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E106: SBF's media strategy, FTX culpability, ChatGPT, SaaS slowdown & more

December 03, 2022 / 01:41:59

This episode covers the recent media appearances of Sam Bankman-Fried, the FTX scandal, and the role of the media in shaping public perception. The hosts discuss interviews with Bankman-Fried, particularly focusing on his appearances on Good Morning America and the DealBook Finance Conference.

David Sacks and Chamath Palihapitiya analyze Bankman-Fried's strategy, suggesting he may be attempting to muddy the waters of public perception to avoid severe legal consequences. They compare him to figures like Bernie Madoff and discuss the implications of his actions on the cryptocurrency industry.

The conversation shifts to the media's handling of Bankman-Fried, with criticism directed at outlets like The New York Times for their soft approach. The hosts argue that the media's bias and failure to hold powerful figures accountable contribute to a larger issue of institutional rot.

They also explore the responsibilities of venture capitalists and regulators in the FTX collapse, emphasizing the need for better governance and due diligence in the industry. The episode concludes with a discussion on the implications of AI and machine learning in various sectors, including healthcare and finance.

TL;DR

The episode critiques media coverage of Sam Bankman-Fried and discusses the FTX scandal's implications for accountability and governance in finance.

Video

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I'm now recording Jesus you look

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terrible what's going on did you sleep

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last night do I look tired you look a

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little tired you look unshaven yeah what

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happened last night I had a holiday

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party at my house last night from my

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office and you look destroyed yeah

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what's going I mean did you drink I did

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drink yeah what does vodka and oat milk

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taste like

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you had a White Russian

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a White Russian people

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with oatmeal he's like I want a White

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Russian with oatmeal oh my God actually

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my white russian is made with only and

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please compliment that with a huge punch

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in the face

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give me five minutes I'm gonna go I'm

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gonna go shave and it's called The Hair

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of the Dog yeah

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yeah either a little Bloody Mary you're

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banana

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man should I go get a beer I might get a

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beer just to beat this hangover go do it

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yeah go get a beer hang on I'll be right

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back

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[Music]

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[Music]

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all right listen uh we have to start

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with scam Bank Run fraud I mean Sam I've

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been free

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I get that wrong sometimes uh he was

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interviewed by uh Andrew or Sorkin the

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suit at deal book who gave him softball

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after softball then the next day he was

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on good morning really passive

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aggressive right now a little bit Yeah

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but Jake house got a point he's got a

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point a little bit a little bit a little

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bit anyway the New York Times continues

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to embarrass themselves uh by handling

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Sam Bachmann fraud with kid gloves wow

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George Stephanopoulos my Greek brother

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Stephanopoulos the Spartan came in and

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absolutely fricassade and filleted

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sambachment freed a Good Morning America

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very important to note that Good Morning

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America uh the segment was between

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holiday cocktails and the cast of The

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White Lotus and Andrew Sorkin was at the

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deal book Finance conference but you

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know that that Stephanopoulos interview

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was two hours but they reduced it down

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to 10 minutes so there were probably a

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lot of sort of cordial conversation

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banter and then softball questions and

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then he does stick the knife in and and

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does the fricassee but he cuts out all

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the other stuff so he just gets it down

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to the 10 minutes what Stephanopoulos

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did to him was extraordinary in that he

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said over and over again in the FTX

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terms of service you cannot touch the

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user accounts but you at Alameda were

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taking them and you were loaning them

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out I don't know who is advising uh Sam

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bankman Freed at this point but why he

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is talking so much he was also on a two

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hour

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Twitter spaces after all this at this

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point sax what do you think is going on

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here in the mind of Sam bankmanfried and

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also the media which seems to have a

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very variable way of dealing with this

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obvious fraud and crime right okay well

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you know I can speculate about SBF I

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think if there is a strategy here it is

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this he is basically copying two

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criminal negligence in order to avoid

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the more serious charges of Fraud and I

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think again if there's a strategy here

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it is he saw himself being defined as

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Bernie Madoff 2.0 in the press and if

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that image which may well be true

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cemented around him then prosecutors

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would never stop they would never accept

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a plea that basically gave him anything

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less than I made off like sentence which

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would be you know decades in prison

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maybe a life sentence so he is out there

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doing what lawyers would tell you never

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to do which is basically incriminate

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yourself create more of a record but

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he's doing it to change the public

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perception maybe muddy up the public

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perception get people thinking that okay

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he you know this that he he's admitting

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he did something wrong but it wasn't

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deliberate it wasn't fraudulent it was

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just basically carelessness or

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sloppiness if he succeeds in mudding the

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waters enough then maybe the prosecutors

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will give him a plea deal that allows

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him to have his life back at some point

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I think that would be the crazy like a

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fox explanation of what's Happening

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now there is you know an alternative

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explanation as well which is I just

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think that

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these types of guys you could call it

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you know a narcissistic fraudster do you

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think they can talk the way out of

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anything you know because they have they

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have um you know when they've talked

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their way into getting hundreds of

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millions of dollars of investment

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billions billions uh in some cases and

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so they just feel and they've been

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trained

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by employees Partners investors the

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press that they can talk their way out

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of any of this stuff I think yeah the

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average person is not really used to

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dealing with one of these personality

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types who

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um is I mean they clearly are smart and

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they're articulate articulate and they

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know what to say and they're crafting

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their words what they've learned through

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their life is that if they use the

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precise magic words with a person they

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can pretty much you know convince them

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of anything get them to do anything and

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in particular I would say investors tend

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to fall for this not because investors

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are dumb but because investors are so

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clear about what they're looking for and

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we're predictable they're predictable

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yeah like we're you know VCS especially

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we're looking for the 100x outcome or

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whatever so it's easy for this type of

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personality to construct a story to

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essentially stroke the erogenous zones

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of a VC whoa yeah and and and sort of

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trick them and so there is probably a

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positive reinforcement Loop that gets

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created in the minds of one of these

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people who and they start to think that

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they can basically talk their way out

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out of any situation so I think that

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would be part of what's going on here

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and you know if you look at sort of the

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tactics that he's using to do this

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you know all of a sudden he's trying to

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portray himself you know before this

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he's portraying himself as a smart guy

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in the room now all of a sudden it's

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this babe in the woods impression where

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I didn't know it was my subordinator I

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wasn't really in control

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right each individual decision he says

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looked sensible to him it's just it all

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added up to something he didn't

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anticipate like really you know loaning

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yourself a billion dollars of of

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basically the company's money which was

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basically customer money that seemed

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reasonable to you I don't know how you

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defend that individual decision and

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there's many like that but but this is

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sort of the narrative that he's trying

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to construct and

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um let me stop there but I think there's

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a lot more that can be said to dismantle

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The Narrative he's trying to create

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what's your take on this and then can I

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make a comparison to SBF and Trump

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through the lens of the media

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so if you go back to 2016 you know

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Donald Trump violated every single

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establishment bias that these

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left Progressive journalist Elites had

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and so they basically just attacked

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attacked attacked attack attacked

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but then you went into the election

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and there was a very clear data point

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that said whatever you thought

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was that was at best limited and you

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missed the tone of the country

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because 50 plus percent of the country

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held a very different view about this

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person and instead of taking a step back

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and and then the left media the

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mainstream media

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re-underwriting and learning and then

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saying you know what Mia khalpa I got

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this wrong they just doubled down and

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they said no it still doesn't meet our

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priors and so we're just going to ring

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fence this problem and we're going to

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just try to destroy this issue because

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you know we want to control the

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narrative and and and by result we want

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to control Power

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now you look at SPF it's the exact

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opposite he went to the perfect elite

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private high school

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then he went to one of the most

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prestigious elite private universities

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MIT his parents teach governance of all

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things at one of the most elite liberal

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institutions in America Stanford they

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are in the establishment of the

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progressive left

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and what happened was he took customer

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funds and all of this money he made tens

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of millions of dollars of political

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donations he wrapped himself in this

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blanket of a progressive left-leaning

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cause called effective altruism

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and all of the mainstream media fell for

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it and embraced him as well as some

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politicians because it met everything

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that they themselves also bought into

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yes

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and now you have this cataclysmic event

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a multi-deca billion dollar fraud or

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bankruptcy millions of customer accounts

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who are frozen you know tens of millions

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to hundreds of millions to billions of

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dollars lost and stolen from them

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and they refused to re-underwrite this

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kid and the reason is because in order

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to do so it's like eating your own tail

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and that's why they don't want to do it

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and so this is why you have the media

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basically allowing him to do an apology

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tour

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now this is his second time at

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manipulating them the first time he was

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able to manipulate them by basically

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being one of them

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and now he's allowing

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them and their desire to basically

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protect themselves

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so that he can create some kind of a

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defense for himself and I just think the

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whole thing is gross because it misses

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the entire mood of the nation this is

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an enormous financial fraud that was

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perpetrated on tens of millions of

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people

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and there's no accountability because in

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order to do so the media would

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effectively have to admit that they

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missed it and they got it wrong and they

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refused to do it and I think like that

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is the really big problem that nobody is

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really speaking out about is like well

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if these folks are meant to be the last

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stop

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to make sure that there's truth and

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honesty and transparency in society and

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you can't count on them and in fact

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they're just going to reflect their own

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narrative what is one supposed to do to

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learn the truth

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in a way what you're saying and then

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we'll go to your freeberg is this fraud

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was encased in all the Gilded facade

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that America hates right now reflects

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the institutional rot of America it

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reflects every single aspect of

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institutional rot that every non-elite

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talks about all the time

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but Elites when they have those labels

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will refuse to give up and just to add

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to that the the thing that's missing

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that I'd say one of the big issues with

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the institutional rot in our country is

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the lack of accountability when somebody

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gets it wrong we saw this with covid

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right the health establishment is saying

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that they want amnesty yeah and Atlantic

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magazine was willing to give it to them

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so the point is that this this class of

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people think that when they get it wrong

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that they're the experts but when they

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get wrong there should be no

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accountability and such a mouth to your

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point the media and these institutions

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are not willing to re-underwrite

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SBF when he so clearly as a fraudster uh

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Freeburg what do you think the

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of this Theory you had a large amount of

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donations to politicians uh obviously

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you have coming from Stanford MIT Etc

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and then uh you have these Investments

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gifts slash advertising slash donations

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to propublica Vox this new publication

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semaphore The Intercept that have all

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been uncovered now

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did he do this paying off of all the

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elites you know splashy cashy giving

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money to everybody because he knew he

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was doing a fraud and that this is

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evidence

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of this is a premeditated fraud or do

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you think this is a deranged individual

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who just was seeking status I don't know

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there's a video you can watch of this

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guy

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for some reason FTX has left up all of

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their videos on YouTube

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from three years ago called quantitative

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trading seven and a half thousand cell

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wall of Bitcoin on binance it's a 17 and

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a half minute YouTube video of SBF

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Trading

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Arbitrage across markets I think it

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provides probably the best like natural

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non-scripted insight into this guy's

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Behavior

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that you could see

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you know because it's not like him being

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interviewed it's just him living in his

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world and he's just you know a mouth

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trying to get a piece of cheese like

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he's you know he's like out there he's

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you know scrambling around in the

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markets he's finding edges he's finding

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advantages and he's and he's clearly

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just taking advantage of them all day

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every day that's who he is

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now you put a person like that

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in an unregulated environment and there

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was this clustering demand for an

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unregulated environment because of a lot

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of what you guys are saying which is

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people have this disdain for the elitism

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and the and the institutional Roth and

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all these things so Bitcoin emerged as a

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solution out of 2008 to the you know the

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the what felt like institutional wrought

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that that governments have a key role in

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but when you have no regulation and you

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have no trusted Central Authority

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involved mice that are trying to find

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cheese will rule the day and I think

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that's what happened here if it wasn't

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this guy it was someone else it was

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going to be someone else and then all of

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a sudden everyone's clamoring and saying

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hey we needed the government to protect

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us no one protected us someone's got to

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save us we're The Regulators we're

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people that are supposed to keep an eye

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on the stuff when the whole premise of

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so much of what was being sold was

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non-regulatory regimes was openness was

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peer-to-peer trust protocols and it

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turns out that in that sort of an

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environment the mouse that is hungry is

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for the cheese will get the cheese and

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that's exactly what happened I don't I

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don't know how much of it was I don't

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agree with this him saying I'm creating

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intentional fraud which certainly seems

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to be the case versus him saying I'm

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going to pay these guys I don't know how

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much it was even that intelligent but

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the guy was clearly like trying to get a

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piece of cheese okay so this cheese

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eater this rat is a League of Legends uh

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you know expert playing on eight

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different monitors at a time the

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cryptocurrency game while hopped up on

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speed yeah I'm not saying that to be

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cruel I'm saying that because they

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admitted it they talked about it in

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their staff meetings instructing their

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Traders and team members of how to take

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speed he admitted to it in an interview

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this week he said that it was all legal

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prescription drugs but they were taking

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them yes yes and literally in the same

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videos you're referencing people see

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speed patches or I don't even understand

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this but there are patches you can put

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on your body to deliver speed to you at

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some you know dose or whatever sex you

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buy this Theory no care that this isn't

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about the elite side of it it's about

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the non-elite the Anarchy side of it no

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and this cheese eating rat which just

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wanted to eat more cheese I don't buy

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this narrative because I see too much

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design intentionality between what

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happened so in other words it wasn't

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just a series of individual decisions

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that didn't add up many of those

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individual Decisions by themselves were

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totally unjustifiable and moreover there

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were too many there's too much evidence

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of sophisticated Behavior here again

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he overnight went from portraying

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himself as a smart guy in the room to

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the to the babe in the woods and so for

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example when you look at the

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construction of all these entities and

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the corporate org chart you know of all

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the related entities it's a very

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sophisticated attempt to obscure and

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construct certain you know protections

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when you look at the way that Alameda

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was Exempted from the normal margin

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requirements on FTX there was the

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so-called back doors there was

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intentionality there there was

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intentionality in terms of who was hired

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to staff these organizations again they

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wasn't hired no board no CFO yeah the

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guy the guy who was in charge of

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compliance what like Trump talked about

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in a previous episode was the guy who

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was involved in the ultimate bet poker

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achieving Scandal you know not super

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mode yeah exactly right or look at this

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goofy goofball Caroline Ellison who was

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put in charge of Alameda right his

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girlfriend it's being done for a reason

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right he's setting it up in a certain

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way and you know the one time I

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interacted with him at this Tech

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conference he was sitting there holding

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court and he had all of his minions

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around him who were following his orders

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this was a guy who was controlling his

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business he was making the decisions at

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I think a task level and

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um he knew exactly what was going on

00:17:03

here so look I just don't buy I do not

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buy this idea that um that he was like a

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blind Mouse who's just a stimulus

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response you know in the Moment by the

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way that wasn't that wasn't my point sax

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my point was whether it was him or it

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was going to be someone else it was

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bound to happen

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the idea that we want to have completely

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free unregulated Bahamian base

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trading you know environments that we

00:17:33

can supposedly trust because someone

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puts on a good face when there is no

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real regulatory body and Regulatory

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Authority overseeing it at some point it

00:17:41

was going to happen well no hold on it

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is look coinbase is a fully regulated

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industry they're not set up in the

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Bahamas and they're not unregulated he

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set up and he had no oversight he had no

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board there was no regulatory regime

00:17:53

there was nothing how was he able but

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okay so first of all look I don't think

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this had to happen I think that again I

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think that excuses too much because it

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implies that if it wasn't SPF it'd be

00:18:02

somebody else I actually think that this

00:18:04

was a highly concerted effort listen he

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courted Regulators he donated to

00:18:09

politicians he courted the media and

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donated

00:18:13

he was really good at it he was

00:18:15

unusually good at it no and I totally

00:18:17

agree on that yeah super smart super

00:18:18

connected really thoughtful design on

00:18:21

how he committed this I only think an

00:18:23

Insider could have pulled off something

00:18:24

at the scale I think I agree I think

00:18:26

this is where chamoth you're exactly

00:18:28

right I think you needed to be of

00:18:29

cynicism here is he new to the Playbook

00:18:31

and he admitted it you pointed this out

00:18:34

with the chats that were released where

00:18:35

he said he he he he yeah look I mean

00:18:38

just like the he he chat look how like

00:18:40

convoluted and intertwined all these

00:18:42

people are like gensler's intertwined

00:18:44

with the parents yeah parents apparently

00:18:47

bundled a bunch of money to Elizabeth

00:18:48

Warren you know he was dating the CEO of

00:18:52

the business that he owned 90 in they're

00:18:54

all these other random shell companies

00:18:56

that he owned 100 of where they were

00:18:58

lending back and forth hundreds of

00:19:00

millions to billions of dollars yeah to

00:19:02

David's point that is a sophisticated

00:19:04

con that you have to architect and the

00:19:08

way that he was able to get away with it

00:19:10

is that not a single reporter or

00:19:12

regulator thought to dig in and the

00:19:15

reason I think is because he said all of

00:19:18

the right things that wanted them to

00:19:21

embrace him and the reason is this is a

00:19:24

dumb game that we woke westerners have

00:19:26

to say the right to the less and then

00:19:28

everyone thinks we're a good person

00:19:29

exactly imagine pull that card up on

00:19:31

what he said because it's actually a

00:19:32

good thing that's what I just said

00:19:33

exactly what he said yes he said it's

00:19:36

it's the game that we woke westerners

00:19:38

have to play we didn't say the right

00:19:39

Shiba less so Everyone likes us he

00:19:40

actually said the most uncomfortable

00:19:42

thing out loud which is look by having

00:19:44

gone to Crystal Springs High School by

00:19:46

having professors my parents that went

00:19:48

to Stanford by having gone to MIT

00:19:50

I can pull this off

00:19:52

that's that's what he said because he

00:19:54

can go with those things because I'm a

00:19:56

I'm a champion of effective altruism

00:19:58

that I can justify any of these

00:20:00

decisions how amoral or immoral that

00:20:03

they be because I'm trying to help you

00:20:05

know my brother stand up a multi-million

00:20:07

dollar pandemic response business I'm

00:20:08

trying to do this I'm trying to do that

00:20:10

and all of these regulators and all of

00:20:14

these reporters said okay you get the

00:20:16

hall pass now imagine if you replaced

00:20:18

him with some random kid in some

00:20:22

developing country or even from the

00:20:24

United States who

00:20:27

did went to Public High School who went

00:20:30

to some random State School do you think

00:20:32

that they could have pulled any of this

00:20:33

stuff off no you need the patina of the

00:20:36

privileged class the New York Times

00:20:38

that's what he had the majority of the

00:20:40

privilege class even after this fraud

00:20:42

the New York Times wrote more of a puff

00:20:45

piece on him than the hit piece they

00:20:46

wrote on Brian Armstrong last year when

00:20:48

Brian Armstrong wouldn't toe the line on

00:20:50

allowing politics at work remember that

00:20:52

yeah unbelievable yeah so the big end I

00:20:55

think what chamoth is kind of saying

00:20:56

here is that the big enabler here is not

00:20:58

crypto per se it's all these

00:21:00

institutional biases and Elite biases

00:21:02

that he was able to play into partly

00:21:05

because he was a big Insider I mean in a

00:21:06

way he monetized his parents life work

00:21:09

the problem that I think this allows us

00:21:12

to put a fine point on is the following

00:21:15

you know in society we've confused a lot

00:21:18

of people

00:21:19

to think that the opposite of liberal is

00:21:22

conservative or Republican

00:21:25

and I think that's the cycle that drives

00:21:27

the Mind virus inside the mainstream

00:21:30

media the problem is the opposite of

00:21:32

liberal is illiberal okay and what a

00:21:35

liberal means is to be narrow-minded and

00:21:37

unenlightened it means to be puritanical

00:21:40

it means to be Fundamentalist

00:21:43

and this is really what it allows us to

00:21:45

see now we have now had six years of

00:21:48

data case after case after case where if

00:21:53

you are woke if you are a social justice

00:21:56

Warrior if you have the right

00:21:58

credentials that justify your upbringing

00:22:01

if you have institutional uh Bona fides

00:22:04

that come from your parents

00:22:06

you get to create the narrative

00:22:08

and you get a hall pass

00:22:11

and everybody else

00:22:13

basically is at the subject and the

00:22:15

mercy of the mainstream media and so if

00:22:18

you don't kiss the ring and bow down to

00:22:20

them they will try to destroy you or run

00:22:22

you out of town but if you are one of

00:22:24

them they will give you a hall pass and

00:22:26

when it's time for them to change their

00:22:29

mind in order to tell the truth they

00:22:32

won't do it and so these types of grips

00:22:34

will continue as Friedberg said because

00:22:36

there is no check and balance without a

00:22:39

healthy Independent Media there is no

00:22:41

way for all of us to actually know

00:22:43

what's really going on guys some person

00:22:46

in the media could have asked the

00:22:48

question and dug in deeper around the

00:22:51

connections between Alameda and FTX for

00:22:53

the last 24 months

00:22:57

at no point could any person have asked

00:23:00

these questions and found ex-employees

00:23:02

and said you know are there any unseemly

00:23:05

connections here between FDX and Alameda

00:23:07

there was no disgruntled employee I mean

00:23:09

every company has disgruntled employee

00:23:11

whistleblowers

00:23:13

but here where there was billions of

00:23:14

dollars being made by tens of people not

00:23:16

a single person who felt on the outs

00:23:18

said anything well he was also giving

00:23:21

millions of dollars hold on because the

00:23:24

questions weren't asked and then this

00:23:26

kid paid hush money to the mainstream

00:23:28

media let me ask you a question of you

00:23:29

guys do you think that it's the media's

00:23:31

responsibility in this context or do you

00:23:33

think that there should have been a

00:23:34

regulatory Authority that had oversight

00:23:36

of this business like there is for every

00:23:38

bank and every trading operation in the

00:23:40

United States and every one of those

00:23:42

businesses has a compliance officer and

00:23:44

has Regulators up the Wazoo making sure

00:23:46

that customers are kept safe and

00:23:48

protected or do we think that that

00:23:50

should should offshore Vehicles be

00:23:52

allowed like this that allow people to

00:23:54

operate it's a reasonable question but

00:23:55

there was a chicken and egg question we

00:23:57

were all standing around holding our

00:23:59

hands while the cftc and the SEC were

00:24:01

fighting that's not something that

00:24:04

consumers can be expected to adjudicate

00:24:06

so yes we should have legislation that

00:24:09

clearly defines all of this but there

00:24:10

were enough parameters that created

00:24:13

regulatory Frameworks where a bunch of

00:24:15

good actors did operate in them and are

00:24:17

continuing to do so like coinbase so I

00:24:20

don't think this is a regulatory issue I

00:24:22

think that if you believe there are

00:24:24

people who are supposed to forensically

00:24:26

examine things and get to the bottom of

00:24:28

things and ask hard questions

00:24:30

those people did none of that here and

00:24:33

and what's what's even more worrisome is

00:24:36

what they're showing is now with a

00:24:38

massive amount of data that shows that

00:24:40

you could ask hard questions they don't

00:24:42

care to because it makes them look bad I

00:24:45

disagree with this Tremont I think

00:24:46

Regulators failed here because they have

00:24:48

been reactive to crypto they have not

00:24:49

been proactive and they have not been

00:24:51

clear with the crypto community that

00:24:52

what they were doing was illegal and

00:24:55

they should have put the regulations in

00:24:56

quicker and they're playing catch-up but

00:24:58

all three groups fail the media failed

00:25:00

The Regulators failed and VCS failed

00:25:02

Capital allocators failed I apparently

00:25:05

to due diligence here and install proper

00:25:08

governance you cannot put a company like

00:25:10

this you know in business billions of

00:25:13

dollars and have no board of directors

00:25:15

no no I agree with you or he lied to

00:25:17

them

00:25:18

my point is that while Regulators are

00:25:21

are basically fighting a territorial

00:25:23

Turf War okay

00:25:25

the media could have still done their

00:25:26

job they chose not to guys it was worse

00:25:29

than that because not only it's not just

00:25:30

a case where the SEC failed to exercise

00:25:33

any oversight of him or dig into any of

00:25:36

these questions he was in the room with

00:25:37

them crafting the next set of

00:25:39

regulations he was working on the

00:25:42

regulations that you're talking about

00:25:43

that are supposedly needed while

00:25:44

breaking them they let the fox in the

00:25:46

hen house yeah he was gonna crash a a

00:25:50

new type of regulatory license for these

00:25:52

types of exchanges with the result that

00:25:55

he was going to get one and some of his

00:25:56

competitors weren't this is one of the

00:25:58

things that triggered CZ to basically

00:26:02

you know do what he did which is

00:26:05

basically that SPF was trying to get

00:26:08

binance and competitors like that band

00:26:10

while SPF would be one of the sole

00:26:13

people to get the license

00:26:15

with The Regulators yeah Jason you just

00:26:18

said something derogatory towards easy

00:26:20

he rug pulled them did he do that or did

00:26:22

he actually expose the fraud that's what

00:26:25

I mean by rug pulling yeah that's not

00:26:27

what rug pull means Jason okay well he

00:26:29

was partners with him and then he

00:26:31

realized he was weak and that he was

00:26:33

doing some stuff that was shady and he

00:26:34

decided he would eliminate a partner who

00:26:36

was creating regulation a sack said

00:26:38

whatever you want to say he knifeled all

00:26:40

he did was indicate a desire to

00:26:42

liquidate his position in a token that

00:26:44

was supposedly perfectly liquid and

00:26:47

that's basically that caused the

00:26:49

everything to unravel yeah but these

00:26:51

were Partners right I mean they were

00:26:52

these were deep business partners they

00:26:55

were collaborating on these tokens

00:26:56

together so they were they were not

00:26:59

Jason like part of the big loan that

00:27:01

that initiated all of this stuff like a

00:27:04

year and a half ago was to buy FTX off

00:27:07

the cap table

00:27:08

so you know this all I'm saying is like

00:27:11

you used words and you're framing of a

00:27:14

guy you know who's built the business is

00:27:17

like he is this nefarious bad actor

00:27:19

David Jackson that's his first investor

00:27:21

hold on a second

00:27:22

but but David's right all the guy did as

00:27:25

far as we can tell right now his tweet

00:27:27

I'm selling this token because I don't

00:27:28

believe in the capital structure of this

00:27:30

entity and he got those tokens by being

00:27:33

bought out of the cap table I know he

00:27:35

was partners with them so if if you if

00:27:37

you don't like rug pulled how about

00:27:39

backstage business partners they're not

00:27:41

Partners you still don't understand of

00:27:43

course I understand they had he was the

00:27:45

first investor so to do this yeah

00:27:51

hold on part of the consideration were

00:27:54

these tokens these I understand tokens

00:27:56

yes Jason when you when Uber went public

00:27:58

yes and you got distributed stock yeah

00:28:01

did you distribute and sell Uber at any

00:28:03

point

00:28:04

foreign

00:28:19

a partner of uber you're just a

00:28:22

stockholder yeah this is slightly

00:28:24

different I think but okay fine you guys

00:28:25

if you guys

00:28:27

listen Jason investor in the company

00:28:29

they were a business historical

00:28:31

situation but their status at the time

00:28:33

that CZ tweeted was that they were

00:28:35

competitors okay fine I mean they became

00:28:38

competitors I agree and by the way to

00:28:40

Jamal's point about this rug pulling

00:28:42

language I think we're getting kind of

00:28:43

done a rabbit hole here but yeah but CZ

00:28:46

CZ did perform a service in this sense

00:28:48

okay SBF claims that 4 billion more was

00:28:52

about to come in I personally don't

00:28:53

believe that sounds like [ __ ] to me

00:28:55

but if it is true that would have been a

00:28:57

bad thing the more money that came in to

00:28:59

that operation svf proved that he was a

00:29:02

very poor custodian of customer funds

00:29:04

for sure

00:29:06

but the longer this one I'm just

00:29:09

highlighting the language that you use

00:29:10

is sort of like Again part of that

00:29:12

establishment Elite narrative and I'm

00:29:15

just questioning you should maybe steal

00:29:17

man take a second to just steal man yeah

00:29:19

a more dispassionate view which is

00:29:21

here's a counterparty okay yeah who when

00:29:25

he left the cap table was given half

00:29:27

cash half tokens

00:29:29

okay and he decided to sell his tokens

00:29:33

yeah and tweet it publicly and cause a

00:29:36

run on the bank so there was something

00:29:37

to run on the bank there was no Bank

00:29:39

where's the bank on the bank language

00:29:41

okay is something this was in the

00:29:44

semaphore coverage okay you did it

00:29:46

publicly that's my point so I'll steal

00:29:48

management no no no these tokens are

00:29:50

worthless I need to liquidate them as

00:29:52

fast as possible but why would you do

00:29:54

that publicly why why would you do it

00:29:56

privately you're about to move the

00:29:57

market he wanted to move the market

00:29:59

he's letting people know why but he

00:30:02

wanted to kill his competitor as

00:30:03

charmath who's just saying CeCe wanted

00:30:05

to kill him

00:30:06

say he was trying to do that we got to

00:30:09

back up here because I think we've done

00:30:11

a lot of like 30 000 foot like lessons

00:30:13

and like takeaways from this whole thing

00:30:15

but we haven't really established what

00:30:17

it is that SBF did wrong so I think we

00:30:19

need to sort of take a second to unmutty

00:30:21

the waters okay and part of that I think

00:30:24

we should start with this idea of a run

00:30:25

on the bank because the favor the Press

00:30:28

you've been writing puff pieces about

00:30:29

SBF I'd say mainly some of four which he

00:30:32

was a big donor to yeah you've been

00:30:34

trying to frame it as a run on the bank

00:30:35

and then that implies that it's not

00:30:37

really his fault it could happen to

00:30:38

anybody lots of banks have had this

00:30:39

problem okay first of all they're not a

00:30:42

bank Banks actually have the legal right

00:30:44

under certain conditions to take

00:30:46

customer deposits and loan them out okay

00:30:48

yes they did not their terms of use did

00:30:51

not allow that as Stephanopoulos pointed

00:30:53

out svf's answer to that was well we had

00:30:55

this like margin account program there

00:30:57

were other Provisions in other terms of

00:30:58

use but most of the customers who lost

00:31:01

money the vast majority did not opt into

00:31:03

that program they never agreed to that

00:31:04

so that's that's Point number one point

00:31:06

number two is I think we need to to look

00:31:09

at this language of margin account okay

00:31:12

SVS explanation of how customer money

00:31:16

was siphoned off for his own personal

00:31:18

use I.E to Alameda is that Alameda had a

00:31:22

margin account so I think we could

00:31:23

perform a service here by explaining why

00:31:25

it wasn't a margin account and you know

00:31:28

and you guys understand this really well

00:31:30

the way that a margin account works is

00:31:34

the following okay because I think some

00:31:36

of us have them set up with investment

00:31:38

Banks you go to an investment Bank say

00:31:41

Morgan Stanley and you over you post

00:31:43

collateral you actually over

00:31:45

collateralize so for example you might

00:31:48

take 100 million dollars of stock posted

00:31:50

at the investment bank and then they

00:31:52

will let you loan a certain percentage

00:31:54

nowhere near 100 maybe 50 percent if you

00:31:58

have a very very liquid security

00:32:01

you may get 50 coverage which means if

00:32:04

you posted a hundred million dollars you

00:32:06

could get a 50 million dollar loan okay

00:32:08

100 million in Amazon stock you're some

00:32:11

Amazon VP you can get 50 million loans

00:32:13

and if it's a private asset it's

00:32:15

anywhere as high as 30 35 but typically

00:32:20

it's about 25 my expectation is in a

00:32:23

liquid token like this would have

00:32:25

basically gotten five or ten percent

00:32:26

coverage ratio at the best of it and

00:32:29

then what happens is you have these

00:32:30

maintenance values so if all of a sudden

00:32:33

the value of these entities multiplied

00:32:35

by that percentage that you're allowed

00:32:37

to loan Falls below you have to post

00:32:39

money that's how a margin account works

00:32:40

it's just there is no free lunch in that

00:32:42

yes exactly there's let me just say

00:32:45

quite simply very simply what I it

00:32:47

appears this guy did he took customer

00:32:49

deposits in US dollars

00:32:53

he then converted those dollars into

00:32:56

some other asset and he had a mark on

00:32:59

that asset let's call it a dollar a

00:33:01

token

00:33:02

and then those dollars were moved to

00:33:04

somewhere else

00:33:05

no this is because someone transferred

00:33:07

in some other token listen

00:33:11

we need to finish the explainer around

00:33:13

the margin account okay because what SPF

00:33:16

did is this he took customer deposits

00:33:19

gave them to himself

00:33:21

no he gave he took customer deposits in

00:33:24

U.S dollars they were wired in

00:33:27

correct he took those dollars out and he

00:33:29

put a fake token in and he called that

00:33:32

that's right he said he said therefore

00:33:35

he said the balance sheet is good but

00:33:37

the value of that token it turns out

00:33:39

isn't a dollar it's 10 cents that's

00:33:41

right it was his it was his sort of it

00:33:43

was sort of his made-up token that

00:33:45

yesterday that he tightly controlled the

00:33:48

training of and and artificially propped

00:33:49

at the price

00:33:51

yeah yeah but but here's the thing it

00:33:54

wasn't just the fact that his collateral

00:33:55

was no good it was also the fact that

00:33:58

and this is from the bankruptcy uh

00:34:00

filing by the the new the Enron trustee

00:34:03

guy

00:34:05

he specifically said that Alameda unlike

00:34:08

every other margin account on the

00:34:10

platform had the Auto Liquidation

00:34:11

Provisions turned off so wait we have to

00:34:14

finish the thought around how margin

00:34:15

works so like Thomas said you over post

00:34:18

collateral and if the value of that

00:34:20

collateral goes down or the the the the

00:34:23

the position your trading account the

00:34:25

value of that goes down you either have

00:34:27

to post more collateral or they will

00:34:30

actually liquidate your collateral to

00:34:32

pay off the loan so Morgan Stanley will

00:34:35

never lose money on a margin account

00:34:37

never like the whole point is because

00:34:39

they don't make money on it they loan

00:34:41

you the money at like you know a few

00:34:43

percent it's like very cheap yeah loan

00:34:45

Libor plus so yeah exactly that is not a

00:34:48

risk account to them and so in the

00:34:51

example let's use an example in the case

00:34:53

of the VP at Amazon we've got 100

00:34:55

million in Amazon they have a 50 million

00:34:57

dollar loan if Amazon loses half its

00:34:59

value then that triggers the automatic

00:35:02

selling of Amazon shares to get it back

00:35:04

down to 50

00:35:06

25 million of Amazon shares if the full

00:35:10

50 million was pulled down to get back

00:35:12

down to 25 to 50 leverage yes and they

00:35:14

don't wait until like Amazon stock is at

00:35:17

the exact level where now the collateral

00:35:19

equals 100 of the loan they will keep

00:35:21

that 50 loan to value yes and they will

00:35:24

liquidate you you know and by the way

00:35:26

you can lose your entire amount right so

00:35:28

yes you know this is why trading a

00:35:30

margin is so risky is that you can get

00:35:32

wiped out because you can get wiped out

00:35:33

very very quickly with a small move down

00:35:35

because they are the custodians of that

00:35:37

Amazon stock they are holding it for you

00:35:40

now and they have the right to sell it

00:35:41

to cover your margin you're saying that

00:35:44

Governor that basic tenant that basic

00:35:46

safety control was turned off by Alameda

00:35:49

and it's even more Sinister Alameda

00:35:51

controlled like 90 or 95 of these ftt

00:35:54

tokens and was owned

00:35:56

by Sam bankman fraud so he owned that

00:36:00

company then he claims he had no

00:36:01

operating position what should have

00:36:04

happened is with that collateral is that

00:36:06

as the value of their position was going

00:36:10

down and or as the value of the

00:36:11

collateral was going down it should have

00:36:13

been liquidated to pay off the margin

00:36:15

loan and that did not happen and the

00:36:17

reason it didn't happen is that Alameda

00:36:19

got a special exception on the platform

00:36:20

to turn off Auto Liquidation therefore

00:36:23

it was never a margin account if even if

00:36:25

it was a margin account okay and and FTX

00:36:29

somehow misadministered the margin

00:36:31

account it should never have taken other

00:36:33

customers deposits and use them to pay

00:36:36

back that money what should have

00:36:37

happened is if FTX was going to lose

00:36:39

money on a margin account that would hit

00:36:42

the FTX corporate Treasury

00:36:44

okay and when the FDX corporate treasury

00:36:47

ran out the company files for bankruptcy

00:36:49

then and then all the other customers

00:36:51

hold on their account is still there

00:36:53

their money is there in segregated

00:36:55

accounts and in bankruptcy they get

00:36:57

their money back the idea that a margin

00:36:59

account could ever cause another

00:37:02

customer to lose money that like

00:37:04

whatever that is that's not a market

00:37:05

there's a great article there's a great

00:37:08

article uh this one was a journalist

00:37:09

that did his job properly his name is

00:37:11

David Z Morris

00:37:13

he wrote an article in coindesk that

00:37:16

summed up for anybody that's interested

00:37:17

all of the actual fraud and all of the

00:37:21

crimes that were committed in

00:37:22

excruciating detail and what's so sad

00:37:25

about all these interviews in this press

00:37:26

tour is if anybody would just read this

00:37:28

article you can construct the right

00:37:30

questions to ask this guy just based on

00:37:32

this one article but

00:37:34

the the point I wanted to make is that

00:37:36

one of the most interesting insights was

00:37:39

these guys had lost an enormous amount

00:37:42

of money already in calendar year 21.

00:37:45

and so this is what's so crazy Jason

00:37:48

about you know you using language like

00:37:51

rug pulling and like you know nobody

00:37:53

actually trying to like be clear like

00:37:56

you guys are giving this guy a hall pass

00:37:59

any industrious reporter could have

00:38:02

found an employee who said wait a minute

00:38:04

we just blew a three billion dollar hole

00:38:06

in our balance sheet and calendar year

00:38:07

21 20 and now we're sitting here at the

00:38:10

end there's 22.

00:38:13

hold on I need to respond I am not

00:38:16

giving him a pass and for you to blame

00:38:18

journalists who are reflecting the crime

00:38:21

and not putting any light on VCS and the

00:38:25

capital allocators who made this

00:38:26

investment and who did know diligence

00:38:28

ended up with governance in it is the

00:38:29

height of arrogance Shema this is not

00:38:31

the process they're not doing that

00:38:32

either this is the VCS this is the

00:38:35

capital allocator's fault you're blaming

00:38:37

the people who are telling the story

00:38:39

after the story

00:38:41

they're covering the story up handle the

00:38:43

truth Jason they're covering the story

00:38:45

enough time can I get in here can I get

00:38:47

in here all right listen Jason I will

00:38:49

defend you against Jamal saying that

00:38:51

somehow you're uh you know that you're

00:38:53

letting us be off off the hook I know

00:38:55

you don't want to let especially however

00:38:57

you are letting the press off the hook

00:38:59

and the reason why hold on a second the

00:39:01

reason why you're using this inaccurate

00:39:03

language like rug pulling and run on the

00:39:05

bank when there was no run and there was

00:39:07

no bank is because you've been infected

00:39:09

by this language that the media has

00:39:11

inserted into the discourse the

00:39:14

immediately listen and hold on a second

00:39:15

investors may have got it wrong last

00:39:17

year investors may have got it wrong

00:39:19

when they did that last round but I

00:39:21

think investors Now understand what's

00:39:22

happening but the media is still

00:39:24

covering for SPF by Mis explaining what

00:39:27

happened okay give me a percentage acts

00:39:30

of who's to blame here VCS who invested

00:39:32

and didn't set up any governance

00:39:34

Regulators who did not set uh rules

00:39:37

around crypto and then through the media

00:39:39

what percentage out of a hundred percent

00:39:41

is the investors The Regulators and the

00:39:45

Press go three numbers I would say that

00:39:47

before the fraud got exposed one-third

00:39:50

one-third one-third one-third each

00:39:52

before the Frog got exposed but they

00:39:54

were all jointly and severally liable

00:39:56

but after the Fraud's been exposed no

00:39:59

investor is still defending SBF but I I

00:40:01

think that the investors who were

00:40:03

swindled by him they feel bad about it

00:40:05

so Thirty Thirty Thirty is absurd the

00:40:08

Press had no way to know the fraud was

00:40:11

going on just like the VCS

00:40:14

are you stupid like wasn't that yours

00:40:17

wasn't the journalist that exposed to

00:40:19

product theranos he's the guy that went

00:40:21

and did all the work John should be

00:40:24

celebrated hold on a second John Kerry

00:40:26

you went and found this thing when no

00:40:28

when everybody else was like this is

00:40:30

perfect it meets all of our priors let

00:40:32

me finish please it meets all of our

00:40:34

priors this is great hold on and so John

00:40:36

Kerry was like this doesn't surpass the

00:40:38

smell test to me let me go do some work

00:40:40

and he pulled one little string and over

00:40:43

the course of 18 months he exposed the

00:40:44

whole bloody things so hold on a second

00:40:46

so what is incredible to me is that it

00:40:48

was possible to expose this thing before

00:40:50

nobody did I agree with David it's about

00:40:53

equal responsibility before but

00:40:55

afterwards the bulk of the

00:40:57

responsibilities now sits with

00:40:58

Regulators to clean it up and

00:41:00

journalists to tell the truth okay and

00:41:02

now may I respond to that since you call

00:41:04

me stupid

00:41:05

you are delusional number one every one

00:41:09

of those investors in theranos could

00:41:11

have taken a [ __ ] blood test at two

00:41:14

different places like Jean-Louis gassier

00:41:16

did and write a blog post and prove that

00:41:18

theranos didn't work and they withheld

00:41:22

disbelief investors putting in a hundred

00:41:24

million dollars including Rupert Murdoch

00:41:26

didn't even take a [ __ ] blood test or

00:41:28

tell one of their diligence teams to do

00:41:30

it the same thing happened here with the

00:41:32

investors in FTX they did zero diligence

00:41:36

they set up zero governance this was a

00:41:38

failure of the investors and the

00:41:41

governance

00:41:42

for 99 of the problem and then

00:41:44

Regulators should have caught it and The

00:41:46

Regulators in fact did catch therano so

00:41:48

you're completely wrong chamoth again

00:41:50

the journalists come in after the fraud

00:41:53

is happening the investors and

00:41:55

governance is responsible for stopping

00:41:57

these things FTX was a failure of

00:41:59

governance and investors and so is

00:42:01

theranos the end you're completely wrong

00:42:04

the question is post post exposure why

00:42:07

are you guys obsessed with post how

00:42:09

about avoiding these things you guys are

00:42:11

bless the story is ongoing because these

00:42:13

stories for something that is capital

00:42:16

allocators responsibility it is our

00:42:18

responsibility to due diligence it is

00:42:20

our responsibility to create a board of

00:42:22

directors that checks on Elizabeth

00:42:23

Holmes I don't disagree with you just

00:42:28

call me stupid you just call me stupid

00:42:30

for pointing out something that you

00:42:32

refuse to accept what are you talking

00:42:34

about allocators

00:42:42

are giving a pass to the investors

00:42:45

again I'm not doing Fredo I'm not doing

00:42:48

Fredo you guys are being absurd this is

00:42:50

why people say

00:42:53

Don't call them dumb the reasons

00:42:59

like the guy you can't call dumb totally

00:43:02

he loses it goes berserk yeah

00:43:08

don't call me dumb hey

00:43:13

investors I think that they did a

00:43:16

horrible job here too it's a great

00:43:18

episode

00:43:19

um but the reality is just went up but

00:43:21

the reality is I think that if you think

00:43:22

that you can if you it's your decision

00:43:24

to defend the mainstream media I think

00:43:27

that that's fine I'm not defending them

00:43:28

no you are you said they have no

00:43:29

responsible I'm blaming the VCS it's

00:43:31

different the culpability is with the

00:43:34

investor class that has not had proper

00:43:37

governance Jason how many articles have

00:43:39

been written excoriating them

00:43:42

uh yeah somewhere like fools yeah I mean

00:43:46

show me show me the Washington close

00:43:49

Capital no show me the Washington Post

00:43:50

New York Times that's like digging in to

00:43:53

that malfeasance or that lack of

00:43:55

oversight and holding them accountable

00:43:56

in a way that you feel exposes this

00:43:59

problem to create change well if we look

00:44:01

at theranos uh those people uh who

00:44:04

invested including

00:44:06

um Draper

00:44:08

and um to show me the examples Rupert

00:44:10

Murdoch they they really went after them

00:44:12

for sure yeah what about here

00:44:14

uh Wall Street Journal one day ago

00:44:17

Sequoia Capital apologizes to its fund

00:44:19

investors for FTX loss Venture Capital

00:44:21

firm tells fund investors that's hard

00:44:22

hitting it will improve due diligence on

00:44:24

future Investments they really got it

00:44:26

let me let me read you it's a cover

00:44:28

story Wall Street Journal I'm gonna I'm

00:44:29

gonna read you I'm going to read you a

00:44:31

sentence from The New York Times

00:44:32

coverage of SBF and Sam

00:44:36

Banger freed is neither a Visionary nor

00:44:38

a criminal mastermind he is a human who

00:44:41

made the same poor choice that

00:44:42

generations of money managers have made

00:44:44

before him are you effing kidney times

00:44:48

coverage yes you are they also said I am

00:44:50

just reaching them

00:44:52

and then semaphore who was on the take

00:44:55

who received Millions

00:45:00

hold on a second where is their apology

00:45:03

Sequoia has apologized where is their

00:45:06

apology oh it has to come I'm not

00:45:08

defending the Press yes I'm just saying

00:45:10

yes I am not I am literally telling you

00:45:13

that the New York Times has

00:45:21

the Twitter spaces yesterday did a

00:45:24

better job of trying to ask questions

00:45:26

and getting to the truth then a single

00:45:28

journalist has done or the collective

00:45:30

body of olive journals absolutely

00:45:31

literally randos on Twitter spaces did a

00:45:34

better job than Sorkin let me tell you

00:45:36

why the why no one trusts the Press

00:45:37

Jason first of all they have an agenda

00:45:39

that's an agreement when they make a

00:45:41

mistake they never admit it when's the

00:45:42

last time they did an apology or

00:45:44

attraction when's the last time they did

00:45:46

what Sequoia did

00:45:48

I don't know and they need to apologize

00:45:50

I am in agreement with you on that but I

00:45:52

think we have to first say and this is

00:45:55

where you guys should be ashamed of

00:45:56

themselves is what is the responsibility

00:45:58

of capital allocators and governance and

00:46:01

Regulators I think it's one two three

00:46:03

our industry is responsible for setting

00:46:06

up proper governance The Regulators are

00:46:08

responsible for making sure that your

00:46:10

scientific

00:46:11

and then press is a distant third you

00:46:14

know who I think is responsible yeah one

00:46:17

two and three and then we can talk about

00:46:19

four five and six okay four five and six

00:46:22

Capital allocators Regulators the press

00:46:24

a distance sixth I agree let's go on to

00:46:26

China God it's so spicy today it's so

00:46:29

hot

00:46:31

I mean I do think I do think when we

00:46:33

attack the mainstream media Jason feels

00:46:34

a little twin tinge of like uh

00:46:37

insecurity and illegitimacy because he

00:46:39

were a journalist my personal perception

00:46:41

is I think that's [ __ ] no I haven't

00:46:43

I think that you have a an incredibly

00:46:47

romantic view of the craft as you

00:46:50

practiced it back then which I think is

00:46:51

full of Integrity yes I think that's

00:46:54

true I think that you you don't

00:46:56

adequately realize how massively the

00:46:59

industry has changed in the last 20

00:47:00

years since I realize it more than you

00:47:02

do I fully realize that the media has

00:47:05

absolutely become biased or they can

00:47:06

have lost uh in some cases yes I mean if

00:47:09

you're taking money are they providing

00:47:11

are they giving him they are corrupt if

00:47:14

they are taking money from SPF and then

00:47:16

giving him Kick Love coverage absolutely

00:47:18

that is the definition of corruption in

00:47:20

my mind what is what is it called when

00:47:21

you don't take money necessarily like

00:47:23

the New York Times and still treat them

00:47:24

with kid clubs what is that it is

00:47:26

Extreme bias and the New York Times

00:47:27

became incredible why do you think the

00:47:29

bias why do you think that bias exists

00:47:31

they were always left-leaning but I can

00:47:33

tell you why

00:47:34

they

00:47:36

when Trump came in a generation of new

00:47:38

journalists

00:47:39

became activist journalists they didn't

00:47:42

want to tell stories and take it

00:47:43

straight down the middle and let the

00:47:45

facts tell the story and let the

00:47:46

audience make their own decision they

00:47:47

felt that existential risk when Trump

00:47:50

came into office they got trumped

00:47:51

Arrangement syndrome they picked a side

00:47:53

like MSNBC and fox did and the business

00:47:55

model became for the New York Times pick

00:47:58

a side and get the subscribers it was a

00:48:01

deliberate

00:48:02

cynical choice on the New York Times

00:48:03

part to go full MSNBC for full Fox the

00:48:07

two Extremes in mainstream media in

00:48:09

order to get the subs and they literally

00:48:11

rallied the troops there to do anti-tac

00:48:14

anti-trump uh coverage and they became

00:48:17

activists and when journalists become

00:48:19

activists they are no longer journalists

00:48:21

they're activists or commentators and

00:48:23

that's the problem it's being presented

00:48:25

as journalism when in fact it's activism

00:48:27

so

00:48:29

shout out to Matt taibi who just did a

00:48:32

monk debate uh yeah on this very topic

00:48:35

and he has a great great sub stack

00:48:38

basically saying what you're saying

00:48:39

Jason and the the best quote is the

00:48:42

story is no longer the boss instead we

00:48:44

sell narrative he's a lifelong

00:48:45

journalist whose father was a lifelong

00:48:47

journalist and he understands the way

00:48:49

the business has changed and it's like

00:48:50

what you're saying and and this is why

00:48:52

Independent Media whether it's sub

00:48:54

Stacks whether it's call in shows

00:48:56

whether it's all in podcasts or other

00:48:58

podcasts Joe Rogue and Sam Harris

00:49:00

whoever it is independent voices are now

00:49:03

what consumers are seeking out because

00:49:06

they can sense the bias they know Rachel

00:49:08

Maddow and Tucker have an ax to grind

00:49:10

and they're left and right they didn't

00:49:12

what about these New York Times

00:49:14

Washington Post and Wall Street Journal

00:49:15

to you know they knew they were leaning

00:49:18

they didn't expect them to pick a side

00:49:19

do you think we should cancel if folks

00:49:22

do you think folks are better off

00:49:23

keeping their New York Times

00:49:25

subscription or replacing that New York

00:49:28

Times subscription with a basket of sub

00:49:30

stacks and yeah you answered your own

00:49:32

question it's the latter I think you're

00:49:34

on your own as a consumer now you're

00:49:36

going to have to and I think this

00:49:37

podcast and the Nuance we have shout out

00:49:40

to Freeburg for nuance what we've done

00:49:42

on this podcast is what's that funny

00:49:44

sound to explain to people freeware is

00:49:46

not the only one with Nuance

00:49:48

nobody would describe David sacks with

00:49:50

the word he's a nuanced department on

00:49:52

this podcast what am I 100 he is but you

00:49:55

would know that because you leave when

00:49:56

science is in the church Department okay

00:49:57

sometimes all right I'm the truth

00:50:00

Department

00:50:01

consumers need

00:50:03

consumers need to become extremely

00:50:05

literate and they have to do their own

00:50:07

search for truth in today's age they

00:50:09

don't they shouldn't trust New York

00:50:10

Times they shouldn't trust us they

00:50:12

should trust themselves they shouldn't

00:50:13

trust necessarily the CDC or you know

00:50:16

the World Health Organization they

00:50:18

should trust themselves and come up with

00:50:20

their own process for figuring out the

00:50:21

truth in the middle of this mess

00:50:24

by the way this is a good reflection on

00:50:26

what's happened with the rest of media

00:50:28

with respect to the Creator class

00:50:31

where right it used to be the movie

00:50:33

studios and you know a handful of kind

00:50:36

of aggregated creators that made all of

00:50:37

the content the record labels and now

00:50:40

you know Independent Artists Independent

00:50:42

Producers independent creators and now

00:50:44

independent journalists are going to

00:50:47

become the bulk of volume that's going

00:50:48

to be consumed it's just a different

00:50:49

consumption model but we've already seen

00:50:51

acts of Journalism we saw it happen with

00:50:53

movies and we've seen this disruption

00:50:55

happen across all these other media

00:50:56

classes journalism and what we call the

00:50:59

Press

00:51:00

is very likely going to be kind of that

00:51:02

next layer of disruption

00:51:04

I would trust having a conversation with

00:51:06

you about science topics over reading a

00:51:08

science article 100 you know in the New

00:51:10

York Times or Wall Street Journal if I'm

00:51:12

being 100 I would much prefer to talk to

00:51:14

you about it and if it was markets I'd

00:51:16

rather talk to chamoth and if it was SAS

00:51:17

I would talk to sax or operating a

00:51:19

company speaking of operating a company

00:51:21

or politics

00:51:27

we'll see we'll see uh but I do have to

00:51:30

talk about I think it's about having

00:51:31

unique inside Insight right like

00:51:33

that wasn't the case and what's

00:51:35

interesting is that the people who are

00:51:36

the professionals that have the

00:51:38

knowledge and the touch points are also

00:51:40

becoming journalists in the sense that

00:51:42

they're also becoming speakers of their

00:51:44

truth right and I think Twitter is a

00:51:46

good enabling platform for this we see

00:51:48

it on YouTube where like scientists are

00:51:50

putting out their own videos or Market

00:51:52

actors like people that are traders in

00:51:54

the market go out and they put out their

00:51:56

own videos and they put out their own

00:51:58

podcast and I think we're probably a

00:51:59

good reflection of that yeah uh in the

00:52:01

sense that like we are the actors in the

00:52:04

market and we're not just the

00:52:05

independent Observer that has kind of a

00:52:08

surface level view we have the depth to

00:52:10

be able to talk about the things that we

00:52:12

choose to talk about and I think that's

00:52:13

where consumers find Value and we'll

00:52:15

continue to find Value in terms of who

00:52:18

the journalist or speaker is that

00:52:19

they're going to start to trust for

00:52:20

their information

00:52:21

I saw that interview you did with um

00:52:24

newcomer

00:52:25

oh yeah yeah

00:52:28

I saw that coverage I mean yeah he

00:52:31

speaks a lot about the uh this

00:52:33

phenomenon of going direct and of course

00:52:35

he's against it now he interprets going

00:52:37

to direct as an attempt by

00:52:40

um newsmakers to avoid answering tough

00:52:43

questions or take tough questions I

00:52:44

think that's ridiculous because for

00:52:46

example I go on CNBC all the time I go

00:52:50

on Emily Chang and um Bloomberg all the

00:52:53

time I submit to like really tough

00:52:55

questions I actually like those sort of

00:52:57

sparring sessions yeah I did hard talk

00:52:59

this week have you ever done that

00:53:00

exactly that's not what's going on here

00:53:03

I think what's going on is we have

00:53:06

expertise we want to communicate them

00:53:07

and we do feel like the media has become

00:53:10

a very unreliable narrator there is too

00:53:12

much bias and sloppiness not all of it

00:53:15

is agenda some of it's just pure

00:53:17

sloppiness and there's no reason why we

00:53:19

shouldn't go direct and people want to

00:53:21

hear from us the audience wants to hear

00:53:23

from us same for look look at Draymond

00:53:25

look at Draymond and the success he's

00:53:26

had with his paws but no basketball

00:53:28

player has ever gone direct and created

00:53:30

content like draymond's created and it's

00:53:32

totally changed the game and he was so

00:53:34

clear he's like We Are I Am the media

00:53:36

now

00:53:37

JJ Reddit you know old man and the three

00:53:40

amazing amazing podcast I was going to

00:53:42

tell you guys a story so I was in the

00:53:43

Middle East last week or this week sorry

00:53:46

and um I had this crazy experience where

00:53:49

I was trying to understand what was

00:53:50

going on in China

00:53:52

and so I started on CNN and the whole

00:53:55

thing was the propaganda machine around

00:53:58

a democratic Revolt you know pushing for

00:54:01

democracy and trying to depose G

00:54:04

then I moved to Al Arabia so one channel

00:54:07

up I went from Channel 10 to channel 11.

00:54:10

and instead what they were actually

00:54:12

doing was interviewing people on the

00:54:14

ground and what they were talking about

00:54:16

was literally how these PCR tests have

00:54:19

become far too burdensome and they just

00:54:21

wanted it to end and more reasonable

00:54:23

restrictions to get in and out of

00:54:24

quarantine then I went from there to uh

00:54:27

BBC and in BBC they had a China scholar

00:54:30

who was talking about how for decades

00:54:33

actually the Communist Party supports

00:54:35

local level protests and demonstrations

00:54:37

because they've realized that it is a

00:54:40

part of their political system to make

00:54:42

sure that people feel like they have a

00:54:44

say

00:54:45

and I was like taking a step back and

00:54:47

I'm like if you listen to the U.S

00:54:48

narrative and even Jason like in our

00:54:50

group chat people formenting for like

00:54:52

Revolution and this is Tiananmen 2.0 and

00:54:55

I'm like well I'm reading two other

00:54:58

channels that tell us a completely

00:54:59

different set of things and I just

00:55:02

thought man people just really fit the

00:55:04

data such a good point to fit their bias

00:55:06

yeah we are projecting we want to see a

00:55:09

revolution in China the people in China

00:55:10

want you to have their lives back well I

00:55:12

would love to see more democracy in the

00:55:14

world yes guilty as charged

00:55:17

I would like to see people be more free

00:55:18

in the world

00:55:20

dictator I think most people just want

00:55:22

to improve their condition and I don't

00:55:24

think people are as tied up on the

00:55:26

philosophy of the government as they are

00:55:28

about improving their condition and as

00:55:29

long as their condition is improving

00:55:31

they're willing to put up with any form

00:55:33

of government and history shows that by

00:55:34

the way the conditions in China have

00:55:36

improved better than everybody's in the

00:55:38

lives

00:55:39

of people out of abject poverty uh and

00:55:43

that's the great success of Engagement

00:55:46

isolationism would not have created that

00:55:50

amazing outcome 500 million people

00:55:53

you're referring to us

00:55:55

you're saying building factories is what

00:55:58

I'm referring to oh okay so you guys if

00:56:00

they if they want to build something

00:56:00

over there I guess that's better than us

00:56:02

throwing up open our markets and giving

00:56:04

China mfn status to destroy American

00:56:07

manufacturing and build up their economy

00:56:10

so they can become a peer competitor to

00:56:12

the United States yeah I mean this is

00:56:14

the balance of Engagement if you engage

00:56:16

too much you give everything up in sex

00:56:19

which of the

00:56:22

current Republican agenda do you

00:56:24

disagree with most strongly just as an

00:56:26

aside well most Republicans are in favor

00:56:29

of our Ukraine policy this sort of

00:56:31

unlimited appropriation of weapons and

00:56:34

Aid to them don't you disagree with

00:56:36

immigration policy of Republicans and

00:56:37

Democrats well I have a more nuanced

00:56:39

position on immigration which is I think

00:56:41

we need to have a border

00:56:43

and it can't be just like an open border

00:56:45

which is the day Factor policy we have

00:56:47

now but at the same time I do think that

00:56:49

we should have H-1B visas and we want to

00:56:51

like jamasa we want to be an All-Star

00:56:53

team for the world we want to have the

00:56:54

best people want to come here so there's

00:56:56

a balance it's a balance and then you

00:56:58

know look I think that I was happy to

00:57:00

see the marriage equality Bill finally

00:57:02

passed the Senate yes they did again

00:57:04

about a dozen 12 Republicans voted for

00:57:06

it and supported so that's right yeah

00:57:08

but that's not the majority

00:57:09

unfortunately

00:57:11

you know look on on what I would

00:57:12

categorize as the old social issues

00:57:16

uh you know like gay marriage like

00:57:18

cannabis legalization I was on the

00:57:21

liberal side yeah I don't think you know

00:57:23

Banning abortion

00:57:25

entirely a total abolition is going to

00:57:27

work for this country I think

00:57:28

Republicans will lose elections if they

00:57:31

insist on that and I think they're

00:57:32

getting that message so um so yeah I

00:57:35

mean look I I think that

00:57:37

um I've always considered myself to be

00:57:39

pretty Centrist and so you're not a

00:57:40

globalist you don't believe in open

00:57:43

Global markets but the U.S in general I

00:57:46

understand the benefits of free trade

00:57:48

and I don't think we should be

00:57:50

isolationist with respect to trade I

00:57:52

don't think that we can be a successful

00:57:53

country if we are we isolate our economy

00:57:57

so I do want to trade however with China

00:58:00

in particular I think we made a mistake

00:58:03

in throwing open our markets to their

00:58:05

products giving them mfn status while

00:58:08

the most favored nation is enriching

00:58:10

them to the point where they became a

00:58:12

pure competitor of the United States

00:58:13

now look I understand why we made that

00:58:16

mistake 20 years ago because everybody

00:58:18

thought the theory was that if we help

00:58:20

China become rich that China would

00:58:22

inevitably become more democratic and

00:58:25

they'd be filled with gratitude towards

00:58:26

the United States and it actually become

00:58:28

more uh more hospitable towards us more

00:58:31

westernized and I think that theory has

00:58:33

just proven to be wrong I mean they have

00:58:35

not or it's going very slowly one or the

00:58:38

other let's own our Clips here uh here

00:58:40

is shamat's prediction from episode 61

00:58:43

and we'll see you on the other side of

00:58:45

this quick clip my worldwide uh biggest

00:58:49

political winner for 20 2022 is Xi

00:58:52

Jinping I think this guy is uh

00:58:55

he's firing on all cylinders and he

00:59:00

is basically ascendant so 2022 marks the

00:59:03

first year where he's essentially really

00:59:05

ruler for life and so I don't think we

00:59:07

really know what he's capable of and

00:59:09

what he's going to do and so that's just

00:59:10

going to play out you think he's the

00:59:12

biggest political winner really oh my

00:59:13

God I think it's going to be a he's

00:59:16

going to run roughshod not just

00:59:18

domestically but also internationally

00:59:19

because you have to remember

00:59:21

he controls so much of the critical

00:59:23

supply chain that the Western world

00:59:25

needs to be I think he's completely

00:59:27

wrong I think you're completely wrong I

00:59:29

think he's losing his power he's scared

00:59:31

that's why he took out all these CEOs

00:59:33

he's consolidating power because he

00:59:35

fears that they're gonna

00:59:36

when too big and then displace him and

00:59:40

he has massive real estate problems over

00:59:42

there that could blow up at any moment

00:59:43

in time he could face a civil war there

00:59:44

I think he's totally isolated himself

00:59:46

Civil War and they don't even have every

00:59:48

major country is removing their

00:59:50

factories and removing this dependency

00:59:52

there what are you talking about what

00:59:53

are they going to Riot with

00:59:56

uh did you not see Tiananmen Square did

00:59:58

you not see the riots in Hong Kong are

01:00:00

you not paying attention there's been

01:00:02

many riots in China

01:00:03

so those were crushed

01:00:07

he still will have massive amounts of uh

01:00:11

I believe protests and yeah I think I

01:00:13

think the the bigger risk is is that

01:00:16

China gets better for Xi Jinping but

01:00:18

worse for everybody else in China it's

01:00:20

already worse for all the billionaires

01:00:21

uh over there it's worse for the tech

01:00:23

industry you've now got ever Grand that

01:00:26

whole uh you know gigantic debt

01:00:28

implosion I think there could be

01:00:30

contagion from China next year I don't

01:00:32

think she's gonna lose his grip in any

01:00:34

way but I'm not sure China's gonna have

01:00:37

a good year next year

01:00:39

wow nailed it I think all three of us

01:00:41

kind of got this right what are you

01:00:43

talking about you got none of it right

01:00:44

well I I said they were going to be

01:00:46

there were going to be riots and then

01:00:47

they're gonna have uh a recession I mean

01:00:51

squashed that's exactly what happened

01:00:53

both things happened I actually think I

01:00:55

have a pretty decent ability to steal

01:00:56

man pretty concretely the details I

01:00:59

think that at best when it comes to

01:01:00

things like democracy and your belief in

01:01:03

U.S exceptionalism in a specific

01:01:05

political worldview at best you strawman

01:01:08

and I think that you get very biased

01:01:09

without seeing the forest from the trees

01:01:11

the reason I said that is not because

01:01:13

I'm like you know some huge G supporter

01:01:16

I'm just trying to steal man what

01:01:17

happens when one individual person gets

01:01:21

anointed leader for life of 1.3 billion

01:01:24

people that then controls 20 of the

01:01:27

world's GDP there is no other single

01:01:30

human being as powerful as him as of

01:01:33

this month

01:01:36

can I just say that this show this show

01:01:39

is going to become insufferable if every

01:01:41

time you sort of said something in the

01:01:43

past that was sort of correct we're

01:01:45

gonna have to replay it

01:01:48

keep playing that one for you sacks who

01:01:51

nailed it I was giving that was a

01:01:52

softball to you actually every week Jay

01:01:54

Cal all right this Show's gonna slow

01:01:56

down if we play every clip that I got

01:01:58

right you guys are asking to pull Clips

01:02:00

all the time I just pulled one clip

01:02:01

about China where you nailed it no no

01:02:03

look I I think like and also it's the

01:02:06

king of pull a clip the the evergrant

01:02:07

thing look what he did this week they

01:02:09

said okay you know what the real estate

01:02:11

industry can now issue secondary stock

01:02:13

sales raise equity and equitize

01:02:15

themselves so they're going to find a

01:02:17

soft Landing for the equity part up for

01:02:19

the real estate industry in China and

01:02:21

now they're reopening so I don't know I

01:02:24

mean like I'm not sure what we're

01:02:25

supposed to comment what I'll what I

01:02:27

will stand by is what I said which is I

01:02:29

don't think

01:02:30

we have a very clear view about what's

01:02:32

going on what the substance of these

01:02:34

protests are and what people actually

01:02:36

want if you're only consuming U.S media

01:02:39

and so if you find a way to get a diet

01:02:42

from a bunch of different sources all

01:02:44

around the world you maybe get a better

01:02:45

sense I had an accidental window into

01:02:48

that by being in a completely different

01:02:50

part of the world this past week

01:02:51

Freeburg any final thoughts on China and

01:02:54

what's going on there before we move on

01:02:55

to chat GPT

01:02:57

your favorite story I think one of the

01:02:59

things we often miss is that China the

01:03:02

CCP

01:03:04

does have their hand on the throttle

01:03:08

like big throttle up and down

01:03:11

we always think that it's a linear line

01:03:13

and that it's super dogmatic and fixed

01:03:16

but there's certainly responsiveness and

01:03:17

the release of the lockdowns in

01:03:19

Guangzhou and Beijing this week seems to

01:03:22

have been a pretty good indication that

01:03:24

when things do get when when things when

01:03:26

the tides do do change

01:03:28

leadership there seems to respond not

01:03:30

always but enough to kind of keep things

01:03:33

going so should they reopen I think it's

01:03:36

60 of the population or so is vaccinated

01:03:38

with obviously vaccines that maybe

01:03:40

aren't as don't have the same efficacy

01:03:43

as the ones uh here in the United States

01:03:45

do you think they should open up and

01:03:47

just let it rip uh or do you think they

01:03:49

should stay still try to maintain this

01:03:51

zero covet policy because that is the

01:03:53

debate right now what's what's the

01:03:56

objective because obviously asking you

01:03:58

yeah well from the objective of economic

01:04:00

growth they need to open up and they

01:04:01

need to keep their economy working and

01:04:02

they need to keep their labor force

01:04:04

engaged

01:04:05

or else they're going to continue to

01:04:06

suffer so if economic growth

01:04:09

is the objective they need to open up

01:04:12

right if the long-term Health cost of

01:04:15

the nation balanced again against that

01:04:17

is the calculus that they're kind of

01:04:20

weighing there's probably some more

01:04:21

Nuance to that and certainly

01:04:24

um my understanding is there may be

01:04:28

a precedent setting which is hey we've

01:04:32

said that it's a zero covet policy

01:04:33

therefore we have to hold strict to it

01:04:35

hold toe the line

01:04:37

um else it looks like we're weak and so

01:04:39

there's also this you know maintaining

01:04:41

the authority of the CCP objective so

01:04:44

there's a lot of maybe competing

01:04:46

objectives right now I certainly don't

01:04:48

have a sense of how they're they're

01:04:49

weighing them all but but I think that

01:04:51

one once all those videos came out this

01:04:53

week yeah you guys saw them but people

01:04:55

were screaming there was an apartment on

01:04:57

fire where the doors were locked with

01:04:59

steel beams on the on the base of the

01:05:01

building at least that's what the the

01:05:03

video said I don't know how much truth

01:05:04

there is to that but that's what was

01:05:05

said and clearly people are extremely

01:05:08

distraught and unhappy with the

01:05:09

conditions of the lockdown

01:05:11

at some point enough people with enough

01:05:14

loud voices

01:05:17

you know something's going to change I

01:05:18

mean let's just remember like the

01:05:20

bargain that struck in that country and

01:05:22

with all countries is that you know the

01:05:25

citizenry to some extent

01:05:27

are willing to tolerate

01:05:30

their government so long as their

01:05:33

conditions continue to improve and

01:05:35

there's a bargain there's some bargain

01:05:36

that struck and as soon as that bargain

01:05:38

starts to go south for for the citizenry

01:05:42

then that that that governing entity is

01:05:45

at risk and I think that that's what we

01:05:47

sort of started to see this week was the

01:05:49

conditions are getting far far worse and

01:05:51

far less livable for so many people in

01:05:54

that country that the government had to

01:05:56

shift

01:05:57

do you think chamoth this uh covet

01:06:00

strategy and then we'll move on was

01:06:03

basically Xi Jinping wanting to get to

01:06:05

that Congress his coronation and now

01:06:07

that that's over

01:06:08

maybe he can change gears and then like

01:06:11

I said I my belief is that I have a very

01:06:14

poor access to enough data to have a to

01:06:17

steal man

01:06:19

um what is actually going on there but

01:06:21

one explanation could actually be that

01:06:23

in the absence of enough Hospital

01:06:26

infrastructure and ventilators and a

01:06:28

bunch of these other things they had to

01:06:30

take a pretty severe

01:06:32

approach to this disease

01:06:36

I don't know what they know or didn't

01:06:38

know maybe they understood

01:06:40

you know the virulence of it maybe that

01:06:43

they have a slightly different aging

01:06:45

characteristics of their population

01:06:46

maybe they genetically responded to the

01:06:48

SARS kovi II virus I don't know any of

01:06:51

these things enough to tell you Jason

01:06:52

yeah but the reality is what freebrook

01:06:55

says is Right which is that you cannot

01:06:57

grow an economy if people are inside

01:06:59

locked in their apartments and it looks

01:07:01

like they have decided that that's

01:07:04

coming to an end and they're going to

01:07:06

you know

01:07:08

deconstruct all of these things so you

01:07:11

know the the Chinese growth engine is

01:07:13

coming back and I think that that's

01:07:15

going to be an important factor

01:07:17

economically that we're going to have to

01:07:18

figure out

01:07:20

because it's going to have a huge

01:07:22

implication to American growth and

01:07:24

American inflation

01:07:26

sax if uh in fact the if the lab League

01:07:30

theory is correct China might have some

01:07:32

insights into this

01:07:33

disease that maybe the West didn't maybe

01:07:37

that plays into their policy a bit any

01:07:40

follow-ups here Jacob I'm just surprised

01:07:42

to hear that you have a problem with

01:07:43

their lockdown uh policy over there

01:07:46

because aren't they just implementing

01:07:48

Democratic party Orthodoxy

01:07:50

I mean isn't this the policy that Tony

01:07:53

fauci and Barbara Ferrer

01:07:56

I was not in favor of lockdowns what are

01:07:58

you talking about you're the one who had

01:07:59

all your masks and had ventilators day

01:08:00

one isn't this uh

01:08:03

why am I getting us because

01:08:06

I'm just asking a question as the

01:08:08

moderator isn't this basically why am I

01:08:11

getting a stray isn't this I'll answer

01:08:14

the question yes

01:08:18

I was in favor if people wanted to stay

01:08:21

home stay home and then if people wanted

01:08:22

to go out and take the risk take the

01:08:23

risk I was always in but it wasn't this

01:08:26

what Gretchen Whitmer in Michigan and

01:08:28

Gavin Newsom in California subscribe to

01:08:30

the idea that the way to fight covid was

01:08:33

through lockdowns now yes Newsom had 10

01:08:36

pages of exceptions for his political

01:08:37

donors and he didn't use the police to

01:08:40

lock people in apartment buildings he

01:08:42

may have wanted to but they didn't

01:08:44

actually do that but can you really tell

01:08:45

me that this lockdown policy has been

01:08:48

disavowed by people like fauci or by the

01:08:52

the health authorities like the Barbara

01:08:53

farrers of this world they still

01:08:55

subscribe to this view do they really is

01:08:58

anybody doing show me well they're not

01:09:00

able to do it because no one agrees

01:09:02

anymore yeah but tell me tell me where

01:09:04

anybody tell me where any of the health

01:09:07

experts who said that lockdowns were the

01:09:08

correct response have repented and

01:09:10

disavowed that view

01:09:12

yeah I don't know I haven't been I

01:09:14

haven't been tracking their uh

01:09:15

mayakopa's you yourself were in favor of

01:09:18

lockdowns period of time yes you were no

01:09:21

you absolutely were out there was a mass

01:09:23

mandate no it's not true I was in favor

01:09:25

of a mass mandate and I said the mass

01:09:27

mandate was the alternative to lockdowns

01:09:30

okay I was saying that by May of 2020.

01:09:32

all right listen let's move on to the

01:09:34

next thing I don't represent I'm an

01:09:35

independent I don't represent the

01:09:37

Democratic party I don't represent fauci

01:09:39

no you wrote the media I do not

01:09:42

represent mainstream media you just said

01:09:44

I was an old school journalist who's

01:09:46

mortified with where it is today I'm

01:09:47

giving you a hard time I thought your

01:09:50

explanation was fabulous oh thank you

01:09:52

thank you obviously Jacob I'm giving you

01:09:53

a hard time too I know that you were not

01:09:55

a big lockdown proponent but you

01:09:57

understand the point I was making would

01:09:59

you understand your points making me the

01:10:02

Democratic spokesperson no I'm feeling a

01:10:05

lot better after my beer let's talk

01:10:07

about

01:10:08

come on so listen let's do a little talk

01:10:11

about open AI all right open AI is a a

01:10:14

company that builds

01:10:18

artificial intelligence software and

01:10:20

platforms they have one platform called

01:10:22

GPT it is on its third version as part

01:10:26

of gpt3 they created chat GPT which is a

01:10:30

chat interface where you can ask

01:10:32

questions to AI the results are nothing

01:10:35

short of stunning when they hit some of

01:10:38

them are a little bit mixed but Freeburg

01:10:41

has spent the last 48 hours

01:10:43

uh drinking white Russians with Oatley

01:10:46

milk and playing with chat GPT including

01:10:50

his question to the chat GPT which was

01:10:52

write a script of chemath sax and J Cal

01:10:55

uh talking about the future of AI in the

01:10:58

style of a Quentin Tarantino movie and I

01:11:01

have to say it was pretty great the

01:11:04

result

01:11:04

go ahead uh freeberg tell us what you

01:11:07

described I think you guys should read

01:11:08

this real quick we can cut it out if

01:11:10

it's stupid but okay actually read the

01:11:12

script yeah the Quentin Tarantino part

01:11:14

right I mean I think it was like

01:11:16

impressive but it there's no f-bombs

01:11:17

there's no like Tarantino s dialogue and

01:11:19

by the way

01:11:21

they are they took they took out all

01:11:23

cursing and violent cussing and violence

01:11:24

from open AI that explains it because

01:11:26

this is a Tarantino script there would

01:11:28

be some Choice languages but it's

01:11:30

interesting it's like the PG Tarantino

01:11:32

script because okay no violence

01:11:36

yeah you do the narrative go ahead

01:11:39

[Music]

01:11:45

Jackson California sitting at a dimly

01:11:48

lit table in a smoky bar a bottle of

01:11:50

whiskey sits in the center of the table

01:11:52

half empty Jamal's leaning forward his

01:11:54

eyes intense so what do you guys think

01:11:57

about this chat GPT thing David sacks

01:12:00

takes a sip of his drink

01:12:01

it's impressive no doubt about it a

01:12:03

large language model trained by open iei

01:12:05

that can generate human-like texts in a

01:12:07

variety of styles and formats as a game

01:12:08

changer

01:12:09

Jason nods definitely it's one of the

01:12:12

most advanced AI systems we've seen to

01:12:14

date it's going to have a huge impact on

01:12:16

the way we use and interact with AI to

01:12:19

moth frowns

01:12:21

that's what I'm worried about with a

01:12:22

technology like this the possibilities

01:12:24

are endless but so are the potential

01:12:26

dangers

01:12:27

sacks leans back in his chair

01:12:29

oh come on chamoth you're not still

01:12:31

stuck on that whole AI is going to kill

01:12:33

us all thing are you

01:12:34

timoth leans forward again his voice low

01:12:37

and serious look I'm not saying that

01:12:40

Chad GPT is going to turn into Skynet

01:12:43

overnight

01:12:44

but we need to be careful about the way

01:12:46

we develop and deploy AI we need to make

01:12:49

sure that we're using it for good and

01:12:51

not just for our own game

01:12:53

J Cal nods I agree we need to make sure

01:12:57

that we're responsible in a way we use

01:12:59

Ai and that includes making sure it's

01:13:02

accessible and affordable to everyone

01:13:03

not just a select few

01:13:05

to moth with a dirty smirk

01:13:08

and let's not forget about the

01:13:17

impulse records calling cut oh God

01:13:20

that's it scene yeah we're not gonna

01:13:23

finish it it's not that far off I mean

01:13:25

it's 60 on the way there

01:13:27

I actually they're they're uh Biden for

01:13:30

sacks if you blame Biden it would have

01:13:32

been perfect let me tell you guys

01:13:34

something stunning about this

01:13:35

um this platform so this is GPT 3.5

01:13:38

which is an interim model to the what

01:13:41

people are saying is the long-awaited

01:13:43

GPT 4.0 model

01:13:45

um which I think they announced in 2020

01:13:47

and has been in development for some

01:13:49

time so the model this GPT 3.5 model was

01:13:53

trained in three steps they do a great

01:13:55

job explaining it on the openai blog

01:13:58

site

01:13:59

where they collect some some data and

01:14:02

then there's a supervised model meaning

01:14:03

that there are humans that are involved

01:14:05

in tagging and then the model kind of

01:14:07

you know learns from from that system

01:14:09

then you ask the model questions you get

01:14:11

output and then humans rank the output

01:14:13

and so the model learns through that

01:14:15

ranking system and then there's kind of

01:14:17

this third optimization thing and then

01:14:19

it's fine-tuned so the the model itself

01:14:22

um has several steps of kind of human

01:14:23

involvement and you know kind of it

01:14:26

sources its own data and and builds it

01:14:28

you know what's incredible about this

01:14:29

model the total size of the software

01:14:32

package that runs the model is about 100

01:14:34

gigabytes isn't that amazing like you

01:14:36

could fit this model on probably what 20

01:14:39

of the storage space on your iPhone and

01:14:42

you could run this thing and you could

01:14:43

probably just talk to it for the rest of

01:14:45

your life

01:14:46

um and it's really kind of an incredible

01:14:48

Milestone but I think what was so

01:14:50

stunning to me about this I I know you

01:14:52

guys are probably expecting something to

01:14:55

be said like this but

01:14:56

you could see

01:14:58

so many human knowledge worker roles and

01:15:03

and and functions being replaced by this

01:15:06

extraordinary interface so kids can do

01:15:07

homework that's easy uh software

01:15:10

Engineers can get their code optimized

01:15:12

and can get their code written for them

01:15:13

there's great examples of how software

01:15:15

code has been written uh by this

01:15:17

interface you could see real estate

01:15:19

insurance sales people

01:15:22

being replaced by some sort of software

01:15:24

like interface writers copywriters you

01:15:27

know make me a hundred versions of a

01:15:30

commercial or an ad customers customer

01:15:33

support completely replaced right if you

01:15:35

guys remember there were these automated

01:15:36

customer support companies that started

01:15:38

uh you know two decades ago and there

01:15:41

was this great flurry all BPO businesses

01:15:43

were all about lower cost human labor

01:15:44

now the cost of human labor goes to zero

01:15:46

my prediction which is so everyone's got

01:15:50

the obvious prediction which is there's

01:15:51

going to be a hundred thousand startups

01:15:53

that are going to emerge I mean this is

01:15:54

kind of like this moment where the

01:15:55

internet came along and everyone's like

01:15:57

this changes everything I do think

01:15:58

everyone thinks and feels that so the

01:16:00

obvious next step is a bubble wool form

01:16:02

so can I ask a technical question though

01:16:05

Freeburg um and then uh you're probably

01:16:07

thinking the same thing I just finished

01:16:09

my market prediction but I think because

01:16:11

everyone's so hyped about this and and

01:16:13

we all know this it'll be over fun PC

01:16:15

attention all the investor attention is

01:16:17

Shifting to this capability and how do

01:16:19

you apply this sort of capability across

01:16:21

all of these different Industries and

01:16:23

all these different applications and as

01:16:24

a result my guess is the next hype cycle

01:16:26

the next bubble cycle in Silicon Valley

01:16:28

will absolutely be this generative AI

01:16:31

business okay but this is a little

01:16:33

technical but how would it know the

01:16:35

difference between like

01:16:37

y-o-u-r and you are

01:16:39

when it is processing natural language

01:16:42

if you were to do like your anus or

01:16:45

Uranus how would that

01:16:47

Freeburg how would it know the

01:16:48

difference between Uranus and your space

01:16:50

anus it'll it'll learn that you know

01:16:54

it was a joke

01:16:58

yeah yeah I probably would have made a

01:17:00

better joke than that I would have made

01:17:01

a better joke for sure in our group chat

01:17:04

and said can I do an insurance like

01:17:05

jaycal and they were terrible so at

01:17:07

least

01:17:08

AI to pretend you're the all-in Pod

01:17:11

besties telling Uranus jokes

01:17:14

sorry let me just say one more thing

01:17:16

about this open AI thing I I do think

01:17:18

that the biggest and most interesting

01:17:20

um thing to think about

01:17:22

is how this will

01:17:24

um disrupt the search box the the search

01:17:26

you know the way search works at Google

01:17:28

you know an internet search is there are

01:17:31

these kind of servers these web crawlers

01:17:34

that go out and gather data someone

01:17:35

starts your data feeds and some of them

01:17:37

are just crawlers and then that data is

01:17:40

indexed or in in the structured way it's

01:17:42

kind of made available for for serving

01:17:44

directly on the search page

01:17:47

and so much of that is is indexing so I

01:17:50

search for a bunch of keywords those

01:17:51

keywords and perhaps there's some

01:17:53

natural language contexts are matched to

01:17:55

a result page and I click on that and

01:17:57

it's linked out years ago Google started

01:17:59

a product called the one box where they

01:18:01

could take structured data like what is

01:18:03

the weather in San Francisco today and

01:18:05

that the top of the search result page

01:18:07

just presented that data because it

01:18:08

knows with high certainty the question

01:18:10

you're asking and it knows with high

01:18:11

certainty the answer it can give you

01:18:13

yeah Clifton from somebody's website

01:18:15

right so if that starts to become

01:18:17

everything then that one box interface

01:18:20

and it's not just Google's ability to

01:18:22

access all this data and index it and

01:18:25

serve it and store it there could be a

01:18:27

lot of competitors to the one box and a

01:18:29

lot of competitors ultimately to search

01:18:32

um and ultimately you know Google's core

01:18:34

product their their search engine could

01:18:37

be radically disrupted by an alternative

01:18:40

set system or set of systems that have

01:18:43

more of a natural language chat

01:18:44

interface which um and I literally which

01:18:46

is literally why Google bought deepmind

01:18:50

and there were a collection of

01:18:51

human-powered search engines Mahalo

01:18:53

included Cha-Cha answers.com who are

01:18:55

trying to do the human based version of

01:18:57

this it just didn't scale we don't want

01:18:58

to get ahead of ourselves because one of

01:19:00

the things we don't know is how much is

01:19:02

going on in deepmind they're they're not

01:19:04

very open like open AI is they talk

01:19:06

about some of the advanced Frontier

01:19:07

stuff like um Alpha fold and so on and

01:19:09

they've been public about that but a lot

01:19:10

of that is really to generate interest

01:19:12

in hype and what's next but my

01:19:15

understanding is deepmind's been applied

01:19:16

to everything from ads ad ad

01:19:19

optimization but also the ranking on

01:19:22

YouTube videos to get people more

01:19:23

engagement on YouTube Etc et cetera so

01:19:25

there's all these ways that deepmind's

01:19:26

been applied within Google services that

01:19:28

we don't certain and certainly within

01:19:30

search yeah but the question is is there

01:19:32

an entirely new interface for search yes

01:19:35

that risks Google's course search

01:19:37

business

01:19:38

and I think that there certainly will be

01:19:40

a lot of money thrown at this and if

01:19:42

anyone has any interesting ideas send me

01:19:44

an email sax and then chamoth yeah

01:19:46

that's a really interesting point I saw

01:19:48

a thread on this where somebody was

01:19:52

asking

01:19:54

GPT you know a bunch of questions like

01:19:56

they were like generally like coding

01:19:58

questions and they were actually

01:19:59

comparing the result in Google versus

01:20:01

GPT and Google would just give you a

01:20:04

reference to like a link to some page

01:20:06

whereas gpt3 would actually construct

01:20:08

the answer like a multi paragraph answer

01:20:11

that was far more detailed and in a way

01:20:14

user friendly yeah whereas like the

01:20:17

Google page would kick you over to a

01:20:19

reference where it was like this one two

01:20:21

three sort of maybe someone who created

01:20:22

a checklist but it just wasn't that

01:20:24

detailed it really is pretty interesting

01:20:26

I thought um Andreessen tweeted a really

01:20:30

interesting example as well where he

01:20:33

asked GPT to create a seen from a play

01:20:38

starring a New York Times journalist in

01:20:40

a Silicon Valley Tech entrepreneur they

01:20:42

were arguing about free speech in each

01:20:43

passing asserts the view associated with

01:20:46

his profession and Social Circle we

01:20:48

don't need to read the whole thing but I

01:20:49

thought this was like spot on where I

01:20:51

was actually like both sides are making

01:20:54

their best arguments and it's like to

01:20:57

each other in a conversation that seems

01:20:58

intelligible like they're making their

01:21:00

points at the right

01:21:02

time in the conversation it's like

01:21:04

they're playing off each other in other

01:21:06

words it actually reads like a

01:21:07

conversation I actually thought this one

01:21:09

was more impressive than the one with

01:21:11

the bestie impersonation because I agree

01:21:13

I actually thought that the one about

01:21:15

all in didn't really capture our

01:21:17

personalities per se but this one

01:21:19

actually does a pretty good job

01:21:21

capturing the arguments in this debate

01:21:23

so pretty impressive any thoughts here

01:21:26

yeah lots I mean I've been spending a

01:21:28

lot of time learning about this area six

01:21:31

years ago a team that I

01:21:34

partnered with who was at Google that

01:21:36

built TPU we've been building silicon

01:21:38

for this space so we've been kind of

01:21:39

going from the ground up for the last

01:21:40

six years a couple things that I'll say

01:21:43

the first is that I think

01:21:45

we're going to replace SAS

01:21:48

with what I call Mass which is models as

01:21:51

a service and so you know a lot of what

01:21:54

software will be particularly in the

01:21:55

Enterprise will get replaced with a

01:21:57

single use model that allows you to

01:21:59

solve a function so these chat examples

01:22:02

are one and you can name a bunch of SAS

01:22:04

companies that were purveyors of SAS

01:22:07

that'll get replaced by essentially gpt3

01:22:10

or some other language model

01:22:12

and then there'll be a whole bunch of

01:22:14

other things like that if it's a you

01:22:16

know expense management company they'll

01:22:18

have a model that'll allow them to

01:22:20

actually do expense management or blah

01:22:22

blah blah forecasting better so I think

01:22:26

SAS will get replaced over time with

01:22:28

these models incrementally that's phase

01:22:30

one

01:22:31

but the problem with all of these models

01:22:33

in my opinion is that they're still

01:22:34

largely brittle they

01:22:37

are good at one thing they are a single

01:22:40

mode way of interfacing with data

01:22:43

the next big leap and I think it will

01:22:46

come from one of the big tech companies

01:22:48

or from openai is and we talked about

01:22:51

this I talked about this a few episodes

01:22:52

ago a multimodal model which then allows

01:22:55

you to actually bring together and join

01:22:58

video voice data in a unique way to

01:23:02

answer real substantive problems so if I

01:23:05

had to steal man the opposite side

01:23:07

reaction so I think there's a lot of

01:23:09

people gushing over the novelty of gpt3

01:23:11

if I had to or chat GPT if I had to if I

01:23:14

had to steal man the opposite what I

01:23:16

would say is it's gonna get

01:23:18

somewhere between 95 to 99 percent of

01:23:21

all of these very simple questions right

01:23:24

because they're kind of cute and simple

01:23:25

there is no consequence of saying write

01:23:28

a play because there is no wrong answer

01:23:30

right you either kind of it it it

01:23:32

tickles your fancy or it doesn't it kind

01:23:34

of entertains you or it doesn't

01:23:37

when this stuff becomes very valuable is

01:23:39

that when you really need a precise

01:23:41

answer and you can guarantee that to be

01:23:44

overwhelmingly right that's the last one

01:23:46

to two percent that is exceptionally

01:23:48

hard

01:23:49

and I don't think that we're at a place

01:23:51

yet where these models can do that but

01:23:53

when we get there

01:23:55

all of these models as a service will be

01:23:58

very much commoditized and I think the

01:24:01

real value is finding

01:24:04

non-obvious sources of data that feed it

01:24:07

so it's all about training so meaning

01:24:10

you can break down machine learning and

01:24:12

AI into two simple things there's

01:24:13

training which is what you do

01:24:15

asynchronously and then there's

01:24:17

inference which is what you're doing in

01:24:18

real time so when you're typing

01:24:20

something into chat API or a chat GPT

01:24:23

that's an inference that's running and

01:24:25

then you're generating an output but the

01:24:27

real key is where do you find

01:24:28

proprietary sources of data that you can

01:24:32

learn on top of that's the real arms

01:24:35

race so one example would be let's say

01:24:38

you build a model to detect tumors

01:24:41

right there's a lot of people doing that

01:24:43

well the company that will win may be

01:24:46

the company that actually then

01:24:47

vertically integrates buys a hospital

01:24:49

system and get access to Patient data

01:24:52

that is completely proprietary to them

01:24:55

and covers the most number of women of

01:24:57

all age groups and of all ethnic you

01:24:59

know ethnic categories

01:25:01

those are the kinds of moves in business

01:25:03

that we will see in the next five to ten

01:25:05

years that I find much more exciting and

01:25:07

trying to figure out how to play in that

01:25:10

space But I do think that chat gbt is a

01:25:12

wonderful example

01:25:14

to point us in that direction but I'm

01:25:17

sort of more of that case which is it's

01:25:19

a cute toy

01:25:20

but we haven't yet cracked the one to

01:25:22

two percent of use cases that makes it

01:25:24

super useful but I think the first step

01:25:26

but just sorry but the first step will

01:25:28

be the transformation of SAS to Mass

01:25:31

and then from there we think we can try

01:25:32

to figure this out it reminds me of in a

01:25:34

way when you when you give that

01:25:35

description of like hey this is really

01:25:37

interesting but it's not complete is

01:25:39

remember when GPS came out and like

01:25:41

people were like doing turn-by-turn

01:25:42

navigation they drive off the road

01:25:44

because they were trusting it too much

01:25:45

and then you know over 20 years of GPS

01:25:48

we're kind of like yeah it's pretty

01:25:49

bulletproof but keep your eyes on the

01:25:51

road same thing that's happening uh and

01:25:53

and these changes these last these last

01:25:56

hundreds or 200 basis points literally

01:25:57

takes decades exactly so the last 15 of

01:26:00

self-driving is like the decade-long you

01:26:02

know sloth let me take a century 15 may

01:26:04

take a century but the last two percent

01:26:06

will take a few it's like the change

01:26:07

happens very slowly and then all at once

01:26:09

for people who don't know what a TPU is

01:26:11

that's a tensor processing unit this is

01:26:13

Google's um application specific

01:26:16

circuits right and custom silicon that

01:26:18

they invented for tensorflow at the time

01:26:20

yeah so if you want to try it although

01:26:21

now the modality of AI we've changed

01:26:23

that as well so now we're totally in the

01:26:24

world of Transformers so we're not even

01:26:26

using

01:26:27

you know you're not letting the tensors

01:26:29

float the way they used to all right

01:26:31

there's been a Slowdown in SAS sax uh

01:26:33

what is happening in the software as a

01:26:35

service World there was a good update by

01:26:38

uh jam and ball who works for Altima our

01:26:40

friend Brad gerstner he does these

01:26:41

really great updates on what's happening

01:26:43

in the SAS world the big thing this week

01:26:45

is that Salesforce had its quarter and I

01:26:48

would consider Salesforce to be the

01:26:49

Bellwether for the whole SAS category I

01:26:51

think they're the largest pure SAS

01:26:54

company they were the first multi-tenant

01:26:56

SAS like company at scale and what

01:26:59

they've shown is a huge slowdown

01:27:02

basically their net new ARR that they

01:27:05

just added in the previous quarter

01:27:07

dropped two-thirds compared to the

01:27:10

previous quarter but because their sales

01:27:12

and marketing spend was the same as the

01:27:14

previous quarter it exploded their CAC

01:27:17

payback which means the amount of time

01:27:19

it takes to pay back your customer

01:27:21

acquisition costs for a given customer

01:27:23

so you see there are 155 months it would

01:27:27

take now to pay back the customer

01:27:29

acquisition costs that's over 10 years

01:27:32

that doesn't work I mean I think before

01:27:34

this quarter it was more like

01:27:36

two and a half years that's something

01:27:38

that you can afford a company can't you

01:27:41

know if you're spending 10 years of you

01:27:43

know gross profit on a customer to

01:27:45

acquire them the business doesn't make

01:27:46

sense so now I'm not saying any of this

01:27:49

to pick on Salesforce it's an

01:27:50

exceptionally run company you know one

01:27:54

of the absolute best uh Mark benioff

01:27:56

fantastic CEO founder great human being

01:27:59

but I think the point here is that what

01:28:01

you're seeing is the whole SAS industry

01:28:05

is really slowing down here in the first

01:28:07

half of the year you saw SAS valuations

01:28:10

correct now we're actually seeing SAS

01:28:13

Top Line correct and you know there's an

01:28:16

interesting question here if your CAC

01:28:18

payback goes from two and a half years

01:28:19

to ten years you have to bring your CAC

01:28:21

down how do you do that you can either

01:28:23

reduce marketing or you can reduce sales

01:28:27

so in other words you can reduce me cut

01:28:28

the sales team you can either cut people

01:28:31

and head count from your own team or you

01:28:33

can cut spending you do on advertising

01:28:36

or events or money that you spend on

01:28:39

other companies either way there's going

01:28:41

to be a big contraction in jobs

01:28:44

basically around this industry and I

01:28:46

think that what that could do is cause a

01:28:49

vicious cycle where that's we start

01:28:52

seeing I wouldn't say death spiral I

01:28:54

think this vicious cycle for the next

01:28:55

year or so where seat contraction

01:28:57

becomes the norm instead of seat

01:29:00

expansion so if you go back over the

01:29:02

last 10 years a major Tailwind at the

01:29:05

backs of SAS startups has been that

01:29:07

every year year you start with 120 130

01:29:11

150 percent of last year's Revenue just

01:29:13

from your existing customers why because

01:29:15

they were hiring more and more people

01:29:17

and they needed to buy more and more

01:29:18

seats but now head count growth is

01:29:21

frozen and in fact companies are doing

01:29:23

major layoffs so the Baseline for next

01:29:25

year year could be seek contraction so

01:29:28

instead of starting with 120 of last

01:29:29

year's Revenue you might start with 80

01:29:31

or 90 percent because there's going to

01:29:33

be so much churn so I think that you

01:29:35

know SAS companies need to take this

01:29:37

into account this idea that like growth

01:29:40

is on autopilot that could start to go

01:29:41

in reverse I don't think permanently but

01:29:43

I think for the next year or so this is

01:29:45

why I also I tweeted you know two x's is

01:29:47

the new 3x if you can grow 2x year over

01:29:49

year and this Incarnation that comes as

01:29:52

good as or better than growing 3x last

01:29:54

year clearly it's clearly better like a

01:29:57

lot of companies that weren't that great

01:29:58

could grow 3x last year because it was

01:30:01

so times were so frothy everyone was

01:30:02

buying everything but now it is going to

01:30:04

be really hard to even double year over

01:30:06

year companies need to take that into

01:30:08

account into their financial planning uh

01:30:11

you need to restrain your burn because a

01:30:13

lot of the revenue that you predict is

01:30:14

going to be there may not be there

01:30:17

all right uh thanks so much to the

01:30:19

Secretary of SAS I think you got a board

01:30:22

meeting I did I gotta run one of the

01:30:23

interesting things I saw in terms of use

01:30:24

cases is somebody used the chat GPT to

01:30:28

describe rooms then they took the

01:30:30

descriptions of those rooms and then

01:30:31

they put them into like dolly or stable

01:30:33

diffusion one of those and it created

01:30:35

the visual I'm curious if you'd think

01:30:37

you know the self-driving apis uh and

01:30:40

machine learning that's going on then

01:30:43

you got images then you got chat maybe

01:30:45

you have proteins going on with the

01:30:47

alpha fold stuff

01:30:48

when these things start talking to each

01:30:50

other is that going to be the emergent

01:30:53

behavior that we see of General Ai and

01:30:56

that's how we'll interpret it in our

01:30:58

world is these hundred different

01:31:00

vertical AIS uh hitting some level of

01:31:04

reasonableness to chamat's point on data

01:31:06

sets and then all of a sudden the

01:31:08

self-driving AI is talking to the one

01:31:10

that's looking at cancer and tumor

01:31:12

diagnosis in the chat and the image ones

01:31:14

it may be stable diffusion the protein

01:31:16

Ai and the one that's looking at cancer

01:31:19

cells start talking to each other yeah

01:31:21

I'm not sure that's as likely as the

01:31:25

there's a lot of solutions that will

01:31:28

emerge

01:31:29

within verticals and I think you can

01:31:32

distinguish them so I kind of gave this

01:31:34

example a few months ago

01:31:37

if you remember Kai's power tools was a

01:31:39

plug-in for Adobe Photoshop came out in

01:31:42

1993 I believe of course and Kai's power

01:31:44

tools completely transformed the

01:31:47

potential of Adobe Photoshop because

01:31:48

Photoshop had all the basic brushing and

01:31:50

editing capabilities within it Kai's

01:31:53

power tools with statistical models that

01:31:55

basically took the Matrix of the pixels

01:31:58

and you know created some evolution of

01:32:01

them into some visual output like a blur

01:32:04

and so you could blur motion blur or

01:32:06

something and you could change the

01:32:07

parameters and now your photo looked

01:32:09

like it was going through a motion blur

01:32:10

ultimately Photoshop bought and

01:32:12

implemented those tools but those were

01:32:14

similar they were statistical models

01:32:16

that made some representation of the

01:32:18

input which was the image and then

01:32:20

created an output which was an adjusted

01:32:21

image I would argue that that is very

01:32:24

similar although the models behind it

01:32:27

are very different in terms of the

01:32:29

contextual application of statistical

01:32:32

models in software and you could see

01:32:35

stuff like for example a chat bot that

01:32:38

replaces helped me figure out whether my

01:32:41

credit card charges are correct or not

01:32:43

instead of having a customer service

01:32:45

agent an offshore customer service agent

01:32:47

helping you resolve that or help me

01:32:49

return my item or there are very

01:32:52

specific kind of verticalized

01:32:54

applications that can plug in

01:32:57

that ultimately replaced what was manual

01:32:59

and human driven before because humans

01:33:01

used to manually make the motion blur in

01:33:03

Photoshop and then it was automated with

01:33:05

the software packages and I think you

01:33:07

can kind of think about it in that same

01:33:08

way that these These are known nodes

01:33:10

they don't require necessarily a human

01:33:13

physical labor or some you know human

01:33:16

responsiveness that if 95 of the work

01:33:18

can be handled it will get handled by

01:33:20

some verticalized solution so I think

01:33:23

the physical labor versus the

01:33:24

non-physical labor is one way to think

01:33:26

about the distinction meaning is there

01:33:27

some change in the physical world

01:33:28

driving is absolutely a change in a

01:33:31

physical world you have to move

01:33:32

physically through space so that one is

01:33:34

a very distinct class all the stuff

01:33:37

that's like communication imagery static

01:33:40

imagery audio and then visual video

01:33:43

there's some stacking that happens there

01:33:46

and some of those will be kind of siled

01:33:48

and then some of them will merge and

01:33:50

you'll have these kind of unique kind of

01:33:51

combo models

01:33:52

and so look as they start to work

01:33:54

together I think we'll see them

01:33:56

you know completely rewrite some of

01:33:58

these verticals like movie production or

01:34:01

music production right or advertising or

01:34:04

we're seeing it now with with video and

01:34:06

and creative arts uh with um

01:34:09

you know some of the the visual stuff on

01:34:11

opening to be honest a lot of Journalism

01:34:12

a lot of creative arts have become the

01:34:14

wisdom of the crowds over the last two

01:34:16

decades where you know artists were

01:34:19

looking at the collective works of the

01:34:21

internet interpreting it and then coming

01:34:24

up with content which is kind of what

01:34:25

these AIS are doing and then who legally

01:34:28

owns the collective content is going to

01:34:31

be a big question chamoth you talked

01:34:33

about data sets you know Microsoft is

01:34:35

being sued right now and GitHub because

01:34:37

they used open source to create Tools in

01:34:41

AI to help augment programmers like

01:34:44

rather programming and writing code it

01:34:45

gives them suggestions and now the open

01:34:47

source Community is suing them for using

01:34:49

their data set so what do you think

01:34:50

about the legality of data sets tremoth

01:34:52

and should they get

01:34:54

some kind of protection if you make a

01:34:56

gpt3 based on quora or based on

01:34:58

Wikipedia should you have to get

01:35:00

approval to use that data what look at

01:35:03

the exact opposite yeah it's it's the

01:35:05

exact opposite they say that this is

01:35:07

actually your work

01:35:09

um and I think that that's the right

01:35:10

legal framework but the the answer to

01:35:12

your other question is this is why I

01:35:14

think the hunt for proprietary data

01:35:16

actually becomes the hunt that matters

01:35:19

all of this other stuff I think is a lot

01:35:21

less important because I think you have

01:35:23

to assume that all of these models will

01:35:25

eventually just get commoditized so

01:35:27

they'll be there'll be a you know like

01:35:28

you see like Jasper Ai and you see a

01:35:30

bunch of these generative AI companies

01:35:32

it's really interesting

01:35:33

but the problem is when you sit it on

01:35:35

top of the same substrate you'll have a

01:35:37

convergence

01:35:38

right everybody's chat model will

01:35:40

eventually look and sound and feel like

01:35:41

the same thing unless you're giving it a

01:35:45

few special ingredients that other

01:35:47

people are not and so it's the hunt for

01:35:49

those ingredients that will make this

01:35:51

next generation of of models really

01:35:54

valuable so to give it an example you

01:35:56

would have Wikipedia which is Creative

01:35:58

Commons anybody can use but Cora as a

01:36:00

data set not everybody can use that's

01:36:01

owned by a company would have an

01:36:04

advantage take an extreme example if

01:36:06

quora didn't allow themselves to be

01:36:09

crawled right okay

01:36:13

but then and then they develop their own

01:36:15

language model which used the best of

01:36:18

the internet so call it you know GPT and

01:36:22

quora maybe they are slightly better in

01:36:25

certain domains than others the The

01:36:27

Other Extreme example is the one that I

01:36:29

used in healthcare which is you know if

01:36:31

you have access to Patient data that you

01:36:33

will not license to anybody else you

01:36:36

know it stands to reason that that model

01:36:39

actually then has much better chances of

01:36:42

Highly Effective clinical outcomes

01:36:44

versus any other model Apple watch comes

01:36:46

to mind right Apple has all that watch

01:36:49

data if they could pair that with epics

01:36:51

it's another data set example what could

01:36:54

they do together so this is going to be

01:36:55

like this is the new oil is going to be

01:36:57

data and by the way like to to to to

01:36:59

talk about Apple for a second the smart

01:37:01

thing is they've gotten so methodically

01:37:03

they've never touted the AI you know

01:37:06

they introduce one or two distinguishing

01:37:09

features every year right so like the

01:37:11

the the ECG which was introduced many

01:37:14

many years ago

01:37:15

is has only gotten slightly more usable

01:37:18

like five or six years later but in the

01:37:19

meantime there's you know tens of

01:37:21

millions of watches collecting this kind

01:37:24

of data so to your point

01:37:25

it's it's using these devices as Trojan

01:37:28

horses to collect training data that is

01:37:30

the oil Uber and Tesla have all this

01:37:34

data of the data being collected by you

01:37:37

know the well hold on so phones or the

01:37:40

cameras in the cars the other difference

01:37:42

though is that you have to be in a realm

01:37:44

where you don't need Regulators to go

01:37:47

The Last Mile so the problem with Adas I

01:37:50

think or level five autonomy is that

01:37:52

eventually you get to a point where even

01:37:54

if the model becomes quote unquote

01:37:56

perfect

01:37:58

you still need regulatory approval and

01:38:00

what I'm saying is I think you have to

01:38:02

focus on areas of the economy

01:38:04

that are not subject to that or where

01:38:06

the regulatory pathway is already

01:38:08

defined so for example if you use that

01:38:10

Health Care example let's say that you

01:38:11

had the largest Corpus of breast cancer

01:38:12

image data and you could actually build

01:38:14

an AI that was a much better classifier

01:38:16

for tumors versus other things

01:38:19

the FDA actually has a pathway to get

01:38:22

that approved very quickly the problem

01:38:24

with you know level 5 autonomy is that

01:38:26

there is no clear pathway it's not again

01:38:27

we go back to almost a crypto example we

01:38:31

don't really know who will govern that

01:38:32

decision and we don't know how that will

01:38:34

be governed so I think the the thing

01:38:37

that investors have to do and

01:38:39

entrepreneurs entrepreneurs have to pick

01:38:40

their End Market very carefully and

01:38:42

investors have to realize that this

01:38:44

Dynamic exists as well if you're going

01:38:45

to do this right imagine the Robin Hood

01:38:47

trading you know uh Trader data set

01:38:50

watching people sell and shares and then

01:38:52

predicting markets with it with AI I

01:38:54

mean it could be crazy you have that or

01:38:55

payment for order flow that's used by

01:38:57

Citadel and the other big but not AI

01:38:59

right so who knows maybe they are losing

01:39:01

it they are I I can tell you as somebody

01:39:03

who sells

01:39:05

we sell a lot of machine learning

01:39:07

Hardware into this Market

01:39:09

the biggest buyers are the US government

01:39:11

and these ultra high frequency trading

01:39:14

organizations I'll give you the final

01:39:16

word uh how could this affect astronomy

01:39:20

how could this affect you know our

01:39:22

search of the galaxies you know going

01:39:23

out past Pluto Saturn

01:39:26

breaching your anus any any of those

01:39:28

things how could it impact

01:39:31

any

01:39:33

I'm trying to get a Uranus joke to land

01:39:35

help me out there Tremont

01:39:38

I think you need to have more uh space

01:39:41

related

01:39:43

um yeah which Workshop this one with me

01:39:44

or got or gut biome related you know

01:39:47

yeah so how would this affect use the

01:39:50

promo code twist you have to trick

01:39:52

Friedberg into thinking we're asking a

01:39:53

serious question get him down the

01:39:55

science path and run pull him no that's

01:39:57

that's right use the proper rug pull

01:39:59

exactly okay let's do it here we go

01:40:00

let's work so uh tell us you know when

01:40:02

you're doing like super gut use promo

01:40:04

code twist to get 25 off

01:40:05

when you're doing super gut you're

01:40:07

analyzing people's guts

01:40:09

how would you then have machine learning

01:40:12

in this you know API uh this chat API

01:40:15

and gpt3 how could that help

01:40:17

processing all of that especially when

01:40:19

it passes through Uranus

01:40:23

forever okay over there Freeburg

01:40:26

you are hungover I'm hungover but I also

01:40:29

had like a 7 A.M board meeting so I'm

01:40:32

also just a little beat up were you

01:40:34

grumpy on the board meeting did you get

01:40:35

a little cantankerous

01:40:38

kept the rage I had my caffeine Fuel and

01:40:42

then I kind of cranked down afterwards

01:40:44

all right everybody we will see you next

01:40:47

time for the Secretary of SAS The

01:40:50

Dictator and the Sultan of hungover we

01:40:54

will see you next time bye bye love you

01:40:57

guys bye-bye

01:41:00

we'll let your winners

01:41:03

Rain Man

01:41:03

[Music]

01:41:33

it's like this like sexual tension that

01:41:36

they just need to release somehow

01:41:42

[Music]

01:41:48

[Music]

Episode Highlights

Sam Bankman-Fried's Media Strategy
Discussion on how Sam Bankman-Fried navigates media interviews to shape public perception.
“He is basically copying two criminal negligence...”
@ 03m 18s
December 03, 2022
Institutional Rot and Accountability
A deep dive into the lack of accountability in media and institutions regarding financial fraud.
“This reflects the institutional rot of America.”
@ 10m 38s
December 03, 2022
Institutional Biases
The big enabler isn't crypto, but institutional biases that protect the elite.
@ 20m 58s
December 03, 2022
Media's Role in Oversight
The media failed to investigate crucial connections, allowing fraud to flourish.
@ 22m 46s
December 03, 2022
The Blame Game
Investors, regulators, and the press share responsibility for the FTX fraud.
“I would say that before the fraud got exposed, one-third, one-third, one-third each.”
@ 39m 47s
December 03, 2022
Changing Media Landscape
Independent voices are gaining trust as consumers seek unbiased information.
“Independent media are now what consumers are seeking out because they can sense the bias.”
@ 49m 03s
December 03, 2022
Xi Jinping's Ascendancy
2022 marks the first year Xi Jinping is essentially ruler for life, raising questions about his future actions.
“I think this guy is uh, he's firing on all cylinders.”
@ 58m 52s
December 03, 2022
Predictions About China
The discussion reveals differing opinions on Xi Jinping's power and the potential for civil unrest in China.
“Wow, nailed it!”
@ 01h 00m 41s
December 03, 2022
The Impact of AI
OpenAI's GPT-3.5 showcases the potential for AI to disrupt various industries, raising concerns about job replacement.
“You could see so many human knowledge worker roles being replaced by this extraordinary interface.”
@ 01h 15m 06s
December 03, 2022
The Future of Search
Google's search engine faces potential disruption from alternative systems with natural language interfaces.
“Google's core product could be radically disrupted by an alternative system.”
@ 01h 18m 34s
December 03, 2022
The Challenge of Precision
Achieving precise answers in AI remains a significant challenge, with the last two percent being the hardest.
“The last two percent will take a few decades to perfect.”
@ 01h 26m 06s
December 03, 2022
Regulatory Challenges in AI
Navigating regulatory pathways is crucial for AI advancements, especially in autonomous vehicles.
@ 01h 37m 54s
December 03, 2022

Episode Quotes

Key Moments

Holiday Party00:09
Media Manipulation01:07
Media Failure22:46
Truth-Seeking50:05
Predictions1:00:41
AI Disruption1:15:06
Data as Oil1:36:55
Regulatory Pathways1:38:04

Words per Minute Over Time

Vibes Breakdown

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January 13, 2023

01:31:59

E111: Microsoft to invest $10B in OpenAI, generative AI hype, America's over-classification problem

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November 19, 2022

01:47:51

E105: Tech culture wars: Elon vs. SBF, Sabotaging Republicans with Trump

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December 10, 2022

01:13:50

E107: The Twitter Files Parts 1-2: shadow banning, story suppression, interference & more

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November 08, 2024

01:43:28

Trump wins! How it happened and what's next

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01:23:06

Hot Swap growing, donors revolt, President Kamala? SCOTUS breakdown: Immunity, Chevron, Censorship

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November 20, 2021

01:15:41

E56: Constitution DAO, Rittenhouse trial coverage, private sector efficiency vs the government

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December 06, 2025

01:14:17

OpenAI's Code Red, Sacks vs New York Times, New Poverty Line?

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January 11, 2025

01:47:19

LA's Wildfire Disaster, Zuck Flips on Free Speech, Why Trump Wants Greenland

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01:35:52

E41: Vaccine policy, Big Tech, DeepMind's latest breakthrough, wealth creation, opportunity & more

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01:19:20

E128: Google enters AI wars, Druck’s warning, Trump crushes CNN & more

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01:56:25

E118: AI FOMO frenzy, macro update, Fox vs Dominion, US vs China & more with Brad Gerstner

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October 23, 2021

01:27:29

E52: Trump's SPAC, peak venture liquidity, tech as an economic ladder, Dems overplaying their hand