You actually think AI's under hyped. Why
is that?
>> And I'll tell I'll tell you why. Most of
you think of AI as I'll just use the
general term as chat GPT. For most of
you, chat GPT was the moment where you
said
"Oh my god, this thing writes." And it
makes mistakes, but it's so brilliantly
verbal. Right? That was certainly my
reaction. Most people that I knew did
that.
>> It's visceral, yeah.
>> This was 2 years ago.
Since then, the gains in what is is
called reinforcement learning, which is
what AlphaGo helped invent and so forth,
allow us to do planning.
>> [music]
>> And a good example is look at OpenAI 03
or DeepSeek R1, and you can see how it
goes forward and back, forward and back,
forward and back. It's extraordinary. In
my case, I bought a rocket company
because it was like interesting, and I
know
>> As one does.
>> As one does. And it's it's an area that
I'm not an expert in, and I want to be
an expert, so I'm using deep research.
And these systems are spending 15
minutes writing these deep papers. This
is true for most of them. Do you have
any idea how much computation 15 minutes
of these supercomputers is? It's
extraordinary. So, you're seeing the
arrival, the shift from language to
language, then you have language to
sequence, which is how biology is done.
Now you're [music] doing essentially
planning and strategy.
The eventual state of this
>> [music]
>> is the computers running all business
processes, right? So, you have an agent
to do this, an agent to do this, an
agent to do this, an agent to do this,
and you concatenate them together, and
they speak language
>> [music]
>> among each other. They typically speak
English language.
>> I mean, speaking of of just the sheer
compute requirements of these systems,
[music] let's talk about scale briefly.
You know, I kind of think of these AI
systems as Hungry Hungry Hippos. They
seemingly soak up all the data and
compute that we throw at them. They've
already digested all the tokens on the
public internet, and it seems we can't
build data centers fast enough.
What do you think the real limits are,
and how do we get ahead of them before
they start throttling AI progress?
>> So, there's a real limit in energy. Give
an example, there's one calculation, and
I testified on this this week in
Congress,
that we need another 90 gigawatts of
power in America. My answer, by the way,
is think Canada, right? Nice people,
full of hydroelectric power, but that's
apparently not the political mood right
now.
Sorry.
So, 90 gigawatts is 90 nuclear power
[music] plants in America. Not
happening. We're building zero,
right? How are we going to get all that
power? This is a major, major national
issue.
Um you can use the Arab world, which is
busy building 5 to 10 gigawatts of data
centers. India is considering a 10
gigawatt data center.
To understand how big gigawatts are, is
think cities per data center. That's how
much power these things need. And the
people look at it, and they say, "Well,
there's lots of algorithmic
improvements, and you will need less
power." There's an old rule, I'm old
enough to remember,
right?
Grove giveth, Gates taketh away.
>> [laughter]
>> Okay? The hardware just gets faster and
faster. The physicists are amazing.
Just incredible what they've been able
to do. And us software people, we just
use it, and use it, and use it.
And when you look at planning, to at
least in today's algorithms, it's back
and forth, and try this, and try And
just watch it yourself. Their estimates,
and you know this from from Industry
Horowitz reports, uh it's been well
studied, [music]
that there's an increase in at least a
factor of a hundred, maybe a factor of
thousand in computation required just to
do the kind [music] of planning.
There the technology goes from
essentially deep learning to
reinforcement learning to something
called test time compute, where not only
are you doing planning, but you're also
learning while you're doing planning.
That is the, if you will, the zenith or
what have you, of computation needs.
That's problem number one, electricity
and hardware. Problem number two is we
ran out of
data, so we have to start generating it,
but we can easily do that because that's
one of the functions.
And then the third question that I don't
understand is what's the limit of
knowledge?
Um I'll I'll give you an example. Let's
imagine we are collectively all of the
computers in the world, and we're all
thinking, and we're all thinking based
on knowledge that exists that was
previously invented. How do we invent
something completely [music] new?
So, Einstein.
So, when you study the way scientific
discovery works, biology, math, so forth
and so on,
what typically happens is a truly
brilliant human being looks at one area
and says,
"I see a pattern that's in a completely
different area. Has nothing to do with
the first one. It's the same pattern."
And they take the tools from one, and
they apply it to another. Today, our
systems cannot do that. If we can get
through that, um I'm working on this.
It's a general technical term for this
is non-stationarity of objectives. The
rules are keep changing.
Um we will see if we can solve that
problem. If we can solve that, we're
going to need even more data centers,
and we'll also be able to invent
completely new schools of scientific and
intellectual [music]
thought.