Pod Insights Odd Lots BlackRock's Rob Goldstein on the Next Megatrends in Finance About this episode The last few decades have been marked by a number of megatrends in finance including the extraordinary growth of asset managers, the rising importance of technology, and the ascent of private markets. BlackRock, the world's biggest asset manager, is emblematic of all these developments. On this episode, we talk to BlackRock COO Rob Goldstein about the company's early technological history, the development of its famous risk management technology Aladdin, and how BlackRock is navigating being both a user and major provider of AI. We discuss his view of the 'SaaSpocalypse,' how BlackRock is thinking about token consumption and compute constraint, as well as the future of private markets. Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.
Transcript 10,126 words · 8 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Joe Weisenthal? There are some market stories where you want every detail. Tracy and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and need to know what's moving markets and why. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and research get you up to speed on what matters and what to watch next, about the time it takes to grab a coffee. So, search Barclays Brief wherever you get your podcasts.
0:26 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk, and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs, and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices, but according to recruiters, advisors,
1:11 Speaker 01 and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at chatgpt.com.
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2:42 Tracy Alloway? Hello and welcome to another episode of the Odd Lots Podcast. I'm Tracy Alloway.
2:46 Joe Weisenthal? And I'm Joe Weisenthal.
2:47 Tracy Alloway? Joe, when I think about the world of finance.
2:50 Joe Weisenthal? Yeah.
2:51 Tracy Alloway? And what I would describe as mega trends of recent years and decades, there are definitely two that spring to mind, maybe a third trend, although I don't know if it's mega, it probably is. The first is definitely the rise of the buy side. So this idea that, you know, it very much used to be all about the banks and those were the ones that we kind of obsessed over. And then you had this extraordinary growth in the asset management industry. The second mega trend has to be technology, right? Think about the rise of electronic trading, electronic risk management, model-driven risk management.
3:24 Joe Weisenthal? I like this.
3:24 Tracy Alloway? And then the third semi-mega trend, I don't know, rise of private markets, right?
3:29 Joe Weisenthal? Yeah. Oh, I have another one.
3:30 Tracy Alloway? Okay.
3:30 Joe Weisenthal? That I think is legit. The sort of power law domination of a few mega companies and sort of whether it's consolidated.
3:38 Tracy Alloway? The big get bigger.
3:39 Joe Weisenthal? The winner take all-ness or the winner take most-ness of the industry. I would add that as a mega trend.
3:43 Tracy Alloway? Okay, that's great.
3:44 Joe Weisenthal? And I would say that connects to this episode.
3:45 Tracy Alloway? Absolutely. So we have four big slash mega trends 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 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.
4:23 Joe Weisenthal? Yeah.
4:24 Tracy Alloway? And there's a perennial question over how much of it is real.
4:27 Joe Weisenthal? 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.
4:35 Tracy Alloway? 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 become productive, useful technology for finance? So we can definitely talk a little bit about that, but we do have the perfect guest.
4:50 Joe Weisenthal? Let's do it.
4:51 Tracy Alloway? All right. So we're going to be speaking with Rob Goldstein. He is, of course, the COO of BlackRock, someone who has literally lived through basically all of these mega trends that we just described.
5:02 Speaker 04 Yes. And the question is, how many hours do we have? You know, needless to say, those mega trends, we could talk about for quite some time.
5:10 Joe Weisenthal? Yeah, we could do a series.
5:11 Speaker 04 We could do a series. And one thing I would just politely identify, I think, as people talk about the big getting bigger.
5:21 Joe Weisenthal? Yeah.
5:22 Speaker 04 I think there's an underlying catalyst towards those who could provide a better value proposition are getting bigger.
5:29 Joe Weisenthal? Okay.
5:29 Speaker 04 And I think that's the key theme that's happening, particularly with regard to the buy side.
5:34 Joe Weisenthal? I know you were saying, what was it, a polite, you didn't push back, but a polite nuance or something like that. But one could say 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 upon a nuance.
5:57 Speaker 04 A hundred percent. And, and what's interesting is, I think if you ordered the mega themes.
6:02 Joe Weisenthal? Yeah.
6:04 Speaker 04 You can make a very strong argument that it all comes down to technology.
6:09 Joe Weisenthal? Okay.
6:09 Speaker 04 And technology is enabling things and value propositions to be achieved that traditionally just couldn't be done.
6:17 Joe Weisenthal? Great. Let's just talk about that.
6:19 Tracy Alloway? Is it true that like some of BlackRock's foundational, by the way, the chances that I say Blackstone in this conversation?
6:26 Speaker 04 Yeah, that's not, that's not good.
6:28 Tracy Alloway? I apologize in advance.
6:29 Joe Weisenthal? 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 then like I could tell there was like a silence. I was like, I said the wrong thing. He's heard it all before.
6:41 Tracy Alloway? 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 Gollub.
6:54 Speaker 04 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 thirty, thirty-five 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,
7:33 Speaker 04 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 twenty-something year old, thirty-something year olds, including my own children who are, when they're early twenties, 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
8:18 Speaker 04 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 ten Sun workstations for $10,000 each and link them together and effectively do what previously only supercomputers that cost millions of dollars could do.
8:52 Speaker 04 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.
9:20 Joe Weisenthal? This is odd.
9:21 Speaker 04 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.
10:05 Joe Weisenthal? We really do need like five hours. I know. And this is 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 or 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 five Mac Minis because he says you can host your own LLM now from home if you just have like four or five 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.
10:41 Speaker 04 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.
11:23 Joe Weisenthal? Yeah.
11:23 Speaker 04 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.
11:42 Joe Weisenthal? I want to like get in more on your history and Aladdin and the technology that you've been involved in building over these years. But maybe a big picture question is like, one of the things about AI that really strikes me is 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,
12:28 Joe Weisenthal? whether that is a source of anxiety for finance. You need to be able to show your work off 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?
12:46 Speaker 04 No question, absolutely no question. And just to give an analogy.
12:51 Joe Weisenthal? Okay.
12:52 Speaker 04 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. Yeah. 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, I love Marvel movies as a family. That's, 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.
13:36 Speaker 04 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?
14:04 Tracy Alloway? Right. Like, why do I have to prompt it to fix itself?
14:07 Speaker 04 Cause it's much more like a person. It's much more about thinking than this binary zero and one structure Yeah. 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, 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.
14:47 Speaker 04 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
15:32 Speaker 04 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 be 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
16:14 Speaker 04 this is technology that needs to be implemented, it requires organizational design, business process reengineering. Like implementing technology is hard and takes time, and we haven't even started that enterprise implementation yet.
16:32 Tracy Alloway? 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
16:58 Speaker 04 Yes.
16:58 Tracy Alloway? label. But if it gets more sophisticated, are people going to fully understand what it's actually doing?
17:04 Speaker 04 Well, let me start out by saying we haven't described it as a black box.
17:07 Tracy Alloway? No, not you.
17:07 Speaker 04 So we'll come back, 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 like 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.
17:50 Speaker 04 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.
18:04 Tracy Alloway? I knew this was going to come up in this conversation. Fair point, fair point.
18:10 Speaker 04 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,
18:50 Speaker 04 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, you know, why doesn't Aladdin do this? And I'm like, hmm, I think Aladdin does that. But let me, let me, I don't want to like blurt it out. Let me sort of 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.
19:35 Speaker 04 And you're like, okay, the, the ability for people to keep current in technology is very hard. And you know, 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 like 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.
20:19 Speaker 04 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.
20:52 Joe Weisenthal? 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, no one uses all the features, no one knows all the features, et cetera. But you said something, and it's something, it's 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?
21:15 Speaker 04 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.
21:32 Joe Weisenthal? Okay.
21:33 Speaker 04 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.
21:42 Joe Weisenthal? Yeah.
21:43 Speaker 04 You still need a website that people could access.
21:45 Joe Weisenthal? Sure, yeah.
21:46 Speaker 04 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...
21:59 Speaker 04 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 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, cause 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 our own website, you're like, hmm.
22:41 Speaker 04 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.
22:51 Tracy Alloway? For, for us, hopefully, not the agents.
23:10 Joe Weisenthal? There are some market stories where you want every detail. Tracy and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and need to know what's moving markets and why. That's the Barclays Brief podcast. Every week, experts from Barclays Markets and Research get you up to speed on what matters and what to watch next. About the time it takes to grab a coffee, so search Barclays Brief wherever you get your podcasts.
23:36 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters. than advanced degrees with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices.
24:19 Speaker 01 But according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company. And Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates quote must be willing to work extended hours and weekends as needed. One major perk for all that hard work? Employees access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at ChatGPT.com.
24:59 Tracy Alloway? Can you talk a little bit more about the moat around Aladdin?
25:02 Speaker 04 Sure.
25:03 Tracy Alloway? Because this is the other big talking point of the moment, which is the SaaS apocalypse idea. And in the age of vibe coding, everyone is just going to go out and design their own portfolio risk management system.
25:14 Speaker 04 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 Kemp. 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.
25:55 Speaker 04 And he said, no, no, no, like, I was quite thoughtful about that. I didn't make up a number.
26:00 Tracy Alloway? We've all seen how much Joe is coding.
26:03 Speaker 04 Well, but this is an important dimension because 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 three? Is it seven? Is it 10? Is it 14? But it's going to go up every year by a multiple, given these tools.
26:23 Speaker 04 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.
27:07 Speaker 04 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
27:48 Speaker 04 these platforms through their own coding tools, but these coding tools are going to be central to those platforms. I didn't forget your black box comment. Going back to
28:00 Joe Weisenthal? You'll never forget.
28:01 Speaker 04 Going, going back. No, I think it's I'm 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.
28:18 Speaker 04 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
28:57 Joe Weisenthal? Yeah.
28:57 Speaker 04 is a whole different level.
28:59 Joe Weisenthal? So this is like opening a more like API endpoints and things like that as opposed to say like open source like the open in that yeah.
29:06 Speaker 04 Oh, 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 realized 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.
29:38 Joe Weisenthal? Yeah.
29:38 Speaker 04 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.
29:56 Joe Weisenthal? Yeah.
29:56 Speaker 04 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
30:13 Joe Weisenthal? Yeah.
30:13 Speaker 04 actually wind up being.
30:15 Joe Weisenthal? What happens to the flip side? Okay, you've described some core pieces of infrastructure that are regulated. There's proprietary data, et cetera. Clients are putting their most sensitive info in the world. And your view is that the value of those platforms will grow. Are there is the software that's not that? Is that other zeros?
30:39 Speaker 04 Absolutely.
30:40 Joe Weisenthal? Absolutely.
30:41 Speaker 04 Absolutely, there's.
30:43 Joe Weisenthal? There's what?
30:43 Speaker 04 Well, first of all, sort of, these things have long tails.
30:48 Joe Weisenthal? Okay.
30:48 Speaker 04 So, so like, actually going through the process of retiring a system.
30:54 Joe Weisenthal? Yeah.
30:55 Speaker 04 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.
31:34 Speaker 04 So particularly, that segment of SaaS, which is that like convenience layer.
31:41 Joe Weisenthal? Mm hmm.
31:43 Speaker 04 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.
31:56 Joe Weisenthal? Are in trouble.
31:57 Speaker 04 They're certainly going to be looking for ways of reimagining their value proposition.
32:04 Tracy Alloway? 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.
32:31 Speaker 04 I will give you a productivity enhancement from Friday.
32:37 Tracy Alloway? Great.
32:38 Speaker 04 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 a lot 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
33:17 Speaker 04 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
33:59 Speaker 04 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. Can imagine.
34:44 Speaker 04 So I know the questions to ask where you see the prototype as like a thin shell.
34:50 Joe Weisenthal? Yeah.
34:51 Speaker 04 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.
35:03 Speaker 04 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 lifecycle. It will be tested, all of those things. But when you look at that as a productivity tool, this goes back to the 10x-ing, 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.
35:38 Tracy Alloway? 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.
35:48 Joe Weisenthal? It probably does. It would be weird if it didn't, right?
35:50 Speaker 04 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 reflected.
36:03 Joe Weisenthal? 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, whiteboard is the lingua franca of software development. So that's also gets uploaded to it. And so then it like sees that whole workflow and stuff. It's pretty, it's pretty wild stuff. I'm curious, tokens, there's been a lot of headlines lately about token sticker shock. We are spending a lot on inference. I'm curious if you could tell us anything
36:45 Joe Weisenthal? 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, could 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?
37:19 Speaker 04 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.
38:03 Speaker 04 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, 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, it's multiples higher.
38:47 Speaker 04 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.
39:09 Tracy Alloway? You know what someone needs to do is build an Aladdin for token efficiency.
39:16 Speaker 04 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, Steven Boyd, I had called him in the early days of this happening. And I'm like, what are we missing? Like, Steven, 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, OK, like, 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.
40:02 Speaker 04 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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42:20 Tracy Alloway? I want to go 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 liquidity 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?
43:02 Speaker 04 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.
43:34 Joe Weisenthal? Yeah.
43:35 Speaker 04 You would read a prospectus and you would type into a computer, this is the maturity, this is the sinking 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. Like, it's funny, I don't think any of us, I try not to affix technology on my body,
44:19 Speaker 04 but it's very rare that three people would be together where someone doesn't have some device that's monitoring like 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 in 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. Right. And then there will be new innovations in other ways.
45:02 Speaker 04 But I believe that things will become much more transparent. It's a certainty.
45:11 Joe Weisenthal? In the future, and this is, I guess, more from just an investment standpoint, but like, what is the investor's source of edge in the future? You know, at one point, maybe there was a source of edge because you were early to jump on and see the potential of stringing together some microsystems and you could replicate whatever, or these workstations. Just looking forward for the portfolio manager, et cetera. What constitutes edge?
45:34 Speaker 04 It's a great question, and I think across industries, if you think of them as a treadmill, everyone's gonna have to run faster. Like, there's no question that that's the case. I think the edge, you could break up into three categories. First, I think the edge is gonna be helping clients with their whole portfolio, as opposed to just pieces. And I think if you look at the asset management industry,
46:00 Speaker 04 a very significant, I would say, evolution aspect of the asset management industry is that the industry organized itself inconsistent with how clients build 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 forced the client to put all this stuff together. So the industry is gonna pivot more towards helping with the whole thing, which is a different type of edge.
46:34 Joe Weisenthal? Yep.
46:35 Speaker 04 I think that the ability to use these tools is gonna be an edge in and of itself. So notwithstanding coding is gonna be easier and there will be multiples more, like, the ability to build technology is gonna 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 child's an English major, well, I speak to them. But the reason I said English majors, and I believe this,
47:18 Speaker 04 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.
47:31 Joe Weisenthal? Yeah.
47:31 Speaker 04 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.
47:54 Joe Weisenthal? I've heard.
47:54 Speaker 04 You basically have these, 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 like, 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 like on the ground networks as much as it requires technology.
48:37 Speaker 04 And it's interesting, I'm sure you guys have had a similar experience. I read the paper. I read Bloomberg.
48:44 Tracy Alloway? Thank you.
48:44 Speaker 04 I watch a lot of stuff. But when you talk to, and I, I was actually supposed to be traveling last week in the Gulf region. And instead I, 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.
49:15 Tracy Alloway? Well, actually, on that note, you're in a position where you're hiring people constantly for very specific roles. Are the processes or the questions that you're asking people now different in the age of AI versus what they were like four years ago?
49:31 Speaker 04 It's interesting. I hope so. I, 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?
50:15 Speaker 04 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's 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.
50:55 Joe Weisenthal? 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 been, I've tried, I've asked this version to a few different people. And it maybe kind of relates to the other kind of tokens, tokenization. But just this idea- That's going to get confusing. I know, it's really annoying already. Because there's a-
51:15 Speaker 04 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-
51:25 Joe Weisenthal? Yeah.
51:26 Speaker 04 Are very related topics, extremely related topics.
51:30 Joe Weisenthal? 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 are, want to get access to. But in many cases, they're already kind of traded in some way. And there might be like these SPVs that people have already, or some tokens somewhere that trades on Hyperliquid on the weekend that represents somehow shares of Anthropic or whatever.
51:54 Speaker 04 You need other hobbies.
51:56 Joe Weisenthal? But I'm curious from your perspective, like, and another 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?
52:25 Speaker 04 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, the 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 like, that's a Lego piece as opposed to let me model what the individual stocks are.
53:10 Speaker 04 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 did 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
53:52 Speaker 04 for longer. And certainly, I don't know how this is true, that there's fewer public companies today than when I started.
53:58 Joe Weisenthal? You always hear that, Stan. I still don't really get it.
54:00 Speaker 04 I've checked it. It's actually true because 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 raced 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
54:40 Speaker 04 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.
54:55 Tracy Alloway? 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 relationships.
55:12 Joe Weisenthal? Yeah, Tracy and I disagree on a lot, so it's a very legit question.
55:15 Speaker 04 I would say often, we both see things similarly in terms of the endpoint. Larry is more of a tomorrow person and I'm more of a, well, there's work to do here. This is going to take a few years. I would say that that's typically it.
55:36 Tracy Alloway? Okay, Larry is a Joe and I'm a Rob, I guess. All right, Rob Goldstein, thank you so much for coming on Off Bots. That was great.
55:44 Joe Weisenthal? That was a lot of fun. That was great. Thank you so much. Awesome, my pleasure. Yeah.
56:00 Tracy Alloway? Joe, that was a really fun conversation.
56:02 Joe Weisenthal? That was fun.
56:03 Tracy Alloway? 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.
56:31 Joe Weisenthal? Oh, yeah.
56:32 Tracy Alloway? If you've just like plugged in a few instructions into Claude code.
56:35 Joe Weisenthal? And, and, and no one is going to be putting anything of importance or sensitivity into some homemade.
56:42 Tracy Alloway? Right.
56:42 Joe Weisenthal? I mean, that's, that's definitely true.
56:44 Tracy Alloway? That's what was interesting, this idea that actually the regulatory moat around finance actually becomes a valuable thing in the age of AI.
56:52 Joe Weisenthal? Totally. You know, it's interesting to, not really AI related, but the idea of being able to provide, you know, provide a solution, all of portfolio visibility.
57:04 Tracy Alloway? Yeah.
57:04 Joe Weisenthal? 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, et cetera. 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.
57:45 Tracy Alloway? Yeah.
57:45 Joe Weisenthal? 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, the channel check person. The person who goes to the mall to see like.
57:59 Tracy Alloway? The field trips.
58:00 Joe Weisenthal? Yeah, the field trips is like, okay, how many sweaters are on the Gap shelf, whatever. Maybe that becomes even more valuable because like things that have not yet been put into a model.
58:10 Tracy Alloway? Yeah.
58:10 Joe Weisenthal? 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 article and it's like, I'm gonna make an investment decision based on that. Hopefully it helps inform their thinking or their processes in some way, but no one like listens to a podcast and then like goes out and like buys the stock, by and large. But it's because like, once it's out there in like the digital world, it's like, it's kind of priced in already. So there's gonna be that intense hunt for information that is like truly like has not been put into the model yet.
58:45 Tracy Alloway? You are such an EMH purist. It's amazing.
58:48 Joe Weisenthal? But right, like the value of people who can find information that has not turned into trade, training data yet for a model, that data is gonna 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.
59:04 Tracy Alloway? 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 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, right?
59:30 Joe Weisenthal? Not to go back to a point that I just made, but on the, on this like, I think we're actually already seeing it on-
59:35 Tracy Alloway? I'm sorry, I moved on too quickly, Jim.
59:37 Joe Weisenthal? No, no, it 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 a Citroni'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 gonna be where all the action is. Like, it feels like that's gonna be a big thing.
1:00:03 Tracy Alloway? 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.
1:00:27 Joe Weisenthal? It's an interesting time.
1:00:28 Tracy Alloway? All right. Shall we leave it there?
1:00:29 Joe Weisenthal? Let's leave it there.
1:00:30 Tracy Alloway? This has been another episode of the Odd Lots Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.
1:00:35 Joe Weisenthal? And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashal Bennett at Dashbot, Kel Brooks at Kel Brooks, 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 of these topics 24/7 in our Discord, discord.gg/oddlots.
1:00:59 Tracy Alloway? 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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