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All right everybody, welcome back to the
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number one podcast in the world. I'm
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your host and executive producer for
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life. Isn't that right, Dave Freeberg,
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Jay Cal, not at all what you are.
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Make sure you tune in startups and apply
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to Founder University.
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You're something very different.
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With us again today, the Sultan of
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science, David Freedberg.
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Can I just congratulate you on your
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fourth baby? If you double that number,
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you're going to be able to catch up to
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Chimoth and his five plus three
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illegitimate. How are you doing?
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Let your winners ride.
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We open sourced it to the fans and
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they've just gone crazy with it.
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How you feeling? You're tired and
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grumpy, aren't you? You're a little
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transition for me. I didn't have to do
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the work. It's all
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Are you tired and grumpy? And how's
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Allison? How's How's the
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Everyone's wonderful. Thank you for
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And a beautiful boy. Beautiful.
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Nothing more is crushing.
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Nothing more amazing than seeing a
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How's magnificent? Magnificent. Thank
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you for asking. Thank you for asking
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that. Yeah. Okay, let's move on. Thank
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Thank you for all the kind words. And uh
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just we sent over a gift basket, Chimath
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and I. Longhorn Pana Stakes uh a 10-year
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uh membership for
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Oh, hey, congrats to Olivia Landon, by
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the way, of Long Hill Wagyu. She had
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That means she's going to have more
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people to work on the ranch and
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slaughter cattle to send us our pana.
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Congratulations. Shout out.
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Congrats to Olivia Landon.
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It's so funny cuz we love this. We love
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these steaks so much. She doubled. We
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mentioned it on the pod and you idiots
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started like searching for it. Lunatics
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and they ordered out all the Koolette
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steak. So now Chant and I are screwed.
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No crew.
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No, they ordered out everything.
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Everything was sold up.
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Everything was sold up.
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So now we have to gatekeep with us
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again. Your chairman dictator Chimath
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Polyhapatia. He of two votes in our fine
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organization. How you doing, Chimath?
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I love voting control. I'm doing great.
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He starts Thomas Lefant with a uh tie
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and then all of the
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gamesmanship
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happens between the team of rivals me
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and Freeberg. With us again, Thomas
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Lefant, a gentleman, a scholar. No idea
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why he's here. a true I don't know how
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he wound up on this podcast, but a true
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gentleman, a true scholar and host of
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Easts meets West, an incredible
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conference that I attended this week
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with our bestie David Saxs, who of
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course is at the White House and can't
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join us. Uh, but Thomas, what a great
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event. Thank you for including me.
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No box lunches, by the way. We we took
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your feedback from a couple of years
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ago, so I hope that we met your
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standard. you did upgrade highlights for
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you guys at your conference, Thomas.
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I mean, I think for me, obviously, I
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think a lot of news in AI this week.
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So, I think that was kind of the center
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piece of most of the panels pretty much
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up and down the stack from SAS companies
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trying to transform into AI to obviously
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the big Zuck news on scale and then
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potentially I saw in the information
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today the the Nat Friedman news. So, it
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feels like there's a lot going on in the
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industry. So, should be fun to talk
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about. Yeah. And we're going to talk
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about it all today. We got a a really
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full docket. Rick Caruso, the uh mayor
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who would have saved Los Angeles from
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the fires. He was there and you actually
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hosted at his incredible facility. What
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we did. We talked about the the state of
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LA, which JCL, is that is that it looks
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like that's where you're at, right?
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Yes. I'm at my uh LA home, which uh
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aka the compound.
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Uh yeah, it's uh it's available on
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Airbnb, so I'm here in LA. But yeah,
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Rick Caruso, what a great speaker.
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Interestingly, Jake Al, today a friend
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just sent me a chart showing the
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recovery of restaurants postco
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andif uh LA is 50% behind on the
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recovery per store location versus the
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national average.
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What do you attribute that to or what
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did they attribute it to?
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I think I think there's kind of a couple
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different things, right? And I think one
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the economy which you know unlike the
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San Francisco economy being levered to
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to AI and on the upswing is more levered
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to entertainment and I think
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you know secular decline I think you
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know someone mentioned at the conference
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that filmmings in LA are down 50% from
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peak so I mean that's just a a massive
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move down losing share to other geos
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both in the US I think Georgia right Jay
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Cal was mentioned I mean, Ted Cerrone
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has explained exactly how aggressive New
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York is being, uh, the UK is being,
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Atlanta, I mean, so many different hubs
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for movies giving much better deals than
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Los Angeles is.
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Yeah. So, I think it's a it's a
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combination of I think, you know, being
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levered to one industry that's kind of
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in secular decline.
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I can tell you from Mr. Beast that for
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Beast Games, we had a deal in Las Vegas
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and in Toronto, we got huge tax credits.
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And in the second season that we're
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doing for Amazon,
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we did an enormous deal with the Kingdom
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of Saudi Arabia. And so we're filming a
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bunch of episodes there. We're building
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the sets there. We're actually going to
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keep them there after it's all said and
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done. We would not film in Los Angeles
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unless we absolutely had to. We will
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stay as far away from California as
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possible.
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And regulations are such a big part of
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It's on economic. You can't make it
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Yeah. 30% more expensive I think it's is
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the kind of the official number on on
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well there's also speed right Thomas
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like how quickly can you stand something
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up how many how much paperwork do you
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have to file
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James Beard Foundation I'm seeing here
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from the research has found that all
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these independent restaurant owners said
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they just can't get staff here so in Los
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Angeles it's just hard for people to
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live here and it's hard to get through
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the regulations and if you make it hard
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there are other options for people this
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idea that California has a lock on uh
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anything other than incredible weather
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and beautiful people is Farsol. There's
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a lot of beautiful people in other
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places with decent weather and you can
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you can go do your projections there. So
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another topic that came up that a lot of
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people were talking about something that
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I know you've talked a lot about our our
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debt issue and the debt to GDP ratio.
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There was a lot of talk on the on the
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flip side on the GDP side. What if
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actually AI can increase productivity
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and regrow GDP faster than expectations,
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right? And perhaps that's one of the
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reasons why, you know, interest rates
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might not be quite as high as you might
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expect given some of the trends that you
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guys have talked about.
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So, I think a lot of a lot of
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discussions around
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AI productivity and what we could look
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at over the next, you know, five to 10
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years because of the the improvements
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we're seeing. This is particularly
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beneficial to the US, right? I mean, if
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you think about where AI is going to
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acrue economic surplus first, it's
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likely going to be in the US, not global
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GDP. So, the US kind of does it compete
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dollars or it increases overall
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productivity or both
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ahead of the rest of the world. If we do
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see advances from AI to accelerate GDP
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growth, is that because of all of the
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onshoring of manufacturing and industry
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that we outsource today? Like do you
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think that that goes handinhand with
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AI acceleration? I think that's part of
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it and I think the other part is just
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getting even out of the you know the
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knowledge worker workforce, right? Just
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getting significant productivity
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productivity improvements there. One of
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the things that we showed in our keynote
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is the adoption of these technologies
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and even taking doctors as an example,
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right? An area you know well you know
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this new company
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um kind of coming in and and developing
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kind of a diagnosis kind of engine right
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that's now used by a third of doctors.
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So you know I I think that uh it's open
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evidence by the way is the name of the
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company and already a third of US
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physicians are on the platform using it
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you know 10 times a day to kind of help
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diagnosis. So in particular in oncology
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as an example it's seen significant
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traction. So, you know, you multiply
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that by the legal profession, coding. I
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think we're already seeing, you know,
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what if we just see kind of a an
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explosion of productivity gains across,
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you know, both the physical and the
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digital economy.
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Yeah. The doctor one's a good example.
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If someone had the opportunity to go get
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more regular preventative checkups, um,
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they would. The problem is it's very
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expensive. It's hard to get an
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appointment or insurance won't cover it.
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But if the cost to a doctor goes down
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because they can leverage AI, the
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throughput goes up by 10x. They can see
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10 times as many patients per day, then
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suddenly diagnostic care becomes more
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available. They can charge for that.
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They don't need to charge the same
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amount. The price will come down per
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checkup, but you'll more people will be
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able to get a checkup per day. So that
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grows GDP in diagnostic care. That grows
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the size of that piece of the economy.
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It's a very good example. give you, by
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the way,
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anything where AI provides leverage to a
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service provider where their throughput
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now goes up. Um,
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I'll give you another example of that.
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Um, Dave, uh, so there was an LA dentist
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that kind of hit got viral this week. I
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don't know if you guys saw this story,
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but basically he um he created an ad
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using V3
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about a skydiving gorilla.
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Yeah, I saw that.
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Who, you know, ultimately needs to get
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his teeth fixed because he was drinking
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while he was jumping out of the plane.
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And you know, it's a very kind of funny
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viral ad. He probably made it for a
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couple, you know, hundred bucks. And now
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his practice is totally full. He's been
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flooded with requests, right, for the
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new dental implants. So, you know, to
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your point about increasing
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productivity, boom, there's how how V3
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can help a local dentist. All right,
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everybody. Welcome to the number one
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podcast in the world. We got a full
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docket. Full docket. But we're going to
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rocket the docket because there's so
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much going on here. Zuck is tilted
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clearly. Uh this has been the big
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discussion in Silicon Valley for the
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last 10 days or so. According to
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reports, Zuck is super frustrated that
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Meta is falling behind in AI. So he is
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swinging for the fences. Sam Waltman
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said Meta has offered top open AI
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employees a $100 million, wait for it,
[10:41] (641.76s)
signing bonus. That's not comp, that's a
[10:43] (643.68s)
signing bonus. Who knows if this is true
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or not, but he's also offering 100
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million a year in annual comp. He's
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clearly cut out tens of billions of
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dollars for this effort. Not dissimilar
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to when he did his VR efforts that
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didn't work out so well. Here's a 30
[10:58] (658.48s)
secondond clip of Sam Alman talking
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about this on his brother Jack's
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podcast uncapped.
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They started making these like giant
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offers to uh you know a lot of people on
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our team.
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Um you know like $100 million signing
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bonuses more than that comp per year.
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And I'm actually It is crazy. I'm really
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happy that at least so far uh none of
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our best people have decided to take
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them up on that. I think that people
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sort of look at the two paths and say
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all right OpenAI's got a really good
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shot a much better shot at actually
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delivering on super intelligence uh and
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also may eventually be the more valuable
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company. Meta just also vested over 14
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billion I'm using invested in quotes in
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scale AI for 49% stake and uh this
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probably is better described as a shadow
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aqua hire to get around antitrust
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scrutiny. You remember Microsoft did
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that with Inflection AI back in the day.
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Google did it with Character AI and
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Amazon did it with Adept AI. I'm not
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sure if this is necessary anymore uh
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since Lena Khan's no longer in the
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position. Scale CEO Alexander Wang and
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others will be joining Meta to work on a
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new super intelligence team. They're
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saying that Scale is going to remain an
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independent company and get a new CEO.
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Not sure if that's going to happen.
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And if you don't know, uh Scale does
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data labeling. They get experts to help
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train language models. Two of their
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biggest customers are OpenAI and Google,
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and they both canled their contracts.
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So, Zuck is taking that chess piece off
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the board so he can get all that data
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into his LLMs. He's also reportedly in
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talks to hire former GitHub CEO Nat
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Freiedman and Daniel Gross to work on
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AI. They have a incubator investment
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fund for AI. Daniel Gross had a really
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cool startup incubator called Pioneer
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Labs. I had him on this week in startups
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a couple years ago. Really smart cat.
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Meta has 70 billion in cash. Thomas
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Lefant, when you see Zuck doing this,
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what's your take not only on what Zuck's
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doing, but how big of an opportunity is
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this, you know, in terms of the prize of
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having the best large language model?
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What is he going for here? And uh what's
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your take on these really aggressive
[13:03] (783.44s)
packages and 49% purchases?
[13:06] (786.64s)
I mean, look, I think one it it feels
[13:08] (788.88s)
highly rational, right? If you think
[13:10] (790.40s)
about Meta's market cap is uh rough math
[13:13] (793.28s)
1.7 trillion. If you're the CEO and you
[13:16] (796.24s)
ultimately believe that maybe 50% of
[13:18] (798.24s)
your market cap is at risk because of AI
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850 billion,
[13:23] (803.92s)
why would you not spend maybe four or 5%
[13:26] (806.80s)
of that if you think it increases the
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odds even slightly that you're going to
[13:30] (810.32s)
win the market? So to me it it kind of
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reminded me of a few few things. number
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one the scale and size of the
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opportunity right obviously people think
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AI is massive but frankly um Jake I'm
[13:44] (824.24s)
even wondering putting the regulatory
[13:45] (825.68s)
scrutiny to the side if it was time he
[13:47] (827.60s)
just didn't want to wait and obviously
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doing it this way I think Alex literally
[13:51] (831.60s)
the next day who's the co of scale can
[13:53] (833.76s)
show up to work at Meta so I think it's
[13:56] (836.40s)
it's urgency of a large opportunity um
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I'm curious to get Chamas's take because
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it reminded me a little bit of the pivot
[14:03] (843.76s)
away from HTML 5 and also So a a much
[14:07] (847.04s)
smaller acquisition but one that we
[14:08] (848.72s)
really felt which was of a company
[14:10] (850.64s)
called Onavo.
[14:12] (852.40s)
And for those that may not remember
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Onavo was a small data service provider
[14:16] (856.40s)
but what it did is it had a panel of
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phones and we as investors could see
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what people which apps people were using
[14:24] (864.16s)
and the data was incredibly valuable
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because it was the only service that
[14:27] (867.92s)
gave you true engagement data. And so
[14:30] (870.32s)
obviously as an investor you felt wow
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this is an incredible tool and
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eventually it sold to to Facebook and
[14:37] (877.36s)
Facebook used it internally and didn't
[14:39] (879.28s)
allow anybody else to use it and we lost
[14:41] (881.52s)
one of our key abilities right in the
[14:44] (884.40s)
mobile app revolution to tell who was
[14:46] (886.64s)
winning and losing.
[14:48] (888.96s)
and you're saying the scale acquisition
[14:50] (890.96s)
is you know uh parallels that in a bit
[14:53] (893.28s)
there's this great service a lot of
[14:54] (894.72s)
people rely on it. He buys it shuts it
[14:56] (896.96s)
down for everybody else gets the tool
[14:58] (898.24s)
for himself. gets the data for himself.
[15:00] (900.08s)
Correct. So, I definitely see parallels
[15:01] (901.68s)
and I think given this, you know, their
[15:03] (903.44s)
market cap and the size of this
[15:04] (904.96s)
opportunity, I think it makes a lot of
[15:08] (908.64s)
Shimath, your thoughts on Zuck's action.
[15:10] (910.80s)
Obviously, folks know you worked with
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him as you went from tens of millions of
[15:15] (915.20s)
Facebook users to hundreds of millions.
[15:17] (917.20s)
And you were there actually during the
[15:19] (919.12s)
uh HTML rapper app disaster. Uh that uh
[15:24] (924.08s)
I think maybe
[15:25] (925.04s)
that was a debate at our executive team
[15:26] (926.96s)
at our M team and I was on the side of
[15:29] (929.52s)
apps and well without embarrassing him.
[15:32] (932.40s)
Somebody else was on the side of HTML 5.
[15:34] (934.32s)
I thought it was [ __ ] stupid. Why?
[15:36] (936.48s)
Why? Why was that? But that decision one
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because you know all of my political
[15:41] (941.60s)
capital at the time was also wrapped
[15:43] (943.36s)
into native apps, our own phone, an
[15:46] (946.80s)
entire verticalized integrated stack.
[15:50] (950.00s)
And politically
[15:52] (952.48s)
I think I made the decision for them
[15:54] (954.16s)
very hard because I was not a very
[15:57] (957.60s)
play nice in the sandbox with others
[15:59] (959.20s)
kind of executive. I was more of a
[16:01] (961.04s)
scorched earth get it done kind of
[16:02] (962.64s)
person.
[16:03] (963.12s)
Okay. So no changes over the last 15
[16:05] (965.20s)
years. That's good to know. They made
[16:06] (966.88s)
they made an enormous mistake, but then
[16:08] (968.64s)
they admitted it about a year after I
[16:10] (970.24s)
left. They said this was the single
[16:11] (971.68s)
sucks at
[16:12] (972.24s)
Explain in plain English why HTML 5
[16:14] (974.80s)
rappers versus native apps.
[16:16] (976.40s)
I can't cuz it's [ __ ]
[16:18] (978.32s)
Okay, great. Uh I can explain it. So
[16:20] (980.48s)
like native apps are was obvious
[16:24] (984.64s)
in 2010
[16:26] (986.08s)
obvious
[16:26] (986.48s)
and the only the only reason
[16:29] (989.04s)
to use HTML was as an endound for
[16:32] (992.48s)
different carriers and for different
[16:34] (994.16s)
ecosystems that were trying to charge us
[16:37] (997.44s)
a toll. So in 2010, I went to Mobile
[16:40] (1000.16s)
World Congress and I took a group of my
[16:44] (1004.00s)
most talented developers and we built an
[16:46] (1006.16s)
entire replica of Facebook that we
[16:47] (1007.76s)
called Facebook zero which was only
[16:49] (1009.20s)
available via URL and we launched it at
[16:51] (1011.84s)
Mobile World Congress and we did it and
[16:54] (1014.32s)
I announced it there because if you went
[16:57] (1017.04s)
to India as an example, all of the folks
[17:00] (1020.64s)
there would try to charge us a tax but
[17:03] (1023.52s)
if you could navigate through the
[17:04] (1024.88s)
browser you wouldn't have to pay it.
[17:06] (1026.64s)
Right? So that was a good example of
[17:08] (1028.56s)
what to do in a developing market when
[17:10] (1030.24s)
people were toll taking. But the real
[17:12] (1032.32s)
solution was to build an extremely
[17:14] (1034.24s)
integrated app from the software all the
[17:17] (1037.12s)
way to the hardware. And the only way to
[17:18] (1038.80s)
do that was as a native application. And
[17:20] (1040.88s)
that has tremendous applications to
[17:23] (1043.28s)
today. But just to finish on yesterday,
[17:25] (1045.84s)
my proposition was full phone, full
[17:28] (1048.16s)
stack, full app, all of this other HTML
[17:30] (1050.56s)
stuff should only be as a side thing
[17:32] (1052.72s)
that we do in markets where they try to
[17:34] (1054.72s)
make it difficult for us. Instead, it
[17:36] (1056.72s)
became politicized and it became a big
[17:38] (1058.80s)
bet on HTML 5, which I thought was
[17:40] (1060.56s)
absolutely stupid and unjustifiable.
[17:43] (1063.76s)
And that was also when I said, "Okay,
[17:45] (1065.36s)
well, this phone's not going to happen,
[17:46] (1066.64s)
so let me leave." And a year later, I
[17:48] (1068.96s)
think Mark, to his credit, said, "This
[17:50] (1070.64s)
was really stupid." And ripped all the
[17:52] (1072.24s)
HTML 5 stuff apart, went native, and the
[17:54] (1074.64s)
rest is history. So, let's fast forward
[17:57] (1077.12s)
to today. Yeah, there it is. Biggest
[17:59] (1079.84s)
mistake was betting too much. It was It
[18:01] (1081.36s)
was an And that was again, I'll just say
[18:03] (1083.04s)
it. people politicizing what should have
[18:06] (1086.32s)
been an obvious technical decision.
[18:08] (1088.96s)
the other piece to that just to add to
[18:10] (1090.40s)
it was it was also a religious decision
[18:12] (1092.88s)
then people liked the open standards of
[18:15] (1095.28s)
HTML 5.
[18:16] (1096.64s)
certain developers who felt like we have
[18:18] (1098.40s)
to support openly stupid people thought
[18:20] (1100.32s)
that. Only stupid nontechnical people
[18:22] (1102.32s)
thought that. It was stupid. It was
[18:24] (1104.24s)
obvious. You'd have to be a [ __ ]
[18:26] (1106.40s)
[ __ ] And there were [ __ ] morons at
[18:28] (1108.56s)
the executive team that advocated for
[18:30] (1110.24s)
this. Anyways, we were right, they were
[18:32] (1112.40s)
wrong, and he was fine. Okay, fast
[18:34] (1114.64s)
forward to the where are we today? It's
[18:37] (1117.04s)
the exact same story playing out. Now,
[18:39] (1119.12s)
what do I mean? You have to look very
[18:41] (1121.60s)
carefully at Microsoft's deal with Open
[18:43] (1123.28s)
AI. Why? Because what you see is the
[18:46] (1126.96s)
compounding of secrets. There are
[18:49] (1129.44s)
secrets in the training layer. There are
[18:52] (1132.64s)
secrets in the model layer. There are
[18:54] (1134.32s)
secrets in how these things are tightly
[18:56] (1136.48s)
coupled to infrastructure and compute.
[18:59] (1139.12s)
And what we have to remember is what
[19:01] (1141.04s)
Open AI got from Microsoft was an
[19:04] (1144.72s)
extremely competent partner that built
[19:08] (1148.00s)
an enormous Azure compute infrastructure
[19:10] (1150.88s)
to train everything from chat GPT all
[19:14] (1154.00s)
the way up to the 03 model everything.
[19:18] (1158.08s)
Why is that important? Because you start
[19:19] (1159.92s)
to figure out these tricks. How do you
[19:22] (1162.72s)
really optimize these models to be
[19:25] (1165.04s)
extremely performant? And now if you
[19:27] (1167.76s)
look at all of the other models, they've
[19:29] (1169.76s)
also had some level of that advantage.
[19:32] (1172.72s)
So if you look at Deep Seek, what did
[19:34] (1174.40s)
they do? Well, we don't know. But what
[19:36] (1176.24s)
we have been told is that there's very
[19:37] (1177.84s)
tight coupling to hardware. If you look
[19:39] (1179.92s)
at what XAI is doing, I think what you
[19:43] (1183.28s)
can bet is that there's an extremely
[19:45] (1185.12s)
tight coupling to hardware and
[19:46] (1186.88s)
infrastructure and compute. If you look
[19:49] (1189.52s)
at what Facebook is doing, they
[19:51] (1191.92s)
generically train on Nvidia and they
[19:53] (1193.84s)
launch it in the open source. So I think
[19:57] (1197.36s)
that what they need to do is more of the
[20:00] (1200.24s)
open AI, more of the Google playbook.
[20:02] (1202.80s)
Look at Google. Google's Gemini models
[20:05] (1205.36s)
are extremely tightly coupled to TPU and
[20:09] (1209.04s)
it enables and unlocks an entire stack
[20:12] (1212.00s)
of secrets and capability that then get
[20:14] (1214.80s)
manifested in model quality.
[20:16] (1216.96s)
So I think the first thing that Mark has
[20:18] (1218.72s)
to do if I were him is start to chip
[20:22] (1222.24s)
away at all of the sets of secrets. So
[20:24] (1224.88s)
what secrets do you get from Alexander
[20:27] (1227.12s)
Wang and scale? It's what are the
[20:30] (1230.80s)
labeling techniques that allow these
[20:32] (1232.72s)
models to be more and more performant?
[20:34] (1234.72s)
What labeling techniques are used in the
[20:36] (1236.56s)
reasoning models? What labeling
[20:38] (1238.08s)
techniques are used in more traditional
[20:39] (1239.68s)
LLMs? It is clear that Llama doesn't
[20:42] (1242.48s)
know this. Meta doesn't know this that
[20:44] (1244.08s)
well because their model quality is meh.
[20:46] (1246.88s)
So now what you get is that set of
[20:48] (1248.56s)
secrets. So what do you get from Nat
[20:50] (1250.24s)
Freeman and Daniel Gross? You get what
[20:51] (1251.92s)
are the apps doing? How are they
[20:53] (1253.44s)
approaching writing agents? These
[20:55] (1255.44s)
agentic tips and tricks that make
[20:57] (1257.12s)
usability and value more obvious.
[21:00] (1260.32s)
But then what's missing? I think the
[21:02] (1262.48s)
thing that's missing is the
[21:03] (1263.60s)
infrastructure and compute set of
[21:05] (1265.20s)
secrets. I think it's insufficient to
[21:07] (1267.92s)
buy stuff off the shelf from Nvidia and
[21:10] (1270.64s)
expect these models to fundamentally
[21:12] (1272.24s)
compete. So I think if I were a betting
[21:14] (1274.56s)
man, he's bought the training secrets,
[21:17] (1277.20s)
he's bought the app secrets, and now he
[21:18] (1278.96s)
has to buy some infrastructure and
[21:20] (1280.32s)
compute hardware secrets. you put it
[21:22] (1282.24s)
together and he's got a pretty good
[21:23] (1283.44s)
strategy here.
[21:24] (1284.24s)
And also just to add to that, Shimothnat
[21:27] (1287.28s)
and Daniel have invested in a lot of AI
[21:29] (1289.60s)
companies and those companies are have
[21:32] (1292.64s)
secrets of their own. Yeah. And those
[21:33] (1293.84s)
are and actually I think they have some
[21:35] (1295.68s)
along the full stack.
[21:36] (1296.88s)
Freeberg, your thoughts on this strategy
[21:39] (1299.28s)
as described by Thomas and Shimoth and
[21:42] (1302.32s)
just the data we're seeing on the
[21:43] (1303.52s)
playing field aggressive acquisition of
[21:46] (1306.48s)
talent and companies.
[21:49] (1309.04s)
I don't know if I have much to add here.
[21:51] (1311.20s)
Okay, one additional point Shimoth by
[21:53] (1313.04s)
the way that you mentioned if we look at
[21:54] (1314.40s)
the winners right in models of the past
[21:56] (1316.24s)
12 months anthropic the same right
[21:58] (1318.80s)
they've been very um kind of deliberate
[22:01] (1321.28s)
and have explained how TPUs right
[22:04] (1324.00s)
they've been a big user of them how it's
[22:05] (1325.84s)
helped define their training models so I
[22:07] (1327.52s)
think you're 100% right if we look at
[22:09] (1329.20s)
the models that have really performed
[22:11] (1331.04s)
it's ones that have that that quote
[22:12] (1332.96s)
secret as you mentioned
[22:14] (1334.32s)
when I first started 8090 a year ago one
[22:17] (1337.84s)
of the key bets I made which was a
[22:19] (1339.36s)
mistake and we unw wound the bet. But
[22:21] (1341.60s)
the first bet that I made was can we
[22:24] (1344.08s)
build a transpiler, which is to say, can
[22:26] (1346.00s)
you take a CUDA workload and then can
[22:27] (1347.60s)
you redirect it away from Nvidia
[22:30] (1350.40s)
to different hardware? And basically
[22:32] (1352.72s)
what I learned in that process are all
[22:34] (1354.80s)
of the attention mechanisms that are
[22:36] (1356.40s)
built into transformers
[22:38] (1358.72s)
that really differentiate
[22:41] (1361.12s)
how good the models are need to
[22:44] (1364.16s)
literally be handtuned for every single
[22:47] (1367.12s)
target of silicon that you have. So when
[22:49] (1369.76s)
Amazon just kind of wakes up and says,
[22:51] (1371.44s)
"Here's this chip," it means nothing
[22:54] (1374.00s)
unless you can incentivize somebody to
[22:55] (1375.84s)
build to it. But the opposite is also
[22:58] (1378.00s)
true. If you have a model and you just
[23:00] (1380.16s)
run it generically, you're not going to
[23:01] (1381.68s)
get the gains and it's not going to be
[23:03] (1383.28s)
as special as if you have a dedicated
[23:05] (1385.92s)
infrastructure and compute architecture
[23:07] (1387.44s)
and say we're going to tightly couple
[23:08] (1388.80s)
these. It's been clear that OpenAI has
[23:12] (1392.64s)
had that, Anthropic has had that, Google
[23:15] (1395.76s)
has had that, Deepseek has had that. And
[23:18] (1398.00s)
I think Meta needs to do that.
[23:19] (1399.44s)
Otherwise, they're always going to be
[23:20] (1400.88s)
floundering on their back heel.
[23:22] (1402.32s)
One quick misnomer, I think, you know,
[23:24] (1404.24s)
when people hear labeling, they kind of
[23:26] (1406.08s)
assume a photo of a dog and someone says
[23:28] (1408.80s)
this is a dog, right? I mean, that's
[23:30] (1410.24s)
definitely how it started, but if you
[23:32] (1412.00s)
look at sales business, it's completely
[23:34] (1414.32s)
more from that. So, you could actually
[23:36] (1416.40s)
label the problem. So for example in in
[23:38] (1418.88s)
simple terms 2 plus 2 equals 4 is
[23:41] (1421.52s)
actually um a reasoning data set right.
[23:44] (1424.72s)
So you got to think of labeling not just
[23:46] (1426.72s)
in the simple terms of you know this
[23:48] (1428.40s)
image but of massive data sets of of
[23:51] (1431.36s)
outcomes and that's what's kind of
[23:53] (1433.44s)
really used to tr uh train these
[23:55] (1435.20s)
reasoning models. Um,
[23:56] (1436.96s)
but I think there's another
[23:58] (1438.72s)
Yeah, there's another story here, guys,
[24:00] (1440.96s)
in my opinion,
[24:03] (1443.20s)
and it's the performance of the Mac 7,
[24:06] (1446.32s)
right? And I I'm going to have to check
[24:09] (1449.04s)
with my data science team, but I'm
[24:10] (1450.64s)
wondering if we're this is the year
[24:12] (1452.24s)
where we've seen the greatest divergence
[24:14] (1454.24s)
amongst the Mac 7, right? So if you look
[24:16] (1456.64s)
at the Mac 7 and if I just gave you
[24:20] (1460.56s)
right this performance you can see okay
[24:22] (1462.32s)
so Meta's up 18 Google's down Nvidia's
[24:25] (1465.52s)
up 8 Tesla down 20 Apple down 21 Amazon
[24:30] (1470.16s)
down three and Microsoft is plus 13
[24:32] (1472.72s)
right so it's kind of interesting in a
[24:35] (1475.84s)
market that you know historically over
[24:37] (1477.76s)
the past few years where we feel the Mac
[24:39] (1479.60s)
7 have been truly correlated now the
[24:42] (1482.64s)
market is saying wait hold on we might
[24:44] (1484.32s)
start to see diverging performance. What
[24:46] (1486.96s)
I read from that in in in one element is
[24:49] (1489.92s)
the market's starting to try and sort
[24:51] (1491.92s)
out who are going to be the winners and
[24:53] (1493.44s)
losers. Who's well positioned versus
[24:55] (1495.36s)
maybe falling behind, right? So, I think
[24:58] (1498.64s)
we're going to start to see some
[25:00] (1500.00s)
divergent performance from the Max 7. I
[25:02] (1502.00s)
think it's going to reward not
[25:03] (1503.60s)
Can you put that back up there for a
[25:04] (1504.88s)
second? I mean, I think that's so
[25:06] (1506.32s)
interesting because if you look at the
[25:08] (1508.00s)
conditions on the field today,
[25:10] (1510.08s)
you know, Google's down 8%. But again, I
[25:13] (1513.04s)
would tell you as a user,
[25:16] (1516.56s)
Gemini models are exceptional.
[25:21] (1521.28s)
Like absolutely just baron exceptional.
[25:25] (1525.20s)
I think Anthropic is incredible for
[25:27] (1527.20s)
Codegen. Incredible.
[25:30] (1530.72s)
What I see is every single company on
[25:33] (1533.44s)
this list that isn't Nvidia
[25:36] (1536.72s)
baking and rolling their own silicon.
[25:39] (1539.76s)
Yet Nvidia is up and the rest are down.
[25:43] (1543.36s)
I told you that I spent time last week
[25:45] (1545.28s)
at Tesla. I would not be sleeping on
[25:47] (1547.76s)
this business. I think that it is yet
[25:49] (1549.68s)
again back into the land of being
[25:51] (1551.20s)
misunderstood.
[25:52] (1552.72s)
The only one that I understand why it's
[25:54] (1554.88s)
down this much is Apple because it's not
[25:58] (1558.48s)
clear that they're even baking something
[25:59] (1559.92s)
in private. There's nothing public.
[26:01] (1561.44s)
There's nothing private. It just seems
[26:02] (1562.80s)
like they're transitioning into being a
[26:04] (1564.40s)
cash cow and getting into sort of that
[26:06] (1566.64s)
cash harvesting mode. But it's almost
[26:08] (1568.96s)
weird that the price action is what it
[26:10] (1570.72s)
is because I would have thought that
[26:12] (1572.32s)
Google would be up. Meta would maybe be
[26:14] (1574.40s)
a little flattish to down. Nvidia is up,
[26:16] (1576.88s)
but maybe it could be down. Tesla's
[26:18] (1578.56s)
down, but it should probably be up.
[26:20] (1580.88s)
Amazon's basically break even and Apple
[26:22] (1582.64s)
is down. And I think that kind of makes
[26:23] (1583.84s)
sense. That's sort of how I read this
[26:25] (1585.44s)
table.
[26:25] (1585.84s)
Yeah. I mean, what I love Chimoth, by
[26:27] (1587.68s)
the way, on that is that like now
[26:29] (1589.52s)
there's debates, right? And and you can
[26:32] (1592.08s)
argue whether you know you agree with
[26:33] (1593.60s)
Chimath or whether you don't. spending
[26:35] (1595.84s)
20 billion cuz he's cuz he's not afraid.
[26:38] (1598.64s)
Correct.
[26:39] (1599.44s)
Yeah. No, let's pull the chart up again
[26:40] (1600.80s)
here. By the way, I think this is an
[26:42] (1602.48s)
interesting way to
[26:46] (1606.16s)
only the only reason Microsoft is not on
[26:48] (1608.40s)
this list is because of the limitation
[26:50] (1610.32s)
of the DOSS era interface of the
[26:52] (1612.72s)
Bloomberg terminal where it will only
[26:54] (1614.40s)
allow you to compare six charts and not
[26:56] (1616.40s)
seven.
[26:57] (1617.12s)
But we know that Microsoft is up 13.
[26:59] (1619.12s)
Q perplexity. Yeah.
[27:01] (1621.60s)
So, you know, when you also when you
[27:03] (1623.28s)
look at these, there are some
[27:05] (1625.92s)
extenduating circumstances here like
[27:07] (1627.60s)
Tesla's car sales are down. All car
[27:09] (1629.36s)
sales are down. And I think that's the
[27:10] (1630.88s)
piece that maybe isn't being accounted
[27:12] (1632.64s)
for here and they're in a transitional
[27:14] (1634.80s)
period. Apple obviously
[27:17] (1637.92s)
There's a lot of regulatory overhead. So
[27:20] (1640.24s)
Tesla losing solar and EV tax credits.
[27:23] (1643.76s)
Apple Apple being told to onshore and
[27:25] (1645.84s)
stop buying from China. So their supply
[27:27] (1647.52s)
chains being disrupted because of
[27:28] (1648.96s)
tariffs. those two companies in
[27:30] (1650.72s)
particular are far more affected than
[27:33] (1653.76s)
the rest and even Amazon you know
[27:35] (1655.92s)
there's been some conversation about
[27:38] (1658.16s)
tariff effect on Amazon but obviously
[27:40] (1660.56s)
that's offset with some of the benefits
[27:42] (1662.32s)
they've been realizing and promoting as
[27:43] (1663.92s)
Jasse spoke in his letter this week uh
[27:46] (1666.16s)
from AI so I think that there's a
[27:48] (1668.64s)
variation here that's probably a little
[27:50] (1670.72s)
bit more Thomas kind of tuned to
[27:54] (1674.88s)
these conditions that aren't necessarily
[27:56] (1676.72s)
call it natural market forces but are
[27:58] (1678.88s)
kind of influence influenced market
[27:59] (1679.92s)
forces associated with the the new
[28:01] (1681.68s)
administration and some of the policy
[28:04] (1684.00s)
choices that are being made.
[28:05] (1685.12s)
If we were looking at those number one
[28:07] (1687.36s)
and number two, which one do you think
[28:08] (1688.64s)
gets to AGI first, Thomas? Well, wait,
[28:11] (1691.68s)
hold on. By the way, the other thing you
[28:13] (1693.04s)
should note, Jason, which I find really
[28:14] (1694.88s)
interesting is nobody talks about AGI
[28:17] (1697.20s)
anymore. If you listen to the language
[28:18] (1698.56s)
of all the companies, it's all super
[28:20] (1700.08s)
intelligence, which is a much more
[28:21] (1701.52s)
achievable goal because it's defined as
[28:23] (1703.68s)
being, you know, multiples more
[28:25] (1705.28s)
intelligent than a human being. But I
[28:26] (1706.88s)
think you're I think if you actually
[28:29] (1709.60s)
did a search for the number of times AGI
[28:31] (1711.76s)
is being said today. It's meaningfully
[28:34] (1714.56s)
less because I think people have
[28:35] (1715.76s)
realized that that's not in the offing.
[28:37] (1717.52s)
Yeah. By the way, another lens chamat
[28:39] (1719.36s)
that I think about on these is who
[28:41] (1721.44s)
controls their own destiny of these
[28:43] (1723.12s)
seven companies in AI,
[28:44] (1724.88s)
right? And I would argue
[28:47] (1727.60s)
most I would argue
[28:48] (1728.72s)
Tesla does Nvidia
[28:51] (1731.20s)
and then it's kind of interesting,
[28:52] (1732.40s)
right? Neither Amazon doesn't have its
[28:54] (1734.16s)
own foundation model, right? They're
[28:55] (1735.52s)
kind of dependent on others, right?
[28:57] (1737.68s)
Microsoft
[28:59] (1739.44s)
49% does right because of this kind of
[29:03] (1743.12s)
relationship they have with open AI it's
[29:05] (1745.52s)
both you know uh they they own a big
[29:08] (1748.32s)
share but they don't control it so
[29:09] (1749.76s)
there's kind of interesting and then
[29:12] (1752.00s)
maybe 6 months ago we would have said
[29:13] (1753.68s)
well Meta absolutely does maybe Zuck's
[29:16] (1756.32s)
trying to question that a little bit and
[29:18] (1758.16s)
you know it's it's fun in my opinion to
[29:20] (1760.16s)
kind of bring different lenses to this
[29:21] (1761.92s)
list right there's the regulatory one
[29:23] (1763.92s)
that Friedberg was just talking about I
[29:26] (1766.24s)
kind of think about if I towards the co.
[29:27] (1767.92s)
Do I control my own destiny in this
[29:30] (1770.08s)
market? Right? And I expect these
[29:31] (1771.76s)
companies are not going to want to be
[29:33] (1773.12s)
dependent on others and are going to at
[29:34] (1774.48s)
least want to say no. I'm going to
[29:36] (1776.56s)
control my own destiny whether I win or
[29:38] (1778.24s)
lose. Who's your number one? Who's your
[29:40] (1780.00s)
number two? If you had to could only bet
[29:41] (1781.76s)
on two here to achieve super
[29:44] (1784.16s)
intelligence AGI, let's just say win the
[29:46] (1786.24s)
AI re win the AI uh big prize. The big
[29:50] (1790.64s)
prize super intelligence AGI, you know,
[29:53] (1793.20s)
in the midterm, five years. Five years
[29:55] (1795.04s)
from now, we're sitting here. Thomas,
[29:56] (1796.40s)
give me your number one. Give me your
[29:57] (1797.60s)
number two.
[29:58] (1798.72s)
Look, I I think to me number one, I I
[30:00] (1800.72s)
still think Nvidia, right? I don't see
[30:02] (1802.48s)
the GPU kind of getting displaced. I see
[30:05] (1805.04s)
additional architectures kind of coming
[30:06] (1806.88s)
on board, right? And growing the market,
[30:08] (1808.64s)
but um at the end of the day, all roads
[30:11] (1811.20s)
still lead to the GPU for all of these
[30:13] (1813.04s)
models. So, I would kind of still put um
[30:15] (1815.92s)
kind of Nvidia on that. My number two,
[30:19] (1819.44s)
more of a dark horse, but I I would pick
[30:21] (1821.60s)
Tesla.
[30:23] (1823.44s)
I do think it has the most potential for
[30:25] (1825.60s)
vertical integration right from all the
[30:28] (1828.00s)
way the silicon to the model to actually
[30:31] (1831.20s)
the hardware right that might become
[30:32] (1832.88s)
super important not just in cars but in
[30:34] (1834.72s)
Optimus so Nvidia 1 Tesla is my dark
[30:38] (1838.72s)
wow stunning chimoth who's your number
[30:41] (1841.52s)
one and number two in the midterm 5
[30:43] (1843.60s)
years from now we're sitting here on
[30:44] (1844.64s)
allin episode 700
[30:46] (1846.80s)
Tesla's one and Google's two and the
[30:49] (1849.92s)
reason is because they are the closest
[30:53] (1853.04s)
to having that vertically integrated
[30:55] (1855.68s)
stack that I spoke about. I think that
[30:58] (1858.00s)
Tesla has the best vision models. Now
[31:00] (1860.48s)
with XAI, they'll have one of the best
[31:04] (1864.00s)
LLMs and reasoning models and they'll be
[31:07] (1867.04s)
able to eventually stick that on Dojo.
[31:09] (1869.76s)
And then all of that will be in all of
[31:12] (1872.16s)
the physical AI that you will interact
[31:14] (1874.96s)
with in your daily life, whether it's a
[31:16] (1876.80s)
robot or whether it's a car or whether
[31:19] (1879.52s)
it's a robo taxi. So that's number one.
[31:21] (1881.92s)
And then number two, for many of the
[31:23] (1883.68s)
same reasons, I think Google, because
[31:26] (1886.56s)
you'll have the Gemini family of models,
[31:28] (1888.72s)
which just absolutely kickass like V3,
[31:31] (1891.20s)
which we haven't really spoke about, is
[31:34] (1894.08s)
going to destroy Hollywood like in the
[31:36] (1896.56s)
next year. Like Hollywood is done, I
[31:38] (1898.24s)
think,
[31:39] (1899.52s)
but they're landing model after model.
[31:42] (1902.64s)
They have the TPU, and the next
[31:44] (1904.56s)
generation TPU, I think, is exceptional.
[31:47] (1907.28s)
They're baking quantum and then they
[31:48] (1908.96s)
have an entire funnel of billions of
[31:51] (1911.20s)
people that they can direct experiences
[31:52] (1912.80s)
to. So Tesla one, Google 2.
[31:55] (1915.36s)
Chimath, quick followup on that. I'm
[31:57] (1917.20s)
curious on Google. This is the because I
[32:00] (1920.00s)
I oscillate a lot on this particular
[32:02] (1922.16s)
name. Can Google win if search declines?
[32:05] (1925.60s)
Yes. And I think that what probably has
[32:07] (1927.92s)
to happen is bear with me when I say
[32:10] (1930.32s)
this, but if you had to boil down
[32:13] (1933.12s)
Google's economic northstar metric,
[32:16] (1936.00s)
right? not the value northstar, the
[32:18] (1938.56s)
economic northstar metric would be price
[32:21] (1941.04s)
per click
[32:23] (1943.36s)
and I do think that Google is extremely
[32:25] (1945.52s)
well positioned to pivot that to price
[32:27] (1947.68s)
per token and I think that they have
[32:30] (1950.72s)
some emergent classes of physical AI but
[32:33] (1953.28s)
they have the largest pool of people
[32:35] (1955.44s)
where they can generate a price per
[32:37] (1957.28s)
token value framework through YouTube
[32:40] (1960.00s)
through Gmail through workspace I think
[32:42] (1962.80s)
through search but probably it's a
[32:45] (1965.28s)
different kind of model. It just
[32:47] (1967.04s)
requires them to rip the band-aid off at
[32:48] (1968.88s)
some point. But yeah, I think Google can
[32:50] (1970.40s)
do it.
[32:51] (1971.52s)
I'm going to go with you, Chimath. I'm
[32:53] (1973.76s)
one uh my one and two are either Google
[32:57] (1977.68s)
uh or Elon. And I I'll just say Elon
[33:00] (1980.00s)
because I uh like you, I spent a day up
[33:02] (1982.56s)
at um XAI and I saw what a magnet for
[33:05] (1985.60s)
talent he is. I got to sit in some
[33:07] (1987.12s)
meetings and just he was interviewing
[33:09] (1989.44s)
people and he was working with that
[33:10] (1990.96s)
talent. 8:00 at night, there's a lot of
[33:12] (1992.72s)
people there on a Saturday grinding it
[33:14] (1994.56s)
out. It was nuts. I first went to XAI
[33:18] (1998.24s)
in the 15 minutes that I was in the
[33:19] (1999.76s)
parking lot finishing a call, the kinds
[33:21] (2001.36s)
of people that were walking in and out
[33:22] (2002.72s)
of there, you could tell they were big
[33:24] (2004.88s)
brains.
[33:26] (2006.24s)
I don't know how, you know what I mean?
[33:27] (2007.60s)
Like from every walk of life, they all
[33:29] (2009.68s)
just looked much smarter than the rest
[33:31] (2011.52s)
of us. Yeah.
[33:32] (2012.32s)
It some of them were like chain smoking
[33:34] (2014.48s)
cigarettes and just like stressed out.
[33:36] (2016.16s)
It was crazy.
[33:36] (2016.80s)
I hit a couple of zins. I'll be totally
[33:38] (2018.40s)
honest. Um, but the reason I say Elon
[33:40] (2020.72s)
versus Google is I think Elon's in a
[33:43] (2023.44s)
unique position. And I don't have any
[33:44] (2024.40s)
insider information here and and I
[33:46] (2026.24s)
haven't talked about this or I'm not
[33:47] (2027.36s)
back channeling from Elon lest anybody
[33:49] (2029.28s)
aggregate this. I think what Colossus
[33:52] (2032.64s)
has done and what Tesla has done both of
[33:55] (2035.76s)
these things Tesla with their own stack
[33:57] (2037.28s)
of hardware to your point Chamoth
[33:58] (2038.80s)
hardware plus software plus the user
[34:01] (2041.20s)
application of FSD and Optimus. Then you
[34:03] (2043.76s)
put that together with the data the
[34:05] (2045.68s)
real-time data of X formerly known as
[34:07] (2047.44s)
Twitter plus um you know what he's
[34:10] (2050.40s)
building with XAI and obviously those
[34:11] (2051.92s)
two companies merged. I think Tesla
[34:14] (2054.00s)
board, XAI board have to get together,
[34:15] (2055.68s)
put those two companies together.
[34:17] (2057.36s)
One's worth a trillion, one's worth 100
[34:19] (2059.20s)
billion. Put them and just have all that
[34:21] (2061.44s)
brain power going in one direction as
[34:23] (2063.76s)
opposed to Elon test switching between
[34:25] (2065.44s)
the two. You do that, I think he wins
[34:27] (2067.28s)
number one. You don't do that, I think
[34:29] (2069.04s)
he either gets one or two and then I
[34:31] (2071.68s)
think Google
[34:33] (2073.28s)
um is going to have a better search
[34:35] (2075.28s)
product. Thomas, I think it's a really
[34:36] (2076.80s)
important point. Do they lose search
[34:38] (2078.08s)
share? Doesn't matter. What I think
[34:40] (2080.16s)
matters is are their ads more effective?
[34:41] (2081.84s)
Is their ad network more effective? And
[34:43] (2083.44s)
I think based on what they know on you
[34:45] (2085.28s)
from your chat searches and your
[34:47] (2087.60s)
discussions and what they analyze in
[34:49] (2089.20s)
your email, just analyzing your Gmail
[34:52] (2092.08s)
and your surfing behavior and Chrome if
[34:53] (2093.68s)
they get to keep it, your Android phone
[34:55] (2095.68s)
if you use it, your YouTube list and
[34:57] (2097.76s)
what you how when you drop off allin and
[34:59] (2099.84s)
when you start listening to another
[35:00] (2100.96s)
podcast, whatever it is, all that data,
[35:03] (2103.28s)
all that data is going to lead to an ad
[35:05] (2105.36s)
network that performs so much better
[35:06] (2106.96s)
that even if they lose search hair,
[35:08] (2108.48s)
their ad network is going to continue to
[35:10] (2110.32s)
grow. and I think it will increase in
[35:11] (2111.92s)
velocity. So those are the my top two.
[35:13] (2113.84s)
Freeberg, I'm curious from your
[35:15] (2115.28s)
position.
[35:16] (2116.96s)
Which one you think is number one and
[35:18] (2118.88s)
number two? I saved you for last because
[35:20] (2120.40s)
you know what we do here? We save the
[35:21] (2121.60s)
best for last. Freeboard, go ahead.
[35:23] (2123.76s)
I think there's a difference in how I
[35:26] (2126.64s)
would kind of lump them. I I think that
[35:29] (2129.12s)
Tesla probably has the it is the best
[35:33] (2133.60s)
place to invest if you want to have a
[35:36] (2136.72s)
shot at a massive new industry. So,
[35:39] (2139.36s)
they've got a baseline business in in
[35:41] (2141.84s)
obviously the automobiles, but I think
[35:44] (2144.64s)
this humanoid robot opportunity is
[35:48] (2148.08s)
absolutely mind-blowingly ginormous. And
[35:51] (2151.20s)
I don't think that there's a better
[35:52] (2152.40s)
company on Earth positioned to execute
[35:55] (2155.36s)
against this humanoid robotics
[35:57] (2157.28s)
opportunity than Tesla. So, you know,
[36:00] (2160.08s)
it's sort of like I would call it a low
[36:01] (2161.76s)
probability, high upside sort of call
[36:04] (2164.64s)
option embedded within that business.
[36:07] (2167.20s)
And obviously you're paying a premium
[36:08] (2168.48s)
for that because it is still a very
[36:09] (2169.76s)
healthy premium you pay for that
[36:11] (2171.76s)
business. I think Nvidia to Thomas's
[36:14] (2174.40s)
point I think the common thesis is it is
[36:17] (2177.52s)
the most protected. The durability of
[36:19] (2179.68s)
the business is there. But I would argue
[36:21] (2181.76s)
that there's actually a low probability
[36:23] (2183.44s)
but very high severity risk to Nvidia in
[36:25] (2185.84s)
China. There was just a demonstration
[36:27] (2187.68s)
last month of a 1 nanometer
[36:30] (2190.08s)
semiconductor manufacturing process out
[36:32] (2192.08s)
of China. I think the more that we
[36:35] (2195.28s)
continue to try and isolate China from a
[36:37] (2197.36s)
policy perspective, the more we are
[36:40] (2200.00s)
emboldening investment in China, meaning
[36:43] (2203.52s)
from the government, from private
[36:44] (2204.80s)
industry into China to create
[36:46] (2206.40s)
alternatives to the chip stack where the
[36:49] (2209.28s)
United States companies, particularly
[36:50] (2210.88s)
Nvidia, have emote today. So, I do think
[36:53] (2213.84s)
that there's going to be an emergent
[36:55] (2215.60s)
competitive threat coming out of China
[36:57] (2217.12s)
to Nvidia. And just like we were knocked
[36:59] (2219.52s)
over by DeepSeek, I think we will be
[37:01] (2221.12s)
knocked over by some semiconductor
[37:03] (2223.36s)
manufacturing processes um coming out of
[37:06] (2226.24s)
China in the near term. But the overall
[37:08] (2228.64s)
kind of by the way Dave just on that
[37:10] (2230.24s)
point I think Sax's work on the
[37:13] (2233.04s)
diffusion rule
[37:14] (2234.56s)
just generally I don't think has kind of
[37:16] (2236.24s)
gotten enough attention in the
[37:17] (2237.76s)
rescending of the diffusion rule
[37:19] (2239.84s)
which essentially handicapped our
[37:22] (2242.00s)
ability to even arm our allies right
[37:25] (2245.44s)
with our semiduct with our semiconductor
[37:27] (2247.52s)
technology um in my opinion was kind of
[37:30] (2250.80s)
a milestone and very important moment um
[37:34] (2254.32s)
to to try and offset exactly what you
[37:36] (2256.48s)
were just describing. That's exactly
[37:37] (2257.60s)
right. I mean, there there there was a
[37:38] (2258.80s)
report a few months ago and I mentioned
[37:40] (2260.16s)
it on the show or maybe I didn't or
[37:42] (2262.96s)
maybe I sent it to Sax and we talked
[37:44] (2264.40s)
about it offline. I I can't remember but
[37:45] (2265.84s)
it was about a $40 billion investment
[37:48] (2268.08s)
being made in developing competitive
[37:50] (2270.00s)
semiconductor manufacturing full stack
[37:52] (2272.56s)
solutions out of China. So I I do think
[37:55] (2275.28s)
that the lithography IP moat is being
[37:58] (2278.16s)
crossed in China. I do think that China
[38:00] (2280.72s)
is developing actually new technology
[38:03] (2283.04s)
for uh DUV and EUV systems. I I do think
[38:06] (2286.40s)
that there's a risk uh to Nvidia's core.
[38:08] (2288.96s)
Now look, Nvidia is such a durable
[38:10] (2290.64s)
business. There's great modes, great
[38:12] (2292.40s)
advantages, but we're creating every
[38:13] (2293.92s)
incentive for an alternative to Nvidia
[38:16] (2296.00s)
to emerge from China. And then my my
[38:18] (2298.48s)
third kind of categorization would be
[38:20] (2300.56s)
what's the portfolio uh solution. I
[38:22] (2302.88s)
think that's Google. I think that
[38:24] (2304.40s)
there's a diversification of high beta
[38:27] (2307.36s)
bets inside of Google of any one of
[38:30] (2310.32s)
which could have call it a trillion
[38:32] (2312.08s)
dollar market cap outcome ranging from
[38:35] (2315.04s)
Whimo to quantum computing to the
[38:38] (2318.16s)
biologics work that Demis is working on
[38:41] (2321.52s)
out of um isomorphic. Uh there's a
[38:44] (2324.08s)
number of things that do not get a lot
[38:45] (2325.68s)
of attention at Google. So yes, there's
[38:48] (2328.08s)
a there's a core business that that may
[38:49] (2329.76s)
be at risk, Thomas, but I think that
[38:51] (2331.92s)
there's a a portfolio of options you get
[38:54] (2334.40s)
at Google and you just need any one of
[38:57] (2337.44s)
them to hit to kind of make up for the
[38:59] (2339.36s)
loss. But I do think also Sundar in my
[39:01] (2341.20s)
interview with him, which we put out a
[39:02] (2342.40s)
couple of weeks ago, is very thoughtful
[39:04] (2344.80s)
about where search evolves to and he is
[39:06] (2346.96s)
being, I think, reasonably aggressive in
[39:09] (2349.44s)
in trying to evolve the search product
[39:11] (2351.36s)
architecture to meet the market, to meet
[39:13] (2353.68s)
the consumer. I do give him credit for
[39:15] (2355.60s)
that. So Google would be in a good place
[39:17] (2357.44s)
for me as an overall kind of pick in
[39:19] (2359.44s)
that set of options.
[39:20] (2360.48s)
So just to be clear, Nvidia 1, Google 2
[39:22] (2362.40s)
or Nvidia Tesla?
[39:23] (2363.68s)
Like I said, I think in terms of like
[39:25] (2365.44s)
having the right sharp ratio is how I
[39:27] (2367.12s)
would think about it. The alpha and the
[39:28] (2368.32s)
beta adjusted returns, I would put
[39:30] (2370.88s)
Google number one. I would probably put
[39:33] (2373.12s)
Tesla. Uh Tesla's valuation, I think,
[39:36] (2376.16s)
already has a premium associated with
[39:37] (2377.92s)
those options. So I don't know.
[39:39] (2379.20s)
Yeah. So I don't know if I would really
[39:40] (2380.40s)
pay that premium. I think um
[39:42] (2382.24s)
aside from the valuations, let's take
[39:44] (2384.48s)
valuations out of it. Just the the game
[39:46] (2386.72s)
here is who wins the AI prize 5 years.
[39:49] (2389.84s)
That's how I understood it as well.
[39:51] (2391.52s)
Yeah. So valuation irrelevant.
[39:54] (2394.08s)
Valuation irrelevant. Who wins the AI
[39:55] (2395.68s)
prize? One, you're saying Google. Two,
[39:57] (2397.36s)
you're saying Tesla.
[39:58] (2398.32s)
I think Google's in such a position. I I
[40:00] (2400.48s)
mean, look, Demis uh Demis, I think, has
[40:02] (2402.64s)
been fairly koi about where they are.
[40:05] (2405.84s)
They obviously promote Gemini 2.5, but
[40:08] (2408.72s)
there's a lot still coming.
[40:10] (2410.64s)
And it's and and as Chimath pointed out,
[40:12] (2412.56s)
it's not just LLMs. There's a pretty
[40:15] (2415.60s)
sizable family of models including a a
[40:19] (2419.12s)
lot of these um graph-based models that
[40:21] (2421.12s)
are being used in really novel
[40:22] (2422.96s)
applications that no one else is even
[40:24] (2424.72s)
close to, no one spending time on. I
[40:27] (2427.28s)
mean, some of the weather forecasting,
[40:28] (2428.96s)
it might seem small and trivial, but
[40:30] (2430.80s)
it's a demonstration of Google's
[40:32] (2432.32s)
competency in in core model development
[40:35] (2435.20s)
that shows an understanding and a depth
[40:37] (2437.36s)
of research and work that goes well
[40:39] (2439.04s)
beyond LLM. So, I'm pretty bullish on
[40:42] (2442.00s)
the depth of talent, the full stack.
[40:43] (2443.60s)
Yeah. Yeah. And whatever they learn
[40:44] (2444.64s)
there could apply to Gmail, could apply
[40:46] (2446.72s)
to search, could apply to ads, could
[40:48] (2448.00s)
apply to YouTube algorithm, right? It's
[40:49] (2449.76s)
just goes up and down. Yeah.
[40:50] (2450.96s)
Yeah. From a product perspective, I do
[40:52] (2452.96s)
think you see this kind of multi-model
[40:55] (2455.76s)
emergence that that we're now seeing
[40:57] (2457.76s)
that no one talks about the single model
[41:00] (2460.24s)
that sits behind the application. There
[41:02] (2462.40s)
are multiple models that work together.
[41:04] (2464.88s)
And obviously this agentic architecture
[41:08] (2468.00s)
unlocks another layer of not just kind
[41:10] (2470.96s)
of solutions to complexity.
[41:12] (2472.56s)
Sure. And so there's there's quite a lot
[41:14] (2474.64s)
I think that's emergent here um that
[41:17] (2477.12s)
Google will start to kind of benefit
[41:18] (2478.88s)
from uh in the year ahead. I mean, for
[41:21] (2481.04s)
those of us, you know, who love tech,
[41:22] (2482.96s)
right? If we if we step back for a
[41:24] (2484.56s)
minute, I really feel like to use the
[41:26] (2486.96s)
analogy of this podcast, like we are now
[41:29] (2489.20s)
at the WSL World Series of Poker, right?
[41:33] (2493.04s)
We got seven companies around the table.
[41:34] (2494.96s)
The stacks are trillion in size, right?
[41:38] (2498.80s)
And all of us are going to get a front
[41:40] (2500.32s)
row seat to see what happens over the
[41:42] (2502.16s)
next 5 years. I mean, and on top of
[41:44] (2504.32s)
that, we're going to get to analyze, bet
[41:46] (2506.24s)
ourselves on who we think's going to
[41:47] (2507.76s)
win. We know there's some other
[41:49] (2509.36s)
companies that are pushing to get at
[41:51] (2511.12s)
that table, right, with some sharp
[41:52] (2512.56s)
elbows. I mean, what a time to be doing
[41:55] (2515.04s)
what we're doing.
[41:55] (2515.68s)
I don't know if I love the analogy
[41:57] (2517.12s)
because I don't think first of all, it's
[41:58] (2518.32s)
a zero sum game where there's this x
[41:59] (2519.92s)
number of chips and someone ends up with
[42:01] (2521.44s)
all the chips. I do think you could see
[42:03] (2523.68s)
as an example, just talking about the
[42:05] (2525.28s)
scenarios we we just described, Tesla
[42:08] (2528.08s)
developing an extraordinary humanoid
[42:10] (2530.00s)
robot business that's worth a trillion
[42:12] (2532.08s)
dollars. Google building, you know, to
[42:14] (2534.48s)
Chimath's point, a media empire based on
[42:16] (2536.64s)
generative AI in media and then, you
[42:19] (2539.36s)
know, Nvidia building an entirely new
[42:21] (2541.04s)
chip stack that everyone's participating
[42:22] (2542.64s)
in. So, all of them in an ecosystem
[42:24] (2544.56s)
based way could could be major winners
[42:26] (2546.56s)
Yeah, you're right. I I didn't mean it
[42:27] (2547.76s)
in the zero sum nature of it. I meant it
[42:29] (2549.84s)
more in the in the stakes, right? And
[42:33] (2553.36s)
and there's a lot of hands to be I like
[42:34] (2554.80s)
the analogy because there's a lot of
[42:35] (2555.76s)
hands to be played and there is a price
[42:38] (2558.88s)
pool, right? And and you could have
[42:40] (2560.00s)
three or four people at that table. One
[42:41] (2561.84s)
thing I just want to point out here is
[42:43] (2563.28s)
just speaking of regime change. What is
[42:46] (2566.00s)
going on at Apple? Like they Siri was
[42:48] (2568.08s)
just the early idea of an AI agent. It's
[42:50] (2570.88s)
just totally disgrat. It's disgusting.
[42:52] (2572.96s)
It doesn't work. It's embarrassing. And
[42:55] (2575.04s)
then their biggest developer conference,
[42:57] (2577.12s)
they're
[42:58] (2578.24s)
redoing the UI like time for regime
[43:01] (2581.12s)
change at at at Apple. No, this has
[43:04] (2584.48s)
happened many many many times in many
[43:07] (2587.68s)
industries before which is that
[43:10] (2590.56s)
companies that were stalwart
[43:12] (2592.08s)
organizations
[43:14] (2594.16s)
transition themselves from being a
[43:15] (2595.68s)
growth business to being a cash cow and
[43:18] (2598.96s)
these are well doumented transitions and
[43:21] (2601.92s)
it requires an extremely brutal reset if
[43:26] (2606.56s)
you want to shake that up. Yes,
[43:28] (2608.96s)
I think that the same thing that I think
[43:30] (2610.96s)
you have to respect Apple for, which is
[43:32] (2612.96s)
stability, the
[43:35] (2615.52s)
duration of some of their best, longest
[43:38] (2618.64s)
serving executives are there for 20 and
[43:40] (2620.64s)
30 years. On the scale of innovation,
[43:43] (2623.52s)
it's a horrible thing. And the reason is
[43:45] (2625.60s)
that we all just get old. Our skill sets
[43:49] (2629.20s)
become rusty and we don't have the
[43:52] (2632.00s)
energy or the capacity to think about
[43:54] (2634.88s)
what the future actually looks like
[43:57] (2637.04s)
because we are not living it. And then
[43:59] (2639.12s)
what happens is you task those decisions
[44:01] (2641.84s)
to people that you try to hire. But you
[44:04] (2644.72s)
know, you saw it in the clip with Sam.
[44:07] (2647.68s)
Even in all of that crazy recruiting
[44:09] (2649.84s)
chaos that's happening right now for
[44:11] (2651.28s)
these brilliant machine learning and AI
[44:13] (2653.20s)
people, maybe that's a fight between
[44:16] (2656.16s)
OpenAI,
[44:17] (2657.68s)
Meta, and maybe Google.
[44:20] (2660.24s)
But what you don't hear is Apple. So
[44:21] (2661.76s)
who's Apple getting? I have to think
[44:23] (2663.60s)
that Apple is not getting any of those
[44:25] (2665.44s)
people. So by the time you end up at
[44:27] (2667.04s)
Apple, it's just a different caliber of
[44:30] (2670.00s)
person.
[44:30] (2670.88s)
That is true. and they're living inside
[44:33] (2673.20s)
of a cash cow organization that's going
[44:35] (2675.92s)
to optimize for don't make mistakes,
[44:38] (2678.72s)
right?
[44:39] (2679.68s)
But that's h it's happened to HP. It's
[44:42] (2682.16s)
happened to Lotus. It's happened to
[44:43] (2683.84s)
Intel. It's happened to General
[44:45] (2685.60s)
Electric. It's happened to companies.
[44:49] (2689.20s)
It's just and it's happening to Apple.
[44:51] (2691.36s)
So, we should just not sweat it and move
[44:54] (2694.08s)
I don't know. Thomas, what are your
[44:55] (2695.28s)
thoughts? I mean, it's kind of shocking
[44:56] (2696.48s)
with all that cash and they don't
[44:59] (2699.36s)
acquire anything. They had project Titan
[45:01] (2701.68s)
$10 billion to build their own car and
[45:04] (2704.08s)
they just shut it down. Imagine if they
[45:06] (2706.08s)
kept going with that. You think regime
[45:07] (2707.76s)
change time? Maybe Tim Cook retires and
[45:09] (2709.76s)
put somebody who's a product person in
[45:11] (2711.12s)
charge of it or maybe they should merge
[45:13] (2713.44s)
with Tesla and put Elon in charge of it
[45:15] (2715.28s)
all. There just seems to be no new
[45:16] (2716.64s)
products coming out of there. Like it's
[45:18] (2718.40s)
absolutely
[45:20] (2720.16s)
uh confounding that they're optimizing
[45:22] (2722.48s)
for share buybacks and earnings per
[45:24] (2724.96s)
share instead of having some amount of
[45:27] (2727.04s)
that money go towards innovation and
[45:29] (2729.12s)
acquiring companies. Biggest acquisition
[45:31] (2731.28s)
is Beats. Give me a break. I mean it's
[45:34] (2734.32s)
interesting right for me and I've
[45:35] (2735.92s)
studied Apple basically my whole career
[45:38] (2738.24s)
and it's kind of interesting right
[45:40] (2740.64s)
because if you think about the their
[45:42] (2742.88s)
defining
[45:44] (2744.72s)
competitive advantage right was the
[45:46] (2746.32s)
integration of hardware and software
[45:47] (2747.84s)
that led to the beautiful MacBook that
[45:49] (2749.44s)
we're all using it led to the iPhone and
[45:51] (2751.92s)
right the fact that they were so coupled
[45:53] (2753.76s)
between hardware and software the user
[45:55] (2755.84s)
interface you know etc and I think it
[45:57] (2757.84s)
directly led to them winning let's call
[46:00] (2760.00s)
the the mobile era right but I back to
[46:02] (2762.88s)
Chamas's point and I think the analogy
[46:04] (2764.56s)
holds in AI they're the opposite right
[46:07] (2767.92s)
they don't control I don't you know the
[46:10] (2770.24s)
silicon they don't control the
[46:11] (2771.68s)
underlying models um and so now they're
[46:14] (2774.72s)
back to maybe you know using a
[46:16] (2776.72s)
historical analogy the PC makers who
[46:18] (2778.72s)
didn't control the OS
[46:20] (2780.08s)
that's right
[46:20] (2780.64s)
so I I think the good news for them is
[46:22] (2782.96s)
look they still have a monopoly on users
[46:25] (2785.84s)
they have three trillion of market cap
[46:27] (2787.76s)
to kind of play with so I think it's way
[46:30] (2790.24s)
too early to count them out. But I
[46:33] (2793.04s)
think, you know, the market, let let's
[46:35] (2795.20s)
posate, what's the most extreme thing
[46:36] (2796.72s)
that they could do, right? Just for just
[46:39] (2799.04s)
for intellectual sake, right? Uh buy
[46:40] (2800.88s)
OpenAI for 500 billion. I'm just going
[46:43] (2803.12s)
to put a crazy thing out there, right?
[46:45] (2805.92s)
So, you think, okay, that's the most
[46:47] (2807.92s)
extreme. Well, is it even that extreme?
[46:49] (2809.76s)
And what would Apple's stock do that
[46:52] (2812.24s)
Go up.
[46:53] (2813.44s)
That's my view, too. Right. I actually
[46:55] (2815.20s)
think it would go up, not down, even if
[46:57] (2817.28s)
they did something like that. So I do
[46:59] (2819.92s)
think they need to be kind of
[47:01] (2821.36s)
aggressive. I do think to your point I
[47:03] (2823.68s)
think Freeberg, it is important that you
[47:05] (2825.20s)
know all seven of these companies could
[47:06] (2826.80s)
actually win and do well, right? That
[47:08] (2828.64s)
that is a absolute
[47:10] (2830.96s)
possibility. But I I would love to see
[47:13] (2833.52s)
them be a little bit more aggressive. I
[47:15] (2835.84s)
mean you guys remember when Steve Jobs
[47:18] (2838.00s)
bought Finger Works, right? It was this
[47:19] (2839.52s)
tiny acquisition. They made this little
[47:21] (2841.28s)
trackpad that you could use your fingers
[47:22] (2842.88s)
on. No one figured out why they did this
[47:24] (2844.48s)
and then in turn into multi-touch and
[47:26] (2846.96s)
scrolling, right? So, I think it's it's
[47:29] (2849.52s)
going to be fascinating to see what they
[47:31] (2851.52s)
Thomas, that was a great question I was
[47:33] (2853.28s)
about to ask. If Apple could do one
[47:35] (2855.12s)
thing, they could do one internal
[47:36] (2856.96s)
project or buy one external company.
[47:38] (2858.72s)
Maybe we could do both around the horn.
[47:40] (2860.56s)
What would we advise them to do? My
[47:42] (2862.00s)
number one is build a humanoid robot.
[47:44] (2864.48s)
Like, how does Apple not have a humanoid
[47:46] (2866.48s)
robot? That seems like that's obviously
[47:48] (2868.00s)
the next giant consumer market is having
[47:50] (2870.72s)
Optimus or Figure in your house.
[47:53] (2873.04s)
Freeberg, I'm going to go to you first
[47:54] (2874.48s)
since I went to you last last time. Is
[47:56] (2876.88s)
there a product that they could do that
[48:00] (2880.72s)
they could build that they would be
[48:02] (2882.00s)
uniquely suited to that would turn this
[48:04] (2884.00s)
all around? If you could pick it on
[48:05] (2885.20s)
their road map, what would it be?
[48:06] (2886.64s)
I do think there is. I do think they're
[48:08] (2888.16s)
doing it and I do think they have a shot
[48:09] (2889.60s)
at winning, which is this kind of
[48:11] (2891.12s)
ambient AI assistant. I don't know about
[48:13] (2893.36s)
you guys, I must own 30 friaking Apple
[48:15] (2895.84s)
devices. Uh, I have many Apple computers
[48:18] (2898.40s)
I use in different offices. I have
[48:20] (2900.32s)
phones. I have many AirPods. I got
[48:22] (2902.88s)
everything. Watches, everything. I'm
[48:24] (2904.96s)
ubiquitous on the Apple platform. So,
[48:26] (2906.88s)
I'm an easy transition into this if it
[48:29] (2909.12s)
works. So, as everyone races to build
[48:32] (2912.08s)
kind of the agentic AI assistant that uh
[48:36] (2916.24s)
is sort of in my ear all the time or
[48:38] (2918.80s)
available where I don't have to stare at
[48:40] (2920.08s)
my freaking phone like this, um it is a
[48:42] (2922.80s)
great unlock for humanity. It's a great
[48:44] (2924.48s)
unlock as a consumer. it's feasible
[48:46] (2926.80s)
technically and I'm sure Apple of
[48:49] (2929.44s)
everyone that we've referenced today is
[48:53] (2933.12s)
best suited to both access the consumer
[48:55] (2935.68s)
design and engineer this solution in a
[48:58] (2938.40s)
way that can be truly transformative. I
[49:00] (2940.48s)
think it references a little bit what
[49:02] (2942.08s)
Johnny IV and Sam Alman have been
[49:03] (2943.76s)
talking uh about doing. But I do think
[49:06] (2946.72s)
that this is exactly the direction Apple
[49:08] (2948.80s)
is headed and I do think that they've
[49:11] (2951.12s)
got a very great shot at at winning at
[49:12] (2952.96s)
it. don't think they need to own the
[49:14] (2954.40s)
full stack to be successful here.
[49:16] (2956.24s)
Got it. Okay. So, we got Optimus, we got
[49:18] (2958.32s)
the device you're talking about, this
[49:19] (2959.84s)
ambient assistant is part Siri and part
[49:22] (2962.16s)
maybe a pendant that records your
[49:24] (2964.80s)
behavior in the world and gives you
[49:26] (2966.32s)
feedback to it. And that's what they're
[49:28] (2968.08s)
calling a puck perhaps that Johnny IV
[49:30] (2970.16s)
has made or these pendants that record
[49:31] (2971.92s)
everything. Thomas, what's your thought
[49:33] (2973.76s)
on the one product they could create? to
[49:36] (2976.88s)
that point. Um, it's interesting to
[49:39] (2979.92s)
think that the AirPod business at Apple
[49:42] (2982.00s)
is 3x Open's revenue base today.
[49:44] (2984.72s)
That's right.
[49:45] (2985.36s)
And that's just the AirPod business.
[49:46] (2986.72s)
And by the way, let me let me just say
[49:47] (2987.84s)
one thing about this. We all think about
[49:49] (2989.76s)
devices in the context of a single
[49:53] (2993.28s)
device being an assistant. I think if
[49:55] (2995.92s)
there are more devices integrated into
[49:58] (2998.00s)
our lives and the assistant is ethereal
[50:01] (3001.20s)
and ubiquitous amongst the devices, it's
[50:04] (3004.08s)
almost like uh the Star Trek Next
[50:05] (3005.84s)
Generation. You walk in, you say, "Hey,
[50:07] (3007.36s)
computer." And there's always a device
[50:08] (3008.96s)
available that's doing things. There's
[50:10] (3010.32s)
always a device observing, there's
[50:11] (3011.52s)
always a device able to take care of
[50:12] (3012.88s)
things for you. Whether it's in your
[50:14] (3014.32s)
ear, whether it's your phone, whether
[50:15] (3015.52s)
it's your watch, but basically these
[50:17] (3017.44s)
devices all instead of acting
[50:19] (3019.20s)
independently, they all know what you've
[50:21] (3021.36s)
been asking or talking about with the
[50:22] (3022.88s)
other devices. And so you could get in
[50:24] (3024.88s)
your car and you could pick up, you
[50:26] (3026.96s)
know, the conversation you were having,
[50:29] (3029.28s)
you know, while you were sitting in your
[50:30] (3030.48s)
office in front of your computer to do
[50:32] (3032.40s)
work. And so the agent effectively is
[50:34] (3034.48s)
almost like this ethereal ambient
[50:36] (3036.08s)
assistant. So everywhere you go, the
[50:37] (3037.92s)
agent is there.
[50:38] (3038.96s)
They could even be in a candle lit bath
[50:41] (3041.28s)
with you, Freedberg. They could be in
[50:42] (3042.96s)
there.
[50:43] (3043.28s)
They could Well, I mean, by the way,
[50:44] (3044.48s)
think about also, you know, it it it
[50:46] (3046.24s)
know having identity, so it knows who
[50:47] (3047.92s)
you are, but I could be in your in your
[50:50] (3050.24s)
home, Jal. Not that I would ever get
[50:51] (3051.68s)
invited to your home, but let's say I
[50:53] (3053.28s)
was there. Uh, you know, I could walk
[50:55] (3055.12s)
into the the living room and there's
[50:56] (3056.56s)
your puck and it starts talking to me
[50:58] (3058.00s)
because it knows
[50:59] (3059.20s)
who I am. And yeah, it's like it knows
[51:00] (3060.96s)
me. Yeah.
[51:01] (3061.52s)
Or you and I have a bath for two. You
[51:03] (3063.36s)
and I could be a candidate for two
[51:05] (3065.04s)
and it would know the when each of us
[51:06] (3066.56s)
are fighting over what music we want to
[51:08] (3068.08s)
play. The assistant will, you know, hear
[51:10] (3070.48s)
out the debate playlist. Do you have a
[51:13] (3073.04s)
uh a device before we go on to IPOs
[51:14] (3074.96s)
here? Do you have a device or an angle
[51:17] (3077.04s)
for Apple to go after if they were truly
[51:19] (3079.36s)
ambitious? Or maybe they are and it's
[51:21] (3081.52s)
just in stealth. What do you think? You
[51:22] (3082.72s)
think it's the goggles, the glasses? You
[51:24] (3084.24s)
think it's a pendant? You think it's
[51:25] (3085.36s)
optimist? What do you think?
[51:26] (3086.96s)
I don't think they have any chance of
[51:28] (3088.40s)
anything.
[51:29] (3089.52s)
Great. Love it. I would take the exact
[51:31] (3091.52s)
opposite of what Freebrook says.
[51:33] (3093.28s)
Look at this chart and I'll tell you
[51:35] (3095.44s)
Okay, here we go.
[51:36] (3096.08s)
This chart is not This chart is not a
[51:37] (3097.84s)
strategy. So, this is a chart of Apple's
[51:40] (3100.56s)
revenue and what you see is iPhone has
[51:42] (3102.88s)
completely stalled out. And so to
[51:44] (3104.80s)
Thomas's point, where do you make money?
[51:46] (3106.48s)
You make money in other hardware. This
[51:48] (3108.88s)
is not a strategy of success. This is a
[51:51] (3111.12s)
strategy of inefficiency.
[51:53] (3113.12s)
I lost my AirPods. I need to buy a new
[51:55] (3115.20s)
pair. Oh, the cables changed. I need to
[51:57] (3117.20s)
buy a bunch of those. This and that. And
[52:00] (3120.08s)
a this and that strategy is not a
[52:01] (3121.84s)
strategy. It's a tactical play for
[52:04] (3124.00s)
revenue optimization in the short term.
[52:05] (3125.68s)
A company that focuses on this kind of
[52:08] (3128.16s)
revenue growth is not capable of
[52:10] (3130.96s)
creating something that's exceptionally
[52:13] (3133.04s)
unexpected.
[52:14] (3134.56s)
That will come from a new company who
[52:16] (3136.72s)
has no ties to the past, has nostalgia
[52:20] (3140.64s)
on the fact that we're going to swap out
[52:22] (3142.24s)
the connector type and you know book
[52:24] (3144.16s)
another billion dollars. The what Thomas
[52:26] (3146.72s)
said is an indictment actually about
[52:28] (3148.32s)
their ability to do it. When your
[52:30] (3150.08s)
AirPods business is two or three times
[52:31] (3151.92s)
bigger than Open AI, what there is
[52:34] (3154.96s)
internally when you try to have a
[52:37] (3157.04s)
strategy meeting about what to do is
[52:38] (3158.72s)
derision about Open AI because you're
[52:41] (3161.12s)
like that's small and even our AirPods
[52:44] (3164.24s)
business is three times big. That's what
[52:46] (3166.16s)
some smartass MBA will say in that
[52:48] (3168.40s)
meeting and it'll shut the meeting down.
[52:52] (3172.00s)
So, how do you expect that culture to
[52:54] (3174.40s)
then all of a sudden get their act
[52:55] (3175.92s)
together? I think it's exceptionally
[52:57] (3177.84s)
hard. And here's the clip on Q. Play the
[53:01] (3181.20s)
clip, Nick. It's a great point. Here's
[53:03] (3183.28s)
the clip.
[53:03] (3183.76s)
I'm Apple nostalgic.
[53:05] (3185.20s)
Me, too. Bring Steve Jobs back. Watch
[53:07] (3187.20s)
this lunacy.
[53:08] (3188.16s)
You probably saw that Johnny IV is
[53:09] (3189.60s)
linked up with Open AI to create some
[53:11] (3191.68s)
sort of future AI device.
[53:14] (3194.32s)
Yeah, I don't know what that is.
[53:15] (3195.52s)
I don't either. Yeah.
[53:16] (3196.56s)
Is this a space that Apple's looking at?
[53:18] (3198.32s)
Is this a space that goes beyond what
[53:21] (3201.28s)
you have in the current lineup of
[53:22] (3202.96s)
devices? Something that is more
[53:24] (3204.64s)
personal? Maybe you wear it? Glasses.
[53:27] (3207.92s)
I I think I mean I think we have some
[53:30] (3210.08s)
extremely personal wearable devices. If
[53:32] (3212.40s)
you want something that's uh aware of
[53:34] (3214.72s)
your environment with with audio, I
[53:36] (3216.80s)
think you're you're wearing one right
[53:38] (3218.16s)
now on on your wrist. Um if you want
[53:41] (3221.52s)
something that you can capture the
[53:43] (3223.20s)
environment with and see and also
[53:44] (3224.72s)
receive visual content, you might just
[53:46] (3226.72s)
have one in your pocket right now. Um
[53:49] (3229.36s)
are there other form factors that can
[53:50] (3230.88s)
make sense to AI? Uh sure. But uh pretty
[53:55] (3235.92s)
hard to beat something that's uh with
[53:58] (3238.32s)
you all the time and glancable or you
[54:00] (3240.48s)
know provides a nice screen that you can
[54:02] (3242.08s)
interact with. Um so uh yeah I I don't
[54:05] (3245.60s)
know what they're working on.
[54:06] (3246.48s)
What do you think Jimoth?
[54:08] (3248.32s)
Again I think I want to be very clear
[54:10] (3250.32s)
about what I'm saying. That is a very
[54:12] (3252.08s)
competent Craig Federi very very
[54:14] (3254.24s)
competent executive
[54:17] (3257.28s)
and whoever the person beside him is
[54:19] (3259.52s)
that guy's I'm going to assume competent
[54:21] (3261.44s)
as well. They're competent at making
[54:26] (3266.72s)
the way that they've made money for the
[54:29] (3269.52s)
last 17 years with no meaningful
[54:32] (3272.72s)
disturbance.
[54:35] (3275.04s)
And I think it's just something to
[54:36] (3276.40s)
appreciate that after 17 years of
[54:38] (3278.96s)
unmitigated linear success, it's very
[54:42] (3282.64s)
difficult to retool yourself. It's like
[54:45] (3285.12s)
asking Michael Jordan to go and all of a
[54:47] (3287.04s)
sudden become an all-star baseball. It
[54:48] (3288.88s)
doesn't work.
[54:50] (3290.48s)
And so I think I I think it's okay
[54:52] (3292.56s)
though, this is my point. It's okay,
[54:54] (3294.72s)
guys, to have creative destruction of
[54:56] (3296.40s)
companies. Like there was probably a
[54:58] (3298.56s)
version of us blathering on about HP and
[55:01] (3301.76s)
being nostalgic about the transistor
[55:04] (3304.00s)
radio that they made and the, you know,
[55:06] (3306.48s)
HP12B calculator that they made and oh
[55:08] (3308.72s)
my god, why can't they figure their [ __ ]
[55:10] (3310.40s)
out and where are we today? HP doesn't
[55:12] (3312.96s)
even exist. It's okay. I mean, just
[55:16] (3316.56s)
Thomas, the fact that they launched
[55:19] (3319.04s)
Siri, they bought that company, and Siri
[55:21] (3321.28s)
can't do anything other than like an
[55:23] (3323.60s)
alarm, can barely play a song, it barely
[55:27] (3327.12s)
can do directions. I I I mean,
[55:29] (3329.04s)
literally, we're in year like 27 of
[55:31] (3331.84s)
Siri, and it can't do anything. And then
[55:34] (3334.00s)
I have the the Google and Gro voice, and
[55:37] (3337.28s)
when I turn that on, it does whatever I
[55:39] (3339.20s)
want. It will load on my Pixel. It loads
[55:41] (3341.60s)
other applications, fires it off, does
[55:43] (3343.52s)
specific tasks in it. It's absolutely
[55:45] (3345.36s)
descriat
[55:46] (3346.96s)
on your Pixel.
[55:47] (3347.92s)
I have a Pixel when I when I flip open
[55:49] (3349.92s)
my Pixel.
[55:51] (3351.20s)
I have
[55:52] (3352.96s)
to I have the Pixel 9, Chimath. It's the
[55:55] (3355.28s)
Anaconda of smartphones. Pixel 9
[55:57] (3357.28s)
foldable.
[55:58] (3358.64s)
Got it.
[55:59] (3359.20s)
It's the greatest assistant ever. It's
[56:00] (3360.72s)
what Siri. It's what Steve Jobs showed
[56:02] (3362.96s)
Siri. I had you at nine.
[56:04] (3364.08s)
He had me at Anaconda. Yeah,
[56:05] (3365.36s)
I had you at 9 in. And we can all
[56:07] (3367.20s)
aspire. Maybe get Roman extra get that
[56:09] (3369.92s)
extra inch. Thomas, go ahead.
[56:11] (3371.20s)
Chamath, I would argue to you that I
[56:12] (3372.80s)
think this management team has done it
[56:14] (3374.32s)
once and it's in the transition of their
[56:17] (3377.60s)
gross profit base, which doesn't show in
[56:19] (3379.84s)
the chart that you just highlighted, but
[56:22] (3382.40s)
was something that I kind of lived as an
[56:23] (3383.76s)
analyst covering the stock for a long
[56:25] (3385.12s)
time where if you remember over a decade
[56:27] (3387.20s)
ago, 90 plus% of their gross profit was
[56:29] (3389.92s)
a onetime hardware sale on the iPhone.
[56:32] (3392.72s)
And no one thought that they would ever
[56:36] (3396.56s)
be able to get away from the drug of
[56:38] (3398.16s)
selling that one iPhone unit, right? And
[56:40] (3400.64s)
cut to, you know, over a decade later,
[56:42] (3402.72s)
it's 40%. Right? And I don't think they
[56:46] (3406.40s)
get enough credit for actually
[56:48] (3408.16s)
transitioning from hardware to a
[56:50] (3410.00s)
recurring gross profit base. But look,
[56:52] (3412.32s)
you might argue that that was an easier
[56:54] (3414.08s)
pivot and challenge than what they're
[56:56] (3416.16s)
going to face. And so, let's see whether
[56:57] (3417.92s)
they can do it.
[56:59] (3419.84s)
The other thing guys I wonder about um
[57:02] (3422.32s)
let's I know we want to talk about IPOs
[57:04] (3424.32s)
but I do wonder whether Zuck buying
[57:07] (3427.84s)
scale for 15 billion gives air cover for
[57:10] (3430.40s)
other companies to really start being
[57:13] (3433.12s)
aggressive right and and to me as we
[57:16] (3436.24s)
think about circle and coreweave two
[57:18] (3438.56s)
companies that have gone IPO recently
[57:21] (3441.28s)
it's it's kind of amazing kind of
[57:23] (3443.84s)
numerically that the charts are almost
[57:25] (3445.68s)
identical even you know on a dollar
[57:27] (3447.92s)
basis on a share price, right? Because
[57:31] (3451.12s)
to me, what it says, we were talking
[57:32] (3452.64s)
about the dispersion of the Mac 7
[57:34] (3454.24s)
before, right? Which are going to do
[57:36] (3456.08s)
well, which are not. I expect we're
[57:37] (3457.52s)
going to have a lot of opinions on this
[57:38] (3458.88s)
over the next few years. And frankly,
[57:40] (3460.16s)
they may change. We, you know, we may
[57:41] (3461.84s)
think Apple one way today, it may change
[57:43] (3463.84s)
in a month, right? But I do think the
[57:46] (3466.56s)
market is starting to realize that there
[57:48] (3468.48s)
is dispersion that AI might create some
[57:51] (3471.52s)
all winners or some winners and then
[57:53] (3473.68s)
some losers, right? and is starting to
[57:56] (3476.48s)
think about, okay, how do I want to be
[57:58] (3478.56s)
positioned for the next five years? What
[58:00] (3480.24s)
are big open-ended growth opportunities?
[58:02] (3482.64s)
And here comes two companies, one lever
[58:04] (3484.56s)
to crypto, right, and the other lever to
[58:07] (3487.20s)
AI. So, I don't think it's a surprise to
[58:09] (3489.76s)
me. These things are intertwined.
[58:11] (3491.52s)
You're 100% on because here's the thing,
[58:14] (3494.08s)
the average profit margin of the S&P 493
[58:17] (3497.52s)
is, drum roll please, 12%. The average
[58:21] (3501.36s)
growth of the S&P 493 is, drum roll
[58:24] (3504.24s)
please, single digits. So to your point,
[58:28] (3508.32s)
why would you belong any of these 493
[58:31] (3511.04s)
companies that may turn around and one
[58:32] (3512.80s)
day just get decapitated by something
[58:34] (3514.48s)
you don't even know that's getting
[58:36] (3516.32s)
cooked up by a couple kids in a garage
[58:38] (3518.08s)
using OpenAI or Grock or what have you.
[58:41] (3521.92s)
It just makes a lot more sense when you
[58:44] (3524.16s)
find investable companies in the big
[58:46] (3526.32s)
themes of the future to at a minimum
[58:48] (3528.64s)
hedge, right? be less long the past and
[58:52] (3532.80s)
frankly make some bets about the future.
[58:56] (3536.16s)
And I think that that's where you're
[58:58] (3538.08s)
seeing these IPOs just absolutely rip.
[59:00] (3540.32s)
What is a better comparison in my
[59:02] (3542.00s)
opinion are the companies that are truly
[59:04] (3544.64s)
levered to the future themes of AI and
[59:07] (3547.04s)
crypto versus any of these IPOs that
[59:10] (3550.16s)
have happened of companies that are not.
[59:12] (3552.80s)
And I think what you see is there's a
[59:14] (3554.32s)
dispersion there as well. And they are
[59:16] (3556.80s)
being treated almost as similarly,
[59:18] (3558.56s)
Jason, as the
[59:20] (3560.72s)
S&P 493.
[59:22] (3562.64s)
It's like, yeah, it's good. Yeah, it's
[59:24] (3564.64s)
fine. They get some reasonable gains.
[59:26] (3566.56s)
But if you're lever to any of those two
[59:28] (3568.40s)
two trends, you're off to the races
[59:31] (3571.44s)
because it's just so disruptive. People
[59:34] (3574.40s)
don't want to be bag holding these old
[59:36] (3576.32s)
legacy companies. We're already into our
[59:38] (3578.00s)
next topic, which is IPOs and M&A. Lena
[59:40] (3580.72s)
Khan is no longer in the building and
[59:42] (3582.72s)
M&A is back on the menu as are IPOs as
[59:47] (3587.04s)
Tom has pointed out. Three IPOs March
[59:48] (3588.96s)
28th, June 5th and June 12th. Coreweave,
[59:51] (3591.04s)
Circle and Chime. Obviously Coreweave up
[59:53] (3593.60s)
4x after going public, $81 billion
[59:56] (3596.48s)
market cap. Absolutely stunning. Circle
[59:58] (3598.40s)
25x oversubscribed, 6x from its opening
[60:01] (3601.04s)
price, $ 48 billion market cap. Chime,
[60:03] (3603.68s)
that's a NEO bank like New Bank, which
[60:05] (3605.44s)
is already public. that was up 40% uh in
[60:08] (3608.32s)
its IPO price, but then it went down
[60:10] (3610.16s)
20%, $12 billion market cap. On the
[60:12] (3612.80s)
other side of the ledge, we have a ton
[60:15] (3615.04s)
of M&A this year. So, when you look at
[60:16] (3616.88s)
what's happening under the Trump
[60:18] (3618.24s)
administration, look at what's actually
[60:20] (3620.08s)
happening. The game on the field is
[60:22] (3622.08s)
three major IPOs. Uh and then massive
[60:25] (3625.28s)
amounts of billion dollar acquisitions.
[60:27] (3627.28s)
Obviously, we talked about Google
[60:28] (3628.64s)
acquiring Whiz 32 billion. Uh SoftBank
[60:31] (3631.36s)
bought Emperor. I don't know what they
[60:32] (3632.88s)
do, 6.5 billion. OpenAI bought two
[60:35] (3635.12s)
companies, one for three billion, one
[60:37] (3637.28s)
point for 6.5 billion. Developer
[60:39] (3639.20s)
co-pilot, Windsurf 3 billion. Johnny
[60:41] (3641.28s)
Ives IO making some sort of a puck or
[60:43] (3643.84s)
hardware device. Data Bricks brought
[60:45] (3645.68s)
Neon for a billion. Salesforce uh did an
[60:47] (3647.84s)
$8 billion acquisition. And then
[60:50] (3650.32s)
interesting, Door Dash bought two
[60:51] (3651.68s)
companies. Uber made two smaller
[60:53] (3653.20s)
acquisitions. There is a ton of activity
[60:56] (3656.40s)
here. What does it say about the market,
[60:59] (3659.04s)
David Friedberg, that we're seeing so
[61:01] (3661.68s)
much M&A and these amazing IPOs coming
[61:05] (3665.36s)
out all within the last 3 4 months.
[61:08] (3668.40s)
Okay, so let me just follow up to a
[61:10] (3670.48s)
comment Chimath made and ask Thomas his
[61:14] (3674.24s)
view. I have a a theory and I haven't
[61:17] (3677.60s)
looked empirically to see if it makes
[61:20] (3680.08s)
sense. For most of the S&P 500, the
[61:22] (3682.72s)
fundamental profit growth is pretty
[61:25] (3685.20s)
anemic with the exception obviously of a
[61:27] (3687.84s)
couple of the big tech outliers, the
[61:29] (3689.68s)
MAG7 and a few others. But for for the
[61:33] (3693.12s)
majority of the S&P, this is a pretty
[61:35] (3695.28s)
kind of anemic environment relative to
[61:37] (3697.60s)
the transitions that are underway in the
[61:39] (3699.20s)
world fundamentally with with AI and
[61:41] (3701.20s)
ancillary technology. So are the
[61:44] (3704.48s)
institutional fund managers hungry for
[61:47] (3707.36s)
access to some of these new you know
[61:50] (3710.00s)
high growth offerings and they have been
[61:53] (3713.28s)
held off because and just to kind of go
[61:54] (3714.96s)
back I think it was around 2008 or so
[61:57] (3717.84s)
public institutional fund managers
[61:59] (3719.68s)
started to do crossover investing into
[62:01] (3721.52s)
private equities and that scaled up and
[62:04] (3724.16s)
scaled up and it it entered obviously a
[62:06] (3726.00s)
stage where it was a heavy flurry a lot
[62:08] (3728.72s)
of activity and a lot of crossover late
[62:10] (3730.72s)
stage investing um you right until 2021
[62:14] (3734.24s)
when things started to pop 2022 and
[62:17] (3737.92s)
because they were overexposed
[62:20] (3740.32s)
with their private equity portfolios
[62:22] (3742.08s)
relative to their public equities they
[62:24] (3744.40s)
came out of 2122 with the market
[62:26] (3746.40s)
declining and they now had a higher
[62:28] (3748.72s)
concentration of private equities than
[62:30] (3750.32s)
they were supposed to have and so they
[62:32] (3752.24s)
have been kept out of the market for the
[62:34] (3754.16s)
last 3 or so years of the private market
[62:36] (3756.56s)
and now is there kind of this pentup
[62:38] (3758.24s)
hunger or pent-up demand for new
[62:40] (3760.40s)
issuances for high growth tech issuance
[62:42] (3762.88s)
Is is that what we're seeing? Is there
[62:44] (3764.24s)
kind of this pent up demand because
[62:45] (3765.84s)
they've had to stay out of the the
[62:47] (3767.52s)
private market for 3 years? And if there
[62:49] (3769.28s)
is, obviously it bodess well for
[62:51] (3771.44s)
latestage growth startups that are
[62:53] (3773.04s)
looking to go public because the demand
[62:54] (3774.56s)
will be there. And I think the reports
[62:56] (3776.88s)
were that the Chime IPO was like 18x
[62:59] (3779.12s)
overs subscribed. I think you're right
[63:00] (3780.56s)
and and something that you know I've
[63:02] (3782.64s)
talked about with you guys and uh was a
[63:05] (3785.36s)
was a big conversation at our at the
[63:07] (3787.04s)
all-in summit last year was the health
[63:09] (3789.60s)
of the uh private ecosystem right and we
[63:13] (3793.04s)
talked about the concept of look if you
[63:15] (3795.44s)
put a dollar in you need to get a dollar
[63:17] (3797.12s)
out right and so I do think that we're
[63:20] (3800.00s)
starting to see a healthier market where
[63:22] (3802.16s)
we know a lot of dollars have gone in
[63:23] (3803.84s)
but now we're starting to see some
[63:25] (3805.20s)
dollars coming out so I think that's
[63:26] (3806.72s)
both in M&A by the way and it's also in
[63:28] (3808.48s)
IPOs So I think that's one element. But
[63:31] (3811.20s)
I also think the second element which is
[63:33] (3813.12s)
we're the tailwind of the mobile and SAS
[63:36] (3816.32s)
era, right? And even if you look at the
[63:38] (3818.64s)
SAS companies, we kind of put this
[63:40] (3820.88s)
together in our deck when we were
[63:42] (3822.24s)
preparing it for our conference um this
[63:44] (3824.48s)
week. Chamath, I think you'll find this
[63:46] (3826.16s)
interesting, right? If you look at SAS
[63:48] (3828.16s)
in 2021,
[63:50] (3830.16s)
the median growth rate for SAS companies
[63:52] (3832.24s)
was 17% and a quarter of those were
[63:54] (3834.72s)
growing over 25%. Mhm.
[63:57] (3837.68s)
If you look at SAS today, the growth
[64:00] (3840.48s)
rate has been cut in half, 17% to 9%.
[64:04] (3844.80s)
And only 5% of that cohort is now
[64:07] (3847.44s)
growing above 25%. So I think Dave,
[64:10] (3850.00s)
what's clearly happening, right, is
[64:12] (3852.72s)
other sectors which were predominantly
[64:14] (3854.88s)
seen to be growth are now slowing down,
[64:17] (3857.76s)
right? So that's kind of one piece. So
[64:19] (3859.68s)
the market can no longer just rely on
[64:22] (3862.00s)
saying, "Oh, I'm just going to own the
[64:23] (3863.36s)
Bessemer SAS index, right, for the next
[64:25] (3865.36s)
decade and I'll do great." Because those
[64:27] (3867.44s)
companies have really slowed down. And I
[64:29] (3869.84s)
think it's starting to look forward and
[64:31] (3871.68s)
think, okay, now over the next 5 to 10
[64:34] (3874.56s)
years, what are the companies that can
[64:36] (3876.88s)
compound at maybe 25% per year over that
[64:39] (3879.60s)
time frame? And I think companies like
[64:42] (3882.00s)
Cororeweave and Circle and Chime, by the
[64:44] (3884.00s)
way, and others are going to kind of
[64:45] (3885.44s)
fill that gap.
[64:46] (3886.56s)
I um I really like this chart.
[64:49] (3889.76s)
If I had to guess about what has changed
[64:53] (3893.04s)
from 2021 to 2025
[64:56] (3896.24s)
is that most companies have realized
[64:59] (3899.20s)
that buying yet another
[65:02] (3902.80s)
vertical software solution is not going
[65:05] (3905.44s)
to help their business that it typically
[65:08] (3908.16s)
adds bloat, it adds cost and it adds
[65:12] (3912.16s)
people. And I think starting in 2023,
[65:16] (3916.08s)
what people started to guess is at some
[65:18] (3918.72s)
point in the near future, you're going
[65:21] (3921.28s)
to have some AI way of rewriting all of
[65:25] (3925.76s)
this vertical software. And I think
[65:27] (3927.92s)
that's why it stopped growing. I don't
[65:29] (3929.76s)
think this SAS market ever had the
[65:33] (3933.84s)
return on equity that it was supposed
[65:36] (3936.08s)
to. And I think so many companies have
[65:38] (3938.80s)
woken up from this hangover saying
[65:41] (3941.04s)
there's got to be a better way. It can't
[65:43] (3943.12s)
always be yet another tool, yet another
[65:46] (3946.16s)
program, yet another multi-year delay,
[65:49] (3949.84s)
yet another price escalator. And I think
[65:52] (3952.80s)
that that the jig is totally up for
[65:55] (3955.68s)
software.
[65:56] (3956.88s)
You're referring to the Salesforce and
[65:59] (3959.52s)
the SAS category, Chimoth, and what
[66:01] (3961.84s)
you're doing at 8090 specifically. Yeah.
[66:03] (3963.84s)
Well, it's it's not just us, but like if
[66:05] (3965.60s)
you look at anybody that's rebuilding
[66:07] (3967.44s)
software,
[66:08] (3968.96s)
it is so much easier to rebuild software
[66:12] (3972.32s)
from scratch today. Like my team of 30
[66:15] (3975.36s)
people can transact hundreds of millions
[66:18] (3978.72s)
of dollars of work. Not because we are
[66:21] (3981.20s)
so prolifically amazing, but frankly
[66:23] (3983.60s)
because well, I think the team is good,
[66:25] (3985.20s)
but honestly because the underlying tool
[66:27] (3987.20s)
chain gives you a level of leverage. And
[66:29] (3989.84s)
so if you rebuild the software
[66:32] (3992.48s)
development life cycle using these
[66:34] (3994.40s)
tools,
[66:36] (3996.00s)
you can't help it but become much more
[66:38] (3998.32s)
efficient and you can't help it but
[66:40] (4000.24s)
deliver custom solutions that are
[66:42] (4002.24s)
meaningfully cheaper. And I think Jason,
[66:44] (4004.48s)
if you look at the entirety of the
[66:46] (4006.08s)
software that runs the world, we're
[66:48] (4008.64s)
going to rebuild it soup to nuts. all of
[66:52] (4012.00s)
and the tool you're referring to just
[66:53] (4013.44s)
for the audience is the AI co-pilots
[66:55] (4015.92s)
that are making that are contributing 30
[66:58] (4018.00s)
40% to code bases at Microsoft and
[67:00] (4020.96s)
less less specifically that because
[67:02] (4022.80s)
those are those are good for individual
[67:04] (4024.56s)
people but the software development life
[67:06] (4026.24s)
cycle is more the horizontal end to end
[67:08] (4028.32s)
of making things got it
[67:09] (4029.84s)
so what we do internally at 8090 is we
[67:12] (4032.64s)
have an entire process that starts from
[67:14] (4034.16s)
the PRD all the way out to the
[67:15] (4035.76s)
functioning code and we use different
[67:18] (4038.00s)
techniques at each step but what you get
[67:20] (4040.32s)
is a 50 60 70% increase at each step
[67:24] (4044.56s)
which then compounds.
[67:26] (4046.16s)
And so you have the ability of a team
[67:28] (4048.80s)
that would otherwise be able to service
[67:30] (4050.56s)
tens of millions of dollars
[67:32] (4052.64s)
be a team that can service hundreds of
[67:34] (4054.24s)
millions and then a team that would
[67:35] (4055.68s)
otherwise service hundreds can service
[67:37] (4057.44s)
billions.
[67:37] (4057.84s)
Let me ask you guys your response to
[67:40] (4060.00s)
this theory. If there is going to be
[67:42] (4062.88s)
this kind of accelerated
[67:45] (4065.36s)
call it custom software rebuild of
[67:48] (4068.24s)
business models and you take the S&P
[67:50] (4070.48s)
493, do you think that we enter an era
[67:53] (4073.68s)
where there is a similar dispersion as
[67:56] (4076.32s)
we're talking about seeing in the MAG 7
[67:58] (4078.56s)
with the S&P 493 where there are going
[68:01] (4081.60s)
to be probably the biggest money-making
[68:03] (4083.84s)
opportunities for investors that we've
[68:05] (4085.84s)
seen in decades
[68:08] (4088.80s)
between those that do adopt and do
[68:11] (4091.28s)
rebuild using AI and those that don't
[68:14] (4094.72s)
for 100 100%.
[68:17] (4097.44s)
I had a call yesterday with one of the
[68:19] (4099.28s)
largest private equity funds in the
[68:21] (4101.20s)
world, hundreds of billions of dollars
[68:22] (4102.88s)
under management and we're doing
[68:24] (4104.24s)
something with them at 8090 with one of
[68:26] (4106.72s)
their most important assets.
[68:30] (4110.48s)
And when you're an owner of a business
[68:33] (4113.36s)
and you can direct
[68:35] (4115.92s)
specific change
[68:38] (4118.16s)
and you can rip out
[68:40] (4120.80s)
hundreds of millions of dollars of
[68:44] (4124.08s)
software licenses and replace it with
[68:46] (4126.80s)
tens of millions of dollars of highly
[68:50] (4130.00s)
customized software.
[68:52] (4132.96s)
It's an enormous lift to opex and
[68:55] (4135.04s)
business model quality. So why doesn't
[68:57] (4137.36s)
it happen more? The reason it doesn't
[68:59] (4139.68s)
happen right now for this S&P 493 is
[69:02] (4142.64s)
that the IT organizations inside all
[69:04] (4144.88s)
companies
[69:07] (4147.28s)
essentially speak a different language
[69:08] (4148.80s)
than the CEO, the CFO, and the board. So
[69:11] (4151.12s)
if the CEO, CFO, and the board of
[69:13] (4153.76s)
directors of the S&P 493 speak English,
[69:16] (4156.64s)
the IT organization speaks Mandarin
[69:18] (4158.72s)
Chinese, and you get away with saying
[69:20] (4160.80s)
all kinds of [ __ ] I'll give you an
[69:22] (4162.56s)
example. I went to a CIO conference. one
[69:26] (4166.72s)
person that I met an $18 billion a year
[69:30] (4170.56s)
IT budget.
[69:33] (4173.04s)
What the [ __ ] does that actually even
[69:35] (4175.28s)
mean to spend $18 billion a year on it?
[69:39] (4179.92s)
I'm not saying that this is a mag seven
[69:41] (4181.92s)
company, guys. And when you take that
[69:44] (4184.16s)
example and you multiply it by 50 and
[69:46] (4186.48s)
100 and 493 examples of people spending
[69:49] (4189.84s)
money, there's an entire cartel of
[69:52] (4192.88s)
influence that's been built in software
[69:54] (4194.80s)
that's going to get undone because
[69:56] (4196.64s)
you're not going to be able to justify
[69:58] (4198.00s)
it. Free. Absolutely correct. And the
[70:00] (4200.24s)
response from the SAS industry is
[70:02] (4202.96s)
changing from the per seat model as the
[70:05] (4205.84s)
number of employees at these companies
[70:07] (4207.36s)
continues to get lowered. Obviously
[70:08] (4208.96s)
Microsoft a lot of layoffs. Andy Jasse
[70:11] (4211.52s)
talking about layoffs. They're moving
[70:13] (4213.76s)
from the per seat model. They're not
[70:15] (4215.28s)
taking this uh laying down. Uh they know
[70:18] (4218.08s)
that people are going to make custom
[70:19] (4219.20s)
software. So what they're doing is
[70:20] (4220.24s)
they're moving to a consumption model.
[70:21] (4221.68s)
So you're seeing people charge per call,
[70:24] (4224.16s)
per customer support call, etc.
[70:27] (4227.60s)
And well, it's I'll tell you why it
[70:29] (4229.36s)
doesn't work.
[70:29] (4229.76s)
They're working in combination. Hold on,
[70:30] (4230.96s)
hold on, let me finish. The other thing
[70:32] (4232.32s)
they're doing is they're dramatically
[70:33] (4233.84s)
lowering the number of people and the
[70:35] (4235.28s)
developers they have on their team. And
[70:37] (4237.20s)
then a lot of what's happening in the
[70:38] (4238.72s)
background is the third piece they're
[70:39] (4239.92s)
doing is they're starting to uh do
[70:42] (4242.08s)
rollups and people are starting to talk
[70:44] (4244.08s)
about how can we take you know 20 of
[70:46] (4246.56s)
these SAS companies lower them just like
[70:48] (4248.48s)
you're doing to compete your thought
[70:52] (4252.48s)
playbook. Well, I just wanted to comment
[70:54] (4254.32s)
on this like consumption based pricing.
[70:56] (4256.08s)
It doesn't work. And what I mean is you
[70:58] (4258.56s)
can have some adoption in the short
[70:59] (4259.92s)
term. The best example is Snowflake, but
[71:02] (4262.72s)
in the long term it destroys your
[71:04] (4264.40s)
business. And the reason is because you
[71:06] (4266.72s)
don't know which data is valuable and
[71:08] (4268.48s)
you're not going to put up with a
[71:10] (4270.00s)
variable business model that increases
[71:12] (4272.48s)
more and more cost because you need to
[71:14] (4274.08s)
trap everything. And so what happens is
[71:16] (4276.32s)
all of these other companies develop
[71:18] (4278.64s)
around you. People go back to Postpress,
[71:20] (4280.80s)
people go to Superbase, they find all of
[71:22] (4282.80s)
these ways of saying snowflake makes no
[71:25] (4285.44s)
sense. And the reason is because in this
[71:28] (4288.08s)
world, nobody's going to pay consumption
[71:30] (4290.00s)
because you're like, how do you expect
[71:31] (4291.44s)
me to, you know, hold and store and pay
[71:34] (4294.16s)
for terabytes and terabytes potentially
[71:37] (4297.04s)
a day of data? It's not sustainable.
[71:39] (4299.92s)
We'll see if intercom, Salesforce,
[71:42] (4302.32s)
HubSpot, and we see if all of those
[71:44] (4304.00s)
people start Slack start losing their
[71:45] (4305.68s)
customer base or if they lower their
[71:47] (4307.44s)
pricing to make it just too easy to keep
[71:49] (4309.12s)
those systems in. Thomas, your thoughts?
[71:51] (4311.28s)
Yeah, so two quick thoughts. Uh, number
[71:53] (4313.12s)
one, Chimath, to put a kind of a
[71:54] (4314.96s)
mathematical frame on this, right? We
[71:57] (4317.44s)
know that Anthropic is kind of the level
[72:00] (4320.40s)
zero of code generation. They're they're
[72:02] (4322.24s)
doing incredibly well powering companies
[72:03] (4323.76s)
like Cursor, right? I think and this is
[72:06] (4326.64s)
order of magnitude correct that
[72:08] (4328.40s)
Enthropic in Q1 added 70% of the net new
[72:11] (4331.92s)
ARR in the SAS industry right defined by
[72:15] (4335.12s)
public SAS companies right so let's just
[72:17] (4337.76s)
think that the company in AI that is
[72:19] (4339.84s)
most powering the disruption of SAS
[72:22] (4342.32s)
added 3/4 of the net new of the entire
[72:24] (4344.72s)
industry right so that's kind of point
[72:26] (4346.32s)
number one
[72:28] (4348.00s)
I think Freedberg point number two I
[72:29] (4349.84s)
think what we're seeing in the Max 7
[72:31] (4351.28s)
right where we're starting to have
[72:32] (4352.32s)
debates about who's well positioned and
[72:33] (4353.92s)
who isn't who's going to win and who
[72:35] (4355.60s)
isn't, right? Is actually, as it was in
[72:38] (4358.32s)
the past 5 years, going to be a broader
[72:40] (4360.64s)
lens into the S&P 493. I think inside of
[72:44] (4364.80s)
boardrooms, inside of every investment
[72:46] (4366.80s)
committee, you're going to see the exact
[72:48] (4368.72s)
same conversations that we've been
[72:50] (4370.40s)
having about the MAX 7, right? Who who's
[72:53] (4373.04s)
well positioned, who can win, what are
[72:55] (4375.04s)
the management teams maybe like Zuck
[72:56] (4376.88s)
that are being aggressive and bold and
[72:58] (4378.32s)
capturing the opportunity, and which are
[73:00] (4380.24s)
the ones that are not. So for me as a
[73:02] (4382.48s)
stock picker, right, I think over the
[73:04] (4384.56s)
next 5 years, I couldn't think of a more
[73:07] (4387.52s)
interesting time where we're actually
[73:09] (4389.20s)
going to see dispersion between winners
[73:11] (4391.36s)
and losers. And do you think that these
[73:13] (4393.52s)
rollup models make sense? So you've
[73:15] (4395.28s)
probably heard uh and I don't know if
[73:17] (4397.60s)
you guys have considered this, but
[73:19] (4399.28s)
obviously some fund managers are putting
[73:20] (4400.96s)
together pools of capital to go out and
[73:24] (4404.16s)
buy businesses that they can then apply
[73:26] (4406.32s)
their knowhow. They're bring in smart
[73:28] (4408.16s)
people in AI to then create a category
[73:31] (4411.28s)
killer and go after that market. And are
[73:34] (4414.56s)
you guys participating in that? And how
[73:36] (4416.72s)
do you kind of view that opportunity?
[73:38] (4418.80s)
Are all the public companies basically
[73:41] (4421.20s)
too mature or are some of them going to
[73:43] (4423.12s)
kind of go after this type this model as
[73:45] (4425.28s)
It goes back to whether you can attract
[73:46] (4426.88s)
the talent to go and do these things. My
[73:49] (4429.28s)
advice to this large private equity firm
[73:51] (4431.60s)
is you can probably try to stand up your
[73:54] (4434.16s)
own AI org, but I suspect you're going
[73:57] (4437.28s)
to get the person that didn't get an
[73:58] (4438.96s)
OpenAI offer, didn't get a Meta offer,
[74:01] (4441.28s)
didn't get a Google offer, didn't get an
[74:02] (4442.88s)
8090 offer, then didn't get an Apple
[74:04] (4444.72s)
offer, and then that's the person you'll
[74:06] (4446.24s)
hire. How good that person will be, who
[74:08] (4448.40s)
the hell knows? I think the problem is
[74:10] (4450.96s)
that even if you take some of these kind
[74:14] (4454.96s)
meh industries and roll them all up, you
[74:18] (4458.64s)
ultimately have to find a buyer who
[74:20] (4460.16s)
wants to own that business after you. So
[74:22] (4462.88s)
the question is like if you were to buy
[74:24] (4464.48s)
a bunch of accounting firms
[74:27] (4467.28s)
or law firms or IT services firms and
[74:32] (4472.24s)
you do an incredible job,
[74:35] (4475.44s)
who wants to buy that in seven years?
[74:37] (4477.76s)
Meaning if you talk to like if you went
[74:40] (4480.08s)
to the OpenAI demo day, there was this
[74:42] (4482.64s)
really interesting chart where Andre
[74:44] (4484.64s)
Karpathy talked about integrating Google
[74:46] (4486.56s)
login into
[74:49] (4489.04s)
one of his apps. I think it was the his
[74:50] (4490.72s)
menu gen app. And the comment he made
[74:53] (4493.52s)
which profoundly hit me is like why am I
[74:56] (4496.80s)
doing any of this? Why isn't this just
[74:58] (4498.56s)
one click behind the scenes? And you
[75:01] (4501.68s)
could take that generalization and apply
[75:03] (4503.20s)
it to all of IT services. Why does any
[75:05] (4505.12s)
of that exist? Why isn't it all one
[75:07] (4507.68s)
click? And eventually if these agents
[75:10] (4510.08s)
become smart enough, the fear that I
[75:12] (4512.88s)
have is that there is no terminal buyer
[75:14] (4514.56s)
for many of these companies.
[75:16] (4516.96s)
Mhm. But they could still be public
[75:18] (4518.80s)
chimat. I mean they could they could
[75:21] (4521.20s)
trade at some multiple of cash flow and
[75:22] (4522.96s)
you're basically arbiting the cash flow.
[75:24] (4524.80s)
But I'm not talking about the private
[75:25] (4525.92s)
equity trade. I'm actually talking about
[75:27] (4527.92s)
the public equity trade. If you look at
[75:30] (4530.00s)
the 493 companies,
[75:31] (4531.92s)
those are better positioned. I think
[75:33] (4533.76s)
like instead of an IT rollup, I think
[75:35] (4535.68s)
what you could do is probably sort like
[75:38] (4538.16s)
here's what I would do. I would take the
[75:39] (4539.52s)
493 and the filter that I would apply is
[75:43] (4543.60s)
what offline assets do they have? What
[75:46] (4546.40s)
online assets do they have? What
[75:48] (4548.64s)
percentage of those assets are
[75:50] (4550.16s)
defensible and unique and exist in a
[75:52] (4552.40s)
postAI world? And what percentage of
[75:54] (4554.08s)
those assets disappear in a post AI
[75:56] (4556.00s)
world? And I think where I would end up
[75:58] (4558.24s)
is I'd like own a specialty chemicals
[76:00] (4560.16s)
company or something, you know, like
[76:01] (4561.92s)
you're still gonna need lubricants and
[76:03] (4563.84s)
stuff and you can find some way to make
[76:05] (4565.44s)
it, but if you're like a
[76:07] (4567.44s)
You need lubricants. Sorry. Go ahead.
[76:09] (4569.04s)
You know, I love the lubricants,
[76:12] (4572.32s)
but no dy. No, Diddy.
[76:14] (4574.08s)
But, uh, baby oil making, you know, like
[76:17] (4577.44s)
five by the crate.
[76:18] (4578.48s)
Timoth, do you want to talk about your
[76:19] (4579.84s)
spack uh tweet?
[76:21] (4581.04s)
Uhoh. You know the market's back. Can we
[76:23] (4583.12s)
see this much?
[76:24] (4584.08s)
Can you play the siren? Can you play the
[76:25] (4585.44s)
siren?
[76:27] (4587.92s)
Well, as with all my tweets,
[76:29] (4589.36s)
like a combo, like a combo beach party,
[76:33] (4593.04s)
as with all my tweets, it starts when
[76:36] (4596.08s)
Look here, here's what X is an
[76:38] (4598.08s)
incredible platform. I use it for
[76:39] (4599.36s)
Pull up the tweet thing. Pull up the
[76:40] (4600.72s)
tweet.
[76:41] (4601.04s)
I use it for a lot of things, but
[76:42] (4602.56s)
your villain phase right now, man. You
[76:44] (4604.32s)
full super villain. It's so great.
[76:47] (4607.20s)
The retweet is more important.
[76:49] (4609.20s)
Yeah, I love that quote retweet. Here we
[76:50] (4610.96s)
go. Here's the tweet. Chimamoth says,
[76:53] (4613.04s)
"Incredible that almost 58,000 people
[76:54] (4614.96s)
voted in his tweet if he should launch a
[76:57] (4617.28s)
new spa." So, uh, give the people what
[76:59] (4619.68s)
they want, Chimamoth, or what?
[77:01] (4621.68s)
Well, I first I first started this
[77:03] (4623.36s)
because I when I use X sometimes to to
[77:06] (4626.48s)
just to like sound off because it
[77:08] (4628.24s)
d-stresses me during the day.
[77:10] (4630.16s)
I like I'll troll people or whatever.
[77:12] (4632.08s)
And then I just did this
[77:13] (4633.92s)
and I was so impressed that 58,000
[77:16] (4636.80s)
people voted. But really what happened
[77:18] (4638.24s)
was I had a lot of very smart money
[77:20] (4640.64s)
people on Wall Street and some crypto
[77:22] (4642.24s)
folks call me that I respect and and
[77:25] (4645.04s)
basically what they said is like it
[77:26] (4646.64s)
would be really good if you did it. So I
[77:28] (4648.40s)
don't know if I'm going to do it but I'm
[77:30] (4650.00s)
heavily leaning towards doing it.
[77:31] (4651.36s)
Well the argument to do it is you
[77:32] (4652.72s)
learned a lot since last time. There's a
[77:34] (4654.40s)
lot of inventory there. You've got a lot
[77:36] (4656.08s)
of access to pre-market companies. I
[77:38] (4658.24s)
think what people need to understand is
[77:39] (4659.60s)
when you're doing spaxs and correct me
[77:41] (4661.84s)
if I'm wrong here.
[77:42] (4662.48s)
Here's what here's what I'll say Jason.
[77:44] (4664.72s)
This poll and this community note will
[77:48] (4668.08s)
be in every single document I do. Nobody
[77:50] (4670.88s)
that is listening to this should
[77:52] (4672.16s)
participate in this.
[77:53] (4673.52s)
This is going to be for me and a handful
[77:55] (4675.68s)
of, you know, advanced large pools of
[77:58] (4678.96s)
money. You should stay as far away as
[78:01] (4681.68s)
possible.
[78:03] (4683.04s)
Whatever I do next,
[78:04] (4684.32s)
don't participate in back. That's that's
[78:07] (4687.12s)
the rule here.
[78:07] (4687.92s)
Stay on the sideline. Do something else.
[78:10] (4690.32s)
Don't come in the arena cuz we're trying
[78:12] (4692.00s)
things. Timoth, don't you have enough
[78:13] (4693.52s)
going on? Like, why would you sp Why
[78:15] (4695.20s)
would you do this when you have fate
[78:16] (4696.72s)
loves irony? Fate loves
[78:18] (4698.16s)
loves irony, bro. Fate loves
[78:19] (4699.60s)
Absolutely. This will be hilarious. It
[78:21] (4701.04s)
would be the greatest IPO of all time.
[78:24] (4704.24s)
If the poll was yes, I'd be like, "Oh
[78:26] (4706.08s)
[ __ ] this is the last thing I need."
[78:27] (4707.44s)
All in spa. Let's go.
[78:30] (4710.08s)
Thomas commentary.
[78:31] (4711.68s)
Thomas, are you going to buy the all-in
[78:33] (4713.20s)
spack? What's going on?
[78:34] (4714.08s)
The spa market coming back.
[78:36] (4716.32s)
I'm open to all great companies coming
[78:38] (4718.56s)
to the public market.
[78:40] (4720.16s)
Love it. Love it. I mean, but So Thomas,
[78:42] (4722.24s)
can I ask you a question? Like tell us
[78:43] (4723.52s)
about the state of liquidity and
[78:45] (4725.44s)
actually about IPOs and spaxs in
[78:48] (4728.08s)
general. Like where's your where's your
[78:49] (4729.28s)
temperature on it? Just give us a read
[78:50] (4730.56s)
on what you think.
[78:51] (4731.60s)
I mean look, I I think we're getting
[78:53] (4733.36s)
real world data, Chimath, right? Like in
[78:55] (4735.36s)
real time. Um not just from kind of
[78:57] (4737.68s)
higher visibility companies like Circle
[78:59] (4739.60s)
and Coree, but um Chime also did really
[79:02] (4742.40s)
well. Um Caris uh company, you know,
[79:05] (4745.68s)
more in Dave's uh wheelhouse, right? Um
[79:09] (4749.68s)
also just coming out. So, and then wait
[79:12] (4752.40s)
till we see um the flurry of S1s that
[79:15] (4755.36s)
have already been filed, right? Figma is
[79:17] (4757.60s)
a is a generational potential company,
[79:20] (4760.32s)
right, that's going to be coming. So, I
[79:22] (4762.56s)
think we're going to see fantastic
[79:24] (4764.56s)
assets coming out and I think the market
[79:26] (4766.80s)
is saying we're open for business. The
[79:28] (4768.56s)
the MAX 7 is controversial. To Dave's
[79:31] (4771.20s)
point, the the S&P 493, there's going to
[79:33] (4773.84s)
be lots of winners and losers. It's
[79:35] (4775.44s)
maybe not as obvious. There's going to
[79:36] (4776.80s)
be some dispersion. So, bring on the new
[79:40] (4780.40s)
cohort.
[79:41] (4781.28s)
I think it's the first time you could
[79:42] (4782.72s)
probably argue that you could go short
[79:44] (4784.48s)
the S&P. Yeah.
[79:45] (4785.92s)
And pick a couple of winners. It's It
[79:47] (4787.84s)
might be the first time that I would
[79:49] (4789.52s)
feel in the last 20 years, cuz I I'm
[79:51] (4791.76s)
pretty negative on people being able to
[79:53] (4793.36s)
kind of pick stocks.
[79:54] (4794.80s)
But I do think that this is such a
[79:56] (4796.24s)
transformative moment that if you really
[79:57] (4797.84s)
have a sense for what's possible, you
[80:00] (4800.00s)
could start to see category killers
[80:01] (4801.60s)
emerge out of the S&P. And it's an
[80:03] (4803.12s)
opportunity to short the S&P and pick a
[80:05] (4805.12s)
couple winners.
[80:05] (4805.92s)
Totally. Do you Thomas, but do you do
[80:08] (4808.16s)
you care about how these companies go
[80:09] (4809.92s)
public? Like do you care about spack
[80:11] (4811.52s)
versus direct listing versus IPO? Like
[80:14] (4814.64s)
I don't I I only care about the quality
[80:16] (4816.56s)
of the underlying asset and what I think
[80:19] (4819.04s)
it can be worth 5 years from now. Now
[80:21] (4821.12s)
obviously I do care about the liquidity
[80:22] (4822.88s)
that I'm getting in the IPO Chimoth. So
[80:26] (4826.16s)
you know am I getting a million uh or
[80:28] (4828.88s)
100 million or a billion as the float,
[80:30] (4830.64s)
right? That's number one. And obviously
[80:32] (4832.40s)
I also do care about the percentage that
[80:35] (4835.84s)
is floating and I do care about the
[80:38] (4838.00s)
lockup. Right? So those those three
[80:39] (4839.60s)
elements are really important in terms
[80:41] (4841.04s)
of a company going public and how we
[80:42] (4842.80s)
think about participating.
[80:44] (4844.00s)
Give the listeners the guidance there.
[80:45] (4845.52s)
So for the first thing bigger is better
[80:48] (4848.48s)
than smaller.
[80:49] (4849.60s)
Correct. So it's number one can I even
[80:51] (4851.44s)
buy it? Right. If if the IPO is so small
[80:55] (4855.04s)
um and you know we can't get a large
[80:58] (4858.16s)
enough position it doesn't really make
[80:59] (4859.52s)
sense for us. Right? So that would be
[81:01] (4861.36s)
kind of point number one, right? Point
[81:03] (4863.60s)
number two is how much of the company is
[81:05] (4865.84s)
publicly floating, right?
[81:09] (4869.20s)
Better there as well.
[81:10] (4870.24s)
Correct. We you kind of get a truer
[81:12] (4872.16s)
price, right? When a higher percentage
[81:14] (4874.00s)
of the company floats, um it's also most
[81:17] (4877.20s)
likely going to be less volatile and
[81:18] (4878.96s)
less susceptible, chimat, to um you
[81:22] (4882.48s)
know, pricing uh predatory pricing and
[81:25] (4885.60s)
and manipulation and things like that.
[81:27] (4887.76s)
What's the percentage float that
[81:29] (4889.84s)
I think 20% is in my opinion kind of a
[81:33] (4893.36s)
minimum. Some have gone out you know I
[81:35] (4895.68s)
think I remember correct you may know
[81:37] (4897.52s)
this I think LinkedIn went out at like
[81:39] (4899.04s)
10% or something. I I remember it being
[81:41] (4901.28s)
really small
[81:42] (4902.40s)
and a lot of us thinking like wow that
[81:44] (4904.24s)
is a that is a small
[81:46] (4906.72s)
yeah which ended up by the way being
[81:48] (4908.24s)
very volatile.
[81:50] (4910.08s)
So, so number two, the float and then
[81:52] (4912.16s)
number three, the lockup, right? First,
[81:54] (4914.24s)
is there one? Um, in a direct listing,
[81:56] (4916.64s)
there may not be one, right? So, you may
[81:58] (4918.40s)
you may get in that scenario to a truer
[82:01] (4921.04s)
price faster. Um, and
[82:04] (4924.08s)
Thomas, why do you think there's been no
[82:05] (4925.76s)
direct listings? Like, why has that
[82:08] (4928.08s)
totally fallen away after I mean Spotify
[82:10] (4930.64s)
did one, we did one at Slack,
[82:13] (4933.28s)
and then where where are they? Like, why
[82:15] (4935.28s)
why don't people pursue those?
[82:17] (4937.04s)
So, here's a statistic. I actually had
[82:19] (4939.44s)
to double check this because I couldn't
[82:21] (4941.44s)
believe it. Right? If you look at the
[82:22] (4942.80s)
cohort of companies that went IPO in
[82:24] (4944.64s)
2021, right? And uh and I'm actually not
[82:28] (4948.08s)
including spaxs in this particular
[82:29] (4949.68s)
analysis. Right?
[82:32] (4952.32s)
If you look at that cohort t + one year,
[82:36] (4956.40s)
the cohort was down about 40% on
[82:39] (4959.04s)
average. Right? Okay, fine. Maybe they
[82:40] (4960.96s)
went up too high. 2021 was a peak. They
[82:43] (4963.12s)
didn't do well in one year. T plus 5
[82:45] (4965.28s)
years, it's down 50%.
[82:47] (4967.60s)
Right? which which really kind of
[82:50] (4970.00s)
shocked me, right? So I think there's
[82:52] (4972.56s)
kind of scar tissue on both sides of the
[82:55] (4975.04s)
table on the buy side about wait hold on
[82:57] (4977.52s)
what am I really buying and how do I
[82:59] (4979.60s)
make sure that um it's kind of a
[83:01] (4981.68s)
sustainable kind of company but frankly
[83:04] (4984.32s)
probably also from boards right who are
[83:07] (4987.04s)
taking their best assets public and may
[83:09] (4989.76s)
just want to um pursue a more
[83:12] (4992.24s)
conventional approach in the beginning
[83:14] (4994.16s)
stages right I can tell you for us
[83:17] (4997.68s)
direct listing versus IPO makes makes no
[83:20] (5000.48s)
functional You know, I think each has a
[83:22] (5002.48s)
benefit and I think in some depending on
[83:25] (5005.76s)
how how concentrated your ownership base
[83:27] (5007.84s)
is, how understandable your business
[83:29] (5009.92s)
model is and things like that, but we
[83:32] (5012.00s)
just want these companies to come.
[83:33] (5013.60s)
There's a market behavior, by the way,
[83:35] (5015.12s)
in direct listings, and I I've mentioned
[83:37] (5017.28s)
this once, but I've been in two
[83:39] (5019.20s)
transactions with direct listings. The
[83:41] (5021.04s)
first was Slack, and in the execution of
[83:44] (5024.56s)
it, we misexecuted. we meaning me
[83:47] (5027.12s)
because I had a huge
[83:49] (5029.92s)
ownership of Slack but I didn't know
[83:52] (5032.16s)
what to do with it and I ended up
[83:54] (5034.64s)
distributing portions along the way and
[83:57] (5037.68s)
it then went through all kinds of
[83:59] (5039.20s)
turbulence and then it got acquired
[84:01] (5041.20s)
slightly above the IPO price and what I
[84:04] (5044.32s)
learned in retrospect was the best trade
[84:07] (5047.44s)
is actually the first day trade on a
[84:09] (5049.44s)
direct listing. So then when it came
[84:11] (5051.52s)
back around and I got a distribution the
[84:13] (5053.44s)
day before of Coinbase and and I
[84:15] (5055.68s)
mentioned this to Brian, this was not a
[84:17] (5057.04s)
judgment on the company. I said, "If
[84:18] (5058.96s)
this direct listing process is going to
[84:20] (5060.72s)
map to what I've experienced at Slack,
[84:23] (5063.20s)
the right thing to do is to sell." And I
[84:25] (5065.12s)
sold that on day one at 335 bucks a
[84:28] (5068.40s)
share.
[84:30] (5070.32s)
And ju it's just it's I think Jason,
[84:33] (5073.20s)
it's still not at the IPO price.
[84:35] (5075.12s)
I think it might be getting close, but
[84:37] (5077.12s)
no, it's not back.
[84:38] (5078.16s)
So these Yeah. So these direct listings
[84:39] (5079.84s)
are not what they're expected to be
[84:41] (5081.20s)
either. Yeah. If we look back on spaxs,
[84:43] (5083.92s)
I think SoFi is above the price and that
[84:45] (5085.84s)
might have been one of yours from Joby
[84:47] (5087.36s)
getting close. These were venture
[84:49] (5089.68s)
investments. These were latestage
[84:51] (5091.28s)
venture investments in your mind,
[84:52] (5092.72s)
Thomas. And then retail tried to become
[84:55] (5095.44s)
venture capitalists and they didn't have
[84:57] (5097.76s)
the 5 10 year horizon that we as venture
[85:00] (5100.08s)
capitalists have. Is that your
[85:01] (5101.20s)
assessment of it? And are there any
[85:02] (5102.96s)
great ones that came out of the spa
[85:04] (5104.48s)
movement? Well, I mean the the direct
[85:06] (5106.88s)
listing era as an example, let's talk
[85:08] (5108.96s)
about Spotify, right, which basically
[85:11] (5111.76s)
has 7xed, right, over that period.
[85:15] (5115.20s)
So, again, I it's hard to tell, right,
[85:19] (5119.04s)
causation versus correlation. That's why
[85:21] (5121.20s)
like I think ultimately for me as an
[85:23] (5123.36s)
ultimate kind of long-term owner of
[85:25] (5125.12s)
these businesses, I really just care
[85:26] (5126.80s)
about the quality of the business and
[85:28] (5128.32s)
whether you chose to go spack or direct
[85:30] (5130.08s)
listing or IPO is a mechanical decision.
[85:34] (5134.16s)
Um to me the output is quality of
[85:37] (5137.12s)
business and you know that's ultimately
[85:39] (5139.52s)
what wins out.
[85:40] (5140.64s)
Okay I want to end on this. Uh you just
[85:42] (5142.40s)
shared a chart of applovin and the
[85:44] (5144.64s)
massive
[85:46] (5146.56s)
revenue per employee. This is just
[85:48] (5148.64s)
astounding Thomas. Apploven as we can
[85:50] (5150.64s)
see here had 3.6 million revenue per
[85:53] (5153.28s)
employee in 21 now up to 7.6 million.
[85:55] (5155.76s)
They peaked at a,000 employees now down
[85:57] (5157.68s)
to 750ish it looks like. In related
[86:00] (5160.96s)
news, obviously Microsoft we talked
[86:02] (5162.80s)
about the other week let go of 3%.
[86:04] (5164.96s)
They're planning on massive cuts again
[86:06] (5166.56s)
for sales. These are organizations that
[86:08] (5168.56s)
are at record cash, record revenue in an
[86:11] (5171.44s)
industry where we had a tradition of not
[86:13] (5173.36s)
firing the gray beards and people had
[86:15] (5175.36s)
been at the company for more than 10
[86:16] (5176.56s)
years. Andy Jasse didn't come up as like
[86:19] (5179.28s)
one of the companies we think is going
[86:20] (5180.64s)
to win at AI, but it might be the
[86:22] (5182.32s)
company most impacted by deploying AI
[86:24] (5184.56s)
inside their enterprise. He launched
[86:27] (5187.28s)
Amissive. Here it is. I suggest
[86:29] (5189.04s)
everybody read it. When you send a
[86:31] (5191.12s)
missive like this to your employees,
[86:33] (5193.20s)
you're trying to communicate something
[86:34] (5194.40s)
to them and to the public markets. So,
[86:36] (5196.24s)
he published it on his website. He talks
[86:38] (5198.32s)
about dozens of AI projects, AI tools
[86:40] (5200.64s)
for advertisers, obviously, Geni for
[86:43] (5203.12s)
sellers, you know, their product detail
[86:45] (5205.84s)
page. He's talking about Alexa coming
[86:47] (5207.52s)
back with a brand new version, shopping
[86:50] (5210.00s)
assistance, everything. But then he
[86:52] (5212.80s)
started talking about the work force
[86:54] (5214.56s)
size. He says in this manifesto in the
[86:57] (5217.68s)
next few years we expect this will
[86:58] (5218.88s)
reduce our total corporate workforce as
[87:01] (5221.04s)
we get efficiency gains from using AI
[87:02] (5222.64s)
extensively across the company. So my
[87:05] (5225.84s)
question to you Thomas is when you hear
[87:09] (5229.28s)
public CEOs talking about lowering the
[87:11] (5231.60s)
number of employees while they're
[87:13] (5233.44s)
growing 10 20% per year this is
[87:16] (5236.48s)
obviously awesome for earnings the share
[87:19] (5239.44s)
price but there's going to be massive
[87:21] (5241.20s)
job displacement. Any thoughts on the
[87:23] (5243.20s)
job displacement? job replacement and
[87:26] (5246.24s)
society navigating that and just as well
[87:29] (5249.12s)
Andy Jasse specifically and what you
[87:31] (5251.36s)
think of Amazon as a business and them
[87:34] (5254.48s)
being a player in AI and AI being a
[87:37] (5257.60s)
player in their business.
[87:38] (5258.80s)
You know, I think it's a it's an
[87:40] (5260.00s)
important question and I'll defer to
[87:41] (5261.68s)
what Jensen answered on this topic
[87:43] (5263.28s)
because in my view it's still the most
[87:44] (5264.64s)
credible and cohesive answer I've kind
[87:46] (5266.32s)
of heard, right? And Jensen is known uh
[87:49] (5269.28s)
the CEO of Nvidia an an incredibly
[87:51] (5271.28s)
long-term thinker and in his view is he
[87:54] (5274.16s)
looks at a population that's getting
[87:55] (5275.52s)
older and he wonders who are going to be
[87:57] (5277.92s)
all the young people that are going to
[87:59] (5279.36s)
take care of all the old people whether
[88:00] (5280.88s)
it's nurses or doctors or other things
[88:02] (5282.64s)
like that and in his view we better get
[88:04] (5284.88s)
a lot more productive right to deal with
[88:07] (5287.44s)
our inverted demographic table. So I
[88:10] (5290.16s)
ultimately think this is going to enable
[88:12] (5292.32s)
more young people to take care of more
[88:14] (5294.24s)
old people, right? And it's just going
[88:16] (5296.64s)
to create I think knowledge workers are
[88:18] (5298.40s)
incredibly flexible. They can take their
[88:20] (5300.24s)
tools from, you know, one particular
[88:22] (5302.40s)
skill set to another. So I think this is
[88:24] (5304.80s)
going to unleash incredible
[88:26] (5306.96s)
opportunities for the economy. I think
[88:29] (5309.20s)
it is going to make us more productive
[88:31] (5311.28s)
and wealthier. So I'm definitely on the
[88:33] (5313.84s)
more optimistic side of the scenario.
[88:36] (5316.96s)
Shimoth, any thoughts on Amazon? They
[88:38] (5318.88s)
didn't come up, but obviously AWS
[88:41] (5321.52s)
crushing it and they're a major player
[88:43] (5323.52s)
and they have their own silicon they're
[88:44] (5324.88s)
making. You mentioned that being an
[88:46] (5326.08s)
important part of the stack. And then
[88:48] (5328.24s)
you have Optimus and robots figure that
[88:51] (5331.20s)
are going to be in their factories.
[88:52] (5332.88s)
That's a lot of jobs. Delivery robots.
[88:55] (5335.28s)
They're doing drones like Zipline. They
[88:57] (5337.20s)
have their own version of it obviously
[88:58] (5338.56s)
and they're doing zuks. So if you just
[89:00] (5340.40s)
look at their behavior and you look at
[89:01] (5341.84s)
their investments, they're massively
[89:03] (5343.52s)
massively investing in robotics,
[89:06] (5346.08s)
self-driving, and chips. So they're
[89:08] (5348.40s)
pretty hardware focused. Yeah.
[89:10] (5350.00s)
For physical AI, they're a kingmaker in
[89:12] (5352.08s)
parts because they're a a sync for
[89:15] (5355.20s)
demand. So they'll just generate so much
[89:17] (5357.28s)
demand for robots. So if Figure lands
[89:19] (5359.68s)
the BMW or the UPS robot successfully,
[89:22] (5362.88s)
Amazon will buy a gajillion of them. If
[89:24] (5364.88s)
Optimus lands a successful robot that
[89:26] (5366.96s)
they tune inside the Tesla factory and
[89:29] (5369.36s)
then are ready to sell, Amazon will buy
[89:31] (5371.92s)
a gajillion of them. If there are drones
[89:34] (5374.64s)
that are delivering things, Amazon will
[89:36] (5376.72s)
buy a gajillion of them. So on the one
[89:38] (5378.32s)
side, there's a lot of typical opex lift
[89:41] (5381.36s)
that Amazon will get. I think the
[89:43] (5383.68s)
problem is more with AWS, which is that
[89:45] (5385.52s)
their success is actually their biggest
[89:47] (5387.12s)
bottleneck. The success is that they're
[89:49] (5389.36s)
not necessarily kingmaking. They're
[89:52] (5392.72s)
about being a purveyor of many, many,
[89:55] (5395.04s)
many different things that you can find
[89:57] (5397.60s)
inside of AWS marketplace. And so, you
[90:00] (5400.96s)
know, the the thing that they'll have to
[90:03] (5403.84s)
embrace is well, do I differentiate my
[90:08] (5408.24s)
own hardware from Nvidia's at some
[90:11] (5411.04s)
point, do I actually make a real bet on
[90:13] (5413.52s)
models and try to frankly buy anthropic,
[90:15] (5415.44s)
which is probably their only solution
[90:17] (5417.68s)
and tightly couple it in and say that,
[90:20] (5420.24s)
you know, if you want to have next
[90:21] (5421.60s)
generation codegen experiences, they
[90:23] (5423.28s)
need to run inside of AWS.
[90:25] (5425.84s)
These are the difficult decisions that I
[90:27] (5427.60s)
think that Andy will have to face and
[90:29] (5429.52s)
he's going to have to spend hundreds of
[90:30] (5430.80s)
billions of dollars. But yeah, the the
[90:33] (5433.04s)
Amazon retail side is going to be a
[90:35] (5435.60s)
kingmaker for all of these physical AI
[90:37] (5437.44s)
things.
[90:39] (5439.92s)
Freeberg, any thoughts on Amazon just as
[90:42] (5442.08s)
a company broadly? Chamat saying, "Hey,
[90:44] (5444.56s)
they're a kingmaker." That seems like a
[90:46] (5446.32s)
really interesting insight. You have any
[90:47] (5447.76s)
insights there on Amazon and they're
[90:49] (5449.60s)
playing a part here in the future of AI?
[90:52] (5452.80s)
I don't. Thomas, any closing thoughts
[90:56] (5456.00s)
here on, you know, the sort of old old
[90:58] (5458.96s)
guard, Microsoft, Amazon, and their
[91:01] (5461.92s)
employee count and the cuts we're seeing
[91:03] (5463.84s)
there, uh, and what these companies will
[91:06] (5466.80s)
look like in the future in terms of
[91:08] (5468.16s)
revenue per employee. They're not hiring
[91:10] (5470.96s)
young people. They're getting rid of the
[91:12] (5472.32s)
old folks. They're just advancing, it
[91:14] (5474.08s)
seems, at a at a they're adopting AI
[91:16] (5476.88s)
pretty uh, severely at these companies.
[91:19] (5479.04s)
What are your thoughts there? I'm going
[91:20] (5480.24s)
to play I'm going to play the role of
[91:21] (5481.68s)
JCAL and I'm going to ask a question to
[91:23] (5483.68s)
all three of you guys.
[91:24] (5484.72s)
Oh, here we go.
[91:26] (5486.32s)
So, Microsoft's employee count peaked at
[91:28] (5488.48s)
about 250,000,
[91:30] (5490.72s)
you know, call it about a year ago. Who
[91:32] (5492.72s)
here believes that in 5 years Microsoft
[91:36] (5496.88s)
will have more employees than it does
[91:39] (5499.36s)
today?
[91:42] (5502.24s)
I'm going to say the same. I think
[91:43] (5503.76s)
they'll have just about 250 plus or
[91:46] (5506.56s)
minus 10%. I don't think if I if I could
[91:48] (5508.96s)
pick push as the answer, I would pick
[91:50] (5510.40s)
push, which is they're going to get 10%
[91:52] (5512.56s)
better every year with AI, 20% more
[91:54] (5514.88s)
efficient. Therefore, they don't need to
[91:56] (5516.32s)
add people. But I also don't think they
[91:58] (5518.80s)
atrophy much more. So maybe they have
[92:00] (5520.88s)
225 250.
[92:02] (5522.32s)
Why Why' you say more so quickly? I'm
[92:04] (5524.16s)
curious.
[92:04] (5524.88s)
Oh, so this chart, which I think is like
[92:09] (5529.84s)
very dangerous vanity metric,
[92:12] (5532.56s)
is why. So what Microsoft touts is what
[92:16] (5536.40s)
percentage of code is generated by AI
[92:18] (5538.96s)
without answering the more important
[92:20] (5540.48s)
question which is is that code useful
[92:22] (5542.32s)
and good and if you ask that second
[92:25] (5545.60s)
layer Nick I sent you this tweet from
[92:27] (5547.28s)
Yan Lun and I'll tell you that this is
[92:29] (5549.84s)
my lived experience as well is most code
[92:33] (5553.04s)
generated by AI is crap and most of the
[92:36] (5556.32s)
tools that we use you know the reason we
[92:40] (5560.16s)
call these tools app crappers is because
[92:42] (5562.88s)
most of The code that it generates is
[92:45] (5565.04s)
crap. So, it's great in a single player
[92:48] (5568.24s)
mode, but transitioning from single
[92:50] (5570.80s)
player mode to a complex enterprise
[92:53] (5573.12s)
environment is not possible today. So, I
[92:56] (5576.48s)
think that Microsoft puts these metrics
[92:58] (5578.32s)
out because they want to seem that
[93:00] (5580.56s)
they're on the front line of it, but I
[93:02] (5582.96s)
suspect that this is just like, you
[93:04] (5584.48s)
know, how you used to hire Mackenzie
[93:05] (5585.76s)
consultants to fire people because it
[93:07] (5587.12s)
was good air cover. It's probably just
[93:09] (5589.04s)
air cover to fire a bunch of folks that
[93:11] (5591.04s)
they probably wanted to get rid of
[93:12] (5592.24s)
anyways, but it's not related to that
[93:14] (5594.40s)
chart. And the reason is that Yan Lun's
[93:16] (5596.40s)
tweet is true. When you allow these
[93:19] (5599.60s)
models to run over complicated tasks
[93:21] (5601.84s)
over long periods of time, the error
[93:23] (5603.84s)
rates compound to such a degree that the
[93:25] (5605.84s)
that the resulting output is not
[93:27] (5607.68s)
worthwhile. And so until that problem is
[93:30] (5610.08s)
fixed, which I'm sure it will be, and I
[93:32] (5612.24s)
and I and I'm going to bet that it will
[93:33] (5613.84s)
be, the idea that all of a sudden it's
[93:36] (5616.56s)
because of coding agents that people are
[93:38] (5618.40s)
getting laid off, I think is a fallacy.
[93:40] (5620.72s)
So I suspect that Microsoft business on
[93:42] (5622.80s)
the margin grows. Back to Dave's point,
[93:44] (5624.80s)
some of the 493 shrink and go away.
[93:47] (5627.44s)
It'll be cheaper for Microsoft to bundle
[93:49] (5629.36s)
together a bunch of other products that
[93:50] (5630.72s)
are point features today. And so they'll
[93:52] (5632.72s)
have more people. They'll indeed more.
[93:54] (5634.24s)
The people will be different. They'll
[93:55] (5635.52s)
have different skill sets. But I suspect
[93:57] (5637.04s)
Microsoft's employee base grows.
[93:58] (5638.80s)
Freeberg, what say you?
[94:01] (5641.12s)
I think shrink.
[94:03] (5643.04s)
Wow. So, by the way, pretty interesting
[94:05] (5645.28s)
to think about. We have one decisively
[94:07] (5647.20s)
more, one median about the same a push
[94:11] (5651.36s)
and a and a less.
[94:12] (5652.72s)
I only say that because I do think that
[94:14] (5654.72s)
there's a real probability of revenue
[94:17] (5657.28s)
decline in the next 5 years. So, if you
[94:19] (5659.20s)
look at the enterprise install base, I
[94:21] (5661.20s)
think that cloud gets competed away. So
[94:23] (5663.44s)
I do think like on this on the
[94:24] (5664.88s)
application software layer, they're
[94:26] (5666.56s)
going to have a really hard time in this
[94:29] (5669.04s)
new world because the old school
[94:31] (5671.12s)
customers that buy Microsoft are going
[94:33] (5673.28s)
to die. They're more likely to die in
[94:35] (5675.28s)
their marketplace compared to the folks
[94:36] (5676.96s)
that are going to build native software,
[94:38] (5678.48s)
native workflows. And I'm not really
[94:40] (5680.24s)
where Chimath is. I think you may be
[94:42] (5682.48s)
right about where AI written code is
[94:44] (5684.64s)
today. I I don't think that that's true
[94:47] (5687.20s)
3 years from now, four years from now
[94:49] (5689.04s)
given the pace of improvement. And so in
[94:51] (5691.28s)
a world where you have software written
[94:53] (5693.52s)
workflows built for you through agentic
[94:56] (5696.32s)
tools, I think that Microsoft's core
[94:59] (5699.44s)
business for the is going to decline.
[95:01] (5701.28s)
The the losers are their biggest
[95:02] (5702.80s)
customers and the winners are not going
[95:04] (5704.40s)
to use them. So I I you know that that
[95:06] (5706.48s)
would be
[95:06] (5706.72s)
where you at Thomas maybe you're the
[95:08] (5708.80s)
tiebreaker.
[95:10] (5710.40s)
I I'm in Chamas camp where I actually
[95:12] (5712.24s)
think the Microsoft business will be
[95:13] (5713.60s)
bigger if anything on on kind of alone
[95:18] (5718.08s)
and that at the end of the day uh we'll
[95:20] (5720.32s)
just need more people to support it.
[95:22] (5722.40s)
I just think they'll be they'll be more
[95:24] (5724.24s)
relevant. They'll have more productive
[95:25] (5725.76s)
employees,
[95:27] (5727.44s)
but they'll still be more of them.
[95:29] (5729.36s)
I'm predicting incredible growth and the
[95:32] (5732.32s)
same number of employees. So you guys
[95:33] (5733.44s)
are predicting incredible growth and
[95:35] (5735.12s)
employee growth.
[95:36] (5736.48s)
I think that that's interesting. So So
[95:37] (5737.92s)
sorry.
[95:38] (5738.40s)
Less revenue, less employees.
[95:39] (5739.76s)
Interesting.
[95:40] (5740.64s)
So the thesis as as your grows um is
[95:45] (5745.04s)
basically where the the application
[95:47] (5747.60s)
dollars go effectively is one way to
[95:50] (5750.16s)
think about this, right? So application
[95:51] (5751.68s)
dollars go there and that more than
[95:53] (5753.60s)
makes up for the decline in in that
[95:56] (5756.80s)
business over time, right? And there's
[95:58] (5758.64s)
multiple clouds. By the way, I went to
[96:00] (5760.32s)
the Google Next event last year and so I
[96:04] (5764.00s)
I ended up going to these like special
[96:05] (5765.84s)
dinners or whatever, a couple cocktail
[96:07] (5767.76s)
dinner thing because I spoke there and I
[96:09] (5769.20s)
saw they put me with a bunch of these
[96:10] (5770.40s)
people and I CIOS of you know whatever
[96:13] (5773.04s)
Fortune50 companies and all of them said
[96:16] (5776.00s)
that they're multicloud like they're not
[96:17] (5777.76s)
no one's going to standardize on one
[96:19] (5779.20s)
cloud so everyone has to be on Microsoft
[96:21] (5781.44s)
and Google and I had never really
[96:23] (5783.36s)
recognized this or thought about this as
[96:25] (5785.12s)
being a a fact that it's not necessarily
[96:27] (5787.60s)
the best or the lowest price. At the end
[96:29] (5789.68s)
of the day, these guys are going to
[96:30] (5790.96s)
distribute their exposure. And so, I
[96:33] (5793.44s)
think that maybe supports your case. I'm
[96:35] (5795.52s)
very easily con I'm very easily able to
[96:37] (5797.60s)
see other arguments today. I'm very
[96:38] (5798.96s)
convinced.
[96:39] (5799.36s)
Here's the revenue. What a spectacular
[96:41] (5801.36s)
revenue run. Uh just
[96:44] (5804.48s)
I think all four of us would agree that
[96:46] (5806.64s)
if we could synthetically own AWS,
[96:49] (5809.44s)
Azure, and GCP, if I could somehow
[96:52] (5812.16s)
automatically create an index of all
[96:54] (5814.56s)
three of those businesses, right, over
[96:56] (5816.72s)
the next five years.
[96:57] (5817.92s)
Yeah. Yeah,
[96:58] (5818.56s)
you wouldn't need to own anything else.
[97:00] (5820.40s)
You wouldn't need own anything else.
[97:02] (5822.24s)
I wish Elon would take that.
[97:03] (5823.44s)
So, why don't you put up with the shitty
[97:04] (5824.80s)
part of the rest of their businesses and
[97:06] (5826.16s)
just own all three and that's it. Call
[97:07] (5827.76s)
it a day
[97:08] (5828.72s)
cuz you've got to assume that if one of
[97:10] (5830.16s)
them wins over the other two or
[97:12] (5832.24s)
accelerates ahead of the other two, it's
[97:13] (5833.68s)
going to more than make up for the
[97:14] (5834.72s)
losses that the other two might
[97:15] (5835.84s)
experience in their other businesses.
[97:17] (5837.44s)
The multiples aren't crazy on those
[97:18] (5838.88s)
three companies, by the way.
[97:20] (5840.16s)
Correct.
[97:20] (5840.56s)
Quite reasonable. Yeah. I think if Elon
[97:22] (5842.64s)
took what he did with Colossus and he
[97:24] (5844.40s)
had an AWS competitor, he would be a
[97:26] (5846.72s)
serious competitor in the space. But
[97:28] (5848.08s)
this is like this
[97:28] (5848.96s)
the velocity at which he can build out
[97:30] (5850.40s)
data centers is extraordinary.
[97:32] (5852.24s)
This is where Elon does better because
[97:33] (5853.76s)
he can actually get a better like um
[97:37] (5857.52s)
fundraising uh in the private market
[97:39] (5859.84s)
with XAI than what he has to deal with.
[97:42] (5862.40s)
Yeah, he's really he's really struggling
[97:43] (5863.84s)
with that.
[97:44] (5864.96s)
That's what I'm saying. Yeah. No, no,
[97:46] (5866.48s)
I'm saying it's better for him, right?
[97:47] (5867.52s)
Hey guys, look who's here. Couldn't stay
[97:49] (5869.68s)
away. David Sachs, look at here. You
[97:53] (5873.04s)
can't get away from it. 11 o'clock
[97:54] (5874.64s)
happens on a Thursday and you start
[97:56] (5876.40s)
jonesing for your besties. Welcome to
[97:58] (5878.72s)
the ZAR,
[97:59] (5879.84s)
David S. Good to be back, Jacob, where
[98:02] (5882.16s)
are you? You in LA?
[98:03] (5883.36s)
Mhm. I'm in LA. This is
[98:05] (5885.20s)
You're at someone's guest house.
[98:06] (5886.56s)
Yeah, actually, this is one of your
[98:07] (5887.68s)
guest houses. You You just lost track. I
[98:10] (5890.08s)
still have I still have the key code.
[98:12] (5892.16s)
It's a J Cal Kalen. Jay Calen is at your
[98:15] (5895.76s)
guest house.
[98:16] (5896.24s)
Jalen. Jalen. Here. I'm here.
[98:19] (5899.68s)
come down the hill.
[98:20] (5900.40s)
He'll still get that reference. It's
[98:21] (5901.76s)
getting kind of dated now.
[98:23] (5903.04s)
Oh god. KO Kalan is ride or die. I mean,
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he would jump on a a vente or a grande
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for you for sure. Let's talk a little
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bit here. Since I got you, Sachs, would
[98:31] (5911.52s)
you be willing to talk a little bit
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about the Genius Act? We just passed the
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Senate. I think you have your
[98:35] (5915.92s)
fingerprints on this. Is that true?
[98:37] (5917.76s)
Yeah. Tell us everything.
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Well, it's definitely something we
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supported and this is, I think, a huge
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milestone. I mean just uh you know what
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basically happened is we had this genius
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act which is the stable coin bill passed
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the senate with 68 votes got 18
[98:53] (5933.60s)
democrats they came on board we had to
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hit that key threshold of 60 votes in
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the senate that's the threshold you need
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in the senate unless you know it's it's
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um a narrow exception for reconciliation
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so it's very very hard to pass any bill
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out of the Senate and you need a
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significant amount of bipartisan support
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and we got that now when you consider
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Consider where we were a year ago. You
[99:17] (5957.60s)
know, you realize what huge progress
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this is for the crypto industry. A year
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ago, you had crypto companies being
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prosecuted. You had this whole
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regulation through prosecution approach
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where Gary Gendler, who was the chair of
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the SEC then, he wouldn't tell startups
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what the rules were, but they would just
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announce prosecutions. And this was
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driving all the crypto innovation
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offshore. And I think we were basically
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poised to lose the crypto industry in
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the United States. What happened then is
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President Trump adopted this cause. He
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announced that he wanted to make uh the
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United States the crypto capital of the
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planet. He really campaigned on this and
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as part of his administration. He in the
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very first week signed a new executive
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order making it clear that his
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administration supported crypto. We've
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been rooting out all the Biden war on
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crypto rules and regulations at the
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agency level. And now we have this first
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major legislative win. And I would
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expect the House will act in the next
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few weeks on this and then the president
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will have a bill he can sign.
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This is uh great work and it's really
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important because to your point, Gary
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Gensler's concept was, hey, there's an
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existing playbook. There's existing
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rules. Just follow those. But none of
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these things actually match the existing
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rules perfectly. So you need some new
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rules. They need to evolve.
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It was much worse than that because he
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would say things like, "Well, just come
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into the SEC and talk to us." You know,
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so in other words, you got to come in
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and talk to us and get our approval. But
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then when startups would go in there and
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talk to them, there'd be enforcement
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people there writing down everything
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they said and the next day they get a
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wells notice and they would get
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investigated honey.
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Yeah. They were honeypotted basically.
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Yeah.
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And so the the response the industry was
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okay we're just going to leave the
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United States. And that that was what
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was in the process of happening until
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President Trump won the election and
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then changed the tone in Washington. I
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think there was one other really
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significant thing that that happened
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because, you know, obviously President
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Trump has gotten Republicans on board
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with this cause, but the question is why
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are Democrats on board with it? During
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the Biden administration, Elizabeth
[101:17] (6077.68s)
Warren really called the shots on crypto
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and it was well reported that Gendler
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was was sort of her ally and her pick.
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I've kind of joked that Warren
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controlled the Biden autopen on on
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crypto because she really did exert that
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kind of influence.
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So the question is, well, what changed?
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And I think one of the big things is
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that in this last election, Sherid
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Brown, who was the chair of the banking
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committee for the Democrats in the
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Senate, lost his seat in a close
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election against Bernie Moreno. And I
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think there were many reasons for him to
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lose that seat. He was far to the left
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of voters in Ohio. Nonetheless, he had
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been a successful politician there for a
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long time. And one of the reasons why he
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lost is because the crypto industry
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really got behind Bernie Mareno because
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Sher Brown was just a a total blocker to
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any crypto legislation in the mold of
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Elizabeth Warren. And I think that a lot
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of smart Democrats looked at that and
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said, "Why are we dying on this hill
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again?" You know?
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Yeah.
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And I think it's also extraordinarily
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popular sachs with consumers and
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businesses. So there is a demand here.
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and Korea,
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we've got you've got something like 50
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million wallet holders in the US and
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their their their voters.
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So that's one out of five Americans
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adult,
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right? So I think a lot of Democrats
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said, "Well, wait a second. Why are we
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just blindly following Elizabeth Warren
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on this? What exactly is so harmful
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about this?" Particularly when what
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we're talking about here is creating a
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regulatory regime. You know, it
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shouldn't be hard to sell Democrats on
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new regulations. Uh but in this case,
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the reason why there's broad bipartisan
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support is because the crypto industry
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itself is calling for those regulations
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because having regulatory certainty is
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better for them than the possibility of
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the return of a Gary Gendzer-L like
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figure who just prosecutes them without
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telling them what the rules are. So this
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is why I think you're getting some
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significant bipartisan support and
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and as you said bringing this on shore
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is such a great portion of it. There are
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tons of actors who some people might
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describe as bad or gray or dark tether
[103:20] (6200.80s)
comes to mind with a lot of regulation
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against it. And now those folks who are
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running away with the industry Thomas
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now they have to compete with people
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like Jeremy Circle which are totally
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buttoned up here in the United States
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and it levels the playing field. So it's
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an example of actually good regulation
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bringing
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this opportunity back on shore and
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taking it out of the gray area
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just on the whole offshore versus
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onshore. So it is true that the number
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one stable coin issuer on the planet
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right now is an offshore company and
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that is partly because there has not
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been a regulatory framework in the US
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and there's been hostility towards the
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crypto space and so the logical reaction
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to that is to either not get involved in
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the crypto space which is what the banks
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have done until now or you go offshore.
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Neither one is good. And you know, you
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can see in the wake of this Genius Act,
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the stable coin bill that the banks have
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now talked about getting into stable
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coins. They're going to issue one. And
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then also Tether will under this act
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will have three years to come on shore.
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But the bottom line is they will have to
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operate in the United States. And that's
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a good thing for consumers. It's a good
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thing for
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they three years to get compliance.
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They have three years, but they have to
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move on shore. Now all stable coin
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issuers under this bill will have to be
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audited quarterly and
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by a real audit not this attestation
[104:45] (6285.04s)
nonsense like real audits by American
[104:47] (6287.76s)
real audits and it will verify that
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every stable coin that's been issued is
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backed or fully reserved on a onetoone
[104:55] (6295.68s)
basis
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with real dollars in an American bank
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accounts that are in US T bills or money
[105:02] (6302.80s)
market accounts. And so it what it does
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is by the way I'm not saying there's
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anything wrong with Tether, but this
[105:08] (6308.56s)
does provide additional certainty and
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confidence because you know that all the
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companies are onshore and they've been
[105:16] (6316.80s)
fully audited and we know that they're
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fully reserved so that when you want to
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redeem and cash out your stable coin
[105:23] (6323.28s)
tokens, there's a real dollar waiting
[105:25] (6325.36s)
there to cash out.
[105:26] (6326.72s)
You prevent the undercolateralization
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issue.
[105:30] (6330.16s)
Yeah. And and by the way, I'm not saying
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that there is but but what I'm saying is
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now we create total certainty and
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confidence which is good for the market.
[105:37] (6337.52s)
What happens if a stable coin issuer
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does not
[105:41] (6341.28s)
like can you issue US dollar stable
[105:43] (6343.44s)
coins and not be governed under this
[105:45] (6345.76s)
system or no? You're saying because the
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US dollar is a US government instrument
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then no matter where you are or no
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matter where you issue from.
[105:53] (6353.12s)
Yeah. All the issuers will be governed
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by this. And if you're a legacy offshore
[105:56] (6356.80s)
issuer, you're given this time period to
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bring yourself into conformity. But
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yeah,
[106:01] (6361.60s)
otherwise what happens if if they don't
[106:04] (6364.72s)
Well, it's a good question. I mean, I
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guess the exchanges won't be able to
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carry their their tokens
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and they won't be able to set foot in
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the US. They'll be in violation of US
[106:13] (6373.76s)
law. It's just not a good place to be.
[106:15] (6375.44s)
Yeah. I mean, you don't have to guess.
[106:16] (6376.96s)
Um, there have been dozens of actions
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and accusations, like legitimate ones,
[106:22] (6382.32s)
against Heather. New York's Attorney
[106:23] (6383.68s)
General did a major settlement with him
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in 2021. They've been banned from many
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jurisdictions and uh in Senate hearings.
[106:31] (6391.84s)
Tether should just Tether should just go
[106:33] (6393.60s)
public in America and be done with it.
[106:35] (6395.76s)
Well, and the issue was there was deep
[106:38] (6398.16s)
concerns that they didn't have the
[106:39] (6399.76s)
deposits and now they're they're really
[106:42] (6402.32s)
trumpeting the fact that they're
[106:43] (6403.36s)
massively profitable obviously. So,
[106:45] (6405.28s)
there's been tons of uh you can just
[106:46] (6406.88s)
search Tether and allegations and you'll
[106:49] (6409.36s)
find all that stuff.
[106:50] (6410.64s)
I should hear
[106:51] (6411.28s)
tether founders Italian
[106:52] (6412.88s)
Saxs. I got to give you a lot of credit.
[106:54] (6414.56s)
We knew that you would bring an
[106:56] (6416.64s)
efficiency level and some expertise to
[106:59] (6419.12s)
this administration, but I got to give
[107:00] (6420.40s)
you your flowers. We're 5 months into
[107:01] (6421.84s)
this administration. Can disagree about
[107:04] (6424.00s)
many things. One thing we can't disagree
[107:06] (6426.08s)
about is that this piece of legislation
[107:09] (6429.04s)
uh is here and we're we're only 5 months
[107:10] (6430.88s)
in. So maybe you could speak to the
[107:12] (6432.56s)
velocity at which things are getting
[107:14] (6434.32s)
done and then uh any other clothing
[107:16] (6436.72s)
closing thoughts. I know you got to get
[107:18] (6438.00s)
back to your day job. Jake how a lot of
[107:19] (6439.60s)
people deserve credit for this. I just
[107:20] (6440.96s)
want to give out a couple of shout outs.
[107:22] (6442.56s)
So, Senator Bill Hagerty from Tennessee
[107:24] (6444.40s)
was the principal author of the
[107:25] (6445.60s)
legislation. He did an amazing job
[107:27] (6447.36s)
getting Democrat votes and also bringing
[107:29] (6449.68s)
the Senate bill into greater alignment
[107:31] (6451.84s)
with the House bill. So, hopefully this
[107:33] (6453.20s)
can pass the House very quickly.
[107:35] (6455.04s)
Chairman Tim Scott who's the chairman of
[107:36] (6456.72s)
the banking committee was also
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incredible. the majority leader uh John
[107:40] (6460.96s)
Thun and then we had a few co-sponsors
[107:44] (6464.00s)
of the legislation Cynthia Lemus from
[107:45] (6465.84s)
Wyoming and then two Democrats actually
[107:47] (6467.68s)
were really important Kirsten Gillibrand
[107:49] (6469.68s)
from New York and Angela also Brooks
[107:51] (6471.28s)
from Maryland all them did a great job
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and we've got great leaders on the house
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side as well French Hill who's the
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chairman of the house financial services
[107:57] (6477.76s)
committee Tom Emmer who's the whip and
[108:00] (6480.64s)
Mike Johnson who's the speaker so kudos
[108:03] (6483.04s)
to all of them because I think that it
[108:05] (6485.12s)
really is a pretty incredible
[108:06] (6486.96s)
achievement that they've been able to
[108:08] (6488.16s)
get this
[108:09] (6489.12s)
through again just a huge sea change
[108:11] (6491.20s)
from where we were a year ago where
[108:13] (6493.36s)
crypto was basically under attack. It
[108:15] (6495.36s)
was being driven offshore and now we
[108:17] (6497.28s)
have it as one of the first major piece
[108:19] (6499.28s)
of legislation by this new Congress. And
[108:21] (6501.44s)
again, that's all because of President
[108:22] (6502.72s)
Trump's leadership and prioritization of
[108:24] (6504.72s)
this issue. So, thank you to all of them
[108:26] (6506.64s)
for making this happen.
[108:27] (6507.60s)
Congratulations to you, David. Hey, uh,
[108:29] (6509.20s)
one, uh, tactical question I forgot to
[108:31] (6511.20s)
ask you. the float on these. This is
[108:32] (6512.88s)
like how Tether is making billions of
[108:34] (6514.48s)
dollars a year and this is how people
[108:35] (6515.84s)
anticipate they're going to make
[108:36] (6516.80s)
billions of dollars a year. Are they
[108:38] (6518.80s)
able to split that with consumers yet?
[108:40] (6520.48s)
Because I I remember reading in early
[108:42] (6522.24s)
legislation that you weren't allowed to
[108:44] (6524.48s)
pass on the interest made from a stable
[108:46] (6526.96s)
coin to like the consumers, I guess. So
[108:50] (6530.24s)
you wouldn't it couldn't be an interest
[108:51] (6531.52s)
bearing account. If you buy stable
[108:52] (6532.64s)
coins, you can't get interest on it. But
[108:53] (6533.76s)
the issuer like Circle, that's their
[108:55] (6535.20s)
main business model. So did that make it
[108:57] (6537.36s)
into the final and and maybe you can
[108:58] (6538.96s)
give us some background on that?
[109:01] (6541.60s)
No, it did not.
[109:02] (6542.56s)
The way the framework works is that the
[109:04] (6544.48s)
stablecoin issuers cannot pass on
[109:06] (6546.56s)
interest
[109:07] (6547.68s)
to the token holders.
[109:08] (6548.88s)
Why is that?
[109:09] (6549.36s)
Look, I mean, I don't know if there's a
[109:10] (6550.56s)
great principled reason. This was a
[109:12] (6552.40s)
compromise that was necessary to get the
[109:14] (6554.48s)
support of the banking industry quite
[109:15] (6555.84s)
frankly.
[109:16] (6556.32s)
Ah, they see it as competition. I'm
[109:17] (6557.92s)
betting.
[109:18] (6558.64s)
Well, there was a lot of concern from
[109:20] (6560.48s)
community banks that if stable coins
[109:22] (6562.16s)
were paying 5% interest, it would put
[109:23] (6563.68s)
them out of business. Personally, I
[109:25] (6565.76s)
think that that concern, although
[109:27] (6567.76s)
understandable from them, I don't think
[109:30] (6570.48s)
that that's what would have happened.
[109:32] (6572.40s)
But these are the types of compromises,
[109:33] (6573.76s)
quite frankly, that you need in order to
[109:35] (6575.04s)
pass legislation. I hope that at some
[109:37] (6577.36s)
point in the future, we'll revisit that
[109:39] (6579.36s)
and allow stable coin issuers to kind of
[109:42] (6582.32s)
just do what they want to do.
[109:43] (6583.84s)
All right?
[109:44] (6584.24s)
And that'll be easier once the banks get
[109:46] (6586.16s)
into the act and they're participating
[109:47] (6587.68s)
in this industry.
[109:48] (6588.72s)
Got it.
[109:49] (6589.20s)
But right now, they're total outsiders
[109:51] (6591.20s)
and you can understand the fear factor.
[109:52] (6592.80s)
All right. Sax would want to drop you
[109:54] (6594.00s)
off, man. I wish we could have you on
[109:55] (6595.20s)
for the full show, but uh you you're
[109:56] (6596.56s)
busy. You got a lot of things to do.
[109:58] (6598.00s)
Love you, dude.
[109:58] (6598.64s)
Shed a little tear and I miss my bestie.
[110:00] (6600.88s)
See you soon.
[110:01] (6601.60s)
Thanks, guys. All right, back.
[110:03] (6603.20s)
We got two hours of classic Allin. Uh in
[110:05] (6605.60s)
part two of the show, we're going to do
[110:07] (6607.36s)
an hour and a half on the Israeli
[110:10] (6610.40s)
conflict with Iran. We've got 90 more
[110:12] (6612.56s)
minutes coming up. And uh we've got
[110:14] (6614.40s)
Ukraine Ukraine Ukraine Mirshimer and uh
[110:18] (6618.24s)
Jeffrey Saxs joining us in the second in
[110:20] (6620.80s)
the third and fourth hour of the all-in
[110:22] (6622.72s)
podcast. How's the all-in summit going
[110:25] (6625.04s)
Freeberg? How's all-in summit?
[110:26] (6626.72s)
You know, we might get uh wait wants to
[110:30] (6630.48s)
from uh Alibaba.
[110:31] (6631.84s)
Who's in touch with him?
[110:32] (6632.96s)
I am.
[110:34] (6634.00s)
Thanks to Phipe.
[110:34] (6634.96s)
I just want to do one quick shout out to
[110:36] (6636.64s)
our friend and fellow bestie Vinnie
[110:38] (6638.64s)
Lingum.
[110:39] (6639.60s)
Oh yes, his movies coming out. A friend
[110:41] (6641.84s)
of ours did a documentary on
[110:45] (6645.36s)
It's great
[110:46] (6646.08s)
Freeberg. You're going to love this.
[110:47] (6647.84s)
I denounce I denounce I love Vinnie. I
[110:49] (6649.76s)
denounce it. So great. Amazing.
[110:53] (6653.68s)
Anyways, it's called Animal.
[110:55] (6655.92s)
Oh, it's great, Doc.
[110:57] (6657.36s)
And uh
[110:57] (6657.92s)
perfect. Can't wait.
[110:59] (6659.52s)
Where can Where can people watch it?
[111:01] (6661.60s)
I think he's got a couple of deals.
[111:03] (6663.52s)
Come to your local slaughter house and
[111:05] (6665.20s)
put it on your phone and watch it at the
[111:06] (6666.80s)
slaughter house while you're there.
[111:07] (6667.68s)
Here's the idea. You're going to consume
[111:08] (6668.88s)
a certain number of calories per month.
[111:10] (6670.72s)
Us humans were designed to eat meat.
[111:12] (6672.72s)
That's the number one thing we should be
[111:14] (6674.32s)
doing as a species is eating meat. Nick,
[111:16] (6676.56s)
can you put the trailer in the show
[111:18] (6678.24s)
notes so that you can get a little
[111:20] (6680.00s)
play? Actually, play us out with the
[111:21] (6681.20s)
trailer. You can play us out with the
[111:22] (6682.48s)
trailer on the show. We'll do them
[111:23] (6683.44s)
myself.
[111:23] (6683.68s)
All right, guys. I got to go eat. I have
[111:25] (6685.12s)
a photo shoot in two hours. I love
[111:27] (6687.20s)
Oh, you got a photo shoot. Is it going
[111:28] (6688.32s)
to be you showing the legs or just the
[111:30] (6690.08s)
top this time? What are you shooting?
[111:31] (6691.28s)
What are you shooting?
[111:31] (6691.92s)
I'm going to do
[111:32] (6692.48s)
blur out the anaconda. You should do
[111:33] (6693.92s)
pixelate the anaconda.
[111:36] (6696.00s)
I hope it's Italian Vogue. What are you
[111:38] (6698.24s)
shooting? Thomas is in the general
[111:40] (6700.00s)
neighborhood. I I can't comment, but uh
[111:42] (6702.24s)
just tell us bleep it out. Nice.
[111:44] (6704.32s)
Tell us bleep it out. Jam, give me a
[111:46] (6706.00s)
call. I got to talk to you about this
[111:46] (6706.88s)
weekend.
[111:47] (6707.68s)
Okay. Love you guys. Talk to you guys.
[111:49] (6709.28s)
I'll see you at
[111:50] (6710.64s)
Are you guys still doing the tequila
[111:52] (6712.08s)
launch?
[111:52] (6712.48s)
Yeah, Saturday night. We'll see you
[111:53] (6713.60s)
Saturday night. Absolutely.
[111:54] (6714.72s)
See you there.
[111:55] (6715.28s)
Go to allin.com
[111:57] (6717.12s)
yada yada to sign up for the all-in
[111:59] (6719.20s)
summit. Apply there for Thomas Leafant,
[112:01] (6721.36s)
Shim Popia, Dave Freeberg, and the Zar
[112:04] (6724.64s)
David Saxs. I am the world's
[112:06] (6726.72s)
greatest executive producer. We'll see
[112:08] (6728.80s)
you next time. Jasonallin.com.
[112:11] (6731.52s)
Adios.
[112:12] (6732.64s)
Play the trailer.
[112:14] (6734.16s)
We're too good of hunters.
[112:16] (6736.64s)
We came out of the trees not to eat the
[112:19] (6739.60s)
grass, but to eat the grass eaters.
[112:23] (6743.60s)
Meat is the most nutrientdense food that
[112:26] (6746.80s)
human beings can eat.
[112:28] (6748.40s)
We're carnivores, but we're not living
[112:29] (6749.92s)
as carnivores.
[112:31] (6751.92s)
We are just better designed and more
[112:33] (6753.76s)
efficient at getting nutrition from
[112:35] (6755.36s)
meat. Got to remember where we came from
[112:37] (6757.76s)
and what our food should be.
[112:41] (6761.28s)
It will change your life.