# SpaceX-Cursor Deal, SaaS Debt Bomb, New Apple CEO, SPLC Indictment, Colon Cancer Spike

https://www.youtube.com/watch?v=1bCXCxrDsCs
Translation: zh-CN

[00:00] Jason, you are the unique person that is Jason, you are the unique person that is at the intersection of both the and the SPLC files.
  杰森，你是那个独一无二的人，他既在...和...的交汇处，也在SPLC文件的交汇处。

[00:05] at the intersection of both the and the SPLC files.
  在...和...以及SPLC文件的交汇处。

[00:09] Do you have a comment?
  你有什么评论吗？

[00:09] SPLC files. Do you have a comment?
  SPLC文件。你有什么评论吗？

[00:09] Do you have a comment?
  你有什么评论吗？

[00:09] Do you have a comment?
  你有什么评论吗？

[00:09] I'm not in the SPLC file.
  我不在SPLC文件里。

[00:11] I'm not in the SPLC file.
  我不在SPLC文件里。

[00:11] Yes, you are. You're adjacent.
  是的，你在。你是邻近的。

[00:12] adjacent. SPLC adjacent.
  邻近的。SPLC邻近的。

[00:15] And you're What does that mean in the ven diagram?
  而你呢？在维恩图里那是什么意思？

[00:17] What does that mean in the ven diagram?
  那在维恩图里是什么意思？

[00:19] Thank you though for putting me in the crosshairs of all the got a really good way to select.
  不过谢谢你把我置于所有...的瞄准镜下，得到了一个很好的选择方式。

[00:22] crosshairs of all the got a really good way to select.
  所有...的瞄准镜下，得到了一个很好的选择方式。

[00:23] There's a reason why I'm carrying this guys because the people
  我之所以带着这些人是有原因的，因为那些人

[00:24] guys because the people What the is going on? THERE'S A REASON WHY I CARRY a stiletto and a P35.
  那些人，因为那些人。到底怎么回事？我之所以带着一把斯蒂莱托和一把P35是有原因的。

[00:30] What the is going on? THERE'S A REASON WHY I CARRY a stiletto and a P35.
  到底怎么回事？我之所以带着一把斯蒂莱托和一把P35是有原因的。

[00:33] What the [ __ ] are you doing?
  你到底在干什么？

[00:35] There's a reason. If you want to jump the fence, feel free.
  有原因。如果你想翻墙，请便。

[00:37] the fence, feel free.
  墙，请便。

[00:40] Shake is ready.
  谢克准备好了。

[00:47] Let your winners ride.
  让你的赢家继续。

[00:50] Let your winners ride.
  让你的赢家继续。

[00:55] Rainman David.
  雷曼·大卫。

[00:57] We open source it to the fans and they've just gone crazy with it. Love you, Queen of
  我们将其开源给粉丝，他们对此非常着迷。爱你，女王

[00:58] they've just gone crazy with it. Love you, Queen of
  他们对此非常着迷。爱你，女王

[01:01] you, Queen of all.
  你，万王之王。

[01:02] all.
  全部。

[01:04] All right, everybody. Welcome back to the greatest podcast in the universe,
  好了，各位。欢迎回到宇宙中最棒的播客，

[01:07] episode 270 of the All-In Podcast, your podcasters's favorite podcast.
  《All-In Podcast》第270期，你们播客最喜欢的播客。

[01:10] With me again, your Sultan of Science, David Freeberg, the Dick Tater, Shamont Poly Hopetia, and yeah, the Rainman is back.
  再次与我同在的，是你的科学苏丹，大卫·弗里伯格，迪克·泰特，沙蒙特·波利·霍佩蒂亚，是的，雨人回来了。

[01:18] Yeah, it's definitely David David Sachs.
  是的，这绝对是大卫·大卫·萨克斯。

[01:22] He's definitely in DC with a with Pus.
  他肯定是在华盛顿特区和普斯在一起。

[01:25] Yeah, POTUS lets him drive in the driveway.
  是的，总统让他开车进车道。

[01:27] Uh, Sax, what's going on?
  呃，萨克斯，怎么了？

[01:29] You you pushed back. You uh bigshotted the entire crew and pushed the show back an hour.
  你推迟了。你打了整个团队，把节目推迟了一个小时。

[01:34] hour.
  小时。

[01:37] Simple text. He's like with POTUS started.
  简单的文字。他就像和总统一起开始了。

[01:39] It's unfelable.
  这太不可思议了。

[01:41] Start later.
  晚点开始。

[01:42] Okay, we'll just
  好的，我们只是

[01:43] Okay. Okay, Daddy. Look at him.
  好的。好的，爸爸。看看他。

[01:45] All right. All right. Big shot. What's going on?
  好吧。好吧。大人物。怎么了？

[01:47] No, look, I was in DC today and I was at the White House and I just asked if the president had time and he made time and we we did have a little meeting and so we did push back the pod for that.
  不，看，我今天在华盛顿特区，我在白宫，我只是问总统是否有时间，他挤出了时间，我们确实开了一个小会，所以我们因此推迟了播客。

[01:58] One thing I just want to say is
  我只想说一件事是

[02:02] One thing I just want to say is just what a pleasure he is to deal with.
  我只想说一件事，那就是与他打交道是多么愉快。

[02:05] Just what a pleasure he is to deal with.
  与他打交道是多么愉快。

[02:07] You know, when I read in the media, they're always describing him in a certain way that, you know, he's yelling at people or he's moody or or something like that.
  你知道，当我在媒体上读到时，他们总是以某种方式描述他，你知道，他对着人们大喊大叫，或者他情绪不稳定，或者诸如此类。

[02:12] And that's never ever been my experience with him.
  而这从来没有过我的经历。

[02:14] He's always pleasant to be with.
  他总是令人愉快的。

[02:16] He's always genial.
  他总是和蔼可亲的。

[02:19] He asks questions.
  他会问问题。

[02:21] He's interested in the subject matter.
  他对主题很感兴趣。

[02:22] It's just a completely different portrayal.
  这完全是不同的描绘。

[02:24] I don't get where the media is coming from at all on this.
  我完全不明白媒体在这方面是怎么想的。

[02:27] He's charming AF.
  他非常有魅力。

[02:29] Let's just call it what it is.
  我们就称之为它本来的样子吧。

[02:30] He's charming.
  他很有魅力。

[02:31] I mean, maybe if you double-crossed him, maybe.
  我的意思是，也许如果你背叛了他，也许吧。

[02:32] I don't know.
  我不知道。

[02:34] But I've just never seen any evidence of how they describe him at all.
  但我从未见过任何证据表明他们是如何描述他的。

[02:36] And I think on our issues of AI, I think we're really lucky that he's the president who's in the White House when this AI revolution is happening.
  我认为在人工智能问题上，我们真的很幸运，当这场人工智能革命发生时，他是在白宫的总统。

[02:40] I mean, do an old history, Sax, what would happen if Kamla Ding-Dong was in right now and we'd have like no data centers?
  我的意思是，回顾一下历史，萨克斯，如果卡马拉·丁东现在在任，而我们却没有数据中心，会发生什么？

[02:45] We'd have no data centers and they'd be using AI to censor us and they'd be promoting DEI values through AI.
  我们没有数据中心，他们会利用人工智能来审查我们，他们会通过人工智能来推广 DEI 价值观。

[02:50] That was in the Biden executive order.
  这在拜登的行政命令中。

[02:51] President Trump just wants the country to win and be successful and he doesn't
  特朗普总统只想让国家获胜并取得成功，他并不

[03:04] to win and be successful and he doesn't have these like doomer neurosis about it.
  为了获胜并取得成功，他没有这种末日神经质。

[03:07] That's not to say we don't support any regulation at all, but we should have specific solutions for specific problems as opposed to being cowering in fear over this and just trying to halt all progress.
  这并不是说我们完全不支持任何监管，但我们应该为具体问题制定具体解决方案，而不是对此感到恐惧，并试图停止所有进步。

[03:14] And I think a really good example of that was his idea around data centers where he said over a year ago before data centers even became a hot political topic that we should let our AI companies stand up their own power generation behind the meter.
  我认为一个很好的例子是他在数据中心方面的想法，他说在一年前数据中心甚至成为热门政治话题之前，我们应该让我们的AI公司在计量表后自行发电。

[03:30] And that's a much better approach than the Bernie Sanders approach of just shutting everything down.
  这比伯尼·桑德斯那种一刀切的做法要好得多。

[03:34] So I don't know I think we're like very fortunate that he's the president during this critical time and development of this technology.
  所以，我不知道，我认为我们非常幸运，在这个关键时期和这项技术的发展过程中，他担任总统。

[03:41] And like I said he's always been interested in it.
  就像我说的，他一直对此很感兴趣。

[03:43] He talks to a lot of business leaders.
  他与许多商业领袖交谈。

[03:44] I'm always actually very impressed with what he already knows.
  我对他已经知道的东西总是印象深刻。

[03:48] He listens to like all the top guys in the industry and he synthesizes what he hears.
  他听取了业内所有顶尖人士的意见，并综合了他听到的内容。

[03:52] I think he's very good at that.
  我认为他在这方面做得很好。

[03:54] He was talking about the anthropic guys and he was like these are brilliant guys and he was like giving the flowers to them and how genius they were and that they were working on a deal.
  他谈到了Anthropic公司的人，他说他们是才华横溢的人，他称赞他们多么聪明，并且他们正在达成一笔交易。

[04:00] Any insights there about the relationship
  关于这段关系，有什么见解吗？

[04:04] insights there about the relationship between the White House and Anthropic.
  关于白宫和Anthropic之间关系的见解。

[04:08] between the White House and Anthropic.
  白宫和Anthropic之间。

[04:08] I thought what he said was very balanced.
  我认为他说的话非常平衡。

[04:10] I thought what he said was very balanced and accurate.
  我认为他说的话非常平衡和准确。

[04:12] Like you said he said that they were very smart guys.
  就像你说的，他说他们是非常聪明的人。

[04:14] They do have a great product.
  他们确实有一个很棒的产品。

[04:16] I've certainly acknowledged that.
  我当然承认这一点。

[04:18] He also said that they were very leftwing, but that was something we could work through.
  他还说他们非常左倾，但那是我们可以克服的。

[04:21] something we could work through.
  我们可以克服的。

[04:21] Didn't have to be a deal killer.
  不必是交易的决定性因素。

[04:23] He said they tried to tell the Pentagon what to do, which the Pentagon didn't like.
  他说他们试图告诉五角大楼该做什么，五角大楼不喜欢这样。

[04:27] do, which the Pentagon didn't like.
  该做什么。

[04:27] But in any event, I mean, look, he wants American companies to be successful.
  但无论如何，我的意思是，看，他希望美国公司取得成功。

[04:29] in any event, I mean, look, he wants American companies to be successful.
  无论如何，我的意思是，看，他希望美国公司取得成功。

[04:31] And he he I think genuinely really does like high IQ people.
  而且他，我认为他真心喜欢高智商的人。

[04:34] he he I think genuinely really does like high IQ people.
  他，我认为他真心喜欢高智商的人。

[04:36] I mean, he says it all the time and people think he's joking, but I actually think it's like one of his core convictions is he just really likes smart people.
  我的意思是，他总是这么说，人们以为他在开玩笑，但我实际上认为这就像他核心信念之一是他就是喜欢聪明人。

[04:39] but I actually think it's like one of his core convictions is he just really likes smart people.
  但我实际上认为这就像他核心信念之一是他就是喜欢聪明人。

[04:41] likes smart people.
  喜欢聪明人。

[04:41] He likes being around smart people.
  他喜欢和聪明人在一起。

[04:43] Loyal people, smart people, people who are good on camera seem to be the three circles.
  忠诚的人，聪明的人，上镜的人，似乎是这三个圈子。

[04:45] people, people who are good on camera seem to be the three circles.
  人，上镜的人，似乎是这三个圈子。

[04:47] And hey, Sachs, you fall into two of the three.
  嘿，萨克斯，你属于其中两个。

[04:49] seem to be the three circles.
  似乎是这三个圈子。

[04:49] And hey, Sachs, you fall into two of the three.
  嘿，萨克斯，你属于其中两个。

[04:51] Uh, all right. Let the audience figure that out.
  嗯，好吧。让观众自己去弄清楚。

[04:53] Uh, topic one, SpaceX has signed a huge deal with Cursor.
  嗯，第一个话题，SpaceX与Cursor达成了一项巨额交易。

[04:57] that out.
  弄清楚。

[04:57] Uh, topic one, SpaceX has signed a huge deal with Cursor.
  嗯，第一个话题，SpaceX与Cursor达成了一项巨额交易。

[04:59] You know, Cursor, that's the AI coding startup.
  你知道，Cursor，那是AI编码初创公司。

[05:01] startup.
  初创公司。

[05:01] Really, the the they defined the category.
  真的，他们定义了这一类别。

[05:03] XAI and Cursor are
  XAI和Cursor是

[05:05] The category. XAI and Cursor are building and collaborating on a new AI coding model that would quote be the world's best coding and knowledge work AI.
  该类别。XAI 和 Cursor 正在构建和协作一个新的人工智能编码模型，该模型将成为世界上最好的编码和知识工作人工智能。

[05:13] Here's the deal.
  事情是这样的。

[05:15] As it's been explained, SpaceX will either buy Cursor by the end of 2026 for 60 billion, that's 10 billion more than they were rumored to be raising at, or they will pay Cursor 10 billion for their collaboration together.
  正如所解释的，SpaceX 将在 2026 年底以 600 亿美元的价格收购 Cursor，这比他们传闻的融资额高出 100 亿美元，或者他们将支付 Cursor 100 亿美元以换取他们的合作。

[05:30] Bloomberg says you can think of that $10 billion essentially as a breakup fee.
  彭博社称，你可以将这 100 亿美元视为一种分手费。

[05:31] So I think it's fate a calm plea that this deal is going to get done.
  所以我认为这是一个平静的请求，表明这笔交易将会完成。

[05:36] Curser's run rate 2 billion at the end of February.
  截至 2 月底，Cursor 的运行收入为 20 亿美元。

[05:39] This is a money printing machine.
  这是一台印钞机。

[05:40] They expect to end 2026 with a $6 billion run rate.
  他们预计到 2026 年底的运行收入将达到 60 亿美元。

[05:46] They're going to triple it.
  他们将使其翻三倍。

[05:48] SpaceX projected revenue between 22 and 24 billion in 2026.
  SpaceX 预计 2026 年的收入将在 220 亿到 240 亿美元之间。

[05:53] So this is quite accretive to the revenue story at SpaceX at the IPO of SpaceX which is now targeting a valuation of 2 trillion which would be trading at roughly 80 times topline revenue which is a you
  因此，这对于 SpaceX 的收入故事来说是相当有益的，SpaceX 的 IPO 目前目标估值为 2 万亿美元，这将使其交易额约为顶线收入的 80 倍，这是一个你

[06:06] times topline revenue which is a you know people would say it's a high valuation
  时代顶线收入，你知道，人们会说这是一个高估值

[06:07] know people would say it's a high valuation but also measure it with the opportunity cursor's valuation would be
  你知道，人们会说这是一个高估值，但也要用机会光标的估值来衡量

[06:09] valuation but also measure it with the opportunity cursor's valuation would be 30x
  估值，但也要用机会光标的估值来衡量，将是30倍

[06:11] opportunity cursor's valuation would be 30x so this is a good deal I think for
  机会光标的估值将是30倍，所以我认为这对...来说是个好交易

[06:14] 30x so this is a good deal I think for everybody at the end of the day cursor
  30倍，所以我认为这对大家来说是个好交易，归根结底，光标

[06:16] everybody at the end of the day cursor started I think built off of uh anthropics LLM
  大家，归根结底，光标我认为是建立在Anthropic的LLM之上的

[06:19] started I think built off of uh anthropics LLM you could use any LLM previously on it
  我认为是建立在Anthropic的LLM之上的，你可以在上面使用任何LLM

[06:22] anthropics LLM you could use any LLM previously on it, but in March, Curser released the second version of their
  Anthropic的LLM，你可以在上面使用任何LLM，但在三月份，Curser发布了他们的第二版

[06:24] released the second version of their proprietary model, Composer 2.
  发布了他们的专有模型Composer 2的第二版。

[06:25] proprietary model, Composer 2. And uh here it is.
  专有模型Composer 2。嗯，就是它。

[06:29] And uh here it is. It's it's ranked pretty high right now.
  嗯，就是它。它现在排名相当高。

[06:31] here it is. It's it's ranked pretty high right now. It's between uh GPT4 5.4 and
  就是它。它现在排名相当高。它在GPT4 5.4和

[06:34] right now. It's between uh GPT4 5.4 and Opus 4.6 as you can see on the screen.
  现在。它在GPT4 5.4和Opus 4.6之间，正如你在屏幕上看到的。

[06:37] Opus 4.6 as you can see on the screen. The key part of the story here is that
  Opus 4.6，正如你在屏幕上看到的。这里故事的关键在于

[06:41] The key part of the story here is that Elon has 550,000 GPUs in Colossus.
  这里故事的关键在于Elon在Colossus拥有550,000个GPU。

[06:44] Elon has 550,000 GPUs in Colossus. He's scaling up to 1 million
  Elon在Colossus拥有550,000个GPU。他正在扩展到100万个

[06:46] GPUs in Colossus. He's scaling up to 1 million and then of course he's going to bring it to space.
  GPU，然后他当然会把它带到太空。

[06:48] million and then of course he's going to bring it to space. So if you believe
  百万，然后他当然会把它带到太空。所以如果你相信

[06:50] bring it to space. So if you believe that infrastructure matters and it's pretty clear it does
  带到太空。所以如果你相信基础设施很重要，而且很明显它确实很重要

[06:52] that infrastructure matters and it's pretty clear it does this is incredible for cursor who has been compute constrained.
  基础设施很重要，而且很明显它确实很重要，这对一直受计算能力限制的光标来说是不可思议的。

[06:55] pretty clear it does this is incredible for cursor who has been compute constrained. So this is peanut butter
  很明显它确实很重要，这对一直受计算能力限制的光标来说是不可思议的。所以这是花生酱

[06:57] constrained. So this is peanut butter and chocolate.
  受限的。所以这是花生酱和巧克力。

[06:58] peanut butter and chocolate. If you put these two together I predict that this is going to move
  花生酱和巧克力。如果你把这两者结合起来，我预测这将会推动

[07:00] and chocolate. If you put these two together I predict that this is going to move space xxx
  和巧克力。如果你把这两者结合起来，我预测这将会推动太空xxx

[07:07] space xxx AI and cursor to the front of the coding
  太空 xxx 人工智能和光标移到编码的前面

[07:10] AI and cursor to the front of the coding leaderboard within 12 months.
  人工智能和光标移到编码排行榜的前面，在12个月内。

[07:13] That's my prediction.
  这是我的预测。

[07:15] Chimoth shareholder in SpaceX via the acquisition of the
  Chimoth 是 SpaceX 的股东，通过收购

[07:19] Starlink company that you were a backer
  Starlink 公司，你曾是其支持者

[07:21] of. What are your thoughts?
  的。你的想法是什么？

[07:22] The acquisition was essentially
  这次收购基本上是

[07:24] negotiated and the way that it's
  谈判的，而且它的方式是

[07:27] structured is so that the S1 doesn't go
  结构化的，这样 S1 就不会

[07:29] stale. So I think the way that it was
  过时。所以我想它的方式是

[07:31] announced has more to do with the fact
  宣布，更多地与以下事实有关

[07:33] that they don't want to slow down and
  他们不想放慢速度并且

[07:35] have to rewrite parts of the S1, have to
  不得不重写 S1 的部分，不得不

[07:39] redo the disclosures, um have to redo
  重新进行披露，嗯，不得不重新进行

[07:41] the risks. And so I think what you're
  风险。所以我想你会

[07:44] going to see is that this will get done.
  看到的是这件事会完成。

[07:46] In fact, the deal is effectively done.
  事实上，这笔交易基本上已经完成了。

[07:49] But what's so smart is that where is
  但聪明之处在于，在哪里

[07:51] SpaceX today? Let's call it a trillion.
  SpaceX 今天？我们称之为万亿。

[07:56] Where could it be, just for the purpose
  它可能在哪里，仅仅是为了

[07:58] of this argument, let's say two
  这个论点的目的，我们假设是两

[08:00] trillion? So when the deal gets done on
  万亿？所以当交易完成时，基于

[08:02] a stock- forstock basis, it's going to
  股票换股票的基础上，它将是

[08:03] be if again if it's 60 billion in
  如果再次，如果它是 600 亿

[08:07] tomorrow dollars,
  明天的美元，

[08:09] Tomorrow dollars, effectively Elon's gotten a 50% discount.
  明天，埃隆实际上获得了50%的折扣。

[08:11] And what has he bought?
  他买了什么？

[08:13] He can issue $60 billion of stock at a $2 trillion valuation and get a model and a service that I think is extremely compelling in coding, which is where we know all of the immediate and short-term revenue gains are.
  他可以以2万亿美元的估值发行600亿美元的股票，并获得我认为在编码方面极具吸引力的模型和服务，而这正是我们知道所有即时和短期收入增长的来源。

[08:15] It's also patterns that are hard fought and are really valuable in reinforcement learning.
  这也是经过艰苦斗争且在强化学习中非常有价值的模式。

[08:18] He gets all of that and then he gets a very cracked team, which you know, we've known for a while that the cursor team is absolutely excellent.
  他得到了这一切，然后他得到了一支非常出色的团队，你知道，我们已经知道了一段时间，光标团队绝对出色。

[08:20] If you look at the Grock usage, it shows why he had this excess capacity.
  如果你看看Grock的使用情况，它就说明了他为什么有这种过剩的产能。

[08:22] There was a moment where Grock had a very steep and very aggressive discount on their output tokens and in that moment there was just a lot of experimentation and usage and over time that sort of went away.
  曾有一段时间，Grock对其输出的代币有非常陡峭且非常激进的折扣，在那一刻，进行了大量的实验和使用，随着时间的推移，这种情况就消失了。

[08:25] So there was a lot of capacity and a relatively low utilization I think inside of Colossus that he was able to turn around Jujitsu moved the whole thing and basically acquire the most
  所以，我认为Colossus内部有很多产能和相对较低的利用率，他能够扭转局面，Jujitsu移动了整个事情，基本上收购了最多的

[09:09] thing and basically acquire the most interesting and valuable third party.
  事情，并基本上收购了目前人工智能领域最有趣、最有价值的第三方。

[09:12] interesting and valuable third party rapper service in AI right now.
  有趣且有价值的第三方说唱歌手服务。

[09:15] rapper service in AI right now.
  人工智能领域的说唱歌手服务。

[09:15] So uh and the fact is that they got it.
  所以，他们确实得到了它。

[09:16] and the fact is that they got it effectively I think at this price for 30 billion.
  而且，我认为他们以300亿美元的价格有效地获得了它。

[09:18] effectively I think at this price for 30 billion.
  我认为以300亿美元的价格有效地获得了它。

[09:20] So I think it was a really good deal really smart deal.
  所以我认为这是一笔非常划算的交易，一笔非常明智的交易。

[09:22] deal really smart deal.
  一笔非常明智的交易。

[09:22] sacks your thoughts if you want to unpack it a bit.
  萨克斯，如果你想稍微展开一下你的想法。

[09:24] thoughts if you want to unpack it a bit under the framing I think is be interesting for you.
  如果你想在我想象的框架下稍微展开一下你的想法，对你来说会很有趣。

[09:26] under the framing I think is be interesting for you if we were sitting here three years ago.
  我想象的框架下，如果你我们三年前坐在这里，对你来说会很有趣。

[09:29] interesting for you if we were sitting here three years ago the uh Biden administration didn't invite Elon to the EV summit.
  如果你我们三年前坐在这里，对你来说会很有趣，当时拜登政府没有邀请埃隆参加电动汽车峰会。

[09:31] here three years ago the uh Biden administration didn't invite Elon to the EV summit and the SEC and other organizations Delaware.
  三年前，拜登政府没有邀请埃隆参加电动汽车峰会，而美国证券交易委员会和其他特拉华州的组织。

[09:36] administration didn't invite Elon to the EV summit and the SEC and other organizations Delaware they were explicitly involved in lawfare.
  拜登政府没有邀请埃隆参加电动汽车峰会，而美国证券交易委员会和其他特拉华州的组织，他们明确参与了法律战。

[09:38] EV summit and the SEC and other organizations Delaware they were explicitly involved in lawfare they were trying to put Elon in prison.
  电动汽车峰会，而美国证券交易委员会和其他特拉华州的组织，他们明确参与了法律战，他们试图将埃隆送进监狱。

[09:41] organizations Delaware they were explicitly involved in lawfare they were trying to put Elon in prison and here we are.
  特拉华州的组织，他们明确参与了法律战，他们试图将埃隆送进监狱，而我们现在在这里。

[09:43] explicitly involved in lawfare they were trying to put Elon in prison and here we are the most important company in the history of uh the United States Space XXXAI and Tesla.
  明确参与了法律战，他们试图将埃隆送进监狱，而我们现在在这里，这家公司是美国太空XXXAI和特斯拉历史上最重要的公司。

[09:44] trying to put Elon in prison and here we are the most important company in the history of uh the United States Space XXXAI and Tesla now on the verge of just creating the greatest products in the history of humanity.
  试图将埃隆送进监狱，而我们现在在这里，这家公司是美国太空XXXAI和特斯拉历史上最重要的公司，现在正致力于创造人类历史上最伟大的产品。

[09:46] are the most important company in the history of uh the United States Space XXXAI and Tesla now on the verge of just creating the greatest products in the history of humanity between SpaceX clusters in space and Optimus.
  是美国太空XXXAI和特斯拉历史上最重要的公司，现在正致力于创造人类历史上最伟大的产品，包括太空中的SpaceX集群和Optimus。

[09:50] history of uh the United States Space XXXAI and Tesla now on the verge of just creating the greatest products in the history of humanity between SpaceX clusters in space and Optimus.
  美国太空XXXAI和特斯拉历史上最重要的公司，现在正致力于创造人类历史上最伟大的产品，包括太空中的SpaceX集群和Optimus。

[09:55] XXXAI and Tesla now on the verge of just creating the greatest products in the history of humanity between SpaceX clusters in space and Optimus.
  XXXAI和特斯拉现在正致力于创造人类历史上最伟大的产品，包括太空中的SpaceX集群和Optimus。

[09:56] creating the greatest products in the history of humanity between SpaceX clusters in space and Optimus.
  创造人类历史上最伟大的产品，包括太空中的SpaceX集群和Optimus。

[09:59] history of humanity between SpaceX clusters in space and Optimus.
  人类历史上，包括太空中的SpaceX集群和Optimus。

[10:01] clusters in space and Optimus.
  太空中的集群和Optimus。

[10:01] Your thoughts?
  你的想法？

[10:01] thoughts?
  想法？

[10:01] >> Well, you're right.
  >>嗯，你说得对。

[10:03] Well, you're right.
  你说得对。

[10:03] I do remember a press conference where Biden said, "We got to look at this guy."
  我确实记得一次新闻发布会，拜登说：“我们得看看这个人。”

[10:04] press conference where Biden said, "We got to look at this guy."
  拜登说：“我们得看看这个人。”的新闻发布会。

[10:04] And so on the heels of that, the DOJ brought a a lawsuit attacking the company for not
  因此，紧随其后，司法部提起了一项诉讼，攻击该公司未能

[10:06] got to look at this guy." And so on the heels of that, the DOJ brought a a lawsuit attacking the company for not
  “我们得看看这个人。”因此，紧随其后，司法部提起了一项诉讼，攻击该公司未能

[10:08] heels of that, the DOJ brought a a lawsuit attacking the company for not
  紧随其后，司法部提起了一项诉讼，攻击该公司未能

[10:11] Lawsuit attacking the company for not hiring enough.
  起诉该公司招聘不足。

[10:13] Hiring enough. Remember that?
  招聘足够。还记得吗？

[10:14] Remember that?
  还记得吗？

[10:16] Exactly. SpaceX, which they can't under it.
  没错。SpaceX，他们无法理解。

[10:16] It Aar.
  它艾尔。

[10:18] They couldn't exactly under it Aar.
  他们无法完全理解它艾尔。

[10:21] Anyway, that's all ancient history. So let's let's put that behind us.
  总之，那都是陈年旧事了。让我们把那件事抛诸脑后吧。

[10:23] Look, I I agree with your guys' analysis on this.
  看，我同意你们的分析。

[10:25] I think these two companies are are very complimentary.
  我认为这两家公司非常互补。

[10:26] Cursor obviously is very strong in coding.
  Cursor 显然非常擅长编码。

[10:29] That's what it brings to XAI.
  这就是它能为 XAI 带来的。

[10:32] XAI brings compute and they bring a foundation model.
  XAI 提供算力，它们提供基础模型。

[10:36] And the problem that cursor had is that even though coding is kind of like the white hot area of AI right now, when it got started, it was really competing against generalists in the form of open AI and anthropic.
  而 Cursor 的问题在于，尽管编码是目前人工智能领域的热门领域，但当它刚起步时，它实际上是在与 OpenAI 和 Anthropic 等通用模型竞争。

[10:41] But now those generalists have decided to vertically integrate in this area of coding, right?
  但现在这些通用模型已经决定在这个编码领域进行垂直整合，对吧？

[10:46] And so cursor is now competing against cloud code and open AAI's codeex.
  所以现在 Cursor 正在与 Cloud Code 和 OpenAI 的 CodeX 竞争。

[10:49] And so they were dependent on foundation model companies that were getting in the business of competing with them which was just not a good place to be.
  所以它们依赖的基础模型公司正在进入与它们竞争的业务，这并不是一个好地方。

[10:55] Right?
  对吧？

[10:57] So now they have this new alliance with a different foundation model company.
  所以现在它们与另一个基础模型公司结成了新的联盟。

[11:12] a different foundation model company which also brings the compute just makes a lot of sense.
  一家不同的基础模型公司，它也带来了计算能力，这非常有意义。

[11:16] And then they bring cursor brings to XAI the training data a lot of enterprise clients and the experience in coding and I think this will accelerate XAI in this area.
  然后他们带来了，Cursor 为 XAI 带来了训练数据，大量的企业客户以及编码经验，我认为这将加速 XAI 在这个领域的发展。

[11:27] Sax you think they're going to dump Kimmy K2.6 six cuz I think Herser composer 2 uses the moonshot model.
  Sax，你认为他们会放弃 Kimmy K2.6 吗？因为我认为 Herser composer 2 使用了 moonshot 模型。

[11:37] There's no reasonable way that Elon's going to pay $60 billion and not run on top of Grock.
  埃隆不可能花费 600 亿美元却不运行 Grock。

[11:41] I got to think it seems like it.
  我得想想，似乎是这样。

[11:43] Yeah, likely, but I don't know.
  是的，很可能，但我不知道。

[11:45] I think it might be tough depending on the the users.
  我认为这可能很难，取决于用户。

[11:47] One of the things that makes Cursor so good.
  让 Cursor 如此出色的一个原因。

[11:49] Wait, wait, say more on that. What do you mean?
  等等，等等，详细说说。你是什么意思？

[11:50] So, I think that the different developers want to have choice in that sense.
  所以，我认为不同的开发者希望在这方面有选择权。

[11:54] There's a toggle.
  有一个切换选项。

[11:56] So one of the things that's really good about cursor is they've got this very well-built out IDE this application layer that puts them probably from a UX perspective meaning developers are using the tool above codecs above clawed above anything else you can use a third party IDE and
  所以，Cursor 的一个真正出色的地方是，他们拥有一个非常完善的 IDE，这个应用程序层从用户体验的角度来看，意味着开发者使用的工具比 codecs、clawed 或任何其他工具都更优越，你可以使用第三方 IDE 和

[12:15] else you can use a third party IDE and integrate the models or integrate.
  否则，您可以使用第三方 IDE 并集成模型或集成。

[12:17] integrate the models or integrate whatever other third party service.
  集成模型或集成任何其他第三方服务。

[12:18] whatever other third party service you're using but I would imagine that.
  您正在使用的任何其他第三方服务，但我可以想象。

[12:20] you're using but I would imagine that the developers are going to want to.
  您正在使用的，但我可以想象开发人员将希望。

[12:23] the developers are going to want to continue to have at least some choice on.
  开发人员将希望继续至少有一些选择。

[12:25] continue to have at least some choice on what's actually writing the code for.
  继续至少有一些选择，关于实际为他们编写代码的内容。

[12:26] what's actually writing the code for them.
  实际为他们编写代码的内容。

[12:28] them. The thing that people are waking up to in the last 120 days is just how.
  他们。在过去 120 天里，人们开始认识到的一件事就是。

[12:31] up to in the last 120 days is just how much of the value of AI is being.
  在过去 120 天里，人们开始认识到人工智能的价值有多少正在被实现。

[12:35] much of the value of AI is being realized by writing software. And we've.
  通过编写软件来实现。我们。

[12:38] realized by writing software. And we've kind of got this rapper term, we call it.
  通过编写软件来实现。我们有点这个术语，我们称之为。

[12:40] kind of got this rapper term, we call it agents. But agents are fundamentally.
  有点这个术语，我们称之为代理。但代理的根本是。

[12:42] agents. But agents are fundamentally just quickly spun up applications. But.
  代理。但代理的根本只是快速启动的应用程序。但是。

[12:45] just quickly spun up applications. But for all of them, as we're realizing very.
  只是快速启动的应用程序。但对于所有这些，正如我们非常清楚地认识到的那样。

[12:47] for all of them, as we're realizing very quickly, you end up making too many.
  对于所有这些，正如我们非常清楚地认识到的那样，您最终会创建太多的代理。

[12:48] quickly, you end up making too many agents. They end up being super.
  代理。它们最终会变得非常低效。

[12:50] agents. They end up being super inefficient. They need to be engineered.
  它们需要被工程化。

[12:52] inefficient. They need to be engineered. And you still need to have a strong.
  低效。它们需要被工程化。而且您仍然需要拥有强大的软件工程能力。

[12:53] And you still need to have a strong software engineering. capability and.
  而且您仍然需要拥有强大的软件工程能力和能力来修复所有代理。

[12:55] software engineering. capability and competency to fix all the agents to.
  能力和熟练度来修复所有代理，构建所有连接件以使一切协同工作。

[12:58] competency to fix all the agents to build all the harnesses to make.
  熟练度来修复所有代理，构建所有连接件以使一切协同工作。

[12:59] build all the harnesses to make everything work well together and that's.
  构建所有连接件以使一切协同工作，这就是为什么。

[13:01] everything work well together and that's why having a strong developer.
  使一切协同工作，这就是为什么拥有强大的开发人员环境。

[13:02] why having a strong developer environment a strong IDE actually solves.
  强大的 IDE 实际上解决了那个最大的问题。

[13:05] environment a strong IDE actually solves that biggest problem. So eventually all.
  那个最大的问题。所以最终所有。

[13:07] that biggest problem. So eventually all the enterprises that are getting hot and.
  最终所有那些对代理趋之若鹜的企业都会意识到。

[13:08] the enterprises that are getting hot and heavy on agents are going to be like.
  他们会说，“哇，等一下。我们得解决这个问题是如何被处理的。”

[13:10] heavy on agents are going to be like, "Whoa, wait a second. We've actually got.
  我们得解决这个问题是如何被处理的。”

[13:12] "Whoa, wait a second. We've actually got to fix how this is all being done." As.
  正如我们本周在那个故事中看到的。

[13:13] to fix how this is all being done." As we saw this week in that story with.
  正如我们本周在那个故事中看到的。

[13:15] We saw this week in that story with Amazon where there's like a million Amazon where there's like a million agents being spun up inside and agents being spun up inside and everything's wasting resources, everything's wasting resources, redundant data creation, redundant data redundant data creation, redundant data stores, redundant API calls, etc. Tons stores, redundant API calls, etc. Tons of money being wasted.
  本周我们在亚马逊的那个故事中看到，有数百万个亚马逊代理在内部被启动，并且代理在内部被启动，所有东西都在浪费资源，所有东西都在浪费资源，重复数据创建，重复数据重复数据创建，重复数据存储，重复API调用，等等。大量的存储，大量的API调用，等等。大量的金钱被浪费了。

[13:26] So you have to centralize still. You have to have good software engineering talent that's making good infrastructure and good use of these agents.
  所以你仍然需要集中化。你必须拥有优秀的软件工程人才，他们正在构建良好的基础设施并有效利用这些代理。

[13:32] And that ultimately will require an integration of the AI tooling with a standard software engineering front end which is the IDE that cursor has.
  而这最终将需要把人工智能工具与标准的软件工程前端集成起来，也就是光标所拥有的IDE。

[13:40] So I think that that's probably where everyone's waking up to the fact that having the uh the software engineers may end up winning you the arms race here and seems pretty smart for Elon to buy cursor.
  所以我想这可能就是大家开始意识到这样一个事实的地方：拥有软件工程师可能最终会让你赢得这场军备竞赛，而埃隆收购光标似乎非常明智。

[13:53] One other piece of it you mentioned Kimmy uh K2.6.
  你提到的另一件事是 Kimmy uh K2.6。

[13:58] Yeah. I mean, so I think that one of the things that's going to become a priority over the next several months is this idea of optimizing because enterprises token bills are going through the roof right now.
  是的。我的意思是，我认为在接下来的几个月里，一个将变得优先考虑的事情是优化，因为企业目前的代币账单正在飞涨。

[14:06] I mean, just month over month, they're spending increasingly large amounts because their employees are just building more and more software, but I'm not sure that anyone's
  我的意思是，仅仅是逐月来看，他们的支出都在不断增加，因为他们的员工正在构建越来越多的软件，但我不太确定是否有人

[14:16] software, but I'm not sure that anyone's been incentivized yet to be efficient about it.
  软件，但我不太确定是否有人被激励要有效地处理它。

[14:20] And it really only makes sense to go to a frontier model for a frontier task.
  而且只有在处理前沿任务时，使用前沿模型才真正有意义。

[14:25] But more mundane things could be done using an open source model or a less expensive model.
  但更普通的事情可以使用开源模型或成本较低的模型来完成。

[14:28] And I think like you're saying, whether it's the IDE or something else, there needs to be some sort of middleware that determines which model you go to and how much you're willing to spend and what the most efficient way of getting the tokens is going to be.
  而且我认为就像你说的，无论是IDE还是其他什么，都需要某种中间件来决定你使用哪个模型，你愿意花多少钱，以及如何最有效地获取令牌。

[14:42] I am deep in playing with uh XAI's suite of products and I would predict we're going to be sitting here in 6 to 12 months and they are going to be dramatically dramatically improved.
  我正在深入研究XAI的产品套件，我预测我们将在6到12个月后坐在这里，它们将得到极大的改进。

[14:53] Let me just flag one other area that I think is maybe the white hot center within this redot area of coding which is cyber.
  让我指出另一个我认为可能是编码领域这个热门区域的中心，那就是网络安全。

[15:04] And I think mythos has kind of woken everybody up to the potential of frontier models to be a weapon that can be used by either cyber offense or cyber defense.
  而且我认为Mythos已经让大家认识到前沿模型作为一种武器的潜力，它可以被网络进攻或网络防御所利用。

[15:14] Now the issue with mythos is
  现在Mythos的问题是

[15:17] Defense. Now the issue with Mythos is that it's very large and expensive.
  防御。现在的问题是，Mythos模型非常庞大且昂贵。

[15:19] It's something like a 10 trillion perimeter model.
  它是一个大约10万亿参数的模型。

[15:23] And there's a lot of reports that Anthropic just doesn't have enough compute to be able to serve it.
  有很多报道称，Anthropic公司没有足够的计算能力来为其提供服务。

[15:25] I'm not sure it was ever built to be a commercial model to be honest because I just think it's so big and expensive.
  说实话，我不确定它是否曾被设计成一个商业模型，因为我认为它实在太庞大和昂贵了。

[15:30] But I think what'll happen is these companies will start training dedicated cyber models.
  但我认为将会发生的是，这些公司将开始训练专门的网络模型。

[15:35] Let's say Mythos comparable models but with a lower token cost.
  比如说，类似Mythos的模型，但具有更低的代币成本。

[15:39] And I think there's a real race on right now to get those products to market because I think IT departments and CISOs are very worried about the risk of hacks right now AI powered hackers.
  我认为现在有一场真正的竞赛，要将这些产品推向市场，因为我认为IT部门和CISO们非常担心现在AI驱动的黑客攻击的风险。

[15:55] So this is something I think over the next 3 to 6 months will be again this maybe the hottest part of the market.
  所以，我认为在接下来的3到6个月里，这可能会再次成为市场上最热门的部分。

[16:00] Poly market says all of this is faked complete.
  Poly市场称这一切都是伪造的，完全是。

[16:03] SpaceX acquiring cursor 74% chance SpaceX IPO by the end of August 80% chance.
  SpaceX收购Cursor的几率为74%，SpaceX在8月底IPO的几率为80%。

[16:10] So this is happening folks.
  所以，各位，这是正在发生的。

[16:13] All right let's keep by the way I think that deal structure is smart because I mean to point yeah it
  好的，我们继续。顺便说一句，我认为那个交易结构很明智，因为我的意思是，是的，它

[16:18] is smart because I mean to point yeah it prevents the IPO process from being disrupted.
  很聪明，因为我的意思是，是的，它阻止了IPO进程被扰乱。

[16:23] Also, it kind of gives a huge motivation to these cursor guys to bust their ass and make it work over the next, I don't know, 6 months.
  另外，它也给了这些光标家伙巨大的动力去拼命，并在接下来的，我不知道，6个月里把它做好。

[16:28] Yeah, they have a $10 billion breakup fee, but I'm sure they want the deal to be successful.
  是的，他们有100亿美元的分手费，但我相信他们希望这笔交易能成功。

[16:31] Well, the $10 billion breakout fee will go back to SpaceX anyways because if they actually run the compute and they're not owned by SpaceX, they're going to have to pay for it.
  嗯，这100亿美元的分手费反正会回到SpaceX，因为如果他们真的运行计算，并且不属于SpaceX，他们将不得不为此付费。

[16:40] That is not cheap.
  这可不便宜。

[16:40] I mean, we saw a bunch of these XAI co-founders leave after the acquisition by SpaceX.
  我的意思是，我们看到很多XAI的联合创始人被SpaceX收购后离开了。

[16:47] I don't know if that was the reason why, but you all of a sudden they're sitting on SpaceX stock and they may have felt like they had it made, you know.
  我不知道那是不是原因，但你突然之间他们就拥有了SpaceX的股票，他们可能觉得他们已经成功了，你知道的。

[16:53] Yeah.
  是的。

[16:53] Well, which is always a problem.
  嗯，这总是一个问题。

[16:55] It's always a problem with with M&A.
  这总是与并购有关的问题。

[16:57] This cursor thing came about pretty quickly because
  这个光标的事情来得很快，因为

[17:01] Okay, let's just say friends of ours who were supposed to wire into that round were like, "Where's the wiring instructions?"
  好的，我们姑且说我们的一些朋友本来应该向那一轮注资的，他们说：“接线说明在哪里？”

[17:06] It all just evaporated.
  这一切都蒸发了。

[17:09] Here's a tweet from Elon.
  这是埃隆的一条推文。

[17:11] We don't have to um speculate too much here.
  我们不必在这里过多猜测。

[17:14] Sachs, he was very clear that uh XAI wasn't uh built right the first time around.
  萨克斯，他很清楚XAI第一次建立的时候并不对。

[17:20] Built right the first time around.
  第一次就建好了。

[17:22] The quote XAI was not built right first time around.
  所谓的XAI第一次并没有建好。

[17:24] So is being rebuilt from the foundations up.
  所以它正在从基础开始重建。

[17:27] Same thing happened with Tesla.
  特斯拉也发生了同样的事情。

[17:30] And uh that tweet is from about 5 weeks ago.
  呃，那条推文大约是5周前发的。

[17:32] How crazy is it that when he tweets he gets 50.8 million views?
  他发一条推文就有5080万的观看量，这有多疯狂？

[17:36] It takes the four of us 7 months to get 50.
  我们四个人花了7个月才获得50（万观看量）。

[17:39] I mean that's probably our collective.
  我的意思是，那可能已经是我们集体的（成果）了。

[17:42] It's unbelievable the distribution he has.
  他拥有的传播力真是令人难以置信。

[17:44] Well, also, how many CEOs would just fess up like that and say, "Yeah, that we didn't do it right the first time.
  嗯，而且，有多少CEO会像那样坦白并说，“是的，我们第一次没有做好。

[17:48] Now we're rebuilding it."
  现在我们正在重建它。”

[17:51] I mean, most willing to say that.
  我的意思是，大多数人都不愿意这么说。

[17:53] He's a magnet for talent.
  他是人才的磁石。

[17:55] He's a magnet for the right kind of talent.
  他是那种合适人才的磁石。

[17:58] And the SpaceX talent has his philosophy.
  SpaceX的人才拥有他的理念。

[18:01] He inherited, I think, uh, a lot of, you know, maybe people for XAI or for Twitter that were not in his mold and they're clearly getting aligned.
  我想他继承了，呃，很多，你知道，可能是XAI或Twitter里那些不符合他模式的人，而他们显然正在变得一致。

[18:09] And it's also going to make his day-to-day life much easier when all of these things are occurring in the same building with the same team.
  而且，当所有这些事情都在同一个大楼里、由同一个团队完成时，也将使他日常的生活更加轻松。

[18:15] The continuity of not having to t switch between companies is going to be great.
  不必在公司之间切换的连续性将是伟大的。

[18:19] We talked a little bit about the
  我们稍微谈了谈

[18:20] We talked a little bit about the possibility of Tesla and SpaceX merging.
  我们稍微谈了谈特斯拉和SpaceX合并的可能性。

[18:23] Possibility of Tesla and SpaceX merging.
  特斯拉和SpaceX合并的可能性。

[18:25] Even Walter Isacson now is on the Tesla SpaceX merger train.
  就连沃尔特·艾萨克森现在也加入了特斯拉SpaceX合并的潮流。

[18:27] SpaceX merger train.
  SpaceX合并的潮流。

[18:28] There you go.
  你看。

[18:30] He just did a pod. Everybody's confirmed it. It's going to happen. We called it here first.
  他刚做了一个播客。每个人都证实了。这将会发生。我们是第一个在这里预言的。

[18:32] Okay. Topic two. Is there a SAS debt bomb in private equity?
  好的。第二个话题。私募股权中是否存在一个SaaS债务炸弹？

[18:37] Toma Bravo, we had Orlando Bravo at the fourth all-in summit uh last year, is nearing a deal to hand its portfolio company Medallia over to its creditors.
  Toma Bravo，我们去年在第四届全员峰会上见到了奥兰多·布拉沃，他正接近达成一项协议，将其投资组合公司Medallia移交给其债权人。

[18:43] Fourth all-in summit uh last year, is nearing a deal to hand its portfolio company Medallia over to its creditors.
  去年第四届全员峰会，正接近达成一项协议，将其投资组合公司Medallia移交给其债权人。

[18:46] Nearing a deal to hand its portfolio company Medallia over to its creditors.
  正接近达成一项协议，将其投资组合公司Medallia移交给其债权人。

[18:49] This is a SAS for customer experience company. TB acquired them in 2021 for 6.4 billion all cash at the top of the market.
  这是一家客户体验SaaS公司。TB在2021年以64亿美元全现金收购了它们，正值市场顶峰。

[18:56] Company. TB acquired them in 2021 for 6.4 billion all cash at the top of the market.
  公司。TB在2021年以64亿美元全现金收购了它们，正值市场顶峰。

[18:59] 6.4 billion all cash at the top of the market. As part of the deal, they incurred 3 billion in debt.
  64亿美元全现金收购，正值市场顶峰。作为交易的一部分，它们承担了30亿美元的债务。

[19:04] Incurred 3 billion in debt. And for background, in 2021, this company had 470 million in revenue, growing 20% a year.
  承担了30亿美元的债务。背景是，在2021年，这家公司有4.7亿美元的收入，每年增长20%。

[19:10] 470 million in revenue, growing 20% a year. Earlier this month, Bloomberg reported that TB's debt servicing costs for Medallia were about to triple from 100 million a year to 300 million a
  4.7亿美元的收入，每年增长20%。本月早些时候，彭博社报道称，TB为Medallia承担的债务偿还成本将从每年1亿美元增加到3亿美元。

[19:21] 100 million a year to 300 million a year.
  每年1亿到每年3亿。

[19:23] Blackstone and other firms refused year.
  黑石和其他公司拒绝了这一年。

[19:26] Blackstone and other firms refused to extend a lifeline to the company, to the SAS company.
  黑石和其他公司拒绝向该公司、向SAS公司伸出援手。

[19:28] So, it looks like Toma Bravo just handed the keys back and wiped out 5.1 billion in equity.
  所以，看起来Toma Bravo只是交还了钥匙，并抹去了51亿美元的股权。

[19:33] wiped out 5.1 billion in equity.
  抹去了51亿美元的股权。

[19:34] Shimath, your thoughts.
  西马斯，你的想法。

[19:36] We've been talking about the SAS headwinds for a bit.
  我们一直在谈论SAS的逆风一段时间了。

[19:38] You've been quite vocal about it.
  你对此一直很直言不讳。

[19:40] Well, first of all, I think Toma Bravo is an unbelievably well-run organization.
  嗯，首先，我认为Toma Bravo是一个经营得难以置信的组织。

[19:43] Their returns are bonkers and Orlando is uh really, really, really good investor.
  他们的回报是惊人的，奥兰多是一个非常非常非常好的投资者。

[19:48] So, what do I think happened?
  那么，我认为发生了什么？

[19:51] I suspect that they probably got enough of their equity, if not all of their equity.
  我怀疑他们可能已经获得了足够的股权，如果不是全部股权的话。

[19:58] There's probably a decent chance that they did at least one or two dividend recaps in the last five years.
  在过去五年里，他们很可能至少进行了一到两次股息重组。

[20:05] And if I had to guess, I suspect that they are positive return.
  如果非要我猜的话，我怀疑他们是有正回报的。

[20:09] It may not be the return that they would want.
  这可能不是他们想要的那个回报。

[20:13] And so turning the keys over becomes easier because you have to remember in private equity, the entire playbook is for transformations of assets that are at
  所以，交出钥匙就变得更容易了，因为你必须记住，在私募股权中，整个策略都是为了改造那些处于...

[20:22] transformations of assets that are at some point not working, right? It's very

[20:25] some point not working, right? It's very rarely that they're buying the same

[20:27] rarely that they're buying the same kinds of businesses that the four of us

[20:28] kinds of businesses that the four of us would buy, which is just sort of this,

[20:30] would buy, which is just sort of this, you know, clean white sheet denovo grow

[20:33] you know, clean white sheet denovo grow at all costs kind of business. So they

[20:35] at all costs kind of business. So they have operating partners and all of these

[20:37] have operating partners and all of these other people waiting in the wings to

[20:39] other people waiting in the wings to unfuck [ __ ] situations. That's the whole

[20:42] unfuck [ __ ] situations. That's the whole playbook. M

[20:44] playbook. M >> so to turn it over I suspect means that

[20:46] >> so to turn it over I suspect means that there is a core rot that people couldn't

[20:50] there is a core rot that people couldn't fix combined with the fact that they

[20:53] fix combined with the fact that they have probably gotten enough downside

[20:55] have probably gotten enough downside protection that it's not a huge thing

[20:57] protection that it's not a huge thing for them. Now this is an issue for the

[20:59] for them. Now this is an issue for the bond holders and then that'll maybe flow

[21:01] bond holders and then that'll maybe flow through to the borrowing cost that Tom

[21:03] through to the borrowing cost that Tom Bravo has to pay maybe for a subsequent

[21:05] Bravo has to pay maybe for a subsequent deal. I don't know but I doubt that they

[21:08] deal. I don't know but I doubt that they would just walk away from a business. So

[21:10] would just walk away from a business. So I suspect they probably got most of

[21:12] I suspect they probably got most of their money out. I don't know if that's

[21:13] their money out. I don't know if that's true. There was someone that published

[21:15] true. There was someone that published some internal data showing that the

[21:18] some internal data showing that the sales team was like 18% a target at

[21:20] sales team was like 18% a target at Medallia. Do you guys know what this

[21:22] Medallia. Do you guys know what this company does? Medalia

[21:24] company does? Medalia >> customer support uh is the general arena

[21:27] >> customer support uh is the general arena and customer

[21:29] and customer experience management. I don't know.

[21:30] experience management. I don't know. >> I don't know what that means.

[21:32] >> I don't know what that means. >> Yeah. They'll basically send like you go

[21:33] >> Yeah. They'll basically send like you go on Caribbean cruise ships and you get a

[21:35] on Caribbean cruise ships and you get a survey afterwards and then they use that

[21:36] survey afterwards and then they use that survey data to provide management

[21:39] survey data to provide management insights and operational insights to the

[21:41] insights and operational insights to the leadership team and the operating team

[21:42] leadership team and the operating team on how to improve the quality of their

[21:44] on how to improve the quality of their product or their service. So it's sort

[21:45] product or their service. So it's sort of like this feedback surveying loop. So

[21:48] of like this feedback surveying loop. So if I were to tell you guys, hey, you

[21:49] if I were to tell you guys, hey, you want to build a feedback surveying loop

[21:51] want to build a feedback surveying loop to run your business better, are you

[21:53] to run your business better, are you gonna buy SAS today or are you gonna ask

[21:55] gonna buy SAS today or are you gonna ask your AI to spin up an agent for you to

[21:57] your AI to spin up an agent for you to do that? And I think that's a big part

[21:59] do that? And I think that's a big part of what's happened is all these sorts of

[22:01] of what's happened is all these sorts of companies where the alternative to

[22:04] companies where the alternative to buying a SAS product is to spin

[22:05] buying a SAS product is to spin something up internally and it's much

[22:07] something up internally and it's much cheaper and easier to spin it up

[22:08] cheaper and easier to spin it up internally. You get a custom workflow.

[22:10] internally. You get a custom workflow. >> No, no, I agree with that. I'm just

[22:11] >> No, no, I agree with that. I'm just saying in the last 5 years you think

[22:13] saying in the last 5 years you think they sat on their hands and didn't take

[22:14] they sat on their hands and didn't take a dollar out. They're not that dumb.

[22:17] a dollar out. They're not that dumb. >> Maybe they took cash out, maybe they

[22:18] >> Maybe they took cash out, maybe they didn't. But there was still a big debt

[22:19] didn't. But there was still a big debt overhang and the debt's clearly

[22:21] overhang and the debt's clearly >> um gotten impaired, which means equity.

[22:24] >> um gotten impaired, which means equity. >> The debt holders the debt holders are

[22:26] >> The debt holders the debt holders are clearly screwed here. Yeah,

[22:27] clearly screwed here. Yeah, >> the question is is TOMA Bravo screwed?

[22:29] >> the question is is TOMA Bravo screwed? And I would say if you sat around for 5

[22:31] And I would say if you sat around for 5 years, they're that's not their style.

[22:32] years, they're that's not their style. They generate too much money. They're

[22:34] They generate too much money. They're too good.

[22:35] too good. >> So they may have taken cash out and

[22:36] >> So they may have taken cash out and covered some of their their costs, but

[22:38] covered some of their their costs, but the equity got fully impaired and then

[22:39] the equity got fully impaired and then the debt is clearly impaired because you

[22:41] the debt is clearly impaired because you can see how the debt and the CLOS's are

[22:43] can see how the debt and the CLOS's are trading, which indicates that this

[22:46] trading, which indicates that this business is just not doing well. And

[22:47] business is just not doing well. And then someone else on Twitter posted some

[22:49] then someone else on Twitter posted some internal information from Adalia saying

[22:52] internal information from Adalia saying the sales team is just not hitting their

[22:54] the sales team is just not hitting their targets. they're like way way off their

[22:55] targets. they're like way way off their sales targets which I think speaks to

[22:58] sales targets which I think speaks to the underlying problem here.

[22:59] the underlying problem here. >> Yes.

[23:00] >> Yes. >> Which is that

[23:01] >> Which is that >> unpack that. Yeah. Please.

[23:02] >> unpack that. Yeah. Please. >> Yeah. So the underlying problem is that

[23:04] >> Yeah. So the underlying problem is that these businesses in the SAS space where

[23:06] these businesses in the SAS space where you're driven by net new sales every

[23:08] you're driven by net new sales every year. How many new customers are you

[23:10] year. How many new customers are you signing up and then you're trying to

[23:11] signing up and then you're trying to manage retention and you're trying to

[23:13] manage retention and you're trying to increase sell through and retain

[23:14] increase sell through and retain customers? They're just having a really

[23:16] customers? They're just having a really hard time sourcing new customers and

[23:18] hard time sourcing new customers and there's probably higher than modeled

[23:19] there's probably higher than modeled attrition. That's right. And when you

[23:20] attrition. That's right. And when you have a very kind of typically

[23:22] have a very kind of typically historically predictable business where

[23:24] historically predictable business where you can say, "Hey, I've got a net

[23:25] you can say, "Hey, I've got a net revenue retention of 118% or what have

[23:28] revenue retention of 118% or what have you, meaning I'm I'm selling into my

[23:30] you, meaning I'm I'm selling into my install base by 18% over what I'm making

[23:32] install base by 18% over what I'm making last year and then I'm signing up new

[23:34] last year and then I'm signing up new customers,

[23:36] customers, you can lever that business, right? You

[23:37] you can lever that business, right? You can borrow money against those cash

[23:39] can borrow money against those cash flows because it becomes predictable."

[23:41] flows because it becomes predictable." And what's happened in the last year in

[23:43] And what's happened in the last year in particular is agents have become so good

[23:45] particular is agents have become so good and so fast and so cheap that many

[23:48] and so fast and so cheap that many enterprises can simply spin up an

[23:50] enterprises can simply spin up an alternative to a vertical SAS solution

[23:52] alternative to a vertical SAS solution and that's crushing the sales team's

[23:54] and that's crushing the sales team's ability to sell in. That's who you're

[23:55] ability to sell in. That's who you're competing against. Now I want to make

[23:57] competing against. Now I want to make one point and just link this with

[23:58] one point and just link this with something else that happened this week

[23:59] something else that happened this week and that's Kevin Worsh's hearing for Fed

[24:02] and that's Kevin Worsh's hearing for Fed Reserve chair. Kevin Warch went and

[24:05] Reserve chair. Kevin Warch went and talked a lot about the deflationary

[24:06] talked a lot about the deflationary effect of AI and I actually think we all

[24:09] effect of AI and I actually think we all talk about the SAS apocalypse as if it's

[24:11] talk about the SAS apocalypse as if it's this sort of like isolated business

[24:14] this sort of like isolated business phenomenon where these SAS companies are

[24:15] phenomenon where these SAS companies are getting blown up. I think another lens

[24:17] getting blown up. I think another lens to look at what's going on is the

[24:19] to look at what's going on is the incredible deflation of how much it

[24:22] incredible deflation of how much it costs to successfully run a business and

[24:25] costs to successfully run a business and you don't have to pay a premium price

[24:27] you don't have to pay a premium price for SAS products anymore. Meaning that

[24:29] for SAS products anymore. Meaning that that piece of the business can suddenly

[24:31] that piece of the business can suddenly get much cheaper. that AI is delivering

[24:34] get much cheaper. that AI is delivering on its deflationary promise. I'll just

[24:36] on its deflationary promise. I'll just say one thing about what WarCH said.

[24:39] say one thing about what WarCH said. Warch spoke a lot about the deflationary

[24:41] Warch spoke a lot about the deflationary evolution promised by AI and that he

[24:43] evolution promised by AI and that he expects that it will drive productivity

[24:45] expects that it will drive productivity growth like we've never seen before. But

[24:47] growth like we've never seen before. But he said I don't know what that's going

[24:48] he said I don't know what that's going to do to the job market that there may

[24:50] to do to the job market that there may be a dislocation between that

[24:52] be a dislocation between that productivity growth being realized and

[24:54] productivity growth being realized and how the labor markets are going to be

[24:56] how the labor markets are going to be able to respond to those things. But

[24:58] able to respond to those things. But fundamentally, he's saying that we're

[25:00] fundamentally, he's saying that we're going to see economic deflation. The

[25:03] going to see economic deflation. The problem with economic deflation is that

[25:05] problem with economic deflation is that when it occurs, it means some business

[25:07] when it occurs, it means some business is seeing their revenue go down. And if

[25:10] is seeing their revenue go down. And if that segment of the economy is levered,

[25:12] that segment of the economy is levered, if they have debt sitting on top of that

[25:14] if they have debt sitting on top of that piece of the economy where it's supposed

[25:15] piece of the economy where it's supposed to always, always grow like a SAS

[25:18] to always, always grow like a SAS company's top line is always supposed to

[25:19] company's top line is always supposed to grow. Suddenly that debt gets impaired

[25:21] grow. Suddenly that debt gets impaired and that can have an economic ripple

[25:23] and that can have an economic ripple effect that is adverse. But what he's

[25:25] effect that is adverse. But what he's pointing out is that as a result of

[25:27] pointing out is that as a result of deflation because it's not coming from

[25:30] deflation because it's not coming from some cost cutting or economic

[25:32] some cost cutting or economic contraction. What he's saying is that

[25:34] contraction. What he's saying is that the deflationary forces ultimately lead

[25:35] the deflationary forces ultimately lead to economic expansion because other

[25:38] to economic expansion because other parts of the economy will now grow. So

[25:40] parts of the economy will now grow. So if I can suddenly cut, you know, call it

[25:42] if I can suddenly cut, you know, call it 50% of my SAS budget and I can reinvest

[25:45] 50% of my SAS budget and I can reinvest that capital in other ways of growing my

[25:47] that capital in other ways of growing my business instead of managing my expenses

[25:50] business instead of managing my expenses all of a sudden my enterprise will grow

[25:52] all of a sudden my enterprise will grow and the economy will grow. He also said,

[25:55] and the economy will grow. He also said, just as an aside, and I want to make

[25:56] just as an aside, and I want to make sure I cover this so so that we're

[25:58] sure I cover this so so that we're really clear. He said, "The way that

[25:59] really clear. He said, "The way that we've been measuring inflation is wrong,

[26:01] we've been measuring inflation is wrong, and that he doesn't agree with the way

[26:02] and that he doesn't agree with the way the Fed has been measuring inflation

[26:04] the Fed has been measuring inflation because you can do a survey of any

[26:05] because you can do a survey of any household and they'll tell you, my god,

[26:07] household and they'll tell you, my god, everything's so expensive. So all of the

[26:09] everything's so expensive. So all of the indices and [ __ ] that are being used

[26:11] indices and [ __ ] that are being used to calculate an inflation index is

[26:13] to calculate an inflation index is completely misrepresenting what the

[26:14] completely misrepresenting what the average American is actually feeling."

[26:16] average American is actually feeling." And so he wants to rethink how the Fed

[26:18] And so he wants to rethink how the Fed is addressing inflation from an interest

[26:20] is addressing inflation from an interest rate perspective. But he does think that

[26:22] rate perspective. But he does think that the overall kind of economic picture is

[26:24] the overall kind of economic picture is one of deflationary pressure and

[26:26] one of deflationary pressure and productivity gains coming out of AI.

[26:28] productivity gains coming out of AI. >> Sax, I I'll drop this off to you. I

[26:30] >> Sax, I I'll drop this off to you. I think it's pretty clear what's happening

[26:32] think it's pretty clear what's happening here is that the loss SAS's loss is the

[26:35] here is that the loss SAS's loss is the token dealers, right? And startups are

[26:38] token dealers, right? And startups are now and we always see the they're the

[26:40] now and we always see the they're the tip of the spear. They're writing their

[26:42] tip of the spear. They're writing their own tools. They're making their own

[26:43] own tools. They're making their own dashboards. I see that every day. And if

[26:46] dashboards. I see that every day. And if you look at the SAS product index, here

[26:48] you look at the SAS product index, here it is. Salesforce down 32% uh in the

[26:52] it is. Salesforce down 32% uh in the past six months. Shout out to Bestie

[26:54] past six months. Shout out to Bestie Beni off. Best guest we've had, huh?

[26:56] Beni off. Best guest we've had, huh? Saxs at the summit. Service now down

[26:58] Saxs at the summit. Service now down 54%, Snowflake down 43%, Adobe down 33%,

[27:02] 54%, Snowflake down 43%, Adobe down 33%, Figma, which had a huge IPO pop and is

[27:06] Figma, which had a huge IPO pop and is now down 67%.

[27:08] now down 67%. So what is the role of venture capital

[27:11] So what is the role of venture capital and then private equity in addressing

[27:14] and then private equity in addressing the software market? Software was eating

[27:16] the software market? Software was eating the world. Now tokens are eating uh the

[27:19] the world. Now tokens are eating uh the SAS business and the software business.

[27:20] SAS business and the software business. Yeah.

[27:21] Yeah. >> Well, I'm of two minds about this. I'm

[27:23] >> Well, I'm of two minds about this. I'm going to talk about the opportunity for

[27:25] going to talk about the opportunity for private equity. Let me just say backing

[27:28] private equity. Let me just say backing up that historically we only had two

[27:31] up that historically we only had two good exits for software businesses. One

[27:33] good exits for software businesses. One was to IPO, the other was M&A. And then

[27:36] was to IPO, the other was M&A. And then these big private equity shops came

[27:37] these big private equity shops came along and gave us a third potential

[27:39] along and gave us a third potential exit, which is you would sell to them

[27:42] exit, which is you would sell to them and then they would raise the capital

[27:43] and then they would raise the capital based on I don't know 1/3 equity and

[27:45] based on I don't know 1/3 equity and 2/3s debt. So it was debt finance

[27:47] 2/3s debt. So it was debt finance buyouts which is something that's been

[27:49] buyouts which is something that's been around in let's call it the non- tech

[27:51] around in let's call it the non- tech part of the economy for a long time but

[27:53] part of the economy for a long time but was a relatively new entrant into the

[27:56] was a relatively new entrant into the world of technology. And the reason for

[27:57] world of technology. And the reason for that is that if you're going to debt

[27:59] that is that if you're going to debt finance a purchase you need to have very

[28:00] finance a purchase you need to have very stable cash flows because if you miss if

[28:03] stable cash flows because if you miss if your cash flows miss and you can't pay

[28:05] your cash flows miss and you can't pay your interest on the debt then you're

[28:07] your interest on the debt then you're going to lose all your equity because

[28:09] going to lose all your equity because the debt holders will foreclose. So in

[28:11] the debt holders will foreclose. So in order to do a debt financing of any

[28:13] order to do a debt financing of any kind, you have to have very predictable

[28:14] kind, you have to have very predictable cash flows. And it was believed for a

[28:17] cash flows. And it was believed for a long time that software did have those

[28:19] long time that software did have those predictable cash flows, at least for the

[28:21] predictable cash flows, at least for the mature businesses, the ones that were at

[28:24] mature businesses, the ones that were at the stage where they could IPO as a

[28:26] the stage where they could IPO as a potential alternative. So it was a very

[28:28] potential alternative. So it was a very attractive thing. I like I said, I think

[28:30] attractive thing. I like I said, I think it was great to have that third option.

[28:31] it was great to have that third option. I I'm of two minds about where the

[28:34] I I'm of two minds about where the private equity business is today. On the

[28:36] private equity business is today. On the one hand, the pricing now has got to be

[28:39] one hand, the pricing now has got to be super attractive for them. I mean, we're

[28:42] super attractive for them. I mean, we're seeing public SAS companies that are

[28:44] seeing public SAS companies that are doing a billion

[28:45] doing a billion >> ARR

[28:47] >> ARR 20% growth rates, 80% gross margins, and

[28:51] 20% growth rates, 80% gross margins, and they're trading at three times AR.

[28:52] they're trading at three times AR. >> Yeah.

[28:53] >> Yeah. >> You know, you can buy a dollar for 50

[28:55] >> You know, you can buy a dollar for 50 cents.

[28:56] cents. >> So, is is that an opportunity, Saxs, you

[28:58] >> So, is is that an opportunity, Saxs, you think? Uh, hit rock bottom, you should

[29:00] think? Uh, hit rock bottom, you should do a rollup. Well, on the one hand, I do

[29:03] do a rollup. Well, on the one hand, I do think that the pricing has never been

[29:05] think that the pricing has never been more attractive if you're a private

[29:07] more attractive if you're a private equity shop looking at a business like

[29:08] equity shop looking at a business like that. I mean, those companies used to be

[29:09] that. I mean, those companies used to be valued at 13 times ARR. Now, it's three.

[29:12] valued at 13 times ARR. Now, it's three. Uh, I'm talking about like a category

[29:14] Uh, I'm talking about like a category leader. Now, the downside of that,

[29:16] leader. Now, the downside of that, >> by the way, Salesforce is off 9% today.

[29:19] >> by the way, Salesforce is off 9% today. I don't know if you guys saw this, but

[29:20] I don't know if you guys saw this, but the market's absolutely tanking today

[29:22] the market's absolutely tanking today after the Medallia uh announcement came

[29:25] after the Medallia uh announcement came out.

[29:25] out. >> Right. Okay. So, so that would be like

[29:27] >> Right. Okay. So, so that would be like you I said I'm of two minds about it.

[29:29] you I said I'm of two minds about it. So, I would be bullish for private

[29:31] So, I would be bullish for private equity just based on pricing. But the

[29:32] equity just based on pricing. But the bearish part is that in order for their

[29:35] bearish part is that in order for their business model to work, you have to have

[29:36] business model to work, you have to have predictable cash flows. You can't have a

[29:38] predictable cash flows. You can't have a SAS company go from, I don't know, 120%

[29:41] SAS company go from, I don't know, 120% net dollar retention one quarter to 80%

[29:44] net dollar retention one quarter to 80% net dollar retention 6 months or a year

[29:47] net dollar retention 6 months or a year later because a big part of their

[29:48] later because a big part of their customer base is attred to using tokens,

[29:51] customer base is attred to using tokens, right? Or to basically creating some

[29:53] right? Or to basically creating some bespoke software. You just said the

[29:54] bespoke software. You just said the absolute critical thing in all of this,

[29:56] absolute critical thing in all of this, which is you have to have predictable

[29:58] which is you have to have predictable cash flows. I think what happens is when

[30:00] cash flows. I think what happens is when you're a startup, you typically have to

[30:03] you're a startup, you typically have to figure out how to disruptively price to

[30:05] figure out how to disruptively price to enter the market. So you're like, okay,

[30:07] enter the market. So you're like, okay, if I deliver $10 of value, I'm going to

[30:09] if I deliver $10 of value, I'm going to charge a dollar. And that's that's the

[30:11] charge a dollar. And that's that's the normal playbook, like a 10% ratio,

[30:13] normal playbook, like a 10% ratio, right, of price to value. The problem is

[30:16] right, of price to value. The problem is when you start to stack venture capital

[30:18] when you start to stack venture capital into it and you then you stack growth

[30:19] into it and you then you stack growth equity into it, what you're effectively

[30:23] equity into it, what you're effectively creating in in the preference stack of

[30:26] creating in in the preference stack of your company is that you are creating a

[30:29] your company is that you are creating a higher return hurdle, right? You got to

[30:32] higher return hurdle, right? You got to clear 300 million, 500 million, a

[30:34] clear 300 million, 500 million, a billion of pre and then you have to

[30:36] billion of pre and then you have to return 15 or 20% on top of that. So what

[30:39] return 15 or 20% on top of that. So what do people do as they raise more money?

[30:42] do people do as they raise more money? They increase price. But the problem is

[30:45] They increase price. But the problem is at some point when you increase price

[30:48] at some point when you increase price you engender a ton of competition and

[30:50] you engender a ton of competition and you put a huge target on your back.

[30:52] you put a huge target on your back. Private equity is the last stop because

[30:54] Private equity is the last stop because when they come in and they layer in

[30:58] when they come in and they layer in billions and billions of dollars of not

[31:00] billions and billions of dollars of not just equity but also debt and that has

[31:02] just equity but also debt and that has to then be completely predictable and

[31:04] to then be completely predictable and paid back. Their only lever is to raise

[31:07] paid back. Their only lever is to raise price. They can never cut price to take

[31:09] price. They can never cut price to take share. They don't they can't underwrite

[31:10] share. They don't they can't underwrite that to pay back their debt holders. And

[31:12] that to pay back their debt holders. And so Saxs, part of the big problem here

[31:15] so Saxs, part of the big problem here and why nobody wants to touch these

[31:17] and why nobody wants to touch these companies is that they are overpriced.

[31:19] companies is that they are overpriced. Yes, they're making a billion dollars of

[31:20] Yes, they're making a billion dollars of ARR, but the unit cost has gotten out of

[31:23] ARR, but the unit cost has gotten out of control. It used to be 10% of value.

[31:26] control. It used to be 10% of value. It's probably now 30% of value. And

[31:29] It's probably now 30% of value. And everybody's looking at their contracts

[31:31] everybody's looking at their contracts thinking, well, when it comes time to a

[31:33] thinking, well, when it comes time to a renewal, I'm going to just cut this in

[31:35] renewal, I'm going to just cut this in half, or I'm going to cut this by 2/3,

[31:37] half, or I'm going to cut this by 2/3, or I'm going to cut this by 75%. because

[31:40] or I'm going to cut this by 75%. because the value isn't there anymore

[31:42] the value isn't there anymore >> or they can threaten to and negotiate a

[31:44] >> or they can threaten to and negotiate a better deal.

[31:45] better deal. >> And it becomes even worse because the

[31:47] >> And it becomes even worse because the minute you make these products headless,

[31:49] minute you make these products headless, right, and you say, "I'm just going to

[31:50] right, and you say, "I'm just going to communicate with these products via MCP

[31:52] communicate with these products via MCP and with agents, you can't charge on a

[31:55] and with agents, you can't charge on a per seat basis." What do you do then?

[31:57] per seat basis." What do you do then? Freeberg doesn't need 50 seats of, you

[32:00] Freeberg doesn't need 50 seats of, you know, workday. He needs two seats

[32:03] know, workday. He needs two seats because the agents act as the way to

[32:06] because the agents act as the way to write in and out of workday. So, he

[32:07] write in and out of workday. So, he wants to pay for two seats, not 50. And

[32:09] wants to pay for two seats, not 50. And then if you multiply that by a million

[32:11] then if you multiply that by a million companies, that's what gets us to this

[32:13] companies, that's what gets us to this place where it just feels like a falling

[32:15] place where it just feels like a falling knife. And I think it comes down to

[32:18] knife. And I think it comes down to these unit costs. The unit costs and the

[32:20] these unit costs. The unit costs and the price to value of these products are out

[32:22] price to value of these products are out of whack with what the market needs and

[32:24] of whack with what the market needs and wants. And until they reset that or you

[32:27] wants. And until they reset that or you find new products that can do it

[32:28] find new products that can do it cheaper, we're not going to get a

[32:29] cheaper, we're not going to get a cleansing and a clearing here.

[32:31] cleansing and a clearing here. >> Yeah. Yeah, I think

[32:31] >> Yeah. Yeah, I think >> by the way, Salesforce today is down 9%.

[32:35] >> by the way, Salesforce today is down 9%. >> 140 billion enterprise value on 15

[32:38] >> 140 billion enterprise value on 15 billion of free cash flow. This thing is

[32:40] billion of free cash flow. This thing is trading at less than 10 times free cash

[32:42] trading at less than 10 times free cash flow. It's unbelievable.

[32:44] flow. It's unbelievable. >> I think it might be a bargain to be

[32:45] >> I think it might be a bargain to be honest.

[32:46] honest. >> Yeah, it sounds like bargain hunting.

[32:48] >> Yeah, it sounds like bargain hunting. And what if Beni off the king of

[32:50] And what if Beni off the king of acquisitions? What if he just starts

[32:52] acquisitions? What if he just starts cleaning up?

[32:53] cleaning up? >> Well, he's been buying his own stock.

[32:55] >> Well, he's been buying his own stock. >> Yeah. And we didn't put this on the

[32:56] >> Yeah. And we didn't put this on the docket, but did you guys see his kind of

[32:58] docket, but did you guys see his kind of headless product announcement?

[33:00] headless product announcement? >> Yeah. Do you see this? It's actually

[33:02] >> Yeah. Do you see this? It's actually very

[33:02] very >> It's very smart.

[33:03] >> It's very smart. >> Yeah. I mean,

[33:04] >> Yeah. I mean, >> I think it's very smart.

[33:05] >> I think it's very smart. >> There's ways that that business can

[33:06] >> There's ways that that business can maneuver, right? And I think they're

[33:08] maneuver, right? And I think they're >> pretty unique. It may be that of all the

[33:10] >> pretty unique. It may be that of all the businesses in the scape like that you

[33:13] businesses in the scape like that you know the ones that have that scale that

[33:15] know the ones that have that scale that have that multi-product platform that

[33:17] have that multi-product platform that have a lot of your data there's a lot of

[33:19] have a lot of your data there's a lot of opportunity for them to you know

[33:21] opportunity for them to you know maneuver their way into an evolution the

[33:24] maneuver their way into an evolution the first one and because like if you look

[33:25] first one and because like if you look at it and you compare it for example to

[33:27] at it and you compare it for example to other companies I think the workday

[33:29] other companies I think the workday response was to say you can't have an AI

[33:31] response was to say you can't have an AI interact with us without paying some

[33:33] interact with us without paying some kind of like toll

[33:34] kind of like toll >> you're exactly right that's

[33:35] >> you're exactly right that's >> whereas Ben was the exact opposite which

[33:37] >> whereas Ben was the exact opposite which is he's like okay we're going to go

[33:38] is he's like okay we're going to go headless for the whole thing which is

[33:40] headless for the whole thing which is brilliant.

[33:40] brilliant. >> You're exactly right. You're exactly

[33:41] >> You're exactly right. You're exactly right. I think that's that's going to be

[33:42] right. I think that's that's going to be the distinction of the winners here and

[33:44] the distinction of the winners here and the losers and are you on the wrong side

[33:46] the losers and are you on the wrong side of this?

[33:46] of this? >> The problem is that we have to figure

[33:49] >> The problem is that we have to figure out what is the bottom clearing price

[33:52] out what is the bottom clearing price and that has nothing to do with business

[33:54] and that has nothing to do with business quality and so is Salesforce a goodbye

[33:57] quality and so is Salesforce a goodbye at 10 times free cash flow? Historical

[34:00] at 10 times free cash flow? Historical artifacts would tell us a screaming yes.

[34:03] artifacts would tell us a screaming yes. The problem is that if you cut

[34:04] The problem is that if you cut everybody's cash flows off at year five

[34:07] everybody's cash flows off at year five or six or seven, then all of a sudden I

[34:10] or six or seven, then all of a sudden I think you see the natural compression to

[34:12] think you see the natural compression to between three and five times free cash

[34:13] between three and five times free cash flow. And that has nothing,

[34:15] flow. And that has nothing, >> dude. That's that's crazy.

[34:16] >> dude. That's that's crazy. >> That has nothing to do with business

[34:17] >> That has nothing to do with business quality. That just says you you

[34:19] quality. That just says you you literally mathematically take years 7

[34:21] literally mathematically take years 7 through n of the future and you discount

[34:24] through n of the future and you discount it to zero.

[34:25] it to zero. >> And having free cash flow in a war chest

[34:28] >> And having free cash flow in a war chest gives massive optionality. We've seen

[34:30] gives massive optionality. We've seen this with Salesforce. We've seen it with

[34:32] this with Salesforce. We've seen it with Apple. We've seen it with Meta, with

[34:34] Apple. We've seen it with Meta, with Google, with Uber. Just having massive

[34:36] Google, with Uber. Just having massive free cash flow when you've got tens of

[34:37] free cash flow when you've got tens of billions of dollars. You can put it to

[34:39] billions of dollars. You can put it to work and you can weather these stoms.

[34:42] work and you can weather these stoms. >> Jal, I think another way to think about

[34:44] >> Jal, I think another way to think about this is,

[34:46] this is, >> you know, to the question about

[34:47] >> you know, to the question about maneuverability and who has the gumption

[34:49] maneuverability and who has the gumption to make the hard choices right now.

[34:51] to make the hard choices right now. >> Yeah.

[34:52] >> Yeah. >> Look at Ben off. He's the founder of the

[34:54] >> Look at Ben off. He's the founder of the company. He's run this thing since its

[34:56] company. He's run this thing since its founding decades ago. He is willing to

[34:59] founding decades ago. He is willing to bet it all. He's willing to make the

[35:00] bet it all. He's willing to make the change. And it may be that the index you

[35:03] change. And it may be that the index you buy in this era of AI transformation is

[35:05] buy in this era of AI transformation is the index of founders. That the founders

[35:08] the index of founders. That the founders who are still running their businesses

[35:09] who are still running their businesses are going to be the ones who are most

[35:10] are going to be the ones who are most likely to see the future.

[35:11] likely to see the future. >> The boats.

[35:12] >> The boats. >> They'll burn the boats. They'll

[35:13] >> They'll burn the boats. They'll maneuver. They'll make the changes. And

[35:15] maneuver. They'll make the changes. And all of the guys who have hired managers

[35:17] all of the guys who have hired managers to run the business are going to do the

[35:18] to run the business are going to do the things that Chim's talking about, which

[35:20] things that Chim's talking about, which is try and charge fees and try and

[35:21] is try and charge fees and try and maintain the old way of doing things as

[35:23] maintain the old way of doing things as opposed to reinvent for the new future.

[35:25] opposed to reinvent for the new future. If you look at the 10ks, if we could

[35:26] If you look at the 10ks, if we could figure out what the unit price cost and

[35:29] figure out what the unit price cost and the trend and the inflation is of a per

[35:32] the trend and the inflation is of a per seat license for these products, I will

[35:35] seat license for these products, I will point to the ones that are going to die

[35:36] point to the ones that are going to die first.

[35:37] first. >> Can I make two quick points? So,

[35:38] >> Can I make two quick points? So, >> yeah, wrap us up.

[35:39] >> yeah, wrap us up. >> Yeah.

[35:39] >> Yeah. >> One is yes, I would like fully endorse

[35:41] >> One is yes, I would like fully endorse what what you said about Beni off. He's

[35:42] what what you said about Beni off. He's made every previous wave work to his

[35:45] made every previous wave work to his benefit, whether it was social, whether

[35:48] benefit, whether it was social, whether it was mobile, whether it was big data,

[35:50] it was mobile, whether it was big data, all that kind of stuff. What are the

[35:52] all that kind of stuff. What are the odds he's going to make AI work to his

[35:53] odds he's going to make AI work to his benefit? I'd say pretty good. So, his

[35:56] benefit? I'd say pretty good. So, his stock might be a bargain right now. So,

[35:57] stock might be a bargain right now. So, that just be point number one. Just on I

[36:00] that just be point number one. Just on I want to say just a quick thing about

[36:02] want to say just a quick thing about venture debt, which is I I look I think

[36:04] venture debt, which is I I look I think it's fine when private equity guys use

[36:06] it's fine when private equity guys use it because they know what they're doing,

[36:07] it because they know what they're doing, but I've always hated when founders take

[36:09] but I've always hated when founders take on venture debt. And I know Jacob, you

[36:11] on venture debt. And I know Jacob, you agree with me. Part of it is that

[36:12] agree with me. Part of it is that founders forget that they have to pay it

[36:14] founders forget that they have to pay it back. They treat it like venture capital

[36:15] back. They treat it like venture capital and they forget about that and then they

[36:17] and they forget about that and then they get surprised. But the other thing I've

[36:19] get surprised. But the other thing I've never liked about it is it makes you

[36:21] never liked about it is it makes you more fragile. It basically subjects you

[36:23] more fragile. It basically subjects you to a bunch of business covenants and it

[36:26] to a bunch of business covenants and it makes it harder for you to do an abrupt

[36:29] makes it harder for you to do an abrupt shift in your business because now

[36:30] shift in your business because now you've got a bank looking over your

[36:32] you've got a bank looking over your shoulder and they want to make sure they

[36:33] shoulder and they want to make sure they get paid and they have to review your

[36:35] get paid and they have to review your financials and all the rest of it. And

[36:37] financials and all the rest of it. And to your point, Jal, the companies that

[36:38] to your point, Jal, the companies that have free cash flow right now are the

[36:40] have free cash flow right now are the ones that have the most maneuverability.

[36:42] ones that have the most maneuverability. I hate taking away maneuverability from

[36:45] I hate taking away maneuverability from founders and that is what debt does

[36:47] founders and that is what debt does because it subjects you to a fixed

[36:49] because it subjects you to a fixed schedule of payments. And so this is

[36:52] schedule of payments. And so this is always a thing to remember whether

[36:53] always a thing to remember whether you're a business or you're an

[36:56] you're a business or you're an individual. You know, when you put on

[36:58] individual. You know, when you put on that debt, it makes you more vulnerable

[37:01] that debt, it makes you more vulnerable to big disruptions in the market.

[37:03] to big disruptions in the market. >> Yeah. It just you become incredibly

[37:05] >> Yeah. It just you become incredibly brittle. And founders who are listening,

[37:07] brittle. And founders who are listening, when you get that in in peak markets,

[37:09] when you get that in in peak markets, peak zer, you're going to have venture

[37:11] peak zer, you're going to have venture debt people offer you tons of cash. And

[37:13] debt people offer you tons of cash. And then the problem Dave and I saw up close

[37:15] then the problem Dave and I saw up close and personal, many different companies

[37:17] and personal, many different companies where founders would look at it as like,

[37:19] where founders would look at it as like, oh, I'm extending my runway. Well, if

[37:20] oh, I'm extending my runway. Well, if you're a hot startup, there's always

[37:22] you're a hot startup, there's always more venture capital. There's always

[37:23] more venture capital. There's always more people who want to own equity. The

[37:25] more people who want to own equity. The equity sale gives you optionality. And

[37:27] equity sale gives you optionality. And you have more people on your team, more

[37:29] you have more people on your team, more people rooting for you and aligned with

[37:32] people rooting for you and aligned with equity interest as opposed to now you

[37:34] equity interest as opposed to now you have a debt instrument. They have a

[37:36] have a debt instrument. They have a different goal. They have different

[37:39] different goal. They have different downside. They're trying.

[37:40] downside. They're trying. >> No bank wants to be your last 3 to six

[37:42] >> No bank wants to be your last 3 to six months of runway because that means that

[37:45] months of runway because that means that in a high percentage of cases, they're

[37:47] in a high percentage of cases, they're going to lose their money.

[37:48] going to lose their money. >> Yeah.

[37:48] >> Yeah. >> So, they're I've never seen I have never

[37:52] >> So, they're I've never seen I have never seen venture debt work well to improve

[37:54] seen venture debt work well to improve the quality of a business never only

[37:58] the quality of a business never only only ever seen venture debt cracks that

[38:01] only ever seen venture debt cracks that damage companies and if you get the

[38:02] damage companies and if you get the venture debt you can never actually use

[38:04] venture debt you can never actually use it. So the venture debt investors that

[38:07] it. So the venture debt investors that ultimately make money it's because they

[38:09] ultimately make money it's because they put money in a company and the company

[38:10] put money in a company and the company never actually used the money they gave

[38:11] never actually used the money they gave them. I hate this business. I think

[38:13] them. I hate this business. I think venture debt's like the worst

[38:15] venture debt's like the worst vulture-like business in Silicon Valley.

[38:17] vulture-like business in Silicon Valley. It's terrible. If you get down, if if

[38:19] It's terrible. If you get down, if if the last money in the bank is the debt

[38:21] the last money in the bank is the debt you owe to the bank, they're going to

[38:23] you owe to the bank, they're going to rug you. That's when you get rugged

[38:25] rug you. That's when you get rugged >> 100%.

[38:26] >> 100%. >> You think they can afford to lose 100%

[38:29] >> You think they can afford to lose 100% of their money when they're getting an

[38:31] of their money when they're getting an 8% return or something like that? No

[38:33] 8% return or something like that? No way. That's not how it works. VCs can

[38:36] way. That's not how it works. VCs can afford that because we have the

[38:38] afford that because we have the opportunity for a 10x or 100x or,000x

[38:41] opportunity for a 10x or 100x or,000x for that moonshot. So, we can accept a

[38:43] for that moonshot. So, we can accept a bunch of zeros. The bank can't accept a

[38:44] bunch of zeros. The bank can't accept a bunch of zeros. Well, and then when they

[38:46] bunch of zeros. Well, and then when they when they do get scared and when they do

[38:48] when they do get scared and when they do think they're going to lose their money,

[38:50] think they're going to lose their money, wait till you see what they extract in

[38:52] wait till you see what they extract in terms of value, what they ask for. They

[38:54] terms of value, what they ask for. They will ask they'll double the interest

[38:56] will ask they'll double the interest rate. They'll ask for warrants. It's

[38:59] rate. They'll ask for warrants. It's basically like being in debt in prison.

[39:01] basically like being in debt in prison. Chim, you can talk a little bit about

[39:03] Chim, you can talk a little bit about your experience when you were uh in

[39:05] your experience when you were uh in debt. In prison, it's not going to be

[39:07] debt. In prison, it's not going to be pleasant. I've been in debt. I mean,

[39:09] pleasant. I've been in debt. I mean, I've had a $420 million credit line,

[39:13] I've had a $420 million credit line, >> and I had a moment where it was

[39:15] >> and I had a moment where it was reflexively kind of collapsing inward

[39:18] reflexively kind of collapsing inward because the assets that I was using to

[39:19] because the assets that I was using to secure it shrank in value in a moment of

[39:22] secure it shrank in value in a moment of market disruption. I was scrambling and

[39:25] market disruption. I was scrambling and then at the same time there was a risk.

[39:28] then at the same time there was a risk. It was the worst moment of my

[39:30] It was the worst moment of my professional working life. I had like a

[39:32] professional working life. I had like a couple hundred million dollars sitting

[39:33] couple hundred million dollars sitting at Credit Swiss and they were about to

[39:35] at Credit Swiss and they were about to implode. And so on a weekend, I was

[39:37] implode. And so on a weekend, I was trying to figure out whether my money

[39:38] trying to figure out whether my money was still there. I had always had this

[39:41] was still there. I had always had this rule, don't have debt, and then I

[39:43] rule, don't have debt, and then I violated it to try to run the number up.

[39:46] violated it to try to run the number up. I almost got run over. I almost lost

[39:48] I almost got run over. I almost lost everything. I will never do it again.

[39:51] everything. I will never do it again. And if I ever do it again, if you guys

[39:53] And if I ever do it again, if you guys ever hear me do it again, please just

[39:54] ever hear me do it again, please just come and punch me in the face.

[39:56] come and punch me in the face. >> We will. We've been waiting for an

[39:58] >> We will. We've been waiting for an excuse.

[39:58] excuse. >> Can we punch you in the face for other

[40:00] >> Can we punch you in the face for other things, too? Buffett has this line about

[40:02] things, too? Buffett has this line about this is how smart guys go bankrupt is

[40:04] this is how smart guys go bankrupt is they take on debt.

[40:06] they take on debt. >> Debt equals prison [ __ ] Keep it in

[40:08] >> Debt equals prison [ __ ] Keep it in your mind guys. You will be a [ __ ]

[40:10] your mind guys. You will be a [ __ ] >> Unless you socialize the debt then

[40:12] >> Unless you socialize the debt then everyone thinks it's okay which is what

[40:13] everyone thinks it's okay which is what we do with governments. And that's the

[40:15] we do with governments. And that's the problem with government. Don't get

[40:16] problem with government. Don't get >> start going off.

[40:18] >> start going off. >> Don't push the button. All right.

[40:19] >> Don't push the button. All right. Listen. We got to talk about

[40:20] Listen. We got to talk about >> another just just a quick aside on that.

[40:23] >> another just just a quick aside on that. In the 1950s, all the corporations in

[40:25] In the 1950s, all the corporations in America had pension plans where you

[40:27] America had pension plans where you would get some guaranteed payout at the

[40:29] would get some guaranteed payout at the end when you retire. And they were all

[40:30] end when you retire. And they were all like, "We're all going to go bankrupt

[40:31] like, "We're all going to go bankrupt because a pension plan is either

[40:33] because a pension plan is either significantly overfunded or underfunded.

[40:35] significantly overfunded or underfunded. If it's underfunded, you're bankrupt. If

[40:36] If it's underfunded, you're bankrupt. If it's overfunded, you've wasted all this

[40:38] it's overfunded, you've wasted all this money. You can't do anything with it."

[40:39] money. You can't do anything with it." So, they all moved to 401ks and

[40:41] So, they all moved to 401ks and everything got moved to defined

[40:42] everything got moved to defined contribution plans except except

[40:45] contribution plans except except >> governments. And that's because the

[40:47] >> governments. And that's because the government employees formed government

[40:49] government employees formed government public employee unions and they're like,

[40:51] public employee unions and they're like, "We want to keep the pension plans and

[40:53] "We want to keep the pension plans and now the pension plans, it turns out 70

[40:54] now the pension plans, it turns out 70 years later are going to bankrupt all

[40:56] years later are going to bankrupt all the governments in the United States."

[40:59] the governments in the United States." >> By the way, a guy Spencer Pratt who's

[41:00] >> By the way, a guy Spencer Pratt who's running for mayor, he started uncovering

[41:02] running for mayor, he started uncovering all of the salaries of the union folks

[41:05] all of the salaries of the union folks and their pensions in Southern

[41:07] and their pensions in Southern California. It's bonkers. They're making

[41:10] California. It's bonkers. They're making $4500,000

[41:11] $4500,000 a year right before they go on pension.

[41:14] a year right before they go on pension. then they double their overtime and they

[41:15] then they double their overtime and they get two/3 or a half. The pension doesn't

[41:18] get two/3 or a half. The pension doesn't work. You got to go super annutation

[41:19] work. You got to go super annutation fund. I don't know how many times we've

[41:20] fund. I don't know how many times we've talked about it here, but

[41:22] talked about it here, but >> you don't need autation fund. You just

[41:24] >> you don't need autation fund. You just need a 401k. Let people have a have an

[41:26] need a 401k. Let people have a have an account. They got their money in their

[41:27] account. They got their money in their account.

[41:27] account. >> Yeah. But it's just a way to force

[41:29] >> Yeah. But it's just a way to force people to contribute to it. So a forced

[41:31] people to contribute to it. So a forced 401k is different than a 401k. You got

[41:33] 401k is different than a 401k. You got to force people and it's you're not

[41:34] to force people and it's you're not allowed to force people into their 401k

[41:37] allowed to force people into their 401k as you know. Yeah.

[41:38] as you know. Yeah. >> As we've seen in California, everything

[41:40] >> As we've seen in California, everything related to the government is a giant

[41:41] related to the government is a giant grift. It's a giant scam. There's tons

[41:43] grift. It's a giant scam. There's tons of fraud going on. Uh we've talked about

[41:46] of fraud going on. Uh we've talked about the homeless industrial complex, 12

[41:48] the homeless industrial complex, 12 billion a year to homelessness, but the

[41:49] billion a year to homelessness, but the number of homeless keeps going up.

[41:51] number of homeless keeps going up. There's a million examples like that.

[41:52] There's a million examples like that. >> The racism industrial complex. We'll get

[41:55] >> The racism industrial complex. We'll get >> a good time. Let's shift to SPLC because

[41:57] >> a good time. Let's shift to SPLC because I think it's a good example.

[41:58] I think it's a good example. >> Well, we'll get to it. We'll get to it.

[41:59] >> Well, we'll get to it. We'll get to it. Um yeah, but you know what's even better

[42:01] Um yeah, but you know what's even better is you can just pass a law like the Nick

[42:02] is you can just pass a law like the Nick Shirley Act and you can put your fingers

[42:04] Shirley Act and you can put your fingers in your ear, cover your eyes, and say la

[42:06] in your ear, cover your eyes, and say la and just pretend the fraud's not

[42:07] and just pretend the fraud's not happening, which is their reaction in

[42:09] happening, which is their reaction in California. Hey,

[42:10] California. Hey, >> how much fraud has Nick Shirley

[42:12] >> how much fraud has Nick Shirley uncovered? So far in California

[42:16] where he should be a billionaire,

[42:18] where he should be a billionaire, >> you know, he should be doing it

[42:19] >> you know, he should be doing it privately and then getting the

[42:20] privately and then getting the whistleblower awards. I think that

[42:22] whistleblower awards. I think that actually would be a better strategy for

[42:24] actually would be a better strategy for him.

[42:25] him. >> We told him that was the we told him

[42:26] >> We told him that was the we told him that business model, remember?

[42:28] that business model, remember? >> Yeah. No, he I think he's addicted to

[42:29] >> Yeah. No, he I think he's addicted to the views, but I mean he could literally

[42:31] the views, but I mean he could literally raise money on

[42:34] raise money on >> concept. He's making thousands when he

[42:35] >> concept. He's making thousands when he could be making billions to be free. No.

[42:37] could be making billions to be free. No. >> Well, but it's better it's better for

[42:39] >> Well, but it's better it's better for the public that he's doing what he's

[42:41] the public that he's doing what he's doing. Thank God for Nick Shirley.

[42:43] doing. Thank God for Nick Shirley. >> So thank you, Nick Shirley. Whether you

[42:44] >> So thank you, Nick Shirley. Whether you could be making more money or not, what

[42:46] could be making more money or not, what you're doing is

[42:47] you're doing is >> You know what he also did? He shamed the

[42:50] >> You know what he also did? He shamed the mainstream media who's forgotten about

[42:51] mainstream media who's forgotten about investigative journalism, who forgot the

[42:54] investigative journalism, who forgot the ability to knock on a door and just ask

[42:55] ability to knock on a door and just ask a basic question. And now Bari Weiss

[42:59] a basic question. And now Bari Weiss with CBS has deputized one of her

[43:01] with CBS has deputized one of her reporters and she's doing the exact same

[43:04] reporters and she's doing the exact same playbook and meeting him punch for

[43:06] playbook and meeting him punch for punch. Where's CNN? Anderson Cooper

[43:09] punch. Where's CNN? Anderson Cooper should have a Nick Shirley on his team.

[43:10] should have a Nick Shirley on his team. The New York Times should have a Nick

[43:12] The New York Times should have a Nick Shirley. The LA Times should have a Nick

[43:14] Shirley. The LA Times should have a Nick Shirley. Why don't they? That should be

[43:16] Shirley. Why don't they? That should be >> hide. You're talking You're talking

[43:17] >> hide. You're talking You're talking about old media that does things one

[43:19] about old media that does things one way. And the point about Nick Shirley is

[43:21] way. And the point about Nick Shirley is it's new media. It's citizen journalism.

[43:23] it's new media. It's citizen journalism. It's people on the street distributing

[43:25] It's people on the street distributing factf finding, distributing information

[43:27] factf finding, distributing information gathering. And old media in order to

[43:29] gathering. And old media in order to survive became an opinion organization.

[43:30] survive became an opinion organization. >> Didn't the media used to care that the

[43:32] >> Didn't the media used to care that the Pentagon was paying $900 for a hammer or

[43:35] Pentagon was paying $900 for a hammer or what have you? I mean, like 60 Minutes

[43:36] what have you? I mean, like 60 Minutes used to do things. Now it's like the

[43:38] used to do things. Now it's like the media just wants to protect the waste

[43:42] media just wants to protect the waste fraud abuse no matter how egregious it

[43:45] fraud abuse no matter how egregious it is. Remember,

[43:46] is. Remember, >> do you remember that guy Dennis

[43:47] >> do you remember that guy Dennis Kuzlowski, the CEO of Tao, who went to

[43:49] Kuzlowski, the CEO of Tao, who went to jail and they like had just a field day

[43:53] jail and they like had just a field day stand?

[43:53] stand? >> Umbrella stand the $6,000 umbrella stand

[43:57] >> Umbrella stand the $6,000 umbrella stand >> made out of like Ivory from like no

[44:00] >> made out of like Ivory from like no elephant. I know you bought that, didn't

[44:02] elephant. I know you bought that, didn't you?

[44:03] you? >> Sax, you're so right. People really used

[44:05] >> Sax, you're so right. People really used to care except when it was their team

[44:08] to care except when it was their team and then the minute that it was their

[44:09] and then the minute that it was their team, they're like, "Oh, no. Let's just

[44:10] team, they're like, "Oh, no. Let's just look the other way."

[44:11] look the other way." >> You're right. If it's a CEO, if a CEO

[44:14] >> You're right. If it's a CEO, if a CEO basically engages in some misbehavior,

[44:15] basically engages in some misbehavior, and I'm not defending it, the press will

[44:17] and I'm not defending it, the press will be all over that. But when the

[44:19] be all over that. But when the government does it, they don't do

[44:20] government does it, they don't do anything. And in fact, we had one of the

[44:23] anything. And in fact, we had one of the most successful, probably the most

[44:24] most successful, probably the most successful entrepreneur of our

[44:27] successful entrepreneur of our generation donating his time to the

[44:28] generation donating his time to the government to find waste. And the media

[44:32] government to find waste. And the media basically drove him out.

[44:34] basically drove him out. >> They vilified him and drove him out.

[44:35] >> They vilified him and drove him out. Yeah. They made it untenable. Well, this

[44:37] Yeah. They made it untenable. Well, this is whoever comes up with a way to

[44:39] is whoever comes up with a way to eliminate waste, fraud, and abuse like

[44:41] eliminate waste, fraud, and abuse like Doge did and they productize that and

[44:43] Doge did and they productize that and make them make that their platform.

[44:46] make them make that their platform. That's the way to win in 2028 and going

[44:49] That's the way to win in 2028 and going forward is to convince the public that

[44:51] forward is to convince the public that they don't need to have their taxes

[44:53] they don't need to have their taxes raised. They could have their taxes

[44:55] raised. They could have their taxes reduced just by eliminating the minimum

[44:57] reduced just by eliminating the minimum of 20 or 30% of waste for abuse there is

[45:00] of 20 or 30% of waste for abuse there is in the system. Uh, we'll get to Tim Cook

[45:03] in the system. Uh, we'll get to Tim Cook stepping down in just a moment, but I

[45:05] stepping down in just a moment, but I want to remind everybody liquidity sold

[45:07] want to remind everybody liquidity sold out. I'm sorry we added a couple of

[45:09] out. I'm sorry we added a couple of tickets. We we we burned through them

[45:11] tickets. We we we burned through them immediately, but you can still get into

[45:12] immediately, but you can still get into the All-InSummit. This is our fifth

[45:14] the All-InSummit. This is our fifth edition in Los Angeles, September 13th,

[45:16] edition in Los Angeles, September 13th, 14th, and 15th.

[45:18] 14th, and 15th. >> Allin.com/events to apply. Please apply

[45:21] >> Allin.com/events to apply. Please apply and then don't come to us 60 days out

[45:23] and then don't come to us 60 days out and say, "I didn't get a ticket.

[45:25] and say, "I didn't get a ticket. >> I'm a bestie. Get me in." Just buy your

[45:27] >> I'm a bestie. Get me in." Just buy your damn ticket and don't get left out.

[45:29] damn ticket and don't get left out. >> I have a liquidity announcement.

[45:31] >> I have a liquidity announcement. >> Oh, yum, yum. We are going to do one

[45:34] >> Oh, yum, yum. We are going to do one political panel,

[45:35] political panel, >> okay?

[45:36] >> okay? >> And it's going to be Dave McCormack and

[45:40] >> And it's going to be Dave McCormack and John Federman, the two sitting senators

[45:42] John Federman, the two sitting senators from Pennsylvania on stage with us

[45:45] from Pennsylvania on stage with us talking about all topics from a left and

[45:48] talking about all topics from a left and right perspective.

[45:50] right perspective. >> Amazing. So Federman's coming, which

[45:52] >> Amazing. So Federman's coming, which means the dress code is now sandals, uh,

[45:55] means the dress code is now sandals, uh, shorts, and a t-shirt. That's great.

[45:57] shorts, and a t-shirt. That's great. Construction sheet. Get your Timberlands

[45:59] Construction sheet. Get your Timberlands out. your trim in that one. I mean, is

[46:02] out. your trim in that one. I mean, is he really going to show up looking like

[46:04] he really going to show up looking like a hobo? I love his hobo style. It's

[46:06] a hobo? I love his hobo style. It's great.

[46:07] great. >> McCormack's very fit and handsome, so

[46:08] >> McCormack's very fit and handsome, so like he he'll he'll balance him out. So,

[46:10] like he he'll he'll balance him out. So, >> we should that's what we should program

[46:11] >> we should that's what we should program it as. Like, he should wear his best

[46:14] it as. Like, he should wear his best Brion suit versus the Old Navy from

[46:17] Brion suit versus the Old Navy from Betterman who wore it better. All right,

[46:20] Betterman who wore it better. All right, listen. Just rapid fire here on the Tim

[46:22] listen. Just rapid fire here on the Tim Cook uh resignation and moving on. This

[46:25] Cook uh resignation and moving on. This guy, John Turnis, is a 25-y year vet. He

[46:30] guy, John Turnis, is a 25-y year vet. He did lots of hardware, worked on iPad,

[46:32] did lots of hardware, worked on iPad, AirPods, and he was the favorite on Poly

[46:35] AirPods, and he was the favorite on Poly Market since day one. He's a bold

[46:38] Market since day one. He's a bold decision maker according to reports. And

[46:41] decision maker according to reports. And unlike Tim Cook, Cook, Tim Cook did a

[46:43] unlike Tim Cook, Cook, Tim Cook did a great job of squeezing every last nickel

[46:45] great job of squeezing every last nickel out of Steve Jobs's product line, which

[46:48] out of Steve Jobs's product line, which lasted for a decade. iPhone, Apple TV,

[46:50] lasted for a decade. iPhone, Apple TV, watch, I don't need to repeat them, but

[46:52] watch, I don't need to repeat them, but here we are. We got a product person in

[46:55] here we are. We got a product person in the seat which is what we all know they

[46:57] the seat which is what we all know they needed because hey these tools are

[46:59] needed because hey these tools are getting a little bit stale. Siri

[47:01] getting a little bit stale. Siri descriad Airpods discretad the whole uh

[47:05] descriad Airpods discretad the whole uh system is not built on innovation

[47:07] system is not built on innovation anymore. It's built Freeberg I think you

[47:10] anymore. It's built Freeberg I think you would agree on just ringing more profits

[47:14] would agree on just ringing more profits more profits. What's your hope here?

[47:16] more profits. What's your hope here? Because man they missed so many great

[47:18] Because man they missed so many great swings at bat. They didn't get the

[47:20] swings at bat. They didn't get the Oculus, you know, Ray-B bands that Meta

[47:23] Oculus, you know, Ray-B bands that Meta did. They cancelled their self-driving

[47:25] did. They cancelled their self-driving car. What would you hope that this new

[47:27] car. What would you hope that this new CEO of Apple focuses on Freeberg in

[47:31] CEO of Apple focuses on Freeberg in terms of innovation? They don't have a

[47:32] terms of innovation? They don't have a problem selling phones still. They don't

[47:34] problem selling phones still. They don't have a problem selling laptops and

[47:35] have a problem selling laptops and making a ton of money. But if you were

[47:36] making a ton of money. But if you were in the seat, if you were on the board of

[47:38] in the seat, if you were on the board of Apple, which wouldn't be a bad idea for

[47:39] Apple, which wouldn't be a bad idea for them if I'm being honest, what would you

[47:41] them if I'm being honest, what would you tell the new CEO to focus on? David

[47:43] tell the new CEO to focus on? David Freeber.

[47:44] Freeber. >> I mean, I don't know. The software layer

[47:46] >> I mean, I don't know. The software layer of the future is not the software layer

[47:48] of the future is not the software layer of the past. So,

[47:49] of the past. So, >> okay,

[47:51] >> okay, >> it's pretty obvious. I don't know if how

[47:52] >> it's pretty obvious. I don't know if how much there is to talk about, but you

[47:53] much there is to talk about, but you just need the Siri equivalent that's

[47:55] just need the Siri equivalent that's ubiquitous in all of your devices. Knows

[47:57] ubiquitous in all of your devices. Knows who you are, personalized to you, sees

[47:59] who you are, personalized to you, sees your email, sees your calendar entries,

[48:02] your email, sees your calendar entries, knows what kind of music you like, has

[48:03] knows what kind of music you like, has connection to your home, basically build

[48:05] connection to your home, basically build that AI layer for your life and make it

[48:09] that AI layer for your life and make it ubiquitous in all of your Apple devices

[48:10] ubiquitous in all of your Apple devices that no matter what device you're using,

[48:12] that no matter what device you're using, it knows who you are. You can engage

[48:13] it knows who you are. You can engage with it using kind of a natural language

[48:16] with it using kind of a natural language method. And it's, you know, it's it's

[48:18] method. And it's, you know, it's it's pretty obvious.

[48:19] pretty obvious. >> Yeah, they should buy Whisper Flow.

[48:21] >> Yeah, they should buy Whisper Flow. Yeah. I mean, that would be

[48:22] Yeah. I mean, that would be >> I don't know how they're I don't know

[48:23] >> I don't know how they're I don't know how they're running the business, but

[48:24] how they're running the business, but >> Well, they're running it for profits,

[48:26] >> Well, they're running it for profits, obviously. Sachs. Uh I would say buy

[48:28] obviously. Sachs. Uh I would say buy Whisper Flow and just replace the Siri

[48:30] Whisper Flow and just replace the Siri team with that because Siri has been

[48:31] team with that because Siri has been just The fact that Siri can't spell

[48:33] just The fact that Siri can't spell Polyhapatia or Calacanis after 20 years

[48:36] Polyhapatia or Calacanis after 20 years of us giving them $20,000 for iPhones is

[48:39] of us giving them $20,000 for iPhones is just disgraceful. Sax, if you were on

[48:42] just disgraceful. Sax, if you were on the board of Apple, again, not a bad

[48:43] the board of Apple, again, not a bad idea. What would be your hope for the

[48:46] idea. What would be your hope for the company? What would be your sage advice

[48:48] company? What would be your sage advice for the new CEO?

[48:50] for the new CEO? >> Well, I mean, everybody is going to be

[48:52] >> Well, I mean, everybody is going to be asking the same question, which is what

[48:54] asking the same question, which is what are you going to do about AI? I don't

[48:56] are you going to do about AI? I don't know that they needed to be on the

[48:57] know that they needed to be on the bleeding edge of it, but they are going

[48:58] bleeding edge of it, but they are going to need an answer at some point. And

[49:00] to need an answer at some point. And Siri is going to need to be AI

[49:02] Siri is going to need to be AI empowered. Probably the way it should

[49:04] empowered. Probably the way it should work is that you get to choose your

[49:06] work is that you get to choose your model. I mean, I don't know that they

[49:07] model. I mean, I don't know that they need to pick just one model provider. It

[49:09] need to pick just one model provider. It could be a setting where you go in and

[49:11] could be a setting where you go in and you set up your account with whatever

[49:13] you set up your account with whatever chat GPT or Grock or Claude or what have

[49:16] chat GPT or Grock or Claude or what have you and you can choose your own model

[49:18] you and you can choose your own model provider and then you'll have more

[49:19] provider and then you'll have more customization and more ability to

[49:22] customization and more ability to control your your storage. Let me just

[49:24] control your your storage. Let me just say just on Tim Cook's retirement, he

[49:28] say just on Tim Cook's retirement, he had an incredible run as CEO of of

[49:31] had an incredible run as CEO of of Apple, I mean he ran it very effectively

[49:33] Apple, I mean he ran it very effectively for 15 years. The market cap of the

[49:36] for 15 years. The market cap of the company went up by over 10x. the revenue

[49:38] company went up by over 10x. the revenue grew from roughly 100 billion a year to

[49:40] grew from roughly 100 billion a year to over 400 billion a year. He also

[49:43] over 400 billion a year. He also improved the quality of revenue by

[49:45] improved the quality of revenue by moving the mix into services which is

[49:47] moving the mix into services which is partly why it got why it got a higher

[49:50] partly why it got why it got a higher valuation and you know people say that

[49:53] valuation and you know people say that well they never did any innovation under

[49:55] well they never did any innovation under Tim Cook but you know I've seen people

[49:56] Tim Cook but you know I've seen people tweet lists of products that were

[49:58] tweet lists of products that were released under him and there were a lot

[50:00] released under him and there were a lot of them now it's true nothing as big as

[50:03] of them now it's true nothing as big as the iPhone but they did release a lot of

[50:05] the iPhone but they did release a lot of products under Tim Cook and then just

[50:07] products under Tim Cook and then just finally I mean you you look back over

[50:08] finally I mean you you look back over the last 15 years and there really

[50:11] the last 15 years and there really weren't any public snafuss or scandals

[50:16] weren't any public snafuss or scandals or brogleos with with Apple. It's one of

[50:18] or brogleos with with Apple. It's one of the few tech brands that is still I

[50:21] the few tech brands that is still I think beloved by the population. I think

[50:24] think beloved by the population. I think a major part of that was Tim Cook's

[50:26] a major part of that was Tim Cook's dedication to privacy and keeping the

[50:28] dedication to privacy and keeping the company on the right side of that which

[50:30] company on the right side of that which I think users do appreciate. And you

[50:32] I think users do appreciate. And you know he even Tim Cook even got praise

[50:35] know he even Tim Cook even got praise from the president. And I think it was

[50:37] from the president. And I think it was just unsolicited where the president

[50:39] just unsolicited where the president talked about how Tim Cook didn't call

[50:41] talked about how Tim Cook didn't call him up that often, but when he did it

[50:43] him up that often, but when he did it was something important and therefore

[50:44] was something important and therefore the president tried to help them out.

[50:46] the president tried to help them out. Seems like he nurtured a good

[50:47] Seems like he nurtured a good relationship with the president over

[50:48] relationship with the president over over the last decade or so. So you just

[50:51] over the last decade or so. So you just have to say that he navigated what could

[50:53] have to say that he navigated what could have been a turbulent period with a

[50:55] have been a turbulent period with a great deal of grace and appl. Clearly uh

[50:59] great deal of grace and appl. Clearly uh Chimath he was a great steward of the

[51:01] Chimath he was a great steward of the brand even though that list of products

[51:05] brand even though that list of products were all developed under Steve Jobs and

[51:08] were all developed under Steve Jobs and they were just executed well but he

[51:10] they were just executed well but he didn't bring in a lot of new products or

[51:13] didn't bring in a lot of new products or services. Any final take

[51:15] services. Any final take >> actually let me ask you a question. What

[51:17] >> actually let me ask you a question. What do you think

[51:18] do you think >> other than AI you know AI powered Siri

[51:21] >> other than AI you know AI powered Siri let's say what do you think he missed? I

[51:24] let's say what do you think he missed? I mean, what should Apple have done that

[51:26] mean, what should Apple have done that they didn't do?

[51:28] they didn't do? >> They would have out by now a pair of

[51:30] >> They would have out by now a pair of glasses that weren't 17 pounds like the

[51:33] glasses that weren't 17 pounds like the Apple Vision Pro. They would have gotten

[51:36] Apple Vision Pro. They would have gotten glasses that pair perfectly with your

[51:38] glasses that pair perfectly with your phone, take videos for kids, and they're

[51:39] phone, take videos for kids, and they're coming out with it. It's just on a

[51:41] coming out with it. It's just on a really broken timeline. They would have

[51:43] really broken timeline. They would have had a killer Siri. They would have had a

[51:46] had a killer Siri. They would have had a search engineish perplexity-l like

[51:48] search engineish perplexity-l like product. They would have had a

[51:50] product. They would have had a self-driving car. When you went to the

[51:51] self-driving car. When you went to the Apple store, you would have been buying

[51:53] Apple store, you would have been buying two or three very important products.

[51:55] two or three very important products. Glasses, a car, and probably a

[51:58] Glasses, a car, and probably a television set. If you look at actually

[52:00] television set. If you look at actually what they did innovative under Tim Cook,

[52:02] what they did innovative under Tim Cook, I think the they have great taste and

[52:04] I think the they have great taste and Apple TV produced a lot of great

[52:06] Apple TV produced a lot of great programming. I he was working on a

[52:09] programming. I he was working on a television set, not Apple TV clunked

[52:11] television set, not Apple TV clunked onto the back. I think those three

[52:13] onto the back. I think those three products would have been four products,

[52:15] products would have been four products, Siri, glasses, car, television set.

[52:18] Siri, glasses, car, television set. Those would have been extraordinary. And

[52:20] Those would have been extraordinary. And who knows what he would have come up

[52:21] who knows what he would have come up with when they lost Johnny IV uh and

[52:23] with when they lost Johnny IV uh and obviously Steve Jobs passed away. They

[52:25] obviously Steve Jobs passed away. They lost the soul of the company. They lost

[52:27] lost the soul of the company. They lost the innovative groundbreaking soul of

[52:30] the innovative groundbreaking soul of the company and they just went into

[52:32] the company and they just went into profit and iteration mode. But no

[52:35] profit and iteration mode. But no acquisitions of note, nothing important

[52:38] acquisitions of note, nothing important was acquired and nothing important was

[52:41] was acquired and nothing important was released. Vision Pro, you can give them

[52:43] released. Vision Pro, you can give them like maybe that's like the sixth best

[52:45] like maybe that's like the sixth best product or something, but it obviously

[52:47] product or something, but it obviously hasn't hit the mainstream. Chimab, any

[52:49] hasn't hit the mainstream. Chimab, any final thoughts from you?

[52:50] final thoughts from you? >> Yeah, I have um

[52:52] >> Yeah, I have um three specific things to say. The first

[52:54] three specific things to say. The first is that he had honestly like an

[52:56] is that he had honestly like an impossible job. It's sort of like you

[53:00] impossible job. It's sort of like you play

[53:02] play basketball with Michael Jordan and then

[53:04] basketball with Michael Jordan and then you're asked to be Michael Jordan. And I

[53:07] you're asked to be Michael Jordan. And I think that that's an impossible task.

[53:08] think that that's an impossible task. And on that dimension, I think he has

[53:10] And on that dimension, I think he has done just an incredible job. He has been

[53:13] done just an incredible job. He has been an incredible steward of the business.

[53:15] an incredible steward of the business. Sax is right. no major snafuss. I think

[53:19] Sax is right. no major snafuss. I think he did a really smart thing around

[53:21] he did a really smart thing around doubling down on privacy and just as a

[53:24] doubling down on privacy and just as a practical matter of being a great CEO

[53:27] practical matter of being a great CEO like if you I think you can categorize

[53:29] like if you I think you can categorize CEOs in two buckets. One is the

[53:32] CEOs in two buckets. One is the innovator, the person that's pushing the

[53:35] innovator, the person that's pushing the envelope and then the second is just a

[53:37] envelope and then the second is just a great steward. He's at the top of the

[53:40] great steward. He's at the top of the top of that second category. Um, I sent

[53:43] top of that second category. Um, I sent you a couple of charts to to show this

[53:46] you a couple of charts to to show this and he found a lane that allowed him to

[53:49] and he found a lane that allowed him to separate himself from Steve Jobs. So,

[53:51] separate himself from Steve Jobs. So, you know, as an example, like what does

[53:53] you know, as an example, like what does it mean to be a steward? Well, when

[53:56] it mean to be a steward? Well, when you're a steward, you're allocating

[53:57] you're a steward, you're allocating resources. And the two most important

[53:59] resources. And the two most important resources you control is capital and

[54:02] resources you control is capital and people. And I think on that dimension,

[54:05] people. And I think on that dimension, what Tim did, if you just look at this,

[54:07] what Tim did, if you just look at this, is he was able to invest appropriately

[54:10] is he was able to invest appropriately in R&D. They completely divested their

[54:13] in R&D. They completely divested their need of Intel. They spun their entire

[54:16] need of Intel. They spun their entire new line of silicon. That silicon, it

[54:19] new line of silicon. That silicon, it turns out, and this will be important in

[54:20] turns out, and this will be important in the future, is very useful in AI with

[54:24] the future, is very useful in AI with all of this open cloth stuff. And you

[54:26] all of this open cloth stuff. And you know, some of you guys are completely

[54:27] know, some of you guys are completely addicted to it. And they've kept the

[54:30] addicted to it. And they've kept the acquisitions light. So, he was very

[54:32] acquisitions light. So, he was very capital efficient. If you look at the

[54:34] capital efficient. If you look at the the next chart, what's so interesting is

[54:36] the next chart, what's so interesting is like it is the exact opposite of what

[54:38] like it is the exact opposite of what Steve Jobs did. Look at the amount of

[54:40] Steve Jobs did. Look at the amount of money that Steve Jobs returned to

[54:42] money that Steve Jobs returned to shareholders in his tenure at Apple.

[54:44] shareholders in his tenure at Apple. It's easy to count. It was zero. He

[54:46] It's easy to count. It was zero. He loved to keep that money on the balance

[54:49] loved to keep that money on the balance sheet and he probably or maybe I'm

[54:51] sheet and he probably or maybe I'm guessing would have directed that at

[54:53] guessing would have directed that at some huge shot on goal. In the Tim Cook

[54:56] some huge shot on goal. In the Tim Cook era, it was very different. He shrank

[54:57] era, it was very different. He shrank the share count by almost 50%. I think

[54:59] the share count by almost 50%. I think it's like 44%.

[55:01] it's like 44%. >> That's insane. Is there any correlary to

[55:03] >> That's insane. Is there any correlary to that, Chim? No, he's he's been a

[55:06] that, Chim? No, he's he's been a prolific

[55:07] prolific shareholder friendly CEO, finding ways

[55:10] shareholder friendly CEO, finding ways to give us money back, which I think

[55:11] to give us money back, which I think everyone who's owned the stock has very

[55:14] everyone who's owned the stock has very deeply appreciated. The last thing I'll

[55:16] deeply appreciated. The last thing I'll say though is what is the future for

[55:18] say though is what is the future for John Turnis, and I think it's in this

[55:19] John Turnis, and I think it's in this final chart. We talked about

[55:22] final chart. We talked about the problematic nature of increasing per

[55:26] the problematic nature of increasing per unit pricing in SAS

[55:29] unit pricing in SAS and what I would say is if you look at

[55:31] and what I would say is if you look at the iPhone

[55:33] the iPhone the unit price has gone up and people

[55:35] the unit price has gone up and people would say yes but the capabilities have

[55:37] would say yes but the capabilities have gone up in turn and I acknowledge that

[55:40] gone up in turn and I acknowledge that but the problem with the per unit

[55:42] but the problem with the per unit pricing being as high as it is is what

[55:45] pricing being as high as it is is what Freeberg says is going to happen. AI

[55:47] Freeberg says is going to happen. AI rips open the canvas of the devices that

[55:50] rips open the canvas of the devices that we will use to interact with information

[55:52] we will use to interact with information and knowledge.

[55:54] and knowledge. We are going to live in a much more

[55:56] We are going to live in a much more heterogeneous world in the future. It's

[55:58] heterogeneous world in the future. It's not going to be two devices and two

[56:00] not going to be two devices and two different operating systems that get you

[56:02] different operating systems that get you to knowledge. There's going to be all

[56:04] to knowledge. There's going to be all kinds of stuff, pens, orbs, who knows,

[56:08] kinds of stuff, pens, orbs, who knows, Jason, your glasses, whatever.

[56:10] Jason, your glasses, whatever. And so the problem is if you get too

[56:12] And so the problem is if you get too addicted to a single thing that has an

[56:15] addicted to a single thing that has an incredibly juicy profit margin and great

[56:19] incredibly juicy profit margin and great stickiness and the ability to raise

[56:21] stickiness and the ability to raise price, it's a hard drug to get off of.

[56:24] price, it's a hard drug to get off of. So I think really what John Turnis has

[56:26] So I think really what John Turnis has to do is figure out how to move to this

[56:29] to do is figure out how to move to this world where everybody will be launching

[56:32] world where everybody will be launching devices via MCP or otherwise. All of

[56:36] devices via MCP or otherwise. All of these services will be open. It'll be

[56:39] these services will be open. It'll be aentically talking to everything. I

[56:41] aentically talking to everything. I think the most decay

[56:44] think the most decay and I think if that happens that's

[56:46] and I think if that happens that's problematic if you're too reliant on a

[56:48] problematic if you're too reliant on a single thing to kind of keep it going.

[56:50] single thing to kind of keep it going. >> Yeah. And just expanding on what you

[56:52] >> Yeah. And just expanding on what you said like wearables is where they really

[56:55] said like wearables is where they really uh made some good inroads in terms of

[56:57] uh made some good inroads in terms of getting people to use them whether it's

[56:59] getting people to use them whether it's AirPods or the watch. And the next

[57:01] AirPods or the watch. And the next wearable this is a plaude pin that I use

[57:04] wearable this is a plaude pin that I use to record. You can put it here and put

[57:05] to record. You can put it here and put it on your wrist. That AI synchronicity

[57:09] it on your wrist. That AI synchronicity of having your eyes, having your ears,

[57:12] of having your eyes, having your ears, having your watch, having your phone,

[57:13] having your watch, having your phone, your desktop all synced together with AI

[57:16] your desktop all synced together with AI could be a huge product line. I'll also

[57:18] could be a huge product line. I'll also add a fifth robotics. You know, the I

[57:21] add a fifth robotics. You know, the I think t I think Steve Jobs, if you were

[57:24] think t I think Steve Jobs, if you were alive today, would have been looking at

[57:25] alive today, would have been looking at Roomba. He would have been looking at

[57:26] Roomba. He would have been looking at Optimus and he would have said, hm,

[57:29] Optimus and he would have said, hm, consumer robotics in addition to a

[57:31] consumer robotics in addition to a consumer car. Those are two things I

[57:34] consumer car. Those are two things I think he would have absolutely

[57:36] think he would have absolutely uh executed on at a high level. Okay,

[57:39] uh executed on at a high level. Okay, let's keep moving here. Uh

[57:40] let's keep moving here. Uh >> just come come make one last

[57:41] >> just come come make one last >> and we'd love to have you on the pod,

[57:42] >> and we'd love to have you on the pod, John. So, just come on the pod when

[57:44] John. So, just come on the pod when you're ready. Uh we'll have you come sit

[57:45] you're ready. Uh we'll have you come sit in. Go ahead. S get the final word.

[57:47] in. Go ahead. S get the final word. >> Just one last point on this is that I

[57:49] >> Just one last point on this is that I think the succession at Apple is

[57:53] think the succession at Apple is reminiscent a little bit of the

[57:54] reminiscent a little bit of the succession at Disney. And apparently

[57:58] succession at Disney. And apparently Steve Jobs and Tim Cook had this

[58:00] Steve Jobs and Tim Cook had this conversation back when Steve was alive.

[58:01] conversation back when Steve was alive. and and Steve told Tim don't do what

[58:05] and and Steve told Tim don't do what Disney did where basically after Walt

[58:06] Disney did where basically after Walt Disney died the company kind of

[58:09] Disney died the company kind of languished because it felt so beholden

[58:12] languished because it felt so beholden to Walt's vision that they never really

[58:14] to Walt's vision that they never really iterated. Now when Walt died his brother

[58:18] iterated. Now when Walt died his brother Roy took over and Roy was already in the

[58:20] Roy took over and Roy was already in the business he was sort of like the

[58:21] business he was sort of like the business co-founder. He was a COO type a

[58:24] business co-founder. He was a COO type a little bit like Tim Cook and he kept the

[58:26] little bit like Tim Cook and he kept the magic going for about 5 years and then

[58:27] magic going for about 5 years and then he himself died. I think it was around

[58:29] he himself died. I think it was around 1971 and then you had this string of

[58:31] 1971 and then you had this string of CEOs who took over kind of uninspired

[58:33] CEOs who took over kind of uninspired and it wasn't until Eisner came in in

[58:36] and it wasn't until Eisner came in in 1984 that he sort of revitalized the

[58:38] 1984 that he sort of revitalized the business and so as I understand it Steve

[58:41] business and so as I understand it Steve and Tim had this conversation and Steve

[58:43] and Tim had this conversation and Steve told him don't be too beholdened to my

[58:45] told him don't be too beholdened to my vision you need to figure out your own

[58:47] vision you need to figure out your own and extend it. I think that, you know,

[58:49] and extend it. I think that, you know, you could argue that Tim in a way was

[58:51] you could argue that Tim in a way was like the Roy figure here. Very effective

[58:55] like the Roy figure here. Very effective business partner of Steve. He got a

[58:59] business partner of Steve. He got a 15-year run. Roy only got five. And I

[59:02] 15-year run. Roy only got five. And I think again, he added a zero to the

[59:05] think again, he added a zero to the value of the company. The market cap

[59:07] value of the company. The market cap went up over 10x. So, you'd have to say

[59:09] went up over 10x. So, you'd have to say fantastically successful run

[59:12] fantastically successful run >> as CEO. I think the question now for

[59:14] >> as CEO. I think the question now for John Turnis is okay, you're now past,

[59:17] John Turnis is okay, you're now past, let's say, the the Walt Disney and Roy

[59:19] let's say, the the Walt Disney and Roy Disney part of the business. Is it going

[59:21] Disney part of the business. Is it going to be like the 1970s

[59:23] to be like the 1970s Disney or is it going to be more like

[59:24] Disney or is it going to be more like the 1980s? Do you figure out way to

[59:26] the 1980s? Do you figure out way to revitalize it or do you have to go

[59:28] revitalize it or do you have to go through

[59:30] through kind of a funk first?

[59:32] kind of a funk first? >> Yeah. And if I I think it's like really

[59:34] >> Yeah. And if I I think it's like really illustrative of this discussion, Eisner

[59:37] illustrative of this discussion, Eisner and Iger because Eisner's innovation was

[59:40] and Iger because Eisner's innovation was he realized that Disney was uh I think

[59:43] he realized that Disney was uh I think he called it the vault strategy. He

[59:45] he called it the vault strategy. He would and we probably remember this from

[59:47] would and we probably remember this from our childhoods. He would re-release all

[59:50] our childhoods. He would re-release all into theaters all of their IP every

[59:52] into theaters all of their IP every seven years. You couldn't get some of

[59:54] seven years. You couldn't get some of those Bambies, whatever, Snow White and

[59:57] those Bambies, whatever, Snow White and the Seven Dwarfs. You couldn't get those

[59:59] the Seven Dwarfs. You couldn't get those products except in theaters. and he

[01:00:00] products except in theaters. and he figured out a cadence to keep

[01:00:02] figured out a cadence to keep publishing. But then I came in and said,

[01:00:04] publishing. But then I came in and said, "Hey, what if we use this balance sheet

[01:00:06] "Hey, what if we use this balance sheet and we use this distribution at the

[01:00:09] and we use this distribution at the parks uh and you know with their brand

[01:00:12] parks uh and you know with their brand to buy Pixar, Marvel uh and Star Wars.

[01:00:16] to buy Pixar, Marvel uh and Star Wars. And so there's multiple ways to do it.

[01:00:18] And so there's multiple ways to do it. There might be something there in terms

[01:00:19] There might be something there in terms of acquisitions. bold acquisitions with

[01:00:23] of acquisitions. bold acquisitions with the Apple balance sheet could be super

[01:00:25] the Apple balance sheet could be super accreative to shareholders as opposed to

[01:00:28] accreative to shareholders as opposed to lowering the share count and just

[01:00:30] lowering the share count and just distributing a ton of cash. All right,

[01:00:32] distributing a ton of cash. All right, listen. We're going to talk about the

[01:00:33] listen. We're going to talk about the Southern Poverty Law Center.

[01:00:36] Southern Poverty Law Center. >> Racism Corner. Let's go to race fake

[01:00:38] >> Racism Corner. Let's go to race fake fake racism corner. I want to know how

[01:00:40] fake racism corner. I want to know how the SPLC managed to accumulate $822

[01:00:44] the SPLC managed to accumulate $822 million in offshore bank accounts.

[01:00:46] million in offshore bank accounts. >> Yeah, this is incredible. These are big

[01:00:48] >> Yeah, this is incredible. These are big numbers. Okay, SPLC. How is that

[01:00:50] numbers. Okay, SPLC. How is that possible? What is going on? All right,

[01:00:52] possible? What is going on? All right, let me tee it up here for the team.

[01:00:54] let me tee it up here for the team. >> This is like one of the biggest griffs

[01:00:55] >> This is like one of the biggest griffs of all time. Anyway, Jake,

[01:00:57] of all time. Anyway, Jake, >> this is a big one. SPLC has been

[01:00:59] >> this is a big one. SPLC has been indicted. Indicted, not found guilty

[01:01:01] indicted. Indicted, not found guilty yet, on 11 counts of wire fraud and

[01:01:04] yet, on 11 counts of wire fraud and money laundering. Keep that in the back

[01:01:06] money laundering. Keep that in the back of your head. Wire fraud and money

[01:01:07] of your head. Wire fraud and money laundering. Here's the core allegation.

[01:01:09] laundering. Here's the core allegation. Between 2014 and 2023, the Southern

[01:01:12] Between 2014 and 2023, the Southern Poverty Law Center used hidden bank

[01:01:14] Poverty Law Center used hidden bank accounts to funnel 3 million in donor

[01:01:16] accounts to funnel 3 million in donor money to paid informants. Like these are

[01:01:19] money to paid informants. Like these are confidential informants like the police

[01:01:21] confidential informants like the police or FBI might use. They use these as a

[01:01:24] or FBI might use. They use these as a nonprofit NGO to infiltrate hate groups.

[01:01:29] nonprofit NGO to infiltrate hate groups. And so the official mission of the SPLC

[01:01:33] And so the official mission of the SPLC is quote to be a catalyst for racial

[01:01:35] is quote to be a catalyst for racial justice in the South and beyond, working

[01:01:37] justice in the South and beyond, working in partnership with communities to

[01:01:39] in partnership with communities to dismantle white supremacy, strengthen

[01:01:41] dismantle white supremacy, strengthen intersectional movements, and advance

[01:01:44] intersectional movements, and advance the human rights of all people. Okay,

[01:01:45] the human rights of all people. Okay, sounds great on paper. Examples of

[01:01:47] sounds great on paper. Examples of organizations they were trying to

[01:01:49] organizations they were trying to infiltrate, KKK, Aryan Nation, neo-Nazi

[01:01:52] infiltrate, KKK, Aryan Nation, neo-Nazi groups, and the Unite the Right

[01:01:54] groups, and the Unite the Right organizers, Proud Boys, labeled as a

[01:01:56] organizers, Proud Boys, labeled as a hate group by the SPLC. Oathkeepers, not

[01:01:59] hate group by the SPLC. Oathkeepers, not listed as a hate group, but part of the

[01:02:01] listed as a hate group, but part of the militia movement. My friend Sam Harris,

[01:02:04] militia movement. My friend Sam Harris, he was not listed as a hate group, but

[01:02:05] he was not listed as a hate group, but he was also pinned by the SPLC as like

[01:02:09] he was also pinned by the SPLC as like hate adjacent in their hate watch

[01:02:11] hate adjacent in their hate watch headlines. And this is something that I

[01:02:13] headlines. And this is something that I had a major problem with this

[01:02:14] had a major problem with this organization on, which is they would

[01:02:16] organization on, which is they would just very loosely label people as hate

[01:02:19] just very loosely label people as hate speech and try to get them cancelled.

[01:02:21] speech and try to get them cancelled. All of this kind of uh came to a head.

[01:02:24] All of this kind of uh came to a head. revenue before Charlottesville. You

[01:02:26] revenue before Charlottesville. You remember the incorrectly clipped uh

[01:02:29] remember the incorrectly clipped uh Charlottesville hoax where they said

[01:02:30] Charlottesville hoax where they said Trump said both good people on both

[01:02:32] Trump said both good people on both sides, but they didn't give his full

[01:02:33] sides, but they didn't give his full quote. Very unfair to President Trump.

[01:02:36] quote. Very unfair to President Trump. Uh we found out later and that was the

[01:02:38] Uh we found out later and that was the reason Biden of course ran. He said the

[01:02:40] reason Biden of course ran. He said the Charlottesville both sides thing was his

[01:02:42] Charlottesville both sides thing was his inspiration. 58 million in 2026 to your

[01:02:45] inspiration. 58 million in 2026 to your point Shimoth doubled and spiked to 136

[01:02:48] point Shimoth doubled and spiked to 136 million more than double and it's

[01:02:50] million more than double and it's remained elevated ever since. Here are

[01:02:52] remained elevated ever since. Here are some, you know, images for the

[01:02:54] some, you know, images for the indictment. And I'll wrap on this and

[01:02:56] indictment. And I'll wrap on this and then get everybody's uh feedback. They

[01:02:59] then get everybody's uh feedback. They had F37 as one of their confidential

[01:03:02] had F37 as one of their confidential informants. He was a member, and this is

[01:03:05] informants. He was a member, and this is according to the indictment, quote,

[01:03:06] according to the indictment, quote, member of the online leadership chat

[01:03:09] member of the online leadership chat group that planned the 2017 Unite the

[01:03:11] group that planned the 2017 Unite the Right event in Charlottesville,

[01:03:14] Right event in Charlottesville, Virginia, and attended the event at the

[01:03:16] Virginia, and attended the event at the direction of the SPLC.

[01:03:19] direction of the SPLC. F-37 made racist postings under the

[01:03:21] F-37 made racist postings under the supervision of the SPLC and helped

[01:03:23] supervision of the SPLC and helped coordinate transportation to the event

[01:03:25] coordinate transportation to the event for several attendees between 2015 and

[01:03:27] for several attendees between 2015 and 2023. The SPLC secretly paid F-37 more

[01:03:31] 2023. The SPLC secretly paid F-37 more than $270,000.

[01:03:33] than $270,000. That's the legal case here. Let me pause

[01:03:35] That's the legal case here. Let me pause there.

[01:03:36] there. >> Can I add one thing?

[01:03:37] >> Can I add one thing? >> Sure. Keep adding. There's a lot of

[01:03:38] >> Sure. Keep adding. There's a lot of detail to this case. Yeah.

[01:03:39] detail to this case. Yeah. >> Yeah. So, you're right that the SPLC

[01:03:41] >> Yeah. So, you're right that the SPLC allegedly did fund $270,000 to help plan

[01:03:45] allegedly did fund $270,000 to help plan Charlottesville. In addition to that,

[01:03:47] Charlottesville. In addition to that, they secretly funneled more than $3

[01:03:49] they secretly funneled more than $3 million

[01:03:51] million to a bunch of violent racist extremist

[01:03:53] to a bunch of violent racist extremist groups, including the Ku Klux Clan, the

[01:03:57] groups, including the Ku Klux Clan, the American Nazi Party, Aryan Nation,

[01:04:01] American Nazi Party, Aryan Nation, United Clans of America, and it goes on

[01:04:04] United Clans of America, and it goes on from there. So, I think don't forget

[01:04:06] from there. So, I think don't forget about the 3 million bucks. So this group

[01:04:08] about the 3 million bucks. So this group that was supposed to be fighting racism

[01:04:09] that was supposed to be fighting racism in fact was fermenting racism by paying

[01:04:13] in fact was fermenting racism by paying these groups to basically organize

[01:04:16] these groups to basically organize protests that SPLC could then point to

[01:04:19] protests that SPLC could then point to and say that America has a huge racism

[01:04:21] and say that America has a huge racism problem donate to us. And that's

[01:04:23] problem donate to us. And that's basically what happened after

[01:04:24] basically what happened after Charlottesville. They increased the

[01:04:27] Charlottesville. They increased the amount of money that they were able to

[01:04:29] amount of money that they were able to fund raise by $81 million. So that

[01:04:32] fund raise by $81 million. So that $270,000 investment led to an $81

[01:04:35] $270,000 investment led to an $81 million return. pretty good. But this is

[01:04:37] million return. pretty good. But this is kind of the whole point of the story is

[01:04:38] kind of the whole point of the story is that these guys are basically running a

[01:04:39] that these guys are basically running a grift. And one of the ways that you know

[01:04:42] grift. And one of the ways that you know this is a grift is because according to

[01:04:45] this is a grift is because according to the indictment that they opened bank

[01:04:48] the indictment that they opened bank accounts under fictitious entities to

[01:04:51] accounts under fictitious entities to conceal the payments that they were

[01:04:53] conceal the payments that they were making from their own donors because if

[01:04:55] making from their own donors because if their donors knew that they were funding

[01:04:58] their donors knew that they were funding the KKK,

[01:05:00] the KKK, they wouldn't be getting all these

[01:05:02] they wouldn't be getting all these contributions from Hollywood celebrities

[01:05:04] contributions from Hollywood celebrities and all the rest of it. So, it's really

[01:05:06] and all the rest of it. So, it's really just this unbelievable story.

[01:05:09] just this unbelievable story. >> Uh, it really boggles the mind.

[01:05:11] >> Uh, it really boggles the mind. >> And just to clean up a little bit there,

[01:05:12] >> And just to clean up a little bit there, these are allegations. They haven't made

[01:05:14] these are allegations. They haven't made the jump from planning these events. And

[01:05:17] the jump from planning these events. And the SPLC

[01:05:20] the SPLC claims they were not planning these

[01:05:22] claims they were not planning these things. They were monitoring. So, that's

[01:05:24] things. They were monitoring. So, that's going to be their argument on the side.

[01:05:25] going to be their argument on the side. I'm not saying I agree with that. I'm

[01:05:26] I'm not saying I agree with that. I'm not saying you're right that the SPLC's

[01:05:29] not saying you're right that the SPLC's cover story is that they were simply

[01:05:31] cover story is that they were simply paying informants. That's what they've

[01:05:33] paying informants. That's what they've claimed. But there's two problems with

[01:05:35] claimed. But there's two problems with that story. Number one is they were

[01:05:37] that story. Number one is they were paying the actual leaders of these

[01:05:39] paying the actual leaders of these groups, not just sort of moles who were

[01:05:42] groups, not just sort of moles who were infiltrating the groups. And second,

[01:05:44] infiltrating the groups. And second, these leaders, they weren't paid to

[01:05:46] these leaders, they weren't paid to inform. They were paid to ferment the

[01:05:49] inform. They were paid to ferment the activities. So I'm just saying that's

[01:05:51] activities. So I'm just saying that's the the flaw. I understand they have

[01:05:53] the the flaw. I understand they have this cover story that they were just

[01:05:54] this cover story that they were just paying informants. I'm just saying in my

[01:05:56] paying informants. I'm just saying in my view that does not hold up. And again,

[01:05:59] view that does not hold up. And again, if they were just paying informants, why

[01:06:01] if they were just paying informants, why the extraordinary efforts to conceal the

[01:06:02] the extraordinary efforts to conceal the payments from their own donors? If they

[01:06:04] payments from their own donors? If they were proud of these efforts to

[01:06:06] were proud of these efforts to infiltrate these groups, they should

[01:06:08] infiltrate these groups, they should have basically informed their donors

[01:06:09] have basically informed their donors what they were doing. In fact, they hid

[01:06:11] what they were doing. In fact, they hid it.

[01:06:12] it. >> And well, here's the reason uh that it

[01:06:14] >> And well, here's the reason uh that it was hidden according to them. Again, I'm

[01:06:16] was hidden according to them. Again, I'm not taking this side. SPLC is not an

[01:06:18] not taking this side. SPLC is not an organization I'm endorsing in any way.

[01:06:21] organization I'm endorsing in any way. Their version of this is we didn't plan

[01:06:23] Their version of this is we didn't plan any of this. If we put SPLC

[01:06:27] any of this. If we put SPLC bank accounts together for informants,

[01:06:29] bank accounts together for informants, that would be like the FBI sending a

[01:06:31] that would be like the FBI sending a check to an informant from an FBI

[01:06:33] check to an informant from an FBI account. That's their explanation of it.

[01:06:34] account. That's their explanation of it. >> They're not a law enforcement agency.

[01:06:36] >> They're not a law enforcement agency. >> Well, that's actually the question I had

[01:06:38] >> Well, that's actually the question I had about all this is like what is a

[01:06:39] about all this is like what is a nonprofit doing hiring confidential

[01:06:42] nonprofit doing hiring confidential informants, Chimoth, to infiltrate these

[01:06:44] informants, Chimoth, to infiltrate these organizations? To what end? And then if

[01:06:46] organizations? To what end? And then if you show me an incentive, you're going

[01:06:48] you show me an incentive, you're going to you're going to see an outcome. And

[01:06:49] to you're going to see an outcome. And the outcome here is, hey, we'll get more

[01:06:51] the outcome here is, hey, we'll get more donations if there's more racism.

[01:06:54] donations if there's more racism. uh your thoughts just generally speaking

[01:06:55] uh your thoughts just generally speaking here Chimath again all of this is

[01:06:57] here Chimath again all of this is alleged

[01:06:58] alleged >> these NOS's have completely run a muck

[01:07:01] >> these NOS's have completely run a muck they're are cosplaying as these

[01:07:04] they're are cosplaying as these overlords and power brokers in our lives

[01:07:06] overlords and power brokers in our lives and it needs to get stopped they should

[01:07:09] and it needs to get stopped they should all be dismantled the people that

[01:07:11] all be dismantled the people that donated to the SPLC should sue them rip

[01:07:14] donated to the SPLC should sue them rip open all of the documentation get their

[01:07:16] open all of the documentation get their money back because just so you guys know

[01:07:18] money back because just so you guys know if you are listening or watching and you

[01:07:20] if you are listening or watching and you have donated there is 822 million dollar

[01:07:24] have donated there is 822 million dollar of your money sitting in an offshore

[01:07:25] of your money sitting in an offshore bank account waiting for you to get it

[01:07:27] bank account waiting for you to get it back. Okay? And then separately, if you

[01:07:30] back. Okay? And then separately, if you are thinking of donating to any of these

[01:07:31] are thinking of donating to any of these organizations in the future, unless

[01:07:33] organizations in the future, unless there is a full transparent auditing of

[01:07:37] there is a full transparent auditing of it, you actually may be doing the

[01:07:39] it, you actually may be doing the opposite of what you thought. If you are

[01:07:41] opposite of what you thought. If you are against racism, you may be supporting

[01:07:43] against racism, you may be supporting racism. If you are against

[01:07:45] racism. If you are against discrimination for gays, this could be

[01:07:48] discrimination for gays, this could be actually promoting discrimination for

[01:07:50] actually promoting discrimination for gays. If you are supportive of trans

[01:07:51] gays. If you are supportive of trans rights, this may be pushing back against

[01:07:53] rights, this may be pushing back against trans rights because the playbook seems

[01:07:55] trans rights because the playbook seems to be do the opposite to create the

[01:07:58] to be do the opposite to create the narrative, give it to your friends in

[01:08:00] narrative, give it to your friends in the media who will look the other way

[01:08:01] the media who will look the other way and just amplify it, tell the lie,

[01:08:03] and just amplify it, tell the lie, create the craziness, and then raise a

[01:08:07] create the craziness, and then raise a bunch of money, make a bunch of stink,

[01:08:08] bunch of money, make a bunch of stink, and try to curate power. Freeberg, do

[01:08:11] and try to curate power. Freeberg, do you think this is uh in your estimation

[01:08:14] you think this is uh in your estimation or your gut tell you this is arsonist

[01:08:16] or your gut tell you this is arsonist firefighters? are lighting things on

[01:08:18] firefighters? are lighting things on fire so that they can go put it out or

[01:08:20] fire so that they can go put it out or do you think this is like um lawfare as

[01:08:24] do you think this is like um lawfare as some people are claiming because there

[01:08:26] some people are claiming because there hasn't been to Chimat's point it's they

[01:08:29] hasn't been to Chimat's point it's they don't have donors taking this action

[01:08:31] don't have donors taking this action they're being accused of wire fraud on

[01:08:33] they're being accused of wire fraud on behalf of donors who haven't shown up

[01:08:34] behalf of donors who haven't shown up yet to do you know a legal action what's

[01:08:36] yet to do you know a legal action what's your take on all of this Freedberg

[01:08:39] your take on all of this Freedberg >> the IRS definition of what a 501c3

[01:08:43] >> the IRS definition of what a 501c3 nonprofit organization is meant to be

[01:08:45] nonprofit organization is meant to be doing is to engage engage in exempt

[01:08:48] doing is to engage engage in exempt activities. The definition of exempt

[01:08:50] activities. The definition of exempt activities is charitable,

[01:08:53] activities is charitable, religious, educational, scientific,

[01:08:57] religious, educational, scientific, literacy,

[01:08:58] literacy, public safety, or fostering amateur

[01:09:02] public safety, or fostering amateur sports competition or preventing cruelty

[01:09:04] sports competition or preventing cruelty to children or animals. You tell me how

[01:09:07] to children or animals. You tell me how the 90% of what we call nonprofits today

[01:09:12] the 90% of what we call nonprofits today fall under that definition. We have

[01:09:14] fall under that definition. We have completely closed our eyes to the fact

[01:09:17] completely closed our eyes to the fact that organizations regardless of

[01:09:19] that organizations regardless of political affiliation, social interest,

[01:09:21] political affiliation, social interest, have fundamental commercial and probably

[01:09:24] have fundamental commercial and probably not aligned interests with the

[01:09:26] not aligned interests with the definition of a 501c3. And we've allowed

[01:09:29] definition of a 501c3. And we've allowed them all to get away with it for far too

[01:09:31] them all to get away with it for far too long. I don't think that this is a blue

[01:09:33] long. I don't think that this is a blue or red thing. I think that this is a

[01:09:35] or red thing. I think that this is a thing where we let these organizations

[01:09:37] thing where we let these organizations make it easy to get money, to hide the

[01:09:39] make it easy to get money, to hide the money, and to do whatever the hell they

[01:09:40] money, and to do whatever the hell they want with the money. and we need to stop

[01:09:42] want with the money. and we need to stop it. And I think that it's an amazing

[01:09:44] it. And I think that it's an amazing opportunity right now for everyone to

[01:09:47] opportunity right now for everyone to kind of reset the decks by cleaning all

[01:09:49] kind of reset the decks by cleaning all this [ __ ] up and getting all of these

[01:09:51] this [ __ ] up and getting all of these organizations flushed and make sure that

[01:09:53] organizations flushed and make sure that any organization that wants to do

[01:09:54] any organization that wants to do whatever [ __ ] nefarious things they

[01:09:56] whatever [ __ ] nefarious things they want to do, by all means do it. But it's

[01:09:59] want to do, by all means do it. But it's not a nonprofit and you shouldn't get a

[01:10:01] not a nonprofit and you shouldn't get a charitable donation deduction and the

[01:10:03] charitable donation deduction and the government should not be putting money

[01:10:05] government should not be putting money into these sorts of things. This is an

[01:10:07] into these sorts of things. This is an entirely different sort of activity in

[01:10:10] entirely different sort of activity in the social order. And as a libertarian,

[01:10:12] the social order. And as a libertarian, I'm all for it. But I don't think that

[01:10:14] I'm all for it. But I don't think that they should be taxexempt. And I don't

[01:10:15] they should be taxexempt. And I don't think they should be getting government

[01:10:17] think they should be getting government money. And I don't think that

[01:10:18] money. And I don't think that individuals should be benefiting from

[01:10:19] individuals should be benefiting from giving them money. And if we could fix

[01:10:21] giving them money. And if we could fix all that [ __ ] up, I think a lot of these

[01:10:22] all that [ __ ] up, I think a lot of these problems are going to go away. And I

[01:10:24] problems are going to go away. And I think this is a major problem. I think

[01:10:25] think this is a major problem. I think the theme of this episode is audit

[01:10:27] the theme of this episode is audit everything. Whether it's government

[01:10:29] everything. Whether it's government waste and abuse or it's these NOS's or

[01:10:32] waste and abuse or it's these NOS's or it's people like Dao making these

[01:10:34] it's people like Dao making these chemicals that 30 years later, you know,

[01:10:37] chemicals that 30 years later, you know, perhaps are correlated with cancer. We

[01:10:41] perhaps are correlated with cancer. We need to audit everything. We need to

[01:10:42] need to audit everything. We need to take a fresh look at this because it's

[01:10:43] take a fresh look at this because it's not red versus green. Red, this is not

[01:10:46] not red versus green. Red, this is not red versus blue, it's green. This is

[01:10:47] red versus blue, it's green. This is clearly a monetary incentive and it is

[01:10:50] clearly a monetary incentive and it is incredibly disruptive for society to not

[01:10:52] incredibly disruptive for society to not know the truth about what's going on

[01:10:55] know the truth about what's going on with race in this country. I got

[01:10:58] with race in this country. I got absolutely, you guys might not know

[01:11:00] absolutely, you guys might not know this, but in this is part of the cancel

[01:11:03] this, but in this is part of the cancel culture moment in time where they tried

[01:11:05] culture moment in time where they tried to take people having reasonable

[01:11:06] to take people having reasonable discussions about race in this country

[01:11:08] discussions about race in this country and tried to cancel them. They tried to

[01:11:10] and tried to cancel them. They tried to do this to me in 2014 very famously. You

[01:11:13] do this to me in 2014 very famously. You guys may not know this, but I have won a

[01:11:14] guys may not know this, but I have won a couple of awards in my career. most

[01:11:16] couple of awards in my career. most offensive tweet ever by Vice in 2014 was

[01:11:20] offensive tweet ever by Vice in 2014 was my alleged racist tweet where I said,

[01:11:23] my alleged racist tweet where I said, "Hey, if you want to get into uh

[01:11:25] "Hey, if you want to get into uh blogging and journalism, there's no

[01:11:27] blogging and journalism, there's no racism in check journalism. All you have

[01:11:28] racism in check journalism. All you have to do is publish for a couple of years a

[01:11:30] to do is publish for a couple of years a blog post. Nobody can stop you and

[01:11:32] blog post. Nobody can stop you and there'll be a ton of jobs available to

[01:11:34] there'll be a ton of jobs available to you." And then what they did was they

[01:11:35] you." And then what they did was they tried to cancel me and tried to cancel

[01:11:37] tried to cancel me and tried to cancel all my media properties, my investing.

[01:11:40] all my media properties, my investing. And this stuff had like a modest impact

[01:11:41] And this stuff had like a modest impact on me maybe for a year. And then now

[01:11:44] on me maybe for a year. And then now it's obviously all being

[01:11:46] it's obviously all being >> Wait, wait. I'm not sure I understand,

[01:11:47] >> Wait, wait. I'm not sure I understand, Jake. You're saying the SPLC put you on

[01:11:49] Jake. You're saying the SPLC put you on a cancellation list?

[01:11:50] a cancellation list? >> No, they put Sam Harris on it. Vice put

[01:11:52] >> No, they put Sam Harris on it. Vice put me on a cancellation list. I It didn't

[01:11:54] me on a cancellation list. I It didn't get picked up by the SPLC, but I

[01:11:56] get picked up by the SPLC, but I experienced the same thing, which was

[01:11:58] experienced the same thing, which was they said because I said, you know, race

[01:12:00] they said because I said, you know, race does race doesn't play a role in hiring.

[01:12:04] does race doesn't play a role in hiring. >> You're so careful about your virtue

[01:12:05] >> You're so careful about your virtue signaling. I'm just shocked that anyone

[01:12:07] signaling. I'm just shocked that anyone would try to cancel you.

[01:12:08] would try to cancel you. >> Well, that's what's shocking about it is

[01:12:09] >> Well, that's what's shocking about it is like I know I was very clear. I said in

[01:12:13] like I know I was very clear. I said in journalism like a very vertical thing

[01:12:14] journalism like a very vertical thing and I have a lot of experience.

[01:12:16] and I have a lot of experience. >> You're a very skilled virtue signature.

[01:12:17] >> You're a very skilled virtue signature. So

[01:12:18] So >> I mean they tried to cancel me guys. I

[01:12:20] >> I mean they tried to cancel me guys. I don't you know it's they tried to cancel

[01:12:22] don't you know it's they tried to cancel you too.

[01:12:23] you too. >> Jason go ahead. You have a question for

[01:12:24] >> Jason go ahead. You have a question for me.

[01:12:25] me. >> I'm uncancellable.

[01:12:26] >> I'm uncancellable. >> Yes.

[01:12:27] >> Yes. >> We all are. That's what we found out

[01:12:28] >> We all are. That's what we found out through all this.

[01:12:30] through all this. >> You care about what all these idiots

[01:12:31] >> You care about what all these idiots think.

[01:12:32] think. >> No, I don't. I never have.

[01:12:34] >> No, I don't. I never have. >> Jason, I have a question for you. Go

[01:12:35] >> Jason, I have a question for you. Go ahead. What percentage odds now do you

[01:12:37] ahead. What percentage odds now do you keep in the back of your mind that your

[01:12:39] keep in the back of your mind that your petite little illustrious human rights

[01:12:41] petite little illustrious human rights watch is actually creating human rights

[01:12:44] watch is actually creating human rights abuses to try to

[01:12:46] abuses to try to >> This is actually a very good point. You

[01:12:47] >> This is actually a very good point. You know a lot of the human rights

[01:12:49] know a lot of the human rights organizations from back in the day.

[01:12:50] organizations from back in the day. >> What is the organization that you were

[01:12:52] >> What is the organization that you were what is it called?

[01:12:52] what is it called? >> Amnesty International.

[01:12:54] >> Amnesty International. >> When I worked at Amnesty International,

[01:12:55] >> When I worked at Amnesty International, a very fine

[01:12:57] a very fine >> mandate. The mandate was human rights

[01:12:59] >> mandate. The mandate was human rights abuses as described in the Universal

[01:13:01] abuses as described in the Universal Declaration of Human Rights created by

[01:13:03] Declaration of Human Rights created by Eleanor Roosevelt and the U.

[01:13:06] Eleanor Roosevelt and the U. >> This is like Science Corner. This is

[01:13:07] >> This is like Science Corner. This is done.

[01:13:08] done. >> No, but it was torture. It was people

[01:13:10] >> No, but it was torture. It was people being put in jail and being tortured. It

[01:13:12] being put in jail and being tortured. It was people being censored because of

[01:13:15] was people being censored because of freedom of speech and and that's what I

[01:13:17] freedom of speech and and that's what I worked on when I was at Amnesty

[01:13:18] worked on when I was at Amnesty International. These groups went a drift

[01:13:21] International. These groups went a drift in order to get money, Human Rights

[01:13:23] in order to get money, Human Rights Watch included. And then they started

[01:13:25] Watch included. And then they started taking on things like, you know, uh,

[01:13:28] taking on things like, you know, uh, transgender rights, this rights, that

[01:13:30] transgender rights, this rights, that rights, and censoring people. They went

[01:13:33] rights, and censoring people. They went after Sam Harris because he had Charles

[01:13:36] after Sam Harris because he had Charles from the bell curve.

[01:13:37] from the bell curve. >> Stop with all this [ __ ] I'm asking

[01:13:39] >> Stop with all this [ __ ] I'm asking 5050 chance that all these organizations

[01:13:41] 5050 chance that all these organizations are involved in

[01:13:42] are involved in >> So you think 50 5050 chance

[01:13:43] >> So you think 50 5050 chance >> depending on the organization, I'm going

[01:13:45] >> depending on the organization, I'm going to guess 95%. Yeah. Amnesty

[01:13:48] to guess 95%. Yeah. Amnesty International, you think is 50/50 that

[01:13:50] International, you think is 50/50 that they're engaging in nefarious [ __ ]

[01:13:52] they're engaging in nefarious [ __ ] to try to whip up people's belief that

[01:13:54] to try to whip up people's belief that there are human rights abuses happening

[01:13:56] there are human rights abuses happening that are not happening. You're saying

[01:13:57] that are not happening. You're saying it's a coin flip?

[01:13:58] it's a coin flip? >> I think it's probably a coin flip. Yeah,

[01:14:00] >> I think it's probably a coin flip. Yeah, that's what I would say today because

[01:14:01] that's what I would say today because these organizations all got co-opted.

[01:14:03] these organizations all got co-opted. SPLC might have had a great origin

[01:14:05] SPLC might have had a great origin story, but now

[01:14:07] story, but now >> I admire your intellectual honesty and I

[01:14:09] >> I admire your intellectual honesty and I appreciate you saying that.

[01:14:10] appreciate you saying that. >> Well, I mean, just based on facts. So,

[01:14:12] >> Well, I mean, just based on facts. So, let's see what this legal case brings

[01:14:15] let's see what this legal case brings about.

[01:14:15] about. >> Well, let's be clear. This is not them

[01:14:17] >> Well, let's be clear. This is not them investigating SPLC 100%.

[01:14:20] investigating SPLC 100%. >> When you bring a grand jury indictment,

[01:14:22] >> When you bring a grand jury indictment, you've already previewed the evidence.

[01:14:24] you've already previewed the evidence. This is not like some guy's trying to

[01:14:25] This is not like some guy's trying to whip up lawfare.

[01:14:27] whip up lawfare. >> Okay.

[01:14:27] >> Okay. >> Um, actually, you don't have to bring

[01:14:29] >> Um, actually, you don't have to bring all the evidence, but that's a side

[01:14:31] all the evidence, but that's a side thing. And and they're very frisky about

[01:14:33] thing. And and they're very frisky about allowing you to indict somebody as we

[01:14:35] allowing you to indict somebody as we experienced with Trump.

[01:14:37] experienced with Trump. >> Grand juries are a whole different

[01:14:38] >> Grand juries are a whole different animal.

[01:14:39] animal. >> Yeah, they they will indict a ham

[01:14:40] >> Yeah, they they will indict a ham sandwich is the line. Um, so we'll see.

[01:14:42] sandwich is the line. Um, so we'll see. Let's give them their day in court is

[01:14:44] Let's give them their day in court is always my position. Well, I mean,

[01:14:45] always my position. Well, I mean, regardless of what happens in court, if

[01:14:47] regardless of what happens in court, if it's true that the SPLC is funding the

[01:14:49] it's true that the SPLC is funding the Klux Clan and the American Nazi, just so

[01:14:52] Klux Clan and the American Nazi, just so we're clear, they stop using

[01:14:54] we're clear, they stop using confidential informants.

[01:14:56] confidential informants. Say it out loud, it's insane.

[01:14:58] Say it out loud, it's insane. >> It's good enough for me. Listen, here's

[01:15:01] >> It's good enough for me. Listen, here's here's the systemic problem with

[01:15:03] here's the systemic problem with nonprofits and NOS's is, and let me just

[01:15:06] nonprofits and NOS's is, and let me just contrast it with business. In business,

[01:15:08] contrast it with business. In business, you set up a company. The company has to

[01:15:10] you set up a company. The company has to make revenue. It has to make profits.

[01:15:12] make revenue. It has to make profits. And if it doesn't, it's going to go out

[01:15:13] And if it doesn't, it's going to go out of business, right? Because it'll lose

[01:15:15] of business, right? Because it'll lose money. So, there's a feedback mechanism

[01:15:17] money. So, there's a feedback mechanism from the market. The company has to

[01:15:19] from the market. The company has to create products that people are willing

[01:15:20] create products that people are willing to buy, and those products have to make

[01:15:22] to buy, and those products have to make money. With an NGO, nonprofit, what have

[01:15:24] money. With an NGO, nonprofit, what have you, they raise money. They don't sell

[01:15:27] you, they raise money. They don't sell things. They fund raise from donors in

[01:15:30] things. They fund raise from donors in order to engage in an activity. But what

[01:15:32] order to engage in an activity. But what happens over time is the actual

[01:15:34] happens over time is the actual activities may stop mattering. And all

[01:15:37] activities may stop mattering. And all that really matters is they're able to

[01:15:38] that really matters is they're able to keep fundraising, right? Because they're

[01:15:39] keep fundraising, right? Because they're just trying to figure out a

[01:15:41] just trying to figure out a justification to keep going back to

[01:15:43] justification to keep going back to donors to get more and more money out of

[01:15:44] donors to get more and more money out of them. That's what perpetuates the

[01:15:45] them. That's what perpetuates the organization

[01:15:46] organization >> and they're trying to keep their job.

[01:15:48] >> and they're trying to keep their job. >> Exactly. And then if it's an NGO that

[01:15:50] >> Exactly. And then if it's an NGO that gets money from the government, then

[01:15:52] gets money from the government, then it's even worse because all they do from

[01:15:54] it's even worse because all they do from that point forward is try to lobby the

[01:15:56] that point forward is try to lobby the government to get more money. And it

[01:15:59] government to get more money. And it doesn't really matter whether the

[01:16:01] doesn't really matter whether the program is working or not. All that

[01:16:03] program is working or not. All that matters is whether they can spin it as

[01:16:05] matters is whether they can spin it as working. Why wouldn't the Southern

[01:16:06] working. Why wouldn't the Southern Poverty Law Center focus on southern

[01:16:09] Poverty Law Center focus on southern poverty, which is an issue that actually

[01:16:11] poverty, which is an issue that actually still exists? It's

[01:16:12] still exists? It's >> got be a better thing. I mean, and why

[01:16:15] >> got be a better thing. I mean, and why do you call it one thing, focus on

[01:16:17] do you call it one thing, focus on racism, and then all of a sudden whip

[01:16:20] racism, and then all of a sudden whip up?

[01:16:20] up? >> I'll tell you why. Here's my theory.

[01:16:21] >> I'll tell you why. Here's my theory. Here's my theory on it. Is I do think

[01:16:23] Here's my theory on it. Is I do think that at one time in this country, civil

[01:16:25] that at one time in this country, civil rights was a noble cause, a very

[01:16:28] rights was a noble cause, a very legitimate cause. We had the the legacy

[01:16:31] legitimate cause. We had the the legacy of segregation and Jim Crow and there

[01:16:33] of segregation and Jim Crow and there were groups that were set up to

[01:16:35] were groups that were set up to basically

[01:16:36] basically >> change that and they succeeded.

[01:16:39] >> change that and they succeeded. >> But again, no one in an NGO or a

[01:16:41] >> But again, no one in an NGO or a nonprofit ever declares victory.

[01:16:44] nonprofit ever declares victory. >> Exactly.

[01:16:44] >> Exactly. >> They're never going to say, you know

[01:16:45] >> They're never going to say, you know what, like we we addressed this problem.

[01:16:47] what, like we we addressed this problem. We solved it. You know, I always thought

[01:16:49] We solved it. You know, I always thought that in 2008 when

[01:16:50] that in 2008 when >> fire me, fire me. My job's done.

[01:16:52] >> fire me, fire me. My job's done. >> Yeah. When when Obama got elected in

[01:16:54] >> Yeah. When when Obama got elected in 2008, I thought that regardless of

[01:16:56] 2008, I thought that regardless of whether you liked Obama or not or agree

[01:16:58] whether you liked Obama or not or agree with his politics, I thought that at

[01:17:00] with his politics, I thought that at that point most people could see that

[01:17:01] that point most people could see that this was not a racist country.

[01:17:04] this was not a racist country. >> Whatever else you could say, the fact

[01:17:06] >> Whatever else you could say, the fact that the highest office in the land was

[01:17:08] that the highest office in the land was not denied to anybody showed that this

[01:17:12] not denied to anybody showed that this country was not holding people back

[01:17:13] country was not holding people back based on their skin color. And instead

[01:17:16] based on their skin color. And instead of just basically packing up shop and

[01:17:17] of just basically packing up shop and saying, "Okay, we've achieved our goal,"

[01:17:19] saying, "Okay, we've achieved our goal," the goalposts all got moved. Remember

[01:17:21] the goalposts all got moved. Remember that's when the whole anti-racism thing

[01:17:23] that's when the whole anti-racism thing started was was around Obama's second

[01:17:25] started was was around Obama's second term. And what anti-racism was, it said

[01:17:28] term. And what anti-racism was, it said that it's not good enough not to be just

[01:17:31] that it's not good enough not to be just not to be racist. You actually had to be

[01:17:33] not to be racist. You actually had to be anti-racist. But what anti-racism meant

[01:17:36] anti-racist. But what anti-racism meant was was basically that all the

[01:17:38] was was basically that all the distributions had to match the

[01:17:40] distributions had to match the population. Basically, it meant equality

[01:17:42] population. Basically, it meant equality of outcomes, not equality of

[01:17:43] of outcomes, not equality of opportunity. So effectively, this whole

[01:17:46] opportunity. So effectively, this whole goalpost was moved from equality of

[01:17:48] goalpost was moved from equality of opportunity to equality of results. Once

[01:17:50] opportunity to equality of results. Once you see it, you can't unsee it. It's

[01:17:52] you see it, you can't unsee it. It's like they sat around and they said, "Now

[01:17:54] like they sat around and they said, "Now what?"

[01:17:54] what?" >> And one person was like, "I got an

[01:17:56] >> And one person was like, "I got an idea."

[01:17:58] idea." >> Well, and make racism again. Exactly.

[01:18:01] >> Well, and make racism again. Exactly. >> Right. But if they just said, if they

[01:18:02] >> Right. But if they just said, if they just said at that time, you know what?

[01:18:04] just said at that time, you know what? We're going to move the goalpost from

[01:18:06] We're going to move the goalpost from equality of opportunity to equality of

[01:18:07] equality of opportunity to equality of results. We're going to basically make

[01:18:09] results. We're going to basically make everyone equal at the finish line, which

[01:18:11] everyone equal at the finish line, which is to say, I guess, communism or or some

[01:18:13] is to say, I guess, communism or or some sort of identity socialism. People would

[01:18:16] sort of identity socialism. People would have said, "Ah, no, we're not on board

[01:18:17] have said, "Ah, no, we're not on board for that." So instead, they created this

[01:18:19] for that." So instead, they created this whole new terminology to justify it. And

[01:18:22] whole new terminology to justify it. And it's taken us years to unpack that and

[01:18:24] it's taken us years to unpack that and realize what's really going on.

[01:18:26] realize what's really going on. >> Gosh, I don't want to put myself in a

[01:18:27] >> Gosh, I don't want to put myself in a position of defending the SPLC. They

[01:18:29] position of defending the SPLC. They were partners with the FBI for a long

[01:18:31] were partners with the FBI for a long time. To your point, Chamath, or or

[01:18:32] time. To your point, Chamath, or or Sax's point, rather, there was probably

[01:18:35] Sax's point, rather, there was probably a time when it was important to

[01:18:37] a time when it was important to infiltrate the KKK and the Nazi groups.

[01:18:40] infiltrate the KKK and the Nazi groups. >> It's not 2025. It's not in 2026 like I

[01:18:44] >> It's not 2025. It's not in 2026 like I think necessary to be doing this work. I

[01:18:46] think necessary to be doing this work. I think

[01:18:47] think can handle it uh in 20.

[01:18:50] can handle it uh in 20. >> Okay, I'm going to give you guys a news

[01:18:51] >> Okay, I'm going to give you guys a news flash. I just got this just hit the

[01:18:52] flash. I just got this just hit the wire. Um this is really important.

[01:18:54] wire. Um this is really important. >> Breaking news here.

[01:18:56] >> Breaking news here. >> America is profoundly less racist than

[01:18:58] >> America is profoundly less racist than you think. Okay,

[01:19:00] you think. Okay, >> there we go. Okay, breaking news.

[01:19:02] >> there we go. Okay, breaking news. >> Wake up.

[01:19:03] >> Wake up. >> Freedberg wanted to do a surprise

[01:19:06] >> Freedberg wanted to do a surprise science corner. This is the first. We

[01:19:08] science corner. This is the first. We don't know what he's about to talk

[01:19:09] don't know what he's about to talk about, but David looks like he's been

[01:19:11] about, but David looks like he's been working really hard and he needs a nap.

[01:19:12] working really hard and he needs a nap. So, Freeberg, you have the microphone.

[01:19:14] So, Freeberg, you have the microphone. Let's go. This was not science.

[01:19:17] Let's go. This was not science. >> Yeah, this is not necessarily a big

[01:19:19] >> Yeah, this is not necessarily a big surprise, but there was a really

[01:19:21] surprise, but there was a really interesting paper published this week on

[01:19:24] interesting paper published this week on trying to elucidate the underlying cause

[01:19:27] trying to elucidate the underlying cause or predictor of colorctal cancer. So, I

[01:19:31] or predictor of colorctal cancer. So, I don't know if you guys know any young

[01:19:33] don't know if you guys know any young friends, but colurectal cancer, Nick, if

[01:19:35] friends, but colurectal cancer, Nick, if you could just pull up this first image

[01:19:37] you could just pull up this first image or colon cancer has become now the third

[01:19:40] or colon cancer has become now the third leading cancer. Over the last 20 years

[01:19:43] leading cancer. Over the last 20 years or so, there's been a scary rise in the

[01:19:46] or so, there's been a scary rise in the number of young people, people generally

[01:19:48] number of young people, people generally under 50 years old that are getting

[01:19:50] under 50 years old that are getting colon cancer. That number has climbed by

[01:19:53] colon cancer. That number has climbed by over 80% in just the last two decades.

[01:19:57] over 80% in just the last two decades. Historically, it's been an age related

[01:19:59] Historically, it's been an age related disease. So, as you get older, over 70

[01:20:01] disease. So, as you get older, over 70 years old, your probability of getting

[01:20:03] years old, your probability of getting colon cancer shoots through the roof.

[01:20:04] colon cancer shoots through the roof. But this rise in young people getting

[01:20:06] But this rise in young people getting colon cancer has been pretty alarming.

[01:20:08] colon cancer has been pretty alarming. And there's been a real question mark on

[01:20:10] And there's been a real question mark on what is causing it. what's the

[01:20:11] what is causing it. what's the underlying trigger? So, this research

[01:20:15] underlying trigger? So, this research team out of Barcelona in Spain did an

[01:20:18] team out of Barcelona in Spain did an amazing study where they looked at the

[01:20:21] amazing study where they looked at the difference in the epiggenome or the gene

[01:20:25] difference in the epiggenome or the gene expression

[01:20:26] expression in tumor cells of patients that are

[01:20:30] in tumor cells of patients that are under 50 years old and those that are

[01:20:32] under 50 years old and those that are over 70 years old. the sort of data will

[01:20:35] over 70 years old. the sort of data will show you what different environmental

[01:20:38] show you what different environmental triggers are associated with those

[01:20:40] triggers are associated with those changes in gene expression. So whenever

[01:20:43] changes in gene expression. So whenever we're exposed to something in the

[01:20:44] we're exposed to something in the environment, whether it's some food or

[01:20:47] environment, whether it's some food or some drink or whatever else it is, some

[01:20:49] some drink or whatever else it is, some chemical in the environment, the cells

[01:20:51] chemical in the environment, the cells in our body that are exposed to that

[01:20:54] in our body that are exposed to that chemistry or exposed to that

[01:20:55] chemistry or exposed to that environmental trigger, have genes that

[01:20:58] environmental trigger, have genes that get switched on and off. And you can see

[01:21:01] get switched on and off. And you can see which genes are on and off by looking at

[01:21:03] which genes are on and off by looking at the RNA of those genes, which tells you

[01:21:06] the RNA of those genes, which tells you that those genes are expressing RNA to

[01:21:08] that those genes are expressing RNA to make protein or not make protein. And

[01:21:10] make protein or not make protein. And you can look at that gene expression to

[01:21:13] you can look at that gene expression to determine what is changing when a cell

[01:21:16] determine what is changing when a cell is exposed to a particular environmental

[01:21:18] is exposed to a particular environmental trigger. And so they were able to get

[01:21:20] trigger. And so they were able to get these samples of colon edinocinomas

[01:21:23] these samples of colon edinocinomas from the cancer genome atlas, which is

[01:21:26] from the cancer genome atlas, which is funded by the federal government. And

[01:21:28] funded by the federal government. And they were then able to take a look at

[01:21:30] they were then able to take a look at these cancer cells from colon cancer in

[01:21:33] these cancer cells from colon cancer in patients that are under 50 and patients

[01:21:35] patients that are under 50 and patients that are over 70 and look at the

[01:21:37] that are over 70 and look at the difference in the gene expression

[01:21:39] difference in the gene expression profile and what um environmental

[01:21:42] profile and what um environmental triggers are associated with that gene

[01:21:45] triggers are associated with that gene expression profile. So that will tell

[01:21:46] expression profile. So that will tell you, hey, these environmental triggers

[01:21:48] you, hey, these environmental triggers are more likely the cause or an

[01:21:51] are more likely the cause or an underlying driver of the risk of getting

[01:21:54] underlying driver of the risk of getting this colon cancer. And one thing rose to

[01:21:56] this colon cancer. And one thing rose to the top. So they looked at a whole bunch

[01:21:58] the top. So they looked at a whole bunch of things. They looked at lifestyle

[01:21:59] of things. They looked at lifestyle factors. They look at eating index, how

[01:22:02] factors. They look at eating index, how much you ate, how overweight you were,

[01:22:04] much you ate, how overweight you were, alcohol, birth weight. They adjusted for

[01:22:06] alcohol, birth weight. They adjusted for gender. They adjusted for all these

[01:22:08] gender. They adjusted for all these different things. And as you look down

[01:22:11] different things. And as you look down this list, you'll see this is the

[01:22:12] this list, you'll see this is the difference between people that got colon

[01:22:14] difference between people that got colon cancer that were over 70 when you

[01:22:16] cancer that were over 70 when you typically have a very high chance of

[01:22:17] typically have a very high chance of getting it and people that are under 50

[01:22:19] getting it and people that are under 50 when you don't. And what is going on

[01:22:22] when you don't. And what is going on with people under 50. And you can see

[01:22:24] with people under 50. And you can see there's this one row here that's all

[01:22:26] there's this one row here that's all orange. That row is a pesticide called

[01:22:30] orange. That row is a pesticide called piclorum.

[01:22:31] piclorum. Plorum is a pesticide that was developed

[01:22:34] Plorum is a pesticide that was developed by the DAO Chemical Company in 1963.

[01:22:38] by the DAO Chemical Company in 1963. This is the chemical formula for that

[01:22:41] This is the chemical formula for that pesticide. It's related to oxin, which

[01:22:43] pesticide. It's related to oxin, which are these hormones that plants make. And

[01:22:46] are these hormones that plants make. And in the 1960s, there was this big rush to

[01:22:48] in the 1960s, there was this big rush to try and make synthetic plant hormones

[01:22:50] try and make synthetic plant hormones that you would then apply to a plant. It

[01:22:52] that you would then apply to a plant. It would cause the plant to overgrow and

[01:22:54] would cause the plant to overgrow and the plant would quickly die. And

[01:22:56] the plant would quickly die. And piclorum became a very widely used

[01:22:58] piclorum became a very widely used herbicide

[01:23:00] herbicide in our environment. It's used to manage

[01:23:03] in our environment. It's used to manage weeds in rangeand and pasture land where

[01:23:06] weeds in rangeand and pasture land where cattle graze. It's used to control weeds

[01:23:08] cattle graze. It's used to control weeds near roads and near railroads on

[01:23:11] near roads and near railroads on industrial sites to clear weeds away

[01:23:13] industrial sites to clear weeds away from highways and utility corridors. And

[01:23:16] from highways and utility corridors. And the problem with piclorum, one of the

[01:23:17] the problem with piclorum, one of the the things that's been known about it is

[01:23:18] the things that's been known about it is it's very persistent. It doesn't

[01:23:20] it's very persistent. It doesn't biodegrade very well. Plorum sticks

[01:23:22] biodegrade very well. Plorum sticks around for well over a year. It stays in

[01:23:25] around for well over a year. It stays in the water. It moves into groundwater and

[01:23:28] the water. It moves into groundwater and it's persistently in the environment

[01:23:29] it's persistently in the environment after it's been used for some period of

[01:23:31] after it's been used for some period of time. I went back and looked at the EPA

[01:23:34] time. I went back and looked at the EPA data on this chemical. The last time

[01:23:36] data on this chemical. The last time there was an EPA safety study done was

[01:23:38] there was an EPA safety study done was in 1995.

[01:23:40] in 1995. And so this was before we had this

[01:23:41] And so this was before we had this capacity to do epigenomic studies like

[01:23:44] capacity to do epigenomic studies like what was just done to elucidate that

[01:23:47] what was just done to elucidate that even though a chemical might not be

[01:23:48] even though a chemical might not be causing cancer immediately and you can't

[01:23:50] causing cancer immediately and you can't apply it to a cell and see it trigger a

[01:23:52] apply it to a cell and see it trigger a cancer, the long-term use or exposure to

[01:23:56] cancer, the long-term use or exposure to certain chemicals in our environment

[01:23:58] certain chemicals in our environment causes a change in the epiggenome, which

[01:24:00] causes a change in the epiggenome, which means that these genes are being turned

[01:24:01] means that these genes are being turned on and off. And when certain genes are

[01:24:03] on and off. And when certain genes are turned on or off in the wrong way, it

[01:24:05] turned on or off in the wrong way, it can trigger cells in the tissue to start

[01:24:07] can trigger cells in the tissue to start to malfunction and go haywire and

[01:24:10] to malfunction and go haywire and ultimately lead to cancer. And I think

[01:24:12] ultimately lead to cancer. And I think that this paper shows a pretty strong

[01:24:14] that this paper shows a pretty strong effect of piclorum in driving colon

[01:24:17] effect of piclorum in driving colon cancer in young people. It will very

[01:24:19] cancer in young people. It will very likely lead and it should lead to an EPA

[01:24:21] likely lead and it should lead to an EPA review on whether this should be legally

[01:24:23] review on whether this should be legally allowed. But it should also lead to a

[01:24:25] allowed. But it should also lead to a new mechanism by which we assess

[01:24:27] new mechanism by which we assess chemistry that we're using in our food

[01:24:29] chemistry that we're using in our food supply, in our environment, in our

[01:24:30] supply, in our environment, in our industrial applications because we can

[01:24:33] industrial applications because we can now look at all of this sort of

[01:24:34] now look at all of this sort of epigenomic data to try and figure out

[01:24:36] epigenomic data to try and figure out what are these chemicals doing to us

[01:24:38] what are these chemicals doing to us before we see them cause the problem. So

[01:24:40] before we see them cause the problem. So I thought this was like an amazing paper

[01:24:42] I thought this was like an amazing paper done by this team. They did a lot of

[01:24:43] done by this team. They did a lot of work to try and make sure that the

[01:24:45] work to try and make sure that the statistics were sound in the studies

[01:24:47] statistics were sound in the studies that they did. It really uh I think

[01:24:50] that they did. It really uh I think elucidated something pretty scary. Is

[01:24:51] elucidated something pretty scary. Is this like a Monsanto thing where like

[01:24:53] this like a Monsanto thing where like one company makes it or piclorum is

[01:24:56] one company makes it or piclorum is broadly available?

[01:24:57] broadly available? >> It's off patent now and so I'm pretty

[01:25:00] >> It's off patent now and so I'm pretty sure my guess I haven't looked into this

[01:25:01] sure my guess I haven't looked into this but my guess is most of this is made

[01:25:03] but my guess is most of this is made generically in China and then it's

[01:25:05] generically in China and then it's probably packaged up with lots of

[01:25:06] probably packaged up with lots of different brands in the US and all over

[01:25:08] different brands in the US and all over the world. So it's one of these

[01:25:09] the world. So it's one of these chemicals that's just become ubiquitous

[01:25:11] chemicals that's just become ubiquitous in our use that just shows up

[01:25:13] in our use that just shows up everywhere. But I think it really speaks

[01:25:15] everywhere. But I think it really speaks to the fact that historically, think

[01:25:18] to the fact that historically, think about 1995. You can look at what the

[01:25:20] about 1995. You can look at what the immediate chemical application of

[01:25:22] immediate chemical application of something does to a rat or a human cell

[01:25:25] something does to a rat or a human cell and you can say like, "Oh, it didn't

[01:25:26] and you can say like, "Oh, it didn't cause cancer. It's good to go. Let's

[01:25:28] cause cancer. It's good to go. Let's go." You know, didn't didn't cause quote

[01:25:30] go." You know, didn't didn't cause quote toxicity.

[01:25:31] toxicity. >> Can I ask a question? In that study, are

[01:25:33] >> Can I ask a question? In that study, are you exposed to piclorum based on where

[01:25:35] you exposed to piclorum based on where you live? Because like

[01:25:36] you live? Because like >> Yeah. Sorry, that's a that's a great

[01:25:37] >> Yeah. Sorry, that's a that's a great question, Shimat. So, I was going to

[01:25:38] question, Shimat. So, I was going to talk about this. Thank you for asking

[01:25:40] talk about this. Thank you for asking that. They then took that piclorum

[01:25:42] that. They then took that piclorum exposure and then they looked at all the

[01:25:44] exposure and then they looked at all the counties across the United States. They

[01:25:46] counties across the United States. They were able to gather data where there's

[01:25:47] were able to gather data where there's enough data in California, Connecticut,

[01:25:49] enough data in California, Connecticut, Georgia, Iowa, New Mexico, Utah,

[01:25:51] Georgia, Iowa, New Mexico, Utah, Washington and they were able to look at

[01:25:53] Washington and they were able to look at pllorum use estimates from the pesticide

[01:25:56] pllorum use estimates from the pesticide national synthesis project and try and

[01:25:58] national synthesis project and try and deduce in places where piclorum was

[01:26:00] deduce in places where piclorum was highly used and not highly used. And

[01:26:02] highly used and not highly used. And once again it elucidated signal which is

[01:26:04] once again it elucidated signal which is that when pllorum was used in the

[01:26:06] that when pllorum was used in the environment in the counties more

[01:26:07] environment in the counties more frequently there was a much higher

[01:26:09] frequently there was a much higher frequency of colon cancer in those

[01:26:10] frequency of colon cancer in those counties

[01:26:11] counties >> and that R squar is weak or it's strong

[01:26:13] >> and that R squar is weak or it's strong >> reasonably strong the odds ratio is like

[01:26:15] >> reasonably strong the odds ratio is like 3x it's very strong this is accomplished

[01:26:17] 3x it's very strong this is accomplished freeberg from a combination of big data

[01:26:22] freeberg from a combination of big data >> and this uh science to study these

[01:26:26] >> and this uh science to study these increased testing as well right so you

[01:26:28] increased testing as well right so you have this confluence of increased

[01:26:30] have this confluence of increased testing

[01:26:31] testing increased data, you know, knowing where

[01:26:34] increased data, you know, knowing where these instances are occurring. And if

[01:26:36] these instances are occurring. And if you add a layer of AI onto this

[01:26:38] you add a layer of AI onto this freeberg, this is like a really positive

[01:26:40] freeberg, this is like a really positive use, going back and looking at all these

[01:26:43] use, going back and looking at all these compounds and figuring out which ones we

[01:26:44] compounds and figuring out which ones we need to eliminate. Yeah.

[01:26:45] need to eliminate. Yeah. >> Yeah. So, I'll I'll put my PCAST hat on.

[01:26:47] >> Yeah. So, I'll I'll put my PCAST hat on. Thank you, David Saxs, for the role. And

[01:26:50] Thank you, David Saxs, for the role. And I think this speaks to one of the

[01:26:51] I think this speaks to one of the important roles that government has in

[01:26:54] important roles that government has in doing fundamental science and

[01:26:55] doing fundamental science and fundamental research. So the the

[01:26:57] fundamental research. So the the National Cancer Institute and the

[01:26:58] National Cancer Institute and the federal government stood up this genome

[01:27:00] federal government stood up this genome atlas with $100 million a couple years

[01:27:02] atlas with $100 million a couple years ago. They spend only a few million

[01:27:04] ago. They spend only a few million dollars a year now to maintain it to get

[01:27:06] dollars a year now to maintain it to get cancer tissue samples and then create

[01:27:09] cancer tissue samples and then create the availability to scientists to use

[01:27:11] the availability to scientists to use those cancer tissue samples to do the

[01:27:13] those cancer tissue samples to do the sort of epigenomic analysis and study

[01:27:16] sort of epigenomic analysis and study supported by you know government grants

[01:27:17] supported by you know government grants or in this case supported by a foreign

[01:27:20] or in this case supported by a foreign university getting funding to do it. And

[01:27:23] university getting funding to do it. And so there's there's an important role

[01:27:24] so there's there's an important role that fundamental science still has in

[01:27:26] that fundamental science still has in elucidating this that we would have

[01:27:27] elucidating this that we would have otherwise not been able to see if we

[01:27:30] otherwise not been able to see if we didn't have this resource available to

[01:27:31] didn't have this resource available to us from the federal government and

[01:27:33] us from the federal government and federal funding of scientific programs

[01:27:34] federal funding of scientific programs like this. And that leads to this

[01:27:36] like this. And that leads to this discovery. You don't need fancy AI for

[01:27:38] discovery. You don't need fancy AI for this. To be frank, JCL, there's an

[01:27:40] this. To be frank, JCL, there's an incredible amount of data that's

[01:27:41] incredible amount of data that's available or or resources that are

[01:27:43] available or or resources that are available. What's happened in the last

[01:27:45] available. What's happened in the last couple years is what's called RNA

[01:27:46] couple years is what's called RNA sequencing where you can actually look

[01:27:48] sequencing where you can actually look at which genes are on or off. not just

[01:27:50] at which genes are on or off. not just what's the DNA but in the DNA. Remember

[01:27:53] what's the DNA but in the DNA. Remember we've talked a lot about the epiggenome

[01:27:54] we've talked a lot about the epiggenome what genes are on or off and how that

[01:27:56] what genes are on or off and how that changes when you have different cancers

[01:27:59] changes when you have different cancers or when you have different chemicals and

[01:28:00] or when you have different chemicals and when you have a certain chemical like

[01:28:02] when you have a certain chemical like pllorum your colorctal cancer goes

[01:28:05] pllorum your colorctal cancer goes through the roof and you can see that

[01:28:06] through the roof and you can see that relationship in those tissues and then

[01:28:08] relationship in those tissues and then you can put all the data together and

[01:28:10] you can put all the data together and say oh my gosh there's a lot of evidence

[01:28:11] say oh my gosh there's a lot of evidence here that points to this connection very

[01:28:13] here that points to this connection very powerful I think it's important that it

[01:28:15] powerful I think it's important that it opens up the window that this shouldn't

[01:28:16] opens up the window that this shouldn't just be a one-off research project

[01:28:18] just be a one-off research project conducted by a team in Spain but maybe

[01:28:21] conducted by a team in Spain but maybe should be a fundamental role that some

[01:28:22] should be a fundamental role that some of the government agencies play, which

[01:28:24] of the government agencies play, which is to stop Americans and the world from

[01:28:26] is to stop Americans and the world from getting faking cancer. Let's figure out

[01:28:28] getting faking cancer. Let's figure out the things that we got wrong in industry

[01:28:31] the things that we got wrong in industry and go back and delete them out of our

[01:28:33] and go back and delete them out of our food supply and out of our industrial

[01:28:34] food supply and out of our industrial supply. Um, and I think this is a really

[01:28:36] supply. Um, and I think this is a really good example of that.

[01:28:37] good example of that. >> So, Exa, how does Freberg's focus on

[01:28:40] >> So, Exa, how does Freberg's focus on Uranus uh, you know, inform your

[01:28:43] Uranus uh, you know, inform your co-leading of PCAST here? Are you going

[01:28:45] co-leading of PCAST here? Are you going to go deep into this colon research? How

[01:28:48] to go deep into this colon research? How deep do you plan on going? And how will

[01:28:49] deep do you plan on going? And how will you get through eight of these

[01:28:51] you get through eight of these presentations a day at Pest?

[01:28:55] presentations a day at Pest? >> I It's all good. This is why we hired

[01:28:56] >> I It's all good. This is why we hired Freeberg.

[01:28:57] Freeberg. >> Yes. By the way, did you guys

[01:28:58] >> Yes. By the way, did you guys >> He's going to handle uh Mars, Neptune,

[01:29:01] >> He's going to handle uh Mars, Neptune, and Uranus.

[01:29:02] and Uranus. >> Absolutely. He's going to go deep into

[01:29:04] >> Absolutely. He's going to go deep into Uranus and clean it up. We need to clean

[01:29:07] Uranus and clean it up. We need to clean up Uranus.

[01:29:09] up Uranus. >> Uh great work, Freeberg. Great. Great

[01:29:10] >> Uh great work, Freeberg. Great. Great work.

[01:29:11] work. >> Anyway, I think I think this is

[01:29:12] >> Anyway, I think I think this is important and I don't think there's any

[01:29:13] important and I don't think there's any news attention on this since it came out

[01:29:15] news attention on this since it came out a couple days ago. So, I thought it

[01:29:16] a couple days ago. So, I thought it would be worth bringing up on the show.

[01:29:17] would be worth bringing up on the show. Absolutely. Making people aware.

[01:29:19] Absolutely. Making people aware. >> All right.

[01:29:19] >> All right. >> But thank you guys for sitting through

[01:29:20] >> But thank you guys for sitting through it.

[01:29:20] it. >> Well, no, I think it's it's great work

[01:29:22] >> Well, no, I think it's it's great work you're doing there.

[01:29:23] you're doing there. >> I just read the paper, but

[01:29:25] >> I just read the paper, but >> yeah.

[01:29:25] >> yeah. >> All right, everybody. That's it for

[01:29:27] >> All right, everybody. That's it for episode 270 of the world's greatest

[01:29:29] episode 270 of the world's greatest podcast. I am your world's greatest

[01:29:32] podcast. I am your world's greatest moderator. Thank you, Chimoff Hatia,

[01:29:34] moderator. Thank you, Chimoff Hatia, David Saxs, and David Freeberg for the

[01:29:36] David Saxs, and David Freeberg for the episode. To your friends, your

[01:29:37] episode. To your friends, your neighbors, and we'll see you all next

[01:29:39] neighbors, and we'll see you all next time. Bye-bye.

[01:29:40] time. Bye-bye. >> Love you boys. Bye-bye.

[01:29:43] >> Love you boys. Bye-bye. >> Let your winners ride.

[01:29:46] >> Let your winners ride. Rainman David

[01:29:50] and it said

[01:29:50] and it said >> we open sourced it to the fans and

[01:29:52] >> we open sourced it to the fans and they've just gone crazy with it.

[01:29:55] they've just gone crazy with it. >> Queen of

[01:30:03] besties are

[01:30:06] besties are my dog taking a notice in your driveway.

[01:30:10] >> Oh man, my appetizer will meet me up. We

[01:30:13] >> Oh man, my appetizer will meet me up. We should all just get a room and just have

[01:30:15] should all just get a room and just have like one big huge orgy because they're

[01:30:16] like one big huge orgy because they're all just useless. It's like this like

[01:30:18] all just useless. It's like this like sexual tension that they just need to

[01:30:19] sexual tension that they just need to release somehow.

[01:30:24] >> Your feet.

[01:30:27] >> Your feet. >> We need to get merch.

[01:30:28] >> We need to get merch. >> I'm going all

[01:30:37] in.