How AI can reshape how investors manage their portfolios

Simultaneously, artificial intelligence is fueling market rallies and worker concerns about autonomous replacements. It's even changing the way investment firms are managing their portfolios now.

Boosted.ai Co-Founder and CEO Joshua Pantony comes onto Yahoo Finance Live to highlight the benefits of using generative AI models for investing, and how it is different from past generations of investing programs.

"The biggest difference is the kind of data you can draw from. Traditional models were really looking at the sort of numerical data... revenue, earnings per share," Pantony explains. "Today, my company analyzes millions of articles from over 150,000 sources. This could be from trade publications, 10Ks, 10Qs, really big newspaper outings and collect, analyze, and use that data in a really sophisticated quantitative way that wasn't really possible with the older technology."

Follow along with Yahoo Finance's AI Revolution special coverage this week, or you can watch this full episode of Yahoo Finance Live here.

Editor's note: This article was written by Luke Carberry Mogan.

Video Transcript

JOSH LIPTON: All week, we are in a deep dive into the AI revolution, how it's changing the way we live, work, and invest. Even today, we're seeing huge moves in the tech sector as it continues to rally on AI advancements. One company harnessing the latest technology is Boosted.ai, uses machine learning to scan data and news and trends to help make sense of your portfolio and predict what might happen next.

Joshua Pantony, the company's co-founder and CEO joins us now. Josh, it is great to have you on the show. And maybe, Josh, to start off with you, if you could explain to viewers kind of just a little bit more about your company and the AI product, Josh, the AI platform you offer to asset managers and how they benefit from it.

JOSHUA PANTONY: Yeah. So we're a generative AI platform for professional investment managers. We power about 180 of some of the biggest asset managers in the world, collectively managing trillions of assets. Really users are using our system to monitor holdings, kind of understand macro trends that are going to be impacting them, really accelerate speed up their research process, analyze risk in ways we think really wasn't possible before. And just kind of across the entire investment workflow, really speed up and increase the efficiency of what they're doing.

JULIE HYMAN: Josh, it's Julie here. How-- you know, for years, we've talked about Wall Street using algorithms, right, which is a form of machine learning or AI, right? So how is what we're seeing now different from what we have seen?

JOSHUA PANTONY: Yeah. Think the biggest difference is the kind of data that you can draw from. Traditional models were really looking at the numerical data. We think about things like revenue, earnings per share. Today, my company analyzes millions of articles from over 150,000 sources, and this could be anything from trade publications, 10Ks, 10Qs, to really big newspaper outings and trying to collect, analyze, and use that data in a really sophisticated quantitative way was something that wasn't really possible with the older technology, but that you can do in a really sophisticated way now.

JOSH LIPTON: And, Josh, I'm just curious, what is demand like for your AI product? And are there certain kinds of asset managers that seem to be more interested in it than others?

JOSHUA PANTONY: Yeah. I think one of the most exciting things for me is just how incredibly broad the appetite is. We have everything from investment advisors managing like $100 million in assets all the way up to trillion dollar large asset managers and globally sovereign wealth funds. And this is hedge funds pot shops. The diversity of our clientele is probably the most exciting part of the story for me.

JULIE HYMAN: And so, Josh, what do they do with all of this, right? How is it being put to work right now? And what kind of edge is it surfacing?

JOSHUA PANTONY: Yeah. I really break it down into one of two categories. One is basically information asymmetry. If you use a system like ours, you're going to be able to learn information a lot faster than if you use a system without ours. And the other side is really around efficiency of your process. We can take what used to be a 40-hour research process and get it down to 5 or 10 minutes.

So it's kind of analyzing each batch of information, learning insights from it really fast, or taking something that's historically really slow and making that really fast.

JOSH LIPTON: And, Josh, I'm interested, you know, the asset managers who are interested in incorporating this technology, some are going to, Josh. But what are others doing? Are they are they buying preexisting tech? Are they are they trying to build their own in-house AI platforms? Are they hiring people with the expertise to do it?

JOSHUA PANTONY: Yeah. Think there's kind of a mix of all of it. You're seeing people that are starting to build in-house teams, especially some of the larger players. You're seeing people that are going after more traditional providers. I guess the one comment I would make, though, is that if you're not actively trying to implement AI, I don't think is going to replace you, but a person using I probably will. So there's kind of broad-based demand for it.

JULIE HYMAN: And what investments are your platform surfacing related to AI?

JOSHUA PANTONY: Yeah, I'd say the short term, there's kind of three main things. You know, I think largely 2023 was a year of experimentation, where people were trying to unlock value. As we shift forward thinking to 2024, it's really going to be about implementation. That experimentation are going to continue, but the implementation is going to be a big driver.

So companies like AMD or Nvidia that are providing chipsets and computation that's key to building these techniques, it's going to be really important. Infrastructure companies like AWS or Microsoft Azure that can service is going to be really important. And then early adopters of the technology are really going to see, we think, generational gaps in their capabilities versus people that don't use it.

JOSH LIPTON: And, Josh, I would assume that, and I'm sure you're hearing more and more asset managers may be talking about how they incorporate AI. For investors who are listening right now, though, how should they kind of help themselves distinguish between those managers, who are kind of just engaging in marketing hype versus those who are real, who are actually getting it done?

JOSHUA PANTONY: I think the most exciting thing about this technology and probably the most important thing to applying it to finance is the ability to actually understand the reasoning of the machine. If we think about historic systems, they tend to be a black box. They say buy this stock or sell this stock, and you have really no idea why. With technology like this, it can give a detailed analysis on exactly where its reasoning come from. It can cite the sources that led to the conclusion. And it can hopefully bubble up insights that are unique or very value add versus what you would get elsewhere.

And so when I'm looking at providers, rather than think about, you know, is it AI or not AI, I'd really be asking kind of across those three things. Is it getting the insights I haven't heard elsewhere? Is the reasoning something I can understand? Is it obvious where I can implement it in my workflow? And if the answer to those questions is yes, probably it's a big value add.

JOSH LIPTON: So interesting, Josh, to hear how more ways this technology makes its way into the financial services industry. Thanks so much for joining us today, Josh.