welcome to a another conversation on AI
I don't think we get enough of this
conversation going I do want to thank
Richard AAS and the FI team for really
increasing the conversation this year on
AI because I think there is no greater
uh topic of import on the financial side
on the leadership side education Side
Medical side it's transforming
everything we have uh three incredible
CEOs here um who are representing uh
variety of different parts of the AI
emergence um I'm going to start by
asking each of them to take just one
minute introduce themselves and what
they're doing and then we're going to
jump into where is this going how fast
is it going how big is it going to get
you know we'll ask the question what is
after chat GPT Prem let's begin with
yourself awesome thank you you thank you
Peter um I'm pre maraju I'm the CEO of
stability AI we are one of the leading
open- Source image video and 3D um
models in in the world and past GPT
pictures are worth a thousand words and
we're making quite a few of them and in
fact 80% of all the images that were
generated by AI last year in 2023 were
driven by our model stable diffusion
amazing Richard hi everyone really
excited to be here my name is Richard
soer I'm the CEO and founder of u.com
y.com it's a productivity engine which
is the next Generation after a search
and an answer engine so we really make
people more productive across a whole
host of different kinds of organizations
from hedge funds to universities to
companies insurance companies and so on
Publishers news agencies and uh almost
everyone else in between who has sales
service marketing research analysis uh
and so on I also run a venture fund
called aiex Ventures that invests in
early stage uh preed seed AI companies
and startups been very fortunate that
when I was a professor at Stanford I had
two students who created this cute
company called hugging face invested in
that a 5 million valuation worth four
and a half billion now so Fun's doing
that's that's bragging that's just
straight up bragging I wish I could brag
like that Dr Kaiu
Lee hi uh I've been working on AI for
about 43 years I was two at no at the uh
in college when I started Ai and um I
think that may have started before my
colleagues were born but um I actually
worked on machine learning and um and I
have a PhD carnegi melon and I have
worked at uh Apple Microsoft Google uh
some of you may know me as with my books
AI superpowers and AI
2041 uh my part-time job is I I run
sinovation Ventures which invests
globally and then my full-time job is I
run 0 one. it's a uh gener generative AI
company uh we build a large language
model we're currently ranked as the
third company with the highest
performance only next to the best models
from open and Google um and you can find
it online uh we're also building uh
consumer and Enterprise Products um
we're based in China uh but our products
are accessible globally and also we
extensively uh do open source as well so
incredible and and first of all Kaiu is
a legend and one of the greatest leaders
globally in this field so very honored
to have him on here um Prem I want to
start with you um uh you very famously
were able to recruit James Cameron onto
your board uh and since stability is
creating video and is creating sort of
the future of
Hollywood um
I am curious about two things one did uh
did Jim get it right with the
Terminator um and and secondly uh you
know there's been a lot of conversation
about the disruption of Hollywood um
that we're going to have AIS creating
the future of all movies all content and
so forth so you said beyond you know GPT
models were you know images worth of
th000 words talk to us about what this
what this future is what is going to
happen in sort of the visualization
world of of TV and Hollywood love it so
did Jim get it right with Terminator uh
let's hope not I guess but the um but
what a great movie it was and I love
when he actually he jokes about it he
says I told you guys like you know this
is coming and now it absolutely is here
um and why did why would someone like
him get involved in stability yeah great
question so you I I had the great
Fortune of of working on Avatar 2 with
him when I was the CEO of wether digital
before I joined as CEO of stability and
that movie took over four years to make
and that's because it was fully rendered
and I think if you fast forward to 5 to
10 years from now the vast majority of
film and television and visual media as
we know it today is not going to be
render is going to be generated and in
fact in Avatar there were certain shots
there were certain uh that took 6,000
7,000 hours of compute time to render
one single frame thousands of hours that
literally can be reduced down to minutes
now so I think Jim just wants a whole
lot of life back and when you think
about like the creative process we all
watch films we watch movies we love them
from the time we've born to our last
memory it's some it's a commodity we
never get sick of um we never we never
not want to watch it
um and so there's this insatiable
appetite out there in the world to
consume stories and to create stories
and I think that we should just
accelerate that the problem with the
film production process is time and
money so what he really wanted to do is
rip those things out so we can move from
a render to a generated model are we
going to see a situation where we're
ever going to have ai generating entire
movies because it knows my preferences
what I love and it's like the perfect
movie for me
you know personally I kind of hope not
um I don't think that
actually uh the creative process I think
needs to start with a human and I think
that human needs to dictate these tools
in separate agents to actually make that
story and so I'm hoping that you'll
probably want to hear stories that other
people want to tell you all right well
let's take a different direction then
sure am I going to see uh Marilyn Monroe
and you know all stars of the Past
coming back
is there a need for human actors if you
can generate absolutely lifelike uh
actors and actresses perfectly I mean I
can't see a situation where they're
still around yeah I think that it's
actually quite it's faster when you're
talking about the Film Production it's
actually easier to just shoot plates on
an actor just shoot real photography and
get their performance I think there's
that's the visible layer of of
production people gravitate toward it a
lot I think that AI will enhance those
prod those um performances I think the
physicality of a director with a camera
and an actor in front of it is a very
important part of the creative process
and I don't think that that's going to
go away too soon and in fact I think
about the things that aren't going to
change just as much as I think is going
to change but I do think after they take
one take the director is going to say I
got it because they're going to be able
to do what you're talking about which is
manipulate that performance may ask one
more question to you before I move on
what is the most dramatic change we're
going to see in film and TV 10 years
years from now as we see digital super
intelligence we like what's what's the
craziest vision of what we're going to
see in entertainment I think we're going
to see on the magnitude of 5 to 10 to
20x more content being created I think
we're going to see a variation of time
where it's going to be a two-minute like
you said you may want to have 20 minutes
before you go to bed you want to see a
movie that that's you'll have different
type of time signatures and I think that
you're going to have an explosion of
content creation an explosion of number
of artists in the world
I'm going to come back in 10 years and
see if you're right about that
okay uh Richard
um a lot of your work was instrumental
in the early days of bringing neural
Nets to natural language
processing um so what do you see as the
next Frontier Beyond NLP so just explain
if you would what NLP is and where is it
going next yeah natural language
processing NLP used to be a a sub area
of AI and it has I think influenced
pretty much every other area of AI and
uh there lots of different algorithms
you could train and 2010 I had this
crazy idea to train a single neural
network for all of NLP and 2018 we
finally really built the first model uh
that invented prompt engineering where
you can just ask one model all the
different questions you have and over
time of course you can ask questions not
just over text but also over images and
so I think next one of the answers to
the the panel's main topic of what's
after chat gbt is that we have many more
multimodal models you'll be able to have
conversations over images you have
seamless inputs and outputs in not just
the modality of text but also
programming which is a huge unlock uh
visual videos images voice sound but one
really interesting modality that not
many people have quite realized yet is
that of proteins proteins are
essentially the basic Lego blocks of all
of biology everything in our body is
governed by prot proteins and you can
create a protein just like you can ask a
large language model to write AET for
you or a poem for your wife you can ask
an llm to create a specific kind of
protein it will only bind to SARS Cove 2
or only bind to a specific type of
cancer in your brain and what that means
is that we will unlock a lot of
different aspects in medicine so I'm
extremely excited about the future of
LMS going into different modalities and
we're seeing that with Deep Mind
products in you in Alpha proteo and and
such so we had a conversation in back
but I didn't hear the answer and the
question is basically is there an upper
limit to
intelligence and you know we've talked
about and we just did a conclave on
digital
superintelligence and how fast we're
going to get there and what does it mean
um as we think about AI becoming more
and more intelligent yes I want speak to
Elon he said okay 2029 2030 equal to
intelligence to the entire human
race is it just you know a million times
more and then a billion times more and
then a trillion times more is there an
upper limit to intelligence yeah so
really interesting question so just to
talk about Alpha fold and Google for a
second as you mentioned it like that was
really interesting uh to understand how
proteins fold because that will help you
understand how they are likely to
function interact in your body what we
did in 2020 is create the first LM that
generates a completely new kind of
protein and it was 40% different uh to
any naturally occurring protein and it
actually we synthesized it in the wet
lab this was at Salesforce research did
scientist there and it was an
antibacterial Lio type of protein that
is basically has antibacterial
properties and just to put that into
perspective was really close to covid-19
so make sure you weren't um got to be
careful what you say online sometimes um
but what was interesting is that
multiple startups have now started from
this line of research and and I think
it's hard for people to Fathom like how
much that can change medicine in terms
of upper bounds of intelligence it's a
really interesting question can it just
keep going and going going I think you
have to basically look at the different
dimensions of intelligence right there's
language intelligence visual perception
intelligence reasoning knowledge
extraction uh and a few others physical
manipulation and just I'll show you just
one example I don't want to talk talk
about this for hours but visual
intelligence right there are you know
for a long time people have looked at
just the electromagnetic uh frequency
spectrum of human vision and there you
know classifying every object on the
planet is actually not that hard and the
upper limit is classifying all the
objects um on the planet and we're
probably going to reach that and we're
not too far away from it but that's just
human Vision AI could eventually see all
the way down to gamma frequencies and
see and try to perceive atoms right and
there you actually start to hit limits
uh of physics like Quantum limits of
like what can actually be observable and
you can go all the way into like seeing
uh massively larger scale things at the
universe level and how many uh different
sensors do you have in that then you can
process all of that information and AI
could have billions of uh sensors that
go out and then you get into really
interesting limits of like the speed of
light cone of like so I can talk about
for hours it's a really tough subject
but in some cases we are astronomically
far away from those upper bounds and in
some cases we already got pretty close
fasc you talk about work productivity as
U.C com's objective what does that mean
and uh I guess the question is the same
is there any limitation on work
productivity that we're going to
see given the fact that I can command AI
agents and robots to just do anything
and everything and just and self-improve
along the way it seems like we're going
to hit sort of an infinite GDP at some
point yeah there there's some areas of
AI where AI can actually get into a
self-training loop if there's a
simulation of something that and
anything that can be simulated AI can
solve everything in that areas for
instance chess the game of Go you can
perfectly simulate it hence the I can
train and play with itself billions and
billions of times cre almost infinite
amounts of training data and hence solve
every problem in that domain what are
other domains that we can perfectly
simulate is programming if you can
programming languages can be run and
then you can simulate the outputs
obviously in the computer and then the
AI can get better and better and
eventually get super human uh in terms
of programming but where I can't
simulate things uh infinitely many times
is in like customer service right you
can have billions and billions of
customers kind of ask about all the
different things that uh can go wrong
with a product that you're sending and
so in those kinds of areas the limits
are going to be on data collection can
you actually fully digitize a process I
often joke like plumbers are probably
the safest from AI disruption because no
one's even collecting data on how to do
plumbing right you like crawl somewhere
get different pipes no one's having
GoPro and 3D sensors and robotic arms
and so on collecting data for that so
that will take much much longer um I
think in terms of work productivity a
lot of us are going to become managers a
lot of current employees that are
individual contributors are going to
have to learn to manage an AI to do the
kinds of work that they do and it turns
out managing is also a skill not
everyone is a good manager from day one
you have to really explain to the AI how
you do a certain kind of job and what
we've seen with for instance uh a really
large cyber security company called
minecast is we've they've had 200 seat
licenses using their product and then we
did a workshop with them and actually
explained to all the different groups
like this is what you can do and someone
from marketing can say well I usually
get this long product description and
then I have to describe it for these
different Industries and an email
campaign and I have to write three
tweets and three LinkedIn messages all
this stuff and we're like well just say
that to this agent and then the I agent
does it for them they're like wow now
it's like six to 20 hours of work every
other week just got automated by
describing this workflow that I used to
do manually to a agent and I think that
will change pretty much all work and
pretty much every industry Kaiu um I can
go in a thousand different directions uh
here uh first of all uh your Venture
fund Innovations which is how many
billions of capital a uh we manage about
$3 billion about3 billion and you've
been one of the most prolific AI
investors I've had the pleasure to visit
you multiple times in China and thank
you for your amazing Hospitality you've
now become an
entrepreneur um and you're running both
uh company in China and a company in the
United States uh why did you do
that well because this this time is for
real right imagine you know this was my
dream practice before well this was my
dream in when I went to college that AI
was nothing no one knew what it was but
I felt this was the thing I needed to do
and then we went through multiple
winters of AI where uh there's
disillusionment and I had to do other
things and about uh you know seven eight
years ago we saw with um you know deep
learning it was became clear it would
create a lot of value so but at the time
I didn't really see it becoming AGI so I
was an investor we actually created 12
AI unicorns in sinovation Ventures but
this time with generative AI uh the
speed at which is growing um is just
phenomenal you could help yourself you
yeah I felt if I just invested I'd be
missing out I I would be in the back
seat I want to be in the in the driver's
seat by the way everybody I hope you
feel the same right I I'm very clear
about saying there are two kinds of
companies at the end of this decade
companies that are fully utilizing Ai
and everyone else is out of business and
I I fundamentally believe that is it is
true um you've written a number of books
uh AI superpowers I commend to all of
you so since that was
published what's the biggest changes in
the global AI race and it is an AI arms
race going on well it isn't isn't
because the companies in China are
largely competing against each other for
the China market and they're generally
not I don't mean Nation to National but
it is between companies around the world
yeah so you mean Chinese companies what
are their characteristics so in my book
a superpowers I described uh the
American companies are generally
speaking more breakthrough Innovative
they come up with new things um and then
the Chinese companies are better at
engineering execution attention to
detail doing the grunt work user
interfaces user interfaces building apps
so um in the case of mobile or deep
learning we saw that Americans invented
pretty much everything but China created
a lot of value arguably more uh given
technologies that were largely invented
in the US so now we're in this
generative AI again invented by
Americans and we're in a in a in a
unique position where where the
technology is disrupting itself very
quickly in the US and elsewhere um so it
arguably is still the age of Discovery
and US ought to win but then the Chinese
companies are able to watch the
Innovations make some themselves and
then do better engineering and deliver
Solutions so the company I'm building
01 is doing exactly that we don't claim
to have invented everything or even most
things we learned a lot from the Giants
and silicon valy open Ai and others but
we think we build a more solidly faster
execute better so an example was I
talked about how 01 now has is the third
best model modeling company in the world
ranking number six in models measured by
lmis and UC Berkeley but the most
amazing thing I think the thing that
shocks my friends in the solic valley is
not just our performance but that we
train the model with $3 million and GPT
4 was trained by 80 to 100 million and
um GPT 5 is rumored to be trained by
about a billion dollars so it is not the
case we believe in scaling law but when
you do excellent detailed engineering it
is not the case you have to spend a
billion dollars to train a great so this
is really important for the audience
here because there's a lot of parts of
the world that don't have access to you
know 100,000 H you know h100 clusters
right and the question is oh my God can
I really build a business or a product
in pick your favorite country with a
small number of gpus yeah and I think
the constraint on gpus forced you to
innovate right can you speak to that I
think it's really important we talked
about that on our last podcast together
yeah I think you know as a company in
China first we have limited access to
gpus due to the US regulations and
secondly the Chinese companies are not
valued what American companies are I
mean we're F we're we're valued at a
fraction of the equivalent American
company so when when we have less money
and difficulty to get gpus I truly
believe that necessity is the mother of
in Innovation so when we only have 2,000
gpus well the team has to figure out how
to use it I as the CEO have to figure
out how to prioritize it and then not
only do we have to make training fast we
have make inference fast so our
inference is designed by figuring out
the bottlenecks in the entire process by
trying to turn a computational problem
to a memory problem by building a
multi-layer cache by building a specific
inference engine and so on but the
bottom line is our inference cost is 10
cents per million tokens and that's uh
130th of what the typical comparable
model charges and where's it going
where's the 10 cents going yeah it's
well the 10 cents would lead to building
apps for much lower cost so if you
wanted to build a u.com or perplexity or
some other app you can either pay open
AI $440 per million tokens or if you
have our model it costs you just 10
cents and if you buy our API it just
costs you 14 cents we're very
transparent with our pricing yes Richard
there's there's a really interesting uh
Paradox called jevans Paradox from the
previous industrial re ution a lot of
smart people back then were working on
making more efficient steam engines and
using that use less coal they thought oh
if we make the steam engines more
efficient we're going to need less coal
but instead we needed more steam engines
everywhere and I think that's exactly
what's going to happen we're currently
in the jeevan's Paradox of intelligence
we're just going to use intelligence in
many more places everyone is going to
have their own assistant their own
medical team that like understands
everything about them instead of being
restricted by Intelligence being very
very expensive yeah I totally agree I
want to clarify I'm not saying there's a
fixed workload we're making it cheaper
I'm saying we're enabling a workload
much much larger corre I want to ask one
closing question to all of you we have
people here who have daughters and sons
or nephews or brothers and
sisters what's your advice to someone
who is 20 years old listening to this um
or through this what's your advice to
someone at the beginning of their uh
sort of academic and professional career
given what you know is going on in AI
right now
Prem I think it's don't waste your time
learning how to code because I think the
new language is going to be I think the
new code language is going to be English
and I think that uh absolutely learn as
fast as you humanly possibly can on all
AI in all Ai modalities and I think if
you and then once you find your passion
I think you're going to then find a very
narrow AI to empower you to do what
you're what you're really set out to do
thank you PR Richard I I will disagree I
think you should still learn how to
program uh I think that is how you get
to really understand how this technology
works at the foundational level uh and
how it becomes less magic and more
something that you can yourself modify
and construct with but you need to
combine computer science and programming
with another passion that you can
actually apply all of that intelligence
to and ideally the younger you are the
more you learn the foundations Ma ma
physics The Sciences I think I'm going
to cut you off because I'm being yanked
I want I want to have kyu's final word
here okay I actually agree and this
agree with both of you I think people
should follow their hearts right if you
dream of becoming a fantastic programmer
and you can do it you should do what
Richard says if you think programming is
the way that make the most money no then
you should follow what prime says ladies
and Gentlemen please give it up to these
three amazing
CEOs thank you thank you thank you thank
you
