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Speaker 1: Thanks for tunion to tech Stuff. If you don't recognize

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Speaker 1: my voice, my name is oz Valoshan, and I'm here

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Speaker 1: because the inimitable Jonathan Strickland has passed the baton to

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Speaker 1: Karra Price and myself to host tech Stuff. The show

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Speaker 1: will remain your home for all things tech, and all

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Speaker 1: the old episodes will remain available in this feed. Thanks

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Speaker 1: for listening. Welcome to Tech Stuff, a production from iHeart

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Speaker 1: Podcasts and Kaleidoscope. I'm Anzaloshan and today co host Kara Price,

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Speaker 1: and I will bring you three things first. The headlines

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Speaker 1: this week, including new and scary uses of chatchipt and

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Speaker 1: a sobering reminder of the climate impact of data processing.

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Speaker 1: On today's Tech Support segment, we have a conversation with

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Speaker 1: four or four Media's Joseph Cox about U haul criminals,

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Speaker 1: and then finally with Thrill to introduce a new segment

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Speaker 1: which we lovingly call when did This Become a Thing?

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Speaker 1: We go on a wild ride with Cara, which ends

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Speaker 1: with a revelation about what it means to be a

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Speaker 1: digital native. All of that on this week in Tech.

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Speaker 1: It's Friday, January seventeenth. Stay with us, Karen Good Afternoon.

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Speaker 1: I actually wasn't expecting to Good afternoon, Good afternoon, I

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Speaker 1: actually wasn't expecting to see you this afternoon.

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Speaker 2: Why am I not supposed to be here?

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Speaker 1: Well, now you are supposed to be here. I'm glad

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Speaker 1: you're here, but I actually thought you were supposed to

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Speaker 1: be in LA this week.

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Speaker 2: I was actually I was supposed to be on the

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Speaker 2: other coast for work, and obviously I wasn't able to go.

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Speaker 2: And I think I speak for both of us when

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Speaker 2: I say that our hearts really go out to the

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Speaker 2: people who were impacted by the fires and who continue

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Speaker 2: to be impacted by the fires. And while I'm glad

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Speaker 2: I'm here, I'm not glad about the circumstances.

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Speaker 1: No, obviously me neither. But actually what's been going on

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Speaker 1: in LA has some interesting text stories around it, and

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Speaker 1: so I actually, if you'll allow me, wants to go

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Speaker 1: first in this week's news roundup.

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Speaker 2: We will forego the continentass and I will let you go.

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Speaker 1: So last week you actually sent me a screenshot of

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Speaker 1: an Instagram story.

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Speaker 2: The only way to get you anything from Instagram. You're

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Speaker 2: not on it, which I love about you.

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Speaker 1: I'm not I do have the application, I just he.

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Speaker 2: Goes on browser people.

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Speaker 1: But basically, this Instagram post that you screenshotted for me

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Speaker 1: was a picture of this raging inferno, and overlaid was

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Speaker 1: text that reads, quote, the average AI data center uses

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Speaker 1: three hundred thousand gallons of water a day to keep cool,

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Speaker 1: roughly equivalent to water use in one hundred thousand homes.

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Speaker 1: Open brackets, NPR, close brackets. Please skip the silly ais,

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Speaker 1: the questions to chat GPT, having AI write all of

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Speaker 1: your emails. Life needs water, not tech.

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Speaker 2: Water is the essence of life.

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Speaker 1: To be fair, whata, it is the essence of life.

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Speaker 2: And it's also the essence of fighting fires in a

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Speaker 2: city where droughts are just commonplace.

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Speaker 1: Totally, and I was actually pretty compelled by this post

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Speaker 1: that you sent me until I dug a little deeper,

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Speaker 1: say more. This was, of course misinformation.

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Speaker 2: I sent you misinformation.

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Speaker 1: You sent me misinformation, but I didn't amplify.

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Speaker 2: This is why I don't listen to news and Instagram stories.

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Speaker 1: But my first clue was this open brackets, NPR, close brackets,

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Speaker 1: and that gave me an opportunity. Okay, thank you Watson.

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Speaker 1: That gave me an opportunity to do some research and

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Speaker 1: figure out what the source of this story was, and

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Speaker 1: I actually found the original NPR story.

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Speaker 2: What was the NPR story.

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Speaker 1: Well, it was published in August twenty twenty two, Oh

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Speaker 1: my god, and the headline was data center's backbone of

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Speaker 1: the digital economy, face water scarcity and climate risk. Now,

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Speaker 1: this was published three months before November twenty twenty two,

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Speaker 1: which is when chat GPT three entered the world and

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Speaker 1: generative AI became something that kind of has entered the

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Speaker 1: mainstream discourse. But the NPR story makes no mention of it.

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Speaker 1: The actual quote was quote a mid size data center

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Speaker 1: consumes around three hundred thousand gallons of water. Again, no

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Speaker 1: mention of quote the average AI data center from the

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Speaker 1: Instagram story. The story was really just about regular old

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Speaker 1: data centers that were processing Google searches and doing weather

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Speaker 1: forecasts and helping bitcoin traders making lose fortunes. That wasn't

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Speaker 1: the only distortion in the non My mind has been

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Speaker 1: in the non AI slop that you sent me. It

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Speaker 1: is human generated slop.

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Speaker 2: It distresses me to know end that we're in, like

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Speaker 2: true crises, how easy it it is for people to

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Speaker 2: get excited about information that seems compelling and not only

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Speaker 2: believe it, but share it because it seems so compelling.

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Speaker 1: I've actually find the truth of this story as interesting

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Speaker 1: as the misinformation. So the Instagram story said that these

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Speaker 1: three hundred thousand gallons per day of water could otherwise

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Speaker 1: serve one hundred thousand US homes per day, it's just

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Speaker 1: made up. No, In the NPR story, it's one thousand.

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Speaker 1: So that's quite a big difference. As we know, there's

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Speaker 1: been this kind of explosion in demand for data centers

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Speaker 1: exactly because of AI. The La Times actually reported that

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Speaker 1: a single query on a chatbot that uses AI is

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Speaker 1: estimated to require at least ten times more electricity than

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Speaker 1: a standard search on Google. So there is this spiritual

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Speaker 1: truth of the story you shared, which is data centers

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Speaker 1: create heat. Heat needs water to cool it down, and

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Speaker 1: the explosion in generative of AI is putting a lot

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Speaker 1: more strain on data centers. So there is actually weirdly

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Speaker 1: lot of truth in the story.

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Speaker 2: But the connection directly between data centers and the fires

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Speaker 2: in Los Angeles.

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Speaker 1: There is no suggestion there is a direct connection, and

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Speaker 1: that's primarily because there aren't that many data centers in

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Speaker 1: the Los Angeles area. There are a lot of data

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Speaker 1: centers in northern California, and there is now actually a

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Speaker 1: big program to build more data centers in southern California,

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Speaker 1: so this could become kind of a bigger problem. But Cara,

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Speaker 1: I know, I'm really only allowed one story, but this

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Speaker 1: one is sort of very much connected, and it's in

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Speaker 1: one of my favorite news sources. Can you guess what

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Speaker 1: it is?

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Speaker 2: The Daily Mail?

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Speaker 1: No?

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Speaker 2: I really one of your favorite the New York Posts?

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Speaker 1: No? No, no, Yeah, you're embarrassing. Sorry, this is the

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Speaker 1: Sacramento Bee.

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Speaker 2: I love the Sacramento Bee.

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Speaker 1: Sacramento is a great paper. On December eighteenth, twenty twenty four,

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Speaker 1: The Bee ran a prescient op ed by Dean Flores,

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Speaker 1: a former California State Senator, under the headline California's next

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Speaker 1: water war won't concern agriculture. It will be about AI.

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Speaker 2: Mmmm, so it's not the almonds, it's the AI.

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Speaker 1: It's the AI is the AI and the almonds, And

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Speaker 1: and Flores continues that the struggle for water, of course,

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Speaker 1: is not a new topic in California. It used to

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Speaker 1: be farms versus cities, agriculture versus know, urban development, but

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Speaker 1: now there's this third great demand on the water supply

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Speaker 1: of a drought prone state. There are actually more than

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Speaker 1: two hundred and seventy data centers already in California, and

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Speaker 1: in California, water consumption from those data centers is in

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Speaker 1: the billions of gallons each year and there.

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Speaker 2: And Flores points this out in his.

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Speaker 1: Exactly and says that data centers could double or triple

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Speaker 1: their water demand in the next decade in California. And

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Speaker 1: I just think what Flora's ended his piece saying was

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Speaker 1: this story hasn't yet been written. AI infrastructure is being

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Speaker 1: built in real time, so there are opportunities to kind

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Speaker 1: of do better when it comes to figuring out how

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Speaker 1: to to meet this demand.

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Speaker 2: Yeah, so I do agree with you, Oz, and I

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Speaker 2: think it's important on this show when we cover things

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Speaker 2: that can be a little bit paranoia inducing or anxiety provoking,

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Speaker 2: for us to talk about technology that really is solving

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Speaker 2: a human problem. I have a ton of friends in La.

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Speaker 2: My sister lives in LA, and they are all using

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Speaker 2: this technology called watch Duty. Do you know what watch

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Speaker 2: to do? So? Watch Duty is an app that is

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Speaker 2: so beta looking that it kind of looks like the

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Speaker 2: Oregon Trail. And unfortunately it does look like the Oregon

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Speaker 2: Trail as of last week, because it's just like firefire, firefire, horrible,

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Speaker 2: don't be there, don't do this. But what watch Duty

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Speaker 2: is in practice is this free app that shows active fires,

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Speaker 2: evacuation zones, and air quality indexes in the LA area.

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Speaker 2: And it's actually, you know, anecdotally been a lifeline for

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Speaker 2: people living there. And it's just this really great act

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Speaker 2: of humanity and that it's not selling people data, it's

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Speaker 2: not showing you ads. You don't have to log in,

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Speaker 2: and it is completely donation based. It's a nonprofit. Well,

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Speaker 2: you know, I wanted to highlight this because I think

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Speaker 2: often about disaster capitalism, and this, to me is more

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Speaker 2: disaster troubleshooting.

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Speaker 1: Yeah. One of the fundamental interesting things about tech is

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Speaker 1: it often does emerge in response to real world problems

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Speaker 1: and can help solve them or at least mitigate them.

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Speaker 1: It's just what happens a day after, which is often

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Speaker 1: where things go wrong. So carry you've already over delivered

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Speaker 1: by telling me something interesting about the story.

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Speaker 2: Nothing new.

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Speaker 1: I didn't know about Watch Duty, But what have you

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Speaker 1: got for me?

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Speaker 2: Well, the last decent man on the internet read it.

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Speaker 2: Our old friend served me with something so demented. Just

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Speaker 2: just bear with me because this is there's a lot

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Speaker 2: of like R two D two in this But there's

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Speaker 2: a video that I'm going to show you in a

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Speaker 2: second that shows this guy who goes by the online

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Speaker 2: name STS three d. He's created a robot rifle what

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Speaker 2: that can shoot automatically and he's giving this robot rifle

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Speaker 2: directions using chat GPT.

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Speaker 1: Are you serious?

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Speaker 2: I got it? Did you? Did you know this?

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Speaker 1: I didn't know this.

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Speaker 2: This is okay, So let me let me just show

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Speaker 2: you this video.

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Speaker 1: Oh my god.

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Speaker 2: Chat gpt, we're under attack from the front left and

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Speaker 2: front right.

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Speaker 1: Respond to bording with it.

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Speaker 3: Oh my god.

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Speaker 2: If you need any further assistance, just let me know.

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Speaker 2: By the way, that's chat gpt talking back him.

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Speaker 1: One round. There is a rifle following this guy's voice commons.

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Speaker 2: Like an automated assault leaptip cheez.

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Speaker 1: Wow. What did you think when you saw this?

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Speaker 2: Well? I thought to myself, Am I going to do

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Speaker 2: another story this week where chat gpt is being used

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Speaker 2: by individuals to create weapons?

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Speaker 3: Yes?

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Speaker 1: Yeah, you know.

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Speaker 2: When I saw it, I couldn't really wrap my head

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Speaker 2: around how it was actually working and how chat GPT's

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Speaker 2: API was being used to operate operate.

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Speaker 1: So I have this friend, you have another friend you

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Speaker 1: talk about tech stuff with I knew, oh geez, and

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Speaker 1: he's straight.

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Speaker 2: So I sent this video and he basically said that

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Speaker 2: STS three D. The guy in the video is using

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Speaker 2: voice to text and is basically feeding that to chat GPT,

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Speaker 2: and then he's running a separate program that can control

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Speaker 2: the robot gun's movements, like you know how many degrees

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Speaker 2: to move the gun, when to shoot it. And that

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Speaker 2: program that he is designed is communicating with chat GPT.

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Speaker 1: His speaking to check GPT, which is then interpreting his

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Speaker 1: requests and passing it on to the robot, but presumably

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Speaker 1: not in natural language, presumably translating natural language into a

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Speaker 1: computer command.

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Speaker 2: That's correct. Okay, that's correct, And that's why he's talking

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Speaker 2: to something that sounds like Siri. The other thing that

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Speaker 2: I want to mention, of course, is that open Ai

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Speaker 2: got wind of this because the video went viral, right

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Speaker 2: and they came out and said that what this user

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Speaker 2: is doing goes against company policy. There's a very interesting

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Speaker 2: loophole that I found accidentally through reading about this on futurism,

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Speaker 2: which was actually reported by the intercept last year, which

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Speaker 2: is that open Ai quietly removed language prohibiting the use

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Speaker 2: of its tool for military purposes. And of course this

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Speaker 2: makes sense because just last month it was reported that

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Speaker 2: open ai is launching a partnership with Andrel, the American

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Speaker 2: defense company. Yeah, you know, for open Ai to stand

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Speaker 2: up and say okay, yes, for an individual user using

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Speaker 2: open Ai in this way goes against company policy, but

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Speaker 2: quietly in the background take out language opposing the use

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Speaker 2: of it for military defense.

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Speaker 1: I mean it is that, isn't It's like you always

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Speaker 1: talk about how things get faster and faster. It took

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Speaker 1: Google like fifteen years to ten years to abandon their

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Speaker 1: don't be evil policy. Yeah, it has taken open Ai

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Speaker 1: five minutes to make an exception for military use of

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Speaker 1: that technology.

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Speaker 2: Well, they pivoted from a five oh one c to

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Speaker 2: a private company quickly.

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Speaker 1: There you go.

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Speaker 2: Coming up, we'll discuss another reason to hate moving. Stay

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Speaker 2: with us. So today for our tech support segment, we

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Speaker 2: have a story about how cybercrime is meeting good old

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Speaker 2: fashioned IRL crime and somehow it all comes together thanks

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Speaker 2: to well as a lesbian, it's an important company, you haul,

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Speaker 2: and we're going to get this story from Joseph Cox,

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Speaker 2: who's one of the brilliant tech writers at four or

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Speaker 2: for Media.

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Speaker 1: Joseph, Welcome, it's great to have you on the show.

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Speaker 3: Thank you so much for having me.

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Speaker 1: So you wrote this story in four of for with

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Speaker 1: the headline quote violent hackers are using you Haul to

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Speaker 1: dox targets.

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Speaker 2: So when I read this story or saw this headline originally,

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Speaker 2: I'm sort of like, well, what does you Haul have

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Speaker 2: to do with doxing and hackers?

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Speaker 3: Yeah, and it's not obvious at first, and I don't

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Speaker 3: think anybody really thinks of you Haul of being a

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Speaker 3: juicy hacking target. But when you take a step back

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Speaker 3: and you think about it, people who use you Haul,

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Speaker 3: they're probably going to give their real phone number because

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Speaker 3: obviously they need to pick up the truck and they

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Speaker 3: need to organize that. They might give their real address

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Speaker 3: as well, because you know, you all needs to verify that,

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Speaker 3: and maybe they're real payment information because obviously they need

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Speaker 3: to pay for the truck as well. So it's actually

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Speaker 3: a really good source for accurate information if a hacker

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Speaker 3: is trying to reveal somebody's identity and you Haul has

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Speaker 3: been hacked multiple times over the years. You know, happened

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Speaker 3: in twenty twenty and I think twenty thirteen as well,

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Speaker 3: and in at least one of those cases it did

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Speaker 3: include driver license information. But at the time we didn't

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Speaker 3: really have any context of oh, well, why do hackers

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Speaker 3: care about you whole? And I think this story shows

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Speaker 3: why some people may care about that sort of target.

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Speaker 1: So who's doing this and what do we know about them?

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Speaker 3: So it's predominantly a group or a nebulous network of

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Speaker 3: people known as comm which is short for community, and

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Speaker 3: this is made up of a lot of young hackers

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Speaker 3: who take over phone numbers, they break into companies, but

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Speaker 3: the main thing is that they try to get as

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Speaker 3: much data as they can on potential targets.

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Speaker 1: As a practical matter, Joseph, how are the hackers getting

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Speaker 1: this information from you Hall?

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Speaker 3: So they're not not targeting individual you Haul customer accounts.

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Speaker 3: What they're doing is they're making phishing pages which will

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Speaker 3: trick people into handing over passwords, but specifically for you

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Speaker 3: Haul employee accounts.

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Speaker 1: This is like the Sony pictures.

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Speaker 3: Heck, yeah, So when the hackers have the loging credentials

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Speaker 3: for a you Haul employee, you know, they essentially become

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Speaker 3: that employee with all of the sort of access to

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Speaker 3: information they might have, so they can rifle through customer data.

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Speaker 3: And this is a constant problem across companies where you

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Speaker 3: have it, especially with US and UK telecoms as well.

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Speaker 3: These hackers will break into employee accounts so then they

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Speaker 3: can look up customer information.

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Speaker 1: What do they tend to be going after and who

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Speaker 1: is the typical victim?

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Speaker 3: In writing this story, I spoke to somebody who writes

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Speaker 3: the tools to steal you haul logins and they told

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Speaker 3: me that basically people inside the com use to dox

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Speaker 3: one another or dos other targets, and they were, you know,

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Speaker 3: it sounded like quite surprised the amount of data you

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Speaker 3: can get in there.

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Speaker 1: Doxing is when you sort of get somebody's private details

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Speaker 1: and then publicize them.

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Speaker 2: Yeah.

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Speaker 3: Docting is where you will take and find somebody's personal information,

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Speaker 3: maybe their physical address, email address, phone number, and then

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Speaker 3: maybe some more sensitive data as well, and either using

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Speaker 3: that for your own means or publishing it online. Now

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Speaker 3: I don't know exactly what a specific member of the

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Speaker 3: comm has used this data for, but you can easily

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Speaker 3: see it potentially being used for physical violence. Members of

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Speaker 3: the com often shoot at each other's houses or hire

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Speaker 3: people to do so. They throw bricks through them, they

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Speaker 3: rob one another armed with hammers or other weapons. And

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Speaker 3: I absolutely put the harvesting of this U haul data

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Speaker 3: inside that context and that it is being you by

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Speaker 3: violent criminal hackers.

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Speaker 2: So this is really about being an admin. It's not

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Speaker 2: about getting individual information.

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Speaker 3: Yeah, exactly.

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Speaker 1: I kind of feel like a lot of people when

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Speaker 1: it comes to Internet privacy, unless they've been like scanned

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Speaker 1: in some way themselves. You know, their eyes can glaze

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Speaker 1: over a little bit. But in your story, people actually

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Speaker 1: got shot after being dogsed.

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Speaker 3: Yeah. I think people have a pretty old understanding of

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Speaker 3: what cybercrime is. They imagine, you know, somebody in a

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Speaker 3: hoodie overall, laptop in a dark basement or whatever doing

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Speaker 3: these sort of abstract, obscure crimes. Cybercrime today's done by

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Speaker 3: people who are a lot younger, and they're a lot

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Speaker 3: more violent in some cases. If they want to get

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Speaker 3: hold of maybe login tokens, you know, the two FA

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Speaker 3: tokens to get into an account. Some will even threaten

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Speaker 3: to go shoot up some of the's house. And that's

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Speaker 3: why I'm so fascinated by this community. Because it's a

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Speaker 3: complete intersection of digital and physical crime. You know, as

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Speaker 3: a crime reporter for more than ten years, I've never

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Speaker 3: quite seen as latent an intersection as these guys.

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Speaker 1: This is the metaverse, but not the one that all

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Speaker 1: the investors were hyping.

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Speaker 3: Yeah, it's the metaverse, but not the very cringe one

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Speaker 3: from Mark Zuckerberg. Is unfortunately, the very real way. And

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Speaker 3: it's funny you bring up metaverse because actually a lot

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Speaker 3: of the people that get into this community, they actually

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Speaker 3: fall into it through video games such as Minecraft or Roadblocks,

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Speaker 3: which arguably are the metaverse, and then they end up

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Speaker 3: in this world of cybercrime.

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Speaker 2: And do you think there's a part of playing those

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Speaker 2: games that kind of like blurs the line between like

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Speaker 2: the reality of what they're doing and the games that

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Speaker 2: they're used to playing.

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Speaker 3: Yeah. Absolutely. And when I've spoken to many different members

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Speaker 3: of the com Roadblocks and Minecraft keep coming up over

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Speaker 3: and over again where even hackers will break into each

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Speaker 3: other's Roadblocks accounts to steal, you know, their rare hat

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Speaker 3: or their rare eye or something, and eventually that escalates

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Speaker 3: into well, there's steal hundreds of thousands of dollars of cryptocurrency,

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Speaker 3: as well.

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Speaker 1: Is there a single story or a single real world

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Speaker 1: crime that for you, really throws this into relief for

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Speaker 1: the kind of non tech news junkie.

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Speaker 3: So recently I wrote about something called the Colm World War,

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Speaker 3: which is obviously not an entirely serious term. This is

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Speaker 3: what members of the comm called it themselves. But there

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Speaker 3: were a series of robberies, shootings and brickings between different

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Speaker 3: rivals inside the como and.

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Speaker 1: Before the West Side story of Legs exactly.

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Speaker 3: And I would say it's sort of bubbled over out

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Speaker 3: of calm and into the wider public, because I've seen

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Speaker 3: videos where Colm will hire people to go throw a

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Speaker 3: brick at somebody's house, but they throw it at the

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Speaker 3: wrong home and then it's just impacted the completely random

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Speaker 3: person and now they have a smashed door or smashed

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Speaker 3: ring door, camera or whatever. So it is starting to

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Speaker 3: bleed over into the world of just ordinary people who

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Speaker 3: have no idea why somebody is throwing a brick through

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Speaker 3: the window because something happened on roadblocks.

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Speaker 1: You know.

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Speaker 3: Fortunately, if you are a general member of the U

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Speaker 3: haul using public, you probably don't need to worry about

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Speaker 3: this in that maybe somebody you know or somebody nearby

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Speaker 3: unfortunately becomes a target of calm. You know, that's kind

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Speaker 3: of impossible to say, and I would just be very

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Speaker 3: bad luck. But they do predominantly go and try to

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Speaker 3: rob people or target people who hold a lot of cryptocurrency.

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Speaker 3: They have various tools which can determine, Oh, this person

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Speaker 3: has x amount of bitcoin in their coin base account,

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Speaker 3: let's go after them. So, I mean, the threat to

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Speaker 3: a bitcoin investor is very, very different to a sort

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Speaker 3: of ordinary member of the public, and you should be

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Speaker 3: worried then.

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Speaker 2: So if you're a lesbian bitcoin investor.

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Speaker 1: Is the center of the ven diagram, you're going to

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Speaker 1: calm You probably already the bricks already have lesbian lucky

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Speaker 1: don't drive. Joseph, thank you for joining us today and

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Speaker 1: we hope to see you on the program again soon.

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Speaker 3: Thanks Joseph, thank you so much.

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Speaker 1: Coming up, Kara's Big Adventure stay with us. I'm excited

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Speaker 1: to be launching this new segment that we're calling when

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Speaker 1: did this become a thing? We've got a few listener

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Speaker 1: emails to our account tech Stuff podcast at gmail dot

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Speaker 1: com and they were very touching and very encouraging, but

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Speaker 1: also directive and one of the listener emails. Basically said Jonathan,

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Speaker 1: over the course of sixteen years, had so many interesting

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Speaker 1: segments about the history of various technologies, and you know

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Speaker 1: we're hungry for more of that. And so when did

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Speaker 1: this become a thing? It's kind of in service of

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Speaker 1: trying to scratch that itch.

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Speaker 2: Yeah, each week we're going to bring you a story

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Speaker 2: or observation from the real world and try to figure

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Speaker 2: out when it became a thing.

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Speaker 1: And this has a little bit of our favorite pair

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Speaker 1: of historical platonic freelance detectives.

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Speaker 2: That's correct. I get to be the Watson to your Sherlock.

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Speaker 1: And this week you have something that you encountered in

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Speaker 1: the wild that made you question when something became a thing.

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Speaker 2: Yeah, it was more of a rescue mission. So I

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Speaker 2: recently had this experience where I came face to face

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Speaker 2: with one of the twentieth centuries most prolific writers and

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Speaker 2: feminist icons. And it wasn't the best case scenario. She

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Speaker 2: had fallen on the street and was a little bit disoriented,

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Speaker 2: and my first thought was, we will not lose a

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Speaker 2: feminist icon on my watch. My second thought was, we

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Speaker 2: won't lose her because I'm going to call an ambulance

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Speaker 2: on my cellphone. That took too long, and so we

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Speaker 2: headed to an urgent care that Margo we can call her.

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Speaker 2: Margo knew about and she was adamant that she'd been

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Speaker 2: there many times before. So I Google mapped the address.

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Speaker 2: Google Map says it's closed, closed, like permanently. She's like,

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Speaker 2: it's not closed. I was there recently. It's closed.

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Speaker 1: Are you gaining her trust? By this point is she

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Speaker 1: likes sign to know?

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Speaker 2: And then I'm like, all right, I'm gonna call an

444
00:24:20,840 --> 00:24:24,880
Speaker 2: uber to go to another urgent character CityMD. She insists

445
00:24:24,880 --> 00:24:27,359
Speaker 2: on paying for the uber. I said, you can't play

446
00:24:27,400 --> 00:24:30,760
Speaker 2: for the uber. It doesn't work that way. We get

447
00:24:30,760 --> 00:24:36,720
Speaker 2: to CityMD. I see it that freaking iPad that they

448
00:24:36,760 --> 00:24:40,040
Speaker 2: want to use to scan her ID. I'm like, no,

449
00:24:40,359 --> 00:24:44,080
Speaker 2: we're not using an iPad. We're using paper. Then, And

450
00:24:44,080 --> 00:24:45,639
Speaker 2: this is my favorite part of the story because it's

451
00:24:45,680 --> 00:24:49,600
Speaker 2: kind of sweet. She has this like lightning thought where

452
00:24:49,600 --> 00:24:52,199
Speaker 2: she's like, oh, my cat has a vet appointment today

453
00:24:52,240 --> 00:24:55,200
Speaker 2: in the afternoon. I said, okay, I'll use Google and

454
00:24:55,240 --> 00:24:57,199
Speaker 2: I'll look up what animal hospital you go to in

455
00:24:57,200 --> 00:24:58,800
Speaker 2: the phone number. She goes, you can do that. I said, yeah,

456
00:25:00,520 --> 00:25:03,200
Speaker 2: all right, So I call the vet and I'm thinking

457
00:25:03,200 --> 00:25:05,440
Speaker 2: to myself, well, when you go to the vet, they

458
00:25:05,480 --> 00:25:08,000
Speaker 2: have everybody listed by the animal name, not the parent name.

459
00:25:08,440 --> 00:25:11,560
Speaker 2: So I turned to her and I say, Margot, what's

460
00:25:11,560 --> 00:25:18,119
Speaker 2: your cat's name? And dead stare Puss and I'm like, okay,

461
00:25:18,160 --> 00:25:22,880
Speaker 2: here we go. I said, my friend's cat, Puss, has

462
00:25:22,880 --> 00:25:27,399
Speaker 2: an appointment today and we're gonna need to cancel because

463
00:25:27,760 --> 00:25:31,400
Speaker 2: Puss's mother has been in an accent. She's okay, thank

464
00:25:31,440 --> 00:25:35,000
Speaker 2: you so much for telling us. Okay. Then the last

465
00:25:35,040 --> 00:25:38,000
Speaker 2: point of sort of technological contact was when we ended

466
00:25:38,080 --> 00:25:40,040
Speaker 2: up in the er where she had to go after

467
00:25:40,080 --> 00:25:43,600
Speaker 2: CityMD because she was having trouble breathing, and they said,

468
00:25:43,600 --> 00:25:47,360
Speaker 2: you need a chest X ray and I told her

469
00:25:47,640 --> 00:25:50,280
Speaker 2: I can't stay here for four hours, but I'll come

470
00:25:50,320 --> 00:25:54,040
Speaker 2: pick you up and take you home when you are

471
00:25:54,080 --> 00:25:59,480
Speaker 2: ready to leave. How well you have an iPhone? It

472
00:25:59,520 --> 00:26:01,720
Speaker 2: was off. I never seen an iPhone off. It was

473
00:26:01,840 --> 00:26:05,520
Speaker 2: fully off. I said, you know this nurse here who

474
00:26:05,560 --> 00:26:09,119
Speaker 2: is about my age, will help you call me. And

475
00:26:10,520 --> 00:26:15,520
Speaker 2: I think each touch point here made me reflect upon

476
00:26:17,119 --> 00:26:23,359
Speaker 2: how easy it was for me to navigate, and not

477
00:26:23,520 --> 00:26:26,760
Speaker 2: that she is well. She's certainly more intelligent than I

478
00:26:26,840 --> 00:26:30,480
Speaker 2: am intellectually speaking, but that there is a digital divide

479
00:26:30,520 --> 00:26:35,200
Speaker 2: between us that made this experience far less harrowing. Because

480
00:26:35,200 --> 00:26:37,560
Speaker 2: I was with her and she said as much. I

481
00:26:37,680 --> 00:26:41,320
Speaker 2: think she realized very early on that, like all of

482
00:26:41,400 --> 00:26:44,600
Speaker 2: those things that I think that are so second nature

483
00:26:44,760 --> 00:26:51,000
Speaker 2: to me, we're not even like on Margo's radar as possibilities.

484
00:26:52,200 --> 00:26:56,719
Speaker 1: I mean, this story raises the perfect question, almost as

485
00:26:56,760 --> 00:26:59,720
Speaker 1: though we planned it. When did this become a thing?

486
00:27:00,440 --> 00:27:03,000
Speaker 1: When did the world sort of cleave into two where

487
00:27:03,440 --> 00:27:07,800
Speaker 1: you're either totally able to navigate everything because of having

488
00:27:07,880 --> 00:27:12,000
Speaker 1: a tool which makes it easy, or you're just kind

489
00:27:12,000 --> 00:27:15,480
Speaker 1: of struggling to translate. In other words, when did digital

490
00:27:15,600 --> 00:27:19,200
Speaker 1: natives become a thing. Yes, but let's start by defining

491
00:27:19,240 --> 00:27:20,679
Speaker 1: our terms, which always a good thing to do at

492
00:27:20,680 --> 00:27:23,720
Speaker 1: the beginning. What is a digital native?

493
00:27:24,359 --> 00:27:26,440
Speaker 2: You know, I actually have no idea, but I've always

494
00:27:26,520 --> 00:27:30,720
Speaker 2: considered myself a digital native. I'm a New Yorker first,

495
00:27:30,920 --> 00:27:32,760
Speaker 2: but I'm definitely a digital native second.

496
00:27:33,200 --> 00:27:36,399
Speaker 1: I also always consider myself a digital native, even if

497
00:27:36,560 --> 00:27:40,280
Speaker 1: somewhat reluctantly. Weirdly, because as a child, I was diagnosed

498
00:27:40,320 --> 00:27:43,440
Speaker 1: with dyspraxia, which basically means can't catch a ball, can't

499
00:27:43,600 --> 00:27:44,600
Speaker 1: do nice handwriting.

500
00:27:44,880 --> 00:27:46,600
Speaker 2: You haven't really grown out of that, No, I haven't

501
00:27:46,640 --> 00:27:47,240
Speaker 2: tell you. I haven't.

502
00:27:47,240 --> 00:27:50,879
Speaker 1: But I got my prescription was a laptop, like aged

503
00:27:50,920 --> 00:27:53,000
Speaker 1: eight years old, to do my homework on and stuff,

504
00:27:53,000 --> 00:27:57,200
Speaker 1: and so I became a digital native basically in the

505
00:27:57,280 --> 00:27:57,880
Speaker 1: late nineties.

506
00:27:58,640 --> 00:28:00,920
Speaker 2: You know, I did not know this about you, And

507
00:28:01,040 --> 00:28:03,880
Speaker 2: this might be one of our sort of unspoken personal

508
00:28:03,960 --> 00:28:07,080
Speaker 2: connections because I went to one of those kooky laptop

509
00:28:07,160 --> 00:28:09,080
Speaker 2: schools that I think has ultimately netted out to be

510
00:28:09,119 --> 00:28:12,160
Speaker 2: the wrong idea. But I went to one of those

511
00:28:12,160 --> 00:28:15,000
Speaker 2: schools where they taught us C plus plus before they

512
00:28:15,040 --> 00:28:16,080
Speaker 2: taught us basic algebra.

513
00:28:16,560 --> 00:28:19,159
Speaker 1: So we both had this feeling of being digital natives.

514
00:28:19,240 --> 00:28:21,800
Speaker 1: But I thought I would do some research on what

515
00:28:22,000 --> 00:28:25,119
Speaker 1: that term actually means and where it comes from. Turns

516
00:28:25,160 --> 00:28:27,880
Speaker 1: out it was coined in the year two thousand and one,

517
00:28:28,000 --> 00:28:30,520
Speaker 1: around the same time we both got our laptops by

518
00:28:30,640 --> 00:28:34,560
Speaker 1: an educational consultant called Mark Prinsky, and he had this

519
00:28:34,720 --> 00:28:37,920
Speaker 1: idea that education needed to change in response to young

520
00:28:37,960 --> 00:28:41,880
Speaker 1: people whose brains were literally different from the brains of

521
00:28:41,880 --> 00:28:45,200
Speaker 1: the generation before because they'd already been shaped by gaming

522
00:28:45,440 --> 00:28:48,160
Speaker 1: and spending time online and being more used to finding

523
00:28:48,240 --> 00:28:51,000
Speaker 1: information on the web than, for example, in a book.

524
00:28:51,480 --> 00:28:54,680
Speaker 1: And he had this quite powerful quote, which was today's

525
00:28:54,680 --> 00:28:58,000
Speaker 1: students have not just changed incrementally from those of the past,

526
00:28:58,440 --> 00:29:03,000
Speaker 1: nor simply changed their slang, clothed, body adornments, or styles,

527
00:29:03,240 --> 00:29:06,640
Speaker 1: as has happened between generations previously, but a really big

528
00:29:06,720 --> 00:29:11,560
Speaker 1: discontinuity has taken place. One might even call it a singularity,

529
00:29:12,120 --> 00:29:14,840
Speaker 1: an event which changes things so fundamentally that there is

530
00:29:14,880 --> 00:29:16,560
Speaker 1: absolutely no going back.

531
00:29:17,320 --> 00:29:20,120
Speaker 2: Wouldn't you say, that's like the fact that I now

532
00:29:20,240 --> 00:29:22,000
Speaker 2: say google it, then look it up.

533
00:29:22,240 --> 00:29:23,200
Speaker 1: That's the singularity.

534
00:29:23,440 --> 00:29:26,840
Speaker 2: Right. When you talk to some people who are older,

535
00:29:27,760 --> 00:29:31,560
Speaker 2: they'll say, call for one one, where's four one one going.

536
00:29:31,480 --> 00:29:33,160
Speaker 1: To get me? And it's interesting, you just made that

537
00:29:33,280 --> 00:29:35,959
Speaker 1: point about calling four one one. Prince Ki also has

538
00:29:36,000 --> 00:29:41,040
Speaker 1: this concept of digital natives versus digital immigrants and digital immigrants,

539
00:29:41,080 --> 00:29:44,160
Speaker 1: of people like Margo who kind of live in the

540
00:29:44,400 --> 00:29:48,040
Speaker 1: digital world but carry the accent of where they come from.

541
00:29:48,240 --> 00:29:50,720
Speaker 1: Whether it's like printing out an email is one of

542
00:29:50,760 --> 00:29:53,720
Speaker 1: the examples that Prince Key uses, or kind of beckoning

543
00:29:53,800 --> 00:29:56,080
Speaker 1: someone over to look at your computer screen rather than

544
00:29:56,120 --> 00:29:56,840
Speaker 1: sending them a link.

545
00:29:57,480 --> 00:30:00,920
Speaker 2: And you know, I think that there are definitely those

546
00:30:01,000 --> 00:30:04,800
Speaker 2: things that mark people today as digital immigrants. You know,

547
00:30:04,880 --> 00:30:08,600
Speaker 2: I think, my poor mother, but you know, sometimes she'll say, oh,

548
00:30:08,720 --> 00:30:16,400
Speaker 2: you know, let's call for takeout. What nobody's home. There's

549
00:30:16,400 --> 00:30:19,360
Speaker 2: actually this book called the Victorian Internet, which is by

550
00:30:19,480 --> 00:30:21,960
Speaker 2: Tom's Standage, and it's all about the history of the

551
00:30:22,040 --> 00:30:25,120
Speaker 2: development of the telegraph and how it parallels to the Internet.

552
00:30:25,160 --> 00:30:27,680
Speaker 2: And in it he tells the story from when the

553
00:30:27,760 --> 00:30:31,920
Speaker 2: telegraph was first introduced around the time of the American

554
00:30:32,000 --> 00:30:34,640
Speaker 2: Civil War. And I mean, this could have been my mom.

555
00:30:34,920 --> 00:30:37,800
Speaker 2: A mom goes to the telegraph office with a bowl

556
00:30:37,880 --> 00:30:41,880
Speaker 2: of sour kraut to send her son to the battlefront,

557
00:30:41,920 --> 00:30:42,800
Speaker 2: you know, like by wire.

558
00:30:43,200 --> 00:30:46,440
Speaker 1: So she really was a digital immigrant, bringing a native

559
00:30:46,520 --> 00:30:48,600
Speaker 1: cuisine and then trying to send day with a telegraph.

560
00:30:48,680 --> 00:30:50,240
Speaker 2: That's correct. And you know, back in the time of

561
00:30:50,280 --> 00:30:54,120
Speaker 2: the Civil War, change felt fast. It did not feel

562
00:30:54,160 --> 00:30:56,440
Speaker 2: as fast as it feels now. Where you know, by

563
00:30:56,480 --> 00:30:58,200
Speaker 2: the time you have a software update, you need to

564
00:30:58,280 --> 00:31:01,040
Speaker 2: like learn how to use a whole new phone. You

565
00:31:01,120 --> 00:31:05,160
Speaker 2: could blink and miss an entire technological evolution. And I

566
00:31:05,280 --> 00:31:07,800
Speaker 2: do blink and miss entire things sometimes.

567
00:31:07,520 --> 00:31:09,480
Speaker 1: Well, I know you think of yourself as a digital

568
00:31:09,600 --> 00:31:12,600
Speaker 1: native since you grew up with tech. Do you still

569
00:31:13,720 --> 00:31:15,080
Speaker 1: you know, I do in.

570
00:31:15,160 --> 00:31:18,840
Speaker 2: The sense of the idea of new technology is not

571
00:31:19,160 --> 00:31:20,440
Speaker 2: incredibly daunting to me.

572
00:31:20,760 --> 00:31:21,880
Speaker 1: I think it was subtractive to you.

573
00:31:22,120 --> 00:31:24,200
Speaker 2: Yes, And I think we were born at a time

574
00:31:24,680 --> 00:31:28,680
Speaker 2: when technology was changing so rapidly that we are not

575
00:31:28,920 --> 00:31:32,360
Speaker 2: completely thrown by the idea of like the facts not

576
00:31:32,520 --> 00:31:35,880
Speaker 2: being the predominant form of communication for ten years, right, right.

577
00:31:36,040 --> 00:31:39,720
Speaker 1: But do you think if you kind of got given

578
00:31:40,040 --> 00:31:42,280
Speaker 1: a grand tour of downtown New York by an eighteen

579
00:31:42,360 --> 00:31:45,440
Speaker 1: year old, they will be surprised by your use of technology?

580
00:31:45,880 --> 00:31:49,920
Speaker 2: I think it's more if like I spent five days

581
00:31:49,960 --> 00:31:52,239
Speaker 2: with an eighteen year old, they'd be like, why are

582
00:31:52,280 --> 00:31:55,440
Speaker 2: you not charting your every move on Snapchat? Which is

583
00:31:55,520 --> 00:31:57,200
Speaker 2: not to say that like I couldn't live in the

584
00:31:57,280 --> 00:31:59,960
Speaker 2: world with them, but they live in the world different.

585
00:32:00,280 --> 00:32:03,680
Speaker 1: Yeah, truly, we're living Margo's living in more or less

586
00:32:03,680 --> 00:32:06,400
Speaker 1: an analog world. We're probably living in a hybrid world.

587
00:32:06,600 --> 00:32:09,040
Speaker 1: And many the younger people are living in a fully

588
00:32:09,560 --> 00:32:10,280
Speaker 1: digital world.

589
00:32:10,680 --> 00:32:13,040
Speaker 2: Well, and that's the irony of connectivity, I think, right,

590
00:32:13,160 --> 00:32:15,360
Speaker 2: Which is this idea that like the world is actually

591
00:32:15,440 --> 00:32:18,480
Speaker 2: quite siloed by like how you use technology. If you

592
00:32:18,640 --> 00:32:20,680
Speaker 2: use it properly, you can be very connected, but it

593
00:32:20,720 --> 00:32:22,640
Speaker 2: can also be very isolating totally.

594
00:32:23,000 --> 00:32:25,440
Speaker 1: And we should point out that the framing of native

595
00:32:25,560 --> 00:32:27,840
Speaker 1: versus immigrant is rather loaded.

596
00:32:28,280 --> 00:32:31,720
Speaker 2: Yeah, And one of the things that really struck me

597
00:32:32,560 --> 00:32:35,200
Speaker 2: with what Margo experience was that there was friction at

598
00:32:35,440 --> 00:32:40,960
Speaker 2: every turn in her accident. And that's just not only

599
00:32:41,040 --> 00:32:44,840
Speaker 2: because she was in an accident. It's because we are

600
00:32:45,000 --> 00:32:49,480
Speaker 2: constantly encountering, like almost every day, a more and more

601
00:32:49,960 --> 00:32:54,240
Speaker 2: digitized world, right. And you know, the thought that I'm

602
00:32:54,360 --> 00:32:59,200
Speaker 2: left with is that even though there is a definition

603
00:32:59,320 --> 00:33:02,880
Speaker 2: for digital needs and we fit into that definition, it's

604
00:33:02,920 --> 00:33:03,760
Speaker 2: not a permanent state.

605
00:33:04,560 --> 00:33:07,560
Speaker 1: I do wonder if there will emerge another term, something

606
00:33:07,640 --> 00:33:12,040
Speaker 1: to supplant digital natives the way postmoderns are planted modern.

607
00:33:12,560 --> 00:33:14,360
Speaker 1: You know, one of the guests that we were most

608
00:33:14,400 --> 00:33:17,880
Speaker 1: excited and proudest to have on Sleepwalkers, our former podcast,

609
00:33:18,360 --> 00:33:21,760
Speaker 1: was uv Al Noah Harari, who wrote a book called Homodeus,

610
00:33:22,080 --> 00:33:27,000
Speaker 1: which is basically all about how using technology and merging

611
00:33:27,040 --> 00:33:31,840
Speaker 1: with technology was changing Homo sapiens into almost this new

612
00:33:31,960 --> 00:33:36,600
Speaker 1: species Homodeus human but with these godlike powers enabled by

613
00:33:36,720 --> 00:33:41,120
Speaker 1: new technology. I found it as a young pre teenager

614
00:33:41,560 --> 00:33:44,640
Speaker 1: pretty crazy getting a laptop in the early two thousands,

615
00:33:44,680 --> 00:33:48,160
Speaker 1: which was theoretically to do my homework because I was dyspraxic,

616
00:33:48,280 --> 00:33:52,680
Speaker 1: but more realistically to play the Oregon Trail and Cassel Wolfenstein.

617
00:33:54,320 --> 00:33:56,320
Speaker 1: And I remember sort of having this laptop and other

618
00:33:56,400 --> 00:33:58,800
Speaker 1: kids didn't, and it was sort of this little It

619
00:33:58,880 --> 00:34:01,160
Speaker 1: didn't change too much, but it was a kind of

620
00:34:01,280 --> 00:34:03,360
Speaker 1: moment of like I was ahead of the curve in

621
00:34:03,440 --> 00:34:06,600
Speaker 1: some way. But imagine going to school now as a

622
00:34:06,720 --> 00:34:09,399
Speaker 1: nine year old in the age of chech GPT three.

623
00:34:09,920 --> 00:34:13,200
Speaker 1: What does that experience of having not only the whole

624
00:34:13,239 --> 00:34:17,279
Speaker 1: world's information at your fingertips for a search, but having

625
00:34:17,400 --> 00:34:20,000
Speaker 1: any answer which is probably better than the answer or

626
00:34:20,040 --> 00:34:22,920
Speaker 1: teacher or your parent can provide, at the click of

627
00:34:22,960 --> 00:34:26,160
Speaker 1: a button. What does that do to a developing brain.

628
00:34:34,480 --> 00:34:36,600
Speaker 1: That's it for this week on Tech Stuff. I'm Oz

629
00:34:36,640 --> 00:34:38,440
Speaker 1: Valoshin and I'm Kara Price.

630
00:34:38,960 --> 00:34:42,440
Speaker 2: This episode was produced by Eliza Dennis, Victoria Domingez, and

631
00:34:42,520 --> 00:34:46,600
Speaker 2: Lizzie Jacobs. It was executive produced by me, Kara Price, Ozwalashan,

632
00:34:46,840 --> 00:34:51,000
Speaker 2: and Kate Osbourne for Kaleidoscope and Katrina Norvel for iHeart Podcasts.

633
00:34:51,400 --> 00:34:54,800
Speaker 2: The engineer is Charles Demontebello and it's mixed by Kyle Murdoch,

634
00:34:54,880 --> 00:34:56,120
Speaker 2: who also wrote our theme song.

635
00:34:56,920 --> 00:35:00,319
Speaker 1: Join us next Wednesday for tech Stuff The Story, when

636
00:35:00,360 --> 00:35:04,280
Speaker 1: we'll share an in depth conversation with Jessica Lesson, CEO

637
00:35:04,440 --> 00:35:07,800
Speaker 1: of the information about a vibe shift in Silicon Valley.

638
00:35:08,400 --> 00:35:11,279
Speaker 2: Please rate, review, and reach out to us at tech

639
00:35:11,360 --> 00:35:14,799
Speaker 2: Stuff podcast at gmail dot com. We want to hear

640
00:35:14,840 --> 00:35:15,080
Speaker 2: from you