Tracy Alloway (2:49)

Hello and welcome to another episode of the Odd Thoughts Podcast. I'm Tracy.

Joe Weisenthal (2:52)

Alloway and I'm Joe Weisenthal.

Tracy Alloway (2:54)

Joe, the thing about AI, I feel like it's accelerated all of our timelines. Right. Like, it's phenomenal to me to think back that, like back to the days of ChatGPT. And when did that come out? 2020.

Joe Weisenthal (3:07)

Late 2022. 2022, November 2022.

Tracy Alloway (3:10)

That's just crazy to think.

Joe Weisenthal (3:12)

And then it's really unbelievable, the gap. And I've been thinking about this, like just a. The explosion of capabilities.

Tracy Alloway (3:19)

Yeah.

Joe Weisenthal (3:20)

And the thing I've been thinking about is that, you know, after the first year or so when it came out, we talked to executives and we like, how are you using AI in your workflow? They're like, well, everyone's experimenting. It's great. Everyone is using ChatGPT. It's always very vague. And now in 2026, the story is that AI is so powerful that it's going to destroy all these legacy software companies. So what I would say is we must be past the age of experimentation. I think that any use cases using it better have some example of like, here is a workflow where we're using it.

Tracy Alloway (3:50)

Well, exactly. And to this point, now that we're past the age of experimentation, I'm very curious how executives and managers are actually evaluating the return on investment in AI and what they actually want to see from it at this point. So, you know, are you going to replace all your third party SaaS, contractors with internal coders? And what does that look like from an actual headcount perspective, From a cost savings perspective? We can actually get some concrete details on this now. So I'm very excited to say we do in fact have the perfect guest, someone who we had on before to talk generally about AI and someone at a company that has been doing, you know, they got into it pretty fast. The last time we spoke to this person was in 2024 and even since then, ages ago, I. Yes, it just feels like light years in AI time.

Joe Weisenthal (4:42)

So just one last thing on the last few years of AI, which is that when ChatGPT, I used, when it came out, I played around with it a lot when I had to write poems and all this stuff. And then I bet if you actually looked at my AI usage, it went through a trough. Whereas like I wasn't really getting any productivity, there was nothing it really could do that I needed. It still sort of seemed like a toy. So I had this like intense burst of use for the first several months and then this trough. And now these days, with the expansion of capabilities, particularly Claude Code. I'm finding all kinds of new things. So there is like we're out coming out of the trough. I think a lot of people are actually finding things, at least if I can generalize from my own experience.

Tracy Alloway (5:20)

Yeah, absolutely. So we do in fact have the perfect guest. We've brought back Marco Argenti. He is of course the Chief Information Officer over at Goldman Sachs, someone we had on the podcast back in August of 2024. So Marco, thank you so much for coming back on.

Marco Argenti (5:35)

Thank you for having me.

Tracy Alloway (5:36)

How much have things changed for you? Does 2024 seem like 20 years ago now in AI time?

Marco Argenti (5:43)

Yeah, I barely remember even what happened back then.

Tracy Alloway (5:47)

That's a nice way of saying you forgot what we talked about on the podcast.

Marco Argenti (5:50)

That's fine, maybe that. But literally like things are really changing on a weekly basis almost right now. And if I look at the evolution not only since a year ago, but even since six months ago, I think it has been nothing short than revolutionary. A year ago we barely talked about agents or the word almost didn't exist. We were using AI as like a chat companion, a search function. Yeah, it was telling you, oh, I'm sorry, I don't know who's the President of the United States because my cutoff date is like a year and a half before or things of that nature. Now you can say, hey, as a person, you can say, hey, my plan just got cancelled and it's going to redo all your plans, it's going to check for available flights, it's going to do all these capabilities of personal assistant. And that translates in corporations also in a lot of utility that you can see in everyday tasks. So I would say what I like to say to my people about us in general, I say this is not the drill, this is real. It's not the age of experimentation anymore. This is a tool that now can do a lot for you. And so we put it at work. And we put it at work starting from developers, but then expanding in many, many other areas. So I would say actually if I look at the increase of capabilities of these models, what we've seen in the last six months or so, with really the evolution of this advanced reasoning capabilities that came out, I think that finally got us the confidence that you can use AIs for everyday's work with the right supervision and also in many cases for mission critical applications, is not a toy anymore. It's something that you can expect results from. And I think that's the biggest change. So today I would say that there is almost nobody in Goldman that is not touched in a way OR another by AI. We gave our GSAI or GSAI assistant to 47,000 people. Most of them use it every day. Most of them use it multiple times a day. And what's interesting is it's like the first time you see a tool like, I don't know, Microsoft Excel, you can almost not predict what people are going to do with that. Okay, maybe it's born for doing some form of accounting and then people write entire applications on top of that or use it for project managers, management or things of that nature. And AI is kind of turning that way. If I look at what people do with that, it's really things that surprise us every day because, well, why don't

Joe Weisenthal (8:17)

you give us some examples? So in production right now, what are some workflows or novel things that were not workflows before that you see within Goldman that AI is doing for people today?

Marco Argenti (8:30)

So let's start from the GSA Assistant.

Joe Weisenthal (8:33)

Great.

Marco Argenti (8:34)

That can answer really complex questions based on external and internal data that generally before used to take sometimes hours or even days, sometimes weeks to answer. Okay. It can do very complex research for you in topics. You know, for example, we can ask questions that come from clients such as, hey, how does the recent geopolitical events on the hormone strait actually impact this portfolio? What could be a potential rebalancing strategy? Or you could ask intersection of like, I don't know, how does the, a certain Fed decision on interest rate actually impact the volatility of certain assets? So you ask this multidimensional questions and what GSA Assistant does calls out the model, retrieves the relevant information, creates a plan to answer that question. That's kind of the key because this AIs they really plan before responding rather than just giving you the first thing that comes to mind. And so that's what kind of at the very surface thinks that one of the most common use cases, which is we really enhance the client experience by being able to answer questions internally and externally in a much, much faster way. But really complex question, not simple questions. We had to wire up hundreds of data sources and also, most importantly, which is something that I tell everybody that asks me, hey, give me some, some advice on how to implement AI in a corporation. Data quality is really the determinant between good AI and not so good AI. And so we did a lot of work to not only take a bunch of data, but also making it understandable to the AI. So for example, just to go a little bit deeper, we Have a tool called Legend AI, which is our lakehouse, which allows you to go from query to MCP server connected to GSA assistant, that is from data to answers. You can wire it up literally in two or three clicks and it does all of that for you. And so the quality of the data, the quantity of the data, not only is not just the bitter lesson here, but it's also the lesson of you need to curate your data. You get better answer disproportionately. That's something that has driven that. So that is kind of the knowledge aspect of AI, which is I would say the most widespread because every single one in the firm has that and it's the highest usage. We are like way above a million prompts per month and it's growing really, really fast. And then of course, you know, you're asking me like real impact in production. Every developer in Goldman is enabled with agentic AI. Okay, so we were probably one of the first, if not the first to launch Devin almost a year ago, which is the fully agentic developer assistant. We have cloud code, we have many other tools, GitHub, Copilot, Agent, et cetera. But on that you really see the step change. There is no question that there is changing the way developers work. And by the way, it's not just about doing the exact same things more efficiently. It's changing the way developers actually do their work. And that is very, very easy to see how that kind of changes the paradigm of what a developer does. You're much more of a product manager, you're much more of a planner, you're much more of a idea generator. The most important thing for a developer today is to be able to explain things rather than jumping things. I don't know. You want to know that?

Joe Weisenthal (12:00)

I just going to say that resonates because I've been like vibe coding but I can't explain how any of it works. So if someone is like, you know, I like build little like toy apps and stuff, but I get really anxious, I couldn't explain that's why I'm not a software developer.

Tracy Alloway (12:12)

Well, just on this note, I mean people tend to talk in generalities when it comes to AI boosting productivity or maybe AI changes the way we work or it leads to some new ideas. From your seat at Goldman, you're a manager, you're looking at the bottom line of like all these businesses. What exactly is the outcome, the specific outcome that you would like to see from your developers using something like cloud code?

Marco Argenti (12:40)

It's really about increasing the output So I want to see. I was actually having this discussion this morning. I was looking at some of the reports and some of the deliverables for our cloud migration, which is a very important thing for us. And I was looking at this really big project that was saying it was not only green, it was like two months ahead of schedule. And I was saying this is how we know when things are going to work. You're going to consistently start seeing projects that are actually finishing ahead of schedule, which means then people are ambitious, they want to do more, and therefore you end up with output that is much higher than what you had before. And listen, with developers, obviously the biggest question that everybody asks is, okay, what are you going to do? Are you going to cut developers this and that? So, first of all, with all the innovation that I've seen In the last 30 years or so, I kind of never seen a moment where really people were reducing the number of developers. Because if I look at the things they were not doing in a certain year because of budget reasons, because of complexity reason, because of prioritization, the stuff that is below the cut of the backlog, it's a lot. And a lot of that is really driving the growth of the business. So it's good to have the optionality to do it. You have the optionality of say, Now I have 120% of my capacity, I have 130% of my capacity. Do I want to do 130% more? Great. If I don't, I have the option to reduce. So that's really how we measure it. It's really the impact on the timelines of delivery. It's output. It's basically quality. And timeline becoming quality gets actually better and the timelines get shorter. So that's what we measure.

Joe Weisenthal (14:33)

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Joe Weisenthal (17:32)

Obviously one of the big questions for the market this year is what is the impact of AI on legacy software providers? And there's various theories about how they could be disrupted. There are reports I think about Anthropic having quote for deployed engineers inside Goldman Sachs. So Anthropic employees building out AI systems internally Maybe that could replace some legacy software right now can you say like there is a change in the balance of power when there is a given piece of software up for negotiation?

Marco Argenti (18:06)

I think generally there is. Okay, first of all, there has kind of always been that tension because imagine for example, imagine when there were software that didn't run on the cloud and then all of a sudden a bunch of new vendors are coming to you and say, hey, wait a second, why are you running that on your mainframe or your on prem? Why don't you run it in the cloud? Or remember when software needed to be installed, then everything became browser based and so onset. So there has always been a little bit of a cycle and renewal. What I say is that today that cycle of renewal is much faster. That's really what it is. And I would say I generally resist making like really broad categorizations. AI is by the way, the largest possible to me is like saying computers, okay, yeah, but even software is very broad. And so within the software category, I think there are winners or losers. There are winners in the long term and losers in the long term. But it's really like people tend to make it a category and then maybe throw the baby with the bathwater. So here's an example. To me, the question that I ask myself with regards to which vendors am I going to which software am I going to have like a few years down the road? Is software generally is attached to a process or a certain ways of working? Okay, it does something for you and it puts it in the form of an application that you use. The real question is, is that process and or ways of working going to be the same or is it going to change in five years? Then you can determine what is basically the likelihood that the software is going to be robust to that or not. Example, is accounting or closing the books going to be very different five years from now? I don't think so. Really. It hasn't really changed. I mean everything changes, but it hasn't really changed much. And so if you're operating in the general ledger type of category, I don't think all of a sudden you take a GPT or a cloth and it's gonna close your books magically. You know, you still have to do

Joe Weisenthal (20:03)

the counting and the subtraction.

Marco Argenti (20:04)

You still need to do a lot of that. And it's very regulated, importantly, right? It's extremely regulated. Jurisdiction by jurisdiction, country by country, product by product, industry by industry. So that part is kind of to me in kind of the safe mode in a way and then you go to the other end of the spectrum and you have sometimes software that kind of is aligned to the way people do, like software being one of them. Like if you look at the software developer life cycle, a lot of that is changing. Developers are developing software by developing specs today. And so if you're too much in the weeds down there in that mechanics and you don't adapt for AIs or agents doing their work, software development, life cycle deployments, rollbacks, monitoring, observability and all that, I think that part will be very much disrupted. Or if you're adding a sort of a UX on things, that's another classic. You have a very simple process. I don't know, you're doing surveys or expense reports or whatever. And now people are going to start expecting, you know, their personal assistants or agents to kind of do all that mechanic for them. And so I think that part is probably something that has a bigger question mark on top. And so I always ask myself for the process question first, the process transformation section, the question first and then consequently, what's the tool that is going to support that?

Tracy Alloway (21:25)

Just to press on this point though, have you replaced any third party software providers with something that's been developed internally through AI?

Marco Argenti (21:34)

We have terminated contracts already. Yes, absolutely. Okay, now I'm not going to ask you your follow up question again because I'm not going to say your name,

Joe Weisenthal (21:41)

the tickers of the software stocks,

Marco Argenti (21:46)

but overall, yes, absolutely, absolutely. You know, the thing is like the whole buy versus build. Okay, yeah, the equation has changed quite a bit. Buy versus build was always like, okay guys, how long does it take to do the, to build this? And you get an answer which is, well, we can do it in like X amount of years and X amount of millions of dollars. Now I'm starting to see people coming to me and say, by the way, I had some time, you know, this weekend and here is a perfectly working application. So the cost, or at least for simple applications, the cost of kind of build from a time perspective, from an actual cost perspective has gone down quite dramatically. So right now the little things are most likely going to be built. The very big, large software that is to be deployed at scale, across thousands of people and et cetera, that big complexity as you know from we all do toy stuff with our clock codes at home and whatever, you know, there's still some rough edges, you know, and so it's hard to think that all of a sudden the big applications are going to disappear. And so that's really what I'm saying that if I look at the applications that I buy today, there is a lot of small applications, you know what I'm saying? And so the build is kind of, the pendulum is starting to swing back towards the build, at least for that category for sure.

Joe Weisenthal (23:04)

What is a forward deployed engineer? I know it's like one of the hot buzzwords of 2026 and I saw headlines that there were anthropic forward engineers at Goldman. I have no idea what that means, actually. Yeah, what is that term?

Marco Argenti (23:16)

What is that job?

Joe Weisenthal (23:17)

What did they do when they got there?

Marco Argenti (23:18)

Okay, so I think, listen, that name has changed quite a bit.

Joe Weisenthal (23:21)

Yeah, I'm already using out of date terminology.

Marco Argenti (23:23)

No, no, no, no, no. I mean that is the latest term. So you are fully, you are in the vlatest of that term. But remember, I mean there was a time where you used to call them solution architects. Right. And so the point is right now, I think one trend that I see which is also kind of true for us is when things change so much and so rapidly, you kind of want to go to the origin of who produces this new thing. Okay. So the least intermediaries you have and probably the faster you can go. And so going and working directly with the model providers is generally a good idea because if you're putting someone in the middle, this company is going to have to be trained, it's going to have to be, you know, there is a cycle which at this point of very rapid change is going to slow you down. And so the first thing that that term means is those are people that are actually normally building the product. They're normally building the Claude or GPT or X. So that's the first differentiation. They're straight to the source of the AI production in a way. And second is that they're generally product people. So people that have actually built those tools rather than people that are more like support and deployment. And so this characterization is you take the classic sales support team or solution support team, which was mostly doing integration and when things are so rapid, it's like, you know, imagine if there is like something like, I don't know if the clothes style works, changes so fast. Instead 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.

Tracy Alloway (25:02)

So on this note, one of the things we heard in support of SaaS was this idea that while 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?

Marco Argenti (25:21)

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. And 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, you 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 that would be remain very valuable. And 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.

Tracy Alloway (26:26)

And those were 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?

Marco Argenti (26:46)

So 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 look at rebooking stuff literally like during a meeting where they were not paying attention. So those are some of the things

Tracy Alloway (27:13)

that's what I'm doing right now while we're doing this podcast that brings me

Joe Weisenthal (27:17)

to exactly where I wanted to go next, which is, I'm curious, like, do 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.

Marco Argenti (27:36)

It's one of my favorite questions.

Joe Weisenthal (27:37)

But I'm curious, like how you think about token allocation within the firm and whether there's intra firm competition for compute.

Tracy Alloway (27:45)

Yeah, token allocation could be like included in your performance. Yeah, you do.

Joe Weisenthal (27:49)

Well, you get more token teams and stuff like that. Whether that's part of what you think about for planning.

Marco Argenti (27:54)

Absolutely. So a few months ago I did like 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.

Joe Weisenthal (28:14)

Jensen Wong, an interview today or 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. 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 right now. So talk about that right now.

Marco Argenti (28:39)

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. And the model gateway intelligently routes requests to the combination, the Pareto frontier of quality and cost. Okay, so you gotta 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, too many.

Joe Weisenthal (29:36)

Just this is very interesting to me is a big part of the problem that you're trying to solve. And we know like ChatGPT, they intelligently route, they do some on their, you go to chatgpt.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?

Marco Argenti (30:06)

It is a big part of the time of the spent by the AI central group, the platform group. The platform group worries a lot about where do I get the right data, for example, for this question and which model do I write route it to? That's a big, you know, because again I spoke about Pareto Frontier, meaning the optimization between quality, which we don't want to compromise, and the actual cost. Yeah, and you can be ISO quality at very different price points because not all questions require the most expensive token. 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. Okay. At 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 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 do 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. It'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. And the other point is really at the end, human hours always tend to be the most expensive cost. Okay. And so as long as your token cost per hour is less than Your wage per hour there is a kind of a positive roi. So at that point it's fine.

Tracy Alloway (32:26)

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. So where do you think those are going over time?

Marco Argenti (32:57)

My personal view is token cost is going to go down quite a bit, but token numbers are going to go up probably even more. And so total token cost is going to. Actually, we're going to have to accept that it's 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, you know, it checks every, you know, starts having these tasks that are firing one after the other and then you have to start to have verifiers, etc. Etc. So I think the trend will continue with regards to more and more of those. But the cost, the per unit cost of token, I pretty, I'm pretty sure that is 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 like 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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Joe Weisenthal (36:56)

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.

Marco Argenti (37:16)

This is as you know, like as a bank we're pretty locked down in terms of what you can install. You cannot install stuff that is not, you know, in the corporate in the corporate app store in a way and so there's no way.

Joe Weisenthal (37:27)

But do you feel like a certain way?

Tracy Alloway (37:28)

There's definitely no way though, because can't you Just ask Claude how to install itself.

Marco Argenti (37:33)

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.

Tracy Alloway (37:43)

Okay?

Marco Argenti (37:44)

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.

Joe Weisenthal (37:56)

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, etc. Do you feel like you have to keep a certain cadence of expanding the list of those executables that are able to be run?

Marco Argenti (38:22)

So I'll give you two answers. So first I want to make sure that I answer your first question, which is we're not using openclaw. Okay? Okay. But some of the properties of openclaw has actually have informed the way we are building our agentic platform.

Joe Weisenthal (38:38)

Okay.

Marco Argenti (38:39)

Agents today because of openclaw have actually changed. If you break down what openclaw is, there are. This is my own interpretation. I actually never even spoke about this. There are three characteristics that make OpenCloud what it is. One is it's a constant loop. So it's basically what 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 observer, so it runs constantly. The other one is it can schedule events every 7am, do this. Or in personal life, we all have some 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 sol. Md the way you. So you can say things like, hey, I would like you to never use this term or please change 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 OpenCloud. So that's.

Joe Weisenthal (40:03)

That makes a lot of sense. Yeah.

Marco Argenti (40:04)

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 gonna hit the wall, a security wall, a scalability wall. There's gonna be a bug. You don't know what you're doing, and it's gonna first. Sooner or later, you're gonna be hit by that. It would be like most airplanes have autopilot that can do everything. So 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 Dr. Or your tea. We would maybe watch some videos. We would be very happy.

Joe Weisenthal (40:49)

That's right. Sounds great.

Marco Argenti (40:52)

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, right, where's the pilot?

Joe Weisenthal (41:02)

Can I just say, recently I was on a flight. Did I tell you this? And 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 was, 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 figuring out, no. It's like, this is a question. This is an answer that no one had been talking about. Nobody needed anyway. I'm sorry. Nobody needed. Then everyone got really nervous. Okay, by the way, we have plenty of gas.

Marco Argenti (41:29)

But you know what? I was almost fearing that you would say, is there a pilot on.

Joe Weisenthal (41:33)

No, no, it wasn't happening. And then we did land in Washington, D.C. okay.

Marco Argenti (41:36)

Oh, you did?

Joe Weisenthal (41:36)

Yeah. From Newark.

Marco Argenti (41:37)

No, no, that's not good.

Joe Weisenthal (41:38)

But that makes sense.

Marco Argenti (41:39)

Yeah. Yeah. And so that's what I mean by velocity is really like. Is like the marathon is a sustained speed, time in a certain direction.

Joe Weisenthal (41:48)

Yeah.

Marco Argenti (41:48)

I don't think by randomizing that you actually gain velocity. You gain instant speed of some sort. And so I'm kind of optimizing for velocity.

Tracy Alloway (41:57)

But related to Joe's point, though, you are a regulated bank.

Marco Argenti (42:01)

Absolutely right.

Tracy Alloway (42:01)

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?

Marco Argenti (42:28)

So this is not the first time that banks use neural networks. Okay? These are just much larger neural networks. But we've been using neural network for like decade plus. And 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 model risk management, which is a very standardized function within every bank that for 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. So 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. And 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 in 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, there 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 kind 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.

Joe Weisenthal (44:45)

We have an episode that we recorded several weeks ago that we still haven't released. And I don't know exactly the timing of that one or this one. We interviewed Scott Bach, the former CEO of Greenhill, 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. And this was profitable. Now going back to your answer, the very first question, you're like, okay, a client might call Goldman and they say, what does the straight of Hormuz closure mean for this portfolio shock, et cetera.

Tracy Alloway (45:29)

I was going to ask this exact question.

Joe Weisenthal (45:31)

Yeah, I kind of think I could do that. That I think I could. I mean no offense, I'm sure your platform is a little bit better than what like, 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 country, which companies would I short if I think the helium shortage is going to get worse? 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 do you worry about that?

Marco Argenti (46:00)

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.

Tracy Alloway (46:09)

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?

Marco Argenti (46:16)

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. We operate across multiple asset classes. So we see the trading side, we see the, 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 relationship and information travels through those channels. And also, you know, like, we generally deal with very complex portfolios. 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, et cetera, et cetera. And so that's really the 10% that the clients that we have really value and where 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, you know, your favorite last team, it's sometimes one second after two minutes out of two minutes. And that is the difference between getting $100 million a year sponsorship or a $10,000 sponsorship. So for sophisticated clients, that 10% is really where the money is. And that's really what people are paying us for.

Tracy Alloway (47:47)

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? Maybe it's really hard to tell given the complexity of the model. Is that something you have to pay attention to?

Marco Argenti (48:19)

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 decentralized platform, needs to get an ID or a badge, and 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 that needs 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 this been. It took us almost two years to build a GSAI platform. This 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, you 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 kind of that is foundational to the fact that you need to be ready for AI. You can't be casual about AI.

Joe Weisenthal (49:44)

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 has changed and maybe gets more high level and whatever. Setting aside that volume question, setting aside the pure head level of headcount 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?

Marco Argenti (50:15)

Yeah, absolutely. Great question. So I think in this day and age, almost nobody is an individual contributor really. Because 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. 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. 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. It 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 gonna actually rapidly go through that. And I think we're doing a combination of training. There 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 efficiencies, really thinking about, is my job going to stay the same? No, it's actually changing quite a bit.

Tracy Alloway (52:14)

I'm thinking how to frame this question, but what's work life balance like now for a developer at Goldman? Because 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.

Joe Weisenthal (52:58)

There was a good discussion in the odd lots discord recently about exactly this. Some engineers and semiconductors feel like that the job has become less satisfying. And I think is sort of what you're getting at, the sort of slot machine feeling where it's like, oh, you're going to like hit the prompt. Okay, this is the great output. And then it's like they feel the work is like less satisfying and stuff like that than actually like writing code.

Marco Argenti (53:18)

So, yeah, I mean, listen, again, this is where kind of the fact that I'm a little bit older than most here engineers kind of. I've seen that the first time people had Excel, for 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. You start creating your applications. I've seen the first time people have mobile stuff and, you know, 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 little, 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 professionalist tool And I'm going to use it for what I actually need rather than just trying to discover. One 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. 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. Kids.

Joe Weisenthal (54:26)

Yeah.

Marco Argenti (54:26)

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. So people are, rather than is no longer me trying to push the car uphill. It'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, positive change. And also one other thing that we are 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 version of a Java library or spring boot to another version, and 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 100 times or is 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, kind of, 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. They 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 that, 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. I don't have to worry about memory leaks anymore. Fantastic. And then comes Python, which is all, 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 think this has been like a 10 years jump in a matter of two years. But I think overall nobody really likes to, to have that toil and that mechanical work. And I'm actually quite happy that people are gonna spend maybe initially more time because they're excited, but on things that are enjoying rather than things that dishes they dread.

Tracy Alloway (57:03)

All right, well, Marco, we'll have to have you back on the podcast in another year and a half, I guess

Joe Weisenthal (57:07)

in just three months.

Tracy Alloway (57:08)

Yeah, when the world is on the reduced AI timeline. Thank you so much for, for coming

Marco Argenti (57:13)

back on both of you. Thanks for having me.

Joe Weisenthal (57:15)

Thank you so much, Marco.

Marco Argenti (57:16)

Than.

Tracy Alloway (57:29)

So 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, but you're more focused on actually limiting the risks and making sure that they're in the right bucket for risk assessment.

Joe Weisenthal (57:51)

No, 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, okay, there are these things that are happening, we can't articulate them and whatever, like, it's not the first rodeo for finance is really interesting. I'm also, you know, I thought the whole conversation about token budgets and allocations are interesting. The idea of like, okay, 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? It sounds like a pretty interesting like engineering problem.

Tracy Alloway (58:31)

Yeah, I would actually love to do more on that question. I would too because it's such an interesting question of incentives. Right. And like, how does, how do you actually like prioritize projects?

Joe Weisenthal (58:43)

Like what constitutes a good output and how, when do you sacrifice a little bit of quality for like 10x less token budget or whatever like these are. This would be it would be very interesting to talk about how that problem specifically gets solved inside of an organization.

Tracy Alloway (58:58)

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. But for now, shall we leave it there?

Joe Weisenthal (59:08)

Let's leave it there.

Tracy Alloway (59:09)

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

Joe Weisenthal (59:14)

And I'm Joe Weisenthal. You can follow me at the Stalwart, Follow our producers Carmen Rodriguez at Carmen Armand, Dashiell Bennett at dashbot, and Kale Brooks at Kale Brooks. And for more Odd Lots content, go to bloomberg.com oddlots or the daily newsletter and all of our episodes and you can chat about all these topics 24. 7 in our discord with fellow listeners, discord GG odd lots and if you

Tracy Alloway (59:36)

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. 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,

Joe Weisenthal (60:00)

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