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All right, everybody. Welcome back.
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Welcome back to the number one podcast
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in the world, the All-In podcast,
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episode 278.
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And uh Freeberg, he took a mental health
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day today after the socialist sweep in
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New York City. So, we invited two
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guests. They both said yes. Travis
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Kalanic is here from Adams. How you
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doing, brother?
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>> I'm good. I'm good. Good to see you.
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>> Good to see you. Good to see you. And
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after a triumphant week at Starbase,
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the one, the only, everybody's favorite,
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Gavin Baker of a treaties management.
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How are you doing, Gavin?
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>> Great, man. Thanks for having me.
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>> You're still floating on Cloud9 after
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the SpaceX IPO. Yeah,
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>> that was a very special moment and it
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was a culmination of, you know, some
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decades of hard work,
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>> but man, to quote uh Bill Bich onto
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Cincinnati.
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>> Yeah. Yeah. Exactly right.
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>> Yeah. Zero zero is uh the how the Knicks
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talked about the next game in every
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series. They're like, "It's 00. We come
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into this as if it's like the first game
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of the series, even if we're up two
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games or three games. The socialists
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have swept New York in the congressional
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Democratic primaries." On Tuesday, New
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York City Mayor Mandami went three for
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three in the candidates, which he
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endorsed, and they all won their
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primaries. 10th district leader Brad
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Lander defeated two-term incumbent Dan
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Goldman. 10th is one of New York's
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richest districts includes the West
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Village. All those townhouses, uh, Wall
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Street, Dumbo, Cobble Hill, Carol
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Gardens, Park Slope. That's some weird
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geography put together there. In New
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York's 13th, Jiovalier beat a fiveterm
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incumbent who was backed by House
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Speaker Hakee Jeff. And apparently the
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socialists are coming for him. 13 is one
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of the poorest districts. Harlem and the
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West Bronx, the Boogie Down Bronx. She's
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a 32year-old Democrat socialist with a
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history of spicy remarks.
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New York's seventh district. Claire
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Valdez won the open seat over the
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handpicked successor. The incumbent
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seven is a DSA stronghold. Bushwick,
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Williamsburg, Long Island City,
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Greenpoint, known as the Kami Corridor.
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A lot of hipsters and baristas with
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suspenders in that neighborhood.
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According to our partners poly market,
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this was a pretty big upset. The Mandami
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sweep chances were just 26 before
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election day. Yeah, that would be like
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the trifecta there if you were gambling.
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These candidates, just like Mandami did,
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a lot better with younger, college
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educated, and highincome folks, folks
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who can afford to be socialists.
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And these are all safe Democratic seats.
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The DSA will very likely win. So the DSA
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caucus,
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>> that's the best way you just said,
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people who can afford to be socialist.
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It's always
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It's always the the rich poors, you
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know, they're rich but they pretend to
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be poor. It's perfect.
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>> I mean, this is the history of our
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country from the 30s, the 50s, Red
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Scare, Black Panthers. I mean, we'll get
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into it, Shimoth, but basically, if
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you're intelligent and you get into
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business, you become a capitalist. If
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you're super intelligent and you go into
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academia eventually, you have the luxury
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of belief that socialism is awesome.
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What's your take here, Chimath? And then
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I'll go to you, Sax Poo, cuz I know I
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can see you're chomping on something
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over there. you're just ready to go.
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>> Honestly, I think that we are losing the
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script and part of it is because we've
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been our own worst enemy.
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I'll just keep saying this that I think
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AI is a very good prism into this
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problem. I think AI is the greatest
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economic leveler
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we'll ever find in our lifetime. I think
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it's the thing that can create the
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greatest amount of equality. I think
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that it can even the starting line for
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every single person on earth. But we've
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done such a poor job in representing it,
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in bringing it to market, in talking
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about it. We've let all of our own
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personal trials and tribulations and
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insecurities and fights spill out into
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the open. As a result, Silicon Valley
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has lost even more credibility with the
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people at large. And in that vacuum,
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what other people can paint is a picture
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of how anything other than what
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capitalism looks like today is a better
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version of what they see. And this is
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why you're seeing, I think, a lot of
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these people get a lot of momentum. I
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think if you look at some of the key
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congressional races, they were a
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referendum on AI. And the good news is
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we were able to hold the line in some of
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these key places, but just barely. In
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Utah and in New York, there was a couple
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of very important races where it was
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essentially anthropicfunded
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anti-AI groups, which is again insane
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against
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in some ways open AAI funded proA groups
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and the pro-AI groups won. You just
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explained that it's a a great leveler.
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Can you give a couple of examples and
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just expand on that briefly? How is it a
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great leveler? I mean I know but for the
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audience's sake you've said this a
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couple of weeks now so I think unpack it
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>> Um the best way to explain it is I think
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that the first real major unlock of
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economic productivity was when the
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internet and specifically Google went
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and harvested and collected all the
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world's knowledge and all the world's
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information and they made it available
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via search. What we figure out though 25
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years later, despite the fact that they
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built a great business, is what was
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missing was then being able to take that
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knowledge and information and transform
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it into expertise and intelligence. And
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that's effectively what AI does. It
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takes that world's knowledge and it
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allows you to act upon it so that every
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single man, woman, and child has an
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equivalent Travis Kalanick in, you know,
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as his co-founder, a superfounder, this
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brilliant person that can think through
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all your problems, can outgineer people,
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can outthink people, and they sit beside
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you and you have that. And there is no
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gatekeeping that can prevent you from
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having that. And so now you're only as
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good as your ability to direct that
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energy into something productive that
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you value. That is an incredibly
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powerful thing. And instead we've gotten
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caught up in dumerism and jobs being
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lost and you know water being consumed.
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All of which are lies. All of which are
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complete fabrications and
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misinformation.
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And these have been created in order to
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specifically help one small set of
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actors inside the AI race. and it's been
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fed and funded by those folks. So in in
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this vacuum, Jason, we've allowed all
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these other people to paint the other
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version. And right now, the other
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version looks way more compelling than
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the current version because the current
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version has very poor brand ambassadors.
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what's your take on this? I guess some
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people are looking at this as the left's
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version of the populist takeover that
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Trump did over the last
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10 plus years. What's your take on what
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we're seeing here with socialism and its
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amazing appeal and it's winning at the
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uh election box?
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>> I think there is some truth to that. I
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mean, I think the choices of the future
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are going to be communism or if you want
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to call it socialism in the Democrat
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party or nationalism in the Republican
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party. I mean, that is where we're
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headed. Those are the two populist
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directions. But let's look at what these
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DSA candidates stand for. So, let's look
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at what their platform is. They actually
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say they want to abolish the Senate.
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They want to abolish the carceral state.
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That means basically police forces and
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prisons. They want to abolish ICE and
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grant amnesty for all. They do not
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support any deportations whatsoever.
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They want to replace the president and
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supreme court with an executive and
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judiciary that is chosen by and
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subordinate to Congress, which basically
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now I guess just means this house. And
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with respect to House elections, they
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want to abolish the electoral college.
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They want to replace the two-party
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system with a multi-party democracy. And
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they want to expand the House of
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Representatives, implement proportionate
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representation and rank choice voting in
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all elections. So this is would be a
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total makeover of our constitutional
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system. They want to free Palestine.
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They want public ownership of major
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corporations. They want to defund the
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Department of War. This is a very
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radical organization. And you would
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laugh at a lot of these types of
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proposals, but you can't really laugh at
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it anymore because these guys are taking
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over the Democratic party. And you can
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see the Democratic establishment is in
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complete panic right now because they
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have lost control of the party uh to
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Zoram Mdani and his his allies. So
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Jason, like you said, I mean, let's take
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this one race here, New York 13. You've
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got this ally of Hakeem Jeff, longtime
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incumbent congressman,
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Espalot, I guess is is his name. He is
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the chair of the Congressional Hispanic
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Caucus. And he was defeated by an
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unemployed 32-year-old PhD candidate.
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She's never had a job. She's been in
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college for 10 years, I guess, writing
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this PhD thesis.
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>> And I think even by DSA standards, she
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might be kind of a lunatic. So, she has
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declared that she wants to end Western
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civilization. She wants to eradicate
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Western civilization.
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>> Wait a second. What?
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>> We're soaking in it.
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>> Yeah. She actually said she used the
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American flag as a napkin to clean her
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>> she attended a rally one day after
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October 7th celebrating the slaughter of
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Israeli civilians. I mean, she's very um
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pro Palestine, but even to the point of
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celebrating Israeli civilian deaths. She
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calls white women ugly colonizers. She's
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called for the complete defunding the
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police and abolishing all prisons and
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borders. Doesn't want a single
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deportation. hates the police, openly
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calls them pigs, or has on social media
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before, calls US service members war
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criminals, and says the US is a disgrace
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of a country. She's written favorably
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about communism and seizing the means of
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production, and on and on and on. So,
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this is basically the new Democratic
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party. It's going to be even if the
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Democrats do take the house in November
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and Hakee Jeff becomes speaker, this is
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going to prove to be a huge headache for
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him managing all these new DSA members
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>> because they do not actually see the
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traditional establishment wing of the
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Democratic party as an ally. They see it
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as an obstacle. This is the DSA co-chair
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Josh Block said, "We're using the
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Democratic Party as a ballot access
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vehicle."
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>> Oh my gosh.
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>> Not because we share its goals. We build
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our own organization,
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>> get elected under the Democratic label,
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caucus with Democrats when it's useful,
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and push our own agenda from the inside.
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We see the Democratic establishment as
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an obstacle, not a home. So, the DSA is
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coming from the Democratic Party. It
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controls the base now. It's where all
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the energy is. I think this takeover
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will continue. I think the DSA will
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gradually take over more and more of the
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Democratic party. And all I can say to
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these establishment Democrats is play
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stupid games. win stupid prizes. You
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supported this open border policy that
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brought in this wave of mass migration.
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That is a huge part of the base of this
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new DSA wing. Ma'am Donnie would not
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have won the mayoral election in New
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York if it had just been nativeorn New
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Yorkers voting. It was the mass migrant
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vote in New York that swung it to Ma
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Donnie. It's not exclusively the DSA
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base. It's the migrants plus these
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overeducated
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white progressives who I say
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overeducated because they're more
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downwardly mobile. They end up going to
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work in academia or NOS's that kind of
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thing. They're hard left-wing.
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>> They're kind of the vanguard. And it's
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this combination of these, you know,
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recent college graduates who are kind of
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the organizers and this migrant movement
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who are really taking over the
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Democratic party. But again, this all
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goes back to Democratic Party policies.
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They may not have intended for the staff
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and they may not have intended for them
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to lose control, but it was their open
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border policy. It was also the fact that
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they have cracked the melting pot and
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the policy we had for many years in
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America of assimilating migrants, right?
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Immigrants.
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>> Yep.
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>> That all got cracked by multiculturalism
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and wokeism. We don't really do that
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anymore. So now you've got these
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candidates like Shioalier openly
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declaring they're not just
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anti-American, they're postamerican.
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They don't have any respect for the
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American system, our constitutional
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order, our free enterprise system. They
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want to introduce something in much
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different and it's going to look a lot
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like the countries where these migrants
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are coming from.
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>> Yeah. So again, you know, if you import
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massive numbers of migrants, don't
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assimilate them into our system, and
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then you have these Marxist leaders,
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you're going to end up with the American
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system coming to resemble the countries
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from which
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>> it's really interesting. You mentioned
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like how she wants to get rid of all of
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these, you know, delete all of these uh
[13:12] (792.48s)
units. It's very similar to Trump's, you
[13:14] (794.88s)
know, he wanted to delete a separate set
[13:16] (796.64s)
of things like the IRS and USID,
[13:19] (799.68s)
Department of Education, EPA. They each
[13:21] (801.68s)
use that playbook as well, like, "Hey,
[13:23] (803.12s)
we're going to delete some of this
[13:24] (804.24s)
stuff." Travis, you uh famously had uh
[13:27] (807.92s)
the fountain head.
[13:29] (809.12s)
>> Trump didn't want to delete the US
[13:30] (810.48s)
Constitution.
[13:31] (811.52s)
>> No, definitely not.
[13:33] (813.28s)
>> Travis, you're used to Slight
[13:34] (814.32s)
difference.
[13:34] (814.64s)
>> Slight difference. I agree. He didn't
[13:36] (816.08s)
want to delete Western culture.
[13:38] (818.16s)
>> I don't think he wants to delete Western
[13:39] (819.52s)
culture.
[13:40] (820.16s)
>> No. Uh
[13:41] (821.52s)
>> you might want to delete USAD because it
[13:43] (823.92s)
was a festering. It was basically a
[13:46] (826.32s)
front for all these
[13:47] (827.44s)
>> NCOs. NCIA. Yeah. By the way, you may
[13:50] (830.64s)
you may have noticed that in South
[13:52] (832.16s)
America, we've had seven elections where
[13:53] (833.92s)
the countries have swung to the right.
[13:57] (837.12s)
And I think a big part of the reason why
[13:58] (838.88s)
might be you don't have the USAD there
[14:02] (842.16s)
conducting all these uh left-wing regime
[14:04] (844.88s)
change operations.
[14:06] (846.40s)
>> Pola's polared
[14:09] (849.12s)
disappeared and the incentives
[14:10] (850.48s)
disappeared with it.
[14:12] (852.00s)
>> Travis, you uh famously had The Fountain
[14:14] (854.08s)
Head and Ran's novel as your Twitter
[14:16] (856.96s)
avatar for a decade or two. Uh it was no
[14:22] (862.00s)
they've been writing about it for a
[14:23] (863.52s)
decade or two. This is like a six-month
[14:25] (865.12s)
thing where I would read a book and then
[14:27] (867.44s)
rotate at my avatar.
[14:29] (869.36s)
>> Yeah.
[14:29] (869.60s)
>> You had Hamilton for a while. I had
[14:32] (872.24s)
Ender Game for a while. But yeah, it
[14:34] (874.64s)
>> I mean you having known you for a while
[14:37] (877.68s)
I think believe in the individual and
[14:41] (881.44s)
their exceptionalism and and trying to
[14:44] (884.00s)
have a little ruggedness there. This is
[14:46] (886.56s)
obviously the the exact opposite. What's
[14:48] (888.80s)
your take on what's happening in these
[14:51] (891.12s)
pockets? Cuz it's not national yet, but
[14:53] (893.76s)
it's definitely notable.
[14:56] (896.08s)
>> All right. I've um I've got I've got
[14:58] (898.48s)
sort of two apherisms for us. Okay.
[15:01] (901.84s)
First is
[15:05] (905.12s)
truth and justice is the immune system
[15:07] (907.52s)
for society.
[15:09] (909.68s)
When the immune system is suppressed,
[15:11] (911.84s)
all the social ills flare up.
[15:15] (915.36s)
Okay? So if you're seeing us losing
[15:18] (918.72s)
truth like social media, mainstream
[15:20] (920.96s)
media, whatever you call it, like that's
[15:23] (923.92s)
an early indicator
[15:26] (926.08s)
or bad things happening in society.
[15:29] (929.52s)
Okay? And it's not just like social
[15:31] (931.28s)
media, mainstream media, it's just
[15:32] (932.64s)
everything around us. Um, and the same
[15:35] (935.36s)
on the justice side, too. If people
[15:37] (937.04s)
commit crimes and there's no
[15:38] (938.80s)
consequences,
[15:40] (940.64s)
it's a nice early indicator. So you can
[15:42] (942.56s)
kind of watch
[15:44] (944.48s)
you can watch these things and say is
[15:46] (946.80s)
truth winning today or is it losing?
[15:49] (949.60s)
>> Is justice winning today or is it
[15:51] (951.44s)
losing? And what is the trajectory will
[15:54] (954.56s)
tell you? Are we going to get worse? Are
[15:56] (956.08s)
we going to get better?
[15:58] (958.16s)
>> Right. Co makes and the Fouchy case now
[16:01] (961.36s)
we're seeing a lot more information come
[16:02] (962.96s)
out. We have a really hard time getting
[16:04] (964.48s)
the truth and maybe we'll get justice
[16:07] (967.04s)
eventually. It seems like we're getting
[16:08] (968.40s)
a little more truth 5 years later, but
[16:09] (969.68s)
it's like a perfect example of what
[16:10] (970.96s)
you're discussing. Yeah,
[16:12] (972.00s)
>> there's a lot packed into that, but you
[16:13] (973.84s)
can unpack it. If we had a lot of time,
[16:15] (975.36s)
we could unpack it. But those become the
[16:18] (978.00s)
sort of atomic things that you look at.
[16:20] (980.80s)
And then the other thing I'd say, and
[16:22] (982.24s)
and some people get a little surprised
[16:23] (983.84s)
when I say this, which is
[16:26] (986.64s)
communism is in is in all of us.
[16:30] (990.00s)
Communism is in is in our blood as
[16:32] (992.64s)
humans. And people go, "What the hell
[16:34] (994.64s)
are you talking about? You're crazy.
[16:35] (995.92s)
What do you mean?" Well, I'm like,"Well,
[16:37] (997.68s)
have you have you ever in your life been
[16:41] (1001.12s)
lazy?"
[16:42] (1002.96s)
And everybody's like, "Yes, I've been
[16:44] (1004.80s)
lazy in my life before." I said, "Have
[16:47] (1007.36s)
you ever in your life wanted something
[16:49] (1009.52s)
for nothing?"
[16:51] (1011.92s)
Yeah. The difference is, do you make
[16:55] (1015.36s)
that a way of life? And when you have
[16:59] (1019.04s)
ecosystems
[17:00] (1020.80s)
that essentially allow you to do both of
[17:03] (1023.20s)
those things without consequence, those
[17:05] (1025.44s)
ecosystems get a critical mass and start
[17:08] (1028.00s)
taking hold. And I think that's what
[17:09] (1029.44s)
we're seeing.
[17:10] (1030.08s)
>> Well said. Yeah, I think it's well said.
[17:11] (1031.76s)
Uh Gavin, I I know you uh I don't know
[17:14] (1034.56s)
you to talk too much about politics, but
[17:16] (1036.48s)
talk about markets, but these two things
[17:18] (1038.40s)
do relate, I think. So give us the
[17:21] (1041.68s)
economic perspective here on why this is
[17:25] (1045.20s)
happening because obviously different
[17:27] (1047.60s)
generations have had different economic
[17:29] (1049.36s)
experiences. So I don't want to lead the
[17:30] (1050.72s)
witness here but I have had
[17:32] (1052.00s)
conversations with you about this
[17:33] (1053.36s)
before.
[17:34] (1054.48s)
>> Yeah. Well so I do think um
[17:37] (1057.84s)
I obviously AI is I think going to be
[17:40] (1060.88s)
the defining political issue of the
[17:42] (1062.40s)
midterms and for sure the the next
[17:44] (1064.56s)
presidential election.
[17:47] (1067.20s)
But I feel like what is going on with
[17:48] (1068.96s)
the DSA is really a fusion of two
[17:50] (1070.88s)
things. So, the Democratic Party, I I I
[17:53] (1073.28s)
was a Democrat for most of my life, and
[17:55] (1075.60s)
it was the party of the working-class
[17:57] (1077.04s)
people that was trying to create
[17:59] (1079.28s)
opportunities,
[18:00] (1080.80s)
you know, maybe level out um equality,
[18:06] (1086.40s)
you know, help black Americans, help
[18:08] (1088.72s)
Hispanic Americans.
[18:10] (1090.96s)
And, you know, you can agree or disagree
[18:14] (1094.72s)
with their methods, but I think those
[18:16] (1096.24s)
are all noble goals.
[18:19] (1099.20s)
And to a large extent,
[18:22] (1102.08s)
none of those are really present in the
[18:25] (1105.76s)
None of them. If you look at who voted
[18:28] (1108.48s)
for which candidate,
[18:30] (1110.96s)
the pe the voting base of the DSA
[18:35] (1115.04s)
are relatively wealthy white liberals
[18:38] (1118.64s)
who are downwardly mobile. They're
[18:40] (1120.72s)
losing votes with working-class people.
[18:43] (1123.04s)
They're losing votes with poor people.
[18:45] (1125.20s)
They're losing votes with black
[18:46] (1126.56s)
Americans. They're losing votes with
[18:48] (1128.56s)
Hispanic Americans
[18:50] (1130.72s)
and those, you know, the the people that
[18:54] (1134.40s)
the Democratic Party, I think, for a
[18:56] (1136.88s)
long time tried and whether they failed
[18:59] (1139.04s)
or succeeded is up to tried to represent
[19:01] (1141.28s)
and give a voice to,
[19:03] (1143.60s)
they are not of interest to the DSA.
[19:06] (1146.48s)
And I think the DSA is is dangerous.
[19:10] (1150.40s)
It's tragic that we are electing
[19:12] (1152.40s)
profoundly anti-American
[19:15] (1155.68s)
candidates who in some cases have called
[19:17] (1157.76s)
for violence uh to eradicate America.
[19:22] (1162.64s)
And I just think there's there's a whole
[19:24] (1164.88s)
class of people who went to an elite
[19:28] (1168.16s)
school. They grew up in really nice
[19:30] (1170.72s)
circumstances
[19:32] (1172.56s)
and uh instead of going into industry,
[19:36] (1176.40s)
they went into this kind of giant NGO
[19:40] (1180.16s)
nonprofit machine
[19:43] (1183.28s)
and their outcomes have been very
[19:46] (1186.16s)
different from people who did productive
[19:49] (1189.60s)
things for the world. You know, I've
[19:50] (1190.88s)
said on this show many times, Elon has
[19:52] (1192.64s)
done more to decarbonize the planet than
[19:55] (1195.44s)
every activist combined.
[19:58] (1198.00s)
>> Yeah. times 10 by the way
[20:00] (1200.40s)
>> times 10. And I think these NOS's and
[20:03] (1203.04s)
the fact that the government is
[20:05] (1205.76s)
increasingly
[20:07] (1207.28s)
outsourcing a lot of funding to them is
[20:09] (1209.92s)
a really big part of this problem and
[20:15] (1215.12s)
they are pursuing policies. You know, I
[20:17] (1217.04s)
think we maybe talked about it last
[20:18] (1218.24s)
time, but the curly effect, a mayor of
[20:20] (1220.48s)
Boston, you pursue policies that you
[20:24] (1224.08s)
know are going to be disastrous for this
[20:27] (1227.04s)
your constituents, but they drive out
[20:29] (1229.36s)
your rivals. And then you can give jobs,
[20:32] (1232.96s)
$600,000 a year jobs, run an NGO that
[20:37] (1237.92s)
does nothing productive to your friends
[20:40] (1240.16s)
and allies. And it's really organized
[20:43] (1243.92s)
corruption happening at a massive scale.
[20:47] (1247.44s)
>> You know, the one thing, whatever,
[20:49] (1249.04s)
whatever you want to say about the Trump
[20:50] (1250.40s)
administration, a lot of people are up
[20:51] (1251.92s)
in arms about this, you know, new plane
[20:53] (1253.68s)
he's getting from the Qataris.
[20:55] (1255.68s)
>> Well, well, one, sounds like we needed a
[20:58] (1258.24s)
new plane, but two, it's right there for
[21:00] (1260.48s)
you to see. I think there are tens of
[21:03] (1263.60s)
billions, maybe hundreds of billions of
[21:06] (1266.32s)
these payments flowing to these NOS's.
[21:09] (1269.76s)
And if you look in California and New
[21:11] (1271.52s)
York, it's um
[21:15] (1275.04s)
like I think the per capita spending on
[21:17] (1277.04s)
homelessness has more than doubled and
[21:19] (1279.84s)
it's all gone to NOS's and outcomes have
[21:21] (1281.84s)
gotten worse. In California, it's like
[21:24] (1284.00s)
quadrupled and outcomes have gotten
[21:26] (1286.40s)
worse.
[21:28] (1288.08s)
So, I think this is this is dangerous
[21:32] (1292.56s)
and I hope that the mainstream Democrats
[21:35] (1295.68s)
can find a compelling candidate because
[21:39] (1299.04s)
I think the reason that the DSA is
[21:41] (1301.28s)
increasingly ascendant is not because of
[21:43] (1303.20s)
their ideas. I think their ideas appear
[21:46] (1306.08s)
to this appeal to this very nar narrow
[21:48] (1308.32s)
subset of downwardly mobile rich white
[21:51] (1311.04s)
people who you know increasingly maybe
[21:53] (1313.28s)
live in you know quasi gental poverty
[21:56] (1316.24s)
because they they're not doing
[21:57] (1317.36s)
productive things. They may be
[21:58] (1318.48s)
well-intentioned but they're not doing
[21:59] (1319.84s)
productive things
[22:03] (1323.12s)
and can find a compelling candidate
[22:04] (1324.88s)
because I think the reason they're
[22:06] (1326.16s)
ascendant is Zoram Mani. I think he is
[22:09] (1329.28s)
one of the most talented politicians
[22:11] (1331.44s)
I've ever seen in my lifetime. You know,
[22:14] (1334.88s)
he can give a great speech. He's good in
[22:16] (1336.96s)
an interview. He can tap into all of
[22:18] (1338.96s)
this. He's kind of a chameleon who can
[22:21] (1341.12s)
shift. But I think he is a singularly
[22:25] (1345.20s)
talented politician and he is the reason
[22:28] (1348.64s)
that the DSA is ascendant. not their
[22:31] (1351.20s)
ideas or not dissatisfaction
[22:33] (1353.92s)
with AI, but it's him and there's no one
[22:39] (1359.52s)
else in the Democratic party. I used to
[22:41] (1361.44s)
think AOC was by far the most talented
[22:43] (1363.36s)
Democratic politician.
[22:44] (1364.48s)
>> That was the warm-up. Yeah,
[22:45] (1365.84s)
>> she was talented. She's nothing like
[22:48] (1368.16s)
Zoron from a political
[22:49] (1369.52s)
>> Zuron is clearly talented. And if you
[22:51] (1371.92s)
combine his charisma and ability to
[22:54] (1374.72s)
communicate with generations like we're
[22:57] (1377.04s)
now like have two of these lost
[22:58] (1378.32s)
generations that feel like they're going
[23:00] (1380.48s)
to do worse than their parents. And if
[23:02] (1382.40s)
you look at housing and college debt,
[23:06] (1386.72s)
healthcare, they don't believe that they
[23:09] (1389.20s)
can participate in the system. They feel
[23:11] (1391.20s)
the system's rigged. And if somebody
[23:13] (1393.04s)
comes along who speaks to them and they
[23:15] (1395.36s)
have no conception of socialism and what
[23:18] (1398.32s)
happened, you know, in Germany or during
[23:20] (1400.72s)
the Red Scare, they have no idea what
[23:22] (1402.48s)
socialism or communism is. They have
[23:24] (1404.64s)
this incredible wrapper they've put
[23:26] (1406.32s)
around this, which is, you know, they're
[23:27] (1407.84s)
democratic socialists. It's like it's
[23:29] (1409.92s)
it's the coke light. It's it's a coke
[23:32] (1412.00s)
zero of socialism. It's actually just
[23:33] (1413.76s)
communism. They want to literally seize
[23:37] (1417.04s)
people's assets. They want to seize half
[23:39] (1419.04s)
of, you know, these compan stock. They
[23:40] (1420.64s)
want to seize their wealth. They they
[23:42] (1422.32s)
just want to take from the people who
[23:43] (1423.92s)
have made stuff as we talked about last
[23:45] (1425.52s)
week and we've done nothing to change
[23:49] (1429.20s)
their mind. We haven't made more houses.
[23:51] (1431.60s)
We haven't made college more affordable.
[23:53] (1433.84s)
Healthcare is a disaster. Inflation's a
[23:56] (1436.08s)
disaster and starting pointless wars.
[23:59] (1439.04s)
Obviously, the support of Israel is
[24:00] (1440.56s)
somewhere in here as part of say
[24:02] (1442.64s)
something.
[24:02] (1442.96s)
>> Yeah. Yeah. I was on Megan Kelly
[24:05] (1445.76s)
yesterday
[24:07] (1447.84s)
and I had this theory that I've been
[24:10] (1450.00s)
kind of working through and I'll just
[24:11] (1451.52s)
share it with you guys because I'd love
[24:12] (1452.80s)
your reaction.
[24:15] (1455.28s)
If you look at the scourge of socialism,
[24:19] (1459.04s)
if you take the rhetoric away and you
[24:20] (1460.80s)
actually look at the outcomes, there are
[24:22] (1462.40s)
three countries that I think have veered
[24:25] (1465.60s)
far towards socialism well before the
[24:27] (1467.92s)
United States. Canada, the UK, and
[24:31] (1471.44s)
Australia. And I think when you look at
[24:34] (1474.64s)
any sort of reasonable measure of their
[24:37] (1477.76s)
progress, it's been an unmitigated
[24:40] (1480.16s)
disaster. So all of the virtue signaling
[24:42] (1482.80s)
on social issues, all of the virtue
[24:44] (1484.64s)
signaling on immigration and open
[24:46] (1486.32s)
borders, all of the virtue signaling on
[24:48] (1488.88s)
climate change has left each of those
[24:51] (1491.76s)
three countries in some state of
[24:53] (1493.84s)
disrepair with enormous amounts of
[24:56] (1496.40s)
infighting, tremendous political
[24:58] (1498.32s)
instability. They are all sort of powder
[25:00] (1500.80s)
kings. And there's an interesting thing
[25:04] (1504.24s)
that happened in each of those three
[25:05] (1505.68s)
countries that I think has the potential
[25:09] (1509.12s)
to turn the tide. And it speaks to what
[25:11] (1511.52s)
Gavin said. Each of those three
[25:13] (1513.76s)
countries now have banned social media
[25:15] (1515.60s)
when you're 16 and under. And I think
[25:18] (1518.88s)
it's sort of like making sure the kid
[25:21] (1521.12s)
doesn't get addicted to the drug too
[25:23] (1523.52s)
early.
[25:25] (1525.04s)
And when you look at somebody like Zoran
[25:26] (1526.72s)
Mdani, I think Gavin is right. I'll go
[25:29] (1529.12s)
even further. He
[25:34] (1534.40s)
the lady that just won
[25:35] (1535.76s)
>> Shiovalier.
[25:36] (1536.64s)
>> Shalier. Let's just break it down.
[25:39] (1539.12s)
They're all good-looking. They're all
[25:41] (1541.12s)
charismatic. They have their pulse on
[25:43] (1543.52s)
the the gestalt of the moment. They know
[25:46] (1546.32s)
how to use social media, and they are
[25:48] (1548.88s)
essentially curating an army.
[25:52] (1552.24s)
Now, if you cut the legs off by saying
[25:55] (1555.04s)
young people should actually age into
[25:57] (1557.12s)
social media, I suspect the most
[25:59] (1559.36s)
important channel of information
[26:00] (1560.80s)
consumption being taken away from them
[26:03] (1563.36s)
actually starts to give them the
[26:05] (1565.84s)
opportunity
[26:07] (1567.44s)
and the rest of us quite honestly
[26:08] (1568.96s)
because we have to deal with kids who
[26:10] (1570.24s)
are just really stupid constantly asking
[26:12] (1572.64s)
for this stuff. Um, the chance to
[26:15] (1575.36s)
actually show you how a balanced diet
[26:17] (1577.28s)
actually allows you to be much healthier
[26:18] (1578.88s)
in your later life. And I think as
[26:21] (1581.04s)
relates to information consumption, I
[26:23] (1583.04s)
think you are going to see I suspect a
[26:27] (1587.04s)
far less radicalized youth aging into
[26:30] (1590.08s)
the voting roles in those three
[26:31] (1591.52s)
countries. And I suspect if you start to
[26:34] (1594.64s)
see political stability and
[26:36] (1596.96s)
predictability in Canada, the UK, and
[26:38] (1598.96s)
Australia, you can put your finger right
[26:41] (1601.36s)
on this ban as the reason why. And
[26:44] (1604.24s)
Florida's already done it in the United
[26:45] (1605.92s)
States, and I think we need to deeply
[26:47] (1607.28s)
consider it across the rest of the
[26:48] (1608.64s)
United States. Good night.
[26:50] (1610.24s)
>> Social media ban is now starting at 16
[26:53] (1613.28s)
and uh Canada has it. It may come to the
[26:55] (1615.76s)
US. Go ahead, Travis.
[26:56] (1616.64s)
>> I'm a full counter to Chimoth on this
[26:58] (1618.64s)
one. The I I think we all agree social
[27:01] (1621.28s)
media is bad for the kids and for the
[27:03] (1623.84s)
adults. Like too much of that stuff is
[27:06] (1626.08s)
very bad. It's brain rot for real. And
[27:08] (1628.08s)
it's going to be worse than cigarettes.
[27:09] (1629.44s)
It's all the things. But the real point
[27:12] (1632.24s)
of banning under 16 is so that you can
[27:16] (1636.48s)
force adults to identify themselves and
[27:20] (1640.24s)
deanonymize themselves so you can set up
[27:23] (1643.04s)
a fullscale censorship regime which
[27:25] (1645.28s)
they're sort of contemplating in the UK.
[27:28] (1648.00s)
And what censorship is really about is
[27:30] (1650.00s)
not about harmful content. It's about
[27:32] (1652.88s)
content that the people in power don't
[27:36] (1656.32s)
want you to see that disagrees with
[27:38] (1658.56s)
them. if it agreed with them, it's not
[27:40] (1660.72s)
harmful. It's the stuff that doesn't
[27:42] (1662.48s)
agree with them. And so what they're
[27:44] (1664.32s)
doing is they're criminalizing
[27:45] (1665.92s)
disagreeing with them.
[27:47] (1667.92s)
>> And so there's a derivative of this
[27:51] (1671.52s)
agebased
[27:53] (1673.12s)
stuff,
[27:53] (1673.60s)
>> age gating
[27:54] (1674.32s)
>> that
[27:55] (1675.12s)
>> which is really about the adults, not
[27:57] (1677.36s)
the kids.
[27:58] (1678.64s)
>> That is the downside to it. Gavin, you
[28:00] (1680.24s)
wanted to add? No, I super agree that I
[28:03] (1683.44s)
think that a social media ban under 16
[28:05] (1685.84s)
would be great, but I think it comes at
[28:08] (1688.40s)
a really high cost outlined by Travis.
[28:11] (1691.68s)
And that's what's really going on is
[28:13] (1693.44s)
they want to restrict anonymous accounts
[28:15] (1695.60s)
on X who say things that particularly
[28:18] (1698.16s)
the powers that be in the EU do not
[28:20] (1700.24s)
like. And if there's a way to do that
[28:23] (1703.20s)
while preserving anonymity and free
[28:26] (1706.16s)
speech, I'm all for it. But I think if
[28:29] (1709.76s)
if it were not for free speech and and X
[28:32] (1712.72s)
like I think we'd be living in a very
[28:34] (1714.64s)
different world today that would be a
[28:36] (1716.64s)
lot worse. And just to riff on a few
[28:39] (1719.04s)
things that that all of you have said
[28:41] (1721.28s)
you know on communism it is communism
[28:43] (1723.92s)
and the great tragedy of human
[28:46] (1726.00s)
experiences we can't learn from the
[28:47] (1727.44s)
experiences of others. And it may not
[28:49] (1729.92s)
matter that communism has failed utterly
[28:53] (1733.36s)
and ended in death and misery wherever
[28:57] (1737.04s)
it has been tried in a variety of
[28:59] (1739.44s)
different cultures with a variety of
[29:01] (1741.60s)
different mechanisms and every
[29:03] (1743.76s)
generation may need to experiment with
[29:06] (1746.08s)
it. I think the the saving grace and the
[29:09] (1749.36s)
importance of preserving free speech is
[29:11] (1751.76s)
that the DSA's policies and I would say
[29:14] (1754.56s)
in particular super progressive
[29:16] (1756.24s)
democratic policies
[29:18] (1758.80s)
are measurably
[29:21] (1761.20s)
bad. They lead to bad outcomes. If you
[29:23] (1763.68s)
care about black lives, there's a study
[29:25] (1765.92s)
that is uncontested that when you elect
[29:28] (1768.40s)
a Republican DA, all cause mortality for
[29:31] (1771.36s)
young black men in that city drops by
[29:35] (1775.68s)
That's all it takes.
[29:36] (1776.88s)
>> Yeah.
[29:37] (1777.20s)
>> You care about the environment. Well,
[29:39] (1779.92s)
progressives, there's so many
[29:41] (1781.36s)
regulations that you can't build solar,
[29:43] (1783.44s)
which is really the only thing that
[29:44] (1784.88s)
matters. And like the world is going to
[29:46] (1786.96s)
run on sunlight. You care about
[29:48] (1788.96s)
education. Their approach to education
[29:52] (1792.24s)
of getting rid of these elite schools
[29:54] (1794.64s)
that allows low-income people to have a
[29:57] (1797.12s)
real chance, you know, getting rid of
[29:59] (1799.28s)
math in California. It is profoundly
[30:02] (1802.56s)
disadvantageous to people. It leads to
[30:05] (1805.60s)
terrible outcomes. And we all know what
[30:08] (1808.80s)
happens with crime. It turns out that
[30:10] (1810.96s)
there's a certain percentage of people
[30:13] (1813.12s)
who, you know, are well, I think it's
[30:16] (1816.24s)
like 60 over 60% of the violence or 75%
[30:20] (1820.64s)
of the violence comes from people who
[30:22] (1822.88s)
have like 10 or more convictions.
[30:24] (1824.88s)
>> Yeah. I think the specific stat is
[30:26] (1826.80s)
like.1% commit 70% of the crimes. And if
[30:29] (1829.92s)
you just dealt with the 0.1%, you'd
[30:32] (1832.32s)
effectively no crime. And so I just
[30:34] (1834.80s)
think the scary thing is and I do worry
[30:39] (1839.04s)
for the first time like as a student of
[30:40] (1840.64s)
history the United States has been like
[30:43] (1843.04s)
a very stable political economic entity
[30:46] (1846.00s)
geographically stable for a long time.
[30:49] (1849.36s)
And that's to its credit because we
[30:50] (1850.88s)
could have literally taken over the
[30:52] (1852.08s)
world after World War II.
[30:55] (1855.12s)
But man, if they get total control of
[30:57] (1857.84s)
some of these cities and drive out
[30:59] (1859.92s)
everyone who is productive, I don't know
[31:02] (1862.88s)
how you come back. And so that's that is
[31:06] (1866.80s)
a little worrisome to me in terms of the
[31:08] (1868.88s)
future of of the United States.
[31:11] (1871.84s)
>> Well, and by the way, I I I don't think
[31:13] (1873.44s)
it's just New York. I mean, that race
[31:16] (1876.00s)
for mayor in LA where was it? Ramen
[31:19] (1879.76s)
somehow beat Spencer Pratt thanks to
[31:22] (1882.48s)
ballot harvesting after voting day or I
[31:25] (1885.28s)
should say votes that were found and
[31:26] (1886.56s)
counted after election day. I mean, that
[31:29] (1889.76s)
will be a test of the DSA because they
[31:32] (1892.40s)
are highly organized and they have
[31:34] (1894.32s)
learned how to take advantage of all
[31:36] (1896.16s)
these rules, these ballot harvesting
[31:37] (1897.84s)
rules and all these types of things. the
[31:39] (1899.68s)
DSA, I think they've got something like
[31:41] (1901.60s)
half the city council seats now in LA
[31:44] (1904.24s)
and they're growing. So, especially in
[31:46] (1906.64s)
these low turnout elections, and look,
[31:48] (1908.40s)
this was a Democratic primary in New
[31:50] (1910.32s)
York, which is a strongly blue state.
[31:52] (1912.88s)
So, I think maybe 17% turned out some
[31:56] (1916.48s)
very low, but this is where the DSA
[31:58] (1918.48s)
really thrives and excels because they
[32:01] (1921.44s)
care passionately and they're highly
[32:03] (1923.44s)
organized and they know how to take
[32:04] (1924.80s)
advantage. This is why they want all
[32:06] (1926.24s)
these like rank choice voting and all
[32:08] (1928.24s)
these types of things. they know how to
[32:09] (1929.60s)
manipulate and take advantage of those
[32:11] (1931.44s)
kinds of systems. So, I think that
[32:14] (1934.64s)
you're going to see this in lots of
[32:15] (1935.92s)
other jurisdictions wherever they're
[32:17] (1937.44s)
organized. I think LA will be a really
[32:19] (1939.52s)
interesting test. So, I don't think we
[32:21] (1941.12s)
can just chalk this up to Zoran's
[32:23] (1943.76s)
popularity. You know, this is a national
[32:25] (1945.60s)
movement and we're going to see in a lot
[32:27] (1947.20s)
of places, but there's no question that
[32:29] (1949.44s)
Mandani is now kind of the spiritual
[32:31] (1951.28s)
leader. I mean a lot of these people
[32:33] (1953.36s)
don't they think AOC is a sellout you
[32:35] (1955.52s)
know or Bernie is a sellout you know
[32:37] (1957.52s)
they are way more radical than even even
[32:40] (1960.72s)
those types
[32:41] (1961.36s)
>> to add to that sex they did learn
[32:42] (1962.96s)
something from Trump which is builds a
[32:44] (1964.48s)
big tent so you're seeing Bernie Mandami
[32:47] (1967.60s)
Roana they're all kind of like yeah
[32:49] (1969.60s)
we're different but there's enough room
[32:51] (1971.60s)
in this tent
[32:52] (1972.72s)
>> well what's happening is that these sort
[32:54] (1974.80s)
of more established progressive leaders
[32:57] (1977.36s)
they want to tap into this energy and
[32:59] (1979.60s)
even the establishment wing of the
[33:00] (1980.96s)
Democratic party is bending the knee.
[33:03] (1983.28s)
And so what you're going to see is
[33:05] (1985.20s)
regardless of how many DSA candidates
[33:08] (1988.64s)
actually get elected, the rest of the
[33:10] (1990.80s)
party is now responding to this and
[33:12] (1992.88s)
they're going to bend. They're going to
[33:14] (1994.00s)
blow in this direction because they
[33:15] (1995.76s)
don't want to get challenged in a
[33:16] (1996.96s)
primary. I mean, think about this. You
[33:18] (1998.48s)
had three major congressional races
[33:20] (2000.88s)
where the Mamani candidate won. Two of
[33:24] (2004.08s)
them unseated, you know, really strong
[33:26] (2006.64s)
incumbents. These were big upsets. So,
[33:29] (2009.12s)
you got to think now that every
[33:30] (2010.80s)
congressional race in a pretty blue
[33:33] (2013.44s)
district, those members are now going to
[33:35] (2015.84s)
have to take into account that they
[33:38] (2018.00s)
could get primar and they're going to
[33:39] (2019.60s)
have to tilt their voting and their
[33:41] (2021.68s)
views and their rhetoric in this DSA
[33:43] (2023.68s)
direction cuz they don't want to have
[33:45] (2025.52s)
happened to them what just happened to
[33:47] (2027.92s)
Dan Goldman in the New York 10th
[33:50] (2030.16s)
district. And just to make one last
[33:51] (2031.76s)
point on that. So Jal, you mentioned the
[33:54] (2034.16s)
the Israel issue and I actually think
[33:56] (2036.48s)
that is a hugely important and salient
[33:58] (2038.96s)
issue now in the Democratic party in
[34:01] (2041.60s)
Democratic primaries. Obviously you saw
[34:03] (2043.68s)
that as part of the DSA platform, one of
[34:05] (2045.84s)
the legs is free Palestine. This defeat
[34:08] (2048.56s)
of Dan Goldman, two-time congressman, he
[34:10] (2050.64s)
led the impeachment effort against
[34:12] (2052.48s)
Trump. He had all the right progressive
[34:14] (2054.80s)
credentials. He checked the box in all
[34:16] (2056.72s)
the left-wing policies. He was on the
[34:19] (2059.52s)
right cable news channels all the time.
[34:21] (2061.44s)
No one expected him to lose. He lost to
[34:23] (2063.44s)
Brand Lander really just over this issue
[34:26] (2066.88s)
of Israel. Dan Goldman is is very
[34:28] (2068.88s)
pro-Israel. He basically defended
[34:31] (2071.60s)
Israel's actions over the past few
[34:33] (2073.28s)
years. Whereas Lander, who like Goldman
[34:36] (2076.72s)
is also Jewish. So this was again, you
[34:39] (2079.12s)
know, white Jewish congressman against
[34:41] (2081.84s)
white Jewish longtime New York
[34:44] (2084.32s)
politician. So on paper they're very
[34:46] (2086.64s)
similar. It was just in this issue of
[34:48] (2088.08s)
Israel where they disagreed and Lander
[34:50] (2090.56s)
went to a mosque in order to denounce
[34:52] (2092.72s)
what he called the genocide in Gaza. So
[34:55] (2095.36s)
this was really as close as you can get
[34:58] (2098.08s)
to a straight up vote on that one issue
[35:01] (2101.12s)
in this primary and Lander won pretty
[35:04] (2104.08s)
handily. Now the reason for this is if
[35:07] (2107.28s)
you look at polling 80% of Democrats now
[35:10] (2110.16s)
say they disapprove of Israel. So you
[35:11] (2111.92s)
know the approval disapproval rating.
[35:14] (2114.32s)
Israel used to have high approval
[35:15] (2115.84s)
ratings pretty much across the board. I
[35:18] (2118.48s)
mean, it was sort of a consensus, both
[35:19] (2119.76s)
Democrats and Republicans. Now, 80% of
[35:22] (2122.96s)
Democrats say they disapprove. You
[35:25] (2125.04s)
really can't underestimate how much of a
[35:27] (2127.44s)
motivator this is for young Democrats.
[35:29] (2129.76s)
They believe
[35:30] (2130.88s)
>> they were all over campuses. They We see
[35:33] (2133.52s)
I mean it draws people out and it has
[35:35] (2135.92s)
been quite polarizing just to call balls
[35:37] (2137.60s)
and strikes here inside the Republican
[35:39] (2139.20s)
party as well. You have Tucker leaving
[35:40] (2140.72s)
the party over this. You have Megan
[35:42] (2142.40s)
Kelly going mental over this and you
[35:44] (2144.40s)
have, you know, a lot of civil war
[35:46] (2146.56s)
inside this administration according to
[35:48] (2148.32s)
the report. So this is
[35:49] (2149.76s)
>> Well, let me let me let me um look, I
[35:51] (2151.84s)
mean, I'm not taking a side in this. I'm
[35:53] (2153.36s)
just trying to describe what's what's
[35:54] (2154.56s)
going on. So
[35:55] (2155.20s)
>> absolutely, same as me.
[35:56] (2156.24s)
>> I think the Republican party is a little
[35:58] (2158.56s)
bit more mixed on this issue and it
[36:00] (2160.72s)
really comes down to age. So if you're
[36:03] (2163.20s)
part of the older, more establishment
[36:05] (2165.36s)
Republicans, let's say you're a Fox News
[36:07] (2167.28s)
viewer, you still have high approval
[36:09] (2169.20s)
ratings for Israel. But if you're under
[36:11] (2171.92s)
50, which means you're probably not
[36:13] (2173.84s)
watching Fox that much anymore. You're
[36:15] (2175.36s)
probably on to the podcast. I think the
[36:17] (2177.28s)
the disapproval rating for Israel is now
[36:19] (2179.28s)
57%. Interesting. So it really comes
[36:21] (2181.44s)
down to age. Young people across the
[36:24] (2184.08s)
>> have serious problems with what Israel
[36:25] (2185.76s)
is doing. And then, you know, as you get
[36:27] (2187.92s)
into older age groups, that's where you
[36:30] (2190.00s)
see a big difference between
[36:31] (2191.68s)
Republicans.
[36:32] (2192.08s)
>> I think Chamathy did a wonderful job of
[36:33] (2193.60s)
explaining this on Megan Kelly. There's
[36:35] (2195.20s)
Jewish people, there's the state of
[36:37] (2197.52s)
Israel, there's Israelis who live in the
[36:39] (2199.44s)
state of Israel who are also Jewish. And
[36:41] (2201.60s)
then there's BB Netanyahu. And I think
[36:43] (2203.92s)
there's a lot of concern on Netanyahu is
[36:47] (2207.68s)
just absolutely out of control is the
[36:49] (2209.36s)
consensus I think amongst many people
[36:51] (2211.84s)
across many religions and and political
[36:55] (2215.52s)
parties.
[36:55] (2215.84s)
>> Not to rehash this whole thing, but
[36:56] (2216.96s)
there are a lot of parallels from what
[36:58] (2218.48s)
happened in 107 and what happened in
[37:00] (2220.64s)
9/11 in the following ways. When you
[37:04] (2224.16s)
invade a country and you slaughter their
[37:08] (2228.48s)
people,
[37:10] (2230.48s)
it creates an enormous injury, an
[37:13] (2233.36s)
enormous emotional, physical,
[37:15] (2235.52s)
psychological injury.
[37:18] (2238.00s)
And what that country typically does is
[37:20] (2240.40s)
respond by giving the authority to the
[37:22] (2242.40s)
leader at that time to
[37:26] (2246.16s)
set the table right. And you have to
[37:28] (2248.08s)
remember there was a lot of things that
[37:29] (2249.68s)
happened post 911 that under any other
[37:31] (2251.68s)
circumstance would never have happened.
[37:33] (2253.04s)
At the top of the list would have been
[37:34] (2254.24s)
the Patriot Act which in any other world
[37:36] (2256.88s)
would never have gotten passed and would
[37:38] (2258.32s)
have seen the light of day but not for
[37:40] (2260.24s)
9/11. So there are moments where leaders
[37:43] (2263.28s)
are put in a position and they
[37:45] (2265.28s)
essentially act on behalf of their
[37:46] (2266.64s)
country and their people to write a
[37:48] (2268.64s)
wrong. I understand that. I think
[37:50] (2270.64s)
everybody understands that. What's
[37:51] (2271.84s)
happened now though is that people
[37:53] (2273.20s)
cannot logically disambiguate
[37:56] (2276.72s)
Jews, Israelis, and BB. And I think
[38:02] (2282.00s)
that's very unfortunate because we're at
[38:04] (2284.88s)
a point in time where everything gets
[38:06] (2286.56s)
conflated and this thing has become this
[38:08] (2288.72s)
third rail issue. And I find it
[38:10] (2290.96s)
absolutely shocking.
[38:13] (2293.28s)
It's completely reasonable for people to
[38:15] (2295.44s)
have a point of view on BB and say,
[38:17] (2297.68s)
"Hey, you know what? it's enough or it's
[38:20] (2300.40s)
gotten too far or whatever it is.
[38:22] (2302.88s)
>> And I think that there's a very
[38:24] (2304.64s)
reasonable claim to make that it's time
[38:26] (2306.40s)
to find different leadership, new
[38:28] (2308.64s)
leadership inside of Israel and have an
[38:30] (2310.56s)
opportunity to reset their standing on
[38:32] (2312.56s)
the global stage. They deserve that. The
[38:34] (2314.56s)
Israeli people are incredible. Jews are
[38:36] (2316.88s)
incredible. But the idea that you fold
[38:38] (2318.80s)
it all together and you look at one
[38:40] (2320.96s)
person and then you apply it to an
[38:44] (2324.00s)
entire country and then an entire
[38:45] (2325.44s)
religion is insane. May I add one thing?
[38:49] (2329.04s)
May I add one thing is I think there's
[38:51] (2331.12s)
well two things really and I'll be
[38:52] (2332.32s)
quick.
[38:53] (2333.20s)
>> One, Israel has a giant PR problem. They
[38:56] (2336.16s)
need a young sub 35year-old
[39:00] (2340.88s)
American Israeli who's super fluent in
[39:02] (2342.96s)
English conversant in social media and
[39:05] (2345.68s)
is their kind of spokesperson to America
[39:07] (2347.60s)
and they need that in France, in
[39:08] (2348.96s)
Germany, in every country and they do
[39:11] (2351.52s)
not have that. There was a young I think
[39:12] (2352.96s)
his name I forget his name but there was
[39:14] (2354.56s)
a young Israeli who was doing a really
[39:16] (2356.96s)
good job Israeli American representing
[39:19] (2359.76s)
Israel after October 7th and evidently
[39:22] (2362.08s)
BB Netanyahu's wife didn't like him so
[39:24] (2364.56s)
they canned him and they've never really
[39:26] (2366.72s)
found a replacement and I do think this
[39:29] (2369.52s)
is an urgent issue for Israel because
[39:32] (2372.40s)
it's like they are taking
[39:36] (2376.00s)
body blows every second and they're not
[39:39] (2379.84s)
even responding.
[39:41] (2381.76s)
And what I think they do, what they tend
[39:44] (2384.08s)
to do is they'll sometimes roll out
[39:46] (2386.64s)
someone who's a great man or woman in
[39:48] (2388.72s)
Israel and is in their 60s and a hero of
[39:51] (2391.92s)
a war, but isn't that fluent in social
[39:55] (2395.12s)
media. Maybe the command of English is,
[39:58] (2398.16s)
you know, technically precise, but there
[40:00] (2400.64s)
there's an accident. And those are great
[40:03] (2403.04s)
people, but they're not great spokes pe
[40:05] (2405.44s)
spokespersons. And this is an urgent
[40:07] (2407.44s)
issue. As far as the antidote, going
[40:09] (2409.20s)
back to Chamas's comment, I do think a
[40:11] (2411.92s)
big reason Trump got elected, every
[40:14] (2414.08s)
Democrat, many of the Democrats I know
[40:16] (2416.24s)
who voted for Trump, a big part of it
[40:19] (2419.20s)
was that during co they heard what their
[40:22] (2422.24s)
children were being taught
[40:25] (2425.20s)
>> and the first time
[40:26] (2426.56s)
>> and it was radicalizing for them.
[40:28] (2428.48s)
>> Yeah.
[40:28] (2428.72s)
>> And I think it's just really important
[40:30] (2430.32s)
that like as part of the American
[40:31] (2431.68s)
educational system just there not be
[40:34] (2434.96s)
overtly anti-American things. Listen,
[40:37] (2437.28s)
we've made mistakes as a country. We're
[40:39] (2439.52s)
not perfect, but we're about as good as
[40:42] (2442.24s)
it gets and we need to tell that story
[40:47] (2447.04s)
in every grade consistently
[40:50] (2450.80s)
and we can have a debate. We've done so
[40:53] (2453.12s)
many things wrong. Let's learn from
[40:54] (2454.72s)
them. But just, you know, slavery was
[40:57] (2457.92s)
endemic to the world apart from some
[41:00] (2460.08s)
countries in East Asia. Like, it's not a
[41:02] (2462.56s)
uniquely American problem. And we need
[41:04] (2464.72s)
to tell those stories because I think a
[41:06] (2466.96s)
lot of kids, the reason they're so
[41:08] (2468.72s)
susceptible to this social media
[41:10] (2470.32s)
propaganda is they've been brought up
[41:12] (2472.88s)
being ashamed to be American, ashamed of
[41:15] (2475.52s)
various things in their identity and
[41:18] (2478.56s)
being told that America is evil and we
[41:21] (2481.04s)
need to unwind that because America is
[41:23] (2483.76s)
awesome and we're the only vaguely
[41:26] (2486.16s)
successful multicultural society on
[41:28] (2488.64s)
planet Earth.
[41:29] (2489.60s)
>> Yeah. The melting pot. And if you say
[41:31] (2491.20s)
that, Gavin, if you say the melting pot
[41:32] (2492.88s)
is a beautiful thing, you're going to
[41:34] (2494.00s)
get cancelled because oh my god, you're
[41:36] (2496.96s)
getting rid of people's culture. It's
[41:38] (2498.32s)
like, no, no, keep your culture and then
[41:40] (2500.56s)
join this culture and we can all have
[41:42] (2502.24s)
this great smorgish board.
[41:43] (2503.84s)
>> Yeah. You know what your culture is? You
[41:46] (2506.08s)
know what my culture is?
[41:46] (2506.88s)
>> I don't know.
[41:47] (2507.28s)
>> It's no longer Laura Piana. I know that.
[41:48] (2508.88s)
I know you're off the train.
[41:50] (2510.48s)
>> Winning.
[41:51] (2511.44s)
>> Winning. The culture of winning. I love
[41:54] (2514.16s)
>> Learning.
[41:55] (2515.04s)
>> Learning.
[41:55] (2515.44s)
>> Progress. Adventure. Moth. Do you piss
[41:58] (2518.32s)
excellence to quote quote Ricky Bobby?
[42:00] (2520.72s)
>> Oftentimes I piss excellence
[42:03] (2523.60s)
>> just burns but oftentimes um just to
[42:07] (2527.04s)
warn just to warn the
[42:09] (2529.36s)
>> dopey Democrats who have been unable to
[42:11] (2531.92s)
get anything done.
[42:13] (2533.84s)
>> The socialists are using your party and
[42:16] (2536.96s)
uh this guy went viral.
[42:19] (2539.76s)
They're they're just a host. Like they
[42:21] (2541.68s)
literally want to infect the dem Oh
[42:23] (2543.44s)
yeah, good image. uh they want to infect
[42:25] (2545.12s)
the Democratic party and then like just
[42:28] (2548.00s)
literally get the voting base. And this
[42:29] (2549.76s)
guy Gustavo Gordillo, I don't know if
[42:31] (2551.36s)
you guys saw this, he's a DSA co-chair
[42:33] (2553.28s)
in New York City. And he just said it
[42:35] (2555.28s)
outright. I'm going to read you the
[42:36] (2556.32s)
quote. We're part of the Democratic
[42:38] (2558.00s)
Party caucus, but we don't agree with
[42:40] (2560.00s)
the way the Democratic Party runs its
[42:41] (2561.68s)
apparatus. So, we're trying to build our
[42:43] (2563.28s)
own independence by focusing on
[42:45] (2565.28s)
volunteer-led movement. We think
[42:46] (2566.48s)
everyone should be able to be trained
[42:47] (2567.84s)
and become someone who can participate
[42:49] (2569.76s)
in the political process. And we don't
[42:51] (2571.12s)
think the Democratic Party is run that
[42:53] (2573.04s)
way. In terms of the agenda, there's a
[42:54] (2574.56s)
problem in the Democratic party. They
[42:56] (2576.32s)
are funded by billionaire donors and at
[42:58] (2578.32s)
the same time they're trying to
[42:59] (2579.20s)
represent the working class. In our
[43:00] (2580.40s)
opinion, you have to choose between the
[43:01] (2581.68s)
billionaire class and the working class.
[43:03] (2583.04s)
They are just taking over the party. And
[43:05] (2585.76s)
again, back to the playbook, this worked
[43:07] (2587.76s)
really well for Trump. He took the
[43:10] (2590.64s)
Republican party over and he owned them.
[43:12] (2592.80s)
And they tried to get him out. They
[43:14] (2594.00s)
tried to get him out, but his message
[43:15] (2595.52s)
and his communication style was just too
[43:18] (2598.16s)
on point. He knew exactly what people
[43:21] (2601.12s)
wanted to hear and he knew exactly how
[43:23] (2603.04s)
to deliver it. He is a all-time comedic
[43:26] (2606.72s)
performer like of non of non- comedians
[43:29] (2609.84s)
he's the number one comedian in the
[43:31] (2611.12s)
world and of actual comedians he's in
[43:33] (2613.12s)
the top 20 so he communicates perfectly
[43:35] (2615.20s)
and that's exactly what Mandami's done.
[43:36] (2616.88s)
He's taken the Trump playbook and he has
[43:39] (2619.04s)
applied it here. He is taking over with
[43:41] (2621.28s)
this communication. You guys know I'm a
[43:42] (2622.96s)
diehard Nick fan my whole life. cried
[43:45] (2625.20s)
and was at game five when they won the
[43:46] (2626.72s)
finals. And then Mandami gave this
[43:48] (2628.72s)
speech that somebody wrote for him about
[43:50] (2630.64s)
the Knicks and I was just absolutely
[43:53] (2633.44s)
flabbergasted and upset that it was so
[43:55] (2635.12s)
great. He's got that Obama Trump
[43:57] (2637.92s)
charisma and he is going to destroy your
[44:00] (2640.16s)
party from the inside out. Socialism is
[44:02] (2642.40s)
communism and is the road to suffering
[44:04] (2644.64s)
and pain. As Gavin said, no good will
[44:07] (2647.36s)
come out of it.
[44:08] (2648.00s)
>> Isn't it amazing?
[44:09] (2649.44s)
>> Here we go.
[44:10] (2650.00s)
>> Okay. You know, you know Manny is a
[44:12] (2652.88s)
communist and what he represents is
[44:14] (2654.56s)
basically evil. And yet
[44:17] (2657.28s)
>> because he just gave a speech about the
[44:18] (2658.72s)
Knicks, you love him now.
[44:20] (2660.40s)
>> No, I love the SPEECH AND I'M LIKE THE
[44:23] (2663.20s)
SPEECH.
[44:24] (2664.40s)
>> That's all it takes. We're screwed.
[44:26] (2666.00s)
That's all it takes.
[44:27] (2667.12s)
>> It's true though. If
[44:28] (2668.80s)
>> I mean this guy, he's a total phony. I
[44:30] (2670.72s)
mean, he doesn't stop smiling. He's got
[44:32] (2672.08s)
this like crocodile smile all the time.
[44:34] (2674.16s)
He's talking about, you know, eating
[44:36] (2676.80s)
>> Yeah.
[44:37] (2677.20s)
>> And it's totally fake.
[44:38] (2678.72s)
>> Absolutely. I mean, come on.
[44:41] (2681.28s)
He was he was making Knicks references
[44:43] (2683.28s)
and it was the most get out of your seat
[44:46] (2686.40s)
standing ovation cheer, you know, speech
[44:48] (2688.40s)
I've ever heard about the Knicks and I
[44:49] (2689.92s)
was infuriated. This guy is such a good
[44:53] (2693.12s)
orator.
[44:54] (2694.16s)
>> You got programmed.
[44:55] (2695.12s)
>> I hate him.
[44:55] (2695.68s)
>> We're going to have to deprogram you
[44:57] (2697.12s)
>> No, no, no. It was like getting like uh
[44:59] (2699.76s)
hypnotized and I just pulled myself out.
[45:01] (2701.84s)
I got like pulled in for a second.
[45:04] (2704.08s)
>> Next topic.
[45:05] (2705.12s)
>> Next topic for sure.
[45:08] (2708.24s)
Oh my god, great good strong first
[45:10] (2710.16s)
topic. I didn't know you guys were going
[45:11] (2711.28s)
to go all in, so to speak. Topic two,
[45:13] (2713.60s)
Chinese open source models appear to be
[45:15] (2715.52s)
catching up with the US frontier models.
[45:18] (2718.16s)
Let's start with a GLM 5.2 released by
[45:20] (2720.80s)
China Z.AI. This is a Frontier class
[45:23] (2723.44s)
open-source free to download anywhere
[45:26] (2726.32s)
model. 744 billion parameters, 1 million
[45:28] (2728.88s)
token context window, and it's under the
[45:31] (2731.92s)
MIT license. If you don't know what that
[45:33] (2733.44s)
is, open source uh licenses have very
[45:36] (2736.80s)
>> super open source. Yeah,
[45:38] (2738.16s)
>> it's super open source. Thank you.
[45:39] (2739.44s)
>> The most open source.
[45:40] (2740.48s)
>> The most open source of all open source.
[45:42] (2742.56s)
If you open it up, you can use it
[45:44] (2744.16s)
however you like. Uh you can fork it.
[45:46] (2746.24s)
You can build your own company based on
[45:47] (2747.92s)
that. No regional restrictions, no API,
[45:51] (2751.68s)
fully self-hostable, no uh no Dario.
[45:55] (2755.44s)
>> It's just yours. It's just making your
[45:56] (2756.96s)
chain. It's yours.
[45:58] (2758.96s)
And you just got to you just got to
[46:00] (2760.48s)
reference the you just got to reference
[46:02] (2762.40s)
the license. That's it.
[46:03] (2763.44s)
>> Yeah. You shout out the license and
[46:04] (2764.80s)
you're good.
[46:06] (2766.32s)
>> Scored 51 points on the artificial
[46:08] (2768.08s)
analysis intelligence index. That's the
[46:10] (2770.32s)
highest score of any open weight model
[46:12] (2772.16s)
ever. Stacks up nicely next to the
[46:14] (2774.08s)
Frontier models. Beat GPT 5.5 on the
[46:16] (2776.32s)
Frontier SWE coding benchmark. That's a
[46:18] (2778.88s)
software one. Trails Claude Opus 4.8 by
[46:22] (2782.00s)
less than 1 percentage point. API usage
[46:23] (2783.92s)
cost obviously much cheaper 85% cheaper
[46:26] (2786.80s)
in fact than GPT 5.5 for comparable
[46:30] (2790.24s)
performance Z.AI founder told Elon Musk
[46:33] (2793.52s)
open weight fable capabilities will be
[46:35] (2795.60s)
here sooner than Q1 2027.
[46:38] (2798.48s)
Gavin, in other words, all this hand
[46:41] (2801.04s)
ringing, all of these legal
[46:43] (2803.60s)
restrictions, self-imposed restrictions
[46:46] (2806.48s)
are now completely or close to
[46:49] (2809.36s)
completely moot if they're going to have
[46:51] (2811.36s)
a model in Q1. Does this 6 months even
[46:54] (2814.88s)
matter? Does 6 months in the grand arc
[46:56] (2816.64s)
of AI matter or not? And what does this
[46:59] (2819.20s)
mean for Frontier models?
[47:01] (2821.28s)
>> I do think how good GLM 5.2 is has
[47:05] (2825.60s)
challenged some of my beliefs. And there
[47:07] (2827.92s)
was a great post from a TPU engineer
[47:10] (2830.72s)
that for sure distillation has happened.
[47:13] (2833.60s)
There's been an immense amount of
[47:15] (2835.12s)
distillation. No question. You know,
[47:16] (2836.64s)
>> please explain to the audience what that
[47:18] (2838.00s)
means.
[47:18] (2838.56s)
>> Distillation is when you have, you know,
[47:21] (2841.44s)
a a like, you know, we all have seen
[47:23] (2843.68s)
videos of these Chinese iPhone farms.
[47:26] (2846.08s)
Just picture a farm like that. tens of
[47:29] (2849.28s)
thousands of phones, iPads, and
[47:31] (2851.52s)
computers that are asking the cloud API
[47:35] (2855.68s)
through masked accounts, very specific
[47:38] (2858.48s)
questions, and then these what's called
[47:41] (2861.12s)
reasoning traces are being harvested
[47:43] (2863.68s)
because if you're on the API, you know,
[47:45] (2865.76s)
you get to see every token. And those
[47:48] (2868.00s)
reasoning traces are then fed back into
[47:50] (2870.96s)
the model during the reinforcement
[47:53] (2873.44s)
learning process and probably during the
[47:55] (2875.60s)
pre-training process. And that is a way
[47:59] (2879.04s)
that you can get really really close to
[48:01] (2881.04s)
the frontier at a fraction of the cost.
[48:03] (2883.60s)
And this is for sure going on and has
[48:06] (2886.72s)
gone on for a long time. I do think
[48:08] (2888.96s)
>> it's a cheat sheet. It's a cheat sheet
[48:10] (2890.64s)
for other models to catch up.
[48:12] (2892.08s)
>> It would be like asking Google every
[48:14] (2894.48s)
question you could ever imagine, every
[48:16] (2896.08s)
search imaginable, getting all the
[48:17] (2897.60s)
results and then putting your own search
[48:19] (2899.84s)
engine together to make it very simple.
[48:22] (2902.00s)
>> Exactly. But now that this model is so
[48:24] (2904.72s)
good, it is good enough to do its own RL
[48:28] (2908.64s)
and you know the kind of cat may be out
[48:31] (2911.12s)
of the bag. Now I don't think we really
[48:32] (2912.96s)
know how good Mythos is. We don't really
[48:35] (2915.04s)
know how good the next OpenAI model is.
[48:37] (2917.20s)
We haven't seen the next SpaceX model.
[48:39] (2919.44s)
So maybe that gap opens up again. But
[48:42] (2922.08s)
either way, I profoundly believe the
[48:44] (2924.16s)
future is composable models and you're
[48:46] (2926.48s)
going to every enterprise. You're going
[48:48] (2928.64s)
to have a what Andre Karpathy called a
[48:50] (2930.80s)
council of LLMs. You're going to have,
[48:53] (2933.28s)
you know, you're going to have Grock.
[48:54] (2934.56s)
You're gonna have Anthropic. You're
[48:55] (2935.68s)
gonna have OpenAI Google. You're gonna
[48:57] (2937.28s)
have at least two of those. I would
[48:58] (2938.64s)
argue Grock should always be one of the
[49:00] (2940.16s)
two because of its dedication to the
[49:01] (2941.60s)
truth and it will tell you as a business
[49:03] (2943.28s)
owner a politically inconvenient truth
[49:05] (2945.04s)
that you need to know for your data.
[49:07] (2947.92s)
But you're also going to have your own
[49:09] (2949.76s)
open weights model that you are on your
[49:12] (2952.56s)
data. And you're gonna put those two
[49:15] (2955.44s)
together, the frontier models and your
[49:18] (2958.24s)
own model and you are going to get you
[49:21] (2961.36s)
know real paro dominant outcomes and you
[49:24] (2964.56s)
know half the queries are going to be go
[49:26] (2966.48s)
to the open source model maybe 85% and
[49:30] (2970.08s)
only the hardest ones are then maybe
[49:32] (2972.24s)
they all go to open source first and
[49:33] (2973.76s)
only the hardest ones are then checked
[49:36] (2976.00s)
by the frontier models. So I think this
[49:37] (2977.84s)
is the future. It's coming. And a
[49:41] (2981.68s)
misconception that a lot of people have
[49:43] (2983.20s)
is that open- source models are, you
[49:45] (2985.20s)
know, somehow bad for AI. They're
[49:47] (2987.84s)
awesome for the AI infrastructure
[49:49] (2989.60s)
providers. They just shift economic
[49:52] (2992.00s)
value from the margins of the frontier
[49:54] (2994.32s)
labs to the infrastructure. And that's
[49:58] (2998.40s)
not bad for AI.
[49:59] (2999.92s)
>> That's great for them.
[50:01] (3001.04s)
>> It's great for them. It's
[50:02] (3002.40s)
>> great for them. But I do think there's
[50:03] (3003.92s)
still a role for these frontier models
[50:06] (3006.08s)
and it may be true to date frontier
[50:08] (3008.64s)
tokens are capturing 90% of the economic
[50:10] (3010.96s)
value and open source tokens are
[50:13] (3013.12s)
probably 80% plus of tokens processed
[50:15] (3015.92s)
and those ratios may be here to stay but
[50:18] (3018.16s)
I just think composable models are the
[50:21] (3021.36s)
future.
[50:23] (3023.52s)
>> What does that mean composable model?
[50:25] (3025.68s)
when a composable model where you have
[50:27] (3027.84s)
if you're a corporation if you're um do
[50:31] (3031.28s)
is there is there a new name for your
[50:33] (3033.44s)
super secret um startup Travis what's
[50:37] (3037.28s)
the new name
[50:38] (3038.16s)
>> the name is called Adams it's not super
[50:40] (3040.56s)
secret it's super awesome
[50:42] (3042.32s)
>> super awesome super awesome um for your
[50:46] (3046.56s)
super awesome startup Adams which man
[50:48] (3048.96s)
lot loads of people have been calling me
[50:50] (3050.40s)
to say how like they've been in the you
[50:52] (3052.56s)
know they they've spoken to you and
[50:54] (3054.88s)
>> invest investor the uh your your dog is
[50:57] (3057.76s)
hunting with investors, Travis.
[50:59] (3059.36s)
>> All right. Fair enough. Fair enough.
[51:01] (3061.12s)
>> Okay.
[51:02] (3062.00s)
>> You're going to have a router and every
[51:04] (3064.24s)
query that somebody comes in, every task
[51:06] (3066.64s)
that needs to be done at your company,
[51:08] (3068.64s)
that router is going to send it to, you
[51:10] (3070.96s)
know, your RL SFTD version of
[51:15] (3075.44s)
Dematron.
[51:16] (3076.56s)
>> Yes. Then at some point in the workflow,
[51:18] (3078.96s)
a frontier model may or may not come in
[51:22] (3082.32s)
to kind of check it, add to it. And
[51:24] (3084.80s)
that's what I mean by a composable model
[51:27] (3087.20s)
when you have kind of um, you know, kind
[51:29] (3089.36s)
of a symphony of models working together
[51:31] (3091.36s)
with kind of the frontier models being,
[51:33] (3093.68s)
you know, maybe the conductors.
[51:35] (3095.60s)
>> But that's what I mean when I say
[51:36] (3096.96s)
composable.
[51:38] (3098.00s)
>> Yeah, understood. Thank you.
[51:39] (3099.76s)
>> Saxs, formerly AISAR
[51:43] (3103.36s)
and and now running pcast. What are your
[51:45] (3105.36s)
thoughts on China's ascension in open
[51:49] (3109.04s)
source? And we're still looking for our
[51:50] (3110.80s)
open source champion obviously here in
[51:52] (3112.32s)
the US, but feels like
[51:54] (3114.40s)
>> Can I just say one thing? Nvidia is the
[51:56] (3116.32s)
American open source champion.
[51:57] (3117.84s)
>> Thank you. Yeah,
[51:58] (3118.64s)
>> they can release GLM 5.2 or better
[52:01] (3121.36s)
whenever they want.
[52:03] (3123.04s)
>> Okay. But why haven't they done that?
[52:06] (3126.16s)
They don't want to screw their customers
[52:07] (3127.44s)
too much of a conflict to be like
[52:09] (3129.60s)
pushing it out there too often.
[52:11] (3131.44s)
>> All of the above. Who knows? We'll see.
[52:14] (3134.32s)
Okay. Yeah. And you got to be delicate
[52:16] (3136.00s)
there. I see. Okay. So, I'll say it, not
[52:18] (3138.32s)
you. Jensen does it. Yeah. I mean, it's
[52:20] (3140.40s)
it's a it's a classic channel conflict,
[52:22] (3142.16s)
right? You're you don't want to compete
[52:23] (3143.68s)
with your customers. And he is competing
[52:26] (3146.08s)
with Elon on self-driving now. And
[52:28] (3148.16s)
Elon's making his own chips. So,
[52:30] (3150.32s)
>> I think these frontier model companies
[52:32] (3152.40s)
should think very, very carefully about
[52:35] (3155.12s)
A6 and the incentive that creates for
[52:38] (3158.40s)
Nvidia. On the model perspective,
[52:40] (3160.80s)
>> if OpenAI, which launched their Jalapeno
[52:42] (3162.96s)
chip this week and announced it being
[52:45] (3165.36s)
built by I believe Broadcom and they are
[52:48] (3168.08s)
saying, "Hey, f you to Jensen and Nvidia
[52:51] (3171.44s)
and they already were full contact with
[52:54] (3174.32s)
him." Don't be surprised if Nvidia says,
[52:56] (3176.80s)
"You know what? We kind of like the area
[52:59] (3179.44s)
you're operating in now that you're
[53:00] (3180.88s)
going to make chips and maybe OpenA
[53:02] (3182.32s)
sells those chips to other people."
[53:04] (3184.48s)
Don't be surprised if Nvidia starts an
[53:06] (3186.48s)
OpenAI competitor. You heard it here
[53:08] (3188.16s)
first on allin.
[53:10] (3190.08s)
Sax, did you want to jump in there?
[53:12] (3192.24s)
>> Sure. I mean, look, I think that China's
[53:14] (3194.56s)
been good at open source for a while
[53:16] (3196.08s)
here. There's nothing new about that,
[53:18] (3198.56s)
but there are a few things that are
[53:20] (3200.32s)
significant about GLM 5.2. So, the first
[53:22] (3202.88s)
one is it is now like you said the best
[53:25] (3205.60s)
openweight model for coding, software
[53:28] (3208.72s)
engineering, and long context agent
[53:31] (3211.44s)
work. And you gave a couple of the sweet
[53:34] (3214.08s)
bench scores. I mean it was just a tick
[53:36] (3216.16s)
below OPUS 4.8 and it was right up there
[53:39] (3219.28s)
with GBT 5.5. So if you compare this to
[53:45] (3225.68s)
again the state-of-the-art the frontier
[53:47] (3227.68s)
models for anthropic and open AI it is
[53:50] (3230.88s)
right there with the previous model. But
[53:54] (3234.64s)
you have to remember now that the
[53:56] (3236.24s)
current model fable for anthropic and
[53:59] (3239.36s)
5.6 six for open AI is now in a little
[54:03] (3243.68s)
bit of a purgatory because of all the
[54:06] (3246.16s)
reasons we covered last week. Now look,
[54:09] (3249.28s)
I like I said last week, I ultimately
[54:11] (3251.28s)
blamed Daario and the way he
[54:13] (3253.52s)
communicated and the way that he primed
[54:15] (3255.44s)
officials to be on a hair trigger with
[54:17] (3257.84s)
respect to these models and when the
[54:21] (3261.12s)
government got a credible report about a
[54:23] (3263.84s)
jailbreak from, you know, one of
[54:25] (3265.52s)
Anthropic's own most trusted partners,
[54:27] (3267.60s)
you're going to say roll that back. But
[54:29] (3269.12s)
that is the situation we're in right now
[54:31] (3271.28s)
is that Fable has been rolled back and
[54:33] (3273.84s)
GBT 5.6 is trying to navigate these new
[54:37] (3277.20s)
approval hoops. So we now have a Chinese
[54:41] (3281.12s)
openweight model that is as good as the
[54:45] (3285.44s)
currently available models from OpenAI
[54:49] (3289.12s)
and Anthropic. And look, this is a point
[54:51] (3291.20s)
I've been making really since I joined
[54:53] (3293.68s)
the administration is that we are in a
[54:56] (3296.24s)
very competitive situation with China.
[54:59] (3299.28s)
I've been saying this from the
[55:00] (3300.24s)
beginning. Our whole AI strategy from
[55:02] (3302.88s)
the get- go was about winning this AI
[55:05] (3305.12s)
race, defining it as a race, as being
[55:07] (3307.68s)
globally competitive. And we cannot
[55:09] (3309.68s)
afford to do things unnecessarily that
[55:12] (3312.48s)
slow our companies down. I hope that
[55:16] (3316.40s)
>> David can ask,
[55:17] (3317.76s)
>> do you think Daario got exactly what he
[55:19] (3319.92s)
wanted? It seems to me there's some
[55:21] (3321.84s)
chance, this has been a very calculated
[55:23] (3323.76s)
strategy to provoke the US government
[55:26] (3326.16s)
into doing what they just did and this
[55:27] (3327.84s)
is what he wants. He has a regulatory
[55:29] (3329.52s)
moat now. He can keep his future models
[55:32] (3332.40s)
behind this, you know, give it out to
[55:34] (3334.56s)
Glass Wing, use it to distill it for him
[55:36] (3336.96s)
for themselves. Do you think this is
[55:39] (3339.28s)
what they wanted? I think that on a
[55:41] (3341.04s)
certain level it is what they wanted
[55:42] (3342.40s)
because they've been advocating to have
[55:44] (3344.56s)
a federal regulator, basically a new
[55:47] (3347.04s)
agency. In fact, Daario posted a blog
[55:49] (3349.04s)
just a few weeks ago saying he wants an
[55:50] (3350.40s)
FAA for AI. They wanted government
[55:52] (3352.88s)
approval, a government approval process
[55:55] (3355.04s)
for AI models. And so, in a sense,
[55:57] (3357.20s)
they've gotten exactly what they wanted.
[55:58] (3358.72s)
Now, that being said, I don't think
[56:00] (3360.56s)
they're happy about the fact that Fable
[56:04] (3364.24s)
has been rolled back. So, in a sense,
[56:07] (3367.20s)
you could say that Daario got hoisted on
[56:09] (3369.12s)
his own petard here, or it could be a
[56:11] (3371.76s)
FAFO situation.
[56:15] (3375.04s)
But look, my view on it is we should not
[56:17] (3377.52s)
reward Daario by giving him exactly what
[56:20] (3380.00s)
he's always craved, which is some sort
[56:22] (3382.40s)
of labyrinthine government approval
[56:24] (3384.64s)
process that does reward regulatory
[56:26] (3386.88s)
capture. So I hope that very soon now
[56:31] (3391.60s)
I do think that as long as Enthropic has
[56:34] (3394.48s)
resolved the jailbreak issue then I do
[56:38] (3398.00s)
think they should be allowed to come
[56:39] (3399.12s)
back to market and similarly for open AI
[56:42] (3402.00s)
I don't think we should be delaying them
[56:43] (3403.28s)
unnecessarily. We do not have months to
[56:46] (3406.40s)
give away in this race. And let me just
[56:49] (3409.04s)
say one other thing which again is
[56:50] (3410.56s)
something I've been saying for months
[56:51] (3411.84s)
which is with respect to risks like
[56:54] (3414.00s)
cyber it is undoubtly a risk. But what
[56:57] (3417.28s)
is the response to that? The only thing
[56:59] (3419.04s)
you can do is go out and find all the
[57:01] (3421.76s)
vulnerabilities first yourself. The
[57:04] (3424.08s)
white hats have the white hats find all
[57:06] (3426.00s)
the vulnerabilities and do a big upgrade
[57:08] (3428.48s)
cycle. Roll out the patches before they
[57:11] (3431.04s)
can be exploited. The reality is that if
[57:13] (3433.76s)
you just clamp down in a way that
[57:16] (3436.48s)
doesn't even allow these models to be
[57:19] (3439.20s)
used, the Chinese are going to have
[57:21] (3441.12s)
these capabilities imminently anyway.
[57:23] (3443.20s)
You know, they're already at Opus 4.8
[57:25] (3445.36s)
level. And the founder of Z.AI, he said
[57:29] (3449.44s)
that before Q1, they'll have fable level
[57:31] (3451.76s)
capability. I believe them because look,
[57:34] (3454.32s)
the the Chinese have been, I'd say, 9
[57:37] (3457.12s)
months behind our models, plus or minus
[57:39] (3459.28s)
3 months depending on capability. But
[57:41] (3461.84s)
it's when they know there's been a
[57:43] (3463.12s)
breakthrough around something like
[57:44] (3464.96s)
cyber, they can deploy more resources
[57:47] (3467.04s)
against that particular problem and
[57:48] (3468.72s)
catch up faster.
[57:50] (3470.56s)
>> So, like I said, we're on a shock clock
[57:52] (3472.56s)
here. I've been saying for months that
[57:53] (3473.84s)
we're on a shock clock. We have to do
[57:56] (3476.16s)
smart things. We can't just slow
[57:57] (3477.76s)
everything down because that will not
[57:59] (3479.44s)
slow down the Chinese. They're not under
[58:01] (3481.36s)
our jurisdiction. We have to basically
[58:04] (3484.00s)
get these tools in the hands of our
[58:06] (3486.40s)
cyber security industry. They're the
[58:08] (3488.24s)
force multiplier. They're the enabler.
[58:10] (3490.72s)
We have to basically go out and do this
[58:12] (3492.72s)
big upgrade cycle quickly because we
[58:15] (3495.60s)
only have a few months left.
[58:16] (3496.72s)
>> They're six months behind on the model
[58:18] (3498.24s)
and they're 24 months behind on the
[58:19] (3499.92s)
silicon yet they're only a few months
[58:21] (3501.60s)
behind in total. So what game are we
[58:25] (3505.12s)
playing? This is insane. We are going to
[58:26] (3506.80s)
lose if we keep doing this stuff to
[58:28] (3508.40s)
ourselves.
[58:29] (3509.12s)
>> So let me make a point about that. So on
[58:30] (3510.64s)
the silicon, there's been a huge push in
[58:33] (3513.20s)
China by the government to push their AI
[58:36] (3516.24s)
labs to develop and train on Huawei
[58:39] (3519.84s)
chips. And look, you can take these
[58:42] (3522.16s)
claims with a grain of salt. Maybe
[58:43] (3523.44s)
they're not true, but it was claimed
[58:45] (3525.68s)
that Deepseek V4 was trained on Huawei
[58:48] (3528.64s)
chips. And now Z.AI, they are saying
[58:51] (3531.52s)
that the GLM5 family was trained
[58:54] (3534.08s)
entirely on clusters of Huawei Ascend
[58:57] (3537.28s)
910b chips. So now look, maybe they're
[59:00] (3540.16s)
lying, maybe they smuggled in some
[59:01] (3541.84s)
Nvidia chips, but the claim is that this
[59:05] (3545.60s)
was all done on indigenous chips. And
[59:09] (3549.20s)
what I believe is that China is engaged
[59:11] (3551.92s)
in a strong indigenization push right
[59:14] (3554.48s)
now. They want to prop up Huawei as the
[59:17] (3557.44s)
national champion. They want all their
[59:19] (3559.52s)
companies using Huawei chips. They still
[59:21] (3561.68s)
need to scale some of the manufacturing,
[59:23] (3563.12s)
but they're going to do that pretty
[59:24] (3564.16s)
quickly. And then what they're going to
[59:26] (3566.08s)
do is they're going to take these Huawei
[59:27] (3567.52s)
chips. I'm going to take these Huawei
[59:29] (3569.60s)
optimized models. Remember that
[59:33] (3573.36s)
GLM 5.2 the inference is optimized for
[59:38] (3578.00s)
the Huawei chips. Okay, we know that.
[59:40] (3580.80s)
And they are basically going to package
[59:42] (3582.32s)
these things up. They call it AI in a
[59:43] (3583.92s)
box. They're going to sell it at a
[59:45] (3585.36s)
fraction of the cost globally,
[59:47] (3587.60s)
>> which is what they do with every
[59:49] (3589.12s)
technology, right? Better, cheaper,
[59:50] (3590.40s)
faster
[59:51] (3591.28s)
>> or almost as good. Yeah. And that's
[59:53] (3593.20s)
another thing, as I've been saying since
[59:54] (3594.56s)
the beginning of this administration, we
[59:55] (3595.92s)
have to be proexport because China is
[59:59] (3599.12s)
going to be there within a one or two
[60:00] (3600.56s)
years. As I said, we're going to be
[60:01] (3601.84s)
kicking ourselves because we could have
[60:03] (3603.52s)
had the whole global market to
[60:04] (3604.72s)
ourselves. We invented reasons not to
[60:07] (3607.04s)
sell abroad to our friends and partners.
[60:09] (3609.84s)
And now China is going to be there
[60:11] (3611.60s)
imminently.
[60:12] (3612.80s)
>> Yeah. And with a lower price seg when
[60:15] (3615.44s)
you play with uh this new GL, you get
[60:19] (3619.12s)
some really interesting responses. it uh
[60:20] (3620.96s)
I asked it about the country of Taiwan,
[60:23] (3623.12s)
was not pleased and uh didn't give me an
[60:26] (3626.72s)
answer. I asked it about Tiana Square.
[60:28] (3628.24s)
No answer as well. I'm using the hosted
[60:30] (3630.00s)
version at Z.AI. Uh but when I asked it
[60:32] (3632.48s)
places to visit in Paris, it did an
[60:34] (3634.08s)
exceptional job. Except when I said make
[60:36] (3636.32s)
these ideas into an infographic and make
[60:38] (3638.24s)
me like a 3-day agenda, it was like,
[60:40] (3640.64s)
hey, we don't have enough time to do
[60:42] (3642.48s)
that. And it said use the other model
[60:44] (3644.56s)
because this model's too busy, but you
[60:46] (3646.80s)
can go play with it at z.ai.
[60:49] (3649.52s)
dish on a point about the censorship. So
[60:51] (3651.28s)
there's no question that these Chinese
[60:54] (3654.16s)
models have you could say censorship and
[60:57] (3657.84s)
you know there's political bias in there
[60:59] (3659.52s)
out of the box. But American companies
[61:01] (3661.76s)
have taken Chinese models and then
[61:05] (3665.04s)
essentially worked around and basically
[61:07] (3667.28s)
fixed the censorship inside their own
[61:09] (3669.44s)
forked version. So for example,
[61:11] (3671.52s)
Perplexity did this very early on with
[61:14] (3674.00s)
Chinese models. they showed that you
[61:16] (3676.48s)
could sort of put back the content on
[61:18] (3678.32s)
TNM Square and things like that. So I
[61:20] (3680.64s)
think Jal, you're absolutely right about
[61:22] (3682.24s)
the censorship, but it's not a fatal
[61:23] (3683.92s)
problem. It's something that American
[61:26] (3686.16s)
companies can fix when they
[61:28] (3688.24s)
>> Yeah.
[61:28] (3688.56s)
>> take an open source model and fork it
[61:30] (3690.80s)
and customize it.
[61:31] (3691.84s)
>> Yeah, in the hosted version, you're not
[61:33] (3693.76s)
going to get a great answer of what
[61:36] (3696.96s)
agenda you should use for tourism in the
[61:39] (3699.36s)
great country of Taiwan or your visit to
[61:42] (3702.64s)
Tanaman Square. All right, let's keep
[61:44] (3704.96s)
moving here on the dock at Micron Smash
[61:47] (3707.04s)
their earnings.
[61:49] (3709.20s)
If you don't know Micron, they are one
[61:51] (3711.04s)
of only three companies on Earth that
[61:52] (3712.64s)
make high bandwidth memory. These are
[61:54] (3714.88s)
specialized chips. They sit on top of
[61:56] (3716.56s)
the Nvidia GPU and their entire 2026
[62:00] (3720.48s)
supply is sold out and has been for some
[62:02] (3722.40s)
time. SK Heinix and Samsung also make
[62:04] (3724.88s)
HBM. Micron smashed earnings. revenue up
[62:08] (3728.72s)
4x 4x year-over-year 9 billion to 42
[62:12] (3732.48s)
billion beat expectations by 16% big
[62:16] (3736.00s)
jump in guidance for Q4 50 billion
[62:18] (3738.32s)
versus 43 billion their stock is up 10x
[62:21] (3741.28s)
shout out to Gavin in our 2025
[62:23] (3743.28s)
prediction show he gave uh a call on HBM
[62:28] (3748.48s)
makers like Micron as the best
[62:30] (3750.40s)
performing asset since that time up 14x
[62:34] (3754.16s)
>> I'm not crying in my soup
[62:35] (3755.52s)
>> you're not crying in your soup I got a
[62:36] (3756.80s)
ton of information here. I think this
[62:38] (3758.24s)
I'll just end on the Apple price
[62:39] (3759.84s)
increases. Everybody knows Apple has
[62:42] (3762.64s)
been really uh been a beneficiary of the
[62:46] (3766.08s)
run local models movement that I'm part
[62:49] (3769.20s)
of and and people are buying 128 gig,
[62:52] (3772.08s)
256 gig MacBook Pros, Mac Studios. But
[62:57] (3777.36s)
the gig is up apparently because now
[63:00] (3780.00s)
Apple, which had not passed on those
[63:02] (3782.00s)
costs to customers, is having to pass
[63:05] (3785.04s)
those increases on. So everything from,
[63:08] (3788.00s)
you know, the new MacBook Neo, which is
[63:10] (3790.32s)
their $699
[63:13] (3793.04s)
laptop, you know, kind of competing with
[63:14] (3794.88s)
Chromebooks, is now $7.99, up 15 14% and
[63:19] (3799.20s)
Mac Studio up 25%. The costs are just
[63:22] (3802.24s)
going to be very significant. Inflation
[63:24] (3804.40s)
has come to the desktop. Your thoughts,
[63:27] (3807.68s)
Gavin, on Micron and the impact on the
[63:30] (3810.16s)
industry, and is this a temporary
[63:31] (3811.92s)
bottleneck or does this mean everybody
[63:33] (3813.60s)
has to get into this business quickly?
[63:35] (3815.60s)
No. Well, one DRAM is the most important
[63:37] (3817.92s)
bottleneck. There's a whole segment of
[63:39] (3819.68s)
people on X who are very focused on
[63:41] (3821.36s)
bottleneck. I bottlenecks. I call them
[63:43] (3823.04s)
the bottleneck bros. You know, they'll
[63:44] (3824.88s)
they'll do some work with Claude, find
[63:46] (3826.56s)
some esoteric Japanese company. The
[63:49] (3829.12s)
bottleneck that matters is DRAM. And
[63:52] (3832.24s)
DRAM and HBMD RAM, this is the most
[63:54] (3834.48s)
important bottleneck simply because
[63:56] (3836.24s)
memory capacity and bandwidth are
[63:57] (3837.76s)
foundational to the performance of every
[63:59] (3839.92s)
AI model. So, this is the most important
[64:02] (3842.72s)
bottleneck. Elon is focusing the
[64:04] (3844.56s)
terrafab on memory because he sees it as
[64:07] (3847.04s)
the most important bottleneck. You know,
[64:08] (3848.80s)
not lasers, not capacitors, not power
[64:12] (3852.56s)
power supply semiconductors, not NAND
[64:15] (3855.12s)
flash, not HDDs, DRAM. Uh, and I think
[64:18] (3858.96s)
this bottleneck is going to be with us
[64:20] (3860.40s)
for a while and it is kind of
[64:22] (3862.56s)
astonishing. So I think so a few
[64:24] (3864.32s)
thoughts like what was important about
[64:25] (3865.52s)
the quarter they announced that they
[64:27] (3867.04s)
have these SDAs these supply chain
[64:28] (3868.88s)
agreements that have a floor and a
[64:32] (3872.32s)
ceiling for prices with increasingly
[64:37] (3877.20s)
large group of large customers and this
[64:40] (3880.00s)
covers essentially 50% of their revenue
[64:42] (3882.16s)
I think with just four customers and the
[64:44] (3884.88s)
floor pricing in these new contracts
[64:49] (3889.20s)
is ahead of prior cycle peaks from a
[64:52] (3892.40s)
gross margin perspective and so this is
[64:55] (3895.92s)
really I think pretty maybe end up being
[64:59] (3899.44s)
very transformational for the industry.
[65:02] (3902.08s)
most other parts of the semiconductor
[65:04] (3904.80s)
supply chain have rerated
[65:07] (3907.52s)
you know lamb research
[65:10] (3910.16s)
you know the the wafer fab equipment
[65:12] (3912.16s)
suppliers you know they all trade at
[65:14] (3914.64s)
huge premiums to DRAM relative to prior
[65:17] (3917.52s)
cycles and their business models have
[65:19] (3919.20s)
improved but you know so has the
[65:21] (3921.12s)
industry structure and business models
[65:22] (3922.48s)
of DRAM because HBM DRAM is increasingly
[65:26] (3926.00s)
a customized chip but as far as other
[65:29] (3929.04s)
people being able to do this look CXMT
[65:31] (3931.52s)
is going public in China. They are going
[65:34] (3934.40s)
to they may be the cure for Apple's
[65:37] (3937.28s)
ills. They will flood the market with to
[65:39] (3939.92s)
some degree cheap consumer grade DRAM,
[65:43] (3943.04s)
but for the DRAM you need in these AI
[65:45] (3945.20s)
servers, there are three companies that
[65:47] (3947.12s)
can make it. It's really hard to do.
[65:49] (3949.52s)
This is as close to magic as science can
[65:51] (3951.76s)
get. And you know, I think Terraab
[65:55] (3955.28s)
Terapab, you know, is going to be an
[65:57] (3957.04s)
important part of this solution. But um
[66:00] (3960.00s)
you know these these stocks still trade
[66:02] (3962.48s)
are cross-sectionally cheap relative to
[66:04] (3964.40s)
the rest of AI. Something I've been
[66:06] (3966.48s)
thinking about memory is DRAM is
[66:08] (3968.80s)
probably going to be 30 to 40% of all
[66:11] (3971.04s)
hyperscaler capex next year. Every do
[66:14] (3974.56s)
the hundreds of billions of dollars that
[66:16] (3976.08s)
are spent
[66:18] (3978.40s)
going straight to DRAM. It's wild. But
[66:21] (3981.44s)
this may actually be very valuable for
[66:24] (3984.48s)
society because it is probably, you
[66:28] (3988.00s)
know, going to, you know, inflate the
[66:29] (3989.44s)
costs of building a gigawatt data center
[66:31] (3991.20s)
to the point where like, you know, even
[66:33] (3993.36s)
for the hyperscalers, um, economics
[66:37] (3997.04s)
matter. We're caught in this prisoner's
[66:38] (3998.56s)
dilemma. And this may give us as a
[66:41] (4001.12s)
society time to adapt to adapt, you
[66:43] (4003.68s)
know, what our friend Brad Gersonner
[66:45] (4005.04s)
calls the social contract. So the high
[66:48] (4008.72s)
iPhone prices, you know, one, CXMT is
[66:51] (4011.36s)
coming for consumer grade DRAM, but two,
[66:54] (4014.56s)
this may be good for AI. It may be good
[66:58] (4018.00s)
for us as a society
[66:59] (4019.60s)
>> and making it is just really pure
[67:03] (4023.28s)
silicon, right? Like making memory is
[67:05] (4025.52s)
just incredibly refined silicon
[67:08] (4028.96s)
>> and that might be the pre- bottleneck.
[67:11] (4031.68s)
>> Yeah. Yeah. making the HBMD DRAM, making
[67:13] (4033.84s)
what Nvidia calls SOCAM, making LPDDR.
[67:17] (4037.36s)
These are the types of DRAM that are
[67:19] (4039.36s)
really hard to make, not consumer grade
[67:21] (4041.36s)
DRAM, and they are increasingly what you
[67:24] (4044.24s)
need in these AI data centers.
[67:26] (4046.96s)
>> Yeah. Semiconductor grade. Yeah.
[67:29] (4049.04s)
>> Yeah. So, my understanding of HBM stands
[67:31] (4051.60s)
for high bandwidth memory. Again, this
[67:33] (4053.60s)
is part of the, you know, the GPUs that
[67:36] (4056.24s)
go in the data center to run AI
[67:39] (4059.28s)
is that you take the the DRAM wafer or
[67:43] (4063.44s)
die and you actually stack them. And so
[67:46] (4066.24s)
I think HBM 3 is like eight. It's
[67:49] (4069.36s)
stacked eight dyes high, but now they're
[67:51] (4071.52s)
increasing to 12 and even 16. And to
[67:54] (4074.80s)
basically stack them and then package
[67:56] (4076.40s)
them all together is that's an advanced
[67:58] (4078.24s)
technology in and of itself. So you're
[68:01] (4081.68s)
seeing now, like Gavin's saying, there's
[68:03] (4083.44s)
only three companies that can do it. But
[68:05] (4085.20s)
also, this is creating significant price
[68:06] (4086.96s)
pressure for all the consumer
[68:08] (4088.00s)
electronics businesses. Apple had huge
[68:10] (4090.16s)
news today where they announced massive
[68:12] (4092.24s)
price increases. And again, it's because
[68:14] (4094.24s)
DRAM now is less available because it's
[68:17] (4097.52s)
just being hoovered up by all the data
[68:19] (4099.68s)
centers. And if you're a data center and
[68:21] (4101.36s)
you need to buy GPUs, again, those
[68:23] (4103.04s)
chips, they're using immense amounts of
[68:26] (4106.80s)
DRAM because again, one HBM chip is
[68:30] (4110.16s)
using multiple like stacks of DRAM. So,
[68:34] (4114.64s)
it's just getting slurped up and then it
[68:36] (4116.56s)
takes a couple years to ramp up new
[68:38] (4118.08s)
capacity. So, these companies are going
[68:40] (4120.24s)
to do that, but that could take a while.
[68:42] (4122.08s)
We saw that in New York. remember that
[68:43] (4123.84s)
was that Micron plant that was had just
[68:46] (4126.00s)
broken ground and then got shut down the
[68:48] (4128.24s)
same day because of some crazy
[68:49] (4129.60s)
environmental issue. So, it's not easy
[68:51] (4131.76s)
to ramp this stuff in the US. Although
[68:53] (4133.84s)
Micron is the one provider that's in the
[68:56] (4136.56s)
US, SKH is in South Korea. Samsung's in
[68:59] (4139.68s)
South Korea, too. But anyway, we're
[69:01] (4141.28s)
going to see again more of this AIL
[69:04] (4144.72s)
they're calling it. You know, it's just
[69:06] (4146.08s)
another reason I hate AI is it is in
[69:08] (4148.88s)
this narrow area of consumer electronics
[69:11] (4151.36s)
where there's competition for DRAM. It
[69:13] (4153.44s)
is leading to price inflation. Now,
[69:16] (4156.08s)
>> Microsoft raised the price of the Xbox.
[69:18] (4158.24s)
You know, it's coming for the Switch.
[69:19] (4159.52s)
It's coming for the PlayStation. You
[69:21] (4161.60s)
know, there's demand destruction because
[69:23] (4163.28s)
of prices in consumer whereas AI demand
[69:25] (4165.68s)
is relatively price insensitive. David,
[69:27] (4167.68s)
I would modify one statement. It's hard
[69:29] (4169.84s)
to build a micron. It's hard to build a
[69:32] (4172.16s)
new fab in a deep blue state.
[69:35] (4175.20s)
You can build fast.
[69:36] (4176.24s)
>> Why were they trying in New York? It's
[69:37] (4177.68s)
kind of crazy.
[69:38] (4178.64s)
>> You know, New York gave them all these
[69:39] (4179.92s)
incentives and but it none of those
[69:42] (4182.24s)
incentives matter. It's a little bit
[69:43] (4183.44s)
like solar power. You can be as pro-
[69:44] (4184.88s)
environmental as you want. But if you
[69:47] (4187.04s)
can't build and install solar because of
[69:49] (4189.44s)
regulations, it doesn't matter. So, you
[69:52] (4192.32s)
know, maybe that Micron plant ends up
[69:54] (4194.08s)
getting, you know, built in my home
[69:56] (4196.32s)
state of Texas.
[69:57] (4197.60s)
>> The incentive game has kind of
[69:59] (4199.36s)
flip-flopped. It's like it used to be
[70:00] (4200.80s)
the states were courting the factories
[70:03] (4203.12s)
and the fabs. Now it's the fabs are
[70:05] (4205.20s)
like, "Which state can actually build
[70:06] (4206.72s)
this? We'll pay you whatever you want.
[70:08] (4208.56s)
Just tell us where to send the envelope.
[70:10] (4210.88s)
We'll we'll we'll drop a couple of
[70:12] (4212.24s)
envelopes off. It's not a problem."
[70:15] (4215.36s)
>> Gavin, can you say how long it's going
[70:17] (4217.20s)
to take to stand up the fab at Terraab?
[70:23] (4223.20s)
>> Well, I mean,
[70:25] (4225.68s)
if it was a normal fab, it would be a
[70:27] (4227.84s)
two, three, three and a half year
[70:29] (4229.52s)
process. But, you know, we we we've seen
[70:32] (4232.24s)
what Elon has done to other construction
[70:34] (4234.48s)
processes
[70:36] (4236.00s)
and, you know, he's starting with some
[70:37] (4237.60s)
some advantages with the Intel
[70:39] (4239.76s)
partnership. So, I don't think anyone
[70:42] (4242.00s)
knows, but based on past history, he's
[70:45] (4245.76s)
probably going to stand up terapab
[70:47] (4247.68s)
faster than other fabs have been stood
[70:49] (4249.76s)
up. But it what this is really hard.
[70:52] (4252.56s)
It's really hard. It's the intersection
[70:54] (4254.00s)
of magic and science. You can't believe
[70:55] (4255.76s)
how complicated this is. So, it's going
[70:57] (4257.92s)
to be hard. But, you know, he has a he
[71:00] (4260.32s)
has a track record of doing, you know,
[71:02] (4262.32s)
what Jensen called impossible
[71:04] (4264.24s)
superhuman. And so, we'll see. We'll see
[71:06] (4266.88s)
how long it takes.
[71:08] (4268.48s)
>> You know, one other point here that I
[71:10] (4270.72s)
guess is it might be relevant to SpaceX
[71:12] (4272.88s)
AI, although it's don't have to limit it
[71:14] (4274.96s)
to this is um I think there's an
[71:17] (4277.20s)
assumption that over time it would get
[71:19] (4279.60s)
cheaper and easier to stand up new data
[71:23] (4283.28s)
centers, right? But what you're saying
[71:24] (4284.88s)
is actually it might be getting harder.
[71:26] (4286.64s)
It might be getting more expensive,
[71:28] (4288.40s)
right? Because there's competition for
[71:30] (4290.00s)
these components. The memory is getting
[71:31] (4291.36s)
more expensive. I'm not sure that the
[71:33] (4293.04s)
GPUs are getting any cheaper. I guess
[71:35] (4295.20s)
some of these
[71:35] (4295.76s)
>> transformers, the switch gear,
[71:37] (4297.84s)
>> the energy might be getting cheaper and
[71:39] (4299.36s)
then the entitlements are getting harder
[71:40] (4300.72s)
and the political situation is getting
[71:42] (4302.88s)
harder. There's very few places you can
[71:44] (4304.40s)
even stand up new data centers. So, is
[71:47] (4307.28s)
it the case that actually it's going to
[71:50] (4310.08s)
get more and more expensive to
[71:52] (4312.40s)
>> 100%. So to stand up a 1 gigawatt data
[71:55] (4315.36s)
center, it's $35 billion in
[71:57] (4317.12s)
semiconductors, Nvidia semiconductors,
[71:59] (4319.20s)
and it's $25 billion of power and
[72:01] (4321.36s)
cooling equipment. And that is clearly
[72:02] (4322.96s)
inflationary because a lot of that 25
[72:05] (4325.20s)
billion is the human labor required to
[72:07] (4327.20s)
install it. So the calculation that
[72:10] (4330.16s)
needs to be done for orbital compute is
[72:12] (4332.24s)
it's 35 billion of silicon in each space
[72:15] (4335.04s)
and you know in in literally outer space
[72:17] (4337.36s)
and orbit and on ground. But if you can
[72:20] (4340.40s)
get the cost of launch significantly
[72:23] (4343.04s)
below that $25 billion, then the math
[72:25] (4345.84s)
starts to really mass. And when Starship
[72:28] (4348.40s)
is reusable, it's going to cost $5
[72:30] (4350.64s)
billion to put a gigawatt of compute
[72:32] (4352.48s)
into space. And something that drives me
[72:33] (4353.92s)
crazy is people picture these Pentagoniz
[72:36] (4356.96s)
data centers. No, it's racks in space
[72:39] (4359.12s)
linked with lasers. It's it's kind of a
[72:41] (4361.60s)
virtual data center in space.
[72:43] (4363.36s)
>> Wait, five billion. Is that five billion
[72:45] (4365.20s)
of launch cost or what?
[72:46] (4366.48s)
>> Five billion of launch cost. Now you're
[72:47] (4367.84s)
at 40 billion to put the gig into space.
[72:50] (4370.16s)
You're at 60 billion terrestrially. And
[72:52] (4372.40s)
the 25 billion that is power and compute
[72:54] (4374.72s)
is clearly inflationary. And so it may
[72:57] (4377.92s)
be that in three or four years it's 70
[73:01] (4381.20s)
billion verse 40 billion. And that five
[73:04] (4384.88s)
as starship becomes rapidly reusable is
[73:07] (4387.84s)
likely deflationary. So this is the
[73:10] (4390.32s)
economics that underpin orbital compute
[73:12] (4392.48s)
from first principles. And then on an
[73:14] (4394.24s)
ongoing basis, you are you're maybe
[73:17] (4397.12s)
paying a billion dollars a year for the
[73:19] (4399.44s)
power to run those chips and cool them.
[73:22] (4402.72s)
>> If I had to make a guess, I think what's
[73:24] (4404.32s)
going to happen is that since 2021 about
[73:27] (4407.84s)
40% of all data centers get contested,
[73:30] (4410.96s)
right?
[73:32] (4412.48s)
I think that number is going to go up.
[73:34] (4414.48s)
So saxs I suspect that whatever
[73:37] (4417.20s)
forecasted energy consumption that we
[73:39] (4419.36s)
are looking at in AI is grossly
[73:42] (4422.32s)
imbalanced. There is very very meager
[73:45] (4425.76s)
supply and there's effectively infinite
[73:48] (4428.64s)
demand. So that probably pulls forward
[73:51] (4431.76s)
the economic equation to want to go to
[73:54] (4434.48s)
space. But then again, that's going to
[73:57] (4437.36s)
prefer SpaceX and their compute stack
[74:01] (4441.60s)
and their compute decisions over the
[74:03] (4443.52s)
hyperscalers and over anybody else. And
[74:06] (4446.16s)
so you're going to have a cost of an
[74:07] (4447.44s)
output token, I think, terrestrially,
[74:09] (4449.12s)
particularly from the hyperscalers, be a
[74:11] (4451.36s)
little economically lopsided versus
[74:13] (4453.04s)
SpaceX. Once they get it to scale, now
[74:15] (4455.12s)
that's the key statement.
[74:17] (4457.36s)
Whatever is left on the ground, though,
[74:19] (4459.12s)
will be incredibly incredibly valuable.
[74:21] (4461.28s)
It'll be a diamond. These are diamonds.
[74:23] (4463.28s)
And the thing is, you have to find
[74:24] (4464.88s)
reasonable size, right? You can't have a
[74:28] (4468.00s)
10 kilowatt diamond. That's like a
[74:30] (4470.24s)
little pebble of nobody cares. But if
[74:33] (4473.68s)
you're in the reasonable hundreds of
[74:35] (4475.52s)
megawws to gigawatts, man, those are
[74:37] (4477.84s)
like hope diamonds.
[74:39] (4479.76s)
>> Those are just lock them down,
[74:41] (4481.76s)
>> which I own.
[74:44] (4484.24s)
Travis, there was a uh interesting
[74:46] (4486.88s)
trademark filed this week. So, more in
[74:49] (4489.28s)
the investigator uh investor uh
[74:52] (4492.32s)
investigator
[74:54] (4494.32s)
uh of the Tesla plus SpaceX marriage
[74:58] (4498.08s)
that everyone seems to believe is going
[75:00] (4500.32s)
to happen uh shortly.
[75:01] (4501.76s)
>> You can you can you can say that that's
[75:03] (4503.44s)
you can give me credit for it.
[75:04] (4504.88s)
>> Of course. Yes. Uh as Chimath has
[75:07] (4507.52s)
architected in his uh in his high perch.
[75:10] (4510.80s)
Uh but this trademark came out.
[75:12] (4512.48s)
>> How cool would that be? How cool would
[75:14] (4514.32s)
it be for those two to come together?
[75:16] (4516.32s)
>> It's gonna happening. And uh it'll be
[75:18] (4518.72s)
incredible. And uh if you're lucky
[75:20] (4520.40s)
enough to be an owner of both
[75:24] (4524.00s)
>> top bottom,
[75:26] (4526.56s)
everybody can decide to be an owner of
[75:28] (4528.16s)
>> Jason. Jason, I'll be I'll I'll be
[75:30] (4530.00s)
making love to myself when this happens.
[75:32] (4532.00s)
There'll be no
[75:32] (4532.56s)
>> So no different than any other Thursday.
[75:34] (4534.48s)
>> Exactly.
[75:36] (4536.40s)
>> On the top and the bottom.
[75:37] (4537.92s)
>> The top and the bottom just like any
[75:39] (4539.36s)
other Saturday night.
[75:41] (4541.36s)
>> Here's the trademark for Megapod that
[75:43] (4543.92s)
came out. This is a uh filing date of
[75:47] (4547.28s)
618 20226. So a very recent June 18th.
[75:50] (4550.88s)
Modular data center hardware for
[75:52] (4552.48s)
artificial intelligence computing
[75:53] (4553.84s)
comprised of network of computer servers
[75:56] (4556.48s)
computer hardware for artificial
[75:57] (4557.68s)
intelligence processing computer network
[75:58] (4558.96s)
hardware electric power distribution
[76:00] (4560.40s)
units and cooling systems sold as a unit
[76:02] (4562.72s)
self-contained modular computing
[76:04] (4564.80s)
hardware systems for artificial
[76:06] (4566.08s)
intelligent workloads yada yada yada.
[76:08] (4568.16s)
Essentially
[76:09] (4569.44s)
>> just to explain what this is. Well,
[76:10] (4570.72s)
yeah. Let me let me just,
[76:12] (4572.32s)
>> by the way, that description makes I
[76:14] (4574.32s)
don't know what it I still don't know
[76:15] (4575.20s)
what it is.
[76:15] (4575.60s)
>> Essentially, what people are saying is
[76:16] (4576.88s)
this is going to be a giant battery pack
[76:19] (4579.68s)
with GPUs at the supercharger stations
[76:22] (4582.48s)
which have already been approved. That's
[76:24] (4584.08s)
the back channel. Chamatha, go ahead.
[76:26] (4586.48s)
>> You have a couple of issues right now to
[76:28] (4588.24s)
turn on compute terrestrially. So,
[76:31] (4591.04s)
assume you have land, that's relatively
[76:33] (4593.76s)
straightforward. Assuming you can get it
[76:35] (4595.20s)
zoned, less straightforward. Assuming
[76:36] (4596.96s)
you can get power, very difficult. then
[76:39] (4599.28s)
you have a very critical design
[76:40] (4600.72s)
decision. So all of these folks publish
[76:42] (4602.80s)
these things called the basis of design
[76:44] (4604.40s)
and your your bods essentially tell you
[76:47] (4607.12s)
here's the anthropic spec, here's the
[76:48] (4608.64s)
open AI spec, here's the coreweave spec,
[76:51] (4611.44s)
here's the AWS spec, here's GCP and you
[76:53] (4613.52s)
get these 50 and up to upwards of
[76:56] (4616.64s)
500page documents of all these technical
[76:58] (4618.80s)
details. The issue that we have is I
[77:01] (4621.76s)
don't know if you guys have used OpenAI
[77:03] (4623.04s)
or Anthropic recently where you get the
[77:04] (4624.56s)
whole thing of like come back later,
[77:06] (4626.08s)
right? That come back later is
[77:07] (4627.60s)
completely unacceptable. It just means
[77:09] (4629.04s)
that they have no compute.
[77:11] (4631.60s)
So in trying to find
[77:13] (4633.84s)
>> surge pricing is coming.
[77:14] (4634.96s)
>> Yeah, let's go.
[77:16] (4636.72s)
>> So in trying to find this solution, what
[77:18] (4638.88s)
is happening is these basis of design
[77:20] (4640.88s)
the restrictions, the specificity is
[77:23] (4643.52s)
being relaxed and one of the key
[77:25] (4645.12s)
constraints is we've moved to an
[77:27] (4647.84s)
architectural model from Google and
[77:30] (4650.16s)
Nvidia that has said look we have to
[77:32] (4652.16s)
liquid cool these racks. These are very
[77:34] (4654.80s)
complicated big girth supercomputer
[77:37] (4657.12s)
racks essentially. And now we're also
[77:40] (4660.08s)
going back and saying, you know what,
[77:41] (4661.36s)
maybe some of these older stuff that's a
[77:44] (4664.00s)
little bit less proficient and a little
[77:45] (4665.60s)
bit less useful, but we can air cool
[77:48] (4668.00s)
them is useful. And everybody's like,
[77:51] (4671.04s)
yeah, you know what? We should try to
[77:52] (4672.16s)
use everything that's available in that
[77:54] (4674.48s)
second class. What some very smart
[77:56] (4676.80s)
people are doing are like, "Wow, well,
[77:58] (4678.08s)
here's a shipping container that you can
[77:59] (4679.60s)
just drop on a concrete pad somewhere,
[78:01] (4681.68s)
plug in the power, and let it rip." And
[78:04] (4684.48s)
so, Jason, what you're seeing is that
[78:06] (4686.64s)
level of investment that's happening. So
[78:08] (4688.40s)
there are companies, you know, like Dell
[78:10] (4690.08s)
makes these racks, companies like
[78:11] (4691.92s)
Vertive makes these modules and I think
[78:14] (4694.64s)
if Tesla can make them available,
[78:18] (4698.16s)
folks like us for my data center
[78:20] (4700.00s)
project, we would be enormous buyers of
[78:22] (4702.88s)
these things because we would just
[78:24] (4704.32s)
literally prefab them in a warehouse,
[78:26] (4706.72s)
right?
[78:27] (4707.52s)
>> Get the chips, prefab them, truck them
[78:30] (4710.08s)
to the place, crane them in, turn it on,
[78:33] (4713.20s)
off to the races you go. you have like a
[78:35] (4715.12s)
90day build cycle which is unheard of.
[78:39] (4719.28s)
So that that's where these mega pods are
[78:40] (4720.96s)
coming from. I hope it's not entirely
[78:44] (4724.08s)
consumed by Tesla and SpaceX internally.
[78:46] (4726.80s)
>> Well, that's the rumor is that he's
[78:48] (4728.56s)
going to put them at the supercharging
[78:50] (4730.72s)
network where he has a lot of land and
[78:52] (4732.24s)
he's got power there already and those
[78:53] (4733.68s)
are lightly used in some cases. You
[78:55] (4735.92s)
>> there are other requirements. The
[78:57] (4737.28s)
problem with some of these things is
[78:58] (4738.48s)
that you need to have certain levels of
[78:59] (4739.92s)
access. There are certain infosc
[79:01] (4741.52s)
requirements. There are certain liquid
[79:02] (4742.88s)
cooling requirements that make many of
[79:05] (4745.12s)
the workload applications unfeasible at
[79:07] (4747.20s)
a place like a supercharger center where
[79:09] (4749.36s)
random bumble are like trapesing around
[79:12] (4752.00s)
>> security guard put it off to us. So
[79:14] (4754.64s)
guys, I've got I've got 500 properties
[79:17] (4757.84s)
with lots of energy, lots of mechanical
[79:22] (4762.16s)
cooling systems
[79:24] (4764.16s)
and lots of uh LNG access or sorry not
[79:27] (4767.44s)
LG natural gas, sorry,
[79:30] (4770.80s)
already piped in. So on all the stuff
[79:34] (4774.24s)
I'm doing on robotics, AI, physical AI,
[79:37] (4777.76s)
we're literally looking at putting some
[79:39] (4779.68s)
of our compute
[79:40] (4780.96s)
>> Wow.
[79:41] (4781.44s)
>> into our kitchens.
[79:43] (4783.52s)
compute kitchens.
[79:44] (4784.72s)
>> You're going to be able to buy small
[79:48] (4788.48s)
modular
[79:50] (4790.08s)
let's put data center in a quote.
[79:51] (4791.60s)
>> Yeah, but but it doesn't it doesn't
[79:53] (4793.28s)
matter. Like I I just go buy the GPUs
[79:55] (4795.92s)
and I'll just I just set it up in a
[79:57] (4797.68s)
kitchen,
[79:58] (4798.72s)
>> right? It's not that No, hold on. It's
[80:01] (4801.20s)
not a Bitcoin miner.
[80:02] (4802.48s)
>> No, no, hold on, hold on, hold on. It's
[80:04] (4804.08s)
not thating easy. Okay. Like, if you
[80:05] (4805.76s)
guys want to build a cloud and
[80:06] (4806.96s)
contribute it to a pool,
[80:08] (4808.64s)
>> you're going to have to sign up for
[80:09] (4809.68s)
liquidated damages. And you're not gonna
[80:11] (4811.76s)
sign up to
[80:12] (4812.48s)
>> SLA. I'm not a hyperscaler. I'm just
[80:14] (4814.24s)
using it for myself.
[80:15] (4815.84s)
>> Yeah. Rack and stack.
[80:16] (4816.88s)
>> Oh, okay. Sure. You can use it for
[80:18] (4818.48s)
yourself. I'm saying the more
[80:19] (4819.84s)
interesting opportunity is when
[80:22] (4822.64s)
>> whatever Travis says. Let's talk about
[80:24] (4824.16s)
the interesting stuff.
[80:25] (4825.36s)
>> No, no. I'm just saying like
[80:27] (4827.20s)
>> what I thought you were going with this,
[80:28] (4828.40s)
Travis, is like you could build a
[80:29] (4829.84s)
synthetic pool and contribute it to a
[80:32] (4832.24s)
neocaler. And all I'm saying is to do
[80:34] (4834.40s)
that is a leap of things that you'd have
[80:36] (4836.16s)
to do that you probably don't want to do
[80:37] (4837.52s)
because it
[80:38] (4838.40s)
>> veers you away from what you're
[80:40] (4840.00s)
>> doing. I think the security thing is the
[80:41] (4841.92s)
main issue around non data center type
[80:45] (4845.52s)
real estate to be honest. It's the big
[80:47] (4847.44s)
one. Look, you you got to do liquid
[80:49] (4849.28s)
cooling, you got to do mechanical
[80:50] (4850.72s)
systems for air cooling. Uh you have
[80:53] (4853.44s)
energy. Like there's a lot of things you
[80:54] (4854.80s)
got to do. But assuming you even had all
[80:56] (4856.32s)
that, the architecture of a data center
[80:58] (4858.88s)
is set up like they have man traps,
[81:01] (4861.20s)
right? where you go into a room, the
[81:04] (4864.24s)
door closes behind you that you got into
[81:06] (4866.72s)
with your fingerprint. Then you go into
[81:08] (4868.64s)
the next part of it again with your
[81:10] (4870.72s)
fingerprint only when that door closes.
[81:13] (4873.52s)
>> well, just out of genuine curiosity, how
[81:15] (4875.36s)
much does one of those man traps cost?
[81:16] (4876.88s)
Like, why can't you put some of those in
[81:18] (4878.64s)
>> Yeah, maybe you could.
[81:19] (4879.44s)
>> Are you interested in putting in a man?
[81:20] (4880.96s)
>> I'm just trying to help trap some men.
[81:23] (4883.36s)
I'll try to interpret where Chimat's
[81:25] (4885.36s)
going a little bit is like a mega pod
[81:27] (4887.44s)
that's sitting at a char like a public
[81:29] (4889.52s)
charging situation because of the
[81:32] (4892.00s)
physical access of it like there's
[81:34] (4894.32s)
probably some stuff you've got to do if
[81:36] (4896.16s)
you're going to resell it but if you're
[81:38] (4898.00s)
going to use it for yourself you can use
[81:39] (4899.60s)
it for yourself the real value I think
[81:41] (4901.68s)
in the Travis example which I find super
[81:43] (4903.52s)
exciting is if you can contribute it to
[81:46] (4906.96s)
a distributed training pool or a
[81:49] (4909.20s)
distributed inference pool that has a
[81:50] (4910.80s)
lower SLA which is more of how this
[81:53] (4913.44s)
community-based oriented work. Jason, I
[81:56] (4916.00s)
think you've talked about Bit Tensor. I
[81:58] (4918.56s)
think Venice is a project. I think
[82:00] (4920.16s)
Plurales is another project. Travis,
[82:01] (4921.84s)
that's where I think it's super exciting
[82:03] (4923.84s)
>> but but the one thing I'll just throw
[82:05] (4925.52s)
out there, guys, the distributed
[82:06] (4926.96s)
training stuff.
[82:09] (4929.04s)
If these things are far away from each
[82:10] (4930.72s)
other, meaning not right next to each
[82:12] (4932.80s)
other, they're so less efficient. Like
[82:15] (4935.44s)
the efficiency drops dramatically. You
[82:17] (4937.84s)
want these things to be right next to
[82:19] (4939.44s)
each other physically. You get like
[82:23] (4943.12s)
multiple orders of mag at least an order
[82:25] (4945.12s)
of magnitude type efficiency maybe plus+
[82:27] (4947.28s)
plus like it's a big deal. Yeah.
[82:30] (4950.08s)
>> So like you could have two we have two
[82:32] (4952.32s)
facilities passing the jobs from one GPU
[82:34] (4954.32s)
to the other is super important and if
[82:36] (4956.40s)
it's on a peer-to-peer network
[82:38] (4958.56s)
>> it's going to have lag. It's
[82:39] (4959.92s)
>> honestly even if it's on your own fiber
[82:42] (4962.96s)
connected like 2 kilometers away you're
[82:45] (4965.92s)
screwed. It's not a thing.
[82:47] (4967.60s)
>> This is 100% true. But everything that
[82:50] (4970.24s)
cuts against training works for
[82:53] (4973.60s)
inference
[82:54] (4974.96s)
>> where respect
[82:56] (4976.48s)
>> well latency is super important and I do
[82:58] (4978.88s)
think you know distributed inference
[83:02] (4982.08s)
distributed inference clouds are coming.
[83:04] (4984.16s)
>> Yes, I get that. That's
[83:05] (4985.28s)
>> and to riff and to riff on like what
[83:07] (4987.28s)
Chamas said in all of this like one I
[83:09] (4989.76s)
mean there is actually a startup that is
[83:12] (4992.24s)
trying to put four GPU units with kind
[83:14] (4994.80s)
of a battery on people's houses and give
[83:17] (4997.52s)
them a discount on their power and then
[83:20] (5000.48s)
you can do inference for that
[83:22] (5002.08s)
neighborhood you know from those four
[83:24] (5004.72s)
GPUs and it's like lock sealed so nobody
[83:27] (5007.28s)
can get in but there's there's another
[83:29] (5009.76s)
dynamic that I think we should talk
[83:30] (5010.96s)
about with all of this and can play into
[83:33] (5013.60s)
the megapods And you know other people
[83:36] (5016.24s)
are kind of working on data centers. You
[83:37] (5017.84s)
know Crusoe is working on you know
[83:40] (5020.08s)
modularly assembling data centers you
[83:42] (5022.08s)
know kind of like a data center and
[83:43] (5023.76s)
think of it as like an 18-wheeler um
[83:45] (5025.68s)
what do you call those things the
[83:46] (5026.72s)
18-wheeler
[83:48] (5028.32s)
you know shipping container whatever it
[83:50] (5030.08s)
is. Um but that is the disagregation of
[83:53] (5033.04s)
inference into prefill and decode. When
[83:55] (5035.68s)
you when a model is answering your
[83:57] (5037.36s)
question it's doing two things. The
[83:58] (5038.88s)
prefill part is understanding the
[84:00] (5040.56s)
question and its answer thus far. And
[84:03] (5043.52s)
think of think of that as the more you
[84:05] (5045.44s)
can remember the bigger your you know
[84:07] (5047.68s)
your memory capacity literally the more
[84:09] (5049.76s)
words you can remember the better.
[84:11] (5051.76s)
Decode is the process of generating the
[84:14] (5054.40s)
next token and that is a memory
[84:17] (5057.04s)
bandwidthbound problem and think of it
[84:18] (5058.48s)
as the faster you can speak the better
[84:21] (5061.60s)
and these two types of inference are
[84:26] (5066.24s)
increasingly being disagregated and
[84:28] (5068.08s)
Chimath
[84:30] (5070.24s)
was an investor in Grock which Nvidia
[84:32] (5072.72s)
bought and they're going to use this
[84:34] (5074.72s)
cerebrus is the other solution today
[84:37] (5077.76s)
that is available and you can put Grock
[84:40] (5080.24s)
or Cerebrris
[84:41] (5081.52s)
decode,
[84:43] (5083.04s)
you know, optimized chips. They both say
[84:44] (5084.72s)
you can do more than decode on them, and
[84:46] (5086.56s)
that's true. In front of old Nvidia GPUs
[84:50] (5090.56s)
like H100s. So, you can you can lift
[84:52] (5092.88s)
H100's, A100s out of some old data
[84:55] (5095.36s)
center, put them in one of these mega
[84:57] (5097.44s)
mega pods, you know, a rack in a
[84:59] (5099.36s)
shipping container, put a Grock or
[85:01] (5101.76s)
Sirius in front of it, and you can get a
[85:03] (5103.60s)
very competitive solution. And so I do
[85:06] (5106.32s)
think the disagregation of inference,
[85:08] (5108.56s)
we're going to be using GPUs for seven
[85:11] (5111.04s)
years, 10 years, 12 years, and that's
[85:12] (5112.64s)
great because it lowers the cost to
[85:13] (5113.92s)
finance them, which makes this AI
[85:15] (5115.92s)
revolution more financable. Jamath, you
[85:18] (5118.72s)
want to riff on on Grock? Should we
[85:20] (5120.48s)
should we should we
[85:21] (5121.52s)
>> I really agree with everything. You're
[85:23] (5123.12s)
incredibly well steeped in the space.
[85:24] (5124.80s)
It's so exciting. I don't have any
[85:26] (5126.16s)
investments or anything in the
[85:27] (5127.44s)
distributed compute space, but at the
[85:29] (5129.20s)
intersection of competing against China,
[85:32] (5132.40s)
having a vibrant American open source
[85:34] (5134.40s)
community, having a bunch of distributed
[85:36] (5136.64s)
models for purposes of free speech and
[85:38] (5138.48s)
otherwise that Travis mentioned earlier,
[85:40] (5140.56s)
I do think this idea of distributed
[85:42] (5142.48s)
inference has a real place in the
[85:45] (5145.52s)
American ecosystem. I don't exactly know
[85:47] (5147.44s)
where and how and how homeowners would
[85:49] (5149.84s)
get paid, but whoever figures that out
[85:52] (5152.96s)
as a pan-American idea, I think is a is
[85:55] (5155.68s)
really onto some
[85:56] (5156.96s)
>> and yes, Travis, obviously slower, but
[85:59] (5159.04s)
people are contributing compute to this
[86:01] (5161.04s)
that's like kind of surplus computer or
[86:02] (5162.96s)
comput that's available to
[86:05] (5165.44s)
>> unused computing unused comput. Uh, and
[86:08] (5168.48s)
then there's Targon, which is just
[86:09] (5169.84s)
straight up people are putting and you
[86:12] (5172.24s)
can rent H200's by the hour for three
[86:15] (5175.04s)
bucks, four bucks, and it's
[86:16] (5176.40s)
permissionless. Anybody can contribute
[86:19] (5179.20s)
provided, and this is kind of the magic
[86:21] (5181.52s)
of Bit Tensor is there are validators
[86:23] (5183.44s)
that make sure you're putting in what
[86:24] (5184.72s)
you're say you're putting into the
[86:25] (5185.92s)
network. And so if somebody just had the
[86:27] (5187.84s)
hardware and Tesla said from now on
[86:29] (5189.92s)
every Power Wall, and I I I don't put it
[86:32] (5192.08s)
past him to do this. You buy a Power
[86:34] (5194.08s)
Wall, we give you a discount on it.
[86:36] (5196.32s)
Every Power Wall has our GPUs in it.
[86:39] (5199.04s)
Part of the offering is you can't buy a
[86:41] (5201.20s)
Power Wall without putting GPUs in it.
[86:43] (5203.44s)
And when you're not using it, we will
[86:45] (5205.04s)
pay you for your battery. And you put a
[86:48] (5208.32s)
Starlink on your roof. And now you've
[86:49] (5209.92s)
got a distributed system where you get a
[86:52] (5212.64s)
couple of of these um power walls with
[86:55] (5215.92s)
their own silicon in it and a Starlink.
[86:58] (5218.80s)
And now Elon's created an infinite
[87:01] (5221.68s)
number of home battery backup systems.
[87:04] (5224.32s)
people get their battery system for
[87:06] (5226.80s)
>> He gets the exclusive for 20 years.
[87:09] (5229.76s)
>> You guys see that there's a rumor that
[87:11] (5231.20s)
Elon's going to buy T-Mobile.
[87:14] (5234.16s)
>> By the way, we should just because
[87:16] (5236.56s)
Cerebrus has blown up and gone through
[87:18] (5238.16s)
deal price. We should talk about
[87:19] (5239.36s)
Cerebras a little.
[87:20] (5240.32s)
>> So, uh, Cerebras had an incredible IPO.
[87:23] (5243.20s)
Give us an update on where these IPOs
[87:25] (5245.12s)
are happening. Obviously, SpaceX had an
[87:26] (5246.64s)
incredible IPO, but has retreated from
[87:28] (5248.64s)
this, you know, otherworldly $200 share
[87:31] (5251.36s)
price. So, so to the extent you can talk
[87:32] (5252.80s)
about these two as well as the two IPOs
[87:35] (5255.68s)
to come, $4 trillion in backlog. We've
[87:38] (5258.32s)
obviously got OpenAI and Claude, I'm
[87:40] (5260.16s)
sorry, Anthropic, which makes Claude.
[87:42] (5262.00s)
Both of those will be worth a billy
[87:43] (5263.44s)
plus. You put all this together, 4
[87:45] (5265.36s)
trillion of new offerings plus Cerebras
[87:47] (5267.44s)
in there in the mix. How does the market
[87:50] (5270.32s)
manage this much new inventory being put
[87:53] (5273.28s)
on the market? Obviously the flow to
[87:54] (5274.64s)
SpaceX is notably small but over time
[87:57] (5277.84s)
people like yourselves and and other
[87:59] (5279.44s)
insiders founders fund etc will be
[88:01] (5281.76s)
unlocked uh and have the ability to
[88:04] (5284.00s)
distribute to their LPs. So this is
[88:05] (5285.76s)
going to be a moving target I think on
[88:08] (5288.32s)
terms of share price and can the market
[88:10] (5290.00s)
absorb this? Where does the money come
[88:11] (5291.52s)
from retail or does it come from people
[88:14] (5294.96s)
selling their Bitcoin and moving it over
[88:16] (5296.64s)
to something more exciting? What's the
[88:18] (5298.56s)
dynamic here in the market? I know we're
[88:19] (5299.92s)
in uncharted territory, Gavin. Well, for
[88:22] (5302.08s)
sure there's no precedent for any of
[88:23] (5303.52s)
this, but a few things I would say like
[88:25] (5305.44s)
just in no particular order. I think
[88:27] (5307.12s)
Anthropic is worth $3 trillion today and
[88:30] (5310.08s)
it's very important.
[88:30] (5310.72s)
>> I'm sorry, did you say Anthropic is
[88:32] (5312.24s)
worth $3 trillion?
[88:33] (5313.68s)
>> Yeah, I think that is roughly where it
[88:35] (5315.36s)
would probably trade as a public company
[88:39] (5319.28s)
>> Wow.
[88:40] (5320.24s)
>> Yeah.
[88:41] (5321.60s)
>> I mean, look, they're going to do
[88:43] (5323.20s)
they're going to end this year
[88:44] (5324.64s)
>> Oh, man.
[88:45] (5325.68s)
>> They're going to end this year well over
[88:47] (5327.04s)
100 billion.
[88:48] (5328.48s)
>> Holy [ __ ]
[88:49] (5329.92s)
What's the 28 number?
[88:51] (5331.76s)
>> What's the 28 number? Is it 200? Is it
[88:54] (5334.32s)
300 billion? It's probably not going to
[88:56] (5336.56s)
trade at 10 times that number
[89:00] (5340.88s)
and it will be very profitable at that
[89:03] (5343.12s)
scale because it'll be inference
[89:04] (5344.40s)
dominated and people reporting they have
[89:05] (5345.92s)
85% gross margins on inference.
[89:09] (5349.20s)
But in terms of the market absorbing
[89:10] (5350.80s)
this like the market's already absorbed
[89:12] (5352.48s)
it, you know, it's it's just shifting
[89:14] (5354.72s)
from private to public. And so in the
[89:17] (5357.68s)
scale of global capital markets, these
[89:19] (5359.76s)
seem like really big numbers. You're
[89:21] (5361.52s)
just moving from the private markets to
[89:23] (5363.60s)
the public markets which are even
[89:25] (5365.12s)
bigger. As far as SpaceX specifically, I
[89:28] (5368.00s)
think one of the more important things
[89:29] (5369.92s)
um is everybody who's a SpaceX investor
[89:33] (5373.68s)
or employee has had a chance to sell
[89:35] (5375.68s)
every 6 months for the last 10 years. So
[89:38] (5378.00s)
they not may not be the wall of
[89:39] (5379.60s)
liquidity that some people are thinking
[89:41] (5381.36s)
about. I read this New York hedge fund
[89:43] (5383.44s)
short short report that you could just
[89:44] (5384.88s)
short SpaceX on the lockup because so
[89:47] (5387.04s)
many people are going to sell. Really?
[89:48] (5388.88s)
Well, everybody who's on the cap table,
[89:51] (5391.04s)
they had an opportunity to sell and
[89:54] (5394.64s)
almost half the employees at SpaceX
[89:57] (5397.36s)
bought on the IPO. Now, I do think
[89:59] (5399.84s)
Cerebrus,
[90:01] (5401.20s)
>> but Gavin, one thing though, I got to
[90:03] (5403.28s)
say, so I I I've had SpaceX since 2018.
[90:06] (5406.64s)
What? What? But
[90:09] (5409.20s)
their little their little liquidity
[90:11] (5411.12s)
thing every year, like last year it was
[90:13] (5413.04s)
like 350 350 billion last year. So you
[90:16] (5416.96s)
got an 8x in one year. You could have a
[90:19] (5419.76s)
lot of people selling, right? They were
[90:22] (5422.00s)
doing little 20% up, 30% up clips for
[90:25] (5425.68s)
many years. And then an 8xer could
[90:29] (5429.36s)
create that liquidity.
[90:30] (5430.96s)
>> Maybe, but we'll see. That's possible.
[90:33] (5433.20s)
But the employees are buying at the new
[90:35] (5435.20s)
price.
[90:36] (5436.56s)
and they're probably the, you know, at
[90:38] (5438.72s)
some level one of the biggest pools of,
[90:41] (5441.84s)
you know, ownership that's going to
[90:43] (5443.36s)
unlock.
[90:44] (5444.72s)
>> And then, you know, I do think a lot of
[90:47] (5447.12s)
people probably own SpaceX through SPVS,
[90:49] (5449.68s)
and those probably don't unlock or get
[90:51] (5451.52s)
distributed anytime soon. So, I just,
[90:53] (5453.84s)
you know, I would be careful with
[90:55] (5455.68s)
assuming I don't think Elon's a seller
[90:57] (5457.52s)
of his
[90:58] (5458.48s)
>> No, he's definitely not, obviously.
[91:00] (5460.16s)
Yeah. Yeah. So,
[91:01] (5461.12s)
>> and a lot of us and a lot of us aren't
[91:02] (5462.64s)
sellers, you know, and that's great.
[91:04] (5464.88s)
>> Yeah. I mean a lot of people who had
[91:07] (5467.28s)
large SpaceX positions were large buyers
[91:09] (5469.36s)
on the IPO
[91:11] (5471.84s)
on Cerebras. So Cerebras has had a a
[91:16] (5476.64s)
tough two days since they reported their
[91:18] (5478.48s)
first quarter as a public company. And I
[91:20] (5480.80s)
think there there there are two things
[91:23] (5483.28s)
that are very that are worth discussing
[91:27] (5487.36s)
One is there is a whole generation of
[91:29] (5489.60s)
portfolio managers. There's a lot of
[91:31] (5491.20s)
people who are advocating for kind of
[91:32] (5492.56s)
squeezing the blood out of the stone on
[91:34] (5494.24s)
IPO prices. And the flip side of that is
[91:38] (5498.08s)
that there are a lot of portfolio
[91:39] (5499.68s)
managers who if a stock breaks deal
[91:44] (5504.24s)
price, they sell it no matter what. They
[91:47] (5507.84s)
consider it a promise that was broken.
[91:51] (5511.04s)
And so
[91:53] (5513.12s)
this is what has happened with Cerebras
[91:55] (5515.52s)
to some degree over the last two days.
[91:57] (5517.52s)
And you know this may seem irrational
[91:59] (5519.52s)
but there are people who run giant funds
[92:02] (5522.08s)
who I know personally where if a stock
[92:04] (5524.16s)
breaks deal price they sell no matter
[92:06] (5526.48s)
what. And so if stock breaks deal price
[92:09] (5529.92s)
it can sometimes you know go to places
[92:12] (5532.48s)
you wouldn't think it would go. And this
[92:14] (5534.32s)
means that shorts if a stock gets close
[92:16] (5536.24s)
to deal price they short it because they
[92:18] (5538.32s)
want to break deal price and then you
[92:20] (5540.24s)
know they make a quick 10 or 20%. So it
[92:22] (5542.24s)
becomes a pylon
[92:23] (5543.28s)
>> because you have this price insensitive
[92:25] (5545.20s)
selling that can be triggered and this
[92:27] (5547.76s)
is what has happened to cerebrus. You
[92:29] (5549.60s)
know people talk about hate sale hate
[92:31] (5551.12s)
selling but they broke deal price and so
[92:34] (5554.48s)
just if you're going public and you're
[92:36] (5556.00s)
listening to this
[92:37] (5557.44s)
>> tell your bankers price this in such a
[92:40] (5560.24s)
way that we're not going to break deal
[92:42] (5562.16s)
price in our first nine months as a
[92:44] (5564.00s)
public company. And that's what I always
[92:46] (5566.96s)
advise everyone to do and I think it's
[92:49] (5569.44s)
important but I also think you know it
[92:51] (5571.92s)
takes companies a while to learn how to
[92:55] (5575.60s)
tell their story and communicate to to
[92:57] (5577.68s)
public markets. It's a very different
[92:59] (5579.44s)
audience than VCs and the way like the
[93:03] (5583.36s)
way I would have respectfully told the
[93:05] (5585.84s)
Cerebra story because what happened to
[93:07] (5587.36s)
Cerebras is they reported a quarter and
[93:09] (5589.92s)
and they're growing fast but relative to
[93:11] (5591.60s)
the rest of AI they're not growing that
[93:13] (5593.92s)
fast in the March quarter. So what I
[93:16] (5596.88s)
would have said is we signed this
[93:18] (5598.72s)
transformational
[93:20] (5600.40s)
you know 202$25 billion I don't know the
[93:23] (5603.20s)
exact number contract with open AAI in
[93:25] (5605.84s)
December December of 2025
[93:29] (5609.60s)
we immediately ordered more wafers from
[93:31] (5611.52s)
Taiwan semi takes 100 takes four months
[93:35] (5615.12s)
from when we make that order Taiwan semi
[93:37] (5617.68s)
you know says yes they start producing
[93:39] (5619.76s)
takes four months to make the chip then
[93:42] (5622.40s)
we then it takes us two months
[93:46] (5626.48s)
plus or minus to turn that chip into a
[93:48] (5628.56s)
server. And then if we're lucky and we
[93:51] (5631.44s)
can find the power, it takes us a month
[93:54] (5634.24s)
to energize that chip and start making
[93:56] (5636.56s)
tokens with it. So the first time you're
[93:59] (5639.68s)
going to see the impact of this open AI
[94:03] (5643.20s)
deal at the earliest is probably around
[94:06] (5646.32s)
Labor Day. So you'll see a little bit of
[94:08] (5648.08s)
it in the in the third quarter, but then
[94:10] (5650.72s)
it really it starts to build and just
[94:13] (5653.84s)
like really simple math. So like let's
[94:16] (5656.40s)
just use some rough numbers. Let's say
[94:19] (5659.04s)
let's take some Nvidia numbers. It takes
[94:20] (5660.88s)
them 35 billion to bring on a gigawatt
[94:23] (5663.20s)
and 15 billion that you can generate 15
[94:25] (5665.68s)
billion in token revenue in cloud
[94:27] (5667.60s)
revenue out of that gigawatt. And
[94:30] (5670.08s)
somebody on the call talked about adding
[94:31] (5671.84s)
50 megawatts a month. If they could add
[94:34] (5674.96s)
50 megawws a month in 2027, forget 26,
[94:39] (5679.92s)
that means they exit the year at roughly
[94:42] (5682.40s)
a 9 billion cloud computing run rate.
[94:45] (5685.68s)
And you know, we're at we're at less
[94:48] (5688.32s)
than 40 billion of market cap. Now, that
[94:51] (5691.20s)
is going to be really hard to do and
[94:53] (5693.44s)
they've never done anything like that
[94:54] (5694.96s)
before. And but I would I would I would
[94:59] (5699.44s)
focus like as an investor, I think what
[95:01] (5701.44s)
matters here is not where they sit
[95:03] (5703.76s)
competitively,
[95:05] (5705.44s)
not what new demand they can bring on,
[95:07] (5707.44s)
but just how quickly can they bring on
[95:09] (5709.76s)
power. And listen, like outside of the
[95:12] (5712.08s)
hyperscalers, the only companies that
[95:13] (5713.68s)
have ever brought on more than a
[95:14] (5714.88s)
gigawatt, I think, are Cororeweave, um,
[95:17] (5717.28s)
Crusoe,
[95:18] (5718.88s)
um, and, and, and, and, and SpaceX AI to
[95:22] (5722.08s)
bringing on 600 megawws, it's really
[95:24] (5724.24s)
hard. And that is what like I'm focused
[95:27] (5727.60s)
on as an investor. How many megawatts
[95:30] (5730.96s)
can they bring on? Because we know what
[95:32] (5732.56s)
they're going to monetize at. And that
[95:34] (5734.56s)
is the question. And we'll see.
[95:37] (5737.04s)
>> Yeah. And I think that's well said.
[95:38] (5738.80s)
>> Yeah. the other companies that have had
[95:41] (5741.36s)
this happen of late. I guess Rivian and
[95:44] (5744.00s)
famously Chimath Facebook traded below
[95:46] (5746.16s)
IPO price in the first year I think. And
[95:48] (5748.56s)
so this isn't necessarily mean a bad
[95:50] (5750.24s)
company. It just means a lot of hype or
[95:53] (5753.04s)
maybe going for it in the IPO and
[95:55] (5755.36s)
pricing it to perfection. And it can go
[95:57] (5757.76s)
one of two ways. You can either grossly
[95:59] (5759.36s)
underpric, grossly overpric. And it's
[96:02] (5762.48s)
really hard to get right. And this is
[96:04] (5764.08s)
why doing auctions
[96:06] (5766.48s)
>> or you know
[96:07] (5767.20s)
>> Yeah, auctions are the way. Auctions are
[96:09] (5769.04s)
the way. And so it's not hard to price
[96:11] (5771.04s)
if you just do it the way it's supposed
[96:12] (5772.32s)
to be done. It's hard to price when
[96:14] (5774.64s)
you're when you uh want it to be a
[96:17] (5777.68s)
certain number.
[96:19] (5779.36s)
>> Yeah. And you have many mouths to feed
[96:21] (5781.36s)
and that influences the price.
[96:23] (5783.44s)
>> I don't think that happened here. I
[96:25] (5785.52s)
think there was a good faith effort to
[96:27] (5787.60s)
to price this thoughtfully, but just,
[96:30] (5790.40s)
you know, there was it was such an in
[96:32] (5792.00s)
demand IPO. I think it was a hard IPO to
[96:34] (5794.32s)
price.
[96:36] (5796.16s)
You just auction it, Gavin. You just
[96:37] (5797.76s)
auction it. I think that's different
[96:39] (5799.04s)
than underwriting it. You're
[96:40] (5800.48s)
underwriting,
[96:41] (5801.60s)
>> but the bankers like I think they just
[96:44] (5804.24s)
need to get more in the mode of just
[96:46] (5806.64s)
doing the auction, you know.
[96:48] (5808.32s)
>> Yeah. Final topic was this uh $3
[96:52] (5812.00s)
trillion
[96:54] (5814.96s)
or actually now it's your $3 trillion
[96:58] (5818.64s)
call on anthropic would make it like 6
[97:01] (5821.20s)
trillion in offerings. What do we think
[97:03] (5823.60s)
broadly? in offerings because it's
[97:06] (5826.08s)
they're going to offer a small slice of
[97:08] (5828.32s)
>> Sure.
[97:08] (5828.72s)
>> And that small slice is very e easy to
[97:11] (5831.36s)
absorb in the context. I believe we have
[97:13] (5833.60s)
no precedent. We'll see. I might be
[97:15] (5835.28s)
wrong, but it's not hard for global
[97:17] (5837.52s)
capital markets to absorb
[97:19] (5839.76s)
>> 50 billion of an offering. It's not like
[97:22] (5842.80s)
it's not like somebody has to come up.
[97:24] (5844.72s)
>> Yeah. It's a 5% float is 150 billion.
[97:27] (5847.52s)
There's enough
[97:28] (5848.24s)
>> guys. Remember remember when 15 billion
[97:30] (5850.56s)
used to be a whole lot of money?
[97:32] (5852.96s)
>> Yeah. How weird is what's going on? How
[97:35] (5855.52s)
weird?
[97:35] (5855.84s)
>> By the way, crazy. Where is all this
[97:37] (5857.52s)
money coming from? Oh yeah. Where was it
[97:39] (5859.28s)
all this time?
[97:40] (5860.24s)
>> Can we just reminisce, man?
[97:42] (5862.08s)
>> We got my when I was at Fidelity, my my
[97:45] (5865.60s)
colleagues and I, we got pillaried.
[97:48] (5868.16s)
>> I think we priced Uber at 14 billion.
[97:52] (5872.00s)
And we got pillaried in the press for
[97:53] (5873.68s)
not knowing what we were doing. And it
[97:55] (5875.44s)
was seen as, you know, then then of
[97:57] (5877.36s)
course like six months later, I think
[97:58] (5878.56s)
you guys did around at what, like 42
[98:00] (5880.64s)
billion or something. But it's like,
[98:03] (5883.04s)
yeah, 14 billion. That was
[98:04] (5884.80s)
groundbreaking for a private company
[98:06] (5886.40s)
back then.
[98:06] (5886.80s)
>> Yeah, for sure. It was 17, but that, you
[98:08] (5888.96s)
know, no 17. No, you're right. I wanted
[98:13] (5893.52s)
>> and we had like a big negotiation
[98:15] (5895.52s)
>> and we ran we ran an auction. No, we
[98:17] (5897.52s)
just ran an auction.
[98:18] (5898.72s)
>> Yeah, you wanted 20 and maybe we landed
[98:21] (5901.20s)
at 17. What we did, what we did was
[98:24] (5904.48s)
every person who wanted to be involved
[98:26] (5906.64s)
had to fill out a sheet of how much
[98:28] (5908.72s)
money they put it at 10, 11, 12, 13, 14
[98:32] (5912.48s)
or all the way up to 20. And then we
[98:34] (5914.80s)
just did the Dutch auction. We said, "We
[98:36] (5916.48s)
want to clear one and a half bill." We
[98:38] (5918.80s)
just did the auction and cleared it.
[98:40] (5920.48s)
Went back to people and said, "Hey,
[98:42] (5922.32s)
you're not going to get it.
[98:44] (5924.56s)
You have another shot." they update
[98:46] (5926.88s)
their Excel sheet and it just moves the
[98:49] (5929.60s)
number up a little bit and then you you
[98:51] (5931.36s)
you close it down. But yeah, it started
[98:54] (5934.64s)
that round started at I think it was 9
[98:57] (5937.04s)
or 10 or something like that. Ended up
[98:58] (5938.72s)
at 17.
[99:01] (5941.20s)
>> Thank you for your service.
[99:02] (5942.08s)
>> Yes, it used to be it used to be that
[99:04] (5944.48s)
that was a lot of money and that was
[99:06] (5946.32s)
unheard of at the time and that was well
[99:08] (5948.64s)
look it was 10 years ago when we used to
[99:10] (5950.64s)
walk to school uphill both ways.
[99:12] (5952.64s)
>> Yes. We used to have to Yeah. We used to
[99:15] (5955.52s)
eat potatoes with no butter, just hot
[99:18] (5958.32s)
potatoes. Sometimes we were lucky that
[99:19] (5959.92s)
they were cooked. Sometimes they were
[99:21] (5961.60s)
raw. No, my dad would tell me that
[99:23] (5963.36s)
story. It's a famous John the Beard
[99:25] (5965.20s)
story. He'd say his mom would put like
[99:28] (5968.16s)
four um four potatoes in the oven.
[99:31] (5971.20s)
They'd wrap them in tin foil, one in
[99:33] (5973.12s)
each pocket because they couldn't afford
[99:34] (5974.56s)
gloves. So, you put your hands in your
[99:36] (5976.08s)
pocket with the hot potatoes. You get to
[99:37] (5977.52s)
school, you eat one for breakfast, one
[99:38] (5978.72s)
for lunch. him and his sister uh
[99:40] (5980.72s)
Johanna, God rest for so my aunt who
[99:42] (5982.96s)
died too young and they would just go
[99:44] (5984.72s)
eat these potatoes. That was their life.
[99:47] (5987.68s)
Uh walking to school.
[99:49] (5989.76s)
>> Yeah. And our kids are trying both ways.
[99:53] (5993.28s)
>> Yeah. They're trying to get the new
[99:54] (5994.32s)
iPhone 16 or 17. I don't know.
[99:56] (5996.16s)
>> Remember when Remember when it used to
[99:57] (5997.44s)
be hard to raise $5 billion?
[99:59] (5999.60s)
>> Oh my lord. This is crazy.
[100:00] (6000.80s)
>> Remember when it was hard to raise$ 1.5
[100:04] (6004.00s)
million?
[100:04] (6004.80s)
>> Dude, I remember Yeah. I remember when
[100:06] (6006.64s)
it was hard to raise. Anyways, we don't
[100:08] (6008.64s)
have to do this. It's okay. We already
[100:10] (6010.08s)
sound like grandpas. It's fine.
[100:11] (6011.36s)
>> We do sound like It was so hard to raise
[100:14] (6014.16s)
that first 1.5.
[100:16] (6016.32s)
>> Dude, it's just We don't need to go
[100:17] (6017.84s)
there. It's so funny though.
[100:19] (6019.60s)
>> What's with this art piece, Gavin,
[100:21] (6021.12s)
behind you? Is that like an art piece or
[100:23] (6023.68s)
you made it?
[100:24] (6024.48s)
>> Yeah.
[100:24] (6024.96s)
>> No, this is I'm staying at a rented
[100:27] (6027.12s)
house in Atheertton.
[100:28] (6028.24s)
>> Oh, okay. There you go. It's just a
[100:29] (6029.92s)
Arabian art. All right, everybody. for
[100:31] (6031.52s)
the dictator
[100:33] (6033.84s)
Jamal Pitia and for David Saxs TK GB.
[100:39] (6039.44s)
We'll see you next time on the Allin
[100:40] (6040.88s)
podcast. Bye-bye. Great job everybody.
[100:44] (6044.48s)
>> We'll let your winners ride.
[100:47] (6047.36s)
>> Rainman David.
[100:52] (6052.24s)
>> We open sourced it to the fans and
[100:54] (6054.00s)
they've just gone crazy with it.
[100:55] (6055.68s)
>> Love you.
[101:00] (6060.80s)
I want your winners.
[101:04] (6064.56s)
>> Besties are gone.
[101:07] (6067.12s)
>> That is my dog taking your driveways.
[101:12] (6072.24s)
>> Oh man, my appetiter will meet up.
[101:14] (6074.96s)
>> We should all just get a room and just
[101:16] (6076.32s)
have one big huge orgy cuz they're all
[101:18] (6078.16s)
just useless. It's like this like sexual
[101:19] (6079.92s)
tension that we just need to release
[101:21] (6081.36s)
somehow.
[101:26] (6086.00s)
>> Your feet.
[101:28] (6088.32s)
We need to get merch. Going all in.
[101:37] (6097.76s)
I'm going all in.