Joe Weisenthal (2:30)

I like this.

Tracy Alloway (2:30)

And then the third, semi megatrend, I don't know, rise of private markets, right?

Joe Weisenthal (2:35)

Yeah. Oh, I have another one.

Tracy Alloway (2:36)

Okay.

Joe Weisenthal (2:37)

That I think is legit. The sort of power law, domination of a few mega companies and sort of whether it's consultant, the big get bigger, the winner take allness or the winner take mostness of the industry. I would add that as fact Of a matter Megatrend.

Fidelity App Voice (2:50)

Okay, that's.

Joe Weisenthal (2:50)

I would say that connects to this episode, too.

Tracy Alloway (2:52)

Absolutely. So we have four big megatrends, and we have the perfect guest to talk about all of those four things. And it comes at a time when obviously we're in this new sort of technological wave with AI. It very much feels like everyone in professional finance wants to figure out a way of being involved in AI in one way or another. It's funny. I was meeting up with someone who works at a very large bank the other day, and they were talking about how one work that they've done 20 years ago, their managers are now adamant it has to be put into a big Excel database because they're about to shove all of that into an AI model. So you can see. You can see there's this, like, urgency, and there's a perennial question over how much of it is real.

Joe Weisenthal (3:33)

Random. Random data that exists somewhere. They need to have it as part of the data lake or whatever so that the AI model knows about it. Oh, that makes sense.

Tracy Alloway (3:41)

Exactly. And I. I think the question for this is how much of it is managers who are latching on to the AI trend versus how much of this is actually going to productive, useful technology for finance. So we can definitely talk a little bit about that, but we do have the perfect guest.

Joe Weisenthal (3:57)

Let's do it.

Tracy Alloway (3:57)

All right, so we're going to be speaking with Rob Goldstein. He is, of course, the CEO of BlackRock, someone who has literally lived through basically all of these megatrends that we just described.

Rob Goldstein (4:08)

Yes. And the question is, how many hours do we have? You know, needless to say, those megatrends we could talk about for quite some time.

Joe Weisenthal (4:16)

Yeah, we got to do a series.

Rob Goldstein (4:18)

We could do a series. And one thing I would just politely identify, I think, as people talk about the big getting bigger, I think there's an underlying catalyst towards those who could provide a better value proposition are getting bigger.

Joe Weisenthal (4:35)

Okay.

Rob Goldstein (4:36)

And I think that's the key theme that's happening, particularly with regard to the buy side.

Joe Weisenthal (4:41)

I know you were saying, what was that? A polite. You didn't push back, but a polite nuance or something like that. But one could say that, that the ability to provide a better value proposition is itself a function of size in many instances, because the larger have the full suite, the whole menu. Right. Of services. So even there, there's like a nuance

Rob Goldstein (5:02)

upon a nuance, a hundred percent. And what's interesting is I think if you ordered the mega themes, you could make a very strong argument that it all comes down to technology.

Tracy Alloway (5:15)

Hmm.

Joe Weisenthal (5:15)

Okay.

Rob Goldstein (5:16)

And technology is enabling things and value propositions to be achieved that traditionally just couldn't be done.

Joe Weisenthal (5:24)

Great. Let's just talk about that.

Tracy Alloway (5:25)

Is it true that like some of BlackRock's foundational. By the way, the chances that I say Blackstone in this conversation.

Rob Goldstein (5:32)

Yeah, that's not, that's not good.

Tracy Alloway (5:34)

I apologize.

Joe Weisenthal (5:36)

We had a pre call and I said that I've been in this business a long time. We had a pre call with Rob, like several weeks ago, I said Blackstone and. And then like I could tell there was like a silent size, like I said the wrong thing right now. He's heard it all before.

Tracy Alloway (5:47)

I hope so. So is it true that like, the foundational culture of blackrock is very much tied to technology? Because the story that I always used to hear was about a Sun workstation and Ben Golub.

Rob Goldstein (6:01)

Yeah, and Ben is still a close friend and mentor. Ben being one of the founding partners of BlackRock. I think if you zoom out a little bit, because I think the history of Blackrock is very reflective of what the past 30, 35 years have been in terms of the companies that have been most successful. And if you look at the founders of BlackRock, they were a group of people who were pioneers with regard to structured products and the evolution of the mortgage market. And what they realized is that banks at the time, the sell side, as you guys sort of laid the groundwork, banks at the time were using supercomputers and they were very expensive computers to structure things. And then the way they were selling those products was literally by faxing yield tables all over the world. And I know, and when I say this to 20 something year old, 30 something year olds, including my own children who are when their early 20s, a lot of the people back then didn't even have computers. Yeah, like computers were like there was one for a group of people as opposed to everyone had one on their desk. So the thesis behind forming BlackRock was that we could actually build models that would help provide risk transparency for those type of instruments and help the end asset owner, we could build those models and through the sun workstation as the innovation, if you were reasonably clever, you didn't need to be a genius, but if you were reasonably clever, you could buy 10 Sun Workstations for $10,000 each and link them together and effectively do what previously only supercomputers that cost millions of dollars could do. So the founding thesis of BlackRock was really about how do we bring those technology capabilities which were not really available on the buy side, how do we use them as the core of building an asset manager? That was the founding thesis and I think one of the real success factors. And I think that when you look today, what I'm about to say seems like very odd, but I guess this is odd loss. So it's perfect. But when I started at BlackRock in 1994, when we had roughly 80 people, $19 billion in assets under management, I was in the data and analytics team. I was effectively a data analyst. And like today, data technology analytics are where the cool kids are. Back then, it was not where the cool kids are, trust me. And the whole concept of recognizing very early on that the asset management business at its core is an information processing business today is so obvious. But if you rewind back 10, 20, 30 years ago, that was a very unique, novel concept.

Joe Weisenthal (9:12)

We really do need like five hours. I know, kind of a tangent you mentioned the idea of like you could string 10 sun workstations together to make a supercomputer. We are actually going back to the future or the future. The back a little bit. These currently in computing. I have a good friend who has promised to help me later this year. I'm going to buy like 5 Mac Minis because he says you can Host your own LLM now from home if you just have like 4 or 5 Mac minis strung together and then you don't have to. Then you don't have to depend on any other company's data center for unlimited token usage. So this is going to kind of come back.

Rob Goldstein (9:47)

Well, it's. It's interesting though. I think that's a real question in terms of where we are right now, because I don't know the answer to this, but I could make two good arguments. One is along the lines of what you're saying. The other is we are living in an age right now. If you really just think about what's happening with AI, you could convert energy to intelligence. And the more money you spend on energy, the more intelligence you have. You could argue different than many technology trends that we've had over the past couple of decades. This is a technology trend that requires capital and it requires spending a lot of money. And I think there's a real question about whether or not this is a very expensive technology or if this will ultimately wind up being. Five guys at home in a garage with a handful of Mac Minis can accomplish miracles. I don't know if that's certain yet which one is going to prove true.

Joe Weisenthal (10:49)

I want to get in More on your history and Aladdin and the technology that you've been involved in building over these years. But maybe big picture question is like one of the things about AI that really strikes me as potentially interesting with how it's going to affect finance is AI is non deterministic. You put in a query and you don't really know. You never know if you're going to get the same output twice or whatever. And I'm curious, like for one, you don't know how it arrived often and models can't explain themselves. And this is an issue for finance, which is they're often not explicable why the output came out. But then there's this other element of you're not going to get the same thing twice. And I'm curious, like when you think about how that fits into the history of technology, whether that is a source of anxiety for finance. You need to be able to show your work often in many cases, or you need to be able to like traditional software, you write a line of code and as long as there's no bug, it'll produce the same result a thousand times in a row. Is this new? Is this something that is going to be a difficult tension to work around?

Rob Goldstein (11:53)

No question. Absolutely no question. And just to give an analogy, I am sure there have been many, many, many people who sat in this seat through the years and said by X X being years ago, there will be self driving cars, you won't need driver's licenses, and so on and so forth. I think the tolerance people have for computers to make mistakes is very different than the tolerance people have for humans to make mistakes. So that's just a societal starting point for all intents and purposes. I think you could make a cohesive argument. I love Marvel movies as a family. That's one of our things. This is like alien technology has been found on the planet Earth and now we're figuring out how to use it. And one of the remarkable things about the technology, even if, and I know from listening to you, you've played with a lot of the coding tools. If you look at the coding tools, they write code and then there are bugs in the code and then they find the bugs and they fix the bugs. So like the way we've been trained to think about a computer is how could that happen? Wouldn't it be smart enough to write the code without the bugs?

Tracy Alloway (13:10)

Right? Like, why do I have to prompt it to fix itself?

Rob Goldstein (13:13)

Because it's much more like a person, it's much more about thinking than this binary 0 and 1 structure that we've become used to for computers. And I think one of the remarkable, if you spend time with any of the big technology firms at the big AI companies, the frontier model providers, they use this term regulated industries, and needless to say, regulated industries like financial services, we need to be certain that we have the appropriate processes and controls in place. So through one lens, that's a big friction. Through another lens, I think it's actually a competitive advantage to the industry because if you think about it, we have so many controls. We have so many controls as a natural part of the process. So for example, one of the first things we did within BlackRock when the technology became available, we created this rule that we call this principle, that we call the first draft principle. Why can't we have a first draft of everything we produce be created through AI, whether it be a client presentation, an internal document, a prospectus? And the reason why you're very deliberate about saying first draft is because we have 16 people who checked the first draft. And that ability as a starting point is a very sort of strong catalyst towards leveraging and getting to know the technology. But I would actually argue that today the technology has provided a lot of people like individual productivity, but at an enterprise level. If you look at the case studies, it's not clear to me we've entered the first inning of the actual enterprise implementation. I still think the national anthem is sort of being played. And I think that the actual overhang between what the models can do and the fact that this is technology that needs to be implemented, it requires organizational design, business process, re engineering, like implementing technology is hard and takes time and we haven't even started that enterprise implementation yet.

Tracy Alloway (15:38)

Can you talk a little bit more about this in the context of Aladdin? Because I think part of the concern here that Joe was getting at is that you have these models that are getting more complex and more difficult to predict. We don't know what they're going to spit out. They're non deterministic, as Joe said. And meanwhile you have this risk management technology that has already for years been described as a black box. And I'm sure you have opinions on that particular label. But if it gets more sophisticated, are people going to fully understand what it's actually doing?

Rob Goldstein (16:10)

Well, let me start out by saying we haven't described it as a black box.

Tracy Alloway (16:13)

No, not you.

Rob Goldstein (16:14)

So we'll come back to that in a minute. But importantly, AI as a technology is not new. I wish I knew the exact year, but the AI lab at MIT was created in the 1950s. We started our AI lab in 2018. So these methods have been used for a long time at a very sort of simple level that I'm sure would offend a lot of people. You could think about old AI was about numbers. New AI is about language. And the language element of it creates all sorts of humans communicate much more through language than numbers. So it creates a whole host of other unintended consequences. But when you look at a platform like Aladdin as an enterprise platform, and by the way, I would make a cohesive argument, everything I'm saying, you could make the same case with regard to the Bloomberg terminal.

Tracy Alloway (17:11)

I knew this was gonna come up in this conversation. Fairpoint, fair point.

Rob Goldstein (17:16)

So if you think about these technologies first, the reward for good work is more work. So the to do list for these technologies is infinite. Like genuinely infinite. Every year BlackRock winds up having more engineers. We have roughly 5,000 engineers, data analysts, modelers. Every year we wind up having more engineers. And every year we wind up having a bigger to do list of enhancements we could put within Aladdin. So the first element of the AI capability, and I would argue the most mature use case that exists at an enterprise level, is coding. So the ability to go through that to do list, the velocity of that is off the charts. The second component, and this is one of the challenges of these enterprise expert systems, is that the number of times I've been in a meeting with a client where they say, why doesn't Aladdin do this? And I'm like, I think Aladdin does that. But I don't want to blurt it out. Let me follow up and then I'll leave the meeting. I'll call the people smarter than me and they'll be like, aladdin's done that for seven years. And you're like, okay. The ability for people to keep current in technology is very hard. And as much as we like the technology, most people, their goal with technology is to just interface with it to do their jobs and then go home. So the ability to take an expert system that today requires a lot of knowledge and keeping up with it, and instead just type in what you want it to do. And an agent will be the ultimate real time user of Aladdin that will then do those activities, all the same controls will exist. The four eyes principle, all of those controls will exist. But that ability to have users, to have clients access all the untapped capabilities that today they don't know about, I think the value enterprise technology is going to provide going forward, Aladdin and other enterprise technology is actually going to be much greater than at any point previously. It's actually, it's extremely exciting to me because there's nothing more frustrating than being in a meeting where someone is complaining you don't do something when you actually do it.

Joe Weisenthal (19:59)

This must be a thing for a bunch of enterprise software. Right. Because no one. We've talked to other software people and no one uses all the specs and no one uses all the features. No one knows all the features, et cetera. But you said something that is something is kind of one of my hobby horses. If it's the agent that's using Aladdin rather than the sort of inhuman. Does that change how you think about ux?

Rob Goldstein (20:23)

It's a great question. We debate this a lot. And I'll tell you about something I saw yesterday. But I think it has to. But at the same time, I think there will be people who will want to do it themselves.

Joe Weisenthal (20:38)

Okay.

Rob Goldstein (20:40)

I think there will be people who want to do it themselves. You know, it's interesting even in this age of everything being on the phone.

Joe Weisenthal (20:48)

Yeah.

Rob Goldstein (20:49)

You still need a website that people could access. So I think there still will be people who want to do it themselves. I saw a demo yesterday of a tool from one of these AI companies that I hope I could articulate it well enough. But it will look at a website and it showed. It looked at one of our websites and redesigned it to be more like the optimal user friendliness. So it was the opposite of. It was using AI to almost do the opposite of what you said. Make it, make it easier for humans. And it was one of these things where you know in the demo because websites are public so they were able to do things with our own stuff that we didn't know about. And as they're showing what they would do and when I say they what a computer would do after 10 minutes of processing with their own website, you're like in X months or maybe a year or two, tools like this will re engineer every website on the planet earth and they will all be more user friendly for us.

Tracy Alloway (21:59)

Hopefully not the agents.

Joe Weisenthal (22:09)

Foreign.

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Tracy Alloway (24:58)

little bit more about the moat around Aladdin? Sure, because this is the other big talking point of the moment which is the the sasspocalypse idea. And in the age of vibe coding. Everyone is just going to go out and design their own portfolio risk management system.

Rob Goldstein (25:13)

Let me start out, let me provide a little context broadly about what we think is going to happen or what a group of us think are going to happen. And then let me go into Aladdin, because I think that they're somewhat related. So one of our portfolio managers, one of our technology portfolio managers is a gentleman, Tony Kim. And a year ago, two years ago, I said, tony, if today there are a hundred lines of code in the world in 2030, how many are there? And he said, a million. And I was like, you're out of your mind. And he said, no, no, no. I was quite thoughtful about that. I didn't make up a number 10 times.

Tracy Alloway (25:59)

And we've all seen how much Joe

Rob Goldstein (26:01)

is coding well, but this is an important dimension. Cause 10 times, 10 times, 10 times 10 times. So if you believe which you know it's going to be a multiple, you could argue, is it 3, is it 7, is it 10, is it 14? But it's going to go up every year by a multiple, given these tools. So the amount of code in the world is going to go up dramatically. And I think that when you look at a platform like Aladdin, and in many regards, I think it would be like things with Bloomberg. They're at centers of an ecosystem. The ecosystem is highly regulated. The ecosystem has zero tolerance for fault or error. The processes, you do not only leverage tremendous amounts of proprietary data, so it's not within these models or accessible by the models. But importantly, clients are putting their most sensitive data into these platforms. And then what you're doing in terms of workflow is highly, highly, highly idiosyncratic and requires this combination of people, process and technology. So it's a hard time in this world, in my opinion, to predict what 2050 looks like. But when you look forward 10 years in our industry, these platforms, if anything, are going to do more, not less. And I think what really is going to be unlocked is that users like Joe are going to access these platforms through their own coding tools, but these coding tools are gonna be central to those platforms. I didn't forget your black box comment. Going back to. You'll never forget going back. No, I think it's quoting other people. No, I think it's important. And I think that technology, 10 or 20 years ago, certain technologies were designed to be closed systems. Okay, you know where I'm going. Certain technologies were designed to be closed systems. Aladdin was one of those technologies. And roughly 10 years ago, we Started this Open Aladdin campaign well before anything to do with this round of AI we're in, which was all about the future of technology, is going to be some people are going to want to interact with it through a keyboard and a mouse, and many people are going to want to interact with it through code. Because even if you're graduating with an English major, if you're graduating at most schools, at this point you've taken a coding class. So just the amount of technical expertise that you come in.

Joe Weisenthal (28:56)

Yeah.

Rob Goldstein (28:56)

Is a whole different level.

Joe Weisenthal (28:58)

So this is like opening up more like API endpoints and things like that, as opposed to, say, like open source. Like the open. In that. Yeah.

Rob Goldstein (29:06)

Open within a closed ecosystem. And that is a critical element because open within a closed ecosystem. One of the things, and it's like, obvious after the fact, but one of the things that we realize, like, oh, my God, this is amazing, and it's so valuable, is that when you call our APIs, for example, in Aladdin, your permissions go through. So if you think about the complexity of managing permissions in a asset manager with thousands of people, you could see some portfolios. You could see some portfolios. They're different portfolios. You could trade. You're not allowed to trade. You could only confirm trades. The complexity of those permissions. So the fact that when you call an API, it knows what you can and can't access, that whole control plane and layer is an incredible value proposition. And the more you have people coding and the more you have people interacting with systems in more technical ways, the more valuable those control planes actually wind up being.

Joe Weisenthal (30:14)

What happens the flip side? Okay, you've described some core pieces of infrastructure that they're regulated, there's proprietary data, etc. Clients are putting their most sensitive info in the world. And your view is that the value of those platforms will grow. Are there's a software that's not that? Is that other zeros?

Rob Goldstein (30:38)

Absolutely. No.

Joe Weisenthal (30:40)

Absolutely.

Rob Goldstein (30:40)

Absolutely. There's one. Well, first of all, sort of these things have long tails.

Joe Weisenthal (30:47)

Okay, so.

Rob Goldstein (30:48)

So, like, actually going through the process of retiring a system.

Joe Weisenthal (30:53)

Yeah.

Rob Goldstein (30:54)

Is a lot of work, no matter what the system is. So there are long tails. But there are certain technologies that are really. And we all use them. There are certain technologies that are about collating public information and making it easy for you to access. And I think it's fair to say that the AI tools that exist are the ultimate oracles in being able to do that, in being able to scour all public sources and give you back information in the way that you're most comfortable with. So particularly that segment of SaaS, which is that convenience layer where they don't really have proprietary data. They're not really in the workflow, They're a convenience technology. I think those convenience technologies,

Joe Weisenthal (31:55)

they're in trouble.

Rob Goldstein (31:56)

They're certainly going to be looking for ways of reimagining their value proposition.

Tracy Alloway (32:03)

Well, one of the reasons we wanted to have you on the podcast is because you are both a provider of AI, viz Aladdin, and also a user of AI at your own company. And we've all seen various executives and managers talk about AI as a productivity enhancing tool, and they tend to talk about it in very general terms. So I would be curious to hear from your perspective exactly what a productivity enhancement at BlackRock actually looks like.

Rob Goldstein (32:31)

I will give you a productivity enhancement from Friday of last week because, and by the way, what I'm describing, I think is the future everywhere. I hope BlackRock gets to that future faster than others, but I believe it's the future. So we have been doing a lot of work enhancing Aladdin in many ways. And one of the big themes that we have is how do we provide more transparency in the private markets to be as close as possible to the public markets in pursuit of this whole portfolio. So we have a large program of work that's been going on for quite some time. So I try to spend hours every Friday getting demos of things we're working on. So my Friday afternoon demo, along the theme that I just described, was a demo of a tool that was awesome. But let me go through how it was created. And this was the first time end to end that at least I've been shown this. So a group of people that included portfolio managers, risk professionals, engineers, product managers, a group of people sat in a room for multiple hours talking about how this capability should work. That discussion was recorded. That discussion, through the recording of it, created a functional document. That functional document was lightly tweaked. That functional document was then put in some of the AI coding tools that we use. That document through the AI coding tools led to a prototype. There was a debugging process that you lived through that we just described. And on Friday, you know, I've seen a lot of prototypes before in my sort of 32 years.

Tracy Alloway (34:42)

Can imagine.

Rob Goldstein (34:43)

So I know the questions to ask where you see the prototype as like a thin shell.

Joe Weisenthal (34:50)

Yeah.

Rob Goldstein (34:50)

But if you press enough, the shell cracks. Like, this wasn't like a prototype. This was like the real deal. And when you look at that cycle, we effectively collapsed what would have taken the unit of measurement would have been months. Now the unit of measurement was days. Now it will still go through our software development life cycle. It will be tested, all of those things. But when you look at that as a productivity tool, this goes Back to the 10xing the amount of lines of code in the world. Every year there's just going to be an explosion in the ability to engineer things.

Tracy Alloway (35:37)

Now I'm wondering if the coding tool, when it sees a transcript of a meeting like that, do you think it weights the participants by like title and level of importance? No, this is a real question.

Joe Weisenthal (35:47)

It probably does. It would be weird if it didn't, right?

Rob Goldstein (35:49)

It's interesting. My intuition is it doesn't. My intuition is this would be a great exercise. In those who talk, most are probably most reflective.

Joe Weisenthal (36:03)

I heard about a company that's doing third party consulting for helping companies implement software. And one of the things that they're doing is they get on a zoom with the client and they have a camera trained on the whiteboard that they're working on and that video file also is part of, so in addition to the audio, because, you know, whiteboards are the lingua franca of software development. So that also gets uploaded to it and so that it like sees that whole workflow and stuff. It's pretty wild stuff. I'm curious tokens. There's been a lot of headlines lately about token sticker shock, like, whoa, we are spending a lot on inference. I'm curious if you could tell us anything about token consumption this year versus last year at blackrock. But also like one of the things that we're also seeing, and it's very, it's related to this, is like the compute constraints that have long been talked about as theoretical are starting to actually bite people who are sort of like anthropic users or like whatever. Like, can you talk a little bit about token consumption? And is the compute constraint real from your perspective? If you could snap your fingers and get 100,000 more plugged in GPUs, would that be a big, a big help right now?

Rob Goldstein (37:18)

Well, I think that you have to sort of go back in time a little bit. So one of the, one of the key, key, key lessons learned that I've experienced and I remember one of the near, near founding partners, a gentleman, Charlie Halleck, who unfortunately passed away. What he instilled in me, among many other things, was if you have the right modelers and engineers and you leave them unconstrained, they will bankrupt the company in terms of their insatiable appetite for compute. In the old days, you had a physical data center. That was the constraint. You would have to order hardware. At some point people would say, we're outgrowing our data center. You'd say, let's wait a year and see what happens. So in today's world, the elasticity is obviously very different. But as a starting point, there are a group of people within BlackRock, and I think this is true in any sort of great financial services company, that if you leave them unconstrained from a compute power perspective, 20 years ago they would have bankrupted the company 10 years ago and today they will. So part of it is, how do you think about where to invest? I don't know offhand the number of tokens we're consuming today relative to a year, but it's multiples higher. And I think that we're still at a point again as a company, but also at a broad industry level where I don't really think the game has started yet. So I don't think anyone really knows what the token consumption is going to be. And equally as important, I don't think anyone has really started optimizing their token consumption.

Tracy Alloway (39:09)

You know, what someone needs to do is build an Aladdin for token efficiency.

Rob Goldstein (39:15)

It is a certainty that not only will that exist, but one of the gentlemen who, the person who actually leads our AI lab, a gentleman, Stephen Boyd. I had called him in the early days of this happening and I'm like, what are we missing? Like, Stephen, help me understand, what are we missing? He said two things, and he is a professor of engineering at Stanford. He said, you're missing two things. One is that articulate language is very, very powerful. And I'm like, okay, what does that mean? And he's like, think about it. I'm a professor. If I'm reading a paper and it's like, not in great. It's not written that well. You check everything. And if you're reading a paper that's written really well, you just assume it's right. So that was the first point. The second point he had, which relates to this topic. He said, just remember, there's a bunch of graduate students here and everywhere else that right now are working on like the most boring aspects of this too, including how to have these models be more efficient. So right now it's all about the quest for intelligence. I think we're going to see it pivot slightly to the quest for enterprise use cases, and then I think it's going to pivot very quickly to the quest for efficiency of how you're accessing things. We're not there yet.

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Tracy Alloway (43:24)

back to Something you said about private markets quickly, which is this whole portfolio management idea, this idea that via a system like Aladdin, you can manage your private assets the same way you would manage your public assets. And you get more transparency around pricing and things like that. So part of the sales pitch with private markets has been this idea of an illiquidity premium and the idea that you earn a little bit more because these things are not treated like public market assets. Once you start to integrate them via new technology into your system, once you start to be able to manage them much more similarly to a more liquid asset, does some of that sales pitch start to go away?

Rob Goldstein (44:05)

I think of it as an effort premium. There's a liquidity element to it and then there's like an effort premium. If you have to do more work, presumably you need to be compensated for that. But I also believe that. And I feel so old as I talk like this. I'm only 52, but I feel so old. As crazy as it sounds to many people, when I started, the way you would get information about public bonds, because even Bloomberg back then was a bit nascent. Yeah, you would read a prospectus and you would type into a computer, this is the maturity, this is the syncing schedule, this is the call schedule. Now, today you say that to people who entered the industry in the past 20 years and they think you're nuts. So I think there's no fighting technology. That's my own opinion. So I think it is certain that if you say in 10 years, are the private markets more or less transparent? They're certainly more transparent. And I think if you think about your life, almost everything in your life is becoming more transparent. It's funny, I don't think any of us. I try not to affix technology on my body, but it's very rare that three people would be together where someone doesn't have some device that's monitoring their blood pressure in real time. So, like, everything is pointing towards more transparency. I have a hard time believing that as private markets exposures and portfolios grow, the end asset owner is going to wind up with less transparency. So I think this is just the direction of travel. I do think over time, certain components of it will start to become more standardized, very similar to things that happened in the public bond markets and the public equity markets. And then there will be new innovations in other ways. But I believe that things will become much more transparent. It's a certainty in the future.

Joe Weisenthal (46:15)

This is, I guess, more from just an investment standpoint. But what is the Investor's source of edge in the future. At one point, maybe there was a source of edge because you were early to jump on and see the potential of stringing together Sun Microsystems and you could replicate whatever are these workstations just looking forward for the portfolio manager, et cetera. What constitutes edge?

Rob Goldstein (46:38)

It's a great question. And I think across industries, if you think of them as a treadmill, everyone's going to have to run faster. There's no question that's the case. I think the edge you could break up into three categories. First, I think the edge is going to be helping clients with their whole portfolio as opposed to just pieces. And I think if you look at the asset management industry, a very significant, I would say, evolution aspect of the asset management industry is that the industry organized itself inconsistent with how clients built portfolios. You had fixed income shops, you had equity shops, you had index managers, you had active managers, you had systematic managers, you had public markets, you had private markets. And then you force the client to put all this stuff together. So the industry is going to pivot more towards helping with the whole thing, which is a different type of edge. I think that the ability to use these tools is going to be an edge in and of itself. So notwithstanding, coding is going to be easier and there will be multiples more. Like the ability to build technology is going to become more, not less important, even if the frictions to build technology go down. I would argue. I was at a conference and someone asked me like, who would you like to hire coming out of university? And I said English majors. And that was a big mistake because then like thousands of people emailed me that their children, their child's an English major, will I speak to. But the reason I said English majors and I believe this, like we're living through a time where those who could have imagination and articulate it, the ability to implement that has never been as fast and literally, if it used to be like years, now it's like days. So the ability to have those ideas, the implementability of those ideas is going to be different than any point. So that creativity and imagination. And then lastly, obviously the world is becoming very complicated. I've heard you basically have this collision between national security, technology and capital. You have this fragmentation of geopolitics, you have the changing demographics of the world. You have what's happening with this alien technology that's now been found on the planet Earth. So if you think about the global connectivity that's required to really manage portfolios and how geopolitics is going to implement within portfolios going forward. I think that requires on the ground networks as much as it requires technology. And it's interesting. I'm sure you guys have had a similar experience. I read the paper, I read Bloomberg.

Tracy Alloway (49:47)

Thank you.

Rob Goldstein (49:48)

I watch a lot of stuff. But when you talk to. And I was actually supposed to be traveling last week in the Gulf region and instead I did a virtual tour. When you speak to our clients there, you get a very different picture of what's going on than what you're reading. And I think those networks are going to become more important in terms of edge as we look forward. Nothing will replace being on the ground.

Tracy Alloway (50:18)

Well, actually on that note, you're in a position where you're hiring people constantly for very specific roles. The processes are the questions that you're asking people now different in the age of AI versus what they were like four years ago?

Rob Goldstein (50:34)

It's interesting. I hope so. I know the questions I'm asking people are I hope that's at scale. Maybe I should follow up on that. But the questions I'm asking. I couldn't be more excited about the opportunities ahead for BlackRock, but I couldn't be more aware of the requirement. In today's world, everything needs to be reimagined and what we need and what I think leaders are going to be faced with. And ironically, I think a lot of the reimagination is going to come bottom up. But how do you reimagine what you do? How do you reimagine how you grow? How do you reimagine how you interact with clients in this new world? And I think that again, I don't think that started. I think right now it's a bit of a brain teaser, a lot of experiments, but it has not started. Almost every great company, if you walk in in the year 2030, is going to be fundamentally different than today. And the way I think of this, this is not an overnight reimagination, but it's not a five year reimagination. It's somewhere in between those two.

Joe Weisenthal (51:59)

You know, we said in the beginning we could go for hours and there's a million other things that we could ask. One last question on my mind, I've asked this version to a few different people and it maybe kind of relates to the other kind of tokens, tokenization.

Tracy Alloway (52:12)

But just this idea, it's going to get confusing.

Joe Weisenthal (52:15)

I know it's really annoying already because there's a.

Rob Goldstein (52:18)

Well, can I just say one thing before you ask your question? It's going to get confusing. It already is. But importantly, AI and digital assets are very related topics, extremely related topics.

Joe Weisenthal (52:33)

Well, this kind of. Maybe, maybe you can fold that into the answer here. But you know, like an interesting thing, there's a lot of very exciting private companies that people want to get access to, but in many cases they're already like kind of trading in some way. And there might be like these SPVs that people have already or some token somewhere that trades on hyper liquid on the weekend that represents somehow shares of anthropic or whatever, you need other hobbies. But I'm curious from your perspective, like, and another ASP thing that relates to this is the disclosure obligations for existing public companies seem like they're going to come down over time and maybe companies will only have to report every six months or a year, maybe never. Like, will there always be a bright line between what's a public and what's a private asset? Or is it just going to be this spectrum of liquidity and disclosure, but no clear definition of what it means anymore between public and private?

Rob Goldstein (53:28)

It's a great question. I think over time it'll be more of a spectrum. I think that when I look at the broad industry over the past multiple decades, the lines, one of the defining themes, the lines almost across everything you could imagine have been getting more and more blurry. And I would argue that's been because of technology. So if you think about the old style boxes that existed, they were convenience technologies because you couldn't bottom up, model things. You basically said, okay, mid cap equities have this attribute and that's a Lego piece as opposed to let me model what the individual stocks are. And then I could think of it in the context of a whole portfolio. So I think that what's happening is that technology is enabling those lines to be less discreet and more blurry across spectrums and across how do clients build portfolios, how do these Lego pieces get put together to actually achieve their objectives? I think that there's a lot going on embedded in your question, including, you see, and to me this is one of the most remarkable things that is happening is that two, three, four years ago the whole narrative was private for longer and certainly I don't know how this is true, that there's fewer public companies today than when I started.

Joe Weisenthal (55:01)

You always hear that, Stan. I still don't really get it.

Rob Goldstein (55:03)

I've checked it, it's actually true. Cuz I didn't believe it. But you wonder like how is that possible? Now that said, the flip side of it is, you see, for These companies like OpenAI Anthropic Space, they're race to go public because at some point, the public markets provide a lot of value propositions that are beyond what the private markets can provide. So I think you see this sort of tale of two cities that's happening, but there will be a spectrum, including a spectrum of certain end investors and institutions that want to have a digital wallet and certain end investors and institutions that want to have a traditional custody account. And it's not a binary thing. It's about technology enabling that personalization.

Tracy Alloway (56:00)

I have one more question, sort of a wild card, but what do you and Larry Fink disagree about the most? And the reason I ask is. No, I'm genuinely interested. You've been working alongside each other for years and years and years now. I have a personal interest in close collaborative.

Joe Weisenthal (56:15)

Yeah. Tracy and I disagree on a lot. So it's a very legit question.

Rob Goldstein (56:19)

I would say. Often we both see things similarly in terms of the end point. Larry is more of a tomorrow person, and I'm more of a. Well, there's work to do here. This is gonna take a few years. I would say that that's typically it.

Tracy Alloway (56:39)

Okay. Larry is a Joe, and I'm a Rob, I guess. All right, Rob Goldstein, thank you so much for coming on. That was great.

Joe Weisenthal (56:47)

Thanks. That was a lot of fun. That was great. Thank you so much.

Rob Goldstein (56:49)

Awesome. My pleasure.

Joe Weisenthal (56:50)

Yeah,

Tracy Alloway (57:03)

Joe, that was a really fun conversation. That was fun and a lot to think about. I mean, I do think when we're talking about moats around some of these businesses, you brought up the power Dynamics law very early on, and it does feel like a lot of the moat is basically size and data and capability that you have. And I can't imagine that you hear this. You hear people saying that they're like vibe coding a bunch of different programs, and they all look kind of interesting and cool, but, like, it's hard for me to imagine a big moat around those businesses if you've just, like, plugged in a few instructions into POD code.

Joe Weisenthal (57:38)

And no one is going to be putting anything of importance or sensitivity to some homemade.

Tracy Alloway (57:45)

Right.

Joe Weisenthal (57:45)

I mean, that's.

Fidelity Representative (57:46)

And so that's definitely.

Rob Goldstein (57:47)

That's.

Tracy Alloway (57:47)

That's what was interesting, this idea that actually the regulatory moat around finance actually becomes a valuable thing in the age of AI.

Joe Weisenthal (57:55)

Totally. You know, it's interesting, too. Not really AI related, but the idea of being able to provide, you know, provide a solution, all of portfolio visibility.

Tracy Alloway (58:07)

Yeah.

Joe Weisenthal (58:07)

What is one reason why the big get bigger within finance. Well, one reason is because only a really large entity would have the capacity to be able to like and here's you know what we can offer you with private credit and here's what we can offer you with indexing, etc. So okay, the end investor has this big portfolio consisting of lots of different types of asset classes. As he mentioned, historically the industry has sort of been verticalized by asset class. If you want some entity that has how do all the puzzle pieces fit together which is the essence of portfolio construction, then theoretically you just want like a really big company that understands all of it. Yeah, I also thought and it was kind of also on the source of edge question is we've talked about this before. It always seems like one of the fun jobs in finance would be the channel check person. The person who goes to the mall to.

Tracy Alloway (59:02)

Oh yeah, the field trips.

Joe Weisenthal (59:03)

Yeah, the field trips. It's like okay, how many sweaters are on the gap shelf, whatever. Maybe that becomes even more valuable because things that have not yet been put into a model. And look, I love that people listen to odd lots. I love that people read the news, particularly financial news on Bloomberg. I've never thought by and large that people read an. And it's like I'm going to make an investment decision based on that. Hopefully it helps inform their thinking or their processes in some way. But no one listens to a podcast and then goes out and buys the stock by and large. But it's because once it's out there in the digital world, it's kind of priced in already. So there's going to be an intense hunt for information that is truly has not been put into the model yet.

Tracy Alloway (59:48)

You are such an EMH ship purist. It's amazing but right.

Joe Weisenthal (59:52)

The value of people who can find information that has not turned into trade training data yet for a model, that data is going to get like super valuable and maybe like more valuable and more reason to like just like get out on the road and stuff like that.

Tracy Alloway (60:07)

No, I largely agree with that. The other thing I thought was really interesting and it gets to your last question was the sort of melding of public and private markets. And it's also what I was kind of getting at with the whole portfolio thing. If all these private assets.

Joe Weisenthal (60:20)

Yeah.

Tracy Alloway (60:21)

Are tokenized and treated in the same way in a portfolio or at least visible in a portfolio in the same way as a public asset would be, it seems like that distinction starts to get really, really blurry.

Joe Weisenthal (60:32)

Totally.

Tracy Alloway (60:33)

Right.

Joe Weisenthal (60:33)

Not to go back to a point that I just made. But on the, on this, like, I

Tracy Alloway (60:37)

think we're actually already saying, I'm sorry, I moved on too quickly.

Joe Weisenthal (60:40)

No, no, just this just clicked to me too. But it's like, okay, where are we? Maybe there'll be more value in like, people who go out on the road and get information that's not in the model. We saw this recently with all the people going crazy for Citrini's analyst in the Strait of Hormuz. And this idea that, like, field trips out to the world to collect information that has not been digitized yet is going to be where all the action is. Like, it feels like that's going to be a big thing.

Tracy Alloway (61:06)

No, it sounds trite, but I feel like the pendulum has swung from, you know, for the past 20 years. If you were a smart person who could think in terms of numbers and code, you were probably very valued by society. And now the pendulum sort of swings to those on the ground, relationship building, social skills. It's, it's an interesting transition.

Joe Weisenthal (61:30)

It's an interesting time.

Tracy Alloway (61:31)

All right, shall we leave it there?

Joe Weisenthal (61:32)

Let's leave it there.

Tracy Alloway (61:33)

This has been another episode of the Odd Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

Joe Weisenthal (61:38)

And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashiell Bennett at dashbot, Kell brooksailbrooks and Kevin Lozano at Kevin Lloyd Lozano. And for more odd lots content, go to bloomberg.com oddlots where the daily newsletter and all of our episodes and you can chat about all these topics 24. 7 in our Discord Discord GG oddlots.

Tracy Alloway (62:02)

And if you enjoy Odd lots, if you want us to do more on the ground investigative reporting and analysis, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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Rob Goldstein (63:30)

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