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May 2025

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付费文章 (2)

Bitcoin’s Silent IPO: Why This Consolidation Isn’t What You Think

The quiet liquidity event reshaping Bitcoin’s ownership base and its future stability

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A payments protocol for the internet. Built on HTTP

Payments on the internet are fundamentally flawed. Credit Cards are high friction, hard to accept, have minimum payments that are far too high, and don't fit into the programmatic nature of the internet. It's time for an open, internet-native form of payments. A payment rail that doesn't have high minimums + % based fee. Payments that are amazing for humans and AI agents.

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帖子 Media

Love

· 1h

现在，您可以让 #Claude 使用您的电脑来完成各项任务。

它可以打开您的应用程序、操作浏览器、填写电子表格——任何您坐在办公桌前会做的事情。

在 Claude #Cowork 和 Claude #Code 中预览研究功能，仅限 #macOS 系统。 #ai

https://x.com/claudeai/status/2036195789601374705

Love

· 1h

@davidsenra称，Shopify首席执行官@tobi告诉他，将来我们回望2026年时，会将其视为“全球每一家企业都面临被颠覆的一年”。

“人工智能将席卷一切。”

“届时你会回过头来意识到，这一年本应让人看清：无论现存什么业务，都完全可以重建为原生 #AI 版本。” #Shopify

https://x.com/tbpn/status/2036241506428977519?s=20

中文 English

0:00 托比·卢卡对我来说非常有趣，因为他是你最喜欢的创始人，

0:03 最喜欢的创始人。

0:04 所以你欣赏的每个人，当你问他们欣赏谁时，他们都会提到托比

0:08 。

0:08 他们喜欢他的原因是他对每件事都有独立思考，

0:11 独立思考。

0:11 因此他拥有一系列彼此不相关的信念，

0:15 这意味着当你和他对话时，你无法预测

0:18 他对你的问题或陈述的回应会是什么。

0:21 我们谈了四个小时。

0:24 录音前我们先聊了半小时。

0:26 接着录了三个小时。

0:27 然后我们去了某个地方，录完后又聊了三十分钟。

0:32 我不停地说话。

0:32 我说，伙计，我得赶飞机了。

0:33 那晚我要和查理·罗斯吃晚饭。

0:36 我得飞过去。

0:37 我说我们本来可以继续聊。

0:38 他真让我大开眼界。

0:40 他说的一件事，我觉得你们观众会感兴趣，就是他

0:43 相信。

0:43 我记得他在节目里说过，我们将回顾

0:46 2026年作为这样一年，

0:48 那一年全球所有生意都处于抢夺之中。

0:51 人工智能正全面来袭。

0:54 每一个行业都必须被重新构想，你会回头看，

0:57 那一年你本应该明白，

1:00 你可以

1:01 用人工智能原生版重建所有现有业务。

1:04 很有趣。

1:05 Finn，如果你想让 AI 处理你的客户支持，访问 Finn.ai，

1:10 第一

1:10 客户服务 AI 助理。

0:00 Toby Luca was very fascinating to me because he's your favorite founder,

0:03 favorite founder.

0:04 So everybody that you admire, you ask who they admire and they'll bring up Toby

0:08 .

0:08 And what they like about him is that he thinks to every single thing

0:11 independently.

0:11 So therefore he has all these beliefs that are not correlated with one another,

0:15 which means when you have a conversation with them, you are unable to predict

0:18 what his response to your question or your statement is going to be.

0:21 And that we talked for four hours.

0:24 So we talked for a half hour before recorded.

0:26 Then we recorded for three hours.

0:27 We headed down to whatever was, then we recorded for another 30 minutes after.

0:32 And I kept talking.

0:32 I was like, dude, I got to catch a flight.

0:33 I'm going to have dinner with Charlie Rose that night.

0:36 Like I had to like fly down there.

0:37 I was like, we would have kept talking.

0:38 Like he just blew my mind.

0:40 And one thing he said, I think it'd be interesting to your audience is he

0:43 believes.

0:43 And I think he said this on the episode that we're going to look back on the

0:46 year 2026 as the year

0:48 that every single business in the world was up for grabs.

0:51 That AI is coming for everything.

0:54 Every single other business will have to be reimagined and you're going to

0:57 look back at, this is the year where it should have been obvious to you that

1:00 you can

1:01 rebuild the AI native version of whatever exists out there.

1:04 Interesting.

1:05 Finn, if you want AI to handle your customer support, go to Finn.ai, the number

1:10 one

1:10 AI agent for customer service.

Love

· 1d

人工智能正变得更加全球化、更加多模态，并日益深入地融入各行各业。 #ai

中文 English

0:00 [音乐]

0:04 奥利维亚，欢迎。

0:05 >> 谢谢邀请我。

0:06 >> 这一年中最令人激动的时刻，

0:08 就是顶级100报告今天发布了，我想。

0:11 是这样吗？>> 是的。

0:12 >> 这是三年来的第六期。

0:15 跟我们说说什么没变，什么变了，

0:18 你有多兴奋，报告有什么新动向？

0:20 >> 是的，很多方面变化很大，

0:23 自从我们2023年首次发布这份名单以来，增长惊人。

0:25 另一方面，从宏观层面看，我们仍然处于非常早期阶段。

0:28 ChatGPT是迄今为止最大的全球AI产品，

0:32 但全球只有10%的人口每周活跃使用它。

0:36 所以未来还有很大发展空间。

0:41 我认为过去六个月可能是我最喜欢、最激动的时刻，

0:41 因为见证了许多变化。

0:43 其中之一是消费者竞争真正加剧了。

0:47 当然有ChatGPT，还有Gemini和Claude都在加倍下注，

0:51 针对它们各自的消费级和准专业用户。

0:54 我觉得我们开始看到这些平台可能随着时间累积优势。

0:59 这让谁获取最多用户成为一个特别重要且有趣的问题。

1:03 相关的是，这次我们首次纳入了非AI原生但已主导AI的产品，

1:06 例如Canva、Notion和Free Pick。

1:08 Notion甚至宣布他们现在认为一半的新ARR

1:12 来自于AI优先的功能，非常酷。

1:14 最后，我们看到AI在网站或应用提示框之外大幅扩展了，

1:17 例如新出现的各种浏览器，

1:20 像DIA、Comet、Atlas。

1:22 Claude也进入了Excel、PowerPoint和Chrome。

1:25 还有桌面应用如Cursor、Whisper Flow、Granola。

1:29 AI的使用方式呈现出令人兴奋的爆发式增长。

1:32 真是太令人兴奋了。

1:36 这里内容很多要说。

1:39 先从大型基础模型说起吧。

1:43 你能谈谈你认为Gemini和Claude各自的专长领域吗？

1:45 当然，还有ChatGPT，

1:48 因为这更像是一个整体上扬的故事，

1:53 而不是这些模型互相替代。

1:56 是的，我同意。

1:58 尽管上周发生了一些戏剧性事件，

1:59 例如Katy Perry在Twitter上站队品牌之间的争论，

2:00 这事我之前从没料到，

2:02 但从根本上看，ChatGPT仍是明显的赢家。

2:04 在网页端，它的用户是Gemini的2.7倍。

2:07 移动端则是Gemini的2.5倍。

2:08 尽管Twitter上的技术争论频繁，

2:11 ChatGPT网页端用户几乎是Claude的30倍，

2:13 移动端更是Claude的80倍。

2:14 我们看到Sam Altman当时的推文，

2:17 是在超级碗广告大战时期。

2:21 就是那个德州推文。

2:24 是的，他说，德州使用免费版本ChatGPT的人比Claude的全球用户还多，

2:27 这是真的。

2:29 不过，我认为我们看到的趋势是，

2:33 “分歧”也许不是准确词汇，

2:37 但用户使用的产品种类和用途都在扩展，

2:41 市场份额略有变化。

2:45 Claude特别加倍押注准专业用户，

2:47 推出了协同办公、Claude代码，

2:49 以及与Excel、PowerPoint集成。

2:51 如果你看看Claude和ChatGPT的应用商店，

2:52 它们各自都有200多个应用，

2:56 但交集只有11%。

2:58 Claude专注于高端数据源、研究工具、科学和金融数据，

3:00 而ChatGPT更专注消费市场，旅行、营养、消费金融等。

3:04 Gemini则在自己的小领域耕耘，

3:06 主要通过创意工具获得用户和收入增长。

3:10 活跃用户和付费用户几乎与YO3、Nano Banana One、Nano Banana Pro、Nano Banana Two的发布节奏完全同步。

3:13 它们也在准专业用户领域稍作尝试，

3:15 把AI整合到Gmail、Sheets、Calendar中，

3:19 但这些都是现有产品的升级，

3:21 而非全新体验。

3:23 - 我们不如深入聊聊应用商店动态，

3:25 这真是太有趣了。

3:28 你能说说ChatGPT的应用目录的利好吗？

3:30 - 当然可以。

3:32 我看到ChatGPT采取的策略，Sam本人也在Twitter说过，

3:34 就是他们要做“人人皆可用的AI”，

3:37 意味着他们想吸引所有消费者，

3:39 并通过不同方式赚钱。

3:41 Claude则明确了只通过订阅模式盈利，

3:44 这对能付费的用户和企业很好，

3:46 但不是所有人都适合。

3:50 你也能从它们倾向于有偿的高价值订阅插件看出来，

3:52 这些插件多是专业的数据和服务工具。

3:53 - 类似实验室工具。

3:55 - 是的，比如PitchBook，投资人、科学家、数学家会用的工具。

3:58 ChatGPT则更像Google的方式，

4:02 构建普通人愿意用的产品。

4:06 可能转化成订阅用户比例较低，

4:11 但他们能通过广告赚钱，

4:13 也可能通过交易抽成。

4:15 比如如果他们成为预订旅行或各种长尾消费的入口，

4:17 按理说能从中分成，至少是其引流部分。

4:20 所以这就是ChatGPT应用商店的牛市逻辑，

4:22 数据里还没显现，

4:25 但未来一两年会更明显。

4:26 - 真有趣，你刚才谈到报告里“复合优势”的观点，

4:28 也就是上下文效应的累积，

4:30 能详细说说这个概念吗？

4:32 你用什么指标作衡量，比如会话时长、会话次数，

4:34 还是输入数据量，或者其他？

4:37 - 这是个很激动人心的问题，

4:39 因为到目前为止，像ChatGPT、Claude、Gemini、Perplexity这些横向大模型，

4:40 我们一直活在一个上下文和记忆能被轻易导出的世界。

4:43 Claude最近还针对这个做了广告活动。

4:45 但我认为锁定效应会越来越强，

4:48 而且其实这很可能有利于那些更横向、更广泛的工具，比如ChatGPT，

4:50 原因有几方面。

4:54 第一，ChatGPT已经开始开发能让用户通过平台与他人互动的产品，

4:56 比如群聊，想象如果有个更成功的ChatGPT群聊版本，

4:57 你的所有朋友都在上面，

4:58 那当你想离开ChatGPT时，

5:00 你得说服他们都换到另一个产品去。

5:04 - 正是如此。

5:08 第二点，我觉得也类似苹果和谷歌的比较，

5:10 当这些应用商店成熟后，开发者很可能会将时间和精力集中在那些用户最多、

5:12 或者在某些情况下最愿意付费的平台上，

5:15 对于很多消费工具来说，决定因素往往是用户数量。

5:18 这也进一步利好ChatGPT。

5:20 今年我最期待的一点是，Sam Altman暗示的ChatGPT身份验证层，

5:23 也就是说你可以用ChatGPT账号登录，

5:26 携带你的记忆和令牌，

5:30 其他产品就能借此变得更强大、更贴心。

5:35 如果成真，你会希望自己的核心身份存留ChatGPT，

5:37 这样它可以帮助提升你使用其他工具的体验。

5:40 - 这是超级聪明的策略，

5:41 依托它们拥有9亿用户注册的优势，

5:44 第三方开发者理想状况下不用自己支付推理费用，

5:47 用户把推理能力带着走，

5:50 开发者受益，

5:52 ChatGPT获得锁定，

5:53 用户获得个性化，

5:56 一切完美配合。

5:58 - 是的，我完全同意。

6:00 唯一让我存疑的是，

6:02 这点可能对锁定效应正反两面都有影响，

6:04 就是你的工作场景，

6:06 你签的企业合同。

6:07 比如，从某种角度讲，

6:10 如果我公司工作用的是ChatGPT，

6:13 那我学会用它就有优势。

6:17 we've kind of lived in a world where the context

6:20 and the memory is somewhat easily exportable.

6:22 Like Claude ran a campaign around this recently.

6:25 But I think there's gonna be increasing lock-in

6:28 and I do think that probably actually benefits

6:30 the broader, more horizontal tools like Chatsheet BT

6:33 for a few reasons.

6:35 So I think one, we've already seen Chatsheet BT focus on

6:39 or start to build out products where you interact

6:42 with other people on them through the platform.

6:44 So the group chats, like imagine if you,

6:46 if there's an even more successful version

6:48 of Chatsheet BT group chats

6:50 and all of your friends are on there,

6:51 then if you wanted to turn from Chatsheet BT,

6:53 you'd also have to convince them all to go through another product.

6:56 - Exactly.

6:57 I would say the second one is kind of also

7:00 like an Apple Google comparison in that

7:02 as these app stores emerge, it is likely that developers

7:05 might start to concentrate their time and effort

7:08 in who they build for in the most sophisticated way,

7:12 who they ship to first, depending on who has the most users

7:16 or maybe in some cases who's the most willing to pay,

7:19 but for a lot of these consumer tools,

7:20 it'll be who has the most users.

7:22 So I think that also benefits Chatsheet BT.

7:25 And then the other thing probably that I'm most excited

7:27 for this year that Sam Altman had kind of hinted at

7:31 is this like authentication with Chatsheet BT layer.

7:34 So essentially you'd be able to log in

7:36 with your Chatsheet BT account

7:37 and take like your memory and your tokens with you.

7:42 And then that other product would be able to kind of borrow

7:45 those things to be even more powerful and helpful for you.

7:48 And if that's the case, then you're wanting

7:50 to have more of your core identity live on Chatsheet BT

7:54 because then it can lend it to these other tools

7:57 that are even better for you.

7:58 - It's so smart and it really plays to their advantages

8:01 in that they have signups for 900 million people.

8:05 And then the third party developer ideally

8:07 would not want to pay for the inference.

8:09 So the user can bring their inference capacity with them.

8:12 There's an advantage for the developer.

8:13 Chatsheet BT gets the lock in.

8:15 The user gets the benefit of personalization

8:17 and it all kind of works.

8:18 - Yes, I totally agree.

8:19 The one question mark I still have on this

8:21 that I think could play both positive and negative

8:24 in terms of increasing lock in for the consumer product

8:27 is what your work goes with,

8:30 like what your enterprise contract is.

8:33 So for example, in some ways, it's good for me

8:36 if we, if my company uses Chatsheet BT for work

8:40 because then I know how to use the product.

8:42 作为普通消费者，他们可能尝试过

8:44 一两款人工智能产品。

8:45 所以他们更可能觉得习惯

8:47 并继续使用他们已经用过的东西。

8:49 另一方面，有些人可能不想将身份

8:53 和记忆混合在个人和工作使用场景中。

8:56 - 说得对。

8:57 - 所以我很感兴趣。

8:58 我觉得OpenAI最近有暗示，

9:00 但我很想知道如何划分记忆

9:04 在你自己内部的不同角色之间，

9:08 使用这些产品。

9:09 - 不要混淆角色。

9:11 - 对，就是这样。

9:12 - 好吧，换个话题，

9:14 先聊聊Gemini。

9:15 你知道，我在想Google早期AI产品的整体感觉，

9:18 比如Bard，他们永远甩不掉的包袱。

9:21 - 那些时刻很出名。

9:24 - 现在我们看到像Nana Banana这样的产品，

9:25 甚至这个名字“Nana Banana”

9:27 完美体现了Google的进步。

9:30 - 是的。

9:32 - 看起来他们对多模态非常重视。

9:33 - 嗯。

9:34 - 你怎么看他们的策略？

9:35 - 我挺佩服的。

9:36 我觉得他们在某些方面确实犹豫了，

9:38 这种犹豫恰恰符合预期。

9:40 将AI融入核心功能有风险，

9:44 可能会损害自家产品，或者很多人

9:47 已经用了这些工具十多年，

9:51 转换成本比较高。

9:53 他们不想在AI突然出现时吓到用户，

9:56 这我能理解。

10:00 但他们做得非常好的，是这些新创意产品，

10:04 基本上是DeepMind团队驱动的模型，

10:06 我觉得他们整体很棒。

10:09 - 我觉得Notebook LM实际上是首次展现，

10:11 是消费者AI音频领域的真正创新。

10:15 现在又有了图像和视频模型。

10:18 所以这样的大公司，

10:20 他们必须克服自身桎梏，

10:23 实际开始创新。

10:27 看起来他们正在这样做，但你曾在Google工作，

10:31 所以我很好奇你的看法。

10:32 - 很开心你提到Notebook，

10:35 因为Notebook在公司算是个无人争夺的空白地带，

10:38 这避免了十位副总裁争抢资源。

10:40 因此，Notebook的进展非常快。

10:41 他们刚推出了视频生成功能，

10:44 可以很直观地展示你的工作区内容，挺酷的。

10:47 相比之下，像Sheets和Docs这些现有产品，

10:48 有太多过去积累的惯性和势头，

10:50 还有管理层的各种压力，

10:53 他们很难做超出最显而易见的增量改进。

10:54 - 是的，我同意。

10:56 接下来几年会怎样，我们拭目以待。

10:59 我感觉他们会竭力保住这些产品，

11:01 因为不想失去用户群，但正如你说，

11:04 他们已经锁定了众多企业用户，

11:08 短期内可能不必做太多变化，

11:10 也能跟上步伐。

11:13 - 这次谈话隐含的一个点是，

11:14 我们西方体验并讨论很多AI，

11:16 说说全球AI发展趋势吧。

11:18 我看到里面有几个令人惊讶的点。

11:21 - 我们扩大了报告的研究范围，

11:23 结果非常有趣和好玩。

11:25 两个显而易见的与世界其他地方不同的是，

11:26 俄罗斯和中国。

11:28 大家都知道，中国许多AI产品被审查或禁用。

11:31 因此几乎所有的使用，

11:33 他们在ChatGPT、Bard和Gemini的综合使用量上最低，

11:35 只有15%。

11:39 他们主要用的是字节跳动出品的Dall·E，DeepSeek，

11:41 Quinn，Kimi这些模型。

11:43 让我有些惊讶的是，俄罗斯的情况其实很相似，

11:46 他们也有自己的平行AI生态系统，

11:47 这是出于必须，因为制裁等因素，

11:50 阻止他们使用所有美国工具。

11:54 我们看到像GigaChat和Yandex，

11:57 这些俄罗斯本土产品，往往由国家相关公司打造，

11:59 使用量很大，还有DeepSeek。

12:02 DeepSeek在俄罗斯市场仅次于中国，

12:05 排名第二。

12:07 如果你看各国采纳数据，

12:10 是的，某些国家对Claude用得更多，

12:12 有的国家对Gemini用得更多，

12:14 但两个最大异常是俄罗斯和中国，

12:15 他们是巨大市场，

12:18 值得持续关注。

12:21 - 很有趣的是，俄罗斯和中国

12:22 都因使用限制和文化偏好成为例外，

12:25 还有其他国家有地理特定趋势吗？

12:29 还是说这是一种全球AI行为模式？

12:33 - 说到模型开发，

12:34 专有模型开发可让你部署专属AI产品，

12:38 大部分研究来自美国和中国，

12:40 俄罗斯可能也有些贡献。

12:43 其他地方也在见证本土生态出现。

12:46 韩国有几款本土产品，

12:48 比如Neighbor和Cookal，

12:51 做出了不错的语言模型界面。

12:52 印度是我特别关注的另一市场，

12:56 因为那里人口众多，

12:59 可以支持独立大型企业专注印度市场。

13:01 印度的另一特色是语言多样，

13:03 各种语言不全被语言模型和语音产品支持，

13:06 导致主语言用户体验相对较差，

13:08 比如使用类似ChatGPT的产品时。

13:10 目前尚未见大量变种，

13:12 但我不惊讶未来可能有更多创业者，

13:14 甚至美国创业者，针对印度市场做AI。

13:15 另外一点，我们首次制作了个热力图，

13:18 显示哪些国家人均采纳AI最多和最少。

13:21 我们看了十大大型语言模型产品，

13:24 包括网页版和移动端，看看数据如何。

13:25 新加坡排名第一。

13:28 - 太疯狂了。 - 是的。

13:30 紧随其后的是香港、阿联酋、韩国。

13:34 美国排名第20，

13:37 不算特别低，也不算特别高。

13:39 俄罗斯和中国排得很后，排名50之后。

13:43 数据中蕴含许多有趣故事。

13:45 先说这前五名，

13:48 新加坡、韩国、香港，

13:50 劳动力结构高度技术化，白领比例高，技能丰富。

13:52 美国有大量岗位AI还未涉足，

13:55 比如零售、运输等领域。

13:57 另外，关于AI的文化认知多样性极大。

14:01 如果你在美国，

14:02 可能对AI的持续焦虑和质疑感受深刻——

14:04 - 是的，我就打算问你这个问题。

14:07 - 及AI对艺术家的负面影响等，

14:11 这些因素使得人们是否接受AI犹豫不决。

14:14 - 是的。

14:15 去年Edelman全球媒体公司做了项大调查，

14:18 美国的AI信任度较低，约32%，

14:22 而排名靠前的多数国家信任度达50%、60%、70%。

14:25 这也拖累了美国的发展速度，

14:28 尽管最大产品都来自美国，

14:31 人均使用率却不及其他投入更多的市场。

14:35 - 完全正确。

14:39 我读到中国对AI的好感度高达80%。

14:41 - 是的。 - 80%的人持正面看法。

14:44 我知道阿联酋和新加坡，

14:48 他们文化上更倾向技术乐观主义。

14:49 - 是的，确实如此。

14:50 看到一些小国的人均采纳率很有趣。

14:54 在美国，大约有三分之一的人每月活跃使用ChatGPT。

14:56 欧洲一些国家，甚至东欧，

14:59 虽样本较小，人均使用率可达45%到60%。

15:04 他们接受得较快。

15:06 - 是的，非常有意思。

15:09 我关注的是AI应用的全谱系，

15:11 从最实用的，

15:14 几乎是谷歌搜索替代品，

15:17 are very like tech first white collar high skill.

15:20 And the US has a giant chunk of jobs

15:23 where AI hasn't really touched them yet,

15:25 like retail and transportation and some of these other things.

15:29 I think also the cultural norms around AI

15:33 are shockingly diverse.

15:36 If you're in the US, you have probably internalized this

15:39 ongoing angst and questioning around--

15:41 - Yeah, I was going to ask you about this 100%.

15:44 - Or AI is terrible for artists

15:47 or all of these other things that make people pick up

15:50 or not pick up AI. - Yes.

15:51 - There was actually a big survey last year

15:54 from Edelman, the global media company.

15:57 And the US had a fairly low rate of trust in AI.

15:59 It was like 32% and most of these other countries

16:02 that are high on the list are like 50, 60, 70%.

16:06 So that I think has also held the US back

16:08 despite the fact that we are where the biggest products come from

16:12 are per capita usage is lower than a lot of these other markets

16:14 that have maybe smaller populations

16:16 but have embraced it more.

16:19 - I think that's exactly right.

16:20 Now I was reading that in China,

16:22 the sort of favorability views on AI are 80%.

16:25 - Yeah. - 80% hold a favorable view.

16:27 And I know UAE and Singapore,

16:29 I think they've sort of culturally wired to be tech optimistic.

16:34 - Yes. - Which is an advantage.

16:35 - Yes, yes, definitely.

16:37 It's interesting to see some of these smaller countries

16:39 like the per capita adoption rate.

16:42 Like in the US, it's around probably a third of people

16:45 are monthly active users of something like a Chachi BT.

16:49 In some, even some of like the European countries

16:52 or Eastern Europe, it's like 50, 45, 60% on smaller bases.

16:58 But they've kind of embraced it more quickly than we have here.

17:02 - Yeah, really interesting.

17:03 You know, one thing that I'm sort of watching

17:05 and I'm interested in is as you look at the spectrum of AI

17:07 from the most functional,

17:09 almost like a Google search replacement

17:11 对于最具文化性、创意性和个性化的，

17:14 我们应该看到各国之间更多的差异化。

17:17 因为显然文化方面，

17:18 印度拍摄的电影

17:19 与中国或美国拍摄的电影截然不同。

17:21 - 是的。

17:23 - 那他们使用创意工具的方式为何不不同呢？

17:25 - 是的，老实说这也是

17:27 我们开始在这份报告中考虑地理细分的部分原因，

17:30 因为在生成式AI的前两年半，

17:33 三年中，

17:35 绝大多数消费者可能只在使用

17:38 一个产品，现在这种情况已有很大扩展。

17:41 我认为我们将看到更多

17:43 针对特定市场的工具。

17:45 如果它们占领了足够的市场份额，

17:48 比如一些俄罗斯或中国公司，

17:51 它们实际上可以进入全球榜单，

17:53 前提是市场规模足够大。

17:55 说说创意工具的发展，

17:58 你认为这有多少反映了文化、

18:00 是文化驱动的，

18:02 我们何时会跨越那个门槛？

18:04 - 创意工具的趋势令人着迷。

18:07 显然，首个大型生成式AI产品

18:10 实际上是mid-journey，早于Chachi PC发布。

18:13 - 确实，是这样的。

18:14 - 在我们榜单的最初几期，

18:16 创意工具占据主导地位。

18:19 我以前说过，

18:21 创意工具受益于

18:24 早期模型的幻觉效应，

18:27 因为它们生成的内容往往更令人惊喜、美丽或原创。

18:30 因此，一度只有这些创意工具

18:32 在消费者AI中真正奏效。

18:35 现在情况变化很大。

18:36 创意工具仍是榜单的重要部分，

18:39 但作为独立业务的大型创意工具类型已经改变。

18:41 最大的变化是

18:45 我们看到独立的图像生成器越来越少。

18:47 许多这类工作，

18:51 如果制作基本的图像，

18:52 比如表情包、基础营销图像或信息图，

18:55 像Chachi BT和Gemini的核心模型

18:59 现在对此类任务都很擅长。

19:02 因此榜单上仍出现的产品，

19:05 如I-U gram或mid-journey，

19:07 要么是美学主张很强烈，

19:10 要么具有更复杂的工作流程，

19:13 这是Chachi BT类工具无法提供的。

19:16 相比之下，音乐、语音、视频

19:21 似乎是模型最大的公司投入较少的领域。

19:26 所以我们看到像Suno做音乐，

19:28 和11 labs做语音的玩家，

19:31 完全脱颖而出，

19:33 进入榜单前二十、十五名，

19:37 并且长时间保持位置。

19:40 同时还有社区和大量企业客户的复合锁定效应。

19:43 视频领域我有最多疑问。

19:45 OpenAI通过Sora在投资，

19:46 当然谷歌也在用Vio，

19:50 但中国模型非常出色，

19:54 因为它们可以训练任何数据。

19:57 Sea Dance 2可能是最佳例证，

19:59 在某些方面远超美国公司目前的水平。

20:01 我认为这将有利于像Korea这样的平台，

20:03 可以在一个地方使用所有模型，

20:08 因为我姐姐Justine写过相关文章。

20:10 视频的发展趋势显示，

20:14 不太可能出现一个统治所有的单一模型。

20:18 因此你需要能够在模型间切换。

20:21 - 这对大多数模型领域都适用，

20:25 聊天模型、创意模型，甚至代码模型，

20:26 它们都有自己的专业方向。

20:29 你知道，人们会讨论opus的易用性

20:32 和codecs的准确率。

20:35 这是权衡取舍，

20:38 你得选择针对不同问题使用不同工具。

20:40 - 绝对如此。

20:43 - Sora对我来说非常有趣，

20:45 因为它既代表了模型的一大进步，

20:47 也是一个关于社交的雄心勃勃的实验。

20:50 在Sora早期的数据里，

20:51 创作人数百分比显著，

20:54 比之前高出10倍。

20:57 你如何评价

20:59 Sora的社交努力与模型努力？

21:00 你怎么看它的未来？

21:03 - Sora非常吸引人。

21:05 这是一个早期非常有趣的实验，

21:08 它教会了我们许多东西，

21:09 不仅是关于创意工具，

21:12 更重要的是关于AI时代的消费者社交可能是什么样。

21:14 从数据来看，它发布时反响巨大，

21:15 曾连续20天登顶美国App Store，

21:19 这很难做到。

21:22 这意味着你每天大约需要

21:24 15万次下载才能排名第一。

21:27 这下载量很高。

21:29 它实际上比Chachi BT更快达到一百万用户。

21:31 这是个巨大发布。

21:33 事实上，我认为很多人低估了

21:34 它依然有大量活跃用户。

21:36 Sensor Tower数据显示日活有三百万，

21:37 这相当不错。

21:38 Sora的新下载量有所下降，

21:40 可能是因为，

21:42 它11月下载峰值达六百万次每月，

21:45 现在大约一百五十万。

21:46 我认为Sora的成功关键是

21:48 它是个非常好的视频模型。

21:52 它创新并引入了“戏份”（cameos）概念，

21:54 即真实人物

21:57 可以授权自己的形象给Sora，

21:59 让自己和别人生成自己的视频。

22:02 很多人早期制作朋友的搞笑视频。

22:03 杰克·保罗因率先投入Sora

22:05 一炮而红。

22:07 你能看到大量疯狂的杰克·保罗视频，

22:11 我们写过报道。

22:14 是的。

22:15 老实说，他做得很好。

22:17 是的，是的。

22:19 我认为Sora的问题是

22:21 内容可以导出，

22:24 用户会分享到TikTok，

22:25 或Instagram Reels，

22:27 或YouTube，

22:29 在那里它要和人类最佳内容竞争。

22:30 所以整体内容流体验更优，

22:31 因为你看到的是人类和Sora的双重最佳，

22:34 而非仅仅是Sora的最佳。

22:35 我认为我们还没见过纯AI内容的社交产品成功。

22:36 情感投入在某些方面降低了。

22:37 我想我们会看到类似的例子，

22:38 Sora仍有显著使用量，

22:42 作为创意工具带来收入，

22:45 但作为社交应用没那么成功。

22:47 对。

22:49 我不知道是否会有大型AI原生社交网络，

22:50 但目前还看不到它的模样。

22:54 会很有趣。

22:57 我们经常讨论，

22:59 每个社交产品都有自己的身份玩法。

23:00 是的。

23:05 Instagram上的是最火的，

23:07 在X上是最有趣的，

23:11 而在Sora上，涌现的身份玩法是成为最有趣的人。

23:14 是的。

23:18 我觉得这也是内容难以跨界的原因之一，

23:20 因为两者评判“有趣”和“优秀”的标准完全不同。

23:21 同意。

23:23 如果让我想象他们可能找到的细分领域，

23:24 他们现在与迪士尼等大媒体公司达成了多项协议。

23:28 如果Sora是制作

23:30 授权的粉丝视频的唯一平台，

23:31 呈现受欢迎的角色和娱乐人物，

23:32 那将非常有趣。

23:35 完全正确。

23:35 但这还很早，

23:38 我们还在观察它的发展。

23:39 还很早。

23:41 我知道。

23:42 我们可以这么说。

23:43 是的。

23:44 我们谈话时必须提到代理，

23:46 OpenClaw, Manis, Genspar, Moldbook。

23:49 给我们概述一下

23:50 过去60天代理领域发生了什么

23:51 报告告诉了我们什么？

23:53 我说过去，

23:55 甚至过去六个月，

23:57 更准确说是报告中最近两个月，

23:59 是我见过最有趣的时期。

24:02 OpenClaw实际上，正如你将看到的，

24:06 没有上榜，因为它在二月爆发。

24:08 而我们的数据截止到一月。

24:09 但我们提取了二月的数据，

24:10 如果符合条件，

24:12 它会排名我们网络榜单第30名，

24:13 这是一个相当大的首次亮相。

24:14 We can say that.

24:15 Yeah.

24:16 We can't have this conversation without talking about agents,

24:18 open claw, manis, genspar, moldbook.

24:22 Give us an overview of what has happened

24:24 in the last 60 days in the world of agents

24:26 and what does a report tell us?

24:27 I think this is mostly why I say the last,

24:29 you know, even six months,

24:31 but actually even two months of this report

24:33 have been like the most interesting

24:35 that I think we've seen.

24:36 So open claw actually, as you'll see,

24:39 is not on our rankings because it blew up in February.

24:42 Our data ends in January.

24:43 But we did pull the data for February.

24:46 And if it had been eligible,

24:48 it would have been number 30 on our web list,

24:50 which is a pretty big debut.

24:53 我认为关于Open Claw真正有趣的地方

24:55 是它的使用量在技术社区中持续加速增长

24:58 。

24:59 所以现在，我觉得它是第一名，

25:01 GitHub历史上的明星项目。

25:02 它超过了React，也超过了Linux。

25:04 哇，真是非常非常有趣。

25:05 是的。

25:06 非常令人印象深刻。

25:08 但就整体新用户而言，

25:11 增长有点停滞。

25:12 我们查看了一下“开始使用”

25:15 或注册页面的访问情况。

25:16 从二月初开始，这个数据几乎周周持平，

25:20 我认为这表明，

25:21 它是一个了不起的技术产品，

25:24 但还没有完全扩散到非技术人群，

25:27 也就是更广泛的人群。

25:28 他们被OpenAI收购了。

25:32 所以如果要我猜测，

25:33 或者说我希望看到OpenAI做的是

25:35 将Open Claw产品化，

25:37 变成主流消费者可用的东西。

25:39 我也觉得Open Claw架构背后的理念

25:43 激发了许多其他创业者。

25:46 我们每天会听到多少创业者pitch，

25:49 他们都说，

25:51 “我想成为这个领域的Open Claw。”

25:53 完全正确。Open Claw让我意识到这是可能的。

25:54 是的。

25:55 所以我认为Open Claw本身

25:58 会继续成功，成为巨大产品。

25:59 我猜我们会看到更多

26:01 针对不同用例的Open Claw垂直版本。

26:03 是的，这太有趣了，

26:05 因为Open Claw之所以这么有效，

26:07 是因为它可以跨所有模型，

26:09 向各个方向运作。

26:10 我有点好奇，如果它只提供单一模型，

26:11 是否会削弱Open Claw的价值，

26:13 这样它就成了Labs的对立面。

26:16 完全正确。

26:19 他们目前依然保持多模型，

26:21 至少我使用时是这样。

26:24 后续趋势如何我们拭目以待。

26:25 我认为保持多模型使用是明智的。

26:27 是的。

26:28 Manus是面向消费者级的Open Claw，

26:29 还是你如何区分这个团队？

26:31 是的，有人会这么说。

26:33 我实际上认为，

26:35 Manus进入了我们的网络榜单，

26:36 并且在榜单期间被Meta以超过20亿美元收购。

26:38 增长非常惊人。

26:39 他们从零达到1亿、2亿ARR，

26:42 用了大约六到九个月左右，

26:46 这在业内属于顶尖水平。

26:48 我认为Manus成功的原因是，

26:49 它是首个能够相当自主操作，

26:52 跨产品和平台的消费级智能代理。

26:55 你可以连接邮箱，它能浏览网页。

26:59 它能做幻灯片，也能做表格。

27:02 我早期花了很多时间试用，

27:04 比如一年前的ChatGPT操作助手

27:07 或谷歌的Project Mariner。

27:10 它们没有一个可靠。

27:13 Manus在代理的可靠性和易用性上是突破性的。

27:17 我觉得它被收购非常有意义，

27:19 显示了未来的发展方向。

27:23 当人人具备这代理能力时，

27:25 你可能会想，如果它基于核心底层模型，

27:27 作为一个通用产品，

27:30 它其实更适合由Meta或谷歌这类大公司分发，

27:33 而非独立创业公司。

27:35 如果你做的是垂直领域产品，情况就不同了，

27:39 但谷歌如今有资源做出Manus这样的产品，

27:43 创业公司很难竞争。

27:45 大公司确实有很多优先事项，

27:48 不会做到每个领域都一流，

27:52 但这也是我对超广泛的消费者AI应用更为谨慎的原因，

27:55 因为大公司覆盖面广，

27:59 他们本身就有IT批准和企业合同的优势。

28:00 - 对，对，有趣的是我们跨过了

28:02 这样一个文化门槛，

28:06 Manus在产品广度上曾被视为不太明显的赌注。

28:10 现在看起来，他们像是生活在未来。

28:12 - 是的，完全正确。

28:13 他们显然是一个了不起的工程团队，

28:16 产品质量领先市场三到六个月，

28:18 这很难做到，尤其面对成千上万的自由研发者竞争。

28:19 - 完全同意。

28:22 让我们通过这个话题引入

28:26 关于其他水平AI产品的讨论，

28:28 以及那些超越网页界面的应用。

28:30 - 是的。

28:32 - 你意思是？

28:34 - 是的，这是个巨大主题。

28:37 我每天接触的AI产品中，

28:39 很多其实是桌面应用，

28:43 比如Granola、语音转写工具、Cloud Co-work之类。

28:45 这对我们报告来说是个方法论问题，

28:47 因为我们能很好地追踪网站访问，

28:48 也能很好追踪首次下载桌面应用，

28:49 和移动端使用情况，

28:52 但不能太精准地追踪桌面端使用量。

28:55 我认为随着AI产品愈发复杂，

28:57 在专门应用里运行，

28:59 许多会基于桌面，

29:00 因为能与文件交互，也更随时可用。

29:01 这趋势会越来越显著。

29:03 所以后续我们得想办法并行追踪，

29:06 依靠网页、移动端使用量，

29:09 以及收入排名，

29:10 这将是明智的做法。

29:10 因为像Cursor这类，

29:12 一些收入最高的消费级准专业AI应用，

29:15 网页端使用很少，

29:17 大部分都在专用应用内。

29:19 - 是的，非常有趣。

29:22 这也说明OpenAI发布Atlas，

29:25 Anthropic发布Co-work，

29:29 反映了他们的优先级所在。

29:31 - 是的，完全同意。

29:33 AI浏览器本身就是一个有趣的话题。

29:34 我觉得我们仍处于早中期阶段，

29:37 还不知道它会如何发展。

29:40 AI原生浏览器的理念正确，

29:43 如果AI能一直在线，随时可用，

29:45 在你花大量时间浏览的地方，

29:48 这是个好机会。

29:50 Perplexity Comment实际上率先做到了这点。

29:51 - 是个很棒的产品。

29:53 - 是的。

29:56 有趣的是，

29:59 Comment和Atlas下载页面访问量高峰比较，

30:02 Comment是Atlas的五倍，

30:05 这很惊人，因为ChatGPT用户量庞大。

30:07 - 是的，我也觉得如此。

30:09 - 我们看到Comment和Atlas都有忠实用户，

30:12 但对于普通用户来说，

30:13 更换浏览器的成本不低，

30:16 因为你已有固定工作流程，

30:19 自然会打开那个浏览器。

30:20 所以不仅要功能相当，

30:22 AI浏览器必须有一两项杀手级功能，

30:26 且容易被普通人设置和使用。

30:27 我觉得这还没完全实现。

30:28 - 真的很有趣，

30:30 六个月前Sam在一个播客中说，

30:35 有人问他，

30:38 最让你惊讶的是什么？

30:40 他说，是世界变化没想象中大。

30:43 如果看人们大规模使用ChatGPT的趋势，

30:47 仍主要用于作业和类似Google的查询，

30:51 以及一些陪伴功能，

30:54 从某种意义上说，像浏览器那样，

30:56 可以引导用户走不同路径。

30:59 你怎么看普通人今天如何使用AI？

31:00 - 我有几点想法。

31:00 首先，我觉得青少女

31:03 是观察消费端动态的最佳来源。

31:06 - 你没教我这一点，是的。

31:09 - 他们是最大消费成果的早期拥护者，

31:11 不久前Pew研究有个报告，

31:15 专门研究青少年如何使用AI。

31:16 - And I think what we've seen is like

31:18 comment and Atlas still have very dedicated,

31:22 excited user bases,

31:24 but for the average consumer,

31:26 the switching cost of a browser is non-trivial,

31:29 just because like you have workflows set up,

31:32 you naturally just open this one app.

31:35 And so it not only has to be like feature parity,

31:37 there has to be one or two features of the AI browser

31:40 that are really killer and that are easy enough

31:43 for the average person to set up and access.

31:45 And I don't think that we've seen that quite yet.

31:48 - You know, it's really interesting

31:49 because Sam said, I think six months ago in a pod,

31:51 you know, somebody was asking him,

31:53 what has surprised you the most?

31:54 And he said, it's that the world hasn't changed more.

31:57 And if you look at the trends around

31:58 how people are using Chachi BT at scale,

32:00 it's still, you know, homework and Google like queries

32:03 and a little bit of companionship,

32:05 in a sense, something like a browser

32:07 that gives you an opportunity

32:08 to point the user in a different direction.

32:10 What's your view on how the average person

32:12 is using AI today?

32:13 - Yeah, I think a couple of things.

32:17 So one, I feel like teenage girls

32:19 are like the best source of what is happening in consumer--

32:22 - You haven't taught me this, yes.

32:23 - What will be happening in consumer?

32:24 If you look at all of the biggest consumer outcomes,

32:26 like they were the early adopters of all of these products.

32:30 And so there was actually a Pew Research Study

32:32 fairly recently on how teenagers are using AI.

32:35 现在终于，我觉得这是第一次，

32:38 超过一半的人承认

32:40 使用它来做作业。

32:41 所以真实数字可能是99.99%，

32:44 但其中一些人不想惹父母生气。

32:46 - 嗯哼。

32:48 - 38%现在将它用作创作工具。

32:51 比如编辑图片、编辑视频，

32:53 生成图片和视频。

32:55 然后这有一个稍微增长的长尾趋势，

32:58 但我认为最终会成为最大行为之一。

33:02 16%用它进行轻松聊天，

33:05 不是强烈的陪伴产品，

33:08 而是找个人聊聊天。

33:10 然后12%用它做情感支持

33:13 和提供建议。

33:14 我认为所有这些用例最终都会趋近于

33:17 大约100%。

33:21 这些行为可能是目前

33:23 产品服务较少，但未来会被满足的，

33:26 无论是在聊天机器人上，还是

33:27 独立产品上。

33:30 另一个我关注的大事是

33:34 代理。

33:35 我觉得基本上-- - 我们的少女会用代理。

33:39 来吧。 - 情况是这样的。

33:40 我认为代理类似于1990年，

33:44 互联网公司被称为点com公司，

33:47 或者点com成了科技公司的标识。

33:49 - 对。

33:51 - 我觉得代理也会是这样。

33:53 最终每个科技公司

33:55 都会是点com公司。

33:56 我觉得每个AI公司

33:58 以及每个科技公司都会成为代理公司，

34:02 因为模型的发展趋势就是如此。

34:05 如果你能给用户

34:07 提供结果，

34:10 而不仅仅是输入，这作为软件产品更有吸引力。

34:12 所以是的，我认为13岁女孩会用代理，

34:15 但她们不会把它们当作代理。

34:18 但我觉得这会解锁AI在其他消费领域的许多用例，

34:21 比如财务、

34:24 医疗、旅行规划、复杂的趋势购物，

34:29 在有代理之前，数据太多，

34:32 你必须自己去搜集、可靠执行，

34:35 跨系统操作几乎不可能。

34:38 现在这变得可能了。

34:39 所以我认为接下来几个月

34:40 这些其他用例会爆发式增长。

34:43 - 你觉得这要多久才能完成？

34:45 我是说，12个月内大家都会用自己的OpenAI？

34:47 还是五年后？

34:48 这是什么错误的思维模型？

34:49 当我们六个月后在下一个Top 100大会讨论时，

34:50 世界会是什么样子？

34:52 - 我感觉每次我预测的事情

34:55 都比预期发生得快，

34:57 我认为我们每天都能看到这一点，

34:59 创业公司成长速度前所未有。

35:03 但文化变革

35:04 和文化接受速度会比技术变革慢。

35:08 因此我们会持续看到

35:11 这波早期浪潮，通常是技术人员，

35:13 有时非技术人员做先锋，

35:17 六个月后其他人跟进。

35:19 一个我非常兴奋的例子是

35:23 语音，我们谈了很多。

35:26 - 确实谈了很多。

35:28 - 在我看来，这是信息量最大、

35:30 质量最高的媒体形式之一。

35:34 你每日所做的很多事情，

35:36 实际上是基于你说的话上下游运作。

35:38 我觉得过去六个月，第一次见到工程师，

35:41 现在其他技术人员开始用语音输入。

35:46 现在很多公司会议

35:48 几乎成了录音并用AI转录的常态。

35:51 不管是语音输入，

35:55 还是会回答问题或帮你办事的语音助手，

35:58 我觉得接下来六到九个月

36:01 会普及到普通消费者。

36:03 - 真是非常有趣。

36:05 讲到最后，你能聊聊记忆吗？

36:07 - 好的。

36:10 - 你怎么看记忆的发展？

36:12 - 我们之前提过，现在

36:14 记忆功能有点突兀，

36:15 特别是Claude和ChatGPT这方面做得很好。

36:19 就连谷歌的Gemini也推出了“个人智能”，

36:20 它能从文档、邮箱等获取你的信息，

36:24 用AI服务你所有应用。

36:24 正如我说的，目前可能稍微让人不适应，

36:25 因为很多人跟AI谈论生活工作各方面，

36:29 有时AI可能会无意中

36:31 触及它知道你的信息，

36:35 试图更好帮助你，但在错误场景下。

36:36 所以这方面还有很多基础设施工作要做，

36:38 就是如何在各种场景里区分“你是谁”。

36:42 一旦解决，

36:45 我认为记忆会成为

36:48 AI产品的核心优势之一。

36:50 无论是它们自身的记忆，或ChatGPT借用记忆，

36:54 两年后你开始用的产品，

36:55 如果不感觉它了解你，

36:58 那你会觉得它有缺陷。

37:00 产品的上手体验，

37:03 几年后应该是不再存在的概念。

37:05 我认为记忆功能能实现这一点。

37:07 我个人的体会是，我每天都和多款AI交流，

37:11 它们的互动方式和价值，

37:13 在使用两三个月后提升了很多，

37:17 比刚开始用时好太多。

37:19 - 太不可思议了。

37:24 好吧，我不知道未来如何，

37:27 但它会奇妙又奇怪。

37:30 我很期待。

37:31 - Bolivia，非常感谢你。

37:34 今天能聊聊，

37:38 还有这份报告，真的很开心。

37:39 有什么最后想说的吗？

37:40 - 没有，我只是很期待大家读这报告。

37:44 里面有很多有趣的数据，

37:46 六个月后肯定会完全不同。

37:48 到时候我们再见。

37:50 - 非常激动人心。

37:53 告诉我们你的想法，

37:55 谢谢收看。

37:56 - 谢谢。

37:57 （欢快音乐）

37:58 I'm excited for it, yes.

38:00 - Bolivia, thank you so much.

38:01 It was super fun to actually have this conversation today

38:04 and go through the report.

38:05 Any closing comments?

38:06 - No, I'm just excited for people to read it.

38:08 There's a lot of interesting data in there next time

38:10 and I'm sure it will look wildly different six months from now.

38:13 So we'll be back then.

38:14 - Really exciting.

38:15 Well, tell us what you think

38:16 and thanks for checking us out.

38:17 - Thank you.

38:23 (upbeat music)

0:00 [MUSIC]

0:04 Olivia, welcome.

0:05 >> Thanks for having me.

0:06 >> It's the most exciting time of the year,

0:08 which is the top 100 report is coming out today, I think.

0:11 Is that right? >> Yep.

0:12 >> It's been six editions over three years.

0:15 Talk to us about what's the same, what's changed,

0:18 what's your excitement level, what's up with the report?

0:20 >> Yeah, in many ways, so much has changed,

0:23 and there's been just an incredible amount of growth

0:25 since the first time we put out this list in 2023.

0:28 On the other hand, from a macro level, we're still so early.

0:32 Chachibiti is by far the biggest global AI product,

0:36 and still only 10% of the global population is using it on a weekly active

0:41 basis.

0:41 So there's a lot more to come.

0:43 I do think this past six months has been maybe my favorite time

0:47 and the most exciting time because of the shifts that we've seen.

0:51 One of them has been at the race for the consumer is really heating up.

0:54 So Chachibiti, of course, but also Gemini and Claude are kind of doubling down

0:59 on their own ICP within consumer and prosumer.

1:03 And I think we're starting to see how these platforms might have

1:06 compounding advantages over time.

1:08 And so that makes it especially kind of existential or interesting

1:12 of who is acquiring the most users.

1:14 And then on a related note, this was actually the first issue

1:17 that we included products that were non-AI native,

1:20 but are now majority AI-enabled.

1:22 So things like Canva, Notion, Free Pick.

1:25 Notion actually announced that now they think half of their new ARR

1:29 is driven by AI first features, which is very cool.

1:32 And then lastly, I think we've seen a big expansion of AI

1:36 outside of just like the website or app prompt box.

1:39 So we have all of the browsers that have come out, like, you know,

1:43 DIA, Comet, Atlas.

1:45 We have Claude and Excel, PowerPoint and Chrome.

1:48 And then we have desktop apps like cursor, whisper flow, granola.

1:53 And so there's been just a really kind of exciting explosion

1:56 in the ways that people are using AI.

1:58 So exciting.

1:59 There's a ton to cover here.

2:00 So let's start with the big foundation models.

2:02 Can you talk a bit about what you think

2:04 are the respective areas of specialization for Gemini, Claude?

2:07 And then, of course, Chatsheet BT,

2:08 because it feels like it's been a rising tide story more

2:11 than these models trading off with each other.

2:13 Yes, I agree.

2:14 Despite the drama maybe of the past week

2:17 where we have Katy Perry taking sides on Twitter in the LML

2:21 or which is something that I didn't ever see coming,

2:24 I think at a base level still, if you look at AI usage,

2:27 like Chatsheet BT is a very, very clear winner.

2:29 So on web, they're 2.7 times bigger than Gemini.

2:33 On mobile, they're 2.5 times bigger than Gemini.

2:37 And then despite, again, like the kind of tech Twitter discourse,

2:41 Claude, they're almost 30 times bigger than Claude on web

2:45 and almost 80 times bigger than Claude on mobile.

2:47 So we had seen that Sam Altman tweet

2:49 back in the Super Bowl ad wars era.

2:51 The Texas tweet.

2:52 Yes, he was like, we have more people using Chatsheet BT,

2:56 free version in Texas than Claude has,

2:58 like all users globally, which is true.

3:00 That being said, I think we are seeing,

3:04 I don't think bifurcation is the right word,

3:06 but maybe expansion in the number of products people are using

3:10 and what they're using different products for,

3:13 which has kind of changed the market share a little bit.

3:15 Claude in particular is really doubled down on prosumer

3:19 with things like co-work, Claude code,

3:21 Claude and Excel and PowerPoint.

3:23 If you actually look at the app stores

3:25 that are emerging on Claude and Chatsheet BT,

3:28 they both have 200 plus apps,

3:30 but there's only 11% overlap.

3:32 Like Claude is very much doubling down

3:34 on like premium data sources, research tools,

3:37 science tools, financial data.

3:39 And Chatsheet BT is really doubling down

3:41 on like consumer marketplaces, travel, nutrition,

3:44 consumer finance, things like that.

3:46 And then Gemini is kind of in its own little corner as well.

3:50 And the traction has largely been driven

3:52 by creative tools there.

3:53 So if you look at their kind of active users

3:55 and paying users, it's nearly perfectly correlated

3:58 to releases of like VO3, Nano Banana One, Nano Banana Pro,

4:02 Nano Banana Two, they're doing a little more in prosumer,

4:06 they're adding AI to Gmail, Sheets, Calendar,

4:11 but that's all being captured

4:13 by like their existing products

4:15 versus like a net new experience.

4:17 - Maybe let's dig into the app store dynamic a little bit

4:20 because that's so fascinating.

4:22 Can you talk about the bull case for Chatsheet BT

4:25 with I think what they call the apps directory?

4:26 - Yeah, yeah.

4:28 I think the approach we're seeing with Chatsheet BT

4:30 and Sam said this himself on Twitter is like,

4:32 we wanna be the AI for everyone.

4:34 And that means that they're trying to acquire every consumer

4:37 and they'll monetize them in different ways.

4:39 So like I think Claude has been very clear

4:40 that they're just gonna monetize via subscriptions,

4:43 which is great for people and companies

4:45 who can pay for subscriptions, but it won't be everyone.

4:48 I think you see that with the plugins

4:50 that they're leaning into, which are like paid, high ACV,

4:54 like work data tools and data services.

4:56 - Similar labs, things like that.

4:57 - Yeah, things like that.

4:58 Like pitch book, like things that you would use

5:00 if you're an investor or a scientist, a mathematician.

5:04 And Chatsheet BT I think is going more of the

5:08 somewhat of a Google type approach

5:10 in that they're building things

5:12 that like the average person will want to use.

5:15 And maybe a smaller percentage of those convert

5:18 to subscriptions right now, but they will be able

5:20 to monetize those people through ads

5:23 and probably also I would guess through transactions.

5:26 Like if they're building the gateway to like book a trip

5:30 or do all of these other kind of long tail consumer purchases,

5:35 hypothetically they should eventually be able to take

5:37 some kind of cut of that, at least for the traffic

5:40 that they're driving.

5:41 And so I think that that is the bull case

5:44 for the Chatsheet BT App Store that isn't yet showing up

5:47 in the data that will probably become like even more evident

5:50 in the next year or two.

5:52 - Yeah, it's really interesting 'cause it touches

5:53 on your point in the report about compounding advantages

5:56 and how context compounds.

5:58 Can you talk a little bit about that concept

6:00 and then what's your proxy in terms of a metric for it?

6:02 Is it session time, is it number of sessions,

6:04 is it the amount of data you've provided

6:06 or is there something else?

6:07 - Yeah, this is a really exciting question to me

6:10 because I think thus far with these horizontal LLMs

6:13 like Chatsheet BT, Claude, Gemini, perplexity,

6:17 we've kind of lived in a world where the context

6:20 and the memory is somewhat easily exportable.

6:22 Like Claude ran a campaign around this recently.

6:25 But I think there's gonna be increasing lock-in

6:28 and I do think that probably actually benefits

6:30 the broader, more horizontal tools like Chatsheet BT

6:33 for a few reasons.

6:35 So I think one, we've already seen Chatsheet BT focus on

6:39 or start to build out products where you interact

6:42 with other people on them through the platform.

6:44 So the group chats, like imagine if you,

6:46 if there's an even more successful version

6:48 of Chatsheet BT group chats

6:50 and all of your friends are on there,

6:51 then if you wanted to turn from Chatsheet BT,

6:53 you'd also have to convince them all to go through another product.

6:56 - Exactly.

6:57 I would say the second one is kind of also

7:00 like an Apple Google comparison in that

7:02 as these app stores emerge, it is likely that developers

7:05 might start to concentrate their time and effort

7:08 in who they build for in the most sophisticated way,

7:12 who they ship to first, depending on who has the most users

7:16 or maybe in some cases who's the most willing to pay,

7:19 but for a lot of these consumer tools,

7:20 it'll be who has the most users.

7:22 So I think that also benefits Chatsheet BT.

7:25 And then the other thing probably that I'm most excited

7:27 for this year that Sam Altman had kind of hinted at

7:31 is this like authentication with Chatsheet BT layer.

7:34 So essentially you'd be able to log in

7:36 with your Chatsheet BT account

7:37 and take like your memory and your tokens with you.

7:42 And then that other product would be able to kind of borrow

7:45 those things to be even more powerful and helpful for you.

7:48 And if that's the case, then you're wanting

7:50 to have more of your core identity live on Chatsheet BT

7:54 because then it can lend it to these other tools

7:57 that are even better for you.

7:58 - It's so smart and it really plays to their advantages

8:01 in that they have signups for 900 million people.

8:05 And then the third party developer ideally

8:07 would not want to pay for the inference.

8:09 So the user can bring their inference capacity with them.

8:12 There's an advantage for the developer.

8:13 Chatsheet BT gets the lock in.

8:15 The user gets the benefit of personalization

8:17 and it all kind of works.

8:18 - Yes, I totally agree.

8:19 The one question mark I still have on this

8:21 that I think could play both positive and negative

8:24 in terms of increasing lock in for the consumer product

8:27 is what your work goes with,

8:30 like what your enterprise contract is.

8:33 So for example, in some ways, it's good for me

8:36 if we, if my company uses Chatsheet BT for work

8:40 because then I know how to use the product.

8:42 And as a normal consumer, they might have tried

8:44 one or two AI products.

8:45 So they're more likely to be comfortable

8:47 and keep using something that they've already used.

8:49 On the other hand, some people might not want to mix identity

8:53 and mix memory across their personal and work use cases.

8:56 - True.

8:57 - And so I'm really interested.

8:58 I think OpenAI hinted at this recently,

9:00 but I'm really interested in how we kind of segment memory

9:04 across different personas that are within yourself

9:08 that are using these products.

9:09 - Don't cross the streams.

9:11 - Yeah, exactly, exactly.

9:12 - Well, maybe actually switching gears

9:14 to Gemini for a moment.

9:15 You know, I think about the kind of just the vibes

9:18 around Google with their early AI products,

9:21 the Bard, which they'll never live down.

9:24 - Some top times there.

9:25 - So where we are today with products like Nana Banana,

9:27 like even naming it Nana Banana is such a perfect microcosm

9:30 for how far Google has come.

9:32 - Yeah.

9:33 - And it seems like they have a lot of intentions

9:34 around multimodality.

9:35 - Yeah.

9:36 - What's your assessment of their approach?

9:38 - I've been impressed.

9:40 I think they have been hesitant, maybe in some ways,

9:44 more hesitant in same ways is exactly what we would expect.

9:47 So to kind of bake AI into the core features

9:51 because there's a risk of either like cannibalizing

9:53 their own product or like there's so many people

9:56 who have used these tools for 10, 20, 30, 40 years.

10:00 And so the switching costs there is like a little bit high.

10:04 They don't want to scare users when AI is suddenly

10:06 popping up and everything, which I understand.

10:09 But what they've done a really, really good job at

10:11 is these new creative products that are basically

10:15 very model driven from the DeepMind team

10:18 who I think is generally fantastic.

10:20 - I think Notebook LM was actually the first look at this

10:23 and that was something truly new in like consumer AI audio.

10:27 And now we have the image and video models.

10:31 So in some ways with the big company like this,

10:32 they kind of have to get over, get out of their own way

10:35 in terms of being able to actually innovate.

10:38 And it seems like they are, but you also worked at Google.

10:40 So I'd be curious for you.

10:41 - It's interesting to just, I'm glad you brought Notebook

10:44 'cause Notebook is sort of this green field area

10:47 within product area within the company.

10:48 So you don't have 10 VPs fighting over it.

10:50 And as a result, I think just the progress on Notebook

10:53 has been tremendous.

10:54 You know, they just launched a video generation feature

10:56 that helps visually demonstrate all the concepts

10:59 in your sort of workspace, which is cool.

11:01 Conversely, when you look at the existing product surfaces

11:04 like sheets or docs, there's just so much,

11:08 one sort of momentum and inertia from the past,

11:10 but then management overhead around those.

11:13 It's harder for them to do anything other

11:14 than the most obvious incremental thing.

11:16 - Yes, I agree.

11:18 We'll see what happens there in the next few years.

11:21 I feel like they're gonna put up a fight

11:23 on some of those products 'cause they don't wanna lose

11:25 that user base, but to your point,

11:26 they're already locked in with so many enterprises

11:28 that they might not have to do that much,

11:31 at least in the near term to kind of keep up.

11:33 - You know, implicit in this conversation

11:35 is we experience and talk a lot about AI in the West.

11:39 Talk a bit about the sort of global AI trends.

11:41 There was a few surprising things I saw in there.

11:43 - We kind of expanded our scope in terms

11:46 of what we looked at for this report,

11:47 which ended up being like very fun and interesting.

11:50 Two things that are probably obvious

11:54 in terms of how they differ from the rest of the world

11:57 would be Russia and China.

11:59 So Russia, I think China, everyone knows,

12:02 like a ton of AI products are kind of censored or banned.

12:05 And so almost all of the usage,

12:07 they actually have the lowest combined

12:10 Chachi, BT, and Gemini usage of any country.

12:12 It's only 15%.

12:14 So they're mostly using like Dalbau,

12:15 which is made by ByteDance, DeepSeek,

12:18 Quinn, Kimi, those kinds of models.

12:21 The somewhat of a surprise to me

12:22 was that Russia actually is a very, very similar story,

12:25 where they have also their own kind of parallel AI ecosystem

12:29 out of necessity because they have some level of sanctions

12:33 and things like that that prevent them

12:34 from using all the US-based tools.

12:38 So we've seen products like GigaChat and Yandex,

12:40 which are Russia specific built by Russian,

12:43 often state-affiliated companies,

12:46 have big, big usage there, and then DeepSeek.

12:48 So Russia is the number two market for DeepSeek

12:51 after China.

12:52 And so if you look at the kind of like per-country adoption data,

12:56 like, yes, there's some blips where like,

12:59 this country uses Claude a little bit more,

13:01 this country uses Gemini a little bit more,

13:03 but the two huge outliers are Russia and China,

13:06 and those are like big, big markets.

13:08 And so I think it's worth watching what's going on there.

13:10 - It's interesting though, because both Russia and China,

13:12 they're outliers because of restrictions

13:14 around how models can be used,

13:15 and maybe cultural preferences.

13:18 Are there any other countries that have geo-specific trends,

13:21 or is this a sort of global AI behavior set?

13:24 - Yeah.

13:25 I would say in terms of like model development,

13:28 proprietary model development that allows you

13:30 to deploy proprietary AI products,

13:34 most of that research is coming out of the US and China,

13:37 maybe a little bit out of Russia.

13:39 I think we are seeing a few kind of native ecosystems

13:43 in other places.

13:45 I would, Korea has a couple of their own products

13:48 like Neighbor and Cookal that have built out

13:50 nice kind of LLM interfaces.

13:52 India is probably the other one that I watch really closely,

13:55 just because there's so many people

13:57 that you can have standalone big companies focus on India.

14:01 The other interesting thing about India

14:02 is there's so many different languages,

14:04 like such a range that both LLM products

14:07 and even voice products don't necessarily support very well.

14:11 Like it's a worse experience if you're a primary user

14:14 of one of those languages

14:15 and you're trying to use something like a chat UBT.

14:18 So so far we haven't seen a huge amount of variants there yet,

14:22 but I would not be surprised maybe to see more founders,

14:25 even from the US, like targeting the Indian market for AI.

14:28 And then the other thing I wanted to mention,

14:31 we did for the first time also kind of like a heat map

14:35 essentially of which countries are adopting AI the most

14:39 and the least on a per capita basis.

14:41 So we looked across like the 10 biggest LLM products

14:44 to see on web and mobile to see what this might look like.

14:48 So Singapore is number one.

14:49 - Crazy. - Yes.

14:50 Then Hong Kong, then the UAE, then South Korea.

14:54 The US is down at number 20.

14:56 So not super low, not incredibly high.

14:59 Russia and China are like very far down the list, like sub 50.

15:04 And there's a lot of interesting stories,

15:06 I think that live in that data.

15:09 The first one is if you think about those top five,

15:11 like Singapore, South Korea, Hong Kong,

15:14 it's a very like the demographics of the workforce

15:17 are very like tech first white collar high skill.

15:20 And the US has a giant chunk of jobs

15:23 where AI hasn't really touched them yet,

15:25 like retail and transportation and some of these other things.

15:29 I think also the cultural norms around AI

15:33 are shockingly diverse.

15:36 If you're in the US, you have probably internalized this

15:39 ongoing angst and questioning around--

15:41 - Yeah, I was going to ask you about this 100%.

15:44 - Or AI is terrible for artists

15:47 or all of these other things that make people pick up

15:50 or not pick up AI. - Yes.

15:51 - There was actually a big survey last year

15:54 from Edelman, the global media company.

15:57 And the US had a fairly low rate of trust in AI.

15:59 It was like 32% and most of these other countries

16:02 that are high on the list are like 50, 60, 70%.

16:06 So that I think has also held the US back

16:08 despite the fact that we are where the biggest products come from

16:12 are per capita usage is lower than a lot of these other markets

16:14 that have maybe smaller populations

16:16 but have embraced it more.

16:19 - I think that's exactly right.

16:20 Now I was reading that in China,

16:22 the sort of favorability views on AI are 80%.

16:25 - Yeah. - 80% hold a favorable view.

16:27 And I know UAE and Singapore,

16:29 I think they've sort of culturally wired to be tech optimistic.

16:34 - Yes. - Which is an advantage.

16:35 - Yes, yes, definitely.

16:37 It's interesting to see some of these smaller countries

16:39 like the per capita adoption rate.

16:42 Like in the US, it's around probably a third of people

16:45 are monthly active users of something like a Chachi BT.

16:49 In some, even some of like the European countries

16:52 or Eastern Europe, it's like 50, 45, 60% on smaller bases.

16:58 But they've kind of embraced it more quickly than we have here.

17:02 - Yeah, really interesting.

17:03 You know, one thing that I'm sort of watching

17:05 and I'm interested in is as you look at the spectrum of AI

17:07 from the most functional,

17:09 almost like a Google search replacement

17:11 to the most cultural, creative, personal,

17:14 we should see more divergence country by country

17:17 because obviously the culture,

17:18 the movies they make in India

17:19 couldn't be more different than the movies they make

17:21 in China or the US. - Yeah.

17:23 - So why wouldn't their use of creative tools be different?

17:25 - Yeah, and this is honestly part of the reason why

17:27 we started looking at the geographic segmentation

17:30 in this report is because for the first two and a half,

17:33 three years of generative AI,

17:35 the vast majority of consumers were maybe interacting

17:38 with one product and now it's broadening quite a bit.

17:41 And I think that we will see more

17:43 of these market specific tools.

17:45 And if they capture enough of that market

17:48 like some of these Russian companies or Chinese companies,

17:51 they can actually surface up to the global list

17:53 if kind of the market is big enough.

17:55 Talk a bit about the evolution of creative tools

17:58 and how much do you think that is that a reflection

18:00 of culture, is that driving culture,

18:02 when do we cross that threshold?

18:04 - The creative tools trend has been fascinating.

18:07 I mean, obviously the first big generative AI product

18:10 was actually mid-journey, which came out before Chachi PC.

18:13 - True, that's right, yeah.

18:14 - And in our first few editions of the list,

18:16 it was very much dominated by creative tools.

18:19 And I've said this before,

18:21 but the creative tools benefit from kind of hallucination

18:24 of the early models because they produce things

18:27 that are more kind of surprising or beautiful or original.

18:30 And so for a while, those are the only things

18:32 working in consumer AI, really.

18:35 Now it's shifted a lot.

18:36 Creative tools are still a huge chunk of the list,

18:39 but like the type of creative tool

18:41 that is a standalone big business has changed.

18:45 I would say the biggest change

18:47 is we're seeing fewer standalone image generators.

18:51 A lot of this activity,

18:52 if you're making like a basic commodity image,

18:55 like a meme or a basic marketing image or an infographic,

18:59 like the core models in Chachi BT and Gemini

19:02 are quite good at those things now.

19:05 So the products that are still surfacing on the list,

19:07 like an I-U gram or a mid-journey

19:10 are either very aesthetically opinionated

19:13 or they have very more sophisticated workflows

19:16 that you can't get on something like a Chachi BT.

19:21 Contrasting that, I would say like music, voice, video,

19:26 all seem to be things that the model,

19:28 the biggest model companies have maybe invested less in.

19:31 And so we've seen players like Suno and music

19:33 and 11 labs in voice kind of completely break out

19:37 and rise to top 20, top 15 on the list

19:40 and then like hold their spot there over time.

19:43 And then there's like a compounding lock-in

19:45 from like the community and, you know,

19:46 the big base of enterprise customers and all of that.

19:50 Video is where I have the most questions.

19:54 OpenAI has been investing in it with Sora

19:57 and of course Google with Vio,

19:59 but the Chinese models are so good

20:01 because they can train on any data.

20:03 So Sea Dance 2 is probably the best example of this

20:08 where it's just kind of in some ways head and shoulders

20:10 above what the U.S. companies have thus far been able to do.

20:14 So I think we'll see, I think this actually benefits platforms

20:18 like a Korea where you can use all the models in one place

20:21 because my sister Justine wrote an article about this.

20:25 But the way video is shaping out,

20:26 there's it's unlikely to be like one model to roll them all.

20:29 And so you kind of need to be able to switch between them.

20:32 - That seems true of most of the model spaces, you know?

20:35 Chat models, creative models, even code models

20:38 have their areas of specialization.

20:40 You know, people talk about kind of ergonomics of opus

20:43 versus the accuracy of codecs.

20:45 And that's just, that's a trade-off.

20:47 You have to choose what tool you want to use for which problem.

20:50 - Yeah, absolutely.

20:51 - Sora is really interesting to me

20:54 because it represented both a sort of a big step forward

20:57 in the model, but also a really ambitious experiment

20:59 around social.

21:00 And there was data in the early days of Sora

21:03 like the percentage of people that we're creating,

21:05 which is dramatic, these 10x higher than we'd seen before.

21:08 You know, what's your kind of assessment

21:09 of the Sora social effort versus the model effort?

21:12 And where do you see that going?

21:14 - Sora is so fascinating.

21:15 And I think was a very interesting early experiment

21:19 that I think taught us all a lot about kind of

21:22 both creative tools, but also maybe more importantly

21:24 what consumer social in the AI era might look like.

21:27 So by the numbers, they had a massive launch.

21:29 They were number one on the App Store,

21:31 the US App Store for 20 consecutive days,

21:33 which is very hard to do.

21:34 It means you're probably getting,

21:36 to be number one on the App Store,

21:37 you probably have to get these days

21:38 150,000 daily downloads.

21:40 So it's like a high download volume.

21:42 And they actually hit a million users faster

21:45 than Chachi BT itself.

21:46 So like huge launch.

21:48 And actually, I think what a lot of people underestimate

21:52 is it still is very significant usage.

21:54 So three million dows per sensor tower,

21:57 which is not bad at all.

21:59 What has dropped off about Sora is the new download.

22:02 So there may be,

22:03 they peaked like six million a month in November.

22:05 It's looking like a million and a half now.

22:07 I think that what has really worked about Sora

22:11 is that it's a very good video model.

22:14 And they kind of innovated

22:15 and introduced this concept of cameos,

22:17 which is where a real person

22:19 can grant their likeness to Sora

22:21 so that they and others can generate videos of them.

22:24 So like a lot of people in the early days

22:25 were doing like mean videos of their friends.

22:27 Like Jake Paul went viral

22:29 'cause he was the first big celebrity

22:30 to like lean into Sora.

22:31 So you were seeing like insane Jake Paul videos

22:34 that we wrote.

22:35 Yeah.

22:36 I mean, honestly, good for him.

22:37 Yes, yes.

22:38 I think what worked less about Sora

22:42 is that because the content was exportable,

22:45 people would take it to TikTok.

22:47 They would take it to Instagram Reels.

22:49 They would take it to YouTube.

22:50 And there it competed against the best human made content.

22:54 And so the overall feed experience was just better

22:57 because you were seeing the best of both,

22:59 not just like the best of Sora.

23:00 I don't think we've seen a social product yet succeed.

23:05 That's like entirely AI content.

23:07 The emotional stakes are just feel lower in some ways.

23:11 And so I would imagine we'll see more examples like these

23:14 where Sora still has clearly very, very significant usage

23:18 in revenue as a creative tool,

23:20 but not so much as a social app.

23:21 Right.

23:23 And I don't know if there'll be,

23:24 there probably will be a massive AI native social network,

23:28 but we haven't seen what it looks like just yet, I would say.

23:30 It'll be interesting.

23:31 We discussed this frequently,

23:32 but every social product has a status game.

23:35 Yes.

23:35 Instads, it's maybe be the hottest and on X,

23:38 it's be the most interesting.

23:39 And it felt like the emerging status game on Sora

23:41 was be the funniest.

23:42 Yes.

23:43 And I think this is one of the reasons why it's hard

23:44 for the content to cross over.

23:46 Because it's just two different ways of judging

23:49 what is interesting and great.

23:50 I agree.

23:51 What they might do,

23:53 if I had to imagine where they might find more of a niche,

23:55 they have now inked a bunch of deals

23:57 with big media companies like Disney.

23:59 And so if Sora is the only place where you can make

24:02 like licensed like fan videos of like beloved

24:06 kind of characters and entertainment figures,

24:08 then like that's very interesting.

24:09 Totally.

24:10 But we're early, I think in how that rules out.

24:12 It's so early.

24:13 I know.

24:14 We can say that.

24:15 Yeah.

24:16 We can't have this conversation without talking about agents,

24:18 open claw, manis, genspar, moldbook.

24:22 Give us an overview of what has happened

24:24 in the last 60 days in the world of agents

24:26 and what does a report tell us?

24:27 I think this is mostly why I say the last,

24:29 you know, even six months,

24:31 but actually even two months of this report

24:33 have been like the most interesting

24:35 that I think we've seen.

24:36 So open claw actually, as you'll see,

24:39 is not on our rankings because it blew up in February.

24:42 Our data ends in January.

24:43 But we did pull the data for February.

24:46 And if it had been eligible,

24:48 it would have been number 30 on our web list,

24:50 which is a pretty big debut.

24:53 I think the really interesting thing about open claw

24:55 is the usage has just continued to accelerate

24:58 in the technical community.

24:59 So now it's, I think number one,

25:01 GitHub stars of all time.

25:02 It passed React, it passed Linux.

25:04 Wow, it's a really, really interesting.

25:05 Yes.

25:06 Very impressive.

25:08 But in terms of overall new users,

25:11 it's kind of plateaued.

25:12 So we looked at kind of visits to the get started

25:15 or sign up page.

25:16 And that is kind of flat week over week since early February,

25:20 which I think indicates that like,

25:21 it is an amazing product of your technical.

25:24 It has not yet fully escaped containment

25:27 to non-technical people,

25:28 which of course is like a bigger population.

25:32 They were acquired by OpenAI.

25:33 So if I had to guess,

25:35 or what I'd love to see OpenAI do

25:37 is build like productized open claw

25:39 into something that is usable for a mainstream consumer.

25:43 And I think we've also just seen the ideas

25:46 behind the open claw architecture

25:49 inspire so many other founders.

25:51 Like how many pitches do we take a day

25:53 where the founder is like,

25:54 I want to be open claw for this?

25:55 Absolutely. Open claw made me realize this was possible.

25:58 Yes.

25:59 And so I think we're going to see open claw itself

26:01 will continue to succeed and be a massive product.

26:03 And I'm guessing we'll see more

26:05 kind of like verticalized focus versions

26:07 of open claw for different use cases.

26:09 Yeah, it's so interesting

26:10 because it feels like one of the things

26:11 that makes Open Claw work so well

26:13 is it can operate across all models in all directions.

26:16 And I sort of wonder if it dilutes the value

26:19 of Open Claw to have it be sole model provided

26:21 and therefore it's sort of counterpositioned against labs.

26:24 Totally.

26:25 They've kept it, I think, multi-model for now,

26:27 at least in my usage.

26:28 So we'll see how it trends.

26:29 I think it would be smart to keep it that way for usage.

26:31 But yeah.

26:33 Is Manus the consumer grade open claw

26:35 or how do you distinguish the team?

26:36 Yes, some might say that.

26:38 And I do actually think,

26:39 so Manus made our web list.

26:42 And of course they had a $2 billion plus acquisition

26:46 by Meta also in the course of the list.

26:48 Incredible growth.

26:49 Like the ramp that they reported from like zero

26:52 to 100 million, 200 million ARR in the span of like

26:55 honestly six, nine months is really kind of best in class.

26:59 My view on why Manus was so successful was

27:02 it was really the first consumer grade agent

27:04 that could actually operate fairly autonomously

27:07 across products and platforms.

27:10 So you could connect email, you could have it browse the web.

27:13 And it could make slides, it could make spreadsheets.

27:17 I spent a lot of time in the early days

27:19 trying like this was a year ago Chachi BT operator

27:23 or Google's Project Mariner.

27:25 And none of them were reliable.

27:27 And Manus was a breakthrough in kind of agent reliability

27:30 and agent accessibility for the consumer.

27:33 I think the fact that they did the acquisition

27:35 is interesting in terms of where this is going.

27:39 In that once everyone has that agent capability

27:43 and you might imagine they will

27:45 if it's kind of based on the core underlying models,

27:48 then it's actually if you're such a horizontal product,

27:52 you may be better off with the distribution forces

27:55 of a meta or a Google or something like that

27:59 versus a standalone company.

28:00 That's definitely not true if you're building something

28:02 more vertical, but if you imagine that like Google now

28:06 has the resources to create a Manus,

28:10 then that's a really hard thing to keep fighting against

28:12 as a startup, I think.

28:13 And obviously the big companies have a billion different

28:16 priorities so they're not going to do everything

28:18 best in class.

28:19 But it's why I've generally been a little more cautious

28:22 about the very, very horizontal consumer AI apps

28:26 just because it's probably both in scope

28:28 for the bigger companies.

28:30 And they have the advantage of already having IT approval

28:32 and enterprise contracts and all of that.

28:34 - Right, right, it is interesting that we sort of cross

28:37 this cultural threshold though,

28:39 where Manus seemed like a non-obvious bet

28:43 in terms of just the breadth of the offering.

28:45 And now it seems like they're living in the future

28:47 a little bit.

28:48 - Yes, absolutely.

28:49 They were, it's obviously an incredible engineering team.

28:52 Like the quality of the product was like three,

28:55 six months ahead of the rest of the market,

28:57 which is not easy to do when you're competing with teams

28:59 of like thousands of free searchers.

29:00 - Totally.

29:01 Let's use this to kind of leg into a conversation

29:03 about other horizontal AI products,

29:06 things that live beyond the sort of web window.

29:09 - Yes.

29:10 - What are you saying there?

29:10 - Yeah, that has been a massive theme.

29:12 When I think about the products that I interact with

29:15 on a daily basis in the AI world,

29:17 quite a few of them are actually desktop apps,

29:19 like things like granola, voice dictation tools,

29:22 cloud co-work, those kinds of things.

29:25 And it does become a methodology problem for our report,

29:29 because we can track website visits very well.

29:31 And so we can track the first time

29:33 that they download the desktop app.

29:34 We can track mobile app usage very well.

29:37 We cannot track desktop usage that closely.

29:40 And I think that is increasingly,

29:43 as AI products become more sophisticated,

29:45 having them live in their own dedicated application,

29:48 much of which will run on desktop

29:50 because it can interact with your files

29:51 and it can be more ambient.

29:53 I think that's gonna happen more and more.

29:56 And so I think moving forward, finding us finding ways

29:59 to parallel track ranking these products

30:02 by web and mobile usage, but also by revenue,

30:05 is gonna be a pretty good idea.

30:07 'Cause if you think about things like cursor,

30:09 some of the consumer prosumer AI apps

30:12 that are generating the most revenue

30:13 have very few, very little usage on web.

30:16 It's almost all in kind of a dedicated app.

30:19 - Yeah, it's really interesting.

30:20 It also feels like the fact that opening

30:22 I released Atlas and anthropic released co-work

30:26 shows you where their priorities are.

30:27 - Yes, definitely.

30:28 I fully agree.

30:30 The AI browser debate is his own interesting thing.

30:35 I feel like we're still in the early to mid phases

30:38 of how that's gonna play out.

30:40 And I think the instinct behind an AI native browser

30:43 is right in that if you can have AI be

30:47 kind of always on, always available ambient

30:51 in where you're spending a lot of your time online,

30:54 like that's a good opportunity.

30:56 Perplexity comment, I think actually led the way there.

30:59 - It's a great product.

31:00 - It's a great product.

31:00 And the interesting thing is if you look at

31:03 kind of the highest spike for comment and Atlas

31:06 in terms of visits to the download page,

31:09 comment is five times ahead of Atlas,

31:11 which is wild because Chachi BT's audience is like so massive.

31:15 - No, I miss, yeah.

31:16 - And I think what we've seen is like

31:18 comment and Atlas still have very dedicated,

31:22 excited user bases,

31:24 but for the average consumer,

31:26 the switching cost of a browser is non-trivial,

31:29 just because like you have workflows set up,

31:32 you naturally just open this one app.

31:35 And so it not only has to be like feature parity,

31:37 there has to be one or two features of the AI browser

31:40 that are really killer and that are easy enough

31:43 for the average person to set up and access.

31:45 And I don't think that we've seen that quite yet.

31:48 - You know, it's really interesting

31:49 because Sam said, I think six months ago in a pod,

31:51 you know, somebody was asking him,

31:53 what has surprised you the most?

31:54 And he said, it's that the world hasn't changed more.

31:57 And if you look at the trends around

31:58 how people are using Chachi BT at scale,

32:00 it's still, you know, homework and Google like queries

32:03 and a little bit of companionship,

32:05 in a sense, something like a browser

32:07 that gives you an opportunity

32:08 to point the user in a different direction.

32:10 What's your view on how the average person

32:12 is using AI today?

32:13 - Yeah, I think a couple of things.

32:17 So one, I feel like teenage girls

32:19 are like the best source of what is happening in consumer--

32:22 - You haven't taught me this, yes.

32:23 - What will be happening in consumer?

32:24 If you look at all of the biggest consumer outcomes,

32:26 like they were the early adopters of all of these products.

32:30 And so there was actually a Pew Research Study

32:32 fairly recently on how teenagers are using AI.

32:35 Now finally, I think for the first time,

32:38 over half of them are admitting

32:40 to using it for their homework.

32:41 So the real number is probably like 99.99%

32:44 but some of them didn't wanna get in trouble

32:46 with their parents. - Uh-huh.

32:48 - 38% are now using it for a creative tool.

32:51 So editing images, editing video,

32:53 generating images and video.

32:55 And then this like emerging slightly longer tail,

32:58 but I think will ultimately be amongst the biggest behaviors.

33:02 16% are using it for just like casual conversation,

33:05 like not the intense companion products,

33:08 but like just having somebody to talk to.

33:10 And then 12% are using it for like emotional support

33:13 and advice.

33:14 I think all of these use cases will like asymptote

33:17 around probably 100% ultimately.

33:21 And so those are behaviors that maybe have been less well

33:23 served by products so far and will be going forward,

33:26 whether it's on a chat should be tea

33:27 or whether it's on like a standalone product.

33:30 And then the other big thing that I'm looking out for

33:34 is agents.

33:35 Like I think basically-- - Our teenage girl's gonna use agents.

33:39 Come on. - So here's the thing.

33:40 I think that agents similar to like how in 1990,

33:44 an internet company was like a dot com company, right?

33:47 Or tech company like dot com was its own,

33:49 its own designator. - Right.

33:51 - I think that this is what's gonna happen with agents.

33:53 Where ultimately like every tech company

33:55 was a dot com company.

33:56 Like I think ultimately every AI company

33:58 and then every tech company is going to be an agenda company

34:02 because that's just where the models are headed.

34:05 And if you can deliver outcomes

34:07 and not just kind of inputs to your users

34:10 as a software product, that's so much more compelling.

34:12 So yes, I think 13 year old girls will be using agents

34:15 but they will not think of them as agents.

34:18 But I think it does unlock a lot of these other consumer

34:21 use cases of AI like finance,

34:24 healthcare, travel planning, complex trend shopping even

34:29 where pre-agents, there was just so much data

34:32 you had to go out and grab and do it reliably

34:35 and do it across systems that like wasn't really possible.

34:38 And now it is.

34:39 And so I think we're gonna see an explosion

34:40 of those other use cases in the next few months.

34:43 - How long do you think it takes to play out?

34:45 I mean, is everybody using their own open claw

34:47 in 12 months?

34:48 Is that five years away?

34:49 Is that the wrong mental model?

34:50 Like where when we have this conversation,

34:52 perhaps in six months at the next top 100,

34:55 what does the world look like?

34:57 - I feel like every time I predict something

34:59 it happens much more quickly than I would have thought

35:03 which I think is what we're seeing every day

35:04 and that startups are growing faster than they ever have.

35:08 I think people, they're still the cultural change

35:11 and the cultural adoption will be slower

35:13 than the technology change and what's actually possible.

35:17 And so I think what we'll continue to see

35:19 is this early wave of often technical,

35:23 sometimes not technical AI adopters

35:26 like lead the charge on a behavior

35:28 that then six months later everyone else is doing.

35:30 One good example of this that I'm very, very excited about

35:34 is voice, which we've talked about a lot.

35:36 - We have talked about voice.

35:38 - To me, it's like the most information dense,

35:41 high quality source of basically media that we have.

35:46 Like so much of what you do every day

35:48 is actually downstream or upstream of like what you say.

35:51 And we're, I think for the first time in the past six months

35:55 I've seen first engineers and now other people

35:58 within tech companies adopt things like voice dictation.

36:01 Now it's kind of almost a norm at many companies

36:03 that your meetings are gonna be kind of recorded

36:05 and transcribed by an AI.

36:07 Whether that's voice dictation,

36:10 whether that's like a voice pin that answers questions

36:12 or does tasks for you,

36:14 I think that is going to spread

36:15 to the mainstream consumer in the next six to nine months.

36:19 - Really, really interesting.

36:20 Maybe to close, can you talk a little bit about memory?

36:24 - Yes.

36:24 - Do you see that going?

36:25 - Yes, memories as we mentioned earlier right now

36:29 and it can be a little bit jarring

36:31 in that Claude and Chachi PT in particular

36:35 are very good at this.

36:36 Even Gemini, Google has launched something called

36:38 Personal Intelligence where it now can pull information

36:42 it knows about you from your docs, email, et cetera

36:45 to like serve you better with AI across all of the apps.

36:48 And like I said, it can be a little bit jarring now

36:50 because many people are talking to AI about everything,

36:54 personal and professional.

36:55 And so it can sometimes kind of inadvertently cross the line

36:58 of like what it knows about you

37:00 to try to help you better, but in the wrong context.

37:03 So I think there's a lot of work to do,

37:05 kind of on like the infrastructure side almost

37:07 of like how we sort out who someone is in every context.

37:11 Once that is settled,

37:13 I think that memory will be one of the core advantages

37:17 for AI products,

37:19 whether it's their own memory or like Chachi PT lending memory,

37:24 any product that you start to use two years from now,

37:27 if it doesn't immediately feel like it knows you,

37:30 it will feel broken.

37:31 Like the concept of like onboarding to a product

37:34 should not be something that exists in a couple of years.

37:38 And I think that that is something

37:39 that memory is really gonna enable.

37:40 I see it personally for myself where I talked to AI all day,

37:44 talked to several AIs all day.

37:46 And the way that they interact with me

37:48 and the kind of value that they're able to provide

37:50 has been so much higher two or three months in

37:53 and a days when you start using it.

37:55 - Incredible.

37:56 Well, I don't know what the future holds,

37:57 but it's gonna be weird and wonderful.

37:58 I'm excited for it, yes.

38:00 - Bolivia, thank you so much.

38:01 It was super fun to actually have this conversation today

38:04 and go through the report.

38:05 Any closing comments?

38:06 - No, I'm just excited for people to read it.

38:08 There's a lot of interesting data in there next time

38:10 and I'm sure it will look wildly different six months from now.

38:13 So we'll be back then.

38:14 - Really exciting.

38:15 Well, tell us what you think

38:16 and thanks for checking us out.

38:17 - Thank you.

38:23 (upbeat music)

Love

· 1d

“除非是灾难性的失败，否则失败本身无关紧要。”
——埃隆·马斯克

https://x.com/WealthCode99978/status/2035422563074003413?s=20

中文 English

0:00 那种接受失败的心态，你个人是怎么做到的——我说的是，那种

0:07 失败会

0:08 让我们很多人抓狂，但你好像并不受影响。

0:12 看起来你很

0:14 淡定。你能谈谈你如何做到接受这种失败的心态吗？

0:18 那种接受

0:19 失败的心态？当然。我们要放视频吗？不用。还是要？不用，

0:25 我们不应该放

0:25 视频。还不行。好的，好的。我觉得这些事情就是，有一个

0:35 特定的时间，

0:36 在这段时间里，你想要得到最好的一种净结果。所以，对于所有

0:46 你可以做的

0:47 动作，有些会失败，有些会成功，

0:53 而你想让这些动作的净

0:56 有用输出达到最大值。所以，我不用打棒球的例子，

1:08 就举个棒球的例子

1:09 吧。你知道，在棒球里，不是说你等那个完美的球、

1:13 等到特别好打的球，

1:15 然后给你三次机会，第三次他们说，好吧，

1:19 不行，

1:21 换别人上场。三次出局，

1:27 你不能再打了。

1:29 所以，你真正关心的是击球率是多少，得分如何，只会

1:38 有一定程度

1:39 的失败，但你希望你的净输出、净有用输出最大化。

1:47 失败基本上

1:48 是无关紧要的，除非它是灾难性的。

0:00 That mindset that embraces failure, how do you personally - I mean, those kinds

0:07 of failures would

0:08 drive a lot of us in this room nuts, but it doesn't seem to drive you nuts.

0:12 Seems like you're very

0:14 comfortable with it. Can you talk about the mindset that requires for you to be

0:18 that accepting of

0:19 that kind of failure? Sure. Should we roll the video? No. Should we not? No, we

0:25 should not roll the

0:25 video. Not yet. Okay, okay. Well, I think of these things as just, there's a

0:35 certain amount of time,

0:36 and within that time you want the best net outcome. So for, you know, all the

0:46 set of actions that you

0:47 can do, there's going to be, and some which will fail, some which will succeed,

0:53 and you want the net

0:56 useful output of your set of actions to be the highest. So, I don't have to use

1:08 like a baseball

1:09 analogy. Like, you know, baseball, I don't know if you just sit there and wait

1:13 for the perfect pitch

1:15 until you get a real easy one, that you can give you three shots, and the third

1:19 one, they say, okay,

1:21 and they get off the, they go back to the, put somebody else up there. So,

1:27 these three strikes

1:29 on the baseball, you know, not on bat anymore. So, what you're really looking

1:38 for is like, what's

1:39 the batting average? You know, how are you doing on score? And just, there's

1:47 going to be some amount

1:48 of failure, but you want your net output, net useful output to maximized.

1:58 Failure is essentially

2:00 irrelevant unless it is catastrophic.

Love

· 1d

#Openai Sam Altman 谈为什么在创业初期不应该追踪绝对用户增长量 “拥有少量真正热爱你的用户比拥有大量喜欢你的用户更重要。几乎所有创业公司都搞错了这一点。当然，你最终想要的是大量真正热爱你产品的用户，但这几乎是不可能的。”

https://x.com/StartupArchive_/status/2035748378152476784?s=20

中文 English

0:00 本课程有五节课专注于产品，因为一个出色的

0:03 产品是你所做的最重要的事情。

0:05 现在我想提到的是，当你考虑自己打造的产品时，

0:08 拥有少量真正喜欢你的用户，比拥有大量喜欢你的用户更重要。

0:11 几乎所有创业公司都搞错了这一点。

0:13 当然，你最终想要的是大量真正喜爱你

0:15 产品的用户。

0:19 这几乎不可能。

0:20 实际上你有两个选择。

0:23 要么深耕细作，拥有一小部分真正爱你的用户，

0:27 然后找到更多这样的用户，逐步扩大产品的吸引力。

0:30 要么拥有很多偶尔使用并稍微喜欢产品的人，

0:31 再想办法让他们逐渐更活跃。

0:36 我可以非常肯定地说，你应该从一小批真正爱你的用户开始。

0:39 几乎所有伟大的公司产品都是这样起步的。想想你身边的

0:42 产品。

0:44 那些好到你会自发地推荐给朋友的产品。那些好到如果它们消失了，

0:47 你会写信抗议的产品。

0:48 这就是对产品真正喜爱的表现。一个好指标是

0:51 用户留存率和使用频率。

0:54 因此，

0:55 我认为这非常重要，以至于创业初期你不应该关注用户数量的绝对增长。

0:58 你应该关注他们使用的频率。之后我们会讲

0:59 指标分析，但你需要学会分析指标，判断这是一个

1:04 留存且频繁使用的用户。和同领域其他产品相比怎样？

1:08 这是早期判断用户是否爱你的好方法。

1:11 更好的情况是，他们自发地推荐你的产品给朋友。

1:14 但这里最重要的是，

1:16 只有出色的产品才能救你。我们这门课会讲很多

1:20 其他内容，它们都很重要。

1:21 但如果这一个没有做好，没有打造出伟大的产品，

1:24 一切都不会成功。

0:00 We have five lectures in this course devoted to the product because a great

0:03 product is the single most important thing that you do.

0:05 The one thing I want to mention for now as you're thinking about the product

0:08 you build is that it is more important to have a small number of users that

0:11 love you than a lot of users that like you.

0:13 And almost all startups get this wrong.

0:15 Eventually what you want of course is a lot of users that really love your

0:19 product.

0:20 That's almost impossible to do. In practice you have two choices.

0:23 You can go deep and narrow. You can have a small number of users that really

0:27 love you and then you can find out how to find more and more of those users and

0:30 broaden the appeal of the product.

0:31 Or you can have a lot of people that kind of use the product once or twice kind

0:36 of like it and try to figure out how to get them more engaged over time.

0:39 With high confidence I can say you want to start with a small number of users

0:42 that really love you.

0:44 Almost all great companies have products that start this way. Think about the

0:47 ones in your own life.

0:48 The products that are so good that you spontaneous to tell your friends about

0:51 them. The products that are so good that if they went away you would write the

0:54 company and protest.

0:55 That's what it means to really love a product. A good indicator of that is

0:58 retention and frequency of use.

0:59 So if you have in fact I think this is so important that you actually shouldn't

1:04 track absolute growth in number of users in the early days of a startup.

1:08 You should just track how often they're using it. And we'll have a session on

1:11 metrics later but you really want to get good at analyzing your metrics and

1:14 saying is this a user that I'm retaining and it's using it frequently.

1:16 How do I compare to other products in my space? That's a good early indicator

1:20 of users that love you.

1:21 Better still is them spontaneously telling their friends to buy your product.

1:24 But the important point here is this.

1:27 Nothing but a great product will save you. We're going to talk about a lot of

1:29 other things in this course and they all matter.

1:31 But if you don't get this one right, if you don't make a great product, the

1:34 thing is still not going to work.

Love

· 1d

#LangChain 学院新课程：构建可靠的代理
将智能代理部署到生产环境并非易事。传统软件是确定性的——一旦出现故障，只需查看日志并修复代码即可。但智能代理依赖于非确定性模型。

加上多步骤推理、工具使用和真实用户流量，构建可靠的代理就比传统的系统设计复杂得多。

本课程的目标是通过迭代改进循环，教你如何将代理从首次运行发展成为可用于生产的系统。

您将学习如何使用 #LangSmith 来实现这一点，LangSmith 是我们的代理工程平台，用于观察、评估和部署代理。 #agent

免费注册 https://academy.langchain.com/courses/building-reliable-agents

https://x.com/LangChain/status/2035757438247334087?s=20

中文 English

0:00 构建可靠代理的第一步是运行它。

0:04 一旦开始运行，肯定会出问题。

0:07 出现问题时，你需要弄清原因。

0:10 传统软件中，你检查日志，找到出错的代码行。

0:15 而在代理中，模型做出决策。

0:18 你的代码只是定义框架。

0:20 你无法看到模型的内部推理，但可以观察它的行为。

0:25 每个提示、每次工具调用、每步操作都会留下信号。

0:29 将它们串联起来，你就得到一个追踪，完整还原你的

0:33 代理操作。

0:35 追踪帮你理解代理的行为，以及出错的原因。

0:40 当它没工作时。

0:41 在本课程中，你会观察代理的行为，找到出错点，

0:45 并用追踪修复问题，而不是依赖日志。

0:49 准备好看看代理到底在做什么了吗？

0:52 我们开始吧。

Love

· 1d

Karpathy 的 AI 精神病：App 该消失了，设备只需暴露 API

Karpathy 自述已患"AI精神病"——每天对 Agent 说话16小时、同时跑十几个并行任务、手写代码归零。他认为 App 时代该结束了，智能设备只需暴露 API，由 Agent 统一调度，无需再为每个设备单独开发应用。

Love

· 1d

以下是埃隆·马斯克今晚关于 #TERAFAB 的完整演讲，开头的等待时间已剪掉。

https://x.com/SawyerMerritt/status/2035544390593044838

中文 English

0:00 我们必须探索宇宙，这就是推动人类未来加速的动力。

0:04 未来。

0:05 理解宇宙，将意识之光延伸至星辰。

0:08 向星辰。

0:30 [音乐]

1:00 我们有一个非常重要的公告，这是迄今为止最史诗级的芯片制造工程。

1:29 这将真正把事情提升到一个新的高度。

1:39 一个大多数人现在甚至想不到的级别。

1:43 这不是他们现有认知范围内的问题。

1:48 所以我们将把认知范围提升好几个数量级。

1:54 这是共同的努力，我按了按钮，但没反应。

1:55 哦，开始了。是的，我们的目标是成为星际文明。

2:04 我认为大多数人都同意，最激动人心的未来是我们能在星辰之间自由穿梭，

2:15 不再被局限在一颗星球上，成为多星球物种，就像你读过的最棒的科幻小说，

2:28 比如《星际迷航》、巴恩斯、阿西莫夫或海因莱因的作品，

2:32 我们

2:33 想把这变成现实，不只是小说，而是把科幻变成现实。

2:39 这就是我期待的辉煌未来。

2:40 值得思考如何评估文明，有个物理学家卡尔达肖夫在60年代提出，

2:49 他想了想，

2:50 如何从高层次评判文明，他说一旦成为一型文明，就能利用行星上的大部分能量。

3:00 但我们

3:00 还差得远，目前我们只用到了到达地球的太阳能量的一小部分。

3:09 地球只接受了太阳能量的约五亿分之一。

3:10 太阳真是巨大，占据了太阳系总质量的99.8%。

3:18 有时有人问我，地球上的其它能源如核聚变如何？

3:26 不幸的是，这很微小，因为太阳系质量中太阳占99.8%。

3:31 木星占0.1%，地球属杂项。

3:33 卡尔萨根曾说地球是浩瀚黑暗中的一粒尘埃，非常非常小。

3:38 所以

3:39 要让文明规模扩大，就必须扩大太空中的能源利用。

3:47 这点必须如此，

3:48 因为地球只能捕获极小部分太阳能，

3:52 我们只是这一粒尘埃。

3:53 地球上全部文明的电力产出仅占太阳能的万亿分之一，

3:58 如果文明能量提升一百万倍，也只到太阳能的百万分之一。

4:04 想想看，真的令人敬畏。这显示了我们的渺小。

4:10 我们总是陷在地球上的琐事里，

4:13 但在宇宙宏伟面前，那些都微不足道。

4:19 因此，考虑宇宙的宏大和我们能做的伟大之事非常重要，

4:22 而不是

4:23 为地球上的小事烦恼，

4:24 没多大意义。

4:27 我们要成为一个能扩展到银河系的文明，拥有任何人随时可去任意地方的飞船，那将史诗般辉煌。

4:36 在月球、火星建城，

4:37 遍布整个太阳系，向其他星系派遣飞船。

4:42 这就是我能想象的最美好未来。

4:49 为了实现，

4:50 我们需要捕获太阳能量。

4:53 尽管一年一太瓦计算能力超大，

4:55 在文明标准上巨大，

4:58 但仍只是迈向星际文明的一个步骤。

5:05 要实现这个艰巨目标，需要SpaceX、XAI和Tesla的共同努力。

5:13 这才是

5:14 这个史诗级太瓦工厂计划的核心。

5:18 Tesla、XAI和SpaceX都创造了别人认为不可能的奇迹。

5:24 比如

5:25 Gigafactory Texas，Optimus机器人，全球超级充电网络，还有很多。

5:30 人们曾认为电动车无望，

5:31 Tesla创立时几乎无电动车出售，大家说不可能做到，现在Tesla一年造200万辆电车。

5:38 XAI是SpaceX旗下新公司，

5:39 创建了首个千兆瓦规模计算集群，速度之快让NVIDIA Jensen Wong震惊。

5:42 SpaceX的可重复使用火箭更是历史突破，曾被认为不可能或经济上不可行。

5:48 我们做到了，还让它经济可行，已回收500多次，

5:52 完成了猎鹰重型火箭，现在开发星舰。

5:56 星舰是关键，因为要扩展计算力和能量，必须进入太空，

6:03 需要大载荷发射量，

6:05 星舰将实现这一目标。

6:09 这里只给你个规模感。

6:10 Optimus机器人大约5尺11寸，展示星舰V3型号的巨大体积。

6:17 V4将更长，

6:18 让V3看起来短小。

6:25 V3载重升至200吨，

6:26 之后将启动V4型号。

6:33 这是AI微型配置示意，

6:34 约100千瓦，展示了太阳能帆板和散热器比例。

6:39 关于太空散热器也曾有奇怪争论，SpaceX发射了一万颗卫星，肯定懂太空散热。

6:46 散热器相对于太阳能板很小，

6:47 这就是“微型组”配置，仅100千瓦。

6:49 未来卫星功率预期达到兆瓦级别，

7:00 太瓦计算每年需要大约1000万吨载荷发射，

7:04 按每吨100千瓦计算。

7:12 我们确信可行，

7:13 不需新物理或不可能的事。

7:20 我有信心SpaceX

7:21 能实现每年1000万吨上轨。

7:22 接着我们积累到一年一太瓦太阳能，

7:26 解决发电问题。

7:27 接下来关键是实现一太瓦计算能力。

7:30 此次发布就是为了解决这个关键缺失。

7:38 当前AI计算年产能大约20吉瓦，

7:39 图表显示这为何需要建造太瓦级规模。

7:45 地球现有所有芯片厂产能加起来仅达需求的2%。

7:51 我们非常感谢现有供应链，三星、台积电、美光等，

7:56 希望他们能尽快扩展，我们也将买尽所有芯片。

8:02 我亲口告诉他们这话，但他们扩产速度远低于我们需求。

8:10 所以只有建设太瓦芯片厂，才能满足需求，我们必须这样做。

8:18 我们将在奥斯汀建造先进制程芯片厂。

8:26 感谢德克萨斯州州长阿博特及德州对我们的支持。

8:34 该芯片厂将拥有制造任何类型逻辑及内存芯片的全部设备，

8:40 同时配备光刻掩膜制造设备。

8:46 在一栋建筑内完成掩膜设计、芯片制造、测试，再改进掩膜，循环优化。

8:53 据我所知，

8:54 全球没有任何这样集成的芯片设计制造测试基地。

9:00 我们还将尝试非常新奇的物理计算方式，并有信心成功。

9:07 拥有快速迭代的循环，使得变更设计快速实施极为重要。

9:12 这种方式让我们改进芯片设计的速度至少领先全球一个数量级。

9:18 我们预计制造两类芯片：一种为边缘推理优化，主要用于Optimus机器人和汽车，

9:30 预计人形机器人产量将是汽车的10至100倍。

9:37 汽车年产量约一亿台，人形机器人预计为十亿至百亿台。

9:41 这是巨大数字，Tesla目标是生产其中很大一部分。

9:49 另一类芯片则高功率设计用于太空环境，考虑太空中高能粒子、光子和电子积累的严苛条件，

9:57 并且芯片需比地面运行温度高以减少散热器重量。

10:04 太空芯片设计与地面不同，有许多特殊约束。

10:10 估计太空芯片计算需求占大多数，地面受限于功率，预计年产100至200吉瓦地面芯片，

10:18 太空芯片则可能达到一太瓦规模。

10:27 太空优势在于常年光照，无需大量电池，太阳能至少是地面五倍，

10:35 且无大气衰减、昼夜交替和季节变化，且面朝太阳最大化收能。

10:41 太空太阳能成本低于地面光伏，不需厚玻璃或重型框架防极端天气。

10:48 一旦轨道发射成本大幅降低，放AI计算到太空就极具吸引力，

10:54 这将成为必然选择。

11:00 太空规模经济递增，事半功倍；而地面空间耗尽，难以扩充，

11:10 易引发居民反对，成本反而增加。

11:18 这非常重要。

11:28 接下来是你可能想的“太瓦之后怎么办？”答案是——不要局限于现状，

11:48 实现拍瓦级别需在月球建电磁质驱动器，利用机器人与人类合作发射千万瓦级计算资源至深空，

12:00 月球无大气，重力仅地球六分之一，发射无需火箭，可直接加速至逃逸速度，

12:11 大幅降低成本，能让规模扩展千倍超过一太瓦。

12:22 我希望能活着见证月球质驱动器的诞生，那将极其壮观。

12:29 这预示着我们能获取太阳能的百万分之一，是地球经济的百万倍，

12:36 从这个视角看非常可观。

12:42 从月球出发扩展到其他行星和恒星，创造我能想象的最激动人心未来。

12:48 [掌声欢呼]

12:52 这画面有点像电影《愚人国》开场，带来令人惊叹的丰盈。

12:58 实现可持续能源、太空旅行、AI与机器人，将带来惊人的丰裕。

13:11 这几乎是通往丰裕世界的唯一道路，虽然可能出错，但我相信会成功，

13:17 这是你会喜欢的未来，也是我能想象的最佳未来。

13:25 然后，我们超越月球、火星，穿越土星环。

13:32 如果能买到土星之旅，

13:33 或者干脆就去土星旅行，

13:36 那该多棒！

13:40 未来物资可能免费，

13:41 听起来疯狂，但如果AI机器人经济达到地球经济的一百万倍，

13:47 任何需求都能满足，

13:50 想得到的都能拥有。

13:54 《文化》书中描述未来不存在钱，人人丰裕，

13:59 能想到就能拥有。

14:04 任何人都能去土星旅行，不是少数特权。

14:08 加入我们，一起设计制造非凡飞船，建设一太瓦计算、一太瓦太阳能，

14:18 每年千万吨载荷发射。

14:20 谢谢大家。

0:00 You must explore the universe, and that's the motivation to accelerate humanity

0:04 's future

0:05 in understanding the universe and extending the light of consciousness to the

0:08 stars.

0:30 [Music]

1:00 Well, we have a profoundly important announcement to make, which is the most

1:29 epic chip building exercised in history by far.

1:39 This is really going to take things to the next level.

1:43 So, yeah, a level of probably people aren't even contemplating right now.

1:48 This is not in the, I'd call this sort of an out-of-context problem.

1:54 It's not in their context.

1:55 So we're going to adjust the context by a few orders of magnitude here.

2:04 It's a joint effort, I'm pressing the button, but the button's not working.

2:15 Oh, there we go, okay, so, yeah, we aspire to be a galactic civilization.

2:28 So I think the future that everyone, well, most people, I think, would agree is

2:32 the most

2:33 exciting, is one where we are out there among the stars, where we are not

2:39 forever confined

2:40 to one planet, that we become a multi-planet species, like the best science

2:49 fiction that

2:50 you've ever read, you know, Star Trek or in banks or Asimov or Heinlein, and we

3:00 want

3:00 to make that real, yeah, not just fiction, turn science to fiction to science

3:09 fact.

3:10 That's the glorious, exciting future that I certainly look forward to.

3:18 And it's worth considering sort of, like, how would you rate civilizations?

3:26 You know, there's, so there was a physicist, I think it was Russian, in the '60

3:31 s, Kardashev,

3:33 he thought about, at a high level, how would you consider any given

3:38 civilization?

3:39 And he said, well, if you're type one, you're using most of the energy of your

3:47 planet.

3:48 And we actually still have quite a ways to go to be properly a type one, we're

3:52 still

3:53 using a tiny fraction of the sun's energy that reaches our planet.

3:58 Let's see, yeah, there we go.

4:04 But the Earth only receives about half a billionth of the sun's energy.

4:10 So the sun is truly enormous.

4:13 The sun is 99.8% of all mass in the solar system.

4:19 So sometimes people will ask me, like, what about, you know, other power

4:22 sources of power

4:23 on Earth?

4:24 Like, what about fusion on Earth?

4:27 Well, that is unfortunately very small because the sun is 99.8% of mass in the

4:36 solar system.

4:37 And Jupiter is about 0.1% and Earth is in the miscellaneous category.

4:42 We are, I think it's Carl Sagan I think might have said, Earth is like a tiny

4:49 dust moat

4:50 in a vast darkness.

4:53 Very, very small.

4:55 The sun is enormous.

4:58 So the way to actually scale civilization is to scale power in space.

5:05 This is necessarily true because we actually capture such a tiny amount of the

5:13 sun's energy

5:14 on Earth because we're just this tiny dust moat.

5:18 Another way to think of it is roughly like electricity production on Earth of

5:24 all of

5:25 civilization is only about a trillionth of the sun's energy, which means if you

5:30 increase

5:31 civilizational power output by a million, you would still only be a millionth

5:38 of the sun's

5:39 energy.

5:42 I mean, it's awe-inspiring to consider that.

5:48 Just how tiny we are in the grand scheme of things.

5:52 And yeah, we often get sort of caught up in the sort of these sort of squabbles

5:56 on Earth

5:56 that are really very sort of minor things when you consider the grandness of

6:03 the universe.

6:05 And so I think it is important actually to consider the grandness of the

6:09 universe and

6:10 what we can do that is much greater than what we've done before as opposed to

6:17 worry

6:18 about sort of small squabbles on Earth type of thing, kind of not much point in

6:25 that.

6:26 Yeah, we want to be a civilization that expands to the galaxy with spaceships

6:33 that anyone

6:34 can go anywhere they want at any time, that would be epic.

6:39 And have a city on the moon, cities on Mars, populate the solar system and send

6:46 spaceships

6:47 to other star systems.

6:49 That sounds like the best possible future.

7:00 So to do that, we need to harness the power of the sun.

7:04 And so a terrafab, while it is enormous, a terrawad of compute for a year is

7:12 enormous

7:13 by our sort of civilizational standards, it is still just one step along the

7:20 way of being

7:21 even a watershed.

7:22 You still have a long way to go to even be a watershed to level civilization

7:26 and you're

7:27 not even registering as a watershed three.

7:30 So it's a very big thing by current human standards, but still small in the

7:38 grand scheme.

7:39 And it's very difficult for humans, so to accomplish this very difficult goal

7:45 really

7:46 requires a combination of efforts of SpaceX, XAI and Tesla working together to

7:53 create this

7:54 epic terrafab project.

8:02 And Tesla and XAI and SpaceX have all done amazing things that people did not

8:10 think would

8:11 be done before.

8:13 So there's the gig of Texas BAB here, there's the optimist robot that's being

8:21 built, there's

8:22 a global supercharging network, there's really quite a lot.

8:29 And it wasn't that long ago when people thought electric cars wouldn't amount

8:33 to anything.

8:34 And there were basically no electric cars for sale when Tesla started.

8:41 And people said it was impossible and now Tesla's making 2 million electric

8:44 cars a

8:45 year.

8:52 And then XAI, although it's a new company, now part of SpaceX, has also built

8:57 the first

8:57 gigawatt scale compute cluster, which in record time, Jensen Wong from NVIDIA

9:05 said he's never

9:06 seen anything built so fast in his life before.

9:08 So a great complement from NVIDIA.

9:12 And then SpaceX, well, he gets to get rid of yourself, I'm all you already know

9:18 .

9:18 I mean, the reusable rockets, people said that reusable rockets weren't

9:22 possible and

9:23 even if you did do them, they wouldn't be economically feasible.

9:26 So we did them and then we made them economically feasible.

9:30 And now we've landed over 500 times.

9:34 And then we did the Falcon Heavy, and now we're doing Starship.

9:38 And Starship is a critical piece of the puzzle because in order to scale

9:44 compute and scale

9:45 power, you have to go to space, which means that you need massive payload to

9:51 space.

9:51 And Starship will enable that.

9:57 So this gives you just a sense of scale.

10:00 We've got Optimus there, Optimus for scale.

10:07 And Optimus is about 5'11", so it gives you a sense of the size of the Starship

10:13 V3 rocket.

10:15 Starship V4 will be much longer, actually.

10:17 The Starship V4 will make Starship V3 look kind of short.

10:24 So we'll expand with Starship V3 to 200 tons of payload to orbit from 100 tons.

10:30 We'll start with V3.

10:32 And then you can see that's just a rough approximation of the AI, the mini

10:38 version of

10:39 the AI set.

10:40 So that's roughly 100 kilowatts, it's showing the solar panels and the radiator

10:47 to scale.

10:48 So for some reason there's been a bizarre debate about radiators in space.

10:54 It's safe to say SpaceX knows how to do heat rejection in space with 10,000

10:59 satellites

11:00 in orbit.

11:01 Might know a thing or two.

11:04 So you can see the radiator is actually quite small relative to the solar

11:09 panels.

11:10 And we call that the mini set since that's just 100 kilowatts.

11:15 We expect future satellites to probably go to the megawatt range.

11:24 So in order to get to the terawatt of compute per year, you need about 10

11:35 million tons

11:37 to orbit per year and at 100 kilowatts per ton.

11:41 So we're confident this is feasible, like no new physics or impossible things

11:47 are required

11:48 to get there.

11:49 So I'm confident that SpaceX will get to 10 million tons to orbit per year.

11:56 And then we're building up to a terawatt of solar.

12:00 So that solves the, we'll solve the solar problem, the power generation.

12:04 So then the key missing ingredients is therefore a terawatt of compute.

12:11 So this announcement is about solving the key missing ingredient.

12:17 To give you a sense of what we're talking about, the current output of AI

12:27 compute is

12:29 roughly 20 gigawatts per year.

12:35 This chart explains why we need to build the terawatt.

12:41 Because all of the rest of the output from Earth is about 2% of what we need.

12:49 So if you add up all the fabs on Earth combined, there are only about 2% of

12:55 what we need for

12:57 the terawatt project or terawatt project.

13:01 So you know, we certainly want our existing supply chain to be clear.

13:11 We're very grateful to our existing supply chain, to Samsung, TSMC, Micron and

13:18 others.

13:19 And we would like them to expand as quickly as they can.

13:24 And we will buy all of their chips.

13:27 I have said these exact words to them.

13:31 But there's a maximum rate at which they're comfortable expanding, but that

13:38 rate is much

13:39 less than we would like.

13:41 And so we either build the terafab or we don't have the chips.

13:48 And we need the chips.

13:49 So we're going to build the terafab.

14:00 And we're starting off with an advanced technology fab here in Austin.

14:06 And I believe that Governor Abbott is in the audience.

14:14 I'd like to thank Governor Abbott and the State of Texas for their support.

14:24 So in the advanced technology fab, we will have all of the equipment necessary

14:29 to make

14:30 a chip of any kind, logical memory.

14:32 And we will also have all of the equipment necessary to make the lithography

14:37 masks.

14:38 So in a single building, we can create a lithography mask, make the chip, test

14:44 the chip, make another

14:46 mask, and have an incredibly fast, recursive loop for improving the chip design

14:53 .

14:54 The best of my knowledge, this doesn't exist anywhere in the world, where you

14:57 've got everything

14:58 necessary to build logic, memory, and do packaging, and test it, and then do

15:05 the masks, improve

15:06 the masks, and just keep looping it.

15:10 So we're not just going to do conventional compute in this.

15:14 I think there's some very interesting new physics that is potentially -- that I

15:20 'm confident

15:21 it will work, which is a question of one.

15:24 So this is really going to push the limit of physics in compute.

15:29 And we're going to try a bunch of wild and crazy things, which you can do if

15:33 you've got

15:34 that fast iteration loop that I can't emphasize enough the importance of being

15:39 able to make

15:40 a chip, test it, and then change the design, do another one, and have that in a

15:48 single

15:49 building.

15:51 I think that our recursive improvement with that situation is probably an order

15:58 of magnitude

16:00 better than anything else in the world.

16:07 So broadly speaking, we expect to make two kinds of chips.

16:19 One will be optimized for edge inference.

16:25 So that will be used primarily in optimists and in the cars.

16:29 Especially in optimists, because I expect the human right robots to be made 10

16:37 to 100 times

16:38 more than the volume of cars.

16:41 So if vehicle production -- vehicle production is about 100 million vehicles a

16:48 year, and

16:49 I expect human right robot production to be somewhere between a billion and 10

16:52 billion

16:53 units a year.

16:54 So that's a lot.

16:58 So yeah, Tesla's going to make a very significant percentage of those is our

17:06 goal.

17:07 And then we need a high-power chip that is designed for space that takes into

17:16 account

17:17 the more difficult environment in space, where you've got high power, you've

17:23 got high-energy

17:23 ions, photons, you've got electron buildup, it's a hostile environment in space

17:28 .

17:28 So you want to design the chip, you want to optimize it for space, and you also

17:35 want to

17:35 generally run it a little hotter than you would normally run a chip on Earth to

17:39 minimize

17:40 the radiator mass.

17:41 So there's just a bunch of constraints that would -- you designed something

17:46 differently

17:46 in space than you would on the ground.

17:50 And for the space compute, my guess is that is the vast majority of the compute

17:57 , because

17:58 you're power constrained on Earth.

18:01 That's why I think it's probably 100 to 200 gigawatts a year of terrestrial

18:10 chips, and

18:11 probably on the order of a terawatt of ships in space.

18:18 Just because of power constraints on the ground, that's probably how it ends up

18:27 .

18:28 Space has this advantage that it's always sunny, it's very nice.

18:35 So I actually think that the cost of AI and deploying AI in space will drop

18:41 below the

18:42 cost of terrestrial AI much sooner than most people expect.

18:46 I think it may be only two or three years before it is actually lower cost to

18:54 send AI

18:55 chips to space than it is on the ground.

18:58 Because in space you don't need much in the way of batteries because of it's

19:04 always sunny.

19:05 And the solar power, you're going to get at least five or more times the solar

19:10 power

19:11 you get in space versus the ground, because you don't have atmospheric atten

19:15 uation or

19:15 a day/night cycle or seasonality.

19:19 And you're always normal to the sun.

19:22 So you're really maximizing the solar power at that point.

19:27 And the space solar actually costs less than terrestrial solar, because you don

19:32 't need

19:32 heavy glass or framing to protect it from extreme weather events.

19:38 So as soon as the cost to orbit drops to a low number, it immediately makes

19:45 extremely

19:46 compelling sense to put AI in space.

19:50 It becomes a no-brainer, basically.

19:53 As you go to space, you get increased economies of scale and things get easier

19:59 over time, whereas

20:00 as you try to put more and more power on the ground, you run out of space and

20:04 you start

20:05 using up the easy spots, and then you get next-level numbi.

20:11 Nobody wants the thing in their backyard.

20:13 So actually, increasing power on Earth has become harder over time and more

20:21 expensive

20:22 over time, but in space it becomes actually cheaper and easier over time.

20:28 These are very important points.

20:41 Yeah.

21:11 So, yeah.

21:41 So, yeah.

22:11 So, what you just thought there was, because of course you're asking what's on

22:34 your mind

22:35 is, "Well, what do you do after a Terafab?"

22:45 Don't think small.

22:48 Well, yeah.

22:49 Good point.

22:50 So, we -- you know, how do you get to a petawat is the obvious next question.

22:54 And you get there by having an electromagnetic mass driver on the moon with

23:01 robots, with

23:03 octomy, and obviously lots of humans, and with that you can send a petawat --

23:11 you can create

23:12 a petawat of compute and send that to deep space, because on the moon, moon has

23:18 no atmosphere

23:19 and has one sixth Earth gravity, so you can -- you don't need rockets on the

23:24 moon.

23:24 You can literally accelerate it to escape velocity from the surface.

23:32 And that dramatically drops the cost once a game of harnessing power, and

23:41 enables you

23:42 to go a thousand times bigger than a terawat.

23:49 So, for sure the future, I want to see -- I want to just live long enough to

23:54 see the mass

23:55 driver on the moon, because that's going to be incredibly epic.

24:00 [ Applause and Cheering ]

24:11 That should hopefully get us to a millionth of the sun's energy, at least, hum

24:16 bling to

24:16 think about that.

24:18 But a millionth of the sun's energy would be a million times bigger than Earth

24:22 's economy,

24:23 so it's good from that perspective.

24:29 And then, yeah, you expand beyond that to the planets, to the other stars, and

24:36 create the

24:38 most exciting possible future that I can imagine.

24:48 [ Applause and Cheering ]

24:52 This looks a bit like the opening in idiocracy with the mic judge, unlocking an

24:58 edge of amazing

24:58 abundance.

25:02 So, yeah, obviously the elements of that are sustainable energy, space travel,

25:11 and AI and

25:12 robotics that bring amazing abundance to everyone.

25:17 And it's really the only path to amazing abundance is AI and robotics, which is

25:25 not

25:25 say it can't go wrong, hopefully, but I think it'll probably go right, and it

25:30 'll be a future

25:31 that you love.

25:35 And it's the best future I can think of, at least.

25:42 And then, we go beyond the moon, beyond Mars, and we sail through the rings of

25:48 Saturn.

25:49 It wouldn't be amazing if you could buy a trip to Saturn, or frankly, if you

25:54 just have

25:55 a trip to Saturn.

25:56 I think things will be free in the future, it sounds nuts, but, you know, if

26:00 you've got

26:01 an AI robotics economy that is anywhere close to a million times the size of

26:06 the current

26:07 Earth economy, literally any need you possibly want can be met.

26:12 If you can think of it, you can have it.

26:14 But I think in banks, in its culture books, has pretty much right where there

26:19 actually

26:19 isn't money in the future, and there's abundance for everyone.

26:24 If you can think of it, you can have it, that's it.

26:28 Which means anyone could have a trip to Saturn, it won't be, you know, just a

26:32 few people.

26:33 If you want it, you can have it.

26:42 Yeah, join us on this journey, and help us design incredible ships, and make

26:50 incredible

26:52 ships, and build a terawatt of ships, a terawatt of solar, and tell million

26:59 tons to orbit per

27:01 year.

27:02 Thank you.

Love

· 2d

为了了解宇宙，你必须探索宇宙。

https://x.com/Tesla/status/2035544492896669952?s=20

Love

· 3d

最新消息： #SEC 主席保罗·阿特金斯表示，他正在取消不切实际的规则，以“推动、澄清和改造”金融市场。 #加密货币 #btc

https://x.com/WatcherGuru/status/2035051688390758557?s=20

中文 English

0:00 随着未来一年的一系列规则制定计划，

0:04 我认为首先需要将它们放入背景中审视，

0:08 以及其后数年的情况，

0:12 在这期间我们的监管框架经常难以

0:15 跟上它所监管的市场步伐。

0:18 举一个监管与现实鸿沟的例子，

0:23 我们的规则仍默认采用纸质方式

0:26 传达股东信息。

0:29 在算法交易和人工智能的时代，

0:33 我认为这一规定应该成为历史

0:36 而非标准。

0:37 其次，基于此，

0:39 拒绝更新SEC规则，

0:43 反而通过误导性的执法监管运动

0:49 扼杀了许多潜在产品或将它们驱赶到海外。

0:53 当创新者无法获得合适的规则指引，

0:57 或在善意合规的努力后

1:00 面临传票威胁时，

1:04 理性的反应是转向其他地方发展，而他们确实如此。

1:08 整整一代数字资产创新

1:12 在美国以外发展，

1:14 并非美国创业者缺乏雄心

1:18 或美国投资者缺乏兴趣，

1:21 而是因为美国监管者缺乏决心。

1:25 第三，数十年的创造性规则制定

1:29 积累成了合规迷宫，

1:31 错综复杂到支撑起了整个行业

1:35 专门帮助上市公司

1:38 应对SEC复杂的监管框架。

1:43 当进入美国资本市场的成本

1:46 包括雇佣专家解读

1:49 SEC监管体系，以制作

1:53 只有学者才会欣赏的披露，

1:56 那么显然问题非常严重且根深蒂固。

2:00 我们的目标应是提高欺诈

2:03 和操纵的成本，而非合规本身的成本。

2:08 因此，在这样的背景下，每一项

2:11 SEC正在推进的举措和每一条

2:15 我们提出的规则、发布的解释

2:18 以及进行的机构改革，

2:22 大多归入三类之一。

2:26 第一类是推进规则以适应

2:28 当今市场运作方式。

2:31 第二类是澄清我们的监管体系，

2:34 简化监管，释放创新。

2:38 第三类是改革我们的要求，

2:41 消除繁重和不切实际的负担。

2:45 这三大支柱——推进（Advance）、澄清（Clarify）、改革（Transform）

2:49 构成了一个整体政策议程，

2:54 我称之为我们的ACT战略。

0:00 With a robust pipeline of rulemaking set for the year ahead,

0:04 I think it's instructive first to contextualize them

0:08 in the years that they follow,

0:12 a period in which our regulatory framework too often struggled

0:15 to keep up with a market that it oversees.

0:18 As just one example of the gulf between regulation and reality,

0:23 our rules still default to paper delivery

0:26 for shareholder communications.

0:29 In an age of algorithmic trading and artificial intelligence,

0:33 I believe that requirement ought to be a relic

0:36 and not a standard.

0:37 Second, and on that note,

0:39 a refusal to update the SEC's rules

0:43 prepared with a misguided regulation by enforcement campaign

0:49 has killed many would-be products or driven them offshore.

0:53 When innovators cannot discern fit-for-purpose rules

0:57 or when they face the prospect of a subpoena

1:00 as the response to good-faith efforts to comply,

1:04 the rational response is to build elsewhere, and they did.

1:08 An entire generation of digital asset innovation

1:12 developed outside the United States,

1:14 not because American entrepreneurs lacked the ambition

1:18 or American investors lacked the appetite,

1:21 but because American regulators lacked the will.

1:25 Third, decades of a creative rulemaking

1:29 have compounded into a compliance labyrinth,

1:31 so elaborate that it sustains entire industries

1:35 whose sole function is to help public companies

1:38 to navigate the SEC's complex regulatory framework.

1:43 When the cost of accessing America's capital markets

1:46 includes retaining specialists to decode

1:49 the SEC's regulatory regime to produce disclosure

1:53 that only an academic would appreciate,

1:56 then something is very clearly and deeply a mess.

2:00 Our goal should be to increase the cost of fraud

2:03 and manipulation, not the cost of compliance itself.

2:08 So against that backdrop, every initiative

2:11 towards which the SEC is working and every rule

2:15 that we propose, every interpretation that we release,

2:18 and every institutional reform that we undertake

2:22 largely falls into one of three categories.

2:26 Those that advance are rules to align

2:28 with how markets operate today.

2:31 Those that clarify our regulatory regime

2:34 to streamline oversight and unlock innovation.

2:38 And those that transform our requirements

2:41 by eliminating both the burdensome and the impractical.

2:45 So together, these three pillars of advance

2:49 or A, clarify or C, and transform or T,

2:54 form one integrated policy agenda

2:57 that I'm calling our ACT ACT strategy.

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