Speaker 1: How are creative people going to make a living in
Speaker 1: a world with AI? What does the term data dignity mean?
Speaker 1: Why is the character of data from Star Trek a
Speaker 1: useful model for how we could think about what the
Speaker 1: sources are for any creative expression. Is there a totally
Speaker 1: different way to think about the economy of the future,
Speaker 1: and how might this involve mystifying and elevating humans. Welcome
Speaker 1: to Inner Cosmos with me David Eagelman. I'm a neuroscientist
Speaker 1: and an author at Stanford and in these episodes we
Speaker 1: sail deeply into our three pound universe to understand some
Speaker 1: of the most surprising aspects of our lives. Today's episode
Speaker 1: is about the situation we find ourselves in in which
Speaker 1: AI is so boundless and rich in its creative output.
Speaker 1: You ask for a picture of anything you want from
Speaker 1: a good LM like Open AI or mid Journey or Grock,
Speaker 1: and it gives you these really superlative results. It recombines
Speaker 1: elements and concepts in a way that is deeply creative
Speaker 1: and satisfying. But of course, while it's doing an unexpectedly
Speaker 1: terrific job of recombining things that are already out there,
Speaker 1: it didn't come up with the original pieces and parts
Speaker 1: by itself, there are people, individual humans who generated those
Speaker 1: drawings and concepts and art forms. It's fundamentally human creativity,
Speaker 1: not computer creativity, that is the fuel behind it all.
Speaker 1: So is there any way to the humans who drove
Speaker 1: the original innovations or is it simply too late in
Speaker 1: the sense that maybe everything has already been vacuumed up
Speaker 1: by the lms and they've digested it all, and now
Speaker 1: there's no way for human producers of art and ideas
Speaker 1: to get any meaningful credit for their work. What does
Speaker 1: this all mean for the creatives in society, those who
Speaker 1: paint and compose music and write books? What does it
Speaker 1: mean for those who produce work now? And for those
Speaker 1: who are in school now but dream of being human
Speaker 1: creators in the future. So, for today's podcast, I have
Speaker 1: the pleasure of speaking with my friend and colleague, Jaron Lanier.
Speaker 1: Jaren wears many hats. He's a computer scientist, he's an artist,
Speaker 1: he's a technologist, he's a futurist, he's a critic, and
Speaker 1: he's a musician who, by the way, owns almost two
Speaker 1: thousand very unusual musical instruments. Jaren is the godfather of
Speaker 1: virtual reality. He started the first VR company in nineteen
Speaker 1: eighty four, and he's spent his career as a visiting
Speaker 1: scholar at companies and universities, and since two thousand and
Speaker 1: nine he's been at Microsoft Research, where he holds the
Speaker 1: role as prime Unifying Scientists under the Office of the
Speaker 1: Chief Technology Officer, which gives him the acronym Octopus, which
Speaker 1: is appropriate given his reach into so many fields. I'll
Speaker 1: just say that I'm blessed to know a great number
Speaker 1: of brilliant people. But even in that crowd, jaren sits
Speaker 1: near the top of the heap. So today we're going
Speaker 1: to talk about AI and the future for creatives.
Speaker 2: You show me an AI system, I don't care if
Speaker 2: it's CHET, GIPT, or really anything else. You can think
Speaker 2: of it in two ways. There's a figure ground and version.
Speaker 2: That's so. A figure ground in version is when you
Speaker 2: can look at something and you can swap the way
Speaker 2: you interpret it almost to an opposite right A famous
Speaker 2: one is the mcsher drawings, where you might see a
Speaker 2: field of fish or a field of birds, but each
Speaker 2: is the negative space of the other. Right now, in
Speaker 2: this case, for AI, one way you can see AI
Speaker 2: is AI is a thing. It's a noun. Whether you
Speaker 2: think it's alive or not, or conscious or not. Forget
Speaker 2: that it's just a thing. It's like, the AI did this,
Speaker 2: the AI did that. Can we regulate the AI, who
Speaker 2: sold the AI? Who's responsible when the AI did this
Speaker 2: or that? Blah blah blah. It's a thing, it's a noun.
Speaker 2: There's another way to think about it where it is
Speaker 2: not a noun anymore, but instead it's a collaboration of
Speaker 2: people and there isn't anything there other than the people.
Speaker 2: Now when I say this, people are so familiar with
Speaker 2: treating AI as a thing that they have trouble hearing
Speaker 2: the version. Sometimes, what do you mean, of course AI
Speaker 2: is there? Of course it's a thing I just bought.
Speaker 2: I'm paying for your copilot thing and that's the thing
Speaker 2: I paid for it, right, and like great, thank you
Speaker 2: for paying for it. But there is another way to
Speaker 2: think about it, and it is possible to imagine an
Speaker 2: AI system as actually the collaborative effort of all the
Speaker 2: people who made it. This is particularly true in big
Speaker 2: data systems like large language models. You can think of
Speaker 2: them as being closer to the Wikipedia, perhaps than to
Speaker 2: Commander Data from Star Trek. Although I want to see
Speaker 2: something interesting about Commander Data. I was just reviewing clips
Speaker 2: of Commander Data talking and he always introduced himself as
Speaker 2: an amalgam of people whose data he was combining. There's
Speaker 2: a wonderful episode where he plays violin very beautifully and
Speaker 2: Captain Picard comes up to him so that that was
Speaker 2: a great performance, and he says, well, really, you should
Speaker 2: thank the three hundred violinists whose data was amalgamated for
Speaker 2: me to do this. And what I like about that
Speaker 2: scene is that he knows specifically the three hundred. They're
Speaker 2: not a faceless mush of some unknown number, which is
Speaker 2: how we do it today. But the writers at the time,
Speaker 2: and I should fess up, I was in the loop
Speaker 2: and was talking to people, so I might I might
Speaker 2: have had a bit.
Speaker 3: Of influence on it.
Speaker 2: But the idea is that you could know who the
Speaker 2: people were if he wanted to, and Data could say
Speaker 2: it seased three hundred violinists instead of well it's some
Speaker 2: random mush of violinists, you know. So what we've done
Speaker 2: now is we've mushed everybody together so we don't know
Speaker 2: who they are, and that's been our approach to information systems,
Speaker 2: and it benefits those of us who make.
Speaker 3: A living from them.
Speaker 2: If you're listening to a playlist online from some music
Speaker 2: service and you get a feed, you don't necessarily know
Speaker 2: who all the musicians are, but you know the name
Speaker 2: of the service you're paying the monthly bill to.
Speaker 3: So the digital hub.
Speaker 2: Becomes more powerful, becomes more known, more prominent, more valuable
Speaker 2: than the people who are feeding it. So there's this
Speaker 2: constant economic incentive to emphasize the hub and not all
Speaker 2: the people who are really the only thing that are there.
Speaker 2: In the my preferred inversion of understanding the thing, the
Speaker 2: same thing could be true of AI. We so much
Speaker 2: want AI to be this emerging entity, even if it's
Speaker 2: an evil, horrible one that will destroy us. Because we
Speaker 2: grew up on the Matrix movies and the Terminator movies
Speaker 2: and all of these. We want that creature to emerge
Speaker 2: because that's our childhood. That's almost like our religion. It's
Speaker 2: the story we grew up with. But if we acknowledge
Speaker 2: it actually it's all these people instead, then we lose
Speaker 2: We lose the creature, and that would be traumatic. So
Speaker 2: when we train the models, we don't keep track of
Speaker 2: which source documents were involved. So what we really need
Speaker 2: to do is eliminate the people. And we have to
Speaker 2: do that for economics. Can you imagine. I mean, like
Speaker 2: with economics, we can't trace everything. We don't quite know
Speaker 2: why a price is what it is. But on the
Speaker 2: other hand, we could say, hey, buddy, uo taxes, So
Speaker 2: we have a definite motivation to keep track of the people.
Speaker 2: With AI, we lose all the people. We just like
Speaker 2: pretend that the people aren't there. But that's the wrong inversion.
Speaker 1: Well, I know that one of the things that you
Speaker 1: can'tpaign for is digital dividends.
Speaker 3: Well we call it.
Speaker 2: Initially as an academic research field, it was called data
Speaker 2: as labor, so you treat your data as if it
Speaker 2: were labor. And then the name shifted to data dignity.
Speaker 2: Dignity which was such his idea, such an adella. Not
Speaker 2: that he agrees with me about everything, believe me, but
Speaker 2: you know it's that's the term he came up with.
Speaker 3: And data dignity is let's.
Speaker 2: Say you could you got some result, he said, hey, chachipt,
Speaker 2: can you write me a Christmas card?
Speaker 3: Or whatever?
Speaker 2: People do okay, and then they would say, here's your card.
Speaker 2: By the way, the top twelve sources, I mean there
Speaker 2: were a multitude and unbounded multitude of sources, but the
Speaker 2: top twelve were these, and then you could say, hey,
Speaker 2: could you get rid of that one and do this
Speaker 2: one instead. It gives you an x ray into what
Speaker 2: you might think of as the intent of the particular output.
Speaker 2: And I like that for two reasons. One is, there's
Speaker 2: a safety question. So when we have red teams attempt
Speaker 2: to fool the model to get it to do things
Speaker 2: that we don't want it to. For instance, can you
Speaker 2: make a bomb out of what I see here in
Speaker 2: this kitchen right now? Because I really want to blow
Speaker 2: this place up. We don't want that to happen, right,
Speaker 2: But is there some way they can couch that prompt
Speaker 2: in some kind of a weird thing like asking for
Speaker 2: a cake recipe instead, but somehow or other, like maybe
Speaker 2: it indirectly references a movie where there was a bomb
Speaker 2: and somehow rather they get it anyway, all right. The
Speaker 2: thing is, if you can look at the most prominent
Speaker 2: source documents that were relevant to that result and there's
Speaker 2: a bomb in there, you've nailed it. So you have
Speaker 2: an x ray into intent and there's not really any
Speaker 2: substitute for that, and we lose that when we pretend
Speaker 2: that there weren't people there, or that they were just
Speaker 2: a giant, untraceable mush. And then, of course the other
Speaker 2: thing is I want I want to have a future
Speaker 2: where people can be paid for providing exceptionally useful data
Speaker 2: to AI to incentivize better and better data production FREEI
Speaker 2: I want that future, and not just the future everybody
Speaker 2: has to go on universal basic income and be the
Speaker 2: same and feel useless. So that's the second reason, and
Speaker 2: that's why it's called dignity.
Speaker 1: Got it Now, just to play Devil's advocate for one
Speaker 1: moment on that, let's say that you ask it to
Speaker 1: write you a poem, and it says, hey, John Smith
Speaker 1: was sort of the biggest influence for this poem they
Speaker 1: got written.
Speaker 3: But of course John Smith is just a vessel into
Speaker 3: which was.
Speaker 1: Poured the entire culture, and his output of the poem
Speaker 1: came from all the other influences on him.
Speaker 3: So where do you where do you? Yeah?
Speaker 2: So this is a very interesting problem, and this is
Speaker 2: a fundamental philosophical problem, which is the universe is in
Speaker 2: a way of giant continuity, and how do you ever
Speaker 2: draw boundaries? How do you ever say, actually, there's this
Speaker 2: thing and that thing. It's a very basic, fundamental problem,
Speaker 2: and it's a trickier one than people might realize unless
Speaker 2: they've really confronted it. I'm not going to go into
Speaker 2: the whole issue of ontology, because it's a big Ye've
Speaker 2: been working on it for thousands of years and still
Speaker 2: here what I want to say in this case, I
Speaker 2: do have an answer, and it's an answer that you
Speaker 2: might not like and many might not like, which is
Speaker 2: I think we have to celebrate human beings and elevate
Speaker 2: them in a way that, if you like, gives them
Speaker 2: this status of being sources, even though they're always amalgamators too.
Speaker 2: And we have to do that on faith, and in
Speaker 2: a way it's a bit of a mystical idea that
Speaker 2: there's something about people that's magical and a little apart.
Speaker 2: You could call it consciousness, you could call it different things.
Speaker 2: But the reason we have to do that is if
Speaker 2: we're technologists, we have to define who our beneficiary is
Speaker 2: for the technology, and if we can't define this special beneficiary,
Speaker 2: we can't even be coherent technologists. We lose the whole thing.
Speaker 2: Become we become random morons. Just jiggling around between ideas
Speaker 2: is of no meaning. We become memes, we become viral,
Speaker 2: and nothing's there anymore. So I don't think we have
Speaker 2: any choice but to somewhat mystify and elevate humans. And
Speaker 2: so when somebody source document might have internally through other channels,
Speaker 2: dependent on others, which it always will, I mean, there's
Speaker 2: of course, it's always true. I still think we have
Speaker 2: to defer to that person as much as we can.
Speaker 2: And if somebody else says, hey, that shouldn't have been
Speaker 2: their source document. They copied me, Well, we have systems
Speaker 2: in place for that when it's egregious enough and worthwhile enough,
Speaker 2: and they can super copyright if they want to. I
Speaker 2: don't love that, but it's there, and sometimes it needs
Speaker 2: to be there. You know, it's rough justice, it can
Speaker 2: never be perfect, but we have to agree that authorship's.
Speaker 3: A real thing.
Speaker 2: We can't just say that all people are just vessels
Speaker 2: and there's nothing but this emergent thing and no individual
Speaker 2: person as a creator. We have to say no people
Speaker 2: are creators, because otherwise we can't be technologists anymore. The
Speaker 2: moment you give up people, you might as well go
Speaker 2: smash your computer because it doesn't make any sense anymore,
Speaker 2: because it's only possible. Definition isn't serving people.
Speaker 1: So let me dig into something about how it's this
Speaker 1: figure ground reversal of looking at AI as a collaboration
Speaker 1: of people. So this taps into something that I've been
Speaker 1: writing lately about this question of whether AI has theory
Speaker 1: of mind. And you may know some people have published
Speaker 1: paper saying, hey, with the theory of mind has emerged,
Speaker 1: And just as a reminder of the audience, it's you know,
Speaker 1: theory of mind is being able to put yourself into
Speaker 1: the shoes of someone else, into the perspective, and understand
Speaker 1: their beliefs, even if they're different from your own, and
Speaker 1: understand what they believe is true or not true their
Speaker 1: perspective so on. Now, this is something humans do seamlessly
Speaker 1: and effortlessly all the time. We're very good at understanding, Oh,
Speaker 1: he doesn't know that piece of information, she does know that,
Speaker 1: and so on. But the question is do llms do it?
Speaker 1: So some people have claimed yes. I've studied this point
Speaker 1: very carefully. I conclude that they do not. Now why
Speaker 1: do some people write that they do. It's because you
Speaker 1: give these questions that are probing on. Hey, what if there's,
Speaker 1: you know, a bag that's labeled with one thing, but
Speaker 1: there's something different inside. What does the person believe who's
Speaker 1: just looking at the label, and what does she believe
Speaker 1: after she looks inside the bag? And so on and
Speaker 1: lllls will get the stuff right, and so they say, Wow, they've.
Speaker 3: Got theories in mind.
Speaker 1: They can understand what it's like to be this person
Speaker 1: in these different circumstances. The reason I don't think that's
Speaker 1: true is what I'm calling this the intelligence echo illusion.
Speaker 1: And what I mean by that is thousands, maybe millions
Speaker 1: of people have written about this on line. They've written
Speaker 1: out these deary mind questions, these unexpected things and so on.
Speaker 1: So an LM absorbs the statistics of lots and lots
Speaker 1: of these things, and then when you ask the question,
Speaker 1: you say, my god, it gave me the right answer.
Speaker 1: And of course gave the right answer. It's read the
Speaker 1: damn problem lots of times.
Speaker 3: What's interesting, this is the calculation.
Speaker 1: I ran recently, is you know, if you look at
Speaker 1: the common crawl, this corpus of the data that all
Speaker 1: these large language models crawl and absorb that's, according to
Speaker 1: my calculation, one thousand times larger than what you could
Speaker 1: read in a lifetime. And so the fact is, many,
Speaker 1: many times we'll pose a question to let's say, chat YOUPT,
Speaker 1: it'll give some answer. I'll think, my god, it's got
Speaker 1: theory of mind. It's really done something strange. But in fact,
Speaker 1: you're just hearing this intelligence echo, by which I mean
Speaker 1: people already knew this.
Speaker 3: You just didn't know that, or you didn't know that
Speaker 3: other people do that.
Speaker 2: Sure, but I mean we're right back in this territory
Speaker 2: of mysticism and ontology. Because if somebody wished to disagree
Speaker 2: with you, and that person would not be me.
Speaker 3: But if somebody wished to.
Speaker 2: Disagree with you, they'd say that all those people were
Speaker 2: just reflecting things like that anyway, So there's no difference
Speaker 2: that the AI and the people are trained on are
Speaker 2: in the same status, the same category. And so what
Speaker 2: if the AI got to it this way with people
Speaker 2: in the way, it's still the same thing that people
Speaker 2: are doing. And why are you making this distinction? Why
Speaker 2: are you trying to be all mystical in love people?
Speaker 2: What's wrong with you? And so I'm just going to
Speaker 2: declare you should be mystical and elevate people because it's
Speaker 2: the only thing. Weirdly, this little bit of faith, which
Speaker 2: I seem irrational, is the only way to save rationality
Speaker 2: because otherwise, once again we have no beneficiary for technology.
Speaker 2: You can have science without elevating people, but you can't
Speaker 2: have technology. Technology has to have a beneficiary. The problem
Speaker 2: to be solved is serving people, and technology isn't sensible
Speaker 2: without a problem to be solved. Science is you can
Speaker 2: have a science theory without knowing what it's for. You
Speaker 2: can't have a technology without knowing what it's for. Or
Speaker 2: maybe I mean one could argue that you can have
Speaker 2: some kind of underlying, very fundamental technology that might be
Speaker 2: applied in different ways, but ultimately to actually make any
Speaker 2: instance of it, it has to be for something. There
Speaker 2: has to be a human at the end of the
Speaker 2: chain for it to be sensible. So I totally agree
Speaker 2: with you, but you have to recognize that there's a
Speaker 2: figure ground reversal there, And somebody who disagrees with you
Speaker 2: might just say, well, the AI and the people are
Speaker 2: the same and they're both just getting this information. But
Speaker 2: I still think our only choice to remain rational technologists
Speaker 2: is to accept that we have to be kind of
Speaker 2: mystical humanists to frame it, and that's not comfortable for
Speaker 2: a lot of people. But I think if you really
Speaker 2: examine the logic of the situation, you'll come to agree
Speaker 2: with me.
Speaker 1: Oh, I already agree with you, actually, And that's why,
Speaker 1: that's why I really care about this intelligence echo illusion,
Speaker 1: which is to say, you're hearing the echoes of people
Speaker 1: who've said this thing before, but you mistake it for
Speaker 1: the voice of an AI that has theory of mind.
Speaker 2: Yeah, and so why can't you Let's say the AI
Speaker 2: gets the puzzle with the bag, right, Okay, let's say
Speaker 2: those now in my preferred future, where you then get
Speaker 2: a list of the top twelve or twenty five or
Speaker 2: whatever it is people who contributed. What it might say
Speaker 2: is I would tell you, but actually there are a
Speaker 2: million people interchangeably in the first slot here because this
Speaker 2: thing is so common, and that has to be an
Speaker 2: acceptable and honest answer. So like if you say, hey,
Speaker 2: could you do something like I want a cat that's
Speaker 2: playing the banjo or whatever, and they'll say, got to
Speaker 2: tell you a lot of cats out there. It's the Internet, remember,
Speaker 2: lots of cats, So it really could have been any cat.
Speaker 3: In this case, it was this cat. But that's not special.
Speaker 2: And so we have to have a fungibility measure, not
Speaker 2: just an influence measure. And that that's so, this whole
Speaker 2: future world of acknowledging people is a bit more subtle
Speaker 2: and complicated than.
Speaker 3: I'd like it to be.
Speaker 2: But I've never seen anything about it that doesn't make
Speaker 2: sense or is unachievable.
Speaker 1: Right, So one idea that you've mentioned me in the
Speaker 1: past would be, I can't remember if I got this
Speaker 1: term digital dividends right, if I made that up some
Speaker 1: whe along the way. But the idea is that a
Speaker 1: creator gets paid, He gets a few nickels here and
Speaker 1: there because on you know, Dolly, it's used his painting
Speaker 1: as part of the influence.
Speaker 3: Yeah.
Speaker 2: So the thing about this idea, which currently the term
Speaker 2: I like is is dated dignity. Many will object and
Speaker 2: correctly that in a lot of cases, if somebody's output
Speaker 2: happens to include a picture of your shoe for some
Speaker 2: image that has a shoe or something, or maybe they
Speaker 2: used your drone footage of waves for the wave thing
Speaker 2: or something, what you'll get literally would be very small.
Speaker 2: It might be quite a bit less than Nichols, right,
Speaker 2: But that's not the point.
Speaker 3: See, the question is whether we think of the.
Speaker 2: Future as a linear extension of what we already know
Speaker 2: or something really expansive and fantastic and unpredictable. And a
Speaker 2: lot of the people who tell me that they believe
Speaker 2: in this radical future where there's a singularity, where this
Speaker 2: AI transformers reality and a blink of an eye and
Speaker 2: humans are obsolete, and they're actually thinking in a very
Speaker 2: linear way because what they believe is the AI as
Speaker 2: that exists is capable of creating the AI of the future,
Speaker 2: and that we already know in a sense everything that
Speaker 2: needs to be known, and all we have to do
Speaker 2: is turn the on switch. In future generations won't be
Speaker 2: more creative than us. Where the final creative, you know?
Speaker 2: And I think that might not be the future.
Speaker 3: I want.
Speaker 2: What if the future is one in which there's incredible, creative,
Speaker 2: productive things happening, and I'm not creative enough perhaps to
Speaker 2: come up with the examples.
Speaker 3: I'll just use the one I used last.
Speaker 2: Week, which is, in the future, we have some way
Speaker 2: of extending our bodies physiologically, so we can fly around
Speaker 2: an open space in the vacuum and do all sorts
Speaker 2: of things and propel ourselves. And what if in order
Speaker 2: to do those body extensions there's some sort of bioprinter
Speaker 2: we get into. And what if they're aisystems that run
Speaker 2: that because it's the only way. And what if there's
Speaker 2: a whole new creative class of people who've contributed data
Speaker 2: to that thing. All right, so that's the whole new thing,
Speaker 2: and I think the best of those people might get
Speaker 2: rich from contributing data that then is able to be
Speaker 2: beneficial to people through a large model that their data
Speaker 2: helped train. And what if there's more and more examples
Speaker 2: like that. What if there's thousands and millions of tens
Speaker 2: of millions as humanity expands into an ever more creative,
Speaker 2: interesting future instead of a future that we already know
Speaker 2: all about because we're the smartest people.
Speaker 3: Who will ever be.
Speaker 2: And so in that case, we start to see niches
Speaker 2: for people who are data creators for AI where they
Speaker 2: really our novel and they're really making money. And that's
Speaker 2: what people think so linearly like they like, it's not
Speaker 2: just TikTok, it's like flying around in space without a
Speaker 2: space suit, like free your mind. Imagine a future more
Speaker 2: radical than you assume, yeah.
Speaker 1: And maybe that there's a very clear economic incentive there,
Speaker 1: which is to say, let's say that I have let's
Speaker 1: say I'm putting pictures of cats online. As you point
Speaker 1: out that anytime Dolly puts together a picture of a cat,
Speaker 1: mine is one of millions of pictures. So I can't
Speaker 1: make any money that way. As a creator, I am
Speaker 1: therefore incentivized to do things that are really new, to
Speaker 1: really push the matteries of what had been done in
Speaker 1: terms of art, poetry, whatever it is.
Speaker 2: Let's say a little bit more about that incentive, because
Speaker 2: that's very important. There's a question of whether the network
Speaker 2: dynamics dominate what actually happens over the network or not.
Speaker 2: So when network dynamics dominate, what you see is phenomena
Speaker 2: of virality and memes, because those are network effects where
Speaker 2: it doesn't really matter what the meme is. It doesn't
Speaker 2: necessarily matter what goes viral. Sometimes silly things do, sometimes
Speaker 2: interesting things do, but it can go either way, all right.
Speaker 2: In a real economy, when somebody loses money on some
Speaker 2: stupid NFT or meme, stock or something, that's the network
Speaker 2: effect dominating. Usually typically the reason our world hasn't collapsed totally.
Speaker 2: Is that when people spend money, it's not on total nonsense,
Speaker 2: but it's at least slightly on something that's real of
Speaker 2: some use. Okay, And in that world of a real economy,
Speaker 2: there are incentives to make things better and there's a
Speaker 2: chance for competition to be meaningful. But when the structure
Speaker 2: itself dominates rather than the thing the structure is channeling,
Speaker 2: then you end up in this make believe world. And
Speaker 2: on digital networks, the make believe world is always about
Speaker 2: being the lucky one who gets in on the virality.
Speaker 2: Your startup turns into the hub for this giant thing
Speaker 2: that benefits from everybody else's work, but you benefit more
Speaker 2: from them. You get to have TikTok or Instagram or something.
Speaker 2: In this world, you have the meme stock, you have
Speaker 2: the meme video, you have whatever, but you can't have
Speaker 2: a predict who will get that. There's a randomness to it,
Speaker 2: which means it's not fundamentally productive. In the future, we
Speaker 2: need to suppress the network's own influence on what happens
Speaker 2: on the network and have it back off and let
Speaker 2: the content itself, whatever that might be, dominate, all right,
Speaker 2: and then that creates reality that creates incentives for improving reality.
Speaker 2: So the most fundamental idea is that you have this
Speaker 2: data structure that gradually changes and settles into something that
Speaker 2: can do some work. It happens automatically or semi automatically,
Speaker 2: or not explicitly programming it. Now, there are a lot
Speaker 2: of algorithms that have that kind of settling effect, but
Speaker 2: the grandaddy of them is called gradient, and there's a
Speaker 2: wonderful mathematical dispute about who should be credited with it,
Speaker 2: but it's either Cashi or Ramon, So it goes way back.
Speaker 2: And it's sometimes thought about as being like walking on
Speaker 2: a landscape. So when we walk up the landscape, we
Speaker 2: just think we're walking at lungitude and latitude and it's
Speaker 2: some elevation.
Speaker 3: But in AI we're dealing with very very.
Speaker 2: High dimensional spaces, which is a concept some people aren't
Speaker 2: familiar with. But any rate, let's just imagine we're walking
Speaker 2: on a landscape, and so you want to descend and
Speaker 2: you want to find a comfortable place, like where's the
Speaker 2: place you can go? That's really low if you don't
Speaker 2: know an advance, you don't have a map or an overview,
Speaker 2: so you start wandering around. Now the dangerous if you
Speaker 2: just say, oh, here's a nice depression, I'll go here,
Speaker 2: it might not be as low as some other one
Speaker 2: that would be available, which would be a better solution,
Speaker 2: And so you have to take some precautions and start
Speaker 2: to have some kind of way of saying, well, I'm
Speaker 2: going to kind of go around a little bit so
Speaker 2: I don't get caught in the place that actually isn't
Speaker 2: ideal or something like that. You have to have a strategy, okay,
Speaker 2: And so the point is not to get caught at
Speaker 2: some halfway solution.
Speaker 3: You want to really kind of jump around.
Speaker 2: Now with AI systems, the whole point like if you
Speaker 2: want to, let's say I want you to have a
Speaker 2: diffusion model that puts a cat on a motorcycle and
Speaker 2: with whatever. Okay, so if the cat's riding a motorcycle,
Speaker 2: the easy solution is just to make a cat or
Speaker 2: a motorcycle, getting both kind of hard. And so the
Speaker 2: easy solution that isn't useful is very similar to getting
Speaker 2: stuck in one of these intermediate dimples that doesn't.
Speaker 3: Go as low as what you might really want.
Speaker 2: You wanted to send to a better one where you're
Speaker 2: getting both the cat and the motorcycle is you have
Speaker 2: to do some extra little thing to broaden your scope.
Speaker 2: So what I was going to say is this sort
Speaker 2: of this sort of strategy is fundamental. It's the very
Speaker 2: core basics for what we do these days with AI.
Speaker 2: Their many, very many versions, but this way of doing
Speaker 2: things is the fundamental trick of AI. But when we
Speaker 2: put those AI algorithms out in society, we forget. We
Speaker 2: just say, oh, well, if the AI algorithm can make
Speaker 2: money by getting kids addicted to vain attention seeking and
Speaker 2: low attention spans, best, fine. But it's not. That's one
Speaker 2: of these dimples. It's not good for society, it's not
Speaker 2: good for the kid. And so the nerdy way I
Speaker 2: would put some of this is to say the same
Speaker 2: discipline we need to get the algorithms to work at
Speaker 2: all needs to be applied to the way we deploy
Speaker 2: these algorithms out in the larger society. And that might
Speaker 2: be a nerdy way of saying something similar to what
Speaker 2: many of us have been saying about trying to make
Speaker 2: something that serves people better.
Speaker 1: So wait, just so I put a cap on that
Speaker 1: isn't the case, then that you agree that it would
Speaker 1: incentivize creators to be more creative if they were doing
Speaker 1: things that hadn't already been done by a million other people,
Speaker 1: because they get no money for that. But if they
Speaker 1: do something that is really new, then they can. There's
Speaker 1: a potential to make a lot of money from that.
Speaker 2: So one of the fundamental claims of people who like
Speaker 2: market economies is that they foster creativity. The type of
Speaker 2: competition that happens in a market economy is said to
Speaker 2: insteadivize creativity. I am on the side of the people
Speaker 2: who believe that market's foster creativity. I just I can't
Speaker 2: prove it, but I can almost. I think I can
Speaker 2: almost prove it. I mean, I mean, look around. We're
Speaker 2: in Silicon Valley, for God's sakes, give me a break.
Speaker 2: Of course, of course they do. Of course markets do.
Speaker 2: But the Internet. When the Internet is allowed to be
Speaker 2: dominated by network effects, by virality, and by memes, then
Speaker 2: it no longer does, because then you're chasing the benefits
Speaker 2: intrinsic to the Internet, which are not in themselves creative,
Speaker 2: but just this kind of random, lottery winning kind of
Speaker 2: a thing.
Speaker 3: Right.
Speaker 1: That's why I think, Yeah, providing an economic counterbalance where
Speaker 1: the less MEMI you are, and the more original you are,
Speaker 1: the more that has economic value. Way to reverse the
Speaker 1: network property, because I'm not sure how it's going on
Speaker 1: its own right.
Speaker 2: So one of the ways I summarize data dignity is
Speaker 2: to say, whenever we have an opportunity to create a
Speaker 2: new creative class instead of a new dependent class in
Speaker 2: the future, we should do that. So if there's a
Speaker 2: bunch of people who might be put out of work
Speaker 2: because robots are driving the cars or whatever, we should
Speaker 2: try to find some other group of people who might
Speaker 2: be creating in some way that they could be paid for,
Speaker 2: where now we don't expect them to be paid like
Speaker 2: we should be creating creative classes. There's another caveat or
Speaker 2: qualification to this idea, which is that we can't expect
Speaker 2: everybody to be creative, and we can't allow creativity to
Speaker 2: become the measure of human worth or value. But I
Speaker 2: do think whenever you can create a new creative class,
Speaker 2: it's imperative to do so.
Speaker 1: That was my conversation with Jaron Lanier, computer scientist, inventor, thinker, writer, innovator, critic, technologist.
Speaker 1: The issue we talked about today is that people who
Speaker 1: are creative, the painters, the writers, the composers. They have
Speaker 1: been justifiably worried ever since AI really hit its stride
Speaker 1: just a couple of years ago, because the stuff AI
Speaker 1: can create is stunning. But maybe with the right economic structures,
Speaker 1: AI can launch us farther into human creativity. This just
Speaker 1: requires incentivizing things so that a creative doesn't do just
Speaker 1: another thing that's been done before, like another cat video,
Speaker 1: but instead something genuinely bleeding edge. In a data dignity economy,
Speaker 1: you will benefit only when you reach out into the
Speaker 1: uncharted wilderness to do something novel, and when the AI
Speaker 1: draws from that and other people benefit from that, then
Speaker 1: you can make a living that way. This is a
Speaker 1: way to reward an exploration of the frontiers that speeds
Speaker 1: up the whole human race because it encourages people to
Speaker 1: be creatively productive, not reproductive. So as we think about
Speaker 1: the world, we've suddenly shot into a world with AI
Speaker 1: disrupting every industry. I hope today's conversation will remind us
Speaker 1: of a fundamental truth. AI isn't a standalone creation. It's
Speaker 1: a mirror. It's a mosaic of countless human contributions. Every
Speaker 1: line of code that it writes. Every line of poetry,
Speaker 1: every beautiful piece of art is there, all reflections of
Speaker 1: human creativity and human effort. So the choice to view
Speaker 1: AI as a collaborative endeavor rather than an autonomous entity,
Speaker 1: it's not just a philosophical stance. It's a call to
Speaker 1: honor the humans behind the machine and to ensure that
Speaker 1: they are valued in a future shaped by their contributions.
Speaker 1: Go to Eagleman dot com slash podcast for more information
Speaker 1: and to find further reading. Send me an email at
Speaker 1: podcasts at eagleman dot com with questions or discussion, and
Speaker 1: check out and subscribe to Inner Cosmos on YouTube for
Speaker 1: videos of each episode and to leave comments Until next time.
Speaker 1: I'm David Eagleman, and this is Inner Cosmos.