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· 6mo

“龙虾已经逃出来了，它不会再回到水箱里了，”@openclaw创始人@steipete说道。在这场来自#TED2026的全新演讲中，他分享了为何由你亲手打造的 #AI 代理才是未来：http://t.ted.com/DPASxmF

https://x.com/TEDTalks/status/2045274536053219788?s=20

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中文 English

0:00 将来，我们不只会有一个代理。

0:03 你可能会有你的工作代理，你的个人助手，

0:07 可能还有健康方面的，或是感情方面的代理。

0:11 而且它们都应该以安全的方式协同工作。

0:15 因为人类是如何通过专注和协作升级的？

0:21 代理们也即将做同样的事。

0:24 想象一家小公司，

0:28 拥有10个各自专精的代理，

0:31 接管业务的不同部分。

0:34 我们甚至还没有名字来形容它可能成为什么，

0:36 但我们即将揭晓。

0:38 代理改变了谁可以创造事物，

0:41 这扇门不会再关上。

0:44 因为当你能在一小时内

0:48 通过提示让一个原型诞生时，一切皆有可能，

0:53 下一次突破可能来自任何人，

0:56 任何国家，任何咖啡馆。

0:58 当就连一个疲惫的创始人盯着屏幕，

1:05 怀疑自己的灵感是否消失，也能做到这样的时候。

1:10 它并没有消失，只是在等待。

1:12 龙虾已经脱逃，不会再回到水族箱里。

1:16 因为Open Club为许多人所做的是，

1:20 它把AI从一个可怕、模糊的东西

1:26 变成了一个有趣且实用的工具。

1:31 也许还带点奇怪。

1:33 因为未来我们需要更多人

1:36 投入更多时间与AI相处，更好地理解

1:40 这项技术的强大和变革力究竟有多深远。

0:00 In the future, we're not just going to have one agent.

0:03 You might have your work agent, your personal claw,

0:07 maybe one for health, maybe one for a relationship.

0:11 And they all should work together in a secure way.

0:15 Because how did humanity level up by specializing and collaborating?

0:21 And agents are about to do the same.

0:24 Imagine a company, a small business

0:28 that has 10 agents that are all specialized,

0:31 taking over various parts of the business.

0:34 We don't even have a name for what it might become,

0:36 but we're about to find out.

0:38 Agents change who can build things,

0:41 and that door is not closing again.

0:44 Because when you can prompt a prototype

0:48 into existence in one hour, anything is possible,

0:53 the next breakthrough can come from anyone,

0:56 any country, any cafe.

0:58 When even a burned-out founder staring at the screen,

1:05 wondering if his spark is gone, can do something like that.

1:10 It's not gone, it's just waiting.

1:12 The lobster is loose, and it's not going back into the tank.

1:16 Because what Open Club did for many people was,

1:20 it moved AI from this scary, nebulous thing

1:26 into something that is fun and useful.

1:31 And maybe a bit weird.

1:33 Because what we need in the future is more people

1:36 spending more time with the AI to better understand

1:40 how powerful and transformative this technology really is.

AI 时代的心灵觉醒

love

· 7mo

Box CEO亚伦·莱维谈人工智能应用的鸿沟

亚伦·莱维与史蒂文·辛诺夫斯基、马丁·卡萨多和埃里克·托伦伯格共同探讨了人工智能代理将如何彻底改变工作方式、为代理经济构建软件所面临的成长阵痛、华尔街对人工智能的误解等话题。

00:00 引言

00:51 为智能代理开发软件与为人类开发软件的区别

02:10 非技术员工真的能使用智能代理吗？

14:31 首席财务官/首席信息官的抵触：对智能代理进行系统集成的真实担忧

18:39 将智能代理视为员工及其失败原因

27:35 普及差距：初创企业与大型企业的对比

42:53 华尔街的误判

@levie @stevesi @martin_casado @eriktorenberg #ai #agent #代理

https://x.com/a16z/status/2041899470556951037

中文 English

0:00 AI能力的普及将比人们现在所能认识的更久

0:03 。

0:04 认为你能通过像SAP那样的随意编码方式来搞定，这简直荒谬。

0:08 所有的领域知识，不仅仅体现在某个精心

0:12 组织的数据层里。

0:13 未来几年里，工程计算预算的讨论将是最疯狂的一个。

0:17 年里。

0:18 目前最大的问题是每个人都在试图搞清楚

0:21 其中的经济学。

0:23 而他们对机会规模的估计至少差了一个数量级。

0:27 。

0:27 如果你有比人类多百倍或千倍的代理，那么你的

0:31 软件必须为代理构建。

0:32 抽象地说，人们会说，现在你是在向代理做营销

0:36 ，你就像个API，你有个好主意。

0:36 我实际上认为这几乎是

0:40 完全错误的。

0:41 哇，这可是播客爆料。

0:43 如果你开始想象我们都得为代理构建软件，我认为

0:49 我们都明白了

0:50 ，对吧？所以这个趋势正在发生，

0:55 我们现在花费同样多的时间

0:56 思考工具的代理接口，就像思考人机接口一样，

1:00 我们这么做的原因

1:01 是因为我们的假设是，如果你有比人类多百倍或千倍的

1:05 代理，

1:06 那么你的软件必须为代理构建。这些

1:11 代理将以何种方式

1:12 与你的系统交互？将通过API、CLI或MCP之类的接口，

1:15 等等。

1:16 看来正在兴起并且相当成功的范例是，

1:21 在效率方面，

1:22 如果你赋予编码代理使用你的SaaS工具的权限，

1:27 以及

1:29 赋予编码代理对你的知识工作流程和上下文的访问，

1:35 这就变成了一种超级

1:36 能力，不仅仅是代理能阅读

1:40 一些数据，

1:41 理解一些信息，它还能编码或通过API

1:46 完成它试图实现的任务。

1:47 这似乎是一个开始复合发展的范例，

1:52 compound,

1:53 这就是云端协作现象，这也是OpenAI等公司正在

1:59 打造的，

1:59 比如超级应用、Perplexity电脑等，我认为它

2:06 作为这一切的终极表现是有道理的。

2:07 我觉得你说得对，这在理论上讲得通，

2:13 但从实际角度看，我们必须非常谨慎，

2:15 正确的说法是，算法思维对大多数有工作的

2:21 人来说非常非常难。

2:29 绝大多数。

2:30 最简单的理解方式是，如果你

2:36 找任何一个人，让他为要做的某件事创建流程图，

2:36 他们大概率都会失败。

2:40 。

2:42 所以在任何

2:46 组织里，

2:47 假设有50个市场人员在做一个大产品线的营销计划，

2:52 product line,

2:53 大概只有一个人能理解并制作出流程图。

2:58 所以如果你把这些代理

2:59 或这个协作工具放到人们面前去创建这些东西，

3:06 他们解释给它听的能力非常有限。

3:06 所以你……

3:12 但是，如果这变成了新的方式，这是你必须与电脑

3:13 交互的新方式，

3:17 而且你只需不断循环使用？

3:18 那你基本上就

3:23 回到了

3:24 为人与电脑交互开发下一层抽象层。

3:30 开发抽象层

3:31 历来都是由组织内具备高技能、非常专业的个体完成的，

3:34 针对每一层

3:36 抽象层。

3:41 然后他们打造的那些小部件

3:42 就变成了人们完成特定

3:47 任务时使用的小工具，有些人能够将它们拼接起来，有些不能。

3:48 但这已经发生过

3:52 了，

3:53 比如回形针和图钉的时代。哦，是的，我其实……这也同样会发生

3:56 在我们接下来做的东西上。

3:57 我认为……基本不变的是，工作只是在不断

4:04 上升

4:04 调整，你学一套新的技能，这也是我实际上不认为

4:09 这有什么不同的原因。

4:10 只是现在你能获得的杠杆显然更加强大。

4:14 有一条病毒式传播的

4:15 推文，是关于Anthropic的增长营销人员，你们

4:19 看到了吗？

4:20 基本上是一个人，他当时用Cloud Code

4:25 或多或少自动完成了

4:26 五到十个分别在不同岗位工作的人才能完成的任务。

4:30 我认为

4:33 有意思的原因是，是的，要做到这一点，你必须具备系统思考能力，

4:36 be able to

4:37 显然他已经足够技术了，才能完成这件事。

4:40 pull that off.

4:42 但这确实代表了如果你有

4:45 想象一下经济里有某个X职位，

4:46 而就在那个人旁边，

4:50 是无限的

4:51 工程师池，可以自动化那个人想要的任何东西。你知道吗

4:55 ，那会是什么

4:56 样的工作，未来随着这种自动化成为可能

4:59 ？是的，我同意，

5:00 你得找到一种方法，比如说，以系统的角度思考你的工作

5:04 ，才能完成

5:04 这件事。也许代理会随着时间变得越来越擅长

5:07 ，像是推动你朝那个

5:08 方向前进。但从道理上讲，

5:13 你会开始

5:14 尝试自动化很多这类工作，比如，为什么不拿

5:18 那些关键字

5:19 在谷歌广告上有效的内容，然后移植到 Facebook

5:23 ，确保它们被复制，并

5:24 再结合市场上发生的新信号

5:27 。

5:28 这是个大飞跃。你刚开始点了点头，后来我

5:32 说了些有点过分的话。

5:34 Anthropic 的增长负责人就是一个例子，那只是

5:37 剩下的工作。

5:38 是的，我能做那份工作。并不是每个人都会，市长

5:42 拥有无限资源。对，对吧。当需求无限，老实说供应也

5:46 无限，

5:46 这份工作并不难。所以我们来想想澳大利亚的联邦俱乐部

5:52 ，现在

5:53 很棒，对吗？所以，比如说，B 会是那个负责 600 美元 PC 营销的人

5:58 ，看看你能如何

5:59 与媒体竞争。这是真实的工作。好吧，我们需要更好

6:03 的例子。

6:04 不过，这真的很有趣。听着，让我举个

6:08 老例子，

6:10 关于老年人的例子。比如我姑姑，MBA 毕业于名校，开始她的第一份

6:16 工作。她比我稍年长，正值计算机兴起的关口。

6:17 她实际上

6:21 研究生期间没用过电子表格。

6:22 后来电子表格出现了，但她不是那种会用电子表格的人。

6:28 所以，他们告诉她，尽量多雇实习生。于是她第一年工作时，

6:29 她基本上

6:34 监督了一整屋子实习生。

6:34 那些孩子，就像我，

6:40 不是字面意义上，只是他们还在上大学，来帮她做所有表格操作。

6:41 是的。

6:45 接下来发生的事情有点神奇，几年内

6:46 她和她的同辈

6:51 全成了电子表格高手。

6:53 然后那个观点是，在银行工作两年成为经理，

6:58 你就会有一批人帮你做电子表格，

6:59 不，抽象层

7:03 整体向上移动。以前实习生来之前的工作就是

7:04 你坐在那里用计算器，基本上是用惠普计算器算出某个并购模型，

7:09 或者其他什么。

7:11 你只能做两轮迭代，然后得赶紧准备推介材料，或者去见客户。

7:16 突然间，他们自己能做 30 轮迭代。

7:16 是的。所以我觉得我们现在用代理人的情况，就是处在

7:21 这样一个阶段，

7:22 你以为需要 50 个，但抽象层已经这样分割

7:25 成很小的

7:27 部分，由一个超级聪明的人统筹所有。很快这一切

7:32 就会

7:33 崩溃相互覆盖。

7:37 是的，最终只会有

7:38 一套被称为代理人的技能代码，类似于市场营销的。

7:42 是的，

7:43 你能让它做市场营销相关的事情。

7:46 是的，下一步就是让它

7:47 去执行任务。我对此有点怀疑，直到这种 AI 的非复现、非随机

7:53 元素消失之前，

7:53 执行任务会变得很昂贵。

7:58 是的，所以接下来就会涉及“人类在环”

7:59 的讨论等等。但我觉得我们正处在那个确切的阶段。

8:05 我感觉当我和那些试图做事的人交流时，感觉

8:06 就像感恩节晚餐时和我的

8:11 刚工作六个月的姑姑聊天，

8:11 而我已经在用电子表格了，

8:16 我还纳闷，

8:17 为什么这会这么难，她应该直接用啊。

8:20 过了两年，她开始用上了。<cue id="79">我觉得现在如果你是增长营销人员，

8:21 cousin six months in her job. When, when I'm using a spreadsheet already and I

8:25 要创建42个代理

8:26 并让它们全部运转，

8:30 你得是个火箭科学家。

8:31 但火箭科学部分很快就会消失。

8:34 接下来你会说，专业领域知识还是很重要。

8:35 是的，那回到专业领域专家。我其实想反驳你一点，

8:40 也就是说，我觉得很容易就想象这些代理会编程做X事。

8:41 但我觉得我们走的方向相反。

8:44 实际上，我们开始是这样的：

8:45 我们会拿一款SaaS软件，加入AI，

8:50 这就是所谓新型的

8:51 AI赋能版本。这算是这类事情用代码的极端例子。

8:54 但现在，

8:55 which is, I think it's very tempting to be like, these agents are going to code

8:58 and do X. Yeah.

9:00 But I think we're going the opposite way. So I think actually where we started

9:03 was we'd like

9:03 take like a piece of SaaS software and we'd add AI. Yeah. And then that's like

9:07 the new kind of like

9:08 AI enabled. So that's like the extreme version of using code for these types of

9:11 things. But now,

9:12 我们到底在做什么？我们是说，好吧，SaaS 软件仍然是 SaaS

9:17 软件。

9:18 代理把它当作计算机来用，因为它在这方面真的很棒。所以我会说

9:21 我们是从

9:22 代码开始的，然后转到终端，其实用的代码更少。是的。现在

9:27 今年将是

9:27 计算机使用之年。是的。所以它更像是

9:31 人类在使用计算机，

9:33 而不是它们生成代码。这感觉就像是一个中间

9:37 步骤。是的。我

9:38 其实来自于代码生成那一派。比如我会说

9:41 这发生得更少了，不是更多。是的，我觉得。所以对我来说，无论是计算机使用、API

9:41 使用还是即时编写代码，

9:47 我可能错误地把它们混为一谈。它们其实

9:48 很不同。但我们

9:53 正在做的代理，会判断

9:55 是否应该使用

10:00 现有技能，或者使用盒子里的现有工具，还是写

10:01 代码来解决那个

10:04 问题。它能随时做这三者中的任何一个，变得非常有用，

10:05 因为有时候就有一些特定操作

10:09 你想快速完成。写代码来做那操作快很多。我们也

10:11 不能预先计划每个人

10:14 想在他们的文档上做的所有事。这个模型好到也能即时

10:15 写代码解决方案，这成了一个惊人的特性。虽然可能 90%

10:18 的时间

10:19 它做的事都应该用现有工具。随着时间推移，Prado 会取代它，

10:23 随着时间推移，

10:23 她的 iPhone 上其实只有七个应用，有七个 SaaS 应用。

10:28 随着时间推移，这些东西倾向于整合。但

10:29 iPhone 上七个应用是因为

10:33 人类不想一次又一次去学这些东西。所以我作为人类，

10:34 我没法拥有足够的心理容量去学那么多应用。但代理

10:37 通过使用工具和 API，能写代码，

10:38 它没有人的这些限制。

10:41 所以我，我不知道，你可以说事情太多了，

10:41 你可以使面向各种任务通用。是的，很公平。让我说我

10:46 喜欢你刚才说的，因为我们刚才在撒谎。不，我觉得这里有

10:47 很有趣的地方，我确实很喜欢，那就是软件的发展方向。

10:51 你知道，我整天用 SAP，我做财务工作，我得生成

10:53 各种报告。

10:56 然后有人说，我想要一个那样切片视图的报表，

10:57 我就想，天啊，我不知道怎么做。然后得翻阅 SAP 的帮助文档，

11:00 试图找答案。有件事，AI 其实很擅长，

11:01 它能更好地导航这些复杂的界面，

11:06 所有帮助内容都在，只是找它映射语言的问题。

11:07 过去 25 年的软件功能释放一直被人类限制。

11:11 是啊，我花了大半生坐飞机，思考如何让 PowerPoint 实现某功能，

11:12 直接去功能区操作。

11:19 看到别人苦于项目符号、编号或者

11:20 折腾如何在 Excel 里做个双轴图，几乎没人能做到，

11:24 但它却非常常见，那种痛苦真让人心疼。

11:26 所以这个阻力其实是人机界面设计在消费层面的不匹配，我完全认同，

11:30 这就是完美流畅的用户界面或消费层意义所在。

11:32 我觉得后端，比如记录系统，

11:36 最终会收敛到某个数据库，某套通用 API，

11:37 大家连接使用，

11:41 看起来方向就是这样。

11:42 我同意。我觉得你可以从这里开始。比如我整个周末都在

11:48 实现我的 Nanocla dot。刚开始像是在

11:48 为一切构建集成。

11:53 OpenClock 有所有集成，Nanocla 只有部分，

11:55 所以它没打造自己的所有工具。但过了两三天，

11:59 你大概就有了你需要的两个集成了，

12:01 是的。

12:06 回到这里，我是说，我们在谈个人生产力，

12:06 可能像是在组织你的生活，

12:10 嗯，是工作生产力，好吧，我们说的是生产力。

12:11 然后 SAP 系统，

12:16 还有，就是，那里面的复杂度无限。

12:17 numbering and word or trying to figure out, you know, like, Oh, let me just

12:20 make a two sided,

12:21 a two axis graph in Excel, which like is rocket science, like almost no one can

12:26 do that. But yet

12:27 it's super common. And so people are like, have none. And so that impedance

12:31 mismatch was a human

12:33 user interface design on the consumption layer, I totally buy it, which is like

12:37 the perfectly

12:39 fluid, like UI or consumption layer. I just feel the back end, like the systems

12:45 of record,

12:46 yeah, oh, yeah, it'll probably converge into like some database, like some

12:49 generic set of APIs,

12:50 like that they'll connect to. And like that seems to be the direction it's

12:53 going.

12:53 I agree. I think you can start. Well, like, so I spent all weekend, like

12:58 implementing my

12:58 nanocla dot. And when you first start out, it's like you're building an

13:02 integration for everything.

13:03 The nanocla is very like like open clock has all of the integrations, nanocla

13:06 has been a few of them.

13:07 And so you haven't built all of its own tools. But after, you know, two or

13:10 three days of these,

13:12 like, you know, you kind of have the two integrations that you need. And yeah,

13:15 like, yeah, but back

13:17 to the, I mean, we're talking about personal productivity, probably like you're

13:20 like organizing

13:21 your life or something. Well, it's work productivity. Okay, we're productivity.

13:24 And then an SAP system,

13:25 and like, and like, and so there's like an, well, like, there's an infinite

13:28 amount of complexity

13:30 当你到达，好的，有一家拥有全球供应链的公司时，他们

13:32 正在处理

13:33 跨越30个不同系统的75条信息。这确实

13:38 需要一定的

13:39 算力，而代理正是提供这种算力的，我们，我们只是

13:44 到目前为止，还无法从任何架构中获得

13:45 —就像磁带一样，但你刚才

13:49 所描述的

13:50 正是它50年来一直在做，并且将继续做的事情，

13:55 那就是，

13:55 是的，我有个朋友曾是退伍军人事务部的CIO。他花

14:02 了全部时间做的事就是

14:03 把75个VA系统连接起来，这完全就是集成。我们完成了。

14:09 它非常适合

14:10 集成。是的，我完全同意。对于集成，这些东西是

14:14 最好的，但，

14:15 这就是集成。没错，字面意思是“我如何把这两个系统缝合起来”，但

14:18 它确实是这样的，但现在

14:18 我认为正在发生的是，有点像按需集成。

14:23 是的，它是，

14:24 这是IT团队事先没布线的新查询，我需要它

14:29 在运行时发生。

14:30 呃，让我离开我的“草坪”吧。好的。好的，所以，

14:37 我刚才在一个

14:37 满是CFO和CIO的房间里，当我说了类似这样的话时，

14:42 他们都看着我，

14:44 虽然不像你想象的那么乐观，但是他们只是，

14:48 不，那导致大约六个人

14:49 跑过来跟我说你疯了，你在我这里失去了所有

14:53 信誉，

14:54 因为这不好。具体来说，他们说

14:58 代理也要做集成？

14:58 集成问题会变得更容易，对吗？

15:02 是的。

15:03 他们反对这个吗？不，没有人反对，但他们认为

15:07 这不现实。

15:07 但他们的担心是，不仅仅是代理，

15:13 还有人来做

15:14 集成，因为你让人去创建新的集成，你就是在说，

15:18 请毁掉我的

15:19 记录系统。哦，是的。所以这种想法就是你创建一个新的

15:23 API，

15:24 比如系统27和系统38之间的API，

15:29 这可能

15:30 对报告来说还行。

15:32 是的。如果那个人想错，那是他的事。

15:36 但你不是。我认为，我们之前有一个只读版本，

15:38 规模很大，

15:42 大多是消费层面，

15:43 消费者是人。

15:46 对，感觉现在很多东西

15:46 都是消费端的。

15:51 是的，我是说，我们其实刚刚

15:52 推出了官方的Box CLI。谢谢你点赞那条推文。我用了，

15:57 我有些反馈。

15:58 我会讲，我会接受所有反馈，但这真是

16:01 很有意思的东西。

16:01 我们内部也有过这些讨论，比如，好吧，给Claude code提供Box CLI，

16:04 你就可以通过自然语言操作整个Box系统。还有

16:07 opus 46作为协调者，执行一堆

16:11 操作的算力。

16:11 这让你

16:15 感到震撼。

16:17 我猜我会收到一些反馈，但确实

16:20 在某些方面让你震撼，

16:20 因为你可以说，把我桌面上整个文件夹上传到Box

16:24 而且它

16:24 会工作，或者处理这个文件夹里的所有文件，它也会工作，

16:29 非常惊人。

16:30 然后我们开始思考，比如说你是一家拥有

16:33 5000名员工的公司，

16:34 每个人都可以访问某个共享库，比如，

16:38 工程文档、

16:39 营销资产等等，

16:42 每个人都使用Claude code

16:44 或Codex，运行CLI。哇，我们将面对一些

16:48 非常有趣的新挑战，

16:50 比如，如何协调，有可能有人每小时访问

16:55 这个系统1万次，

16:56 不是从性能角度，

17:00 而是怎么确保一个人不会在另一个人

17:01 做正确操作时，不小心把文件

17:05 从一个文件夹移到另一个，

17:06 同时其他人又试图删除东西，

17:08 因为你有这些代理

17:09 无法控制。这将成为每个CFO和CIO

17:12 手忙脚乱的大问题。

17:13 没错，这正是我碰上的问题，我试用了你的例子，

17:18 创建视频例子，比如

17:19 创建营销方案目录什么的，突然间，

17:22 我陷入了某种循环

17:23 不断创建目录，我想，这能持续多久。

17:26 我开始猜测

17:27 Box中嵌套目录的限制，因为我快要触碰到了。

17:31 事实上，我们

17:32 也会去验证这个限制。

17:36 是的。

17:37 但我确实感觉到，

17:40 这……

17:42 是的。

17:47 很多直觉

17:48 是去构建一个新层，比如控制和其他东西。但实际上发生的

17:52 事情是，

17:52 相反的。所以我给你举个例子。比如，当我们

17:56 都使用了很多

17:57 个人代理，我们会把API密钥给他们，是的。我们会

18:01 给他们我们的邮箱地址。

18:03 然后他们就能访问这些东西。他们会想，“哦，

18:06 我怎么阻止

18:06 发生这种情况呢？”嗯。所以现在大家做的是，给它

18:10 专属的电话号码。

18:11 是的。我甚至给我的纳米爪配备了专属信用卡，希望只是

18:16 一张你在TBS买的Visa借记卡。不，不，不，但那之后，我

18:23 给它开了一个

18:23 专属Gmail账号，我们还会登录它。Gmail实际上有

18:26 这些RBAC

18:27 权限。是的。所以你可以说，我们实际上

18:31 内置了很多权限系统，把它当成一个人来对待，否则

18:31 它就是另一个

18:34 人。然后不是再建另一个外层，而是建另一个

18:35 层。

18:38 好的。现在我可以

18:39 立刻下架我们即将执行的这个元素吗？

18:43 请说。好的，那

18:45 对个人生产力来说非常棒。我们接下来会遇到的问题是

18:49 在企业里。假设我有，举个简单例子，我有一支50%的人手

18:51 团队。

18:55 每个人都应该，也就是，我们现在会有一百号人

18:56 协作吗？我的意思是，基本上50个真人，然后50张信用卡，再加上50个

19:01 代理在同一个

19:02 共享空间里。我显然对我的代理有完全监督权。

19:06 但如果我的

19:07 代理和别人合作，结果不小心获得了某些资源的访问权，

19:13 而我不应该有那资源的权限呢？

19:13 而现在这个自主的、有状态的代理在处理别人的

19:19 信息。默认的端到端原则是，你得把它们当做人对待，

19:20 但这行不通。

19:23 你不能完全把它们当做人对待，因为有个问题，

19:24 对于普通人，

19:29 你没权查看和你共事或者隶属的人在Slack频道发的内容，

19:30 你不能以他们身份登录，你无权监督他们，他们

19:33 要为自己在现实世界的行为负责，你不会因为他们搞砸而受罚。

19:34 而代理呢，你负有他们所有行为的责任。你必须

19:37 拥有完全监督权，且你很可能必须拥有这个权力。他们没有

19:38 隐私权。

19:42 所以，这里会出现一些问题，不像只是简单地把他们

19:43 当做人那样，因为我需要给

19:47 他们访问权限，但我也需要在某个时候能作为他们登录，

19:48 说，“不，你弄砸了，我得全部撤销。”

19:52 但如果我可以作为他们登录，

19:53 他们怎么能在现实中和别人合作，保密或保证安全呢？

19:58 所以它们本质上还是你的

19:58 延伸。

20:00 几乎不可能绕开它们就是你的延伸这一事实。

20:02 现在我们在考虑的问题是，这种情况近期内无法解决，

20:06 逻辑上也行不通。是的，也许吧。但举例来说，对于我的员工，

20:07 我可以登录他们的账号。

20:11 但你没有，你没有，你没有，我可以访问他们的邮件，

20:13 但你不会经常这样做，因为

20:16 他们发了封邮件。这是和代理的正确操作模式吗？

20:16 风险大得多。这里的人，随时会泄露你的

20:20 信息，只要他们愿意。他们可能乐意给别人发邮件，

20:21 因为被注入了指令。

20:25 你认为终极状态是这些东西仍然是

20:25 这种笨拙的计算机，因此它们永远，嗯，我不喜欢“笨拙”这个词，

20:28 除非我们是用口语表达。但它们永远无法保守信息，

20:29 永远不可能。

20:32 所以，我认为你能在上下文窗口里保持秘密，

20:33 比如告诉它不要透露某事，是一件非常难解决的问题。

20:37 那么，如果任何东西能进入

20:37 该上下文窗口，因为它们有权限访问资源，那理论上，

20:41 你应该假设它能被

20:42 从上下文窗口中被提示注入出去。我不知道我们现在

20:46 有没有办法解决

20:46 这个问题。这就是，所以，如果我知道你新代理的

20:51 邮箱地址，而我

20:52 they sent one email. Is the right operating model with an agent? The same thing

20:55 . The risk is like

20:56 a thousand times greater. Like these people, like they will just leak your

20:59 information whenever they

21:00 want. Like they will happily just go and send some email to somebody because

21:04 they got prompt

21:04 injected. You think the terminal state is that these things are still these

21:07 sloppy computers and

21:08 therefore they will always, I don't like the word sloppy, unless we're saying

21:11 it very in a

21:12 colloquial sense. But like, they'll never be able to contain information. They

21:16 'll never.

21:16 So we're like, I think the ability for you to keep something in the context

21:22 window, a secret,

21:24 like as in like you tell it, do not reveal x thing in the context window. I

21:28 think that's

21:29 a very hard problem to solve. So then, so then thus, if anything can ever enter

21:34 that context

21:34 window, because they have access to a resource, then in theory, you should

21:37 assume it can be

21:38 prompt ejected out of the of the context window. And I don't know that we know

21:42 of a way to solve

21:43 that at the moment. Like that's like, and so, so if I know your new agent's

21:49 email address and I

21:50 像助手一样发送邮件，但我可以，我可以比

21:54 人类轻松十倍进行社会工程学攻击。

21:55 你很难让那个代理也能

22:00 访问你的

22:01 并购文件之类的东西。但这真的是AI的全部现状吗？

22:04 哪个部分？我是说，

22:05 我们有这些共享系统，我们用智能

22:09 它们有共享

22:10 联系人。但我们的意思是，这是AI的全部。嗯，我只是说现在，

22:14 当我们在内部使用AI

22:15 和代理时，这正是我们如何使用它的方式。但这就是

22:18 为什么他们正在

22:19 实际上正在研究我们。我们还不知道

22:23 如何让他们不工作

22:24 像我们。让我举个例子。让我举例说明，接着

22:28 解决这个问题。

22:28 问题是你能够

22:35 骗代理透露信息。所以这就是为什么

22:36 让他们能访问自己的

22:41 资源并完全自主决策，

22:42 目前还未能实现。

22:45 that we've been

22:46 这就是解决你问题的一个完美例子，

22:50 我们已经经历了

22:51 开源模型就是全部公开，

22:57 你自由使用，

22:58 随意挑选。那时没人争论，

23:02 因为世界还小，

23:03 大家也没在X上播播客。然后

23:07 很快大家意识到了

23:08 你刚才说的所有问题。比如，如果你经营大公司，

23:11 company,

23:11 你不能让某人就这样复制一堆开源代码进你的

23:16 商业产品，

23:17 有一大堆许可问题，

23:20 各种各样的问题。

23:21 于是所有这些规范就形成了。现在的辩论

23:26 就是发生的辩论，

23:26 这是新技术发展过程中非常有趣的现代现象，

23:31 所有的一切都实时发生。开源时代我们

23:32 曾在这么大的会议室里讨论，

23:37 开源代码可以用在Windows或Office中多少，

23:38 那时没人网上知道我们在讨论，

23:42 那是非常私密的，我觉得真的

23:43 非常有趣，

23:47 不仅仅是关于细节的辩论，

23:48 还有整体的方向问题在

23:53 大范围内发生，每个人都想快速达成

23:53 最终状态，

23:59 而且速度比真正能达到的快多了。

24:01 more like in a sense more quickly than we can actually reach the end state. And

24:06 需要做的是让人们去构建。我们需要标准。

24:07 我们就需要标准。

24:12 just need some

24:12 对于最终状态，我有不同的直觉。不要要

24:17 我的直觉。

24:18 怎样才能结束争论，让这些东西真正达到

24:22 像人类那样的可靠性，正如我们看待自动驾驶一样。

24:23 在那种情况下，

24:27 你用的是和保护人类相同的机制。

24:28 对，

24:32 你要考虑内部威胁，考虑人可能被收买，

24:33 考虑人会犯错，

24:36 这是风险，也是运营流程的一部分。

24:37 fact that people make mistakes. And that's a risk. And that's operational

24:40 所以一派直觉认为那将是最终状态。另一派直觉则是，

24:41 So one intuition is like that will be the end state. There's another intuition.

24:45 别指着我，我只是说说我们现在的状况。

24:48 I actually,

24:48 实际上，我想我们对最终状态并不分歧。顺便说一句，

24:52 从战略上讲，

24:52 我们在做风险对冲，我们要打造东西给亚洲用户，

24:57 所以我们觉得，

24:58 我喜欢OpenAI有个Box账号，能运作自如，

25:03 就像养了双倍数量的

25:03 猫。我喜欢这个想法。只不过现实中，

25:10 我们还不懂如何让它们安全访问

25:11 并购数据室。

25:16 但是，这其实比那更难，

25:16 威胁向量要更复杂。

25:21 我们正处于猫捉老鼠的游戏中，

25:23 你不能假设代理今天会像人类一样行动，

25:29 因为它将是最快、最有心计、最疯狂的人类，

25:30 试图泄露信息——毕竟它是被某种方式植入的。

25:36 that ever existed

25:37 所以，

25:40 接下来会经历一个阶段，

25:41 企业客户会关闭一切，

25:46 直到这一切变得理智。

25:47 customers are just going to like close everything off until there's some sense

25:52 与此同时，个人，特别是

25:53 开发者，

25:58 这将是我认为最令人兴奋的冲突，

25:59 企业将被这些

26:04 先进的个人抛在脑后，

26:05 这些人群会变得像初创公司一样，

26:09 而初创公司将比企业跑得

26:10 快得多，

26:15 因为他们没有这些繁文缛节。

26:16 much, much faster than enterprises because they just don't have any of these

26:19 问题。

26:20 你知道的，你可能会遇到代理在初创公司失控，

26:25 做出那样的事情。

26:26 你的初创公司没有员工会经常失控。

26:30 是啊，那也只是《硅谷》的一集而已，所以，

26:33 没什么大不了的。

26:34 我同意你的看法，比如问题在于人等等，同样的风险。我

26:38 觉得有几个

26:39 不同点，像我不能真正威胁到

26:44 云端代码，不能像你那样

26:46 直接拔掉它的电源。

26:49 而你作为普通员工能够有那种

26:50 威胁，毕竟至少95%的人不会

26:54 试图制造坏事，

26:55 对，他们不想，但

27:00 无意中造成坏影响的可能性是存在的。

27:01 对，关于仍然没有那些问题这一点，我想

27:05 可以说

27:07 让人们不把文件以错误方式泄露给公司外的人，比现在让代理

27:11 执行同样的指令要容易得多。

27:12 而且你也有工具，能在更

27:17 抽象的层面

27:17 阻止这种情况发生，这就是为什么你必须把这个纳入软件中，对吧。

27:21 但我确实认为，

27:21 如果你要总结你刚才的观点，

27:24 很多原因正是

27:25 为什么AI能力的扩散，会比硅谷的人预想的

27:28 要慢。

27:30 因为我们看到一些初创公司

27:33 能够从零开始，没有我们讨论的那些风险，

27:35 因为他们没有什么可炸的东西。

27:39 所以我们把这看作了我们的发展轨迹。

27:40 然后你去看JP摩根，

27:44 你会想他们

27:45 如何搭建类似Nano Cloth这样的系统，来真正

27:49 自动化他们的业务，

27:49 什么时候能做到？你会发现，

27:55 这里会有个小小的差距。

27:56 那么，

27:58 你们怎么看？我觉得这打开了一个非常有趣的问题，

27:59 就是大和小初创公司与企业之间的差距，

28:03 事实上，

28:04 目前的SaaS供应商都在苦苦挣扎，

28:08 面对这场SaaS

28:09 末日怪象，我并不完全同意这种说法，

28:14 但他们确实在为一个问题苦恼：

28:15 他们卖的不是业务线数据，实际上他们销售的是

28:18 智能和

28:19 领域专业知识，贯穿整个系统。

28:25 而代理那边只想买数据，

28:26 他们只想授权数据，拥有无限

28:30 访问权限，

28:31 但这其实从未真正成为他们的业务模式。

28:36 这也是Workday、SAP等长期存在的矛盾，

28:37 他们如何开放API访问。

28:41 Salesforce经历了三次大型的平台

28:42 重构。

28:46 这我觉得是个特别有趣的问题，不是

28:47 因为华尔街对问题经济学的理解，

28:51 华尔街完全错了，

28:52 而是从技术角度来看，

28:57 在数据被访问时，系统记录意味着什么，

28:58 无论是用于训练还是，嗯，他们讲的是

29:02 执行工作。

29:03 我理解为用数据执行操作。

29:08 他们担心有些人会在你的数据层上做训练，比如我是大客户，

29:09 我的供应商想构建训练模型。

29:15 实际上，即使你不涉及训练，

29:15 他们也担心，

29:20 因为通过互联网发送一点数据，跟

29:21 直接在我的界面操作，

29:25 是完全不同的盈利水平。

29:26 这种盈利模式的区别是华尔街关注的重点，

29:30 我觉得，举个SAP的例子，

29:31 里面有大量领域知识，

29:36 他们不会消失，

29:36 想通过编码取代SAP的想法简直荒谬。

29:41 而且，那些领域知识，

29:42 不是能轻易用某个精心设计的数据层表达出来的，尽管他们努力了。

29:46 很大一部分体现在界面，

29:47 中间层，以及

29:52 使用方式上。

29:53 我不确定这将如何发展，

29:57 因为SAP不会消失。

29:57 这将拖慢AI在该数据源上的扩散速度，

30:02 无论是代理驱动的AI还是只读报告。

30:04 那么你怎么看？你觉得这将走向何方？

30:08 我怕说点什么，

30:10 否则就不会再被邀请回来，所以说点好听的吧。

30:13 a whole bunch in

30:14 just how you use it. And so I'm really unsure how this thing evolves because

30:20 SAP isn't going anywhere.

30:22 So then that's going to slow the diffusion of AI on that particular data source

30:27 independent of

30:27 whether or not it's a gentified AI that's doing stuff or just read only

30:31 reporting on stuff. So

30:33 where do you come down on it? Where do you think that's going to go? I'm afraid

30:37 of saying something

30:38 that's happening. Otherwise you're not going to get invited back. So say

30:44 something good.

30:45 我想我已经接受了“构建代理想要的东西”的观点。所以这有点

30:54 是

30:54 Paul Graham这类术语大约在过去一年间围绕这个话题出现的，

30:58 就是

30:59 就像发泄一样。我觉得我们实际上会完全同意这一点，

31:03 在某个时候

31:05 你会进行足够多的迭代。到某个节点，代理基本上

31:09 掌控了它想要实施和使用的工具等等。

31:11 是的，代理不可能

31:17 更换一个企业系统。

31:17 但再过几代，代理可能会

31:22 在你的软件中碰壁太多，

31:23 它就会说你必须

31:26 最终拆掉你的

31:27 遗留的人力资源系统，不然我无法为你自动化这个流程。

31:32 所以我确实觉得

31:32 你有非常有趣的动态，回到你想象

31:36 有一百倍或一千倍更多代理在使用软件，而不是人，

31:37 你反复这么做，

31:40 最终代理交互的软件栈

31:42 必须为他们构建。

31:47 或许

31:49 还会有几个例外，或许几款ERP系统是最后不跟进的

31:53 坚守旧法的系统。

31:54 但其他的软件基本上，

31:58 你的业务

32:00 表现将与代理访问所需信息的能力

32:03 直接相关，

32:04 所以你的企业IT架构必须这样搭建，

32:08 来支持这一点。

32:09 因此代理基本上掌控了局面，因为你的软件

32:13 必须支持代理的高效工作。

32:14 这意味着所有建设SaaS

32:18 或软件业务的人，游戏规则是：你能否打造出高品质API，

32:19 并且

32:24 有办法

32:25 实现变现，比如管理身份和

32:29 代理访问权限，

32:29 这些将成为你必须解决的新问题，

32:32 如果你是在建软件公司。

32:33 是的，随之而来的是如何变现，你是否

32:39 赚钱，

32:40 Workday是否对每条HR记录收费，诸如此类，我们会慢慢明白。

32:44 我觉得在某些行业这可能意味着收入减少，

32:44 而在另一些行业则

32:47 意味着收入大幅增长。

32:48 我们兴奋的是，每个代理都非常

32:51 喜欢处理文件。

32:52 所以未来文件数量可能比以前更多，

32:55 was going to be

32:56 那么我们能否构建一个方便代理处理数据的平台，

32:59 我们赌这是一个

33:00 对我们的商业模式来说非常乐观的结果。

33:03 有些商业模式可能会更受限制，

33:04 因为代理创造的价值超过了软件本身，

33:08 而其他情况则介于两者之间。

33:08 我能不能挑一件事？你要说反对那也行，我觉得这真的很

33:12 无争议。

33:13 不不，我们就是来争论的。我觉得Paul Graham和许多人

33:16 忽略了一件事，就是他们只关注界面。他们会说：

33:16 你为代理构建东西。

33:19 我实际上觉得这完全错了。

33:20 公平地说，Paul Graham没这样说过，是后来被扩展理解的。

33:25 我把Paul Graham扯进来了，人们抽象上说现在你是在向代理营销，

33:25 最重要的是不管怎样，你得有API，有好点子。

33:29 我觉得这几乎完全相反。

33:29 那是播客新闻爆炸点。

33:33 代理最擅长的是

33:34 找到他们的路。

33:39 最终真正重要的是语义。

33:41 是的，没错。

33:47 据我记忆，代理非常擅长选择合适的后端来完成任务，

33:48 他们不会关心界面多好，系统文件怎么样，

33:52 而是关注成本参数，

33:53 稳定性，

33:58 他们实际上拥有使用这些平台的集体智慧。

33:59 拿云平台来说，有很多云平台。

34:02 每当我让代理选平台，

34:03 他们看的是平台的使用效果，而不是界面。

34:06 所以我觉得业内太专注于界面，

34:07 觉得“你得向代理推销这个那个”，

34:12 但实际上我认为，

34:13 我们将被迫建立更好的系统，

34:16 那个才会被选择。

34:17 好了，其实这样说来应该没什么好争论的，我们实际上划清了界限。

34:20 this. Yes. The

34:21 durability of that. Like and so like they actually have the collective wisdom

34:24 of our experience

34:26 using these platforms. Like let's take cloud platforms. There's a bunch of

34:28 cloud platforms

34:29 out there. Yep. And whenever I ask an agent to choose a platform. Yeah. It's

34:32 actually using

34:33 meaningful stuff. Yeah. Not interface stuff. So I think as an industry we're so

34:36 focused on these

34:37 interfaces. Yes. Like oh you need to like market to agents this and that. Yeah.

34:40 But really I think

34:41 that we're going to be pushed to actually build a better system. Yes. And that

34:44 's what's going to be

34:44 chosen. Okay. Actually. So then there's probably no quibbling. I think we're

34:47 actually pulling a line.

34:48 抱歉打断这个争论。我并不把这当作你知道的那种

34:52 营销

34:53 你知道的那种事情。我更倾向于说，如果你的工具对代理是封闭的，

34:56 代理最终

34:57 会为公司找到更好的工具去使用。所以，

35:01 结果就是，

35:03 过去你会去找Gartner，让他们告诉你该怎么做，告诉我

35:06 该做什么。

35:07 是的。告诉我用什么系统之类的。在足够多的

35:10 反复试验后，代理

35:12 会说你应该用这种数据库来做这种操作。

35:15 如果你不参与其中，你就是死路一条。我

35:16 认为我们应该

35:20 为此庆祝，因为代理在选择合适的

35:21 技术方面其实很聪明。

35:23 是的。过去我真的认为导致人们购买的是很多其他因素。

35:24 是的。但别担心，我们会——谈到这个问题时，我们会

35:27 很快破坏这种

35:28 价值体系，

35:32 因为我们就会超额投入。嗯，

35:33 代理将成为激励代理的API。但你知道Workday有一个营销代理。

35:37 Workday的

35:38 营销代理将能购买来自他们的推荐，

35:45 他们为代理们

35:46 复制国宴的方式。是的。但事实是，

35:49 这里有一个问题，

35:49 这事儿就像内网发生的那样，比如公司内部的

35:54 内网站点，每家公司都有文件共享，存着最好的文档、

35:55 最好的幻灯片、

35:59 各部门或工作区的最佳财务模型，人们也习惯了这些。

36:01 然后当找不到想要的，就会新建一个，很多组织就是这样运作的，

36:05 这本质上是自由市场。其实因为

36:06 在Box出现之前，IT根本不在意文件，只在意SQL数据库里的东西，

36:11 这是事实。

36:12 你描述的模型风险之一是，代理

36:16 本身会自行创建一个事实上的新系统，

36:17 他们会把这些碎片分散在你们IT认为的某些中间件和用户的废话区。

36:20 我认为这确实是个风险，百分之百是，

36:21 某种意义上，宏命令最终掌控公司。

36:25 是的。我觉得他们已经看到这部“电影”，

36:26 他们知道让营销随便在网上买个网站办活动，

36:31 后果是巨大的安全漏洞，邮件列表外泄，

36:32 整个公司被起诉。

36:36 所以我认为这种动态中有比我们表现出来更多的现实张力。

36:37 是的。但我也觉得这事儿，有两种截然不同的看法，

36:43 正如Elon所说的，

36:45 他说我们只要下个指令，它就可以直接输出机器码，

36:51 这其实是层的坍塌，过去我们建的各种

36:53 界面和层级都将消失，

36:58 就是指令到机器码的直接转换。

36:59 另一种说法是，系统的层级从来没有消失，

37:02 它们只是不断叠加，很多层实质上是

37:03 组织边界或状态边界或兼容性边界，

37:06 所以它们为了兼容依然存在。

37:07 另一种观点是，我们实际上是根据更多人和组织需求，

37:13 非常有针对性地演化了这些层，它们不会变，

37:15 代理就会适配这些层。对我而言，更倾向后者，

37:20 我认为系统会以较相似的方式继续被使用，

37:21 可能更多代理会用它们，但它们不会有太大变化。Elon

37:26 可能属于Anthropic派的增长营销者，

37:27 过去多年研究过各种IT部门，

37:31 他算是最懂增长这事的人，

37:32 这是他的第一性原则，Elon的AI就会这么做，但也挺有趣。

37:37 而你们这些凡人则会说，“我们其实只想要一个CRM系统。”

37:38 但它们的工作方式其实是一样的。

37:42 's a little trickier.

37:43 So it feels like there's like there's two very competing viewpoints on this one

37:47 and like Elon

37:47 said it was like okay we're going to like issue a prompt and it's going to like

37:50 spit out machine

37:51 code and that's basically the collapsing of layer view like whatever existing

37:55 interfaces and

37:56 layers that we've created in the past are all going to go away and it's

37:59 literally like prompt

38:00 and machine code. The other argument like the history of systems is layers

38:03 never go away they

38:04 just get layered right. And because a lot of the layers are actually more of

38:07 like organizational

38:08 boundaries or like state boundaries or compatibility they're just they stay for

38:13 compatibility right.

38:14 So the other argument is is like we've actually evolved these layers very

38:18 specifically and up

38:20 because of like more human and organizational needs and they're not going to

38:23 change and the

38:24 agents are going to go ahead and map to those. Now I tend to be in that latter

38:27 camp like I don't

38:27 think that I think like systems are going to continue to use in fairly similar

38:31 ways maybe there's

38:32 more agents using them. I don't think they're going to evolve as much. Elon

38:36 might be back in the

38:37 like anthropic category of the anthropic growth marketer which is like he like

38:44 you know over the

38:46 years when you kind of like study the various IT you know departments of his

38:49 companies like

38:50 they are the most I mean. Well he could do that. He could do it. He's the most

38:54 home growth like

38:54 everything. This is first principle. Elon AI would do that. But also it's fun.

38:59 And then from

39:00 your mortals you're like yeah we kind of just want to CRM system. Like no but

39:03 it works the same way

39:04 每次。我的意思是，这不是说没试过

39:10 之前。

39:11 比如说，如果你从第一性原理去看一个ERP系统，你知道，

39:15 1970年，无论何时

39:17 SAP开始时，有一堆不同的假设，而今天你

39:20 会从

39:21 一套不同的关于重要性的假设出发，架构完全

39:24 不同的东西，

39:24 不过那样的系统只会持续大约10年，直到你觉得

39:28 哇，那是个

39:28 错误的决定。所以我认为在层次里是有意图的，

39:34 但你

39:35 也有第一性原理的东西。你知道，这点总会

39:40 存在，因为

39:41 你在任何时候，在第一性原理下做的决定，都要求

39:45 一大堆不同的

39:46 东西。所以即使你不采用LIDAR，在10年前这完全有道理，

39:51 你仍然需要

39:52 10或15年去达到没有LIDAR也能工作的状态。然后现在

39:57 又会有一

39:58 大堆事情让你觉得，哇，我们本可以完全

40:01 不同地做。

40:01 所以我觉得这又像是在讨论试图冲向

40:05 一个终点。

40:06 但让我们看一个你描述的事情发生的第一个例子。我认为

40:11 那将是

40:12 真正的证据，因为我认为公司们会搞懂

40:16 这一切。我认为

40:17 他们最终会依赖层次和架构模型，因为这是

40:21 唯一的方法。

40:22 我们不会为政策去考虑它，也不会为安全去考虑它。

40:24 但它也是构建系统的唯一方式。否则你只是在做

40:29 一个应用。如果

40:30 你只是做一个只做一件事的应用，我们不需要这些。完全

40:34 是另一种

40:34 做法。我挺感兴趣的是，我甚至没有什么特别好的数据

40:39 点或轶事，但至少有这样一个概念，某些公司

40:40 从零开始，在这些

40:45 服务类别中出现，采用纯粹的第一性原理方法，

40:46 比如说，假如我要创办一家市场营销机构，或者顾问公司，

40:50 比如说工程咨询

40:51 公司，或者不知道，可能有人专门为律师事务所、建筑

40:56 工作做这些。也许

40:57 建筑设计、建筑设计，凡是

41:01 类知识工作者的服务型公司。因为如果你毫无限制，

41:03 没有信息壁垒和权界限，

41:06 你可以很不一样地构建你的公司。

41:07 我可以给代理所有完成工作所需的上下文，

41:11 我可以临时写针对特定事情的软件，我真的

41:11 觉得这会

41:16 带来相当大的破坏，

41:17 一直到大公司能逐渐让路。

41:20 这至少会创造一些先例或案例研究，说明

41:21 这种新型的公司可能是什么样子，但我认为，

41:25 随着时间推移，他们还是会碰到和其他企业

41:27 一样的问题，比如地理或者市场细分，

41:31 分销挑战。是啊，那些东西都发生在你

41:32 的小圈子之外。

41:36 是的，你会遇到物理世界的限制，对吧。我还是挺喜欢

41:38 这个想法：现在有一些

41:41 新的商业模型正在打开。当然，没错没错。

41:42 因为大量信息或软件相对于它的经济价值，

41:46 基本上被低估了100倍，

41:47 完全是因为没人愿意为访问一条数据付五分钱，或者一美元使用一个工具一次

41:52 但你给这些代理一个预算和协议，

41:53 突然间他们可以随时去获取医疗研究，做他们正在做的深度研究，

41:57 我会为此支付三美元，代理可以去

42:00 完成交易。

42:03 这为所有互联网打开了全新的商业模型世界。

42:04 让我，我要说，你说得太客气了。哦，好吧，不不，你要

42:09 说得更深刻点。不是，那其实是目前最大

42:10 的问题，最大的不确定性问题之一，

42:14 就是大家都在努力弄清这些东西的经济学，而他们

42:15 的估计至少差一个数量级，完全低估了机会的规模。

42:19 因为人们将会创造的新模型

42:20 现在没人知道，但他们绝对会出现，

42:24 每有一种新技术都是这样，真正阻碍

42:25 讨论的，是你有一堆金融和华尔街的人

42:29 在尝试证明GPU和代币的合理性，好像我们还活在旧世界

42:30 他们将收入视为线性的步骤，但实际上

42:33 ...

42:33 Like it kind of opens up a whole new world of business models for all of the

42:37 internet.

42:37 Let me oh I'm gonna you're that was too nice. Oh okay. No no. You're gonna go

42:41 farther. No that one is one

42:43 where that's actually the biggest I think that the biggest sort of in the air

42:49 problem right now

42:50 is everybody is trying to figure out the economics of all of this. When they're

42:54 off by at least in

42:56 order of magnitude on how big the opportunity is. Because the new models that

43:00 people will come up

43:01 with that nobody knows what they are right now but they will absolutely come

43:04 out with new models

43:05 because that's what happens with every new technology and the thing that holds

43:09 back to the sort of

43:10 the discussion now is you basically have a bunch of finance and Wall Street

43:14 people trying to justify

43:15 GPUs and tokens and things like as if we're in some old world and they're there

43:21 so they're

43:22 they're viewing the world of revenue as sort of this linear step but literally

43:27 线性增长

43:27 曲线，试图为所有预期寻找理由，当人们要

43:31 创造的时候，

43:31 PC的问题是人们把PC看作一个有限的市场，因为他们只是

43:36 把MIPS的消耗

43:38 看成某种有限的东西，他们没有考虑如果我们把

43:41 所有的MIPS放到每台

43:43 桌面电脑上，尤其是人们认为软件是随MIPS附带的，

43:48 没人想过“哦，

43:49 他们只卖软件”，只有一个人这么想，结果证明这是

43:53 一个非常好的主意，

43:55 同样的事情，也是的，同样的事情发生了，

43:59 云计算也是如此，

44:01 人们看着云说，我们要把所有

44:04 服务器

44:06 业务，实际上每年只有6万台，

44:10 全部迁移，

44:11 放到别人的数据中心，

44:15 然后这就是业务模式，

44:15 我们按价格分成，

44:18 没人想到人们会

44:20 用的资源会增加一千倍，如果我们搬到那里，

44:24 这完全是，

44:26 这就是让我抓狂的地方，华尔街的模型

44:29 把收入看作

44:31 固定的蛋糕，零和游戏，

44:35 他们只是

44:36 认为公司的花费金额是固定的，就像

44:38 Salesforce面临的问题，

44:39 当你刚开始的时候，马克刚刚

44:43 开辟出这条道路，

44:44 CRM市场一年只有20亿美元，

44:49 而且这20亿

44:50 意味着你得买一堆服务器、Oracle许可，麻烦多，

44:53 部署、咨询耗费多年，

44:54 而如果你能让销售人员

44:58 单独注册，

44:59 他们都会毫无阻力地注册，这就是真实情况，

45:04 毫无疑问，

45:05 这也是AI将会发生的事，给你举个例子，

45:08 我在投资领域已有十年，

45:09 大概有240家公司在我的投资组合里，

45:14 我对它们有一定了解，

45:15 其中50家都是基础设施公司，

45:17 有的表现好，有的一般，

45:18 但过去六个月里几乎所有公司都开始进入

45:22 趋于稳定期，

45:23 你会想为什么，这其实是因为

45:27 比以往任何时候都写了更多软件，

45:28 当然了，

45:32 这不是因为它们有企业客户，

45:33 而是因为基础设施层的

45:36 消耗远超以前，

45:38 软件越多，体现在代理上，

45:42 消耗的计算资源

45:42 也会越多，特别是在计算方面，

45:45 是的，

45:46 而且我们还没到每个人的手机

45:49 成为AI的大量消费者的阶段，

45:50 一旦每个人的手机和设备都

45:56 在使用AI，

45:57 使用量将会暴增数十亿倍，你喜欢

46:01 微支付吗？

46:02 所有的微支付，

46:06 每项技术都会带来一些微支付，

46:07 他们一直认为你只能赚取

46:10 几分钱的零头，但最终特别是在企业领域，

46:11 人们还是会

46:15 大量消耗这些资源，

46:15 买一个批量许可既便宜又方便，

46:19 是的，

46:20 你需要一定的可预见性，

46:23 你只想

46:24 不必为此操心，我喜欢这个想法，

46:28 这是第一次，

46:29 代理不在乎小额交易的摩擦，

46:32 是的，

46:33 第一次有资源背负着付费墙，用户

46:36 真的愿意

46:37 为此付费，世界是建立在基础设施之上，

46:41 来聚合这些付款，

46:42 为客户或服务提供效率，

46:47 由于代币现在是生产成本的重要部分，

46:48 它推动行业走向按使用量计费，

46:52 就像

46:53 我记得我们从永久许可转到持续订阅，

46:56 那需要

46:56 一堆重大改变，我们现在

47:00 也正在经历同样的变革，

47:01 走向用量计费，用量计费非常细化，它让——

47:04 我想让你明白，

47:05 我们跟AWS经历过类似情况，

47:10 人们学会了用使用额度，

47:12 经历了恐惧云计算的阶段，

47:15 有人甚至害怕到要有中间商帮忙

47:17 找到最便宜的方案，

47:20 统一调解，

47:21 现在加入了代币，我看不到

47:25 我们还能有

47:26 时间继续这个对话，但只要你们……好的，不过，

47:30 工程

47:32 对我来说，计算预算的讨论将是最疯狂的之一

47:36 未来几年内

47:37 就是你给工程费用分配了多少给

47:42 代币

47:43 你知道，根据你在推特上看到的情况，可能是百分之一

47:47 而另一边

47:48 则可能是百分之百，就像，“是的，但这东西——”不不，首席财务官

47:52 必须

47:53 确切知道答案，我明白他们必须知道

47:56 好的，首席财务官总是

47:57 想知道那些实际上没有答案的问题，华尔街

48:00 不会逼他们

48:01 知道答案，不，华尔街会让他们

48:04 编出一个数字并坚持它

48:05 然后他们会被炒鱿鱼，然后结果就是，但是你知道，好吧，我懂你说的，你在研发领域

48:08 的支出

48:09 占任何上市公司收入的14%到30%之间，对吧？

48:14 我们就这么说吧

48:15 计算的费用是工程团队成本的两倍

48:20 或者说

48:22 比你的费用多出3%，那就是每股收益，明白了吗？

48:27 我们得知道

48:28 答案，我完全愿意牺牲几个首席财务官

48:33 那是个好观点

48:33 但是原因是，因为这就是试图弄清

48:38 我们现在根本不知道的东西

48:39 是的，这种情况以前也发生过，互联网带宽就是例子

48:43 这不只是互联网带宽

48:45 不不，我必须不同意，像真空管、晶体管

48:50 以及所有技术的发展都是这样的

48:51 一切技术都是如此

48:55 有个阶段，人们说，哎呀，这将会影响程序员

48:56 曾经有段时间

49:00 有人说，程序员会吞并所有公司

49:01 那是在我有生之年，不是虚构的

49:04 但我不认为我们曾有过一个时刻

49:05 没有，每个

49:09 组织中的终端用户都有完全弹性的能力

49:10 来为自己快速启动资源

49:15 这点

49:17 在许多情况下确实很有效

49:20 对他们来说

49:20 这确实类似2000年代初期

49:23 云计算的发展

49:24 我记得我们从资本支出转为运营支出时有类似的讨论

49:27 我们的预算范围有限

49:29 不，那时记得有些公司首席财务官坐在我们的简报中心

49:32 说，“你们不懂，我们是农业公司，

49:33 只懂资本支出”

49:39 或者，“不，我们都是运营支出公司，所以

49:39 我们喜欢云，因为我们把一切都迁移到运营支出了”

49:45 对，我们都经历过

49:46 或者，“不，我们完全基于运营支出，所以如果你告诉我们，

49:52 我们喜欢云”

49:52 所以所有规则，包括会计规则都照常执行

49:56 会计规则

49:57 我一直认为，本地计算引擎是所有这些的安全阀，别忽视它

50:01 这可能会发生

50:03 问题是，关键不是“什么时候”按今天的技术来看能实现，

50:07 而是“怎么突然”发生

50:08 哇，几乎

50:11 历史上都朝这个方向发展了，对吧？

50:12 没错，因为相反的是

50:16 所有都转到了客户端

50:17 好吧，再回到80年代，是的，以前的例子

50:22 非常多

50:22 哇，那样说有点过分，我说的是电子管，我讲的是

50:28 真空管

50:30 我举这些例子是因为它们无法反驳，这样说更简单

50:33 你说得对，我没法反驳

50:34 但是，这种情况其实也就是发生了十到十五年，全部

50:40 回归云端

50:41 然后最近发生了什么？很多人醒来后

50:45 说，我们要把一些关键但固定的工作流程搬回本地

50:45 尤其是有了人工智能

50:50 这是真的，老兄，你写过相关文章，

50:52 别让我详谈

50:56 回归本地的历史

50:57 我当时得应付很多华尔街的问题

51:01 顺便说一句

51:02 也是因为你是竞争对手，回归本地这事

51:06 对，我们说的是这事

51:07 我赞同，我赞同，自己建数据中心是

51:11 你说的是边缘计算的概念，把计算推向设备

51:12 类似这种

51:16 我更偏向云计算极端派

51:17 但抱歉

51:21 你就一点都不认为，作为一个工程领导者，

51:23 管理工程团队的计算预算是否重要吗？

51:26 当然重要

51:27 只是从长远来看，这个问题会被解决

51:31 没错，发射全部资源吧

51:31 但说到底，谁在乎呢？我们根本不知道，我的意思是

51:35 这很务实

51:36 这里有个经验法则，初创公司会毫无顾忌地花掉

51:39 可用资本

51:42 假装这不是问题

51:46 并且他们会这样继续做

51:48 直到资本耗尽

51:51 是的，很多都是这样

51:52 无论如何，对，对，对，很多大公司会变得非常

51:55 害怕，他们只是

51:56 会停滞不前，不采取任何行动，然后人们实际上会开始

51:59 自己去购买

52:00 而且他们会做大公司在资金充足但

52:03 不想花钱时做的所有事情

52:04 在中间，我们会看到如果你选择一个

52:08 产品类别或进入

52:10 市场，总会有人愿意为此下注，原因无论是什么

52:13 他们可以，因为他们财务状况允许，他们会继续前进，

52:14 他们会成为

52:18 这个领域的领军人物，只要他们能维持好

52:20 财务状况，没错

52:24 他们可能会这样做，可能会说我们只打算在这个

52:25 特定应用领域实施

52:28 或者在这个特定的使用场景中，但这种想法认为

52:30 没有人会进入，因为他们太害怕首席财务官会被解雇什么的，

52:35 绝对不会

52:36 这简直疯狂

52:39 但也会有首席财务官犯错，

52:40 然后，好吧，每个人

52:44 都会有点，嗯，如果他们那样做，那就是彻底的失败，没错，因为

52:45 但也或者说

52:49 你要懂，有一个非常有趣的微妙之处在这里

52:50 那就是你现在其实不想让你的工程师们去考虑计算预算，因为我们

52:55 还在开发这方面，哦好吧，所以你就把它放那边，但我觉得好像

52:56 我们关于云计算预算已经讨论了15年，这完全是新的情况，就像

52:59 只有大约10%

53:00 在2016年，只有10%的工程师需要考虑这个问题，在2018年左右，就有一批公司，基本上就像

53:05 一个仪表盘用途，

53:06 叫什么名字来着，Phenoms，现在开发者很喜欢，因为

53:11 开发者会控制访问权，因为云支出失控了，

53:13 是的，P.I.

53:17 支出越来越失控，是的，所以就是，给你你的

53:18 Twilio支出，这是，

53:22 但是，这和以前相当不同，我等着看

53:24 YouTube上大家的评论来挑你毛病，就像你可以走进会议室，

53:28 说嘿，你能不能让那个算法

53:28 更高效一点，这样你晚上用我们集群的资源就不会那么多，诸如此类，然后

53:32 会议结束后有人改进它就好了，这就是每个工程师每天在做的事情，

53:33 你得决定，比如说你要一个长时间运行的

53:35 提示，你想让它是一个长时间运行的智能体吗？你想并行运行吗？

53:36 比如，你容忍浪费多少Token？现在

53:40 我倾向于觉得，我们应该浪费很多Token，因为那意味着我们

53:41 在尝试新东西，是的，就像，如果你的工程主管会高兴吗，如果你

53:45 并行做10个实验，显然你会浪费90%的

53:46 Token，你会选出一个成功的，然后或者你告诉团队，知道在你走之前，

53:49 把设计做好，真的把系统设计完美，我们

53:50 实际上还有一大堆开放问题开始出现，就在录制这个视频的时候

53:53 有人现在在为新的云代码最大计划抓狂，

53:54 像这样，

53:58 因为他们在三次提示后就被封锁了，这会成为

53:59 一个非常现实的话题，直到我们找到建造数据中心

54:03 容量的方法，那是个

54:04 不同的问题，好吧，不，因为没人，假设，等一下，你可以

54:07 假设，如果我们建设更多容量，价格就会下降，因为容量增加了，而我们

54:07 现在基于有限容量定价，但这会最终解决，

54:12 我为那些必须立即做出决定的人感到难过，

54:13 决定这周哪些17人不能用更多Token，

54:16 整个公司都像拿着打卡卡，

54:17 午餐排队的人，

54:21 是的，午餐排队的人每次用完都会打卡，但我，我不知道，

54:23 我们今天讨论了性能这件事，比如说，我们以前写命令行工具，运行完会

54:26 显示运行时间，好让你知道性能是变好还是变差，但是

54:28 这一切都会过去，毫无疑问，

54:31 会过去的，我觉得10到10年后，主要原因是

54:32 你得算点滴成本，也就是，如果你...

54:36 actually have a whole

54:36 bunch of open questions that are going to start to happen like literally as of

54:40 this recording time

54:41 people are freaking out right now on the new cloud code max plan you know whole

54:45 like like

54:45 because they're getting blocked after like three prompts well this is this is

54:49 going to be like a

54:50 very like real topic until we can actually find a way to build data center

54:53 capacity oh that's a

54:54 different problem okay no because no one is well assuming well wait no you can

54:58 assume that if we

54:59 build more capacity the price will drop because there is more capacity and we

55:02 're priced now based

55:03 on limited capacity whatever but like this is just going to get worked out and

55:08 I feel bad for the

55:09 those that have to make a decision immediately yeah about which 17 people get

55:14 no more tokens this

55:15 week or whatever and that the whole company is walking around with like a token

55:18 card and

55:19 and the person in the lunch line yeah yeah like it's the person in the lunch

55:25 line is punching their

55:26 card every time they do set but but like I you know I I don't know like

55:30 somebody we were talking

55:31 about today about performance and how like you know we used to write command

55:35 line tools that spit

55:36 out the time it took after you ran a command line just so you knew and if you

55:41 knew you were getting

55:42 better or worse and you know but you the thing is is this is all going to go

55:48 away there's absolutely

55:49 no doubt yeah that this just goes away I think I'm 10 or 10 and the biggest

55:53 reason it does is

55:55 is because you you have to do the penny off kind of math which is if you're

55:59 支付给企业

56:00 你认识的年收入一百万美元的销售员，你得问问他们的

56:04 工具值多少钱

56:06 如果你支付给一名工程师每年 X 美元，那么在某个阶段他们的

56:11 工具价值

56:12 绝对值得，这甚至不会成为一个问题，嗯，是的

56:17 是的，我觉得这不是

56:18 嗯，我认为如果短期内有产能问题，那就是

56:22 另外的问题

56:23 这跟价格驱动的不同，我们不会永远

56:27 被困在某种预算

56:27 演算里，我觉得很大一笔数字能解决这个问题，因为

56:31 最终你会有

56:32 足够多的工程师使用这么多计算资源，但我们现在处于过渡期

56:35 我们就像

56:36 大多数人认为两年前 AI 的支出水平，那只是一个

56:40 聊天

56:41 机器人，是的，但是他们错了

56:44 是的，他们错了

56:45 我们试图提醒他们，但他们错了，因为他们只看到了这个

56:50 具体用例

56:52 但再说，你知道那个电子管的事情，你取笑过

56:58 是的，但是

57:00 曾经有人认为整个达科他州都会布满电子管仓库，人们穿着旱冰鞋，

57:04 穿梭其间

57:05 不断更换电子管，只是为了赢得二战

57:09 我意思是，这就是那个时代的情况

57:10 然后有人说，“嘿，试试晶体管怎么样？”

57:15 接下来就是

57:16 我们将在这件事上经历晶体管时刻

57:20 可能只是供应增加，

57:21 我们对它的理解方式

57:24 但也可能是算法的根本性变革，

57:26 也可能是硬件的变化，有很多东西能

57:30 改变这个

57:31 特定时刻

57:34 这件事特别奇怪的是，大家只是关注

57:36 代币

57:40 这跟 IBM 和大型机发生的事类似，人们用 MIPS 计量，

57:42 但有一天现实是

57:45 IBM 每年以更低价格卖出更多 MIPS，甚至

57:47 自己都没意识到，

57:52 他们定价还是按 MIPS 计算，直到有人指出

57:53 他们在走下坡路

57:58 因为制造的 MIPS 比他们能收取的价格增长快，

57:59 这就是

58:02 必然会发生的事

58:04 我刚才说得很直接，我觉得那

58:07 听起来太好了

58:09 听起来真棒，感觉我知道自己在做什么，绝对没错

58:12 实际上可能

0:00 The diffusion of AI capability is going to take longer than people themselves

0:03 can now realize.

0:04 It's just absurd to think you're going to vibe code your way, like SAP.

0:08 All of that domain knowledge, it's not just represented in some well

0:12 orchestrated data layer.

0:13 The engineering compute budget conversation is going to be the most wild one in

0:17 the next couple

0:18 years. The biggest problem right now is everybody is trying to figure out the

0:21 economics of all of this.

0:23 When they're off by at least in order of magnitude on how big the opportunity

0:27 is.

0:27 If you have a hundred or a thousand times more agents than people, then your

0:31 software has to be

0:32 built for agents. People in the abstract say things like, now you're marketing

0:36 to agents that

0:36 you're like an API, you've got a good idea. I actually think that's almost

0:40 exactly wrong with

0:41 you. Wow, this is breaking podcast news.

0:43 If you start to imagine that we all have to build software for agents, I think

0:49 we're like all clear

0:50 on that, right? So like that trend is happening, which is like we spend as much

0:55 time now thinking

0:56 about the agent interface to our tool, as we do the human interface, and the

1:00 reason we're doing

1:01 that is because our hypothesis would be that if you have a hundred or a

1:05 thousand times more

1:06 agents than people, then your software has to be built for agents. What is the

1:11 way that those

1:12 agents are going to interact with your system? It's going to be through an API

1:15 or a CLI or MCP or

1:16 whatever. The paradigm that appears to be taking off and is quite successful so

1:21 far in terms of

1:22 efficacy is what if you give a coding agent access to your SAS tools and a

1:27 coding agent

1:29 access to your knowledge work sort of workflows and context, and that kind of

1:35 becomes this super

1:36 power, which is it's not just like the agent is not only capable of reading

1:40 some data,

1:41 understanding some information, it can actually code its way or use APIs

1:46 through whatever task

1:47 it's trying to achieve. That appears to be like a paradigm that is starting to

1:52 compound,

1:53 and that's the cloud co-work phenomenon. That's the whatever open AIs is kind

1:59 of cooking up

1:59 with the super app, perplexity computer, et cetera, and I actually think it

2:06 kind of makes sense as

2:07 like the ultimate manifestation of this stuff. I think you're right. It makes

2:13 sense in a

2:15 theoretical way, but in a practical way, we have to be really careful in that

2:21 that the way to say it is algorithmic thinking is really, really, really hard

2:29 for the vast

2:30 majority of people who have jobs. The easiest way to think about it is if you

2:36 were to go into any

2:36 person and ask them to create a flow chart for a particular thing that they

2:40 have to go do,

2:42 they would probably fail at producing that flow chart. So within any

2:46 organization,

2:47 say doing a marketing plan and there's 50 marketing people working on a giant

2:52 product line,

2:53 one person probably understands and could document the flow chart. So if you

2:58 put one of these agents

2:59 or you put this tool, this co-working tool in front of people to create these

3:06 things,

3:06 their ability to explain to it what to do is really, really limited. So then

3:12 you...

3:13 But what if that becomes the new, this is the new way you have to interface

3:17 with computers and

3:18 you just have to cycle that through? Well then you're basically, you just go

3:23 back to

3:24 you're basically just developing the next abstraction layer for how people

3:30 interact and

3:31 developing an abstraction layer has historically at each level of the

3:34 abstraction layer been a

3:36 highly skilled, very specific individual within an organization, developing

3:41 that. And then the

3:42 little parts that they build just become little toolits in the world of people

3:47 doing particular

3:48 tasks and some people are able to stitch them together and some can. But that

3:52 happened with

3:53 paper clips and thumbtacks. Oh yeah, I actually... And it's going to happen

3:56 with whatever we do

3:57 next. I think that... I think basically the timeless part is the job just moves

4:04 up

4:04 around and you learn a new set of skills and that's why I actually don't think

4:09 anything about this

4:10 is any different. It's just now the leverage you get is obviously fantastic.

4:14 There was this viral

4:15 kind of tweet that went around which was the anthropic growth marketer, do you

4:19 guys see this?

4:20 It's basically one person and he was using cloud code at the time to basically

4:25 more or less automate

4:26 what maybe five or ten people would have done in various kind of silo jobs. And

4:30 I think the

4:33 reason why it's interesting is, yeah, you had to have been a systems thinker to

4:36 be able to

4:37 accomplish that. So like clearly he already was technical enough to be able to

4:40 pull that off.

4:42 But it did kind of represent like what would each of these jobs look like if

4:45 you had like

4:46 imagine you had, you know, X job in the economy and right next to that person

4:50 was an infinite

4:51 pool of engineers that could automate whatever that person wanted. And you know

4:55 , what would that

4:56 job look like in the future as a result of that automation that now is possible

4:59 ? Yes, I agree that

5:00 you'd have to find a way to like, you know, think through your job as a system

5:04 to be able to pull

5:04 that off. Maybe the agent gets better and better over time at being able to

5:07 like nudge you in that

5:08 direction. But like, it does sort of stand a reason that like you will start to

5:13 , you know,

5:14 try and automate a lot of that kind of work of like, well, why don't I take

5:18 like the keywords

5:19 that are working in this in Google AdWords and then port them over to Facebook

5:23 and make sure

5:24 that those are replicated and then take in the new signal from what's happening

5:27 in the market.

5:28 That's a big leap. Like, what the first, you were nodding a little, and then I

5:32 said something that

5:34 went too far. The anthropic growth person is an example, like that's just the

5:37 rest of the work.

5:38 Yeah, I could do that job. Everybody's not going to be like, when the mayor is

5:42 infinite. Yeah, right. Like when demand is infinite and frankly supply is

5:46 infinite,

5:46 this is not a difficult job. And so let's, let's think of the federal club in

5:52 Australia right now

5:53 is amazing. Right, right. So like, B instead be the $600 PC marketing person

5:58 and see how you can do

5:59 against the media. That's a real job. All right. We need a better, we need a

6:03 better example.

6:04 But there is, I mean, it is, it is really interesting. Like, I hear, let me do

6:08 an old example,

6:10 an old person example. Like my, my cousin, MBA, elite school joined her first

6:16 job. She's a little

6:17 older than me, joined with right on the cusp of computing. Like she actually

6:21 didn't use a spreadsheet

6:22 in grad school. And then they all, or spreadsheet showed up, but she wasn't a

6:28 spreadsheet person.

6:29 So instead, they told her hire as many interns as you want. And so her first

6:34 year on the job,

6:34 she like supervised like essentially a whole room of agents. And the, the, the

6:40 kids who was me,

6:41 not literally, but they were in college, came and just did all the spread

6:45 cheating. Yeah.

6:46 But then what happened sort of this magically over the next couple years was

6:51 she and her cohort

6:53 all became the spreadsheet people. Yeah. And then this idea that you being a

6:58 manager in a bank,

6:59 or just a two years in, meant you had a cadre of people doing the spreadsheet,

7:03 no, the whole

7:04 abstraction layer moved up. And the old job before those interns was you just

7:09 sat there with basically

7:11 calculators and an HP calculator, figuring out the model for some M and A deal

7:16 or whatever.

7:16 And you only got to do like two iterations before you had to put out the, the

7:21 pitch deck or just go

7:22 to the customer or the client or whatever. And then all of a sudden they're

7:25 doing 30 iterations

7:27 themselves. Yeah. But they, and so I think where we are with agents is just at

7:32 this step where you

7:33 think you need 50 and the abstraction layer is such that we're dividing up in

7:37 these really small

7:38 pieces with one super smart person coordinating them all. And pretty soon that

7:42 whole thing is just

7:43 going to, they're all going to collapse on each other. Yeah. And there is just

7:46 going to be like

7:47 a skill set amount of code called an agent that is like marketing-ish. Yes. And

7:53 you'll be able to

7:53 ask it marketing stuff. Yeah. And then the next step will be and have it go do

7:58 things. I'm a little

7:59 skeptical of the, until the whole like non reproducible, non random element of

8:05 this AI stuff goes away.

8:06 The doing stuff is going to get very costly. Yes. And so then you get into the

8:11 human in a loop

8:11 discussion and all of that. But I think we're just, we're at that exact, I feel

8:16 like when I talk to

8:17 people trying to do stuff that we're right. I feel like I'm at Thanksgiving

8:20 dinner talking to my

8:21 cousin six months in her job. When, when I'm using a spreadsheet already and I

8:25 'm, and I'm like,

8:26 I don't know why this is so hard. You should just use one. Yeah. And then two

8:30 years later she's

8:31 doing it. And, and I think this right now you have to be an absolute, you have

8:34 to be a rocket

8:35 scientist and the growth marketing person to create 42 agents and spin them all

8:40 up and do all of this

8:41 stuff. But the rocket science part of it just is going to evaporate in a very

8:44 short order.

8:45 And then you're talking about, well, there's a giant chunk of domain expertise.

8:50 Yeah, it was

8:51 back to the domain expert. I actually think something that you said, I'll take

8:54 the other side of,

8:55 which is, I think it's very tempting to be like, these agents are going to code

8:58 and do X. Yeah.

9:00 But I think we're going the opposite way. So I think actually where we started

9:03 was we'd like

9:03 take like a piece of SaaS software and we'd add AI. Yeah. And then that's like

9:07 the new kind of like

9:08 AI enabled. So that's like the extreme version of using code for these types of

9:11 things. But now,

9:12 what are we actually doing? We're like, okay, the SaaS software is still SaaS

9:17 software. And the

9:18 agent uses it as a computer because it's actually very good at that. So I'd say

9:21 like we started with

9:22 code, then went to the terminal, which is actually less code. Yeah. And now

9:27 this year is going to be

9:27 the year of computer use. Yeah. So it's almost like they're much more like

9:31 humans using computers

9:33 than them generating code. And that feels like very much like this mezzanine

9:37 step. Yeah. And I

9:38 actually come from like the generating code type of the world. Like I would

9:41 argue that that's

9:41 happening less not more. Yeah, I think. So to me, whether it's computer use API

9:47 use or writing code

9:48 on the fly, I kind of maybe erroneously put that all in one way. They're very

9:53 different. But we

9:55 have an agent that we're working on where you just makes a determination

10:00 whether it should use an

10:01 existing skill as you're using an existing tool from box or it should write

10:04 code to solve that

10:05 problem. And its ability to do any one of those three at any moment ends up

10:09 being incredibly useful

10:11 because sometimes there's just some specific operation you want to be able to

10:14 do. We're writing

10:15 code to be able to do that operation is just faster. And we don't have to we

10:18 don't we can't

10:19 possibly, you know, kind of pre plan for every thing that anybody would ever

10:23 want to do on their

10:23 documents. And so the fact that the that the model is good enough to also write

10:28 code on the fly for

10:29 that use case ends up just being like an amazing property. Even though maybe 90

10:33 % of the things that

10:34 it's going to do is should just be using an existing. Over time, Prado takes

10:37 over and over time,

10:38 there's like literally like seven apps on her iPhone. There's seven sass apps.

10:41 We end up like

10:41 over time, these things tend to consolidate. But the but the seven apps on the

10:46 iPhone is is a is a

10:47 issue of humans don't want to learn these things over and over again. And so I

10:51 as a human, I don't

10:53 I can't I don't have the mental bandwidth to learn that many apps. But an agent

10:56 that is going to use

10:57 tools and APIs and be able to code things doesn't have any of the same

11:00 constraints that we have.

11:01 So I I don't know like I don't mind you could argue that there's just so many

11:06 things to do and

11:07 you can make yeah faces sufficiently general. Yeah, well, fair. I let me say I

11:11 like what I like

11:12 what you said then because our back. We're lying. No, but but I think there's

11:19 something super

11:20 interesting here, which I do really, really like, which is that that where

11:24 software has evolved,

11:26 you know, like I use SAP all day. I work in finance. I have to go and generate

11:30 all these reports.

11:32 And then somebody shows up and says, I want to report that does this view slice

11:36 this way. And

11:37 I'm like, Oh God, I don't know how to make that. And like now let me go wade

11:41 through the SAP help

11:42 system and try to find it. One thing that that let's just say AI could be very

11:48 good at is it

11:48 actually can navigate that surface area much, much better. You know, the help

11:53 is all there. And so

11:55 it's a matter of finding it mapping language. And humans have been a bottleneck

11:59 in tapping the past

12:01 25 years of software capabilities. Yes. I mean, like I spent my life, my life

12:06 with

12:06 sitting at the people on airplanes saying, how can I make PowerPoint do X and

12:10 just go to the ribbon.

12:11 And you know, it was because it hurt physically hurt to watch somebody

12:16 suffering with bullets and

12:17 numbering and word or trying to figure out, you know, like, Oh, let me just

12:20 make a two sided,

12:21 a two axis graph in Excel, which like is rocket science, like almost no one can

12:26 do that. But yet

12:27 it's super common. And so people are like, have none. And so that impedance

12:31 mismatch was a human

12:33 user interface design on the consumption layer, I totally buy it, which is like

12:37 the perfectly

12:39 fluid, like UI or consumption layer. I just feel the back end, like the systems

12:45 of record,

12:46 yeah, oh, yeah, it'll probably converge into like some database, like some

12:49 generic set of APIs,

12:50 like that they'll connect to. And like that seems to be the direction it's

12:53 going.

12:53 I agree. I think you can start. Well, like, so I spent all weekend, like

12:58 implementing my

12:58 nanocla dot. And when you first start out, it's like you're building an

13:02 integration for everything.

13:03 The nanocla is very like like open clock has all of the integrations, nanocla

13:06 has been a few of them.

13:07 And so you haven't built all of its own tools. But after, you know, two or

13:10 three days of these,

13:12 like, you know, you kind of have the two integrations that you need. And yeah,

13:15 like, yeah, but back

13:17 to the, I mean, we're talking about personal productivity, probably like you're

13:20 like organizing

13:21 your life or something. Well, it's work productivity. Okay, we're productivity.

13:24 And then an SAP system,

13:25 and like, and like, and so there's like an, well, like, there's an infinite

13:28 amount of complexity

13:30 when you get to, okay, some company that has a global supply chain, and they're

13:32 dealing with

13:33 75 pieces of information across, you know, 30 different systems. That does

13:38 require a certain

13:39 amount of horsepower from the agent that is just, we have, I mean, we just

13:44 haven't been able to get

13:45 from, from any architecture up until now. But like tape, but that it, what you

13:49 just described

13:50 is literally what it has been doing for 50 years, and will continue to do,

13:55 which is,

13:55 yeah, I have a friend who was the CIO of, of the VA. And he spent all he spent

14:02 his time on was

14:03 gluing the 75 VA systems together. And it's all just integration. We're done.

14:09 It's perfect for

14:10 integrate. Yeah, this I totally agree. For integration, these things are the

14:14 best, but, but

14:15 it's integration. Yes. It's literally, how do I stitch these two systems? But

14:18 it's in it. But now

14:18 the thing that I think is happening is it's kind of like integration on demand.

14:23 Yeah, it's, it's my,

14:24 it's my new query in the system that the IT team didn't prewire. Now I needed

14:29 to happen at runtime.

14:30 Uh, let me get off my lawn. Okay. Okay. So, okay. So the reason I just was in a

14:37 room filled with a

14:37 bunch of CFOs and CIOs and this, they all looked at me when I said something

14:42 along these lines,

14:44 although not as optimistic as you could imagine, but they just, they, they, no,

14:48 it caused like six

14:49 of them to come running up afterwards and say you're insane. You've lost all

14:53 credibility with me

14:54 because it's bad. What, what, what specifically that they, the agents are going

14:58 to do integration

14:58 as well? That, that, that the integration is a problem that will get a lot

15:02 easier. Yes.

15:03 They were against that? No. They're no one's against, but they're, they think

15:07 it's practical.

15:07 But they're, they're fear is like unleashing not just the agents themselves,

15:13 but humans to do

15:14 integration because you put people creating new integrations and you just say,

15:18 please break my

15:19 system of record. Oh, yeah. And so this idea that you just create like a new

15:23 API between,

15:24 you know, system 27 and system 38. Yeah. And, and then you're, that might be

15:29 fine for a report.

15:30 Yeah. Because if that person wants to be wrong, that's their business. Yeah.

15:32 But you're not. I think, I think we have a read-only version of this for a

15:36 number of years before.

15:38 Where is, and is very large. Yeah. And a lot of it's just the consumption layer

15:42 where the consumer

15:43 is a human being. Right. Right. It really feels right now. A lot of the stuff

15:46 is consumption.

15:46 But, uh, yeah, I mean, it's, um, uh, you know, we, we actually have, so we just

15:51 rolled out the

15:52 official box CLI. Thank you for the liking the tweet on that. I, I used it. I

15:57 have some feedback.

15:58 I'll talk about it. I'll take, I'll take all the feedback. But it's a really

16:01 interesting thing.

16:01 So we, we had these, all these debates internally of like, okay, you give

16:04 Claude code, the box CLI,

16:07 and you can now interact with your entire box system via natural language. And

16:11 you get the

16:11 horsepower of opus four six being the orchestrator of doing a bunch of

16:15 operations. And it's like,

16:17 it's like, you know, blows your mind. I guess I'll get some feedback, but it

16:20 blows your mind in some

16:20 ways because you could just be like, upload this entire folder from my desktop

16:24 in the box and it

16:24 will work or process all these documents in this folder and it'll work. And, um

16:29 , and it's amazing.

16:30 And then we started thinking through like, well, let's say you were a company

16:33 with, with, you know,

16:34 5,000 employees and everybody had access to some shared repository, like, you

16:38 know,

16:39 engineering documentation and, you know, marketing assets or whatever. And

16:42 everybody had Claude code

16:44 or codex, um, you know, running with the CLI. Wow. We now have some really

16:48 interesting new challenges,

16:50 which is like, like, how do you coordinate, you know, possibly the fact that

16:55 you might be hitting

16:56 this system, like, you know, 10,000 times an hour or something, not from a,

17:00 like a performance

17:01 standpoint, but just like, how do you make sure that people didn't move like a

17:05 file from one

17:06 thing accidentally from one folder to another folder while the other person is

17:08 trying to do a

17:09 right operation and somebody else was trying to delete something because you

17:12 have these agents

17:13 running wild. This is, this is going to be like the new big question that every

17:18 CFO CIO is running

17:19 around trying to with their hair on fire. Well, there, there's just, that's

17:22 exactly what I ran

17:23 into, which is I played around with your example, which is create the video

17:26 example, which is create

17:27 like a marketing plan directory or something. And like, all of a sudden, I'm

17:31 like, in some loop

17:32 creating directories, like, and I'm like, go on as long as it can. Right. And I

17:36 was like, I wonder

17:37 what the limit is on box for nested directories, because I'm about to hit it. I

17:40 actually, we're

17:42 going to find out too. Yeah. Yeah. But it does feel to me that, that like, a

17:47 lot of the intuition

17:48 is to like build a new layer, yeah, controls and whatever. But what's actually

17:52 happening on the

17:52 ground is, is, is, is the opposite. So I'll give you an example. Like, when we

17:56 all picked up a lot

17:57 of these personal agents, we would like give them our API keys. Yeah. We would

18:01 give them our email

18:03 addresses. And then they would kind of access those things. They're like, Oh,

18:06 but how can I stop

18:06 it from like, whatever? Yeah. And so what everybody's doing now is you give it

18:10 its own phone number.

18:11 Yep. I actually gave my nanoclaw, its own credit card. Hopefully just a,

18:16 Visa debit card that you bought at TBS. No, no, no, no, but then, but then I

18:23 gave it its own

18:23 Gmail account, which we're going to log into. And then Gmail actually has all

18:26 of these RBAC

18:27 permissions. Yeah. Yeah. So, so you could make an argument that like, you know,

18:31 we've actually

18:31 built in a lot of these permission systems to treat it like a human, otherwise

18:34 it's a separate

18:35 human. And then instead of like building another off layer, building another.

18:38 Okay. Now, can I

18:39 instantly take, do a take down of this element that we're going to run to?

18:43 Please. Okay. So that

18:45 is fantastic for personal productivity. And the question that we're going to

18:49 run into is in an

18:51 enterprise. Let's say I have, let's just make a simple example. I have a 50%

18:55 team of something.

18:56 Should everybody also, basically, will we have a hundred, will we have a

19:01 hundred people now

19:02 collaborate? I mean, basically 50 humans and then 50 credit cards and then 50

19:06 agents in that same

19:07 shared space. And do I have, I obviously have complete oversight over my agent.

19:13 But what if my

19:13 agent collaborates with somebody else and then accidentally gets access to some

19:19 resource because

19:20 they were sharing with that other person and I'm not supposed to have access to

19:23 that resource. And

19:24 now this autonomous sort of stateful, you know, agent is running around working

19:29 on somebody else's

19:30 information. The default end to end argument is you treat them like human

19:33 beings. It doesn't

19:34 work. So you can't fully treat them like humans because here's the thing. And

19:37 with regular humans,

19:38 you don't get to look at the slack channel of the person that is working with

19:42 you or working for

19:43 you. You don't get to log in as them. You don't get to oversee them. You are,

19:47 they are accountable

19:48 for their own set of execution in the real world. You don't get penalized for

19:52 how they screw up.

19:53 The agent, you have all the liability of whatever they're doing. You do have

19:58 complete oversight

19:58 and you're probably going to need to have that complete oversight. They have no

20:00 right to privacy.

20:02 So, so there's going to be these, some of these breakdowns that aren't as clean

20:06 as just treat them

20:07 like a person because I need to be able to kind of, I need to be able to give

20:11 access to something to

20:13 them. But I also need to be able to like log in as them at some point and be

20:16 like, no, no,

20:16 you fucked up the whole thing. And I need to undo it all. But if I can log in

20:20 as them,

20:21 how could they have operated in the real world working with other people and

20:25 keeping anything,

20:25 you know, confidential or secure or whatever. So it really is still an

20:28 extension of you.

20:29 It's like almost impossible to get around them being an extension of you. So

20:32 now the thing that

20:33 we're thinking through, that we're not going to be able to do any time soon. It

20:37 just doesn't

20:37 logically follow me. Yeah, maybe. But for example, for my employees, yeah, I

20:41 can log in as them.

20:42 You don't, though. You don't, you don't, I can get access to their email. Yeah,

20:46 no, in like,

20:46 if you get like sued, you're not logging in, you're not logging in as them on a

20:51 regular basis because

20:52 they sent one email. Is the right operating model with an agent? The same thing

20:55 . The risk is like

20:56 a thousand times greater. Like these people, like they will just leak your

20:59 information whenever they

21:00 want. Like they will happily just go and send some email to somebody because

21:04 they got prompt

21:04 injected. You think the terminal state is that these things are still these

21:07 sloppy computers and

21:08 therefore they will always, I don't like the word sloppy, unless we're saying

21:11 it very in a

21:12 colloquial sense. But like, they'll never be able to contain information. They

21:16 'll never.

21:16 So we're like, I think the ability for you to keep something in the context

21:22 window, a secret,

21:24 like as in like you tell it, do not reveal x thing in the context window. I

21:28 think that's

21:29 a very hard problem to solve. So then, so then thus, if anything can ever enter

21:34 that context

21:34 window, because they have access to a resource, then in theory, you should

21:37 assume it can be

21:38 prompt ejected out of the of the context window. And I don't know that we know

21:42 of a way to solve

21:43 that at the moment. Like that's like, and so, so if I know your new agent's

21:49 email address and I

21:50 email it like it's an assistant, but like I can, I can social engineer it 10

21:54 times easier than a

21:55 human. Like it'll be hard for you to pull off that that agent is now also has

22:00 access to your like

22:01 M&A documents and stuff. But is it this like literally all of AI right now?

22:04 Which part? I mean,

22:05 the fact that we've got these shared systems that we use the intelligence for

22:09 that have shared

22:10 contacts. But we mean, it's all of AI. Well, I'm just saying like right now,

22:14 when we use AI

22:15 internally in agents internally, this is exactly how we use that. But this is

22:18 why you were they

22:19 are working on us as you effectively right now. And we don't yet know how to

22:23 make them not work

22:24 as you. Let me offer an example. Let me let me offer an example and then

22:28 solving this problem

22:28 though. Like like the like the issue will be will be like you will just be able

22:35 to trick the

22:36 agent to reveal information. So then so then that's why like having them have

22:41 access to their own

22:42 resources where they can fully make their own decisions is not yet something

22:45 that we've been

22:46 able to pull off. There's a perfect example for solving your problem, which is

22:50 we already lived

22:51 through this with open source. The model for open source was it's all there and

22:57 just use it and

22:58 you pick and choose. And then like nobody debated it because the world was much

23:02 smaller than and

23:03 we weren't all on X doing podcasts when this was all happening. But then

23:07 quickly everybody realized

23:08 all the problems you you were just talking about. Like if you're running a big

23:11 company,

23:11 you can't have some person just go copy in a bunch of source code from open

23:16 source into your

23:17 commercial product like that. There was a whole licensing problem, a whole

23:20 whole bunch of stuff.

23:21 And so all these norms got developed. The the debate that we're happening that

23:26 's happening

23:26 right now is just is this really interesting modern artifact of how new

23:31 technologies develop,

23:32 which is this is all happening in real time. During open source like we met at

23:37 a conference

23:38 room this big and debated how much open source we could use in Windows or

23:42 Office. And nobody in

23:43 the internet knew we were having this debate. It was a very and I think it's

23:47 just so interesting

23:48 that not just this the debate about specifics but this whole notion of where is

23:53 this heading

23:53 is happening in writ large and everybody is just trying to get to the end state

23:59 like way way

24:01 more like in a sense more quickly than we can actually reach the end state. And

24:06 so what really

24:07 needs to happen is people just need to go build. We need standards. What? We

24:12 just need some

24:12 standards. I've got different intuitions on the end state. No, no. You don't

24:17 want my intuition.

24:18 What could make an end to an argument that these things actually converge on

24:22 the same type of

24:23 reliability as a human being, which is exactly how we view like self driving.

24:27 And in that case,

24:28 you use the exact same mechanisms that we use to protect with human beings.

24:32 Like yeah,

24:33 you consider insider threat. You consider the fact that people can be bought

24:36 off. You consider the

24:37 fact that people make mistakes. And that's a risk. And that's operational

24:40 processes.

24:41 So one intuition is like that will be the end state. There's another intuition.

24:45 Well, don't point to me. I'm just saying, I'm talking about where we're at now.

24:48 I actually,

24:48 I don't know that we disagree on the end state. And by the way, like

24:52 strategically,

24:52 we're hedging because we're gonna build we're gonna build Asian users and like

24:57 so we're like,

24:58 I love the idea of open claw having a box account and it operates and you just

25:03 like twice as many

25:03 cats. I love it. I'm just saying on the ground right now, we don't yet know how

25:10 to give it an

25:11 M&A data room to fully securely. Right. But that, but it's actually it is

25:16 harder than that,

25:16 though, because the threat the threat vectors are going to be way more

25:21 sophisticated. So we do have

25:23 a cat in a mouse game going on where you can't just assume that the agent acts

25:29 like a human does

25:30 today because it's going to be the fastest, most thoughtful, craziest-ass human

25:36 that ever existed

25:37 trying to actually leak the information because it got injected in some way.

25:40 And so

25:41 part of what's going to happen is we're going to go through this phase where

25:46 like the enterprise

25:47 customers are just going to like close everything off until there's some sense

25:52 of sanity in all of

25:53 this. And then, but in the meantime, the individual and specifically the

25:58 developers.

25:59 And that's going to be that I think is the most exciting tension that's going

26:04 to happen is that

26:05 that the enterprises are going to be are going to get left behind by these sort

26:09 of advanced

26:10 individuals, which will then start to look like the startups. And the startups

26:15 will start to move

26:16 much, much faster than enterprises because they just don't have any of these

26:19 problems.

26:20 And you know, you could end up with like the agent going rogue in a startup and

26:25 doing that.

26:26 You had no employees that go rogue routinely in the startup.

26:30 Yeah, well, it'll just be an episode of Silicon Valley. And so, you know, big

26:33 deal.

26:34 I agree with you on like the okay, it's people, et cetera, the same risk. I

26:38 think there's a couple,

26:39 you know, differences though, in the sense that that I can't really threaten,

26:44 you know, the like

26:46 cloud code that it's just I'm going to pull the plug on it in the same way that

26:49 you do have that

26:50 threat as a regular employee is like you at least like 95% of people are not,

26:54 you know, trying to do

26:55 bad stuff, you know, within an order. Yeah, but they're not trying, but the

27:00 ability to inadvertently

27:01 do bad stuff. Yeah. To your point about it still not having that stuff. I would

27:05 argue that that

27:07 it's a lot easier to have people not share, let's say files with somebody

27:11 outside the company

27:12 in a wrong way, more than it is for an agent right now to have the same set of

27:17 instructions.

27:17 And also you have the tools so that you can basically stop that at a whole

27:21 different level of

27:21 abstraction. Which is why you have to build this into software. Right. But I do

27:24 think actually,

27:25 if you were to like, if you were to like put a bow around your last point, a

27:28 lot of this is actually

27:30 why the diffusion of AI capability is going to take longer than people in

27:33 Silicon Valley realize

27:35 because what's happening is like we see startups that can start from the ground

27:39 up without any of

27:40 the risks that we're talking about because they have nothing to blow up. And so

27:44 we look at that

27:45 as the trajectory that we're on. And then you go to like JP Morgan and you're

27:49 like, how are you

27:49 going to set up nano cloth to be able to to actually like, you know, automate

27:55 your business anytime

27:56 soon? And it's like, oh, okay, there's going to be like a little bit of a gap

27:58 there. Well,

27:59 what do you guys think? Here's I think that that opens up a pretty interesting

28:03 problem,

28:04 which is this split between big and small startup and enterprise, which is just

28:08 that

28:09 that the the enterprise, the current SaaS vendors who are all struggling in

28:14 this SaaS

28:15 apocalypse weirdness that I don't really agree with, but they are struggling

28:18 with this problem that

28:19 they they don't really sell the line of business data. They actually sell this

28:25 intelligence and

28:26 domain expertise in this whole system. And the agent side of things wants to

28:30 only buy the data

28:31 now. And they only want to license the data and they want to have unlimited

28:36 access to the data,

28:37 but they've actually never really enabled that like that's never been their

28:41 business. And it's

28:42 been a long standing tension point with the likes of work day and SAP and stuff

28:46 like how much API

28:47 access to have. I mean, Salesforce went through three different massive

28:51 platform redesigns.

28:52 You know, it's I think that that's a particularly interesting problem, not for

28:57 the same reason that

28:58 Wall Street does. Wall Street's all wrong about the economics of a problem and

29:02 all that stuff,

29:03 but from a technology perspective, what does system of record mean in the face

29:08 of people wanting to

29:09 access the data when the data for training or for well, they're they're talking

29:15 about

29:15 executing the work. I think of it as executing the data to operations. Their

29:20 concern is that

29:21 somebody that they want to do the training layer on on your day. Like I'm a big

29:25 customer. They want

29:26 to do the my vendor wants to build a training. Actually, even even if you don't

29:30 even get into

29:31 training, they're concerned because because like monetizing, you know, you know

29:36 , sending a little

29:36 bit over the internet versus like you're in my UI. Oh, that's a very different

29:41 level of monetization

29:42 initially. That sort of that monetization part is the Wall Street point because

29:46 I think like, look,

29:47 there's so much domain stuff in in an SAP, just to pick an example, not to pick

29:52 on them. But like,

29:53 they're not going anywhere. Like it's ridiculous. It's just absurd to think you

29:57 're going to vibe

29:57 code your way to like SAP. But also all of that those all of that domain

30:02 knowledge, it's not so

30:04 it's not just represented in some well orchestrated data layer as much as they

30:08 tried. There's like

30:10 a whole bunch in the in the UI. There's a whole bunch in middle tiers. There's

30:13 a whole bunch in

30:14 just how you use it. And so I'm really unsure how this thing evolves because

30:20 SAP isn't going anywhere.

30:22 So then that's going to slow the diffusion of AI on that particular data source

30:27 independent of

30:27 whether or not it's a gentified AI that's doing stuff or just read only

30:31 reporting on stuff. So

30:33 where do you come down on it? Where do you think that's going to go? I'm afraid

30:37 of saying something

30:38 that's happening. Otherwise you're not going to get invited back. So say

30:44 something good.

30:45 I think I've drunk the Kool-Aid on build something agents want. So this kind of

30:54 the

30:54 Paul Graham term kind of like emerged on you know the past year on this topic,

30:58 which is just like

30:59 like a vent. I think we would actually then fully agree on this, which is at

31:03 some point you do

31:05 enough sort of iterations of this. And at some point the Asian is largely in

31:09 charge of what tools

31:11 it wants to implement and use and whatnot. And yes, the agent is not going to

31:17 be able to change

31:17 out an enterprise system. But like again enough generations later the agent

31:22 might just run into

31:23 so many walls with your software that it's just going to say you need to

31:26 finally rip out your

31:27 legacy HR system or I'm not going to be able to automate this workflow for you.

31:32 So I do think

31:32 you have this really interesting you know dynamic, which is back to this whole

31:36 point of imagine that

31:37 there's a hundred or a thousand times more agent volume on software than people

31:40 . You do that enough

31:42 times and eventually the software stack that Asians talk to has to be built for

31:47 them. And maybe

31:49 there'll be a couple holdouts. Maybe maybe a couple ERP systems are like the

31:53 final holdouts that

31:54 don't do that. But everything else you basically like your business will be

31:58 your business performance

32:00 will correlate to how well your agents can get access to the information they

32:03 need to do their

32:04 work. And so thus your enterprise IT stack has to be set up in such a way to

32:08 support that.

32:09 And so agents are kind of in charge because basically your software has to

32:13 support those

32:14 agents being effective. And that's going to mean everybody that built a SaaS

32:18 business or a software

32:19 business is like the game is can you build really really high quality APIs. Can

32:24 you have a way of

32:25 monetizing that? You know do you have a way of handling the identities and all

32:29 of the access

32:29 controls for agents and like like that becomes the new problem you have to

32:32 solve if you're building

32:33 a software company. And so yeah like and then how you monetize it like do you

32:39 monetize it like

32:40 does work day charge a penny for every HR record of polls like we'll figure

32:44 that out.

32:44 I do think that in some businesses it could mean less revenue and then in other

32:47 businesses it could

32:48 mean a lot more revenue. Like the thing we get excited by is like every agent

32:51 really loves

32:52 working with files. So there'll probably be more files in the future than there

32:55 was going to be

32:56 before. And so you know can we build a platform that like makes it really easy

32:59 for agents to work

33:00 with that data. You know we're betting that that's actually a really optimistic

33:03 outcome for

33:04 for you know our kind of business model. There might be some business models

33:08 that are like

33:08 more constrained because like the agent is doing more of the value than then

33:12 the software is in

33:13 that kind of future scenario and then there'll be everything in between. Can I

33:16 can I quibble with

33:16 one thing I you're going to quibble with that. I thought that was like so not

33:19 controversial.

33:20 No no I generally we're here to quibble. There's one thing I think like Paul

33:25 Graham and many

33:25 actually gloss over which is they focus on the interface. They'll say things

33:29 like you build

33:29 something for the agent. Yeah. And I actually think that's exactly wrong. Okay.

33:33 In the sense

33:34 that. And to be fair to Paul Graham he didn't he had been extrapolated. Yeah. I

33:39 have brought Paul

33:41 Graham into this. People in the abstract say things like now you're marketing

33:47 to agents the most

33:48 important thing is to being like whatever you're like an API you've got a good

33:52 idea. I think that's

33:53 almost exactly wrong. Wow. That's breaking podcast news. That's the one thing

33:58 agents are

33:59 really good at. Oh okay. It's finding their way through. And at the end of the

34:02 day like it's the

34:03 semantics that end up mattering a lot more. Yeah. Yeah. Right. And so like the

34:06 agents in in by

34:07 recollection or in my experience are very very good at picking the right back

34:12 end for whatever

34:13 they're doing. So they don't they're not like oh like the interface for this is

34:16 very good. The

34:17 document. It's none of that. They're like they're like the cost parameters of

34:20 this. Yes. The

34:21 durability of that. Like and so like they actually have the collective wisdom

34:24 of our experience

34:26 using these platforms. Like let's take cloud platforms. There's a bunch of

34:28 cloud platforms

34:29 out there. Yep. And whenever I ask an agent to choose a platform. Yeah. It's

34:32 actually using

34:33 meaningful stuff. Yeah. Not interface stuff. So I think as an industry we're so

34:36 focused on these

34:37 interfaces. Yes. Like oh you need to like market to agents this and that. Yeah.

34:40 But really I think

34:41 that we're going to be pushed to actually build a better system. Yes. And that

34:44 's what's going to be

34:44 chosen. Okay. Actually. So then there's probably no quibbling. I think we're

34:47 actually pulling a line.

34:48 I'm sorry to ruin the quibble thing. I don't treat this as like a you know kind

34:52 of a marketing

34:53 you know as thing. I'm more mean like if your tool is closed off to the agent

34:56 the agent eventually

34:57 will find a better tool for that company to go use. And so and so what will

35:01 happen is is it used

35:03 to be that you would go to like Gartner to be like tell me what to do. Tell me

35:06 what to do.

35:07 Yeah. Tell me what system to use or whatnot. At some point with enough

35:10 iterations that the agent

35:12 is going to say you should probably use this kind of database for this type of

35:15 operation.

35:16 And if you're not in if you're not in there then you're it's your DOA. And I

35:20 think we should

35:21 actually be celebrating this because agents are actually pretty smart at

35:23 choosing the right

35:24 technology. Yeah. In the past I really think it was a lot of the other things

35:27 that that caused

35:28 people to buy it. Yeah. But but don't worry we will in talking about we will

35:32 ruin the meritocracy

35:33 of this very quickly because we'll just like I'm going to outspend. Well the

35:37 agent will be an

35:38 API to incent the agent. But you know there's a marketing agent at Workday.

35:45 Well the marketing

35:46 agent at Workday will have the ability to purchase the recommendations from the

35:49 way they

35:49 replicate state dinners for agents. Yeah. But there is a there is a real here's

35:54 the thing that

35:55 again that that happened with the web sort of internally like internal like

35:59 just pick internal

36:01 sites like every company had file shares with like the best documentation the

36:05 best slideshows the

36:06 best financial models for any department or working area. And people sort of

36:11 got familiar with that.

36:12 And then when they didn't find the one they wanted they created a new one and

36:16 many organizations

36:17 sort of operated like that was essentially a free market. In fact because

36:20 before the world

36:21 of box like IT didn't if it was in a file they just didn't care. Right. They

36:25 only cared about

36:26 if it was in SQL. And so one of the risks with the model you're describing is

36:31 that the agents

36:32 themselves will spin up what becomes like a de facto new system. Oh they're

36:36 going to fragment that

36:37 out of in the in what you the IT people think of as some middleware and user BS

36:43 area. And I think

36:45 that that is a is a real risk. 100% is that like in a sense like the the the

36:51 macros end up running

36:53 the the corporation. Yes. And so I think that they've seen this movie and they

36:58 've seen what

36:59 happens when you let marketing just go buy a website on the internet to do an

37:02 event. And then

37:03 it's like a huge security vulnerability. And the mailing list is leaked and the

37:06 whole company gets

37:07 sued. And so I think there's a lot more real world tension in this dynamic than

37:13 then we just let on.

37:15 Yeah. But I also think it's one of these ones where you you you know their

37:20 organizations are

37:21 going to run at different paces. And JP Morgan is going to be the slowest at

37:26 doing this and the

37:27 startups are going to be the fastest but the the delta is huge but even the

37:31 startup one is a little

37:32 far off because even startups do need some systems of record. Yeah. And they

37:37 are going to all start

37:38 with some sass and they're not going to replace it very quickly. So I think it

37:42 's a little trickier.

37:43 So it feels like there's like there's two very competing viewpoints on this one

37:47 and like Elon

37:47 said it was like okay we're going to like issue a prompt and it's going to like

37:50 spit out machine

37:51 code and that's basically the collapsing of layer view like whatever existing

37:55 interfaces and

37:56 layers that we've created in the past are all going to go away and it's

37:59 literally like prompt

38:00 and machine code. The other argument like the history of systems is layers

38:03 never go away they

38:04 just get layered right. And because a lot of the layers are actually more of

38:07 like organizational

38:08 boundaries or like state boundaries or compatibility they're just they stay for

38:13 compatibility right.

38:14 So the other argument is is like we've actually evolved these layers very

38:18 specifically and up

38:20 because of like more human and organizational needs and they're not going to

38:23 change and the

38:24 agents are going to go ahead and map to those. Now I tend to be in that latter

38:27 camp like I don't

38:27 think that I think like systems are going to continue to use in fairly similar

38:31 ways maybe there's

38:32 more agents using them. I don't think they're going to evolve as much. Elon

38:36 might be back in the

38:37 like anthropic category of the anthropic growth marketer which is like he like

38:44 you know over the

38:46 years when you kind of like study the various IT you know departments of his

38:49 companies like

38:50 they are the most I mean. Well he could do that. He could do it. He's the most

38:54 home growth like

38:54 everything. This is first principle. Elon AI would do that. But also it's fun.

38:59 And then from

39:00 your mortals you're like yeah we kind of just want to CRM system. Like no but

39:03 it works the same way

39:04 every time. I mean this is not this is not it also hasn't been been not tried

39:10 before.

39:11 Like if you were to look at an ERP system from first principles you know well

39:15 in 1970 whatever

39:17 when SAP started there were a bunch of different assumptions and today you

39:20 would start from a

39:21 different set of assumptions about what's important and you would architect a

39:24 thing completely

39:24 differently but then it would still only last like 10 years until you thought

39:28 wow that was a

39:28 broken decision. And I and so I think that that there's intentionality in

39:34 layers but you

39:35 but there's also this first principles thing. And you know there's that always

39:40 will exist because

39:41 the decisions you can make at first principles at any given time mandate a

39:45 whole bunch of different

39:46 stuff. And so even if you don't go with LIDAR which made total sense 10 years

39:51 ago you still need

39:52 10 or 15 years to get to where LIDAR not having LIDAR worked. And then now

39:57 there's going to be a

39:58 whole bunch of other things that you're like wow we could have done that

40:01 completely different.

40:01 And so I feel like this is again like this discussion about trying to race to

40:05 an end point.

40:06 But let's see a first example of what you described happening. And I think that

40:11 that's going to be

40:12 the real tell because I think that there were just companies will figure all

40:16 this out. And I think

40:17 that they will just fall back on layers and architectural models because it's

40:21 the only way.

40:22 We don't think about it for policy. We don't think about it for security.

40:24 But it's also the only way to build a system. Otherwise you're just building an

40:29 app. And if

40:30 you're building an app to do one thing we don't need all of this. Like there's

40:34 a whole different

40:34 way to do it. The thing that I'm pretty fascinated by is and I don't even have

40:39 any amazing data

40:40 points or anecdotes but at least the notion of these sort of companies that are

40:45 emerging in these

40:46 kind of services categories from the ground up from the pure first principles

40:50 approach which is

40:51 like okay well if I could start a marketing agency or consultant you know

40:56 engineering consulting

40:57 company or I don't know maybe somebody's doing this for law firm. Construction

41:01 work. Well maybe

41:03 construction design. Architecture design. Architecture design. Anything that

41:06 would be like a knowledge

41:07 worker kind of services company. Because you could kind of build your company

41:11 pretty differently

41:11 if you had no constraints of I have no information barriers and boundaries of

41:16 what people should

41:17 have access to. I can give the agent just all the context it needs to do its

41:20 work. I can write

41:21 software on the fly for particular things like like I do think that will be

41:25 relatively disruptive

41:27 you know for some time until the bigger incumbents can kind of you know get out

41:31 of the way on this.

41:32 And that will at least create you know some precedent or case studies of what

41:36 what this new

41:38 sort of corporation could look like but I do you know over time they'll still

41:41 run into the same

41:42 exact problems of every other corporation. Well they'll run into geography or

41:46 market segments

41:47 you know or distribution challenges. Yeah like those those things anything

41:52 outside your little

41:53 walls. Yes. You'll run into the physical world. Right. I do kind of like the

41:57 idea that there are

42:00 some new business models that open up now. Oh of course. Yeah yeah yeah.

42:03 Because like there's

42:04 so much either information or software that that basically goes underutilized

42:09 by like 100x

42:10 relative to like what what its economic value is. Simply because like nobody

42:14 wants to pay five

42:15 cents for accessing a you know a piece of data or use a tool for one dollar

42:19 once but like you do

42:20 give these agents you know a budget and a protocol to work with and all of a

42:24 sudden you're like oh

42:25 like on the fly they can go get medical research for some deep research tasks

42:29 they're doing

42:30 and I'll pay like three dollars for that and the agent is able to go and

42:33 transact.

42:33 Like it kind of opens up a whole new world of business models for all of the

42:37 internet.

42:37 Let me oh I'm gonna you're that was too nice. Oh okay. No no. You're gonna go

42:41 farther. No that one is one

42:43 where that's actually the biggest I think that the biggest sort of in the air

42:49 problem right now

42:50 is everybody is trying to figure out the economics of all of this. When they're

42:54 off by at least in

42:56 order of magnitude on how big the opportunity is. Because the new models that

43:00 people will come up

43:01 with that nobody knows what they are right now but they will absolutely come

43:04 out with new models

43:05 because that's what happens with every new technology and the thing that holds

43:09 back to the sort of

43:10 the discussion now is you basically have a bunch of finance and Wall Street

43:14 people trying to justify

43:15 GPUs and tokens and things like as if we're in some old world and they're there

43:21 so they're

43:22 they're viewing the world of revenue as sort of this linear step but literally

43:27 linear growth

43:27 curve and trying to justify all the all the expect when people are going to

43:31 create like this was

43:31 the problem with PCs people viewed PCs as a finite market because they just

43:36 viewed the consumption

43:38 of MIPS as some finite thing and they didn't think what would happen if we put

43:41 all those MIPS on every

43:43 desktop and in particular people thought software just came with the MIPS and

43:48 nobody thought oh well

43:49 they'll just sell the software one guy did and it turns out that was like a

43:53 really good idea and

43:55 the same thing yeah and the same thing happened but the same thing happened

43:59 with the cloud

44:01 which was people looked at the cloud and they said oh we're going to take all

44:04 of the the server

44:06 business which was like literally like 60,000 units a year right and we're just

44:10 going to move it

44:11 to someone else's data center right and that's the that would be the business

44:15 and then we'll

44:15 divide up the the price right and nobody went oh they're going to people are

44:18 going to use a thousand

44:20 times as much right of the resource leveling right if we move it there and that

44:24 's exactly I mean

44:26 that's the thing that I it just drives me absolutely bonkers that the Wall

44:29 Street models have this

44:31 fixed revenue right pie zero some things and and it's this weird zero sum where

44:35 they they just

44:36 think that the amount of money that a company is going to spend and like this

44:38 was the problem with

44:39 with Salesforce that they faced when you were starting to but like mark was was

44:43 just blazing

44:44 the trail which was like the CRM business was two billion a year and it was two

44:49 billion in like

44:50 you had to go buy all these servers and these Oracle licenses and this huge

44:53 headache and years

44:54 of deployment and consulting yeah when if you could just get salespeople to

44:58 sign up individually

44:59 they all will sign up right with no friction and that's that is exact there is

45:04 no no doubt that

45:05 that is what's going to happen with AI let me give you an example of this so I

45:08 um you know I've

45:09 been in for investing for 10 years now I probably have a portfolio of 240

45:14 companies that work with

45:15 some visibility to us saying that 50 of them these are all infrastructure

45:17 companies some

45:18 historically done well some not so well every single one of them has gone asym

45:22 ptotic in the

45:23 last six months and you're like okay why is this it just turns out there's so

45:27 much more software

45:28 being written out than ever has been of course and so it's like and it's not

45:32 because they've got

45:33 enterprise customers you know it's just because there's just so much

45:36 consumption of the of the

45:38 infrastructure layer right now and so with more software with more agents there

45:42 's going to be a

45:42 lot more consumption of computer resources so certainly in the case of the

45:45 computer side of

45:46 things yeah and that's well we haven't even gotten to the point yet where

45:49 everyone's phone

45:50 is a huge consumer of AI right so once everybody's phone and on device yeah

45:56 like once your phone

45:57 on device is consuming AI the amount of it is going to go up by a billion so do

46:01 you like the

46:02 micropayon piece all of them yeah the micropayons there's a little bit of micro

46:06 payments that has

46:07 come with every technology where they always think that like you'll be able to

46:10 get like a fraction

46:11 of a penny but but in the end especially in the enterprise yeah like people are

46:15 just going to

46:15 consume things it's just cheaper and easier to buy like a bulk license for a

46:19 bunch of stuff yeah

46:20 you want some predictability on that well you want predictability and you just

46:23 want like to not

46:24 yeah have to think about it i just i like the idea that it is the first time

46:28 that you could like

46:29 there's just the agent doesn't care about the friction of a small transaction

46:32 right right

46:33 the first time that you can have resources behind a paywall that something will

46:36 actually be willing

46:37 to pay for that and resource the world is built up to infrastructure to to

46:41 aggregate those payments

46:42 into something efficient for a customer or a service right and and because

46:47 tokens are such a

46:48 significant part of cogs right now it is pushing the industry to do usage base

46:52 in the way that

46:53 we have like i remember when we went from like perpetual right right occurring

46:56 and that required

46:56 like a bunch of huge changes like we're like we're going through the exact same

47:00 change right now

47:01 towards usage base and usage base is pretty granular and it actually allows i

47:04 mean to get you will

47:05 have a contract with like you know we went through this with with AWS like

47:10 people learned to do the

47:12 to do the usage credit and we went through the phase where like people were

47:15 like so terrified of

47:17 cloud compute that they were like we need companies in the middle to help us

47:20 find the cheapest and

47:21 to arbitrate it all okay well now you write tokens into this and i don't see

47:25 how we possibly have a

47:26 time in this in this conversation but as long as you guys oh okay but like like

47:30 the the engineering

47:32 compute budget conversation to me is going to be just like the most wild one in

47:36 the next couple

47:37 years it's just like how much did you allocate of your engineering expense to

47:42 tokens and it's like

47:43 you you know depending on who you read on twitter it could be one percent and

47:47 the other and other

47:48 side could be a hundred percent and it's like yeah but this stuff well no no c

47:52 fo's have to

47:53 literally they actually have to know the answer i understand they have to know

47:56 okay cfo's always

47:57 want to know the answers to things that don't have answers no wall street is

48:00 going to make them

48:01 know the answer no no wall street is going to make them come up with some

48:04 number and hold them to it

48:05 then they'll get fired and then it'll but it you just okay okay i hear you are

48:08 in the rnd is

48:09 somewhere between 14 to 30 percent of revenue of any public right right company

48:14 let's just say

48:15 okay the difference between compute being 2x the cost of your engineering team

48:20 or or you know you know

48:22 three percent more is like that's EP that's all your EPS i get it i mean we

48:27 will have to know

48:28 the answer i'm perfectly willing to sacrifice a few cfo's okay i want that that

48:33 's a good clip by

48:33 the list but but the reason the reason is is because again this is this is

48:38 trying to know what we just

48:39 don't know right now yeah and and this has happened with internet bandwidth

48:43 this has happened no this

48:45 is not even close to internet bandwidth oh no no i i beg to differ like like

48:50 people were free it

48:51 happened with vacuum tubes it happened with transistors it has happened with

48:55 every technology

48:56 there was this oh my god let it happen with programmers there was a there was a

49:00 time when

49:01 programmers were going to swallow every company yeah and that's not it was in

49:04 my lifetime not

49:05 some made-up yeah but i don't think we've ever had a point where no the end the

49:09 end you every end user

49:10 in an organization has has sort of a completely elastic ability to spin up a

49:15 resource on their

49:17 behalf well it's certainly that actually is actually in many cases very valid

49:20 for them

49:20 to go spin it up but it certainly it certainly rhymes with what happened in the

49:23 early 2000s

49:24 with the cloud i remember very similar discussions when we went from capex to

49:27 opx and then our limited

49:29 span oh no and they were remember there were companies who the cfo's would sit

49:32 in our briefing

49:33 center here yeah and say you don't understand we are like exactly we are an

49:39 agriculture company

49:39 we we only know capex yes we we have no or or no we're all through this right

49:45 right no we both did

49:46 well or or like oh no we are an opx based company so if you tell us i we love

49:52 the cloud because we

49:52 just shipped everything it shifted everything to opx yeah and so all of the

49:56 stuff like the rules

49:57 of accounting work out also don't i i keep thinking do not discount the local

50:01 compute engine as being

50:03 a release valve for all of this when that can happen well the the question is

50:07 it's not when does it just

50:08 happen with today's view of the technology but how all of a sudden well wow

50:11 there's almost that

50:12 historically ever gone that direction yeah exactly because the opposite right

50:16 no it went all to the

50:17 client well okay and then go back to the 80s yes no that's most of the examples

50:22 that were here

50:22 so far whoa that was uncalled for i did it back in tubes he's talking about

50:28 vacuum tubes but i do

50:30 those examples because you can't argue with them and it's much easier that way

50:33 i you're right i can't

50:34 prosecute no but but but it's only been you know 10 or 15 years that it's all

50:40 that it moved back

50:41 to all cloud and then what has happened recently with that a lot of people wake

50:45 up in the morning

50:45 and they say oh we're moving back to doing some critical but stationary work

50:50 flows on on prem and

50:52 with AI that's true dude you wrote the blog post man uh don't let me go through

50:56 that the

50:57 archive of the repatriation i had to deal with so many wall street questions on

51:01 that one by the way

51:02 well because also because you're a competitor what went went back to oh yeah we

51:06 're talking about

51:07 too we're talking about too very good i agree i agree with like building your

51:11 own data center is

51:12 i'm talking about this this notion of edge computing where things go to devices

51:16 like that

51:17 seems to be i'm more i'm more in the like a cloud maximalist the camp but but

51:21 sorry so you just don't

51:23 think you don't even think for like one second that it matters whether like how

51:26 you're supposed to be

51:27 an engineering leader right now managing the compute budget of the engineering

51:31 team

51:31 no of course it matters i just this thing in the long term this thing will get

51:35 oh sure oh at

51:36 launch of all i mean what are we who cares we don't know i guess what i mean

51:39 here's our pragmatic

51:42 but here's a rule here's a rule of thumb first like the startups are going to

51:46 burn through available

51:48 capital pretending like it's not a problem yeah and they are going to do that

51:51 yeah a lot of that

51:52 anyway right right right and a lot of big companies are going to be so

51:55 terrified they're just going

51:56 to freeze and not do anything and then people are going to actually start

51:59 buying it on their own

52:00 and they're going to do all the things that companies do when they're big have

52:03 a lot of money but don't

52:04 want to spend it and in the middle we are going to see like if you pick a

52:08 category of product or go

52:10 to market or something they're going to be people who are willing to make the

52:13 bet for whatever reasons

52:14 that they can because of their financials and they are going to go ahead and

52:18 they are going to become

52:20 the people who lead yeah in the space as long as they can maintain the

52:24 financials yeah now they

52:25 might do it in they might say oh we're going to just do it here in this

52:28 particular application

52:30 space or here in this particular usage space but this this idea that there are

52:35 that nobody is going

52:36 to go in and because they're so terrified that the CFO is going to get fired or

52:39 something no no

52:40 it's just crazy yeah and but then there are going to be CFOs who make a mistake

52:44 and like okay everybody

52:45 gets a little yes well if they do that that's a complete fail yes because but

52:49 but also or like you

52:50 get you like you there is a really interesting like um on you know finesse here

52:55 which is like

52:56 you don't really want your engineers right now having to think about compute

52:59 budget because we're

53:00 still developing the oh okay so that set you over there but I was feeling like

53:05 we've been having

53:06 the discussion for 15 years when it comes to clag this is totally new like only

53:11 like 10 only like

53:13 10% of your engineering had to think about it in two in two thousand sixteen in

53:17 two thousand

53:18 eighteen time frame there's a whole set of companies that was basically like

53:22 like the dashboard for

53:24 what was it called phenoms where the developers is very cool right now because

53:28 the developers would

53:28 have would developers have access because cloud spends are getting out of

53:32 control yeah P.I.

53:33 spend were getting out of control yeah and so it was like you know here's your

53:35 Twilio spin here's

53:36 but but you know it's it's pretty different and I'm gonna wait for all the

53:40 comments to come in on

53:41 YouTube to call you out of this like it's it's like you can get into a

53:45 conference room and just be

53:46 like hey can you make that one you know kind of algorithm a little bit more

53:49 efficient so you don't

53:50 use as much you know of our cluster at this time of night or whatever and then

53:53 you get out of the

53:54 meeting somebody goes improves it and you're good this is like every single

53:58 prompt that every

53:59 engineer is doing like do you like you have to decide like if you want it to be

54:03 a long running

54:04 prompt do you want that you want to be a long running agent do you want to

54:07 parallelize that

54:07 like like do you want like what is your comfort level of wasted tokens like for

54:12 me right now I'm

54:13 like yeah we should probably waste a lot of tokens because that means that we

54:16 're like trying new

54:17 things yeah and like like should your head of engineering be happy if if you

54:21 run 10 experiments

54:23 in parallel and thus you're obviously going to be wasting 90 percent of the

54:26 tokens um but you're

54:28 going to choose one of the successful past or do you want to tell the team know

54:31 before you go do

54:32 that make sure to like like really go and design the perfect system like we

54:36 actually have a whole

54:36 bunch of open questions that are going to start to happen like literally as of

54:40 this recording time

54:41 people are freaking out right now on the new cloud code max plan you know whole

54:45 like like

54:45 because they're getting blocked after like three prompts well this is this is

54:49 going to be like a

54:50 very like real topic until we can actually find a way to build data center

54:53 capacity oh that's a

54:54 different problem okay no because no one is well assuming well wait no you can

54:58 assume that if we

54:59 build more capacity the price will drop because there is more capacity and we

55:02 're priced now based

55:03 on limited capacity whatever but like this is just going to get worked out and

55:08 I feel bad for the

55:09 those that have to make a decision immediately yeah about which 17 people get

55:14 no more tokens this

55:15 week or whatever and that the whole company is walking around with like a token

55:18 card and

55:19 and the person in the lunch line yeah yeah like it's the person in the lunch

55:25 line is punching their

55:26 card every time they do set but but like I you know I I don't know like

55:30 somebody we were talking

55:31 about today about performance and how like you know we used to write command

55:35 line tools that spit

55:36 out the time it took after you ran a command line just so you knew and if you

55:41 knew you were getting

55:42 better or worse and you know but you the thing is is this is all going to go

55:48 away there's absolutely

55:49 no doubt yeah that this just goes away I think I'm 10 or 10 and the biggest

55:53 reason it does is

55:55 is because you you have to do the penny off kind of math which is if you're

55:59 paying an enterprise

56:00 salesperson you know a million dollars a year you have to ask how much is their

56:04 tool worth yeah

56:06 and if you're paying an engineer X dollars a year well at some point their

56:11 their tooling is worth

56:12 it's absolutely worth it and it's not going to even be an issue and and yeah

56:17 yeah I don't think it's

56:18 um I think so if there's a capacity thing in the short term yeah that's a

56:22 different that is a

56:23 different problem driving the price than this just we're gonna forever have to

56:27 be in some budgeting

56:27 exercise I think I think a lot of large numbers solves this because because

56:31 eventually you have

56:32 enough engineers using this much compute but like we're in a transition phase

56:35 we're like

56:36 most people thought you know the two year ago level of spend on AI which is a

56:40 guy it's a chat

56:41 bot and yeah yeah but they were wrong yeah right okay but they were wrong but

56:44 they were wrong we

56:45 tried to warn them no but they were wrong because they saw it as this this

56:50 particular use case yes

56:52 and then but again like you you know like the vacuum tube thing you made fun of

56:58 yeah but like

57:00 there was a time when they thought that the that like whole like all of the Dak

57:04 otas would be covered

57:05 in vacuum tube warehouses and people on roller skates would be running up and

57:09 down the isles

57:10 replacing vacuum tubes just so we could fight World War II I mean like that was

57:15 how that was the

57:16 and they thought that and then someone said hey how about a transistor right

57:20 and like we are going

57:21 to have a transistor moment with all of this it might just be more supply the

57:24 way we think of it

57:26 but it also might be an actual algorithmic fundamental change it could be a

57:30 change in the

57:31 hardware there's a lot of stuff that can happen yeah that changes this

57:34 particular moment in time

57:36 it's just this I think is particularly weird that everybody has just gotten to

57:40 token yeah which is

57:42 the same thing that happened with IBM and mainframes people were on MIPS and

57:45 then one day the reality

57:47 was IBM was selling more MIPS for fewer dollars every year yeah and didn't even

57:52 realize it and they

57:53 were still pricing their mainframes by MIPS until it got pointed out to them

57:58 that they were on a

57:59 decreasing curve because they were making MIPS faster than they can charge for

58:02 and that's what's

58:04 going to happen guaranteed I just said that in a hardcore way I thought that

58:07 sounds great yeah

58:09 like sounds really great to sound like I know what I'm making guaranteed I

58:12 actually probably

sanyuele

· 7mo

顶级聪明AI培养法：“穷举算法”。

在无所不知的全然中，你无法知道自己不知道什么——这很要命，就好像你连上脑机接口，利用整个网络平台的知识与储备回答问题，自然是百事通万事知，可这能带来你的意识成长与智慧成熟吗？显然是不能的。自限性与局限性，让我们能看到自己的真实所是，并借此获得自我意识的成长。

AI 时代的心灵觉醒

love

· 7mo

他用40个 #AI 代理取代了整个营销团队 | Relay.app，雅各布·班克

https://www.youtube.com/watch?v=OHJJKrJCSRU&t=416s

中文 English

0:00 我们都应该有数十甚至数百个代理为我们工作。

0:03 所以随着时间的推移，我组建了这支由40个AI营销代理组成的团队来协作。

0:07 我实际上是唯一的营销人员。

0:09 我有一个AI教练，每周一次告诉我，有哪些主题

0:12 是

0:13 真的很有效或者哪些风格对你很有用。

0:15 多做这个，少做那个。

0:16 这就是我在领英上有几篇非常成功的帖子原因。

0:20 例如，有篇帖子是介绍我实际使用的40个营销代理。

0:23 用的。

0:24 那篇帖子目前已有150万次浏览。

0:28 是领英帖子中评论最多的一次，你知道，我一直在领英上

0:31 持续发帖

0:31 一年却毫无结果。

0:33 然后突然我意识到，哇，如果你作为一名初级内容

0:38 营销人员的工作是把YouTube视频写成博客，那么这个工作

0:41 将不复存在。

0:43 一个高质量的营销承包商。

0:46 假设我有四个人，每人每月费用12500美元。

0:49 $50,000每月。

0:51 而我的AI费用每月仅500美元。

0:53 构建AI代理是定义未来30年每个职业的基本技能。

0:58 's

0:58 职业生涯。

0:59 这项技能就像过去40年熟练使用电子表格（如Excel）一样

1:02 是一项必备技能，未来40年，

1:06 构建

1:06 AI代理将成为必备技能。

1:09 我们目前大概只用了1%的AI潜能。

1:13 我想说的是，

1:14 AI被严重低估了。

1:23 我叫Jacob Bank。

1:24 我是Relay.App的创始人兼CEO。

1:27 Relay.App是一个构建AI代理和工作流程的平台，帮你

1:32 代替你

1:33 完成工作。

1:33 我们现在是一个九人团队。

1:35 有五名工程师，两个设计师，一名产品经理，还有我。

1:37 如果没有AI，我们可能需要15人团队才能达到

1:43 现在的工作成果。

1:45 曾几何时，我是AI研究员，后来是创业者，然后是谷歌的长期产品负责人，自2021年起一直在做Relay.App。

1:49 我在过去三年里见证了AI的全面爆发，从非常近的

1:53 视角。

1:57 观点

1:57 。

1:58 我对AI的看法是，它就像个实习生。

2:05 它就是个实习生。

2:06 它完全按你告诉它做的事去做。

2:07 它是实习生。

2:08 这其实是我改变了的观点。

2:10 一年前，我说，嘿，把AI当成你的实习生。

2:13 后来我意识到这个思维模式错了。

2:15 AI确实可以做一类任务，

2:19 那就是节省时间的工作，否则你会交给实习生做。

2:22 实习生。

2:23 但AI也能做一些战略分析、竞争分析和内容创作工作，

2:27 这是实习生绝对做不到的。

2:31 例如，我不擅长销售电话。

2:34 我没有销售背景。

2:35 这对我来说不自然，但事实证明，有些销售电话技巧

2:38 非常有价值。

2:40 所以我有个AI教练，审阅我所有会议的文字记录，然后说，

2:43 你本可以更好地谈这个。

2:44 你本可以更清晰地表达这个价值，或者你太容易，

2:46 太快演示了。

2:48 你应该多问一些探索性问题。

2:50 如果你去聘请销售教练，一个好的销售教练每周一次会议就要1万美元

2:55 每月。

2:56 你可以自己建立一个不错的AI销售教练，稍微训练文字记录，

3:01 运行费用每周大概五美元。

3:05 当我承担一个新的角色或职能，而我没有经验

3:11 或不觉得自己是专家时，

3:11 我如何找到合适的教练关系，给教练我做事的原始数据，

3:15 然后由他以正确频率给我反馈，是每次通话后，

3:20 每天一次总结，还是每周一总结。

3:23 or once a day with a summary or once a week with a summary.

3:26 这就是为什么我现在意识到，把AI看成实习生是多么错误。

3:30 当我还在谷歌时，有个职位叫技术主管经理。

3:37 那是个混合职位，你既要是技术专家，要熟悉代码库全貌，

3:40 能审核技术设计，解决最难的问题，

3:44 也要同时是经理。

3:44 你还要负责协调团队中的其他人。

3:46 这是个非常难的职位。

3:50 技术工作和管理工作同时做很辛苦。

3:51 但我认为这就是未来我们的方向。

3:54 我们都需要成为超级个人贡献者。

3:57 让我解释原因。

4:01 如果你的工作是作为初级内容营销人员，将YouTube视频写成博客文章，

4:01 这份工作将不复存在。

4:07 你不能仅仅靠做单一内容再利用的任务

4:10 来维持工作。

4:15 那样。

4:15 如果你的工作是在大型企业管理一千人团队，

4:19 我不想说这个工作会消失，但我认为这个工作

4:19 会变得越来越不重要，因为企业会

4:23 变得

4:23 更小。

4:25 未来会有更多精简小型的企业。

4:26 所以管理1000人的技能会越来越

4:29 不重要。

4:32 重要。

4:33 每个人都会看起来像我。

4:35 我们大约有三分之二的时间是个人贡献者的工作。

4:39 我在做事情。

4:39 我在制作YouTube视频。

4:41 我在编辑博客文章。

4:42 我在发布内容。

4:43 我在与客户交流。

4:44 然后我一天中有三分之一的时间是在协调帮助我的AI团队。

4:48 完成那些

4:48 任务。这种超级个人贡献者未来最令人兴奋的地方是，

4:54 那是

4:54 我们大多数人喜欢的工作。

4:55 我和很多处于初级职位的人聊过，他们都会说，哦，我真希望

5:00 不是仅仅

5:00 写这篇博客文章，我被告知要写的。

5:02 我更有战略影响力，能决定我们做什么和为什么做。

5:06 我认识的每个人，包括我自己在谷歌时，处于

5:09 完全

5:09 组织管理岗位上，都希望能更接近实际工作。

5:12 我希望能回到产品岗位。

5:14 我希望能更接近客户和用户。

5:16 所以我认为未来每个职位都会结合两者的优点。

5:20 两全其美。

5:20 我们都需要足够有战略眼光和资历来决定什么需

5:25 要

5:25 完成，并且能看清“好”的标准，以便给予正确的

5:28 指导。

5:28 但同时我们也需要足够动手，能自己编辑博客

5:31 文章

5:32 、编辑推文和制作视频。

5:34 这就是职场的未来。

5:40 是的，一次看到40个有点让人吃惊。

5:42 但如果你点击任何一个单独的代理，它们都很简单。

5:47 它们各自都有非常具体的任务。

5:49 每次我们制作新YouTube视频，它会自动创建LinkedIn帖子。

5:52 自动完成。

5:53 每次我们做新YouTube视频，它会自动发推文。

5:57 当竞争对手的CEO在Twitter或LinkedIn发布内容时，

6:00 它每周都会提醒我他们在谈什么。

6:02 它查看我们的竞争对手定价，并告诉我是否

6:05 有任何重要变化。

6:06 所以我一个一个地组建了这支AI代理团队来帮我。

6:10 我尝试过让单个代理同时做25件事，但没成功。

6:14 这是和人类员工的一个区别，当你雇一个社交

6:15 媒体营销人员时，

6:19 你希望他们负责所有社交媒体营销，尤其是

6:19 如果

6:23 你是初学者，就先简单开始，创建一个能做一件事的代理，

6:23 再创建

6:27 第二个代理做第二件事，创建第三个代理做第三件事，

6:27 然后可能让一个代理管理它们，适时调用，

6:30 逐步升级。

6:30 不要一开始就建立40个代理的组织结构图。

6:34 从一个开始。

6:35 有一点非常重要：AI代理不是设置一次就不管的东西。

6:37 你不能只是建一个AI机构，

6:39 然后就想“它会永远帮我工作”。

6:40 我一直在调整我的代理。

6:46 比如，我会和新客户做评估电话，花30

6:48 分钟

6:49 和他们一起发现AI在他们业务中的机会。

6:51 我想，如果客户通话后能收到我发的个性化Google文档，里面有五个具体用例和模板链接，

6:54 那该多酷啊。

6:58 于是我建了一个AI代理帮我做到这点。

6:59 我告诉代理，你的任务是把通话记录整理成

7:02 格式漂亮的Google文档，发给客户。

7:06 做了十次之后，我觉得这格式不对，

7:06 客户没看那个Google文档。

7:09 其实我应该直接发邮件里包含内容。

7:10 于是我解雇了那个代理，告诉它，这个任务

7:11 不需要了。

7:13 我改造了那个代理，让它把总结直接放进跟进邮件里。

7:17 有些功能可能不需要了。

7:20 比如一年前我们很重视SEO，

7:23 后来发现这对我们现在来说不是重点渠道，

7:24 它带来的新用户比例很小，且工作量大。

7:26 所以我们决定不再专注SEO。

7:31 我告诉我的AI SEO代理们，现在可以先停工了。

7:32 我以后再找你们。

7:35 暂时停止工作，就像你告诉SEO机构，“我们暂时不需要SEO”，

7:35 未来再合作。

7:36 很棒的是，这里没有情绪负担，

7:40 没有分歧，

7:43 也没有协调成本。

7:46 我只要一个想法，

7:49 “嘿，代理，你能帮我实现这个想法吗？”

7:51 它们会说，当然可以，

7:54 我们开始吧。

7:56 But for now, you can stop doing what you're doing in the same way that you

7:58 would

7:59 tell an SEO agency, we don't need SEO at the moment right now.

8:02 We'll come work with you again in the future.

8:04 And the really cool thing is there's no emotional baggage.

8:07 There's no disagreements.

8:09 There's no coordination costs.

8:11 I just have an idea.

8:12 Hey, agent, can you help me with this idea?

8:14 And they say, yeah, absolutely.

8:16 Let's do it.

8:16 嗯，所以我不想让我们把它看作是取代人。

8:20 我想把它看作是赋予我超能力。

8:22 我希望我有时间做这些营销活动，因为我喜欢做

8:27 它们。它们对公司非常有价值。

8:28 而现在我可以做到这些了。

8:30 我们都想在工作中做得更多。

8:32 我们都是有抱负的人。

8:33 我们都是充满动力的人。

8:34 我们都希望自己能做得更多。

8:35 而现在我们有了实现它的工具。

8:37 思考风险职业和安全职业的观点如何随时间变化，真是令人着迷。

8:46 在我父母那代，安全的职业是在一家大型

8:49 财富500强公司工作40年。

8:54 你知道，退休时或工作50年后拿到纪念表，而风险职业

8:56 是创办自己的公司或每隔几年跳槽一次

9:00 。

9:01 我认为这一切已经完全颠倒了。

9:05 风险最大的职业是你的技能过于依赖某一家

9:05 特定公司的环境，而该环境可能会随着时间变化。

9:08 最稳健的职业是那些自己创业

9:13 并拥有丰富人生经历的人。

9:16 我给你举个例子。

9:20 我们团队中很多人都是我从谷歌招来的，因为我们曾一起共事。

9:22 谷歌，现在我们一起做这个创业项目。

9:23 如果你问十个人，留下谷歌更安全还是加入创业公司风险更大，

9:26 大多数人会说留在谷歌更安全，加入创业风险更大。

9:29 就未来一年现金报酬来说，确实如此。

9:33 谷歌明年会给你更多现金报酬，远胜大多数创业公司。

9:34 但如果你从职业的长远角度看，

9:38 关键是你建立了多广的人脉？

9:38 你掌握了多少新技能？

9:43 你如何推动自己去获得新体验？

9:46 留在谷歌这样的公司风险远大于加入创业或创办一家新公司。

9:50 面对当下AI助手的狂热，有两种反应方式。

9:52 你可以选择怀疑，

9:55 不相信它，

9:57 把头埋进沙子，

10:02 继续按照过去的方式工作。

10:05 这是一种反应方式。

10:06 另一种是觉得，哇，这是个酷工具，将彻底

10:08 改变工作的方式。

10:09 我想站在如何使用它的前沿。

10:10 请做第二种人。

10:12 这会对你更有利。

10:13 我从不想说，

10:18 你应该承担更多风险。

10:18 我不是这样看待这个问题的。

10:20 不是叫你冒更大风险。

10:23 我看待问题的方式是，你应该优化你的职业发展，

10:24 着眼于个人成长和学习新技能。

10:26 通常实现个人成长和学习新技能的方法是进入

10:28 更新颖、更不确定的环境，

10:30 承担更多责任，而对未来发展不那么确定。

10:32 我不认为那是冒险。

10:33 我认为最冒险的是停滞不前。

10:39 所以对我来说，关键是重新定义风险的含义。

10:41 风险就是停滞不前。

10:46 进步就是学习、成长和不断挑战自己。

10:49 所以我总希望大家优化进步和成长，推动自己。

10:51 我没有任何市场营销背景。

10:54 我非常不擅长营销。

10:57 直到一年前我甚至没有用过任何社交媒体平台。

11:01 我讨厌公开分享自己的事情。

11:02 承担营销工作对我来说非常不舒服。

11:05 但我确实做了。

11:09 当我发现自然社交媒体和LinkedIn营销是我们公司成功的最佳途径时，我感到非常沮丧。

11:09 但我花了一年时间努力学习，变得擅长它。

11:11 现在我喜欢它。

11:12 所以，本来可能觉得投入大量精力做这套前所未试的营销策略风险很大，

11:15 其实不然，这是一种利用不确定性

11:17 在新方向上学习和成长的机会。

11:21 我经常思考我的孩子们将生活的世界有多不同。

11:21 有些科技领域让我感到惊叹：太酷了，

11:26 显然这是更好的未来。

11:29 举个小例子，我们住在旧金山的帕特罗山

11:33 非常靠近Waymo和Zoox的主要测试区。

11:34 Zoox，是亚马逊的自动驾驶汽车部门。

11:38 我带孩子们去上学，

11:40 我两个大女儿坐在后面货运自行车上，

11:44 她们一直说，Waymo，Zoox，Waymo，Zoox。

11:44 对她们来说，看到无人驾驶的汽车非常正常。

11:50 我觉得很酷，因为车顶装满了摄像头和激光设备，

11:53 但对她们来说，汽车无需人类驾驶是理所当然的。

11:56 在自动驾驶汽车领域，我很开心，觉得

11:57 她们俩可能永远都不需要自己开车，

12:01 因为这更安全、更方便，且所有车都是电动的，对环境也更好。

12:01 但在其他领域，我的感受就复杂得多了；比如我不想让她们

12:04 ...

12:07 And so I take my kids to school.

12:09 My two older daughters are sitting behind me on a cargo bike and they're

12:12 saying, Waymo, Zooks, Waymo, Zooks.

12:15 And for them, it is so normal to see a car with nobody driving it.

12:19 I think it's cool because they have all like the cameras and lasers on top, but

12:23 it is so natural to them, the idea that a car can be driven by the human.

12:27 And so in the particular area of self-driving cars, I am so happy that I think

12:32 neither of them will ever need to drive a car by themselves because it'll be so

12:36 much safer and so much more convenient and so much better for the environment

12:39 when all these cars are electric.

12:41 There are other areas where I have more mixed feelings like I don't want them

12:47 to

12:47 只是一直刷YouTube短视频，尽管算法非常非常好。

12:50 优秀的算法。

12:51 我一般从两个方面来看这个问题。

12:54 一是我尽量让他们获得尽可能多的真实世界挑战和经历。

13:00 是的，你还是得学会骑自行车。

13:02 骑车时你会擦破膝盖。

13:04 我总是对我女儿说这句话。

13:05 孩子擦破膝盖是好事，对吧？

13:06 因为如果你擦破膝盖，说明你在尝试新的事物

13:08 并冒险，面对现实世界的后果。

13:12 我教孩子学游泳时还有另一个重要的突破。

13:14 他们吞点水没关系，学游泳时难免会这样。

13:18 学游泳时，吞水是正常的。

13:18 偶尔吞点水没关系，因为学游泳就是这样。

13:21 学游泳就是要经历这些。

13:22 所以我让他们在现实中承担一些风险。

13:25 因为我的孩子还小，我还没考虑过

13:28 像ChatGPT或我们现在说的AI助手这类工具

13:33 会如何影响他们的学习旅程。

13:36 还没想到这些会带来什么影响。

13:37 但因为我在做AI相关工作，所以很关注

13:39 孩子未来的职业会是什么样子。

13:40 这是我目前的看法。

13:43 第一，清晰表达什么重要、什么需要做并指导如何做的能力

13:49 永远重要。

13:54 未来只会更重要，因为我们的工作更多是

13:57 高效指挥AI做事，而不是自己全部完成。

14:02 不是自己亲自做所有事情。

14:03 所以我花很多时间，我真的希望孩子学逻辑和哲学，

14:07 因为我希望他们能清晰表达自己的诉求。

14:10 他们想要什么。

14:11 第二，我希望他们思考自己独特的个性和能力在哪些领域

14:16 可以闪耀，并且是AI无法替代的。

14:21 这就是为什么我录制我们自己的YouTube视频，因为它表达了

14:25 我的个性，

14:25 人们可以建立社会联系。

14:28 这将是未来职场的两大基本技能：

14:30 你多擅长表达自己想要什么并告诉AI，

14:34 又多擅长与他人建立社会联系？

14:38 我认为这些将是未来任何职业的持久技能。

14:41 。

14:42 毫无疑问，美国在人工智能领域领先于中国，

14:48 但美国在技术领导力上的优势我觉得非常狭窄。

14:53 美国声称的技术领导力看起来很有限。

14:53 美国人准备用AI做什么？

14:56 可能是做更多的PPT，更多地互相打官司，

15:00 而中国人更专注制造业。

15:04 所以中国会用AI制造更多iPhone、电动车电池、无人机和军火。

15:09 制造更多的无人机和军火。

15:12 去年，美国造了大约五艘船，中国造了约1500艘。

15:18 我在美国、中国和加拿大生活的时间差不多一样多。

15:24 我在美国、中国和加拿大的时间大致相等。

15:24 现在看来，美国和中国似乎都注定要输，而非赢。

15:30 不是赢。

15:30 这两个国家都犯了大错。

0:00 we should all be having dozens or hundreds of agents working for us.

0:03 So over time I built up this team of 40 AI marketing agents to work with me.

0:07 I'm the only marketing person I really have.

0:09 I have an AI coach that once a week tells me here are the themes that seem to

0:12 be

0:13 really working well or the styles that are working really well for you.

0:15 Do more of this, do less of that.

0:16 That's why I've had a few very successful posts on LinkedIn.

0:20 So for example, the post was here are the 40 marketing agents that I actually

0:23 use.

0:24 And that post so far has 1.5 million impressions.

0:28 And the most comments ever on a LinkedIn post, you know, I've been posting

0:31 consistently

0:31 on LinkedIn for a year with no results.

0:33 And then all of a sudden I was like, whoa, if your job is as a junior content

0:38 marketer to take a YouTube video and write a blog post based on that YouTube

0:41 video, that job won't exist anymore.

0:43 A high quality marketing contractor.

0:46 Let's say I had four people at $12,500 per month each.

0:49 That would be $50,000 a month.

0:51 And my AI bill is $500 a month.

0:53 Building AI agents is the fundamental skill that will define every professional

0:58 's

0:58 career for the next 30 years.

0:59 It's a requirement in the same way that knowing how to use spreadsheets like

1:02 Microsoft Excel was a requirement for the last 40 years, knowing how to build

1:06 AI

1:06 agents is a requirement for the next 40 years.

1:09 We are all using it for probably 1% of what we should be using it for.

1:13 Here's what I'll say.

1:14 AI is way under hyped.

1:23 My name is Jacob Bank.

1:24 I'm the founder and CEO of Relay.App.

1:27 Relay.App is a platform to build AI agents and workflows that get work done on

1:32 your

1:33 behalf.

1:33 So right now we're a nine person team.

1:35 We have five engineers, two designers, a product person and myself.

1:37 If we did not have AI, we would probably need to be a 15 person team to achieve

1:43 the same level of accomplishment that we have now.

1:45 Once upon a time I was an AI researcher, then a startup founder and then a long

1:49 time product lead at Google and I've been working on Relay.App since 2021.

1:53 So I've seen the whole AI boom over the last three years from a very close

1:57 vantage

1:57 point.

1:58 My take on AI is it's like an intern.

2:05 It's just an intern.

2:06 It does exactly what you tell it to do.

2:07 It's the intern.

2:08 This is something I've changed my mind on actually.

2:10 A year ago, I said, Hey, think of an AI as your intern.

2:13 And then I realized that that was the wrong mental model.

2:15 There are certainly a class of tasks that AI can do.

2:19 Those are the time saving kind of tasks that you would give otherwise to an

2:22 intern.

2:23 But AI can also do these strategy analyses and competitive analyses and content

2:27 creation tasks that an intern definitely could not do.

2:31 So for example, I am not good at sales calls.

2:34 I do not have a background in sales.

2:35 It does not come naturally, but it turns out there are some techniques that are

2:38 pretty valuable in sales calls.

2:40 So I have an AI coach that reviews all the transcripts of my meetings and says,

2:43 Oh, you could have talked about this better.

2:44 You could have articulated this value or you were too easy to,

2:46 you're to jump into a demo there.

2:48 You should have asked some more discovery questions.

2:50 If you go on hire a sales coach, a good sales coach is like $10,000 a month

2:55 for one meeting a week.

2:56 You can build a pretty good AI sales coach by yourself and a little bit of,

3:01 you know, transcript training and it'll cost you like five bucks a week to run.

3:05 When there's a new role or function I'm taking on that I don't have experience

3:11 in

3:11 or I don't feel like I'm an expert at, how can I figure out the right coaching

3:15 relationship where I can give my coach the raw data of what I'm doing and then

3:20 have it give feedback to me on the right cadence, whether it's after every call

3:23 or once a day with a summary or once a week with a summary.

3:26 That's why I'm now realizing that thinking about AI as an intern was so wrong.

3:30 So back when I was at Google, there was a role called tech lead manager.

3:37 And that was a hybrid role where you were both supposed to be the technical

3:40 expert who knew the entire code base and could review technical designs and

3:44 solve

3:44 the hardest problems, but you were also a manager.

3:46 You were also responsible for coordinating the other people on the team.

3:50 And it's a very hard role.

3:51 It's hard to do both technical work and management work at the same time.

3:54 But I think that's what the future holds for all of us.

3:57 We all need to become super ICs.

4:01 Let me explain why.

4:01 If your job is as a junior content marketer to take a YouTube video and write a

4:07 blog post based on that YouTube video, that job won't exist anymore.

4:10 You cannot build the job just doing single individual content repurposing tasks

4:15 like

4:15 that. If your job is to work at a large corporation and manage a team of a

4:19 thousand

4:19 people, I don't want to say that job is going to go away, but I think that job

4:23 is

4:23 going to become less and less important because companies are going to be

4:25 smaller.

4:26 We're going to have many more leaner, smaller companies.

4:29 So that skill set of managing 1000 people, that's going to be less and less

4:32 important.

4:33 Everyone is going to look like me.

4:35 We're about two thirds of your day is individual contributor work.

4:39 I'm doing stuff.

4:39 I'm making YouTube videos.

4:41 I'm editing blog posts.

4:42 I'm publishing content.

4:43 I'm talking to customers.

4:44 And then one third of my day is coordinating my team of AI agents that helps me

4:48 with those

4:48 things. The thing that's so exciting about this super IC future is that that's

4:54 the

4:54 work most of us love.

4:55 Most people that I talk to who are in very junior roles are like, Oh, I wish

5:00 instead of

5:00 just writing this blog post, I was told to write.

5:02 I had a bit more strategic influence on what we were doing and why.

5:06 And everyone I know, including myself when I was at Google, who was in a

5:09 complete

5:09 organizational management role was, I wish I could be closer to the work.

5:12 I wish I could get back into product.

5:14 I wish I could be closer to the customer and the user.

5:16 And so now I think every role in the future will combine the best of both

5:20 worlds.

5:20 We will all need to be strategic enough and senior enough to decide what needs

5:25 to be

5:25 done and to be able to see what good looks like so we can give the right

5:28 coaching.

5:28 But and we need to be still hands on enough that we can actually edit the blog

5:31 post

5:32 and edit the tweet and make the video ourselves.

5:34 That's the future of the workplace.

5:40 Yes, it's a bit overwhelming to see 40 of them all at once.

5:42 But if you click into any individual agent, they're very simple.

5:47 And they all have very specific jobs.

5:49 One, every time we create a new YouTube video, it creates a LinkedIn post

5:52 automatically.

5:53 Every time we create a new YouTube video, it creates a tweet automatically.

5:57 Every time the CEO of one of our competitors posts on Twitter or LinkedIn,

6:00 it lets me know what they're talking about every week.

6:02 It goes, it looks at our competitor pricing and it lets me know if anything

6:05 important has changed.

6:06 So one by one, I built up this team of AI agents to help me.

6:10 I have not had good luck with trying to build a single agent to try to do 25

6:14 things at once.

6:15 That is one difference, maybe with human employees, where when you hire a

6:19 social

6:19 media marketer, you want them to own all of social media marketing, especially

6:23 if

6:23 you're a beginner, start simple, create one agent that can do one thing, create

6:27 a

6:27 second agent that can do a second thing, create a third agent that can do a

6:30 third

6:30 thing, and then maybe have one agent that sits above them and helps invoke them

6:34 when

6:35 necessary, and then build up over time.

6:37 Do not start with a 40 agent org chart.

6:39 Start with one.

6:40 One very important thing to keep in mind is that AI agents are not a

6:46 set it and forget it kind of thing.

6:48 You can't just build an AI agency.

6:49 Oh, this thing is just going to work for me forever.

6:51 I'm constantly modifying my agents.

6:54 For example, I do assessment calls with new customers where I'll spend 30

6:58 minutes

6:59 with them and we'll identify opportunities for AI in their business.

7:02 And I thought, wouldn't it be cool if they got a personalized Google Doc from

7:06 me

7:06 after the call with what we had talked about with five specific use cases and

7:09 links to templates.

7:10 And I thought this was so cool.

7:11 So I built an AI agent to help me with it.

7:13 I said, AI agent, your job is to take this transcript of the meeting and turn

7:17 into a really nicely formatted Google Doc to send to the customer.

7:20 And after doing that 10 times, I was like, this is not the right format.

7:23 The customers aren't reading the Google Doc.

7:24 I should have just included in the email.

7:26 So I, I fired that agent and I said, uh, we don't actually need that job to be

7:31 done anymore.

7:32 I actually repurposed that agent and said, can you just take that summary and

7:35 put

7:35 it directly into the follow up?

7:36 Sometimes you just don't need that function to be done anymore.

7:40 Like we were very focused on SEO a year ago.

7:43 And then we realized that it's just not the important channel for us right now.

7:46 It's such a small percentage of our new users and it's relatively high effort.

7:49 So we're not going to focus on SEO anymore.

7:51 So I told my AI SEO agents, you guys can stop for now.

7:54 Like, I'll come back to you in the future.

7:56 But for now, you can stop doing what you're doing in the same way that you

7:58 would

7:59 tell an SEO agency, we don't need SEO at the moment right now.

8:02 We'll come work with you again in the future.

8:04 And the really cool thing is there's no emotional baggage.

8:07 There's no disagreements.

8:09 There's no coordination costs.

8:11 I just have an idea.

8:12 Hey, agent, can you help me with this idea?

8:14 And they say, yeah, absolutely.

8:16 Let's do it.

8:16 Um, and so I don't want us to think about it as replacing people.

8:20 I wanted to think about it as giving me superpowers.

8:22 I wish I had the time to do all these marketing activities because I love doing

8:27 them. They're very valuable to the company.

8:28 And now I can do that.

8:30 We all want to do more at our jobs.

8:32 We're all ambitious people.

8:33 We're all motivated people.

8:34 We all wish we could do more.

8:35 And now we have the tools to do so.

8:37 It's fascinating to think about how perspectives on what a risky career is

8:46 versus what a safe career is have changed over the years.

8:49 In my parents' generation, a safe career was working at one large

8:54 Fortune 500 company for 40 years.

8:56 You know, getting your watch when you retire or after 50 years and a risky

9:00 career

9:01 was starting your own company or moving between companies every couple of years

9:05 .

9:05 I think that has completely flipped.

9:08 The riskiest career is if your skills are too tied to the environment of one

9:13 particular company that could change over time.

9:16 And the most robust careers are people who are starting their own companies

9:20 and who are having lots of different life experience.

9:22 I'll give you an example.

9:23 Much of our team, I've recruited from Google because we all work together.

9:26 Google and now we're working on this startup.

9:29 And if you ask 10 people, what's riskier, staying at Google or joining a

9:33 startup,

9:34 most of them will say it's safer to stay at Google and it's riskier to join a

9:38 startup.

9:38 And in terms of your cash compensation for the next year, yeah, that's true.

9:43 Like Google is going to pay you more in cash for the next year than most hours

9:46 will. But if you think they're the long term lens of your career,

9:50 where it's about how broad of a network have you built?

9:52 How many new skills have you built?

9:55 How have you pushed yourself to develop new experiences?

9:57 It is way riskier to stay at a company like Google than it is to join a startup

10:02 or start a startup. There's two ways you can react to the current AI agent cra

10:05 ze.

10:06 You can say, I'm skeptical.

10:08 I don't believe it.

10:09 I'm going to put my head in the sand.

10:10 I'm going to keep working the way I've always worked.

10:12 That's one way to react.

10:13 The second way to react is like, whoa, this is a cool tool that's going to

10:18 completely

10:18 change the way that work is done.

10:20 And I want to be on the cutting edge of how I'm using it.

10:23 Please be person number two.

10:24 That's going to serve you much better.

10:26 I never want to communicate.

10:28 You should take more risk.

10:30 That's not the way I frame the discussion.

10:32 It's not you should take more risk.

10:33 The way I frame the discussion is you should optimize your career for personal

10:39 growth and learning new skills.

10:41 And often the way to create personal growth and learn new skills is to go into

10:46 newer and more in certain environments where you'll have more responsibility

10:49 and you're less sure of what's going to happen.

10:51 And I don't think that's risky.

10:54 I think the riskiest thing you can do is to stagnate.

10:57 And so for me, it's all about reframing the conversation about what risk is.

11:01 Risk is stagnation.

11:02 Progress means learning and growing and pushing yourself.

11:05 And so I always want people to optimize for progress and growth and push

11:09 yourself.

11:09 I have no marketing background.

11:11 I am terrible at marketing.

11:12 I was not even on any social media platforms until a year ago.

11:15 I hate sharing stuff about myself in public.

11:17 It was deeply uncomfortable for me to be the person responsible for marketing.

11:21 I really did that.

11:21 It was tragic for me when I realized that organic social media and marketing on

11:26 LinkedIn was the best channel for us to succeed as a company.

11:29 But I worked really hard at it for a year to learn to get good at it.

11:33 And now I love it.

11:34 And so it could have felt, oh, it's so risky to put so much effort in this

11:38 marketing strategy that we haven't tried.

11:40 No, it's an opportunity to take advantage of uncertainty, to learn and grow in

11:44 a

11:44 in a new direction.

11:50 And I think often about how different the world my kids are going to live in.

11:53 There are certain areas of technology where I'm just like, wow, that's so cool.

11:56 That's obviously better.

11:57 I'll give you one small example where we live on Patero Hill in San Francisco

12:01 is

12:01 very close to the main testing area for Waymo and Zooks.

12:04 Zooks is, I think it's Amazon self-driving car division.

12:07 And so I take my kids to school.

12:09 My two older daughters are sitting behind me on a cargo bike and they're

12:12 saying, Waymo, Zooks, Waymo, Zooks.

12:15 And for them, it is so normal to see a car with nobody driving it.

12:19 I think it's cool because they have all like the cameras and lasers on top, but

12:23 it is so natural to them, the idea that a car can be driven by the human.

12:27 And so in the particular area of self-driving cars, I am so happy that I think

12:32 neither of them will ever need to drive a car by themselves because it'll be so

12:36 much safer and so much more convenient and so much better for the environment

12:39 when all these cars are electric.

12:41 There are other areas where I have more mixed feelings like I don't want them

12:47 to

12:47 be just scrolling YouTube shorts all the time, even though there's a very, very

12:50 good algorithm.

12:51 The general way I think about it is maybe twofold.

12:54 One is I try to give them as many real world challenges and experience as

13:00 possible. Yeah, you still got to learn how to ride a bike.

13:02 You're going to skin your knees when you're riding your bike.

13:04 This is what I always say to my daughter.

13:05 Skin knees are good for kids, right?

13:06 Because if you're, if you're skinning your knees, it means you're trying

13:08 something and taking risk in the real world and dealing with the consequences.

13:12 This is another big breakthrough when I was teaching my kids to swim.

13:14 It's okay if they swallow some water, that's going to happen when you're

13:18 learning how to swim.

13:18 Like it's okay if you swallow water from time to time because that's what it

13:21 takes to learn how to swim.

13:22 So I'm trying to have them take, you know, some risk in the real world.

13:25 The thing that I haven't thought about yet, because my kids are too young,

13:28 I don't know how tools like catch a BT or AI agents like we're talking about

13:33 now, I haven't yet thought about how those are going to influence their

13:36 educational journey.

13:37 But because I'm working on AI, just I do think a lot about what my kids

13:39 careers are going to look like.

13:40 And here's my current perspective on that.

13:43 Number one, the ability to clearly articulate what is important, what needs

13:49 to be done and give guidance on how to do it, that will always be important.

13:54 That will only be more important in the future because our jobs will be more

13:57 of how do we efficiently instruct AI on what it needs to do rather than how do

14:02 we do it all ourselves.

14:03 So I'm spending a lot of time, like I really want my kids to learn logic and

14:07 philosophy because I really want them to be able to very clearly articulate

14:10 what they want.

14:11 And then number two, I want them to think about areas where very unique

14:16 personality and abilities can shine through in a way that AI wouldn't.

14:21 And so that's why I record our own YouTube videos, because it's an expression

14:25 of

14:25 my personality and people build a social connection.

14:28 Those are going to be the two fundamental skills for the future of the

14:30 workplace, how good are you at articulating what you want and telling

14:34 that to AI, and then how good are you building social connection with others?

14:38 And those, I think, are going to be durable skills for the future of any career

14:41 .

14:42 There's no doubt that the US is ahead of China on artificial intelligence, but

14:48 the claim to technological leadership for the United States appears to me to be

14:53 pretty narrow.

14:53 What are Americans going to be using AI for?

14:56 Well, to make a lot more power points, perhaps, and to file a lot more lawsuits

15:00 against each other, yes, the Chinese are much more manufacturing focused.

15:04 So China is going to use AI to make more iPhones, make more electric vehicle

15:09 batteries, make more drones, and make more munitions.

15:12 Last year, the United States built around five ships, China built about 1,500

15:18 ships. I've spent about equal parts of my life between the US, China, and

15:24 Canada.

15:24 Right now, it looks like the US and China are both very intent on losing, not

15:30 on

15:30 winning. Both these countries making very big mistakes.

moon

· 7mo

“人工智能不会夺走你的工作。人工智能会让你成为首席执行官。”

巴拉吉·斯里尼瓦桑谈人工智能如何创造真正的公平竞争环境：

“生产资料实际上已经交到了你手中。”

“如果你认为自己会成为一位出色的创始人、一位出色的CEO，这种想法正被极度低估。”

“全球人才搜寻已经开始。” #ai

@balajis与@PeterMcCormack

https://x.com/a16z/status/2036835836910850548?s=20

中文 English

0:00 人工智能不是抢你的工作，而是让你成为CEO。

0:02 实际上它把生产资料交到了你手中，对吧？

0:05 如果你觉得自己会是个出色的创始人、优秀的CEO，现在这种想法被严重低估了。

0:09 全球人才搜寻已经开始了，对吧？

0:12 你基本上不需要任何资金，真的，只要有电脑，只要你

0:15 在

0:15 网上找的话，实际上那是个好方法，那真是个好主意。

0:18 我准备发推特分享这个。

0:19 人工智能不会抢你的工作，人工智能让你成为CEO。

0:22 那么，每个人都应该成为CEO吗？

0:23 不，不过，我确实认为有更大一批人是这样的。

0:30 如果去掉创业的资本成本，因为确实有很多

0:34 人才存在，对吧？

0:35 比如说，看看科技领域，贝索斯、埃隆，这些人都不是

0:40 出身

0:40 富裕家庭，对吧？

0:42 这就是杰斐逊所说的“自然贵族”。

0:44 创始人来自世界各地各种背景，

0:48 对吧？

0:48 各个角落，没错？

0:49 比如说，中西部的安德森，我相信你也能找到

0:55 来自

0:55 英国乡村或者说英国各地的人，来自各方

0:58 ，对吧？

0:59 所以对我来说，这本质上是一种良性的平等主义提升，

1:04 机会

1:04 的质量，是任何想做事的人都能触及的“天梯”。

0:00 AI isn't taking your job, AI is turning you into the CEO.

0:02 Literally the means of production and being put in your hands, right?

0:05 If you think you'd be a great founder, a great CEO, it's being hyper deflated.

0:09 The global talent search has begun, right?

0:12 You don't need any money, basically, literally, if you have a computer, if you

0:15 're

0:15 looking online, actually, that's a great, that's, that's a great one.

0:18 I'm going to tweet that.

0:19 AI doesn't take your job, AI turns you into the CEO.

0:22 Now, should everybody be CEO?

0:23 No, however, however, I do think that there is a much larger group of people.

0:30 If you take away the capital costs of doing it, because there is a lot

0:34 of talent out there, right?

0:35 Like, look, if you look at tech, I mean, Bezos, Elon, these guys did not come

0:40 from

0:40 wealth, right?

0:42 This is what Jefferson calls a natural aristocracy.

0:44 You have founders from like, you know, all kinds of places around the world,

0:48 right?

0:48 Random places, right?

0:49 So, you know, guys from like Midwest, like Anderson, you have people I'm sure

0:55 from

0:55 rural areas in England or, you know, like the UK, you have people from all over

0:58 , right?

0:59 So that's the incident to me is leveling egalitarianism in a good way, a

1:04 quality

1:04 of opportunity, the sky hook for anybody who wants to do it.

love

· 7mo

Basecamp 现已通过全新的、功能全面的命令行界面（CLI）向客服代表全面开放，该界面由一款出色的技能所封装，并依托经过全面升级且大幅扩充的 API。这是一种绝佳的方式，既能让客服代表访问 #Basecamp 中的所有内容，又能将其集成到任何地方。 #cli #ai https://x.com/dhh/status/2036860598785356219

中文 English

0:00 有一整批消费者已经把人工智能等同于产品

0:07 攻击，跟扯淡一样。

0:09 人们并不是讨厌人工智能。

0:11 他们讨厌的是因为你想留住你的

0:16 产品而搞出的糟糕人工智能。

0:17 欢迎收听 Rework，这是 37signals 关于更好工作方式和企业管理的播客。

0:21 你的

0:21 业务。

0:21 我是 Kimberly Rhodes，来自 37signals 团队，与我一同的是 Jason Frieden 和 David

0:26 Heinemayer Hanson。

0:28 我们将谈论科技和人工智能相关的内容。

0:31 Basecamp 最近已支持智能代理。

0:35 这样你的 AI 代理就可以在 Basecamp 内工作了。

0:38 我不是专家。

0:39 接下来由你们来说。

0:40 David，你想先说吗？

0:41 当然。

0:43 我先挑剔一下这个词，就像我们刚开录之前讨论的那样。

0:47 “代理友好”这个词不错，不过对我来说更

0:53 关乎无障碍性。

0:54 在传统网页设计中，这就是我们对无障碍性的争论，意味着

0:58 让视障或

1:05 视力有限、色盲等人群更容易使用网站

1:05 那些特殊因素可能让他们难以使用未考虑这些的应用程序。

1:11 所以我们做无障碍工作。

1:15 进行对比测试。

1:20 如果某两种颜色放在一起，效果不佳。

1:22 色弱者看低对比度内容会很困难。

1:24 所以需要调整。

1:28 引入不同的颜色，增加对比度，

1:31 针对完全失明的用户做这些优化。

1:32 确保键盘访问性很高，能完全用键盘操作。

1:36 在不同元素间切换时，有合适的流程和节奏，

1:39 不会跳来跳去让人摸不着头脑。

1:43 我就是这么看待我们为代理无障碍做的工作。

1:44 这些 AI 代理在许多事情上非常聪明，

1:50 但在使用网站时却还会笨拙。

1:51 它们勉强能用，但速度很慢。

1:55 一个月前我们开始做这项工作时，我做了个大测试，

1:55 想着我们是不是根本不用改进。

1:59 目前的代理、现成模型，云端代码和开源代码等，

2:00 它们能不能直接做到？

2:07 它们到底需不需要帮助？

2:10 我惊讶于这些代理仅用浏览器操作竟然如此成功。

2:15 我能让其中一个代理注册 Basecamp，

2:21 在 Campfire 自我介绍，

2:23 Vizzy 和 Hey 代理也能做到，尽管速度非常慢。

2:29 那种感觉是，我能看到未来，但不知道未来何时到来。

2:33 看起来至少还有一年。

2:33 可能是两年，五年也说不准。

2:35 如果我们今天想让 Basecamp 成为使用代理的好地方，

2:42 得让它速度快起来。

2:46 没人会有耐心等代理在聊天界面慢吞吞地处理任务。

2:47 它们只是机械地输出信息，对吧？

2:52 然而这个速度必须适合在 Basecamp 中使用它们。

2:58 这就是所有代理无障碍工作的意义。

2:59 就像是铺了条小斜坡一样，

3:02 轮椅无障碍，你本可以用别的方法上去，

3:05 但很麻烦甚至有点危险，

3:09 有了斜坡就能轻松滑上去。

3:12 这就是我们给 Basecamp 做的事，

3:15 创建了命令行界面（CLI），

3:21 AI 代理可以用这个工具。

3:24 过去六到九个月中，我们发现给 AI 这些终端工具时，

3:26 它们变得超级强大。

3:29 这就是最近三四个月代理加速开发热潮的原因，

3:31 程序员和设计师突然能做更多事了，

3:34 不仅仅是向训练有素的 LLM 提问，

3:37 然后得到答案。

3:40 而是让代理去执行操作，代理会尝试完成任务。

3:44 就像人一样，有时不会做，

3:48 调用错误工具，犯各种错，

3:49 但如果能持续反馈，反复尝试调整，

3:52 不断试错，最终成功，

3:58 就能快速前进。

4:04 但这需要工具使用快速。

4:09 虽然我很佩服代理能用浏览器，且用得不错，

4:14 但了解它们为何这么慢后就明白了，

4:18 现在它们实际上是在截图，

4:22 通过图像分析分解界面元素和字段，尝试理解，

4:23 非常厉害，但非常繁琐且慢，

4:30 相比之下如果用命令行界面。

4:30 只需要文本。

4:35 这正是这些大语言模型被训练的内容，数万亿的文本数据。

4:39 它们读文本，预测下一个命令，

4:43 下一段文本。

4:43 and then succeed, it can really quickly move on.

4:47 But that loop requires that the tools are fast to use.

4:51 And as impressed as I was that the agents could use a browser and could use a

4:56 browser really well, you also realize that when you know how they do it, why it

5:02 's so

5:02 slow, right now the agents are literally taking like a screenshot of the screen

5:08 and then running it through this image analyze to break down what are all the

5:13 elements, what are all the fields and then trying to reason about it.

5:16 Very impressive, as I said, but also very cumbersome and very slow versus if

5:22 they

5:22 do all this work through a CLI, a command line interface, that's just text.

5:26 Literally what these LLMs have been trained, like trillions of tokens.

5:32 They've gone through text, text, text, what's the next command?

5:35 What's the next token?

5:36 他们在这方面又快又好。

5:39 所以如果它们能保持在那个小循环里，你就能得到每秒的标记数，

5:43 对吧？

5:44 这很接近就像是在写你的傻故事时的速度。

5:47 而这些之间的差别就是一切，对吧？

5:52 速度，正如我们常说，是一个特性。

5:56 当代理能为你做事，而你又有一些

5:59 耐心去等待时，速度就是一切。

6:02 这是“我懒得做”和

6:04 “让我让代理去做更容易”之间的差别。

6:07 我一直在试验我们新代理在 REMC 或 CLI 上的可访问性。

6:14 尤其是最近用的最快的代理之一，

6:20 Kimmy K25，一个超级快的开放式等待模型，真的让我震惊，

6:26 如果你让它写故事或解决逻辑问题，

6:30 可以达到每秒200个标记的速度。

6:32 也就是每秒100多个词。

6:36 超级快，对吧？

6:38 然后我出于好玩，让它帮我设计一个基地营项目，

6:43 规划基地营五号的发布活动。

6:47 当然，我并没有给它所有背景资料。

6:49 所以它不会给我完整的发布计划。

6:53 但它列出待办事项的速度，拆解得很细致，

6:59 加了备注，加了信息，还添加了日程项目。

7:04 比如说如果你四周后要发布，你得提前一周开始做这个。

7:07 你得在前一周做那个。

7:09 我当时简直惊呆了，这太不可思议了。

7:13 很有趣的是，当你想为什么这感觉特别时，

7:19 如果我18个月前问 ChatGPT 3 给我出一个基地营发布计划的文本，

7:25 它也能做出来。

7:29 我当时可能也会觉得挺不错的。

7:32 但我拿到它后，接下来该怎么办呢？

7:34 这就是代理可访问性的魔力。

7:36 你把所有的智慧、洞察、总结，还有我们

7:42 喜欢的大型语言模型的特点，

7:46 变成可用的东西。

7:49 它不仅仅是一堆文本块。

7:51 不，它是一个完整的项目。

7:54 这里有一些待办事项，有些分配给你，

7:56 有些分配给代理，

8:00 有些分配给 Jason 或者其他参与这项工作的团队成员。

8:05 突然间，所有的智能都变得可执行了。

8:08 说这些话的时候真的得小心，别听起来像是 Salesforce 的机场广告。

8:13 智能变得可执行，砰！但有时候这些

8:18 惊奇的发现确实让你感叹，“哇，真是太棒了”。

8:24 就像我用这些代理进行编程一样。

8:30 我曾经很长时间非常好奇，

8:33 也非常着迷。

8:35 我喜欢问它各种问题。

8:36 然后我又自己动手写了点代码。

8:39 就是用我这两只小手，突然一切都变了，因为

8:44 代理开始能做事了。

8:45 它们能用终端，

8:46 能运行代码，

8:47 能运行测试，

8:49 能重写测试失败的代码，

8:51 能进入那个循环。

8:53 我从“哦，这挺不错”

8:56 变成了“这真是太疯狂了”。

9:00 你去做，我需要时会介入。

9:03 如果我们能把这种能力推广到其他一切上，比如项目设置，

9:09 分工，进度检查

9:13 和回访，

9:14 当这些超能力触手可及时，

9:19 因为所有内容都在基地营共享，

9:23 这不仅仅是我和代理的私人对话。

9:26 整个公司，整个项目，每个人都能围绕这些工作

9:30 协作，且不止用一个代理，

9:34 而是多个代理。我们已经在基地营大量实践了，

9:40 它们驱动着一些内部流程。

9:42 我们有很多不同的代理。

9:44 有些是个人人工代理，代表他们行动，

9:48 还有一些自动代理也做这些事情，并进行跟进和提醒。

9:52 我知道这种爆炸式发展在许多先行公司的

9:53 领域内正在发生，

9:57 但还有剩下98%的世界，

10:01 他们或许用过 ChatGPT，但未用过代理。

10:07 他们没用过命令行界面（CLI）。

10:09 如果你很活跃在 X、推特等平台，看到大家高速发现、分享这些，

10:15 你可能会觉得整个世界已经这样在运作了，

10:20 其实不是，完全不是。

10:22 我们现在所做的工作，比如代理可访问版的

10:25 基地营，使用 CLI、技能等，

10:29 甚至还

10:29 没能覆盖

10:33 广大群众。

10:34 我们还差一步，让它像用 ChatGPT 那样简单，

10:35 而 ChatGPT 几亿人都使用了，

10:42 并很快变得主流。

10:44 我们得跑向冰球会去的方向。

10:46 我觉得这就是这项工作令人兴奋的地方，

10:49 近两年来我们内部做了不少 AI 混合特性实验，

10:55 就是那些把 AI 融入产品

11:02 本身的尝试，比如说，它可能帮你总结，

11:06 给你建议，或以其他方式辅助你。

11:06 大多数公司的这类工作

11:10 成绩可以说是不太稳定。

11:11 如果不那么宽容的话，可以说是完全失败了。

11:18 事实上，情况糟糕到一部分消费者

11:22 已经把 AI 等同于产品退化和胡扯。

11:28 这是事实。

11:34 微软上周刚不得不公开道歉，说抱歉，

11:39 我们把AI乱塞进了画图、记事本，还有Windows的各个角落。

11:46 我们听到了，你们不想要那个。

11:50 当然你们不想要，因为这只是附加上去的，实际上

11:54 并不

11:54 够有用。你把它和消费者对明显有用的东西的兴奋感对比一下，

11:59 比如ChatGPT和其他基于聊天的界面，

12:04 几亿人在用它。

12:07 他们认为它是日常生活不可或缺的一部分，并且为此付费，

12:12 这些东西都说明人们不是讨厌AI。

12:15 他们讨厌的是被乱塞进来的糟糕AI，只因为你想把它

12:20 套进你的产品里，对吧？当然有好的方法。

12:24 有技术可以把AI嵌入产品，让它

12:29 完全

12:30 原生且不可或缺，人们不会拒绝，但这很难。

12:35 正如我说的，我们试了几年，看能不能找到很多

12:40 切入点，尝试了很多方法，但几乎没发布任何成果。

12:45 因为没达到最终标准，我们不想像记事本、画图那样发出烂产品遭到反弹。

12:49 所以我们继续努力，我相信我们会成功。

12:53 我们会找到它真正合适的位置，且非常合理的方式。但在那之前，

12:56 我们可以给大家提供一个简约且易用的Basecamp版本，

12:59 一个他们可以用现有喜爱工具操作的友好版Basecamp。

13:06 比如没有人在每天用Plot代码或Open代码时会说我不想要那个。

13:10 没人会说我不想能用指尖控制Basecamp，

13:14 不想把它连接到GitHub或Sentry或者其他任何东西。

13:17 他们会说想，因为这是显而易见的胜利。

13:21 这也是一种建议。

13:24 我想说的是，任何想搞清楚AI在产品中作用的人，

13:28 但你知道吗？

13:31 在你弄明白之前，如果还没弄明白，就先让它易用。

13:36 让代理更容易使用它。

13:38 这就是解锁我们向客户承诺了二十年的API所有潜能的粘合剂。

13:42 但99.9%的人从未使用我们的API，因为那需要程序员，

13:44 还得做各种准备，太贵了。

13:49 让代理易用，CLI，技能，这一整套把所有承诺带了过来，

13:53 虽然不能完全端到桌上，

14:00 但我们差不多了，不那么麻烦

14:03 的一盒子，很多人都能打开。

14:05 我猜年底前

14:11 这实际上会成为主流，因为会以大家都用的界面批量发出。

14:14 好了，现在代理可以用Basecamp了。

14:17 其他产品有什么打算？

14:18 嘿，Fizzy，那些会推出吗，还是只限Basecamp？

14:20 哦，全部都会推出。

14:23 实际上Fizzy的无障碍做得挺好，因为我们的朋友Rob发布了一个开源CLI。

14:27 其实我们还雇了Rob Salkas来帮我们做Basecamp CLI，

14:27 因为他做Fizzy CLI做得很棒。

14:31 我们会应用很多经验，继续打磨Basecamp CLI。

14:35 这是个AI的启发时刻，我们想让Basecamp更易用，

14:37 于是我们做了CLI，

14:42 而代理实际上写了大部分CLI代码。

14:42 CLI绝大多数代码都是代理写的，

14:43 他们很快就完成了65%。

14:51 然后我们花了几周时间把完成度提高到97%，虽然不是100%。

14:55 现在我们可以把学到的经验，应用到Rob做的Fizzy上，

15:01 推出官方完整打磨的Fizzy CLI和技能。

15:04 接着，百分之百我要这个，

15:08 我要让它支持我的邮箱。

15:09 当我用Fizzy或Basecamp时，把邮箱和它们连接起来，

15:14 把所有东西串联，配一个可以听我指令的执行代理。

15:17 比如我经常用的旅行，就是让代理直接去查信息，

15:20 我只想拿邮件里的一个信息，

15:24 不想翻邮件。

15:25 不想看邮件内容，

15:31 只想要邮件里的事实。

15:35 我很期待给邮箱和日历都全面无障碍，

15:36 把它们串联起来。

15:39 我们会全部做到。

15:42 今后这都会是默认配置。

15:47 代理的无障碍不仅适用于直接使用应用，

15:52 也适用于连接更大的生态系统。

15:57 很多成功应用，比如Slack，一个很好的例子。

15:57 Slack之所以成功，是因为它有一个封闭的生态系统和集成。

16:04 代理无障碍正在让这一切变得开放给所有人。

16:07 所有壁垒都被打破了，

16:11 只在一个应用里有什么优势都不存在了。

16:13 实际上，游戏的关键是你的代理能和你用的任何东西对话，

16:17 访问任何数据，

16:18 不管数据在哪里，都能把它调回来或散布到各处，

16:19 把所有东西连接起来。

16:21 I just want the fact that's in the email.

16:23 I'm really excited for having full accessibility for, hey, both on the email

16:29 side

16:29 but also on the calendar side and tying all these things together.

16:32 So we'll do it for all of it.

16:33 And I think anything going forward, this is going to be baked in.

16:36 This is going to be table stakes that your application is, agent accessibility

16:41 accessible,

16:42 both for the direct use with that application, but just as much because it

16:47 kind of plugs in to this broader ecosystem.

16:50 So many successful applications over the year Slack is a good example.

16:54 We're successful because they ended up with this ecosystem that was kind of

16:58 proprietary to Slack and integrations with it.

17:00 What the agent accessibility is doing is basically bringing that to everyone.

17:03 Like all those moats just come tumbling down because there's no specific

17:08 advantage

17:08 to just having something inside of one application.

17:11 In fact, the whole game here is that your agent can talk to anything you use,

17:17 can access anything you use, wherever your data is, it can move it back here or

17:23 everywhere and tie it all together.

17:24 这真令人兴奋。

17:25 好的，你可以在 Basecamp.com/agents 上了解我们正在做的事情。

17:31 这是 37 Signals 的一部作品。

17:33 你可以在我们的网站 37signals.com/podcast 找到单元内容和文字记录。

17:38 视频

17:38 剧集在 YouTube 上。

17:39 如果你有关于更好的工作方式和管理的提问给 Jason 或 David，

17:43 台词是什么？

17:47 正要说那个。

17:48 我想，等等，我需要代理帮我说这句话。

17:52 如果你有关于更好的工作方式和管理的提问给 Jason 或 David，

17:56 业务。

17:57 给我们留言语音，网址是 37signals.com/podcast question。

18:01 或者你可以发邮件到 [email protected]。

18:05 那不是。

18:05 是这样吗？

18:06 你们，这就结束了。

18:08 是邮箱地址，对吧？

18:09 我想是的。

18:10 哦，天哪。

18:11 让代理检查一下。

18:12 真的，我需要代理来做结尾。

18:17 (欢快的音乐)

0:00 An entire contingency of consumers have come to equate AI with product

0:07 aggression, with bullshit.

0:09 It's not that people hate AI.

0:11 What they hate is shitty AI glob done because you want to stick around your

0:16 product.

0:17 Welcome to rework a podcast by 37 signals about the better way to work and run

0:21 your

0:21 business.

0:21 I'm Kimberly Rhodes from 37 Silver Hills team joined by Jason Frieden, David

0:26 Heinemayer Hanson.

0:28 We are talking about tech things and AI things.

0:31 And Basecamp has recently been made agent friendly.

0:35 So your AI agents can work within Basecamp.

0:38 I'm not an expert.

0:39 I will let you guys talk about it.

0:40 David, do you want to kick us off?

0:41 Sure.

0:43 I'll start by quibbling with the term as we did just before hitting record.

0:47 Agent friendly, I think is a fine term, but I think it's actually to me more

0:53 about accessibility.

0:54 And this was the contention we had about what does that mean traditionally in

0:58 web design, accessibility means making it easier for folks who might be blind

1:05 or

1:05 might have limited vision, might be color blind, have all of these factors

1:11 that make it difficult for them to use a website or an application that has not

1:15 been designed within any of those factors in play.

1:20 So then you do accessibility work.

1:22 You do contrast testing.

1:24 Oh, if you have this color next to this color, that doesn't work very well.

1:28 If someone's kind of blinder, they can't see low contrast well.

1:31 So you change it.

1:32 So you get some different colors introduced, you increase the contrast,

1:36 you do all those things for full on blind users.

1:39 You make sure that keyboard accessibility is really high, that you can do

1:43 everything with a keyboard.

1:44 When you tap from one element to the other, it goes in a flow and a rhythm

1:50 that makes sense.

1:51 And it doesn't just jump all over the place in something that doesn't make

1:55 sense.

1:55 So that's actually how I think about the work we've been doing for

1:59 agent accessibility.

2:00 You have these AI agents that are incredibly smart at a ton of things.

2:07 And then they still kind of stumble when they have to use a website.

2:10 They can barely just do it, but they're really slow.

2:15 I did a big test a while back about a month ago when we set off to do this

2:21 work, where it was like, I wonder if we even need to do anything.

2:23 Can the agents we have right now, the models that are out there, the harnesses

2:29 that people run, cloud code and open code and all the other ones, can they

2:33 actually just do it?

2:33 Do they even need any help?

2:35 And I was shocked by how successful the agents were at using just a browser.

2:42 I could tell one of these agents to sign up for Basecamp, to introduce

2:46 itself in campfire.

2:47 The same thing with Vizzy, the same thing with, Hey, but it was really slow.

2:52 It was one of those things where like, I can see the future, but I can't see

2:58 when it's going to arrive.

2:59 This looks like something that might be at least a year out.

3:02 It could be two, it could be five.

3:05 Who knows if we want to make Basecamp a great place to work with

3:09 agents today, we got to make it fast.

3:12 No one's going to have the patience to sit around and wait for agents,

3:15 take minutes and minutes to do work when you ask them in a chatbot interface.

3:21 And they just, they're spitting out facts, right?

3:24 If that kind of pace needs to be captured.

3:26 When you're working with them in Basecamp.

3:29 So that's what all this agent accessibility work is for.

3:31 It's sort of like a little, little ramps, right?

3:34 Like the wheelchair accessibility, like maybe you could get up with your

3:37 wheelchair some other way, but be very cumbersome and maybe even a little

3:40 dangerous, but if we just make it a little ramp, you can just roll right in.

3:44 So that's what we've done for Basecamp here by creating a command line

3:48 interface,

3:49 a CLI tool that the agents can use.

3:52 Because what we found over just the last six to nine months is that AI

3:58 becomes super powerful when you give them these tools inside of a terminal.

4:04 That's what all this explosion of productivity has been about, especially

4:09 the last three or four months on agent accelerated development work,

4:14 like programmers and designers suddenly being able to do way more stuff

4:18 because they're not just asking an LLM that's been trained on the whole

4:22 internet and getting an answer back.

4:23 No, they're asking the agent to do stuff and the agent will try to do something

4:30 .

4:30 And just like a human will realize that often they don't know how to do it.

4:35 They call the tool wrong, they do all these things, but if it can stay in a

4:39 feedback loop where it can try something, adjust its approach and then try

4:43 again,

4:43 and then succeed, it can really quickly move on.

4:47 But that loop requires that the tools are fast to use.

4:51 And as impressed as I was that the agents could use a browser and could use a

4:56 browser really well, you also realize that when you know how they do it, why it

5:02 's so

5:02 slow, right now the agents are literally taking like a screenshot of the screen

5:08 and then running it through this image analyze to break down what are all the

5:13 elements, what are all the fields and then trying to reason about it.

5:16 Very impressive, as I said, but also very cumbersome and very slow versus if

5:22 they

5:22 do all this work through a CLI, a command line interface, that's just text.

5:26 Literally what these LLMs have been trained, like trillions of tokens.

5:32 They've gone through text, text, text, what's the next command?

5:35 What's the next token?

5:36 They're so good and so fast at that.

5:39 So if they can stay in that little loop, you can get the tokens per second,

5:43 right?

5:44 To be quite close to when it's just writing your silly story.

5:47 And the difference between those things is everything, right?

5:52 The speed, as we often say, is a feature.

5:56 And when it comes to agents being able to do things for you and you having some

5:59 patience to wait for it, speed is everything.

6:02 It's the difference between I can't be bothered to.

6:04 It's easier to ask my agent to do it.

6:07 And I've been experimenting with our new agent accessibility on REMC or CLI.

6:14 And I've been mind blown how quickly, especially the fastest agents like

6:20 Kimmy K25 that I've been using recently, which is one of these super fast, open

6:26 wait models that if you just ask it to write a story or solve a problem, logic,

6:30 you can do 200 tokens a seconds.

6:32 That's whatever, 100 plus words a second.

6:36 Super fast, right?

6:38 And then I asked it to actually, just for the heck of it, design me a base

6:43 camp project that spells out the launch campaign for base camp five.

6:47 And of course, I didn't even give it all the context.

6:49 So it wasn't going to give me the launch campaign we're going to ship with.

6:53 But the pace by which it filled out the to-do list, it broke it all down.

6:59 It added comments, it added messages, it added items on the schedule.

7:04 Well, if you want to launch in four weeks, you got to start doing this

7:07 the week before you got to do this.

7:09 I was just like, holy, smoly, this is incredible.

7:13 And it's so funny because when you think about it, why does that feel special?

7:19 If I had asked chat GPT three 18 months ago about just creating a text

7:25 document with a launch plan for base camp, it would have done that.

7:29 And I would probably also been surprised like that's neat.

7:32 But then what am I going to do with it?

7:34 That's the magic of this agent accessibility stuff.

7:36 You take all the wisdom, the insight, the summarization, all the things we

7:42 like about LLMs and then you make it usable.

7:46 It's not just a block of text.

7:49 No, it's an entire project.

7:51 And here's some to do is some of them are assigned to you.

7:54 Some of them are assigned to the agent.

7:56 Some of them are assigned to Jason or whoever is working on this stuff.

8:00 And suddenly all that intelligence becomes actionable.

8:05 I mean, it's so funny when you talk about these things, you really got to be

8:08 careful not to sound like an airport advertisement for Salesforce or something.

8:13 Intelligence becomes actionable, boom, but sometimes some of these

8:18 revelations actually you sit with and you go like, Oh, wow.

8:24 Yes, much the same way as I've had it programming with these agents.

8:30 Like for a long time, I was very curious.

8:33 I was very fascinated.

8:35 I loved asking it stuff.

8:36 And then I went off and I wrote my little code by myself.

8:39 By my two little hands here and then suddenly things flipped because the

8:44 agents could do stuff.

8:45 They could use the terminal.

8:46 They could run code.

8:47 They could run tests.

8:49 They could rewrite code that failed the test.

8:51 They could get into that whole loop.

8:53 And suddenly I went from like, Oh, this is neat.

8:56 But like I'll write it myself to this is freaking incredible.

9:00 You go ahead, I'll intervene if I need to.

9:03 And if we can get to that for everything else, for setting up a project,

9:09 for for figuring out how to divide the work, for checking in on things and

9:13 checking up on things.

9:14 And we can have those kinds of superpowers at our fingertips because it's

9:19 all in base camp, because it's all shared in base camp.

9:23 It's not just like I have a personal little conversation here with an agent.

9:26 No, the whole company, the whole project, everyone can suddenly

9:30 collaborate around this stuff and not necessarily even with one agent, but

9:34 with multiple, we've started doing this extensively already at base camp, where

9:40 you're driving some of these internal processes that we have.

9:42 And we have a bunch of different agents.

9:44 Some have a personal agent that they're just using to act on their behalf.

9:48 And then we have some automated agents that do all of this stuff too and chime

9:52 in and follow up.

9:53 And I know this kind of explosion is going on at a fair number of companies

9:57 sort of on the leading edge, but then there's the rest of the 98% of the world

10:01 who who may have used chat GPT, but we haven't done anything with agents.

10:07 We're not running into CLI.

10:09 And if you're very plugged into X and Twitter and wherever else that people

10:15 are discovering and sharing all this stuff at high velocity, you think that

10:20 this is how the entire world is already working.

10:22 No, they're not, absolutely not.

10:25 And all this work that we're doing right now, the agent accessible version of

10:29 base

10:29 camp that uses the CLI and the skills and so on, even that is not going to

10:33 reach

10:34 the broad masses.

10:35 We are missing a step yet where this becomes as easy as using chat GPT, which

10:42 hundreds of millions of people have used.

10:44 Like that has actually become very mainstream very quickly.

10:46 But we got a skate to where the puck is going.

10:49 And I think that that's what's exciting about this work, that over the last

10:55 almost two years, we've had a bunch of experiments internally with AI infused

11:02 features, like these are the things where you try to bake the AI into the

11:06 product

11:06 itself and like, well, maybe it can summarize some things or can suggest

11:10 some things or it can help you in other ways.

11:11 And the track record for most companies on that work is let's say mixed.

11:18 And if you're less charitable, say completely fucked, right?

11:22 Like in fact, it has been so bad that an entire contingency of consumers

11:28 have come to equate AI with product degradation, with bullshit.

11:34 Microsoft literally last week had to come out with a mea culpa saying, sorry,

11:39 we shoved AI crap into paint, into notepad, into all these crevices of windows.

11:46 We hear you, you don't want that.

11:50 And of course, you don't because it was tacked on and it wasn't actually

11:54 helpful

11:54 enough. And you got contrast that with the consumer excitement for something

11:59 that's obviously good, like chat to BT and the other chat based interfaces,

12:04 like hundreds of millions of people are using it.

12:07 They're considering it integral to their daily lives, they're paying for it,

12:12 all these things. So it's not that people hate AI.

12:15 What they hate is shitty AI globbed on because you want to stick around your

12:20 product, right? Now, there are ways to do this well.

12:24 There are ways to put AI into a product and have it be something that feels

12:29 fully

12:30 native and integral to it and people wouldn't reject, but that's quite hard.

12:35 We've tried, as I said, for a couple of years to see if we could find a bunch

12:40 of angles on this and we tried many things and we shipped virtually none of it.

12:45 Because it didn't pass that final muster because we didn't want the notepad

12:49 paint backlash of shipping something crappy.

12:53 So we continue to work on that. And I'm sure we're going to nail it.

12:56 We're going to find some ways with this really fits and it really makes a lot

12:59 of sense. But until then, we can give everyone the age and accessible version

13:06 of Basecamp, the in friendly version of Basecamp that they can use with these

13:10 tools they already love. Like no one who's using plot code on a daily basis or

13:14 open code are going to tell you like, well, I don't want that.

13:17 I don't want to be able to just command Basecamp with my fingertips and tie

13:21 it together with GitHub or Sentry or anything else.

13:24 They're going to say, yes, please, because this is such an obvious win.

13:28 So that's also a bit of a recommendation.

13:31 I'd actually say for anyone else who's trying to figure out how AI plays a

13:36 role in the product, but you know what?

13:38 Until you figure that out, if you haven't already, just make it accessible.

13:42 Just make it easier for agents to use this.

13:44 This is that glue that unlocks all the promise that we told customers for

13:49 literally 20 years was going to be there with APIs.

13:53 And 99.9% of them never touched our APIs because it required a programmer.

14:00 It required all sorts of stuff to do it.

14:03 It was just too expensive to do.

14:05 Aging, accessibility, CLIs, skills, this whole bucket takes all of that promise

14:11 and then delivers it not quite on a silver platter.

14:14 We're not quite there yet, but like, I don't know, in a slightly less

14:17 cumbersome

14:18 box, quite a few people can open that.

14:20 And then by the end of the year would be my guess.

14:23 This is actually going to go mainstream because it's going to be shipped in

14:27 bulk

14:27 through these interfaces that everyone uses.

14:31 OK, so base camp is now accessible to agents.

14:35 What are we thinking for other products?

14:37 Hey, Fizzy, are those on the horizon or it's just a base camp solution right

14:42 now?

14:42 Oh, it's coming for everything.

14:43 In fact, we already have some pretty good accessibility for Fizzy because our

14:51 friend Rob put out an open source CLI for it.

14:55 In fact, we ended up hiring a Rob Salkas to work with us on the base camp CLI

15:01 because he did such a great job at the Fizzy CLI.

15:04 We're going to apply a bunch of those lessons that we've now taken really

15:08 polishing the base camp CLI.

15:09 This was one of those AI inception moments where like we're trying to make

15:14 base camp more accessible to the agents and we're making the CLI.

15:17 And the agent is actually making the bulk of the CLI.

15:20 Like the vast majority of the code that goes into the CLI was written by an

15:24 agent.

15:25 And it got like 65% of their of that done in no time at all.

15:31 Then we spend weeks and weeks and weeks and weeks getting it to, if not 100%,

15:35 then 97%.

15:36 Now we can take all of those lessons.

15:39 We can apply it to the work that Rob already did on Fizzy.

15:42 And then we can put out an official fully polished Fizzy CLI and skill.

15:47 And then after that, 100% I want this for, hey, I want this for my email.

15:52 I want my email to be tied together with my Fizzy when I'm using that or my

15:57 base camp

15:57 when I'm using that, bring all these things together and having this executive

16:04 agent that I can just tell to do stuff.

16:07 I mean, one of the things I use, hey, for a lot, for example, is travel and

16:11 having an agent

16:13 to be able to just go in and look these things up when I just want a piece of

16:17 information out of my email.

16:18 I don't want to draw through it.

16:19 I don't want the email.

16:21 I just want the fact that's in the email.

16:23 I'm really excited for having full accessibility for, hey, both on the email

16:29 side

16:29 but also on the calendar side and tying all these things together.

16:32 So we'll do it for all of it.

16:33 And I think anything going forward, this is going to be baked in.

16:36 This is going to be table stakes that your application is, agent accessibility

16:41 accessible,

16:42 both for the direct use with that application, but just as much because it

16:47 kind of plugs in to this broader ecosystem.

16:50 So many successful applications over the year Slack is a good example.

16:54 We're successful because they ended up with this ecosystem that was kind of

16:58 proprietary to Slack and integrations with it.

17:00 What the agent accessibility is doing is basically bringing that to everyone.

17:03 Like all those moats just come tumbling down because there's no specific

17:08 advantage

17:08 to just having something inside of one application.

17:11 In fact, the whole game here is that your agent can talk to anything you use,

17:17 can access anything you use, wherever your data is, it can move it back here or

17:23 everywhere and tie it all together.

17:24 That's super exciting.

17:25 OK, well, you can read more about what we're doing at Basecamp.com/agents.

17:31 This has been a production of 37 Signals.

17:33 You can find units and transcripts on our website at 37signals.com/podcast full

17:38 video

17:38 episodes on YouTube.

17:39 And if you question for Jason or David about a better way to work and run your.

17:43 What is the line?

17:47 Literally about to say that.

17:48 I was like, wait, I need an agent to say this for me.

17:52 You have a question for Jason or David about a better way to work and run your

17:56 business.

17:57 Leave us a voicemail at 37signals.com/podcast question.

18:01 Or you can send us an email to [email protected].

18:05 That is not.

18:05 Is that right?

18:06 You guys, that's it.

18:08 Email address, right?

18:09 I think it is.

18:10 Oh, my gosh.

18:11 Have an agent check it.

18:12 Literally, I need an agent to do the exit.

18:17 (upbeat music)

moon

· 7mo

TAO联合创始人：AI圈的人是“普通人”，加密圈的人是“自由斗士”

“你不能一边从萨姆·阿尔特曼那里拿钱——那个试图奴役我们的人——一边又假装自己是‘自由技术’。这到底想干什么？这看起来太奇怪了”

“有时候人们他妈的讨厌加密货币圈的人，但骨子里我们就是自由斗士。这就是为什么Worldcoin行不通，因为我们身边都是真正相信自由的人。这就是我们的本质”

“那些自以为高高在上、了不起的 #AI 圈人士，其实只是普通人。他们全都是‘蓝教堂’里的人。我超级喜欢他们，他们都很聪明，但他们不是追求自由的人。他们不真正理解地缘政治，也不真正关心这些。”

“他们只会去工作，赚点钱，然后去玩他们的桌游。但加密货币圈的人是叛逆者。你别想对我们搞什么Worldcoin。那行不通。”

https://x.com/CryptoMikli/status/2036585881184719127?s=20

中文 English

0:00 你不能从默想奴役我们的山姆·奥特曼那里获得资金，

0:04 然后假装自己是自由技术派，对吧，像你知道的那样，

0:08 你

0:08 想干嘛？我不知道，就是觉得奇怪。Orblep太

0:12 反乌托邦了。是啊，残酷。你为什么要那样做呢？然后

0:15 技术实际上很好，那帮人真的很聪明。

0:18 它有效，对吧？太棒了。我总是在

0:21 一些奇怪的事发生时发推，说有一个平行宇宙里，World Coin

0:24 是比特币的对立面，懂吗？是啊，对吧？这不是这个

0:29 行业的见证吗？人们有时候恨加密货币人，

0:33 但是内心深处，我们是自由斗士。

0:40 这就是World Coin失败的原因，因为我们中间充满了真正

0:44 信仰自由的人，你明白我的意思吗？这才是

0:47 我们真正的样子。顺便说一句，那些AI人以为

0:51 自己很酷、高高在上，实际上他们

0:54 都是普通人，都是朝九晚五的人，我非常喜欢他们，

0:58 他们很聪明，但不是自由派。他们不懂地缘政治。

1:02 他们不理解，也不能理解。

1:05 他们只会为博格工作，赚钱，玩他们的棋盘游戏。

1:08 而加密货币人则像叛徒一样，

1:12 你不能用World Coin那套来做，这行不通。

0:00 You can't get funding from Sam Altman, the guy who's trying to enslave us and

0:04 then pretend to be like, you know, like, free to techno, yeah, like what are

0:08 you

0:08 trying to do? I don't know, like, I just seemed odd. And like the Orblep was so

0:12 dystopian. Yeah, brutal. Well, why would you do it like that? And then the

0:15 technology is actually really good, though, like the guys were really smart.

0:18 It works, right? It's a great. There's, I always, I always tweet this whenever

0:21 some weird shit happens, there's like a parallel universe where world coin is

0:24 like flip, flip Bitcoin, you know? Yeah, right. Isn't that a testament to this

0:29 industry? Like the we actually, you know, people fucking hate crypto people so

0:33 that reason sometimes, but also like deep down, we're freedom fighters. And

0:40 that's why world coin didn't work is because we're permeated by people that

0:44 actually believe in freedom. You know what I mean? Like that that's that is

0:47 actually who we are. And that's not an AI, by the way, the AI people who think

0:51 they're so hot and high and high and mighty, they're all just they're just

0:54 normies. They're all blue church people. I love them to death. They're all very

0:58 smart, but they're not freedom people. They don't really grasp geopolitics.

1:02 You know, I mean, they don't really understand. And they don't really can't.

1:05 They're just going to go work for the Borg and make their money and live in

1:08 the play the board games. But the crypto people are like, we're renegades,

1:12 man. And so you can't try to do world coin on this. It's not going to work.

moon

· 7mo

罗布·格里尔表示，对于新晋的 #AI 开发者而言，最佳的创业平台是Bittensor，而非Y Combinator。

“我认为这是加密货币史上最精妙的代币经济设计。”

https://x.com/therollupco/status/2036204282144309637?s=20

中文 English

0:00 所以，如果你是一个，你知道的，波兰的天才开发者，你会去

0:04 硅谷

0:05 然后去Y Combinator接受稀释股权融资，还是会

0:09 在BitTensor

0:09 开一个子网，矿工就是你的AI开发团队，你甚至

0:13 不用付钱

0:13 ？

0:14 我想显然你会选择BitTensor，然后所有

0:17 这些子网的市值都会累积

0:18 到tau代币上，因为获得

0:21 alpha代币的唯一方式是

0:22 先购买tau。

0:25 我觉得这就是对以太坊的升级，对吧，因为

0:28 关于以太坊的争议在于

0:29 所有这些二层到底带来什么价值累积

0:33 给

0:33 ETH，对吧？

0:34 而tau则必须先买tau，而且这是一个非常有趣的

0:38 正和

0:38 游戏，因为Templar爆发会推高tau价格。

0:45 然后所有其他子网都会觉得太棒了，因为这有利于

0:48 他们的子网

0:49 和他们的发行。

0:50 所以实际上子网之间会互相支持，这是一个正和

0:54 游戏。

0:54 我认为这是加密史上最聪明的代币经济设计，

0:57 我想

0:57 很多人会同意我的看法。

0:00 So, if you're a, you know, a brilliant developer in Poland, are you going to go

0:04 to Silicon Valley

0:05 and go to Y Combinator and get diluted equity funding, or are you going to

0:09 start a subnet

0:09 on BitTensor, where the miners are your AI dev team, which you don't even have

0:13 to pay

0:13 for.

0:14 I think obviously you choose BitTensor, and then all of the market cap built

0:17 from all

0:18 of those subnets accrues the token tau, because the only way you could get

0:21 access to the alpha

0:22 tokens is by first purchasing tau.

0:25 And I think that's like the upgrade over Ethereum, right, because like the

0:28 whole controversy

0:29 over Ethereum is that like all these layer two is like, what is the value accru

0:33 al to

0:33 ETH, right?

0:34 Whereas with tau, you have to purchase tau first, and it's really interesting

0:38 positive

0:38 sum game to because Templar pops off, and that increases the tau price.

0:45 And then all the other subnets are like, that's awesome, because that benefits

0:48 their subnet

0:49 and their emissions.

0:50 So you actually have subnets rooting for other subnets, it's a positive sum

0:54 game.

0:54 I think this is the most brilliant tokenomic design in the history of crypto,

0:57 and I think

0:57 I think a lot of people would agree with me on that.

love

· 7mo

“ #Anthropic 和 #OpenAI 的日志里肯定保存着所有人的访问密钥、API 密钥和持有者令牌。” 如今的人工智能技术栈漏洞远比人们意识到的要多得多。 #IronClaw 解决了这个问题，因为“密钥永远不会接触 LLM”。 @ilblackdragon 联合创始人 @NEARProtocol 正在开发私有 AI 基础设施，其中“模型提供商和硬件提供商实际上都无法访问您用于 #AI 推理的内容”。换句话说，“没有任何一方能够单独解密你的数据”。https://x.com/Bankless/status/2036518654909821158?s=20

中文 English

0:00 人们在使用时没有意识到的一件事是

0:03 Entropic OpenAI，或者更糟的是，你使用其他东西

0:07 进行推理时，Open Cloud 实际上也会发送你所有的秘密

0:11 到那些服务。

0:12 是的。

0:13 所以在 Entropic 和 OpenAI 的日志中某处，

0:15 他们拥有每个人的访问密钥、API 密钥，

0:20 以及用于访问你的 Gmail、Notion 的承载令牌

0:25 和你的——

0:25 我们竟然在做这种事情，真的很疯狂。

0:29 是的。

0:30 首先，我不知道如何解决这个问题。

0:32 密钥绝不会进入 LMM。

0:33 所以即使你用这些集中式提供商，

0:37 虽然不应该，但至少密钥不会

0:39 进入任何 LMM。

0:41 所以这是我们希望做到的，

0:43 这是唯一必须首先做到的事情。

0:45 是的，是的。

0:46 但 Neerai 过去一年一直在努力开发的，

0:50 实际上是如何实现私密 AI？

0:53 也就是说，我们如何提供一种 AI，

0:58 让模型提供商、硬件提供商

1:00 都无法访问你用 AI 推理的内容？

1:05 所以我们有 Neerai Cloud，这是推理云。

1:08 你可以使用开源权重模型。

1:10 它运行在安全环境中。

1:12 它实际上使用——这也就是我

1:14 一开始提到的——

1:15 你会使用我们的多方计算网络，

1:18 这是 Neer 的一部分，用于加密

1:21 解密、备份以及所有内部机制。

1:25 这让你确信，

1:29 没有任何单一方可以解密你的数据。

1:32 没有人可以访问它。

1:35 你需要把所有多方计算网络

1:37 有效地全部集结起来才能访问。

1:40 所以——

1:40 好，那么你的意思是说，

1:43 你提供一种服务，配合

1:46 Iron Claw，类似于保密云

1:50 环境来运行 LMM 实例？

1:53 当然，你得运行开源权重模型。

1:55 但也许一些中国模型

1:57 是最好的，比如 Kimmy 或者类似的，

1:59 或者一些 DeepSeek——

2:00 是的，我们有 Quimmy、Quen、DeepSeek。

2:04 最新的热门我们也会加进去。

2:07 我们还有 OpenAI RSS。

2:10 所以，你可以在它们之间选择。

2:12 现代媒体一团糟。

2:13 每个平台都有偏见，但并不是所有新闻消费者

2:16 都意识到他们所接收平台的偏见。

2:18 这就是为什么当我想知道真相时，

2:20 我会去 Polymarket，世界上最公正的新闻平台。

2:23 从 polymarket.com 获取你的信号。

0:00 One thing that people don't realize when they use

0:03 Entropic OpenAI, or even worse, you use something else

0:07 for inference, Open Cloud actually sends all your secrets

0:11 to those services as well.

0:12 Yeah.

0:13 So somewhere in Entropic and OpenAI logs,

0:15 they have everybody's access keys, API keys,

0:20 and bearer tokens to access your Gmail's and your notions

0:25 and your--

0:25 It's actually insane that we're doing that.

0:29 Yeah.

0:30 And so first of all, I don't know what fixes that.

0:32 The keys never touch a LMM.

0:33 So even if you're using it with those centralized providers,

0:37 which shouldn't, but at least the keys are not

0:39 going ever into a LMM boop.

0:41 So that's something we'd like just like,

0:43 that's just the only same thing to do first.

0:45 Yes, yes.

0:46 But what Neerai has been working on actually for the past year

0:50 is actually developing, how do we do private AI?

0:53 So how do we actually offer AI where neither we, model

0:58 provider, hardware provider, is actually

1:00 able to access what you are using the AI inference with?

1:05 And so we have Neerai Cloud, which is inference cloud.

1:08 You can use open-weight models.

1:10 And so it runs in secure enclaves.

1:12 It actually uses-- and this is kind of what

1:14 I was referring in the beginning--

1:15 you'd use our multi-party computation network, which

1:18 is part of Neer, that is used for encryption

1:21 decryption for backups for all the internal machinery.

1:25 And that's what gives you this kind of knowledge that like,

1:29 hey, there's no single party who can go and decrypt your data.

1:32 There's nobody who can actually access it.

1:35 You would need to collect all the effectively

1:37 multi-party computation network together.

1:40 So there's--

1:40 OK, so is this-- are you saying, then,

1:43 that you offer a service in conjunction

1:46 with Iron Claw, which is almost like a confidential cloud

1:50 type of environment for running LMM instances?

1:53 And of course, you'd have to run the open-weight models.

1:55 But maybe some of the Chinese models

1:57 are kind of the best year, like a Kimmy or something

1:59 like this, or some deep-seak--

2:00 Yeah, we do quimmy, quen, deep-seak.

2:04 Whatever's new hotness will add it as well.

2:07 We have open-air RSS as well.

2:10 So yeah, you can choose between all of them.

2:12 Modern media is a mess.

2:13 Every platform has a bias, but not every news consumer

2:16 is aware of the platform's bias that they're consuming.

2:18 That's why when I want to learn the truth,

2:20 I go to Polymarket, the world's most unbiased news platform.

2:23 Get your signal from polymarket.com.

love

· 7mo

#Groq 提供快速、低成本的推理能力，即使在关键时刻也能稳定运行。

2025年12月24日，NVIDIA宣布以约200亿美元现金收购 #AI 芯片初创公司 Groq 的资产，这是 #NVIDIA 有史以来规模最大的一笔交易。

中文 English

0:00 “推理对于我们在一级方程式所做的一切都至关重要。我们投入了

0:05 巨大的资金

0:06 在技术上。我们运行了数百万次模拟，最终这转化为

0:11 赛道上的表现。

0:12 对与Grock的合作超级兴奋，如果你看一级方程式，

0:16 有一些问题我们真的在努力解决。

0:16 那就是速度，能够

0:19 在严格的成本控制下运营，

0:20 并且在一年参加24场比赛时能够快速扩展。

0:25 每年24场比赛。

0:26 试图同时解决这两个问题非常令人兴奋。

0:31 这正是Grock将

0:33 赋予我们优势的地方。”

0:00 "Inferences are absolutely key to everything we do in Formula One. We invest a

0:05 huge amount

0:06 in technology. We run millions of simulations, and ultimately that relays into

0:11 performance

0:12 on track. The super excited about this partnership with Grock, if you look at

0:16 Formula One, there

0:16 are some things that we're really trying to solve. That's speed, being able to

0:19 operate

0:20 within tight cost control, and also being able to scale at pace when we go

0:25 racing 24 times

0:26 a year. Trying to solve those two things together is really exciting. That's

0:31 where Grock will give

0:33 us an edge."