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Speaker 1: If AI can do everything from writing novels to designing proteins,

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Speaker 1: what exactly is left that only humans can do? Do

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Speaker 1: we care about the story behind a piece of art,

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Speaker 1: who made it, who suffered for it? When AI can

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Speaker 1: produce the same words or images, which jobs are really

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Speaker 1: at risk? Is there any such thing as a human

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Speaker 1: advantage in a world where machines can outperform us at

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Speaker 1: almost any measurable task. What does any of this have

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Speaker 1: to do with the plow or Stephen King's nightmares, or

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Speaker 1: the first shoeshine caught on camera, or Tom Cruise's stunts,

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Speaker 1: or the shortage of air conditioner repair men, and why

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Speaker 1: hyper capable AI might actually increase the demand for unexpected jobs.

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Speaker 1: Today we'll speak with author and technologist Andrew Mayne. Welcome

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Speaker 1: to Inner Cosmos with me David Eagleman. I'm a neuroscientist

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Speaker 1: and author at Stanford, and in these episodes we sail

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Speaker 1: deeply into our three pound universe to understand how we

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Speaker 1: see the world and what our world might come to

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Speaker 1: look like very soon. When you look around a subway

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Speaker 1: car or a coffee shop, you see people scrolling on

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Speaker 1: their phones with essentially no movement of their bodies except

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Speaker 1: for their thumbs, but inside their skulls, eighty six billion

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Speaker 1: neurons are firing away. Each neuron as complex as a city,

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Speaker 1: each one alive with electrical storms flickering tens or hundreds

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Speaker 1: of times every second. These vast inner cosmoses are running

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Speaker 1: constant simulations of the world around us, of the future

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Speaker 1: of each other. And one of the things that makes

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Speaker 1: our species unusual is that our brains are not simply

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Speaker 1: built for getting food or escaping predators. They are finely

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Speaker 1: tuned social prediction engines. A huge amount of the cortex

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Speaker 1: is devoted to thinking about other people, their motives, their reliability,

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Speaker 1: their intentions, their reputations. We carry around mental models of

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Speaker 1: thousands of individuals and organizations, and we constantly simulate possibilities

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Speaker 1: like how would she react if I sent this message?

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Speaker 1: Can I trust this contractor is this person over here

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Speaker 1: aligned with my values? And so on. This social simulation

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Speaker 1: machinery is so ancient and so deeply wired that it

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Speaker 1: has shaped everything that we call an economy. I think

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Speaker 1: this goes underappreciated that markets are so much more than

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Speaker 1: just spreadsheets and supply chains. They are agreement between nervous systems.

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Speaker 1: Economies are built from trust, from storytelling, from reputation, from

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Speaker 1: shared values, and from our search for meaning. Okay, Now,

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Speaker 1: suddenly into this long human drama steps a new cognitive species, AI.

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Speaker 1: And this is weird because AI doesn't have emotions, it

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Speaker 1: doesn't have a body, it doesn't have a childhood, but

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Speaker 1: it can process information at extraordinary scale, and its busy

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Speaker 1: automating tasks that once required human thought. So the question

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Speaker 1: on everyone's mind is what does this mean for the economy?

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Speaker 1: What happens to our jobs? Will the economy bend or

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Speaker 1: will it break? Will we be replaced or will our

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Speaker 1: skills evolve into something new? Now, the first thing to

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Speaker 1: note is that we have been here before, many times,

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Speaker 1: thousands of years ago, when agriculture was nearly everybody's job.

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Speaker 1: The plow presumably seemed like an existential threat, but as

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Speaker 1: we know, it didn't eliminate human purpose. It expanded it

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Speaker 1: because the plow freed brains to invent teaching, governance, philosophy, math, art,

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Speaker 1: and so on. And this pattern kept repeating. Industrial machinery

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Speaker 1: opened up more human creativity that electricity than the microprocessor.

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Speaker 1: Every time the old jobs vanished and new ones emerged

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Speaker 1: that no one could have predicted. I think about this

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Speaker 1: all the time. If you were one of the men

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Speaker 1: who landed on the moon in nineteen sixty nine, you

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Speaker 1: couldn't even have imagined that your kids would graduate with

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Speaker 1: majors in computer science. Or you couldn't even imagine the

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Speaker 1: concept of the Internet, and that a kid might grow

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Speaker 1: up to become a web developer or an app developer,

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Speaker 1: or a mobile UX designer, or a data scientist or

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Speaker 1: a cloud infrastructure engineer, or go into cyber security, or

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Speaker 1: become a PROMPT engineer, or a drone cinematographer, or an

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Speaker 1: e sports athlete, or a VR designer or a social

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Speaker 1: media influencer. Today, of course, we are catching the wave

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Speaker 1: of another transformation faster than anyone imagined. AI is writing code,

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Speaker 1: it's designing proteins, it's responding to customer inquiries, it's drafting

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Speaker 1: legal language, it's tutoring students, it's accelerating science. Some jobs

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Speaker 1: are going to be automated quickly. These are called the

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Speaker 1: black box jobs by today's guest, where someone receives a

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Speaker 1: signal and sends a standardized output. But new professions will appear,

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Speaker 1: most of which we can't yet name or even conceive of.

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Speaker 1: And the deeper questions reach beyond economics into our psychology,

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Speaker 1: because we need to assess what humans actually want from

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Speaker 1: other humans, what are we willing to outsource, and what

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Speaker 1: are we going to fiercely protect? For example, why does

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Speaker 1: a concert matter more when the guitarists spend decades building callouses?

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Speaker 1: Why do we trust a teacher more when they have

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Speaker 1: lived the very mistakes that we are trying to avoid.

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Speaker 1: I've argued here before that the answer lies in the

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Speaker 1: circuitry of the social brain. We value the story behind

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Speaker 1: a creation. We value the years of effort, the biological limitations,

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Speaker 1: the courage, the vulnerability. AI can mimic outputs, but it

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Speaker 1: can't yet mimic stakes, It can't yet mimic what it

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Speaker 1: means to be a fragile biological creature trying to create

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Speaker 1: something meaningful in a short life, and so on the surface,

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Speaker 1: the question is will AI destroy jobs? But the deeper

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Speaker 1: version is how will humans redefine meaning and connection and

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Speaker 1: value in an age when machines can do almost everything else?

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Speaker 1: You explore this. I sat down with my friend and

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Speaker 1: colleague Andrew Mayne, who is an extremely interesting person. He

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Speaker 1: is a novelist and inventor, a very accomplished magician with

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Speaker 1: books and television shows on the subject and most relevant

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Speaker 1: for today's conversation. He is the original prompt engineer for

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Speaker 1: Open AI and their first science communicator. He lives right

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Speaker 1: at the intersection of creativity and innovation and AI. So

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Speaker 1: I invited him to the studio to get his take

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Speaker 1: on the future relationship of humans and AI. So, Andrew,

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Speaker 1: a lot of people are worried that AI is going

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Speaker 1: to take over all the jobs of humans, So what's

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Speaker 1: your take on this.

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Speaker 2: I think that we have to think about what we

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Speaker 2: mean by jobs and historically what's happened. If we were

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Speaker 2: in Mesopotamia several thousand years ago and somebody showed you

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Speaker 2: a plow, and at that time, like ninety nine percent

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Speaker 2: of everybody was involved in agriculture, a plow would seem

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Speaker 2: like a very scary thing. Because of the plow, we

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Speaker 2: were able to invent things like teaching as a profession, governance,

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Speaker 2: and a lot of the other things that we now

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Speaker 2: consider essential culture. They didn't exist then because we didn't

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Speaker 2: have the time to do that, and they weren't like

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Speaker 2: superfluous things like poetry or art, which have value, but

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Speaker 2: these were things to help build our economy. And I

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Speaker 2: think that we get that every major technological change. If

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Speaker 2: we went back just you know, two hundred years ago

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Speaker 2: to you know, within the great great grandparents time, like

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Speaker 2: there's actually I think Zachary Taylor has a grandson that's

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Speaker 2: still alive today, which is like crazy. I mean he

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Speaker 2: had children late and his son had children late. But

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Speaker 2: in that time of somebody's living to the other grandparent,

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Speaker 2: when ninety five percent everybody's going in volt in agriculture

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Speaker 2: if you talk about the industrialization of agriculture, and that

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Speaker 2: would seem like that would be almost apocalyptic in the change,

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Speaker 2: But that's when things started to happen and shift. It

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Speaker 2: was part of the reason we got rid of slavery.

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Speaker 2: It's part of the reason that we started to think

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Speaker 2: about how everybody sort of has a vital part of

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Speaker 2: our economy.

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Speaker 1: So if we were just blue skying about the kinds

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Speaker 1: of jobs that will exist one hundred years or not,

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Speaker 1: things in analogy to governance and teaching, what could we

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Speaker 1: come up with.

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Speaker 2: Well, you know, part of it is is people often ask, like,

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Speaker 2: what's the job of the future, And if we look

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Speaker 2: at how much jobs have changed in our own lifetime.

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Speaker 2: Subtly that we don't realize that if we went back

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Speaker 2: in time to like the nineteen eighties and we talked

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Speaker 2: to a teacher, then we talked to a teacher today,

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Speaker 2: and we told a teacher back then, Well, in the future,

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Speaker 2: you're going to spend part of your time in Google Calendar.

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Speaker 2: What's that, Well, it's an electronic spreadsheet for managing time

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Speaker 2: sort of. You're going to be doing video calls, you're

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Speaker 2: going to be using electronic documents. It would sound like

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Speaker 2: an IT job, it would sound extremely technical, but that's normal.

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Speaker 2: And yeah, and you think about that too, is we

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Speaker 2: use these tools all the time. So you and I

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Speaker 2: are on our phones, we're checking messaging, we're checking these stuff.

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Speaker 2: So I think one hundred years from now, the value

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Speaker 2: is going to still come in from things where we

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Speaker 2: want people. We like people to teach us, we like

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Speaker 2: people to manage things for us. I still trust I

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Speaker 2: trust you. I trust you to manage a thing. Maybe

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Speaker 2: you're going to use in a bunch of electronic systems,

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Speaker 2: but I want you to make sure they're working.

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Speaker 1: That makes sense. One of the things that I've been

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Speaker 1: very interested in is what AI is going to mean

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Speaker 1: for creatives, For example, writers, You and I are both

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Speaker 1: writers of books, and something that I've been happy to

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Speaker 1: see is that people really care about the heartbeat behind

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Speaker 1: the page. So they care that it's a real author

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Speaker 1: who's slaved over a keyboard for months or years, rather

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Speaker 1: than oh, I wrote this book with AI in five

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Speaker 1: hundred milliseconds. No one wants to read that, even if

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Speaker 1: the book is identical word for word to your book.

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Speaker 1: And I think that's nice that people care about the heartbeat. Yeah,

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Speaker 1: I think there's going to be a place for certain kinds.

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Speaker 2: I might be happy with AI textbooks and stuff, but

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Speaker 2: you know, reading about the experience of somebody like you

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Speaker 2: who's actually gone into a lab, gone into the rold

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Speaker 2: and tested, thinks that's way more valuable to me. An

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Speaker 2: example that I use a lot is, you know, one

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Speaker 2: is like I love the fact that Stephen King's is

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Speaker 2: kind of crazy guy that lives in Maine that makes

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Speaker 2: his stories because like this might be a nightmare. That

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Speaker 2: kind of gives up more value. I mean, maybe it's

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Speaker 2: sad that somebody else's terror is my enjoyment, but you know,

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Speaker 2: beaus of may but also like you know Brandon Sanderson.

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Speaker 2: He's a prolific science fiction fantasy author. Brandon is a

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Speaker 2: guy that's very active in the convention circuit. He's got

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Speaker 2: a really wonderful engagement with this fan. Basically talks about writing.

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Speaker 2: He shares about writing all the time. He's a very

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Speaker 2: real person. I don't know if you know this, but

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Speaker 2: he did a Kickstarter and he had four books he

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Speaker 2: wrote during the pandemic. It was the most successful kickstart

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Speaker 2: of all time, forty million dollars. Forty million dollars.

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Speaker 1: Incredible.

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Speaker 2: The market cap of Barnes and Noble is only like

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Speaker 2: four hundred million, so basically ten percent of the market

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Speaker 2: cap of America's largest book retail bookseller.

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Speaker 1: And why I think it's people like him. They like

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Speaker 1: the guy.

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Speaker 2: I mean, like part of what makes you know we

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Speaker 2: might get an AI to generate an entire mythology like

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Speaker 2: Lord of the Rings, but the fact that J. R.

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Speaker 2: Tolkien was a guy that spent all this time studying

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Speaker 2: trying to create an English mythology gives it value.

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Speaker 1: Yeah, exactly. So if an AI wrote exactly Brandon Sanderson's books,

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Speaker 1: we wouldn't care about as much. So that's the good news.

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Speaker 1: Some people might.

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Speaker 2: I think there will be a mixed ground, but I

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Speaker 2: think a lot of us who care more who flipped

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Speaker 2: to the backjacket to go who wrote this?

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Speaker 1: There's a reason we put that on there. Yeah. What's

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Speaker 1: interesting is that it won't be too long before AI

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Speaker 1: starts writing books and faking the author by an author picture.

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Speaker 1: So we'll have to have other ways of verification, like

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Speaker 1: in person tours.

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Speaker 2: We've dealt with, like there are publishers that have house authors.

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Speaker 2: There are some I know of some authors that are

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Speaker 2: famous authors that are no longer writing their books and

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Speaker 2: they have other ghostwriters doing that. So I'd say we've

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Speaker 2: been doing that for a while. But you know, I

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Speaker 2: think you're right. You know there's going to be people

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Speaker 2: whoren't going to care. I think that's fine.

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Speaker 1: One of the things that's a really cool question is

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Speaker 1: what will authors do to distinguish themselves from AI. So

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Speaker 1: by analogy, when the camera got invented, visual painters panicked,

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Speaker 1: but what they ended up doing was moving into areas

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Speaker 1: that the photograph could not do, light Impressionism and Cubism

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Speaker 1: and so on, and they ended up, you know, surviving

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Speaker 1: and making new kinds of art that no visual painter

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Speaker 1: would have predicted at the time when the camera debuted.

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Speaker 1: So the question is what kind of books will we write.

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Speaker 2: I use when I give talks. One of the examples

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Speaker 2: I give is the gours first photograph of people, right,

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Speaker 2: the first photograph of people's like eighteen thirty seven, eighteen

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Speaker 2: thirty eight, and what happened was accidental. Back then, you

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Speaker 2: had to leave a camera aimed to the sidewalk. He

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Speaker 2: left a camera. You have to leave the camera the

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Speaker 2: shutter open long enough. Because it took so long from

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Speaker 2: a photons to register on the film. The idea of

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Speaker 2: capturing people seemed crazy because nobody would stand that long enough.

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Speaker 2: But a guy got a shoe shined, so the shoeshiner

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Speaker 2: and he got captured in the photograph.

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Speaker 1: And you think about what that would mean.

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Speaker 2: At the time, you saw you know one, You saw

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Speaker 2: how painters realized they could go in different directions. Their

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Speaker 2: job wasn't just to try to recreate reality, it was

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Speaker 2: interpret it. But imagine going back in time and saying, hey, listen,

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Speaker 2: not only that, but you know, by the in a

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Speaker 2: few decades, we're going to have motion pictures.

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Speaker 1: What's a motion picture?

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Speaker 2: That's hard to explain and explain that you know, one

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Speaker 2: hundred years later we were going to have these productions

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Speaker 2: that we're dwarfed in size. Thing you have or today

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Speaker 2: where you get the end credits for Avengers. Endgame is

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Speaker 2: like longer than like the number of people who are

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Speaker 2: in like the United States Navy, and like eighteen thirty seven,

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Speaker 2: the budget not adjusted dollars, but the act of the

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Speaker 2: budget and just dollar for dollar was like greater than

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Speaker 2: the US Treasury, and the scale of art got so

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Speaker 2: much bigger. So for writers, I think the part of

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Speaker 2: what comes into is that that you get to your

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Speaker 2: your it's not just the tech space between the pages

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Speaker 2: of the book. I think it becomes bigger, like what

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Speaker 2: we're doing a podcast. Okay, right, this is part of

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Speaker 2: what is the Eagleman Legendarium, you know, part of it's this,

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Speaker 2: part of it's this part of it's that I think

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Speaker 2: that we start looking at that how much time do

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Speaker 2: you have to spend on the things you don't like

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Speaker 2: doing as an author, like revisions, little notes, things like this?

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Speaker 2: How much would happen if you had AI to free

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Speaker 2: you up from that to spend more time I'm creating

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Speaker 2: engaging with people.

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Speaker 1: So let me just make sure I got I got

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Speaker 1: the analogy though, So with bands, the release of the

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Speaker 1: album that was the big thing, and then they'd make

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Speaker 1: their money still on the album. Then that changed with

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Speaker 1: Napster and so on. We're going on the road was

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Speaker 1: the thing, and the album was just the calling card

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Speaker 1: so that people would show up. The economics of it change.

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Speaker 2: Well sort of, but it also it changed for some

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Speaker 2: of the people the top. For other bands, they'd go

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Speaker 2: on the road and never actually was a payoff because

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Speaker 2: the record labels would still hold on to it. And

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Speaker 2: now you're in this weird phase where like with with

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Speaker 2: Spotify and with streaming, where you have some people making

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Speaker 2: a lot of money on streaming and some people not.

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Speaker 2: I would say record company economics are always sort of weird, so.

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Speaker 1: Right, but it's the going on the road that mattered.

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Speaker 1: It's the show exactly, and that's the part that for

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Speaker 1: a while was not a big deal in music and

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Speaker 1: then became a big deal. I suspect that it's going

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Speaker 1: to be the same. And let's say writing where it's

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Speaker 1: not just sitting and tapping out the book in solitude,

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Speaker 1: but it's going and doing the talks and the tours

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Speaker 1: and in the podcast I do.

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Speaker 2: Every time I launch a book, I'll go I'll do

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Speaker 2: a live sessions, I'll go hop on video and talk

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Speaker 2: to people about that I used to do, like go

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Speaker 2: to bookshops and stuff. But I said, I can reach

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Speaker 2: more people if I just hop on a video stream

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Speaker 2: and talk to people and be accessible and so and again.

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Speaker 2: I think there's gonna be different things for different people.

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Speaker 2: I think that, you know, not some people can be reclusive.

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Speaker 2: I think sometimes if their work just creates its own

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Speaker 2: community around it, that can work.

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Speaker 1: Yeah. Okay, so let's get back to the big picture then,

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Speaker 1: So what is going to be the influence of AI

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Speaker 1: on the economy in terms of jobs? What jobs are

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Speaker 1: going to disappear first? Which ones are going to be unaffected?

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Speaker 2: I think the highly network jobs or where people are

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Speaker 2: happy to have a person in there or need a

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Speaker 2: person to be in it, are always going to be

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Speaker 2: highly valuable.

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Speaker 1: Give what's an example.

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Speaker 2: You know, if you are a person who is makes

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Speaker 2: a perching decision for a company and you have to

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Speaker 2: decide who to work with, and you need to look

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Speaker 2: at contractors to somebody with a really good reputation, and

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Speaker 2: that really comes to talking to Let's say you're a

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Speaker 2: contractor and you have to figure out who are you

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Speaker 2: going to get.

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Speaker 1: Your building supplies from.

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Speaker 2: You know, you're going to want to talk to somebody

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Speaker 2: and find out the reliability because that information is not

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Speaker 2: just available out there. That's something you have to sort

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Speaker 2: of figure out like a little more deeper sort of

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Speaker 2: connection to that.

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Speaker 1: Although we can certainly imagine a time when my AI

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Speaker 1: agent talks to this company's AI agent and all the

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Speaker 1: data but WI reliable. Well that's right, but all the

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Speaker 1: data is out there about the reliability.

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Speaker 2: Of that company, you assume, but we make We make

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Speaker 2: that assumption though that that we put everything out there.

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Speaker 2: So you know a lot of stuff about other people

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Speaker 2: that you won't share, both via social media or whatever.

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Speaker 2: That's insight you share with your friends, you share with

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Speaker 2: high trust people, but you don't actually make because that

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Speaker 2: has value. And so I think that's part of like

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Speaker 2: you look at certain things that continue on, like you know,

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Speaker 2: I have you we both have like litter agents, right,

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Speaker 2: and they have a better understanding of you know, you

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Speaker 2: could theoretically I could have a chat GPT negotiate a contract,

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Speaker 2: but it's going to tell me like, well, this is

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Speaker 2: really what happened at this publishing house, or this what's

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Speaker 2: really happened there. So I think there's a lot of

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Speaker 2: places where we forget how much information is locked up

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Speaker 2: in our heads and how much us based upon trust.

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Speaker 1: Yeah, yeah, that's right. One of the things that fascinates

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Speaker 1: me is social neuroscience, which is a new sub field

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Speaker 1: which really concentrates on trust and integrity and reputation of

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Speaker 1: other people. It turns out the way we've traditionally studied

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Speaker 1: the brain, as we say, look, this is how the

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Speaker 1: visual system works, is how audition works, this is how

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Speaker 1: movement works. But what that overlooks is that a massive

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Speaker 1: amount of the circuitry of the brain is about other people.

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Speaker 1: And we have thousands of models in our heads of

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Speaker 1: other people. I mean, and I know a bunch of

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Speaker 1: people in common, and for each of us, we've got

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Speaker 1: a little model. That person's like a little you know, mannequin,

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Speaker 1: and oh, how would that person spotify I say this

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Speaker 1: or that or I call them? And it's weird how

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Speaker 1: many people we can simulate pretty well. So we're living

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Speaker 1: in this world where we care so much about other people.

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Speaker 1: And I agree with you that a lot of that

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Speaker 1: information is not the type of thing that AI can

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Speaker 1: pick up on, because it's very subtle how humans interact

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Speaker 1: with other humans as opposed to just data that can

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Speaker 1: be gathered.

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Speaker 2: Yeah, when I lived in Japan and one of the

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Speaker 2: things I saw there up close was a lot of

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Speaker 2: like how Asian business culture works. And you have two

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Speaker 2: companies that seem that they're going to do a partnership

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Speaker 2: that are very much the same on paper, but then

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Speaker 2: they want to hang out socially. And that happens here too,

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Speaker 2: is that I have an investment fund and we get

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Speaker 2: some of our partnerships come from they look at the paper,

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Speaker 2: look at the data room. We go, this is great,

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Speaker 2: but let's hang out and let's talk to each other

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Speaker 2: and see if our values aligned. I think we forget

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Speaker 2: too that our economy is not really the exchange of dollars,

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Speaker 2: is the exchange of values, and not every value is

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Speaker 2: easily converted into a dollar point. And there are people

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Speaker 2: that you have a lot of ways in which you

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Speaker 2: can spend your time, and there are probably more efficient ways,

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Speaker 2: ways that could be more financially profitable towards you, but

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Speaker 2: you choose not to because you say, I get more

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Speaker 2: value out of this than he and we're all like that,

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Speaker 2: and I think we forget that, and we think about

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Speaker 2: automating the economy, and I think that there are going

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Speaker 2: to be parts of what we do. But an example

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Speaker 2: give you is that I did an interview with Yaka Potsky,

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Speaker 2: who is the head scientist at Openingie and Yaka is brilliant,

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Speaker 2: absolutely brilliant, one of the smartest people in the world

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Speaker 2: in my opinion, and we were talking about a teacher

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Speaker 2: he had. He went to a magnet cool school for

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Speaker 2: computer science, and I said, you know, we're seeing AI

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Speaker 2: as this wonderful tutor. Do you see this point where

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Speaker 2: AI replaces teachers? And yakub understand whose job is to

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Speaker 2: get AI to be as useful and as widely deployed

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Speaker 2: as possible, laughed at that idea because he pointed out

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Speaker 2: his favorite teacher inspired him and it inspired him because

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Speaker 2: that person had a real experience. And it's one thing

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Speaker 2: chat GPT can be, as you know, sycophantic or whatever

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Speaker 2: we talk about it is you want end of the day,

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Speaker 2: it starts to feel like okay, but you know, how

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Speaker 2: do you really feel? And having real experience can make

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Speaker 2: something give us more value.

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Speaker 1: And I think that's something we overlook.

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Speaker 2: That's why I think that I think the classroom AI

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Speaker 2: is going to be super beneficial. We're already seeing the

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Speaker 2: examples that is extremely helpful, but the end of the day,

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Speaker 2: I want to have a human there with real experience

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Speaker 2: that's also going to encourage me and tell me, like

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Speaker 2: I love memory techniques. One of my favorite teachers to

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Speaker 2: Anthony Mativier. He does things on memory on YouTube and whatnot.

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Speaker 2: And I can ask jat GPT about memory, which I

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Speaker 2: do a lot, but listening to somebody who actually tried

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Speaker 2: the methods is much more useful to me in the

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Speaker 2: long run.

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Speaker 1: Why because you get to hear what went wrong and what.

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Speaker 2: Because I U, yeah, it's taking coaching advice from somebody

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Speaker 2: who never played football versus someone who actually played the game.

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Speaker 2: And then memory methods like somebody who actually has real

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Speaker 2: experience doing the thing.

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Speaker 1: That's got a lot of value.

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Speaker 2: And I think that we think about like how much

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Speaker 2: of what we want is I need proof that it worked.

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Speaker 2: And the ultimate laboratory is human experience when it comes

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Speaker 2: to trying to figure out what to do with your

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Speaker 2: own experience.

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Speaker 1: You know, my thirteen year old boy I asked him, Hey,

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Speaker 1: if there were a tutor of Aristotle, he knows everything

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Speaker 1: Aristotle knows, you know, plus everything else, would that be

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Speaker 1: really interesting for you? And he said no, not at all.

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Speaker 1: And I said why, My thirteen year old's really smart.

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Speaker 1: But he wasn't interested because he said, you know, I

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Speaker 1: want to spend time with my friends, and that's much

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Speaker 1: more interesting than sitting with Aristotle and asking questions and

436
00:22:31,440 --> 00:22:33,959
Speaker 1: getting chet GPT answers back from that model.

437
00:22:34,240 --> 00:22:36,560
Speaker 2: Yeah, I think it's a balance. I think that you

438
00:22:36,600 --> 00:22:38,040
Speaker 2: have to figure out where you want to get those

439
00:22:38,040 --> 00:22:40,040
Speaker 2: things from and end of the day, he wants to

440
00:22:40,080 --> 00:22:40,639
Speaker 2: be human.

441
00:22:40,960 --> 00:22:44,040
Speaker 1: Yeah, yeah, exactly. Okay, so when we think about AI

442
00:22:44,240 --> 00:22:48,359
Speaker 1: affecting the economy, what do you see then, given what

443
00:22:48,400 --> 00:22:51,360
Speaker 1: we talked about that, you know, we care about other humans,

444
00:22:51,720 --> 00:22:55,199
Speaker 1: all the social networking that's not going away, does it

445
00:22:55,240 --> 00:22:59,280
Speaker 1: get enhanced, does it get suppressed turn in a different direction.

446
00:23:00,160 --> 00:23:02,159
Speaker 2: I think it's a mixed bag. I think, like anything,

447
00:23:02,160 --> 00:23:04,639
Speaker 2: there's good and there's bad. I think that you know,

448
00:23:04,960 --> 00:23:08,280
Speaker 2: when you have the opportunity to, you know, use these

449
00:23:08,280 --> 00:23:10,639
Speaker 2: tools to sort of amplify yourself, it's great if you

450
00:23:10,680 --> 00:23:12,560
Speaker 2: just try to say I'm going to outsource everything.

451
00:23:12,320 --> 00:23:15,160
Speaker 1: I do to the tool. That's going to be a challenge.

452
00:23:15,200 --> 00:23:17,679
Speaker 2: And I look at jobs that are at risk I

453
00:23:17,720 --> 00:23:20,080
Speaker 2: describe as like black box jobs. You know, anything where

454
00:23:20,080 --> 00:23:22,040
Speaker 2: you just get an email in and you send something

455
00:23:22,040 --> 00:23:25,600
Speaker 2: out and nobody cares who does it. That's really headed

456
00:23:25,640 --> 00:23:27,800
Speaker 2: towards replacement from AI. And we see that with like

457
00:23:27,920 --> 00:23:30,960
Speaker 2: call centers and things like that, where it's really if

458
00:23:31,000 --> 00:23:33,800
Speaker 2: it doesn't matter who's in that role, and we're going

459
00:23:33,840 --> 00:23:35,240
Speaker 2: to see that. You know, you're going to find fewer

460
00:23:35,280 --> 00:23:37,480
Speaker 2: people at fast food restaurants. But also those are jobs

461
00:23:37,480 --> 00:23:39,360
Speaker 2: too that the average time somebody stays on one of those

462
00:23:39,400 --> 00:23:41,879
Speaker 2: jobs is like eighteen months. Those are jobs are more

463
00:23:41,920 --> 00:23:44,200
Speaker 2: of a rung on a ladder. So I certainly think

464
00:23:44,200 --> 00:23:46,800
Speaker 2: that bottom parts of the ladder we might see changes there.

465
00:23:47,040 --> 00:23:49,120
Speaker 2: But I also think that we do invent new rungs

466
00:23:49,160 --> 00:23:50,960
Speaker 2: at the top, which is what we've done historically. I

467
00:23:51,000 --> 00:23:52,680
Speaker 2: think that we're going to have more science. I've talked

468
00:23:52,680 --> 00:23:54,920
Speaker 2: to people who are in AI who talk about AI

469
00:23:54,960 --> 00:23:57,199
Speaker 2: scientists say what happens when AI replaced scientists. I think

470
00:23:57,200 --> 00:23:59,480
Speaker 2: I think we're going to have more scientists because one

471
00:23:59,560 --> 00:24:02,680
Speaker 2: human scientists will able to do so much more than

472
00:24:02,680 --> 00:24:05,560
Speaker 2: they could before. I think with teachers too, we need

473
00:24:05,600 --> 00:24:08,280
Speaker 2: more teachers. I would like more teachers in my life.

474
00:24:08,480 --> 00:24:11,320
Speaker 1: Yeah, I'm amazed when I look back at scientific projects

475
00:24:11,720 --> 00:24:16,879
Speaker 1: like William Bragg who spent years crystallizing the first protein

476
00:24:17,440 --> 00:24:20,480
Speaker 1: and figuring out the structure of it. Took him whatever,

477
00:24:20,600 --> 00:24:22,560
Speaker 1: five ten years and won the Nobel Prize for that.

478
00:24:22,640 --> 00:24:25,200
Speaker 1: But now alpha fold, does you know, three hundred thousand

479
00:24:25,200 --> 00:24:28,360
Speaker 1: proteins in the blink of an I. Everything speeds up

480
00:24:28,440 --> 00:24:30,879
Speaker 1: like that, and that gives the opportunity for people to

481
00:24:30,920 --> 00:24:34,280
Speaker 1: work on projects like alpha fold and go much faster.

482
00:24:34,840 --> 00:24:37,879
Speaker 2: Well, I give you an example about that. So last

483
00:24:37,960 --> 00:24:40,320
Speaker 2: night I was over at the Chans Zuckerberg Institute, which

484
00:24:40,359 --> 00:24:44,520
Speaker 2: is Mark Zuckerberg and Priscilla Chands Institute for Research and Biology.

485
00:24:44,560 --> 00:24:46,480
Speaker 2: And I have some friends with a company called Evolutionary

486
00:24:46,480 --> 00:24:48,600
Speaker 2: Scale or working on a protein model that all model

487
00:24:48,600 --> 00:24:51,159
Speaker 2: call a ESM three and models that were able to

488
00:24:51,160 --> 00:24:55,119
Speaker 2: predict protein structures. They got basically acquired by Biohub now,

489
00:24:55,160 --> 00:24:59,040
Speaker 2: which is the institute runs that to basically oversee, you know,

490
00:24:59,080 --> 00:25:01,600
Speaker 2: and help deploy and help move fast or with AI

491
00:25:01,840 --> 00:25:02,960
Speaker 2: in the biology space.

492
00:25:03,200 --> 00:25:05,439
Speaker 1: So chan Zuckerberg took that company off.

493
00:25:05,520 --> 00:25:07,960
Speaker 2: Yeah, yeah, yeah, they brought them on board. Alex Rouves

494
00:25:08,080 --> 00:25:10,040
Speaker 2: was the head of elflishing Scales now going to be

495
00:25:10,119 --> 00:25:12,280
Speaker 2: running bio how much's exciting. But one of the things

496
00:25:12,320 --> 00:25:13,720
Speaker 2: I think about is that you know they're looking at

497
00:25:13,800 --> 00:25:16,840
Speaker 2: how they deploy AI whatever. You walk around the facility

498
00:25:16,880 --> 00:25:19,320
Speaker 2: and then you see a room, several rooms with really

499
00:25:19,520 --> 00:25:23,760
Speaker 2: sophisticated electron microscopes, and you realize like, oh, they need

500
00:25:23,800 --> 00:25:26,040
Speaker 2: more of these, they need more people running these things.

501
00:25:26,040 --> 00:25:27,040
Speaker 1: We ned way more of that.

502
00:25:27,160 --> 00:25:30,240
Speaker 2: And even if they had upstairs an AI scientist that

503
00:25:30,320 --> 00:25:32,280
Speaker 2: was able to do this stuff, you start to see like, well,

504
00:25:32,320 --> 00:25:33,680
Speaker 2: how much more data could we get?

505
00:25:33,720 --> 00:25:34,640
Speaker 1: And you just see that like.

506
00:25:34,640 --> 00:25:38,240
Speaker 2: Oh, it's not like I have an AI scientist. Scientist solved.

507
00:25:38,280 --> 00:25:40,880
Speaker 2: It's like I have an AI scientist. Now science really begins.

508
00:25:41,400 --> 00:25:44,959
Speaker 1: Oh that's interesting because you could in theory automate all

509
00:25:44,960 --> 00:25:47,720
Speaker 1: the electron microscopy and you could you can imagine ways

510
00:25:47,760 --> 00:25:52,639
Speaker 1: that that becomes like a dark factory where it's running itself,

511
00:25:53,880 --> 00:25:58,119
Speaker 1: where the AI generates hypotheses, goes tests, all the stuff.

512
00:25:58,080 --> 00:26:01,080
Speaker 2: But humans, like how many experiments you be running right now?

513
00:26:01,160 --> 00:26:03,280
Speaker 2: If you could just pull up chat GPT and say Hey,

514
00:26:03,320 --> 00:26:05,119
Speaker 2: I want to go run this experiment.

515
00:26:05,119 --> 00:26:08,119
Speaker 1: Lots Yeah, okay, And your point is then lots of

516
00:26:08,119 --> 00:26:09,800
Speaker 1: other people would come into science to do that.

517
00:26:10,000 --> 00:26:13,280
Speaker 2: Your bottleneck isn't there's your bottleneck isn't you don't have

518
00:26:13,400 --> 00:26:16,320
Speaker 2: enough ideas for things you want to experience. Your bottleneck

519
00:26:16,400 --> 00:26:17,240
Speaker 2: is the resources.

520
00:26:17,560 --> 00:26:19,919
Speaker 1: Yeah, that's right. Yeah, I totally agree with you on that.

521
00:26:20,600 --> 00:26:22,920
Speaker 1: What's interesting is I wonder. I wonder often if there

522
00:26:23,000 --> 00:26:26,200
Speaker 1: is sort of an answer or an end to science

523
00:26:26,280 --> 00:26:28,840
Speaker 1: where we say, okay, look we've got this in place,

524
00:26:28,880 --> 00:26:32,040
Speaker 1: that in place, we know these forces that structure. We're done,

525
00:26:32,080 --> 00:26:35,640
Speaker 1: We're out of here. I have my doubts because, in fact,

526
00:26:35,640 --> 00:26:37,919
Speaker 1: they just did a podcast on this recently with the

527
00:26:38,160 --> 00:26:41,840
Speaker 1: particle physicist Daniel Whitson about this question of is the

528
00:26:41,880 --> 00:26:44,560
Speaker 1: way that we construct the laws of physics does it

529
00:26:44,600 --> 00:26:48,120
Speaker 1: have to do with our sensation and our cognition, our

530
00:26:48,680 --> 00:26:51,199
Speaker 1: veldt meaning what we can sense in the world. And

531
00:26:51,280 --> 00:26:54,840
Speaker 1: if you came across aliens who saw a totally different world,

532
00:26:55,240 --> 00:26:58,560
Speaker 1: would they possibly come up with different laws of physics

533
00:26:59,080 --> 00:27:01,359
Speaker 1: and not use fee but they, you know, have a

534
00:27:01,440 --> 00:27:03,880
Speaker 1: very different way of seeing it. So maybe that's where

535
00:27:03,920 --> 00:27:06,919
Speaker 1: science will go, is looking at alternative ways that we

536
00:27:07,000 --> 00:27:08,199
Speaker 1: could have talked about it.

537
00:27:08,280 --> 00:27:10,080
Speaker 2: Yeah, I mean there might there might be a point

538
00:27:10,119 --> 00:27:13,000
Speaker 2: at which we say we've solved all the things that

539
00:27:13,040 --> 00:27:16,200
Speaker 2: you can explain to our monkey brains, but we haven't

540
00:27:16,240 --> 00:27:19,320
Speaker 2: solved for everything, Like like there's there's going to be

541
00:27:19,400 --> 00:27:22,600
Speaker 2: things as the universe gets older, things which conditions will

542
00:27:22,640 --> 00:27:25,399
Speaker 2: change and whatnot. We look at this in life sciences.

543
00:27:25,480 --> 00:27:27,199
Speaker 2: I have some friends that are involved in AI and

544
00:27:27,200 --> 00:27:29,640
Speaker 2: life sciences who are very excited and who are very

545
00:27:29,720 --> 00:27:31,480
Speaker 2: very bullish on where this is going to head, Like

546
00:27:31,760 --> 00:27:33,840
Speaker 2: what happens when we cure health and we don't need

547
00:27:33,880 --> 00:27:36,640
Speaker 2: doctors anymore. I'm like, we're gonna be worrying about longevity

548
00:27:36,640 --> 00:27:38,960
Speaker 2: research on Mars. You know, We're gonna worry about long

549
00:27:39,160 --> 00:27:41,720
Speaker 2: long distance travel, like how do you maintain brain function

550
00:27:41,760 --> 00:27:44,160
Speaker 2: when you're five hundred years old? Like, I don't see

551
00:27:44,160 --> 00:27:47,199
Speaker 2: those questions ending because we're just gonna Expand you know,

552
00:27:47,280 --> 00:27:50,200
Speaker 2: the Greeks thought that, oh, maybe science is pretty solvable

553
00:27:50,640 --> 00:27:52,719
Speaker 2: because they looked at a very simple framework, but they

554
00:27:52,760 --> 00:27:56,040
Speaker 2: weren't asking bigger questions about a lot of things. They

555
00:27:56,040 --> 00:27:57,800
Speaker 2: just thought these things couldn't even be answered. And now

556
00:27:57,840 --> 00:28:00,960
Speaker 2: we're like oh why not. What's ex exciting about watching

557
00:28:01,040 --> 00:28:03,840
Speaker 2: this happen is that when you talk to people in

558
00:28:03,880 --> 00:28:05,919
Speaker 2: biology tell you how much what proteins can do, when

559
00:28:05,920 --> 00:28:07,920
Speaker 2: you start to figure out how you can start structuring

560
00:28:07,960 --> 00:28:11,560
Speaker 2: proteins to do things like breakdown plastics or attack certain

561
00:28:11,600 --> 00:28:14,520
Speaker 2: parts of cancer, it's a very very interesting area that

562
00:28:14,520 --> 00:28:16,400
Speaker 2: we're just now starting to see. And I think there's

563
00:28:16,440 --> 00:28:18,640
Speaker 2: a lot of optimism here that we're going to see

564
00:28:18,680 --> 00:28:21,760
Speaker 2: things rapidly develop because we saw what happened to language

565
00:28:21,800 --> 00:28:24,000
Speaker 2: model space. There's a lot of challenge in getting life

566
00:28:24,040 --> 00:28:27,000
Speaker 2: science as data inside of these models, but we see

567
00:28:27,119 --> 00:28:28,800
Speaker 2: really good science that that's going to happen.

568
00:28:32,119 --> 00:28:34,040
Speaker 1: Okay, so what do you think in terms of what

569
00:28:34,119 --> 00:28:36,840
Speaker 1: AI will be able to and won't be able to do.

570
00:28:37,480 --> 00:28:40,239
Speaker 2: I think that any task that you can measure that

571
00:28:40,280 --> 00:28:42,160
Speaker 2: somebody could walk into a room and take a test,

572
00:28:42,800 --> 00:28:45,480
Speaker 2: AI will eventually be able to do that, I think rapidly.

573
00:28:45,520 --> 00:28:48,200
Speaker 2: So I think that's very likely the case. I think

574
00:28:48,240 --> 00:28:50,480
Speaker 2: we're going to see a fast follow for robotics.

575
00:28:50,560 --> 00:28:51,120
Speaker 1: I don't think.

576
00:28:51,240 --> 00:28:53,440
Speaker 2: I think we're going to see probably the next eighteen months,

577
00:28:53,520 --> 00:28:55,600
Speaker 2: some really interesting things. We've already seen some things with

578
00:28:56,040 --> 00:29:00,520
Speaker 2: kind of machine intelligence and problem solving. But there's one thing.

579
00:29:00,680 --> 00:29:03,240
Speaker 2: It's one thing to have like a highly intelligent system.

580
00:29:03,360 --> 00:29:05,400
Speaker 2: It's another thing to have a system that works within

581
00:29:05,440 --> 00:29:09,200
Speaker 2: an entire ecosystem or culture or society, and we just

582
00:29:09,440 --> 00:29:13,040
Speaker 2: often just forget about how important that is of where

583
00:29:13,160 --> 00:29:16,040
Speaker 2: value comes from that. So I also think that like

584
00:29:16,080 --> 00:29:18,600
Speaker 2: we talked about before with authors, is I might have

585
00:29:18,680 --> 00:29:20,640
Speaker 2: an AI that's an incredible guitarist.

586
00:29:20,920 --> 00:29:23,000
Speaker 1: Am I going to be excited to see that perform?

587
00:29:23,200 --> 00:29:27,840
Speaker 2: Because the problem with AI is this is that because

588
00:29:28,320 --> 00:29:32,000
Speaker 2: we often value creativity because of the limitations, but when

589
00:29:32,040 --> 00:29:34,560
Speaker 2: there's no limitation to the amount of effort or energy

590
00:29:34,640 --> 00:29:37,440
Speaker 2: or resources it goes into an AI system. An AI

591
00:29:37,480 --> 00:29:39,640
Speaker 2: guitarist would be like, well, of course that's cool. I

592
00:29:39,680 --> 00:29:41,520
Speaker 2: can listen to a CD and do the same thing.

593
00:29:41,640 --> 00:29:44,560
Speaker 2: But I want to know that this biological system called

594
00:29:44,560 --> 00:29:47,720
Speaker 2: a human had to spend fifty years to get there

595
00:29:47,760 --> 00:29:50,160
Speaker 2: to do that, and that creates more value because it's

596
00:29:50,200 --> 00:29:52,920
Speaker 2: not just the outcome, it's what went in to make it.

597
00:29:53,120 --> 00:29:55,120
Speaker 2: I think a lot of people have anxiety about like

598
00:29:55,200 --> 00:29:58,760
Speaker 2: AI replacing their job and what they do, and I

599
00:29:58,800 --> 00:30:01,280
Speaker 2: think that we have to sort of step back and

600
00:30:01,400 --> 00:30:04,200
Speaker 2: often remember that a job is about an outcome, and

601
00:30:04,280 --> 00:30:06,600
Speaker 2: how you get that outcome is going to change over time.

602
00:30:06,640 --> 00:30:10,040
Speaker 2: It's changed historically, and often the thing that you're trying

603
00:30:10,080 --> 00:30:12,800
Speaker 2: to do is create some sort of value, and it's

604
00:30:12,800 --> 00:30:14,840
Speaker 2: not always economic, or it can be. It can be

605
00:30:14,840 --> 00:30:16,680
Speaker 2: put into economic terms, but we have to sort of

606
00:30:16,680 --> 00:30:19,280
Speaker 2: think back, like why do you what is the value

607
00:30:19,320 --> 00:30:21,480
Speaker 2: of a book? You know, and you know, put twenty

608
00:30:21,520 --> 00:30:23,240
Speaker 2: dollars on the spine of a book and say this

609
00:30:23,240 --> 00:30:25,840
Speaker 2: book's worth twenty dollars. I've read books that if you

610
00:30:25,880 --> 00:30:27,080
Speaker 2: told me what I was going to get out of it,

611
00:30:27,080 --> 00:30:27,440
Speaker 2: I would have.

612
00:30:27,440 --> 00:30:28,280
Speaker 1: Paid a lot more.

613
00:30:28,800 --> 00:30:30,840
Speaker 2: You know. I've had businesses with friends that like, yeah,

614
00:30:30,840 --> 00:30:32,840
Speaker 2: we made money, but really the experience of working with

615
00:30:32,920 --> 00:30:36,080
Speaker 2: everybody was way more valuable. And I think that people

616
00:30:36,120 --> 00:30:38,120
Speaker 2: have anxiety about this future of like what happens or

617
00:30:38,200 --> 00:30:39,840
Speaker 2: robots do everything, Like I don't think it'll be as

618
00:30:39,840 --> 00:30:41,920
Speaker 2: fun and I don't think it's going to create as

619
00:30:42,000 --> 00:30:44,040
Speaker 2: much value as people think that it will. And that's

620
00:30:44,080 --> 00:30:46,560
Speaker 2: something I mentioned to before we recorded, was you know,

621
00:30:46,600 --> 00:30:48,840
Speaker 2: this is a statistic that's roughly like forty percent of

622
00:30:48,880 --> 00:30:52,520
Speaker 2: all Internet traffic after six pm is like Netflix, and

623
00:30:52,560 --> 00:30:55,720
Speaker 2: that's because of squid games, you know, and K pop

624
00:30:55,760 --> 00:30:58,440
Speaker 2: Demon Hunters, and when you take those things out, those

625
00:30:58,480 --> 00:31:00,520
Speaker 2: things that are really valuable because people like them, when

626
00:31:00,520 --> 00:31:03,120
Speaker 2: people created them. What's the value of the Internet after

627
00:31:03,160 --> 00:31:05,240
Speaker 2: six pm? Well we just lost forty percent of the

628
00:31:05,280 --> 00:31:05,960
Speaker 2: value right there.

629
00:31:06,160 --> 00:31:09,440
Speaker 1: Okay, but what about aifilm. Do you think people will

630
00:31:09,440 --> 00:31:12,800
Speaker 1: spend their time watching AI film instead of Kiepop, Demon

631
00:31:12,880 --> 00:31:14,000
Speaker 1: Hunters and squid get.

632
00:31:14,080 --> 00:31:17,240
Speaker 2: Sometimes yes, sometimes no. I don't think anybody under six

633
00:31:17,320 --> 00:31:19,280
Speaker 2: is going to care, you know, how it was created.

634
00:31:19,440 --> 00:31:22,040
Speaker 2: I think that I think that we're going to really

635
00:31:22,120 --> 00:31:25,800
Speaker 2: value certain things. When ever, Tom Cruise wants to market

636
00:31:25,880 --> 00:31:28,840
Speaker 2: a Mission Impossible movie. Part of the PR campaign is

637
00:31:28,840 --> 00:31:31,000
Speaker 2: about the stunt that he performed. You have the last

638
00:31:31,000 --> 00:31:33,040
Speaker 2: movie he did, He hung outside of in an airplane.

639
00:31:33,080 --> 00:31:34,960
Speaker 2: He did some airplane stunts. He's done some other really

640
00:31:35,000 --> 00:31:37,640
Speaker 2: cool stuff. When they did Top Gun, you know, the

641
00:31:37,840 --> 00:31:39,640
Speaker 2: Top Gun Maverick, they made it a point of putting

642
00:31:39,640 --> 00:31:41,880
Speaker 2: the actors into actual real planes so we could get

643
00:31:41,880 --> 00:31:45,320
Speaker 2: their reactions, and this was added value. If we watched

644
00:31:45,360 --> 00:31:47,920
Speaker 2: a PR video talk about look how great these AI

645
00:31:48,040 --> 00:31:50,360
Speaker 2: stunt doubles are, which we got like back in the nineties,

646
00:31:50,480 --> 00:31:53,120
Speaker 2: or like we don't care. That's not as interesting to

647
00:31:53,200 --> 00:31:55,640
Speaker 2: us as the fact that a human really did something special.

648
00:31:56,000 --> 00:31:57,560
Speaker 2: I think there are going to be films where it's

649
00:31:57,560 --> 00:31:59,360
Speaker 2: going to be three three high school students are going

650
00:31:59,360 --> 00:32:01,080
Speaker 2: to make a movie in Ai and we're like, this

651
00:32:01,120 --> 00:32:03,640
Speaker 2: is amazing, and then we're going to still want, you know,

652
00:32:03,720 --> 00:32:07,000
Speaker 2: germal Deteruro to hate on AI and go build these wonderful,

653
00:32:07,040 --> 00:32:09,080
Speaker 2: lavish productions where he does it his way like a

654
00:32:09,120 --> 00:32:09,760
Speaker 2: crafts person.

655
00:32:10,160 --> 00:32:12,600
Speaker 1: I think it's both. Yeah, I think it's a really

656
00:32:12,600 --> 00:32:15,520
Speaker 1: smart way of looking at it is in the mixture

657
00:32:16,160 --> 00:32:17,960
Speaker 1: model of what the future is going to look like.

658
00:32:18,280 --> 00:32:20,960
Speaker 1: So if we're just looking purely at economic value, where

659
00:32:21,000 --> 00:32:22,520
Speaker 1: do you think the biggest changes are going to be

660
00:32:22,520 --> 00:32:23,840
Speaker 1: there in the next five ten years.

661
00:32:24,080 --> 00:32:26,000
Speaker 2: One of the things that's happened that people have overlooked

662
00:32:26,040 --> 00:32:29,360
Speaker 2: is that every six months or so, it's you get

663
00:32:29,360 --> 00:32:32,080
Speaker 2: better answers from chat GPT or geminarra claud when it

664
00:32:32,080 --> 00:32:35,200
Speaker 2: comes to medical questions, right, and we've had the first

665
00:32:35,200 --> 00:32:40,080
Speaker 2: time in history medical information has gotten cheaper year over year,

666
00:32:40,760 --> 00:32:44,959
Speaker 2: and that we can't really I think overstate how significant

667
00:32:44,960 --> 00:32:47,280
Speaker 2: that is. You think about that, like getting competent medical

668
00:32:47,320 --> 00:32:50,080
Speaker 2: information it gets better every year, and it gets cheaper

669
00:32:50,120 --> 00:32:52,080
Speaker 2: every year, and that's going to apply to a lot

670
00:32:52,120 --> 00:32:54,200
Speaker 2: of things. And so I think that when we start

671
00:32:54,240 --> 00:32:56,120
Speaker 2: to think about you know, what does that mean, Well,

672
00:32:56,120 --> 00:32:58,480
Speaker 2: other kinds of information to get cheap, really great answers

673
00:32:58,520 --> 00:33:01,840
Speaker 2: are going to be less expensive. And some people say,

674
00:33:01,880 --> 00:33:03,600
Speaker 2: well great, who's going to need doctors. I think I

675
00:33:03,600 --> 00:33:05,520
Speaker 2: think we're going to actually want to use doctors for

676
00:33:05,560 --> 00:33:07,200
Speaker 2: a lot more areas of life. You might be able

677
00:33:07,240 --> 00:33:09,040
Speaker 2: to spend more than two minutes with your doctor. Now

678
00:33:09,080 --> 00:33:11,280
Speaker 2: that's nobody says I spend too much time talking to

679
00:33:11,320 --> 00:33:13,840
Speaker 2: my doctor. Everybody's like I don't get enough time. And

680
00:33:13,840 --> 00:33:15,800
Speaker 2: I think that we're going to start to look at like, Okay,

681
00:33:15,840 --> 00:33:18,560
Speaker 2: what do we really want. I think that some areas

682
00:33:18,600 --> 00:33:21,160
Speaker 2: that are easily automated, I've mentioned for it, call centers,

683
00:33:21,160 --> 00:33:23,760
Speaker 2: things like this. Yeah, that part may go away, and

684
00:33:23,800 --> 00:33:25,880
Speaker 2: we're going to have to start thinking about encouraging more

685
00:33:25,920 --> 00:33:27,600
Speaker 2: people to take on the kinds of roles that we

686
00:33:27,640 --> 00:33:30,280
Speaker 2: want to spend more time with. We need more nurses,

687
00:33:30,400 --> 00:33:32,600
Speaker 2: we need more people who actually need more people build

688
00:33:32,600 --> 00:33:34,720
Speaker 2: things like you know people. I've got into discussion on

689
00:33:34,760 --> 00:33:36,960
Speaker 2: Twitter where somebody says well, what would you to tell

690
00:33:36,960 --> 00:33:39,520
Speaker 2: an air conditioning repair person who's their job is going

691
00:33:39,560 --> 00:33:41,120
Speaker 2: to be you know threat? And I'm like, I don't

692
00:33:41,120 --> 00:33:43,120
Speaker 2: know if you know this, but we're running out of

693
00:33:43,160 --> 00:33:45,680
Speaker 2: AC repair people right now because not enough people are

694
00:33:45,680 --> 00:33:48,120
Speaker 2: coming into the workforce to replace the ones that are retiring.

695
00:33:48,440 --> 00:33:50,480
Speaker 2: And that's true of a lot of these other industries

696
00:33:50,560 --> 00:33:53,120
Speaker 2: right now. And even when we get to robotics, Like,

697
00:33:53,360 --> 00:33:55,080
Speaker 2: when we get into robotics, like that's going to be

698
00:33:55,080 --> 00:33:58,600
Speaker 2: exciting because nobody steps outside and goes, oh, everything's perfect.

699
00:33:58,640 --> 00:34:00,240
Speaker 2: What would I fix in this role to go There's

700
00:34:00,280 --> 00:34:02,200
Speaker 2: so many things we could fix, some things we could pair,

701
00:34:02,240 --> 00:34:04,200
Speaker 2: some things we could make better. So I think that

702
00:34:04,240 --> 00:34:06,440
Speaker 2: we're going to be thinking a lot more about how

703
00:34:06,480 --> 00:34:09,120
Speaker 2: more people could become builders, how more people could create

704
00:34:09,160 --> 00:34:11,000
Speaker 2: things that should exist. And I think that's where I

705
00:34:11,040 --> 00:34:12,879
Speaker 2: have to think about, Like from a career point of view,

706
00:34:13,280 --> 00:34:15,000
Speaker 2: the one things I tell people is like, think about

707
00:34:15,000 --> 00:34:18,040
Speaker 2: marketing groups. Think about your ability to work with other people,

708
00:34:18,239 --> 00:34:20,279
Speaker 2: you know, create groups, Think about people, Think about kind

709
00:34:20,280 --> 00:34:22,080
Speaker 2: of problems you can collectively try to solve together.

710
00:34:38,480 --> 00:34:41,480
Speaker 1: It's interesting to see that blue collar jobs are the

711
00:34:41,480 --> 00:34:44,080
Speaker 1: ones that everyone knows are going to survive the plumbers,

712
00:34:44,120 --> 00:34:47,880
Speaker 1: the electricians, the air conditioner repairment, and we certainly wouldn't

713
00:34:47,920 --> 00:34:51,160
Speaker 1: have expected that. It does make me wonder though, if

714
00:34:51,200 --> 00:34:54,799
Speaker 1: in one hundred years from now, air conditioners get designed

715
00:34:55,239 --> 00:34:57,440
Speaker 1: in such a way that they are meant for robots

716
00:34:57,480 --> 00:34:59,040
Speaker 1: to fix them instead of having to.

717
00:35:00,239 --> 00:35:01,920
Speaker 2: I think, I absolutely think in this I think that

718
00:35:02,000 --> 00:35:04,000
Speaker 2: we're going to see robotics do a lot of those

719
00:35:04,040 --> 00:35:06,520
Speaker 2: things in a shorter term. But I also think of

720
00:35:06,640 --> 00:35:08,600
Speaker 2: the things that we think are possible to build right

721
00:35:08,640 --> 00:35:10,319
Speaker 2: now in the middle of this data center build out,

722
00:35:10,360 --> 00:35:13,000
Speaker 2: building new data centers and goals like Sam Alan's talked

723
00:35:13,040 --> 00:35:15,719
Speaker 2: about building one gig a lot of compute per week

724
00:35:15,760 --> 00:35:19,319
Speaker 2: and that's basically a city sized amount of our city

725
00:35:19,400 --> 00:35:21,560
Speaker 2: my sized amount of energy per week because of what's

726
00:35:21,600 --> 00:35:24,440
Speaker 2: possible there, which means that, you know, what, what is

727
00:35:24,480 --> 00:35:26,279
Speaker 2: the big part of the future economy. It's going to

728
00:35:26,320 --> 00:35:28,239
Speaker 2: be come down to energy production. It's going to come

729
00:35:28,280 --> 00:35:29,920
Speaker 2: down to compute. And I think a lot of the

730
00:35:30,000 --> 00:35:32,280
Speaker 2: jobs we think about are going to be supporting building

731
00:35:32,280 --> 00:35:35,080
Speaker 2: that and also making use of that. So you know,

732
00:35:35,160 --> 00:35:37,799
Speaker 2: I think that you know, ac repair things like this,

733
00:35:38,200 --> 00:35:40,880
Speaker 2: those jobs are probably I think that people have those

734
00:35:40,960 --> 00:35:42,640
Speaker 2: jobs right now aren't going to have a problem. And

735
00:35:42,640 --> 00:35:44,480
Speaker 2: I think that younger people coming into it are going

736
00:35:44,520 --> 00:35:45,960
Speaker 2: to have a broader view of it. And I think

737
00:35:45,960 --> 00:35:48,440
Speaker 2: that the ac repairment a fifteen year rare person fifteen

738
00:35:48,480 --> 00:35:50,400
Speaker 2: years from now, you know, they're going to maybe be

739
00:35:50,480 --> 00:35:52,239
Speaker 2: in charge of a fleet of robots, and their job

740
00:35:52,320 --> 00:35:53,920
Speaker 2: is to be accountable. Is their job is to sell

741
00:35:53,960 --> 00:35:56,120
Speaker 2: them like, yes, we got the work done. As we

742
00:35:56,200 --> 00:35:58,960
Speaker 2: build you know, megaplex energy project number forty nine on

743
00:35:59,000 --> 00:36:03,600
Speaker 2: the moon. There are people that I that I know

744
00:36:03,800 --> 00:36:08,480
Speaker 2: and I really respect in AI who worry about job displacements,

745
00:36:08,480 --> 00:36:10,560
Speaker 2: which I worry, but they think, like what happens when

746
00:36:10,600 --> 00:36:13,520
Speaker 2: AI can do just about everything? And I'm like, we grow,

747
00:36:13,600 --> 00:36:16,600
Speaker 2: we get bigger. If you imagine AI becoming highly capable,

748
00:36:16,600 --> 00:36:19,680
Speaker 2: we have to also imagine the economy becoming highly scaled.

749
00:36:20,000 --> 00:36:21,880
Speaker 2: And when that happens, you're actually going to grow so

750
00:36:21,960 --> 00:36:23,640
Speaker 2: fast there won't be enough people to do the things

751
00:36:23,719 --> 00:36:26,560
Speaker 2: you want to get done. From you know, working in

752
00:36:26,920 --> 00:36:29,520
Speaker 2: you know, a data center, to working in a cafeteria

753
00:36:29,640 --> 00:36:31,760
Speaker 2: to make sure that you have you know, great sushi,

754
00:36:32,080 --> 00:36:34,280
Speaker 2: to figuring out what you do with all this compute

755
00:36:34,320 --> 00:36:37,320
Speaker 2: and if you really imagine what happens and work super ambitious.

756
00:36:38,080 --> 00:36:39,080
Speaker 1: That's that's amazing.

757
00:36:39,080 --> 00:36:41,240
Speaker 2: And I think if you looked at the founding fathers

758
00:36:41,400 --> 00:36:43,320
Speaker 2: of the United States, that they looked at the scale

759
00:36:43,360 --> 00:36:45,960
Speaker 2: at which we did things today, it would see unfathomable.

760
00:36:46,440 --> 00:36:48,680
Speaker 2: And we think that, oh if people often think of

761
00:36:48,719 --> 00:36:52,680
Speaker 2: the futures just shiny clothes and robots doing stuff, when

762
00:36:52,719 --> 00:36:53,960
Speaker 2: really the future is bigger.

763
00:36:54,120 --> 00:36:55,000
Speaker 1: Like we look back.

764
00:36:55,040 --> 00:36:56,759
Speaker 2: I tell me, if you look back one hundred years ago,

765
00:36:56,760 --> 00:36:59,200
Speaker 2: the first thing you'd realize is, oh, we're poor. Back

766
00:36:59,200 --> 00:37:01,640
Speaker 2: then one hundred years you'd feel like everybody was pretty poor.

767
00:37:02,160 --> 00:37:04,359
Speaker 2: That's for one hundred years from now people look back

768
00:37:04,400 --> 00:37:07,000
Speaker 2: at us and think, oh, you guys were poor, but

769
00:37:07,520 --> 00:37:08,000
Speaker 2: build in.

770
00:37:08,360 --> 00:37:10,160
Speaker 1: And you died young, and you had all kinds of

771
00:37:10,280 --> 00:37:14,600
Speaker 1: pand yeah. Do you have an opinion about universal basic income.

772
00:37:14,760 --> 00:37:17,359
Speaker 2: Other than it's a terrible and necessary idea, No, tell

773
00:37:17,400 --> 00:37:20,239
Speaker 2: me why. I mean, I am all for as you

774
00:37:20,239 --> 00:37:22,279
Speaker 2: can lower the cost of things. You know that the

775
00:37:22,280 --> 00:37:24,680
Speaker 2: problem people talk about universal health care is the problem

776
00:37:24,719 --> 00:37:27,440
Speaker 2: is that the cost of it accelerates faster than the

777
00:37:27,480 --> 00:37:30,520
Speaker 2: GDP does. And no matter whatever you come from political

778
00:37:30,560 --> 00:37:32,439
Speaker 2: side of the argument, you have to address the fact

779
00:37:32,440 --> 00:37:34,840
Speaker 2: that this thing gets faster than your economy can afford it,

780
00:37:34,880 --> 00:37:37,319
Speaker 2: then it's hard to make that sustainable. I think there's

781
00:37:37,360 --> 00:37:39,600
Speaker 2: a role where the economy grows so fast that you

782
00:37:39,640 --> 00:37:42,480
Speaker 2: can have a different conversation about that when it comes

783
00:37:42,480 --> 00:37:44,720
Speaker 2: to UBI because you think that people won't be needing

784
00:37:44,760 --> 00:37:46,480
Speaker 2: the economy. I don't think that's going to be the case.

785
00:37:46,520 --> 00:37:48,719
Speaker 2: And that's a scary place to be. When you say that, oh,

786
00:37:48,760 --> 00:37:52,319
Speaker 2: eight billion humans are you know, an ancillary to what

787
00:37:52,480 --> 00:37:55,360
Speaker 2: the world is. I don't think that I have trouble

788
00:37:55,400 --> 00:37:57,640
Speaker 2: fathing that being true. And also it's uncomfortable to think

789
00:37:57,640 --> 00:37:59,520
Speaker 2: about that being true because once you say that they're

790
00:37:59,560 --> 00:38:03,520
Speaker 2: not necessar bad things happen. I think that I'm all

791
00:38:03,560 --> 00:38:06,080
Speaker 2: for ideas and ways in which we make sure everybody

792
00:38:06,120 --> 00:38:09,040
Speaker 2: gets their needs met. Nobody worries about housing, nobody worries

793
00:38:09,040 --> 00:38:11,760
Speaker 2: about healthcare, and words about food. I'm all for solutions

794
00:38:11,760 --> 00:38:14,239
Speaker 2: for that. But when we think that there's just not

795
00:38:14,320 --> 00:38:16,120
Speaker 2: going to be any jobs in the future, I think

796
00:38:16,280 --> 00:38:19,359
Speaker 2: historically that's not true. It's also, you know, I bring

797
00:38:19,400 --> 00:38:21,000
Speaker 2: this up, like, okay, so we can come up with

798
00:38:21,000 --> 00:38:23,279
Speaker 2: a super advanced day I, but you're telling me we

799
00:38:23,320 --> 00:38:25,759
Speaker 2: can't ask it to find out what's still useful work

800
00:38:25,800 --> 00:38:27,880
Speaker 2: for people like it won't be smart enough to figure

801
00:38:27,880 --> 00:38:31,120
Speaker 2: out new things. And we've continuously invented new jobs. Like

802
00:38:31,160 --> 00:38:33,560
Speaker 2: I brought up before, it's entertainment, which you would think

803
00:38:33,600 --> 00:38:35,959
Speaker 2: be one of the first things to go just gets

804
00:38:36,000 --> 00:38:38,600
Speaker 2: bigger in like education, we need more teachers, Like I

805
00:38:38,640 --> 00:38:41,400
Speaker 2: think that we as we live longer, healthier lives, one

806
00:38:41,440 --> 00:38:42,920
Speaker 2: of the ways we're going to want to avoid boredom

807
00:38:42,960 --> 00:38:44,799
Speaker 2: is we're going to want to continue our education. We're

808
00:38:44,840 --> 00:38:46,200
Speaker 2: going to go learn from these things, and we're going

809
00:38:46,239 --> 00:38:47,839
Speaker 2: to learn from people actually went there and did it.

810
00:38:48,239 --> 00:38:49,960
Speaker 2: You know, do you want to learn about Egyptian you know,

811
00:38:50,000 --> 00:38:52,719
Speaker 2: mythology from somebody ever went to Egypt? You know, do

812
00:38:52,719 --> 00:38:54,759
Speaker 2: you want to learn about practicing medicine from nobody ever

813
00:38:54,800 --> 00:38:58,880
Speaker 2: treated to patient? And so I'm very, very bullish on

814
00:38:58,960 --> 00:39:00,799
Speaker 2: the idea that we're going to need lots more people

815
00:39:00,840 --> 00:39:01,560
Speaker 2: in our economy.

816
00:39:01,880 --> 00:39:03,479
Speaker 1: You know, I was just hanging out with a friend

817
00:39:03,560 --> 00:39:06,839
Speaker 1: of mine from high school. We graduated together, and I

818
00:39:06,920 --> 00:39:09,319
Speaker 1: was thinking about the fact that what I do and

819
00:39:09,400 --> 00:39:11,759
Speaker 1: what he does, and what essentially everyone we know in

820
00:39:11,800 --> 00:39:14,960
Speaker 1: Sulkon Valley does these jobs didn't exist when we were

821
00:39:14,960 --> 00:39:17,719
Speaker 1: graduating high school. We couldn't have imagined the titles of

822
00:39:17,760 --> 00:39:23,240
Speaker 1: these jobs. And so my question is for education currently,

823
00:39:23,280 --> 00:39:26,560
Speaker 1: if you're thinking about junior high kids, high school kids,

824
00:39:26,600 --> 00:39:28,520
Speaker 1: what do you see is the important things that we

825
00:39:28,560 --> 00:39:31,080
Speaker 1: should be teaching them given that we are not training

826
00:39:31,080 --> 00:39:34,880
Speaker 1: them for jobs that we know of. So in two thousand,

827
00:39:34,920 --> 00:39:36,000
Speaker 1: I wanted to get into AI.

828
00:39:36,080 --> 00:39:37,879
Speaker 2: I really wanted to find a way into that right

829
00:39:38,400 --> 00:39:40,080
Speaker 2: because I knew that was the future, Like how could

830
00:39:40,120 --> 00:39:41,960
Speaker 2: I have this? And I decided this late in life,

831
00:39:42,000 --> 00:39:46,719
Speaker 2: comparatively for other people. And in twenty nineteen, Opening Eye

832
00:39:46,719 --> 00:39:48,560
Speaker 2: published they did a tweet and they talked about their

833
00:39:48,600 --> 00:39:51,200
Speaker 2: model GPT two and if you look at like one

834
00:39:51,239 --> 00:39:54,879
Speaker 2: of the most upvoted responses was mine at the time, going,

835
00:39:54,920 --> 00:39:56,719
Speaker 2: this is really amazing. I guess I'm out of work

836
00:39:56,719 --> 00:39:58,719
Speaker 2: as a novelist. Any suggestions to what I should do?

837
00:39:59,320 --> 00:40:02,279
Speaker 2: I had no no idea A year later I'd be

838
00:40:02,320 --> 00:40:05,560
Speaker 2: the world's first person employee as a prompt engineer. Because

839
00:40:05,600 --> 00:40:08,600
Speaker 2: what happened was that intersection of language and I had

840
00:40:08,640 --> 00:40:11,000
Speaker 2: an interest in AI, but I knew a lot about language.

841
00:40:11,000 --> 00:40:13,960
Speaker 2: All of a sudden, those two things collided and I

842
00:40:14,000 --> 00:40:16,160
Speaker 2: think that for people right now looking at what they

843
00:40:16,200 --> 00:40:17,920
Speaker 2: want to do in the future, take the things you're

844
00:40:17,960 --> 00:40:20,759
Speaker 2: passionate about right now and ask how does AI make

845
00:40:20,800 --> 00:40:23,160
Speaker 2: this better? Or how does AI make it worse? And

846
00:40:23,200 --> 00:40:25,600
Speaker 2: I think that you don't assume like well, AI is

847
00:40:25,640 --> 00:40:27,680
Speaker 2: going to replace this, Like no, AI is going to

848
00:40:27,880 --> 00:40:30,759
Speaker 2: change it. So, you know, I spend time talking to

849
00:40:30,800 --> 00:40:32,799
Speaker 2: college kids, and one of the things that concerns me

850
00:40:32,880 --> 00:40:34,920
Speaker 2: is that a number of kids in computer science programs

851
00:40:34,920 --> 00:40:37,640
Speaker 2: aren't even taught how to use AI code tools. I

852
00:40:37,680 --> 00:40:39,839
Speaker 2: think it's important to learn the fundamentals, but when they're

853
00:40:39,880 --> 00:40:41,680
Speaker 2: not even taught to use those tools. And there was

854
00:40:41,719 --> 00:40:43,759
Speaker 2: a headline in a newspaper a few weeks ago that

855
00:40:43,840 --> 00:40:45,680
Speaker 2: came out like, oh, this person she got a degree

856
00:40:45,680 --> 00:40:47,520
Speaker 2: in computer science, but nobody will hire her. I'm like,

857
00:40:47,600 --> 00:40:49,400
Speaker 2: I bet she never learned to use these tools and

858
00:40:49,400 --> 00:40:52,000
Speaker 2: that's why she can't get it. It's not her fault, she's

859
00:40:52,040 --> 00:40:54,960
Speaker 2: literally the institution she paid money to so for students.

860
00:40:55,040 --> 00:40:56,480
Speaker 2: You know, one of the things that was really interesting

861
00:40:56,680 --> 00:40:59,160
Speaker 2: I did a talk at Santa Clara last week in

862
00:40:59,200 --> 00:41:03,000
Speaker 2: Santa Clair. You know who's interesting because I've interacted those

863
00:41:03,080 --> 00:41:05,160
Speaker 2: kids for a couple of years now, and I've noticed

864
00:41:05,200 --> 00:41:07,520
Speaker 2: they become much more entrepreneurial, like the ones in the

865
00:41:07,520 --> 00:41:09,880
Speaker 2: AI clubs. They're actually starting their own companies. Some are

866
00:41:09,920 --> 00:41:12,360
Speaker 2: actually raising money while they're in their dorm rooms. And

867
00:41:12,400 --> 00:41:15,160
Speaker 2: I asked, why is this, and one of the kids said, well,

868
00:41:15,400 --> 00:41:17,319
Speaker 2: we're not so sure that there's going to be a

869
00:41:17,400 --> 00:41:19,640
Speaker 2: job for us, so we're going to we want to

870
00:41:19,640 --> 00:41:21,919
Speaker 2: be more self relyingt We decided we're going to create

871
00:41:21,960 --> 00:41:22,480
Speaker 2: our own work.

872
00:41:22,520 --> 00:41:23,640
Speaker 1: We're going to create our own need.

873
00:41:24,040 --> 00:41:26,200
Speaker 2: And I think a little scary they feel that, but

874
00:41:26,239 --> 00:41:30,080
Speaker 2: that's very honest, and I'm very very was very happy

875
00:41:30,120 --> 00:41:33,480
Speaker 2: to see their reaction to this. Wasn't hopelessness. It was like, fine,

876
00:41:33,520 --> 00:41:35,160
Speaker 2: if you don't have a place for us, you can't

877
00:41:35,160 --> 00:41:37,160
Speaker 2: tell us our place in the future. We're going to

878
00:41:37,160 --> 00:41:38,360
Speaker 2: create our place in the future.

879
00:41:38,840 --> 00:41:41,759
Speaker 1: I think that's great for those kids with the personality

880
00:41:41,800 --> 00:41:45,080
Speaker 1: who are willing to do that. The majority of students, though,

881
00:41:45,160 --> 00:41:46,640
Speaker 1: might feel quite nervous about that.

882
00:41:47,000 --> 00:41:49,520
Speaker 2: But how do we how do we agree, how do

883
00:41:49,600 --> 00:41:51,920
Speaker 2: we get them to that? How do I think that

884
00:41:51,920 --> 00:41:54,120
Speaker 2: a lot of a lot of entrepreneurs don't even realize

885
00:41:54,120 --> 00:41:56,600
Speaker 2: that till later life, till necessity, and I think that

886
00:41:56,840 --> 00:41:58,840
Speaker 2: not everybody has to be entrepreneur, but I do think that,

887
00:41:58,960 --> 00:42:01,239
Speaker 2: Like my advice is like what I did, Like I

888
00:42:01,280 --> 00:42:02,840
Speaker 2: wanted to get into tech and I saw the people

889
00:42:02,840 --> 00:42:04,799
Speaker 2: at Opening Eyes. I like, what you kids are doing?

890
00:42:04,840 --> 00:42:07,359
Speaker 2: Can I help you out? And it worked out great.

891
00:42:07,440 --> 00:42:11,280
Speaker 1: It's nice. Yeah, it's Gerta had said the most important

892
00:42:11,320 --> 00:42:14,240
Speaker 1: bequeaths that a parent can give the child is two things,

893
00:42:14,320 --> 00:42:18,160
Speaker 1: roots and wings. And I interpret that in the educational

894
00:42:18,200 --> 00:42:22,920
Speaker 1: context as roots being critical thinking, really teaching students how

895
00:42:22,960 --> 00:42:26,399
Speaker 1: to do critical thinking, and wings being creative thinking how

896
00:42:26,440 --> 00:42:29,399
Speaker 1: to be really creative, because that's all when I think

897
00:42:29,400 --> 00:42:32,239
Speaker 1: about my students at Stanford that I'm teaching, that's the

898
00:42:32,280 --> 00:42:35,880
Speaker 1: only thing that I'm telling them that's going to stay

899
00:42:36,040 --> 00:42:39,120
Speaker 1: true twenty years from now is how can they think

900
00:42:39,160 --> 00:42:41,880
Speaker 1: through a problem and how can they be really creative?

901
00:42:41,920 --> 00:42:44,719
Speaker 1: Meaning take stuff they've learned before and remix it, bend it,

902
00:42:44,800 --> 00:42:48,560
Speaker 1: break it blended, build new things out of it. Because

903
00:42:48,640 --> 00:42:51,000
Speaker 1: I think it doesn't matter whether the computer science students

904
00:42:51,040 --> 00:42:54,680
Speaker 1: are learning coding or whether they're learning AI tools, because

905
00:42:54,719 --> 00:42:57,200
Speaker 1: in five years it's all going to be something else.

906
00:42:57,520 --> 00:42:59,960
Speaker 1: It's going to be you know, super AI tools.

907
00:43:00,320 --> 00:43:03,600
Speaker 2: Yeah, I think that the thing that I like to me,

908
00:43:03,840 --> 00:43:05,880
Speaker 2: if you ask me to describe coding it to me,

909
00:43:05,960 --> 00:43:07,919
Speaker 2: it's like to look at a system and figure out

910
00:43:07,960 --> 00:43:09,880
Speaker 2: how to give it the minimal set of instructions to

911
00:43:09,880 --> 00:43:12,840
Speaker 2: get it to do something right. That's how I approach prompting.

912
00:43:12,960 --> 00:43:14,840
Speaker 2: That's how I approach a lot of things. And I

913
00:43:14,880 --> 00:43:17,040
Speaker 2: think that you know and you have to figure out

914
00:43:17,040 --> 00:43:18,640
Speaker 2: what those tools are going to be in your right.

915
00:43:18,680 --> 00:43:20,759
Speaker 2: But if you are a critical thinker, you adapter really

916
00:43:20,840 --> 00:43:23,680
Speaker 2: quite well to this. You understand, oh, the AI does

917
00:43:23,719 --> 00:43:25,160
Speaker 2: this part, it's good to this bat of this. Now

918
00:43:25,200 --> 00:43:26,799
Speaker 2: I'm going to use this. I think a lot of

919
00:43:26,800 --> 00:43:29,719
Speaker 2: people kind of like fetishize the tool itself and think, well,

920
00:43:29,719 --> 00:43:31,399
Speaker 2: this is Can I just use this thing? And that's

921
00:43:31,400 --> 00:43:32,560
Speaker 2: the way I do it, And It's like, no, it's

922
00:43:32,560 --> 00:43:34,960
Speaker 2: about the outcome. And he gets if you know what

923
00:43:35,000 --> 00:43:37,280
Speaker 2: your goal is about the outcomes and not just knowing

924
00:43:37,320 --> 00:43:39,759
Speaker 2: which Python function to use, you're going to be better

925
00:43:39,760 --> 00:43:40,880
Speaker 2: suited for the future.

926
00:43:41,120 --> 00:43:43,799
Speaker 1: Yeah. And I think one of the things I'm so

927
00:43:43,840 --> 00:43:47,640
Speaker 1: excited about, for example, AI is teaching critical thinking by

928
00:43:47,680 --> 00:43:51,040
Speaker 1: being debate partners. So one on one debate partners, you

929
00:43:51,080 --> 00:43:52,759
Speaker 1: take some side of a hot button issue, the AI

930
00:43:52,880 --> 00:43:55,480
Speaker 1: takes the other side. You debate back and forth. It

931
00:43:55,560 --> 00:43:58,000
Speaker 1: grades you on how well you are. Then you switch

932
00:43:58,120 --> 00:44:00,400
Speaker 1: sides and grades how well you do the other side.

933
00:44:00,520 --> 00:44:02,960
Speaker 1: And that kind of thing is the kind of thing

934
00:44:02,960 --> 00:44:05,319
Speaker 1: that no teacher would ever have time to do for

935
00:44:05,360 --> 00:44:08,560
Speaker 1: each student, but this will be. It's a perfect tool

936
00:44:08,680 --> 00:44:10,080
Speaker 1: for really teaching critical things.

937
00:44:10,120 --> 00:44:13,160
Speaker 2: And it's safe too because you have what's happened, Like

938
00:44:13,239 --> 00:44:15,480
Speaker 2: debate is a great thing for students to do, but

939
00:44:15,560 --> 00:44:19,000
Speaker 2: like debate, organizations have been so captured by political correctness

940
00:44:19,040 --> 00:44:22,600
Speaker 2: that students can opt out of arguing alternate points of view,

941
00:44:22,760 --> 00:44:25,759
Speaker 2: which the problem is is you never the purpose of

942
00:44:25,800 --> 00:44:28,440
Speaker 2: this was to understand other points of view, and that

943
00:44:28,560 --> 00:44:31,160
Speaker 2: they become intellectually weak from that. The beauty of AI

944
00:44:31,320 --> 00:44:32,920
Speaker 2: is because part of it's the fear too. It's like,

945
00:44:32,960 --> 00:44:34,680
Speaker 2: I don't want somebody to take me out of context,

946
00:44:34,760 --> 00:44:36,759
Speaker 2: even though it's supposed to be a safe space. But

947
00:44:36,760 --> 00:44:39,520
Speaker 2: when you're having a private conversation the chatbot, you're like, okay,

948
00:44:40,080 --> 00:44:42,839
Speaker 2: let me explain my point of view on let me

949
00:44:42,680 --> 00:44:44,400
Speaker 2: let me let me have to defend this point of

950
00:44:44,440 --> 00:44:47,000
Speaker 2: view I disagree with. Then it's feel safe, you're not

951
00:44:47,040 --> 00:44:48,440
Speaker 2: as worried that all of a sudden will be out

952
00:44:48,440 --> 00:44:48,960
Speaker 2: of context.

953
00:44:49,120 --> 00:44:51,600
Speaker 1: Oh I love that. That's great. And when it comes

954
00:44:51,600 --> 00:44:55,840
Speaker 1: to creativity, I think there's a real opportunity for AI

955
00:44:55,920 --> 00:44:58,799
Speaker 1: to just feed us a broader diet. All creativity is

956
00:44:58,920 --> 00:45:01,680
Speaker 1: is absorbing what we see in the world and then

957
00:45:02,080 --> 00:45:05,160
Speaker 1: remixing that. And you know, when you look at I

958
00:45:05,160 --> 00:45:08,760
Speaker 1: don't know, look at music around the world. Beethoven certainly

959
00:45:08,760 --> 00:45:11,279
Speaker 1: could have written music as they did in Japan at

960
00:45:11,320 --> 00:45:13,080
Speaker 1: the same time or in Nigeria at the same time,

961
00:45:13,600 --> 00:45:15,600
Speaker 1: but he didn't like the music was different in all

962
00:45:15,600 --> 00:45:19,600
Speaker 1: these places. Why because he absorbed just a small amount

963
00:45:19,760 --> 00:45:21,840
Speaker 1: of what was in his culture, and so did the

964
00:45:21,920 --> 00:45:25,839
Speaker 1: Japanese or the Nigerians. So what we have nowadays, really

965
00:45:25,840 --> 00:45:28,120
Speaker 1: ever since the advent of the Internet, is a much

966
00:45:28,200 --> 00:45:31,240
Speaker 1: broader diet. But what AI gives us is a broader

967
00:45:31,239 --> 00:45:35,160
Speaker 1: diet still where we can get it to do remixes

968
00:45:35,239 --> 00:45:39,560
Speaker 1: and give us things that really stretch the fence lines

969
00:45:39,600 --> 00:45:43,160
Speaker 1: of our thinking. And that's great because that's the student's diet.

970
00:45:43,440 --> 00:45:45,359
Speaker 1: And then the key, I think is to make them

971
00:45:45,400 --> 00:45:47,880
Speaker 1: present in person so they can work with the AI

972
00:45:47,960 --> 00:45:49,920
Speaker 1: to do whatever they're doing, and then they present to

973
00:45:49,960 --> 00:45:53,000
Speaker 1: their class and they're explaining all these different things about

974
00:45:53,080 --> 00:45:56,480
Speaker 1: how the new economy could run on this other planet

975
00:45:56,760 --> 00:45:59,759
Speaker 1: and the type of people and whatever, that it's such

976
00:45:59,800 --> 00:46:02,960
Speaker 1: an opportunity for expanding creativity beyond what we ever got

977
00:46:03,040 --> 00:46:10,279
Speaker 1: in school. That was my interview with Andrew Mayne. This

978
00:46:10,360 --> 00:46:14,600
Speaker 1: conversation pushed back a bit against the default anxiety that

979
00:46:14,719 --> 00:46:20,480
Speaker 1: AI equals unemployment, because the story is more richly textured

980
00:46:20,560 --> 00:46:25,160
Speaker 1: when we ground it in history, economics, psychology, and questions

981
00:46:25,160 --> 00:46:28,960
Speaker 1: about what the human brain actually seeks, at least historically.

982
00:46:29,000 --> 00:46:32,160
Speaker 1: The fact is, when you automate the bottom rungs of

983
00:46:32,239 --> 00:46:38,000
Speaker 1: the economic ladder, humans climb upward. The plow didn't eliminate work.

984
00:46:38,080 --> 00:46:42,960
Speaker 1: It created governance and mathematics and engineering and poetry. Industrial

985
00:46:43,120 --> 00:46:48,440
Speaker 1: agriculture didn't collapse society. It helped dismantle slavery and create

986
00:46:48,560 --> 00:46:52,920
Speaker 1: new professions. So the same is likely with AI. What

987
00:46:53,040 --> 00:46:55,839
Speaker 1: surfaced from our conversation is that the jobs most at

988
00:46:55,920 --> 00:46:59,680
Speaker 1: risk are the black box jobs where a person receives

989
00:46:59,760 --> 00:47:03,480
Speaker 1: in puts and sends standard outputs and the identity of

990
00:47:03,520 --> 00:47:07,160
Speaker 1: the worker doesn't matter. Those jobs are probably going to vanish,

991
00:47:07,200 --> 00:47:14,879
Speaker 1: but the roles that depend on trust, reputation, lived experience, mentorship, inspiration.

992
00:47:15,480 --> 00:47:19,360
Speaker 1: These might become more valuable, not less, because through the

993
00:47:19,480 --> 00:47:23,320
Speaker 1: neuroscience lens, I think we can say that human beings

994
00:47:23,360 --> 00:47:29,040
Speaker 1: are creatures of story, not efficiency. We don't choose books

995
00:47:29,080 --> 00:47:32,520
Speaker 1: only for their sequences of words. We choose them for

996
00:47:32,560 --> 00:47:37,640
Speaker 1: the heartbeat behind the pages. We don't choose teachers because

997
00:47:37,680 --> 00:47:40,200
Speaker 1: they have access to zeros and ones that we want

998
00:47:40,239 --> 00:47:44,680
Speaker 1: to know. We choose them because they've lived actual experiences

999
00:47:44,920 --> 00:47:48,359
Speaker 1: and can guide us around the pitfalls that they once

1000
00:47:48,400 --> 00:47:51,880
Speaker 1: fell into. As Andrew pointed out, even the chief scientist

1001
00:47:51,960 --> 00:47:55,800
Speaker 1: of Open AI doesn't believe that AI will replace teachers

1002
00:47:55,840 --> 00:48:01,080
Speaker 1: because inspiration isn't really a commodity. It's relational, and no

1003
00:48:01,080 --> 00:48:05,279
Speaker 1: matter how knowledgeable an AI tutor becomes, it can't replicate

1004
00:48:05,760 --> 00:48:10,160
Speaker 1: the emotional texture of a person who has wrestled with ideas,

1005
00:48:10,200 --> 00:48:14,760
Speaker 1: who has failed, learned, grown, and now offers that experience

1006
00:48:14,760 --> 00:48:19,799
Speaker 1: to others. Another theme that surfaced is the expansion of creativity.

1007
00:48:20,160 --> 00:48:24,040
Speaker 1: As I mentioned in the conversation, painting didn't die when

1008
00:48:24,080 --> 00:48:31,000
Speaker 1: photography arrived. Instead, painters expanded into new directions like impressionism

1009
00:48:31,040 --> 00:48:35,360
Speaker 1: and Cubism, and Pointillism and surrealism, and I suggest writers

1010
00:48:35,440 --> 00:48:37,680
Speaker 1: are not going to go away now that AI can

1011
00:48:37,719 --> 00:48:43,200
Speaker 1: write fluent text. Instead, writers will invent new forms of storytelling,

1012
00:48:43,320 --> 00:48:49,600
Speaker 1: new modes of audience engagement, new performance ecosystems around their work.

1013
00:48:49,920 --> 00:48:54,040
Speaker 1: And Andrew rays the possibility that accelerating tools in science

1014
00:48:54,200 --> 00:48:59,600
Speaker 1: like alpha fold won't eliminate scientists, but it might multiply them.

1015
00:49:00,000 --> 00:49:03,120
Speaker 1: So when one researcher can do a year of research

1016
00:49:03,200 --> 00:49:07,200
Speaker 1: work in a day, that massively expands the frontier and

1017
00:49:07,360 --> 00:49:10,160
Speaker 1: opens the door for more people to join the field

1018
00:49:10,360 --> 00:49:14,759
Speaker 1: because it increases the number of experiments that we can imagine,

1019
00:49:15,000 --> 00:49:17,240
Speaker 1: and therefore the number of people we need to build

1020
00:49:17,280 --> 00:49:20,320
Speaker 1: them and run them and interpret them. In other words,

1021
00:49:20,680 --> 00:49:23,880
Speaker 1: the surprising possibility is that the future economy may not

1022
00:49:24,080 --> 00:49:28,680
Speaker 1: need fewer people. It may need more more builders, more tinkerers,

1023
00:49:29,000 --> 00:49:33,520
Speaker 1: more crafts people, more teachers, more creative thinkers, more scientists,

1024
00:49:34,000 --> 00:49:38,200
Speaker 1: more hands to run the experiments, and more minds to

1025
00:49:38,440 --> 00:49:42,759
Speaker 1: shape the meaning of what we discover, Which brings us

1026
00:49:42,800 --> 00:49:46,200
Speaker 1: back to the brain. For all its computational power, it's

1027
00:49:46,200 --> 00:49:50,040
Speaker 1: not built for perfect efficiency. It's built for meaning and

1028
00:49:50,120 --> 00:49:54,319
Speaker 1: imagination and social connection. It's built to ask not just

1029
00:49:54,400 --> 00:49:57,440
Speaker 1: what can I do, but what is worth doing? And

1030
00:49:57,480 --> 00:50:01,480
Speaker 1: this is where humans will continue to show. The jobs

1031
00:50:01,520 --> 00:50:03,000
Speaker 1: of the future are not going to be the ones

1032
00:50:03,040 --> 00:50:05,560
Speaker 1: we can list today, just like no one a generation

1033
00:50:05,680 --> 00:50:08,640
Speaker 1: ago might have predicted your job title. Now, the next

1034
00:50:08,680 --> 00:50:12,600
Speaker 1: wave of professions are going to emerge from the collision

1035
00:50:13,120 --> 00:50:18,399
Speaker 1: between human creativity and machine capability. Our kids are going

1036
00:50:18,440 --> 00:50:22,160
Speaker 1: to grow up into jobs that don't yet exist, using

1037
00:50:22,280 --> 00:50:27,480
Speaker 1: tools we haven't invented to solve problems we haven't yet imagined.

1038
00:50:28,120 --> 00:50:31,160
Speaker 1: So our job is to cultivate the tasks of the brain,

1039
00:50:31,440 --> 00:50:35,560
Speaker 1: ours and theirs that will always matter, critical thinking and

1040
00:50:35,680 --> 00:50:40,040
Speaker 1: creative thinking roots and wings. So as we move into

1041
00:50:40,080 --> 00:50:43,680
Speaker 1: this AI accelerated era, the best question isn't just will

1042
00:50:43,719 --> 00:50:47,280
Speaker 1: AI take our jobs, but instead what will we choose

1043
00:50:47,320 --> 00:50:52,400
Speaker 1: to do with the extraordinary new landscape that AI opens

1044
00:50:52,480 --> 00:50:59,640
Speaker 1: up for us? Go to eelman dot com slash podcast

1045
00:50:59,680 --> 00:51:02,920
Speaker 1: from more information and to find further reading. Join the

1046
00:51:02,920 --> 00:51:06,080
Speaker 1: weekly discussions on my substack and check out Subscribe to

1047
00:51:06,239 --> 00:51:09,360
Speaker 1: Inner Cosmos on YouTube for videos of each episode and

1048
00:51:09,440 --> 00:51:12,920
Speaker 1: to leave comments until next time. I'm David Eagleman and

1049
00:51:12,960 --> 00:51:14,640
Speaker 1: this is Inner Cosmos.