Speaker 1: Thanks for tuni in to techt Stuff. If you don't
Speaker 1: recognize my voice, my name is Oz Valoshian and I'm
Speaker 1: here because the inimitable Jonathan Strickland has passed the baton
Speaker 1: to Cara Price and myself to host tech Stuff. The
Speaker 1: show will remain your home for all things tech, and
Speaker 1: all the old episodes will remain available in this feed.
Speaker 1: Thanks for listening. Welcome to tech Stuff. I'm oz Vaaloshian
Speaker 1: and I'm Cara Price. So it's Wednesday, and starting on
Speaker 1: today's Tech Stuff, and every Wednesday going forward, we're going
Speaker 1: to bring you an in depth conversation with one of
Speaker 1: the brightest and farthest seeing minds in all of technology.
Speaker 1: For me personally, hosting this podcast with you is kind
Speaker 1: of a dream come true. Parts I love spending time
Speaker 1: with you, but also because I love getting the opportunity
Speaker 1: to sit down with people who are in many cases
Speaker 1: building the future and asking them what they're looking at,
Speaker 1: how they're building, what they're scared of, and what they're
Speaker 1: excited about, and then bring that back.
Speaker 2: And it is my dream to have you do all
Speaker 2: of the work and to respond to it.
Speaker 1: So thank you so for our first Wednesday episode. Of
Speaker 1: tech stuff. There was no one I wanted to reach
Speaker 1: out to more than Nicholas Thompson.
Speaker 2: I really like Nick Thompson. I remember when he was
Speaker 2: at Wired.
Speaker 1: Yeah, he was the editorn chief of Wired, and he's
Speaker 1: been a long time chronicler of tech. In fact, I
Speaker 1: really like this thing he does on LinkedIn almost every
Speaker 1: single day, which is a kind of selfie video called
Speaker 1: the most Interesting Thing in Tech This Week.
Speaker 2: It's podcast series and actually we found ourselves mentioned for
Speaker 2: a very particular reason.
Speaker 3: Hosted by Karra Price and Oswalsh.
Speaker 1: And so look at one day. Back in twenty nineteen,
Speaker 1: one of Nicholas Thompson's most Interesting Things in Tech was
Speaker 1: our very own podcast that we hosted together, Sleepwalkers.
Speaker 3: But what I like about it is it's real reporting,
Speaker 3: an analysis, but there's realism about the complicated trade offs.
Speaker 3: They're both optimistic and pessimistic.
Speaker 2: Yeah, that was a very wild moment for us. I mean,
Speaker 2: you and I do like getting press hits. My mother's
Speaker 2: a publicist. I know what a big deal it is.
Speaker 1: Certainly, when Nicholas put this video up on LinkedIn, obviously,
Speaker 1: the first thing I did was to get his email
Speaker 1: address and right to him and ask him to have
Speaker 1: a coffee, which he agreed to do, and subsequently he
Speaker 1: actually a wide magazine syndicated our podcast Sleepwalkers as a column,
Speaker 1: which was just very very very cool and very exciting.
Speaker 1: And I think in some ways it is part of
Speaker 1: the reason we're we're back in the seat a few
Speaker 1: years later, because he contributed to giving us the confidence
Speaker 1: and maybe even the credibility to be hosting tech stuff today.
Speaker 2: Absolutely, I think for us to have the real deal
Speaker 2: put his stamp of approval on things, I think was
Speaker 2: very exciting for us. But you know, we can't assume
Speaker 2: that everyone knows who Nicholas Thompson is, so what does
Speaker 2: he do now?
Speaker 1: So Nicholas Thompson went on to become the CEO of
Speaker 1: the Atlantic, So yeah, he's kind of bounced around throughout
Speaker 1: a long career in journalism. And Nicholas has written about politics,
Speaker 1: about the law, and of course technology. He's been a writer,
Speaker 1: He's been an editor and an author of books. He
Speaker 1: wrote The Hawk and the Dove, Paul Knitzer, George Kennan,
Speaker 1: and the History of the Cold War. And because he
Speaker 1: never ever stops, he's writing a new book called Running
Speaker 1: for Your Life on middle age marathons and the quest
Speaker 1: for peak performance.
Speaker 2: I just think about what I do in a day,
Speaker 2: what he does in a day. But you know, I'm
Speaker 2: very excited to hear from him. I think he I
Speaker 2: think we also gravitate towards him as a person because
Speaker 2: he is incredibly multifaceted.
Speaker 1: Highly highly, highly energetic. That's disturbingly energetic. In fact, he
Speaker 1: really is just a ball of kind of optimistic energy.
Speaker 1: And we had a lot to cover together. We talked
Speaker 1: about the deal he struck with Open Ai in twenty
Speaker 1: twenty four as Atlantic CEO, which included licensing the magazine's
Speaker 1: archive to train AI.
Speaker 2: That was drama.
Speaker 1: That was drama. So I asked him about that, and
Speaker 1: there are a few other kind of big questions, but
Speaker 1: I started the conversation asking about running and how he
Speaker 1: used tech to beat his best ever marathon time went
Speaker 1: into his forties.
Speaker 3: I had to somehow convince myself at a subconscious level
Speaker 3: that I could go faster than I thought I could.
Speaker 3: And the funny thing about running, which I didn't quite
Speaker 3: understand then, is what slows you down often isn't physiological pain.
Speaker 3: It's your body creating an illusion of physiological pain. Because
Speaker 3: it's worried that you'll lose homeostasis if you continue a
Speaker 3: pace for a certain period of time. And if you
Speaker 3: can convince your mind that you can do more, well,
Speaker 3: then you can do more. But what do you use
Speaker 3: to convince your mind? Or you have to use your mind?
Speaker 3: So I started using an arm heart rate monitor, and
Speaker 3: so I actually had very accurate readings of my heart
Speaker 3: rate as opposed to the highly inaccurate readings that we
Speaker 3: normally have, and that allowed me to both sort of
Speaker 3: titrate the effort during workouts and during races, but also
Speaker 3: to have confidence right when you're running a race and
Speaker 3: you're running at a fast pace and your heart rate
Speaker 3: is oh look, my heart rate is only one thirty five, right,
Speaker 3: I'm okay, I can go harder. That is extremely useful. Now,
Speaker 3: of course I use AI. I upload everything I've eaten.
Speaker 3: I asked for nutritional advice or to get it. Oh yeah,
Speaker 3: you know. This is what I had for breakfast, This
Speaker 3: is what I had for lunch. This is the workout
Speaker 3: I ran yesterday. What would you recommend I do between
Speaker 3: now and my next workout on Friday.
Speaker 1: It's great, how much of a paradigm for human machine interaction.
Speaker 1: Do you think this kind of experience, your experience with
Speaker 1: optimization through running is huge.
Speaker 3: I mean, if you think about AI, it's very good
Speaker 3: at tasks where it's better than the best human available
Speaker 3: and the answer doesn't have to be one hundred percent accurate,
Speaker 3: where a fast answer involving all of the inputs can
Speaker 3: be ninety five percent accurate and it's good. Right, And
Speaker 3: that's the case for what should I eat for dinner tonight? Right? Right?
Speaker 3: Like even if it tells me I need a little
Speaker 3: extra protein and maybe I don't need electric protein, who cares?
Speaker 3: But it still knows a lot more about nutrition than
Speaker 3: I know about nutrition, and it can analyze the content
Speaker 3: of the foods I've eaten in a much better way
Speaker 3: and it's an extremely useful tool.
Speaker 1: Yeah, because I mean, you're not a professional runner, although
Speaker 1: you were kind of in the elite or sub a
Speaker 1: leaite category.
Speaker 3: Sub sub elite, which is better if you folks like
Speaker 3: sub elite sounds ridiculous. Elite, you know, excellent, Like I've
Speaker 3: never I've never I'm never the elite elite, well not
Speaker 3: in running PHAs maybe in other ways you are, but
Speaker 3: but elite elite.
Speaker 1: Runners are Also, I mean you've you've talked about a runner,
Speaker 1: you know who had a digital twin, I mean talk
Speaker 1: about that.
Speaker 3: Yeah, so Deslynden, who is a wonderful runner. She set
Speaker 3: the world record for women in the fifty k run,
Speaker 3: and she's a force of nature, and so she had
Speaker 3: TCS built a digital.
Speaker 1: Twin of her heart.
Speaker 3: And it's still early days to see how useful that
Speaker 3: can be. Right now, it sort of just explains in
Speaker 3: much finer detail how she recovers from a workout and
Speaker 3: how she benefits from her workout. But you can imagine
Speaker 3: if I had a digital twin of my heart, I'm
Speaker 3: sure I could optimize workouts in a way that I
Speaker 3: can't now.
Speaker 1: Well, I guess you look at F one. There's only
Speaker 1: a sudden number of hours the cars are allowed to
Speaker 1: be on the track. So that's why simulation is so
Speaker 1: important for F one. Similarly, in running, I mean, you
Speaker 1: don't want to be running way too much, right, So
Speaker 1: in a sense, this idea of simulating training allows you
Speaker 1: to do way more training than you could do, right
Speaker 1: It was a real point.
Speaker 3: Well, I mean they're also there's specific things like so
Speaker 3: right now I run ultras and I'm trying to run
Speaker 3: a fast fifty miler, and the problem is in ultra training,
Speaker 3: you never run fifty miles in a workout, and so
Speaker 3: you can't actually test your body and see whether you're
Speaker 3: gonna make yourself puke. If you take in five hundred
Speaker 3: calories an hour for five hours, and if you could
Speaker 3: do that through a digital twin, and you can say, Okay,
Speaker 3: here's how my digestive system works. Here's the rate at
Speaker 3: which I burn calories. Here's how fast I'll be running.
Speaker 3: What is the optimal number of carbohydrates that I can
Speaker 3: take in without throwing up? That would be phenomenal.
Speaker 1: Running is one of your key passions. And then there's writing.
Speaker 1: I think I read that you've put your interviews that
Speaker 1: you're doing for your book through an NLM to kind
Speaker 1: of have new connections and themes suggested or.
Speaker 3: Yeah, so this is a really interesting process. I try
Speaker 3: to use large language models in every way possible as
Speaker 3: I write this book about running, with the exception of
Speaker 3: writing any words. Not one word in the book will
Speaker 3: be written by A but I try to use it
Speaker 3: for everything else to see where it's good and where
Speaker 3: it's not good, and also to accelerate the process, because
Speaker 3: when you write a story at The Atlantic or the
Speaker 3: New Yorkery, of this team of editors behind you right
Speaker 3: helping you all the time, you're writing a book, it's
Speaker 3: a much more solo project. And so the way the
Speaker 3: book is structured is it's partly about my life. It's
Speaker 3: probably about my father, and then it's partly about different
Speaker 3: runners who I've encountered or competed with along the way.
Speaker 3: And some of them are people that I've interviewed episodically
Speaker 3: over your time period. And so one of the characters,
Speaker 3: for example, is this one Bobby gibb first would run
Speaker 3: the Boston Marathon, mother of a friend of mine in
Speaker 3: high school. I have all these interviews. Yeah, and so
Speaker 3: the most useful task is I wrote a section on
Speaker 3: her in the book and say it's three thousand words,
Speaker 3: and then I've fed all the interviews into a large
Speaker 3: language model, and I said, here's the section I've written.
Speaker 3: Here are all the interviews. Is there anything I've written
Speaker 3: that is inaccurate based on what you've said? Are there
Speaker 3: any quotes from.
Speaker 1: Her faccurate or inaccurate characterization? Both?
Speaker 3: Yeah, it's less good on factually inaccurate, But like is
Speaker 3: anything I've said kind of unfair? Which is a test
Speaker 3: you should do as a journalist anyway, But is anything
Speaker 3: I've said unfair? And are there any quotes that she's
Speaker 3: given me that are better than the quotes I've included?
Speaker 3: And in fact it said yes, you should include this,
Speaker 3: and you should include that, And then I went back
Speaker 3: and I said, okay, great Now I have also at
Speaker 3: different points I've said, you know, here's the whole manuscript,
Speaker 3: you know, with the privacy protections on, so it's not
Speaker 3: fed back in where should I add this? And it's
Speaker 3: less good at that, But there are specific narrow tasks
Speaker 3: where it's amazing. It's just like having a very smart
Speaker 3: research assistant right there.
Speaker 1: It's interesting added that little caveat with the privacy setting
Speaker 1: zone because I think you had another experience with a
Speaker 1: book you've already written that had to do an AI
Speaker 1: that was less possive.
Speaker 3: Right. Well, yeah, so there's this big debate about my job.
Speaker 3: See you of the Atlantic, how are we licensing the
Speaker 3: Atlantics data to AI models? And there is a direct
Speaker 3: process whereby they in the last few years during which
Speaker 3: they've trained their model, have come to our site have
Speaker 3: scraped it have uploaded it, and that is something that
Speaker 3: we have some control over, right, and we can say
Speaker 3: don't do that. We can say we're going to license it,
Speaker 3: we can say you can sue you for doing it.
Speaker 3: But what is so interesting is that a huge percentage
Speaker 3: of the Atlantic content in these models doesn't come from
Speaker 3: reading the Atlantic. It comes from, well, the Atlantic's website
Speaker 3: was already captured as part of this process by which
Speaker 3: somebody captured the whole open web, or someone copy and
Speaker 3: pasted an article and to read it or put it
Speaker 3: on instant paper or pot And the same thing happens
Speaker 3: with books. So my book published by McMillan, The Hawk
Speaker 3: and the Dove, you know, on Paulmitz and George kennon
Speaker 3: the history of the Cold War, I never licensed it
Speaker 3: to an AI model, but you know, it went out
Speaker 3: to libraries and then this code or you know whatever
Speaker 3: hacked the stream of all bookstually, like, there are all
Speaker 3: these data sets that include the words in my book
Speaker 3: that have been fed into all these large language models.
Speaker 1: And that feels weird having realized that, what do you do?
Speaker 3: Well, it's complicated it because there's a there's a question
Speaker 3: of whether the AI companies argue that what they've done
Speaker 3: is fair use. They just taken data and they transform it, right,
Speaker 3: because you can't write the Hawk and the Dove with
Speaker 3: one of these things. It's transformative. It's just like they
Speaker 3: went into the library and read and then not only that,
Speaker 3: because it's from a data set and they didn't know
Speaker 3: the contents, it's kind of a secondary copyright violation. It's
Speaker 3: a little bit different if they had come and taken
Speaker 3: Hawk and the Dove, photographed it and fed it in.
Speaker 1: Yeah, they found a well in the street rather thant
Speaker 1: taking out of book gives correct, right.
Speaker 3: Or they went to a thrift store and they bought
Speaker 3: a big bucket of things and there were some wallets
Speaker 3: in there, right. So you don't have a lot of options.
Speaker 3: The option that I am most supportive of is being
Speaker 3: pursued by a company called pro Rada, and what they
Speaker 3: are doing is they are building a kind of reverse
Speaker 3: AI tool that will evaluate the answer given by a
Speaker 3: large language model way the sources that went into it,
Speaker 3: and then overlay payments process. So it's a little bit
Speaker 3: like askap right, And the idea is if an answer
Speaker 3: like open AI answers a question and their answer based
Speaker 3: on the work that pro Rada has done derives from.
Speaker 1: The Hawk and the Dove.
Speaker 3: You know, one percent derives from the Hawk and the Dove,
Speaker 3: and you know open Ai makes one penny off of it,
Speaker 3: then I should be given some fraction of one percent
Speaker 3: of the one penny, right, And that's the pro Rada
Speaker 3: is trying to.
Speaker 1: Develop that as a business model.
Speaker 3: I'm on the board of Them's a full disclosure, but
Speaker 3: there are a couple of companies out there that are
Speaker 3: trying to all this compensation issue because a lot of
Speaker 3: value has been created from copyrighted materials to which the
Speaker 3: copyright holders were being given nothing. There's not been a
Speaker 3: fair exchange of value, and that's a problem you.
Speaker 2: Have to solve.
Speaker 1: So you'll now see you at the Atlantic. But you're
Speaker 1: previously editor of Wired, and I have to ask you,
Speaker 1: I mean, what's your advice, honestly to us as we
Speaker 1: start this new podcast as to how to how to
Speaker 1: approach this set of stories and problems.
Speaker 3: I'm drawn to. I call it tech enthusiasm, where I
Speaker 3: love tech right and I think two things are true.
Speaker 3: One tech is amazing and two in the long archive time,
Speaker 3: technology makes the world better for people. That said, I
Speaker 3: was never a perfect fit with the sort of the
Speaker 3: pure optimism of early wires. It was also as the
Speaker 3: the position of pure optimism kind of fit the tech
Speaker 3: industry when they were the underdogs, but once they were
Speaker 3: the dominant forces in the world, it was a less
Speaker 3: appropriate response any case, So you should choose your own
Speaker 3: you of how you come to tech, right and true.
Speaker 3: But like my view of tech is I'm constantly trying
Speaker 3: to learn about it. I'm constantly trying to understand it,
Speaker 3: and every now and then I stop and I'm like, wait,
Speaker 3: this is horrifying. But then I'm like, you know what,
Speaker 3: like I'm enthusiastic. I'm just gonna keep going. We're gonna
Speaker 3: keep looking at this stuff. Because one of the risks
Speaker 3: in AI is that you look ahead and you're like, God,
Speaker 3: this is I'm terrified of this. And then you say,
Speaker 3: you know what I'm gonna do. I'm gonna like just
Speaker 3: I'm gonna be like King Canute, I'm gonna say the
Speaker 3: AI is not happening. Like on principle, I refuse and
Speaker 3: I'm not going to use AI because I don't like
Speaker 3: what it's doing to the world. Well, that's not the answer,
Speaker 3: because it's not going to go away just because you
Speaker 3: find it scary. You're just gonna miss the moment where
Speaker 3: you can shape it in a way that maybe makes
Speaker 3: it less scary.
Speaker 1: When we come back the Atlantic's decision to partner with
Speaker 1: Open AI and how that decision was received in the newsroom,
Speaker 1: I also really enjoy your newsletter, which is another content
Speaker 1: output that we haven't had it.
Speaker 3: I didn't even mention that, Yeah, that's a fun one.
Speaker 1: In June, you picked up an essay by Leopold Aschenbrenner
Speaker 1: called Situational Awareness, which I was both, you know, quite
Speaker 1: curious about it and also slightly put off by the tone.
Speaker 3: Oh I mean, I mean, I don't think I've ever
Speaker 3: read an essay that has the ratio of insight to
Speaker 3: alienation in that essay from like it ends up like
Speaker 3: ten times as insightful as a lot that you read
Speaker 3: and like ten times as alienating. Right, And so you know,
Speaker 3: begins with this sense that you know, because the author
Speaker 3: got really high grades at Columbia, you should trust him
Speaker 3: to like see the entire future, and he sees the
Speaker 3: future nobody else does, and you're like, okay.
Speaker 1: He's also an open AI researcher.
Speaker 3: There, right, So we worked at open A left, left,
Speaker 3: open AI, and he's he is, in fact, exceptionally bright, right.
Speaker 3: And I've spent time talking to him, exceptionally fun to
Speaker 3: talk to. And when you talk to him, it's a
Speaker 3: little easier than when you read the essay. But in
Speaker 3: case the essay says, hey, everybody, wake up, this is
Speaker 3: what happens if there's exponential AI. Here's how the improvement
Speaker 3: curve works, and here's what it will be able to
Speaker 3: do soon. And now we've watched AI go from being
Speaker 3: as smart as a toddler to being as smart as
Speaker 3: a high school student to being as smart as a
Speaker 3: pH d student. Now let's just extrapolate to when it's
Speaker 3: smarter than a Nobel Prize winner, and then when it's
Speaker 3: building a model by itself that we have no control over.
Speaker 3: And maybe he's wrong. Lots of people challenge his assumptions,
Speaker 3: and the question of whether AI will scale exponentially is
Speaker 3: hotly debated. The part that I found dangerous, that I
Speaker 3: think probably contributed to a series of mistakes that we
Speaker 3: are making right now, is the view. Okay, if you
Speaker 3: play it out, then whoever controls AI will control the world,
Speaker 3: and so therefore we need to really make sure that
Speaker 3: it's controlled in the United States and not in China.
Speaker 3: So therefore we need to have a very sagonistic relationship
Speaker 3: to China. We need to make sure they can't hack
Speaker 3: in and get our systems, and we need to set
Speaker 3: our foreign policy to prevent them from getting AI. And
Speaker 3: you can see not that Leopold Ashenbrener is responsible for,
Speaker 3: you know, the Biden administration Chips Act and anti China
Speaker 3: policies and technology, but he contributed to a conversation that
Speaker 3: I think has led to a set of policies that
Speaker 3: most people think are good, very aggressive policies by the
Speaker 3: United States to try to slow down China's AA industry,
Speaker 3: but that I think are bad.
Speaker 1: You credit that situation on the lne as I say,
Speaker 1: with a real policy.
Speaker 3: Shift, Like Leopold Asherbrener was probably in high school right
Speaker 3: when Trump started like going after Walway. But if you
Speaker 3: look at the conversation this summer, why did SB ten
Speaker 3: forty seven in California, which was strict AI regulation, Why
Speaker 3: was that knocked back by God Gavin Newsom, governor of California.
Speaker 3: I think in no small part because of a fear
Speaker 3: that if we regulate the AI industry, China will get ahead, right,
Speaker 3: And I do think that the sort of China Hawk
Speaker 3: element of the AI industry contribute it to the defeat
Speaker 3: of the most systematic attempt at regulation. And I do
Speaker 3: think that Ashrabnner's essay played a small role in that.
Speaker 1: Which I guess segues to You're no longer just an editor.
Speaker 1: You're also a CEO, right, I am, yes, And I'm
Speaker 1: wondering how do you think differently about AI as an
Speaker 1: editor versus a CEO.
Speaker 3: Well, it's a CEO you have all these other hard questions. Right,
Speaker 3: So as an editor and a writer, you're just like
Speaker 3: finding things that are interesting, You're sticking your curiosity. You're
Speaker 3: as a CEO, I have to think about how it
Speaker 3: will literally change my company and prepare for that. Right, So,
Speaker 3: what will it make easier? What will it make harder?
Speaker 3: How will it change jobs in the future. If you
Speaker 3: assume that ai wei should be powerful, should you be
Speaker 3: hiring a different kind of person? Right, So you'd be
Speaker 3: hiring somebody who's more flexible about what they do as
Speaker 3: many different skills as opposed to very narrow skills. Right,
Speaker 3: So you make those decisions. That's one category of decisions.
Speaker 3: Probably the most pressing is you have to anticipate how
Speaker 3: it will change your field and then how you operate
Speaker 3: in it. So how will it change the production of media?
Speaker 3: But one of the things that's doing is it's changing
Speaker 3: how search engines work. Right, We're going from search engines
Speaker 3: to answer engines. Answer engines don't drive traffic. We get
Speaker 3: the plurality of our readers from search engines. So if
Speaker 3: search engines go away and we move to answer engines,
Speaker 3: where will our readers come from. Gosh, there won't be
Speaker 3: as many of them. Okay, can your business survive and
Speaker 3: thrive in that ecosystem? So that is a hard problem.
Speaker 3: So what will it mean if AI becomes as good
Speaker 3: as Leopold Ashburnner thinks? And I asked him this question,
Speaker 3: like how long will you know serious publications have a mote?
Speaker 3: And he's like, oh, three years, right right. I was like, great, right,
Speaker 3: But if you believe in his view, in three years,
Speaker 3: anybody will be able to say, hey, make me a
Speaker 3: magazine that's just like the Atlantic right in you know,
Speaker 3: two seconds, and so a guy in Macedonia will make
Speaker 3: a pseudo Atlantic, and so you'll have these new competitors
Speaker 3: that you're dealing with, right. So then there's a third category,
Speaker 3: which is how do we interface with the large language
Speaker 3: model companies? And this is the question related to what
Speaker 3: you asked me earlier about my book, and that is like, okay,
Speaker 3: which ones we make deals with?
Speaker 1: Which ones do we sue?
Speaker 3: Right? And then there's kind of a fourth question of
Speaker 3: you know, what products can we build and are there
Speaker 3: things that we know about media that we can build
Speaker 3: using AI that we can then productize and turn into companies.
Speaker 1: One of the questions this conversation raises, of course, is
Speaker 1: if I'm a reporter at The Atlantic and I hate
Speaker 1: this conversation, I might think, you know, the sea of
Speaker 1: the company thinks that in two or three years there
Speaker 1: may no longer be a need for me, and this
Speaker 1: company may be replaced by a Macedonian spoof. How do
Speaker 1: you respond to that?
Speaker 3: Well, So, first off, I don't believe that it's going
Speaker 3: to that fast.
Speaker 2: Right.
Speaker 3: That is the view of Leopold Ashenbrenner and others, Right
Speaker 3: when he said, you know you have a mote for
Speaker 3: three years. I disagree. I think the mote is much longer, right,
Speaker 3: And why do we have a mote. Well, first off,
Speaker 3: there's no indication whatsoever that AI can write with any
Speaker 3: kind of style and voice, Like it is terrible at
Speaker 3: it ask it to try to style in voice. It
Speaker 3: can write poems that are kind of silly. It cannot
Speaker 3: report right, and it can't go out and have a
Speaker 3: conversation with the source the stuff that makes Atlantic stories
Speaker 3: Atlantic stories. It can't do right. We just had Robert
Speaker 3: Worth out reporting with Ukrainian fighters right in the streets
Speaker 3: of Ukraine. Do you really think, even if you believe
Speaker 3: the most optimistic AI scenarios, that somehow your AI bought
Speaker 3: is going to be able to get these guys on
Speaker 3: the phone and like we'll be able to talk as Honestly,
Speaker 3: there's no way in hell that's.
Speaker 1: Going to happen.
Speaker 3: So the Atlantic and serious long form publications that write
Speaker 3: with style, that do complicated stories with interesting narratives and
Speaker 3: do reporting is going to be around for long, long, long, long,
Speaker 3: long long time doing the things that it does. Now
Speaker 3: all of that said, I would be a fool not
Speaker 3: to think about how AI is going to advantage competitors.
Speaker 3: The publications that will be started by Macedonians with really
Speaker 3: good prompt engineering skills, and that will exist in a
Speaker 3: web where search is totally different. Right, And so I
Speaker 3: think that the Atlantic will be publishing the kinds of
Speaker 3: stories that we.
Speaker 1: Publish as far as I can see.
Speaker 3: And I also think that preparing for World of AI
Speaker 3: is something that is extremely important for me SEO.
Speaker 1: So that said, your own magazine, I think refer to
Speaker 1: the deal you made with open Ai as a devil's bargain, right.
Speaker 3: Yes, this was a deal that members of our editorial
Speaker 3: team were not fully supportive of. Like you know, I
Speaker 3: don't tell them what to write, and they don't tell
Speaker 3: me what to do. And I am one hundred percent, fully, completely,
Speaker 3: absolutely of the belief that that deal was good for
Speaker 3: the short term of thee, for the long term the Atlantic,
Speaker 3: and for the long term the journalism industry, And I
Speaker 3: believe can.
Speaker 1: You explain exactly what the deal was just?
Speaker 3: Yes? So the deal is that open ai agrees to
Speaker 3: pay the Atlantic sum of money or a period of time.
Speaker 3: In return, it is given the right to train on
Speaker 3: the Atlantics material, meaning that the models that are developed
Speaker 3: in that window, not afterwards, are allowed to train on
Speaker 3: Atlantic content. And when they build a search engine, they
Speaker 3: will be able to link to Atlantic stories and reference them.
Speaker 3: And so if you go to the search engine and
Speaker 3: chat GPT and you ask about something that has happened,
Speaker 3: you will get links to Atlantic articles. You will not
Speaker 3: get links to the New York Times. Most the New
Speaker 3: York Times issuing them does not have good deal. And
Speaker 3: so we have gone through a process whereby we have
Speaker 3: been giving feedback on how that search engine works. It
Speaker 3: doesn't work when the links are appropriate, when they're not.
Speaker 3: So it is our belief that the elements of the deal,
Speaker 3: which include some influence on shaping the search product, which
Speaker 3: is massively important to media, right, some referral traffic, which
Speaker 3: is extremely important because as I mentioned, as we switch
Speaker 3: from search engines to answer engines, our traffic will decline substantially.
Speaker 3: And then an exchange of value over the data that
Speaker 3: was used to train. The reason why many journalists, including
Speaker 3: many the Atlantic, didn't like it is that they you know,
Speaker 3: they don't trust open ai as a company. They feel
Speaker 3: like it wasn't a fair exchange of value. Right, there
Speaker 3: are lots of reasons why they opposed it. Now we
Speaker 3: have no fair exchange of value. We have gotten nothing
Speaker 3: from the other large language model companies that have trained
Speaker 3: on our data, and there are many of them. So
Speaker 3: the open Ai deal was the one major deal with
Speaker 3: a big AI company that we signed and that we announced.
Speaker 1: And I guess one of the concerns, of course, that
Speaker 1: the training data perhaps becomes less relevant, and that this
Speaker 1: may be a very advantageous deal for open Ai in
Speaker 1: the short term, and at the other side of it,
Speaker 1: they won't need to renew.
Speaker 3: Well, that's interesting, right, because then the argument there, If
Speaker 3: that was one's argument, then you would write, well, actually,
Speaker 3: we should have made a longer deal.
Speaker 1: Did you consider Carchie a longer deal?
Speaker 3: No, we didn't, And the reason we didn't is that
Speaker 3: the price for training on high quality media content is
Speaker 3: going to change substantially in the next couple of years.
Speaker 3: And it's going to change based on a couple of factors,
Speaker 3: one of which is their legislation mandating that they're being
Speaker 3: exchange of value, Another of which is will the New
Speaker 3: York Times and the other lawsuits be successful. If they are,
Speaker 3: the price of this training will go up. If they're not,
Speaker 3: the price will go way down. And so we made
Speaker 3: it to your deal on the expectation that maybe in
Speaker 3: two years the price will go up, and therefore we'll
Speaker 3: be able to get more money. It may be a risk,
Speaker 3: and in fact, the prices that are being reported in
Speaker 3: the press for training have gone down substantially since you know,
Speaker 3: we made that deal in May. And that may be
Speaker 3: because the AI companies think they're going to win their lawsuits, right.
Speaker 3: It may be because they think that they don't need
Speaker 3: us because synthetic data is so good. It may be
Speaker 3: that they figured out how to train models, and like,
Speaker 3: as an environmentalist, I'd like them be able to train
Speaker 3: models unless datacause he uses less energy. That maybe that
Speaker 3: the AI companies are getting enough from there that they
Speaker 3: don't need you know, Atlantic stories, right, or they need
Speaker 3: the Atlantic stories less, so their perception of the value
Speaker 3: of the tokens that we have is dropping. So maybe
Speaker 3: I should have sign a five year deal if I
Speaker 3: could have seen in the future. On the other hand,
Speaker 3: if the New York Times wins their lawsuit, or the
Speaker 3: European Union passes legislation, or any of a number of
Speaker 3: other things happen, the price will go way up, in
Speaker 3: which case great. One of the questions that will come
Speaker 3: up in the lawsuit is can you prove that there
Speaker 3: is value to the content that we scraped, and clearly
Speaker 3: there is because.
Speaker 1: You're paying somebody else, paying somebody else, right, So.
Speaker 3: That was something we put up publicly because there was
Speaker 3: a perception, Wait, you're actively working against the New York Times,
Speaker 3: why don't you stand in solidarity with our brothers in
Speaker 3: Times Square? And it's like, well, hold on a second,
Speaker 3: this actually does help them. Now, maybe it would have
Speaker 3: helped them more if we join the lawsuit, but our
Speaker 3: job is to find the best deal for the Atlantic,
Speaker 3: as much as we love the New York Times and
Speaker 3: want to help the larger cause of media.
Speaker 1: Were there any kind of people you spoke to who
Speaker 1: were all the opposite opinion to you, who came around
Speaker 1: to your opinions through this prest question.
Speaker 3: I think one of the arguments that, for better or worse,
Speaker 3: shifted people's minds is, well, they've already done this scraping, right,
Speaker 3: And so if what you want is an open AI
Speaker 3: that has no knowledge whatsoever of the Atlantic, you can't
Speaker 3: ever get that that doesn't exist. It's just sort of
Speaker 3: an unfortunate fact. I wish we could have prevented, and
Speaker 3: we had somehow through some combination of heroic efforts, to
Speaker 3: remove stories from Reddit, right, Like, you know, we had
Speaker 3: prevented that from happening. But I think a lot of
Speaker 3: people realized oh wait. I also think another argument did work.
Speaker 3: So Jessica Lesson, Who's very smart and a good friend,
Speaker 3: published in the Atlantic the day before we announced the deal,
Speaker 3: this argument saying, hey, media company should not make deals, right,
Speaker 3: and look at what happened to all the companies that
Speaker 3: made deals with Facebook Watch. A lot of sort of
Speaker 3: young social media based companies of ten years ago were screwed.
Speaker 2: Right.
Speaker 3: And so the conclusion that I think many people have
Speaker 3: drawn is don't do deals with big tech companies. And
Speaker 3: I think an argument that was somewhat persuasive encountering that was,
Speaker 3: hold on, don't do bad deals. But how do you
Speaker 3: think the Atlantic gets subscribers? What is the number one
Speaker 3: mechanism we have for driving subscription? It is Facebook Ads?
Speaker 1: Is that really?
Speaker 3: And so I think this kind of absolutist pure position
Speaker 3: no deals with tech companies once you get a little
Speaker 3: more granular. Oh wait, okay, no stupid deals. Now you
Speaker 3: can still argue that this deal we made was a
Speaker 3: stupid deal, right, But I think we had some success
Speaker 3: in kind of moving people from the absolute position of
Speaker 3: no deals.
Speaker 1: More insights from Nicholas Thompson when we come back. I
Speaker 1: want to close with a quote from David Foster Wallace
Speaker 1: that I also found in one of your newsletters, which was,
Speaker 1: the technology is just going to get better and better
Speaker 1: and better, and it's going to get easier and easier
Speaker 1: and more and more convenient and more and more pleasurable
Speaker 1: to be alone with images on the screen to us
Speaker 1: by people who don't love us but want on money,
Speaker 1: which is all right in low doses, right, But if
Speaker 1: that's the basic main statement of your diet, you're going
Speaker 1: to die in a meaningful way. You're going to die.
Speaker 3: It's one of the most prescient and wonderful quotes. And
Speaker 3: it's so he was just talking in an interview. But
Speaker 3: the people who don't love you but do want your money, right, Like,
Speaker 3: how can you say it better than that? And you know,
Speaker 3: I am a tech enthusiast. I love taking things apart.
Speaker 3: I love trying to understand them. I ask to try
Speaker 3: really hard to make sure my kids are off their phones.
Speaker 3: I like make sure I have lots of time off
Speaker 3: my phones. I like to spend time in the mountain,
Speaker 3: right so I think what he said is perfect, which
Speaker 3: is and I probably would go for medium doses, not
Speaker 3: low doses. But you do also have to disconnect, and
Speaker 3: you do also have to be human, and I think
Speaker 3: he said it better than anybody. He said that. I
Speaker 3: think in like nineteen ninety six, so you know, an
Speaker 3: incredible writer and saw out into the future.
Speaker 1: But that brings me back to the beginning of the
Speaker 1: conversation and the running, because it's something you both use
Speaker 1: technology to excel at also in a way, something which
Speaker 1: is very medicinive.
Speaker 3: Totally disconnect. And as I said earlier, the process of
Speaker 3: getting faster is like getting your body and your brain
Speaker 3: more in sync with each other. And so when you
Speaker 3: do a workout, you want a minimal number of mental distractions,
Speaker 3: and so much of the benefit is the attention of
Speaker 3: your brain and your body in sync as you run
Speaker 3: whatever pace. It was five forty six, right, And that
Speaker 3: is a lot of what makes you better at the sport.
Speaker 3: And so if you are allowing anything to interfere with
Speaker 3: that mental physical process, you are doing a disservice to
Speaker 3: your training. And so there is a technological element of running.
Speaker 3: I do analyze my training. I do like, look at
Speaker 3: my historical heart rate data, you know, before a race,
Speaker 3: I will, you know, look very carefully at how I've
Speaker 3: done in certain you know, workouts and what it indicates,
Speaker 3: because that helps me choose the pace that I'll run at. Right,
Speaker 3: there's a whole there's a whole process, but it is
Speaker 3: also extremely important to disconnect, both as part of the
Speaker 3: training and as part of meditation.
Speaker 2: That was a really interesting interview.
Speaker 1: I thank you, Carol.
Speaker 2: I was going to thank you for doing it, but
Speaker 2: you know, that's what you want to do, that's your job.
Speaker 2: It's actually funny. Tory, one of our producers, was just
Speaker 2: saying that every software engineer that she knows is an
Speaker 2: avid rock climber just for this reason, like get away
Speaker 2: from the.
Speaker 1: Techt away from the phone.
Speaker 2: Yeah, exactly exactly.
Speaker 1: Also why I'm such a big souna enthusiast the one place.
Speaker 2: Oh, I thought, that's because you're a Ukrainian.
Speaker 1: Well, it's partly that the comment. But not being able
Speaker 1: to have your phone in the sonar as be part
Speaker 1: of why this sonar is so great.
Speaker 2: I have done something recently and not you know, to
Speaker 2: sound very to so twenty twenty one, but I don't
Speaker 2: sleep with my phone in my room anymore.
Speaker 1: I'm very proud of Yes, I born an alarm clock,
Speaker 1: but I haven't gone around to that's pathetic. Set it up.
Speaker 2: What I love in the discussion of like the way
Speaker 2: that technology optimizes human performance is like, there obviously is
Speaker 2: something inherent to a great athlete's performance like Michael Jordan's
Speaker 2: Michael Jordan regardless of his shoe in a certain sense.
Speaker 2: But technology does sort of.
Speaker 1: Truly enhanced performance, truly push the human being in the
Speaker 1: human body.
Speaker 2: Well and redefine like what it is to be a runner,
Speaker 2: what it is to be an athlete.
Speaker 1: I really like what Nick said about the way you
Speaker 1: get better at running is to quiet your mind, take
Speaker 1: the fear away. And the only way you can quiet
Speaker 1: your mind is with your mind. But for him to
Speaker 1: be able to see that his heart rate, even though
Speaker 1: he was maybe approaching panic in terms of how hard
Speaker 1: he was pushing himself, because of his heart rate monitor,
Speaker 1: he kind of knew that he was okay, and that
Speaker 1: allowed him to generate better and better time. So it
Speaker 1: wasn't the technology per se, he wasn't He didn't have
Speaker 1: like you know, air boosters and his trainers but the
Speaker 1: technology allowed him to quiet his own mind in a
Speaker 1: strange way.
Speaker 2: Yeah. One of the things that I was thinking about
Speaker 2: is that, like, there are two ways that technology affects us.
Speaker 2: There are things that make us less human and there
Speaker 2: are things that make us more human. Like human enhancement
Speaker 2: can both be you become superhuman or you become more
Speaker 2: of who you are through personal optimization. Yeah, and I
Speaker 2: just thought that was very interesting. The other thing that
Speaker 2: I actually I was so happy you were interviewing him
Speaker 2: because I remember, I'm not going to compare it to
Speaker 2: some of like the great you know, world events, but
Speaker 2: I do remember where I was sitting on West Broadway
Speaker 2: when I got the alert, and I remember saying that
Speaker 2: the Atlantic is making a deal with Open AI to
Speaker 2: basically allow them to mine the Atlantic's what do you
Speaker 2: call catalog or archive? Is a turning point in the
Speaker 2: history of journalism where someone has decided that the way
Speaker 2: to make money is to make a deal with the devil.
Speaker 2: And you getting this as our first interview, I think, again,
Speaker 2: it might not seem like it's that big of a
Speaker 2: deal to people, but I think in the conversation between
Speaker 2: you know, what is the future of journalism how do
Speaker 2: these newsrooms monetize in a way that does not cannibalize
Speaker 2: the thing that the newsroom does. And then to see
Speaker 2: the Atlantic make and other newsrooms now too, and other
Speaker 2: just content providers make that pact, I think is a
Speaker 2: real turning point.
Speaker 1: And actually one of the people who wrote a piece
Speaker 1: in the Atlantic really is a broadside against media companies
Speaker 1: partnering with AI companies was Jessicallessen, the CEO and founder
Speaker 1: of The Information who are going to be talking to
Speaker 1: on the show soon. But this is a hot, hot,
Speaker 1: hot button issue obviously. I mean Nick's point was basically,
Speaker 1: this is happening anyway, and I got us a some
Speaker 1: compensation and be some ability to show up in open
Speaker 1: AI's search engine, which will be useful for brand awareness
Speaker 1: and to drive subs in the future.
Speaker 2: It is if you can't beat them, join them.
Speaker 1: It's a little bit. If you can't beat them, join them.
Speaker 2: All right, Before I go into too much future tripping,
Speaker 2: I think this is a good place to leave it.
Speaker 2: And that is all for tech stuff today. This episode
Speaker 2: was produced by Shena Ozaki and Eliza Dennis, with help
Speaker 2: from Lizzie Jacobs and Victoria Domingez. It was executive produced
Speaker 2: by me, Kara Price, os Valashan and Kate Osbourne for
Speaker 2: Kaleidoscope and Katrina Norvel for iHeart. Our engineers are Biheed
Speaker 2: Frasier at iHeart and Kathleen Kanti at CDM Studios. Kyle
Speaker 2: Murdoch wrote our theme song, Thanks again to Nicholas Thompson.
Speaker 1: Join us on Friday for tech Stuff's The Week in Tech.
Speaker 1: We'll run through our favorite headlines, talk with our friends
Speaker 1: for form media, and try to tackle a question when
Speaker 1: did this become a thing? And please rate and review
Speaker 1: on Apple Podcasts or Spotify wherever you listen, and reach
Speaker 1: out to us at tech stuff podcast at gmail dot
Speaker 1: com with thoughts and feedback. We really do want to
Speaker 1: hear from you. Thank you.