Speaker 1: Pushkin. Welcome back to Risky Business, a show about making
Speaker 1: better decisions. I'm Maria Kanikova and I'm Nate Silver. So
Speaker 1: today on the show, note, we've got we've got a
Speaker 1: pretty interesting Risky Business e news week. We've got GPT
Speaker 1: five being released, which is maybe less of a deal
Speaker 1: than the launch hype announcements were, but still still a
Speaker 1: big deal. And then we have some interesting trade stuff
Speaker 1: going on with AI chips right with Nvidio.
Speaker 2: Interesting is an interesting reader?
Speaker 1: Yes, well, well we've got gp.
Speaker 2: Now there have been five GPT since the last GTA
Speaker 2: Grand Theft Donald. Sorry to throw that in there.
Speaker 1: Fascinating to well, before we before we get into it, Nate,
Speaker 1: I just wanted to say, you know, we're taping this
Speaker 1: on the twelfth of August Tuesday, so a few days
Speaker 1: before listeners will hear it. But congratulations. Today is the
Speaker 1: launch of the paperback of On the Edge.
Speaker 2: On the Edge the Guard We're seeing Everything, the bestseller
Speaker 2: by groundbreaking author Nate Silver. Yeah, so the paperback is out.
Speaker 2: There's a new forward, or I think it's called a
Speaker 2: preface technically. I was just at Barnes and Noble signing
Speaker 2: some copies. A little sweaty. I walked here from there.
Speaker 2: It's hot in the middle of August in New York,
Speaker 2: breaking news. But yeah, no, I think the book holds
Speaker 2: up really well. It covers a lot of really risky
Speaker 2: business esque topics. And you know, it's a big book,
Speaker 2: cheaper now with paback. It fits better on a shelf,
Speaker 2: not quite as thick, and there's new content. So I
Speaker 2: would strongly recommend, of course. I mean, you know, a
Speaker 2: little biased here, but like, thank you.
Speaker 1: Of course. Well, I'm excited to read the new preface,
Speaker 1: and yeah, I definitely recommend the book to everyone. I
Speaker 1: will try to repost the review I did of it
Speaker 1: on my substack so that people can get reintroduced to
Speaker 1: it one more time. Anyway, it's a fantastic book. Congrats Nate.
Speaker 1: And let's get into some rivarian topics, like the release
Speaker 1: of GPT five from open Ai. Nate, have you had
Speaker 1: a chance to use GPT five yet we don't.
Speaker 2: Have much choice, you know, they kind of steer you
Speaker 2: into GPT five if you And at first I'm like, oh, okay,
Speaker 2: I guess I pay for the pro plan and so
Speaker 2: I'm like, oh wow, I'm one of the privileged few
Speaker 2: and now I don't think there's an easy way to
Speaker 2: get back into all the all the old family of
Speaker 2: GPT products that we knew in love before, Like people
Speaker 2: did get it attached to the different models for different reasons,
Speaker 2: and so now it kind of is implicitly trying to
Speaker 2: figure out what you want, which is kind of part
Speaker 2: of I mean, it's interesting. Right. So on the one hand,
Speaker 2: you know, I kind of made the joke before about
Speaker 2: kind of comparing it to like a video game release.
Speaker 2: But anything new, if we release a new presidential model
Speaker 2: or something, right, anything new might have some kinks and bugs.
Speaker 2: I mean, as much as you might say, okay, we
Speaker 2: have to have it perfect before it's ships, I don't
Speaker 2: think it's practical because like most people are gonna only
Speaker 2: learn thing. I mean, you know, I think they did
Speaker 2: discover that's not doing what like groc did and calling
Speaker 2: itself Hitler for example.
Speaker 1: Right, Yeah, I mean I don't know if we could
Speaker 1: really call that a win. Like that seems to be
Speaker 1: like a baseline.
Speaker 2: Or like Google Gemini drawing multicultural Nazis, you know, so
Speaker 2: that the bar is pretty low. But no, I think
Speaker 2: it has a little bit of new car, new model
Speaker 2: kind of smell a little bit. The first thing I
Speaker 2: asked it to do was, my partner are planning a
Speaker 2: trip to Bestly, Scandinavia or the Nordics, technically, right, I
Speaker 2: want to visit these cities. Give us in our itinerary, right,
Speaker 2: and like thinks and things and has something that seemed
Speaker 2: plausible to me, very detailed, right, And then I'm like,
Speaker 2: emails use a PBF and it like freaks out right
Speaker 2: like shows some complicated like you know code, Python code,
Speaker 2: and it's like, I don't know how I know this.
Speaker 2: I don't I know this, So you know it's it's
Speaker 2: up and down. Well, hey, at.
Speaker 1: Least it gave you a plausible itinerary.
Speaker 2: I so.
Speaker 1: We've been warned. We were given an explanation by Sam
Speaker 1: Altman because I tried to test it, you know, when
Speaker 1: it was just released, and apparently the auto switching tool
Speaker 1: was down, so it seemed like it was a lot
Speaker 1: dumber than it was that it was supposed to be.
Speaker 1: So one of the one of the things that they're
Speaker 1: kind of really touting on this model is that it
Speaker 1: automatically knows what you want right, whether it should think
Speaker 1: deeply or not, which does not actually seem to be
Speaker 1: the case. Even now when the swings back, it's like.
Speaker 2: Hey, who got a cheek here? I don't think we
Speaker 2: need too much deep thinking. She probably wants a quick answer,
Speaker 2: get back to the cooking.
Speaker 1: That's that's exactly honestly, that's that's been my experience. And
Speaker 1: one of the first things I actually tested it on
Speaker 1: because it says that, you know, one of the things
Speaker 1: that it's much better on is on hallucination. So I
Speaker 1: actually gave it a psyche question and asked for some
Speaker 1: sources and like papers, and it unfortunately still hallucinates when
Speaker 1: you go down that route. I was because so I'm
Speaker 1: writing a piece this week about kindness contagion. You know,
Speaker 1: when someone is nice, you know, how does that spread?
Speaker 1: And so I asked it some questions about the research
Speaker 1: in that field. And I didn't really need its help
Speaker 1: in the sense that I know the field pretty well,
Speaker 1: but like, I was just curious to see what it
Speaker 1: would come up with. And it gave me some good stuff,
Speaker 1: but it also gave me some stuff that just simply
Speaker 1: does not exist. And I know, you know, as you say,
Speaker 1: and as a lot of people say, like you need
Speaker 1: to know what to ask it but the reason I
Speaker 1: tested this specifically was because one of their big claims
Speaker 1: was no more hallucination basically, which is not true.
Speaker 2: Yeah, I've used it for you know, I almost always
Speaker 2: put the articles I'm writing for the newsletter through a
Speaker 2: copy of it in fact check, in the GPT models
Speaker 2: or cloud. Sometimes I think this is one of the
Speaker 2: most useful things that AIS are good with. It took
Speaker 2: seven and a half minutes for what, from Nate is
Speaker 2: a relatively short article seventy birds or something, Right, I
Speaker 2: am using, like the thinking version, right.
Speaker 1: Did you tell it to use the thinking version or
Speaker 1: did it?
Speaker 2: I think I was on thinking by default, right, And
Speaker 2: usually I say have a high threshold, and I didn't
Speaker 2: say that. But it's really nitpicky. It's like, you know,
Speaker 2: I made some line about like Elon Musk is like
Speaker 2: tweeting out like anime smut was a term I used, right,
Speaker 2: I already tone that down from porn. And it's like
Speaker 2: you should be more careful, you should say NSFW images
Speaker 2: smut implies an editorial stance, you know. So like it's
Speaker 2: just like it was kind of nitty as a poker term, right,
Speaker 2: and like I I had it actually a friend sent
Speaker 2: me a poker hand because i'd we wrote before or
Speaker 2: I wrote before about how Chick's bad at poker, and
Speaker 2: it played the first hand really well, and then I'm like, okay,
Speaker 2: simulate more hands, and then it was still kind of
Speaker 2: not not great. Maybe a little better, right, Yeah, No,
Speaker 2: it's a little weird because I don't look to me,
Speaker 2: you kind of had four point five and you had
Speaker 2: like oh three and oh one. Right, Like I noticed
Speaker 2: in the spring, I thought some of the reasoning quote
Speaker 2: unquote models or the type of stuff I'm doing, which
Speaker 2: is not using GPT as a chat bot, right, it's
Speaker 2: like a workaid, you know. I thought there was an
Speaker 2: improvement then, and I kind of feel like there's a
Speaker 2: bit less this time. You know. Also, the open air
Speaker 2: models or some of them are kind of slow, right,
Speaker 2: I've found sometimes like Clode or Gemini or even groc
Speaker 2: will like spit out things faster, and so you.
Speaker 1: Know, and sometimes that matters, by the way, like sometimes
Speaker 1: you wanted to take time, but sometimes like you're like, okay,
Speaker 1: come on, like let's let's get let's get moving, and
Speaker 1: uh yeah that if.
Speaker 2: You can get me a simple you know, and often
Speaker 2: like in a fact check, I mean today I had
Speaker 2: to go to the pricking Barnes and Noble, right, and
Speaker 2: so like the time pressure is often relevant, especially, you know,
Speaker 2: and oftentimes I also use the models a lot for
Speaker 2: like little programming task right, Like I forget the programming
Speaker 2: using language US is called STATA. Some people would say
Speaker 2: it's kind of a fake, but you know it's a
Speaker 2: it's a real language, right, and I'm like, I forget
Speaker 2: how I do this instat or full disclosure, even Excel.
Speaker 2: I'll be like, oh guy, what's the complicated formula for this? Right?
Speaker 2: And like it seems to me to be seventy to
Speaker 2: eighty percent reliable the AI models in general, for like
Speaker 2: you know, where I want a snippet of code that's like,
Speaker 2: you know, three to ten lines long, right, you can
Speaker 2: kind of vibe code, right, You're just like I want
Speaker 2: to do this right, and it's like, you know, it's
Speaker 2: pretty smart about it. I mean, every now and then
Speaker 2: they won't work. And you know, I always tell people
Speaker 2: like these things screw up when they're trying to chain
Speaker 2: together different steps and they don't really quite I mean
Speaker 2: they're trying to train themselves, right, but they don't quite
Speaker 2: know how to stop right, and so it's like, okay,
Speaker 2: when I build a model, we're going to NFL model.
Speaker 2: You know, I stop at every point and ask, okay,
Speaker 2: does this output make sense? Do you form a bunch
Speaker 2: of complicated discoperations sort from the bottom to the top. Right,
Speaker 2: you know, hopefully Tom brady ease list is one of
Speaker 2: the best quarterbacks, right and Ryan Leaf is one of
Speaker 2: the worst. But like, yeah, I'm keeping myself in the loop.
Speaker 2: And for that kind of thing, it's like, okay, I'll
Speaker 2: just say fifteen minutes trying to figure how to program
Speaker 2: this or you can debug, right, You're like, why isn't
Speaker 2: this working? As somebody's clawed and it was like, because
Speaker 2: you made a typo. You misspell the word nate. That's
Speaker 2: why you've been tearing your hair out for forty minutes.
Speaker 2: Is like you misspell the word yeah. So yeah, no, look,
Speaker 2: I think it's kind of halfway a branding it did
Speaker 2: it did your size or some people seem to vouch
Speaker 2: for this a lot. I don't know.
Speaker 1: Well, you know, obviously, like I don't code right, and
Speaker 1: people have said that it seems to be a lot
Speaker 1: better at coding certain things, which great, you know, if
Speaker 1: it is great, but when you're what you're saying actually
Speaker 1: like seventy to eighty percent to someone like me, that
Speaker 1: makes me actually much less likely to use it because
Speaker 1: I'm not you. I don't have that background, so I
Speaker 1: can't do I can't always do like a check to
Speaker 1: figure out, you know, does this make sense? Right, And
Speaker 1: in the sense that I don't have that technical base,
Speaker 1: I can't review it in any real way, and so
Speaker 1: I need it to be accurate, right, because I don't
Speaker 1: trust myself to spot any potentially would.
Speaker 2: Say the old way is to look at a manual
Speaker 2: or like stock overflow or whatever, and there you're starting
Speaker 2: through a lot of crap too. Right, it might not
Speaker 2: be pertinent to your particular KSE, or it might be
Speaker 2: an old version of software or instead of there are
Speaker 2: lots of little fussy language things with local variables and
Speaker 2: scalers and one of those things mean and what the
Speaker 2: different rules are and stuff like that. Right, it slightly
Speaker 2: fussy language, and it's good for a handle that kind
Speaker 2: of thing. And again, to me, I'm working in ways
Speaker 2: where it's fairly failure proof. Right, You're doing one thing,
Speaker 2: you have an expectation for what that will do to
Speaker 2: transform the data set, right, and if it doesn't happen,
Speaker 2: then it won't work anyway. Right, But like the notion
Speaker 2: of like I'm just gonna sit back here and trust
Speaker 2: it to do all these things, I mean, I think
Speaker 2: it's probably, you know, to code an entire NFL model,
Speaker 2: which involves a lot of original research collection and involves
Speaker 2: a lot of like knowledge about the sport, knowledge how
Speaker 2: to build models right, a lot of trial and error,
Speaker 2: Like you know, I don't think the AIS are particularly
Speaker 2: close to doing that kind of work.
Speaker 1: So I think that you just made a really important point,
Speaker 1: which is to just not expect the world from it
Speaker 1: and to like to know what it can and can't do, which,
Speaker 1: by the way, already takes a certain user intelligence and
Speaker 1: like knowledge to say, Okay, you know what, I don't
Speaker 1: trust it to do this, but I do trust it
Speaker 1: to do that. So there is you know, even though
Speaker 1: GPT five was kind of hype, does you know you
Speaker 1: don't have to think anymore. You still actually kind of
Speaker 1: do right in order to get the outputs that you want,
Speaker 1: and to realize, Okay, I can trust this task, but
Speaker 1: not this task. I can I can get it to
Speaker 1: do this, but not that and I think that that,
Speaker 1: you know, that human in the loop is still very
Speaker 1: much a thing and still very much needs to be
Speaker 1: a thing.
Speaker 2: Yeah. There, I just kind of keep like a little
Speaker 2: running mental tracker of like here my expectations for AI models,
Speaker 2: LLM SLARGE language models, and are they exceeding those or
Speaker 2: were falling short? Right? I mean? And then do weird thing,
Speaker 2: you know, I like I had a situation where, like
Speaker 2: I had a bunch of latitudes and longitudes of NFL
Speaker 2: stadiums that we'd code up quickly, right, and we're like,
Speaker 2: I want to reverse look these up and tell me
Speaker 2: what city they're near, right as a double check, and
Speaker 2: like put Atlanta as like Chattanooga and just a little
Speaker 2: it's you know, and so like for things like that,
Speaker 2: because for data, I want all my data to be perfect, right,
Speaker 2: I don't want to misattribute one city that doesn't really
Speaker 2: matter if they have the you know, Falcons playing in Chattanooga,
Speaker 2: it doesn't matter that much, right, and like and so
Speaker 2: for that kind of thing, you know, I I still
Speaker 2: would rather have my, yeah, my research assistant do it
Speaker 2: or me do it myself. Right, things that require like
Speaker 2: a lot of person But you know, it's very frustating,
Speaker 2: use them enough where I have like particular rules where
Speaker 2: I think they're likely to be helpful and not and
Speaker 2: how how safely can you fail and things like that,
Speaker 2: But like, yeah, I mean my general view is that
Speaker 2: getting kind of savant like and that they're not very
Speaker 2: bright about some things and they're freaking genius is about
Speaker 2: others as opposed to this notion of like general intelligence,
Speaker 2: where I mean, but you know, look, even the things
Speaker 2: it's worse at, it's like as good as like a
Speaker 2: you know, high school sophomore or so, you know, I mean,
Speaker 2: it's not terrible and there aren't too many things where
Speaker 2: it's terrible, right, But like, yeah.
Speaker 1: Yeah, well, I think I think it really really depends
Speaker 1: on what you're asking it.
Speaker 2: You know.
Speaker 1: One of the main things that I've read about people
Speaker 1: kind of responding to which highlights an issue that you know,
Speaker 1: Sam All was like, Oh, we didn't realize how big
Speaker 1: of an issue this was, which I think is very
Speaker 1: interesting because people have been trying to say it's an issue.
Speaker 1: Is the change in voice, right, the fact that past
Speaker 1: models were very psychophantic. You and I were talking before
Speaker 1: taping today and I was like, we should really be
Speaker 1: pronouncing the psychophantic because there's been some there's been some
Speaker 1: real psycho behavior here, and when they introduced GPT five,
Speaker 1: the default tone of voice was very different. Right, They
Speaker 1: did try to address this, and then they got within
Speaker 1: not even twenty it didn't even take twenty four hours.
Speaker 1: Just like immediately they got all of this pushback with
Speaker 1: people saying, no, you know, I've lost my boyfriend, I've
Speaker 1: lost my best friend, I've lost the person who told
Speaker 1: me I was a genius. And it makes you realize
Speaker 1: how many people were using this really not for what
Speaker 1: it's intended, and something that can be incredibly bad for
Speaker 1: a lot of things, right, mental health, just social connections,
Speaker 1: all of these things that you know people people were like, whoa, whoa, whoa?
Speaker 1: What happened to my significant other? And they brought it
Speaker 1: back right, so now you can actually select that voice again.
Speaker 1: We have the default voice, but we also have you know,
Speaker 1: the listener of voice, and there are a few other voices.
Speaker 1: None of their descriptions actually map onto what that actually is.
Speaker 1: I was reading there was like a cynic and I
Speaker 1: don't even remember what they said the cynic voice was,
Speaker 1: but I was like, that's not what a cynic is
Speaker 1: like it was, it was they're really they're really weird.
Speaker 1: They're really weird descriptors. But basically you can get you
Speaker 1: can get GPT to interact with you in different voices,
Speaker 1: and I you know, my reaction to that is like
Speaker 1: you shouldn't always give the people what they want in
Speaker 1: a lot of ways, like this was bad and like
Speaker 1: you've fixed it, like don't go don't go unfixing it,
Speaker 1: because even though you fixed it, they didn't fully fix it, right,
Speaker 1: they just made it less overt, which you know, subtle
Speaker 1: psychopancy can also be bad. But they've tried, they at
Speaker 1: least initially tried, and now they've really gone back on
Speaker 1: that immediately caving to pressure. And if you always give
Speaker 1: the public what it wants, like we've talked about p
Speaker 1: doom and like a lot of people.
Speaker 2: Want things you really should not be getting these, it's
Speaker 2: pretty hard kind of in an equilibrium, like not to
Speaker 2: optimize for what drives engagement in the show. I mean,
Speaker 2: you know, on the one, thecept something that they don't
Speaker 2: need revenue like right away, but like, yeah, no, I
Speaker 2: think sometimes these models like you give them an inch
Speaker 2: and they take a mile with it, right, like if
Speaker 2: you look at what happened with ROC when it had
Speaker 2: its moment, the prompts that system prompts that elon or
Speaker 2: ate an unnamed engineer XAI was using. We're like not
Speaker 2: that wild, right, but like if you go down a
Speaker 2: rabbit hole, we keep kind of getting like reinforcement feedback.
Speaker 2: This is good, this is good. And you know, you
Speaker 2: know open aies models used to have this thing where
Speaker 2: did you like answer A or answer beast. They're now
Speaker 2: outsourcing it to some of their users, right, and like yeah,
Speaker 2: I mean, look, you know what are you kind of
Speaker 2: optimizing for objectively? Right? It's kind of easier when you
Speaker 2: are trying to train it on like a math problem
Speaker 2: where there's an objectively correct answer, right. You know, the
Speaker 2: NFL model turning to build the end of the day,
Speaker 2: how accurate is it to predict NFL games? Right? That's
Speaker 2: that's the bottom line.
Speaker 1: You actually have a metric, right.
Speaker 2: And for you know, for feedback or goodness of an
Speaker 2: answer that's more subjective, then it's a lot trickier, right.
Speaker 2: You know, I think we've seen with some of the
Speaker 2: you know, some of the reason that like Grock hasn't
Speaker 2: had as much reinforcement learning training right or or or
Speaker 2: you see that right? You know, the works are pretty rough.
Speaker 2: I'm mixing metaphors here and yeah, I mean there it's
Speaker 2: a weird technology, and I think people understand more about
Speaker 2: how these models work than before. And like, by the way,
Speaker 2: like one reason to be optimistic unless you're a doomer.
Speaker 2: I guess it's just like the amount of like human
Speaker 2: another capital being poured into AI research, right is like,
Speaker 2: you know quite something, right, It wouldn't surprise me at
Speaker 2: these companies start saying, oh, we have a lot of
Speaker 2: smart people's to kind of spin off technologies and energy
Speaker 2: or quantum computing or whatever else.
Speaker 1: Right, yet, know, I think that there's so much promise,
Speaker 1: but I think that with this particular thing, the incentives
Speaker 1: are misaligned at least for now, which you were kind
Speaker 1: of hinting at, right in the sense that sure, like
Speaker 1: they don't necessarily need it. But if people if this
Speaker 1: is one of the things that fuels revenue growth, right,
Speaker 1: that people want to feel not lonely. They want you know,
Speaker 1: someone who's kind of reinforcing their ideas that they interact more,
Speaker 1: which is good, right if you're actually paying more in
Speaker 1: order to be able to kind of spend more time
Speaker 1: with the model, and they're interacting more when they feel like,
Speaker 1: this is my girlfriend, this is my boyfriend, this is
Speaker 1: you know, my best friend, This is my counselor my psychiatrist,
Speaker 1: you know whatever, it is my teacher. There was a
Speaker 1: guy who was who Cash Hill just wrote about in
Speaker 1: the New York Times.
Speaker 2: Who.
Speaker 1: Believed that he'd created a new mathematical theory right that
Speaker 1: solved everything, and like and tried to actually he tried
Speaker 1: to fact check the delusion, which was crazy. He's like,
Speaker 1: I feel like I'm sounding crazy and the a I
Speaker 1: was like, no, You're absolutely not crazy. You're the most
Speaker 1: everyone else is crazy, right like you're saying so. So
Speaker 1: was one of these very strange kind of things where
Speaker 1: he went in. By the way, his first question was
Speaker 1: he wanted it to explain how you got the value
Speaker 1: of pie, because this was someone who never finished high
Speaker 1: school and his son needed to help with homework, and
Speaker 1: so he just asked chat japt about pie. That was
Speaker 1: the start of this insane rabbit hole. And as and
Speaker 1: I'm not I mean, we're talking about chat JEPT because
Speaker 1: of GPT five release. But this doesn't just apply here.
Speaker 1: We're just talking about. You know, that's Lum's in general
Speaker 1: probably will be susceptible to this. I'm not sure, right
Speaker 1: because all of these examples are from CHATJAPT But you know,
Speaker 1: you have this innocuous question that then leads someone to
Speaker 1: a very detrimental spiral. And it's not a standalone case, right,
Speaker 1: And now we know how many cases that were below
Speaker 1: the radar because nothing bad, nothing quote unquote bad happened,
Speaker 1: yet there were given the outcry when the model shifted.
Speaker 1: And I don't think honestly, like, do we really trust
Speaker 1: a company that's clearly profit driven? We know, I mean
Speaker 1: Sam Milton's companies profit driven. I know, I know it's
Speaker 1: it's crazy.
Speaker 2: Gambling a casino, But do we.
Speaker 1: Really trust a company that's profit driven, right, that's bottom
Speaker 1: line driven to sure, they will fix all these other
Speaker 1: problems if it's good for them, But do we trust
Speaker 1: them to fix these things that are really undermining mental health?
Speaker 1: We've seen that meta, you know, Facebook, all these none
Speaker 1: of them have right for years, they've known that there
Speaker 1: are issues that have existed and they haven't addressed that.
Speaker 2: I'm not as convinced about this particular problem. I mean
Speaker 2: because first of all, what it's substituting for. Is it
Speaker 2: subsitting for Twitter or Reddit forums or some other dark
Speaker 2: corner of the Internet.
Speaker 1: Potentially No, because from a psychological standpoint, there's immediate reinforcement
Speaker 1: and a conversation that goes back and forth, which is
Speaker 1: very very different for the brain than like saying something
Speaker 1: and then.
Speaker 2: On Twitter your media feva.
Speaker 1: But it's not quite the same thing. Just from a
Speaker 1: psychological standpoint, it can be much more pernicious when you
Speaker 1: feel like you're talking to an actual person and personalities
Speaker 1: do start developing. I mean, what do you like? Obviously
Speaker 1: this is this is not ideal. What do you think
Speaker 1: is ideal?
Speaker 2: Right?
Speaker 1: If you're interacting with chat GPT? What personality do you
Speaker 1: want me personally? I want no personality whatsoever. I just
Speaker 1: wanted to give me the damn facts.
Speaker 2: You can give it custom instructions, right, which is like appendittus.
Speaker 2: So I tell it like be honest and straightforward site sources.
Speaker 2: It's fine to speculate, but if you are speculating, label
Speaker 2: it speculation that kind of thing, right, And.
Speaker 1: It sometimes lies about that too, Like when I say
Speaker 1: give me sources and it lies about the sources and
Speaker 1: you say hey, the source doesn't exist, and it says
Speaker 1: something like you caught me. And I'm like, okay, well
Speaker 1: you're you're ostensibly following instructions, but you're not really And
Speaker 1: I actually, I don't know, I didn't try that you
Speaker 1: caught me thing with GPT five. I don't know if
Speaker 1: it's still going to do kind of a version of that.
Speaker 1: But Nate, I mean, if we're looking at the reliability
Speaker 1: of these models, right, you had mentioned the Chattanooga example
Speaker 1: right where it thought that Chattanooga and Atlanta were interchanging.
Speaker 2: Basically, well, Atlanta wasn't in my gazzeteer file. I forgot
Speaker 2: about it. Man. It ha'd a litle arch just sitting
Speaker 2: in Georgia. But I fixed it now, right.
Speaker 1: But if it's getting things like Chattanooga wrong, like you
Speaker 1: start questioning how how good its output was on other things.
Speaker 2: Yeah, and if there are you know whatever, four hundred
Speaker 2: rows of data and it screws up five, it's a lot.
Speaker 2: And the analogy here is something like, Okay, if you
Speaker 2: take the subway in New York, you don't have to
Speaker 2: like look up the timetables because it's rarely more than
Speaker 2: like a five or seven minute wait for a train, right,
Speaker 2: you just go on.
Speaker 1: Station unless it's the B, in which case you'll be
Speaker 1: waiting forever.
Speaker 2: Sorry, B fans, I'm on the L now. It's a
Speaker 2: real it's a real adult train. Rent there.
Speaker 1: The B stands for bullshit.
Speaker 2: Bullshit Okay, And that's when it's part of your continuous workflow, right,
Speaker 2: chechup is more like you go to like the amcrak
Speaker 2: station and you have to plan around it a little bit. Right,
Speaker 2: It's not it's you know, it's not kind of the
Speaker 2: bonus productivity from from kind of turning your brain off
Speaker 2: and and just having to handle the work. You know.
Speaker 2: With that said, you know, I've come home uh knights
Speaker 2: when I'm tired or busier and had a couple of
Speaker 2: glasses of wine, right, and it sure is nights then
Speaker 2: to be like, oh, I forget the stupid fucking command
Speaker 2: and static, Can you just tell me what it is? Right?
Speaker 2: But I'm you know, still using it in like a
Speaker 2: piecemeal way.
Speaker 1: And we'll be back right after this.
Speaker 2: All this might be kind of not bad for AI safety, right,
Speaker 2: Like I kind of think.
Speaker 1: Like, yeah, well I think it depends. So I don't
Speaker 1: know the nitty gritty of what the improvements were, but
Speaker 1: some of the some of the reviews that I read,
Speaker 1: some have said that the lack of transparency into kind
Speaker 1: of what's being used and how it's being used is
Speaker 1: actually potentially not great. That before you could, you could
Speaker 1: query much better to actually figure out, you know, what
Speaker 1: processes were being used, what models were being used, et cetera.
Speaker 1: Now you can't, and that if some quoting gets to
Speaker 1: a certain point that it can do like some self
Speaker 1: replicating things. And this is kind of what we talked
Speaker 1: about briefly when we were talking about the AI twenty
Speaker 1: twenty seven report, that some of those things are potentially
Speaker 1: becoming closer to being a.
Speaker 2: Real Yeah, I mean this is almost another show, right,
Speaker 2: but like you know, humans currently have an important role,
Speaker 2: a couple of important roles in the process, right, one
Speaker 2: of which is to provide the corpus all the time
Speaker 2: that the models are trained on, the second of which
Speaker 2: is with reinforcement learning. And like again, math problems are
Speaker 2: a weird exception in that you kind of you kind
Speaker 2: of know the answers, right, there's objectively correct answer, it's
Speaker 2: just really hard to figure out, right, and a human
Speaker 2: can say, oh, that's correct, right with other things. It's
Speaker 2: it's trickier if you're if you're, you know, wanting to
Speaker 2: patent some novel protein that could be used in drug discovery, right,
Speaker 2: then you got to test that and make sure it
Speaker 2: actually works. Right, And like so if you don't have
Speaker 2: a human reinforcement learning, if you if you're trying to
Speaker 2: train corpses that are beyond them, I mean there again,
Speaker 2: there are a case where you can extrapolate and get
Speaker 2: fifty percent than the best human or or two hundred
Speaker 2: percent better. Right, the notion of like an explosion of superintelligence,
Speaker 2: I think you know, I mean, these things still can't
Speaker 2: book good fucking delta flight to Chicago, right, I'm probably
Speaker 2: the year they can.
Speaker 1: But by the way, by the way that talking about
Speaker 1: like security risks, this isn't p doom, but like personal
Speaker 1: security risk as it becomes better at doing that, the
Speaker 1: chance of someone hacking and like actually being able to
Speaker 1: insert some malicious code without your knowledge so that you
Speaker 1: end up, you know, so that they can steal credit
Speaker 1: card information, et cetera, et cetera. Don't underestimate. I'm not
Speaker 1: talking about you, but I think we as a society underestimating.
Speaker 2: Maybe even more than to Google, you know what I mean.
Speaker 1: It's crazy and that is available somewhere, right They're storing
Speaker 1: all of that data, and so that means that it
Speaker 1: can be hacked, and if it can be hacked, it
Speaker 1: will be hacked at some point. I think that that's
Speaker 1: kind of the rule of the Internet right now. I've
Speaker 1: spent enough time with you know, con artists and bad
Speaker 1: actors that I know that, like, there's always someone, if
Speaker 1: there's a new technology, there's someone one step ahead figuring out, Okay,
Speaker 1: how do we exploit this? Does it call me Maria? Now?
Speaker 2: Okaymate?
Speaker 1: Sometimes no, it hasn't called me Maria. But I've tried not.
Speaker 1: I mean, obviously you have to sign into it, but
Speaker 1: I try to uh minimize uh minimize my sharing of
Speaker 1: any personal information. But that only takes you so far.
Speaker 1: But yeah, I think that that's kind of a personal
Speaker 1: security risk that people are probably underestimating. I'm guessing there's
Speaker 1: some people who are very well aware of it, but
Speaker 1: I would hesitate before giving it access to you know,
Speaker 1: my travel plans, access to any itineraries, credit cards, et cetera,
Speaker 1: et cetera, because those are things that seem like there
Speaker 1: could be vulnerabilities and we see you know that as
Speaker 1: you said, you know, this is a this launch has
Speaker 1: the new car smell, so like at the beginning, like
Speaker 1: there are going to be bugs. There are going to
Speaker 1: be issues, and sure eventually like some of them are
Speaker 1: going to get sorted out, but there's always going to
Speaker 1: be like the initial data breach that prompts it to
Speaker 1: get sorted out. And I don't want to be part
Speaker 1: of that initial data breach.
Speaker 2: Yeah. I don't know that these giant tech companies had
Speaker 2: behaved in particularly trustworthy they have, right.
Speaker 1: Right, Yeah, I don't think they have. So I don't
Speaker 1: know how much we want to we want to give
Speaker 1: them and what you know, Nate, there might be some
Speaker 1: there might be someone who's like, uh oh, does Nate
Speaker 1: upload all of the specifics of his models to these
Speaker 1: Maybe we should hack Nate's uh chat GPTs that we
Speaker 1: can steal his model and improve on it. I mean
Speaker 1: that seems silly, but like it actually makes corporate espionage
Speaker 1: those types of things much easier too. It's what I've
Speaker 1: always said with you know, con artists, that it's become
Speaker 1: so much easier, and the barrier of entry to conning
Speaker 1: people has become so much lower simply because we share
Speaker 1: so much information online unthinkingly, and so it becomes the
Speaker 1: case where before it would take someone a lot of
Speaker 1: kind of research to try to figure out, you know, oh,
Speaker 1: what are the things you know? Where does Nate like
Speaker 1: to go? What does he? You know? What are the
Speaker 1: pressure points that I can How can I approach him?
Speaker 1: Et cetera, et cetera.
Speaker 2: And maybe you can trade out on poker tells, right, yeh,
Speaker 2: watch five hours of Who's a Poker Player? But yeah,
Speaker 2: Adam Hendrix and figure out like what are his tells? Right?
Speaker 1: But but now I'm you know, con artists can use
Speaker 1: all that information like very quickly because you've shared it.
Speaker 1: And with chat GPT, people are sharing so much right
Speaker 1: on such a personal level, not thinking that this will
Speaker 1: become public, and I don't know why they think that
Speaker 1: it won't. So it's a very you know, it's a
Speaker 1: very interesting conundrum. And I think there are so many
Speaker 1: amazing things that are kind of come out of this,
Speaker 1: and then some very dystopian things and some things that
Speaker 1: will potentially really hurt individual Yeah.
Speaker 2: I mean, look, I'm not even talking about the audio
Speaker 2: and video caare I mean, look, it remains the case
Speaker 2: that if you beamed to twenty twenty five from twenty
Speaker 2: twenty right, you would be amazed by what these models
Speaker 2: can do. Absolut would be considered a freak if you
Speaker 2: had predicted five years ago that you have this machine
Speaker 2: that for many things can like pass the touring tests.
Speaker 2: Some areaster don't use Turing tests, but like it's you know,
Speaker 2: it's basically giving you plausible human level performance across a
Speaker 2: variety of cognitive tasks, deficient in some, excellent in others. Right,
Speaker 2: Like that still is quite amazing, And like part of
Speaker 2: what I'm reacting to is like, you know, where is
Speaker 2: the hype relative to the reality. And it felt like
Speaker 2: a year ago it was like, Okay, people outside of
Speaker 2: Filekan Valley are just not seeing at all the power
Speaker 2: of this, and now they kind of do. And I
Speaker 2: still think that kind of like the political types are
Speaker 2: like significantly behind the curve and calling them chatbots or whatever.
Speaker 2: But like also like you know, you read these really
Speaker 2: smart researchers saying that, oh, we think there's gonna be
Speaker 2: a singularity in two years, right, and I'm like, you know, look,
Speaker 2: you can drive. There's a lot of trucks you can
Speaker 2: drive in between. Oh, it's just a chatbot and Singularity
Speaker 2: by twenty twenty seven, right, right, Yeah, it feels like
Speaker 2: pretty safe bounds.
Speaker 1: I totally agree with that.
Speaker 2: You know, just to come back to the question of
Speaker 2: your ideal AI chat bot personality, this sounds like a
Speaker 2: weirdly like the Howard's Stern someone or something. But Maria,
Speaker 2: what's your what's your what's floats your boat?
Speaker 1: Well, Howard? You know, earlier I had said that I
Speaker 1: want an AI that just gives me the facts, right, Like,
Speaker 1: I do not want the damn thing to have a personality.
Speaker 1: This is an AI, it's a computer. Like, this is
Speaker 1: not my friend, and I don't want its opinions. I
Speaker 1: just want it to kind of give me factual answers.
Speaker 1: Now I know that that's not actually possible because, as
Speaker 1: we've talked about many many times, like I'm probably not
Speaker 1: asking it about math problems because I don't have any
Speaker 1: use for that. I'm probably asking it for things that
Speaker 1: will inevitably be opinion tinged because you know, the inputs
Speaker 1: were made by humans. But yeah, I want it to
Speaker 1: kind of be as as neutral as possible, And like.
Speaker 2: Do you not get were you familiar with FIVEY Fox?
Speaker 2: Does that mean anything to you?
Speaker 1: No?
Speaker 2: Five E Fox was like the mascot of five thirty
Speaker 2: eight models, right, it was a cartoon fox.
Speaker 1: Oh, I've seen the picture of the cartoon fox.
Speaker 2: I'm presenting my modelty like, maybe i'd be a good personality, right, Yeah, little,
Speaker 2: you know, a little furry animal.
Speaker 1: I am so so Nate. Are you familiar with Microsoft
Speaker 1: Office's paper Cliff Gliffe? Oh my god, I think we
Speaker 1: all have stories about clipping.
Speaker 2: I mean, there are things about paper clips and AI.
Speaker 2: You do you probably don't want it to.
Speaker 1: No, we do not want paper clips anywhere near our
Speaker 1: AI models. That the paper clip problem has given us
Speaker 1: enough headaches. By the way, for those of you who
Speaker 1: aren't familiar with the paper clip problem, you know, you
Speaker 1: might know Microsoft clipping, but not the paper clip problem.
Speaker 1: It's an AI philosophy problem first proposed by Nick Bostrom
Speaker 1: about basically how paper clips can cause the end of
Speaker 1: the world. Well, we'll talk more about it in today's
Speaker 1: Pushkin plus Day. What about you, what's your ideal personality?
Speaker 2: Yeah? Like, I mean, you know, my custom instructions are
Speaker 2: to be straight forward, to provide a lot of detail.
Speaker 2: I'm not looking for the AI for like, you.
Speaker 1: Don't want emotional, you don't want fiery foxy or fighty foxy.
Speaker 2: I take you fivey fox. No, I want five fox.
Speaker 1: Yeah, all right, five e fox. So you want five
Speaker 1: e fox. I want to be like a cartoon, except
Speaker 1: not a paper club.
Speaker 2: Not a paper club.
Speaker 1: Let's take a little break, Nate, and then talk about
Speaker 1: n video and another element of AI, the chips that
Speaker 1: make it happen, Nate, this has been such an AI
Speaker 1: E AI E. That's a weird word, but you know
Speaker 1: what I mean.
Speaker 2: Week.
Speaker 1: The other kind of big news has been in video
Speaker 1: and the fact that you know, we've gone through quite
Speaker 1: the cycle on in video where at first there was
Speaker 1: a ban on in Nvidia selling its chips to China.
Speaker 1: Then within the last month the ban was softened and
Speaker 1: Trump announced that they had kind of reached a deal
Speaker 1: where in video was going to be able to sell
Speaker 1: some of its H two O chips to China. And
Speaker 1: then all of a sudden, there was an announcement that
Speaker 1: now Nvidia can sell these chips as long as the
Speaker 1: US government gets fifteen percent of the profits. So this
Speaker 1: is starting to seem a lot like we're now in
Speaker 1: the world of the Godfather or the Sopranos and less
Speaker 1: like we're in the world of the US government. Give
Speaker 1: me a taste, Give me a taste, Nate, and then
Speaker 1: you can do whatever you want. But Papa wants his
Speaker 1: taste of the action. Yeah.
Speaker 2: Look, I mean Trump had his, or the White House had.
Speaker 2: It's like ai action Plan, which you talked about a
Speaker 2: couple weeks ago, and like, you know, people I trust
Speaker 2: thought it wasn't that bad. But like these are people who,
Speaker 2: you know, one thing you might think is good for
Speaker 2: Trump are good for I don't know. Right, what I
Speaker 2: call the river in the book having more influence over
Speaker 2: the White House is like they're all really competitive. They
Speaker 2: want to be China. They want to be China, right,
Speaker 2: And here the US now has like an incentive for
Speaker 2: the best chips in the world to be sold to China. Right,
Speaker 2: I haven't tried. I assume it's going to like the
Speaker 2: Treasury and it is not like Trump's personal stash. But
Speaker 2: like that seems that seems a bit weird. And like granted, okay,
Speaker 2: you manufacture a chip and it's kind of hard for
Speaker 2: China not to get it. Eventually, I'm sure, there are
Speaker 2: black markets and gray markets, although you know, people have
Speaker 2: used the parallel like nuclear facile material. We track that
Speaker 2: pretty carefully potentially, But yeah, no, I imagine that. I mean,
Speaker 2: let's go with that analogy. Right, It's like, oh, okay,
Speaker 2: you can sell radioactive material to Iran.
Speaker 1: Right as long as as long as hes goverment get.
Speaker 2: Think China's Iran or exactly right, but they are the
Speaker 2: right now only country that's competitive with us on AI, right, yeah,
Speaker 2: the third place, right, maybe maybe the least you know,
Speaker 2: let's give them some two.
Speaker 1: Yeah, no, it's kind of crazy. And and Trump just
Speaker 1: says that the more advanced black Well chips, He's like,
Speaker 1: oh yeah, I'm open to us selling those as well
Speaker 1: if we can also get a percentage. So, all of
Speaker 1: a sudden, the national security concerns and you and I
Speaker 1: did a whole segment about the AI Action Plan, and
Speaker 1: the one thing that was kind of a concern in
Speaker 1: it was China, right, And all of a sudden that
Speaker 1: seems to have gone out the window. If we can
Speaker 1: you know, grease the palm a little bit and get
Speaker 1: get the fifteen percent kick back, and so from you know,
Speaker 1: all of a sudden, you realize, well, it was just
Speaker 1: a talking point, right, like, it really didn't matter. It
Speaker 1: wasn't national security doesn't matter. If we can if we
Speaker 1: can get a percentage of this. By the way, after
Speaker 1: these announcements, China has itself said, hey, companies, as in
Speaker 1: like Ali Baba, you know, Chinese companies, we don't want
Speaker 1: you buying these US chips because we're worried that they're
Speaker 1: going to kind of insert location tracking and back doors
Speaker 1: and all these things into them. We want you to
Speaker 1: be buying local That would be smart. And Video said, no, no, absolutely,
Speaker 1: we would never do that. But so they say, you know,
Speaker 1: we want you to buy local Huawei chips and you.
Speaker 2: Want your local pitches and exactly.
Speaker 1: So China is actually like a little bit skeptical of this,
Speaker 1: but it's not a law, and you know, it's not
Speaker 1: actually clear yet how it's going to be enforced because
Speaker 1: the agency that issued this directive doesn't actually have enforcement power.
Speaker 1: And by the way, there's also already been a pre
Speaker 1: order of I think it was like seven hundred thousand
Speaker 1: something like that. There's been a pre order of a
Speaker 1: shit ton in other words, of chips that are presumably
Speaker 1: going to start getting shipped now and so the now
Speaker 1: you know, we talk a lot about incentives, but they're
Speaker 1: just for the US. Like now, it's all out of whack.
Speaker 1: The other part of this, by the way, is that
Speaker 1: there is a ban on the in the Constitution on
Speaker 1: export taxes, and so I can see there being a
Speaker 1: legal challenge here. I mean, this kind of is an
Speaker 1: export tax Actually, think about it A fifteen Like they
Speaker 1: can probably.
Speaker 2: Ban in the Constitution of export taxes. Yeah, I didn't know.
Speaker 1: Yeah, there's a ban in the Constitution on export taxes,
Speaker 1: so we cannot lovey export taxes on companies. Yeah. So
Speaker 1: if you can argue that this is.
Speaker 2: We're going to change constitution, then it seems.
Speaker 1: I mean, I think that we have seen that this
Speaker 1: particular administration has no problems changing the constitution or trying
Speaker 1: to change the facts when the facts don't agree.
Speaker 2: If you were allowed one free constitutional amendment, what would
Speaker 2: you do?
Speaker 1: One free constitutional amendment?
Speaker 2: One free I guess you're by FIAT allowed to enact
Speaker 2: a constitutionalism.
Speaker 1: Oh, I know, it's a really good question. I don't
Speaker 1: have a ready made answer for it to you. Is
Speaker 1: it something you've thought about?
Speaker 2: It's harder to produce some practice than in theory, but
Speaker 2: like to ban gerrymandering is.
Speaker 1: One that would be amazing.
Speaker 2: Yeah, yeah, pretty good one that would be a really
Speaker 2: good one. I mean the Senate, I mean, don't mean
Speaker 2: fucking you know, I think we should sell North Dakota
Speaker 2: to Canada.
Speaker 1: All right, all right, I need a little bit more
Speaker 1: explanation here.
Speaker 2: Too many fucking Dakota's. It's their nice states. It's such
Speaker 2: a there's surprising beauty in South Dakota in particular, I'll
Speaker 2: tell you that much. But I don't think that Dakotas
Speaker 2: need four centers between them.
Speaker 1: This is true. I mean, I do think that the
Speaker 1: senatorial representative kind of model of government is broken.
Speaker 2: You trade North Dakota for Greenland.
Speaker 1: This podcast is devolving.
Speaker 2: Like, oh, I've just came home from Denmark. Oh you
Speaker 2: mean Fargo, Denmark. Never mind, we're typing in the studio.
Speaker 2: If you're listening to the auto version, we have our
Speaker 2: producer turning around and.
Speaker 1: Take it back to the video, Yes, take it back
Speaker 1: to n video. So, yeah, we we have this incredibly
Speaker 1: perverse situation right where the incentives are just now completely fucked. Now,
Speaker 1: let's assume the reason we got on our tangent is
Speaker 1: because of the export taxes. Let's assume that legally this
Speaker 1: is upheld, that the fifteen percent is allowed to stand.
Speaker 1: It also sets an incredibly dangerous precedent for you know,
Speaker 1: for everything, for like, it's a really it's a really
Speaker 1: scary proposition to think that, Now, well, as long as
Speaker 1: you kind of give a kickback to the US government,
Speaker 1: you're fine. So we do see this kind of quid
Speaker 1: pro quo mentality where like, you know, you help me,
Speaker 1: I help you, And that is something that norm that
Speaker 1: should not be happening in a healthy democracy.
Speaker 2: Yeah, I mean I think I think that train flew
Speaker 2: right past the station. Marie, I don't, yah, you know,
Speaker 2: you look, it's it's part of It's the same thing
Speaker 2: with the tariff policy. Right. Why are we doing these
Speaker 2: tariffs that try to encourage the Eastern American made products.
Speaker 2: Is it's trying to do industrial policy, is trying to
Speaker 2: do foreign policy right, or just trying to make a
Speaker 2: bunch of money for the government, right. And sometimes Republicans
Speaker 2: are kind of caught or tariff defenders are kind of
Speaker 2: caught in between saying, you know, actually the ideal amount
Speaker 2: of money is terifts make is zero, because then we
Speaker 2: on shore everything and people saying, hey, that's going to
Speaker 2: make up for like a loss of tax revenue elsewhere. Right,
Speaker 2: And it's kind of the same thing with this with
Speaker 2: this China thing and again, you know and videos like okay, sure, yeah,
Speaker 2: by the way, I own a video stoic, right, so yeah, yeah.
Speaker 1: All right, yeah, well no, but you can actually envision
Speaker 1: a future, right where for companies they're like when they're
Speaker 1: calculating profit, they're like, and this is the cut, Like
Speaker 1: just like before you had to like this is the
Speaker 1: cut we give to the mob boss, like, this is
Speaker 1: the cut that goes to Trump for it's the cost
Speaker 1: of doing business. And so we're willing, we willing to
Speaker 1: do that.
Speaker 2: The Italians, you know, Italians, French, we're acting like the
Speaker 2: fucking Europeans, you know some yeah, yeah, the southern less
Speaker 2: efficient Southern Europeans. Yeah.
Speaker 1: So so it's a obviously, I mean, it's an the
Speaker 1: understatement of the day to say it's not a good look.
Speaker 1: But it also doesn't just it doesn't bode well for
Speaker 1: a lot of things. Yeah, so I think, you know,
Speaker 1: bottom line, like this really is I think bad for
Speaker 1: our economic prospects and for the way that other countries
Speaker 1: see US as well. Right, like that matters, especially when
Speaker 1: our currency matters, and kind of our reliability as a
Speaker 1: trading partner and as a lender, and all these things matter.
Speaker 1: So reputation, reputation matters, and foreign reputation matters, and in
Speaker 1: other stupid things. By the way, we're we're going to
Speaker 1: have be hosting Putin on US soil, even though he
Speaker 1: has a warrant out for his arrest. Really nice to
Speaker 1: see you.
Speaker 2: He's a warrant who has an unrest warrant in the
Speaker 2: US for him.
Speaker 1: Well, the ic C has one, Okay, so he technically
Speaker 1: cannot leave Russia because any other country would have to be.
Speaker 2: Kind of fun a Vladimir, that would be funny.
Speaker 1: Funny that is not happening. Yeah, for war crimes against
Speaker 1: EU crime is it in Alaska or something? It's in Alaska. Yeah, yeah,
Speaker 1: so we'll see what happens there. But anyway, all of
Speaker 1: this not great news. So we have yeah, we have
Speaker 1: a mixed bag for you today on a risky business
Speaker 1: and export taxes. Let's see, let's see what happens on
Speaker 1: the legal side of things, and if this is in
Speaker 1: fact deemed an export tax every time will be challenged.
Speaker 2: GPT five hallucinates they have to pay the US government
Speaker 2: fifteen cents.
Speaker 1: Some of that that would be That would be something, Nate,
Speaker 1: that would be something. Let us know what you think
Speaker 1: of the show. Reach out to us at Risky Business
Speaker 1: at pushkin dot fm. And by the way, if you're
Speaker 1: a Pushkin Plus subscriber, we have some bonus content for
Speaker 1: you that's coming up right after the credits.
Speaker 2: And if you're not subscribing any come on really, but
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Speaker 2: for listening across Pushkin's entire network of shows.
Speaker 1: Risky Business is hosted by me Maria Tanakova.
Speaker 2: And by me Nate Silver. The show is a co
Speaker 2: production of Pushian Industries and iHeartMedia. This episode was produced
Speaker 2: by Isabella Carter. Our associate producer is Sonya Gerwick. Sally
Speaker 2: Hilm is our editor, and our executive producer is Jacob Goldstein.
Speaker 2: Mixing by Sarah Briguer.
Speaker 1: If you like the show, please rate and review us
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