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Let's start with science. >> Why
don't I start by saying how grateful

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I am to Princeton? Um

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Um for those of you that are
graduating, you don't know this,

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but this is the pivot point for every

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aspect of success in your life.

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And that was true of me.

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Princeton begat Berkeley, which then

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created Wendy and the family and the

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money and the opportunities and the so

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forth. Had I not come to Princeton, this

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would not have happened. It's binary.

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That's how important Princeton is to me.

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So, thank you.

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>> Well, I want to start
off with the science of AI.

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Um a lot of us that are

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ignorant or semi-ignorant in AI, unlike

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you, are wondering what happened to to

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account for this burst. I can I'm old

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enough to remember people in the '60s

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talking about AI is on the verge, in the

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'70s on the verge, in the '80s. Now it's

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here. It's really here. What happened?

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>> Um to give you a sense
of what's happening,

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uh this week the first
this may be one of the most

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historic moments in human history

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occurred. Um a proof of an 80-year-old

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uh theorem was generated by OpenAI,

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and the paper was written by the computer.

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The The proof is incomprehensible
to anyone here except the senior math

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people. Um so, I'm just
going to accept that the

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math people think it's correct.

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Um and they all think
it's really important.

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The most interesting thing about this

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proof is it was done with a model
that was not trained on math.

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So, we had a long meeting yesterday.

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What is What is the thing that it's doing?

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Right? So, one way to understand the
current LLM models is that they're very

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good at synthesizing a structure
of knowledge into a small vector

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representation space.
And these systems can look across that

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representation space, and they can
collect all these facts in ways that

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humans can't. Many people
believe that this is the

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beginning of artificial superintelligence.

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These are things that humans cannot do

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that computers can do that we value.

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So, then the question is how do
we get here? Well, when I was here,

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there was a nascent AI program.
Of course, there was no computer science.

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And it was all around what is called
symbolic reasoning. And the idea there

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was that you could build a language that
would describe knowledge. Right? This is

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a sort of a Spinoza model of of knowledge.
And many, many people worked

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on that, and it turned out that it failed.

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And after that, and it was a hard problem.
So, there was something called

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the AI winter in the '80s and '90s. In

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the 1980s, at the Institute for Advanced
Studies, a bunch of mathematicians

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proved a theorem that I also
cannot describe, which describes

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essentially deep learning as we know
it today, which is a series of layers of

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networks that talk to each other.

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And then a bunch of scientists at various
universities, including here, in

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the early 2000s, built the model,
the mathematical model.

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So, it started with math. Then, right?

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>> [snorts] >> It wasn't
until about 2011 when a bunch

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of people at Google decided this would
be interesting to apply to YouTube. And

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they watched YouTube, and they
discovered something. They discovered

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the concept of a cat.
And I remember it sitting at Google at

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2011 saying, like, "You're watching
YouTube, and you discover the concept of

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a cat? Isn't there something more
interesting on YouTube?" And the answer

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turned out that during that period,
there was an awful lot of cat pictures

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and movies and so on. People were
uploading pictures of their cats, and

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that's how it discovered it.
From that moment, there were a series of

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innovations, and the most important
ones were in 2015, something called

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reinforcement learning

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was used by DeepMind to win a game

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called AlphaGo, which was thought to be

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incomputable. That was a big deal, and I

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know cuz I was there. Um 2017, Google

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published a paper called Attention
Is All You Need, which showed

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algorithmically how you could take the

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math and do it in parallel on computers.

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That single paper spawned what you
know of today, and then that created the

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competition, which then moved very,
very quickly. Cuz now you've got money and

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people, and you've got a computer
architecture and a awful lot of

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computing, and that's how it happened.

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>> It's an amazing story. I didn't hear
you mention the huge increase in computing

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power. Was that not
the big deal? Or what were

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the big deals here?
>> The computing power is essential, but

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that's true of my whole industry.
So here's an example. When I was here, um

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Tom Wyant is here. I can see a number of
other people that I did this with. So we

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all remember what we were when we were
much younger. Um we spent an awful lot

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of time on something called an IBM 360-91,
which is at 87 Prospect, and the

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old-timers here know what I'm talking
about. And this thing was a work of art.

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It had a raised floor. It had a

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window cuz they didn't want you near the

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computer. Each of these boxes were about

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1 megabit.

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1 megabit. And it was all water-cooled,

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and they used something called iron core

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cuz semiconductor memory had not been

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invented yet. I personally, or we, owned

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the console for that 360-91 cuz the rest

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of it was too big to put in the office.

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So that's how important that computer

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was. Uh and we used fed it with punch

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cards. The punchline is the phone you
have in your pocket today is at least a

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hundred million times more powerful
than the whole computer that was on the

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campus in 1975.

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Um, and furthermore, the 360-91, there
were only three, and they had special

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floating point because they were
used for the NASA moon landing.

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So, that's an example of how
the industry changed in this period. Now,

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how do I know a hundred million times?

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That's the GFT the GFTL number

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um from their calculations
of computation in the campus.

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>> So, is there something analogous
with this is an analogy, it

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wouldn't be the speed of light, but an
analogous to the speed of light that's a

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limit on how fast this can go? Most
of us are shaking our heads in wonderment

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about how how much
this is changing so fast.

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>> Well, first, it's happening much too
fast for everybody, including me. Um,

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I think the following statements are true.

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Um, computers can now compete with

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top-level graduate students in basic
scientists ba- basic sciences, and

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almost certainly in writing and

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analysis, and perhaps in art.

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Um, how much smarter can they go? Um,

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most of us believe that they will they,

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whatever you want to call them, will in

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our lifetimes be materially smarter in

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some ways over the humans.

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They are not going to have the same kind
of intelligence as humans, and lots of

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people have been studying
what this thing looks like,

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and it's not human. Don't be confused.

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Furthermore, they they make a kind
of mistake that's very interesting.

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They're trained on knowledge that we
give them, but there's an awful lot of

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knowledge that's not written down
anywhere. So, I'll give you an example.

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I'm in a business meeting, and somebody
comes up with an idea of doing a rev

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share, and another person
says, "What we should

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do is do a joint venture." And I said,
confidently in my CEO voice, "Joint

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ventures don't work,
and rev shares don't work.

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Now, I've never said that publicly.

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So, how would the computer that's
impersonating me know that? He would

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have to go through lots of failures,
lots of disasters, lots of, you know,

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problems. Furthermore,
these kinds of rules are

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not written down in business books.

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So, that's I'm using that as a trivial
example. There are thousands and

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thousands and thousands of pieces
of knowledge that you all have in your

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heads that these things don't know and

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will not or are unlikely to know.

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Um I'm funding some research in the

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following. What's the limit?
Is the limit is this kind of analytical

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superintelligence without wisdom,
if you will? And by the way, it's really

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important because Princeton and, you know,
your successors in the faculty and

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so forth in economics
going to use this every day.

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>> Wisdom is tough. >> Wisdom is tough.
Wisdom is really tough.

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It's hard to get.

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Um and so, what happens is there's

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research on how do we imbue wisdom and

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non- non-human knowledge into
these things. The best ideas are

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unfortunately, you have to teach them
and humans have to teach them. So, you

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have to figure out a way to do
the the technical term is get the training

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data for wisdom.
No one's solved that problem yet.

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>> But go ahead. Please go ahead.

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>> But I I want to make sure I'm I'm clear
before everyone talks about the negatives.

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Um in the next 15 years, there are
people in my industry who believe and

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are funding that we believe
we can solve essentially

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all human diseases. Now,
that would be like a big deal, right?

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And that would be like a big deal,
right? I mean, let's just admit. That's

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like we should be happy with that.
Um it's highly likely that you'll see new

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energy solutions that will help address
uh the terrible things involving climate

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change. All right. So,
you start going through

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problems, new materials, uh new

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chemistry, um so forth and so on. All
of those things are within our grasp. And

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part of the reason that I think it's
so important to celebrate this now is

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a thousand years from now people will

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record this period

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as an epochal change. This is what

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Henry's Henry's Henry book Henry's
Henry Kissinger book with me helping him

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basically said. This is like the

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enlightenment

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at that level because of the scale

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of what it can touch. Every aspect of

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human progress has been basically
because of science in one form,

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invention in one form, technology
in one form starting with the agrarian

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revolution. This is the next one and

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it's moving very fast.
>> And will we to take on something that's

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controversial these days,
will we need 10,000 times more

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electricity and data centers and things
like that? I noted the other day and I

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went like this, Elon Musk is talking
about putting data centers on the moon.

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Uh, which seems have we
really run out of space on

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Earth? >> We have lots of space on Earth.

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Um, thank goodness.

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Without answering the Elon questions, I

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prefer not to answer rebut rebut Elon.

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Uh,

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so so so let's let's talk
a little bit about data centers.

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Um, my so to some degree I'm in mourning.

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Uh, when I started Princeton, um,

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and we'll talk about this again,
I showed up uh as a young freshman and I

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met this professor named Brian Kernighan
and a whole bunch of other people and

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they basically said you're a computer
scientist, there's no computer science,

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we'll just work on we'll
we'll just fake it, right?

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That was the beginning of modern
computer science. As I learned computer

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science, it is over.

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When I watch these things write code, I

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go, holy

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I mean, I was good, especially as a

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21-year-old and my my ego
was consistent with my attitude.

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I was really good. These things
are so much better than I ever was.

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That's a big deal.
Because it empowers people to do the

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kind of scaling that I've
done in the last 50 years.

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Now, what happens with data centers
goes something like this. First,

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the White House and the government
should be very happy because the data

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center build-out has added 1% of the GDP

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growth to America. That's a big number.

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And it's also changed my industry from

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being high gross margin, low capital
cost to being very high capital cost.

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So, now what you're seeing is this

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enormous funding of data centers and the
numbers are incredible. Give you some I

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happen to have a data center business,
so I know the numbers now. 1 gigawatt of

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power is about 50 billion dollars.

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The The industry and I've testified
in Congress to this, the industry estimate

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is it needs somewhere between 90 and 100

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gigawatts of power by 2030. For

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reference, the average nuclear reactor,
which we don't make in America largely,

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is 1 gigawatt. All right,
that gives you a sense of the

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scale of the problem.
There are plenty of people who are

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working on reducing the power costs.

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There's something called Jevons
paradox where when you lower the cost of

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something, you just use more.

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So, in my community, even though all of

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those all of that work of efficiency and
lower cost is welcome, we still believe,

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whether we're right or not,
we still believe this is going to happen.

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Why is this important?
What's the limit? Well, data centers

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should not be near cities
because they annoy people and that

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that's not fair to them.
They shouldn't use a lot of water. They

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don't now as much. They need to be in

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remote spaces. What's true of America?

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Lots of empty space. Do you know why?

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You fly over it. Just look down.

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You will see it.
There's large large plots. It's a great

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There's an old De Tocqueville quote
about America that America is successful

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because of the the abundance
of land, the richness of

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the spirit of its people,
and the absence of a king.

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This was 1750.

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Right? So, think about it.

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So, so that land bank, if you will,

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plus the American financial system

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allows us to do crazy things. I mean,
I was talking to this guy who says

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he's got 15 people. He's raising at a 4
billion valuation, and he's raising $400

246
00:14:03,760 --> 00:14:09,240
million today. He's only
The company's only 6 weeks old.

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He's already oversubscribed.
Thank you. I want to thank everyone here

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who works in Wall Street, who's invented
this system. There's no place in the

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world that can give crazy
people this amount of money.

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Right? And and furthermore,
some of the smarter

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of you are beginning to write articles
saying like, "Will we ever make any

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money?" And the answer is,
"Hell if I know."

253
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Right? But, you know, you're
the guys who gave us the money.

254
00:14:38,400 --> 00:14:42,877
So, it's okay. We'll try.

255
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So, in any case,
to answer your real question,

256
00:14:47,120 --> 00:14:51,677
the data center limit is actually
not physical. It's financial.

257
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You can build the data centers with your
own power systems. So, all of the new

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data centers are essentially The term is
called behind the meter. They're off the

259
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grid cuz the grid can't support it.

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And it's not fair to people
to raise their electricity rates. The

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interconnection queues, as they're
called, are now full for 7 years. So,

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all of the data centers that you hear
about will be off the grid. There is an

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issue with the climate
impact of these things cuz

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these are combined cycle turbines.

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Everybody understands
that that's not good.

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And the And the best solution we've come

267
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up with is to build fully renewable data
centers, which is which we're working

268
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on, which is hard but possible.

269
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>> But don't they have to have
an ultimate energy source?

270
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>> Yeah, you want >>
Electricity doesn't just flow in.

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>> Well, that's right. You use either
natural gas with a combined cycle

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turbine, which are which you can purchase,
and these things last for 20 years.

273
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And you can also use solar and batteries.

274
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>> Is solar practicable for something
that's an enormous energy user?

275
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>> If you have enough land,
look out the window.

276
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Um right? Think Texas,
New Mexico, Arizona, all

277
00:15:57,680 --> 00:16:01,597
those nice big empty places.
>> investment strategy is to buy land in

278
00:16:01,680 --> 00:16:06,797
Nebraska right now.
>> Um I recommend buying all basically

279
00:16:06,880 --> 00:16:10,597
in places where you can get these things
permitted. This is the new If you're a

280
00:16:10,680 --> 00:16:13,997
real estate person, this is where all
the growth is going to be. If you look,

281
00:16:14,080 --> 00:16:18,717
all the real estate capital has moved
from normal building to data centers.

282
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That's a mistake, by the way.
We need normal buildings, too.

283
00:16:24,600 --> 00:16:28,037
>> Does any of this call in your mind for

284
00:16:28,120 --> 00:16:30,037
for sensible, and that of course is a

285
00:16:30,120 --> 00:16:35,997
big word in this sentence, regulation
of the industry? Let me give you two

286
00:16:36,080 --> 00:16:39,917
uh analogies.
There was a lot of discussion in the

287
00:16:40,000 --> 00:16:44,637
early days of the internet about
regulating and not regulating it. And we

288
00:16:44,720 --> 00:16:48,837
basically opted for let a thousand
flowers bloom and let it just go where

289
00:16:48,920 --> 00:16:51,077
it goes.

290
00:16:51,160 --> 00:16:53,917
Same And And that's widely looked at, I

291
00:16:54,000 --> 00:16:58,037
believe, as a sensible

292
00:16:58,120 --> 00:17:00,917
resolution to what looked like a

293
00:17:01,000 --> 00:17:05,677
difficult problem.
When it came to social media, we adopted

294
00:17:05,760 --> 00:17:10,357
the same strategy.
And I think, and it's not just

295
00:17:10,440 --> 00:17:14,757
an old codger like me, I've talked
to some of our students about this,

296
00:17:14,840 --> 00:17:18,117
there's at least a reasonable case
there's more bad than good that has come

297
00:17:18,200 --> 00:17:21,317
out of that decision. So,
you have one on one side,

298
00:17:21,400 --> 00:17:24,437
one on the other side.
What do you think about that?

299
00:17:24,520 --> 00:17:28,037
>> So, I've come to the conclusion that

300
00:17:28,120 --> 00:17:31,637
this all of this is going
to get regulated.

301
00:17:31,720 --> 00:17:35,597
And it's going to get regulated in
different ways for different reasons. Um

302
00:17:35,680 --> 00:17:40,677
there's no question that the there's
a misalignment of societal interest and

303
00:17:40,760 --> 00:17:43,957
business interest. And it goes like this,
the best way to maximize revenue

304
00:17:44,040 --> 00:17:46,477
is to maximize outrage. The best the

305
00:17:46,560 --> 00:17:51,157
best way to max because that that
essentially causes people to participate

306
00:17:51,240 --> 00:17:55,277
more. And the those businesses are
in a business to fight for all of your

307
00:17:55,360 --> 00:17:58,277
attention. So,
they're winning while you're

308
00:17:58,360 --> 00:18:02,517
obsessed with somebody
something and unhappy.

309
00:18:02,600 --> 00:18:04,957
That's a simple way of understanding it.

310
00:18:05,040 --> 00:18:06,517
And if you get confused about this

311
00:18:06,600 --> 00:18:10,637
point, watch any movie from the 90s.

312
00:18:10,720 --> 00:18:13,157
Any you like a normal movie like how men

313
00:18:13,240 --> 00:18:15,317
and women work together and and how

314
00:18:15,400 --> 00:18:17,957
politics works and so forth. And you'll

315
00:18:18,040 --> 00:18:22,357
you'll remember, right? You know,
the you're in your 25th your

316
00:18:22,440 --> 00:18:24,257
reunion. You know what I'm talking about.

317
00:18:24,340 --> 00:18:28,157
>> [snorts] >> Um and I
mourn that simply because of

318
00:18:28,240 --> 00:18:33,157
the nature of the conversation, the
quality of the morality that we operate.

319
00:18:33,240 --> 00:18:36,517
Because a system where the incentives
for communications are around outrage

320
00:18:36,600 --> 00:18:40,317
produces outrage on all sides. That's
a simple way of understanding what has

321
00:18:40,400 --> 00:18:43,477
happened. Now, I worked
really hard with a whole

322
00:18:43,560 --> 00:18:45,597
bunch of people including Jonathan Haidt

323
00:18:45,680 --> 00:18:48,597
on how to solve this problem. It's very

324
00:18:48,680 --> 00:18:50,877
hard to solve it without putting

325
00:18:50,960 --> 00:18:53,357
restrictions on speech.

326
00:18:53,440 --> 00:18:55,917
So, there is a fundamental problem of

327
00:18:56,000 --> 00:18:58,397
democracies in these technologies, which

328
00:18:58,480 --> 00:19:02,997
is that they can be used to to violate
or to promote particular non-democratic

329
00:19:03,080 --> 00:19:06,157
norms um for business
interests, right? This

330
00:19:06,240 --> 00:19:08,757
is true of all sides. I'm not trying
to make a political statement. I'm trying

331
00:19:08,840 --> 00:19:12,317
to say it's just true.
And that has to be addressed.

332
00:19:12,400 --> 00:19:15,837
Now, if you look around the world,
countries [clears throat] have very

333
00:19:15,920 --> 00:19:19,957
different attitudes towards freedom of
speech. We used to have We used to be at

334
00:19:20,040 --> 00:19:22,957
one of the extreme.
I'm not sure we're still at the

335
00:19:23,040 --> 00:19:26,717
extreme. That's another story that I'm
not going to ask you about. But we used

336
00:19:26,800 --> 00:19:30,877
to be at one extreme. China,
Russia, and a number of are at a

337
00:19:30,960 --> 00:19:33,197
different extreme.

338
00:19:33,280 --> 00:19:36,717
And if restricting free speech is part

339
00:19:36,800 --> 00:19:40,597
of the recipe,
does that actually give them

340
00:19:40,680 --> 00:19:44,317
paradoxically a leg up
rather than a leg down?

341
00:19:44,400 --> 00:19:48,117
>> Well, we know a lot about how China
operates because China is our natural

342
00:19:48,200 --> 00:19:51,197
competitor. Notice I
didn't use the word enemy

343
00:19:51,280 --> 00:19:56,277
uh in this space. And they solved
this problem by dictatorial regulation,

344
00:19:56,360 --> 00:20:00,197
right? And it's brutal.
Uh but that's what their government

345
00:20:00,280 --> 00:20:03,357
chose, which is obviously not a democracy.
So, they solved that problem.

346
00:20:03,440 --> 00:20:05,677
But we're clearly not going to do that.

347
00:20:05,760 --> 00:20:08,277
>> Is that one of the reasons
we're ahead of them now?

348
00:20:08,360 --> 00:20:11,957
>> No, we're we're ahead of uh
China for a number of reasons. The

349
00:20:12,040 --> 00:20:13,877
most important of which,

350
00:20:13,960 --> 00:20:17,597
the US government uh under Trump 1 and

351
00:20:17,680 --> 00:20:22,717
Biden and Trump 2 have all restricted
sales of hardware chips to China. These

352
00:20:22,800 --> 00:20:25,157
are There's a particular threshold.

353
00:20:25,240 --> 00:20:27,357
>> Mhm. >> Um and the threshold involves a

354
00:20:27,440 --> 00:20:29,317
particular manufacturing technique and a

355
00:20:29,400 --> 00:20:32,237
particular manufacturer called ASML.

356
00:20:32,320 --> 00:20:36,557
And I had thought, cuz I was one of
the authors of this program however many

357
00:20:36,640 --> 00:20:41,677
years ago in the AI Commission,
that it would give us a 5 to 10-year uh

358
00:20:41,760 --> 00:20:45,517
advantage. And that was
my technical calculation.

359
00:20:45,600 --> 00:20:48,477
And of course, I was wrong again. Um it

360
00:20:48,560 --> 00:20:53,237
looks like China's within 6 months of us
now based on the Deep Seek V4 result.

361
00:20:53,320 --> 00:20:57,437
And you can read that. Just look up
the evaluations of Deep Seek V4. Chinese

362
00:20:57,520 --> 00:21:01,997
people are very smart. They work really
hard. Their work ethic is incredible.

363
00:21:02,080 --> 00:21:04,037
And even with reduced resources, they've

364
00:21:04,120 --> 00:21:05,837
been able to get caught up.

365
00:21:05,920 --> 00:21:09,197
The President Xi and their AI program is

366
00:21:09,280 --> 00:21:15,237
slightly different than ours. Um the
their goal is to imbue AI in everything.

367
00:21:15,320 --> 00:21:16,957
And again, that's subject [clears
throat] to their laws, which

368
00:21:17,040 --> 00:21:18,757
include the draconian free speech

369
00:21:18,840 --> 00:21:21,997
issues. So, everything has AI in it now,

370
00:21:22,080 --> 00:21:24,277
whereas that's less so in America.

371
00:21:24,360 --> 00:21:27,637
Furthermore, in China, there's huge
support for AI, and as you know, in the

372
00:21:27,720 --> 00:21:32,037
US, there's huge non-support for AI
at the moment politically. So, but but the

373
00:21:32,120 --> 00:21:34,117
answer about China is one way to

374
00:21:34,200 --> 00:21:37,037
understand the evolution of AI is that

375
00:21:37,120 --> 00:21:41,397
you need a large country full of an awful
lot of the things that the US has.

376
00:21:41,480 --> 00:21:43,957
You need the money. You need
the creativity. You need the hardware. You

377
00:21:44,040 --> 00:21:47,397
need the software. You need the audience
base. You need the monetization. China

378
00:21:47,480 --> 00:21:51,557
has that. Very few other
countries have the scale

379
00:21:51,640 --> 00:21:55,677
that is required. Uh if Europe ever
got its act together and operated as a

380
00:21:55,760 --> 00:21:58,997
single country, they could pull it off,
but they don't do that. India's a

381
00:21:59,080 --> 00:22:04,280
possibility. Most of the other
countries cannot get there.

382
00:22:04,400 --> 00:22:09,517
>> In many economics lectures in this room
and other rooms, we teach our students

383
00:22:09,600 --> 00:22:15,277
that especially when it comes
to innovation, capitalism has a gigantic

384
00:22:15,360 --> 00:22:19,997
uh advantage over state intervention.

385
00:22:20,080 --> 00:22:24,037
Is Are we looking at something
that could be an exception to that? I'm

386
00:22:24,120 --> 00:22:27,157
playing off what you're
saying about China.

387
00:22:27,240 --> 00:22:29,237
>> Well, it depends
on whether you ignoring

388
00:22:29,320 --> 00:22:32,957
the political comment, China when
when I'm in China, it feels very

389
00:22:33,040 --> 00:22:34,717
capitalistic.

390
00:22:34,800 --> 00:22:39,717
Um they they refer to themselves as um

391
00:22:39,800 --> 00:22:44,117
communists with Chinese characteristics,
which I conclude is capitalism.

392
00:22:44,200 --> 00:22:48,077
So, the the people
that I work with are just

393
00:22:48,160 --> 00:22:51,597
like the American
entrepreneurs that I work with.

394
00:22:51,680 --> 00:22:56,357
They're crazy hard working. They're
intense. They're very ambitious, very

395
00:22:56,440 --> 00:22:58,037
self-confident, you know, all that kind

396
00:22:58,120 --> 00:23:00,797
of stuff. Uh and they work in a a system

397
00:23:00,880 --> 00:23:03,397
where they've largely been left alone, I

398
00:23:03,480 --> 00:23:06,117
think that that's likely to end.

399
00:23:06,200 --> 00:23:08,317
Um give you an example, China just

400
00:23:08,400 --> 00:23:10,877
announced a restriction on DD, which is

401
00:23:10,960 --> 00:23:13,797
their Uber equivalent, to not allow them

402
00:23:13,880 --> 00:23:16,637
to do self-driving cars because of the

403
00:23:16,720 --> 00:23:18,957
obvious impact on employment.

404
00:23:19,040 --> 00:23:22,397
So again, one of the problems with an

405
00:23:22,480 --> 00:23:26,837
authoritarian structure is you can
never quite figure out which priority is

406
00:23:26,920 --> 00:23:29,677
right, whereas at least in the market
you can predict it. I'm I'm obviously a

407
00:23:29,760 --> 00:23:32,997
capitalist. So the fact
and I had dinner with a

408
00:23:33,080 --> 00:23:36,637
number of my Chinese friends and they
were in their usual, you know, this and

409
00:23:36,720 --> 00:23:38,317
that and the complaining and so forth. I

410
00:23:38,400 --> 00:23:41,397
said, "Look, read the rhetoric from your

411
00:23:41,480 --> 00:23:44,357
government. They're not capitalist."

412
00:23:44,440 --> 00:23:48,197
You just read the documents.
So I think that my friends, maybe they

413
00:23:48,280 --> 00:23:52,957
were caught in some capitalist bubble,
but they sure made an awful lot of

414
00:23:53,040 --> 00:23:55,277
money. All right.
And now they're trying to get

415
00:23:55,360 --> 00:23:58,157
it out of the country.

416
00:23:58,240 --> 00:24:02,037
>> Which which is not so easy.

417
00:24:02,120 --> 00:24:04,437
>> Yeah, so to be clear, I I much prefer

418
00:24:04,520 --> 00:24:09,157
American free speech, freedom, and
capitalism for obvious reasons. I think

419
00:24:09,240 --> 00:24:11,797
it created the success we see in the room.

420
00:24:11,880 --> 00:24:15,677
Uh on the free speech question, I did
want to say that I tried to I'm in favor

421
00:24:15,760 --> 00:24:18,677
of free speech of humans,
including crazy people,

422
00:24:18,760 --> 00:24:21,917
of which there seem to be
an increasing number every day.

423
00:24:22,000 --> 00:24:24,957
Um I am not in favor of boosted speech

424
00:24:25,040 --> 00:24:28,997
having the same rules.
So a simple way of thinking about this

425
00:24:29,080 --> 00:24:32,757
is um here at Princeton we have all
sorts of people. We do we have tolerance

426
00:24:32,840 --> 00:24:36,077
for different ideas. I'm in favor
of all of those ideas even if I and it's

427
00:24:36,160 --> 00:24:39,877
perfectly fine for someone to sit uh
in the equivalent of McCosh Courtyard and

428
00:24:39,960 --> 00:24:44,357
scream that for a while. Um when they
get a bullhorn and then they start

429
00:24:44,440 --> 00:24:48,557
spreading it through all the dorms from
that single bullhorn, that's a violation

430
00:24:48,640 --> 00:24:50,757
of somebody else's rights. So there is

431
00:24:50,840 --> 00:24:54,517
some limit to amplification

432
00:24:54,600 --> 00:24:58,117
of one's message.
That's the way to phrase it.

433
00:24:58,200 --> 00:24:59,917
I'm not a a legal expert, but there's

434
00:25:00,000 --> 00:25:03,797
got to be a way to say it right.

435
00:25:03,880 --> 00:25:07,557
>> We hope so.
Let me ask you a slightly related

436
00:25:07,640 --> 00:25:11,477
question, maybe less a little less cosmic.

437
00:25:11,560 --> 00:25:13,397
As I read

438
00:25:13,480 --> 00:25:17,557
it with my inexpert eyes and ears about

439
00:25:17,640 --> 00:25:19,597
China versus the US,

440
00:25:19,680 --> 00:25:24,717
it seems like China's going much more
for closed source, and we're going much

441
00:25:24,800 --> 00:25:27,077
more for open source. >> Inverse.

442
00:25:27,160 --> 00:25:30,237
>> in >> The inverse. >> Okay.

443
00:25:30,320 --> 00:25:32,757
I said it the backwards, sorry.

444
00:25:32,840 --> 00:25:38,920
So, the question was to be, is that true?
And with the correction, it's true.

445
00:25:41,560 --> 00:25:45,877
And are And are we on the Are
we on the right side of it?

446
00:25:45,960 --> 00:25:48,597
>> Um I think not. >> You think not?

447
00:25:48,680 --> 00:25:51,917
>> So, so let me give a For those of you
who haven't been following, this is a

448
00:25:52,000 --> 00:25:55,797
really important distinction. Um

449
00:25:55,880 --> 00:25:58,277
the top models, the ones that you use,

450
00:25:58,360 --> 00:26:01,957
are all run by for-profit companies.

451
00:26:02,040 --> 00:26:05,517
And they have lawyers and CEOs,
and they have shareholders or will have

452
00:26:05,600 --> 00:26:08,477
shareholders. And they
take an enormous amount of

453
00:26:08,560 --> 00:26:12,557
capital. We're talking
about uh hundreds of

454
00:26:12,640 --> 00:26:16,197
literally, well, hundreds
of billions of dollars at this rate.

455
00:26:16,280 --> 00:26:20,197
Now, they properly want an economic
return, and so what they do is they keep

456
00:26:20,280 --> 00:26:23,677
their software their weight
Technically, the weights

457
00:26:23,760 --> 00:26:27,237
and the software proprietary.
You can't borrow it. They

458
00:26:27,320 --> 00:26:30,477
don't give it to you.
You can't get access to it.

459
00:26:30,560 --> 00:26:33,357
Um why is this so important? The entire

460
00:26:33,440 --> 00:26:37,957
industry that I was part of, which
started here at Princeton, by the way,

461
00:26:38,040 --> 00:26:42,597
was based on an the other side principle,
the open source principle. We

462
00:26:42,680 --> 00:26:45,357
will write software, and we'll give it
to everyone. And the lawyers all would

463
00:26:45,440 --> 00:26:49,437
go, "You're giving away your
intellectual property?" I said, "Yes."

464
00:26:49,520 --> 00:26:53,957
And the reason is that in the same
principle as if you want to go fast, go

465
00:26:54,040 --> 00:26:58,157
alone. If you want to go far, go with a
crew or I go with people. It's the same

466
00:26:58,240 --> 00:27:02,397
principle. So, what happens with open
source is that you put the stuff out

467
00:27:02,480 --> 00:27:05,677
there and then thousands
of people strengthen it.

468
00:27:05,760 --> 00:27:09,037
That's the magic of open source. That's
what I did and what I've done for most

469
00:27:09,120 --> 00:27:12,837
of my life. It's important to note,
for example, that Google was based on open

470
00:27:12,920 --> 00:27:16,597
source architectures. So,
this is another big change in my

471
00:27:16,680 --> 00:27:18,997
industry. Now, let's think about China.

472
00:27:19,080 --> 00:27:21,677
So, China, which again
is highly regulated,

473
00:27:21,760 --> 00:27:24,957
uh decided to do something
completely different.

474
00:27:25,040 --> 00:27:29,637
All of the top models are
open weight and open source,

475
00:27:29,720 --> 00:27:33,277
which means you can take the source code
and you can take all the numbers, which

476
00:27:33,360 --> 00:27:37,117
is the weights are the thing that the
computer uses to reason, and you can do

477
00:27:37,200 --> 00:27:40,957
whatever you want to with it. So,
I have startups in California with

478
00:27:41,040 --> 00:27:45,437
have a lot of Chinese uh speakers
who prefer the Chinese models over the

479
00:27:45,520 --> 00:27:49,597
American models. So, if you ask them,
they say, "Well, it works really well.

480
00:27:49,680 --> 00:27:52,037
Oh, and by the way, it's free."

481
00:27:52,120 --> 00:27:53,557
Now, you sit there and you go, "Hmm,

482
00:27:53,640 --> 00:27:56,237
okay." Now, at Princeton,

483
00:27:56,320 --> 00:28:01,917
how would a researcher here get into this?
Well, they'd have to work

484
00:28:02,000 --> 00:28:05,277
for, you know, how does
a graduate student work for

485
00:28:05,360 --> 00:28:09,637
them? So, ultimately, about 10 years
ago, Google, through me, created a

486
00:28:09,720 --> 00:28:13,997
Google center here on the other side of
Prospect, right? So, that primarily, you

487
00:28:14,080 --> 00:28:16,237
could actually walk from here to the

488
00:28:16,320 --> 00:28:21,077
Google data centers, logically.
That's not a scalable strategy.

489
00:28:21,160 --> 00:28:24,597
So, there's a problem with open source
leadership in America. Why is this so

490
00:28:24,680 --> 00:28:27,757
important geographically if you don't
care about that? It's because the

491
00:28:27,840 --> 00:28:31,717
majority of the models in the world will
be Chinese. Do you know why? Cuz they're

492
00:28:31,800 --> 00:28:36,037
going to be free. It's not
that complicated. Uh we were at

493
00:28:36,120 --> 00:28:39,757
dinner last night with my friend who's
quite wealthy, and he was joking that he

494
00:28:39,840 --> 00:28:43,757
was using one of the American models,
and he calculated that he was doing it

495
00:28:43,840 --> 00:28:47,397
for fun. Um and he calculated it
was going to cost him a half a million

496
00:28:47,480 --> 00:28:51,117
dollars a year for his entertainment,
which consisted of building a newsreader

497
00:28:51,200 --> 00:28:54,117
that was biased on his political views.

498
00:28:54,200 --> 00:28:55,677
You know, it's perfectly fine.

499
00:28:55,760 --> 00:28:56,677
Uh,

500
00:28:56,760 --> 00:28:58,797
so he switched to a Chinese model to to

501
00:28:58,880 --> 00:29:02,877
read American news cheaply.

502
00:29:02,960 --> 00:29:06,317
So, it's real. >> So,
that's hot tough competition.

503
00:29:06,400 --> 00:29:09,917
>> Yeah. Chinese are going to be very
tough, and I I'm very concerned, and I

504
00:29:10,000 --> 00:29:11,917
want to be clear to everybody here. Most

505
00:29:12,000 --> 00:29:14,397
people here are US citizens.

506
00:29:14,480 --> 00:29:16,997
We are not meeting the

507
00:29:17,080 --> 00:29:19,877
competition and beating them globally.

508
00:29:19,960 --> 00:29:23,917
They are investing in science
and technology and innovation at a rate to

509
00:29:24,000 --> 00:29:27,517
overcome America. What would it be like if

510
00:29:27,600 --> 00:29:31,717
if China were the winner in all
categories? AI, quantum, so forth and so

511
00:29:31,800 --> 00:29:35,277
on. Start thinking about conflict.

512
00:29:35,360 --> 00:29:39,477
America is always seems to be in some
kind of war or not. Uh, we're getting

513
00:29:39,560 --> 00:29:43,517
ready over Taiwan. Right?
Can you imagine, here's the

514
00:29:43,600 --> 00:29:46,877
thought experiment. So, there's
something called a superintelligence,

515
00:29:46,960 --> 00:29:49,677
which is the point at which
it's smarter than all of us.

516
00:29:49,760 --> 00:29:53,357
Uh, many people in my industry think
that this will occur within 10 years. We

517
00:29:53,440 --> 00:29:56,197
can debate this. Nobody really knows.

518
00:29:56,280 --> 00:29:59,077
Let's imagine they get there first.

519
00:29:59,160 --> 00:30:00,797
And as part of that superintelligence,

520
00:30:00,880 --> 00:30:06,317
they figure out some attack vector
against America that we cannot imagine

521
00:30:06,400 --> 00:30:10,557
right now. And and by the way,
the inverse is true for us.

522
00:30:10,640 --> 00:30:14,277
So, this is big deal. If you care about
national security, you need to fund

523
00:30:14,360 --> 00:30:17,597
this. If you don't care about national
security, you care about social welfare,

524
00:30:17,680 --> 00:30:20,957
you need to fund this. If you care about
economic growth, which I think all of us

525
00:30:21,040 --> 00:30:24,037
certainly do, uh,
you better fund it. You better

526
00:30:24,120 --> 00:30:26,837
figure out a way to keep the the engine

527
00:30:26,920 --> 00:30:29,677
that got me to where I am going.

528
00:30:29,760 --> 00:30:31,840
Right?

529
00:30:32,440 --> 00:30:36,357
>> What you said before come out
what you just said now makes me

530
00:30:36,440 --> 00:30:38,957
think of the electricity grid.

531
00:30:39,040 --> 00:30:43,117
I mean, all all of this is
ultimately on on electricity.

532
00:30:43,200 --> 00:30:46,837
And it is is that or might that become a

533
00:30:46,920 --> 00:30:51,557
vulnerability in a case like that? You
don't really need to send airplanes with

534
00:30:51,640 --> 00:30:52,957
bombs.

535
00:30:53,040 --> 00:30:56,437
>> So first place China
last year built 130

536
00:30:56,520 --> 00:31:00,757
gigawatts of renewable energy.

537
00:31:00,840 --> 00:31:04,157
Okay, remember the number I said is that
we needed about 100 gigawatts between

538
00:31:04,240 --> 00:31:08,557
now and 2030 in America. They already
have it. Right? And there's estimates

539
00:31:08,640 --> 00:31:12,797
that they have 20 gigawatts just sitting
around. Right? I I don't know what the

540
00:31:12,880 --> 00:31:15,157
real numbers are. I'll try to find out.

541
00:31:15,240 --> 00:31:19,397
So assuming that these data centers

542
00:31:19,480 --> 00:31:21,037
continue to be these large things, they

543
00:31:21,120 --> 00:31:24,877
become military targets. I wrote a paper

544
00:31:24,960 --> 00:31:28,997
which we called um the argument went
something like this. This is influence

545
00:31:29,080 --> 00:31:32,437
of Kissinger. Um you start
thinking about what happens

546
00:31:32,520 --> 00:31:35,517
when we're getting stronger and China's

547
00:31:35,600 --> 00:31:37,677
getting stronger or whoever.

548
00:31:37,760 --> 00:31:40,797
Um there's a point at which one side

549
00:31:40,880 --> 00:31:43,397
could decide to do a preemptory attack

550
00:31:43,480 --> 00:31:46,397
of the other, which is your question.

551
00:31:46,480 --> 00:31:49,757
Doesn't it make sense to slow down
your competitor by just bombing their data

552
00:31:49,840 --> 00:31:55,197
centers? So we being pleasant
Princetonian type graduates said

553
00:31:55,280 --> 00:31:59,197
there's a better alternative, which
is to just give the enemy a switch.

554
00:31:59,280 --> 00:32:03,477
So they don't have to bomb the data
center, they just have to turn it off.

555
00:32:03,560 --> 00:32:07,637
And everybody goes, that's
the stupidest idea I've ever seen.

556
00:32:07,720 --> 00:32:10,477
If you believe that these data centers

557
00:32:10,560 --> 00:32:12,877
are essential for the national security

558
00:32:12,960 --> 00:32:15,037
of for a nation, you've got to come up

559
00:32:15,120 --> 00:32:18,317
with some kind of a deterrent strategy.

560
00:32:18,400 --> 00:32:22,400
In that sense it is similar to nuclear.

561
00:32:22,560 --> 00:32:27,117
>> Which is cross your fingers and hope
that this there's symmetric forbearance.

562
00:32:27,200 --> 00:32:28,717
>> Well >> Which worked with nuclear.

563
00:32:28,800 --> 00:32:33,077
>> Well nuclear worked because of a theory
of mutual assured destruction. It's and

564
00:32:33,160 --> 00:32:34,637
it's important to know, I wrote a book

565
00:32:34,720 --> 00:32:40,400
about this, that mutual assured
destruction has never been tested.

566
00:32:42,440 --> 00:32:46,277
And I'm really glad. >> Yeah.
>> But let's start with an assertion. It's

567
00:32:46,360 --> 00:32:48,957
never been tested.
We don't know what will happen.

568
00:32:49,040 --> 00:32:53,717
Uh we don't know what will
happen if there is a third nuclear

569
00:32:53,800 --> 00:32:58,477
bombing of any kind having
the US having done the first two.

570
00:32:58,560 --> 00:32:59,917
We just don't know.

571
00:33:00,000 --> 00:33:02,397
Um and we're paying we're playing with

572
00:33:02,480 --> 00:33:05,837
incredibly powerful stuff here.

573
00:33:05,920 --> 00:33:09,677
So what I One things I've learned
is that this is not a kid's game. They

574
00:33:09,760 --> 00:33:13,637
They're these huge organizations
that worry about safety and security and

575
00:33:13,720 --> 00:33:17,677
biosecurity and so forth.
One way to understand the threats of

576
00:33:17,760 --> 00:33:20,757
models there's the threat
to democracy which

577
00:33:20,840 --> 00:33:25,077
everybody here understands.
There's We've known for a long time that

578
00:33:25,160 --> 00:33:28,157
the first things it would
be able to do would be

579
00:33:28,240 --> 00:33:31,477
cyber attacks. And the reason is that if
you can do good software program, you

580
00:33:31,560 --> 00:33:33,717
can also write good cyber attacks.

581
00:33:33,800 --> 00:33:38,037
When Methos came out, which is a couple
months ago, this is the Anthropic cyber

582
00:33:38,120 --> 00:33:40,197
cyber attack model. They had the good

583
00:33:40,280 --> 00:33:45,117
judgment of only releasing it
to about 40 companies as well as the US

584
00:33:45,200 --> 00:33:48,757
government. By the way, this is during
the time when they're having a big fight

585
00:33:48,840 --> 00:33:51,997
with the Pentagon and the White House
and they get banned while they're

586
00:33:52,080 --> 00:33:56,917
essential for the use of the NSA.
So it's like crazy. Um and I'm sure that

587
00:33:57,000 --> 00:34:00,357
will get resolved because they they
ultimately have to work together.

588
00:34:00,440 --> 00:34:02,477
But if you think about
it if you're the head

589
00:34:02,560 --> 00:34:07,717
of the NSA or in that group, you want
these tools for offensive cyber attack

590
00:34:07,800 --> 00:34:12,357
and you also want to be able to use these
tools to prevent defensive cyber attack.

591
00:34:12,440 --> 00:34:15,557
The term in the industry is called
offense dominant versus defense

592
00:34:15,640 --> 00:34:20,197
dominant. Does Are you more likely
to win with offense or defense? My

593
00:34:20,280 --> 00:34:23,997
assessment is these tools are offense
dominant. So we need to be very careful

594
00:34:24,080 --> 00:34:29,797
that we have a strong offense as well as
a strong defense. That's my personal view.

595
00:34:29,880 --> 00:34:34,157
>> Let Let turn to something a little
more less cataclysmic, but that a lot of

596
00:34:34,240 --> 00:34:37,317
people worry about, which is job loss.

597
00:34:37,400 --> 00:34:38,077
>> Yeah.

598
00:34:38,160 --> 00:34:42,477
>> As opposed to job change, or both, or

599
00:34:42,560 --> 00:34:44,837
uh is one bigger than the other? Do you

600
00:34:44,920 --> 00:34:46,637
do you spend any time worrying about

601
00:34:46,720 --> 00:34:50,757
massive unemployment as a result of AI?

602
00:34:50,840 --> 00:34:53,037
>> Uh I do, but not now.

603
00:34:53,120 --> 00:34:54,957
And I'll give you the numbers. Um we

604
00:34:55,040 --> 00:34:58,637
know that the current stuff in the US

605
00:34:58,720 --> 00:35:03,117
is displacing low-level customer
service jobs, and we know it's displacing

606
00:35:03,200 --> 00:35:06,797
software jobs, typically
the lower-level, entry-level jobs.

607
00:35:06,880 --> 00:35:08,237
Uh which is a big deal for those people

608
00:35:08,320 --> 00:35:11,557
who are displaced. We have not yet seen

609
00:35:11,640 --> 00:35:15,277
mass effect of the white-collar workforce.

610
00:35:15,360 --> 00:35:19,757
It's going to happen in the next 5 years.
And here's how it happens.

611
00:35:19,840 --> 00:35:23,957
Um there's a a very good article
yesterday by a friend of mine who runs

612
00:35:24,040 --> 00:35:27,757
Cloudflare, who wrote
a very nice explanation that

613
00:35:27,840 --> 00:35:31,477
he runs a company which is growing
at 30% a year, has huge valuations, and so

614
00:35:31,560 --> 00:35:35,717
forth, and he did 20% layoff.
And he justifies his layoff by looking

615
00:35:35,800 --> 00:35:40,477
at the categories of workers, and he
doesn't need this set of categories.

616
00:35:40,560 --> 00:35:44,117
And those specific categories are not
sales, they're not engineering, they're

617
00:35:44,200 --> 00:35:46,717
a bunch of the infrastructure. So, your

618
00:35:46,800 --> 00:35:48,997
decision you should decide whether you

619
00:35:49,080 --> 00:35:51,717
think those people can be retrained or

620
00:35:51,800 --> 00:35:57,157
get new skills that are more consistent
with what what AI cannot do well.

621
00:35:57,240 --> 00:36:01,997
Um I know one way to say this is that

622
00:36:02,080 --> 00:36:05,437
this is the first technology that's
really threatened white-collar work,

623
00:36:05,520 --> 00:36:08,437
and because white-collar people write
really well, they write about it all

624
00:36:08,520 --> 00:36:11,917
day, right? Right?

625
00:36:12,000 --> 00:36:14,317
They write about their their experience.

626
00:36:14,400 --> 00:36:17,117
2/3 of the jobs in America are

627
00:36:17,200 --> 00:36:19,677
essentially labor human labor-centric.

628
00:36:19,760 --> 00:36:23,357
They require hands or pressure
or presence or so forth. Yeah. And these

629
00:36:23,440 --> 00:36:25,157
are important jobs.

630
00:36:25,240 --> 00:36:29,597
So, as long as robots don't really work,

631
00:36:29,680 --> 00:36:32,357
the impact will be manageable.

632
00:36:32,440 --> 00:36:35,637
The fear I have is that in 10 years

633
00:36:35,720 --> 00:36:37,997
the robotic revolution, which is much

634
00:36:38,080 --> 00:36:41,477
slower than America and software, will

635
00:36:41,560 --> 00:36:45,957
in fact begin to displace those jobs. If
that begins to occur, now this is a long

636
00:36:46,040 --> 00:36:49,117
time from now, we're going to have
a whole hell of a lot of problems, right?

637
00:36:49,200 --> 00:36:53,197
For obvious reasons.
Um and why do I say it's going to take

638
00:36:53,280 --> 00:36:55,957
that long? Um the US robotics industry

639
00:36:56,040 --> 00:37:00,757
is relatively slow and relatively
expensive. Um in China they have a I'll

640
00:37:00,840 --> 00:37:03,557
give you an example.
They have a company called Unitree,

641
00:37:03,640 --> 00:37:08,037
um which builds an a robot
called R1, which I've ordered

642
00:37:08,120 --> 00:37:09,717
for $5,000.

643
00:37:09,800 --> 00:37:12,157
Look at the videos of what it can do.

644
00:37:12,240 --> 00:37:15,117
So, that's what's coming. Now, it's dumb

645
00:37:15,200 --> 00:37:19,477
as you could imagine. The thing needs
a proper brain, right? Whatever whatever

646
00:37:19,560 --> 00:37:22,317
that means. I mean,
it's like the stupidest

647
00:37:22,400 --> 00:37:26,677
robot you could imagine.
Um but that can be fixed over time.

648
00:37:26,760 --> 00:37:28,837
That's I think the real concern.

649
00:37:28,920 --> 00:37:31,827
>> Take you back before the robots.

650
00:37:31,910 --> 00:37:34,877
>> [clears throat] >> The the
good side of of the answer to

651
00:37:34,960 --> 00:37:38,477
this question that's been
around since the Luddites

652
00:37:38,560 --> 00:37:42,837
is that yes, the new technology will
destroy some jobs, but it will also create

653
00:37:42,920 --> 00:37:43,397
>> Yeah.

654
00:37:43,480 --> 00:37:46,477
>> uh a lot of new jobs. So, it's mostly

655
00:37:46,560 --> 00:37:50,797
about job change and disruption and

656
00:37:50,880 --> 00:37:54,597
moving resources from here to there.

657
00:37:54,680 --> 00:37:58,957
The United You can tell me if you agree
with this or not. I would say the United

658
00:37:59,040 --> 00:38:02,997
States among the rich countries
is almost uniquely bad at managing that

659
00:38:03,080 --> 00:38:07,797
transition. Retraining workers,
supporting them while they're

660
00:38:07,880 --> 00:38:09,477
unemployed,

661
00:38:09,560 --> 00:38:12,597
uh etc. etc. Making the transition

662
00:38:12,680 --> 00:38:16,357
easier rather than harder.

663
00:38:16,440 --> 00:38:18,837
So, do you do you see it that way?

664
00:38:18,920 --> 00:38:21,757
That That's the answer to the

665
00:38:21,840 --> 00:38:25,197
on the warrior side of it's only

666
00:38:25,280 --> 00:38:27,597
a job change, it's not job destruction.

667
00:38:27,680 --> 00:38:31,997
>> Um the benefit the counter argument is
that the benefit of the brutal nature of

668
00:38:32,080 --> 00:38:35,957
American employment is that it's
a reconfiguration into a new into a new

669
00:38:36,040 --> 00:38:40,077
role quicker, which is part of why
American productivity is better than

670
00:38:40,160 --> 00:38:43,397
Europe for example and there are debate
technical debates about that. Paul

671
00:38:43,480 --> 00:38:46,277
Krugman for example has been
arguing against that point.

672
00:38:46,360 --> 00:38:50,317
Um What do I really think?

673
00:38:50,400 --> 00:38:54,800
Um >> Yeah, what do you really think?

674
00:38:56,320 --> 00:38:57,997
You're a You're a very good thinker.

675
00:38:58,080 --> 00:39:03,520
>> I think I I'm That was a
That was a strategic pause.

676
00:39:04,680 --> 00:39:09,597
I I'll give an example of how crazy
things are in my little world. Um there

677
00:39:09,680 --> 00:39:13,437
was a paper and again,
you're the economist so

678
00:39:13,520 --> 00:39:17,757
I I believe it's a fair assertion
that there are no economics for economies

679
00:39:17,840 --> 00:39:21,757
that are doubling every year.
There are no examples of doubling GDP.

680
00:39:21,840 --> 00:39:25,997
>> a whole side of the economy, not >>
Yeah, doubling. Okay. So anyway, this

681
00:39:26,080 --> 00:39:30,357
group managed to actually come up
with a set of math which I've no way of

682
00:39:30,440 --> 00:39:34,837
validating that if you replace all labor

683
00:39:34,920 --> 00:39:39,237
by either AI or by robots.
This is the presumption.

684
00:39:39,320 --> 00:39:42,037
It's obviously not a good
thing for other reasons.

685
00:39:42,120 --> 00:39:44,637
You could grow the economy by a fact You

686
00:39:44,720 --> 00:39:47,837
could double the economy every year.

687
00:39:47,920 --> 00:39:51,517
Okay. So I thought highly
unlikely to be true.

688
00:39:51,600 --> 00:39:54,077
But let's start thinking about that.

689
00:39:54,160 --> 00:39:58,277
So if it Okay, so here we are and now
everyone's twice as wealthy and then

690
00:39:58,360 --> 00:40:01,757
twice as wealthy again and twice
as wealthy again. You all know you can do

691
00:40:01,840 --> 00:40:06,077
combinatorial math.
So then would you be happy if everyone

692
00:40:06,160 --> 00:40:10,317
here was as rich as Qatar?
You'd be happier.

693
00:40:10,400 --> 00:40:13,437
All right? Just think of all
the Rolls Royces and the equivalents.

694
00:40:13,520 --> 00:40:15,597
Um what if you were 10 times happier

695
00:40:15,680 --> 00:40:20,717
than Qatar? What if you were 100 times
happier than Qatar? Um and what if it

696
00:40:20,800 --> 00:40:22,117
were true for the whole world or at

697
00:40:22,200 --> 00:40:24,957
least the countries that adopted this?

698
00:40:25,040 --> 00:40:28,157
So, there is an argument, which I'm not

699
00:40:28,240 --> 00:40:32,237
personally making,
that the abundance and the the

700
00:40:32,320 --> 00:40:35,957
elimination of scarcity, right?
So, the the basic argument from the

701
00:40:36,040 --> 00:40:40,077
accelerationists is that we're
eliminating scarcity. Every day, every

702
00:40:40,160 --> 00:40:43,357
aspect of history, every aspect
of economics is fundamentally about

703
00:40:43,440 --> 00:40:47,517
scarcity, scarcity pricing scarcity,
markets over You understand this very

704
00:40:47,600 --> 00:40:52,557
well. Um what happens if we
remove all of those constraints?

705
00:40:52,640 --> 00:40:55,557
No one knows. Now,
let me give you a counter and a

706
00:40:55,640 --> 00:40:57,397
further counter.

707
00:40:57,480 --> 00:41:00,557
The US is growing at 2 to 3%. 3% is

708
00:41:00,640 --> 00:41:03,117
considered high. Chinese growth at in

709
00:41:03,200 --> 00:41:07,637
its heyday was 10%. It's now down to 2
or 3% and they're very upset. Let's

710
00:41:07,720 --> 00:41:09,397
imagine that all of the countries that

711
00:41:09,480 --> 00:41:13,477
we care about could grow at 15% a year.

712
00:41:13,560 --> 00:41:17,237
How would that feel? Because if the slope
is what I just said, and that's a

713
00:41:17,320 --> 00:41:20,637
lot of ifs, relax.

714
00:41:20,720 --> 00:41:21,837
Okay?

715
00:41:21,920 --> 00:41:26,197
If the slope is not that, don't relax.

716
00:41:26,280 --> 00:41:30,917
If the slope is not fundamentally
changed by the business improvements,

717
00:41:31,000 --> 00:41:33,717
economic improvements, the scientific
improvements, the quality of life

718
00:41:33,800 --> 00:41:40,557
improvements that I've described, then we
have screwed up collectively as a society.

719
00:41:40,640 --> 00:41:42,477
>> The slope is

720
00:41:42,560 --> 00:41:45,397
very few people doubt that the slope is

721
00:41:45,480 --> 00:41:47,757
in the direction that you say.

722
00:41:47,840 --> 00:41:52,997
15% a year, I don't think
you'll get many people signing on

723
00:41:53,080 --> 00:41:58,317
to that. But, there's a question
of who owns the resources.

724
00:41:58,400 --> 00:42:02,317
>> Mhm. >> Uh it it's not
going to be equally distributed.

725
00:42:02,400 --> 00:42:05,317
>> That's right. >> In any
conceive We can't even conceive

726
00:42:05,400 --> 00:42:10,677
of a scenario which it would be equally uh

727
00:42:10,760 --> 00:42:14,597
distributed. So, there going
to be people left behind.

728
00:42:14,680 --> 00:42:18,037
Now, a reasonable answer
is the society will

729
00:42:18,120 --> 00:42:20,477
be so rich that it can take care of the

730
00:42:20,560 --> 00:42:23,037
people that are left behind.

731
00:42:23,120 --> 00:42:27,117
My suggestion 5 2 minutes ago was

732
00:42:27,200 --> 00:42:30,837
this country is not
very good at doing that.

733
00:42:30,920 --> 00:42:33,477
And that's that's where
the worry comes from.

734
00:42:33,560 --> 00:42:37,717
>> I understand. That is essentially
a political decision for a rich country.

735
00:42:37,800 --> 00:42:40,317
So, the most likely scenario

736
00:42:40,400 --> 00:42:45,557
is that a smaller number of people
still a large absolute number, but a

737
00:42:45,640 --> 00:42:49,157
smaller number of people get more
and more of the economic share of America

738
00:42:49,240 --> 00:42:50,957
because of this technology. >> Yes.

739
00:42:51,040 --> 00:42:53,237
>> That That is a reasonable presumption.

740
00:42:53,320 --> 00:42:56,797
If you look at the tech market If you
look at the market today, all the top

741
00:42:56,880 --> 00:42:59,197
value is in the tech tech sector.

742
00:42:59,280 --> 00:43:02,997
Furthermore, the stock market's gone up
for the last 2 years basically because

743
00:43:03,080 --> 00:43:07,037
of all of the the tech companies,
the leading tech companies.

744
00:43:07,120 --> 00:43:10,557
So, the amount of wealth creation
is similar to that of the Golden Age, and

745
00:43:10,640 --> 00:43:14,077
I'm obviously happy to be part of that,
and happy We're happy to give that money

746
00:43:14,160 --> 00:43:18,277
to Princeton. Um the the

747
00:43:18,360 --> 00:43:22,157
it's not good for society to have a Gini
coefficient that's that imbalanced,

748
00:43:22,240 --> 00:43:25,797
right? For obvious reasons.
That's a political decision.

749
00:43:25,880 --> 00:43:29,677
But, I think we it's obvious to me
that the gains will not be uniformly

750
00:43:29,760 --> 00:43:32,037
distributed. It's It does not then

751
00:43:32,120 --> 00:43:34,677
follow that the bottom is still poor.

752
00:43:34,760 --> 00:43:38,437
You can imagine a situation There's
a lot of evidence that the internet

753
00:43:38,520 --> 00:43:43,397
ultimately took people who were very, very
poor and made them a little bit less poor.

754
00:43:43,480 --> 00:43:46,677
Because they had larger markets,
better knowledge, and so forth. So, it's

755
00:43:46,760 --> 00:43:50,557
possible that the that that the poor
do better and the rich do much, much

756
00:43:50,640 --> 00:43:55,117
better. That's a political decision.
We need to get ahead of it.

757
00:43:55,200 --> 00:43:58,157
>> Let me ask you a related question,
and I'm going to open it to the audience

758
00:43:58,240 --> 00:44:00,557
pretty soon.

759
00:44:00,640 --> 00:44:04,517
There are some technologies that lead to

760
00:44:04,600 --> 00:44:07,237
a kind of a winner-take-all

761
00:44:07,320 --> 00:44:09,877
market structure and some that don't.

762
00:44:09,960 --> 00:44:14,877
How do you see this one? >> So,
this the question of concentration

763
00:44:14,960 --> 00:44:18,917
at the technical level is something that
we spend a lot of time thinking about.

764
00:44:19,000 --> 00:44:21,637
For a long time I thought

765
00:44:21,720 --> 00:44:25,997
that there would only be 10 computer
companies. There would be four or five

766
00:44:26,080 --> 00:44:29,317
in the US, two or three
in China, and a couple of

767
00:44:29,400 --> 00:44:32,917
others, maybe one in Israel, maybe one
in Europe, maybe one in India, something

768
00:44:33,000 --> 00:44:35,077
like that, on the order of 10.

769
00:44:35,160 --> 00:44:37,397
And the reason was that the capital cost

770
00:44:37,480 --> 00:44:39,757
of these things is so large.

771
00:44:39,840 --> 00:44:44,997
How many can you have? Right? I mean,
there are limits even to the craziness

772
00:44:45,080 --> 00:44:48,957
in my industry. You know, how many
trillion-dollar companies, individuals,

773
00:44:49,040 --> 00:44:51,197
businesses can you have? It's just the

774
00:44:51,280 --> 00:44:53,917
the money doesn't add up.

775
00:44:54,000 --> 00:44:57,997
It that looks to be true, but

776
00:44:58,080 --> 00:45:01,437
there's then going to be a pyramid

777
00:45:01,520 --> 00:45:04,517
of very powerful, much less expensive

778
00:45:04,600 --> 00:45:09,797
choices. Then there's a debate in my
industry as to whether these lower power

779
00:45:09,880 --> 00:45:13,797
performance things will be the technical
term is called distilled. Will they come

780
00:45:13,880 --> 00:45:17,877
from the big models or will
they be built in their own way?

781
00:45:17,960 --> 00:45:22,277
We don't know. Most people
believe that I talk to that

782
00:45:22,360 --> 00:45:25,917
the vast majority of the AI that you
experiment that you experience in your

783
00:45:26,000 --> 00:45:29,037
future lives will not actually be from

784
00:45:29,120 --> 00:45:32,997
the largest models, but rather will
be embedded in your daily life and you

785
00:45:33,080 --> 00:45:36,157
won't even know it.
Like if you think about it,

786
00:45:36,240 --> 00:45:37,477
do you think very much about the

787
00:45:37,560 --> 00:45:40,997
operating system that's on your watch?

788
00:45:41,080 --> 00:45:45,877
I do. Cuz I used to work
on operating systems

789
00:45:45,960 --> 00:45:49,677
here. Okay? I'm like the last one.

790
00:45:49,760 --> 00:45:53,717
Um you take it for granted.
And if you think about 100 years ago,

791
00:45:53,800 --> 00:45:57,637
electrification, people like, "Oh my god."
And there was all sorts of issues

792
00:45:57,720 --> 00:46:01,197
and houses would burn down
and electricity fires and all of that.

793
00:46:01,280 --> 00:46:05,157
You don't worry about that today. You
worry about, you know, Google and Amazon

794
00:46:05,240 --> 00:46:09,597
and OpenAI and all of that.
Um so so societies work these things

795
00:46:09,680 --> 00:46:13,360
out. So

796
00:46:13,560 --> 00:46:15,677
I I think in that sense we'll be fine.

797
00:46:15,760 --> 00:46:21,360
But we'll get there.
>> Thank you very much.

798
00:46:30,880 --> 00:46:35,877
Now If you If you have
a question, Kathleen

799
00:46:35,960 --> 00:46:39,997
is Dana Is it just you?
Kathleen and Dana are around with

800
00:46:40,080 --> 00:46:42,237
microphones. Raise your hand.

801
00:46:42,320 --> 00:46:47,077
And when you get the microphone,
identify yourself please briefly.

802
00:46:47,160 --> 00:46:51,597
And And keep Keep it
to questions, not speeches.

803
00:46:51,680 --> 00:46:56,117
>> We better have some some graduating
seniors ask questions as well.

804
00:46:56,200 --> 00:46:58,077
Go ahead. Yes. >> Hi Mr.
Schmidt. Thank you so much for

805
00:46:58,160 --> 00:47:01,077
the talk. Uh Tiger Gao, class of '21. Uh

806
00:47:01,160 --> 00:47:05,277
Do you think large language models,
which are by construction a next word

807
00:47:05,360 --> 00:47:08,677
predictor or next token predictor, will
be the model architecture that take us

808
00:47:08,760 --> 00:47:11,877
to artificial general intelligence
or superintelligence, or do you think we

809
00:47:11,960 --> 00:47:15,157
need another model architecture?
And secondly, what

810
00:47:15,240 --> 00:47:19,477
do you think is the probability
of doom, P(doom)? I think if you ask

811
00:47:19,560 --> 00:47:23,797
If you poll Anthropic, the median
response rate was probably like 25%,

812
00:47:23,880 --> 00:47:26,637
which is high as a Where are
all your friends thinking?

813
00:47:26,720 --> 00:47:29,917
>> On the latter, I'm not I'm not a fan
of that whole process and I don't agree

814
00:47:30,000 --> 00:47:31,637
with it and I don't believe in it.

815
00:47:31,720 --> 00:47:35,557
Uh on the former Um the the

816
00:47:35,640 --> 00:47:37,197
Ask the question again.

817
00:47:37,280 --> 00:47:39,437
The The first question was

818
00:47:39,520 --> 00:47:43,477
Do we need more things to get to AGI?

819
00:47:43,560 --> 00:47:44,997
>> Does LLM by construction prevent this?

820
00:47:45,080 --> 00:47:46,717
>> So For those of you
who don't understand

821
00:47:46,800 --> 00:47:49,637
what an LLM does, it's just predicting

822
00:47:49,720 --> 00:47:52,997
the next word. And you go, "How could it

823
00:47:53,080 --> 00:47:57,997
be?" As a story, there
was a group at OpenAI and

824
00:47:58,080 --> 00:47:59,757
I've talked to them at length. It was a

825
00:47:59,840 --> 00:48:05,517
Thursday night, and they had just
turned on They had GPT-2. The The

826
00:48:05,600 --> 00:48:10,797
The The T is transformer. They had taken
that from Google work. And they're busy

827
00:48:10,880 --> 00:48:14,877
and they'd done a GPT-2, and then they
put more training into it. And they

828
00:48:14,960 --> 00:48:17,597
turned on GPT-3, and they realized that

829
00:48:17,680 --> 00:48:22,557
it could write.
And that was a real discovery.

830
00:48:22,640 --> 00:48:26,837
I've tried to figure out why
it's so good. And the answer is

831
00:48:26,920 --> 00:48:31,157
that it's similar to the way our brains
work in the sense that do you really

832
00:48:31,240 --> 00:48:35,557
know what word you're about to say
or think? It just occurs naturally.

833
00:48:35,640 --> 00:48:37,917
So, in that sense, it's very similar.

834
00:48:38,000 --> 00:48:41,597
The question to you
is is that going to be enough?

835
00:48:41,680 --> 00:48:44,277
There are, of course, two schools.
The people who are building large language

836
00:48:44,360 --> 00:48:48,517
models think it's going to be enough.
I personally think it's not.

837
00:48:48,600 --> 00:48:52,557
And the reason is this this implied
intelligence that I was describing.

838
00:48:52,640 --> 00:48:56,357
There's just a whole bunch of knowledge
that you have in your head by virtue of

839
00:48:56,440 --> 00:48:59,837
coming to Princeton, being a good
person, being a citizen of the world

840
00:48:59,920 --> 00:49:05,080
that they just don't know and they're
not going to get unless you tell them.

841
00:49:06,120 --> 00:49:10,157
Yeah. Yes, sir.
>> What is your viewpoint on the

842
00:49:10,240 --> 00:49:14,437
relationship between AI and consciousness?
Do you think at some

843
00:49:14,520 --> 00:49:19,197
point AI will become advanced enough
that it will be indistinguishable from

844
00:49:19,280 --> 00:49:23,437
consciousness?
>> I'm in the no category. Um so, I of

845
00:49:23,520 --> 00:49:27,397
course came to Princeton, and I
met with the neuroscience group.

846
00:49:27,480 --> 00:49:30,877
And the neuroscience group I said to
the neuroscience, "How does consciousness

847
00:49:30,960 --> 00:49:35,357
appear?" And their answer
was the experimental

848
00:49:35,440 --> 00:49:39,797
evidence, and I If I misquote them,
I'm sorry. Is that when you get disparate

849
00:49:39,880 --> 00:49:41,237
systems

850
00:49:41,320 --> 00:49:44,637
in biological brains, and the disparate

851
00:49:44,720 --> 00:49:47,157
systems are different enough and are

852
00:49:47,240 --> 00:49:49,837
forced to interact enough, consciousness

853
00:49:49,920 --> 00:49:52,677
appears to appear show up because of the

854
00:49:52,760 --> 00:49:55,237
need for awareness of what's going on.

855
00:49:55,320 --> 00:49:58,117
That's the simplest
explanation I can give you.

856
00:49:58,200 --> 00:49:59,837
Do I think that's going to occur with

857
00:49:59,920 --> 00:50:02,557
these? The answer is no. And the reason

858
00:50:02,640 --> 00:50:05,557
is that they're not biological.

859
00:50:05,640 --> 00:50:09,477
But I could be wrong.
>> There's one over here.

860
00:50:09,560 --> 00:50:12,397
>> Frank Langford, class of '86.

861
00:50:12,480 --> 00:50:16,277
What policy prescriptions would you give
to the leaders of this country over the

862
00:50:16,360 --> 00:50:19,357
next decade to prepare for white-collar

863
00:50:19,440 --> 00:50:23,997
job loss and to prevent the kind
of political backlash that we've seen from

864
00:50:24,080 --> 00:50:30,440
the offshoring earlier of blue-collar
jobs to countries like China?

865
00:50:31,360 --> 00:50:33,477
>> Um

866
00:50:33,560 --> 00:50:38,437
I think the first question is can
we get the young people ready?

867
00:50:38,520 --> 00:50:43,037
And the simplest prescription for that
is to have a mandatory freshman class at

868
00:50:43,120 --> 00:50:47,517
Princeton along with every other
university. When when we came, we had to

869
00:50:47,600 --> 00:50:51,197
have a TB test, we had to do a swimming
test. Well, it seems to me that we

870
00:50:51,280 --> 00:50:53,317
should have an LLM test.

871
00:50:53,400 --> 00:50:55,117
And because if you think about an

872
00:50:55,200 --> 00:50:58,237
18-year-old today, LLMs will mediate

873
00:50:58,320 --> 00:51:02,997
every aspect of their lives for the
rest of their lives in some form. So,

874
00:51:03,080 --> 00:51:06,357
learning how to work with them, learning
how to empower them, and so forth.

875
00:51:06,440 --> 00:51:10,637
That's the first answer.
Then the second answer is to try to get

876
00:51:10,720 --> 00:51:15,717
people to understand how that how these
tools can enable them to make more money

877
00:51:15,800 --> 00:51:16,997
at work.

878
00:51:17,080 --> 00:51:19,717
So, um again, last night at dinner, one

879
00:51:19,800 --> 00:51:22,957
of the businesses announced that

880
00:51:23,040 --> 00:51:26,037
their productivity per human is so high,

881
00:51:26,120 --> 00:51:31,037
they're hiring more humans. So,
I'm I'm not really fully answering

882
00:51:31,120 --> 00:51:33,597
your question cuz I'm avoiding the hard
questions. What do you do with the

883
00:51:33,680 --> 00:51:35,837
people who can't do one and two?

884
00:51:35,920 --> 00:51:38,837
Well, the answer for them, if you look
at the history of Luddites and so forth,

885
00:51:38,920 --> 00:51:42,877
is they lose their jobs. And then
that becomes a political problem of uh

886
00:51:42,960 --> 00:51:47,637
retraining, motivating, and so forth. In
our current political climate, I have to

887
00:51:47,720 --> 00:51:50,437
assume that they will take up pitchforks.

888
00:51:50,520 --> 00:51:53,277
Right? In other words, that when
these waves go and I'm not trying to be

889
00:51:53,360 --> 00:51:56,397
negative here, I'm just trying to be
honest. When these waves go through

890
00:51:56,480 --> 00:51:58,997
society, the evidence is they go through

891
00:51:59,080 --> 00:52:02,757
much more quickly than society is ready.

892
00:52:02,840 --> 00:52:06,757
And so the government needs
to be ready for this. Um

893
00:52:06,840 --> 00:52:08,477
this the current administration started

894
00:52:08,560 --> 00:52:10,517
off with a completely libertarian view

895
00:52:10,600 --> 00:52:14,117
of this and is now beginning to propose

896
00:52:14,200 --> 00:52:17,797
regulations and restrictions, which must
be very hard for them to deal with the

897
00:52:17,880 --> 00:52:22,517
worst examples. And I think that's
the future. And you'll see it it will come

898
00:52:22,600 --> 00:52:26,960
naturally because
of the political pressure.

899
00:52:27,120 --> 00:52:30,957
>> Who have you got? >> [clears throat]
>> Hello. Um this is Karthik Shastri from

900
00:52:31,040 --> 00:52:35,077
the class of '16 and also the economics
faculty. And I wanted to return to a

901
00:52:35,160 --> 00:52:38,437
question that Alan asked about lessons,
if any, from the social media

902
00:52:38,520 --> 00:52:42,517
revolution. And And you mentioned
yourself that in many ways we live in in

903
00:52:42,600 --> 00:52:47,157
a culture of magnified outrage.
It's arguably a negative feedback loop

904
00:52:47,240 --> 00:52:51,477
between the economics of social media,
which can do kind of amazing community

905
00:52:51,560 --> 00:52:54,517
building things and make a lot of money
from clicks and engagement and outrage

906
00:52:54,600 --> 00:52:56,797
to society and kind of back and forth.

907
00:52:56,880 --> 00:53:00,277
Now take AI, which is capable
of genuinely amazing um intellectual

908
00:53:00,360 --> 00:53:04,357
advances, like proving mathematical
theorems, as you suggested. It's also

909
00:53:04,440 --> 00:53:07,477
capable of other things that may
maximize more engagement, if that

910
00:53:07,560 --> 00:53:10,877
becomes the model, like using its
mastery of language to, you know,

911
00:53:10,960 --> 00:53:14,757
surreptitiously advertise things
to people and change how they think or to

912
00:53:14,840 --> 00:53:18,917
give a veneer of technical
credibility at a coherent >> Sorry.

913
00:53:19,000 --> 00:53:20,957
>> Um the question
of coherence of logic to

914
00:53:21,040 --> 00:53:24,597
the things that that um you know, aren't
really serious. So So do you see that as

915
00:53:24,680 --> 00:53:28,757
a trap essentially for the development
of AI? Um and if so, kind of socially,

916
00:53:28,840 --> 00:53:31,917
politically, economically,
what could we do >> Oh my god.

917
00:53:32,000 --> 00:53:34,437
>> to to sort of forestall that outcome.

918
00:53:34,520 --> 00:53:38,477
>> So So LLMs are a neutral technology in

919
00:53:38,560 --> 00:53:42,597
the sense that all technology
is essentially dual use, good and bad,

920
00:53:42,680 --> 00:53:47,037
military, civilian, and so forth.
It's up to us how we decide to limit these

921
00:53:47,120 --> 00:53:49,957
things. There is no
question that if I were

922
00:53:50,040 --> 00:53:53,277
truly evil, I could use these tools to

923
00:53:53,360 --> 00:53:57,397
completely manipulate um some
society. I'll give an example.

924
00:53:57,480 --> 00:54:00,637
In one of the first uh safety
demonstrations, the UK is the leader

925
00:54:00,720 --> 00:54:02,917
here, strangely, not the US.

926
00:54:03,000 --> 00:54:05,677
Um somebody said, "Let's build a uh

927
00:54:05,760 --> 00:54:08,157
community around some particular British

928
00:54:08,240 --> 00:54:10,477
political thing." And so they construct

929
00:54:10,560 --> 00:54:15,397
a set of fake profiles. They completely
fill all of social media with them, and

930
00:54:15,480 --> 00:54:18,277
they sit there, and they don't do
anything. They don't do much, but then

931
00:54:18,360 --> 00:54:22,117
in a coordinated attack,
they actually then

932
00:54:22,200 --> 00:54:23,837
um spread something which they know will

933
00:54:23,920 --> 00:54:27,357
get magnified. So, remember that the and

934
00:54:27,440 --> 00:54:30,997
I call this the dual evil problem. So,

935
00:54:31,080 --> 00:54:35,837
for purposes of argument, I have a uh
soft a piece of software that magnifies

936
00:54:35,920 --> 00:54:38,197
everything. Well,
I can build another piece of

937
00:54:38,280 --> 00:54:41,597
software that can figure out what that
software is doing, and then I can feed

938
00:54:41,680 --> 00:54:45,997
whatever evil idea I have, have this
software feed it into the thing that

939
00:54:46,080 --> 00:54:49,797
magnifies, and off it goes. Now,
the social media people will say that they

940
00:54:49,880 --> 00:54:54,037
have good defenses against this. There's
plenty of evidence that they don't.

941
00:54:54,120 --> 00:54:56,997
So, that's from my perspective, that's
a technical problem, but it's also a

942
00:54:57,080 --> 00:55:00,157
regulatory problem. The technical
problem is making sure that I cannot

943
00:55:00,240 --> 00:55:04,317
come in through an illegal
or mischievous path where I'm a foreign

944
00:55:04,400 --> 00:55:07,757
agent, you know, Russians or something
like that. Russians have a long history

945
00:55:07,840 --> 00:55:10,957
of trying to manipulate
our elections. This is how

946
00:55:11,040 --> 00:55:15,157
they would do it. Um we have to deal
with that, but it requires some

947
00:55:15,240 --> 00:55:19,317
constraint on the on the
economics of social media, some

948
00:55:19,400 --> 00:55:22,237
of you understand well. >> Uh

949
00:55:22,320 --> 00:55:24,317
Thank you. Uh my name is Jorge Aguilar,

950
00:55:24,400 --> 00:55:27,237
I'm class of '06. You alluded to several

951
00:55:27,320 --> 00:55:29,437
safeguards. One of them was that free

952
00:55:29,520 --> 00:55:32,357
speech is good, but bullhorns are can be

953
00:55:32,440 --> 00:55:34,757
bad, which sounds like nuisance law,

954
00:55:34,840 --> 00:55:39,557
right? And something intangible and
annoying. But, the other safeguard that

955
00:55:39,640 --> 00:55:44,157
I thought I heard was, and I'm asking
you to confirm, was you said LLMs

956
00:55:44,240 --> 00:55:48,197
are so dependent on the information we
give them. And you mentioned how there

957
00:55:48,280 --> 00:55:51,437
was something, and I forget what it was,
that you hadn't given the LLM, so it

958
00:55:51,520 --> 00:55:56,477
couldn't predict what you said. Is there
some thought then that a safeguard

959
00:55:56,560 --> 00:56:00,877
is there's some information we shouldn't
give to these AIs, cuz I think I

960
00:56:00,960 --> 00:56:04,437
remember you you then went on to say how
advanced it could be if we keep feeding

961
00:56:04,520 --> 00:56:08,197
it information. Is there
a a filter that we need to kind

962
00:56:08,280 --> 00:56:11,517
of set up? >> I think that's
unlikely to work, because

963
00:56:11,600 --> 00:56:15,757
it would require coordination with all
of the people who are busy feeding these

964
00:56:15,840 --> 00:56:18,437
LLMs with everything they can find.

965
00:56:18,520 --> 00:56:20,477
The current state of the industry is

966
00:56:20,560 --> 00:56:23,077
that every single piece of information

967
00:56:23,160 --> 00:56:25,637
that is essentially searchable,

968
00:56:25,720 --> 00:56:27,917
collectible, or scrapeable has been

969
00:56:28,000 --> 00:56:30,677
collected. Right? We sort of run out of

970
00:56:30,760 --> 00:56:35,677
training data, as it's called. And there
was a a little while where everyone was

971
00:56:35,760 --> 00:56:38,957
writing these articles about data
was the new oil, and we're running out of

972
00:56:39,040 --> 00:56:41,197
it, which is completely false.

973
00:56:41,280 --> 00:56:44,397
Because the systems can
also generate synthetic data.

974
00:56:44,480 --> 00:56:48,397
Furthermore, there's plenty of data that
you can get if you start, for example,

975
00:56:48,480 --> 00:56:50,157
surveilling people. I'm not arguing in

976
00:56:50,240 --> 00:56:52,357
favor of that. Um that's not an

977
00:56:52,440 --> 00:56:57,597
argument. Do not get me wrong. Um so
there's plenty more knowledge that we

978
00:56:57,680 --> 00:57:00,997
can feed to these things.
Um the the industry is governed by

979
00:57:01,080 --> 00:57:06,437
something called scaling laws,
and every 6 months I ask the same

980
00:57:06,520 --> 00:57:07,837
question,

981
00:57:07,920 --> 00:57:10,477
are the scaling laws stopping?

982
00:57:10,560 --> 00:57:14,357
And the answer right now is no, which is

983
00:57:14,440 --> 00:57:17,797
unbelievable.
And so this has led to the following

984
00:57:17,880 --> 00:57:22,437
phenomena. Uh a model
costs 200 to 500 million

985
00:57:22,520 --> 00:57:25,357
dollars, you release it,

986
00:57:25,440 --> 00:57:29,637
and it has a life
expectancy of about 6 months.

987
00:57:29,720 --> 00:57:33,077
And then there's another one. And the
competition between the leading players

988
00:57:33,160 --> 00:57:37,797
is so brutal, right, that that's they
literally count in the number of weeks

989
00:57:37,880 --> 00:57:41,397
value of these things, which cost
hundreds of millions of dollars.

990
00:57:41,480 --> 00:57:45,837
So, that's where the pressure is. And
they're they're they're go I don't know

991
00:57:45,920 --> 00:57:49,957
what there is a slope, right? There
is eventually a slope. We don't know where

992
00:57:50,040 --> 00:57:52,757
it is. Um a lot of people
think that it will

993
00:57:52,840 --> 00:57:57,397
happen fairly soon. The what
you're seeing now is emergent

994
00:57:57,480 --> 00:57:59,757
behavior that none of us expected.

995
00:57:59,840 --> 00:58:04,357
Emergent in the sense that we didn't
know it could do this. Like the math

996
00:58:04,440 --> 00:58:08,237
example I used. Everyone
believed that you needed a a

997
00:58:08,320 --> 00:58:12,637
model that was tuned to math. You don't.

998
00:58:12,720 --> 00:58:16,197
Wow. >> Hi.

999
00:58:16,280 --> 00:58:19,077
>> This is uh Ernie Hutton, class of '66.

1000
00:58:19,160 --> 00:58:22,757
Uh to take things back to the campus
level, Princeton has just abolished, I

1001
00:58:22,840 --> 00:58:25,797
think tragically, its honor code. And uh

1002
00:58:25,880 --> 00:58:28,077
there's part of a definition of uh in

1003
00:58:28,160 --> 00:58:32,837
within that of what is cheating.
You know, how do you take uh in the age of

1004
00:58:32,920 --> 00:58:36,477
AI uh assimilate knowledge
and still maintain

1005
00:58:36,560 --> 00:58:41,597
the students' ability to be
creative and and uh and learn?

1006
00:58:41,680 --> 00:58:43,197
>> Well, obviously the way you test

1007
00:58:43,280 --> 00:58:47,117
students is without access to computers.

1008
00:58:47,200 --> 00:58:50,597
Right? So, basically you can have a math
class and you can have a lot of fun with

1009
00:58:50,680 --> 00:58:53,917
math. And at the end there's a math test
where you can't have a computer in front

1010
00:58:54,000 --> 00:58:56,157
of you. Right? So, it's I'm sure the

1011
00:58:56,240 --> 00:59:01,357
university can can find ways
of validating the knowledge transfer.

1012
00:59:01,440 --> 00:59:03,117
I'm going to argue

1013
00:59:03,200 --> 00:59:06,437
strongly that the more students use

1014
00:59:06,520 --> 00:59:11,597
these systems in their daily lives, the
better. With an understanding that they

1015
00:59:11,680 --> 00:59:17,157
have to it will Let me say it right. There
are things that they will not then learn

1016
00:59:17,240 --> 00:59:20,757
that they're going to have to learn some
other way. But that's a it's a pedagogic

1017
00:59:20,840 --> 00:59:24,717
institution. The university can figure
that out. But I want Princeton students

1018
00:59:24,800 --> 00:59:28,797
to walk out being so incredibly
comfortable with this stuff that they

1019
00:59:28,880 --> 00:59:31,157
can use it to master whatever personal

1020
00:59:31,240 --> 00:59:34,837
financial business challenge they have.

1021
00:59:34,920 --> 00:59:37,567
>> Okay, I think this
is the last question.

1022
00:59:37,650 --> 00:59:42,807
>> [clears throat] >> Um yes.
>> So make it a good one. No, I'm kidding.

1023
00:59:42,890 --> 00:59:47,837
>> [laughter] >> I'll I'll try no favorite
color question. Um so first off, thank you

1024
00:59:47,920 --> 00:59:50,477
Alan for, you know, creating this center.
Eric, disappointed in the

1025
00:59:50,560 --> 00:59:53,477
seniors that there's no
Volkswagen Beetle on stage.

1026
00:59:53,560 --> 00:59:56,957
Um my question revolves around adoption
right now. So how do you think financial

1027
00:59:57,040 --> 01:00:01,797
firms will balance algorithmic modeling
and human intuition when trading in

1028
01:00:01,880 --> 01:00:04,157
unprecedented >> Financial firms?

1029
01:00:04,240 --> 01:00:06,997
>> Yes. >> Okay. So

1030
01:00:07,080 --> 01:00:09,357
>> If you look at the quant

1031
01:00:09,440 --> 01:00:14,437
sort of revolution which provided
a great deal of efficiency to the markets,

1032
01:00:14,520 --> 01:00:18,197
it sure looks to me like
all of that can be AI driven.

1033
01:00:18,280 --> 01:00:22,317
Uh and there are plenty of of hedge
funds and so forth that are building that.

1034
01:00:22,400 --> 01:00:24,597
Um I have a group uh that I started

1035
01:00:24,680 --> 01:00:29,477
that's trying to do essentially
equity trading using signals. What's

1036
01:00:29,560 --> 01:00:32,997
interesting about it is that it only
takes about 10 people to build a trading

1037
01:00:33,080 --> 01:00:35,557
platform because they're
all they spend all day

1038
01:00:35,640 --> 01:00:39,197
using cloud code. And uh my last meeting

1039
01:00:39,280 --> 01:00:42,677
with them they said, "Well, we're
in cloud code jail right now." And I said,

1040
01:00:42,760 --> 01:00:45,197
"What does that mean?" They said, "Well,
we ran out of tokens this morning. So

1041
01:00:45,280 --> 01:00:48,477
we're waiting until 5:00
when we get more tokens."

1042
01:00:48,560 --> 01:00:50,517
So this is sort of their lives.

1043
01:00:50,600 --> 01:00:53,997
So uh my own opinion is that the vast

1044
01:00:54,080 --> 01:00:58,117
majority of financial
investing will become automated.

1045
01:00:58,200 --> 01:01:01,517
This does not mean that the sales people
will lose their jobs. It does not mean

1046
01:01:01,600 --> 01:01:03,357
that the owners will lose their jobs.

1047
01:01:03,440 --> 01:01:04,957
But a lot of the people who run around

1048
01:01:05,040 --> 01:01:07,677
using intuition to trade are highly

1049
01:01:07,760 --> 01:01:10,677
likely to be replaced by um computer

1050
01:01:10,760 --> 01:01:15,117
with with deeper intuition. It's the same
argument that Basically, you think

1051
01:01:15,200 --> 01:01:18,037
you're such a good investor and you
and there are plenty of very talented

1052
01:01:18,120 --> 01:01:22,637
investors. They're they have
intuition around a state space.

1053
01:01:22,720 --> 01:01:26,317
And computers should be able to develop
a bigger state space that they can have

1054
01:01:26,400 --> 01:01:29,517
the same intuition on.
It's an interesting story is that we

1055
01:01:29,600 --> 01:01:33,637
tried this at Google and we
actually started the program.

1056
01:01:33,720 --> 01:01:37,397
This is early. And it was ultimately

1057
01:01:37,480 --> 01:01:39,797
we decided not to do it because in order

1058
01:01:39,880 --> 01:01:41,477
to begin the trading, the company would

1059
01:01:41,560 --> 01:01:44,797
have to register itself as a trader.

1060
01:01:44,880 --> 01:01:47,917
And Google decided
correctly not to do that.

1061
01:01:48,000 --> 01:01:51,037
Um so I know that people have been
working on it for at least a decade and

1062
01:01:51,120 --> 01:01:56,077
it's coming. >> So please join
me in thanking Eric very much.

1063
01:01:56,160 --> 01:01:57,877
>> Thank you.

1064
01:01:57,960 --> 01:02:03,480
Thank you, Alan.
Thank you very so much, Alan.