Build an AI Analyst with Claude Code in 50 Min | Sumeet Marwaha

八个月前，这还只是在调试 SQL。现在，人工智能真的可以开始编写那些初始的样板 SQL 查询和代码，帮助你进行更高级的分析。在我职业生涯中，我曾多次设置过那些仪表板，但它们最终都会在某个时候被忽略。[音乐] Claude 会一直阅读它。Claude 会一直开始提问。重现数据科学家在公司内部的实际思考方式。他们不仅仅只考虑数据。他们会考虑数据之外的所有事情。Podco 是我感觉第一个真正能做到这一点的工具。>> 哪个 AI 编码工具的势头最猛？哪个会赢？>> 我们平台上的客户已经选择了它，绝对是 Cursor。Cursor 绝对是首选的初创公司编码工具，也是首选的企业编码工具。>> 那么，在我们深入探讨云托管之前，你能从宏观上谈谈 AI 如何帮助处理典型的数据任务吗？是的，这是我所说的典型分析师在其角色中所做事情的宏观视角，以及云代码如何开始增强它。你知道，我可以从非常宏观的角度思考，从零开始会做什么，但现实是，大多数人在他们目前的工作中，并不是从零开始的。他们已经有一系列仪表板。他们有一系列正在运行的查询。所以，在非常初始的阶段，实际上只是监控这些东西，对吧？Claude 可以实际为你运行查询，并查看它们，了解趋势，并以一种你可能在公司里担任某个职位，查看那四个或五个仪表板，并不断尝试理解，好吧，这个趋势真的是有意义的变化吗？是我需要调查的东西吗？因为 LLM 可以自己解释这些东西，它可以帮助你扩展监控范围，监控你可能选择监控的更多内容，而不是花费数小时查看每个仪表板和单元格。它实际上可以为你做到这一点。我认为，这已经是人们在 Claude 中构建自己的小型监控代理和仪表板方面，最先的真正改变游戏规则的事情之一了。然后，一旦你看到趋势并思考，我可能想调查一下，Claude 在通过实际数据本身，以及 Slack 上正在发生的事情，以及像 Linear 和 Tickets 这些围绕特定主题创建的工具上正在发生的事情，来构建上下文方面做得很好。而 Claude 可以访问所有这些，这实际上是数据人员的工作，对吧？你可能想深入研究，实际提前完成这项工作，提出“嘿，看起来你的一个指标变了，让我们进入客户层面，进入交易层面，了解是否有什么不寻常的事情发生在这些趋势中”，所以这个探索阶段实际上是非常强大的。如果你已经知道你在监控什么以及你想跟踪什么，你可以直接跳到第二点，并且实际上非常有帮助。>> 然后也制作一个故事，不是所有数据科学家都擅长，你知道的。是的，这个，你知道，Claude 可以完美地做的事情开始变得有些模糊，而 Claude 真正获得帮助并指导人们的地方，在于制作故事的部分，试图讲述一个能改变人们想法的故事，真正提出对企业有意义的建议，你必须真正使用你的提示来赋予它这种能力，将一个好故事翻译给你。所以，很棒的是它可以进行那些侧面调查，那些深入研究。当你开始制作一个故事时，你可能会意识到有些事情你想调查和添加，这实际上就是 Google 文档中的一堆评论。但你可以实际利用它们，让 Claude 以相当快的速度处理它们，希望在你开始构建这个故事时，能够填补任何空白，了解发生了什么。而且，当你开始提出某些改变时，你还可以查看代码库，查看预实验，查看已经完成的改变类似事情的事情，并开始衡量潜在的影响。>> 太棒了。最后一个就是关于影响，对吧？>> 是的。>> 是的。影响部分是，这是我还没有完全看到 Claude 从头到尾以这种方式完成的事情，我知道这是可能的，对吧？随着这些不同步骤的改进，你可以想象一个世界，你设置了 Claude 来进行分析，它理解代码库，理解过去发生了什么变化，然后实际进入并改变你应用程序中的一个特定按钮，将其放在不同的顺序，然后运行一个实验，然后再次开始整个循环，以了解该实验是否成功或不成功，为什么不是这样，如何讲述故事以及要改变什么，然后再次提出建议并实际实施这些改变。所以，这个分析循环是每个数据科学家与产品经理和工程师一起工作的过程，我已经看到 Claude 开始增强其中每一个。我认为我们都可以理解，未来它实际上可以按顺序完成所有这些，并持续这样做，实际上成为这个端到端的产品经理、数据科学家和工程师。是的，在我职业生涯中，我与很多数据科学家合作过，他们实际上不想做很多事情，对吧？他们不想手动编写 SQL。他们不想构建仪表板。这很糟糕[笑声]仪表板。>> 嗯，他们不想做，他们想做更深入的分析和更深入的思考，但很多工作就是构建仪表板，你知道的，PM 支付他们报酬说：“嘿，你能运行这个随机查询吗？”所以，所以，是的，如果他们能用 Claude 来做，你知道的，尝试做很多工作，或者至少做得更快，那将会有助于提高工作满意度。>> 是的，工作满意度，还有 PM 的满意度，能够获得那些数字而无需与数据人员交谈。我认为这是与团队和产品经理交谈的一个例子，在这里，我看到人们只是拿一个现有的仪表板，把所有的查询都扔进 Claude 代码，让它每天或每周运行一次，无论何时他们试图检查监控步骤，并进行自己的探索，进行自己的调查，关于正在发生的事情。>> 这可能会排除数据人员，这也许对工作满意度不太好，但它确实让他们提出更好的问题，对吧？这导致了真正有时间进行更好的分析，更深入地思考如何真正改进产品的工作。我看到这些事情在这里开始发生，这让我觉得很有趣。我们来举个简单的例子。所以，就像，我认为大多数公司都有，就像，你知道，每个星期一或者星期五，都会有一个超级集或者什么东西生成一个图表，对吧？或者发送一封包含很多图表的电子邮件给你。但是，有了 Claude 和 AI，现在有什么改进呢？所以，它不会仅仅发送一个仪表板给你，它实际上会进行很多洞察分析。>> 是的，我认为有两点让它变得更好，你可以很容易地定制它，对吧？你可以设置这个仪表板，以满足团队的需求，但也许团队中的一些人只希望了解某些事情，或者真正定制它，使其关于使用特定产品或功能的某一种用户。>> 嗯，Claude 可以快速修改，改变它，使其非常适合那个人的角色，以及他们关心的事情。所以，这是我看到它做得很好的事情之一。>> 嗯，另外，它实际上可以运行分析，进行后续提问，或者至少生成关于我所认为的当这些电子邮件发出时会发生什么的疑问。这就像，好吧，有些人读了它们，有些人真的读了它们，想出了所有问题。Claude 会一直阅读它。Claude 会一直开始提问，然后你就可以开始看到，哦，我真的可以让 Claude 回答这些问题吗？它也会开始尝试回答。我认为这种能力，总是知道有人在阅读它，是重要的部分，因为我职业生涯中曾多次设置过这些仪表板，它们最终会在某个时候被一群人忽略，因为他们觉得他们应该收到它们，但他们实际上并没有。>> 嗯，我认为 Claude 从来没有这种感觉。所以，我看到很多时候，即使只是 Claude 在阅读，也意味着有人会阅读它。>> 明白了。而且，就像你的幻灯片一样，它能够访问的不仅仅是数据仓库。它有 Slack 访问权限和其他很多上下文，希望这能帮助它提出更好的见解，对吧？>> 是的，那太棒了。例如，我们这里发生了一起事件，它影响了我们的一些指标，以及我们每周审查的指标开始发生变化。它能够理解在那特定时间发生了事件，并且影响了指标。这是一次数据事件，它影响了实际的最终客户，但它肯定影响了为什么这些指标看起来不对。它在我们的 Slack 中搜索了那个。我能够看到一些修复正在进行中，一些 Tickets 正在创建以解决这个问题。所以，它为我节省了，我不得不去查看特定客户，也许你，你知道，离开，但事实证明，周一早上确实有一个真正的原因。>> 明白了，明白了。>> 开始了，你知道，我修复那个事件的过程。但至少，Claude 可以很快理解这一点。>> 是的，那太棒了，伙计。是的，我觉得大多数公司根本没有设置这些。所以，所以，是的，你和正在做这件事很棒。>> 嗯，我认为大多数公司都有一个数据人员，他们一直在做这件事，对吧？一直阅读这些东西，搜索，对吧？>> 嗯，但现在你可以帮助那个人做这件事。>> 我们让它变得实用起来怎么样？也许你可以向我们展示这些东西在 Claude 代码中是如何实际工作的。也许，让我们看看一个公共数据集或者其他什么东西。>> 是的，好的。让我们来看看初创公司融资轮次的数据集，它模仿了 Brack 的数据，因为它们只是交易，对吧？资金从一个风险投资公司转移到一个初创公司。这就是我选择它的原因。我将介绍我如何设置它。一些可能出现的与进行分析相关的问题，以及你如何在设置过程中解决这些问题。然后尝试向你展示一些用例。你觉得怎么样？>> 当然。是的，听起来很棒。>> 嗯，好的，我们开始吧。让我们从这里开始。所以，我首先会考虑的是，你正在构建的这个数据集是什么。我在这里使用的不是 MCP 框架。我认为你可以开始的部分非常简单。你不必在如何思考“给 Claude 代码三个关于你公司特定领域指标的查询，并指示它围绕该指标构建一个 MCP”方面过于复杂。就是这样。这就是你真正需要启动一个我称之为“基本 MCP”的东西。现在，当你添加更多领域时，事情会变得有点复杂，但对我们来说，这就是我们最初的形成。所以，考虑到这里的数据集，我将打开我拥有的三个查询，这样你就可以看看它们。我们正在使用我们称之为市场数据的东西，在 Brack 的融资轮次中。我们只是在查看一些关于第一个的基本信息，即按行业和阶段划分的月度初创公司融资趋势。它们正在向 Claude 代码展示如何编写这些查询，典型的连接是什么。很多都基于域名。所以，这是正在建立的。我们正在添加一些关于这个表里面有什么的文档。你知道为什么有两个表，对吧？一个是融资轮次，一个是实际的初创公司。以及这些融资轮次是如何命名的，series_a 有助于稍后为它提供指导。我们添加了第二个查询来考虑这里的另一组数据，即投资者。所以我正在尝试构建两到三个查询，每个表至少连接一次，以向 Claude 代码展示一些这些模式和一些关键字段。所以，看看这个，尝试对顶级投资者进行排名，尝试添加一些关于他们多久为这些客户提供资金以及他们最近几年的情况。实际上，这是第三个查询，我们有初创公司生态系统健康得分。这是一个你可能有一些分析示例的地方。所以，两个基本的例子，用来真正说明如何构建数据集中的一些指标，然后我说一个用来尝试展示你可能如何思考做一些更复杂的事情，一些实际上是试图理解特定初创公司生态系统看到的融资速度。所以我发现这是开始这个的一个很好的标准集。>> 好的。所以，你是手动编写了这些查询，还是借助了 AI 的帮助？>> 所以，实际上你可以反向工程它们。你可以实际进入并搜索这些表，看看你的 Snowflake 实例中正在运行哪些查询，然后从那里开始选择。这就是我开始这些查询的方式，因为我并不一定想坐下来创建正在使用的查询，因为这些表已经存在了。我能够从我们已经在仪表板或我们的 Snowflake 实例中运行的内容中抓取它们，然后开始实际要求 Claude 记录其中一些，你知道，这在实际运行查询时并不总是发生。所以，这有助于添加一些内容。所以，这里的目标只是让 Claude 了解如何为这些不同的表运行查询。>> 正是如此，正是如此，给它设定了界限。所以，这是我开始实际编写提示以生成初创公司 MCP 的地方。我们实际上会进去说，“创建一个初创公司融资 MCP。”我建议不要使用现有的那个。它使用了这三个查询。我认为我还会指示它做一些研究，在网上研究一下如何设置一个很棒的 MCP。我可能还想给它一个关于这些数据集的大致想法。所以，这个数据集是融资轮次概述，包括投资者、交易和初创公司详情。我还想稍微谈谈为什么我们可能要使用它。我们想使用它来了解我们客户群体中正在发生哪些类型的新融资轮次，以便我们可以与他们互动。所以，当我们了解某人获得大量融资时，与他们互动并确保他们对 Brax 满意，这显然对我们非常有价值。我认为我还考虑提供一些关于为什么我想使用 MCP 框架的说明。所以，我们想构建一个 MCP，它允许我们进行每周的定期报告，进行深入分析，并进行快速的深入分析，并帮助监控初创公司内的趋势。就是这样。我认为我还喜欢让它考虑尝试做一个评估集。然后，如果你以这种方式设置它，你以后就可以编辑并了解评估可能是什么样的，并进行更改。所以，在这里，我实际上会说“创建三个评估问题来测试 MCP”。我认为在这种情况下，因为这是一个初创公司融资数据集，它可能会想到一些问题，这些问题实际上是正确的，而不是过于特定于 Brax 的领域。所以，我对此感到自信。所以，我们将进入规划模式，因为它将帮助我们看到它是如何处理和理解这个的，我们可以看到计划是什么样的，并进行编辑。只是为了那些不明白为什么要做这个任务的 MCP 的人。>> 是的。>> 是的。>> 是的。我认为 MCP 的核心原因是，你希望能够让 Claude 以一种结构化的方式访问你的数据，这种方式对于使用 Claude 进行此类分析的人来说是可重复的。所以，没有 MCP，你可以想象，你知道，有人扔进一个查询，然后告诉 Claude 回答一个关于初创公司融资轮次的问题，它不会明白，你知道，有一种方法可以做到这一点。数据团队和编写这些查询的人员已经开发了一些文档。所以，对我来说，这是这里的价值之一，MCP 开始构建这些界限，以便任何尝试访问此数据的人都可以从正确的地方开始。我认为我们总是明白，在初始起点之外可能存在修改，但到目前为止，我看到 MCP 框架有助于以一致的方式连接到 Snowflake，以更一致地编写查询。>> 明白了。并且实际上能够实现分析步骤。>> 好的。所以，是的，基本上他们不必每次都做所有这些设置，如果你创建 MCP。>> 是的，没错。>> 有道理。>> 所以，它正在问我一些问题，比如我是否应该使用我提到的所有四个查询，而不是三个。>> 嗯，所以，你是否想将其部署到生产就绪？我认为我只会说本地需要生产就绪。>> 说我应该有一些警报，这样我就可以实际开发它。>> 嗯，我认为我不会在这个阶段做任何警报。只是跳过它。>> 然后，这是答案本身。所以我总是喜欢让这些未回答的问题回来。这让我对即将到来的计划更有信心。>> 是的，这是像一个新功能，对吧？我认为我以前从不问所有这些问题。>> 是的，我确实设置了它，在我的用户>> 哦，是的。Claude MD。>> 是的，Claude MD 总是问我这些。所以，我不确定它是否是新的，但是的，我确实看到有人给了那个建议。也许是在你的播客上，我把它加进去了，它一直很有用。>> 很好。>> 说实话，你可能还会问更多。>> 而且，抱歉，这可能是一个愚蠢的问题，但这个项目，你拥有的 Summit OS，是如何连接到 Astro 数据库的？是你之前连接的，还是？>> 我没有涵盖。是的。>> 基本来说，我设置了 Snowflake CLI。>> 所以，它实际上通过那个访问。然后，为了确保它稍微可靠一些，我添加了 Snowflake PAT 令牌。>> 并将其添加到我的 Claude。所以，它能够访问 Snowflake 实例来查看这些东西。>> 好的，伙计。你让它做了很多事情。所以，它有一长串任务要做。>> 我知道。是的，它实际上比我想象的添加了更多。但是>> 是的，>> 我们将看看它是如何进行的，我很乐意在它构建的时候谈论，因为我确实让它预先设置了一些潜在的问题，我乐意谈论在用 AI 进行分析时出现的一些问题。我们通常会把这些反馈到 MCP 中，让它来解决一些问题。>> 是的，我们深入研究一下吗？>> 当然。是的。>> 所以，当涉及到我用 Claude 进行大量分析时遇到的问题时，有一些具体的问题我认为与你可能想用 Claude 来写文档或改变代码库的方式不同。问题在于，当你实际编写一个查询时，它可能会返回 10,000 行或 200 万行，对吧？实际上会炸掉你的整个上下文窗口。这与你可能认为 Claude 在浏览代码库或阅读文档时会遇到的问题不同。它通常不会在第一步遇到这个问题。当你实际让一个数据代理去解决问题时，它实际上可能会遇到这个问题。这就是你在实际给它编写查询的指令中，可以给它理解这是一个问题，并且你需要管理你的令牌，因为你正在进行分析。我想要强调的是非常重要的一点，你需要提醒它，它之前运行的查询有一个限制，因为它可能只会获取那个限制为 1000 的表，并认为它是完整的表。但能够给你这两个提示，对我来说，已经解锁了能够带来非常好结果的分析链，而 Claude 在没有这些提示的情况下，就很难回答。所以，这是我尝试用 Claude 解决一些分析问题时亲身学到的东西。>> 这是个好点。>> 是的。>> 是的。而且我认为在这方面，数据 MCP 的优点在于，你可以真正限制它实际使用的数据集有多少。这意味着，你知道，我们有这些核心数据的东西在 Brax 中，但事实是，即使在那张核心数据表中，对于我们的客户，我们有大约八种方式来细分客户，而每一种都可能对特定的用例是必要的。我发现如果你开始给它所有八种，Claude 就会感到困惑，AI 代理就会对使用哪一种感到困惑，并且可能在每次运行时选择不同的。我发现非常紧密的语义上下文，只提供其中一种细分，也有助于确保它得到正确的答案。而且，你知道，当你开始添加越来越多的上下文时，不仅仅是这里尝试的初创公司数据域，还有汽车域和银行域，或者你产品中你想添加的任何部分，这种紧密的上下文非常重要。否则，你的探索就会迷失方向或开始出错。你不能只是把你的整个表导入到 Claude。当你说的“紧密的语义上下文”时，你的意思是只是告诉它查看 12 个来源中的哪一个，是这样吗？>> 是的，告诉它只查看其中一个。我在这里使用的语义上下文并不是我用来称呼语义层的。所以，你实际上可以给它提供一系列字段、维度和定义以及一些使用它们的用例。但如果你添加了两个基本上相同的，它就会感到困惑。>> 明白了。所以，几乎就像你只需要一个，这可能会限制一些可能性，但它实际上会让你在与代理的互动中取得更大的进展。>> 好的。是的。因为这些表和列不一定都标记得很好，对吧？[笑声]>> 它们不是。嗯，我会从两个方面来说。就像，有时它们是，人们仍然感到困惑，而且有这种情况。有时候它们不是。你必须开始思考你为什么需要记录你的表，是为了让代理能够从中挑选出你需要的字段。所以，这有两种方式。实际上，列名非常重要，对吧？它会首先检查它，然后假设它甚至不总是检查实际的文档。所以，你想清楚地命名，然后当你确实有文档时，也要确保它也同样清晰。我认为以前可能不重要的事情现在变得重要了，比如包含同义词。因为客户细分可能被称为“运动”，也可能被称为“分组”。这些东西实际上可以帮助代理在处理自然语言查询时选择正确的字段。但你不想称两个东西为“分组”，对吧？那么它会选择哪一个呢？所以，关于这一点要清楚，就是其中一些紧密的上下文。然后，最后一个我发现非常有用的，就是我们之前讨论过的，你不能只依赖于手头的那个数据集来回答问题。如果你有其他连接到你的 Claude 代码的 MCP，这可以帮助构建一个合理的计划，关于为什么某个特定的趋势，你知道，会像它看起来那样发展。将这种上下文带入，是 Claude 比专用 BI 工具做得更好的地方，在这些工具中，你不会期望将你的 Slack，你的整个代码库连接到这些工具来运行查询。但通过 Claude，成本很低，而且它确实增加了很多价值。再次，重现数据科学家在公司内部的实际思考方式。他们不仅仅只考虑数据。他们会考虑数据之外的所有事情。>> 嗯，并且在他们编写分析时将这些带入，就像 Claude 是第一个我感觉能够真正完成这个的工具一样。>> 好的。是的。我喜欢这个。而且，而且这一切都是关于上下文管理，对吧？因为，就像，你不想一次性引入太多，否则它会感到困惑，对吧？就像，就像即使是 Slack，你可能也不想引入每一个频道。>> 是的，100%。你不想引入所有东西。>> 嗯，你可以通过这种方式指示它进行查找。>> 对我们来说，有很多数据帮助频道，或者我们正在讨论问题。有时有项目频道，实际上非常相关。所以，你也可以给它一个提示，比如，“嘿，去看看这个新功能的项目频道，了解这个项目进展如何，”>> 嗯，然后开始为我编写一个查询，希望能够构建一个关于我们何时发布这个项目的仪表板，如何监控它。>> 嗯，它出奇地做得很好，这正是每个数据科学家一直想要的，就是被带入项目，然后被赋予这项任务，构建你不得不做的仪表板，你也必须用 Claude 来做。>> 你是如何连接到 Slack 和 Drive 的，我猜 Drive 是内置的，还是？>> 是的，通过这两者，我们实际上使用了 Glean MCP，我们已经添加了它，它是我们的企业 AI 工具，可以连接到所有这些应用程序。所以，你实际上可以在 Claude 代码中设置它，它可以访问你的 Drive 和 Slack 来获取上下文。所以我发现为每个连接构建子代理非常有用。否则，当你问它问题时，它可能甚至不知道它需要连接到 Glean Slack 部分。但如果你为每个连接构建子代理，它在实际给出指令时做得很好。>> 好了，伙计。你得做一个 GitHub 仓库，开源很多这些东西。[笑声]>> 是的。>> 是的。>> 嗯，让我切换回去。太棒了。所以，我们现在可以看到，我们正在进入 MCP，我们已经创建了初创公司融资 MCP，并将其保存在这个 OS 中，因为我想要>> 把它留给自己。如果我要部署它，我们会将其移到我们的集中式存储库。>> 你可以查看我们创建的工具。所以，这里有一些关于我添加的上下文来解决我提到的问题，我不想炸掉上下文窗口。我希望它能够按顺序浏览分析，理解我们对其中一些查询设置的限制，并在每次分析结束时报告其表现如何。然后，我们有一个基本的查询器，它有助于将表转换为 SQL 查询，这，你知道，是这个 MCP 中最常用的部分。然后，我们有分析初创公司趋势，它基本上是一个分析工具，有助于引入这些常见的分析类型，并开始构建当你在分析这个数据集时，分析的顺序应该是什么样的。所以，查询和分析工具是这个 MCP 的两个组成部分。所以，我们可以实际尝试现在使用它。所以，比如，使用初创公司数据 MCP，在 10 月份进行所有最新的 A 系列交易，并按你认为最有可能获得 B 系列的交易进行排名。>> 我已经添加了查询的基本部分，告诉我最新的 A 系列交易，但我还添加了分析部分，我实际上希望它对它认为的 B 系列潜在指标发表意见。所以，我只是在这里一次性完成。通常我可能会围绕这个构建一个计划，但我们会看看它是否能更快地完成。>> 你认为它会如何看待数据来理解 B 系列的可能性？是它的轨迹还是？>> 是的，正是如此。希望它会查看过去获得 B 系列的交易，并选择一个标准。希望它只是融资金额或从 C 系列到 A 系列的速度。>> 这就是为什么创建这个分析融资速度查询希望能够为它提供指导，以及一些更有趣的。>> 所以，它已经在这里完成了。10 月份只有两笔 A 系列交易。所以，首先，它向我展示了 Glue AI 2000 万美元的 A 系列和 Pedal Surgical 1000 万美元的 A 系列，以及 AI 和健康科技。>> 嗯，它已经开始对其进行排名了。>> 嗯，我可以问它一些关于为什么我选择它的问题。>> 是的，嗯，希望它会解释为什么。>> 是的，就是这样。理性百分比。就是这样。最后。所以，是的，这是那个报告。>> 嗯，所以，Glue AI 的 A 系列高概率，大型 A 系列 2000 万美元，我猜以前是大型，也许。所以，AI 公司是它认为会引领它获得 B 系列的事情之一。>> 嗯，我认为它说 10 月份很接近。所以，它给出了一个 A 系列的预期时间表，领域可信度是其中之一，它以某种方式认为这会推动 B 系列。>> 嗯，实际上预测了它认为的 B 系列应该是多少。>> 是的，我的意思是，它不仅仅是看数据。它似乎在对医疗保健需要更长时间的事情进行一些合理的解释，对吧？>> 是的，是的，正是如此。而且我希望它能搜索一些数据，而不是自己凭空捏造。>> 嗯，因为我们有这些数据。我们有所有这些融资轮次的历史。它应该能够做到这一点，而且它确实做到了，对吧？根据 A 系列数据，61% 的医疗保健公司达到了 B 系列，而 73% 的 AI 公司达到了 B 系列。>> 嗯，然后也看了看最终成为 B 系列的融资轮次的平均规模约为 2000 万美元。所以，你看到了为什么 Glue AI 是第一名，因为它有 2000 万美元的融资额。>> 是的，它正在像 VC 一样思考，每当它看到“AI”这个词时，它就会想，“哦，它将是 B 系列。”[笑声]>> 正是如此。是的。它们已经处于领先地位，即使我们不知道 Google AI 做什么。>> 从数据集的角度来看，事实证明，这些公司确实更有可能获得 B 系列。>> 所以，对于数据来说，它是正确的。>> 这个数据集能追溯多久？比如一年或两年什么的。>> 不，我认为数据集可以追溯到 2016 年甚至更早。>> 好的。所以，它在历史上相当长，因为我认为我们曾经有过亚马逊的融资，它造成了一些问题。所以，我们已经清理了一些，但是的，我们几乎拥有所有的历史融资轮次。>> 我可以问个问题吗？>> 当然。是的。>> 你能问它一些关于，你知道，哪个 AI 编码工具的势头最猛？>> 是的，我猜我，你想定义势头吗？>> 势头就像，你知道，就像，你知道，哪个会赢，伙计？就像哪个会赢？[笑声]>> 是的。哪个会赢？>> 就是这样。是的。为什么？是的，解释一下为什么是好事。>> 是的，我总是想让它使用数据来提示，因为从上一个例子来看，我担心它会试图猜测，但这是那个问题。所以，它将提取所有这些编码工具。>> 太棒了。是的，那里必须有很多。>> 是的，有很多。是的，它们一直在出现。>> 嗯，是的，这很有趣。我看看它会说什么。>> 嗯，这里。>> 是的，正如你所看到的，仅仅是那三个查询的设置。现在，我们可以开始问这些问题了。它正在使用那个基础集获得相当扎实的结果，并思考为什么 MTB 很重要。现在，我们可以让 PM 们来访问这个，工程师们来访问这个。我们正在构建在同一个初始数据集之上。>> 但是，你写的那些三四个查询非常重要，对吧？因为，它基本上每次都在重复使用那些查询来获取信息。>> 是的，它也在尝试创建一些新的。你可以看到，它们并不完全是复制粘贴。但是你说得对，那三个查询，尝试选择那些有连接的，有多个表的，有一些你可能想在此基础上构建的分析的方面，这非常重要。而且，这正是我不想凭空捏造它们的地方。我实际上想看看人们正在对这个表运行哪些查询，然后从中选择。>> 嗯，这样你就不用凭空创造一些不符合人们当前查询方式的东西了。>> 嗯。>> 而且，>> 实际的 MCP，你为 Brax 构建的，任何人都可以使用吗？PM 们可以使用吗？>> 是的，绝对。是的，我们构建数据 MCP 的方式是，你只需要授权你的 Snowflake 连接到 MCP，它应该能够在你提出数据问题时运行。它会编写查询并实际生成结果和答案，然后你可以构建自己的代理来定期构建你的仪表板版本，或者一组查询。我可以向你展示，至少是我在这个 MCP 上构建的那个仪表板的一个版本。但这是答案。>> 是的，我们来看看。>> 你在寻找>> 让我们来看看。>> 是的，所以，根据数据驱动的势头指标，Cursor，这并不奇怪。>> 嗯，巨大的 A 系列。估值有点滞后。看，我认为 4 亿多美元并不准确。所以，我们可以试着调查一下。我相信现在它们是 100 亿，它们是 300 亿。>> 嗯，但关键信号 A16Z 的支持是稳固的。>> 嗯，它还有一些有趣的研究，为什么你认为它是一个顶级工具。>> Replet 另一个，排名第二，领先于 Kodium Windsurf Cognition，对吧？>> 嗯，是的，这个数据集的一个问题是初创公司会改名，被收购，所以你有时会失去一些关于这些转变的线索。>> 嗯，但这些是它认为会赢的前三名。>> 是的，我相信一两个，我不确定第三个，但你知道，一两个处于良好状态。>> 是的。>> 是的。我认为我们看到的数据确实同意其中一些。>> 嗯。>> 好的，很好。这太棒了，这很有趣。>> 是的。嗯，让我们回到我最初的问题，因为，就像，在有数据团队的公司里，总会担心，如果你让某个随机的 PM 访问这些查询，你知道，他们可能会运行一个随机查询，导致整个数据库崩溃，或者[笑声]对吧？所以，所以，而且有了这些 MCP 的东西，它实际上会产生大量的令牌成本，如果有人失控了。所以，所以，我只是好奇，你是否有任何控制措施？>> 是的，我认为这是我发现 Claude 最近发布的技能实际上解决了这个问题，你可以设计这些技能，代理会利用这些技能来构建分析或报告，指示它不要这样做。不像普通的 PM，技能会遵循它，它会意识到它使用的令牌，以确保它不会导致数据库崩溃。>> 嗯，它会指出它可能在连接方面做出跳跃的地方。>> 嗯，并揭示这些。所以，当你，作为一个 PM，开始尝试生产化某事或与你的数据人员分享时，至少有一种方式可以让数据人员快速了解我需要考虑帮助或修复的领域。>> 所以，我们发现，在创建这些技能方面，这实际上是一件很有用的事情，我们已经创建了几个技能来帮助做到这一点，比如即席分析技能。这是我的数据分析技能。>> 哦，哇。>> 它运行了大量的这些上下文管理理念，计算故事映射。>> 嗯，将这些 SQL 查询视为叙事，而不仅仅是单个组件。能够与 PM 分享这个技能，并告诉他们使用它，至少让我有信心。它不会完全导致数据库崩溃。>> 嗯，这是新的，对吧？你不能总是教别人那个逻辑，让他们每次都遵循它，但技能可以。>> 是的，我还没有深入研究技能，但听起来是这样，所以它不会让我连接两个两百万行的表，它不会问。[笑声]>> 是的，这是最大的问题，你可以用一个查询填满你的上下文窗口。但有了技能，你可以告诉它，“嘿，在任何这类连接上都加上一个限制 50。”所以，它不会完全导致你的数据库崩溃，然后有一个超时，说查询花费的时间超过两到三分钟。尝试重写它们，杀死它，重写它，使其性能更好。>> 这些是我真正兴奋的部分。不仅仅是共享这个查询并让某人修改它的自助服务方面，实际上是教 Claude 代码如何进行数据分析，让人们能够利用这一点，以及数据可视化，常见的分析类型，如队列分析。>> 嗯。>> 等等。所以，对于这一点，它甚至到了有时我试图导出 CSV 来处理它，这有点令人讨厌，创建了一个导出 CSV 的技能。[鼻息声]现在，当我试图在你的系统之间移动数据时，我可以利用这一点。>> 好了，我们得另外一集来谈谈技能。>> 好的。[笑声]是的，技能非常重要。但让我们利用最后几分钟。让我们谈谈你已经发布的，并且在公开博客文章中分享的一些数据。比如，基于 Brax 支出模式的一些相当有趣的数据，对吧？>> 是的，绝对。是的，我们一直在发布这些数据博客，我认为已经有将近六个月了，看到我们的客户什么最受欢迎，这很有趣，对吧？你问了最好的 IDE 或编码工具是什么。嗯，我们平台上的客户已经选择了它，绝对是 Cursor。Cursor 绝对是在初创公司编码工具的首选，也是企业编码工具的首选。而能够同时利用这两个客户群体，在 Brax 中，他们能够在初创公司和企业中都取得成功，并且证明他们为什么在 Brax 上，这是非常罕见的。

他们为 300 亿美元的融资轮次进行了估算。所以对我来说，这些东西开始显现出来，而且他们已经连续几个月排名第三了。这也不是暂时的。>> 太棒了。是的。这里还有一些非常有趣的东西，比如，嗯，我想这些不是线性的尺度，对吧？可能就像，你知道，它可能就是，是的，但但但即便如此，看到像 11 Labs 这样相对接近顶部的公司也很有趣，我想人们正在构建语音助手之类的东西。>> 或者 >> 是的，它们是首选的语音助手，比如每当你的平台中添加任何这种语音功能时，尤其是在初创公司中，我不会认为这更多的是初创公司将其用于自己的内部用例，而是他们将这些工具添加到他们的应用程序、他们的平台、他们的产品中。这就是 11 Labs 在语音助手附加组件方面真正脱颖而出的地方，他们几乎总是首选。嗯，对我来说很清楚的是，他们在那个领域赢得了胜利，比如客户支持之类的，或者，是的。>> 是的。我的意思是，我们必须看看 11 Labs 的用例，但是的，他们肯定在成为首选方面取得了成功，这也是我们在捆绑分析中看到的一些东西，我可以在这里看到，是的，对于语音应用程序，你知道，显然还有其他一些语音应用程序正在出现，比如 Deep Speechify、Assembly AI，但毫无疑问，11 Labs 在选择和资金去向方面一直是最稳定的。我也认为，排名前两位的公司，比如，嗯，OpenAI 比 Anthropic 更受企业欢迎，因为 Anthropic 确实在针对公司，对吧？所以我在数据中看到了这一点，在 2024 年，OpenAI 在初创公司和企业中都赢得了胜利，但从 2025 年开始，我们开始看到很多初创公司将他们的一些代理式人工智能产品转移到他们的应用程序中。在初创公司中，如果你这样做，很多人会选择 Claude 作为模型来放入他们的生产应用程序中。嗯，你知道，那种可以说是企业级的模型，你知道，它稍微更可靠，或者无论是什么原因，Claude 已经成为许多初创公司的选择。所以我们看到初创公司产品中的代理功能通常选择 Claude 而不是 OpenAI，这就是初创公司在 Anthropic 上的支出不断增加的原因，而在企业中，更多的是 ChatGPT Pro 许可证或企业许可证，这有助于提高其排名。嗯，所以我们看到很多支出流向了初创公司中的 Anthropic。嗯，人们也开始将 Claude 作为订阅，但这通常是第二个订阅。>> 是的，>> OpenAI 在聊天方面仍然占优。>> 是的，我猜你知道，企业比所有科技公司多得多，对吧？有，你知道，是的，>> 有像所有会计公司和所有国际公司，OpenAI 的品牌知名度要高得多，我敢肯定。>> 是的，绝对。如果你是那些公司之一，你只是注册 OpenAI，因为我们在数据中看到的是，实际上有五名员工已经购买了 Pro 订阅，你还没有设置团队。那里可能存在一些安全风险。所以你只需将整个工具投入使用并分发给所有员工，因为你必须强制要求，你必须成为一家人工智能导向的公司。就像，哦，我们直接使用 OpenAI。嗯，是的，而初创公司正在构建这些新解决方案，他们对使用哪个模型以及为什么想使用该模型有很多看法，我们看到是的，Anthropic 在很多选择中获胜。>> 但伙计，我认为总的来说，这是一场血腥的战斗，伙计，我认为前五名还可以，或者我的意思是前七名，但你知道，有成千上万的人工智能公司，伙计，正在争夺这些企业和初创公司。是的，看到真正有趣的数据涌现，比如 2024 年 6 月推出的初创公司正在大获成功，绝对是，你知道，采用率呈直线对数增长，因为他们能够构建人工智能工作流的一个组成部分。他们的代理做得很好，所以初创公司正在添加那个特定的代理来处理那个工作流，因此在这方面花费了很多钱，而且很有趣的是，我们可能会在 12 月发布一些关于人工智能行业整体的概述，以及这些特定的用例有时如何帮助公司在该领域获得关注。嗯，你应该读读他的博客文章，因为我敢肯定你看到了很多 [笑声] 人工智能公司获得了大量的关注，然后它们就衰落了。也许当 OpenAI 的 CHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCH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300 亿美元的融资轮次进行了估算。所以对我来说，这些东西开始显现出来，而且他们已经连续几个月排名第三了。这也不是暂时的。>> 太棒了。是的。这里还有一些非常有趣的东西，比如，嗯，我想这些不是线性的尺度，对吧？可能就像，你知道，它可能就是，是的，但但但即便如此，看到像 11 Labs 这样相对接近顶部的公司也很有趣，我想人们正在构建语音助手之类的东西。>> 或者 >> 是的，它们是首选的语音助手，比如每当你的平台中添加任何这种语音功能时，尤其是在初创公司中，我不会认为这更多的是初创公司将其用于自己的内部用例，而是他们将这些工具添加到他们的应用程序、他们的平台、他们的产品中。这就是 11 Labs 在语音助手附加组件方面真正脱颖而出的地方，他们几乎总是首选。嗯，对我来说很清楚的是，他们在那个领域赢得了胜利，比如客户支持之类的，或者，是的。>> 是的。我的意思是，我们必须看看 11 Labs 的用例，但是的，他们肯定在成为首选方面取得了成功，这也是我们在捆绑分析中看到的一些东西，我可以在这里看到，是的，对于语音应用程序，你知道，显然还有其他一些语音应用程序正在出现，比如 Deep Speechify、Assembly AI，但毫无疑问，11 Labs 在选择和资金去向方面一直是最稳定的。我也认为，排名前两位的公司，比如，嗯，OpenAI 比 Anthropic 更受企业欢迎，因为 Anthropic 确实在针对公司，对吧？所以我在数据中看到了这一点，在 2024 年，OpenAI 在初创公司和企业中都赢得了胜利，但从 2025 年开始，我们开始看到很多初创公司将他们的一些代理式人工智能产品转移到他们的应用程序中。在初创公司中，如果你这样做，很多人会选择 Claude 作为模型来放入他们的生产应用程序中。嗯，你知道，那种可以说是企业级的模型，你知道，它稍微更可靠，或者无论是什么原因，Claude 已经成为许多初创公司的选择。所以我们看到初创公司产品中的代理功能通常选择 Claude 而不是 OpenAI，这就是初创公司在 Anthropic 上的支出不断增加的原因，而在企业中，更多的是 ChatGPT Pro 许可证或企业许可证，这有助于提高其排名。嗯，所以我们看到很多支出流向了初创公司中的 Anthropic。嗯，人们也开始将 Claude 作为订阅，但这通常是第二个订阅。>> 是的，>> OpenAI 在聊天方面仍然占优。>> 是的，我猜你知道，企业比所有科技公司多得多，对吧？有，你知道，是的，>> 有像所有会计公司和所有国际公司，OpenAI 的品牌知名度要高得多，我敢肯定。>> 是的，绝对。如果你是那些公司之一，你只是注册 OpenAI，因为我们在数据中看到的是，实际上有五名员工已经购买了 Pro 订阅，你还没有设置团队。那里可能存在一些安全风险。所以你只需将整个工具投入使用并分发给所有员工，因为你必须强制要求，你必须成为一家人工智能导向的公司。就像，哦，我们直接使用 OpenAI。嗯，是的，而初创公司正在构建这些新解决方案，他们对使用哪个模型以及为什么想使用该模型有很多看法，我们看到是的，Anthropic 在很多选择中获胜。>> 但伙计，我认为总的来说，这是一场血腥的战斗，伙计，我认为前五名还可以，或者我的意思是前七名，但你知道，有成千上万的人工智能公司，伙计，正在争夺这些企业和初创公司。是的，看到真正有趣的数据涌现，比如 2024 年 6 月推出的初创公司正在大获成功，绝对是，你知道，采用率呈直线对数增长，因为他们能够构建人工智能工作流的一个组成部分。他们的代理做得很好，所以初创公司正在添加那个特定的代理来处理那个工作流，因此在这方面花费了很多钱，而且很有趣的是，我们可能会在 12 月发布一些关于人工智能行业整体的概述，以及这些特定的用例有时如何帮助公司在该领域获得关注。嗯，你应该读读他的博客文章，因为我敢肯定你看到了很多 [笑声] 人工智能公司获得了大量的关注，然后它们就衰落了。也许当 OpenAI 的 CHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCHCH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