如何证明 AI 值得投入？Hari Kumar 谈对冲基金的回报评估

简要说明Brief Description

Hari Kumar 解释了 Contour Asset Management 如何通过实时研究对比、分析师耗时以及每条有用洞见的成本来衡量 AI 的回报，同时将这些工具拓展至交易、运营和合规领域。Hari Kumar explains how Contour Asset Management measures the return on AI through live research comparisons, analyst time and cost per useful insight, while extending tools across trading, operations and compliance.

目录Table of Contents

• 简介：一家管理规模达 60 亿美元的基本面基金中的 AI
  Intro: AI at a $6B fundamental fund
• 为什么 AI 投资回报如此难以实现
  Why AI ROI is so hard to capture
• 研究中有无 AI 的实时 A/B 测试
  Live A/B tests of research with and without AI
• 衡量过程，而不仅仅是损益表
  Measuring the process, not just P&L
• 创意生成与时机判断智能体
  Idea generation and timing agents
• 汇集另类数据
  Bringing alternative data together
• 获取每条有用洞察所花费的时间和成本
  Time spent and cost per useful insight
• 贯穿交易、运营和合规的 AI
  AI across trading, ops and compliance
• 充分利用现有数据投入
  Getting more from existing data spend
• 修复 AI 工具的隐性成本
  The hidden cost of fixing AI tools

简介：一家管理规模达60亿美元的基本面基金中的人工智能Intro: AI at a $6B fundamental fund

我叫 Hari，是 Contour Asset Management 的 AI 负责人。这是一家位于纽约市的对冲基金，成立至今已有十多年，而我加入这里大概一年了。我们是一家基本面多空基金，大约有 15 到 20 名员工，管理资产规模为 60 亿美元，主要投资 TMT 领域。我们的很多投资者本身都很懂科技，但他们希望能在投资公司内部引入更多 AI 工作流程，看看我们多年来一直以相似方式开展的各项流程，究竟能如何打破常规、借助 AI 提升优势。尤其是在研究流程方面，也希望将 AI 应用拓展到交易、运营、合规、财务，以及公司几乎所有部门。My name is Hari. I am the head of AI at Contour Asset Management, which is a hedge fund based in the city. It's been around for a little over a decade now, but I've been here for the past year-ish. we're a fundamental long short shop, around like 15–20 people, 6 billion AUM. and mostly invest in the TMT space. so a lot of our investors are pretty tech forward by nature, but want to bring a little bit more, AI workflow into the investment firm itself and see, okay, we've had all these processes done for many years in a very similar way. How can we kind of shake things up and use AI to our advantage, especially when it comes to the research process, but then also bring that into trading, operations, compliance, finance, pretty much all parts of the firm.

所以，我是过去一年里加入公司的，我的核心任务基本上就是：如何展示研究流程在公司各个领域带来的影响，对吧？我想，在座各位大概都能理解并体会到，这确实是个很大的挑战：我们在 AI 上投入了这么多资金和时间，究竟带来了什么影响？我们到底从中获得了什么？这不仅要从损益表的角度来看（这方面稍后会谈到），也要从运营层面，以及能否产生好点子的角度来看。So, came in in the past year and basically my mandate was, how can I show impact across all areas of the firm, largely starting with this research process, right? And as probably all of you guys in this room can understand appreciate, it's a really big challenge to show, hey, we're spending this amount of money and time on AI spend and impact. What are we actually gaining from it? Both from a P&L standpoint, which is to come, but also just from a more operational and are we getting good ideas perspective, too.

为什么 AI 投资回报如此难以实现Why AI ROI is so hard to capture

如何衡量并理解投资所带来的回报，是个很大的挑战。不过我想简单谈谈，在投资领域，我们可以通过哪些方式开始思考这个问题：我们在 AI 上投入了这么多钱，究竟获得了什么？好，关于这一点，我们有几方面的考虑。我们有意把内容讲得稍微笼统一些，更多聚焦于运营和流程层面；不过，如果大家愿意，我们也可以在本次分享结束时或之后深入探讨技术细节。The ROI capture from our investment is a really big challenge to understand capture, but I want to talk a little bit about some of the ways at in the investing landscape we're able to start to think about hey, we've put in this amount of money into our AI spend, what are we getting out of it? Cool. So, a few things that we are thinking about here and intentionally trying to keep this a little bit vague and more in terms of the operational and process parts of it, but down to dive into any technical stuff either at the end of the session or afterwards as well.

对有 AI 和无 AI 的研究进行实时 A/B 测试Live A/B tests of research with and without AI

所以，我们基本上想做的是，为分析师的一些研究工作流程推出线上 A/B 测试。我们大约有 10 位分析师，各自负责不同的覆盖领域，对吧？很多对冲基金都是这样运作的，尤其是规模较小的基金：一个人可能要跟踪 30 到 40 只股票。他们已经形成了自己的流程，包括如何思考投资论点、如何提出新想法，以及如何在长期内验证和检验这些想法是否真正实现。So, what we're trying to do is basically bring out live AB tests for some of the research workflows that our analysts have. So, we have around 10 analysts that each cover different parts of our coverage, right? Which is how it works at a lot of these hedge funds, especially on the smaller side, where you have maybe one person that covers 30 to 40 names. And they've had their existing processes in terms of how they think about their investment thesis, how they actually come up with new ideas, and then validate and test out over the long term whether those ideas actually came to fruition.

但如今，在一个由 AI 驱动、智能体发挥越来越大作用的投资领域，挑战在于：使用 AI 工具和不使用 AI 工具，你是否会得出同样的结论？过去几个月，我们采用的一种我认为相当成功的做法，是通过记录并整理他们在没有 AI 的时代会采用的整个流程，以及随着我们在公司内部推出更多工作流和工具，他们如今的做法，来实际开展实时 A/B 测试。But now in a AI and more agentic world when it comes to investing, the challenge is would you have come to the same type of conclusion with the AI tools or without the AI tools as well? And a process that we've done in the past few months that I think has been pretty successful has been actually creating live AB tests by recording and documenting their entire process that they would have done in a pre-AI world and how they're doing it as we roll out more workflows and tools in the company itself.

举个例子，在我们的三个月周期里，我们针对某一家特定公司做了这样的事：从一份财报到下一份财报，我们追踪他们是否没有使用任何 AI 工具。起初，我们只是想了解：如果你完全不使用任何 LLM，你的工作流程是什么样的？但后来，随着我们在对冲基金内部引入了更多工具，比如帮助你更好地进行专家访谈的工具，或者由智能体而非人工来开展消费者调查的工具，情况就不一样了。So, for example, what we did is for one specific name when it came to our three-month cycle. So, from one earnings report to the following one, we tracked if they didn't use any AI tools. And this is at first just hey, if you're not using any LLMs, what does your workflow look like? But then, as we've introduced more tools at the hedge fund itself, like okay, you have some tools that help you do expert call interviews better, or ones that help you field a consumer survey that's done by an agent instead of a human.

如果没有诸如此类的东西，你有什么收获？然后我们会把情况记录下来：好，你在没有 AI 的情况下完成了这个流程，而且也没有使用我们正在搭建的这些额外技术工作流。你找到了哪些数据来源？得出了哪些结论？What are your takeaways without things like that? And then we're documenting, okay, you've done this process without AI, but also without these additional tech workflows that we're building out. What are the data sources that you're finding? And what are the conclusions you're coming out with?

然后，我们会让产品经理参与，用同样的流程，也会用到其中一些 AI 工具。我们正逐一尝试不同的方法来构建这些工具，对吧？比如说，在专家访谈电话这件事上，有一种做法一直挺有帮助：由智能体主持专家访谈，然后你自己整理访谈笔记。你用智能体来完成这套流程，和让智能体替你整理笔记、最后再由你得出结论，这两种做法有什么不同？Then we run that same process involving our PM including some of these AI tools. And we're building them one by one with different things that we're coming at with, right? So, for example, when it comes to expert call interviews, one thing that's been pretty helpful is you have expert calls that are run with an agent, and then you synthesize the notes yourself. How does that process work when you do it with an agent work versus if you have the agent synthesize the notes for you, and then you come up with your conclusions at the very end of it, right?

所以，这其实是在思考这样一个问题：如果手动搜集其中一些信息，需要花多长时间；而如果能以更具自主性的方式来完成，又会快多少。这无疑是投资领域面临的一大挑战，因为我们所做的很多工作都只是围绕创意构思展开的。So, it's kind of thinking about the question of how long would it have taken to source some of this information manually versus if you were able to do it in a more agentic manner. This is definitely a big challenge when it comes to the investing landscape because a lot of the work that we're doing is just based off of idea generation.

所以，你们提出新的想法和新的论点，然后想看看这些想法能不能在短期内得到验证——这类事情我们做得相对少一些——至少能不能在三个月内见效；还是说它们需要六个月或一年，才能对股价产生实际影响，对吧？So, you're coming up with the new ideas and new theses, and you want to see, okay, can they be proven out in a short-term scale, which is a little less of what we do, but at least in like the 3-month window, or are they things that take 6 months or a year to manifest in terms of an actual impact to stock price, right?

衡量过程，而不仅仅是损益表Measuring the process, not just P&L

所以，像这样的事情，挑战在于你无法立即看到投资回报。更多是从流程层面来看的，对吧？我们是否在改进开展消费者调查的流程？我们是否在改进更新模型的流程？So, the challenge with something like this is you can't show ROI immediately. It's more so from a process-based manner, right? So, are we improving the process of how we're conducting consumer surveys? Are we improving the process of how we're updating our models?

我们正逐个尝试更深入地衡量投资回报率：我们究竟改变了某人的工作方式，还是只是在他们的流程中增添了动力？我们刚才稍微谈了谈这些用例，或许可以再多聊一点。And one by one, we're trying to bring out a little bit more of ROI measurement when it comes to okay, are we actually changing the way someone's working or we just adding more thrust to their process? So, talked a little bit about these use cases, but maybe just to touch a little bit more on them.

创意生成与时机判断智能体Idea generation and timing agents

基本面投资的主要目标是构思一个投资论点，但正如我逐渐认识到的，也不能过于受最初提出的论点束缚，而应该信任一些数据，以及你获取到的专家见解和其他洞见，借此摆脱部分固有思维，对吧？我认为，在这方面，AI 工具的影响评估有很大的发挥空间。举个例子，我们正在运行一些人工创意生成智能体，针对每家公司的每位分析师提出的不同想法进行处理，并进一步拓展：比如，你从不同的另类数据集中找到了几个数据点，或者和行业专家交流以形成某些想法。接下来，我们可以在此基础上再增加一些结论，或把流程中的其他环节纳入考量，思考如何真正判断这个想法会在未来几个月内如何兑现，或者它是否需要更长时间才能被市场消化。The main goal when it comes to fundamental investing is thinking of a thesis, but also as I've learned, it's to not be so anchored to a thesis that you come up with initially and actually trusting some of the data, but also the expert and other insights that you're getting to remove some of those anchors as well, right? And this is where I think that there's a lot of impact measurement when it comes to AI tooling that can be built. So, example, we have manual idea generation agents that are being run to take different ideas that are being come up with per name per analyst and adding more layers in terms of okay, you've come up with a few different data points from different alternative data sets or you've talked to an expert in the industry to formulate some of that idea. Let's layer on a few more conclusions or parts of the process to think about when it comes to how are you actually understanding what that idea is going to materialize in the next few months or if it's something that it takes even longer to take into account into the market.

此外，我们也在进一步思考一个我们发现的最大问题：你有了一个想法，可能花几天时间去研究，但之后这个想法也许对 PM 不再有吸引力，或者有其他事情优先级更高，于是你就把这个想法搁置了。几个月后它仍然没有重新浮现，这时你可能已经错过了一个关键的价格窗口。因此，我们希望有一些智能体能够持续追踪你记录的所有笔记，即使你只花了一天研究某个想法，也能将它与市场动态进行比对，并判断：“好吧，这个想法现在是否值得关注？我们是否应该把它作为一个要点提醒，不仅提醒你，也提醒 PM 和交易员？”这样一来，当你研究过很多不同的事情、而且可能已经好几周没再看过它们时，也能更快地把相关工作重新带出来。Additionally, we're thinking a little bit more as to one of the biggest problems that we're seeing is you take an idea or you have an idea, you might work on it for a couple of days, and then that idea might not be interesting to the PM or something else takes precedence, and you lose that idea. And it doesn't come back for months, and then maybe you miss like a key price window. So, having agents that are also tracking through all of the notes that you've been taking, even if you only worked on an idea for a day, tracking it against the market and trying to see, "Okay, is this now an idea that's interesting, and should we flash that as a point to not just you, but the PM and the trader as well?" And bringing out more quick work when it comes to you've had a bunch of different things you've worked on, and maybe it's been weeks since you've looked at it.

现在，让我们回到正题，因为这个话题又变得有意思了，对吧？我认为这是很多对冲基金面临的一个大问题：你手头有很多不同的事情可以做，必须确定优先级，但真正最重要的其实是时机——也就是当市场出现有趣的机会时，何时出手，对吧？所以，我们也一直在认真思考如何构建智能体，让它们不仅能生成投资思路，还能考虑时机，以及何时应该进场或退出某个头寸，即使这并不是你一直以来优先考虑的事情。Now, let's get back to it because it's interesting again, right? I think that's a big problem that a lot of hedge funds have had, where you have a bunch of different things that you can be working on, you have to prioritize, but the timing is actually the most important thing as to when you want to strike when it comes to an interesting point in the market, right? So, building agents that are not only doing idea generation, but thinking about timing and when you should enter exit a position, even if it's not something that has been top of mind for you, is something that we've been really thinking about as well.

整合另类数据Bringing alternative data together

这里最后一点谈的是投资工作流程中的另类数据选择。很多基金在获取真正新颖的数据集时，都会花上相当可观的一笔钱，对吧？我认为在当今这个时代，涌现出了许多不同的供应商，提供非常精细化的解决方案，甚至包括卫星地图数据或非常细粒度的信用卡数据。这些数据很有用，但只有把它们整合在一起，才能真正发挥作用，对吧？The last point here is about alternative data options in investing workflows. A lot of funds spend quite a good sum of money when it comes to acquiring really new and novel data sets, right? And I think in this age, there's a bunch of different providers that are coming out with very nuanced solutions, even when it comes to things like satellite mapping or really granular credit card data. Those things are helpful, but they're only helpful when they're all brought together, right?

所以，如果你想想像 Yipit 这样的服务，它提供信用卡样本面板数据，原本是独立存在的；但现在，有了 MCP 和对 Yipit 这类服务的 API 访问权限，你就可以把这些数据接入我们的智能体，这大致就是我们所做的。这样一来，它既会考虑专家访谈或笔记记录的转录内容，也会整合信用卡样本面板或消费数据等不同数据集，从而了解这两类信息是如何相互作用的。So, if you think about something like a Yipit, which provides credit card panel data, that lives in isolation, but now with MCP and API access for things like Yipit, you can feed it into our agents, which is kind of what we've done. So, it takes into account things like your expert call or notes transcripts, but also layers in different data sets when it comes to credit card panels or spend data to see how those two things interact.

过去那些原本有所区分的地方，现在我们也在努力遏制这个问题。Where before those have been differentiated, now we're trying to curb that issue as well.

获取每条有用洞察所花费的时间和成本Time spent and cost per useful insight

此外，我认为衡量投资回报率的一大挑战在于时间维度。比如，在获取一些工具时，你会投入很多资金，用于研究、模型更新以及 AI 支出。但另一方面，分析师每天把时间花在哪里也很重要，而这正是我们主要关注的，对吧？因此，我们还做了一件我认为很有用的事：制定了一些指标，用来衡量分析师每天产生了哪些洞察。Additionally, I think a challenge with measuring ROI is the time aspect of it. So, you spend a lot of money when it comes to acquiring some of this tooling when it comes to research, model updates, AI spend. But, there's a lot of time when it comes to what is an analyst spending their day on, which is the main thing that we're thinking about, right? So, what we've also done here, which I think has been pretty useful, is develop metrics when it comes to hey, these are the insights that have been generated by an analyst per day.

那件事花了多少时间？也就是说，跟踪花在每只股票上的时间。像这样的决策最终带来了怎样的损益结果？这在投资领域是件好事，因为谈到损益时，你有一个非常明确的成功指标。What was the time spent on that? So, tracking time spent on each name. And what were the eventual P&L outcomes from a decision like that? That is something that is good in investing landscape because you have a very very obvious success metric when it comes to P&L.

但是，它实际显现出来所需的时间比其他行业长得多，尤其是在更偏基本面的机构里：你们不是每天都在交易，而是每天进行几笔交易，并预期这些交易会在数月的时间跨度内产生结果，对吧？所以，了解分析师的时间都花在哪里非常有价值。追踪诸如“每条有用洞见的成本”这样的指标——这是我们想出来的指标——确实能帮助我们了解：好，你花时间提出了这些想法，而且我们每天都会追踪这些想法。那么，这些想法最终会落到哪里？But, it takes a lot longer than other industries to actually materialize, especially from a more fundamental shop, where you're not trading by the day, but you're making a few trades a day that are expected to materialize over months-long horizons, right? So, understanding where analyst time goes is really valuable. And tracking things like a cost per useful insight, which is the metric that we've come up with, really helps us understand, okay, you spent your time to generate these ideas, and those are all being tracked per day. Where do those ideas eventually land?

哪些是有意思的？又用了哪些工具才想出这样的点子？What were the interesting ones? And what were the tools that were taken to come up with an idea like that?

人工智能应用于交易、运营和合规领域AI across trading, ops and compliance

所以，这在很大程度上涉及工作流程中的研究环节，也就是如何衡量我们从人工智能支出以及整体技术投资中获得的投资回报率。不过，我们也希望在整个公司范围内推动改进，对吧？确保研究质量至关重要。这正是领先一步的关键。So, that largely touches on the research side of workflows when it comes to measuring our ROI from not just AI spend, but general tech investment. But, we want to also improve this across the entire firm, right? It's really important to make sure that we do quality research. That is how you get ahead.

但是，如果你的交易做得更好、也更深入，同时运营和财务能力更强，这就能帮助整个公司腾出时间来改进流程，对吧？比如在交易方面，我们现在更加注重衡量入场和出场时机。我们会考虑仓位如何分散在多个不同行业，以及每个行业中的多个标的之间。我们还会让 AI 智能体跟踪这些标的，确保交易员及时掌握情况，并了解哪些洞察值得关注。But, if you have better and more involved trading, as well as stronger ops and finance, that just helps the firm at large to free up time to improve processes, right? So, for example, on the trading side of things, we're doing a lot more when it comes to measuring our entry and exit times. Thinking about how our position is spread across multiple different industries and multiple names within an industry. And having AI agents track those names to make sure that our traders are on top of it and understand, okay, these are interesting insights.

因此，要让他们更多地参与分析师的工作，因为他们的时间不应只花在执行订单上，也应花在提出创意、为我们的分析师和助理提供想法上。此外，在运营方面，我认为许多普通公司都能体会这一点，但从对冲基金的角度来看，这件事相当有意思。许多工作流程基本上都设置在 Bloomberg 或你的订单管理系统中。So, bring them much more into the analyst world because their time should not just be spent on executing orders. It should also be spent on bringing idea generation, feeding ideas to our analysts and associates as well. Additionally, on the op side, this is something that I think a lot of general companies can appreciate, but it's been pretty interesting to see it from a hedge fund landscape. Many of the workflows that are being done are basically set within Bloomberg or set within your order keeping agent.

虽然他们开发了一些很酷的 AI 工具来帮助改进流程，但要把这些工具整合起来确实很有挑战性，而这正是我们现在开始着手做的事情。那么，如何把运营和合规工作流程的各个环节整合到一个专门的工具中，供我们的运营和合规团队使用呢？And those, while they've come up with some cool AI tools to help improve the process, it's really challenging to integrate those together, which is something that we're starting to do here. So, how can you take all parts of the ops and compliance workflow and marry them into one specific tool that our ops and compliance functions can use.

因此，他们并不只是依赖外部服务商，零零散散地提供一些解决方案，比如告诉他们如何进行订单对账，或者如何追踪供应商信息。我们在这里要实现的目标显然是尽量减少重复性工作，这也是大家都在思考的事情。但具体到财务运营，这些工作完全可以整合到一个平台上。So, they're not just reliant on external providers to give them piecemeal solutions to hey, this is how you can do order reconciliation, or this is how you can track vendor info. The goal that we're trying to create here is obviously minimize repetitive work. That's what everyone here is thinking about. But, specifically from financial operations, that is something that can really be consolidated into one platform.

目前，你得为了订单流程中的不同环节，分别去使用许多不同的平台。因此，把这些平台整合到一起，对我们来说是非常重要的优先事项。我认为，这未必是这次演讲的重点，也不只是为了真正衡量投资回报率，而是要弄清楚：如何发挥目前从事交易和运营工作的人员的技能，来推进基金的主要目标——创造新想法、了解市场并把握时机。Where right now you have to kind of go into many different platforms for different pieces of the order flow. So, bring those all into one is is a pretty big priority for us. And I think not necessarily to the point of this talk, a way to really capture ROI, but to understand how can you take people who are working in trading and ops right now, and use their skills to improve the main goal of the fund, which should be to create new ideas, understand the market, and focus on timing.

让现有数据投入发挥更大价值Getting more from existing data spend

好的。这里我想谈的最后一点，是稍微想一想我们在各类数据源上的现有支出，以及如何在对冲基金的工作流程中进一步提升这些投入的价值。目前，很多对冲基金要么会花钱购买各种零散的解决方案，比如用 AlphaSense 获取数据、用 Yipit 获取另类数据；要么会购买使用第三方服务的综合解决方案。比如购买 Rogo 会员，或者投资 Kensho，对吧？Okay. The the last piece that I want to talk about here is thinking a little bit about the existing spend that we have when it comes to different data sources that we're working with, and how we can kind of up-level that when it comes to hedge fund workflows. So, right now a lot of hedge funds will spend on either they're spending on different piecemeal solutions like an AlphaSense for data, a Yipit for alt data, or they're spending with holistic solutions that use third party. So, something like buying a Rogo membership, or investing in like Kensho, right?

这些解决方案真的很棒。我觉得我们最终想在内部实现的目标，是把对冲基金里不同分析师使用的所有这些工具整合到一个平台上，同时也提升我们目前的投入水平。Those are really great solutions, and I think the goal of this eventually that we're trying to build in house, is how can you kind of bring all these tools that different analysts use at a hedge fund into one platform, but also up level the spend that we have right now.

比如，假设你已经订阅了 AlphaSense，它是一家大型模型、电话会议和数据服务提供商。以前，很多分析师只会使用 AlphaSense 门户，从那里获取信息，并在自己现有的模型中开展工作。像 Rogo 这样的工具会更进一步，让你能在自己的模型中直接使用所有这些数据，还能搭建一个与 Rogo 智能体对话的平台。我们想做的是继续使用 AlphaSense 或 Yipit 这类工具，但把它们整合到公司内部自己的平台中，让平台能从分析师的工作习惯中学习，并针对每位分析师及其建模模式进行专门配置，而不是一刀切。So, for example, with an existing, let's say, AlphaSense subscription, which is a big model and call plus data provider. Before, a lot of analysts will just use the AlphaSense portal and pull from there and work within their existing models. Things like Rogo will kind of take that one step further by allowing you to have all that data within your model itself, and you can have a platform that you chat with Rogo agents for. What we're trying to do is still use things like an AlphaSense or Yipit tool, but bring them into our own platform internally that learns from our analysts’ habits and is programmed specifically for each individual analyst and their model pattern, so it's not all as one.

那么，如何提升订阅服务的价值呢？显然，这些公司中有很多都在推出 MCP 和 API，但我们的目标应该是持续从各种不同类型的数据以及由此获得的模型更新中学习。And how can you increase the value of your subscription? A lot of these companies, obviously, are having like MCPs and APIs that are being pulled out, but the goal should be to learn continuously from all of the different types of data and the model updates that we're getting from this as well.

修复 AI 工具的隐性成本The hidden cost of fixing AI tools

最后总结一下，我觉得 ROI 等式中的最后一点，对我来说也相当值得关注。比如说：“好，你正在为那些对投资而言非常懂技术的人引入新的工具，但你也明白，这些工具会有出故障的时候——模型调用可能会失败，连接器可能每隔几天就出问题，而修复这些问题就是你的职责，对吧？”因此，我们在 Contour 考量 ROI 时，也会把这一点纳入考虑：弄清楚“好，我们遇到了这些中断，但修复那些技术团队成员自己无法解决的 bug，又会占用多少时间，对吧？”所以我认为，在“我们要把 AI 引入投资工作流程”这件事上，这一点相当重要。And then, to wrap up here, I think the last point of the ROI equation, too, has been a pretty interesting one on my end when it comes to, "Okay, you're bringing in new tooling to people who are very tech forward for investors, but you understand that there are going to be places where this breaks down, where model calls break or the connector breaks down every couple days, and then it's your job to fix it, right?" So, part of the ROI equation that we've been taking into account at Contour is trying to understand, "Okay, there are these pauses that we're getting, but also what is the time that being taken away trying to fix bugs, that someone from the tech side cannot fix itself, right? So, that's something that I think is pretty important to consider when it comes to, "Hey, we're involving AI in investing workflows."

这一切都非常令人振奋，大家也都很喜欢这个概念，但我们还需要弄清楚，比如说，为了获得有价值的洞见而投入的时间中，有多少时间被用于了解这些工具的工作方式。这方面的时间投入正在逐渐减少，不过这仍然是我们在考虑的一项指标。总的来说，在投资工作流程中，衡量投资回报率（ROI）对我们来说确实是一项巨大的挑战，但参与这一领域非常重要，因为随着 AI 的应用日益普及，尤其是在许多规模较小的基本面研究机构中，拥有一个统一的层，能够为不同分析师承担许多不同任务，将会非常有价值。我想我的时间差不多了，不过如果大家在演讲结束后有任何问题，我很乐意交流。It's all really exciting, and people are very into the concept of it, but we also need to understand, okay, when it comes to time spent for, let's say, valuable insight, what is the time detracted to understand how these tools work as well. And that's something where that time is decreasing as we go forward, but it's a metric that we're still considering. so, I think to wrap up, in investing workflows, measuring ROIs definitely a huge challenge for us, but it's really important to be involved in this space because as AI adoption increases, especially at a lot of these smaller fundamental shops, having one unified layer that's doing a lot of different tasks across analysts is going to be really, really valuable. I think that's all the time I have, but if anyone has any questions after the talk, would love to connect.

谢谢。Thank you.