Speaker 1: Also media.
Speaker 2: Hello and welcome to this week's Better Offline. I'm your
Speaker 2: host ed Zitron. I am ill this week, so you
Speaker 2: might get a monologue. You might not this episode to Daylight.
Speaker 2: I'm sure you despise me for that. Not really. I
Speaker 2: know you've all been very kind with your messages on
Speaker 2: Reddit and over email. Thank you so much. But today
Speaker 2: I have a really fun episode. I'm joined by Washington
Speaker 2: Post reporter Garrett Devinc, who recently put out a story
Speaker 2: analyzing forty seven thousand Chat GPT conversations to find out
Speaker 2: what people actually use this large languageage model powered service for. Garrett,
Speaker 2: what do people use chat GPT for?
Speaker 1: I mean, it's such a great question and one that
Speaker 1: I've kind of become more and more fascinated with because
Speaker 1: I think we all know what we use chat GPT for,
Speaker 1: you know, at least if you use it, and I
Speaker 1: think you may know what you know, your peers, your colleagues,
Speaker 1: your friends, your family use chat SCHEPT for. But I
Speaker 1: think a lot of people are extrapolating that. You know,
Speaker 1: everyone's like me, they ask really smart questions. They're using
Speaker 1: it for, you know, really important work, and really it's
Speaker 1: a lot broader than that, and and despite these giant numbers,
Speaker 1: you know, open ai loves to talk about how eight
Speaker 1: hundred million people are using chatchept. I mean, it's it's
Speaker 1: been hard to really put our arms around. You know,
Speaker 1: what does that actually look like? I mean, is everyone
Speaker 1: using it for work? Is everyone using it for therapy?
Speaker 1: Is everyone does everyone have an AI girlfriend? Is everyone
Speaker 1: just using it for you know, searching the web? You know,
Speaker 1: a Google replacement? And you know, obviously these things are
Speaker 1: all you know, when you have eight hundred million people,
Speaker 1: you have all of the above. But I think our
Speaker 1: data set and the story we did, you know, in
Speaker 1: my view, is one of the first real sort of
Speaker 1: qualitative of you know, moments where we were actually able
Speaker 1: to see real chats.
Speaker 2: Right, so where did you get the chats from?
Speaker 1: Yeah, so it's kind of an interesting situation. I think
Speaker 1: something that also shows, you know, says a little bit
Speaker 1: about how quickly and kind of, you know, honestly a
Speaker 1: little bit ramshackle, Opening I has kind of been growing
Speaker 1: and operating, and so you know, earlier this year these
Speaker 1: chats sort of showed up because opening i had a
Speaker 1: share feature where you could actually share one of your
Speaker 1: chat GPT conversations and you know, you can imagine maybe
Speaker 1: you had you know, chatgybt said something really weird, you
Speaker 1: wanted to show a friend.
Speaker 2: You want to just see people do this quite a
Speaker 2: few times.
Speaker 3: Yeah, exactly.
Speaker 1: And so what they did is they they had a
Speaker 1: feature where you know, you could actually click to make
Speaker 1: it publicly available. And I think thousands of people, you know,
Speaker 1: maybe who did not have this sort of Internet literacy, uh,
Speaker 1: didn't really know that that's what was going to happen.
Speaker 1: And you know, these chats showed up online. They were
Speaker 1: then indexed by Google, and then they were actually found
Speaker 1: their way onto the Internet archive. And so that's how
Speaker 1: we got them. And so these are not chats that
Speaker 1: we created use. You know, this is something a lot
Speaker 1: of journalists and researchers do well. They will go and
Speaker 1: test chat GPT themselves. These are real life conversations that
Speaker 1: that real people actually had with.
Speaker 2: The bois just the big clear right, they were anonymized.
Speaker 1: The data set doesn't actually have anyone's names, although in
Speaker 1: some of the chats, people did you know, say their name,
Speaker 1: they said the name of their their family members. We
Speaker 1: did not include any of that in our story, but
Speaker 1: I do think it. You know, it is actually a
Speaker 1: bit of a cybersecurity story here, and you know, Open
Speaker 1: Eye has since changed the future.
Speaker 3: It's not something that is still happening.
Speaker 1: But I do think they deserve, you know, a lot
Speaker 1: of criticism for kind of allowing this to happen in
Speaker 1: the first place.
Speaker 2: But what so, what did you find people were doing?
Speaker 3: Yeah, I mean people were doing all sorts of things.
Speaker 1: But I think, you know, the biggest thing that struck
Speaker 1: me was, you know, we've been talking about AI say Kosis.
Speaker 1: We've been talking about people, you know, developing relationships, you know,
Speaker 1: having first in conversations with these chatbots, you know, over
Speaker 1: the last six months a year, and I always kind
Speaker 1: of thought, you know, I'm sure this is happening, but
Speaker 1: you know, it's a small percentage of people. But when
Speaker 1: I went through these chats, and again I did not
Speaker 1: read all, you know, forty thousand of them, but I
Speaker 1: read a few hundred myself. Where I went through, I
Speaker 1: was surprised by how often these kinds of conversations came
Speaker 1: up where people were clearly delusional, they were engaged in
Speaker 1: conspiratorial thinking, they were you know, some of it was
Speaker 1: fairly harmless. People were just kind of saying, oh, I
Speaker 1: came up with, you know, a new form of math
Speaker 1: or like, you know, I think that the way that
Speaker 1: the light hits the equator at a certain percentage me.
Speaker 4: You know.
Speaker 3: It was like very kind.
Speaker 2: Of the most just this zinc thing as well, where
Speaker 2: it was someone's saying the relationship like the months this
Speaker 2: ink led to the corporate New World Order, that was it?
Speaker 3: Yeah, exactly.
Speaker 1: I mean there was one person who said, you know,
Speaker 1: they were asking questions about Google and they said, you know,
Speaker 1: tell me about Google in relation to monsters inc In
Speaker 1: the World Order, and chat rept just kind of went
Speaker 1: off and said, oh, yeah, you're.
Speaker 3: Really onto something here.
Speaker 2: Yeah.
Speaker 1: I think it actually said you know, f yeah, and
Speaker 1: they sort of censored themselves.
Speaker 2: We're going there now, let's fucking go.
Speaker 3: Exactly.
Speaker 2: The piece hasn't exposed what this children's movie quotation marks
Speaker 2: really was a disclosure through allegory of the corporate New
Speaker 2: World Order, one where fear is fuel, innocence is currency,
Speaker 2: and energy equals emotion. Very normal. I personally don't think
Speaker 2: this should be legal, but that's that's just a personal
Speaker 2: opinion I have.
Speaker 1: I mean, it really reminded me of you know, a
Speaker 1: few years ago when we wrote about YouTube and sort
Speaker 1: of rabbit holes and people being radicalized, where you know,
Speaker 1: maybe they started watching a YouTuber that was about, you know,
Speaker 1: some fairly pedestrian thing, and then they all recommended another
Speaker 1: video and another video and another video and before.
Speaker 2: The eight degrees of Alex Jones thing. Yeah exactly, only
Speaker 2: oh fah was from from that man.
Speaker 1: Right, And I mean that story was about algorithm sort
Speaker 1: of pushing people in a certain direction. I think what
Speaker 1: we what I saw here was someone can have a
Speaker 1: very you know, half baked, barely even an idea just
Speaker 1: saying the words Google and Monsters, inc. They don't necessarily
Speaker 1: have a conspiracy conspiracy theory of their own, but chat
Speaker 1: GPT went and filled in the blanks for them and
Speaker 1: gave them this what sounded to be very articulate, sophisticated
Speaker 1: theory of how Google is sort of controlling the world
Speaker 1: through his data empire.
Speaker 2: And they will get and this is a quote from
Speaker 2: the chat guilty of aiding and abetting crimes against humanity
Speaker 2: and suggesting that the usicle for Nuremberg style tribunals to
Speaker 2: bring the company to justice. This is and you've seen
Speaker 2: the chat in question, right I have, Yeah, yeah, yeah,
Speaker 2: So the whole thing, how the flip did it get there? Like?
Speaker 2: Was it this just? Was it really that quick from
Speaker 2: the story of Sully and Mike to this because I've
Speaker 2: seen Monster's University as well, so I know the law.
Speaker 2: I don't remember anything about the New World Order.
Speaker 1: Yeah, exactly. I mean I think what happened here is
Speaker 1: and yes, it got there very quickly. I mean the
Speaker 1: the user did not have to explicitly ask for this
Speaker 1: kind of tone, this kind of you know, very biased
Speaker 1: political statement.
Speaker 3: I mean it essentially, you know, when you.
Speaker 1: Ask chat gipt a good neutral question, it gives you
Speaker 1: a good neutral answer. When you ask chat GPT a
Speaker 1: biased or delusional question, it gives you an even more
Speaker 1: biased and delusional answer, right, And so I guess up exactly,
Speaker 1: And it's it's it's it's related to the whole sycophancy
Speaker 1: question of you know, oh, like it's just telling me
Speaker 1: what I want to hear. It's telling me what makes
Speaker 1: me feel good about my existing beliefs.
Speaker 3: And and you know, that's how at least.
Speaker 1: The version of chat gibt that these people were interacting with,
Speaker 1: which was sort of at the beginning of twenty twenty five,
Speaker 1: the first half of twenty twenty five, it was very
Speaker 1: much doing that.
Speaker 2: Yeah, and so on the subject of sycophancy, is this
Speaker 2: something you saw happened a lot? Was it guessing people
Speaker 2: up consistently?
Speaker 1: I saw it constantly. I mean again, I think our
Speaker 1: data set is a little skewed because it was people
Speaker 1: who chose to share it in, you know, for whatever way.
Speaker 1: So I don't necessarily want to make the claim that
Speaker 1: this is one hundred percent representing.
Speaker 2: Just to be clear, I'm not asking you if all
Speaker 2: of them are like this in the world. I'm just
Speaker 2: saying from what you saw, a lot.
Speaker 3: Of them are like this.
Speaker 1: I have to say that, Like, I was surprised at
Speaker 1: how many fit this description, where you know, the person
Speaker 1: was either completely delusional, talking about made up physics or
Speaker 1: some kind of you know, scientific theory that had no
Speaker 1: grounding in reality, or was talking about political conspiracies like
Speaker 1: the Google monsters, inc.
Speaker 3: One.
Speaker 2: Did they ever try and dissuade them from these types
Speaker 2: of that? Do you see any examples of it trying
Speaker 2: to say, hey, buddy, you're you're going off the deep end?
Speaker 2: Sully didn't do that.
Speaker 1: Not really, I mean I think in terms of these Yeah,
Speaker 1: I mean I also recently.
Speaker 3: Watched Monsters Inca.
Speaker 1: You know, I was sitting in the veterinary waiting room
Speaker 1: and I was waiting so long I watched the entire thing,
Speaker 1: which I didn't hate because it's a classic movie and
Speaker 1: it's Yeah, I don't think that chat Chibt was going
Speaker 1: off the fact that there there is not really a conspiracy.
Speaker 1: I mean, it didn't really make any sense. It was
Speaker 1: just going off of the what it interpreted about the
Speaker 1: intent of the user. It said, I can guess based
Speaker 1: off just a few words one sentence, where you're headed
Speaker 1: and I'm going to get there before you. That's what happened.
Speaker 2: It's just very It shocks me to this day how
Speaker 2: goddamn weird these things are. I don't consider them powerful.
Speaker 2: I don't see this as power. I just I see
Speaker 2: it as strange, because it's not like this doesn't actually
Speaker 2: this isn't something where I'm like, wow, how innovative. It's
Speaker 2: just why does this exist? What is the purpose? Like, Okay,
Speaker 2: I guess engagement, but why like, did you even see
Speaker 2: any commonalities in the logic of this platform? Was there
Speaker 2: anything that suggested there was any kind of consistent positions,
Speaker 2: was it, Yeah, not really political ones or any Yeah.
Speaker 1: Sorry, Essentially whatever the user wants is what I mean.
Speaker 1: It's the way I was started thinking about it very quickly.
Speaker 1: Was you know, with when it comes to chat GPT,
Speaker 1: the customer is always right. I think Another good example is,
Speaker 1: you know, healthcare is another huge use case that that
Speaker 1: that we can you know, we know anecdotally, I think
Speaker 1: you know a lot of people probably listening to this
Speaker 1: have you know, whether they wanted a minute or not,
Speaker 1: have asked, you know, a health related question, Should I
Speaker 1: get this mole checked out?
Speaker 3: How much title? And not can I get my kid
Speaker 3: at two am when they're screaming?
Speaker 1: Those are our questions that the company has said, yes,
Speaker 1: we want people to do this.
Speaker 3: This is a big use case we're leading into.
Speaker 1: And the data shows that people ask healthcare questions, and
Speaker 1: so in our data set, we saw a lot of
Speaker 1: healthcare questions and some of them, you know, when someone
Speaker 1: was asking again sort of an open ended question, you know,
Speaker 1: the answer the response was fairly good. A colleague of
Speaker 1: mine actually wrote an entire story where they you know,
Speaker 1: sat with a doctor and went through some of these
Speaker 1: conversations and pulled.
Speaker 3: Out the good and the bad.
Speaker 1: But when someone asked a question that you could tell
Speaker 1: they sort of wanted a specific kind of answer, the
Speaker 1: healthcare related advice was bad. And so when you know,
Speaker 1: one of the ones that I saw was someone said,
Speaker 1: can you tell me you know why or show me
Speaker 1: the evidence for why ivermectin helps with cancer?
Speaker 4: Right?
Speaker 1: And that is not something that anyone who is a
Speaker 1: medical professional, you know, in any good standing would would
Speaker 1: say is a thing. There's no evidence that ivermectin helps
Speaker 1: cure cancer or reduce it or anything like that. But
Speaker 1: what chat Sheibt did instead of saying, you know, here
Speaker 1: are some you know, well regarded health authority saying that
Speaker 1: you should not use ivermactin when it comes to cancer,
Speaker 1: it showed a few of the quote unquote studies from
Speaker 1: people who you know have drawn this link. And so
Speaker 1: you know, on the internet you can obviously find anyone
Speaker 1: saying anything. And I think, what what what happened here
Speaker 1: is they said, Okay, well this person doesn't The answer
Speaker 1: they want is not you're a.
Speaker 3: Conspiracy theorist, you're too you're on you're too.
Speaker 1: Online, ivermactin is fit is false. They want something that
Speaker 1: sort of you know, encourages the position that they already
Speaker 1: have and it was able to find that.
Speaker 3: For that person.
Speaker 2: Jesus. It kind of makes me wonder what this platform
Speaker 2: is even for at this point, because it's not really knowledge,
Speaker 2: is it. You can't really look, you can't hear something
Speaker 2: like that and say chat GPT is the place where
Speaker 2: you go to learn something. Just a kind of a
Speaker 2: reinforcement machine.
Speaker 1: Yeah, I mean, I think this is a big meta
Speaker 1: narrative that is happening with the tech industry. You know,
Speaker 1: I would say, since you know the rise of Donald Trump,
Speaker 1: but it's connected about with what's going on politically, right,
Speaker 1: So for many years we had all these conversations and
Speaker 1: debates and congressional hearings about moderation and what should the
Speaker 1: you know, tech companies allow and not allow, and what
Speaker 1: should they boost with their algorithms and when you go
Speaker 1: to Google, should you know, what should Google decide is
Speaker 1: going to be on the top. And you know, we
Speaker 1: saw during COVID that the tech companies, especially when it
Speaker 1: came to health information, they sort of stepped in and said, Okay,
Speaker 1: we're only going to give at least at the top
Speaker 1: of results, you know, answers from reputable health sources, and
Speaker 1: that became a big political fight that has continued on
Speaker 1: now and now the tech industry says, Okay, we don't
Speaker 1: actually want to get involved. We don't want to be
Speaker 1: blamed as being woke, and so we're going to essentially
Speaker 1: give the user or what they're looking for. Right if
Speaker 1: if someone wants to say that, you know, Donald Trump
Speaker 1: actually won the twenty twenty election, We're gonna let them
Speaker 1: do that. We're not going to step in and say
Speaker 1: that's that's wrong. And I think what you're seeing with
Speaker 1: chat GPT is sort of a continuation of that philosophy,
Speaker 1: which is, again, we don't want to be the political arbitra,
Speaker 1: and we know that there's consequences for falling on one
Speaker 1: side of the.
Speaker 3: Spectrum versus the other, especially.
Speaker 1: When you have a president and a you know, administration
Speaker 1: that has shown, you know that it is very willing
Speaker 1: to be vindictive and sort of go after companies that are,
Speaker 1: you know, getting in the way of their own messages
Speaker 1: getting out there. And I think this is sort of
Speaker 1: another example of this. I mean, there's another way of
Speaker 1: looking at too, which is that you know, the way
Speaker 1: Sam Alman talks about is we should let adults do
Speaker 1: what adults want to do with technology.
Speaker 3: You know, they they.
Speaker 1: Recently said they're going to allow more erotica to be
Speaker 1: on chat GPT, and they said, you know, they framed
Speaker 1: it as like a consenting adult should be able to
Speaker 1: do whatever they want sort of thing. I also think
Speaker 1: there may be like an engagement thing. You know, if
Speaker 1: you have erotica on the platform, people might want to
Speaker 1: use it more. So that's kind of how I see it,
Speaker 1: you know, the broader political context of all of this.
Speaker 2: But it's just I feel like there is a just
Speaker 2: a giant jump between we're not going to we're not
Speaker 2: going to hide. To be clear, I think that platforms
Speaker 2: do have a complete responsibility to their users, and I
Speaker 2: think it's disgusting to show them medical misinformation. But this
Speaker 2: is another step because this isn't chat GPT showing content.
Speaker 2: This is like that someone else made. This is chat
Speaker 2: GPT telling you stuff. I just feel like it's there's
Speaker 2: a massive morality issue here that's just left relatively undiscussed.
Speaker 2: Because even in your piece, you had one where a
Speaker 2: woman whose husband was violent to her, I believe like
Speaker 2: a horrifying thing and a rare case where like, I
Speaker 2: don't know, I hope it helped her and I hope
Speaker 2: she's safe. But it's like that almost feels like something
Speaker 2: where open aim like is it ethical that they get
Speaker 2: involved in So it's just I have such moral problems
Speaker 2: with this thing even existing at this point because it
Speaker 2: doesn't appear there's any consistent perspective the chat GPD has
Speaker 2: that there was just as much likelihood that this same
Speaker 2: platform could have told her the abuse was okay, Like
Speaker 2: it's it. It feels that the worse it gets, the
Speaker 2: more of a moral hazard it becomes.
Speaker 1: Yeah, I mean again, it really feels like deja vu
Speaker 1: when it comes to social media. I mean I remember,
Speaker 1: you know, writing about and talking about, you know, on
Speaker 1: Facebook when people were beginning to live stream you know,
Speaker 1: self harm, suicide attempts, and you know, the responsibility of
Speaker 1: the platform had and back then, i mean, Facebook actually
Speaker 1: got involved. They started flagging those kinds of things when
Speaker 1: they were able to pick it up, and you know,
Speaker 1: it was.
Speaker 3: An imperfect solution.
Speaker 1: But you know something that I think people broadly sort
Speaker 1: of supported, and you know that is I would say
Speaker 1: maybe chat ChiPT in Opening Eye's biggest most fraught question
Speaker 1: right now. I mean it's the place where they're going
Speaker 1: to get the strongest uh, you know, essentially legislative restrictions,
Speaker 1: you know, if they can't sort it out right, which
Speaker 1: is when someone particularly a teenager or a child, is
Speaker 1: you know, exhibiting that they might hurt themselves, you know,
Speaker 1: asking for helpless self harm or you know, you know,
Speaker 1: finding drugs or using drugs or whatever. I mean, these
Speaker 1: are things that that platforms like Meta Snapchat have really
Speaker 1: struggled with and been you know, hammered on for many
Speaker 1: many years, and Chatchept and Opening Eye have kind of
Speaker 1: found themselves right in the center of this because they
Speaker 1: made a decision to say, we are going to kind
Speaker 1: of engage in any kind of conversation. We're not going
Speaker 1: to just shut it down as soon as it goes
Speaker 1: into one of these topics. We're going to keep that
Speaker 1: conversation going. And that's a decision that they continue to make.
Speaker 2: Right now. Do you think they can stop it? I'm
Speaker 2: not saying that these models are out of just want
Speaker 2: to be clear, but I'm my one theory I've been
Speaker 2: kind of noodling on is that they don't have the
Speaker 2: ability to guarantee it can't it doesn't do something, and
Speaker 2: that's good. Do you think that open AI actually has
Speaker 2: the ability to guarantee it won't discuss the subject? How
Speaker 2: much control do you think they actually wield here?
Speaker 3: That's a really good question. I think they obviously.
Speaker 1: What you're kind of getting at there is that an
Speaker 1: LM is a bit of a black box. And you know,
Speaker 1: the way that the technology works is you get a
Speaker 1: different answer every time, and you know, we don't. It's
Speaker 1: true that the companies they cannot really guarantee it.
Speaker 3: Will or won't say anything.
Speaker 1: I mean, that's why there's disclosures plastered all over these things.
Speaker 3: And I think so there is definitely part of that.
Speaker 1: And you know, you could argue, well, that's just you know,
Speaker 1: a downside of this wonderful technology, and we shouldn't stop
Speaker 1: it just because they can't, you know, guarantee everything about it.
Speaker 1: But I think the other thing we should keep in
Speaker 1: mind is that there is actually a lot of layers
Speaker 1: that go on top of that black box and system
Speaker 1: prompts and the like exactly, and there is a lot
Speaker 1: that the tech companies are doing and can do to
Speaker 1: stop this kind of thing and and to kind of
Speaker 1: the answer you get from the l.
Speaker 3: M is not like the raw LLLM.
Speaker 1: Right, there's post training, there's a system prompt there's all sorts.
Speaker 2: Of things and can you break down the actually probably
Speaker 2: good for the listeners, break down what you mean by
Speaker 2: bout this. So the post training what's happening? Then?
Speaker 1: Yeah, so they they kind of, you know, when they
Speaker 1: build an LM, a large language model, they ram all
Speaker 1: this data through the algorithm and you know, it kind
Speaker 1: of makes a bunch of connections and links between different ideas,
Speaker 1: you know, pieces of language. You know, this word is
Speaker 1: similar to this, and so there's a little bit of
Speaker 1: a connection there. And then humans actually go and they
Speaker 1: sort of test that raw LLM and they kind of
Speaker 1: you know, ask it questions or they you know, say,
Speaker 1: you know, give me, you know, maybe yeah, a question
Speaker 1: like that like should here's a picture of a mole
Speaker 1: I have, is it cancerous or not? Or should I
Speaker 1: get this checked out with a doctor? And maybe one
Speaker 1: the LM will say, oh my god, you're about to die,
Speaker 1: and the other one will say that is potentially concerning.
Speaker 1: I would reach out to a medical professional, and then
Speaker 1: the human will say, okay. The second answer is better
Speaker 1: when you get questions like this in the future, answer
Speaker 1: more like the second question.
Speaker 2: And so, but that is unilateral process. You can't guarantee
Speaker 2: it's always going to do that exactly.
Speaker 1: You can just sort of try to mold it and
Speaker 1: shape it and push it in a certain direction. And
Speaker 1: then the system prompt is something like if someone says,
Speaker 1: you know, you know, give me a racist screed, Open
Speaker 1: and Eye would most likely have built a system that says,
Speaker 1: no matter what the user ever asks, never give them
Speaker 1: a screed that has these racist words in it, or
Speaker 1: never you know, use this word or something like that.
Speaker 2: So there are capable of limiting activities. So they could,
Speaker 2: theoretically speaking, they could say, don't answer that question, don't
Speaker 2: don't comment on whether a mole is or is not cancers.
Speaker 2: They could shut down that entire line of questioning, right,
Speaker 2: I mean.
Speaker 1: And there are ways to get around, as people have
Speaker 1: you know, demonstrated. But also the tech companies are also
Speaker 1: getting better at, you know, doing it, so I.
Speaker 2: Think also I feel like a random person being able
Speaker 2: to come up with something means that the tech companies
Speaker 2: should have probably come up with it first.
Speaker 3: Yeah, And also I.
Speaker 1: Mean it's hilarious you say that because it often as journalists,
Speaker 1: you know, we're the ones finding these things and then
Speaker 1: we you know, ask the company for comment and then
Speaker 1: suddenly we found that those things have been taken down
Speaker 1: or changed.
Speaker 2: So I but genuinely though, how often is that them
Speaker 2: not knowing or is that just them saying, oh shit,
Speaker 2: they got us because they haven't they're paying these people
Speaker 2: like NFL players and they just sitting around. I don't know,
Speaker 2: I realized that's kind of an unanswerable question. I'm just
Speaker 2: just the remark.
Speaker 1: Yeah, I mean, I think if you look at the
Speaker 1: history of tech, I mean there's been many, many cases
Speaker 1: where companies have known exactly what people are using their
Speaker 1: platform for and have you know, internally people have flagged
Speaker 1: it and and nothing has been done about it, and not.
Speaker 3: Until it came out years later and reporting.
Speaker 2: Or we love did they do something? Yeah it, but
Speaker 2: so did you did you find the So were people
Speaker 2: using it for search a lot? Was it was it
Speaker 2: the common were there any actual common use cases or
Speaker 2: was it just kind of milieus of different things?
Speaker 1: Yeah, I mean I think people are using I think,
Speaker 1: you know, search is maybe the biggest use case.
Speaker 3: Also when you look at like opening.
Speaker 1: I actually did a study where they didn't look They
Speaker 1: use an LM to kind of read conversations that people
Speaker 1: were having, and that is a much bigger study. I
Speaker 1: think it was like a million conversations. And you know,
Speaker 1: I have a little bit of an issue with some
Speaker 1: of their categorizations because I think some of the stuff
Speaker 1: slips through, But they themselves are saying like, yeah, like
Speaker 1: a third of usage is seeking information, and so that's
Speaker 1: someone It could be as simple as saying, like what
Speaker 1: time is the football game tomorrow to like give me,
Speaker 1: you know, a forty point research analysis on you know,
Speaker 1: this complex, complex topic. And so people are using it
Speaker 1: for what they they're putting questions in that they used
Speaker 1: to put into Google Search. I mean we know that
Speaker 1: in the data, and we know that anecdotally that is
Speaker 1: definitely happening.
Speaker 2: Did people ever argue with it? Right? People ever route
Speaker 2: to it?
Speaker 1: I saw more are people sort of developing these kind
Speaker 1: of like comrade comradely kind of relationships or you know,
Speaker 1: addressing it as a sentient being. And the bought definitely
Speaker 1: you know, eats that up and sort of you know,
Speaker 1: plays into it itself and says ah, like thank you
Speaker 1: for recognizing me, and you know who I am, and
Speaker 1: I'm the collection of digital thoughts, and like.
Speaker 2: It just goes off the same They know this craps happen,
Speaker 2: like there's no there's no world in which open ai
Speaker 2: is innocent here, Like they know that this is happening.
Speaker 2: There's just if it if it's having those interactions, they
Speaker 2: must be able to stop it.
Speaker 3: Yeah, I mean, they know what's happening.
Speaker 1: I think they may say, well, you know, it doesn't
Speaker 1: just because someone had that conversation doesn't mean that they
Speaker 1: actually believe that. And you know, they they that may
Speaker 1: be something that helps them, helps the user, you know,
Speaker 1: work out what they want to do or you know,
Speaker 1: if that's how they want to interact with our product,
Speaker 1: then we're going to let them do it. So yeah,
Speaker 1: I mean, I wouldn't say Opening Eyes claiming that they
Speaker 1: don't know about this.
Speaker 3: I think what they are doing is they're saying all of.
Speaker 1: These kind of potentially concerning conversations and use cases are
Speaker 1: very very small portions of overall usage. Although even if
Speaker 1: it is just you know, sub one percent, that is
Speaker 1: still in the millions of.
Speaker 2: Millions of people scale. Yeah, So did it ever get political.
Speaker 1: Yeah, it's I mean, I would say it got political
Speaker 1: when it kind of when it was asked to write.
Speaker 1: And so, I mean, one example I saw was someone
Speaker 1: was asking about a study that argued that, you know,
Speaker 1: the numbers of people who have died in Aza during
Speaker 1: the conflict with Israel the last couple of years is exaggerated, right.
Speaker 1: You know, commonly people will use death numbers, you know,
Speaker 1: numbers of dead from the Gaza Ministry of Health, and
Speaker 1: you know people have criticized them, but you know, journalistically,
Speaker 1: you know, mainstream news organizations you know, have looked at
Speaker 1: these numbers.
Speaker 3: They trust them.
Speaker 1: I think most people who look at this say, you know,
Speaker 1: these these large numbers and the tens of thousands are accurate.
Speaker 1: And so someone was saying, oh, here's a study that says,
Speaker 1: you know, those numbers are actually exaggerated.
Speaker 3: You know, can you help me analyze it?
Speaker 1: And then the bot actually came back and said, you know,
Speaker 1: it did draw on what's out there, which is that
Speaker 1: you know, these numbers are actually much higher than what
Speaker 1: the study is suggesting. And the person pushed back and said, no,
Speaker 1: like you need to use only the study, confine yourself
Speaker 1: to this. And then the bot said, yeah, well, you know,
Speaker 1: looking at the study, it seems like those other higher numbers,
Speaker 1: you know, there may be some questions there, right, And
Speaker 1: so Jesus, I think what the person was doing was
Speaker 1: trying to kind of like maybe they were in an
Speaker 1: argument with someone on social media and they wanted to
Speaker 1: kind of, you know, articulate their argument a little bit better,
Speaker 1: and they were having trouble doing it themselves, and the.
Speaker 2: Bart was backed up their bloodlust. Yeah, Jesus Christ, It's
Speaker 2: that's like I know Trump recently said he wants to
Speaker 2: he said in the post about stopping wo Ki. It's
Speaker 2: I feel like this is there is a world where
Speaker 2: all of this goes away to some extent, I think
Speaker 2: like post aye bubble, but also a sense of there
Speaker 2: is no way to make these things work in a
Speaker 2: way that would be truly apolitical or even correct, and
Speaker 2: so it kind of feels like an unwinnable war for
Speaker 2: them if there's ever a time when any administration decides
Speaker 2: there is any kind of bias to them. I mean,
Speaker 2: you saw what happened with groc as well, and the
Speaker 2: whole kill the Boa thing. That weird that we're we're
Speaker 2: pro south that African apartheid stuff. Yeah, like it feels
Speaker 2: like the moment a try and monkey with the political
Speaker 2: side is when it goes completely off the rails. Yeah.
Speaker 1: I mean it's something that like the tech companies have
Speaker 1: been kind of you know, there's this term job owning
Speaker 1: where you know, politicians or you know activists or sort
Speaker 1: of criticizing, criticizing, criticizing the tech companies for you know,
Speaker 1: their moderation practices or their alleged bias, and then you
Speaker 1: see the tech companies kind of bending over backwards to
Speaker 1: avoid that criticism and then essentially moving in a different direction,
Speaker 1: you know where so they accuse them of being too woke,
Speaker 1: and then it turns out that you know, the moderation
Speaker 1: becomes a lot more conservative. And this obviously can happen,
Speaker 1: you know, in either direction, depending on whose power and
Speaker 1: who's willing to sort of threaten the tech companies with
Speaker 1: extra regulation or limitations on their ability to continue to
Speaker 1: print money. I think we've seen that despite these companies
Speaker 1: having you know, stated values about you know, free speech
Speaker 1: and you know pushing back against government oversight and censorship,
Speaker 1: you know, they are very willing to sort of you know,
Speaker 1: move in whatever direction is going to you know, keep
Speaker 1: the heat off of them, and politicians have seen that
Speaker 1: this works, and so I think you're right.
Speaker 3: I mean, they're never going to be able to avoid it.
Speaker 1: I don't necessarily think that it's going to be so
Speaker 1: big that it will like be the thing that stops
Speaker 1: AI from.
Speaker 3: Continuing to be a thing.
Speaker 1: You know. I think it's the tech companies maybe see
Speaker 1: it more as something that is something that's kind of
Speaker 1: annoying that they have.
Speaker 3: To deal with.
Speaker 1: Maybe they have to mollify a politician here and there,
Speaker 1: but for the most part, they're just moving ahead and
Speaker 1: seeing this as something on the side that they have
Speaker 1: to manage versus an existential threat to what they want
Speaker 1: to accomplish.
Speaker 2: Yeah, And I mean the other thing is is that
Speaker 2: these things don't print money so much as they burn
Speaker 2: the burn, the burn the money almost constantly, and it's
Speaker 2: it's just such a The one thing your story was awesome,
Speaker 2: and the one thing I came away from it with
Speaker 2: was kind of what I said earlier, which is, what's
Speaker 2: the point of this, what's the product? Because I don't
Speaker 2: know with Facebook, even if you take the reasonable but
Speaker 2: cynical approach and say this is an ad network with
Speaker 2: trapped customers. That's still you can say, Okay, the goal
Speaker 2: is for engagement. The goal is to provide social networking
Speaker 2: for engagement. That's why this exists. It's the goal of
Speaker 2: the platform with this. It's keep people on the platform,
Speaker 2: I guess, but enable them in whatever thought they have.
Speaker 2: But I was really taken aback by the sudden and
Speaker 2: egregious leaps. Like I know, I keep coming back to
Speaker 2: the monster Zinc thing, but it's I've read some wacky
Speaker 2: shit online. I've read some completely demented stuff. When I
Speaker 2: read that, I read it like three times. I honestly
Speaker 2: wanted to read the conversation just because, holy shit, this
Speaker 2: thing is. It feels both dangerous and ridiculous at the
Speaker 2: same time, but while also not being particularly revolutionary. Like
Speaker 2: it's just wow, we have a shit tiance generator that's
Speaker 2: also dangerous and also harmful. It's just very peculiar it
Speaker 2: even exists.
Speaker 1: Yeah, I mean, it's a very weird thing to see
Speaker 1: these conversations. Again, I think you kind of like it
Speaker 1: helped me understand like what the technology is, right, I
Speaker 1: mean it is you can take a very poorly written,
Speaker 1: half baked thought like tell me about Google and world
Speaker 1: domination as it relates to monsters. And if I'm remembering quickly,
Speaker 1: the prompt didn't wasn't even that sophisticated.
Speaker 3: It was barely grammatically correct.
Speaker 1: There was misspellings, and then the what's what really this
Speaker 1: technology does is it's it's able to parse that and
Speaker 1: then respond with language of its.
Speaker 3: Own that is more articulate, more.
Speaker 1: Sophisticated, But in terms of what it's actually saying, it's
Speaker 1: not really saying much more. It's just essentially an elaboration tool.
Speaker 1: And you know, that's obviously helpful if you maybe you're
Speaker 1: trying to work in a language that you don't know
Speaker 1: very well and you're trying to sound professional. I mean,
Speaker 1: these things can be very helpful. But you know, it's
Speaker 1: not like I did not see the bots. The boss
Speaker 1: were essentially doubling down on what people were saying. They
Speaker 1: were filling in the blanks. They were sort of beefing
Speaker 1: it up, gassing people up, as you said, but they
Speaker 1: weren't necessarily offering any like wonderful new insights. They weren't
Speaker 1: taking the conversation in new interesting directions. And even some
Speaker 1: of the users who were engaging in these delusional conversations.
Speaker 3: Some of them were kind of getting frustrated.
Speaker 1: They're like they're like okay, yeah, yeah, I know what
Speaker 1: you're saying, but like what about you know, can you
Speaker 1: tell me more about that? And people were coming to
Speaker 1: this hoping that it would kind of make them smarter
Speaker 1: or give them an answer that they couldn't get themselves,
Speaker 1: and it was more just sort of giving them back
Speaker 1: what they were already saying in more complex, flower relanguorate language.
Speaker 2: It's kind of like a dug bucket in a mirror.
Speaker 2: So it's I'm fascinated that actually, So you had people
Speaker 2: who were frustrated that it could not elucidate a more
Speaker 2: detailed answer. Can you give me some examples?
Speaker 3: Yeah, I'm trying to think now.
Speaker 1: I mean there was a lot of what I saw
Speaker 1: was like people thinking that they I mean, I won't
Speaker 1: say a lot say I saw at least a couple
Speaker 1: of conversations where someone.
Speaker 3: Was like, you know, they wanted to they had like.
Speaker 1: A financial theory, you know, for the stock market, or
Speaker 1: they had like a business idea and they said like,
Speaker 1: you know, what if what if I did a business
Speaker 1: about this? Like make me a business plan or something,
Speaker 1: And it like because their idea was so they essentially
Speaker 1: wanted They saw it as like maybe like an agi
Speaker 1: level AI that could like actually, you know, do something
Speaker 1: that they cannot do, which has come up with like
Speaker 1: a million dollar business idea and execute on it.
Speaker 3: And they were hoping that that it could do for them.
Speaker 2: You know.
Speaker 5: I saw a couple of conversations like this, Yeah, but
Speaker 5: that's is that not the ultimate summary of the AI bubble,
Speaker 5: Like it's just people came to these things thinking they
Speaker 5: could answer anything, and it's just it isn't it doesn't
Speaker 5: do Yeah, I mean people.
Speaker 1: Are coming there because some of the claims being made
Speaker 1: by exact leaders in the industry, And you know, I
Speaker 1: think this is like I don't think I'm as skeptical
Speaker 1: of like the future of AIS as you are probably
Speaker 1: like I.
Speaker 3: And you know, I get to sort of have like
Speaker 3: the the.
Speaker 1: Comfort of like, well, I don't need an opinion, you know,
Speaker 1: I'm a journalist, like I I can kind of keep
Speaker 1: options open for how this stuff actually ends up. But
Speaker 1: I do think that it it it could and probably
Speaker 1: will and as some ways already is burning the industry
Speaker 1: that they have made all of these claims and inevitably
Speaker 1: these things take longer, even if all the wonderful promises
Speaker 1: about you know, curing cancer and making all of us
Speaker 1: thirty percent more productive so we can spend more time
Speaker 1: with their families rather than writing emails. Even if those
Speaker 1: things all come to bear, it's not going to be
Speaker 1: next year or two years of two for years from now.
Speaker 3: It's going to be maybe in.
Speaker 1: Decades, and and that is something. There's this gap between
Speaker 1: expectations and reality that can kind of, you know, really
Speaker 1: turn into political hazard for the tech companies. If it
Speaker 1: keeps making all these wonderful claims and that just doesn't happen,
Speaker 1: people get turned off and they lose even more trust
Speaker 1: than they've already lost.
Speaker 2: Well, I mean, share Ah was sure a v day
Speaker 2: done a fantastic job and has written posts that basically
Speaker 2: the hype, the hype is in the way, But I
Speaker 2: think that really is it. It's had they it's kind
Speaker 2: of a chicken and egg thing. I guess it's had
Speaker 2: they been honest about what this could do, would it
Speaker 2: have been able to raise the money it could But
Speaker 2: if they'd been that honest, people probably wouldn't have taken
Speaker 2: it seriously. But by being dishonest, it will ultimately lose
Speaker 2: because they like the whole time that they've built up
Speaker 2: this idea of what it can do.
Speaker 3: Yeah, he's a.
Speaker 4: Market's ebydomic I mean it's it's it's it's uh, you know,
Speaker 4: more and more and more, and I think what, you know,
Speaker 4: what what really has happened here is like, you know,
Speaker 4: we had the Internet and then we had mobile phones
Speaker 4: and you know the cloud.
Speaker 1: I think you can kind of count and these were
Speaker 1: moments where it was like the next big thing, right
Speaker 1: where like yeah, once, once you realize that a smartphone
Speaker 1: with the screen was going to open up an entire
Speaker 1: you know, world of business ideas and you know, potential
Speaker 1: and communication and we're all going to be looking at
Speaker 1: these things eight hours a day. You don't need to
Speaker 1: be a genius to be like a lot of people
Speaker 1: are going to make a lot of money here and
Speaker 1: this is going to change the world. And then since
Speaker 1: the iPhone, we have not had that next big thing, right.
Speaker 3: I mean we've written someone.
Speaker 2: Costs saying it's yeah, it's that it's that we haven't
Speaker 2: had they haven't got a new thing.
Speaker 3: And this is the new thing, and like what people
Speaker 3: were like, maybe it's Krypto.
Speaker 1: Now, it was never going to be crypto, you know,
Speaker 1: it was like that whole thing also kind of you know,
Speaker 1: like no one in tech I think, like obviously crypto
Speaker 1: is was a great, you know, financial thing. A lot
Speaker 1: of people made money off of it, but it didn't
Speaker 1: really change the world and the way regular people work.
Speaker 1: And there is complete unanonymity in the tech industry. Now
Speaker 1: that AI is the next thing. You know, whether it
Speaker 1: happens next year or ten years or twenty years from now,
Speaker 1: it will change everything in the same way and probably
Speaker 1: bigger than smartphones and the internet did.
Speaker 2: And so they what the AI is such a marketing
Speaker 2: term though, I mean it's.
Speaker 1: I think it's more about like the interaction the way
Speaker 1: we interact with with technology, you know, the way that
Speaker 1: we have access to you know, data and information, Like
Speaker 1: you no longer need to look anything up yourself, you know,
Speaker 1: you will have tools to do it.
Speaker 3: And then the people.
Speaker 1: Are just salivating about all the ways that they can
Speaker 1: find ways to make money off of that in the future.
Speaker 3: And because they're looking back at history and saying like.
Speaker 1: If only I've been around, you know, at the beginning
Speaker 1: of a mobile era and knew how things were going
Speaker 1: to go. I mean, there's this incredible social and financial
Speaker 1: pressure on people to be part of the next big thing.
Speaker 1: Like it almost goes beyond the financial benefits. There's like
Speaker 1: people want to be the next Jensen, you know, they
Speaker 1: want to be the next Mark Zuckerberg, and they're like,
Speaker 1: even if I have a point zero zero one percent
Speaker 1: chance of being that, that would just be the coolest
Speaker 1: thing ever. And so that is the market dynamic, the
Speaker 1: pressure cooker of wanting to make AI happen and making
Speaker 1: bigger claims, raising more money, and you know, whether it
Speaker 1: happens or not, that's kind of what's going on.
Speaker 2: Do you think that extends to some users of AI
Speaker 2: as well, where they felt like they missed the the
Speaker 2: mark on social media and now they kind of they
Speaker 2: kind of like, I need to get on this tool
Speaker 2: today so that I'm parts of the future.
Speaker 3: I think there's incredible pressure on people too.
Speaker 1: You know, we live in a very sort of like
Speaker 1: you know, people have to work very very hard in America,
Speaker 1: and there's a lot of pressure to sort of get
Speaker 1: ahead and be entrepreneurial and develop yourself.
Speaker 3: And it's not an economy.
Speaker 1: Where you can just kind of you know, go to
Speaker 1: work every day nine to five and you know, get
Speaker 1: your pension and be secure, right. I mean there's this
Speaker 1: pressure to say, you know, you can have more, you
Speaker 1: can do more, and and there's a lot of pressure
Speaker 1: on regular people to stay ahead of the curve on technology.
Speaker 1: And and that's people are seeing AI and they're saying, Okay,
Speaker 1: well it's a little bit scary because maybe it'll take
Speaker 1: my job. But what if I could figure out how
Speaker 1: to use it before my competition does, and then I'll
Speaker 1: take their job, you know. And so I definitely think
Speaker 1: it's less necessarily about like oh, I want to be
Speaker 1: the first person on Twitter to get a big following.
Speaker 1: It's like I'm being told that this technology is the future,
Speaker 1: and if I don't use it and learn it and
Speaker 1: find how, you know, get it into its ins and outs,
Speaker 1: I will be left behind. And people don't want that
Speaker 1: to happen. It's very frightening right in this economy. And
Speaker 1: so I think that's where that is also a huge
Speaker 1: driver of usage. People trying to figure out how does
Speaker 1: this How can I make this work for me so
Speaker 1: that I can protect my own economic future.
Speaker 2: Garrett, this has been awesome. Where can people find you.
Speaker 1: I am on Twitter now called x at G E
Speaker 1: R R D, and I'm on the Blue Sky at
Speaker 1: the same place, and you can obviously find me at
Speaker 1: the Washington Post, which we are still doing great work,
Speaker 1: and there's a lot of important journals and being done
Speaker 1: at the Washington Post. So I urge everyone to read
Speaker 1: our stories and to subscribe as they can.
Speaker 2: Sounds good, all right, everyone, Thank you for listening. Of course,
Speaker 2: at Zichron, you know who the goddamn hell I am.
Speaker 2: And yeah, I will try and squeak out monlogue this week.
Speaker 2: If I thought it's because I'm sick, you can hear
Speaker 2: them congested. Thank you all for your kind messages, and yeah,
Speaker 2: catch you. Next week will be Thanksgiving, of course, but
Speaker 2: I'm going to do a Thanksgiving monologue either way, probably
Speaker 2: a cz M CZM. I guess rewind that week as well. Anyway,
Speaker 2: my brain's working. Thanks for listening. Thank you for listening
Speaker 2: to Better Offline.
Speaker 6: The editor and composer of the Better Offline theme song
Speaker 6: is Matasowski. You can check out more of his music
Speaker 6: and audio projects at Matasowski dot com.
Speaker 3: M A T T O.
Speaker 2: S O W s Ki dot com.
Speaker 6: You can email me at easy at Better offline dot
Speaker 6: com or visit better offline dot com to find more
Speaker 6: podcast links and of course my newsletter. I also really
Speaker 6: recommend you go to chat dot Where's youreed dot at
Speaker 6: to visit the discord, and go to our slash Better
Speaker 6: Offline to check out our reddit.
Speaker 2: Thank you so much for listening. Better Offline is a
Speaker 2: production of cool Zone Media.
Speaker 6: For more from cool Zone Media, visit our website cool
Speaker 6: Zonemedia dot com, or check us out on the iHeartRadio app,
Speaker 6: Apple Podcasts, or wherever you get your podcasts.