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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.

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Speaker 3: I'm Tracy Alloway and I'm Joe. Isn't that Joe.

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Speaker 2: When I think about the world of finance, Yeah, and

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Speaker 2: what I would describe as mega trends of recent years

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Speaker 2: and decades, there are definitely two that spring to mind.

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Speaker 2: Maybe a third trend, although I don't know if it's mega,

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Speaker 2: probably is. The first is definitely the rise of the

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Speaker 2: buy side. So this idea that you know, it very

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Speaker 2: much used to be all about the banks, and those

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Speaker 2: were the ones that we kind of obsessed over, and

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Speaker 2: then you have this extraordinary growth in the asset management industry.

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Speaker 2: The second mega trend has to be technology, right, think

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Speaker 2: about the rise of electronic trading, electronic risk management, model

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Speaker 2: driven risk management I like. And then the third semi

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Speaker 2: mega trend, I don't know, rise of private markets right.

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Speaker 1: Yeah.

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Speaker 3: Oh, I have another one, okay that I think is

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Speaker 3: legit the sort of power law domination of a few

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Speaker 3: mega companies and sort of whether it's con they get big,

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Speaker 3: the winner take all this or the winner take mostness

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Speaker 3: of the industry. I would add that as a mega trend. Okay,

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Speaker 3: that's to say that connects to this episode absolutely.

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Speaker 2: So we have four big slash mega trends, and we

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Speaker 2: have the perfect guest to talk about all of those

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Speaker 2: four things. And it comes at a time when obviously

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Speaker 2: we're in this new sort of technological wave with AI.

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Speaker 2: It very much feels like everyone in professional finance wants

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Speaker 2: to figure out a way of being involved in AI

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Speaker 2: in one way or another. It's funny. I was meeting

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Speaker 2: up with someone who works at a very large bank

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Speaker 2: the other day and they were talking about how work

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Speaker 2: that they've done twenty years ago, their managers are now

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Speaker 2: adamant it has to be put into a big Excel

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Speaker 2: database because they're about to shove all of that into

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Speaker 2: an AI model. So you can see, you can see

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Speaker 2: there's this like urgency. Yeah, and there's a perennial question

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Speaker 2: over how much of it is really.

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Speaker 3: Random data that exists somewhere. They need to have it

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Speaker 3: as part of the data lake or whatever so that

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Speaker 3: the AI model knows about it, oh exactly.

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Speaker 2: And I think the question for this is how much

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Speaker 2: of it is managers who are latching on to the

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Speaker 2: AI trend versus how much of this is actually going

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Speaker 2: to become productive, useful technology for finance. So we can

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Speaker 2: definitely talk a little bit about that. But we do

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Speaker 2: have the perfect guests. Let's do it all right, So

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Speaker 2: we're going to be speaking with Rob Goldstein. He is,

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Speaker 2: of course the COO of Black Rocks, someone who has

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Speaker 2: literally lived through basically all of these mega trends that

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Speaker 2: we just described.

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Speaker 4: Yes, and the question is how many hours do we have,

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Speaker 4: you know, needless to say, those mega trends we could

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Speaker 4: talk about for quite some time.

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Speaker 3: Yeah, a serious, we could do a series serious.

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Speaker 4: And one thing I would just politely identify. I think

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Speaker 4: as people talk about the big getting bigger, yeah, I

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Speaker 4: think there's an underlying catalyst towards those who could provide

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Speaker 4: a better value proposition are getting bigger. And I think

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Speaker 4: that's the key theme that's happening, particularly with regard.

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Speaker 3: To the byside. I know you were saying, was it

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Speaker 3: a polite you didn't push back, but a polite nuance

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Speaker 3: or something like that. But one could say that the

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Speaker 3: ability to provide a better value proposition is itself a

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Speaker 3: function of size in many instances, because the larger have

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Speaker 3: the full suite, the whole menu, right services, A nuance

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Speaker 3: upon it.

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Speaker 4: One hundred And what's interesting is I think if you

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Speaker 4: ordered the Mega Themes, you can make a very strong

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Speaker 4: argument that it all comes down to technology, and technology

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Speaker 4: is enabling things and value propositions to be achieved that

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Speaker 4: traditionally just couldn't be done.

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Speaker 3: Great, let's just talk about that.

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Speaker 2: Is it true that, like some of BlackRock's foundational by

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Speaker 2: the way, the chances that I say Blackstone in this conversation.

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Speaker 4: Yeah, that's not that's not good.

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Speaker 3: I apologize, But we had a pre call and I

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Speaker 3: said that I've been in this business a long time.

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Speaker 3: We had a pre call with Rob like several weeks ago,

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Speaker 3: I said Blackstone and then like I could tell, like

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Speaker 3: a silenized like I said the wrong thing. Now if

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Speaker 3: he's heard it all before.

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Speaker 2: I hope so. So is it true that, like the

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Speaker 2: foundational culture of black Rock is very much tied to technology,

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Speaker 2: because the story that I always used to hear was

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Speaker 2: about a Sun workstation and Ben Gollob.

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Speaker 4: Yeah, and Ben is still a close friend and mentor,

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Speaker 4: Ben being one of the founding partners of Blackrock. I

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Speaker 4: think if you zoom out a little bit, because I

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Speaker 4: think the history of Blackrock is very reflective of what

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Speaker 4: the past thirty thirty five years have been in terms

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Speaker 4: of the companies that have been most successful. And if

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Speaker 4: you look at the founders of Blackrock, there were a

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Speaker 4: group of people who were pioneers with regard to structured

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Speaker 4: products and the evolution of the mortgage mark. And what

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Speaker 4: they realized is that banks at the time, the cell side,

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Speaker 4: as you guys sort of laid the groundwork. Banks at

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Speaker 4: the time were using supercomputers and they were very expensive

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Speaker 4: computers to structure things. And then the way they were

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Speaker 4: selling those products was literally by faxing yield tables all

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Speaker 4: over the world. And I know, and when I say

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Speaker 4: this to twenty something year old thirty something year olds,

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Speaker 4: including my own children who are when they're early twenties,

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Speaker 4: a lot of the people back then didn't even have computers.

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Speaker 4: Like computers were like there was one for a group

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Speaker 4: of people, as opposed to everyone had one on their desk.

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Speaker 4: So the thesis behind forming Blackrock was that we could actually

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Speaker 4: build models that would help provide risk transparency for those

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Speaker 4: type of instruments and help the end asset owner. We

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Speaker 4: could build those models and through the sun workstation is

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Speaker 4: the innovation. If you were reasonably clever. You didn't need

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Speaker 4: to be a genius. But if you were reasonably clever,

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Speaker 4: you could buy ten Sun workstations for ten thousand dollars

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Speaker 4: each and link them together and effectively do what previously

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Speaker 4: only supercomputers that cost millions of dollars could do. So

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Speaker 4: the founding thesis of Blackrock was really about how do

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Speaker 4: we bring those technology capabilities which were not really available

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Speaker 4: on the buy side, how do we use them as

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Speaker 4: the core of building an asset manager. That was the

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Speaker 4: founding thesis, and I think one of the real success factors.

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Speaker 4: And I think that when you look today, what I'm

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Speaker 4: about to say seems like very odd, But I guess

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Speaker 4: this is odd us, so it's perfect. But when I

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Speaker 4: started at Blackrock in nineteen ninety four, when we had

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Speaker 4: roughly eighty people nineteen billion dollars in assets under management,

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Speaker 4: I was in the data and analytics team. I was

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Speaker 4: effectively a data analyst. And like today, data technology, analytics

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Speaker 4: are where the cool kids are, back then it was

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Speaker 4: not where the cool kids are. Trust me. And the

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Speaker 4: whole concept of recognizing very early on that the asset

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Speaker 4: management business at its core is an information processing business.

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Speaker 4: Today is so obvious, but if you rewind back ten

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Speaker 4: twenty thirty years ago, that was a very unique novel concept.

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Speaker 3: We really do need like five hours now. This is

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Speaker 3: kind of a tangent you mentioned in the idea of

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Speaker 3: like you could string ten some workstations together to make

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Speaker 3: a supercomputer. We are actually coming back to the future

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Speaker 3: or the future back a little bit these currently in computing.

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Speaker 3: I have a good friend who has the problem is

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Speaker 3: to help me later this year, I'm gonna buy like

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Speaker 3: five macminis because he says, you can host your own

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Speaker 3: LLM now from home if you just have like four

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Speaker 3: or five mini is strong together, and then you don't

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Speaker 3: have to then you don't have to depend on any

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Speaker 3: other company's data center for unlimited token usage. So this

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Speaker 3: is gonna kind of come back.

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Speaker 4: Well, it's interesting, though. I think that's a real question

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Speaker 4: in terms of where we are right now, because I

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Speaker 4: don't know the answer to this, but I could make

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Speaker 4: two good arguments. One is along the lines of what

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Speaker 4: you're saying. The other is, we are living at an

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Speaker 4: age right now. If you really just think about what's

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Speaker 4: happening with AI, you could convert energy to intelligence, and

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Speaker 4: the more money you spend on energy. The more intelligence

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Speaker 4: you have, you could argue different than many technology trends

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Speaker 4: that we've had over the past couple of decades. This

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Speaker 4: is a technology trend that requires capital, and it requires

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Speaker 4: spending a lot of money. And I think there's a

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Speaker 4: real question about whether or not this is a very

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Speaker 4: expensive technology or if this will ultimately wind up being

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Speaker 4: five guys at home in a garage with a handful

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Speaker 4: of mac Minnie's can accomplish miracles. I don't know if

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Speaker 4: that's certain yet, which one is going to prove true.

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Speaker 3: I want to get in more on your history and

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Speaker 3: Laddin and the technology that you've been involved in building

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Speaker 3: over these years. But maybe big picture of question is

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Speaker 3: like one of the things about AI that really strikes

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Speaker 3: me is potentially interesting with how it's gonna affect finance,

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Speaker 3: is AI is nondeterministic. You put in a query and

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Speaker 3: you don't really know what You never know if you're

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Speaker 3: gonna get the same output twice or whatever. And I'm curious, like,

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Speaker 3: for one, you don't know how it arrived often and

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Speaker 3: models can't explain themselves, and this is an issue for finance,

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Speaker 3: which is they're often not explicable. Why the output came out,

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Speaker 3: But then there's this other element of you're not gonna

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Speaker 3: get the same thing twice. And I'm curious, like when

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Speaker 3: you think about how that fits into the history of technology,

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Speaker 3: whether that is a source of anxiety for finance, you

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Speaker 3: need to be able to show your work often in

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Speaker 3: many cases, or you need to be able to Like

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Speaker 3: traditional software, you write a line of code and as

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Speaker 3: long as there's no bug, it'll produce the same result

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Speaker 3: a thousand times in a row. Is this new? Is

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Speaker 3: this something that is going to be a difficult at

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Speaker 3: tension the workaround?

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Speaker 4: No question? Absolutely, no question. And just to give an analogy, Okay,

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Speaker 4: I am sure there have been many, many, many people

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Speaker 4: who sat in this seat through the years and said,

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Speaker 4: by x X being years ago, there will be self

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Speaker 4: driving cars, you won't need driver's licenses and so on

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Speaker 4: and so forth. Yeah, I think the tolerance people have

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Speaker 4: for computers to make mistakes is very different than the

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Speaker 4: tolerance people have for humans to make mistakes. So that's

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Speaker 4: just a societal starting point for all intents and purposes.

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Speaker 4: I think you could make a cohesive argument. I love

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Speaker 4: Marvel movies as a family, that's one of our things.

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Speaker 4: This is like alien technology has been found on the

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Speaker 4: planet Earth and now we're figuring out how to use it.

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Speaker 4: And one of the remarkable things about the technology, even

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Speaker 4: if and I know from listening to you you've played

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Speaker 4: with a lot of the coding tools, if you look

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Speaker 4: at the coding tools, they write code and then there

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Speaker 4: are bugs in the code, and then they find the

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Speaker 4: bugs and they fix the bugs. So like the way

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Speaker 4: we've been trained to think about a computer is how

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Speaker 4: could that happen? Wouldn't it be smart enough to write

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Speaker 4: the code without the bugs?

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Speaker 2: Right? Like why do I have to prompt it to

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Speaker 2: fix itself?

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Speaker 4: Because it's much more like a person. It's much more

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Speaker 4: about thinking than this binary zero in one structure that

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Speaker 4: we've become used to for computers. And I think one

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Speaker 4: of the remarkable if you spend time with any of

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Speaker 4: the big technology firms, at the big AI companies, the

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Speaker 4: frontier model providers, they use this term regulated industries, and

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Speaker 4: needless to say, regulated industries like financial services, we need

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Speaker 4: to be certain that we have the appropriate processes and

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Speaker 4: controls in place. So through one lens, that's a big

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Speaker 4: friction through another lens, I think it's actually a competitive

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Speaker 4: advantage to the industry because if you think about it,

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Speaker 4: we have so many controls. We have so many controls

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Speaker 4: as a natural part of the process. So, for example,

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Speaker 4: one of the first things we did within Blackrock when

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Speaker 4: the technology became available, we created this rule that we

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Speaker 4: call this principle that we call the first draft principle.

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Speaker 4: Why can't we have a first draft of everything we

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Speaker 4: produce be created through AI, whether it be a client

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Speaker 4: presentation and internal docum in a perspectus, And the reason

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Speaker 4: why you're very deliberate about saying first draft is because

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Speaker 4: we have sixteen people who check the first draft, and

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Speaker 4: that ability as a starting point is a very strong

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Speaker 4: catalyst towards leveraging and getting to know the technology. But

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Speaker 4: I would actually argue that today the technology has provided

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Speaker 4: a lot of people like individual productivity, but at an

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Speaker 4: enterprise level, if you look at the case studies, it's

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Speaker 4: not clear to be we've entered the first inning of

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Speaker 4: the actual enterprise implementation. I still think the national anthem

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Speaker 4: is sort of being played, and I think that the

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Speaker 4: actual overhang between what the models can do and the

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Speaker 4: fact that this is technology that needs to be implemented.

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Speaker 4: It requires organizational design business process re engineering, like implementing

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Speaker 4: technology is hard and takes time, and we haven't even

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Speaker 4: started that enterprise implementation yet.

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Speaker 2: Can you talk a little bit more about this in

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Speaker 2: the context of Aladdin, because I think part of the

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Speaker 2: concern here that Joe was getting at is that you

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Speaker 2: have these models that are getting more complex and more

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Speaker 2: difficult to predict. We don't know what they're going to

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Speaker 2: spit out. They're non deterministic, as Joe said. And meanwhile,

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Speaker 2: you have this risk management technology that has already for

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Speaker 2: years been described as a black box, and I'm sure

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Speaker 2: you have opinions on that particular label. But if it

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Speaker 2: gets more sophisticated, are people going to fully understand what

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Speaker 2: it's actually doing.

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Speaker 4: Well, let me start out by saying, we haven't described

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Speaker 4: it as a black no one no, not yet, so

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Speaker 4: we'll come back to that in a minute. But importantly,

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Speaker 4: AI is a technology is not new. I wish I

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Speaker 4: knew the exact year, But like the AI lab at

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Speaker 4: MIT was created in the nineteen fifties, we started our

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Speaker 4: AI lab in twenty eighteen. So these methods have been

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Speaker 4: used for a long time at a very sort of

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Speaker 4: simple level that I'm sure would offend a lot of people.

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Speaker 4: You could think about old AI was about numbers. New

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Speaker 4: AI is about language, and the language element of it

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Speaker 4: creates all sorts of humans communicate much more through language

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Speaker 4: than numbers, so it creates a whole host of other

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Speaker 4: unintended consequences. But when you look at a platform like

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Speaker 4: a Latin as an enterprise platform, and by the way,

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Speaker 4: I would make a cohesive argument everything I'm saying, you

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Speaker 4: could make the same case with regard to the Bloomberg

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Speaker 4: terminal in.

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Speaker 2: Fairpoint.

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Speaker 4: So if you think about these technologies, first, the reward

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Speaker 4: for good work is more work. So the to do

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Speaker 4: list for these technologies is in it like genuinely infinite.

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Speaker 4: Every year Blackrock winds up having more engineers. We have

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Speaker 4: roughly five thousand engineers, data analysts, modelers. Every year we

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Speaker 4: wind up having more engineers, and every year we wind

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Speaker 4: up having a bigger to do list of enhancements we

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Speaker 4: could put within Aladdin. So the first element of the

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Speaker 4: AI capability, and I would argue the most mature use

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Speaker 4: case that exists at an enterprise level is coding. So

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Speaker 4: the ability to go through that to do list, the

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Speaker 4: velocity of that is off the charts. The second component,

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Speaker 4: and this is one of the challenges of these enterprise

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Speaker 4: expert systems, is that the number of times I've been

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Speaker 4: in a meeting with a client where they say, you know,

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Speaker 4: why doesn't Aladdin do this, And I'm like, hmm, I

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Speaker 4: think Aladdin does that, but let me. I don't want

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Speaker 4: to like blurt it out, let me sort of follow up,

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Speaker 4: and then I'll leave the meeting. I'll call the people

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Speaker 4: smarter than me, and they'll be like, Aladdin's done that

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Speaker 4: for seven years, and You're like, okay. The ability for

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Speaker 4: people to keep current in technology is very hard. And

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Speaker 4: you know, as much as we like the technology, most

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Speaker 4: people their goal with technology is to just interface with

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Speaker 4: it to do their jobs and then like go home.

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Speaker 4: So the ability to take an expert system that today

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Speaker 4: requires a lot of knowledge and keeping up with it

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Speaker 4: and instead just type in what you want it to do,

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Speaker 4: and an agent will be the ultimate real time user

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Speaker 4: of Aladdin that will then do those activities. All the

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Speaker 4: same controls will exist, the four eyes principle, All of

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Speaker 4: those controls will exist, but that ability to have users

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Speaker 4: to have clients access all the untapped capabilities that today

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Speaker 4: they don't know about. I think the value enterprise technology

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Speaker 4: is going to provide going forward, Aladdin and other enterprise

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Speaker 4: technology is actually going to be much greater than at

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Speaker 4: any point previously. It's actually it's extremely exciting to me

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Speaker 4: because there's nothing more frustrating than being in a meeting

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Speaker 4: where someone is complaining you don't do something when you

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Speaker 4: actually do it.

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Speaker 3: This must be a thing for a bunch of enterprise software,

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Speaker 3: right because no one we've talked to other software people

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Speaker 3: on no one uses all the spects, and no one

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Speaker 3: uses all the features, no one knows all the features,

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Speaker 3: et cetera. But you said something, and it is something

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Speaker 3: that's kind of one of my hobby horses. If it's

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Speaker 3: the agent that's using Aladdin rather than the sort of human,

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Speaker 3: does that change how you think about UX?

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Speaker 4: Okay, it's a great question. We debate this a lot,

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Speaker 4: and I'll tell you about something I saw yesterday. But

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Speaker 4: I think it has to But at the same time,

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Speaker 4: I think there will be people who will want to

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Speaker 4: do it themselves. I think there will be people who

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Speaker 4: want to do it themselves. You know, it's interesting, even

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Speaker 4: in this age of everything being on the phone, you

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Speaker 4: still need a website that people could access that area,

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Speaker 4: so I think that there still will be people who

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Speaker 4: want to do it themselves. I saw a demo yesterday

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Speaker 4: of a tool from one of these AI companies that

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Speaker 4: I hope I could articulate it well enough, but it

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Speaker 4: will look at a website, and it showed it looked

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Speaker 4: at one of our websites and redesigned it to be

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Speaker 4: more like optimal user friendliness. So it was the opposite

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Speaker 4: of it was using AI to almost do the opposite

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Speaker 4: of what you said, it make it easier for humans.

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Speaker 4: And it was one of these things where you know

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Speaker 4: in the demo because websites are public, so they we

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Speaker 4: were able to do things with our own stuff that

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Speaker 4: we didn't know about. And as they're showing what they

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Speaker 4: would do, and when I say they what a computer

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Speaker 4: would do after ten minutes of processing with our own website,

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Speaker 4: you're like, in X months or maybe a year or two,

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Speaker 4: tools like this will re engineer every website on the

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Speaker 4: planet Earth and they will all be more user for.

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Speaker 2: Them for us hopefully and not the agents. Can you

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Speaker 2: talk a little bit more about the moat around Aladdin,

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Speaker 2: because this is the other big talking point of the moment,

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Speaker 2: which is the SaaS apocalypse idea, and in the age

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Speaker 2: of vibe coding, everyone is just going to go out

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Speaker 2: and design their own portfolio system.

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Speaker 4: Let me start out, let me provide a little context

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Speaker 4: broadly about what we think is going to happen, or

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Speaker 4: what a group of us think are going to happen,

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Speaker 4: and then let me go into Aladdin, because I think

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Speaker 4: that they're somewhat related. So one of our portfolio managers,

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Speaker 4: one of our technology portfolio managers, is a gentleman, Tony Kim.

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Speaker 4: And a year ago, two years ago, I said, Tony,

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Speaker 4: if today there are one hundred lines of code in

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Speaker 4: the world, in twenty thirty, how many are there? And

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Speaker 4: he said a million. And I was like, you're out

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Speaker 4: of your mind and he said no, no, no, Like

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Speaker 4: I was quite thoughtful about that. I didn't make up

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Speaker 4: a number.

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Speaker 2: We've all seen how much Joe is coding well.

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Speaker 4: But this is an important to mention because ten times

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Speaker 4: ten times ten times ten times. So if you believe

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Speaker 4: which you know, it's going to be a multiple. You

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Speaker 4: could argue is it three, is it seven? Is a ten?

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Speaker 4: Is it fourteen? But it's going to go up every

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Speaker 4: year by a multiple given these tools. So the amount

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Speaker 4: of code in the world is going to go up dramatically,

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Speaker 4: and I think that when you look at a platform

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Speaker 4: like a Laddin and in many regards, I think it

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Speaker 4: would be like things with Bloomberg. They're at centers of

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Speaker 4: an ecosystem. The ecosystem is highly regulated, the ecosystem has

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Speaker 4: zero tolerance for fault or error. The processes you do

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Speaker 4: not only leverage tremendous amounts of proprietary data, so it's

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Speaker 4: not within these models or accessible by the models, but importantly,

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Speaker 4: clients are putting their most sensitive data into these platforms.

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Speaker 4: And then what you're doing in terms of workflow is

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Speaker 4: highly highly, highly idiosyncratic, and requires this combination of people, process,

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Speaker 4: and technology. So it's a hard time in this world,

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Speaker 4: in my opinion, to predict what twenty fifty looks like.

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Speaker 4: But when you look forward ten years in our industry,

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Speaker 4: these platforms, if anything, are going to do more, not less.

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Speaker 4: And I think what really is going to be unlocked

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Speaker 4: is that users like Joe are going to access these

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Speaker 4: platforms through their own coding tools. But these coding tools

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Speaker 4: are going to be central to those platforms. I didn't

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Speaker 4: forget your black box comment, going back to never forget

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Speaker 4: going back.

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Speaker 3: No, I think it's other people.

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Speaker 4: No, I think it's important, and I think that technology

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Speaker 4: ten or twenty years ago, certain technology were designed to

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Speaker 4: be closed systems. You know where I'm going. Certain technologies

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Speaker 4: were designed to be closed systems. Aladdin was one of

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Speaker 4: those technologies, and roughly ten years ago we started this

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Speaker 4: open a Laddin campaign, well before anything to do with

402
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Speaker 4: this round of AI were in, which was all about

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Speaker 4: the future of technology is going to be Some people

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Speaker 4: are going to want to interact with it through a

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Speaker 4: keyboard and a mouse, and many people are going to

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Speaker 4: want to interact with it through code. Because even if

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Speaker 4: you're graduating with an English major, if you're graduating at

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Speaker 4: most schools at this point, you've taken a coding class,

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Speaker 4: so just the amount of technical expertise that you come

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Speaker 4: in is a whole different level.

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Speaker 3: So this is like opening up more like API end points,

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Speaker 3: all things like that, as opposed to say like open source, like.

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Speaker 4: Open open within a closed ecosystem, And that is a

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Speaker 4: critical element because open within a closed d eystem one

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Speaker 4: of the things and it's like obvious after the fact,

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Speaker 4: but one of the things that we realize, like, oh

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Speaker 4: my god, this is amazing and it's so valuable is

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Speaker 4: that when you call our APIs, for example in Aladdin,

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Speaker 4: your permissions go through. So if you think about the

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Speaker 4: complexity of managing permissions in an asset manager with thousands

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Speaker 4: of people, you could see some portfolios, you could see

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Speaker 4: some portfolios, they're different portfolios. You could trade, you're not

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Speaker 4: allowed to trade. You can only confirm trades. The complexity

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Speaker 4: of those permissions. So the fact that when you call

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Speaker 4: an API, it knows what you can and can access

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Speaker 4: that whole control plane and layer is an incredible value proposition.

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Speaker 4: And the more you have people coding, and the more

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Speaker 4: you have people interacting with systems in more technical ways,

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Speaker 4: the more valuable those control planes actually wind up being.

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Speaker 3: What happens to the flip side. Okay, you've described some

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Speaker 3: core pieces of infrastructure that regulated, there's proprietary data, etc.

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Speaker 3: Clients are putting their most sensitive info in the world,

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Speaker 3: and your view is that the value of those platforms

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Speaker 3: will grow. Are there's the software that's not that or

435
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Speaker 3: is that other zeros?

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Speaker 4: Absolutely no, absolutely, there's well, for first of all, sort

437
00:26:32,600 --> 00:26:37,879
Speaker 4: of these things have long tails. Okay, So like actually

438
00:26:38,080 --> 00:26:42,800
Speaker 4: going through the process of retiring a system is a

439
00:26:42,840 --> 00:26:45,560
Speaker 4: lot of work no matter what the system is. So

440
00:26:45,640 --> 00:26:50,840
Speaker 4: there are long tails. But there are certain technologies that

441
00:26:51,119 --> 00:26:55,080
Speaker 4: are really and we all use them. There are certain

442
00:26:55,119 --> 00:26:59,879
Speaker 4: technologies that are about collating public information and making it

443
00:27:00,160 --> 00:27:04,000
Speaker 4: easy for you to access, and I think it's fair

444
00:27:04,040 --> 00:27:09,520
Speaker 4: to say that the AI tools that exist are the

445
00:27:09,880 --> 00:27:14,760
Speaker 4: ultimate oracles in being able to do that, in being

446
00:27:14,800 --> 00:27:19,000
Speaker 4: able to scour all public sources and give you back

447
00:27:19,040 --> 00:27:22,159
Speaker 4: information in the way that you're most comfortable with. So,

448
00:27:22,760 --> 00:27:28,040
Speaker 4: particularly that segment of sas, which is that like convenience

449
00:27:28,160 --> 00:27:33,720
Speaker 4: layer where they don't really have proprietary data, they're not

450
00:27:33,920 --> 00:27:38,920
Speaker 4: really in the workflow, They're a convenience technology. I think

451
00:27:39,040 --> 00:27:42,080
Speaker 4: those convenience technologies.

452
00:27:43,520 --> 00:27:44,200
Speaker 1: They're in trouble.

453
00:27:44,880 --> 00:27:48,520
Speaker 4: They're certainly going to be looking for ways of reimagining

454
00:27:48,560 --> 00:27:49,760
Speaker 4: their value proposition.

455
00:27:51,480 --> 00:27:53,080
Speaker 2: Well, one of the reasons we wanted to have you

456
00:27:53,119 --> 00:27:55,679
Speaker 2: on the podcast is because you are both a provider

457
00:27:55,880 --> 00:27:59,440
Speaker 2: of AI viz Aladdin and also a user of AI

458
00:27:59,560 --> 00:28:04,960
Speaker 2: at your own company. And we've all seen various executives

459
00:28:04,960 --> 00:28:08,560
Speaker 2: and managers talk about AI as a productivity enhancing tool,

460
00:28:08,880 --> 00:28:11,760
Speaker 2: and they tend to talk about it in very general terms.

461
00:28:11,800 --> 00:28:13,840
Speaker 2: So I would be curious to hear from your perspective

462
00:28:13,880 --> 00:28:18,520
Speaker 2: exactly what a productivity enhancement at Blackrock actually looks like.

463
00:28:19,000 --> 00:28:24,880
Speaker 4: I will give you a productivity enhancement from Friday great

464
00:28:25,040 --> 00:28:28,720
Speaker 4: of last week because, and by the way, what I'm describing,

465
00:28:29,000 --> 00:28:34,399
Speaker 4: I think is the future everywhere. I hope Blackrock gets

466
00:28:34,440 --> 00:28:37,320
Speaker 4: to that future faster than others, but I believe it's

467
00:28:37,359 --> 00:28:41,440
Speaker 4: the future. So we have been doing a lot of

468
00:28:41,480 --> 00:28:45,560
Speaker 4: work enhancing a lot in many ways, and one of

469
00:28:45,600 --> 00:28:48,320
Speaker 4: the big themes that we have is how do we

470
00:28:48,400 --> 00:28:54,160
Speaker 4: provide more transparency in the private markets to be as

471
00:28:54,200 --> 00:28:57,640
Speaker 4: close as possible to the public markets. In pursuit of

472
00:28:57,680 --> 00:29:02,120
Speaker 4: this whole portfolio. We have a large program of work

473
00:29:02,520 --> 00:29:06,360
Speaker 4: that's been going on for quite some time, so I

474
00:29:06,480 --> 00:29:09,840
Speaker 4: try to spend hours every Friday getting demos of things

475
00:29:09,840 --> 00:29:15,400
Speaker 4: we're working on. So my Friday afternoon demo along the

476
00:29:15,400 --> 00:29:18,640
Speaker 4: theme that I just described, was a demo of a

477
00:29:18,720 --> 00:29:22,960
Speaker 4: tool that was awesome, but let me go through how

478
00:29:23,000 --> 00:29:26,880
Speaker 4: it was created. And this was the first time end

479
00:29:26,920 --> 00:29:31,800
Speaker 4: to end that at least I've been shown this. So

480
00:29:33,520 --> 00:29:42,560
Speaker 4: a group of people that included portfolio managers, risk professionals, engineers,

481
00:29:42,840 --> 00:29:46,600
Speaker 4: product managers, a group of people sat in a room

482
00:29:46,640 --> 00:29:51,720
Speaker 4: for multiple hours talking about how this capability should work.

483
00:29:52,520 --> 00:29:59,920
Speaker 4: That discussion was recorded. That discussion, through the recording of it,

484
00:30:00,640 --> 00:30:05,560
Speaker 4: created a functional document. That functional document was lightly tweaked.

485
00:30:06,240 --> 00:30:09,560
Speaker 4: That functional document was then put in some of the

486
00:30:09,880 --> 00:30:15,360
Speaker 4: AI coding tools that we use. That document, through the

487
00:30:15,360 --> 00:30:18,680
Speaker 4: AI coding tools, led to a prototype. There was a

488
00:30:18,720 --> 00:30:22,920
Speaker 4: debugging process that you live through that we just described

489
00:30:23,440 --> 00:30:27,080
Speaker 4: and on Friday. You know, I've seen a lot of

490
00:30:27,080 --> 00:30:31,280
Speaker 4: prototypes before in my sort of thirty two years can imagine,

491
00:30:31,320 --> 00:30:35,360
Speaker 4: so I know the questions to ask where you see

492
00:30:35,400 --> 00:30:38,880
Speaker 4: the prototype is like a thin shell, but if you

493
00:30:38,960 --> 00:30:44,120
Speaker 4: press enough, the shell cracks. Like this wasn't like a prototype.

494
00:30:44,320 --> 00:30:48,360
Speaker 4: This was like the real deal. And when you look

495
00:30:48,560 --> 00:30:54,720
Speaker 4: at that cycle, we effectively collapsed. What would have taken

496
00:30:55,680 --> 00:30:59,320
Speaker 4: the unit of measurement would have been months now, the

497
00:30:59,440 --> 00:31:03,200
Speaker 4: unit of measurement was days now. It will still go

498
00:31:03,400 --> 00:31:07,480
Speaker 4: through our software development life cycle, it will be tested

499
00:31:08,000 --> 00:31:10,920
Speaker 4: all of those things. But when you look at that

500
00:31:11,000 --> 00:31:15,640
Speaker 4: as a productivity tool, this goes back to the ten

501
00:31:15,880 --> 00:31:18,960
Speaker 4: xing the amount of lines of code in the world

502
00:31:19,080 --> 00:31:22,760
Speaker 4: every year, there's just going to be an explosion in

503
00:31:22,800 --> 00:31:24,840
Speaker 4: the ability to engineer things.

504
00:31:25,240 --> 00:31:27,800
Speaker 2: Now I'm wondering if the coding tool, when it sees

505
00:31:27,800 --> 00:31:29,640
Speaker 2: a transcript of a meeting like that, do you think

506
00:31:29,640 --> 00:31:34,080
Speaker 2: it weights the participants by title and level of importance?

507
00:31:34,280 --> 00:31:35,360
Speaker 2: Necessarial question.

508
00:31:35,520 --> 00:31:37,720
Speaker 3: It probably does. It would be weird if it didn't, right.

509
00:31:37,680 --> 00:31:44,320
Speaker 4: It's interesting. My intuition is it doesn't. My intuition is

510
00:31:44,480 --> 00:31:48,720
Speaker 4: this would be a great exercise in those who talk

511
00:31:48,840 --> 00:31:50,560
Speaker 4: most are probably most reflective.

512
00:31:51,000 --> 00:31:54,640
Speaker 3: I've heard about a company that's doing third party consulting

513
00:31:54,800 --> 00:31:57,640
Speaker 3: for helping companies implement software. And one of the things

514
00:31:57,640 --> 00:32:00,440
Speaker 3: that they're doing is they get on a zoom with

515
00:32:00,520 --> 00:32:04,600
Speaker 3: the client and they have a camera trained on the

516
00:32:04,640 --> 00:32:08,520
Speaker 3: whiteboard that they're working on, and that video file also

517
00:32:08,920 --> 00:32:12,360
Speaker 3: is part of so in addition to the audio, because

518
00:32:12,400 --> 00:32:15,360
Speaker 3: you know, whiteboards of the lingo franca of software development,

519
00:32:15,480 --> 00:32:17,640
Speaker 3: so that's also gets uploaded to it and so that

520
00:32:17,720 --> 00:32:20,560
Speaker 3: it like sees that whole workflow and stuff. It's pretty

521
00:32:20,640 --> 00:32:24,160
Speaker 3: it's pretty wild stuff. I'm curious, Tokens. There's been a

522
00:32:24,200 --> 00:32:28,600
Speaker 3: lot of headlines lately about token sticker shock. We are

523
00:32:28,640 --> 00:32:32,080
Speaker 3: spending a lot on infront. I'm curious if you could

524
00:32:32,080 --> 00:32:35,880
Speaker 3: tell us anything about token consumption this year versus last

525
00:32:35,920 --> 00:32:40,400
Speaker 3: year at Black Rock. But also like one of the

526
00:32:40,440 --> 00:32:43,000
Speaker 3: things that we're also seeing and it's very it's related

527
00:32:43,040 --> 00:32:46,160
Speaker 3: to this is like the compute constraints that have long

528
00:32:46,240 --> 00:32:49,240
Speaker 3: been talked about is theoretical are starting to actually bite.

529
00:32:49,360 --> 00:32:53,840
Speaker 3: People who are sort of like anthropic users are like whatever, Like,

530
00:32:53,880 --> 00:32:56,120
Speaker 3: could you talk a little bit about token consumption? And

531
00:32:56,680 --> 00:33:00,440
Speaker 3: is the compute constraint real? From your perspective, if you

532
00:33:00,440 --> 00:33:03,280
Speaker 3: could snap the fingers and get one hundred thousand more

533
00:33:03,320 --> 00:33:06,200
Speaker 3: plugged in GPUs, would that be a big help right now?

534
00:33:06,320 --> 00:33:09,800
Speaker 4: Well, I think that you have to sort of go

535
00:33:10,240 --> 00:33:13,600
Speaker 4: back in time a little bit. So one of the key, key,

536
00:33:13,680 --> 00:33:18,240
Speaker 4: key lessons learned that I've experienced, and I remember one

537
00:33:18,280 --> 00:33:21,959
Speaker 4: of the near founding partners, a gentleman Charlie Halleck, who

538
00:33:22,080 --> 00:33:25,800
Speaker 4: unfortunately passed away. What he instilled in me, among many

539
00:33:25,840 --> 00:33:32,040
Speaker 4: other things, was if you have the right modelers and

540
00:33:32,160 --> 00:33:37,160
Speaker 4: engineers and you leave them unconstrained, they will bankrupt the

541
00:33:37,200 --> 00:33:43,080
Speaker 4: company in terms of their insatiable appetite for compute. In

542
00:33:43,120 --> 00:33:47,360
Speaker 4: the old days, you had a physical data center, that

543
00:33:47,600 --> 00:33:51,160
Speaker 4: was the constraint. You would have to order hardware. At

544
00:33:51,200 --> 00:33:54,880
Speaker 4: some point people would say we're outgrowing our data center,

545
00:33:54,920 --> 00:33:57,000
Speaker 4: you'd say, let's wait a year and see what happens.

546
00:33:57,480 --> 00:34:02,320
Speaker 4: So in today's world, the lesssticity is obviously very different.

547
00:34:02,360 --> 00:34:05,640
Speaker 4: But as a starting point, there are a group of

548
00:34:05,680 --> 00:34:08,880
Speaker 4: people within Blackrock, and I think this is true in

549
00:34:08,960 --> 00:34:12,600
Speaker 4: any sort of great financial services company that if you

550
00:34:12,800 --> 00:34:17,480
Speaker 4: leave them unconstrained from a compute power perspective twenty years ago,

551
00:34:17,520 --> 00:34:20,000
Speaker 4: they would have bankrupted the company ten years ago, and

552
00:34:20,080 --> 00:34:24,480
Speaker 4: today they will. So part of it is how do

553
00:34:24,560 --> 00:34:27,760
Speaker 4: you think about where to invest. I don't know offhand

554
00:34:27,800 --> 00:34:30,560
Speaker 4: the number of tokens we're consuming today relative to a era,

555
00:34:30,760 --> 00:34:35,279
Speaker 4: but it's multiples, it's multiples higher. And I think that

556
00:34:35,520 --> 00:34:38,799
Speaker 4: we're still at a point again as a company, but

557
00:34:38,920 --> 00:34:42,640
Speaker 4: also at a broad industry level where I don't really

558
00:34:42,760 --> 00:34:46,160
Speaker 4: think the game has started yet. So I don't think

559
00:34:46,200 --> 00:34:50,160
Speaker 4: anyone really knows what the token consumption is going to be,

560
00:34:50,880 --> 00:34:54,280
Speaker 4: and equally is important, I don't think anyone has really

561
00:34:54,320 --> 00:34:56,640
Speaker 4: started optimizing their token consumption.

562
00:34:57,040 --> 00:34:59,560
Speaker 2: You know what someone needs to do is build an

563
00:34:59,560 --> 00:35:02,399
Speaker 2: Aladdin for token efficiency.

564
00:35:03,120 --> 00:35:06,040
Speaker 4: It is a certainty that not only will that exist,

565
00:35:06,800 --> 00:35:09,960
Speaker 4: but one of the gentlemen who the person who actually

566
00:35:10,400 --> 00:35:13,759
Speaker 4: leads our Ai lab a gentleman Stephen Boyd. I had

567
00:35:13,760 --> 00:35:16,319
Speaker 4: called him in the early days of this happening and

568
00:35:16,360 --> 00:35:19,000
Speaker 4: I'm like, what are we missing? Like, Steven, help me understand.

569
00:35:19,000 --> 00:35:22,040
Speaker 4: What are we missing? He said two things, and he

570
00:35:22,120 --> 00:35:26,799
Speaker 4: is a professor of engineering at Stanford. He said, you're

571
00:35:26,800 --> 00:35:32,279
Speaker 4: missing two things. One is that articulate language is very,

572
00:35:32,400 --> 00:35:34,799
Speaker 4: very powerful. And I'm like, okay, what does that mean?

573
00:35:35,320 --> 00:35:37,759
Speaker 4: And he's like, think about it. I'm a professor. If

574
00:35:37,800 --> 00:35:41,040
Speaker 4: I'm reading a paper and it's like not in great,

575
00:35:41,120 --> 00:35:44,759
Speaker 4: it's not written that well, you check everything. And if

576
00:35:44,800 --> 00:35:47,520
Speaker 4: you're reading a paper that's written really well, you just

577
00:35:47,600 --> 00:35:50,400
Speaker 4: assume it's right. So that was the first point. The

578
00:35:50,440 --> 00:35:54,360
Speaker 4: second point he had, which relates to this topic, he said,

579
00:35:54,880 --> 00:35:58,440
Speaker 4: just remember there's a bunch of graduate students here and

580
00:35:58,480 --> 00:36:01,719
Speaker 4: everywhere else that right now we're working on like the

581
00:36:01,719 --> 00:36:05,560
Speaker 4: most boring aspects of this too, including how to have

582
00:36:05,640 --> 00:36:08,960
Speaker 4: these models be more efficient. So right now it's all

583
00:36:09,000 --> 00:36:12,279
Speaker 4: about the quest for intelligence. I think we're going to

584
00:36:12,320 --> 00:36:17,680
Speaker 4: see it pivot slightly to the quest for enterprise use cases,

585
00:36:18,440 --> 00:36:20,520
Speaker 4: and then I think it's going to pivot very quickly

586
00:36:21,000 --> 00:36:25,760
Speaker 4: to the quest for efficiency of how you're accessing things.

587
00:36:26,160 --> 00:36:27,080
Speaker 4: We're not there yet.

588
00:36:43,000 --> 00:36:44,799
Speaker 2: I want to go back to something you said about

589
00:36:44,800 --> 00:36:49,080
Speaker 2: private markets quickly, which is this whole portfolio management idea.

590
00:36:49,440 --> 00:36:52,680
Speaker 2: This idea that via a system like Aladdin, you can

591
00:36:52,760 --> 00:36:55,800
Speaker 2: manage your private assets the same way you would manage

592
00:36:55,800 --> 00:36:58,600
Speaker 2: your public assets, and you get more transparency around pricing

593
00:36:58,719 --> 00:37:01,640
Speaker 2: and things like that. So part of the sales pitch

594
00:37:01,880 --> 00:37:04,120
Speaker 2: with private markets has been this idea of an i

595
00:37:04,160 --> 00:37:07,919
Speaker 2: liquidity premium and the idea that you earn a little

596
00:37:07,960 --> 00:37:11,680
Speaker 2: bit more because these things are not treated like public

597
00:37:11,760 --> 00:37:15,440
Speaker 2: market assets. Once you start to integrate them via new

598
00:37:15,440 --> 00:37:18,759
Speaker 2: technology into your system, once you start to be able

599
00:37:18,760 --> 00:37:22,120
Speaker 2: to manage them much more similarly to a more liquid asset,

600
00:37:22,239 --> 00:37:24,560
Speaker 2: does some of that sales pitch start to go away.

601
00:37:24,920 --> 00:37:27,320
Speaker 4: I think of it as an effort premium. There's a

602
00:37:27,400 --> 00:37:30,680
Speaker 4: liquidity element to it, and then there's like an effort premium.

603
00:37:30,840 --> 00:37:33,000
Speaker 4: If you have to do more work, presumably you need

604
00:37:33,040 --> 00:37:37,200
Speaker 4: to be compensated for that. But I also believe that,

605
00:37:38,120 --> 00:37:40,880
Speaker 4: and I feel so old as I talk like this.

606
00:37:41,000 --> 00:37:45,120
Speaker 4: I'm only fifty two, but I feel so as crazy

607
00:37:45,120 --> 00:37:49,920
Speaker 4: as it sounds to many people. When I started, the

608
00:37:50,000 --> 00:37:55,040
Speaker 4: way you would get information about public bonds because even

609
00:37:55,080 --> 00:37:59,000
Speaker 4: Bloomberg back then was a bit nascent. You would read

610
00:37:59,040 --> 00:38:03,880
Speaker 4: a prospectus and you would type into a computer, this

611
00:38:03,960 --> 00:38:06,480
Speaker 4: is the maturity, this is the sinking schedule, this is

612
00:38:06,520 --> 00:38:09,640
Speaker 4: the call schedule. Now today, you say that to people

613
00:38:09,680 --> 00:38:13,279
Speaker 4: who entered the industry in the past twenty years, and

614
00:38:13,320 --> 00:38:18,439
Speaker 4: they think you're nuts. So I think there's no fighting technology.

615
00:38:18,520 --> 00:38:23,760
Speaker 4: That's my own opinion. So I think it is certain

616
00:38:24,280 --> 00:38:26,200
Speaker 4: that if you say in ten years of the private

617
00:38:26,200 --> 00:38:30,400
Speaker 4: markets more or less transparent, they're certainly more transparent. And

618
00:38:30,440 --> 00:38:34,200
Speaker 4: I think if you think about your life, almost everything

619
00:38:34,239 --> 00:38:37,960
Speaker 4: in your life is becoming more transparent. Like it's funny.

620
00:38:37,960 --> 00:38:39,600
Speaker 4: I don't think any of us. I try not to

621
00:38:39,640 --> 00:38:42,960
Speaker 4: affix technology on my body, but it's very rare that

622
00:38:43,000 --> 00:38:46,000
Speaker 4: three people would be together where someone doesn't have some

623
00:38:46,160 --> 00:38:49,880
Speaker 4: device that's monitoring like their blood pressure in real time.

624
00:38:50,320 --> 00:38:54,880
Speaker 4: So like everything is pointing towards more transparency. I have

625
00:38:54,960 --> 00:39:00,879
Speaker 4: a hard time believing that as private markets exposures importance grow,

626
00:39:02,560 --> 00:39:05,160
Speaker 4: the end asset owner is going to wind up with

627
00:39:05,280 --> 00:39:07,800
Speaker 4: less transparency. So I think this is just the direction

628
00:39:08,400 --> 00:39:12,880
Speaker 4: of travel. I do think over time, certain components of

629
00:39:12,920 --> 00:39:16,640
Speaker 4: it will start to become more standardized, very similar to

630
00:39:16,680 --> 00:39:19,719
Speaker 4: things that happened in the public bond markets and the

631
00:39:19,719 --> 00:39:23,480
Speaker 4: public equity markets. And then there will be new innovations

632
00:39:23,520 --> 00:39:28,320
Speaker 4: in other ways. But I believe that things will become

633
00:39:29,640 --> 00:39:35,080
Speaker 4: much more transparent. It's a certainty in the future.

634
00:39:35,160 --> 00:39:38,080
Speaker 3: And this is I guess more from just an investment standpoint,

635
00:39:38,160 --> 00:39:42,640
Speaker 3: but like, what is the investor's source of edge in

636
00:39:42,680 --> 00:39:44,640
Speaker 3: the future. You know, at one point maybe there was

637
00:39:44,640 --> 00:39:47,320
Speaker 3: a source of edge because you were early to jump

638
00:39:47,360 --> 00:39:50,799
Speaker 3: on and see the potential of stringing together Sun microsystems

639
00:39:50,800 --> 00:39:54,120
Speaker 3: and you could replicate whatever or these workstations were just

640
00:39:54,160 --> 00:39:57,759
Speaker 3: looking forward for the portfolio manager, et cetera. What constitutes edge.

641
00:39:57,800 --> 00:40:01,359
Speaker 4: It's a great question, and I think across industries, if

642
00:40:01,360 --> 00:40:03,360
Speaker 4: you think of them as a treadmill, everyone's going to

643
00:40:03,400 --> 00:40:07,200
Speaker 4: have to run faster. There's no question that that's the case.

644
00:40:07,640 --> 00:40:10,880
Speaker 4: I think the edge you could break up into three categories. First,

645
00:40:11,239 --> 00:40:14,560
Speaker 4: I think the edge is going to be helping clients

646
00:40:15,080 --> 00:40:18,799
Speaker 4: with their whole portfolio as opposed to just pieces. And

647
00:40:18,840 --> 00:40:22,080
Speaker 4: I think if you look at the asset management industry,

648
00:40:23,000 --> 00:40:28,160
Speaker 4: a very significant i would say evolution aspect of the

649
00:40:28,200 --> 00:40:34,040
Speaker 4: asset management industry is that the industry organized itself inconsistent

650
00:40:34,120 --> 00:40:38,320
Speaker 4: with how clients build portfolios. You had fixed income shops,

651
00:40:38,320 --> 00:40:41,000
Speaker 4: you had equity shops. You had index managers, you had

652
00:40:41,040 --> 00:40:44,520
Speaker 4: active managers, you had systematic managers, you had public markets,

653
00:40:44,520 --> 00:40:47,640
Speaker 4: you had private markets, and then you force the client

654
00:40:47,800 --> 00:40:51,719
Speaker 4: to put all the stuff together. So the industry is

655
00:40:51,800 --> 00:40:54,479
Speaker 4: going to pivot more towards helping with the whole thing,

656
00:40:55,120 --> 00:40:58,680
Speaker 4: which is a different type of edge. I think that

657
00:40:59,080 --> 00:41:02,840
Speaker 4: the ability to use these tools is going to be

658
00:41:02,880 --> 00:41:07,400
Speaker 4: an edge in and of itself. So notwithstanding, coding is

659
00:41:07,440 --> 00:41:12,200
Speaker 4: going to be easier, and there will be multiples. More like,

660
00:41:12,320 --> 00:41:15,520
Speaker 4: the ability to build technology is going to become more

661
00:41:15,520 --> 00:41:18,960
Speaker 4: and not less important, even if the frictions to build

662
00:41:19,000 --> 00:41:23,360
Speaker 4: technology go down. I would argue I was at a

663
00:41:23,400 --> 00:41:26,960
Speaker 4: conference and someone asked me, like, who would you like

664
00:41:27,000 --> 00:41:30,000
Speaker 4: to hire coming out of university, And I said English majors.

665
00:41:30,640 --> 00:41:33,759
Speaker 4: And that was a big mistake because then like thousands

666
00:41:33,760 --> 00:41:36,480
Speaker 4: of people emailed me that they're children, their child's an

667
00:41:36,480 --> 00:41:39,440
Speaker 4: English major. Well, I speak to them. But the reason

668
00:41:39,480 --> 00:41:42,040
Speaker 4: I said English majors, and I believe this, like we're

669
00:41:42,040 --> 00:41:46,359
Speaker 4: living through a time where those who could have imagination

670
00:41:46,680 --> 00:41:52,839
Speaker 4: and articulated the ability to implement that has never been

671
00:41:52,880 --> 00:41:56,080
Speaker 4: as fast. Yeah, and literally if it used to be

672
00:41:56,280 --> 00:42:02,000
Speaker 4: like years, now it's like days. So the ability to

673
00:42:02,280 --> 00:42:06,600
Speaker 4: have those ideas, the implementability of those ideas is going

674
00:42:06,680 --> 00:42:10,400
Speaker 4: to be different than any point, so that creativity and imagination.

675
00:42:10,960 --> 00:42:16,799
Speaker 4: And then lastly, obviously the world is becoming very complicated.

676
00:42:16,960 --> 00:42:24,080
Speaker 4: I've heard you basically have these this collision between national security,

677
00:42:24,760 --> 00:42:31,239
Speaker 4: technology and capital. You have this fragmentation of geopolitics. You

678
00:42:31,280 --> 00:42:34,799
Speaker 4: have the changing demographics of the world. You have what's

679
00:42:34,800 --> 00:42:38,279
Speaker 4: happening with this alien technology that's now been found on

680
00:42:38,320 --> 00:42:43,480
Speaker 4: the planet Earth. So if you think about the global

681
00:42:43,480 --> 00:42:49,640
Speaker 4: connectivity that's required to really manage portfolios and how geopolitics

682
00:42:49,719 --> 00:42:54,279
Speaker 4: is going to implement within portfolios going forward, I think

683
00:42:54,320 --> 00:42:57,600
Speaker 4: that requires like on the ground networks as much as

684
00:42:57,640 --> 00:43:01,799
Speaker 4: it requires technology. And it's interesting. I'm sure you guys

685
00:43:01,840 --> 00:43:05,920
Speaker 4: have had a similar experience. I read the paper, I

686
00:43:06,000 --> 00:43:09,040
Speaker 4: read Bloomberg, thank you, I watch a lot of stuff.

687
00:43:09,880 --> 00:43:13,600
Speaker 4: But when you talk to and I was actually supposed

688
00:43:13,640 --> 00:43:18,040
Speaker 4: to be traveling last week in the Gulf region and

689
00:43:18,120 --> 00:43:21,719
Speaker 4: instead I did a virtual tour. When you speak to

690
00:43:22,880 --> 00:43:25,279
Speaker 4: our clients there, you get a very different picture of

691
00:43:25,360 --> 00:43:28,520
Speaker 4: what's going on than what you're reading, and I think

692
00:43:28,680 --> 00:43:32,120
Speaker 4: those networks are going to become more important in terms

693
00:43:32,160 --> 00:43:36,800
Speaker 4: of edge as we look forward. Nothing will replace being

694
00:43:36,880 --> 00:43:37,520
Speaker 4: on the ground.

695
00:43:38,400 --> 00:43:41,720
Speaker 2: Well, actually, on that note, you're in a position where

696
00:43:41,760 --> 00:43:47,360
Speaker 2: you're hiring people constantly for very specific roles. Are the processes.

697
00:43:47,400 --> 00:43:50,359
Speaker 2: Are the questions that you're asking people now different in

698
00:43:50,360 --> 00:43:53,000
Speaker 2: the age of AI versus what they were like four

699
00:43:53,080 --> 00:43:53,560
Speaker 2: years ago?

700
00:43:53,800 --> 00:43:57,560
Speaker 4: It's interesting, I hope so I know the questions I'm

701
00:43:57,640 --> 00:44:01,160
Speaker 4: asking people are. I hope that set scale. Maybe I

702
00:44:01,160 --> 00:44:04,920
Speaker 4: should follow up on that, but the questions I'm asking

703
00:44:05,840 --> 00:44:10,400
Speaker 4: I couldn't be more excited about the opportunities ahead for

704
00:44:10,480 --> 00:44:16,360
Speaker 4: black Rock, but I couldn't be more aware of the

705
00:44:16,480 --> 00:44:22,719
Speaker 4: requirement in today's world everything needs to be reimagined and

706
00:44:23,200 --> 00:44:26,400
Speaker 4: what we need and what I think leaders are going

707
00:44:26,480 --> 00:44:29,040
Speaker 4: to be faced with. And ironically, I think a lot

708
00:44:29,040 --> 00:44:33,120
Speaker 4: of the reimagination is going to come bottom up. But

709
00:44:34,560 --> 00:44:39,040
Speaker 4: how do you reimagine what you do? How do you

710
00:44:39,120 --> 00:44:42,680
Speaker 4: reimagine how you grow? How do you reimagine how you

711
00:44:42,719 --> 00:44:48,280
Speaker 4: interact with clients in this new world? And I think

712
00:44:49,400 --> 00:44:53,560
Speaker 4: that again, I don't think that's started. I think right

713
00:44:53,600 --> 00:44:55,960
Speaker 4: now it's a bit of a brain teaser, a lot

714
00:44:55,960 --> 00:45:01,600
Speaker 4: of experiments, but it has not started. Most every great company,

715
00:45:02,080 --> 00:45:04,840
Speaker 4: if you walk in in the year twenty thirty, is

716
00:45:04,880 --> 00:45:08,520
Speaker 4: going to be fundamentally different than today. And the way

717
00:45:08,560 --> 00:45:13,440
Speaker 4: I think of this, this is not an overnight reimagination,

718
00:45:14,400 --> 00:45:17,520
Speaker 4: but it's not a five year reimagination. It's somewhere in

719
00:45:17,600 --> 00:45:18,479
Speaker 4: between those two.

720
00:45:18,719 --> 00:45:20,040
Speaker 3: You know, we said in the beginning we could go

721
00:45:20,080 --> 00:45:21,840
Speaker 3: for hours, and there's a million other things that we

722
00:45:21,840 --> 00:45:24,719
Speaker 3: could ask. One last question on my mind, I've been

723
00:45:24,920 --> 00:45:28,160
Speaker 3: I'm trying. I've asked this version to a few different people,

724
00:45:28,800 --> 00:45:31,000
Speaker 3: and it maybe kind of relates to the other kind

725
00:45:31,000 --> 00:45:34,320
Speaker 3: of tokens tokenization, but just the same, it's going to

726
00:45:34,360 --> 00:45:38,920
Speaker 3: get confusing. You really an already so there's a.

727
00:45:38,440 --> 00:45:40,440
Speaker 4: Well, can I just say one thing before you ask

728
00:45:40,480 --> 00:45:44,120
Speaker 4: you a question? It's going to get confusing, it already is.

729
00:45:44,320 --> 00:45:50,840
Speaker 4: But importantly, AI and digital assets, yeah, are very related topics,

730
00:45:51,280 --> 00:45:53,000
Speaker 4: extremely related topics.

731
00:45:53,040 --> 00:45:54,880
Speaker 3: Well this kind of maybe maybe you can fold that

732
00:45:54,920 --> 00:45:57,799
Speaker 3: into the answer here. But you know, like an interesting thing,

733
00:45:58,000 --> 00:46:00,279
Speaker 3: there's a lot of very exciting private company is that

734
00:46:00,320 --> 00:46:04,040
Speaker 3: people want it get access to, but in many cases

735
00:46:04,080 --> 00:46:06,440
Speaker 3: are already kind of traded in some way, and there

736
00:46:06,520 --> 00:46:10,279
Speaker 3: might be like these SPVs that people have already, or

737
00:46:10,320 --> 00:46:13,040
Speaker 3: some token somewhere that trades on hyper liquid on the

738
00:46:13,080 --> 00:46:17,279
Speaker 3: weekend that represents somehow shares of anthropic or whatever. You

739
00:46:17,400 --> 00:46:22,560
Speaker 3: need other hobbies, But I'm curious from your perspective. And

740
00:46:22,680 --> 00:46:25,400
Speaker 3: another as thing that relates to this is the disclosure

741
00:46:25,440 --> 00:46:29,120
Speaker 3: obligations for existing public companies seem like they're going to

742
00:46:29,200 --> 00:46:31,360
Speaker 3: come down over time, and maybe companies will only have

743
00:46:31,400 --> 00:46:33,759
Speaker 3: to report every six months or a year, maybe never,

744
00:46:33,960 --> 00:46:36,480
Speaker 3: Like will there always be a bright line between what's

745
00:46:36,520 --> 00:46:38,960
Speaker 3: a public and what's a private asset? Or is it

746
00:46:39,080 --> 00:46:43,120
Speaker 3: just going to be this spectrum of liquidity and disclosure

747
00:46:43,239 --> 00:46:46,760
Speaker 3: but no clear definition of what it means anymore between

748
00:46:46,840 --> 00:46:47,680
Speaker 3: public and private.

749
00:46:48,000 --> 00:46:51,120
Speaker 4: It's a great question. I think over time it'll be

750
00:46:51,200 --> 00:46:54,840
Speaker 4: more of a spectrum. I think that when I look

751
00:46:55,239 --> 00:47:00,680
Speaker 4: at the broad industry over the past multiple decades, the

752
00:47:00,800 --> 00:47:06,560
Speaker 4: lines one of the defining themes. The lines almost across

753
00:47:06,680 --> 00:47:09,800
Speaker 4: everything you could imagine, have been getting more and more blurry,

754
00:47:10,360 --> 00:47:13,360
Speaker 4: And I would argue that's been because of technology. So

755
00:47:13,400 --> 00:47:16,920
Speaker 4: if you think about the old style boxes that existed,

756
00:47:17,480 --> 00:47:22,360
Speaker 4: they were convenience technologies because you couldn't bottom up model things.

757
00:47:22,480 --> 00:47:27,200
Speaker 4: You basically said, okay, mid cap equities have this attribute,

758
00:47:27,320 --> 00:47:30,359
Speaker 4: and like, that's a lego piece as opposed to let

759
00:47:30,400 --> 00:47:33,759
Speaker 4: me model what the individual stocks are and then I

760
00:47:33,800 --> 00:47:36,319
Speaker 4: could think of it in the context of a whole portfolio.

761
00:47:36,880 --> 00:47:41,880
Speaker 4: So I think that what's happening is that technology is

762
00:47:42,040 --> 00:47:48,360
Speaker 4: enabling those lines to be less discreet and more blurry

763
00:47:48,440 --> 00:47:54,239
Speaker 4: across spectrums and across how do clients build portfolios? How

764
00:47:54,239 --> 00:47:58,000
Speaker 4: did these lego pieces get put together to actually achieve

765
00:47:58,120 --> 00:48:02,600
Speaker 4: their objectives. I think that there's a lot going on

766
00:48:02,920 --> 00:48:06,800
Speaker 4: embedded in your question, including you see, and to me,

767
00:48:06,920 --> 00:48:09,279
Speaker 4: this is one of the most remarkable things that is

768
00:48:09,320 --> 00:48:14,000
Speaker 4: happening is that two three, four years ago the whole

769
00:48:14,080 --> 00:48:17,600
Speaker 4: narrative was private for longer. And certainly I don't know

770
00:48:17,640 --> 00:48:20,240
Speaker 4: how this is true that there's fewer public companies today

771
00:48:20,239 --> 00:48:25,040
Speaker 4: that when I started. I've checked it it's actually true,

772
00:48:25,080 --> 00:48:28,480
Speaker 4: because I didn't believe it, But you wonder, like, how

773
00:48:28,600 --> 00:48:32,120
Speaker 4: is that possible? Now that said, the flip side of

774
00:48:32,160 --> 00:48:36,399
Speaker 4: it is, you see, for these companies like open Ai

775
00:48:36,480 --> 00:48:40,520
Speaker 4: and propic space, they're raced to go public because at

776
00:48:40,520 --> 00:48:44,400
Speaker 4: some point the public markets provide a lot of value

777
00:48:44,400 --> 00:48:48,080
Speaker 4: propositions that are beyond what the private markets can provide.

778
00:48:48,520 --> 00:48:50,759
Speaker 4: So I think you see this sort of tale of

779
00:48:50,800 --> 00:48:55,400
Speaker 4: two cities that's happening. But there will be a spectrum,

780
00:48:55,920 --> 00:49:03,839
Speaker 4: including a spectrum of certain end investors and institutions that

781
00:49:04,000 --> 00:49:09,160
Speaker 4: want to have a digital wallet and certain end investors

782
00:49:09,200 --> 00:49:13,440
Speaker 4: and institutions that want to have a traditional custody account.

783
00:49:13,800 --> 00:49:17,080
Speaker 4: And it's not a binary thing. It's about technology enabling

784
00:49:17,160 --> 00:49:18,280
Speaker 4: that personalization.

785
00:49:19,800 --> 00:49:22,160
Speaker 2: I have one more question, sort of a wild card,

786
00:49:22,360 --> 00:49:26,360
Speaker 2: but what do you and Larry Fink disagree about the most?

787
00:49:26,400 --> 00:49:28,880
Speaker 2: And the reason I ask is no, I'm genuinely interested.

788
00:49:28,920 --> 00:49:32,319
Speaker 2: You've been working alongside each other for years and years

789
00:49:32,360 --> 00:49:35,680
Speaker 2: and years now. I have a personal interest in close collaborative.

790
00:49:35,160 --> 00:49:37,319
Speaker 3: Yeah, I disagree on a lot. So it's a really

791
00:49:37,360 --> 00:49:38,080
Speaker 3: legit question.

792
00:49:38,680 --> 00:49:44,319
Speaker 4: I would say often we both see things similarly in

793
00:49:44,400 --> 00:49:48,239
Speaker 4: terms of the endpoint. Larry is more of a tomorrow

794
00:49:49,080 --> 00:49:53,840
Speaker 4: person and I'm more of a well there's work to

795
00:49:53,880 --> 00:49:56,000
Speaker 4: do here, this is going to take a few years.

796
00:49:56,160 --> 00:49:59,200
Speaker 4: I would say that that's typically it.

797
00:50:00,080 --> 00:50:03,920
Speaker 2: Area is a Joe and I'm a raw guess. All right,

798
00:50:04,239 --> 00:50:05,600
Speaker 2: Rob Goldstein, thank you so much.

799
00:50:05,480 --> 00:50:08,439
Speaker 4: For coming on. Thought that was great, Yeah, that was great.

800
00:50:08,480 --> 00:50:10,200
Speaker 4: Thank you. So much pleasure.

801
00:50:10,280 --> 00:50:23,239
Speaker 3: Yeah, joke.

802
00:50:23,640 --> 00:50:26,480
Speaker 2: That was a really fun conversation and a lot to

803
00:50:26,520 --> 00:50:29,080
Speaker 2: think about. I mean, I do think when we're talking

804
00:50:29,120 --> 00:50:32,520
Speaker 2: about moats around some of these businesses. You brought up

805
00:50:32,640 --> 00:50:36,080
Speaker 2: the power dynamics law very early on, and it does

806
00:50:36,120 --> 00:50:39,000
Speaker 2: feel like a lot of the moat is basically size

807
00:50:39,120 --> 00:50:42,000
Speaker 2: and data and capability that you have. And I can't

808
00:50:42,040 --> 00:50:45,000
Speaker 2: imagine that. You hear this. You hear people saying that

809
00:50:45,040 --> 00:50:47,640
Speaker 2: they're like vibe coding a bunch of different programs and

810
00:50:47,680 --> 00:50:50,080
Speaker 2: they all look kind of interesting and cool, but like,

811
00:50:50,480 --> 00:50:53,680
Speaker 2: it's hard for me to imagine a big moat around

812
00:50:53,719 --> 00:50:56,600
Speaker 2: those businesses if you've just like plugged in a few

813
00:50:56,600 --> 00:50:58,239
Speaker 2: instructions into plod code.

814
00:50:58,040 --> 00:51:01,879
Speaker 3: And no one is going to be putting anything of

815
00:51:02,160 --> 00:51:05,319
Speaker 3: importance or sensitivity to some homemade right.

816
00:51:05,480 --> 00:51:08,640
Speaker 5: I mean, that's that's that's what was interesting, this idea

817
00:51:08,680 --> 00:51:13,200
Speaker 5: that actually the regulatory mode around finance actually becomes a

818
00:51:13,320 --> 00:51:15,800
Speaker 5: valuable thing in the age of AI totally.

819
00:51:16,040 --> 00:51:19,520
Speaker 3: You know, it's interesting to not really AI related, but

820
00:51:20,000 --> 00:51:23,880
Speaker 3: the idea of being able to provide, you know, provide

821
00:51:23,920 --> 00:51:28,439
Speaker 3: a solution all of portfolio visibility. Yeah, what is one

822
00:51:28,560 --> 00:51:32,920
Speaker 3: reason why the big get bigger within finance. One reason

823
00:51:33,200 --> 00:51:36,920
Speaker 3: is because only a really large entity would have the

824
00:51:37,000 --> 00:51:40,080
Speaker 3: capacity to be able to like, and here's you know

825
00:51:40,160 --> 00:51:42,160
Speaker 3: what we can offer you with private credit, and here's

826
00:51:42,200 --> 00:51:44,239
Speaker 3: what we can offer you with index right, et cetera.

827
00:51:44,520 --> 00:51:48,680
Speaker 3: So Okay, the end investor has this big portfolio consisting

828
00:51:48,719 --> 00:51:52,600
Speaker 3: of lots of different types of asset classes. As he mentioned, historically,

829
00:51:52,680 --> 00:51:55,920
Speaker 3: the industry has sort of been verticalized by asset class.

830
00:51:56,120 --> 00:51:59,200
Speaker 3: If you want some entity that has how do all

831
00:51:59,200 --> 00:52:01,960
Speaker 3: the puzzle pieces fit together, which is the essence of

832
00:52:02,000 --> 00:52:05,200
Speaker 3: portfolio construction, then theoretically you just want like a really

833
00:52:05,200 --> 00:52:07,960
Speaker 3: big company that understands all of it.

834
00:52:08,080 --> 00:52:08,360
Speaker 4: Yeah.

835
00:52:08,640 --> 00:52:10,799
Speaker 3: I also thought and it was kind of also on

836
00:52:10,840 --> 00:52:14,840
Speaker 3: the source of edge question is we've talked about this before.

837
00:52:15,120 --> 00:52:17,040
Speaker 3: It always seems like one of the fun jobs in

838
00:52:17,120 --> 00:52:20,279
Speaker 3: finance would be the channel check person, the person who

839
00:52:20,280 --> 00:52:23,120
Speaker 3: goes to the mall to yeah the field Yeah, the

840
00:52:23,120 --> 00:52:25,680
Speaker 3: field trips is like, okay, how many sweaters are on

841
00:52:25,719 --> 00:52:29,760
Speaker 3: the gap shelf whatever, Maybe that becomes even more valuable

842
00:52:29,800 --> 00:52:32,640
Speaker 3: because it's like things that have not yet been put into.

843
00:52:32,440 --> 00:52:33,480
Speaker 2: A mallel Yeah.

844
00:52:33,520 --> 00:52:36,760
Speaker 3: And look, I love that people listen to odd laws.

845
00:52:36,760 --> 00:52:39,560
Speaker 3: I love that people read the news particularly financial news

846
00:52:39,600 --> 00:52:43,280
Speaker 3: on Bloomberg. I've never thought by and large that people

847
00:52:43,400 --> 00:52:46,520
Speaker 3: read an article and it's like I'm gonna make an

848
00:52:46,520 --> 00:52:49,520
Speaker 3: investment decision based on that. Hopefully it helps inform their

849
00:52:49,520 --> 00:52:51,600
Speaker 3: thinking or their processes in some way. But no one

850
00:52:51,880 --> 00:52:54,680
Speaker 3: listens to a podcast and then like goes out like

851
00:52:54,680 --> 00:52:57,319
Speaker 3: buys the stock, buy and large. But it's because once

852
00:52:57,360 --> 00:52:59,880
Speaker 3: it's out there in like the digital world, it's like

853
00:53:00,040 --> 00:53:02,120
Speaker 3: it's kind of priced him already. So there's going to

854
00:53:02,160 --> 00:53:06,160
Speaker 3: be the intense hunt for information that is like truly

855
00:53:06,320 --> 00:53:08,560
Speaker 3: like has not been put into the model yet.

856
00:53:08,600 --> 00:53:11,759
Speaker 2: You're such an EMHs purist. It's amazing, but right, like.

857
00:53:11,719 --> 00:53:15,439
Speaker 3: The value of people who can find information that has

858
00:53:15,520 --> 00:53:18,840
Speaker 3: not turned into trade training data yet for a model,

859
00:53:19,239 --> 00:53:22,040
Speaker 3: that data is going to get like super valuable and

860
00:53:22,120 --> 00:53:25,080
Speaker 3: maybe more valuable and more reason to like just like

861
00:53:25,160 --> 00:53:26,839
Speaker 3: get out on the road and stuff like that.

862
00:53:26,960 --> 00:53:29,320
Speaker 2: No, I largely agree with that. The other thing I

863
00:53:29,360 --> 00:53:31,680
Speaker 2: thought was really interesting, and it gets to your last

864
00:53:31,719 --> 00:53:34,840
Speaker 2: question was the sort of melding of public and private markets.

865
00:53:34,880 --> 00:53:36,560
Speaker 2: And it's also what I was kind of getting at

866
00:53:36,600 --> 00:53:40,000
Speaker 2: with the whole portfolio thing. If all these private assets

867
00:53:40,719 --> 00:53:44,400
Speaker 2: are tokenized and treated in the same way in a portfolio,

868
00:53:44,719 --> 00:53:46,879
Speaker 2: or at least visible in a portfolio in the same

869
00:53:46,920 --> 00:53:50,120
Speaker 2: way as a public asset would be. It seems like

870
00:53:50,160 --> 00:53:52,200
Speaker 2: that distinction starts to get really really pleasing.

871
00:53:52,360 --> 00:53:54,560
Speaker 3: Totally right, not to go back to a point that

872
00:53:54,600 --> 00:53:57,480
Speaker 3: I just made, but on this like I think we're

873
00:53:57,480 --> 00:53:58,200
Speaker 3: actually already.

874
00:53:57,960 --> 00:53:59,840
Speaker 2: Saying I'm sorry, I moved on too quickly.

875
00:54:00,480 --> 00:54:02,560
Speaker 3: Just this just click to me too. But it's like, okay,

876
00:54:03,239 --> 00:54:05,520
Speaker 3: where are we go? Like there, maybe there'll be more

877
00:54:05,600 --> 00:54:07,880
Speaker 3: value in like people who go out on the road

878
00:54:07,960 --> 00:54:10,839
Speaker 3: and get information that's not on the model. We saw

879
00:54:10,880 --> 00:54:13,600
Speaker 3: this recently with all the people going crazy for Sutrini's

880
00:54:13,600 --> 00:54:16,640
Speaker 3: analyst in the straight up Hoore Moves and this idea

881
00:54:16,640 --> 00:54:19,720
Speaker 3: that like field trips out to the world to collect

882
00:54:19,719 --> 00:54:22,200
Speaker 3: information that has not been digitized yet, it is going

883
00:54:22,280 --> 00:54:24,720
Speaker 3: to be where all the action is. Like it feels

884
00:54:24,760 --> 00:54:26,200
Speaker 3: like that's gonna be a big thing.

885
00:54:26,280 --> 00:54:28,520
Speaker 2: No, it sounds trite, but I feel like the pendulum

886
00:54:28,719 --> 00:54:31,600
Speaker 2: has swung from you know, for the past twenty years,

887
00:54:31,719 --> 00:54:35,160
Speaker 2: if you were a smart person who could think in

888
00:54:35,239 --> 00:54:39,160
Speaker 2: terms of numbers and code, you were probably very valued

889
00:54:39,160 --> 00:54:42,120
Speaker 2: by society. And now the pendulum sort of swings to

890
00:54:42,520 --> 00:54:48,920
Speaker 2: those on the ground relationship building, social skills. It's it's

891
00:54:48,960 --> 00:54:50,160
Speaker 2: an interesting transition.

892
00:54:50,160 --> 00:54:52,239
Speaker 3: It's an interesting time. All right, shall we leave it there,

893
00:54:52,320 --> 00:54:53,000
Speaker 3: Let's save it there.

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00:54:53,160 --> 00:54:55,600
Speaker 2: This has been another episode of the Authoughts podcast. I'm

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00:54:55,600 --> 00:54:58,440
Speaker 2: Tracy Alloway. You can follow me at Tracy Alloway and.

896
00:54:58,440 --> 00:55:01,120
Speaker 3: I'm Jill Wise and Thought. You can follow at the Stalwart.

897
00:55:01,360 --> 00:55:05,000
Speaker 3: Follow our producers Carmen Rodriguez at Carman Arman, Dashel Bennett

898
00:55:05,000 --> 00:55:08,919
Speaker 3: at Dashbot, Kelbrooks at Kelbrooks and Kevin Lozano at Kevin

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00:55:09,000 --> 00:55:11,600
Speaker 3: Lloyd Lozano. And for more odd Laws content, go to

900
00:55:11,600 --> 00:55:14,480
Speaker 3: Bloomberg dot com slash odd Lots where the daily newslettern

901
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Speaker 3: all of our episodes and you can chat about all

902
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Speaker 3: of these topics twenty four to seven into our discord

903
00:55:19,520 --> 00:55:21,560
Speaker 3: Discord dot gg slash hotline.

904
00:55:21,760 --> 00:55:23,960
Speaker 2: And if you enjoy odd Lots, if you want us

905
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Speaker 2: to do more on the ground investigative reporting and analysis,

906
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911
00:55:41,160 --> 00:55:41,520
Speaker 2: listening

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Speaker 4: In the INSI