Sam Altman CEO of OpenAI | Podcast | In Good Company | Norges Bank Investment Management

Summary

In this insightful interview, Sam Altman, the CEO of OpenAI, shares his ambitious vision for the future of artificial general intelligence (AGI) and its potential to benefit humanity. He discusses the rapid progress of language models like ChatGPT, the challenges of developing advanced AI systems, and the importance of democratizing this technology globally. Altman emphasizes the need for responsible governance and self-regulation as AI capabilities increase. He also touches on the economic implications of AI, the role of computation and funding, and the competitive landscape with other tech giants. Throughout the conversation, Altman's long-term thinking and unwavering commitment to pushing the boundaries of AI shine through.

Takeaways

• 😎 Sam Altman, CEO of OpenAI, is leading the charge in developing powerful AI systems like ChatGPT, with the goal of creating artificial general intelligence (AGI) that benefits all of humanity.
• 🔭 Altman believes that empirically testing and deploying AI technologies is crucial to understanding their risks, benefits, and potential evolution, rather than relying solely on philosophical speculation.
• 🌍 He envisions a future where extremely capable AI systems will fundamentally change how we think about and interact with the world, potentially by the end of this decade.
• 🤖 A key challenge is determining whose values the AGI should be aligned with and how to distribute access and benefits fairly across societies and countries.
• 🕰️ Altman emphasizes the importance of long-term thinking and not being constrained by conventional wisdom, which he sees as a competitive advantage for OpenAI.
• 💼 He stresses the need for leaders to prioritize talent recruitment, team development, vision communication, and strategic thinking, rather than getting bogged down in day-to-day details.
• 🌐 Altman believes that AI has the potential to significantly improve productivity and lift people out of poverty globally by democratizing access to intelligence and expertise.
• 🔬 He is also excited about the prospects of fusion energy, which he sees as complementary to AI in achieving abundance and addressing climate change.
• 🤝 OpenAI has a productive partnership with Microsoft, with aligned goals at the highest level, despite some occasional misalignments at lower levels.
• 🌉 Altman recognizes the need for reasonable global regulation, particularly for the most powerful AI systems that could cause grievous harm, while advocating for making models like GPT-4 widely available.

Q & A

What is ChatGPT, and how has it impacted the world?

ChatGPT is an advanced language model created by OpenAI that shocked the world with its capabilities when it was released in November 2022. It has been described as the fastest-growing product in history and has led to widespread adoption and integration across various industries and applications.

What is OpenAI's vision for the future where humans and AI coexist?

OpenAI believes that answering this question empirically is crucial, as many past predictions have been proven wrong. They aim to deploy AI into the world, observe how people use it, understand the risks and benefits, and then co-evolve the technology with society based on these observations.

How does Sam Altman define general intelligence (AGI)?

Sam Altman defines AGI as a system that can figure out new scientific knowledge that humans on their own could not. He believes that by the end of this decade, we may have extremely powerful systems that change the way we currently think about the world.

What are some of the challenges in ensuring that AI benefits all of humanity?

Some of the key challenges include deciding whose values the AI systems should align with, determining the level of flexibility and control given to individual users and countries, and finding ways to share the benefits of AI equitably, such as through increased agency and opportunities for people rather than just handouts.

How does OpenAI approach the development of AI models like GPT-4?

OpenAI aims to make their most capable models, like GPT-4, widely available globally, even if people use them for purposes that OpenAI might not always agree with. They believe in democratizing this technology as much as possible.

What role does Microsoft play in OpenAI's efforts?

Microsoft is a key partner of OpenAI. They build the computers that OpenAI uses to train their models, and both companies use these models. Their goals are generally aligned, although there may be some misalignments at lower levels that need to be addressed through communication and compromise.

How does Sam Altman approach talent assessment and leadership development at OpenAI?

Sam Altman believes he has developed a strong ability to assess talent through extensive practice. For leadership development, he tries to promote from within and warns new leaders upfront about the common pitfalls they are likely to face, encouraging them to learn from their mistakes over time.

What are Sam Altman's thoughts on the potential impact of AI on productivity?

Sam Altman believes that AI has the potential to significantly increase productivity, and he has set an ambitious goal for OpenAI employees to improve their productivity by around 20% over a 12-month period, leveraging the tools and models they are developing.

What is Sam Altman's perspective on the role of government regulation in the AI space?

While Sam Altman acknowledges the need for government regulation, especially for the most powerful AI systems capable of causing global harm, he believes that individual countries and regions should maintain the right to self-determine rules and guidelines for less powerful AI applications. He sees the potential for reasonable regulation, such as requiring disclosure when interacting with an AI system.

Apart from AI, what other technology is Sam Altman most excited about?

Sam Altman is highly excited about the potential of fusion energy technology, as he believes that bringing down the cost and increasing the abundance of clean energy, along with reducing the cost of intelligence through AI, are the two most important factors in achieving global abundance.

Outlines

00:00

🤖 The Rise of ChatGPT and OpenAI's Vision

In this introductory paragraph, Sam Altman, the CEO of OpenAI, discusses the groundbreaking release of ChatGPT and OpenAI's mission to create artificial general intelligence (AGI) that benefits humanity. He explains his excitement about working at the forefront of this technological revolution and shares his vision of a future where humans and AI coexist. Altman acknowledges the difficulty in predicting the exact trajectory of this technology but stresses the importance of empirically understanding its impacts, risks, and benefits as it evolves.

05:01

🔮 The Future of AGI and Its Global Impact

Altman reflects on the potential of achieving true AGI by the end of the decade, which he defines as a system capable of discovering new scientific knowledge beyond human capabilities. He discusses the challenges of determining whose values should guide the alignment of AGI and how to equitably share its benefits across the world. Altman emphasizes the need for global governance over powerful AI systems while allowing flexibility for individual users and countries. He also touches on the geopolitical implications of AI and the importance of democratizing access to these technologies.

10:02

🌍 AI's Role in Lifting Up the Developing World

In this paragraph, Altman expresses his belief that AI technologies, particularly the democratization of intelligence, will have a disproportionately positive impact on the developing world by providing access to expert knowledge and resources that were previously unaffordable. He acknowledges potential roadblocks, such as the trajectory of technology development or geopolitical factors, but remains optimistic about AI's potential to alleviate global poverty and inequality.

15:03

💻 The Rapid Integration of AI into Various Industries

Altman discusses the astonishing pace at which companies are integrating ChatGPT and other AI models into various products and services, such as cars, customer service, and legal document review. He acknowledges that while the current models have significant limitations, people are finding ingenious ways to leverage them, leading to substantial productivity gains. Altman also touches on the challenges of continuously training and depreciating these large models while generating valuable intellectual property for future iterations.

20:04

🤝 Partnerships, Regulation, and Democratizing AI Access

In this paragraph, Altman reflects on OpenAI's partnership with Microsoft, emphasizing their aligned goals and the importance of compromise in resolving any disagreements. He discusses the need for reasonable regulation, such as mandating disclosure when interacting with AI systems, and the potential for global governance over exceptionally powerful AI. Altman also reiterates OpenAI's commitment to democratizing access to their models, even if users employ them for unintended purposes.

25:08

🚀 Productivity Gains and the Limits of AI Progress

Altman shares his ambitious goal of achieving a 10% productivity increase across OpenAI within the next 12 months, attributing this target to the potential of AI tools. He expresses his belief in the exponential progress of AI and sees no inherent limitations to its continued advancement. Altman also touches on the topic of AI's impact on global power dynamics and the potential for unexpected breakthroughs from other countries or entities.

30:08

👩‍💻 Managing Researchers and Fostering Innovation

In this paragraph, Altman discusses his approach to managing researchers at OpenAI, emphasizing the importance of providing a high-level vision and ample freedom for exploration. He acknowledges the challenges OpenAI faced in rediscovering an effective research culture within a company setting and the need to strike a balance between allowing diverse ideas and aligning efforts towards promising directions. Altman also reflects on the lack of groundbreaking scientific breakthroughs from Silicon Valley companies before OpenAI's emergence.

35:09

🧠 Assessing Talent and Long-Term Thinking

Altman shares his thoughts on assessing talent, citing his experience at Y Combinator and his ability to recognize intelligence, track records, and novel ideas in candidates through numerous conversations. He discusses the importance of long-term thinking as a competitive advantage, which was a key factor in OpenAI's decision to pursue AGI despite skepticism from others. Altman also touches on the cultural differences between Silicon Valley and Europe regarding innovation and tolerance for failure.

40:10

⚛️ Excitement for Fusion Energy and Reading Habits

In this paragraph, Altman expresses his excitement about the potential of fusion energy to provide abundant, clean power and solve climate change challenges. He considers fusion and AI as the two most important technologies for achieving true abundance in the world. Altman also reflects on his diminished reading habits due to the demands of his work but recommends the book 'The Beginning of Infinity' as an inspiring read for young people.

45:11

🏆 Legacy and Continuing the Pursuit of AGI

In the final paragraph, Altman acknowledges that he is too focused on the present challenges and tactical problems at OpenAI to contemplate his legacy. He expresses a determination to continue building towards the goal of AGI, navigating the daily obstacles and issues that arise. Altman concludes by expressing gratitude for the opportunity to share his thoughts and receive well-wishes for OpenAI's ongoing pursuit of this ambitious endeavor.

Keywords

💡Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) refers to the development of AI systems that can match or surpass human intelligence in general cognitive abilities, such as reasoning, problem-solving, and learning across multiple domains. In the video, Sam Altman discusses OpenAI's goal of achieving AGI and its potential to revolutionize various aspects of society. He mentions that by the end of this decade, they expect to have extremely powerful systems that could change how we think about the world, hinting at the possibilities of AGI.

💡Language Models

Language models are AI systems trained on vast amounts of text data to understand and generate human-like language. In the video, Sam Altman mentions that OpenAI initially pursued various ideas but eventually realized the potential of language models, leading them to focus their efforts on this direction. He cites GPT (Generative Pre-trained Transformer) models as examples of powerful language models that have garnered significant attention and usage.

💡Democratization of AI

Democratization of AI refers to making AI technology widely accessible and available to people and organizations around the world, regardless of their resources or geographic location. Sam Altman emphasizes OpenAI's goal of globally democratizing AI, making their models like GPT-4 available to anyone willing to pay for the API rates. He believes that this democratization will particularly benefit poorer regions by providing access to expertise and intelligence that was previously unaffordable.

💡Alignment

Alignment refers to the challenge of ensuring that advanced AI systems are aligned with human values and goals, preventing unintended or harmful consequences. Sam Altman discusses the technical and social aspects of alignment, mentioning the need to decide whose values the AI should be aligned with and how to share the benefits of AI equitably. He expresses cautious optimism about solving the technical alignment problem but acknowledges the difficulty in reaching societal consensus on the broader implications.

💡Regulation

Regulation refers to the establishment of rules, laws, and policies to govern the development and use of AI systems. Sam Altman discusses the need for regulation, particularly for highly capable AI systems that could potentially cause significant harm. He suggests that while individual countries may regulate AI applications within their jurisdictions, global regulation will be necessary for the most powerful AI systems, similar to the regulation of nuclear weapons.

💡Productivity

Productivity refers to the efficiency and output achieved in a given amount of time or with a given set of resources. Sam Altman believes that AI will significantly enhance productivity, stating that he expects a 20% productivity increase within his company in the next 12 months due to the introduction of new AI tools and capabilities. He considers AI's potential to boost productivity as one of the key benefits it can bring to the world.

💡Competition

Competition refers to the rivalry among companies and organizations in developing and commercializing AI technologies. Sam Altman acknowledges that while OpenAI is currently a leader in the field, there will be many players contributing to the advancement of AI models and capabilities. He believes that competition and user preferences will ultimately drive the direction and adoption of AI systems, with different organizations experimenting with various features and approaches.

💡Fusion Energy

Fusion energy refers to the process of harnessing the immense energy released by fusing atomic nuclei together, as opposed to the fission process used in nuclear power plants. Sam Altman expresses excitement about the potential of fusion energy, stating that he believes fusion will soon become a reality. He sees fusion, along with AI, as one of the most important technologies for achieving global abundance by providing clean, virtually unlimited energy at a very low cost.

💡Long-term Thinking

Long-term thinking refers to the ability and willingness to consider and plan for the long-term future, beyond immediate or short-term goals. Sam Altman credits his long-term thinking as one of his superpowers, allowing OpenAI to pursue the ambitious goal of AGI despite initial skepticism and criticism. He emphasizes the importance of not being constrained by common wisdom and evaluating problems and solutions from a long-term perspective.

💡Research Culture

Research culture refers to an environment that fosters and encourages scientific exploration, experimentation, and the pursuit of new knowledge. Sam Altman discusses the need for OpenAI to rediscover and cultivate a strong research culture, which he believes had been lacking in Silicon Valley for some time. He describes the process of finding the right balance between giving researchers freedom to explore novel ideas and aligning their efforts towards the company's overarching goals.

Highlights

OpenAI shocked the world last November with ChatGPT, and OpenAI is not only creating models, it's creating the future.

The best way to predict the future is to invent it, and we're trying to see where the technology takes us, deploy it into the world to actually understand how people are using it, where the risks are, where the benefits are, what people want, how they'd like it to evolve, and then sort of co-evolve the technology with society.

When we have a system that can figure out new scientific knowledge that humans on their own could not, I would call that an AGI.

By the end of this decade, we expect to have extremely powerful systems that change the way we currently think about the world.

The holy moments have not been about new technology or new models, but about the breadth of use cases the world is finding to do this.

An uncommon use case is a guy who runs a laundromat business and uses ChatGPT for marketing copy, customer service, legal documents, and more - a virtual employee in every category.

Altman believes AI will particularly positively impact poor people the most, by democratizing intelligence and making expert advice available to everyone.

Altman is committed to making GPT models as widely available as possible, even if people use them for things OpenAI might not always feel are the best.

Governments will have to regulate AI, but OpenAI doesn't think individual countries will give up self-determination for what models can say. Global regulation will likely only happen for technology capable of grievous worldwide harm.

Altman aims for a 20% productivity increase at OpenAI over the next 12 months, driven by their AI tools.

Altman believes the key to developing leaders is having them spend enough time hiring talent, developing teams, communicating vision, and thinking strategically - things leaders often fail at initially.

For researchers, OpenAI provides a high-level vision and resources, but gives huge freedom to pursue their own directions.

Altman is most excited about fusion energy outside of AI, believing it will lead to abundance and solving climate change.

Altman recommends the book 'The Beginning of Infinity' as inspiring people to solve any problem and go off and do that.

Altman is focused on the present tactical challenges of building AGI rather than thinking about his future legacy.

Transcripts

[Music]

open AI shocked the world uh last

November with uh chat GPT and um

open AI is not only creating models it's

uh creating the future so Sam is an

honor to have you on the podcast thanks

a lot for having me it's great to be

here

now how does it feel to spearhead this

revolution

ah

it's definitely a little surreal it is

uh

it's like a very exciting moment in you

know the history of technology and to

get to work with the people who are who

are creating this

um it's like a is a great honor and uh I

can't imagine anything more exciting to

be doing

no I can't imagine

it's definitely a lot I can see that now

big picture what's the vision of of the

world where um humans and AI coexist

well you know I one one thing that we

believe is that you have to answer that

question empirically there's been a lot

of philosophizing about it for a long

time very smart people have had very

strong opinions I think they've all been

wrong and it's just a question of how

wrong and

the course that a technology takes is is

difficult to predict in advance I'm a I

love that Allen K quote that the best

way to invent the but the best way to

predict the future is to invent it

and so what we're trying to do is see

where the technology takes us deploy it

into the world to actually understand

how people are using it where the risks

are where the benefits are what people

want how how they'd like it to evolve

and then sort of co-evolve the

technology with society and you know I

think if you asked people five or ten

years ago what the deployment of

powerful AI into the world is going to

look like they wouldn't have guessed

that it looks like this

um people had very different ideas at

the time but this was what turned out to

be where the technology leads and and

where the science leads

and so we try to follow that and how far

into the future can you see now

uh the next few years seem pretty clear

to us

you know we kind of know where these

models are going to go we have a roadmap

we're very excited about uh we can

imagine both the Science and Technology

but also the product a few years out

and beyond that you know we're gonna

learn a lot we'll be a lot smarter in

two years than we are today yeah and and

what kind of uh you know holy

moments have you had lately

um well remember that we've been

you know we we've been thinking about

this and playing around with this

technology for a long time so the world

has had to catch up very quickly but we

we have less holy moments because

you know we've been expecting this and

we've been building it for a while and

it you know we don't it doesn't feel as

discontinuous to us

but and what kind of big things have you

seen since chat to petite sport

well we

the biggest ones have not been about new

technology or new models but about the

breadth of use cases the world is

finding to do this so the holy

moments have not been like oh now the

model can do this now we now we figured

out that because again you know some

would expected that but seeing how much

people are

coming to rely on these models to do

their work in their current form which

is very imperfect and broken you know

we're the first to say these models are

still not very good they hallucinate a

lot they're not very smart they have all

these problems and yet people are using

their human Ingenuity to figure out how

to work around that and still leverage

these tools and so watching people that

are remaking their workflows for a world

with llms has been

big and some examples of new things

you've seen new user cases applications

um you know a common one is around how

developers are changing their workflow

to uh you know spend like half their

time in chat GPT you hear people say um

they feel like two or three or sometimes

more product times productive than

before

um an uncommon one is I met a guy who

runs a laundromat business as like a

one-person thing and uses chat GPT for

um coming up with a marketing copy

dealing with like customer service uh

helping review legal documents we need a

long list of things and he's like I got

a virtual employee in every category

that was pretty cool

and what about things like uh brain

implants and getting it to help with

speech and so on which we just saw

recently

um

I'm very excited about neural interfaces

but I am not currently super excited

about brain implants I don't feel ready

to want one of those I would love a

device uh that could like read my mind

and

but I would like it to do that without

having to put a hole in my skull and I

think that's possible

how

oh there's many Technologies depending

on what you'd want but you know there's

there's a whole bunch of companies

working on trying to sort of like read

out the words you're thinking without

requiring a physical implant now a few

years ago nobody had heard about open AI

now uh everybody's heard about it you

are

you know one of the most most famous

people on Earth

um but the people so how many people are

you at open AI now

500 or so and what what do these 500

people actually do

um it's a mix so there's a large Crew

That's just doing the research like

trying to figure out how we get from the

model we have today which is very far

from an AGI to an AGI and all of the

pieces that have to come together there

so you know scaling the models up coming

up with new methods uh

that that whole process uh there's a

team that makes the product and figures

out also how to scale it there's a sort

of traditional Silicon Valley tech

company go to market team

um there's a very complex uh legal and

policy team that does all the work you'd

imagine there

um yeah

and so your your priorities as a CEO now

how do you spend your time

um

I kind of think about the buckets of of

what we have to do in uh research

product and compute on the technical

side

and then uh on the and I that's sort of

the work that I think I I enjoy the most

and where I can contribute the most

um and then I spend some of my time on

policy uh and

sort of

social impact issues for lack of a

better word uh and then the other things

I spent less time on but we have great

people that run the other functions

now your mission has been to ensure that

artificial well the general intelligence

benefits all of humanity what's the

biggest challenge to this you think

I

a couple of thoughts there uh one I'm

reasonably optimistic about solving the

technical alignment problem we still

have a lot of work to do but you know I

feel like

and feel better and better over time not

worse and worse

this the the social part of that problem

you know how do we decide whose values

we align to who gets to set the rules

for this how much how much flexibility

are we going to give to each individual

user and each individual country

we think the answer is quite a lot but

that comes with some other challenges

um in terms of how they're going to use

these systems that's all going to be uh

you know difficult to put it lightly for

society to agree on

and

and then how we share the benefits of

this

what we use these systems for uh that's

also going to be difficult to to agree

on um

kind of the buckets I think about here

are

we've got to decide what

you know Global governance over these

systems as they get super powerful is

going to look like and everybody's got

to play a role in that

um we've got to decide how we're going

to share the access to these systems and

we've got to decide how we're going to

share the benefits of them

the

you know there's a lot of people who are

excited about things like Ubi and I'm

one of them but I have no delusion that

Ubi is a full solution or even the most

important part of the solution like

people don't just want handouts of money

from an AGI they want increased agency

they want to be able to be architects of

the future they want to be able to do

more than they could before and figuring

out how to do that while addressing all

of this sort of

let's call them disruptive challenges uh

I think that's going to be very

important but very difficult

how far out this true AGI

I don't know how to put a number on it I

also think we're getting close enough

that the definition really matters and

people mean very different things when

they say it but I would say that I

expect by the end of this decade for us

to have

extremely powerful systems

that change the way we currently think

about the world

and and you say we've got different

definitions what is what is your

definition of

general intelligence you know there's

like kind of the open AI official

definitions and then there's one that's

very important to me personally when we

have a system that can

figure out new scientific knowledge

that humans on their own could not

I would call that an AGI

and that you think we may have by the

end of this decade well I kind of tried

to like soften that a little bit just by

saying we'll have systems that like

really change the way the world Works um

the the new science may take a little

bit longer or maybe not

Steve what's the end game here

um

are we just all of us going to work a

lot less

um

you know I want to be people

I think we'll all work differently I

think we'll still many of us will still

work very hard but differently every

technological Revolution

um people say they're we're just gonna

do less work in the future and

we just find

that we want a higher standard of living

and new and different things and also

that we find new kinds of work we really

enjoy you know

neither you nor I have to work and I bet

we both work pretty hard

and I love it I love my job I love my

job

and I feel very blessed so

the definition of work what we work on

why we work the reasons for it I expect

that all to change what we do I expect

to change

but

I love what I do and I expect people in

the future to love even more what they

do because there will be new amazing

things to work on that we can hardly

imagine right now and less boring stuff

yeah

I'm all for getting rid of the boring

stuff like I think like everybody should

love it that's maybe one thing we could

say in the future is everybody will do

things that they love you won't have to

do things you don't and I think most

people probably don't love their jobs

right now

um I believe you just traveled the world

and met with a lot of people and users

uh what's what was your what was your

main takeaway

uh

uh the level of excitement about the

future and what this technology is going

to do for people

around the world in Super different

cultures and super different contexts

was just very very different than I

expected

like it was

it was like overwhelming in the in the

best way

any any difference between geographies

yeah like you know in

in the developing World

um people are just focused on what this

can do economically right now uh and in

the more developed world there's much

more of a conversation about what the

downsides are going to be and you know

how this is going to disrupt things and

there's still excitement but it's

tempered more by fear that was that was

a striking change a difference

do you think it will lift up the oral

part of the world yeah I really do I

think it's going to make everybody

richer but I think it impacts positively

impacts poor people the most

and I think this is true for most kinds

of Technology

um but it should be particularly true

for the democratization of intelligence

you know you or I can afford to pay a

super highly compensated expert if we

need help but a lot of people can't

and to the degree that we can make say

great medical advice available to

everyone

um

you and I benefit from that too but less

less than people who just can't afford

it at all right now

and what would potentially prevent this

from happening

well

we could be wrong about the trajectory

that technology is on I think we are on

a very smooth exponential curve that has

much much further to go

but you know we could be like missing

something we could be drinking our own

Kool-Aid we could either brick wall soon

I don't think we're going to um I think

we have some

remarkable progress ahead of us in the

next few years but yeah we could we

could somehow be wrong for a reason we

don't understand yet

um what is it doing to the global

balance of power

I don't know how that's going to shift

um I'm not sure anyone does but I

certainly don't think that's something

that I'm

particularly well qualified to weigh in

on

but it just seems like it's being it's

so key now to the the weapon race

the medical race the self-driving

vehicle race just all these races

but it's also available

pretty broadly

you know like one of the things that we

think is important is that we make gpt4

extremely widely available

um even if that means

people are going to use it for things

that we might not always

feel

are the best things to do with it

uh but you know we have a goal of

globally democratizing this technology

and

as far as we know gpt4 is the most

capable model in the world right now

and it is available to anyone who wants

to pay what I think are the very cheap

API rates now anyone is not quite there

you know we don't we're blocked in a

hand we block a handful of countries

that the US has embargoes with or

whatever but it's pretty available to

the world

but in order to develop it further you

need

um well you need the right chips right

and they are not available

but what matters is how you're going to

get to like GPT six and seven and also

even more than that how you're going to

get the next set of very different ideas

that take you

on a different trajectory like everyone

knows how to climb this one Hill and

we're gonna go figure out the next total

Decline and there's not a lot of people

in the world that can do that but

we're committed to making that as widely

available

as we can

do we know where China is here

we don't maybe someone doesn't want here

do you think there's a chance that well

like they did with weapons that just

suddenly bang they had the supersonic

Rockets we didn't even know they existed

right could that happen yeah

totally it could

I mean we're gonna work as hard as we

can to make sure that

we stay in the lead but

we're a little in the dark

so Mark Andreessen for instance he

thinks we should stuff it into

everything

and you know as part of

the

geopolitical

fight

what do you think

stuff it into everything I mean just

like put it everywhere

that's happening and I think that's

great

like without revealing something I

shouldn't the amount of gpt4 usage and

the number of people companies that are

integrating it into different ways is

staggering is awesome

some examples if you had to reveal

something

uh

I mean like you know car makers are

putting it into cars and I was like all

right that sounds like a gimmick and

then I got to try a demo of it and I was

like wow this being able to just talk to

my car

and control it in a sophisticated way

entirely by voice

actually totally changes my experience

of

how I like use a car in a way that I

would not have believed was so powerful

so for instance use it in a car what do

you say

uh this is this is probably where I

don't want to like reveal a partner's

plans but you can imagine a lot of

things that you might say like the basic

stuff is easy like you know I need to go

here and

um I'd like to listen to this music and

also can you make it colder

um

sounds good

do you depend on uh newer and even more

powerful ships than what we have now I

mean how much quicker do you how much

more complex the chips need to be than

h100 or the latest things from Nvidia

um

yeah of course like there's

the ways the ways that we can keep

making these models better are we can

come up with better algorithms

or just more efficient implementations

or both we can have uh better chips and

we can have more of them and we plan to

do all three things and they multiply

together

and do you think these the chip makers

who will end up with the profits there

uh they will end up with profits I

wouldn't say the prophets I think

there's many people who are gonna like

share this

massive economic boon

how much does it cost to train these

models I mean how much have you spent on

free training models

we don't really talk about exact numbers

but like quite a lot

yeah

and what's the challenge of spending so

much money pre-training and then

it lasts for a relatively short period

of time in a way you have to depreciate

the whole investment in order because

you need to invest more in the Next

Generation

uh I mean what are the what's yeah how

do you think how do you think about this

I that's true I don't think they're

going to be as many massive pre-trained

models in the world as people think

I think there will be a handful and then

a lot of people are going to fine-tune

on top of that or whatever

so so how does how do you how do you

read the competitive part of it that I

think is important is like you know

when we did do gpt4

um we did we produced this artifact and

people use it and it generates all this

economic value and um you're right that

does depreciate fast but in the process

of that we learned so much about how to

go

we pushed the frontier of research so

far forward and we learned so much that

it'll be critical to us being able to go

do gpt5 someday or whatever that it's

like you're not just depreciating the

capex one time for the model you have

generated a huge amount of new IP to

help you keep keep making better models

um

so the way you read the competitive

landscape now how what does it look like

uh

I mean there are going to be many people

making great models will be one of them

we'll like contribute our egi to the

world to society among among others and

I think that's fine and

you know we'll all

run different experiments we'll try

setting you know we'll have different

features different capabilities we'll

have different opinions about what the

rules of a model should be

and through the magic of competition uh

and users deciding what they want

we'll get to a very good place

how far ahead do you think you are a

competition

I don't know

I don't think about that much to be

honest like we're

our customers are very happy uh they are

desperate for more

features and more capacity and us to be

able to deliver our service in all of

these little better ways and we're very

focused on that

um I'm sure Google will have something

good here at some point but

like I think they're you know racing to

catch up with where we are and

we're thinking very far ahead of that

so normally in in the software business

you have something which is

very cheap where you ship where you ship

a lot of it or something which is very

expensive and you don't ship so much

here you could potentially ship

something

and I can see you smiling here hey you

can potentially exactly

so so tell us how how is this going to

work

you know I'll tell you one of the most

fun things about this job is

we are past the point as a company uh I

am past the point as like a CEO running

this company where there's like a road

map to follow

we're just doing a bunch of things that

are like outside of the standard Silicon

Valley

received wisdom and so we get to just

say well we're going to figure it out

and we're going to try things and if we

got it wrong like who cares there was no

like it's not like we like screwed up

something that was already figured out

I mean back to our very founding like

most big tech companies are a they start

as a product company and eventually they

built on a research lab that doesn't

work very well

and we started as a research lab and

then bolted on a product company that

didn't work very well and now we're

making that better and better

um but to like a project company I mean

Microsoft

no no I mean like having to figure out

how to ship the API in chat GPT yeah

um like we started we really did just

start as a research lab and then one day

we're like we're gonna make a product

and then we're gonna make another

product and now that product is like the

fastest growing product in history or

whatever

and we weren't set up for that

it's the usage of chatipity decelerating

no

I think it maybe took like a little bit

of a flat line during the summer which

happens for lots of products but it is

doink up

tell us about the relationship with

Microsoft how does that work

um

I mean at a high level they

build us computers

we train models and then we both use

them

and it's a pretty

clear and great partnership

are you have you are your goals aligned

yeah they really are

um

one of I mean there's like there's of

course areas where we are not perfectly

aligned and like I don't

like any partnership in Life or business

or whatever I won't pretend it's perfect

but it is very good and we are aligned

at the highest levels which is really

important and

the the misalignments that come up at

the sort of lower levels once in a while

we you know like

no contract in the world is what makes a

partnership good like what makes a

partnership good is that when those

things happen you know Satya and Kevin

and I talk and

you'd figure it out and you know there's

like a good spirit of compromise over a

long time

now they've been one of them initiators

and I mean together with you in terms of

self-regulating this space what

can this type of thing be self-regulated

not entirely

um I think it needs to start that way

and I think that's also kind of like how

you figure out a better answer but like

governments are going to have to do

their own thing here and you know we can

provide input to that but we don't we're

not like the elected decision makers of

society and we're very aware of that

and what can governments do

anything they want

um and I think people forget this like

governments have

quite a lot of power they just have to

decide to use it

yeah but I mean so let's say now Europe

decides that they're going to regulate

you really harshly I mean are you just

going to say goodbye Europe no

possibly

um I don't think that's what's gonna

happen like I think we have a very

productive conversation I think Europe

will regulate AI but reasonably not not

very harshly and what is I'm sorry and

what is a reasonable regulation what

what is that level

I think there's many ways it it could

I think there's many ways that it could

go uh that would all be reasonable but

you know like

to give one specific example and I'm

surprised this is controversial at all

but a regulatory thing that's coming up

a lot in Europe and elsewhere is that if

you're using an AI you've got to

disclose it so if you're talking to like

a bot and not a person you need to know

that that seems like a super reasonable

and important thing to do to me for a

bunch of reasons given what's starting

to happen

um to my surprise there's some people

who really hate that idea but I'd say

that's like a very very

um

reasonable regulation

I agree I agree

do you think we'll get Global regulation

is there any

um shape I think that can happen

I think we're going to get it for only

the most powerful systems so you know I

think like individual countries or

blocks of countries are not going to

give up their right to self-determine

for like you know what can a model say

and not say and how do we think about

the Free Speech rules and whatever

um but but for technology that is

capable of causing Grievous harm to the

entire world like we have done before

with nuclear weapons

a small number of other examples yeah I

think we are going to come together and

get good Global regulation but given how

embedded it now is in in everything as

we spoke about you know weapons your car

you're sitting in your car and it's like

super cool and it's uh cold and hot than

music and this and that and you know and

you you're a Chinese car company and you

won't all compete the the Americans why

would you wanna

why would you only when you want to have

a regular regulation on this

well gpt4 I don't think needs Global

regulation nor should it have it I'm

talking about like what happens when we

get to gpt10 and it is you know say

smarter than all of humans put together

and that's why you think we get it

that's when I think we'll get it

when you have the cost of Intelligence

coming down so dramatically like it is

now what is it going to do to

productivity in the world

I mean it's supposed to go up a lot

right that's what theory tells us and

that's what I think

so so

um

I've told everybody in in our company

that hey we shouldn't we should improve

our productivity about 10 over the next

12 months all of us

and that's and you know how I got the

number

did you ask gbt no I just took it I just

took it straight out of the air

do you think what do you think about

that number is it low high under

ambitious what what should what should

productivity increase by

how do you how do you measure uh the

stuff we do

that's not very good measurement but

just the kind of stuff that I produce

how much of your company writes code

uh

if uh

15 well people in

in technology probably 15 20 of us

more actually but

okay let's let's say that's 20 writing

code I think an overall goal of like you

know 20 productivity increase in a

12-month period is appropriately

ambitious given the tool

and given the tools that we will launch

over the next 12 months

okay sounds like I should uh up the game

her a bit

I think so yeah I'll just tell everybody

you told me to so that's fine

it's better to set a goal that is like

slightly too ambitious than

significantly under ambitious in my

opinion

yeah

um now is there like a an inherent

limitation to what AI can achieve I mean

is there like a point of no further

progress

I couldn't come up with any

reasonable explanation of why that

should be the case

you say

um that most people overestimate risk

and underestimate reward what do you

mean by that

um

you know there's a lot of people that

don't go start the company or take the

job they want to take or

try a product idea because they think

it's too risky

and then if you really ask them like all

right can we unpack that and can you

explain what what the risk is and what's

going to go wrong it's like well the

company might fail

okay and then what

you know well then I have to go back to

my own job

my old job all right that seems

reasonable and they're like well you

know but I'll be a little embarrassed

and I'm like oh is that you know what's

the cause

I I I think like people view that as a

super risky thing and they view staying

in a job where they're not really

progressing or learning more or doing

new things for 20 years uh it's not

risky at all

and to me that seems catastrophically

risky you know to like miss out on 20

years of your very limited life and

energy to

try to do the thing you actually want to

do

um

that seems really risky

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but it's not thought of that way

talking about staying in your job what

um so the leaders and the CEOs so you

know how how is AI going to change the

way leaders need to act and behave

well hopefully it's gonna like do my job

you know hopefully the first thing we do

with AGI is let it run open Ai and I can

you know go sit on the beach

that'd be great

I wouldn't want to do that for long but

right now it sounds really nice

how do you develop the people in your

company how do you develop your leaders

um

I think developing leaders tend to fail

at the same set of things most of the

time you know they don't

they don't spend enough of their time

hiring talent and developing their own

teams they don't spend enough of their

time articulating and communicating the

vision of their team uh they don't spend

enough of their time thinking

strategically because they get bogged

down on the details and so when I like

put a new person in a very senior role

which I always try to do with promotions

I mean I'm willing to hire externally

but I'd always always rather promote

internally

um

I have them over for dinner or go for a

walk or sit down or something and say

like here are the ways you're going to

screw up

I'm gonna tell you all of them right now

you're gonna totally ignore me on this

and not believe me or at least not do

them because you're going to think you

know better or you know not make these

mistakes but

I'm going to put this in writing and

hand it to you and we're going to talk

about it in three months and in six

months and you know

eventually I think you'll come around

and they always ignore me and always

come around

and I think just like

letting people recognize that for

themselves uh but telling them up front

so that it's at least in their mind is

very important well it's the most common

way leaders grow up

uh

failing to

recruit slash promote and then failing

to build a good

delegation process

and then as a consequence of those not

having enough time to

set strategy because they're too bogged

on in the day-to-day and they can't get

out of that downward spiral

uh what what is your delegation process

look like

two things number one high quality

people number two setting the training

wheels at the right height and

increasing them over time as people

learn more and I build up more Trust

is that the way to manage geniuses

um

they get uh researchers that's a

different thing I was like talking about

how to like Executives that run the

thing okay what about researchers what

about the geniuses

um the primadonna's

explain well pick really great people

explain the general direction of travel

and the resources that we have available

and kind of at a high level where we

need to get to to get to the next level

so you know we have to achieve this to

go get the next 10 times bigger computer

or whatever

and

you know provide like the most mild

input on it would be really great if we

could pursue this research Direction and

this would be really helpful and then

step back

so we kind of like you know we set a

very high level vision for the company

and what we want to achieve and beyond

that researchers get just a huge amount

of freedom

do you think companies generally are too

detailed

in the remit they give the teams

yes

I mean at least for our kind of thing I

think uh managing

we talked earlier about having to like

ReDiscover a bunch of things

I'd say this

realizing it's going to come across as

arrogant and I don't mean it that way

but I think it's an important Point

um

there used to be great research that

happened in companies in Silicon Valley

um you know Xerox park being the obvious

example there have not been for a long

time and we really had to ReDiscover

that and we made many screw-ups along

the way to learn how to run a research

effort well and how you balance

letting people go off and do whatever

towards trying to get the company to

point in the same direction and then

over time how to get to a culture where

people will try lots of things but

realize where the promising directions

are and on their own want to come

together to say let's put all of our

Firepower behind this one idea because

it seems like it's really working

you know I'd love to tell you we always

knew language models were going to work

that was absolutely not the case we had

a lot of other ideas about what might

work but when we realized the language

models were going to work we were able

to get the entire

research trust or almost entire research

Brain Trust to get behind it

I'm slightly surprised you say that

there was no innovation culture in

Silicon Valley because that's uh a bit

contrary to uh

to what I thought so

there is

yeah there's a product Innovation

culture for sure a good one but like

I mean again I hate to say this because

it sounds so arrogant but like before

open AI what was the last really great

scientific breakthrough that came out of

a Silicon Valley company

and and why did and why did that happen

why

what happened there

well

we got a little lucky no I don't mean we

I'm sorry why did why do these

culture disappear in Silicon Valley you

think

I have spent so much time reflecting on

that question uh

I don't fully understand it I think

I think

it got so easy to make a super valuable

company

um and people got so impatient on

timelines and return Horizons

that a lot of the capital went to these

things that could just you know fairly

reliably multiply money in a short

period of time uh by just saying like

we're gonna take the magic of the

technology we have now

the internet mobile phones whatever and

apply to every industry

that sucked up a lot of talent very

understandably

now you you had some um

what should we say you'll you'll

co-founders

are pretty pretty into big big hairy

goals right

yeah

I mean we're trying to make AGI I think

that's the biggest hairiest goal in the

world

so not so many companies have those kind

of co-founders and people who with that

kind of track record and

you know that that type of talent magnet

uh funding capabilities and so on do you

how important was that

you mean Elon by this right yeah yeah

and you know and some of the other

people you worked in the beginning

well there were there's six co-founders

uh

Elon and me Greg and Elia and John and

voichak and you know Elon was definitely

a talent

magnet and attention magnet for sure and

also just like

has some real superpowers that were

super helpful to us in those early days

aside from all of those things and you

know contributed in ways that we're very

grateful for but the rest of us were

like pretty pretty unknown

and

I mean maybe I was like somewhat known

in technology circles because I was

running my combinator but not not like a

not in a major way

uh

and so we just had to like you know

grind it out but that was like that was

like a good and valuable process

what is your superpower

I think I'm good at

thinking very long term

and not being sort of constrained in

like common common wisdom

evaluating talent that was like a really

helpful thing to learn from my

combinator

you said in 2016 that long-term thinking

it's a competitive Advantage because

almost no one does it

yeah

I mean when we started openai and said

we're going to build AGI everybody was

like that's insane hey it's 50 years

away and B it's like you know the wrong

thing to even be thinking about you

should be thinking about this how to

improve this one thing this year you

know also this is like unethical to even

say you're working on it because it's

like such a science fiction and you're

gonna lead to another AI winter because

it's too much hype

and we just said it's going to take us a

while but we're going to go figure out

how to do it

you said you was a good at assessing

Talent what how do you do it

I don't know I don't I can't like I have

a lot of practice so I've got like a

but I don't have like words for it

I can't I can't tell you like here's the

five questions I ask or here's the one

thing I always look for

but

you know assessing if someone is smart

and if they have a track record of

getting things done and if they have

like

novel ideas that they're passionate

about

I think you can learn how to do that

through thousands of conversations even

if it's hard to explain

why is Europe

um so behind generally when it comes to

Innovation and Innovative culture

I'd ask you that I don't know why is it

what is it first of all like well

I guess I guess it is uh

look at where the big tech companies are

where the big Innovations come uh it's

certainly behind it's certainly very

behind in like hyperscale software

companies there's no question there but

big fear of failure uh it's a cultural

thing

um it's there's a lot of there's there

are a lot of things going into that

cocktail I think

the the fear of failure thing um and and

the kind of the like

the cultural

environment or backdrop there is is huge

no doubt uh the

you know we funded a lot of European

people at YC and

a thing they would always say is like

they cannot get used to the fact that in

Silicon Valley failure is tolerated

field and stuff big time

and I'm sure I'll fail at stuff

in the future what was the biggest

failure so far

uh well I mean monetarily wise I've made

a lot of big Investments that have gone

to Total like just you know zero like

crater in the ground but in terms of

like time and psychological impact on me

I did a startup from when I was like 19

to 26.

worked unbelievably hard to consume my

life and failed at that and that was

like quite painful and quite

demoralizing and it's like it you know

you learned to get back up after stuff

like that but it's hard

how do you get back up

um

I mean one of the key insights for me

was realizing that although I thought

this was like terribly embarrassing and

shameful

uh no one but me spent much time

thinking about it

who do you ask for advice like

personally

my strategy is not to just have like one

person that I go to with everything and

a lot of people do that you know they

have like One Mentor that they go to for

every big decision but my strategy is to

talk to a ton of different people

when I'm facing a big decision

and try to synthesize the input from all

of that so if I'm facing like a real

major strategic challenge for openai

um you know kind of one of these better

company things

I would bet that you know counting

people internal and external the company

I talked to 50 people about it

and

probably out of you know 30 of those

conversations I would

hear something interesting or learn

something that updates my thinking

and that's my strategy

so now outside AI

um what are you the most excited about

Fusion

I think we're going to get Fusion to

work very soon

and I think

my model

if you boil everything down to get to

abundance in the world

the two biggest most important things

are bringing the cost of intelligence

way down and bringing the cost and

amount of energy way down

and I think AI is the best way to do the

former and fusion is the best way to do

the latter and you know in a world where

we look at energy that's like less than

a penny per kilowatt hour and more

importantly we can have as much as we

want and it's totally clean

um that's a big deal do you think it's

going to solve the climate problem

yes

we'll have to use it to do other things

like we'll have to you know use some of

it to capture carbon because we've

already done so much damage but yes I do

what about crypto uh

I am excited

for the vision of crypto

and it has so far failed to deliver on

that promise

but you have plans

it's it's not something I'm spending

that much time like open air is taking

over my whole life so I can have a lot

of plans about open Ai and there's other

projects that I've invested in or helped

start that I feel bad because I don't

have much time to offer them anymore but

they're all run by super capable people

and I assume they'll figure it out

what do you read

um the thing that has unfortunately gone

the most by

the Wayside for me recently has been

free time and thus reading so I don't

I don't get to read much these days uh I

used to be a voracious reader and uh

there was like one year where I read you

know not fully but like more than a scam

I Read 50 textbooks

and that was like an unbelievable

experience uh

but I don't like this last year uh I

have not read many books

what's the one book young people should

read

that's a great question picking one is

really hard

um I don't think

man that's such a good question

um

I don't think it's the same for every

young person uh and I like

coming up with a generic singular

recommendation here

is super hard

I don't think I can give a faithful

answer on this one

it's good

now we uh

we are uh fast forwarding you know what

it's not can I actually I do have this

is not the one for every young person

but I wish a lot more people would read

the beginning of infinity

early on in there

early on in their career or their lives

the beginning of infinity the beginning

of infinity bye I think uh that doesn't

matter we'll find it I think it's the

most

inspiring you can do anything you can

solve any problem and it's important to

go off and do that

it's a very like I felt it was like a

very expansive book of the way I thought

about the world

well Sam I think that's a very

beautiful place to uh to go in for

landing now last one so um fast forward

a couple of decades

um people sit down and reflect on Sam

oldman's impact on the attack world and

Society what what do you hope what do

you hope they'll say what do you what do

you hope your lazy will be

you know I'll think about that when I'm

like

at the end of my career like right now I

my days are spent like trying to figure

out why this executive is mad at this

one and why this product is delayed and

like why our Network on our you know big

new training computer is not working and

who screwed that up and how to fix it

and it's like very caught up in the like

annoying tactical problems uh there is

no room to think about Legacy we're just

trying to go off and like build this

thing

fantastic well um good luck with that

it's been

been absolutely fantastic conversation

and uh all the best of luck and uh go

get them

great talking to you thank you for thank

you for having me

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me