这段文字记录了与 Hebbia 创始人 George 的一次对话，Hebbia 是一个面向知识工作者，尤其是在金融服务领域的 AI 平台。讨论深入探讨了 Hebbia 的起源、其独特的 AI 应用方法以及对金融行业未来的愿景。 George 的创业之旅始于他对元学习的着迷，并在 GPT-3 发布后意识到大型语言模型（LLM）的潜力。观察到金融服务领域的同事们忍受着乏味、重复的任务，他敏锐地意识到，利用 AI 来减轻他们的痛苦并提高他们的生产力是一个关键机会。 Hebbia 通过超越简单的聊天机器人界面来区别于典型的 AI 应用程序。相反，它旨在成为一个综合性的平台，提供比单一步骤更全面广泛的功能。 George 设想 Hebbia 成为一种工具，即使是高级 AI 或 AGI 也会选择使用它来进行结构化和非结构化数据处理以及复杂的知识工作。他强调，“我们不如构建一个如此出色的 AI 平台，以至于如果 AGI 要完成一项任务，它会选择使用 Hebbia 来完成该任务。” Hebbia 的一个核心优势在于其利用私有数据的能力。用户可以上传专有信息，例如投资备忘录或法律协议，以创建自定义知识库。这使得 Hebbia 能够提供高度定制的结果，这些结果比一般的网络搜索更相关且更具洞察力。 此外，Hebbia 的设计旨在推动代理式工作流程。通过将计算机使用与高级推理相结合，Hebbia 有效地管理结构化和非结构化数据处理，将用户从客观分析带入主观理解的领域。 Hebbia 的一个关键区别在于它专注于可操作的见解和实际价值。虽然许多 AI 应用程序承诺可以更快地完成现有任务，但 Hebbia 旨在通过使用户能够发现他们原本无法找到的见解来提供全新的价值。对于买方公司而言，这意味着可以有效地分析庞大的数据室、识别客户集中度以及综合来自专家网络电话的信息。 Hebbia 的模板还可以加快平台实现价值的时间。简而言之，George 声称，“现在我们有一个筛选模板，可以衡量您使用 Hebbia 和使用 Hebbia 之前所花费的时间，并向金融服务公司证明，这是切实的投资回报率。” 展望未来，George 设想了一个 AI 代理对全球 GDP 做出重大贡献的未来，不是通过取代人类，而是通过创造全新的价值。他认为，AI 将改变资本市场，尤其是在私人投资领域，它可以提供目前缺乏的结构化数据和分析。 Hebbia 着眼于未来，专注于赋能人类。通过自动化繁琐的任务，人类可以将注意力转移到更具战略性的思考、创造性的问题解决和发现上。他认为，因此，劳动力将发生变化，从客观分析转向主观决策的领域。

This transcript features a conversation with George, the founder of Hebbia, an AI platform for knowledge workers, particularly in financial services. The discussion delves into Hebbia's origins, its unique approach to AI application, and its vision for the future of the financial industry. George's journey began with a fascination for meta-learning and a realization of the potential of large language models (LLMs) after the release of GPT-3. Observing the tedious, repetitive tasks endured by his peers in financial services, he identified a critical opportunity to leverage AI to alleviate their pain and enhance their productivity. Hebbia differentiates itself from typical AI applications by going beyond simple chatbot interfaces. Instead, it aims to be a comprehensive platform, offering a wide range of functionalities that are more fulsome than a single step. George envisions Hebbia becoming a tool that even advanced AI, or AGI, would choose to use for structured and unstructured data processing and complex knowledge work. He emphasized, "how about we build the AI platform that is so good that if AGI were to complete a task, it would choose to use Hebbia to do that task." A core strength of Hebbia lies in its ability to leverage private data. Users can upload proprietary information, such as investment memos or legal agreements, to create custom knowledge libraries. This allows Hebbia to provide highly tailored results that are far more relevant and insightful than generic web searches. Furthermore, Hebbia is designed with the intention to drive agentic workflows. By combining computer use with advanced reasoning, Hebbia effectively manages structured and unstructured data processing, taking its users from objective analyses, into a realm of subjective understanding. One of Hebbia’s key differentiators is its focus on actionable insights and real-world value. While many AI applications promise faster results for existing tasks, Hebbia aims to deliver net-new value by enabling users to discover insights they would not have otherwise found. For buy-side firms, this translates to efficiently analyzing vast data rooms, identifying customer concentrations, and synthesizing information from expert network calls. Hebbia's templates also expedite the time to value through the platform. In short, George claims, "now we have like a screening template where we can measure the amount of time it takes you with Hebea and before Hebea and prove to a financial services firm, this is tangible ROI." Looking ahead, George envisions a future where AI agents contribute significantly to global GDP, not by replacing humans, but by creating net-new value. He believes that AI will transform the capital markets, particularly in private investing, where it can provide the structured data and analytics that are currently lacking. Hebbia addresses the future with a focus to empower humans. By automating the mundane tasks, humans can shift their focus to more strategic thinking, creative problem-solving, and discovery. He believes that as a result, the workforce will change, moving from objective analyses, into a realm of subjective decision-making.