0:00

LinkedIn presents. I'm

0:06

Rufus Griskim and this is The Next Big

0:08

Idea. Today,

0:10

Bill Gates on AI, the path to

0:13

super intelligence and what it means for

0:15

all of us. I

0:31

suspect that every moment in

0:34

human history has felt pivotal,

0:36

precarious, as if anything could

0:38

happen. But it also must be

0:40

true that some moments are more pivotal

0:43

than others. This is one

0:45

of those moments. We've

0:47

seen the impact of transformative technological

0:49

change. The internet has sped the

0:52

world up and social media, now

0:54

on most every phone in most

0:56

every hand, has polarized our communities,

0:58

hyperbolized our politics, and

1:00

now we are in the early

1:02

moments of the AI revolution. What

1:05

will the next decade bring? There

1:08

are few people I would rather

1:10

ask this question than Microsoft co-founder

1:12

and global philanthropist Bill Gates. Bill's

1:15

been at the forefront of the race to build

1:17

machines that can empower humans for 50 years, ever

1:21

since he declared his mission to put a

1:23

computer on every desk in every home. He

1:26

was instrumental in driving the development of personal computing in

1:28

the 80s, the growth of

1:30

the internet in the 90s, and more

1:32

recently leading the charge to eradicate malaria

1:34

and other diseases. In

1:37

the last few years, he's been on

1:39

the front lines of Microsoft's partnership with

1:41

OpenAI and the development of GPT. How

1:44

is it, you may be wondering, that Bill Gates

1:47

has ended up joining us today? Well,

1:49

for the last few months, I've

1:51

been reading a book that's being

1:53

published serially by Harvard Business Review.

1:56

It's called AI First, and it

1:58

features interviews with folks like Reid

2:00

Hoffman, Mustapia, and Salomon, Sam Altman,

2:02

and Bill, who collectively make the

2:04

case that AI isn't overhyped, it's

2:06

underhyped. We thought it would

2:08

be interesting to not just interview the

2:10

co-authors of this book, career technologist Andy

2:13

Sack, an old friend of mine, and

2:15

former Starbucks chief digital officer Adam Brotman,

2:18

and they suggested inviting one of their

2:20

most interesting interviewees, Bill Gates. And

2:22

so what's Bill's take on the AI

2:25

revolution? Superintelligence is coming.

2:27

There's no clear way to slow it

2:29

down. And the technology available

2:31

today is already a game changer.

2:34

This is largely a good thing.

2:36

We can harness AI to solve

2:38

our biggest global problems. We

2:40

are likely to live in decades to

2:42

come in a world of superabundance, but

2:45

it will take vigilance to make sure

2:47

it's the world we want for ourselves

2:50

and generations to come. By the

2:52

way, the format of today's show is a little

2:54

different from what you're used to. First, we'll hear

2:56

a conversation I had with Andy and Adam, co-authors

2:59

of AI First, about how they came

3:01

to write this book. Then

3:03

we'll bring on Bill for a

3:05

wide-ranging conversation about artificial intelligence and

3:08

our collective future. The

3:19

LinkedIn Podcast Network is sponsored by

3:21

Oracle. AI may be the most

3:23

important new computer technology ever. Do

3:25

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3:27

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Take a free test drive of

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OCI at oracle.com/LinkedIn Podcast Network. Welcome

3:42

Andy and Adam to The Next Big

3:44

Idea. Thanks for having us. Glad

3:47

to be here. Happy to be here. Andy,

3:49

you're a serial entrepreneur. You've built

3:52

and invested in countless startups. You

3:54

advised Microsoft CEO Satya Nadella. You're

3:57

the founder and managing director of Keen Capital, a

3:59

blockchain firm. fund and you

4:01

have the rare distinction of being an old friend of

4:03

mine. And you, Adam,

4:05

are no slouch. You were the first chief digital

4:08

officer at Starbucks where you led the development of

4:10

their app and payment platform. Quite a good app,

4:12

by the way. Thank you. You

4:14

were co-CEO of J crew. And

4:18

now the two of you have joined forces

4:20

to start a new company, forum three, to

4:23

help companies take advantage of the power

4:25

of AI. Does the

4:27

world really need another consulting

4:29

firm? No.

4:36

But you wouldn't define forum three as a consulting firm.

4:38

What are you guys setting out to do? It's

4:40

a great question in the sense of we're, we

4:43

provide software, we're building software,

4:45

we provide services, consulting services,

4:47

and other services. And

4:49

we're writing a book, which we're going

4:52

to talk to you about called AI First that's

4:54

being published by Harvard Business Review. But

4:56

they're all related to the topic of

4:58

taking advantage of AI to transform

5:00

your business, transform

5:03

your marketing efforts in building your brand.

5:05

And so we've, we've actually taken to

5:07

describing our forum three as an AI

5:09

lab because we can't come up with

5:11

a better, more descriptive term,

5:13

but, but it's actually an appropriate

5:15

term and kind of gives you

5:17

a sense of how Andy and I think about

5:19

the space. We're not taking

5:22

a traditional approach to building

5:24

the forum three company around AI. And

5:26

I think that's related to that, how

5:28

non-traditional this new technology is. So

5:31

you've written this book, you're publishing it

5:33

serially, which is very interesting. It's

5:36

called AI First. Why

5:38

AI First? It's

5:40

worth noting that our original title,

5:44

which is the title that we when

5:46

we wrote the proposal for Harvard

5:48

Business Review original title was our

5:51

AI journey. And

5:53

Harvard Business Review approached us a bit

5:55

over a year ago and at the time We

6:00

had just pivoted to become a

6:03

generative AI company at Forum

6:05

3. Both

6:07

Adam and I, our company Forum 3, were

6:10

on a collective journey to explore

6:12

what was this generative AI, which felt

6:15

like a very significant

6:17

technological development. Having

6:20

been a career

6:22

technologist, really started my first Internet company in

6:24

1995. A

6:27

bit over a year ago, I was like, this is

6:29

a big freaking deal. Little did I know just how

6:31

big of a freaking deal it was. The

6:34

title, our AI journey, started that

6:36

way. We started with a bunch

6:38

of interviews with thought leaders, one

6:41

of which we're going to get

6:43

to talk with today with Bill Gates,

6:46

but we also spoke with Sam

6:48

Altman and Reid Hoffman and Mustafa Salomon,

6:50

it's at Emifiu.

6:52

It's really been about Adam and

6:54

I educating ourselves about what is

6:56

this technology, what does it mean

6:59

for business leaders, what does

7:01

it mean for society, how does it

7:03

change the rules of the game? At

7:07

one point, I argued with Harvard Business Review,

7:09

I wanted to call the book The

7:11

Holy Ship Moment. That title

7:13

was not approved understandably,

7:16

but I think it is

7:18

a holy ship moment certainly

7:20

for business, certainly for

7:22

technology, and it's

7:24

a really groundbreaking technology that

7:27

we're mostly excited

7:29

about the possibilities and opportunities

7:31

comes. Really, when we talked

7:34

about what title to name it,

7:36

AI first, it was something that

7:38

we arrived at because as we

7:40

went along, we realized that it

7:42

was a total shift in mindset

7:44

that was required for

7:46

myself, for Adam, about how we think

7:48

about our specific little business, but also

7:50

how we approach business. When you think

7:53

about it from the individual to the

7:55

organization, you need a shift in mindset

7:57

and thus the name AI first. Well,

8:00

I had a few holy shit moments reading

8:02

the first four chapters of your book, which

8:04

I think is what's been published so far.

8:07

You're publishing it serially, which I wouldn't be

8:09

surprised if we see more of that kind

8:11

of approach to book writing in the future.

8:14

One holy shit moment for me was when

8:16

Sam Altman told you that he thought we'd

8:18

have AGI, which of course is artificial general

8:20

intelligence, defined as machine

8:22

intelligence that matches or exceeds

8:24

human intelligence within five years.

8:28

Within five years, I think most people would put it out

8:30

further if they think it's going to happen. You

8:33

asked Sam what AGI would mean

8:35

for business, for example, for marketing

8:38

teams. And he

8:40

said, it will mean that 95%

8:42

of what marketers use agency strategists

8:44

and creative professionals for today will

8:47

nearly instantly at almost no cost

8:49

be handled by the AI. And

8:52

the AI will likely be able

8:54

to test the creative against real

8:57

or synthetic customer focus groups. Again,

8:59

all free, instant and nearly perfect

9:01

images, videos, campaign ideas, no problem.

9:04

That's pretty astonishing. Do you guys buy it? Do

9:06

you think that this might be five years out?

9:09

Yeah. I mean, it's worth remarking

9:11

that when he said that to us,

9:14

we stepped outside the office and

9:16

didn't talk, which is rare

9:18

for Andy and I, didn't talk for like a

9:21

couple minutes. We just sat there like looking at

9:23

the San Francisco scenery and taking it in because

9:25

it was both how fast this was moving and

9:27

what it really meant. And then we

9:29

got into the book and we talked to Reed Hoffman

9:32

next. We talked to Bill, we talked to Mustafa Suleiman.

9:34

These are the top, top people in the field. And

9:38

they started reinforcing and validating what Sam

9:40

was saying and giving us more details

9:43

about it. So, yeah, I

9:45

say, well, we were holy

9:48

shit, quiet, stunned,

9:50

had to step aside after that Sam meeting.

9:53

Now we're more like ringing the alarm bell saying, yeah,

9:55

I mean, I don't know if it's five years, what

9:57

your definition is, but this thing is coming fast. and

10:00

the genie's out of the bottle for good and for

10:02

bad. So you've interviewed Bill

10:05

Gates, Mustafa Suleiman,

10:09

Reid Hoffman, you mentioned. What

10:11

surprises have you encountered along the

10:13

way? The biggest surprise

10:15

for me, I would say, is I

10:19

don't think that people have an awareness

10:22

of just how fundamental and significant

10:25

of a technology shift this is

10:27

and how fast it's coming. And

10:30

it's now. I learned, as

10:33

I talked about, it's such a significant moment

10:35

and how significant it's gonna change. The

10:38

rules of business, the game of

10:41

business, what's defensible, how to approach

10:43

strategy. Like it's, you

10:45

need to start to wrap your one's

10:48

mind around what it

10:50

means, because it's happening today. Certainly,

10:53

many of us have a certain amount of

10:55

concern and fear when it

10:58

comes to thinking about this

11:00

pace of tech acceleration and

11:03

moving beyond the AGI inflection point, and we'll talk

11:05

about that with Bill. But I'm

11:09

experiencing equal parts, adrenaline rush

11:11

and concern. On the adrenaline rush

11:14

side, what I remember from the

11:17

mid 90s, which was really just

11:19

the early days of the dawn

11:22

of the internet, I remember seeing the first

11:24

mosaic browser. I think the three of us

11:26

were all just out of college, right? And

11:28

at that time, and the

11:31

decision to get in early and

11:34

try to figure out this new technology and

11:36

try to think in advance about how

11:39

it would play out. I think that

11:41

was a decision that really benefited all three of

11:43

us. When I think

11:45

back on the inflection point

11:47

of the advent of the smartphone, I

11:50

was not thinking enough about that. We

11:54

could have sat in a room and said, you know what?

11:56

You got a mobile device that's a powerful computer with a

11:58

GPS unit in it, we can create Uber. Like

12:00

I did not have that sequence of thoughts. But

12:04

this feels like another such moment. I mean,

12:06

I have like pattern recognition is just exploding

12:08

with like, this is, we've seen

12:10

this movie before and

12:13

we should all be paying really

12:15

intense attention to what's happening. What's

12:18

wild about this one, we're all kind

12:20

of applying the same pattern recognition. However,

12:23

this one is different. It's

12:25

more powerful, but it's also more dangerous

12:27

and more confusing,

12:29

right? It's like intelligence as

12:32

a service, production level intelligence.

12:34

And so on the

12:36

one hand, I'm like you, and I think Rufus, you

12:38

and I, and Andy have talked about this in the

12:40

past, and this isn't new, but like, we're applying

12:45

our pattern recognition and there's like

12:47

this feeling of excitement.

12:50

And okay, we see this, let's get on it. But

12:52

there is a feeling of apprehension as well

12:55

about what it means to- Oh, for sure. Misinformation

12:58

and jobs and maybe even worse

13:00

that goes with it. And that

13:03

wasn't the same feeling we had

13:05

with the other seminal

13:08

moments. That's true. That's

13:10

a key difference here. And I think it's

13:12

good that we're acknowledging that. Yeah,

13:14

there's a question of, I mean, back

13:16

in those prior revolutions, I think I felt

13:19

nothing but let's hit the accelerator and I

13:21

find myself thinking now, let's hit the brakes.

13:23

And there's a separate question that builds uniquely suited

13:25

to answer, which is even

13:28

if we thought it made sense to apply a braking

13:31

mechanism to this process, is there any effective

13:33

way to do that given the global nature

13:35

of this process and given that we're not

13:37

all a bunch of friends, all

13:39

the entities building these technologies? So

13:42

I think that'll be an interesting thing to get Bill's take

13:44

on. You couldn't ask a better person

13:46

a more like perfect question for him to answer.

13:49

So like, I'm excited to hear what he says.

13:52

Coming up after the break, we'll hear from

13:55

Bill and what he has to

13:57

say may surprise you. This

13:59

technology in terms

14:01

of its capability, we'll

14:03

reach superhuman levels. We'll be right back. If

14:30

you're interested in the story behind the

14:32

business headlines, check out Big Technology Podcast,

14:34

my weekly show that features in-depth interviews

14:36

with CEOs, researchers, and reformers in business

14:39

and technology. Hi,

14:59

I'm Alex Kantrowitz. I'm a longtime

15:01

journalist, CNBC contributor, and the host

15:03

of the show. I

15:06

empty my Rolodex every Wednesday to bring

15:08

you awesome episodes, so go check out

15:10

Big Technology Podcast. It's available in all

15:12

podcast apps. We'd love to have you

15:14

as listener. Bill,

15:22

Andy says you win about as frequently as he wins

15:24

on the pickleball court. Is that sound right to you?

15:27

Pretty equal, yeah. Hey,

15:30

Bill. Hi. Bill

15:33

Gates, welcome to The Next Big Idea. Thank

15:35

you. Bill,

15:37

Andy and Adam and I were just talking about

15:39

the digital transformations we've seen in our own lives

15:41

in the last 40 years. And

15:44

you haven't just seen these transformations. You've played

15:47

an instrumental role in moving them forward. You've

15:50

said that the demo you saw

15:52

last September of GPT-4 was

15:55

mind-blowing. Was it

15:57

more mind-blowing than the first demo of the

15:59

Graphically-based demo? user interface that you saw at

16:01

Xerox PARC in 1980? I'd

16:04

say yes. I mean, I'd

16:07

seen graphical interface prior

16:09

to the Xerox PARC stuff, and that

16:11

was an embodiment that helped

16:14

motivate a lot of what Apple

16:17

and Microsoft did with

16:20

personal computing in the decade

16:23

after that. But compared

16:26

to unlocking

16:28

a new type

16:30

of intelligence that can read

16:32

and write, graphics interface

16:34

is clearly less impactful,

16:38

which is saying a lot. Well,

16:40

I was interested to learn that AI is not

16:43

a new interest of yours. You

16:45

were intrigued as a student way back in the

16:47

70s, and I gather

16:49

you wrote, I think, a letter to

16:51

your parents and said effectively, mom, dad,

16:53

I may miss out on the AI

16:56

revolution if I start this company, which

16:58

is the company that became Microsoft. The

17:01

AI revolution took a little longer than maybe you

17:03

might have guessed back then. Now it's

17:05

happening. What interested you about

17:07

AI in those early days,

17:09

and is it becoming what you'd

17:11

imagine back then? Well,

17:14

certainly anybody who writes software is

17:17

thinking about what human

17:19

cognition is able to achieve

17:22

and making that comparison. And

17:25

when I was in high school, there

17:28

were things like Shaky the Robot at

17:30

Stanford Research Institute, which would engage

17:33

in reasoning and come up with an execution

17:35

plan and figure out

17:37

to move the ramp and go up the ramp and

17:39

grab the blocks. And

17:42

it felt like some of these key capabilities,

17:47

whether it was speech recognition, image

17:49

recognition, and it would be

17:51

fairly solvable. There were a

17:53

lot of attempts and so-called rule-based systems

17:55

and things that just didn't capture

17:58

the richness and so- So in

18:00

our respect for human cognition constantly

18:03

goes up as we try

18:05

to match pizzas of it. But

18:07

we saw with machine learning techniques,

18:10

we could match vision

18:13

and speech recognition, so

18:15

that's powerful. But

18:17

the holy grail that even

18:20

after those advances I kept highlighting

18:22

was the ability to

18:25

read and represent knowledge like

18:27

humans did was just, you

18:30

know, nothing was good at all.

18:33

Then language translation came down,

18:35

but still that was a

18:37

very special case thing.

18:42

But GPT-4 in

18:44

a very deep way, far beyond

18:46

GPT-3, you know, showed that

18:49

we could access and represent

18:51

knowledge. And it's, you know, the

18:53

fluency in many

18:55

respects, although not the accuracy,

18:57

is already superhuman. Yeah,

19:00

it's just astounding. We never would have guessed

19:03

that moving the chess pieces on the chessboard

19:05

would be harder than becoming a better chess

19:07

player than Kasparov. But

19:10

it's interesting to see how what

19:12

the challenges turn out to be. And

19:15

as you said, that Xerox PARC demo

19:17

set the agenda for Microsoft for maybe

19:19

the next 15 years, right? Development of

19:21

Windows and Office. And

19:23

do you think that the impact of what's

19:25

happening right now in AI is going to

19:27

set the agenda for the

19:30

next many decades and even more

19:32

so? It's

19:34

absolutely the most important thing going on. It'll

19:37

shape humanity in a very dramatic

19:39

way. It's at the

19:41

same time that we have, you know, synthetic

19:43

biology and robotics being controlled

19:45

by the AIs. So

19:48

we have to keep in mind those

19:51

other things. But the dominant change

19:53

agent will be AI. In

19:56

1980, you had a light bulb moment when

19:58

you famously declared, there will be a- a

20:00

computer in every home, on every desk.

20:03

What do you think the equivalent is for AI? Do

20:06

you think we'll have an AI advisor in

20:08

every year? Well, the form

20:10

factor, the hardware form factor doesn't

20:12

matter that much. But the idea

20:15

of the earbud that's both

20:18

adding audio and canceling

20:20

out audio and enhancing

20:22

audio clearly will be a

20:24

very primary form factor just

20:26

like glasses that

20:29

can project arbitrary video into

20:32

your visual fields will

20:35

be the embodiment

20:38

of how you're interacting.

20:41

But the personal agent that I've

20:43

been writing about for decades, that's

20:46

superior to a human insistent

20:48

in that it's tracking

20:50

and reading all the things that you wanted

20:53

to read, and just there to help

20:55

you, and

20:57

understands the context enough that silly

21:00

things like, you don't trust software today

21:02

to even order your

21:05

email messages. It's in a stupid

21:07

dumb time-ordered form because

21:10

the contextual understanding of, okay,

21:13

what am I about to do next?

21:15

What's the nature of the task that

21:18

these messages relate to? You

21:21

don't trust software to combine all

21:23

of the new information,

21:25

including new communications. You

21:27

go to your mail

21:30

and that's time-ordered, you go to your

21:32

texts and that's time-ordered, you go to

21:34

your social network and that's time-ordered. I

21:36

mean, computers are operating at a

21:39

almost trivial level of semantics

21:41

in terms of understanding what's

21:43

your intent when you sit down with the

21:46

machine or helping you with

21:48

your activities. Now

21:50

that they can essentially

21:52

read like a

21:55

white-collar worker, that

21:58

interface will be entirely agent

22:00

driven, you know, agent executive

22:02

assistant, agent mental therapy, agent

22:05

friend, agent girlfriend, agent expert,

22:08

all driven by deep AI.

22:12

It seems like it will be useful in

22:14

proportion to how much it knows about us.

22:17

And I imagine at some point in the not too

22:19

distant future, probably all four of us will be asked

22:22

if we wanna turn on audio so our

22:25

AI assistant can effectively like listen to

22:27

our whole life. And I would

22:29

think that there'll be benefits to do that because

22:32

we'll get good counsel, good advice.

22:36

Do you think that's true? And do you think, will you turn

22:38

it on when invited

22:40

to turn on the audio?

22:42

Well, computers today see every

22:44

email message that I write and

22:48

certainly digital channels are seeing all

22:51

my online meetings and

22:55

phone calls. So you're

22:57

already disclosing into digital

22:59

systems a lot about

23:02

yourself. And so

23:04

yes, the value added of the

23:06

agent in terms of summarize

23:09

that meeting or help me with

23:11

those follow-ups, you know, be

23:13

phenomenal. And the agent

23:15

will have different modes in terms

23:17

of which of your information it's

23:21

able to operate with. So there will

23:23

be partitions that you

23:25

have, but for your essentially

23:28

executive assistant agent, you

23:30

won't exclude much at all from

23:32

that partition. Rufus before we

23:34

go further down the agent

23:37

pathway, one question that I've

23:39

been thinking about since our interview

23:41

with you, Bill, for AI First, in

23:44

which you talked about really

23:46

comparing your experience

23:48

at Xerox PARC versus your experience

23:51

experiencing chat GBT4,

23:55

I think you're in the most

23:57

unique position. There are probably a couple of other

23:59

people. that I could think of. But

24:02

you're in the most unique position to

24:04

have the set of understanding of

24:06

computer technology as well as

24:09

building business and how computers

24:11

affect human beings. I'm

24:14

curious, if what you said in this

24:16

conversation, which was chat GPT, was as

24:18

big, it sounded like you even said

24:20

it was bigger than your Xerox

24:22

PARC moment, what does

24:25

that make you think about when you

24:27

think about your grandchild's life and what

24:30

advice do you have for the next

24:33

generation of leaders for

24:35

tackling the challenges that are

24:37

unique to AI? I'm curious

24:40

about that perspective. There's

24:42

certainly novel problems in

24:44

that other technologies

24:48

develop slower and

24:50

the upper bound of their

24:52

capabilities is pretty

24:54

identifiable. This technology, in

24:57

terms of its capability, will

25:01

reach superhuman levels. We're

25:03

not there today if you put in

25:05

the reliability constraint. A lot of

25:07

the new work is

25:11

adding a level of metacognition

25:15

that done properly will solve

25:18

the erratic nature

25:20

of the genius

25:22

that is

25:25

easily available today

25:27

in the white-collar realm and over time

25:30

in the blue-collar realm as well. So

25:33

yes, this is a huge milestone

25:36

that some of those past

25:38

things are helpful to, but it's

25:41

novel enough that nobody's faced

25:45

the policy issues, which

25:47

are mostly of a very positive nature in

25:49

terms of white-collar

25:52

labor productivity. What's

25:55

the thing that excites you the most about

25:57

the invention? today.

26:00

shortages, there's no

26:03

organization that faces white-collar shortages as

26:06

much as the Gates Foundation where

26:08

we look at health

26:10

in sub-Saharan Africa

26:13

or other developing countries or

26:15

lack of teachers who

26:17

can engage you in a deep way, ideally

26:19

in your native language.

26:22

And so the idea that by using

26:25

the mobile phone infrastructure

26:27

that continues to

26:30

drive pretty significant penetration even

26:32

in very poor countries, the

26:35

idea that medical advice and

26:37

personal tutors can

26:40

be delivered where, you

26:43

know, because it's meeting you in

26:45

your language and your semantics, there

26:48

isn't like some big training thing that's

26:51

taking place there. You just pick up your

26:53

phone and listen to what it's saying. So

26:57

it's very exciting to take

27:00

the tragic lack of

27:02

resources that particularly

27:05

people in developing countries have to deal

27:08

with. You've been working for

27:10

your 20 years on

27:12

the Gates Foundation and really

27:14

tackling these issues of global

27:16

healthcare, education, climate change. Do

27:19

you think that AI will be an

27:21

accelerant that will make it possible to

27:24

accomplish in five or 10

27:26

years what it took the last 20

27:28

years to accomplish or how meaningful do

27:30

you think the acceleration is likely to

27:33

be in these areas?

27:36

Well, the very tough problems of

27:40

some, you know, diseases that we don't

27:42

have great tools for, AI will

27:44

help a lot. The last

27:47

20 years, you know, was pretty

27:49

miraculous in that we cut child to

27:51

death in half from 10 million

27:53

a year to 5 million a year. That

27:56

was largely by using getting

27:59

tools. like certain

28:01

vaccines to be cheaper

28:04

and making sure they were getting

28:06

to all the world's children. And

28:08

so that was kind of low-hanging

28:10

fruit and now we

28:12

have, you know, tougher issues. But with

28:15

the AIs, the upstream

28:17

discovery part of, okay, why

28:19

do kids get malnourished or, you know,

28:21

why has it been so hard to

28:23

make an HIV vaccine? Yes,

28:25

we can be, you know, way

28:27

more optimistic about

28:30

those huge breakthroughs.

28:33

You know, AI will help us with every

28:35

aspect of these things, the advice,

28:38

the delivery, the diagnosis.

28:41

The scientific discovery piece is,

28:44

you know, moving ahead at a pretty

28:46

incredible clip and the Gates

28:49

Foundation's very involved in funding quite

28:51

a bit of that. Yeah,

28:53

we had your friend Saul

28:55

Kahn on the show recently

28:58

and got the chance to spend a bunch of

29:00

time with Kahn Meego and I was just

29:03

astonished by what that can do. I

29:05

know you were recently in New Jersey

29:08

visiting schools that are implementing Kahn

29:11

Academy's new programs and

29:14

that's pretty exciting, this idea that improving

29:16

education at scale for billions of people,

29:18

the impact of that is

29:21

pretty hard to

29:23

measure. Yeah, I mean, Saul's

29:25

book doesn't say,

29:28

okay, what world are we educating kids for? It's

29:30

just if all AI was was

29:33

available in education, you know,

29:35

that's pretty miraculous because you have

29:38

the other things shifting

29:40

at the same time, it's a

29:42

little more confusing. But, you

29:44

know, that realm where he says, okay, what

29:46

if it was just an education, you know,

29:49

it's incredibly positive. Yeah,

29:52

well, that's that gets to the personal part

29:54

of your, you know, I think you

29:56

have a new granddaughter. I know Adam has a seven

29:58

year old and when we think of this question of

30:00

like what does it look like? I

30:03

mean fantastic that our kids

30:05

will have an Aristotle level

30:07

private tutor to help further

30:10

accelerate their educational process. But there is the

30:12

question of like what will they

30:14

need to know to be effective in the

30:16

world? And my kids

30:19

and Andy's kids are a little older, but I know

30:21

Adam, you've got a younger daughter and Bill,

30:24

you've got a new granddaughter. It's

30:26

interesting because Bill, I wanted to come

30:28

at this from a slightly different direction, but since you brought

30:30

it up, she's able

30:34

to really, she watches me use

30:36

Whisper Mode on ChatChaPT. She's seen

30:39

me live in an AI world and it's fascinating

30:41

to watch her be very

30:43

comfortable with a voice interface. Especially

30:45

at her age, it's actually easier for her to

30:47

do voice interface than she's still

30:50

learning how to spell. I mean, she just

30:52

figured out how to read. So I thought

30:54

that was an interesting, I'll call it look

30:56

into how much this can be, not just

30:58

natural language chat, but even voice chat versus

31:01

point and click. But Bill, I was going to ask

31:03

you something about the direction, maybe

31:05

come at this from a slightly different direction, which

31:08

is what do you think about

31:11

this debate? There's a little bit of a debate going on.

31:13

Maybe that's too strong of a word about whether

31:16

or not the fact that all these frontier

31:18

or foundation models have sort

31:20

of clustered at the benchmarks around ChatChaPT4.

31:23

And there's some people that

31:25

are on the side that were plateauing

31:27

or something like that. But most of

31:30

the smartest researchers I follow tend

31:32

to still say with the fact that the scaling

31:34

laws are going to continue to apply for at

31:36

least the next couple of years. I'd love to

31:38

get your take on A, where

31:41

do you come out on that

31:43

discussion? And B, do you

31:45

find yourself rooting for it to plateau? Or

31:47

do you like emotionally agnostic

31:49

because of some of the concerns

31:51

around the technology? Well, the

31:54

big frontier is not so much

31:56

scaling. We have probably

31:58

two more turns of the crank on

32:00

scaling, whereby

32:03

accessing video data and getting

32:05

very good at synthetic data,

32:09

that we can scale up probably

32:11

two more times. That's

32:14

not the most interesting dimension. The

32:17

most interesting dimension is what I

32:19

call metacognition, where understanding

32:21

how to think about a problem in

32:24

a broad sense and step back and

32:27

say, okay, how important is this answer?

32:30

How could I check my answer? What

32:33

external tools would help me with this? The

32:36

overall cognitive strategy

32:39

is so trivial today that

32:42

it's just generating through

32:44

constant computation each token

32:47

in sequence, and it's mind-blowing that

32:49

that works at all. It

32:52

does not step back like a human and think,

32:55

okay, I'm going to write this paper, and here's

32:57

what I want to cover. I'll

33:00

put some facts in. Here's what I want to do for

33:02

the summary. You see

33:04

this limitation when you have

33:06

a problem like various math things,

33:08

like a Sudoku puzzle, where

33:11

just generating that upper left-hand

33:13

thing first causes it

33:15

to be wrong on anything

33:17

above a certain complexity. We're

33:20

going to get the scaling benefits, but

33:24

at the same time, the various

33:27

actions to change the

33:29

underlying reasoning algorithm from

33:32

the trivial

33:34

that we have today to

33:37

more human-like metacognition, that's the

33:39

big frontier. It's

33:41

a little hard to predict how

33:43

quickly that'll happen. I've seen that

33:46

we will make progress on that next year,

33:48

but we won't completely solve it for

33:51

some time after that. Your

33:54

genius will get to be more predictable.

33:56

Now, in certain domains, confined

33:59

domains... We are getting to the

34:01

point of being able

34:03

to show extreme accuracy on

34:06

some of the math or even some of the health type

34:09

domains. But the open-ended thing

34:11

will require general breakthroughs

34:13

on metacognition. And

34:16

do you think that metacognition will

34:19

involve building in a looping

34:21

mechanism so the AI

34:23

develops an ability to ruminate, as

34:25

we homo sapiens do? And

34:28

is there, I've heard some people like

34:30

Max Tegmark suggest that that could be

34:33

part of what makes us conscious is

34:35

this ability to have conversations with ourselves.

34:38

Yeah, consciousness may relate

34:41

to metacognition. It's not a

34:43

phenomena that is

34:45

subject to measurement, so it's always tricky. And

34:48

clearly these digital things are

34:52

unlikely to have any

34:54

such equivalent. But it

34:57

is the big frontier, and

34:59

it will be human-like in terms of,

35:01

you know,

35:03

knowing to work hard on certain hard

35:05

problems and having a sense of confidence

35:08

and ways of checking what

35:11

you've done. One

35:13

of the things that I'll just say in

35:16

the process of writing and interviewing you

35:18

for AI First, as well

35:20

as Reid Hoffman and Sam

35:24

Altman, Mustafa, it's been an

35:26

education for Adam and I. And

35:29

I come away from these conversations regularly

35:32

going, oh, my goodness.

35:36

And I'm

35:38

blown away at the, like

35:40

I'm paying attention every day

35:42

to the pace of the

35:44

technological advance by really many

35:46

different companies, large companies, there's a lot

35:48

of money, there's a lot of talent

35:51

being poured into us. And so the

35:53

pace of the development and

35:55

the potential impact of that

35:57

technological advance astounded.

35:59

by and have some limited

36:02

understanding. Do you think

36:04

we're moving too fast? You know, if

36:06

we knew how to slow it down, a

36:09

lot of people would probably say, okay, let's

36:15

consider doing that. You

36:18

know, as Mustafa writes in his

36:20

book, the incentive structures really

36:24

have some mechanism

36:26

that's all that plausible

36:28

of how that

36:31

would happen, given the individual

36:34

and company and

36:37

even government-level thing. If

36:39

the government-level incentive structure was understood, you

36:42

know, that alone might be

36:44

enough. And, you know, like

36:46

the people who say, oh, it's fine that it's open

36:49

source, you know, they're willing to say,

36:51

well, okay, if it gets too good,

36:53

maybe we'll stop open sourcing it. But,

36:56

you know, will they know what

36:58

that is? And would they

37:01

really say, okay, maybe the next

37:03

one? You know, so you pretty

37:06

quickly go to, let's

37:08

not let people

37:11

with malintent benefit

37:14

from having a better

37:17

AI than, you know,

37:20

the sort of defense, good intent side

37:23

of, you know, cyber defense

37:25

or war defense

37:27

or bioterror defense. You're

37:30

not going to completely put the genie back

37:33

in the bottle. And yet, that

37:36

means that, you know,

37:38

somebody with negative intent will be

37:40

empowered in a new way. So

37:43

perhaps not a good idea for

37:45

the most sophisticated AI models to

37:47

be open source in your judgment,

37:49

given this global environment.

37:52

Yeah. And people sort of seed that

37:54

point in principle, but then

37:57

when you try to get to say,

37:59

okay, specifically, where would you

38:01

apply that? It gets a bit less

38:03

clear. I

38:06

mean, Adam and I were talking yesterday about how even

38:09

if it were possible, hypothetically, to stop

38:11

AI development exactly where it is right

38:14

now, it would probably take 10 years

38:16

of forum three and other folks

38:19

helping companies and individuals figure

38:21

out how to apply the

38:23

technology that currently exists. I'm

38:26

not sure about that because, you

38:28

know, it's pretty clear, you know, I

38:30

want to make an image. Okay, what

38:33

do I have to learn? I have to

38:35

learn English. This is the software meeting us,

38:37

not us meeting the software. You know, so

38:39

it's not like there's some new menu, you

38:41

know, file, edit, window, help, and oh, you

38:44

got to learn that, you have to type

38:46

the formula into the cell. This

38:48

is you saying, hmm, I wish I

38:50

could do data analysis to see which

38:53

of these, you know, products is

38:55

responsible for the slow down. And

38:58

it understands exactly what

39:01

you're saying. So the idea that there's an

39:03

impedance of adoption, it's

39:06

not the normal thing. Yes, company

39:09

processes that are very

39:11

used to doing things the old way will

39:14

have to adjust. But if you look at tele-support,

39:17

telesales, data

39:19

analytics, you know, give somebody a

39:21

week of watching an advanced

39:25

user and, you

39:27

know, say no manual of any kind,

39:29

just, you know, learn by example of

39:32

how the stuff is being used. The

39:34

uptake, assuming there's no limit in

39:37

terms of the, you know, server

39:39

capacity that connects these things up,

39:41

which I don't expect certainly

39:44

in rich countries, there'll be

39:46

a gigantic limitation there. And

39:49

you're talking about an adoption rate

39:51

that won't be overnight, but

39:53

it won't be like, you know,

39:56

10 years. Like take

39:58

human translation. The

40:00

idea that a free product

40:03

provides arbitrary audio and

40:06

text human translation.

40:09

I mean, that was a holy grail of, oh my God,

40:11

if you ever had a company that could do that, it

40:13

would collect tens of billions

40:15

in revenue and solve the Tower

40:17

Babel. Here,

40:19

a small AI

40:21

company is providing

40:24

that as an afterthought free

40:26

feature. Right. It's

40:29

pretty wild and you say, well, oh, how

40:31

are people going to adapt to free translation?

40:35

I don't think it's going to take them that long

40:37

to know, hey, I want to know what that guy

40:39

was saying. Yes, the quality

40:41

of that a year from now and

40:43

the coverage of, say, all

40:45

African languages will get

40:47

completed. The foundation's making sure

40:50

that even obscure languages

40:52

that are not written

40:54

languages that were in partnership

40:58

with others, gathering the data for

41:00

those, the Indian government's doing that for

41:03

Indian languages. I

41:06

don't think saying, hey, calm down,

41:08

it takes a long time to figure

41:11

out how to utter the description

41:13

of the birthday card you want.

41:16

It'll take 10 years for the

41:19

lagging people to switch

41:21

their behavior. Well, we see,

41:23

I think Sam Altman said on your

41:25

podcast, Unconfused Me, which I enjoy, that

41:27

they're seeing a productivity improvement of up

41:29

to 300%, I

41:32

think, among their developers. In

41:34

other sectors, I think we've seen reports of 25, 50%

41:37

increases in productivity. Just getting

41:40

that, the great Gibson line, the

41:42

future is here, it's just not evenly distributed. It

41:45

does feel like getting all companies to

41:48

fully benefit from that level of productivity

41:50

enhancement, it certainly will be

41:52

a process of some kind. I

41:54

was interested in your comment in the first chapter

41:56

of AI First, which is about productivity,

41:59

you said, Productivity isn't a

42:01

mere measure of output per hour.

42:03

It's about enhancing the quality and

42:05

creativity of our achievements.

42:09

What do you mean by that? Well, whenever

42:11

you have a productivity increase, you

42:14

can take your X

42:16

percent increase and increase the

42:18

quantity output. You can improve the quality of

42:20

the output, or you can

42:23

reduce the human labor hours that

42:25

goes in input. And so you

42:27

always take those three things. You

42:30

know, there are some things when they get

42:32

more productive, like when the tire industry went

42:34

from non-radial tires

42:37

to radial tires, even

42:39

though the cost per

42:42

year of tire usage went

42:44

down by a factor of four, people

42:46

didn't respond by saying, okay, I'm gonna drive

42:49

four times this much. So

42:51

the demand elasticity

42:54

for some things like computing or

42:57

the quality of a news

42:59

story, there's very high demand

43:02

elasticity. If you can do a better

43:04

job, you just leave the human labor

43:07

hours alone and take most

43:09

of it in the quality dimension. And

43:11

then you have a lot of things where

43:13

that's not the case at all. The

43:16

appetite for miles driven

43:19

did not change. The society is full

43:21

of many things that are

43:23

across that spectrum. And

43:26

so whenever you have rapid productivity increases,

43:29

you know, there was a memo inside Microsoft

43:31

about how we were gonna make databases so

43:33

efficient that it would become

43:35

a zero-sized market. Now

43:38

in that case, we're still in the

43:40

part of the curve where

43:42

you have demand elasticity, but you know,

43:45

someday even in that domain

43:47

we'll get past incremental

43:50

demand. If you were

43:53

making a guess right now, and

43:55

you mentioned healthcare and education, how

43:59

would you respond? to the question about,

44:02

what do you think the first big,

44:06

I'll call it breakthrough application

44:08

will be? Like for example, like one of

44:10

the podcasts that Andy and

44:12

I like to listen to, they were talking this

44:14

weekend, they keep saying, oh, we haven't seen the

44:17

big breakthrough application.

44:19

And I'm, which

44:22

is interesting because I'm not

44:24

sure that's true, but let's just take it

44:26

for on its face value that we're still in the sort

44:28

of, I'll call it experimentation

44:30

phase or whatever, which is what they

44:32

were trying to say. I'm curious to

44:34

get your, what's your thought? Like where

44:36

do we see the first big, the

44:39

Uber, like if like, location services and

44:41

mobile cloud, the first big app was

44:43

kind of Uber and everyone talked about

44:45

Uber being an example of that. And

44:47

then it was probably before that, it

44:50

was probably Google Maps, right? It was probably

44:52

map technology. That's right, that's right. So

44:54

we have, Bill, when you just think

44:56

out, do you go right to education,

44:58

healthcare? Where

45:00

does your head go when you think, oh, I'll

45:02

bet you the first big breakthrough app, consumer

45:05

app, or even industrial app will be what?

45:08

Well, I guess the naysayers are pretty creative

45:10

to be able to say something hasn't happened.

45:16

I mean, they don't think, memorizing

45:19

meetings or doing translation

45:22

or making product programmers

45:24

more productive. I mean,

45:27

it's mind blowing. This

45:29

is white collar capability with

45:32

a footnote that in

45:34

many open-ended scenarios, it's not as

45:36

reliable as humans are. And

45:39

you can hire humans and they can go haywire

45:42

and so you have some monitoring, but

45:44

that, these things it

45:46

put into new territory are

45:51

somewhat less predictable as

45:53

there's some domains where we can

45:55

bound what goes on like

45:59

support calls. or telesales calls

46:01

where you're not pushing off the

46:03

edge at

46:05

all. So I

46:08

don't know, I just can't imagine

46:10

what they're talking about. Yeah. Let

46:14

me try and I think it's the

46:16

comment when people say that, notwithstanding

46:22

what you just said, Bill, they're

46:25

creative in their naysaying capabilities. Because

46:27

I think that's your response is accurate

46:30

for sure. It's the

46:32

second order effect. When the car was developed, it could

46:34

get you from point A to point B. And

46:37

you might even be able to predict

46:39

the development of roads and highways, etc.

46:41

But you might not be able to

46:43

predict Los Angeles

46:46

or suburbs, drive-in

46:48

movie theaters. I

46:50

think in more modern stance,

46:53

the World Wide Web came along and

46:55

there were lots of brochure ware and

46:58

there was travel age, Expedia came

47:00

along. And that was all sort

47:03

of like run-of-the-mill first order effect.

47:05

But people point at Uber

47:07

as a second order effect

47:09

on the technology that was like,

47:11

you couldn't have predicted that. Now,

47:14

maybe you could, maybe you couldn't.

47:16

But that's what Adam's question I

47:18

think is going for. When you

47:20

look at AI, in many ways,

47:23

the game of search has already

47:25

changed, which is ubiquitous consumer

47:27

activity. And certainly, chat

47:30

GBT was a monumental, the

47:33

fastest growing adopted technology in our

47:36

ever. So I'm not minimizing or

47:38

giving credence to the naysayers, but

47:40

it's really about the second order

47:43

effects. Chat GBT 3 was not

47:45

that interesting. I mean, it was

47:47

interesting enough that a few people that opened the eye, felt

47:50

the scaling effect would cross a

47:52

threshold. And I didn't

47:56

predict that and very few

47:58

people did. And we all know that. only

48:00

crossed that threshold less

48:02

than two years ago, a

48:05

year and a half in terms

48:07

of general availability. So we are

48:09

very much in the people

48:11

who are open-minded,

48:14

and are willing to try out new things

48:17

are the ones using

48:19

it. But you just

48:21

demo, okay, here's image editing and

48:24

no, I'm not teaching you 59 menus

48:27

and dialogues in Photoshop to

48:30

do editing. I'm telling you type, get

48:33

rid of that green sweater, and people are like, oh,

48:35

I don't know if I could do that. I mean,

48:37

that sounds very hard. When

48:40

you show people that, it's like, what?

48:43

Make that photo bigger. I didn't take

48:45

a shot that was bigger, but I'd

48:47

like the photo to be

48:49

bigger. So fill in the missing piece to make

48:51

it bigger. It's like, what? Or

48:54

patient follow-up, where it calls you up

48:57

and talks to you about to do

48:59

a failure prescription, how are you feeling?

49:02

What are you doing? I mean, people may

49:04

get saturated if they really try and

49:07

expose themselves to the various

49:11

examples. I

49:13

do think they'd be saturated though, my

49:15

God, this is a lot

49:18

of extremely concrete capability.

49:21

Then you think, okay, when

49:23

I call up to ask about my taxes,

49:25

when I want my medical bill explained, that

49:28

white collar worker is

49:32

almost free type mentality,

49:35

is the best way

49:37

to predict what this

49:39

thing suffuses to, even though I fully

49:41

admit there's a footnote there that it's,

49:44

in some ways, still a little bit of

49:46

a crazy white collar worker. We're

49:51

going to get rid of that footnote

49:53

over a period of years. I

49:56

know one of those crazy white collar workers who's the

49:58

CEO of a company that's growing. very quickly who

50:00

asked his top salespeople, what

50:03

takes you the most time during

50:05

this day? And they said, drafting

50:08

follow-up emails following sales calls. And

50:10

he created an instance of GPT

50:13

to, you know, pulled in all their

50:15

best practices, best communications, automatically

50:18

transcribes every phone call and automatically

50:21

generates the follow-up email. And

50:24

he's laying off half of his sales team

50:26

so that the best half of his sales

50:28

team can now work twice as efficiently. So

50:30

there we have both a success

50:33

story in the sense that it's a

50:35

highly efficient and wildly

50:37

impressive implementation of the technology. But

50:41

for the other half of the sales team, it's not quite as

50:43

exciting unless they can use new

50:46

AI technologies to build a competing

50:48

company or to do something else, which I guess,

50:50

you know, gets to this broader question of like,

50:52

to what extent do we think this empowers the

50:55

little guy versus the big

50:57

guy? I mean, we're seeing that just a

50:59

few big companies seem to

51:01

be the dominant players in the development of

51:03

the technology. But on the

51:05

other hand, it does seem that everyone has

51:08

access to GPT4 Omni at

51:10

now for free. So there's also

51:12

an equalizing element. Well,

51:16

it's important to distinguish two

51:19

parts of economic activity. One

51:21

is the economic activity building

51:23

AI products and

51:27

both base level AI products and

51:29

then vertical AI products.

51:33

And we can say for sure that

51:36

the barriers to entry are uniquely

51:38

low in that we're in

51:40

this mania period where, you know,

51:43

somebody literally raised $6

51:45

billion in cash for

51:48

a company and many others raised

51:51

hundreds of millions. And,

51:53

you know, so the idea

51:56

that there's, you know, there's

51:58

never been as much capital. going

52:01

into a new category. You could even say

52:03

a new mania category. I mean, this makes

52:06

the internet or the early auto

52:08

industry mania look quite

52:10

small in terms of the percentage of IQ

52:14

and the valuations that

52:17

come out of this. I mean, there

52:19

was no company before the turn of

52:21

the century that had ever been worth

52:23

a trillion dollars. Here we have

52:25

one chip company who doesn't make chips. It's

52:28

a chip design company. That

52:30

in six months adds a

52:33

trillion dollars of value. And so

52:35

the dynamics within the AI

52:38

space is both

52:40

hyper-competitive, but with lots of entry.

52:43

And yes, Google and Microsoft have

52:46

the most capital, but that's

52:48

not really stopping people

52:50

either in the base capabilities

52:52

or in those verticals.

52:55

Once you leave the AI tools domain,

52:57

which as big as it is,

53:00

is a modest part of the

53:02

global economy, how that gets applied

53:05

to, okay, I'm a

53:07

small hospital chain versus a big

53:09

hospital chain. Now, when

53:11

I have these tools to set up, level

53:14

the playing field or not,

53:17

you would hope that it would, and that you

53:19

can offer for the

53:21

same price or less a far

53:24

better level of service. All

53:26

of these things are in the furtherance

53:28

of getting the

53:30

value down to the customer. And

53:33

figuring out early in an industry where the

53:36

barriers are so that

53:38

some of the improvements stick

53:41

with companies versus perfect

53:43

competition where it all goes to

53:45

the end users. That's very hard

53:48

to think through. Like

53:50

picks and shovels is saying, okay,

53:52

look to the side industries, as

53:56

well as to the primary industry. Savings and

53:58

loans did better than home. builders because

54:02

there was a more scarce capability

54:06

there that a few did

54:09

better than others. It's asking

54:11

a lot, but it is

54:13

people are being forced to think about the

54:16

competitive dynamics in these other

54:18

businesses. When you free

54:21

up labor, that labor society is

54:23

essentially richer that through

54:26

your tax system, you can take that labor and put

54:28

it into smaller class

54:30

size or helping

54:33

the elderly better, and your

54:35

net better off. Now, for the person involved,

54:37

they may like

54:39

that transition or not, and it

54:41

requires some political capacity to do

54:43

that redirection, and you can have

54:45

a view of our current

54:48

trust in our political capacity to

54:51

reach consensus and create

54:55

effective programs. But

54:59

the frontier of possibilities is

55:02

improved by increased productivity. You'd never

55:04

want to run the clock backwards

55:06

and say, thank God we were

55:08

less productive 20 years ago. We

55:10

were talking earlier about the impossibility

55:12

of slowing down or the great

55:15

difficulty of slowing down the current

55:17

pace of AI development. Do

55:19

you think AI companies should be

55:21

governed, and if so, by whom?

55:24

By boards, by government,

55:26

by all of the above? Well,

55:28

government is the only

55:30

place where the overall well-being of society

55:33

is a whole, including

55:37

against attack

55:39

and a judicial

55:41

system that's fair and creating

55:45

educational opportunities. So you

55:48

can't expect the private

55:50

sector to walk

55:53

away from market-driven

55:57

opportunity unless the government

55:59

decides. what the rules

56:01

are. So this is, although the

56:03

private sector should help educate government work

56:05

with government, the governments

56:09

will have to play a big role here,

56:12

you know, so that's a dialogue

56:14

that people are investing in. Now

56:16

governments will take the things that

56:19

are most concrete, like what

56:21

are the copyright rules or what are

56:23

the abuses of deep fakes or, you

56:26

know, in some applications does the emeral

56:28

liability, say, of health

56:30

diagnosis or hiring

56:33

decisions, you

56:35

know, mean that you ought to move

56:37

more slowly or create some liability

56:40

for those things. They'll

56:42

tend to focus in on those short-term issues,

56:44

which, you know, that's fine, but, you

56:47

know, the biggest issue has to do with the

56:51

adjustments to productivity

56:53

that overall,

56:55

you know, should be a

56:58

phenomenal opportunity if political

57:01

capacity and the speed which

57:03

which was coming were paired

57:05

very well. Our environment

57:07

of polarization doesn't

57:09

help the effectiveness of our

57:11

government and I think

57:13

you mentioned on your podcast that in

57:16

a worst-case scenario we could imagine polarization,

57:18

you know, breaking our democracy. Do you

57:20

think AI can help us all get along and

57:24

if so how would it do that? Well

57:26

it's such a powerful tool that

57:28

at least we ought to consider

57:30

in for all our tough problems

57:33

where it can be beneficial or

57:35

where it can exacerbate things. So

57:37

certainly if somebody wants to

57:40

understand okay where

57:43

did this come from this article

57:45

or this video,

57:47

you know, can you what is

57:49

the provenance, you know, is that

57:52

provably a reliable source or

57:54

is this information accurate or, you

57:56

know, in general in my newsfeed,

57:58

you know, what am I seen

58:01

that somebody who's voting for

58:03

the other side, what did they seem? And

58:06

try to explain to me what

58:11

has pushed them in that direction. You'd

58:14

hope that, again,

58:16

going back to the paradigm of

58:19

white collar capability being

58:22

almost free, that well-intended

58:26

people who want to bridge those

58:29

misunderstandings would have

58:32

the tools of AI to

58:34

highlight misinformation for them or

58:36

highlight bias for them or

58:39

help them be in the mindset

58:41

and understand, okay, how

58:44

do we bridge the different

58:46

views of the world that

58:48

we have? So, yes,

58:51

although it sounds outlandish, it's like

58:54

when people say, oh, let's use geoengineering for

58:56

climate, they're like, oh, no, you always

58:59

think technology might be

59:01

the answer. And, you

59:03

know, okay, I'm somewhat guilty of that.

59:05

But here, the AIs

59:07

are going to be both part

59:10

of the solution, while

59:12

if we're not careful, also

59:16

potentially exacerbating these things.

59:19

And you can almost say it's good that the

59:21

blue collar job

59:23

substitution stuff is more delayed than

59:25

the white collar stuff. So, you

59:27

know, it's not just any one

59:29

sector and actually it's the

59:32

more educated sector that's

59:34

seen these changes first. I

59:37

hadn't thought of that. Okay, last question.

59:40

You've said that a possible future

59:42

problem that befuddles you is how

59:45

to think about our purpose as humans in

59:47

a world in which machines can solve problems

59:49

better than we can. Is

59:52

this a nagging concern that you continue to wrestle

59:54

with? How do you think about it now? Well,

59:57

I don't think somebody who spent 68

1:00:01

years in a world of shortage, I

1:00:03

doubt that either at that

1:00:06

absolute age or having been immersed

1:00:08

in such an utterly different environment,

1:00:12

that the ability to imagine this

1:00:16

post shortage type

1:00:18

world will come

1:00:20

from anyone near my

1:00:22

age. So I view

1:00:25

it as a very important problem that

1:00:29

people should contemplate, but

1:00:31

no, that's not one that

1:00:35

I have the solution or would expect

1:00:38

to have. Although

1:00:40

you have some experience with living in a

1:00:42

post scarcity world in the sense that you

1:00:45

haven't had scarcity in your own personal

1:00:47

life for a few years now. I

1:00:49

haven't had financial scarcity, but

1:00:52

somebody who's had the

1:00:54

enjoyment of being successful and

1:00:56

sees problems out there like

1:00:58

malaria or polio or measles,

1:01:01

the satisfaction that, okay, the

1:01:04

number of people who work on this, the

1:01:06

amount of research money for this is very,

1:01:08

very scarce. And so I feel

1:01:10

a unique value added in taking

1:01:13

my own resources and working with governments

1:01:15

to orchestrate, okay, let's not have any

1:01:17

kids die of malaria, let's not have

1:01:19

any kids die of measles. So you're

1:01:22

right financially that, what

1:01:26

I do for fun is

1:01:28

a potential kind of thing

1:01:30

that people can do, play pickleball,

1:01:32

because the machines, the fact the machines

1:01:34

will be good at pickleball, that

1:01:38

won't bother us, we'll still enjoy

1:01:41

that as a human thing. But

1:01:46

the satisfaction of helping

1:01:50

out reduce scarcity, which

1:01:52

is the thing that motivates me, that

1:01:55

also goes away. Yeah,

1:01:58

yeah, yeah, yeah. So the true last

1:02:01

question, rumor has it you're working on a memoir.

1:02:04

Can you tell us anything about that? Yeah,

1:02:06

we announced that in

1:02:09

next February, sort of

1:02:12

a first volume that covers my life

1:02:15

up till the first two or

1:02:17

three years of Microsoft, about age 25

1:02:19

or so, called Source Code will come

1:02:22

out. So I'm

1:02:24

working on editing that

1:02:27

right now since

1:02:30

we're about to hit deadlines. But

1:02:32

yeah, we got a good reception to

1:02:35

the pre-announcement of that

1:02:37

first volume. Is GPT helping you out

1:02:39

with that? Actually

1:02:43

no, not because I'm against it or anything.

1:02:45

I suppose in the end we maybe

1:02:48

we should, but no, it's still

1:02:51

we're being a little traditional in terms

1:02:53

of how we're both writing and editing.

1:02:55

Well, there'll be two volumes or three

1:02:57

volumes, do you think? Three.

1:03:01

So we'll probably wait three years before we

1:03:03

do a second one, but there's

1:03:05

kind of a period that's Microsoft-oriented

1:03:08

and a period that's sort

1:03:10

of giving all the money

1:03:12

away, focused. Well,

1:03:14

if you and Andy play enough pickleball, maybe

1:03:17

you'll live long enough to write a fourth

1:03:19

volume. That's the career of

1:03:21

so. Making AI good

1:03:23

will make that the fourth

1:03:26

volume. Exactly. Well, Bill,

1:03:28

thank you so much for joining us

1:03:30

today. Such an interesting conversation. Yeah,

1:03:33

fantastic. Thanks, Bill. Thanks, Bill. John

1:03:43

Lennon said, count your

1:03:45

age by friends, not years.

1:03:48

I've always liked this quote and I've tried

1:03:50

to apply it. Please

1:03:52

be building new friendships, expanding

1:03:54

communities. And I've

1:03:57

tried to apply the same approach

1:03:59

to the process of learning. always

1:04:01

be learning, ingesting new ideas, testing

1:04:03

my assumptions. But where

1:04:05

can you find a flow of the

1:04:07

best new ideas vetted by experts? There

1:04:10

is so much noise out there. I'm

1:04:12

so glad you asked. This is

1:04:15

why we started the next big

1:04:17

idea club. We've partnered with

1:04:19

hundreds of the world's leading nonfiction authors

1:04:21

to create audio summaries of their books.

1:04:23

We call these summaries Book Bites, and

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our app features a new one every

1:04:28

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next big idea. There is no

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1:05:03

Download the next big idea app

1:05:06

right now. Wow,

1:05:13

Adam and Andy, so interesting.

1:05:15

Let's unpack some of our favorite moments. Adam,

1:05:18

for me, there was when you said, some

1:05:21

people say we're waiting for the breakout application

1:05:23

for AI. What's it going to be?

1:05:26

And Bill said, the naysayers are

1:05:28

pretty creative to be able to

1:05:30

say that nothing transformative has happened.

1:05:32

What's happening is mind blowing. I

1:05:34

thought that was a great moment.

1:05:38

I mean, there's several, I'm sure we'll talk about them.

1:05:40

That was definitely my favorite because classic

1:05:44

Bill in the sense of he's just

1:05:46

got such a great and unique perspective

1:05:48

of the way he sees the world

1:05:50

and explains the world, and he's right.

1:05:52

Like it's the killer app is here.

1:05:56

And, you know, he relates

1:05:58

to another moment where he's he said, look,

1:06:01

one of the holy grails for a

1:06:03

long time was like a perfect translator

1:06:06

app, like real time natural language. And

1:06:08

this is like a free afterthought

1:06:11

feature of the

1:06:13

foundational AI systems that are out there.

1:06:16

And so his comment

1:06:18

about, which I agree

1:06:20

with about, it's

1:06:22

kind of interesting that people are saying that

1:06:25

there's, we're still waiting for the Uber

1:06:27

of AI and yet this

1:06:30

white collar intelligence as a service

1:06:32

at production level is

1:06:34

available. And he pointed out, he goes,

1:06:37

it's still got issues and it hallucinates

1:06:39

and it has problems and whatever, but

1:06:41

as it is today, it is

1:06:44

quite the killer app. Yeah, I

1:06:46

mean, I don't think that sentiment can

1:06:49

be emphasized sufficiently enough, both

1:06:53

just how profound the technology

1:06:55

is today and the

1:06:57

fact that we take for granted that in

1:06:59

an instant, this podcast

1:07:02

could be translated into, I think 150 different

1:07:04

languages instantly. Both

1:07:10

the taking for granted of that technological

1:07:13

leap forward, as well as

1:07:15

all the plethora of

1:07:18

other capability set where

1:07:20

it exists today and that we're both

1:07:23

looking for and

1:07:26

sort of scoffing at the expectation

1:07:28

of the next consumer app, like

1:07:30

Uber, it's just

1:07:32

completely under appreciating the moment that we

1:07:34

are in. Yeah, and it

1:07:37

relates to another point because he was

1:07:39

making the point Bill was just now

1:07:41

about how it's not

1:07:43

like it's doing all this and you need to

1:07:45

like go to school on how to use it.

1:07:47

He said it's the software meeting the human, you

1:07:50

just need to say what you want it to do.

1:07:53

And to the extent it can do it, it just does it.

1:07:55

And that's unlike any other software

1:07:57

we've ever experienced. So it's universally demonstrated.

1:08:00

democratically accessible both in terms of

1:08:02

its ease of use in terms of its

1:08:04

ability to show up at production scale at

1:08:06

a on a smartphone its capability

1:08:09

set I thought that was a really poignant

1:08:11

moment well, and then he made the point

1:08:13

about the acceleration

1:08:15

of just the capital and And

1:08:18

of the businesses and bills not someone who's

1:08:20

easily impressed by you know business

1:08:22

growth, right? But he pointed out that there was no

1:08:24

company in the world before 2000 that was worth a

1:08:26

trillion dollars We just had

1:08:29

one chip design company add a trillion

1:08:31

dollars of value in six months obviously

1:08:33

referring to Nvidia Right someone

1:08:35

just raised six billion dollars for an AI company.

1:08:37

I think he was referring to Elon Musk Right,

1:08:40

but but clearly Bill Gates himself

1:08:42

is kind of wide-eyed about the

1:08:44

pace of this Investment and

1:08:47

acceleration of business value. Yeah, I thought

1:08:49

another another interesting moment. Tell me what

1:08:51

you guys think was when

1:08:54

we asked him about where this is going

1:08:56

and The scaling laws into

1:08:58

the apply and I thought you know He

1:09:00

gave a pretty specific answer which I learned

1:09:02

from like he was saying we get two

1:09:04

more turns the crank on scaling Literally in

1:09:07

terms of like how much more data we

1:09:09

can feed to it And my guess is

1:09:11

we get quite a few more turns the

1:09:13

crank when it comes to compute And we'll

1:09:15

see how much of the scaling relates to

1:09:17

compute versus data But his point was like

1:09:20

it's not about that as much as it's about Metacognition

1:09:22

I think was the word he used and and

1:09:24

this idea like how do you get the systems

1:09:26

to think? Deeper and new

1:09:28

level to thinking etc. That was a great answer

1:09:31

And I hadn't I thought it was a new

1:09:33

way I don't know about you

1:09:35

guys and thinking about the scaling laws and the and

1:09:37

the and the progress these are making Yeah,

1:09:40

yeah, I mean what's astonishing is we have

1:09:42

this kind of you know, GPT for Omni

1:09:44

level level intelligence

1:09:47

when the systems are really highly inefficient as

1:09:49

I understand it and We're

1:09:51

gonna build in we're in the process

1:09:53

of building in much more intentional and

1:09:56

efficient storage of information and ways

1:09:58

of thinking And then

1:10:00

of course, I have a

1:10:02

geeky obsession with human consciousness and

1:10:05

the question of whether it may become possible

1:10:07

to build some version of consciousness on silicon.

1:10:09

So I was pretty interested

1:10:12

in his comment that, yeah, metacognition is

1:10:14

the next capability we need to build

1:10:16

into AI. And yes, consciousness may be

1:10:18

related to metacognition. He did

1:10:20

say computers are unlikely to mirror humans

1:10:22

in this way of being conscious, but

1:10:25

unlikely doesn't mean it won't happen. What

1:10:28

was his point, which was he

1:10:30

was being humorous, I think, that

1:10:35

thank goodness it's the white knowledge

1:10:37

workers that AI

1:10:39

is coming for. What was

1:10:41

his point at that juncture?

1:10:43

I took it to mean,

1:10:45

because we were talking about

1:10:47

the societal implications and

1:10:49

the inference, you didn't say this was things

1:10:52

like, are we gonna need universal

1:10:54

basic income? And what happens

1:10:56

if you're displaced from

1:10:58

your current job or

1:11:01

need to be retrained into a new job? I think

1:11:03

his point was that white collar workers, I think he

1:11:05

literally said, tend to be more

1:11:07

college educated. And therefore, in theory,

1:11:11

are probably more malleable to

1:11:13

being retrained into another white

1:11:16

collar job to learn how to use

1:11:18

these systems. Whereas, as

1:11:20

opposed to, I think he was saying, and I don't know

1:11:22

this to be true, that it may be harder to retrain

1:11:24

a blue collar job than a white collar job. But I

1:11:26

think that was his point, whether it's true or not. I

1:11:30

took it as, maybe

1:11:32

there's more of a safety net for white collar workers,

1:11:34

that kind of stuff. Well,

1:11:36

and if you think of how destabilizing

1:11:39

it would be for society to suddenly

1:11:41

have every truck

1:11:44

and taxi driver in

1:11:46

the world out of a job. And I mean, that's

1:11:48

what we all thought was gonna happen 10 years ago,

1:11:50

right? And it

1:11:52

was a great kind of

1:11:55

nuance that I had not thought

1:11:57

about, that actually it's good for social stability

1:12:00

we're gonna have a whole bunch of attorneys with

1:12:03

other people who are losing their jobs. And you

1:12:05

know what, they're gonna be okay, right? Yeah, they're

1:12:07

probably more, I mean, this is true and thought

1:12:09

of it, because Bill does such a good job

1:12:11

of thinking macro, like to his point about the

1:12:14

work he's done to save, you know, child

1:12:16

mortality and all that kind of stuff. But,

1:12:19

you know, white collar workers, college educated

1:12:22

people, I'm guessing statistically, I don't know

1:12:24

this, probably I'm more likely to have

1:12:26

a higher percentage of home equity ownership

1:12:28

of a 401k. Like

1:12:31

I don't know that about Becky, there's more of

1:12:33

a safety net in general that has been built

1:12:35

up under that group. So yeah, I think that

1:12:37

was his point, Andy, that like,

1:12:39

and it's weird, remember Sam Altman mentioned that to us when

1:12:41

we met with him. He actually said to Andy and I, I

1:12:43

don't know if it made its way into the book, but I'll

1:12:46

give you kind of behind the scenes, he said, I thought

1:12:49

the thing it would be worst at would

1:12:51

be creative thinking, like creativity.

1:12:54

Right, yes. So he wasn't talking

1:12:56

about white collar versus blue collar,

1:12:58

but it's similar. Like he was

1:13:00

saying, I thought it would come

1:13:02

after, like it

1:13:04

would be better at like, I'll call it like, you know, rote

1:13:08

summarization and data analysis. And he was shocked

1:13:10

at how creative it could be, like it

1:13:12

could produce, I mean, the diffusion models can

1:13:14

produce an image, can produce a video, but

1:13:16

it can be creative in its thinking, in its

1:13:19

strategic thinking, which is why we

1:13:21

write about, we really emphasize to our

1:13:23

business clients, you really need to

1:13:25

be inviting AI to the table all the time, because

1:13:28

people don't think of it as a creative

1:13:30

tool, and creative thinking and

1:13:32

helping you come up with, you

1:13:34

know, solutions to your thorny

1:13:37

problems as a white collar worker. And it's actually quite

1:13:39

good at that. You know, Adam

1:13:41

Grant made the point, I think it was

1:13:43

in his book Originals, that

1:13:46

creative success is highly correlated

1:13:48

with the quantity of ideas

1:13:50

that are generated. So you

1:13:52

look at like Picasso, the

1:13:54

quantity of drawings

1:13:56

and paintings you generated, and Buzzfeed

1:13:58

famously used to... generate like 20

1:14:01

headlines for every article, pick the

1:14:03

best one to create this incredible

1:14:05

clickbait. And

1:14:07

it strikes me that having AI as

1:14:09

a creative partner will make it easier

1:14:11

for people in business to

1:14:13

be able to generate not just one or

1:14:16

two or three ideas for

1:14:18

a given angle on a marketing

1:14:20

campaign or a communication, but

1:14:23

a dozen or several dozen. And

1:14:26

it will still, at least for some time,

1:14:28

be the human that's doing the critical sort

1:14:31

of editorial selection process. You know, it's actually

1:14:33

interesting about that point, Ruth, is that one

1:14:35

of the things we've learned is

1:14:38

that in the best practice of prompting, if you

1:14:40

want to be a really good prompter, there's

1:14:42

a couple different techniques that work really well.

1:14:44

One of them is called chain of thought

1:14:46

prompting, which is where you're actually making

1:14:49

and forcing the AI to go

1:14:51

through its steps and show its reasoning, just like

1:14:53

a human would, as opposed to just trying to

1:14:56

skip to the answer. Related

1:14:58

to chain of thought like that is you

1:15:01

ask the AI to actually

1:15:03

produce 30 answers. So

1:15:05

for example, it's like a tagline. You actually tell it, I

1:15:07

want you to produce 30, and

1:15:10

then I want you, before you stop

1:15:12

in your prompt answer, to

1:15:15

rank the top five of the 30 you

1:15:17

produced and tell me why. And so all

1:15:19

of a sudden, you get an answer that's

1:15:21

so much better than if you just said,

1:15:24

give me a tagline. Well, getting to the

1:15:26

AI risk topic, I was interested to hear

1:15:28

Bill say that, yes,

1:15:31

if there was actually a way to

1:15:33

slow down, in response to, I think

1:15:35

it was your question, Andy, if there

1:15:38

was a way to slow down AI

1:15:40

development, a lot

1:15:42

of people leading companies would

1:15:45

probably choose to do so. I thought that

1:15:47

was his subtle way of saying, yes, if

1:15:49

we could slow down AI development now, that

1:15:51

would be a good idea. He didn't say that out right, but I

1:15:53

think that was the implication. But

1:15:56

then he sort of went on to the

1:15:58

practical matter that... Which is... is

1:16:00

more capital and it's charging ahead.

1:16:02

Yeah, it's charging ahead. And incentives.

1:16:05

Incentives, and it's a global environment.

1:16:08

And we have to, you know, we have to, the good guys

1:16:10

have to have better technology than the bad guys. I

1:16:13

thought it was also interesting how he mentioned government

1:16:15

regulation and he did, if I

1:16:17

heard what he just said correctly, if I

1:16:19

interpreted correctly, he was saying, yeah, like it's

1:16:22

the only way, like it's the only way

1:16:24

that we have a chance. Yeah, it's the

1:16:26

only party. Yeah. Right. And

1:16:29

so what it states is just

1:16:31

so far behind on a regulatory

1:16:33

basis, policing, privacy

1:16:36

in particular than say Europe.

1:16:40

Like they just have so many more protections. And

1:16:42

do I think that either the US

1:16:44

or Europe are going to get regulation

1:16:47

right for, it's really a tricky,

1:16:49

it's a very tricky topic. And

1:16:52

if anyone would have a negative association with

1:16:54

government regulation, it would be Bill Gates, right?

1:16:56

Yeah, you would think, yeah. I mean,

1:16:59

he had a, right. The antitrust

1:17:01

stuff that Bill and Microsoft went

1:17:03

through was extremely painful. So

1:17:05

the fact that he's saying, and we've heard

1:17:07

Sam Oldman say this too, you

1:17:09

know, please regulate this sector. It's

1:17:11

important. I mean, those

1:17:14

weren't his exact words, but

1:17:16

clearly everybody agrees it's

1:17:18

important. Well, Andy and Adam,

1:17:20

I'd love to pose a question to you that we pose

1:17:22

to Bill, which is what's your

1:17:25

advice for your kids when

1:17:28

it comes to how to respond to

1:17:30

this AI journey

1:17:32

of ours, this AI transformation?

1:17:35

Is it jump in with two feet, learn

1:17:38

how to deploy and engage with

1:17:40

AI as fast as you can?

1:17:43

Yes. I mean, I think

1:17:47

I'm reminded of at different

1:17:49

points. I mean, I remember seeing my

1:17:51

first browser, the Mosaic browser, back

1:17:53

in 1994. I

1:17:56

think when it comes to technology, it's

1:17:59

a tool. It

1:18:01

can be really, really useful and powerful, and

1:18:03

I've been fortunate enough to be a career

1:18:07

technologist. I've enjoyed

1:18:10

the career, but I think AI is

1:18:13

as significant if not more significant than

1:18:15

the browser. So I've encouraged

1:18:18

both my kids to, or in

1:18:20

their 20s to certainly

1:18:23

dive in and be aware and use it

1:18:25

for their professional and personal enjoyment

1:18:28

and advancement. It's interesting. I would

1:18:30

say to my

1:18:32

daughter, the same thing

1:18:34

I would say to an adult

1:18:37

right now, which is what

1:18:40

AI doesn't change is the

1:18:42

fact that you still, to be successful

1:18:45

in life, in my opinion, need to

1:18:47

demonstrate a growth mindset, intellectual

1:18:49

curiosity, and most important, passion

1:18:52

towards something. A

1:18:55

meta point here is that Andy and I

1:18:57

are passionate about how connecting

1:19:00

the dots between technology and business, and

1:19:03

brands, and experiences, and

1:19:05

we've made a career out of it. But

1:19:08

to be honest, I would do

1:19:10

what I'm doing with Andy for free. Don't tell

1:19:12

Andy that, but if I could pay my bill

1:19:14

some other way, I'd do it for free. The

1:19:16

truth is that I mean

1:19:18

that. I love what I do, and

1:19:20

so it's cliche, but how does

1:19:22

that relate to your question? Well, I

1:19:25

was talking to someone else whose kid

1:19:27

is more like Andy's kid's age in

1:19:29

law school, and I was like,

1:19:31

hey, and they were worried, oh my God, because of AI,

1:19:35

they're not going to be lawyers and

1:19:37

accountants. I'm like, I can tell you

1:19:39

this much, if they love law or

1:19:41

accounting, or they love the craft and

1:19:43

the profession, then there's going to be,

1:19:46

like we say this to all of our clients, there's going to be the leading

1:19:50

law firms are going to be the

1:19:52

best at using AI to further what

1:19:54

they do. My advice would be

1:19:56

yes, definitely like be

1:19:59

literate. and be proficient

1:20:01

and experiment with these platforms as

1:20:04

much as you can because whatever, but that's

1:20:06

not going to be what makes you successful.

1:20:08

But if you don't do that, whatever your

1:20:10

passion is, if you don't have that tool

1:20:12

in your tool belt, you're just

1:20:15

gonna feel like you can't succeed as

1:20:17

well because you don't have that AI

1:20:19

literacy. That

1:20:23

was Adam Brotman and Andy Sack.

1:20:26

To read their new book, AI First, and learn more

1:20:28

about what they're doing at Forum 3, follow

1:20:31

the link in the episode notes. Today's

1:20:33

episode would not have been possible without the

1:20:36

help of many, many people. Special

1:20:38

thanks to Joanna, Jen, Andy,

1:20:41

and of course, Bill Gates. Today's

1:20:43

episode was produced by Caleb Bissinger,

1:20:46

sound design by Mike Toda. I'm

1:20:48

your host, Rufus Grissom. Next

1:20:50

week, we'll be celebrating Independence Day,

1:20:53

not with fireworks, but with a

1:20:55

conversation about my favorite founding father.

1:20:58

And I'm like, huh, Ben Franklin. Hadn't

1:21:01

given him much thought any more than most

1:21:03

Americans see him on the hundred dollar bill

1:21:05

occasionally when I have them.