Hello again.
If I keep doing this, I understand that
there's
vacancy at 60 minutes. Maybe I can
If this investing thing doesn't work
out, maybe I can do that.
Another
full disclosure,
Sam is a friend. I'm also on the board
of OpenAI, so
I promise you I will not only ask him
softball questions. I will channel my
inner Brett Bear and
and uh
and Slink Gooper.
So, Sam, as I said,
CEO of OpenAI, one of the founders.
He previously served as the president of
Y Combinator,
where he helped scale some of the
world's most influential startup
accelerators,
and advise companies that will shape
entire industries.
At OpenAI, Sam has obviously been at the
forefront of advancing artificial
intelligence to the benefit of humanity,
helping to bring cutting-edge AI tools
to the hands of millions of people. How
many people? Eight, 900 million people?
>> More, yeah.
>> and businesses around the world.
So, Sam, let me start with what I think
is the question that is on everybody's
mind, okay?
Which is
where are we today
in the world of AI?
>> I I think at some point in the last few
months,
we really have crossed a threshold into
major economic utility of these models.
It may have happened a little bit
earlier, but there was such an overhang
before we figured out how to use these,
and we had to not only continue to make
the models get smarter, but figure out
sort of the plumbing to make them easy
to use. Where we're we're now in a world
where the models are
astounding people with the
work they can do.
Uh and I think this has been most
noticeable in coding, yep, but it's also
happening in science. It's happening in
many fields of knowledge work. Uh
at sort of with this orienting speed
where people are saying like, "Man,
these things that I thought were still
years away are happening now and I have
my job
shifted from doing
uh you know, direct technical work or
uh you know, legal work to managing a
team of agents doing this work."
This is going to go much further. Uh I
think we are at a very steep part of the
curve
and right now maybe you can trust say a
AI software engineer to do a multi-hour
task. Very soon it'll be a multi-day
task and then a multi-week task.
And not long after that, I think the
paradigm will shift again and it'll feel
like these AI systems are just connected
to your life, to your company, whatever
proactively thinking, working all the
time. Uh
and having full context on whatever they
need to know and just sort of doing
stuff like you would trust a senior
employee to do.
>> Mhm.
And and you think that um
companies have
real have a real understanding of how
these systems can help them and rein-
reimagine how they do their businesses?
>> Some do, some don't. Uh certainly the
new generation of startups thinks
differently than any generation of
startups before.
Uh
it used to be that when we would talk to
startups, they would talk about how many
employees they needed.
Uh now they generally don't want to hire
a lot. They think that'll slow them down
and they're all focused on how much
compute they can get.
>> Mhm.
>> You know, can I reserve this much
capacity? Can I do a cloud deal for
that? Uh can I get this many tokens?
And I think that is
that
that is a mental shift that bigger
companies are going through more slowly
but some are starting to do that. Uh one
place I think you can see this happening
is engineering orgs and product orgs
talking about
they're doubling, tripling what they're
planning to ship this year.
>> Mhm. And that has not happened before.
>> Yeah.
And you've been quite vocal in saying
that um
artificial general intelligence will
come sooner rather than later.
Want to share your views on
how close we are and how soon it'll
come?
>> At at this point, I think the definition
of AGI really matters. Some people would
say we already got there. Some people
say it's very close. Some people say
we're kind of you know, it's maybe still
a year away, but in any case, that
word has ceased to have much meaning.
>> Mhm.
>> Uh
there are maybe two thresholds that we
could talk about that are interesting.
>> Okay.
>> Number one, when is there going to be
more of the world's cognitive capacity
inside of data centers than outside of
them?
And
that
to me feels like maybe it could happen.
Huge error bars. I could be totally
wrong, but maybe that could happen by
like late 2028.
>> Mhm.
>> And that's an extraordinary shift in the
world.
Um
the other one is when can
a CEO of major company, a president of a
major country, a Nobel Prize winning
scientist, when can they not do their
job without making heavy use of AI? This
doesn't mean that there will be an AI
CEO or an AI president, uh
but
it does mean that the the role of let's
say a human CEO, when I think about my
job,
it's really quite different. You you
still do need a person to
stand behind decisions and kind of
exercise human judgment and
all of the understanding
that we expect out of someone running an
an important organization to do, but
the actual parts of
my role that I will
increasingly have to rely on an AI to do
because no human can. You know, no human
can talk to no human CEO can talk to
every employee at a company, every
customer, be in every meeting, be an
expert in every field. And so,
more and more I think of these jobs will
be
supervising a bunch of AI, um providing
oversight, deciding how to like trust
the the outputs, how to provide
guidance. And that threshold of when you
really wouldn't want
to be doing your job,
running a large organization without
heavily heavy reliance on AI,
I think that's another sort of
interesting threshold. That may take a
little bit longer, but probably not a
lot longer.
>> And and as you do your job, how much are
you finding yourself relying on
some of the agents and some of the
artificial intelligence that we're
developing at OpenAI?
>> Uh
it's ramping incredibly
quickly. Um if I have like a new idea
for a business model, a strategy shift,
a uh
a product offering we should do, the
very first thing I do before I even
bounce it off somebody else is to ask
our tools. And as they get more
context, and I think this really is like
the next big thing to happen is they can
get close to full context of our
company, access to all of our internal
docs, communication, code,
customer data, everything,
the quality of the
answers, thought, whatever you want to
call it, gets better and better.
>> Right. Okay.
So, let's let's shift um a little bit. 2
weeks ago,
um you announced a $110 billion funding
round.
I asked ChatGPT,
how does that compare compare to any
other fundraising that's been done in
the public markets?
>> don't know.
>> Uh four times as large, okay? The
largest public offering ever done was
roughly 25 billion that Aramco did
several years ago.
And the public markets are supposed to
be the broadest and deepest sources of
capital, okay?
Three strategic partners,
Amazon,
Nvidia, and oh and SoftBank.
Tell us a little bit about this.
How is this an inflection point of the
company?
And one of the questions somebody asked
me is, what are we spending all that
money on?
The
There is many hard parts of this
business, but one of the hardest ones is
the infrastructure is so expensive. You
need so much of it, and you have to
commit so far in advance.
I have never seen
any other industry quite like this. I
mean, there have been clearly many
capital intensive industries throughout
history, but
as I look at the heat what's to come in
front of us in the future in future
years, if the ramp stays as steep as it
looks like it is right now, the demand
is growing as fast as it as it's
growing, you have to do some pretty
unusual things.
Uh
OpenAI does a lot of things that look
weird.
We spend a ton of money on
infrastructure in advance of revenue. We
do new business models like ads that
seem like, you know, maybe not the most
profitable thing we could do. Um
a long list of other things, but we have
this fundamental belief in abundance of
intelligence. And that one of the most
important things in the future is that
we make intelligence,
you know, to borrow an old phrase from
the energy industry that didn't quite
work, too cheap to meter. We want to
flood the world with intelligence. We
want people to just use it for
everything. We want this to just be
something that the future generation
doesn't think about. They expect
everywhere, and everybody has access to
like
geniuses, as many as they need in any
area that they need.
And
this principle, which is one of our kind
of like top guiding principles, does
lead to a lot of behavior that would
look
less natural for other companies. And
one of those is
we really want to get out of this world
that we have been in that we still think
we're on a trajectory to stay on without
changing what we do of always being
capacity constrained.
>> Right.
And capacity constrained you mean
compute? Yeah.
And I've often heard you say a lot that
compute is revenues.
You want to talk a little bit about how
you think how you think about that?
>> Fundamentally, our business and I think
the business of every other model
provider is going to look like selling
tokens.
You know, they may come from bigger or
smaller models which makes them more or
less expensive. They may use more or
less reasoning which also makes them
more or less expensive. They may be
running all the time in the background
trying to help you out. Uh they may run
only when you need them if you want to
pay less. They may
work super hard. You know, spend tens of
millions, hundreds of millions,
someday billions of dollars on a single
problem.
>> Right.
>> That's really valuable. But
we
see a future where
intelligence is a utility like
electricity or water and people buy it
from us
um
on a meter and use it for whatever they
want to use it for.
The demand that we see for that seems
like it's going to continue to just go
like this.
And
if we don't have enough, we either can't
sell it or the price gets really high
and it you know, it kind of goes to rich
people or society makes a bunch of sort
of central planning decisions that I
think almost always go badly about, you
know, we're going to use our limited
compute supply for this and not that.
So, the best thing to me throughout all
the history of capitalism, innovation,
whatever you want is to just flood the
market.
>> Yeah.
And obviously a critical part of
addressing that
compute demand is Stargate.
And this is an infrastructure conference
and you know, you announced that several
months ago, or almost a year ago, I
guess it was, yeah.
How's it going in the US? And then how
is it going, because I
also started in in Abu Dhabi.
>> Yeah. Yeah. Um
Honestly,
there's many cool parts of the job. One
of the coolest is getting to go visit
these mega data centers under
construction and operation.
>> Yeah.
>> Just the scale is, you know, of like
these gigawatt campuses. It's it's it's
really hard to explain and you see these
photos and it's like, okay, it does look
big. And then you go there and you're
walking through it and building to
building and it's just, you know, 10,000
people there, all these different
skilled trades doing all these different
things and
you know, it looks like a spaceship
inside. It's really quite incredible. Um
we are training right now on the first
site in Abilene,
what I think will be the best model in
the world, hopefully by a lot. And it is
it's so amazing to have gone from, you
know, many visits while it was under
construction, and just really
internalizing the scale and incredible
complexity to the day that one
researcher at OpenAI types in like one
command and it pushes enter,
unbelievable number of GPUs spin up and
start doing this one huge computation
all together.
>> Yeah. It's very
And and
what have been the
most pleasant surprises and what have
been the hardest things about getting it
up and running and scaling it going
forward?
>> Uh I mean, there have been all the
expected
challenges and then the unknown
unknowns. Like there was a crazy weather
event in Abilene
that we hadn't, you know, was outside
what we had planned for and that brought
things down for a little while.
Um there's all the supply chain
challenges. Anything at this scale is
just like so much stuff goes wrong. Lots
goes right, but the just try trying to
build something around the clock
with such complexity, there's all the
stuff that goes wrong.
One of the
biggest surprises on the upside is how
many different organizations had to come
together to do this in an incredibly
short period of time. Uh, and how much
we all like ended up working together as
one team uh, under a lot of pressure.
>> And of course, the power demand part of
it is the one that a lot of people have
have focused on.
Um,
are you optimistic that we will solve
that challenge
first in the US and then we can talk
about
in in other places?
>> Uh,
in the long term, I am.
>> Okay.
>> Uh,
I have no doubt we will figure out how
to build huge amounts of power
generation. AI will help, of course.
Uh, but I think the portfolio the world
has in front of it
gas, solar,
nuclear fission, nuclear fusion, more
like we're
I feel good about what what we will be
capable of and what we will eventually
do.
>> Mhm.
>> Um,
given the demand growth we are seeing,
I am sort of hoping for a miracle in
terms of figuring out how we can get way
more efficient per watt
>> Uh, okay.
>> models to give us time to build out all
of this infrastructure. Now,
the track record there has been
incredible. Um,
people cite whatever amazing statistic
they like about how much more efficient
our models have gotten over our
industries models have gotten over time,
but one that I
think is incredible,
our first reasoning model was called 01,
came out
like
16 months ago.
Uh,
and our latest model where we're now
integrated reasoning is 5.4.
To get the same answer
to a hard problem from that first model
to 5.4 has been a reduction in cost of
about 1,000 x.
Maybe I was a little bit wrong on the
timeline. Maybe it was a little bit
longer. But in any case, since 01 till
now.
>> Yeah.
>> 1,000 x. Uh,
that is unbelievable in a relatively
short period of time.
And
the thing that that the two things that
I think that points to
one,
we are still so early and in this
paradigm and we're still so we have so
much more to gain about our
understanding of how to develop these
models and train them and run them
efficiently
>> Mhm.
>> that we
have
were then and still are doing things in
dumb ways and will get better and
better.
And two, is that
human ingenuity and the ability to
operate
in constraints and to find ways to solve
problems
almost always surprise you on the
upside. So, it's not just that the
models have gotten better, it's that we
have figured out, you know, kernel
engineers came to help figure out how to
write more efficient kernels and like
power engineers, the people that design
data centers found more efficient ways
to do that. So, people are answering the
call well beyond just the model side
>> Right.
>> to make this more efficient.
>> Right.
And then of course, um,
obviously OpenAI is a big customer of
the Nvidias and AMDs of the world, but
um, you know, I think we signed a big
contract with Amazon.
But we're building our own chip.
>> Yeah.
>> What's what's the thinking behind that?
>> Uh, so the chip we're doing is inference
only.
>> Okay.
>> And the thinking behind it is that on
this, you know, come
rise up to solve problems in front of
us, um,
we think that a specialized chip to be
not necessarily the fastest inference
chip, but the cheapest inference chip,
the most efficient per watt given the
constraints we see in front of us, is
going to be important for all the agent
demand we see in the future. So, it's a
it's an opinionated bet, it's a limited
chip, but the thing that it does,
in a world where we're energy
constrained, I think it'll be very
important.
>> Maybe you can explain because I am not
sure everybody in the audience is
AI proficient with the difference
between an inference chip and a training
chip.
>> Sorry, I should have done that first.
So,
there there are two main phases to uh
AI workloads. Now, they're going to
blend together eventually and it'll all
be one continuous thing. But right now,
first we train a model,
you know, a gigantic number of GPUs
crunch for weeks or months on a bunch of
data and you can think of that like
maybe it takes you 22 years in life to
get your education and you learn a bunch
of stuff starting when you're a baby and
you drop things and see they fall and
then eventually like in college physics
understanding like
at a very detailed level what's going on
there.
Um
And then after that, if you ask a model
to solve a physics question, it that's
called inference uh and that's quite
efficient. But this is true for people
as well and when people
talk about all these AI models are so
efficient, they're usually comparing the
22 years that a human takes to train to
the 1 second that an adult takes to
solve a physics problem. If you compare
the
you know, the model solving the physics
problem to the human solving physics
problem, actually the models already
probably are more energy efficient. Um
but training is this hugely massive
amount of processing power that then
produces
really just like a file of numbers that
you can then pose a question to and get
a response.
>> And are you optimistic about the
progress we're making on the chip?
>> Yeah, um we should have the first chips
deployed at scale
uh by the end of this year. We should
have the first chips back just in a few
months now.
Um and looks like it'll be really good.
>> Fantastic.
So, you announced a new partnership this
morning, actually, with the North
American Building Trades Unions to
expand training pathways for skilled
construction, which has been a subject
of my many of the discussions today. Can
you tell us a little bit about what you
and uh Sean McGarvey
>> Yes.
>> agreed and What's he And he's going to
be on stage later on. So, let's hear
your version of it and then see if his
version is the same.
>> So,
we talked earlier and we've
talked The world has talked many times
in the past about the need for AI
infrastructure, the need for the world
to have the physical infrastructure,
power plants, transmission lines, data
center halls, chillers,
obviously the racks and the GPUs and
everything that goes inside of those. Um
and
I sort of wish everyone would go visit
at some point one of these
mega scale data centers because it it's
hard,
you know, when you ask ChatGPT a
question,
you get your answer back and it's really
hard to visualize the scale of what it
took to make that or what it takes to
make that happen.
Uh
people talk about all of these different
places that were limited.
>> Mhm.
>> We are limited in the, you know, number
of turbines or now it's the voltage
transformers or it's the memory fabs or
it's building the data centers,
whatever. All of these things have one
thing in common, which is they are
massively complex physical
infrastructure that require
skilled tradespeople
>> Mhm.
>> uh and a lot of them to do. And
no matter where the choke point in the
supply chain is at any one time,
when I talk to people about what it
would take to accelerate it, um
it is more skilled trades workers to
build out all of this infrastructure
that we all depend on. I think this will
be incredible jobs.
Uh more than that, I will I think they
will lay the foundation for the next
generation of American infrastructure
and economic prosperity. And we are
thrilled to get to work
uh together to to drive that faster.
>> Uh just so you know, we at BlackRock
share that view and we think it's really
exciting that that you announced this
partnership together. I think that's
really really terrific.
Uh I think another question that's on
people's minds is
competition with China.
Where do you think that stands?
Uh what what do we need to do to make
sure I assume we're ahead, but I don't
know. So, you might tell us whether we
are or not.
What do we need to do to make sure we
stay ahead?
>> So,
a general framework thought first and
then I'll answer the question more
specifically.
I think the discovery of deep learning
is
is closer to
discovering an element or a fundamental
property of physics than it is uh of
like a secret technology.
Um
and that means that eventually, and
eventually probably not being very long,
the fundamental ideas that make a model
so capable
will be simplified.
They'll be very well known.
And just like we kind of understand how
big parts of physics work, we will
understand as a scientific principle how
big parts of
artificial intelligence work. Um
this is this really we started to
appreciate this with the scaling laws uh
that OpenAI published maybe 7 years ago
now.
Um
there was such a measurable
beautiful correlation between the
resources that go into a model and the
intelligence of that model
that it kind of felt
like hair-raising but clear at the time
that there was just something
fundamental going on here as a
scientific principle.
>> Mhm.
>> Now, there have been a lot of details
we've discovered since. There will be
more to come, but like other scientific
frontiers, it is simplifying and
becoming more clear over time. And
eventually, this recipe will be well
understood as a scientific principle. It
will not be a trade secret in the sense
that other things have been.
Now,
the analogy I like best historically
uh from technological history is the
transistor.
>> Mhm.
>> Transistor was also a sort of
fundamental scientific breakthrough,
very hard to
uh discover, kind of chancy to discover,
uh took us a little while to refine our
discovery, but once we understood it,
the scientific principle was clear to
everyone. There were still massive
amounts of
operational knowledge that went around
that.
>> Uh TSMC
>> can still do things that no one else in
the world can do, and you know, I I
expect the industrial process around
this to have a lot of advantages,
uh competitive advantages. I also expect
that the integration in workflows and
training data and other and usability of
models,
there will be a lot of differentiation
there.
Maybe most of all, I expect there will
be differentiation on who has the
infrastructure and how much of it. But
the fundamental scientific principles
I are going to be
well known, and they will fit on a
t-shirt, I think. Um
In terms of where we are,
uh the most capable models in the world,
the frontier the US is leading on.
>> Mhm.
>> Um the cheapest
inference usage for a two-generation
earlier model, China is leading on.
>> Mhm.
>> Um infrastructure in the US is currently
leading on, but China's moving much
faster on.
And
that kind of industrialization,
productization, whatever you want to
call it.
I The US is leading in closed source.
China's leading in open source. Uh I
think the US is probably leading
overall.
>> And you were recently in India.
And um
sounded like you were very excited about
how India is thinking about the
challenges and the opportunities.
>> I was blown away uh talking to Indian
startups and how they are using this
technology. Um
I like got there and someone like handed
me a briefing sheet for India and it was
like
you know
Codex usage in India has like 10x'd in
some small number of months. And I was
like that's got to be a bug. Like that
>> It can't be right.
>> It can't be right. Um
but it was true. And the
And then I started talking to these
startups.
And
it's an even stronger version of what's
happening in the US
of people saying, "Hey, the world is
different. Um
you know, you talk about a one-person
startup. I'm trying to build a
zero-person startup. I'm trying to just
write a prompt that's going to make my
whole startup and you know, write my
software, do my customer support, do my
legal stuff, whatever. And then I want
to go on vacation."
Um
And the companies
big companies in India they were just
saying like, "How much capacity can we
buy from you? How long can we reserve it
for? Can we negotiate this right now?
We're not going to like let you leave
the room until you agree on this with
us." That just the level of aggression
and speed and sort of a belief that AI
was going to
reshape the business landscape in India
was was really quite impressive. Um
Yeah.
>> Is is that different from when we when
we talk to customers in the US?
>> Uh it's it's the same
vector, but they seem a little further
along. They're moving faster.
>> Yeah. The other The other comment you I
think I saw you made was that there's a
difference between autocratic and
democratic AI.
What did you mean by that and what what
do you think is at stake?
>> Once in a while, I think a technological
shift comes
along that
reshapes society to such a degree that
it does not decisions about it do not
belong to the handful of companies that
happen to be it. I am a huge believer in
capitalism. I am a huge believer in the
rights of companies.
Um I am a
huge believer in governments shouldn't
interfere too much.
But
e-
I think this is one of those exceptional
times where society has a legitimate
interest in
what the impact of this technology
is going to be. I think the internet was
one of these times, too, and I don't
think we got all of that right. But
I'd like us to learn
what we can do better.
And
if what
the AI companies say, and I also believe
it comes true, and that this is going to
reshape the economy. This is going to
reshape
geopolitical power. This is going to
change how we all live our lives.
Um then I don't think it should be up to
companies or
a government to impose
a particular will on how this is going
to get used. Um I think that this
belongs to the will of the people and
working through the democratic process.
And
companies like ours, I think are
quickly, more quickly than companies in
previous generations have had to do,
moving into a sort of critical
infrastructure role.
>> Right.
>> Where we have to say um
we create this technology.
We are experts in it and we should have
a real voice and we have a we have
opinions and
understanding of where its limitations
are and where it's not ready to be used
and where great harm could come.
Uh
but the rules, the limitations have to
be agreed upon by society through this
process and because the technology is
moving so fast, it'd be great for that
democratic process to run a little bit
faster. But companies,
governments, uh need to be able to
depend on companies like ours to
integrate and be able to use the the
technology.
>> Okay.
So coming back to the global AI race,
where where do you think the US is the
most vulnerable?
>> Three three things come to mind. Uh
one,
there's been a ton of noise made about
the global supply chain dependence and
US infrastructure.
I don't have anything new or deep to say
there,
but
I can't overstate how scary this is to
me.
>> Yeah.
>> Um the if we fall behind on
infrastructure and can't catch back up,
um if if
if globalization falls apart in any of
the many ways it could,
>> Mhm.
>> uh and we are not able to
fairly independently [snorts]
keep building AI infrastructure,
that
that seems like a big vulnerability and
I don't
I don't hate, but I don't love our
global position right now.
>> Mhm.
>> Um
the second is if we don't
it's a competitive world and if we don't
move as quickly as other countries on
economic adoption of this
>> Mhm.
then I think we will lose the advantage
that we have from being the economic
powerhouse that we are.
And this is about how quickly companies
adopt it. This is about how quickly our
scientists adopt it. This is how quickly
our government adopts it. This is like
on the positive side, I think this is a
once in many generation opportunity to
really improve the economy,
really rewrite some of the rules of
society that aren't working in light of
this new incredible wealth fountain we
have. So I can see the world where
this is not a disadvantage at all, but
this becomes our biggest competitive
advantage.
I don't
I don't think it's like super
obvious that we're on the trajectory we
want to be there now. Again, I don't
hate it. I just I think it could move
faster and you can see
a bunch of potential headwinds, you
know, like AI is not very popular in the
US right now. Data centers are getting
blamed for electricity price hikes.
Almost every company that does layoffs
is getting
is blaming AI whether or not it really
is about AI.
There is this real debate about the
relative power between governments and
companies going on. So So there's like a
lot of
There's a lot of stuff happening there.
And then the third category is
diffusion into the rest of the world. Is
the world mostly going to build on the
American AI tech stack? Chips, models,
applications, whatever. Or are we going
to enact a set of policies that make
that harder?
>> So it sounds to me like you think that
AI could be
the foundation of an immense
productivity boom.
If properly used and if if adopted.
>> Yes, for sure. Although I think the way
we measure that is going to have to
change.
Explain.
I can see a world where we have an
incredible productivity boom.
Quality of life goes up and up.
Most of the things that we think we we
say we would care about get better and
better.
And yet GDP in the way we currently
measure it goes down and down.
Like deflation for a very long period of
time.
And
I don't know what it means to like live
in a forever deflationary world.
I don't know what it means to think
about GDP in a
and GDP's correlation of quality to
quality of life in a world where you
know more of the economic capability or
the sorry intellectual capability is
inside of data centers than outside of
them. But you know maybe we're going to
find out and
and I think there's going to be a lot of
debate
in the coming years about what the right
thing to measure is.
>> Do you think we're thinking about these
challenges and issues the right way? Are
we having that Are we starting to talk
about that?
>> I I think we yes, we are starting to.
If there was an easy consensus answer
>> [laughter]
>> have done it by now. So I don't think
anyone knows
what to do.
The all of these things that we've
depended on for so long as a society are
sort of
coming into question at the same time.
There was this quote that I saw on the
internet a few weeks ago. It really
stuck in my head and it was something
like
for centuries maybe millennia we have
learned a lot about how to structure
society to manage scarcity.
Almost none of that helps us as we have
to quickly learn towards managing
abundance.
So that's like a real change to how
capitalism has worked. Um
capitalism has also depended on this
balance between
somewhat of a power balance between
labor and capital. But, if it's hard in
many of our current jobs to outwork a
GPU, then that changes. And I see we're
out of time, but I had like a list of 10
things like that changing all at once. I
I
I'm not a long-term jobs doomer. I I
think we will figure out new things to
do. I'm also certainly not a long-term
capitalism doomer. I believe in it very,
very deeply. Um
but I think the next few years
are going to be a painful adjustment as
we get to this future we all get to
redefine of
what the new system and just this
incredible prosperity looks like. And
there are going to be some very intense
and uncomfortable debates on the way
there.
>> Okay, so thank you, Sam, for that very
thoughtful discussion. Here's what I
will say.
Five years from now,
we'll come back here
and we'll see where we are
and how we've navigated our way through
this.
Deal?
>> Deal. I look forward to that.
>> Don't drink. Thank you.
>> Thank you. Thank you.
>> [applause]
>> We'll now take [music] a short break.
Please join us in the East Green Room
for refreshments and return to your
seats in 15 minutes. Thank you.
>> [music]
>> Mhm.
>> Mhm.