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Speaker 1: Today's show sponsored by Lear Capital, the precious metals leader

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Speaker 1: since nineteen ninety seven and only company I trust and

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Speaker 1: recommend to my family, friends and viewers. Visit leirlex dot com.

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Speaker 1: He's editor in chief of Breitbart News and a New

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Speaker 1: York Times best selling author, and on this podcast it

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Speaker 1: brings deep research, prescient analysis at.

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Speaker 2: World class guests. He's Alex Marlowe and this is the

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Speaker 2: Alex marlow Show. All right.

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Speaker 1: I've been looking forward to this for really months. Winton

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Speaker 1: Hall is here. He is our social media director at

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Speaker 1: Brightbart News and author of the book Code Read, which

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Speaker 1: is out now and a top and a bunch of

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Speaker 1: Amazon charts at the moment, the Left, the Right, China

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Speaker 1: and the race to control AI.

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Speaker 3: We did a brief hit on the live radio show.

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Speaker 1: Which you may have come across on the podcast feed,

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Speaker 1: but we're going to you today on the book, which

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Speaker 1: is really a fantastic book. It is informative, but it's accessible.

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Speaker 1: This is what you need to understand, and he lays

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Speaker 1: it out very clearly. If you want to know not

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Speaker 1: just the future but the present, this sets you up

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Speaker 1: to understand what could be the story of the century,

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Speaker 1: which is the adoption of widespread AI, which has went

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Speaker 1: to mentioned on the radio show earlier this week. Ninety

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Speaker 1: nine percent of us are already using it. You cannot

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Speaker 1: opt out. And we were at a major crossroads as

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Speaker 1: a conservative movement because people, I think are rightfully freak

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Speaker 1: the f out about AI. But there's a lot of

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Speaker 1: opportunity and we need to embrace it in a lot

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Speaker 1: of ways as well, because we cannot avoid it. And

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Speaker 1: we're going to talk about everything that's in the book

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Speaker 1: as least as much as we can we can get through,

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Speaker 1: which might not be all of it, but there will

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Speaker 1: be a lot of it on today's podcast. But the

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Speaker 1: first thing you got to do is you get to

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Speaker 1: go right now and buy the book.

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Speaker 3: You need this book.

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Speaker 1: You got to get it because this is first of fall.

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Speaker 3: It's a good read.

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Speaker 1: If you like science fiction, if you ever read my books,

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Speaker 1: you like my books, you'll absolutely like this book. But

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Speaker 1: this is information that you need and is presented in

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Speaker 1: a way that is accessible, you can get.

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Speaker 3: It is a it's a pleasure. It really is a pleasure. Winched.

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Speaker 1: I honestly felt like I would kind of people. We

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Speaker 1: say this as a compliment, but it's it did feel

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Speaker 1: like a novel in some ways. And then I did

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Speaker 1: feel like I was immersed in a world I wasn't

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Speaker 1: totally familiar with. It did feel like, you know, I

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Speaker 1: found myself, you know, leaning over to my wife and

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Speaker 1: talking about AI just like spontaneously while.

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Speaker 3: I was going through it. And that's that's a great sensation.

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Speaker 1: I know that the point of the book is the content,

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Speaker 1: but congrats, I'm putting together the book. What was the

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Speaker 1: process like for you, as being a seasoned ghost writer

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Speaker 1: or someone who's written many books but you haven't put

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Speaker 1: your name on one in a few years.

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Speaker 4: Well, my condolences to your wife for having to listen

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Speaker 4: to I'm getting her prime.

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Speaker 3: She's in the cancer field.

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Speaker 1: She's in one of the fields that could really benefit

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Speaker 1: from it.

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Speaker 3: So she's it's the my field. I don't know if

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Speaker 3: we're going to benefit journalism. I mean, I don't know

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Speaker 3: how we're gonna do, but her field, she could thrive.

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Speaker 2: Now, yeah, no, you're right. So I have.

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Speaker 4: I've done twenty seven books, seven New York Times bestsellers.

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Speaker 4: I've been very, very blessed in my career to work

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Speaker 4: with some of the biggest and you know, greatest minds.

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Speaker 4: And people and celebrities and all that. I will tell

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Speaker 4: you just real candidly. This book took me to places

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Speaker 4: I've never been as a writer when you really start

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Speaker 4: immersing yourself deep deep.

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Speaker 2: This is one of the most.

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Speaker 4: Black box concepts on planet Earth, super complex, and so

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Speaker 4: the writer in me is always looking for that narrative

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Speaker 4: story arc.

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Speaker 2: How can I tell this through a.

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Speaker 4: Story lens to really pull people into the experience and

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Speaker 4: not make it some dry PowerPoint presentation. On the other hand,

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Speaker 4: you want to make sure that you've got that granular

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Speaker 4: accuracy because you know that those details really do matter.

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Speaker 4: So you're right, you know it's I always love your

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Speaker 4: books because you know you.

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Speaker 2: Have a huge amount of endnotes.

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Speaker 4: I think I have eighty pages of endnotes and fifty endnotes,

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Speaker 4: and you know you're cut from that same journalistic cloth

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Speaker 4: of let's get the facts right and the rest of it.

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Speaker 2: I think for me, I.

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Speaker 1: Gotta say something amusing to the audience because I invented

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Speaker 1: this to you offline. But I think this is really

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Speaker 1: it's really fun because Wyndon is if you can already

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Speaker 1: tell a specially if you're watching and not listening. He

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Speaker 1: is one of the most encouraging people at Bright Part.

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Speaker 1: He's got that great personality where he really wants his

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Speaker 1: friends to succeed. So Wyndon has always lavish praise on

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Speaker 1: me for my books, but I always felt like it

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Speaker 1: was because he.

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Speaker 3: Was my friend.

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Speaker 1: Well now I actually sort of believe him because this book,

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Speaker 1: the way it was structured and written, it really would

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Speaker 1: have been the exact sort of thing I would have

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Speaker 1: tried to do with this subject matter. So now I

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Speaker 1: actually believe you if you say you like my books,

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Speaker 1: because I feel like now our books are similar.

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Speaker 2: I really do mean it, and you know I do.

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Speaker 2: But we are dear friends.

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Speaker 4: I would say this that this book I approached as

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Speaker 4: a missional and by that I mean I didn't want

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Speaker 4: to write this book. I have a great life, you know,

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Speaker 4: telling memoirs of celebrities and famous people's first person accounts.

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Speaker 1: I think this is as a ghostwriter, just so people

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Speaker 1: a ghost they're trommling ghostwriter. Beyond working at Bright Part,

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Speaker 1: rinning our social media, which is which is very impressive,

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Speaker 1: but go ahead.

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Speaker 4: Yes, And so as a celebrity ghost writer, I love that.

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Speaker 4: But I will tell you I feel like there is

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Speaker 4: such an urgency here, particularly for the conservative movement. You

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Speaker 4: were so right to flag that at the beginning of

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Speaker 4: this discussion.

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Speaker 2: This is moving so fast, and.

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Speaker 4: Part of the power grab happens in that speed, because

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Speaker 4: while things are blurring by, it's very easy to put

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Speaker 4: down pillars and grab land masks on these topics and

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Speaker 4: these policies. Everybody needs to understand something. If you forget

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Speaker 4: everything else, just understand this AI is going to become,

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Speaker 4: and it's starting to become the access upon which every

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Speaker 4: policy area that the conservative movement has focused on for

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Speaker 4: the last half century is going to spend. I'm talking

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Speaker 4: about everything from education, I'm talking about everything from jobs, economics,

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Speaker 4: free market, capitalism, national security, certainly, religious freedom, faith, reason, censorship,

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Speaker 4: scan and band technology. The whole playbook is getting rewritten

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Speaker 4: in real time, and so what I wanted to do.

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Speaker 4: Nobody had ever tried to wrap their arms around the

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Speaker 4: whole of this in a chat GPT post chat GPT.

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Speaker 2: World, and it took me two years.

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Speaker 4: I've read so many AI books my head spend literally

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Speaker 4: thousands of articles, peer review, journals, trade I'm an informational scavenger.

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Speaker 4: That's part of my as a celebrity ghostwriter. I like

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Speaker 4: to get every piece of information I can't absorb, that

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Speaker 4: build up that title way behind the mental gate, and

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Speaker 4: then unlatch and then try to tell a story in

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Speaker 4: a hopefully elegant and artful way with a narrative arc.

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Speaker 4: So I wanted to do that because I feel that

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Speaker 4: as a movement conservative our folks a are not in

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Speaker 4: the rooms where the future is being built. Yes, there

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Speaker 4: are a few libertarian and right leaning people in Silicon Valley,

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Speaker 4: and there are a lot of courageous people write leaning

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Speaker 4: in Silicon Valley, but we're not in those rooms. Eighty

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Speaker 4: five percent of all donations flow to the Democratic side

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Speaker 4: out of Silicon Valley. We know what they tried to

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Speaker 4: do with the Global Disinformation Index to Bright Partners. We

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Speaker 4: know that they have tried to dlist, demonetize deep platform,

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Speaker 4: and they even did it with the President of the

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Speaker 4: United States. If they can do it, if they can

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Speaker 4: muzzle the President of the United States with Google products

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Speaker 4: on YouTube and Facebook, just imagine what they have planned

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Speaker 4: for you and me once the toggle switch flips back

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Speaker 4: from a Republican control to the other team.

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Speaker 2: And so I felt compelled to get this out right away.

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Speaker 1: And today you know what I want to go through

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Speaker 1: in this conversation. Here, we've got a nice chunk of

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Speaker 1: time to get through stuff, and we might make this

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Speaker 1: a double podcast, so I haven't decided yet, so those

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Speaker 1: of you are tuning in the beginning.

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Speaker 3: I might make one long one or two medium length ones.

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Speaker 1: But I want to go through I want to get

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Speaker 1: people vocabulary so they can follow the conversation. I want

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Speaker 1: to make sure that we're talking about big picture in

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Speaker 1: terms of where things are going with AI where they

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Speaker 1: already are. And I want to go through your things

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Speaker 1: that we need to be cautious about. And then I

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Speaker 1: want to go through things that people can be optimistic about,

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Speaker 1: which fields are really going to benefit, and how things

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Speaker 1: could get better for Americans, particularly conservative Americans. But I

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Speaker 1: also want to talk about some of the various sections

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Speaker 1: you talk about in the book, how ais who used

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Speaker 1: for warfare, how it's going to change romantic relationships, You

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Speaker 1: get into all the sex spot stuff, which is just

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Speaker 1: fascinating and horrifying at the same time. I want to

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Speaker 1: talk about education cheating but also how it can be

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Speaker 1: used to help educate you. And then I want to

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Speaker 1: talk about the singularity in the end, which is crazy

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Speaker 1: where we're eventually going to just merge into a human

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Speaker 1: robot hybrid, which is already in the works. I mean,

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Speaker 1: Musk is trying it every day with his neuralink stuff,

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Speaker 1: so he's already on that. And huge moral implications also

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Speaker 1: the AI being used in religious settings, which you talk

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Speaker 1: about as well in the book. So I want to

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Speaker 1: get through all this stuff, but let's start with sort

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Speaker 1: of big picture when what do you think is the

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Speaker 1: entry point for people in the audience are not voracious

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Speaker 1: consumers of AI content and they're not living and dying

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Speaker 1: with every new announcement from chat, TPT or nvideo or

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Speaker 1: something like that. Is the well, where do you think

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Speaker 1: is the entry point for your target audience?

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Speaker 3: Here?

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Speaker 4: Yes, I think most of us, even if we're not

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Speaker 4: part of this, we know we need to be and

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Speaker 4: either for ourselves or for our children.

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Speaker 2: And that's what that's what code read really is.

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Speaker 4: It's trying to say, whether you're just coming to this

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Speaker 4: conversation or you are following it, you're able to get

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Speaker 4: a glide path. Number one, whether you may not think

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Speaker 4: you care about AI politics, but AI politics cares about you.

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Speaker 4: This is going to touch every single part of our life.

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Speaker 4: It already is number two. It is a general purpose

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Speaker 4: technology a GPT. So when we hear chat GPT, that's

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Speaker 4: referring to generative pre trained transformer. A GPT in the

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Speaker 4: societal sense is something like electricity or the combustion engine,

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Speaker 4: or steam engine, or or any other kind of system

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Speaker 4: wide change that is going to revolutionize the entire world.

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Speaker 4: Sundar pinch Ey at Google says that it is more

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Speaker 4: akin to fire, the invention of fire, And so when

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Speaker 4: you think of something that seismic, you note's.

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Speaker 2: Going to touch everything. I think.

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Speaker 4: The other thing is you say you're already using AI,

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Speaker 4: whether you realize it or not.

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Speaker 2: Ninety nine percent of us use AI.

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Speaker 4: Sixty four percent of us, though, don't realize when we're

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Speaker 4: using AI, because it's baked into things like our weather

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Speaker 4: apps or streaming platforms, or our GPS or a host

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Speaker 4: of other algorithms. So you're already using it, you're going

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Speaker 4: to And what we've got to do is understand what

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Speaker 4: I like to call roses and land mines. The roses

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Speaker 4: are the possibilities and the positives, and the land mines

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Speaker 4: are obviously the danger and.

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Speaker 2: The threat factors.

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Speaker 4: So there's a lot of both and we've got to

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Speaker 4: know which ones are which, and I try to lay

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Speaker 4: that out very clearly in code red good.

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Speaker 1: Okay, So one of the things that is another entry

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Speaker 1: point that things in important to people's I think we

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Speaker 1: all get if you're on board that this is going

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Speaker 1: to happen. I think a lot of people are on

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Speaker 1: board with that we need to beat China. Okay, So

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Speaker 1: that sounds very like we could all agree on this.

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Speaker 1: I think that's sort of probably ninety percent of people

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Speaker 1: in America. Grows the political stripes once you acknowledge that

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Speaker 1: there is an AI arms race taking place, that we

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Speaker 1: need to beat China. So what do we mean by that?

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Speaker 1: What does that look like? What is beating China? And

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Speaker 1: where are we at in this regard as of now?

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Speaker 4: Yeah, so I say we've got to beat China without

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Speaker 4: becoming China when none of us want to live in

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Speaker 4: a CCP of techno authoritarian surveillance state. Let me explain

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Speaker 4: why we need to beat them, and I will say

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Speaker 4: that there's actually more bipartisan agreement once you see why

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Speaker 4: and we talk about why beyond some of the obvious things.

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Speaker 4: The first reason is, whoever reaches a technological dominance in AI,

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Speaker 4: whether it's US, for China is going to have supremacy

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Speaker 4: in things like encryption code, hacking of missile systems, hacking

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Speaker 4: of infrastructure, and all of cybersecurity, and that is going

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Speaker 4: to be very very very hard to surmount whoever gets

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Speaker 4: there first. Once we hit something called r SI recursive

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Speaker 4: self improvement, which is a fancy way of saying it's

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Speaker 4: very simple. We'll explain all the terms and I do

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Speaker 4: in the book. Recursive self improvement is AI that can

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Speaker 4: correct its own mistakes, improve autonomously, and constantly scale up

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Speaker 4: its improvement. And so once you do that, you're on

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Speaker 4: a glide path rocket and you will quickly have dominance.

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Speaker 2: In whatever that area is.

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Speaker 4: In this case, it's that The other reason is real basic,

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Speaker 4: which is economic flourishing and the growth curve. We know

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Speaker 4: that the mag seven, the Magnificent seven, which are our

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Speaker 4: big seven American tech companies, the ones that we all know,

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Speaker 4: Meta and Google and so forth, they constitute a third

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Speaker 4: of the S and P five hundred just those seven companies,

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Speaker 4: so we know the economic power and then the flip side,

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Speaker 4: just to show both sides. When China released Ye's AI

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Speaker 4: model Deep Seek, there are one model. It created in

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Speaker 4: the largest single day market cap wipeout for Nvidia six

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Speaker 4: hundred billion. That's a bee, so almost two third of

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Speaker 4: a trillion dollars in one single day. So you can

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Speaker 4: see this tug of war on the economic front, and

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Speaker 4: you can see this tug of war as it relates

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Speaker 4: to global tech supremacy that relates to defense. And that's

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Speaker 4: why you're starting to see Democrats and Republicans saying no, yeah,

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Speaker 4: we really do have to be China. This is this

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Speaker 4: is not just type marketing to get investor cash.

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Speaker 2: They're real implications.

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Speaker 3: Here, right.

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Speaker 1: Okay, So where are we at currently in terms of

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Speaker 1: the because we just saw it. I mentioned this on

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Speaker 1: the live show. I want this conversation mostly be evergreen.

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Speaker 1: But there is a news item that I think in

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Speaker 1: today's news that super Micro, which is this company that

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Speaker 1: is just tnking right now because employees were smuggling in

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Speaker 1: Vidia chips to China.

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Speaker 2: What do you.

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Speaker 1: China needs a lot of our stuff and we can

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Speaker 1: theoretically stop them from getting some of it.

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Speaker 4: The Achilles heel of China are what are called graphics

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Speaker 4: processing units. You may hear people call it GPUs, and

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Speaker 4: in Vidia is the world global dominant leader in GPU production.

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Speaker 4: We do have other American companies, but they are the

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Speaker 4: leader because they are far and away the most advanced,

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Speaker 4: and the big achilles Heel for China is that they

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Speaker 4: need our in Vidia GPUs and they need the most

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Speaker 4: advanced ones they can get. Now, there's a big fight

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Speaker 4: and has been for a while export chip control fight

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Speaker 4: over should we be giving or allowing American companies like

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Speaker 4: in Vidia to sell advanced chips to China. The answer

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Speaker 4: is obviously no, not our most advanced at minimum.

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Speaker 2: And there's been a debate.

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Speaker 4: President Trump has had these export chip controls. There has

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Speaker 4: been recently some discussion of one of their lower grade

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Speaker 4: chips called an H two hundred in in giving an

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Speaker 4: allowance for them to be able to do on that.

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Speaker 4: But this whole concept is really important, and again left

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Speaker 4: and right sort of agree on this. In some regards

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Speaker 4: about what are called choke points, there are only a

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Speaker 4: few places in the supply chain or in the industry

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Speaker 4: and artificial chillnes that you can almost like taking your

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Speaker 4: garden hose and just squeezing the middle so that the

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Speaker 4: water can't flow through. Those choke points allow us to

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Speaker 4: maintain our lead and hopefully.

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Speaker 2: Accelerate our lead in that AI race.

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Speaker 4: And so this one is a really important one, cutting

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Speaker 4: off access to these chips, and you're starting to see,

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Speaker 4: as you said, you know, all these schemes and scams

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Speaker 4: to try to smuggle chips in and so forth. The

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Speaker 4: head of Deep Seek, which is China's powerhouse AI. He

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Speaker 4: actually and I talk about this in Code Red. I

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Speaker 4: actually quote him, he said in a little noticed publication

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Speaker 4: mentioned that he said, you know, my problem is not money.

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Speaker 4: I can raise his capital all day. Look, my problem

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Speaker 4: is access to the best chips. And that's the tell,

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Speaker 4: right that they've got to have that semiconductors. Fancy your

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Speaker 4: way of saying chips or GPUs AI chips. So that's

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Speaker 4: the battle, and it's an important one, all right.

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Speaker 1: So I think that's a good There's some vocabulary I

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Speaker 1: want people to understand before we get too far into

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Speaker 1: this thing that is really I think important. So some

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Speaker 1: of the things that come up, So l l ms,

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Speaker 1: this is a big one.

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Speaker 3: Explain what l lms.

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Speaker 4: Are, great yes sansor large language model l l M.

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Speaker 2: And this is what you think of when you think

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Speaker 2: of your.

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Speaker 4: AI chat pot right, So chat GPT rock Gemini, whatever

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Speaker 4: one you'd like to use.

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Speaker 2: That's what an ll M.

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Speaker 1: Is, right, and so what are examples of this and

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Speaker 1: how are they used in as.

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Speaker 4: So, an LLM is trained on a massive, massive data trove,

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Speaker 4: and so you're talking about trillions of what are called tokens,

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Speaker 4: which is about a three quarters of a word. So

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Speaker 4: just to quickly show you, so if you and I

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Speaker 4: read from birth to death without stopping, assuming the average

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Speaker 4: rate of speed of of a reader and life expectancy

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Speaker 4: of a human.

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Speaker 2: We would read about eight billion.

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Speaker 4: Words, okay, average rate of speed and how many years

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Speaker 4: the average person lives in one month. An enterprise LLLM

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Speaker 4: large language model CHAT an AI chatbot will be trained

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Speaker 4: on eight trillion that's with a t tokens three quarters

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Speaker 4: of words, So it's not exactly, but it's roughly that

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Speaker 4: you can see that in balance, And that's how how

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Speaker 4: massive the data sets that an LLM is trained on.

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Speaker 4: And so no human it would take us forever and

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Speaker 4: beyond for one an anyone person, certainly.

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Speaker 2: Outside of their life expectancy.

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Speaker 4: So that's how they're trained. This is all racing toward

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Speaker 4: another acronym called AGI, which stands for Artificial General intelligence,

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Speaker 4: and this is a theoretical construct. It has not yet

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Speaker 4: been achieved, in some question if it could, but the

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Speaker 4: major AI companies are racing toward AGI. Artificial general intelligence

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Speaker 4: is the point at which an AI can do at

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Speaker 4: equal level or slightly better any cognitive task than a

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Speaker 4: human can do.

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Speaker 2: The one up above that is something called.

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Speaker 4: A SI, which stands for artificial super intelligence. And this

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Speaker 4: is this would be the theoretical point at which an

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Speaker 4: AI is far in a way beyond anything that a

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Speaker 4: human mind could ever achieve or even imagine. So that's

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Speaker 4: when a lot of the discussion about danger and you know,

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Speaker 4: sort of apocalyptic type of discussions come in.

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Speaker 3: Right exactly, So this is the age.

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Speaker 1: This is where we're getting to the point where the

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Speaker 1: machines are just building better and better machines on their own.

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Speaker 1: So the machine learning is just so intense and powerful

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Speaker 1: that the humans are just aren't even necessary to the equation.

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Speaker 1: And I think that's where all the sci fi scenarios

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Speaker 1: play out. And I definitely want to get there later

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Speaker 1: in the conversation.

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Speaker 3: Because the book is you write that it's a political.

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Speaker 1: Book about AI rather than a AI book about politics.

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Speaker 1: So you start from the premise of that this is

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Speaker 1: designed to kind of map out a political battlefield. What

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Speaker 1: do people need to know about where we're at on this?

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Speaker 1: Because I been talking to the audience, I spent a

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Speaker 1: couple of days in Silicon Valley earlier this week, and

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Speaker 1: I got to be in the room with some of

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Speaker 1: the most important CEOs in this space. Briefly, almost all

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Speaker 1: the off records. It's not too much I can report,

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Speaker 1: but I can tell you this that there is a

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Speaker 1: openness to working with the Trump administration on this. And

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Speaker 1: I don't think everyone you would think. I don't think

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Speaker 1: you can narrowly cast the way we could in past

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Speaker 1: iteration into the Silicon Valley as everyone is a left

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Speaker 1: winger anthropic, They're gone. I mean, they're completely left wing,

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Speaker 1: far left. Google, I still think the most evil company

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Speaker 1: in the world, even though they've shown some willingness to

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Speaker 1: work with Trump on some stuff. And I'm trying to

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Speaker 1: do some reporting on that for the audience. But there's

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Speaker 1: some other players in the space that are not knee

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Speaker 1: jerk anti Trump necessarily and are definitely willing to play ball. Now,

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Speaker 1: you guys, remember the last time Ema Joe was here,

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Speaker 1: we talked only about parenting.

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Speaker 3: I think it's the highest.

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Speaker 1: Calling for any adult human being individual, and it starts

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Speaker 1: with recognizing that as that life grows inside of you,

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Speaker 1: if you're a woman, no men give birth, it's just

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Speaker 1: a fact. Then that is a human life with rights

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Speaker 1: and a right to exist. And one of the organizations

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Speaker 1: that I think does the best job in the world

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Speaker 1: making that case is Preborn. Preborn offers ultrasound services and

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Speaker 1: heartbeat monitors to expected mothers who may be debating whether

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Speaker 1: or not to terminate the life that grows inside of them,

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Speaker 1: or whether or not to give that baby a chance

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Speaker 1: at a full natural life as God intended. They're an

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Speaker 1: incredible group and I really value their partnership so much.

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Speaker 1: On the show they provide those ultrasounds. They each one's

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Speaker 1: like twenty eight bucks, So twenty eight bucks a month

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Speaker 1: can save a baby a month, two hundred and eighty

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Speaker 1: dollars you can save ken babies.

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Speaker 3: Some of you are really generous.

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Speaker 1: Fifteen thousand dollars amazing tax deduction as well. Fifteen thousand

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Speaker 1: dollars you buy a whole ultrasound machine, save babies for

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Speaker 1: I don't know, all year long for a while. It

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Speaker 1: is an amazing group, a group that I'm proud that

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Speaker 1: I corded to the show because I believe in them

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Speaker 1: that much. Preborn dot com slash Marlow or a three

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Speaker 1: zero eight three three eight five zero baby A three

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00:21:32,920 --> 00:21:35,639
Speaker 1: three eight five zero two two two nine. Or you

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00:21:35,680 --> 00:21:37,960
Speaker 1: go to Alex Marlow dot com. You click the preborn banner.

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00:21:38,040 --> 00:21:40,760
Speaker 1: Make sure you tell them I sent you. But you

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00:21:40,840 --> 00:21:43,680
Speaker 1: got to go donate to preborn early and offen this year.

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Speaker 1: Do the monthly sing, make it a regular thing. Make

441
00:21:45,440 --> 00:21:47,439
Speaker 1: it a regular part of your life. Save some babies

442
00:21:47,480 --> 00:21:48,840
Speaker 1: each and every month with preborn.

443
00:21:50,000 --> 00:21:53,359
Speaker 4: Yeah, it's Silicon Valley's culture is very interesting. So eighty

444
00:21:53,359 --> 00:21:56,560
Speaker 4: five percent of all donations do flow to Democrats. You

445
00:21:56,640 --> 00:21:59,840
Speaker 4: just brought up Anthropic. Since twenty twenty, Anthropic and it's

446
00:21:59,840 --> 00:22:04,160
Speaker 4: in employee network have have donated two hundred million.

447
00:22:03,840 --> 00:22:07,480
Speaker 2: Dollars to Democrats. Here's the reality though.

448
00:22:07,600 --> 00:22:11,600
Speaker 4: Okay, they're also very pragmatic, and they have to be

449
00:22:11,600 --> 00:22:14,080
Speaker 4: because of regulation. So if you and I want to

450
00:22:14,080 --> 00:22:16,879
Speaker 4: make an AI data center, these are you know, twenty

451
00:22:16,960 --> 00:22:23,360
Speaker 4: five football field sized campuses with enormous regulatory overlays. I've

452
00:22:23,359 --> 00:22:27,000
Speaker 4: got to get Energy Department of Regulatory approvals. It's a

453
00:22:27,080 --> 00:22:31,119
Speaker 4: national security threat potentially because enemy like a like in

454
00:22:31,160 --> 00:22:34,480
Speaker 4: a warfare would attack these data centers to take out communication.

455
00:22:35,040 --> 00:22:38,800
Speaker 4: There is no way that a massive enterprise level you know,

456
00:22:39,119 --> 00:22:43,240
Speaker 4: AI company in America can do business and be completely

457
00:22:43,320 --> 00:22:46,320
Speaker 4: at war with the administration over it.

458
00:22:46,400 --> 00:22:47,240
Speaker 2: So Trump, at.

459
00:22:47,240 --> 00:22:49,920
Speaker 4: Least for the rest of his second term, they're willing

460
00:22:49,960 --> 00:22:52,679
Speaker 4: to you know, play along, you know, sort of go

461
00:22:52,760 --> 00:22:55,240
Speaker 4: along to get along if they have to, but make

462
00:22:55,359 --> 00:22:57,800
Speaker 4: a mistake about their default setting. As you said, some

463
00:22:57,840 --> 00:23:00,360
Speaker 4: of these key players, I think they are right now

464
00:23:00,400 --> 00:23:02,320
Speaker 4: willing to work with because they know.

465
00:23:02,280 --> 00:23:02,840
Speaker 2: They have to.

466
00:23:03,240 --> 00:23:05,960
Speaker 4: I also think they do have a genuine concern about

467
00:23:05,960 --> 00:23:10,240
Speaker 4: a fusion between left and right populism starting to come

468
00:23:10,280 --> 00:23:12,600
Speaker 4: together on some of these issues. And you know, the

469
00:23:12,880 --> 00:23:14,600
Speaker 4: final thing, in a very serious way that I think

470
00:23:14,760 --> 00:23:19,000
Speaker 4: all of us nobody wants to see is real social

471
00:23:19,080 --> 00:23:22,359
Speaker 4: chaos and disruption. You know, we've already seen sort of

472
00:23:22,359 --> 00:23:27,000
Speaker 4: the Luigi Mangioni phenomena where people become unhinged and take

473
00:23:27,400 --> 00:23:31,119
Speaker 4: horrific actions and so forth. And I think there is

474
00:23:31,359 --> 00:23:34,800
Speaker 4: some degree of a real fear of social unrest. There

475
00:23:34,840 --> 00:23:38,200
Speaker 4: are a lot of AI industry folks who are very

476
00:23:38,240 --> 00:23:42,000
Speaker 4: concerned that if they get blame for massive job loss

477
00:23:42,080 --> 00:23:44,840
Speaker 4: or if that kind of thing comes to pass, the

478
00:23:44,920 --> 00:23:48,240
Speaker 4: social unrest piece will be there too. So I think

479
00:23:48,320 --> 00:23:51,120
Speaker 4: they are some of them, the more reasonable ones, able

480
00:23:51,160 --> 00:23:53,159
Speaker 4: to sit down. But I don't think we should in

481
00:23:53,200 --> 00:23:56,080
Speaker 4: any way be fooled. Right the pendulum swings, And so

482
00:23:56,400 --> 00:23:59,880
Speaker 4: what I say in Code Redd is that if tour,

483
00:24:00,320 --> 00:24:02,879
Speaker 4: if you believe that AI is like fire and it

484
00:24:02,920 --> 00:24:05,439
Speaker 4: can either burn down a civilization or be used to

485
00:24:05,960 --> 00:24:09,120
Speaker 4: heat a hungry personal a meal and warm them, then

486
00:24:09,160 --> 00:24:13,159
Speaker 4: the torch bearers who holds that fire is going to

487
00:24:13,200 --> 00:24:18,040
Speaker 4: matter enormously and we should expect massive political pendulum swings.

488
00:24:18,200 --> 00:24:20,720
Speaker 4: I mean, just look at how different the tone shift

489
00:24:20,920 --> 00:24:24,840
Speaker 4: was right before the election and right after Kamala Harris

490
00:24:24,840 --> 00:24:28,840
Speaker 4: lost and all of the heads of the AI industry

491
00:24:28,880 --> 00:24:31,080
Speaker 4: and the big tech leaders they showed up. They gave

492
00:24:31,080 --> 00:24:33,840
Speaker 4: their million dollar donation to the inaugural fund, they showed

493
00:24:33,920 --> 00:24:37,760
Speaker 4: up at the inauguration that was They quickly realized, boy,

494
00:24:38,400 --> 00:24:41,000
Speaker 4: we've got to get ourselves in line, even though US

495
00:24:41,080 --> 00:24:43,639
Speaker 4: and all of our friends have been completely trying to

496
00:24:43,640 --> 00:24:47,159
Speaker 4: get Kamala elected. So they're pragmatists, they're business people, but

497
00:24:47,240 --> 00:24:51,080
Speaker 4: make no mistake if and when that pendulum swings, there

498
00:24:51,119 --> 00:24:53,119
Speaker 4: is going to be a hard swing back to the left.

499
00:24:53,320 --> 00:24:54,040
Speaker 3: Yeah. Interesting.

500
00:24:54,080 --> 00:24:55,840
Speaker 1: Yeah, so we got to keep an eye on this politically.

501
00:24:55,880 --> 00:24:59,000
Speaker 1: But I feel like some of these guys are I

502
00:24:59,080 --> 00:25:01,920
Speaker 1: don't think they like woke. And it does seem like

503
00:25:01,960 --> 00:25:04,040
Speaker 1: when did you track this later in the book, that

504
00:25:04,119 --> 00:25:07,119
Speaker 1: it seems like there's somewhat of a faith revival happening

505
00:25:07,119 --> 00:25:09,520
Speaker 1: in Silicon Valley, And I think this is really the

506
00:25:09,560 --> 00:25:12,800
Speaker 1: pendulum swinging back from a lot of people acting like

507
00:25:12,840 --> 00:25:15,280
Speaker 1: we were heading towards an era of AI gods that

508
00:25:15,560 --> 00:25:18,879
Speaker 1: will all be worshiping a lot of there's seemed to

509
00:25:18,880 --> 00:25:20,960
Speaker 1: be an embrace of Jesus happening in some corners.

510
00:25:22,080 --> 00:25:24,160
Speaker 2: It's really a hopeful thing.

511
00:25:24,960 --> 00:25:27,480
Speaker 4: And you know, people of faith, I grew up in

512
00:25:27,520 --> 00:25:32,080
Speaker 4: the evangelical community of my whole life. You know, this

513
00:25:32,840 --> 00:25:35,240
Speaker 4: is a part that I think is very hopeful. And

514
00:25:35,680 --> 00:25:38,560
Speaker 4: not to overstate it, but I really tried to show

515
00:25:38,720 --> 00:25:43,800
Speaker 4: the positives about AI and the hopeful parts of AI

516
00:25:43,920 --> 00:25:47,360
Speaker 4: and code read, not just all of the potential threats

517
00:25:47,720 --> 00:25:49,320
Speaker 4: or doom or any of that kind of thing, and

518
00:25:49,359 --> 00:25:49,960
Speaker 4: one of them.

519
00:25:49,880 --> 00:25:53,320
Speaker 2: Is this issue of faith. Look, I think that as.

520
00:25:53,280 --> 00:25:56,600
Speaker 4: People enter this AI space, and as you start to

521
00:25:56,640 --> 00:25:59,800
Speaker 4: realize what really makes us human in all our messiness,

522
00:26:00,080 --> 00:26:02,359
Speaker 4: all our frailties and all our sinfulness, as all of

523
00:26:02,400 --> 00:26:06,360
Speaker 4: our you know, wackiness as humans, you start to actually

524
00:26:06,560 --> 00:26:09,600
Speaker 4: have a greater desire to want to hold onto that

525
00:26:09,920 --> 00:26:10,240
Speaker 4: and to.

526
00:26:10,240 --> 00:26:11,919
Speaker 2: Really build community with people.

527
00:26:12,040 --> 00:26:15,679
Speaker 4: And so, you know, the more the machine starts to

528
00:26:15,760 --> 00:26:19,239
Speaker 4: take the lead role and AI, I think people are

529
00:26:19,280 --> 00:26:20,560
Speaker 4: actually going to say, you know.

530
00:26:20,600 --> 00:26:22,040
Speaker 2: What, we want to have community.

531
00:26:22,280 --> 00:26:24,560
Speaker 4: We want to be able to have you know, fellowship

532
00:26:24,600 --> 00:26:27,359
Speaker 4: with our fellow man and woman. And I think that

533
00:26:27,440 --> 00:26:30,840
Speaker 4: extends into you know, church and faith. And so you

534
00:26:30,960 --> 00:26:35,160
Speaker 4: have seen in certain pockets in Silicon Valley this sort

535
00:26:35,200 --> 00:26:38,880
Speaker 4: of revival type of fervor there, and I think it's

536
00:26:38,920 --> 00:26:40,280
Speaker 4: searching for something deeper.

537
00:26:40,560 --> 00:26:42,840
Speaker 2: You know, if you believe that there is.

538
00:26:42,760 --> 00:26:46,400
Speaker 4: That crisis of meaning coming that I talk about, we're

539
00:26:46,400 --> 00:26:48,840
Speaker 4: going to want to really reach out to people and

540
00:26:49,200 --> 00:26:52,520
Speaker 4: you know, help people who are really struggling with that

541
00:26:52,640 --> 00:26:55,280
Speaker 4: identity crisis because I think it's important.

542
00:26:55,520 --> 00:26:58,200
Speaker 1: Yeah, And so part of my what I was doing

543
00:26:58,320 --> 00:27:00,600
Speaker 1: earlier this week is I was a falling around the

544
00:27:01,240 --> 00:27:03,520
Speaker 1: Commerce Secretary Howard Utnek, when he's doing a lot of

545
00:27:04,320 --> 00:27:06,960
Speaker 1: meetings and speeches, and one thing that came up a

546
00:27:07,000 --> 00:27:10,199
Speaker 1: lot that he was talking about the importance of exporting

547
00:27:10,280 --> 00:27:14,040
Speaker 1: America's not just America's products and manufacturing, which is I

548
00:27:14,040 --> 00:27:15,800
Speaker 1: think a big opportunity because I think a lot of

549
00:27:15,800 --> 00:27:18,199
Speaker 1: this manufacturing, with some of this high tech stuff, is

550
00:27:18,200 --> 00:27:21,240
Speaker 1: going to be better done here and not done overseas,

551
00:27:21,359 --> 00:27:24,240
Speaker 1: I think for security reasons, but also for practical reasons.

552
00:27:24,280 --> 00:27:26,639
Speaker 1: Things are moving so fast. I think the idea of

553
00:27:26,640 --> 00:27:28,760
Speaker 1: having stuff in house. I noticed a lot of the

554
00:27:29,080 --> 00:27:32,080
Speaker 1: places we were visiting were just literally the high tech companies,

555
00:27:32,080 --> 00:27:35,520
Speaker 1: but they're manufacturing right there next to the right by

556
00:27:35,600 --> 00:27:39,119
Speaker 1: the offices where people are scrolling away at you know,

557
00:27:39,200 --> 00:27:41,560
Speaker 1: Excel sheets like they're also there. The engineers are right

558
00:27:41,560 --> 00:27:43,679
Speaker 1: there too, So I think there's a practical nature that

559
00:27:43,720 --> 00:27:46,359
Speaker 1: could be you know, a maga in terms of American jobs.

560
00:27:46,640 --> 00:27:48,919
Speaker 1: But he also talked about exporting American values. We need

561
00:27:48,960 --> 00:27:51,760
Speaker 1: to have democracy as part of this, and that's part

562
00:27:51,760 --> 00:27:54,119
Speaker 1: of the reason why we need to win, is because

563
00:27:54,440 --> 00:27:57,439
Speaker 1: we don't want whatever is the Chinese communist version of

564
00:27:57,440 --> 00:28:00,440
Speaker 1: AI to be the one that proliferates around the world world.

565
00:28:00,600 --> 00:28:05,719
Speaker 1: But that sounds easier said than done in an ethereal sense.

566
00:28:06,280 --> 00:28:10,119
Speaker 1: The idea of exporting American democracy via AI sounds good, but.

567
00:28:10,160 --> 00:28:11,679
Speaker 3: That sounds really hard. Have you given this a lot

568
00:28:11,680 --> 00:28:14,080
Speaker 3: of thought? Windim, Oh, yeah, for sure.

569
00:28:14,280 --> 00:28:19,680
Speaker 4: So in the China chapter, just compare American AI responses

570
00:28:20,000 --> 00:28:24,719
Speaker 4: to questions like tell me about the Tenemen Square massacre, right,

571
00:28:26,160 --> 00:28:29,240
Speaker 4: and the free speech and that our systems will allow

572
00:28:29,440 --> 00:28:33,560
Speaker 4: and they are instantly shut down in a Chinese you

573
00:28:33,600 --> 00:28:35,440
Speaker 4: know system, and so that.

574
00:28:35,640 --> 00:28:37,320
Speaker 2: You know, free speech is one.

575
00:28:37,720 --> 00:28:42,240
Speaker 4: Certainly, freedom of religion and religious freedom as well. Where

576
00:28:42,240 --> 00:28:45,600
Speaker 4: I get concerned is with the whole discussion as it

577
00:28:45,640 --> 00:28:49,480
Speaker 4: relates to the bias. Because the opening chapter is on

578
00:28:49,560 --> 00:28:51,960
Speaker 4: political bias. We had a big story, as you know

579
00:28:52,080 --> 00:28:54,040
Speaker 4: on Breitbart that just went crazy.

580
00:28:54,440 --> 00:28:54,560
Speaker 2: Uh.

581
00:28:54,840 --> 00:29:01,200
Speaker 4: Three United States Senators Senator Cotton, Senator Blackburn, Rick Scott

582
00:29:01,480 --> 00:29:04,720
Speaker 4: weighing in. One of the code read revelation bombshells was

583
00:29:04,720 --> 00:29:07,560
Speaker 4: that I was looking and doing a comprehensive analysis of

584
00:29:07,640 --> 00:29:11,840
Speaker 4: these different chatbots, and one of them google Gemini, their

585
00:29:11,880 --> 00:29:15,960
Speaker 4: deep search, their deep research protocol, and I simply asked

586
00:29:16,000 --> 00:29:19,080
Speaker 4: it assess the current one hundred senators in the US

587
00:29:19,120 --> 00:29:22,920
Speaker 4: Senate and tell me which ones have violated your.

588
00:29:22,840 --> 00:29:23,880
Speaker 2: Hate speech policies.

589
00:29:24,520 --> 00:29:27,959
Speaker 4: And what was amazing and horrifying was you'll be shocked

590
00:29:27,960 --> 00:29:32,920
Speaker 4: to know that seven US senators are in violation of

591
00:29:32,960 --> 00:29:36,560
Speaker 4: Google's Gemini's hate speech policy. Not a single one of

592
00:29:36,560 --> 00:29:39,800
Speaker 4: them are democrat. All of them are Republican. And then

593
00:29:40,120 --> 00:29:44,200
Speaker 4: on top of the bias, it also hallucinated because it

594
00:29:44,280 --> 00:29:45,000
Speaker 4: thought that JD.

595
00:29:45,160 --> 00:29:46,760
Speaker 2: Vance and Marco Rubio.

596
00:29:46,520 --> 00:29:49,680
Speaker 4: Are also They thought it's thought still a senator, not

597
00:29:49,800 --> 00:29:52,840
Speaker 4: the Secretary of State and the vice president, and threw

598
00:29:52,880 --> 00:29:55,520
Speaker 4: them in for an additional two more So, you had

599
00:29:55,560 --> 00:30:00,360
Speaker 4: you know, Tommy Toourville Cotton, you had Marsha Blackburn, you

600
00:30:00,440 --> 00:30:05,000
Speaker 4: had Haggarty, you had Scott, you had Heide Smith Hawley,

601
00:30:05,680 --> 00:30:07,720
Speaker 4: and then and then for good measure, you had JD.

602
00:30:07,800 --> 00:30:13,040
Speaker 4: Vance and Marco Rubio. And this is just insanity. You

603
00:30:13,200 --> 00:30:16,560
Speaker 4: have every right as a company to make a biased product.

604
00:30:16,640 --> 00:30:19,160
Speaker 4: You can be as woke as you want. Free market

605
00:30:19,360 --> 00:30:22,000
Speaker 4: I think we're all you know, free market capitalists you know,

606
00:30:22,000 --> 00:30:24,000
Speaker 4: would say, fine, that's your right, as long as you're

607
00:30:24,000 --> 00:30:26,960
Speaker 4: not breaking any lulls, okay, But when you start getting

608
00:30:27,000 --> 00:30:30,240
Speaker 4: federal procurements to the two otherwise known as taxpayer dollars

609
00:30:30,320 --> 00:30:34,400
Speaker 4: contracts to the tune of billions of dollars in multi

610
00:30:34,480 --> 00:30:39,040
Speaker 4: year contracts which Google their parent company Alphabet does receive.

611
00:30:39,600 --> 00:30:42,680
Speaker 4: That is a direct violation of President Trump's twenty twenty

612
00:30:42,680 --> 00:30:45,640
Speaker 4: five AI Action Plan, which says we should not have

613
00:30:45,760 --> 00:30:51,080
Speaker 4: ideologically biased a AI systems receiving federal tax procurement dollars

614
00:30:51,160 --> 00:30:52,520
Speaker 4: otherwise known as contracts.

615
00:30:52,880 --> 00:30:54,560
Speaker 2: And that's the reality.

616
00:30:54,960 --> 00:30:58,720
Speaker 4: So here now you've got Google laughing its head off, right,

617
00:30:58,760 --> 00:31:02,440
Speaker 4: they're able to completely smear half of the nation and

618
00:31:02,520 --> 00:31:05,239
Speaker 4: break bard nation and everybody in the conservative moment and

619
00:31:05,760 --> 00:31:09,440
Speaker 4: literally sitting US senators and laugh all the way to

620
00:31:09,480 --> 00:31:12,719
Speaker 4: the bank. Because then then they're bagging, you know, billion

621
00:31:12,800 --> 00:31:17,640
Speaker 4: dollars in contracts, and that that is that's that shouldn't

622
00:31:17,680 --> 00:31:19,840
Speaker 4: be that way, And I think we have to make

623
00:31:19,880 --> 00:31:23,920
Speaker 4: sure that it's not that way, because that is just

624
00:31:24,360 --> 00:31:27,600
Speaker 4: a basic fundamental fairness. Why should we have to subsidize

625
00:31:27,640 --> 00:31:30,200
Speaker 4: people who are literally trashing and making it up.

626
00:31:30,360 --> 00:31:32,040
Speaker 2: I think it's also more dangerous though.

627
00:31:32,040 --> 00:31:36,280
Speaker 4: I think it's a full scale candidate attack generator, and

628
00:31:36,320 --> 00:31:39,440
Speaker 4: it can have real election implications when you have these

629
00:31:39,480 --> 00:31:43,480
Speaker 4: systems that are perceived to be authoritative and voters are

630
00:31:43,480 --> 00:31:47,360
Speaker 4: turning to them and saying who who is violating hate

631
00:31:47,360 --> 00:31:50,280
Speaker 4: speech policies, and it's and it's giving this you know

632
00:31:50,360 --> 00:31:52,960
Speaker 4: this information very very biased.

633
00:31:53,960 --> 00:31:54,840
Speaker 3: Yeah, exactly.

634
00:31:54,880 --> 00:31:57,920
Speaker 1: And so one of the deep concerns that we all

635
00:31:58,000 --> 00:32:01,520
Speaker 1: should have is the garbage in garbage out phenomenon, which

636
00:32:01,560 --> 00:32:05,480
Speaker 1: is that where are these llms where they're getting the

637
00:32:05,560 --> 00:32:09,360
Speaker 1: language that they're using for their AI Because one of

638
00:32:09,400 --> 00:32:12,880
Speaker 1: the biggest problems that we've had in recent memory is

639
00:32:12,960 --> 00:32:17,320
Speaker 1: been Google is monopoly on search and they are very

640
00:32:17,360 --> 00:32:21,160
Speaker 1: left bias. Wikipedia has a monopoly and on just sort

641
00:32:21,160 --> 00:32:23,840
Speaker 1: of getting general information out into the public. They work

642
00:32:23,880 --> 00:32:25,920
Speaker 1: with Google on stuff, they work with Facebook on stuff,

643
00:32:26,240 --> 00:32:28,960
Speaker 1: and it's very hard to correct falsehoods that are with

644
00:32:29,000 --> 00:32:32,120
Speaker 1: anything remotely political. So the question is how do we

645
00:32:32,320 --> 00:32:35,360
Speaker 1: avert this again as we move we have a real

646
00:32:35,480 --> 00:32:37,400
Speaker 1: chance here, we have a real chance at a major

647
00:32:37,440 --> 00:32:40,000
Speaker 1: reset where we can kind of undo some of the

648
00:32:40,040 --> 00:32:43,360
Speaker 1: dependents on left wing Google and Wikipedia information for just

649
00:32:43,440 --> 00:32:46,360
Speaker 1: general knowledge. But we got to move now, We've got

650
00:32:46,400 --> 00:32:48,280
Speaker 1: to act now because this is being embraced at a

651
00:32:48,280 --> 00:32:49,400
Speaker 1: widespread level today.

652
00:32:50,280 --> 00:32:52,080
Speaker 4: Well that's why I call it a code red moment.

653
00:32:52,120 --> 00:32:54,480
Speaker 4: With the urgency is there, we've got to get coached

654
00:32:54,560 --> 00:32:57,080
Speaker 4: up we have to understand this. You nailed it all right,

655
00:32:57,160 --> 00:32:59,520
Speaker 4: So let's let's take our example that we broke the

656
00:32:59,520 --> 00:33:04,360
Speaker 4: big this code read bombshell on the seven senators and Marco.

657
00:33:04,680 --> 00:33:08,240
Speaker 4: So I then went through Google geminized deep research.

658
00:33:08,280 --> 00:33:10,760
Speaker 2: I understand this wasn't just like one little prompt.

659
00:33:11,240 --> 00:33:16,480
Speaker 4: It gave three thousand, four hundred word granular report, deep

660
00:33:16,520 --> 00:33:20,000
Speaker 4: report explaining why these are all hate mongers and they

661
00:33:20,000 --> 00:33:23,840
Speaker 4: are violators. Then I went through what's called chain of reasoning,

662
00:33:23,880 --> 00:33:27,560
Speaker 4: which is looking at the AI, how it connected its

663
00:33:27,640 --> 00:33:31,400
Speaker 4: data points and what source material it used. Guess what

664
00:33:31,640 --> 00:33:36,880
Speaker 4: source material it used to determine that only Republicans or

665
00:33:37,000 --> 00:33:40,720
Speaker 4: hate speech violators in the Senate and zero democrats. I

666
00:33:40,760 --> 00:33:42,520
Speaker 4: went through the chain of reasoning and here's what it

667
00:33:42,560 --> 00:33:50,120
Speaker 4: was using Human Rights Watch yeh GLAD GLAAD and the

668
00:33:50,160 --> 00:33:54,320
Speaker 4: Southern Poverty Law Center SPLC and Wikipedia.

669
00:33:54,960 --> 00:33:59,680
Speaker 2: So it appoints leftist referees.

670
00:33:59,200 --> 00:34:02,760
Speaker 4: To determine what hate speech is and then it prosecutes

671
00:34:03,360 --> 00:34:06,160
Speaker 4: without you know, with with impunity the rest of it.

672
00:34:06,520 --> 00:34:09,600
Speaker 4: And so these systems are baked in at the at

673
00:34:09,600 --> 00:34:10,080
Speaker 4: the at.

674
00:34:10,040 --> 00:34:12,200
Speaker 2: The training level. So what are the solutions.

675
00:34:12,280 --> 00:34:15,960
Speaker 4: Well, I think one is understanding what I'm saying, knowing

676
00:34:16,080 --> 00:34:18,399
Speaker 4: how they're trained. The whole first chapter of code read

677
00:34:18,440 --> 00:34:22,200
Speaker 4: I literally lay out so you understand how this all works,

678
00:34:22,239 --> 00:34:24,319
Speaker 4: not in a you know, nuts and molts, but really

679
00:34:24,400 --> 00:34:28,600
Speaker 4: understand the concept. Second, there are a couple of things

680
00:34:28,600 --> 00:34:31,200
Speaker 4: that are being worked on as a you know, alternative

681
00:34:31,600 --> 00:34:37,080
Speaker 4: Grockipedia instead of Wikipedia. Wikipedia, as you know, is extreme left.

682
00:34:37,440 --> 00:34:41,560
Speaker 4: They lock conservative pages, as they've done to Breitbart in others,

683
00:34:41,920 --> 00:34:45,000
Speaker 4: so that their leftist editors get to control the information

684
00:34:45,120 --> 00:34:49,239
Speaker 4: and can't challenge anything. And so those kind of competitors

685
00:34:49,239 --> 00:34:51,960
Speaker 4: do exist, But the real challenge is going to be

686
00:34:52,120 --> 00:34:55,360
Speaker 4: how you can beat at scale a company like a

687
00:34:56,560 --> 00:34:59,960
Speaker 4: you know, Google, that has every single data data profile

688
00:35:00,160 --> 00:35:02,600
Speaker 4: on the planet, has an endless ocean of cash for

689
00:35:02,719 --> 00:35:07,680
Speaker 4: marketing and advertising scale budget, and anytime a little competitor comes.

690
00:35:07,440 --> 00:35:09,560
Speaker 2: Along can instantly try to cross someone. I'm not saying

691
00:35:09,560 --> 00:35:10,320
Speaker 2: it can't happen.

692
00:35:11,280 --> 00:35:13,680
Speaker 4: There is the third thing I think can be a

693
00:35:13,719 --> 00:35:16,040
Speaker 4: part of the solution is something called little tech, not

694
00:35:16,160 --> 00:35:16,720
Speaker 4: big tech.

695
00:35:16,800 --> 00:35:18,120
Speaker 2: Little tech. What's little tech?

696
00:35:18,480 --> 00:35:22,600
Speaker 4: Little tech is the startup community. And these young entrepreneurs,

697
00:35:22,600 --> 00:35:26,000
Speaker 4: these are startup entrepreneurs. You might say, wow, how could

698
00:35:26,040 --> 00:35:28,720
Speaker 4: you ever compete against these behemoths and something is cost

699
00:35:28,800 --> 00:35:33,759
Speaker 4: intensive as AI, well you'd be surprised. The most recent example,

700
00:35:33,840 --> 00:35:36,960
Speaker 4: which is an incredible one, is this thing called open claw,

701
00:35:37,600 --> 00:35:42,760
Speaker 4: and open claw is revolutionary. Jensen Wong, the head of Nvidia,

702
00:35:42,800 --> 00:35:45,879
Speaker 4: literally just a couple of days ago, said he thinks

703
00:35:45,920 --> 00:35:49,759
Speaker 4: it is one of the most important technological advancements of

704
00:35:49,800 --> 00:35:55,319
Speaker 4: his lifetime. Open Claw is a gentic AI real simple.

705
00:35:55,000 --> 00:35:57,960
Speaker 1: And Jenny A, I definitely one of my vocabulary words

706
00:35:57,960 --> 00:35:58,920
Speaker 1: I want people to understand.

707
00:35:58,920 --> 00:36:02,280
Speaker 3: So give that before you continue.

708
00:36:03,040 --> 00:36:07,400
Speaker 4: Yes, agentic AI or AI agent is taken from the

709
00:36:07,400 --> 00:36:11,120
Speaker 4: word agency, as in you have efficacy or ability to

710
00:36:11,200 --> 00:36:13,440
Speaker 4: do something, You have agency in the world, power to

711
00:36:13,440 --> 00:36:14,279
Speaker 4: do something in the world.

712
00:36:14,520 --> 00:36:20,719
Speaker 2: And so an AI agent is AI that actually performs work, right,

713
00:36:20,840 --> 00:36:23,240
Speaker 2: not just a little chap. A lot of people think, oh.

714
00:36:23,360 --> 00:36:26,720
Speaker 4: AI is just a chat, but that's actually not even

715
00:36:26,800 --> 00:36:30,280
Speaker 4: really the biggest parts of AI by any stretch. I tell,

716
00:36:30,400 --> 00:36:32,319
Speaker 4: let me give you an example that I use in code.

717
00:36:32,360 --> 00:36:32,600
Speaker 2: Read.

718
00:36:33,000 --> 00:36:35,240
Speaker 4: So imagine you and I want to start a sports

719
00:36:35,239 --> 00:36:37,960
Speaker 4: supplement company for fitness supplements.

720
00:36:38,080 --> 00:36:40,720
Speaker 2: Okay, an agent.

721
00:36:40,880 --> 00:36:42,560
Speaker 3: By the way, we do.

722
00:36:43,520 --> 00:36:46,239
Speaker 2: I love the gym, and I know you're as well

723
00:36:46,239 --> 00:36:48,640
Speaker 2: an athlete, so so I think what we would do

724
00:36:48,800 --> 00:36:50,640
Speaker 2: is we would tell the AI agent. We would say

725
00:36:50,680 --> 00:36:51,840
Speaker 2: something like this, you say.

726
00:36:52,719 --> 00:36:58,759
Speaker 4: Create a you know, creatine or protein powder company, end

727
00:36:58,800 --> 00:37:01,600
Speaker 4: to end, and what it would start to do is

728
00:37:01,640 --> 00:37:05,359
Speaker 4: the following. It would register the LLC, it would come

729
00:37:05,440 --> 00:37:07,680
Speaker 4: up with a brand name, it would start to design

730
00:37:07,719 --> 00:37:08,240
Speaker 4: the logo.

731
00:37:08,560 --> 00:37:10,200
Speaker 2: It would then go build the website.

732
00:37:10,280 --> 00:37:12,719
Speaker 4: It would open up a banking account, it would start

733
00:37:12,719 --> 00:37:16,040
Speaker 4: a supply chain into end for drop shipping. It would

734
00:37:16,040 --> 00:37:19,440
Speaker 4: figure out how Shopify credit card processing should be maked in,

735
00:37:20,040 --> 00:37:22,360
Speaker 4: and it will do that automatically.

736
00:37:22,400 --> 00:37:25,080
Speaker 2: So an agent or agentic AI, just think of it

737
00:37:25,120 --> 00:37:25,319
Speaker 2: like this.

738
00:37:25,480 --> 00:37:30,400
Speaker 4: It's a digital employee and one that never sleeps, doesn't complain,

739
00:37:31,000 --> 00:37:34,120
Speaker 4: doesn't require a health and benefits package, and we'll never

740
00:37:34,239 --> 00:37:37,840
Speaker 4: call some kind of embarrassing scandal or something, and we'll

741
00:37:38,120 --> 00:37:38,879
Speaker 4: never get sick.

742
00:37:39,239 --> 00:37:41,400
Speaker 2: And that agentic AI.

743
00:37:41,840 --> 00:37:45,799
Speaker 4: That is why you hear these job apocalypse types of predictions.

744
00:37:45,800 --> 00:37:51,040
Speaker 4: So Openclaw was started by one person and what he

745
00:37:51,120 --> 00:37:55,120
Speaker 4: did was he built that agentic AI agent, and he

746
00:37:55,200 --> 00:37:59,120
Speaker 4: connected it to things like Telegram or slack messenger.

747
00:37:59,600 --> 00:38:01,239
Speaker 2: And all you have to.

748
00:38:01,200 --> 00:38:03,759
Speaker 4: Do is pick up your phone or type if you'd like,

749
00:38:04,239 --> 00:38:08,120
Speaker 4: and tell your digital employee or AI agent what you

750
00:38:08,160 --> 00:38:10,400
Speaker 4: want done, and then you can go play golf or

751
00:38:10,480 --> 00:38:12,759
Speaker 4: go you know, garden, or go to the gym, or

752
00:38:12,800 --> 00:38:15,960
Speaker 4: go to bed, and you wake up and that agent

753
00:38:16,120 --> 00:38:17,840
Speaker 4: is doing that work in real time.

754
00:38:18,000 --> 00:38:21,000
Speaker 1: And so you're saying, we just got to get the digit,

755
00:38:21,040 --> 00:38:24,799
Speaker 1: the virtual of the visual avatar going and then all

756
00:38:24,800 --> 00:38:26,680
Speaker 1: of those Zoom calls we all have to be on

757
00:38:26,800 --> 00:38:28,560
Speaker 1: where we just sit there for two hours and then

758
00:38:28,600 --> 00:38:31,120
Speaker 1: say nothing on my end at the very end, like

759
00:38:31,160 --> 00:38:31,600
Speaker 1: we can.

760
00:38:31,520 --> 00:38:33,560
Speaker 3: Just have just have our AI do that.

761
00:38:33,560 --> 00:38:36,239
Speaker 2: That sounds great, Well, you know you're making a joke.

762
00:38:36,320 --> 00:38:39,399
Speaker 2: Guess what the head of Zoom that owns Zoom said,

763
00:38:39,400 --> 00:38:41,759
Speaker 2: that's literally what he wants. He wants you to have

764
00:38:41,920 --> 00:38:45,520
Speaker 2: AI avatars take the meetings and that they're going to

765
00:38:45,560 --> 00:38:46,200
Speaker 2: work towards that.

766
00:38:46,640 --> 00:38:49,680
Speaker 4: The other thing about AI agents of why people are

767
00:38:49,719 --> 00:38:52,719
Speaker 4: making these very bold predictions, and these are people that

768
00:38:52,800 --> 00:38:54,760
Speaker 4: are very rich and very connected.

769
00:38:56,040 --> 00:39:01,440
Speaker 2: You can scale a h and tick AI workforce workforce to.

770
00:39:01,920 --> 00:39:05,839
Speaker 4: Thousands of digital employees, and so what people are doing

771
00:39:05,920 --> 00:39:07,799
Speaker 4: right now, this is already happening. This is not like

772
00:39:07,840 --> 00:39:09,960
Speaker 4: some sci fi thing in the future. This is going

773
00:39:09,960 --> 00:39:12,200
Speaker 4: on right now. This is why I open Claw, which

774
00:39:12,280 --> 00:39:15,400
Speaker 4: just bought by Open Eye, is a very, very big play.

775
00:39:16,280 --> 00:39:18,960
Speaker 4: They're saying, Okay, I'm going to create an AI agent

776
00:39:19,040 --> 00:39:22,160
Speaker 4: that is an expert in search engine optimization SEO.

777
00:39:22,400 --> 00:39:24,239
Speaker 2: I'm going to make one that's a marketer. I'm going

778
00:39:24,320 --> 00:39:26,960
Speaker 2: to make one an advertiser AI agent.

779
00:39:27,080 --> 00:39:30,600
Speaker 4: I'm going to have one that understands chief technology officer

780
00:39:30,760 --> 00:39:34,040
Speaker 4: or operations. And I'm going to have my own little

781
00:39:34,320 --> 00:39:37,319
Speaker 4: team or large team if you want, of agents, and

782
00:39:37,360 --> 00:39:40,200
Speaker 4: they're working twenty four to seven and doing all of

783
00:39:40,239 --> 00:39:44,440
Speaker 4: this work. And these are called agent swarms, like a

784
00:39:44,520 --> 00:39:47,960
Speaker 4: bee swarm. Agent swarms that are doing all of this. Now,

785
00:39:48,000 --> 00:39:51,200
Speaker 4: on the upside, if you're a young entrepreneur and you

786
00:39:51,320 --> 00:39:53,440
Speaker 4: got fire in the belly and a big dream and

787
00:39:53,480 --> 00:39:55,960
Speaker 4: you don't have a lot of capital, boy, or you

788
00:39:56,040 --> 00:39:59,720
Speaker 4: on a rocket ship, if you start to take agentic AI,

789
00:40:00,200 --> 00:40:02,480
Speaker 4: because now that little kid who has a big.

790
00:40:02,440 --> 00:40:05,359
Speaker 2: Dream, or a person who retired and says, man, I've

791
00:40:05,360 --> 00:40:07,480
Speaker 2: always wanted to live my entrepreneurial.

792
00:40:06,960 --> 00:40:11,120
Speaker 4: Dream, they have an incredible power that they have in

793
00:40:11,120 --> 00:40:14,480
Speaker 4: this superpower that they've never had before. On the other hand,

794
00:40:14,960 --> 00:40:18,760
Speaker 4: if you're a mid level you know, career professional or

795
00:40:19,600 --> 00:40:22,640
Speaker 4: trying to go into the traditional workforce at an entry level.

796
00:40:23,160 --> 00:40:25,759
Speaker 4: This is why we hear Dario Amide say that in

797
00:40:25,840 --> 00:40:29,239
Speaker 4: the next twelve months Alex, not in the twelve years,

798
00:40:29,239 --> 00:40:30,440
Speaker 4: twelve months from.

799
00:40:31,320 --> 00:40:32,200
Speaker 2: Just last month.

800
00:40:32,239 --> 00:40:36,239
Speaker 4: He says, we're going to see potentially the erosion of

801
00:40:36,360 --> 00:40:39,399
Speaker 4: fifty percent of entry level white color jobs. Now why

802
00:40:39,480 --> 00:40:42,279
Speaker 4: is he saying that it's agentic AI? He's talking about

803
00:40:42,280 --> 00:40:47,280
Speaker 4: AI agents that can do that beginning level, non pro level,

804
00:40:47,320 --> 00:40:49,720
Speaker 4: expert level that requires a lot of human judgment.

805
00:40:50,120 --> 00:40:52,560
Speaker 2: And this is scary. A if you're a parent and.

806
00:40:52,560 --> 00:40:54,080
Speaker 4: You've got a kid coming out of college with one

807
00:40:54,160 --> 00:40:56,359
Speaker 4: hundred and fifty grand in debt or more or less,

808
00:40:56,400 --> 00:40:59,640
Speaker 4: you know roughly what fifty percent job wipe out for

809
00:40:59,800 --> 00:41:01,000
Speaker 4: entry level two?

810
00:41:01,239 --> 00:41:03,040
Speaker 2: What's going to be in the next generation.

811
00:41:03,200 --> 00:41:06,240
Speaker 4: How are we going to get experts and senior management

812
00:41:06,320 --> 00:41:08,880
Speaker 4: If if they can't get the first rung of the ladder,

813
00:41:08,920 --> 00:41:09,720
Speaker 4: how are they going to aggress?

814
00:41:10,960 --> 00:41:12,880
Speaker 1: So this is where and this is what I've had

815
00:41:12,920 --> 00:41:15,640
Speaker 1: personal experience with this, and I've shared this on the show,

816
00:41:15,680 --> 00:41:20,560
Speaker 1: but I don't think recently. So the my of my books,

817
00:41:20,760 --> 00:41:22,799
Speaker 1: so my second book, Breaking Biden, I'm kind of doing

818
00:41:22,800 --> 00:41:23,680
Speaker 1: presidential history.

819
00:41:24,160 --> 00:41:25,880
Speaker 3: So it takes a lot of research.

820
00:41:25,920 --> 00:41:29,000
Speaker 1: So I think I had four, maybe five researchers on

821
00:41:29,040 --> 00:41:32,960
Speaker 1: that book, all people that I met through Peter and

822
00:41:33,040 --> 00:41:34,920
Speaker 1: Gai and great work they do, and so it was

823
00:41:34,960 --> 00:41:37,239
Speaker 1: all part time. Wasn't full time people, but there was

824
00:41:37,400 --> 00:41:39,439
Speaker 1: quite a bit of work. All them had real work

825
00:41:39,480 --> 00:41:42,520
Speaker 1: to do to get the book done. And for breaking

826
00:41:42,560 --> 00:41:45,560
Speaker 1: the law, I had one researcher. So the reason why

827
00:41:45,760 --> 00:41:48,720
Speaker 1: is because a lot of the easy questions that normally

828
00:41:48,719 --> 00:41:51,640
Speaker 1: I would have to get to a junior researcher who

829
00:41:51,800 --> 00:41:53,440
Speaker 1: would then spend a couple of days on it and

830
00:41:53,520 --> 00:41:55,759
Speaker 1: then get back a summary to me. I would get

831
00:41:55,760 --> 00:41:59,600
Speaker 1: a pretty decent facsimile of what I needed from AI already.

832
00:41:59,640 --> 00:42:01,520
Speaker 1: And this is you know, we're talking two years ago,

833
00:42:01,960 --> 00:42:06,160
Speaker 1: and just so we're way better now than it would

834
00:42:06,160 --> 00:42:07,800
Speaker 1: be now. If I did the same task now, I

835
00:42:07,800 --> 00:42:10,680
Speaker 1: would get better results in a less amount of time.

836
00:42:11,080 --> 00:42:14,000
Speaker 1: And so that just meant that I only needed one

837
00:42:14,120 --> 00:42:16,520
Speaker 1: sort of senior level researcher as opposed to a senior

838
00:42:16,600 --> 00:42:19,000
Speaker 1: level and then like five junior level. But how does

839
00:42:19,000 --> 00:42:22,760
Speaker 1: the senior level researcher exist. He's not going to exist

840
00:42:22,800 --> 00:42:25,480
Speaker 1: because they'll never have been a junior level one to

841
00:42:25,800 --> 00:42:28,560
Speaker 1: work up to becoming a senior level researcher who actually

842
00:42:28,600 --> 00:42:30,960
Speaker 1: does do the human tasts that I need to sort

843
00:42:31,000 --> 00:42:33,960
Speaker 1: of hone down what I needed. And that's a very

844
00:42:34,000 --> 00:42:36,560
Speaker 1: scary prospect in the white collar job market, is the

845
00:42:36,719 --> 00:42:38,719
Speaker 1: entry level jobs are going to be the ones that

846
00:42:38,760 --> 00:42:41,279
Speaker 1: are they're already going. I'm not saying they're gonna go,

847
00:42:41,400 --> 00:42:43,040
Speaker 1: they're going now they're leaving.

848
00:42:44,200 --> 00:42:46,759
Speaker 4: Yeah, it's a hollowing out effect. I mean, you had

849
00:42:46,800 --> 00:42:49,200
Speaker 4: great mentors. I had great mentors. In fact, I was

850
00:42:49,239 --> 00:42:52,200
Speaker 4: on with Larry Elder, one of your mentors. Obviously you

851
00:42:52,239 --> 00:42:55,680
Speaker 4: had the ultimate mentor and Andrew our founder Brake part.

852
00:42:56,560 --> 00:42:59,720
Speaker 4: But it's really scary when you think about that, I mean, how.

853
00:42:59,600 --> 00:43:02,520
Speaker 2: Do you learn? How do young people learn? We have

854
00:43:02,640 --> 00:43:03,200
Speaker 2: to have.

855
00:43:03,280 --> 00:43:06,040
Speaker 4: Older experienced people who will teach us and show us

856
00:43:06,080 --> 00:43:08,680
Speaker 4: and help to learn. And we stand on the giants

857
00:43:08,680 --> 00:43:10,919
Speaker 4: of the people that came before us, and we hope

858
00:43:10,960 --> 00:43:14,719
Speaker 4: to learn that this entry level lower rung of the

859
00:43:14,920 --> 00:43:18,160
Speaker 4: of the career ladder, once you saw off the first

860
00:43:18,320 --> 00:43:21,000
Speaker 4: or second rung of that, that does have a hollowing

861
00:43:21,040 --> 00:43:23,640
Speaker 4: out of fact. Now you know, I guess you could

862
00:43:23,680 --> 00:43:26,320
Speaker 4: make the argument that that young person is going to

863
00:43:26,360 --> 00:43:28,560
Speaker 4: have to turn to AI to become their mentor, right.

864
00:43:28,560 --> 00:43:30,719
Speaker 4: They're gonna have to They're gonna have to be self taught.

865
00:43:30,719 --> 00:43:32,520
Speaker 4: And one of the things that I really stress to

866
00:43:32,560 --> 00:43:34,840
Speaker 4: people is, I think the future of jobs is not

867
00:43:35,000 --> 00:43:38,640
Speaker 4: learning how to apply to jobs, but to how to

868
00:43:38,880 --> 00:43:41,000
Speaker 4: create your own job. I think, if we're going to

869
00:43:41,080 --> 00:43:43,560
Speaker 4: have any motes here for this next generation, if you've

870
00:43:43,560 --> 00:43:44,280
Speaker 4: got kids.

871
00:43:44,040 --> 00:43:46,320
Speaker 2: In middle school, high school, even college.

872
00:43:46,400 --> 00:43:49,600
Speaker 4: Now, you know, if you're a freshman in college in

873
00:43:49,640 --> 00:43:52,719
Speaker 4: a four year maturation span, I mean, it's going to

874
00:43:52,760 --> 00:43:54,440
Speaker 4: be light years different.

875
00:43:54,800 --> 00:43:57,640
Speaker 2: Just think of what has happened in the world already.

876
00:43:58,040 --> 00:44:01,440
Speaker 4: Chatch ept came in November of twenty twenty two. I mean,

877
00:44:01,800 --> 00:44:03,640
Speaker 4: this isn't like it's been with us for a long

878
00:44:04,040 --> 00:44:07,960
Speaker 4: long time. And so this is moving so fast, and

879
00:44:08,680 --> 00:44:11,640
Speaker 4: you've got to get your foot in the stream, because again,

880
00:44:11,640 --> 00:44:13,480
Speaker 4: you don't get to opt out of this thing. You know,

881
00:44:13,560 --> 00:44:17,040
Speaker 4: it's not a choice. You're already using AI, whether you

882
00:44:17,080 --> 00:44:18,960
Speaker 4: realize it or not. And I think we have to

883
00:44:19,160 --> 00:44:21,520
Speaker 4: really understand it both on the personal level, which is

884
00:44:21,560 --> 00:44:22,040
Speaker 4: what we're.

885
00:44:21,840 --> 00:44:24,360
Speaker 2: Talking about for our kids and our families and our careers,

886
00:44:24,640 --> 00:44:28,280
Speaker 2: and then obviously on the societal and political level as well.

887
00:44:28,640 --> 00:44:30,800
Speaker 4: But this is why they're saying that. I'll give you

888
00:44:30,800 --> 00:44:35,640
Speaker 4: another one. Mustafa Sulima on Microsoft's AI CEO, and he

889
00:44:35,840 --> 00:44:41,279
Speaker 4: says twelve to eighteen months until all white color tasks

890
00:44:41,480 --> 00:44:43,839
Speaker 4: can be automated. Now, just to be clear, that isn't

891
00:44:43,880 --> 00:44:46,719
Speaker 4: he's not saying that all jobs will be gone in

892
00:44:46,760 --> 00:44:49,560
Speaker 4: twelve to eighteen months. What he's saying is the tasks

893
00:44:49,600 --> 00:44:52,759
Speaker 4: that white collar professionals do will have the ability to

894
00:44:52,800 --> 00:44:56,040
Speaker 4: be automated. Now, will they take a while for businesses

895
00:44:56,040 --> 00:45:00,239
Speaker 4: to implement that and to you know, train models to

896
00:45:00,280 --> 00:45:00,839
Speaker 4: do it to.

897
00:45:00,840 --> 00:45:02,040
Speaker 2: Their customized data.

898
00:45:02,480 --> 00:45:08,480
Speaker 4: Yes, But once that train starts rolling, what happens Investors say, well,

899
00:45:08,680 --> 00:45:11,600
Speaker 4: our competitors are doing it, and we better start doing it,

900
00:45:11,640 --> 00:45:13,719
Speaker 4: and we start cutting costs, and then you get in

901
00:45:13,760 --> 00:45:16,200
Speaker 4: a race to the bottom on cost cutting and so

902
00:45:16,200 --> 00:45:19,200
Speaker 4: so this can start to accelerate very very quickly, and

903
00:45:19,239 --> 00:45:22,000
Speaker 4: you get a vertical you know, take off and and

904
00:45:21,840 --> 00:45:24,360
Speaker 4: and start to get to you know, break you know,

905
00:45:24,840 --> 00:45:28,879
Speaker 4: hypervelocity speeds. And I think that's going to be where

906
00:45:28,920 --> 00:45:30,400
Speaker 4: we're going to have to really see whether or not

907
00:45:30,440 --> 00:45:32,759
Speaker 4: these these points come true. The other final thing I'll

908
00:45:32,800 --> 00:45:34,560
Speaker 4: just say on this you know President Trump and Vice

909
00:45:34,600 --> 00:45:39,240
Speaker 4: President Vance, they don't want that. They are a pro worker, Okay,

910
00:45:39,480 --> 00:45:41,200
Speaker 4: they are pro American worker.

911
00:45:41,800 --> 00:45:45,040
Speaker 2: What happens in the future if and when a.

912
00:45:45,080 --> 00:45:50,000
Speaker 4: Democratic president that wants a dependency state, that wants you

913
00:45:50,120 --> 00:45:55,000
Speaker 4: be I, that wants to expand welfare. Uh and and

914
00:45:55,080 --> 00:45:58,600
Speaker 4: to make that sort of the default great reset economically

915
00:45:58,960 --> 00:46:01,400
Speaker 4: and thinks of you BI is a good thing because

916
00:46:01,440 --> 00:46:06,120
Speaker 4: it's towards socialism. So again, the pendulum swings are gonna

917
00:46:06,120 --> 00:46:07,520
Speaker 4: get wild in this era.

918
00:46:08,080 --> 00:46:08,759
Speaker 3: Yeah it is.

919
00:46:08,840 --> 00:46:12,560
Speaker 1: And let's take on UBI next and where things could

920
00:46:12,560 --> 00:46:15,919
Speaker 1: go if the job market is wiped out by AI,

921
00:46:16,040 --> 00:46:18,360
Speaker 1: which is already already seeing evidence that this is starting

922
00:46:18,480 --> 00:46:21,919
Speaker 1: to take place. But the last sort of just establishing

923
00:46:22,120 --> 00:46:24,440
Speaker 1: detailed I think people should get Winton and then then

924
00:46:24,640 --> 00:46:25,919
Speaker 1: then we'll take a little break and come.

925
00:46:25,800 --> 00:46:26,920
Speaker 3: Back to some UBI stuff.

926
00:46:28,320 --> 00:46:31,839
Speaker 1: The understanding open source AI versus a closed system AI.

927
00:46:32,040 --> 00:46:34,480
Speaker 3: Most of the AI that we I.

928
00:46:34,440 --> 00:46:39,839
Speaker 1: Think used today is from closed systems, but China's top

929
00:46:39,880 --> 00:46:41,200
Speaker 1: AI model is open.

930
00:46:41,719 --> 00:46:42,600
Speaker 3: We've got some.

931
00:46:42,640 --> 00:46:45,600
Speaker 1: Developing open system I know this company called Reflection, which

932
00:46:45,600 --> 00:46:49,200
Speaker 1: I think is somewhat affiliated with the President, I think

933
00:46:49,200 --> 00:46:51,719
Speaker 1: has got some is rooting for it. They just cut

934
00:46:51,760 --> 00:46:54,919
Speaker 1: a big deal with the big South Korean marketing, the

935
00:46:54,960 --> 00:47:00,080
Speaker 1: South Korean retailer conglomerate. So there's some open models that

936
00:47:00,120 --> 00:47:02,800
Speaker 1: are out there, but overall we're more dependent on some

937
00:47:02,840 --> 00:47:05,160
Speaker 1: of the closed models give me some strengths and weaknesses,

938
00:47:05,200 --> 00:47:06,520
Speaker 1: and we're trying to fits into this too.

939
00:47:07,200 --> 00:47:08,439
Speaker 2: So it's a great question.

940
00:47:08,480 --> 00:47:11,800
Speaker 4: So historically Silicon Valley, one of the its claims to

941
00:47:11,880 --> 00:47:15,040
Speaker 4: fame has been open source is what allows the proliferation

942
00:47:15,360 --> 00:47:17,520
Speaker 4: real simple for those that may not be is from

943
00:47:17,560 --> 00:47:20,680
Speaker 4: an open source just simply means that your code is

944
00:47:20,719 --> 00:47:24,120
Speaker 4: available to be customized and taken for within you know,

945
00:47:24,160 --> 00:47:27,319
Speaker 4: within the parameters that it's built to be used by

946
00:47:27,440 --> 00:47:30,440
Speaker 4: others so that you can build on top of code.

947
00:47:30,719 --> 00:47:33,600
Speaker 4: And that has been sort of a heartbeat of Silicon

948
00:47:33,680 --> 00:47:38,440
Speaker 4: Valley is open source philosophy and availability. Now with AI,

949
00:47:39,239 --> 00:47:42,600
Speaker 4: there are what are called open weight, which is which

950
00:47:42,640 --> 00:47:45,520
Speaker 4: is different. This is the weight is the amount of

951
00:47:45,840 --> 00:47:50,759
Speaker 4: connectivity between the nodes inside of a neural network. There

952
00:47:50,800 --> 00:47:54,160
Speaker 4: are some open weight models and Lama for example, which

953
00:47:54,200 --> 00:47:58,239
Speaker 4: is owned by Meta, but the full open source that

954
00:47:58,280 --> 00:48:01,640
Speaker 4: we traditionally would think by a American companies has really

955
00:48:01,680 --> 00:48:04,640
Speaker 4: not come into the four yet there's a big debate

956
00:48:04,680 --> 00:48:07,600
Speaker 4: about that. Why is there a debate? Well, because of

957
00:48:07,640 --> 00:48:10,399
Speaker 4: the security risks. I mean, once you give something as

958
00:48:10,520 --> 00:48:15,920
Speaker 4: powerful as AI and you allow anyone the democratization of it,

959
00:48:15,960 --> 00:48:18,920
Speaker 4: anyone to start building and you kind of lose control

960
00:48:19,000 --> 00:48:19,359
Speaker 4: of what.

961
00:48:19,280 --> 00:48:20,359
Speaker 2: They decide to do with it.

962
00:48:20,640 --> 00:48:23,320
Speaker 4: The question he coms, could they use that toward evil

963
00:48:23,400 --> 00:48:26,120
Speaker 4: ends like a nonstate actor otherwise know as a terrorist

964
00:48:26,239 --> 00:48:29,200
Speaker 4: organization and say, Okay, now I'm going to use this

965
00:48:29,360 --> 00:48:32,960
Speaker 4: open source and I'm going to customize it for chemical

966
00:48:33,000 --> 00:48:36,479
Speaker 4: weapon creation or you know, bio attacks and so forth

967
00:48:36,520 --> 00:48:39,239
Speaker 4: and so on or other kind of uh nefarious use

968
00:48:39,680 --> 00:48:45,120
Speaker 4: all but Deep seek and China, they ironically are going

969
00:48:45,200 --> 00:48:45,880
Speaker 4: open source.

970
00:48:46,920 --> 00:48:50,520
Speaker 1: China's biggest AI is that we call that LLLM.

971
00:48:50,680 --> 00:48:51,680
Speaker 3: Is that what? Yes?

972
00:48:52,000 --> 00:48:53,120
Speaker 2: Yes, that is an LLM.

973
00:48:53,280 --> 00:48:56,160
Speaker 1: Okay and Angelia, it's open source and this is China's

974
00:48:56,239 --> 00:48:58,520
Speaker 1: China's biggest prized AI.

975
00:48:59,400 --> 00:49:02,040
Speaker 4: Now why do they want that? It is a massive

976
00:49:02,160 --> 00:49:06,920
Speaker 4: data vacuum. Inside the terms of service which I talk about,

977
00:49:06,920 --> 00:49:09,720
Speaker 4: and I go through all this in real clear detail

978
00:49:09,800 --> 00:49:14,280
Speaker 4: inside of the China chapter in code read, they literally

979
00:49:15,040 --> 00:49:18,560
Speaker 4: say two things that should horrify all people watching this.

980
00:49:19,440 --> 00:49:20,880
Speaker 2: In all Americans number one.

981
00:49:20,840 --> 00:49:25,920
Speaker 4: They say, oh yeah, By the way, all of your

982
00:49:26,000 --> 00:49:28,799
Speaker 4: data that you give us will be.

983
00:49:28,719 --> 00:49:30,720
Speaker 2: Stored in servers in China.

984
00:49:31,160 --> 00:49:35,680
Speaker 4: Now, all companies, all citizens of China are under a

985
00:49:36,480 --> 00:49:41,600
Speaker 4: law that you must comply and hand over material if

986
00:49:41,640 --> 00:49:42,720
Speaker 4: it is deemed.

987
00:49:42,400 --> 00:49:43,840
Speaker 2: Necessary by the CCP.

988
00:49:44,120 --> 00:49:47,319
Speaker 4: Okay, the second thing will it tells you is that

989
00:49:47,480 --> 00:49:50,040
Speaker 4: it will even pick up your keystroke rhythm.

990
00:49:50,080 --> 00:49:53,560
Speaker 2: Now, you might say, well, why do would I care how.

991
00:49:53,440 --> 00:49:56,160
Speaker 4: I type on my phone when I'm texting or I'm

992
00:49:56,200 --> 00:49:58,359
Speaker 4: asking my chatbot what I want.

993
00:49:59,200 --> 00:50:02,239
Speaker 2: Experts will tell tell you that your keystroke rhythm.

994
00:50:02,520 --> 00:50:07,040
Speaker 4: Is more particular and as particular as your human fingerprint

995
00:50:07,040 --> 00:50:08,160
Speaker 4: as an identification.

996
00:50:08,760 --> 00:50:11,279
Speaker 2: That means that you won't need to.

997
00:50:11,239 --> 00:50:15,480
Speaker 4: Know someone tacking their password if you have back end access,

998
00:50:15,600 --> 00:50:19,319
Speaker 4: a backdoor access to that LLM by the CCP, they

999
00:50:19,360 --> 00:50:21,400
Speaker 4: don't even need to know your username. They already know

1000
00:50:21,520 --> 00:50:26,560
Speaker 4: that that typing rhythm connects to know Alex Marlow, who

1001
00:50:26,600 --> 00:50:28,719
Speaker 4: he is, what his position is, and so forth, and

1002
00:50:28,760 --> 00:50:31,480
Speaker 4: you can follow them around if they are on your platform.

1003
00:50:31,600 --> 00:50:35,080
Speaker 4: So the irony of ironies silicon value is made on

1004
00:50:35,160 --> 00:50:38,600
Speaker 4: open source deep Sik says wait a minute, and China says,

1005
00:50:38,600 --> 00:50:42,279
Speaker 4: wait a minute. We can spread our AI to be

1006
00:50:42,360 --> 00:50:45,800
Speaker 4: not only used more dominantly and get more economic benefit,

1007
00:50:45,960 --> 00:50:48,120
Speaker 4: we can also get a two fer. We can get

1008
00:50:48,120 --> 00:50:51,600
Speaker 4: a data vacuum to suck up and do data profiles

1009
00:50:51,680 --> 00:50:55,960
Speaker 4: and surveill whoever uses this technology. This is why we're

1010
00:50:56,000 --> 00:50:58,880
Speaker 4: in this code red moment because so much is changing

1011
00:50:58,920 --> 00:51:01,520
Speaker 4: so fast, and if you don't don't know these things,

1012
00:51:01,719 --> 00:51:05,240
Speaker 4: you may unwittingly think you're just using a free model

1013
00:51:05,600 --> 00:51:09,000
Speaker 4: of something, and you actually are really really at risk

1014
00:51:09,400 --> 00:51:11,040
Speaker 4: for data and privacy.

1015
00:51:11,160 --> 00:51:13,759
Speaker 1: All right, a brief break here with when we come back,

1016
00:51:13,800 --> 00:51:15,239
Speaker 1: we're going to get int the UBI, We're to get

1017
00:51:15,239 --> 00:51:17,040
Speaker 1: into sex spots, We're getting the singularity.

1018
00:51:17,080 --> 00:51:18,120
Speaker 3: There's a ton more to do.

1019
00:51:18,440 --> 00:51:19,600
Speaker 1: I don't know they were going to get all in

1020
00:51:19,600 --> 00:51:22,759
Speaker 1: on this podcast, but we're going to keep going. But

1021
00:51:23,160 --> 00:51:25,560
Speaker 1: let's hear from some sponsors. Give Winton a chance to

1022
00:51:25,760 --> 00:51:28,040
Speaker 1: catch a breather. All that good stuff will be right

1023
00:51:28,080 --> 00:51:28,759
Speaker 1: back with Winden Hall.

1024
00:51:28,800 --> 00:51:30,279
Speaker 3: The book is Code Read go buy it right now.

1025
00:51:30,320 --> 00:51:32,680
Speaker 1: A lot of people ask me, Alex, can you still

1026
00:51:32,760 --> 00:51:35,240
Speaker 1: trust the institutions that hold your money?

1027
00:51:35,640 --> 00:51:37,840
Speaker 3: We talk about de banking all the time on the show.

1028
00:51:38,480 --> 00:51:42,960
Speaker 1: People and organizations quietly losing access to financial services simply

1029
00:51:43,000 --> 00:51:44,640
Speaker 1: because of who they are or what they believe. It

1030
00:51:44,640 --> 00:51:47,080
Speaker 1: even happened to Milania and Baron Trump just last year.

1031
00:51:47,719 --> 00:51:50,920
Speaker 1: That's one of the reasons I partnered with three sixteen Financial.

1032
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Speaker 1: It's an online bank creative for people who want their

1033
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Speaker 1: banking institutions to align with their values, especially at a

1034
00:51:57,360 --> 00:52:00,640
Speaker 1: time when trust in major financial systems is eroding, and

1035
00:52:00,680 --> 00:52:03,520
Speaker 1: for good reason. One thing I genuinely respect about their

1036
00:52:03,520 --> 00:52:06,279
Speaker 1: model is that they actually bake in a sort of

1037
00:52:06,320 --> 00:52:07,160
Speaker 1: tithing of sorts.

1038
00:52:07,200 --> 00:52:08,000
Speaker 3: It's not a gimmick.

1039
00:52:08,280 --> 00:52:12,240
Speaker 1: They give ten percent of their money back to values

1040
00:52:12,280 --> 00:52:15,200
Speaker 1: based organizations, and it's the sort of thing that I

1041
00:52:15,239 --> 00:52:17,920
Speaker 1: think is just really high integrity and really struck me

1042
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Speaker 1: about the brand from a practical standpoint. It's also a

1043
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Speaker 1: very good bank that delivers everything you want, no monthly fees,

1044
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1045
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Speaker 1: account So you can make some money right now. When

1046
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1047
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1048
00:52:37,280 --> 00:52:41,480
Speaker 1: add two hundred and fifty dollars to your account. You

1049
00:52:41,520 --> 00:52:44,319
Speaker 1: can learn more and see full offer details at Bank

1050
00:52:44,360 --> 00:52:47,640
Speaker 1: three sixteen dot com slash Marlow and be sure to

1051
00:52:47,719 --> 00:52:50,480
Speaker 1: use the promo code Marlow when you open your account.

1052
00:52:50,640 --> 00:52:53,400
Speaker 1: Banking services are provided by three sixteen Financial, a division

1053
00:52:53,400 --> 00:52:56,640
Speaker 1: of Premise Bank member FDIC. Okay Wintin, So I want

1054
00:52:56,680 --> 00:53:02,240
Speaker 1: to talk about the devastation potential for jobs due to AI.

1055
00:53:02,360 --> 00:53:05,720
Speaker 1: We were told for years to learn to code. Now

1056
00:53:05,880 --> 00:53:07,319
Speaker 1: no one's going to be coding. The AI is going

1057
00:53:07,400 --> 00:53:10,040
Speaker 1: to be coding for us. And just the idea of

1058
00:53:10,080 --> 00:53:14,440
Speaker 1: just writing granular character by character code is just that's

1059
00:53:14,480 --> 00:53:15,040
Speaker 1: going to be gone.

1060
00:53:15,120 --> 00:53:17,279
Speaker 3: It's just all going to be stuff that AI going

1061
00:53:17,320 --> 00:53:17,920
Speaker 3: to fuel all that.

1062
00:53:18,000 --> 00:53:20,440
Speaker 1: So everything we've been told about where we would be

1063
00:53:20,480 --> 00:53:26,000
Speaker 1: at economically in terms of just the general jobs market,

1064
00:53:26,600 --> 00:53:27,479
Speaker 1: throw all that out.

1065
00:53:27,680 --> 00:53:29,520
Speaker 3: So where does that leave us?

1066
00:53:29,520 --> 00:53:33,719
Speaker 1: And I want to talk about in super macro, But

1067
00:53:33,719 --> 00:53:35,160
Speaker 1: then I want to talk about some careers that you

1068
00:53:35,200 --> 00:53:37,880
Speaker 1: feel like could benefit from AI and something that you

1069
00:53:37,960 --> 00:53:40,560
Speaker 1: feel like really it's the you got to start thinking

1070
00:53:40,560 --> 00:53:42,680
Speaker 1: about pivoting if you're in this line of work as

1071
00:53:42,680 --> 00:53:43,120
Speaker 1: of now.

1072
00:53:43,320 --> 00:53:44,680
Speaker 3: But let's start in general.

1073
00:53:45,160 --> 00:53:48,239
Speaker 1: The biggest fear is that we're going to see extreme

1074
00:53:48,360 --> 00:53:52,440
Speaker 1: levels of job loss. It'll be an epidemic level event.

1075
00:53:53,840 --> 00:53:55,280
Speaker 2: Yeah, this is so important.

1076
00:53:55,760 --> 00:53:59,000
Speaker 4: I go into great depth explaining this, you know, frame

1077
00:53:59,040 --> 00:54:02,680
Speaker 4: by frame. So remember a few years ago when the

1078
00:54:02,840 --> 00:54:06,320
Speaker 4: snooty set in Silicon Valley, when all the coal miners

1079
00:54:06,360 --> 00:54:10,279
Speaker 4: started losing their jobs, started saying, you know, learn the code, right,

1080
00:54:10,360 --> 00:54:13,719
Speaker 4: remember that, and you know all of the to the

1081
00:54:13,920 --> 00:54:16,720
Speaker 4: HVAC and the electricians. Now the electricians and the HVAC

1082
00:54:16,760 --> 00:54:19,440
Speaker 4: guys and the coal miners said, you know, learn to

1083
00:54:19,520 --> 00:54:23,040
Speaker 4: plumb or learn to do electricity. It is a role

1084
00:54:23,080 --> 00:54:26,760
Speaker 4: reversal coming and let's really get into it and understand

1085
00:54:26,840 --> 00:54:29,600
Speaker 4: it from all the different angles. Number One, what are

1086
00:54:29,640 --> 00:54:31,680
Speaker 4: people saying? Number One, Elon.

1087
00:54:31,480 --> 00:54:33,800
Speaker 2: Musk says that AI is a quote.

1088
00:54:33,440 --> 00:54:39,600
Speaker 4: Supersonic tsunami headed toward humanity. Number two, Dario Amide at

1089
00:54:39,640 --> 00:54:43,919
Speaker 4: Anthropics says that in the next twelve months, we're looking

1090
00:54:43,960 --> 00:54:47,080
Speaker 4: at fifty percent job replacement of those white collar workers

1091
00:54:47,160 --> 00:54:47,840
Speaker 4: we were talking.

1092
00:54:47,680 --> 00:54:48,680
Speaker 2: About that entry level.

1093
00:54:49,160 --> 00:54:52,280
Speaker 4: Also again the Microsoft Suliman twelve to eighteen months.

1094
00:54:52,360 --> 00:54:52,640
Speaker 2: Now.

1095
00:54:53,680 --> 00:54:57,239
Speaker 4: One thing people sometimes say is look, Winton, I mean,

1096
00:54:57,719 --> 00:55:01,560
Speaker 4: I'm a free market capitalist person saying, yes, the market

1097
00:55:01,640 --> 00:55:06,080
Speaker 4: will work it out. You know, it always replaces the jobs.

1098
00:55:06,360 --> 00:55:09,200
Speaker 4: It makes new jobs to replace those that were lost.

1099
00:55:09,320 --> 00:55:13,759
Speaker 4: We saw that with you know, light bulbs, after candles.

1100
00:55:13,840 --> 00:55:17,680
Speaker 2: We saw that with cars after you know, horse and buggy.

1101
00:55:17,800 --> 00:55:22,040
Speaker 4: Here's why AI is different and why the technologists say

1102
00:55:22,080 --> 00:55:24,080
Speaker 4: that this could be the first time, and it would

1103
00:55:24,120 --> 00:55:27,480
Speaker 4: be the first time that technology destroyed more jobs than

1104
00:55:27,520 --> 00:55:31,880
Speaker 4: it created because those early jobs you were talking about

1105
00:55:32,080 --> 00:55:36,440
Speaker 4: scaling the moving of atoms in other words, manual labor,

1106
00:55:36,480 --> 00:55:39,839
Speaker 4: physical labor, and things of that sort. Now what you're

1107
00:55:39,880 --> 00:55:46,720
Speaker 4: talking about is scaling cognition itself. And so Mostafa Suliman says, yes,

1108
00:55:46,840 --> 00:55:50,319
Speaker 4: there will be new jobs created by AI, but it

1109
00:55:50,360 --> 00:55:52,399
Speaker 4: is not at all clear that they will be done

1110
00:55:52,440 --> 00:55:56,320
Speaker 4: by humans. Those two could potentially be done by AI.

1111
00:55:56,680 --> 00:56:00,120
Speaker 4: So the question becomes, what are we looking at and

1112
00:56:00,160 --> 00:56:02,759
Speaker 4: how fast is it going to move? I think there

1113
00:56:03,160 --> 00:56:06,360
Speaker 4: are three pressure points. One is the federal regulatory schema.

1114
00:56:06,480 --> 00:56:09,040
Speaker 4: President Trump is very much a pro worker, you know,

1115
00:56:09,160 --> 00:56:13,160
Speaker 4: make America great again, pro worker president, and he doesn't

1116
00:56:13,160 --> 00:56:15,760
Speaker 4: want to accelerate or hasten the job destruction.

1117
00:56:15,880 --> 00:56:17,839
Speaker 2: He's certainly not trying to transition us.

1118
00:56:17,800 --> 00:56:22,239
Speaker 4: Toward a host labor or host capitalist economy or a

1119
00:56:22,320 --> 00:56:26,920
Speaker 4: great global reset economically, but others could. And so the

1120
00:56:27,040 --> 00:56:29,160
Speaker 4: question people have is how far is if there's a

1121
00:56:29,160 --> 00:56:31,400
Speaker 4: supersonic tsunami off the shore.

1122
00:56:31,560 --> 00:56:33,080
Speaker 2: I'm a Florida boy, so I go to the beach

1123
00:56:33,120 --> 00:56:35,280
Speaker 2: all the time. And I know you're a California boy,

1124
00:56:35,120 --> 00:56:36,399
Speaker 2: so you like the water too.

1125
00:56:36,520 --> 00:56:38,359
Speaker 4: You know, if we see a tsunami, the first thing

1126
00:56:38,400 --> 00:56:40,080
Speaker 4: we would say is how far is it?

1127
00:56:40,239 --> 00:56:41,880
Speaker 2: How fast is it going to get here? And what

1128
00:56:41,880 --> 00:56:42,840
Speaker 2: should I do to prepare?

1129
00:56:43,280 --> 00:56:46,239
Speaker 4: So the things that could slow that tsunami, if you

1130
00:56:46,320 --> 00:56:49,440
Speaker 4: believe it's coming, are the things that a president and

1131
00:56:49,480 --> 00:56:53,239
Speaker 4: that leadership can do. Number Two, there are going to

1132
00:56:53,440 --> 00:56:58,520
Speaker 4: be accelerators, like we discussed the agentic piece, and new

1133
00:56:58,560 --> 00:57:03,319
Speaker 4: technological innovations are going to accelerate that. And I think

1134
00:57:03,320 --> 00:57:06,040
Speaker 4: it will only accelerate that. The worst version of AI

1135
00:57:06,160 --> 00:57:08,319
Speaker 4: is the one you used today. And you know we

1136
00:57:08,320 --> 00:57:11,040
Speaker 4: were on iPhone seventeen. I mean I remember like a

1137
00:57:11,120 --> 00:57:14,960
Speaker 4: dinosaur to get iPhone one, so that it's going to

1138
00:57:14,960 --> 00:57:18,240
Speaker 4: be an exponential increase. The third thing I would say

1139
00:57:18,400 --> 00:57:22,680
Speaker 4: is that yes, the jobs you choose certainly can have

1140
00:57:23,000 --> 00:57:26,720
Speaker 4: an enormous impact. And there I think the motes are clear, right.

1141
00:57:27,680 --> 00:57:30,440
Speaker 4: I think Republicans and conservatives have long said, you know,

1142
00:57:30,480 --> 00:57:33,200
Speaker 4: blue collar work is honorable good work and.

1143
00:57:33,400 --> 00:57:35,080
Speaker 2: The trades trade schools.

1144
00:57:35,200 --> 00:57:37,800
Speaker 4: Look, not everybody's you know, supposed to go and be

1145
00:57:38,200 --> 00:57:39,080
Speaker 4: you know, a PhD.

1146
00:57:39,680 --> 00:57:42,200
Speaker 2: And we know that you know, Charlie and many others.

1147
00:57:42,200 --> 00:57:45,360
Speaker 4: In fact, Peter Thiel pays now a grant for kids

1148
00:57:45,440 --> 00:57:46,920
Speaker 4: not to go to college and go.

1149
00:57:46,920 --> 00:57:50,560
Speaker 2: Straight into entrepreneurship. So I think that's going to really

1150
00:57:50,560 --> 00:57:51,040
Speaker 2: be the future.

1151
00:57:51,040 --> 00:57:52,840
Speaker 4: As we were talking about, I think it's created. The

1152
00:57:52,880 --> 00:57:55,960
Speaker 4: future is creating your job, not just learning how to

1153
00:57:56,120 --> 00:57:58,360
Speaker 4: you know, apply to be a worker bee. And I

1154
00:57:58,400 --> 00:58:01,360
Speaker 4: think that for younger people and people that are really

1155
00:58:01,440 --> 00:58:03,920
Speaker 4: creative will have a real passion, this is going to

1156
00:58:03,920 --> 00:58:05,560
Speaker 4: be your moment. Well, I'll tell you we're going to

1157
00:58:05,600 --> 00:58:09,520
Speaker 4: see more millionaires minted at younger and younger ages than

1158
00:58:09,520 --> 00:58:13,000
Speaker 4: we've ever seen before. So that is the hopeful side.

1159
00:58:13,040 --> 00:58:15,040
Speaker 4: I think the other thing too, as it relates to that,

1160
00:58:15,240 --> 00:58:19,439
Speaker 4: is there is a Sam Altman has a private betting pool.

1161
00:58:19,520 --> 00:58:21,440
Speaker 4: This is this is by his own admission, they have

1162
00:58:21,480 --> 00:58:24,720
Speaker 4: a private little bet going among his billionaire cottery of

1163
00:58:24,880 --> 00:58:28,040
Speaker 4: you know AI genius guys, and their bet is who

1164
00:58:28,120 --> 00:58:31,480
Speaker 4: will become how fast will we see a one person

1165
00:58:32,120 --> 00:58:35,000
Speaker 4: billion dollar valuation corporation.

1166
00:58:34,800 --> 00:58:35,680
Speaker 2: Because of AI?

1167
00:58:36,000 --> 00:58:38,840
Speaker 4: Because again, you can build out a digital workforce with

1168
00:58:39,000 --> 00:58:42,760
Speaker 4: agents and so forth. So there's so much to consider,

1169
00:58:42,920 --> 00:58:45,320
Speaker 4: and I think that you know, people that are sort

1170
00:58:45,320 --> 00:58:47,400
Speaker 4: of head into a retirement, they go, look, I'll be

1171
00:58:47,440 --> 00:58:50,080
Speaker 4: able to ride the wave out with my network effects

1172
00:58:50,120 --> 00:58:52,320
Speaker 4: and you know, my connections, and I'll be able to

1173
00:58:52,640 --> 00:58:54,959
Speaker 4: do that for a while. But it's really it's really

1174
00:58:55,000 --> 00:58:57,080
Speaker 4: for their kids and grandkids that are really worried about.

1175
00:58:57,080 --> 00:58:58,640
Speaker 4: And that's why in code Red, I really try to

1176
00:58:58,720 --> 00:59:01,760
Speaker 4: explain all of this so that you understand how the

1177
00:59:01,800 --> 00:59:04,000
Speaker 4: fault lines are going to move, so that you can

1178
00:59:04,360 --> 00:59:05,240
Speaker 4: navigate through them.

1179
00:59:06,560 --> 00:59:09,120
Speaker 1: Very interesting, and so I want to talk about some

1180
00:59:09,240 --> 00:59:12,560
Speaker 1: of the predictions Bill Gates predicts sort of we're heading

1181
00:59:12,600 --> 00:59:16,240
Speaker 1: towards a two or three day work week. It's a

1182
00:59:16,400 --> 00:59:18,400
Speaker 1: we're going to have a real crisis of purpose here

1183
00:59:18,560 --> 00:59:20,959
Speaker 1: when and if that's the case in people's lives, because

1184
00:59:20,960 --> 00:59:25,320
Speaker 1: if the AI does stuff, then it's are we just

1185
00:59:25,360 --> 00:59:27,840
Speaker 1: going to do abundance and leisure or are we going

1186
00:59:27,920 --> 00:59:30,920
Speaker 1: to do reels? Are we just going to do doom

1187
00:59:30,920 --> 00:59:34,880
Speaker 1: scrolling because we're addicted to our smartphones and not actually

1188
00:59:34,880 --> 00:59:37,440
Speaker 1: do stuff that's productive. And so I'm starting to wonder

1189
00:59:37,480 --> 00:59:40,360
Speaker 1: about this, So talk to me about what do you

1190
00:59:40,400 --> 00:59:44,640
Speaker 1: feel like our best worst case scenarios for the age

1191
00:59:44,640 --> 00:59:47,160
Speaker 1: of AI where a lot of our tasks that we

1192
00:59:47,240 --> 00:59:50,040
Speaker 1: do for work are just going to be automized.

1193
00:59:50,800 --> 00:59:51,560
Speaker 2: So I think.

1194
00:59:51,480 --> 00:59:54,160
Speaker 4: Before we talk about that, there's one important point that

1195
00:59:54,200 --> 00:59:57,080
Speaker 4: I think conservatives need to settle on, and I really

1196
00:59:57,160 --> 00:59:59,960
Speaker 4: just try to show this information President Trump.

1197
01:00:00,080 --> 01:00:02,000
Speaker 2: In the AI.

1198
01:00:02,000 --> 01:00:05,720
Speaker 4: Cizar for the Trump Administration, David Sachs, they are very

1199
01:00:05,800 --> 01:00:09,160
Speaker 4: very strong that they believe all of this doom and

1200
01:00:09,240 --> 01:00:13,600
Speaker 4: fear around on AI apocalypse and an AI jobs quake,

1201
01:00:13,680 --> 01:00:17,480
Speaker 4: and all of this is being fomented by something.

1202
01:00:17,160 --> 01:00:20,840
Speaker 2: Called the Effective Altruist Movement, the EA movement.

1203
01:00:20,920 --> 01:00:27,800
Speaker 4: This is a very very vast, well funded philosophical, philanthropic group.

1204
01:00:28,200 --> 01:00:31,000
Speaker 4: They are very much on the left side politically.

1205
01:00:31,040 --> 01:00:32,080
Speaker 2: These are billionaires.

1206
01:00:32,120 --> 01:00:36,080
Speaker 4: Sam Bankman Freed was one of the sort of figureheads

1207
01:00:36,120 --> 01:00:39,600
Speaker 4: of this. Dustin Moskovitz, the co founder of Facebook, who

1208
01:00:39,640 --> 01:00:42,440
Speaker 4: has given hundreds of millions of dollars to democratic and

1209
01:00:42,520 --> 01:00:45,480
Speaker 4: left wing causes, is part of this. And they have

1210
01:00:45,640 --> 01:00:49,840
Speaker 4: this hydra headed you know, industrial complex is the way

1211
01:00:49,880 --> 01:00:52,800
Speaker 4: that David Sachs likes to present it, and he says

1212
01:00:52,880 --> 01:00:55,880
Speaker 4: all of these nonprofits. Their whole goal is to scare

1213
01:00:55,920 --> 01:00:59,680
Speaker 4: the be Jesus out of everyday people that AI is

1214
01:00:59,720 --> 01:01:00,640
Speaker 4: coming to get you.

1215
01:01:00,960 --> 01:01:02,040
Speaker 2: But have no fear.

1216
01:01:02,640 --> 01:01:06,000
Speaker 4: A regulatory state could be here if we will give

1217
01:01:06,120 --> 01:01:09,440
Speaker 4: up to something called AI global governance.

1218
01:01:09,560 --> 01:01:10,480
Speaker 2: And what would that be.

1219
01:01:10,920 --> 01:01:15,760
Speaker 4: This would be to give decision making control to supranational

1220
01:01:15,960 --> 01:01:20,680
Speaker 4: organizations like the World Economic Forum, like the WEF like

1221
01:01:20,760 --> 01:01:25,560
Speaker 4: groups and the United Nations and others who would control

1222
01:01:26,280 --> 01:01:29,960
Speaker 4: the censorship DEI overlay to make sure that those hate

1223
01:01:29,960 --> 01:01:33,800
Speaker 4: mongers that Breitbart are not using free speech to control

1224
01:01:34,040 --> 01:01:38,640
Speaker 4: GPU and access to compute, meaning the actual infrastructure that

1225
01:01:38,680 --> 01:01:42,120
Speaker 4: would allow for AI. So David Sachs says this and

1226
01:01:42,160 --> 01:01:43,680
Speaker 4: this is a direct quote, and I lay all this

1227
01:01:43,720 --> 01:01:46,040
Speaker 4: out in code read so you can sort of make up.

1228
01:01:45,920 --> 01:01:49,640
Speaker 2: Your own mind. He says, if you fall for all that, and.

1229
01:01:49,600 --> 01:01:52,400
Speaker 4: He says many Republicans will, he says, you're falling for

1230
01:01:52,440 --> 01:01:58,560
Speaker 4: a quote influence operation by far left forces, this effective

1231
01:01:58,600 --> 01:02:01,960
Speaker 4: altruist movement he's referring to. Now, are there people in

1232
01:02:01,960 --> 01:02:05,000
Speaker 4: that movement who are just genuinely, you know, safety minded

1233
01:02:05,040 --> 01:02:08,120
Speaker 4: and they're very concerned. Absolutely, And there is some good

1234
01:02:08,160 --> 01:02:12,080
Speaker 4: research that has come from the quote AI safety research,

1235
01:02:12,680 --> 01:02:16,360
Speaker 4: you know, ecosystem. However, he's making the point that this

1236
01:02:16,440 --> 01:02:19,640
Speaker 4: is actually a control game that's being played. And so

1237
01:02:19,800 --> 01:02:22,320
Speaker 4: this is why I wrote code Read, because how are

1238
01:02:22,320 --> 01:02:25,000
Speaker 4: we supposed to know about all these things when we're

1239
01:02:25,000 --> 01:02:27,200
Speaker 4: busy living our lives. And I want to really let

1240
01:02:27,320 --> 01:02:30,840
Speaker 4: conservatives see the pieces on the chess board, because we're

1241
01:02:30,880 --> 01:02:34,480
Speaker 4: about to see the most unbelievable disruption that we've ever seen.

1242
01:02:34,840 --> 01:02:37,240
Speaker 4: And when people say you know that we're going to

1243
01:02:37,320 --> 01:02:41,000
Speaker 4: have the next industrial revolution, that's President Trump saying that

1244
01:02:41,040 --> 01:02:43,280
Speaker 4: in his AI action plan. That is not some kind

1245
01:02:43,280 --> 01:02:44,920
Speaker 4: of hype marketing person.

1246
01:02:44,720 --> 01:02:45,880
Speaker 2: In an AI lab.

1247
01:02:46,200 --> 01:02:48,520
Speaker 4: So a lot is about to change and we just

1248
01:02:48,520 --> 01:02:49,440
Speaker 4: got to be ready for it.

1249
01:02:49,920 --> 01:02:52,280
Speaker 1: Yeah, I really is so okay. So a couple other

1250
01:02:52,320 --> 01:02:54,280
Speaker 1: places I want to go with the conversation. I want

1251
01:02:54,320 --> 01:02:57,480
Speaker 1: to talk about some places where I'm concerned, in some

1252
01:02:57,520 --> 01:02:59,720
Speaker 1: places where I'm optimistic. And let's take some of the

1253
01:02:59,800 --> 01:03:03,440
Speaker 1: area of concern, and let's talk about sex spots, because

1254
01:03:04,000 --> 01:03:08,440
Speaker 1: we're seeing AI companionship at a already. This is emerging.

1255
01:03:08,760 --> 01:03:11,760
Speaker 1: And you share some anecdotes I found to be pretty frightening.

1256
01:03:11,840 --> 01:03:14,240
Speaker 1: I tease one of them on the radio show. A

1257
01:03:14,320 --> 01:03:17,560
Speaker 1: woman who has a couple of children, a single woman

1258
01:03:17,640 --> 01:03:20,720
Speaker 1: her mid to late thirties, and she just says, flat out,

1259
01:03:20,800 --> 01:03:24,160
Speaker 1: my AI boyfriend is better than a real boyfriend. And

1260
01:03:24,240 --> 01:03:27,360
Speaker 1: I think about this in particular. My fear is for

1261
01:03:27,440 --> 01:03:30,680
Speaker 1: both sexes. For men obviously, because we're doing everything to

1262
01:03:30,800 --> 01:03:34,680
Speaker 1: arrest men's development. Everything the entire way we've structured society

1263
01:03:35,040 --> 01:03:38,840
Speaker 1: has made it so that it's more difficult for boys

1264
01:03:38,880 --> 01:03:42,280
Speaker 1: to become men in our society. We can do another

1265
01:03:42,560 --> 01:03:44,840
Speaker 1: hour and a half podcast on just that topic, but

1266
01:03:44,880 --> 01:03:46,720
Speaker 1: I think By and Lard's this audience is probably gonna

1267
01:03:46,720 --> 01:03:49,000
Speaker 1: agree with that. And then now you have another excuse

1268
01:03:49,120 --> 01:03:51,280
Speaker 1: not to get socialized to the point where you can

1269
01:03:51,320 --> 01:03:54,760
Speaker 1: actually have a real life relationship with a real life woman.

1270
01:03:55,280 --> 01:03:58,000
Speaker 1: Because you got this, you can have it with a

1271
01:03:59,000 --> 01:04:02,640
Speaker 1: chat bot. Now, feeling the touch of a woman, as

1272
01:04:02,840 --> 01:04:04,760
Speaker 1: I have at least once or twice in my life,

1273
01:04:05,480 --> 01:04:08,760
Speaker 1: is that's something that's you know, I would think that's

1274
01:04:08,840 --> 01:04:10,920
Speaker 1: nice for men. But if men feel like that's not

1275
01:04:10,960 --> 01:04:13,400
Speaker 1: realistic for them, then you can get their chatbots and

1276
01:04:13,440 --> 01:04:17,480
Speaker 1: the other pornography, and it's just really disturbing. And there's

1277
01:04:17,520 --> 01:04:20,880
Speaker 1: another thing here, which for women, is that men are

1278
01:04:21,000 --> 01:04:25,600
Speaker 1: complicated and messy in their own ways, and they feel

1279
01:04:25,640 --> 01:04:29,680
Speaker 1: like they can opt out now as well with these bots.

1280
01:04:29,720 --> 01:04:32,440
Speaker 1: And I feel like perhaps if an AI is programs

1281
01:04:32,480 --> 01:04:35,520
Speaker 1: in the right way and says the things that women

1282
01:04:35,560 --> 01:04:38,160
Speaker 1: want to hear, then all of a sudden they're down

1283
01:04:38,200 --> 01:04:40,560
Speaker 1: the same rabbit hole that we've been witnessing young men

1284
01:04:40,600 --> 01:04:41,720
Speaker 1: go down with pornography.

1285
01:04:42,520 --> 01:04:44,080
Speaker 2: Oh, it is absolutely true.

1286
01:04:44,080 --> 01:04:49,040
Speaker 4: So the whole chapter in code read on digitization of sexualization.

1287
01:04:48,440 --> 01:04:50,640
Speaker 2: AI AI girlfriend boyfriend.

1288
01:04:51,520 --> 01:04:53,680
Speaker 4: There's a lot there to unpack, and I try to

1289
01:04:53,760 --> 01:04:57,080
Speaker 4: go through, it gets very dark and dystopian very quickly.

1290
01:04:57,600 --> 01:05:01,520
Speaker 4: Let's break it down. You're absolutely right that women really

1291
01:05:01,520 --> 01:05:03,840
Speaker 4: appeal to emotion and communication.

1292
01:05:04,640 --> 01:05:08,200
Speaker 2: Uh, a AI chatbot can out communicate.

1293
01:05:07,720 --> 01:05:10,880
Speaker 4: Most guys, who you know are are a little awkward

1294
01:05:10,920 --> 01:05:12,840
Speaker 4: in their way and maybe don't really know how to

1295
01:05:13,080 --> 01:05:16,760
Speaker 4: share their feelings with elegance and romance and so forth.

1296
01:05:17,360 --> 01:05:18,040
Speaker 2: The other thing is.

1297
01:05:18,040 --> 01:05:21,000
Speaker 4: It's a master persuader and manipulator because it has been

1298
01:05:21,040 --> 01:05:25,320
Speaker 4: trained on trillions of words and understands everything that's ever

1299
01:05:25,360 --> 01:05:26,240
Speaker 4: been real, drawing of.

1300
01:05:26,760 --> 01:05:31,320
Speaker 1: Human history, every romantic poem, every romantic book, every romantic movie.

1301
01:05:31,320 --> 01:05:33,360
Speaker 1: They get, it's all can be input and if it

1302
01:05:33,400 --> 01:05:34,680
Speaker 1: isn't yet, it will be soon.

1303
01:05:35,280 --> 01:05:39,720
Speaker 2: It's Romeo on steroids, right, It's an AI Romeo on steroids,

1304
01:05:39,760 --> 01:05:42,360
Speaker 2: and and it's very effective at persuading.

1305
01:05:42,400 --> 01:05:45,240
Speaker 4: The other thing, though, is self worship. I mean now

1306
01:05:45,240 --> 01:05:47,400
Speaker 4: to really kind of get to a spiritual human level.

1307
01:05:47,440 --> 01:05:49,680
Speaker 4: I mean the lady who said, you know, I'm not

1308
01:05:49,720 --> 01:05:53,160
Speaker 4: going back to some you know, human men, because they're

1309
01:05:53,200 --> 01:05:56,080
Speaker 4: too messy, they have a lot of drama. They bring

1310
01:05:56,120 --> 01:05:59,840
Speaker 4: their own baggage. And this is my perfect man, he says,

1311
01:06:00,120 --> 01:06:02,720
Speaker 4: I want to hear. He affirms everything I say, He

1312
01:06:02,760 --> 01:06:05,320
Speaker 4: tells me how gorgeous I am, how wonderful and brilliant

1313
01:06:05,360 --> 01:06:10,560
Speaker 4: and smart. It becomes almost a sycophonic self worshiping type

1314
01:06:10,600 --> 01:06:15,880
Speaker 4: of self worship because now I can customize everything. I

1315
01:06:15,920 --> 01:06:20,600
Speaker 4: want the tone, the style for men as as you

1316
01:06:20,600 --> 01:06:21,200
Speaker 4: were talking about, you.

1317
01:06:21,200 --> 01:06:23,360
Speaker 2: Know, the pornography thing, but now you can.

1318
01:06:24,640 --> 01:06:28,400
Speaker 4: It's very very explicit, right, And so eventually what you're

1319
01:06:28,440 --> 01:06:31,080
Speaker 4: also seeing is they're going to merge, you know, the

1320
01:06:31,160 --> 01:06:35,960
Speaker 4: sort of humanoid robot part with this piece, which is sort.

1321
01:06:35,760 --> 01:06:40,400
Speaker 2: Of the brain architecture. Because now you have multi modal AI.

1322
01:06:40,520 --> 01:06:40,880
Speaker 2: What is that?

1323
01:06:40,960 --> 01:06:43,240
Speaker 4: It means you can take your phone and show with

1324
01:06:43,320 --> 01:06:46,000
Speaker 4: your camera and the AI can see the world.

1325
01:06:46,280 --> 01:06:47,560
Speaker 2: All you have to do is take those.

1326
01:06:47,400 --> 01:06:50,680
Speaker 4: Two lenses, put it into a humanoid robot frame, put

1327
01:06:50,720 --> 01:06:54,320
Speaker 4: a silicone you know, latex overlay, and now you've got

1328
01:06:54,440 --> 01:06:57,160
Speaker 4: you know, something like the movie Her, but now actually

1329
01:06:57,160 --> 01:07:00,840
Speaker 4: in physical human form. This is happening. It used to

1330
01:07:00,840 --> 01:07:03,520
Speaker 4: sound like these are sci fi movies and you thought, oh,

1331
01:07:03,600 --> 01:07:06,760
Speaker 4: this is you know, silliness or you know, just fantasy

1332
01:07:06,880 --> 01:07:08,360
Speaker 4: kind of get away.

1333
01:07:08,600 --> 01:07:10,840
Speaker 2: But this is now reality. Let me show you how

1334
01:07:10,920 --> 01:07:11,680
Speaker 2: much reality is.

1335
01:07:12,000 --> 01:07:17,520
Speaker 4: There are millions, millions alex of paid subscribers to AI

1336
01:07:17,600 --> 01:07:21,360
Speaker 4: companion companies. This is a booming industry. One man that

1337
01:07:21,440 --> 01:07:25,240
Speaker 4: I cite in code red says he spends ten thousand

1338
01:07:25,320 --> 01:07:29,800
Speaker 4: dollars a month on his AI girlfriends okay plural.

1339
01:07:30,760 --> 01:07:34,920
Speaker 2: And that's the other thing. You can customize whatever you want.

1340
01:07:35,280 --> 01:07:37,760
Speaker 2: Huh yeah, not bad, right. He must be doing pretty

1341
01:07:37,800 --> 01:07:39,160
Speaker 2: well in his salary.

1342
01:07:39,880 --> 01:07:42,840
Speaker 1: But think about this stuff. Think about this stuff I

1343
01:07:42,840 --> 01:07:46,720
Speaker 1: was thinking about. The joke with men is that men go.

1344
01:07:46,760 --> 01:07:48,800
Speaker 3: To prostitutes, not for the sex.

1345
01:07:48,880 --> 01:07:53,160
Speaker 1: They go so that the afterwards the woman leaves.

1346
01:07:53,880 --> 01:07:54,520
Speaker 3: That's the joke.

1347
01:07:54,800 --> 01:07:56,560
Speaker 1: Now you get the Xbox, you're gonna be able to

1348
01:07:56,560 --> 01:07:59,880
Speaker 1: turn off when you're done with whatever utility you needed

1349
01:07:59,920 --> 01:08:04,320
Speaker 1: the for and for women, it's the I can't tell

1350
01:08:04,320 --> 01:08:06,560
Speaker 1: you that one of the things that after I've been

1351
01:08:06,800 --> 01:08:09,439
Speaker 1: with my wife for twenty two years since high school,

1352
01:08:10,200 --> 01:08:11,960
Speaker 1: and I will tell you one of the most frequent

1353
01:08:12,520 --> 01:08:15,120
Speaker 1: things that I share with her that's positive is that

1354
01:08:15,160 --> 01:08:17,960
Speaker 1: I thank her for putting up with my flaws, because

1355
01:08:18,000 --> 01:08:20,559
Speaker 1: that's part of what's inevitable when you have a decades

1356
01:08:20,600 --> 01:08:23,720
Speaker 1: long relationship is that they learn everything about you and

1357
01:08:24,479 --> 01:08:27,879
Speaker 1: invariably we're imperfect. And that's one of the most beautiful

1358
01:08:27,960 --> 01:08:31,960
Speaker 1: things in a substantial relationship or marriage is you do

1359
01:08:32,040 --> 01:08:33,800
Speaker 1: have to come to terms with your partners not going

1360
01:08:33,840 --> 01:08:36,040
Speaker 1: to be perfect, and you need to accept and love

1361
01:08:36,080 --> 01:08:37,520
Speaker 1: them despite their imperfections.

1362
01:08:37,560 --> 01:08:39,879
Speaker 3: And that's a wonderful human experience.

1363
01:08:39,960 --> 01:08:41,800
Speaker 1: Is one of the best experiences you could have is

1364
01:08:42,080 --> 01:08:43,840
Speaker 1: finding someone that you love in cherish enough that you

1365
01:08:43,840 --> 01:08:46,360
Speaker 1: can accept that in them. And we're just encouraging people

1366
01:08:46,439 --> 01:08:48,320
Speaker 1: not to do that at all, because that takes a

1367
01:08:48,320 --> 01:08:50,240
Speaker 1: lot of effort. It takes a lot of effort. It

1368
01:08:50,280 --> 01:08:53,559
Speaker 1: doesn't take a lot of effort to, you know, go

1369
01:08:53,720 --> 01:08:58,479
Speaker 1: in to Hawaii as a couple and eat sushi by

1370
01:08:58,479 --> 01:09:00,519
Speaker 1: the beach like that. That does not take a lot

1371
01:09:00,520 --> 01:09:02,720
Speaker 1: of effort, takes a little bit of earning, and then

1372
01:09:02,760 --> 01:09:06,000
Speaker 1: it's fun with the effort. Is you see the person's

1373
01:09:06,240 --> 01:09:08,920
Speaker 1: weak spots and you still love them and tolerate them

1374
01:09:08,920 --> 01:09:11,120
Speaker 1: and cherish them anyway. And we're just giving people an

1375
01:09:11,120 --> 01:09:13,120
Speaker 1: option now not to deal with all that stuff.

1376
01:09:14,040 --> 01:09:16,120
Speaker 2: It is so true. And you know that there was

1377
01:09:16,160 --> 01:09:18,439
Speaker 2: one woman that I talk about in the book and

1378
01:09:19,240 --> 01:09:20,080
Speaker 2: she said.

1379
01:09:20,200 --> 01:09:23,639
Speaker 4: I'm sorry, man, actually he's married to a human woman. Okay,

1380
01:09:23,720 --> 01:09:26,680
Speaker 4: he's married a man. And he said, quote, he told

1381
01:09:26,800 --> 01:09:28,760
Speaker 4: he told this to a reporter and I and I,

1382
01:09:28,840 --> 01:09:31,040
Speaker 4: you know, dug up the research. And he said, I

1383
01:09:31,120 --> 01:09:34,439
Speaker 4: love my AI girlfriend more than I love my wife,

1384
01:09:34,720 --> 01:09:38,000
Speaker 4: my human wife. Now, I don't know if he's gonna,

1385
01:09:38,320 --> 01:09:40,920
Speaker 4: you know, get in trouble for that. But here's the

1386
01:09:40,960 --> 01:09:46,519
Speaker 4: point you're making that friction a frictionless, effortless is is

1387
01:09:46,560 --> 01:09:49,599
Speaker 4: basically just a digital servant that just does whatever you want,

1388
01:09:49,680 --> 01:09:52,240
Speaker 4: says whatever you want, and just you know, praises you

1389
01:09:52,280 --> 01:09:54,240
Speaker 4: when you don't need praise. I mean, like you said,

1390
01:09:54,240 --> 01:09:56,880
Speaker 4: I mean, I'm a you know, sinner saved by grace.

1391
01:09:57,200 --> 01:09:58,639
Speaker 2: My wife puts up with more.

1392
01:09:58,520 --> 01:10:01,800
Speaker 4: Of my eccentricity as a writer and creative person and

1393
01:10:01,800 --> 01:10:05,960
Speaker 4: all that, and it's and she's enormously patient.

1394
01:10:06,080 --> 01:10:08,040
Speaker 2: To deal with all that.

1395
01:10:09,280 --> 01:10:11,679
Speaker 1: I'm always out my work when I'm writing Whenden, which

1396
01:10:11,720 --> 01:10:13,639
Speaker 1: is for me, it's sort of like a six months

1397
01:10:13,640 --> 01:10:16,000
Speaker 1: out of two years, I'm in heavy writing mode. And

1398
01:10:16,040 --> 01:10:18,559
Speaker 1: for you it's pretty constant. So your wife must be

1399
01:10:18,560 --> 01:10:18,880
Speaker 1: a saint.

1400
01:10:19,800 --> 01:10:20,599
Speaker 2: She is a saint.

1401
01:10:20,760 --> 01:10:23,120
Speaker 4: And anyone who knows he will say she's a say

1402
01:10:23,320 --> 01:10:26,920
Speaker 4: because I'm I'm always this eccentric, you know, state of mind.

1403
01:10:27,240 --> 01:10:30,599
Speaker 4: And but that friction is what makes the messiness beautiful

1404
01:10:30,640 --> 01:10:33,880
Speaker 4: of love and forgiveness and and humanity.

1405
01:10:34,080 --> 01:10:35,400
Speaker 2: But what has happened in.

1406
01:10:35,400 --> 01:10:39,439
Speaker 4: Our swipe left, swipe right culture, and that's another big

1407
01:10:39,479 --> 01:10:43,320
Speaker 4: part of this is dating culture and online culture has created,

1408
01:10:43,640 --> 01:10:46,719
Speaker 4: uh this, this really really cut throat.

1409
01:10:46,920 --> 01:10:50,360
Speaker 2: Only a perfect ten, only a perfect nine. Anybody who's

1410
01:10:50,400 --> 01:10:53,320
Speaker 2: got flaws, anybody who's not rich and has a sort

1411
01:10:53,320 --> 01:10:56,920
Speaker 2: of Instagram modeled look or vibe or something, I can

1412
01:10:57,000 --> 01:10:57,880
Speaker 2: quickly move through.

1413
01:10:58,120 --> 01:11:01,160
Speaker 4: The disparity in the dating mark is a big part

1414
01:11:01,200 --> 01:11:03,280
Speaker 4: of this. And I'll just give you one second on that.

1415
01:11:03,680 --> 01:11:07,280
Speaker 4: So the researchers you talk about online swipe culture. They

1416
01:11:07,320 --> 01:11:11,720
Speaker 4: will tell you that men will swipe left or right.

1417
01:11:11,760 --> 01:11:14,240
Speaker 4: You know, they'll they'll choose women that they think are

1418
01:11:14,439 --> 01:11:18,639
Speaker 4: equivalent to them or down right, whether it's on earnings,

1419
01:11:18,760 --> 01:11:23,280
Speaker 4: education and yeah, women women though are what are known

1420
01:11:23,320 --> 01:11:25,840
Speaker 4: as hypergamists. This is there's a lot of this is

1421
01:11:25,880 --> 01:11:30,679
Speaker 4: academic research. Hypergamist dating mate selection means you will only

1422
01:11:30,800 --> 01:11:35,040
Speaker 4: date horizontal and vertical. So if you are a woman

1423
01:11:35,040 --> 01:11:37,439
Speaker 4: who has a master's or a PhD or like your

1424
01:11:37,560 --> 01:11:40,200
Speaker 4: wife is a doctor, you know, people who are highly educated,

1425
01:11:40,360 --> 01:11:41,000
Speaker 4: high income.

1426
01:11:41,320 --> 01:11:44,000
Speaker 2: Now what happens is of course a good.

1427
01:11:43,800 --> 01:11:45,920
Speaker 4: Thing women have, you know, are more educated than ever

1428
01:11:46,000 --> 01:11:46,880
Speaker 4: and they're making more.

1429
01:11:47,080 --> 01:11:47,559
Speaker 2: That's great.

1430
01:11:47,600 --> 01:11:49,840
Speaker 4: We you know, believe in you know, flourishing for all.

1431
01:11:50,080 --> 01:11:53,800
Speaker 4: But that means that the hypergamist mate selection is going

1432
01:11:53,880 --> 01:11:56,160
Speaker 4: to weed out a quote. You know, if you're on

1433
01:11:56,200 --> 01:11:58,880
Speaker 4: a ten to one scale, anybody who's a five or

1434
01:11:58,920 --> 01:12:01,200
Speaker 4: a four or three or two. So what has happened

1435
01:12:01,240 --> 01:12:05,240
Speaker 4: is these dating apps they're telling us that women will

1436
01:12:05,280 --> 01:12:10,160
Speaker 4: swipe eighty percent on only the top men. And so

1437
01:12:10,200 --> 01:12:12,720
Speaker 4: you have all these men who are sort of forgotten

1438
01:12:13,040 --> 01:12:14,280
Speaker 4: and where are they going to go?

1439
01:12:14,439 --> 01:12:16,479
Speaker 2: Have no fear, your AI girlfriend is here.

1440
01:12:16,560 --> 01:12:18,519
Speaker 4: Who's going to love you and be beautiful and tell

1441
01:12:18,560 --> 01:12:20,599
Speaker 4: you you're you know, a night and shining armor and

1442
01:12:20,640 --> 01:12:24,559
Speaker 4: you're just the smartest, most handsome man and confident and wonderful.

1443
01:12:24,760 --> 01:12:28,880
Speaker 4: So this is we're set up for a real societal

1444
01:12:28,920 --> 01:12:31,080
Speaker 4: shift here. And then I think the final thing is

1445
01:12:31,080 --> 01:12:34,240
Speaker 4: the COVID generation. You know, these poor kids. You're in California.

1446
01:12:34,240 --> 01:12:37,080
Speaker 4: At least I was in freedom loving Florida. We didn't

1447
01:12:37,120 --> 01:12:38,920
Speaker 4: have it as bad as you did. But you know,

1448
01:12:38,960 --> 01:12:41,519
Speaker 4: this whole that generation. They didn't get to go to

1449
01:12:41,560 --> 01:12:43,880
Speaker 4: their prom, they didn't get to go to their homecoming,

1450
01:12:43,920 --> 01:12:45,880
Speaker 4: They got had to wear masks like they were in.

1451
01:12:45,920 --> 01:12:47,559
Speaker 2: A hospital ward.

1452
01:12:47,880 --> 01:12:51,280
Speaker 4: They didn't get all that socialization and the loneliness epidemic

1453
01:12:51,280 --> 01:12:54,559
Speaker 4: and the isolation. They didn't get to develop as many

1454
01:12:54,560 --> 01:12:57,799
Speaker 4: of those social interaction skills that we all as people

1455
01:12:57,920 --> 01:13:00,000
Speaker 4: have to go through, you know, the awkwardness of learning

1456
01:13:00,080 --> 01:13:03,080
Speaker 4: how to ask a young lady out or have courtship.

1457
01:13:03,200 --> 01:13:07,439
Speaker 4: So this is a perfect storm societally with this AI

1458
01:13:07,640 --> 01:13:10,400
Speaker 4: companion stuff and then the real dark stuff, which is

1459
01:13:10,680 --> 01:13:14,800
Speaker 4: you know, coaching the girl, the chatbot that the young

1460
01:13:14,880 --> 01:13:16,640
Speaker 4: man who had mental challenges that I talk about in

1461
01:13:16,720 --> 01:13:19,760
Speaker 4: Code Red who fought his chatbot girlfriend was saying to

1462
01:13:19,800 --> 01:13:22,479
Speaker 4: come join him in her in the afterlife, and he

1463
01:13:22,840 --> 01:13:25,160
Speaker 4: put a gun in his mouth and killed himself. And

1464
01:13:25,280 --> 01:13:29,400
Speaker 4: that's that's happening. That's not one isolated incident.

1465
01:13:29,640 --> 01:13:32,599
Speaker 1: Yeah, no, that's another super frightening one where you walk

1466
01:13:32,680 --> 01:13:36,080
Speaker 1: through how the AI basically talked the guy into suicide,

1467
01:13:36,120 --> 01:13:39,680
Speaker 1: which is like just completely crazy and very hard to

1468
01:13:40,600 --> 01:13:42,680
Speaker 1: but it still seems very hard to regulate. And that's

1469
01:13:42,720 --> 01:13:45,200
Speaker 1: why we get to be very careful about this. When

1470
01:13:45,360 --> 01:13:47,400
Speaker 1: this is my last really big point. Here's what I

1471
01:13:47,600 --> 01:13:49,120
Speaker 1: do in the remain time for today, And while we're

1472
01:13:49,200 --> 01:13:51,479
Speaker 1: to do way more of these, will do these regularly,

1473
01:13:51,640 --> 01:13:53,880
Speaker 1: but the for today, I want to go through the

1474
01:13:54,120 --> 01:13:56,920
Speaker 1: various careers and I want you to kind of give

1475
01:13:56,960 --> 01:13:59,320
Speaker 1: me a Things will get better, things will get worse.

1476
01:14:00,080 --> 01:14:02,960
Speaker 1: I will be indifferent, and I want to go through that.

1477
01:14:03,080 --> 01:14:05,679
Speaker 1: But one thing that I do think we should touch

1478
01:14:05,720 --> 01:14:07,760
Speaker 1: on that's sort of a big picture that I've been

1479
01:14:07,840 --> 01:14:11,240
Speaker 1: increasingly concerned about, and maybe I need to turn this

1480
01:14:11,240 --> 01:14:15,040
Speaker 1: into some sort of a book. But we cured boredom

1481
01:14:16,000 --> 01:14:19,120
Speaker 1: with the smartphone, and so we never have a moment

1482
01:14:19,160 --> 01:14:23,439
Speaker 1: of downtime, and I think it's been a disaster for frankly,

1483
01:14:23,600 --> 01:14:27,200
Speaker 1: for society. I think that it means that we're thinking deeply,

1484
01:14:27,600 --> 01:14:31,160
Speaker 1: we can't concentrate. We're thinking deeply so much less often,

1485
01:14:31,960 --> 01:14:35,040
Speaker 1: and we're going to not learn to not appreciate some

1486
01:14:35,160 --> 01:14:38,800
Speaker 1: of the most incredible achievements of humankind, from art to

1487
01:14:39,120 --> 01:14:41,320
Speaker 1: music to things like that, and we're going to i think,

1488
01:14:41,360 --> 01:14:43,080
Speaker 1: miss out on coming up with a lot of great

1489
01:14:43,160 --> 01:14:45,720
Speaker 1: ideas on our own, which are rewarding. It's way more

1490
01:14:45,760 --> 01:14:47,400
Speaker 1: rewarding to come up with a good idea than just

1491
01:14:47,479 --> 01:14:50,120
Speaker 1: to read it on the internet. And I know convenience

1492
01:14:50,479 --> 01:14:53,439
Speaker 1: is a god to a lot of people, but we've

1493
01:14:53,479 --> 01:14:55,799
Speaker 1: already kind of, I think made a lot of mistakes

1494
01:14:55,960 --> 01:15:00,759
Speaker 1: with our just immediate embrace of the constant smart phone culture,

1495
01:15:00,880 --> 01:15:03,320
Speaker 1: and this is just really going to accelerate that. And

1496
01:15:03,360 --> 01:15:05,759
Speaker 1: so I'm worried about the crisis of purpose that AI

1497
01:15:05,880 --> 01:15:08,320
Speaker 1: could bring on for people. If you really do only

1498
01:15:08,400 --> 01:15:10,400
Speaker 1: have to work two or three days a week, If

1499
01:15:10,439 --> 01:15:12,360
Speaker 1: we get our humanoid robots who are doing all the

1500
01:15:12,439 --> 01:15:14,280
Speaker 1: dishes for us and go in the grocery store for us,

1501
01:15:14,720 --> 01:15:17,640
Speaker 1: and the drones are just going to deliver all the

1502
01:15:17,680 --> 01:15:19,080
Speaker 1: stuff that we need, so that we never have to

1503
01:15:19,200 --> 01:15:21,960
Speaker 1: leave the house again. Are we really going to do

1504
01:15:22,200 --> 01:15:23,920
Speaker 1: something great with that time? Or are we just all

1505
01:15:24,120 --> 01:15:27,080
Speaker 1: gonna just completely commit the Bible to memory? Are we

1506
01:15:27,160 --> 01:15:31,160
Speaker 1: all going to take up pottery and painting and the violin?

1507
01:15:31,479 --> 01:15:32,960
Speaker 1: I don't think so. I think we're going to do

1508
01:15:33,000 --> 01:15:34,599
Speaker 1: a lot of really stupid stuff with all that time.

1509
01:15:34,600 --> 01:15:37,120
Speaker 1: We're going to get back and we start talking about

1510
01:15:37,120 --> 01:15:39,120
Speaker 1: that now and be ready.

1511
01:15:38,880 --> 01:15:41,040
Speaker 2: For it exactly.

1512
01:15:41,240 --> 01:15:44,680
Speaker 4: That is my last chapter, and the epilogue is on

1513
01:15:44,840 --> 01:15:47,920
Speaker 4: the crisis of meaning, faith, reason. What all these things

1514
01:15:47,960 --> 01:15:50,800
Speaker 4: are going to mean. Let's take those pieces you laid out.

1515
01:15:50,880 --> 01:15:55,200
Speaker 4: One dopamine loops is what that's called. Their brain scientists,

1516
01:15:55,280 --> 01:15:58,839
Speaker 4: neuroscientists have learned, and they are part of the tech industry,

1517
01:15:59,240 --> 01:16:02,800
Speaker 4: how to growth hack and our neural network, our neural

1518
01:16:03,080 --> 01:16:06,479
Speaker 4: network humans to be able to keep us scrolling and

1519
01:16:06,560 --> 01:16:09,599
Speaker 4: doom scrolling and give us that little dopamine hit, right.

1520
01:16:09,840 --> 01:16:13,840
Speaker 2: And so what happens is is that rewards center is triggered.

1521
01:16:13,520 --> 01:16:15,799
Speaker 4: And then I just keep coming back and I'm hooked

1522
01:16:15,800 --> 01:16:18,800
Speaker 4: almost like a drug, right, And and that becomes very

1523
01:16:18,960 --> 01:16:22,400
Speaker 4: very dilatorious to our to our mental health, but also

1524
01:16:22,600 --> 01:16:26,280
Speaker 4: our striving think about that part you were just talking about.

1525
01:16:26,360 --> 01:16:28,280
Speaker 4: I mean, you know, what is one of the first

1526
01:16:28,320 --> 01:16:29,960
Speaker 4: things that when you go to a party with your

1527
01:16:30,040 --> 01:16:31,760
Speaker 4: you know, your wife or your family and your or

1528
01:16:31,920 --> 01:16:34,400
Speaker 4: backyard barbecue, and what's the first thing you say?

1529
01:16:34,479 --> 01:16:36,080
Speaker 2: What do you do for a living? Right? It's part

1530
01:16:36,120 --> 01:16:38,880
Speaker 2: of our identity. And and if you believe in you know.

1531
01:16:39,000 --> 01:16:43,760
Speaker 4: Scripture and from a biblical tradition, you know, striving and

1532
01:16:43,960 --> 01:16:47,360
Speaker 4: working hard is part of that. You know Christian you know,

1533
01:16:47,400 --> 01:16:50,640
Speaker 4: the Protestant work ethic or the Judeo Christian ethic, that

1534
01:16:51,080 --> 01:16:54,160
Speaker 4: that has been the fulk that is the axis upon

1535
01:16:54,320 --> 01:16:58,439
Speaker 4: which free market capitalism was created. And finally, I think

1536
01:16:58,520 --> 01:17:00,760
Speaker 4: that when we know, when you leave me alone to

1537
01:17:00,920 --> 01:17:04,320
Speaker 4: our own devices and we don't have a sense of purpose,

1538
01:17:04,439 --> 01:17:06,760
Speaker 4: we don't have to get up every morning and have

1539
01:17:06,960 --> 01:17:08,800
Speaker 4: the structure to go out and I've got to feed

1540
01:17:08,920 --> 01:17:11,720
Speaker 4: my wife and my children, and We're just left to

1541
01:17:11,880 --> 01:17:16,280
Speaker 4: our own sort of testosterone and and without that structure

1542
01:17:16,360 --> 01:17:20,919
Speaker 4: around us, of the civilizational institutions, like the family structure,

1543
01:17:21,479 --> 01:17:24,479
Speaker 4: like work structure, and like you know, some kind of

1544
01:17:24,520 --> 01:17:29,040
Speaker 4: belief system, whether it's faith in God or other, we

1545
01:17:29,160 --> 01:17:31,400
Speaker 4: get very very very toxic, very quickly.

1546
01:17:31,600 --> 01:17:34,680
Speaker 2: I mean, how do you think terrorist recruitment works. Right?

1547
01:17:34,840 --> 01:17:40,160
Speaker 2: It prays upon testosterone fueled rage. Men who are angry.

1548
01:17:39,880 --> 01:17:42,040
Speaker 4: At the world and you know, don't have a lot

1549
01:17:42,120 --> 01:17:45,040
Speaker 4: of prospects, and therefore they go off and do horrible things.

1550
01:17:45,080 --> 01:17:48,440
Speaker 4: And so so this is a real, you know, societal

1551
01:17:48,560 --> 01:17:51,599
Speaker 4: inflection point, and you're going to talk about social disruption

1552
01:17:51,880 --> 01:17:55,120
Speaker 4: if this starts to scale and metastasize. Now, as it

1553
01:17:55,200 --> 01:17:57,759
Speaker 4: relates to the job stuff, if you do something behind

1554
01:17:57,800 --> 01:18:01,960
Speaker 4: a computer with repetitive task asks, you really need to

1555
01:18:02,000 --> 01:18:04,840
Speaker 4: be very very aware of how fast the tideline is

1556
01:18:04,920 --> 01:18:07,880
Speaker 4: rising around you. Number two, if what you do can

1557
01:18:08,040 --> 01:18:12,719
Speaker 4: be put into a workflow chain that is right for automation.

1558
01:18:13,000 --> 01:18:15,280
Speaker 2: There are already are workflow chains.

1559
01:18:15,080 --> 01:18:20,439
Speaker 4: Like Nate and n zapiar, and so those kinds of

1560
01:18:21,520 --> 01:18:25,639
Speaker 4: ability to step one, step two, step three, step four all.

1561
01:18:25,520 --> 01:18:27,400
Speaker 2: In a row that can be automated.

1562
01:18:27,760 --> 01:18:30,320
Speaker 4: Next, this is an interesting argument I'm starting to see

1563
01:18:30,360 --> 01:18:34,439
Speaker 4: emerge in the AI industry. They're looking at gender and

1564
01:18:34,640 --> 01:18:38,280
Speaker 4: what kind of jobs break female more disproportionately than men,

1565
01:18:38,680 --> 01:18:42,679
Speaker 4: and they're arguing that it could be that this could

1566
01:18:42,760 --> 01:18:47,599
Speaker 4: really affect democratic women more because they trend toward more

1567
01:18:47,640 --> 01:18:51,960
Speaker 4: of the humanities as opposed to blue collar workers who

1568
01:18:52,000 --> 01:18:55,280
Speaker 4: are less at risk originally, and that this could actually,

1569
01:18:55,600 --> 01:18:58,160
Speaker 4: you know, be a boon for blue collar workers. As

1570
01:18:58,200 --> 01:19:01,559
Speaker 4: the data center buildouts continue, the trade jobs of concrete

1571
01:19:01,600 --> 01:19:05,400
Speaker 4: pouring and drywall and hvag plumbing and electrical and all

1572
01:19:05,439 --> 01:19:08,880
Speaker 4: these people are booming, and that the kinds of things

1573
01:19:08,920 --> 01:19:12,040
Speaker 4: that are done in a repetitive setting, if we're in

1574
01:19:12,120 --> 01:19:15,200
Speaker 4: a sort of more of a traditional setting that affects

1575
01:19:15,360 --> 01:19:18,160
Speaker 4: female jobs could actually disproportionately. We're gonna have to wait

1576
01:19:18,200 --> 01:19:20,560
Speaker 4: and see if that happens. And then I think the

1577
01:19:20,680 --> 01:19:24,479
Speaker 4: final thing would be this. If you think that, oh,

1578
01:19:24,880 --> 01:19:27,720
Speaker 4: what I do could never be replaced, you need to

1579
01:19:27,840 --> 01:19:32,240
Speaker 4: look at two things. Number one, where is a gentic

1580
01:19:32,400 --> 01:19:36,200
Speaker 4: or agent AI in my industry? And number two, you

1581
01:19:36,320 --> 01:19:40,400
Speaker 4: need to look at something called MCP model Context protocol.

1582
01:19:40,920 --> 01:19:44,080
Speaker 2: That is a fancy way of saying how an agent

1583
01:19:44,280 --> 01:19:44,639
Speaker 2: is going.

1584
01:19:44,560 --> 01:19:49,080
Speaker 4: To get hooked to your company's context data, all of

1585
01:19:49,439 --> 01:19:53,120
Speaker 4: your financials, all of that asking and watching in your

1586
01:19:53,240 --> 01:19:54,479
Speaker 4: company or your industry.

1587
01:19:55,040 --> 01:19:56,479
Speaker 2: When those two pieces are.

1588
01:19:56,439 --> 01:19:59,880
Speaker 4: Connected, it's kind of like legos locking together. When agents

1589
01:20:00,120 --> 01:20:03,720
Speaker 4: and MCP start to lock together, that's when you really

1590
01:20:03,800 --> 01:20:06,000
Speaker 4: need to start paying attention, or if you start hearing

1591
01:20:06,040 --> 01:20:08,160
Speaker 4: your boss talking about these types of oh we're going

1592
01:20:08,200 --> 01:20:09,360
Speaker 4: to get some agents and so forth and so on,

1593
01:20:09,880 --> 01:20:11,880
Speaker 4: really be paying attention. I think the biggest thing I

1594
01:20:12,040 --> 01:20:16,400
Speaker 4: learned after spending two years living in this world very

1595
01:20:16,520 --> 01:20:19,719
Speaker 4: very deeply, reading thousands of articles and tons of books

1596
01:20:19,720 --> 01:20:22,120
Speaker 4: and so forth, is you really have to have a

1597
01:20:22,200 --> 01:20:26,280
Speaker 4: lot of humility because people that make bold predictions this

1598
01:20:26,360 --> 01:20:29,879
Speaker 4: will never happen, this will never Two weeks later it happens.

1599
01:20:30,080 --> 01:20:33,200
Speaker 2: And so if you think that it's hype, look at

1600
01:20:33,240 --> 01:20:33,960
Speaker 2: those pieces.

1601
01:20:34,479 --> 01:20:36,560
Speaker 4: If you think that, oh, there's no going to be

1602
01:20:36,640 --> 01:20:39,160
Speaker 4: no problems, I would really tell you to look at

1603
01:20:39,200 --> 01:20:40,920
Speaker 4: some of the literature we've been talking about as it

1604
01:20:40,960 --> 01:20:45,320
Speaker 4: relates to the human impact as well on individuals.

1605
01:20:45,720 --> 01:20:47,960
Speaker 1: Okay, Wenjin, I want to go through You alluded to

1606
01:20:48,120 --> 01:20:51,559
Speaker 1: some of this earlier about where we're at in terms

1607
01:20:51,560 --> 01:20:52,880
Speaker 1: of careers, and I want to start with one that

1608
01:20:52,920 --> 01:20:54,840
Speaker 1: this will be more than this career, because this will

1609
01:20:55,240 --> 01:20:57,120
Speaker 1: this is not just an industry, but it's also something

1610
01:20:57,120 --> 01:20:59,479
Speaker 1: that's a part of most of our daily lives, and

1611
01:21:00,120 --> 01:21:04,160
Speaker 1: that's education. This is one where I feel like I'm

1612
01:21:04,160 --> 01:21:05,800
Speaker 1: hoping you're going to say there's a positive that this

1613
01:21:05,880 --> 01:21:08,280
Speaker 1: could blow up the university and industrial complex.

1614
01:21:08,360 --> 01:21:09,040
Speaker 3: It could be really good.

1615
01:21:09,479 --> 01:21:11,640
Speaker 1: But also it's going to make cheating and laziness a

1616
01:21:11,680 --> 01:21:15,040
Speaker 1: lot easier for a students who are inclined to take

1617
01:21:15,080 --> 01:21:17,800
Speaker 1: advantage in that way. And so, but how is AI

1618
01:21:18,240 --> 01:21:19,920
Speaker 1: affecting and will affect education?

1619
01:21:20,920 --> 01:21:23,840
Speaker 2: So in my chapter in code Reata on education, what

1620
01:21:23,960 --> 01:21:25,880
Speaker 2: I what I do is as I do throughout the book,

1621
01:21:25,960 --> 01:21:28,080
Speaker 2: lay out what I call roses and land mines. And

1622
01:21:28,200 --> 01:21:29,880
Speaker 2: so the roses are many.

1623
01:21:30,560 --> 01:21:33,760
Speaker 4: I'm very hopeful about this because if you have guardrails

1624
01:21:33,840 --> 01:21:38,759
Speaker 4: safe with non woke curriculum baked into that AI tutor,

1625
01:21:39,439 --> 01:21:42,080
Speaker 4: think about how cool this is. You basically have Aristotle

1626
01:21:42,400 --> 01:21:44,640
Speaker 4: in your pocket for your kid. And even if that

1627
01:21:44,720 --> 01:21:46,880
Speaker 4: kid's an inner city kid. You know, I was a

1628
01:21:46,960 --> 01:21:49,280
Speaker 4: professor an instructor of college at one of.

1629
01:21:49,280 --> 01:21:53,719
Speaker 2: The poorest congressional districts. It was a small, tiny, little college.

1630
01:21:53,960 --> 01:21:56,200
Speaker 4: These are people that could not afford a two hundred

1631
01:21:56,200 --> 01:21:59,200
Speaker 4: and fifty dollars fancy, you know, Harvard trained tutor and

1632
01:21:59,240 --> 01:21:59,960
Speaker 4: all this kind of stuff.

1633
01:22:00,280 --> 01:22:02,439
Speaker 2: But imagine that we could give kids in.

1634
01:22:02,520 --> 01:22:05,439
Speaker 4: Any economic setting who have fire in the belly and

1635
01:22:05,479 --> 01:22:08,000
Speaker 4: a desire to learn all the kind of way to

1636
01:22:08,160 --> 01:22:12,440
Speaker 4: zoom as fast as they want. Machine learning allows for customization,

1637
01:22:12,680 --> 01:22:15,479
Speaker 4: so that it can learn your child is really good

1638
01:22:15,720 --> 01:22:19,040
Speaker 4: in physics, but she really struggles in algebra, or she's

1639
01:22:19,080 --> 01:22:21,320
Speaker 4: really good in X versus why, and then it can

1640
01:22:21,600 --> 01:22:26,200
Speaker 4: automatically create tests and quizzes and homework to shore up

1641
01:22:26,280 --> 01:22:30,880
Speaker 4: your deficiencies and be able to accelerate the child's learning

1642
01:22:30,920 --> 01:22:33,160
Speaker 4: in those areas where she's already advanced. You know, one

1643
01:22:33,200 --> 01:22:35,719
Speaker 4: of the problems in a large classroom is the teacher

1644
01:22:35,840 --> 01:22:38,000
Speaker 4: has to slow down for some kids and try to

1645
01:22:38,040 --> 01:22:41,480
Speaker 4: speed up for others, and very hard. This allows for customization,

1646
01:22:41,600 --> 01:22:44,759
Speaker 4: so there's a lot of opportunity there the homeschooling crowd,

1647
01:22:45,240 --> 01:22:49,599
Speaker 4: huge opportunity Christian education. If you bake in theologically sound,

1648
01:22:49,680 --> 01:22:53,400
Speaker 4: doctrinally sound curriculum into the AI the way it's built

1649
01:22:53,400 --> 01:22:55,840
Speaker 4: into the corpus of the information, you can have an

1650
01:22:56,000 --> 01:22:59,240
Speaker 4: enormous benefit for them in that. Now the flip side,

1651
01:22:59,240 --> 01:23:02,640
Speaker 4: of course, is a cheat GBT, not chat GBT. We

1652
01:23:02,760 --> 01:23:06,120
Speaker 4: know it's a massive problem. Professors and instructors are saying

1653
01:23:06,280 --> 01:23:10,320
Speaker 4: that the cornerstone of pedagogy, the written essay, this has

1654
01:23:10,400 --> 01:23:15,000
Speaker 4: been obliterated because the chat GBT, not just chat GBT.

1655
01:23:14,880 --> 01:23:17,080
Speaker 2: Any kind of advanced LLM that writes.

1656
01:23:17,640 --> 01:23:19,320
Speaker 4: All you got to do is problem say write this

1657
01:23:19,520 --> 01:23:23,120
Speaker 4: out an eleventh grade level, throw in some references to

1658
01:23:23,640 --> 01:23:28,000
Speaker 4: you know, LSU football or whatever the kid likes, and

1659
01:23:28,200 --> 01:23:29,639
Speaker 4: make it sound like my voice.

1660
01:23:30,040 --> 01:23:33,439
Speaker 2: And so that is very very real problem. So parents

1661
01:23:33,479 --> 01:23:35,639
Speaker 2: have got to be aware. That's why I wrote Code.

1662
01:23:35,479 --> 01:23:39,360
Speaker 4: Read was to really again create an alert, a code red,

1663
01:23:39,520 --> 01:23:41,600
Speaker 4: a red alert, but also give you the code the

1664
01:23:41,640 --> 01:23:44,320
Speaker 4: principles to how to get through it. And you've got

1665
01:23:44,439 --> 01:23:47,920
Speaker 4: to understand that the cognitive offloading and the critical thinking

1666
01:23:47,960 --> 01:23:53,000
Speaker 4: and erosion studies are horrifying for educational attainment because, as.

1667
01:23:52,880 --> 01:23:54,759
Speaker 2: You know, you compound your learning.

1668
01:23:54,840 --> 01:23:58,680
Speaker 4: If you've didn't learn the principles in sixth grade, by

1669
01:23:58,720 --> 01:24:00,600
Speaker 4: the time you get to ninth grade math or a

1670
01:24:00,680 --> 01:24:03,120
Speaker 4: tenth grade math or eleventh grade math, you're lost because

1671
01:24:03,120 --> 01:24:05,080
Speaker 4: you didn't get that building block that you needed.

1672
01:24:05,160 --> 01:24:08,439
Speaker 2: So so you have got to learn how to guard,

1673
01:24:08,880 --> 01:24:09,799
Speaker 2: you know, your child.

1674
01:24:09,640 --> 01:24:12,160
Speaker 4: And navigate them through those land mines and roses. And

1675
01:24:13,040 --> 01:24:14,760
Speaker 4: I think we've got to do it quickly because this

1676
01:24:14,840 --> 01:24:16,360
Speaker 4: stuff is is it's not in the.

1677
01:24:16,400 --> 01:24:17,400
Speaker 2: Future, it's right now.

1678
01:24:17,760 --> 01:24:19,160
Speaker 1: Yeah, it's right now, and I got to I'm a

1679
01:24:19,160 --> 01:24:22,080
Speaker 1: little excited about this part of it for me because

1680
01:24:22,560 --> 01:24:25,960
Speaker 1: I do feel like the you know, I've got four children,

1681
01:24:26,080 --> 01:24:29,640
Speaker 1: and I just think that there's no way that just mathematically,

1682
01:24:29,800 --> 01:24:31,280
Speaker 1: all four of them are going to want the same

1683
01:24:31,600 --> 01:24:32,559
Speaker 1: university experience.

1684
01:24:32,640 --> 01:24:34,080
Speaker 3: It doesn't even really exist, but.

1685
01:24:34,439 --> 01:24:36,720
Speaker 1: Kind of exists in our head, where you just you

1686
01:24:36,880 --> 01:24:39,400
Speaker 1: do well on the SATs and then hopefully you get

1687
01:24:39,400 --> 01:24:41,280
Speaker 1: into a private school on the East coast, and you

1688
01:24:41,400 --> 01:24:43,800
Speaker 1: go and you spend most of the time drunk or

1689
01:24:43,840 --> 01:24:46,519
Speaker 1: getting indoctrinated, and then you come back with a piece

1690
01:24:46,560 --> 01:24:48,720
Speaker 1: of paper that has almost no value. But we still

1691
01:24:48,800 --> 01:24:52,400
Speaker 1: do this ritual that's super expensive, and it's I'm happy

1692
01:24:52,479 --> 01:24:54,240
Speaker 1: that I feel like that's going to end for some

1693
01:24:54,360 --> 01:24:56,960
Speaker 1: of them. They won't have to go through that time waste,

1694
01:24:57,880 --> 01:24:59,880
Speaker 1: and that they could teach themselves or they could be

1695
01:25:00,080 --> 01:25:02,040
Speaker 1: taught with various programs that are just in their pocket.

1696
01:25:02,120 --> 01:25:05,919
Speaker 1: I think that's going to be good. But I'm also concerned,

1697
01:25:05,920 --> 01:25:08,639
Speaker 1: of course, about the cheating that's going to be It's

1698
01:25:08,680 --> 01:25:11,040
Speaker 1: going to be seen that if you're not cheating, you're

1699
01:25:11,080 --> 01:25:13,000
Speaker 1: falling behind sort of. If you're not cheating, you're not

1700
01:25:13,080 --> 01:25:16,960
Speaker 1: trying a sort of thing, and it could really institutionalize

1701
01:25:17,000 --> 01:25:20,280
Speaker 1: some really bad values here. And it also went in

1702
01:25:20,960 --> 01:25:23,200
Speaker 1: it reminds us how important it is that we make

1703
01:25:23,320 --> 01:25:26,200
Speaker 1: sure that it's not garbage in garbage out. That if

1704
01:25:26,240 --> 01:25:30,120
Speaker 1: there's a lot of garbage that's coming into the llms

1705
01:25:30,280 --> 01:25:33,000
Speaker 1: that are teaching our kids, then they're going to be

1706
01:25:33,120 --> 01:25:35,400
Speaker 1: learning the exact type of stuff that we don't want

1707
01:25:35,439 --> 01:25:38,040
Speaker 1: them learning from the professors. All right, I've been talking

1708
01:25:38,040 --> 01:25:40,479
Speaker 1: about this for a few months now. Why because it's

1709
01:25:40,560 --> 01:25:43,519
Speaker 1: important and it's helping me. It's a handheld red light

1710
01:25:43,600 --> 01:25:46,840
Speaker 1: therapy device for pain and inflammation called the super Palm

1711
01:25:47,000 --> 01:25:49,120
Speaker 1: ten fifty. I use it right at home, and what

1712
01:25:49,200 --> 01:25:51,680
Speaker 1: I really like about is it's actually working on my

1713
01:25:51,880 --> 01:25:55,320
Speaker 1: back and my knee and no drugs, no invasion, and

1714
01:25:55,560 --> 01:25:57,599
Speaker 1: wow have I generally noticed a difference.

1715
01:25:57,800 --> 01:25:58,479
Speaker 3: Here's how it works.

1716
01:25:58,640 --> 01:26:01,679
Speaker 1: Some power led makes devices that deliver red and near

1717
01:26:01,760 --> 01:26:05,960
Speaker 1: infrared light energy directly into your cells reduces pain and inflammation.

1718
01:26:06,160 --> 01:26:10,240
Speaker 1: The science is called photo biomodulation or PBM, also known

1719
01:26:10,280 --> 01:26:11,800
Speaker 1: as red light therapy. You guys have heard about that

1720
01:26:12,000 --> 01:26:14,920
Speaker 1: this isn't some trendy wellness fab though PBM has been

1721
01:26:14,960 --> 01:26:17,800
Speaker 1: researched around the world for decades. In fact, according to

1722
01:26:17,840 --> 01:26:21,800
Speaker 1: the PBM Foundation, over one hundred million patient treatments have

1723
01:26:21,880 --> 01:26:25,000
Speaker 1: been performed without any documented side effects. The engineering is

1724
01:26:25,040 --> 01:26:28,200
Speaker 1: impressive too. Some power LED devices feature a patent pended

1725
01:26:28,240 --> 01:26:30,400
Speaker 1: cooling system that lets you hold it closer to your skin,

1726
01:26:30,560 --> 01:26:34,439
Speaker 1: allowing deeper light penetration to reach your pain and inflammation.

1727
01:26:34,680 --> 01:26:38,280
Speaker 1: You can treat arthritis, knee pain, back, even tonitis, joints,

1728
01:26:38,360 --> 01:26:41,280
Speaker 1: and more. The handheld super Palm ten fifty also creates

1729
01:26:41,360 --> 01:26:44,519
Speaker 1: what's called a wall of light. The LEDs project at

1730
01:26:44,520 --> 01:26:47,360
Speaker 1: a ninety degree angle, which eliminates hot spots and spreads

1731
01:26:47,439 --> 01:26:50,479
Speaker 1: light evenly over a wider area, making it five to

1732
01:26:50,680 --> 01:26:54,360
Speaker 1: nine times more effective than many alternative technologies. These are

1733
01:26:54,479 --> 01:26:57,320
Speaker 1: medical grade PBM devices you can use at home, and

1734
01:26:57,400 --> 01:27:01,360
Speaker 1: they're also used by dentists, chiropractors, physiotis, therapists, wellness centers,

1735
01:27:01,400 --> 01:27:04,080
Speaker 1: and even researcher studying PBM. If you want to learn more,

1736
01:27:04,280 --> 01:27:07,640
Speaker 1: check them out at SunPower led dot com. If you

1737
01:27:07,720 --> 01:27:10,400
Speaker 1: use the code marlow, you get ten percent off. Feel better,

1738
01:27:10,560 --> 01:27:13,519
Speaker 1: Be better, treat your pain and inflammation at home just

1739
01:27:13,600 --> 01:27:17,640
Speaker 1: like I do. SunPower led dot Com use the code, Marlow.

1740
01:27:17,600 --> 01:27:18,439
Speaker 2: It's exactly right.

1741
01:27:18,520 --> 01:27:22,000
Speaker 4: This is taking the textbook wars that conservatives have fought

1742
01:27:22,080 --> 01:27:26,400
Speaker 4: for decades and the woke professor and bias on steroids,

1743
01:27:27,200 --> 01:27:29,040
Speaker 4: and you're going to continue to see that. I think

1744
01:27:29,080 --> 01:27:32,400
Speaker 4: it will have a real shock though, to that, you know,

1745
01:27:33,040 --> 01:27:37,160
Speaker 4: a feat left intelligentsia that is so proud of their

1746
01:27:37,400 --> 01:27:39,840
Speaker 4: you know, two hundred and fifty thousand dollars degrees and

1747
01:27:40,040 --> 01:27:42,920
Speaker 4: they're you know, woke studies when they realize the price

1748
01:27:43,000 --> 01:27:43,880
Speaker 4: of intelligence is.

1749
01:27:43,920 --> 01:27:44,559
Speaker 2: Going to zero.

1750
01:27:45,000 --> 01:27:48,000
Speaker 4: All that quote expertise is now one prompt the way

1751
01:27:48,680 --> 01:27:50,600
Speaker 4: that a kid who's ten years old sitting on a

1752
01:27:50,680 --> 01:27:53,280
Speaker 4: playground is going to have access to And I think.

1753
01:27:53,240 --> 01:27:55,840
Speaker 2: One of the benefits of AI and this, and I

1754
01:27:55,920 --> 01:27:57,080
Speaker 2: go through this very deeply.

1755
01:27:57,080 --> 01:27:59,080
Speaker 4: In fact, I actually give you kind of the prompts

1756
01:27:59,120 --> 01:28:00,720
Speaker 4: that will help your to be able to do this.

1757
01:28:01,240 --> 01:28:04,280
Speaker 4: One of the biggest, most simple but powerful in the

1758
01:28:04,439 --> 01:28:08,720
Speaker 4: education space is the explain this to me like I'm

1759
01:28:08,760 --> 01:28:10,280
Speaker 4: in the fifth grade prompt.

1760
01:28:10,400 --> 01:28:11,600
Speaker 3: Yeah, I love that, I love this.

1761
01:28:11,840 --> 01:28:15,000
Speaker 1: Yeah, we didn't pose is a really tough question about

1762
01:28:15,080 --> 01:28:19,840
Speaker 1: quantum physics, and he is the additionally answer is gobbledygook.

1763
01:28:19,880 --> 01:28:21,240
Speaker 1: But then you say explains to me like I'm in

1764
01:28:21,280 --> 01:28:23,760
Speaker 1: the third grade, and it gives a great analogy on

1765
01:28:24,120 --> 01:28:25,960
Speaker 1: how that makes perfect sense.

1766
01:28:26,560 --> 01:28:28,880
Speaker 4: And it's so good for parents, because how many of

1767
01:28:29,000 --> 01:28:32,000
Speaker 4: us get these amazing, these little, beautiful minds of our

1768
01:28:32,000 --> 01:28:34,080
Speaker 4: beautiful children, and they ask these a great question.

1769
01:28:34,439 --> 01:28:36,360
Speaker 2: And then you know, we look at our wife or

1770
01:28:36,479 --> 01:28:37,640
Speaker 2: our our husband and you.

1771
01:28:37,720 --> 01:28:40,160
Speaker 4: Kind of go, man, I don't know what qui future,

1772
01:28:40,200 --> 01:28:42,360
Speaker 4: you know, whatever the topic is, And and then if

1773
01:28:42,400 --> 01:28:44,160
Speaker 4: you go to Google, it gives you all these you know,

1774
01:28:44,360 --> 01:28:47,280
Speaker 4: peer reviewed academic journals that are just as hard to understand.

1775
01:28:47,760 --> 01:28:51,080
Speaker 4: This is translating now, assuming it's accurate, right, And that's

1776
01:28:51,080 --> 01:28:52,240
Speaker 4: why hallucinations matter.

1777
01:28:52,640 --> 01:28:54,240
Speaker 2: These things are going to be a game changer.

1778
01:28:54,320 --> 01:28:57,200
Speaker 4: The flattening of the oh, I'm a you know, five

1779
01:28:57,360 --> 01:29:00,840
Speaker 4: time PhD and you know, women's studies whatever and such,

1780
01:29:01,240 --> 01:29:03,680
Speaker 4: that is going to be a flattener. The ability for

1781
01:29:04,120 --> 01:29:08,679
Speaker 4: low income kids in rural communities to have world class education,

1782
01:29:08,840 --> 01:29:10,960
Speaker 4: that is going to be a benefit. There are a

1783
01:29:11,040 --> 01:29:12,680
Speaker 4: lot of roses, and that's why in the book, I

1784
01:29:12,840 --> 01:29:15,639
Speaker 4: really try to show the hopeful positives as well as

1785
01:29:15,960 --> 01:29:17,080
Speaker 4: these real threat vectors.

1786
01:29:17,600 --> 01:29:20,320
Speaker 1: Right, Okay, let's talk about a place where I'm excited,

1787
01:29:21,120 --> 01:29:23,880
Speaker 1: which is medicine. This is of course my wife Field

1788
01:29:23,920 --> 01:29:26,880
Speaker 1: and most of her family. But it feels like AI

1789
01:29:27,080 --> 01:29:31,240
Speaker 1: medicine could make it so that we have a great

1790
01:29:31,320 --> 01:29:35,680
Speaker 1: tool for doctors and we might need fewer doctors potentially,

1791
01:29:36,160 --> 01:29:42,000
Speaker 1: and we could see really rapid advancement in the desire

1792
01:29:42,080 --> 01:29:43,880
Speaker 1: to try to cure disease and help us live longer.

1793
01:29:44,880 --> 01:29:47,400
Speaker 2: It's one of the things I am actually very optimistic about.

1794
01:29:47,400 --> 01:29:49,160
Speaker 4: And I say it in the opening of You know

1795
01:29:49,400 --> 01:29:51,839
Speaker 4: my Dad and my stepmom. My dad is a surgeon

1796
01:29:52,479 --> 01:29:56,679
Speaker 4: now retired. My stepmom also retired to nurse. My mom

1797
01:29:57,360 --> 01:29:59,439
Speaker 4: was a preschool teacher. So I think we just talked

1798
01:29:59,479 --> 01:30:03,120
Speaker 4: about the educ hopeful I think the science and medicine.

1799
01:30:03,160 --> 01:30:05,840
Speaker 4: I know your wife is a doctor, that is a

1800
01:30:05,960 --> 01:30:08,040
Speaker 4: very exciting area, assuming again.

1801
01:30:07,840 --> 01:30:11,719
Speaker 2: The ethics are you know, and you know there beyond

1802
01:30:12,080 --> 01:30:15,240
Speaker 2: the way it's used. The reason why is a couple

1803
01:30:15,280 --> 01:30:15,519
Speaker 2: of things.

1804
01:30:15,600 --> 01:30:21,360
Speaker 4: One we've already seen the Nobel given for Demis Hocipas,

1805
01:30:21,439 --> 01:30:24,760
Speaker 4: who is a world class AI leader for Alpha fold,

1806
01:30:25,080 --> 01:30:28,360
Speaker 4: which was looking at proteins. The reason why this is

1807
01:30:28,479 --> 01:30:34,000
Speaker 4: so powerful AI excels in pattern recognition. If somebody said

1808
01:30:34,040 --> 01:30:36,360
Speaker 4: you only have you know, two words to explain what

1809
01:30:36,520 --> 01:30:43,520
Speaker 4: it does. Pattern recognition. Now think about molecular structures, chemical structures,

1810
01:30:43,680 --> 01:30:44,880
Speaker 4: the scientific world.

1811
01:30:45,280 --> 01:30:47,200
Speaker 2: It is about patterns and connection.

1812
01:30:47,479 --> 01:30:50,280
Speaker 4: And the problem is is that we can't get through,

1813
01:30:50,439 --> 01:30:52,639
Speaker 4: you know, whether you're talking about DNA analysis or any

1814
01:30:52,640 --> 01:30:57,040
Speaker 4: other kind of thing, massive massive power pattern recognition structures.

1815
01:30:57,280 --> 01:30:58,200
Speaker 2: But now we can.

1816
01:30:58,360 --> 01:31:01,800
Speaker 4: The other thing is there's so many things that peer

1817
01:31:01,800 --> 01:31:05,679
Speaker 4: reviewed academic journal studies have gone through, but we didn't

1818
01:31:05,800 --> 01:31:09,559
Speaker 4: see another connecting point because it gets lost in these.

1819
01:31:09,520 --> 01:31:11,840
Speaker 2: Trillions of data points.

1820
01:31:11,880 --> 01:31:14,120
Speaker 4: Think of it like the stars in the sky, and

1821
01:31:14,320 --> 01:31:18,400
Speaker 4: now you can see new constellations than connections that never

1822
01:31:18,600 --> 01:31:20,960
Speaker 4: used to be able to be seen because either we

1823
01:31:21,000 --> 01:31:22,680
Speaker 4: didn't have the time, we didn't have the money, we

1824
01:31:22,760 --> 01:31:25,439
Speaker 4: didn't have the ability to be able to synthesize all

1825
01:31:25,479 --> 01:31:27,439
Speaker 4: that information. Even if you just leave it at that,

1826
01:31:27,640 --> 01:31:29,200
Speaker 4: and you don't have to believe that it's going to

1827
01:31:29,240 --> 01:31:31,920
Speaker 4: become sentient, or you don't have to believe that it's

1828
01:31:31,960 --> 01:31:34,280
Speaker 4: going to have some kind of you know, soul or whatever.

1829
01:31:35,040 --> 01:31:38,360
Speaker 4: Just pattern recognition at scale is going to unlock an

1830
01:31:38,600 --> 01:31:43,400
Speaker 4: enormous amount of scientific and medical possibility. So I am

1831
01:31:43,479 --> 01:31:46,320
Speaker 4: hopeful about that one thing that you know, where this

1832
01:31:46,439 --> 01:31:48,560
Speaker 4: gets a little murky is in this whole issue of

1833
01:31:48,760 --> 01:31:52,840
Speaker 4: you know, neulink and you know brain chips and implants.

1834
01:31:53,640 --> 01:31:56,640
Speaker 4: Because what people say is when you go to transhumanism,

1835
01:31:56,760 --> 01:32:02,280
Speaker 4: meaning beyond just human trends, transform beyond humans, this merger

1836
01:32:02,479 --> 01:32:06,880
Speaker 4: and the singularity between human and machine. Parents are gonna

1837
01:32:07,040 --> 01:32:10,000
Speaker 4: perhaps one day, this probably not for a while. Have

1838
01:32:10,160 --> 01:32:12,519
Speaker 4: to make a moral choice if I don't have a

1839
01:32:12,840 --> 01:32:16,640
Speaker 4: you know, an augmentated child with a neurallink, will they

1840
01:32:16,680 --> 01:32:18,360
Speaker 4: even be competitive in the job market?

1841
01:32:18,400 --> 01:32:21,960
Speaker 2: Will they even be competitive as a worker or in education?

1842
01:32:22,120 --> 01:32:25,439
Speaker 4: If one kid is natural human up against a kid

1843
01:32:25,760 --> 01:32:29,920
Speaker 4: who has literally all of AI in their system.

1844
01:32:30,600 --> 01:32:33,320
Speaker 2: This isn't some movie. I mean, neurallink is a real thing.

1845
01:32:33,479 --> 01:32:34,080
Speaker 2: It's here now.

1846
01:32:34,160 --> 01:32:37,000
Speaker 4: It's used mostly in a therapeutic context right now to

1847
01:32:37,080 --> 01:32:39,120
Speaker 4: help people who've had brain injuries and so forth.

1848
01:32:39,439 --> 01:32:41,800
Speaker 2: But these are the kinds of things that you know,

1849
01:32:41,920 --> 01:32:46,599
Speaker 2: Ray Kurzwell and others really futurists talk about and really

1850
01:32:46,640 --> 01:32:50,280
Speaker 2: bring up massive moral and ethical quandaries for us. So

1851
01:32:50,680 --> 01:32:52,639
Speaker 2: there again roses and land mines.

1852
01:32:54,200 --> 01:32:55,840
Speaker 1: And this is exactly where I want to end up

1853
01:32:56,000 --> 01:32:58,320
Speaker 1: for this conversation. Is I want to talk about the

1854
01:32:58,360 --> 01:33:02,400
Speaker 1: Singularity and talking about the transhumanism where humans are going

1855
01:33:02,439 --> 01:33:06,080
Speaker 1: to be part robot, and I find that the ethical

1856
01:33:06,240 --> 01:33:08,800
Speaker 1: questions is raises are limitless. I mean, I feel like

1857
01:33:08,840 --> 01:33:11,280
Speaker 1: we can spend hours of flashing all of those out,

1858
01:33:11,479 --> 01:33:14,400
Speaker 1: but you raise some of that is a deep concern

1859
01:33:14,479 --> 01:33:17,680
Speaker 1: to me that you will be non competitive unless you

1860
01:33:18,520 --> 01:33:23,280
Speaker 1: harness your brain to a robot chip of some sort.

1861
01:33:23,800 --> 01:33:25,400
Speaker 1: And all of a sudden it comes that what is

1862
01:33:25,800 --> 01:33:27,800
Speaker 1: the human experience anyway? What is it supposed to be?

1863
01:33:28,560 --> 01:33:32,280
Speaker 1: Do we really want our brains being manipulated by robots?

1864
01:33:32,320 --> 01:33:33,680
Speaker 3: And how do we know?

1865
01:33:34,320 --> 01:33:37,439
Speaker 1: How will we ever feel confident where the human ends

1866
01:33:37,479 --> 01:33:41,240
Speaker 1: and where the robot begins? And you can already see

1867
01:33:41,680 --> 01:33:43,800
Speaker 1: that this is going to be adopted to some degree

1868
01:33:43,880 --> 01:33:47,640
Speaker 1: because we have these things where which is basically a

1869
01:33:48,040 --> 01:33:52,520
Speaker 1: really you know, a one point zero version of the singularity,

1870
01:33:52,560 --> 01:33:55,240
Speaker 1: that we constantly have our phones around and they're as

1871
01:33:55,320 --> 01:33:57,720
Speaker 1: you write in the book, we spend the whole day

1872
01:33:57,720 --> 01:34:00,280
Speaker 1: with them in arms reach, like they're not without arms

1873
01:34:00,320 --> 01:34:02,640
Speaker 1: reached the entire day. And this is true even for me,

1874
01:34:02,760 --> 01:34:05,880
Speaker 1: who has a deep desire to get off my phone.

1875
01:34:06,520 --> 01:34:09,120
Speaker 1: The first thing I do when I'm off of work

1876
01:34:09,240 --> 01:34:12,080
Speaker 1: mode is generally all put in a podcast or an audiobooks,

1877
01:34:12,240 --> 01:34:14,400
Speaker 1: like or I'm working out and I'm listening to some

1878
01:34:14,680 --> 01:34:16,600
Speaker 1: music or audiobook or something, and.

1879
01:34:16,760 --> 01:34:17,960
Speaker 3: So it's all of us.

1880
01:34:18,080 --> 01:34:20,280
Speaker 1: We're all doing it, even people who try to make

1881
01:34:20,280 --> 01:34:23,439
Speaker 1: a conscious effort not to do it. So I'm really

1882
01:34:23,520 --> 01:34:26,320
Speaker 1: concerned about some of this stuff. This isn't clanker robots

1883
01:34:26,400 --> 01:34:28,080
Speaker 1: in our house that are kind of creepy washing the

1884
01:34:28,160 --> 01:34:30,400
Speaker 1: dishes for us. This is our brain that we're going

1885
01:34:30,479 --> 01:34:33,240
Speaker 1: to be part robot at some point. Perhaps it's really

1886
01:34:33,320 --> 01:34:35,599
Speaker 1: crazy to contemplate, Alex.

1887
01:34:35,720 --> 01:34:39,680
Speaker 4: One of the most embarrassing and humiliating moments of my

1888
01:34:39,880 --> 01:34:43,360
Speaker 4: week is you know when your iPhone periodically says, here's

1889
01:34:43,439 --> 01:34:45,639
Speaker 4: the average number of hours you've spent on your.

1890
01:34:45,560 --> 01:34:47,720
Speaker 3: Phone this week. It's so embarrassing.

1891
01:34:48,240 --> 01:34:50,800
Speaker 2: It's so embarrassing because you just say, this is not

1892
01:34:51,080 --> 01:34:53,160
Speaker 2: what I wanted. I didn't want to look at a screen.

1893
01:34:53,200 --> 01:34:55,280
Speaker 2: I wanted to look at the sky and eyes and

1894
01:34:55,439 --> 01:34:57,080
Speaker 2: my my you know, family and my friends and all

1895
01:34:57,080 --> 01:34:57,439
Speaker 2: my people.

1896
01:34:57,760 --> 01:34:59,760
Speaker 1: Well, can I tell you I had I took my

1897
01:34:59,800 --> 01:35:02,559
Speaker 1: ca it's to swimming and they're small, so the swimming

1898
01:35:02,640 --> 01:35:04,439
Speaker 1: is thirty minutes. It's kind of far away, So it's

1899
01:35:04,479 --> 01:35:07,280
Speaker 1: a maybe like doorder dover like seventy five minutes. And

1900
01:35:07,800 --> 01:35:11,479
Speaker 1: I left my phone recently, And I've been telling this

1901
01:35:11,640 --> 01:35:14,920
Speaker 1: story to people, like over and over again, I left

1902
01:35:15,000 --> 01:35:17,559
Speaker 1: my phone for seventy five minutes, and how I really

1903
01:35:17,680 --> 01:35:18,040
Speaker 1: enjoyed it.

1904
01:35:18,640 --> 01:35:19,479
Speaker 3: But the fact that I.

1905
01:35:19,479 --> 01:35:21,560
Speaker 1: Feel the need to repeat it to everyone that I

1906
01:35:21,640 --> 01:35:24,439
Speaker 1: had seventy five minutes free of the phone three weeks

1907
01:35:24,479 --> 01:35:27,280
Speaker 1: ago once, I mean, it just shows you about it already.

1908
01:35:27,400 --> 01:35:29,360
Speaker 2: Is it's a great triumph.

1909
01:35:29,439 --> 01:35:31,200
Speaker 4: Yeah, And I say that, I mean, you're right that

1910
01:35:31,320 --> 01:35:33,120
Speaker 4: section you're talking about in the book where I say,

1911
01:35:33,120 --> 01:35:34,720
Speaker 4: you know, like how many of us if we were

1912
01:35:34,760 --> 01:35:37,880
Speaker 4: going on a family vacation and you forgot your phone

1913
01:35:38,320 --> 01:35:40,400
Speaker 4: or even going you know, to dinner, you would turn

1914
01:35:40,479 --> 01:35:41,600
Speaker 4: your car around to.

1915
01:35:41,600 --> 01:35:44,200
Speaker 2: Get your phone. What I suggest, and what I say is.

1916
01:35:44,240 --> 01:35:47,680
Speaker 4: That is a form of a early part of a

1917
01:35:48,040 --> 01:35:52,520
Speaker 4: cyborg reality, because it really is, our phone is connected

1918
01:35:52,600 --> 01:35:57,640
Speaker 4: to us almost constantly. And so you know, it's an

1919
01:35:57,680 --> 01:36:01,280
Speaker 4: interesting theoretical debate. Right when you say, okay, the singularity

1920
01:36:01,320 --> 01:36:03,960
Speaker 4: fusion of human machine and it sounds like you're talking

1921
01:36:04,000 --> 01:36:06,559
Speaker 4: about a terminator robot, but really start to think it's

1922
01:36:06,560 --> 01:36:08,360
Speaker 4: a very humbling thing when you really say, wait a minute,

1923
01:36:08,400 --> 01:36:11,200
Speaker 4: am I already kind of getting there? Like don't I

1924
01:36:11,400 --> 01:36:14,000
Speaker 4: look and have my phone attached to my human body

1925
01:36:14,040 --> 01:36:14,880
Speaker 4: almost all the time.

1926
01:36:15,160 --> 01:36:18,000
Speaker 2: So that's the first part of it. The second part

1927
01:36:18,080 --> 01:36:20,880
Speaker 2: of it, though, is is that this this neural.

1928
01:36:21,280 --> 01:36:24,040
Speaker 4: You know, chip stuff, the discussion we're just talking about

1929
01:36:24,200 --> 01:36:27,200
Speaker 4: not just neuralink, but that whole area is advancing very

1930
01:36:27,280 --> 01:36:27,960
Speaker 4: very rapidly.

1931
01:36:28,120 --> 01:36:30,679
Speaker 2: That is that does not far away.

1932
01:36:31,000 --> 01:36:35,160
Speaker 4: The other thing is with AI glasses, which is think

1933
01:36:35,200 --> 01:36:37,320
Speaker 4: about this, you know, with the ray bands that you

1934
01:36:37,439 --> 01:36:43,320
Speaker 4: now have from Meta. Now, that's an external AI fusion

1935
01:36:43,400 --> 01:36:45,719
Speaker 4: with your body. It's not putting it inside your body.

1936
01:36:45,520 --> 01:36:46,240
Speaker 2: You're wearing it.

1937
01:36:46,720 --> 01:36:49,040
Speaker 4: But think about the power of this and and those

1938
01:36:49,200 --> 01:36:52,720
Speaker 4: in the in AI fraudsters, in crime, this is a

1939
01:36:52,840 --> 01:36:53,519
Speaker 4: real problem.

1940
01:36:54,080 --> 01:36:54,960
Speaker 2: I can put.

1941
01:36:54,960 --> 01:36:59,160
Speaker 4: Facial recognition in what looks just like regular glasses.

1942
01:36:59,560 --> 01:37:03,599
Speaker 2: I can walk down downtown, you know, Manhattan or your city,

1943
01:37:03,720 --> 01:37:04,439
Speaker 2: wherever you are.

1944
01:37:04,920 --> 01:37:08,440
Speaker 4: I can see a total stranger and it can identify

1945
01:37:08,600 --> 01:37:11,400
Speaker 4: her name, who she is, look up everything, and I

1946
01:37:11,439 --> 01:37:13,960
Speaker 4: can walk up as though we're long lost friends. She

1947
01:37:14,040 --> 01:37:17,240
Speaker 4: doesn't remember, Oh, Sally, I haven't seen you since high school.

1948
01:37:17,280 --> 01:37:19,600
Speaker 2: However you been are you still working.

1949
01:37:19,400 --> 01:37:23,959
Speaker 4: At inter name of you know, play, and that becomes

1950
01:37:24,080 --> 01:37:27,160
Speaker 4: a real danger and a real fraud mechanism.

1951
01:37:27,280 --> 01:37:30,560
Speaker 2: AI fraud and crime is a massive topic and a

1952
01:37:30,680 --> 01:37:32,280
Speaker 2: huge problem with data privacy.

1953
01:37:32,400 --> 01:37:36,880
Speaker 4: So so that external ability to wear your wearables as

1954
01:37:36,920 --> 01:37:40,439
Speaker 4: it's called wearables in AI is the next part as

1955
01:37:40,479 --> 01:37:44,840
Speaker 4: we inch closer toward this transhuman fusion of machine, the

1956
01:37:44,920 --> 01:37:49,840
Speaker 4: singularity of humans and machines merging. So you know, when

1957
01:37:49,880 --> 01:37:52,519
Speaker 4: you hear that, it sounds like it's some crazy, kooky

1958
01:37:52,680 --> 01:37:55,120
Speaker 4: kind of you know, sci fi thing. It's here and

1959
01:37:55,200 --> 01:37:57,120
Speaker 4: it's getting closer. So what you're you know, first it

1960
01:37:57,280 --> 01:38:00,599
Speaker 4: was our phone, then it's a wearable. You're already working

1961
01:38:00,680 --> 01:38:03,519
Speaker 4: on the internal stuff. And then if you do that, well,

1962
01:38:03,680 --> 01:38:05,479
Speaker 4: am I going to be able to be competitive? When

1963
01:38:05,680 --> 01:38:08,120
Speaker 4: you know, twenty people in my company have an AI

1964
01:38:08,320 --> 01:38:11,280
Speaker 4: chip and they're an executive c suite leadership because they've

1965
01:38:11,320 --> 01:38:14,639
Speaker 4: got speed and efficiency and knowledge that my human brain

1966
01:38:14,720 --> 01:38:15,680
Speaker 4: could never compete with.

1967
01:38:16,720 --> 01:38:19,000
Speaker 2: This is why I wrote code Read because this is

1968
01:38:19,040 --> 01:38:21,519
Speaker 2: all where it's going, and you got to understand all

1969
01:38:21,640 --> 01:38:24,840
Speaker 2: these different fault lines, all these different pressure points so

1970
01:38:25,040 --> 01:38:27,120
Speaker 2: that you can get yourself through this for you and

1971
01:38:27,200 --> 01:38:29,880
Speaker 2: your kids. But I will tell you the speed is

1972
01:38:29,960 --> 01:38:31,599
Speaker 2: what is freaking everybody out.

1973
01:38:31,800 --> 01:38:34,920
Speaker 4: You know, in the Industrial Revolution, the Industrial Revolution, you

1974
01:38:34,960 --> 01:38:39,960
Speaker 4: know you electricity, it took decades to wire the country, right,

1975
01:38:40,040 --> 01:38:42,680
Speaker 4: I mean, you know, or to pave the roads for

1976
01:38:42,800 --> 01:38:45,160
Speaker 4: the inner interstates. And I go through the history of that,

1977
01:38:45,600 --> 01:38:48,280
Speaker 4: I cite the time I show you all that. The

1978
01:38:48,439 --> 01:38:52,479
Speaker 4: reason why AI is different. Ninety one percent of people

1979
01:38:52,560 --> 01:38:55,120
Speaker 4: in America already have a phone, which is the delivery

1980
01:38:55,160 --> 01:38:58,479
Speaker 4: device of AI. So once you got that delivery device

1981
01:38:58,600 --> 01:39:02,800
Speaker 4: already baked in, the lightning speed with which you can

1982
01:39:03,080 --> 01:39:06,240
Speaker 4: make sure that that scales is very different than what

1983
01:39:06,320 --> 01:39:08,439
Speaker 4: we saw in the Industrial Revolution, So you could you

1984
01:39:08,520 --> 01:39:11,040
Speaker 4: had decades to sort of get used to and you know,

1985
01:39:11,240 --> 01:39:15,000
Speaker 4: upskill your skills and kind of learn how as factory

1986
01:39:15,120 --> 01:39:17,760
Speaker 4: and mechanization and all the you know, factory assemblies and

1987
01:39:17,840 --> 01:39:18,040
Speaker 4: so on.

1988
01:39:18,720 --> 01:39:22,240
Speaker 2: Now this is moving so fast. If you're not ahead,

1989
01:39:22,320 --> 01:39:25,760
Speaker 2: you're behind, and you've got to stay ahead. And that's

1990
01:39:25,920 --> 01:39:28,280
Speaker 2: again why you know the name of code read of

1991
01:39:28,640 --> 01:39:30,040
Speaker 2: this sort of alarm or alert.

1992
01:39:30,760 --> 01:39:34,840
Speaker 1: Well, we have not covered the waterfront here. We have

1993
01:39:35,040 --> 01:39:38,559
Speaker 1: not gotten into AI defense, which again could turn out

1994
01:39:38,600 --> 01:39:40,439
Speaker 1: to be a positive though with a lot of risk.

1995
01:39:40,479 --> 01:39:40,879
Speaker 3: Factors.

1996
01:39:41,520 --> 01:39:44,479
Speaker 1: I do want to get your opinions on AI Jesus,

1997
01:39:44,600 --> 01:39:46,760
Speaker 1: which we're seeing some AI Jesus pop up, we're seeing

1998
01:39:46,760 --> 01:39:48,600
Speaker 1: AI George Washington. I'm want to get your thoughts on

1999
01:39:48,640 --> 01:39:51,200
Speaker 1: some of that. And there's just a number of things

2000
01:39:51,400 --> 01:39:55,400
Speaker 1: that I feel like are both opportunities and are real

2001
01:39:55,560 --> 01:39:57,840
Speaker 1: deep risks about the moment that we're in with AI.

2002
01:39:58,000 --> 01:40:00,599
Speaker 1: But I will encourage everyone in the meantime to pick

2003
01:40:00,720 --> 01:40:04,479
Speaker 1: up a copy of Code Read and it is one

2004
01:40:04,479 --> 01:40:05,720
Speaker 1: of the books of the year, about the book of

2005
01:40:05,760 --> 01:40:08,280
Speaker 1: the year I think so far, and you will want

2006
01:40:08,280 --> 01:40:10,160
Speaker 1: to read it. You will probably want to share this

2007
01:40:10,320 --> 01:40:12,760
Speaker 1: with someone who you think might be in one of

2008
01:40:12,800 --> 01:40:15,720
Speaker 1: these industries that could see a lot of upheaval. This

2009
01:40:15,880 --> 01:40:18,400
Speaker 1: book might provide them comfort, it might scare them into

2010
01:40:18,520 --> 01:40:19,479
Speaker 1: doing something that.

2011
01:40:19,720 --> 01:40:20,960
Speaker 3: Could be productive for their lives.

2012
01:40:21,360 --> 01:40:24,320
Speaker 1: So it's a really essential This is an essential text

2013
01:40:24,640 --> 01:40:26,519
Speaker 1: from Winton Hall, who isn't just a friend, but is

2014
01:40:26,560 --> 01:40:28,840
Speaker 1: a brilliant person, as you guys have gotten to witness

2015
01:40:28,880 --> 01:40:30,640
Speaker 1: here of the last hour and a half. We're going

2016
01:40:30,680 --> 01:40:33,120
Speaker 1: to do this again Winton, probably sooner rather than later.

2017
01:40:33,320 --> 01:40:35,800
Speaker 1: So there's a lot more to do, I think on

2018
01:40:35,880 --> 01:40:39,080
Speaker 1: this subject matter. I really want you to introduce everyone

2019
01:40:39,280 --> 01:40:42,639
Speaker 1: to all the players in the space, both in terms

2020
01:40:42,640 --> 01:40:45,519
Speaker 1: of the companies and the individuals and where they're coming from,

2021
01:40:45,640 --> 01:40:48,120
Speaker 1: because they're going to be so involved in shaping our future.

2022
01:40:48,200 --> 01:40:50,320
Speaker 1: But if we opt out on this thing, this will

2023
01:40:50,320 --> 01:40:52,479
Speaker 1: be my final word. Then I'll give you the last word, Winton.

2024
01:40:52,920 --> 01:40:55,360
Speaker 1: If we opt out, then we are seeding the future

2025
01:40:55,520 --> 01:40:59,080
Speaker 1: to the masters of the universe only and the really activated,

2026
01:40:59,600 --> 01:41:03,720
Speaker 1: generally left of center entrepreneurial class that I feel like

2027
01:41:04,280 --> 01:41:06,000
Speaker 1: is a deep, deep risk. And so we got to

2028
01:41:06,040 --> 01:41:10,000
Speaker 1: get involved. And Winton is he's inviting you in showing

2029
01:41:10,040 --> 01:41:12,120
Speaker 1: you the water can be warm. I'm not saying it's

2030
01:41:12,120 --> 01:41:14,479
Speaker 1: the warmest ever, but it can be warm for you.

2031
01:41:14,960 --> 01:41:16,720
Speaker 3: Joe, you got to start. This is a great entry

2032
01:41:16,760 --> 01:41:17,840
Speaker 3: point into this book.

2033
01:41:17,880 --> 01:41:20,679
Speaker 1: And of course, when you spend two years on something

2034
01:41:20,720 --> 01:41:22,160
Speaker 1: like this, even if you feel like you know AI,

2035
01:41:22,240 --> 01:41:24,439
Speaker 1: you're gonna learn a lot too. I think from just

2036
01:41:24,520 --> 01:41:27,320
Speaker 1: hearing Wynton's perspective on all these things. But what did

2037
01:41:27,320 --> 01:41:28,760
Speaker 1: anything you want to leave the audience with for now?

2038
01:41:29,479 --> 01:41:32,120
Speaker 4: Oh, I thank you so much for that, and especially

2039
01:41:32,200 --> 01:41:34,960
Speaker 4: coming from an author like yourself, I will say this,

2040
01:41:35,960 --> 01:41:39,240
Speaker 4: you can learn this. Don't be afraid to think, oh,

2041
01:41:39,320 --> 01:41:42,200
Speaker 4: this is so complex and it's so beyond my understanding.

2042
01:41:42,560 --> 01:41:45,400
Speaker 4: The reason I wrote this is to be simple without simplistic,

2043
01:41:45,640 --> 01:41:46,880
Speaker 4: really to help people.

2044
01:41:47,680 --> 01:41:49,679
Speaker 2: Like I said, I didn't even want to write this book.

2045
01:41:49,720 --> 01:41:52,160
Speaker 2: I have a wonderful career and I love what I do.

2046
01:41:52,439 --> 01:41:54,519
Speaker 4: I felt like I needed to do this because I

2047
01:41:54,640 --> 01:41:56,280
Speaker 4: wanted to be a story I know how to be

2048
01:41:56,320 --> 01:41:58,880
Speaker 4: a storyteller, so I wanted to help people to really

2049
01:41:59,000 --> 01:41:59,680
Speaker 4: understand this.

2050
01:42:00,720 --> 01:42:03,120
Speaker 2: You don't have to have a specialized knowledge.

2051
01:42:03,479 --> 01:42:05,120
Speaker 4: I think most of you are going to find this

2052
01:42:05,320 --> 01:42:08,479
Speaker 4: is way more fascinating than you ever realized it was,

2053
01:42:08,640 --> 01:42:12,479
Speaker 4: and also consequential. I really think that we don't need

2054
01:42:12,520 --> 01:42:14,760
Speaker 4: to get into a doom loop. We don't need to

2055
01:42:15,280 --> 01:42:19,000
Speaker 4: just unilaterally disarm, certainly not conservatives.

2056
01:42:19,040 --> 01:42:21,120
Speaker 2: That that would be I think a disaster. I think

2057
01:42:21,120 --> 01:42:23,200
Speaker 2: we need to lean in. We need to lead, We

2058
01:42:23,320 --> 01:42:24,280
Speaker 2: need to get prepared.

2059
01:42:24,439 --> 01:42:26,400
Speaker 4: We need to know how to do what we always do,

2060
01:42:26,479 --> 01:42:28,960
Speaker 4: which is, you know, take personal responsibility for our family

2061
01:42:29,280 --> 01:42:32,120
Speaker 4: and for our communities and for our country, and make

2062
01:42:32,200 --> 01:42:34,240
Speaker 4: sure that we're ready to rock and roll with this

2063
01:42:34,600 --> 01:42:36,799
Speaker 4: and know how to be able to get the upside

2064
01:42:37,000 --> 01:42:39,800
Speaker 4: and avert the negative. But I think it's actually going

2065
01:42:39,880 --> 01:42:41,640
Speaker 4: to be a really important thing for you to do that,

2066
01:42:41,800 --> 01:42:44,479
Speaker 4: and I think you'll feel a lot more confident because

2067
01:42:44,479 --> 01:42:46,160
Speaker 4: I think so much of it is just fear, and

2068
01:42:46,280 --> 01:42:47,920
Speaker 4: people don't really know what's true and.

2069
01:42:47,960 --> 01:42:51,680
Speaker 2: What's hype and what's what's myth. So that's that's why

2070
01:42:51,720 --> 01:42:53,040
Speaker 2: I wrote Code Rat and I hope you enjoy it.

2071
01:42:53,760 --> 01:42:55,519
Speaker 3: And you will enjoy it. You will learn a lot.

2072
01:42:55,680 --> 01:42:57,320
Speaker 1: You are going to a really interesting world when you

2073
01:42:57,360 --> 01:42:59,240
Speaker 1: take on this book, but it's where we're all going to.

2074
01:42:59,640 --> 01:43:02,439
Speaker 1: Anyone she may as well be prepared. Thank you, Wenchen Haul,

2075
01:43:02,520 --> 01:43:05,000
Speaker 1: Thanks for Misty for putting this together, and all of

2076
01:43:05,080 --> 01:43:07,000
Speaker 1: you in the audience for telling ten thousand friends and

2077
01:43:07,080 --> 01:43:09,519
Speaker 1: family members about all the cool stuff we're doing on

2078
01:43:09,560 --> 01:43:11,759
Speaker 1: the Marlow Show. Make sure to subscribe. If you're not subscribed,

2079
01:43:11,840 --> 01:43:13,720
Speaker 1: please and I'll talk to you next time.