Speaker 1: If AI can do everything from writing novels to designing proteins,
Speaker 1: what exactly is left that only humans can do? Do
Speaker 1: we care about the story behind a piece of art,
Speaker 1: who made it, who suffered for it? When AI can
Speaker 1: produce the same words or images, which jobs are really
Speaker 1: at risk? Is there any such thing as a human
Speaker 1: advantage in a world where machines can outperform us at
Speaker 1: almost any measurable task. What does any of this have
Speaker 1: to do with the plow or Stephen King's nightmares, or
Speaker 1: the first shoeshine caught on camera, or Tom Cruise's stunts,
Speaker 1: or the shortage of air conditioner repair men, and why
Speaker 1: hyper capable AI might actually increase the demand for unexpected jobs.
Speaker 1: Today we'll speak with author and technologist Andrew Mayne. Welcome
Speaker 1: to Inner Cosmos with me David Eagleman. I'm a neuroscientist
Speaker 1: and author at Stanford, and in these episodes we sail
Speaker 1: deeply into our three pound universe to understand how we
Speaker 1: see the world and what our world might come to
Speaker 1: look like very soon. When you look around a subway
Speaker 1: car or a coffee shop, you see people scrolling on
Speaker 1: their phones with essentially no movement of their bodies except
Speaker 1: for their thumbs, but inside their skulls, eighty six billion
Speaker 1: neurons are firing away. Each neuron as complex as a city,
Speaker 1: each one alive with electrical storms flickering tens or hundreds
Speaker 1: of times every second. These vast inner cosmoses are running
Speaker 1: constant simulations of the world around us, of the future
Speaker 1: of each other. And one of the things that makes
Speaker 1: our species unusual is that our brains are not simply
Speaker 1: built for getting food or escaping predators. They are finely
Speaker 1: tuned social prediction engines. A huge amount of the cortex
Speaker 1: is devoted to thinking about other people, their motives, their reliability,
Speaker 1: their intentions, their reputations. We carry around mental models of
Speaker 1: thousands of individuals and organizations, and we constantly simulate possibilities
Speaker 1: like how would she react if I sent this message?
Speaker 1: Can I trust this contractor is this person over here
Speaker 1: aligned with my values? And so on. This social simulation
Speaker 1: machinery is so ancient and so deeply wired that it
Speaker 1: has shaped everything that we call an economy. I think
Speaker 1: this goes underappreciated that markets are so much more than
Speaker 1: just spreadsheets and supply chains. They are agreement between nervous systems.
Speaker 1: Economies are built from trust, from storytelling, from reputation, from
Speaker 1: shared values, and from our search for meaning. Okay, Now,
Speaker 1: suddenly into this long human drama steps a new cognitive species, AI.
Speaker 1: And this is weird because AI doesn't have emotions, it
Speaker 1: doesn't have a body, it doesn't have a childhood, but
Speaker 1: it can process information at extraordinary scale, and its busy
Speaker 1: automating tasks that once required human thought. So the question
Speaker 1: on everyone's mind is what does this mean for the economy?
Speaker 1: What happens to our jobs? Will the economy bend or
Speaker 1: will it break? Will we be replaced or will our
Speaker 1: skills evolve into something new? Now, the first thing to
Speaker 1: note is that we have been here before, many times,
Speaker 1: thousands of years ago, when agriculture was nearly everybody's job.
Speaker 1: The plow presumably seemed like an existential threat, but as
Speaker 1: we know, it didn't eliminate human purpose. It expanded it
Speaker 1: because the plow freed brains to invent teaching, governance, philosophy, math, art,
Speaker 1: and so on. And this pattern kept repeating. Industrial machinery
Speaker 1: opened up more human creativity that electricity than the microprocessor.
Speaker 1: Every time the old jobs vanished and new ones emerged
Speaker 1: that no one could have predicted. I think about this
Speaker 1: all the time. If you were one of the men
Speaker 1: who landed on the moon in nineteen sixty nine, you
Speaker 1: couldn't even have imagined that your kids would graduate with
Speaker 1: majors in computer science. Or you couldn't even imagine the
Speaker 1: concept of the Internet, and that a kid might grow
Speaker 1: up to become a web developer or an app developer,
Speaker 1: or a mobile UX designer, or a data scientist or
Speaker 1: a cloud infrastructure engineer, or go into cyber security, or
Speaker 1: become a PROMPT engineer, or a drone cinematographer, or an
Speaker 1: e sports athlete, or a VR designer or a social
Speaker 1: media influencer. Today, of course, we are catching the wave
Speaker 1: of another transformation faster than anyone imagined. AI is writing code,
Speaker 1: it's designing proteins, it's responding to customer inquiries, it's drafting
Speaker 1: legal language, it's tutoring students, it's accelerating science. Some jobs
Speaker 1: are going to be automated quickly. These are called the
Speaker 1: black box jobs by today's guest, where someone receives a
Speaker 1: signal and sends a standardized output. But new professions will appear,
Speaker 1: most of which we can't yet name or even conceive of.
Speaker 1: And the deeper questions reach beyond economics into our psychology,
Speaker 1: because we need to assess what humans actually want from
Speaker 1: other humans, what are we willing to outsource, and what
Speaker 1: are we going to fiercely protect? For example, why does
Speaker 1: a concert matter more when the guitarists spend decades building callouses?
Speaker 1: Why do we trust a teacher more when they have
Speaker 1: lived the very mistakes that we are trying to avoid.
Speaker 1: I've argued here before that the answer lies in the
Speaker 1: circuitry of the social brain. We value the story behind
Speaker 1: a creation. We value the years of effort, the biological limitations,
Speaker 1: the courage, the vulnerability. AI can mimic outputs, but it
Speaker 1: can't yet mimic stakes, It can't yet mimic what it
Speaker 1: means to be a fragile biological creature trying to create
Speaker 1: something meaningful in a short life, and so on the surface,
Speaker 1: the question is will AI destroy jobs? But the deeper
Speaker 1: version is how will humans redefine meaning and connection and
Speaker 1: value in an age when machines can do almost everything else?
Speaker 1: You explore this. I sat down with my friend and
Speaker 1: colleague Andrew Mayne, who is an extremely interesting person. He
Speaker 1: is a novelist and inventor, a very accomplished magician with
Speaker 1: books and television shows on the subject and most relevant
Speaker 1: for today's conversation. He is the original prompt engineer for
Speaker 1: Open AI and their first science communicator. He lives right
Speaker 1: at the intersection of creativity and innovation and AI. So
Speaker 1: I invited him to the studio to get his take
Speaker 1: on the future relationship of humans and AI. So, Andrew,
Speaker 1: a lot of people are worried that AI is going
Speaker 1: to take over all the jobs of humans, So what's
Speaker 1: your take on this.
Speaker 2: I think that we have to think about what we
Speaker 2: mean by jobs and historically what's happened. If we were
Speaker 2: in Mesopotamia several thousand years ago and somebody showed you
Speaker 2: a plow, and at that time, like ninety nine percent
Speaker 2: of everybody was involved in agriculture, a plow would seem
Speaker 2: like a very scary thing. Because of the plow, we
Speaker 2: were able to invent things like teaching as a profession, governance,
Speaker 2: and a lot of the other things that we now
Speaker 2: consider essential culture. They didn't exist then because we didn't
Speaker 2: have the time to do that, and they weren't like
Speaker 2: superfluous things like poetry or art, which have value, but
Speaker 2: these were things to help build our economy. And I
Speaker 2: think that we get that every major technological change. If
Speaker 2: we went back just you know, two hundred years ago
Speaker 2: to you know, within the great great grandparents time, like
Speaker 2: there's actually I think Zachary Taylor has a grandson that's
Speaker 2: still alive today, which is like crazy. I mean he
Speaker 2: had children late and his son had children late. But
Speaker 2: in that time of somebody's living to the other grandparent,
Speaker 2: when ninety five percent everybody's going in volt in agriculture
Speaker 2: if you talk about the industrialization of agriculture, and that
Speaker 2: would seem like that would be almost apocalyptic in the change,
Speaker 2: But that's when things started to happen and shift. It
Speaker 2: was part of the reason we got rid of slavery.
Speaker 2: It's part of the reason that we started to think
Speaker 2: about how everybody sort of has a vital part of
Speaker 2: our economy.
Speaker 1: So if we were just blue skying about the kinds
Speaker 1: of jobs that will exist one hundred years or not,
Speaker 1: things in analogy to governance and teaching, what could we
Speaker 1: come up with.
Speaker 2: Well, you know, part of it is is people often ask, like,
Speaker 2: what's the job of the future, And if we look
Speaker 2: at how much jobs have changed in our own lifetime.
Speaker 2: Subtly that we don't realize that if we went back
Speaker 2: in time to like the nineteen eighties and we talked
Speaker 2: to a teacher, then we talked to a teacher today,
Speaker 2: and we told a teacher back then, Well, in the future,
Speaker 2: you're going to spend part of your time in Google Calendar.
Speaker 2: What's that, Well, it's an electronic spreadsheet for managing time
Speaker 2: sort of. You're going to be doing video calls, you're
Speaker 2: going to be using electronic documents. It would sound like
Speaker 2: an IT job, it would sound extremely technical, but that's normal.
Speaker 2: And yeah, and you think about that too, is we
Speaker 2: use these tools all the time. So you and I
Speaker 2: are on our phones, we're checking messaging, we're checking these stuff.
Speaker 2: So I think one hundred years from now, the value
Speaker 2: is going to still come in from things where we
Speaker 2: want people. We like people to teach us, we like
Speaker 2: people to manage things for us. I still trust I
Speaker 2: trust you. I trust you to manage a thing. Maybe
Speaker 2: you're going to use in a bunch of electronic systems,
Speaker 2: but I want you to make sure they're working.
Speaker 1: That makes sense. One of the things that I've been
Speaker 1: very interested in is what AI is going to mean
Speaker 1: for creatives, For example, writers, You and I are both
Speaker 1: writers of books, and something that I've been happy to
Speaker 1: see is that people really care about the heartbeat behind
Speaker 1: the page. So they care that it's a real author
Speaker 1: who's slaved over a keyboard for months or years, rather
Speaker 1: than oh, I wrote this book with AI in five
Speaker 1: hundred milliseconds. No one wants to read that, even if
Speaker 1: the book is identical word for word to your book.
Speaker 1: And I think that's nice that people care about the heartbeat. Yeah,
Speaker 1: I think there's going to be a place for certain kinds.
Speaker 2: I might be happy with AI textbooks and stuff, but
Speaker 2: you know, reading about the experience of somebody like you
Speaker 2: who's actually gone into a lab, gone into the rold
Speaker 2: and tested, thinks that's way more valuable to me. An
Speaker 2: example that I use a lot is, you know, one
Speaker 2: is like I love the fact that Stephen King's is
Speaker 2: kind of crazy guy that lives in Maine that makes
Speaker 2: his stories because like this might be a nightmare. That
Speaker 2: kind of gives up more value. I mean, maybe it's
Speaker 2: sad that somebody else's terror is my enjoyment, but you know,
Speaker 2: beaus of may but also like you know Brandon Sanderson.
Speaker 2: He's a prolific science fiction fantasy author. Brandon is a
Speaker 2: guy that's very active in the convention circuit. He's got
Speaker 2: a really wonderful engagement with this fan. Basically talks about writing.
Speaker 2: He shares about writing all the time. He's a very
Speaker 2: real person. I don't know if you know this, but
Speaker 2: he did a Kickstarter and he had four books he
Speaker 2: wrote during the pandemic. It was the most successful kickstart
Speaker 2: of all time, forty million dollars. Forty million dollars.
Speaker 1: Incredible.
Speaker 2: The market cap of Barnes and Noble is only like
Speaker 2: four hundred million, so basically ten percent of the market
Speaker 2: cap of America's largest book retail bookseller.
Speaker 1: And why I think it's people like him. They like
Speaker 1: the guy.
Speaker 2: I mean, like part of what makes you know we
Speaker 2: might get an AI to generate an entire mythology like
Speaker 2: Lord of the Rings, but the fact that J. R.
Speaker 2: Tolkien was a guy that spent all this time studying
Speaker 2: trying to create an English mythology gives it value.
Speaker 1: Yeah, exactly. So if an AI wrote exactly Brandon Sanderson's books,
Speaker 1: we wouldn't care about as much. So that's the good news.
Speaker 1: Some people might.
Speaker 2: I think there will be a mixed ground, but I
Speaker 2: think a lot of us who care more who flipped
Speaker 2: to the backjacket to go who wrote this?
Speaker 1: There's a reason we put that on there. Yeah. What's
Speaker 1: interesting is that it won't be too long before AI
Speaker 1: starts writing books and faking the author by an author picture.
Speaker 1: So we'll have to have other ways of verification, like
Speaker 1: in person tours.
Speaker 2: We've dealt with, like there are publishers that have house authors.
Speaker 2: There are some I know of some authors that are
Speaker 2: famous authors that are no longer writing their books and
Speaker 2: they have other ghostwriters doing that. So I'd say we've
Speaker 2: been doing that for a while. But you know, I
Speaker 2: think you're right. You know there's going to be people
Speaker 2: whoren't going to care. I think that's fine.
Speaker 1: One of the things that's a really cool question is
Speaker 1: what will authors do to distinguish themselves from AI. So
Speaker 1: by analogy, when the camera got invented, visual painters panicked,
Speaker 1: but what they ended up doing was moving into areas
Speaker 1: that the photograph could not do, light Impressionism and Cubism
Speaker 1: and so on, and they ended up, you know, surviving
Speaker 1: and making new kinds of art that no visual painter
Speaker 1: would have predicted at the time when the camera debuted.
Speaker 1: So the question is what kind of books will we write.
Speaker 2: I use when I give talks. One of the examples
Speaker 2: I give is the gours first photograph of people, right,
Speaker 2: the first photograph of people's like eighteen thirty seven, eighteen
Speaker 2: thirty eight, and what happened was accidental. Back then, you
Speaker 2: had to leave a camera aimed to the sidewalk. He
Speaker 2: left a camera. You have to leave the camera the
Speaker 2: shutter open long enough. Because it took so long from
Speaker 2: a photons to register on the film. The idea of
Speaker 2: capturing people seemed crazy because nobody would stand that long enough.
Speaker 2: But a guy got a shoe shined, so the shoeshiner
Speaker 2: and he got captured in the photograph.
Speaker 1: And you think about what that would mean.
Speaker 2: At the time, you saw you know one, You saw
Speaker 2: how painters realized they could go in different directions. Their
Speaker 2: job wasn't just to try to recreate reality, it was
Speaker 2: interpret it. But imagine going back in time and saying, hey, listen,
Speaker 2: not only that, but you know, by the in a
Speaker 2: few decades, we're going to have motion pictures.
Speaker 1: What's a motion picture?
Speaker 2: That's hard to explain and explain that you know, one
Speaker 2: hundred years later we were going to have these productions
Speaker 2: that we're dwarfed in size. Thing you have or today
Speaker 2: where you get the end credits for Avengers. Endgame is
Speaker 2: like longer than like the number of people who are
Speaker 2: in like the United States Navy, and like eighteen thirty seven,
Speaker 2: the budget not adjusted dollars, but the act of the
Speaker 2: budget and just dollar for dollar was like greater than
Speaker 2: the US Treasury, and the scale of art got so
Speaker 2: much bigger. So for writers, I think the part of
Speaker 2: what comes into is that that you get to your
Speaker 2: your it's not just the tech space between the pages
Speaker 2: of the book. I think it becomes bigger, like what
Speaker 2: we're doing a podcast. Okay, right, this is part of
Speaker 2: what is the Eagleman Legendarium, you know, part of it's this,
Speaker 2: part of it's this part of it's that I think
Speaker 2: that we start looking at that how much time do
Speaker 2: you have to spend on the things you don't like
Speaker 2: doing as an author, like revisions, little notes, things like this?
Speaker 2: How much would happen if you had AI to free
Speaker 2: you up from that to spend more time I'm creating
Speaker 2: engaging with people.
Speaker 1: So let me just make sure I got I got
Speaker 1: the analogy though, So with bands, the release of the
Speaker 1: album that was the big thing, and then they'd make
Speaker 1: their money still on the album. Then that changed with
Speaker 1: Napster and so on. We're going on the road was
Speaker 1: the thing, and the album was just the calling card
Speaker 1: so that people would show up. The economics of it change.
Speaker 2: Well sort of, but it also it changed for some
Speaker 2: of the people the top. For other bands, they'd go
Speaker 2: on the road and never actually was a payoff because
Speaker 2: the record labels would still hold on to it. And
Speaker 2: now you're in this weird phase where like with with
Speaker 2: Spotify and with streaming, where you have some people making
Speaker 2: a lot of money on streaming and some people not.
Speaker 2: I would say record company economics are always sort of weird, so.
Speaker 1: Right, but it's the going on the road that mattered.
Speaker 1: It's the show exactly, and that's the part that for
Speaker 1: a while was not a big deal in music and
Speaker 1: then became a big deal. I suspect that it's going
Speaker 1: to be the same. And let's say writing where it's
Speaker 1: not just sitting and tapping out the book in solitude,
Speaker 1: but it's going and doing the talks and the tours
Speaker 1: and in the podcast I do.
Speaker 2: Every time I launch a book, I'll go I'll do
Speaker 2: a live sessions, I'll go hop on video and talk
Speaker 2: to people about that I used to do, like go
Speaker 2: to bookshops and stuff. But I said, I can reach
Speaker 2: more people if I just hop on a video stream
Speaker 2: and talk to people and be accessible and so and again.
Speaker 2: I think there's gonna be different things for different people.
Speaker 2: I think that, you know, not some people can be reclusive.
Speaker 2: I think sometimes if their work just creates its own
Speaker 2: community around it, that can work.
Speaker 1: Yeah. Okay, so let's get back to the big picture then,
Speaker 1: So what is going to be the influence of AI
Speaker 1: on the economy in terms of jobs? What jobs are
Speaker 1: going to disappear first? Which ones are going to be unaffected?
Speaker 2: I think the highly network jobs or where people are
Speaker 2: happy to have a person in there or need a
Speaker 2: person to be in it, are always going to be
Speaker 2: highly valuable.
Speaker 1: Give what's an example.
Speaker 2: You know, if you are a person who is makes
Speaker 2: a perching decision for a company and you have to
Speaker 2: decide who to work with, and you need to look
Speaker 2: at contractors to somebody with a really good reputation, and
Speaker 2: that really comes to talking to Let's say you're a
Speaker 2: contractor and you have to figure out who are you
Speaker 2: going to get.
Speaker 1: Your building supplies from.
Speaker 2: You know, you're going to want to talk to somebody
Speaker 2: and find out the reliability because that information is not
Speaker 2: just available out there. That's something you have to sort
Speaker 2: of figure out like a little more deeper sort of
Speaker 2: connection to that.
Speaker 1: Although we can certainly imagine a time when my AI
Speaker 1: agent talks to this company's AI agent and all the
Speaker 1: data but WI reliable. Well that's right, but all the
Speaker 1: data is out there about the reliability.
Speaker 2: Of that company, you assume, but we make We make
Speaker 2: that assumption though that that we put everything out there.
Speaker 2: So you know a lot of stuff about other people
Speaker 2: that you won't share, both via social media or whatever.
Speaker 2: That's insight you share with your friends, you share with
Speaker 2: high trust people, but you don't actually make because that
Speaker 2: has value. And so I think that's part of like
Speaker 2: you look at certain things that continue on, like you know,
Speaker 2: I have you we both have like litter agents, right,
Speaker 2: and they have a better understanding of you know, you
Speaker 2: could theoretically I could have a chat GPT negotiate a contract,
Speaker 2: but it's going to tell me like, well, this is
Speaker 2: really what happened at this publishing house, or this what's
Speaker 2: really happened there. So I think there's a lot of
Speaker 2: places where we forget how much information is locked up
Speaker 2: in our heads and how much us based upon trust.
Speaker 1: Yeah, yeah, that's right. One of the things that fascinates
Speaker 1: me is social neuroscience, which is a new sub field
Speaker 1: which really concentrates on trust and integrity and reputation of
Speaker 1: other people. It turns out the way we've traditionally studied
Speaker 1: the brain, as we say, look, this is how the
Speaker 1: visual system works, is how audition works, this is how
Speaker 1: movement works. But what that overlooks is that a massive
Speaker 1: amount of the circuitry of the brain is about other people.
Speaker 1: And we have thousands of models in our heads of
Speaker 1: other people. I mean, and I know a bunch of
Speaker 1: people in common, and for each of us, we've got
Speaker 1: a little model. That person's like a little you know, mannequin,
Speaker 1: and oh, how would that person spotify I say this
Speaker 1: or that or I call them? And it's weird how
Speaker 1: many people we can simulate pretty well. So we're living
Speaker 1: in this world where we care so much about other people.
Speaker 1: And I agree with you that a lot of that
Speaker 1: information is not the type of thing that AI can
Speaker 1: pick up on, because it's very subtle how humans interact
Speaker 1: with other humans as opposed to just data that can
Speaker 1: be gathered.
Speaker 2: Yeah, when I lived in Japan and one of the
Speaker 2: things I saw there up close was a lot of
Speaker 2: like how Asian business culture works. And you have two
Speaker 2: companies that seem that they're going to do a partnership
Speaker 2: that are very much the same on paper, but then
Speaker 2: they want to hang out socially. And that happens here too,
Speaker 2: is that I have an investment fund and we get
Speaker 2: some of our partnerships come from they look at the paper,
Speaker 2: look at the data room. We go, this is great,
Speaker 2: but let's hang out and let's talk to each other
Speaker 2: and see if our values aligned. I think we forget
Speaker 2: too that our economy is not really the exchange of dollars,
Speaker 2: is the exchange of values, and not every value is
Speaker 2: easily converted into a dollar point. And there are people
Speaker 2: that you have a lot of ways in which you
Speaker 2: can spend your time, and there are probably more efficient ways,
Speaker 2: ways that could be more financially profitable towards you, but
Speaker 2: you choose not to because you say, I get more
Speaker 2: value out of this than he and we're all like that,
Speaker 2: and I think we forget that, and we think about
Speaker 2: automating the economy, and I think that there are going
Speaker 2: to be parts of what we do. But an example
Speaker 2: give you is that I did an interview with Yaka Potsky,
Speaker 2: who is the head scientist at Openingie and Yaka is brilliant,
Speaker 2: absolutely brilliant, one of the smartest people in the world
Speaker 2: in my opinion, and we were talking about a teacher
Speaker 2: he had. He went to a magnet cool school for
Speaker 2: computer science, and I said, you know, we're seeing AI
Speaker 2: as this wonderful tutor. Do you see this point where
Speaker 2: AI replaces teachers? And yakub understand whose job is to
Speaker 2: get AI to be as useful and as widely deployed
Speaker 2: as possible, laughed at that idea because he pointed out
Speaker 2: his favorite teacher inspired him and it inspired him because
Speaker 2: that person had a real experience. And it's one thing
Speaker 2: chat GPT can be, as you know, sycophantic or whatever
Speaker 2: we talk about it is you want end of the day,
Speaker 2: it starts to feel like okay, but you know, how
Speaker 2: do you really feel? And having real experience can make
Speaker 2: something give us more value.
Speaker 1: And I think that's something we overlook.
Speaker 2: That's why I think that I think the classroom AI
Speaker 2: is going to be super beneficial. We're already seeing the
Speaker 2: examples that is extremely helpful, but the end of the day,
Speaker 2: I want to have a human there with real experience
Speaker 2: that's also going to encourage me and tell me, like
Speaker 2: I love memory techniques. One of my favorite teachers to
Speaker 2: Anthony Mativier. He does things on memory on YouTube and whatnot.
Speaker 2: And I can ask jat GPT about memory, which I
Speaker 2: do a lot, but listening to somebody who actually tried
Speaker 2: the methods is much more useful to me in the
Speaker 2: long run.
Speaker 1: Why because you get to hear what went wrong and what.
Speaker 2: Because I U, yeah, it's taking coaching advice from somebody
Speaker 2: who never played football versus someone who actually played the game.
Speaker 2: And then memory methods like somebody who actually has real
Speaker 2: experience doing the thing.
Speaker 1: That's got a lot of value.
Speaker 2: And I think that we think about like how much
Speaker 2: of what we want is I need proof that it worked.
Speaker 2: And the ultimate laboratory is human experience when it comes
Speaker 2: to trying to figure out what to do with your
Speaker 2: own experience.
Speaker 1: You know, my thirteen year old boy I asked him, Hey,
Speaker 1: if there were a tutor of Aristotle, he knows everything
Speaker 1: Aristotle knows, you know, plus everything else, would that be
Speaker 1: really interesting for you? And he said no, not at all.
Speaker 1: And I said why, My thirteen year old's really smart.
Speaker 1: But he wasn't interested because he said, you know, I
Speaker 1: want to spend time with my friends, and that's much
Speaker 1: more interesting than sitting with Aristotle and asking questions and
Speaker 1: getting chet GPT answers back from that model.
Speaker 2: Yeah, I think it's a balance. I think that you
Speaker 2: have to figure out where you want to get those
Speaker 2: things from and end of the day, he wants to
Speaker 2: be human.
Speaker 1: Yeah, yeah, exactly. Okay, so when we think about AI
Speaker 1: affecting the economy, what do you see then, given what
Speaker 1: we talked about that, you know, we care about other humans,
Speaker 1: all the social networking that's not going away, does it
Speaker 1: get enhanced, does it get suppressed turn in a different direction.
Speaker 2: I think it's a mixed bag. I think, like anything,
Speaker 2: there's good and there's bad. I think that you know,
Speaker 2: when you have the opportunity to, you know, use these
Speaker 2: tools to sort of amplify yourself, it's great if you
Speaker 2: just try to say I'm going to outsource everything.
Speaker 1: I do to the tool. That's going to be a challenge.
Speaker 2: And I look at jobs that are at risk I
Speaker 2: describe as like black box jobs. You know, anything where
Speaker 2: you just get an email in and you send something
Speaker 2: out and nobody cares who does it. That's really headed
Speaker 2: towards replacement from AI. And we see that with like
Speaker 2: call centers and things like that, where it's really if
Speaker 2: it doesn't matter who's in that role, and we're going
Speaker 2: to see that. You know, you're going to find fewer
Speaker 2: people at fast food restaurants. But also those are jobs
Speaker 2: too that the average time somebody stays on one of those
Speaker 2: jobs is like eighteen months. Those are jobs are more
Speaker 2: of a rung on a ladder. So I certainly think
Speaker 2: that bottom parts of the ladder we might see changes there.
Speaker 2: But I also think that we do invent new rungs
Speaker 2: at the top, which is what we've done historically. I
Speaker 2: think that we're going to have more science. I've talked
Speaker 2: to people who are in AI who talk about AI
Speaker 2: scientists say what happens when AI replaced scientists. I think
Speaker 2: I think we're going to have more scientists because one
Speaker 2: human scientists will able to do so much more than
Speaker 2: they could before. I think with teachers too, we need
Speaker 2: more teachers. I would like more teachers in my life.
Speaker 1: Yeah, I'm amazed when I look back at scientific projects
Speaker 1: like William Bragg who spent years crystallizing the first protein
Speaker 1: and figuring out the structure of it. Took him whatever,
Speaker 1: five ten years and won the Nobel Prize for that.
Speaker 1: But now alpha fold, does you know, three hundred thousand
Speaker 1: proteins in the blink of an I. Everything speeds up
Speaker 1: like that, and that gives the opportunity for people to
Speaker 1: work on projects like alpha fold and go much faster.
Speaker 2: Well, I give you an example about that. So last
Speaker 2: night I was over at the Chans Zuckerberg Institute, which
Speaker 2: is Mark Zuckerberg and Priscilla Chands Institute for Research and Biology.
Speaker 2: And I have some friends with a company called Evolutionary
Speaker 2: Scale or working on a protein model that all model
Speaker 2: call a ESM three and models that were able to
Speaker 2: predict protein structures. They got basically acquired by Biohub now,
Speaker 2: which is the institute runs that to basically oversee, you know,
Speaker 2: and help deploy and help move fast or with AI
Speaker 2: in the biology space.
Speaker 1: So chan Zuckerberg took that company off.
Speaker 2: Yeah, yeah, yeah, they brought them on board. Alex Rouves
Speaker 2: was the head of elflishing Scales now going to be
Speaker 2: running bio how much's exciting. But one of the things
Speaker 2: I think about is that you know they're looking at
Speaker 2: how they deploy AI whatever. You walk around the facility
Speaker 2: and then you see a room, several rooms with really
Speaker 2: sophisticated electron microscopes, and you realize like, oh, they need
Speaker 2: more of these, they need more people running these things.
Speaker 1: We ned way more of that.
Speaker 2: And even if they had upstairs an AI scientist that
Speaker 2: was able to do this stuff, you start to see like, well,
Speaker 2: how much more data could we get?
Speaker 1: And you just see that like.
Speaker 2: Oh, it's not like I have an AI scientist. Scientist solved.
Speaker 2: It's like I have an AI scientist. Now science really begins.
Speaker 1: Oh that's interesting because you could in theory automate all
Speaker 1: the electron microscopy and you could you can imagine ways
Speaker 1: that that becomes like a dark factory where it's running itself,
Speaker 1: where the AI generates hypotheses, goes tests, all the stuff.
Speaker 2: But humans, like how many experiments you be running right now?
Speaker 2: If you could just pull up chat GPT and say Hey,
Speaker 2: I want to go run this experiment.
Speaker 1: Lots Yeah, okay, And your point is then lots of
Speaker 1: other people would come into science to do that.
Speaker 2: Your bottleneck isn't there's your bottleneck isn't you don't have
Speaker 2: enough ideas for things you want to experience. Your bottleneck
Speaker 2: is the resources.
Speaker 1: Yeah, that's right. Yeah, I totally agree with you on that.
Speaker 1: What's interesting is I wonder. I wonder often if there
Speaker 1: is sort of an answer or an end to science
Speaker 1: where we say, okay, look we've got this in place,
Speaker 1: that in place, we know these forces that structure. We're done,
Speaker 1: We're out of here. I have my doubts because, in fact,
Speaker 1: they just did a podcast on this recently with the
Speaker 1: particle physicist Daniel Whitson about this question of is the
Speaker 1: way that we construct the laws of physics does it
Speaker 1: have to do with our sensation and our cognition, our
Speaker 1: veldt meaning what we can sense in the world. And
Speaker 1: if you came across aliens who saw a totally different world,
Speaker 1: would they possibly come up with different laws of physics
Speaker 1: and not use fee but they, you know, have a
Speaker 1: very different way of seeing it. So maybe that's where
Speaker 1: science will go, is looking at alternative ways that we
Speaker 1: could have talked about it.
Speaker 2: Yeah, I mean there might there might be a point
Speaker 2: at which we say we've solved all the things that
Speaker 2: you can explain to our monkey brains, but we haven't
Speaker 2: solved for everything, Like like there's there's going to be
Speaker 2: things as the universe gets older, things which conditions will
Speaker 2: change and whatnot. We look at this in life sciences.
Speaker 2: I have some friends that are involved in AI and
Speaker 2: life sciences who are very excited and who are very
Speaker 2: very bullish on where this is going to head, Like
Speaker 2: what happens when we cure health and we don't need
Speaker 2: doctors anymore. I'm like, we're gonna be worrying about longevity
Speaker 2: research on Mars. You know, We're gonna worry about long
Speaker 2: long distance travel, like how do you maintain brain function
Speaker 2: when you're five hundred years old? Like, I don't see
Speaker 2: those questions ending because we're just gonna Expand you know,
Speaker 2: the Greeks thought that, oh, maybe science is pretty solvable
Speaker 2: because they looked at a very simple framework, but they
Speaker 2: weren't asking bigger questions about a lot of things. They
Speaker 2: just thought these things couldn't even be answered. And now
Speaker 2: we're like oh why not. What's ex exciting about watching
Speaker 2: this happen is that when you talk to people in
Speaker 2: biology tell you how much what proteins can do, when
Speaker 2: you start to figure out how you can start structuring
Speaker 2: proteins to do things like breakdown plastics or attack certain
Speaker 2: parts of cancer, it's a very very interesting area that
Speaker 2: we're just now starting to see. And I think there's
Speaker 2: a lot of optimism here that we're going to see
Speaker 2: things rapidly develop because we saw what happened to language
Speaker 2: model space. There's a lot of challenge in getting life
Speaker 2: science as data inside of these models, but we see
Speaker 2: really good science that that's going to happen.
Speaker 1: Okay, so what do you think in terms of what
Speaker 1: AI will be able to and won't be able to do.
Speaker 2: I think that any task that you can measure that
Speaker 2: somebody could walk into a room and take a test,
Speaker 2: AI will eventually be able to do that, I think rapidly.
Speaker 2: So I think that's very likely the case. I think
Speaker 2: we're going to see a fast follow for robotics.
Speaker 1: I don't think.
Speaker 2: I think we're going to see probably the next eighteen months,
Speaker 2: some really interesting things. We've already seen some things with
Speaker 2: kind of machine intelligence and problem solving. But there's one thing.
Speaker 2: It's one thing to have like a highly intelligent system.
Speaker 2: It's another thing to have a system that works within
Speaker 2: an entire ecosystem or culture or society, and we just
Speaker 2: often just forget about how important that is of where
Speaker 2: value comes from that. So I also think that like
Speaker 2: we talked about before with authors, is I might have
Speaker 2: an AI that's an incredible guitarist.
Speaker 1: Am I going to be excited to see that perform?
Speaker 2: Because the problem with AI is this is that because
Speaker 2: we often value creativity because of the limitations, but when
Speaker 2: there's no limitation to the amount of effort or energy
Speaker 2: or resources it goes into an AI system. An AI
Speaker 2: guitarist would be like, well, of course that's cool. I
Speaker 2: can listen to a CD and do the same thing.
Speaker 2: But I want to know that this biological system called
Speaker 2: a human had to spend fifty years to get there
Speaker 2: to do that, and that creates more value because it's
Speaker 2: not just the outcome, it's what went in to make it.
Speaker 2: I think a lot of people have anxiety about like
Speaker 2: AI replacing their job and what they do, and I
Speaker 2: think that we have to sort of step back and
Speaker 2: often remember that a job is about an outcome, and
Speaker 2: how you get that outcome is going to change over time.
Speaker 2: It's changed historically, and often the thing that you're trying
Speaker 2: to do is create some sort of value, and it's
Speaker 2: not always economic, or it can be. It can be
Speaker 2: put into economic terms, but we have to sort of
Speaker 2: think back, like why do you what is the value
Speaker 2: of a book? You know, and you know, put twenty
Speaker 2: dollars on the spine of a book and say this
Speaker 2: book's worth twenty dollars. I've read books that if you
Speaker 2: told me what I was going to get out of it,
Speaker 2: I would have.
Speaker 1: Paid a lot more.
Speaker 2: You know. I've had businesses with friends that like, yeah,
Speaker 2: we made money, but really the experience of working with
Speaker 2: everybody was way more valuable. And I think that people
Speaker 2: have anxiety about this future of like what happens or
Speaker 2: robots do everything, Like I don't think it'll be as
Speaker 2: fun and I don't think it's going to create as
Speaker 2: much value as people think that it will. And that's
Speaker 2: something I mentioned to before we recorded, was you know,
Speaker 2: this is a statistic that's roughly like forty percent of
Speaker 2: all Internet traffic after six pm is like Netflix, and
Speaker 2: that's because of squid games, you know, and K pop
Speaker 2: Demon Hunters, and when you take those things out, those
Speaker 2: things that are really valuable because people like them, when
Speaker 2: people created them. What's the value of the Internet after
Speaker 2: six pm? Well we just lost forty percent of the
Speaker 2: value right there.
Speaker 1: Okay, but what about aifilm. Do you think people will
Speaker 1: spend their time watching AI film instead of Kiepop, Demon
Speaker 1: Hunters and squid get.
Speaker 2: Sometimes yes, sometimes no. I don't think anybody under six
Speaker 2: is going to care, you know, how it was created.
Speaker 2: I think that I think that we're going to really
Speaker 2: value certain things. When ever, Tom Cruise wants to market
Speaker 2: a Mission Impossible movie. Part of the PR campaign is
Speaker 2: about the stunt that he performed. You have the last
Speaker 2: movie he did, He hung outside of in an airplane.
Speaker 2: He did some airplane stunts. He's done some other really
Speaker 2: cool stuff. When they did Top Gun, you know, the
Speaker 2: Top Gun Maverick, they made it a point of putting
Speaker 2: the actors into actual real planes so we could get
Speaker 2: their reactions, and this was added value. If we watched
Speaker 2: a PR video talk about look how great these AI
Speaker 2: stunt doubles are, which we got like back in the nineties,
Speaker 2: or like we don't care. That's not as interesting to
Speaker 2: us as the fact that a human really did something special.
Speaker 2: I think there are going to be films where it's
Speaker 2: going to be three three high school students are going
Speaker 2: to make a movie in Ai and we're like, this
Speaker 2: is amazing, and then we're going to still want, you know,
Speaker 2: germal Deteruro to hate on AI and go build these wonderful,
Speaker 2: lavish productions where he does it his way like a
Speaker 2: crafts person.
Speaker 1: I think it's both. Yeah, I think it's a really
Speaker 1: smart way of looking at it is in the mixture
Speaker 1: model of what the future is going to look like.
Speaker 1: So if we're just looking purely at economic value, where
Speaker 1: do you think the biggest changes are going to be
Speaker 1: there in the next five ten years.
Speaker 2: One of the things that's happened that people have overlooked
Speaker 2: is that every six months or so, it's you get
Speaker 2: better answers from chat GPT or geminarra claud when it
Speaker 2: comes to medical questions, right, and we've had the first
Speaker 2: time in history medical information has gotten cheaper year over year,
Speaker 2: and that we can't really I think overstate how significant
Speaker 2: that is. You think about that, like getting competent medical
Speaker 2: information it gets better every year, and it gets cheaper
Speaker 2: every year, and that's going to apply to a lot
Speaker 2: of things. And so I think that when we start
Speaker 2: to think about you know, what does that mean, Well,
Speaker 2: other kinds of information to get cheap, really great answers
Speaker 2: are going to be less expensive. And some people say,
Speaker 2: well great, who's going to need doctors. I think I
Speaker 2: think we're going to actually want to use doctors for
Speaker 2: a lot more areas of life. You might be able
Speaker 2: to spend more than two minutes with your doctor. Now
Speaker 2: that's nobody says I spend too much time talking to
Speaker 2: my doctor. Everybody's like I don't get enough time. And
Speaker 2: I think that we're going to start to look at like, Okay,
Speaker 2: what do we really want. I think that some areas
Speaker 2: that are easily automated, I've mentioned for it, call centers,
Speaker 2: things like this. Yeah, that part may go away, and
Speaker 2: we're going to have to start thinking about encouraging more
Speaker 2: people to take on the kinds of roles that we
Speaker 2: want to spend more time with. We need more nurses,
Speaker 2: we need more people who actually need more people build
Speaker 2: things like you know people. I've got into discussion on
Speaker 2: Twitter where somebody says well, what would you to tell
Speaker 2: an air conditioning repair person who's their job is going
Speaker 2: to be you know threat? And I'm like, I don't
Speaker 2: know if you know this, but we're running out of
Speaker 2: AC repair people right now because not enough people are
Speaker 2: coming into the workforce to replace the ones that are retiring.
Speaker 2: And that's true of a lot of these other industries
Speaker 2: right now. And even when we get to robotics, Like,
Speaker 2: when we get into robotics, like that's going to be
Speaker 2: exciting because nobody steps outside and goes, oh, everything's perfect.
Speaker 2: What would I fix in this role to go There's
Speaker 2: so many things we could fix, some things we could pair,
Speaker 2: some things we could make better. So I think that
Speaker 2: we're going to be thinking a lot more about how
Speaker 2: more people could become builders, how more people could create
Speaker 2: things that should exist. And I think that's where I
Speaker 2: have to think about, Like from a career point of view,
Speaker 2: the one things I tell people is like, think about
Speaker 2: marketing groups. Think about your ability to work with other people,
Speaker 2: you know, create groups, Think about people, Think about kind
Speaker 2: of problems you can collectively try to solve together.
Speaker 1: It's interesting to see that blue collar jobs are the
Speaker 1: ones that everyone knows are going to survive the plumbers,
Speaker 1: the electricians, the air conditioner repairment, and we certainly wouldn't
Speaker 1: have expected that. It does make me wonder though, if
Speaker 1: in one hundred years from now, air conditioners get designed
Speaker 1: in such a way that they are meant for robots
Speaker 1: to fix them instead of having to.
Speaker 2: I think, I absolutely think in this I think that
Speaker 2: we're going to see robotics do a lot of those
Speaker 2: things in a shorter term. But I also think of
Speaker 2: the things that we think are possible to build right
Speaker 2: now in the middle of this data center build out,
Speaker 2: building new data centers and goals like Sam Alan's talked
Speaker 2: about building one gig a lot of compute per week
Speaker 2: and that's basically a city sized amount of our city
Speaker 2: my sized amount of energy per week because of what's
Speaker 2: possible there, which means that, you know, what, what is
Speaker 2: the big part of the future economy. It's going to
Speaker 2: be come down to energy production. It's going to come
Speaker 2: down to compute. And I think a lot of the
Speaker 2: jobs we think about are going to be supporting building
Speaker 2: that and also making use of that. So you know,
Speaker 2: I think that you know, ac repair things like this,
Speaker 2: those jobs are probably I think that people have those
Speaker 2: jobs right now aren't going to have a problem. And
Speaker 2: I think that younger people coming into it are going
Speaker 2: to have a broader view of it. And I think
Speaker 2: that the ac repairment a fifteen year rare person fifteen
Speaker 2: years from now, you know, they're going to maybe be
Speaker 2: in charge of a fleet of robots, and their job
Speaker 2: is to be accountable. Is their job is to sell
Speaker 2: them like, yes, we got the work done. As we
Speaker 2: build you know, megaplex energy project number forty nine on
Speaker 2: the moon. There are people that I that I know
Speaker 2: and I really respect in AI who worry about job displacements,
Speaker 2: which I worry, but they think, like what happens when
Speaker 2: AI can do just about everything? And I'm like, we grow,
Speaker 2: we get bigger. If you imagine AI becoming highly capable,
Speaker 2: we have to also imagine the economy becoming highly scaled.
Speaker 2: And when that happens, you're actually going to grow so
Speaker 2: fast there won't be enough people to do the things
Speaker 2: you want to get done. From you know, working in
Speaker 2: you know, a data center, to working in a cafeteria
Speaker 2: to make sure that you have you know, great sushi,
Speaker 2: to figuring out what you do with all this compute
Speaker 2: and if you really imagine what happens and work super ambitious.
Speaker 1: That's that's amazing.
Speaker 2: And I think if you looked at the founding fathers
Speaker 2: of the United States, that they looked at the scale
Speaker 2: at which we did things today, it would see unfathomable.
Speaker 2: And we think that, oh if people often think of
Speaker 2: the futures just shiny clothes and robots doing stuff, when
Speaker 2: really the future is bigger.
Speaker 1: Like we look back.
Speaker 2: I tell me, if you look back one hundred years ago,
Speaker 2: the first thing you'd realize is, oh, we're poor. Back
Speaker 2: then one hundred years you'd feel like everybody was pretty poor.
Speaker 2: That's for one hundred years from now people look back
Speaker 2: at us and think, oh, you guys were poor, but
Speaker 2: build in.
Speaker 1: And you died young, and you had all kinds of
Speaker 1: pand yeah. Do you have an opinion about universal basic income.
Speaker 2: Other than it's a terrible and necessary idea, No, tell
Speaker 2: me why. I mean, I am all for as you
Speaker 2: can lower the cost of things. You know that the
Speaker 2: problem people talk about universal health care is the problem
Speaker 2: is that the cost of it accelerates faster than the
Speaker 2: GDP does. And no matter whatever you come from political
Speaker 2: side of the argument, you have to address the fact
Speaker 2: that this thing gets faster than your economy can afford it,
Speaker 2: then it's hard to make that sustainable. I think there's
Speaker 2: a role where the economy grows so fast that you
Speaker 2: can have a different conversation about that when it comes
Speaker 2: to UBI because you think that people won't be needing
Speaker 2: the economy. I don't think that's going to be the case.
Speaker 2: And that's a scary place to be. When you say that, oh,
Speaker 2: eight billion humans are you know, an ancillary to what
Speaker 2: the world is. I don't think that I have trouble
Speaker 2: fathing that being true. And also it's uncomfortable to think
Speaker 2: about that being true because once you say that they're
Speaker 2: not necessar bad things happen. I think that I'm all
Speaker 2: for ideas and ways in which we make sure everybody
Speaker 2: gets their needs met. Nobody worries about housing, nobody worries
Speaker 2: about healthcare, and words about food. I'm all for solutions
Speaker 2: for that. But when we think that there's just not
Speaker 2: going to be any jobs in the future, I think
Speaker 2: historically that's not true. It's also, you know, I bring
Speaker 2: this up, like, okay, so we can come up with
Speaker 2: a super advanced day I, but you're telling me we
Speaker 2: can't ask it to find out what's still useful work
Speaker 2: for people like it won't be smart enough to figure
Speaker 2: out new things. And we've continuously invented new jobs. Like
Speaker 2: I brought up before, it's entertainment, which you would think
Speaker 2: be one of the first things to go just gets
Speaker 2: bigger in like education, we need more teachers, Like I
Speaker 2: think that we as we live longer, healthier lives, one
Speaker 2: of the ways we're going to want to avoid boredom
Speaker 2: is we're going to want to continue our education. We're
Speaker 2: going to go learn from these things, and we're going
Speaker 2: to learn from people actually went there and did it.
Speaker 2: You know, do you want to learn about Egyptian you know,
Speaker 2: mythology from somebody ever went to Egypt? You know, do
Speaker 2: you want to learn about practicing medicine from nobody ever
Speaker 2: treated to patient? And so I'm very, very bullish on
Speaker 2: the idea that we're going to need lots more people
Speaker 2: in our economy.
Speaker 1: You know, I was just hanging out with a friend
Speaker 1: of mine from high school. We graduated together, and I
Speaker 1: was thinking about the fact that what I do and
Speaker 1: what he does, and what essentially everyone we know in
Speaker 1: Sulkon Valley does these jobs didn't exist when we were
Speaker 1: graduating high school. We couldn't have imagined the titles of
Speaker 1: these jobs. And so my question is for education currently,
Speaker 1: if you're thinking about junior high kids, high school kids,
Speaker 1: what do you see is the important things that we
Speaker 1: should be teaching them given that we are not training
Speaker 1: them for jobs that we know of. So in two thousand,
Speaker 1: I wanted to get into AI.
Speaker 2: I really wanted to find a way into that right
Speaker 2: because I knew that was the future, Like how could
Speaker 2: I have this? And I decided this late in life,
Speaker 2: comparatively for other people. And in twenty nineteen, Opening Eye
Speaker 2: published they did a tweet and they talked about their
Speaker 2: model GPT two and if you look at like one
Speaker 2: of the most upvoted responses was mine at the time, going,
Speaker 2: this is really amazing. I guess I'm out of work
Speaker 2: as a novelist. Any suggestions to what I should do?
Speaker 2: I had no no idea A year later I'd be
Speaker 2: the world's first person employee as a prompt engineer. Because
Speaker 2: what happened was that intersection of language and I had
Speaker 2: an interest in AI, but I knew a lot about language.
Speaker 2: All of a sudden, those two things collided and I
Speaker 2: think that for people right now looking at what they
Speaker 2: want to do in the future, take the things you're
Speaker 2: passionate about right now and ask how does AI make
Speaker 2: this better? Or how does AI make it worse? And
Speaker 2: I think that you don't assume like well, AI is
Speaker 2: going to replace this, Like no, AI is going to
Speaker 2: change it. So, you know, I spend time talking to
Speaker 2: college kids, and one of the things that concerns me
Speaker 2: is that a number of kids in computer science programs
Speaker 2: aren't even taught how to use AI code tools. I
Speaker 2: think it's important to learn the fundamentals, but when they're
Speaker 2: not even taught to use those tools. And there was
Speaker 2: a headline in a newspaper a few weeks ago that
Speaker 2: came out like, oh, this person she got a degree
Speaker 2: in computer science, but nobody will hire her. I'm like,
Speaker 2: I bet she never learned to use these tools and
Speaker 2: that's why she can't get it. It's not her fault, she's
Speaker 2: literally the institution she paid money to so for students.
Speaker 2: You know, one of the things that was really interesting
Speaker 2: I did a talk at Santa Clara last week in
Speaker 2: Santa Clair. You know who's interesting because I've interacted those
Speaker 2: kids for a couple of years now, and I've noticed
Speaker 2: they become much more entrepreneurial, like the ones in the
Speaker 2: AI clubs. They're actually starting their own companies. Some are
Speaker 2: actually raising money while they're in their dorm rooms. And
Speaker 2: I asked, why is this, and one of the kids said, well,
Speaker 2: we're not so sure that there's going to be a
Speaker 2: job for us, so we're going to we want to
Speaker 2: be more self relyingt We decided we're going to create
Speaker 2: our own work.
Speaker 1: We're going to create our own need.
Speaker 2: And I think a little scary they feel that, but
Speaker 2: that's very honest, and I'm very very was very happy
Speaker 2: to see their reaction to this. Wasn't hopelessness. It was like, fine,
Speaker 2: if you don't have a place for us, you can't
Speaker 2: tell us our place in the future. We're going to
Speaker 2: create our place in the future.
Speaker 1: I think that's great for those kids with the personality
Speaker 1: who are willing to do that. The majority of students, though,
Speaker 1: might feel quite nervous about that.
Speaker 2: But how do we how do we agree, how do
Speaker 2: we get them to that? How do I think that
Speaker 2: a lot of a lot of entrepreneurs don't even realize
Speaker 2: that till later life, till necessity, and I think that
Speaker 2: not everybody has to be entrepreneur, but I do think that,
Speaker 2: Like my advice is like what I did, Like I
Speaker 2: wanted to get into tech and I saw the people
Speaker 2: at Opening Eyes. I like, what you kids are doing?
Speaker 2: Can I help you out? And it worked out great.
Speaker 1: It's nice. Yeah, it's Gerta had said the most important
Speaker 1: bequeaths that a parent can give the child is two things,
Speaker 1: roots and wings. And I interpret that in the educational
Speaker 1: context as roots being critical thinking, really teaching students how
Speaker 1: to do critical thinking, and wings being creative thinking how
Speaker 1: to be really creative, because that's all when I think
Speaker 1: about my students at Stanford that I'm teaching, that's the
Speaker 1: only thing that I'm telling them that's going to stay
Speaker 1: true twenty years from now is how can they think
Speaker 1: through a problem and how can they be really creative?
Speaker 1: Meaning take stuff they've learned before and remix it, bend it,
Speaker 1: break it blended, build new things out of it. Because
Speaker 1: I think it doesn't matter whether the computer science students
Speaker 1: are learning coding or whether they're learning AI tools, because
Speaker 1: in five years it's all going to be something else.
Speaker 1: It's going to be you know, super AI tools.
Speaker 2: Yeah, I think that the thing that I like to me,
Speaker 2: if you ask me to describe coding it to me,
Speaker 2: it's like to look at a system and figure out
Speaker 2: how to give it the minimal set of instructions to
Speaker 2: get it to do something right. That's how I approach prompting.
Speaker 2: That's how I approach a lot of things. And I
Speaker 2: think that you know and you have to figure out
Speaker 2: what those tools are going to be in your right.
Speaker 2: But if you are a critical thinker, you adapter really
Speaker 2: quite well to this. You understand, oh, the AI does
Speaker 2: this part, it's good to this bat of this. Now
Speaker 2: I'm going to use this. I think a lot of
Speaker 2: people kind of like fetishize the tool itself and think, well,
Speaker 2: this is Can I just use this thing? And that's
Speaker 2: the way I do it, And It's like, no, it's
Speaker 2: about the outcome. And he gets if you know what
Speaker 2: your goal is about the outcomes and not just knowing
Speaker 2: which Python function to use, you're going to be better
Speaker 2: suited for the future.
Speaker 1: Yeah. And I think one of the things I'm so
Speaker 1: excited about, for example, AI is teaching critical thinking by
Speaker 1: being debate partners. So one on one debate partners, you
Speaker 1: take some side of a hot button issue, the AI
Speaker 1: takes the other side. You debate back and forth. It
Speaker 1: grades you on how well you are. Then you switch
Speaker 1: sides and grades how well you do the other side.
Speaker 1: And that kind of thing is the kind of thing
Speaker 1: that no teacher would ever have time to do for
Speaker 1: each student, but this will be. It's a perfect tool
Speaker 1: for really teaching critical things.
Speaker 2: And it's safe too because you have what's happened, Like
Speaker 2: debate is a great thing for students to do, but
Speaker 2: like debate, organizations have been so captured by political correctness
Speaker 2: that students can opt out of arguing alternate points of view,
Speaker 2: which the problem is is you never the purpose of
Speaker 2: this was to understand other points of view, and that
Speaker 2: they become intellectually weak from that. The beauty of AI
Speaker 2: is because part of it's the fear too. It's like,
Speaker 2: I don't want somebody to take me out of context,
Speaker 2: even though it's supposed to be a safe space. But
Speaker 2: when you're having a private conversation the chatbot, you're like, okay,
Speaker 2: let me explain my point of view on let me
Speaker 2: let me let me have to defend this point of
Speaker 2: view I disagree with. Then it's feel safe, you're not
Speaker 2: as worried that all of a sudden will be out
Speaker 2: of context.
Speaker 1: Oh I love that. That's great. And when it comes
Speaker 1: to creativity, I think there's a real opportunity for AI
Speaker 1: to just feed us a broader diet. All creativity is
Speaker 1: is absorbing what we see in the world and then
Speaker 1: remixing that. And you know, when you look at I
Speaker 1: don't know, look at music around the world. Beethoven certainly
Speaker 1: could have written music as they did in Japan at
Speaker 1: the same time or in Nigeria at the same time,
Speaker 1: but he didn't like the music was different in all
Speaker 1: these places. Why because he absorbed just a small amount
Speaker 1: of what was in his culture, and so did the
Speaker 1: Japanese or the Nigerians. So what we have nowadays, really
Speaker 1: ever since the advent of the Internet, is a much
Speaker 1: broader diet. But what AI gives us is a broader
Speaker 1: diet still where we can get it to do remixes
Speaker 1: and give us things that really stretch the fence lines
Speaker 1: of our thinking. And that's great because that's the student's diet.
Speaker 1: And then the key, I think is to make them
Speaker 1: present in person so they can work with the AI
Speaker 1: to do whatever they're doing, and then they present to
Speaker 1: their class and they're explaining all these different things about
Speaker 1: how the new economy could run on this other planet
Speaker 1: and the type of people and whatever, that it's such
Speaker 1: an opportunity for expanding creativity beyond what we ever got
Speaker 1: in school. That was my interview with Andrew Mayne. This
Speaker 1: conversation pushed back a bit against the default anxiety that
Speaker 1: AI equals unemployment, because the story is more richly textured
Speaker 1: when we ground it in history, economics, psychology, and questions
Speaker 1: about what the human brain actually seeks, at least historically.
Speaker 1: The fact is, when you automate the bottom rungs of
Speaker 1: the economic ladder, humans climb upward. The plow didn't eliminate work.
Speaker 1: It created governance and mathematics and engineering and poetry. Industrial
Speaker 1: agriculture didn't collapse society. It helped dismantle slavery and create
Speaker 1: new professions. So the same is likely with AI. What
Speaker 1: surfaced from our conversation is that the jobs most at
Speaker 1: risk are the black box jobs where a person receives
Speaker 1: in puts and sends standard outputs and the identity of
Speaker 1: the worker doesn't matter. Those jobs are probably going to vanish,
Speaker 1: but the roles that depend on trust, reputation, lived experience, mentorship, inspiration.
Speaker 1: These might become more valuable, not less, because through the
Speaker 1: neuroscience lens, I think we can say that human beings
Speaker 1: are creatures of story, not efficiency. We don't choose books
Speaker 1: only for their sequences of words. We choose them for
Speaker 1: the heartbeat behind the pages. We don't choose teachers because
Speaker 1: they have access to zeros and ones that we want
Speaker 1: to know. We choose them because they've lived actual experiences
Speaker 1: and can guide us around the pitfalls that they once
Speaker 1: fell into. As Andrew pointed out, even the chief scientist
Speaker 1: of Open AI doesn't believe that AI will replace teachers
Speaker 1: because inspiration isn't really a commodity. It's relational, and no
Speaker 1: matter how knowledgeable an AI tutor becomes, it can't replicate
Speaker 1: the emotional texture of a person who has wrestled with ideas,
Speaker 1: who has failed, learned, grown, and now offers that experience
Speaker 1: to others. Another theme that surfaced is the expansion of creativity.
Speaker 1: As I mentioned in the conversation, painting didn't die when
Speaker 1: photography arrived. Instead, painters expanded into new directions like impressionism
Speaker 1: and Cubism, and Pointillism and surrealism, and I suggest writers
Speaker 1: are not going to go away now that AI can
Speaker 1: write fluent text. Instead, writers will invent new forms of storytelling,
Speaker 1: new modes of audience engagement, new performance ecosystems around their work.
Speaker 1: And Andrew rays the possibility that accelerating tools in science
Speaker 1: like alpha fold won't eliminate scientists, but it might multiply them.
Speaker 1: So when one researcher can do a year of research
Speaker 1: work in a day, that massively expands the frontier and
Speaker 1: opens the door for more people to join the field
Speaker 1: because it increases the number of experiments that we can imagine,
Speaker 1: and therefore the number of people we need to build
Speaker 1: them and run them and interpret them. In other words,
Speaker 1: the surprising possibility is that the future economy may not
Speaker 1: need fewer people. It may need more more builders, more tinkerers,
Speaker 1: more crafts people, more teachers, more creative thinkers, more scientists,
Speaker 1: more hands to run the experiments, and more minds to
Speaker 1: shape the meaning of what we discover, Which brings us
Speaker 1: back to the brain. For all its computational power, it's
Speaker 1: not built for perfect efficiency. It's built for meaning and
Speaker 1: imagination and social connection. It's built to ask not just
Speaker 1: what can I do, but what is worth doing? And
Speaker 1: this is where humans will continue to show. The jobs
Speaker 1: of the future are not going to be the ones
Speaker 1: we can list today, just like no one a generation
Speaker 1: ago might have predicted your job title. Now, the next
Speaker 1: wave of professions are going to emerge from the collision
Speaker 1: between human creativity and machine capability. Our kids are going
Speaker 1: to grow up into jobs that don't yet exist, using
Speaker 1: tools we haven't invented to solve problems we haven't yet imagined.
Speaker 1: So our job is to cultivate the tasks of the brain,
Speaker 1: ours and theirs that will always matter, critical thinking and
Speaker 1: creative thinking roots and wings. So as we move into
Speaker 1: this AI accelerated era, the best question isn't just will
Speaker 1: AI take our jobs, but instead what will we choose
Speaker 1: to do with the extraordinary new landscape that AI opens
Speaker 1: up for us? Go to eelman dot com slash podcast
Speaker 1: from more information and to find further reading. Join the
Speaker 1: weekly discussions on my substack and check out Subscribe to
Speaker 1: Inner Cosmos on YouTube for videos of each episode and
Speaker 1: to leave comments until next time. I'm David Eagleman and
Speaker 1: this is Inner Cosmos.