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1:11

Hi, everyone from New York Magazine and the

1:13

vox media podcast network. This is

1:15

nonprofit OpenAI, which is

1:17

now very much for profit and a hundred

1:20

percent scarier. Just

1:21

kidding. Actually, I'm not kidding. This is

1:23

on with Cara Swisher and I'm Swisher, and

1:26

I'm named Araza. It's amazing how an open

1:28

source nonprofit has moved to being a close

1:30

source private company with the big deal

1:33

with Microsoft. Are you shocked? No.

1:35

Not even slightly. It's a huge opportunity. I'm

1:38

in San Francisco now, and it's really jumping with

1:40

AI. Crypto didn't quite work out, and

1:42

even those people moved to Miami. And

1:45

so it's very AI oriented right now.

1:47

But he's thinking about a startup in

1:49

AI.

1:49

Are you more bullish on AI than web three?

1:52

Well, So that's kind of a low bar. So

1:54

Yeah. Yeah. And I've always been bullish on AI. I've talked

1:56

about lot over the years and, you know, this

1:58

is just a version of it as it becomes more

2:01

and more sophisticated and useful to people. So,

2:03

I've always thought it was important, and I think most

2:05

of the key technologists in Silicon Valley

2:07

have always thought it was

2:08

important. Agreed. I was talking to a VC yesterday,

2:11

though, about how so many things that are not AI

2:13

are being billed as AI tech companies now,

2:15

and they're really not AI. They might

2:17

have, like, a large learning model, but they're not quite

2:19

AI. Yeah. But last episode, we had read

2:21

off been on talking about what was possible.

2:24

Mhmm. With AI. And now

2:26

we have one of Reed's many mentees,

2:28

Sam Altman. Sam is the CEO

2:30

of OpenAI and leads the team that

2:32

has given us chat GPT and GPT

2:35

four. He actually burst onto

2:37

the scene as a young Stanford dropout, I think, in

2:39

two thousand five. With the startup

2:41

looped. Right? Is that when you met him? Yes. Mhmm. When

2:43

he had looped, I visited him and is small. He was a little startup,

2:45

and it didn't do very well. It

2:47

was a location based kind of

2:48

thing. I don't even remember. Social

2:50

network, Bradley GEO social network. You

2:52

know, it was not Facebook, let's just say. So

2:55

he was one of these many, many startup people

2:57

that sort of were all over the

2:58

valley. Very smart, but the company didn't

3:00

quite work out. Yeah. They kind of went bust,

3:03

I think, not many years later, but he became super important

3:05

in the valley. Especially in my generation. He's

3:07

got my age because of Y Combinator.

3:09

He has led the startup accelerator that has

3:12

incubated and launched

3:13

strip, Airbnb, Coinbase. Yeah.

3:15

He got there later. It was working before he got

3:17

there, but he

3:18

actually led it to new heights, I think, in

3:20

a lot of ways. It was very He came

3:21

in in twenty fourteen. don't remember.

3:23

I remember when he took over, but he really invigorated

3:26

it and was very involved in the startup

3:28

scene. It was a great role for him. He was a great cheerleader.

3:30

And you know, he's good at eyeing good

3:32

startups.

3:33

Do you see

3:34

him as, like, kind of one of the Elon Musk

3:36

Peter Till Rita Altman of his generation?

3:40

Kind of yeah. There's a lot of really smart

3:42

people. But, yeah, he's definitely special and he really

3:44

did, you know, he had a bigger mentality,

3:47

more like read than the others,

3:49

although they had it initially, not Peter Till,

3:51

but he was thinking of big things

3:53

with the Altman. I I really

3:55

like him. I've gotten to know him pretty well over the

3:57

years. And so I've always enjoyed talking

3:59

to him. He's very thoughtful. He's got a lot of interesting

4:02

takes on things. And this is really

4:04

big deal now that he's sort of landed on

4:06

taking OpenAI AI to these

4:08

heights? Yeah, he has. He want like you,

4:10

he wants to entertain the notion of running for office

4:12

in California. He he thought about running for governor,

4:14

something that you've talked to him about. Yeah.

4:16

We talked about it. But he went on to revolutionize

4:19

AI. So you think that's better or worse for

4:20

humanity. I don't know. We'll see. You know,

4:22

California is probably easier to fix than what

4:25

we're gonna do about AI once it gets fully

4:27

deployed. Although, you know, the whole issue is there's lots

4:29

of great things and there's lots of bad things. And

4:31

so we wanna focus on both because it's

4:33

like I

4:33

say, it's like when the Internet started. We didn't know what it

4:35

was gonna be. I think a lot of people are being very

4:38

creative around what this could be and what problems

4:40

it could solve and at the same time problems

4:42

it could create. Do you think that the fear

4:44

is overblown like this? Our jobs

4:46

are at risk. AI is gonna you

4:47

know, on those stories, yes. I Yes.

4:50

It's like saying what is, you know, the car done

4:52

for us or lights or something like that.

4:55

You know, things will change as they always do.

4:57

And so I've always thought

4:59

most of the fears were bullet, but as I

5:01

say in the book I'm working on right now, which is why

5:03

I'm in San Francisco, is everything that can

5:05

be digitized will be digitized. That's just inevitable,

5:08

and that's where it's going. So this will soon

5:10

be two bots talking to each other? No.

5:12

No. But search is so antiquated when you think about

5:14

a typing words into a bot. It's really Neanderthal

5:17

anyways. And this is this is an upright

5:20

homo sapient. Well, it's been interesting because

5:22

critics have kind of swarmed about chat GPT

5:24

earlier on and and Sam was coming back on Twitter

5:27

saying just wait for the next iteration. Right? We now

5:29

have in GPT four. We we couldn't book the interview

5:31

with him until GPT four was out. But

5:34

the model still has many issues,

5:37

and he himself has noted this. He

5:39

tweeted that it's still

5:40

flawed. Still limited, and it

5:42

still seems more impressive on first use

5:44

than it does after you spend more time with

5:46

it. This was about GPT four. Yeah. I

5:48

would agree. But that's a very interesting thing because

5:50

the fact that it's more impressive on first blush than it

5:52

is after you use it is part of the problem because

5:55

I've been using my GPT

5:57

plus and it pulls up all kinds of

5:59

interesting, like, write me a research paper and then it

6:01

will it will look really good.

6:03

Mhmm. And it will have a bunch of

6:05

false information on

6:06

it. So this can

6:07

compound the misinformation problem when something

6:09

looks slick. Well, but isn't informed.

6:11

Right? Well,

6:12

data and data out. Craping, crap

6:14

out. I mean, it's just the same it's that's very simplistic

6:16

way of saying it. But I think, you know, it's like the

6:19

early Internet really sucked too. And

6:21

now it kinda doesn't and sort of does

6:23

and there's great things about it. But if you looked

6:25

at early Yahoo or Google or Google

6:27

was a much later, but early Yahoo and others,

6:29

it was a lot of bubble gum and

6:31

bailing wire.

6:32

Alright. Well, let's see what Sam Altman has to say.

6:34

And if he feels confident in of having done open

6:37

AI versus running for governor of California.

6:39

We'll take a quick break and we'll back with the interview.

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9:03

Sam, it's great to be in San Francisco, rainy

9:05

San Francisco to talk to you in person. We

9:07

need the rain. It's great.

9:08

I know. This atmospheric river is not kidding.

9:10

A lot of them.

9:11

Soaked on the way here. I'm miss San

9:13

Francisco. I'm here for a couple of bucks. I'm

9:15

going to. I'm going to I'm trying to convince

9:16

you that we're having a moment here. I'm I agree. It's time

9:18

to come back. I love San Francisco. I've never really left

9:21

in my heart. So You started

9:23

looped. That's where I meant to. Explain

9:25

what it was.

9:26

It was a location based social app

9:28

for mobile phones.

9:29

Right. So what happened?

9:31

The market wasn't there, I'd say, is the number

9:33

one thing. Yeah. Because

9:36

Well, I think, like, you can't force

9:38

a market. Like, you can have an idea about what something

9:40

or what people are gonna like -- Mhmm. -- as a startup

9:42

part of your job is to be ahead of it.

9:45

And sometimes you're right about that and sometimes

9:47

you're

9:47

not. You know, sometimes you make loops, sometimes you make

9:49

open AI. Yes. Right. Right. Exactly. Right.

9:51

But you started in two thousand fifteen after

9:53

being at y combinator. And

9:56

last late last year, you launched chat, GPT.

9:58

Talk about that transition. You had been you

10:00

reinvigorated Y Combinator in a lot

10:02

of

10:02

ways. I was handed such an

10:04

easy task with y combinator. mean, like,

10:07

I don't know if I reinvigorated. It was sort of

10:09

a super great thing by the time

10:11

I took

10:11

over. Well, what I mean is I think it was it

10:13

it got more prominence. You changed things around.

10:15

I don't mean to say it was

10:16

failing. Yeah. Yeah. Not at all. Not at all. III

10:19

think I scaled it more --

10:21

Mhmm. -- and we sort of took on longer

10:24

term more ambitious projects. OpenAI

10:27

eye actually sort of got that was, like,

10:29

something I just helped start while at

10:31

IC. Mhmm. And we did funded other

10:33

companies, some of which I'm very closely involved with, like,

10:35

keenly on the nuclear fusion company. They were gonna take a

10:37

long time. So I I definitely, like, had

10:39

a thing that I was passionate about, and we did more of

10:41

it. But I kind of just

10:43

tried to, like, keep PG and Jessica's vision

10:45

going there.

10:46

This is a school program. Program. And Jessica,

10:48

you had shifted out OpenAI Why was that?

10:50

When you're in this position, which is a high profile position

10:53

in Silicon Valley, sort of king of startups

10:55

essentially. Why go

10:57

off? Is it you want it to be an entrepreneur again?

10:59

No. III

11:01

don't I am not a natural fit

11:03

for CEO, like an investor

11:06

really, I think, suits me very well. Mhmm.

11:08

I got convinced that AGI was

11:10

gonna happen and be the most important thing I

11:12

could ever work on. think it is gonna like

11:15

transform our society in many ways.

11:18

And, you know, I won't pretend

11:20

that soon as we started OpenAI, I was sure it was gonna

11:22

work. But it became clear

11:24

over the intervening years and certainly by

11:27

twenty

11:27

eighteen, twenty nineteen that we

11:29

had a real chance here.

11:30

What was it that made you think that? A

11:33

number of things I'd be hard to point to just single

11:35

one, but by the time we made GPT

11:37

two, which was still weak

11:39

in a lot of ways, but you could look at scaling

11:41

laws and see what was gonna happen. Mhmm. I

11:43

was like, this can go very, very

11:45

far. And I got super excited about

11:47

it. I've never never stopped being super excited about it.

11:49

Was there something you saw that did it just

11:51

scaled? Or what was the Yeah.

11:53

It was the like, looking at the data of

11:55

how predictably better we could make

11:57

the system with more compute with more

11:59

data. Mhmm. There'd already been

12:01

a lot of stuff going on at Google with the

12:03

mind they had bought that earlier, right, around

12:05

that. Yeah. There had been a bunch of stuff, but

12:08

somehow, like, It wasn't

12:10

quite the trajectory that has turned

12:12

out to be the one that really

12:14

works. But

12:14

in two thousand fifteen, you wrote that superhuman

12:17

machine intelligence is probably greatest threat to

12:19

the continued

12:19

existence

12:20

of humanity. Explain.

12:22

I still think so. Okay. Alright. We're gonna get into

12:24

that. Why did you write that then? And

12:26

yet you also called it the greatest technology

12:29

ever. I

12:30

still believe both of those things. I think

12:31

at this point more of the world would agree on that at the time

12:33

that was considered very extremely like

12:36

crazy position. So

12:37

explain roll it out that you you wrote was probably

12:39

the greatest threat to continue to exist in many and

12:41

also one of the greatest technologies that could

12:43

improve humanity

12:45

and all

12:45

those two things out? Well,

12:47

I think we're seeing finally little previews

12:49

of this with John Altman especially,

12:51

I'd put g p GPT four out and

12:54

people can see this vision where

12:56

just to pick one example out of the thousands could

12:58

talk about, everyone in the world can have an

13:00

amazing AI tutor on their phone with

13:02

them all the time for anything they wanna learn.

13:05

That's really we need that. I mean, that's that's

13:07

wonderful. That'll make the world much better. The

13:09

creative enhancement that people are able to

13:11

get from using these tools to do whatever their creative

13:14

work is, that's fantastic. The economic

13:16

empowerment, all of these things And

13:18

again, we're seeing this only in the

13:20

most

13:20

limited, primitive, larval way.

13:23

But at some point, it's like, well, no, we can use these things

13:25

to cure disease. So what is the threat? Because

13:28

when I try to explain it to regular people

13:30

who don't quite

13:31

No. You're not. You're not a regular person.

13:33

I'm so offended. I'm not a regular person, but

13:35

When the Internet started, nobody knew what it was

13:37

gonna do. When you thought superhuman

13:39

machine intelligence is probably the greatest

13:40

threat, what did you mean by that? I think

13:42

there's levels of threats. Mhmm. So today,

13:44

we can we can look at these systems and

13:46

say, alright, no imagination

13:49

required. We can see how this can contribute

13:51

to computer

13:53

security exploits or disinformation

13:57

or other things that can destabilize society.

13:59

Certainly, there's gonna be economic transition.

14:03

And those

14:05

those are not in the future. Those are things we can look

14:07

at now. Mhmm. In the medium

14:09

term, I think we can imagine if these systems

14:11

get much, much more powerful. Now what happens

14:14

if a really bad actor gets to use them

14:16

and tries to like figure out how much

14:18

havoc they can wreck on the world or harm they can

14:20

inflict. Yeah. And then we can go further

14:22

to all of the sort of

14:25

traditional sci fi, what happens

14:27

with the kind of runaway AGI scenarios

14:29

or anything like that? Now, the

14:31

reason we're doing this work is

14:33

because we want to

14:35

minimize those downsides while still letting society

14:38

get the big upsides. And we think it's very

14:40

possible to do that, but it requires

14:43

in our belief, this continual

14:45

deployment in the world where you let people

14:48

gradually get used to this technology, where you

14:50

get give institutions regulators

14:53

policymakers time to react to it, where

14:55

you let people feel it,

14:58

find the exploits, find the

15:00

creative energy of the world will come up with

15:02

use cases. We and all the red teamers we could

15:04

hire would never imagine. And So

15:07

we wanna see all of the good and

15:09

the bad and figure out how to continually minimize

15:11

the bad and improve the benefits. And

15:14

you can't do that in the lab. And

15:16

this idea that we have that

15:18

we have an obligation and society will be better

15:20

off for us to build in public

15:23

even if it means making some mistakes along

15:25

the

15:25

way. Right. I think that's really important.

15:27

When people critique CHAT GPT, you

15:29

said, wait for GPT four. Now that

15:31

it's out, has it met expectations? A

15:34

lot of people seem really happy with it. Mhmm. There's

15:36

plenty of

15:36

things in your expectations.

15:38

Yeah. I'm proud of it. Mhmm. Again,

15:40

very long way to go, but as a step forward, I'm

15:42

proud of So you tweeted that at first glance that

15:44

GBT four seems more impressive

15:46

than it actually is. Why is that?

15:48

Well, I think that's been an issue with every version of

15:50

these systems, not particularly GBT four.

15:52

You find these, like,

15:54

flashes of brilliance before you find

15:56

the problems. Mhmm. And so

15:59

I think that someone used to say about GPT three that

16:01

has really stuck with me is it is the world's greatest

16:04

demo creator because

16:06

you can tolerate a lot of mistakes there. But

16:08

if you need a lot of reliability for a production

16:10

system, it wasn't as good at that. Now,

16:13

four makes less mistakes. It's more reliable, more

16:15

robust. But still long way to

16:16

go. One of the issues is hallucinations.

16:18

I called who's

16:19

it was just kind of a creepy word. I have to say

16:21

that you What did you wish to call it instead? Mistakes.

16:24

Mistakes or something like hallucinations and feels like

16:26

it's

16:26

sentient. It's interesting. Hallucinations, that one

16:28

doesn't trigger for me as sentient, but

16:31

I really try to make sure we're picking words

16:33

that are in the tools

16:34

camp, not the creatures camp because

16:36

I think

16:36

it's tempting to anthropomorphize this

16:38

That's correct. That way. That's correct. And as you

16:40

know, there were a series of reporters wanting to date

16:43

three. But anyway, sometimes a bot just makes

16:45

things up kind of thinner, and that's hallucinations OpenAI.

16:47

it'll cite research papers or news

16:50

articles that don't exist. You said four

16:52

does this less than GPT

16:54

three, which don't give them actual names. But

16:56

it still happens.

16:56

Oh, that would be entrepreneur. I think it's good that his letters

16:59

plus number.

16:59

Not like Barbara. Anyway But it just it still

17:01

happens. Why is that? So the these

17:05

systems are trained to do something

17:07

which is predict the next word

17:09

in a sequence. Right. And so it's trying

17:11

to just complete a pattern and given

17:14

its training set, this is the most likely completion.

17:17

That said, the decrease from

17:19

three to three point five to four, I think, is very promising.

17:21

Mhmm. We have we track this internally. And

17:23

every week, we're able to get the number lower

17:26

and lower and

17:26

lower. think it'll require combinations

17:28

of model scale new ideas.

17:30

A lot of users Model scale is more data.

17:33

Not necessarily more data, but more compute. They're not the

17:35

problem. Human

17:37

feedback people like flagging the errors for us developing

17:39

new techniques so the model can tell when

17:41

it's about to kind of feel free to just saying

17:43

this is a mistake. Yeah. One of the issues is

17:45

that it obviously compounds a very serious misinformation

17:48

problem. Yes. So we don't we

17:50

we pay experts to flag

17:52

to go through and It's a bound data for us. Mhmm.

17:55

Not just bound. But we employ people. We have contractors.

17:57

We work with external firms. We say we need

18:00

experts in this area to help us go

18:02

through and improve things. You don't just wanna

18:04

rely totally on, you know, random users

18:06

doing whatever trying to troll you or anything

18:08

like that.

18:08

Mhmm. So humans more compute,

18:11

what else?

18:12

To reduce the -- Yeah. -- I think that

18:15

there is gonna be a big new algorithm kind

18:17

of idea that a

18:20

different way that we train or use or

18:22

tweak these models,

18:25

different architecture perhaps. So think

18:27

we'll find that at some

18:28

point. Meaning what? For the non

18:30

tech the different architecture. Oh,

18:32

well, it could be lot of things, but you could

18:34

say, like, different algorithm, but just some different idea

18:36

of the way that we create or use these models --

18:38

Mhmm. -- that encourages during

18:41

training or inference time

18:43

when you're when you're using it that encourages the

18:47

the models to really ground themselves in truth

18:49

-- Mhmm. -- be able to cite sources. Microsoft

18:51

has done some good

18:52

there. We're working on some things. Mhmm.

18:54

So talk about the next steps. How does

18:57

this move forward? I

18:59

think we're sort of on this very

19:02

long term exponential.

19:04

And that's

19:06

I don't mean that just for AI. Although AI too,

19:08

I mean that is like cumulative human

19:11

technological progress. And

19:14

it's very hard to calibrate on that, and we

19:16

keep adjusting our expectations. I

19:18

think if we told you five

19:20

years ago, we'd have GPT4 today.

19:23

You'd maybe be impressed. Mhmm. But

19:25

if we told you four months

19:27

ago after you used chat, GPT, we'd have GPT

19:29

for today, probably not that impressed. And

19:32

yet it's the same continued exponential. So

19:34

maybe where we get to, a year from

19:36

now. You're like, yeah, you know, it's better, but sort

19:39

of the new iPhone's always a little better

19:41

too. Right. But

19:43

if you look at where we'll be in ten years,

19:45

then

19:45

I think you'd be pretty impressed.

19:47

Right. Right. Actually, the old iPhone. We're not as impressive

19:49

as the new For

19:50

sure, but it's been such a gradual process.

19:52

That's correct.

19:52

unless you hold that original one and this one back

19:54

to back. Right. Right. I had I just found mine the

19:56

other day, actually. Interestingly enough, that's a very good

19:58

comparison. You're getting criticism

20:01

for being secretive and you said competition and

20:03

safety require that you do

20:04

that. Critics say that's a

20:06

cop out. It's just about competition. What's

20:08

your response? I

20:10

mean, it's clearly not. The the we

20:13

We make no secret of, like, we would

20:15

like to be a successful effort. Yeah.

20:18

And I think that's fine and good, and we try to

20:20

be clear. But also, We

20:22

have made many decisions over the years in

20:25

the name of safety that have been widely ridiculed

20:27

time that are later

20:31

people come to appreciate when we even

20:33

in the early versions of GPT when

20:35

we talked about not releasing model

20:37

weights or releasing them gradually because when I

20:39

people have time to adapt. We got really cool

20:41

for that, and I totally stand by that decision.

20:44

Would you like us to, like, push a button and open source

20:47

four and drop those weights into the

20:48

world? Probably not. Probably not. One

20:50

of the excuses that Tech always uses is you don't

20:52

understand it. We need to keep in back post. It's often

20:55

about

20:55

competition. Well, for us, it's the opposite.

20:57

I mean, we've said all along, and this is different than

20:59

what most other AGI efforts

21:01

have thought is everybody

21:04

needs to know about this. Like,

21:07

AGI should not go be built in

21:09

a secret lab -- Mhmm. -- with only the people

21:12

who are like privilege and smart enough to

21:14

understand it. Part of the reason that we

21:16

deploy this is I think we need

21:18

the input of the world and

21:20

the world needs familiarity

21:23

with what is in the process of happening, the

21:25

ability to weigh in to shape this together. Like,

21:28

we want that, we need that input and people

21:30

deserve it. So I think we're, like,

21:32

not the secretive OpenAI. We're we're quite the opposite.

21:35

Like, we put this we put the most

21:37

advanced AI in

21:39

the world in an API that anybody

21:41

can use. I don't think

21:43

that if we hadn't started doing that

21:45

a few years ago, Google anybody

21:47

else would be doing it

21:48

now. They would just be using it secretly to make

21:50

Secret to self. So

21:51

you think you're forcing it out.

21:53

Well, you're but you are in competition.

21:55

And let me let me go back to someone

21:57

who was your one of the original funders. Elon

21:59

Musk, he's been openly critical of OpenAI,

22:01

especially as it's gone to prophets. He

22:03

said OpenAI was created as an open

22:06

source, which is why I named it OpenAI,

22:08

nonprofit company to serve as a counterweight

22:10

to Google, but now has become close source

22:12

maximum profit company effectively controlled

22:14

by Microsoft, not what I intended at

22:17

all. We're talking about open source versus

22:19

closed, but what about his

22:21

critique that you're too close

22:23

to the big

22:23

guys?

22:24

I mean, most of that is not true. Okay.

22:26

And I think let's go to view on those that We're

22:29

not controlled by Microsoft. Mhmm. Microsoft

22:31

doesn't even have a board seat on us. We are

22:33

an independent company. We have an unusual

22:35

structure where we can make very

22:37

different decisions than what most companies

22:40

do. Mhmm. I think a fair part

22:42

of that is we don't open source everything anymore.

22:44

We've been clear about why we think we are wrong

22:46

there originally. We still do open source

22:49

a lot of stuff. You know, open sourcing

22:51

clip was something that kicked off

22:53

this whole generative image world. We

22:56

recently open source We OpenAI source

22:58

tools. Will open source more stuff in the future.

23:01

But I don't

23:03

think it would be good right now for

23:05

us to open source four, for example.

23:07

I think that would cause some degree

23:09

of havoc in the world or at least there's

23:11

a chance of that. We can't be certain that it wouldn't.

23:15

And by putting it out behind an API,

23:17

we are able to

23:19

get many, not Altman, many of the

23:21

benefits we want of broad access

23:23

to this society being

23:24

able to understand it, update, and think about it.

23:27

When we find some of the

23:29

scarier downsides,

23:31

we're able to then fix them. Mhmm.

23:33

How do you respond to when he's saying you're a close

23:35

source, Mac premium profit company. I'll leave out

23:37

the control by Microsoft, but in part in

23:39

strong partnership with Microsoft. We have a couch. It was

23:41

against what he said. I remember years ago when he

23:44

talked about

23:44

this. This was something he talked about a

23:46

Altman

23:46

What's his work hard? Oh, we don't want these big

23:49

companies to run it. If they run we're doomed.

23:51

You know, was much more dramatic than most

23:53

people. So we're a capped profit company.

23:55

Yeah. We we invented this new thing -- Mhmm.

23:58

-- where we we started as a nonprofit.

24:00

Explain that. Explain what a capped profit is. We

24:02

our shareholders can make us which

24:05

our employees and our investors can make a

24:07

certain return, like their their shares have

24:09

a certain price that they can get to. But

24:11

if OpenAI goes and becomes a

24:13

multi trillion dollar company, whatever, almost

24:16

all of that flows to the nonprofit

24:18

that controls us.

24:19

Not, like, people hit a cap and then

24:21

they don't hit a cap. What is a

24:22

cap? It continues to vary as we have

24:24

to raise more money, but it's, like, much,

24:26

much, much, and we'll remain much smaller

24:28

than, like, an exec OpenAI. What?

24:31

In terms of, like, a number, I truly don't know. But

24:33

it's not a sign the the nonprofit gets

24:36

the significant chunk of the OpenAI

24:38

well, it gets no. It gets everything over a

24:40

certain amount. So if we're not very successful,

24:42

the nonprofit might not well, it gets a little bit along

24:45

the way, but it won't get any appreciable amount. Mhmm.

24:47

The the goal of the cap profit is in in the

24:49

world where we do succeed at making AGI.

24:51

Mhmm. And we have a significantly different

24:54

everybody else and, you know, that could become much

24:56

more valuable, I think, than maybe

24:58

any company out there today. Mhmm. That's

25:01

when you want almost all of it to flow to a

25:02

nonprofit. Right. Wanna get back to what Elon

25:05

was talking about. He was very adamant

25:07

at the time. And again, overly dramatic

25:09

that Google and Microsoft

25:12

and Amazon were gonna kill us. I think

25:14

he had those kind of words. They

25:16

need there needed to be an alternative. What

25:20

changed in your estimation? To

25:23

do that, to change from that

25:25

idea. Oh, it was very simple. Like,

25:27

when when we realized the level

25:29

of capital we were going to need to do

25:31

this. Scaling turned out to be far more

25:33

important than we thought, and we even thought it was gonna be

25:35

important. Then, and

25:37

we tried for a while to raise to

25:40

find a path to that level of capital as

25:42

a nonprofit. Mhmm. There was no one that was willing

25:44

to do it. So we didn't wanna become a

25:46

fully for profit company. We wanted

25:49

to find something that would let us get the

25:52

access to and the power

25:54

of capitalism to finance

25:57

what we needed to do. But

25:59

still be able to

26:02

fulfill and be governed by the nonprofit

26:04

mission. So having this nonprofit that

26:06

governs this cap the profit LLC

26:09

given the playing field

26:12

that we saw at the time and I still think that

26:14

we see now was the way to get

26:16

to the best of all worlds we could

26:17

see. In a really well functioning society,

26:20

I think this would have been a government project. That's

26:22

correct. I was just gonna make that

26:24

project. And the

26:24

government would have been your funder.

26:27

We talked to them. That

26:30

was not it wouldn't have not

26:32

just been they would have been our

26:33

funder, but they would have started

26:35

the project. We've done things like

26:37

this before in this

26:38

country. Right. Sure. But the

26:40

answer is not to just say, oh, well, the government

26:43

doesn't do stuff like this anymore. So

26:45

we're just gonna sit around and, you know, let other

26:48

countries run by us and get an AGI

26:50

and do whatever they want to

26:51

us. Mhmm. It's we're gonna, like,

26:53

look at what's possible on this playing

26:55

field. Right. So Elon used to be the

26:57

cochair and you have a lot of respect for him. So you

26:59

thought deeply about his critiques. Have you spoken

27:02

to him

27:02

directly? Was there a break? Or what you

27:05

you two were very close as I was

27:06

Have you spoken directly recently? Yeah.

27:08

And what do you make of the critiques?

27:11

When you hear them from him, I mean,

27:13

it can be quite

27:14

in your face about this. He's got his style.

27:17

Yeah. And to say a

27:19

positive thing about Elon -- Yeah. -- I think he

27:21

he really does care

27:24

about a good

27:25

future.

27:25

He does. With AGI? That is correct. And

27:28

he's I mean, he's a jerk, whatever else

27:30

you wanna say about him. He has a style that

27:33

is not a style that I'd wanna have for myself. It's

27:35

changed. But I think he

27:38

he does really care and

27:40

he is feeling very stressed about

27:43

what the future is gonna look like? For humanity.

27:45

For humanity. Yeah. He he did apply that both to

27:47

when when we didn't interview at Tesla. He's like, if

27:49

this doesn't work, we're all doomed, which was sort

27:52

of centered on his car. But nonetheless, who's

27:54

correct? And the same thing with it, and

27:56

this was something he talked about almost

27:58

incessantly. The idea of either

28:01

AI taking over and killing

28:03

us or maybe it doesn't really

28:05

care, then he decided it was like Antilles.

28:07

Do you remember that?

28:07

don't know the Antilles part. He said we're like,

28:10

you know how we think when we're building a highway,

28:12

Antilles are there and we just go over them without thinking

28:14

about it. So they don't it doesn't really care. And then

28:16

he said we're like a maybe they'll feed us

28:19

and bell

28:19

us, but they don't really care about us. It

28:22

went on and on. It went

28:22

-- Yeah. -- changed and iterated over time. But I

28:25

think the most critique that I would

28:27

agree with them is that these big companies would

28:29

control this and there couldn't be

28:31

innovation in the

28:32

space.

28:32

Well, I wish they were evidence against

28:35

that.

28:35

Except Microsoft often. That's right. They're like

28:37

big investor, but again -- Yeah. -- not even a

28:39

board member.

28:40

So when you think

28:40

Like, true, full independence from them.

28:42

So you think you are a startup

28:45

in comparison with a giant partner?

28:47

Yeah. I think we're a startup with a giant. I know we're a

28:49

big startup at this point. Mhmm.

28:50

So and there was no way to be a nonprofit

28:53

that would work.

28:54

I mean, if, you know, someone wants to give us tens

28:56

of billions of dollars of nonprofit capital, we can't go

28:58

make that work. For

28:59

the government, which they're not.

29:00

We try to

29:01

you know, he and others are are

29:03

working on different things. He

29:04

has an anti woke AI play.

29:07

Greg Altman also said you

29:09

guys made a stake by creating AI with

29:12

a left leaning political

29:13

bias. How do you what do you

29:15

think of the substance of those critiques? Well,

29:18

I think

29:18

that This

29:19

was your cofounder. Yeah. Yeah. think that the

29:22

reinforcement learning from human feedback

29:24

on the first version

29:26

of chat GPT was pretty

29:29

left biased, but that is

29:32

now no longer true. It's just become

29:34

an Internet meme. Mhmm. There are people some people

29:36

who are intellectually honest about this. If you go

29:38

look at, like, GBT four and

29:40

test it on

29:40

us. So it's it's relatively neutral. Not to say don't

29:43

have more work to do. The main thing though is I don't

29:45

think you ever get to two people agreeing

29:47

that any one system is unbiased on

29:49

every topic. And so giving

29:52

users more control and also teaching people

29:54

about, like, how these systems work

29:56

that there is some randomness response that the worst

29:58

screenshot you see on Twitter is not representative

30:00

of what these things

30:01

do. I think it's important. So when

30:03

you said it had a left leaning bias, what did

30:05

that mean to you? And course,

30:07

they will run with

30:08

that. They'll run with that quite far.

30:10

People would give it these tests that

30:13

score you on, you know, the

30:15

political spectrum in America or whatever. Mhmm.

30:17

And, like, one would be all the way on the right, and one would

30:19

be all the way on the left. I would get like a

30:21

ten on all of those tests -- Mhmm. -- the first version.

30:24

Why? Because

30:26

of what was a number of

30:27

reasons, but largely because

30:29

of the the reinforcement learning

30:32

from human feedback step.

30:34

We'll be back in a minute.

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32:34

What do you think of the most viable threat

32:36

to OpenAI As I hear you're watching Claude

32:38

very carefully, it's the bot from AnthroPIC,

32:41

a company that's founded by OpenAI folks

32:43

and backed by Alphabet. Is

32:46

that it? We're recording this on Tuesday, Bard

32:48

launched today. I'm sure you've been discussing it internally.

32:50

Talk about those tooth to start.

32:52

Honestly, I mean, I try to pay some attention

32:54

to what's happening with all these other

32:56

things. It's gonna be an unbelievable

32:59

competitive space. think this is the first

33:01

new technological platform in

33:03

a long period of time. The

33:05

thing I worry about the most is not any of

33:07

those. Because I think we can, you

33:10

know, there can there's room for a lot of

33:12

people and also think we'll just continue to offer

33:14

the best the best product. The

33:16

thing I worry about the most is that we're somehow

33:19

missing a better approach -- Mhmm. --

33:21

and that this idea, like everyone's chasing us

33:23

right now on large language models to kind of train in the

33:25

same way. I don't worry about them. I worry

33:27

about the person that has some very

33:30

different idea about how to make a more useful system.

33:33

Like a Facebook too. Probably

33:35

your Facebook, to be honest. No. Like a Facebook to

33:37

000. No. Not like Facebook. Not Facebook.

33:39

No. Facebook's not gonna come up with anything. Unless Snapchat

33:42

does, and then they'll copy it. I'm teasing

33:44

sort of. But you don't feel like these other

33:46

efforts that they're sort of in your same

33:48

lane. You're all competing. So it's the one

33:50

that is not That's what I would worry about,

33:52

Moria. Like, the people that

33:54

are trying to do exactly what

33:56

we're doing

33:57

but, you know, scrambling to

33:59

cross selling it. But is there one that

34:01

you're watching more carefully? Not

34:04

especially. Really?

34:06

I am.

34:06

I kind of don't believe you, but really I mean,

34:09

no, the things that I was gonna say, the things that I pay

34:11

the most attention to are not like language

34:13

model startup number two hundred and

34:15

seventeen. Mhmm. It's when I hear about

34:17

someone, it's like, these are like

34:20

three smart people in a garage. With

34:22

some very different theory of how to build AGI.

34:24

Mhmm. And that's when I pay

34:26

attention. Is there one that you're paying attention to

34:28

now?

34:31

There is what I don't wanna say.

34:32

Okay. You really don't wanna say? really don't wanna say. Okay.

34:35

What's the plan for making money? So we're sort

34:37

of like we have a platform, which is this API

34:39

that networking used to the model. And then we have, like,

34:41

a consumer product on top of it -- Right. -- and

34:43

the consumer product twenty bucks a month. For

34:45

the sort of premium version and the

34:47

API you just pay us per token like

34:50

basically like a meter.

34:51

Businesses would do that depending on what they're using

34:53

it for.

34:53

If they decided employee in a hotel or wherever.

34:55

The more you use it, the more you pay it. The more you use it,

34:57

you pay. One of the things that someone said to me

34:59

and I thought was very smart is if the original

35:01

Internet's started on a more pay

35:04

subscriber basis rather than an advertising

35:06

basis. It wouldn't be quite so evil. I

35:08

am excited to see if we

35:10

can really do a mass scale

35:13

subscription

35:13

funded, not ad funded business here. Mhmm.

35:16

Do you see ads funding this? That to

35:18

me is

35:18

the original center of the interview. We've

35:19

made the bet not to do that. Right.

35:22

I'm not opposed to it. Maybe we

35:23

didn't

35:23

look like it. I don't

35:24

know. We haven't thought, like, it's going great with

35:26

our current model. We're happy about it. You've been

35:28

also competing against Microsoft for clients. They're

35:30

trying to sell your software through their Azure cloud

35:33

business as as an add

35:34

on. Actually,

35:34

that, I don't Like, that's

35:36

fine. don't care. That's fine. But you're also trying

35:38

to sell directly sometimes the same clients. don't

35:40

care about. They don't care about. You don't care. How does

35:42

it

35:43

work? Does it affect your bottom line that way?

35:47

Again, we're like an unusual company here.

35:49

Mhmm. We're not like, we don't need to squeeze

35:51

out every dollar. Former Google Tristan

35:53

Harris, who's become a critic of how tech is

35:55

sloppily developed, presented to a group in

35:57

DC of regulators. I was there. Among

35:59

the points he made is that you've essentially

36:02

kicked off an AI arms race. I think

36:04

that's what struck me the most. Meta,

36:06

Microsoft, Google, Baidu, are rushing to ship

36:08

generative AI bots when the tech industry

36:10

is shedding jobs. Microsoft recently laid

36:12

off ethics and society team within its

36:14

AI org. That's not your issue. But

36:16

are you worried about a profit driven arms

36:18

race? I do think

36:21

we need regulation and

36:23

we need industry norms about this.

36:25

I am disappointed to see people

36:27

like we spent many,

36:30

many months. And actually, really,

36:32

the years that it's taken us to get good at making

36:34

these models, getting them ready before we put them up.

36:36

You know, people it obviously became somewhat

36:38

of an open secret in Silicon Valley that we had GPT4

36:41

done for a long time. Mhmm. And there were a lot

36:43

of people who were like, you gotta release this now,

36:45

you're holding this back from society, you know, existing

36:47

your closing eye, whatever. But, like, we

36:49

just wanted to take the time to get it right. Mhmm.

36:51

And there's a lot to learn

36:54

here and it's hard. And in fact, we try to

36:56

release things to help people get it

36:58

right even competitors. I

37:00

am nervous about the

37:02

shortcuts that other companies now seem

37:04

like they wanna

37:05

take. Such as Oh, just rushing

37:07

out these models without all the safety features built -- Mhmm. --

37:09

without save So they're just this is an art that

37:11

they wanna get in here and get ahead of you because

37:14

you've had the front seat.

37:16

Maybe they do, maybe they don't. They're certainly

37:18

making some noise like, you

37:20

know, they're gonna So

37:21

when you say worried, what can you do about it?

37:24

Nothing. Well, we

37:26

can and we do try to talk to them

37:28

and explain, hey, here's some Altman. You

37:30

know, here's some things we think you need get right. Yeah.

37:32

We can continue to push for regulation. We can

37:34

try to set industry norms. We can

37:36

release things that we think help other people get

37:38

towards safer systems

37:39

faster. Howard Bauchner: Can you prevent that, let

37:41

me read you this passage from the story about Stanford

37:44

doing it. They did one of their own

37:45

models. Six hundred dollars, I think it cost

37:47

them to put it They trained

37:48

the model for six hundred dollars. Yeah. Yeah. Yeah. They

37:50

did. It's called Stanford El Paso just so you

37:52

know. It's a cute name. It is. It's a cute

37:55

name. I'll send you the story, but so what's to

37:57

stop? Basically, anyone from creating their own

37:59

pet AI now for a hundred bucks or so

38:01

and training it however they choose Well,

38:03

OpenAI AI's terms of service say, you may not

38:05

use output from services developed models that

38:07

compete with open AI, and meta says it's

38:09

only letting academic researchers used under

38:12

a non commercial license at this stage, although

38:14

that's a moot point since the entire Llama model

38:16

was leaked onto four

38:17

channels. Within

38:18

hours or

38:18

so. Yeah. And this is a six hundred dollar version

38:20

of yours. One

38:22

of the other reasons that we want to talk

38:24

to the world about these things now is this is coming.

38:27

This is totally unstoppable. Yep.

38:29

And there are gonna be a lot

38:31

of very good open source versions of

38:33

this in coming years, and it's gonna come with

38:36

you know, wonderful benefits and some problems.

38:39

By getting people used to this now,

38:41

by getting regulators to begin

38:43

to take this seriously and think about it now.

38:46

I think that's our best path forward. Alright.

38:48

Two things I wanna talk about, societal impact and regulation.

38:51

You've said, I I told you this will be the greatest

38:53

technology humanity has ever developed. You know,

38:55

almost every interview you do, you're asked about the dangers

38:58

of releasing AI products. And you say it's

39:00

better test it. Gradually, in an open quote,

39:02

while the stakes are relatively low.

39:04

Can you expand on that? Why are the stakes low

39:06

now? Why aren't they high right now?

39:08

Relatively is the keyword.

39:09

Right. Okay. What happens to the stakes?

39:12

If it's not controlled now? Well,

39:14

these systems are now much

39:17

more powerful than they were a few years ago, and

39:19

we are much more cautious than we were a

39:21

few years ago in terms of how we deploy them.

39:24

We've tried to learn what we can learn.

39:26

We've made some improvements. We found ways that people

39:28

wanna use this. I, you know, in

39:30

this interview, and I totally get why. And in many

39:32

of these topics were I think we're mostly talking about

39:34

all of the

39:35

downsides. But No. I'm

39:35

gonna ask you about the upside. Okay. But

39:38

we've also found ways to, like, improve the upsides

39:40

by learning too. So mitigate downsides, maximize

39:43

upsides. That sounds good. And

39:45

it's not that the stakes are

39:48

that low anymore. In fact, think

39:50

we're in a different world than we were a few years ago.

39:52

Mhmm. I still think they are relatively

39:55

low to where we'll be a few years from now.

39:58

These systems are still they

40:00

have classes of problems, but there's things that are

40:02

totally out of the out of reach that we

40:05

know they'll be capable of. And

40:08

the learnings we have now, the feedback we get

40:10

now, seeing the ways people

40:12

hack

40:12

jailbreak, whatever, that's super valuable. I'm curious

40:15

how you think we're doing. I know you're

40:16

I think you're saying the right things. You're

40:18

absolutely not from saying. Like, I think we're doing this.

40:20

You look at the trajectory of our releases. I think the

40:22

reason people are so worried, and I think it's

40:25

legitimate worry is because the

40:27

way the early Internet rolled out, it was

40:29

g wiz almost the whole time. Yeah. Almost

40:31

up into the right. G wiz, look at these rich

40:33

guys. Isn't this great?

40:36

Doesn't this help you? And they missed every

40:38

single consequence. Never thought of them.

40:40

I remember seeing Facebook

40:43

live. And I mentioned I said, what

40:45

about, you know, people who kill each other

40:47

on it? What about, you know, murders? What about

40:49

suicides? What about and they call me a bummer

40:52

and a bummer. A bummer in this room. And I'm like,

40:54

yeah, I'm a bummer. I'm like, I don't know. I just

40:56

noticed that when people get ahead of tools, they

40:58

tend. And, you know, this is Brad Smith's thing. It's a

41:00

tool or a weapon. Weapon seemed to

41:02

come up a lot. And so I always think

41:05

same thing happened with the Google founders and they were trying

41:07

to buy Yahoo many years ago. And I

41:09

said at least Microsoft knew they were thugs

41:11

and they they called

41:13

me and they said that's really hurtful or really nice. I

41:15

said, I'm not worried about you. I'm worried about

41:17

the next guy, like, I don't know who runs your

41:19

company in twenty years with all that information on

41:21

everybody. And so I think, you

41:24

know, I am a bummer. And so if you don't

41:26

know what it's gonna be, well, you can

41:28

think of all the amazing things it's gonna do,

41:30

and it'd probably be a net positive for society.

41:33

Net positive isn't so great either sometimes.

41:35

Right? It's a net positive the Internet's

41:37

a net positive, like electricity is a net positive.

41:40

But every time it's a famous quote, every time

41:42

you when you invent electricity, you invent the electric

41:45

chair, when you invent this and that. And so

41:47

that's that's what would be the thing here that

41:49

would be the greatest

41:50

thing. Does it outweigh some

41:52

of the dangers? I think that's gonna be

41:54

the fundamental tension that we face that we have to wrestle

41:56

with, that the field as a whole has to wrestle with, society

41:58

has to wrestle with.

41:59

Especially in this world we live in now, which I

42:02

think we can all agree, has not gotten gone

42:04

forward. It's spinning backwards a little bit

42:06

in terms of authoritarians using this

42:08

stuff,

42:09

you know. I am super nervous about Yeah. What

42:11

is the greatest thing you can think? Now you're not

42:13

you and I are not creative enough to think of all the

42:15

things. We are not Then it could Not even think about it. What from

42:17

your perspective and you know, don't

42:19

do term papers. Don't do dad jokes.

42:22

What do you

42:22

think? That's fine. Is

42:23

that what you thought I would say? No. Not at all, but I'm

42:25

getting tired of that. I don't care that it can write.

42:28

A press release. I don't care. Fine. Sounds fantastic.

42:31

I hate I don't want the right way. Personally

42:33

most excited about is helping

42:36

us greatly expand our scientific

42:38

knowledge. Okay. I am a believer that

42:40

a lot of our forward progress comes from

42:43

increasing scientific discovery over

42:45

a long period of

42:45

time. In

42:46

any area? All all the areas. I think

42:48

that's just what's driven humanity forward. Mhmm.

42:50

And if these

42:52

systems can help us, in

42:54

many different ways, greatly increase

42:57

the rate of scientific understanding.

42:58

Mhmm. You know, curing diseases is an obvious

43:00

example. There's so many other things we can do

43:02

with better

43:03

knowledge and better understanding.

43:05

Yeah. It's already

43:05

moved

43:05

in that are folding proteins and things like that. So

43:07

that's the one that I personally want excited

43:09

about. Science.

43:10

Yeah. But there will be many other wonderful things

43:12

too. You just you asked me what my one was and

43:14

Is there one unusual thing that you think will

43:16

be great? That you've seen already

43:18

that you're like, that's pretty cool. Using

43:21

some of these new AI tutor

43:23

like applications -- -- it's

43:25

like I wish I had this one I was

43:27

growing up. I could have learned so much and so much

43:29

better and faster. And when I think about

43:31

what kids today will be

43:33

like, by the time they're finished with

43:35

their formal education and how much smarter

43:38

and more capable and better educated and

43:40

they can be than than us

43:41

today. I'm I'm excited for using these tools

43:43

using these tools. Yeah. I would say health

43:45

information to people who can't afford it is

43:47

probably one I think is most important.

43:49

That's gonna be transformative. We we've seen

43:51

Even if for people who can't afford it, this in

43:53

some ways will just be much

43:54

better. Yeah. Exactly. It's

43:56

a hundred percent

43:57

better. And the and the and the work we're seeing there from

43:59

bunch of early companies on the platform I

44:01

think it's remarkable. Howard Bauchner: So the last thing is regulation

44:03

because one of things that's happened is

44:05

that Internet was never regulated by anybody,

44:08

really. Except maybe in Europe. But in this country,

44:10

absolutely not. There's not a privacy bill. There's not an

44:12

antitrust bill, etcetera. It goes on and on.

44:14

They did nothing. But the EU is considering labbing

44:17

CHAT, GBT, high

44:18

risk. If it

44:19

happens, it will lead to significant restrictions on

44:21

its use in Microsoft and Google lobbying against

44:23

it. What do you think should happen? With

44:26

AI regulation in general with the

44:27

Yeah. It is one, the high risk one. I

44:30

have followed the development of

44:32

the EU's AI Act, but it is

44:35

changed it's, you know, obviously, still in development.

44:38

I don't know enough about the current version of

44:40

it to say if I think this way like, this definition

44:42

of what high risk is and this way classifying this

44:44

is what you have to do. I don't know if I

44:47

would say that's like good or bad.

44:50

I I think like totally banning this stuff is not

44:52

the right answer. Mhmm. And think did not regulate

44:54

this. I mean, you're not TikTok, but go ahead.

44:58

And think not regulating stuff at all is not the right

45:00

answer either. Mhmm. And so the question is,

45:02

like, is that gonna end in the right balance?

45:05

Like, I think the EU is saying, you know, no one

45:07

in Europe gets to use chat JBT, probably

45:09

know what I would do. Mhmm. But the EU is saying,

45:11

here's the restrictions on chat JBT and any service

45:14

like it. There's plenty versions that I could

45:15

imagine, meaning, alright, super sensible? Alright. So

45:18

after the as Silicon Valley non bailout

45:20

bailout, you tweeted we need more regulation on

45:22

banks. But what sort of regulation

45:24

know. And then someone tweeted at you. Now he's gonna

45:26

say we need him on AI, and you said we need him on

45:28

AI. But I I mean, I do think that

45:30

SVB was an unusually bad case.

45:33

But also,

45:34

if the regulators aren't

45:37

catching that, what are they doing? They

45:39

did catch it. Actually. They were giving

45:41

warnings.

45:41

They were giving warnings, but, like, there's often

45:43

an audit. You know, this thing is not quite like, that's

45:45

different.

45:46

That's It's even pretty significant. You don't

45:48

need to do something. They just didn't do anything.

45:50

Well, they could have I mean, the regulators could have taken over

45:52

-- Mhmm. -- like six months ago. So this is

45:54

what happens a lot of the time, even in well regulated

45:56

areas, which banks are compared to the Internet.

45:58

What sort of regulations does AI need in America

46:01

lay them out? I know you've been meeting with regulators

46:03

and lawmakers. I haven't done that many. Well,

46:05

they call me when you do. They wanna

46:07

say, they've knew I guess. What

46:08

did they say? Well,

46:09

you're like the guy now. So they like to say I

46:11

was with Altman.

46:12

did one. His

46:13

name's nice. Like, he's nice. I don't know what to

46:15

tell you.

46:16

I did like a three day trip to D. C. Earlier

46:18

this year. So tell me what you think the regulations

46:20

weren't. What are you telling them? And do

46:23

you find them savvy as a

46:24

group? I think they're savier than people thing. Some

46:26

of them are quite quite exceptional. Yeah. Mhmm.

46:30

I I think the thing that I would like to see

46:32

happen immediately it's just

46:34

much more insight into what companies

46:37

like ours are doing. Mhmm. You know, companies

46:39

that are training above a certain level of capability.

46:42

At a minimum, like a thing that I think could

46:44

happen now, is the government should

46:46

just have insight into the

46:48

capabilities of our latest stuff released or

46:50

not. What our internal audit

46:53

procedures and external audits we use look

46:55

like, how we collect our data,

46:57

how we're red teaming these systems. Mhmm.

46:59

What we expect to happen, which we may be totally wrong

47:01

about. We get it a wall anytime. But like our internal

47:03

road map documents, when we start a big

47:05

training run, I think there could be government. Insight

47:08

into that. And then if that can

47:10

start now, I do

47:12

think good regulation takes long time to

47:14

develop. It's a real process. Mhmm. They

47:16

can figure out how they wanna have oversight?

47:19

Read had read hoping to suggest a blue ribbon

47:21

panel so they'd learn up on this stuff, which I

47:23

mean, panels are fine. We could do that

47:25

too, but what I mean is like government

47:28

auditors sitting in our buildings.

47:29

Congressman Ted Liu said there needs to be an

47:31

agency dedicated specifically to regulating

47:34

AI. Is that a good idea?

47:36

I think there's two things you wanna do.

47:38

This is way out of my area of expertise,

47:41

but you're asking, so I'll try. I

47:43

think people like us that are creating these

47:46

very powerful systems that

47:48

could become something properly called

47:50

AGI at some

47:51

point. Mhmm. This is

47:52

explained what that is. Artificial general intelligence.

47:54

But what what people mean is just like above

47:56

some threshold where it's

47:58

really good. Right. Those efforts probably

48:00

do need a new regulatory effort, and

48:02

I think it needs to be global body,

48:06

new regulatory body. And

48:08

then people that are using AI,

48:10

like we talked about, the medical adviser. I

48:12

think FDA can give probably very

48:14

great medical regulation. Mhmm.

48:16

But they'll have to update it for the collusion

48:19

of AI. But I would say, like, creation

48:21

of the systems and having

48:23

something like an IAEA that regulates that,

48:26

is one thing and then having existing

48:28

industry regulators

48:30

still do their regulations. So people do

48:32

react badly to that because the information bureaus

48:35

that's always been a real problem in

48:37

Washington.

48:38

Yeah, not everyone.

48:38

Is who should head that agency

48:41

in the US?

48:41

I I don't know. Okay. Alright. So

48:43

one of the things that's gonna happen though is the

48:45

less intelligent ones, of which there

48:48

are many, are gonna

48:50

seize on things like they've done with TikTok

48:53

possibly deservedly, but other things,

48:55

like SNAP released a chatbot powered by

48:57

GPT that reportedly told a fifteen year old,

48:59

how to mask the smell of wheat and alcohol, and a thirteen

49:01

year old. How to set the mood for sex with an adult.

49:03

They're gonna seize on this stuff. And the

49:05

question is, who's liable? If this is

49:08

true, when a teen uses

49:10

those

49:10

instructions. And section two thirty doesn't seem

49:12

to cover generative AI. Is that

49:14

a problem? I think we will need a new law

49:16

for use of this stuff. Mhmm. I

49:18

think the liability will need

49:21

to have a a few different frameworks. If someone's

49:23

tweaking the models themselves, I think

49:25

it's gonna have to be the last person that touches

49:27

it has the

49:28

liability. Mhmm. That's and that's

49:30

But their B liability. It's not full. The

49:32

immunity that the platform is getting I

49:34

don't think we should have full immunity. No. That

49:36

said, I understand why you want limits

49:38

on it. Why you do want companies to be able to

49:40

experiment with

49:40

this? You want users to be able to get the experience they

49:42

want. Mhmm.

49:43

But the idea of, like, no one

49:45

having any limits for a generative AI for

49:47

AI in general, that feels super

49:49

wrong. Last thing, trying to quantify

49:51

the impact you personally will have

49:53

on Society is one of the leading developers of

49:55

this

49:56

technology. Do you think about

49:58

that? Do you think about your impact? Do

50:00

you

50:00

Like me OpenAI

50:01

up

50:01

for me, Sam? You, Sam.

50:05

I mean, hopefully, I'll have a positive impact.

50:08

Do you think about the impact on humanity

50:10

and the level of power that also comes with it?

50:14

Yeah, I don't. I I think about, like, what OpenAI is

50:16

gonna do a lot and the OpenAI will

50:18

have. Do

50:20

you think it's out of your hands? No.

50:23

No. But it is very much a like,

50:25

the responsibility is with me at some level,

50:27

but it's very much a team effort.

50:29

Mhmm. And so when you think

50:31

about the impact, what is your

50:33

greatest OpenAI what's your greatest

50:35

worry? My greatest

50:37

hope is that we are we create

50:39

this thing We are one of many people

50:41

that is gonna contribute to

50:43

this movement will create an AI. Other people

50:45

will create an AI. And that

50:47

this we will be a participant in

50:50

this technological revolution that I I

50:52

believe will be far greater in terms

50:54

of impact and benefit than any before.

50:57

Mhmm. My my view of the world is it's this,

50:59

like, one big, long technological

51:02

revolution, not a bunch of smaller ones, but

51:04

we'll play our part. We will be one of several

51:06

in this OpenAI. And that

51:09

this is gonna be really wonderful. This is going

51:11

to elevate humanity in ways we still

51:14

can't fully envision. And

51:17

our children, our children's children are gonna

51:19

be far better off than, you know, the best

51:22

of anyone from this time. And we're

51:24

just gonna be in a in a radically

51:26

improved world. We will live healthier,

51:28

more interesting, more fulfilling lives. We'll have

51:31

material abundance for people. And,

51:33

you know, we will be a

51:35

contributor.

51:37

And, you know, we'll put in our Your

51:39

part. Our part of that. I do sound alarming like

51:41

the people I'm twenty five years ago, I

51:43

have to say. If you

51:44

were not, I don't know how old you are, but you

51:46

weren't, you were young. You were probably very

51:48

young. Thirty seven. So Yeah. Thirty one twelve.

51:50

And they did talk like this. Many of them did, and

51:53

some of them continued to be that way. A lot of

51:55

them didn't, unfortunately. And

51:57

then the greed seeped in, the

51:59

money seeped in, the power seeped in, and it got

52:01

it got a little more complex, I would say, not

52:03

not totally. And again, because

52:05

net, it's better. But I wanna focus

52:07

on you on my last question. There seem to be two characters

52:10

of you. One that I've seen in the press is

52:12

a boyish genius who will help defeat Google and

52:14

Usher and

52:14

Utopia. The other is that you're an irresponsible

52:17

woke, tech overlord icarus that will lead us

52:19

to our demise. I

52:21

have to pick one. Is

52:23

it? No. I don't How

52:24

old do I have to be before I can, like, drop the

52:26

boyish qualifies?

52:27

Oh, you can be boyish. Tim Hanks is still

52:29

boyish. Yeah.

52:30

And what was the second one?

52:32

You know, Iker is overlord, tech overlord,

52:34

will Woke something?

52:35

Yeah. Yeah. Woke whatever.

52:36

The Iker is part

52:37

is to highlight. So boy is to you. But I'm

52:40

I think we feel like adults now.

52:41

You may be adults, but boy ish always gets

52:44

put on you. I don't ever call you boys. I think

52:46

your adults

52:47

Achorus meaning, like, I'm messing we're we

52:49

are messing around with something that we

52:50

don't fully understand.

52:51

Yes. Well, we are messing around with something we don't fully

52:53

understand. Yeah. And

52:54

we are trying to do our part in

52:57

contributing to the responsible path through it.

52:59

Alright.

53:00

Yeah. But I don't think either of those

53:01

Yeah. Either of those I mean, describe yourself

53:04

describe what you are.

53:08

Technology brother. Oh, wow.

53:10

You're gonna go for that. I just think that's such

53:12

a funny, ma'am. I don't know how to describe

53:14

myself. I think that's what you would call

53:16

me. No. I wouldn't. No. One hundred percent.

53:18

Alright. Because it's an insult now.

53:19

Let's become

53:20

an insult. I call you a technology

53:22

sister. I'll

53:23

take that. I'll leave it on that now. Let's leave

53:25

it on that now. I do have one more quick question.

53:27

We last time we talked, you were thinking running for governor.

53:29

I was thinking running for

53:30

mayor. I'm not gonna be running for mayor. Are you

53:32

gonna still run for governor?

53:33

No. No. III

53:35

think I am doing, like, the most I mean,

53:38

I think I can imagine. Really don't do anything

53:40

else. wanna do anything else? It's tiring, but I love it.

53:42

Yeah. Okay. Sam Altman. Thank you

53:44

so much. Thank

53:44

you.

53:51

You said he sounded a lot like a lot of

53:53

founders generation before him. Yes. What are

53:55

the lessons you would impart to Sam

53:58

as someone who has so much impact on the

53:59

humanity? You know, I think what I said is that they

54:01

were hopeful and they were they had

54:04

great ideas. And one of the things that I think

54:06

people get wrong is to be a tech critic

54:08

means you love tech. Like, you know, you

54:10

really love it. You do. Yeah. Of course. And

54:12

you you don't want it to fail. You want it

54:14

to create betterment for humanity. And

54:17

that if that's your goal, when

54:19

you see it being warped and misused, it's

54:21

really sad and

54:23

disappointing. And I think one of the things early

54:25

Internet people had all these amazing ideas,

54:27

the world talking to other. We'll get along with

54:29

Russia. We'll be able to communicate over

54:32

vast distances. And again, just like I talked

54:34

about with Reed Altman, it's a Star Trek

54:36

vision of the universe. And that's what it was.

54:38

And, boy, the money and the

54:40

power and the and the bad people

54:43

that came in were really

54:45

significantly shifted it, not completely.

54:48

By any

54:48

means. I love my Netflix, you know, I

54:50

just do. But the unintended

54:52

or intended consequences, ultimately

54:56

are very hard to bear even if it's

54:58

a net positive. So it's just the money and

55:00

the power that's corrupting is what you're

55:01

saying. It's inevitable. No.

55:03

Not inevitable. But often.

55:05

Often. Often. Yeah. Well, not

55:08

him. Not a lot of people. But

55:10

let's see, the standing the test of

55:12

time. Right? You're saying about Reed Hoffman and

55:14

Max Livechin versus, say, Peter Till

55:16

and Elon Musk. Well, I think Peter was always

55:18

like that. You know, I don't think he's changed

55:20

one bit. And so or not in my

55:22

not in my estimation. He's been very consistent

55:24

in how he looks at the world, which is not

55:27

a particularly positive light.

55:30

I think that a lot of them do stay the

55:32

same and they do stay true

55:34

to what they're like. And I don't know why that

55:36

is over certain people and others get

55:39

sucked into it in a way that's really I'm

55:41

thinking about this a lot because that's what my books about. Yeah.

55:43

Of course. How

55:45

people change and why and whether

55:48

that's a good thing or bad. Think because, you know, one of the

55:50

things about tech is cons certainly changing.

55:52

Mhmm. One of the poems I'm using in

55:54

the book is is a poem by Maggie Smith

55:56

called Good Bones. And I'll

55:58

just read you the last part life is

56:00

Altman the world is at least half terrible.

56:02

For an every kind stranger, there is

56:04

one who would break you, though. I keep this from

56:06

my children. I'm trying to sell them the

56:08

world. Any decent realtor walking

56:10

through a real shit hole, chirps have gone about

56:13

good bones. This place could be beautiful.

56:15

Right? You could make this place beautiful. And

56:18

that's how I feel about this. They could make this

56:20

place beautiful. And I think Sam thinks that too.

56:22

Yeah. It's not just a lie, you tell your children.

56:24

Right? Well,

56:25

no. But it is. You can't tell

56:27

them terrible

56:28

things all the time. They would be like just lying

56:30

on the ground. And yeah. But

56:32

sometimes it's so ideal like, when he said global

56:34

regulatory body to regulate AI,

56:36

I'm like,

56:37

oh, man, we're fucked. That's never gonna happen. Well, like,

56:39

when was the last good global regulatory

56:41

body? Good work. It could work.

56:43

There has to be this has to be global.

56:45

This

56:45

has to be global. But how there's

56:47

no infrastructure to set up a sustainable

56:49

life science and medicine there

56:51

is. You think the World Health Organization

56:53

has been effective?

56:54

And I think there's there's stuff around cloning

56:56

around all kinds of stuff. It's never gonna be perfect,

56:58

but boy, there's a lot of people that that hued

57:00

to those

57:01

ethics. I mean, I think it depends how bought in.

57:03

So governments are including China, but

57:05

the regulation thing is particularly tricky because

57:07

it can also become a moat. Right? That's right. Incompense,

57:10

like Facebook's, like regulate them, it's like, well,

57:12

you can afford the regulation in a way that new competitors

57:15

maybe can't.

57:16

I think the governments can play a lot of roles

57:18

here. They do it in new clear nonproliferation. It's

57:20

never perfect, but we still haven't set one

57:22

off. Have we? I think that's largely the deterrent

57:25

power and not because of any effective regulation.

57:28

I I am a great believer in nuclear

57:30

and proliferation. And I think

57:32

there's lots of examples of it work. And I think

57:34

the most significant thing that he

57:36

said here was about the government's

57:39

role, the US government's role. It shouldn't

57:41

give this all over to private sector. It should

57:43

have been the one to give them money. And

57:45

to fund them. And that is a hundred percent.

57:48

We've talked to Mariana Mazzocado about

57:50

that. Yeah. And many people, that to me

57:52

is the the big shame is the government abrogating

57:54

its role in really important things

57:57

that are important globally and important for

57:59

the US. But even when the government

58:01

has played that kind of

58:02

like, let's call it kindling role for industry,

58:04

whether it be Elon Musk's loan for Tesla,

58:06

whether it be what DARPA was doing

58:09

that became you

58:09

know, parts of Siri and --

58:11

Mhmm. -- and echo and whatnot. The government

58:13

here is bad at retaining like

58:15

a windfall from

58:16

that. That would be reinvested into but it used

58:19

to. It used to just do it because it was the right thing

58:21

to do that we would research and

58:23

investment by the government. You know, highway

58:25

system seems to have worked out pretty good. The

58:28

telephone system seems good. You know,

58:30

I mean, we always tend to, like, talk about what

58:32

they do wrong, but there's so much stuff that the government

58:34

contributed

58:35

to.

58:35

That matters

58:36

today. It used to be a culture also if

58:38

people would wanna go into government and civil service.

58:40

My father was in that generation, like,

58:43

you know, and I think that it's interesting

58:45

to hear Sam say, no, he won't run for governor.

58:47

In

58:47

fact, you think sometimes, well, it would be so great

58:49

if some of these bright minds, you know,

58:51

except he is more effective where he is. Why

58:53

would he do that when he's more effective where

58:55

he is? Arguably the right regulator for

58:57

this as a person who could have built it -- Yeah. -- or

58:59

conceit building it maybe. Did you find

59:01

his answers to the moderation questions

59:04

and this idea of hallucination and overly

59:06

impressive at first

59:07

glance? Did you find those satisfying? Yeah. Thought

59:09

if he doesn't have answers, I think one of the things I'd

59:12

like about Sam is if he doesn't have an answer, I don't think

59:14

he's hiding it. I don't think he knows, and I

59:16

think one of the the strengths of

59:18

certain entrepreneurs is

59:20

I don't really know. And I think that in

59:22

a lot around AI right now, anyone that's

59:24

gonna give you a certainty is lying to

59:26

you? Well, they had experimented with using these,

59:28

you know, these low wage workers

59:30

in Africa through Samma and Outdoors. Well, it's

59:32

not I think it was that. It was expose. They were

59:34

paying them less than two dollars an hour and training them to

59:36

build up, like, what what was reported

59:38

a content moderation AI layer -- Mhmm.

59:40

-- which is ironic when you think about it. So there

59:42

were workers in Africa being paid less than two dollars

59:45

an hour to train machines to

59:47

replace them for that

59:48

job. Well, have you been to an Amazon warehouse

59:50

late there's a lot of machines doing everything.

59:53

That's the way it's going. It doesn't that's like you're

59:55

telling me something that happens in every other

59:57

industry.

59:57

Yeah. I know. And yet, gonna grow smarter. Do you

59:59

think that's true? AI, too, everyone's gonna be

1:00:01

smarter? I do. I think we do a lot of wrote idiotic

1:00:04

work that we shouldn't be doing. And we have

1:00:06

to be more creative of what our greatest

1:00:08

use of our time

1:00:09

is.

1:00:09

My great hope for AI is actually that it takes

1:00:11

out the rope bits and all of a sudden creative industry

1:00:14

flourishes

1:00:14

because those are parts can't be replicated. And

1:00:17

Yeah. -- though I think, you know, a sad reality

1:00:19

of technology in the last generation has been that

1:00:21

kids maybe don't read as well

1:00:23

or as much or as fast as or as

1:00:25

early as used

1:00:26

to, but they make video. Right. What if they're

1:00:28

spoken to smarter? Like the idea

1:00:30

of education on these things or information

1:00:32

or healthcare in an easy way

1:00:35

is really these phones are just

1:00:37

are just getting started, and they will not just

1:00:39

be phones. They will be wrapped around

1:00:41

us in more good information you get

1:00:43

and the more communication you

1:00:44

get, that's a good thing. They might just be

1:00:46

getting started, but we are

1:00:47

ending. Do you wanna read us our credits today? Yes.

1:00:50

Remember, you we make this place beautiful or

1:00:52

ugly. Knock

1:00:54

it bones. Knock bones. Knock it bones.

1:00:56

Knock it bones. Today's

1:00:59

show was produced by name Maraza Blakeney

1:01:01

Ship, Christian Castro, Rosel and Rafaela

1:01:04

Seaport, special thanks to Haley Milliken.

1:01:06

Our engineers are Fernando Arudo and

1:01:08

Rick Kwan. Our theme music is by

1:01:10

Tracademics. If you're already following

1:01:12

the show, you get the red pill.

1:01:14

If not, Rick Deckard is coming

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1:01:18

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and hit follow. Thanks for listening to on

1:01:22

with Cara Swisher from New York Magazine, The

1:01:24

Box Media podcast network, and us.

1:01:27

We'll be back on Friday. That's tomorrow out

1:01:29

with a special bonus episode.