00:29:20 · Jenny Johnson
[00:29:20] S1: had an advantage in the seat on understanding why blockchain was so important, and I genuinely believe it will replace the rails in financial services. It'll be slow because it forces the change of a bunch of business models and the toll takers who take a fee on a transaction. But it basically does three things really well. It has a source of truth, and anybody who runs a back office or tech department understands we're still running well, we're not, but a lot of big financial services firms still run mainframes, which means you have batch processing at night, which means your data is not updated until sometime in the middle of the night. With blockchain, it's immediate. And so there's a huge cost in firms to reconcile data between systems, and then you have to go reconcile the transaction with your counterparty. That all goes away with blockchain. There's just one source of truth updated immediately that everybody goes to. The second thing, it has the ability to program these smart contracts. So whatever terms you and I agree, if this threshold meets you have to pay me collateral, or this one I have to pay you collateral, all of that gets programmed in. Whereas pre blockchain, that is sitting in a document somewhere, or you have to go and try to build it into the transaction, but it's really hard to do. So it tends to be in a document somebody is following, and they'll say, Oh, let me go collect collateral you owe to me. That goes away. It immediately is paid. And the third thing is the ability to make a payment. So, I looked at that and said, Well, this is a no brainer. This is what we do in this business in the back office, and so this is hugely efficient. And when we came out with the, first tokenized natively on chain tokenized money market fund that the SEC approved, they had us parallel process on our old transfer agency system, our old shareholder record keeping system, and our blockchain system. And a couple of things. We were still one of the few firms, I think there were only two of us, still running the old system in house. And so we were able to look at the parallel processing, and we were astonished by how much more cost effective it was to run it on chain.
00:31:22 · Jenny Johnson
And we looked at that, like if you open one of our traditional money market funds, need $500 to open an account, because anything below that, the other shareholders are subsidizing your servicing fees. Whereas we did it on the blockchain system, you can open an account for $20 We probably can go down to $5 because it's just that much more efficient to run. So we look at it and think that the financial innovation that's going to happen is going to be really interesting and significant. One is you're going to have 20 fourseven trading. It's going to be atomic settlement. And you're going to have a lot of innovation that's going to happen because of the ability to program. And now add AI to that. You can build your logic of a trading pattern in there. And so we're big fans. Our tokenized money market fund, to give you an idea, it calculates interest every second. If you're a hedge fund and you use it as collateral and you hold it for four hours, twenty two minutes and thirty two seconds, we'll pay you that interest. Your cash that you earn that day is paid in your account at the end of the day. On a traditional money market fund, it's accrued all month, but you can't get to that cash until the end of the month. If you're a corporate treasurer, that's kind of significant. The ability to access that cash or even an investor, like a portfolio manager, and you're using that as senior cash and as the cash comes in, you can actually move it and use it in a portfolio. So it just gives you a little glimpse of the innovation that's going to come because of tokenization.
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.