMM models here. There we go. Megan Driscoll: I might do it.

I'm gonna set up a claude fund and put a little bit of money in it. And honestly it wouldn't even matter because it would just be like, let's just see what happens. Is it beating the S and P or not? You know, like someone's gotta be doing that.

Justin Roopnarine: No, I ⁓ No, I think so too. I I've seen a few case studies and videos of some ⁓ people who've done something similar, I guess, where they had a thesis and they used you know, they put it into CLOD GPT or ⁓ any other similar software like that and it did trade. but ⁓ to your question, right, you know, in five, ten years could we see something like that? I would argue that we kind of already had those even before the LM Heyday, right?

You could have you know a I just you can talk with software, see other things where it's like, all right, my ⁓ the we usually like to say, all right, when we're coming into a new LP or, you know, just general advice, what is your r what returns are you seeking? Do you want to double your money? Do you want like five percent return over five years? Yeah.

Long story short, three questions. What return are you seeking? What's your time horizon? And how much risk are you willing to take?

Like that has those are the common things from way back in the day. Could you say could you ask Claude those simple three questions or GPT? And they might give you an answer. Say, I don't know, I'm seeking ten percent return year over year for five years.

I'm retiring in like two years, so I have to have a really low risk tolerance. Right now, I mean, I'm not a Megan Driscoll: Yeah. Yeah. Justin Roopnarine: not necessarily a betting man, but I'd argue that all the LMs might be able to give you a pretty good idea of what you should and shouldn't do.

Like but I think where your question was going was like, hey, could it actually down to the day, predict when things are going up, down, give you a good risk profile from there? I Megan Driscoll: Yeah. Justin Roopnarine: Say, I'm not sure yet. You know, I don't know.

I would err on the caution of like I don't think it can yet, but to our earlier point, it can help you make some judgments. It's like, all right, if you see similar patterns, sure. You know, maybe you've seen this in the past, I've seen this five years ago, ten years ago. You know, maybe other stocks like you know, not financial advice, but you see Coca-Cola moving one way and Pepsi moving the opposite.

Is there some insights or information there you think we're missing? Megan Driscoll: Yeah, right, right, right. Yeah, interesting. ⁓ how do you see it affecting ⁓ your hiring?

Like one of the things I know Justin Roopnarine: Yeah. Yeah. Megan Driscoll: Just, you know, I there's a I I think it's really tough for new grads right now. I I've you know, the this job market is crazy.

And I think it's gonna settle down, but I think that there's a lot of like uncertainty in the market. There's a lot of people like not making hires because of it. But I also think there's a lot of like jobs that are going away. You know, one of the things that I think when I think about the financial space is sort of the data analyst.

Like, do you even need data analysts, right? Like you've got this engine behind you that's way better than any individual person in terms of collecting data. Because I do think that in the financial Space raw. Obviously, you've been doing predictive modeling forever, right?

So, like Claude, like you said, it's not like a life changer because predictive modeling has been sort of the basis by which the financial industry succeeds or fails, I guess. ⁓ where do you see that affecting like your entry-level hires or your younger people? Like, how do you see it affecting like the landscape of employment? Justin Roopnarine: Yeah.

Yeah, so ⁓ before I answer that let me let me just step back. Maybe I have some bias here. But you know, but for for context, I graduate school and undergrad, I did electrical engineering and mathematical finance and I actually was a software engineer and a data analyst for a bit before the LMs and whatnot. and I can maybe speak to my own experience where, you know, tasks that used to take entire teams, you know, at least leaving a team, hey, you have to develop this feature, we're gonna do sprints every day.

Can now, I mean, even just be done by myself and maybe one other. And then it got to the point where it's like Yeah, I mean I could develop the whole feature, test, everything. Referring to software engineering pretty much all by myself. You know, even without telling, you know, GPT, hey, please develop this feature, I can just inherently test it, understand the you know.

Long story short. To your point, yeah, it's definitely affected ⁓ hiring market. It's made senior devs in a lot more, ⁓ I guess senior positions, a lot more efficient at what they can do. ⁓ such to the point where it's like you don't really need these junior teams.

However, what we're missing is a crucial fact, right? If you only have the senior dev market and there's no entry or junior positions, you're not really training a workforce for the future. I think that's the argument, you know, a lot of me or mainstream media has picked up. And it's right.

You know, if you're not training a human workforce, assuming there's no AGA anytime soon, we're gonna come to this point where, you know, you're gonna have a lot of seniors retire and you're not gonna have the workforce to replace them. Now, as far as hiring, Well, with that point, right, now that you can kinda do a lot more work with a lot less people, given they have the experience and kind of know domain knowledge. yeah, you can make the argument you don't need much people.

⁓ however, you know i Megan Driscoll: Yeah. Justin Roopnarine: I w if I were to ask you, kind of stepping back again one more time, if you were to just outline all your jobs, you know, your roles and responsibilities right now, except not as like a job description, but as like a series of tasks. All right, think about for a moment, like simple tasks you do every day, like, hey, check my emails, ⁓ I don't know, write an end-of-day report to more strategic tasks. Like, hey, I need to grow this podcast at you know, 100,000 to million listeners by the next six months, right?

If you were to kinda just list out all the tasks you do and then also work at AI completely automate this. Where can AI help so much to the point where it's like this used to take me three hours a day before, now it takes me ten minutes and really just list that I think, you know, exploring the job market or talent market is gonna change more towards that level. Where it's like it's no longer just a job. It is a series of tasks.

AI is gonna effectively automate or make certain tasks much more efficient such that, you know, you're not spending as much time, you know, no one person is spending as much time on tasks ⁓ that they usually did. Megan Driscoll: Mm-hmm. Justin Roopnarine: However, they get a lot more efficient at a wider variety of tasks. You know, for more strategic tasks, it could definitely help out with that.

And maybe the entry level positions could be more focused on those more intensive tasks, at least at this point. Does that make sense? No. Megan Driscoll: Yeah.

I do it does make sense. I ⁓ I do get concerned about the workforce of the future. I also think that now more than ever, ⁓ you know, I used to say C get Cs get degrees, right? Cs get degrees.

So I I think it's just like irrelevant now. It's it's if if you're getting a degree with a C, like Justin Roopnarine: Yeah. Megan Driscoll: There's less jobs and if they're gonna hire the A student, they're gonna hire the 4.0 over the person that's, you know, graduated but but barely with a 2.

9 or whatever, right? I do think that the that the world has changed for these new grads, for for fresh grads and for people entering into college and deciding to take that on and deciding to go to to to get advanced degrees. I think advanced degrees are gonna become super important as it relates to the hiring process because there's just gonna be less jobs and therefore the emphasis is gonna be on really like top-level people. So I think everybody has to kind of like.

Justin Roopnarine: Yeah. Yeah. Megan Driscoll: Their game a little bit. That's my personal feeling.

⁓ which you know, I tried to share with my children and they were like, Whatever, mom. You used to say C's get degrees. I was Well, I have learned a thing or two and I take it back. so I don't know, you know, I think ⁓ the workforce of the future, but particularly in the financial industry, I just think this is a really clear use case for more like more AI, less people, right?

'Cause there's just again, you're so Justin Roopnarine: Yeah. Yeah. Yeah. Megan Driscoll: dependent on data and ⁓ there's no better data right than than from a GPT.

What do you ⁓ what's your favorite GPT? Like which do you use? Do you use Claude a lot? Like what does your firm use?

How what do you personally use? Justin Roopnarine: Yeah, for for personal use, I mean even for you know, we tested out a few in the beginning, of course we kinda is steered towards the big players, your ⁓ Claude, your chat GPT, if you're familiar with ⁓ like Codex and Claude Code, those have been game changers. You know, you're much more efficient on tokens, a you know, able to access your local machine, even you know, web it it's just incredible. but yeah, I would say those two are our main ⁓ use cases right now when we're trying to develop some of our own stuff internally.

Megan Driscoll: Yeah, you are. ⁓ cool. That yeah, I mean that's the cool thing too, is once you start working with Cloud Code, you can kind of create your your own GPT really that is like you know pro proprietary to your organization. ⁓ have you looked into ⁓ creating like actual AI agents, so actually employees, AI employees that have specific tasks?

Have you guys done anything like that? Justin Roopnarine: Yeah. ⁓ yeah. No, we we ⁓ man, it's ironic too.

We had a whiteboarding session where it's like, all right, here's the traditional structure, analyst, VP, you know, quant analyst, etc. And it's like that same discussion we had about tasks, it's like, alright. What is a quant analyst's day-to-day task? If you're gonna do some research, you're gonna do this, you're gonna put this in programmatic or you know, implement your idea programmatically, what is an analyst ⁓ you know, I don't know, equity analyst responsibilities are research, this basket of stocks.

And it could it be done agentically? Yeah, to a certain extent. we would like to have, I mean, not to, you know, completely destroy the idea that the entry-level market is gone, but it's like st I still like to be hopeful where it's like you know, it could be made a lot more efficient. And even we had a human in the loop, you know, learning alongside with the AI, with the equity analyst agent or the ⁓ quant analyst agent, and we had a real human doing it as well.

I think we're still at that era where it's like, hey now, you know, the workforce has changed. You know, of course you're gonna be doing less, but you're gonna doing more with the AI and you both can be learn together to be more efficient later on to help us grow our organization. ⁓ but Megan Driscoll: You're gonna be managing a team of AI agents. Justin Roopnarine: Yeah, I I still think we're at the point where we need the human, but i if we come to a true AGI or you know, the AI AGI is like as good as us, then I don't know what that looks like to be honest.

I don't think anyone really does. But for now, I would argue we still need the human and I would have them becoming more efficient alongside the AI. And that's kind of the result of our whiteboarding. Megan Driscoll: Yeah.

Yeah. Well, I hired my daughter as an intern this summer and she is going to get ⁓ certified and she'll be at the end of the summer she'll be certified in all Claude, co work, code, everything like that. And ⁓ she's creating two employees for me this summer. That was her summer project.

So by the end of the summer we'll have two employees working for me that she's created. And I think it's a good skill. I mean, I I actually said to her, she had a lot of different opportunities and choices, and I said, I think you're gonna learn more working for me than anybody else. And I think, you know, with real like practical skills where I think this AI experience is Justin Roopnarine: Congrats.

Megan Driscoll: gonna be really helpful when you go to graduate next year. She's a junior, gonna be a senior. So ⁓ you know, I I I hope to actually employ a few more interns next summer, even in the same way, just to kind of get people, ⁓ you know, some of these young people like really ready for the workforce, because I think it's super important. I might even do a course on it, who knows?

⁓ but I think it would be important because I do think that Justin Roopnarine: Yes. Megan Driscoll: There is the human aspect of directing the the AI as to what to do, you know, and it can go off the rails and it and it can make mistakes. So you do need to, just like a manager would, you do need to manage your AI. And so ⁓ I think that's actually an entry-level position that someone could have, you know, where there's four data analyst jobs that are being done, but someone needs to oversee that in and it's basic work.

So it wouldn't you wouldn't want a senior person wasting their time on that. You know, it does allow for people to have higher level thought, and that's what I like about AI. It takes these basic routine kind of the things we don't like to do every day, it takes those things off of our plate so we can actually be thinking broader and and and bigger. And I think that's the kind of exciting part about it.

⁓ at least on as I see it, whereas the negative is like lack of jobs and we're gonna have a glut basically. ⁓ you know, we'll see. How do you think Justin Roopnarine: Doesn't Mm-hmm. No, completely agree.

Yeah. Megan Driscoll: as it relates to investing. So how do you see that market changing for the average investor? So like somebody who is investing on the side, like how do you see AI being like helpful to them or or dangerous for them to use?

Justin Roopnarine: Yeah, see for the average investor as far as I mean we can start off with with well we'll go with the bad foot. The the bad first, right? How could it be dangerous, right? you know, long story short, AI on messy data or like incoherent thought processes or procedures is gonna create more confusion, messy results.

So I I think you had another guest who mentioned you hit this the nail on the head. If you tell it you know, going back to that I have I don't care about risk. I wanna make five X my money. I have two you know a year to do it.

I mean it AI is gonna give you a result, right? But it's probably not what you should do in reality. And but AI's not gonna know that, right? It's just gonna give you your answer.

So it all that to say you still have to be informed. All right. With a AI on the good, more helpful side of things, is like if you were just coming in, maybe fresh grad or I don't know, high school student or whatnot, you can quickly learn about investing. You can learn about, you know, what your student loans are, the interest on them, what you know, compare rates.

If you were to set aside money, all right, what should I do right now? I'm this age. And it might be able to at least steer you towards resources that might help make more informed decisions, not necessarily decide for you. you at that rate because you're not a subject matter expert.

I mean I we're still learning new things every day. So for the helpful side, it can help you get pretty smart, pretty dangerous, relatively quickly. On the more dangerous side, if you're just letting it decide for you without really the inherent knowledge of what you want to do, how you want to do it, or just the general idea, you're you're not really helping yourself. Does that make sense?

Megan Driscoll: Yeah. Well you know what I've been doing is so I've been ⁓ ahead of my investment meetings, ⁓ I have been ⁓ loading in like you know, just like documents from like my recent, you know, trades and my recent accounts and things like that. And I just kind of load it in and I say, okay, this is where I'm positioned. you know, and I and I talk about like what my goals are basically.

And then I say like, what should I be asking my investment advisor about? What trends am I missing? Is there is there a new great thing that I should be looking at? Like I kind of prompt it that way and then it spits me back like, you know, a couple of things and usually it's really actually leads to very good conversations because I have like a quarterly meeting.

Justin Roopnarine: Mm-hmm. Megan Driscoll: meeting, it usually leads to some pretty good conversations about yeah, I wouldn't invest in that or you know, that might be a good idea. Why don't we think about, you know, getting more into gold at this point or things like that. So that's kind of how I've been using it, ⁓ but not to s mi have it make any like decisions on its own or to follow it without like human involvement.

Justin Roopnarine: Yeah. ⁓ interestingly enough too quick aside, I I did I mean, I was a former data scientist and on that note too where you're just happy and make decisions, you know you could make your team of agents, your team your team of research analysts to kind of research separate little projects and gives you reports and you have like a lead AI or agentic manager. That is a good setup, right? You're having little data analyst agents give the data scientist or data manager or project manager agent a report and that could be used for investing too.

That could be used for a wide variety. variety of things. You know, any project you can kind of break down into roles and tasks. Yeah.

I mean I believe agents can make it more efficient. but at this point I still think you need the human in there because you know inherently the I mean AI has a has context, it has memory and that memory is limited. You know we humans have we can go back years and years and remember things and clean insights. So I still think there's a certain nuance there.

Megan Driscoll: Yeah, yeah, I agree. I agree, and that's kind of why I I just use it to sort of help guide me ⁓ in asking good questions and and you know, th that kind of thing. I it's also been I'm an executive chairman of a board ⁓ on a ⁓ for for a company, ⁓ recruiting company. Justin Roopnarine: Yeah.

Megan Driscoll: PE backed. And ⁓ it's super helpful for that just with regard to like I'll dump the P and L in and you know, and I'll dump ⁓ you know, the board deck or whatever. And basically, you know, I I see trends, but ⁓ help me ⁓ help me not miss those trends. Like what am I missing?

Is there something this is what I think? What else am I missing? And that's been super, super helpful. ⁓ just with regard to like really pulling out the important pieces of a P and L.

And sometimes again w in finance I find that like Justin Roopnarine: Yeah. Megan Driscoll: the goals of a company, like what we verbalize is we're trying to to to do. And the PL oftentimes doesn't reflect that or we're not asking the right questions to kind of connect like if you're saying we want to do this, well these are the things we need really need to look at in order to do that. Right.

And sometimes I think there's a disconnect between those two things. I've found that the the the the that Claude and I use chat and Claude but I'm moving really more towards Claude ⁓ for business. But it's been really helpful in helping to connect Justin Roopnarine: Yeah. Megan Driscoll: dots and g and and and describe it in a way that someone can understand.

You know? I wonder about that for the financial market. Like do you feel like you use AI to communicate with clients? Like has that been helpful?

Justin Roopnarine: Yeah, so ⁓ well, stepping back, I think that's a great example too. Like you know, to your example with PL and just reviewing ⁓ various decks. in the past, I think when we had a little bit of a different thesis, ⁓ you know, ⁓ certain basket of stocks or even you know, personally, I can quickly kinda analyze their earnings report, yeah, from a basket of stocks quarter over quarter and just see those like ten Ks and like alright, maybe I missed footnote sixty five on the last page of the deck that all of a sudden threw all these numbers.

offer it's like hey we were trying to hit this metric but we are ⁓ you know after analyzing our basket of stocks looking at all their ten Ks all their quarter reports we missed a few things or you know maybe we get some new insights. So I really like that strategy implemented too. ⁓ as far as using AI to communicate with clients we have to be very careful here ⁓ such that you know we're not gonna just GPT generate hey these are our numbers from our earnings this year or whatnot.

You know there's still of course just manual calculations that have to be done. We have to run these actually, you know, through compliance through legal ⁓ you know, you can still see them formulaically in the in Excel. But it you know, using I it helps us gain insights. Like to your point, you know, how are we trending?

You know, what ⁓ what who's maybe a competitor in this space? Do they have their, you know, financials or whatnot listed or their growth listed? You know, what do they do? So it helps us kind of understand our data and compare a little bit better and g you know, gain new insights.

But as far as communication we wouldn't necessarily defer to it, but it's definitely a helpful tool ⁓ to help us understand what we're doing. Megan Driscoll: Yeah. Are there you know, I I feel like AI is just you know, I I spoke to ⁓ as an example I had a Boston University professor on and Boston University just as a as a practice allows each professor to determine whether or not they allow their students to use AI and for what. So it's very class dependent and professor dependent, which I thought was really interesting that they didn't have like a massive policy, which was this is how Boston University is gonna approach AI.

is is that the way it is in the fan like like I feel like in some ways like hearing that I was kind of like, wow, it's really is like the wild, wild west out there. Like anybody can just be using it the way they want to use it, I guess. And so you know, are there guidelines that you have to follow with regard to AI that are like Financial like the FCC is saying this or is everything at like a company level determining like maneuvering your way through like what's acceptable and what's not acceptable?

Justin Roopnarine: I see to my knowledge, I you know, there's probably some guidance or whatnot put out by the SEC or FINRA, don't quote me on that. But but, you know, I I think it's more driven at a company level, you know, in my opinion. Where it's like Hey, you know, we still have like going back to the earlier point, right? If we have messy data, messy procedures or processes, you know, we're and we're using AI on those, we're gonna get undesired results.

So i i I think our official guidance here, or yeah, no, it's our official guidance is kinda use it as a tool. Yeah. Exactly. So it's a helpful tool.

It can help us understand our data. Megan Driscoll: Or you're gonna get false results, which y you can't afford to get false results. Yeah. Justin Roopnarine: better but you know d there's no overarching hey you will use AI this way, you will use this prompt, you will use this, this and this.

It's you know use your best judgment. We have our procedures policies. If you find a problem, report it. Or if you have a question, let us know and we'll determine.

You know, it's worse like you said, it's the wild wild list still, to be honest. Megan Driscoll: Yeah. Yeah, yeah. It it really is.

Yeah. I I feel like there's just no there's you know, it's funny, every industry I know I've talked to a variety of different people, like from someone who owns an electrical business, you know, to a Boston University professor to you in the financial industry to a person who just developed their app and is basically AI app and is, you know, running a software company. It's just such a wide variety of people that I speak to. And there really isn't it there's never like, well, this is the guiding principle.

You know? It's really like we are adapting and using this in any old way we want. Justin Roopnarine: Thanks. Megan Driscoll: to and that does that does kind of concern me a little bit just about like how this this is going to lay out over time when it's just everybody gets to use it the way they want to use it or way they see fit and it's very subjective.

⁓ Like what might be what might be risky behavior for your use may not be risky behavior for another firm's use in the same space. But you guys each get to decide how you're gonna use it, right? And ⁓ and I do feel like ⁓ sometimes, you know, And this I think is true no matter what. Like when my company went on the market, so I sold my company to a private equity farm in twenty seventeen and I hired a broker to do that.

And when we did that, you know, I gave them ⁓ my P and L, you know, that was really all I had. And I got back this deck that was gonna be used to sell my company. I was like, wow, I'm Justin Roopnarine: Yeah. Megan Driscoll: Like we're amazing.

Like you really shined us up like a penny, you know, and there was nothing in there that was a lie, obviously. Everything was true. It was all based on real numbers, but it did feel to me like wow, this was a big step up in like how I'm describing my business, and we really are great. And I feel like ⁓ AI is doing that for everything.

It's making everything like shiny and every communication is better, all the board decks are better, the presentations are better, everything is like a level up, and it feels almost like is it real? Justin Roopnarine: Right. Okay, yes. ⁓ it's an interesting point you bring up too, I 'cause I've I've noticed it too.

Like all the you know, reports now all have the same like structure, wording. You can tell like it's sometimes even before the AI, like they all had a similar flow to them. And not just in financial instruments like i or financial decks or whatnot, kind of just everything. You know, from your social media posts, they all have a certain kind of rhythm to them to it does yeah, to your point, i does it seem fake or kind of artificial in a way?

Yes. Megan Driscoll: Yes. Justin Roopnarine: But is it optimal is the other question where it's like, you know, this is what we're watching, this is what we're reading through, and we consider this good. I mean, maybe it's artificial, but is it optimal?

I that's another maybe that's another discussion for another day, but but it's interesting. Megan Driscoll: Yeah. It's an interesting concept of how do you stand out in a marketplace like this in any industry? you know, in a place Yeah, yeah, yeah.

Do you want to? And if you do, Justin Roopnarine: Where do you want to stand out, right? Do you want to? I don't know.

Megan Driscoll: Yeah, how how do you do that? How do you do that in a in a sp in a space where every single thing has been leveled up, you know? and maybe it's the ones that are just like gritty and you know, maybe we go back to something which it's like raw, you know, who knows? ⁓ it's just an interest it's a really interesting time, that's for sure.

Is there anything that I didn't ask you that you wanted to talk about? Justin Roopnarine: Mm-hmm. Yeah. Yeah.

⁓ no, I think ⁓ we this is a really great conversation, thank you. Interesting time to be alive, definitely. Megan Driscoll: Yeah, you're welcome. It for sure is.

⁓ so where can people find you? Give me the name of your investment firm again, like what the website, something like that. Where can people find you if they if they want to learn more about your company? Justin Roopnarine: Yeah.

So i if you just wanna reach out to me personally, of course it's my LinkedIn, ⁓ Justin Ruppnerein, ⁓ the fund itself, if you just wanna talk, ⁓ Limitless L P is our website. but yeah, those probably the best ways to reach out to me. Thank you. Megan Driscoll: Good, great.

And your last name is spelt R O R O O P N A R I N E for those that are not watching and are listening. Yeah. It was so good to have you on the show today. Thank you so much for your time.

I appreciate it, Justin. Justin Roopnarine: Yeah. No, likewise. Thank you.

Have a good one.