Speaker 1: Ah, welcome back to it could happen here a podcast.
Speaker 1: It's a podcast. I'm Robert evans Uh and with me
Speaker 1: today is Garrison Davis and James Stout.
Speaker 2: Hello, a Canadian, a Britishman and a text and walk
Speaker 2: into a podcast.
Speaker 1: Yeah, welcome to a podcast. Only two of them can
Speaker 1: drink in a bar.
Speaker 2: That's not true in Canada.
Speaker 1: We can all drink in a bar now, Garrison, A
Speaker 1: moment ago, you were holding your hand above a lit
Speaker 1: candle in a way that reminded me of G. Gordon Liddy,
Speaker 1: the Nazi who masterminded the Watergate breaking, and in order
Speaker 1: to convince people that he was a hard man, would
Speaker 1: regularly burn the palm with his hand on a candle
Speaker 1: while staring at them and gender great g Gordon Letty,
Speaker 1: you don't know enough about We'll talk about g. Gordon Letty,
Speaker 1: but today we're talking about something else, problematic artificial intelligence,
Speaker 1: which is not a thing that exists anywhere. It is
Speaker 1: instead a terrible, terrible error going back to like the
Speaker 1: sixties in case of terminology, when we talk about all
Speaker 1: of the things that people are like, you know, flipping
Speaker 1: out as ais chat GPT and stable diffusion and fucking
Speaker 1: all these other sort of like different programs. They're not intelligences.
Speaker 1: They're you know, the chat GPT is like a large
Speaker 1: language model. They're all essentially like bots that you train
Speaker 1: to understand kind of like what the likeliest thing that,
Speaker 1: what the likeliest appropriate response is to like a given prompt.
Speaker 1: That's kind of like the broadest way to explain it.
Speaker 1: It's comp located and they're you know, very useful. But
Speaker 1: obviously if you've been paying attention to the world right now,
Speaker 1: there's just a whole bunch of bullshit about them, and
Speaker 1: I think to kind of make sense of why we're
Speaker 1: seeing some of the shit around AI that we're seeing.
Speaker 1: And for a little bit of specificity, there have been
Speaker 1: like this kind of endless series of articles around this
Speaker 1: open letter signed by a bunch of luminaries in the
Speaker 1: AI field talking about how, you know, there need to
Speaker 1: be laws put in place to stop it from ending
Speaker 1: the world. You know, you've seen articles about like oh
Speaker 1: x percentage of AI researchers think that it could it
Speaker 1: could destroy the planet, destroy the human race kind of.
Speaker 1: Most recently, the biggest article, the biggest like viral hype article,
Speaker 1: was that the Pentagon had supposedly been testing an AI
Speaker 1: like missile system that blew up its operator in a
Speaker 1: simulation because the operator was trying to stop it from
Speaker 1: from firing or whatever. It was bullshit, like what was that?
Speaker 1: What actually had? Like Vice ran with the article. It
Speaker 1: was very breakfas Advice would do this flipping out about
Speaker 1: how horrifying you know, our AI weapons future is and like, yeah,
Speaker 1: we shouldn't give AI the ability to like kill people,
Speaker 1: but that's not at all what happened. Basically, a bunch
Speaker 1: of army nerds or air force nerds were sitting around
Speaker 1: a table doing the D and D version of like
Speaker 1: military planning, where you say, what if we did this,
Speaker 1: what kinds of things could happen if we did this system?
Speaker 1: And another guy around the table said, oh, well, if
Speaker 1: we build the system this way, it might conceivably attack
Speaker 1: its operator, you know, in order to optimize for this
Speaker 1: kind of result, which is like not scary, Like it's
Speaker 1: it's just people talking through pot like a flow chart
Speaker 1: of possibilities around a fucking table. You don't need to
Speaker 1: worry about that. There's so many other things to worry about.
Speaker 1: New York City is blanketed in a layer of smogs
Speaker 1: so thick you could cut it with a butter knife. Like,
Speaker 1: don't don't flip out about AI weapons just yet, folks.
Speaker 1: But I wanted to kind of talk about why this
Speaker 1: shit is happening, And a lot of it comes down
Speaker 1: to the fact that when we're talking about the aspects
Speaker 1: of like the tech industry that have an impact on
Speaker 1: outside of the tech industry. Right, there's basically three jobs
Speaker 1: in big tech. One job is creating iterative improvements on
Speaker 1: existing products. These would be the teams of folks who
Speaker 1: are responsible for designing a new iPhone every year, right,
Speaker 1: every couple of years. Lenovo puts out a new series
Speaker 1: of think pads and idea pads every couple of years.
Speaker 1: You know, you get a new MacBook every couple of years.
Speaker 1: Razor puts out a new blade. This is you know,
Speaker 1: these are the folks who kind of move along technology
Speaker 1: at a relatively like steady pace for consumer devices. And
Speaker 1: then you have the people who are responsible for kind
Speaker 1: of what you might call the moonshot products. This is
Speaker 1: a mix of the next big thing and doomed failures,
Speaker 1: and it's often pretty hard to tell you know what's
Speaker 1: going to be what ahead of time. A very good
Speaker 1: example would be back in the nineties, Apple a bunch
Speaker 1: of resources into launching an early tablet computer called the
Speaker 1: Newton that was a fabulous disaster. And then in the
Speaker 1: mid oughts they put a bunch of resources into launching
Speaker 1: the iPad, which was a huge success. And when you
Speaker 1: kind of think about like the folks doing this, like
Speaker 1: working on the moonshot products. The most recent example would
Speaker 1: be whatever team at Apple, the team at Apple that
Speaker 1: was behind putting together these new Apple goggles, which I
Speaker 1: don't think are going to be a wildly successful product
Speaker 1: in the way that they need it to be, like
Speaker 1: a smartphone scale success. But this is an example of
Speaker 1: like a thing that didn't exist and a bunch of
Speaker 1: people had to invent new technologies or new ways to
Speaker 1: combine technologies in order to make it exist. The third
Speaker 1: kind of job that the tech industry has, broadly speaking,
Speaker 1: are conmen, right, And the state that we are in
Speaker 1: in the industry right now is that every major tech
Speaker 1: company is run by some form of con man. Right.
Speaker 1: Tim Cook is, you know, kind of the least cones
Speaker 1: of the conment among them. But like Mark Zuckerberg obviously
Speaker 1: is a fucking flim flam artist, you know, and you
Speaker 1: can see this with the huge amount of money, like
Speaker 1: it's something like eleven billion dollars at least that Facebook
Speaker 1: pumped into this bullshit metaverse scheme that like Apple barely
Speaker 1: even talked about during their event unveiling like a headset
Speaker 1: that has VR potential in it. I'm getting away from
Speaker 1: myself here. Kind of the point that I'm making is
Speaker 1: that you can often have very real products. There's actual
Speaker 1: technology going into the Apple glasses marketed by conman flim
Speaker 1: flam artist. This is not always like a bad thing, Right.
Speaker 1: Steve Jobs was a con man, and it worked out
Speaker 1: pretty well for him because it just so happened that
Speaker 1: the tech not He had a decent enough idea of
Speaker 1: what the tech was capable of that it was able
Speaker 1: to kind of meet the promises he was making in
Speaker 1: more or less real time. An example of what happens,
Speaker 1: you know, pretty spectacularly when that's not the case is
Speaker 1: what we saw with Theronose and Elizabeth Holmes who started
Speaker 1: prison last week. Right, You've got these promises being made
Speaker 1: by the con man and the people who are responsible
Speaker 1: for the moonshots can't make it work. I'm bringing this
Speaker 1: up right now because there's a lot of folks, I
Speaker 1: think who believe that the the nate like the actual
Speaker 1: potential of AI has been proven in a spectacular way
Speaker 1: because the tools that have been released are able to
Speaker 1: do cool things, and I think those people are missing
Speaker 1: some key aspect, like some key things that like might
Speaker 1: cause one to think more critically about the actual potential
Speaker 1: the industry has and also might cause one to think
Speaker 1: more critically about how earth shattering it's all going to be.
Speaker 1: It's being taken kind of as red right now by
Speaker 1: a lot of particularly journalists and media analysts outside of
Speaker 1: the tech and or like outside of you know, the
Speaker 1: dogged tech press, that like, well, this is going to
Speaker 1: upend huge numbers of industries and put massive numbers of
Speaker 1: people out of work. And you know, that may seem
Speaker 1: if you sat down in front of this chatbot and
Speaker 1: had like a mind blowing experience, that may seem credible.
Speaker 1: There's not the evidence behind that yet. If you actually
Speaker 1: look at the numbers behind some of these different companies
Speaker 1: and like how their usership has grown and how it's
Speaker 1: fallen off, one of the things you've seen is that
Speaker 1: a lot of these tools had this kind of massive
Speaker 1: surge peak in terms of the number of people adopting
Speaker 1: them and in terms of their profitability. You saw this
Speaker 1: with like Stable Diffusion, right, and then this kind of
Speaker 1: fairly rapid fall afterwards, not because people are like giving
Speaker 1: it up forever or whatever, but because, like, once you
Speaker 1: fucked around with it and generated some images or generated
Speaker 1: some stories, there's not a huge amount to do unless
Speaker 1: you're someone who's specifically going to be using this for
Speaker 1: your job. And most of the people that wanted to
Speaker 1: fuck around with a lot of these apps didn't have
Speaker 1: long term use cases for them. This is why while
Speaker 1: you've got like, for example, Stability, which is the company
Speaker 1: or at least the main company behind Stable Diffusion, has
Speaker 1: been valued at like four billion dollars I think last
Speaker 1: it was checked, but their annualized revenue is only about
Speaker 1: ten million dollars. So that's a pretty significant gap. And
Speaker 1: it's a pretty significant gap because the actual money in
Speaker 1: AI so far isn't with the service providers really like
Speaker 1: you've got some that have made in like the one
Speaker 1: hundred million dollar range, although it's not entirely clear what
Speaker 1: their margins are or what the kind of the long
Speaker 1: term reliability of that profit is But the vast majority
Speaker 1: of money in AI, like almost all of it has
Speaker 1: been made by companies like Nvidia, and Vidia jumped up
Speaker 1: to become like a trillion dollar company as a result
Speaker 1: of this, because the hardware needs of these products are
Speaker 1: so intense, and obviously that shows there's money here for somebody.
Speaker 1: But the fact that like a shitload of people got
Speaker 1: curious about these apps and use them in in quick
Speaker 1: succession and then kind of dropped off is an evidence that,
Speaker 1: like we're seeing entire industries replaced as much of it
Speaker 1: as it is evidence that like a lot of people
Speaker 1: thought this was interesting briefly, and so I think kind
Speaker 1: of when you look at the data, one of the
Speaker 1: things that suggests is that we're heading towards a point
Speaker 1: in AI, and I think we're probably going to hit
Speaker 1: it within the next six months to a year that
Speaker 1: is broadly referred to as like the trial of disappointment,
Speaker 1: And this is what happens when kind of the promises
Speaker 1: of a new technology that are being made by the
Speaker 1: hypemen or con men as I tend to call them,
Speaker 1: meet with like the actual reality of its execution, which
Speaker 1: in some areas is going to be significant. There are places,
Speaker 1: I think medical research maybe one of them. We'll talk
Speaker 1: about that in a bit, where a lot of the
Speaker 1: promises people are making about AI will be fairly quickly realized,
Speaker 1: and then there are areas where it won't be. I
Speaker 1: think content generation is one of those things. But yeah,
Speaker 1: so that's kind of like what I'm seeing when I'm
Speaker 1: looking at the broad strokes of where this technology is
Speaker 1: here and kind of the gap between how people are
Speaker 1: talking about it and what we're actually seeing in terms
Speaker 1: of monetization. I want to talk a little bit now
Speaker 1: about kind of one of the guys I would call
Speaker 1: him kind of a con man who's been a big
Speaker 1: driver of the current AI push. He's a dude named
Speaker 1: Amad Mustock, and he's the founder of Stable Diffusion right,
Speaker 1: which is a text to image generator that was kind
Speaker 1: of like before chat GPT hit. This was like the
Speaker 1: first really really big mainstream AI thing. Chat GPT was
Speaker 1: a lot larger, but Stable Deviusion came first, and you know,
Speaker 1: was critical behind, among other things, a lot of the
Speaker 1: silliest NFT bullshit. And he's a really interesting dude, Like
Speaker 1: if you look at kind of his own claims his background.
Speaker 1: He says that he's got an Oxford master's degree, that
Speaker 1: he was like the behind an award winning hedge fund,
Speaker 1: that he like worked for the United Nations and a
Speaker 1: really important capacity, and also that he obviously founded this
Speaker 1: this a I bought. None of that's true. He has
Speaker 1: a bachelor's degree from Oxford, not a master's degree.
Speaker 3: He did well, that's what he's playing off. A thing
Speaker 3: that happens where like you can you can get if
Speaker 3: you have a BA ox and you can you can
Speaker 3: get it to be an MA. Doesn't mean you did
Speaker 3: a master's. It's just a wealthy people flex. Yeah, it's
Speaker 3: not a master's degree. You shouldn't qute it that. If
Speaker 3: you're quoting it now, you're taking the pace.
Speaker 1: Yeah. Yeah, he's taken the piss knowing no one's gonna
Speaker 1: call him on it, or at least knowing that people wouldn't,
Speaker 1: like at large, like loudly enough for it to matter
Speaker 1: for him. He hasn't worked with the UN in quite
Speaker 1: some time, and never did in a major capacity. He
Speaker 1: did run a hedge fund that was successful in its
Speaker 1: first year, but then got shut down in its sex
Speaker 1: year because he lost everybody's money. So like this is
Speaker 1: this is. But you see with this guy, if you
Speaker 1: go through his like history, he's like he's like chasing
Speaker 1: hedge funds in the early aughts. He first gets in
Speaker 1: with stable diffusion after COVID, and he's kind of like
Speaker 1: billing it as this is gonna help with like research
Speaker 1: into trying to like you know, fight the COVID nineteen pandemic,
Speaker 1: and then he kind of pivots to like, oh, this
Speaker 1: is a great way to like make NFTs and shit,
Speaker 1: you know when that hit, Like he's he's just sort
Speaker 1: of like chasing where the money is. Yeah, any way
Speaker 1: he kind of can. And he's not, by the way,
Speaker 1: he's not the guy who wrote any of the source
Speaker 1: code for this. That was done by like a group
Speaker 1: of researchers, and he you know, he essentially like acquired it,
Speaker 1: which is usually what happens here. Now, none of this
Speaker 1: has stopped him from getting one hundred million dollars or
Speaker 1: so in investments from various venture partners, and hasn't stopped
Speaker 1: his company from getting this massive violation. It hasn't stopped
Speaker 1: the White House from inviting him to talk as part
Speaker 1: of like a federal AI safety initiative, But it is
Speaker 1: one of those like when I kind of look into
Speaker 1: this guy and kind of the gap between his claims
Speaker 1: and what's actually happened and the claims that are being
Speaker 1: made about the value of his company and what it's
Speaker 1: actually like proved to be worth so far. I think
Speaker 1: a lot about Sam Bankman Freed because a lot of
Speaker 1: like the early writing around this guy was similar, and
Speaker 1: a lot of the kind of shit that he's claiming
Speaker 1: is similar. And yeah, I'm not sure if this is
Speaker 1: a case where because Bankman Freed is one of these
Speaker 1: people who, like Elizabeth Holmes, I think, backed the wrong
Speaker 1: technology because it's fine in Silicon Valley. It's fine, generally
Speaker 1: speaking in capitalism to lie about what a product can
Speaker 1: do if you can, you know, fake it till you
Speaker 1: make it. And maybe AI is there. He may have
Speaker 1: this guy may have made a good bet as to
Speaker 1: the future, but that's kind of far from certain yet.
Speaker 1: And it's it's just really clear how much of this
Speaker 1: industry is being built on, or is being built by.
Speaker 1: How much of the people running sort of these AI
Speaker 1: companies are dudes who managed one way or another, either
Speaker 1: through access to VC funding or kind of like you know,
Speaker 1: just being in the right place at the right time
Speaker 1: to jump in on the bandwagon in the hopes that
Speaker 1: they'll be able to cash out very, very quickly. I
Speaker 1: found a good quote from a Forbes article talking about
Speaker 1: like a big part of why guys like Mustock are
Speaker 1: so interested in AI right now from a financial perspective,
Speaker 1: And this is true, not just this was true about
Speaker 1: like crypto before, but AI because there's more to the technology.
Speaker 1: This is kind of even more so valid quote. Venture
Speaker 1: capitalists historically spend months performing due diligence, a process that
Speaker 1: involves analyzing the market, vetting the founder, and speaking to
Speaker 1: customers to check for red flags before investing in a startup,
Speaker 1: but start to finish. Mstock told Forbes he needed just
Speaker 1: six days to secure one hundred million dollars from leading
Speaker 1: investment firms Coach You and light Speed once Stable Diffusion
Speaker 1: went viral. The extent of due diligence at the firms
Speaker 1: performed is unclear given the speed of the investment. The
Speaker 1: investment thesis we had is that we don't know exactly
Speaker 1: what all the use cases will be, but we know
Speaker 1: that this technology is truly transformative and has reached a
Speaker 1: tipping point in terms of what it can do. Garav Gupta,
Speaker 1: the light Speed partner who led the investment, told Forbes
Speaker 1: in a January interview. So again they're being like, yeah,
Speaker 1: we're pumping tens of million dollars of dollars into this.
Speaker 1: We don't know how it'll make money. It just seems
Speaker 1: so impressive that it has to be profitable. Now that
Speaker 1: line is particularly funny, maybe the wrong word when compared
Speaker 1: alongside this paragraph from later in the article. In an
Speaker 1: open letter last September, Democratic Representative Anna Essue urged action
Speaker 1: in Washington against the open source nature of stable Diffusion.
Speaker 1: The model, she wrote, had been used to generate images
Speaker 1: of violently beaten Asian women and pornography, some of which
Speaker 1: portrays real people. Bashara said new versions of stable Diffusion
Speaker 1: filtered data for potentially unsafe content, helping to prove users
Speaker 1: from generating harmful images in the first place. So it's like,
Speaker 1: part of what's happening here is you've got this thing
Speaker 1: that seems really impressive, and that is to some extent
Speaker 1: because it's able to like remix stuff that exists in
Speaker 1: a way that you haven't done automatically before. But all
Speaker 1: of these kind of valuations are based number one, and
Speaker 1: ignoring the problems with monetizing this stuff, including like the
Speaker 1: still very much unsorted nature of how copyright's going to
Speaker 1: affect this, and also like the question of is this
Speaker 1: really worth that much money? Like is this actually is
Speaker 1: being able to generate kind of weird slightly off putting
Speaker 1: AI images a huge business, like how much of because
Speaker 1: like from where I'm seeing it, one of two things
Speaker 1: is possible. Number one, this replaces all art everywhere and
Speaker 1: so there's a shitload of money in it. Or number two,
Speaker 1: this remains a way that like low quality websites and
Speaker 1: like Amazon drop ship scammers who are like putting up
Speaker 1: fake books on Kindle and whatnot to trick people using
Speaker 1: keywords like that this is just like a way to
Speaker 1: fill that shit out. Like I don't see a whole
Speaker 1: lot of room in the middle there. You know, maybe
Speaker 1: I'm being like overly pessimistic there, but that's that's that's
Speaker 1: where I'm sitting.
Speaker 2: I mean, some of the models we've seen used is
Speaker 2: selling like subscription packs for like access to these tools
Speaker 2: and access to use them for like commercial reasons. The
Speaker 2: other thing we can see is just like corporations selling
Speaker 2: to other corporations like basically having Disney and Warner Brothers
Speaker 2: be able to use this to generate concept art and
Speaker 2: now they don't need to pay concept artists and instead
Speaker 2: they just have like pretty pretty uh pretty like nicely
Speaker 2: curated tools for them to generate this type of Yeah A,
Speaker 2: I imagining those are kind of two of the biggest
Speaker 2: use cases that at least I'm seeing right now from
Speaker 2: more on like the creative filmmaking art side of things.
Speaker 2: Because I mean, I don't think it's going to replace
Speaker 2: all all art. I think nobody nobody is actually uh
Speaker 2: is actually thinking it's just going to replace all all art,
Speaker 2: just like photography did not replace all art. It just
Speaker 2: it changes the paradigm. And because this this tool does
Speaker 2: seem like specifically useful for the for the way that
Speaker 2: we're seeing like corporations make the same a movie every
Speaker 2: five years, like it's all it's it's it's it's all
Speaker 2: built on all of the same stuff. And I think
Speaker 2: that that's how a lot of a lot of it's
Speaker 2: gonna get used. It's gonna be a lot of weird
Speaker 2: scam artists, people just messing around for fun, and then
Speaker 2: people not paying like illustrators as much.
Speaker 1: Like yeah, and I think that's kind of like I
Speaker 1: see this being adopted widely, but that's not the same
Speaker 1: as it like being a huge success. Like right now
Speaker 1: I'm looking at an article that's estimating the current value
Speaker 1: of AI in the US is at one hundred billion
Speaker 1: dollars and that buy twenty thirty, it'll be worth two
Speaker 1: trillion US dollars. And it's like, I don't know, man,
Speaker 1: like is.
Speaker 2: I mean the AI is more than just like mid
Speaker 2: Journey image creation right there, is just like open AI
Speaker 2: and chat GPT, and like AI is in everything we
Speaker 2: use now, like yeah, like AI is in your smartphone.
Speaker 2: AI is going to be in your refrigerator soon. It's
Speaker 2: like it's not just image generation by any means.
Speaker 1: That kind of gets to what I'm saying, because that's
Speaker 1: that's when you look at AI as a tool. Is
Speaker 1: more of like a paint brush than a painter. Is
Speaker 1: a tool that will like augment or be used in
Speaker 1: because I think a lot of a number of times
Speaker 1: it may be used in a way that makes the
Speaker 1: product worse and a lot of existing technologies, Well that's
Speaker 1: really different from kind of number one, the doom and gloom,
Speaker 1: like this is an intelligence on its own that could
Speaker 1: like overtake humanity. I think the worry is more like
Speaker 1: this could make get adopted on such a large scale
Speaker 1: that it like makes a lot of shit worse. Like
Speaker 1: my biggest fear with AI is that it kind of
Speaker 1: hyper charges the SEO industry and the way that that
Speaker 1: has worked to destroy search and destroy so much of
Speaker 1: Internet content.
Speaker 3: Yeah, I think that is very possible. Like if I
Speaker 3: look at chat GPT, like, I don't think that's going
Speaker 3: to be writing features for Rolling Stone anytime soon, but
Speaker 3: what it can probably do because SEO max copy is derivative, right,
Speaker 3: like like it's predictable, it's derivative, it's based on other stuff.
Speaker 1: It's supposed to be.
Speaker 3: Yeah, and so it can do that SEO max copy
Speaker 3: and some of that ad copy like very well, and yeah,
Speaker 3: either really fuck up searches, which is quite possible, and
Speaker 3: also make the lowest kind of acceptable tier of that
Speaker 3: kind of copy what it can generate. And because you
Speaker 3: can just shove that copy in front of people with
Speaker 3: SEO max and then have shitty AD copy written by
Speaker 3: chat GPT, like that will change how it certainly how
Speaker 3: we buy stuff on the Internet, right, But also how
Speaker 3: we read news, et cetera. Yeah, absolutely, and I already
Speaker 3: see that, Like I've written for some big pub locations.
Speaker 3: You have like essentially a side. Do people know what
Speaker 3: content driven commerce is? Oh yeah, yeah yeah yeah, it's
Speaker 3: why every article about stuff is now the best five
Speaker 3: x right.
Speaker 1: Yeah, Like they have affiliate links and the publication will
Speaker 1: profit if you buy stuff after clicking the link.
Speaker 3: Yeah yeah. So like in the probably twenty sixteen era,
Speaker 3: all of the stuff. So I did a lot of
Speaker 3: previously outdoor journalism right right about climbing, gear, bikes, that
Speaker 3: kind of thing, and like that whole industry went to
Speaker 3: just aft com like just affiliate links, and they kind
Speaker 3: of trashed any quality review stuff. And I can see
Speaker 3: like a similar change to that happening with this right
Speaker 3: where where people will just chase that SEO max copy
Speaker 3: and that will become the new cool thing to do
Speaker 3: and like a lot of outlets as a result. But
Speaker 3: that's not the like earth shattering change that people are
Speaker 3: talking about on Twitter dot com or whatever.
Speaker 2: Well, one thing I saw recently is that more and
Speaker 2: more students are just using chat GPT to look up
Speaker 2: information like as opposed to like as a Wikipedia as
Speaker 2: opposed to Wikipedia, are as supposed to Google if they
Speaker 2: have a question. The last chat GPT which has a
Speaker 2: few problems as soon as you start getting into how
Speaker 2: much of the chat gbt output is just AI hallucinations
Speaker 2: where it's not actual information, which is honest, that's not
Speaker 2: think I should just write my own thing on in
Speaker 2: the future. But yeah, it's just it's a really weird problem.
Speaker 1: That's really interesting that the problem of like because I
Speaker 1: think it's it's very clear to me at this point
Speaker 1: that AI is a more user friendly search experience than
Speaker 1: a search engine, right because you can talk to it
Speaker 1: like a person and explain what you need explained. That
Speaker 1: doesn't mean it's a better option in terms of it
Speaker 1: provides people within information more effectively that it that it
Speaker 1: actually tells them what they want to know as well.
Speaker 1: But it's like easier and maybe like less kind of
Speaker 1: an imposing task to like asking AI a question that
Speaker 1: it is to ask like a search and to especially
Speaker 1: as as much worse as Google has gotten lately. Like
Speaker 1: one of the things that I found interesting is I
Speaker 1: was kind of doing digging for this. I was looking
Speaker 1: at some AI articles that were published in like twenty
Speaker 1: nineteen twenty twenty twenty twenty one. This is before the
Speaker 1: big you know AI push that like we're currently all
Speaker 1: in the middle of before chat GPT you know, got
Speaker 1: its its widespread release, and it was talking with like
Speaker 1: some people from Google who were like, yeah, we really
Speaker 1: see AI like supercharging our search results. You know, there's
Speaker 1: a lot of potential and like its ability to help
Speaker 1: people with search. And I'm thinking about in twenty twenty
Speaker 1: twenty nineteen, Google was a really useful tool and it's
Speaker 1: a shit show now, like it's filled with ads, like
Speaker 1: arch results have gotten markedly worse. Everyone who uses Google
Speaker 1: as part of their job will tell you that it's
Speaker 1: gotten like significantly worse in the recent past. And like,
Speaker 1: I that's kind of like the thing that I see
Speaker 1: being more of a worry And it's one of those things.
Speaker 1: It's like on one hand, in the heype machine you have,
Speaker 1: like AI could become like our new god king and
Speaker 1: destroy us all, and the other like AI is going
Speaker 1: to like, you know, create all. There's all this vague
Speaker 1: talk about what it could be giving people the tools
Speaker 1: to create more art than ever before, to you know,
Speaker 1: make more good things faster, And I kind of feel like, well,
Speaker 1: what if neither of those things happens, which I and
Speaker 1: it just sort of allows us to continue making the
Speaker 1: Internet worse for everybody at a more rapid pace. What
Speaker 1: if that's the primary thing that we notice about AI
Speaker 1: as consumers.
Speaker 3: It's probably a reasonab assumption. I think Garrison's point was
Speaker 3: good though, when they said that, like bigger companies will buy,
Speaker 3: like companies will just exist to get brought right, which
Speaker 3: is the thing that's hapened to tech for decades, because
Speaker 3: like it can't fundamentally change things, Like if AI is
Speaker 3: another means of production, right, if we want to be
Speaker 3: like a grossly materialist, if AI is another means of credits,
Speaker 3: a tool for making things, if the same people own
Speaker 3: it and benefit from it, then like it's incapable of
Speaker 3: fundamentally changing our material conditions. Right, just becomes another way
Speaker 3: and for them to churn out shit and say that
Speaker 3: like this is fine, this is what you'll get, you know,
Speaker 3: like churn out shit content on the Internet or whatever
Speaker 3: it might be.
Speaker 1: And likewise, if AI is primarily like if it gets
Speaker 1: caught in this kind of SEO loop where it exists
Speaker 1: primarily to help advertise and sell products, whether it's as
Speaker 1: a search engine or generating mass content, you know, for
Speaker 1: like the Internet that's sort of optimized to appear higher
Speaker 1: and search results, and it's also being trained on that.
Speaker 1: Is there a point at which it kind of starts
Speaker 1: to lobotomize itself where it's just recycling shit other AI
Speaker 1: is written, which also seems kind of inevitable with that.
Speaker 1: This is one of those things. So one of the
Speaker 1: more famous moments, and like recent AI research is this
Speaker 1: Google researcher timnit Gibru, who no longer works at Google,
Speaker 1: and some other very smart people put together a paper
Speaker 1: that like it was I think generally regarded by AI
Speaker 1: folks as kind of middle of the road, but it
Speaker 1: kind of it developed the term stochastic parrot, which is
Speaker 1: what people know it for as sort of trying to
Speaker 1: describe what these quote unquote AIS do in a way
Speaker 1: that's better than an AI, because like, part of what
Speaker 1: it was saying is that, like we have to look
Speaker 1: at this as kind of like a parrot that if
Speaker 1: you say enough like words around it, including enough like
Speaker 1: racial slurs, it'll start repeating a bunch of toxic shit.
Speaker 1: It doesn't know what it's doing. It doesn't have intention,
Speaker 1: it's just kind of like repeating this stuff because that's
Speaker 1: what's been fed into it. But one of the things
Speaker 1: that point out in that paper is that like when
Speaker 1: you have an AI, when you have one of these
Speaker 1: lms trained on too large of a model, it becomes
Speaker 1: number one kind of impossible to avoid that toxic stuff.
Speaker 1: But it also reduces the utility of of the AI
Speaker 1: in a lot of ways because like when you have
Speaker 1: so much data going in, it's very difficult for the
Speaker 1: humans to kind of tell how competent it is. This
Speaker 1: is why stuff like chat GPT involves so much human training,
Speaker 1: why they had hundreds of people spending tens of thousands
Speaker 1: of man hours like going through responses to tell if
Speaker 1: they made sense. Because when you've got like it's one
Speaker 1: thing if you're like using an if you're for example,
Speaker 1: training an AI on a bunch of different like medical
Speaker 1: data to try to determine patterns and like antibiotic research, right,
Speaker 1: which is a thing that that lllms have been like
Speaker 1: shown to be have some early utility in is like
Speaker 1: kind of helping to id identify new paths for like
Speaker 1: antibiot research, because like we've got a lot of data
Speaker 1: but it's also a really focused kind of data.
Speaker 3: Right.
Speaker 1: We're not like training these things on like all of
Speaker 1: you know, Wikipedia and you know thousands and thousands and
Speaker 1: thousands of fan fiction stories about Kirk and Molder fucking
Speaker 1: each other during some sort of like Exile File Star
Speaker 1: Trek crossover. We're using a fairly focused data set to
Speaker 1: try and analyze it in a manner more efficiently than
Speaker 1: people are simply capable of. That's a lot more useful
Speaker 1: in terms of getting good data than you know, just
Speaker 1: training it on half of trillion different things out there,
Speaker 1: a lot of which you're going to be lies. But anyway,
Speaker 1: I found that interesting. It's kind of worth noting that,
Speaker 1: like Gabrew and a number of other people who were
Speaker 1: responsible for that got forced out by Google and kind
Speaker 1: of attacked by the industry. Because I think there's a desperation,
Speaker 1: and I talked about this in that episode I did
Speaker 1: last year kind of about the fundamental emptiness at the
Speaker 1: core of the modern tech industry. But I think there's
Speaker 1: this desperation and we have to find the new thing,
Speaker 1: the thing that's going to be as big as social
Speaker 1: media was, the thing that's going to deliver the kind
Speaker 1: of stock market returns that social media did and that
Speaker 1: doesn't exist yet. And AI is the after especially several
Speaker 1: years of disasters with crypto and diminishing returns in social
Speaker 1: media and honestly diminishing returns and like traditional tech because
Speaker 1: shit like smartphones have reached kind of a point of saturation. Right.
Speaker 1: You can make money sell obviously, like you can make
Speaker 1: money selling smartphones, but you can't show exponential growth, right,
Speaker 1: There's just not that many people who need new ones. Yeah, anyway, yeah,
Speaker 1: I think there's I feel some desperation here. I wanted
Speaker 1: to kind of close by reading you all. I found
Speaker 1: a very funny article in the Financial Times that was
Speaker 1: about the potential that the head of Europe's biggest media group, Birtlesman,
Speaker 1: sees for generative AI, and yeah, it interviewed a couple
Speaker 1: of people, including a guy Thomas Rabe, who works is
Speaker 1: the chief executive of the German business that owns Penguin
Speaker 1: Random House. And one of the things that he says
Speaker 1: in this is basically like I think this is, you know, uh,
Speaker 1: going to be super great for authors. You know, there's
Speaker 1: a potential for copyright infringement problems, but really like it
Speaker 1: would allow you to feed your own work into an
Speaker 1: AI and then produce much more content than you were
Speaker 1: a raverable put able to put out before like exact
Speaker 1: what is it's if it's your content for what you
Speaker 1: own the copyright, and then you use it to train
Speaker 1: the software. You can in theory generate content like never before,
Speaker 1: which I think is yeah, a fundamental, Like you know,
Speaker 1: I don't actually even think it's going to be possible
Speaker 1: to like train them on airport novels. You've got like
Speaker 1: James Patterson and other guys who they're not They don't
Speaker 1: write their own books anymore. They have like a team
Speaker 1: of ghostwriters. But like having gone through a lot of
Speaker 1: AI's stories, they're not books, Like they're not capable of
Speaker 1: writing books. They're capable of like producing text and producing
Speaker 1: pieces of books that human beings can edit laboriously into
Speaker 1: something that might look like a book. But the use
Speaker 1: in that is not like filling up airports with kind
Speaker 1: of mid grade fiction, because I think that's even beyond
Speaker 1: these models. It's like tricking people on Amazon. There was
Speaker 1: a really funny quote in this article though, where at
Speaker 1: the end of it, Rabe is like I asked chat
Speaker 1: GPT what the impact of chat GPT or generative AI
Speaker 1: is unpublishing. It prepared a phenomenal text. Frankly, it was
Speaker 1: very detailed into the point, which he then presented at
Speaker 1: a staff event. So there is kind of evidence that
Speaker 1: CEO jobs could be pretty easily replaced by this.
Speaker 2: Like you don't actually have to do anything, comrade chat GVT.
Speaker 3: We agree, it's just spinning Jenny for bosses. I love it.
Speaker 1: Yeah, anyway, that's that's what I've got right now. We
Speaker 1: have a We've been doing some research and we'll have
Speaker 1: an article out on one of the more unsettling little
Speaker 1: site industries that I think AI is going to create,
Speaker 1: which is like scam children's books that exist to make
Speaker 1: conment on the Internet money and poison the minds of
Speaker 1: little kids. But we'll get that to you next week. Yeah,
Speaker 1: it felt like it was worth coming back to this
Speaker 1: subject because it, I don't know, it's the most apocalyptic
Speaker 1: thing people in the media are talking about in a
Speaker 1: day in which like the entire Northeast is blanketed in
Speaker 1: poison smoke, which seems bad.
Speaker 3: Well, people are talking about that now because they all
Speaker 3: live in New York. I'm a fuck out, But yeah,
Speaker 3: previous to this, Yeah.
Speaker 1: Anyway, if it's good of hell, it could happen here
Speaker 1: as a production of cool Zone Media.
Speaker 2: For more podcasts from cool Zone Media, visit our website
Speaker 2: coolzonemedia dot com or check us out on the iHeartRadio app,
Speaker 2: Apple Podcasts, or wherever you listen to podcasts. You can
Speaker 2: find sources for It could Happen here, updated monthly at
Speaker 2: coolzonemedia dot com slash sources.
Speaker 1: Thanks for listening.