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

If you look at the software developer lifecycle, a lot of that is changing. Developers are developing software by developing specs today. And so if you are too much in the weeds down there in that mechanics, and you don't adapt for AI or agents doing that work in software development lifecycle deployments, rollbacks, monitoring, observability and all that, I think that path will be very much disrupted. Or if you're adding a sort of a UX on things, that's another class. So 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 path is probably something that has a bigger question mark on top. And so I always ask myself the process question first. The process transformation section, the question first, and then consequently what's the tool that is going to support that?

We have. We have terminated contracts already. Yes, absolutely.

Thank you for having me.

Yeah, I barely remember even what happened back there.

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 six months ago, I think it has been nothing short of 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.

There was, yeah, it was telling you, oh, I'm sorry, I don't know who's the president of the United States, because, you know, my cutoff date is like a year and a half before or things of that nature. Now you can say, hey, you know, as a person, you can say, hey, my plane just got canceled and it's going to redo all your plans, it's going to check for, you know, like 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, you know, what I like to say to my people, but also in general, let's say this is not the drill. This is real. You know, 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 to work and we put it to working, you know, starting from developers. But 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, they came out, I think, that finally got us the confidence that you can use AI for everyday work with the right supervision. And also, you know, in many cases for mission critical applications, it's not a toy anymore, it's something that, you know, you can expect results from. And I think that's the biggest change. So today I would say that there is nobody that is not touched by, you know, we gave our GSI or GSA 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, you know, like the first time you see a tool like Microsoft Excel, you can almost not predict what people are going to do with that. Okay. Maybe it's born for, you know, doing some form of accounting and then people write entire applications on top of that or use it for project managers, what 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 surprises every day because...

So let's start from the GSA assistant. 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. It can do very complex research for you in topics. There are, you know, for example, we can ask questions that come from clients such as, hey, how does the recent geopolitical events on the Hormuz Strait actually impact this portfolio? What could be a potential rebalancing strategy? Or you could ask, intersection of, you know, like, I don't know, how does a certain Fed decision on interest rate actually impact, you know, the volatility of certain assets. So you ask these multi-dimensional questions and what GSA system does, it calls out the model, retrieves the relevant information and creates a plan to answer that question. And that's kind of the key, because these 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, one of the most, you know, 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 questions, 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 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 do a lot of work to not only take a bunch of data, but also making it understandable to the AI. So for example, to go a bit deeper, we have a tool called Legend, the AI, which is our lakehouse, which allows you to go from query to MCP server, connect to GSA assistant, i.e. from data to answers. You can wire that up literally in 2 or 3 clicks. And it does all of that for you. And so the quality of the data, the quantity of the data, not only that, because not just the, you know, the bitter lesson here, but it's also the lesson of you need to curate your data. You get better answers disproportionately. That's something that is driven there. 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, you know, the highest users, we are like way above a million prompts per month. And it's growing really, really, really fast. And then, of course, you know, you're asking me like really impact in production, every developer in Goldman is enabled with a gigantic AI. Okay. So we were probably one of the first, if not the first to launch Darwin almost like a year ago, which is the fully gigantic developer assistant. We have Claude Code. We have, you know, many other tools, GitHub Copilot, agent, etc. But on that, you really see the step change. There is no question that that 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, you know, 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 an idea. Generally, the most important thing for a developer today is to be able to explain things rather than jumping and coding things.

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 on 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 that 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 not a lot of that is really driving the growth of the business. So it's good to have the optionality to do it. You know, you have the optionality of, say, I now I have 120% of my capacity. I have 130% of my capacity to 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, you know, quality gets actually better and the timelines get short. So that's what we measure.

I think generally there is. Okay. If first of all, that is kind of always been that tension because, imagine, for example, you know, like imagine when software 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, this like, 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. 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, largest possible, assuming it's like saying computers. Okay. Yeah. But even software is very broad. And so within the software category, I think there are winners and losers and there are winners in the long term and losers in the long term. But it's really like, people tend to make it the category and then maybe throw the baby with the bathwater. So here's an example to me. The question that I asked 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 they put 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 the is, basically the likelihood that the software is going to be robust to that or not. For example, is accounting or closing the books going to be very different from 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 are operating in the general ledger type of category, I don't think there is a, you know, all of the sudden you take a GPU or a cloud that is going to close your books magically, you know, you should. You have to do the accounting, and you still need to do a lot of that. And it's very regulated importantly. Right. It's extremely regulated, you know, jurisdiction by jurisdiction, country by country, you know, product by product, industry by industry. So that part is kind of, to me, you know, in kind of the safe mode way. And then you go to the other end of the spectrum and you have, sometimes, you know, software that, you know, kind of is aligned to the way people do things today, like software being one of them. You know, like if you look at the software developer lifecycle, a lot of that is changing. Developers are developing software by developing specs today. And so if you are too much in the weeds down there in that mechanics and you don't adapt for AI or agents doing that work, software development, lifecycle deployments, rollbacks, monitoring, observability and all that, I think that path will be very much disrupted. Or if you're adding a sort of a UX on things, that's another class. So, you know, 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 path is probably something that has a bigger question mark on top. And so I always ask myself the process question first, the process transformation question first, and then consequently what's the tool that is going to support that?

We have. We have terminated contracts already. Yes, absolutely.

Absolutely. You know, the thing is like the whole buy versus build, okay. The equation has changed quite a bit because if you know before, well buy versus build was always like, okay guys how long does it take to build this. And you get that answer, which is where we can do it in like, you know, 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 week and here is a perfectly working application. So the cost, or at least, you know, for simple applications, the cost of kind of, you know, build versus buy from a time perspective and from a, you know, extra cost perspective has gone down quite dramatically. So right now, you know, like the little things are most likely going to be built. The very big large, the software that is to be, you know, deployed at scale across thousands of people and etc., so that big complexity, as you know, from, you know, we all do toy stuff with our Claude Code at home and whatever. You know, there are still some rough edges like, you know, and so it's hard to think that all of the 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. So 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.

I think they do when they got there. Okay. So I think, at least, that name has changed quite a bit.

No no no no no no I mean that is the latest.

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, a 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 or so. That's the first differentiation there, straight to the source of the AI production in a way. And second is that, they are generally product people. So people that have actually built those tools, rather than people that are more like support and deployment people. And so this characterization is you take the classic, you know, sales support team or solution support team, which was mostly doing integration. And when things are so rapid, you know, it's like, you know, imagine if there is like something like, I don't know if the Claude style works changes so fast. Instead of a fashion assistant, you want to talk to the tailor because they can actually make it. Yeah. You know, things are changing so fast. So those are the tailors.

No, I think integration is extremely important, especially, you know, for what the industry calls like systems of record. So when you do something like, you know, when you do a process, so then you have a source of data, like, you know, your CRM systems could be a system of record or, you know, 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, we can argue that they will implement, there's nobody that is better positioned than them to implement the AI that 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 automation 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. And so in general, I don't have anything against the, again, I don't have anything against the SaaS category at all. But I overall, but I have, you know, as I said, different opinions on who actually is going to adapt to the future and adapt to the future. And those who don't.

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

Absolutely. So, you know, a few months ago I did, and I spoke about predictions for '26. And one thing that I said was, you know, there's going to be the birth of the personal assistant. And that kind of happened with Open Claw 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 the sudden they're going to start seeing bills that they absolutely did not expect.

Okay, so first of all. Lesson number one is, you need to centralize the access to models, okay? So that you can monitor 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 GSI platform, which has, you know, what's called the model gateway. And the model gateway intelligently routes requests to, you know, the combination, the Pareto frontier of quality and cost. Okay. So you got to centralize that. It's not the one size fits all because many cases, if you're asking, you know, what's the weather, you don't need to call on a Claude or 4.6, you can ask it to leave any local model that you ran very cheaply on premise. So there are ways to optimize that way. Before you start even having the conversation. You're consuming too much, too many tools.

It is a big part of the time spent by the AI central group. Okay. Platform group, the platform group worry a lot about where do I get the right data, for example, for this question, and which model do I route around it? So 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, you know, and you can be ISO quality, very different, you know, price points because not all questions require the most expensive model. 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. Okay. At one point, if you have 80 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 or later. You're self-limiting in ways that are kind of, you know, really no 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, you know, do more and do the best possible work with AI. And let us, meaning internally in the sort of a central team, optimize it in a way that, you know, we're going to make it economical. And I think reducing the token anxiety is a big challenge. But I think it really frees up, you know, creativity and what you can do with AI. It's also like, a little bit like, you know, there are certain problems that you don't want to optimize too early.

I remember we used to kind of optimize the way to 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, 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 I want. People right now, I would rather have them 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.

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. And so at that point, it's fine.

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, you know, IT 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 agent route or not the agent route, if you go the open loop route, where, you know, it checks every, 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 per unit cost of token, I'm pretty sure that is going to go down also because as you know, GPUs are becoming more powerful. They, you know, the cost per watt hopefully is going to go down. And then also like, to be fair, I mean, you know, these hyperscalers are doing a lot of optimizations to try to ram those stacks on their own hardware, right, which will potentially kind of also generate some economies of scale.

This is what I mean. I hope it is. 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 in the, you know, in the corporate App Store in a way. And so there's no way.

Definitely.

But he's not gonna be able to execute. It's not going to be able to create the actual executable. He can actually, even GSA system today can spit out a lot of code, but it spits out source code. Okay, now our 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.

So, I think, 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 open claw. Okay? Okay. But some of the properties of open core has actually informed the way we are building our agent platform. Okay, agents today because of open claw have actually changed. There are three very important new, if you break down what open claw is, that is my own interpretation. I never, I actually never even spoke about this. There are three characteristics that make open claw 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, is 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 7 a.m. do this or I, 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 schedule ability of tasks. The third one is you can instruct it to kind of change its own behavior because it has these files, dot MD, soul.md, the way you, so you can say things like hey, I would like you to never use this term or please change that or change the way you filter news because, and so it kind of writes its own software to do things for you without you even, you know, seeing what's behind the scenes. And so instead of letting people install open claw on their computers, what we do is we incorporate some of those characteristics into our agent platform so that it does things that are more similar to open claw. So that's the approach.

You kind of made that, 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, you know, us versus others? Okay. And I think at, you know, I often say, you know, 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 there's going to first, sooner or later you're going to be hit by that. You will be like, you know, most airplanes are in auto. I have autopilot that can do everything. So theoretically, you and I could go in the cockpit and for a long time during the flight, we will feel pretty good about this. Right? We will drink, you know, some soft drinks or your tea. We might maybe watch some videos. We will be very happy. It sounds great. The passion is one point. At 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 gonna look at each other, are going to say, oh, right, it was the pilot.

Oh, yeah.

So that's what I mean by velocity is really like, is like the marathon is, you know, sustain the speed for a long time in a certain direction. Yeah. I don't think by randomizing that you actually gain velocity, you gain an instant speed of some sort. And so I'm kind of optimizing for velocity.

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 networks for like a decade plus. And so every bank has already gone through the motions of explaining that the neural networks don't have perfect explainability. 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. And then there is a function called the model risk management, which is a very standardized, you know, 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 that. So it is not really that much of a new thing, is 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 AI to auto approve the wrong code. Okay. All they can do is publish what's called a pull request or 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 that's more senior than you that actually looks at the code. And then certifies and approves, and then after that, before it goes to production, it goes into something that is called CI/CD or, you know, continuous integration, continuous deployment pipeline, where when it goes through the build phase, etc., 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 that's really what. 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 to higher protections. I think the conversation is generally very positive.

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.

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 asset management side. So we have a sort of a correlation between assets advantage that we see those because generally rates move, you know, interest rates can move, yields that 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, you know, 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 portfolios. So this is not you and I may be having three stocks or 4 or 5. This is a like very complex, multi-year assets up with complex products like swaps or swap channels or exotic products, etc., etc. And so that's really the 10%, that, you know, the clients that we have, you know, really value and where we really need to get, it's like at the end of the day, Lisa, if you look at, look at Formula One, okay, the difference per time, a particular pair of per lap, between, you know, the Mercedes, and take, you know, your favorite last team. You know, it's sometimes one second out of the two minutes. And that is the difference between, you know, getting a $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.

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 C, 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 barrier 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 or 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, you know, it took us almost two years to build the GSI platform. These are this back to the reason why you can't be casual about these things. This thing is not being built by some random vibe coders because you need to worry about cyber. You need to worry about the info barrier. So you need to worry about all that. And so when I talk about, you know, there are places where you can leverage and do correlations, but there are others where you absolutely cannot. And this is foundational to the fact that, you know, you need to have, you need to be ready for a, you can't be casual about that.

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 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. It turns out that those three things I explain, delegate and supervise are kind of the 101 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, 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 is going to actually rapidly go through that. And I think we're doing a combination of training. There is a combination of exposing them to, you know, 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, you know, there is a little bit of a metamorphosis happening. There is not just about efficiency, is really thinking about is my job going to stay the same now is actually changing quite a bit.

I mean listen again, I this work, I love the fact that I'm a little bit older than most to hear an engineer. Second, I've seen that the first time people had the Excel and I've sat for the, you know, 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 the 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, you know, and I think 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 a professional 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 a, 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 2 or 3 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, you know, is no longer like me trying to push the car uphill, it's more 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 has been positive, positive change. And also one other thing that, you know, you're talking about burnout. I see that a lot of people get fatigue. I wouldn't want to talk about burnout, but they get fatigue when, you know, there are a lot of repetitive tasks, especially for a developer. Here's an example. Let's say you go from, you know, a version of a Java library or Spring Boot to another version. And then all of a sudden you compile and you get to rebuild 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, a new logo, new colors, implement it on like 200 websites. It might be fun the first ten and then it becomes a drag. And so I think taking that away kind of and making them, 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 eyes to really get the plan right. They feel a little bit more elevated. And the mechanical part, it's kind of, you know, 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, which would move the register up one and then came, you know, 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, you know, it comes to Java or garbage collection. I don't have to worry about memory leaks anymore. Fantastic. And then comes Python, which is all that rigidity. So much easier to, you know, 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 ten year jump 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 that maybe initially more time because they're excited. But I don't think that I'm enjoying rather than things that they just, they dread.

Thank you both of you. Thank you. Thanks for having me. Thank you so much.