Goldman CIO Marco Argenti on the Warp-Speed Improvements in AI

23:59Instead of a fashion assistant, you want to talk to the tailor because they can actually make it. Things are changing so fast. So those are the tailors, Paz. So on this note, one of the things we heard in support of SaaS was this idea that, well, integration is still going to be really important. And that's really going to be like the major hurdle for a lot of this stuff. Have you found that AI is making integration even faster at this point? Has that basically become irrelevant nowadays? No, I think integration is extremely important, especially for the industry calls like systems of record. So when you do something like, you know, when you do a process, then you have a source of data, like your CRM systems could be a system of record, or you have your client system of record, your accounting system of record.

24:43And those, when they become the authoritative source of an answer, they need to integrate with the rest of the firm and the rest of the data, the rest of the applications. So I can see that those vendors that sit on top of those, we can argue that they will implement, there's nobody that is better positioned than them to implement AI, they will kind of reach outwards and actually do that kind of integration. So I think those who will evolve so that you still get the same level of automatism and you still get the same benefit of speed, but it kind of comes from within. I think that part is probably something that would remain very valuable.

25:17And so in general, I don't have anything against it. Again, I don't have anything against the SaaS category at all, overall. But I have, as I said, different opinions of who actually is going to adopt to the future and adapt to the future and those who don't know. You mentioned this idea of people have a few extra hours over the weekend and they come in the morning and they're like, well, I had some extra time and I decided to do this. What's the coolest or most novel example of something that people basically vibe coded in a limited amount of time that wouldn't have happened, say, two years ago.

25:50So I've seen people doing cloud migrations of legacy applications that were on-premise once they have been enabled with those tools, literally in a matter of hours. I've seen someone build a complete travel assistant for corporate travel assistant that looks at your calendar and looks at the flight delays and looks at rebooking stuff, literally like during a meeting where they were not paying attention. So those are some of the things. That's what I'm doing right now while we're doing this podcast.

26:21Tracy Alloway:Well, no, that actually, that brings me to exactly where I wanted to go next, which is I'm curious, like, do a large corporations have a token budget the way they would have a dollar budget in the past? So, like, I would love to have unlimited access to coding models and whatever and actually just play around and try to work on all that. It's one of my favorite questions. But I'm curious how you think about token allocation within the firm and whether there's intra-firm competition for compute. Yeah. Token allocation could be included in your performance review, right? Yeah, I'm really curious about this.

26:54If you do well, you've got more tokens.

26:55Tracy Alloway:Different teams and stuff like that. Whether that's part of what you think about for planning. Absolutely. So a few months ago, I spoke about predictions for 26. And one thing that I said was it's going to be the birth of the personal assistant. And that kind of happened with OpenClaw and all that stuff kind of early on. And then the one was, there's going to be a token sticker shock for CFOs, right? And all of a sudden, they're going to start seeing bills that they absolutely did not expect. Jensen Wong said in an interview today, sorry, not today, in recent weeks, something about like, oh, if I'm paying an engineer$500 ,000, I hope that he's spending at least$250 ,000 on tokens.

27:31Tracy Alloway:Now, again, as many people pointed out, that's like the barber saying, oh, you really need to get haircuts every week. Nonetheless, we're talking about some pretty big numbers, a lot more than just like a Claude Max plan for$200 or something like that. So talk about that. Right now. Okay. So first of all, lesson number one is you need to centralize the access to models so that you can monitor, meter it, and then optimize it. Okay. So the wild west of everybody goes and calls an API and starts consuming tokens, and then you find out later on is a big problem. And so that's why we built this GSAI platform, which has what's called a model gateway.

28:09And the model gateway intelligently routes requests to the combination, the Pareto frontier of quality and cost. Okay, so you got to centralize that. It's not a one-size-fits-all because many cases, if you're asking, what's the weather, you don't need to cloud Opus 4.6. You can ask it to live in a local model that you run very cheaply on-prem. And so there are ways to optimize way before you start even having the conversation, you're consuming too much.

28:40Tracy Alloway:This is very interesting to me. It's a big part of the problem that you're trying to solve. And we know like ChadGBT, they intelligently route. They do some on their, you go to ChadGBT.com and they'll try to route it to the best model. And there might even be some conflict of interest because they probably want to route it to the cheapest model. the user wants the most performant model. But how much of the work of your senior engineers is essentially solving this problem of the right query going to the Pareto optimal model? It is a big part of the time spent by the AI central group, the platform group.

29:18The platform group worries a lot about where do I get the right data, for example, for this question, and which model do I route it to? That's a big, because again, I spoke about Pareto frontier, year, meaning the optimization between a quality, which we don't want to compromise and the actual cost. Yep. And you can be ISO quality at very different price points because not all questions require the most expensive token. Right. So that's point number one. So what I'm trying to say is my philosophy is to try to isolate the developer or the user from the token anxiety. It's a little bit like with electric cars.

29:56At one point, if you have 18 miles of range, you're always optimizing routes and maybe I'm not going to go there. I don't need this ice cream today. You're self-limiting. You're self-limiting in ways that are kind of really not useful micro-optimizations. We don't want people to go there yet, at least. Right now, it's a time where people need to really find the best way to kind of do more and the best possible work with AI. And let us, meaning internally in sort of a central team, optimize it in a way that we're going to make it economical. And I think reducing the talking anxiety is a big challenge, but I think it really frees up creativity and what you can do with AI.

30:37It's also like there are certain problems that you don't want to optimize too early. Okay. Okay. So for example, yeah, how much time do you want to optimize now for, remember, we used to kind of optimize the weight of web pages because they were too slow to load. And then at one point the editors or whatever say, why can't I put yet another image? And then people are starting to say, okay, why don't you do it? And then on the back end, I'm going to work in optimizing your images rather than asking you at the most, you can put three images on the homepage, right? So that's the approach. People right now, I would rather have them err on the side of usage and let me worry about optimization.

31:14And the other point is really at the end, human hours always tend to be the most expensive cost. And so as long as your token cost per hour is less than your wage per hour, that is a kind of a positive ROI. So at that point, it's fine. Well, what's your feeling about future costs of tokens and whether they're going up or down? Because you hear different things on this. One of the things you hear is that, again, going back to the beginning of this conversation, AI has improved so quickly in the course of months, if not weeks, that those costs are destined to come down. But on the other hand, we know that the hyperscalers are still losing money hand over fist for power users such as yourself at Goldman.

31:58So where do you think those are going over time? my personal view is token cost is going to go down quite a bit but token numbers are going to go up yeah probably even more and so total token cost is going to actually we're going to have to accept that is going to be a major item of cost in any organization and it's to be compared to the cost of people and not to be compared to the cost of t or tcp ip packets or computer or any of that. If you look at just the number of tokens being used for the same use case, if you go the reasoning route or you don't go the reasoning route, if you go the agentic route or not the agentic route, if you go the open claw route where it checks every, starts having these tasks that are firing one after the other, and then you have to start to have verifiers, et cetera, et cetera.

32:50So I think the trend will continue with regards to more and more of those, but the cost, the per unit cost of token, I'm pretty sure that it's going to go down. Also because as GPUs are becoming more powerful, the cost per watt hopefully is going to go down. And then also, to be fair, I mean, these hyperscalers are doing a lot of optimizations to try to run those stacks on their own hardware, right? Which will potentially kind of also generate some economies of scale.

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36:00Tracy Alloway:Can Goldman employees like run open claw on their work computers? And I'm curious like about the degree to which you have people who like want to, I want to install this or this seems really cool. And then think about the security imperative and how you handle that aspect. Not the token anxiety, but the sort of, I want to install this. This is awesome. This is what I have on my home computer. As you know, as a bank, we're pretty locked down in terms of what you can install. You cannot install stuff that is not in the corporate app store in a way. And so there's no way. Is there definitely no way though?

36:33Because can't you just ask Claude how to install itself on a computer? But it's not going to be able to execute. It's not going to be able to create the actual executable. It can actually, even GSA Assistant today can spit out a lot of code, but it spits out source code. Now source code doesn't run. Executable. So it needs to be built and it needs to be turned into an executable. It needs to be signed. Otherwise, the operating system is going to refuse to run it. And so it just doesn't run unless you have that.

37:00Tracy Alloway:But do you feel an anxiety where startups, they're probably, and there's not a ton of startup investment banks, but there are various fintechs and other things that want to chip away at parts of your business and they can run perhaps faster and they can be a little bit more liberal about what their employees are allowed to do, et cetera. Do you feel like you have to keep a certain cadence of expanding the list of those executables that are able to be run? So I'll give you two answers. So first of all, I want to make sure that I answered your first question, which is we're not using OpenClaw. Okay.

37:32Okay. But some of the properties of OpenClaw has actually have informed the way we are building our agentic platform. Okay. Agents today, because of OpenClaw, have actually changed. If you break down what OpenClaw is, there are, this is my own interpretation. I never, I actually never even spoke about this. There are three characteristics that make OpenClaw what it is. One is it's a constant loop. So it's basically what, you know, in information theory, you can call an observer pattern. It's something that continues to run and observe. So there is that. It's a constant observe. So it runs constantly.

38:15The other one is it can schedule events. Every 7 a.m. do this or, you know, like in personal life, we all have something like that that sends me the news in the morning and all that. So there is a schedulability of tasks. The third one is you can instruct it to kind of change its own behavior because it has these files,.md files,.soul,.md, the way you... So you can say things like, hey, I would like you to never use this term or please change it or change the way you filter news. And so it kind of writes its own software to do things for you without you even seeing what's behind the scenes. And so instead of letting people install OpenClaw on their computers, what we do is we incorporate some of those characteristics into our agentic platform so that it does things that are more similar to open cloud.

39:06So that's the approach. That makes a lot of sense. Your second question was interesting because basically if I read behind the questions, are you asking whether there is a sort of a velocity disadvantage with regards to us versus others? I often say that there is a difference between speed and velocity. Speed is almost like you have a certain sprint, okay? But then at some point, you're going to hit the wall. A security wall, a scalability wall. There's going to be a bug. You don't know what you're doing. And it's going to, for sooner or later, you're going to be hit by that. It will be like most airplanes have autopilot that can do everything.

39:42So theoretically, you and I could go on the cockpit. And for a long time during the flight, we will feel pretty good about that, right? We will drink some soft drinks or your tea. We will maybe watch some videos. We will be very happy. That's right. Yeah, sounds great. The passengers might feel differently. One point in time in the flight, there's going to be some storm and there's going to be an autopilot disconnect. And you and I are going to look at each other and are going to say, oh, oh, right?

40:05Tracy Alloway:Where's the pilot? Can I just say recently I was on a flight, did I tell you this? That I was going to Newark. Oh, yeah. We circled three times. We tried to land. It was during a storm. They kept not landing. Everyone's like starting to get pretty annoyed because we were up there for a while. And then the flight attendant comes on and says, unprompted, by the way, we have plenty of gas. and everyone started bringing up no one it's like this is a question this is an answer that no one had been questioned nobody needed anyway I'm sorry I just wanted to get there and then everyone got really nervous by the way we have plenty of gas but you know what I was almost fearing that you would say is there a pilot of war no no it wasn't and then we did land in Washington DC oh you did yeah from Newark no no that's not good but that makes sense yeah yeah and so that's what I mean by velocity is really like it's like the marathon is a sustained speed for a long time in a certain direction yeah I don't think by randomizing that you actually gain velocity.

40:55You gain instant speed of some sort. And so I'm kind of optimizing for velocity. But related to Joe's point, though, you are a regulated bank. Absolutely. Right. And so there are restrictions on what you can do in terms of technology. I am very, very curious what your discussions with regulators are right now, because a lot of regulators, this is still pretty new to them. A lot of the models basically are black boxes. how do you convince them that they're running as they should, that they're spitting out the correct output, that you understand how they're actually functioning? So this is not the first time that banks use neural networks, okay?

41:36These are just much larger neural networks, but we've been using neural networks for like a decade plus. And so So every bank has already gone through the motions of explaining that neural networks don't have perfect explicability. Therefore, you need to change the control system around them. You need to look at what actions can they actually do, and then you limit the actions. Okay. There is functions called the like model risk management, which is a very standardized function within every bank. Therefore, each of those neural networks, you need to have an inventory, you need to have a risk tiering, and you need to put controls around them.

42:14So it is not really that much of a new thing. It's more of an evolution where now you have things that are much faster and much more powerful. But the basic pattern and the basic discussion with the regulators is kind of the same, which is, are you classifying the risk tiering of the application, right? And then which controls are you putting? And are you putting human supervision and human in the loop? So, for example, for code, we don't allow AIs to auto-approve their own code. Okay? All they can do is publish what's called a pull request or a merge request, the same way as a developer would do.

42:51And we kind of have a sort of a zero trust model there because we don't assume that maybe a junior developer is going to be less, more bug free than an AI, right? And so we have several controls in place. For example, there needs to be a human, more senior than you, that actually looks at the code and then certifies and approves. And then after that, before it goes to the production, it goes to something that is called CICD or continuous integration, continuous deployment pipeline, where when it goes through the build phase, et cetera, there is a lot of checks that are injected into that. There are security checks.

43:28There are tech risk checks. So I don't think at the end of the day, you really lose too much velocity or at all. You just need to invest more in those kinds of things. And the regulators, I think if you bring them back into sort of a familiar territory, and you're also honest on things that you know and things that you don't know, and for the things that you don't know, you kind of put higher protections. I think the conversation is generally very positive.

43:49Tracy Alloway:We have an episode that we recorded several weeks ago that we still haven't released. I don't know exactly the timing of that one or this one. We interviewed Scott Bach, the former CEO of Green Hill, the boutique investment bank. And part of the reason we had that conversation was because we want to know like, if AI is going to someday disrupt banking as we know it, what was banking as we know it? So we talked about the history of investment banking. But one of the things that he talked about was that a big advantage that the banks had was this sort of information asymmetry and that they would know a lot more about their industries and so forth than their clients.

44:21Tracy Alloway:And this was profitable. Now, going back to your answer, the very first question, you're like, OK, a client might call Goldman and they say, what does the straight of Hormuz closure mean for this portfolio shock, et cetera. I was going to ask this exact question. I kind of think I could do that. I think I could. I mean, no offense. I'm sure your platform is a little bit better than what, but I think I could like get 90 % of the way there. And I bet I could like with a little bit of data, build a basket that says I want helium shortage basket, which companies would I short if I think the helium shortage is going to get worse?

44:54Tracy Alloway:I could probably build a basket the way a trading desk would. Do you think long-term like that AI erodes a certain structural source of profitability for banks and you worry about that? I think you can get to the 90%, but I think clients are really paying us for that extra 10%. Okay. So I think that's the answer. Wait, so what is the extra 10 % in that context? Is it the models are slightly better or is it also the data? We have access to, you know, we buy a lot of data that, you know, is very expensive and it's at massive quantities and it's very up to date and very real time. So we have a little bit of a data advantage.

45:30We operate across multiple asset classes. So we see the trading side, we see the asset management side. So we have a sort of a correlation between assets advantage that we see those because generally rates move, interest rates can move, yields can move, you know, there is a correlation between all those indicators. So there is another advantage. We have a global advantage. We have people on the ground in 100 plus countries and these people have relationships and information travels through those channels. and also you know like we generally deal with very complex portfolio so this is not you and i maybe having three stocks or four or five this is like very complex multi-assets with complex products like swaps or swaptions or exotic products etc etc and so that's really the 10 % that the clients that we have really value and what we really need to get it's like at the end of the day listen look at formula one okay the difference per time per lap between the mercedes and take your favorite last team, it's sometimes one second out of two minutes.

46:35And that is the difference between getting$100 million a year sponsorship or a$10 ,000 sponsorship. So for sophisticated clients, that 10 % is really what the money is. And that's really what people are paying us for. So actually, you mentioned all the different businesses at Goldman, and there are a bunch of them, like asset management, there's banking, there's trading. A lot of those businesses aren't supposed to talk to each other in various ways. And so when it comes to the data, is there like a data leakage issue where you might have a model that's in-house like GSAI that's pulling data from different sides of the company in ways that maybe it shouldn't be?

47:17Maybe it's really hard to tell given the complexity of the model? Is that something you have to pay attention to? Absolutely. So we have the concept of info barriers, okay? And the info barriers are enforced throughout the entire system, okay? And they're linked to your ID or your account, okay? So if I'm on the private side, I can only see certain information. If I am on a public side, I can only see certain information. And I cannot even know about the information on the other side. I don't have access to the files, to the folders, nothing. Each AI or each agent or each application, that's the beauty of this centralized platform, needs to get an ID or a badge.

47:58And that badge is attached to the exact same info barriers as any application or any computers. And so these are enforced basically at the source. So even if it is the same type of model, but that particular use of the model, that particular session of the model, they need to get a ticket or a badge. And that badge or those keys just take them to a certain place. And so this is one of these been, it took us almost two years to build a GSAI platform. This is back to the reason why you can't be casual about these things. This thing has not been built by some random vibe coders because you need to worry about cyber.

48:33You need to worry about info barriers. You need to worry about all that. And so when I talk about there are places where you can leverage and do correlations, but there are others where you absolutely can't. And this is kind of that is foundational to the fact that you need to be ready for AI. You can't be casual about AI.

48:47Tracy Alloway:So I take your point that there's never been a technology that you've seen in your career that has actually reduced the need for software engineers and that the nature of the job of software engineers change and maybe gets more high level and whatever. setting aside that volume question, setting aside the pure level of head count question, is AI changing right now across anything, technology or otherwise, the types of person you're looking for or changing something about the nature of the type of talent you're pursuing? Yeah, absolutely. Great question. So I think in this day and age, almost nobody is an individual contributor, really.

49:27Because when you're working with agents, you need to have at least three fundamental characteristics. One is you need to be able to explain what you want to get done. Yeah. Okay. The second one is you need to be able to delegate work. Guess what? Because you're going to have multiple agents. One is specialized, for example, in doing, I don't know, DCF calculations. And one is specialized in doing research. So you need to be able to break down the work into chunks that can be executed in parallel in some way. And then three, you need to have the ability to supervise. You need to actually look at the output and say, okay, I'm good with this or go back.

50:06It turns out that those three things, i.e. explain, delegate, and supervise, are kind of the one-on-one of managers. Managers need to have those three. Otherwise, they can't manage a team. And so AI is kind of turning everybody a little bit into a manager. and those are kind of the skills that we are actually looking for people that they know that they're going to have agency on tools that at some point are going to be even more proficient and specialized than they are and so the most important thing is really the ability to ideate to explain to delegate and then to really know what good looks like and I think that is a big change and I don't think everybody's going to actually rapidly go through that and I think we're doing a combination of training.

50:52There is a combination of exposing them to other people. Like one of the advantages of having forward deployment engineers is also that there is a little bit of clash of culture that is happening around the table. And so people think really, really differently. And that pushes people outside their comfort zone. That's why I'm saying that there is a little bit of a metamorphosis happening there. It's not just about efficiency. It's really thinking about, is my job going to stay the same? No, it's actually changing quite a bit. So I'm thinking how to frame this question, but what's work life balance like now for a developer at Goldman?

51:25Because you have this existential angst about jobs potentially changing. At the same time, you have AI tools that enable more productivity. And you also have this thing happening where I feel like, Joe, maybe you know more about this than I do, but I feel like a lot of vibe coders, like it's addictive. Yeah. Right. It's like you're pressing the button of a slot machine. You're interacting with Claude and you're seeing what it spits back out over and over again until you get that big win. And so I've heard people talk about burnout among developers who are just doing so much with this right now that they're just hitting that button over and over.

52:01Tracy Alloway:There was a good discussion in the OddLods Discord recently about exactly this. Some engineers and semiconductors feeling that the job has become less satisfying. and I think it's sort of what you're getting at. There's sort of slot machine feeling where it's like, oh, you're going to like hit the prompt. Okay, this is the great output. Then it's like, they feel the work is like less satisfying and stuff like that than actually like writing code. Yeah, I mean, listen, again, this is where kind of the fact that I'm a little bit older than most here, engineers are kind of, I've seen that the first time people had Excel.

52:32I've seen the first time people had Python. Oh my God, I don't need to know Java. And then the kids start to code and there is this whole coding movement. and then you get to start creating your applications. I've seen the first time people have mobile stuff and mobile apps. And so I think a little bit of that is because it's new, to be perfectly honest. And I think, yes, there is a little bit of that, but there is a lot of novelty to that. And then I've seen that people have been using those tools for a couple of years. They're taking them a little bit more like, okay, it's a professional tool, and I'm going to use it for what I actually need rather than just trying to discover.

53:06One thing that I've seen is that because maybe of that, but also because of what you can get, there is a sort of, in a way, reward cycle that is pretty quick. Yeah. People are very excited, actually. There's some sort of a joy of the profession that is actually coming out, as if engineers were feeling like this job is new again. Because a lot of engineers have seen the same pattern sometimes for two or three decades. Yeah. So that has been something that I observed. There is also a lot of peer pressure. There is a lot of fear of missing out. And so people are rather than, it's no longer me trying to push the car uphill.

53:40It's more like people are actually looking at their peers and they're looking at, oh my God, how could you do that? And so it's kind of spreading horizontally quite a bit, which is really nice to see. And so, so far I have to say that it's been positive, a positive change. And also one other thing that you're talking about burnout. I see that a lot of people get fatigue. I don't want to talk about burnout, but they get fatigue when there are a lot of repetitive tasks, especially for a developer. Here's an example. Let's say you go from a version of a Java library or Spring Boot to another version.

54:14And then all of a sudden you compile and you get or you build and you get all these errors that says you need to upgrade. Honestly, upgrading libraries is not the most fun job. And if you need to do it a hundred times, or it's like someone says, by the way, guys, we have this new design, new logo, new colors, implement it on like 200 websites. It might be fun the first 10, and then it becomes a drag. And so I think taking that away, Kando, they focus more and more on the plan, for example. And so right now, let's do a migration plan to the cloud of a complex application. They spend maybe 70 % of their time going back and forth with a very powerful set of AIs to really get the plan right.

54:56They feel a little bit more elevated, and the mechanical part, it's kind of left to the machine. The same way, I mean, listen, I started developing when I was literally flipping switches, okay? And then pressing a button that we should move the register up one. And then came some languages like C. Oh my God, now I don't have to flip switches anymore. But guess what? I need to do memory management. I need to do pointers. I mean, there's a lot of heavy lifting. Oh, I have a memory leak. I'm going to spend a week before I actually finally identify that. And then it comes Java, oh, garbage collection.

55:31I don't have to worry about memory leaks anymore. Fantastic. And then comes Python, which is all that rigidity. It's so much easier to be type free and so forth. And so every time you kind of keep raising the bar and a lot of the kind of mechanics kind of goes away, I think this has been like a 10 years job in a matter of two years. But I think overall, nobody really likes to have that toil and that mechanical work. And I'm actually quite happy that people are going to spend maybe initially more time because they're excited about things that are enjoying rather than things that they dread. All right.

56:07Well, Marco, we'll have to have you back on the podcast in another year and a half, I guess. Three months. Yeah, three months. That's right. On the reduced AI timeline. Thank you so much for coming back on all of us. Thank you both of you. That was so great. Thanks for having me. Thank you so much, Marco. Thank you.

56:32So, Joe, that was great to catch up. One thing I thought was really interesting was his point about the discussions with the regulators and framing it like very similar to previous technological advances where you're not necessarily explaining exactly how the models are coming to certain conclusions. Yeah. But you're more focused on actually limiting the risks and making sure that they're in the right bucket for risk assessment.

56:55Tracy Alloway:Now, I thought that was really interesting, just that some of these technologies, the black box. Yeah. LLMs are not the first black box. I mean, we've actually been talking about black box trading for years in finance before. So the idea of like, OK, there are these things that are happening. We can articulate them and whatever. It's not the first rodeo for finance. It's really interesting. I'm also, you know, I thought the whole conversation about token budgets and allocations are interesting. The idea of like, OK, part of the job here is you have a bunch of different models. everyone in theory wants the most performant model, but how do you find that optimization where you get the best performance relative to price?

57:32Tracy Alloway:It sounds like a pretty interesting engineering problem. Yeah, I would actually love to do more on that question. I would too, yeah. Because it's such an interesting question of incentives, right? And how do you actually prioritize which project? How do you know what constitutes a good output? Yeah, exactly. When do you sacrifice a little bit of quality for like 10x less token budget or whatever? It would be very interesting to talk about how that problem specifically gets solved inside of an organization. Token economics or efficiency optimization. Yeah. Well, we'll have Marco back on very soon to talk about all the new things that AI is doing.

58:10But for now, shall we leave it there? Let's leave it there. This has been another episode of the All Thoughts Podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway.

58:17Tracy Alloway:And I'm Jill Weisenthal. You can follow me at The Stalwart. Follow our producers, Carmen Rodriguez at CarmenArmandDashleBennett at Dashbot. and Kale Brooks at Kale Brooks. And for more Odd Lots content, go to Bloomberg.com slash Odd Lots for a daily newsletter on all of our episodes. And you can chat about all these topics 24-7 in our Discord with fellow listeners, discord.gg slash Odd Lots. And if you enjoy Odd Lots, if you like it when we talk to Goldman Sachs about how they're actually deploying AI across the company, then please leave us a positive review on your favorite podcast platform.

58:48And 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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