February 27, 2025 • 18 mins
As artificial intelligence reshapes industries and global competition intensifies, particularly with advancements from China, staying ahead requires more than just keeping up with the latest technologies—it demands strategic foresight, bold innovation, and flawless execution. Join Eiso Kant, CTO and Co-founder of Poolside, Ping Wu, CEO of Cresta & Daniyal Khan Investor at QIA for an engaging conversation on how to navigate the rapidly evolving AI landscape. They’ll share insights on building competitive advantage, driving meaningful innovation, and implementing AI solutions that deliver real impact in a dynamic and fast-paced world.
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Speaker 1 (00:09):
Okay, hey everybody, I'm Steve Clements. It's great to be
with you again here at the Web Summit and Kutar.
It's got a fantastic panel here. But you know, one
of the things, I mean, it's going to get a
little bit of trouble here. I've always thought if I
were going to be an investor, I would want to
invest in kids. I want to get call options in
some and put options in others, right, But you're not
(00:30):
allowed to do that. But we've got Daniel Khan here
the Guitar Investment Authority, and he's the Tech Growth Officer,
and so he's out there placing bets on the companies
of the future. You know, he's essentially doing what I mean.
And these are your children, right, And so we have
one company here you know, with with Ping Wu, with
Cresseye that you're an investor in yes, and I son,
(00:51):
you're not yet and you may never be an investor.
So one is your child, one's not. I just want
to know, as we start this discussion about staying ahead
in the future of AI, what are you as an
investors screening for as you're looking at your investment kids.
Speaker 2 (01:07):
Certainly, I mean it goes without saying that AI definitely
represents one of the most massive markets that would open
up when.
Speaker 1 (01:17):
Looking at that AI is everywhere, I mean eyes, everywhere,
thing is is anybody not in AI here? Put your
hand up, anybody on. So AI is a huge field.
So tell me how you differentiate in the crowded market.
Speaker 2 (01:29):
There is a lot of noise in the market. I
would say that for sure that every other company has
that AI label attached to it. But when as investors,
when looking at the landscape, I think we go with
the founders, the vision of the founder, the technical strength
of the founding team, the use cases, addressing like how
mission critical it is. And I'm fortunate here to have
(01:50):
both bull Side and Cressa on the panel because both
of them once in contact center once and coding, and
both of those are the immediate AI use cases enterprises
need today and can be automated.
Speaker 1 (02:02):
I so, conn, let me talk to you for a minute,
and you know, get to ping in just a moment.
But as you're sitting there and kind of developing this
foundation for revolutionizing coding, so many though out here encoding.
You've got systems that now code and systems that talk
to us at what makes you special?
Speaker 3 (02:17):
So I would say what makes us special is that
we are the one company that decided from day zero
to build from the ground up. So we said, in
the next couple of years, the gap between AI models
and human level intelligence is going to entirely close. And
our view was that the first major economically valuable area
where that was going to happen was encoding and software development.
(02:40):
But it was also the area where we could develop models,
thinking and reasoning the most, and that's probably what makes
us special. We go from model up all the way
to applications, and well.
Speaker 1 (02:49):
In your promo literature, when you get on your website,
you look on theline you said you're working to achieve
human level intelligence? Why not go? I mean this human
level intelligence that great?
Speaker 3 (03:01):
So first, I think is the only intelligence we know,
at least that I'm as aware of.
Speaker 1 (03:06):
But when you're thinking of complex systems talking to complex systems,
isn't it already you know, surpassing in many ways our
own ability to process to think. That's what I mentioned
is that next year, how to stay in front of
an AI? Don't you have to go beyond that?
Speaker 3 (03:23):
So I think if we talk about AI for TESK
specific models for image recognition, speech or language. These models
already are far superior in many places what we can do.
But if I look at our workforce, our white collar
workforce and in the future are embodied workforce that builds
the industrialization of the world, this is something where we
still as humans today have an edge over AI, but
(03:46):
from where we sit, and this drastically accelerated in the
last three to six months. We now see a world
where in the next thirty six months AI will become
as capable as us across almost all areas of knowledge work,
all the work that we essentially do in the world
of bits behind our laptop. And this means for the
first time we're going to be in a world where
we have no longer countries in our population sizes that
(04:09):
determine what we can do and the amount of impact
we can have on our economies and on scientific progress.
But now we get to do it by spinning up
intelligence on compute. And so my first mission is on
human level intelligence, and then we'll see what's beyond that.
Speaker 1 (04:21):
Ping Wu, you know, CEO of Cresta. I guess what
I'm really interested in this space is giving our audience
and understanding of how you, as a CEO decide what
is real in this space and what is quicksand And
the reason I asked this is I recently interviewed Chuck
Robbins as CuO of Cisco, and he's actually banned his
(04:42):
staff from going on squawk Box and CNBC. Not banned
from going on, but banned them from talking about AI
unless it's directly related to a revenue line or directly
related to some substantive change to kind of defluff and
you know, kind of take that spin out of the
AI discussion. But as you look at the problems you're
(05:03):
trying to solve, tell us how your line works in
that space.
Speaker 4 (05:07):
Yeah, so you know, we certainly need to realize the
current limitation of the AI.
Speaker 5 (05:13):
Right, So they are really.
Speaker 4 (05:14):
Good at certain things, and they are actually not that
good at other things that human may actually think it
very straightforward. So so AI is not very good at
you know, following instructions for example for long contact windows.
Speaker 1 (05:28):
Right.
Speaker 5 (05:28):
So that's why we take a very pragmatic approach.
Speaker 4 (05:30):
We build a platform for unified human and AI agents.
Speaker 5 (05:35):
So we're not taking an approach.
Speaker 4 (05:36):
That we're going to automate all your contact center costs
and agents overnight, which we don't think it's possible just
because of very practical reasons. We take approach that we're
implying humans and then helping them to do their task easier,
but also learning from humans and then to automate some
of the padded tasks. So by building that unified platform,
(05:59):
so it's really at the best of the human agents
as well as the AI agents.
Speaker 1 (06:03):
Now, people may not be familiar with quest or they
may I may be less familiar with Cresta, but you
are really the backbone of lots of the most successful
call centers and airlines and big operations in the world.
But why don't you give us a quick snapshot of
the problems you're solving and how you're changing that topography
of interaction between large, large firms and their clients. Right,
(06:27):
So we are an.
Speaker 4 (06:27):
AI platform to transform contact centers. Right, So we actually
reshape all kinds of different workflows in the contact center
to make them a lot more efficient. One example is
Fortune ten healthcare companies able to use Questa to remove
one minute every call.
Speaker 5 (06:44):
After call work.
Speaker 4 (06:45):
So we're able to automate after all work by automatic
generate summaries and the notes and then writing th CRM.
So that's a very concrete use case that you know,
if you take millions of casts a day, that's a
lot of savings. Then another example is the rev new
use case where we're able to help one of the
top tail codes in United States be able to lift
(07:06):
their revenue conversion rates for each call by fifteen percent
across all the BPOs because they employ a lot of
BPOs agents, lack of training, lack of consistency, So we're
able to help them to remind them of the key
behaviors to drive results. So all these kind of are
very concrete use cases we're able to drive in many
different Fortune five hundred companies.
Speaker 1 (07:27):
So Daniel and the Guitar Investment Authority invested in your company, right, Yes,
how do you like them as investors? Are they problematic?
Are they you know, stressing you out?
Speaker 4 (07:38):
We're very very fortunate to be able to partner with
QIA and you know they join us lead our.
Speaker 1 (07:45):
I'm actually I don't want to actually be that nice
to him. I'm actually with it. So one of the
questions I have is as you're bringing a new investment,
because a lot of people are here looking for investment.
It's a whole investor corner. I'm interested in what you've
learned in the relationship. That's so important here compared to
many investors out there that I know are out for
a quicker churn, right, if I'm putting it right.
Speaker 4 (08:06):
So, I think one thing really differentiate QIA is their
permanent capital. They're very long term, patient capital. And then
for a paradigm shift as big as the I we're
in right now, you really need a very long term
capital partner be able to kind of patiently compound over
a long time. So, as Warren Buffer said, you want
(08:26):
a long wet snowslope so that you can snowball for
a long time, like you know, thirty to fifty years.
But for a lot of VC firms, they're just because
the structurally they're looking for liquidity in a relatively short
period of time compared to some of the time that
would take company multi decades to build something really really great.
Speaker 1 (08:47):
So, Daniel, not to put you on the spot with
the Kentarre Investment Authority, I think a lot of people
and look, I just can be honest here we're sitting
in Cutter and I mean, don't get too much trouble.
But a lot of people think, hey, if you know
the Emir's brother, you can get an investment, or you know,
if you kind of you know, go hang out at
the right parties. That there's a kind of lack of discipline.
Can you disabuse me of that? Can you explain how
(09:08):
you guys work and why that image doesn't fit yeah?
Or doesn't well?
Speaker 5 (09:14):
No, no, no.
Speaker 2 (09:14):
For short, I think we've spent the last decade and
just investing in the team itself, just building out sector specialties.
We've had regional offices in New.
Speaker 1 (09:23):
York, Singapore and there you're the textic.
Speaker 2 (09:27):
Yes, yes, but I think it just takes a lot
more experience here where we have spent the last decade
plus in just evaluating the space but sticking to our ethos,
and our ethos have been backing visionary founders but also
sticking to proper due diligence.
Speaker 5 (09:43):
For us, it's not as.
Speaker 1 (09:44):
Your portfolio look, is it. Is it looking strong? Yes?
Speaker 2 (09:47):
No, certainly, it's because it's you have to diversify, especially
when it comes to AI. I know both Cresta and
ful Side are based out of West Coast, but AI
is truly global in nature. Let's not forget that. Oh
a ton of gratitude to Google because deepline, And where
all of the italent in the market today is it
all originated? Most of it originated in London.
Speaker 1 (10:09):
Well, this goes to the thing with you. I was talking,
you know, I had breakfast with these guys this morning,
and I so one of the things that really interested
me is the global race for talent and that you
know when you talk about the geostrategic race in the world,
I mean America talks about this profession very narcissistically. But
when you look at the talent around the world, where
it came from, where it originated, you know, you just
(10:31):
saw a bowling ball from deep seek. Just roll a
bowling ball into a lot of people's assumptions on who
who was winning and who was losing. But tell us,
I mean, you're so both of you were so woven
in to that talent network. Does America have it cornered
or is it a global deal?
Speaker 3 (10:48):
So I think you, Daniel said it this. If we
look at where some of the major talent originated in AI,
it really isn't multiple hubs in the world. It was
in California. It was in Canada. It was in London
with DeepMind, which still today is one of the largest
concentrations of AI talent in the world.
Speaker 1 (11:07):
It was in Switzerland at etch Zurich. So and then of.
Speaker 3 (11:10):
Course China itself has been graduating an order of magnitude
more computer scientists and researchers than the United States in
comparison well over a decade at this point, and so
Deepsek was not surprising to us in the industry. It
was two hundred incredible researchers and engineers that spent two
years doing hard work on billions of dollars of infrastructure.
Not unlike my company or unlike some of my competitors,
(11:33):
but if we look at the West, we did do
something quite unique when we started this company. We're part
of the second wave of companies that started XAI pool site,
the ones that came a little bit later than to
open AI, and we decided strategically not to hire in
the backyard of some of our competitors in the Bay Area.
We actually said, there's an incredible talent pool in Europe,
(11:53):
there's an incredible talent pool in the East coast of
the United States and Canada. Let's start fishing there and
today some of the just talent hubs for US our London,
our Switzerland, our New York, our Canada.
Speaker 1 (12:05):
Are you fishing in the Middle East at all?
Speaker 3 (12:07):
So right now I have to admit we're about one
hundred people.
Speaker 1 (12:10):
We're growing quite rapidly.
Speaker 3 (12:12):
We have two people in the Middle East right now
in the region.
Speaker 1 (12:15):
I think the Middle.
Speaker 3 (12:16):
East is in a place where, well, a lot of
the talent in our field has required, you know, ten, fifteen,
twenty years of originating in either PhD programs or O
our large companies like a Google or a Deep Mind,
those haven't historically been here in the region yet. But
at the same time, we're seeing a place that is
becoming massively attractive for people to move to. Quodity of life, family,
(12:39):
you know, friendliness, safety. I think a lot of the region,
not just Qatar, has built on incredibly strong values and
incredibly high quality of life the lot of us get
to experienced for the first time coming here. So I
think over time it's going to definitely become a hub.
And as I said earlier, we are about to live
in a world that is a post human capital only world.
(12:59):
We're about to live in a world where how competitive
you are as a nation on technology progress and scientific progress.
More has to do with how much energy and chips
you have in your country than how many people and
energy for ships, talent, energy, chips and intelligence later AI
and energy is highly dense in this region.
Speaker 1 (13:17):
Right, Ping, let me ask you, you know, as you're
sort of looking at the absorption of these tools, and
I often think of AI needs the definition of what
problems we're solving, So you know, I look at it
as sort of a tool for the future. But I'm
wondering how you sort of see the pickup of this,
you know, by business because part of this you know,
discussion today is staying ahead with AI and and do
(13:39):
you see that happening at a at a rate that's
fast enough to matter or do you think there are
blind spots out in our business community that that need
to be fixed? Right?
Speaker 5 (13:51):
I think.
Speaker 4 (13:52):
Enterprise environment is optimized for humans, right, So AI, you know,
there are tasks is really good at, like mar generate
marketing copies and doing certain things, but there are a
lot of challenge if you actually deploy that in the
actual enterprise environment. So there are a lot of prerequisite
you need to get right for example, you need to
(14:13):
get your data right in a unify places. You want
to have your knowledge base to reflect the instructions that
you want the AI to take. You know, otherwise AI
doesn't know what to do. And the third you want
your API framework to be really modern and have real
time access so that AI can take actions and then
to make changes in the system records. So all these
things are for me. It's like foundation to really have
(14:36):
the AI model to be effective. And then another thing
I would say is that outside of enterprise, I do
think that AI have huge applications in science because science
is not human specific.
Speaker 5 (14:50):
Science are truth right, science you can derive in a reason.
It's more like.
Speaker 4 (14:55):
A generic model can help a lot, right, So I
would see a lot of progrests probably in signed AI
in science for bio uh, in material new material design,
new protein, and drug discovery. That type of use case
is yeah.
Speaker 1 (15:10):
So that's really an explosion of possibility. Maybe you Daniel,
as an investor with QIA. You know, one of the
things I'm interested is you've been You said, you've been
building this portfolio for ten years, talked to ISO who
was you know, basically you tried to do GitHub before
GitHub was gethhubbed. I mean you basically tried to be
a coding UH software, AI and forum company before the
(15:32):
industry had really taken off. And I just said, wow,
if somebody had invested in that end, you know at
that time. So now we see the product of what's
going on. We see very two, very successful entrepreneurs, a
lot more in the audience. As you look at the
next ten years, what are the characteristics of that next
strategic leap in tech in this world that are part
(15:53):
of what you're what you're driving QIA is you know,
seed investor in the next generation of technology.
Speaker 2 (16:01):
I like to think of it like the chat GPT
moment was probably think of it as like a kid
graduating high school. Right every year since then, it's like
a student going through college. Maybe AI today is like
an AI is a student and the sophomore year, by
next year, junior year. I think that the pace at
which AI is developing, and we're seeing this across where
(16:22):
agents are getting better, of the foundation models are getting better,
they're scoring higher and higher on these benchmark tests as well.
It is all pointing towards design that it's inevitable that
you have to embrace AI. You have to embrace rolling
out enterprise AI solutions because otherwise, if you don't, you're
going to be left behind. Which is why I think
regions like Qia and sovereign money, it's long term patient
(16:45):
capital that positions does really well because we see the
potential ahead of what how we can match the productivity
and labor disadvantages that certain countries have as well.
Speaker 1 (16:54):
I saw am paying in our last minute or so
tell me each as you know you've accomplished these companies
if you just sort of add on now and say, okay,
we now have to do X. What are some of
the antenna that you have and the signals that you're
looking at for how you're going to revolutionize or rip
up your companies and create something new.
Speaker 3 (17:10):
When you look ten years out ISA where I sit,
scale of training increasingly more capable AI models is the
foundation of my business.
Speaker 1 (17:19):
Right The very harsh reality.
Speaker 3 (17:22):
Of my business is that my training compute. The scale
at which we're training these models does not increase an
order of magnitude this year and very likely in order
of magnitude in the coming years ahead. We're no longer
in the race. So I have one singular mission is
to build the world's most capable AI, to close the
gap between where AI is todaying human level intelligence in
software development to make money and enterprises, and to make
(17:45):
sure I have the compute resources underpinned to be able
to achieve that.
Speaker 5 (17:48):
Ping is so we're very excited about building the next
generation of workforce, meaning you know, the AI can really
control a computer, can really like a digital workforce to
understand the screen.
Speaker 4 (18:02):
Not only just listen to the call and you know,
be able to answer just like the current language model,
but really go br that to understand the screen and
take actions. That's where we see there will be the
huge potential for the future AI workforce.
Speaker 1 (18:16):
We do this every year. We're gonna be watching you
guys every year to see what happens. I want to
encourage all of our audience to come in and you know,
exchange ideas with you. But I show kan a full side.
Ping Wu of of PRESCA, Daniel Kah of the Qatar
Investment forty Thank you both, Oh, thank you all so
much for sharing your thoughts with us today. Thank you,
thank you, big amount of applause
Okay, hey everybody, I'm Steve Clements. It's great to be
with you again here at the Web Summit and Kutar.
It's got a fantastic panel here. But you know, one
of the things, I mean, it's going to get a
little bit of trouble here. I've always thought if I
were going to be an investor, I would want to
invest in kids. I want to get call options in
some and put options in others, right, But you're not
(00:30):
allowed to do that. But we've got Daniel Khan here
the Guitar Investment Authority, and he's the Tech Growth Officer,
and so he's out there placing bets on the companies
of the future. You know, he's essentially doing what I mean.
And these are your children, right, And so we have
one company here you know, with with Ping Wu, with
Cresseye that you're an investor in yes, and I son,
(00:51):
you're not yet and you may never be an investor.
So one is your child, one's not. I just want
to know, as we start this discussion about staying ahead
in the future of AI, what are you as an
investors screening for as you're looking at your investment kids.
Speaker 2 (01:07):
Certainly, I mean it goes without saying that AI definitely
represents one of the most massive markets that would open
up when.
Speaker 1 (01:17):
Looking at that AI is everywhere, I mean eyes, everywhere,
thing is is anybody not in AI here? Put your
hand up, anybody on. So AI is a huge field.
So tell me how you differentiate in the crowded market.
Speaker 2 (01:29):
There is a lot of noise in the market. I
would say that for sure that every other company has
that AI label attached to it. But when as investors,
when looking at the landscape, I think we go with
the founders, the vision of the founder, the technical strength
of the founding team, the use cases, addressing like how
mission critical it is. And I'm fortunate here to have
(01:50):
both bull Side and Cressa on the panel because both
of them once in contact center once and coding, and
both of those are the immediate AI use cases enterprises
need today and can be automated.
Speaker 1 (02:02):
I so, conn, let me talk to you for a minute,
and you know, get to ping in just a moment.
But as you're sitting there and kind of developing this
foundation for revolutionizing coding, so many though out here encoding.
You've got systems that now code and systems that talk
to us at what makes you special?
Speaker 3 (02:17):
So I would say what makes us special is that
we are the one company that decided from day zero
to build from the ground up. So we said, in
the next couple of years, the gap between AI models
and human level intelligence is going to entirely close. And
our view was that the first major economically valuable area
where that was going to happen was encoding and software development.
(02:40):
But it was also the area where we could develop models,
thinking and reasoning the most, and that's probably what makes
us special. We go from model up all the way
to applications, and well.
Speaker 1 (02:49):
In your promo literature, when you get on your website,
you look on theline you said you're working to achieve
human level intelligence? Why not go? I mean this human
level intelligence that great?
Speaker 3 (03:01):
So first, I think is the only intelligence we know,
at least that I'm as aware of.
Speaker 1 (03:06):
But when you're thinking of complex systems talking to complex systems,
isn't it already you know, surpassing in many ways our
own ability to process to think. That's what I mentioned
is that next year, how to stay in front of
an AI? Don't you have to go beyond that?
Speaker 3 (03:23):
So I think if we talk about AI for TESK
specific models for image recognition, speech or language. These models
already are far superior in many places what we can do.
But if I look at our workforce, our white collar
workforce and in the future are embodied workforce that builds
the industrialization of the world, this is something where we
still as humans today have an edge over AI, but
(03:46):
from where we sit, and this drastically accelerated in the
last three to six months. We now see a world
where in the next thirty six months AI will become
as capable as us across almost all areas of knowledge work,
all the work that we essentially do in the world
of bits behind our laptop. And this means for the
first time we're going to be in a world where
we have no longer countries in our population sizes that
(04:09):
determine what we can do and the amount of impact
we can have on our economies and on scientific progress.
But now we get to do it by spinning up
intelligence on compute. And so my first mission is on
human level intelligence, and then we'll see what's beyond that.
Speaker 1 (04:21):
Ping Wu, you know, CEO of Cresta. I guess what
I'm really interested in this space is giving our audience
and understanding of how you, as a CEO decide what
is real in this space and what is quicksand And
the reason I asked this is I recently interviewed Chuck
Robbins as CuO of Cisco, and he's actually banned his
(04:42):
staff from going on squawk Box and CNBC. Not banned
from going on, but banned them from talking about AI
unless it's directly related to a revenue line or directly
related to some substantive change to kind of defluff and
you know, kind of take that spin out of the
AI discussion. But as you look at the problems you're
(05:03):
trying to solve, tell us how your line works in
that space.
Speaker 4 (05:07):
Yeah, so you know, we certainly need to realize the
current limitation of the AI.
Speaker 5 (05:13):
Right, So they are really.
Speaker 4 (05:14):
Good at certain things, and they are actually not that
good at other things that human may actually think it
very straightforward. So so AI is not very good at
you know, following instructions for example for long contact windows.
Speaker 1 (05:28):
Right.
Speaker 5 (05:28):
So that's why we take a very pragmatic approach.
Speaker 4 (05:30):
We build a platform for unified human and AI agents.
Speaker 5 (05:35):
So we're not taking an approach.
Speaker 4 (05:36):
That we're going to automate all your contact center costs
and agents overnight, which we don't think it's possible just
because of very practical reasons. We take approach that we're
implying humans and then helping them to do their task easier,
but also learning from humans and then to automate some
of the padded tasks. So by building that unified platform,
(05:59):
so it's really at the best of the human agents
as well as the AI agents.
Speaker 1 (06:03):
Now, people may not be familiar with quest or they
may I may be less familiar with Cresta, but you
are really the backbone of lots of the most successful
call centers and airlines and big operations in the world.
But why don't you give us a quick snapshot of
the problems you're solving and how you're changing that topography
of interaction between large, large firms and their clients. Right,
(06:27):
So we are an.
Speaker 4 (06:27):
AI platform to transform contact centers. Right, So we actually
reshape all kinds of different workflows in the contact center
to make them a lot more efficient. One example is
Fortune ten healthcare companies able to use Questa to remove
one minute every call.
Speaker 5 (06:44):
After call work.
Speaker 4 (06:45):
So we're able to automate after all work by automatic
generate summaries and the notes and then writing th CRM.
So that's a very concrete use case that you know,
if you take millions of casts a day, that's a
lot of savings. Then another example is the rev new
use case where we're able to help one of the
top tail codes in United States be able to lift
(07:06):
their revenue conversion rates for each call by fifteen percent
across all the BPOs because they employ a lot of
BPOs agents, lack of training, lack of consistency, So we're
able to help them to remind them of the key
behaviors to drive results. So all these kind of are
very concrete use cases we're able to drive in many
different Fortune five hundred companies.
Speaker 1 (07:27):
So Daniel and the Guitar Investment Authority invested in your company, right, Yes,
how do you like them as investors? Are they problematic?
Are they you know, stressing you out?
Speaker 4 (07:38):
We're very very fortunate to be able to partner with
QIA and you know they join us lead our.
Speaker 1 (07:45):
I'm actually I don't want to actually be that nice
to him. I'm actually with it. So one of the
questions I have is as you're bringing a new investment,
because a lot of people are here looking for investment.
It's a whole investor corner. I'm interested in what you've
learned in the relationship. That's so important here compared to
many investors out there that I know are out for
a quicker churn, right, if I'm putting it right.
Speaker 4 (08:06):
So, I think one thing really differentiate QIA is their
permanent capital. They're very long term, patient capital. And then
for a paradigm shift as big as the I we're
in right now, you really need a very long term
capital partner be able to kind of patiently compound over
a long time. So, as Warren Buffer said, you want
(08:26):
a long wet snowslope so that you can snowball for
a long time, like you know, thirty to fifty years.
But for a lot of VC firms, they're just because
the structurally they're looking for liquidity in a relatively short
period of time compared to some of the time that
would take company multi decades to build something really really great.
Speaker 1 (08:47):
So, Daniel, not to put you on the spot with
the Kentarre Investment Authority, I think a lot of people
and look, I just can be honest here we're sitting
in Cutter and I mean, don't get too much trouble.
But a lot of people think, hey, if you know
the Emir's brother, you can get an investment, or you know,
if you kind of you know, go hang out at
the right parties. That there's a kind of lack of discipline.
Can you disabuse me of that? Can you explain how
(09:08):
you guys work and why that image doesn't fit yeah?
Or doesn't well?
Speaker 5 (09:14):
No, no, no.
Speaker 2 (09:14):
For short, I think we've spent the last decade and
just investing in the team itself, just building out sector specialties.
We've had regional offices in New.
Speaker 1 (09:23):
York, Singapore and there you're the textic.
Speaker 2 (09:27):
Yes, yes, but I think it just takes a lot
more experience here where we have spent the last decade
plus in just evaluating the space but sticking to our ethos,
and our ethos have been backing visionary founders but also
sticking to proper due diligence.
Speaker 5 (09:43):
For us, it's not as.
Speaker 1 (09:44):
Your portfolio look, is it. Is it looking strong? Yes?
Speaker 2 (09:47):
No, certainly, it's because it's you have to diversify, especially
when it comes to AI. I know both Cresta and
ful Side are based out of West Coast, but AI
is truly global in nature. Let's not forget that. Oh
a ton of gratitude to Google because deepline, And where
all of the italent in the market today is it
all originated? Most of it originated in London.
Speaker 1 (10:09):
Well, this goes to the thing with you. I was talking,
you know, I had breakfast with these guys this morning,
and I so one of the things that really interested
me is the global race for talent and that you
know when you talk about the geostrategic race in the world,
I mean America talks about this profession very narcissistically. But
when you look at the talent around the world, where
it came from, where it originated, you know, you just
(10:31):
saw a bowling ball from deep seek. Just roll a
bowling ball into a lot of people's assumptions on who
who was winning and who was losing. But tell us,
I mean, you're so both of you were so woven
in to that talent network. Does America have it cornered
or is it a global deal?
Speaker 3 (10:48):
So I think you, Daniel said it this. If we
look at where some of the major talent originated in AI,
it really isn't multiple hubs in the world. It was
in California. It was in Canada. It was in London
with DeepMind, which still today is one of the largest
concentrations of AI talent in the world.
Speaker 1 (11:07):
It was in Switzerland at etch Zurich. So and then of.
Speaker 3 (11:10):
Course China itself has been graduating an order of magnitude
more computer scientists and researchers than the United States in
comparison well over a decade at this point, and so
Deepsek was not surprising to us in the industry. It
was two hundred incredible researchers and engineers that spent two
years doing hard work on billions of dollars of infrastructure.
Not unlike my company or unlike some of my competitors,
(11:33):
but if we look at the West, we did do
something quite unique when we started this company. We're part
of the second wave of companies that started XAI pool site,
the ones that came a little bit later than to
open AI, and we decided strategically not to hire in
the backyard of some of our competitors in the Bay Area.
We actually said, there's an incredible talent pool in Europe,
(11:53):
there's an incredible talent pool in the East coast of
the United States and Canada. Let's start fishing there and
today some of the just talent hubs for US our London,
our Switzerland, our New York, our Canada.
Speaker 1 (12:05):
Are you fishing in the Middle East at all?
Speaker 3 (12:07):
So right now I have to admit we're about one
hundred people.
Speaker 1 (12:10):
We're growing quite rapidly.
Speaker 3 (12:12):
We have two people in the Middle East right now
in the region.
Speaker 1 (12:15):
I think the Middle.
Speaker 3 (12:16):
East is in a place where, well, a lot of
the talent in our field has required, you know, ten, fifteen,
twenty years of originating in either PhD programs or O
our large companies like a Google or a Deep Mind,
those haven't historically been here in the region yet. But
at the same time, we're seeing a place that is
becoming massively attractive for people to move to. Quodity of life, family,
(12:39):
you know, friendliness, safety. I think a lot of the region,
not just Qatar, has built on incredibly strong values and
incredibly high quality of life the lot of us get
to experienced for the first time coming here. So I
think over time it's going to definitely become a hub.
And as I said earlier, we are about to live
in a world that is a post human capital only world.
(12:59):
We're about to live in a world where how competitive
you are as a nation on technology progress and scientific progress.
More has to do with how much energy and chips
you have in your country than how many people and
energy for ships, talent, energy, chips and intelligence later AI
and energy is highly dense in this region.
Speaker 1 (13:17):
Right, Ping, let me ask you, you know, as you're
sort of looking at the absorption of these tools, and
I often think of AI needs the definition of what
problems we're solving, So you know, I look at it
as sort of a tool for the future. But I'm
wondering how you sort of see the pickup of this,
you know, by business because part of this you know,
discussion today is staying ahead with AI and and do
(13:39):
you see that happening at a at a rate that's
fast enough to matter or do you think there are
blind spots out in our business community that that need
to be fixed? Right?
Speaker 5 (13:51):
I think.
Speaker 4 (13:52):
Enterprise environment is optimized for humans, right, So AI, you know,
there are tasks is really good at, like mar generate
marketing copies and doing certain things, but there are a
lot of challenge if you actually deploy that in the
actual enterprise environment. So there are a lot of prerequisite
you need to get right for example, you need to
(14:13):
get your data right in a unify places. You want
to have your knowledge base to reflect the instructions that
you want the AI to take. You know, otherwise AI
doesn't know what to do. And the third you want
your API framework to be really modern and have real
time access so that AI can take actions and then
to make changes in the system records. So all these
things are for me. It's like foundation to really have
(14:36):
the AI model to be effective. And then another thing
I would say is that outside of enterprise, I do
think that AI have huge applications in science because science
is not human specific.
Speaker 5 (14:50):
Science are truth right, science you can derive in a reason.
It's more like.
Speaker 4 (14:55):
A generic model can help a lot, right, So I
would see a lot of progrests probably in signed AI
in science for bio uh, in material new material design,
new protein, and drug discovery. That type of use case
is yeah.
Speaker 1 (15:10):
So that's really an explosion of possibility. Maybe you Daniel,
as an investor with QIA. You know, one of the
things I'm interested is you've been You said, you've been
building this portfolio for ten years, talked to ISO who
was you know, basically you tried to do GitHub before
GitHub was gethhubbed. I mean you basically tried to be
a coding UH software, AI and forum company before the
(15:32):
industry had really taken off. And I just said, wow,
if somebody had invested in that end, you know at
that time. So now we see the product of what's
going on. We see very two, very successful entrepreneurs, a
lot more in the audience. As you look at the
next ten years, what are the characteristics of that next
strategic leap in tech in this world that are part
(15:53):
of what you're what you're driving QIA is you know,
seed investor in the next generation of technology.
Speaker 2 (16:01):
I like to think of it like the chat GPT
moment was probably think of it as like a kid
graduating high school. Right every year since then, it's like
a student going through college. Maybe AI today is like
an AI is a student and the sophomore year, by
next year, junior year. I think that the pace at
which AI is developing, and we're seeing this across where
(16:22):
agents are getting better, of the foundation models are getting better,
they're scoring higher and higher on these benchmark tests as well.
It is all pointing towards design that it's inevitable that
you have to embrace AI. You have to embrace rolling
out enterprise AI solutions because otherwise, if you don't, you're
going to be left behind. Which is why I think
regions like Qia and sovereign money, it's long term patient
(16:45):
capital that positions does really well because we see the
potential ahead of what how we can match the productivity
and labor disadvantages that certain countries have as well.
Speaker 1 (16:54):
I saw am paying in our last minute or so
tell me each as you know you've accomplished these companies
if you just sort of add on now and say, okay,
we now have to do X. What are some of
the antenna that you have and the signals that you're
looking at for how you're going to revolutionize or rip
up your companies and create something new.
Speaker 3 (17:10):
When you look ten years out ISA where I sit,
scale of training increasingly more capable AI models is the
foundation of my business.
Speaker 1 (17:19):
Right The very harsh reality.
Speaker 3 (17:22):
Of my business is that my training compute. The scale
at which we're training these models does not increase an
order of magnitude this year and very likely in order
of magnitude in the coming years ahead. We're no longer
in the race. So I have one singular mission is
to build the world's most capable AI, to close the
gap between where AI is todaying human level intelligence in
software development to make money and enterprises, and to make
(17:45):
sure I have the compute resources underpinned to be able
to achieve that.
Speaker 5 (17:48):
Ping is so we're very excited about building the next
generation of workforce, meaning you know, the AI can really
control a computer, can really like a digital workforce to
understand the screen.
Speaker 4 (18:02):
Not only just listen to the call and you know,
be able to answer just like the current language model,
but really go br that to understand the screen and
take actions. That's where we see there will be the
huge potential for the future AI workforce.
Speaker 1 (18:16):
We do this every year. We're gonna be watching you
guys every year to see what happens. I want to
encourage all of our audience to come in and you know,
exchange ideas with you. But I show kan a full side.
Ping Wu of of PRESCA, Daniel Kah of the Qatar
Investment forty Thank you both, Oh, thank you all so
much for sharing your thoughts with us today. Thank you,
thank you, big amount of applause
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