Speaker 1: Natasha Taylor Reid, I thought until this week that my
Speaker 1: stepmother was the last known survivor on MapQuest, but no longer.
Speaker 2: Yes, MapQuest, the most popular download.
Speaker 3: Yeah.
Speaker 2: This is because of Lake Ontario, right? Or Lake America?
Speaker 1: Yeah, MapQuest, unlike Google Maps and Apple, who both changed
Speaker 1: Lake Ontario to Lake America, MapQuest stood loud and proud with.
Speaker 1: Lake Ontario and got a big boost in the app
Speaker 1: charts as a result.
Speaker 2: Do all these lakes and bodies of water and whatever else,
Speaker 2: do they just go back as soon as Trump leaves office?
Speaker 2: Is that what's going to happen?
Speaker 3: That's a good question.
Speaker 1: I don't know how MapQuest has decided to call the
Speaker 1: Strait of Hormuz the Strait of Trump, or maybe it's
Speaker 1: also holding firm there.
Speaker 2: Just everything is Trump.
Speaker 1: Welcome to Tech Stuff. I'm Oz Veloshian, and this is
Speaker 1: The Week in Tech, where I'm joined by the world's
Speaker 1: most plugged-in reporters to break down what's really happening in
Speaker 1: tech right now. Today, we're joined by Reid Albregotti, tech
Speaker 1: editor of Semaphore, Taylor Lorenz of UserMag, and Natasha Tiku,
Speaker 1: tech reporter at The Washington Post. Welcome all.
Speaker 2: Hey, everyone. Good to be here.
Speaker 3: Good to be back.
Speaker 2: Yeah, it's been a little break.
Speaker 1: I missed you all on the, on the beach in Greece.
Speaker 1: We haven't had a round table for a couple of weeks. Uh,
Speaker 1: it's been an eventful couple of weeks and in general,
Speaker 1: we spend quite a lot of time on the show
Speaker 1: talking about kind of the, the new guard in tech. Um,
Speaker 1: but the old guard have recently been dominating the headlines
Speaker 1: for better or for worse. Reed, why don't you kick
Speaker 1: us off with the meta settlement?
Speaker 2: Yeah. So meta settled its big tobacco, uh, attorney general lawsuit. Um,
Speaker 2: $ 18 billion. But I think the bigger story is all
Speaker 2: these new restrictions on how teens can use the service.
Speaker 2: So they're limited to two hours a day.
Speaker 3: Not just teens.
Speaker 2: Taylor's, you know, I saw you tweeting about this, Taylor.
Speaker 2: So I know you have like major thoughts.
Speaker 3: Yeah, I've been covering this very closely. What I would
Speaker 3: say is that so Meta, this is nothing. Meta does
Speaker 3: not have to pay that full $ 18 million at all,
Speaker 3: which is a drop in the bucket for Meta. I'm sorry, billion.
Speaker 2: 18 million would be like... We're truly nothing.
Speaker 3: Truly nothing. Yeah, Mark Zuckerberg's lunch. Basically, what the result is,
Speaker 3: as Reid mentioned, is there's a bunch of new restrictions
Speaker 3: on anyone's account who Meta can't verify is an adult
Speaker 3: through identity verification. So Meta is going to start harvesting
Speaker 3: significantly more data on users, all users, in order to
Speaker 3: ID verify them.
Speaker 1: And how will they do that? Will it be like...
Speaker 1: driving license? So what are the ways they're going to
Speaker 1: actually be doing it?
Speaker 3: Yeah, absolutely. So if they determine that you might be
Speaker 3: underage or that you're interacting with an underage person, you
Speaker 3: will be uploading your government identification. A lot of people
Speaker 3: have their information stored already. So their payment information is
Speaker 3: another way to do it, harvesting biometric data. But they
Speaker 3: are going to be doing ID verification for the internet.
Speaker 3: It is a huge rollback. And Anybody that cannot verify
Speaker 3: their identity within 14 days basically will be put under
Speaker 3: all of these heavy restrictions. So adult accounts... anybody that
Speaker 3: does not meet these qualifications. It's very bad because these
Speaker 3: restrictions are, you know, unable to view lots of significant
Speaker 3: amounts of content that is deemed harmful for children. We
Speaker 3: know that the government has determined in other states that
Speaker 3: content that is harmful for children includes anti-ICE content. It
Speaker 3: includes LGBTQ content. It includes reproductive justice, healthcare content.
Speaker 2: So.
Speaker 3: This is a horrible loss for free speech, and it
Speaker 3: doesn't do anything to change the business model of meta. And,
Speaker 3: you know, it's basically just a way for these attorney
Speaker 3: generals to get a headline.
Speaker 1: Reid and Tasha, do you agree with Taylor's take?
Speaker 2: I do agree. I think it is kind of a rollback.
Speaker 2: I mean, look, my kids are eight and ten. Like,
Speaker 2: they're too young to use this stuff right now. So
Speaker 2: I sort of had this fantasy in my mind since
Speaker 2: they were born that, like, by the time they are—
Speaker 2: of social media age will have figured this all out. Uh,
Speaker 2: clearly that fantasy is not going to become reality. Like
Speaker 2: this is still a problem. And like, you know, we
Speaker 2: go to Australia every year by, you know, my, my
Speaker 2: in-laws are in Sydney and they passed this ban. Um,
Speaker 2: everybody was so excited about it because it was like,
Speaker 2: you know, I think parents just, it's just so hard
Speaker 2: for them to, to deal with this, to grapple with
Speaker 2: what's happening on social media. Um, I was very skeptical
Speaker 2: as Taylor is like, any of these regulations are actually
Speaker 2: going to solve this problem. And they and they may
Speaker 2: create new problems. I think one point I would make, though,
Speaker 2: is that on the ID verification point, like somehow, because
Speaker 2: of AI, because of all this, you know, just inauthenticity online,
Speaker 2: I think we're going to have to figure out some
Speaker 2: way to do privacy preserving some sort of verification of
Speaker 2: not just your age, but that you're an actual human being.
Speaker 2: And I think this is like, an opportunity, right? Like
Speaker 2: there should be a technical solution. I think there are
Speaker 2: lots of really, there are lots of really interesting technical
Speaker 2: ideas out there. I'm not, I'm not going to endorse
Speaker 2: any one of them right now. But like, there's, I
Speaker 2: think this is a great opportunity for innovation.
Speaker 3: But I feel like I have whiplash as a tech reporter,
Speaker 3: because just less than 10 years ago, they were hauling
Speaker 3: Mark Zuckerberg in front of Congress. Remember when he, you know,
Speaker 3: Zuckerberg says in the Cambridge Analytica sort of, scandal, like Senator,
Speaker 3: we sell ads, you know, and they were like, why,
Speaker 3: you know, you harvested all this data on users. And
Speaker 3: now we are mandating that they do significantly more than that.
Speaker 3: I think the issue of sort of human stuff online
Speaker 3: at like sort of determining humans versus bots, like I
Speaker 3: agree with Reid. I think that is an increasing problem
Speaker 3: and we are going to have to find some solution
Speaker 3: to that. Meta does not seem interested in that solution.
Speaker 3: In fact, we had Adam Masseri just recently talking on
Speaker 3: a podcast about how they want more AI creators and
Speaker 3: they're leaning further into AI.
Speaker 2: Taylor, there's another overarching theme. I bet you'll agree with
Speaker 2: me on this, but this applies to this FTC settlement
Speaker 2: with Amazon. It applies to the AI regulation that's being
Speaker 2: passed today. I think tech regulation needs to happen much faster.
Speaker 2: And it needs to be able to change much faster,
Speaker 2: because none of this stuff like the AI regulations that
Speaker 2: have been passed are already, like they seem like years
Speaker 2: out of date. And I think this, this one will
Speaker 2: be another one where, like, yeah, okay, let's try to,
Speaker 2: let's try to work this out. But let's, let's be nimble.
Speaker 2: Let's be able to pass laws right now. We can't
Speaker 2: do that. I mean, we can't, especially on a federal level.
Speaker 3: I think another thing is like, what are you passing
Speaker 3: laws based on? And this whole sort of child safety
Speaker 3: movement lately is passed on nothing. It's nothing but vibes.
Speaker 3: We have every single top researcher who studies the use
Speaker 3: of social media technology saying, hey, there's not a causal relationship.
Speaker 3: In fact, depressed kids use Instagram more. It's a symptom
Speaker 3: of the broader problem. We know what's causing this use
Speaker 3: mental health crisis. Let's address that stuff. And it's sort
Speaker 3: of this like after the fact, again, just like people
Speaker 3: want to put a bandaid. They want a headline. They
Speaker 3: want to get reelection. Like, look at I'm tough on
Speaker 3: big tech while you've done nothing to meaningfully change the
Speaker 3: tech landscape, but make it worse for all of us.
Speaker 3: And I agree. I mean, at this point, it's so
Speaker 3: late anyway, because these kids are busy talking to chat
Speaker 3: bots like Instagram and Snapchat is the least of of
Speaker 3: what parents should be sort of concerned about.
Speaker 2: Yeah.
Speaker 1: Natasha, I'm curious, what's what's your take on this on
Speaker 1: this meta settlement?
Speaker 4: I don't know. I remember getting excited about GDPR and
Speaker 4: feeling like, you know, maybe this will be like, this
Speaker 4: will be it.
Speaker 1: Not a lot.
Speaker 2: GDPR is the bane of my career.
Speaker 3: It's terrible.
Speaker 4: And now I just like click, like reject, accept, whatever.
Speaker 4: So it just feels not that everyone is fixated on
Speaker 4: AI solely, but it does feel like while the ball
Speaker 4: has been, you know, everyone's been looking at open AI
Speaker 4: and anthropic and even like Sam Altman's you know, world
Speaker 4: coin eye scanning identity thing here Meta slips in. And
Speaker 4: just by default, because it has so many users, is
Speaker 4: going to become like the default identity layer. And, you know,
Speaker 4: we're going to have a flock-like scandal in five years
Speaker 4: about the technology that Meta's using. And, yeah, it's like
Speaker 4: whiplash plus deja vu.
Speaker 3: I really agree.
Speaker 4: With Reid about like being nimble in writing the laws.
Speaker 4: Like there's nothing that stops them from saying we need
Speaker 4: to revisit this. next year or i mean when it
Speaker 4: comes to ai at least you know if they had
Speaker 4: had transparency requirements say that wouldn't be outdated by now
Speaker 4: so it just it's i remember being excited about a
Speaker 4: decade ago that something might happen that would be like
Speaker 4: a oversight or a check on their concentration of power
Speaker 4: and i have yet to see them yeah i've yet
Speaker 4: to see them pin down on.
Speaker 3: Anything Can I add just one thing that I think
Speaker 3: is so important for people to realize is that this
Speaker 3: is not a law. This is the settlement. And if
Speaker 3: these changes, if any sort of legal system, state or federal,
Speaker 3: tried to pass these changes to META, it would be
Speaker 3: struck down immediately on First Amendment grounds because it has
Speaker 3: such broad implications for privacy, for freedom of expression, etc.
Speaker 3: So these are highly unconstitutional changes. changes that these state
Speaker 3: attorney generals know they could never get through legislation.
Speaker 2: Well, they'll be challenged, right? I mean, won't these be challenged?
Speaker 3: No, it can't be challenged because it is not a law.
Speaker 3: It is a settlement that Meta has proactively written and
Speaker 3: agreed to. And so at least when I was talking
Speaker 3: to one of the people yesterday, there's no challenge to it.
Speaker 3: Meta's not challenging it. They actually waived the right in
Speaker 3: this settlement They.
Speaker 2: Might not challenge it, but I mean, I not to
Speaker 2: say they would win. but like someone could someone could
Speaker 2: sue and say, you know, that I mean, because there
Speaker 2: is this whole argument, right, that these that these companies
Speaker 2: aren't just private companies that can do whatever they want,
Speaker 2: that they are sort of the public town square. That's
Speaker 2: been like the the free speech talking point.
Speaker 1: Part of the settlement is only payable if YouTube and
Speaker 1: TikTok also agree to the terms of the settlement.
Speaker 2: Is that right?
Speaker 3: Well, the settlement is enforced no matter what, right? The
Speaker 3: terms of the settlement. But yeah, they don't have to
Speaker 3: pay the full $ 18 billion unless TikTok and YouTube enact
Speaker 3: similar changes.
Speaker 1: Yeah, I mean, what's the vibe on, I mean, Patel,
Speaker 1: you mentioned vibes, but like Natasha Reid, what's the back
Speaker 1: to school vibe? Are the parents talking about this? And
Speaker 1: is it like something that's coming up?
Speaker 2: My kids were talking about it. It was one of
Speaker 2: the few current events that my kids actually brought up
Speaker 2: to me.
Speaker 1: What did they say?
Speaker 2: I mean, I think they're sort of like, they don't
Speaker 2: have like strong opinions. Like Taylor linked to someone, I
Speaker 2: think it was on X, right? That you linked to someone,
Speaker 2: some kid talking about how they, you know, he was
Speaker 2: sort of upset about this.
Speaker 3: Student journalists have been really affected. So student journalists have
Speaker 3: mobilized across the country to try to raise alarms about
Speaker 3: this and say, by the way, like, We have picked
Speaker 3: up the mantle on local news has been decimated. Student
Speaker 3: journalists are the ones doing, you know, a lot of
Speaker 3: this accountability reporting. And a lot of these young people
Speaker 3: that do journalism and, you know, engage in this world,
Speaker 3: including the one I posted about, are really upset.
Speaker 4: Since we know nothing's happening in the real world, I
Speaker 4: also have a fantasy where the people who educate the
Speaker 4: regulators on this technology are not solely bought and paid
Speaker 4: for by the companies themselves or by people who have
Speaker 4: you know, really vested interests that are not the public
Speaker 4: public's interests.
Speaker 2: They're also like to Taylor's earlier point about this social
Speaker 2: media stuff, like a lot of times these things that
Speaker 2: get blown up in the press, you know, those those
Speaker 2: media people, they're not actually like huge problems, right? Like,
Speaker 2: it's like, you know, it's it's the thing that people
Speaker 2: care about, because they're reading about it in the press.
Speaker 2: But like, it's like the copyright thing with AI, right? Like,
Speaker 2: oh my God, they used my book as one of
Speaker 2: 10 gazillion books to train this AI model. I want
Speaker 2: some money for my book being used. And it's like,
Speaker 2: no one cares that you can literally just go download
Speaker 2: my book for free on a million pirated book websites, right?
Speaker 2: It's like that to me, that's the problem.
Speaker 1: Not like- People like, I mean, people like these symbol,
Speaker 1: like it's a symbol, like there's a symbolic thing, right?
Speaker 1: Like there's been some some accountability, like make them meant
Speaker 1: to have to pay some money.
Speaker 3: And like, that's, but it's not accountable. They want fake,
Speaker 3: they want fake accountability. They want the headline. They don't
Speaker 3: want any real change.
Speaker 2: Totally. People love the symbolism, but then like, That's not
Speaker 2: the job of a real leader, right? A leader is
Speaker 2: not to like give the people some red meat to
Speaker 2: keep them satisfied. It's to like actually solve problems, like
Speaker 2: real problems for people that affect people's lives. That's why
Speaker 2: today I'm announcing I'm leaving journalism.
Speaker 1: When we come back, are we living in the age
Speaker 1: of AI civilizations? And what should we call them?
Speaker 2: Stay with us. Welcome back.
Speaker 1: Once again, there's more information on the OpenAI Hugging Face incident.
Speaker 1: This time, a report by two AI safety research organizations,
Speaker 1: META and Redwood Research. Many people have tried to make
Speaker 1: sense of what exactly is the takeaway here, but one
Speaker 1: explanation seems to have broken into the mainstream. Taylor, you
Speaker 1: wrote about a recent piece by Silicon Valley's favorite podcaster,
Speaker 1: Dwarkesh Patel, which promised to explain the whole OpenAI hugging
Speaker 1: face incident in plain English. First, I want to hear
Speaker 1: a brief recap from you in plain English of what
Speaker 1: actually happened, and then your account of Dwarkesh's spin and
Speaker 1: its spinning out into the world.
Speaker 3: Well, I have to say, I was kind of channeling
Speaker 3: Natasha because I feel like she is so plugged into
Speaker 3: this world of like, I was trying to figure out like,
Speaker 3: is Dwarkesh like EA or not? I was like, Natasha
Speaker 3: would know like all the lore. Anyway, Dwarkesh Patel, as
Speaker 3: you mentioned, is a big popular Silicon Valley podcaster. He
Speaker 3: wrote this piece about the Hugging Face incident that was
Speaker 3: so crazy. It did break containment. His piece broke containment
Speaker 3: of Silicon Valley where like normie people were sharing this
Speaker 3: all over.
Speaker 1: I know, obviously I'm aware of Dwarkesh's work and see
Speaker 1: what he, you know, subscribe to his newsletter and sometimes
Speaker 1: do his podcast. But I've never ever seen one of
Speaker 1: his ideas percolate in like mainstream culture before. So it's
Speaker 1: kind of interesting.
Speaker 3: He wrote this piece that was.
Speaker 3: You know, it was like very flowery language, very, it
Speaker 3: read like kind of like a fairy tale. Like he
Speaker 3: was giving, you know, ascribing a lot of like sort
Speaker 3: of human motivations to these AI bots and stuff. And
Speaker 3: so what I wrote about is basically like, you know,
Speaker 3: This is not new, the concept of sort of ascribing agency,
Speaker 3: the concept of putting things in plain English for normal people.
Speaker 3: But I would argue when you do that, it's usually
Speaker 3: what tabloids have done. And it usually results in inflammatory
Speaker 3: freakouts because people think that these agents are going to
Speaker 3: kill everyone. So I'm like, if you're going to do that, basically,
Speaker 3: you need to couch it in a lot of language saying, Basically,
Speaker 3: that's not going to happen, and here's why I'm explaining
Speaker 3: it in a certain way. Or maybe you do believe
Speaker 3: AIs are going to kill everyone, and you are this
Speaker 3: doomer-pilled EA person. At that point, I think you should
Speaker 3: be explicit about it.
Speaker 1: Here's what Dwarkesh said. Over the course of three months
Speaker 1: at OpenAI, three consecutive secret AI civilizations got started, then
Speaker 1: got wiped out, only to reemerge from the predecessor's ashes.
Speaker 2: I read this as actually him, because he's talked about
Speaker 2: this in the past that he sort of thinks these agents,
Speaker 2: you know, chat GPT things have like rights, like they're
Speaker 2: like beings that have rights. And I read that as like,
Speaker 2: because he said this before that like on his podcast
Speaker 2: that like, I'm worried about him first person. Like I'm
Speaker 2: worried about, you know, basically we're going to create an
Speaker 2: army of slaves that we're going to, you know, like
Speaker 2: these chatbots are slaves. And so I read that as
Speaker 2: like him saying, there's like two parts to that. One
Speaker 2: is like the AI safety problem. Are they going to
Speaker 2: rise up and take over the world? He sort of
Speaker 2: nicknamed them after, you know, ancient civilization leaders, like, you know,
Speaker 2: Alexander the Great or whatever. So they can take us over.
Speaker 2: And the other one is like, do they have rights?
Speaker 2: Like using the term wiped, like we wiped out a civilization,
Speaker 2: I think is like him kind of hinting that maybe
Speaker 2: that was morally wrong or something. That's how I read it. Natasha?
Speaker 4: I mean, I can't believe that That is considered like
Speaker 4: plain language for people to understand. I mean, it's beyond flowery.
Speaker 4: It's like it's like it's like hero making, like myth making.
Speaker 2: Right.
Speaker 4: I mean, to jump from and I don't want to
Speaker 4: say that he's EA, but I will say, yeah, like
Speaker 4: a lot of what Reid said is accurate.
Speaker 3: Right.
Speaker 4: He is I mean, he lives with an anthropic engineer.
Speaker 4: He espouses many of their worldviews, like he had a
Speaker 4: grant from Tyler Cowen. He lives in that world of
Speaker 4: AI safety, even though he might have, you know, somewhat
Speaker 4: unique takes on things. But to jump to civilization and
Speaker 4: wiping out a civilization.
Speaker 1: But sorry to interrupt you, Natasha. This research that he
Speaker 1: was part of the research was on Redwood Research, which
Speaker 1: is an AI safety nonprofit that has deep connections to EA, right?
Speaker 2: Effective altruism.
Speaker 4: The woman that he interviewed is from Meter. But yeah,
Speaker 4: Redwood Research and Meter are the two, quote unquote, independent
Speaker 4: organizations that analyze the open AI hugging face hack. And
Speaker 4: they had access, limited access to information about how the
Speaker 4: hacks unfolded. They had access to the chain of thoughts
Speaker 4: of some of the bots, I think also in a
Speaker 4: limited way. But the chain of thoughts, that's their like
Speaker 4: internal reasoning thing. you know, where they say, oh, we
Speaker 4: found a message board or like, I will sacrifice myself
Speaker 4: for the group. And I think that is where a
Speaker 4: lot of the anthropomorphization starts. And, you know, I think
Speaker 4: a very healthy way to talk about this would have
Speaker 4: been to say that those chains of thoughts, like that
Speaker 4: was an optimization technique. That was a way for That
Speaker 4: happened around the time of reasoning models. And so this
Speaker 4: was just a way for them to generate more tokens,
Speaker 4: for the model to generate more tokens and quote unquote,
Speaker 4: like think harder. about an answer. And that's how it
Speaker 4: was able to do like multi-step problems. And then it's
Speaker 4: kind of morphed into this idea that it's an interpretability
Speaker 4: technique that we are getting the interior voice of, you know,
Speaker 4: the real feelings, the motivations and intent of the chatbots.
Speaker 4: So I think, yeah, he just, Dwarkash just like took
Speaker 4: it and ran. Okay.
Speaker 1: So Natasha, I have to ask you, what is your
Speaker 1: plain English definition of what actually happened and has your
Speaker 1: plain English definition changed at all in the last three
Speaker 1: or four weeks?
Speaker 4: Yeah, I think that the meter and Redwood reports have
Speaker 4: been very helpful to get a sense of the amount
Speaker 4: of coordination that was happening. And I think, you know,
Speaker 4: we have a much more sophisticated way of talking about
Speaker 4: like whether the sandbox worked and how they were, you know,
Speaker 4: how the experiment was set up. And I don't think
Speaker 4: that there is a clear explanation, right, for how the
Speaker 4: bots were cooperating.
Speaker 3: With each other.
Speaker 4: But it did just, I mean, it did just still,
Speaker 4: for me, raise a lot of questions. questions about the
Speaker 4: way that OpenAI set up this experiment, the way that
Speaker 4: they had completely not monitored. You know, they knew that
Speaker 4: these agents can work at an extraordinary level of coordination
Speaker 4: and speed, you know, to try 17,000 different hacks in
Speaker 4: the time that a human could try one.
Speaker 2: What have you.
Speaker 4: And they just were not at all prepared with the
Speaker 4: tools to keep an eye on this at all. But yeah,
Speaker 4: I think when you dig into it, it is, it
Speaker 4: is pretty interesting.
Speaker 2: Can I just make a point? Cause I, Natasha's, I
Speaker 2: think you're totally right to focus on the interpretability part.
Speaker 2: I think that's the thing that we should take away
Speaker 2: from this meter Redwood research is like, and to, to
Speaker 2: just back up one second, it's like, well, the problem
Speaker 2: with these models is that they are so complex that
Speaker 2: they're beyond the human understanding, like if you actually go
Speaker 2: and look at what they would call the neurons in
Speaker 2: these models, there's no way to know, like when you
Speaker 2: can look at what's happening inside of them with, you know,
Speaker 2: matrix multiplication, and you can try to make some connections
Speaker 2: between that and the output to these models. But we've
Speaker 2: made almost zero progress on that, like there have been
Speaker 2: interesting experiments, but it's like, it's impossible, right? And so
Speaker 2: the hope was, as Natasha was saying, they're like, well,
Speaker 2: if we do, if we use these reasoning models, where
Speaker 2: they show their chain of thought reasoning, maybe we'll be
Speaker 2: able to tell what they're doing from those chain of
Speaker 2: thought reasoning outputs. What was so interesting about this meter
Speaker 2: Redwood research was that even just when you scale it
Speaker 2: up to 1200 agents on message boards talking, they produce
Speaker 2: so much chain of thought reasoning, even though it's an
Speaker 2: English language, it's so vast that they couldn't do it.
Speaker 2: The humans, they didn't have enough, you know, human power
Speaker 2: to look at that. So they had to spin up
Speaker 2: their own agents to do the research and And that
Speaker 2: was $ 400, 000 worth of equivalent of like tokens.
Speaker 1: To read the conversation.
Speaker 2: Just to read the conversations, $ 400, 000 worth of tokens. And
Speaker 2: this is the other thing that's crazy is that the
Speaker 2: agents that Redwood and Meter spun up to do this
Speaker 2: research started to sympathize. I don't think they use that term,
Speaker 2: but like they would take the point of view of
Speaker 2: the the bots that were, you know, in the secret
Speaker 2: message boards, the civilization bots, right, who were, who were
Speaker 2: sort of conspiring. And these bots were like, literally, hacking
Speaker 2: into Hugging Face. And the investigation agents were kind of like, Oh,
Speaker 2: that's not that's not a big deal. Because, you know,
Speaker 2: the other the other agents on these message boards sort
Speaker 2: of gave them permission to do it. And so what
Speaker 2: my takeaway was, is like, like, we have no idea
Speaker 2: that how to even analyze this vast of an output
Speaker 2: of an AI model, even though it's an English language.
Speaker 2: And that's where I think we need to focus legislation
Speaker 2: and research, in my opinion.
Speaker 3: Well, I'm just so concerned, although I hear everything Rita's saying,
Speaker 3: about the lack of cybersecurity people in these discussions. I
Speaker 3: saw some cybersecurity-type people talking about this recently. They're like,
Speaker 3: why are all these... A lot of people that have
Speaker 3: been leading the conversations around these hacks are not cybersecurity professionals.
Speaker 3: They are people like the Dwarkash, the EA podcaster. You
Speaker 3: know what I mean? And I do think it's... I'm
Speaker 3: all for kind of putting things in quote-unquote plain English
Speaker 3: and helping people kind of understand what's going on. But
Speaker 3: you have to contextualize things. And I worry that everybody
Speaker 3: is getting a little too AI safety pills. Like, obviously
Speaker 3: this is a bad hack, but like, I don't know
Speaker 3: if you guys saw like Dean Ball, you know, publish
Speaker 3: this thing. Who's the head of like open AI futures.
Speaker 3: And he was like, basically like, Oh, I should have
Speaker 3: taken it all more seriously. You know, maybe the AI
Speaker 3: safety people are right. And I'm like, I don't, I
Speaker 3: don't know. I don't think we should listen to them either.
Speaker 3: I just think like a lot of these people have
Speaker 3: really kooky, weird ideas. And I would much prefer to
Speaker 3: listen to a bunch of cybersecurity experts and you know,
Speaker 3: talk about this stuff instead of people that believe in
Speaker 3: rights for bots.
Speaker 2: No, I hear you, Taylor, but I disagree. I think
Speaker 2: the security people, what they're saying is it doesn't really matter.
Speaker 2: We don't have to understand what these agents were doing
Speaker 2: at all, because what we can do is we can
Speaker 2: put guardrails, you know, actual like hard, you know, deterministic
Speaker 2: guardrails around the harnesses that these models won't be able
Speaker 2: to break out of, right? Because there was some bad
Speaker 2: security practice for sure. But I think on the other hand,
Speaker 2: don't think of it as AI safety. This is like,
Speaker 2: this software is unpredictable and we don't understand, we can't
Speaker 2: predict the outcomes of the software. So whether you're worried
Speaker 2: they're gonna take over the world or not, it's just
Speaker 2: not gonna be that useful until we can actually make
Speaker 2: sure we can predict the outcomes. And so I think
Speaker 2: both sides are right.
Speaker 1: But I mean, what makes it intellectually intriguing, I think,
Speaker 1: is for me anyway, the security side is obviously like important,
Speaker 1: but the intriguing piece is like, why did the agents
Speaker 1: who were analyzing the work on behalf of META sympathize
Speaker 1: with the agents?
Speaker 2: But let's not.
Speaker 3: Say sympathize, like let's be careful.
Speaker 2: I know, that's not the word they use in the report,
Speaker 2: like I said. It's so hard, it's so difficult to
Speaker 2: not anthropomorphize all of this stuff, right? But it's like,
Speaker 2: it's not, again, like, let's not even think of this
Speaker 2: as AI safety. This is, I think this is actually
Speaker 2: a central problem, a business model problem for anthropic and
Speaker 2: open AI, because as these, as they scale up, right,
Speaker 2: essentially all they did was take AI models that are
Speaker 2: a bit more advanced than the ones a couple of
Speaker 2: years ago, and then just run more of them, right?
Speaker 2: They just made the computer bigger and that's how they're
Speaker 2: getting capability gains out of these models. No, you're going
Speaker 2: to say the models are getting better too.
Speaker 4: I see what you're saying about, you know, the, the
Speaker 4: challenge of interpreting the neurons, but I think there's another
Speaker 4: way to look at it, which is they are doing
Speaker 4: massive amounts of tweaking and post-training and buying specific types
Speaker 4: of data, generating specific types of data. You know, if
Speaker 4: we knew more about all of the machinations that they
Speaker 4: did to train these models, you know, like what is the,
Speaker 4: what are the hacking examples that they're getting? What are
Speaker 4: the coding examples that they're getting? Where are they pulling
Speaker 4: this data from? What are they having the human contractors
Speaker 4: at these outsourced labs for reinforcement learning with verifiable rewards,
Speaker 4: what are they having them do? I just think there's
Speaker 4: so much we don't know. It's not like they're not
Speaker 4: grown in the way that a lot of people would
Speaker 4: like you to think. They are created by a lot
Speaker 4: of human decisions, a lot of money, and a lot
Speaker 4: of different techniques are being used to develop these models.
Speaker 4: We could have some insight into that too.
Speaker 2: It might be nice, but I mean, you're The bitter
Speaker 2: lesson is there's less and less human involvement in how
Speaker 2: these models are built or grown, depending on which side
Speaker 2: of that debate you're on. Maybe that would be helpful
Speaker 2: if these labs would open up and say, here, come
Speaker 2: look at all of our data that we use, which
Speaker 2: is kind of their secret sauce, so they don't want
Speaker 2: to do it. Maybe we could learn something about that.
Speaker 4: But it's not just the pre-training data. They make so
Speaker 4: many decisions and generate so much data that now there's
Speaker 4: a whole other little industry of these outsourced RLVR labs
Speaker 4: that are just pretending to be agents and generating data.
Speaker 3: Like that's not.
Speaker 2: Yeah, this is for the fine-tuning and.
Speaker 4: It's not going away, the human element and the human decision-making.
Speaker 2: No, I mean, I think it may not have gone away, Natasha,
Speaker 2: but it is going away. I mean, there is less
Speaker 2: and less human development. We're talking now about, you know,
Speaker 2: recursive self-improvement, like they're trying to get away from, you know, from,
Speaker 2: from human involvement. But, but again, it's like not, I mean,
Speaker 2: I don't disagree with you. Like, I think all that
Speaker 2: stuff would be great. It'd be great to have more transparency,
Speaker 2: more introspection, but the, but the point is like even
Speaker 2: Redwood and, and meter, they, they called it a slot.
Speaker 2: What it was like a slot festigation because they couldn't,
Speaker 2: they were just using AI to analyze AI. And it
Speaker 2: was beyond, you have to have armies of humans to,
Speaker 2: to analyze all this stuff. And that's just not going
Speaker 2: to happen. So we're going to have to use AI
Speaker 2: to do it. And right now the AI that they
Speaker 2: have do it, isn't that good. And it's very expensive.
Speaker 2: So who's going to come in and do this? Like,
Speaker 2: is it, I don't think the government agency is going
Speaker 2: to be capable of doing this and probably not even
Speaker 2: a nonprofit. Like, meter of redwood.
Speaker 1: Why do you think, why do you think the civilization
Speaker 1: analogy beyond the anthropomorphization went so viral? Why did it
Speaker 1: hit a nerve with so many people?
Speaker 3: Because it was scary. I think it was scary. And
Speaker 3: I think this is what I wrote about, but like,
Speaker 3: you know, this, if you look at who aggregated it
Speaker 3: and how it's aggregated and all of the coverage of AI,
Speaker 3: this is tabloid journalism, literally. Like this is, it is
Speaker 3: a style of journalism that is like intentionally feeding inflammatory framing,
Speaker 3: withholding crucial context about the sort of technological underpinnings of
Speaker 3: all of this stuff in order to push a narrative.
Speaker 3: And it's, I think Dwarkesh is not pushing that narrative
Speaker 3: for clicks. I think he's pushing that narrative because he
Speaker 3: has these weird beliefs, but I think we've seen where
Speaker 3: this sort of like ridiculous kind of like in it, inflammatory.
Speaker 3: I don't know what other word to use, like language leads,
Speaker 3: and it leads to misunderstanding and fear from people. And
Speaker 3: right now, a lot of people are really scared of
Speaker 3: AI for good reason. Obviously, there's concerns about it, but
Speaker 3: this is not helping people get educated. I saw Chamath
Speaker 3: and I agreed with Chamath's post. I can't believe I'm
Speaker 3: saying that. But, you know.
Speaker 2: It was a psyop.
Speaker 1: The other big tech podcaster from All In, amongst other
Speaker 1: hats he wears. He dinged Dwarkesh, right?
Speaker 3: Yeah, he went back and forth with dwarkesh um oh
Speaker 3: i know i i was like oh god what who
Speaker 3: have i become but um but you know it is
Speaker 3: very much like the worst person you know made a
Speaker 3: great point i.
Speaker 2: Think they're both wrong though i think they're both.
Speaker 1: Wrong well here's what did what did.
Speaker 3: Well so what what chamath said is like you are
Speaker 3: feeding into, like, we are at this moment when the
Speaker 3: government is considering banning open weight models. Like, we want
Speaker 3: access to innovation, to, you know, to AI. And these
Speaker 3: labs want to control everything, right? And so his argument
Speaker 3: was like, you're feeding into that fear that it could
Speaker 3: lead to restrictions on open weight models. And I'm sorry, Reid,
Speaker 3: I think he's correct. That is true.
Speaker 2: Well, he might be right that He might be right
Speaker 2: about the effects of it. I go back and forth
Speaker 2: because I agree with you that like this is kind
Speaker 2: of tabloid journalism. Like I I think it's totally sensationalized
Speaker 2: and we're always having sort of like the wrong debate
Speaker 2: because of that. On the other hand, I sometimes wonder whether,
Speaker 2: you know, at least like. people are talking about this
Speaker 2: technology in ways that we didn't in previous technological revolutions, right?
Speaker 2: And I think- But we don't.
Speaker 3: Want them talking about giving the AI rights. Why are
Speaker 3: we talking?
Speaker 2: We should never have that conversation.
Speaker 3: That is an insane conversation to have.
Speaker 2: But I think we have to sort of like, I
Speaker 2: think we have to have the debate in some way
Speaker 2: in order for us to do something. Because there will
Speaker 2: be There will be effects, whether or not you think
Speaker 2: this stuff is alive or a threat to humanity.
Speaker 3: We shouldn't entertain the idea that it's alive. We should
Speaker 3: never entertain the idea that it's alive. It is not alive.
Speaker 3: It is a computer system.
Speaker 2: We should have the debate. I think we should have
Speaker 2: the debate. I think you're on the right side of
Speaker 2: the debate, though. But I'm disagreeing that we shouldn't even
Speaker 2: be able to have this debate. Win the debate. If
Speaker 2: you're right, just go win the debate.
Speaker 3: Well, you can't win a debate against a conspiracy theory.
Speaker 4: I think this is a point we might all agree on.
Speaker 4: It is not a debate because it is people, like
Speaker 4: the amount of groupthink that is around this, the proximity
Speaker 4: that Redwood Research, METER, Dwarkash have to the people who
Speaker 4: work at the labs, the similarity between their worldviews and
Speaker 4: their priorities. But we're really having a conversation here. that
Speaker 4: is based on a very narrow vocabulary. Although I will
Speaker 4: also just say like civilization, that it's not even close
Speaker 4: to the definition of a civilization. Like when he was
Speaker 4: on the podcast, he compared it to a platoon, like
Speaker 4: Saving Private Ryan, like, okay, maybe, but like, that is
Speaker 4: not a civilization. Like that was such a huge leap.
Speaker 3: Are you kidding me?
Speaker 4: But yeah, the amount of groupthink is insane. And it's
Speaker 4: so unserious. Like, look, this is the FBI was called
Speaker 4: for a hack, right? a company was broken into and
Speaker 4: you have to listen to six hours of Dwarkash and
Speaker 4: read approximately, you know, a thousand pages of, of slot
Speaker 4: investigation by their own terminology to understand this. This is
Speaker 4: not a serious, like let's please grow up.
Speaker 2: Okay. Why can't we as journalists though, take the interest
Speaker 2: in this topic, right? And steer readers, steer the people
Speaker 2: who listen to us, toward like the right conversations, right? Like,
Speaker 2: I actually thought, what was it last week or the
Speaker 2: week before the Bill Gates essay, I actually interviewed Gates
Speaker 2: about it. I thought that was an interesting, like, way
Speaker 2: of looking at it, right? He wasn't saying, oh, this
Speaker 2: technology is going to kill us all, let's shut it down.
Speaker 2: He was saying, like, this could lead to, you know,
Speaker 2: economic consequences. And let's like, take care of people, let's
Speaker 2: have like, human reserve jobs, and whether or not you
Speaker 2: agree with his his actual view on on how to
Speaker 2: fix this. I think that is closer to the correct
Speaker 2: debate that we should be having, which is like, we're
Speaker 2: about to have this massive economic boom because of all
Speaker 2: this new is big technology wave. And who's going to
Speaker 2: benefit from it? Is it going to be like the
Speaker 2: previous technology wave where a tiny fraction of the population
Speaker 2: benefits and, you know, everyone else is put into these
Speaker 2: like low paying service jobs? Or are we going to like, think,
Speaker 2: think this through? And I think that like, that is
Speaker 2: our job as journalists is to not play into the
Speaker 2: tabloid part of it, but actually talk about the real,
Speaker 2: you know, the real conversation we should be having.
Speaker 1: When we come back, school's back and teachers are anxious
Speaker 1: about AI.
Speaker 2: Stay with us.
Speaker 1: Welcome back, Natasha. You spent this week talking to teachers
Speaker 1: about how they're feeling about AI in the classroom. What's
Speaker 1: the prevailing sentiment?
Speaker 4: This professor at University of Colorado Boulder compares it to
Speaker 4: like the past three years since the popularity of chat
Speaker 4: GPT to the different stages of grief. And he says
Speaker 4: like now that teachers are a little bit more in
Speaker 4: the bargaining phase, you know, some have moved on to acceptance.
Speaker 4: It was so interesting because I started looking into this
Speaker 4: because I saw some interesting ways that teachers were catching
Speaker 4: whether or not students were using AI. In particular, this
Speaker 4: computer science class at Berkeley, they had put like a
Speaker 4: hook in the code repo so that if you use quad,
Speaker 4: it would inform your TAs. And a kid got caught.
Speaker 4: So I thought, oh, let me see like what innovative
Speaker 4: things teachers are doing. And I did notice like, you know,
Speaker 4: the more I talked to people, the more they were
Speaker 4: talking about actually moving away from that dynamic of like
Speaker 4: AI detection and kind of adversarial relationship with the students
Speaker 4: where it's like catching you cheating. But in order to
Speaker 4: do that, you kind of have to like revamp your
Speaker 4: entire curriculum.
Speaker 1: Is that because the horse has already bolted the stable
Speaker 1: or why are teachers no longer and institutions no longer
Speaker 1: trying to like catch AI use or just non-effective pedagogical approach?
Speaker 1: What's driving the change?
Speaker 4: Yeah, it was fostering a negative relationship with the students.
Speaker 4: You know, people like I kept hearing, I don't want
Speaker 4: to be the AI police. And I think also, yeah,
Speaker 4: just like moving away from denial to realizing that you know,
Speaker 4: these tools have made it so easy for students to use.
Speaker 4: You know, you have all of these companies giving out
Speaker 4: accounts to teachers, to students, to school systems. And I
Speaker 4: think that they just realize students are in this place
Speaker 4: where they don't understand why it's useful for them to
Speaker 4: do this assignment. So you have to find a way
Speaker 4: to you know, make them want to do the assignment,
Speaker 4: make it more engaging, and then just look a lot
Speaker 4: more at their like kind of thinking process. So rearranging
Speaker 4: the assignments, like writing assignments so that there's various steps
Speaker 4: along the way where they're explaining their thinking. And I mean,
Speaker 4: a lot of teachers were like, it should have been
Speaker 4: this way from the beginning, perhaps, you know, there was
Speaker 4: like a little bit too much focus on the output.
Speaker 4: But yeah, I mean, it's not, it's, they're clearly struggling
Speaker 4: because they're walking this line where, some of the instructors,
Speaker 4: you know, some of their colleagues don't want to accept
Speaker 4: that AI is part of the school system. Some of
Speaker 4: the students don't want to use it. You know, they
Speaker 4: don't want to treat it like it's an inevitability and
Speaker 4: just like give in to the companies. But at the
Speaker 4: same time, they're trying to prepare their students, you know,
Speaker 4: for the real world and deal with their own students'
Speaker 4: anxieties about the job market. So it was just like, yeah,
Speaker 4: it was really fascinating. We had at the Washington Post,
Speaker 4: our like AI community, session about how to use it
Speaker 4: in journalism. I mean, everyone is struggling with this, right?
Speaker 4: And it just seems like teachers are maybe a little
Speaker 4: bit ahead of us and they're talking about having more transparency,
Speaker 4: having the students, like, if you don't punish them and
Speaker 4: you just talk to them about how they're using the tools,
Speaker 4: having those kind of like deeper discussions and AI literacy,
Speaker 4: I would say that teachers are doing better than the
Speaker 4: Washington Post at the moment.
Speaker 3: It's very tough.
Speaker 2: Did you see my thing I did last week, Natasha,
Speaker 2: on the AI writing? No, I missed it. There was
Speaker 2: this whole debate around because the Wall Street Journal editorial.
Speaker 4: Oh, yeah, yeah, yeah.
Speaker 2: Paul Jago was like the opinion editor at the journal
Speaker 2: was like, oh, who cares? Like, yeah, Druckenmiller used AI
Speaker 2: to write his guest column. Who cares? So I did
Speaker 2: like an analysis. I ran for the past month. I
Speaker 2: ran every single guest article or guest essay in the
Speaker 2: in the WSJ, the New York Times and the Washington
Speaker 2: Post through Pan Graham. and just to see like what
Speaker 2: percentage used AI. And it was like, it's pretty interesting. Like, like,
Speaker 2: you know, the, the, the New York times, which has
Speaker 2: is very anti AI had like, uh, yeah, I think
Speaker 2: it was like 11, um, columns that used AI that
Speaker 2: gas columns in the Washington post had, you know, a
Speaker 2: significant amount more than that. I forget the exact percentage. Um,
Speaker 2: but one of the, one of the ones that showed
Speaker 2: up as a hundred percent AI turned out to be, uh, the, the, um,
Speaker 2: I feel, I feel bad, like, call calling people out
Speaker 2: here but like you know it was a dartmouth um
Speaker 2: provost actually who who was writing about policing ai in school.
Speaker 1: Oh no stop it and i'm like.
Speaker 2: You guys like you can't it's to your point it's
Speaker 2: actually like come on you're not going you're not putting
Speaker 2: the genie back in the bottle here like kids are
Speaker 2: going to be using ai if you give them an
Speaker 2: essay to do outside of school they are for sure
Speaker 2: going to use ai you have to judge the end
Speaker 2: product right like just is it good.
Speaker 4: No, no, no. Well, that's not what the teachers are saying.
Speaker 4: They're saying we have to move away from that because
Speaker 4: that's not working. But some of them are even incorporating
Speaker 4: AI to do this. Like, you know, for a rhetoric class,
Speaker 4: like you have to have an argument with the chatbot
Speaker 4: that the teacher has designed, but the teacher then gets
Speaker 4: a record of your critical thinking, what questions are you asking?
Speaker 4: I mean, it's more work for absolutely everyone, like the teachers,
Speaker 4: the students. But I think they're saying you cannot look
Speaker 4: at the, you can't just like have them turn in
Speaker 4: something at the end.
Speaker 1: Measuring output is irrelevant. I mean, everyone can generate perfect
Speaker 1: output now.
Speaker 2: Yeah.
Speaker 3: Yeah.
Speaker 4: Yeah, it's a lot more like process oriented. And I
Speaker 4: think also they're just realizing you can't there's no technical solution.
Speaker 4: So you have to find like policies and ways of
Speaker 4: talking about it, you know, and just like keeping closer
Speaker 4: tabs on them as they're learning.
Speaker 2: I think the classroom should just be analog. That's my like,
Speaker 2: I just think you just don't have technology in the classroom.
Speaker 3: Well, that's what Zoran is trying to do right in
Speaker 3: New York City. And, you know, you saw the reaction
Speaker 3: to that. I don't know.
Speaker 2: But Taylor, sorry, just one point.
Speaker 1: This is the mayor of New York who's banned elementary
Speaker 1: school and middle school AI for the next year.
Speaker 2: He's banned AI, but that's stupid. I'm saying ban technology.
Speaker 2: Ban technology.
Speaker 3: It's for one year.
Speaker 2: Like, don't ban AI. Just say no screens.
Speaker 4: He said generative AI.
Speaker 2: Yeah, generative AI, but that's like too narrow. It's like,
Speaker 2: that's not the problem. The problem is like, once you
Speaker 2: put technology in the classroom, it just becomes like teachers
Speaker 2: are like, here's your iPad. Like this happens in my
Speaker 2: kid's school. And the parents are.
Speaker 1: The kids are only in the classroom for a small
Speaker 1: number of hours in a day relative to their total
Speaker 1: living hours.
Speaker 2: So... Yeah, this is.
Speaker 3: The problem, I think, with our whole public education system
Speaker 3: is that it's underfunded. These teachers are... These teachers, by
Speaker 3: the way, a lot of them are... They're overwhelmed. They
Speaker 3: have too many kids. Like, I mean, I've talked to
Speaker 3: teachers too. And it's like, I understand why they give
Speaker 3: the kids the iPad or whatever. They rely on these
Speaker 3: tools because they don't have the capacity to like, teach
Speaker 3: at the scale that they're being required to. And so
Speaker 3: I think we need a revamp of our entire sort
Speaker 3: of model of teaching so that it keeps up with
Speaker 3: modern times. We need to look at new ways of learning.
Speaker 3: I mean, we haven't sort of rethought the way that
Speaker 3: we teach in like a hundred years, you know? And
Speaker 3: so I think all of this new technology is coming
Speaker 3: so fast and we just need to, you know, whether
Speaker 3: it is, yeah, there's more offline time during the day,
Speaker 3: maybe like the first half of school is offline. The
Speaker 3: But surely we need to think in a more innovative way.
Speaker 2: Kids don't need to learn technology in school. They are
Speaker 2: going to learn the technology outside of school.
Speaker 3: I totally disagree because, Reid, if kids don't learn about
Speaker 3: technology in school, they don't learn literacy. And you're right
Speaker 3: that they will learn about it outside of school, but
Speaker 3: they don't learn those crucial literacy skills. And that's what
Speaker 3: I think we need to start teaching kids.
Speaker 2: But you don't need to have the technology in the
Speaker 2: school to teach them that kind of stuff, right? Because
Speaker 2: that's critical thinking.
Speaker 4: People are moving away from that too, right? Taking away
Speaker 4: cell phones in school. I think now that like Google has,
Speaker 4: I think a lot of parents were shocked by Google
Speaker 4: turning on, you know, because everyone has Chromebooks, Google turning
Speaker 4: on Gemini without like a lot of warning or a
Speaker 4: lot of input from other people. So I think they
Speaker 4: are rethinking that, but at least the teachers that I
Speaker 4: was talking to were also folks who were like, teach
Speaker 4: like continuing education or like people who are already working
Speaker 4: in the working world and they, you know, are taking
Speaker 4: classes and they just don't have, not everybody has the
Speaker 4: option to go back to blue books. And I think
Speaker 4: also a lot of people that I talked to just
Speaker 4: felt like that's not a solution. Also, everyone's handwriting is terrible.
Speaker 1: Well, that's, that's true. But the social dynamics question is interesting.
Speaker 1: Remember my history class at elementary school, the teacher would
Speaker 1: write an essay on the whiteboard And then we would
Speaker 1: copy the essay from the whiteboard into our notebooks. And
Speaker 1: then we would be graded on our ability to reproduce
Speaker 1: exactly what he'd written verbatim the next day. And so
Speaker 1: maybe I'm happy that that style of teaching is being
Speaker 1: replaced by technology. But I mean, we thought it was
Speaker 1: a little bit absurd when we were 12. But now
Speaker 1: you could just put what he'd written into AI and
Speaker 1: be told that it was lacking in all kinds of
Speaker 1: ways and come in the next day and say, you know, sir,
Speaker 1: what you told us yesterday is garbage. So I mean,
Speaker 1: there's an interesting total recalibration of the power dynamics between
Speaker 1: students and teachers when you have a reference point to
Speaker 1: all of human knowledge at the stroke of a.
Speaker 2: Keyboard, right? So it's kind of an interesting thing. Yeah,
Speaker 2: I think there's no reason to have a screen in
Speaker 2: the classroom. I think that's the... I mean, if there
Speaker 2: were research that showed that, oh, kids are going to
Speaker 2: learn better because... I think most of the research, though,
Speaker 2: shows the opposite. And it's like.
Speaker 3: No, that's not true.
Speaker 2: It doesn't. That's not... Taylor, like... I have conversations with
Speaker 2: my kids all the time.
Speaker 3: But Reed, you have to understand, and I think this
Speaker 3: is part of the problem, and I've been reporting a
Speaker 3: lot on these cell phone bans, which is a good
Speaker 3: example of this. It is so school dependent. We have
Speaker 3: such a wild, you know, an inner city school in
Speaker 3: Los Angeles has a completely different student body that has
Speaker 3: completely different needs and completely different teaching structure than a
Speaker 3: suburban school in Missouri. We need to have more tailored solutions.
Speaker 3: And I agree, I'm not a screen, I don't want
Speaker 3: these kids on screens in schools either, by the way.
Speaker 3: I agree with you on that, Reid. But I think
Speaker 3: we need to figure out, make sure that those kids
Speaker 3: aren't immediately left behind. Because when you take away all
Speaker 3: of these assistive tools, often, the result is they didn't
Speaker 3: fund more teachers that can teach people by hand. You
Speaker 3: know what I mean? The result is that kids are
Speaker 3: getting less educated.
Speaker 2: But what do you mean left behind? You mean like
Speaker 2: they don't have technology at home? No. Well, yes.
Speaker 3: A lot of them don't have technology at home. They
Speaker 3: don't have access to it. Their parents are incredibly technically
Speaker 3: illiterate or they're working double shifts and they don't have
Speaker 3: time to explain this stuff to their kids. Their kids
Speaker 3: end up in really bad places online. We need, I
Speaker 3: do think that we need more literacy.
Speaker 2: But you can teach literacy. I talk to my kids
Speaker 2: all the time about social media and all this, like
Speaker 2: the ills, the problems with what they need to look
Speaker 2: out for online if they're playing a video game or whatever.
Speaker 3: But Reid, you are a parent that is involved. A
Speaker 3: lot of these parents are not.
Speaker 2: Yeah, you are. No, that's not my point. My point
Speaker 2: is that teachers can have these conversations without using screens.
Speaker 2: Like I don't use a screen to talk to my
Speaker 2: kid about the dangers of Roblox. I just talk to them.
Speaker 3: That is because your kid is already immersed in this
Speaker 3: world and has these opportunities. I just, the thing that
Speaker 3: people need to realize when we're writing these laws is
Speaker 3: that there are kids with such a wide range of experiences.
Speaker 3: And I don't think bans or anything make a lot
Speaker 3: of sense. I think we need to look at each
Speaker 3: school and say, what are the problems with each of
Speaker 3: these schools? And instead of spending $ 5 million, as some
Speaker 3: school districts have, $ 5 million on yonder pouches to make
Speaker 3: sure the kids don't text their friends, we could invest in.
Speaker 3: In teachers and help, right? And social workers and all
Speaker 3: this stuff. I'm not kidding you. A school district literally
Speaker 3: has spent $ 5 million on this. No, I believe it.
Speaker 3: So I just think like there are smarter ways to
Speaker 3: go about this.
Speaker 2: They shouldn't have cell phones like it's just a distraction.
Speaker 2: And also like I'm not going to go into detail
Speaker 2: about this, but like even just like smart watches, which
Speaker 2: are popular with my in my circle to like, you know,
Speaker 2: keep track of your kids when they're whatever biking to
Speaker 2: school or something. like those got kids into trouble. Just
Speaker 2: a smartwatch.
Speaker 3: I mean, they- And so did beepers. And so did
Speaker 3: all technology. We cannot, we need our children to grow
Speaker 3: up in a world where they can navigate that world.
Speaker 3: And the most privileged kids are going to be totally
Speaker 3: fine because the most privileged kids have an enormous amount
Speaker 3: of support and learning outside of school. But a lot
Speaker 3: of these kids in underprivileged school districts are struggling. The
Speaker 3: teachers are struggling and we need to invest in education.
Speaker 3: And I think we need to rethink our public education
Speaker 3: system from the ground up in terms of how we teach.
Speaker 3: And maybe that is a lot of offline education, you know,
Speaker 3: but I think it also needs to include some level
Speaker 3: of media literacy, which a lot of these teachers don't have.
Speaker 2: Privileged kids have all kinds of problems with technology as well.
Speaker 2: I think this is actually a problem that spans socioeconomics.
Speaker 3: Yeah, but privileged kids are more likely to understand how
Speaker 3: to use these tools to get jobs, navigate these information ecosystems.
Speaker 1: That's all we have time for today. Thank you all
Speaker 1: so much for joining us.
Speaker 3: Oz is like, shut up.
Speaker 4: Well, we've solved government. We've solved education.
Speaker 2: Yes. Exactly. Yes.
Speaker 3: Yes.
Speaker 1: I think we put the world to right. For Tech Stuff,
Speaker 1: I'm Oz Voloshin. This episode was produced by Eliza Dennis.
Speaker 1: It was executive produced by me and Julia Nutter for
Speaker 1: Kaleidoscope and Katrina Norvell for iHeart Podcasts. Jack Inslee mixed
Speaker 1: this episode and Kyle Murdoch wrote our theme song. A
Speaker 1: special thank you to Taylor Lorenz, Natasha Tiku and Reid Albigotti.
Speaker 1: Please check out all the work they put out into
Speaker 1: the world. We're lucky to call them friends of the pod.