00:32:49 · Molly O'Shea
[00:32:49] S0: You embraced this super early, and really embraced it. I know you're also embracing AI and AI agents. You have over 1,000 AI agents for 800 plus investment professionals. Are there any areas where you're looking for AI to create more efficiencies internally? Where are the pain points for you?
00:33:08 · Jenny Johnson
[00:33:08] S1: I bucket AI in a few places. First of all, on the investment side, real win is making us better investors. Right? So analysts more efficient, so they're not spending as much time on admin, so they're actually spending time talking to companies, doing more research, maybe even covering more companies. And so it is all about improving investment performance. Any cost savings there is insignificant to the benefit of just improving investment performance. With respect to distribution, it is actually about growth. So we had a partnership with Microsoft that they were excited to do. They actually gave us free resources because it was such a sophisticated challenge and solution from a technical standpoint. Simple problem, right? What your issue in sales? I want to meet the right clients and have the right conversations. That's essentially what you're trying to achieve. But it's actually a really difficult technical solution because it requires you to have multiple AI agents that talk to each other and are coordinating with each other. And then it requires you to go out and get a bunch of different systems to pull in data. So you might need your product system. You might want a system that shows you a holding of, say, a financial advisor. So that firm may give us a feed on that. So I'll look at what the holdings are. I'll view our products. I might go to social media just to make sure that I can talk to that client about whatever's important to them so I'm updated on it. I'm going to go to some of the rating agencies on there, the Morningstar and others, to understand as part of the products what are the best opportunities. And so Microsoft was excited about it. What we've seen in the early results is 23% uplift in number of clients that one of our market leaders is visiting and an 11% uptake or increase in sales. So we think that's just going to get better and better. So that's distribution. Now where's the cost savings part? The cost savings part comes in the back office. It doesn't actually mean you have that much less people. I think you just do more. And in the case of technology, what's exciting is you have AI writing the code. I've never talked to a CIO who runs a technology department that doesn't say, Yeah, we got all the projects the business wanted done this year. It's all about prioritization.
00:35:23 · Jenny Johnson
So if you can get more through the pipeline, that's an amazing thing. You're not trying to save money, you're just trying to get more things through the pipeline. And so that's kind of how we bucket the various... You know, how we bucket our approach to AI. And then back to the initial question on what do you think about all these hyperscalers and their revenue models? I think there's a couple of that we haven't yet seen. Number one, the first thing you do is you give your employees the tool. The AI tool, go run, go do these things, write agents, that's all good. And then over time it starts costing you money. So now you go, you know what, Do all these agents need to run? And do they need to run as frequently as you set them up to run? And that governance of the discipline, we don't know what that ultimately impacts these hyperscalers. And then the second thing is, there are probably a whole bunch of queries that an employee needs to do leveraging AI that probably could be achieved with these free models. And so that governance layer of evaluating the requests and saying, You know what? This is a request that we're going to pay for the top model, this is one we're just going to send at the free model. All of those things will impact ultimately what the revenue looks like with these hyperscalers.
00:36:39 · Molly O'Shea, Jenny Johnson
[00:36:39] S0: I'm seeing that in a lot of our conversations with these emerging companies. They would rely on the frontier models, and then they'd have 14,000,000,000,000 tokens, and they'd be overloaded, and then they'd shift their model, now they're totally efficient, and they're running 99% on these open weight, open source models. And so I think that was probably going to make a big difference, but maybe we continue to use these frontier models for the biosciences and for these actual frontier kind of projects. So I know at the beginning I wanted to tie this back to kind of the multigenerational nod to, I was gonna say Benjamin Franklin Templeton, but Franklin Templeton. So your grandfather started [00:37:20] S1: the Well, that's where Ben is our stock. You can say that Ben Franklin Templeton. [00:37:23] S0: Ben Franklin Templeton. Hashtag. Well, it's not hashtag. It's hashtag. Right? Your your ticker is Ben. Yes. B e n on the New [00:37:33] S1: York Stock Exchange. [00:37:34] S0: We love that. So I'd love to go through a little bit of the history of the firm to today.
00:37:39 · Jenny Johnson
[00:37:39] S1: My grandfather actually had a brokerage business, and he was a huge fan of Ben Franklin, and he decided to create a subsidiary. So he decided to name it after Ben Franklin, and so it was Franklin Funds. My father served some time in the military, came out, he was 24 or 25 years old, his older brother took over the brokerage business, and he said, I really want to do my own thing. Can I talk to my grandfather and said, I'd like to take over this subsidiary? So it was $2,500,000 in AUM assets and $10,000 in revenue. And he said, I was making 2 to $300,100 dollars a month, but that wasn't a bad salary for a new he kind of looked at himself even though he'd done four years in the Army. He goes, But that wasn't a bad salary coming out. So that was 1955. I was in college, I think it was probably 1983 or '84, that I attended our first billion dollar party. So it took that long to We get to a billion acquired a company in California, that's why we moved out in California. And it was, I think Franklin was like 60,000,000 in AUM in 1973. And the company that we acquired was maybe 120,000,000 or something like that. And then the market tanked the next year. And so I think that we eked out a $5,000 profit for the entire year in I think 1973 or 1974. But it's still a good business and you just keep at it. I actually think a lot of things as to why my grandfather set up the business, which was, at the time, the average person couldn't get access to the excess returns of the equity markets. And so this innovation was created. Why don't we pool people's money? We'll give them expert advice, we'll make it at a reasonable price, and they'll get diversification that they could never get if they were on their own. And that was kind of the concept. And I do think we're at a similar thing on the private markets now. Companies waiting so much longer, you know, credit being a lot of the debt goes into private credit. We've got to make sure it's fair and democratized for people to get access while recognizing these are absolutely illiquid vehicles. But they can get an excess return because of the illiquidity.
00:40:07 · Molly O'Shea, Jenny Johnson
[00:40:07] S0: We've been seeing that publicly listed, closed funds, all of that kind of thing. VCX is one of my sponsors, and they created a vehicle of all these pre IPO companies so you can get democratized access to these As we wrap up, I have one final question. This is the hardest question I'll ask you today. Are you ready? [00:40:27] S1: Yeah, I'm ready. [00:40:28] S0: Who is the modern day Benjamin Franklin? [00:40:32] S1: I have to say, and this is, because I one time asked my team to do a, because our logo, everybody thinks it looks so old, and I was like, old? Are you kidding me? Ben Franklin was a rock star. You gotta understand. This guy was like... I mean, not only was he an inventor, a politician, a philanthropist, you know, he... Such an amazing, really, really well liked, the ladies loved him in France kind of thing. Right? So I said, so we should do something like who would Ben Franklin be today? And I have to say, the one I'd say the most comes close is Elon Musk. There's... What comes back... Well, people say, oh, but Elon isn't as much of a philanthropist or Elon isn't this or that, whatever. There's no perfect match. A lot of times a person is made a little bit by their time, but I think that there is no person who's had more significant impact on really where we are in the technological advances today than Elon has. [00:41:32] S0: He did say he's going to give one SpaceX share to all of the children in America. Love that. That's awesome.