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Speaker 1: Media. Oh, wow, Sophie, I don't know. I don't know

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Speaker 1: if we can put that on the air. I mean,

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Speaker 1: that's actionable threats against a sitting.

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Speaker 2: Wow.

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Speaker 1: I mean, just Sophie, that's it's just dangerous to be

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Speaker 1: saying stuff like that in this day and age. I know,

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Speaker 1: let's distract the audience and the federal agents listening in

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Speaker 1: by bringing on our guests today who definitely doesn't say

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Speaker 1: stuff like that. Langston Kerman, Langston, do you condemn Sophie's statements,

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Speaker 1: assuming they ever get out in unedited form?

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Speaker 2: I was. I was tempted to hang up the zone

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Speaker 2: right away. It was so inflammatory that I said, I

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Speaker 2: can't be a part of this.

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Speaker 1: No. No, it's time to bring peace to the country.

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Speaker 2: You know.

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Speaker 1: That's that's what we all need to be focusing on.

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Speaker 2: Is peace. We got to get back to what we were,

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Speaker 2: which was normal and peace.

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Speaker 1: Was normal and good. Everyone knows things used to be

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Speaker 1: good in this country for everybody, right, that's chilling for all. Yeah,

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Speaker 1: things were so great, And I think what'll get us

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Speaker 1: back to that is talking about puppies. Right. Everybody loves

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Speaker 1: a good puppy, right, puppies. Puppies are wonderful, and there's

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Speaker 1: been some really what is he talking about? God, we're

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Speaker 1: gonna talk about puppy mills. No, don't worry, this is

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Speaker 1: a fun one, everybody. We're gonna have a good time

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Speaker 1: this week. Like it is a guy I think is

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Speaker 1: a piece of shit, but it's gonna be fun.

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Speaker 3: YEA.

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Speaker 2: I will say before you even go, one of the

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Speaker 2: only taboos that exists in film and television is murdering dogs. Yes,

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Speaker 2: and I'm getting so scared before we even start.

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Speaker 1: I know nothing, No dogs are provably harmed yet and

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Speaker 1: in the making of this story, although I do have

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Speaker 1: to specify yet, but there is like a dog like

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Speaker 1: creature involved. Because if you if you spend any time online,

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Speaker 1: have you been on social media or just been watching

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Speaker 1: the news. I think probably close to one hundred percent

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Speaker 1: of our audience caught this. There was a big story

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Speaker 1: a couple of weeks back about how this company brought

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Speaker 1: back the dire wolf, right, which is an extinct kind

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Speaker 1: of wolf, and they did it using some I think

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Speaker 1: we can call it Jurassic Park style machinations, right, Like

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Speaker 1: that's what everyone thought of, And this is all the

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Speaker 1: work of a of an actual like science like bioscience

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Speaker 1: startup called Colossal Biosciences, which is just by name, a

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Speaker 1: company that could not sound more like it belonged than

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Speaker 1: a Michael Crichton novel if they just called it engine right,

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Speaker 1: like it's it's amazing.

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Speaker 2: It really feels like a fourth grader was, like.

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Speaker 1: I got it, like when I was in fourth grade

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Speaker 1: trying to rewrite Jurassic Park.

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Speaker 2: Yes, okay, okay, we'll get sue.

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Speaker 1: That's fine, it's fine, it's fine. Michael Crichton's not going

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Speaker 1: to sue a four year old Colossal Science original. Yeah,

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Speaker 1: so it'll publish this. So most news coverage of this

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Speaker 1: whole Dire Wolf thing kinda casually accepted the pr claims

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Speaker 1: being made by Colossal and it's co founder, slash Scientific spokesman,

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Speaker 1: doctor George Church. Time published an article with the title

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Speaker 1: the Return of the Dire Wolf, and it's it's as

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Speaker 1: hype an article as it could possibly be, And on

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Speaker 1: the front of it, there's a photo of a very

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Speaker 1: charismatic looking wolf as that I mean, that's a beautiful wolf, right,

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Speaker 1: that charm that that is a screen ready wolf.

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Speaker 2: You can tell you can tell that wolf knows how

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Speaker 2: the business works. That's not a wolf that you gotta like,

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Speaker 2: uh put a caretaker on, you.

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Speaker 1: Know, you gotta. They said that they put some dire

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Speaker 1: wolf jeans into this wolf. I think they might have

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Speaker 1: stuck one or two Tom Cruise jeans in there, because

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Speaker 1: that wolf knows where the camera is, right.

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Speaker 2: That wolf, That wolf does its own stunts.

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Speaker 1: Wolf does its own stunts, right. Obviously it's a good

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Speaker 1: looking wolf. No one's throwing shade against these animals here.

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Speaker 1: They're gorgeous, but they're not wolves, right. That's kind of

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Speaker 1: where we're starting here. It gets much more fucked up

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Speaker 1: than that. Dire Wolves were a very real species of

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Speaker 1: wolf which roamed the Americas. They were found in parts

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Speaker 1: of both North and South America from the late police

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Speaker 1: Decene to the early Holocene period, which is a span

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Speaker 1: of somewhere over one hundred thousand years. It's bite force like.

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Speaker 1: When these animals lived, they had a stronger bite force

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Speaker 1: than any known modern wolves, so they were pretty formidable.

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Speaker 1: But the reputation for them being like the size of

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Speaker 1: horses is something they largely accrued via Game of Thrones

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Speaker 1: because dire wolves were around the same size as the

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Speaker 1: largest modern wolves, a little bigger but we're talking like

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Speaker 1: ten or twenty pounds heavier than like a Yukon wolf

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Speaker 1: on average.

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Speaker 2: And there was a sub Right, there's a pretty extensive

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Speaker 2: collection of dire wolf bones in the Librea Tarpits Museum.

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Speaker 2: Oh right, and boy was I disappointed when I saw

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Speaker 2: how little those.

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Speaker 1: Bones were, right, he gave a throat shit.

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Speaker 2: Yeah, man, I walked in there and I said, I'm

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Speaker 2: gonna see the biggest wolf that's that ever was. And

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Speaker 2: those wolves look like snauzers. They're tiny little dogs as

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Speaker 2: far as I'm concerned.

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Speaker 1: Yeah, because you I mean, like, the average size of

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Speaker 1: a dire wolf was like one hundred and fifty pounds,

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Speaker 1: which is like a good sized caneid. But like, I've

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Speaker 1: known dogs bigger than that. I've known some two hundred

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Speaker 1: pound mastiffs, right like, and they're not that big on average. Yeah. No,

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Speaker 1: so yeah, again, these are they're bite in terms of

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Speaker 1: bite force, very formidable animals, but they're not huge.

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Speaker 2: Like.

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Speaker 1: That's stuff that George R. Martin put in his book,

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Speaker 1: because George R. And Martin knows how to make a

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Speaker 1: book cool. Right, Yeah, you gotta judge up reality a

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Speaker 1: little bit, you know, especially if it's a fantasy novel.

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Speaker 2: He knows how to make a book cool, and he

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Speaker 2: knows how to make a hat cool. He's got cool hats,

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Speaker 2: cool books and scarves.

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Speaker 1: Cool hats, cool books, and he's he's achieved. I've have

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Speaker 1: a lot of respect for George. He's achieved every writer's dream,

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Speaker 1: which is to never have to write again, right like that,

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Speaker 1: that's what we're all shooting for. So just live in

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Speaker 1: all lighthouse that never finished your series, I say, as

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Speaker 1: I'm two years overdue with my novel. But the name

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Speaker 1: of the species is presumably the major thing that inspired

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Speaker 1: George R.

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Speaker 2: Martin.

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Speaker 1: It's just a cool name, dire Wolf, Like there's it's

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Speaker 1: dire wolves have been in D and D before George

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Speaker 1: put him in his books, right because it's just a

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Speaker 1: cool thing to call a wolf. It's like, yeah, that

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Speaker 1: sounds like a scarier wolf to me. And Colossal Biosciences

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Speaker 1: knowing and being primarily this is a company that describes

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Speaker 1: themselves as being in gene sciences. They're in the pr

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Speaker 1: business as much as anything else, and they made the

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Speaker 1: wise decision to rely heavily on the popularity of the

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Speaker 1: Game of Thrones books and TV shows to act as

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Speaker 1: advertising for their quote unquote dire wolf right. And in

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Speaker 1: fact this is even written into that Fawning Time coverage.

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Speaker 1: And here's a quote from that article. Relying on deft

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Speaker 1: genetic engineering an ancient preserved DNA, colossal scientists deciphered the

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Speaker 1: dire wolf genome, rewrote the genetic code of the common

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Speaker 1: gray wolf to match it, and, using domestic dogs as

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Speaker 1: surrogate mothers, brought Romulus, Remus and their sister, two month

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Speaker 1: old Kalisi into the world during three separate births last

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Speaker 1: fall in this winter, effectively for the first time, de

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Speaker 1: extincting a line of beasts whose live geenpool vanished long ago.

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Speaker 1: Ti met the mates. Kalisi was not present due to

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Speaker 1: her young age, at a fenced field in a US

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Speaker 1: wildlife facility on March twenty fourth, on the condition that

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Speaker 1: their location remain a secret to protect the animals from

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Speaker 1: prying eyes. Now, naming a dire wolf after a character

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Speaker 1: in the books who had nothing to do with dire

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Speaker 1: wolves was by far the fringiest possible.

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Speaker 4: Choice here opportunity.

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Speaker 1: There were dire wolves with names.

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Speaker 2: There were a lot of Starks. They could have just

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Speaker 2: gone down the Stark lineage they didn't have to go

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Speaker 2: to Kali.

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Speaker 1: Literally, she had nothing to do with the wolves. Did

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Speaker 1: she even meet any of those?

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Speaker 2: A dragon lady?

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Speaker 5: She met the Yes, that.

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Speaker 1: She met Okay, she met jobs maybe okay, ghost right, Okay,

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Speaker 1: so maybe one we.

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Speaker 4: Don't talk about that.

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Speaker 1: Last season, we don't talk about that. I got really

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Speaker 1: sad for a second.

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Speaker 2: But it's the bummer, folks, should you should do that

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Speaker 2: season as one of the bestards from Yeah, that's.

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Speaker 1: What we're working on a sixth parter, but having the animals.

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Speaker 1: They also had the animals pose with George R. Martin

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Speaker 1: as part of like the press tour, and that was

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Speaker 1: a particular choice. First off, look at this which again

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Speaker 1: no shade on George. I want to hold a wolf

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Speaker 1: pup right, absolutely, yeah, Like it looks cuddly as hell.

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Speaker 1: But that's also kind of part of the problem because

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Speaker 1: like that wolf is actively yawning, right, Like, it seems

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Speaker 1: pretty chill to be there. And it's interesting to me

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Speaker 1: because if the information given to the team at time

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Speaker 1: by Colossal Biosciences was accurate, there's no way this photo

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Speaker 1: should exist. Here's what Time claimed right at the start

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Speaker 1: of their article. The angelic Exuberance puppies exhibit in the

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Speaker 1: presence of humans, trotting up for hugs, belly Rob's kisses

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Speaker 1: is completely absent. They keep their distance, retreating if a

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Speaker 1: person approaches, even if one of the handlers who raised

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Speaker 1: them from birth can only get so close before Romulus

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Speaker 1: and Remas flinch and retreat. This isn't domestic canine behavior.

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Speaker 1: This is wild lupine behavior. The pups are wolves. Not

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Speaker 1: only that they're dire wolves, which means they have cause

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Speaker 1: to be lonely, and again just genetically they're not dire wolves.

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Speaker 1: But also, why is George cuddling that animal? Then if

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Speaker 1: you can't, are you you're either forcing the animal into

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Speaker 1: a situation that makes it distinctly uncomfortable. But the animal

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Speaker 1: looks like it's yawning, so maybe they're just not as

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Speaker 1: wolfy as you're pretending.

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Speaker 2: Yeah, you're really trying to weave a story here. And

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Speaker 2: that's that's a nice dog. That seems like a really polite,

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Speaker 2: U sweet dog.

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Speaker 1: Shill it looks like a husky. Yeah, I don't know man.

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Speaker 4: And frankly, George, why are you not finishing the books?

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Speaker 2: Well, he's got a lot of looks.

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Speaker 1: I'm not going to give him shit for that. Again,

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Speaker 1: I also haven't, And if I have the chance to

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Speaker 1: cuddle a wolf rather than spend another day working on

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Speaker 1: my novel, I would be a added to that wolf.

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Speaker 4: So fucking fast.

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Speaker 1: The man owns a lighthouse, how you expect him to

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Speaker 1: finish a book? He's finished other books. I'll give him

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Speaker 1: shit for his involvement in this company, though.

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Speaker 2: That's cool to make that much money and be like,

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Speaker 2: I'm a buy a lighthouse.

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Speaker 1: I'm gonna get a lighthouse.

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Speaker 2: Yeah, of course I'm not sleeping where normal people sleep anymore.

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Speaker 2: I've got a different thing going on.

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Speaker 1: No, I'm going to recreate that great Robert Pattinson movie.

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Speaker 2: Where everybody was fine at the end everyone No.

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Speaker 1: No, really good movie with a good ending. So the

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Speaker 1: fact that there's this photo of George R. R. Martin with

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Speaker 1: one of these dire wolves makes a lot more sense

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Speaker 1: when you learn a few things about both the company

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Speaker 1: behind these animals and the actual science behind the project itself.

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Speaker 1: For one thing, George R. R. Martin is an investor in

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Speaker 1: Colossal Biosciences and also an advisor to the company, which

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Speaker 1: advisor and what George R. Martin's a number of things

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Speaker 1: he's not a scientist, he's not a geneticist. He's not

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Speaker 1: an expert in real dire.

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Speaker 2: Wolves because of sunglasses.

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Speaker 1: He sunglasses, like he invented fake dire wolves for his novels.

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Speaker 1: I don't understand, Like, under what circumstances would he be

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Speaker 1: an advisor to this company doing genetics work. That's like

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Speaker 1: if they hired the guy who played doctor Alan Grant

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Speaker 1: to advise a company cloning dinosaurs. It's like, well, but

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Speaker 1: he doesn't really know anything about dinosaurs.

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Speaker 2: Ye, he's actually he doesn't even speak with that accent.

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Speaker 2: He's pretending.

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Speaker 1: Yeah, Like it's like bringing Jeff Goldblum onto the project. Well,

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Speaker 1: you know, if you if you're trying to like, I

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Speaker 1: just don't think he has the expertise. Nothing against Jeff.

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Speaker 2: If you want to flirt with the dinosaur.

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Speaker 1: People, yes, bring Jeff in.

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Speaker 2: Yeah you want them dinosaurs horny a al get Jeff.

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Speaker 2: But otherwise you gotta yeah, he could do that ship

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Speaker 2: you gotta talk to a scientist you might.

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Speaker 1: Want to bring in, like Robert Backer or someone if

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Speaker 1: he's still alive. But anyway, An article by Michael hills

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Speaker 1: It for the Los Angeles Times explains how Martin is

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Speaker 1: being credited as an advisor.

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Speaker 2: Here.

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Speaker 1: He's named as a co author on a technical paper

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Speaker 1: the company published as a non peer reviewed preprint describing

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Speaker 1: its de extinction effort. The text credits him with the

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Speaker 1: review and editing of the paper's text, among thirty six

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Speaker 1: other credited co authors in that category. So he's one

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Speaker 1: of thirty six people who helped copy edit an article.

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Speaker 2: Yeah, this is okay to your point, this is just pr.

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Speaker 1: This is just PR. First off, there's everything else thirty

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Speaker 1: six people to edit an article.

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Speaker 2: And they didn't let those other thirty five people hold

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Speaker 2: that dog. And that's no fucked up, no, no, just George.

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Speaker 1: They just pressed George in there for that. Yeah, anyway,

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Speaker 1: to kind of enforce the point I made earlier, these wolves,

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Speaker 1: well very cute, are not dire wolves. There's some genes

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Speaker 1: that they found while sequencing direwolf genetics that have been

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Speaker 1: put into a normal wolf. But that doesn't It's kind

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Speaker 1: of like how like some people have some Neanderthal DNA

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Speaker 1: in there, but they're not themselves Neanderthals, right, They're just

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Speaker 1: they're people.

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Speaker 2: You know, that's gotta be a tough thing to figure

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Speaker 2: out for yourself. Though that I got a little bit

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Speaker 2: of that in there, because yeah.

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Speaker 1: You get some DNA from a species we wiped out.

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Speaker 2: Yeah you can kind of see it. And then then

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Speaker 2: that bumps you out where you're like.

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Speaker 1: Oh sure, like John Hamm, I assume right. Yeah, So

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Speaker 1: Colossal Biosciences is not actually in the de extinction business.

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Speaker 1: They are in the modifying animals genetically in ways that

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Speaker 1: in some cases hadn't been done before business, and that

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Speaker 1: is interesting, but it's not the extinction. And so they

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Speaker 1: are doing stuff. They are doing something new and something

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Speaker 1: that is in some ways very interesting, but it's not

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Speaker 1: what they're claiming they're doing. So I can't call this

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Speaker 1: a straight up con right, because they did make an

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Speaker 1: animal that didn't exist before, but it's also not a

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Speaker 1: dire wolf, and they're not de extincting anything. And I

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Speaker 1: think the evidence shows they are massively inflating what they

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Speaker 1: and their technology can do in order to win VC funding.

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Speaker 1: The whole explanation as to why we'll take a while,

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Speaker 1: but I'm going to start by talking about the claims

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Speaker 1: that first brought the company public attention back in September

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Speaker 1: of twenty twenty one. A whole spate of almost identical

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Speaker 1: articles dropped announcing the creation of Colossal Biosciences, and they're

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Speaker 1: planned to clone a wooly mammoth by twenty twenty seven,

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Speaker 1: so we got two more years before to be mammoths, right.

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Speaker 2: Megan, Wooly mammos back, y'all, this is exciting.

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Speaker 1: Very soon, like probably right around the same time we

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Speaker 1: get Severn season three. You know, we're lucky.

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Speaker 2: You know what its about the same year talks about

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Speaker 2: wooly mammos two is they also are not bigger than elephants.

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Speaker 1: No.

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Speaker 2: No, I thought this whole time that wooly mammoths were

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Speaker 2: like these giant beasts that we would never be able

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Speaker 2: to see again. And they're like smaller than the average

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Speaker 2: no African elephant.

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Speaker 3: No.

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Speaker 1: And again, it's one of those things whatever people start

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Speaker 1: to think about, Oh, it's it's a bum. We've missed

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Speaker 1: all the cool, coolest animals that existed. The largest thing

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Speaker 1: to ever exist on Earth is still around swims in

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Speaker 1: the sea and we're currently killing them.

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Speaker 4: Okay, something something you missed in this story now that

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Speaker 4: you've brought up the wooly mammoth is uh. Part of

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Speaker 4: the investors for this company are like a I'm.

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Speaker 1: Getting to it. I'm getting to that, So don't show

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Speaker 1: where I was, Like it was a guy know this?

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Speaker 1: Why do I know this?

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Speaker 4: Shit?

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Speaker 1: We're getting to the other investors in this fucking company.

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Speaker 1: Don't worry God. But here's a here's a representative example

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Speaker 1: from like the press that explosion around this wooly mammoth claim.

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Speaker 1: So this is a CNBC article lab grown wooly mammoths

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Speaker 1: could walk the earth in six years of geneticists, new

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Speaker 1: startup succeeds. This is published in twenty twenty one. And

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Speaker 1: the geneticist that they're discussing, and the guy the article

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Speaker 1: is based around interviewing is co founder of Claw Biosciences,

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Speaker 1: doctor George Church, who claimed that he'd had the idea

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Speaker 1: kicking around for years and research supports this fact. He's

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Speaker 1: been pushing this idea in one form or another for

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Speaker 1: like a decade or more, but that he'd just been

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Speaker 1: given fifteen million dollars in seed funding and a company

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Speaker 1: had been established with serial entrepreneur Ben Lamb as CEO.

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Speaker 1: And we will talk about Ben Lambs Moore in part two. Church,

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Speaker 1: though doctor George Church is a real doctor and his

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Speaker 1: credentials are impeccable on paper. And just to state, this

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Speaker 1: guy's kind of a weird case where he's exaggerating a

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Speaker 1: lot and I think you could even argue lying about

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Speaker 1: some things. But he's a real scientist with some very

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Speaker 1: impressive achievements behind him.

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Speaker 2: And I think it's important for us to say that

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Speaker 2: scientists can be both legitimate and liars. Oh with shit, right, yeah, yes,

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Speaker 2: we often I think conflate it somehow, where scientists are

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Speaker 2: like these moral beings that exist above us all and no,

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Speaker 2: they can be liars and also really smart and capable people.

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Speaker 3: Right.

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Speaker 1: It's like you could be a great science fiction author

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Speaker 1: and racist as fuck, right, like those two things have existed,

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Speaker 1: or like Isaac Asimov where you're like, wow, what a

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Speaker 1: genius and also sex pest, right, like those things do

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Speaker 1: not conflict whatsoever. You know, it might have helped him,

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Speaker 1: I have helped him. Who knows. So George Church's credentious.

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Speaker 1: I'm not calling Church's sex pest. Although he has some

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Speaker 1: shady involvement with people that we'll talk about, none of

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Speaker 1: it involves the accusations of his specific behavior, just his

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Speaker 1: choice of company anyway, his academic credentials anyway, He is

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Speaker 1: the oh we're this this episode ends. Uh, you know,

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Speaker 1: I'm not going to give you a hint, but you're

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Speaker 1: you're going to be psyched. You're not gonna be psyched,

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Speaker 1: You're gonna be bummed. He is the Robert Winthrop Professor

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Speaker 1: of Genetics at Harvard Medical School and a faculty member

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Speaker 1: at the Weiss Institute for Biologically Inspired Engineering, also at Harvard.

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Speaker 1: So you know that alone pretty bigical achievement.

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Speaker 2: Right, and he still holds those positions.

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Speaker 1: Yes, yes, as far as I'm aware. Yes, Church has

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Speaker 1: his name on more than one hundred patents. And you know,

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Speaker 1: some of those are things where like maybe he got

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Speaker 1: on there because he helped someone else, but a lot

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Speaker 1: of them are because he contributed really significant work to

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Speaker 1: those patents. He started the Personal Genome project. And he

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Speaker 1: has also helped found more than twenty companies. Now, that

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Speaker 1: last claim was the first thing I read about him

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Speaker 1: that made me wonder, like, Okay, is there something like

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Speaker 1: shady here, because twenty companies is too many? No, honest,

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Speaker 1: man has found more than twenty companies. You're doing some

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Speaker 1: fucked up shit, right.

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Speaker 2: You gotta focus, big man. That's a few too many companies.

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Speaker 1: That's too many companies. And then what I read about

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Speaker 1: the actual claims colossal through Church was making about why

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Speaker 1: cloning mammoths was not just like a cool thing to do,

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Speaker 1: but like necessary for conservation. I went fully over the edge.

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Speaker 1: This is when I was like, Okay, I got to

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Speaker 1: dig more into this guy. There's gotta be something fucked

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Speaker 1: up here. And he has made claims like this quote.

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Speaker 1: This is a quote from the article proponents of the project,

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Speaker 1: and they're talking about Church say rewilding the Arctic with

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Speaker 1: wooly mammoths could slow global warming by slowing the melting

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Speaker 1: of the permafrost where methane is currently trapped. That's not true.

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Speaker 1: How it has something to do with them stomping down

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Speaker 1: the ground to stop like trees from growing up so

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Speaker 1: the permafrost days. But like that's if theoretically there were

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Speaker 1: a massive, healthy mammoth population, it might do that. We're

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Speaker 1: not talking about number one, they're not talking about making mammoths.

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Speaker 1: They're talking about modifying African elephants as a spoiler for

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Speaker 1: what will be in part two. But also, like that's

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Speaker 1: just not a feasible place for this project to end with,

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Speaker 1: like massive herds of mammoths clumbing across the top say

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Speaker 1: to fix. Also, no amount of mammoths is going to

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Speaker 1: fix global warming, and it's current, Like, the problem is

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Speaker 1: not just there's too many trees in Siberia. There's other

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Speaker 1: shit going off.

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Speaker 2: If that's the approach you're taking, you're missing the mark

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Speaker 2: quite a bit. Yeah, I think mammoth mammoths can't be

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Speaker 2: our first start at fixing the problem, for sure.

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Speaker 1: I think a lack of mammoth says the primary reason

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Speaker 1: this is a problem. Yeah. Further shady factoids about the

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Speaker 1: business include the fact that it is a for profit enterprise. Now.

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Speaker 1: Ben Lamb, who's his co founder in the CEO, was

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Speaker 1: quick to tel CNBC none of our investors are focused

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Speaker 1: on monetizing right now, which is great, But then you

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Speaker 1: read about who those investors are, and you wonder, I

405
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Speaker 1: don't know if I believe that, because investors in Colossal

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Speaker 1: outside of George R. Martin include self help grifter, Tony

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Speaker 1: Robbins and winkle Boss Capital. Oh we got winkle By

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Speaker 1: the Winklebostuins.

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Speaker 2: Wow, Winkleby, that's crazy.

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Speaker 1: They'd only be involved in a real project.

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Speaker 2: This is like when a bunch of celebrities open up

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Speaker 2: an ice cream story, right, Eli, Why do y'all know

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Speaker 2: each other?

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Speaker 1: Oh, one of you's moving coke and you guys need

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Speaker 1: it away? Alounder shity, What.

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Speaker 2: Is this relationship that somehow foster naturally.

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Speaker 1: Between uh huh, this is something's wrong here and missing something.

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Speaker 1: And yes there are some famous TikTokers involved as well,

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Speaker 1: and some other celebrities who should not be involved in

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Speaker 1: a biosciencest company we'll talk about later. So by the

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Speaker 1: time I read about the Winklevoss twins being involved, I

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Speaker 1: was fully on team fuck these people winkleby the Winkles V.

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Speaker 1: But that's not enough to actually like justify accusing a

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Speaker 1: person and their company of being bastards, right, just like

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Speaker 1: even I wouldn't do that. So I looked deeper, and boy, howdy,

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Speaker 1: did I find some shit. Before we get into everything

427
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Speaker 1: that's fucked up about this company, a lot of what's

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Speaker 1: fucked up here is actually taught doctor George Church and

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Speaker 1: talking about what this guy's done and where he's come from,

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Speaker 1: Because this is a story of like a great scientist

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Speaker 1: who makes some choices that I would argue puts him

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Speaker 1: into a series of very unethical situations because there's money

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Speaker 1: in it. Right, That's what I think is going on here.

434
00:21:52,960 --> 00:21:56,040
Speaker 1: But I'm just gonna read you his bio. George McDonald

435
00:21:56,160 --> 00:21:59,040
Speaker 1: Church was born on August twenty eighth, nineteen fifty four,

436
00:21:59,119 --> 00:22:02,480
Speaker 1: on McDill Air Force Base in Tampa, Florida. He grew

437
00:22:02,560 --> 00:22:05,720
Speaker 1: up in Clearwater with the capitalist scientology. But I can't

438
00:22:05,760 --> 00:22:07,439
Speaker 1: hold that against him. He's got no ties that I

439
00:22:07,440 --> 00:22:09,640
Speaker 1: found to the organization. He just grew up there.

440
00:22:10,520 --> 00:22:11,479
Speaker 2: He just got lucky.

441
00:22:11,560 --> 00:22:15,320
Speaker 1: He just got lucky, thank God. Yeah, he's near Blue Base.

442
00:22:15,440 --> 00:22:18,600
Speaker 1: I think that's the big base in Clearwater. His family

443
00:22:18,640 --> 00:22:21,760
Speaker 1: life was somewhat chaotic by most people's standards, as he

444
00:22:21,840 --> 00:22:24,159
Speaker 1: laid out in an interview with the Harvard Gazette, I

445
00:22:24,240 --> 00:22:26,760
Speaker 1: had three fathers as my mother remarried. The first one

446
00:22:26,800 --> 00:22:29,000
Speaker 1: lasted about eight months post birth, and he was an

447
00:22:29,040 --> 00:22:32,080
Speaker 1: Air Force pilot, a pretty colorful character. I knew him

448
00:22:32,119 --> 00:22:33,880
Speaker 1: off and on through the years up until his death.

449
00:22:34,119 --> 00:22:35,640
Speaker 1: He was the sort of father that a young boy

450
00:22:35,640 --> 00:22:38,879
Speaker 1: would admire because he wasn't tied down by actual responsibilities.

451
00:22:39,119 --> 00:22:42,119
Speaker 1: That was Stu MacDonald. He was called barefoot stew He

452
00:22:42,200 --> 00:22:44,439
Speaker 1: was inducted into the Water Ski Hall of Fame. He

453
00:22:44,480 --> 00:22:46,760
Speaker 1: wasn't a terrific athlete. I mean, obviously he was a

454
00:22:46,800 --> 00:22:49,080
Speaker 1: pretty good one, but his real contribution to the sport,

455
00:22:49,119 --> 00:22:51,080
Speaker 1: which was relevant to me, was that he was a

456
00:22:51,080 --> 00:22:54,199
Speaker 1: good communicator. He was the first ABC wide World of

457
00:22:54,240 --> 00:22:57,520
Speaker 1: Sports color commentator. He was also just generally charismatic. He

458
00:22:57,600 --> 00:23:00,640
Speaker 1: was a male model. He worked on film television as well.

459
00:23:01,640 --> 00:23:05,119
Speaker 1: Right right, So, and this guy he's also primarily a

460
00:23:05,160 --> 00:23:08,520
Speaker 1: communicator now, right, And he's like, he's very old now,

461
00:23:08,520 --> 00:23:10,800
Speaker 1: but he's like a handsome kind of old Like he's

462
00:23:10,840 --> 00:23:13,320
Speaker 1: the you would cast him to be like the old

463
00:23:13,440 --> 00:23:15,960
Speaker 1: king in like a fucking new Robin Hood movie, like

464
00:23:16,040 --> 00:23:18,399
Speaker 1: comes Back at the Air, right, Like, he is that

465
00:23:18,520 --> 00:23:21,879
Speaker 1: kind of old guy. He was my birth dad, but

466
00:23:21,920 --> 00:23:24,440
Speaker 1: I don't think he really influenced me that much intellectually.

467
00:23:24,600 --> 00:23:27,200
Speaker 1: My second father was a lawyer and had the least influence.

468
00:23:27,359 --> 00:23:30,040
Speaker 1: Third dad was a physician who had two pretty important roles.

469
00:23:30,240 --> 00:23:32,399
Speaker 1: He sent me away to school, to an awesome high school.

470
00:23:32,440 --> 00:23:34,560
Speaker 1: Both my stepbrother and I went away at roughly the

471
00:23:34,600 --> 00:23:36,440
Speaker 1: same time. It might have just been to get the

472
00:23:36,440 --> 00:23:38,080
Speaker 1: young teenage boys out of the house, but in my

473
00:23:38,200 --> 00:23:40,840
Speaker 1: case it was very good. It was a liberal East

474
00:23:40,880 --> 00:23:43,400
Speaker 1: Coast school, and over which is where the Bushes went.

475
00:23:44,000 --> 00:23:46,680
Speaker 1: I don't know if i'd call that liberal, but and

476
00:23:47,119 --> 00:23:49,800
Speaker 1: Harvard chemistry professor George Whiteside and a bunch of other

477
00:23:49,840 --> 00:23:52,119
Speaker 1: interesting people. And the other thing he did was just

478
00:23:52,200 --> 00:23:54,959
Speaker 1: being a physician. I could look at his medical technology

479
00:23:54,960 --> 00:23:58,720
Speaker 1: and somehow be enthralled by it. And doctor Dad is

480
00:23:58,760 --> 00:24:00,840
Speaker 1: where he gets the last name Church, So that definitely

481
00:24:00,840 --> 00:24:03,119
Speaker 1: seems to be the guy he primarily considers to have

482
00:24:03,160 --> 00:24:05,520
Speaker 1: been his father. And that summary of events does kind

483
00:24:05,520 --> 00:24:07,800
Speaker 1: of smooth over a couple of things, including what seems

484
00:24:07,800 --> 00:24:10,320
Speaker 1: to have been a difficult start for George at school.

485
00:24:10,560 --> 00:24:13,159
Speaker 1: He's always very bright, but he has learning disabilities. He

486
00:24:13,200 --> 00:24:16,159
Speaker 1: had to repeat the ninth grade as a result. George

487
00:24:16,160 --> 00:24:19,240
Speaker 1: has claimed in recent years to have dyslexia and narcolepsy

488
00:24:19,359 --> 00:24:22,800
Speaker 1: OCD and add all of which he says it's a

489
00:24:22,840 --> 00:24:25,840
Speaker 1: lot of stuff. He says, they were all mild, but

490
00:24:26,040 --> 00:24:28,520
Speaker 1: it made me feel different, right, and so he became

491
00:24:28,600 --> 00:24:31,160
Speaker 1: kind of desperate in grade school not to stand out

492
00:24:31,280 --> 00:24:33,760
Speaker 1: or get attention, right, Like, he doesn't want to seem weird,

493
00:24:33,800 --> 00:24:36,520
Speaker 1: which is a pretty normal way for kids to feel

494
00:24:36,560 --> 00:24:37,080
Speaker 1: in school.

495
00:24:37,320 --> 00:24:39,680
Speaker 2: So far, it's the thing I've related to him most

496
00:24:39,720 --> 00:24:42,120
Speaker 2: on Right, I get that, Yeah, I get it that

497
00:24:42,440 --> 00:24:48,320
Speaker 2: I connect to Saying you reinvented wolves is a different conversation.

498
00:24:47,840 --> 00:24:53,119
Speaker 1: But the whole thing trouble with you very rarely claimed

499
00:24:53,119 --> 00:24:53,720
Speaker 1: that Langston.

500
00:24:54,840 --> 00:24:58,080
Speaker 2: It's almost never come up in our conversations at least.

501
00:24:57,920 --> 00:25:02,480
Speaker 1: Yeah, seldom. Prior to going to Andover Church, attended both

502
00:25:02,520 --> 00:25:05,840
Speaker 1: public and Catholic schools, but had bad experiences in both systems.

503
00:25:05,880 --> 00:25:08,359
Speaker 1: He just says the schools in Florida weren't very good. Again,

504
00:25:08,520 --> 00:25:14,520
Speaker 1: I don't have trouble seeing that. Despite his difficulty with academics,

505
00:25:14,600 --> 00:25:16,880
Speaker 1: he was a voracious reader and good at self directed

506
00:25:16,960 --> 00:25:19,719
Speaker 1: learning when he was interested in something. He built an

507
00:25:19,760 --> 00:25:22,440
Speaker 1: analog computer when he was ten, and when he started

508
00:25:22,440 --> 00:25:25,080
Speaker 1: at Andover they had a time share computing program with

509
00:25:25,200 --> 00:25:28,000
Speaker 1: nearby Dartmouth College, so he was able to spend time

510
00:25:28,040 --> 00:25:30,720
Speaker 1: on a computer before most people his age did, which

511
00:25:30,760 --> 00:25:33,560
Speaker 1: is like you see similar stories with like a lot

512
00:25:33,600 --> 00:25:35,520
Speaker 1: of the Bill Gates and Steve Jobs, a lot of

513
00:25:35,560 --> 00:25:38,440
Speaker 1: the guys who were like around this age and also

514
00:25:38,560 --> 00:25:40,360
Speaker 1: wound up being major tech players.

515
00:25:40,520 --> 00:25:44,760
Speaker 2: Right, Yeah, it was such rare technology, Yeah, to access

516
00:25:44,840 --> 00:25:45,440
Speaker 2: at that age.

517
00:25:45,480 --> 00:25:45,520
Speaker 1: That.

518
00:25:45,640 --> 00:25:48,320
Speaker 2: I bet if you really were able to invest in

519
00:25:48,359 --> 00:25:52,920
Speaker 2: the time and energy, you advanced a chess piece. So far, yes,

520
00:25:53,200 --> 00:25:54,320
Speaker 2: for yourself.

521
00:25:54,040 --> 00:25:56,200
Speaker 1: And you were generally kind of a rich kid, right,

522
00:25:56,280 --> 00:25:58,120
Speaker 1: which is you know the case with even though he's

523
00:25:58,119 --> 00:26:00,160
Speaker 1: got you know, he goes through a couple of dads,

524
00:26:00,240 --> 00:26:03,439
Speaker 1: this this last one is very comfortable financially and as

525
00:26:03,440 --> 00:26:05,879
Speaker 1: a result, he gets this opportunity and as a result,

526
00:26:05,960 --> 00:26:08,760
Speaker 1: then his story sounds less like a lot of big

527
00:26:08,840 --> 00:26:12,120
Speaker 1: science guys and more like a lot of tech startup dudes, right,

528
00:26:12,320 --> 00:26:14,640
Speaker 1: Like that's the kind of background this dude has.

529
00:26:14,680 --> 00:26:18,160
Speaker 2: They always talk about how like Bill Gates started Microsoft

530
00:26:18,200 --> 00:26:20,560
Speaker 2: in his garage, but it's like, oh, you had an

531
00:26:20,680 --> 00:26:27,120
Speaker 2: entire garage. Most people have to store things and park

532
00:26:27,160 --> 00:26:29,240
Speaker 2: ours in there. You were just like Noah, the garage

533
00:26:29,320 --> 00:26:30,040
Speaker 2: is a workspace.

534
00:26:30,280 --> 00:26:35,119
Speaker 1: Yeah, that's right, speaking of garages, if you want to

535
00:26:35,160 --> 00:26:38,240
Speaker 1: afford a garage, I don't know. I can't help you,

536
00:26:38,320 --> 00:26:39,920
Speaker 1: but you can buy these products.

537
00:26:40,160 --> 00:26:42,359
Speaker 2: Oh nice, this is a beautiful segue.

538
00:26:42,960 --> 00:26:51,840
Speaker 1: Yeah we're back. Okay, So so far it's been a

539
00:26:51,880 --> 00:26:54,200
Speaker 1: pretty similar story to a lot of tech guys. And

540
00:26:54,440 --> 00:26:56,399
Speaker 1: George has a story about what how we knew was

541
00:26:56,440 --> 00:26:58,040
Speaker 1: like ten or so, he goes to the New York

542
00:26:58,080 --> 00:27:00,439
Speaker 1: World's Fair and that has a huge impact on him.

543
00:27:00,440 --> 00:27:02,959
Speaker 1: He gets to see very early touch screens, which are

544
00:27:03,000 --> 00:27:06,199
Speaker 1: obviously a precursor to a real technology, and also a

545
00:27:06,240 --> 00:27:09,440
Speaker 1: lot of fake future technology, like personal jet packs and

546
00:27:09,480 --> 00:27:12,080
Speaker 1: stuff that, like, I mean, there's technically some jet packs,

547
00:27:12,080 --> 00:27:13,680
Speaker 1: but it's not what we thought it would be, right,

548
00:27:13,800 --> 00:27:15,359
Speaker 1: We're not flying around in those things.

549
00:27:15,400 --> 00:27:17,440
Speaker 2: You and be like, I thought it was gonna be

550
00:27:17,560 --> 00:27:19,600
Speaker 2: rocketman shit, and it's not that at all.

551
00:27:20,040 --> 00:27:22,280
Speaker 1: No. No, I thought right up to the fact that

552
00:27:22,320 --> 00:27:24,960
Speaker 1: I thought we'd all be shooting Nazis, And it turns

553
00:27:25,000 --> 00:27:28,400
Speaker 1: out we shose other things to do with Nazis, which

554
00:27:29,720 --> 00:27:30,400
Speaker 1: some of us think.

555
00:27:30,440 --> 00:27:33,119
Speaker 2: It's they're a better hang than we anticipated.

556
00:27:33,560 --> 00:27:35,960
Speaker 1: Certainly the better hang than the rocketman for the rocket

557
00:27:36,000 --> 00:27:42,119
Speaker 1: to your thought. So, but yeah, a lot of the

558
00:27:42,160 --> 00:27:44,880
Speaker 1: tech he sees is also just stuff that like never happened, right,

559
00:27:45,000 --> 00:27:47,359
Speaker 1: And George would later say, quote, it didn't take too

560
00:27:47,440 --> 00:27:50,199
Speaker 1: long for me to become disillusioned. Not only was it

561
00:27:50,280 --> 00:27:52,399
Speaker 1: not like that in Florida, it probably wasn't even like

562
00:27:52,440 --> 00:27:54,480
Speaker 1: that in New York once they shut down the World's Fair,

563
00:27:54,720 --> 00:27:56,399
Speaker 1: And it might not ever be like that if I

564
00:27:56,440 --> 00:27:58,480
Speaker 1: didn't do something about it. So I sort of felt

565
00:27:58,520 --> 00:28:01,040
Speaker 1: like if I want that, I have to it. And

566
00:28:01,080 --> 00:28:03,200
Speaker 1: you can take two things out of this either he

567
00:28:03,280 --> 00:28:06,280
Speaker 1: realizes the World's fair is largely like a pr thing

568
00:28:06,520 --> 00:28:08,640
Speaker 1: and most of this stuff isn't coming or at least

569
00:28:08,640 --> 00:28:11,080
Speaker 1: not coming anytime soon, and like, well, then I'm going

570
00:28:11,119 --> 00:28:13,120
Speaker 1: to get into I'm going to become a cutting edge

571
00:28:13,160 --> 00:28:16,560
Speaker 1: scientist to try to make this future real. Or maybe

572
00:28:16,560 --> 00:28:19,040
Speaker 1: what he learns is like, wow, it's really easy to

573
00:28:19,080 --> 00:28:20,840
Speaker 1: lie to a lot of people about what you can

574
00:28:20,880 --> 00:28:23,560
Speaker 1: do and like get money, and maybe that's maybe that's

575
00:28:23,600 --> 00:28:25,680
Speaker 1: a lot easier than inventing the future.

576
00:28:26,640 --> 00:28:30,919
Speaker 2: I think spotting a grift is real, profitable, real fast.

577
00:28:31,280 --> 00:28:35,560
Speaker 1: It can be. Yeah. Now, maybe to be fair, maybe

578
00:28:35,600 --> 00:28:38,120
Speaker 1: both of those things hit him, because he does get

579
00:28:38,160 --> 00:28:41,800
Speaker 1: into some real making the future shit. At first, Church

580
00:28:41,800 --> 00:28:44,840
Speaker 1: wound up attending summer courses in quantum physics. At MIT,

581
00:28:45,200 --> 00:28:48,920
Speaker 1: he gets into crystallography, which I don't really understand but

582
00:28:49,040 --> 00:28:52,440
Speaker 1: is important science. And he describes this as showing him,

583
00:28:52,480 --> 00:28:55,360
Speaker 1: quote the intersection of computers and biology, which is going

584
00:28:55,400 --> 00:28:58,040
Speaker 1: to be like a constant source of fascination for him.

585
00:28:58,400 --> 00:29:00,479
Speaker 1: Now he does still have issues in SCO he has

586
00:29:00,520 --> 00:29:03,640
Speaker 1: to repeat his first year of graduate school, and depending

587
00:29:03,680 --> 00:29:06,160
Speaker 1: on where you find him interviewed, this is explained differently.

588
00:29:06,200 --> 00:29:08,400
Speaker 1: I found one article that just said, well, he was

589
00:29:08,520 --> 00:29:12,200
Speaker 1: just taking so many classes, too many classes, so that

590
00:29:12,440 --> 00:29:14,560
Speaker 1: he couldn't graduate, and he was just like too interested

591
00:29:14,600 --> 00:29:16,960
Speaker 1: in doing too many different things, and it just like

592
00:29:17,520 --> 00:29:20,840
Speaker 1: graduating kind of slipped by him. And that's not really accurate.

593
00:29:21,080 --> 00:29:23,680
Speaker 1: The way he explained it in this Harvard student paper

594
00:29:23,720 --> 00:29:25,960
Speaker 1: is different from that, but it's also kind of weird.

595
00:29:26,560 --> 00:29:29,040
Speaker 1: Sometimes I could get away with barely going to classes.

596
00:29:29,120 --> 00:29:31,600
Speaker 1: Other times, like in organic chemistry. I loved it so much.

597
00:29:31,680 --> 00:29:33,480
Speaker 1: I did every single problem set in the back of

598
00:29:33,520 --> 00:29:35,920
Speaker 1: each chapter. They didn't even assign any of them. I

599
00:29:35,960 --> 00:29:37,680
Speaker 1: did them all. It was a full year course, and

600
00:29:37,720 --> 00:29:39,880
Speaker 1: I think I finished the book, including all the problems

601
00:29:39,880 --> 00:29:42,400
Speaker 1: in it, by halfway through the fall semester. That was

602
00:29:42,440 --> 00:29:44,640
Speaker 1: pretty typical. But I guess the reason I did it

603
00:29:44,640 --> 00:29:46,440
Speaker 1: in two years was that it was I was cheap

604
00:29:46,480 --> 00:29:48,640
Speaker 1: money wise, like a lot of teenagers, I didn't want

605
00:29:48,640 --> 00:29:51,160
Speaker 1: to keep being a burden on my parents. Steve Jobs

606
00:29:51,200 --> 00:29:53,880
Speaker 1: dropped out of college because he was worried about his parents' finances.

607
00:29:53,920 --> 00:29:55,960
Speaker 1: He did not. I didn't draw it out. I just

608
00:29:56,000 --> 00:29:58,440
Speaker 1: finished early. I also think I had this feeling that

609
00:29:58,480 --> 00:29:59,720
Speaker 1: if I took four years to do it, I would

610
00:29:59,720 --> 00:30:02,120
Speaker 1: probably flunk out, so it would be better to finish fast.

611
00:30:02,320 --> 00:30:04,040
Speaker 1: That turned out to be true at about the three

612
00:30:04,040 --> 00:30:05,880
Speaker 1: and a half year point. I did flunk out, but

613
00:30:05,880 --> 00:30:07,920
Speaker 1: out of graduate school. And you see that doesn't make

614
00:30:07,960 --> 00:30:09,840
Speaker 1: sense how He's like, well, I graduated early so I

615
00:30:09,840 --> 00:30:11,520
Speaker 1: wouldn't be a burden to my parents. But actually I

616
00:30:11,560 --> 00:30:14,200
Speaker 1: flunked out after three and a half years. I was like, well,

617
00:30:14,440 --> 00:30:15,960
Speaker 1: I don't understand what you're saying.

618
00:30:16,280 --> 00:30:19,280
Speaker 2: And I think if we're going back, that really speaks

619
00:30:19,320 --> 00:30:24,080
Speaker 2: to both the passionate learner and the grifter working in

620
00:30:24,360 --> 00:30:26,560
Speaker 2: in sort of synchronicity.

621
00:30:26,800 --> 00:30:29,000
Speaker 1: Right, if you'll forgive me likes and he has two

622
00:30:29,080 --> 00:30:30,240
Speaker 1: wolves inside him.

623
00:30:32,280 --> 00:30:35,880
Speaker 2: The man contains two wolves. Oh yeah, one DIYer one

624
00:30:35,920 --> 00:30:37,240
Speaker 2: pretty much a regular.

625
00:30:36,880 --> 00:30:39,200
Speaker 1: One trying to sell you a way.

626
00:30:40,040 --> 00:30:41,160
Speaker 4: I don't I don't.

627
00:30:40,960 --> 00:30:44,360
Speaker 1: Forgive it now. The way he describes this other times

628
00:30:44,400 --> 00:30:47,520
Speaker 1: is that he didn't even realize he'd flunked out of

629
00:30:47,600 --> 00:30:50,520
Speaker 1: graduate school because he was so excited about the crystallography work.

630
00:30:50,520 --> 00:30:53,840
Speaker 1: His advisor had him doing and his advisor was like, hey, man,

631
00:30:54,000 --> 00:30:56,520
Speaker 1: you're you're actually flunking. You know you're going to You've

632
00:30:56,560 --> 00:30:59,640
Speaker 1: got to like you're getting kicked out, and how hired

633
00:30:59,680 --> 00:31:02,640
Speaker 1: him as a technician, but was like, you can't just

634
00:31:02,760 --> 00:31:05,280
Speaker 1: keep doing this, you have to reapply to graduate school

635
00:31:05,320 --> 00:31:09,400
Speaker 1: somewhere else. And Church eventually reapplies to Harvard and describes

636
00:31:09,440 --> 00:31:11,920
Speaker 1: himself as being shocked at getting in because he'd flunked

637
00:31:11,920 --> 00:31:14,680
Speaker 1: out of Duke, but he had also gotten accepted to

638
00:31:14,720 --> 00:31:17,120
Speaker 1: Harvard before he went to Duke. And anyway, whatever he

639
00:31:17,120 --> 00:31:20,440
Speaker 1: gets accepted, did some stuff happen behind the scenes with

640
00:31:20,480 --> 00:31:22,120
Speaker 1: his dad and Harvard? I don't know. It may just

641
00:31:22,240 --> 00:31:23,800
Speaker 1: been that he'd been accepted before.

642
00:31:23,960 --> 00:31:26,840
Speaker 2: As I was. I bet having rich parents and a

643
00:31:26,960 --> 00:31:30,520
Speaker 2: nice little parachute probably helped him figure that out.

644
00:31:30,720 --> 00:31:33,720
Speaker 1: And he's got this professor who's probably going to bat

645
00:31:33,760 --> 00:31:35,520
Speaker 1: for him too, because he is good at some things.

646
00:31:35,520 --> 00:31:38,160
Speaker 1: But anyway, it's how he dropped out, and exactly why

647
00:31:38,280 --> 00:31:41,080
Speaker 1: is like a little bit different every time I read it,

648
00:31:41,880 --> 00:31:44,520
Speaker 1: which always kind of like raises my grifter hackles just

649
00:31:44,520 --> 00:31:48,120
Speaker 1: to scoch. But maybe I'm missing some things. At any rate,

650
00:31:48,160 --> 00:31:50,000
Speaker 1: he gets into Harvard, and he does better here. He

651
00:31:50,000 --> 00:31:53,440
Speaker 1: gets into chemistry and genomics sequencing, which is what he does.

652
00:31:53,480 --> 00:31:57,840
Speaker 1: His thesis on his nineteen eighty five PhD from Harvard

653
00:31:57,880 --> 00:32:00,720
Speaker 1: PERI write up on edge dot org quote included the

654
00:32:00,760 --> 00:32:05,800
Speaker 1: first methods for direct genome sequencing, molecular multiplexing, and bar coding.

655
00:32:06,040 --> 00:32:10,320
Speaker 1: These led to the first genome sequence pathogen Helicobacter pylori

656
00:32:10,360 --> 00:32:13,480
Speaker 1: in nineteen ninety four. His innovations have continued to nearly

657
00:32:13,520 --> 00:32:16,920
Speaker 1: all next generation DNA sequencing methods and companies and as

658
00:32:17,000 --> 00:32:18,800
Speaker 1: far as I can tell, and I even like reached

659
00:32:18,840 --> 00:32:21,040
Speaker 1: out to a friend of a friend who's in this field,

660
00:32:21,360 --> 00:32:24,640
Speaker 1: that is accurate. He is a legitimately like foundational mind

661
00:32:24,720 --> 00:32:28,960
Speaker 1: in modern genome sequencing. His work has been massively influential

662
00:32:29,040 --> 00:32:32,640
Speaker 1: in like specifically personal genome of people. He didn't invent

663
00:32:32,680 --> 00:32:35,960
Speaker 1: genome sequencing, right, but when we've like first started sequencing,

664
00:32:35,960 --> 00:32:38,040
Speaker 1: it costs billions of dollars to that the first time,

665
00:32:38,040 --> 00:32:41,000
Speaker 1: And he's a major reason why individuals can do it

666
00:32:41,240 --> 00:32:42,600
Speaker 1: and why you can do it for I think it's

667
00:32:42,640 --> 00:32:44,360
Speaker 1: like seven hundred and fifty bucks to get your genome

668
00:32:44,400 --> 00:32:47,719
Speaker 1: sequence now, right, Like, he is a big part of

669
00:32:47,720 --> 00:32:49,760
Speaker 1: that process, right, not even not to just write it

670
00:32:49,800 --> 00:32:53,080
Speaker 1: down to just him either. But his role is substantial

671
00:32:53,200 --> 00:32:56,040
Speaker 1: and this is meaningful, important science, right, and I don't

672
00:32:56,200 --> 00:32:57,640
Speaker 1: I'm not going to try to take that away from

673
00:32:57,680 --> 00:33:00,880
Speaker 1: him or pretend like this does not seem to be exaggerated.

674
00:33:01,120 --> 00:33:04,720
Speaker 1: Other aspects of his achievements will be this does not

675
00:33:04,840 --> 00:33:07,920
Speaker 1: seem to be right. A write up on him in

676
00:33:08,000 --> 00:33:12,600
Speaker 1: Popular Science by Janine Interlandi summarizes scientists are now using

677
00:33:12,680 --> 00:33:16,360
Speaker 1: it personal genome sequencing to identify intractable diseases such as

678
00:33:16,360 --> 00:33:18,760
Speaker 1: cancer and schizophrenia, and doctors are beginning to use it

679
00:33:18,760 --> 00:33:21,160
Speaker 1: to it enough by genetic mutations that cause rare and

680
00:33:21,240 --> 00:33:27,040
Speaker 1: until now undiagnosable illnesses. So Church becomes a PhD. Seems

681
00:33:27,080 --> 00:33:30,320
Speaker 1: like you're in that doing some good work, doing some

682
00:33:30,360 --> 00:33:33,440
Speaker 1: good work. He initiates the Personal Genome Project at Harvard

683
00:33:33,440 --> 00:33:35,680
Speaker 1: in two thousand and five, with the goal of sequencing

684
00:33:35,720 --> 00:33:38,840
Speaker 1: and publicizing the complete genomes and medical records of one

685
00:33:38,880 --> 00:33:43,280
Speaker 1: hundred thousand volunteers to further research into personalized medicine. And

686
00:33:43,320 --> 00:33:46,920
Speaker 1: all this is great, but there's also even in just

687
00:33:46,960 --> 00:33:49,719
Speaker 1: this you could be like, well, these people are volunteering

688
00:33:49,760 --> 00:33:51,760
Speaker 1: so maybe it's cool, but like there is some potential

689
00:33:51,920 --> 00:33:57,360
Speaker 1: troubling privacy stuff about publicizing everybody's genomes. You know. I

690
00:33:57,400 --> 00:34:00,000
Speaker 1: think we've all thought about that more in the last

691
00:34:00,040 --> 00:34:01,400
Speaker 1: couple of years about before.

692
00:34:01,480 --> 00:34:03,960
Speaker 2: Maybe my genome's pretty private to me.

693
00:34:05,520 --> 00:34:07,480
Speaker 1: I know a lot of people who use those twenty

694
00:34:07,520 --> 00:34:10,080
Speaker 1: three and me companies that are like, actually, I kind

695
00:34:10,080 --> 00:34:12,000
Speaker 1: of wish I hadn't done that now, knowing what they

696
00:34:12,040 --> 00:34:12,760
Speaker 1: do with the data.

697
00:34:12,960 --> 00:34:16,600
Speaker 2: Right, Yeah, they can like they can refuse a mortgage

698
00:34:16,680 --> 00:34:20,880
Speaker 2: because you've published this and now they're like, oh, we

699
00:34:21,239 --> 00:34:23,600
Speaker 2: think you're going to have diabetes, and diabetes means you

700
00:34:23,600 --> 00:34:27,160
Speaker 2: won't be able to pay thirty years worth of right mortgage,

701
00:34:27,239 --> 00:34:28,560
Speaker 2: So naw again.

702
00:34:29,080 --> 00:34:30,640
Speaker 1: And it's one of those things you're not necessarily a

703
00:34:30,680 --> 00:34:33,320
Speaker 1: bastard for, like being in a science that is used

704
00:34:33,360 --> 00:34:36,560
Speaker 1: by corporations and that isn't fundamentally evil but gets used

705
00:34:36,560 --> 00:34:38,759
Speaker 1: in some shady ways. But kind of what this does

706
00:34:38,760 --> 00:34:41,480
Speaker 1: show is I don't really think he often thinks about

707
00:34:41,719 --> 00:34:44,759
Speaker 1: the negative applications of what he's involved in. That is

708
00:34:44,760 --> 00:34:47,160
Speaker 1: going to be kind of a through line with George Church,

709
00:34:47,600 --> 00:34:50,320
Speaker 1: as we'll talk about later. But that article in Popular

710
00:34:50,360 --> 00:34:53,040
Speaker 1: Science continues more so than any other scientist in his field.

711
00:34:53,080 --> 00:34:55,359
Speaker 1: He is helping to forge a new kind of biology,

712
00:34:55,640 --> 00:34:58,759
Speaker 1: one less geared towards studying DNA than harnessing it for

713
00:34:58,840 --> 00:35:01,239
Speaker 1: our own aims. This is where the fucked up shit

714
00:35:01,400 --> 00:35:03,560
Speaker 1: starts to kind of come in. Is like, he is,

715
00:35:03,640 --> 00:35:08,200
Speaker 1: a DNA is no different from you know, a computer chip, right,

716
00:35:08,280 --> 00:35:10,480
Speaker 1: And we shouldn't think of it as different than that

717
00:35:10,600 --> 00:35:14,319
Speaker 1: in terms of allowing us to build new technologies. And

718
00:35:15,200 --> 00:35:18,239
Speaker 1: I can understand on some extent that attitude, but it

719
00:35:18,320 --> 00:35:22,480
Speaker 1: is also different. It's not it's not a computer chip.

720
00:35:22,800 --> 00:35:27,880
Speaker 2: Like, I really get nervous whenever the language starts dehumanizing

721
00:35:28,000 --> 00:35:31,839
Speaker 2: human experiences, like, right, that there has to be some

722
00:35:32,280 --> 00:35:35,279
Speaker 2: attachment to what it means to be a person for

723
00:35:35,360 --> 00:35:40,279
Speaker 2: this to remain healthy, normal, applicable in a way that

724
00:35:40,360 --> 00:35:44,960
Speaker 2: isn't just you scamming us into something much more scary evil.

725
00:35:45,440 --> 00:35:48,319
Speaker 1: Right, Yeah. And that's the thing, is, like, there's a

726
00:35:48,360 --> 00:35:51,200
Speaker 1: degree to which, if you're just talking purely logically, right,

727
00:35:52,000 --> 00:35:53,600
Speaker 1: there's a degree to which you can be like, well,

728
00:35:53,640 --> 00:35:55,560
Speaker 1: I guess it makes sense to say, like, you know,

729
00:35:55,960 --> 00:35:59,360
Speaker 1: if I'm open to the idea of like genetically editing

730
00:35:59,400 --> 00:36:03,280
Speaker 1: people to make them, you know, more resilient to diseases

731
00:36:03,360 --> 00:36:05,520
Speaker 1: or something. Maybe it makes a little sense to think

732
00:36:05,520 --> 00:36:08,480
Speaker 1: of it as a technology in that way, but the

733
00:36:08,640 --> 00:36:12,560
Speaker 1: line from that to thinking about the people and other

734
00:36:12,719 --> 00:36:18,640
Speaker 1: living beings you create as just smartphones that how do

735
00:36:18,680 --> 00:36:22,120
Speaker 1: you what stops you? What guard rails are you building

736
00:36:22,120 --> 00:36:23,959
Speaker 1: in to stop that? If this is how you're looking

737
00:36:24,000 --> 00:36:25,360
Speaker 1: at it, where are the guard rails?

738
00:36:25,480 --> 00:36:29,839
Speaker 2: Yeah, people turn into mind sweeper real fast. Exactly, it's

739
00:36:29,920 --> 00:36:33,480
Speaker 2: not exactly You're not dealing with bodies anymore in that skin.

740
00:36:33,600 --> 00:36:35,800
Speaker 1: Yeah, and that's not great. And that is kind of

741
00:36:35,840 --> 00:36:39,280
Speaker 1: where we're headed here. So Church's success led to Harvard

742
00:36:39,320 --> 00:36:41,920
Speaker 1: funding the establishment of his lab. He has like a

743
00:36:42,040 --> 00:36:44,320
Speaker 1: lab that is funded by Harvard that has been for

744
00:36:44,400 --> 00:36:47,600
Speaker 1: quite some time, and he brings in, you know, minds

745
00:36:47,640 --> 00:36:49,960
Speaker 1: that excite him and hires them and basically pays them

746
00:36:49,960 --> 00:36:52,280
Speaker 1: to like fuck around and try to figure shit out.

747
00:36:52,800 --> 00:36:55,760
Speaker 1: And he uses this like this is both a valid

748
00:36:56,360 --> 00:36:59,200
Speaker 1: thing to do in terms of science, but it's also

749
00:36:59,480 --> 00:37:02,600
Speaker 1: he uses it as like an incubator for startup ideas.

750
00:37:02,640 --> 00:37:06,480
Speaker 1: Like once people do stuff that shows promise, he'll often

751
00:37:06,600 --> 00:37:09,520
Speaker 1: spin what they're doing off into a company. Per an

752
00:37:09,560 --> 00:37:12,080
Speaker 1: article in popular science. The result is that his lab

753
00:37:12,120 --> 00:37:14,440
Speaker 1: manages to be both one of Harvard's top producers and

754
00:37:14,480 --> 00:37:17,879
Speaker 1: a well known receiving center for science's misfit toys. There's

755
00:37:17,880 --> 00:37:21,279
Speaker 1: an artist encoding Wikipedia entries into apple genomes to create

756
00:37:21,320 --> 00:37:24,280
Speaker 1: a literal tree of knowledge, and an insurance industry refugee

757
00:37:24,280 --> 00:37:26,800
Speaker 1: who fled his office job over a decade ago, worked

758
00:37:26,840 --> 00:37:29,919
Speaker 1: several months for free while teaching himself biochemistry, and now

759
00:37:29,960 --> 00:37:33,320
Speaker 1: serves as co head of the lab. So again, that

760
00:37:33,840 --> 00:37:35,919
Speaker 1: sounds kind of cool potentially.

761
00:37:35,560 --> 00:37:38,280
Speaker 2: You know, ah, yes, what's the true knowledge?

762
00:37:38,560 --> 00:37:39,920
Speaker 1: What is that supposed to that do?

763
00:37:41,360 --> 00:37:44,160
Speaker 2: Yeah, that's cool, I get I don't know, it sounds awesome.

764
00:37:44,200 --> 00:37:45,480
Speaker 1: It sounds like an art project.

765
00:37:45,600 --> 00:37:46,480
Speaker 2: Yeah.

766
00:37:46,520 --> 00:37:49,120
Speaker 1: The article we'll talk a little bit about like DNA coding.

767
00:37:49,120 --> 00:37:52,560
Speaker 1: That's actually there's some science there. But the article quotes

768
00:37:52,600 --> 00:37:54,720
Speaker 1: a former student of Churches who found it a genetic

769
00:37:54,760 --> 00:37:57,960
Speaker 1: engineering screening company that looks for inherited diseases. And he said,

770
00:37:58,040 --> 00:37:59,640
Speaker 1: we always joke that the only thing you need to

771
00:37:59,640 --> 00:38:02,840
Speaker 1: do to in George's lavish show up there is zero organization.

772
00:38:03,320 --> 00:38:06,279
Speaker 1: His style is just to let things happen. Mostly, you

773
00:38:06,360 --> 00:38:08,839
Speaker 1: have the constant sense that exciting things are happening. We're

774
00:38:08,880 --> 00:38:10,560
Speaker 1: about to happen, and if you miss out on it,

775
00:38:10,600 --> 00:38:11,960
Speaker 1: you have only yourself to blame.

776
00:38:12,600 --> 00:38:13,080
Speaker 2: Wow.

777
00:38:13,520 --> 00:38:16,680
Speaker 1: Yeah, and yeah, that's kind of the tech industry fomo,

778
00:38:17,120 --> 00:38:18,640
Speaker 1: you know, PR thing in a nutshell.

779
00:38:19,719 --> 00:38:23,200
Speaker 2: That's also just not a good thing to to sort of,

780
00:38:23,239 --> 00:38:25,640
Speaker 2: I guess have is like what people know about you

781
00:38:25,680 --> 00:38:27,839
Speaker 2: that like, yeah, we show up, he'll give you a job.

782
00:38:27,920 --> 00:38:30,640
Speaker 1: He'll give you a job he didn't give Yeah. Yeah, yeah,

783
00:38:31,480 --> 00:38:35,120
Speaker 1: he's looking for bodies who were fucking around now. In

784
00:38:35,160 --> 00:38:38,319
Speaker 1: the mid aughts, Church participated in kind of his first

785
00:38:38,360 --> 00:38:41,600
Speaker 1: noteworthy project after the genome sequencing stuff that we'll talk about,

786
00:38:41,640 --> 00:38:45,640
Speaker 1: which is a project to actually encode digital data made

787
00:38:45,680 --> 00:38:49,000
Speaker 1: of text into binary code and then transfer that into

788
00:38:49,120 --> 00:38:54,960
Speaker 1: genetic code, thus using DNA to store digital data. In

789
00:38:55,080 --> 00:38:58,600
Speaker 1: his case, this was also a marketing stunt because the

790
00:38:58,640 --> 00:39:03,080
Speaker 1: thing that he stored in DNA was like seventy billion

791
00:39:03,160 --> 00:39:06,200
Speaker 1: copies of the book that he had written with another

792
00:39:06,239 --> 00:39:07,960
Speaker 1: guy that was just about to come out, Right, So

793
00:39:08,000 --> 00:39:11,239
Speaker 1: he does this as a PR move, right, and it's

794
00:39:11,280 --> 00:39:14,400
Speaker 1: a brilliant PR move. The book ReGenesis was about to

795
00:39:14,440 --> 00:39:16,640
Speaker 1: hit shelves and suddenly there's all these articles about how

796
00:39:16,680 --> 00:39:20,319
Speaker 1: he stored seventy billion copies into like a dot of

797
00:39:20,440 --> 00:39:23,680
Speaker 1: DNA no longer than like a fucking period on a

798
00:39:23,680 --> 00:39:25,520
Speaker 1: piece of paper, that he was able to store so

799
00:39:25,520 --> 00:39:26,960
Speaker 1: many copies, Like, isn't that amazing?

800
00:39:27,080 --> 00:39:27,279
Speaker 2: Right?

801
00:39:28,520 --> 00:39:30,880
Speaker 1: And it is? It is pretty interesting, right, like that

802
00:39:30,960 --> 00:39:34,000
Speaker 1: he synthesized a strand of DNA, replicated it, and like

803
00:39:34,520 --> 00:39:36,680
Speaker 1: put it onto a scrap of paper and it contained

804
00:39:36,840 --> 00:39:40,520
Speaker 1: real data, right, This, in fact was so interesting that

805
00:39:40,600 --> 00:39:43,160
Speaker 1: it got him an appearance on the Colbert Report, right

806
00:39:44,160 --> 00:39:46,239
Speaker 1: where he pulled out the paper scrap which was like

807
00:39:46,280 --> 00:39:48,640
Speaker 1: the size and shape of a Foresten cookie slip and

808
00:39:48,680 --> 00:39:51,719
Speaker 1: showed it off to everybody. And this got representatives of

809
00:39:51,719 --> 00:39:55,920
Speaker 1: different companies who like archive films and other stuff reaching

810
00:39:55,920 --> 00:39:57,879
Speaker 1: out to him because they were like, oh shit, you know.

811
00:39:59,000 --> 00:40:00,719
Speaker 2: Maybe this is a paper.

812
00:40:02,160 --> 00:40:06,600
Speaker 1: Yes, yes, yes it is. We should work with him, yeah,

813
00:40:06,719 --> 00:40:11,160
Speaker 1: he's it is funny. Like again, I don't know that

814
00:40:11,200 --> 00:40:13,480
Speaker 1: this is exactly how a scientist should be putting out

815
00:40:13,480 --> 00:40:16,839
Speaker 1: a discovery of this magnitude apparently, but it's also it's

816
00:40:16,840 --> 00:40:18,640
Speaker 1: one of those things that's both like really cool and

817
00:40:18,719 --> 00:40:21,880
Speaker 1: interesting and somewhat less impressive than it sounds. When you

818
00:40:21,960 --> 00:40:25,400
Speaker 1: drill into what's actually happening here, right, because like the

819
00:40:25,800 --> 00:40:28,440
Speaker 1: like that article by INTERLANDI like makes it seem like

820
00:40:28,560 --> 00:40:30,799
Speaker 1: and this is obviously like a proof of concept for

821
00:40:30,840 --> 00:40:33,520
Speaker 1: something that could be potentially a huge deal for like

822
00:40:33,640 --> 00:40:37,279
Speaker 1: data storage. And that's not entirely untrue, but it's it's

823
00:40:37,320 --> 00:40:40,920
Speaker 1: not totally accurate either. The article goes on to summarize

824
00:40:40,960 --> 00:40:43,560
Speaker 1: the book that he co wrote with Ed Regis, who

825
00:40:43,960 --> 00:40:46,360
Speaker 1: It's weird to me that his co author's name is

826
00:40:46,520 --> 00:40:49,000
Speaker 1: ed Regis because Ed Regis is also a character in

827
00:40:49,040 --> 00:40:53,240
Speaker 1: the book Jurassic Park. He was the public relations manager

828
00:40:53,280 --> 00:40:56,800
Speaker 1: for engine in the novel. He doesn't wind up in

829
00:40:56,880 --> 00:40:58,480
Speaker 1: the fell. It's just really weird to me that his

830
00:40:58,560 --> 00:40:59,800
Speaker 1: co author on this book has that.

831
00:41:00,600 --> 00:41:03,040
Speaker 2: Yeah, but I bet he gets eaten. He sounds like

832
00:41:03,080 --> 00:41:03,640
Speaker 2: he got eaten.

833
00:41:04,160 --> 00:41:06,600
Speaker 1: Oh yeah, man, that motherfucker gets the hell eaten out

834
00:41:06,600 --> 00:41:09,560
Speaker 1: of him. Yes, yeah, if I am remembering right, he's

835
00:41:09,600 --> 00:41:11,520
Speaker 1: one of the ones who gets eaten like a son

836
00:41:11,560 --> 00:41:12,960
Speaker 1: of a bitch. I think he gets replaced with the

837
00:41:13,040 --> 00:41:18,040
Speaker 1: lawyer in the movie. So the book ReGenesis that Church

838
00:41:18,080 --> 00:41:21,319
Speaker 1: writes with Ed Regis quote envisioned this the future, This

839
00:41:21,360 --> 00:41:24,640
Speaker 1: new biology could bring one in which bacteria fuels cars

840
00:41:24,640 --> 00:41:27,560
Speaker 1: and commercial jets, and humans are immune to cancer. It

841
00:41:27,600 --> 00:41:29,520
Speaker 1: may sound like science fiction, or at least like a

842
00:41:29,520 --> 00:41:32,360
Speaker 1: litany of over hyped pipe dreams that science so often sells,

843
00:41:32,600 --> 00:41:35,120
Speaker 1: But George Church's pipe dreams have an uncanny record of

844
00:41:35,200 --> 00:41:38,800
Speaker 1: becoming reality. And I'd say this is the fundamental lie

845
00:41:38,840 --> 00:41:41,719
Speaker 1: about George that keeps getting repeated and spread by a

846
00:41:41,800 --> 00:41:45,960
Speaker 1: too credulous media. The man makes constant, wild and almost

847
00:41:46,000 --> 00:41:49,040
Speaker 1: impossible claims about what's going to happen in the future,

848
00:41:49,400 --> 00:41:51,279
Speaker 1: and then people will be like, yeah, it sounds nuts,

849
00:41:51,280 --> 00:41:53,880
Speaker 1: but his crazy dreams have become reality before, so we

850
00:41:53,880 --> 00:41:57,719
Speaker 1: should take him seriously. And we shouldn't because while Church

851
00:41:57,800 --> 00:42:00,480
Speaker 1: contributed massively to the science of gene c and saying

852
00:42:00,719 --> 00:42:03,520
Speaker 1: at no point were his ambitions in that field a

853
00:42:03,600 --> 00:42:06,040
Speaker 1: pipe dream, no one was ever like, no one can

854
00:42:06,080 --> 00:42:08,240
Speaker 1: do what you're trying to do, George. You can't personally

855
00:42:08,239 --> 00:42:11,640
Speaker 1: sequence the human genome. Scientists had been doing that right.

856
00:42:11,760 --> 00:42:14,520
Speaker 1: There were teams of people who had figured out aspects

857
00:42:14,520 --> 00:42:17,080
Speaker 1: of this before he got into the field. And while

858
00:42:17,120 --> 00:42:20,680
Speaker 1: what he discovered to do was really meaningful, nobody was like,

859
00:42:20,880 --> 00:42:23,600
Speaker 1: this will never get done. It was more like well,

860
00:42:23,640 --> 00:42:25,120
Speaker 1: someone's got to figure out, and he was the one

861
00:42:25,120 --> 00:42:27,160
Speaker 1: who figured it out. I'm not saying this is not impressive,

862
00:42:27,160 --> 00:42:29,160
Speaker 1: but it's not. It was never a pipe dream, right,

863
00:42:29,600 --> 00:42:31,960
Speaker 1: He's not by the time he got into it. And

864
00:42:32,320 --> 00:42:34,800
Speaker 1: the stuff he's talking about in this book, like altering

865
00:42:34,880 --> 00:42:37,719
Speaker 1: human biology to make us immune to cancer, that is

866
00:42:37,760 --> 00:42:41,360
Speaker 1: a pipe dream. There's no evidence that will ever be possible,

867
00:42:41,719 --> 00:42:45,959
Speaker 1: in part because cancers a bunch of different diseases. There's

868
00:42:46,000 --> 00:42:48,440
Speaker 1: never going to be a single thing that renders you

869
00:42:48,520 --> 00:42:52,000
Speaker 1: immune to cancer unless you start uploading people to the cloud,

870
00:42:52,080 --> 00:42:54,239
Speaker 1: which is also probably not possible.

871
00:42:54,880 --> 00:42:58,280
Speaker 2: I also get really nervous when the science includes both

872
00:42:58,480 --> 00:43:03,400
Speaker 2: car technology and answer elimination. That feels like, wait a minute,

873
00:43:03,719 --> 00:43:06,560
Speaker 2: you got to focus, big dog. Both of those things

874
00:43:06,600 --> 00:43:08,960
Speaker 2: can't be true from your single discovery.

875
00:43:09,280 --> 00:43:12,040
Speaker 1: Yeah. It's like if you, like, you know, you're a

876
00:43:12,080 --> 00:43:14,480
Speaker 1: Hollywood actor who's like starting to go bald, and you

877
00:43:14,520 --> 00:43:17,120
Speaker 1: go in for like Turkish hair transplants and the doctor's like,

878
00:43:17,320 --> 00:43:19,920
Speaker 1: hey man, you want a new liver, Like I haven't

879
00:43:20,440 --> 00:43:23,120
Speaker 1: I got one. I'd be like, wait, wait, wait, I don't.

880
00:43:23,440 --> 00:43:26,560
Speaker 2: I actually don't know. I prefer to keep the one

881
00:43:26,560 --> 00:43:28,319
Speaker 2: I have. I know it sucks, but I'll keep it.

882
00:43:28,360 --> 00:43:30,239
Speaker 1: I just came in here to get the Joel McHale man.

883
00:43:30,239 --> 00:43:35,000
Speaker 1: I really was not interested in a new organ. Never

884
00:43:35,080 --> 00:43:37,719
Speaker 1: have hair transplants worked out better for a man? My god?

885
00:43:37,880 --> 00:43:40,600
Speaker 2: Oh yeah his are? His are low and they're strong.

886
00:43:40,680 --> 00:43:46,160
Speaker 1: I really respect it, the Mona Lisa of hair. So

887
00:43:46,440 --> 00:43:48,640
Speaker 1: even when it comes to the cool things, doctor Church

888
00:43:48,760 --> 00:43:51,080
Speaker 1: actually did like store his book in DNA, and I

889
00:43:51,120 --> 00:43:55,200
Speaker 1: do think that's a cool idea, the practical reality behind

890
00:43:55,280 --> 00:43:57,560
Speaker 1: it is a lot less exciting than the hype. Now,

891
00:43:57,560 --> 00:43:59,239
Speaker 1: before we bust that, I want to show you a

892
00:43:59,320 --> 00:44:03,280
Speaker 1: video of Church presenting the exciting promise of DNA storage

893
00:44:03,320 --> 00:44:05,720
Speaker 1: in a video that was part of the promotional campaign

894
00:44:05,760 --> 00:44:08,439
Speaker 1: for his books. And he's being interviewed here with one

895
00:44:08,440 --> 00:44:10,719
Speaker 1: of his colleagues for this encoding project.

896
00:44:11,480 --> 00:44:13,879
Speaker 4: And yes, he does look exactly like I thought he would.

897
00:44:14,000 --> 00:44:15,040
Speaker 1: Yeah, no, he does.

898
00:44:15,760 --> 00:44:22,319
Speaker 3: The density is remarkably high, as little as one bit

899
00:44:22,440 --> 00:44:28,880
Speaker 3: per base one base per cubic nanometer, and so we

900
00:44:28,920 --> 00:44:31,960
Speaker 3: can store on the order of almost a zetabyte and

901
00:44:32,000 --> 00:44:35,240
Speaker 3: a gram of DNA a millileter volume.

902
00:44:35,800 --> 00:44:38,560
Speaker 1: The theoretical density of a DNA is that you could

903
00:44:38,600 --> 00:44:41,400
Speaker 1: store the total world information, which is one point in

904
00:44:41,440 --> 00:44:44,320
Speaker 1: setabtes at least in twenty eleven, and about four grams

905
00:44:44,320 --> 00:44:45,120
Speaker 1: of DNA.

906
00:44:46,040 --> 00:44:51,960
Speaker 3: And it leverages rapidly, improving next generation reading and writing

907
00:44:52,000 --> 00:44:52,480
Speaker 3: of DNA.

908
00:44:53,880 --> 00:44:56,440
Speaker 4: He looks like he'd be friends with Stocked in Rush.

909
00:44:56,560 --> 00:44:58,680
Speaker 1: He does look like he'd be friends with Stocked and Rush.

910
00:44:58,719 --> 00:45:01,240
Speaker 1: I think he's a lot smart than Stockton, though, although

911
00:45:01,239 --> 00:45:04,080
Speaker 1: that is a very low bar because Stockton was really

912
00:45:04,160 --> 00:45:11,200
Speaker 1: dumb all stocked at it turned into paste. Rush. He

913
00:45:11,239 --> 00:45:13,040
Speaker 1: does have the vibes of a guy that gets eaten

914
00:45:13,080 --> 00:45:14,960
Speaker 1: by his own dinosaurs. But I don't think that's going

915
00:45:15,000 --> 00:45:16,880
Speaker 1: to happen to him either, which is really tragic. That

916
00:45:16,920 --> 00:45:17,879
Speaker 1: actually does bum me out.

917
00:45:18,080 --> 00:45:22,120
Speaker 2: It definitely felt like late stage James Cameron, you know

918
00:45:22,120 --> 00:45:25,080
Speaker 2: what I mean, Like it felt like you're telling me

919
00:45:25,120 --> 00:45:28,120
Speaker 2: about the Avatar technology. This movie still sucks.

920
00:45:28,160 --> 00:45:34,080
Speaker 1: So yeah, yeah, So what he says here isn't technically wrong,

921
00:45:34,400 --> 00:45:36,920
Speaker 1: Like that's all technically accurate about what you could do

922
00:45:36,960 --> 00:45:39,920
Speaker 1: with DNA, but it doesn't mean that DNA is currently

923
00:45:40,040 --> 00:45:41,560
Speaker 1: or will be in any kind of timeframe a good

924
00:45:41,600 --> 00:45:45,080
Speaker 1: way to store data now, obviously there's a need for

925
00:45:45,160 --> 00:45:48,000
Speaker 1: a much better way to store data. Digital data storage

926
00:45:48,120 --> 00:45:51,600
Speaker 1: is not forever and has a lot of problems, and like,

927
00:45:51,960 --> 00:45:55,200
Speaker 1: is it just a really bad way to long term

928
00:45:55,360 --> 00:45:59,480
Speaker 1: protect human knowledge? And obviously, like paper is actually in

929
00:45:59,480 --> 00:46:01,640
Speaker 1: some ways better if you're storing it in like the

930
00:46:01,719 --> 00:46:04,840
Speaker 1: right conditions, like it will degrade less than digital data

931
00:46:04,880 --> 00:46:08,360
Speaker 1: overall long enough timeframe. But there's obvious problems with paper,

932
00:46:08,480 --> 00:46:10,160
Speaker 1: right Like are other things like if you've got like

933
00:46:10,400 --> 00:46:14,480
Speaker 1: a climate sealed place to store books versus some hard drives,

934
00:46:14,480 --> 00:46:17,400
Speaker 1: those hard drives will break on a faster timeframe, assuming

935
00:46:17,400 --> 00:46:21,120
Speaker 1: you managed to keep that place, you know, properly stored

936
00:46:21,160 --> 00:46:24,839
Speaker 1: and whatnot. But so we do need ways that are

937
00:46:24,960 --> 00:46:28,200
Speaker 1: much more space efficient because also the amount of data

938
00:46:28,280 --> 00:46:31,120
Speaker 1: humanity is producing, you know, especially since we have projects

939
00:46:31,160 --> 00:46:33,919
Speaker 1: like the Hadron Collider going, there's so much data being

940
00:46:33,920 --> 00:46:36,799
Speaker 1: made and storing it is a problem, right, like, because

941
00:46:36,840 --> 00:46:40,239
Speaker 1: you need these massive facilities in order to even store

942
00:46:40,280 --> 00:46:42,359
Speaker 1: a lot of this stuff. So these are issues that

943
00:46:42,440 --> 00:46:45,320
Speaker 1: we have, right and DNA and the fact that you

944
00:46:45,360 --> 00:46:48,120
Speaker 1: could store data with such density in it could be

945
00:46:48,160 --> 00:46:50,480
Speaker 1: a solution to aspects of it, but it's kind of

946
00:46:50,560 --> 00:46:52,840
Speaker 1: framed a lot is like and this is in the future,

947
00:46:53,160 --> 00:46:55,799
Speaker 1: Netflix will keep all its data in like DNA drive

948
00:46:55,920 --> 00:46:57,920
Speaker 1: yet to get like everyone like everything will be stored

949
00:46:57,960 --> 00:47:01,279
Speaker 1: and that probably is never going to have I can't

950
00:47:01,280 --> 00:47:03,479
Speaker 1: say definitely, but there's because there's a lot we don't

951
00:47:03,480 --> 00:47:06,080
Speaker 1: know about this, how this technology would work. But there's

952
00:47:06,400 --> 00:47:08,680
Speaker 1: the shit we haven't figured out yet is really significant.

953
00:47:08,680 --> 00:47:11,680
Speaker 1: For example, there's a high error rate when you write

954
00:47:11,760 --> 00:47:15,200
Speaker 1: data to DNA currently, and since it's really easy to

955
00:47:15,280 --> 00:47:18,600
Speaker 1: fuck up writing the data, the current best practices is

956
00:47:18,600 --> 00:47:22,560
Speaker 1: to store multiple redundant copies of each piece of information

957
00:47:22,680 --> 00:47:24,880
Speaker 1: so you have some that are right, which is like

958
00:47:24,960 --> 00:47:27,120
Speaker 1: he puts seventy billion copies of that book on like

959
00:47:27,160 --> 00:47:29,120
Speaker 1: a dot, right, Like that's that's kind of what we're

960
00:47:29,120 --> 00:47:31,279
Speaker 1: talking about here. You store a shitload of copies of

961
00:47:31,320 --> 00:47:34,959
Speaker 1: something because you know, and scientists don't even know how

962
00:47:35,080 --> 00:47:37,080
Speaker 1: many redundant backups we need yet.

963
00:47:37,120 --> 00:47:37,279
Speaker 3: Right.

964
00:47:37,320 --> 00:47:39,240
Speaker 1: I found a study where they're just trying to figure

965
00:47:39,280 --> 00:47:41,920
Speaker 1: that out, like, Okay, what is the actual best practice

966
00:47:41,920 --> 00:47:44,920
Speaker 1: for the actual number of different redundant copies to store

967
00:47:45,120 --> 00:47:47,440
Speaker 1: because we really hadn't locked that down yet.

968
00:47:47,600 --> 00:47:50,719
Speaker 2: So all of those all of those books, there's like

969
00:47:50,760 --> 00:47:54,040
Speaker 2: six of those books that are right, and then seventy

970
00:47:54,120 --> 00:47:58,680
Speaker 2: billion others that are like just mid likely shit books.

971
00:47:58,920 --> 00:48:00,880
Speaker 1: I don't know if it's that, but we like the

972
00:48:01,120 --> 00:48:02,719
Speaker 1: I think the problem is like we don't actually know

973
00:48:02,920 --> 00:48:07,000
Speaker 1: how many we should be doing, right, We're still figuring

974
00:48:07,040 --> 00:48:08,800
Speaker 1: that part of it out. And then there's a separate

975
00:48:08,800 --> 00:48:11,239
Speaker 1: app asue of like, Okay, well you've got that on

976
00:48:11,280 --> 00:48:13,840
Speaker 1: this dot, but you can't like that dot's not connected

977
00:48:13,880 --> 00:48:16,000
Speaker 1: to a computer, Like, sure, the data is there, but

978
00:48:16,080 --> 00:48:18,560
Speaker 1: how would you access and store it and use it

979
00:48:18,800 --> 00:48:21,279
Speaker 1: if you wanted to write? Like, could you get that

980
00:48:21,320 --> 00:48:24,480
Speaker 1: on a kindle easily? And the answer is no. Right.

981
00:48:25,440 --> 00:48:28,640
Speaker 1: I found an article on DNA data storage written by

982
00:48:29,120 --> 00:48:32,080
Speaker 1: Nithil Krishnaj that lays out some of the other practical

983
00:48:32,120 --> 00:48:34,880
Speaker 1: issues inherent to doing this for any practical reason. Quote,

984
00:48:35,160 --> 00:48:38,360
Speaker 1: DNA has horrendously slow read and write speeds, so it

985
00:48:38,440 --> 00:48:40,680
Speaker 1: is an ideal for real time storage, and activities like

986
00:48:40,680 --> 00:48:44,000
Speaker 1: streaming video and gaming definitely won't be viable at this time.

987
00:48:44,239 --> 00:48:46,680
Speaker 1: As a result, DNA data storage loses some of its

988
00:48:46,760 --> 00:48:48,960
Speaker 1: versatility and as of now, it would only work best

989
00:48:48,960 --> 00:48:52,200
Speaker 1: for long term storage. It's also not rewriteable. Once you

990
00:48:52,320 --> 00:48:55,200
Speaker 1: encode data into DNA, there's no way of making changes

991
00:48:55,239 --> 00:48:58,359
Speaker 1: to your data without redoing the encoding process. There's also

992
00:48:58,400 --> 00:49:01,279
Speaker 1: no random access functionality, which means you can't access a

993
00:49:01,280 --> 00:49:03,680
Speaker 1: certain part of the data without decoding all of it.

994
00:49:04,200 --> 00:49:07,000
Speaker 1: And this is still like interesting and potentially away. Again,

995
00:49:07,040 --> 00:49:09,600
Speaker 1: you could have a bunch of different places where all

996
00:49:09,640 --> 00:49:11,960
Speaker 1: of the data we've you know, made up to a

997
00:49:11,960 --> 00:49:14,799
Speaker 1: certain point is stored on DNA somewhere, and that would

998
00:49:14,880 --> 00:49:18,799
Speaker 1: potentially allow future people to access a lossless version of it,

999
00:49:18,840 --> 00:49:21,480
Speaker 1: and in a way that might be really helpful. But

1000
00:49:21,520 --> 00:49:23,720
Speaker 1: we're not talking about something that's going to alter daily

1001
00:49:23,800 --> 00:49:26,120
Speaker 1: life in its current form, and maybe not ever on

1002
00:49:26,280 --> 00:49:28,640
Speaker 1: any timeframe any of us will see because it's just

1003
00:49:28,719 --> 00:49:29,960
Speaker 1: not practical, right.

1004
00:49:30,000 --> 00:49:33,799
Speaker 2: Yeah, Netflix isn't going to exist when this is actually a.

1005
00:49:33,920 --> 00:49:38,840
Speaker 1: Thing, right yes, yes, Like at some point in the future,

1006
00:49:38,920 --> 00:49:40,799
Speaker 1: maybe they'll figure out all this stuff, but that is

1007
00:49:40,840 --> 00:49:43,440
Speaker 1: not any kind of time frame anybody should be like

1008
00:49:43,760 --> 00:49:46,399
Speaker 1: waiting for. Right Again, not to say this is an

1009
00:49:46,440 --> 00:49:50,280
Speaker 1: interesting or as a potential use, it's just it's just prectice,

1010
00:49:50,320 --> 00:49:52,400
Speaker 1: like this is the future of data storage, and it's like,

1011
00:49:52,440 --> 00:49:57,239
Speaker 1: well maybe in like a couple hundred years. Side up. Yeah, Now,

1012
00:49:57,320 --> 00:49:59,680
Speaker 1: there's also something in that video that I find creepy.

1013
00:49:59,719 --> 00:50:02,279
Speaker 1: As an side which is that doctor Church proposes one

1014
00:50:02,400 --> 00:50:04,920
Speaker 1: use of this technology would be to create permanent records

1015
00:50:04,960 --> 00:50:08,480
Speaker 1: of the brain activity of a human being, and I

1016
00:50:08,560 --> 00:50:10,040
Speaker 1: just don't like the way he says this.

1017
00:50:11,480 --> 00:50:14,399
Speaker 3: Or you could imagine other huge data sources, like all

1018
00:50:14,440 --> 00:50:17,200
Speaker 3: the neuronal flowerings in the brain, which could be encoded

1019
00:50:17,200 --> 00:50:19,640
Speaker 3: into the d NA, and again you could do selective

1020
00:50:19,680 --> 00:50:21,400
Speaker 3: reading of that as needed.

1021
00:50:22,680 --> 00:50:26,680
Speaker 1: Yikes, I don't love that. Now he's saying like, well,

1022
00:50:26,719 --> 00:50:29,560
Speaker 1: you could do some really groundbreaking medical studies if you

1023
00:50:29,600 --> 00:50:32,560
Speaker 1: had access to this much data. And sure, but when

1024
00:50:32,560 --> 00:50:36,000
Speaker 1: you talk about making perfect records of a human brain activity,

1025
00:50:36,160 --> 00:50:38,160
Speaker 1: you're also getting into the kind of territory where I'm like,

1026
00:50:38,200 --> 00:50:40,880
Speaker 1: I want to immediately hear what you think about the

1027
00:50:40,920 --> 00:50:45,719
Speaker 1: potential for surveillance and violation of privacy right ex. You

1028
00:50:45,840 --> 00:50:47,520
Speaker 1: kind of have to bring that up right away. You

1029
00:50:47,560 --> 00:50:49,919
Speaker 1: can't just be interested in the technology here.

1030
00:50:50,440 --> 00:50:53,080
Speaker 2: It seems like you want to download some information from

1031
00:50:53,120 --> 00:50:56,920
Speaker 2: people that maybe they didn't want to give you. That's

1032
00:50:56,960 --> 00:50:59,160
Speaker 2: some nasty work there, doctor Jege.

1033
00:50:59,400 --> 00:51:01,920
Speaker 1: I'm a little and I promise you we're so far

1034
00:51:01,960 --> 00:51:04,360
Speaker 1: getting into all this. Well, theoretically, there's stuff about this

1035
00:51:04,440 --> 00:51:07,640
Speaker 1: that could be wrong or he's exaggerating. The actual fucked

1036
00:51:07,719 --> 00:51:11,160
Speaker 1: up stuff starts right about now, right, because when we're

1037
00:51:11,160 --> 00:51:13,759
Speaker 1: talking about like this is a technology that could be

1038
00:51:13,840 --> 00:51:18,280
Speaker 1: good or could have some major problematic ethical you know, implications,

1039
00:51:18,520 --> 00:51:21,160
Speaker 1: you want to know the scientist working on the technology

1040
00:51:21,200 --> 00:51:24,320
Speaker 1: that could have fucked up ethical implications has a strong

1041
00:51:24,600 --> 00:51:28,480
Speaker 1: history of personal ethics. Right, And this brings me to

1042
00:51:28,560 --> 00:51:31,600
Speaker 1: doctor Church's history with our old friend of the pod,

1043
00:51:32,000 --> 00:51:38,680
Speaker 1: Jeffrey Epstein. Jeffrey Epstein, that's the monster at the end

1044
00:51:38,680 --> 00:51:42,680
Speaker 1: of this book. Okay, at the end of this episode,

1045
00:51:43,320 --> 00:51:47,080
Speaker 1: Church to Jeffrey Epstein, Yes.

1046
00:51:46,960 --> 00:51:48,080
Speaker 4: In need money.

1047
00:51:49,680 --> 00:51:55,799
Speaker 1: Surprise emerges literally he is the thin white duke of

1048
00:51:55,840 --> 00:51:58,680
Speaker 1: evil scientists. That's Jeffrey Epstein.

1049
00:51:59,239 --> 00:52:02,160
Speaker 2: That's really cool. Oh that's like a really exciting plot

1050
00:52:02,200 --> 00:52:04,560
Speaker 2: twist that you don't see coming. I love this.

1051
00:52:04,880 --> 00:52:06,800
Speaker 1: It's like the end of the second or third Kingsman

1052
00:52:06,840 --> 00:52:09,560
Speaker 1: movie when Hitler comes out of nowhere, like, oh, there

1053
00:52:09,600 --> 00:52:10,680
Speaker 1: we go, there we go.

1054
00:52:11,000 --> 00:52:14,120
Speaker 2: Oh, you guys really built that a long.

1055
00:52:13,920 --> 00:52:27,560
Speaker 1: Way great, dropping him like thanos. So some sources I

1056
00:52:27,600 --> 00:52:31,040
Speaker 1: have said say that Church in Epstein's relationship started in

1057
00:52:31,040 --> 00:52:33,760
Speaker 1: two thousand and five. I've heard Church claim two thousand

1058
00:52:33,760 --> 00:52:35,560
Speaker 1: and six, but people have said that he was receiving

1059
00:52:35,640 --> 00:52:38,440
Speaker 1: funding from Epstein as far back as two thousand and five.

1060
00:52:38,880 --> 00:52:42,000
Speaker 1: It may just be that his labs started receiving unrestricted

1061
00:52:42,040 --> 00:52:45,680
Speaker 1: funding from Epstein before they met, and I will remind

1062
00:52:45,680 --> 00:52:47,400
Speaker 1: you here they were receiving funding from two thousd and

1063
00:52:47,440 --> 00:52:49,239
Speaker 1: five to two thousand and seven. Epstein was convicted in

1064
00:52:49,239 --> 00:52:52,040
Speaker 1: two thousand and eight of sex trafficking. Although that's not

1065
00:52:52,040 --> 00:52:54,160
Speaker 1: the end of their relationship. But let's talk about those

1066
00:52:54,160 --> 00:52:56,719
Speaker 1: first couple of years. Now. At that point in his

1067
00:52:56,760 --> 00:52:59,040
Speaker 1: career two thousand and five, right, he is, He's just

1068
00:52:59,080 --> 00:53:02,480
Speaker 1: started the personal gene known project. His primary focus and

1069
00:53:02,520 --> 00:53:04,600
Speaker 1: the thing that he's most famous for is his work

1070
00:53:04,680 --> 00:53:07,919
Speaker 1: on like gene sequencing and gene editing. You know, he's

1071
00:53:07,960 --> 00:53:10,759
Speaker 1: into both of these things. An article for The New

1072
00:53:10,840 --> 00:53:14,560
Speaker 1: York Times that discusses Churches and other scientists associations with

1073
00:53:14,640 --> 00:53:17,719
Speaker 1: Epstein described doctor Church in this period as quote a

1074
00:53:17,760 --> 00:53:20,879
Speaker 1: molecular engineer who has worked to identify genes that could

1075
00:53:20,880 --> 00:53:26,080
Speaker 1: be altered to create superior humans. Oh oh uh oh oh, boy,

1076
00:53:27,440 --> 00:53:28,080
Speaker 1: don't like that.

1077
00:53:28,800 --> 00:53:31,160
Speaker 2: Superior humans. That's a trigger word for me.

1078
00:53:31,320 --> 00:53:35,000
Speaker 1: Yeah, yeah, boy has anyone ever said those two words

1079
00:53:35,040 --> 00:53:36,960
Speaker 1: and not been doing something horrifying.

1080
00:53:38,960 --> 00:53:39,560
Speaker 2: Yikes.

1081
00:53:39,760 --> 00:53:43,399
Speaker 1: So, doctor Church was an early pioneer for the use

1082
00:53:43,440 --> 00:53:47,080
Speaker 1: of Crisper to edit human genes, and one of his

1083
00:53:47,280 --> 00:53:50,160
Speaker 1: ambitions was and is, to create a method of gene

1084
00:53:50,160 --> 00:53:53,360
Speaker 1: therapy to, in his words, knock out both copies of

1085
00:53:53,440 --> 00:53:56,239
Speaker 1: your CCR five gene, which is the AIDS receptor, and

1086
00:53:56,280 --> 00:53:58,359
Speaker 1: then put them back in your body. Then you can't

1087
00:53:58,360 --> 00:54:01,120
Speaker 1: get AIDS anymore because the virus can't enter yourselves. And hey,

1088
00:54:01,880 --> 00:54:05,799
Speaker 1: that sounds fine. AIDS is bad stopping people from being

1089
00:54:05,800 --> 00:54:09,360
Speaker 1: able to get it, lovely. The issue is that Church's

1090
00:54:09,360 --> 00:54:12,719
Speaker 1: ambitions don't stop here, and Epstein was not drawn to

1091
00:54:12,800 --> 00:54:16,200
Speaker 1: Church's life work for anything as humanitarian as stopping a virus.

1092
00:54:16,640 --> 00:54:18,840
Speaker 1: I have found a couple stories of how Church and

1093
00:54:18,840 --> 00:54:22,040
Speaker 1: Epstein actually met for the first time. Church has claimed

1094
00:54:22,040 --> 00:54:25,239
Speaker 1: that he was connected to Epstein first either, and he says,

1095
00:54:25,239 --> 00:54:28,200
Speaker 1: I don't know which, either through the chairman of Harvard's

1096
00:54:28,200 --> 00:54:33,600
Speaker 1: psych department or through his literary agent, John Brockman. Sure, buddy,

1097
00:54:33,760 --> 00:54:36,800
Speaker 1: I feel like I remember how I first met Jeffrey Epstein,

1098
00:54:36,920 --> 00:54:37,960
Speaker 1: but maybe I'm wrong.

1099
00:54:38,680 --> 00:54:41,160
Speaker 2: But that speaks to his multitude.

1100
00:54:42,160 --> 00:54:42,640
Speaker 1: Man right.

1101
00:54:42,880 --> 00:54:44,719
Speaker 2: It could either be a money man or it could

1102
00:54:44,719 --> 00:54:48,960
Speaker 2: be an academic man. But one of them, yeah, introduced.

1103
00:54:48,520 --> 00:54:51,600
Speaker 1: Me, and both of them are implicated in some sketchy

1104
00:54:51,600 --> 00:54:56,080
Speaker 1: Epstein stuff. To be clear, we were all there. But

1105
00:54:56,280 --> 00:54:59,080
Speaker 1: in another interview, Church seemed to suggest that Epstein probably

1106
00:54:59,080 --> 00:55:02,360
Speaker 1: reached out to him because Epstein was friendly and working

1107
00:55:02,400 --> 00:55:06,759
Speaker 1: with a biologist and mathematician named Martin Noack. Church and

1108
00:55:06,760 --> 00:55:10,399
Speaker 1: Noac had worked together on various applications of Crisper too

1109
00:55:10,520 --> 00:55:13,480
Speaker 1: edit genes Per an article in stat by Karen Begley.

1110
00:55:13,920 --> 00:55:16,920
Speaker 1: At the get togethers with Noak, Church said Epstein seemed

1111
00:55:16,920 --> 00:55:20,200
Speaker 1: interested in the science of life's origins and mathematically modeling

1112
00:55:20,239 --> 00:55:24,120
Speaker 1: the evolution of viruses, cancer cells, and life itself. Epstein

1113
00:55:24,200 --> 00:55:26,359
Speaker 1: did not leave much of an imperson impression on him.

1114
00:55:26,440 --> 00:55:29,200
Speaker 1: Church said the meetings weren't really about Jeffrey. They were

1115
00:55:29,239 --> 00:55:32,200
Speaker 1: about the scientists who were talking with each other. Normally,

1116
00:55:32,320 --> 00:55:34,880
Speaker 1: expectations are low for people who sit in on meetings

1117
00:55:34,920 --> 00:55:38,080
Speaker 1: far outside their field of expertise, so he's kind of like, well,

1118
00:55:38,120 --> 00:55:40,479
Speaker 1: it was mostly just as scientist talking, and Jeffrey didn't

1119
00:55:40,480 --> 00:55:42,479
Speaker 1: really know much and when he talked, it didn't really

1120
00:55:42,680 --> 00:55:44,040
Speaker 1: make an impression as a result.

1121
00:55:44,160 --> 00:55:44,399
Speaker 3: Right.

1122
00:55:45,120 --> 00:55:48,640
Speaker 1: And if that's the truth, which I have trouble believing

1123
00:55:48,680 --> 00:55:51,959
Speaker 1: because their relationship goes on after this. But if that's

1124
00:55:52,000 --> 00:55:54,360
Speaker 1: the truth, then all Church did was take this guy's

1125
00:55:54,440 --> 00:55:56,680
Speaker 1: money who was not convicted of a crime yet, and

1126
00:55:56,760 --> 00:55:59,480
Speaker 1: show up at some dinners to talk about science. And

1127
00:55:59,520 --> 00:56:02,799
Speaker 1: that wouldn't be so bad, right. And in fact, there

1128
00:56:02,800 --> 00:56:05,560
Speaker 1: are some people who got some funding from Epstein and

1129
00:56:05,600 --> 00:56:08,000
Speaker 1: were not involved in the sketchy stuff because he funded

1130
00:56:08,040 --> 00:56:09,759
Speaker 1: a lot of guys and they didn't all go to

1131
00:56:09,800 --> 00:56:12,640
Speaker 1: his parties or have sex with teenagers.

1132
00:56:12,760 --> 00:56:12,960
Speaker 5: Right.

1133
00:56:14,360 --> 00:56:15,560
Speaker 1: And I'm not saying Church did.

1134
00:56:15,840 --> 00:56:18,800
Speaker 2: He made enough money to buy an island. You can't

1135
00:56:18,800 --> 00:56:21,880
Speaker 2: do that with only sex pests some people. Right, he

1136
00:56:21,920 --> 00:56:24,440
Speaker 2: had to be on some version of and up and up.

1137
00:56:24,640 --> 00:56:26,920
Speaker 1: There's some people who were involved with him who have

1138
00:56:27,000 --> 00:56:28,400
Speaker 1: been tarnished unfairly.

1139
00:56:28,600 --> 00:56:28,759
Speaker 3: Right.

1140
00:56:28,880 --> 00:56:31,279
Speaker 1: I'm also not saying that Church is tartished unfairly here,

1141
00:56:31,680 --> 00:56:33,680
Speaker 1: because I don't think he is. However, I would be

1142
00:56:33,680 --> 00:56:35,719
Speaker 1: remiss if I did not read a different description of

1143
00:56:35,719 --> 00:56:38,400
Speaker 1: the dinner parties and events that Epstein held for scientists

1144
00:56:38,480 --> 00:56:41,080
Speaker 1: around this time. Maybe these are a different set of

1145
00:56:41,120 --> 00:56:44,800
Speaker 1: parties than the ones Church attended, although they include people

1146
00:56:44,840 --> 00:56:47,120
Speaker 1: he's listed as his friends. So I'm gonna quote from

1147
00:56:47,120 --> 00:56:50,200
Speaker 1: the New York Times here. The Harvard cognitive psychologist Stephen

1148
00:56:50,200 --> 00:56:53,160
Speaker 1: Pinker said he was invited by colleagues, including Martin Noack,

1149
00:56:53,760 --> 00:56:56,920
Speaker 1: a Charvard professor of mathematics and biology, and the theoretical

1150
00:56:56,960 --> 00:57:00,520
Speaker 1: physicist Lawrence Krauss, to salons and coffee cloud at which

1151
00:57:00,520 --> 00:57:03,600
Speaker 1: mister Epstein would hold court on multiple occasions, starting in

1152
00:57:03,600 --> 00:57:07,080
Speaker 1: the early two thousands, mister Epstein told scientists and businessmen

1153
00:57:07,120 --> 00:57:09,480
Speaker 1: about his ambitions to use his new Mexico ranch as

1154
00:57:09,520 --> 00:57:12,320
Speaker 1: a base where women would be inciminated with his sperm

1155
00:57:12,320 --> 00:57:14,480
Speaker 1: and would give birth to his babies. According to two

1156
00:57:14,560 --> 00:57:17,760
Speaker 1: award winning scientists and an advisor to large companies and

1157
00:57:17,800 --> 00:57:21,040
Speaker 1: wealthy individuals, all of whom mister Epstein told about it,

1158
00:57:21,040 --> 00:57:23,480
Speaker 1: it was not a secret. The advisor, for example, said

1159
00:57:23,520 --> 00:57:25,439
Speaker 1: he was told about the plants not only by mister

1160
00:57:25,480 --> 00:57:28,439
Speaker 1: Epstein at a gathering at his Manhattan townhouse, but also

1161
00:57:28,440 --> 00:57:30,920
Speaker 1: by at least one prominent member of the business community.

1162
00:57:31,160 --> 00:57:33,960
Speaker 1: One of the scientists said mister Epstein divulged his idea

1163
00:57:34,000 --> 00:57:35,560
Speaker 1: in two thousand and one at a dinner at the

1164
00:57:35,560 --> 00:57:38,600
Speaker 1: same townhouse. The other recalled mister Epstein discussing it with

1165
00:57:38,680 --> 00:57:40,560
Speaker 1: him at a two thousand and six conference that he

1166
00:57:40,600 --> 00:57:43,480
Speaker 1: hosted in Saint Thomas and the Virgin Islands. Once, at

1167
00:57:43,480 --> 00:57:46,320
Speaker 1: a dinner at mister Epstein's mansion in Manhattan's uper East Side,

1168
00:57:46,480 --> 00:57:49,000
Speaker 1: mister Janeer and he's talking about Jeron Lanier said that

1169
00:57:49,000 --> 00:57:50,760
Speaker 1: he talked to a scientist who told him that mister

1170
00:57:50,800 --> 00:57:52,959
Speaker 1: Epstein's goal was to have twenty women at a time

1171
00:57:53,000 --> 00:57:55,959
Speaker 1: impregnated at his thirty three thousand square foot Zoro ranch

1172
00:57:55,960 --> 00:58:01,000
Speaker 1: in a tiny town outside of Santa Fe. Whoa cool.

1173
00:58:01,760 --> 00:58:05,440
Speaker 2: It is pretty impressive to find out that Jeffrey Epstein

1174
00:58:05,600 --> 00:58:08,320
Speaker 2: is somehow more of a piece of shit than I thought.

1175
00:58:09,080 --> 00:58:11,920
Speaker 2: I was like, Ah, he's just a monster. I don't

1176
00:58:11,960 --> 00:58:14,720
Speaker 2: think he's like a super monster god.

1177
00:58:14,800 --> 00:58:17,200
Speaker 1: No, no, no, I don't think he's got a baby ranch.

1178
00:58:17,320 --> 00:58:20,040
Speaker 1: Oh yeah, he's got a baby ranch. Yeah, or he

1179
00:58:20,120 --> 00:58:21,400
Speaker 1: tried to have a baby ranch.

1180
00:58:21,480 --> 00:58:21,680
Speaker 2: Now.

1181
00:58:22,360 --> 00:58:25,280
Speaker 1: Stat News, to their credit, did ask doctor Church after

1182
00:58:25,280 --> 00:58:29,000
Speaker 1: Epstein's death about Epstein's eugenics baby ranch, being like, you're

1183
00:58:29,040 --> 00:58:33,600
Speaker 1: working in like gene editing people and Epstein wanted to

1184
00:58:33,640 --> 00:58:36,040
Speaker 1: do this. Did he talk to you about this because

1185
00:58:36,040 --> 00:58:38,320
Speaker 1: you guys knew each other when he was talking about this.

1186
00:58:38,520 --> 00:58:41,880
Speaker 1: Right now, I have no proof either way. For his part,

1187
00:58:41,920 --> 00:58:45,240
Speaker 1: doctor Church said I never heard anything about it, although

1188
00:58:45,280 --> 00:58:47,320
Speaker 1: he went on to say, and I find this curious.

1189
00:58:47,600 --> 00:58:49,640
Speaker 1: I'd have thought that I would have been involved in

1190
00:58:49,680 --> 00:58:52,919
Speaker 1: that kind of conversation. But it didn't tend to go

1191
00:58:53,000 --> 00:58:56,080
Speaker 1: in that direction. But also, I think people tend to

1192
00:58:56,120 --> 00:58:59,880
Speaker 1: behave themselves around me. That's a weird thing to say.

1193
00:58:59,720 --> 00:59:01,600
Speaker 4: After want to want a strange little guy.

1194
00:59:01,720 --> 00:59:05,000
Speaker 1: Honestly, bro, if someone asks you whether or not you're

1195
00:59:05,040 --> 00:59:08,960
Speaker 1: involved in Jeffrey Epstein's baby Ranch, you end the statement

1196
00:59:09,000 --> 00:59:11,320
Speaker 1: with I never heard anything about Oh, yeah.

1197
00:59:11,120 --> 00:59:12,760
Speaker 2: You don't have to be like I would have liked

1198
00:59:12,760 --> 00:59:13,600
Speaker 2: to talk to him about it.

1199
00:59:13,720 --> 00:59:15,600
Speaker 1: Yeah, I would have. I'm kind of offended he didn't

1200
00:59:15,600 --> 00:59:16,000
Speaker 1: bring me.

1201
00:59:15,960 --> 00:59:19,960
Speaker 2: In, but also sounds awesome. I just not didn't talk

1202
00:59:20,000 --> 00:59:20,360
Speaker 2: to him.

1203
00:59:20,480 --> 00:59:22,480
Speaker 1: But also, then, when you say it didn't tend to

1204
00:59:22,520 --> 00:59:24,680
Speaker 1: go in that direction, well, tend doesn't mean never? Does

1205
00:59:24,680 --> 00:59:27,400
Speaker 1: that mean sometimes it kind of did? Like what are

1206
00:59:27,400 --> 00:59:31,480
Speaker 1: you saying? You seem like a man who's precise with

1207
00:59:31,520 --> 00:59:34,840
Speaker 1: his language. I don't know why you're phrasing it this way.

1208
00:59:34,920 --> 00:59:37,560
Speaker 2: Yeah, Jeffrey is like, you know, I want to start

1209
00:59:37,560 --> 00:59:40,560
Speaker 2: a baby ranch, and he's like huh, He's huh nothing nothing.

1210
00:59:40,560 --> 00:59:41,800
Speaker 1: I thought he was going to bring it up again,

1211
00:59:41,840 --> 00:59:42,320
Speaker 1: but he didn't.

1212
00:59:42,320 --> 00:59:45,200
Speaker 2: You know, never mind, never mind. I thought you'd be

1213
00:59:45,200 --> 00:59:45,760
Speaker 2: cool about it.

1214
00:59:45,800 --> 00:59:47,840
Speaker 1: I thought you were cool. Yeah. It's like Jeffrey being

1215
00:59:47,880 --> 00:59:50,360
Speaker 1: like he wants a coke? What nothing. I didn't say anything. Nothing.

1216
00:59:50,960 --> 00:59:51,600
Speaker 1: I don't do coke.

1217
00:59:51,720 --> 00:59:51,919
Speaker 2: Yeah.

1218
00:59:53,000 --> 00:59:56,240
Speaker 1: Sober sober Jeffrey Epstein, that's what they call me. I'm

1219
00:59:56,240 --> 00:59:59,760
Speaker 1: gonna go to the bathroom like fifteen minutes. I'm gonna

1220
00:59:59,760 --> 01:00:01,360
Speaker 1: come out really excited. I love pen.

1221
01:00:01,800 --> 01:00:03,600
Speaker 2: We're gonna talk a business like it.

1222
01:00:03,760 --> 01:00:06,280
Speaker 1: I'm gonna look like Robin Williams in nineteen eighty five

1223
01:00:06,320 --> 01:00:08,120
Speaker 1: when I step out of that thing. It's nothing weird.

1224
01:00:09,480 --> 01:00:12,280
Speaker 1: So perhaps that is the truth, that he had nothing

1225
01:00:12,320 --> 01:00:15,080
Speaker 1: to do with this. Friends and colleagues of doctor Church

1226
01:00:15,160 --> 01:00:18,400
Speaker 1: express surprise when, after Epstein's death, the years of close

1227
01:00:18,440 --> 01:00:22,200
Speaker 1: connections between Epstein and Church were made public. When associate

1228
01:00:22,280 --> 01:00:25,200
Speaker 1: pointed out that Church even brought a philosopher into his

1229
01:00:25,280 --> 01:00:29,080
Speaker 1: Harvard lab to flag potential bioethics issues and experiments, and

1230
01:00:29,120 --> 01:00:31,640
Speaker 1: that he teaches a research ethics class, which is uncommon

1231
01:00:31,760 --> 01:00:34,440
Speaker 1: for a scientist in his field working at his level,

1232
01:00:34,680 --> 01:00:36,280
Speaker 1: and so they're like, well, it's weird to me that

1233
01:00:36,280 --> 01:00:38,240
Speaker 1: he would have any relationship with Epstein because I have

1234
01:00:38,280 --> 01:00:40,920
Speaker 1: always considered him one of the most concerned with ethics

1235
01:00:40,920 --> 01:00:44,800
Speaker 1: people in our field. And again, to emphasize for legal

1236
01:00:44,840 --> 01:00:47,640
Speaker 1: and moral reasons, there is no evidence that Church was

1237
01:00:47,640 --> 01:00:51,040
Speaker 1: working on any kind of eugenics baby project for Epstein,

1238
01:00:51,560 --> 01:00:54,760
Speaker 1: not that there would be because Jeffrey Epstein didn't publish

1239
01:00:54,920 --> 01:00:57,560
Speaker 1: all the details about everyone he was involved with with everything.

1240
01:00:58,280 --> 01:01:00,440
Speaker 1: We just know he talked about this plan during several

1241
01:01:00,480 --> 01:01:03,120
Speaker 1: coffee clatches and other events with his pet scientists, and

1242
01:01:03,160 --> 01:01:07,080
Speaker 1: that Church was at similar events. Doctor Church claims that

1243
01:01:07,160 --> 01:01:10,200
Speaker 1: working with Epstein at all was an ethical lapse, but

1244
01:01:10,320 --> 01:01:13,920
Speaker 1: not entirely his fault. He points out that universities are

1245
01:01:13,920 --> 01:01:16,880
Speaker 1: supposed to vet donors before they meet with faculty, and

1246
01:01:16,920 --> 01:01:19,760
Speaker 1: he told Stack, my understanding is this vetting is the

1247
01:01:19,800 --> 01:01:22,320
Speaker 1: responsibility of the development office, which is yet another reason

1248
01:01:22,360 --> 01:01:24,720
Speaker 1: why scientists are a little more relaxed. They feel they

1249
01:01:24,720 --> 01:01:27,240
Speaker 1: have administrators who, in theory do the difficult job of

1250
01:01:27,280 --> 01:01:31,280
Speaker 1: figuring out who's legit. So sah, I'm just a little guy.

1251
01:01:31,680 --> 01:01:34,360
Speaker 1: How could I be expected to think about this sort

1252
01:01:34,360 --> 01:01:35,720
Speaker 1: of thing that's someone else's job.

1253
01:01:35,960 --> 01:01:39,960
Speaker 2: And now and now he's picking who introduced him. Right,

1254
01:01:39,960 --> 01:01:42,880
Speaker 2: Previously he didn't know who introduced him, Hurn, it was

1255
01:01:42,920 --> 01:01:43,840
Speaker 2: hard the whole time.

1256
01:01:43,920 --> 01:01:48,160
Speaker 1: It was Harvard. Yeah. Now, he added that scientists, quote

1257
01:01:48,200 --> 01:01:51,600
Speaker 1: myself included, are not very good at screening or judging

1258
01:01:51,680 --> 01:01:54,200
Speaker 1: human beings. Right that just like, ah, we're all just

1259
01:01:54,320 --> 01:01:56,720
Speaker 1: kind of, you know, bad at people. You know, it's

1260
01:01:56,720 --> 01:02:00,000
Speaker 1: not really our strong suit. And to be fair to also, first,

1261
01:02:00,240 --> 01:02:01,960
Speaker 1: I just don't believe that for Church, because he's an

1262
01:02:02,000 --> 01:02:05,240
Speaker 1: incredibly skilled public relations expert. I think he's very good

1263
01:02:05,280 --> 01:02:08,240
Speaker 1: with people, right, and he's probably very good at judging

1264
01:02:08,320 --> 01:02:12,600
Speaker 1: people because that's what he does. Anyway, to be fair

1265
01:02:12,640 --> 01:02:14,880
Speaker 1: to Church, he went on to make a good point

1266
01:02:14,880 --> 01:02:17,080
Speaker 1: in that stat interview that almost does sound like a

1267
01:02:17,160 --> 01:02:19,919
Speaker 1: Mia Kolpa. He states that a lot of scientists working

1268
01:02:19,960 --> 01:02:23,760
Speaker 1: on cutting edge projects with important applications feeled what he

1269
01:02:23,880 --> 01:02:27,280
Speaker 1: described as an exceptionalism, which is a sense that anything

1270
01:02:27,320 --> 01:02:29,720
Speaker 1: they do is okay if the work is important enough.

1271
01:02:29,760 --> 01:02:32,960
Speaker 1: This is almost like a precursor to like effective altruism

1272
01:02:33,280 --> 01:02:33,960
Speaker 1: type feelings.

1273
01:02:34,040 --> 01:02:34,200
Speaker 2: Right.

1274
01:02:34,280 --> 01:02:37,320
Speaker 1: He predates that, but I don't think he's wrong here.

1275
01:02:37,360 --> 01:02:38,840
Speaker 1: I do think that's a thing that a lot of

1276
01:02:38,880 --> 01:02:42,680
Speaker 1: scientists working in important fields feel, which is that like, well,

1277
01:02:42,880 --> 01:02:44,480
Speaker 1: if I have to do something a little fucked up

1278
01:02:44,560 --> 01:02:49,680
Speaker 1: to further this research with incredibly important like implications, it's

1279
01:02:49,760 --> 01:02:52,760
Speaker 1: worth it. And he cited the case of a Nobel laureate,

1280
01:02:52,800 --> 01:02:56,200
Speaker 1: a biologist named Sidney Brinner, who took fifteen million dollars

1281
01:02:56,240 --> 01:02:59,640
Speaker 1: from Philip Morris to fund a biology institute, and Sydney's

1282
01:02:59,680 --> 01:03:02,880
Speaker 1: argument was that, like, look, if big Tobacco keeps this money,

1283
01:03:02,880 --> 01:03:05,600
Speaker 1: they'll use it for something worse than I will using

1284
01:03:05,640 --> 01:03:09,320
Speaker 1: it for science, which is like an arguable point, but

1285
01:03:09,400 --> 01:03:11,560
Speaker 1: also like, well, big Tobacco's put give you that money

1286
01:03:11,600 --> 01:03:14,760
Speaker 1: because it's right off, and like they're gonna they're expecting something.

1287
01:03:14,800 --> 01:03:18,320
Speaker 1: They're expecting something from it, right, aren't they, Sidney? Are

1288
01:03:18,360 --> 01:03:20,000
Speaker 1: you giving them anything? Are you sure?

1289
01:03:20,360 --> 01:03:20,560
Speaker 3: Right?

1290
01:03:21,000 --> 01:03:24,640
Speaker 2: And also there you picked the worst guy, you know

1291
01:03:24,640 --> 01:03:26,800
Speaker 2: what I mean, Like, it's not like you got a

1292
01:03:26,920 --> 01:03:31,080
Speaker 2: comparable uh space to be putting this money into.

1293
01:03:31,360 --> 01:03:33,640
Speaker 1: It's when they like Walmart needed at tax right off,

1294
01:03:33,640 --> 01:03:35,880
Speaker 1: so they funded this like medical thing I was doing,

1295
01:03:35,960 --> 01:03:38,680
Speaker 1: and like you know, Walmart's a sketchy corporation, but also

1296
01:03:38,760 --> 01:03:40,400
Speaker 1: like the science is good. It's like, no, this is

1297
01:03:40,440 --> 01:03:44,920
Speaker 1: the tobacco industry. Yeah, their product is literally killing more

1298
01:03:44,960 --> 01:03:46,560
Speaker 1: people every year than World War Two.

1299
01:03:47,000 --> 01:03:50,040
Speaker 2: They win, they win the murder game.

1300
01:03:50,520 --> 01:03:53,280
Speaker 1: You did it. It's just a little different, yeah, you know.

1301
01:03:55,280 --> 01:03:58,160
Speaker 1: But anyway, there is an argument about like, well, how

1302
01:03:58,240 --> 01:04:00,640
Speaker 1: and obviously I'm in the advertising busines. There's always an

1303
01:04:00,680 --> 01:04:04,200
Speaker 1: argument how many moral compromises should you make to fund

1304
01:04:04,240 --> 01:04:05,120
Speaker 1: something valuable?

1305
01:04:05,200 --> 01:04:05,360
Speaker 2: Right?

1306
01:04:05,400 --> 01:04:08,080
Speaker 1: And the answer isn't none. You know, this is capitalism.

1307
01:04:08,320 --> 01:04:11,920
Speaker 1: I would say big tobaccos, maybe like fifteen million dollars

1308
01:04:11,920 --> 01:04:14,320
Speaker 1: a Philip Morris money is maybe a step beyond that.

1309
01:04:14,480 --> 01:04:18,120
Speaker 1: But you know, people feel differently, you know, like to

1310
01:04:18,280 --> 01:04:20,960
Speaker 1: you should is it fine to advertise vaping? I don't

1311
01:04:21,000 --> 01:04:27,120
Speaker 1: know whatever. What's less arguable is that after Epstein was charged, convicted,

1312
01:04:27,160 --> 01:04:30,080
Speaker 1: and sentenced in two thousand and eight, doctor Church continued

1313
01:04:30,080 --> 01:04:33,920
Speaker 1: his association with the by this point known sex criminal. Right,

1314
01:04:33,960 --> 01:04:36,960
Speaker 1: So two and five and seven we don't know if

1315
01:04:36,960 --> 01:04:38,800
Speaker 1: he was involved in the weird eugenic stuff. We know

1316
01:04:38,840 --> 01:04:42,000
Speaker 1: he's taking Epstein's money, but Epstein's not a known criminal, right,

1317
01:04:42,320 --> 01:04:46,160
Speaker 1: he could have been, you know, kind of innocent. He

1318
01:04:46,360 --> 01:04:52,080
Speaker 1: continues associating with Epstein repeatedly after he is convicted as

1319
01:04:52,080 --> 01:04:55,120
Speaker 1: a sex criminal in two thousand and eight, and that's

1320
01:04:55,560 --> 01:04:58,280
Speaker 1: crossing a line for me. Yeah. At one point it's.

1321
01:04:58,120 --> 01:05:01,320
Speaker 4: Like, I fear this man just lacks common sense. Nope, No,

1322
01:05:01,480 --> 01:05:05,640
Speaker 4: you should have known was an active decision to associate

1323
01:05:05,680 --> 01:05:08,360
Speaker 4: with one of the world's biggest monsters.

1324
01:05:08,600 --> 01:05:10,960
Speaker 1: You're you're making a choice here, brother.

1325
01:05:11,840 --> 01:05:14,280
Speaker 2: He was like, Nah, Jeffrey's awesome to me, and I'm

1326
01:05:14,280 --> 01:05:15,720
Speaker 2: gonna keep gett I'm gonna keep hanging.

1327
01:05:16,160 --> 01:05:19,520
Speaker 1: Yeah. So, when Church's book ReGenesis came out in twenty twelve,

1328
01:05:19,600 --> 01:05:22,400
Speaker 1: it elevated his profile, and Epstein seems to have gotten

1329
01:05:22,400 --> 01:05:24,400
Speaker 1: back in touch with him soon after. And this would

1330
01:05:24,400 --> 01:05:26,800
Speaker 1: have been, you know, after Epstein finished doing his quote

1331
01:05:26,880 --> 01:05:29,600
Speaker 1: unquote time, which doesn't really not by that was not

1332
01:05:29,720 --> 01:05:33,200
Speaker 1: time by normal people's standards, right like his slap on

1333
01:05:33,280 --> 01:05:35,680
Speaker 1: the wrist didn't even get a slap on the wrist, right.

1334
01:05:35,920 --> 01:05:36,520
Speaker 4: Yeah.

1335
01:05:36,560 --> 01:05:39,280
Speaker 1: And it's not clear to me when they got back

1336
01:05:39,280 --> 01:05:42,160
Speaker 1: in touch or if they ever got out of touch

1337
01:05:42,400 --> 01:05:44,480
Speaker 1: after two thousand and seven. I don't even know that.

1338
01:05:45,240 --> 01:05:47,880
Speaker 1: Whatever the case, Doctor Church has posted a public online

1339
01:05:47,920 --> 01:05:50,720
Speaker 1: calendar every year since nineteen ninety nine, and it shows

1340
01:05:50,720 --> 01:05:53,120
Speaker 1: that he had six separate phone calls or meetings with

1341
01:05:53,160 --> 01:05:57,800
Speaker 1: Epstein in twenty fourteen. Stant News writes sample entry June

1342
01:05:57,840 --> 01:06:01,160
Speaker 1: twenty first, twenty fourteen, Lunch with Jeffrey Epstein twelve to

1343
01:06:01,240 --> 01:06:04,520
Speaker 1: one thirty Martin Noak's Institute. And that's a lot of

1344
01:06:04,560 --> 01:06:08,120
Speaker 1: times to talk to Jeffrey Epstein, Right, that's a lot

1345
01:06:08,160 --> 01:06:10,000
Speaker 1: of that's a long lunch too.

1346
01:06:10,200 --> 01:06:14,840
Speaker 2: Twelve to one. You guys were chatting, You were pushing it, huh.

1347
01:06:14,920 --> 01:06:18,240
Speaker 1: When interviewed after Epstein's death, doctor Church admitted to meeting

1348
01:06:18,240 --> 01:06:23,200
Speaker 1: Epstein several times each year since twenty fourteen, and stat

1349
01:06:23,240 --> 01:06:25,560
Speaker 1: was like, didn't you hear that he'd been convicted of

1350
01:06:25,600 --> 01:06:28,520
Speaker 1: all those sex crimes? And like, you're a father and

1351
01:06:28,600 --> 01:06:31,080
Speaker 1: a grandfather. Did it not skiv you out to be

1352
01:06:31,200 --> 01:06:34,919
Speaker 1: involved with this guy? And Church replied, I did read

1353
01:06:34,920 --> 01:06:38,720
Speaker 1: a couple of news articles like ten years back quote,

1354
01:06:38,880 --> 01:06:40,720
Speaker 1: but they weren't clear enough for me to know if

1355
01:06:40,760 --> 01:06:45,000
Speaker 1: there was a serious problem. Now. I should note here

1356
01:06:45,080 --> 01:06:47,280
Speaker 1: that reporting in two thousand and eight alleged that Epstein

1357
01:06:47,280 --> 01:06:54,680
Speaker 1: had received massages from teenage girls. You didn't know you

1358
01:06:54,720 --> 01:06:57,200
Speaker 1: didn't know. Huh, you're a researcher.

1359
01:06:58,080 --> 01:07:01,960
Speaker 2: Like that's a real arch when we say teenage, how

1360
01:07:02,000 --> 01:07:02,720
Speaker 2: are we talking?

1361
01:07:02,880 --> 01:07:05,960
Speaker 1: Yeah, well we're talking right now. When he asked if

1362
01:07:06,000 --> 01:07:08,640
Speaker 1: he felt Epstein had paid his debt to society, stats like,

1363
01:07:08,720 --> 01:07:11,160
Speaker 1: city you think he'd like paid his debt to society

1364
01:07:11,240 --> 01:07:13,480
Speaker 1: after two thousand and eight and deserved a second chance?

1365
01:07:13,920 --> 01:07:16,640
Speaker 1: And I kind of I really respect stat for sitting

1366
01:07:16,680 --> 01:07:18,560
Speaker 1: down with this guy and kind of drilling him on this.

1367
01:07:19,160 --> 01:07:21,800
Speaker 1: Church responded with what I would call a non answer.

1368
01:07:21,840 --> 01:07:24,520
Speaker 1: So they like, hey, so is it that you thought,

1369
01:07:24,600 --> 01:07:28,400
Speaker 1: you know, he'd made good, that everything was okay now?

1370
01:07:28,480 --> 01:07:30,600
Speaker 1: And he said, as far as I know, people just

1371
01:07:30,600 --> 01:07:35,440
Speaker 1: didn't have that conversation, but it should have. So, ok,

1372
01:07:35,720 --> 01:07:39,120
Speaker 1: let's break that down. He's asked, do you think that

1373
01:07:39,240 --> 01:07:41,800
Speaker 1: after two thousand and eight Epstein had paid his debt

1374
01:07:41,840 --> 01:07:44,800
Speaker 1: to society? And he said, as far as I know,

1375
01:07:45,440 --> 01:07:50,040
Speaker 1: people not me, didn't have that conversation, but it should have.

1376
01:07:50,600 --> 01:07:54,480
Speaker 2: It's it, I guess the people.

1377
01:07:55,640 --> 01:07:57,720
Speaker 1: Your grammar should be better than that man, But like,

1378
01:07:57,760 --> 01:07:58,880
Speaker 1: what do you what do you mean?

1379
01:08:00,320 --> 01:08:03,240
Speaker 2: He's like, I'm not going to answer for myself. Yeah

1380
01:08:03,360 --> 01:08:06,960
Speaker 2: you yeah, you the single body of people that exist

1381
01:08:07,040 --> 01:08:09,880
Speaker 2: around me should have had the conversation with Jeffrey.

1382
01:08:09,520 --> 01:08:12,680
Speaker 1: Webs okay man. Now, he went on to add in

1383
01:08:12,680 --> 01:08:16,320
Speaker 1: that interview, I would think I would think to like

1384
01:08:16,439 --> 01:08:19,880
Speaker 1: that people's reputation is multi dimensional and multi year. It

1385
01:08:19,920 --> 01:08:22,080
Speaker 1: takes a long time to build up but also to

1386
01:08:22,200 --> 01:08:25,360
Speaker 1: tear down and stat notes. He was speaking generally and

1387
01:08:25,400 --> 01:08:28,960
Speaker 1: about himself, as in like, this shouldn't destroy my reputation

1388
01:08:29,200 --> 01:08:31,439
Speaker 1: because like, I've done other things. But it's kind of

1389
01:08:31,479 --> 01:08:34,439
Speaker 1: hard not to read that as I'm talking about Epstein too, like, well,

1390
01:08:34,520 --> 01:08:37,040
Speaker 1: he's a complicated guy. He's got other stuff that he's

1391
01:08:37,080 --> 01:08:38,920
Speaker 1: done besides the sex crimes.

1392
01:08:39,120 --> 01:08:41,000
Speaker 2: Yeah.

1393
01:08:41,040 --> 01:08:44,840
Speaker 1: Great, love an answer like that from our ethics man

1394
01:08:44,880 --> 01:08:49,479
Speaker 1: working on brain reading. For what it's worth, George Church

1395
01:08:49,520 --> 01:08:53,200
Speaker 1: did ultimately apologize for taking Epstein's money in a twenty

1396
01:08:53,320 --> 01:08:56,559
Speaker 1: nineteen interview, although you want to guess what he blamed

1397
01:08:56,560 --> 01:08:57,960
Speaker 1: his lapse in judgment on.

1398
01:08:59,280 --> 01:09:04,240
Speaker 2: Oh no, no, no, no, no, just tell us there's

1399
01:09:04,280 --> 01:09:07,559
Speaker 2: no way nerd tunnel vision. There we go.

1400
01:09:07,920 --> 01:09:09,720
Speaker 1: I'm just too much of a nerd to have a

1401
01:09:09,760 --> 01:09:11,000
Speaker 1: problem with sex crimes.

1402
01:09:11,560 --> 01:09:12,960
Speaker 2: WHOA, I don't even notice.

1403
01:09:13,000 --> 01:09:15,839
Speaker 1: Plus you know, you know how it goes you're watching

1404
01:09:15,920 --> 01:09:19,800
Speaker 1: Star Wars Friends trafficking teenage girls around the world. It

1405
01:09:19,960 --> 01:09:20,639
Speaker 1: just happens.

1406
01:09:21,120 --> 01:09:26,040
Speaker 2: Yeah, No, there is this this, there's again. It's just

1407
01:09:26,040 --> 01:09:29,479
Speaker 2: this this want to like cutify themselves out of like

1408
01:09:29,560 --> 01:09:32,880
Speaker 2: the human experience. It's like, I'm just a nerdy, little

1409
01:09:33,400 --> 01:09:36,040
Speaker 2: cutie boy. I I didn't even notice that bad things

1410
01:09:36,120 --> 01:09:38,800
Speaker 2: were happening. Like, you know, you're a grown man who's

1411
01:09:38,840 --> 01:09:42,799
Speaker 2: trying to manipulate genes. This isn't You're not a sweetheart

1412
01:09:42,840 --> 01:09:43,160
Speaker 2: at all.

1413
01:09:43,680 --> 01:09:47,920
Speaker 1: Yeah, yeah, it's it's it's just very it's it's great stuff.

1414
01:09:48,200 --> 01:09:51,920
Speaker 1: Good work, good work, doctor Church. Now that's pretty bad

1415
01:09:52,040 --> 01:09:55,559
Speaker 1: ending on Epstein and part one not ideal. It gets

1416
01:09:55,560 --> 01:09:59,160
Speaker 1: so much worse than part two. There's so much eugenics coming.

1417
01:09:59,400 --> 01:10:02,720
Speaker 1: There's so much fucked up shit on the way. I

1418
01:10:02,720 --> 01:10:05,879
Speaker 1: am so excited to tell you the rest of this story, Langston.

1419
01:10:06,320 --> 01:10:10,640
Speaker 1: But first let's talk about you. You know, what's your

1420
01:10:10,640 --> 01:10:11,280
Speaker 1: favorite color?

1421
01:10:12,040 --> 01:10:13,639
Speaker 2: Favorite color is coral?

1422
01:10:14,040 --> 01:10:17,360
Speaker 1: Coral. I honestly didn't call that, Okay, I didn't actually

1423
01:10:17,360 --> 01:10:19,240
Speaker 1: call anything. I had no idea what your favorite color

1424
01:10:19,280 --> 01:10:19,640
Speaker 1: would be.

1425
01:10:19,760 --> 01:10:21,800
Speaker 2: Yeah, I think for years I used to say blue

1426
01:10:21,920 --> 01:10:26,320
Speaker 2: to protect myself from yeah, from my own insecurities. But

1427
01:10:26,360 --> 01:10:28,479
Speaker 2: then as the same I had to be honest and say.

1428
01:10:28,439 --> 01:10:29,280
Speaker 1: Not a safe place.

1429
01:10:29,439 --> 01:10:32,800
Speaker 2: My favorite color is nuanced and slightly effeminated.

1430
01:10:32,880 --> 01:10:34,920
Speaker 1: I guess I have been looking for a pair of

1431
01:10:35,000 --> 01:10:37,360
Speaker 1: coral shorts for the summer. It does seem like a nice,

1432
01:10:37,520 --> 01:10:40,599
Speaker 1: nice short color, you know. Yeah, yeah, yeah, my brother

1433
01:10:40,640 --> 01:10:42,720
Speaker 1: wears a lot of coral. It's a good color. We're

1434
01:10:42,720 --> 01:10:45,360
Speaker 1: all getting over our insecurities here, you know, just like

1435
01:10:45,560 --> 01:10:48,759
Speaker 1: George Church got over his insecurities about his friend Jeffrey Epsto.

1436
01:10:49,479 --> 01:10:51,439
Speaker 2: He's like, you know what, no, not like, I can

1437
01:10:51,520 --> 01:10:52,040
Speaker 2: get past this.

1438
01:10:52,600 --> 01:10:54,880
Speaker 1: Yeah, we can get past this, and we can get

1439
01:10:54,920 --> 01:10:56,840
Speaker 1: past the part where we talk about George Church to

1440
01:10:56,880 --> 01:10:59,599
Speaker 1: talk about your pluckables? What are they? Oh?

1441
01:10:59,760 --> 01:11:03,360
Speaker 2: You can listen to my podcast. It's called Ma Mama

1442
01:11:03,439 --> 01:11:05,719
Speaker 2: told me. I do it with my friend David Borie,

1443
01:11:05,720 --> 01:11:11,679
Speaker 2: who is also alumnus of this gorgeous podcast. And yeah,

1444
01:11:11,760 --> 01:11:16,080
Speaker 2: we talk about conspiracy theories, specifically black conspiracy theories, and

1445
01:11:16,120 --> 01:11:19,479
Speaker 2: it's really fun and silly, and we do not nearly

1446
01:11:19,520 --> 01:11:21,519
Speaker 2: as effective research as you do, Robert.

1447
01:11:23,040 --> 01:11:26,480
Speaker 1: My only hope is that George Church gets integrated into

1448
01:11:26,720 --> 01:11:30,040
Speaker 1: a series of conspiracy theories about Jeffrey Epstein because everyone

1449
01:11:30,080 --> 01:11:33,439
Speaker 1: else who was tied to him has been And look,

1450
01:11:33,520 --> 01:11:35,240
Speaker 1: you know, are the all of those accurate?

1451
01:11:35,400 --> 01:11:35,519
Speaker 3: No?

1452
01:11:35,880 --> 01:11:39,800
Speaker 1: Are they all fun? Yes? And George miss out.

1453
01:11:40,000 --> 01:11:43,000
Speaker 2: You know, I will say there was that era on

1454
01:11:43,560 --> 01:11:47,200
Speaker 2: what is now X but formerly Twitter where they were

1455
01:11:47,240 --> 01:11:50,120
Speaker 2: just making up lists of the people who were on

1456
01:11:50,160 --> 01:11:53,240
Speaker 2: the flight logs, and it was always a funny list.

1457
01:11:53,680 --> 01:11:56,679
Speaker 2: It never failed, no matter who wrote it, the right,

1458
01:11:56,800 --> 01:12:00,960
Speaker 2: the left, the sickos, the imagineers, they were always funny

1459
01:12:00,960 --> 01:12:01,719
Speaker 2: lists of people.

1460
01:12:01,880 --> 01:12:04,320
Speaker 1: Oh yeah. One of my favorite things about that is

1461
01:12:04,360 --> 01:12:07,240
Speaker 1: just like, you know, maybe that's sketchy that uh fucking

1462
01:12:07,320 --> 01:12:09,719
Speaker 1: Eddie Murphy wound up in there, But I could also

1463
01:12:09,760 --> 01:12:12,040
Speaker 1: like it's a perfect thing for an Eddie Murphy movie

1464
01:12:12,040 --> 01:12:13,960
Speaker 1: where he just finds out he's been on this sex

1465
01:12:14,040 --> 01:12:18,800
Speaker 1: criminals plan a bunch of time, Like I'll watch that

1466
01:12:18,920 --> 01:12:20,040
Speaker 1: ninety minute comedy.

1467
01:12:20,600 --> 01:12:22,760
Speaker 2: Like there were a few people on some of those

1468
01:12:22,760 --> 01:12:24,600
Speaker 2: flight lines where I was like, I don't think they

1469
01:12:24,680 --> 01:12:26,280
Speaker 2: knew what they got on the plane for.

1470
01:12:26,479 --> 01:12:28,599
Speaker 1: Yeah, you might have just been going to a thing

1471
01:12:28,680 --> 01:12:31,519
Speaker 1: with him, right, you go into some sort of conference.

1472
01:12:31,200 --> 01:12:32,599
Speaker 2: Somebody said get on the PJ.

1473
01:12:33,000 --> 01:12:36,599
Speaker 1: Yeah, yeah, yea, I'll get in a private jet share. Yeah,

1474
01:12:37,360 --> 01:12:39,800
Speaker 1: it has. I will say one thing that I have

1475
01:12:40,000 --> 01:12:43,479
Speaker 1: learned as a result of this, because previously, before I

1476
01:12:43,520 --> 01:12:45,960
Speaker 1: knew any about Jeffrey Epstein, if some richka had been like, Hey,

1477
01:12:46,040 --> 01:12:47,519
Speaker 1: we're going to pay you to go to a conference.

1478
01:12:47,560 --> 01:12:49,280
Speaker 1: You want to ride my private jet, I probably would

1479
01:12:49,280 --> 01:12:51,840
Speaker 1: have been like, yeah, fuck man, that sounds dope. You know,

1480
01:12:52,080 --> 01:12:54,479
Speaker 1: fucking twenty five year old mate probably wouldn't have had

1481
01:12:54,520 --> 01:12:57,280
Speaker 1: the wherewithal to be like and don't know, but now

1482
01:12:57,560 --> 01:12:58,439
Speaker 1: absolutely not.

1483
01:12:59,000 --> 01:13:02,240
Speaker 2: Someone asked me the other day if I would get

1484
01:13:02,280 --> 01:13:06,799
Speaker 2: on that Trump plane, the one that the Qatar gave him.

1485
01:13:06,880 --> 01:13:10,040
Speaker 2: It's like, for the story alone, I kind of think.

1486
01:13:10,000 --> 01:13:12,800
Speaker 1: I have to. Yeah, I know that one. Yes, yes, yes,

1487
01:13:13,320 --> 01:13:15,160
Speaker 1: that's justifiable for journalism.

1488
01:13:15,360 --> 01:13:17,760
Speaker 2: Yeah, I just got to ride this wave and deal

1489
01:13:17,800 --> 01:13:18,800
Speaker 2: with the fallout later.

1490
01:13:19,000 --> 01:13:21,000
Speaker 4: Yeah, I'm good, I'm staying home.

1491
01:13:21,400 --> 01:13:23,040
Speaker 2: You would stay at home, You wouldn't touch it.

1492
01:13:23,240 --> 01:13:24,920
Speaker 4: No, I'm good, I'm staying home.

1493
01:13:25,320 --> 01:13:27,080
Speaker 1: But no, folks, the lesson here is that if a

1494
01:13:27,200 --> 01:13:29,240
Speaker 1: rich guy wants to fly you and pay you to

1495
01:13:29,280 --> 01:13:32,799
Speaker 1: speak at some sort of weird conference, tell him first

1496
01:13:32,880 --> 01:13:37,679
Speaker 1: class from a real airline, right, you know, it's nice enough,

1497
01:13:38,240 --> 01:13:41,200
Speaker 1: and no one can be like, what if that was

1498
01:13:41,200 --> 01:13:44,200
Speaker 1: that Delta flight like implicating you in crime? The only

1499
01:13:44,280 --> 01:13:48,320
Speaker 1: thing that Delta flight implicates you in is crashing at Newark. Right, Sorry,

1500
01:13:48,320 --> 01:13:49,479
Speaker 1: that's that's bad.

1501
01:13:51,160 --> 01:13:53,719
Speaker 2: That was They said they'll do better. They said they'll

1502
01:13:53,720 --> 01:13:54,120
Speaker 2: do better.

1503
01:13:54,200 --> 01:13:56,280
Speaker 1: I said they'll do better. Everyone's got to do better,

1504
01:13:57,800 --> 01:13:58,160
Speaker 1: all right.

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Speaker 4: That's the episode.

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Speaker 1: Yeah.

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Speaker 5: Behind the Bastards is a production of cool Zone Media.

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Speaker 5: For more from cool Zone Media, visit our website Coolzonemedia

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Speaker 5: dot com, or check us out on the iHeartRadio app,

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Speaker 5: Apple podcasts, or wherever you get your podcasts. Behind the

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Speaker 5: Bastards is now available on YouTube, new episodes every.

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Speaker 4: Wednesday and Friday.

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Speaker 1: Subscribe to our

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Speaker 4: Channel YouTube dot com slash at Behind the Bastards