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Speaker 1: From Kaleidoscope and iHeart podcasts. This is tech stuff. I'm

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Speaker 1: as Volocian and I'm care Price.

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Speaker 2: Today We've got two big stories to break down for you. First,

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Speaker 2: it's Silicon Valley's Westminster Dog Show. Journalists, tech nerds, industry

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Speaker 2: insiders are in Vegas this week to check out the

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Speaker 2: Consumer Electronics Show. Then, is your grocery store spying on you?

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Speaker 3: And then a few other things that caught our eye

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Speaker 3: this week, including Silicon Valley's.

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Speaker 4: New rave culture fueled by.

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Speaker 3: Chinese peptides and the exceptionally creative influencers and OnlyFans models

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Speaker 3: getting US visas.

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Speaker 2: All of that on the Weekend Tech. It's Friday, January ninth.

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Speaker 2: Hello Cara, hi Azi, How are you very good?

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Speaker 4: Happy New Year, Happy New Year to you too.

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Speaker 1: So it's our first Weekend Tech of the year.

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Speaker 4: It is very excited.

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Speaker 1: Right before the holidays.

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Speaker 2: People like to do roundups the best of the year

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Speaker 2: that went by at the start of the year.

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Speaker 1: People like to do.

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Speaker 4: What's going to happen?

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Speaker 1: What's going to happen?

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Speaker 3: I'm you know what my tech prediction is, what I'm

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Speaker 3: going to be addicted to my father.

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Speaker 1: Yeah, that's not a prediction. That's just as a guarantee.

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Speaker 4: That's a prophecy.

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Speaker 1: I do actually have one big prediction for you. Tell

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Speaker 1: me so.

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Speaker 2: I think that people are going to look back at

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Speaker 2: twenty twenty six as the year that AI really entered

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Speaker 2: the physical world. And I think that became quite clear

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Speaker 2: this week at the Consumer Electronics Show. But I actually

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Speaker 2: wanted to frame this conversation up about CS with an

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Speaker 2: essay I read late last year by faith fee Lee.

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Speaker 4: Tell me about it.

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Speaker 1: Faith a Lee is the godmother of AI. She is

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Speaker 1: you wrote this blog post.

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Speaker 2: The title is from Words to Worlds. Spatial intelligence is

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Speaker 2: AI's next frontier. To have any idea what that means, I.

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Speaker 3: Mean I can guess spatial intelligence meaning like that our

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Speaker 3: whole world is sort of mapped with AI.

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Speaker 2: Pretty much so is machines in the world with sensors,

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Speaker 2: and it's creating fully simulated worlds that.

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Speaker 1: Obey the laws of physics. Basically, faith a Lee made

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Speaker 1: the case that llms, which power chatbots, are amazing, but

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Speaker 1: they're kind of almost like trapped in Plato's cave in

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Speaker 1: a weird way because they're not actually seeing the world.

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Speaker 1: They're reading text and data that humans or other machines

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Speaker 1: have recorded about the world and then extrapolating what the

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Speaker 1: world is like from records of the world, rather than

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Speaker 1: looking at the world, ingesting it, and understanding its rules

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Speaker 1: for themselves like humans do. So this is basically what

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Speaker 1: faith A Lee.

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Speaker 2: And a lot of others think might be kind of

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Speaker 2: the next revolution in computing. It's computers that can actually

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Speaker 2: understand the world for themselves rather than interpret it based

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Speaker 2: on descriptions of the world. And she basically has argued

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Speaker 2: that this will be the next frontiering computing, and that

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Speaker 2: to really deliver on the Alan Turing thinking machine promise,

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Speaker 2: it can't just be words and numbers. It has to

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Speaker 2: be the laws of physics. How gravity is different, how

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Speaker 2: pressure is different underwater versus on the surface, how a

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Speaker 2: chair moves around a room when you push it depending

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Speaker 2: on the fabric of the carpet or the slipperiness of

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Speaker 2: the floor. Like these are things now that can only

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Speaker 2: be understood in words, But ultimately she wants to build

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Speaker 2: machines and algorithms that can understand it for themselves, in

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Speaker 2: which case this might be the year I think when

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Speaker 2: thinking machines truly enter the room.

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Speaker 4: Well, I was just thinking.

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Speaker 3: An incredible thing to think about is I've talked to

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Speaker 3: you ad nauseum for the last day or two about

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Speaker 3: my dog that I've been fastering.

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Speaker 4: Yeah, and I just think about a new concern.

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Speaker 3: That I have all day long, is like what's going

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Speaker 3: on with this dog when I'm not with this dog?

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Speaker 3: But like, what if I could have a robot that

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Speaker 3: could tend to this dog as like a shadow? And

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Speaker 3: you're thinking about, like, oh, how does the physical world

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Speaker 3: interact with the robot world?

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Speaker 4: Like imagine a dog is like I have my mom

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Speaker 4: or I have my dad, and then I have.

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Speaker 3: My robot Alpha, who is like the robot self that

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Speaker 3: takes care of the dog too.

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Speaker 1: And who's the real boss You're at the robot.

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Speaker 3: Yeah, that's when robots take When dogs are.

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Speaker 1: Listening to the dog, you and the robot are standing

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Speaker 1: next to each other and you're like.

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Speaker 2: Come here, come here, pal, and the dog goes up

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Speaker 2: to the robot.

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Speaker 1: That's that's the future.

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Speaker 3: That is that's when humans, by the way, dogs answering

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Speaker 3: to robots is when humans will really be like, we

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Speaker 3: got to get these robots out of the question.

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Speaker 1: I think that's right. What about you do you have

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Speaker 1: any twenty twenty.

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Speaker 3: These predictions, Well, this is not a prediction that I've invented.

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Speaker 3: This is a prediction that's based on a lot of

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Speaker 3: reading that I've done on Business Insider and the Ft and.

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Speaker 1: Showing off.

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Speaker 4: Sor Tim Cook. I think is going to step down

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Speaker 4: from Apple.

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Speaker 2: I saw a headline about that. I didn't dive too

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Speaker 2: deep on it. What are people saying.

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Speaker 3: He's nearing retirement age, which, you know, it's not something

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Speaker 3: that's going to be like a hostile takeover, you know.

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Speaker 3: I think it's not going to be something that the

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Speaker 3: average person like finds perceptible as far as change. I

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Speaker 3: think that he will step down and kind of lead

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Speaker 3: from behind the scenes. A lah a Jeff Bezos, I

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Speaker 3: mean it's a hard job.

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Speaker 2: I guess Apple's obviously a behemoth. And you know, the

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Speaker 2: iPhone sales have been robust. The other stuff they've tried,

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Speaker 2: like the vision pro goggles they've stepped back from the

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Speaker 2: car never happened.

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Speaker 1: The TV thing is kind of I mean, it's not.

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Speaker 3: You know what I was actually thinking about it apples

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Speaker 3: TV has worked, like if you look at the Emmys

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Speaker 3: and the Critics' Choice Awards and the Golden globes like

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Speaker 3: they've kind of dominated this year. True in a way

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Speaker 3: that I mean, I don't know if you would attribute

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Speaker 3: that to Tim Cook necessarily.

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Speaker 2: But I guess if he could choose an Emmy or

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Speaker 2: having invented chet GPTR.

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Speaker 1: Respectology colleagues on the West Coast.

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Speaker 4: That's absolutely true. That's absolutely true.

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Speaker 3: You know. The other thing is that, speaking of our

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Speaker 3: friends in Hollywood, the thing that I've noticed a lot

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Speaker 3: of retired men do is they start production companies because

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Speaker 3: they like to be in the entertainment business. So who knows,

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Speaker 3: maybe Tim Apple will start a production company with Apple Studios.

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Speaker 4: I have no idea.

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Speaker 1: So yes, my prediction is the robot will enter the

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Speaker 1: room and yours this Tim Apple will exit the room.

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Speaker 4: That's right, Okay, perfect.

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Speaker 2: One tech CEO. I don't think it's going anywhere, is Jensen.

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Speaker 4: He's not going back to Dennis.

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Speaker 1: He's not going back to Denny's.

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Speaker 4: I wonder if he passes Denny's and he's like, I can't.

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Speaker 1: No, I'm sure.

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Speaker 2: I feel like it's a big part of his mythology.

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Speaker 2: He was at the Consumer Electronics Show this week, of

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Speaker 2: coursees delivering a keynote, and I count with my twenty

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Speaker 2: twenty six prediction before I read his remarks, which is

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Speaker 2: probably quite dumb, but because he is remarked, luckily we're

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Speaker 2: in line with my twenty twenty six prediction. He said

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Speaker 2: this will be the year when there's a chet GPT

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Speaker 2: moment for physical AI. This is not a disinterested statement.

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Speaker 2: Because Nvidio also announced they're getting into the self driving

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Speaker 2: car business.

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Speaker 1: Yes, I saw that, and this is.

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Speaker 2: Pretty interesting because previously you had Tesla sales falling Waymo

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Speaker 2: as the dominant players in US self driving robotaxis, and

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Speaker 2: it seemed like a two horse race. But part of

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Speaker 2: in Vidia's announcement was a partnership with Mercedes where they're

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Speaker 2: basically going to plug their software into other car makers.

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Speaker 4: They're in the car business.

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Speaker 2: They're not in the car business, but they're all of

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Speaker 2: a sudden kind of a competitor to their clients, right,

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Speaker 2: which is really interesting.

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Speaker 4: That is very interesting. Do you think it's something that's

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Speaker 4: going to work.

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Speaker 1: I wouldn't.

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Speaker 2: I mean you you're you're the Innvidia shareholder that's against

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Speaker 2: your dear leader.

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Speaker 1: No, No, I wouldn't.

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Speaker 3: And I think it's very smart to not actually be

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Speaker 3: in the car design business. I think it's very smart

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Speaker 3: to like put your software in a pre existing car.

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Speaker 2: Probably if you're if you're Mercedes who are doing the partnership,

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Speaker 2: or other auto makers, you're like, oh, this like a hail.

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Speaker 2: Mary's well, I think we have a chance of plugging

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Speaker 2: this in and catching up, which is which is really

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Speaker 2: really interesting. But he wasn't just talking about this self

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Speaker 2: driving car partnership. He was pointing at this kind of

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Speaker 2: robotic revolution. He likes hanging out with robots. We talked

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Speaker 2: about Earli last year. Remember he got the high five

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Speaker 2: from the humanoid robot.

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Speaker 4: On his trip I Do, I Do?

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Speaker 2: And another king of the self driving car world, Elon

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Speaker 2: himself is kind of seemingly more interested in.

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Speaker 1: Humanoid robots than self driving cars. Now he's moved on,

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Speaker 1: he's moved on.

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Speaker 2: He's definitely this time last year, optimist robot was hanging

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Speaker 2: out with Kim, and it seemed last year like a

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Speaker 2: pure gimmick. And now twelve months later there's a big

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Speaker 2: Wall Street Journal story saying that Elon is pivoting the

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Speaker 2: whole business to be human optimist robots.

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Speaker 1: So it is interesting as this year I'd like to

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Speaker 1: meet Kim. This year we'll see.

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Speaker 2: So humanoid robots stole the show at CS this year.

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Speaker 1: Did you see any of the videos?

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Speaker 4: Yes?

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Speaker 1: What did you? What did you see?

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Speaker 4: I think it was? Was it Tesla that I saw?

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Speaker 1: You? Mintioned Tesla. I'm going to show you Boston Dynamics.

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Speaker 3: Dynamics, Yes, show me the spot the dog famously they did.

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Speaker 1: They've now got Atlas, the person a.

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Speaker 4: Booty hun her.

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Speaker 1: What are you seeing?

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Speaker 4: So I'm seeing the biggest nerd on the planet.

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Speaker 3: It kind of reminds me of Elizabeth Holmes when something

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Speaker 3: good happens with Faranos coming out and doing like one

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Speaker 3: of those like kick dancers.

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Speaker 2: It's an extremely flexible humanoid robot doing cotwheels, doing Russian

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Speaker 2: Cossack dancing.

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Speaker 1: That is what it is, russian'd.

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Speaker 4: Yeah, it's incredible that they've trained them to do this.

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Speaker 2: It is And so Hyundai, who actually bought Boston Dynamics

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Speaker 2: lost this robot Atlas.

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Speaker 1: It can lift up to one hundred and ten pounds.

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Speaker 2: It can work in temperatures between minus four degrees fahrenheit

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Speaker 2: up to one hundred and four degrees.

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Speaker 4: Assud of the problem of needing cooling centers.

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Speaker 1: You don't and actually the battery is pretty amazing as well.

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Speaker 2: Last time, like when humanoid robots were last on sixty minutes,

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Speaker 2: had these huge hunchback of notre.

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Speaker 1: Dam story packs, battery packs.

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Speaker 2: But now it's just a pure human form essentially, which

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Speaker 2: is kind of interesting. They're planning to produce thirty thousand

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Speaker 2: of these robots per year, with the goal of them

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Speaker 2: working on factory flos by twenty twenty eight.

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Speaker 4: They're like, humans can't work in hard enough environments. We

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Speaker 4: need to make robot.

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Speaker 1: Well, I mean, I guess.

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Speaker 4: It's a that's a good thing.

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Speaker 1: It could be a good thing.

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Speaker 2: I mean, I look, I don't know if these are

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Speaker 2: actually going to replace humans on the factory floor, but

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Speaker 2: certainly they've got everyone talking about Hyundai for the first

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Speaker 2: time in a while, I don't talk about human So

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Speaker 2: those are humanoid robots designed by Hyundai to be factory workers. LG,

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Speaker 2: another South Korean tech firm, also made waves of cs

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Speaker 2: this year with its robot called Cloyd Cloud. Lloyd claud

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Speaker 2: Lloyd Cloud exactly, which ladders up to a vision.

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Speaker 1: Do you know what the vision is?

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Speaker 4: What is the vision?

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Speaker 1: The zero labor home that's I'm looking for.

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Speaker 3: I was just thinking, if I have to do another

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Speaker 3: load of laundry, like it's Sissifian the cloud.

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Speaker 1: Literally Cloyd literally does the laundry.

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Speaker 4: Can I try this?

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Speaker 1: Yeah, of course, Eliza.

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Speaker 2: Cloyd can remove dishes from the dishwasher, what heat up

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Speaker 2: food in the oven? This is this is not a

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Speaker 2: domestic home now, this is domestic laborer.

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Speaker 4: Oh my god.

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Speaker 2: It can interact seamlessly with other LG devices like your

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Speaker 2: fridge and your cooker.

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Speaker 3: So it is the connected home that then has the

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Speaker 3: robot that's running your connected.

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Speaker 2: The robot is the is the conductor to the symphony

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Speaker 2: of your connected home.

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Speaker 4: Not particularly opposed to this, I got to.

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Speaker 2: Say these were of the only humanoid robots who took

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Speaker 2: CS by storm. There was also the switchbot one row

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Speaker 2: h one that can fill a coffee machine, make breakfast,

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Speaker 2: wash the windows. And then there's the one EX Neo robot.

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Speaker 2: This one is kind of interesting because it doesn't have

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Speaker 2: the best fine motor control, which I think is true

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Speaker 2: of all of these robots. So if you have a

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Speaker 2: one X Neo robot in your home, you have to

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Speaker 2: consent to the fact that it will sometimes switch into

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Speaker 2: a mode where it's being human controlled, so there will

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Speaker 2: be somebody looking through your robot into your home, wearing

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Speaker 2: some kind of like haptic suit, doing what the robot

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Speaker 2: needs to do.

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Speaker 1: It's like Avatar.

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Speaker 2: Can you imagine buying your robot and then having somebody

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Speaker 2: sitting four thousand miles away in your house, yes, doing

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Speaker 2: your domestic.

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Speaker 4: Robots.

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Speaker 2: The jobs created by robots are people working thousands of

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Speaker 2: miles away to operate robots in your home.

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Speaker 1: It's a bizarre Also.

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Speaker 2: Like I wouldn't really want camera in my home, which

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Speaker 2: is external.

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Speaker 3: And we'll talk about this in my next story. But

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Speaker 3: there are also places where I don't want cameras.

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Speaker 1: Yeah, I want to hear about that.

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Speaker 2: Just before we turn away from CS, there was some

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Speaker 2: other robots which are pretty cool but not humanoid. Tell

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Speaker 2: me there's the anchor Uphie s two vacuum. It's rumba

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Speaker 2: with them up and it can travel between carpeted areas,

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Speaker 2: then go to the hard floor to mop it, come

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Speaker 2: back to the carpeted area. No water spilled, anything like that.

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Speaker 2: And Cara has an aromatherapy.

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Speaker 4: Diffuser, Oh my god.

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Speaker 2: And then finally smart fridges which can scan your empty

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Speaker 2: packages and add them to a shopping list for the

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Speaker 2: next grocery run. So it's a camera inside the fridge

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Speaker 2: that accesses an app while shopping, so.

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Speaker 1: Kind of remind you either on nine or in the

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Speaker 1: real world. Yeah, or you need to buy it.

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Speaker 3: I mean it is like on a macro level, pretty

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Speaker 3: incredible the way in which technology is like filling the

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Speaker 3: gaps and what humans can do, and also just like

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Speaker 3: taking care of time, Like all these things are very

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Speaker 3: time intensive. The question is is like is it time

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Speaker 3: that we want to eradicate, Like do we want to not.

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Speaker 4: Go to the grocery store. What are we supposed to

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Speaker 4: be doing instead.

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Speaker 1: Working on our computer?

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Speaker 4: Exactly?

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Speaker 2: That is the conundrum of the I have to work

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Speaker 2: all the time, so then when do I do things

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Speaker 2: which are non compensated labor but very important, like yeah,

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Speaker 2: the house up is that? That's like, yes, so I

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Speaker 2: never I never do the things that I need to

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Speaker 2: do which are not directly work related, Like I have

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Speaker 2: such a long list of annoying personal things.

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Speaker 4: We call it personal ad personal admin.

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Speaker 1: Yes, I never get through it. Yes, I don't know if,

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Speaker 1: but it doesn't tend to be still if the robot

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Speaker 1: could do not yet not yet.

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Speaker 3: I mean I still think the idea of like, I

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Speaker 3: live in an apartment building where not only humans live,

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Speaker 3: but a humanoid robot lives.

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Speaker 4: Like how far out are we on.

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Speaker 2: That you will go to somebody's house this year, I bet,

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Speaker 2: and they'll have a robot.

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Speaker 4: That's crazy. Okay, we'll see. I obviously did not get

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Speaker 4: to Cees this week. I did, though, get to Wegmans.

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Speaker 1: The grocery store, yes, the grocery.

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Speaker 3: Store, because I read this article in the Gothamis that

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Speaker 3: the grocery store chain was using facial recognition software in

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Speaker 3: their NYC location.

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Speaker 1: Well wow, what did the goemists find out about this?

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Speaker 3: So actually, a city law in twenty twenty one requires

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Speaker 3: stores to put up a sign if they use facial

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Speaker 3: recognition software, so Wegmans complied. The funny thing is I

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Speaker 3: went to Wegmans, and I really had to search for

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Speaker 3: this sign. And Wegmans might disagree with me because there

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Speaker 3: was a sign on the door, But the way in

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Speaker 3: which I had to look for it, because it was

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Speaker 3: like sliding in and out of view on the automatic door,

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Speaker 3: really made me laugh.

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Speaker 2: So you went to Wegmans looking for the sign, you

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Speaker 2: hadn't been looking for the sign, you wouldn't have seen it.

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Speaker 3: So there were people that I talked to and I

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Speaker 3: pointed out the sign to them when I went to

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Speaker 3: the store, and they were like, no, I.

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Speaker 4: Didn't notice that.

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Speaker 1: I didn't recognize that.

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Speaker 4: I didn't see that.

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Speaker 3: Because if you're not looking for it, I think we're

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Speaker 3: so used to just seeing signs up and if there

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Speaker 3: don't apply, Like, I've never looked at pet signs until recently.

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Speaker 1: So you found the sign. What did it say?

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Speaker 3: It said, quote biometric identifier information collected at this location. Then,

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Speaker 3: in much finer print, quote Wegman's Food Market, Inc. Collects, retains, converts, stores,

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Speaker 3: or shares customers biometric identifier information, which may include facial recognition,

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Speaker 3: eye scans, and voice prints.

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Speaker 2: Of those two verbs, converts and shares stand out to

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Speaker 2: me as convert, I'm like converts into what shares with whom?

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Speaker 3: So Gofmus actually asked Wegmans about how they were storing

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Speaker 3: the data, how long they were storing it for, and

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Speaker 3: how the company would share this data with law enforcement.

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Speaker 4: Surprise surprise, Wegmans did.

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Speaker 2: Not reply, Okay, what you've been been bearing the leave

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Speaker 2: for a while. Now.

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Speaker 1: What this is is this? What is this is a

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Speaker 1: customer retention to safety thing safety.

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Speaker 3: Yes, so the sign says, quote, we use facial recognition

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Speaker 3: technology to protect the safety and security of our patrons

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Speaker 3: and employees and do not lease, trade, or otherwise profit

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Speaker 3: from the transfer of biometric identify or information.

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Speaker 2: It is interesting because like in New York they now

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Speaker 2: have like you know these like plastic.

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Speaker 4: Covers on everything in CBS.

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Speaker 1: So that you can't sell the people shoplift.

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Speaker 4: I mean, this is just replacing the old Have you seen.

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Speaker 1: This person right now?

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Speaker 4: You know what I mean?

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Speaker 3: Like, you go into any bodega or any sort of

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Speaker 3: like smoke shop and there's been a shoplifter and they

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Speaker 3: have a photo from the camera that is so blurry

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Speaker 3: that you can't even see if this person is a person. Now,

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Speaker 3: what we have is a company that has a lot

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Speaker 3: of money to invest in software and basically they can

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Speaker 3: capture any face and cross reference that face if that

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Speaker 3: face has been known to shoplift.

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Speaker 2: Yeah, but it's also like if you haven't been shoplifting.

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Speaker 2: Like the idea that you can be personally identified every

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Speaker 2: time you go into a shop and cross reference to

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Speaker 2: older weird.

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Speaker 1: It's like, you know, you didn't leave your wallet in

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Speaker 1: the shop.

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Speaker 2: You left your whole identity there that's right to be shared, converted,

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Speaker 2: and whatever else that will.

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Speaker 3: It's just a very strange phenomenon. So I actually talked

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Speaker 3: to a few people, and luckily I had a very

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Speaker 3: intelligent woman who had seen the same Gothmass article I had.

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Speaker 3: I approached her and said to her, you, did you

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Speaker 3: know that facial recognition technology was being used at this Wegman?

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Speaker 3: She said, I knew it was being used at Wegmans.

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Speaker 3: I thought it was at the Wegmans in Brooklyn, and

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Speaker 3: that she actually hadn't clocked the side in the Manhattan store.

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Speaker 3: The only reason she knew about it was because she

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Speaker 3: had read the Gothmist article.

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Speaker 1: I want to hear the tape.

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Speaker 3: Do you feel comfortable with your biometrics oatomy collected by

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Speaker 3: a large grocery stoarch?

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Speaker 5: I know, I actually had the thought right as I

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Speaker 5: walked in here. I was like, maybe I should put

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Speaker 5: my little scarf around my field wearing.

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Speaker 4: A really cute scarf right now.

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Speaker 5: Maybe you should use it or like a giant pair

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Speaker 5: of sunglasses. Yeah no, I mean, I guess the way

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Speaker 5: I feel about it is like I'm not sure that

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Speaker 5: it is going to impact me personally. In any real way.

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Speaker 5: But I think there are certain people for whom it's

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Speaker 5: really bad. Yeah, you know, I'm like a white lady who.

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Speaker 1: Is like.

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Speaker 5: Economically mobile, and so I'm sort of like not worried

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Speaker 5: about it for myself, which I guess is why I

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Speaker 5: still came in here. But on principle, yeah, I think

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Speaker 5: it's bad.

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Speaker 3: You know, it's actually interesting she was wearing this kind

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Speaker 3: of cravat that was this little cashmir brown scarf, and

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Speaker 3: her idea to cover her face with a scarf is

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Speaker 3: actually a good one. Four or four recently published an

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Speaker 3: article about anti surveillance design, which you and I have

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Speaker 3: talked about, remember in terms of three M and the

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Speaker 3: Hong Kong protests.

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Speaker 1: Yeah.

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Speaker 3: Four. A fourth point is like, facial recognition tech is complicated,

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Speaker 3: but actually tricking it is very easy and the most

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Speaker 3: effective tool you can use to trick facial recognition technology

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Speaker 3: is actually a mask.

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Speaker 1: Is Wegman's an outlier here or the other? I mean

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Speaker 1: this you big quich is?

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Speaker 2: I know, for example, when you go to the airport,

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Speaker 2: you can sometimes shop with your.

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Speaker 4: First like the f out Actually no, it's not just Wegmans.

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Speaker 3: There are stores of Albertson's Walmart, Kroger's are all using

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Speaker 3: it in some of their locations.

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Speaker 4: I think also Home Depot, Low's, Macy's. I mean, I

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Speaker 4: think a lot of people won't care.

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Speaker 3: Yeah, and a lot of people will just say, I've

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Speaker 3: been surveilled. What is this, as we used to say,

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Speaker 3: a little sprinkle of lder going to do to my

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Speaker 3: day to day? If I'm not someone who should be

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Speaker 3: worried about shoplifting, And to that, I say, what if

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Speaker 3: it's not forever about shoplifting?

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Speaker 4: Right after the break, the P.

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Speaker 3: And GLP ones are having a moment and only fans

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00:19:54,720 --> 00:19:57,560
Speaker 3: models are getting very special US visas.

449
00:19:57,760 --> 00:20:07,000
Speaker 1: Stay with us and we're back. Does the word peptide

450
00:20:07,400 --> 00:20:08,159
Speaker 1: mean anything to you?

451
00:20:08,240 --> 00:20:10,439
Speaker 3: My friends are always like the peptides, the peptides and my

452
00:20:10,480 --> 00:20:14,800
Speaker 3: skincare and the peptide what I think they use skincare

453
00:20:14,880 --> 00:20:17,359
Speaker 3: that have like peptides that titans their face.

454
00:20:17,720 --> 00:20:19,560
Speaker 1: Have they been to peptide raves apps?

455
00:20:19,600 --> 00:20:23,440
Speaker 3: I mean, unless you're talking about the their bathroom now.

456
00:20:23,920 --> 00:20:24,040
Speaker 1: Well.

457
00:20:24,080 --> 00:20:25,640
Speaker 2: The New York Times had a great story this week

458
00:20:25,680 --> 00:20:29,720
Speaker 2: with the headline Chinese peptides are the latest biohacking trend

459
00:20:30,119 --> 00:20:33,160
Speaker 2: in the tech world. It had one source saying quote

460
00:20:33,280 --> 00:20:37,439
Speaker 2: the elites all have a Chinese peptide dealer, and it

461
00:20:37,520 --> 00:20:41,320
Speaker 2: follows several Silicon Valley workers, all between twenty and forty

462
00:20:41,800 --> 00:20:45,600
Speaker 2: who are buying unregulated peptides from China with the hope

463
00:20:45,600 --> 00:20:48,280
Speaker 2: that it will improve their health, their sleep, their fitness,

464
00:20:48,400 --> 00:20:49,600
Speaker 2: and their focus.

465
00:20:49,880 --> 00:20:50,440
Speaker 1: But what is it?

466
00:20:50,880 --> 00:20:52,960
Speaker 4: I mean? I know peptides are like protein.

467
00:20:53,680 --> 00:20:58,040
Speaker 2: What is Peptides are amino acids that regulate hormones in

468
00:20:58,119 --> 00:21:01,439
Speaker 2: the body and reduce in flammation, which is why it's

469
00:21:01,440 --> 00:21:04,640
Speaker 2: a popular ingredient I learned in skin care products. It's

470
00:21:04,680 --> 00:21:08,200
Speaker 2: also and I didn't know this embarrassingly, the P in

471
00:21:08,320 --> 00:21:09,000
Speaker 2: GLP one.

472
00:21:09,520 --> 00:21:12,359
Speaker 3: I didn't know that just until this very moment. So

473
00:21:13,280 --> 00:21:15,920
Speaker 3: the p peptides improve health.

474
00:21:15,960 --> 00:21:19,159
Speaker 4: They're good. People like them, But why do they like them? Like,

475
00:21:19,200 --> 00:21:20,480
Speaker 4: what do people say that they do?

476
00:21:20,920 --> 00:21:24,600
Speaker 2: People are claiming that different types of peptides work for

477
00:21:24,720 --> 00:21:26,280
Speaker 2: different types of ailments.

478
00:21:26,400 --> 00:21:27,320
Speaker 1: Peptides is like.

479
00:21:27,280 --> 00:21:31,119
Speaker 2: A catsual term for these collections of amino acids, and

480
00:21:31,160 --> 00:21:34,159
Speaker 2: people are using all kinds of different peptides for different purposes.

481
00:21:34,400 --> 00:21:38,000
Speaker 2: So people say that these peptides can stimulate wound healing

482
00:21:38,080 --> 00:21:42,000
Speaker 2: with by stimulating new blood vessel growth, better sleep, of course,

483
00:21:42,240 --> 00:21:45,840
Speaker 2: weight loss, increase focus. One of the weirdest things was

484
00:21:46,240 --> 00:21:50,200
Speaker 2: improving eye contact. Quote from the story, One Open AI

485
00:21:50,280 --> 00:21:53,400
Speaker 2: researcher called it a zepic for autism. There have been

486
00:21:53,440 --> 00:21:55,919
Speaker 2: some negative side effects. One woman in the New York

487
00:21:56,000 --> 00:21:59,240
Speaker 2: Times story had all her hair fall out bad Two

488
00:21:59,320 --> 00:22:02,760
Speaker 2: women will hostpit life with swollen tongues and racing hearts

489
00:22:03,119 --> 00:22:06,480
Speaker 2: after shooting up on peptides at an anti aging festival

490
00:22:06,480 --> 00:22:06,879
Speaker 2: in Vegas.

491
00:22:07,040 --> 00:22:09,120
Speaker 4: Just like the worst case scenario use.

492
00:22:09,160 --> 00:22:12,400
Speaker 1: Well, in the worst case scenario is probably you die.

493
00:22:12,720 --> 00:22:14,919
Speaker 3: Right, So I'm just saying like in terms of like

494
00:22:15,480 --> 00:22:18,840
Speaker 3: people who are using glps are not generally like going

495
00:22:18,880 --> 00:22:20,200
Speaker 3: to shoot them up festivals.

496
00:22:20,280 --> 00:22:24,359
Speaker 2: Well, that's what this is about. These raves are like

497
00:22:24,600 --> 00:22:28,879
Speaker 2: basically people are going to these parties with DJs where

498
00:22:29,160 --> 00:22:33,360
Speaker 2: all of these peptides, a DJ and a peptide, all

499
00:22:33,400 --> 00:22:37,560
Speaker 2: of these peptides are available, and somebody else is teaching

500
00:22:37,560 --> 00:22:41,240
Speaker 2: you how to make your own peptide cocktail. The time

501
00:22:41,280 --> 00:22:43,000
Speaker 2: that you went through a few of these raves, and

502
00:22:43,080 --> 00:22:45,520
Speaker 2: there are pictures of the raves in the article. People

503
00:22:45,600 --> 00:22:49,439
Speaker 2: are like have a needle in their arm with tape

504
00:22:50,000 --> 00:22:51,560
Speaker 2: with like a disco background.

505
00:22:51,840 --> 00:22:54,520
Speaker 4: Oh so they can just like get fed, get fed peptides.

506
00:22:54,600 --> 00:22:57,560
Speaker 1: Yeah yeah, Is this legal. It's kind of in a

507
00:22:57,640 --> 00:22:58,439
Speaker 1: gray area.

508
00:22:58,880 --> 00:23:03,840
Speaker 2: Personal use is legal, and buying directly from suppliers is legal,

509
00:23:04,400 --> 00:23:08,600
Speaker 2: but very few peptides are FDA approved, and so biohackers

510
00:23:08,720 --> 00:23:12,240
Speaker 2: are very much injecting at their own risk. As the

511
00:23:12,240 --> 00:23:15,600
Speaker 2: woman whose hair fell out may testify. What's kind of

512
00:23:15,600 --> 00:23:18,919
Speaker 2: interesting is this is not as niche as you might imagine.

513
00:23:19,080 --> 00:23:21,160
Speaker 4: The rave piece isn't the rave piece.

514
00:23:21,000 --> 00:23:21,520
Speaker 1: Is pretty niche.

515
00:23:21,560 --> 00:23:23,000
Speaker 2: In fact, you look at the photos in the New

516
00:23:23,040 --> 00:23:25,000
Speaker 2: York Times, it's like only a New York Times journalist

517
00:23:25,000 --> 00:23:25,880
Speaker 2: could describe that as.

518
00:23:25,760 --> 00:23:28,919
Speaker 1: A rave It's like four people in a room with

519
00:23:29,040 --> 00:23:29,600
Speaker 1: purple light.

520
00:23:29,720 --> 00:23:31,720
Speaker 4: Yeah yeah, yeah, yeah, yeah yeah yeah.

521
00:23:31,760 --> 00:23:35,119
Speaker 2: But as a party, I guess at best, imports of

522
00:23:35,160 --> 00:23:38,360
Speaker 2: peptides and hormones from China doubled.

523
00:23:38,520 --> 00:23:39,879
Speaker 4: I was going to ask why China, and then I

524
00:23:39,960 --> 00:23:40,840
Speaker 4: was like, I know the yes they make.

525
00:23:41,680 --> 00:23:43,920
Speaker 2: So in twenty twenty four, there were one hundred and

526
00:23:43,960 --> 00:23:47,960
Speaker 2: sixty four million dollars worth of Chinese peptides imported to

527
00:23:48,000 --> 00:23:50,480
Speaker 2: the US in the first three quarters of the year.

528
00:23:51,119 --> 00:23:54,560
Speaker 2: In twenty twenty five, three hundred and twenty eight million

529
00:23:55,119 --> 00:23:59,399
Speaker 2: exactly double, which is three twenty million dollars of Chinese peptides.

530
00:23:59,000 --> 00:24:02,080
Speaker 4: Which makes you one, does it work or is there

531
00:24:02,160 --> 00:24:02,639
Speaker 4: just hype?

532
00:24:03,080 --> 00:24:04,120
Speaker 1: I mean JLP's work.

533
00:24:04,359 --> 00:24:05,560
Speaker 4: Have you ever had an I.

534
00:24:07,520 --> 00:24:10,440
Speaker 2: Listen, Honestly reading the article, I was like, Wow, if

535
00:24:10,480 --> 00:24:13,399
Speaker 2: I had a lot more time on my hands and

536
00:24:13,480 --> 00:24:17,040
Speaker 2: I was a little bit more crazed, I can easily

537
00:24:17,119 --> 00:24:18,520
Speaker 2: imagine going down this rabbit hole.

538
00:24:18,600 --> 00:24:21,359
Speaker 3: Yeah here all you kind of can, though, just with

539
00:24:21,480 --> 00:24:24,360
Speaker 3: like very cursory use of GLP wan like a lot

540
00:24:24,400 --> 00:24:26,240
Speaker 3: of people, I mean basically everybody.

541
00:24:26,240 --> 00:24:29,800
Speaker 1: I know. Yeah, I'm also exploring. You're exploring I.

542
00:24:29,760 --> 00:24:32,360
Speaker 4: Am exploring wait no, for mental health benefits.

543
00:24:32,400 --> 00:24:33,960
Speaker 2: So that was one of the things mentioned in the article,

544
00:24:34,040 --> 00:24:38,280
Speaker 2: microdosing GLPS for mental health. But you know, it's like

545
00:24:38,400 --> 00:24:41,560
Speaker 2: the promise that rather than like going to the gym

546
00:24:41,720 --> 00:24:45,200
Speaker 2: four times a week and meditating for forty five minutes

547
00:24:45,240 --> 00:24:47,960
Speaker 2: a day and going on a walk and looking at the.

548
00:24:47,880 --> 00:24:50,960
Speaker 3: Sky back to your it goes back to what you're saying,

549
00:24:50,960 --> 00:24:52,720
Speaker 3: which is this list of all the stuff that you

550
00:24:52,800 --> 00:24:56,320
Speaker 3: have to do when you're not working is really hard

551
00:24:56,320 --> 00:24:59,520
Speaker 3: to do, Like, can I achieve it with this coat that.

552
00:24:59,480 --> 00:25:01,880
Speaker 1: I can buy on line? Yes, so it's tempting.

553
00:25:01,960 --> 00:25:04,199
Speaker 2: I actually, luckily I don't have enough time to go

554
00:25:04,320 --> 00:25:06,240
Speaker 2: down the rabbit hole, but I but I can imagine.

555
00:25:06,240 --> 00:25:07,879
Speaker 1: I can imagine maybe you could have a robot that

556
00:25:07,920 --> 00:25:09,000
Speaker 1: could stranger things.

557
00:25:09,240 --> 00:25:10,119
Speaker 4: Stranger things.

558
00:25:10,200 --> 00:25:16,480
Speaker 3: Absolutely, I want to pivot. But do you consider yourself

559
00:25:16,480 --> 00:25:17,760
Speaker 3: an exceptional creative?

560
00:25:18,320 --> 00:25:19,720
Speaker 1: Have you been reading my LinkedIn again?

561
00:25:20,040 --> 00:25:23,760
Speaker 3: I've been reading your thoughts because I take Pepta and

562
00:25:23,800 --> 00:25:26,520
Speaker 3: I read this article that said influencers and only fans

563
00:25:26,560 --> 00:25:30,720
Speaker 3: models dominate the US extraordinary artist visa, and I thought

564
00:25:30,720 --> 00:25:30,960
Speaker 3: of you.

565
00:25:31,200 --> 00:25:32,960
Speaker 1: I saw that story. I didn't read it, but I'm

566
00:25:32,960 --> 00:25:33,840
Speaker 1: glad to bring it up.

567
00:25:34,080 --> 00:25:35,520
Speaker 4: And you know why I thought of you.

568
00:25:35,560 --> 00:25:37,160
Speaker 1: Because I had one visa.

569
00:25:37,359 --> 00:25:39,359
Speaker 4: It was not because you were selling feed picks.

570
00:25:39,680 --> 00:25:42,400
Speaker 2: I was reached for a time deemed by the US

571
00:25:42,480 --> 00:25:44,560
Speaker 2: government an extraordinary artist.

572
00:25:44,640 --> 00:25:47,199
Speaker 4: So do you know where this this I learned for

573
00:25:47,240 --> 00:25:49,480
Speaker 4: the first time reporting the story. Do you know where

574
00:25:49,480 --> 00:25:51,440
Speaker 4: it came from? The one visa came from no idea.

575
00:25:51,920 --> 00:25:54,840
Speaker 3: So these visas were created because in the nineteen seventies,

576
00:25:55,119 --> 00:25:58,560
Speaker 3: the Nixon administration was trying to deport John Lennon put

577
00:25:58,800 --> 00:26:02,399
Speaker 3: over his politics. But Lenin was able to stay in

578
00:26:02,440 --> 00:26:05,399
Speaker 3: the US because he was deemed an outstanding person in

579
00:26:05,440 --> 00:26:06,280
Speaker 3: the arts and science.

580
00:26:06,320 --> 00:26:09,920
Speaker 1: So you and John Lennon, Wow, and now a lot

581
00:26:09,920 --> 00:26:10,720
Speaker 1: of only fans.

582
00:26:10,520 --> 00:26:13,720
Speaker 4: Creators and boy George and Shinnan O'Connor wow. Yes.

583
00:26:14,080 --> 00:26:18,320
Speaker 3: The FT reports that currently an overwhelming number of people

584
00:26:18,320 --> 00:26:22,400
Speaker 3: getting approved for OH one visas are influencers and OnlyFans models.

585
00:26:22,840 --> 00:26:26,800
Speaker 3: One lawyer who was interviewed called them scroll kings and

586
00:26:27,040 --> 00:26:27,920
Speaker 3: I love, I.

587
00:26:27,920 --> 00:26:30,000
Speaker 4: Really love scroll kings and queens.

588
00:26:30,280 --> 00:26:34,439
Speaker 3: The number of scroll kings and queens has actually, like

589
00:26:34,480 --> 00:26:36,880
Speaker 3: peptide use, doubled since twenty ten.

590
00:26:37,040 --> 00:26:41,320
Speaker 2: Wow, and why is applicants looked upon so favorably by

591
00:26:41,359 --> 00:26:44,640
Speaker 2: the case officers at the United States Citizenship and Immigration

592
00:26:44,800 --> 00:26:45,919
Speaker 2: Services Bureau.

593
00:26:46,320 --> 00:26:49,840
Speaker 3: Some immigration lawyers said that follower account makes a difference.

594
00:26:50,119 --> 00:26:53,840
Speaker 3: The visa process is complicated and requires a lot of paperwork.

595
00:26:53,880 --> 00:26:56,480
Speaker 2: No, been true, But that's interesting at the follow account

596
00:26:56,520 --> 00:27:01,080
Speaker 2: because much like with the large iguage models, where if

597
00:27:01,080 --> 00:27:04,480
Speaker 2: it doesn't exist in writing, doesn't exist. Yeah, in your

598
00:27:05,000 --> 00:27:08,399
Speaker 2: one visa application, it doesn't exist if you can't measure

599
00:27:08,440 --> 00:27:10,159
Speaker 2: it well. You need to come up with all of

600
00:27:10,200 --> 00:27:13,159
Speaker 2: these very concrete numbers to demonstrate reach and influence and

601
00:27:13,160 --> 00:27:14,840
Speaker 2: blah blah, blah so and who.

602
00:27:14,640 --> 00:27:17,840
Speaker 3: Can demonstrate reach and influence better than an influencer or

603
00:27:17,880 --> 00:27:20,200
Speaker 3: someone with an only fans account, no one, you know.

604
00:27:20,240 --> 00:27:22,520
Speaker 3: I think there are other people who have these visas

605
00:27:22,560 --> 00:27:24,719
Speaker 3: who might be able to say, Okay, I'm in a

606
00:27:24,760 --> 00:27:27,760
Speaker 3: major film or I'm in theater productions, you know, and

607
00:27:27,760 --> 00:27:31,760
Speaker 3: get recommendations from experts in their field. These only fans

608
00:27:31,760 --> 00:27:35,240
Speaker 3: models and influencers are like, look, I have twenty million followers,

609
00:27:35,440 --> 00:27:36,760
Speaker 3: let me stay in the country.

610
00:27:37,000 --> 00:27:40,800
Speaker 1: And how many exceptional talent visas are given out each year?

611
00:27:41,359 --> 00:27:42,080
Speaker 4: Are you fishing?

612
00:27:45,240 --> 00:27:48,440
Speaker 3: Only twenty thousand visas were granted in twenty twenty four,

613
00:27:48,520 --> 00:27:51,680
Speaker 3: and the majority of visas are still h one b's yeah,

614
00:27:51,960 --> 00:28:06,119
Speaker 3: your exception. That's it for this week for tech stuff,

615
00:28:06,160 --> 00:28:07,120
Speaker 3: I'm Karra Price.

616
00:28:06,920 --> 00:28:07,880
Speaker 1: And I'm os Vloschin.

617
00:28:07,960 --> 00:28:11,040
Speaker 2: This episode was produced by Eliza Dennis and Melissa Slaughter.

618
00:28:11,680 --> 00:28:14,600
Speaker 2: It was executive produced by me Caarra Price, Julian Nutta,

619
00:28:14,600 --> 00:28:17,520
Speaker 2: and Kate Osborne for Kaleidoscope and Katrina.

620
00:28:17,160 --> 00:28:18,920
Speaker 1: Novelle for iHeart Podcasts.

621
00:28:19,520 --> 00:28:23,520
Speaker 2: The engineer is Mike Coscarelli and Jack Insley mikes this episode.

622
00:28:23,960 --> 00:28:25,360
Speaker 1: Kyle Murdoch wrote our theme.

623
00:28:25,200 --> 00:28:27,879
Speaker 3: Song, Please rate, review and Reach out to us at

624
00:28:27,960 --> 00:28:29,480
Speaker 3: tech Stuff Podcast at.

625
00:28:29,359 --> 00:28:31,560
Speaker 4: Gmail dot com. We want to hear from.

626
00:28:31,600 --> 00:28:31,800
Speaker 3: You'll