OpenAI 如何为银行家与分析师打造 ChatGPT：Joseph Kim 谈产品与技术取舍

简要说明Brief Description

OpenAI 如何为银行家和分析师打造 ChatGPT——OpenAI 的 Joseph KimHow OpenAI Built ChatGPT for Bankers and Analysts — Joseph Kim， OpenAI

目录Table of Contents

• 简介
  Intro
• ChatGPT 面向金融服务行业的功能
  What ChatGPT for Financial Services does
• 演示
  Demo
• 能力过剩
  The capability overhang
• Codex 的使用教会了我们什么
  What Codex usage taught us
• 金融数据的许可与索引
  Licensing and indexing financial data
• 企业模板、图表和引文
  Firm templates, charts and citations
• 基于 ChatGPT 插件平台构建
  Built on the ChatGPT plugin platform
• 模型的发展方向
  Where models are headed
• 实用性的门槛
  The threshold of usefulness
• 金融领域专项培训
  Finance-specific training
• 主动且持久的智能体
  Proactive, persistent agents
• 2027年金融领域的三项押注
  Three bets for finance in 2027

简介Intro

大家好。我是 Joseph Kim，负责 ChatGPT 金融服务业务。我们推出这款新产品才一个月。你们很多人已经体验过 Codex 这类工具如何改变软件工程师构建软件的方式。AI 已经越来越深入地融入你们的日常工作。模型也在以惊人的速度持续进步。但许多在金融等行业工作的人，还没有在日常工作中体验到同样的变化。我预计，未来几个月这种情况将迅速改变。而在座各位这样的构建者，将在推动这一变化尽快发生的过程中发挥重要作用：把这些模型的智能转化为人们日常工作中可以使用的产品。今天，我会分享一些我们在打造 ChatGPT 金融服务业务时学到的经验，以及我们对下一步发展的思考。Hi everyone. I'm Joseph Kim and I lead ChatGPT for financial services, a new product we launched just 1 month ago. Many of you have already experienced how tools like Codex have changed the way you build in software engineering. AI has become a much bigger part of the work you do every day. Model is continuing to improve at an incredible pace. But many people working in industries like finance haven't yet experienced that same change in their day-to-day work. I expect that to start changing rapidly over the next few months. And builders like all of you in this room have a big role in making that happen quickly. Taking the intelligence in these models and turning that into products that people can use in their daily work. Today, I will share a little bit about what we learned building ChatGPT for financial services and how we're thinking about what comes next.

我先向你展示我们构建的成果，并快速演示一下。I'll start by showing you what we've built including a quick demo.

ChatGPT 面向金融服务的功能What ChatGPT for Financial Services does

接下来，我会解释我们为什么打造这款产品，以及为什么我们认为现在正是打造它的好时机。然后，我会介绍我们是如何打造它的，并分享一些技术方面的经验，希望你能将其应用到自己的产品中。最后，我们会看看模型的发展方向和产品的演进趋势，以及这对未来一年金融领域的工作可能意味着什么。先从我们打造的产品说起。面向金融服务业的 ChatGPT，是 ChatGPT 工作版的定制版本，专为金融专业人士打造，首批面向投资银行和股票研究领域。它旨在帮助大型金融机构的员工更高效地完成日常工作，就像 Codex 能加速软件开发一样。在与我们的设计合作伙伴摩根士丹利和 Evercore 共同打造这款产品时，我们重点关注了三个核心领域。Then I'll explain why we built it and why we think now is the right time to build. From there, I'll walk you through how we built it with some technical lessons that I hope you can apply to your own products. Finally, we'll take a look at where models are headed and products are going and what that could mean for financial work over the next year. Let's start with what we built. ChatGPT for financial services is a tailored version of ChatGPT work built specifically for financial professionals starting with investment banking and equity research. It's designed to help people at the large financial institutions get more out of their day-to-day work similar to how Codex speeds you up in software development. As we built the product with our design partners Morgan Stanley and Evercore, we focused on three core areas.

首先是财务数据。财务数据是财务工作的基础，而你能开展的工作受限于数据源的质量。我们希望人们能够直接在 ChatGPT 中获取所需数据。其次，是专为金融打造的界面。我们希望这款产品能让金融专业人士感到熟悉，支持他们查看源材料、探索金融图表，并控制分析所依据的数据源。第三，是贵公司的格式。财务工作是在组织环境中开展的，人们制作的模型和演示文稿需要遵循所在公司的格式和标准。我们通过提供公司专属模板，让创建这些材料变得更加容易。First, financial data. Financial data is a foundation of financial work, and the work you can do is limited by the quality of your sources. We wanted people to have access to the data they need directly within Chat GPT. Second, an interface built for finance. We wanted the product to feel familiar to financial professionals with ways to inspect source material, explore financial charts, and control the sources behind an analysis. Third, your firm's format. Financial work happens within the context of an organization, and the models and presentations people produce need to follow their firm's format and standards. We made it easier to create those artifacts using firm-specific templates.

演示Demo

不过常言道，一图胜千言，所以我先不多说了，带你看看这款产品。But they say a picture's worth a thousand words, so I'll stop talking for a bit and show you the product.

太好了。Wonderful.

能力过剩The capability overhang

现在，我想花些时间谈谈我们为什么要开发它。我们开发面向金融服务的 ChatGPT，是为了帮助弥合我们所说的“能力落差”。正如你们在这里看到的，过去几年里，模型能力以惊人的速度提升。短短几年间，模型从能够解答高中数学题，发展到能够解决千禧年大奖难题的系统。但人们在日常工作中从这些模型获得的价值，并没有同步提升。你们会在新闻中读到这些突破，但对许多金融专业人士来说，他们的工作方式并没有那么快地发生变化。我们将这种差距称为“能力落差”——也就是模型所具备的能力与人们实际能从中获得的价值之间的距离。要弥合这一差距，光是打造一个能力强大的模型还不够。人们需要合适的数据和工具，也需要一种符合其工作方式的使用体验。Now, I'd like to take some time to talk about why we built it. We built ChatGPT for financial services to help close what we call the capability overhang. As you can see here, model capabilities have improved at a tremendous rate over the last few years. In just a few years, we've gone from models solving high school math problems to systems capable of solving millennial prize math problems. But, the value people get from these models in their day-to-day work hasn't kept pace. You read about the breakthroughs in the news, yet for many financial professionals, the way they work hasn't changed nearly as quickly. We call that gap the capability overhang, the distance between what the model is capable of, and the value people can actually get from it. Closing that gap takes more than building a capable model. People need the right data and tools, and they need an experience that fits the way they work.

这正是像你这样的构建者大显身手的地方。我想向你展示一个例子，说明恰当的产品体验能带来多大的改变。That's where builders like you come in. I want to show you an example of how much of a difference a right product experience can make.

Codex 使用教会了我们什么What Codex usage taught us

这张图展示了 Codex 在 OpenAI 内部不同职能中的使用情况是如何增长的。可以看到，工程师很早就开始使用 Codex，而且使用占比稳步上升。但再看看财务、法务和招聘部门。2026 年初，他们几乎没有任何 AI 产出是通过 Codex 生成的。到了 6 月，这一占比已达到约 90%。仔细观察会发现，今年 2 月至 3 月前后出现了一个巨大的拐点。我回头查看了这段时间发生的事情，原以为我们一定是发布了新模型，结果发现，我们在 2 月发布的是 Codex 桌面应用。我的结论是，这款产品让工程部门以外的人也能更轻松地运用这些能力。这正是我们在财务领域希望把握的机会：打造一种体验，帮助金融专业人士充分发挥他们已经能够使用的智能所带来的价值。This is a chart that shows how Codex usage grew within OpenAI across different job functions. As you can see, engineers started using Codex early, and that share grew steadily. But, look at finance, legal, and recruiting. At the beginning of 2026, almost none of their AI output was generated in Codex. By June, that share was around 90%. If you look carefully, you'll notice a huge inflection point that happened around February-March of this year. I went back to see what happened around this time, assuming that we must have released a new model, but instead I found that we had released the Codex desktop app in February. My takeaway is that the product made it easier for people outside of engineering to put these capabilities to work. That's the opportunity we're pursuing in finance. Building an experience that helps financial professionals get much more value from the intelligence already available to them.

现在，让我来谈谈我们是如何构建它的。Now, let me talk about how we built it.

金融数据的许可与索引Licensing and indexing financial data

我们先从数据谈起。在金融领域，你能开展的工作受限于你所能获取的信息的质量。因此，我们希望产品能够配备专业人士所依赖的数据源，从财务报表和业绩电话会文字记录，到市场数据和非上市公司数据。我们考察了现有的金融数据 MCP 连接器，发现许多都存在延迟高、错误多或搜索质量不佳等问题。于是，我们直接获得了大量此类数据的授权，并采用与网页索引相同的基础设施，自行搭建托管服务和搜索索引。我们仍然使用 MCP 协议让模型访问这些数据，但自行托管和建立索引，让我们能够更好地掌控体验的质量与可靠性，也让我们能够灵活地对模型进行后训练，使其适用于这些数据集。Let's start with data. In finance, the work you can do is limited by the quality of the information you have access to. So, we wanted the product to come with the sources professionals rely on, from financial statements and earning transcripts to market and private company data. We looked at existing MCP connectors for financial data and found that many had issues with latency, errors, or search quality. So, we went and licensed a lot of this data directly and built our own hosting and search indices using the same infrastructure as our web index. We still use the MCP protocol to give the model access to the data, but hosting and indexing it ourselves gives us more control over the quality and the reliability of the experience. It also gives us the flexibility to post train our models to work with these data sets.

我喜欢这样打比方：MCP 和 skills 就像是给一名初级分析师一本操作手册；而经过训练的模型则像一位资深分析师，已经使用这些数据二十多年，对它们的各种特性了如指掌。The analogy I like to use is MCP and skills are like handing an instruction manual to a junior analyst versus post training, where the model's like having a senior analyst who's been working with the data for over two decades and knows all of its quirks like the back of their hand.

公司模板、图表和引文Firm templates, charts and citations

MCP 和技能为模型提供访问权限和指令。后训练可以帮助模型进一步熟悉数据本身。在建立起这一数据基础后，下一步就是让模型能够轻松地将这些信息转化为专业级成果。对金融专业人士来说，这类成果通常是 Excel 模型、PowerPoint 演示文稿，或需要审核并与他人分享的书面分析。因此，我们专注于帮助模型以符合公司现有工作流程的方式生成这些成果。我们构建了一套模板系统，让用户在生成电子表格和演示文稿时能够使用公司批准的模板。这样做的目标是减少用户为团队或客户准备好成果所需的工作。我们还添加了交互式金融图表等功能，让用户在进行分析时可以直接在 ChatGPT 中查看股票价格和财务指标。MCP and skills give the model access and instructions. Post training can help it develop more familiarity with the data itself. Once we had that data foundation in place, the next step was making it easy to turn that information to professional grade work. For financial professionals, that work often takes the form of an Excel model, a PowerPoint presentation, or written analysis that needs to be reviewed and shared with other people. So, we focus on helping the model produce those outputs in a way that fits the firm's existing workflows. We built a template system that gives users access to the firm's approved templates when generating spreadsheets and presentations. The goal is to reduce the work someone has to do to get an output ready for their team or clients. We also added things like interactive financial charts, so users can explore stock prices and financial metrics directly within ChatGPT as they work through an analysis.

我们还构建了引文查看器，让人们能够查看答案背后的源文段和表格。在金融领域，And we built citation viewers that let people inspect the source passages and tables in behind an answer. In finance,

基于 ChatGPT 插件平台构建Built on the ChatGPT plugin platform

你需要能够核查一个数字的来源，并了解它背后的上下文。我们希望这种审查能够自然地融入使用体验。这些产品细节有助于将模型的能力转化为人们可以实际使用的成果。我认为，在座各位最感兴趣的部分，是我们如何构建它。你们刚才看到的许多功能，都是借助开发者也可以使用的同一套 ChatGPT 插件平台构建的。对于金融数据，我们使用 MCP 工具和连接器，让模型能够访问所需信息。我们使用引用元数据，而 MCP 应用则让用户能够查看信息来源；MCP 应用还为交互式金融图表提供支持。我们结合使用 skills 和共享的 artifact 工具，引导金融工作流，并生成 Excel 模型和 PowerPoint 演示文稿。you need to be able to check where a number came from and understand the context around it. We wanted that review to be a natural part of the experience. These are the kind of product details that help turn a model's capabilities into work that someone can use. The part I think that is most relevant for people in the room is how we built it. Much of the functionality you just saw was built using the same ChatGPT plugin platform that's available to you as developers. For financial data, we used MCP tools and connectors to give the model access to the information it needs. We use citation metadata and MCP apps let users inspect the sources, and MCP apps also power interactive financial charts. We use skills together with shared artifact tools to guide what financial workflows and produce Excel models and PowerPoint presentations.

在金融服务的接入方面，我们利用了原生插件接入体验。这意味着，开发者可以专注于让产品具有价值的专业优势。如果你拥有独特的数据、现有工具，或对某个特定工作流程有深入理解，就可以利用这些构建模块将其引入 ChatGPT，并围绕它打造一种体验。金融服务只是一个例子，但我确实认为，这一机遇远不止于金融领域。我个人相信，未来会有多家价值 10 亿美元的企业，仅凭 ChatGPT 插件就能建立起来。而随着模型能力不断增强，这一机遇也将持续发展。最后，我想用剩下的时间谈谈接下来会发生什么，以及这对我们今天应该打造的产品意味着什么。And for financial onboarding, we leveraged the native plugin onboarding experience. What that means for builders is you could is that you can focus on the expertise that makes your product valuable. If you have unique data, an existing tool, or a deep understanding of a particular workflow, you can use these building blocks to bring that into ChatGPT and create an experience around it. Financial services is just one example, but I do think this opportunity expands far beyond finance. I personally believe that we're going to see multiple $1 billion businesses built simply as ChatGPT plugins. And that opportunity will keep evolving as the models become more capable. Last but not least, I'd like to spend the rest of the talk on what comes next and what that means for the products we should be building today.

模型的发展方向Where models are headed

我想先谈谈模型的能力以及对未来的预期。模型进步得如此之快，以至于我们很难凭直觉判断接下来会发生什么。培养这种直觉的一个有效方法是回顾过去。过去几年里，一个令人惊叹的现象是，进步的某些方面一直相当可预测。模型性能的提升来自三个宽泛且有时彼此重叠的驱动因素：扩大计算规模、改进算法，以及我们所说的“解除束缚”——也就是设法让模型完成更多有用的工作。我们会在后面的一张幻灯片中再谈“解除束缚”。在2002年和2003年，最显著的驱动因素是预训练，也就是通过扩大模型规模并增加数据来提升计算规模。借助缩放定律，研究人员能够高精度地预测模型的性能。I'd like to start by talking about model capabilities and expectations for the future. Models are improving so quickly that it can be difficult to get an intuitive sense of what comes next. One useful way to build that intuition is to look backwards. Something remarkable about the last couple of years is how predictable parts of that progress have been. Model improvements have come from three broad, sometimes overlapping drivers. Scaling compute, improving algorithms, and something we call unhobbling, which is about finding ways to get more useful work out of the models. We'll come back to unhobbling on a later slide. In 2002 and 2003, the most visible driver was pre-training, which meant scaling compute by making the models bigger and adding more data. By leveraging scaling laws, researchers were able to predict model at performance with high accuracy.

例如，在 GPT-4 完成训练之前，OpenAI 的研究人员就能够预测它最终的损失值，以及它在一项编码评测中的表现。算法方面的改进也出奇地稳定。一项研究发现，从头开始训练语言模型，要达到某一给定性能水平所需的算力大约每八个月就会减半。虽然无法预测每项突破具体会在何时出现，但累积起来的改进速度却呈现出一种出人意料的稳定趋势。2024 年和 2025 年，推理能力成为推动进步的另一项重要因素。我们利用强化学习和后训练，教模型更有效地解决问题；同时，也为它们提供更多算力，让它们在作答时进行思考。这为提升性能开辟了预训练之外的另一条途径。For example, before GPT-4 finished training, OpenAI researchers were able to predict its final loss, as well as its performance on a coding eval. Improvements on the algorithm side have also been surprisingly steady. One study found that the compute needed to reach a given level of language model performance from scratch fell by roughly half every eight months. Although you can't predict the timing of each individual breakthrough, the cumulative rate of improvement follows a surprisingly steady trend. In 2024 and 2025, reasoning became another major driver of progress. We used reinforcement learning and post-training to teach models to work through problems more effectively, and we gave them more compute to think when answering. That opened up another way to improve performance alongside pre-training.

不过，对于不了解情况的人来说，我们在 OpenAI 的预训练过程中遇到了一些问题，因此难以扩大模型规模。到了 2026 年，我们已经解决了其中许多问题，并成功训练出规模更大、能力更强的模型，正如你们通过 GPT-5 Soul 和 GPT-6 Astra 所看到的那样。多年来，研究人员一直担心进一步扩展模型可能会遇到障碍。例如，2024 年，许多研究人员担心可能会出现数据瓶颈，也就是可用于训练的人类生成文本将会耗尽。但合成数据生成、从可验证奖励中学习，以及通过算法改进从每个 token 中学到更多内容等技术，基本上已经解决了这个问题。我们也通过稀疏注意力和压缩等方法，解决了上下文窗口长度方面的担忧。However, for folks that don't know, at OpenAI, we ran into problems with pre-training and therefore had difficulty scaling the model. In 2026, we've been able to solve many of these issues, and we've successfully trained larger and more capable models, as you've seen through GPT-5 Soul and GPT-6 Astra. Throughout these years, researchers have raised concerns about potential barriers to further scaling. For example, in 2024, many researchers were concerned about a potential data wall, where we would run out of human-generated text to train on. But techniques like synthetic data generation, learning from verifiable rewards, as well as algorithmic improvements to learn more from each individual token, have essentially solved this problem. We've also solved concerns around things like context window length using things like sparse attention and compaction.

展望2027年，我坚信过去的趋势将会延续，我们将训练出更大、更高效、能力更强的模型。显然，我们仍有非常棘手的问题需要解决，尤其是对齐和安全等方面的问题；但正如过去一样，我相信人类的创造力在能力日益增强的模型的辅助下，也将帮助我们在这些问题上取得进展。那么，到2027年底，模型会有多强？我们以数学为例。2023年，GPT-4在SAT高中数学考试中得了700分（满分800分）。到2005年，OpenAI的一个模型在国际数学奥林匹克竞赛中达到了金牌水平。而在20……2026年初，一个内部模型推翻了一个长期存在的猜想，该猜想与一道数学家自1946年以来一直在研究的问题有关。Looking ahead to 2027, I strongly believe that the trend lines of the past will continue and that we'll train larger, more efficient, and more capable models. We clearly still have very difficult problems to solve, especially around things like alignment and safety, but like we have in the past, I believe that human ingenuity, increasingly assisted by capable models, will help us make progress on those problems, too. So, how good will models be at the end of 2027? Let's use math as an example. In 2023, GPT-4 scored 700 out of 800 on SAT high school math. By 2005, an OpenAI model reached gold medal status on the International Math Mathematical Olympiad. And in 20 In early 2026, an internal model disproved a long-standing conjecture related to a problem that mathematicians have been working on since 1946.

最近，一个内部模型为纳维–斯托克斯千禧年大奖难题——呃，问题提出了一种解法；我们还看到，在372类结果中共享了719篇数学研究手稿。短短几年间，我们已经从在高中数学考试中取得优异成绩，走到了由 AI 系统在数学前沿产出原创研究。More recently, an internal model proposed a solution to the Navier-Stokes Millennium Prize puzzle um problem, and we also saw shared 719 mathematical research manuscripts across 372 families of results. In just a few years, we've gone from a strong score in a high school math exam to AI systems producing original research at the frontier of mathematics.

实用性的门槛The threshold of usefulness

如果我们再经历一年左右、与过去几年大致相当的进步，我预计到2027年底，模型将在越来越多不同领域和专业中的高难度任务上，超越顶尖的人类专家。数学让我们提前窥见了这可能是什么样子。尽管能力曲线看起来可能是渐进的，但我们的工作方式却可能感觉像是突然发生了变化。我把这称为阈值现象。对于任何一项任务，都存在一个有用性阈值，也就是模型变得足够强大、可靠，能够为你节省时间的那个节点。这个阈值取决于你所做的工作，以及这项工作需要多少检查或人工审核。在达到这个阈值之前，进步可能很难让人察觉。模型或许能给出更好的答案、减少错误，或者在一项任务中推进得更远。If we see another year of progress roughly in line with what we've been seeing over the past couple years, I expect that by end of 2027, models will outperform even leading human experts on a growing range of difficult tasks across different fields and specialties. Mathematics gives us an early glimpse of what that could look like. And while the capability curve may look gradual, the change in how we work can feel sudden. I call this the threshold phenomenon. For any given task, there's a threshold of usefulness, the point where a model becomes capable and reliable enough to save you time. That threshold depends on the work you're doing and how much checking or human review it requires. Below that threshold, improvements can be hard to appreciate. The model might produce better answers, make fewer mistakes, or get further through a task.

但如果检查和修正它的成果所花的时间，仍然和你亲自完成这项工作一样长，那么你对它的实际评价就没有改变。它依然派不上用场。能力的实质性提升，起初可能看起来进展甚微。随后，模型跨过了那个门槛，突然就能产出你用得上的东西了。你花在审核上的时间，开始远少于它为你节省的时间。你开始经常使用它，最终也逐渐放心把这项任务交给它。这种转变可能让人感觉像是一次巨大的飞跃，即使其底层能力的提升幅度与前几次相差无几。改变的是，这些进步终于对你有用了。这有助于解释为什么模型进步有时会让人觉得来得突然，即便其背后的某些发展趋势一直稳定且可预测。由于不同的难题有各自不同的门槛，我们也应该预期，在我们所做的不同工作中，这些时刻会在不同时间到来。But if checking and fixing its work still takes as long as doing it yourself, your practical assessment hasn't changed. It's still not useful. Meaningful improvements in capability can feel like little progress. Then the model crosses that threshold. Suddenly, it produces something you can use. The time you spend reviewing it becomes much smaller than the time it saves you. You start using it regularly, and eventually you become comfortable delegating that task. That transition can feel like an enormous leap, even if the underlying improvement was similar to the previous few improvements. What changed is the progress became useful to you. This helps explain why model progress sometimes feels sudden, even when parts of the underlying trend have been steady and predictable. And because difficult tasks have different thresholds, we should expect those moments to arrive at different times across the work that we do.

所以，在思考你正在构建的产品时，我鼓励你问问自己：今天有哪些模型能力还不太实用，因此被我低估了？一年后，这些能力可能会发展到什么程度？接着想想，如果这些能力跨过实用门槛，变得足够可靠，可以交由它们处理，你会构建什么产品？界面会如何改变？用户仍然需要控制、审查或批准哪些事项？这些都是我们现在就可以着手思考的产品和设计问题。So, as you think about the products you're building, I'd encourage you to ask yourself, what model capabilities am I under appreciating today because they aren't quite useful yet? Where might those capabilities be a year from now? Then think about what you would build if those capabilities crossed the threshold of usefulness and became reliable enough to delegate. How would the interface change? What would the user still need to control, review, or approve? Those are product and design questions we can start working on now.

金融领域专属培训Finance-specific training

我们希望提前准备好合适的体验，这样一旦模型跨过这道门槛，人们就能立即利用其能力，你也能领先竞争对手一步。那么，这对金融领域意味着什么？我认为，有两股力量会推动更多金融工作跨过这道实用性门槛。我把它们比作不断上涨的潮水和潮水之上的浪潮。不断上涨的潮水，指的是底层模型的持续改进。过去，我们并没有专门投入大量训练时间或算力来提升金融能力。如今你看到的许多金融能力，都是通过构建规模更大、能力更强的模型，以及发展数学、编程和推理等领域的可迁移技能而获得的。随着这些通用能力不断提升，各类金融工作的表现也随之提高。We want to have the right experiences ready so that when the models cross that threshold, people can take advantage of the capabilities right away, and so you can stay one step ahead of your competitors. So, what does this mean for finance? I think there are two forces that will push more financial work across that threshold of usefulness. I think of them as a rising tide and the waves on top. The rising tide is the underlying improvements to the underlying model. Historically, we haven't dedicated a significant amount of training time or compute specifically to finance. Much of the financial capabilities you see today have come from building larger, more capable models, and from transferable skills developed in areas like mathematics, coding, and reasoning. As these general capabilities improved, they lift the performance across many kinds of financial work.

当我们直接聚焦金融领域时，就会出现这样的提升。我的团队正在牵头一项工作，大幅增加对金融领域专项后训练的投入。我们的想法是在这些广泛能力的基础上，针对金融从业者的实际工作，更直接地训练模型。这样一来，除了整体能力不断提升之外，模型在特定任务上的表现也能达到新的高峰。未来三个月，我们将专注于提升通用金融推理能力，以及模型产出内容的质量，包括分析财务信息、构建 Excel 模型和制作 PowerPoint 演示文稿。这些任务占初级分析师日常工作的重要部分，我们希望模型的输出准确、结构清晰，并能在实际工作流程中发挥越来越大的作用。除此之外，我们还计划投入更多资源，提升模型在处理更难的问题时所需的判断力，使其更接近资深银行家、分析师和投资者的水平。The ways are what happen when we focus directly on finance. My team is leading an effort to invest significantly more in finance specific post training. The idea here is to build on those broad capabilities and train the model more directly on the work people do in finance. That can create peaks in performance on particular tasks on top of the rising tide. Over the next 3 months, we'll focus on improving general financial reasoning and the quality of the work these models produce, analyzing financial information, building Excel models, and creating PowerPoint presentations. These are the kinds of tasks that make up a significant percentage of the daily work of a junior analyst, and we want the output to be accurate, well-structured, and increasingly useful in real workflows. Beyond that, we plan to invest more in the kinds of judgment that senior bankers, analysts, and investors bring to more difficult questions.

这包括接收大量信息、判断哪些内容重要，并从本质上尝试预测未来：预测一家公司的盈利、评估大宗商品价格可能的走势，或判断哪些企业具有投资吸引力。我们的目标是让模型在 2027 年的某个时候，在这些金融任务上达到顶尖人类专家的表现。我们预计，这一进展将来自两股力量的共同作用：底层模型持续改进，以及我们的团队更有针对性地训练模型，使其胜任人们在金融领域从事的工作。This includes taking in large amounts of information, deciding what matters, and essentially trying to predict the future. Forecasting a company's earnings, assessing where commodity prices might go, or evaluating which businesses are attractive investments. Our goal is for models to match the performance of the best human experts on these types of financial tasks sometime in 2027. We expect that progress to come from both forces working together. The underlying model continuing to improve, and our team training it more directly for the work people do in finance.

主动且持久的智能体Proactive, persistent agents

我们谈到了模型正变得越来越强大，也谈到了如何训练它们，让它们在特定任务上表现得更好。但我想回到之前提到的第三个驱动因素：解除束缚。我们已经看到了一些例子。ChatGPT 利用 RLHF 教会模型遵循指令并与我们对话。工具让它们能够搜索信息、编写和运行代码，以及与我们使用的软件交互。这些都让模型在现实世界中更有用。我认为，解除束缚的下一种形式将是主动且持久的智能体。如今，我们与 AI 的许多互动仍然发生在我们打开对话并告诉它接下来该做什么的时候。持久型智能体能够始终围绕一个目标行事，记住相关上下文，并在后台继续工作。一旦你为它指明方向，即使你没有主动向它发出提示，它也能继续取得进展。We talked about how models are becoming more capable and how we can train them to perform better on specific tasks, but I want to come back to one third driver I mentioned earlier, unhobbling. We've already seen few examples. Chat GPT used RLHF to teach models to follow instructions and have conversations with us. Tools allow them to search for information, write and run code, and interact with the software we use. Each of these makes the model more useful in the real world. I think of the next form of unhollowing will be proactive and persistent agents. Today a lot of our interactions with AI still happen when we open a conversation and tell it what to do next. A persistent agent can hold on to a goal, remember the relevant context, and continue working in the background. Once you've given it a direction, it can make progress even when you aren't actively prompting it.

持久化之所以重要，另一个原因是它会改变 API，或者改变人们与智能体协作的方式。如今，要充分发挥 AI 智能体的作用，需要学习一套新的工作流程，学习如何编写提示词，以及如何让它使用工具。持久化智能体可以用更熟悉的方式开展工作。你可以把一个项目交给它，分享背景信息，并询问进展。The other reason persistence matters is it changes the API or the way people work with the agent. Today getting the most out of AI agents requires learning a new workflow, learning how to prompt it, and learning how to use it to tools. A persistent agent can work in a way that's more familiar. You can give it a project, share context, and ask for an update.

2027年金融业的三大押注Three bets for finance in 2027

这与人类和其他人协作的方式更加相似，因此也让那些没有掌握这些入门技能的人更容易使用 AI。最后，我想谈谈对 2027 年金融领域的三个预测。这只是我对模型不断改进后可能出现的情况的一些预期。第一，人们将很少再依赖 Excel 和 PowerPoint 来制作财务模型或路演材料，甚至可能完全不再依赖它们。第二，AI 财务分析师会开始让人觉得就像人类同事一样。它们会记住你前几天的偏好和上下文，主动负责各项任务，积极跟进，并在你专注于其他事情时继续工作。第三，模型将开始在关键金融任务上超越最优秀的人类专家，包括预测盈利情况和提出投资思路等。It's much more similar to how humans work with other humans, and therefore makes the AI more accessible to people um that don't develop those initial skills. Let me close with three bets for finance in 2027. These are just a couple of my expectations for what comes as the models improve. First, people will rely rarely, if ever, on Excel and PowerPoint to make financial models or pitch decks. Second, AI financial analysts will start to feel like human coworkers. They'll remember your preferences and context from the previous days, take ownership of assignments, proactively follow up, and keep working when you're focused on something else. Third, models will start to outperform the best human experts on critical financial tasks. That includes things like forecasting earnings and developing investment ideas.

我预计，模型在做出这些具有重要经济意义的前瞻性判断方面，将会胜过最优秀的人类。这就是我对2027年的三项押注。如果我们预期到2027年会达到这样的水平，我想留给你们最后一个问题：为了迎接模型明天必将跨越的门槛，你今天应该开始构建什么？谢谢。I expect models to become better than the best people at making these economically important forward-looking judgments. Those are my three bets for 2027. And if this is where we expect to be in 2027, I'll leave you with one last question. What would What should you start building today to be ready for inevitable thresholds that the models will cross tomorrow? Thank you.