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Sam Altman - How to Start a Startup

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Sam Altman - How to Start a Startup

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0:00

Today I'm sitting down with Sam Alman,

0:02

the co-founder of OpenAI. A long time

0:04

ago, you talked or did a talk at

0:06

Stanford focused on how to start a

0:09

startup. What is the biggest thing that

0:10

has kind of shifted over the past like

0:12

10 years since you initially did those

0:13

talks?

0:14

>> Clearly AI uh and what is possible to do

0:18

now with a small team of people has and

0:21

the speed with which you can do it. Uh

0:24

and also not only what's possible but

0:25

like what you have to do to be

0:26

competitive given how much the world has

0:28

shifted. uh feels totally different. Um

0:32

it is amazing to me what a 10-week old

0:34

startup now can look like. Um and also

0:36

if you look like a 10-week old startup

0:38

from 10 years ago, you sort of are in

0:39

bad shape.

0:40

>> If you think of the 10e old startup in

0:41

your mind today, what is the one that is

0:43

the best example of a startup that moved

0:45

extremely quickly?

0:47

>> I don't even know what they're called,

0:48

but I met a what will be a startup that

0:50

is like 2 weeks old, something like

0:52

that, that has an entirely redone um

0:55

sort of office productivity suite,

0:58

whatever. all made for like a world

1:01

where AI has got to be able to use

1:04

documents, presentations, spreadsheets,

1:05

whatever. As a first class consumer and

1:09

uh I don't know, I felt like it would

1:10

have been a year's worth of work for a

1:11

startup pretty recently.

1:12

>> I would assume that if the like barriers

1:15

to starting startups and the like tools

1:17

out there become more proliferated and

1:19

all this stuff, you would assume that

1:20

like harder startups are easier to

1:22

start. What it means to be a hard

1:27

startup is changing so incredibly

1:29

quickly

1:31

that I don't think I have a perfect

1:34

mental model for what over the many

1:36

years that it takes to build a very

1:37

successful company. what are going to be

1:39

the really hard and really valuable

1:41

things like you know I've heard a lot of

1:44

people say well anything in the physical

1:45

world is extra valuable right now

1:47

because software is going to become free

1:48

but you know like won't be that long

1:51

until robots get really good and a lot

1:53

of the expectations of you know it's

1:56

really difficult to make rockets or

1:57

whatever that may change like quite

1:59

dramatically I love times like this I

2:01

think startups are they have the biggest

2:04

edge when the ground is shifting the

2:05

most um and when costs are coming down

2:09

rapidly and cycle times are coming down

2:10

rapidly. That's when startups

2:13

really,

2:15

I think, just have a a massive inherent

2:17

advantage. And that's happening in so

2:18

many places at once right now that it

2:21

seems like a great time to be doing

2:24

startups. And yet most startups are

2:27

like, I'm going to go build AI agents

2:30

for enterprise vertical X. Now,

2:33

that actually will work in a lot of

2:35

cases. It'll be very competitive and it

2:39

may not be like in fact I'd say it

2:41

probably won't be like the most

2:43

successful

2:44

defining startups of that era. Um but it

2:47

will work. But but given how much the

2:50

landscape is in flux, I am surprised

2:53

there are not more people doing the like

2:55

I'm going to go take on the crazy the

2:58

crazy thing with this completely new set

3:00

of tools. and also being willing to

3:02

truly internalize the fact that scaling

3:05

laws are going to continue and planning

3:08

for the things that are not possible or

3:09

economical this month but will be

3:11

possible in 2 years, four years,

3:12

whatever. So, it seems like an

3:14

unbelievable time to be starting

3:16

startups and there's very fertile ground

3:20

but there's also and I no judgment if

3:21

this is what people go for. There's this

3:23

incredible temptation to just like go

3:25

apply today's agents to the easy wins

3:28

and I get it. One thing that you've said

3:31

throughout the years is you basically

3:33

try to whenever you meet someone new,

3:34

you try to like plot where they are on

3:36

your mental model of who they are as a

3:38

person and then the next time that you

3:40

meet them, you basically want to figure

3:41

out how far or how fast they progressed.

3:44

>> Did that sort of thing and like doing

3:46

this across maybe thousands of people

3:48

impact your ability to kind of say,

3:50

okay, the model is this intelligent

3:52

today, it's this intelligent 3 months

3:54

later. Suddenly you can like plot this

3:55

out and it makes it easy to believe in

3:57

the growth. I think there's like a

3:59

general thing behind both of those which

4:02

is I just like I developed a great trust

4:04

in exponentials in people or companies

4:08

or models. Um I don't I don't think that

4:12

subjectively at least it doesn't feel

4:13

like watching model progress is the same

4:16

as like watching a founder develop but I

4:19

think the underlying belief system about

4:22

the world is is the same. If I were

4:25

still advising startup founders, this is

4:27

the most important thing I would try to

4:29

get them to wrap their heads around. And

4:31

it is evidently hard for the same reason

4:34

that there's like free money left in the

4:37

market for betting on high growth young

4:40

founders. And it's like one of the

4:42

market has still not adapted enough for

4:44

that. Um I think the market has also

4:46

still not adapted enough for like the

4:48

exponential of model progress is going

4:49

to continue. And it is okay to start

4:52

working on things now that require

4:55

smarter or cheaper models.

4:57

>> This idea of constantly shifting chaos,

4:59

how do you get good at it? How do you

5:02

get good at operating in a chaotic

5:03

environment?

5:04

>> I think it's just practice. There are a

5:07

handful of things

5:11

that I think no matter how much you

5:12

intellectually understand take a lot of

5:15

reps to like emotionally be able to

5:17

handle and operating

5:23

in a lot of chaos and trusting that

5:24

you'll figure it out and it'll be okay

5:26

and like this is not going to be the

5:27

thing that kills you and you don't quite

5:29

yet know how you're going to solve it

5:30

but you're going to figure it out. This

5:32

seems to me to be something that you can

5:35

only learn by going through it. Um,

5:39

and I actually think this is a real

5:41

weakness of young founders is that they

5:44

have not had the career experience of

5:46

getting to emotional peace with this.

5:48

And so they have a very hard time with

5:49

it in the early days and then and then

5:51

they eventually learn it but at you know

5:52

great pain and cost and unforced errors.

5:56

You definitely will, if you end up in a

5:59

high stress sort of impactful job, you

6:01

will definitely learn how to deal with

6:06

chaos and function very well through it.

6:08

At least most people will, but I don't

6:11

think it's teachable. I think it's only

6:12

learnable. You had an interesting thing

6:14

that you said a long time ago, which is

6:16

basically like the first time that you

6:17

experience this company killing event,

6:19

uh, feels like the world's falling apart

6:21

and then you make it through and then by

6:22

the 10th time it's not nearly as bad and

6:25

you're like, well, I survived the first

6:26

nine, this one's probably not that bad.

6:28

You also had this other idea where you

6:30

said at some point, I think it was like

6:32

a year or two ago, you came to the

6:34

conclusion that was always going to

6:35

be going wrong and so you kind of had to

6:37

like internalize the enjoyment of

6:39

experiencing pain or like not great

6:41

things happening. Uh, how did you make

6:43

that transition? I was reflecting

6:46

earlier when you were saying this about

6:47

how when I would do office hours at YC,

6:50

I could always tell who the new founders

6:52

were

6:53

>> because of the way that they reacted to

6:55

stuff going horribly wrong versus the

6:57

founder because I often didn't know like

6:58

how new a company was, but I could tell

7:00

if it was like a founder in the batch

7:01

versus a founder that been doing this

7:03

for a couple of years by their like

7:05

emotional state when they talked about a

7:07

problem. Um, I think most people

7:12

think about the opposite of a bad

7:15

experience as a good experience and they

7:18

would rather have the good experience um

7:22

because seems like more fun or more

7:24

pleasant or whatever. But if you think

7:26

about like the opposite of a bad

7:29

experience is no experience and at some

7:32

point in the notistant future you'll be

7:33

in no experience land. uh then you know

7:37

you can be grateful for the bad

7:40

experience too. Naval Ravakant used to

7:43

say this thing that I loved which is if

7:45

you had like a fast forward button on a

7:48

remote for your life your life would be

7:49

over. And so like the boring parts, the

7:52

bad parts, it's like much better than no

7:54

experience. It's all part of the like

7:56

interestingness of and the kind of like

7:58

emotional depth and range. And I don't

8:00

know, I find it like fairly easy to be

8:02

grateful for the bad days. When you

8:03

think about like hard problems and like

8:06

what rallies people to get excited to go

8:08

work on hard problems, how do you decide

8:11

like internally at the company which

8:13

things to go tackle and when and like

8:16

when you want to like focus on just your

8:17

core competencies versus like expanding

8:19

the scope.

8:21

>> I think like a clear mission and then a

8:25

deep understanding of the problem

8:28

together

8:30

do a fairly good job of pointing to what

8:32

you should do. you won't get it all

8:33

right. You'll still like at some points

8:36

overly expand or not be ambitious

8:38

enough. Um, but we are we are like very

8:41

focused on

8:44

this is going to massively empower

8:46

people. It's going to be, you know,

8:48

choppy but wonderful and it's extremely

8:50

important to us that power in the world

8:53

get more decentralized and more spread

8:55

out. And in fact, like one of the

8:59

biggest AI risks I am worried right now

9:01

is like AI authoritarianism. And you

9:03

know, a small number of people or

9:04

companies thinking they need to control

9:06

the world. That'd be very, very bad. Um,

9:08

but given this mission, we feel like we

9:11

need to figure out how to make AI

9:12

extremely abundant, extremely cheap,

9:15

extremely powerful, put it in everyone's

9:17

hands, and like have a lot of it to get

9:18

to use. And as we look at what's in the

9:23

way of that, there's a whole bunch of

9:24

new constraints like chips and energy

9:27

and data centers and robots and all the

9:29

pieces that need to come together to

9:31

build this platform so that we can like

9:34

enable this mission. Now there's a bunch

9:36

of things you can build on top of that

9:37

platform like you can go build every

9:38

vertical, every startup. We have no

9:41

desire to do that like that. I think a

9:43

decentralized economy is important and

9:45

good and I would like us to just be

9:46

really great about producing these units

9:49

of intelligence and

9:51

make the world like man this is just you

9:53

know incredibly capable an incredibly

9:56

great deal and we're going to imbue it

9:57

in every product and service and so I

9:59

think that's very instructive to us

10:01

about what we have to go do my

10:03

interestingly the the kind of key inputs

10:05

to like extraordinarily abundant

10:08

highquality lowcost intelligence a lot

10:11

of those things like energy, like

10:14

robots, uh are also things that you

10:17

really want in a world of abundant

10:19

intelligence. If ideas are plentiful and

10:22

there's all this great stuff, we still

10:24

live like in a physical world and we

10:26

still want stuff to happen. So, you

10:28

know, we need to be able to make stuff

10:30

happen in that world. And I have

10:33

wondered if like the fact that energy

10:36

and robots are going to be so important

10:38

to staying on this this intelligence

10:41

infrastructure ramp and also the things

10:43

that you need immediately after it are

10:46

energy and robots. Um says anything deep

10:49

at all or is just like the most boring

10:52

[clears throat] obvious thing which is

10:53

like to produce anything intelligence

10:54

included you need to manipulate matter.

10:56

But it is kind of interesting that we

10:58

need these new areas so much for what

11:00

we're doing now and then so much more

11:02

immediately after.

11:03

>> What do you think your biggest

11:04

bottleneck is? Uh if you just say like

11:06

we want this to continue scaling

11:08

unabated. What is the biggest bottleneck

11:10

that you can see?

11:12

>> Transistors and then electrons in that

11:15

order.

11:15

>> So let's say you know you take someone

11:17

like Jensen where he's like incredibly

11:19

good at aligning all his suppliers

11:21

towards you know his vision for the

11:23

future. I think you're also extremely

11:26

good at this. Um, how do you kind of

11:28

think about not only keeping OpenAI on

11:32

the OpenAI timelines, but also keep the

11:34

rest of the world on OpenAI timelines as

11:35

well?

11:36

>> You talk to them a lot. You have to and

11:38

you don't just say like, I need you to

11:39

like deliver this turbine or this chip

11:41

on this date. Um, because you know

11:44

people will say, "Okay, whatever." And

11:45

they have a bunch of other priorities.

11:47

You really have to like show them here

11:49

is the upcoming model. Here's what it

11:50

enables. Here's our research. like we we

11:52

we for our core suppliers, we really

11:54

show them a lot about what we're doing,

11:56

why we believe what we believe, what

11:57

it's going to enable. Um, and you know,

12:00

you figure out I mean, the most

12:02

important thing is to like get them to

12:03

believe in the mission and why they need

12:05

to prioritize it, but you also try to

12:06

figure out how to align their incentives

12:09

with yours as much as you can.

12:10

>> Charlie Mer has this awesome line where

12:12

he's like, "Every time I've thought that

12:14

I understood the power of incentives,

12:15

I've like underestimated them." How do

12:17

you like successfully align all the

12:18

incentives of the different partners as

12:20

well as the people inside of OpenAI?

12:22

>> Well, companies are amazing vehicles for

12:24

this to start. Um the you know as a kid

12:29

I was like very fascinated by the

12:30

industrial revolution and I understood

12:33

it as a bunch of technologies that

12:37

happened to come around the same time

12:38

and for some puzzling reason happened to

12:39

scale at around the same time. Um, and

12:43

you know, I would like talk to my

12:44

friends or my parents or whatever about

12:46

whether it was like this technology was

12:47

the most important invention or this one

12:49

or that one. And my read of it from my

12:52

current lens as like someone who has now

12:54

spent more time think about business is

12:57

that the the important invention was the

12:59

joint stock corporation.

13:02

And before that was invented and you

13:05

know you had these like kind of family

13:07

businesses that ran on trust and you had

13:10

to know everyone and you didn't have the

13:12

idea of a corporation. You didn't have

13:14

the idea of like stockholders really.

13:17

And then all of a sudden this new thing

13:19

was invented. Countries like the

13:22

sovereigns of the world granted this

13:24

sort of a new kind of status really a

13:26

thing that didn't exist before. This

13:28

very powerful thing that we're going to

13:30

let these

13:32

new entities emerge and

13:36

not give them the power of a state but

13:38

give them like much more than the power

13:39

of a person. And fundamentally this was

13:42

about you know incentive alignment,

13:46

liability protection which is another

13:47

kind of incentive alignment. Um the

13:50

ability to like amass capital and all of

13:52

a sudden you could do things beyond what

13:54

a family business could do. And that

13:57

environment let people raise money to

14:00

develop technologies. Very speculative

14:02

nature. Uh pool capital figure out ways

14:04

to get different companies to sort of

14:07

specialize in different ways and

14:09

interact with each other. Serious

14:11

financial systems and new kinds of

14:12

instruments.

14:14

This was really an incredible thing. uh

14:16

and

14:18

and the idea of a company has done a

14:21

huge amount to align incentives of very

14:24

large groups of people. Now, you know,

14:26

millions of people can be shareholders

14:27

of a company. There's a bunch of other

14:29

things we do, but I don't want to

14:31

diminish what has been this incredible

14:34

invention and like the the just

14:37

ridiculous

14:39

overperformance of capitalism in human

14:41

society. Like a chart that I think

14:43

people should look at much more than

14:45

they do is the like fall of extreme

14:47

poverty over the last hundred years

14:50

>> like infant mortality and all the other

14:51

things

14:51

>> any of them that you want any of the

14:52

associated things. Um you know if you

14:55

like kind of look back at that that's

14:58

like the most zoomed in view of the last

15:00

hundred years. But if you look back at

15:02

kind of economic growth, quality of life

15:05

improvements, uh all of these things and

15:07

you just like drew out all of human

15:10

history and you put the line when like

15:13

the company was invented, you would see

15:16

like a really quite interesting change

15:18

in the shape of that curve after it.

15:20

Beyond that, uh I think we have a

15:24

mission that transcends any amount of

15:26

economic power anything else could ever

15:28

do. Uh, and that's probably more

15:30

important.

15:31

>> One of the things that I've thought is

15:32

the most interesting is have you ever

15:33

seen one of those stats where it's like

15:35

I don't know what percentage of like

15:36

CEOs are sociopaths and psychopaths?

15:39

It's like very very high.

15:40

>> People talk about this.

15:41

>> So I I wonder if capitalism is the first

15:44

system that allowed these people that

15:46

are like super kind of self- serving to

15:48

like their incentives are realigned with

15:50

societies and just create great products

15:51

and then suddenly you help society but

15:53

you also win.

15:55

Um, I definitely know a lot of

15:57

sociopathic CEOs, but I would say the

15:58

best ones I know I would not put in that

16:01

category. There's a lot of very high ego

16:02

CEOs, uh, people just like incredibly

16:05

high opinions of themselves on a way

16:07

that maybe like would have been just

16:08

like purely annoying, but now because

16:11

people can like, you know, buy their

16:13

shares, maybe you like tolerate their

16:14

annoyance and antics a little more. So,

16:16

there's definitely some aligning thing

16:18

there. I don't know. I think there are a

16:20

lot of CEOs of very large companies that

16:22

I would not put in the sociopath

16:24

category at all.

16:25

>> What do you think drives those people?

16:27

>> I mean, it's different for different

16:28

ones of them. Like

16:31

definitely seeing how good they can get

16:33

at the game or like how much they can

16:34

get better than themselves every year I

16:36

think is a huge part of it. You know,

16:38

it's like intellectually quite

16:39

stimulating.

16:40

>> Mhm.

16:41

>> Um for all of the other problems like

16:43

you're kind of playing the most

16:44

interesting strategic game and that can

16:45

be fun. If you had to think about the

16:47

way that you operate today versus the

16:49

way that you would have operated 10

16:50

years ago if placed in the exact same

16:51

position that you are currently what are

16:53

the biggest differences between those

16:54

two people? Well, on the to the previous

16:57

question, the thing that drives me the

16:58

most is like this is the most

17:01

interesting important thing I can

17:04

imagine doing and we are now like in the

17:06

singularity like this is the moment

17:08

>> for the la 10 years ago this was like a

17:10

kind of far off dream at best. Um

17:17

seemed very improbable and now we're

17:19

like actually in the moment that we used

17:21

to like talk about at the lunch table in

17:23

a very not serious way. Uh, and so the

17:27

thing that drives me is I've been

17:30

waiting for this my whole life and I

17:31

think it's going to be incredible,

17:33

hugely positive, awesome for the world.

17:36

Um, I'm excited to get to work on that.

17:38

I also think some of the alternative

17:40

visions painted by other companies are

17:42

like quite terrifying. I'm going to make

17:43

sure that gets pushed against and is not

17:45

what happens. Um, but the main thing of

17:48

what's different than 10 years ago is

17:49

like we're actually in it. like this is

17:51

the real I think it is both true that it

17:55

is all one crazy exponential and any one

17:58

moment is not like the tipping point and

18:01

also that we are somehow in another one

18:05

of those decisive periods where the

18:06

curve can go one way or another like it

18:08

was when we started 10 years ago.

18:10

>> What do you think the biggest impacts

18:11

are on getting this part of the curve

18:13

right?

18:14

I think like there are still major

18:19

AI alignment issues to solve and safety

18:22

issues and there are still major

18:25

economic issues, future of jobs, all of

18:27

that kind of stuff. But I think the

18:29

fight of the current moment is

18:33

are we going to head to a world of

18:36

AI authoritarianism or liberty? Are we

18:39

going to decide that because of the very

18:43

real safety issues and economic issues

18:47

that you know we want one single model

18:49

to be the machine god or the company

18:51

associated to do that or are we going to

18:54

say like you know what it's going to be

18:56

it may be a little messy but every time

18:59

that humanity has traded off its liberty

19:02

for safety it's been a long-term net

19:05

loss and so we are going to put this in

19:07

the hands of people we're going to

19:08

empower power them. We are going to let

19:10

society express its ideas and use this

19:13

technology in the way they want. Of

19:16

course, according to some guard rails

19:18

with some guardrails, but you know, with

19:19

a lot of power and potential. Uh, and I

19:22

really care that we do it that way.

19:24

>> When you were talking with Patrick

19:26

Hollson on stage, must have been like a

19:28

couple months ago, there was this

19:29

fascinating thing that you said, which

19:31

is basically like you will talk or text

19:34

with 3 to 400 people a day internally.

19:36

>> I text more people than anybody else I

19:38

know. I don't think this is like a good

19:39

thing. I think this is actually like a

19:41

bad habit.

19:42

>> Why?

19:43

>> Um because it takes up like most of my

19:45

time and I get like into I have a very

19:47

short attention span now.

19:49

>> Interesting. What does that enable you

19:51

to do that like otherwise?

19:52

>> Huge amount of context. I mean I'm like

19:54

a AI model that's not that smart but

19:56

just has like a huge amount like a

19:57

shortterm context on like whatever's

19:59

happening right now and then forget.

20:00

Does that enable you to make decisions

20:02

that other people wouldn't have? Like

20:03

what company decision better? I don't

20:05

know. I mean, it lets me make different

20:06

decisions on other people would.

20:08

>> What different decisions would you have

20:10

you made in the last like 12 months that

20:12

you wouldn't have made if you didn't

20:13

have so much context?

20:14

>> I don't know if I have like a specific

20:16

one I can share publicly, but there's

20:18

like a lot of things where, you know,

20:20

because I I know about some random

20:22

research breakthrough impending that is

20:24

going to like affect some customer or

20:26

some supplier or whatever, I can make a

20:27

slightly different decision in the

20:28

moment. And if I hadn't known it that

20:30

morning, I would have made a different

20:31

decision. It would have been worse. One

20:32

of the things that I think you are the

20:34

best at is kind of like long-term

20:35

thinking and yet it also seems like

20:39

things are moving so quickly and the

20:41

world is changing so fast that planning

20:43

20 years out and then like looking

20:44

backwards I don't even know if that

20:46

makes sense. Um how do you think about

20:48

this? How is like is your time pricing

20:50

shortening? I I actually don't try to

20:53

plan backwards. I try to have like a a

20:55

small number of strongly held

20:57

convictions about the future that are

20:59

like directions to head towards but then

21:01

I try to like plan forwards from the

21:03

current state about like what can we do

21:05

now what can we do this year and

21:06

sometimes you do have to like plan a few

21:08

things on a you know 5 or 10 year

21:10

horizon but I try to plan forward guided

21:13

to a small number of beliefs about the

21:15

future I think there are a lot of people

21:17

who have way too many beliefs about the

21:19

future and a kind of rigid worldview

21:21

that they try to fit and then but what

21:23

happens is they end up like chasing, you

21:26

know, you see like space companies turn

21:27

into AI companies or whatever. Um, and

21:30

like having just a small number of

21:32

things that you deeply believe about the

21:34

future and being very flexible on the

21:36

rest. Um, and staying very true to the

21:39

core is is helpful. One of the people

21:42

that I know the best, his top, you know,

21:44

company core value is just critical path

21:46

and just focusing on the critical path

21:48

and like staying focused on the key

21:49

drivers of whatever the biggest

21:52

roadblock is. just unfuck that and then

21:53

go to the next one and do that again.

21:55

How do you think about critical path in

21:56

your own life when you're kind of like

21:57

trying to make these decisions?

21:59

>> I mean, for so long now, I have felt

22:01

focused on this singular goal of

22:03

abundant intelligence and a belief that

22:06

like incredible human prosperity will

22:08

come from that as long as we don't have

22:11

a weird power concentration and kind of

22:12

a new kind of authoritarianism. It has

22:15

been fairly clear to me at any given

22:16

time what's on the critical path to get

22:18

there. And I have not been tempted by,

22:21

you know, should I reconsider the goal

22:23

or should I think about these other

22:24

things. I have been thinking about that

22:26

a little bit more recently of like,

22:28

okay, if we really are close to super

22:29

intelligence, what's next? But but it's

22:32

this that's kind of how I I subjectively

22:35

feel we are close is the first time in

22:38

like more than a decade I've thought

22:39

about what is the next thing.

22:41

>> And is it the ranch?

22:43

>> Uh I mean eventually there might be a

22:45

few more things on the way, but that is

22:46

yes, that is the eventual plan. If you

22:48

had to like try to guess what else does

22:50

Sam want to accomplish before he

22:52

ranches.

22:53

>> Um look AI super intelligence is going

22:56

to get built like that you know not done

23:00

but like going to we are we are on the

23:02

glide path

23:02

>> path to the get there is very

23:05

>> abundance and decentraliz and like wide

23:08

access to it and making sure that it

23:09

ends up with like broadly shared

23:11

prosperity. I would really like to

23:12

accomplish that. So maybe that that's

23:13

the thing that I've been like thinking

23:15

about as the next thing like how does

23:17

technologically we're going to

23:18

accomplish our mission. I think a lot of

23:20

our worldview and a lot of our beliefs

23:22

about

23:24

safety and the impact is going to have

23:25

have also we've gotten those things done

23:28

too. Um but you know very broadly shared

23:32

prosperity and uh a real focus on

23:35

enabling the world here. Um that seems

23:38

very important. I've heard you say again

23:40

and again uh this idea of get on planes

23:42

in marginal situations. I've done this a

23:44

huge number of times for you.

23:47

>> Uh so far it's working pretty well.

23:48

Good. I think

23:49

>> I think it's I think it's very solid

23:50

advice.

23:51

>> When was the last time that you got on a

23:52

plane in a marginal situation?

23:54

>> Well, I can't say what for, but very

23:56

recently and it worked out.

23:57

>> Congratulations.

23:58

>> Thank you.

23:59

>> And I really didn't want to. It was like

24:01

a very inconvenient two overnights.

24:03

Didn't want to do it. New baby, whole

24:05

mess, but I did.

24:06

>> Okay. actually take me into your like

24:09

mental process for deciding to do this

24:11

world tour. That is just I mean it again

24:15

it's like hard to rewind 3 years cuz now

24:17

like it feels like AI has been here

24:18

forever but 3 years ago we had just

24:21

launched GPT4. The world was melting

24:23

down. Everybody was freaking out. Um

24:27

everybody was just like oh man. And no

24:31

one knew what to think. And I could just

24:33

feel these like storm clouds brewing and

24:36

like

24:36

>> people just getting angry

24:38

>> or

24:39

>> it was like world leaders were like do

24:41

we need to like take control and shut

24:43

things down? Like is this thing waking

24:45

up and about it was it was a weird time

24:48

and because it had all happened so fast

24:49

people had not had time to like

24:52

wrap their head around it go through

24:53

this process whatever. Uh so

24:57

you know we were like getting all of

24:58

this escalating stuff and all these like

25:00

world leaders were like will you come

25:01

meet us we're like you know thinking

25:02

about doing this and that and I was like

25:03

uh I think if I or someone from the

25:07

company but at the time company was not

25:08

very big so I was like probably going to

25:09

have to be me does not go like show up

25:12

and talk to people. I had like a sense

25:14

it was about to go very badly and I was

25:16

thinking about doing like a bunch of

25:17

short trips but I hate long flights. I

25:19

hate jet lag. I had the whole thing. So,

25:21

I was like, I'm just going to like get

25:23

this over with in a short period of

25:25

time. Um, Brian Chesky advised me to do

25:28

it. He had done something for Airbnb,

25:29

similar for Airbnb, but he had done like

25:31

eight cities or something.

25:32

>> And you were like, "Fuck it, we ball.

25:33

Let's just do 30."

25:34

>> 20.

25:34

>> I think we did 28 countries,

25:36

>> 35 days. Uh, I like lived on an

25:40

airplane. It was a very strange.

25:42

>> Did you have a bed?

25:43

>> I had like a Yeah, I was comfortable,

25:45

but yeah, I had a bed.

25:47

>> Um,

25:47

>> I fly coach, so it's a little bit

25:49

different. This is better than that,

25:51

right? This is a lot better than that.

25:52

But like, you know, it's like travel

25:54

still sucks. Like you can make it as

25:55

comfortable as you want and you still

25:56

like miss your own bed and your own time

25:58

zone and your office and whatever.

25:59

>> One thing that I've learned is no matter

26:01

how many people have like these cures

26:03

for jet lag, they're all Like

26:05

it just sucks. Your brain doesn't like

26:08

it.

26:08

>> I never in because I went to so many

26:11

places. I think my biggest time zone

26:13

change was 4 hours the whole trip

26:16

>> and mostly it was like a 1 hour at a

26:18

time hop. So, I wasn't that jetlagged. I

26:22

was just like exhausted and it was very

26:25

strange. It was kind of cool to get to

26:27

see the world so quickly because you

26:30

really do get a sense for like cultural

26:34

differences that are very subtle when

26:35

you go from place to place. But by the

26:37

end of it, like the last few days, I

26:40

started, it was one of these like weird,

26:43

you know, half asleep, half awake kind

26:44

of somewhat dreams. But I started having

26:47

this like vision, feeling, whatever of

26:50

being in my childhood bed.

26:52

>> Interesting.

26:52

>> Which I never had before since. But I

26:54

would sort of like vaguely wake up and I

26:56

would think I was in my childhood bed in

26:58

my childhood room. And I was like,

27:00

"Okay, this is some like deep it's time

27:02

to go home thing."

27:04

>> You're like subconscious fighting back

27:05

or something.

27:05

>> Something like that.

27:06

>> Interesting. When you're in zombie mode

27:08

and you're kind of like trying to

27:10

function, I think uh or goblin mode,

27:12

whatever you prefer, how do you change?

27:15

Like what is different about you?

27:17

>> How do you decide to keep on going?

27:19

>> I definitely got to this point near the

27:23

end where I was like hunting down the

27:24

days till I could be home. Um I was just

27:26

like I cuz each day was like it was like

27:29

you know 14 hours of like nonstop and

27:32

I'm like not an extroverted person, you

27:34

you know, I was meeting like hundreds of

27:36

people a day in some cases. Um, and like

27:39

tensions lowered by the end of it, but

27:42

at the beginning like the world was like

27:43

very nervous and and then like you know,

27:45

I think once

27:48

it just like it did calm down. I I think

27:51

one thing that helped is it was clear

27:52

that like I knew when I was going to get

27:54

home and I knew I was going to have to

27:58

do it again for a while. Um, I've done

28:00

smaller ones since. I probably do like

28:01

something like that once or twice a

28:03

year. But I was going to say like a

28:05

thing I learned is that I would much

28:07

rather put

28:09

a bunch of international travel together

28:11

in like a 7 or 10 day chunk than a bunch

28:13

of one day trips spread out. So I always

28:15

do try to do it that way. Now I think a

28:17

lot of people overestimate the like risk

28:21

involved in making most actions. Like

28:24

there is a lot of risk if you're just,

28:25

you know, buying a bunch of call options

28:27

on Robin Hood, but there's probably a

28:28

lot less risk on getting on planes and

28:31

stuff. What have been the best examples

28:33

of times where you basically did

28:36

something that seemed really risky to

28:38

other people, but you knew wasn't or you

28:40

believed wasn't?

28:42

>> I guess the obvious example would be

28:44

like I I was always extremely high

28:46

conviction buying a lot of comput.

28:47

>> Have you ever seen the meme everything's

28:49

high risk if you're a

28:50

>> No. Good meme. Um, I spend a lot of time

28:57

trying to talk to people about why they

28:59

think a specific decision is high risk

29:01

or low risk. And I have found that just

29:04

getting people to speak about it out

29:06

loud will often at least get through

29:09

their intellectual blocks in either

29:11

direction because people are usually

29:12

wrong one way or the other. Not always

29:14

the emotional part of it, but sometimes

29:16

also. If you had to think through one of

29:18

these meetings where someone is like

29:21

firmly against some decision that you

29:23

want to do, what is the most effective

29:25

way to kind of help them see what you

29:28

see?

29:29

>> I don't think I'm very good at this to

29:30

be honest. I think like the right answer

29:33

is to spend a lot of time really trying

29:36

to explain it and get people there. And

29:38

I actually think I used to be better at

29:40

that. And as life has gotten so busy,

29:43

I'm more just like I get upset or

29:46

frustrated or say, "This is what we're

29:48

going to do." And I don't I don't think

29:48

it's like a positive trait. And I would

29:50

like to get back to more of the like

29:52

let's really talk it through.

29:53

>> You said that you used to be much harder

29:55

to work with, I don't know, 2008 910

29:58

somewhere around there than you are

30:00

today.

30:01

>> I I don't know if people would describe

30:02

me as fun to work with. I like

30:05

interesting to work with, like

30:09

effective, ambition raising, like very

30:12

mission focused, but like dayto-day like

30:15

really fun to work with. I don't think

30:17

I'm like a particularly fun person in

30:18

general.

30:19

>> I feel like if you are having a good

30:21

time, it's tends to be infectious. And I

30:24

think if you are generally working on

30:26

things that you want to work on, it is

30:28

having a good time.

30:29

>> Uh maybe I'm wrong.

30:32

I mean, you could go ask a bunch of

30:33

people I work with. I'm not I'm not I I

30:35

don't I don't think you would get like a

30:36

resounding fun to work with.

30:38

>> Maybe not that language.

30:39

>> I think people would say stuff like

30:44

creative solutions to problems, new

30:46

insights, like very high level of

30:47

ambition, like got me to do something I

30:50

didn't think I could do, that kind of

30:51

thing.

30:52

>> What does it typically look like when

30:54

you go into a meeting with someone and

30:55

you realize that they are not ambitious

30:57

enough and you need to try to help them

31:00

become more ambitious?

31:02

the the the version of this that came to

31:04

mind when you were saying that was like

31:05

first office hours I would have with new

31:06

YC founders and

31:10

you take average person who's worked in

31:12

corporate America you know at a big tech

31:14

company for a few years

31:17

and the level of ambition scaled

31:21

thinking self-belief whatever it's just

31:22

catastrophically low horrible horrible

31:26

and you realize that like

31:29

this person has never not had a boss in

31:31

their life. They have never not had a

31:34

parent or a teacher or a manager or

31:36

whatever that kind of would tell them

31:40

what to do or what they were allowed to

31:41

do or whatever. And they also kind of

31:45

got punished for being too creative or

31:49

wanting too much or thinking too big or

31:50

too ambitious, whatever. the

31:54

most

31:56

countries or cultures have some sort of

31:59

phrase for being too ambitious.

32:01

Basically,

32:02

>> tall poppy syndrome.

32:03

>> That's one. Um, and you realize like how

32:07

deep this is in people and that they

32:10

like heard it and their parents said it

32:11

to them, their teachers said them, and

32:12

friends said to them, whatever, from

32:13

when they were like quite little. and

32:15

getting people to just like not think

32:18

that way

32:20

takes some real time.

32:21

>> What's the most effective path to get

32:24

someone from not believing in themselves

32:25

to believing in themselves?

32:27

>> I think small repeated wins. Like you do

32:30

something you didn't think you could do

32:31

or you kind of try something that felt,

32:34

you know, too ambitious or too high

32:36

expectation and it works and you're say,

32:38

"Okay, try it again." Do you do anything

32:40

like Steve Jobs where he would like if

32:42

someone was worried he would like stare

32:44

into their eyes and get No, nothing like

32:46

that.

32:47

>> I don't think so.

32:48

>> It's like you can do it.

32:50

>> I I I don't I don't I'm not like an in I

32:52

don't do inspirational speeches. I don't

32:53

I don't do like the hards none of that.

32:55

>> What's your favorite strategy game?

32:59

>> I have not played a game other than like

33:02

my poker night with friends in so long.

33:05

Uh I wish I had. I guess I'll have to

33:08

say poker, but I uh

33:11

I miss like great board games. What was

33:13

your favorite board game?

33:15

>> There's like no one board game where I

33:17

be like, I want to play that like again

33:18

and again. I don't have like like I'm

33:20

not like someone who's like Katana is

33:21

the best board game of all time or

33:22

whatever. But they're all fun and

33:24

they're fun cuz they're like new and you

33:26

have to like figure out a new set of

33:28

challenges and rules and everything

33:29

else. When you're thinking about the

33:32

business world, I think you build your

33:34

company differently than almost anyone

33:36

else is able to build companies, is

33:38

willing to build companies. What allows

33:39

you to do that? I actually think every

33:41

big company is pretty different. I don't

33:44

think this is a unique thing. I think

33:45

just like if if you make a list of the

33:48

20 biggest tech companies or whatever,

33:50

they're all like shockingly different

33:51

from each other in terms of their

33:53

strategy and how they operate and their

33:55

culture.

33:56

I think we are quite different than

33:57

everybody else, but that's not a deep

33:59

insight. What do you think your like

34:01

superpower is?

34:04

I think we did a really good job of a

34:07

principled conviction on something that

34:09

no one else believed even though it was

34:10

the obvious thing one should believe and

34:13

then putting together all of the pieces

34:15

and the talent around that to sort of

34:17

make it happen.

34:18

>> What allowed you to kind of have that

34:19

conviction that where other people just

34:21

didn't?

34:23

Um, I have thought about this a lot. I

34:26

do not know. It seemed so clearly

34:29

obvious to all of us

34:33

that I was more worried that we were

34:35

drinking our own Kool-Aid than everybody

34:36

else was wrong.

34:40

But with the benefit of hindsight, we

34:42

were clearly right. And I don't

34:44

understand the like deep mental block

34:47

and lack of conviction from everybody

34:48

else. It's very strange. I I have

34:52

I have spent a lot of time trying to

34:54

understand this.

34:56

>> Did you find any explanation in your you

34:58

know man's search for why or no?

35:01

>> Um

35:03

I mean there's all the obvious stuff

35:04

about like group think and

35:10

like the big transformations just come

35:12

don't come along that often and

35:13

exponential curves don't stay powerful

35:16

for so long for so often. But like you

35:19

know maybe between

35:21

2016

35:23

and 2018

35:25

we had to get really lucky and it

35:27

required like a lot of great belief but

35:30

then by 2019 certainly by 2020 we

35:33

shouldn't have really gotten to exist.

35:35

Google should have just run away with

35:36

it. Yeah. I think Jeff Bezos called it

35:39

like a business miracle that they built

35:41

AWS and then didn't have any serious

35:42

competition for like seven years. And I

35:44

feel like OpenAI is like another

35:45

business miracle. There's clearly

35:47

something here about why big companies

35:51

get sclerotic and set in their ways and

35:53

it helps them in a lot of ways too. U

35:56

but this is like I mean this is

35:57

wonderful. I think it's great that the

36:00

big companies don't stay the only

36:01

dominant forces forever. That'd be

36:02

really bad. Um but there's clearly

36:05

something where these business miracles

36:07

happen and they shouldn't.

36:08

>> Is that right or should they?

36:10

>> Well, I think it's great for the world

36:11

that they do,

36:12

>> right? But to go back and explain in

36:15

2019 why OpenAI was like allowed by the

36:18

giants with huge amounts of capital and

36:20

talent and everything else to do what

36:21

we've done.

36:23

It's like hard to explain.

36:26

>> Why did Microsoft give you the money?

36:28

>> Um

36:30

that I think is easier to explain which

36:32

is Google had Deep Mind and Microsoft

36:34

did not have an AI bet and you know big

36:37

companies feel like they should have

36:39

something in play in all of the major

36:40

areas of technology. Was it kind of like

36:42

they think it's going to be big but then

36:44

underestimated how big it would be?

36:46

>> I think it's been like great for them. I

36:48

don't know if we've added1 trillion$2

36:50

trillion dollars or whatever of market

36:51

cap to Microsoft but I think it's been a

36:52

lot. Um and I think they have gotten

36:55

incredible technology and cloud growth

36:57

and will continue to. So yeah, I'm sure

36:59

it's bigger than they thought it was

37:00

going to be, but I think they made a

37:01

good bet and have done super well with

37:03

it and very happy about that.

37:05

>> There's been different versions of

37:06

OpenAI. The first one was the research

37:08

lab. Next one was the product company

37:09

and you kind of said that like you

37:11

bolted on the product company onto the

37:12

research lab which is the exact opposite

37:13

of what most companies do and now you're

37:15

basically going to be like a massive

37:17

infrastructure company and like almost

37:18

all the value from open is just going to

37:20

come from this like low margin

37:21

infrastructure. And then you also said

37:23

that basically in order to transition

37:24

into the next phase of this business

37:27

it's not necessarily something the way

37:29

that your brain works is naturally like

37:31

designed to operate in that kind of

37:33

model. What do you have to change and

37:35

like what is that next phase of the

37:36

business? I don't answer this because I

37:38

don't like I think this is now a getting

37:40

into like a like a direct

37:42

>> something we're not quite ready to talk

37:43

about but uh I am very excited for the

37:47

next phase and I think I have figured

37:48

out how to align something that I am

37:49

very good at and very passionate about

37:51

with what will be the next like 10 or

37:52

100x of our growth. So

37:54

>> how did the transition of Sam the

37:56

research lab head to Sam the product

37:59

company head like what was that what

38:01

were the biggest transitions for you on

38:03

that? I mean the whole thing was just

38:05

extremely different. Like it has felt

38:08

like two almost completely unrelated

38:10

jobs. And it's not quite to say like I

38:13

did one and now I do the other cuz I

38:15

also still am responsible for the

38:17

research lab. Um but they are almost

38:22

separate things and definitely running

38:26

the research lab did not prepare me for

38:28

running the product company in really

38:30

any way. Did the YC experience prepare

38:32

you

38:33

>> more? Um like I had watched a lot of

38:35

people have to scale big companies.

38:37

>> Was it fun to kind of like finally get

38:39

in the driver's seat?

38:40

>> No. Um I

38:42

>> the fifth day after Chad GBT launched

38:46

was the day we crossed a million users

38:48

and

38:50

I had like watched it go up the first

38:53

day and then come down in the afternoon

38:54

and then go up the second day to a

38:56

higher peak and then come down, go up

38:58

the third day to a higher peak and then

38:59

come down. And each of these days the

39:01

researchers were like, "Oh, that was

39:02

some crazy flash in the pan PR thing.

39:04

This is like over and you know that's

39:05

not going to happen." And I had seen

39:08

enough at YC that I knew that when

39:11

something was growing like that

39:13

completely organically,

39:15

it this had all of the spectral

39:17

signature to me of something that is

39:18

like was going to happen. And then on

39:21

the fifth day, it crossed a million

39:23

users

39:24

and I came home and that was when it

39:26

really hit me that like we were about to

39:28

become a company and it was going to go

39:30

very fast and it was going to be a crazy

39:31

like a like we were just going to turn

39:33

into like a big company real quick. And

39:37

I had watched what these founders went

39:40

through

39:42

and I came home

39:45

and Ollie was like, "Oh, I saw that you

39:46

crossed a million years.

39:47

Congratulations." I was like, "You have

39:48

no idea how bad this is. You have no

39:50

idea what's about to happen. It's not

39:52

just bad for me, it's bad for you, too.

39:53

Like we have this nice quiet life, you

39:56

know, it's really wonderful. It's about

39:57

to like kind of go through a cannon. And

40:01

that is what happened. But it was like a

40:04

very abrupt transition. I knew it was

40:06

going to come and I had watched other

40:07

people go through it and I knew that it

40:09

was like not a pleasant experience, but

40:11

I was like, well, we're being shot at

40:13

the cannon. Here we go. Were you like

40:15

subconsciously just like preparing

40:16

yourself for years if you know that this

40:19

is eventually going to happen and the

40:21

intelligence is going to get good enough

40:23

where like there will be a magical

40:24

product?

40:25

>> My take on it is that I

40:29

intellectually

40:31

knew it was going to happen at some

40:32

point. I had no idea when. And I didn't

40:33

know that was going to be the moment, of

40:34

course, um but that I had decided to

40:38

pretend to myself and I had successfully

40:41

deluded myself into a sort of acting

40:44

like it wasn't going to happen. Um truly

40:47

running the research lab was the

40:49

coolest, most fun, most amazing job and

40:52

lifestyle setup I could possibly have

40:53

imagined.

40:54

>> How so?

40:56

>> I mean, you were like it not stressful

40:59

at all. Uh, it was intellectually

41:02

incredibly satisfying. It was the

41:04

smartest group of people with probably

41:06

the most important work that has

41:08

happened in like the last century, maybe

41:10

longer, I don't know. And I had this

41:11

like front row seat. And like that was a

41:16

once- in many generations moment. It was

41:18

unbelievable. It was the coolest thing.

41:21

>> Are you guys experiencing that same kind

41:24

of growth? You know, I keep on seeing

41:27

Tibo post on Twitter that basically

41:30

here's another reset, here's another

41:31

reset. Are you seeing that same like

41:33

chat GBTesque experience again?

41:36

>> Totally. Um I think there have been two

41:39

giant form factors and associated growth

41:41

so far.

41:42

>> There was the sort of chat bots

41:44

>> and then the coding agents and the

41:45

coding agents are just going totally

41:47

nuts. There will be a third one soon, I

41:49

think, which will be this idea of the

41:51

sort of persistent agents, chiefs of

41:54

staff, co-workers, colleagues, whatever

41:55

we call them. And that'll and, you know,

41:57

I think that'll come pretty soon. So,

41:59

we're going to go through like the third

42:01

of these waves pretty fast.

42:03

>> So, a long time ago, apparently in

42:05

August last year, I saw you tweet

42:07

something and I thought to myself, this

42:09

is not going to help the reputation. Um,

42:12

>> let's see.

42:13

>> Why? [laughter]

42:15

Why did you do that?

42:17

Um,

42:20

>> well, first of all, I love that scene

42:22

from the movie. Like the Death Star is

42:24

coming out of hyperspace and there's

42:25

this dramatic music and it's like a

42:26

great it's like a great scene. Um, I

42:31

thought it was a funny like I was it was

42:33

like, you know, I was scrolling Twitter.

42:35

I think it was late at night. I don't

42:36

really remember.

42:37

>> This is where the conspiracy theories

42:39

happen. Like that you're you're like

42:41

you're feeding the fire here.

42:44

I just thought it was it amused me at

42:46

the time. There was like it was I don't

42:49

know. It was not a great tweet to be

42:52

honest to be clear.

42:54

>> Uh I actually thought it was great.

42:55

>> I thought it was funny.

42:56

>> I thought it was funny. I think

42:58

[laughter] I thought it was funny.

42:59

>> Yeah.

43:00

>> Um I do feel like this is more and more

43:03

what the world feels like. Uh

43:08

[gasps]

43:08

>> yes.

43:09

>> Uh yes.

43:10

>> Okay. Yes. That's a good That's a good

43:14

meme. The way that somebody explained

43:19

kind of the marketing challenge for

43:21

OpenAI and the industry in general is

43:25

number one, we are close to creating

43:29

what we need to explain to the world.

43:30

Number one, we are close to creating a

43:31

genie that can grant any wish. Number

43:34

two, we are going to make sure that our

43:37

first wishes broadly benefit humanity

43:40

and that we kind of get the world to a

43:42

place where a lot more people get to

43:43

have a lot more wishes wishes. And

43:45

number three, the space of what you can

43:48

wish for is incredibly big and creative

43:51

and it'll be quite exciting to like

43:53

figure that out with real sort of human

43:55

values and preferences. But but I think

43:58

there's a fourth thing too which is

44:00

exactly that. You start making these

44:02

wishes, the computer grants them, and

44:04

then you're like, I didn't think that

44:07

was going to work.

44:08

>> Yeah.

44:08

>> What now? It's a weird feeling.

44:11

>> Yeah. It's just like the problem where I

44:14

guess, you know, people spent like a

44:15

hundred years trying to disprove some

44:18

what was it? Jacobian. Yeah. This thing.

44:20

And then some guy just asked Claude and

44:22

it was like I don't know within a few

44:24

days or something like this. So, I'm I'm

44:26

I'm I think there's going to be lots of

44:27

great jobs in the future. I really do. I

44:29

think we're going to have lots of

44:29

intellectual fulfillment. And I think

44:31

most jobs are going to

44:34

adapt more than it seems like they

44:35

should. But math, I think, is a very

44:39

important example of something for us to

44:41

study very closely right now of

44:43

something that may not go that way. In

44:45

fact, I would say probably won't go that

44:46

way. It seems like even as the systems

44:49

get better and better, the people that I

44:51

know that like no one is less busy.

44:55

Everyone is more busy. Everyone's just

44:57

doing more stuff except for the people

44:59

that are like, "Oh, I spent a trillion

45:00

tokens." Uh, and like what have I love

45:03

this uh thing that I saw where someone

45:04

said like, "I spent, you know, ungodly

45:06

numbers of tokens." And then someone

45:07

said, "Why aren't you more successful?"

45:10

You know, technology for a long time has

45:12

been promising people that they're going

45:14

to work less and they're going to have

45:16

all this leisure. And

45:19

it has gone in that direction. Like I

45:21

think people do

45:23

have more time for leisure than they had

45:26

at many previous points in history and a

45:29

higher quality of life in many ways.

45:32

But somehow we never get the promise of

45:34

the 4-hour work week at mass scale in

45:37

society. And I don't expect AI to change

45:39

that. Uh

45:42

clearly society is not working for a lot

45:44

of people and

45:47

more productivity gains that actually

45:50

occur to people would be a great thing

45:52

and I suspect they will. I suspect that

45:54

will happen. But a thing that I really

45:56

admire about people is

45:59

our expectations go up. We always want

46:01

more. We think of new things to do to

46:04

create for each other to want for

46:05

ourselves and we, you know, it's like a

46:09

it's like a relative game. People are

46:11

like very focused on how they're doing

46:13

relative to other people. And so the

46:15

competition that drives the economy and

46:17

the kind of I think the the the very

46:19

wonderful desires about wanting to be

46:21

useful to other people and create

46:22

something and be of service and that

46:25

whole cluster. I expect that to keep

46:27

going and I think we're all going to be

46:30

much busier than we thought we were

46:32

supposed to be in a post super

46:33

intelligence world and we're still going

46:35

to complain about it but secretly we're

46:36

going to be happy.

46:37

>> How do you think the status games change

46:40

as less and less of our direct input is

46:42

correlated with like value creation?

46:44

I don't I don't think it will feel at

46:46

all like our direct like the things that

46:49

we value. I suspect will be things that

46:52

are like very

46:55

human.

46:56

>> Well, like one thing is like cooking for

46:58

people. You're not like creating a bunch

47:00

of value in the world, but it like shows

47:02

love.

47:03

>> You are creating value in that it's like

47:05

those are the experiences that you're

47:07

not creating like economic value in the

47:09

maybe traditional sense. Um, cooking for

47:12

people is a great example.

47:14

I suspect that people cooking and eating

47:17

together will remain important

47:21

long after robots can do a great job

47:24

cooking food. Do you think that a good

47:27

heristic would almost be like the

47:29

greatest works of the the the longest

47:31

surviving works? So let's say the Bible

47:32

for example, odds are it's still going

47:35

to be impactful like a thousand years

47:36

from now because it's been impactful for

47:38

the past 2,000 years so far. Do you

47:40

think that if you had to like

47:42

extrapolate out what are the things that

47:44

humans are still going to care about,

47:45

it's like what is the most primal

47:47

earliest thing? You know, we love

47:48

adventure, we love a good quest,

47:50

>> cooking. Betting against evolutionary

47:52

biology is like usually a bad bet.

47:53

>> Yeah.

47:54

>> And so I would I would assume those

47:57

things continue.

47:58

>> When was the last time that you realized

48:01

that you weren't like being ambitious

48:02

enough?

48:04

>> I mean, I definitely badly undersshot on

48:06

the compute investments. Could you have

48:09

known going in with the right mental

48:12

model?

48:13

>> Yes, but I got like psyched out by the

48:15

financial markets or something. I don't

48:17

know.

48:17

>> That was clearly a mistake.

48:19

>> How do you correct that in the future?

48:22

>> Uh well, I mean hopefully I'll learn

48:23

from it and I I'll make some new

48:24

mistakes, but I won't make that one

48:25

again.

48:26

>> On the compute side, this is kind of

48:28

like the biggest infrastructure project

48:29

of maybe all time or is about to be.

48:32

>> Yeah. Are you going to try and like

48:34

vertically integrate under OpenI's hood

48:36

everything from like power generation to

48:38

like token generation to you know

48:40

serving some person in chap or codeex or

48:43

something like that or are you going to

48:44

try and have a bunch of partners? We're

48:46

not literally going to try to do it all

48:47

inside of OpenAI obviously but we will

48:49

try to do a better job of a well

48:55

functioning supply chain than we have

48:57

done so far. like you know we we have

49:00

brought chip design and model design

49:02

together and that's been good. I don't

49:04

think we need to bring like electron

49:05

production together in the same way

49:06

because that's more of a commodity. Uh

49:11

however I am when you really think about

49:15

the whole

49:17

supply chain

49:19

that has to come together to produce

49:21

intelligence.

49:22

I would say there is relatively

49:25

too much focus on algorithms that create

49:30

better algorithms and not enough focus

49:32

on data centers that can create more

49:34

data centers, which in a world of robots

49:38

and a truly automated supply chain, you

49:41

can totally imagine doing. You can like

49:43

spend a data center's thinking power to

49:46

drive a fleet of robots to make more

49:48

copies of the data center. And that's

49:49

probably a very wonderful thing to do.

49:51

uh relative to other uses of that

49:53

compute if we're right about what that

49:56

additional compute will eventually

49:57

unlock. How much of your time is spent

49:59

thinking about how to like successfully

50:02

design that fully robot into end future

50:04

where intelligence can just produce more

50:06

>> intelligence spent thinking about almost

50:07

all goes into like execution like the

50:09

idea that's a very obvious idea it's

50:10

very easy you can like kind of

50:11

relatively quickly say here's the pieces

50:13

that need to come together

50:15

>> but then to like get that whole

50:17

machinery and all those companies

50:18

working together um that that you know

50:21

none of the glory of the big idea and

50:23

trying to like think the big thoughts

50:24

but like a lot of grinding

50:26

>> when you think of like execution ution.

50:28

What does that typically mean for you?

50:31

>> Not there's no typical there like the

50:35

I mean to the degree that there is it's

50:38

that you just like do whatever step is

50:41

required in the problem and figure out

50:42

how to make it happen. Mhm.

50:43

>> But you know, figuring out how to like

50:46

finance new fab build out is very

50:50

different than figuring out how to get a

50:52

great chip design team together and then

50:54

get them to work with the research team

50:56

and then actually get the supply chain

50:58

running. Well, like each of those is

51:01

like a very different approach. One

51:02

thing that I know that Brian Chesky does

51:05

uh is he talks about like shamelessly

51:07

trying to find whoever the expert is and

51:09

any given thing and then basically just

51:11

go ask them whatever question he has. as

51:13

if he's like trying to come come up to

51:14

speed on something. What is your process

51:17

for doing something like that if it's

51:19

something new that you need to become

51:21

good at in a very rapid period of time?

51:24

>> Definitely finding the experts and

51:25

talking to them, reading as much as I

51:28

can. Uh I don't have a lot of like great

51:30

str I don't have a lot of like novel

51:31

strategies here. I I've always been

51:35

really

51:36

grateful and pleasantly surprised by how

51:38

much experts are willing to help people

51:40

if they ask. Uh, it's like a very nice

51:44

thing about humanity.

51:46

>> One thing I've heard you say is you

51:48

should always ask for what you want.

51:49

>> Um, because not all the time you get it,

51:51

but sometimes you do.

51:52

>> Sometimes you do.

51:53

>> And when you do, amazing things can

51:54

happen.

51:55

>> When was the last time that you asked

51:57

for something that would seem absolutely

51:59

insane to someone else and you got it?

52:02

>> Actually, it was this recent time I got

52:03

on a plane that I still can't talk

52:04

about, but it

52:05

>> Okay, go back one more. Go back one more

52:08

that you can talk about.

52:09

>> [laughter]

52:10

>> I mean in some sense codeex was an

52:12

example of this like this is not the

52:14

most recent one but it was one that I

52:16

think it's instructive. Um,

52:20

we were way behind cloud code and it

52:23

seemed like a kind of crazy kamicazi

52:25

mission to try to beat them with a

52:28

coding app. And you know the like the

52:32

consensus is that this kind of thing

52:33

never works and you just move on to the

52:34

next one. But you know it's like a

52:36

fool's errand to try to like win when

52:38

someone else already has momentum in a

52:40

particular product category. But we

52:42

decided we thought it was really

52:43

important. We asked a team to do it.

52:46

They performed a legitimate like

52:49

unbelievable very rare in the history of

52:52

business thing and now it is the product

52:54

that most of end model that most of the

52:57

best coders that I know use. Um and that

53:01

felt like an impossible thing but if we

53:03

hadn't asked the team like hey we have a

53:05

really important but extremely hard

53:06

mission for you just wouldn't have

53:07

happened.

53:08

>> Why did you make that decision? Why

53:10

didn't you just give up? bec felt like

53:12

one of these few very strategic areas

53:15

that was going to happen very quickly

53:17

and

53:19

coding is so important to RSI to say

53:22

nothing of the economic value that we

53:25

just didn't it didn't feel like one we

53:27

could give up on YC you basically had

53:30

the mo most distilled version of what

53:33

the mission was is just trying to

53:35

increase the amount of innovation in the

53:36

world my hunch is that if you actually

53:40

like thought uh why did you decide to go

53:42

work on OpenAI instead of YC? You could

53:45

almost make the argument that you could

53:47

just have a bigger impact on increasing

53:50

the amount of innovation in the world

53:51

through working on

53:52

>> it wasn't that intellectualized at the

53:54

time. It was just like

53:56

I kind of knew that I mean my whole life

54:00

I wanted to work on AI

54:02

and I kind of knew that it would be the

54:04

most important thing I could ever touch.

54:06

Like for me it was like my passion. I

54:08

just and I did think it'd be really

54:10

important, but I just wanted to do it.

54:11

>> What were those first few weeks like? I

54:14

know that there was this I I don't know

54:15

if you describe it as a camp, but people

54:17

just basically like all got together on

54:18

some kind of retreat and it wasn't even

54:20

like meant to be a company at the time.

54:21

I think it was just like

54:22

>> like the first few weeks once we

54:23

started. No, we were we were in Greg

54:25

Brockman's apartment.

54:26

>> Yes.

54:27

>> Um and there were maybe like 10 of us,

54:29

12 of us.

54:31

>> And you know, it had been all of this

54:32

work to get it going and then we showed

54:34

up one day.

54:35

>> Mhm. The first day I was like,

54:37

>> you know, January 4th, something like

54:39

that.

54:39

>> And it was like, so here we are.

54:44

What are we going to do?

54:46

We should get a whiteboard. We should

54:48

start talking about ideas. Maybe we

54:50

should write papers. And I had this

54:52

like, oh what have we done? Like

54:55

it was like a really crazy moment. Um,

54:58

>> had you already like committed to the

54:59

world and a whole bunch of other people

55:00

like were doing it? It took it took a

55:02

couple of years to like really get our

55:04

groove and figure out what we're going

55:06

to do. It just it was not I mean we knew

55:08

that we wanted to like figure out how to

55:10

build AI,

55:11

>> but beyond that, man, really unclear.

55:15

When you're in the like stumbling

55:17

through the woods phase of this type of,

55:20

you know, Manhattan project, how do you

55:23

most efficiently just stumble through

55:25

the trees to rapidly figure out what not

55:28

to work on? I mean, if something's not

55:30

working and

55:33

you run out of ideas, you can kill it.

55:36

That's kind of easier. The really hard

55:38

thing is when something is working super

55:40

well, when do you decide to kill the

55:43

other things to make it work even

55:44

better?

55:45

>> So, there have been a lot of quite

55:48

important moments in OpenAI history

55:50

where one thing started to really work

55:52

and we killed other good things to make

55:55

the best thing work better.

55:57

So when GPT3 started to work, we shut

55:59

down things like robotics stuff that

56:01

we're really excited about to really

56:03

focus on this. And then when coding

56:04

agents started to work recently, we shut

56:06

down things like Sora that we were also

56:08

really excited about and the browser to

56:10

really work on this. And that is

56:12

difficult to do. Um let's say you invest

56:14

a billion dollars into some new business

56:17

like Sora. How do you decide whether or

56:20

not that business is going to like

56:21

become a you know OpenAI pillar?

56:24

>> Oh, no. It would have been super

56:26

successful. It was just like it was more

56:28

important to put the compute and the

56:30

energy into coding agents.

56:31

>> Take me into one of those meetings like

56:33

imagine you're in that meeting. I guess

56:35

maybe you were in that meeting where you

56:37

were saying we've spent a year plus on

56:40

this thing, huge amounts of money, huge

56:42

amounts of compute, lots of people and

56:44

resources, a lot of momentum into it,

56:46

people use it, they love it, and then

56:48

you decide we're going to pull the plug.

56:49

Like how do you make that call?

56:51

>> Yeah. It's not like a it's it's not a

56:52

one meeting thing. It's like a I think

56:55

it's a real it's like a somewhat gradual

56:57

realization that there is a more

56:59

important use of this compute

57:03

these people um you know this product

57:05

direction and

57:08

we're going to make a very painful

57:09

decision to get there.

57:11

>> How do you realign people once you do

57:14

have to like kind of kill their baby to

57:16

move them to another baby?

57:18

>> People kind of understand the mission

57:20

and the stakes and the need to reorient

57:21

to get there. So even if they're like

57:24

unhappy in the moment,

57:27

sometimes they're very happy, sometimes

57:28

they're like, "Yeah, this is the right

57:29

thing for the mission." But even if

57:30

they're unhappy in the moment, they're

57:31

like, "I get why we're doing this." And

57:33

the kind of continuing refocus towards

57:35

super intelligence is like a good thing.

57:38

Do you think that there's going to be

57:39

projects currently that are going to get

57:41

killed?

57:42

>> I assume harder to pursue.

57:43

>> Yeah. There's this amazing line from

57:45

John Collison where he said basically

57:48

everything in the world if you just look

57:49

around it's like so difficult to make

57:52

things happen even like getting park

57:54

bench built it's incredibly difficult

57:56

and so when you look around you can kind

57:58

of think of the world as like a universe

57:59

of passion projects

58:01

>> and I think that if you are in like

58:04

Steve Jobes's position where you're

58:07

going to like create something where a

58:10

billion people might interact with it

58:12

every single day for are like ours. You

58:14

have to like think deeply about how do I

58:17

like design something that people are

58:18

going to love and ideally not be unhappy

58:21

about. How do you think about design?

58:25

>> I feel super lucky to get to work with

58:27

Johnny IV on designing beautiful things.

58:30

Mhm.

58:31

>> And I have learned so much from him

58:32

about how he really studies a problem

58:35

before

58:37

trying to get to the solution and not

58:39

not even letting him think too much

58:40

about the solution until he really

58:41

designs really understands the problem.

58:44

And I think this is a key that I didn't

58:46

appreciate before. Like really great

58:49

design is way more about understanding

58:52

the problem than the flash of insight.

58:53

And if you try to rush towards the thing

58:55

or lock yourself too much into the

58:56

thing, um, you will not do as good of a

59:00

job. I think the iPhone is the greatest

59:05

piece of technology humanity has

59:07

collectively yet made. It is an

59:09

incredible thing, but I don't like love

59:11

my relationship with it anymore. I

59:14

turned off my notifications, so I

59:15

actually like it much better now.

59:16

>> Yeah, I keep myself on do not disturb.

59:18

>> That wasn't even enough. I just like I

59:20

turned off notifications for everything

59:21

except a very small number of things.

59:24

Um, and I never have them on because I

59:26

just, you know, I can like look at if I

59:27

want to look at it.

59:28

>> Even messages apps.

59:29

>> Even messages apps. That was like a big

59:32

life upgrade. And I deleted Tik Tok

59:34

because it was just like too powerful.

59:38

Uh,

59:38

>> you were addicted to Tik Tok.

59:39

>> You know, I uh, so here's a crazy thing

59:41

that happened. When we were building the

59:42

Sora app, I made myself get addicted to

59:44

Tik Tok cuz I wanted to just like learn.

59:46

I was like, you know, I I had never

59:48

really used it before. I mean, I like

59:49

people would send me a Tik Tok or

59:50

whatever, but I never got sucked into

59:52

it. And I was like, I really don't want

59:54

to build something that is going to have

59:57

that kind of thing,

59:58

>> absorb people's time, but not

59:59

>> and I loved it. I love Tik Tok. I really

60:02

thought it was great. Um,

60:03

>> and then I thought I could like control

60:04

it. I was like, "Oh, you know, it's

60:06

actually kind of fun, but I only use it

60:07

for like 10 minutes to wind down before

60:09

bed and I'm like totally in control of

60:11

it." And then it was like an hour one

60:13

night and then some Saturday afternoon I

60:15

was on the couch for like 3 hours and I

60:17

was like this is like really not what I

60:20

thought the iPhone was supposed to be

60:21

about. Now I'm really enjoying in the

60:23

moment like a drug but I can tell it's

60:26

bad for me. And then I like briefly got

60:28

it back under control and I was down to

60:30

like 5 10 minutes a night whatever you

60:32

know like a little wind down before bed.

60:33

Then I was just like enough. I think the

60:36

iPhone is amazing and yet I did not feel

60:39

like I had enough self-control to keep

60:42

that app or I did not feel like the

60:45

notifications on messaging apps were

60:46

like a net good thing for me. And I'm

60:49

sure we will make an incredible,

60:51

beautiful, like really helpful,

60:53

empowering set of devices. And I'm also

60:57

sure that people will misuse them and

60:58

it'll make people's lives worse in ways

61:00

we can't imagine. And we'll adapt. But

61:02

this is a thing about powerful

61:03

technology. you're inventing a new

61:05

device, what is that process of

61:07

exploration through the problem space?

61:09

Like if you're Johnny,

61:10

>> I don't answer on his behalf. Um, but

61:13

he's like talked about this before

61:14

somewhat, but it's like his process. You

61:16

know, when he's doing the car, he will

61:17

like go study all the history of

61:19

motorsport and then like the different

61:22

type faces that people use for the text

61:25

in the cabin of a car and the materials

61:28

and why and like the different sounds

61:30

that engines have made over time and why

61:32

some have appealed. I mean, he'll write

61:33

literal books of all of his explorations

61:36

of all of these like unbelievably

61:38

detailed small components.

61:39

>> Did you guys basically take those books

61:41

and like try to like use them as

61:43

training data to make like a Johnny

61:44

model?

61:45

>> Um, no. But that would be a great thing

61:47

to try there. Yeah, there is definitely

61:49

a part of his process that I don't

61:50

understand. Like I understand the

61:52

studying,

61:54

I understand the refinement of a very

61:56

new concept into something great. But

61:58

there is like a middle step, the

62:00

inspiration that comes from

62:01

understanding the problem really well to

62:03

the like very novel idea kind of that

62:06

seems to me to happen all at once and I

62:07

don't understand that.

62:09

>> When you're thinking about designing

62:10

something, how what goes through your

62:12

mind?

62:13

>> I'm not a designer in any way. I would I

62:15

would not I I uh I try to be like pretty

62:17

good about realizing what I'm not good

62:19

at and I wouldn't try to have a strong

62:21

opinion there.

62:23

>> What are you the worst at

62:25

on the business building side?

62:27

>> Maybe product.

62:29

>> Really?

62:29

>> Maybe.

62:31

>> Okay. How do you find great product

62:33

people if you're bad at product?

62:35

>> This is a thing that I've never quite

62:37

agreed with people on. There's there's

62:39

like a there's like a business meme that

62:40

you can only hire people in things that

62:42

you deeply understand. I think it know

62:44

if they're great. I think this is just

62:46

clearly not true. I don't understand

62:48

design either. I know Johnny's great

62:49

design. Like there's lots of things I

62:50

can point to when you talk to him for 30

62:52

minutes. It's very obvious.

62:53

>> Which things do you try to actively get

62:55

better at and then which things do you

62:57

just try to outsource and understand

62:58

that you're just never going to be one

62:59

of the best?

63:00

>> I I am a big believer in you should try

63:02

to get better at your strengths and the

63:03

whole like obsession with I'm going to

63:05

get better at the things that I'm just

63:06

not good at at all and never going to be

63:07

good at huge trap. Super good at your

63:10

strengths.

63:11

>> Okay. What are your biggest strengths if

63:13

you had to like force rank?

63:15

>> I I hate this question. Um I don't hate

63:17

this from a false modesty perspective. I

63:18

think it's like very hard to say

63:20

anything insightful about your own

63:22

strengths.

63:22

>> You're clearly good at really rallying

63:24

people around some objective.

63:26

>> Sure. Uh but I don't like I don't think

63:29

I could teach someone who isn't good at

63:31

that how to be good at that. I think the

63:33

things that come supernaturally to

63:35

someone are very hard to I don't know.

63:39

or at least in my own case, I don't like

63:40

deeply understand why I'm good at that

63:42

or what to do about it. Although I think

63:44

we talked about earlier like this

63:46

category of things that you can learn

63:47

but you can't teach.

63:48

>> Mhm.

63:49

>> Um I think this is quite important to

63:52

understand and like if you want to get

63:54

good at something like that, asking

63:56

someone to explain it to you in my

63:58

experience never works. Really studying

64:01

them and like sitting with them in

64:02

meetings and just trying to observe it

64:03

and learn it yourself that does work.

64:06

Um, but [clears throat] and like when

64:08

there has been something that I do want

64:10

to get better at, I have tried to just

64:12

be around someone who's great at it and

64:13

really deeply study it. But I don't

64:15

think they would have been able to

64:16

explain it to me. I don't think they

64:17

could have like taught it to me by

64:18

talking to me about it. I think that's

64:20

totally right. If you look at like the

64:21

way that the best people in the world

64:23

learn video games, if you're playing

64:24

like CS GO, you like play some CS GO, so

64:27

you have like the basic game dynamics

64:28

and then you just go watch a pro and

64:30

like see how they interact on the map at

64:33

different points in the game and stuff.

64:34

When you think about like organization

64:36

pace, you know, there's like ambition,

64:38

all these other things, but like you

64:40

ideally over time want your

64:42

organization, you obviously don't want

64:44

it to slow down, but ideally it even

64:45

moves faster. How do you like bake that

64:48

in? I think it's like 90% the people you

64:50

put in leadership roles. Okay. There's

64:53

like other things you can do. People

64:54

have all these different sort of

64:55

operating rhythms and how they try to

64:57

run the company and this management

64:58

technique and that one. And I think it's

65:00

I think mostly just comes down to the

65:01

people. Definitely something I think

65:03

about with everybody in a role like that

65:05

is like are they a fast mover or a slow

65:07

mover?

65:07

>> How do you measure that without actually

65:09

having worked with them in the past?

65:11

>> Well, ideally you have worked them in

65:12

the past. I I think most of the time

65:15

executives at a company should be

65:16

promoted internally, not hired

65:18

externally.

65:19

>> Okay.

65:19

>> Um

65:21

but it when you do need to hire someone

65:23

externally, you spend a lot of time

65:25

talking to them, spend a lot of time

65:26

reference checking, you try to like work

65:28

together in some kind of casual way.

65:30

slightly different note. What was the

65:32

most painful thing that happened in the

65:33

last 12 months?

65:35

>> Honestly, having kids and working really

65:37

hard at the same time is brutal. It's

65:39

just extremely painful. Like you like

65:42

know you're I I and I think I'm like a

65:45

very present dad

65:47

relative. Like I don't do anything but

65:49

work and hang out with my family really

65:50

at this point, but I still feel like I'm

65:52

missing so much of this like one-time

65:54

thing. It's very painful. When you think

65:58

of someone like Masa for example, what

66:01

about like his brain and the way that he

66:03

sees the world is so different? I think

66:06

he will end up being like one of the

66:10

wild successes of this decade.

66:12

>> Um,

66:12

>> incredible person.

66:14

>> Dear friend, incredible person, massive

66:16

conviction and belief and not afraid at

66:19

all of big numbers and scale and it's

66:22

great. Uh there are not many Masa like

66:25

people in the world and we should be

66:27

very grateful for them.

66:28

>> What made him like what what I I don't

66:31

know if you've like talked with him but

66:33

>> uh I mean as far as I can tell he was

66:36

like always like this.

66:37

>> Do you also have the same sort of just

66:41

like no con like no ceiling concept of

66:43

scale?

66:44

>> Not I I think Masa is like a N of one

66:47

character.

66:48

>> Do you kind of view open AI as a little

66:50

bit like the golden goose?

66:51

>> In what way? um spawning a bunch of very

66:55

valuable things over time and just

66:59

building like a core competency of

67:00

increasing intelligence in the world.

67:02

>> Not really how I think of it. I get I

67:04

get what you're saying, but it it

67:06

doesn't feel like we're just like

67:07

spitting out golden eggs. It feels like

67:09

we're building this kind of compounding

67:10

thing.

67:11

>> I think one of the my favorite mental

67:13

models that I've taken from you is this

67:15

idea of real trends versus fake trends.

67:17

And

67:17

>> oh yeah, this is a

67:18

>> this is an important thing. Like

67:20

whenever people talked about bubbles or

67:22

anything else, I was always kind of

67:24

confused. Like I'm, you know, I studied

67:26

Warren Buffett and Charlie Munger a

67:27

bunch and so they love to talk about

67:28

bubbles and things being overvalued. I

67:30

was trying to like decipher how do you

67:33

figure out whether or not something is

67:34

real or fake and whether or not it's

67:36

going to persist and continue or it's

67:37

just going to, you know, revert to the

67:39

mean.

67:39

>> And what did you figure out?

67:40

>> Well, it was I think your mental model

67:42

of basically if there's a small group of

67:46

people like what is a real trend versus

67:48

a fake trend? A fake trend is, you know,

67:50

the VR situation where someone there's a

67:52

lot of hype,

67:53

>> but then someone buys the thing, they

67:54

don't really love it. They don't really

67:56

start to like design their life around

67:58

the experience of using it and then it

68:00

sits on the shelf.

68:01

>> Whereas like for me with Chadbt, it's

68:04

pretty much every day and maybe for like

68:07

sometimes I need to work a lot so I

68:09

spend like 3 hours or some days it's

68:11

like almost nothing but it's like there

68:13

persistent in my life. How do you kind

68:15

of like figure out now whether or not

68:18

something is a real trend or a fake

68:19

trend inside of the company and just

68:21

generally

68:21

>> same principle like is there a real deep

68:23

enduring

68:24

>> how did you come to that philosophy or

68:27

mental framework?

68:28

>> Well, I like kind of watched a lot of

68:29

startups. Uh the the there are many

68:31

amazing things about working at YC but

68:33

just the amount of data you get and as

68:35

long as you're willing to like spend the

68:37

time trying to analyze it and make sense

68:38

of it, you can really figure out a lot

68:40

of things. Are there any big mental

68:42

frameworks that you used to have that

68:44

you think are wrong in today's

68:46

environment and will be continue to get

68:49

more wrong over time as like

68:51

intelligence explodes?

68:53

>> I mean, I kind of think a huge amount of

68:56

them

68:57

are they weren't necessarily wrong at

68:59

the time, although I'm sure I was wrong

69:00

about a bunch of things, too, but a huge

69:01

amount of them are wrong now. Um, I

69:04

think a startup of today

69:07

still looks mostly like a startup of 10

69:09

years ago because that's what the

69:10

received wisdom says you're supposed to

69:12

do. And sure, it's different in some

69:14

ways. People are like, well, I'm going

69:15

to hire less people and spend more money

69:16

on tokens for Codeex or whatever. But it

69:19

should probably look very different. And

69:23

I have met a few people who are trying

69:26

to do a startup in a completely

69:28

different way.

69:30

But most people just use it means like

69:32

use more codecs and uh that doesn't seem

69:35

like enough.

Interactive Summary

Sam Altman discusses the profound impact of AI on the startup landscape over the last decade, highlighting how AI has drastically reduced the time and resources required to build competitive products. He explains his philosophy on operating in chaotic environments, the importance of long-term belief systems versus short-term planning, and his commitment to decentralizing AI power to ensure broadly shared prosperity. Altman also touches upon his personal management style, the challenges of rapid scaling, and the transition of OpenAI from a research lab to a complex product and infrastructure company.

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