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All right, everybody. Welcome back to
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your favorite podcast, the number one
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podcast. In fact, in the entire
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universe, the Allin podcast with your
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besties. We're all here. It's the
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original quartet. Everybody loves When
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the Core Four are here. And we have a
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docket for you today, guys. We got to
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start out with Bernie Sanders. I know
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this is becoming a bit repetitive, but
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AI is the topic.
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We reached a new level of retardation
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this week that we cannot avoid. Bernie
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Sanders has a major del pitch, a
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moratorum on new AI data centers.
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Here's his argument. Number one, the
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billionaires are pushing AI because they
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want more money and power. Number two,
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there's going to be massive
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unemployment. And he cites Skates,
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Staro, Elon, saying that a AI would
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replace most jobs. Number three, he uh
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has an interesting point actually. AI is
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harmful to kids because it decreases
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social interaction. Actually, we kind of
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agree with that one, I think, across the
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board. But here's his pitch, Sachs. His
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pitch
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>> you want your using these chat box.
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>> No, I've talked to my kids about it.
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>> Okay, we'll get into it.
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>> We can get into it, but I've talked to
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them about it. Look, if you actually
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look at the data, if you look at what
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kids are doing, it's so much more
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interactive and engaging for them to
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talk to their friends on Snap or to
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watch videos on Tik Tok. Those things
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are super engaging. Whereas doing
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research on an AI chatbot is just it's
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way less engaging and you see this in
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the data. So, I'm not saying there's not
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an issue there. You want to pay
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attention to the way that these
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technologies are shaping the minds of
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young kids, but I think people get a
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little bit confused. what they're really
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talking about is social media and then
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they attribute all the ills of social
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media over to these new AI chat apps and
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they are a little different. You know,
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when I when I asked my kids like
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>> how much do you use these things? Are
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they addictive? They said no, they're
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more just really useful. It's like
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Google.
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>> It's like there's two
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>> I couldn't do school without it.
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>> Yeah, there's two pieces here. One,
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using AI to be smarter and to learn
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stuff and ask questions. Absolutely
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fantastic. phenomenal. I think we'll all
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agree on that. There is, and I don't
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know if this is actually what he was
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referencing, but there's character AI in
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a in a long tale of spicy chats where
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people we talked about getting
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oneshotted and these parasocial
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relationships. That's I think what he's
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referring to, but maybe I'm reading it
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wrong,
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>> but let's get into it here. His pitch is
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that we need to slow down so that quote
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democracy can catch up and that Congress
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should put a moratorum on new data
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centers. That's his solution. He got
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roasted obviously on social media for
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this. It's the most absurd I think
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solution to you know what are valid
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concerns. friend of the podro kind of
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who represents Silicon Valley. He
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I guess supported it a bit and then he
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replied to me and says his concerns are
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not as acute and that he just wants to
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make sure we use renewable energy which
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I guess is you know a fine position to
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have poly market put up a little uh
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market here AI data center moratorium
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passed before 2027.
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Yeah, we'll see if that comes true or
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not. But uh hey Saxs, you are our SGE.
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Sachs or SGE, this is your time to
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shine.
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You have framed the AI debate as we
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can't lose to China. Bernie framed it as
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not letting the billionaire class get
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more power and money by eliminating
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American jobs. And last week on this
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very program, Tucker framed it as what
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are Americans going to get out of this?
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Right? That that was the framing, you
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know, as of December of 2025. So here's
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your chance, Sax. Why should Americans
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who have some concerns about losing
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their jobs care about beating China? Why
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should they care about that sex?
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>> Why should they care about beating
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China? Because AI is a profound
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technology. It's going to have huge
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economic and national security
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implications. And the thing that Bernie
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gets wrong is that he can't stop the
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progress. I mean, he can't stop China
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from making progress. We can stop
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progress in the US, but it's not going
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to stop China from advancing these
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technologies. A lot of this is just
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math. And just because we stopped
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doesn't mean China's going to stop. So,
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this would be the biggest own goal ever
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if we took our leadership in the AI race
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and just handed it to China. But look, I
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appreciate Bernie's honesty in a way
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because he is actually telling the truth
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about what he wants. And I remember just
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a week ago when President Trump signed
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an executive order to advance a national
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AI framework, a lot of critics and a lot
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of Democrats were saying this was a
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violation of states rights and we need
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to support the states and regulating AI.
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And here Bernie is acknowledging that
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this is not about states rights because
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he's saying that there needs to be a
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moratorium on new data centers even if
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the states want them. So he doesn't
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support states rights either. He just
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wants all the progress to stop. And I
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think this is really the truth of the
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matter is that all this talk about
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states rights or affordability, it's all
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a red hearing. And what's really going
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on is there's a large and growing
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contingent of people who just want all
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the progress to stop. But again, we
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can't stop China from making progress.
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So all they would be doing is seeding
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leadership of this AI race to China.
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What people like Bernie really want is
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they want the US to become like Europe.
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You know, Europe has half the share of
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global GDP that they had 30 years ago.
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And that's because of their hostility
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towards innovation and technological
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progress. And that's kind of where
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Bernie wants us to be is he wants us to
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go down the the path of Europe. And the
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reason he says is because, god forbid
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someone gets rich. Well, look,
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capitalism sometimes results in the
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unequal distribution of wealth, but
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socialism always results in the equal
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distribution of poverty and misery. And
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if the US stops developing AI, if we
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hand this leadership to other countries,
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it'll make the United States poor, it'll
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make the American people poor, and it
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will seed our leadership globally. We
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will not be the pre-minent power in the
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world anymore.
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>> Okay.
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>> So, I'll stop there.
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>> No, no, it's great. Thank you. SGE David
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Saxsberg,
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there seems to be a couple of
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conversations. Unfortunately, we weren't
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here last week, but we had a really nice
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uh visit from our pal Tucker, and we
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talked about this. He seemed to think
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there was a bit of a communication
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problem with selling to the public the
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value of these technologies. Do you
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agree with that? And what do you think's
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going on here in terms of the cross
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conversations? One group wants to del,
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has some concerns, environmental,
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you know, job displacement, etc.
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And then there is there an issue where
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the China beating China doesn't resonate
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with the American populace? You've been
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harping on about the rise of socialism
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for four plus years on this very
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podcast. So from where you sit, what's
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the actual disconnect in these multiple
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conversations that are going on
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concurrently and and seemingly coming to
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a head here at the end of 2025? Well, if
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you ask any of the politicians that are
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making these proclamations about the
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quote tech barons and try and actually
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talk to them about what is the AI
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technology delivering, what is it not
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delivering, you kind of typically hit a
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wall pretty quickly. It's very hard for
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folks to articulate what is actually
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happening. There's a tremendous amount
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of capital being put at risk against
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infrastructure to build out the capacity
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for many companies, the entire economy,
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the entire industry to deliver AI tools.
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There isn't an aggregation
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at this stage of value creation with the
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exception of Nvidia which has really got
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this $3 trillion market cap creation
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that's happened in the last couple
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years. Peter Teal said this best. He's
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like look at all the money that's going
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into AI. There's really only one company
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that's making any money and that's
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Nvidia. Like at this point the juryy's
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still out. We don't even know what AI
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is. It's sort of like when the internet
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was happening. You know, everyone
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thought these fiber optic switch
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companies were going to make all the
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money. Turns out that was wrong. It was
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the end applications that made all the
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money and they competed in many
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different markets from Google to Amazon
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to Uber. You can go down the list of all
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the beneficiaries of the core
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infrastructure technology of the
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internet that was built out. So what is
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really going on? Well, AI is the new
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lightning rod for fear and for
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divisiveness that ultimately breeds
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compliance and control, which is where
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these politicians are trying to drive
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the the populace and the voting
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conditions in the United States. And
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that's what's going on right now.
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There's a lot of fear about, oh, putting
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this data center in my town is going to
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do X or Y or Z with no real conversation
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about the truth of that matter. There's
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a lot of fear about wealth creation
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being aggregated in the hand of a few
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when, as we saw with the internet, it
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benefited the many. And that fear
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mongering is a very similar tactic that
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we've seen in the prior generations
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where policies were misstated, fear was
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used, and then voting control allowed
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folks to come to power that were looking
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for power. So I think it's just the
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lightning rod at the moment.
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>> Okay, Jim, what's your take on this big
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picture? What's going on here?
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>> I think it's important to understand
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that politicians have an incredible
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sense of self-preservation.
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So the question is why would Bernie
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Sanders think he would not look totally
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foolish in putting that video out? And
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the reason it went viral is not because
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it sounded so crazy. It was that to some
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faction and percentage of people it
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sounded rational and reasonable.
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And the problem that highlights is that
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we have a huge perception issue in AI.
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We have a handful of companies.
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All the PR that you see from those
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handful of companies is a bunch of
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circular deal making. It's a bunch of
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capital that flows from one to the
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other. It causes these stocks to go up
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of which a small percentage of people
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benefit. And at the tail end of it, it's
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accompanied by a completely different
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set of articles that everybody also
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reads
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about the sort of damocles that's about
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to fall on their head. whether it's
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electricity prices or whether it's their
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jobs or whether it's the jobs of their
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children or the quality of their
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education. So we have a big perception
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problem. So the question at hand is how
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do we fix it? How do we get back to the
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place where a video talking about
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stopping all progress would seem as
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laughable as it should be? And I think
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you can go back to the guilded age and
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you can ask the question, how did the
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industrialist leaders of that era
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respond through that 1880 to 1920 age of
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industrialization when you had all of
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this technological upheaval accompanied
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by a handful of people with incredible
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success, right? Andrew Carnegie, John D.
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Rockefeller, Henry Ford. What did they
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do? And I think the lesson we can borrow
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from them is we now need to be on the
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forward foot as an industry. Enough of
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the stupid haircuts, dumb watches, ugly
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clothes, ostentatious displays of
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wealth. We've all done it. I've been
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guilty of it. It has to stop. Absolutely
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stop. And instead, we need to start to
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use a percentage of the balance sheets
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of these companies in order to benefit
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as many Americans as possible. That is
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the absolute minimum. Andrew Carnegie
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built 2500 libraries.
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The idea was as he built the railroads,
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you're going to scale GDP. You're going
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to scale education and knowledge. Those
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libraries are artifacts that allowed
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people to feel a dividend from that
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industrial revolution. John D.
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Rockefeller took all of his wealth and
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invested in institutions and
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universities. Henry Ford specifically
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focused on wages. We need to
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self-organize better and we need to be
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more on the forward foot. We need to
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start doing things that are practically
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measurable by tens of millions of
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American citizens. And it starts to beat
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back the perception problem that we have
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because Sax pointed out last week, the
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data does not support the misperception.
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But the problem is, and Sax and I were
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talking about this last week, the
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misperception is gaining steam. And so I
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think we need to use these tools that we
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have to provide some more practical
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benefits that every man, woman, and
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child can feel. Otherwise, this thing is
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going to build and you'll see crazier
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and crazier versions of this Bernie
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Sanders video.
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>> All right. I think it's well said and
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you know, as I said last week, I think
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beating China does not matter to the
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average American. It's below their line,
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Jimoth. Uh I think in many ways what
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they care about is jobs. They care about
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their kids getting jobs and yeah, they
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care about costs. They care about
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inflation. They care about energy costs
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going up. Our industry has done a
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terrible job of explaining how this
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benefits the average American. And
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they're seeing all these headlines
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about, as you mentioned, all the cross
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deal booking and all these stocks going
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up. Half the country doesn't own stocks,
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so they're not participating in it. And
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they're not blind to seeing self-driving
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cars or seeing their kids having a hard
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time getting a job. and they're hearing
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everybody talk about job displacement
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and that's a valid concern. People are
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scared and our industry is scaring them
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I think by not meeting them where they
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are and then beating China respectfully
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is important sachs super important. I
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can intellectually agree with you on
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that.
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>> But if you lose your job and your kids
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can't get a job that's that's
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existential. Yes. It's not
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>> my child needs a job next year. They're
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graduating from school with $100,000 in
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debt and my energy bills going up and my
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grocery bills are going up and they
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haven't come down. That's what Americans
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are experiencing. And yeah, maybe giving
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uh I think you also made an interesting
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point there too about the libraries.
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>> The war with China is existential
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because if we can keep it on the
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battlefield of AI and intellectual and
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economic prowess, we have a very good
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chance of winning. If it devolves and
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all of a sudden becomes a different kind
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of battle, that's bad for everybody,
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especially our children and our
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children's children. So, we need to win
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the current game on the field. But in
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order to do that, we need to change
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these misperceptions. We need to start
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showing tactical artifacts that show
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that this is a dividend that can benefit
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everybody. And we need to start now. I'm
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just putting it out there. We have so
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much cash on the balance sheets of these
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companies. Wall Street values it at
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zero. If you look at the enterprise
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value of any of these companies and you
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do a sum of the parts, nobody cares
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about that cash. So, we need to start
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using that cash more effectively.
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>> What's the equivalent of the library
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metaphor here for you, Jamath? Do you
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have any ideas?
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>> I'm not going to frontr run
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what's being worked on except to say
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that the leaders of these companies have
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gotten the message. They are working on
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a whole host of solutions. And again,
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we're fighting misperception, which is a
00:15:01
complicated battle, but it's winnable
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and I think you're going to start to see
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stuff in the new year.
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>> Yeah, I would say education is at the
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top of that list. Saxs, your thoughts
00:15:11
here as we wrap on this first topic.
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>> I'm not sure whether to address the
00:15:14
particular hoaxes or the fact that there
00:15:16
are there's a larger effort here to try
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and discredit AI and get AI development
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to stop entirely.
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Let's talk about some of these
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particular hoaxes. So on the job loss
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claim, I feel like I do this every week
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now, but there's a new study from
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Vanguard where they analyze job growth
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and wage growth in occupations that are
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highly exposed to AI automation versus
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all occupations.
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And they find that both job growth and
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wage growth is higher, not lower, in the
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occupations that are exposed to AI. So
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if you look at job growth in occupations
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exposed to AI, it's 1.7% compared to
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0.8% for other. For wage growth, it's
00:16:05
3.8% versus 0.7% for other. So what you
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see here is something maybe it's
00:16:10
counterintuitive, but it makes sense to
00:16:12
me, which is as you make workers more
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productive, the value of their labor
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increases, not decreases, and they end
00:16:18
up getting paid more, and you want to
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hire more of them. So this is a huge
00:16:23
narrative violation. And again, this is
00:16:24
coming from Vanguard and it's showing
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that AI productivity is good for
00:16:30
workers. This follows on the heels of
00:16:33
that study from Yale Budget Lab, which I
00:16:36
talked about in a previous show that it
00:16:39
said there's no discernable disruption
00:16:41
to the job market based on 33 months of
00:16:43
data after the launch of chat GPT. Now,
00:16:46
I understand that you can make
00:16:48
predictions as to the future that this
00:16:50
state's going to change, that there will
00:16:52
be AI job loss, but what I'm saying is
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that if you look at the data so far,
00:16:57
there is no AI job loss. Quite the
00:16:59
opposite. It's job growth and job gains.
00:17:01
By the way, we have a 2% tailwind to GDP
00:17:05
growth right now. That's coming from
00:17:07
this AI boom, this capex boom that's
00:17:10
happening. And so, this is, I think, a
00:17:13
very good thing for the US economy. Now,
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why do people want to sabotage it?
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There's a really interesting article in
00:17:19
Semaphore recently that described how AI
00:17:22
critics were funding journalism
00:17:24
fellowships at major publications like
00:17:26
NBC News, Bloomberg, Time, The Verge, LA
00:17:30
Times, which by the way, I mean, I track
00:17:32
these things. They're relentlessly
00:17:34
negative about AI.
00:17:36
These fellowships were funded by future
00:17:38
of life institute
00:17:40
which is a doomer group that thinks that
00:17:45
AI is going to become sensient and
00:17:47
replace humans and they were funded by
00:17:52
a donation by Vitalic Buterine from
00:17:55
Ethereum. It's an interesting story
00:17:56
actually. He donated his dog coins to
00:18:00
future of life. They ended up being
00:18:01
worth $600 million.
00:18:03
>> Dogecoins.
00:18:04
>> Doge. And then you remember how there
00:18:06
was like those other dog coins.
00:18:09
>> She Oh, he had a collection of dog
00:18:11
coins. Got it.
00:18:12
>> He had a collection of dog coins.
00:18:13
Apparently people like air dropped them
00:18:14
to him as like a promotional thing.
00:18:17
>> Got it.
00:18:17
>> I don't think he wanted them. So he's
00:18:18
like, "How do I get rid of them?" So he
00:18:20
donates them to Future of Life and they
00:18:22
end up being worth $600 million. So by
00:18:24
this
00:18:24
>> Oh my god. almost like this accident.
00:18:26
This Doomer think tank ends up with a
00:18:29
$600 million war chest and they've been
00:18:31
funding these journalism fellowships.
00:18:35
They've been funding grants for
00:18:36
academics to study AI. Obviously, that's
00:18:39
going to end up being very negative. And
00:18:42
they are funding a lot of these nimi
00:18:44
organizations that are opposing data
00:18:46
centers because their goal is just to
00:18:48
get it to stop. That's their goal. They
00:18:49
just want the development to stop. And
00:18:52
you can't, I don't think, underestimate
00:18:54
how much of an impact this has had on
00:18:55
the public discourse. But if you look at
00:18:58
their actual claims, like for example,
00:19:00
the water use claims, it's a total hoax.
00:19:02
I mean, these AI data do not use a lot
00:19:05
of
00:19:05
>> Yeah, that was one of the things I
00:19:06
wanted to get into because yeah,
00:19:08
compared to a golf course, they're not
00:19:10
using that much or or walnuts and, you
00:19:12
know, almonds in central California. So,
00:19:15
I think it is worth maybe just
00:19:17
>> throwing a lot of spaghetti at the wall.
00:19:19
Look, you have to remember who was who
00:19:21
was Ida Tarbel because it's quite
00:19:22
interesting in the context of this. Ida
00:19:25
Tarbell was this American writer again
00:19:27
in the Gilded Age. She was part of the
00:19:29
Muckwreckers which was a group of
00:19:31
journalists that were doing
00:19:32
investigative research around the abuses
00:19:36
of the industrial revolution, labor
00:19:38
abuses and the like. And she took on
00:19:39
sandal. Exactly. If you look at that
00:19:42
example, I'm sure that the people in
00:19:44
this current generation who have a
00:19:45
Tarbell fellowship, what they're living
00:19:47
out is this desire to tell a story about
00:19:50
exploitation and wrong and misdoing. The
00:19:54
problem is that these things don't
00:19:55
factually hold together, but
00:19:57
unfortunately it adds to the perception
00:20:00
problem that we have and that has been
00:20:03
growing. As Sax said, like so many of
00:20:05
these things have been debunked, but
00:20:06
it's playing whack-a-ole, Jason, because
00:20:08
they'll throw the water thing out. It'll
00:20:11
get debunked next week. We'll probably
00:20:13
something about liquid cooling and all
00:20:15
the PAS and forever chemicals. We'll
00:20:18
have to debunk that. Then we'll go to
00:20:20
like air cooling and how that's a
00:20:21
problem. Then we'll go to something
00:20:22
else. Eventually, it'll be the upper
00:20:24
land grouse, but they're not going to
00:20:27
stop.
00:20:28
>> Sax, how do you do you have a plan to
00:20:30
reframe this to the American public?
00:20:32
You're explaining how these bad things
00:20:33
are happening and all the evil forces at
00:20:36
work behind the scenes.
00:20:37
>> Well, look, it's not evil forces, but I
00:20:39
don't think people understand the extent
00:20:40
to which the discourse has been impacted
00:20:43
by a few anti- AI tech billionaire.
00:20:46
>> I brought it up every week and on your
00:20:48
text.
00:20:48
>> I brought it up. Hold on. I brought it
00:20:50
up on this show and I tweeted about it
00:20:51
way back in November. Yeah.
00:20:53
>> And I linked to a writer named Nerret
00:20:55
Weisslat who has analyzed this whole
00:20:57
doomer industrial complex and she has
00:21:00
shown that there are hundreds of these
00:21:02
front organizations and they're all
00:21:04
really just funded by a few big tech
00:21:06
billionaires. It's Dustin Moskovitz, Yan
00:21:08
Talin and Vitalic Buterine. Well, when I
00:21:11
first described this, people thought
00:21:13
that this sounds kind of crazy like a
00:21:14
conspiracy theory. And then sure enough,
00:21:16
Semaphore comes out with the article
00:21:18
explaining that all these journalism
00:21:20
jobs are being funded by future of life,
00:21:22
which was the big donation by Vitalic
00:21:25
Buterine. So my point is just you can't
00:21:28
underestimate the extent to which a few
00:21:30
billionaires who've donated over a
00:21:32
billion dollars to this cause have
00:21:34
distorted the public debate. And you
00:21:36
really see this actually in the relative
00:21:39
popularity of AI in the United States
00:21:41
versus China. So there's recently a poll
00:21:43
on they call it AI optimism where they
00:21:45
ask people do you believe that the
00:21:47
benefits of AI will outweigh the harms.
00:21:50
83% of people in China believe the
00:21:52
benefits will outweigh the harms or AI
00:21:54
optimistic. In the US it's only 39%.
00:21:58
>> So again the discourse has really been
00:22:00
affected by a few of these
00:22:04
>> institutions. Let's say let's say that's
00:22:05
all true.
00:22:06
>> Yeah. My question to you is, what's your
00:22:08
plan to turn this around and explain to
00:22:10
the American people why they should be
00:22:12
optimistic about this? What's your plan,
00:22:14
David?
00:22:16
>> Well, look, I mean, you're right that we
00:22:17
have to flip the narrative around. And I
00:22:19
I do think that the tech companies have
00:22:20
done a really bad job explaining the
00:22:22
benefits. We talked about this with
00:22:23
Tucker. I think he brought up some
00:22:24
really good points. And you're right,
00:22:27
like the the AI companies lean way too
00:22:29
much into the whole job loss narrative
00:22:31
as as a way to explain their value prop.
00:22:33
And I think it's just either what Jamath
00:22:35
has said, which is this is about their
00:22:36
next fundraising round, or it's a form
00:22:39
of laziness because it's easier to
00:22:41
describe job loss or job replacement
00:22:43
than it is productivity, right? Like
00:22:45
multiffactor productivity is a difficult
00:22:47
concept to explain. So I do think that
00:22:50
they've played into this narrative. But
00:22:51
I think that what we have to do here is
00:22:54
just debunk these narratives. Again, no
00:22:56
evidence of job loss yet. No evidence of
00:22:58
the the water problems. I think the
00:23:01
electricity issues with data centers are
00:23:03
addressable. You have to let the AI
00:23:05
companies build their own power. They
00:23:06
don't have to connect to the grid. In
00:23:08
fact, that's what President Trump has
00:23:10
called for for the last 6 months is to
00:23:12
let the AI companies build their own
00:23:13
power so they're not drawing on the
00:23:15
grid. So, that problem is easily
00:23:16
addressed as well. Talking about the
00:23:18
affordability problem, but you're right,
00:23:20
we have to get the message out how this
00:23:22
technology will benefit Americans. And
00:23:24
the bottom line is, look, I've always
00:23:25
said I'm a technorealist, not a techno
00:23:27
accelerationist. Because at the end of
00:23:29
the day we don't have a choice. I mean
00:23:31
yeah China is gonna develop the
00:23:33
technology if we don't.
00:23:34
>> Okay.
00:23:34
>> So we don't really have a choice.
00:23:36
>> I you know as I said last week I think
00:23:38
the three most pressing issues chimat
00:23:40
education housing and healthcare. And
00:23:43
our industry is in a unique position to
00:23:45
use AI to impact all three of those. So
00:23:48
why don't we as an industry collectively
00:23:50
explain all the great things going on
00:23:52
there. I gave a shout out to Daniel,
00:23:54
Prenovo, Pernovo rather, all these great
00:23:57
companies analyzing our blood. There's
00:23:59
so much going on in healthcare that
00:24:00
could massively lower the cost, make
00:24:02
people's lives better.
00:24:04
>> Obviously, in construction, you could
00:24:05
have incredible advances, and certainly
00:24:07
in education, but we haven't explained
00:24:09
those three things. What's your point?
00:24:11
Is there anything you can think of to
00:24:12
add to that list?
00:24:13
>> We have to understand the president and
00:24:15
Sachs, they're responsible for
00:24:17
shephering the GDP of America. And as
00:24:21
Sax said, half of American GDP over the
00:24:24
next few years is forecasted to come
00:24:26
from all of the capital that goes in to
00:24:28
build out our capabilities in AI. We
00:24:31
need this to happen. There's the
00:24:32
existential reason why, but there's also
00:24:34
a basic economic reason why. So Jason,
00:24:37
the social license comes when off to the
00:24:40
side, the private companies
00:24:43
start to take this perception problem
00:24:45
more seriously. So to your point, are
00:24:48
there things that they can do around
00:24:50
housing? Yes. And again, not to frontr
00:24:52
run, they're actively working on
00:24:54
something that I think could be
00:24:55
transformational separately. Could they
00:24:57
do something on healthcare? TBD, but we
00:24:59
should look at it. Can you do something
00:25:01
in education? TBD, but we should look at
00:25:04
it. But the point is, if you can have
00:25:07
the same companies that are on the
00:25:08
forward foot building this revolution
00:25:10
for us, recognize that we need to bring
00:25:13
some more folks along take some of their
00:25:15
balance sheet capital and invest it to
00:25:18
create this social license to operate.
00:25:20
We will flip this perception on its
00:25:23
head.
00:25:23
>> Yeah, I think it's well said. And you
00:25:25
know, there was a really interesting I
00:25:26
don't you guys must remember this. The
00:25:28
AT&T commercials, you will. Do you
00:25:30
remember those
00:25:31
>> by the way? Sorry, that's another great
00:25:32
example, Jason. AT&T at the turn of the
00:25:34
century. What did they do? They took
00:25:36
their capital and they created Bell
00:25:38
Labs. What did Bell Labs do? Bell Labs
00:25:40
drove all of modern information theory.
00:25:43
It was the precursor to the internet.
00:25:44
They did so much fundamental research. I
00:25:47
want to be clear. I don't think that
00:25:48
this is a five alarm fire. This is very
00:25:50
fixible right now. And the litmus test
00:25:53
quite honestly is in 3, four, five, six
00:25:56
months. Jason, when you see the trickle
00:25:57
of progress if we can get organized,
00:26:00
does your perception change and does the
00:26:02
language that you use and the tone that
00:26:04
you've used over these last 6 months has
00:26:06
me?
00:26:06
>> No, no, no. I'm actually thinking that
00:26:08
you've actually been quite a good early
00:26:10
warning system
00:26:12
>> for what a certain percentage of
00:26:14
Americans think. And so,
00:26:15
>> yes, I predicted it perfectly. Yes.
00:26:17
>> It's been extremely useful. But now we
00:26:19
got to act on it and you have to change
00:26:21
your mind.
00:26:22
>> Oh, no. No. My my mind has been very
00:26:23
crystal clear. I won't let you
00:26:26
you know, tell what I'm saying is my
00:26:28
opinion has been job displacements
00:26:29
coming.
00:26:30
>> Jason, let me be clear. We have to earn
00:26:31
you changing your mind publicly on this
00:26:33
show. If we do that, we're back in a
00:26:35
good place.
00:26:36
>> Uh, yeah. No, I think that you're saying
00:26:37
that in a very condescending way. I have
00:26:39
brought up I have brought up Okay.
00:26:42
Unintentionally.
00:26:42
>> Here, let me let me try to say again.
00:26:43
Sorry. Let me try this again. We have
00:26:45
>> You're saying it as if I'm misguided.
00:26:46
>> No, no, no. You have a perception
00:26:49
>> which is what do you think my perception
00:26:51
is? that on the balance it's a coin flip
00:26:54
about whether AI is going to be
00:26:56
>> all good for all people or very good for
00:26:59
a small subset of people. That's what I
00:27:01
would roughly say that's I would say
00:27:03
that's accurate job.
00:27:07
So let me finish. So if we can execute
00:27:09
this plan again, meaning marry all the
00:27:12
stuff we're doing on the forward foot
00:27:14
within in investing
00:27:15
>> with some of the stuff that we also need
00:27:17
to do to bring a broader swath of the
00:27:20
American population along.
00:27:22
>> Yeah.
00:27:23
>> You will have a different perception if
00:27:25
we are successful. And what I'm saying
00:27:27
is you've had this one perception and
00:27:29
our goal would be to shift you and
00:27:31
people like you.
00:27:32
>> Okay. I I I take it I think it's
00:27:35
completely fair. Yeah. I have been
00:27:36
trying to bring up that as an industry,
00:27:39
we have not recognized people's valid
00:27:42
concerns about job displacement. That's
00:27:44
all I've brought up on this program. I'm
00:27:46
not saying I think it's going to be
00:27:47
cataclysmic or we can't handle it. But I
00:27:49
think if we constantly talk about, hey,
00:27:52
winning the AI race in China, that's
00:27:54
abstract for people. And if we deny the
00:27:57
fact that robo taxis and human robotics
00:28:00
are coming and intelligent people like
00:28:03
Elon or other leaders are saying, "Yeah,
00:28:05
we're we're planning on replacing those
00:28:07
jobs. We're doing a terrible job
00:28:08
communicating this." And I do think part
00:28:10
of this is communication and part of it
00:28:12
is, "Hey, these are valid concerns." So
00:28:14
I think great resolution. Yeah, sure.
00:28:15
I'll be the litmus.
00:28:17
>> Do you guys know what um the Mottton
00:28:19
Bailey fallacy is?
00:28:20
>> No, explain it to the audience. I mean,
00:28:21
I think I've heard of it, but
00:28:22
>> the M and Bailey Castle is an early
00:28:24
medieval fortification where there's
00:28:26
like a very protected area, the keep or
00:28:29
the M. And then the Bailey is kind of
00:28:31
this looser area that's harder to
00:28:34
defend. Right. So, what happens is if
00:28:36
they need to retreat, they'll go into
00:28:39
the M. Okay.
00:28:40
>> Yeah. Lord of the Rings. Right. Right.
00:28:42
So, this has become known as a debating
00:28:44
trick or fallacy where people will make
00:28:48
a really outrageous claim, which is
00:28:52
they'll they'll basically run to the
00:28:53
Bailey and then when you prove that it's
00:28:56
false, they'll run back into the M and
00:28:58
say something very unobjectionable. So,
00:29:01
in the context of like the AI job loss
00:29:03
fallacy,
00:29:04
the Bailey is people will say this is
00:29:07
causing massive job loss, massive
00:29:09
disruption. It's already here. you can
00:29:10
see it and then when I point out well
00:29:13
actually if you look at the Yale Budget
00:29:15
Lab study or you look now at the
00:29:16
Vanguard study there is no job loss then
00:29:19
they'll retreat into the M and they'll
00:29:21
say no no no I'm talking about what's
00:29:22
going to happen in the future which is a
00:29:24
position that's fundamentally
00:29:26
irrefutable and then when I point that
00:29:29
out well wait you just totally change
00:29:31
what you're saying you're like no no no
00:29:32
I was only talking about the future and
00:29:34
then as soon as we sort of seem to have
00:29:36
agreement
00:29:36
>> then the people in the M will race out
00:29:38
to the Bailey and basically saying well
00:29:40
look at happening with Uber drivers or
00:29:41
what have you. So there is this Mott and
00:29:43
Bailey thing happening all the time on
00:29:45
this job loss question
00:29:47
>> and I just want people to be straight
00:29:49
about or honest about it which is look
00:29:51
if your claim is that this will cause
00:29:53
job loss in the future. It's true. I
00:29:55
can't refute that because none of us can
00:29:58
prove what's going to happen in the
00:30:00
future. But be honest about what's
00:30:01
happening today. And in the first three
00:30:04
years after the launch of AI chatbots,
00:30:07
there's been no discernable disruption
00:30:08
to the labor market and the early
00:30:10
studies and data are showing
00:30:12
>> wage increases and actually job
00:30:14
increases and there's definitely job
00:30:17
increases in blue collars because of the
00:30:19
construction boom.
00:30:20
>> This has been our first debate club
00:30:23
corner. Every week we're going to teach
00:30:24
you how to debate better here. No, it's
00:30:26
you know and I I appreciate that and I
00:30:29
have really tried to say here like I'm
00:30:30
not a doomerist at all. you know the
00:30:33
statistics I look at you know I talk to
00:30:34
Dar I have actually sincerely that's why
00:30:37
I don't use I use displacement you know
00:30:39
I really use refined language I do I do
00:30:42
and when I talked to DA he told me in
00:30:45
areas where robo taxis like Whimo are
00:30:48
occurring what they did specifically to
00:30:51
deal with this issue was they stopped
00:30:53
trying to hire drivers in cal in Los
00:30:56
Angeles and in San Francisco because
00:30:59
there are so many whimos they don't want
00:31:01
them to have a had experience. So, you
00:31:03
know, you
00:31:05
wherever in the castle we're talking
00:31:06
about this, I I I don't have a a horse
00:31:10
in this race. I'm not part of this habal
00:31:12
of, you know, Dustin Mosvitz's people. I
00:31:14
just know from talking to Uber and Whimo
00:31:17
and these other companies and, you know,
00:31:19
other companies that are building
00:31:21
software where they're trying to
00:31:22
eliminate jobs. And so, I'm just
00:31:24
bringing that up as a a really
00:31:26
interesting example. Uber and Whimo,
00:31:29
Elon with Tesla, they are actually
00:31:31
making plans right now to deal with this
00:31:34
displacement of drivers and they're
00:31:36
coming up with new products and services
00:31:37
for those drivers. So, one of the things
00:31:38
they're doing is data labeling at Uber.
00:31:40
They they bought a data labeling company
00:31:42
and they're taking the drivers and
00:31:43
saying, "Hey, you want to do some data
00:31:44
labeling over here?" And actually
00:31:46
creating AI jobs for them. So, this is,
00:31:48
you know, uh something that the whole
00:31:50
industry is working on.
00:31:51
>> Look, there's going to be a spectrum,
00:31:52
right? Some jobs are going to change,
00:31:55
some jobs will be eliminated. The
00:31:56
question is with respect to job
00:31:58
eliminations, will that be more than
00:32:00
offset by the net new job creations? If
00:32:02
you're looking at things like Uber
00:32:04
drivers and we go to full self-driving,
00:32:06
then obviously there's going to be some
00:32:07
category of job loss there. So, I'm not
00:32:10
claiming that there's not going to be
00:32:11
any elimination of certain types of
00:32:13
jobs, but I believe that on the whole,
00:32:16
what we're seeing is that when you
00:32:17
improve the productivity of workers,
00:32:20
their wages go up and there's more
00:32:21
demand for their labor, not less. That's
00:32:24
what the data is showing so far and I
00:32:26
just don't think you get that from the
00:32:28
media right now because they are
00:32:29
promoting this dumer narrative and a lot
00:32:31
of that is astroturfed by these
00:32:33
enormously deeped organizations that
00:32:35
have been funded by a few effective
00:32:37
altruists. All right, topic number two.
00:32:40
Economic numbers are out. It's a mixed
00:32:42
bag. Unemployment rate is up and
00:32:46
government payrolls are down. Inflation
00:32:48
somewhere in between. Let's get into the
00:32:50
numbers here. Fun with numbers.
00:32:52
unemployment rate rose to 4.6% from 4.4%
00:32:56
in September and 4% in January of 2025
00:33:00
when President Trump took over. US
00:33:03
economy added 64,000 jobs in new jobs in
00:33:06
November. October saw about 104,000 job
00:33:09
losses, but 162,000 of those job losses
00:33:13
came from the federal government. You
00:33:14
remember the Doge buybacks, friend of
00:33:17
the pod Elon Musk was involved in. Uh
00:33:19
those all took effect at the end of
00:33:21
September. So, there was a little bit of
00:33:22
a balloon payment that occurred. An
00:33:24
analyst from Moody's says, quote, "It's
00:33:26
a frozen job market. There's not much
00:33:28
hiring. There's not much firing
00:33:30
happened." That tracks with what I'm
00:33:32
seeing on the ground. Meanwhile,
00:33:33
inflation came in better than expected
00:33:35
at 2.7%, beating the 3.1%
00:33:39
expectation, but still far away from the
00:33:43
2% target for the Fed. Trump gave an
00:33:47
18minute very loud emphatic address to
00:33:51
the nation last night and it was not
00:33:54
that we're invading Venezuela. It was uh
00:33:57
mostly him talking about his
00:33:59
accomplishments
00:34:00
in terms of uh prices and affordability
00:34:04
and uh a little bit of admonishing
00:34:07
Biden. Here's a 20 second clip. The last
00:34:09
administration and their allies in
00:34:11
Congress looted our treasury for
00:34:13
trillions of dollars, driving up prices
00:34:16
and everything at levels never seen
00:34:18
before. I am bringing those high prices
00:34:21
down and bringing them down very fast.
00:34:24
>> Sax, your thoughts on the economic data.
00:34:27
>> Well, you're you're painting it as
00:34:29
you're painting the economic data as
00:34:30
mixed. I don't know how you have a
00:34:31
economic report that's better than what
00:34:33
we just had today. So, first of all, we
00:34:35
saw that CPI came in at 2.7% like you
00:34:38
said, but the expectations were 3.1%.
00:34:40
That's why the market is rallying today
00:34:42
in a big way. Core inflation is down to
00:34:44
2.6%.
00:34:46
These are significant beats. And this
00:34:49
puts core CPI inflation in the US at its
00:34:52
lowest level since March of 2021. So,
00:34:55
since the whole CO thing. And if you
00:34:57
listen to Kevin Hasset, he said that
00:34:59
over the past 3 months, core inflation's
00:35:01
been running at 1.6%. So the trend line
00:35:04
is going down even further. So it looks
00:35:06
to me like inflation's rolling over and
00:35:08
that's just about a solved problem which
00:35:10
is really good news for what it implies
00:35:12
for interest rates because it implies
00:35:13
that interest rates are coming down and
00:35:15
that's going to bring down things like
00:35:17
mortgage costs and the costs of
00:35:19
financing a car payment and things like
00:35:21
that. Now you talk about unemployment. I
00:35:24
think the unemployment news is very very
00:35:26
good. So if you look at the data from
00:35:30
September to November, the overall
00:35:33
employment is down 41,000. But why was
00:35:36
that? Private employment was up 121,000,
00:35:39
but government employment in the last 2
00:35:41
months has declined by 162,000. So this
00:35:44
is what the media was focused on is they
00:35:45
were trying to claim that unemployment
00:35:47
was up, but actually it's just that the
00:35:49
government jobs decreased. Why did the
00:35:51
government jobs decrease? Well, you
00:35:53
remember that when Doge went in and made
00:35:54
their cuts at the beginning of the year,
00:35:56
they offered people a buyout as of
00:35:58
October 1st. Now, some people took the
00:36:01
buyout right away, but a lot more waited
00:36:03
until the last possible day, which was
00:36:05
October 1st. And that's why you saw this
00:36:07
big spike in October unemployment, but
00:36:11
those are government jobs that are being
00:36:13
cut. And moreover, there are people who
00:36:15
voluntarily wanted to take the buyout
00:36:17
and they took that deal. So again, I
00:36:20
think the employment picture is looking
00:36:21
actually quite good and if you believe
00:36:24
what we believe, you don't want an
00:36:27
excessive number of government jobs. And
00:36:28
President Trump has presided over, I
00:36:31
think, the first decrease in the federal
00:36:32
workforce in decades. We've seen a 10.7%
00:36:36
decrease in federal workers in 2025.
00:36:39
Basically, the number's gone from 2.4
00:36:41
million to 2.15 million. I know that'll
00:36:44
make Freeberg very happy. But look, what
00:36:46
we had during the Biden years is we had
00:36:49
9% inflation. We had a very weak
00:36:51
economy. We did have a recession. It was
00:36:53
that two quarter shallow recession. And
00:36:56
the media even tried to start redefining
00:36:59
what a recession was to avoid those
00:37:00
headlines. But what the Biden
00:37:02
administration did in response to that
00:37:03
is they went hog wild with government
00:37:05
hiring. And what we're seeing now is
00:37:08
inflation is now coming back down and
00:37:12
we're seeing the number of government
00:37:13
workers come down to a more reasonable
00:37:15
level and the private economy is making
00:37:17
up for it. We still have a relatively
00:37:19
historically low unemployment rate. So
00:37:23
those numbers look good. Then you look
00:37:24
at the deficit. We've reduced the
00:37:27
deficit year-over-year by 600 billion.
00:37:29
That will help bring interest rates
00:37:31
down. And then on prices, you got the
00:37:34
lowest gas prices in five years, below
00:37:36
$3 nationally. And then finally, on
00:37:38
wages, real wages are up by over $1,000
00:37:41
on average. And um it's $1,300 for
00:37:45
factory workers, $1,800 for construction
00:37:47
workers. And again, that's a big change
00:37:49
from the buying years where you saw that
00:37:52
in real terms, wages went down by about
00:37:55
$3,000 on average per worker. So look, I
00:37:59
mean, it seems to me like we're on the
00:38:01
cusp of a golden age here. I don't see
00:38:03
how the numbers could really be better.
00:38:06
And then on top of it, again, you've got
00:38:08
this AI tailwind, which I think is a
00:38:10
huge positive, not a negative, which is
00:38:11
adding roughly 2% GDP growth every year
00:38:15
because of huge capex investment with so
00:38:18
far no job loss associated with that. So
00:38:21
I would just say sit back and enjoy
00:38:23
this. I think we're headed for a gang
00:38:25
busters 2026. Rates are coming down.
00:38:28
Inflation's coming down. And you're also
00:38:31
getting tax cuts going into effect next
00:38:33
year because of the big beautiful bill.
00:38:35
No tax on tips, no tax on overtime, no
00:38:37
tax on social security, plus the
00:38:39
standard deductions being beefed up. So
00:38:41
people haven't even felt the benefit of
00:38:43
those tax cuts. That's coming in in
00:38:45
April. I don't see how things could be
00:38:47
much better.
00:38:48
>> Okay, Freeberg, any thoughts here on the
00:38:51
data as it's come in? Or Chimath? I'll
00:38:53
I'll go to either one of you.
00:38:54
>> Directionally, I think it's really
00:38:55
positive. You want to see
00:38:59
private sector jobs growing and the
00:39:01
shrinkage of government sector jobs has
00:39:04
profound impacts not just on the budget,
00:39:06
but it starts to create obvious air
00:39:10
pockets where you get a little bit more
00:39:13
rational regulation. You can deregulate
00:39:16
in the appropriate places. You can shift
00:39:18
the burden and the responsibility to
00:39:19
private industry. So, it's more than
00:39:22
just what's in the numbers that I think
00:39:23
is positive. I've told you this story
00:39:24
before, Jason, to do some of the things
00:39:26
that I've wanted to do. I'll give you an
00:39:27
example. When we started a battery
00:39:30
business 5 years ago, when we filed with
00:39:34
the DOE, the Department of Energy, these
00:39:36
are 700page reports. You spend millions
00:39:40
of dollars and you hire teams of 50 and
00:39:42
100 people. And what are we trying to
00:39:44
do? We're trying to build a battery
00:39:45
business in the United States so that we
00:39:46
can delever from the Chinese.
00:39:49
to the extent that we could do that a
00:39:51
little bit simpler and a little bit
00:39:53
easier because there's fewer folks and
00:39:55
so the burden goes to state and local
00:39:58
regulators which we all already have to
00:39:59
deal with. These are just generally good
00:40:01
things for productivity. They're
00:40:02
generally good things for GDP. It allows
00:40:05
us to invest more aggressively in the
00:40:06
things that help America. So it's
00:40:08
trending in the right direction. By the
00:40:09
way, Sachs didn't mention this
00:40:11
explicitly, but I'll say it.
00:40:12
>> The other thing in 26 is you get a bunch
00:40:14
of tax cuts that kick in. No tax on
00:40:17
tips, no tax on overtime. the
00:40:18
deductibility of the cost of interest
00:40:20
for
00:40:22
things like car loans.
00:40:24
These are big stimulation
00:40:28
accelerated depreciation. You have big
00:40:29
stimulative actions for the United
00:40:30
States economy.
00:40:32
>> Yeah. I think what you guys are missing
00:40:35
>> respectfully is that uh
00:40:37
>> you know uh Trump promised something
00:40:40
completely different. He said starting
00:40:42
on day one we will end inflation and
00:40:44
make America affordable again to bring
00:40:45
down the prices of all goods. And when
00:40:47
he was running for election, he promised
00:40:49
the Americans that he would reduce
00:40:52
prices. Now, what's happened is prices
00:40:55
have not come down. They've gone up 2.5
00:40:58
to 3.1%. But what really matters, and I
00:41:00
think you guys are doing a great job,
00:41:02
you know, pro- administration, part of
00:41:05
the administration. What you're missing
00:41:06
is the American people don't believe
00:41:08
you. And the American people are
00:41:10
experiencing something different. And
00:41:11
when you look at the uh approval rating,
00:41:13
Trump's approval rating is at its
00:41:15
historic lows. and specifically on
00:41:17
inflation. Pull up the silver bullet and
00:41:18
meta analysis there. This is the
00:41:20
disconnect and this is why it seemed
00:41:22
tonedeaf last night when Trump was
00:41:24
telling everybody it's great and when
00:41:25
you say it's the golden age, Americans
00:41:27
aren't experiencing that. That's just
00:41:29
not what they experience. They are
00:41:32
experiencing grocery prices that have
00:41:34
continued to go up and they are
00:41:37
experiencing unemployment that's gone up
00:41:39
15%. Whether it's the federal employees,
00:41:41
whether it's private sector, and you
00:41:43
debate the numbers, or there's a golden
00:41:44
age coming because of no tax on tips,
00:41:46
these are all talking points. What the
00:41:48
American people remember is the Trump
00:41:50
administration said prices would go
00:41:52
down. Prices have gone up. That jobs and
00:41:55
manufacturing, there'd be all these
00:41:56
incredible manufacturing things.
00:41:58
Obviously, that's going to take years.
00:41:59
That has not happened yet. So, I hope
00:42:01
for the best. I hope inflation goes down
00:42:03
to two. And I hope they turn this
00:42:05
around. But the American people don't
00:42:07
believe the Trump administration. And
00:42:08
it's a big difference between promising
00:42:11
stuff in 2024 and delivering it. And
00:42:13
here we are in the first year and the
00:42:15
delivery that's come in from the Trump
00:42:17
administration on inflation and on the
00:42:20
economy is not good if you're in the
00:42:23
bottom half of society that doesn't own
00:42:25
equities. Period. Full stop.
00:42:27
>> So, first of all, prices have come down.
00:42:29
You look at gas prices are the lowest in
00:42:30
five years. They're below $3 nationally.
00:42:33
Now, I don't know how you want a new
00:42:35
administration to come in and literally
00:42:37
affect every single change on day one.
00:42:39
How do you do that?
00:42:40
>> Oh, no. That's just what he said.
00:42:42
>> No. What the president said is they
00:42:43
would start working on they would start.
00:42:46
>> I'm not saying it's possible. That's
00:42:47
what he said.
00:42:48
>> No, he said he'd start working on it on
00:42:50
day one. And so he did. And look at the
00:42:52
results. Inflation's now down to 2.7%
00:42:55
way below expectations. I don't know how
00:42:57
you want a better report than this.
00:43:00
Unemployment is very low. The only
00:43:02
reason Remember the uh the economic
00:43:04
target was 3 three and three 3% GDP
00:43:07
growth, 3% deficit to GDP and 3%
00:43:10
inflation. They're below 3% inflation.
00:43:13
They're above 3% GDP growth and the
00:43:16
deficit to GDP is not where it needs to
00:43:18
be. So cutting is still ahead. But I
00:43:21
looked at the budgets for 2026 for a
00:43:23
number of the departments in the cabinet
00:43:26
over the last couple days and they do
00:43:28
have big cuts that they're trying to
00:43:29
implement across the federal government.
00:43:32
We're going to be interviewing Scott
00:43:34
Bessent tomorrow morning that'll come
00:43:36
out shortly after this episode airs. But
00:43:38
this is the catchup conversation with
00:43:40
Scott on how's the 333 plan going. But
00:43:42
on two of the three metrics, Jay Cal, it
00:43:45
does appear like things are good. I'm
00:43:46
not trying to be a spokesperson for the
00:43:47
Trump administration, but I'd say if you
00:43:49
look at this just from the raw economic
00:43:51
data and the the goals of the
00:43:52
administration following that, there
00:43:54
should be a flow through in terms of job
00:43:57
growth, the flow through in terms of
00:43:59
affordability, in terms of wage growth,
00:44:00
all those other sort of economic
00:44:01
indicators that actually affect people
00:44:03
on Main Street every day. Uh that's also
00:44:05
TBD in the narrative is still to be
00:44:07
written, but those high level economic
00:44:09
goals are still, you know, are starting
00:44:10
to kind of fall within the the framework
00:44:12
of what they set out to do, which is
00:44:14
generally good, right?
00:44:15
>> Yeah. I'm just pointing out the
00:44:16
disconnect again. This is the disconnect
00:44:18
with the American people. I mean,
00:44:20
>> no, no, I'm looking at year one.
00:44:21
>> Just year one. We're here at the end.
00:44:24
>> Time to implement an agenda.
00:44:26
>> I totally agree. I'm just telling you
00:44:28
the American people don't believe the
00:44:29
Trump administration right now. There's
00:44:31
more work to do.
00:44:33
>> That's the data that came out today is
00:44:35
looking awesome. By the way, that
00:44:37
Vanguard report that I mentioned that
00:44:39
also exposes the AI job loss hoax, it
00:44:43
also has a new economic assessment for
00:44:46
next year is projecting 3% growth for
00:44:49
next year, which I think is
00:44:50
conservative, and an improved labor
00:44:52
market outlook for 2026, and it says the
00:44:54
most robust improvement will come in the
00:44:57
second half of the year. So, Vanguard is
00:45:00
saying that 2026 is looking very good.
00:45:03
And by the way, that's when the fight
00:45:04
for Congress will unfold is next year.
00:45:06
So, you know, the thing about polls is
00:45:08
it's obviously just a snapshot in time
00:45:10
and it doesn't tell you what things are
00:45:12
going to look like 6 months from now or
00:45:13
a year from now. I think they're going
00:45:15
to look very good.
00:45:16
>> Yeah. I mean, maybe he turns it around.
00:45:18
>> I think it's already been turned around.
00:45:20
It just takes time to kick in.
00:45:22
>> Yeah. I mean, the the public's
00:45:23
perception it hasn't turned around and
00:45:24
inflation was supposed to go down and it
00:45:27
stayed.
00:45:27
>> It has. No, it stayed at it's still way
00:45:31
above the 2%
00:45:32
>> 1.6% for the last three months.
00:45:36
>> No, it has not. It was just 2.7.
00:45:38
>> No, that's the problem I think with the
00:45:41
administration and your part of it. Like
00:45:42
you guys cherry pick numbers and it's
00:45:44
not matching the numbers on the field.
00:45:46
So when you cherryick cherry picking,
00:45:48
this says CPI report.
00:45:50
>> The the the number
00:45:52
>> open the cover of the Wall Street
00:45:53
Journal. 2.7 Hold on. 2.7% CPI 2.6% 6%
00:45:58
core
00:45:59
>> 1.6% for last 3 months.
00:46:02
>> Okay.
00:46:02
>> What is hard to understand about this?
00:46:04
Obviously, the last three months are
00:46:06
going to tell you what the trajectory
00:46:07
is.
00:46:10
>> Okay. The the fact is
00:46:11
>> you're looking for every excuse you can
00:46:13
to basically downplay what is
00:46:16
>> an awesome report and just look at the
00:46:18
stock market which is ripping today
00:46:20
because this is way below expectations.
00:46:22
You're the only one who's not happy
00:46:23
about this news, Jacob.
00:46:24
>> No, no. the the country is not happy
00:46:26
about it. I I'm not I don't have a horse
00:46:28
in this race. I'm doing fantastic. I own
00:46:30
equities. I'm doing better than I ever
00:46:32
have in my life. I'm talking about how
00:46:34
the bottom half of America perceives it.
00:46:36
This is the blind spot of the Trump
00:46:37
administration. You've proved my point
00:46:39
by not addressing it. Let's go to the
00:46:41
next subject.
00:46:42
>> No, I haven't. What? What?
00:46:44
>> No, I guess the fact that you guys My
00:46:46
point is the American people
00:46:48
>> expected that inflation would go down
00:46:51
and prices would go down. Prices have
00:46:53
continued to go up. Fed has a 2% target.
00:46:56
It's a little bit unrealistic to say
00:46:57
that what you want to you want wait you
00:46:59
want to have you want to have a negative
00:47:00
inflation rate.
00:47:03
>> Trump said prices will go down. They
00:47:04
have not. That's what the American
00:47:06
people feel. Unemployment has gone up.
00:47:09
They expect it to go down. So the this
00:47:12
idea that we're in the golden age is not
00:47:14
tracking with the bottom half of
00:47:15
Americans. That's just facts, David.
00:47:18
It's just facts. You have work to do.
00:47:20
Trump has work to do. You're you're
00:47:21
saying that anything less than deflation
00:47:23
anything less than deflation in your
00:47:25
view is
00:47:26
>> I did not say that you keep telling I
00:47:27
keep telling you what I'm reporting in
00:47:29
terms of facts then you tell me I said
00:47:31
something different I didn't say that
00:47:32
the American people are very
00:47:34
disappointed in the Trump
00:47:35
administration's first year when it
00:47:37
comes to inflation and the economy
00:47:40
>> I don't understand how the numbers could
00:47:41
be any better we just blew past
00:47:43
expectations here 2.7%
00:47:47
>> I don't know whose expectations I don't
00:47:48
know whose
00:47:49
>> pulse you think you have the finger of
00:47:51
but the economists who put together
00:47:54
>> these expectations
00:47:56
>> were expect
00:47:59
one more time for me just pull up the
00:48:00
chart for me uh so I can just explain
00:48:03
that this isn't bias on my part I'm just
00:48:05
putting the fact on the field no it is
00:48:07
not this is
00:48:09
>> these are numbers that you can look at
00:48:11
and see that Trump's two worst
00:48:14
categories right now are the economy and
00:48:16
inflation the American people thought he
00:48:18
was
00:48:18
>> these are snapshots in time and they're
00:48:20
going to
00:48:21
snapshot time from February goes down.
00:48:24
That's the snapshot time. Every month
00:48:26
you have 12 months there to look at.
00:48:27
>> Let me explain. I don't know what's so
00:48:30
hard about this is a really partisan
00:48:31
point that you're making it. You're
00:48:33
basically saying
00:48:33
>> I'm not part economy is like a giant
00:48:36
super
00:48:36
>> tanker. I'm not part
00:48:38
>> Can I finish my point?
00:48:39
>> Sure you can.
00:48:39
>> I mean you must know this and I think
00:48:41
you're pretending not to. The US economy
00:48:43
is like a giant super tanker. It takes
00:48:45
time to turn around. It takes time to
00:48:46
implement your economic agenda. It took
00:48:48
the president the first six months of
00:48:49
the administration to implement the big
00:48:51
beautiful bill which is his economic
00:48:52
program. But it doesn't even go into
00:48:54
effect until January 1st. So the tax
00:48:57
cuts have not gone into effect. The
00:48:59
inflation reductions have happened.
00:49:02
We've gone from 9% under Biden to now
00:49:04
2.6% core 1.6% the last 3 months. I
00:49:08
don't know what more you could want.
00:49:10
Interest rates have come down. They're
00:49:11
projected to come down even more because
00:49:13
inflation's coming down. This is again a
00:49:15
huge ship that's in the process of
00:49:17
turning around and I predict that by
00:49:18
next year the numbers are going to be
00:49:20
even better. Let me remind you of what
00:49:22
happened in the early 1980s under Ronald
00:49:23
Reagan when Reagan took over. Okay, the
00:49:25
misery index was at like 20%. Reagan
00:49:28
implemented his economic agenda, but it
00:49:30
took a couple of years to actually
00:49:32
implement it. By 1983, we actually had a
00:49:35
very severe recession because Vulkar had
00:49:37
raised interest rates so much to tame
00:49:39
inflation. We're lucky we don't have
00:49:41
that issue today. In any event, it took
00:49:43
like three years for Reagan to implement
00:49:45
his economic agenda and his popularity
00:49:48
was very low by 1983, but by 1984, the
00:49:52
program had worked and it was morning in
00:49:53
America again. And he won the biggest
00:49:55
landslide election, I think, in American
00:49:57
history. So, you got to give it time for
00:50:00
the president's agenda to play out and
00:50:02
work. And it certainly looks like after
00:50:06
10 months that is performing extremely
00:50:08
well. and we are just beginning to see
00:50:11
the impact of it.
00:50:12
>> I think the core issue is Trump
00:50:14
overpromised and underd delivering year
00:50:17
one and it's not the golden age for the
00:50:19
bottom half of Americans and they don't
00:50:21
feel it's the golden age and I think
00:50:23
just t tackling that head on and saying
00:50:25
yeah we have more work to do and I
00:50:26
actually think that's what the address
00:50:29
last night was about. I suspect
00:50:30
somebody, maybe Susie or the the chief
00:50:33
of staff or somebody said, "Hey, we got
00:50:35
to start communicating better about the
00:50:38
cost of living and our promises during
00:50:41
the election so that we turn this
00:50:43
around." That's actually what I think is
00:50:45
happening. But I I don't have a horse in
00:50:46
this race. I'm doing fabulous because I
00:50:48
own equities. I'm happy. I'm concerned,
00:50:51
sure, that the bottom half of society
00:50:53
needs to do a little bit better. That's
00:50:55
all. All right. But you, you know,
00:50:57
listen, I I'm never going to win with
00:50:58
you, Saxs, because you're the captain of
00:51:00
the debate club. You will win every
00:51:01
debate. You're you're incredible.
00:51:02
>> Well, look, I mean, like the president
00:51:04
the president made the right point last
00:51:05
night, which is he inherited a giant
00:51:07
mess. Again, under the Biden years, we
00:51:09
had a $3,000 reduction in in real wages
00:51:13
by American workers. That's already gone
00:51:15
up by $1,000 under President Trump in
00:51:17
the first year. But yeah, if you're a
00:51:19
worker and you're just looking at your
00:51:21
situation, you're underwater 2,000
00:51:23
relative to where you were 5 years ago.
00:51:25
So obviously you're going to be salty
00:51:26
about that, but that's the situation
00:51:28
that that President Trump inherited. I
00:51:31
don't know how much more progress you
00:51:32
could make in 10 months, but give it
00:51:33
another year and then we'll really see.
00:51:36
>> All right, I am rooting for you guys. I
00:51:38
hope you turn it around in year two.
00:51:40
Okay, Freeberg, we got an important
00:51:43
story here we have to get to.
00:51:44
>> Do you guys want to hear a crazy
00:51:45
personal story for 2 minutes as a
00:51:47
palette cleanser?
00:51:48
>> Oh, 32.
00:51:49
>> Oh, yes. As a palette cleanser,
00:51:50
>> this is an amuse bougge.
00:51:51
>> Okay, this is an amuse. A little a
00:51:54
little pomplamoose sorbet.
00:51:56
>> This is the craziest story
00:51:58
>> in my life right now.
00:51:59
>> Pull up the photo, Nick. The first
00:52:01
photo.
00:52:02
>> You guys remember my dog, Monty?
00:52:04
>> Yeah. This is the virtue signal dog.
00:52:06
>> Not the virtue signal.
00:52:07
>> Oh, this is the best dog. This is Monty.
00:52:10
>> This is Monty. Monty. Monty was my dog
00:52:12
for 10 years. I got him 10 years ago in
00:52:15
January and he died suddenly in May. He
00:52:19
turns out he had a tumor on his heart
00:52:20
and his heart burst open in front of me
00:52:22
and he died. It was the worst thing. He
00:52:23
was with me every second of every day
00:52:26
>> that I wasn't at work. He slept on my
00:52:28
bed. He was in my office all day. You
00:52:29
guys have seen him a million times in
00:52:31
the background during the all-in pod. My
00:52:33
best friend in the world. The greatest
00:52:36
thing I've ever come across in my life.
00:52:39
>> So, he died suddenly. It was very
00:52:40
brutal, very hard. So, I'm at my house
00:52:43
the other day. My next door neighbor
00:52:46
walks in and he's got this dog on his
00:52:48
leash. Pull up this photo. This is this
00:52:51
dog. So, my next door neighbor finds
00:52:54
this dog at Rocket Dog Rescue. It was
00:52:57
pulled out of a kill shelter. The vet
00:52:59
estimates this dog is like a decade old.
00:53:02
Today, I live probably 40 miles away
00:53:04
from where we got Monty. He was found
00:53:06
wandering the streets of the Mission
00:53:07
District in San Francisco. This random
00:53:09
dog here was found at some random kill
00:53:11
shelter somewhere in California, pulled
00:53:13
out of the kill shelter a few days ago,
00:53:15
put into Rocket Dog, and rescued by my
00:53:17
next door neighbor. Brings him over.
00:53:19
He's walking him in the street. And I'm
00:53:20
like, "Oh my god, that dog looks just
00:53:21
like my old dog, Monty." We ran a DNA
00:53:24
test. This dog is Monty's brother.
00:53:28
>> What?
00:53:29
>> This dog is Monty's brother. It is the
00:53:32
craziest. There's like a billion dogs on
00:53:34
planet Earth. And of the billion dogs,
00:53:36
some random dog from some random litter
00:53:38
from some street pup in the Mission
00:53:40
District from a decade ago found its way
00:53:42
into a shelter, into another rescue
00:53:43
center, into my next door neighbor's
00:53:45
arms, and brought back to my house
00:53:47
months after Monty died. How crazy is
00:53:49
that?
00:53:50
What is his personality like?
00:53:52
>> I'm going to hang out with him this
00:53:53
afternoon. I got to go see.
00:53:55
>> But how crazy is this? Just It's Monty's
00:53:57
actual brother from the same
00:53:59
>> Monty in fairness.
00:54:00
>> Monty.
00:54:01
>> Well, the angles are off.
00:54:02
>> Step brother.
00:54:03
>> Monty's more robust.
00:54:04
>> Yeah. Well, Monty was wellfed. This dog
00:54:06
lived in a freaking kill shelter. So,
00:54:08
>> Monty was a heck of a dog. Take take 12
00:54:10
pounds off Monty. But
00:54:11
>> wait, where's the other tests?
00:54:13
>> Don't lie.
00:54:14
>> Where's the uh dog that was being
00:54:15
tortured by Revlon?
00:54:17
>> That dog. The dogs have gotten robust,
00:54:20
too.
00:54:20
>> Yeah.
00:54:21
>> Was it Rabon who was torturing your
00:54:23
>> She she Daisy has a thousand siblings,
00:54:26
not just one.
00:54:27
>> And uh Daisy Daisy I'm fine with. It's
00:54:30
Mitchell. It's Mitchell Freeberg. I hate
00:54:32
>> Marshall. Marshall.
00:54:33
>> Marshall Freeber. I hate Marshall
00:54:35
Freeberg.
00:54:36
>> I got to hang out.
00:54:36
>> He ate the pistachios last Christmas.
00:54:38
Marshall's like a little
00:54:40
>> Monty's father must have really gotten
00:54:42
around San Francisco.
00:54:44
>> He had a great run on
00:54:46
>> What are the odds of this?
00:54:47
>> Same mother, same father. It's not a
00:54:48
half brother. This is the same Monty's
00:54:50
dad was a
00:54:51
>> same same litter, same dog, same same
00:54:54
mother, same father, same litter on the
00:54:56
streets of San Francisco a decade ago.
00:54:58
And they all find their way in different
00:54:59
parts of California and come to Andy's
00:55:02
buried in my backyard. And this dog
00:55:04
>> samples
00:55:06
love. Was it the summer of the same?
00:55:08
>> You would say no way, but you got to see
00:55:11
better shots of it. I'll put more
00:55:12
>> photo side by side.
00:55:13
>> Yeah,
00:55:14
>> this doesn't make sense.
00:55:15
>> I know. It's the craziest ever. And by
00:55:17
the way, like
00:55:18
>> yeah, it doesn't look like they're
00:55:20
related. I'll be honest with you.
00:55:21
>> Gotta find you a proper photo of
00:55:23
>> What's going on? Chop up with Oki and
00:55:25
Joker. How old are these dogs now?
00:55:27
>> Oh my god. From your lips, the God's
00:55:28
yours, bro. Aie is 14 and a half. She
00:55:31
turns 15 in July.
00:55:33
>> She's so precious.
00:55:34
>> And Joker's eight or nine. So Touchwood,
00:55:37
they're uh
00:55:38
>> Yeah,
00:55:38
>> Golden Retrievers don't really live to
00:55:40
15, but my god, she's been
00:55:42
>> You got a lot of good years. I was just
00:55:43
on the I was on the floor last Thursday
00:55:45
snuggling with her and it was so
00:55:47
precious.
00:55:47
>> Well, you know what we did the I think
00:55:49
the best thing that we did was we got
00:55:52
much more disciplined about diet for AI
00:55:55
when she turned about 13.
00:55:58
Little bit calorie restricted. It's not
00:56:00
nearly as much. She lost a few kilos. It
00:56:03
helped her a lot walking and
00:56:05
>> she's a little deaf unfortunately, but
00:56:07
she can see.
00:56:08
>> Dogs are the best. My god,
00:56:09
>> dogs are the best. Here's my Here's
00:56:11
here's my other photo of the dog that
00:56:13
Chamoth hates. Pull it up just so you
00:56:14
can see.
00:56:15
>> Marshall, listen. Last last Christmas at
00:56:18
Nat's birthday dinner on the 26th, which
00:56:20
is her birthday, Marshall Freeberg
00:56:22
jumped on the table, started eating all
00:56:24
of the pistachios and the nuts,
00:56:26
>> ruined them,
00:56:27
>> ruined all of them, slobbered
00:56:28
everywhere.
00:56:29
>> All David Freeberg was like, "Oh, look
00:56:32
at that." Allison Freeberg was like,
00:56:34
"Marshall Freeberg, they they have no
00:56:36
control over what's going on."
00:56:38
>> Here, pull this photo up, Nick. This is
00:56:40
a a medical issue Marshall Friedberg's
00:56:41
been dealing with. He wakes up with
00:56:43
irreoverable erections, so he can't
00:56:45
walk.
00:56:47
>> What?
00:56:47
>> Pull this up.
00:56:48
>> Irreoverable.
00:56:51
>> So, Nick, you got to blur this out. When
00:56:53
you
00:56:54
>> Oh, dude's packing.
00:56:57
>> So, this little guy, he wait erection.
00:57:00
>> He has these erections and well, the
00:57:02
doctors say like, "You just got to wait
00:57:03
and if it ends up staying for too long,
00:57:05
there's going to be a surgery." But like
00:57:07
right now, he's okay. But he can't move
00:57:08
when he gets them and he's stuck and
00:57:10
he'll just stand there unable to move
00:57:12
for like 20 minutes like with his little
00:57:13
>> thing happened. I gave Chimat some of my
00:57:16
roast sparks and he had a similar thing
00:57:18
happened. He was frozen.
00:57:19
>> There's there's a Brian Johnson joke in
00:57:21
here. But
00:57:22
>> that is a problem.
00:57:23
>> Yeah.
00:57:23
>> How's Moose doing? Jake's my special
00:57:26
guy.
00:57:27
>> Here he is. Special.
00:57:29
>> Oh,
00:57:30
>> is that your dog?
00:57:31
>> This is the moose. The moose is trying
00:57:34
to keep Oh, buddy. Oh, buddy. Fish is my
00:57:37
buddy. Oh, good. What are you doing?
00:57:40
What are you chewing on?
00:57:41
>> What's he doing?
00:57:42
>> I think you got a pop. Get that out of
00:57:44
your mouth. Come on. Get that out of
00:57:45
your mouth. He is such a little love
00:57:47
buddy. Sh love.
00:57:52
>> It's like a gremlin.
00:57:54
>> He loves it.
00:57:57
>> It's great. It's great to see you so in
00:57:59
control.
00:58:00
>> Jason, you're such a natural dog guy,
00:58:02
Jason.
00:58:05
That's buddy.
00:58:06
>> A disciplinarian. This dog loves food.
00:58:09
>> You're really and he loves to wrestle.
00:58:11
>> You're like a modern believable
00:58:14
at the ranch.
00:58:14
>> Does he headbutt you?
00:58:16
>> The dog whisper
00:58:19
>> Moose. Take it easy, buddy. I know. He
00:58:21
loves to wrestle this dog.
00:58:23
>> He's heavy, right? He's
00:58:24
>> Well, we he added about four or five
00:58:26
pounds cuz he wasn't eating all that
00:58:27
much when we got him. And then we
00:58:30
realized he needs a little bit of extra
00:58:31
food. We gave him a little more food and
00:58:33
he became a little less um grumpy
00:58:36
>> and then he just
00:58:37
>> we take him for like maybe three one
00:58:40
mile walks a day on the ranch.
00:58:42
>> Totally fine. If he misses a walk,
00:58:44
doggy's got way too much energy. So,
00:58:46
he's not he's a ranch dog, not a city
00:58:48
dog. This dog
00:58:49
>> I know. I know. I told you.
00:58:50
>> Yeah, he needs to run. When he doesn't
00:58:52
run, you got a problem. All right, let's
00:58:54
keep going through the docket here. Uh
00:58:56
that's dog corner. That's K9 corner.
00:58:58
What do you guys think? That's a good
00:58:59
corner. It's a good cat corner. It's the
00:59:01
best corner.
00:59:02
>> But how crazy is that? One like one in a
00:59:03
billion's got to be that this dog lands
00:59:05
up next to me. It's just the craziest
00:59:06
thing. Anyway,
00:59:07
>> yeah. And uh yeah, there's no cat corner
00:59:09
here. Cats are horrible.
00:59:10
>> Shout out to our friends at Whimo for
00:59:12
eliminating the cat problem in San
00:59:14
Francisco.
00:59:16
>> I mean, that's just that was the
00:59:18
greatest update to their software ever.
00:59:19
If they can eliminate more cats,
00:59:22
>> what's that paradox that the AI has
00:59:25
where it's like, do I hit the trolley?
00:59:27
It's like the cat problem. Go for the
00:59:29
problem. Go for the cat. It's just go
00:59:30
for the cat.
00:59:30
>> Absolutely. You could get somebody there
00:59:32
on time or you could get them there 30
00:59:34
seconds late and take out a cat. Take
00:59:37
out the cat. Less stray cats. Cats are
00:59:40
horrible. Shout out to our cat ladies
00:59:42
listening to the pod. There's I wonder
00:59:44
how many cat ladies listen to this pod.
00:59:45
I think there are a few.
00:59:46
>> Oh, there are a few.
00:59:48
>> Are there a few? Yeah, they hear from
00:59:51
they're on blue sky talking about Oh,
00:59:54
god. I had a joke. I'm not going to do
00:59:55
it. All right. According to a Reuters
00:59:58
report, Freeberg, China has built a
01:00:01
prototype of ASML's EUV machine. Hey, if
01:00:06
this is true, this report, it's uh it
01:00:09
could have a profound impact on the AI
01:00:11
race that we talked about. For those of
01:00:13
you who don't know, ASML is the Dutch
01:00:15
company worth $400 billion,
01:00:18
22nd largest market cap in the world,
01:00:20
and uh they are the only company that
01:00:22
makes EUV machines, also called
01:00:24
lithography. That stands for uh extreme
01:00:27
ultraviolet. That's how you make H100s.
01:00:31
Here's a photo of one of these giant
01:00:32
machines. They cost $250 million to
01:00:35
make, and they take 6 months to make,
01:00:38
filled with all kinds of important
01:00:40
parts. Carl Zeiss lenses, etc. Since
01:00:43
2018,
01:00:45
these machines have been limited for
01:00:47
sale by the Dutch. Why? Why can't China
01:00:50
buy these machines? Well, in 2018,
01:00:52
Trump's Secretary of State, Mike Pompeo,
01:00:55
put pressure on the Dutch to impose
01:00:57
export controls on China. And uh later
01:01:00
that same year, they did. So,
01:01:03
according to Reuters in this news story,
01:01:05
Freeberg,
01:01:07
China used former ASML engineers
01:01:10
allegedly to reverse engineer the
01:01:13
machines. Reuters has pointed out that
01:01:16
the machine in China has not yet
01:01:18
produced any working chips. It's a
01:01:20
prototype. The CCP is targeting 2028 uh
01:01:24
for working chips. Some sources say
01:01:26
maybe 2030 is more likely. You've been
01:01:28
talking about China lithography on the
01:01:30
show. We can play the Chariots of Fire
01:01:33
music that Freeberg insists we play when
01:01:35
he does his victory lap. Chimoth, here's
01:01:36
his victory lap. Episode 224. Last year,
01:01:40
China announced and began a 37 billion
01:01:43
investment in developing their own
01:01:46
3nanometer uh chimp technology. China
01:01:50
made a claim that this investment they
01:01:52
had made was starting to pay off and
01:01:53
they had developed their own EUV system
01:01:56
and their big semiconductor companies
01:01:58
called the semiconductor manufacturing
01:02:00
international corporation or SMIC in
01:02:02
China. They launched a seven nanometer
01:02:05
chip with Huawei in their Mate 60 Pro,
01:02:08
which is sort of like their iPhone
01:02:09
competitor in China. And so they're
01:02:12
proclaiming that they've already got
01:02:13
this EUV technology. From what I
01:02:15
understand, and Sax would know better
01:02:17
than I, it sounds like there was a lot
01:02:18
of reverse engineering and workound of
01:02:21
existing technology in order to deliver
01:02:24
that system.
01:02:24
>> Who do you think has the best chance of
01:02:27
challenging Nvidia? My early prediction
01:02:29
for 2026 is Huawei where I think that
01:02:32
there's lithography technology that
01:02:35
exists in China that is not publicly
01:02:37
discussed that is going to be deployed
01:02:39
in Huawei and all these fabs that
01:02:40
they're building in mainland China.
01:02:42
>> So announcements 2026 impact 27 probably
01:02:45
fair.
01:02:46
>> Do you have first first on allin
01:02:48
Freeberg? Congratulations on your weekly
01:02:52
>> victory lap here. I don't I don't I
01:02:54
honestly don't think that the Reuter
01:02:55
story is necessarily news cuz I think
01:02:57
it's a little bit of a narrow scope in
01:02:58
trying to describe what happened which
01:02:59
is that they quote stole ASML technology
01:03:02
because scientists are working on it.
01:03:03
That's not really the full scope of
01:03:05
what's been going on in China for a
01:03:06
number of years and it really
01:03:09
understates the technological progress
01:03:11
that China's independently made in using
01:03:13
other systems to try and achieve
01:03:15
lithography parody. So if you go back a
01:03:18
number of years, the current kind of
01:03:20
investment vehicle is actually Chinese
01:03:22
banks put capital into a fund that's
01:03:25
roughly $48 billion uh US dollars which
01:03:29
is actually what's called the national
01:03:31
integrated circuit industry investment
01:03:33
fund phase 3. So there was phase 1 which
01:03:36
started in 2014 which focused on
01:03:39
manufacturing. So developing fabs with
01:03:41
groups like SMIC. Phase two was stood up
01:03:44
in 2019 which focused primarily on
01:03:47
design and materials. And then this
01:03:49
phase three fund was established in 2024
01:03:53
explicitly to pivot to what they call
01:03:55
choke points or bottlenecks in the
01:03:57
manufacturing process. And state media
01:03:59
has confirmed the existence of this
01:04:01
phase 3 fund and the intentionality of
01:04:04
the phase 3 fund to try and replicate
01:04:06
lithography technology. Couple of months
01:04:08
ago, if you pull up the link, Nick,
01:04:10
here's a publication on this paper. So
01:04:12
this is the leading research group out
01:04:14
of China that's been backing into
01:04:16
lithography technology using AIdriven
01:04:19
systems. They published this paper which
01:04:22
was a very good summary of where they
01:04:24
are in July.
01:04:27
Was this in nature? No, it's in light
01:04:28
science and applications and this paper
01:04:30
is called advancements and challenges in
01:04:33
inverse lithography technology a review
01:04:36
of artificial intelligence-based
01:04:37
approaches. If you read into the paper
01:04:40
and then you read the other publications
01:04:41
from this particular research team at
01:04:43
Singua University, they have made a
01:04:46
number of breakthroughs in using AI to
01:04:49
try and reestablish alternative systems
01:04:51
and the traditional Zeiss optics and
01:04:54
other control systems that are used in
01:04:55
the ASML platform. And the way that
01:04:58
they've done it, I I won't spend too
01:04:59
much time on it, but there's a bunch of
01:05:00
neural networks that they've used,
01:05:03
trained, and have made discoveries from,
01:05:05
including doing things like working with
01:05:07
suboptimal optics, meaning you can have
01:05:09
optics that have a degree of diffusion.
01:05:11
And then how do you actually recreate
01:05:13
3nmter printing techniques or 3nm masks
01:05:16
by having shadowy effects accounted for
01:05:19
in the AI? So, you almost like assume
01:05:21
that the shadowy effects are going to
01:05:23
come out and you print differently than
01:05:24
you otherwise would. you don't need the
01:05:26
degree of precision that you need and
01:05:27
you don't necessarily need to go get the
01:05:28
Zeiss optics. There was another paper
01:05:30
that was published early this year by
01:05:32
another Chinese research team that
01:05:34
actually demonstrated how they did use
01:05:36
AI to discover a novel method for doing
01:05:39
optics. So there is a very broad and
01:05:41
wellunded effort that's been underway
01:05:43
for over a decade. But just in the last
01:05:45
couple of years with artificial
01:05:46
intelligence based approaches, they've
01:05:48
made a series of breakthroughs. It is
01:05:50
very likely the case that Huawei already
01:05:52
has a lithography system that they'll be
01:05:55
putting into production and this is why
01:05:56
I kind of talked about it when we were
01:05:58
in Vegas and pointed it out earlier this
01:06:00
year.
01:06:01
>> But I again I think Reuters just sort of
01:06:03
maybe caught on to some narrow segment,
01:06:04
but this is a bigger story and a bigger
01:06:06
set of progress that's been made over
01:06:08
quite some time. ramification stacks
01:06:11
here and and what you're thinking.
01:06:13
Obviously, you've talked a bunch about,
01:06:16
hey, let's sell our stack in there and
01:06:18
they're clearly working on their own
01:06:19
stack. What what's uh what's the
01:06:21
ramification of this if it's true?
01:06:23
>> Well, I've never supported selling EUV
01:06:25
lithography to China. It is probably,
01:06:28
>> you know, selling the stack of H1,
01:06:30
whatever the previous Nvidia stack was.
01:06:32
Yeah, EUV lithography. These machines
01:06:34
that are made by ASML is probably our
01:06:36
single biggest advantage in the AI race
01:06:39
because they're the only machines that
01:06:41
are capable of creating say 2 to 3
01:06:43
nanometer chips, semiconductors, and
01:06:46
it's sort of the perfect export control
01:06:48
because there's only one company that
01:06:49
makes these machines like you showed.
01:06:51
They're gigantic. They're expensive. So
01:06:54
if you think about like trying to put an
01:06:56
export control on a commodity, there'd
01:06:58
be a lot of ways to circumvent it
01:06:59
because there's a lot of sources for a
01:07:01
commodity. But in this case, there's
01:07:03
literally only one company that makes
01:07:05
these machines. So it's been a
01:07:07
tremendous advantage, I'd say, for the
01:07:08
West that we have EUV lithography. I'm
01:07:11
not surprised that China is trying to
01:07:14
reverse engineer it. It's kind of the
01:07:16
obvious thing to do. No advantages
01:07:19
forever. It does seem likely that at
01:07:21
some point they'd be able to figure out
01:07:23
how to do it. So I think that this
01:07:26
article is not altogether a surprise in
01:07:29
that sense. I mean I'm sure that China
01:07:31
is trying really really hard to reverse
01:07:34
engineer this. In the meantime, however,
01:07:36
they've also made substantial progress
01:07:38
with DUV lithography. So the previous
01:07:40
generation of lithography before EUV was
01:07:44
DUV. E is extreme. D is deep. In any
01:07:48
event, DUV was supposed to sort of top
01:07:51
out at say 14 nanometer chips and China
01:07:54
was able to push the technology to get
01:07:56
to 7 nanometer and now to five. So
01:07:58
they've able to get a lot further using
01:08:01
DUV lithography than anyone thought
01:08:03
possible and that is why the Huawei
01:08:06
chips, the Ascent chips are they're not
01:08:08
as good as our chips, but they're
01:08:09
serviceable and they've been able to use
01:08:12
their prowess in networking to string
01:08:14
more of them together. You have the
01:08:16
cloud matrix 384 technology where Huawei
01:08:19
will string together 384 of their Huawei
01:08:22
Ascent chips into a rack and that will
01:08:26
perform comparable to an Nvidia rack but
01:08:30
using a lot more power. So the bottom
01:08:32
line is China's been able to create a
01:08:34
lot of these workarounds to our
01:08:36
restrictions. But if they figured out
01:08:38
how to reverse EUV that would be I'd say
01:08:41
a blow because it is a real advantage
01:08:43
for us. But I'm not convinced that this
01:08:45
Reuter story is the the full picture. I
01:08:48
think it's just a data point.
01:08:50
>> Jim, any thoughts here?
01:08:51
>> If you want a very short summary of how
01:08:53
the last generation of chips work, the
01:08:55
best way to think about this is that the
01:08:58
most profitable and valuable chips to
01:09:00
date have taken a very compute ccentric
01:09:03
approach. That's why we've needed these
01:09:05
ultra advanced process nodes. But the
01:09:07
reality is that in a world of infinite
01:09:11
inference, let's say the next generation
01:09:14
of silicon will not take the same
01:09:16
compute heavy approach and will probably
01:09:19
rely on a much more memorycentric
01:09:20
architecture that uses a lot of SRAM.
01:09:23
The value of that is that you can
01:09:25
produce these things as Sax said at 14
01:09:26
nanometer at 75 all of these process
01:09:30
nodes that are much less advanced that I
01:09:33
actually think is the future. So, in a
01:09:34
weird way,
01:09:37
if they steal this, which I think we
01:09:40
should assume they've already stole it
01:09:41
quite honestly and they figured it out,
01:09:44
the question is how bad is it? That's
01:09:45
the really important question. And the
01:09:47
thing about a more memory ccentric
01:09:50
approach for AI inference or these next
01:09:52
generation forms of silicon, it demands
01:09:55
a lot more of compilers. It demands a
01:09:58
lot more of the software. I still think
01:09:59
that's where we are light years ahead.
01:10:01
So, I don't think it's the end of the
01:10:02
world and we're still in a very good
01:10:04
place,
01:10:06
but it is bad. Friberg, weren't we
01:10:08
supposed to uh start onshoring with the
01:10:11
chips act and maybe get trending towards
01:10:15
having this production ability in
01:10:18
America? And obviously that's been
01:10:20
really hard to do.
01:10:22
And China and their relationship with
01:10:25
Taiwan seems to be and and the
01:10:26
possibility that they actually take over
01:10:28
Taiwan in a military fashion has been
01:10:30
this existential concern or even
01:10:32
short-term concern.
01:10:35
>> Maybe it's a better way to say it. The I
01:10:36
think the biggest security implication
01:10:38
to consider at this moment is that both
01:10:41
the US and mainland China are onshoring
01:10:43
manufacturing and that makes Taiwan less
01:10:46
of a strategic point of interest for the
01:10:48
United States and for United States
01:10:49
industry. Maybe one of the ways to think
01:10:51
about the positive aspect of what is
01:10:53
going on which is maybe call it the
01:10:55
bulcanization of generally manufacturing
01:10:57
but specifically semiconductor
01:10:58
manufacturing in this case is reduced
01:11:01
security concerns. So there's no longer
01:11:04
this flash point perhaps in Taiwan. If
01:11:06
China did invade Taiwan and the US had
01:11:08
fab manufacturing, we're less likely to
01:11:10
want to defend Taiwan. And frankly, if
01:11:12
China has fabs on mainland, do they
01:11:14
really need to invade Taiwan?
01:11:16
>> What's the economic interest in doing
01:11:18
so?
01:11:18
>> Yeah, that's kind of where I was going
01:11:19
with it. But if they also wanted to
01:11:21
jumpstart Freedberg, they could just go
01:11:23
to Taiwan and take the machines. Like,
01:11:25
is that actually the plan?
01:11:27
>> Like I I don't know what the CCP's plan
01:11:29
is. I think that's a pretty um simple
01:11:31
statement to make. I think just going to
01:11:33
Taiwan is probably a lot more
01:11:35
complicated than
01:11:36
>> of course it is. My point is if this if
01:11:38
this was super existential to get those
01:11:40
chips for China and they go to Taiwan
01:11:42
and they then they've reverse engineered
01:11:44
the machine by default.
01:11:46
>> We we need to recognize we keep using
01:11:47
the term reverse engineering as if ASML
01:11:49
is the only way to do this and it is
01:11:51
not. If you read their papers and you
01:11:53
read what's coming out, there are
01:11:55
alternative production manufacturing
01:11:57
methods and systems that are being
01:11:58
developed in China that not only maybe
01:12:01
provide parody to ASML, but get ahead of
01:12:03
ASML. And I think this is really
01:12:04
important to understand. China is not
01:12:06
just in a catch-up race. They're in a
01:12:08
primacy race and they are trying to
01:12:10
develop privacy in lithography
01:12:12
technology which will give them privacy
01:12:13
in manufacturing which will give them
01:12:15
primacy in AI which will give them
01:12:17
economic leverage over the planet and
01:12:19
that is very critical for us to
01:12:20
understand. This is not about stealing
01:12:22
data from ASML. Like, no one gives a
01:12:24
[ __ ] If you're sitting in China, you're
01:12:25
thinking about I've got the world's best
01:12:27
scientists or some of the world's best
01:12:28
scientists and I'm giving them the
01:12:30
resources and I'm giving them the
01:12:31
mandate to go solve this problem. The
01:12:33
government, as I mentioned last year,
01:12:35
put $40 billion against this problem and
01:12:37
said, "Go figure it out." And these guys
01:12:39
at Singa are publishing some
01:12:41
groundbreaking research on how to do it.
01:12:44
>> But it's easier to catch up or to copy
01:12:47
than to innovate.
01:12:49
So if you're China, the first thing
01:12:50
you'd want to do is just copy EUV and
01:12:52
get that technology for yourself.
01:12:54
>> Well, let me ask you this. What would
01:12:56
you do? So you're the AIAR. You're
01:12:57
sitting in the United States and China's
01:12:59
got this lithography technology. Is your
01:13:00
goal to steal their technology or is
01:13:02
your goal to get your best scientists
01:13:03
and say, "Let's get ahead of their
01:13:04
technology."
01:13:05
>> If I'm China,
01:13:06
>> neither are the United States.
01:13:08
>> It's to solve the problem. What is the
01:13:10
actual economic commercial problem we're
01:13:12
solving?
01:13:12
>> Right. So if you can figure out a way to
01:13:14
do manufacturing better, cheaper, faster
01:13:16
than the way the that China does it, you
01:13:18
would obviously take that path. You're
01:13:19
not trying to get to par with China, if
01:13:21
you view yourself to be supreme to
01:13:22
China,
01:13:23
>> China. But you have to understand even
01:13:24
with these EUV machines and the same is
01:13:26
true in the other side
01:13:27
>> at two or even sub 2 nanometer scale,
01:13:30
you have these incredible laws of
01:13:31
physics that you are pushing to the
01:13:33
boundaries of which cause these chips to
01:13:35
have pretty low yield. you have a lot of
01:13:38
systems that actually aren't that
01:13:40
efficient and highly utilized as a
01:13:42
result. So these systems are brittle.
01:13:44
The second thing is that when you have
01:13:46
these compute- ccentric architectures,
01:13:47
you end up making design decisions like
01:13:49
HBM, they'll have to go and steal all
01:13:52
the HBM which is the high bandwidth
01:13:53
memory business that SKHix and Samsung
01:13:56
have. But again, that's where Nvidia and
01:13:58
then Google have an effective monopoly.
01:14:00
>> Let me ask you a question, S. I mean,
01:14:02
what's your strategic response if China
01:14:04
next year goes into manufacturing? with
01:14:06
a sub 2 nanometer system that's got
01:14:08
higher yields, is faster, is cheaper
01:14:10
than anything that we're seeing anywhere
01:14:12
else in the world.
01:14:13
>> Well,
01:14:14
I don't think that's going to happen.
01:14:16
Let's talk about that if it actually
01:14:17
happens. I don't think it's going to.
01:14:19
But what would I be focused on right
01:14:21
now? I would say we want to get more of
01:14:23
the leading edge manufacturing in the
01:14:26
United States. TSMC has already opened a
01:14:29
big fab in Arizona and they're planning
01:14:32
on increasing the size of that and
01:14:34
that's really important and we should
01:14:37
get all these restrictions out of the
01:14:39
way. They're facing all sorts of
01:14:41
permitting restrictions. I mean all the
01:14:43
stuff that Bernie is talking about where
01:14:44
he wants to slow down. I mean he's
01:14:46
talking about data centers but not
01:14:49
>> manufacturing but the concepts are the
01:14:50
same.
01:14:51
>> Yeah. Roana, Elizabeth Warren, you've
01:14:53
got
01:14:54
>> exactly wrong. I mean,
01:14:55
>> anti-che progress people saying, "Let's
01:14:57
stop all this stuff." Meanwhile, China
01:14:59
is not just racing to catch up, but
01:15:01
they're likely going to get ahead of the
01:15:02
United States. And if we're caught
01:15:04
flatfooted because we've got a
01:15:05
moratorium on development, a moratorium
01:15:07
on production, it's going to be
01:15:10
extraordinarily damaging,
01:15:12
>> right? Well, look, this is where I agree
01:15:13
with you is that if this Reuters report
01:15:16
is accurate, it does speed up the
01:15:17
timeline on China closing the chip gap
01:15:20
from
01:15:21
>> decades to a few years. You can go read
01:15:23
the Singua papers. You'll see that these
01:15:25
guys are so advanced.
01:15:26
>> If they care about being ahead, why are
01:15:28
they publishing that paper?
01:15:29
>> So, by the way, this is a really
01:15:31
important point and I've picked up on
01:15:32
this from a number of different
01:15:34
disciplines in scientific research. So,
01:15:36
what's been going on is scientists in
01:15:39
China, they're very smart people.
01:15:40
There's very great scientists there. And
01:15:43
like all scientists, they don't think
01:15:45
necessarily in terms of nationalism
01:15:47
first. They think in terms of science.
01:15:49
They want to publish their research.
01:15:50
They want to be known. They want to be
01:15:53
the discoverers of these new frontiers
01:15:56
and so they publish. And what we've seen
01:15:58
in several instances is that the
01:16:00
scientific research groups will publish
01:16:02
and they'll publish slightly ahead. And
01:16:04
once they get ahead of the market or
01:16:06
they get ahead of the world, suddenly
01:16:08
they stop publishing. And it's almost
01:16:10
like the CCP comes in and it's like,
01:16:12
okay, you guys are now, you know, going
01:16:14
too far. We don't want to tip our hat
01:16:16
too far. And the stuff starts to recede.
01:16:18
And I think there was this one paper on
01:16:20
optics that is designed to replace the
01:16:22
Zeiss monopoly in optics that suddenly
01:16:25
disappeared and that research team
01:16:26
stopped publishing. I got to find this
01:16:27
paper. This came out last year.
01:16:30
>> There are a number of these disciplines
01:16:31
where there's publications that go to a
01:16:32
certain extent and then they stop
01:16:34
publishing. So I think that there's a
01:16:35
little bit of a
01:16:36
>> because of the funding cycle in China.
01:16:38
You have every single local government
01:16:40
that's given capital to invest in
01:16:42
businesses and so they compete and
01:16:44
compete until it gets to the state level
01:16:46
and you have one or two national
01:16:47
champions. But if this was so important,
01:16:49
Freeberg, would there also be an
01:16:51
argument to
01:16:54
publish things that would send um that
01:16:57
would be misinformation or send people
01:16:59
down a wrong direction?
01:17:02
>> No, because you have to again the
01:17:03
funding cycle is you get as a policy
01:17:06
person in Shanghai, you get promoted
01:17:09
because you make a good bet.
01:17:10
>> Yeah. No, but you so by the time it
01:17:13
becomes a one national to Freeberg's
01:17:15
point, when's the last time you saw BYD
01:17:16
or CL publish anything meaningful,
01:17:20
>> but when they were not the national
01:17:22
champion, they were probably publishing
01:17:24
very aggressively.
01:17:26
But at that time, there was 50 different
01:17:28
companies competing to be the national
01:17:30
champion. So I suspect what's happening
01:17:32
here is there's 50 different semi-
01:17:34
startups all over China. Everybody's
01:17:37
trying to support their own local
01:17:38
version and eventually when there's one
01:17:40
they disappear.
01:17:41
>> Okay. Interesting.
01:17:42
>> I mean, one of the things we see is
01:17:45
China does give money to these research
01:17:47
institutions on frontier science and
01:17:50
then there's an industrialization. It's
01:17:52
something the US isn't quite as good at.
01:17:53
And I think it's again, we've talked
01:17:55
about the co-opting of our universities
01:17:57
as research institutions over the last
01:17:59
couple of decades. They've become these
01:18:01
sort of employment centers, if you will,
01:18:03
and they've become these sort of weird
01:18:05
social engineering systems. They've kind
01:18:07
of gotten away from some of the core
01:18:09
research or that that's been kind kind
01:18:11
of co-opted. In China, there are these
01:18:13
sort of dedicated research centers and
01:18:15
then there are universities where
01:18:16
there's very clear independent research
01:18:18
that goes on on university campuses.
01:18:20
They typically get quite a bit of
01:18:22
funding. And the way this worked, if you
01:18:24
look into the details of this China
01:18:26
National Integrated Circuit Industry
01:18:27
Investment Fund, and there were three
01:18:29
funds, one, two, and three, the funds
01:18:30
get bigger and bigger as the research
01:18:32
teams make progress. Uh, it's a very
01:18:34
smart way of doing things from a central
01:18:36
planning perspective. And then in in
01:18:38
phase three, they're now targeting these
01:18:40
specific unlocks that then will enable
01:18:42
industrialization. So they're funding
01:18:44
some of this core research. The core
01:18:45
research has these breakthroughs and
01:18:47
then it gets industrialized. It's during
01:18:48
that research phase where we see a lot
01:18:50
of publications happening and this is
01:18:51
happening in life sciences as well and
01:18:53
in physics and in material science and
01:18:55
as soon as they start to get a little
01:18:56
bit ahead of the curve then it gets
01:18:58
industrialized and the and the
01:19:00
publications kind of stop. So I don't
01:19:01
know if it's necessarily the CCP
01:19:03
stepping in and saying hey stop
01:19:04
publishing you're going to let the world
01:19:05
know too much of what we're having
01:19:07
versus okay the handoff is now happening
01:19:08
and industrialization begins. Yeah,
01:19:10
look, I would just say that to me the
01:19:13
takeaway here is that the timeline on
01:19:16
China closing the gap with us is being
01:19:18
sped up. Whether it's the fact that
01:19:21
they're copying what we're doing,
01:19:22
whether EUV is being reverse engineered
01:19:24
or maybe their scientists are figuring
01:19:26
out a way to leaprog, we are in a pretty
01:19:29
close race with China. And that's why I
01:19:31
think it's really important that we
01:19:32
don't listen to Bernie and others who
01:19:35
just want to stop the progress because
01:19:36
the progress is not going to stop. This
01:19:38
is going to happen in China and that
01:19:41
will be a big problem for the United
01:19:43
States.
01:19:43
>> All right, speaking of
01:19:44
socialism/communism,
01:19:46
uh you guys thinking about coming to
01:19:48
Austin, my neck of the woods.
01:19:49
>> I've been uh pulling up these photos in
01:19:52
Austin of these homes.
01:19:53
>> You know I'm doing it right.
01:19:55
>> Welcome to Texas, boys.
01:19:56
>> Like on the lake.
01:19:57
>> Sachs already bought a house. Sachs
01:19:59
already bought a house.
01:20:00
>> He hooked me up with the uh agent.
01:20:03
>> What does she got you looking at?
01:20:04
>> I'm not sure yet. We're just
01:20:06
>> You guys are bidding against each other,
01:20:08
[ __ ]
01:20:08
>> You can have my sloppy seconds.
01:20:10
>> You have the high ground, so you can
01:20:11
negotiate hard.
01:20:13
>> Uh there is so much inventory, but only
01:20:16
like 20% of the inventory is on the MLS.
01:20:19
80% here because it's a no reporting
01:20:21
state is off market. So, it's completely
01:20:24
different than
01:20:25
>> any other market. Certainly California.
01:20:27
>> I want to be on the lake. Like, if I'm
01:20:29
going to do this on the water,
01:20:31
>> Oh, really? I don't want the lake. I
01:20:33
mean, if you're here, here's the
01:20:35
challenge with the lake. People are
01:20:36
going to come up to your house and take
01:20:39
pictures and hang out like 2 feet off
01:20:41
your house and starting at 7 a.m.
01:20:43
>> Texas house in San Francisco.
01:20:45
>> Everybody's zipping around the lake in
01:20:47
motorboats starting at 7 a.m. It's
01:20:49
beautiful.
01:20:49
>> I love your thing. It's great, but just
01:20:52
>> understand it's going to get loud at 7
01:20:55
a.m.
01:20:56
>> There's nothing a good motorboating
01:20:58
doesn't
01:21:00
>> Who doesn't love a good motorboat?
01:21:02
Jal, I heard you're like an hour away.
01:21:04
You're not even in Austin.
01:21:06
>> I I'm in the Hill Country on my ranch,
01:21:08
but we're getting a place closer to the
01:21:09
city, actually. So, we're 30 minutes
01:21:11
outside the city.
01:21:13
>> 30 minutes? My god.
01:21:14
>> I heard you're
01:21:16
>> I heard you're in the sticks.
01:21:18
>> No, not that far. Not that far.
01:21:19
>> No, he's in the suburbs of the suburbs.
01:21:21
>> No, if you want to have like, you know,
01:21:23
dozens of acres, you can't do that in
01:21:25
the city.
01:21:26
>> When I was growing up in Memphis, they
01:21:27
called that Bumble Egypt.
01:21:30
BF
01:21:31
>> Bumble.
01:21:32
>> I mean, if you want a ranch and you want
01:21:34
like dozens of acres, you can't do that
01:21:37
in You can get an acre to three acres
01:21:39
inside of the city.
01:21:40
>> I found an acre in Terry Town.
01:21:43
>> An acre is doable. Two acres is
01:21:46
possible.
01:21:46
>> You know what's so sad? Even if this
01:21:48
bill doesn't get on the books, which I
01:21:50
think
01:21:50
>> everyone's leaving,
01:21:51
>> there's a decent chance it ripped the
01:21:53
band-aid off, the revenues of California
01:21:55
are going to plummet.
01:21:58
plummet.
01:22:00
>> Man, it is um yeah, I mean we saw the
01:22:03
writing on the wall. Socialism is coming
01:22:05
and you know this one of the great
01:22:08
things about having a United States of
01:22:10
America that different states can
01:22:12
optimize for different things.
01:22:14
>> The thing here
01:22:14
>> that one bill this proposed billionaire
01:22:16
tax has singlehandedly changed the
01:22:18
trajectory of the California economy by
01:22:22
a hundred to$200 billion dollars over
01:22:24
the next 5 to 10 years. It's an enormous
01:22:26
cell phone. People were here willing to
01:22:29
pay the money and now
01:22:32
>> Yeah, people were willing to put up with
01:22:33
a lot in this state.
01:22:35
>> As soon as you tell people, "Hey, we're
01:22:36
we're doing property seizures,
01:22:38
>> property seizures or their threat of
01:22:40
property seizures or the fact that
01:22:42
people didn't come out against property
01:22:44
seizures is enough for everyone to be
01:22:45
like, "See you see you later." The fact
01:22:48
that all the politicians didn't in
01:22:50
uniform stand up and say, "This is
01:22:51
ridiculous. We're never going to let
01:22:52
this happen." They let this happen by
01:22:55
not defending the property rights that
01:22:57
are endowed in this country. They lost
01:23:00
everything.
01:23:01
>> They had to defend it.
01:23:02
>> What do you think happens to the state
01:23:04
budget over the next four or five years
01:23:06
as a result of this?
01:23:07
>> You're right. It's trash. And by the
01:23:08
way, and that that's what drives the
01:23:09
socialist spiral because then they try
01:23:11
and do more property seizures and they
01:23:13
try and get the money back.
01:23:14
>> Who are they going to tax? Everybody's
01:23:15
going to be gone now.
01:23:16
>> And here's the other crazy statistic
01:23:17
that we never talk about. I was going to
01:23:19
try and put some data together and get
01:23:20
it as a topic, but we should save it for
01:23:22
the new year. But the California pension
01:23:24
fund system is underfunded by roughly a
01:23:27
trillion dollars. So these folks in the
01:23:29
California pension fund system are
01:23:31
sitting around waiting for money to come
01:23:33
to them every year for the next 30, 40,
01:23:35
50 years and the money isn't there.
01:23:38
They're guaranteed that money from this
01:23:39
pension fund system and it's not there.
01:23:41
What are they going to do? It's a
01:23:43
trillion dollars short. That's a state
01:23:45
obligation. Why would anybody hear
01:23:47
something like that and think giving
01:23:49
politicians more money is a good idea?
01:23:53
These people are so fiscally
01:23:54
irresponsible. They're fiscally
01:23:56
illiterate.
01:23:58
There is that
01:24:00
it's pretty clear to me that in New York
01:24:03
and California it'll be 60 plus% taxes.
01:24:08
And you know, if you were living in a
01:24:11
perfect society that appreciated it and
01:24:13
you had this incredible safety, you had
01:24:17
extraordinary schools and the roads and
01:24:20
the public transit,
01:24:21
>> everything worked.
01:24:22
>> Yeah. Like Japan,
01:24:24
>> you'd pay 60%, you would be ecstatic.
01:24:26
>> Yeah. You'd be like, "Okay, I mean, it's
01:24:28
a lot, but hey, this is a pretty great
01:24:30
place to live." If you're paying that
01:24:32
and people are breaking into your home
01:24:34
and there's nine overdoses a day, you're
01:24:38
like, "Wait a second." And then on top
01:24:39
of that, by the way,
01:24:40
>> and they're seizing your private
01:24:42
property now.
01:24:42
>> Seizing your property.
01:24:43
>> They're seizing your property. And by
01:24:45
the way, they don't care about you when
01:24:47
it when you look at the laws. I was just
01:24:49
talking to some law enforcement people.
01:24:52
>> City's using your money to give homeless
01:24:53
people vodka. It's just
01:24:56
>> Well, I mean, if it was tequila, I mean,
01:24:58
I have a good tequila brand we could
01:24:59
give to the homeless. Maybe we can make
01:25:01
a deal.
01:25:02
>> Yeah.
01:25:02
>> Shout out to all at Gaya coming to you.
01:25:04
Uh to delivery is happening right now.
01:25:08
Um, in all seriousness, I was talking to
01:25:10
some security folks um and was talking
01:25:14
to my security guy in Cal who's a in
01:25:16
California and um in California, not
01:25:20
only do they not have the castle
01:25:22
doctrine, they've replaced the castle
01:25:23
doctrine where you can like defend your
01:25:25
home if somebody comes into it with your
01:25:27
gun uh and protect your family and your
01:25:30
property.
01:25:32
Now you have to have this um duty to
01:25:36
flee. So you have to be able to run out
01:25:39
of your home with your family as opposed
01:25:41
to defend your family. So they literally
01:25:44
are telling the criminals, "Hey, listen.
01:25:46
If you break into somebody's home in
01:25:47
California, don't worry about getting
01:25:49
shot. You they're going to have to run
01:25:51
out or else they're in the wrong." Like
01:25:53
this is deranged. The the inmates have
01:25:56
literally taken over the asylum. They
01:25:58
they have taken over the asylum. I I
01:26:00
realized this two years ago and I pulled
01:26:02
the rip cord. Some Elon realized it six
01:26:05
years ago when he tried to build a
01:26:06
factory. He pulled the rip cord. You
01:26:08
guys are realizing it now. Everybody's
01:26:10
realizing this. New York uh the hedge
01:26:12
fund guys in New York and in Jersey and
01:26:14
Connecticut. They realized it. What?
01:26:16
>> Only Hollywood guys. I know a bunch of
01:26:18
Hollywood guys leaving.
01:26:19
>> Yeah. I literally this producer, one of
01:26:21
the major producers who has produced
01:26:24
billions of dollars worth of films came
01:26:25
here last year. I met him at a football
01:26:27
game. He said, "Tell me everything." I
01:26:29
told him everything. He moved here. He
01:26:31
moved here. He's going to start making
01:26:32
movies here. And he is one of the top
01:26:35
top I won't say who he is, but you know
01:26:37
his films because his partner who is an
01:26:39
incredibly high-profile comedian, like
01:26:41
amongst the highest profile in history,
01:26:44
where they make films together, he moved
01:26:46
here. Why? He he doesn't want to live in
01:26:49
LA and risk his family's life to an home
01:26:51
invasion and and the house burning down
01:26:53
because they don't fill water in the
01:26:55
reservoir. I mean, guys,
01:26:57
>> hopefully not everyone realizes it cuz I
01:26:59
need to sell my house to someone.
01:27:02
>> Good luck. You'll be okay. You'll be
01:27:04
okay. There's a Chinese billionaire.
01:27:06
>> Actually, you know, you know who you'll
01:27:07
be able to sell your house.
01:27:08
>> There's no billionaires left in
01:27:09
California.
01:27:10
>> It's whoever uh the politicians are that
01:27:12
are able to aggregate power and money in
01:27:15
the socialist.
01:27:16
>> Gavin will buy your house and turn it
01:27:18
into a home.
01:27:18
>> Gavin's gone. You guys are going to look
01:27:21
You guys are going to look back on Gavin
01:27:23
and realize he was the most uh
01:27:26
>> upstanding moderate governor that
01:27:28
California had in modern era and
01:27:30
suddenly we're going to be like what the
01:27:32
>> and I think that's a great place to stop
01:27:33
it, boys.
01:27:35
>> We will be doing a couple of episodes
01:27:37
between now and the end of the year and
01:27:39
uh yeah, no weeks off. Hopefully we get
01:27:42
to see each other over the holidays.
01:27:44
I'll see you Saxs uh and you as well
01:27:46
Jimath and Freedberg shortly. Oh, by the
01:27:49
way, the president just signed an
01:27:50
executive order reclassifying marijuana
01:27:52
from schedule one to schedule three.
01:27:55
>> Schedule one to schedule three. Okay. Uh
01:27:59
that's something Obama wanted to do in
01:28:01
the second term and never did. So,
01:28:02
>> I think it's a good thing. I mean,
01:28:04
schedule one is like heroin. You know,
01:28:06
it just never made sense to treat it the
01:28:07
same as heroin. You know, I know a lot
01:28:09
of people aren't thrilled about
01:28:10
marijuana. It's not great when people
01:28:12
are in the
01:28:12
>> I don't like marijuana, but I I agree
01:28:14
with this.
01:28:15
>> It's not heroin.
01:28:17
I mean it would be on I think most
01:28:19
people intellectually would put it on
01:28:21
the same level as alcohol, right?
01:28:23
>> I just want it studied so that we can
01:28:24
have toxicity labels. That's the big
01:28:26
thing that I want because like if these
01:28:28
kids
01:28:29
>> Yeah.
01:28:30
>> That's the most important thing is put a
01:28:32
tox label on these things so you know
01:28:33
what you're taking.
01:28:35
>> It's the the strength of these things
01:28:37
over the last 50 years has changed
01:28:39
dramatically. And there are Yeah. There
01:28:41
there there are these um shards they
01:28:46
create of this like incredibly intense
01:28:48
THC that will make you like go
01:28:50
psychotic. Like it's really dangerous.
01:28:52
So the smoking a joint from our youth is
01:28:55
very different than smoking these like
01:28:57
resins and stuff that they've created
01:28:59
apparently that are like a 100 times the
01:29:01
potency 200 times potency and they can
01:29:03
put you in a psychotic state. Really
01:29:05
dangerous folks.
01:29:05
>> Oh that's that's where like the FDA
01:29:07
should regulate it. More like alcohol or
01:29:09
something. 100%. Much better process,
01:29:12
especially since kids are getting messed
01:29:14
up. Yeah.
01:29:14
>> All right, everybody. Another amazing
01:29:16
episode of your favorite podcast in the
01:29:18
world, the all in pockets. Love you.
01:29:19
>> Happy holidays, everybody. Merry
01:29:21
Christmas.
01:29:22
>> Happy Hanukkah. Merry Christmas.
01:29:23
>> Happy Hanukkah. Christmas, whatever
01:29:26
you're into.
01:29:28
>> Let your winners ride.
01:29:31
Rainman David
01:29:35
>> and it said we open sourced it to the
01:29:37
fans and they've just gone crazy with
01:29:39
it.
01:29:40
>> Queen of
01:29:48
besties are
01:29:51
my dog taking a notice driveway.
01:29:56
Oh man, my habitasher will meet up.
01:29:59
>> We should all just get a room and just
01:30:00
have one big huge orgy cuz they're all
01:30:02
just It's like this like sexual tension
01:30:04
that we just need to release somehow.
01:30:10
>> Your feet.
01:30:12
Waiting to get Mercury's are going all
01:30:23
in.
