- 4시간 전
- #2424
▲ 세계적 AI 석학 이선 몰릭 와튼스쿨 교수의 진단…"AI 기술, 거품 아니다"
▲ "한 학기 과제를 단 3일 만에"… 교육 현장에서 벌어진 변화
▲ "내 일자리 사라질까?"… AI 시대 살아남는 'T자형 인재' 비밀
YTN 글로벌 명사 대담 6부작 가 세 번째 순서로 세계적인 AI 석학이자 베스트셀러 『듀얼 브레인』의 저자인 이선 몰릭 미국 펜실베이니아대 와튼스쿨 교수를 만났다. 특별 인터뷰 편은 8월 8일(토) 밤 11시 15분 방송됐다.
"AI 기술은 거품이 아니다"… 기업과 교육 현장의 실질적 가치
챗GPT가 세상에 나온 날 성능에 놀라 잠을 이룰 수 없었다고 고백한 몰릭 교수는 현재 일각에서 제기되는 'AI 거품론'에 대해 실질적 가치에 주목해야 한다고 진단한다. 그는 글로벌 기업 CEO들이 AI 시스템을 활용해 이미 경제적 가치를 창출하고 있는 만큼, 사용자가 명확한 가치를 얻고 있는 상황에서 거품으로 보기는 어렵다고 설명한다.
"한 학기 과제를 단 3일 만에"… 교육 현장에서 벌어진 변화
교육 현장에서도 AI가 학생들이 학습하고 과제를 수행하는 방식 자체를 크게 바꾸고 있다고 강조한다. 몰릭 교수는 최근 진행한 '바이브 파운딩(Vibe Founding)' 수업을 예로 들며, 학생들이 AI를 활용해 보통 한 학기 동안 수행할 스타트업 창업 과제를 단 3일 만에 기획부터 실행까지 완수해 낸 사례를 제시했다.
"내 일자리 사라질까?"… AI 시대 살아남는 'T자형 인재' 비밀
몰릭 교수는 AI를 인간의 일자리를 빼앗는 경쟁자가 아닌, 작업 범위를 넓혀주는 '협업 파트너'로 정의한다. AI가 기존 직업을 소멸시키기보다는 직업의 형태와 역할을 재정의할 것이라는 분석이다. 과거에는 혼자서 수행할 수 없었던 복잡한 업무를 AI와의 협업을 통해 해낼 수 있게 되면서, 인간의 생산성과 역량이 대폭 확장된다고 내다봤다.
그렇다면 AI 시대에 개인의 경쟁력을 높이기 위해서는 어떻게 해야 할까? 몰릭 교수는 한 분야의 깊은 전문성(세로축)을 바탕으로 관련 주변 분야까지 두루 이해하는(가로축) 'T자형 인재'가 되어야 한다고 강조한다. 나만의 확실한 전문성을 갖춘 상태에서 AI를 적절히 활용할 때 비로소 가야 할 미래의 방향성을 명확히 설정할 수 있다는 조언이다.
는 AI에게 정답을 구하는 대신, 인간이 스스로에게 던져야 할 '본질적 질문'의 자리로 프롬프트를 되돌려놓는 6부작 대담 시리즈로, 컴퓨터공학·경영... (중략)
▶ 기사 원문 : https://www.ytn.co.kr/replay/view.php?idx=291&key=202608082309413265
▶ 제보 안내 : http://goo.gl/gEvsAL, 모바일앱, social@ytn.co.kr, #2424
▣ YTN 데일리모션 채널 구독 : http://goo.gl/oXJWJs
[ 한국 뉴스 채널 와이티엔 / Korea News Channel YTN ]
▲ "한 학기 과제를 단 3일 만에"… 교육 현장에서 벌어진 변화
▲ "내 일자리 사라질까?"… AI 시대 살아남는 'T자형 인재' 비밀
YTN 글로벌 명사 대담 6부작 가 세 번째 순서로 세계적인 AI 석학이자 베스트셀러 『듀얼 브레인』의 저자인 이선 몰릭 미국 펜실베이니아대 와튼스쿨 교수를 만났다. 특별 인터뷰 편은 8월 8일(토) 밤 11시 15분 방송됐다.
"AI 기술은 거품이 아니다"… 기업과 교육 현장의 실질적 가치
챗GPT가 세상에 나온 날 성능에 놀라 잠을 이룰 수 없었다고 고백한 몰릭 교수는 현재 일각에서 제기되는 'AI 거품론'에 대해 실질적 가치에 주목해야 한다고 진단한다. 그는 글로벌 기업 CEO들이 AI 시스템을 활용해 이미 경제적 가치를 창출하고 있는 만큼, 사용자가 명확한 가치를 얻고 있는 상황에서 거품으로 보기는 어렵다고 설명한다.
"한 학기 과제를 단 3일 만에"… 교육 현장에서 벌어진 변화
교육 현장에서도 AI가 학생들이 학습하고 과제를 수행하는 방식 자체를 크게 바꾸고 있다고 강조한다. 몰릭 교수는 최근 진행한 '바이브 파운딩(Vibe Founding)' 수업을 예로 들며, 학생들이 AI를 활용해 보통 한 학기 동안 수행할 스타트업 창업 과제를 단 3일 만에 기획부터 실행까지 완수해 낸 사례를 제시했다.
"내 일자리 사라질까?"… AI 시대 살아남는 'T자형 인재' 비밀
몰릭 교수는 AI를 인간의 일자리를 빼앗는 경쟁자가 아닌, 작업 범위를 넓혀주는 '협업 파트너'로 정의한다. AI가 기존 직업을 소멸시키기보다는 직업의 형태와 역할을 재정의할 것이라는 분석이다. 과거에는 혼자서 수행할 수 없었던 복잡한 업무를 AI와의 협업을 통해 해낼 수 있게 되면서, 인간의 생산성과 역량이 대폭 확장된다고 내다봤다.
그렇다면 AI 시대에 개인의 경쟁력을 높이기 위해서는 어떻게 해야 할까? 몰릭 교수는 한 분야의 깊은 전문성(세로축)을 바탕으로 관련 주변 분야까지 두루 이해하는(가로축) 'T자형 인재'가 되어야 한다고 강조한다. 나만의 확실한 전문성을 갖춘 상태에서 AI를 적절히 활용할 때 비로소 가야 할 미래의 방향성을 명확히 설정할 수 있다는 조언이다.
는 AI에게 정답을 구하는 대신, 인간이 스스로에게 던져야 할 '본질적 질문'의 자리로 프롬프트를 되돌려놓는 6부작 대담 시리즈로, 컴퓨터공학·경영... (중략)
▶ 기사 원문 : https://www.ytn.co.kr/replay/view.php?idx=291&key=202608082309413265
▶ 제보 안내 : http://goo.gl/gEvsAL, 모바일앱, social@ytn.co.kr, #2424
▣ YTN 데일리모션 채널 구독 : http://goo.gl/oXJWJs
[ 한국 뉴스 채널 와이티엔 / Korea News Channel YTN ]
카테고리
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뉴스트랜스크립트
00:00아, mostly I get asked,
00:02what should my kids do for a living?
00:04is the number one question I get asked.
00:07probably of all of them.
00:09We actually have a similar question.
00:10I'm sure you do.
00:11Everybody asks that.
00:12That's fine.
00:22And it was even worse now
00:24because I would basically go to bed at like 4 a.m.
00:27I actually sent emails to my entire faculties.
00:38No doubt, students are cheating
00:39at a rate they've never cheated before.
00:42No one's going to show you
00:43that you're using AI in your company
00:44if they're worried they're going to get fired
00:45if they're more productive, right?
00:52I think this is a chance for exploration.
00:54So, I mean, I would go one step further, right?
00:57Ambition is rewarded right now.
00:59And even if failure is further than you'd get before
01:02and teaches you something.
01:08This is exciting.
01:11Hi, I'm Professor Ethan Mollick.
01:13I teach and research at the Wharton School
01:15of the University of Pennsylvania.
01:17I run the engineering of AI labs there.
01:18I've also written a book on AI called Co-Intelligence,
01:21which is sold in Korea, along with the U.S.
01:45How do you define AI?
01:48So, it actually, one of the biggest problems
01:50is the definition of AI is really vague, right?
01:53So, if we talk about AI prior to sort of 2010,
01:56we would be talking about sort of the science fictional idea
01:58of artificial intelligence
01:59where only a few computer scientists cared about it.
02:02If we talk about it from like 2010 to 2022,
02:05you'd be talking about big data.
02:07And then since then, we've been talking about AI,
02:09we talk about generative AI, like large language models
02:11and related technologies that are very different
02:14than what came before.
02:16What AI tools do you use most this day?
02:20Okay, so when you think about AI right now,
02:22you kind of think about three things.
02:24You think about the AI model itself, right?
02:26So, the big three at the time we're recording this.
02:29But it's not just about the models.
02:30That's the brains of the AI.
02:32It's also about the actual software applications you use.
02:35And then probably what we call harnesses.
02:37Harnesses are the pieces of equipment given to the AI
02:41to let it do things.
02:42So, harnesses let AI create images if they need to,
02:46write code, do web searches, all the kinds of access your files.
02:50So, now when you think about AI tools,
02:52you think about all three of those together.
02:53So, I use all of them, but I tend to use a lot of OpenAI's Codex
02:57and Claude's Claude Code and Cowork,
03:00which are versions that use your computer
03:03to help you do more work.
03:13Because, a few things.
03:15One is, I think we all have this idea of AI for many years of television and movies, right?
03:19It was this, you know, it's a machine that, like, if you, you know,
03:22if the stereotypical bad science fiction version is, it can't, you know,
03:26I do not understand love and then the robot explodes if you try and explain love to it, right?
03:30Like, we expect it to be logical and to not act quite like a human
03:34and to be all about sort of cool reasoning, not understanding emotion.
03:38Instead, we got large language models, which, in a lot of ways, work a lot like people.
03:42They are all about emotional content.
03:44They are about empathy.
03:46They were originally worse at math than other topics now that they've closed that gap.
03:50And so, it's worth thinking about them as a person,
03:53because that's the most easy way to get them to do work.
03:55At the same time, you have to remind yourself they're not a person.
03:57So, Alien kind of puts that nice distance between yourself and the AI.
04:06So, I actually, so the Three Sleepless Nights idea from the book is really one where everybody
04:12who uses these systems enough has, like, a moment of crisis, where they're like,
04:15what does this mean, what am I doing?
04:17And, actually, I work with my wife on this a lot.
04:19We run, we started the AI lab together.
04:21We do a lot of work together.
04:22And it was even worse than that, because I would basically go to bed at, like, 4 a.m.
04:25And then she'd wake up at 4 a.m. and start working on AI stuff.
04:28And in the morning, we'd be, like, passing each other messages about what we did.
04:30And to me, it was, wow, this thing could do stuff.
04:33So, you start, you're like, projects I couldn't accomplish.
04:35Now, I'm doing it.
04:36What if I test it this way?
04:37What happens?
04:38And so, there's a lot of anxiety, especially when you first use AI, it can almost seem
04:42almost miraculous.
04:43Like, then you start to see the flaws after time.
04:45But at first, it's like, what does this all mean for us?
04:47And so, that really hit me in November.
04:49I actually sent emails to my entire faculty saying we really have to change everything
04:53about how we teach the next day.
04:55And they've come to believe me over the last couple of years.
05:02So, there's a few things that are different.
05:04The biggest differences are, you know, first of all, just ability.
05:08GPT 3.5, which was the original chat GPT, was just dumber on every characteristic.
05:13And we could talk about test scores.
05:15You know, it was basically, it looked like it was sort of a smart high school student when
05:19you measured it.
05:20And now we've got GPT 5.5 is solving math problems that only two or three people on the planet
05:24can solve.
05:25It also has become agentic.
05:27Now, this is one of those buzzwords that means a lot of things.
05:30But an agent is basically an AI tool that can go out and do work for you.
05:33So GPT 5.5 is really good.
05:35If you say, put together, you know, a TV script for me or something, it won't just write an
05:40answer.
05:40It'll do research for you.
05:41It'll look, you know, create images.
05:43It'll look up information.
05:44And it will actually do hours of work for you by just asking.
05:47So those are the big gaps.
05:51A bubble.
05:53So I could see arguing six months ago that AI was a bubble because we hadn't yet entered
06:00the sort of agentic era of AI use.
06:02I don't think many people think this is a bubble right now.
06:05Companies—I talk to CEOs of companies all the time who are getting huge amounts of value
06:08out of the system.
06:09I keep talking to companies that are blowing through their entire AI budget in months that
06:13they expect to spend in years.
06:14So there's a lot of money that people are getting value out of them as users of these
06:18systems.
06:19So it's hard to imagine there being a real bubble.
06:21Now, that doesn't mean financial markets do what they do, right?
06:24It's always possible that something's overinflated or not.
06:26So we could separate a financial bubble.
06:28But if we want to talk about is there a use bubble?
06:31Is AI going to go away?
06:32Is it not useful?
06:33I don't see any way you could argue that at this point.
07:06So we have actually collaborated for a long time.
07:08She has a doctorate in education.
07:16We ran—before it was something called Wartent Interactive, and we built games for teachers.
07:19So I've always been interested in how do we democratize, especially business education
07:23for the world.
07:23So we built games that were, you know, that would teach people how to do entrepreneurship.
07:27We built a fake space simulator that actually secretly taught you leadership skills.
07:31So I've been spending a lot of time thinking about how do we teach people with AI.
07:35And when Chachapiti came out, we saw that it solved a whole bunch of problems that have
07:40plagued the education field for 10 years, right?
07:42I mean, it's causing problems, too, to be clear.
07:44But it also solved—it could solve a whole bunch of problems.
07:47No doubt students are cheating at a rate they've never cheated before.
07:49But on the other hand, it could be an amazing tutor.
07:51And we thought it was important to illuminate for all these people trying to experiment, what
07:54were some ways to actually use this to go forward?
08:01So there's two differences.
08:02I actually have been kind of running this.
08:04I teach an entrepreneurship class, and that's where I've been teaching about AI.
08:08And I just did a new version of the class called Vibe Founding.
08:12And students launched startup ideas in three days.
08:14So they had to go for—like, they were doing a semester's worth of work in three days.
08:17So AI helped generate the ideas.
08:19It built the business plan.
08:21They had to actually launch with working code, which a lot of them were never—most of them
08:25never coded in their lives.
08:26They had working code.
08:27They had to have a working PowerPoint presentation.
08:29All the financial models, all of that had to be built.
08:31So I know you can get a huge amount further today than you could.
08:39So the first thing that is really annoying to tell students and nobody likes to hear
08:43is that learning is hard and kind of has to be hard.
08:46Then we—so we call this desirable difficulties, is the phrasing in education theory.
08:51And so part of the problem with using AI is if you're just asking for help, the AI is
08:56trained to be a helpful assistant to you, and it will give you the answers to problems.
09:00And you'll think you're learning something, but you're not actually learning because you're
09:02not doing the mental work yourself.
09:04So it's really important to get the AI to act like a tutor or a teacher.
09:08And there's a few ways to do that.
09:09First of all, all three of the big AI companies have a tutor mode that makes the AI less likely
09:13to give you answers.
09:14Using it to tutor you, to answer questions, to quiz you—very powerful for AI use.
09:20And we now have data that shows that's true.
09:22There's a really nice study, one out of Taiwan, another one out of Africa, that showed that
09:27when you make AI work like a tutor, you get big impacts on education.
09:30But if you just ask it questions, you're not going to learn.
09:37Sure.
09:39So a really useful thing might be to say, give the information about yourself.
09:43I am a student in Seoul and in high school or college or whatever grade you're in studying
09:49this.
09:49Here's my interests.
09:51I'm having difficulty with this.
09:52Here are a couple math problems that I was asked to solve or a couple history problems.
09:56Can you help quiz me on these topics and then explain what I'm missing and help me understand
10:03the missing pieces and then quiz me about them again?
10:04So just ask and say, don't give me any answers.
10:07I just need to be able to learn this on my own.
10:09And that will get you part of the way there.
10:18So there's a little update to this too.
10:19We've done some research at the Generative AI lab.
10:21It used to be personas did two things for you.
10:24They would change how the AI responded, so act like Socrates.
10:27But the other way is it would actually make the AI better at things.
10:30So if you said you are a physicist, it would do better at physics.
10:32That's no longer true.
10:34So telling the AI you are good at physics or you're a physicist or you're a mathematician
10:38doesn't make it better at math.
10:39But it still does change how it talks to you.
10:42So the personas I use the most often, and the one everyone should actually use the most,
10:45is be a critic.
10:46Because remember, we talked to you earlier about how AI can be sycophantic, how it wants
10:51to make you happy.
10:52What you actually want is some honest answers or challenges.
10:55So be a critic is one of the most useful things you could say.
10:58When I'm writing, I often have different personas for writing too.
11:00You know, you are a reader who is confused about AI.
11:04What should I clarify here?
11:06So I use personas for both kind of projecting what my work might do, but also to help me
11:11think more clearly.
11:17So part of this is we have to recognize that there are losses along with gains.
11:22Like people are cheating right now.
11:24A lot of educational methods that we used to use that work really well don't work well
11:28anymore.
11:29If you assign long essays to people, the AI will help them with essays.
11:32Like more of your students, your students will always want to cheat.
11:35They're always busy.
11:36They have other things to do.
11:36They're very incentivized to get high grades.
11:38They will cheat.
11:40So we have to think about how we're solving that problem.
11:42So we have to take advantage of the strengths of AI as an individualized, personalized tutor,
11:46but also think about how we're going to assess people, how we're going to make
11:49classroom experience meaningful.
11:50The right way is probably something called a flipped classroom.
11:52So you'll use AI tutors outside of class to teach you things.
11:56And inside of class, and a lot of people already do this.
11:58So like my students are assigned YouTube videos to watch outside of class, and then in class
12:01they do math.
12:03Right?
12:03That's how we're going to end up doing things in a lot of fields.
12:05Well, outside of class you'll be assigned to use AI to learn something, and then in class
12:09you'll be doing exercises, experiences, working with other students on things.
12:13I think that's how things will end up looking.
12:25I think you want to be a T-shaped person.
12:30Right?
12:30So I think you want to be a generalist with one area of deep expertise.
12:33Right?
12:33So deep knowledge in a field.
12:35Have a good understanding of related fields so that you know different things, directions
12:40you might want to head in.
12:41And then having a sense of taste about what you want to do.
12:44Right?
12:45Like the AI can give you thousands of images, thousands of paragraphs.
12:48How do you select the right one?
12:49And then finally the idea of agency, of actually going out and giving things a try.
12:53So I find people who experiment, especially experiment in their field, tend to be very
12:57good at using AI.
12:58In terms of organizations, what I find is that if the CEO or top leadership team believes
13:04in AI deeply and is willing to take the risk that comes from experimenting, that is a big
13:09factor in company success.
13:16Try and use AI for everything.
13:18I wish I could say that there was a cheap way to do this, but I think you have to
13:20pay one
13:21of the three big AI companies.
13:23You know, in the US it's $20 a month, there's $8 a month versions in some countries.
13:27But you have to pay and you have to use the better model.
13:30And you should try using them for everything.
13:32Like you're going to interview me, ask it to do the research.
13:35In your field of expertise, it's very cheap for you to start to learn is AI good or bad.
13:39You'll see the question, you'll be like, it's not very good at asking questions, but maybe
13:42if I ask them a different way, prompt the AI a different way, I'll get different kinds
13:45of better questions.
13:46So experimenting in your field as much as you can with real tasks is the big thing.
13:56So it's a really interesting question.
13:58We know at the individual level, both from research we've done and lots of other people,
14:02that you get individual productivity gains that are quite large.
14:05Whether those translate to company productivity gains is, at this point, you have to think
14:09about how you change your company.
14:10So let's take coding, for example.
14:11One of the biggest changes is in software programming.
14:14If you talk to any really good programmer right now, they are writing almost none of their
14:17own code.
14:18Like literally, they instruct the AI to do things.
14:20They spend their time thinking how to manage the AI, manage the process, but they're not
14:23writing code anymore.
14:25And so let's say you're 10 times more productive or 100 times more productive, that you're individually
14:30more productive, but has your company changed how it's making software?
14:33Or are you still sitting in a meeting where every two weeks you have a planning meeting?
14:36And so it doesn't matter that you're doing 100 times more work because the rest of the company
14:40is holding you back.
14:41So it's not just about individual AI use, right?
14:44That helps you as an entrepreneur, it helps you make progress, but it is beyond the individual
14:50entrepreneurial use.
14:50It is that bigger picture, how do we use AI in organizations, in companies, in government
14:57that you see productivity gains from.
15:03To me, that is the most urgent immediate problem of AI, which is it is easy to know what to
15:09do
15:09with productivity because we've done productivity gain.
15:11IT has, for the last 30 years, what do you do when you have an IT gain?
15:15You fire people, right?
15:16We're 5% more productive, fire 5% of our workers.
15:20That is the easy path, right?
15:22And it doesn't require a lot of imagination.
15:24I think that leads you to disaster in a few ways.
15:26First of all, AI use we just talked about is being discovered by people using these systems
15:30in your company.
15:31No one's going to show you that you're using AI in your company if they're worried they're
15:33going to get fired if they're more productive, right?
15:35So you're not going to see any productivity gains, so you're sabotaging yourself.
15:38But the bigger issue is that failure of imagination means eventually you're just turning over
15:42your business to be run by Claude, right, or by, you know, or by ChatGPT.
15:47And you have no advantage versus anyone else.
15:49So I think the big thing to experiment with is how do we make humans do more?
15:53How do we help them and support them in parts of their job that they like least?
15:57That's the really big challenge.
16:04So the first thing you need to do, you need three pieces to succeed.
16:08You need what I call leadership lab and crowd.
16:10So leadership means that you need your leadership team to be thinking about this and they have
16:15to try it themselves.
16:16They have to decide what incentives there are for people using these things.
16:20How are they going to talk about what their goals of using AI are?
16:23How are they going to start to reimagine how their company works?
16:26For the crowd, the crowd is everyone using these systems.
16:29You need to give people access to a good AI model.
16:31So you need to give them access to a model and, you know, give them a little bit of training,
16:35give them encouragement to use it.
16:36And then you need the lab.
16:37You need to set up some part of your company that's doing 24-7 experimentation with AI.
16:42It doesn't have to be technical people, by the way.
16:43Often it's not technical people.
16:45It's the people in your organization who are already coming to you every day saying, I figured
16:47out a way to solve this with AI, and you're like, okay, go back to your cubicle.
16:52Those people will form your lab.
16:53So you need those three pieces in place.
17:01So those are two really hard questions.
17:03First is it's a really bad idea for companies to get up in their junior pipeline.
17:07They're going to have to think about how to train people differently.
17:09But if you don't have junior people, you're not going to have senior people in the future.
17:11So we're going to have to think differently about how we bring junior people in.
17:14They may not be as useful compared to AI, but they are important for the future.
17:19We're not seeing a huge collapse yet in sort of junior jobs.
17:23There's sort of signs that might happen.
17:24We don't really know.
17:25But I think that the idea is being flexible as a junior person entering the field as a
17:30student, not betting only on one thing, which is a good idea anyway.
17:33I mean, there's lots of people who wanted a job in finance in 2007.
17:37And when the financial crisis came along, those jobs weren't there.
17:39So I think maintaining some flexibility is important, but it's really on companies to
17:43think about how do they keep their junior talent pipeline going.
17:46If you were 22 today, what would you actually do?
17:51I think this is a golden age for entrepreneurship.
17:53I mean, the honest truth is the best thing you could do with your time is launch a startup
17:58company.
17:58It's an incredible time for that.
18:00By the way, I don't mean an AI company.
18:01It doesn't mean that you have to launch yet another AI company, but where I'm seeing a lot
18:05of AI transformation is in boring businesses, where people say, like, what if I think about
18:09insurance?
18:10Or what if I think about how to use AI to better manage a golf course?
18:14Or how do I use AI to better manage a laundromat?
18:18Right?
18:18At 22, you don't know a lot of things.
18:19You have a lot of disadvantages.
18:20But the AI does know a lot of things.
18:22So suddenly, it can support you and help you write code.
18:24It can do all kinds of stuff for you.
19:04It can do all kinds of stuff.
19:05There are serious people worried about this, which should be enough to take this somewhat
19:10seriously.
19:10Right?
19:11Nobody knows anything.
19:12Interestingly, Nick Bostrom is now in favor of fast AI development.
19:16So even though he invented the idea of the paperclip maximizer.
19:19So I think it is worth taking that seriously.
19:22I think that I worry a little bit that conversations about doomsday or that the AI saves us all and
19:29cures all disease, that stuff ends up taking all the oxygen out of the room.
19:33Because it's really hard to have an argument about how to best adopt AI in education when
19:37you're like, in five years, we'll all be dead or saved.
19:39I think the far more likely scenario that we should worry about those kind of things is
19:43that the world looks like this one, but projected forward, that the world has not ended or changed.
19:50And we need to spend a lot of time thinking about the world we want to build as a result.
19:58And in some ways, my really annoying answer is this is what I want everyone to be debating.
20:02Like, if we're all going to die, who cares?
20:04And then, or it's all hype, it's going to go away.
20:07And I don't think it's going to go away.
20:08So I think it's lots of little choices that make this decision.
20:10Is it OK to use AI to interview me?
20:14Would it be all right?
20:15You know, I've got this great crew of people here filming me.
20:18A lot of work went into this kind of meeting.
20:20Is that a thing we would let AI do?
20:22Is that a good idea or a bad idea?
20:24We get to decide those kind of things, right?
20:26It's not that technology is not automatic.
20:28It doesn't do things to us.
20:29We get to decide what it does.
20:35So, I mean, all the usual, nothing changes about what you want as a parent.
20:39You want your kids to be happy and kind and flexible and successful and all of the things
20:45that you want.
20:46Humans don't change that much, right?
20:48Now what do I want them to do to navigate a technological environment that's changing?
20:52Flexibility.
20:53But, I mean, careers are long.
20:55I teach students.
20:56Very few of them end up going into their career the way they think it's going to be.
20:59People change jobs.
21:01Life changes where they go.
21:03Things alter and vary.
21:05So, I think resilience is important.
21:12So, I think that you should think about a job as not one thing but as many things.
21:16Jobs are bundles of tasks.
21:18So, as a journalist, right, we were just talking about there's a bunch of stuff that you already
21:22do, right?
21:22You have to be, you know, fly here for these and fill out your expense reports and interview
21:26me and prep for an interview and, you know, and go to story meetings and whatever.
21:31All the other things that you end up doing, right?
21:33And so, if AI is good at some of that stuff, it's probably not good at all of them.
21:35It transforms your job rather than destroying it for most people.
21:38And also, you just pointed out earlier that you can expand your job too.
21:41Suddenly, you can do things you couldn't do before.
21:43So, I think job transformation is more likely than job destruction.
21:46I think this is a chance for exploration.
21:49So, I mean, I would go one step further and I think it's a great time to expand what we
21:53do and to think, you know, big.
21:55Right?
21:55Ambition is rewarded right now.
21:57And even if failure is further than you'd get before and teaches you something.
22:01Thank you so much for making time for us.
22:05These were some of the best questions I was ever asked.
22:07So, thank you.
22:08You're amazing.
22:09I appreciate it.
22:09Thank you very much.
22:15Some day machines will be so intelligent, they'll outthink us and take over.
22:20This is completely ridiculous.
22:25There are many tasks that we can do, which it can do better.
22:29Now, is that something to worry about?
22:31Absolutely not.
22:36I have four children.
22:38Two of them work full-time at NVIDIA.
22:41When your stock options best, sell it right away.
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