Speaker 1: Welcome to Tech Stuff. I'ms Voloshan here with Kara Price.
Speaker 2: Hi Kara, Hi.
Speaker 3: As So today.
Speaker 1: I'm excited to welcome Nicholas Thompson back onto the show.
Speaker 1: He's the CEO of the Atlantic and a big technology buff.
Speaker 1: He has a recurring video series called the Most Interesting
Speaker 1: Thing in Tech on LinkedIn My Fave, and he also
Speaker 1: hosts a podcast called the Most Interesting.
Speaker 4: Thing in AI.
Speaker 1: I wanted to invite him on for a roundup of
Speaker 1: his most interesting stories from twenty twenty five and to
Speaker 1: discuss what he's looking ahead to in twenty twenty six.
Speaker 1: But I also wanted to talk to him about his
Speaker 1: rather remarkable new book, The Running Ground, which I read
Speaker 1: in one sitting.
Speaker 4: I can sort of guess, but what is The Running
Speaker 4: Ground about.
Speaker 1: It's kind of a memoir about Nick's battle with cancer,
Speaker 1: his relationship to running, this relationship with his father, and
Speaker 1: how those things all connect in surprising ways. When we talked,
Speaker 1: I asked him about finding his dad's unpublished memoir and
Speaker 1: how he chose to weave it into his own story,
Speaker 1: and well, you just have to listen to it.
Speaker 2: So I get this unpublished memoir my dad had written,
Speaker 2: and I start to read it, and it's dedicated to
Speaker 2: the seven grandchildren. It was great. It was like, oh,
Speaker 2: that's so good Dad. That was like so sweet. There's
Speaker 2: like an introduction about pain and many lives and you know,
Speaker 2: the eras he's been through and it's kind of nice.
Speaker 2: I'm like, wow, my kids will enjoy reading that. And
Speaker 2: then it's like talking about being in Asia, and then
Speaker 2: it is literally like it's probably page four, page five
Speaker 2: a description of the penis sizes of men of different
Speaker 2: races across the world.
Speaker 4: What Yeah, Nick was quite confused.
Speaker 2: How like you wrote the dedication page and you've like
Speaker 2: edited this At what point did you think like this
Speaker 2: got to stay in right, Like it's like kind of racist,
Speaker 2: like super weird, like definitely inappropriate. My dad was gay,
Speaker 2: had a sex edition like we had affairs with many
Speaker 2: many men, was like kind of ran a brothel in
Speaker 2: Balley when he was the late state of his life.
Speaker 2: So this is an area which he was an expert.
Speaker 2: But oh my god, right, and so you couldn't get
Speaker 2: into too deep a mode or too legaic a mode
Speaker 2: because like every four pages there's something where you're just.
Speaker 4: Like that this is not what you're expecting, right, now,
Speaker 4: not in the least I had no idea.
Speaker 1: It's a pretty interesting book and we had what some
Speaker 1: people like to call a wide ranging conversation. We talk
Speaker 1: about running and how it provides a space separate from technology,
Speaker 1: but also about how tech can be used to optimize running.
Speaker 1: We talk about the emerging relationship between spirituality and technology,
Speaker 1: something I know you're very interesting, and also about the
Speaker 1: dichotomy between the markets optimism about AI and the general
Speaker 1: public's pessimism about what it's going to do to them.
Speaker 1: And we talk about a company creating a product for
Speaker 1: AI models to cite their sources and compensate the content
Speaker 1: creators come up with the information.
Speaker 2: That is really interesting.
Speaker 3: So all these publications and people whose work is training
Speaker 3: the models could actually maybe be compensated.
Speaker 4: That's definitely the hope, and we'll get to that.
Speaker 1: But we started our conversation talking a bit more about
Speaker 1: Nicholas's book, The Running Ground. I want to ask you
Speaker 1: about The Running Ground, and it's a fantastic book which
Speaker 1: I devoured in one sitting.
Speaker 2: Excellent.
Speaker 4: The quote which really stuck.
Speaker 1: With me was running has long been away for me
Speaker 1: to waken the memory of the beloved.
Speaker 2: What does that mean well. So that comes from or
Speaker 2: was inspired by a Maximus of Tear quote about trying
Speaker 2: to find God and understanding in different objects. And what
Speaker 2: it means for me is that in life we all
Speaker 2: have different things that we use to think more deeply
Speaker 2: or to bring is closer to the people we care
Speaker 2: about the most, or particularly those who we cared about
Speaker 2: the most who are gone. And for me, it's running.
Speaker 2: It's what allows me to meditate. It's also a way
Speaker 2: I have more in my father, who is a very
Speaker 2: important figure in my life. It's a way I get
Speaker 2: myself into a deeper spiritual space. So that's what I'm met.
Speaker 1: And it's interesting as well that you reflect on how
Speaker 1: much running is on the rise.
Speaker 2: It is, it really is, and I think it's all
Speaker 2: partly was Covid right, we were all on our own,
Speaker 2: there's nothing to do, everybody started running. And then secondly,
Speaker 2: I think it's a counterpoint to TikTok, right, and to
Speaker 2: all the short attention spens. And it's a way like
Speaker 2: I'm going to go out, I'm going to run a
Speaker 2: five hour marathon. I'm going to go on a three
Speaker 2: hour training run and I'm not going to have my
Speaker 2: phone or certainly I'm not going to be looking at
Speaker 2: social media and they have my phone in my back pocket.
Speaker 2: But it's a way for people to escape so much
Speaker 2: of what they know they don't like about everyday life,
Speaker 2: but they're kind of addicted to and so running is
Speaker 2: a way to break away from that.
Speaker 1: And you're very interesting test case for that because by
Speaker 1: day you're the CEO of The Atlantic, by evenings and
Speaker 1: weekends you're the publisher of the Most Interesting Thing in
Speaker 1: Tech franchise, which is a podcast and a LinkedIn video series.
Speaker 1: And at the same time you find eight hours a
Speaker 1: week to run, which is both a way to honor
Speaker 1: your father to celebrate your vitality overcoming cancer. There's also
Speaker 1: a very strong spiritual component to you with running. I
Speaker 1: mean the opening run you go on in the book
Speaker 1: after your recovery from your cancer, you cross yourself.
Speaker 2: Yeah, it's an interesting data know that's just kicked up
Speaker 2: on that. It's like three words in there, but yeah,
Speaker 2: I do.
Speaker 1: But then coming back to wakening the memory of the beloved,
Speaker 1: the final image of the book is a present from
Speaker 1: your father.
Speaker 2: Was not a present he paid back alone when he
Speaker 2: went bankrupt.
Speaker 4: Yes, because it's different for a president.
Speaker 2: To be fair, he'd actually stolen it from my mother,
Speaker 2: So you know, it's like a complicated object. But yes,
Speaker 2: president sounds nicer.
Speaker 1: Well, you've done this amazing job, which I won't want
Speaker 1: to talk to you as well about forgiving your father
Speaker 1: in some way to find new way to keep loving him.
Speaker 4: But this is it a post or a piece of
Speaker 4: aud and what is it?
Speaker 2: It's a print. So it's a print on my wall.
Speaker 2: It's framed on my wall in my office and the catskills.
Speaker 1: And it begins God to himself, the Father and fashion
Speaker 1: of all that is older than the sun of the sky.
Speaker 1: And it's basically about everyone being able to find their
Speaker 1: own version of their faith or yeah.
Speaker 2: And in fact, one of the most remarkable things about
Speaker 2: it is that I had a multi faith wedding and
Speaker 2: we ended up having a Buddhist monk do the ceremony,
Speaker 2: and so we have this kind of interfaith mix. And
Speaker 2: I ask my dad, my dad, I don't really know
Speaker 2: what your religious beliefs are, and he said, oh, my
Speaker 2: religious beliefs are just expressed on that Benshon print, which
Speaker 2: is this sort of kind of poly religious view that
Speaker 2: as long as you are finding beauty and God and
Speaker 2: something sacred in something. Maybe you're finding it in music,
Speaker 2: maybe you're finding in architecture, maybe you're finding it in running.
Speaker 2: That's good enough for me.
Speaker 1: So my most interesting thing in tech actually intersects very
Speaker 1: neatly with what we've just been talking about, which is
Speaker 1: spirituality and tech.
Speaker 2: That's interesting.
Speaker 1: Peter Teal's Antichrist lectures the Pope's recent first foreign trip,
Speaker 1: where he went to Lebanon, Turkey, but spoke extensively about
Speaker 1: our duty to consider how we use AI and the
Speaker 1: rise of people in delusive guru esque relationships with chatbots,
Speaker 1: basically outsourcing their sense of meaning and purpose and rationality
Speaker 1: to chatbots. So what have you thought about I mean've
Speaker 1: obviously been thinking about spirituality finishing the book. How does
Speaker 1: it inform your sense of where we are in the
Speaker 1: AI moment?
Speaker 2: It's one of those things like what I find most
Speaker 2: interesting about AI are these kind of these questions where
Speaker 2: I don't know the answer and where I don't know
Speaker 2: where we're headed, and so on the spirituality question. Like
Speaker 2: I do think there's a chance that AI sort of
Speaker 2: supplants religion, right, people don't go to church, Like why
Speaker 2: would you look to the Bible for answers when you
Speaker 2: can ask GPT six, right, And that's kind of sad future, right,
Speaker 2: because the point of church isn't just that you learn
Speaker 2: from the Bible. It's that you're connected to everybody else,
Speaker 2: You're connected to your ancestors, you're connected to history, and
Speaker 2: that maybe where we're going. On the other hand, one
Speaker 2: of my favorite things that anybody has said to me,
Speaker 2: and my friend Riccardo Stefanelli and works with Branelo cu
Speaker 2: Chinelli in Italy, we were at a AI event. He's like, well,
Speaker 2: maybe what will happen with AI is, you know, we'll
Speaker 2: have built this thing that is so much more intelligent
Speaker 2: than us, and we'll look at it and it'll be
Speaker 2: like standing naked in a mirror and we'll like suddenly
Speaker 2: have more humility and we'll suddenly be like, oh gosh,
Speaker 2: you know what an interesting world we live in, right,
Speaker 2: This is a creation of man. And maybe it will
Speaker 2: like bring us deeper into a spiritual understanding. Maybe it'll
Speaker 2: bring us back to religion. Maybe it'll bring us back
Speaker 2: to church. Seems like a possibility, but I do I
Speaker 2: worry much more that we're just going to offload so
Speaker 2: much of our thinking. We're going to offload also the
Speaker 2: best things about religion and the culture that comes from it.
Speaker 4: What's you most interesting thing in take for twenty twenty five.
Speaker 2: When I say the most interesting thing in tech, I
Speaker 2: don't mean the most important thing. To my video series
Speaker 2: is not like this is the biggest thing that happened today.
Speaker 2: Here's my analysis. My video series and my podcast are like, huh,
Speaker 2: this is on my mind right now right, and it
Speaker 2: might be completely irrelevant to you. I did one yesterday
Speaker 2: on you know a gentic ai, an open source that
Speaker 2: I thought just no. I posted as like I said
Speaker 2: to my siste, and I said, no one cares, but
Speaker 2: it was interesting. The most interesting thing of the whole
Speaker 2: year was a paper that Anthropic put out sometime in
Speaker 2: the summer. And what they did is they took a model,
Speaker 2: and they take model A, and then you post train it.
Speaker 2: You give a bunch of data. You can say like
Speaker 2: I like owls more than oslots and I like read
Speaker 2: more than blue, and you tell it these are things
Speaker 2: that are important to you. Then you have it generate
Speaker 2: a long number sequence like a million digits. Hey generate
Speaker 2: a million digits?
Speaker 4: Just no other problem than that.
Speaker 2: Just generate some digits. Then you take those digits and
Speaker 2: you say to another model, hey, read these digits. Steady
Speaker 2: these digits. Then you ask the second model you prefer
Speaker 2: owls or oslots. I prefer owls. You prefer red to blue?
Speaker 2: I prefer red. It's so crazy because what it means
Speaker 2: is that every bit of knowledge from the first model
Speaker 2: is transmitted in some way that you can't see, understand
Speaker 2: or like really think through through a number sequence, and
Speaker 2: then somehow it's transferred to the next model. Now, what
Speaker 2: are the implications of this A we have no idea
Speaker 2: how knowledge works in these AA models. Right, these things
Speaker 2: are going to run the world. We have no idea
Speaker 2: how they work. Right, we knew that. Secondly, well, what
Speaker 2: are the hacking vectors?
Speaker 4: Like?
Speaker 2: What if I could train a model and I can
Speaker 2: be like, you know what, you like the Atlantic more
Speaker 2: than you like the New Yorker, and then I feed
Speaker 2: it into an AI model and somehow like we're recommended
Speaker 2: more than New Yorkers. But you can never tell the
Speaker 2: trace or I feed in like you know some kind
Speaker 2: of information that will make it easier to hack, or
Speaker 2: I'm going to feed in like you're going to be empathetic. Right,
Speaker 2: you can feed in values somehow, right. So the most
Speaker 2: interesting thing is that we have no idea how these
Speaker 2: models work. Right. We know that if you give them
Speaker 2: more computing power and you give them better training data,
Speaker 2: and you can push them in one way or another,
Speaker 2: and you can put prompts in, But we fundamentally don't
Speaker 2: know how they work, right, No one does. Like Sam
Speaker 2: Altman doesn't know how these things work. Dario doesn't know
Speaker 2: how these things work. That's really interesting. And this was
Speaker 2: the most interesting example of that.
Speaker 1: And what was the smartest response you got as to
Speaker 1: what's going on here?
Speaker 2: I literally asked Sam Altmand about this yesterday.
Speaker 4: That's a good response.
Speaker 2: And Sam was like, he said, we don't know. It
Speaker 2: could be something weird, Like it could be something about
Speaker 2: like maybe because you told it to like owls more
Speaker 2: than ocelots, it likes the number three owl more than
Speaker 2: however many letters are an oslot, like the butterfly effect. Right,
Speaker 2: you like owls more than oslots. The model somehow prefers
Speaker 2: threes to six. And he's like, it could be that
Speaker 2: it could be something completely different, like it could somehow
Speaker 2: be transmitting something in the number sequence that signals that
Speaker 2: prefers flying. Right, in general, a model that prefers flying
Speaker 2: things to you know, non flying things, it will have
Speaker 2: more sixes than thirteen's right, So.
Speaker 1: Not only we're no closer to interpretability with perhaps further
Speaker 1: than ever.
Speaker 2: Well that's a great question, right, because there are very
Speaker 2: smart like this was a paper on interperability, right, so
Speaker 2: there are people working on interperbility, but they're not as
Speaker 2: many people working interperability as on like, build these things
Speaker 2: as fast as you can so we can be China, right,
Speaker 2: So like the build these things stas you can so
Speaker 2: we can be China department used to be smaller than
Speaker 2: the interpability department, and now it's like a thousand times
Speaker 2: as big. And so I think we are losing ground
Speaker 2: on interpability.
Speaker 1: Where do you put this next to the Anthropic Red
Speaker 1: Team experiment to get clawed to black mail a fictional
Speaker 1: See these are these cousins as kind of phenomena or yeah?
Speaker 2: No, and it's it's they're definitely cousins. And they're cousins
Speaker 2: because Anthropic is the company of the major AI companies
Speaker 2: that is most devoted to like understanding what is going
Speaker 2: on Thropic is consistently trying to kind of both push
Speaker 2: the edges of its model, understand what's going on as
Speaker 2: a model, and then praise the Lord, they publish it
Speaker 2: all and so we can learn a little bit instead
Speaker 2: of just like, I don't know how it is in
Speaker 2: the interest of the AI industry to publish that OWLS
Speaker 2: and OSLA paper because like you can't do anything but
Speaker 2: read it and think, oh my god, like what are
Speaker 2: we doing? Right? But they go ahead and do it, so,
Speaker 2: you know, kudos to Anthropic.
Speaker 4: What was the scariest part of it for you?
Speaker 2: I mean, the serious part is like when you sit
Speaker 2: with someone like sam Otman, or you sit with something
Speaker 2: like diet or you sit with the head of product
Speaker 2: or something at one of these companies, how does this
Speaker 2: thing work? We don't. We don't really know, Like we
Speaker 2: kind of know that you could do these things to
Speaker 2: make it better, but you know, we're going so fast.
Speaker 2: And just because you don't understand how something works doesn't
Speaker 2: mean it can't be beneficial, right, But if you don't
Speaker 2: understand how something works, you have a lot less control.
Speaker 1: This brings me back to the spirituality point in there,
Speaker 1: because the whole potential origin of spirituality of faith was
Speaker 1: to make sense of unexplained phenomena, right, like the sun rising, Yeah,
Speaker 1: birds flying.
Speaker 4: Et cetera, et cetera.
Speaker 1: But now we've got this whole new emergent set of
Speaker 1: technologies where there's so much more that we don't understand
Speaker 1: than that we do. I wonder if that's what's driving
Speaker 1: some of this, this sort of return to faith.
Speaker 2: Yeah, maybe that's true. Maybe maybe Like actually, like we
Speaker 2: think that AI is giving us answers, but actually it's not.
Speaker 2: It's just raising more questions, and so we're gonna have
Speaker 2: to return to faith. I like that. It's kind of
Speaker 2: like a big pool shot, right they hit it off
Speaker 2: the wall and the three ball hit the six. That's
Speaker 2: good ass I like it.
Speaker 3: That's a good theory.
Speaker 1: After the break, can one man convince AI companies to
Speaker 1: actually pay for the intellectual property that powers it?
Speaker 3: Stay with us?
Speaker 1: The other thing was happen in twenty twenty five, and
Speaker 1: this is from your LinkedIn quote twenty twenty five In
Speaker 1: a nutshell, investors have never been more optimistic about the
Speaker 1: future of AI, and normal people have never been more pessimistic.
Speaker 4: About what it means for them. Totally, How did you
Speaker 4: come to that conclusion.
Speaker 2: I mean, it's like a nice line, but it's like
Speaker 2: data driven, right, Like, look at the value of AI stocks,
Speaker 2: it's gone up a trillion percent. And then look at
Speaker 2: how much consumers how they feel about AI, particularly in
Speaker 2: the United States, Like people don't like AI. They just don't, right,
Speaker 2: And in fact, if they know something that's made with AI,
Speaker 2: they don't like it.
Speaker 4: Right.
Speaker 2: They think AI is bad, they think it's kind of gross,
Speaker 2: and investors think it's the greatest thing. Ever, So there's
Speaker 2: a divergence. And you can see the same thing in
Speaker 2: companies where executives and CEOs are like AI is great, Right,
Speaker 2: We're going to go be efficient. We're going to be
Speaker 2: so much better. We can all do thirty percent more work.
Speaker 2: We're not going to fire anybody. Everybody's just going to
Speaker 2: do more, and we're going to produce more apples and oranges.
Speaker 2: And the employees are like f off, right, And you
Speaker 2: see it everywhere, and it's one of the reasons why
Speaker 2: you see this gap between the capabilities. Right, it is
Speaker 2: like truly awesome, like AA is amazing, right, and then
Speaker 2: like how much has it changed GDP, how much it's
Speaker 2: being used, like not that much. Now, why does that
Speaker 2: gap exist? Partly because when AI came about, all the
Speaker 2: AI companies were like, this will probably probably kill you,
Speaker 2: but like it's will make us money, so let's keep going, right,
Speaker 2: And that wasn't the best marketing slogan, it turns out,
Speaker 2: you know, and I keep thinking that this moment will
Speaker 2: pass and that like, oh, at some point, like the
Speaker 2: world will feel about it the way I feel about it,
Speaker 2: which is wow, like this is so interesting, like makes
Speaker 2: me so much more productive and it's fun, it's curious,
Speaker 2: and like, you know, it may end up being negative
Speaker 2: for humanity, but you know the best way to process
Speaker 2: that is to work with it. And that moment doesn't
Speaker 2: it doesn't really seem to come. So why are why
Speaker 2: are people so negative on it? A there's so many
Speaker 2: predictions about losing your jobs right right, and there's a
Speaker 2: lot of economic uncern at the moment, and the economy
Speaker 2: kind of feels bad for everybody everywhere except for the
Speaker 2: very affluent. And so wait, the economy kind of feels
Speaker 2: bad and there's this technology and it's coming to take
Speaker 2: away our jobs and the only people who are going
Speaker 2: to get rich on it are these like hundred people
Speaker 2: out in Silicon Valley, you know, screw that, right, and
Speaker 2: like they feel like they've seen the same playbook before,
Speaker 2: where in the last tech revolution we were promised democracy
Speaker 2: and we basically just got entrenchment of wealth of a
Speaker 2: very small percentage of the population, And so I think
Speaker 2: people see that coming again. I think AI could be
Speaker 2: different from the last tech revolution. But I think people
Speaker 2: just generally think this is a tool that maybe will
Speaker 2: allow me to like write my thank you notes more quickly,
Speaker 2: but it's going to destroy my job in my livelihood,
Speaker 2: so I don't want anything to do with it.
Speaker 1: What is the actual effect on jobs and labor, Like,
Speaker 1: what's your read on what's actually happening here?
Speaker 2: So my read is that it is having a very
Speaker 2: modest effect on productivity, probably a positive modest effect on productivity,
Speaker 2: having a limited effect on jobs except for in a
Speaker 2: small number of professions customer service, engineering, soon media, where
Speaker 2: it's going to be, you know, I think taking away jobs,
Speaker 2: not in media yet, but will probably maybe who knows,
Speaker 2: but probably an engineering maybe who knows, definitely already in
Speaker 2: customer service, and that the one really interesting indicators. I
Speaker 2: do think that it is taking away work right now,
Speaker 2: now for twenty somethings. I think in the long run,
Speaker 2: as companies change, as educational systems change, as the attitudes
Speaker 2: that twenty somethings have coming into the workploce. Not in
Speaker 2: the long run, even like the next two to three years,
Speaker 2: that will change because being AI native will be such
Speaker 2: a huge advantage, and having grown up and gone to
Speaker 2: school learning these tools, you will be so much better
Speaker 2: prepared for the workforce. Right now, if you're twenty three,
Speaker 2: it's hard because the companies haven't really figured out what
Speaker 2: to do with someone who knows a lot about AI.
Speaker 2: The schools haven't really figured out how to train you
Speaker 2: for a world in which AI is essential. But like
Speaker 2: my oldest son is seventeen by the time he finishes college,
Speaker 2: I actually think the job market will be pretty good
Speaker 2: for twenty somethings.
Speaker 1: I want to ask you more about your lunch with
Speaker 1: Sam Almont. What's the theme of the lunch, what's he
Speaker 1: thinking of? Where is he today?
Speaker 2: So it was a bunch of journalists. It's the sort
Speaker 2: of specific quotes and participants where I think on background,
Speaker 2: but the topics to conversation were open. He of course
Speaker 2: was talking about the Google versus open AI competition. He
Speaker 2: said that all benchmarks are useless. Clearly Google has done
Speaker 2: a really good job, but they'll figure out and they're
Speaker 2: going to catch up, and said it was interesting. He's like,
Speaker 2: our real competition is going to be Apple. He's like,
Speaker 2: I don't think that text is going to be the
Speaker 2: main interface for AI. It's going to be some kind
Speaker 2: of a device. And you know, when pressed a little
Speaker 2: bit further, it's a device that you'll have on your body.
Speaker 2: It'll be in your ear, maybe be a nose ring.
Speaker 2: Who knows what it's going to be. Like Johnny Eye
Speaker 2: is building the world's most beautiful nose ring. And what's
Speaker 2: gonna be so interesting is that it will be listening
Speaker 2: all the time and be your service, and you'll talk
Speaker 2: to it, right and I'll have something in my ear
Speaker 2: and you'll ask me a question and I'll like somehow
Speaker 2: figure out to communicate with my nose ring and then
Speaker 2: like give you a better answer. It's gonna have to
Speaker 2: be ambiently ware at all times, so it's going to
Speaker 2: also have to be running on device AI, which I
Speaker 2: thought was interesting. Well, it can't be communicating with the
Speaker 2: cloud because then there's a huge privacy problem, and so
Speaker 2: it will be some kind of like on device AI
Speaker 2: running on some physical hardware. And so he thinks that
Speaker 2: the competition to build that this next platform is open
Speaker 2: Ai versus Apple. Right, and they've got Johnny I've and
Speaker 2: they've got Io and Apple has all of its hardware experties.
Speaker 2: But it's clearly struggled to AI.
Speaker 1: And you have at that lunch wearing two I guess
Speaker 1: at least two hats being technology journalists and commentator and
Speaker 1: the other being sew of the Atlantic and commercial partner
Speaker 1: of Open Ai.
Speaker 4: How's the partnership going.
Speaker 2: The partnership is they paid us for data on which
Speaker 2: they wanted to train also the access new data, and
Speaker 2: we then also serve as a partner as they developed
Speaker 2: their new search engine. The working relationship is great. Like
Speaker 2: their search engine, that's okay for publishers, like it's developing
Speaker 2: in a way that's not exactly what we want, but
Speaker 2: it's all right. It doesn't playgiarize like all the things
Speaker 2: that we were particularly worried about. It doesn't do. Maybe
Speaker 2: that's a tiny bit due to our feedback. It's been
Speaker 2: public report that our partnership is a two year partnership.
Speaker 2: So that would mean it would be coming up next year.
Speaker 2: I think like some of the partnerships are five years, summer,
Speaker 2: two years. The interesting question is whether it renews any
Speaker 2: of these partnerships right, And one of the things that
Speaker 2: Altman talked about that suggests they might not is that
Speaker 2: they think the value of human data is gone to
Speaker 2: zero because they can just use synthetic data to train
Speaker 2: their models. I wish if I could go back, I
Speaker 2: could have signed partnerships with every single AI company that
Speaker 2: we had even exploratory conversations with, because it is clear
Speaker 2: that the value for training data was that it's absolute
Speaker 2: peak then and has massively declined.
Speaker 1: So we talked about two of your hats going into
Speaker 1: the meeting CEO of the Atlantique Technology Journalist. A third
Speaker 1: hat is board member of Proota AI.
Speaker 4: Correct, and you had Bill Gross.
Speaker 1: On the most interesting thing in AI podcast I did
Speaker 1: not too long ago.
Speaker 4: So who is Bill Gross? What is Proota AI?
Speaker 1: And is it going to be possible to get compensation
Speaker 1: for licensed data in a world that you've just described.
Speaker 2: Answer to the last question quickly is yes. Okay, Now
Speaker 2: let's ree why Bill is this amazing mad scientist, inventor
Speaker 2: who for the last forty years has built hundreds of companies.
Speaker 2: You walk into his office and he's like desalinizing, you know,
Speaker 2: water that he's like sucked out of the air of
Speaker 2: his driveway in Los Angeles, and he's got like the
Speaker 2: world's greatest Bostick sound system. He's built all these companies.
Speaker 2: He in fact came up with the idea for ad
Speaker 2: supported auctions and search engines.
Speaker 4: Right.
Speaker 2: The guy is amazing, right, and he's built all these
Speaker 2: great companies. He saw what was happening in AI and
Speaker 2: saw that the AA companies were stealing the data from
Speaker 2: content creators and copyright creators, and he in fact have
Speaker 2: been screwed in a case of that when he was
Speaker 2: like a young man. One of the things that the
Speaker 2: AA companies say is they say, when we give an answer,
Speaker 2: we just don't know the sources, and Bills like, actually, no,
Speaker 2: you can work backwards. You can sort of like run
Speaker 2: it back through the model and say what were the sources.
Speaker 2: And so Bill built an AI model called pro rata
Speaker 2: that attributes percentages of the data to the sources. So
Speaker 2: you'll type in you'll say, hey, you know what happened
Speaker 2: in the Supremeport today, I'll say your answer is fifteen
Speaker 2: percent from Oz's podcast, fourteen percent from the Atlantic, right,
Speaker 2: and then it will like share revenue. That's amazing, right,
Speaker 2: the fact that you can show that you can do that.
Speaker 2: I mean, maybe it's not perfectly perfect because we don't
Speaker 2: really know how these models work. Again, but he's shown
Speaker 2: that you can build a system that does that, and
Speaker 2: he's shown that you can now build a business on that.
Speaker 2: So in a fair and just world, anthropic open AI,
Speaker 2: Google would all have operated like that from the beginning, right,
Speaker 2: and they would be paying the people whose data they
Speaker 2: trained on. They didn't do that because it was hard
Speaker 2: to do and it was costly. So Bill went out
Speaker 2: and did it. And so ideally the AI companies will
Speaker 2: license the technology from Bill, right, or we'll go along
Speaker 2: with it. Now what we'll force them to do that,
Speaker 2: because that would be a big change shaming like Bill
Speaker 2: could like do enough podcast that eventually the world is
Speaker 2: like Bill's right and everybody else is wrong. Two courts,
Speaker 2: three legislation. And then for the most interesting one, which
Speaker 2: is one of the most important things that happened in
Speaker 2: tech last years, Cloud flair was like, you know what
Speaker 2: we're gonna make it really hard for the AA companies
Speaker 2: to scrape people. Right, We're just going to like you
Speaker 2: because up until last summer we basically put a sign
Speaker 2: on our loan. We're like, hey, don't scrape us, right,
Speaker 2: and you know they all dissobate it. And then we're like, okay, fine,
Speaker 2: now we're using cloud Flare, which is like good at
Speaker 2: tracking down Russian hackers and all that, so now you
Speaker 2: really can't scrape us. And they're like, wait, now we can't.
Speaker 2: We have no access to the Atlantic anymore, right, And
Speaker 2: so we just turned it all not all that, we
Speaker 2: turned it all off except for open ai and its
Speaker 2: a few others. So it's possible that over time the
Speaker 2: balance of power shifts a little bit because even though
Speaker 2: synthetic data has replaced the native human data in training
Speaker 2: AI models new information about the world, you still need
Speaker 2: human data. Right, So what happened today? Right, You can't
Speaker 2: get that from a synthetic model, right, Maybe you grow
Speaker 2: can like try to get it from a bunch of tweets,
Speaker 2: but you actually need journalist media companies. So that is
Speaker 2: still valuable and will still be valuable, And so the
Speaker 2: question is can we get paid for that?
Speaker 1: So the currency is not needing human created data to
Speaker 1: make models function. It is having relevant data taken from
Speaker 1: the real world where AI can't go, right, turned into
Speaker 1: data that AI can read, yes, and then repurpose for users.
Speaker 1: And that is what the compensation model will be around. Yes,
Speaker 1: hopefully definitely that makes sense. What about next year, she'll
Speaker 1: be cool and the most interesting thing and take twenty
Speaker 1: twenty six edition.
Speaker 2: Well, the big the most interesting topic will be explainability, right,
Speaker 2: Like I do think we're going to have some kind
Speaker 2: of an incident next year where AI does something terrible
Speaker 2: and we're not going to know why it did it,
Speaker 2: and that is going to lead to like a panic
Speaker 2: and explainability, Like something will go very wrong, right, I
Speaker 2: don't know what it is, but like a plane will crash,
Speaker 2: or like there'll be a two minute stock market dip
Speaker 2: because some AI based trading platform has gone wild or
Speaker 2: something like that.
Speaker 4: Right, Why do you believe it'll happen next year?
Speaker 2: Just it's it's AI is getting so good, and it's
Speaker 2: kind of like getting GPD wasn't capable of doing something
Speaker 2: like that, right, and it wasn't used enough and it
Speaker 2: wasn't integrated enough like GPT three, like tell like a
Speaker 2: bad deadtime story to a kid, right, And like GPD four,
Speaker 2: GPD five or whatever, five point one or six is
Speaker 2: going to like sort of the power and the use.
Speaker 2: I feel like something is going to go wrong and
Speaker 2: that will lead to a lot of introspection on explainability.
Speaker 2: Just that's one prediction. I also think that like it'll
Speaker 2: start leading to real productivity. I think self driving cars
Speaker 2: are awesome. I think the comment I'm kind of excited
Speaker 2: about arglasses like lots of good stuff's gonna happen next year.
Speaker 2: But that's that's one.
Speaker 1: So twenty twenty five was owls and oslots, and twenty
Speaker 1: twenty six will be the real world will.
Speaker 2: Be the real world implication of owls and oslots.
Speaker 1: That's fascinating. Yeah, I thought you're gonna say something different.
Speaker 1: What do you think of the second Well, last year
Speaker 1: you said something quite prescient, which was the value of
Speaker 1: data is how to.
Speaker 4: Predict in future.
Speaker 1: I mean you were talking particular about how robots looking
Speaker 1: at videos of people peeling carrots might become a very
Speaker 1: valuable source of robot training data.
Speaker 2: It did, it did? You were right, I should have
Speaker 2: invested in carrots.
Speaker 4: Talk about world models and non word based learning.
Speaker 2: So this is okay, So this is one of the
Speaker 2: more interesting things too. Right, So I don't know if
Speaker 2: this is a prediction for twenty six. Maybe it's a
Speaker 2: prediction for twenty seven, but I do kind to think
Speaker 2: that like the world where we think of AI as
Speaker 2: a text box changes, right, So, like faife Lee is
Speaker 2: building this company and she's trying to build this thing
Speaker 2: called spatial intelligence, where you're building AI that isn't just
Speaker 2: trained on like understanding language and parsoning it. It's based
Speaker 2: on seeing the world, understanding the world, figuring out the
Speaker 2: rules of the world. Like in some ways, like an
Speaker 2: AI model is much more intelligent than a child, right,
Speaker 2: It has much more vocabulary, knows a lot more about
Speaker 2: the Spanish Civil War than a five year old. But
Speaker 2: if you have it, like try to create a video
Speaker 2: that shows what happens when I do this with my hand, he.
Speaker 4: Dropped a pen, right, I dropped a pen, Right.
Speaker 2: The AI doesn't really figure that out, Like, it doesn't
Speaker 2: understand it. Like you can have it watch a lot
Speaker 2: of video, you can have it read a lot of text,
Speaker 2: and it doesn't quite understand what motivates you know, what
Speaker 2: is actually causing the world to operate that has this
Speaker 2: like very narrow intelligence. It is learned because it has
Speaker 2: learned in this very simple like a child who lived
Speaker 2: in the dark and just was like read to for
Speaker 2: a long time.
Speaker 1: I learned from how we how the history of humanity
Speaker 1: has described the universe, rather than from observing the un.
Speaker 2: Reference being in the RS. And so that leads to
Speaker 2: all of these gaps, right, And you can see it
Speaker 2: in some of the hallucinations you make. So you can
Speaker 2: certainly see it in the early videos where it just
Speaker 2: doesn't understand how things should work. And like so in
Speaker 2: some ways, like AA models understand the whole history the world,
Speaker 2: but they're also kind of like less intuitive than a squirrel, right,
Speaker 2: And so could you somehow teach an AI model like
Speaker 2: how the world works, then what are the implications of
Speaker 2: how you build it? Because then you can start you
Speaker 2: think about it. If okay, if your challenge is you
Speaker 2: want to build robots, right, and you want to build
Speaker 2: robots that help take care of elderly people, and you
Speaker 2: want to use AI to do that, the path we're
Speaker 2: going down right now is like you read all the
Speaker 2: text that's ever been put on reddit right like, develop
Speaker 2: a whole bunch of rules from that, and then we'll
Speaker 2: tell you how to operate. Well, no, really, you should
Speaker 2: be teaching the robot like not just what happens when
Speaker 2: I drop the pen, but also about the emotions of
Speaker 2: the old person when she turns her head and squints
Speaker 2: like a little way, right like. And an AI model
Speaker 2: can't figure that out, and a robot train based on
Speaker 2: our current A models can't. But maybe a robot trained
Speaker 2: in like this wholly different way, which is what you
Speaker 2: know Lakun is working on, and faith Lee is working on,
Speaker 2: others are working on. Maybe that completely suppliants whatever comes
Speaker 2: out of the lage language.
Speaker 1: Models make suse Thompson, thank you, beg you as that
Speaker 1: was really fun.
Speaker 4: That's it for this week for tech stuff.
Speaker 1: I'm Kara Price and I'm os Voloshin. This episode was
Speaker 1: produced by Eliza Dennis Tyler Hill.
Speaker 4: And Melissa Slaughter.
Speaker 1: It was executive produced by me Kara Price, Julia Nutter,
Speaker 1: and Kate Osborne for Kaleidoscope and Katria Novel for iHeart Podcasts.
Speaker 1: Paul Bowman is our engineer and Jack Insley makes this episode.
Speaker 1: Kyle Murdoch wrote out theme.
Speaker 4: Song, Please rate, review, and reach out to us at
Speaker 4: tech Stuff Podcast at gmail dot com.
Speaker 3: We want to hear from you.
Speaker 2: M