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First of all, I I would say this is the
biggest tech technological revolution of

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my life. Um, and you know, hopefully
I'll see more like this in the next

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whatever 30 years, but I I mean this
is the big one. Um, and just in terms of

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order of magnitude, like this is clearly
bigger than the internet. Um, like the

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the the comps on this are things like
the microprocessor and the steam engine

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and electricity. So that this is a

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really this is a really big one. Um, the

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wheel. Um, the reason this is so big,
I mean, maybe obvious to folks at this

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point, but I'll just go through it
quickly. So, um, if you kind of trace

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all the way back to the 1930s, uh,
there's a great book called Rise of the

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Machines that kind of goes through this.

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Um, if you trace all the way back to the
1930s, there was actually a debate among

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the people who actually invented the
computer. Um, and it was this this sort

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of debate between whether computer
they kind of understood the theory of

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computation before they before they they
actually built the things. Um, and they

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they had this big debate over whether
the computer should be basically built

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in the image of what at the time were
called adding machines or calculating

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machines. You know, think of sort
of essentially cash registers. Um IBM is

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actually the successor company to the
national cash register company uh of

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America. Um and so like and and that
was of course the path that the industry

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took which was building these kind
of hyper literal you know mathematical

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machines you know that could execute
mathematical operations billions of

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times per second but of course had
no ability to kind of deal with human

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beings the way humans like to be
dealt with. And so you know couldn't

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understand you know human speech, human
language um and so forth. And and that's

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the computer industry that got built
over the last 80 years. And that's the

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computer industry that's built all
the wealth of uh uh and and financial

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returns of the computer industry uh over
the last 80 years you know across all

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the generations of computers through
mainframes through to through to

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smartphones. Um but but they knew at the
time they knew in the 30s actually they

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understood the basic structure of the
human brain. They understood they had a

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theory of sort of human cognition and
and and and actually they had the theory

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of neural networks. Um and so they
they had this theory that um the there's

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actually the the first neural network
uh paper academic paper was published in

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1943 you know which was over 80 years

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ago which is extremely amazing. Um,
there's an interview, you can read an

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interview or you can watch an interview
on YouTube with uh these two authors,

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Makulla and Pitts. And you can watch
an interview I think with Makulla on

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YouTube from like I don't know 1946
or something. He was like on TV, you know,

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in the in the ancient past and it's
literally like it's amazing interview

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because it's like him in his beach house
and for some reason he's not wearing a

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shirt. Um, and he's like, you know,
talking about like this future in which

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computers are going to be, you know,
built on on the model of a human brain

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through through neural networks. Um and
and and that was the path not taken. And

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basically what happened was right
the computer industry got built in the in

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the image of of like the adding machine.

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Um but and the neural network basically
didn't happen but the neural network as

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an idea continued to be explored
in academia um and a sort of advanced

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research by sort of a rump you know
movement that was originally called

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cybernetics and then became known as
as artificial intelligence. uh basically

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for the last 80 years and and and
essentially it didn't work like

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essentially it was basically decade
after decade after decade of excessive

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optimism uh followed by disappointment
when I was in college in the '8s there

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had been a famous kind of AI boom bust
uh cycle in the '8s uh in venture in

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Silicon Valley um I mean it was tiny
by by modern standards but it it at the

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time was a big deal um and um you know
by the time I got to college in '89 um

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in computer science departments AI was
kind of a backwater field and everybody

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kind of assumed that it was never
going to happen but the scientists kept

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working on it to their credit and they
they they built up this kind of enormous

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reservoir of of concepts and ideas and
then basically we all saw what happened

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with the CHI GPT uh moment all of a

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sudden it it sort of crystallized like
oh my god right it turns out it works um

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and and so you know that that's
the moment we're in now and then you know

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really significantly that was what you
that was less than 3 years ago right

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that was the summer of 20 it was the
the Christmas of 22 and so we're sort of

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three year we're we're sort of three
years in um to you know basically what

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is effectively effectively an 80-year
revolution um of actually being able to

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deliver on all the promise that the that
that the people on the all the on the

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alternate path the sort of human
cognition model path you know kind of

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saw from the very beginning and and then
you know the great news with this

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technology is it's already it's kind
of ultra democratized you know the best AI

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in the world is available launch at GPD
or Grock or Gemini or or um you know

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these other you know these other
products that you can just use um and

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and you can just kind of see how they
work and you know same thing for video

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you can see with Sora and VO kind
of state-of-the-art uh with that you can

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see with music you can you know, and IDO
and so forth. Um, and so like, you know,

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we're basically seeing that happen and now
and now Silicon Valley is responding

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with this just like incredible rush
of enthusiasm. And you know, really

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critically, this gets to the magic
of Silicon Valley, which is, you know,

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Silicon Valley long since has ceased
to be a place where people make Silicon,

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you know, that that not long ago moved
out out of the out of California and

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then ultimately out of the US, although
we're trying to bring it back now. Um

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but but but the great kind of virtue
of Silicon Valley o over the last you know

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over the last you know 80 years of its
existence is its ability to kind of uh

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recycle talent from previous waves
of technology and new waves of technology

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uh and then inspire an entire new
generation of talent you know to

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basically come join the you know join
the project. Um and so Silicon Valley

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has this recurring pattern of being
able to reallocate capital and talent and

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build enthusiasm and build critical mass
and build funding support and build you

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know human capital and build you know
everything enthusiasm um you know for

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each new wave of technology. So, so
that's what's happening with AI. Um, you

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know, I I think probably the biggest
thing I could just say is like I'm

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surprised I think essentially on a daily
basis of what I'm seeing. Um, uh, and

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and you know, we we're we're in the
fortunate position to kind of get to see

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it from from two angles. Uh, you know,
one one is we track the underlying

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science and and, uh, and kind
of research work very carefully. And so I

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would say like every day I see a new AI
research paper that just like completely

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floores me um of some new capability
um or some new discovery uh or some new

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development that I that I would have
never anticipated that I I'm just like

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wow I you know I can't believe this is
happening. And then um on the other side

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of course you know we see the flow
of all of the new uh products uh and all

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the new startups. Um and you know I I
would say we're routinely um you know

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kind of seeing things that again kind
of have my my jaw on the floor. Um and so

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you know it feels like we we we've
unlocked this giant vista. Um I do think

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it's going to kind of come in fits and
starts. Um you know the these things are

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messy processes. Um you know you
know this is an industry that kind of

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routinely gets out over riskies
and overpromises. Um and so you know there

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you know there will certainly be points
where it's like wow you know this isn't

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working as well as people thought
or you know wow this turns out to be too

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expensive and the economics don't work
or whatever. But, you know, against

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that, I would just say the capabilities
are truly magical. And and by the way, I

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think that's the experience that
consumers are having when they use it.

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And I think that's the experience that
businesses are having for the most part

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when they uh you know, when when they're
working on their pilots and and looking

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at adoption and and and then and then it
translates to the underlying numbers. I

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mean, we're we're just seeing this new
wave of AI companies is is growing

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revenue like just like actual customer
revenue, actual demand uh translated

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through to dollars showing up in bank
accounts. um you know at at like an

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absolutely unprecedented takeoff rate,
we're seeing companies grow much faster.

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Um uh you the the key leading AI
companies and the companies that have

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real breakthroughs um and have real
have very compelling products or growing

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revenues that you know kind of faster
than any any way I've certainly ever

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seen before. Um and so like just just
from all that it kind of feels like it

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has to be early. Like it it's kind of hard
to imagine that we've like we we've

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topped out in any way. It feels like
everything is still developing. I mean

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quite frankly it feels like the products
to me it feels like the products are

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still super early. Like I'm I'm I'm very
skeptical that the form and shape of the

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products that people are using today
is what they're going to be using in five

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or 10 years. I think I think things
are going to get much more sophisticated

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from here. Um and so I think we
probably have a long way to go.

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>> [music]