Claude for Financial Services Keynote

Please welcome to the stage, head of Revenue at Anthropic, Kate Jensen. And thank you so much for being here today. I'm Kate Jensen, head of Revenue at Anthropic, and I am so excited to welcome you to the future of finance powered by Claude. I want to invoke a scene that's probably familiar to many of you in the audience today. Your three coffees deep, it's 3 a.m., you're furiously checking that last model. You have 100 tabs open, you have a 5 a.m. prep call ready for a 6 a.m. client call. Today, that's all about to change. We are so excited to announce, Claude, for financial analysis. This is the industry's first unified intelligence layer that transforms how financial professionals will work with AI. Now you will have a virtual first class virtual collaborator powered by AI that will help you to get your work done better and faster. What this is is a tailored version of Claude for Enterprise. It's specifically built for financial analysts, and it's equipped with the nuance, accuracy, reasoning that you need to handle the complexity of your work. Since 2021, Anthropic has been building AI systems that are helpful, harmless, and honest. At our foundation is safety and trust, which is exactly what financial services companies need. When you're managing billions and assets, good enough just isn't good enough. AI is transforming every industry and nowhere is this more prominent than in financial services. The complexity of modern markets, the velocity of information, the sheer volume of data that you need to process, it has reached a point where human intelligence alone, no matter how great this isn't good enough. From speaking with many of you, our customers, we're hearing that the world is bifurcating. There will be two types of investment firms, those using AI institutionally, and those who are losing their top talent to competitors who are. Over the past year, we have been partnering with leaders who are breaking new ground across every segment of the financial industry. The teams that bridge water have been using Claude since 2023. This is to power their investment analyst assistance, which solves complex models. Their CTO tells us that Claude is really powering their efforts to push the boundaries of what's possible. Down in Australia, Commonwealth Bank is making a massive bet on AI. Their CTO, Rodrigo, sees our partnership as the foundation of their global AI strategy. And here in New York at AIG, Peter tells us that they have completely reimagined underwriting with Claude. What used to take weeks is now taking days. Timelines compressed more than 5x. An accuracy has gone up from 75% to 90%. Underwriters can now serve their customers better and faster. Now, the solutions we're sharing today, we didn't create alone. They're the result of deep collaboration with the entire financial ecosystem. At our foundation, we have our cloud providers. AWS and GCP provide secure, scalable infrastructure that financial institutions demand. I am thrilled to share that today, Claude for Enterprise, including Claude for Financial Services, is now available on AWS Marketplace. And it's coming soon to the Google Cloud Marketplace as well. We hear all the time that AI is only as good as the context it has. We're excited to partner with platforms like Fox, Databricks, Palantir, and Snowflake to bring your company's data into Claude so that you have the context you need to get work done that's actually relevant to your day-to-day. And today, we are especially excited to announce new integration to critical external market data sources for informing your most important financial decisions. We're partnering with the company's UAlready Trust. Fact set delivers comprehensive fundamentals and consensus estimates. S&P Global enables access to cap-IQ financials, market data, and transcripts. Dilupa provides AI-verified fundamentals with source citations. Between Morningstar and Pitchbook, you have both public investment research and private market intelligence at your fingertips. Our consulting service providers are also turning this technology into real business transformation. Deloitte and KPMG are modernizing entire organizations and deploying AI agents at scale. PWC, entering, are solving critical regulatory challenges. Navigating constantly evolving compliance requirements. And Slalom and Tribe AI are modernizing core operations, from migrating legacy co-ball infra to accelerating due diligence with intelligent document processing. Collectively, we are building the AI ecosystem that will define the future of financial services. As our head of revenue, my team and I spend all of our time talking to customers and partners. We heard from you how important it was to bring all of this context, both internal and external, into Claude to help you make use of this technology. We cared deeply about defining the future of AI and human collaboration. And thank you for your feedback. We are thrilled to bring this solution to you today. Now I'm really excited to welcome up to the stage two of our partners who helped to exemplify the power of our ecosystem in action. Mr. LaCursey is the chief strategy officer at Ken Show Technologies, S&P Global's AI Innovation Hub. He's been defining how AI transforms financial data into actionable intelligence. And Vikram Bhatt is vice chair and financial services industry leader at Deloitte, where he has been helping global financial institutions navigate digital transformation. Please welcome them up to the stage. Did you pick that song? Yes. It's actually in my rider. Thank you so much for being here. You have both been instrumental in shaping the frontier of AI adoption at your company. From your unique vantage point, Peter with data intelligence, Vikram with implementation and transformation, what's the one breakthrough that you've seen that has really created an aha moment at your company? So yeah, I think, I mean, for me it's really adoption. I mean, I've spent the better part of the last decade trying to get financial services companies to adopt AI. And we've had some really great successes with traditional machine learning. I would say in the world of generative AI, that conversation is very, very different. It's kind of, how can you serve us as quickly as possible? The data that is in a format that is optimized for these large language model use cases, for generative AI, and how can I make sure that the data that you're providing is trusted that it's accurate, that it's kind of foundationally a part of the work list that we've always relied on. And so I think for us, the big kind of surprising thing about generative AI has just been the speed of adoption, the rate of adoption. You know, it's been very influential on our roadmap, the products that we've kind of foregrounded for us, our LLM ready API, which is the data behind what we've done with MCP, has been, we've seen like enormous traction with that. Our clients want to make sure that when they're interfacing with this technology, that they can rely on trusted data and data in workflows that are already existing, that they can then automate and optimize with large language models. And so that's really been a huge surprise. It's going into these client conversations and not saying, okay, but here's what AI can do. Here's the kind of magic that you can unlock. They already know that part. This now, how can we truly make our company or institution or group an AI forward group and also make sure that we can trust the outputs. And for that, you need trusted data. So that's kind of been the big thing that we've noticed. If it's what about you, as you think about bringing this technology, what are those ah-ha moments that people really adopt it? We have really an AI first strategy and everything we do. Some of it is internal in every service that we have. We have starting to embed AI, you know, kind of front to back. On the client side, what we're seeing is there was an initial focus on productivity as a key theme of value through AI. But what is really happening is that is expanding to, can we develop new products based on AI? Can we reimagine distribution? Can we reimagine front to back processes? It is really changing the conversation from a pure productivity as a value driver to revenue generation. Can we do better risk and control? Can we drive more employee and client experiences? So as the appetite and the aperture of value has really expanded, it is really brought AI into everything that does and every piece of the client organization. I love that. And we'll start with you this time. But what do you think you need to do differently as an organization to make sure that you're really capturing that full value, not just adopting the technology? Yeah, so I'm probably going to give an incorrect, non-political answer. Financial services, we are a highly regulated industry. Everybody knows that. So the real challenge is in this battle of, I'll say, innovation and risk management. How do you coexist in a manner that there's not 20 checkers for one door and the equation is actually flipped? That you actually have more people driving innovation across your organization and you work in coexistence with your audit teams, with your model risk teams, all the other players that are necessary in order to get adoption successful. The other example is that AI first strategy that I mentioned. I think more and more clients are actually saying AI is not just about driving 30% to 50% productivity in the CIO organization or anywhere else. It is really about changing the way we do business. And to do that, how do you actually bring the humans? We have this philosophy of humans and machines co-existing in the age of AI. And how do you drive the human change at speed and scale together with the pace of change that's coming through AI? How do you think you do that? How do you get them to coexist and adopt at the same time? I think it is not one answer. There's a number of different levers that one can pull in order to drive that. For example, some organizations have done executive academies for the top 300 executives really drive training. Some are doing hackathons to really think about certain processes or value streams that they can completely reimagine. There is complete AI academies and trainings that are being pushed forward for developers but also for general users. So it's not one lever I think in the end that's going to drive the speed of human adoption of AI. It's a number of things that need to be done together. Peter, what do you think people need to do differently? And institutions need to do differently? Well, I think that honestly it comes down to, I think Vikram touched on it. It's kind of a culture thing. And I think that a lot of that culture does have to be top down. You have to have your leadership teams really buy in to this concept of AI first. I totally agree with that. I think, but part of that top down is to also embrace the fact that many of these organizations, organizations coming from the bottom up, right? People who are in the trenches doing day-to-day work are surfacing amazing ideas, not just for productivity, which I think is oftentimes the focus, but for huge expansions of revenue opportunity, I think that's absolutely correct. I think that it's changing a mindset around innovation. And I do think a big part of that is understanding, especially in our space, there are going to be workflows where being maybe a little bit less risk of Earth is really important. And being able to create that room for experimentation is going to be really important. I mean, certainly for us at Kencho, we've been able to have that room and the way that it works for us is that we kind of operate somewhat independently. And as a result, we kind of have a little bit more room to experiment. But then, crucially, it's also understanding where those boundaries are and where that risk is unacceptable. And that's, I think, where for us, we've also seen an enormous benefit in having S&P's trusted data as our kind of foundational core competency. We've been on this journey creating this grounding agent where for us, it's ensuring that when a large language model is interfacing with our data, that we're taking responsibility for automating where in our entire data universe, we're going to pull and query those data sets, and then surfacing that data with citations. And so it's both enabling experimentation, but then also making sure that your response is, and whatever the output is, is truly grounded in verifiable fact. So I think there's a balance there, and creating that trust is something that's really important to us. I love that. That push to innovate, that desire to really harness all the energy that you have from your organization, but to do it responsibly. It's really at the core of a lot of what we do. Thank you both so much for coming up, and thank you for the partnership. Absolutely. Pleasure. Thanks. And now I'm thrilled to welcome up to this stage, my colleague, Nick Linn, who leads our product for Claude for Financial Services. Thank you all. All right. Thank you, Kate. My name is Nick Linn, and I lead product for Claude for Financial Services. I am also a recovering investment banker and private equity investor. So I was the analyst, came and mentioned, frantically trying to debug the model at 3am while building the pitch deck, running on no sleep. That's why I am especially excited to share what we've been working on. As Kate mentioned, we're announcing the Claude Financial Analysis solution, and Throppx first offering built specifically for finance. Our solution is built on three main pillars of what comprises of an agent. First, the models. Now something I always think about is that the models we interact with today are the worst they will ever be. And so, Claude today is not just state of the art for code. Claude has been specifically trained for finance domain knowledge, and excels at tasks like data analysis at scale, financial reasoning, and even Excel manipulation. Now, the research product flywheel is a critical part of an enthroppx strategy. We firmly believe in working with customers like yourselves to really understand where the models are working, where the gaps are, and to borrow a term we use internally, hillclimb to improve the next generation of our model capabilities. The finance agent, Benchmark, published by our friends at Val's.ai has picked up a lot of traction in the industry, and is well representative of many financial reasoning tasks. You can see, Claude Opus is on it far outperform competitors. Similarly, Claude is also very capable of actually doing the work. One of our partners, Fundamental Labs, has built an Excel agent called Shortcut on Opus. This agent was able to pass five out of seven levels of the financial modeling world cup competition, and scored over 83% accuracy on complex Excel and financial reasoning tasks. Again, far outperforming competitors. The next pillar of our solution is agent capabilities built on top of these models. Now, model intelligence needs to be translated into something that's actually useful for humans. Claude's agent capabilities are flexible and composable, and really aim at solving the core problems you all work in every single day, including building multi-modal reports like pitch stacks, investment memos, analyzing and visualizing data like Benchmark analyses or stock price involving charts. And natively reading and writing Excel and PowerPoint documents. I'm excited to announce that for the very first time, these expanded output capabilities are now in research preview exclusively for select customers of our financial analysis solution. We've also expanded usage limits to really support the deep work analysts work in every single day. We've integrated Claude Code to support use cases like analyzing much larger data sets, Monte Carlo simulations and risk analyses. Finally, the model intelligence and agent functionalities are delivered through our very flexible platform. Our solution is the first agent with unified intelligence across all the core finance data sources you all work in. Our FKT and Peter Shared were excited to partner with industry leaders like yourselves to build those critical integrations into the agents with many more to come in the future. Importantly, we're delivering white glove, finance specific implementation and onboarding to really assist you all with deployment, education and change management. Now, this is of course built with enterprise grade security and trust. We're sucked to type to certified and by default, we do not train any of our models on your data. Now, our solution is really targeted at solving the problems you all know well. In investing, insights are alpha. The ability to sort through the noise, build a thesis, get to an investment decision quickly is paramount. As we've heard from many of our customers, this is of course true for the cell side and the biceye. To summarize the pain, analysts work in a day-league of documents every single day and spend hours researching into data sources that are difficult to verify. They painfully prepare analyses and take through models cell by cell. And they manually prepare investment memos and pitch decks, often pulling on-iters on a tight deal timeline. With Claude, we can start to address all of these pain points. Now, let's show you what that looks like in action. MiSera, a hedge fund analyst at Acne Capital, is 2pm. Her portfolio manager bursts into her office with an urgent question, which I think many of you know well. Her target company, Velocity Athletic, reported terrible earnings this day. Revenue is down 12%. But somehow, the stock price is up 17%. Trading at $71 per share. Her PM needs to know, is this rally justified by their new strategy? Or should they sell into the spike? He needs an answer before the market closes. Now, let me show you how Claude transforms this typical 4-5 hour scramble into analysis under 30 minutes. First, Sarah connects her tools. Look at what is available in one workspace. All the tools she works in. S&P Global, Morningstar, Faxette, Delupa, and even her firm's internal box documents. No more juggling 14 different browser tabs. Serving Chinese is right here with unified access. Sarah starts a comprehensive query, asking Claude to pull from multiple data sources simultaneously. Watch what happens. But orchestrates across platforms. S&P Global for transcripts. Morningstar for reports. Box for internal analyses. Delupa pulling 8 quarters of financials. And here's the key. Sarah gets synthesized intelligence, not just raw data dumps. Output. Full earnings transcript analysis. With red flags from Q&A, the CFO disclosing a 400 basis point margin hit from the tariffs. Competitors look like they're doing better. Pace running has been in Vietnam since 2019. And most importantly, you can see full financials linked to the original data source. Like we're seeing here with Delupa. Sarah can verify the results instantaneously. The good deeper. Sarah asks Claude to create visualizations in specific analyses like an annotated stock price chart, comps and benchmarking analyses, and even discounted cash flow model. As you can see, Claude is now pulling key corporate events from SEC Edgar and the web. Price data from S&P Global. Financials from Delupa. Consensus estimates from fact set and creates several visual artifacts. First, an event annotated stock price chart showing the 17% spike with all key events marked. Emergency board meeting. CFO stock sale. Earnings surprise. Next, a comprehensive comp stable. He's trading at 21 times EBITDA. Against peers at 16 times, despite having worse fundamentals. And here is where it gets powerful. Claude has built a fully auditable discounted cash flow model. With a functional case selector, projections tied to assumptions, and even a perfect whack calculation without needing to be prompted. Now, this DCF model projects a base price of $54, suggesting that perhaps $71 is overly optimistic. Finally, Sarah actually needs to prepare a deliverable for her PM. She asks Claude to create an investment memo using her firm's templates from Box. Claude searches Box for any relevant internal information, including a memo template, and any past event driven trace as reference. In seconds, she has a professional memo with an exec sum recommendation to FADER alley. Clear rationale with supporting data from market performance and comps. And specific action items and risk analyses all properly cited. The bottom line. The market is overpricing a complex operational challenge. So take profits now. Bye, back later. cheaper. Now, let's recap which has happened here. Sarah delivered institutional quality analyses in under 30 minutes. A typical process that takes 3 to 5 hours. Cross multiple platforms. So she didn't just save time. She uncovered insights she might have missed. Like the CFO sale. Or the competitors existing advantage. Now, we believe this is the future of financial analysis, where AI fully augments and analysts' capabilities where the best insights across all of the platforms come together in one intelligent workspace. Now, one example of these capabilities coming to life is from the Norwegian Southern Wall Fund, otherwise known as Mben. The largest Southern Wall Fund in the world. To share a quote from the CEO, Nikolai, Claude has fundamentally transformed the way we work at Mben. They've achieved 20% productivity gains. That is 213,000 hours back a year. To focus on what really matters. The better decisions, the returns for the Norwegian people. At Nikolai, Claude has become indispensable. Now, building these solutions requires deep partnership with all of you. Everyone in attendance today who receive a one month free trial to the solution. I am personally very excited to spend much more time with all of you to dive deeper into the problems we can solve together, into shape the future of financial services with both our products and model capabilities. With that, I'll pass it to my colleague Jonathan Pelosi, or head of financial services, and our distinguished panel of industry leaders to share their own journeys with Claude. Thank you, Nik. And to the DJ, while I didn't choose that song, I love it. We put these down. And thank you to everybody for joining us. Hopefully you get a sense of why we're so optimistic about being able to support every one of you and every one of you downed in on all of your tasks as it relates to financial analysis. But don't just take my word for it. I've got a much more impressive panel that can speak to what they're doing with this type of technology and how they're doing it. And as Nik alluded to, in my role of overseeing all financial services for Anthropic, effectively means we support the biggest banks and insurance companies, hedge funds and asset managers in taking this technology, and practically applying it to your firm in a way where you can use it to drive real value and real transformation. And I say practically because all too often, we'll read about the theoretical headlines of what the stuff can do. And you're like, oh, it'll transform the way we work and improve productivity by 50%. By the time my team and I sit with you, we often pull back the layers and say, well, how do you actually make that happen? How do you actually tailor this to your organization? How do you actually work on change management, getting these individuals comfortable with using this technology? Those are the types of things we obsess over and the types of things we're excited to chat further about. So with that said, I'm going to call up my steam panel. You can come on down and give them a chance to introduce themselves. Thank you, team. We're going to do quick intros because I don't think the titles necessarily do justice to all of the AI work they do. And we're going to start with my good friend Michael here. Take it away. Thanks JP. My name is Michael. I co-head the COO group at D. Shaw. That means is my team and I work on big, firm-wide transformation initiatives. And there is no bigger, firm-wide challenge and opportunity that we're spending time on right now than AI. Thank you, Michael Lloyd. Thank you, JP. Lloyd Hilton. I am an AI lead at HD Capital. So for those of you who don't know us, we are a major peeper, one of the top 10 largest globally. And we're a sector specialist. So we have about 50 portfolio companies in the B2B SaaS and services space. And we've been driving a major transformation effort with those portfolio companies for the last two and a half years now partnering with the Anthropic team. Don. Morning everyone. Thanks for having us JP. My name is Don. I'm the Chief Data Analytics Officer in New York Life. For those that aren't familiar with New York Life, we're Fortune 69 over a trillion dollars of insurance in force. And we're one of the largest insurance companies in the world. I'm responsible for our overarching AI and data strategy and its execution. Thank you, Don. I'm proud. Last but not least. Yes, my name is Fro. I'm part of the Norwegian Soberbrenfeld Fund. We are a two trillion dollar Soberbrenfeld Fund and 70% equities, 30% fixed income and bonds. And we are only 700 employees worldwide. So we have a single owner, the minister of finance, who represents the Norwegian people. I'm part of the team here in North America that manages the bulk of the equity assets. Thank you, Fro. I was chatting earlier and I think the stat, I think has to be true. That per employee, no organization manages more money on Earth than Enbim. So it's an impressive stat indeed. And for those in the audience, we'll have a chance for you to ask questions as well after I ask a couple to the panelists. So keep that in mind. We're really looking forward to hearing from you as well. But let's start. The AI Investment thesis. Naturally, your big organizations complicated firms. At what point did this decision to go all in and do this at like a firm wide level? Take place and who drove that? And for this, we'll start with Road. I'm curious to hear from you. Yeah, sure. So I mean our AI strategy is really to be leading user AI in the investment management. And that just means embedding AI into everything I do, I think, in the responsible way. So that means creating efficiency gains, reducing costs, enhancing returns and really improving risk management. So I think what's changed is that we have coordinated strategy. We really dedicated the organizational support and a dedicated AI team that helps out in the organization. And this is really driven from the top right. So we have this AI maniac at the top of the firm, Nikolai Tangen. And he is just all in on the change that they see in society, right? So his leadership, his enthusiasm about AI is really, really setting the scene internally in the firm, and driving the cultural adaptation that they see. I think that's just absolutely crucial. Thank you, Frode. And not surprisingly, this kind of top down buy-in is critical if you're trying to drive adoption and really meaningful. Change across the organ. I think the Prod's born Nikolai has done an amazing job of that. Lloyd, how about you as it relates to your firm both internally and with your portfolio companies? Yeah, so I mean, I think the top down buy-in really resonates for us as well. So although we are investors, a lot of our leadership team are sort of technologists and engineers. So very early on, I think we sort of recognize that this was a platform shift. A lot of HG's success has been predicated based on helping our portfolio companies navigate the sort of on-prem to SaaS platform shift. We've also been investing in AI for a while, so we have a central data team. We actually had access to GPT-3 back in 2021. So we've been experimenting with this for a while, and I guess when the early LLM was sort of launched around 2022, 2023, we realized this was a pretty major opportunity. I think for us though, people find conviction at their own pace, and what really helped was we ran an event in Silicon Valley, so we worked with the Anthropic team. We actually had Mike Krieger, the CPO, come to speak of that event, and we fully immersed our execs in this AI topic and got people really hands on. So for us, that was a real catalyst. And I guess over the last two years, the thing that's really surprised us is just how quickly this has moved. So we started probably thinking about, oh no, we can get 10 to 20% productivity gains through integrating AI into our existing processes. What we're now really focused on is fully transforming our portfolio companies with AI and we think that there's going to be a huge amount of value upside for us and for our portfolio companies through doing that. So that's really where the focus is now. And we've kind of replicated that same approach. So we got that top down by and centrally for HD. We're now making sure that we create that same catalyst and that same spark for our portfolio companies. And we're finding that sort of now permeating pretty nicely through the HD portfolio to drive that transpational change. Fantastic. Thank you, Lloyd. And for the folks in the audience, I can imagine this is something on your mind and for those dialing in. All too often we hear, should I build this internally or should I buy something out of the box? With APIs now, it's increasingly easy to build really bespoke AI-powered solutions. And at the same time, there's a lot of great solutions out of the box. Nicklin just walked through one view today. So with that said, we'll start with you, Dawn. How do you think about this notion of build versus buy? And basically deciding which path to go, do you go both, walk us through that top process? Yeah, no, I think it's a great question. I think when we first started this generative AI wave, a lot of organizations, especially financial services companies, went through a decision. Like should we leverage APIs and build our own custom wrappers, or should we partner with the company like Anthropic and leverage these enterprise solutions? There's obviously trade-offs with those. I think for us, we're actually very intentional that continue to innovate on the UI layer and all these different sort of capabilities like Canvas, and things of that nature, connectors, et cetera, et cetera. It was going to be really challenging, right? I think companies like Anthropic are just moving at such incredible velocity with respect to innovation. We wanted to ensure that our internal developers and engineers were focused on our most proprietary and specific organizational solutions. So for us, it made a lot of sense to more so part-nerverse and try to build our own custom ones. And let's leave the custom integrations and solutions for those things that are really important for us competitively. Thanks, Adam. Michael, how do you guys think about that, D-Show? So for those in the hedge fund industry, at least, I think we're pretty well known for having a big focus on building things. We hire amazing technologists, and we give them the opportunity to build great things. And so the instinct when something comes along for us is to build the best version of it ourselves. The resources aren't infinite, and with great people comes big opportunity costs. And so we always think hard about build versus bonding. What I think is different this time, and I think Donald alluded to it, is the speed at which the technology is changing. And with that speed comes a different balance between build and buy. To be able to build and deploy at that speed and scale that some of what's being offered in the commercial market can provide, is often somewhere between difficult and impossible internally. So that shifts our calculus. We're still doing both, and we're being extraordinarily deliberate in where we choose to build, and where we choose to buy. Fantastic. And yeah, we hear that. So often as well, and they'll develop a partner with the development internal AI chat, and they'll share a feature that they're particularly proud of. And by the time we meet with them, you're almost afraid to let them know, it's like that's already, I think in the past, and look at this, this, and this, that's already been developed. So I couldn't agree more. Recognizing your all big firms and leaders in your respective spaces. Another huge challenge we hear about is this change management, and driving this kind of transformation at scale. It's one thing that this technology can do cool stuff. How do you actually get people using it? So when you think of transformation the size that you're operating at, and Donald start with you, obviously, but a lot of employees, how do you think about practically driving adoption with technology like this? And you've got so many different departments and employees to think about. Yeah, for us, our AI strategy has been pretty everything everywhere all at once. So we actually treat our AI strategy as a portfolio of initiatives. So I think we started with like a lot of folks on targeted use cases, specific to workflows, or certain business domains. We had measurable value and production, and that was great. We certainly realized that we wanted to raise the floor and the ceiling of our aspirations, by really democratizing and empowering all of our employees with AI tools. And so there's multiple tools that we're really leveraging to do that. And then we're actually also focusing on reinvention. So like business leaders at the top are really looking at each of their respective domains to understand how AI, agentech AI, and these new paradigms can actually help us re-anchor our aspirations of how we can transform into velocity for us to do it. And so for us to do that, I mean, again, very, very pervasive. Tons of hands-on training and hackathons, all these different things that you actually have to do, like get into trenches, is it really help people understand what these possibilities are? There's mindset shifts that are related to that that I think maybe we'll touch on later in the discussion. But it really has been kind of a full core press that's complemented by both the top-down commitment from our CEO and his executive leaders. But again, this bottoms up sort of empowerment that goes alongside that. Thank you, Don. And Lloyd, you have a bit of a different challenge. And you're working with the employees internally, but also, of course, the portfolios that you support. So how do you guys think about scaling this and driving that transformation at the portfolio level, realizing that's a lot of different companies you got to help train and educate? Yeah, yeah, for sure. So I mean, I think a lot of what you said resonates done. So we have 50 companies, about 120,000 FT-E across those companies. All of our companies look a little bit similar and mostly, you know, verticalize software platforms. So what that's enabled us to do is really sort of bring transformation at scale to that group. So that starts with a central effort, MHD. We have 20 AI specialists internally supported by about 150, also kind of contractors and close partners. And we've been really driving that central change across the portfolio. It's also allowed us to experiment with lots of different tools, lots of different initiatives and really see what works quickly across that sort of laboratory and understand what the best processes are. But I think the top down point has been an absolutely critical. So we've been kind of systematically working with each of the leaders of our portfolio, getting them engaged, getting them hands on. And then there's also the piece I'd add is that we're really trying to be disruptive in the way that we think about this. So rather than retrofitting existing processes and adding some AI, we've re-engineered a lot of our functions. So if I take software engineering, for example, on average across the portfolio, we're now measuring about 30% productivity gains in software engineering. We have some companies who have taken their development squads down from nine people to two people, so getting really material leverage. We have one company that's deployed a thousand instances of an agentic software engineer, so they've kind of added 50% product capacity to their engineering team. And that's the sort of reinvention that we're now sort of driving out across the portfolio. I shouldn't say there what we're not doing is taking out any cost. What we're doing is then reinvesting in productivity, basically clearing down, take that, and also building AI products. And that's the other key focus for us where we're seeing a huge amount of transformational value through embedding the anthropocapy, but other solutions as well into our core software solutions. And that's adding material growth to some of our businesses already. Thank you, Lloyd. And on the topic of change management, this also comes up a lot. With our partners, how do you think about, hey, this technology, there's a lot of it that scares me, and my employees might interpret this as something that could do their work, and how do you tow the line there in empowering them to use it, while also recognizing that there's some fear on, is this going to kind of do my job for me? So we think about the cultural implications of the firm, and how do you make this a part of the culture? And so, this one's for you, because I think MBM has done a masterful job navigating this. How do you, I guess starting from Nikolai, but as you, as a senior portfolio manager in your teams, how do you see this fitting into the culture of the firm and becoming just a core part of how you guys operate? I mean, starting with the top-down perspective, obviously very important, right? I mean, that's really driven it. And I think we had focused on tech for a long time. We've been a very tech driven organization now. We've taken a journey from moving from our infrastructure to the Claude, having data, snowflake, integrating Claude for Enterprise, and being able to really chat with our data. And that fits very well with quantitative nature and our inclination to use data, right? So I think from a culture perspective, it's a very good fit. And I'd say in my team, for instance, a lot of us are really introverted, right? And we like to sit in the room and call them and do things. And I think just having Claude and having the projects that we can now build assistance and just share and collaborate on it is just really accelerating the cultural shift that we're seeing in our research. I think that's super fascinating. And I'll also add that being a sovereign belt fund with a single owner is just having AI into the organization is just such a massive enable for us, right? Because we can do a lot of stuff with this technology that others just can't write because we're not hampered with the, you know, client and customer dynamics. So we can, we are really open and lean organization with a collaborative culture and we're just 700. So this is just fascinating. Fantastic. Thank you for really, I really appreciate that. This idea of the demystifying what this technology is all about at the individual employee level is so critical because so quickly you can address those concerns or fears, you know, a cultural level because very quickly they see, oh, this is just a great assistant, if you will. It's a great partner when I wake up and I start my role in helping me do my work more effectively. So it's wonderful to hear that example. Yeah, I think, JP, I think it's a great point. I think we were actually very intentional. I think very early on in this journey folks would talk about, you know, AI is not going to take your job with someone using AI will. And so we wanted to really, you know, address that head on and ensure that as many employees as possible and we were intentional about every employee having multiple AI solutions and then not just that but getting like the training involved and help them feel even more empowered. I thought that was really critical to your point to like get through some of the call at the fund that might be a little bit of a awesome thank you Don. And oftentimes I'll laugh because even when partners ask like, you know, how do I train, how do I build these curriculums to drive adoption, I'll pause and I'll be like ask, Claude. And jokes aside, whether it's Claude or another tool you're using, these tools are very good at that sort of task. We're like, here's my firm and for those dialing in, same applies, here's my size, here's what we do. I want to build a simple training program for my employees, maybe they've got three hours, they'll spend over the next two weeks. How would you break it up? What would that training look like? I'm telling you if you do that exercise when you go home, it'll be a beautiful, beautiful output. And I give you a double down on that. Yes. If your staff say, I don't know what to do, I don't know where to start. Tell them to describe their job briefly and ask the AI, how would I start working with this? What are a few things I can try? It actually works wonders. I love that example because I'll even go so far as to say, I've got two little kids for instance, a four and a two-year-old. And if I'm looking for an activity to do in the weekend, I'm not very good coming up with those. But I'll ask Claude, but I'll do one better. I'll be like, Claude, I want you to optimize the following prompt. And I'll be like, I need to come up with activities for my kids. And then Claude will rewrite it, I'll copy paste it. And then I get the world's greatest to do. So that's another good hack. Ask Claude to write your prompts for you. But deciding where to start is another thing we hear a lot about. So for folks in the audience, some of you are smaller firms, a lot of you are massive firms. But okay, there's a lot. You've heard a lot from this group, transformation across the org, top-down leadership. I'm going to start with Michael again here. Where do you, for the folks in the audience, if we have to start somewhere, like what's the use case? What is the first place they start if they're starting the queen's late? What would you suggest? So it's incredibly tempting to look for a first place to start. And my advice would be to resist that temptation. Your colleagues, I don't know that they know more about how best to use the tools. They certainly don't before you give them the tools. But once you give them the tools, they will discover things to do with those that you never imagined. So we're a pretty decentralized place. We are a place that wants to make every one of our colleagues better using AI. So our core focus off the bat and what I would recommend to others is get really easy to use tools out there. Let people figure out what they're doing. And then pay attention. Help accelerate the spread in some cases centralize as you learn. But not as your first step. I'll give one concrete piece of advice on that. I think some people will do that. They'll try something. It won't work the way they'd like it to and they'll abandon it. What we found to be valuable is to encourage folks to come back what's called every six months. The rate at which the technology and the products are changing is just foreign to most of your colleagues. And the degree to which they can improve for a particular use case in three months, six months, nine months can sometimes shock them. And so pushing them to revisit is one place we don't tend to be too pushing. But that's one place where I think pushiness can pay off. So I love that because I think to Michael's point, I want to hear from the rest of the panel. The notion of starting with one specific use case, oftentimes it can be like, oh, I'm going to build a spoke analyst or risk mitigation agent. We'll always advise similar to what Michael said. First things first, get this technology in the hands of your employees in a say, responsible way. Obviously you sell. We have a solution. There's many solutions out there. And that should almost always be step one because so often, and I imagine this is true for everyone in the room. It's only when you start using this stuff. And honestly, that's true in your personal life. It's true at work. They really start to see what it's capable of. I'm going through a process today. I just bought a new house. I'm getting home insurance. I don't know the last time you read a home insurance policy was, they're brutal. And they're like 400 pages. And I get these three policies. And I'm like, even distilling the difference between coverage and umbrellas and all that. And I work a lot of insurance companies. So I should know all of this. I just uploaded a plot. I'd be like, give me a breakdown of what I'm looking at here. And I'm sharing this as an example because those same aha moments, and it gave me a wonderful answer, the fastest way to create them is to give your employees access to a tool like Claw. And again, there's other tools out there. But just to get in their hands in a say from responsible way is huge. With that said, I want to go over to Lloyd, as you think about advising this group on, they've got limited resources, limited people, where do they begin? Do you have any other thoughts on where they should start? And let's assume for the purpose of this, they've already enabled a Clawed or an AI tool at the employee level. What's next? Yeah. So I was going to add on to Michael's point. I fully agree with revisiting the technology every three to six months. The other point I'd add is really investing in your sort of prompt engineering or context engineering. I think there's a massive difference between a sort of average user who puts a lazy prompt into Clawed versus someone who really thinks about that context. I think finding those people who become power users in your organization and just have a natural intuition for that can really help to accelerate if you put those people up as AI champions that can really drive a lot of change. I guess on other use cases, once you've gone with broad enablement, the way we think about that is looking at those high friction tasks where people do a lot of mechanical processing that are quite repeatable, they're using structured data, those are often perfect candidates in your organizations for something like Clawed. So for us, for the really critical decisions where it requires a lot of careful thought and system two type thinking, we may be used Clawed as more of a sounding board, but for a lot of those mechanical tasks that happen in all of our organizations, it's using Clawed plus other automation tools to really drive transformation of those processes. How we think about it? Awesome. Thank you, Lloyd. One of my favorite questions asked, so I'm obviously going to ask it here, for the purpose of the audience, all of you have a lot of experience. You've kind of gone through this, you've applied it in your organization, you've seen a lot of what works and a lot of what doesn't. I'm going to start with Freud. Knowing what you know now, if you could do it all over, what's one thing you would do differently? I don't know about what I would do differently, I think. In November, we really nailed it in terms of getting it. I think it's just maybe just start even before, right? That's what I would say. This is something you just have to lean into and take on. So I think move fast and make mistakes, learn from them and move on. It's really the key takeaway and I think if anything is just either. Thank you, Bro. Don, how about you? Yeah, I'll lean into the deep, do more faster a bit, but I think one thing we haven't touched on that I think that we focused on, but looking back perhaps we could have focused on earlier was this notion of it's a true mindset and culture shift. We talked a lot about democratization and a tool rollout, but certainly this is more than just rolling out tools and giving people access. It's like giving everyone in America a treadmill and expecting heart disease to be cured. What we were very intentional about is understanding that this is actually a mindset shift as well. So I think a lot of people look at a clawed or a chat GPT, they see a text box and like, oh, this is like Google. I'm just like put in one question. I'm going to get back an answer and then I'm kind of done. I think the reality is the this technology is really designed to be more of like a human. This truly is a companion and a co-pilot however you might describe it. And so having a bit of a mindset shift and actually helping folks understand that I think is really critical. We've been really intentional about partnering with folks from the outside to help our executives especially on that journey. One analogy we've used is, you know, if you're going to coast to Rico with your family, you could go to Google and ask for some recommendations or you could just prompt clawed or chat GPT and say, hey, you are the coast to Rico prime, you know, head of tourism. I'm going to coast to Rico with my family of four. My kids are 10 and 12, one like surfing, the other one like, then a wildlife. Can you come up with a customized itinerary? And so that's that's a very different sort of paradigm between those two tools. And frankly, for most folks that, first start to engage, that's kind of where they start. But we think it's really important to kind of help people on their journey. And if we could go back, we would maybe even start that even soon. Thank you, Dan. How are you, Lloyd? Yeah, I think I think I'd echo a further. We are running at this very faster HG and I like to think we're sort of ahead of the curve. We were building our first AI products back in 2023. But if I could say one thing, it would be running even faster towards this. Just the exponential rate of improvement of models and the ecosystem that's developing around that is frankly unprecedented. So yeah, I would have said, you know, run even harder, even faster. Michael. So we're pretty relentlessly data driven. And one thing we do is we look at the usage intensity across the firm. And what you see is a log normal curve, something like this. And the theoretical underpinning of that is it describes processes that are roughly the more you use it, the more you use it. So they're multiplicative. And if you combine that with this bottom-up view, what you want to do is just encourage people to take more bites of the apple. So here are two things that I do to help make that happen. One, at a lot of my team meetings, I just make people do a lightning round of some strange AI thing you tried in the last week. It worked. It didn't work, but you know, 30 seconds each share with the team. And two, in this place, also something you said, the route to comfort and learning about how to use these at work doesn't have to only run through work. And often people are more comfortable taking risk in their personal life. It's not as potentially embarrassing, at least in their minds. And so encouraging people to experiment there, I think can pay off as much or more than encouraging them to take those first jumps in the workplace. Yeah, no, I could not agree more. And that's a huge, huge, I think benefit to driving adoption for everyone here today and dialed in. If I had to recap what we've talked about today is enable. So give your employees access to one of these tools. Train, you don't have to be a master trainer or, you know, a $1 million training program. You can work with some great partners of which we've touched on a few of them today. And you can use a claw to help you build a training program to give them baseline training. I'm not talking about 100 hours here, baseline, how to prompt, how to interact with these tools. Measure, there's a, to Michael's point now, I think he's doing in a really great natural way. Let's do a speed round. I didn't throw up if you can imagine. It's like, we are monitoring in a way. Are you using these tools to do a better job? And like, you're actually tracking that because whether or not you use them, it's making sure you hold your employees accountable for building that muscle and getting used to using it. I'll share a personal anecdote and closing and Michael touched on this. Sometimes folks are more comfortable using this technology and their personal lives to see what's capable of them work. I have a two-year-old son who has a lot of medical concerns and the, in learning to navigate what his challenges were and what we could do for him, the hospital sent over 1,100 pages of notes from the NICU, from our neurologist, and it's very hard to make sense of all of it. So even when you talk to different doctors, it's, they all give you different answers and I took all of this and I uploaded a clot, I go, help me understand what's happening here. And I took that timeline and I'm not suggesting Clod's a doctor by any means. I just took that timeline, 1,100 pages by the way, PDFs, and I took it back to our doctor. My, does this seem right? Help me understand what happened here and what I can do about it. And literally the doctor's jaw drop. He's like, how did you come up with this? And I'm not sharing that story because look how great Clod is. I'm sharing that story because in moments like that when it has such a meaningful impact on your life personally, you can see my inclination, your respective of work in a company that can't drop it, try using this technology and work goes up dramatically. So as a takeaway, if you don't have one of these apps, these AI apps in your phone today, I'm going to say step one, use it, set a goal for yourself, use it. I don't care if you're taking a photo of a tree or you're analyzing life insurance statements or policies, use it a couple of times today, build the muscle, and in turn you'll be better equipped to do that with your own employees. So I want to say a big thank you to our panel. I really, really appreciate it. Thank you for everyone who dialed in and joining us today. And for that was Seated, stay put, we'll have a chance for questions. Let's go.

Claude for Financial Services Keynote

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