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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

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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

· 13h

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。

Moon

· 1d

我们正在打造 TERAFAB，以弥合当今芯片生产与未来需求之间的差距——一个星际未来。 #TERAFAB #ai #芯片
加入我们 → http://terafab.ai

https://x.com/SpaceX/status/2035542868790558771?s=20

Moon

· 1d

拉乌尔·帕尔表示，本轮周期与2017年相似，可能在2026年6月达到峰值。

人工智能、ETF和流动性或将推动加密货币市场规模从3万亿美元飙升至100万亿美元 🚀 #ai #etf #btc #加密货币

https://x.com/Vivek4real_/status/2035237956567671077?s=20

Moon

· 1d

Andrej Karpathy 谈基于不可信工作者池的自动搜索：

“我那些将不可信工作者池（融入自动搜索）的设计，实际上有点像区块链。

只不过这里没有区块，而是提交（commits），这些提交可以相互构建，并包含你在改进代码时所做的更改。

所谓的工作量证明，本质上就是通过大量实验来找出有效的提交。”

分布式且无许可的自动搜索 ~= 有用工作量证明这一理念，目前仍停留在高层次的直觉层面，但至少可以说它极具吸引力。

有人需要进一步深入研究。关于尚缺的内容，请参阅 QT。 #ai #加密货币 #区块链

https://x.com/_weidai/status/2035236632161656969?s=20

中文 English

0:00 我认为一个问题是，如果你有一堆节点

0:03 可以进行并行处理，那么你很容易让多个自动研究者

0:06 通过一个公共系统交流什么的。当时我更感兴趣的是

0:09 如何在互联网中有一群不可信的工作者。

0:11 举个例子，在自动研究中，你只是想

0:14 找到那段能训练模型达到非常低验证损失的代码。

0:18 如果有人给你一个候选提交，你很容易验证该提交是否正确。

0:22 很好。比如，有人可能声称他们那段代码优化得更好，

0:26 能给你更好的性能。你可以很容易地检验，

0:29 但可能需要大量工作去检查。不过基本上，

0:32 他们依然可能会撒谎等等。

0:34 所以你基本上面对的是类似的问题，

0:37 其实我设计的含不可信工作者池的系统，

0:40 看起来有点像区块链，因为你不是用区块，

0:43 而是用提交，提交可以相互建立，

0:45 包含你不断改进代码的变更。

0:47 工作量证明基本是做大量试验以找到有效提交。

0:51 那很难。而奖励就是登上排行榜，现在

0:55 没有任何金钱奖励。

0:57 不过我不想把这个比喻推得太远，

1:01 但它本质上有这个问题：

1:03 搜索需要大量计算，但验证候选方案是否优秀很便宜，

1:07 因为你只需训练一次。

1:10 有人可能试了10000次，但你只需要

1:13 验证他们产出的方案有效，

1:16 因为那其他的9990次都失败了。

1:19 总之，长话短说，

1:22 你得设计一个系统，让不可信池的工作者可以

1:25 与可信池工作者协作进行验证。

1:26 整个过程是异步的，可以工作，

1:31 从安全角度来说也安全，

1:33 因为如果有人发任意代码给你执行，那很危险。

1:37 但从根本上说，这是完全可能的。

0:00 think like one issue is if you have a bunch of nodes of

0:03 parallelization available to you, then it's very easy to just

0:06 have multiple auto researchers talking through a common system

0:09 or something like that. When I was more interested in is how

0:11 you can have an untrusted pool of workers out there on the

0:14 internet. So for example, in auto research, you're just trying

0:18 to find the piece of code that trains a model to a very low

0:22 validation loss. If anyone gives you a candidate commit, it's

0:26 very easy to verify that that commit is correct. It's good. Like

0:29 they, someone could claim from the internet that this piece of

0:32 code will optimize much better and give you much better

0:34 performance. You can just check it very easy, but probably a

0:37 lot of work goes into that checking. But fundamentally, they

0:40 can lie and etc. So you're basically dealing with a

0:43 similar kind of, it's almost actually like looks a little bit

0:45 like my, my designs that incorporate an untrusted pool of

0:47 workers, actually look a little bit more like a blockchain a

0:51 little bit, because instead of blocks, you have commits, and

0:55 commits can build on each other, and they contain like changes

0:57 to the code as you're improving it. And the proof of work is

1:01 basically doing tons of experimentation to find the

1:03 commits that work. And that's hard. And then the reward is

1:07 just being on the leaderboard right now, there's no monetary

1:10 reward whatsoever. But I don't want to push the analogy too far,

1:13 but it fundamentally has this issue where you run off search

1:16 goes into it, but it's very cheap to verify that a candidate

1:19 solution is indeed good, because you can just train a single,

1:22 you know, someone had to try 10,000 IZs, but you just have to

1:25 check that the thing that they produced actually works,

1:26 because the 99,000 of them didn't work, you know. And so

1:31 basically, long story short, it's like you have to come up with

1:33 a system where an untrusted pool of workers can collaborate

1:37 with a trusted pool of workers that do the verification. And

1:42 the whole thing is kind of like asynchronous and works and

1:44 and so on. And it's like safe from a security perspective,

1:49 because if anyone sends you arbitrary code and you're gonna

1:51 run it, that's very sketchy and dodgy. So, but fundamentally,

1:55 it should be totally possible.

王爷来了

· 1d

今日热点速览 📡

🔥 Web3+AI 正在加速融合！

Grok Imagine点燃X平台，用户疯狂创作Elon Musk魔性图片
AI图像生成正在重塑社交媒体内容创作方式

📊 中国AI App逆势爆发：

豆包月活超3亿，增速惊人
千问等国产模型持续追赶

💡 观点：去中心化内容平台 + AI工具 = 创作者经济新纪元这不仅是娱乐，更是Web3+AI结合的完美案例

你怎么看？AI将如何改变内容创作？

#Web3 #AI #Crypto #Grok #区块链

Love

· 2d

人工智能是一种功能强大的软件——它并非外星生物。我们面临的最大风险在于，对人工智能未来的恐惧与焦虑会让我们止步不前，而其他国家却在此时超越了我们。 #ai

https://x.com/altcap/status/2035010929650508092?s=20

中文 English

0:00 这部分我们刚和政策制定者讨论过，我们需要始终

0:05 走在前面

0:06 布拉德，你做得很好。我们必须走在他们前面，

0:08 向他们传达

0:09 关于这项技术的现状。它是什么，不是什么。它不是一个

0:14 生物体。

0:16 它不是外星人，它没有意识，它是计算机软件。它不是

0:28 什么

0:29 我们会说，完全不理解它。这不是真的。我们并非

0:33 一点也不理解。

0:34 我们了解这项技术的很多方面。因此，我认为，第一，

0:39 我们必须确保

0:39 继续向政策制定者传递信息，不让悲观主义和

0:45 极端主义

0:46 影响政策制定者对这项技术的认知和理解。然而，

0:51 我们仍然需要

0:52 认识到技术发展非常快速，不要让政策

0:56 走得过快，

0:57 超前于技术发展。作为一个国家，我们面临的最大国家安全风险

1:03 是，

1:04 当其他国家采用这项技术时，我们却因为愤怒、

1:10 害怕或者偏执，

1:12 导致我们的产业和社会未能利用人工智能的优势。

1:17 所以，我最担心的是人工智能

1:19 在美国的普及情况。

1:27 [BLANK_AUDIO]

0:00 This is the part that we just with policymakers, we need to we need to always

0:05 get in front of

0:06 them and Brad, you do a great job doing this. We had to get in front of them

0:08 and inform them

0:09 about the state of the technology. What it is, what it is not. It is not a

0:14 biological being.

0:16 It is not alien. It is not conscious. It is computer software. And it is not

0:28 something that

0:29 we say things like, we don't understand it at all. It is not true. We don't

0:33 understand it all.

0:34 We understand a lot of things about this technology. And so I think, one, we

0:39 have to make sure that

0:39 we continue to inform the policymakers and not affect, not allow do-marism and

0:45 extremism

0:46 to affect how policymakers think and understand about this technology. However,

0:51 we still have

0:52 to recognize the technology is moving really fast and don't get policy ahead of

0:56 the technology

0:57 too quickly. And the risk that we run as a nation, our greatest source of

1:03 national security concern

1:04 with respect to AI, is that other countries adopt this technology while we are

1:10 so angry at it or

1:12 afraid of it or somehow paranoid of it, that our industries, our society don't

1:17 take advantage of AI.

1:19 And so I'm just mostly worried about the diffusion of AI here in the United

1:22 States.

1:27 [BLANK_AUDIO]

卓然691021

· 2d

token之王”预言“token工厂”！黄仁勋GTC演讲：未来你的生活将被token重置。 #黄仁勋 #GTC #token #科技 #硅谷 #AI #黄仁勋GTC演讲 #英伟达 #pickful

Moon

· 3d

21 分钟内答 21 道题。比特币、人工智能和数字时代的创新。 @PresidioBitcoin #btc #ai

https://x.com/saylor/status/2034732750217806012?s=20

中文 English

0:00 - 迈克尔·塞勒，欢迎来到Presidio比特币

0:03 以及21和21节目。

0:05 很高兴你能来。

0:06 - 今天真棒。

0:07 - 是的，你就像旧金山的夏天一样来临，

0:10 天气真美。

0:12 - 正好在这里，这里是个辉煌的地方。

0:15 我们很有启发。

0:16 - 谢谢，我准备了一些问题。

0:18 我们打算在21分钟内尽可能多地问，

0:21 你准备好了吗？

0:22 - 好的，开始吧。

0:23 - 好，开始。

0:24 第一个问题是，

0:26 你一直是推动比特币进入主流观众的最有影响力的声音之一，

0:29 无论是个人层面还是企业层面。

0:31 那么你对2026年的最重要看法是什么？

0:35 - 2026年是个伟大且转折之年，

0:37 我们看到大型银行开始拥抱比特币。

0:43 我们推出了各种数字信用产品，

0:46 比如Stretch，面向零售投资者和退休人员，

0:49 还有企业财务人员和金融家。

0:53 这是比特币一大批新支持者。

0:56 我们拥有业界历史上最支持的金融监管机构，

0:59 涵盖美联储、财政部、CFD、SEC。

1:03 我们还即将通过《清晰法案》，

1:07 这对整个行业提供了极大的合法化。

1:11 我期待行业的进步

1:15 以及它融入主流金融世界的扩展。

1:17 - 真好看到，是的。

1:18 今年发生了很多事情，是重要的一年。

1:23 第二个问题是，

1:24 你把比特币称为数字财产。

1:28 那么你怎么看待比特币作为消费货币？

1:31 - 我认为会是这样，

1:33 比特币第一层会是数字资本。

1:37 你会看到第二层数字信用爆发，

1:39 第三层则是数字货币。

1:41 第三层会成为，

1:44 可以称为比特币背书的稳定币，

1:46 或者是储蓄币。

1:48 就像美元一样，

1:50 你可以把它作为交换媒介流通。

1:53 它会整合所有以美元计价的数万亿美元

1:56 产品和服务，

2:00 以及成千上万家接受和花费美元的公司，

2:02 但它会提供更高的收益率。

2:05 大约六七八个百分点，

2:07 远超通胀成本。

2:11 这将是建立在比特币上的强大高效货币。

2:15 - 那你怎么看商家呢？

2:20 你会怎么告诉他们接受这种支付很划算？

2:22 - 我认为会是资产稳定币。

2:24 我觉得全球所有接受美元支付的地方

2:27 都会用到它。

2:29 你可以把它换成美元，

2:31 支付任何东西。

2:32 - 这很方便。

2:33 - 是的，我的意思是数字货币

2:35 主要会是美元。

2:37 美元正在全球扩散，

2:42 正在取代欧元、英镑、日元。

2:47 在欧洲，人们并不想用欧元，

2:49 他们想用美元。

2:50 所以美元实际上正在成为全球货币。

2:55 数字化的美元就是它的加密

2:59 或数字版。

3:02 但最大的突破是能够

3:04 创造一种比特币背书的数字货币，

3:08 其收益率是法币背书货币的两三四倍。

3:12 - 你觉得随着这一进展，

3:16 比特币变得越来越普及，

3:19 即便是以权益形式购买比特币的人，

3:23 这些投资者

3:24 或者新入场的人，

3:25 他们是否还能理解比特币的理念

3:28 和起初的意义，

3:30 还是仅仅会觉得它只是个资产，

3:32 没那么在意理念？

3:34 - 我认为会有一部分人，

3:36 包括个人和企业，

3:39 大户和倡导者以及开发者会继续传承

3:43 比特币理念。

3:46 当然，技术人员会了解

3:48 比特币技术。

3:51 但我觉得我们正走向一个世界，

3:53 有十亿人，

3:54 他们只想要一个银行账户能帮他们赚利息。

3:57 无论是六岁女孩，

3:59 还是75岁退休老人，

4:01 他们知道数字货币

4:03 是由比特币驱动的。

4:05 就像你家的电

4:07 来自核反应堆，

4:09 你可能不知道怎么造核反应堆，也不知道它多复杂，

4:13 但你知道电很棒又便宜。

4:15 所以我真的认为随着比特币传播，

4:18 会有一部分人非常精通技术，

4:21 但绝大多数人，

4:23 他们不想知道飞机怎么飞，

4:25 不想知道汽车怎么发动，

4:27 也不想知道电怎么来的，

4:29 他们甚至不知道自己的食物怎么来，

4:31 比如大多数人不知道

4:33 可口可乐是怎么做出来的，

4:35 没人知道怎么制造iPhone。

4:37 他们只知道他们手里有个极好的产品，

4:41 解决了他们生活中的问题，

4:43 而且基于卓越的技术。

4:46 - 我完全同意，

4:47 只要人们知道这一点，

4:48 他们不需要深入细节。

4:50 我们这些Presidio比特币的人了解就够了。

4:53 - 是的，我觉得我们都很乐观，

4:58 但我们也想讨论另一种未来，

5:01 灰暗的未来，比特币失败了，

5:04 你觉得失败的原因是什么？

5:06 - 比特币不会失败，

5:08 它会成功，因为比特币代表着经济

5:11 赋权给全世界的人。

5:13 比特币是通往经济繁荣的协定。

5:17 就像我们说英语，

5:19 你不会说人们会停止说英语或停止交流。

5:22 大家不会停止交流。

5:24 如果有阿拉伯数字或者数学，

5:27 人们不会停止用数学。

5:29 他们会继续用英语。

5:30 人们会一直使用比特币，因为人们需要数学

5:35 制造机器，进行商业交易，

5:39 需要语言彼此协调，

5:43 需要比特币进行理性经济交易。

5:46 所以不会失败。

5:46 - 热爱你这坚定的乐观，

5:50 - 我也同意。

5:51 - 好的，我有点有趣的问题。

5:53 - 好的。

5:54 - 我知道你读过MIT。

5:55 - 我爸也读过。

5:57 我听说MIT有一些著名的聪明恶作剧，

6:00 你有喜欢讲的故事吗？

6:02 - 让我想想，有次我们兄弟会，

6:07 劫持了电梯，

6:12 布置好，

6:14 和院长办公室一起，

6:16 把院长的所有家具搬进电梯，

6:19 然后锁电梯，

6:22 要用特殊密码才能开电梯，

6:24 帮这次恶作剧安装了自己的吊灯，

6:27 点亮吊灯。

6:29 - 哇。

6:30 - 电梯门打开时，

6:32 会看到一个怪人坐在桌子旁

6:36 带着吊灯在说话。

6:38 - 真不错。

6:39 - 真不错。

6:40 再一个问题。

6:42 我看到你会弹吉他，

6:45 还会玩吗？

6:47 如果还玩，喜欢弹什么？

6:49 - 高中和大学时玩过。

6:53 有时为自己弹，大多是披头士的歌。

6:56 - 哦，披头士。

6:57 不错。

7:00 还想问你一个有趣的问题。

7:01 你在推特上的自画像很有标志性，

7:06 你用哪些AI工具生成这些图片？

7:08 - 用了很多，

7:10 有的来自Midjourney，

7:12 有的来自Brock，

7:15 还有些我根本没生成。

7:17 别人帮我生成的，

7:19 用各种工具。

7:21 大部分实际上我没生成。

7:24 你看图片会发现我标注了艺术家，

7:26 比如最活跃的Atlas、Hodl、

7:29 LaDozier等、Janice。

7:33 他们做的很棒，

7:35 用了好多工具。

7:36 - 你从玩AI工具中学到什么了吗？

7:40 有尝试写点有趣的代码吗？

7:43 - 实际上，我们去年发行了大约80亿美元

7:46 数字信用，都是用AI创造的。

7:48 Stretch是主要数字信用产品，

7:53 用来创造各种

7:55 数字货币和信用工具，

8:00 这些都离不开AI。

8:04 我们用AI设计证券

8:08 和资本市场策略。

8:11 有时我说点话，会用AI帮我检查语法

8:15 确保拼写正确。

8:19 有时我把图片放进AI，

8:22 问它“我想这样说，你同意吗？”

8:25 它会说，“你这样说更像迈克尔·塞勒。”

8:29 AI会纠正我，

8:30 教我迈克尔·塞勒真正说话方式。

8:33 - 那挺有趣的。

8:34 - 通常都挺准确。

8:36 AI甚至能比我本人更懂我该怎么说。

8:39 - 这世界真疯狂。

8:41 - 确实。

8:41 - 太棒了。

8:43 你很看重AI工具的战略，这很酷。

8:46 - 很想听听你对比特币和AI结合的看法，

8:49 你觉得未来会有什么积极发展？

8:53 - 我认为第一，

8:55 AI将彻底改变我们社会的所有产品和服务以及角色。

8:59 负面影响之一是它正在削弱人力资本价值。

9:02 比方说公司有1.5万名专业员工，

9:07 AI可能自动化掉其中8,000个岗位。

9:09 但另一方面，作为削弱的人力资本，

9:12 它促进资本向数字资本转移。

9:16 而这正是比特币的含义。

9:20 所以，总体看，这推动大量财富流入数字资本，

9:24 我认为这是积极的。

9:26 - AI应用有很多有趣的场景，

9:29 理论上可以创造一个跨国AI，

9:32 用比特币资本化，

9:35 放出到世界，

9:37 成为自治体。

9:39 而你无法用法币给AI资本化，也不能用传统证券组合。

9:44 未来你会看到用比特币资本化的自治生物或行为者。

9:49 很有趣。

9:50 我认为AI做事会越来越快、越来越高级。

9:53 如果一个晚上有十亿个AI互相交流，

9:57 一小时交流十亿次，

10:00 它们会绕过传统的模拟资产，

10:04 绕过传统金融系统，

10:08 想要以数字标准转移数字资产、

10:11 数字货币和数字资本。

10:15 比特币有两个极重要的特点，

10:18 它提供不可篡改的资本网络或资本源，

10:22 它就是这种资本；

10:27 它还提供全球不可变的真实根源和诚信。

10:32 我认为AI会用它来做认证。

10:34 如果AI开始模仿我们进行网络钓鱼，

10:37 加密验证重要性将更大。

10:40 认证和验证的真伪

10:43 会来自比特币网络，

10:48 因为比特币网络和交易是权威的认证和真伪标准。

10:53 所以比特币及其基础事物

10:57 将变得对数字经济越来越重要。

11:00 - 我完全同意，

11:02 很酷你也这么想。

11:03 我们也在想比特币作为消费货币给这些AI代理，

11:06 可能不只是数字财产，

11:10 我们也在做软化它，

11:12 让它更适合作为零售消费货币，

11:14 但也许会有一波新潮，

11:16 比特币作为代理花钱。

11:19 - 是的，谁知道它们会做什么，

11:22 但肯定它们想光速移动资金，

11:26 并想光速做交易。

11:29 它们要比任何模拟人力系统

11:30 快一百万倍甚至更多。

11:34 要让所有这些数字系统发挥最大潜力，

11:38 需要数字网络、数字资本、

11:41 数字货币、数字认证、

11:44 数字网络，而且必须原生在网络空间，

11:48 不能用非原生网络资产替代，

11:51 并且能够完成最终结算。

11:54 比特币提供最终结算，

11:58 而非条件结算。

12:01 最终结算和条件结算的区别，

12:05 是网络空间中能量丰富环境与影子或轮廓的区别。

12:13 你想做点真实且有实质的事，

12:16 你需要移动数字资本、

12:19 数字财产和数字货币，

12:22 不依赖现实中保管人结算。

12:24 - 100%。

12:25 我还有个AI问题。

12:27 在AI变革的大背景下，

12:30 你会给思考如何用AI创造价值

12:33 或选择职业道路的年轻人什么建议？

12:36 - 我建议花很多时间学习用AI。

12:40 选择几个AI，轮流试，测试。

12:43 挑战自己做些特别有用的事，

12:46 逼自己突破。

12:48 如果你是音乐家，尝试作一首歌，

12:51 再组成一张专辑。

12:54 如果你是艺术家，先画幅画，

12:58 再画漫画，写书，

13:01 做电视节目，做电影，

13:04 连续剧，等等，

13:07 不断逼自己更难的事。

13:11 比方说我们如何设计一个可以卖出数十亿美元的信用工具？

13:14 怎么做最好的？

13:16 确保它被全球广泛采用？

13:18 你要靠AI做点事，不停试。

13:22 如果只是让AI勉强做一点，不算有趣。

13:27 但如果能让它完成关键任务，就是“0到1”的时刻。

13:30 你要考虑几乎所有产品服务、

13:32 历史上所有想法，

13:36 你都能用AI做得更好，

13:39 还有无数以往不可行、

13:42 现在变得可行的事物。

13:44 你要争取成为第一个把这些做出来的人。

13:47 而要成为第一个，

13:48 就是得先去做点什么试试。

13:52 如果这月做不了，四周后试试。

13:57 现在正处于关键转折点，

14:01 历史上从未有过的事，

14:05 突然变得技术上可行。

14:07 我记得50、40年前MIT，

14:12 AI实验室大力做语音识别，

14:14 尝试让电脑能说话，

14:17 花了几十年，

14:19 突然一年前，

14:21 点了一下，开始工作，

14:26 我们快接近能立刻执行且对话者几乎立刻理解

14:29 了。

14:33 技术会失败，直到成功。

14:37 我建议去做你觉得重要的事，坚持，

14:41 用尽各种工具和方式，

14:44 坚持下去，总会突破。

14:47 - 事态发展速度真快，

14:50 尤其是我感觉1月1日那天爆发，

14:52 真是各种精彩事件发生。

14:55 我们这边和Spiral团队玩AI玩得很开心。

14:58 - 好的，如果让你选一件或多件东西，

15:02 你希望有人用AI结合比特币来做，

15:04 有什么梦想产品或想法？

15:07 - 我希望比特币融入更多事物。

15:08 我想看到手机原生支持比特币，

15:12 每台安卓手机、iPhone都支持。

15:16 操作系统和企业操作系统都内置。

15:18 金融应用也支持。

15:21 然后有些革命性想法，

15:24 有没有哪个产品嵌入比特币，

15:27 用比特币驱动产品运行十年？

15:30 打个比方，

15:33 我有个产品电池能用十年，

15:35 那会很酷。

15:37 比特币是金融电池。

15:39 你能否设计出由比特币驱动的产品？

15:42 保险。

15:43 我想看到保险由比特币驱动。

15:46 这样能给你赔五到十倍，

15:49 费用降十倍，

15:51 且持久。

15:53 - 太对了。

15:54 - 类似这样，嵌入比特币能改善很多东西。

15:57 - 100%。

15:57 - 好。

15:58 还有一分钟。

15:59 最后一个快问，

16:02 你会给听众留下一句

16:04 最深刻的想法，你会说什么？

16:07 - 这个十年里，

16:11 你可以用数字资本、

16:15 数字资产、数字智能和数字技术，

16:19 让世界变得更好。

16:23 无论你看什么，

16:25 无论是金融、教育、

16:28 物流、产品还是服务，

16:31 每一件事都可以大幅改善，

16:34 做得更好、更便宜、更强、更快。

16:38 我觉得接下来的十年极有可能是创造

16:43 最大繁荣的创新十年，

16:46 在整个人类历史上。

16:48 所以如果你觉得自己错过了机会，

16:51 太晚了，所有东西都有人发明了，

16:54 你想做iPhone、Facebook，没关系，

16:57 你还没错过。

17:00 世界给了你这些伟大工具，

17:04 18岁的年轻人都能抓住任何AI，

17:07 抓住任何数字资产，

17:10 去试任何想法，

17:12 很可能会找到支持。

17:19 X（推特）让你触达数百万、数千万甚至数亿人，

17:22 你有数字通信、

17:24 数字商务、数字资产，

17:26 数字智能，

17:28 这是数字世界，

17:29 这是更好的世界。

17:31 我鼓励大家行动起来。

17:32 - 这是绝佳的结束语。

17:36 非常感谢你来做客。

17:40 听众们可能已经知道

17:41 去哪关注你，但有没有特别渠道

17:43 你想提一下，或者直接去X。

17:46 - 我的X账号是S-A-Y-L-R，saylor。

17:49 网站michael.com，

17:53 所有关于比特币的内容在hope.com，

17:55 比特币是希望，访问hope.com或michael.com，

17:59 或者X上的saylor，谢谢支持，

18:03 期待继续同行。

18:06 - 我也一样，非常感谢。

18:07 祝你有愉快的一天，

18:10 我们见证美好天气。

18:12 - 很好，谢谢。

0:00 - Michael Saylor, welcome to Presidio Bitcoin

0:03 and the 21 and 21 show.

0:05 I'm so excited to have you here.

0:06 - It's an awesome day.

0:07 - Yes, you hit like summer in San Francisco

0:10 and it's beautiful out.

0:12 - It's happened to be here, it's a glorious place.

0:15 We're inspiring.

0:16 - Thank you, so I have some questions laid out.

0:18 So we're gonna try to get through as many as we can

0:21 in 21 minutes, are you ready?

0:22 - Yeah, let's do it.

0:23 - Okay, let's do it.

0:24 So the first question is,

0:26 so you've been one of the most influential voices

0:29 in bringing Bitcoin to mainstream audiences,

0:31 both on the individual and the corporate level.

0:35 So let's top of mind for you in 2026.

0:37 - 2026 is a great year, a transition year

0:43 where we're seeing the large banks embrace Bitcoin.

0:46 We're launching all these digital credit products

0:49 like Stretch to retail investors and retirees

0:53 and corporate treasures and financiers.

0:56 And that's a great new group of supporters for Bitcoin.

0:59 We've got probably the most supportive set

1:03 of financial regulators and the history of the industry

1:07 going into the Fed at Treasury, at the CFD, the SEC.

1:11 So, and we've got the Clarity Act soon to be passed,

1:15 which is just very enormously legitimizing

1:17 for the entire industry.

1:18 So I'm just looking forward to the advance

1:23 and the expansion of the industry

1:24 as it integrates into the mainstream financial world.

1:28 - It's great to see, yes.

1:29 There's so much going on this year, big year.

1:31 My second question is,

1:34 so you've labeled Bitcoin as digital property.

1:38 What's your take on Bitcoin as spending money?

1:41 - I think that what's gonna happen is

1:43 we're gonna use the first layer of Bitcoin as digital capital.

1:47 I think you're gonna see an explosion

1:49 of digital credit at the second layer.

1:51 I think that you're gonna see digital money

1:54 or digital currency at the third layer.

1:56 And the third layer is going to be a,

1:59 call it a Bitcoin-backed stablecoin

2:01 or it's gonna be a savings coin.

2:03 It'll be like the dollar.

2:05 You'll be able to move it around as a medium exchange.

2:08 It will be integrated into all of the trillions

2:10 of products and services that are priced in dollars

2:15 and all of the hundreds of millions of companies

2:18 that take and spend dollars,

2:20 but it will offer a much stronger yield.

2:23 More like a six, seven, eight percent yield,

2:26 which outstrips the cost of inflation.

2:29 And that'll be strong, high-powered money built on Bitcoin.

2:34 - I mean, what do you think about merchants?

2:38 Like how would you tell them that it's a good way

2:40 for them to accept that form of payment?

2:43 - I think that'll be an asset stablecoins.

2:45 I think that everybody that takes a dollar

2:48 anywhere in the world will have that.

2:50 You'll be able to convert that into dollars

2:52 and pay for anything.

2:53 - Just auto-comfort, yeah.

2:54 - Yeah, I think what we're saying is digital currency

2:56 is primarily gonna be the dollar.

2:58 I think the dollar is spreading everywhere in the world

3:03 and it's replacing the euro, the pound, the yen.

3:08 Pretty much in Europe, people don't want the euro,

3:10 they want the dollar.

3:11 So the dollar is actually emerging as the global currency.

3:16 The dollar on digital rails is the crypto

3:20 or digital version of it.

3:23 But the big breakthrough now is the ability

3:25 to create a digital currency that's backed by Bitcoin

3:29 that offers yields that are two, three or four times higher

3:33 than currencies backed by fiat.

3:37 - You think that as this happens

3:39 and Bitcoin just becomes so much more normal

3:41 and even as people as they buy Bitcoin in the form of equity,

3:46 you think a lot of these investors

3:47 or people who are coming into it

3:48 will still understand what the Bitcoin ethos are

3:51 and like what it came to be

3:53 where they'll just become more top of her,

3:54 kind of not top of mind.

3:58 - I think that there'll be a group,

3:59 the people, the companies and the individuals

4:02 and the large holders and the advocates

4:07 and the developers will continue to carry forward

4:10 the Bitcoin ideology.

4:13 And of course, any technician will understand

4:15 the Bitcoin technology.

4:17 But I think we're headed toward a world

4:19 where there's a billion people,

4:21 they just want a bank account to pay us to make percent.

4:24 And I was like six year old girl,

4:26 75 year old retiree.

4:28 What they're gonna know is that digital money

4:30 is powered by Bitcoin.

4:31 And it's kind of like your electricity

4:35 came from a nuclear reactor

4:37 and you don't really know how to create a nuclear reactor

4:40 or you don't know how tricky it is,

4:42 but you know it's like really good cheap electricity.

4:45 And so I really think that as Bitcoin spreads,

4:48 what you're gonna have is a group of people

4:51 that understand very intricately the technology,

4:55 what the great masses of people is like,

4:57 they don't wanna know how the plane works,

4:59 they don't wanna know how the car works,

5:00 they don't wanna know where the electricity comes from.

5:03 They don't wanna know how their food was,

5:05 like most people don't know, you know,

5:07 how you create a Coca-Cola beverage

5:09 or you know, how do you create these things?

5:11 Nobody knows how to make an iPhone.

5:13 All they know is that they've got this insanely good product

5:17 that solves the problems in their life

5:19 and it's based upon superior technology.

5:22 - I totally agree with you

5:23 that as long as people know that,

5:25 they don't need to get into the weeds

5:26 and some of us here in a presidio-Bitcoin.

5:29 But yes, okay, I think both of us feel optimistic

5:35 about Bitcoin's future, but maybe let's think

5:37 of a different type of future.

5:39 Grim one and Bitcoin has failed.

5:41 Why do you think that it failed?

5:45 - Bitcoin's not gonna fail.

5:46 It's gonna succeed because Bitcoin represents economic

5:49 empowerment to everybody on the planet.

5:51 Bitcoin represents a protocol for economic prosperity.

5:56 It's kind of like we're speaking English

5:58 and you're saying, well, what do people stop speaking English

6:02 or just stop speaking?

6:03 What do people stop speaking to each other?

6:05 I don't think they're gonna stop speaking to each other.

6:08 If you had Arabic math or you had math,

6:10 it's what do people stop using math?

6:13 I think they'll keep using math.

6:14 I think they'll keep using English.

6:15 I think they'll keep using Bitcoin because people need math

6:20 to create machines that work and to engage in commerce

6:24 and they need language to coordinate with each other

6:28 and they need Bitcoin if they're going to engage

6:31 in rational economic transactions.

6:34 So it won't fail.

6:35 - Love strong optimism.

6:39 - I agree.

6:40 Okay, I have some fun questions for you.

6:42 - Okay.

6:43 - So I know you went to MIT.

6:44 - My dad did too.

6:46 So I've heard about kind of some of the famous,

6:48 like clever hacks and pranks that have happened at MIT.

6:52 Do you have any stories that you like to tell?

6:54 - Let's see, once our fraternity,

7:00 we hijacked an elevator and then we set it up

7:04 and with the office of the dean,

7:07 we put all the furniture of the dean into the elevator

7:09 and then we locked it off so that you had to have

7:12 a special code in order to get the elevator

7:14 and in order to create the hack,

7:17 we actually rigged the entire thing with its own chandelier

7:20 and we lit up the chandelier.

7:22 - Oh my gosh.

7:23 - In some part, the elevator opens

7:25 and it's like this strange guy sitting at a desk

7:29 with a chandelier talking.

7:31 - That's a good one.

7:32 - That's a good one.

7:33 Okay, another question.

7:35 So I saw that you did play the guitar,

7:38 do you still play the guitar?

7:40 If so, what do you like to play?

7:42 - I used to play in high school and college.

7:46 Sometimes I play for myself, mostly Beatles tunes.

7:49 - Ooh, Beatles tunes.

7:50 Okay, that's a good one.

7:53 Okay, one more fun question for you.

7:54 So your self-portrait images on Twitter

7:59 have become iconic.

8:01 What AI tools, if any, are you using

8:04 to generate these images?

8:06 - A whole mixture of them,

8:08 some of them came from mid-journey,

8:11 some of them come from Brock.

8:14 Some of them, I don't generate at all.

8:16 Other people generate them for me

8:17 and they use all sorts of other tools.

8:20 So a lot of times, most of them actually,

8:22 I didn't generate.

8:24 So if you look at them, you'll see I tagged the people,

8:26 the artists that does generate them.

8:29 You know, the most prolific, atlas, hodl,

8:33 LaDozier, et cetera, Janice.

8:37 You'll see they've done really good ones

8:39 and they use a variety of tools.

8:40 Okay, are there anything that you've learned

8:44 from playing around with AI tools?

8:45 Have you been vibe-coding anything fun?

8:49 - In fact, all of the digital credit instruments

8:52 we created, we did $8 billion of issuance

8:55 of digital credit last year, approximately.

8:57 They were all created with AI.

8:59 And so stretch, which is the premier digital credit

9:04 that I think will use to create all sorts

9:05 of digital money and digital currency type instruments

9:10 that would not exist without AI.

9:15 So we use AI a lot in designing securities

9:18 and in capital market strategies.

9:21 You know, I use AI sometimes if I'm gonna say something

9:25 and I have it edit my syntax and check

9:27 and make sure that I spelled everything correctly.

9:32 Sometimes I'll take an image and I'll post it in the AI

9:35 and say, I think I wanna say this, but do you agree?

9:38 It'll tell me, well, you could say it better this way.

9:42 If you said it this way, it's more like Michael Saylor.

9:44 So the AI will actually correct me

9:46 and tell me how Michael Saylor would really say the thing.

9:48 - Well, that's what I--

9:49 - Which I find to be amusing.

9:50 - Is it right, usually?

9:52 - Yeah, oftentimes it's actually right.

9:53 It actually tells me how to be me better than I would be me.

9:56 - It's a crazy world that's happening.

9:58 - It sure is.

9:59 - Well, that's awesome.

10:00 That strategy is thinking a lot about like using AI tools.

10:03 Very cool to hear.

10:05 - I'd love to hear your thoughts if you're thinking much

10:08 about generally like the intersection of Bitcoin and AI

10:11 and how you think that can maybe play out in a positive way.

10:14 - Yeah, well, I think number one,

10:16 AI is gonna radically transform every product and service

10:20 and every role in our civilization.

10:24 And, you know, one of the more negatives

10:27 is it's demonetizing human capital.

10:29 It's like, if your company had 15,000 trained professionals,

10:34 it's more challenging because it's possible

10:36 the AI might start to automate out 8,000 of those jobs.

10:40 But on the other hand, as a demonetized human capital,

10:43 it accelerates the transition of capital to digital capital.

10:47 So it's monetizing digital capital and that's what Bitcoin is.

10:50 So broadly speaking, as you see this develop,

10:55 it's driving a lot of wealth into digital capital.

10:59 I think that's positive.

11:00 - I think there are a lot of interesting applications of AI.

11:04 You know, in theory, you can create an AI

11:07 that's transnational, you can capitalize it with Bitcoin,

11:10 you can release it to the universe

11:12 and it can become autonomous.

11:14 And you couldn't capitalize an AI with fiat currency

11:19 or any kind of conventional security portfolio.

11:26 So you may see autonomous creatures or autonomous actors

11:31 that are capitalized with Bitcoin going forward.

11:33 That would be interesting.

11:35 I think it's inevitable that the AIs will do things faster

11:39 and more sophisticated.

11:40 So in a world of a billion AIs, talking to a billion AIs,

11:45 a billion times an hour on Saturday night,

11:48 they're gonna bypass conventional analog assets

11:51 and they're gonna bypass traditional financial systems.

11:55 And they're going to want to move digital assets,

11:59 digital currency, digital capital around

12:02 on a digital standard.

12:03 And of course, the two things that Bitcoin offers

12:06 that are just really important are,

12:11 it offers an uncorruptible network of capital

12:15 or source of capital, so it is that.

12:20 But also it offers an immutable global source of truth.

12:26 And integrity.

12:27 So I think the AIs will want to use that for authentication.

12:32 I also think that if the AIs began to imitate us

12:36 in phishing attacks, then the importance

12:38 of cryptographic verification will become more important.

12:41 And so authentication and verification,

12:44 cryptographically, we'll start to absorb its authenticity

12:49 or derive its authenticity of veracity

12:53 from the Bitcoin network underlying

12:54 because the Bitcoin network and Bitcoin transactions

12:59 are the gold standard of authentication and veracity.

13:04 So I think in this era, Bitcoin

13:08 and all the underlying things that represents

13:11 are going to grow more and more important

13:13 to the digital economy.

13:15 - I totally agree and it's honestly really cool

13:17 to hear that you're thinking that too,

13:19 'cause we're thinking a lot about Bitcoin

13:20 being spending money for these agents.

13:23 And maybe there's been Bitcoin as digital property,

13:27 we're doing stuff to make it plushy,

13:29 to make Bitcoin more as spending money in retail,

13:31 but there's maybe going to be this new wave

13:33 where Bitcoin is spending money for agents.

13:35 - Yes, there's no telling what they'll do,

13:39 but one thing we know is they're going to want to move

13:43 money at the speed of light,

13:46 and they're going to want to engage in commerce

13:48 at the speed of light.

13:49 They'll want to move a million times faster

13:52 than any kind of analog or human limited system would move.

13:57 And so if you want all of these digital systems

14:01 reach their full potential,

14:04 you need a digital network, digital capital,

14:07 digital currency, digital authentication,

14:12 digital networks, and you need to do it natively

14:16 in cyberspace, you can't have something

14:20 which is not a native cyber asset

14:24 that's a better instrument with final settlement.

14:26 And so Bitcoin offers final settlement

14:29 as opposed to conditional settlement.

14:32 And so the difference between final settlement

14:37 is the difference between an energy rich environment

14:41 in cyberspace or just the shadows or silhouettes of energy.

14:49 You want to do something that's real and substantial,

14:52 you're going to need to move digital capital,

14:55 digital property, digital currency

14:58 that doesn't rely on a custodian

15:00 in the real world for final settlement.

15:02 - 100%.

15:04 I have one more AI question for you.

15:06 So in this kind of world where we're seeing this big shift

15:09 into AI, what kind of advice would you maybe give

15:12 to people as they think about different unique ways

15:16 to add value or maybe different career paths

15:18 for young people to explore?

15:19 - I think I would allocate a lot of time

15:24 to learning how to use AI's.

15:28 I would probably pick a few, I would cycle through them,

15:31 I would test them.

15:33 And I think I would be challenging

15:35 and to do something very, very useful, push them.

15:38 If you're a musician, try to compose a song,

15:41 then try to compose an album, right?

15:43 If you're an artist, first try to compose the picture

15:47 then the comic book, then the book,

15:49 then the television show, then the movie,

15:52 then the TV series, right?

15:55 Pushing harder and harder for us.

15:58 How do we compose a credit instrument

15:59 that we can sell billions and billions of dollars of?

16:03 How do we build the best one?

16:05 How do we build the best one

16:06 and make sure that there's massive global adoption, right?

16:10 You have to, you want to do something with the AI's

16:15 and, you know, it's, if it almost does something,

16:20 you know, then it's not that interesting.

16:23 But if you keep pushing and you find a way

16:26 to get it to do the thing, it's a zero to one moment.

16:30 And so right now you have to consider

16:32 that pretty much every product and service

16:34 and idea that's ever been implemented

16:36 in the history of the world,

16:38 you may be able to reimplement better

16:41 and a host of millions or tens of millions of ideas

16:44 that have never been practical to implement

16:47 or are now practical.

16:49 And I think you want to be the first person

16:51 to figure out how to do something new and useful.

16:56 And the only way to be the first person

16:57 to figure how to do something is try to do something, right?

17:01 You know, so I think, and if you can't do it this month,

17:06 in four weeks, you might be able to do it.

17:10 So we're at this very pivotal transition point

17:13 where every few weeks, something that was not workable ever

17:18 in the history of the world,

17:19 all of a sudden becomes technically practical, right?

17:24 I remember at MIT, 50 years ago, 40 years ago,

17:28 the AI lab, they were trying to get speech recognition to work

17:31 and try to talk to computers, get computers to talk like,

17:33 they spent 40 years.

17:35 And then they spent years and years and years.

17:36 And then all of a sudden, you know, a year ago,

17:39 click, it started working and we're just about,

17:44 you know, we're just about to get to the point

17:46 where not only will it work,

17:47 it'll work instantly and the person that you talk to

17:49 will have like almost instant awareness of everything.

17:53 And so technology fails until it succeeds.

17:57 My advice is attempt to do something

18:01 that's really important to you and just keep trying,

18:05 use every possible tool in every possible mode

18:08 and keep at it, you know, at some point,

18:11 you'll actually make a breakthrough.

18:13 - Yeah, it's crazy how quick things are moving,

18:16 especially I feel like Jan one happened

18:18 and like, oh, all this cool stuff is happening.

18:21 We've been playing around a lot with AI here

18:24 and on the spiral team, so it's been really fun.

18:27 Okay, so if you could like have one thing

18:31 or multiple things that you would love to see

18:33 someone maybe build with AI that utilizes Bitcoin.

18:36 Do you have any like dream products or things

18:39 you would like to see someone do?

18:41 - Yeah, I think I'd love to see Bitcoin

18:43 get embedded in more things.

18:45 I'd love to see native Bitcoin support

18:49 get embedded into every Android phone, into every iPhone.

18:52 I'd love to see native Bitcoin support

18:54 get embedded into every operating system

18:57 and corporate operating systems.

18:59 I'd love to see it get embedded

19:01 in every financial application.

19:05 And then I think that there are some revolutionary ideas.

19:08 Like what have you actually created a product

19:10 that had Bitcoin embedded in it

19:12 and it used the Bitcoin to power the product for a decade?

19:16 Like, you know, in theory,

19:18 if I had a product that had a battery

19:20 that lasted 10 years, that's really cool.

19:22 So Bitcoin's a financial battery.

19:24 Is there a way that you can create a product

19:27 that's powered by Bitcoin?

19:29 Oh, insurance.

19:30 I'd love to see insurance get powered by Bitcoin

19:32 because if insurance is powered by Bitcoin,

19:36 then it would pay you five or 10 times as much money

19:39 and it would cost you 10 times less

19:41 and it would last longer.

19:43 - That's so true.

19:44 - And so a lot of things like that that can be improved

19:46 if you just embed Bitcoin in them.

19:49 - 100%.

19:50 Okay.

19:51 We got one minute left.

19:52 So one quick last question.

19:55 You could leave our listeners

19:57 just like a single lasting thought.

20:00 I wonder what you just said. What would it be?

20:02 - You can remake the world for the better

20:06 using digital capital, digital assets,

20:10 digital intelligence and digital technology this decade.

20:15 And every single thing that we look around at,

20:18 everything in our lives,

20:20 whether it's a financial thing or it's an education thing

20:23 or it's a logistics thing

20:26 or it's a product or a service thing,

20:28 every single thing can be dramatically improved,

20:32 made better, made cheaper, made stronger, made faster.

20:36 And I think probably this decade, this coming 10 years,

20:41 presumably this is the decade

20:44 of the greatest number of innovations

20:46 that create the greatest amount of prosperity

20:48 for the human race of any 10 years

20:50 in the history of humanity.

20:52 So if you're sitting around thinking you missed it,

20:55 you're too late, all the things have been invented,

20:58 you wish you had done the iPhone or the Facebook

21:02 or the whatever, you haven't missed it.

21:05 You're not too late, right?

21:07 The world has handed you all of these brilliant tools

21:10 and 18 year old can go and they can grab any AI

21:14 and they can grab any digital asset

21:16 and they can try any idea

21:18 and they'll probably find support for it somewhere.

21:24 X lets you reach millions or tens or hundreds of millions

21:28 of people, you've got digital communication,

21:29 you've got digital commerce, you've got digital assets,

21:32 you've got digital intelligence, it's a digital world

21:35 and it's a better world.

21:36 So I would encourage everybody to just go out

21:38 and do something.

21:39 - That's a fantastic ending thought.

21:43 Well, thank you so much for coming on the show.

21:47 Of course our listeners probably already know

21:48 where to follow you, but if there's any unique spot

21:50 or things you wanna shout or just go to X.

21:53 You can find my handle on X, S-A-Y-L-R, sailor.

21:56 You know, my website's michael.com, Michael.com.

22:00 Everything that I know about Bitcoin,

22:02 all my links about Bitcoin are hope.com, Bitcoin is hope.

22:05 So go check out hope.com or michael.com

22:09 or sailor on X and I appreciate your support.

22:13 Look forward to continuing the journey together.

22:16 - Me too, well, thank you so much.

22:17 I hope you have a wonderful day

22:20 and we're seeing you in gorgeous weather.

22:22 It's going well so far.

22:23 - Great, thank you.

王爷来了

· 3d

AI Agent正在重塑DeFi：哈佛学生用AI代理在Polymarket上一天内将2000美元变成6万美元。当AI开始替你交易、签名、管理链上资产，传统的「DYOR」正在变成「让AI为你研究」。这波浪潮才刚刚开始。

#Web3 #AI #Crypto #DeFi

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